diff --git a/0.18.0/.buildinfo b/0.18.0/.buildinfo
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-# Sphinx build info version 1
-# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 7c363d7e29e4b85fb651711b18647047
-tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/0.18.0/.doctrees/nbsphinx/examples/COG.ipynb b/0.18.0/.doctrees/nbsphinx/examples/COG.ipynb
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--- a/0.18.0/.doctrees/nbsphinx/examples/COG.ipynb
+++ /dev/null
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Cloud Optimized GeoTiff (COG)\n",
-    "\n",
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from https://openaerialmap.org/\n",
-    "cog_url = (\n",
-    "    \"https://oin-hotosm.s3.amazonaws.com/\"\n",
-    "    \"5d7dad0becaf880008a9bc88/0/5d7dad0becaf880008a9bc89.tif\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rioxarray.open_rasterio(cog_url, masked=True, overview_level=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray (band: 3, y: 312, x: 688)>\n",
-       "[643968 values with dtype=float64]\n",
-       "Coordinates:\n",
-       "  * band         (band) int64 1 2 3\n",
-       "  * y            (y) float64 4.34e+06 4.34e+06 4.34e+06 ... 4.339e+06 4.339e+06\n",
-       "  * x            (x) float64 -1.333e+07 -1.333e+07 ... -1.333e+07 -1.333e+07\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    transform:     (1.194328566955879, 0.0, -13334019.180693429, 0.0, -1.1943...\n",
-       "    scales:        (1.0, 1.0, 1.0)\n",
-       "    offsets:       (0.0, 0.0, 0.0)\n",
-       "    grid_mapping:  spatial_ref"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fa30cc3aeb8>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rds.astype(\"int\").plot.imshow(rgb=\"band\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
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diff --git a/0.18.0/.doctrees/nbsphinx/examples/clip_box.ipynb b/0.18.0/.doctrees/nbsphinx/examples/clip_box.ipynb
deleted file mode 100644
index 7543b350..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/clip_box.ipynb
+++ /dev/null
@@ -1,717 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Clip Box"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
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-       "  display: none;\n",
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-       "}\n",
-       "\n",
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-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
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-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
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-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 200, x: 200)&gt;\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-30c63a87-57cb-472f-837d-4f925d7c1c64' class='xr-array-in' type='checkbox' checked><label for='section-30c63a87-57cb-472f-837d-4f925d7c1c64' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan 687.0 687.0 ... 491.0 504.0 504.0 515.0 469.0 485.0</span></div><div class='xr-array-data'><pre>array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-af2aad65-513d-46c2-8e63-fda2854beb0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af2aad65-513d-46c2-8e63-fda2854beb0c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.05e+06 5.05e+06 ... 5.004e+06</div><input id='attrs-30d9748d-4a6f-413c-9e83-c0ff31dcab89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30d9748d-4a6f-413c-9e83-c0ff31dcab89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00a56479-fbb4-4223-92f1-b83428597e50' class='xr-var-data-in' type='checkbox'><label for='data-00a56479-fbb4-4223-92f1-b83428597e50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([5049992.781974, 5049761.125615, 5049529.469257, 5049297.812899,\n",
-       "       5049066.156541, 5048834.500182, 5048602.843824, 5048371.187466,\n",
-       "       5048139.531108, 5047907.874749, 5047676.218391, 5047444.562033,\n",
-       "       5047212.905674, 5046981.249316, 5046749.592958, 5046517.9366  ,\n",
-       "       5046286.280241, 5046054.623883, 5045822.967525, 5045591.311167,\n",
-       "       5045359.654808, 5045127.99845 , 5044896.342092, 5044664.685734,\n",
-       "       5044433.029375, 5044201.373017, 5043969.716659, 5043738.0603  ,\n",
-       "       5043506.403942, 5043274.747584, 5043043.091226, 5042811.434867,\n",
-       "       5042579.778509, 5042348.122151, 5042116.465793, 5041884.809434,\n",
-       "       5041653.153076, 5041421.496718, 5041189.84036 , 5040958.184001,\n",
-       "       5040726.527643, 5040494.871285, 5040263.214927, 5040031.558568,\n",
-       "       5039799.90221 , 5039568.245852, 5039336.589493, 5039104.933135,\n",
-       "       5038873.276777, 5038641.620419, 5038409.96406 , 5038178.307702,\n",
-       "       5037946.651344, 5037714.994986, 5037483.338627, 5037251.682269,\n",
-       "       5037020.025911, 5036788.369553, 5036556.713194, 5036325.056836,\n",
-       "       5036093.400478, 5035861.74412 , 5035630.087761, 5035398.431403,\n",
-       "       5035166.775045, 5034935.118686, 5034703.462328, 5034471.80597 ,\n",
-       "       5034240.149612, 5034008.493253, 5033776.836895, 5033545.180537,\n",
-       "       5033313.524179, 5033081.86782 , 5032850.211462, 5032618.555104,\n",
-       "       5032386.898746, 5032155.242387, 5031923.586029, 5031691.929671,\n",
-       "       5031460.273313, 5031228.616954, 5030996.960596, 5030765.304238,\n",
-       "       5030533.647879, 5030301.991521, 5030070.335163, 5029838.678805,\n",
-       "       5029607.022446, 5029375.366088, 5029143.70973 , 5028912.053372,\n",
-       "       5028680.397013, 5028448.740655, 5028217.084297, 5027985.427939,\n",
-       "       5027753.77158 , 5027522.115222, 5027290.458864, 5027058.802506,\n",
-       "       5026827.146147, 5026595.489789, 5026363.833431, 5026132.177072,\n",
-       "       5025900.520714, 5025668.864356, 5025437.207998, 5025205.551639,\n",
-       "       5024973.895281, 5024742.238923, 5024510.582565, 5024278.926206,\n",
-       "       5024047.269848, 5023815.61349 , 5023583.957132, 5023352.300773,\n",
-       "       5023120.644415, 5022888.988057, 5022657.331698, 5022425.67534 ,\n",
-       "       5022194.018982, 5021962.362624, 5021730.706265, 5021499.049907,\n",
-       "       5021267.393549, 5021035.737191, 5020804.080832, 5020572.424474,\n",
-       "       5020340.768116, 5020109.111758, 5019877.455399, 5019645.799041,\n",
-       "       5019414.142683, 5019182.486325, 5018950.829966, 5018719.173608,\n",
-       "       5018487.51725 , 5018255.860891, 5018024.204533, 5017792.548175,\n",
-       "       5017560.891817, 5017329.235458, 5017097.5791  , 5016865.922742,\n",
-       "       5016634.266384, 5016402.610025, 5016170.953667, 5015939.297309,\n",
-       "       5015707.640951, 5015475.984592, 5015244.328234, 5015012.671876,\n",
-       "       5014781.015518, 5014549.359159, 5014317.702801, 5014086.046443,\n",
-       "       5013854.390084, 5013622.733726, 5013391.077368, 5013159.42101 ,\n",
-       "       5012927.764651, 5012696.108293, 5012464.451935, 5012232.795577,\n",
-       "       5012001.139218, 5011769.48286 , 5011537.826502, 5011306.170144,\n",
-       "       5011074.513785, 5010842.857427, 5010611.201069, 5010379.544711,\n",
-       "       5010147.888352, 5009916.231994, 5009684.575636, 5009452.919277,\n",
-       "       5009221.262919, 5008989.606561, 5008757.950203, 5008526.293844,\n",
-       "       5008294.637486, 5008062.981128, 5007831.32477 , 5007599.668411,\n",
-       "       5007368.012053, 5007136.355695, 5006904.699337, 5006673.042978,\n",
-       "       5006441.38662 , 5006209.730262, 5005978.073904, 5005746.417545,\n",
-       "       5005514.761187, 5005283.104829, 5005051.44847 , 5004819.792112,\n",
-       "       5004588.135754, 5004356.479396, 5004124.823037, 5003893.166679])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.274e+06 ... -7.228e+06</div><input id='attrs-ee56b91c-468c-4d4d-ad20-772a210aedbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee56b91c-468c-4d4d-ad20-772a210aedbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a47375a5-e337-405a-8fc2-ef4176e4ba9b' class='xr-var-data-in' type='checkbox'><label for='data-a47375a5-e337-405a-8fc2-ef4176e4ba9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([-7273893.821307, -7273662.164949, -7273430.508591, -7273198.852232,\n",
-       "       -7272967.195874, -7272735.539516, -7272503.883158, -7272272.226799,\n",
-       "       -7272040.570441, -7271808.914083, -7271577.257725, -7271345.601366,\n",
-       "       -7271113.945008, -7270882.28865 , -7270650.632291, -7270418.975933,\n",
-       "       -7270187.319575, -7269955.663217, -7269724.006858, -7269492.3505  ,\n",
-       "       -7269260.694142, -7269029.037784, -7268797.381425, -7268565.725067,\n",
-       "       -7268334.068709, -7268102.412351, -7267870.755992, -7267639.099634,\n",
-       "       -7267407.443276, -7267175.786918, -7266944.130559, -7266712.474201,\n",
-       "       -7266480.817843, -7266249.161484, -7266017.505126, -7265785.848768,\n",
-       "       -7265554.19241 , -7265322.536051, -7265090.879693, -7264859.223335,\n",
-       "       -7264627.566977, -7264395.910618, -7264164.25426 , -7263932.597902,\n",
-       "       -7263700.941544, -7263469.285185, -7263237.628827, -7263005.972469,\n",
-       "       -7262774.31611 , -7262542.659752, -7262311.003394, -7262079.347036,\n",
-       "       -7261847.690677, -7261616.034319, -7261384.377961, -7261152.721603,\n",
-       "       -7260921.065244, -7260689.408886, -7260457.752528, -7260226.09617 ,\n",
-       "       -7259994.439811, -7259762.783453, -7259531.127095, -7259299.470737,\n",
-       "       -7259067.814378, -7258836.15802 , -7258604.501662, -7258372.845303,\n",
-       "       -7258141.188945, -7257909.532587, -7257677.876229, -7257446.21987 ,\n",
-       "       -7257214.563512, -7256982.907154, -7256751.250796, -7256519.594437,\n",
-       "       -7256287.938079, -7256056.281721, -7255824.625363, -7255592.969004,\n",
-       "       -7255361.312646, -7255129.656288, -7254897.99993 , -7254666.343571,\n",
-       "       -7254434.687213, -7254203.030855, -7253971.374496, -7253739.718138,\n",
-       "       -7253508.06178 , -7253276.405422, -7253044.749063, -7252813.092705,\n",
-       "       -7252581.436347, -7252349.779989, -7252118.12363 , -7251886.467272,\n",
-       "       -7251654.810914, -7251423.154556, -7251191.498197, -7250959.841839,\n",
-       "       -7250728.185481, -7250496.529122, -7250264.872764, -7250033.216406,\n",
-       "       -7249801.560048, -7249569.903689, -7249338.247331, -7249106.590973,\n",
-       "       -7248874.934615, -7248643.278256, -7248411.621898, -7248179.96554 ,\n",
-       "       -7247948.309182, -7247716.652823, -7247484.996465, -7247253.340107,\n",
-       "       -7247021.683749, -7246790.02739 , -7246558.371032, -7246326.714674,\n",
-       "       -7246095.058315, -7245863.401957, -7245631.745599, -7245400.089241,\n",
-       "       -7245168.432882, -7244936.776524, -7244705.120166, -7244473.463808,\n",
-       "       -7244241.807449, -7244010.151091, -7243778.494733, -7243546.838375,\n",
-       "       -7243315.182016, -7243083.525658, -7242851.8693  , -7242620.212942,\n",
-       "       -7242388.556583, -7242156.900225, -7241925.243867, -7241693.587508,\n",
-       "       -7241461.93115 , -7241230.274792, -7240998.618434, -7240766.962075,\n",
-       "       -7240535.305717, -7240303.649359, -7240071.993001, -7239840.336642,\n",
-       "       -7239608.680284, -7239377.023926, -7239145.367568, -7238913.711209,\n",
-       "       -7238682.054851, -7238450.398493, -7238218.742135, -7237987.085776,\n",
-       "       -7237755.429418, -7237523.77306 , -7237292.116701, -7237060.460343,\n",
-       "       -7236828.803985, -7236597.147627, -7236365.491268, -7236133.83491 ,\n",
-       "       -7235902.178552, -7235670.522194, -7235438.865835, -7235207.209477,\n",
-       "       -7234975.553119, -7234743.896761, -7234512.240402, -7234280.584044,\n",
-       "       -7234048.927686, -7233817.271327, -7233585.614969, -7233353.958611,\n",
-       "       -7233122.302253, -7232890.645894, -7232658.989536, -7232427.333178,\n",
-       "       -7232195.67682 , -7231964.020461, -7231732.364103, -7231500.707745,\n",
-       "       -7231269.051387, -7231037.395028, -7230805.73867 , -7230574.082312,\n",
-       "       -7230342.425954, -7230110.769595, -7229879.113237, -7229647.456879,\n",
-       "       -7229415.80052 , -7229184.144162, -7228952.487804, -7228720.831446,\n",
-       "       -7228489.175087, -7228257.518729, -7228025.862371, -7227794.206013])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-385e9570-f830-4782-83c0-4339e8e097a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-385e9570-f830-4782-83c0-4339e8e097a0' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>+a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +units=m +x_0=0 +y_0=0</dd><dt><span>res :</span></dt><dd>[231.65635826 231.65635826]</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodata :</span></dt><dd>-28672.0</dd><dt><span>transform :</span></dt><dd>[ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e+00\n",
-       " -2.31656358e+02  5.05010861e+06  0.00000000e+00  0.00000000e+00\n",
-       "  1.00000000e+00]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f6aa6241360>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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uB6zEZc3X3oZ8TcMAaE+UxpfBIMLinV6QTS4l7PraBaEzRsLIqEbskkvYpjhgZEZaxoJerHGdceJN4vzAMaRIY4AQ2tg7BiN08FVy+w7zBPMzGdKVD7FI0rZ3y+8zDIMIQki2MwroX/PJ7qZLSJcGy60gLGORXv3rBqSB6QN88YIIk9j2+jN8sr0bXqlgVWL2vgvRtSc2J8LhAIO2sMn5tQT5pEU98EqiHCknGDtH/LtynadtOeYwm1j61YjfO8/DXrcITjIsnKvAM5w/qNG74kMuojAAYHKJt9t60j+gfOIl5/qsAbUG1TofoC0U8iMbgrMQWLJelMkSpAcrrM6xBDp6KIQSAZuf4d9JqI6fs8Zyyy9Rs/RzpRwpTh7b95OH+NzrhShQipLQ5Tp/v1r3obim6xP1xX6soASYIUptNSAUBwazc8E2wSWI4SRJ5qZHGL1Q4ejhQGsFnk7niO9LaBCE66LuKxR7/r43XeU88OUWK/dwXeoUyGworekQEAAWZK3Ktdc9wvBa60Jg1DJ8t+L0EPIJ/z0eJXitwwCozUlO4807EgWzNsDO129heLXG/tt4BZD9TxZT24nht70b7M662HNleLLb+d4WZJEadnKsWBCIlZuuTITyYPggoRx5Id30PMyzt6MBopdBGMOYbFNQFAMOkSgAhzCaQsJrPnwCAPOzGbK5cd5OUhlMLyikVniWGz5EszhNyKYvt8AkETw/o7D9iaXbd31mhMWZwgl98ZoclFMRDAUWJdh6D+HFg5tecNQ9xfcqTOL3yFnl1MZ1DS4MIddilcvskhdOojAAYPSiIJ3sB3azbgCT1Z1AiNtagMaG5HrXVzA2/5IsG6DRqLd8nK/pJVjZuHrnKAY86JSQrHQkuGfnUvcckwqYnlfOExGPsLbzqn+Nn53LgWQMupCQkQj1MO9R7Ps8SGPnbTSUD0kCbCTIvayGXrgCPvcWhsOml3L3eT7VWJxS6N22QADrOYtHYlJESqKzV6Lp85psuspBkAFgcF2jHCuHssOMvdaZeNcKOHok8DY7Bv2XlFuzAKJcG88bg85hfPkhUu5+jTe/ynjzVYSfjJNxMk7Gm3IYGLT3+O/VBhH9JBHtENGnj33+nxLRM0T0JBH93eDzHyGi5+x33x58/j4i+kP73X9LQSOhVxpvWU+jHqTY+fot6Bw4ejjD0CIuZucV8olx1mu6NFhuK5f4Fis4XXq0Upv5grukZutNEpE6Y7itWEUStxePYXqBH4EkIru34zqHNmdIqFi/89OE+Wlfl9Hb4fCYDn6jE3JIkO5+G3kxq/UMw5cqNH1r8U40u/59STZzcjNMcMrIpmzJSwhHwnBhmOLgHV1noRf7OsLLpyuDzlEbWaPlOHHWbpvHxYOjF9lMlnDU4lRcOwDEiBeTAKMXSxw9zK6hWOMSYikODXbeH6DcdimK0x/3MACgf8NvYIgwu+RN73RpUA0U+jd9Bji77RNciwfXXBw+nbeo+z7U1uZkiyN97qIpkghim1T+WuXcZN5IkWeYqFcN0LttIbEjQtP3v1M1z1UJ4bQdTvxKWNKFJ4vQ/YlhuWUAXZbcSGiNJ6sY9hzmlpLSYHildXDZ1VYGFYSjBi+tQK0tcM0Uml7q8oJuzQV5wP7NJmJboNZgeJWflUkJg+vAlW8NCjtbn9Oo+4Ry7C9z8RU8oTf/jQ8phMW4UvD4mocB2vu0KwA/BeAfAfiQfEBE3wymWnqPMaa0hc4goncC+D4A7wJwDsCvE9HjxpgWzN/3gwA+AkanfgeAX7rbgd+ySkO1DBVNKo2mUMhmPJnXn2lRjRMXHirXKIpp5hMD0iYSfMVejcbG4XVKEUoFsKEgCS8lhCaJk4jLU4hQMd3bxiGARKg5oZFYKKSVTU2PIb3i5uuUMHrJC7qma/MFQQioXE99FbcipzAA3q9qPcqrfz2IM9u8SdPz524SoH/D5hrWVCRwJw9y/UnnwId9lpsJskCYhOGS7l7LdRkSdTgv4Qkfew5DOvnURMV/oxc5/rD+tH9gh2/rRfDi7Y+HcXNC0/Mxa1GQIsiLvQadHR84nz3KQW8nDI1BHtRhJIsAPgQgXfnvVhsZqoHyISDDOZdQSLc5OSHdFoQ2oJxRrQ2ZBkojXRpH95JUXPcwP2sBHBVX1jtmgCLO12RznmOu2p04eR4mfUN4bLYwXFndkTnAfwUtBnAoV67HKL6/RQDHThctiuustYrrAFqDdp0nkygMgKvjdVCd3XQUsrnxTAjGxHUVCaHpKhcSlqK/9Sfj65VBGigOgOm38yIa/DZPdjHcAGD0onbX+jKo8hc4DO5feMoY82EievDYx38ZwN82xpR2G4tEwfcA+Fn7+QtE9ByAD1hm8JEx5ncBgIg+BOB7caI07jxIGySVx3OLcNV5TBvRva2RlCaG0SrC0saOR5crpzAAXvgh740r4pOkmyTZxRo/0CgOvPBUdYw5bwu2pqWoEGDrXixsnVJkbZMBVpupg16KshB4I2lEgrd3o0I2TFyhnkAsxbM6+M4FBr/FkkSs2jCpHy7G4dUGndtLXP+jQ3vt/Pn8jD/g6Ip+GcpIhliUkhjv7nGdRyggurcD4ZKy8BRvIFmy1D94h5d8zAdlrf0FX7zcE1Ub5Eex4gr5ikxCuPat6xhdtrQTDdfT+HtqouLDtpdj9ZAttjuqI1qQyQPsHYoFy7kG7+mIRyHCOCkNqPF8SFxN7T1KaiwCKhHjRiFd6MgzWG2Qy0mkS96ntnO76rKnE1rvs3OEwnaIyecMWAjnbzlWUaGk1L8AL0cg9W630Akhn3pXNT0KlKqsjylrsmqr71kYenzRUoMCCA2LP1fJ0YVDjLX+rQbzM7FoC713URYbA2stfPsCBx895YyH0Ys+igCwd39/BqG9G0HXax+PA/hGIvoxACsAf8MY8/tgfr6PBNsJZ19tXx///K7jras0WiCb8IRebXqzP1kxFM/BM0txjYPq2iCJNnkwj+CCUtMghUWGLAY9EHzqWEgkJkM06O62WNpj6IQFoyzmthOjp0wCNCk5AS3fSbI5qSzef3ZnG6ceJtF1AoRqACw/6C1srmfgMbhqMP6czyjml2+71+3pNQDAhkUCHT3M06sICsDCZGeybFAPMrRWYYv4Xa0FfE7K8y11Jjoq6Bq8yJpNVbyByRIsznXRC5LnIO9tCWghtO6N8gK7zQDdUxhe4Qe09y6eF3vv5nuUTYD+Le2Sslx5rNC/wuexOu1DV0cPd9Dd0zh8mH9brck181/VsCUv4ajubY22IIf4qvoKTR/IjzyKLjRm8pmGTskR+VVDQpsp5w2SRsSFRYYNIlH8hfW+ltt+++LAI85EYYiC5Op846qym67C7Fz4nDj807vtlejguUO0I7ZCqGpBbQuT2dqbwlaoD700X27z/TZkASjB9YaeshhJIVRdzsmfkH+ZrngNiNfV+zcDDll+J2uJg4+ewsZT8frQGd1HZeFPqT4ehnjlsUVEITneT9yJhPXYSAGsA/gggK8B8HNE9DBembPvlT5/1YO8pUe1lr2MABDwIQix5EJBEyF0jhECCnIonBtpGVe+hmRrquHfeCZVi/kPitKqgYoWo8SFAeDwUQv3lDoSqYIOOIKyykN2Scdek4R3wqK8cgNIPuOtdSn4AuLQT/rSbSBN0J5Z5310eTqJsgDiYrj1z65ATVgtz0Ii9IYWW4nLp8j5jW1V8fB5thDpBa62ah+/BFU1WFzw5xp6CrIkRBCu1hPoNK5gzw9rTB7kWLbO2LKfPMjnVewZTB8gZAFKKFsYjJ86BABMHx+hc9CgsdfRuxpgQMHnNLhuj3WdPciQvBHGW+vlWCEtjcs/Ucu1De65EJMxiuXfWAUjXFVJyduKQVIP4vyTKI+IcNIAw5d4LiW1QTVMnEAmw/9yC40t15NISM/OKtRDX+w6vMrX0b3GH1DVOIXBByCYJHUexuICh6Wiyu9gjXT3AvqYURKFAQU5GFN/BHNa2BdW/lqSoBARsB7rh5gTcKtsI3JGwCISA9j9/RgG+Hw8jd0voJHcVQD/3BZBf5SINIAtvDJn31X7+vjndx1vWaWhM8JyO4fOOEkd4uMBH1rhD71bz9/FsV7AL26x2I9TbrvXCUUCOkyo2y1A2qAc+xWqM2B2jt+vPccnttrKon2HcddsZqJzjGjVwYpDCup0l72g0BORAjHAJuZtfnBwvWVFYYfUXwh8cnC1RLmWRYnD3o3KWcOiMKSugWqD1VbqFHJTJMcS+r5+AQDqUQf5E8+DyMKHn72K+dc/El2b3E+APZlyI4/u5fj5EumM72EzyJEsa6w/bROollb88G0+aZMfebhqb7dF7/LU7+v32LPX22vus7bHz0Ws5CjU2AFg72V311juK6swlwbpUqOWwkbi7UUJdHdtuLEX8GkZDw3nC/DzjjTzm7mwV8FhzHAuqtpT7jcJIV1oD3dO2dKXotJ0ZTA7q1yuoem9PLm8+UmvXVfnR9GaEa9e6N9Va5iY046kNtB5kHcLilmzuaXOCc59do49MRkbT2kX2oPh8xUvqrfDLNNZELpLKrhwF2let9NLAR9VkLfL/CN/zUPfu6fxhYx/AeBbAPwmET0OIAewC+bs+2ki+nvgRPhjAD5qjGmJaEpEHwTwewD+PIB/+GoHecsqDecCd4BqmLoJKQVkoXVRjhNv+RNFqA8JTYnFXo4UioM2snjDCmeHZrH7CAvv3PYd5ZA/1ZCicJa48JH1QzFZXhYoAI+H19HxBNWT1MbRqvN1EAZBx5J8rp0i7F+eQp9aw/57GHpSHLSoRok7XrmWRZZj74ZHaQFs6ReHnhtKRtfyTc3OJ4Dx1nFureN05jUibW/i8KvYQszmGje/xk/hs7/XoB4kKG7zD48eYUu3d4sFVrpsncIAgOxgCRiDts+SfH6x667L3Y/U77+4tYI6CNyOnDWcuunjb6qwWuHMaUweUFHyOSkDNJK15KUAL1tyhbcMo/CyyvZ0qbGydN/JigWdeAZJxcWO4lE4YsGRT+ZS6b0+zq35ubxaVygOtTNo0qXmwjt7CtOL7CUJi+72J2tMLmUulJfOG1Qb3rMQhSEoQrPGEzYuNA095swpoKaI57OszTDHUY98jk1CS93b/JyrUYLZeYWSHWCU64TR8/64kuAXT2W1wWG9ha/xdbVHANAMcF/G5+lp3HUQ0c8A+CZwGOsqgB8F8JMAftLCcCsAf8F6HU8S0c8BeApcHvrDFjkFcPL8pwB0wQnwuybBgbew0iDNQiefAdMLictF6IxpxSVu3XbIFr/5ZKpRFFmQ6dKgZwWfTuAqngGf1BNlwR6MicnqxkmURCwOtCfVgyRg/bnrzHtC3X2GsUq1bDbTcR+EytgeFl5ZlKMEnQmfb5urCObJVdasLICYvO/g3WNm9LXTrRolrj8HwIo4XWlOwQHOchQlkS7ZupTFvdxOozDc2nMsiMJGVIutBIVVRMOn9pzCAICr35Jg8wkfRxfrfv+d3kwcBkiybH8RNT2S+Hrbl9xLjXLNa+N0qbHxpA85pddtYqkXhF0WAQlZ0cHyEU4A5UcN+jcyV4Us92xqq8ypYaYBMTyXGzzPXJMkS1ciXljb4US0FNRRy0JPwjLLTQ5XiVeoGkbUHaciFwNE1Xws6Y+hKj7nbMbPZvc9PTQFoOyzGFzTmJ9VGD3vJ+L6sysHPlhtF6DGILFoLrKgAaPCzkYB2o0ISWNw+Kj3mJebgsjS9p6E9DH+uc0vAGd+1z/X3h9ew+I9552BBLB36O+ViQACbYdYKQZw8OV2APQ4sKdrn81xgsovdBgQ2vtUGmeM+f5X+OrPvcL2Pwbgx+7w+ccAvPvzOfZbVmkAFn1ChP6N1odwDMeXXSVuqbmWwVpg0ofCoU6MJ40DeDFW4zRI1sUoFaN4cYcTNqrSPWBrL6wfMBRTZOvMx6hHVxqgNR6RRAAycjQQ5Vo8SVPLqOvCHEOF2UXPVtq1VPFhfw9ZvMVhG31H2kT5GZMA5VrilIJJuZI9VJAh2ohaBhUIRHhyyQoQgReD0NvxdSblB7cjCpDNJ4z9XRrtc/2z3rwP6yZcjYxVFtPH13B8DJ73noS6vovmobNeWXTZLdAdazUfTNCe3XDbt0XAAbadufAIwJGQaow4pwGvTJKVcZxIgG/CVAdWbjaDy9O0RczXVA8AaHJ1GumSw5xiDC22EgYC1H7/1Bpn3YdWPwCMXqztdfhrOvUJf1+TWY1yq/BcWvY5t4VAfq33aZ+datmYkuudnU0AJFEeQ5TF4SN2jdn02fAqr0/JRQ2v8k56f2iZBKsKvY+9gPJbH+W3QxXX8jScBA9DuEIUyfcCaPpB6GrFJyU0JXfrnvj5DE6Ev/nrqd/SSoM0YJQByMdLqWWrJmwNapTPWejUFlHd8qtfNb5znvAmCc1zUvLiDa3nsCDKKClCs655N+b2l7agUpexWqeX0SHolDC4xou8tuEgsSAdI+0sdvcl+Tyz6TFXH2C9GOEGMuQL3NquipSJa9NqZ5FYyWLxSbLWeUkELE77KSehC8fDRPxMwvDf/jti61HOCfCeRRjGGV32N6caZ8huA7C5itVZlsDzc3y8bB4/Cwmzqes2NqEU0hdvAbn3PnQR9CZ551mstlIMrpR2v2H3KbsLq/z7NzX6N31YcbmpXgaoaHpBKNKe1vCqv7bFKXLQFo7DeyRRPWCFIfdEpwy3lWNkC65tcEi0Qx1du04J2US7jo2sVID+dT4hoalPZvy+3CqQ1NqFH9uckEI5KGzWMKeYCGplkViuCLYyjgPK3YNACYa1UW3BtU/C5ty9apMM4vH1ujh4v+/EevhH2TXY+hVW8jJP6+B6w+K+esSKorBVDU0/5psLSStf2yC0J0rjTTzICjnDXcAk3GPIw2QBtvDDimzSwOBahWRpQyLdBNA+JNUW3HMi5PEh7anVs2UM+UxKjqmK4JNknhRF6RRRuElnXEshbTIlhzC96GPGTTdWLKGl7yqK7fWMnzNR3Qifky+eUqWOCARD5FY9lJvij0PtMaSZ8XH0pohBAH4j/5IXsyDI4oI06eOx+VTtrnn8QhVBLauxF/CGgNlj685qH1yrMT+XOcHKdN2NU179l2aMXlPBws4z52HwzSGsTnH4a2XJBA/ebr/XHhiRVAaDayZKzNeD2CsLC+lEIYZU7iEiqO5RxKYsKD15/p392Dtsenw+RVBTo2pmNwC88pC50d2xUFr77PKpRrFbuXNIVg3KrcJ5ltVagmxGLs+QVNyOtrBed1so6MQDE5jZwHjkkzYoDuO1IPO8f5PnkTrGniDPefrYGKM/3MWV7/EU++kf83ml4a+w9ycEhAAw/lztikXHl0uMLwO5Ldz83PdvOIUBMCKsXAfSmdw33JdhAOgvA+amt67SOBkn42ScjC/yeJ2L+74o462rNIyv8AXgIKgmV2hzirDe6dInktNZi2QVV7lWGx3npqsmppYwipyXAXDhWudIOwtO6EIkZ5EuuUd2GDpYBjw4rrPaIEDFtLE3URwYZ5WDgL2vsC8A9B85AgB0/3fvn+vEF5XlU+Oa3gAAGeOqenVCUZVz62gW/LGXmyoqwEsqCrb35wSwlxU2ewJi5lShZB9/js9nekFh86nahSe6V4HFA96c3HsXH7h3019L3SfXZ6FcY9ZV8cIGNxoU17357lBQI59IaDb6UAtJcBHK7Z7zwhyFfBBmCqHO6VJHBWqhNyeV2p0j/1n3dosq4AkL4/1CE+LoaIi4JWwADSdtHAutWMdCj0Oa57hQt08eyLk/SCPhrQRkEvRu8Q6pNaBaI7E9zZuxpcPf5Hu8Wleo+oy4Ang+kvE5kDbj7n3iSVRDFSXqO0d8DPHIjfJs0q6HzTGoeAjH/sxf2Ybc+PHDhwCA2afthDkPtH2N8TN8L8efq9H7/efR+337Y0UwZ7awOseFG+f/bY3VZoq5ZV2oLOpqtc3ntv1J3JdhzEl46k09yBhQ64vqjKBWKg1Aufi2ztnNbq2QbzYzDC77ldr2MySl5jBVMEIYa4jc0CuPawc4TGEojstL8xiAhUu4WCREESb1dIpIwrSZ39f0IiE/BLL3HQIAuv98bPcbo7NkJJUG1QH6K7guCQ/Ib0mzIlgGnQZDAj2BZzrFIIcMKrDbjhfinYMYJXbq944weXyEtSc4Eb32BIDpHGaDFcXigVHUjyGbA9k0Rsp0Jhodq4hm5xKc+bAPY9CtPejz2xFktnnoDA4f4xNe++zCKwzAhaVCGLFAtGWElC6q0o6a3CSEKkucMdC1Cevw9yH9d9MjpAvPcSbzJeTa0pnPkaiawzsUUG2sP71w0GpqDVanChdWHV6poVqD7IBv/uJCD72Xpl4BJoS2n6PNWJJXaxkns4Owa9hDpnPEAA65vqSy7YGtcpSwqadq54p21xlzS7l7sdxIXoYYDA2Tw0cV/sgHPLHUv3uWa3XMmlWQNeHcvwXkYroffQ4gBQThvsN3r+HGH+ftz/5Gyugze6/Li3ySZ379PlX1BUOfeBpv3tFmhPmZ1NUIiOWvWh0L0ZWGDigMyAAm98ys2uU++G8+1bywg8VrgpyEKISoDafyXcf4IP5lUnI1a5iYPz7CmGtbxMy0gkQa/jOxyGPkFhlmDHXvW295yhBl4QofA8FYJ3F1e9jyk1vXUoQA6t/0DaOaPtOSyFh7eoLDd4yw9pQ3v9d+4zlAOhUWHegzHq0k90Lu3cCiasTTC68BALY/PodRCuqGLVDsFlD7U0y+7gG3Tdhq9/BtPQsEsPj/tXh/x0c5VoB14FRt0A96oRc7S46ba/5s7yvHSGoPmNAJLOWJnYcN1zokAdliqDBMYueQA1jw35BCxaSeA63a6CBdtM56zw8ruw0fv39ljmqzh9QmuskYqGWDes1ngauhB0J0b2suyBNGgYKs927PNeVzdGyxBugcIioeTIP7091tPSdYGysMgBWFjPwD+/jU7bOYvLDGH6xXMHMv4HvXFJKyQe93nuVrWeftbnz7WQCMUBSFAQC3v5pPsN3ga+9cye0xcV+HAaEyb36R++a/gtc4Fqd51kvFrao0OtcnoKWl5X5oE6SVF5ilxmord4lvkxCmFzwVyWpdoXOoneCv+yqquUhqtryrIAkaC9oY4necEhuwISlX6eurcgHuyNbmHuK7/hmGcs5PBUogZDK1iVIhCZTEplxv2KDp+CiHCibxnkLdB/LKh2jSFbe1zSwNyHIrQXe3RdeCk/qf2cX+157G2tPWFWgN1j59BHU9yErqWEvWax0H62w6hGymHUy46VAUGkznLQ4fy7H9cU54Khtquf0nvTToHAaW/lpcsyIFlpJABdiilnWflCZKwjOxpb0229O99ylbNX52E9AaU8uUm8/4vgqU27HZWmG52gSGL/pEerpkw2Z2PqbQGNzw55/O26iz4OKsT+CrWkN3FYodfljUaG4YZfezPNtDstKoNllJqErbkJGvG2Fv2At9CTkB/jrc8VpgvhUUGy5YGUhdTtNjBmRB2oUwdFUDMMaBClbrKipm7fz8Om6/30DV9pg7Ha6O3+X3m7bT4vyPPAaA66CqIWH6IO9v+iDh0Ydu4vIfhAwaQPHCnbsMRn3ZX8M4SYS/yQeBJ2qdE7p7GsPnfDC9OuNJapbbGaq+cgSEbaHQ2W8wPxvQeCi4xZeuTFxc1xhki1joh1XeOqC/BthwTKq4cpeaeBsYb3VmxxAeqjVQQTFSujSAMehbiHA1TND0ySmLuk84ejjD0Qc5GL7xb5OodzRTwxu7b75eCU9lC4M2D5hbW1ZIYY2JsOsCwNbv3gZ2D4B1NsdNJ8X6J/ewvMjvixtzqOs7MHXg+eQZkPHJlG9nS7HuegROWhpXE5BXwOK0ly7Sxe6l72QteeoTrFDSAJEVenyiMCTE5vp1BwWO/ev+x9MHOYwlvF9hfiFdNMifu+mQWOrWAeZf5QlETRJ3X5yfBXo7MTmkzoAqk6JNbmsroTcRqJKjANizWtqKdBHmnUP/MNJZ7fjBYBJUY8+GXI4TdIipXdw5qgDS208YEh2EibK5dnUYSQ2XEwC4KDBdeiWYLZk1V5RQPmnRdpQzMEj7OZzPNaq+cvd1cZq/G9rOiqoxWH9SOfTZ4pzB4KUATmvbBof0MXUfKG7zNme+gykPsgd9Pqt5bohQnkuBH5/rfSrUANC+vjQiX5Tx1lUamgVqccAWVbVp49U9FRWLzR40GFwGsqVNXPcIVT9z9A9JxUJGoI9G2UUQCKOk8lDDuh/DSKXuI5xLIY1zUiKCz3aONKqRL94T4SHssRJCCdlkDXn6kTb3zKkAcPiuFjSsUTzDF7Q4w1w7stjDuLrcN7k2OU/BtA9utFitq6iGpbg+h1pZabprV2LN0mH2FdtIlhq9y4fBDTGgxC52CX0N+/ZeeWUho+4poOer91dBmE+IJE99wi96VWl0rJJZnE5RDV/ugRWeXiuiZMltFb0oCzmP1nIoJZVB/6p/uGZzjN2v4axq73ZMn9J0mXF2ftYfa3bB8xz1dowLnQJskaer+Np1Slic4eeaLjQLXqssitt8HlJwmKwarzAALM7m0Am5EF665ByDnb5oU6b4F4VsEjsnbDGfhFnLNbmvbIiJB5xPTFTv03TIwXLlntV9rwTrHkXM0OnK9yMv9th4cmCRFa8jyZmd+jh7QCGFTdPzNSGTiwrb33bNfVe2tg3AcwGDofHgBJnPQuZ4v8b9rAj/Uo63rtJoDVNtSw2BTW4uN9gVnj3IE3hwWXEiOiA3gyI3YTtHOiJ+g+GqWyFjS0qDahhbVEb5zmGCnpGCNk4mxoqCjPFcVCNJNIrFqZEstQtXdA7amCCRYutbvIXbH/DCcPCJAuMXeIFMHvCUKnyfgOO5O7HgV1tAd4eVhYzxc0snqPLbC58/AIAtFqDTd3pzulpLkJ7mxZt/+kUgjWknAGD5wJr7qOnGNPNZYGnrXKFz6AsX6x5hcL1BNvEuQD3KMbe9pKllpRjWjhS3PRtqPeRueuPn+fdtodAWufOe+N4mruah7hL23s0K5cxv7TuFAQC770oBBUdxv7S1aKFCTgJivGpAqAap+949k/Da5549WcJy+R5PTJMlaIvU5eqaXoblqcw9S5+bqN19DQcZYDVSUI1HPwmJIsDer6p9aDVdWU/B7r9zyDkPKfZLKo26r5wx0nTZ8Cltqo1RbRKOSpCWxoUd207cv1ynhO6ucSFYFyILwB0hqGD+jgrzK9sQjXjh3D5uPHEGSGxNTUnQiVcWgtYTpRN6hK916BP01Jt4GHDnsF6C3fco9G7yx5OHDYpdoHtTSsSZd0dGUgFt7q1A147Tfr/cTNDdbwNaZU4QynuhinCd92wC0edMjEt+A3DhHwkBOdRNWASWEVohIFxp11wKAFbbvtpaRjYz2PgDFs71AE5hAMDopZaJHG2IjXwY24UyJg96wV6Ngan1DLaeWERUGmr3kENLA88FpQdFlGwevhhox7GV1jO7em1RXSj0m56P0w//gB+asYSDR+/mJLko1KQyrpIZYIUBeMTS/EwCVfuqeADY/+oWw2dt/4wZsPasP796wEWYoVdY7GtML/jqeyG9e/7PbETFeGJgOsu8jkOO+VHMrCrKIux8R63/jVxjsW8JAw9WQEKg0iayyxo66znup+Up65FYlF5nb4W2m0bKYhkQJqqahfHtr/YhO1UBvVv++6Qyro1xNtdYnEodUm56McHgWuu9sVxZSh3jzkMngdIkn0/jvinkKEqyuYlyJvL8QsDKcjNxynxxSmH6cOAhXs1RXSqR3uLnf/PWGSQluWdCLZC0MUlhuL5CNONrGRqE6pUShG+i8ZZVGkyNnuLgHRJW4s+3njgeHjIR1l5e++Y0L+eSYmoPfi0hElkAcpyw33IIt3XHsbK+6RJ6t+I4c1L5BGw+bSO3fH6WqcZdzwjD24fnFy7A3o5BNVKOmNAlPkMEmbXyHHV0aO0GglH6Y/R+/3nerLLCXpQAgIPveaffb+XblUbbCbNsHUNoys0O+s8H5vhiAbO9junbN+w5s0Ur9yOfNA4NBHikUsg7tFoHmp4VbAON07/tE1QihJaBpwZj0LvJoZ/ZpZ5TGIDtQ2IvWR37K1cZooI6t8MwG59zWLdB2qOG1p+JjYFit4LOFSsLO3QnBVleLZ0pzM8X0b4AoLPnlaDk5QBguUXIg2PPzgMAuX4ZRgHd3QDFp/n5yT2an0ltTZM9v8M2WjfpSsOQcuipcqS4x0Xv5coAkLCt8a9bX4PiuhUGa2j6ACBaQNVAOve9S9Il0O74Ra1Kcbfsue7xcyoCUMT0XOoMvWqM+zZOEuFv4iE9jNefjl1cgOG40sdaLCNJ0kloJHRZ6wFFgjQ0JpKKIoZOgF3nfBYrilbJeZFjmOXjxwSFRgklgz12T0WMu541VUX7CNt6xv09+K+49v0bFopJfkFK3Lx3S0fQx85B3PLzZcoCiKDH9XsfjorfOvuWsvwF6+YlCddgCAne/gTtxW33DIod26nPhrzMGc94C7DCaMNmOvbUFufY/D14TJA69usEWJ3y5zN4LrGoHf7h7FwCnEsiyPD53/SkSG1O6O0YJ1zqMjQu7DHC/iBhXYWEtHqh8eCNjKZLWHiWDJRjhbXnKqdk02sc5zIDm4wfdaN8R9PP0DlsI+bX7u0qUiTLrQDVVzJce3bJJtgPCMt3rZA/7/mb8olxYa3eLnsREn5KVyZqRlaOOBQlHS9rG1b0NCI850SZJVUMIImg5WL/hGCSQGG4PKEotJbXq9CIOOqzJoAzp3AoPoDvu6wZWesrO72Ow3+/0GEMTor73szDKIa+ZnO2KKMisaCdKtke0yH0dbVJEWa+2Dco1/2EDKmU05WJwj3ZgovPxKIvx0yJLW5690aNapy4Yi63H+HwKRSyWesSmGJ1SULRx4UDKGggqMPmM4Bnwd142lug6cS/vv2+cXQ/hi/515OHCf1bwPj3b/C5WGVhWhu6Uwrm8Qfc9vMLBTOvWmXRffGQv7Dbm+11pzAAoHnwNOphhtxa08lNTqTriyxNqW4xfcybgdWQKealHS3nIDqWUZW9otbLTCQV0Lvm+3ALn5f0tVCVr0oHOHwxvxCE2qwCCBP/Al0+eiBBPvXhKMDawfbyshkrhq5torTcIBQHAReTYcHatejjtefEzLZK5dwGkknM2d127lCIGTALLE4HZIunlfMKAF4Ps0sGzchCYkdAcsvXaIxe4OMOr/Az1pY5wfXfkNyQiY0sF9osNaph4oQ9tcfnZWjUEIcBlVwXN4gKmyQ1XQ8oyeYc3jt8hxgXhHoYQ9ezmfeaXO/6C+E6D+7TOcTXcN8GnRT3vZmHtLNcbRLK9cSxaprkGA15Qiz4cz/Zw5BMsd+iHCt0bK2EzgggRKgQwNN0tB1C0/GWlBAkSgy1HibHEEqEzlQH1BUaTVc5q0tZeuuQ3E41ca/miNCvz+eztFZssccWZEj0d/BYIFnhrWHxviYP8/uNpzUL3Km/IaIwAAAXz2H+wMD36LYKLZsGq/HgCBiyKU+rGtXFdYfYqYd8TqtTbE1Xjw4wfmaKm1/nqUN0CvR3wnsN7L5Hqpg5sR1WiKtDf2iBiErYY2HfhzQo4bMWb03CRN09OEsbYIUxt3U/6SpulCWKSUKNjHTydDBJxZa/HG95iv+K17k4m7ue6DKqUx79Q63UPwhaSmN5yl/IaoMiU51i55d7hN8mzOytTeYKm58CROp3bzegNu6uB6IglGvnyExyFuIeBIohVxhesWiqHiud0DuR+5WUBjDGtyeQuh97+XXXGmb2VA7e20AtE5gtNhbKMwbqha5b0zq363rNn7rOQ69M4+ArDTq3bFfBhgkgxy/cJxfDDk6jnngaJ+NknIyTcTLucZxAbt/kQ+CkRvlmN23OVp+DElrr2jWpb30vAIARJyFPjiSUfaWsglEUVR7nU+OQIkJdLU2RhH5BrN+kZrTOai2mH4mS5wZobdREp4TOUezac09wu22fvQwJNwBxr435qSTKyajacwwtbFOhrU9ZXq4MWPu1Z2FKH85S62swlvBv9nYLObXWZjZrkU0qpJ+96k+9aUDWYK4u8vY3vq5n7xMwuN5EcfmbXx94GQl3LnT9PITMcc2fv87ZmwQ46S9wYcCHKVofhWHUkLVw0xV/J4WQ/ev8g3yX4xyTt/OBJFQY8k4BHIrMFnIsE8XwAfb6JO6uas4riLU9slX0w+eDxg5B6K4a51C1ceGw/MB2Q3wwaAQVEF22BVvqoYehE38PqhEwv6RBtsp6cJkAGNdQC4pgFPl6pITieyleZNBki7cV4EjCOSfrqWRzjWzuiUKbXuLOzRCQBEZ+umix2khdF8b9d2SgFphdsmttpZCc87km9TQX3KxO2e9P2ZqVlb83SRGQjr7YQ1IC/ev8nlpg/LnSwfDD0PVrGQaE+gQ99eYfmWVCbazQlUUkieqmw30ApG0nwInvULA0BWH9WZ6g1BqUY+XCRdWYMLimo/4UAFz4qBwpVEPl3HTVAGoVFCoR/5dauZzNtWtuJINDXj6EFCbpk8oAxicOVWtw5iM1skPe4fJ8zyHBAMtdVcfxXBEOZ397ivnFPkYffs59Fya9aTzC4it81XPb4eY5RdDZML22B2OEsVADF884DMFyi2MdeQCQml5MXZV0iO7he2hx9EE4sRr5PghAHNeen47DT9mclULUHW/umXYF/hyCCPLduWvmlB81qEapZ0o2vmalHCk0BYcWAWCxHfN3pYsYlTN6Ke5nAQCjZ+dQS6+QV+cDhZlz/VBU0DbIXNOkcj3F6CWNg8dZSPlrstseY8gt9g36N8jN89EV3xfDjUAnNgVFfbyFVidkQYYxTtGpll8L3xTVBknAPcX1S/ZnCSFZafe+GqXIJy0OH5OeMVw5371h0XDvKNH/iOfGaS3sV5QFAGRZiyzja6rKFLpWSK/6MGyx59Fb/Wsl5uc66FhFnJT3xzswuH91GkT0kwD+JIAdY8y7j333NwD8VwC2jTG79rMfAfADAFoAf9UY8yv28/fB9wj/VwD+mu0r/orjLas0ODdhXMJZ4qXJii3rEJY6P+c7+xmKK7rzI4PubUSx3aZLLuHatd3UBEWSrlo03STiguruti5u23aUjef62HC5lnglNGB6BYkZ65SRW5ITaQpiz8A+dqF5EG+hd9MrDLmedKkxvSC8JWydu/qTDDj1+16KD3/9aWhb0U1JAmRB3Pwd5xh+vBXUajRAsrKFZy/swATw2+Y9DwMAFqfZ1JcGQSFdehPULohidYl5UcS1f1a9HeN6TQsnlxPECSsMsf5Btrp4IPu3H1tYaFJzXL2z7zXo9LGxoxsXASmopemF1OVP5JiC8Scd50rqITC8GtTTWODC6AUvyZPZygEdRGGstlhw5hOGWrsCtM0OoI3zeuQaRi/yMfbfoTB8Mb63OvMeqyiT/k07rxKpwg6g5AFNyNrn+B6IwjQpRczOMALuCJLN2jiakqTUTCMikGtjnKfR9BIsNxM3Z+dnFNpcRYaMAAQAYO33ef5094QSReHomxagl/hi1cNzrHa7QJcPkF3j7aU2i9oYBSgFmulK8lN3laGfx6D72U/jpwD8IwAfio5AdBHAnwDwUvDZOwF8H4B3ATgH4NeJ6HFjTAvgvwfwgwA+AlYa3wHgl+524Les0jCKWUTFowihmtWQIiswNA66tsBZ3PGeLfKT8FK5xgVgooTSFYeXZP/tRobldoJiLywY9JMyXWnMTyVOaIulJ2ELQ1z4JyEbWfSy4JKa0SdyziJ0Qwjj8ryXxGR4n1J9m09bzM6m2P645+KiFywFQ6th6hhSQp0OVu9i4rfaUnKEfbC7t70Q1OsjzL865F+yilCq3EsTsf02Pd+7HPDKogo8o6YPh4phUIBGwSArpIsWB4/lMRlgsGaFSmZ4xX/fZj7hD9jKZVunoSqD1UbiwhW92y1gDKYXAnqOQLC6Kmk7ereCgsxU7g9v0AWivhydGxOUgWex2kwBItd6tNlOoroCaIPpJZ95P3rUYPsTBvvv8Pdq6oFsrkjPcVlZRRiCJgx5GhVGSTHEGPCFnn6eJdE6SUrvZYRD5oj0j69zG9prgzWS85pYnPIKN3x26TKupZLiWwEopAuN0T9NcfVbrJfzqQEKwNGokAa6t/zvi32N1ZonX5S1XY7uj1cg4356GsaYDxPRg3f46u8D+M8B/B/BZ98D4GeNMSWAF4joOQAfIKLLAEbGmN8FACL6EIDvxYnSuPPgHssmrny2I1IYFIc7hCRNrNv5qdQpDgDo7bSA8VBW8WRaIZ5bMuRWS3/rhXZxXQCYPuD7VwOe70j4jY4Pt2i7Eju2gtjmCUzGtAi55QDSKSFZtoHATtx5yNj+vUN/n67c8Mc6rjAGfDPSee3+zs93I7hwOfYhgO4eX5sU2QFsfYbbhzDQ4Usai1MKfStsBWkkln09JEbI2J+LAJSWtJNLOZLS36N6iKhQkjTDRCUf1QaKGWCldrzqGAD6O22wD0SKKGS+NgnQSh1MFyj24RpcSc8S4dNSlUZ25F3Y1YUxyBgsTnlFUA3IeZRJaVxICIBTGEeP+vPd/78uUO3z/c930oitVWhJwva9Jo1zLiFcN6mA8Qtx24BQ/uWTBsmyQbnB0lzOzRk/XRUhzUzC/c5dv3fl273K3Aw9M8krARweNsp7kuWawvozfuKoqkU1znHhN9hgaXoJDh5PXvZsZOiUEZJRbYi587avdXwensYWEX0seP8TxpifuNsPiOi7AVwzxjxBscI+D/YkZFy1n9X29fHP7zreskpDp3FxU+eQ/1aDmKo8m3Nj+XDCkUakKKRKG2B3XSfkmDHFKpWJVw0I/VsxfXfbVc5tF4p2ifeqSltoYyDsjA9/SP5CKm0lh2HEYtesSFRjE++7fHHVWua2T1YanX0fsqLL11zfB9whvEnSQ9smvEMK7giubIVQz1GuCIbfuHuSLrQLoehMYXFKoXtbalIInYlxIZ6QehzgMGGbwykVsTQPH8ntcY55ibsMYJDEPysFryyAOBTROeIeD6UN0SSO8dXvNMx1hcZGsnp5mG3vKxTGn7NJ+Zs6ssTTeYV6vXBFlfNzVsGK4DYxtUW60Dh6OHNC+fBt9ryDOL4oDADYfJI3lMJFH34MkuW5ipRCuvSCVKesQCUnZwjIDyo0FhadLFnzyvzia/aLJlROQMAPFigm8SRVbbA47XmqVMXXLyAQof0Qj2D4YgWdKkcZc/h4H+NnFyi3LeNvy56/GCSuq6F4rkMBSvj7u9wKDJv7xFtoDH0+nsauMeb997oxEfUA/JcAvu1OX9/pdO7y+V3HW1ZphIMVCL8u9jmZKvFtQ5bJNkggbjwTxF+0FdJ28c1P8y0V9tL+jbg7He+DkATCKVn6pJ9qWIirgDLi8LHcoWyy+THklLIWdiDc05WBsiXmhuLis8mDHSQ1MLjCwqUepOg96b0JaAMMB9B7HBeiNIVp+PfCPisFezQvcfDVviq77quoaleE7JXv5mu59C8ITaEiMrnlhvckSBv0b7bBPgh1jyJvqun52gmxRCcP8AadQ0Kbk0fhKA7rhaAGICiInLRYbiYRlXs4RLA5AZtwDU6oZKI+DwdeUSQr/idCt+kB488Z9G76g2XTGtTYsNCQFZ1TFh0CFKI6hvywdfs7epi3mzwsghbQ573C2FyfQf/mtj/Wwrfs5RPk++Pqh3IFk8JxmAlKMARVGEVOoUsTJ+EFM4rYuAkKDB2JJ3wnS8nNCWV/bTtD5jMTgQCivOFMwpLkvssCzxgADh/vOE9TtcD0oa4T/J0jg2Lf30eZN1J7lVS2CNcev+kQKxT7k6R+VTl6T8MAryd66hEADwEQL+MCgE8Q0QfAHkTAsIYLAK7bzy/c4fO7jres0iDDcdJyneObYtUU+xrFvqUGAReMkQGGV7zgbQrlLSc7n1IbE1YbCaaXCMMPenbXxe9uu6Tb8EqNepC4BTU/lwUNbnjMzqUBg6cNNdmv+zc4AardhGf+H0l8UmOwOO0fa90nzM+lPuFZGXR3G6RPXub3qxLYWHfbt2c2kMxWUEFHPppx5rh96BxIa8wvCdxogKYgH04QbsTAMrv9jQ1My9+/+KeA4obCwOYQJC4ti3twvUGb+RBJNtNISnKehuRnRDCLcg9DFzqFm9WyH0fVIWGsiT/BqPDPtiSV5K+z7MPwU2AoOsSdNQhUxT09AAvLHvhr6V+Pc1fZjOdT2+ObNjvPN+NOxHwAKwwAOLAIIqlUb7a9EgrqO9H8y20owFHYOKhssE3bCZss2UsNnl1T+OI91QDDKyH1sr0fIQx43ScaXO/xY4o3pDppc+XufxsoDAYoxGCUuudDhaS52FAoVQ4fK0Bt7LWYxDeAUg1HC+QeFAcadZ8ipoPVuqdpZwh2TNV+f8br1yPcGPOHAE65I3G+4v3GmF0i+kUAP01Efw+cCH8MwEeNMS0RTYnogwB+D8CfB/APX+1Yb1mloVOvMABvvYuykImSLuOe021HgVrjcgzpooWqDaaX/IIZfvA2Dj+5bX8PbH0qbsGZrrSL71LL1pdTUl3bdyD3ScHhNR31dWi65DyLdGUi178aJkhXBvOzfnKmh8DoeRb8JlFIn7vmSQELoHrktOsFzh5TF8UN9uWTgxmOvvkxt6+k0s5TYOoJ8vTcRyzEJ48EwvFWhuFlfj0/z9uI15SUvKAH12MajrAr4mpNBagaRM+G74UXBukitvwluR/mTPo3W3QOgr7fm/65CYIp6oWyMuiu/AcHb0ucMPLKTax1ihRDUhrHUtsWFPXakOc3veSLRAQSDACjKzqiAxdDIKQ16RwCnU9a5t5Dg91vbzH+d6xRJWchlr0I6+Ptgl3zrsYATeAZFRTxYiWVweJ0jsFLPnfQDLIo5xKerwtFNvF5yN/FaQaDiLIIE9vZwnBIUCC3Q0Jvp3WJ6f6NGkYBh49y+K3txM+su6ejZ86Eh35urDa4E6fLjfXZO5VQY3FgkJS+9ipsPPVaBifC748GIqKfAfBN4NzHVQA/aoz5H+94XGOeJKKfA/AUmIrrhy1yCgD+Mjzk9pfwKklw4A2mNKx2nIKxxM3xmB6x3/UPAHwXgAWAv2iM+UTwfQLgY+Bk0J+867FabniTrszLaKrbnHxR39JwwlQS15Lgq0UYKLQFMP8eRhv1O5VTGACw/cmGW2faRLjEhEm4qOYmtrJAWJymKCHcDajLObbsv0uWGtUodQtgds7zLAFsrY5/+8XwJsJsr4Mqa4Gu95Asa5QbvKLqHse160c4yT29MHTMq6QNqmHq8P1JaZAdBedi+yn0r1nBcAZOYQBA/yrnhnytgqBULOrKIEruth0mjpw8+HLBIiOk+Uhq/idCXMId0osEQBSxpUaje2uF5Rkf+zcqtjCbrp8LB2+zxV7fwAdtX+xj8wkfG+eQhk/OhrTmo2d4fujCC9m9d/cjbirJqwHwyCHpfDfnpH3mQW2oe8DgOh9Pp8DGvy7i8GAZswi3HRUJ8aTykODeTvsyhZKujG+qZIEUq1NB/ioIXanWwKTeI2rsPArRWHXPe8jU8j2KvK+Q800DHVsHolqDZVBgt/cunggSclKzmFSw7pGtsfH7a7pxvqZcIzQ97yHnR97r1CmHrBytyX3sxXS/KsKNMd//Kt8/eOz9jwH4sTts9zEA7z7++d3GG0pp2PHNUpByh/GdYNfqMQBfC8YYf23w/V8D8DSA0ct/Gg/V+kmn0wDl0aE4Z0Ac05TvXSczCQ2vNHZ+yEv4o0UXW3/oF6psnwTeQLmRRYVNSeDJZHMNUIrhFWsCmpiBVyWEOkmckth9r01ECnnbLA6DjH//BszCn59+2yW0nQT5LVt7QYRqrePCCKoxmJ/xE7t32yefjfKV6nzfCNkSrqGULDIRfutPG4CCXIIU1QUMv+FiNxQnUvffaTvkCcV1E9dtFPtxF0JRZnJvRAiJdZvNmTZd6kJMYsNIfUl0x+ETIE6I1kMAb5uhfdEXkpVjXxk9uO6fcf9Gg+J6UJOyzsL28BEvdEOFEeXJAAfjFU8gpBkHPPGi78NiC+4CGG5S+lyZeMYup0Fx7+3Z+SRSrjLnBLTQ9BRzokliPEHUj90oLsjrXvdavO3lmD3Az1C8loUNoGw+xd6A730SnBvi0Fw1UC7cJ6N322D8rL+/0weDiUEcMhX4djb3hoeMfBrA1EtEfG9hnRMQ16q8lmFA983T+FKONxsRyvcA+JDh8REAa0R0FgCI6AKA/wuA/+FLeYIn42ScjJNxp2EMJ8Lv5d8bebyqp0FE/zcAfwecZCH7zxhjXtWa/wKGAfCrRGQA/JM74JLPA7gSvBdc8Q0A/w24qGWIVxhE9IPg6kfkvXVniYYJt8H1JqJmECirhKMMcWvYowf8gy0C3ovOP+PD965z4DuxqJX5A946pdbThKRLDZN6hBA1BsMrNXpPc8lre2oEnSdo+pbx1brpi1MeymsI6AnufmUw/vQBcJWTNQaAqSooW1ORHC2RADC5LXRaNVhu971FmhMG17XzrKqgV0jdo4i9tZJ8uP2eNMNaxSo2CX/W+EtH58BgcN3nFMImSdqG8FaWTqS3w/Uk8/PetgmL/Rx0MvAMqfHegvcaJdTI9NzhmgwbZhWHwmMlVjEnU4URGAD0832kKxv7vu25w/javLeQ7bN3V217C7gO+pEXB60rQgTYkk8X2t2D/i0uOHN5U7vyMus0ZnNfse6OP9G+Q+RMR7U9AgUPk/rCkwYEiXIhBnBem98+9I4hNRVB9r1zUKLt+Rji/ELXWe/1kFscbz7l99E5jHNx/lyAVdAtT65x/bM+Ed95+jraixwGbnoZujs1Vpu2O2GtUQ88ZHe5pRg6b72zseX1Cr034XyLrklo3pvY+3wt48vB07iX8NTfBfCnjDFPv94nA+AbjDHXiegUgF8jos8YYz4cfH9HXDERCQfLx4nom15p51YJ/QQADMcXjMRfk6DoSBaPSxwKoVrfT+rlZuLatU4eAjIA0yeYIEnZLnDLUxwCGVxuWGGEFNG1cfuVxSKx3s5BwwrDcjolV3ehHz7tKsOPJ2uzKbvf0ucAAHS/g2STEVFmZxdqHOj3xRJHH7gQwV7T0lM4gOLGQFJIBdgENvkQUbqEo4SXbZueR9xMv34BeraPtc/yNsVBi+51Tyyns4SRMrahUPnYaXtPbCHkwqAaEEbP2zBiRtxwx16qSVgYeVSUicJLZPh8pXBR58x9tNi29zylqICsGnLPaheCsU2TOkGwtArqMrKFQZvbgk47VM2vb39wDUD8nU59cd7kUoLRS6177iLApXi07qkoFAeyKK1AbqsmyA+tTKQM52f4wsIGYKoxUWGl0K0AHpE2vBKEVhsPmzUkhIo2v2QVUv8qazHSJkJSlVsdpCuNo4f9DR5cjfmmQuMsnHMu/uGgzr4WB2CFgWEfySHPpaY3xmoz8zkZIpfXALzxIjD6xbZC/4ZG6tBYcQMoCcnV97E3OCDhqTdbcOfl416Uxq0vksKAMea6/btDRL8A4AMAQqXxSnjjfw/AdxPRdwEoAIyI6J8ZY/7c3Y6nWt+HQhZH1H7UjqaXREVrACsLgAXn8iOb6AdWYz4xTpDNHuojm7UuISh8OlKJnc1alOsp+tcCOGMw6odPo+lnLkGcLRSqAUW1Bd09jaZn93dUI3nB113QxhpQdHD0Hq6nUI3B9IJyv1cNYCp4dUy8wMLOaCEiqRp52o5sZrDaIBxvVjP5Spbq3U/1UewD65/22dtm2IEqbdOljHm2dr+ZH+nwaoXphRyVzRFUYwI1Qae91COuAK62zmYmIs5TTZCjOHavdUZoCkTsrG2XyeoAoDzmO6saESVKugKGnw4K7Gx1t294pHD0IBsLnQnH5X2TIo3rf6rF8FOsUat1YHc9QW7nzcZUR7QVpA2S0nOY6dTm3oKkbJhfUrWBab0n2re1OSrIb602Ayh2l1y/CT53VhhhcWPbIXf9DM9NnXAvDrVTGLyBQRkACuQZ9K8H9ysAOQh66eBxPqf8CAh7dqcLzz7c29EYvORP9vCPPoC1P9hFeW7sPgtb4c7OWaSZfc7ZkmtrOlH+K1jnRFCtrxPRGedEJN9yP52D+8g99SUb96I0PkZE/yuAfwHASTZjzD+/nydCRH0Ayhgzta+/DcDfOrbZLwL4K0T0s+AE+JEx5gaAH7H/YD2Nv/FqCgNgN1QW2XHBJ5ZU22FcfzXkWyXWkdCDj56b4dYHR44sDeCEr/MEbP1G2HlPp+Q+B4Bir0Ez4P33nrwF0+8AfV4xEpaan7XHTxhZJconn+oIcZSULczmOpotNlmF1kFQMjoFQH4hNIWlUQiQK/WQHA0JtYAOqMOTQNDojBx9uNxDQ0Dvs9bVoDik0Ax5w4N3BNxXGlicFhhmB03fC3ERWNL2tHPAXobjkqrj8Mhx7iC+XoqsSK6Ol+vkv3Ktwo4aIpS6QVFY7xbTtA9e9AnYetxxSuPoQYW154N5QF44XfleO1++kwt2Dn/jDFTpBdn8FHNJCQW+nHMT3vsqBg50d4PaIXsOYV1G2yF0LEpuuZ1G6LO2YPCENHnK5y+v4eB7I543h3M2nwwoyCs/h5dn+ZlKyEfVjPaSdaAaE4XLVmsK5XpQRJv68FE+jenqVxsKkwcGjuBxsaWw+BOnooLZPCyCtEALCR2WY4XebqiwCDpTTkmIh+Kg3oosZY1XkPdj3E/I7Zdy3IvSGIHhrWF5ugFwX5UGgNMAfsFWM6YAftoY88tE9EMAYIz5cTAL43cBeM6e01/6go9GHK7oTDTqrvJ48SKJeJjySYvJA97UzuYGqjEYPedRIpufXrk4vM5iBWRSAjW+74EonYXtqta/2cAEbjqaBjQHlm9jmIng88Nq2bC/uE4JdWBBUmuAYe6YZqkFYDw9yXJLoTgwblEmJcNKQ8+izT2VSrUeKEArTEW46g5fq9vXkrvoSeguXRi0mUJzjhXYcpNJ7Za2L4dqOPTgkEAJ83xJK93iyGB6idAJvLjeTuvyTPPTKUzi8zuCygqrhXUKFxpcbqmIBl3a8oryAGwOxm7Tv8a/k4r6zu4SnV1g+jBv0DlsMD/r54ZAkwEWwIxC4+c++hQw/M6bOPyNM/5ado+h5uCfrRAfNsH5YuZhx1uf5kkmc0eEfAibNYqcd3Gcowxgdl9jKWHyGdeFiEyjlvNZYU5j7bnSVXxntiWwkABKSCdEGjVdskyMQFIyv5MoRYAZiaX+aPJAgn7AC5ZPjeNvE6Uulf9AnNtKag4TunoZ2zpA0FjZjF/LvWkzJkccXKvtfUiiPiDTCxmMijnG7s94i4SnjDFfuGD+PIYx5nkA773D5z8evDYAfvhV9vObAH7zXo7pmtrXxhHJNR1xS3mGrTYSrH9midlFlmwbH2FLsT63xsdT5BQGwJ5AU3iLTzilVPBXiO8AYLmVYnA1MJlSadfJvx++2KIZpCjX/KM6fMwv/PVnZD/82dHDfC7j5wVuakkRbaxfWEqT0gubVVAwllQsRKsNG0LKjWvMk00SLM8Z6JT3MXxBWZoSScJzyCEshpxdyHxzpCEA5T2FNrVehVVGTTeuqUhLg43P+Di4UeQUBuBhmWFCs+4TVOvfh0JMlGG4bifvqrF11kv79Kc3nWAWEryQDkMUBgDMzmVYbZCzlpPKOMvcgQGCFTb9V2dQiEItPf094Oei1GcAbHmLok5qYO1Zb404IyQ4tzrIV4A45yL3fn7W5yYADquG4SkQoe5TFJ6qRoTxZS84s6B3PNncjel7pdn0lPu9nIsvhGTBngesuWHBaqgwpJhOzpX7qSOCwYa4z6YgdHd14IEa5BNu3BQOMQYHexXaTuJC0Z0DjaOHOy5XlpYASoPlxn3OaRigfisoDQtl/YcAvgH82P4duFHH1bv+8A0+qDVIpzWq9TxCR3QOGtTDxMdkba5h7WlbnDXuoe3njvAPiFElktgOFUN3t43ehwnLwWVrsQmOfp2tcglXAcBiK3H1CJOHJO7K3938BpvrOBSqdKDYBUrrOUi8X1A5ZLmyXOW7FeKumNAw5cXoBVu30YYd6TT6t8jVLmQWNCYJUNI6ChmVI64Wlypm8UikQxoMF6gpqZHJOA4t/S7KMUWJ5ON4eUejHjhqIelcOVSA8Z6RXO/igt/m9LlDtFY6pT/NYIbNT7ISKU/1kS4aHLnaCi6eEwG/sp3xpE4lqYKiTGJBH5Z9hHxKhlix5Mcq/cO5Eeatejsa1TiJwj068UpUtRyalHCrJNanD0n9TRx+MlaphHUXQOzRhgqjs1+h7WVoC96wc2uOeq2IqUN0TKCpFj7EqLMYiZXNNEziQ4dh7kXuQUj42A28sqRiLyRiBuj5EGs2bwFFKCzQpRxz9KDYC8gU+wkAu1atcgjDd6T9+e5/Be7beEt4GgD+KYCfBvBn7Ps/Zz/7E6/XSX1RBhFMppDNGkdbDnhYpFvMREinZcC3xDFWGSLUBK0iwnz8vF/xTU851MxyI4mK72YXWZKGfEjNsIM6qKQVRQFwgyTAC6B6zApj9EJ8eSI0dMYLU4RCW3C/DxG4dR8oNwykE+b6p7k9bBFU485tKM3lLuza0xnHjR2luKIoFj0/Z5FB1lru7COiEhcrWIQuAOS7HkFkCGg7vk9DfmSivgqdI4PlBjml0zk0Ua+NycOWMDJI5Ifhm+3H9qD+501ntI6fOgIUKwsZt7+qi6O38bPZ+phyaCeA4/HlOO6AF4IGomFiD8dZ3MH+QkTbajNQrogT4ACHJfNJ69BQjsrFPve9dynuOhjkqsOwqaumdh5wrDCMQmRpp4skOs7qTN+xHISjM/Eedj1IYtr70jjlIN6b44vqxgq0d6tyPT+afhqtG5OQbZlsEYdHJs7JDTzwQUY1UijXfKI+HI49V4yBNYpg1v37ZB5/uRT33YvS2DbG/NPg/U8R0X/2Op3PF23ohFCO0yh/AXgrOQ/4ier1AvmUTabJA3mEpDIkuQM7aTO2eMK+1iEqpumxVSRue7ZgV9o1fjnVj4RL0yOMn/fkbflM4/Ax5RZU71byMnSHEO8BtmdIThFVSd33ie5qzaCz77/rHGmY1OcJAC+IsiVFAhiwFmJQp2GUF8zj5w2q4NpVbREqgdKoh8GibeIEaFLb5KjdvhrHMXatgMEN7b43is/v6FH7fWEwfQhYe0rQWHxMaRM6+vk+ZheB8VM+812e6jvQQTUipzAA5tkCgF5AClwcxFXkkq8B4orv3i0WzFIbVPcUqhE5qpFyTfm6F3iFEVKRmMQrltHlGoePBL02RtzvYu9dsSAXaz2fHMu1BXU5xz8DbF6gMujftOy4KcGk5KnRMxV5ftQAaaldOJaP2TiDobShItfHxXq6IYNsG3XL7KAj1CXrCTcLC0JxIVz48HFCuiCsPesNJSA2FiLSyWC+AECiTewFLQzMnp9rURjvNQ79FkFP7RLRnwPwM/b99wPYe/1O6Ys3yISd8TzqQzUGizPeV42aClkLPbTgQqGRrEzEoSMKQ5K9oixCtFUY2zYq5ggq9jVmZ9PIClt/xkM5uT+EifbVdAkxeRwcFXvTi3MY2YRrFTae9vtfbHl6id6OiRZzUnl0kidys6GuJF74TUFRWKIcM5omDrt4z0JbXirxioxiCzu0BIt944SJUcQsu3bx776HoAuNr/vazwAAfvcj73AKAwA2P12je22KxjaGaosU3dsNrn/zmtsmLNabXTJIlgrZ1AoX+8znQZua4Yt++6YgF7KT5LoodwmvSOil7jNUWQSv8xRszmN0mW9S5TrdxZ7I7ldk7p7JOHpI+V70VawkBAAwuBrMlSAsKwANMS76N23zr0K5bdtcOYrzpsvJ5hCSTI1xiXlV6QjZ5qhkwrSL9tfNNSH+NbWelj0puY5i8kAA1ij89iLUQ+MsvDbSMbUO0+0TDr7Cz/nRs8rRBwkoREt9UghGeA3jrYSe+o/AvWj/Pvi6f8d+9qYeBD9pdUZRXiKyuDoUKQ3AFrUFmG9qjcOv5zMTWW+dCXefCxdwNvfJXWoNdzETQSn1InbSZ7MG6882WFlCQZ1RJJg7Rzoq8jKKF1mMogmuWwP96/5LgRP2LwdV7Uc9zGxfh6Q0LvzSu8kar1zP/LkaRhHJOHibb68qlraE0vo3WCGLAiQN7L8tdYJP1azIO/ZUkoqvo28ryE1C3LNDamsIAPm8zrd+zacBAL/1S4yn6C6AzScXqEZ8vt1rvOOwadT0fGyZhxYs2WdWrdu5sQ5QRUiXNl+0FzedWp7yoTiTMHQ0NC7C55DN2Xt0oIAOC8uwf0ZYJT25pDC/1GJwWSYK/6mD2pLjnkTbAZqBhWYfEro7cRX44nTiwjrimYqyOH6+1cDed4km3eZnHiL/Qk9AO+Wigu/jvjJhn5kwl1OOCMVBbP0bBQyv8LkKEEQQVK4bY7BMQ2ZaWR8hwaJ4owArDMAXGGYzVhThGr9f4y2R0zDGvATgu78I5/LFHcYAhoXS8RqNci1IWttnHMaqqQGm53mCjV7SULUJ2mdqtJups2wW2zYZa+VUsectZd4vYbmZoCthCHtOqYUAGiKU6/4xGcXU0EsLqXX0zdr/Pd7KMqJFsefVmQi6ipAfeaFfbfVQjlMnvHq3Sg/DrDXabhokLe25ChWIIhT7qVucZgOWrVTOhZXqTPI/uRWyJr7Xktw1CZBNDNpA8JAJKC8UcOtryUFqf/2j78ap31MY2x0OXyqh5hU6ttHR4Veso82B3a+KPTMZ3RuxcSDnvQqEoap9FXm5xkVpy1P+N2J5F3sCMPAoNlWboP7Cei8BYV9SeWG32kiimPz8kg3VbAbHCmRafhAjtao1QbgF5x5Y34vTPElKW+E+uNpCtXGHvbbjQ1CiqPvXfAKn7fmJxn1QTPRbILb+48JB/iv3kowX2p0JG1LOi6I41LT2rEyYY/fCvj98hLD2nKc2Fw9NEI03P5Bg42kD5yErZh4IiyWLgOkgbGL2WoYxhObLWWkQ0X9ujPm7RPQPEYPdAADGmL/6up7ZyTgZJ+NkfJmNL/fwlFCHfOwu27xph0kITU8hWcV8OLXlmAqLoVTjwwyG4gQn997QzvpuuwrZQjsLLV0xpfPwpRj+IvsXKO78jIX/bQNnf7uMceiBpd3bYau+2LeoLZu0jay4wEIVD2n7o+zLayGUsxZtvdZBOquhOxIjt8neWxw3yK4duH0t3nEK6bzB/JyfNukKDsHTFISDtytnYuQTvl/iRZFmmKgjijTkitoAOBil5DzyqUFSaXc9ktsI192Z3zHo29a1++/qY/3JI+d5QAOmSDGxvUHanDsJdtcsmWCVovhESPDEQ0ImTY9zAYklKKw2WqijJAqplBvA9ieFT0pFBX7cwEu7ewN4z6Y31Y5HTIahINxj/xw9YsOeFlLddoJE80HoRXDdy2or+P4wQC4tmSRT5ttyi8Nng6thHUZQYd6X4wkCTsKp/nl1g5Cm7nawPNcNoN1xiChdxe1cTcp5trDv+fAl7/LPzntERLIyHOK0P287DOWePBIg2SY+b1NtGOx8gLD1B+7riIPrkZ8/wvN/euw9FnDyO0TBhfOyLe6Pd/Bln9Mwxvz/7MuFMebnw++I6M/c4SdvytF2iInz7PyZn7OT3s6xNufFFXqVW5/24SEJNS23+X1x2GK1nkQJyrXPBZ37HHKIXwg/Tlj5u/M+v2C6t02kEOT3oiwADq9JkVc1JGRz43tbHLHfvniQg9/FzsopDADo3Jph733rzgWXgigpWMyusbKQsf/2ImKXrYaE1boIGF68EjYxxDUSh48qdy3h4N4hFMf6AxCBUba1bkCTokqDXPJBxkAFXfVO/fZtmCxxVc7Tx0doO4QjC1luu0B3bYmq4mvMnuqxcg5i50npwzxJBaweXaLd53vxtncx9vIzz3ImvLiRYjPgourd9iERncb5g/AZyhheqSMfPqwul1BNGGpM50A658+7tzksJsl5CX/2r1qAxICvY2F71VNL6F/jxlgysqlxdDaqNlht+eM7mHMg1Lu3vVTNbhzBdFLoLs/V2YOsmOXZU8vkkT2b++C6J4pCaCbxBlH/eonpRT/vo/tl53zY2yQsIJUCyM0n+VpufKNihWE3SUqDYrdCMufzf/5PM8Z7ESDdBjfCcBRPcAm7Lk/fGar7hYwva6URjB8B8PP38Nmba9gYqOQCZEK2GVvPYaFPiFySDmbdfck58OdFQPMsXgBgEVqB5S8LYHIpoILOGRElI2qzWbNX1LUehlFA200idFQ19BTaScUKI1xUSeX5qepRjqar0H+BV9re+9YBANNLLDCyOfcclxj25KvOREI9zP+s1gnJCmgt+kkqoyUfohpmz5VkcVLanIS9dO5C6GPLqrI5kEOPjjIJeU4gYuFG1pPIbs9gOhl0h6dxvdWHzhJMHrKkgENC9Y1TtJ9lKFN9rkTyyREKK/tUFaOP2oLPT+bE/AMskR98G7MAPPvERbQ9jc2P+R8tN5VTdCEvlKoN0nnrLHbJUUXd8YLX04vW0wtAFPnUYPiRgN9p0+c5dMY5lzpwlMLiwe5tnisr5qlENuGKfHlGAByNBgDMz9m2sXZ+132FzmGL4qp3naiMY/tUNqjP+Ux8eG2kDeanPeqvPlZPAsSEi/NzHedJijcWJsKPHlSu6DOfGlBrHHtyPtXO4weAi7+qozzgwHqi1/8YK4til1s9y9h4pnGoMIDzMZ29Egdvf7kX+lrGl32dBhF9J5jn6TwR/bfBVyNwn9k39aDWID9qHbeTwD4BVhzbn+IYxHI7c4oCYDQTAFTjwCpLKEogRl2/bL2G0CyL9XnqD/yKWW1mDnlCrbFhGeN+392poZqg6nzkH5uEwQR2axSHIsKkZ35QOY6gpqtQjRJU712310qAilE7IY1K3VORYI3hxV4ZAMLzhJfRYwg1SLbUWG4kXsjWOhIMaqDQ5hT8nq+jc4PDICZLQHWLdsCWnyiM1XbHHp/DL/t/hE+o6FfQTw9d8d/4Y0yIKBBNo5gvSu7d/IxnlQUAdbmL819zDS89edZ9NvysBzk0BUX90PMjLxBOf5QFcvdWQNjYDwENFIUdRVmEFfD5pHG92yWsdPAuK1BbTtyL5S41Mo4A0O66CGjddWgIHRkcPuo/GL3E81qUWzpvkR35OUq7h/yiY3+T51g9tBFRdSQr40KDc7uubn2dOzrWngoADS1Q9cnxO9V95aHWlmFAvI2jB7k3hhhy5ZhQ7JvIk+vdLF11ejlOIkPn5tdysUrl9VtUOFkPEqjKRB5tuem9nnCOvqZhcN8S4UT0kwCkJcS77Wf/FYA/BaAC8DkAf8kYc2i/+xEAPwBupf1XjTG/Yj9/H3yP8H8FZvu4K2zsbp7GdXA+47sBfDz4fArg//l5XeEbcOiMnMKoxuTw8QATs7nXH36RXzQ2fPPeBwB46mfZV4Q6CWKgoixCBEY2qaBz2296M3PhHYA9GBUVSLXQGaEUugYTI7m6e5rpJ4K52L1dIf8sV6DVD5/G0aOej0HCYWHB0vozKyRBM6Tpo16DJqUPuQjZ3GrTW7uApbV25++L7ZKSqeeF1wsARi94DGpbpGj6ic+jXC9hEoX8tn0YSoFmSxgrqNq1HLqTQKcSN88Y4SOKuUvY//oKZE3e9hlWGMVtu7uGz1UQTW2Ha1xEWEEzzLayVmg2AXb+9Xms2aqkahzTWZQbQOcgvvcSIhFBEyqKcF7MT6Xo7vm+3P2bLfKj2sGZ8wkzFUjflcPHCW3PRJXh1dhXo0uY6vAbWNCbho+19eHAuCmDugkLsR3csKy4GSEJnlN2tILa94tCn9mCOpigPeuLfMLrcZxTrhobOHq7RnZ055CSeCAhgaHkT7J5HJLNJxxalGvPJ5yfkXuTT+qYzqTl+zS9GCspQcONXtJYbCuHIJT8RRhW1Sk5VoTjLYC/0HGfcxo/BS6F+FDw2a8B+BFjTENEfwccEfqbRPROAN8H4F0AzgH4dSJ63BjTgltm/yCAj4CVxncA+KW7HfhuOY0nADxh+1rM7QFARAmAziv97s0yjIJrLF/sG1eLsfmpOdLng5JfUbo9dlWLZ29j9l4fGOYaD+MIBZPKRJ358olG99bKWS6dvRK645WEeAQS7lKNATdCsh7EpHULkY/HC14gvqIExNofXCudwgCAepxjteYZPjMrB8IezKHCmD3YBzU+rh/OcQkfSA/w5TYA4y04R9xX+vAU4435fbpoUK3lTuE2BcOB155liZftzQGlYDIreIoMKlVoe1KjkliqEuuVacJiO+FGTOBirOEf5g6Wmh+xsAjDJunSe3Gq5RCbg38mfN1dW5BnFN8vgQBvPt2iHCcufLg6xffw7G/5/WdB3+wQ6jw/l6J3u3WULE0PmPYSdPd43/3rJbJbM2T22KtLHEq5/o1e6KezoMNjG9OXzB+wIbzGC8rRJ3P3bDpHBgYBpf5cnokP/S1PBQCHzQzj/Rn02BsQ1YPbgPUC6lGKNvNeoYQQp+f9XN36BEE81nyi0XRVFMYNO2a2HYr6opQjDxUX7rDltvwlnPmIn7PS8ExYcSUc3D+Wp6jGNpc1azGetR5On6vIMwfZXjuvQyTpfikNY8yHiejBY5/9avD2I+A+QwC3yf5ZY0wJ4AUieg7AB4joMoCRMeZ3AYCIPgTge/GFKo1g/CqAbwUgZkfXfvb19/DbN/zIpwZrAY2EumHNSqssjO2AN3k3W1hJFaNA0oWOGrbAGC6ws4sp3y8jV3d5uuCmTMJISoTebhvQM1DEVqpzBWp9VbZYiEy7zaMTVDHn146gz3hrcP8xfsSDoBlORIOSAJPHvGBYrSt097XPq/Ri/Hqb+4pyauPitd7tmE/JKEI6bzF5SARfxkR5VmCtPbsEjEFyaNlk8xSmSF0+pVrL0XZ8vChdciX8IuDCUpWv3BURNbBcQTphhdG/4a3/7k7pED5772YCQkHdqJrzAqJYJcchz1sUxsy2AevsKowu+1BeUuqopqTNyAkygFvLSmJ48kDqFAbAHkn96JpD4e2/ne/Z6Hl/fycPI9jeoOkDJrPzdMRClFQAcgiK06TtbVj70TnwAnxxJsXeexiNBgDjP9wFOhnaEc9dLqQMcm0rjWTl6zcALkaMjxkjkMgAc1sf0jnUUYV7d7d9OcVIJbkNvufFvr/+0PqXe+y8rspEeUFJbIf1SDojV4DIH/hqdn2swPZ+jc8zp7FFRCFy9Sfu0P76buM/AvC/2tfnwUpExlX7WW1fH//8ruNelEZhjHF+qjFmRkT3N0P0JRiq4R4T40/ehh4WXlkAgFLAyMOZRGHISJfaWUGqMUBjgvdswYq1szxdMG+OnSuCVunesmGERKEaZejse8mcjLySkdCLFMx1phrLTRUVO/VvBq1ehyxkl7aHhTSuCftlqMZ7WemSlZzAP6sRk7sJdDQUAmFcmX8LDAPIJsAKI0wIN/3EMc/OT/Mxxs/ztSYHNkZmPQtTpNCZcvmXepBwQtmG/kxCqAeJg0+WY2UpIng3+cxgccrHxnVuabWtEh49v8Tue3uOj6kaA03XoGOhqzoFUuP7N0gzILkHnaMW2Qzo7gpCSSGftD6JGsgDef6icJuCBaPkMXo7XBQqlr9Yvbfe7x9s72aM2hte9q9nF3n/HQe77aAeAOVjfMDxRwrUvbACmxWGVHIDtrB0i2/W3nvsXmx1/+riGpIyDLkmEUGheAnTANBRDT0lv4Qmw8R+E+SLZucS9G96yn7VwnmkuUXyhkrlzO95z6LYWaLcLJwHnk80ir0acwvTrXvkQpAAw+DD0Bsg1DrWULOHcUWsmmnrRYllk2PVv69hmHtXGrvGmPd/Iccgov8SnHf+X+SjO53KXT6/67gXpTEnoq82xnzCntD7ACxf5Tdv+JEsGow/eRtYLKEWxy5nNMDiYQ+vSFcBj47x8fBofwG3jU4pRskAAbRRI1k1MEkQb9UG1VrHvkbUO0P20wmSfpK0BCzK5cUD9375CMdmyjEvqKRkL+U4XYMsKmHsVYHsD2sN6q73otoOo516O/E+wiEd2/ieAMVtnx8avMDSnZbHFqH0ZujlaLupq5VJl9o2bbLIroXmcIq99YNrNdqO8rTyKXuOEjIxCYdzVpYvbOf7CpisRWeH9990DdJl0OSJLDrMXtbgZhMl5rWFiIae5nLTJ/YX24lvGZozb1csKFMnoPo3WyS1ce/Fu7wTqWA4pPo8KWO0VD3g59b5WOG+D/dRjhR6uzETAcC5EgB48P8sYYjQ2fHwKp0HiXtLxaJsJ8K8ArKjEtNLrGGrIbD2XEDFk8ZeCMBGh1D292/GQlwn8fZkgPFlP3eyQ//aJIT8sPTMu4lCPUqR29Bga42QTsAc3XY9/Uyy0lHIVzyb3o5fV8Utn/SbPB506XqN4/UmLCSivwBOkP/xIKF9FXduk33Vvj7++V3HvSiN/wzAzxOR7OwsgP/gHn73xh5aA8uVf534SbS6tOZem5TQBG0q24LDRSHkNS09C61qjxHBucYwQbx2oxMhMpKg1qAap0gq78mACEmloxBRyC7av7pCfWaE7IaX9Mut1Pck6HHrSkEISQ8IEW6dQ6Hm4O3FylOBqy8jm3MPadfSs7Gw2GAdhEn6dNGi6acOcQYA1GhQ6T0j0+s4NFTbT0E65s2qhl4Qc89scuGGtlDQiY/zNwU3ERILsRxzvkKEa/emYgJEabYzI3R3TUTSR9pTadQDzqGIxVv3CNUouEe1QdMjFzJsi+B5t8D0AjkFlx/FSkDoUEKesrZD6N+0Xs1hG3FPSZOtUFFU4+D1VovOUeL6twNcRyMeaWei0QmYm6shu4wX/7UXxsWVQ/e62eiDao16nY0ZCZ+GjZiq9QIbT/M+y3VmWz7OeSZrQa5zcF3YJ8ndB7d9MI/CczVEqNYLVyyYzj2QBGC4cDVUbk0Kn9pxpSXDdcMUJuiVsf3cbT3RDl+jKIswN/VahjFAq+9gCdynQUTfAeBvAvhjxpiQm/cXAfw0Ef09cCL8MQAfNca0RDQlog8C+D0Afx7cO+mu4164p36fiN4O4G3gJfAZY8z989e+lEMUsVJoLjKgvRlkEaS1vQM7p1ExYibs40yarUfH3inwzG4SbBP8tqvQdJVz02WChm79ai3FwFJUi8IQ1lqjCPm1Q5SXNuzxNIfD7G+rAaEewiWLjQKYZDA4hyKAoaaIehOkS18oKIiWEIIcWmyrdYXe7ZfDk6WXtMkSUBUUOvb4xiVT9vTKrTWYhDA7F9yrBs5Crvts5S9OZ/4eEALyR+KalYAEMFkBG59hSdvmCtNLOVbWiezuGfR32oBVV4S/KAmFckzO+l+e4nCXMAJ0DonDdeSP59iMV4gc/XoADK75By8CMExmp8dCKPm0jZB1IfQ5n/Jvj97B92b42QRa+Z7fQhMvo+0QFmfyiJU2W2p0LvtEQXPKY1IlDycKOJ1z4lg8YqPYmy6D8yODVzSe3JwK8iJp0GKVtHGJ6nSpox436aqFIUI69TdL1RqThzlKLmsi8qRCIkVCRKYoyiL0lE1C6N3gebI40wHQcb1Z0sBAfG3j/tVpENHPAPgmcO7jKoAfBaOlOgB+zbbN/ogx5oeMMU8S0c8BeAoctvphATYB+MvwkNtfwqskwYF769zXA/DXATxgjPmPiegxInqbMeZffn6XeTJOxsk4GW/t8XnkNF5lP+b77/Dx/3iX7X8MwI/d4fOPAXj353Pse+3c93EAUqZzFVwN/mWjNJpL2+717Ly1Yq1R455xYGyEoalsqUFB6KkNPAoA0GJ5OeoLuJacgEXsVNpZYKoFYIzzNKq+gs6BySV+VP2bLXsK+97qWrx927n/TVe5UAbAmPZ6GHtGqvFJSTJxzYZg6ZMAJy8V2q6qPAibhCHatedLVMM0aquZzEoYGxtX8xK63wH6Nn/TaNCqwuKhNbf9ak258JGc88Ima7nC3Lhz48p37eLzwvAr8fz1ZxvUXYV6yMefn05RHGgXXkoqrtGQ5zG7QCg3fQ/0/ksE3fHnkc4tYst6HnWf4aAyR+pBQI2uYm+uu2vDO3OffNWZ9zC5sx8565zBCQkWpwPLfOXDU6517gu+LmJ0JeQSN1BNEPqy8yMLaoB6n7ntv18fgGqN+aWeuzcmCP3VwwRNV6Gz7ynRDRABQkIIrfT/Di3+ekARqmntuYDrqpe43FE9UMgPW6RB2DZZNtAWICGea/8GT5TBVYN6lMXhv4AqR9uwk2cmONZ0yaL59r6Cr73NGOQhgAnxTF7ruM91Gl+ycS9K4xFjzH9ARN8PAMaYJRG9+a/cDr21hmYQYOGtQIoSkuTDVKrxfD0AkC5btB3lJiZpW3h0rBeAQDE9cZ0cjzfcfxsv/vVnG6jWV44vNxkh5Jri2PMoNzkmnVRx/45qQFZgSa0EMLhmIphtGE4DuGZA4J+iEMNrDAkd245yITcJTRX7FtJ4c4r8JpwCNFkCWjWglRUOAjKy1e3Lcz0APUeVnlQmEjKSYBbEVkgfz9tbuvE0VpISTqu7Cmnp80ODqzVm5zMXblhsK5TrvialHlmFYWGr9Yifk0ODEQvrKEFtfFy+u+NDj0nJCkyQV2GFP8BCLQxH6ZScoHb3Oo8VBuBzRt09g+l5nwMBEHWhdM2bQu6qIBzTu7504UEATDaYUkBTwhQuco6C+qps24A2I0uRA/c+LMgTA0MH4ct06aGwqgUmD+VO4fZvti63lM01Q89Ty++2W0bILUHXRYSIU79gdZ6gLZQ7ZwnrdW34arWRRAW0bYdQDellnf7knod5vdc0jI+Iv5nHvSiNioi6sLY2ET0CoLz7T94kgwhqVQHooXiWra7i03xpR9/woNssLTVSe8XFjSXKrQLp0sbZbf5DPImwwhnwlBqiLESouITxihsRybj6rQrdG8pZrL1bIij9aYeeTt1VqAc+odr05Lh++xBu27/ZRgpBPKuQkkEHgsakhLLnz0/n5OLiAs/Mb3q20zBmDSKG07a26tgmvBfnPWJb6CYAFpLdoL2pS9bbQkSpu5DKYDIxAzALPW/Nt7lCNm3QFIm7VpMCBw+KxQmmULH3Ol0QyqAQUjwXY0+pHvJn2sraZPXKrUBXW+x5iLIeXmXlLwq7ODSo+8pXjheEpkfo3bIEmJsJdyq0SHB3DwJFMgiaaQlySIT1+DrniWYP+RqcUFBW4xzdWYn5Qz6P0RbeINA5cX2QeCi2tijkUTPkFap4SK7avlAo1xQyobdJuFth6rxEBlBI8WE1Spx3v/Y5Hc330tLEZFPvmSTLxhkfACflw+sAGG0HMHIsnN+DKxUWZ7JorqragNpQSQeedpDDe63jrdLu9UcB/DKAi0T0vwD4BgB/8fU8qS/KSBTQ70JfuY78ynW0FUvZZHsTZnOM0WcYh0kHU5jZHPqxS/z9dInedIn6lIfhqVIHFdctJ/VsSOQ48oK9EOPqIlbrCrPHGlBlkRu2f7W4zG2H6cOboOBvse1fixXkKT2sRRt4FoNrwWKzCkdgrMMXeaVGjZ4SciGoCAlmBYgULuqU0L1yFC0+XeQgbUNxdlEbazHSqsHigYELtQFxcaBDvdjdyWINC9JC65IaAyRx//P5GYXhFX6dzVqmHBEE01xj/x3K1y4YFvzCySSNi5KFJH/lc3+O6TzwNDR7QSI4mx6weidfkLraRbEbtHklIJ+1IO09zqZLUU/xted8mLIz1VEDI4C92Cwgzwxh0t3Lh3Yj6ykUOZAQBi+xVivXO2gL5WsTUsLigZGv8O6roLlWQA0emMbpQjuBQQaOxgPwglrCbasNNp6kPbLMo/BZNl1fsFcPgOEVr+zzgKZdINhCH8MXnLrEeKgw3L0ybFABbEAttxI31ycP8gNfnPXn0t3xv00q45qdhdf2WocBva7oqS/WuBf01K8R0ScAfBC8/P6aMWb3VX72xh9lBX35KpAomKqGKiwqZDoDFkvQgC00M2PJoZ65zL87dxr1qSHqgRWEx6pGZdEIbG9xpsP8/8cx69YT2H9fg/FTASPn1RbVKG4PG0Jsmy5FPaqzCe+32DfRNjJceMQqi84uC7XiKUZQl4+fRVsoF4bQGaFNfGw9VBpcV0DOqwKA8twInVt8jwTXL9TkSKyCuhAUSgYKo+kDoysBrYQ975BfKRQyOiEkra/D0DlXGU8sx5Bq2HtYnLIKOGWkl3hTADD+nMFq3Xtlqg4guEvuYS4V4joDsinQv8bvqbGd8+wpibJZXLC1CxfmyD7tjYnhFe0s9XTJPbZDjzNUGN3bJoJlz2zPEsmFkIEV8Nait1Z0SGQJItSb3rOoh6mbn6o1UHNPSRMWrwHsoeTTFo3txiew4zaAmx83kk3A/wTECn1wrQY1BtMHLG+YhHetYirXyOWWAGD8vFcSbi4mAonVjspFrlmn6uXKwp5fEoQkAc6lAF5ZALHCyKaswEJGg/6NOvJi79f4sg5PEdHbjTGfIaKvth8JodElIroIYN8Y8+Lrfoav1yACZVbwv/1h4LKVDEkCU1UwNtFMScLbnjvtfprtTAGwcGi6CSey7dCpQrpoLGzPHspwHwiABVHYqzi/nWK5DWz/QQA/bGOahJBWZLnNQk54niSUtDgTCNfUV+aqxtgEpgf4q5v7WL7rnL8XxnsRpGGbSvlwmyiOxlpclVWYvZ3aKQwASPZZCuixDz+tzvRx+EgIYfaHLfa4IE5CGsoSzYWxfkMhFJbQFokL6WRzjf3HEy/EM2BxzuDUx3l/Rw8qRN2swIrBtd7dZcEvinW1zrmokD5c7o+MfOIV/uIsHzOd8jHK6310pR/6TUu6Z8+tGlFUvQ4A238QQEhbg6ab4OhhG8eXnJQ9FmmDbNq63FhbKGQzT+ndnh+h7SbID/kHkvyHsCfXGtUojSqjQ6Uh4S2X1A+ee7itq3QvNagxyGwuIZsC9TBzvxFl0g1gsIttH/fsHBlkC+OVXxA6i9gW5LOw/3gan5vcDwkZt7niFswSPp0SZufSiG9tGBgrK9sbJ4KS95P7RlQYjvuFnvpSjrt5Gn8dzH74X7/C95tE9IQx5j+8/6f1+g/dL1B+8O3o3JxyGOUC01/rF5mKhYJiPyo6MLfYuaLTW3FoqjYwRG5RqarlpGawCEIvI1sYZAvPNQVl0VphYi607itOAAsFhwi0sIjLpEBpjShJ+i1O8TE3PtsiO6qQHvo40Ox9FwIKBW/NAYw0UaV2fSAAREI/bKSUTSrobobkkEMgemhx81teaRw9HLCsAlAlMH7Rx+3TZcxomq60E1xipa82LV5+YaBaYH5K7meCtucZXlfbvJ/d93hFkU0Q0Z1nc5+HIGPzGvbwnSOD5Rb5upS5rVuxiLK0NFhueHK73g32+qp162nsK3esNiPMTyVRsaNRwNYfeuUd1u5MBLUnldzEIUoh0mx6CsPGk+gxJ1bilAhpNiBWm4L+4yLJsHA0rIsQlJ94Pkv7u/4Nb/GHXobOCDCIGItDQV8PLamkXQfNKEUShG0BYHjVu8+rzVj06Mx740mpUY39vTEU82QhIfY6rTfU9BOnMAAE9CL+s1BhzC4k2HjKExZ29hsszmSOi4oNFb827hc1ujFf5krDGPOD9u83v9I2RPSrr/Tdm2WUZ4YoXjyAvsrhGqcsgji9qQO+nkGBJkgMuyI+idnmSURal00apIGrLEVLgkha+2zJ5Gl2gSZLjbBn0/x0Cp3FvRCKPU+aaFKGfobhrGQFbHw2qMJuDY7etQ6Arfi6rxzTbFIyGkwHAmK1mTolRcE1AnFSUJSFsNJS26Idegl98HiO4sBLjWKvQdNTzrocLjRgjLOK0xXDl+Vw9SBFOVZRsaFOPdqp6fPibmxEpnddkGX+fLnCm18PrxosNz2MVOi5RTnNzzA5oyjmYk+jHpBDW+mU0LutMXnA83QBQO+q8EkZFwqRAsBQaG4/UXmvqaMsbUmghJcGzl4gpvaW597dMUx4aPtPUGMixc7kjSZC+tAxSz18xjJEKQFMsx8KTiAOD+aTxqGYVKm5GM8W5LU5C/bwnJJVGDqzf6z1LvchNKgkPNkcg62nKxPRgKQr7RSGHMcQRXm56UUF1bz8evmgwP47cgdfTksTKRWTEGDMfVMW4XhLQG6JKANXDf5R+9FvAvgnxpjaGPNtr+O5va7DpIRyLcX4CUZNURrfCtP6Sak21tzr1VY32o47vRnX7hVA1Pc4v3wb5aOnnLIQC0uESbHL0l4FLUwBjyoSWmhJcKvG1iJYYVP2BSLJ2wkdtOwvndeYXfLnvNhObdLRNtspDQuCQLiJwgBidJBcV+/yEd+jLAGVDYz0F8+TqNuZCPtizyowYyJrV+6DJD11Qlhtpu7ezCyTr4REOhPjPCgA6FhkUWjNkwbSoDfK8GpwYcZwYvoYYlwE7cYzGvMzvitiPWCLVmpaDFl2YXl2DtkkcCufj+nuGaQrYGZj5+vPNlFCNZsy+kfgo2vPrbDayr2l3uWYvyi2fKqjNr1NT0U1Dx3XwztA/ezMUW903X1JAqJN02HlGdKRNwW5a5ft5N4kyzaCvc4uiGcUeBuDJPKYwyEQYMdoULOikfBaUuFlAX+ZB4vtBN09HSXHQzJFaU08O+/Pr9g3DjXoW9fa59rnjpP9oMdNPfDPXVBaEQvufRpavwWUBrhJRwbgv7Pv/0P72f/99TqpL8YgzZ26Vg+yBd7ZCXL7bQuyyVzqcBB+9TjnNCShF8bd676Py7uGQp+2TZAubkKVLVbrXrJFbTEFhSLEctZyEyGw6CpkQfOa3k6D5XbqCvhUzf8GnwvoO4KeDvUwQ7oyrkBOkrwhNcJx42f9ucZZ38utxJ3L4CU2zWgp/VIV9Cho8LTZweB645K4pBnHL/kSdYyOwSSEqp84CLBQUkjOJ5/FzKhCrtd2ZHFLSNBvEyZXqQWm5z3rbv8mKwyPx2cIqITHVuvMXyTCOCnZwxPfJykNBte0Q74BrDBieo8gYVsZdK1iEc9OFGibK2Sr1oWA6kGCNvdCO5+xFxbWXsSeBYMVZHtnwVul073O7lK2bylatntsqQe038mqdZQ58rwFiUbNsZBpN8Fi2+cShGk4NDbCHAlTzieORbfYb1ENY+hr58g4Yk2uxfF5k+PosTZnTjUAGL4Y0n3wCCnoO4cmeq4A84Z5tuL4mTkqHGPc+9Bju0/N9mBAX97hqWB8jTHmvcH73yCiJ16vE/qiDeNDRJ3rE+CCTXRfY+ydKIv2kXMv+6lq45hsm5OzqiWhOP1q/l1xu8TiTIH5udDKNC6+KyEEWbxNl2PmMuFHL7ZIlq2zjsWjGb3oe0QYhSjBGYbHxKUXzHq2iHuY8wX5l22h7IK3eYSVQS+gXk9vHUWJ7nrUccJMQi2jlyyIoOXQEwnrrOIQiSTZBUp5+GhYXBn3ZF8EfUNEWXg6cKAaG+QVv8+nHKpzifIFC5v+LfEEGL7crvlrC4f0Mxcoq0njeHZSWY6inSCu3xgnGJsiIH6csQckCiuf6gh9lKxaV38AsJdFLSeY3fk05lghoXGJ7vzA7sda3PUwR1K2PrxkW+M2g9wdrx5nbr4llY441vKZZqEehB9Dr2V6SXqI+0rvMLQ2P5NGhYR0bIqtNhJ0Dv21idHiObgIVRAqqwbKezmlcdXfgC9sDRWFUeT6oLySXGZgBLcLIO1zNskduKVCBXg/x5cBeOqelEZLRI8YYz4HAET0MLjP7Jt7kC8MU9sDZJ98nj/OMyDPUL2TmYSTRY163HETjDTTOxxn75SmSLMLCqMXWxdmWJwpooKszNF0WKRHkaAeJBFCioJ+HPmkiRa34PSPs4NKHUdSG1dhDXCcHMDL+ii4YxkA2rjQyfQir/y+bdrU3a2QXz1027frA1eHUY8tDbfErokt3lDYAIDJfPyaPR7rUYzlnvhtw3CJ1KMI4mrwooVOPqbtcQmj52PUGBCjn0YveoWrbN8TYfwVyzK0aHV2jKl35SlVhGK9sAI7XWpML3hJmU99MVznSMMkQZU2WSUg4adhhmoQQ6vzSRNV3+uQtG/RIJsB6VXrEXc60INA6aQdmCRFugioOQa56xJ5HCotnm+Y2E4X2jW46t+oo3DU0BoCh4/55Nrk0SAfcwT0b8BJRU4ke6XZOYxhs+U4AbXHYL+vENpKVwblehYhplZribt32YJh1DLv2g53dxRGAZ3CwayBgFJlcex4wTx5XcaXeyI8GH8DwL8houfBttIDAP7S63pWJ+NknIyT8eU4vgxcjbsqDdsP/L1g/vWQGv3Lg0YEFhFChOqrHnGfkTForYXWdhKk8xptR1ziGCOeVAaLLeUQPQBw9ECCzMJA86mBqgz61yWRba2hIkaIuA5mQmBohyBCQhdaNQb5UR1s403japhw/2dpV5sQOoeta2XJx/CtLQ0BSMjF3AHvZQBAfmPiugGaJEHbu8OUCYynNleAQ8b42g55n890lLBUjU/2u/oH62FIw6G28JAi8TIAYPASt54VypU2531I3mbtWe9lAJ74sAjgm22h8ErMDi4MFeyjMzEuDLNaT0Am7m5Y2OSqavl6JbxFjYlDmsIhtRt0pPvsLRgLWwYRmrFP6BgFZLemaE+t++u52PMNssSbtSeXELeQDYdQjwNBCCeoyNcRv5PdTwBlVa3Bxmd46S+3M0weJWRHvKOBrcKXedx2CDr13h+NCMWBiQrmmKbfby+j7sXcbZ0jS1YZfNa7VTvUXVJq9G9qN4dDLwNgUEG68olwqXaPcosh0CsgkeTf37+E+Je9p2GbdHy3MebvA/jUF+mcvihDWl2q2sTQQAAwQPESI4SazR6afubjt4bDRyI4FluWFsKu9WQBpzCAIGFu4vchBj7sBNf0CE0vcTkSiQ2nAcQwnOA6VcimLZqAGA4mbBLFIRMXQz5WfGUyQrmWuLBXNgMGnztyfS90z4cjTM4KUxSqxM/DRRUlTzMOGbjF7HiMglNVcELb1SUE66oeGrSnODRydApQu7kLSQnkVZQL3yf/Wvib3LHsbI96K3Q8HUdTENJlXIAIItSu/wIjwqQpE1dZ+/CVCsItbfLy7o6kPUNvz/arloru7MXb0Ftjf+7n+kiXLVQptQgZaHsIann7w7cxzljCmP2dBtT4MGNbqOja25ygKhMxLOczX7EudDXdfblZLFTLjYB3LPVMAeVYYe0z8b2KiuEI6O77zoU6IyxOJa7CHYjhvuEa4OZewPh5n8eoh6kj9xRW6agNcF8FAAYTJdwFepuHAJGez5kIsMGQv5cR19t9Iiw0eOugp36HiP4RuEm5ixZL+9c361CVQf9G4yeeK+QxyHa8VMt251ie2nCC0bGVSoOWJbD0xeKoR8DgRrAwJFdh50o9UI5qnM9DR3hy36rU7zO2gnhf4qnolGBScsIjWWkktYmI5aIKa0v9EOY1ujuBtXvDXrurTfFKQ+pT4oY2geWYEKanA1ZTq/jmZ+KFIu1kXZLXrsn5GYaZzgPsgc4DD2uXz2XyiBXSJSGpfHEfYL0LO6SBUREkYIViBGBG4JDKg1pOdgvvV9O1BWdGzpv30w/agjYdFSWvDx7zS6rYCzxSe4u7O4z80SkhnVXQBbscy3ecRVK2WFr4qOTc8iMRhC1WW7kzNkTI9m/5+atajcTG9bWtA/GJb96t5CbaboI2I5d7y+YaOqFI0R1vYRwet80AqEBJE6PkXMGc9b6ieRYoDOFGE1isqkIAQwwOISPep62Hua2RLtpoHlLrkWuSOxEFl09b7iJpKVLqHitUuabOocZq3fee1wkxFYk1iu5bjsPglbP0b6JxL0rj6+3fvxV8ZgB8y/0/nS/eIMO05iC2MPI9b6KG7UgnX8kaQayius8ol9CiLm6/HC8/eMlLssXZIipiCpOdgiAKlUSx27hCLLGIJFzA1mrMrUONgQqUHmmPTunsrlBudFzICGBoqxM612vk+z7a6DyMACHVjNhcVLWGzlREnhhaiIJGEuuz7RCarn+fLIFmwP8AboGaLrzHAAAH720wPMOKa/biCNk5/1zUpGOP6bdf+6x/DpXrTmhhz0HyHwBm51NUAw/pFerrEOlD2n9e9yiiExflEHl9wTxYBIy9xb6xwAB+bwhY+8zUtSlVtcHqlL/H1VoCapKAzsUgP2zdtSy3coC43wjvkJXhnag+AF+n42jqU0SJ8OJ2iWqUOc9EFEbngO9ZPUzZC/EOK5/nIHjegVcn81eu1xFmvkKBnCiLJAh0y9rRKUX9XNqcFU4YTpK2y/y9OtZm1nqGASgggrzLrQlrWia+L4tAwFX1Cif/GsaXNfeUjLtVhL+Zh6E4hlueZne/+ByjU1aPbEXbh8IjqhS1a2P9syxp0kP+O3vEU42wxca/L3aWqMcdpyzCXALAsdqwcjcpNdrcW7OpndCF5ZJanepGRUjVQKHtENafZqewWrP9yNuwiMtrqGxSw6QK6aGXALrI0fa9hxHFugvlvCFHxmcX+OSSpSB/yCKvbqgofFFuskUpzYnSJd+/5WlrlfZbpzAAwIwaVLMcxWV/LvkhsHn15QgrgEOEkwcT9G7F1rWM4kCDGk/1IQR1pS040zmiCnYYVoqhZ6FK7axzuQ+iLMh4Vtt0adB0CeNnA6W3NwUNOYGzeIA1peR8qGELOTvyIRiTkPMsRJlkwTxMSu24x8gABHKoPKMIqvLWcrIyyA5Kl5OaXeKbIEoonxkML3tDR7UG04t53Jq4QxErcRhGXG3w3+GVQIkPSIByLrcxO+eVXpy30E55Jy1ToEgflZB/yx06EPjZ3IepAA79kjYexl7Ylr32fMWDEoVb9xTyo8DAsIr29SAs/LJPhAMAEW2C6dH/CPiS/x2Av2WM2Xudz+11H7L4TUqun3V1gRONVdCdziQU0WdIIx2AhTwA5Nc55qJ7HZgijXpWFFcm0ENrJfdzUOPzKKI0RPBWY6YNEaimVMx64WJQ7CxRbVjrvzGgtnVJVgkvTB7xbKdFkGzVucLGpybuvVqwZNUdb4npQGHU49xRTMtw/cwzTlguN/39mD2kHa0GwJZkaXO36pgQb7rA/HwQfhrWmF71ynb90zFYAEBUlLXcUFxbYT0VMkDvlomeVd33iW5RDoMg0b/ciJPy86BT3viyWJyBgJo1LqcjiWWxUJPKRMbF+h8eQR14/O/qUd8h0hkLgeBN5v68xLOUehmJ18v1qzoOf8HYXtoiOAuFup+4Loo6V2jGORan+dnqBCjHcfJ5FdSNzE9L3soforvrua/aDh9zuRl7HvK+LdiTlPPVKbMR1zZtU+z6LoRy/jKcJyBpxDQGglBropygQMDLgK+qXFMobG8WnUmflRhMIiObtq7nubsfgTEXQuZf27h/xX1E9JMA/iSAHWPMu+1nG+A0woMALgP4940xB/a7HwHwA+Byib9qjPkV+/n74HuE/yswi/ldVdu9hKd+FsCHAfxp+/7P2hP71nu9wHsdRHQZwBR8YY0x5v3HvicA/wDAdwFYAPiLxphPEFFhz7EDvqb/zRjzo3c9lubwlM4U0BiHNKm7CkkdIzV4O4m9GhS7jausBYDi8gGMjU2T1miKIExxhQW0axijmH9KCuLSlYniyFIjIItitaZQHOpICXHHO7s78UAkNBQ0zZExP+cVwsAWFaqpX7Em94tN93NUY79903n5JJdcwXIb6O768JNqgLWnFSor9yVkEYYg8qOYQDBZBYLrgCVU9yb/cLXF24ulqVNOWkteQqdsjSZBrUMWCN7jXtzopRr1IIloUsJzq2zYbLUtfxN09oGtP7TJ6mntCsv43DVmF7KIt0hI8jo780hhzN57BoAP58lfiaM7YIINOwrdRtgXJQzjdW8bVKPEdxUEC7fpRZ57quY5UdnQ4nKDkM+8x3ycV0lnwOxc4pomSR5IlGrHIo9EsKqGzy0PQA3VAK5QdHnaYHnaU/fnh3AKA+A5kwSFnFHjMvEAAor/5WZIEaJdshzg57zaUA4dJT08ZM12dxomNQzCipHFb9dyHa1DD8g4Xqj4BQ8DmPuXCP8pAP8IwIeCz/4LAP/aGPO3iei/sO//JhG9E8D3AXgXgHMAfp2IHjfGtGB2jx8E8BGw0vgOAL90twPfi9LYMMb8f4L3/18i+t57uaovcHzzXfp1fCcY/vsYgK8FX/DXgjsJfosxZma5sv4dEf2SMeYjr3QQoziZvDiVIp/qWHBrj6hRlea+1K5yVaPpp+h98qrbvnngFBIrhOuNHkABbFYBppOhPtZZTFxvEEUCvmOrtQ8f9VbfYlsBNgwzutLEhYBFbIkCTDVyfIiy6Fyb+IsEYIqM+3bLtUvC03oXkeAlRusst4OPWuOK6bioyoeFpMOfOgbQTmzERogFRYD1rrOyCIv9QuuwKbhaO/Q2Nj/TOOGhE2YbbkUI9Bl6GTKkVgNy+5RrFYiuKLnOvtsco8vaeZ1778qw/mwT5TGo9cWM2Vy7OHo76GD6iJfyTU9yFfaetIgAEeK91dbDSJcGyy3lLOz5OfbqRpfF/OZqcYcospBeCa8tThFWm75KWrUsjEWgyrnIsxJlO3yB40+6SFCuZxjcsIzEFnUV97Xwila1rLwlTNXZl7yCbAz0bnLFPsDGABDP2xAmG/aQkfsrnoNRxOuHZFuCqrwiHr3UcGI7WAeGfP5Q1lvYg7ztJEAnVjbhfbpv437l1I35MBE9eOzj7wHwTfb1/wTmCfyb9vOftaUSLxDRcwA+YI30kTHmdwGAiD4E4HtxH5TGvyGi7wPwc/b9vwfg/3yljYnoF+9hn/vGmL94D9sdH98D4EPWffoIEa0R0VljzA0AYvNk9t9dHw9bZWxRL3oxFDDinbGCKITl9v7ginvdPHia3Xrr2uuUlUBx9chtU210HRFhNUzjRvWGIam9m95cbgZJRKURxZUtlFIWSLoygLmzopDf9m80UFUgoKogXNXtoA08o6aXskUUNo0KT1f5sELnyMTJyRwOkixDQlMAsDqtkR3F1dcAUJ3i80lfyDAIYuLFQYvFqQSlEM8lQN0DRi8JJYqOaDY8Tt9/phOKEtTCXwX4yvHhi1Ywn6Xo3AUFN71kUTs7zJUk1fXHezSw0szsvfA1AwDfE64XsACFw/ZlvdebrormRt3zymb8vLFMshaokFEUspGaB+fB3I5BCbw/n9iW8xBjoulxKEuqwHWmUI6VO39JTItibTvkFAbgDQGn1BA3A5OcghhLy2243BPAykugzLx9oJwsMkoADNnCRHOybxVbqNCGl70nvTzVcezDQMxPxteSoFwLoMiTFtU4iar179+4Z09ji4g+Frz/CWPMT7zKb05bWQhjzA0iEjD6ebAnIeOq/ay2r49/ftdxL0rjPwH31vif7fsEwJyI/jqfmxkd2/4duDuZIQH4x6/wnQHwq0RkwEy6x2/SeQBXgvdykTdsIeLHATwK4B8bY37vZQcm+kGwK4ZsuO5QOEnt0VG9W00Uw+QksnEWOi1LoOgAKzafq1EG0sYJCbFiVhe8Lx52V/Md2HiMnmNdR3XQrOaMv6WqjmktJPwUFqjlh5XrZ+B/aAWtMUgXDfIbPo9h+oXrsieJUYHwmozQdFREytf4aFiEGFyeOoaAsWt2dilAvmzXoIkNmVSEtmvQdm1IbZqg3qxRXBNByxavKO2jh2zOwAo+arzCACyVhKUGAYCVxNcDRRbGrqcXCenCK4umxwpDhO/oMitBud+SAxnZ3EbTUxEdi/CHiVcmRI2AzwWECCNH8heMKDbfGqesV+tMsNfxtgfyiXYerGoorptYZ3Zgx5uVsLcinkQ5Vujf9Mc3ijD83MRBfrNpjWqj45h0p5ekdzy/b4q47qQpgF7QW0WnrBBDRaYTcoJcp4S65wsxTXKMYTfwnmGsF2g/UkuDpuOvtc15HQmXlTPogpCxMN/yvRSobkinHtwLO3/C/hthW92wj/1rHvfuaeweD8+/hnEnTWXu8vldx72gp4avts2x8V8aY/7t3TYgov/3K3z1DcaY61ZD/hoRfcYY8+Hwp3c6RXueLYCvJKI1AL9ARO82xnw62pCV0E8AQPfsRSPCcPSMnyxNVyGbe6ijkT7YMqc7GRaPbrjtdcITuLjtYzBtN40qsNvUWzFiDYtyWZ7pIj9qkFqlYfLUU4mDLUCdxfj53u3WCe/8sEK15hdIuZawQgnRJXtzn7ew/ULkvav3CIRXW5BD6Sw3KeqRvbzoza/xp61gmfhjzc4H171dI7vuzy2bAosLLZINvldmA6A9bwpvfcoqXJszUQ3H8YsgWEnHwiOAD61kcwAExweVT01MH37ICkF6UQPMGSYWr3hNElbr32q4h8YNb7VS0JK12ujYfXhWX9+KlqIWvMVBG9XHtDmhzROfK6sNqoADa3jVJ3HdsTVc7/l82jAU2z4/yT0Uh/6YqyDJ37/RQGfK0X4DAK0aJNagqbb6SBYtDt7O15RPDHcTDDwlCpzZ3o4oE6+ojfLeRd0nUBN4nakn2QSA3s0S1Xruktc69d6P3IsQuh3mnhx1/jlZVwnGL8aedlqayMDp3W5R7PqdqFWL8jQj2bJJbZt1CQtEnAsLDbTXPF5f9NQtibwQ0VkA0vn8KoCLwXYXAFy3n1+4w+d3HfcdU2aM+bnwPRH1X22b4PPr9u8OgF8A8IFjm7zSxYf7OATH8r7j8zvzk3EyTsbJeB2HAbvq9/LvCxu/COAv2Nd/AcD/EXz+fUTUIaKHwDnhj9pQ1pSIPmhBRn8++M0rjvvod8WDiL4ewP8AYADuK/5eAP+JMeb/8Qrb9wEoY8zUvv42xAWFAF/8XyGinwUnwI+sVt0GUBtjDomoC0Z2/Z1XO0eTAr2bTAshSbPu1QlW54bofuam3zDPnWU+e9sGJ2Nd0rSCSRSqdbaoVakjL0Msf+k0VkiFbuAeU6tdXUSy5POQ6lUQW2AumZsQ0pm3qsoNxtKHMdmmUM46To4WHrkFoLVJ73pwrOlUME9DyOreVwTQ44srYJFi+LS09vQ1FwB7GZ0joGOrmM21HMtTx1hsbyUoG+GyMtj6hIKYX+KFhfHn7o43zSR/IIWVS9tzWrrZyXUMrll6+ksJklVcOKlTYP8dFhF0wLH1pQUZDK7wMULur97N0oUTyeaFmqF91rXB7KL3pFRtkFpjNi2Zc0xYZAUdFXoO4WvVCqJPwjn28yC/FqJ/Vhv8DKYBj1e28MV3TZc9sfHz7BkaW+Us0HJqNZAqd23L0xmWG8qhpLhlo7f+m27cwljOL3G0935NAEDvVot6oNz1ZAuD0ZN7QGN7pzy4gWTVIpGcCXydhU45/CUsyL1b8f1yfFZBri+EfadLwywI9vYObjbR/Fay9lqZd2mUD3Hz0IasQ0r31zrMfdoVEf0MOOm9RURXwWURfxvAzxHRDwB4CcCfAQBjzJNE9HMAngLQAPhhG5kBuMHeT4Eht7+EV0mCA3dRGkT0kDHmhS/wmgDg7wP4drCghzHmCSL6o3fZ/jQ4rCTn9dPGmF8moh+yv/9xMCTsuwA8B4bc/iX727MA/ieb11AAfs4Y8y/vdnLJClj/TIvOUYM2V+g9yzEQ081R/O4z3ou8cBazt637HtopE7F1gmKgZpD62HlCWGwnDgYqoSapOF+dHdiEpy26KjWSFSGbsLRZbVuX2RYs1YMk6pVBjUE9St0iWNj+Gt2gR0b3VlDh3RqAKKrDMGkSJbDDIiYRUp0D2UeflQWAwUcss6AkUxfAahOQ1Sm5AodmShmJJO/bAig3/bWwwkCUcFxtUFSU1wlCX1LlPrnkw0HlmleY9RDY/kSL6aWAen3s60NUHcN9VxscPukHvions331/eD3gi8taWUzZpaA2fk8okjpHBFgz1HuY4h0U42J27sG8GBJ1odKAORDaeVYoRznvo2s5lxSNwiBtRk5wMXG03xTJeyoE0Jnv3L5H1IKCsDqDAcCOvsNdJJFIJAwr+Xmt/2jc84xaKm+XnKr2SxoGhVChHs3S9Snhg7OLXPO8T4lMSqu3PChrdpOu3BehHNGnmlYI1MPlLu/hrjuSQAf1BpU4xyLoIXA8YZNALD7jcFB/nfcn3Gf6jSMMd//Cl/98VfY/scA/NgdPv8YgHd/Pse+m6fxvwF4HxH9a2PMHU/k1YYx5grFrTVfMThojHkezKh7/PMfD14bAD98h20+BeCrPp9zo9Ygn7ToPLcDGAO9yzhLyjLQeOTq/RcPjJGstEvUCffT4pQI4QzFXuM8h6aXon+zccV2xc4SVLeYWfhlsuJKX7EaRRkdvp1TR9mCazJkbqXzlhstiRWWx5DYfKajhd69uUIy8WW7oizakc8dhAy7TVfF5Ik9YPjCAqttXonnfqsFiPdx9BAAE9KE8P0Qi0/izuHij9BNA4OtT5CnnLCzISwyCwVDz/IqhcnsyaU0EkadA2B2SS4WuP2VCbRt1pQuKCooVLXUF/D7/g37N0CeCUIHANb/7YucAxJamQ5QPuaJxo4e5msZWPxJujDO2lZ3kA1scMRJ17132OcjoAxJn5DlNbOEmElle0YElurwaoPudX7We+/lixLSPYZ1+2vp3Vzh6JGuywOxYOyhux8yBZDLUQgYQBLhOosFdVIxUsvBjRca6bx1BY91nyK4dC11UAGE+3hiXeZFbZ+PzCcy/C987sWev2dtDiy2Cb3b/tx0SlFXwOFLGuncX0A+qZFP+L1Ux0sSPymNK27kE8B9G6Gx9mYdd1Maioh+FMDjFikVDWPM33uVfV+xISpDRDmAvwrg6S/8VO/vUKsaxVPXAABmEfJDa6AbUFKnXCnaf8nDYKaP9t3Dz2aaOazsEOtFFufswUHMNTVIUPc4CQoA89MZerdjCy0U4qJUxJoWQRsiPdJ5QHNhQyhUNu5vyCPVFknkWZRr3Ko2DOHsvG+Atc9ZSzUnlzzd+lSDpptg713HkFrCNzTihS7nSG0cQth4kv+GVOJhoV02N+jfDK6lNmh6KkIlZTPf+7lzwJ8NL/Pf3Q80yA4Sh1yqcuOKywA4CgxRFgD4eHYTVWoUpUb3ycC7KCuYM5vu7eHDHcwtKNHdM3uNxzsBJpWJ+IvC5yq1HysLiswmHlkEBKzJgUGgGuD0R9mdW213nMIAvKcplvtyK0F3t0XnwArGiwWSymB+NoS1+uLBpqDIq+t/rkTTT12DqqTk8JDMa9IG+TxGS7XduE1qRM0BYLUVz5vZeXJkk9VaPFeYUJBf64H9zshxgPkFoG+VdZvHSC6ADRHxPNKlwXI7RXMxKHK9HvQbt89NvPvDR3LMzwH5FZ5I688YvIj7MIy/hjfzuJvS+D5woUcK4PNFUAHAD4Grt8+DE9i/ijt4CV+yoTXMagXYmgXXE3x9DJQlzJhB6MXtFdpuinrdW+rD5+YwqV1MsxXqrb7raZHNDcqRivICqtJuAgtx2vSCD82Q9vj6fBrXHizO8PYhgZtqPQV2cbuEUeSsKHXEys10ecKbPEXb84slWTZourlTQqt1Ata99Z+s4BQGAPReOIIubC+R7QKTBzLfVjNhtlPphwEgqlCWBSLeg3z3ShXZkrMIQzhGEYbX+IertQS9nQY9iwlZbqdoCsLuB4JQ4TgolLxlkUaHsi/+GyqmEE2UX77NMfcmuIjc37udr2c+snrM59e9QegcxCy6EborUBicr9CuiK8cp7j91Qr1ug1DrgOjD5PrNSLIK/Ec6oFXGAAweIY94+k7vUKbn4lpV5Zbvkf34WMWpRVcWrHnq6dHl7lupNjxioga7VyOapQgKU3Qia9G00siNuUyCGclFeflwlxBU3jI+MFXNehdTp1XEQ5pYZxZO80ksUKVtVTY4ki9a70r67lI9bjU95QjigyV7r5260eGKAwA6O5pbD2xxOThHu7veE1J7jfMeEWlYYx5BsDfIaJPGWNeNTkSDptb+G+MMX/2tZ7g6zaMYfrvOHwGs38I07YwZz2sNn/muoOqtjd3kDzoAVzN/5+9P4+2NLvuAsHfOd9w5/vGeC/mMedMZaZm2zKybDB4WpYBW+DCNIMLmtUGQ1V1lW3obqqg3MtNUa420I0RGGNXWdjGZZVNIzwJS8KalYOU8xAZc8Sbhzvfbzr9xz77nH1eREqhiCcppYiz1lv3vnu/+43n7PG3f3upC51VjvjNaIV0UAmOHoV46MNZkzkqzpt7hXMW4eR1EEQrq6RlJ4eE+KrKQA/9/6wwAIL/Fo3IVSpncyn1SRCVwSYC5l8SwnNzAj2xnEV1SY0eobleuda2AHkNmVgIlYTbWxkmSe7KWphcBTykkWPQHAokum+ZCC+pqExwf228o4BKROL6ZXG+jNsTj7h9tXL3nPthp+fXEQybu0BeoDzum3WkQ68seMhagyj34SmjVQCIyGsatd0C5/4s36AKp+65inOvHAIAHPwYbTt71nsMRgmyxDVg+0EvxOafpeM2L3kPuLYZY+th0uBG2XCTrLGJDUq74tuXFKoIaK3Y/Vy1Obcl+n26nRG4g1MZEYWiGCRQNCMY5UOHWSdCFamgKLWsKVQlXVfWVdh4q6xSVZgsV+i+bIsg6x7GO15QAXEkTy9mwTXKUrkzI7DtYyKpRoqGhwgn45DokYdk/TXKc4m1LtOEld7/vo1vcE+DxyeUUj8LgJPYHwURFu6+3g9s86YDSqnUGJO93nZf06EUoDlDW0I17WKzoSr19MsAgCrLUGoFFbE0qFC8+hqiR+53u9p8U9MlPpNRRZ6GbQKTNzXKNAk4hFxHOJBnIbujDY4kFPqyk0sXxhErAl6wchiqSiMkG0NnkSKJMTzVRW2Lbjv3wJgueE+pd8ILXWWAzvnS4fE5DDc8PeO2YZZbvgYZq5b5CF7crOjKiHpnSHqIZGhcnN41mhILSca5VUWxabbeudivf0ZY8EJhpJdSFA0gO0bXnl5JMV3O0X3NT/No6skinVWtmMQoByIRQrqXBPrwKIUrh0tcu+PPVyKKdOEtcUO0syhESGr3ZAoCrwCn7lnB5r8/Cg7TRxl5ITL5XCVA/4jtTseJdYuOWv2mDtpXKrQukPdRNmOMDtU8SV+sMDyknCBurlFx4c4Z2xNFkUXvuJ9qMXZP+7BslVqmWKbzMAaNjVzk8sjidx0nSwpXuUS7CdFUZarQvBQFIH+Zb2qs+7qKeOS9BIAYeOOJQdoTtCBaOVLP3okURZ2KB/nYRgHGnqqy6L3alqhRqTyNDF9DW4SgR4e9+ywr1W97fAX00Fd73IzS+NcAngXwPvv/XwTwiwD+zJf43XkAH7e0IrJ505fKhXx1hjGkLGZJOJoOmaWq2YSaTlGuE4mvTlOYIkeV0QyPz5yCadaw+dY52r60CTQrO6azGqrw4ScAUJVyyCJVhO1cWYAxfUNrrUQVKyc8irpC+3LuBGe6MUTVSBw7rR5RCGN8gq6jShUu/1ABXp1qLcGRj4UztX05xCPIPgwXv5viBRJRNBEs8VUCtC9bYV95xBSdK722VsPiOQ4z8DVwqEDnhLhhdJhRxDjKI5ul6Tk8RK/xBNh+WCiJbQ295j2L8Rl7TxILSjg5xuJHGuCHw14Bh7/07igMRymF8tC8Q0+tva1ht6evdYkgnBKNgJ1TEeZeDTmOAGC0JMJ/oPDKdB5onaNr2X3yKGJQKMTd21i5xLPPNdhrs82iuOJ77iVKPE8XaQPHPCy8m9lXS5Qi7Hn1XX65513LvzS2nx2KUetVzvhx3QmFUuwfS50Bs7e63Si6f46ZViEwftK+QdZRPk/BFDLs1Amjqr5JhsfokP3tgNBPXHDLlOdMix9lBoMTAmU3UGis++fGXQ35d7WdAr0TwhuvA0ufG2BwzLtlMlyVH7qebfmWhsE3dnhKjDPGmD8r/v8flFJP38Tvrto/DZ8TeeM4Z2kCnDgMlAbZwQ6SVUHX2Wwgmp8FAFS7ZKbEZ065r7cen3PJXubg0XbtGw2gJnD2BVlOPFfaHG7gxWVDMFMRpso6vmdFc8OGbnZ9VXK01oPp2gmel05hAMCl79TATgqdidBISwdWFgRNCieZJQlhlHlF4RY/aKFLbiHOEcimSPEYLnlaNOk6x/PWi1kxKOsqEETTGQ1YAdW5YLvK2Xhz/wgt1kLkTJqX/H0yEVn943u8yWpyRcxjAOY+0giEWzSpoHOD+vktv8Pc52/Ko3TRZ/8CW9MFom6G9ifsvTbhtda2CMEkgQUcptSlcQqD70Xa8yABzqtI4SQFmaQc56GFnN45rbH4TOmURTSt0FzJMDpo632mxikMANg9GaN1Bdh50O9k/pnIhe5YWU1FmBXw3kLeoTArDxMR3XgVeRTfZE75Cnz7wkqRaEC8F+pg2A1/f+ZetJDqE/Tjpi2VKhNq58rzcOe0homA0UmRzB5Ebp/NVdq/5AUbL8YuF7JzbxqcAwBsP+BrkHunFA5+usTYwtnTwf6JrW909BSPsVLqW40xfwQASql3IWhH/7rjeWPMv5MfKKV+6BbO8SsyTBwhX6Rkt4kU8mV6XyUUMKh9dtttG50+if4jJFAmsxp529cSNDYq1LepVwDgw+cNESZv7SETVIWnXmf2TQ4TTGciogmxE7p+eQBVllBjpiONUS10HFfV+MQMdu5JsHu/3WGbjjX3cS+AalteMJqYYJucO5n61I3fRsir4Wl/7t0XbGjDnhvnRViQ1jfD/bCA3BuOYzhjlIVtaXun6Jwl9UeVQCRfDcaLPmmfzdEK7HzBtoF9MMfSH8Xgac2hPEk2WX9NcJI4KC23kSXBEXX99O5+1FufzIYrvbC1t8ao2euOpr4gDVB7IKr02rBGQBWroNZntJwinhA/Fm1AL8MTAlAxm6HxeZKy9S0KZTJqqmhFLskMwNWqlFb/xRMKB535DUGM2TJuHkxmFQBPGdNYY0Zam3faMtRG+HV6SwytZ8XAhsR6Bi60quj58eidUoAKOy+yUO9eCCnhOa+1fY9HJg6PCG+9R3VHc8/T/9wffPt+W6OSatQ3RPfNpjc4eMi2v2XD4Mp7YuQLVrFv7EEL3s64Q5TG3wDwy0opXg7b8KXqX2z8FIB/dxOffU0Gd+6LJgUpEMvpA0NWW/6o9ywmi6mzEIoGKQyOx5c1BRjjQzJKIWt7wda+WhCRnA1JuUVk60DiSQVEylW08n7bL3qlhUi55lA6rwIytp17YugMOPxR+l+XEaZd7dz/2k7lLCbAwxo5GT33ErGn7tzj0VzcjhUA2mdjF3tOhiTQmd5al6HlPVkk65uPzbmMRBQncowe8IvYIXpKCsdJ6GU0DXtANNcMMptHyeaAxqpf7Ic/HEHnPjZet3DT2iurfoe5UOCcBxJgiLXvmQA2eRufIwHNymLmXGXvHb2uvs0rDICsZlYOfA7SGxseM0h73rwdHvR5pvp2hf5xHbDsSqWjZ2141ArlvfU5AAI4bXUDEML8i2MXiuT+MdJACDjGbCU/C3qu4WABPpkjb5hzUK0V4ukKGhaJ04snlJuTNTzdc8bNFVX6anajSWE5T6CtoEuF0UEbgqsb6KmCLukiGcrNFeNZG8EcYmOGPVZV0bPibZh5V957VhgAUCy/MdOyX6txM4SFnwfwmFKqa//vfbHtlVLfDaraPqKU+ifiqy44C/gGGMoYRJMC+Qy7qha2lxANuuvlXFAxHs9/dlXZmlHGUocIizntiZBOBERjj6ZShvbJyiObTaALg+Yq3Zr6lR5MGrseF3qaI1/w1u7oUB06r1xIZOlJCltt3+cLlLKut3J7J4mQTy6iwVEFPbWLfbXCcFk7qo94QkpDUn9wC1NdGAwP6kCYSRZXtjLnXxDkjU0fDx4tRi5ZCRCqBRChihqhfeQ+AWBgrW+dEQyTGwXNvkTn5Np3uip8f4K1s0JhsGdhe4kgSYBGHbtvWba/N2g803BKIh6TJ5aIyGUsvJbaJilOFnwyfMPCdfNRcSHaYP3t9hxXNNJeiL468pEB+qfoBg2XKQTTeY2r3RuobQNNkS+SAp89C5l8luGsxWfGGAkllTcpXCRDaHJ/DHEtXc9yWzQnDIJSNCAYLseo9avAcyzqytXMNNeoJw0r1fkXqSCP59bgsHLny0V8ri9LbivE7bHigUaVGMy+zFBbOg8Gn9B9UOhc9IZdUQdyWRS6KYpQLbLYIRYzhe5zCfqPekDFfg21f02YvmbjprmnvpSyEOMqgM8B+H4QVTmPPoD/6uZP7e64O+6Ou+MbaNwBxX23NKxn8nml1Afs/o/bmo831DCRQjaXYjIXobYbWkjDQz6G6Tr42eRtY62ALk2AUqlS5VAn2vYvlhafZK5nBBFXiTP9SNz31nmVxtBTspbzhSbKVPuw0qgk61o24FEKs6/Q79cfq6Gs+5BP51KFaGocfQN5Gf7U+kc1JetFGKN12VMqUPLSH2vpCb/hxqPMk0Xfz5wlr6eq+YufCrpvTsxyiMVo8h4MI2havsoboOR8lYbQzFQUaTkqc/uMGmu043hLmPwTcbFRREipmuiK+IBHAOyetgg2m7OIpgZ5W2HmNZFwFaSB2hY9SAp0HlVM1u7cc3yywPYjBvUVCzee0LUxbcniF7yXAVivKfetZwFAfeeWSybuXOli5nm/fDl3wWi2KvX8UwAwWbg+XyS9DM7F8P3nZyo9VKMFH1RTESRaGOFVrHwvkq5CMvbh1smsdnUWAJDZ0CQXHQL+eUb23jikVR2AgQN3tC9y3Qgflzwfbg/L9T+yoC+eAGrdX0cVA92LNh/U0Mg6KmhY1Vqt0Llsc2OjAq9gn8ZdpfFFx3cB+McAUgCnlFKPg+o7vv8reMybHgRrZVikdm54PPYuOOBj/xIKCnhBqvMKertE0eZmNhlipVA2bAMh0WuYfhfCX+NBBlVUqBr0e5PGKFsxKu78llLsmGPRUVYFPR10YbD5pibGi/w/hXDmXiRJO52LMVqK3Pm2LxvkLYXpjN8eQHCOsvp8OquCuEV90ysELsBqXxL9JoxXhKNlWoXcJyKaGGKdZei/Vo6ADyCsfhWH7WSTgY+rq3IPVYc9reaKVwzJNVE+ZFgKCabe5QVMDxKYr6pprD0uloCi+LdUyO0rlaP/YEABkwK2baW6LDaUCVyjfIilSoCZl7QleKQhw1nTuRp07qvlOUS28C2ebfnKOY99Pvq7CkDpwpy7p3QAf557MSfEkZ0qDAEuhZCfinNpWkXJpJBG+fcAgQrG81HAzFvUPb+ULsKanc6VCmWqHDOC0VS0yY2tqpR+s/AcA0C0C/lOZ7SD2wLUh76Kw5CpzD/EE2p9y/mUWi8khowndN0cgk4HNM8m3MK2oOZbHF7lCn+m6pFFmrc77gj0lFKqCeC/AXkMf00pdS+A+78UiyyA/x7UD+MjAGCMefoGPW2/ZsNEvoBIkuSxlSIL0vKmQjwRngV8zFpPbVXxthechSAHdJPXLm4mkautUuBelSXKboOoqgHkXVrV2TxDJyuYitAxgEUCxdpRqG++iaxTCTs9+lsRRsuWfiQzqG9XTpiNlqlvdH3b3wdq4eqvtxDWqLSMOpdLFM3INfeJpnQfuPgNCPdlYgQd1oq6CmCO0zmgvimQZBEJ5GTojy8LCfmZSJ4snVM7T97//LVdpyxMqw606sgO0D1KVwdOYQDAzukYaU8inmh/3Qv+XirhZfGzniyJ3EA78ucvq+EVxdrZep0coFwNe01lGgo+fr6c46hvAzv3+h2u99pIFyaY/yBDQykvxiCDxecrqMKDALiqnhtSXTcUgRY4P1TFCvF0jwcbKaTWWGKgBhsnaS9cIwBZ9vL39R1PvOmoy8U9kvmZeOLbuaoSmH25coYc31dmjmYYuAcZKBQNiQRU6Jz325cJeePcqGm8QJQonMQvUwUYDw82sWXFrYVrdl/GnaA0QIV8TwD4Zvv/ZRAC6kspjcIYs7uH5fYNN+JxaFHx4DBSFQOta7mjmFa5CcI14+U6mpdHGB0hwaRM6NLrjBaz64xXGdTWJ6ha3H+jILpou8u8ExF6q821Dhr9YwrzL1qoZqKw9UCMKLNd8/7UOtbXZ4Cp3X9a4dq3aBz5mO1bMEt0D667niFLjDmOoikJNrOHlpWRJjIJLDvBAVYoGhWEmwBvbccTUggyRDWd9dZWfdNbqXRQokbnEWWh0mBlMVoMaUw8y67B7uNLqO3QDmtrQ6cwAGB6qIO8FaF/zJJFHrUJ9Rf9NXUveoXBPUdiW0vBSkMWQ6rSd+NTpXFeEVNayPCS0Z6ZlcNAfO2MHOqfEFDTx3exPaQH0f79NtKBQWPNh5zGS4kDv7NyY2NGGapzkcWIMvTHwt3XINC8doKagRqWWHG8oAIPnBLj/n8Ojynhiay/KUb7Ku0/65LB4LomKgTe7fCgR80VTWDmrK+CrxJFaEdGhE3onstQ23Q+DGOWNe9NH3hGhCgB1BUpDl7H9W0KT7vq/tyzINC9wP6NO0RpnDHG/Dml1A8DgDFmrG5OEzyrlPovAETWO/lxAJ+4jXPd10EEcjbcoYDajmiKVBhMbeiK+2EwT02UVahijbztZ1L/npazrlVpEPdzV3RlIqJjiDckaF9BD2mG54skFJhKHQoo08gJ3ukc0Lno6Rx4oi/9wEUAQKpLVFDYXCVoSHo5xdIT/lr4/Lklaa1nEI9KR/oXjyoMDsfO6i2aNv4rrGCOW1cpLXypgLKuAq/VKiFFIENIkhaDKcmZE8jRvEteOLGo2EOJBWRXNpuq7ZqA6yjdKQKBPl1qQRnfdzu31rwrAruqgla1td0K0xm/JDg8wZxE2UyM4UGN9lX6nAUsC8yqpd17hq+y0q1iYOFZwUxsKTBGov/0dE451NLg7WPUAdR/x7tBab9E7yQ9jGRkMDikMDjEBWj2mkRDKsm/1Ni0CsHenrKuHOUIQPNKNjJKhhWgFAaHZGjSvXU8XPxsOCfGqL7ecfrd1sP0+eLTVdg3ZU4DChgeth+I566LsA9JPDHUE4rrc9o+xAUARY1YgmU9T9FQDgY/WkqQ9j0btW8xa73vA1HQP9w1YbJRhxv1dr+VQQblG9uIvplxM0ojs93wDAAopc4AmH7xnwAA/haAv2e3/bcAfhfAP7zF89z/UdFkZAHnchqjKlAiVU1D5QYx98to+DwIQERoMrRTRQrT+dRRUgNA1BO3K9YYnGyjtkPfDw+mUJURSkZhMufhh7OvEOstu8qTWY3GusHOL3vSxM33eBNr4VnrVtu5GWcV4lGBdukt0fGBxAmtMtVobHjhMdG2N7P9fRXD1WXEY+8dAD6GLS3Q6ZxCOeYiBa8oAKC0yqEmmiLq3Dhh0lwrsXsydtXJbGlyqKVMKYbO3kc8LFE2NNIdn6iOhznK5o27EjIfV7Ppw2nNa/7exf0Mw+NNp6CzjgUJ2Oc7PKhhtA/56IJCcbI2gT00PfXcRwAwc9agaOig/7suPVcVQMpn/DhJ38bTDQCNgNF1KBTM6jcRFGfuOS9co4lxXt14kQUe/Z4VMDPhulCM/floKYIqw8ZQWw/562pfoFcm/qOK6zIAhAQcTQrIZg0Wn/bzRVaoM+MAV30XDWDuFRs+mo8QT33hpwv7CcUS5QawYcyi5mHhANPb+PBTc73E4GAME4XFqfxcjSbFwdEFNjKYsFD2br/tcYd4Gv89gN8BcEwp9SsA3gXfMe91hzFmBFIaf+92TvArNWhiUQ2GS5gCZOnXtHfzcw5TeS6evuDlL+oqUBpcfSzRUNlS0+UgxssksNii1YUJrON4bNAZVm4xVjGdK1v+9R1i1O2dsYJ1rHD0t2IfEjMVtQ61Hkk8KjBeqrnq46wbU6UuL3YFwHjuos6FHMNDsSu8qxLfGKi5ZpC1lUumsocgE7AyNJeMDJKRrf61o3vOBB3WpBdhIoXupTJAskkCQ8DWSZRs5ZaIx6UjbwQQKAxOEsvWujqr0HmN8klVLSLiuz4pjsFJ0o5TiypqrBubl7D/b1QYLelQ0IvzyzoIQi7NFRHft8qCPVggDAkODtP7+nOkdVqrJhAwg4Mxdh72tPkqt1byQXFvBmIe2WfWusrtg3VAnT6Zs4WELEDtM5EFgtLb7FzO7XaWnbnj5y/gPYOh8Ezmn0VAYAh4672+TgqIPer553NnHDjkln2Uab9CFcHRvBsdtgQ2MdA/rlC37DBpz9jGZfS/7PTIQ7IRFE2aw3IdljU446G5sX+S/o5IhBtjfk8p9QSAbwItib9tjNn4Ej+DUuptAP4ugJPyOMaYR1/vN1/NoQzRIiA3JITYbbe5B14EtZ0SqjTIOzcObLIFx4vHxArpjrdes4UGtu9LQSAyYO6lDNO5OEAkta56BcN9OTyx2x5rC0Rqd+gTgum1AuprJCXGy3VUCsgZsdTS6B+LMLLUHbWdCtHE50y4ARPndfJOhOmsciSDuvAtV6uYrDNWKHv7HDQ2K4eAAYD+UbqI+ef8Z60rYwyPNtw9qxLl+JoaWxRbHoucxc6DFQ48EYYJ6uuiM2Hqn0tZj6lQixOXxiCbiTERnE6ttRKp9fKu/jFS4I9+93kAwOB/uRcDQTS5e0ahfdl7NpN5UhjseQFhXF8qz9ZKiemsdontsqEDq9xohe0HZG6E9sVkkHQ/vNS+9J2cUxMggb5/vxf9lHUUZs7mjuwRoGfNSo4VAuc2jA7zSXmH5tXyp60bqLzCAGj6Gq3c/pguf+F5ER4TnkXpkE2WGSFRqG8UqFtJMlqO3dxhj4SZl/NGhMkBuG3HSzQvXZfEAk5h8LVI71yVNFfZoy5TUprsIVcJodV4f+kgLHSciDYCtz32SWkopf4rAP+l3eMzIEO+CeDXQDL3PID3GWO27fY/BeBHQd1Tf9wY87u3euybQU9xu9f/cIPPvtj4FQD/LeiCqi+x7Vd9mEgh62ikO6VPUsMLZ5njADzkluPb7Su26RF7JPxaVORV2LTP7imvLHjEk8q1ezVaYbKQut9vPBoBlRceugQ6F71SiQY5ipnU0ZJc+6YGogwYz5PZ39jyvT0AWjhR5pVb1taIah5lA0NuONeZFHMKjU3fFEoZTxXCyoKFYzIMY+jRtAK0R0i1rxYkWKzia10ZY7xcd4iy4UFCsXAytkxVIGh2HmRYEr2kW1PorMR0wYeXxoKqu7FOVvXeBjs8WvZcr30z/X58pMQ7H30Vn3niXtrg3UA09PF5fgaMqIvHxoV9AFuLEQOta8bdn9aKVWwbGeobQLJF2rf34FxwbYQ8UoE137zm+3HwuW48wnQfFgYqFEW663+btykUJS393dNJ4NUBwtpWYVfAKAOaq8Izet6CCTY8KrBsJW7+s5fk8kNrVYCmalyh657atsHjxTg83sRg597E5ZSKpkLR9J5ENAlRbUUTGPiILHQuWuMihBKjRTQ+fC+rBJh9rXLbGK2QjAxGy0KpSOSbpnXnKtRFiPW2htkfT0MpdQSUI37I5ph/HdQ07yEAHzbG/IxS6icB/CSAn1BKPWS/fxjAYQB/oJS6zxhzS8ma11UaSqk6SHMtKqXm4B3vrj3wlxrrxpjfvpWT+mqO4WGmSxaFR10dNEeSCB4Yg9quCCmVJujWFvUzFLN1bN9fD37reowbOIUBwNKBF1j5ZguxHQHQoVLS09LVZoyPNKFKg94DHvaZ9oyDF05mqeaEkSBpr0Jt1+P0mWaER2ulwPBQ7M6TFUMm+KdikaPgpj0AKSQ6PwuptYlmVgomUqhteoU3PlgHTNhhTpc+Fh5NCS/PSuTEf7CEcX2fH6qSCAn3dj7ZsOdnz70dQYsaljIlRA73wd6+J0KVACNBAvj0H9yP1G1Pr7UdelUlKFl78MaWJisL2Qmwfpkkeb7QcgoDAOobOUaHUmfZVwnVKEhkmUy4jm0NweQxq8HWa0h3tFuFyQ34GcaLyplnyXBPgyi2WUyY/HbfTw3Gi7GDCA8PxygaCktWaZQtUs51W0Cpqgq9My2nqAFfvwJ4ZTGds2GsMqzr4DAYe9I6R1Bcp0vjvIeioTCFT3RXcdjxkb2OVNRxxGMPkigTZfttWEPuPoPmNc/aa2IqGvU08mRUcegsvcG9vuWxf+GpGEBDKZWD5PRVELffe+z3vwQqd/gJAO8F8KvGmCmAc0qpV0HlEJ+81QO/3vg/A/g7IAXxBLzS6AH4/9zEvv++UupfAfgwROLcGPObt3Ki+z0qDUw72rnz197tBUM0VJh7ieF4NoZrERU6ozi8E4xKIcpKh4YqZmmxyDCNTC5G4wqlgPNxT/H2JT+bWleLwIOpahG230Qxka13TTH7mRp23krHU0mFR8+cw6v/4gG6LrsIGX+fdSl23T9B+1aFQmPN34fpXIx4sgdOa7xFqioEcfq8pXxb1tI2iEq5eK8KFGiVagyO1T3YwIYLWCkYjYA11WhaU61rAkQwKR380UQa42XvWagCiArjcfxTX4jnzh3A7kn/WdoD0mctjHQptFBZOMiq8/5xcfGKEtwNGyapEoW5l8au0VX98i7UhM49vbKD0QMHXAX56JBtmSsK4AJWgQToH4sxI1rtbn73BNqSJvIVsLLg5krOWq5C3qnBcWBwXGP5s6GS54c5nVFoXymcEts5kwT3zEQkmDcfJzO7bkNlrcv+/DoXvKk/XUgRj0rnLQBAITw+RpWxYK5vEcKpfc0rHYmYkiFKVdhEvL1dRdPmm+yz4/nFr3lTo9YrXQOsyYJGMjDonfb3RxaVzr9oUNR8qK5MAcniIKveb3fIvMqXGItKqc+J/99vjHk/ABhjriil/jGAiyDQ9e/ZNMKyMeaa3eaaUorbTh4B8Cmxr8v2s1sar6s0jDE/B+DnlFJ/yxjzT29h338FwAOg7gYuTQvgDaE07o674+64O97AY8MY87YbfWEjP+8FcArADoB/p5T6kS+yrxu5yrfs89xMIvyfKqUeAcXL6uLzX/4SP33MGPOmWz2xr/RQhmB7RsOGaywaaahx8NMi3LA6Rv90K2hiVKWaEuiA672tCq7nKNE/1Qgs6JpwbwPUEoDmKtUWyMpjkygUCVlso8UIRVOh9y0MhNfI/8Qu0mcp4Hvmj53HFz74IEBErehcIjw8o0uSgUH/mHZ9C8YLxLHDPcFHBywlu4h9lzXl3X/twwLMI8SokmREPaMZm88MsBxOGnXrRLUumhTJ2HGZhlTZTMuRbvnYQ9HxZl48ytE5l2M6T3GMvBUFy6FoatS2S1dDwzmEhefpfHZPJSGlS05/Mq8gE9FsxbuGQuyh2U3mXh4jGuWIRrT/yZEuGmepmIE5rbYepHONMkKocfgP8I2PAKBzka69d9J7UvWnGy6u71ro2hVY1q6nYJH75ti5DNdN5mKX/K1vVxZRdb1nBvj5IMO29a0cZZOBGhrxqMDUclpVkULWiV3Iq2gQSy+j8pKh9TKEqHo9L4Mo8D1SbDKrg+dW2zaIx5WreahvFkFeUhlgIup5th6pAG0A3n5Do3lNQnkNlPHM0GVC5+q9I+zf2J/w1J8AcM4Ysw4ASqnfBPAtAFaVUoesl3EIAM/YywBERghHQeGsWxo3kwj/+6A42UMAPgTguwH8EYAvpTQ+pZR6yBjz/K2e3FdyEGc/JcySgcH8F2hCtS9nKOsajRWP0Gms5q71qM5NUAXsaL07vr0cCSNGU9FnnJNw7TC5M1pie2DzorCfjyy8kBsdGTvhjxzcxtWXlrg5HTb/9Qm04Ll2VEULtXPJ8iSVBrrwgmjmbBYcv5YqjA8IgsFZiuk7oSOIDGu9Cllboy5AAkYrl+BM+gzvtLxbqSI4s4h169yHpFhh8O+Hh+KQwqOqkOxOHC0LK4vBEX89WRdoX6V7W7OhxOmenA2PvSCBbMaet4CqOkUBirGrEpg5R9tNZ1RQdbz+eANV0sDCs/Th9v0ptu8/FFybjLNLmKhL4ho+boz1b88x9yl/XbUdn19J+xV2T2s0BdO7pJBnhTE86j+TDY5GS8yF5r+fdn2HSJ3TXHMkf/ZF1qAw7BygfFXeSRAPPEBE5kiSAYd1/dzi2iB3TmLeSWGadagwzwEQpgaqlPc0fN5Gq4DqY9qlde3bFBvoiUaV8jwFpjNA57LNedQUsq5oRRuHvUzkM7ytsU+JcFBY6pssxdMYwB8HMYsPQb2Ofsa+/pbd/rcBfEAp9bOgdMO9AD5zqwe/mTqNHwTwGICnjDF/RSm1DOBf3cTvvhXAX1JKnQPlNBQA80aB3PKIpkDnUi6a2heIR8LCrXyvah4mCmG18fYIJvXbmFgokMygqCkoCw1NBiXVSYhaBF0YBxPlBChTc0wBDN7iY8fZB5Zx8kqO3dM2cZ6FfD/JoAysSz0t0VgtMTxiY+PjAiaN0DvpzafRMhCNeXsLrbWWpi7CtpnN1RylLXBUpUEyuL5FClv61JfEd2+jBeMJIfkY3EskyitEwwLGCqeikUIZ4woqAaB3IvHUG137ob388WKMMg25s8aL1zd92n5MVP+3c+BVuhfxMIxfN1eoloDRY9HUYLTk+73zdTCIAQgT1EbDUVkwUIGVharI69x8sz2XVom4nqP/Hrvh+RZqO2FurLHujRQWcEwTsrf19OzLNO8KWxA4WbTWvj3V/lEqEuXBcFsJkpCFnLSP2An+aGqgs8oBQpJBSf1pOnRTGMEWCeJAmXNhstDU5vuKmgq79RU+qeBqlLZDY4XH8FAcoNoaGwbDw8rlCZvXSDk6j1cBKgH6x/z/PO/5/7wLxIJCZ9/GPigNY8ynlVK/AeBJUI+ipwC8H0AbwK8rpX4UpFh+yG7/nEVYPW+3/7FbRU4BN6c0xsaYSilV2EZMawBOf6kfgVhu39BDF8DMa7Yj2kiEnwS1d96NA8ZMx/EjCsZMLArK6gnaF8aYLpJVXCbKIqa4AlcFC5wp0hmTzjA/B2VcN+h+0JuHqUUOsScBAOlu7jqxxf0c0wM1B6EdHKHz4CK60bEmoVhsqGnrQR2EJboXS0ApF2qKJj65qackJBjuC61Q1iMku16BjpdqAfIs7VWIwPBhg6Km0bbJ1Ol8jMaqSHrbAkhmCAaAoh4hcy1Jw8K67nlPbc0jF4KHFSErCxY+ivunz2doP+GVZ9oHxstAQ1jz4wUV1GUAvp86W/qRsPhjCQOt+ZAPb+s61VXwCgPA0cOb2Bo0kb1MWqZ1xaKJhJUrGXKdl8aRPGULVq3SGi4r1HaktQ1M5okxQN4TTkxzXU4sYbOiTiQalcSLJppQyWK4aEIPhms54nFJnGpi6DJEbKXDCgNRTMjFogvPk2HFSKvBYS7688fL2x7hyHNCVoXPv1C6fuekMDy3VZWEAI+0ZwkkBQgg2fXFgck+9QhX+LIS4V90GGP+PoC/v+fjKcjruNH2Pw3gp/fj2DejND6nlJoF8C9BKKoBvohro5R60hjzFmPMhS+1zZd7svs5osygc7kADE14Kagq4epKNBBwffGeykuoqnKp/qg3xuj0rENbAUAi+JGMChVUsjNB/55uUFsg3XhWHozAYghj64pg1W0J/iKrMGRsPBkY33uiImQK05qnOySA5l8RVBx9f31lPXKQ2rIRCg1XSX3MC96soz2aZUQU1FHmf5MMS5fz4Fc9tcrCxssnC/7cZR9npr6Qz0SZkI48EuE0KIJrMpQzHgHbj/hzmf9IHVnHhx+YjntocSW17T2FfEIh3Oiz4XGD9BUrjO0x2UAYHAWaq0DvlBDKB4dY6JDbsjUgicmtRXeXgYO/5y9+Mq+dcAOAqm1ZhkW4rGLUDyx/UzuEsbLCADy8dyDa78r8RXM1Q9GKg/4haa9w976sRU5RAASFpldhEYmwZNEk6nPe38xrpYVoW69S8I8xBFs+12RgHHVP0VCo9SSjr0Kt5w2G9uUSRUM7hapLg2Tsfw8VQnZZufM8qGI63t5q9n0Z+7mvr9G4mUT4/8W+/Xml1O8A6BpjvvBFfvKgUuqLfa8AzHwZ5/iVGZUnIaxqkUvYcSiFXWl2ozk2Hg9zxLuif8Q4g2mkvoUogNr6FMmurSzvJkEIh38rBd/gaITeGf/7e37Nz2hWGtyWFggVxni5hmRQOqVT1DRQA+qbvlI9Hleuj8fwYIr6tnGeTTy2br+9/nQ3Ryk8rULEpNMeXQezvw4PNlDFylX5TrsaZd1br7XdCqoyAfRS58aFn/geZPMUOqtiFRBBZl0VeBYuYS34MjORJ+Aw0vgQ7bd7liizHb4/JQEw9wwdf+ObSWndc4oIkEZ/eCxI1HMSWoYpZi6UQZHe2tv9+ze97TXA4l2e+8QZNK8R4SSP3Xd5jVZr0LFPdagY4drmDMpJjM4z/jnvCn+ePZVEvJY1go7ztQEiUQ4E7KwHnrY5BmYCmBCJpfQs6luFC22aSBH9CsvZPfVI8YDOPzC2hMIwCSsDf0MnsxG4gF9VRD7ZtCEyphcBwip7HkXTJ9E7l+mB7txjmyQNQ+Xo+LUEIWPeVC6MWzRUoHDLOnFfMW9Yc4UUhgca3LhO58se+5fT+JqOm2rCZCsQT/D2Sql3G2M+9jqbP3ATu9wf2sjbGCZWrrqVE7gAfLy+EnUTIpmqJyVQVVBTDqxnUFnmusEVC+3AU0l3MlS1CMkWCQxjLbL+GW/CSoWx+KTCzr0Nl0eob0yRz6SIreKJB4AqK4wO0wzPW5r+mNNnO/R/s45G/2jkqEBYSMiiLDlGB2soawpbf4akU+d3a24xsTLhWHiZKujCOMGddUhQdS/4oirZwCaaEsMu5yim85Y9VoSzpKCrbxGChtFaPBzRnJLU3iToBscN0l065mSRYtWsNHr30nkt/gg5wfWshnqc47VnrWtxoELjmj8WexGz52Qs3Z/H6jcBgAkEwXOfOEP7XieLNTtjjQRloFa8R5Zb6f6J7JT77MBHfNNtjtHLqm8pGPk9K4toSjke2agoEV4XKwvmuBocjjF7tnIFb60VUgIy5CSHskaHRPlVqQ7mOoxxyoKfGc/LKIPrzQGQgkmGlQN80Pn6+p36donxQsgZJRF+rDBoZ+RhcuiPUWmSw4wBJYC/p4OT9FrfBDqXfDX+dI741UaH2W3bR0l/JygNpdT/C8CfAyVR+KkbADdUGl8sLPVGHDqvUNa1Q19EOTHOMlFhc7VAbX0Eo218dGy9APYs4jjoDFel2iGgAM/LX8wI6otFb02uv0Vj9gV/PtzpzNE2H6yjrCu0rdLgkIC0+vKGcg1opvM6IG1xlNXWsyhqCotf8KZz3k2RdQV76g/RD/Idi1h6Z45jH6JrGB2IAjrtKCO6ERY08cSjt+gkVWAeROMKRitROW67JloBqAtvRQKeaI4VnS5IMHA83yhKMMuCuUMfL7H2VmsM9ID2n/CVjLqMcKA1wCDz0vfyHx1zFeEc2groNIQSNpqqynnEA6BsVWidp/vz2vkz6FhFk7eA4ePeI6yGCdApEe8IIdlTwLMUlyG2V0FTYkMoI8u9UNskhcqJ9doOId1kmIWLDgEvaCfz/KoCOPHs2QrxsHRV0UVDIxmWYdMpAahgT7QUoVCZjCZlqpxxk3XSwHtYe6cBoHHkD0XIqqHDRk528/o2sedyO1ajEaDexgtRoBzDc6D50FqtnEeYdRSiiedQK2oK/ZP+d+MDhKDjEFmZAkXLc3vVtvfJ0wDuDKUB4AdAnfpuhg7962ao0iDtFagiComwtVjFCsmgcvHS+prvsAdYiysvgt7TZr7rd8ycPNJiq0Ih37rqhUn3pQlW3zWP1ipnacmC57huMjIYLyiMLd9S77EMx/4PHbQYDfpI27wyKwu2smXfCfZS+D5EWeUEwKF/W0eZKFz5k7T9sQ8pR3vSHZaYzseuRwPBbYEq8UJdClmdlagS5YAETC/veoeAQlgMwd1+UGEyH2N8iPYx+wJxFLkQXVshb/g2s5NFRu7462eFAQDv/uEnAACfWjnpPrv44RP+2NtAQyxitlB9i1JPyAf4KmXulQEAjas6SH7zMBpQ6145NdbCmg+AvALfh4K+ZOVf25ad6Oh3s2cr19wJoMSvVBoSRccIssImvGdfMkiGFWZftbu7AaR2tOyNmc55OjHuKEn7FLkmrQKloQtCU2WztE08NkH9y+wLHJIU+bxh5bxMee6DgzGaG2Xg1clQ03CJkVf0G86zyTUnGzQVTaC1YkLur4FH33XP2XvC9UgFfc+hPtlz5XbHnRKeeg1U1f0NpTQAn7/QpXEWQDyukG6KnEVmLftcrICxkBItMk/KLglix8kv4u6MCgJs/qQ0iPq0j+lyG7OvZq72IJ6YoC8Bh356j/lAb/+oL9IqWoB5q8d5DjfpfOaf9I9W9stIxiSk+Ty5xwTnCeKtIfKFFu7713YhJtop0GwuRTz2DaEKyz0lE6g6C8NjXNfC9yTvRKhx72W736vfZhVtapDXC7SfotVa1Ok3MiyRjIGd+4Swyn0/7erBIf7qw77P1y8+b5tNvuyJtJrb/txcXw6mOUl8T2vACyFWFt0foHqoK09Txry+rgJeoua6v/Zde45l1zbB6gL1y4kT8mwpFzZKufjeS7j4qWN427d7t/NTn70frUt07JlztG8GA9D9ETDTdbIWmLIcCO/T1iPAgad8XJ9yS8qx4KZ9ExSwMvKJkWm17YI8SHv4fDYm2hgRxs1mYpf8ZjoXRku1VkoMDonYo6I5yPd764HYdRY0mhTDzHlLzdOMgmtt23CxDD/1Tsp6EEsuecB/P5kLlQhA7W4B8t504WszXCdIu8y56PS2h8EbkLr1yx83ozRGAJ5WSu3lkPrxr9hZfZWGqgyFSoT2j8YFymYckM2hNMBQBIhbHuoxuodwkEyd3lyZQhUVlLB6inbiE4xaoaxFyLoEsNeFCYrVprMa9c3KCSpV0iJY/rDfhvs0A8B03gDnOtDH/PmmG/F17KbcSzoeFoiHCOGQQsGNj89AF8aFwfJm6gQDW7mVhFqOfVigtm0Xs6jOzWYiNFe8wtOZccnuzUc0TAyYVFTgv1R3i9oVstnDce+IsiUSnDMi31Ro/MvPfytqz5MCzw6XSLcFOWJGsFXJvRVPjEOmJSCBwYl7hqVyNX5vbQ7qagjRhaLqZR4MIa2adF71yxLF5o/LCebF915yH/0PP/Sr7v3f+4/vs9dE//ePaTQ2fC+S6YxC56KHKxtNCmPrAX/v2QMEgANP7QV6KEyE11LbLamjpFX6VawCKPN4OUHzWuY8iWlXA13tGJizmTjo/Fe3hoRUFLqEU1J5k5oubT3gRVDnkkAlSg9wN6c8hJ2H+UwCVRjEViFd/K4I7QseLKAqKtirb9ntbREvD0agpcL2C4v5qM/HzDnx4T6NO8XT+G379w05kkGJeOgFT9SzbvlETJj+AEjs4q+lgDEYPkQxIZVTrw22pscHUqS90qGVuIqWF1syKFAm2nfqs0k/XkyMepKxXunm68I4WgwAGH+LTWCeJ0mbAGh7OeSsaWZTzZsposyI7nSNAOLLycrhMZ+olzkDSVXRXKsCCvnhYVqN7cskSKZzCWZe8JncfI6E+WiJjt1YI4Ec2wRxNFaOORYAdu61SdSpOH7HuJ4SxUwJVSm0XvaCOR76cF1tPUI8hOugF41sHQVDNRt70Fn2NnBIarygMHzbGPE5H86bfUkgiKwiDu6PiFRGvQhF0z7niOLmEhnUu6fC7mXilPt/fvMH8VMf+UH3XfeCvUei9kBCt7W9BxK+TZ4Ro/bod0ufDaWUbL/bEC1OWVnI0OFwWbuWqUYrDI/UgiK6xc8Lj7yiDnmMFJzOJShT5Tyj/jGNtE/KgsfaW2LMnGVgRo7aujd8sgWxoV0rzDSgcoPRcoyNt9h72y3QfxPQfInmX30rrAdRJf3JOpdo6sEEaZ+83pogquxcKdA/atfseB8l/Z2gNIwxv/TVOJG74+64O+6Ob/TxDe1pKKV+3RjzPqXUM7iBfnyj0YF8uWMvBQZ7GAA8nBYgL6PmE4Sj+xZhYoXte/2t0xnQvkpWW9qjTn+c04imJabzaXCs2rboMWHbv86+Sp/x76aiLqMSRldZt20t7RM5+i8TTGdjrL2d/u+cJ+I1xsxnXYrvSou6vj5Ffd1ez0FL/tfmwilGSomkoehI17kkKDgq6gTH4SZdmKALYfu1HirbTQ/wXQmZHymfK1E76634zgW6qN5pzjUBp951AS++6tu3qImPM7TOxYDx/EwAoYUkVQd0GI7afHOJ2edtF8NdE1BTTLsaUApDwT81/wd18M3me+iSzE2qFq/t0PejgwqTBbvtMELZrqDHdD/TXULnMO1HNlfBzHiP8R/+8p9HEyGfVLrrUW/tlQJlopyHmPYr5O3IU7bYmqOlJ8PCycFxf3/LxPN8RXsiL1knCpLiw2U7DzhMakI4dOdSiem89/Bq27TDom7PZ1xh57QniGRKFc5bjA7BeRkAQd1Hx+nmxEOPmgKowHTz4RTzL1giTEuNMm/rbWq7MXbOaCeQp3Mhz1fzGp9jeM3MvuBycnbtpf0SwyW/voMGT7c7vpGVBoC/bV+/76txIl+TYQyigW0qs2MDzo06TE2WIc9D7Q4weviQ+2jr/tjHS5sKs69eH/vk8BQApDu5W2DNq2MMj3otcKPEeU+0J+WFykiO2bMFyrp2hYlZN0bWVZh9yR6rX2E6o8MEZVujZfmd6htZUKnLQqQQIbDprHJCpbFZYWoFVWu1cAuNrl1TF0IbpinqlK+pXfPZYT0sMT1MMZtkWOLaN6dOqHefiLB7xu9vcMwmj894yXn+YyegFuigtY0I04UK9XUfYpFtPosGKQhOjHOuYeXb/LNovxK7IsDGJoXpBgf5+ips36+CqnIZJtw9RdtxlXo8Imit7KfOo2rYxPWaf66y6jkeaGBQg4msEN+DdgOA5rq/30zux8nfvSR6PI9kuKp3SqDk7GU4DqypAZTC9r020X6+CpLN3BteWsbty9eXV1UOLJBivKjRPU8H2HyYbgYjzZrXwur1xS+E+woKB+01MIPu9j0xdAns3JfYc6dwk+REk4AEruxnY8FE1DyMFaE/Jr1mHR3U+wytUuL9y5DsbQ2Db2ylIZp5fF3VXdz0UPCVydtiBVZVkBjOF5rAQtMld3fuiZzCAIDFZyaYLCRIe946oloEL0FkpWzPFvU5epCBQfPyCCOhSKLcwwNZWbQvi3j21L+fzGnUepWLx6e7BdLdEEMvLcjxUs39DqA4dFHXQeHY3EsZJpbor31xAsYeZTMJoFVAY71zOnbCubZTorY6gKlZ0rpmgrg3DRrzyFqB3mlFHd1s3D+r2eRtj4RF42KMdBeorzNFBYLudXx/WBhFGRW7MYw162jsPFRBTyysc6TRECR8nLQeW1r5yQGNqmYQ2U6F9U3yPjgBr/OQ1kS2EwWAbEl4k6sx4lFonUsyw8kSnUe6YxVOSvUhbCF3z1vU1TIXm4ZoIRMRPQvPBfY4pMEhY/G+yM7PHVYYALD2Nvq+sSIQWQKpJecTAAwOW+CHzXmMFzWKOrD1gM0DjIiOha18wPcvl4PXRpUoJ1Br8AoDABaey7D9QBrAi+OJ73fOrMascA88xRQpzOZQUWJ8x9dpmCjsyJkJxcBJfGbqDQAMtzEUvvHDU318Eb1ojOm+3ne3OpRS50EUbSWAYm8TEqWUAvBzAL4HhOr6y8aYJ5VSx0BU7QdBwZD32yZSr3+srEByYd1/YENQpmmJBtv2NdUompGrVVj8AiX7uGiPeZK2HvSTfOacQPRklaPdAATSSEA78zkvsTObsF58mhRZtDvG7psW0VzxScf+yYYTAkzhwUoLKiRErFLl6D8AYPcUHYvhpMODMVqrBTLQAmNETvcVWikqLzE+QmqDqUp6JyycMidr21V0K4Xp3BzqG97zWn9LE4mtf5jMq8BTyQ6TtI8aFnU1jaB3EqRXPD1EPPH0Drqw98/Boz1cFvDwZCMsfnkv8tkyYMyFAspv3QUsSWDnMeqFsfsiFUhMDhqkmxKNRCENroVJBwAGglxyNcZ4CX5UPrletKk6vbTXkvQVmiue/LC5atDYrNCgU0BZ08g6Cv0TAmoqAA5O+FsDJxlWyDqC8mXoPWH32aByIASArPP+aYZeX1/zIhVFmfJakLUZcAgsLvSMhWDvivATP3e22vNWBJ0Lb0GEvwbH6oCCgwDvnklJYYtnKeljWBlISnwAaF+xhbJWMRWi5kZ6KbLlLkCFhPHUuHsh0YC3O76hlYYxpgMASql/AGAFwP8KUpZ/AcB+tVq/0fh2Y8zG63z33SAu+HsBvBPAP7evBYD/xiqQDoAnlFK/f1O9PBjFVLfNZWoJTKIdxTNAVB48WAjs3EMuwNCG26V7LIuSahtjTOc7qG+I/MmkQDZnqc0nVYBY6Z6bQE8LRLs+RtK+5MM1u/e2gvPg0JB0742Y5PWNDFWqMbXorfp2hWRQoneSjt9aLdA8t4smFzjlJap2PaxL0WylGmw+KNA17xyj/UkfAjH2NIeH/L0rE6C0tNvNNYMyVcjs7EmupcgXCpQZnYseR4AyziJPBqQwZEyZ25wCnjacLc7G1CBra0fHPTqgkPQ0lt5J5u72hw8h6wLVO+gAtbTAfXOb+PYHiNzgw5sP4oXVZarUprsRUKWzB8Ovad9gOqOcUgOAznl/fv2TQujWgPIAcOApG1bsUJMiDq9xAZls3jVa8qEyrvZurnHdkPXOhKIomspZ81mbWF25IhwAhke0i+szxLR1ScCnhcAnLiblcjDtSyZUGBnNnbGlAclbZBBIFlfpYbueKwOu46D6G3e9yvegYU9y515/8yWC0BWQunyJChRG73iE9krlTV5lOdiEwOZ1AyCoHAeoclwXnhx0L+38bY1vZKUhxp8yxrxT/P/PlVKfBvCPvkLn9MXGewH8sjHGgJo8zXKnKgAcTusrpV4A9cB9faVhDFV225qLquGtfT0tUBPMq5NF/x1j0Yc+Nxsk2GbPFagioHnZZo+VQveVgQtXUW1I4qjL+8cSJCOD1hW/YqPexNM2zLcwWbq+dVhTckcpT82hSuJCSrfJiteTHHriqdyLdgJdVJh91Xou9jhSSejeGKMzVtpECn3BhDp63CuzjlUYDCPN2wrNtdJZbkWDek+4nuBePtBxcqD7YuyIAesb9O1eNlnef2uFFnr3eZK0pk5CZf0t3unlbowAMF0A1D0DrH6WgtxRHSgfGgKFpVofpnhyu4njTdrfydYmXnzqnhsqCnn+DKut71gqE3u+MtxRNKkXu4R5ssIAaB7EE6p4B3woZHSA5sl4EaiLeeWK8jJ/jPraCHUbt++fJuku8xL17coVirLwn7F08hxaGy2LNgCC0besw3beo/+HRxTKuu3VDbgcGed8klFYlGoUABXeE2lMxWN6Tmx5c60MQIbOxqONoHZCQnV5AkmaknhsXAFf284TqcCkMtalQdHUQb2RZNTtH1eYec3sr7LgcYcojVIp9RcA/Crokn8YXznCQQPg95RSBsC/4EbqYhwBIJx01yDdRU6VUicBvBnAp/fuXCn11wH8dQCox12nMPJDM46QDQCMmN2cA5BsnQDQFIgcyZeUbpLw10N6rdr1IL8RjYmGvX+MPmts2EUsid8EdYMqKjSujjBdYMURQRcG6Y7oQzEpMFkiAc607aNDtH37XI5KNIii2HfkKsGjSYGqmUJN/SOdHPLSQyoMl2B+yprWnFy1gq/Wq6BKj0ga1mIkIxMgsWQCNxkQjxQnLKOpITSUoG2IV/y9YF6j/v1zdB7XJhgeaTjLM28p4imaFzmplSbMQVstPdGILjVRHrBaqEfP4P/4z29329cib9WqioSrDKlFE6CyNsTgkLVMbSnKVFQdR1NSGIwI4/Nz39tTGBzl566D0FDHNhCSyfXdMwpR7oW8Mk3naWy+yeaoiLAXzbUKuyf9nJp7mYv7/P7KmnLIIabyl61NA6FrP+f6h6wTQZehoggUBBsIwtqv75SYWvqbvc2kpjMaM2e9tcChJQDoH6PftMVckAKdz5MZc4sa9YuR+SRJo1M0qDHY4IjfyXTBOK6p2Vd9jm5fh/kGD0+J8V+A8gg/BxITH7effSXGu4wxV5VSSwB+Xyn14h423RvpfvcYlFJtAP87gL9jjOldtyEpofcDwEzzkOGQlFQYnKuYzpO5ycqidUX0mKhp9E/Qb1lhsGVvEo1kre8IDtUoQxRrd4xsnlZf95yAkkgLbDdD1fSeDSsL2YNCT3IUHdt72lZus7LIZlOUNe3CVcPjbVSxCoqd6Lpi91q/5jG1gzMd7J4WgkkIjmwGSM42XFw+moZ9ruMhUbQPDwoCxG+pMP+U9YIMkHlGDyQD2r9ErqjSOEGic6JwZ6rrdBBhOqOcQNm5rxkmxROFdGiCxkOnH77i3l/7fWqTXIxF1l8MVYYIH9icOyfvq5i2cW1IuTrbMqiqcg/6SSSBpQADgK0HFRrrYUhIdunjY2w+7u9N66KIyVvF3FjnE647hQEAgyMaUR4WB6a9MqjyBryyyLp7BJoKq6Q5TCaLE6cd7eDUnHRvrEljpsTgOM1fzuExqq+sUfisf1x4ALnXWFLxRxMggictrO2GSKj2tQpVHPabl6Gx2naBKtUOSKAKIJsLFQYAzL4i0Fgi5zg+cFNk4Dc19qsJ09dyfNG7oZSKQK0B3/vVOBljzFX7uqaU+iCAdyBk072M12mQrpRKQArjV4wxv3mzx8wXW+id9ELEKHLrWVmoCmheCxWGqowT+qoiiKAkS2P0EABUDXq//XDH7R8A5p4biG38Y+AwUb5o27OKmg+Awk1VLXEVvJuPdjD34gjZrIz/+tXPbLkcVuN8wsLzopL7tE9RscKQaCoObURTEiRsiecdYO69V4H/ibK53PZULl5WGDzSgY9Jjw5Q10CZrJXXKq1sgJRC61rlBF3aJ4ZiWaW8fi/AdsT3fMcT+I+/77EU7MzJ+HjwnjmHrCAvmiFzrHYJcJHgzQ3KUZi7cOefeuQao4zW3yxCOAAKu/3SUxOMDqaBN7Lxlgq6oP9bF8O6ifaFMao0wurbrFHRg+j/AEe+d+09Iuw40Tj0n/21Rplnss26OsjNGE3XEnt7ArqE53MyBILYWy3N+bnWlSl0YdB9jbTNeLmGoq6cMgRIMbB3JZmFWWHIZyOV7uCwRn3LhKwJlXEKrWgoIPe0NtBkNLHC6h+l+h7OQ86eJUOlLri3JGR9X8c3uqdhjCmVUm/9apyIUqoFQNucRAvAnwTwD/Zs9tsA/qZS6ldBCfBdY8w1i6r6BQAvGGN+9iYPCJNEjuaDhfn4gML4QOToLNqXM9cLAwBSYzA52NpjgSdId0miRIMpFbTZsXNfK9g/QP05MutBRFMqBoy3aHUW8zY0xKCSSCGaeKVUtlKowmDrER9C2nq46SghVOURWIAnapPHjydwPcZ1Tm09uTc5K4jiPsvue6HpagmKJlDfUC60sPQdZMVHP0EkUUcAXPqE1+mnfnMH2aIPRjMF+8aj4vwO51j8NGkHDn/xuUoSOYAgm6NljbmXSTpc+bYUyS4hkwCyhv/0d/qo5Ac//E4gMaivCqHe8LBZo4BIIJziMX3GOY3aDjCZA2L2LGyiX4ZwJAw2GXpPo7leYTyvA9ZfZYClJy1EdUEjb5KyAIDt+2ingxP2Xs2WaJ/3knL2bInesQhLT3gpvnNP3RU2MmpLCU9HUpow7JiVklE2kW+td+4KyNfO1yGLAOUcKmu2CVTKIbqwSdPosPWEBcx2KujNmdadPSFJxVJa2LRUCpO2n3cMea/iGytrDvlyLxdVhfkUJvCUazgZVQ4dqAqDKDNONjD4YD/GfoWnbDfVfwXgEZAq+qsAXgLwawBOAjgP4H3GmG27/U8B+FFQauHHjTG/e6vHvhm/6yml1G8D+HcA3Iz9cqz5mxzLAD5I8h8xgA8YY35HKfU37PF+HsCHQHDbV0GQ279if/suAH8RwDNKqaftZ3/XGPOh1ztYFWlkC01k3Qhpr8L2fR4PL/mPAACRcuGlyUES1uMlbwYzjTRAlrKalth+dMZ9Np1RAa9NJPIHqCjhlh8gyUfhGQGL3ByhqidOaXBrVbeAdcghZCLqrdw/6vehc1+xDiAgliuaQL8ZBTTcrDAAIJ8v0D4ru7P57bb+AzUvknHyxQt+Jb72g7M4+pEM536Qz6VCvBO5xPHkcI761QQDq2e6r5kgLFHFFDZhwbj1EH1X/z76YB7Ayqq/z3/60adRGoWnNqnkvLZh4Z0C6xc93MPUMgGrtEK1kSDZlQHyMKdR2xXCRVH+RvbllkpD0ndPuwrNjdKFVHSBwMrmaz7312xo5KNeYQBA59UIjS0TQENbKxU2Hqf5p7MQjMFhOskeAFC9CEAeQ2slTO7KOcKKUHoW0tJP7fyVeZtkiKC/CuCFIsNbZRhJFjeylT9esMi8KQJPRxJusuHDiizrqiDsFk1Dinh5HgB1DMw6Kqg7GS/owCBprVWulwg3l+LnJZP0tzUM9tPT+DkAv2OM+UGlVAqgCeDvAviwMeZnlFI/CeAnAfyEUuohAH8ewMMADgP4A6XUfcaYW0ra3IzSmAewCeA7xGcGwL4qDWPMawAeu8HnPy/eGwA/doNt/gg3znfcHXfH3XF3vHHGPigNpVQXwLsB/GUAMMZkADKl1HsBvMdu9ksAPgLgJ0Co01+1PZHOKaVeBYX+P3krx78ZwsK/8qW2+bocyrus2/dFWHjBK9328xuYHp0FACS2pzcX+yWDAv2T3iRKeyUmB7xvzCUSMqnZPx45hE08Nhgvpkgsv048LACtXOdAwCaHN8nar2yyvn/CH1PmAKQlCvhKb9kYSHoiDLFkq06V1Ownf0CQHhmFYkDHlV4G9x8ofGQMo0MCHTRfoHshcnH7bCnHa+9TaL9K+5jOG8RnfC5HDSgW0n1NWI0y0TpLrzsP2yZQR3r49qOv4OW+r6B79KGr7v1rA8qAr37Etm+1HhDHzevftoHRJEXUJBOzKiI0rwjPJgUWntvLGhvCNWVTpp17VJB8Luoq8MRGS4Iny4Y4rvwx4bUdHaPVJhM5/94pss0WOs9YVJ0Nweye8vuQ1vR4yVre7GHExADMIz3dR/l8N/AchoeUqwvJG7Y40Y667ZLXO+6PN3NBzJuliGpmRBgoGVwvAWXrXgnhrWLyjqQHIOtvJgtAyz5KVZkgkc1zWVLQy3BTWaM+IZGr81CAMQ6pxXBa2cBKehm6NBgvRCJnVQVeobym2xkKX1Z4alEp9Tnx//sFmvQ0gHUAv6iUegzAEyDap2XB5HHNAooAihx/SuyLUae3NG6m3etRAP8UFAIyAP4IwN82xly+1YO+kUYyKHH8Qx5opcYktZIdm8A70kZtK3PEfgAC7iaXALefTedT+sxusvlwhLLuG+KkOwoLz+eukKlKI+yeTtE97xPrfFwAmMxHUCW55DwkYolx+xwvVpUJ8OeN9RAxE48pBCRDBdP5ygO4bPyiuWCV1gLQ+B0KjrNADKqs50XyMKlw9fsqtJ6je5UtAe1XBandhkL9lQ42v5Wk3+xnaLuhgD6OzvjV/M4HXgMAfO6T97nPfuvzj7v3Bw/tYHvcxNrzlIiPjo6gX2gRPzyI8G/0jhFmu3Qto0mKyiikL5PGrG+GSfvZV0qUadhHYv27J6g/R9sn37yF6RPzTsGaqw3sdoDmNRs7N8qFa6oYqGQU0srimVf8ZztxHfo+mmc7qx0oDZT2uQyaKiAvZIXBIal4BECJ0KAB6vf0UFV0LtnZDlAzjkJd5yFPF8OaZQOtsq7RssWTDC6Qis9oYOashzfr0s9/NmRknmBvCKl/XAeKr75t0Ld8Yy2v++35GpSy8ZJohFQ0CEAhlblMXPN7FtC1nkEmempwkt9BqctQiQwORbbwcv+hTl9Ggn1jLyOGGDGAtwD4W8aYTyulfg4Uinrdw97gs1v2eW4mPPWLAD4A4Ifs/z9iP/vOWz3oG2GoyiAel4h3JwFip/+oFUCC32n3jGAKrSkkw8otltEBJrujSV3FKlhozVWgfwJId/yxB4cTj3vPqb5hOs/9sOm401nRdzzxMd5kSJw7LCwYJ8+TvqgrNIVnke4WKBppgD5pbBgHwV35ZmD2RQ28SNKKaS3yls/2Gs/ViPYlz/kTT4D5JxP0T9pE+XKG1nM1l4OofSZBe6XApm20w8Vqix9lE9NgdEigaQ4Y6F4Mo/18/twn73O5iWJjDvUakN9Pgnb1ZfIsEssVhZdbSAStx/idQ8AoDAXEtnq5A9T9fQWAhsX3sxDZfNRf78PHrjm83tnNBbz7e5/G77/0AJ39YobuU6Klq+iQOJn3uQwAKBox8qZP4g6PKCS7CtPPUjJpfoOqt7UVqiYOFVrRJO8w3fWfqQqorDUfvYW+GF+jD5JcBQK6rJPC4XnYsTDi2q4XxsNGismCWAvHItRsa93xAYXORQFJ7TPnlGiyJGDdaZ+qvWUifP6FHLunLTeVreloWCafKvFJc6Yvaa6GIXdW5uwFcMK7TJWrOAcEjFgg0aSRlAzo/jr4d5sq39lr5LUivY19GfuX07gM4LIxhlEfvwFSGqtc7KyUOgRgTWx/Q9TprYybURoHjDG/KP7/N0qpv3OrB3yjjewAzSam2QAIDx5lNw5JMAcPo1Xal73CAGjCFnVPJz44RoublURzJUTfMLpGKqmsGwUWm0wKumIq9sSrkPY8yg3ytnZ1JWUzQn3HN5oaHK0Fi3v5UwBQYUeQ19W2gdo2XfP8ixmuWSht+xJbpx5r3z9lHMMuXkox94KPh0xtJf2Rj/qKvtGRpksOZzMKkbCmVQ5UNYP6Gn3/hQ/dj5qkpbDyuRK9t5O+P+9oAvTvz6Es2qtTzzEc1lC9SJnwKgqr99kSllDLKlGOWqPWM3h5chrti3Z7AH945nG3aBY/X6F1dYzRMp2P5HXi0IwJPBnPCNy5XGL3VM0pOBOF8N7pLH0WFKjtURj9U/7m6Ne6qGoVkl3/g+w+D9BoPUGGD1eEA3A06wAwPEjPmL2bogmnMABg/iW6R0XQMz0KwkSA94KjjBBI7IHrnDo2coiJlaocQahVwsxL+l8m5llhAOQhlfXroeaMvqKQmgqJB433ipiRwF2LCT0PiYC73bEf6CljzIpS6pJS6n5jzEsA/jiI/eJ5AH8JwM/Y19+yP/ltAB9QSv0sKBF+L4DP3Orxb0ZpbCilfgTAv7X//zAoMf51PYxWKOuRg9nFEz8xarveymBCQKZnGC+QwmjZwi0TAbunPFU6o0Emc/R9PEBAS1Gl4YTkiZp1hWcRewuVFQbHY1UFmtRigWRd5Sw3/t3wiD8od9Lj/claAF6IRz4murCVBqXti7D69tTtc/Nxg4WnVaBEW1cUUnuPWlfGUKKaPenTD0dHvJm3IwoHq5RgsqPDItwnCIJUFUJIqwTIOxXSbYEu2/Hf9x6mG9uaI2FZGYXkFZF/GsDSZLNFCzQ2S+QtEYKJga7tGRJNKqQ9z2VVpgpdEec3GigakVMM9e3KtVtVBpgsl4hGFrZpcydsBY8ORKhv+7a+VUTwXw5bMoEgw5CBsDKd55AWxk3rkr+O8ZvoHsTn6fqnC0BT2JZZR6OoKeyctqzHC6S0uHI6HQCdc4Lz7B7SgjKfJgeRSfpeJN1LZRDGrRKFaFq5DpVAGBpLhpXgoQpfB4c1GuuVm/O1XfJieO3yOcl8mPTS0r6xPGEWXq/DfAv3aw+KGXN/7pKa5bbH/qGn/haAX7HIqddASFIN4NeVUj8K4CJsdMgY85xS6tdBSqUA1d7dcrn7zSiNvwrgnwH4X+z/H7effV0PZWyIalQiGpeOQDBvaKLp6DN+XCOaGpdgVhXFwnlwYpBd6+Y60D+iXO3Adcctw9+NlnTA1kowUz9J8yYlL6XwoF4C9nurAJgSIe8aLDwTJhnX3uxdG6YGr2+Joi+xQFxfBuuZLH8WOP99NE0Wn6Jj9C00tHUNaF0VMEbbUKomKE7yTuw8m5ENZbhaAEs1Xl+38MyTJZqXveBjq5fbtwI2lGZHFZOw5TyAsl/pj5HpWDSBpnhWjj7eKjldUtWyh1oq5LGvpq8ShVSEb6ooQtHUbj/8u/XHQy+NR+Nq5GnTZ4CdhnZw49p2CIv2jKr+2RsNUbBGpTt87LwJtM/rEFoqeK7Ys+AkslF0vS50akMl8jdF0zMgLH+aToh5z2q9EmWqvdJQ2FOBTc+CE/h5S9v15e81EOYm5JBMskbR+pSe2/CwqAC3fT1Ke291Tj29+V5x4SJXoRd1HfJiaaoDkYpDUubXt0pM5qMgtLZfY7/qNIwxTwO4Uc7jj7/O9j8N4Kf349g3g566COD79+Ngb7hRUc1E2YgQTdi6LJF14qBALm8qEZtGQNPMC0eiPaTCKGuUxFz8Ai2e+rrlprICZ+uhZkDvzZ6HJGgzEdC5RL8fHNaoYu/RsNU8OO6333rQn0vdhjxcncieSatzA134HA0qyncULT81Fp72BWGc0AcIDSNDXVz/IRdoc7Vw1mUyMpackSTZyjc1g6R651VfKyNfWwJyIdFJqgSiEi55OzoDdJ71G8y/WGF0QAf3V4IIGitjlI0YRceDCNLd0lGy5J0Eu6cFbb0NY8y+SpPBpAobj8a+5mC5gtnluLvt7yFWWJR5T7Csq1DgW+qTvcV5/H80AbQRvUK64b1IhpQ/4+3LWoigiycGOvce9NReSyLCg7Nn/cFHh0npSHr0rKNCBuFhKATrO54skucFK4MSdC84FzKdsQaEvT/pIKwhUaXx/e0tTQsjwdhbk+dVJX5NNNcNarslesf9zc9EoSPfI4keq+1ULkkdTSq0rlauGFX2o7mtIUJiX8/jjkdPZTMJdG6grCuddeiWcGItb6iAIkQuWh4z58og7CJhicd/13YGFK66FLTMNsvCib0M9mZMHCZY2SKVgrC5lqN4RbSkPeiPz4uS0TCtawbxxLikok4M0l4FlftjDI/UXSw9byjsPOD3JxsJtVYs2aKtvK3tVgFbaNPmetpX6VVnFXReOl6vuZcL7JyOX7fCxugQZkpxcmlt+8Y6AHDstzV0UaCwYYsqppCRpMFmpQ0A2WwN0bR0vVIAIB6XrjlX2YjQ3Cix+aClVqnT+UirWFXA8JTtQfJ84pRyMgQGx40LH6mKeavstVmjQFZtx0NAuSSstZiF4I8m3itMewpQXvjvHdGUlArDYjn04jziNcop8ehcIuRY5O6VClB4PCT5pInhclLxxPieLoDr4TI85Odl0VCun4nO6fk6Y6zuk9k6N4HRVt/04UQesgh0vETGERMWAkw5Q+ezfT83jBLhski5sCJvxySeRTN2CgMgJbJvY/+dl6/6uGPRU0ZRAo0XYTSmiTM9loYCeaMM6Aq47aZEklB/ZPq8tkN/3Ys+wKpMSLFQNqLgf8ATGG48Vguw9TDUG4EtPlY+ycgfX3ZrGy3Tb1hx6SmIfM6eznhBoXPZuPjw6EBE1yROpyZqTKpUofuavY6S9t+1zK15WyMR7KFZRwX3rrLoGS26xanKtpwFsPKOpvvMfV/65K8yIQEgxcWNS0zWt0kRMUOwt25F1fLAC7JoapB3U+cV6pyq72sbPmG89aiX4luP8DkJT2VHudg7VyrPPyFYjAWKp3lVhBnbIZdTYQ9TE+Ez6RnMnK3QO64dEo1CLjJHAILZChitpECpYrp/LPiTUYXCkgTyaF/x/7CymM6FZJUcTu0f87kaus6wn/l4UWG8GOPA0/4iylSjvmlRhalG0YiCvIFkxQ27MZIC4ZBw2iPeKNfQivOLouFV2vfw47RfBUqnucKoEXtvIhWw3hoNNNamyGcEf5s8tX0S9F9mncYbdty56ClDQmM6G6N1dYKd+3zCVFIfdC9aAWljvckwTCQ3NjLoaYlDnyBpUDZjGK1cvBsA9NRbryzg2HupYg2dVdi5l6R8PAoRN452XCBLZDOc8XyE4Z4ynSryhXgAIWZ2T1plZ9vG8oLURVjXYWJg/TF//TNnxblMDToXfWgu7VdBWC4ZUXhC0i5Ek9JdczwssPGYj7txeKAQaDJpeRdNoHM+RBDpQrS7VXwPaP+DozY8IWjry1T82FDi2wmPVLkufACw/XAXqgQ232Q3V0BVr6AsaWDzKjHHcrik1jOA9uyq8QTYvUcoigWv8Vqv0UbMEFzWKOzGnlQyolavLEirRAUNp4wOARL++vz7Ws8AVv8lwwqjxSggFJQ8UuxxcPOiaTcOFL4T0E2+pwZDAY82inICUgi2VqqgSBXw871MiYbd1zWFYZ+aQLD1TpESTgVFvs59kV7eIiQVswjHwgMD4FiRPQGlCZRlaq9T5iyGR/0kZMUmPdB9G+brX2vcueipWGE6S5e/9lYvyPYuTO6FHCArMt/LOe1p6GmJsilQIatDlC2/modHvUKadjVmzk8Dfqm8E6G1YiGxtn8FT2jH5SMUhXTzJdU4j9lXPWeRUdSlbOEFqywWYhT10BqXcXcuHqtSOvD2g0DngkWn7OH3ydsaVeRDZWyBh8WE/r1TGGLdUJ6D3vcsTTZj6mdesQrThufytg46IO7cS8JFhmiU8bUmrh+5ON7Omdh5YYtfyDE4mmBwlHaQtQnJpSofUmpdjpygp2v0kOMqgmtlCwBbor6jbJHA4Yp6ExGkmBVuPLToMcFjVdupgufCygnw8ODREntJtvUsG9EFPUdpvQdcWDNeWNO52zCo9dqKhgrmeFkngkAJc5VhV046M3sv3Rtx7rbrHX8ma5sAP58bggyQr2XmtRyT+ch517wWchH6hFACFI5VDkmnqtB7ZQ9VKs2i7r139ro512FUiJ7az3GneBoSPWUAfALfAOipsgZXmxDEjQUtNkA5jKIOVxAWTyg5OPOa7YyXG5T12IVg4t1JoDB276EdBQtqJg5CDZJAcPde+7ktcFt4WgXhsaxDyUtWFlVKoaf5F0v7vQ68kpRhr8vC9a48lFdVJFA4Xsw5k3mrZAZHo6Cyt0y9UuVwQCXYRGW+RpVA77gwhe1XzXV/fqMD2tGFsHDmuggg9LC4V8P2A36fk/kQatm6Gsa1AU9HEmWh4Ft/MymdyEenMPuSv9fpoMJAoHaiCQEKgri+cGTkfhpXaWlJA6RswPVO4SI+F1JLwjnCSV1WqEVdYXAcmJ70mju9UHO5ryQjRcCeQzw1GC2poO4C8Dm5Ej6Wz/tP91RhqwrOK+udYOFvr1uHhhSfO8NgTUQFqBxKKupRcK9aXLgnFInKuGAwDtB907kIoyXtaOTp+vx7VhYMlQaI7mfnDP2Ai12lMSPnDNOEsNfWucLdJ/lasD/DIta+3sedjZ66O+6Ou+Pu+CoO9RWIeH21x82gp34JhJbasf/PAfifjTFf994GAEznSPW3L0rX2ZPyFS2gsSqSs/zQXcUqQVZ55LN1lI0YoyUL12MeJBnnF17GZD6i5jbzfn/QBu3zZOVMZ8miZ3dbF2HfA8B7GQAsHFeje4FMqcmcDeGI2g/Zp4DrOyaLNhS0pTAvyBtnX84dxUmZAuvv9ubl3GdTpD3fm5mOLyx1LgRX/tyl1cYULFwJnYwMsrYKKuBlEWPfei0yji9zQOkOIaqyWfq/vh42JkqGtD3DW9nD1DLeLRKk195jAJSIbZV1PAw7zSUDBLxFZcPXaURTarDE3lM2a1ylOd0bSwcTe88ga/vvOZTiKC1adL5J3cJ9X6MJyvNrOue9AACY1qjXOod2shnKvTB6Kx7bAr8mo+rYJfLWuAyVdWxujyGzgyMRjPLPMBkaKpRsS9r9MAcCAM210v3fvDpBtkDegARLcEGtTMpHE/+8mFFBVqzLwsfEtgWee9mHEIaHvZvCvcUlolHOqZ1TERqbZv88DDHulPDUo6wwAMAYs62UevNX7pS+OkNn1If5yEfHWHtLMwgjBKRr1F8IHVtQZLTlhzI+YTmd8zOOk8ASZTKd8ZQGeVNhuCzgfFMKf3ECON1RqG8oj4KxE5fDKnnX1kcI93rzIT+721eM/Uww7wpYKiXNFfIunWdjRWGyaLD0Od6G6B84kV80tYMcTmYjzH4uDSZ+3vQcRw6xZe8fL27Xn8LQe07Cc0tRqcQkbxaHbpitNB4bDI56OgmjgeFxv716+wDmsz7BMTlgu8vZkBsLLhlGkgojGlPOyStkrzAAqtNQlQjL2NPmIrFo7OPieQsYnC6BpoUdP1NDWSOSPoCUAQtHHlWqgloFwCsdndPxkqfoYNxYSEKwZbU2PyPuf8EQV1fbYQjxNPdqSIHDg5WBbHwkmZthAKgwh1Km2hFoTgSjLHA9FUfz6oS6TVqjYHwgccV4E6ssJKBiOud/y4gyVxCbKKjKeK4oEyHtFS7vB4gufgAK2yQtAIBEEn1F4bZ0v/pouIPgjkmEa6XUnOgANX+Tv3tDj2hauVaUnculI17LukBXNBJKewWKZsixIyk/ZPUwQPFYIOxEpkrf39gfX7zPgMSSpbn+2KwUElsNLOK5Ooez3p2gtoJstKRcsRlACmP7AX8uc29ex/orCx55dKhCNNRgCciCobK1CLoE+kfE4ts1gfXP7UflufO9UmXojlcRFePJorTaThXsL4zr03Fl1XIsBD7Xo7SO+CqtyYLfV5stexagxA3o7m3RDlFmZYPOlwkYo4FGuuvPl40JI+69LORsXvPfqRLovBKhSugaTETCKDBOxPuiqVDfEgilyqLL7PXqgu6DFvOmLixtVwAnYLBQXnFXCV23y4EMDGq9CrGAJA8O+2Utc1cA1UWMF6Ng3hKnmpjnIv/EDLGS9E/WcfRPN1HbKpwXC3jGACBUqFWkMHO2cmirrKMCBtpoQt/FAoYu60PGixqzr+auvqa+jz60XgAAWoRJREFUVQXcV5ILjvYPtFeEwuqEa/d2xp3iafzPAD6hlPoN0PJ7H/apHP1rOapUY3i4Dl2YgKlz/vkc8aRElYS0BnutErefmJOtHlM+WhbhmkiEaeAL3ZjFNmurYHFy+EB+pnNh3SoEyTQO7QStOMUiWPuOHMnVFHNvXnef1Y4MkZ+3nQJzheYqMFpmHh8LDhDokZqAPqb90lFNTOY09B4loLCHWyvC9UJbCB6uaAeA4bIXsAAp1gCLv0OhKFYWxbEJFIDxOeumGc/ZBPhwhlQ6XVFfY7TCZFYFEOcTf+4svvD0KQBA45oVMlbQ8rORXFlzz+8Ja1olbmKrFMSziqbGKVYOH3IxqVEKg2MewcTWNHs9ZUTFaVIJD44oH2Kzn8ccjmpTxTmHy4aHqSZEVqSXU4XxMj283ZO2kl1Qa6jqesScNJ5kNXl9K4RfqypUGCZSyLqRgJoD4yV/443Cnp7f4qAReWEyxFrFPvTG87F/TOxPnGfRBDYeTZxCTYaGYPH2cJMFmltynUoKk1LM79sed4LSMMb8sm0G8h0gmfBnjDHPf8XP7Ks4ZA/geMKxD/+9jNPXN2lVx0Na9L0zLUxntXOfp3MKk6UKjStWiQwAqBBzLmnPTQQMl32l9dQKuLLmt595xR+fXXJZEZuJdqZ5mwRO7zE6v2978GXgQSCzvvdTV486hQGQZZp1wn4GMucSTzwVetorMTrgp8x0luLojuqCv9gTYmGB1lgHpqIOo6pRv5Fsho7XPUv3Q+YhposG3VcYykufFS1RLCgYb1UJZDMG8UjUSrS8t8OCmKGWWz9ASYBfeccvAAD+x4vf5xQGAIzuswi557zVOjjjpfb8k3TFknZ7smy9lIkK0VTrCNqNsiU8FAZG0veeVNGy+RphoJSJcr1CpnMkWF0orQjDkK2VEjv3RC6/o8o9QrkkxlquOboRV1oQxsspNMpKai+ZJA+5VqKpcUqCPXLJGCBzV/HU5xDiUQUoFRAFSli4Q9TZvGI8Nhge9EKeFSPXbhR7epNwGI9zW8D18y7K91lZ4M4q7oNVEt9QisJomsC1XbI6WLDJOCiPxoZPIEQjUho79/tVJif0ZIkmdGaVSDKkkA7XdeTtmCajXQ88ccfLopBL0mHvKIwPUP7FHWM+FIppHxjanhf5bIV8Fjh+1PNsZ6KJ92SQYu6swtgelyuKW2sy5iuI9EaVsyg51iyt/7yJIBwmoYzMYto/LmLlR0Xob0ejaPrr6p0xqK8JaLJNzg+p5TfKhoHRBsZSn2M3CRKgrCy46jrrEESaBcbmm2njv/ZtfwgA+KUX3ukUBgA8d/UgoqUxirGXFveeWMHKnNB01zpOWaQ2ns8hpXzGIJoIoSgEVdqvkDeVC4vEIwNdhr25jQgnqSrkH2O6FLbe90Kk6RhewQ8PUbdI6ckVDeVyDhwGZK9tb31SlRiY2FeQV7aa3RUyjulcOYGcdTUV3HFvEpuPYg94OheSK067BM7gHitRZoLwkuw7X9ZUoGwI2h3Cu2W9yGSBmB64Tw7zrzkKF6YPueKPIWmBihZRwU5Ok5tVuyBiw7czjPlymjC9YcfXfW7ilocii5NdakZc8GBrtEqViysDQNlMsHuq5mLIuiB3l5Er9Q2N/smQZE+y1vJicIveClxpobUu+N/Wtw3KFNdZXbzA+bhNm7BX1zTaP3gV//DMb7nt/9HF73Lv5z5NC+DA5wU5nQgTJL0CSQ9ucufdxCmL/lGfVOXzkAqjteoLCnko43NEgyPW+9oR3E2l8tTUGVxyHADiIdWPlA2hUOsilt2zCdOBENQDf2+ZlmPrMfrNd73z8zhW38aPzX0eAPBj3/J5/MALf879NrftZ7kfBwC8cnEZnVmaAOoPZ9GFV2LpS8DOmRAhVNhzbV0O6ywkrQVA4ZpJ1wt9DgtJdpmZc/5adQ4MDmlnbCRD2Fgg/c/8TCxIG5sG065280aV5Bmw4M7b3qsFKAxlNFDW7flfCRXweImsb342rEy4JcBoWaHe96zLVUwGmRT2uSiSZGXBHkjeUIimyt1HGerKmwR+kMl0qTB6JyK0rlZB8l02VuMhPT3ZwlVV5IXynB+0lFMYQIisuu3x9a8z7lyloUqCV6oKSPrEdMvDKD+B4nGFohG5BHcVh5W6rRUi6ZNCf/Zl4xLhURZa5q4PgogVGyF8a9uUI5BJzqyjMDxqIbGbyuUGAKC3kGP+ySSIAV84fwD/p/P/JQDgkXtJe730KRt2OUHn0LZW1mgpRjqoHHImmmhEkxKbD4sqdkaumNAaZZoLx6ALXvB+m/EBkSjXQPucdspl4zHtFAZgBZqBC6kAQNmUpb0K8Y6AYdqOfRxGUeWe3iUxMDhV4U9989P+nHWGX9x9EADwW1ephHvjd4iHpQNSNDtvY45tg9nP1gDY/vBDg94phbl3WJ6SdwDZS4tAacN3u34OsAfkkF6MzjH+fyCkvJc8UvVt6i+h91Qmu+JEE7K05m0FDMNYvhSsLMwljFZngqvKPjMJPZfhL2eU2HUxnVWobRuXnJ57hQohQ8CI8BpnKLTFRXlVQt6WQ2eJ3hqcH5SgiFrPh7qSQQkohd4JPxfW3qpdiJU9PM7v7OV5qxKFeGpceGza1YEHEI+oFfHgGPZ93BHhKaXU3wTwK4ye+kYZqjJIRhV5GzXtJmg0rYIHOz6Q2KYt1mKrgNaq8YvZUL5i801igotk7HTOwyVpB9wzwx+DOuXRe3ahhwe9Vc8KAwAmb6YVUQy8ZC4aeyi4m16yv/iZkwC8R1Kl1AFu+z5m8yXhw8RyADA8UnNCLeuG8N6lJ722GxwliTKW8Erl+1Vwcl2uk5YIIxz+eIGt+zzLbc65GfmDSoZ71HXfyxBQ0aL7JSv8Wxc0nr6XXIM/d/wJ/JMnvt3/9jLFglpi++3HCiCn8z51ahU7nz0SwEqjMdD7KFkBk4N0LfVNkfOyeIOyEZ5bmRAEmAVbNDGIJl7xZh0dHCfdIW0ztIim4bIK6EzSXVu/I++V8Uqqislb5jxGPDEYL6gATQUQtxdA+Y7xog9HxWPjmJHl4GuqbRPNfemo2BUamybgEeud9LkEnVuYsTUwyiSk+8g62s1h1wBN5Ggk8STnMthY6J0Oc3IMfJAhXUnXn/YtrNwqk/p2idFy5KDTfM7czgAABAXbrQ8D4A4JTx0E8Fml1JMA/jWA3zXm6x9sXCVUL5H2K0SZx3hXsUYyrBzfP23rk8OyHwMAXPtj9MpUBknPC34A17ujhiY7J0950Q5syKO+GeY0pnPAW7/1ZXzuIpk99x0iK/elJ0748xNPUedA63MN17ioig2Svl/8zRW6BvaW6ptEWDg4TFIknppACciCsYVnSGFEY5IUM68U6J9qwEl9RRYlL1BOlkqvaTKv0b1Iv9+yisspC+yxlDMgzXzNCiPHAmoOCZdUpBSlsFB/waPG/smH/5Q9/1DIc/hhdIh+d+rUqvt+58ESS5+xhIiHQxLBZHg9DY30ArOunxf8PDhPkTcVWisy/GRs0SaHtTQGhyIHfDAR3Q/Xb6VlGw0JwZqL4kBGbnHRaFkjhRJb7yTt0z45lJi1VADW4KLP61l07fm5TpE3Fsw6BzoXPHBjcFihc8UEsGyjw6I8DilFk3AepANKjLM3rwuD4ZJfJPMv2NzSQeFF5XDJ8cRdc5g3ZAguh9BkO93O5TwArOzb+LqXnDeHnvq/KaX+7wD+JKil4D+zrQN/wRizLwr4azGqmHiLBsc0Zs76J1klCqMDsYOcckiBYacu7nmIJ1QFzGVQRyjuNNhooHk+xsx5LxCKunLJUlUA3UsVcIm+653QyFtANucT6NJ6fMdbXwEA/NkHnnaf/cZ/+mb3vratAiUzmaXX7mM+Eb777IILO/DCqeyiHB1QgVAfHNZBeOngp71EjPvkcgyPt9x1jQQjcGHrHGSorGwCu9wcx9airL7NVpjb+LmeSsSN/y33lHALzVxvWdN+/Efzz+8BDPzHJUysII8aHtnEI28DeYc+X3iE7llvSjvcOj/rFAbgk8pcAa+L0JvYfsSgsWKND3sPR5YAsr4GQAFt0elQhl9YGDOCZzoboUzhyBMnyxX0REHn/jedCz7cxIrPcUyNBOwXnkeK0VlVEho32QzQuqZc75YDX7BcZm1WmDqoFapiRUVwIvcgOcWGB8lz4hqc7iUCnDjYrL2tMuEtk/oyXDVcitDYqgLW2VrfU9TzOciOmlLpsEfDhmE6qIJwMgM2ajt+/3krpHHfr7GfiXClVATgcwCuGGO+z9bQ/RqAkwDOA3ifqK/7KQA/CqId+3FjzO/e6nFvqmrFehYr9q8AMAfgN5RS/+hWD3x33B13x91xpw1lbu7vJsffBvCC+P8nAXzYGHMvgA/b/6GUegjAnwfwMIDvAvD/tQrnlsbN5DR+HMBfArAB4F8B+G+NMblSSgN4BcB/d6sH/1oOE1Ho58hHc+jCJ9kAoH80cdYLh6WY+qKxQcVtHIaor2vEF+oYnCRTb/FphaJBGHgAVJVbU87N33q8RPeSQu+EKB5qeKhmZeszvvXtNBdWxhS7+fTLb3PbdwWHEcdhh6e9WfT9b/48PrF6EgB5GYAP6YyWFFQR9laQsWuGI7LVtnNPHe3Ltjal770M2pctCGMoY2QTvtbKjqahNct9P4xAJ0mrn9FT8jdK0H+z9ShfTeQ9GMCzwwI+gV+0/PFq28olkx0Vtj3frWcW0X3Yx2NmXoqCc+HQkrRAxwd8bUbVKDG0eINoEEFnijwMO7iXCeDRcHwt0YTi7D4JLO4JgOYljemigYkt+GBLYTrn0U1VSl4TAypMTJY708bXdq6n+x6J/hi1bXvfbeS5qFOOgT2N5ppB/4QKQjgUXuLtFcqaQtona725SmEkBzGeEEIqrFXxa2D21cKFi5JRhbypAxYFmmu22NKiqCQyqxC8X+x5BoWJIj3D/XKaayE6LW+RHP1K0aLvJ8ut7aj6vaBC6//afvxeAO+x738JwEcA/IT9/FeNMVMA55RSrwJ4B4BP3sqxbyansQgq6LsgPzTGVEqp77uVg74RRtI3OPoRKwxHPkiezaWuFzQA6FyhqPt+BpM5jeZ6iZnXaIJxARBXkBcNbrXJiXWDnQcrpwyS7Qgbj/oGMoPjYW1CrUfvVx7xgf7VfgfdeZIOg9dmAXhlMbmfwkePnqBM4MnWFv7DSw/765wotK6GBYBlk3xUwKO4OIHqmiBZpdG+nGOyQNNkstBB1lVOoDHyR4aHZGhqdA9JvGQlxCxyC1T/P71yaMXlLF4nHBXUsWz7KmxG5vRPi6rv2KB5RfBHCQBCOVtBVSpI9Be/73Gosf3cCfCCwkcsnBzyiWvNMh1cW7rj0UftKzfmhvIoK8olua6NDtLqz23mnL8ZHO5silbARglYs0WlSUocx/kF6s7IuQAAGBzU6FzxPS+qRGE873nFirpC56IJwkkSBi77t9AFUTiIEUrR1CBvREGjp4Vn/Y0v69p1oxwuRchmlFtzWSfk5Jp2Q2MFoDkh+6zXdsNcn6TE5xCtpBJhbjkeUW4cQ4E859sZCghIEm9z/L9BBrtY2Vg2xlwDAGPMNaUU4zaPAPiU2O6y/eyWxs3kNP4fX+S7F17vuzf6UJVBNC5QtGL0T3ip1z+uqchKEMUlA9FlrCKkB1tkJlKYzqpgccpGQVe+LUXcV8iS6yvNASDpEypGCq6tb8ow2ppz/0/Wm9ATmuCdcz7GzeOREx468p8u3Yv4tUYovJdC4SuP5Tq0CfqIfKbCtlWc05nExfIZQsnb5l3qgZ0d8Tv8qW/+kHv//1sjSGt12q/4F546iXjgkWEAMD1AJxENdZDk5j7XzrOws1UKLtlXYfd+5tKwF1uoQGGMjtD33IkPACJRPZ48uAtcnXFGQzZDwoY9meksnQMntSdHckS7MUwibq54y+SRPHThLfMyVXueUehhAMDsa6ImJatQ1LS7B831CtHEuORzlBlEU4OZ1+w12kMzLQxwfXW5Mr4zZOdyZZt72TyBXRKSF0zSglDuw7g8w/hAjNpO6Tr36cJAF76HRVmnFqsepUj7HR0kq0sqhmRo0Ngw7v/uxQJlojASye/6duVqPSaz5PnLCnV5Hx3TtEiET+aVy+/EY1LY/H2UeYUhz3Vfxs1zIC5aJg4e7zfGvB8ArLG+Zox5Qin1npvY1/UwuNvwee7YOg1C4RgUDSqAGh72kFrAQyd1YVlomeJ6Dii2taNlViUw/0KO2hZJL/Y4xktk2mQLtKj0DAnWHCmaVyKHGEp3yWLiEMdkGUivpMADtL/JOpnfsy/S+WUdYHi8woF7fNbv/PY8KrvC8mdJ4sn6B+kJuOY/gsuqaMJReQDU4rT9iofkcgX6+ACFGeS2RdtA79Kq/Inv+i0AwGpO5/D2uQv4pefeiQcOERrphadOIumFCJfJMhMmXj9kQh7wxWlSaQwPIRTasXhfKeQtIJ+lZ3DoDCW61571hTPJgz7eMuo1gDdnqJ+1FOx1Ot542R8fIGXBw8TGLT/pZXTPKluRbty16tK31lUlgRBYuHMhYkc0EpJhkqEtwGxueGrx2nbuvB4TK0zmfb2OUYDOKxcums5GqJIQbRWPPWqQQz2VwHdEGQLEEuDh1HlLBay3nYtTlLXIGRYlqDBTeiPpAK7qu0oUipYnAjUKmLngpb5R4tjTCmXivRRHAyLCV7UtoLVKv+8fjYO5w8eQTAoEo6f39W1Cr0lYry48BFoqy9sdX4ansWGMedvrfPcuAN+vlPoeUHu4rlLqfwOwqpQ6ZL2MQwA4OHoZgKw6OQrgKm5xqG8A9OwtjfbCMfPId/0dbDyqUbYqxH2aWbXtsMqZJj6FdHi0rpiAryrgqbKWVO8kLfLBUQNzZAJ1jST37IsKWRdorIcwQY6rF83QrU53KUSxl1/Kff/4DgBg+sys+yzpeUtTFcBEFBc2r9L+5aIaHSNkDgCUXWuptvz1Jee81kkGCMJ3crAl6/pL3DNC9Iq/ce1LVE0tIaoSMePCC8Iak/BkZYDpggjHtEpE2zFKwUWlOxmqbVuM17PPVIR4xocE7NIKEy7Km5zyCgOgEM/ghEFiv5f3lAdTnQBw95C3nX3ZBLUXqjTO8ubuf1IcdS6KbnXWg5BdEVlhAEC6k6OsR0EvF6MUBkf8g5WeBfexiIf0OjiaBnBZVyEt9Hdz1R+vrBNaSSqKrK1R36ZtnAchFN3gUOzOwfU+55zZdoXpjHY9ROo7Xvmm/RJV7BVqFSnkbe+Z5U3KS0kqFVYYAJB16KDcGhfYW/RIHmTnsj9Xpv8HfHGhrCp/8l/91098ESF+U6PbOWLe/tYfu6lt/9NH/95NHc96Gv9Xi576nwBsGmN+Rin1kwDmjTH/nVLqYQAfAOUxDoOS5PcaY26pJdQd62mUKdA/RgqjsaJdgs9o2+SIKcor+quLUEOtV7m2lvWNDBuP+uCq0RYTb+dgc0UhfanhYtvsVsuKcakEiL8nJMFThceQD48A02Pe0i1eIQleF4oO8LHlwaMZkpUUTWFXlA0v+EbHKpjEoLTWuokNdC2cSzNv9bUOO08f8J+/yu48/b/5prDPNCuMtoUX13cq1He85TadCYnkHBmerOOqvGJhpWJaQpg1Khe6wwG7g4RuvrZFepJOXXo6SQ/IZ3zPlMZqStBMe+8HJ2xYbk6EHte89OEQ4dxpMvd7zyyEvUZaCvNfoAczONkO2o0uP0En1T/uFbKs1k77BpM5HZAMTuY0uufIVSzrlixRMBnItsGsbBz5pAJggIKTvfbzkajYlzUT7aslytTDuYs6kRvGtsaFi11lmwCq47CJ+u0SzfUStW2aELunSZE7zqsDEYqmzxUSrN22ZbVhOA4LUTW9QuUaSBFwhUNQ8cRgMh/5xLjh++mvR+ciV6ZozrLCamyUmMyL67C3hOehhP/e7vgKV4T/DIBfV0r9KICLAH4IAIwxz9kyiedB6Ncfu1WFAdzBSkMVhCipb2qMl/1Ecf0p7ATjMBRbUFTJ6yfR4GjN9ToAAFgeG5nMBeAK2kYHogAjroswx8DEdQ2BupHmaLoDpDsJRkekBascj1C6Q5N9+CDtNL1i2VSP2fDCjsL4oLBO0wowCibxnyktkpWz3hU4ObON5/peaQC0+DgEMfMKCdwxb8Jr2K7H8UIoBKMpfSeT0zACUTQNE5zFjPWCBpZCPTbQmRd6jS+QVJEKNx6KJkn2PidCwXYuGM/PxNXJ1pOKB9SsKun7Y8hCRFYWW5ftD+YKzD/l6WaWP+lDX+1zFC8s26KDXDdB3dYGTGwhmVS60uNiz3DzEa9kuiKck7d0kLDN2ooUg30Gk7kIVSQUgw3PpgP/m0S8z2wPCUbWcRMnRv3xucn+8Z0LPsFUNCLUtnNUsc3FXcxR1rXr2VHWaA1yTkWXxvG9RbmBCXJbBmiHdDWNzQKFRVtxUaP0nssa3NpjY4RzFlk3NFaUARqbJUYHwoJeZhSWivW2xz5HdowxHwGhpGCM2QTwx19nu5/GPrW0uGOVRpUCg2PeCmV+KO5ot/QZiWgS3eFy4tuRJH95Ay7BlQwQLMTREqFANh/020t+pNqWRbrYw7F1xYpFej0AMHzbGFU/he54TZPUC+jPkTSbPkoXFF31woWtP4CguSrTgIC9tg4OMJn4FTff9WXgG1teSm49t4gaqK+Du/amDxuwsGbETTSl+yEVRchNxB/Si844js7nTa/sCfRnACXyBpwLqW8I72EgOvVFFFpkZRFP6M8RGo4NJoJHjBPcUljXtoQlnpFCYcE+eGqBckenScKxwgCEwhBCQhUV4h16PuOjdF+dsmDoKieiU0WFh84apu9lIabsAwMT0tjvhY1yriIR7Wzl73VhkHWVg7OOOxpZx987l3uI/Gtt2xsMtV2i5GHYLBSwc6buitma6yWyjnZro5yGsG/Zaa+saajSuPBZFSu0VguHSOTP4ynnR3SADOP5VCX+VYISyhr9zb0sPNaaco2f+kc14nHo8e7LMOHc+nodd6zSAEDuuhV0TJ9cpQpzzyOw7vOW9lbQJIQWZt1QCUBRws3DPi32XuyvbBi0L9AHrRWLPrGhhSqhOgqesLogaorOOylEVAeweHSIFy4QF3q9Rav61HedAwC88PQJqEIhHvoDlnWDqmmtdGuZLx3fdt9v7nroVLVex8Z63Sd1l6fQ17x1rAuPoopHYWitSrk5jrh3NxB8LBiTIQliroJmAcXMqSaiEBbf39b5KPBK2IKUVCf8O8BXKLOQHy+GhHoMfsgFcozpLQBS5tzxDqDEeDQFslnab/OKRnPNoLlGB2xdzVDbsFaIVQKm5gVz0a2jaNp4+zI3X6Lv9Disrm5slWhshegnabxQDkKF+bREOaODw0A8jxoWEusS3UoFqKDGVolpN8LgsD/f2q7vf2I4NLSn8RLf68ERjcGRmhOKSZ/2yUosb2tUiXJKK5oA9a3cAUeqSLk+4bJfOP0PGK0QlewtZyibMQZHuGe8QjwxDkwwXlABA3OVAINjyhl2HAXYeDT0LJjyZfxWushyRFpn6aP7KCa/AXLId67SUCTkVEkK48CTPvA9OFYPCsiKuvYNXWo6aOVatMhqcYVFNbsgVvxil1h7sn6Uc7WLhgq4nmq7Bp0LE0wXvCbafgz4zuULdG5lij986iH33WRIWu/s50/RBwtVwDU1nbeLdCA8nTnRH0RXKPr+WFFJNNQMRY1EEjzt7Slss95ZAJMVSW7XUvagjZ9bSo35Z61AVaQQmD48Hnll4faXeWUytL+Xie10IDwxDt9bQTle0GitVI66QluOMWbR5VDkXqXDw2jYxk72XITCAEioljWF7jl/PydLPjaWzUbovEY7LxsWWmpzAEbT+bICLRoK6cD34WYyvRsmq/neTI1PFschIy731w64nbSoD8krABEaWyK5Pqgccy6HaiTst6wpDGx9SPcCKQzZsEta0XMvj1E0Y5RCacYjA0c2mLHysHmMRuSYpo0C6puZ8zxMpGAiFeQDZC6nisMwKUDrjEPGfA+YqZfIHD01PHskvL0630Sx9BXgEAH2rbjvaznuWKVhNEFLAaD7hEGVsgWYuO8BWhRZV2F8gl1jINn19N3MWxQJ/qTGZoUo4yKrkDV3OqNJeLkJTu1eeZtkWGGymDoLbetBjYcfPIfXBjSjnzt/GMv/2S+Y6axCmXh0V31NY/LQGJVlwdVj68EIRWGyCKuX5wAA8W4EzJSIhn6fqaialiPvALXHt5F/hn7LHsB03l9g85oI93BntCNWIV9rYP5Zvz9dGlRQTlkAnicIII+Gq8gBr7AkJTiM/zxvWS9HeHVFXTmlw8qeLUqAFJSEPwPAziMkZRpXIuRtoLCIsgJA82Lk6gMYkcT91OkcxPumwtYj5IpFU1IAnEAGrPXMobw89BAAReEcq0T4c85bVHGYVFUVCVeZb5OJ99GSwsz5MmxctFY45cQCW/IzqdILVKZpb1/1OQiU/hitFWowxN5S3k3seYpwYmEceqtoapTzibtftV4VEB4anbpCw9pOARTGeWnj5XrA4MCKi42NqgZkS96SmXkupp4Z1tsqawpFTQXw86wTGkStlyXee/8kvaq+/uNTd6zSSHcNjv/HEnlLIxmUHr43rDBeiIhZk7ftGUwtxpsFC9NspDsWrmkFZPsixfATW/RkNC0sF/LoEUWCnIc690JjeCgK2mDqAlj95ZPu/3teHmM6X7rFVt8y6B/VLuww965VzAC4NvSVzZ1zCjhHWqC5VuHqd1SkLECFbslm7IQXU1j7XtL+POsPk7R89Htecp+9sLGE/NVZ2rZboN8F5g7aIpHn5mFOjJA+663v3ik4gkhqXhUuyKloVrTzVpL29S6dnHmug8aqD7FwolyGlyYHvMKffdkEvU8cbbjE8Ec+5DI6CGSLpQMFjE5W0EKZNi/SewlFHRyO0L1k+74LJFHeVNClCUJ18biC0TZ529RBwr60xISRIDEsa8DYKiSd+x4bbgjyO66K7x8RRWmCFbd9rUJZU05om0iFfSZs2ESVYv/Go+ySrLIdLj3aKR6Vbn/T+Rhlonw74tkIectTs1cxUHYV8haJnLRPPcVZ4Oct7UJ1rCi5QjxvRy4EBZCyNADq1kvKOhrjRe08wto2EA9il//JugREcaE+m/+R5I+xgKHzPHEIwwP7VKdh8OUU971hxz7CAm5/KKXOK6WeUUo9vacakr9XSql/opR6VSn1BaXUW8R3/1optaaUenbv7+6Ou+PuuDu+1kPBQJmb+3sjjzeip/HtxpiN1/nuuwHca//eCeCf21cA+DcA/hmAX/5yDlbbLpB3IpfohqIEXjQmk2CyGGMyrxxM0yODfE4imniun3RAbrZEPDXXSw81TBSiHJ6um5N3S9LdFhbQiMIY7PkMjpHpz27/4JhC413+dhWlxs6gifplfwKSmG3rAY36VR1QcgTEbiBLS9KKZDa+u9ygk31hw1cL9i/MALbGI+rH6N67jZ1N++ODU8x9tOlixey1MKngeEGhe1GGUxSi3AQVuPXuFOVLNn4UUx6F6z44Ce9gsCpEZxV1ejbSs6hieLRWAZSRgEsDUK0cjZfoRLnfe/2a9crsqcpkcTT11dqqMu65VClQQTn0UH2rDEJXylCLYHmte58DEIZM5LZ7O/rFglIE8Hkg2f+jtuVDNpWl++Dud93zRXB+FDoLe1onY+/pxCPqnicT+rXdyiGcigah6lyleUzUIxIWPF7wAJHuxcK3WI4VYIyD/erMIB77eVLFCtE07HkTj/y9iicGCXzYqrlmMJ73FeUMI+Y1wPBcBjyYyAIibPhVkjTe9niDK4SbGW9EpfHFxnsB/LKlav+UUmqWy+aNMR9TSp282R1VicLwYOwqWuWkrF/tY3yEJFHWUUEykdpU+grXZGjQWMuR2yrUwRErQOz6jCcGu6cEPLcN5KIWoWiG2H+A8iTMz9S0vE8ctqlsxzMmaxsdKxEXERZaPpu7+8QBtyB0Duzco53ALi38lrvNcaI3SGDD97oAgGPHvVJa2fLwpWKrTiRsgstp9PQ8Zi0Zo6pEXsOee32d6lh4jBeFIGXiP3u/Fz6Ron88BfZUmgfV8QJNxWGr2Vd8fiDKTIDvV8bj7qOpsUy8Vok9OEH9JVEHcdYS44kQXTwW6Ky1Clk7bBXK9722YwJlxVXKMqdQpioIzwU9HpgAMPLbNtav7+suFYlEEMUToixpCB606Xwsfu9DNQDQOxGjrAGtaz5nYWJgYs+7c7UIQmPb95FEnTkv9i+EeNYOecF0QSEoRr9VEQDlmYg3mzFq1jBrbFTeiAPlP9Je6ZLourT3imtQZolHinveZG2icOH8C88nB0eehi2FdU7r0AEsFCX6ma5kXwvy7iqNfR8GwO8ppQyAf8EEXWIcgWtfBMCzNV77cg+kKlpY7DHUr3qypo23zbn32+/IMPNUGsYiDVwiTucGk8XELb6iFlrprWukZHixcA2BFNLR1P+mSgBdKDSsnJ7OqTANp4Gl772Esy8ddufSTHOce4kguHE/kiUYTrlwPQoL+Km/ROgcGAtq9UNHtrD6PHkT3/vuJ9znrw4WqaHKFknRxPbrZgu5ij17L0CQ1+Y6sPp2rxiY/BAgS3C8qF3jH8AW+1mUUP94SOmSDOn+SkoXqYAcH5jdXTwJBfdeinUAGIlub7VX6zCRL6ysUk+HLvfLnpsuqFqYk9v5nu53zfW9EFnPmwRDdSIs+Dj3wsZGbTdEkdW3S5hYBRDcwEuxVrW05IcHFYaWELBzxdZfWKSeKkmhs6D0SDL/LGSx2+CQNYZKeX2+t/xkTgcdIQHKEfG97hD4z3ngZYqgfa1EsHHNBV+Tqqg4kZ8fKfuQNVjWpXCluOxPLzm3dBGCIbIZqgVyfdKvVKhiuLqNfRsmvH9fr+ONpjTeZYy5ail9f18p9aIx5mPi+xtlpG76KSil/jqAvw4ASXsO065GbadE49krbput95wAAGy8m4RoZ26E6jtGGJ6lLNvc80TElllB0blUoKpprL1ZhCwmHkbKFhMLNBYwnATtn6oCS33mFerH4TquATjyvnPu+1+554MAgN8/TkL9ieEpfHbzOM48QDwhVz5yLBA2qqQeHcyjo0ognzFum7n7NtEf1XFqbgcAMCkS/JMHfhV4gL7/7PgU/v0qsdW+9BTdm/h1Ou3VNmnBdi/ZmgA7u2ZfDhWFO7eKBLAsHBsLuurWVfLMpECJp96C5UXeuhoqHRcist+z8NB5yH5aNFR4/hYVtbf2hBU8Q6d7J2l/s69WIRpKeYQWk+/FQ34l9gB57YCveYkmYRFnUVcBGAOg5Plez8wZPVuV3Z/gtpK1QWm4dCaLQPuifxYz50tUkQoUhTQsmCuNlVI8Mdh8WLve3FyPE1T3i0NO5un+O0oX5ckz3baiSLTSgBGhvtmXw3sxnfHec9oj1Bb3DmFgBIM5TETta3me6YxCVq5eaGANALtGU1sP49rBCnDGbY+7nsb+DmPMVfu6ppT6IIhgSyqN22JrtJ7L+wFgpnHILH+MTMrJg4cd1Hb1O7kSzC8eVhiADz90LnjLvHc8cjHkKiHIH6zSmNiF17BoqygDBsf9OVXNEjAKc0/b2LAmRFRmhc9973sZAPBrpz8MAPj53XvweP0CjsQk4f7Nzrtw6eP+lsQFCAopLGQY5f5nVtbWKR+o/aF7n3Lvv3/mKawUXVzKKdD/j5/4TlQZ3YtkTAsv9CaEZbtM57xzxi+yKvEhD1WR0pR0FdLqLxPl6NABH26RzXakJc/IHLZMa73Kkc0BFDrMOiEflqwpcTmRln+tUh/D5nvG4UOCtfpixNW36aA2ofuaf19YbrKhZTumPNb1No+kkJGhKlYYzIpbRcrlC/hcgNCbmcz5OdvYNMH1Gk0Kmz+PbR2OJEGczmpnsTPZHyvSKlZorpdO0G49aHMi9wiDQNDM83EDcsphSBFT1D18uoqBxKIG42lINqhyKh50rXJNyNMlqU4AG0LUvnAymtj6KaukjAImolkXPwMOUZd1TQalDRFK7+n2hrmrNPZzKKVaALQxpm/f/0kA/2DPZr8N4G8qpX4VlADf5aYjtzomx2YBADv32QTfaoKi6+m6R6/OoHXZT5r2FVoN3JgICAVfZJWFEwY1WsC8+GFoEo8P+Qrt+acjt3iztsbO/f5497VXcV9jBfd+5C+7zw4ueIG//tmDAakfhxmKpp2cKjwfkxhUMwUOdn3c5+Prp/G2RYr6/c3nfxj3za/jk2dPue+Zv4qpI9jC61wKBYwyJMwkfUM08T21oynQWqmCuH4Vi17P9pXvZ94M+59HE9pGhg0kPfbwoEb7SoVSFHklgzBkEjRhstfh6MRtMZ/zNGxiXSZCdQ40LH8jJ9A5xDOdDWk+XD9sUNJ/b2zcKP+8WBly/izrUEGpYwy219RcpZNtXaUCUD319yIZyOuk3iSlCM8Nj2lXlc/XvPHIjeltmODSP+sSw0P+Ycy+YrD5GFBfFeE9EUocLSMAiAC26tpe59jSwo8O0nd66kONqiSqGlZQqgTSoa/jMIqeIysmVYZcU1UShkGJc0q75xqP9hSkchW9UERFU2F0MHLb78swuKs09nksA/igojBKDOADxpjfUUr9DQAwxvw8gA8B+B4Ar4Js+b/CP1ZK/VtQq8NFpdRlAH/fGPMLr3cwfaJE8xd28dJnDlAjJJH4nXkurDYFPBqldzymAr+hR2Lo3MdIi6ZtsznvjzWZVy7Esv0Yzdb6FdpxbYfI0pxVlBkAynFgfeBj7wootwFg86WGO6+9yqJKwyQ2u/zcVjWaKOSL3rp85SKt2nMXKNwV9WN8EovOajSxCejLUfkFV+wRugBbtPS+tksJURkCqlLl6OM5vut6SuxZvFkXrpCOR61XBZxAxHBK27dt3F6ej7QoTUTWLguBwbFw22Rgc0DidjtuMFzPlcWhGb4P9W3P/TRaUqgin5fgeyKLJhvCS3MJb+tNJKPKeTZ8TTozaF31pnvS85JvskDaQSKoippHb7E1vv2guLZtcaGGBLf0ymSNzs4ZW6Mi0FiH/sjfPAoXKUcKmPaB7fuUu3dsCEiDKCBnNMBo2YcVW1dNYFzsng45wOwpu+0DduWMDBWZx5DGB3uoHDo2EXFusZIaLVFinXvSyN/e9vgGqNN4wygNY8xrAB67wec/L94bADckpDfG/PCXc7zRsIanPnMPgLCHdPOyciEoQOYm6FVV9J5DJizAZDhodMjvs7GiMDgtCA+bBfRaDbltZLT4bOWqbQFy+1UBJLt+kaS7YXId8IK7SvaEomAVhJicsehJUdUMqu0aXunbUmvLaBtZ5lhlAC1yFjpTzkJjCzsa870xZPnbxVnFlkF07O9ZIqCQyShsF2qYgE4ogazjLfL6JgAddlyrbRfIuiFyiI83mdcBWWRpLVtOdkdTUhgcdqjtAvWtAmtv8SZ2PN4jzLW8F/TqiBmzEB02Ud444OvkHEqZhgR9rRWm0bCIn64OuKdcAyJxb+qbuaPlGC8laK5kyGboXujCOB4ned6MKGqul7jybn/zk4EiT5CfVWSLTK2CZ+XG51+z3lYQKuyHfT9UQYoNADYf0cg7BruMlrZcaHw8IMxVyXauxEysHEBi95QOu03aSvXIrgFd0Nzic4umFjbe5PwhwX/5uUqUGmAND+Gx6gLBPJWQ9dsdb/QajJsZbxil8dUeM50RvuvdTwIA/vP/9lb3OYdWIjFJA+x/w4ddAG+N8WuVhEpo+vYhEgC5RRyZYYJDf+R7MW8+RLNTJl8BoGzaMMQljWgqEEpWSfB5Gk0EiDy4XwRXrFeJtZQULxiGedmFkxokm3GwKBtrXmjUtz31RWOjApR35wGKtfO2qiJSQicoa2E+IbdCXlqQEu3DMXsJVwb88ZJ+gbwTO6XClcNRfmPrtX+MHlz3vOCL2i4wnaNpX7d1C82VG1ukjHbJWVBWdO+HD3nXqX6u5vp6B0ivqcF4QQXKPvbNFp2ykJ6BLsx1LKjyXo+XfKwpygym84kL6ekiDIeZyNLZiPDV/HN+v5tvrtC44uskiGZDwIvt54y6AkKQwnQOmApEU2PVK2PeX7qrrmsoxqO5Th6iBHzI8Fj7WumU6MILVEMyOOyPP/Na5T3WipBUbLwUDeUYiwFaq9HUh/jKlLxKXtfTGUVklCL/Mns2B6zirV97HXKyL3cYAOXXv6txxyoNHv/p/H3At/Yx2aXV0noxQZSFljwg6LqT8HVvIZwqSXDz9gZAPolRX/HS6Nq3GjRWaELmXZrotS3vmlM4yMIObXFagEqBD0EZDduXwCuLoiHCGqVlZ7Xf1zapJ7mHomq0rngaioZlmIVwzbnOoUoU0l7pLNqyRl6BK8pKPO00n7uEcDrabxE73noEWPw8vecQSizizZJWvXeCpEoAZe14QcJKZuceIYhzr4waazmKZuQE8eCw5ecSx4Ow7DnsJetC5LZ1S83C9QBAWNNhIi+I2Dthq7WsKQwP6iCEQsfy96a1KgS2gMoClkFWIKLyprI9udWe39jc3EHqHbL5ZgE9Lf28ZmSYe/5AUH8ymaWkv1QCAYFgC+id0YHgzeb9+c++oJG1Q+BEa6Vy9SLVHknUOxb5GhBF4brmGnsSnMznxDWHmWjzshZCass6FQ9KhTw46LsK6gJINr1x1FyrHHUJANRvK2sqx91E+Nf12B028aFPPQ40aCY1XrMEaylgkuurQJ0gnLJ177+TLjfz37i8woUmtLjLTHA4XrJhg6sKVS08njI+LFAlVAXteJMsukcqmaIpED+NMHGXd+i3jlsqs1TTdsEweqVz2aJ1UuqIxha7qkTho1IwWnlaeEt5zrUMugrRQBwq6h/1Qlwy/u7ea0NyD7FQh7PaAcLeG+2T3Ywo6h/3Crh1zUsCXuTc72Myr4IY/O5puqmS24uT9AD12kgHJuAaksKMFcbix1L7vyGySSFcgzoKETbUOSXKJxYyW9uiecSKKe1bC1ggqHZP+n011ygUyHmfvKWD58T089LTYlZgAJgslZgsAfNf8J5tR1TjS2XFY3BQXEubBDErlcYW1SgND3mlBITV97Mv+N+3r9LNY1SZiUSBITwdPuBDVbwtQPOSuahGi+Th8LOJx9dzmBml0L3oJ2NtZYDKMg0PjrdQ3y6dMVHFBLioCWSeifw9vfLtM8AT2J9xV2ncHXfH3XF33B03Pe4qja/joYzzMurnE2cBT+eoHShbOzdqLCQtSJfbsJ5HlAHZrHGJaBNTaMi1kZ0qVIlB86pIKPbgobFcJrJsj58QGycn55sre8JPFZ2bjKeXdZEjsdY/W99lqhCPPTV790IGk2jPmDoEJoJuQpceEQSlAhc/HhvXyAgAoj7QWi099NEmHDnhGU0JJMD3trapkHd9Zz6AkGQ8HDV23x80F+GX6QzlBNqXbe4jCy3d1orBcNkn5vmeyuS2rDkp6rYXtg1HRWOysN3+rhoqShMhKvK2fKzcnWfXdglUfEwEAAsOU0m+J5nEZxSd9JTiiXHzhI/F1jAQehk791tkmmCtZS8DoGLTvd5FlShk9v7lLQAmvP60bxyEl8OG7GUmPdugy3raSZ+ez8IL/mZJz0GV5CXK4kj3naE8AwMgOHcxnpcoCnFeA5rPEpkWTz3MnfM6ZdNPDhkiVZUJCiWrRGF4SLt5kwjP/baGQcBMfKtDKXUMxLF3ECRp3m+M+Tml1DyAXwNwEsB5AO8zxmzb3/wUgB8FBWB/3Bjzu7d6/DtWaahcoXY5gSrgKDsAX8xUiPBTPAqFkYzbsrJwtQVdE1R4RyO4hjs8kqFCbhdXYy0MgUjyPABE3yxc8fEiULQN0l0x6Uv/u6xLoQ8XMjMktDmpWN8iaCEL4ul8jObVKUaHa/Y6bOUs7770setpRwFGOcHgrotz6n2DrK2DOoqsq5yQzWboXKWY6J7175kHjMMGTL0hwz+ZSPbOnjUoamFYoazgOIq42HAgykGlwFcF9TN3kGITclmVdWrQJYsRG5v+2vpHI0IcWcSQhPcCpDCaKxC/9cVxLHRrO17SZ4K7SZVA96KvacmbGv0TIpRmFbGEQ48O+ff1DYXpHNByZAeR5aOy/9nDSpAHFy0Cfl47xNwkFHY79zKZoP+sc7mi8ltYJFZWBY2jpMHhcoaiL0Z9x4Yfj2qCrXNdhraFoSK3JOtPUtt0jIEAVUKADJ632UyMyaK/OKOB2rYwTmIqnJQIKllEeiMiyVsbBjD7kggvAPw3xpgnlVIdAE8opX4fwF8G8GFjzM8opX4SwE8C+Aml1EMA/jyAhwEcBvAHSqn7jDG31ND2jlUaPMYnckyWIsw9z9QQtApkA5udM+FtUhUCJSAX3uyLygp2+p+FvcxZyJyDUZY0cNZ/JguV4jEpMGlFcjKbzjNczI0Vis+WVjjUN0mosWfCePfuRY9Mmi6kbv+jJVuxzSCrmgqSqxLlxefQuWDj3BtFwK2jM+I+YlSMUXQdXE9BBWgmiNMDIeWFMt66TnvUEEsmU6McogjMCu9ZD70MUDRTOLZigIRQMriepmRsYbRcbCYhmv2jXrDzPeudsseb7OEdu+zfswfFifusA3Qu+YeatyKoIkRvBUWQtnZBdo2UiXLyHEO0muxwyGAGVyGuwkR21qZqfPl8GYEH+Hss649U4ec+e7Ly+IODft2wIVHfFEy7iQKsoGaFAQBLT4ywfX8zWFf9k+Jc50q0LkSYOed/MxB5s9YqKcfxovCYBTIt3S0dRQmP4cHIeW9GkSHiumvWw21veewTesoWNF+z7/tKqRdAHHzvBdWqAcAvAfgIgJ+wn/+qMWYK4JxS6lUQ28Ynb+X4d6zSUAWQbgNT6/KyxdQ7nmL7O8Y48ms0CXdPx+g/IOMRGp2z2i2+dIdgqY4p1QqcvOMnR2MldMvLmkhE75LCkAuktuUFsipskZFY4OylAEC1mAEGaL5sPYXEejd2/3kbyOGtfbZMGT5Z367CRWFDA44cbhIWeUlrjxPsac/TL8TD0oWVpnMxaj3jlMB0xlOKAB6iOe14gjplPJ2EzimEwlZffTNHfRMYHrarW4UJXBaolQhTSMHZXK8wOKidZ1Lr0flziITvGdckQIUgBydQ7SXoHEFnQVVQEy7AP89YUOjLoUqgd9Kby/HIEG2KYFsua9qdGwMhZMU54AXa+jvod3pMB26skmHB84hhwx3BNzUWnlFtx2C0rJynDVAY1tVt2DnAZI4APT8mDdxL6WJUiHxrrlhjrCm8qQroXM799gN63zvdIIp8EULK5rxRfPDjCkDl6jAm8yrwuJorOUykHHeU0SGKj8NdsW2UNjicIh4bB+xoXyOjybXire2T0gD2Padhmb3fDODTAJaZIcMYc81y+AGkUD4lfsZEr7c07lilkYwNDnx+Cm0lzPZ3eOlQf6aBTduG21UfZ0Kqi7e8iFlZjI5QJW9t028kY63O1WUE0iwVErmqaGv1Kmux6SIMX5X1sOK7fpZ2zlYhu+0lQ4RjiqVbqqoAQkvfKxK+gjgvyv0xg5CFFRwyPLf0ZBYotOlcHJyvUd5irm9TCMchqCobwhH8R0XD92PXuUFzvXK04oBQGKD7Fo8MhofEotbe2m2uEIyyue4FRmu1chanz7kYe63XCwd5/SYmJaIrfz9aV4GpDWk5ZQO4wjQZ1gR8bcn4gCYCPyFYdQEHZ2alUxc9vI1WjjyvaJAHOBUGBCsMwFv+wyPCa8vhenzzM5CQ37LmUVCcI5Mkf8nI9wthhc/XYyIV1JyoihTa7KtisiggHtt2r3V6prKoj3vF6NwEYa10UOHgx5XIQ5iAh4uRV80V7+aUMn9iiF7dUe9XBnlTO6XR2CoxOhA5zzJvqCC3Vt+8fe/AjZtXGot7GtG9fy/rt1KqDeB/B/B3jDE9y6Zxo3GjL25Ze92xSqNoKGy+iQTuPe97xX3+7B/cC8Ari5Pfew4AMCpo9W986CjlO6wFNzxqrZGWn1jNK7J2gBg+naCytQxB5bDyi6esk8UqY++S02d32QrgdT8PFp4rfB8PQ+EsFvAmokSzpB+H8nkDjsOzsjIRxfD582Toi66SESmT2VdpcerCQMn+ENwrW0xeZeCmJ3swnMxWFXEKcShQVRRW4ES156Si3/dPpEHVNN9TGRKRljgVRZrAq5Jxdc5FSOinISMWAOU3VB4msKH992wAyHCZzH+oyogKa+uJCrruaCpCbzbpy/kbvnZHsqcViqb3cJurJbbvixyXVGODvuN51LDKpqz7k592vbJQxhDFh92+f0wFxgCHUCXleJQJlmJjhW+Qs/DbtmxL3Lzjj1/WtGsPy33V2VMyOpzz7IEAwHQ2ChLXZU2hqPtWsqzAmHQUIEUzXiDFxIpNJt2nMwomoolVpjSXpGdEoA+fGN+fYb4cpbFhjHnb632plEpACuNXjDG/aT9e5f5CSqlDANgvvC2i173jjlUaVbvC6F0kKT/3wknUr9CE49zD6T9NGdqi0k5hAMDo0PWolLJZob4h3A/ANcPJm2Gs22i2fPhE7PYiDBKQqTF7rjW22xc5zFDabQ0mc5GzWIcHqe5DxqMlpUKUEQmerPzN2z7s1LYkhByuU8YLZc6/9I/S/UjGJmgMlLcjS/rnQzJl4kNdac9QF0IRKihTXwg5XtBorRhn3VYRFaAxlUZRJ1AAh4n2emGsMLzADXMAeYtyKOxZcPhB0pRMu8olw50QZfmlQ4CE5oJIAaQIUTn+c1aEXFeSdUIkGp+nnAfkTUTu98nIuII2AEFMvxCKGPA9ul3eoqIcjRPsKjw/mbDnIRUGzwG9h9qeQ5MTq5i58HIv+IMNiulcGJ7iIXMd7CXwPANsk6Q9OQY5bwImgOMKQBSE0gaH/PrMZmmdcz1ObTdEkrWu0TVEE+9F7cswAKrb91oUuRS/AOAFY8zPiq9+G8BfAvAz9vW3xOcfUEr9LCgRfi+Az9zq8e9YpcEj65G1we64/vYtnJz1gd1YVzj3ooel1CyRH1dyA0A0FrFha8kFfRtEcpSZP7ndqwsDSDZXgUgBrq+WZYUB+MU4FM2E0l1BtDgB0j2FT1xdDBDaKul7zypvK+RN/7+ufIjCKDp3Djfp3ATJRr5uXmQmIq+JBZXRCsnYOAFKv5dhBhJ+XHwX5QirkI3dpwnvCyuV6QzBazkkNJkPWXLZkpyK/ggm8pXkdH2GYtoAeid0wOFV2zaobSM459qOCHONpEANFZguwl4iRil7Lf4zDjkBlj+pMgE82GivHNiwkP01arseHcVWNc9DVdlQmJB/siDOWKNBCl/mo6LzCT0hVYWJdFmlTz8OLfQqsfdX9Ajh8BBg6WjqPo/I+5BDCvaiLtoNWG8s7dP++ogDhQEFtFcqbD0gvDyxxqazIbQ5b2kyhthruyIZO29z7IPSAPAuAH8RwDNKqaftZ38XpCx+XSn1owAuAvghADDGPKeU+nUAz4OQVz92q8gp4A5WGkoZJEkBrLdQ3wD0e3zGtB55q+fTTxGpYW3DSx9HOw7BxW/nVdEIK7CjKVn9LlZs6TmkdSqt19FhGwtv0TOdeyoMireuVRTOssKEq4aD+gmBzooyEiT1HdrftBthcDRMxANe8OZNBPTmpgprQAAvSGTLTXe8HDBWqRDFSEj+FrjnBuhc8n2l84ZCOvCCbzJHpHqSbbRo+HBZmdL9dCAC43tjyGtifiNGCDkq9MhStNjj13YoGc3Cqb5FiBs+H/6dZN6V1eXSUDCavBqpKGQYrHBAB38DZy6UPqRihWu666n4JTKN3wetbCuDyJ7j9jGF5ko4L/ge0f7pjWtpWlLSnsN/heswaBX4NAw/ce2DRCFJlBnnpaTnlQwrFz5LbJhqMufn90QYM1JhuKp3SddjfDX97NkSqvQggoOfyYJz4XNcetLfjGhSYXyAjs1GQzIQxKKVcQ20evd3gSexD8PsS52GMeaPcOM8BQD88df5zU8D+OnbPjjuYKVhJhHyFymL2H90CmzRjHznvefx2c/e67brXGSaBCEc2n7yde/ZRfGR+bA1p6BE4Mkrw0UyOdr+3hW848AF9/+/f/kR5IMUC5/2G3Uu+R7kbOUzeVuUhcLKNdkZe0u3vlOif8Q/6tlXjfNMZs9S3cZIWM8yJNS6VrkFrCpKQLK1azSF4QJPyPhFWKYqQIUVTRvXZkFbJ+ElaeGryCekOc7NOYWyRvvwSWIEzXaq1CoIlncg4SJx9ib28mi8SPuQyWBVeaglw2Ol5xT0+K6RUOzaLn2jA1HgGUjPgxWGrMOJJn7lz1ygG95aoX2pIvQKajsi7AOmvvDsyHx+G2/yN/xP/7WP4N988lsBAIufovnD9RllzYYFGYDhPAk+t1C46dIg6QvPIPHoJD4vCmVKD7yCqthNtYJ5KHnD/MSRCqOxYVDFCNu5TsPnOJ0jZcEjb2kH2eZ7VaVhyNgpQBt2CrtIGhRNj/wCgNHSHhTD7Q4DmP2p0/iajjtXacQG2QFaQT/yuA/v/buX34z48AiNj1HgnPpTALUdtsCopHL2mIfK6Hdvo/z4HACiX5au+u5J25FPKJX83d50/57Dz2Ejb+Pfv/yI++zQf4qR9oTwAFDfoHMd2Z7PMoywtxNeMjQuxkzn7B8zC7KDn/LB87W3eFcn7ZHQb676AxQtj3SpYg/fVRUCnqb6NtWDyDqLMlEObZYMqe0shzXKVGF4yHNZ1bcpn8FC22ib9HQ1KbAIHH/texVWVYNrhKUMKTWJXtOlR7oBFI5iL6xMVaAUGIIphb/OvWCMplSQyMolnhiHQqtvVUHoiKG5kkhP5iSiCTEfS2CBhKfqnHMh/jMp9JjRl4v5fuBveIUBAK01ukbZzlV6SeMFTeEtyUorKvFZYTAVO0DGD+c42MiQz16LXAArU/YsdGECGHL7aoGpLW50ZJ/y2QqFoQuDxrqH98rroHOMrvs8KBC11xCGff25MFiBowP7OvbB0/hajztWadwdd8fdcXd81cdd7qmv/9GcH+M3zz6G8nkLmbl/gMbH2gG1OKqwwjvuRRg8T55F84Ed52UAVNlaJQqDw7K01ycNx0tABOBH7vssAOADrxKqrvNRCo/Vdo1rbA8Aw0P0iGTfCV14OoutBzXalxCEMgrRQS1vqSCnoEqgfWmCy9/hvYvpcZ8VPP2/0u+2HqrZ3wOzr7K7r8i6tacymadeH9KLMsqHFfKW7ZRnLb4q9mEQgJLw1K/Z7m9BIZsvsfCU9WxiqpzmHEY0AWB89XzWJW+HLdJ0m64vEZ5IJOL0VQqU2ueTkqEJ8Pgcttg95cMSMmeQ9ktknSgIucm+3IUIp3CuSYIg6uv+fecS5aZkHJ2u2SawayGFiokUsravNWAvwzEIV2Fy93d/+t04BJnTqKDyCi3bZzvvUD2NBEVwZTsAzD9P58XPq6ppQORUXM6LCznbColIcjvkFb/EKsgzlHWFeBJ256tviGrxVLlmWwwrZq8mb4UAB4Z4S29CFvOpwsDEKuC+ijLj7hfPV86/pD2DtG8wsiy/cy/uk6A3Zr8S4V/TcccqjWisMPtMgryZoGh5jh2stzFZvJ6DysXN61XQ2W7y+TloUTEdKAuQsqiUp7MwCihe7OLX/rPNV33bLtLfm3Hb68ygqOsAKWIiFfRpSAcGWw/6IrD+CYW5l2xVdqqCbm9ceCZ7Pqy/ueGur3cP/e74B62QawODQzL/UboKWoLqKoce4kVX3/LQRKV9FXKt9/9v78yDLKvqO/75vqX37lmcBSYMDouggIiRSbDARNBQJpXCSplI1FIxmqqkokIsTDQkBCUmLlGTaJSijCEhwRBMXEJFXBBJXMGFHRxggAAzOMNMT9PLdPdbfvnjnHPvuU3P9Jt+3dOve86n6tW7767n3Pvu+Z1zfpsLGhcsb0wqBJabWu2mjqbX+6m4adG1t8zYseFe+LnsyH+gOp4r5sc2O4ERQoM0u11jFsK9h0YmmD/blNt/1fbgZ9KkMuIDNgLTq7sYPbaaT7X4hjFY5Uys9yl6vZlpvUcFQRGC90EuLGIjgr7dVtQVWB4csnukQWm62NC5Bfe1f12l0MCObi65Bjk6XRxWJAi2oLBulqE+WMmmNcd9gxibdw89GukjgoNlOL7i/Baqo+6A2kDZ/S+jKab4fxfKFes9pgeVefvn/4nokK7idFawErSS+z0VxYbq2dvIrK3C84mn9qZWlzPdRqlmUDPGN7p7HaaPQ2ciy8Me8rX7Dtem/43mgBcIa8zbaKljOGKFhpXylzyOCzW5FmqrLAvV0ehv0LW7QiNy3ot9NMJLF0xtTS7r18jxPj9H1AsGrziu5hZOla+sKgTBC/O0ceKfyiSU/X/t6bP8izDgJnmrD/ZmAiOURwaj3hM49NJix6YgKFy5S7C3i90vcr97dxWD8jWryhqAZzaXiol3eorKydCrjXUs3c/kYUqmVrtcHUGoNLqh9pw8OU7fjnKhEQxWT0FohNzsoZfbv9OdLySosooT9uHlr+z3yvigGC+7ZDxB4dn3+CRWLTG91o+qekvOsdHn2BjwPhVTq6PosFEwxp59DdgHwyfmr1EQFqV6nhAL8tzaExsjE9lIAR+c3spTXncwVC2MHnv3NpgaKmX5K/Kb5L6GfG6MOHtfeTp/PqHOQc9SSDxFnu8iphyHNOl1jnm1gfxeNKtReBrfs8/udUU0evJYWKFcISRHvVtZgEpXj8gKq0yWVjm7fnduih2spIIFWa2v5EaYdW8h6J/X5HO8kHiqxjNbcmuR4RPd9nDPwn8u/G8n15QYeLL2rPhU7WNpemo50+xtMnnafvru6GX82Ejhd/QEFWBqOO/a1yNrqTDKCDGJYqUsOIExsbGS2cBPrHdOXJWotxxbV2UhLIaC6aMVeq+VSWcpEloHTZbQuil678inl2r9uUVSll/bCykrFy2Yhs+ZdGcazl+iWKk++GTdTTFFIbuDIAuNfcESrEJB6V4ds6whaXSJiQ35yMJKzqggbHe+CxVKvmFTw/thhHe5AtWRaP+aS+sZtjd82PrsGfj1cc7yrrFm1qPMst35ezTy/AF2n2kc9W1/vh7Rva9B15ifXutTwdegMmkFi7DKpFMshymsuMHu2QO9UfgS5CyoYkHRv6PokFOeajC1Nn8uI8dXWP2wq/ze53nlbZRtLvZED17v4d5UJl248Lr3CK/3OsupOOzI4BPPbsDi3vr0UJ7OtdZXotZXyowGACoTxekkV7B8qqfhE3qBGx3X+pR5qgPPmi7KTJsNUD7amRoqF6aegllyEODBeTUYfNT6VAgzP350lfJUPhU2taqUCwxy36B4JBMLx9hqrC2MpAhfCYydOk3Xk11wspvTmPa5vPsez2/NdOTIF8J3hBj79d7c9wJg94uq9D9VzP5WnciH46V60Sol9DDHj8obizjkR+iZ7t8YHbO9N/NaHnwsHJO/wJWJOsMnuxa+Ot5k9Niod/tgD1Prmpl/QG3A6N4rNtzhjg89xfE4a1s07aJGHo+pOu6dxmInsXI+7G90uV5/lt+87I4JodWzwH9xJ7eZh1gxuWNDb70yGRzc/PbgdRzO0/BmusG6aRqm+0vZ/Pf+tU4fMezjijW7jcGHSowd4353jRhWVu67YL5n7O9J1tP1U0yh0QvCwuVOcfsOPFmn0VUqjBaCaS7kz717Rz7MbazKOwLBwe3p06JXNPYgXwtTa5UJ8Mp40Sej3uuEY+wDtO8F+Q4bbnMFCx7mzbKoTOTBJkNol9gRMo4RVvaOedOrffiahs3qOR1bMHWNmbsn5KOIMAKu9YvuEOlWRdPmkh9RTEWpfZtd+ajJxUuzgoluPLLOAmp25aPvyTWR6bec0OmOIu3GuqZgPbggJJPbZYyJZq3sBAag+5ymu5tiz7s05QRF5tBWLSZl6R52jWDoUdX7YeQEFUJwlyPlr5quFx8rDa0kVj3sLhDCLARhUfexqPp2uN+1gWLioBBeO+71Ta+qZvb+o8e4Rxz7cqz/Uf5CjW52yzvPioTk+jpD95NdL9CzB/p2NRg/On+JrBR5PTfcixvnXo79SCr7ndI7bO/d5UYeuVLVjeDCtKHzEI88vodUULxn/h1Rboi+Xc2s8WpU3LqgNA6+BIOPhLlsl3Apj+TqBEbQ1WRKYn+dep+yOEeQeyiH8vXtbBai1Faj5dD4xqEpqqP5SKOxqpddW/M5yYKZcTjHhrwh69pTpmdPfm8bXSFMSH4vIE88BTC0LW90g0l3rEeJ/RrUdD3y2Gs8jvtVxgmMOL2tmnkOi2CaHHQg4JTvWXQB/wzDlFCpblkwxuwakUlxs5zHmsq85uNYiM3IYdELjKDTqvc4wR+iHASFd6yPCXo5cAKo2V0q6FsWAgMsjTSWMQ1RGqlQH2hSGSsV59LjqVZzjXaIbFqacr3noAcJveY4w1k10pH07HUK4LjnVB1rZr2nZneJek8p030Ep70Q3jt2VoNcYIS8H0h0P5UnhNjxitWs2VbPhIUaTt8S8iD077SCEFv9kFveeW7+0gw+UMlemKFHcw/pvl1FBzM1i0l4QkC7LP7RmqLl1r5fcDuvvi0Pbd7/VJ4NbnKtczgLI416rwtnPuHt5stTuCi2IYTGBid4Qha8ZgXGN5Yyv5AQ5yko7hs9LrZWuNeNbi9IQpj7sSbl6cipTGHkkm835f8Fk6j35dcHCr3tqai3279zurC9MlaDhtEYdFJh9DinDImFdHkybxifOdnrPMb8VGG3FUKChwitcZiP2Bqq2wuYnmh6LBYY00MVKpPNgoVTLCADWaDLdVXnYR/+hlGoGFe+3LfI1avs5vOjlljRCDOe4gxxuMqRwUnXaJ5B0uT+d1k4m0ZxlBXKnWV5lN8ejRgLCvy686+JRxqxwAg+Mm1jlkYayxk1cMICP08cMpr5lyAebQRTzXj7fh+pvjbo4/DEUyxRh6kUhdQAP73SV8osPsIUR+ixlWvFhsNKMPRYpHz1gd16duRzYvU1ffxsq2t8Gj3w9OmVLPRGuVFMnFPrc73pnuHiFNgqn4SqZ48xsb44Xx72nR5yVl1Zb7E0M4yFvzeRd3m9H/Zv8lMxj3WhZn6+ZkWMHpOPTLqHAcH6213hR0/od+bFXgdU73a9/XDvG12uUQ06l1ofUMqNDGr9JdSwLOLvwOPOeXBgR4gvokwXAG4qrxH1tjPv62CBa+75xE57LnSJCvcJcm/y2dLXAtQHqkxs7CqEBmlUyfQ7Pft8sMUgvLe5mzS+yU+9TXrhsz8vY5zG19WPAj37cvPjsaMryMgaSjWt6JhXL8a9AjcSCx2q4PhYispf8IYfaxTCk8/cHqzIYmERDBCg2HEL1kxx3Ko4x0UcXwtClAJlI9BSzQmJ4jmjkcU0YHl9GyU3EgqjxgULWMjKsJ6SrQBt/nyQtBt4bJFOvw54es69lj+pniuLVM8D81wzWz/3bgdG0k3+2q3wtJm9qp3rLRZHrNBYTCT98GCx8FcKqZ4ri1TPRCuU5t4lkUgkEglHEhqJRCKRaJkkNBaHq+feZUWQ6rmySPVMzEnSaSQSiUSiZdJII5FIJBItk4RGIpFIJFomCY15Iul6SXf4z6NRgvd4n82SbpF0v6R7JV18KMd3Au3W029/h6Sf+m0fPmyFPwQW4HleIenJ6By/dlgr0CIL8Tz9PpdKMkmt+h0cNhbgWV4p6S5//NckbTqsFeh0zCx92vwAHwUun2X90cDP++VBYBtwSqvHd9pnPvUEzgW+AXT73xuWuh6LVM8rgEuXuuyLXU+/bjPwVZxz7LqlrsciPMuhaL93AlctdT066ZNGGm0iScBrgc/N3GZmO83sx355FLgf+LlWj+8k2qjn7wMfNLMpv33X4Snx/Gj3eS4X2qznx4E/ohCxrfOYbx3NLAo3Sj8dXs/DTRIa7fMy4Gdm9uDBdpK0BXgx8IP5HN8BzLeeJwEvk/QDSbdK2rq4xWybdp7n2/20xmclrVnEMi4E86qnpAuAJ83szkUvYfvM+1lK+oCkx4E3AJcvZiGXG0dswMJWkPQN4KhZNl1mZl/yy69jjlGCpAHgP4BLZvRiWjp+sVnkelaANcBZwFbg3yUdb37sfzhZ5Hp+GrgS1yu9Ejct8jsLUe5DZbHqKakPuAw4fyHLOx8W+900s8uAyyS9F3g78OcLUvCVwFLPjy3nD65B/BlwzEH2qeLmf981n+M74dNOPYGbgJdHvx8G1i91nRbjeUb7bAHuWer6LHQ9gRcCu4BH/acO/B9w1FLXaRGf5XM7+VkuxSdNT7XHK4EHzOyJ2Tb6OdV/AO43s48d6vEdRDv1/CJwnt/vJKCLzo2kOu96Sjo6+vkbwD2LVsr2mVc9zexuM9tgZlvMbAvwBE6Z/NThKPQh0s6zfF708wLggUUr5TIkCY32+G1mDH8lbZL03/7n2cAbgfMOYIr5rOM7lHbq+VngeEn3AP8GvNl8F64DaaeeH5Z0t6S7cBZjf3jYSn3otPu/XQ60U8cPSrrHP8vzgWeZHB/JpDAiiUQikWiZNNJIJBKJRMskoZFIJBKJlklCI5FIJBItk4RGIpFIJFomCY1EIrFi8d75u7z1Xiv7v1bSfT6I4XWLXb7lSBIaiY5F0pj/3iTp822c5xLvzbwQZXq+N8/8iaQTFuKc0bk/I+mUeRx3RmwSK+kCSe9ZyLItY64BXtXKjt4/473A2WZ2KnDJ4hVr+ZJMbhMdgaSKmdVnrBszs4EFOPejwJlm1rZToW+Me81sXmElZqvnApTpIlz93r6Q510p+NhSN5rZaf73CcDfA+uBCeB3zewBubD928zsM0tW2GVAGmkkCkja6oPu9Ujq98P002bZ701+vzslXevXPVfSzX79zZKOnWP9NZI+JukW4EOSjpP0PUm3S7oyutaWML0g6SJJ/ynpJkkPKsrPIenTkn7oy/w+v+6dwCbgFn8dJJ3vr/NjSTf4+EMz63eGpO/7Mn9B0hrfm78EeFs414xjxiR91J/3Zknr/fpvSfpLSbcCF0t6hR+p3O2nT7qj/c48WBn98/muv++3SVoFvB+40I+ALvT36JMt3Pu/8+faLuk3D+Fvsty5GniHmb0EuBT4lF9/EnCSpO/4Z9/SCOWIY6njmKRP532AvwD+Gtcbe+8s208FforPpQCs9d//hfP4Bhes74tzrL8GuBEo+99fBt7kl/8AGPPLW/Dxf4CLgO3AKqAHl9Nh84xylIFvAaf7349GZV0H/A/Q73//MbPnW7gL+GW//H7gb/zyFRwgbwYuWOEb/PLlwCf98reAT/nlHuBx4CT/+59xwfLCfmceqIy4ECzbga1+/RAuxtJF4VrRPQrXPti9vwHXcTwFeGip/3eL+H+O/z8DwH7gjuhzv992I/AFXEyq43BhUlYvdfk77ZNGGonZeD/wK7gGbLZMe+cBnzc/3WNme/36lwJBeXgtcM4c6wFuMLOGXz6bPPTDtQcp381mNmJmk8B9uKByAK+V9GPgJzjBNpt+4Cy//jtyGd3eHB0PgO+9rzazW/2qfwJ+6SDlCTSB6/3yv1CsZ1h/MvCImW07yLkPVMaTgZ1mdju4vA8291TXwe79F82saWb3ARvnrt6KoATsM7Mzos8L/LYngC+ZWc3MHsF1jJ53wDMdoaTQ6InZWIvrkVVxPePxGdtFa4lpDrRPvH7muVs571S03AAqko7DTTVsNbNhSdfgyj4TAV83s9e1cJ12ma2eauG4Wcso6XTaTwgUHx/fx1bKtewxF+L9EUm/ZWY3SBJuRHonLrjm64Br5NLYnoQb2SUi0kgjMRtXA38G/CvwoVm234zr1T8HQNJav/67uEBx4JLXfHuO9TP5zoz9DoUhXMM8Imkj8KvRtlFcSk+A7wNnSzrRl71PLvpuhpmNAMOSXuZXvRG4lbkpAUE38Hpmr+cDwJZw/QOc+0BlfADYJJ/IStKgpMqM+s2k1Xu/IpH0OeB7wMmSnpD0Vtx9eKukO4F7gVf73b8K7JF0H3AL8G4z27MU5e5k0kgjUUDSm4C6mV0nqQx8V9J5ZvbNsI+Z3SvpA8Ctkhq46aCLcPmUPyvp3cBu4C3+kAOtn8nFwHWSLsYlxmkZM7tT0k9wjcB2nAAKXA18RdJOMztXztroc0EBDfwpLkd0zJuBq+RMdbcfpMwx48Cpkn4EjAAXzlLOSUlvAW7wDf7twFXFXWz3bGU0s22SLgQ+IakXNzf/SlwD9x4/lfVXMy7Z6r1fkRxkRPksJbc5xca7/CdxAJLJbSKxQKhNE2FJdwMX+Pn0RKIjSdNTiUQHIOnrwN1JYCQ6nTTSSCQSiUTLpJFGIpFIJFomCY3EikDOa3y/Vwa3e65CLKdDOO5CSQ9JurHdMiQSnUoSGomVxMNmdsYCnOcMYFah4S2eZsXMrgfetgDXTyQ6lmRym1hxyAWouwnnk3AWcCfwj8D7gA24UB+3SeoHPgG8EPcuXAF8BecR3yvpHJwJ6wtw8au2AE97k+CrgGP9JS8xs9jEN5FYsaSRRmKlciLwt8DpwPNxznbn4LzG/8TvcxnwTTPbCpwLfATnBX85cL0PMRHCf7wEeLWZvd6f9+P+uNcAKSpq4oghjTQSK5VHzOxuAEn34uJVmfeF2OL3OR+4QNKl/ncP+ehhJl82s/1++ZXAKS4CBQBDkgbNbHShK5FIdBpJaCRWKnFcpWb0u0n+vxfwGjP7aXygpF+c5XxxjKwS8NJIiCQSRwxpeipxJPNV4B0+aB2SXuzXHyyWE8DXgCzhkaQzFquAiUSnkYRG4kjmSpwO4y65JE8h8dMtuOmnO3ysp5m8EzjTJza6D/i9w1PcRGLpSR7hiRWBZqT0XMJyvByXpOnXl7IcicRikUYaiZVCA1i1EM5988WPSj4FDC9VGRKJxSaNNBKJRCLRMmmkkUgkEomWSUIjkUgkEi2ThEYikUgkWiYJjUQikUi0TBIaiUQikWiZ/we5ybK//6OGUQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clip using a bounding box\n",
-    "\n",
-    "See docs for `rio.clip_box`:\n",
-    "\n",
-    "  - [DataArray.clip_box](../rioxarray.rst#rioxarray.raster_array.RasterArray.clip_box)\n",
-    "  - [Dataset.clip_box](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.clip_box)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xdsc = xds.rio.clip_box(\n",
-    "    minx=-7272967.1958741,\n",
-    "    miny=5048602.84382404,\n",
-    "    maxx=-7272503.88315758,\n",
-    "    maxy=5049066.15654056,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f6a9e1c64a0>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xdsc.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also clip using bounds in a CRS different from the dataset if you pass in the `crs` kwarg (requires rioxarray 0.12+):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xdscn = xds.rio.clip_box(\n",
-    "    minx=-93.1558,\n",
-    "    miny=45.403,\n",
-    "    maxx=-93.1557,\n",
-    "    maxy=45.4065,\n",
-    "    crs=\"EPSG:4326\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f6a9c8815a0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xdscn.plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/clip_geom.ipynb b/0.18.0/.doctrees/nbsphinx/examples/clip_geom.ipynb
deleted file mode 100644
index f6b17bc1..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/clip_geom.ipynb
+++ /dev/null
@@ -1,339 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Clip\n",
-    "\n",
-    "API Reference for `rio.clip`:\n",
-    "\n",
-    "  - [DataArray.clip](../rioxarray.rst#rioxarray.raster_array.RasterArray.clip)\n",
-    "  - [Dataset.clip](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.clip)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset\n",
-    "\n",
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "  \n",
-    "Notes:\n",
-    "\n",
-    " - `masked=True` will convert from integer to `float64` and fill with `NaN`. If this behavior is not desired, you can skip this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    masked=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc957fb1ac0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clip using a geometry\n",
-    "\n",
-    "By default, it assumes that the CRS of the geometry is the same as the CRS\n",
-    "of the dataset. If it is different, make sure to pass in the CRS of the geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometries = [\n",
-    "    {\n",
-    "        'type': 'Polygon',\n",
-    "        'coordinates': [[\n",
-    "            [425499.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615331.540546387]\n",
-    "        ]]\n",
-    "    }\n",
-    "]\n",
-    "clipped = xds.rio.clip(geometries)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc957829d90>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clipped.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clipped.rio.to_raster(\"clipped.tif\", compress='LZMA', tiled=True, dtype=\"int32\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clip using a GeoDataFrame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas\n",
-    "from shapely.geometry import box"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geodf = geopandas.GeoDataFrame(\n",
-    "    geometry=[\n",
-    "        box(425499.18381405267, 4615331.540546387, 425526.18381405267, 4615478.540546387)\n",
-    "    ],\n",
-    "    crs=\"EPSG:26915\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clipped = xds.rio.clip(geodf.geometry.values, geodf.crs, drop=False, invert=True)\n",
-    "# Note: If you have rasterio < 1.2 you will need convert the geometries to dict-like objects if the projection\n",
-    "# of the geometries differ from the raster. For example:\n",
-    "#\n",
-    "# from shapely.geometry import mapping\n",
-    "#  geometries = geodf.geometry.apply(mapping)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc9572aa100>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clipped.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clipped.rio.to_raster(\"clipped_invert.tif\", compress='LZMA', tiled=True, dtype=\"int32\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clipping larger rasters\n",
-    "\n",
-    "Note: Loading from disk will likely only work directly after opening a raster with [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "\n",
-    "The clip operation needs the full raster loaded with the default method.\n",
-    "This can be an issue if you don't have enough memory (RAM) on you machine.\n",
-    "If this is something you have run into, it is recommended to use the\n",
-    "`from_disk=True` option. This option uses [rasterio.mask.mask](https://rasterio.readthedocs.io/en/latest/topics/masking-by-shapefile.html) when loading the data if possible.\n",
-    "\n",
-    "But be careful, these two methods, as they use different core functions, can have **different outputs**: small discrepencies may appear on the borders (1 pixel added or removed on some borders, see issue [#310](https://github.com/corteva/rioxarray/issues/310))\n",
-    "\n",
-    "Alternatively, you can also use `rio.clip_box` followed by `rio.clip` for a more\n",
-    "consistent memory efficient clip operation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometries = [\n",
-    "    {\n",
-    "        'type': 'Polygon',\n",
-    "        'coordinates': [[\n",
-    "            [425499.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615331.540546387]\n",
-    "        ]]\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "clipped = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    masked=True,\n",
-    ").rio.clip(geometries, from_disk=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc9571db850>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clipped.plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/convert_to_raster.ipynb b/0.18.0/.doctrees/nbsphinx/examples/convert_to_raster.ipynb
deleted file mode 100644
index b7b70d58..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/convert_to_raster.ipynb
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@@ -1,677 +0,0 @@
-{
- "cells": [
-  {
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-   "metadata": {},
-   "source": [
-    "# Example - Convert dataset to raster (GeoTiff)\n",
-    "\n",
-    "Often, it is desirable to take a variable (band) out of your dataset and export it to a raster.\n",
-    "This is possible with the `rio.to_raster()`method. It does most of the work for you so you don't\n",
-    "have to.\n",
-    "\n",
-    "Note: The `rio.to_raster()` method only works on a 2-dimensional or 3-dimensional `xarray.DataArray` or a 2-dimensional `xarray.Dataset`.\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_array.RasterArray.to_raster)\n",
-    "- Dataset: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
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-       "\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * time         (time) object 2016-12-19 10:27:29.687763 2016-12-29 12:52:42...\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06\n",
-       "    spatial_ref  int32 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 ...\n",
-       "    green        (time, y, x) float64 ...\n",
-       "Attributes:\n",
-       "    coordinates:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c445223f-de4e-4b1e-8098-cbee34b218b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c445223f-de4e-4b1e-8098-cbee34b218b7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-28147fcc-df3f-4a34-95e9-c553e0ce5379' class='xr-section-summary-in' type='checkbox'  checked><label for='section-28147fcc-df3f-4a34-95e9-c553e0ce5379' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2016-12-19 10:27:29.687763 2016-...</div><input id='attrs-e088320f-bca6-467e-8cb8-e8986a40cb2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e088320f-bca6-467e-8cb8-e8986a40cb2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-059f464e-7159-4921-a87b-f7cd4f581362' class='xr-var-data-in' type='checkbox'><label for='data-059f464e-7159-4921-a87b-f7cd4f581362' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(2016, 12, 19, 10, 27, 29, 687763, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2016, 12, 29, 12, 52, 42, 347451, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.663e+05 4.663e+05 ... 4.663e+05</div><input id='attrs-b437c71c-6f09-48b3-ad9a-90d13232f2ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b437c71c-6f09-48b3-ad9a-90d13232f2ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57e77136-7143-44d9-b7cf-73fbe2ee23a9' class='xr-var-data-in' type='checkbox'><label for='data-57e77136-7143-44d9-b7cf-73fbe2ee23a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5, 466285.5,\n",
-       "       466288.5, 466291.5, 466294.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.085e+06 8.085e+06 ... 8.085e+06</div><input id='attrs-4c2f981f-ffc5-4814-8a22-ebf2f86a7aef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c2f981f-ffc5-4814-8a22-ebf2f86a7aef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1df8c2f-d47d-47d7-91ab-beeb0f27b13a' class='xr-var-data-in' type='checkbox'><label for='data-f1df8c2f-d47d-47d7-91ab-beeb0f27b13a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,\n",
-       "       8084680.5, 8084677.5, 8084674.5, 8084671.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-da9d5c75-1701-48fa-ba98-c71fc592eebc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da9d5c75-1701-48fa-ba98-c71fc592eebc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ea59b80-4a53-40ca-a226-83a68cd832d4' class='xr-var-data-in' type='checkbox'><label for='data-2ea59b80-4a53-40ca-a226-83a68cd832d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 22S</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-51.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>10000000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>466266.0 3.0 0.0 8084700.0 0.0 -3.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9028421c-78d8-4324-8c53-b922b911a026' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9028421c-78d8-4324-8c53-b922b911a026' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fa584176-aba1-477b-a671-19b6df4dcaae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa584176-aba1-477b-a671-19b6df4dcaae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca424900-74f9-432d-806d-13c47b426ed2' class='xr-var-data-in' type='checkbox'><label for='data-ca424900-74f9-432d-806d-13c47b426ed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>NETCDF_DIM_EXTRA :</span></dt><dd>{time}</dd><dt><span>NETCDF_DIM_time_DEF :</span></dt><dd>[2. 6.]</dd><dt><span>NETCDF_DIM_time_VALUES :</span></dt><dd>[     0.       872712.659688]</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>(&#x27;DN&#x27;, &#x27;DN&#x27;)</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31195140-ecc1-4c84-9a7c-8a4b5cac2d66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31195140-ecc1-4c84-9a7c-8a4b5cac2d66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c047550-03f6-40db-8ad2-26cd7cc23c76' class='xr-var-data-in' type='checkbox'><label for='data-7c047550-03f6-40db-8ad2-26cd7cc23c76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>NETCDF_DIM_EXTRA :</span></dt><dd>{time}</dd><dt><span>NETCDF_DIM_time_DEF :</span></dt><dd>[2. 6.]</dd><dt><span>NETCDF_DIM_time_VALUES :</span></dt><dd>[     0.       872712.659688]</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>(&#x27;DN&#x27;, &#x27;DN&#x27;)</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[200 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3faed2e-e71f-483f-b9bb-c4f9d96230fe' class='xr-section-summary-in' type='checkbox'  ><label for='section-b3faed2e-e71f-483f-b9bb-c4f9d96230fe' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-464f11fb-83f1-46e4-8aea-e2df9c3d5021' class='xr-index-data-in' type='checkbox'/><label for='index-464f11fb-83f1-46e4-8aea-e2df9c3d5021' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2016-12-19 10:27:29.687763, 2016-12-29 12:52:42.347451],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=2,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ab3c0dc-2aab-409d-b42b-cd0f4e962bf8' class='xr-index-data-in' type='checkbox'/><label for='index-9ab3c0dc-2aab-409d-b42b-cd0f4e962bf8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5,\n",
-       "              466285.5, 466288.5, 466291.5, 466294.5],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a08603e-8475-4cf3-917c-37933b01c87c' class='xr-index-data-in' type='checkbox'/><label for='index-8a08603e-8475-4cf3-917c-37933b01c87c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,\n",
-       "              8084680.5, 8084677.5, 8084674.5, 8084671.5],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81c7d954-b5ff-4a73-a2e0-f373fd0ae1d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81c7d954-b5ff-4a73-a2e0-f373fd0ae1d9' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coordinates :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * time         (time) object 2016-12-19 10:27:29.687763 2016-12-29 12:52:42...\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06\n",
-       "    spatial_ref  int32 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 ...\n",
-       "    green        (time, y, x) float64 ...\n",
-       "Attributes:\n",
-       "    coordinates:  spatial_ref"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/input/PLANET_SCOPE_3D.nc\",\n",
-    ")\n",
-    "rds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Converting Dataset to raster\n",
-    "\n",
-    "Dataset: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# note how one time slice was selected on export to make the dataset 2D\n",
-    "rds.isel(time=0).rio.to_raster(\"planet_scope.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"blue\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": false, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info planet_scope.tif"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Converting DataArray to raster\n",
-    "\n",
-    "DataArray: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_array.RasterArray.to_raster)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# note how selecting one variable allowed for multiple time steps in a single raster\n",
-    "rds.green.rio.to_raster(\"planet_scope_green.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"green\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": false, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info planet_scope_green.tif"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Converting DataArray to raster in a different format\n",
-    "Example here, an ER Mapper grid.\n",
-    "Look at gdal for possible formats that you can use to write to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# you will get a two file raster: the .ers file with the metdata and the data with no extension\n",
-    "rds.blue.rio.to_raster(\"planet_scope_green.ers\", driver=\"ERS\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Change the compression of the raster and explicitly make it a Geotiff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds.blue.rio.to_raster(\"planet_scope_green_LZW_compression.tif\", driver=\"GTiff\", compress=\"LZW\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Change the basic datatype of the raster (in this example, also saving space going to 32 bit)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('float64')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.blue.dtype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds.blue.astype('float32').rio.to_raster(\"planet_scope_green_LZW_compression.tif\", driver=\"GTiff\", compress=\"LZW\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Memory efficient raster writing\n",
-    "\n",
-    "Useful for reading and writing larger rasters to disk.\n",
-    "\n",
-    "Note: This will increase the time it takes to generate the raster.\n",
-    "\n",
-    "Also see:\n",
-    "\n",
-    "- [Reading and Writing with Dask](dask_read_write.ipynb)\n",
-    "- [Reading COGs in Parallel](read-locks.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/input/PLANET_SCOPE_3D.nc\",\n",
-    "    lock=False,  # disable internal caching\n",
-    "    cache=False,  # don't keep data loaded in memory. pull from disk every time\n",
-    ")\n",
-    "\n",
-    "rds.green.rio.to_raster(\n",
-    "    \"planet_scope_tiled.tif\",\n",
-    "    tiled=True,  # GDAL: By default striped TIFF files are created. This option can be used to force creation of tiled TIFF files.\n",
-    "    windowed=True,  # rioxarray: read & write one window at a time\n",
-    ") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"blockxsize\": 256, \"blockysize\": 256, \"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"green\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": true, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info planet_scope_tiled.tif"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/dask_read_write.ipynb b/0.18.0/.doctrees/nbsphinx/examples/dask_read_write.ipynb
deleted file mode 100644
index 4773066d..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/dask_read_write.ipynb
+++ /dev/null
@@ -1,137 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reading and Writing with Dask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import multiprocessing\n",
-    "# Linux/OSX:\n",
-    "import multiprocessing.popen_spawn_posix\n",
-    "# Windows:\n",
-    "# import multiprocessing.popen_spawn_win32\n",
-    "import threading\n",
-    "\n",
-    "from dask.distributed import Client, LocalCluster, Lock\n",
-    "\n",
-    "import rioxarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tips for using dask locks:\n",
-    "- Be careful about what lock you use for your process. It is required to have a lock for each worker, so the more fine-grained the better.\n",
-    "- The reading and writing processes need the same type of lock. They don't have to share the same lock, but they do nead a lock of the same type.\n",
-    "\n",
-    "See docs for:\n",
-    "\n",
-    "- [Reading COGs in Parallel](read-locks.ipynb)\n",
-    "- [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "- DataArray: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_array.RasterArray.to_raster)\n",
-    "- Dataset: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### No distributed computing example\n",
-    "Note: Without a lock provided, `to_raster` does not use dask to write to disk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    chunks=True,\n",
-    ")\n",
-    "xds.rio.to_raster(\"simple_write.tif\", tiled=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multithreaded example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    chunks=True,\n",
-    "    lock=False,\n",
-    "    # lock=threading.Lock(), # when too many file handles open\n",
-    "xds.rio.to_raster(\n",
-    "    \"dask_thread.tif\", tiled=True, lock=threading.Lock(),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multiple worker example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with LocalCluster() as cluster, Client(cluster) as client:\n",
-    "    xds = rioxarray.open_rasterio(\n",
-    "        \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "        chunks=True,\n",
-    "        lock=False,\n",
-    "        # lock=Lock(\"rio-read\", client=client), # when too many file handles open\n",
-    "    )\n",
-    "    xds.rio.to_raster(\n",
-    "        \"dask_multiworker.tif\",\n",
-    "        tiled=True,\n",
-    "        lock=Lock(\"rio\", client=client),\n",
-    "    )"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/interpolate_na.ipynb b/0.18.0/.doctrees/nbsphinx/examples/interpolate_na.ipynb
deleted file mode 100644
index 1b30f330..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/interpolate_na.ipynb
+++ /dev/null
@@ -1,216 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Interpolate Missing Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"MODIS_ARRAY.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f3bf197f978>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.isel(x=slice(0, 20), y=slice(0, 20)).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fill missing with interpolate_na\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.interpolate_na()](../rioxarray.rst#rioxarray.raster_array.RasterArray.interpolate_na)\n",
-    "- Dataset: [rio.interpolate_na()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.interpolate_na)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filled = xds.rio.interpolate_na()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[673., 558., 687., ..., 656., 656., 554.],\n",
-       "       [673., 558., 558., ..., 694., 694., 642.],\n",
-       "       [673., 558., 558., ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y            (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x            (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    transform:     (231.6563582639561, 0.0, -7274009.649486291, 0.0, -231.656...\n",
-       "    _FillValue:    -28672.0\n",
-       "    grid_mapping:  spatial_ref"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filled"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f3bf1865860>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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nqTynsHmU2FJgN2CppA5gB2BVCj8kV5SpqfwrgXGSOtLVyFRgWW/12PTsNpWXJClj7NV3VkpXbkGWzLDRo0unWXrYqAo5VTP8hb5rt7d74+rSaf74zPjeIzXJJSsP6bO8+nIJk2deKH+F35dLSnUM39R7pBbIRmc1p6KS9ib7Al6zB/CvZKNrryWburEEOC7d+kfSWWR9xZuA0yNibqM8ilwz/RPw1oiYFRGfIGsI/qVA4cdIGlt7DhxB1kl/E1AbLTUL+AlARLw7IqZFxDTgYuDLEXFJSl+75TUe+FvgOyl9/lzHAr+IiADmAkdIGp/SHAHMTe/dnuK+Jn8zs3ZQm2zYjD6RiPhdGti0P3Ag8BIwhzoDnCTtA8wE9gWOAv5TUsMWrUifyLBut6+eo1jjMxmYk25NdQBXR8Qtku4FrpN0MvAU8LEC5/q6pP3S8/Mi4vfp+XeBqyQtJrsCmQkQEaskfRG4N5dmVXp+BnCNpC8B96dzmJm1jRbdzjoceDwi/iDpaDbfrbmS7E7NGWSDla6JiA3Ak+lv69uAurdJijQit0iaC/wwvT4e+P96SxQRTwD79RD+XKpMo7TndHt9Qp1466nTCEXEZcBldcr1tkb5m5n1l5ILME6qzcFLZqf+3J7MZPPf8R4HOJENNLorl6bu4KOaIh3r/yDpGOBgsj6R2RExp7d0ZmZWTYnRWStzc/DqSpOqPwyc1VvUHsIadlAWWjsrIm4EbiwS18zMqosQnc0f4vsB4LcR8Ux6XW+AU22wUk2vg4/qllTSr9LjWkkv5I61kl6oXBUzM2uoBZMNT2DzrSyoM8Aphc9MS0pNB/YC7ml04rpXIhHxrvTomXZmZn2k2ZtSSdqWbBL33+SCL6CHAU4RsVDSdcAjZLMHTouIhmOdC63iGxEf7y3MzMyao5mNSES8RLZaRz6s7gCniDgfOL/o+Yv0ieybf5Em9R1YNAMzMytuoG1K1ahP5CxJa4E35/tDgGfwBD0zs5bpQoWOdtCoT+TfgH+T9G8R0duwsEFt2KoXKy9JUiqfCkuRVPWHzx9QOo2qrK8CTHnv0tJpVr20bbXMKpg05sU+SQNwzC73l07z1QXvq5RXFUuWTeqzvHhhROkkVcYsqeLqJWsX9d3SNnkR0DnINqW6R9IOtReSxqUl283MrAUG0lLwRRqRsyPi+dqLiFhDtuGTmZk1WTPXzuoLhdbOqpjOzMwqiDZpIIoo0hjMl/Q14FtkQ5g/A9zX0lKZmQ1h7dJpXkSR21mfAV4hW3v+OuBl4LRWFsrMbKiKGFh9IkUWYHwROFPSdhGxrg/KZGY2hIlNg2l0lqR3SnqEbBo8kvaT9J8tL5mZ2RAVoUJHOyjS3F0EHEm2GRUR8QDwnlYWysxsqKqtnTVobmcBRMTTaYfCmv7ZfNjMbLCLrF9koCjSiDwt6Z1ApI1NTgcebW2xzMyGroE0OqtII3Iq8HWyLRKXArfi0Vm9U/mOsT/9dfmlSKqafviS0mnWd1abHrT7dmv6JM385bv1HqkHHcO6Sqc5eucHKuV1+RPvKJ1mwvbll1hZuXhi75F6Mm5j+TQVli+pqmtE+a/oI9ZX66TeNLJ/LgdigHWsFxmdtRL4qz4oi5mZMUhuZ0n6x4j4iqRvsuUeuwGsAr4fEY+3soBmZkNNu4y8KqLRlUit32N+nfcnAj8C9mtqiczMhrCIQdKIRMRP0+OVAJLGZi83TziUVG09bDMzq6tdhu8WUWSy4Zsk3Q88DDwi6T5J+wJExLdbXUAzs6EmotjRDooMt5kN/H1E3A4g6RDgv4F3trBcZmZDUiC6BtPoLGBMrQEBiIg7JI1pYZnMzIa0NrnIKKRII/KEpH8BrkqvTwSebF2RzMyGsAHWsV7kmukkYEeykVg/AiYBn2ploczMhrQoeLSBhlcikoYDX4iI0/uoPGZmQ14zr0QkjQO+A7yJrOk5Cfgd2R5R04AlwHERsTrFPws4mWyNxNMjYm6j8zdsRCJik6QDt64KA59GjaRj2utLpfn9OduVzme735ROAsAuxywpnaazQsddleVBAH756BtKpxkz7uXSaaaOK79USlVfvf/9ldJt2jC8dBq9UH65mY4XK3bMvjiydJJhFVZKAdgwofzv04i15es1amXpJEn/3FIKoKurqXl/HbglIo5N6x9uC3wBuC0iLpB0JnAmcIakfYCZwL7ALsDPJb0hIuouulvkt/N+STcB1wOvzguJiB9VrpKZmfUsgCZdiUjanmzrjk8CRMQrwCuSjgYOSdGuBO4AzgCOBq6JiA3Ak5IWA28D7qyXR5FGZALZXiKH5cKCrH/EzMyarIlzQPYAngUul7QfcB/wWWByRCzP8orlknZK8XcF7sqlX5rC6iqyAKM70c3M+lLxRmSSpPzSVLMjYnbudQfwFuAzEXG3pK+T3bqqp6dLoIal6bURkbQH2T21t6eT3Ql8LiI8zNfMrOlKbX27MiJmNHh/KbA0Iu5Or28ga0SekTQlXYVMAVbk4uf3VJgKLGtUgCK9VFcD1wFTyDpargeuKZDOzMyqaNIQ34j4E9nGgnunoMOBR4CbgFkpbBbwk/T8JmCmpJGSpgN7Afc0yqNIn4gi4qrc6+9L+rsC6czMrKyAaO7orM8AP0gjs54gm+c3DLhO0snAU8DHACJioaTryBqaTuC0RiOzoFgjcnsaAnYNWdt3PPB/JE1Ima6qVC0zM6ujeY1IRCwAerrldXid+OcD5xc9f5FG5Pj0+Dfdwk8ia1T2KJqZmZkV0Caz0YvotU8kIqY3OBo2IJKWSHpI0oLaCAJJEyTNk7QoPY7vlmZ3SeskfT4XdkI6z4OSbpE0KYWPlHStpMWS7pY0LZdmVspjkaRZufDpKe6ilHaboh+WmVmfGEDLnvTFesOHRsT+uREEZ5LNlNwLuI0th5tdBNxceyGpg2x02KER8WbgQaDWJ3MysDoiXp/SXZjSTADOBg4imyhzdq6xuhC4KOW/Op3DzKw91CYbFjnaQPn1FLZevZmSSPoIWcdPfsdEpWOMpOeA7YHFuXOdk57fAFwiScCRwLxaf42kecBRkq4hmzT5l7n8zwH+q1GB1+8yrPQyJjuMLb9sB0dWSAMs/vW00mmmveOp0mkW/WHn0mkAhq0aUTrNyxXSvPhnL5VOA7B85bjSaaosX1LVyJXlv+sNf6VaXp3blk/TUe1jp+Ol8vXqqvAXa+PY8mkAOkdVS9cM7bLhVBF1f4qSDk6P5RfT2SyAW9NuiKeksNfMlAR2SvmMIWtMzn3NCSI2Ap8GHiIbr7wP8N309q7A0yleJ/A82d7vr4YntVmXE4E1KW4+fAuSTpE0X9L8rhe8C7CZ9aEuFTvaQKOvAt9Ij3XXTCng4Ih4C/AB4DRJ72kQ91yy20zr8oGSRpA1IgeQzVN5EDir9nYP54kK4VsGRsyOiBkRMWPY9t6Dy8z6jqLY0Q4aXRxulHQ5sKukb3R/s8jy8BGxLD2ukDSHrH+i3kzJg4BjJX0FGAd0SVoP3J3O8ThAGsNc60epza5cmvpOdgBWpfBDckWZSnbbbCUwTlJHuhrpdTammVmfaqNO8yIaXYl8CJgLrCdbtKv70ZCkMZLG1p4DRwAPU2emZES8OyKmRcQ04GLgyxFxCfBHYB9JO6Y07wceTc/z5zoW+EVERCr3EZLGpw71I4C56b3bU9zX5G9m1h4Kdqq3e8d6RKwErpH0aEQ8UOHck4E5WT83HcDVEXGLpHvpYaZkg3Isk3Qu8D+SNgJ/IC1rTNY3clVarngV2Tr4RMQqSV8E7k3xzstNijwj1etLwP1s7l8xM2sPA+hKpMhYh+fSraiDyar2K+CzEbG0UaKIeALYr4fw56gzUzIX55xury8FLu0h3nrqNEIRcRlwWZ1yva1R/mZm/ara/m/9osgYu8vJbhvtQjaS6acpzMzMmm2AzRMp0ojsFBGXR0RnOq4AduwtkZmZVTOQRmcVaUSelXSipOHpOJFsp0MzM2uFQbbsyUnAccCfgOVkI5tOamWhzMxsYCiyPe5TwIf7oCxtS+uH0fG7cutBbHyh/PoR6yeWTgLAtsvLp1nxo91Lpxm+e7WvPpvGlu8lnLrnit4jdbNsRfnlS6oa+VS1dTvV2Xuc7qouYVLFuEXlf8YvTq52b35ThWVFoj8WauoH7XKrqogh8iMxMxsggrZZ0qQINyJmZu3GVyJmZlbVQLqd1WvHuqTJkr4r6eb0ep8029zMzFphkI3OuoJsLapd0uvfA59rVYHMzIa8QdaITIqI60gT8dPqt5taWiozsyGq6ETDdrnlVaRP5EVJE0ntnqS3k23+ZGZmrTDIRmf9PdnaWXtK+jXZkicNV941M7Pq2uUqo4gijchC4L3A3mQ7A/6OYrfBzMysikHWiNyZtrhdWAuQ9FvgLS0rVZvRJtjmhdbns+0z1dJtqDDTfeOY8r+lVWaeV7X8gZ1LpxEw4oXytwGqzIKOil+josJE9/V7VJuyPvm2EaXTVJ19XsXG7cqn6RpZ/vd22IZqdRrxYqVkW6/J/R2SlgBryfqyOyNihqQJwLXANGAJcFxErE7xzwJOTvFPj4i5jc5f97+PpJ3Jln4fLekANu9Pvj1Qfk0Psxar0oAMVlUaEGsjzb8SOTRtNFhzJnBbRFwg6cz0+gxJ+5Bt7rcv2Yjcn0t6Q0TUHUzV6DvYkWQ7CE4FvpYLXwt8oVI1zMysV2r9Rf/RwCHp+ZXAHWS7vh4NXBMRG4An066xbwPurHeiRtvjXglcKemYiLixOeU2M7MmmiRpfu717IiY3S1OALdKCuDb6f3JEbEcICKWS9opxd0VuCuXdmkKq6vIKr43Svog2eXNqFz4eb2lNTOzCorfzloZETN6iXNwRCxLDcU8SY81iNvTPeGGpSmy7MmlwPHAZ1IGHwNe11s6MzOroMmTDSNiWXpcAcwhuz31jKQpAOmxtvfCUmC3XPKpwLJG5y8yxuSdEfEJYHVEnAu8o1smZmbWTE1a9kTSGElja8+BI4CHyeb+zUrRZgE/Sc9vAmZKGilpOrAXcE+jPIoMbnw5Pb4kaReyrXGnF0hnZmZVNG901mRgjiTI/t5fHRG3SLoXuC4tpvsUaQJ5RCyUdB3wCNAJnNZoZFbtpL35maRxwL8DvyWr3ncqVsjMzBoQzRudFRFPAPv1EP4ccHidNOcD5xfNo0jH+hfT0xsl/QwYFRFeO8vMrBXaaHHFIgrN1ZX0TrKZjR3pNRHxvRaWy8xs6BpMjYikq4A9gQVsXgI+gCHTiAx/BcY+3frZP6v26bslyUauqbA8yPPDK+X1yrjy/yP6cvb5yNV9lhUv7FdhCZMKK7o+c2hn+XyAMRNeqpSuiq4nt++TfEY/Wy1dV3/u+zqYGhFgBrBPRAygapmZDVwD6XZWka++DwPlV8MzM7NqBtDOhkWuRCYBj0i6B9hQC4yID7esVGZmQ1X0ydpZTVOkETmn1YUwM7OcNrnKKKLIEN9f9kVBzMwsM5D6RBrtJ/KriHiXpLW8tl0UEBHRN0MrzMyGmsHQiETEu9Lj2L4rjpnZENdGneZFNLoSmdAoYUSsan5xzMyGNjFIbmcB95G1hwJ2B1an5+PIFuzyIoxmZi0wkBqRuvNEImJ6ROwBzAX+IiImRcRE4EPAj/qqgGZmQ84gmyfy1og4tfYiIm6W9MVGCQzW7Fl+CZMRa1tQkDo6R/ddXiNXlV+2Y9Oo3uM0y/qGN26bbEPfLG0zZvKLldK9+KftmlyS+jpe6Zulbfp1+ZKq2qSBKKLIx7tS0j8D3yer2olke4qYmVmzDbBVfIt8LToB2JFsW8U56fkJRU4uaYmkhyQtqG0mL2mCpHmSFqXH8d3S7C5pnaTPp9djU/rasVLSxem9kZKulbRY0t2SpuXOMyvlsUjSrFz49BR3UUq7TZG6mJn1mQF0O6thIyJpOHBWRHw2Ig6IiLdExOdKjsw6NCL2z20mfyZwW0TsBdyWXuddBNxcexERa1P6/SNif+APbO6TOZls297Xp3QXpnJPAM4GDiLbT/jsXGN1IXBRyn91OoeZWdtQV7GjHTRsRNK2iAc2Oc+jgSvT8yuBj9TekPQR4AlgYU8JJe0F7AT83x7OdQNwuLJ9II9yXZJzAAAUO0lEQVQE5kXEqohYDcwDjkrvHZbibpG/mVk7UBQ72kGRPpH7Jd0EXA+82lsXEUVGaAVwq6QAvh0Rs4HJEbE8nWO5pJ3g1U3kzwDeD3y+zvlOAK7NLUu/K/B0OlenpOeBifnwZGkKmwisiYjObuFbkHQKcArANtuO7ymKmVnztdGtqiKKNCITyDrSD8uFBcWG+R4cEctSQzFP0mMN4p5LdptpXdpUviczgY/nXvcUMSqEbxmYNXizAbabsNsA+pGa2YA3gP7iFFmA8VNVTx4Ry9LjCklzyPonnpE0JV2FTAFWpOgHAcdK+grZhMYuSesj4hIASfsBHRFxXy6LpcBuwFJJHcAOwKoUfkgu3lTgDmAlME5SR7oamQosq1o/M7NmG2gz1nsdnSVpqqQ5klZIekbSjZKmFkg3RtLY2nPgCLINrm4CaqOlZgE/AYiId0fEtIiYBlwMfLnWgCQnAD/slk3+XMcCv0i3uuYCR0ganzrUjwDmpvduT3Ffk7+ZWbtQVxQ62kGR21mXA1cDH0uvT0xh7+8l3WRgTro11QFcHRG3SLoXuE7SyWTLp3yswTnyjgP+vFvYd4GrJC0muwKZCdm6XmlC5L0p3nm5EWVnANdI+hJwfzqHmVl7GIR9IjtGxOW511dI+lxviSLiCWC/HsKfAw7vJe05PYTt0UPYeuo0QhFxGXBZnXK9rVH+Zmb9qdm3s9J0jfnAHyPiQ2kaxLXANGAJcFwayYqks8imPmwCTo+IuY3OXXTG+olsvpV0AkNsxnrnaFj55nLLVWwaXf63YNtl1ZaBWD+pfJqubfruq07Hi32zvEXVcfOdY/vusxi+dnif5PPKcztUSjeiQprOsdU++BHrKiUrbdgrfZNPUzX/V/KzwKNAbR+o2ny9CySdmV6fIWkfsjs6+wK7AD+X9IY03aNHRf4ynkR2K+lP6Tg2hZmZWQs0c55I6sP+IPCdXHC9+XpHA9dExIaIeBJYTC93boqMznoK+HCx4pqZ2VZr7pXIxcA/AvkNBnucr0c2b+6uXLy6c+lqWjY6y8zMKohSy55MkjQ/d5ySP5WkDwEruk2NaKTwXLqaVo7OMjOzkkrOE1mZW5ewJwcDH5b058AoYHtJ36f+fL3a3LuaXufSFekT2TEiLo+IznRcQbaSr5mZtUJEsaPX08RZETE1zb+bSTaX7kTqzNdL4TPTCunTgb2Aexrl4dFZZmZtpg9mrF9AD/P1ImKhpOuAR4BO4LRGI7OgWCNyEnAJ2VLrAfwGj84yM2uNFk02jIg7yJZ/ajhfLyLOB84vel6PzjIzazPtsldIEUVGZ10paVzu9XhJW8wENzOz5hhIm1IVuZ315ohYU3sREaslHdDCMpmZDV1BoU7zdlGkERkmaXxuXZUJBdMNGjG8/NIOXeM2ls5nw+7l0wAMH1b+F65ryXblM6r4e71pVN/8hxi+Z8V1NCp8FlFx9ZKOtX2zBMzGcX33NbVrVLW8Xtq9fLrtFpX/0zOsYbdwexpIS8EX+Yl8FfiNpBvI/owcR4lOFzMzK2kwNSIR8T1J88l2NhTw0Yh4pOUlMzMbggbaplSFrg1To+GGw8ys1aJ9NpwqYkj1bZiZDQgDpw1xI2Jm1m4G0u2sIvNE/i7tU25mZq0WQFcUO9pAkQUYdwbulXSdpKOUNk03M7MWiYJHG+i1EYmIfyZbyfG7wCeBRZK+LGnPFpfNzGxIaubOhq1WaOPwiAg2b4/bCYwHbpD0lRaWzcxsSFJXFDraQa8d65JOJ1tvfiXZHr3/EBEbJQ0DFpFtu2hmZs3QRreqiigyOmsS2QTDP+QDI6Irbb046A3bCKNWFLpo2+xPI0vnM/rda0unAVi1eELpNDGm/JITo5ZVW+vj5deVX85l1Lj1pdNsWD6mdBoARpb/HztqZcnfh6SrwkcYe79YOk3VYZcbV4wunWbcw9Vy6yz/X4RRqytlVcnLO/UepxWyyYYDpxUpMmP9Xxu892hzi2NmZrTJCr1FeJ6ImVmbGVRXImZm1ocGYZ+ImZn1mfYZeVWEGxEzs3bj21lmZlZJtM/Wt0W4ETEzaze+EjEzs8oGThviRsTMrN2oa+Dcz6o27dbMzFojyCYbFjl6IWmUpHskPSBpoaRzU/gESfMkLUqP43NpzpK0WNLvJB3ZWx6+EikgBF0jSqap0Dy/eNek8omAERWWj6gyI7bK8iUAw14sv9ZH5+rtSqfZfmm1XQpeGVc+zfqdN1XKq5LVFX7AG6t9P9xhUd99r+wov7INL0wvn2ZE+VVjANhU5f9VE4ho5mTDDcBhEbFO0gjgV5JuBj4K3BYRF0g6EzgTOEPSPsBMYF9gF+Dnkt4QEXV/4X0lYmbWbiKKHb2eJiIi1qWXI9IRwNHAlSn8SuAj6fnRwDURsSEingQWA29rlIcbETOzdlO8EZkkaX7uOKX7qSQNl7QAWAHMi4i7gckRsTzLKpYDteUmdwWeziVfmsLq8u0sM7N2UusTKWZlRMxoeLrsVtT+ksYBcyS9qUH0nu4JN7zkcSNiZtZmWjE6KyLWSLoDOAp4RtKUiFguaQrZVQpkVx675ZJNBZY1Om9Lb2dJWiLpIUkLJM1PYXVHBaT3d5e0TtLnc2HbSJot6feSHpN0TAofKenaNJLgbknTcmlmpTwWSZqVC5+e4i5Kabdp5WdgZlZOwVtZBfpEJO2YrkCQNBp4H/AYcBPZZoOkx5+k5zcBM9Pf1ulkW6Pf0yiPvugTOTQi9s9dcp1JNipgL+C29DrvIuDmbmH/BKyIiDcA+wC/TOEnA6sj4vUp3YWQNVTA2cBBZJ1CZ+caqwuBi1L+q9M5zMzaQ9C0RgSYAtwu6UHgXrI+kZ8BFwDvl7QIeH96TUQsBK4DHgFuAU5rNDIL+ud21tHAIen5lcAdwBkAkj4CPAF0H5R3EvBGyHZUJNuqt3auc9LzG4BLJAk4kuzDWpXOOw84StI1wGHAX+byPwf4rybVzcxs6zXpblZEPAgc0EP4c8DhddKcD5xfNI9WX4kEcKuk+3KjBnocFSBpDFljcm7+BLVLMeCLkn4r6XpJk1PYqyMJIqITeB6YSP0RBhOBNSluPnwLkk6pjXjY9FLFgeZmZhUootDRDlrdiBwcEW8BPgCcJuk9DeKeS3abaV238A6yzp1fp3PdCfxHeq/eSIKy4VsGRsyOiBkRMWP4thX37jYzq6J5t7NarqW3syJiWXpcIWkOWf9EvVEBBwHHSvoKMA7okrQe+BbwEjAnxbuezf0YtZEESyV1ADsAq1L4IbmiTCW7bbYSGCepI12N9DrywMysT0XApoGzdlbLGpF0e2pYRKxNz48AzmPzqIALyI0KiIh359KeA6yLiEvS65+SNQq/ILuP90iKWjvXncCxwC8iIiTNBb6c60w/AjgrvXd7insNrx2VUL8uAcNeKVf/rgqfbFT8aVRZYmVYhVU7tl9Ycu2XpOySMQDDOnuP012UX10FgI3bVfhGN7risifry/+wxi/ou67LES+V/yw27FBtuZmXdq6UrLT1O1b7xj7ihWr1aoo2ucooopW/nZPJJrbU8rk6Im6RdC9wnaSTgaeAjxU41xnAVZIuBp4FPpXCv5vCF5NdgcwEiIhVkr5INhoB4LxaJ3s61zWSvgTcn85hZtY+3IhARDwB7NdDeN1RAbk453R7/Qdgi/6UiFhPnUYoIi4DLqtTroZrwZiZ9ZsAvMe6mZlVExDuEzEzsyoCd6ybmdlWcJ+ImZlV5kbEzMyqaZ+JhEW4ETEzaycBtGAp+FZxI2Jm1m58JTK4xDDoLLt8VoUvEiPWlk8DoCpfWipMxq06I7z0Zwds2L3kEgE168r/SseoCrPPN1abzbzzr8p/iCPXlJ++v35CtR9Wld+lUaur/cF7Zfvyn+GGSeXz6teZ55V42ROz/lGhATFrOwHheSJmZlaZZ6ybmVll7hMxM7NKIjw6y8zMtoKvRMzMrJogNlXcr6YfuBExM2snXgrezMy2iof4mplZFQGEr0TMzKyS8KZUZma2FQZSx7piAA0l6y+S1gK/6+9y9IFJwMr+LkQfcD0Hl3ar5+siYseqiSXdQlanIlZGxFFV82oGNyIFSJofETP6uxyt5noOLq6n9YVh/V0AMzMbuNyImJlZZW5Eipnd3wXoI67n4OJ6Wsu5T8TMzCrzlYiZmVXmRsTMzCob1I2IpGslLUjHEkkLUvj7Jd0n6aH0eFgKH5uLv0DSSkkXdzvnsZJC0oxc2CxJi9IxKxc+XdLdKfxaSdukcEn6hqTFkh6U9JZ2qaekU1P8BZJ+JWmfQVrPv5f0SCrXbZJe1w71bHId3yPpt5I6JR3bLZ/B9LMcmc63OJV9WrvUc0iIiCFxAF8F/jU9PwDYJT1/E/DHOmnuA96Tez0W+B/gLmBGCpsAPJEex6fn49N71wEz0/NLgU+n538O3AwIeDtwd7vUE9g+F/5h4JZBWs9DgW3T808D17ZbPZtQx2nAm4HvAcfm4rRNHZtUz78FLk3PZ7bjz3IwH/1egD6pZPYL8TSwV533ngNGdgvfK6VRLuxi4EPAHWxuRE4Avp2L8+0UJrJZtB0p/B3A3HycXJrfAVPapZ65904Abh4C9TwA+HU71bOZdQSu4LWNSFvUsVn1BOYC70jPO1Id1E71HMzHoL6dlfNu4JmIWNTDe8cA90fEhm7hJ5B9o8l+m6UDgN0i4mfd4u1K9gtdszSFTQTWRERnt/BGabbWVtcTQNJpkh4HvgKc3kuZB2w9c04m+/YJ7VPPZtcxr13qCM2p56tlS2V/nqwu7VTPQWvAL8Ao6efAzj289U8R8ZP0/ATghz2k3Re4EDiih/QzgY+neMOAi4BP9lSEHsKiQXijNHX1RT1fLUjEt4BvSfpL4J+BWQ3KPGDrmdKcCMwA3ttLmZtWz76uY09F6CFsIP8sy9anqfUc6gZ8IxIR72v0vqQO4KPAgd3CpwJzgE9ExOPd3tuP7FL3vhQ0luz+7B2SIPuPcZOkD5N9Uzkkl3wq2e2ulcA4SR3pG89UYFmKsxTYrVuaZTTQR/Xs7hrgv3JlPqRbme9gANdT0vuAfwLem/u22/J69tPPMm+w/SxrZVuazrkDsKqv6jnk9ff9tFYfwFHAL7uFjQMeAI6pk+YC4NwG57yD13asP0nWcTc+PZ+Q3rue13be/W16/kFe23l3T7vUk9y9aeAvgPmDtJ4HAI/T7V58O9Sz2b+zbNkn0u91bPLP8jRe27F+XTvVc7Af/V6Allcw+w90arewfwZeBBbkjp1y7z8BvLHBOe8gNSLp9UnA4nR8Khe+B3BPCr+e1EGYfkG/lf6IPZQ/V3/XE/g6sDDFvR3Yd5DW8+fAM7n4N7VLPZtYx7eSfbN+kayDemG71LHJ9RyVyro4lX2PdqrnYD+87ImZmVU2VEZnmZlZC7gRMTOzytyImJlZZW5EzMysMjciZmZWmRsRGxTSSrCT0vPfbMV5PilplyaVace0Uuz9kt7djHPmzv1hSWdWTPuFbq8rf15mHuJrA05upnE+bAnZmP6VW3nuO4DPR8T8rTlPOtdM4AMRMati+i3q2QyS1kXEds0+rw1NvhKxyiS9Ne25MErSGEkLJb2ph3ifSPEekHRVCnudsn08avt57N5L+BWSvibpduBCSRMl3Zq+5X+b3JpHktalx0Mk3SHpBkmPSfqB0ro1kv5V0r2SHpY0O+0jcSzZOlo/ULZnxWhJB0r6pbK9LeZKmtJD/bYos6T9yRaw/PPaubqlWSLpQkn3pOP1deo5QdKP07nvkvTmFO+Tki5Jz3eUdGOqz72SDk7h20m6XNneHA9KOkbSBcDoVKYfdPu8JOnf02fykKTje/sczfp9tqOPgX0AXwL+g2yW71k9vL8v2XLak9Lr2rITPwVmpecnAT/uJfwK4GfA8PT6G2zeg+KDZIvk1fJYlx4PIVvRdSrZF6Y7gXfly5GeXwX8RXp+B5uXtBkB/AbYMb0+HrishzrWK/MngUvqfG5LyBYiBPgE8LM69fwmcHZ6fhiwoPu5gatz9dodeDQ9vxC4OJfn+PznkwuvfV7HAPOA4cBk4ClgSqPP0YePAb8Ao/W784B7gfVsXjY+7zDghki3mSJiVQp/B9nie5D9Ef9KL+EA10fEpvT8PbV4EfF/JK2uU757ImIpgLLd86YBvwIOlfSPwLZkaywtJGsM8vYmW3hzXvriPRxY3kMejcrcyA9zjxflwvP1fBfZH3ci4hfpCmyHbud5H7BP7uJge0ljU/jMWmBE1PuMat4F/DDl/YykX5ItnfIC9T9HG+LciNjWmgBsR/atfRTZukd5othS2vXi5MO7n7vIefN7UWwCOiSNAv6T7IrjaUnnkJW9O5GtN/WOAvmULVf3ePXqWWRp8mFkmzK9nA9Mt5zKdHo2ukW1xedY4rw2iLlPxLbWbOBfgB+Q3T7p7jbgOEkTASRNSOG/YfO35L9i87faeuHd/U96H0kfIFultahag7FS0nZAfv/xtWRL/0N2G25HSe9I+YxQts9Fd0XL3N3xucc768TJ1/MQYGVEvNAtzq3A39VepP6YnsJrn9FGSSPq5HW8pOGSdiS72runYF1siHIjYpVJ+gTQGRFXky3R/VZJh+XjRMRC4Hzgl5IeAL6W3jod+JSkB8k2GPpsL+HdnQu8R9JvyTYueqpouSNiDfDfZKu0/pjsdlzNFcCl6ZbNcLIG5sJU9gXAO3s4ZdEydzdS0t0p/v+qE+ccYEY69wVkG4S9WpVc/jNS5/kjwKkp/EvA+NRR/gDZvvKQNfwP1jrWc+YAD5Itxf4L4B8j4k8F62JDlIf4mvUDbeWQZEn/G9g+Is5uasHMSvJ9TbMBRtKpZKOzPtpLVLOW85WImZlV5j4RMzOrzI2ImZlV5kbEzMwqcyNiZmaVuRExM7PK/n/CpPlvwVV8sQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "filled.isel(x=slice(0, 20), y=slice(0, 20)).plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/merge.ipynb b/0.18.0/.doctrees/nbsphinx/examples/merge.ipynb
deleted file mode 100644
index 08f07723..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/merge.ipynb
+++ /dev/null
@@ -1,130 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Merge"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "from rioxarray.merge import merge_arrays\n",
-    "# Note: You can merge datasets with the merge_datasets method\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset\n",
-    "\n",
-    "API reference:\n",
-    "\n",
-    "- [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "- [rioxarray.merge.merge_arrays](../rioxarray.rst#rioxarray.merge.merge_arrays)\n",
-    "- [rioxarray.merge.merge_datasets](../rioxarray.rst#rioxarray.merge.merge_datasets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dem_test = \"../../test/test_data/input/MODIS_ARRAY.nc\"\n",
-    "rds = rioxarray.open_rasterio(dem_test)\n",
-    "arrays = [\n",
-    "    rds.isel(x=slice(100), y=slice(100)),\n",
-    "    rds.isel(x=slice(100, 200), y=slice(100, 200)),\n",
-    "    rds.isel(x=slice(100), y=slice(100, 200)),\n",
-    "    rds.isel(x=slice(100, 200), y=slice(100)),\n",
-    "]\n",
-    "merged = merge_arrays(arrays)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Lu8z4aGbG6fZZNZzyjefhd50rsv/xafFe2ucnmJ4SjsxsUGFwxrkOq48A6djNzWRdFk618AE3D/r5xMt+JuacNfw5XxKvSq/PfCFCvuC2b2yLUaT3dZzLtVcPgSrxcPXojYEo8d5F55b63rGGblXJzJZiNHcqJBNvPudOc6hSUa+wZQwQP5QWzWsbcc0X3W+LLu0LnUYoOoRrX3Lna33rm5Oif9bLK0bZQLoA/rSgm5EA+DFm/gUi+mYAYOYfAPDzENjzMxDo8zea374WwA8SUQ2J4H+PoQy5qTCA6Yrc0GuPzBHtufDGkd+Rz3dfJ66LWpqqEABYRQMAvefDxbsxkBvKLrAsSgBwD7H/0KdTtjF0TsKHziqphgn1GC/Iz+FwDCRm0Y0qoP2pHfvd7NQCGnsldh7M7P5ny4R05J6uuRcuKVtyTFW0GhopW5H9W6XiEam0tt0DpEqufU0mbbImD3X/effgRp73UXZiFO3IhljSTS/MUdZmn8a6ndTIF+JAwXBMqDTMVhOyYYXZUhyOZcMsWMuSlMm96xKVbN+n5hbw92+9IgDzXoTWBhD7ZDdwChUMzBcjO2etTUZzY4JoIjdOudwBmqlTmuaazxfdXMZz+QeIlzdfIOspDU5H4BjonZfxTU62MV2J7b58Ba6KZnDKLarR/nSBd5/Vxh7RY+ULotRt6NhcvslaFGzf2N6/DxeOBQP9szKOOGfsnYnRGLj7rrFX7wu/RkBbx+EMOTvcgu29QByG8TSk6kcUNJwKAKg4UDCBQTDSEJ6X50wJSx+Sm3J8sMjXCwqDUb2CSk0+3fKKUTbM/CyETXb/5z/gvWYIF9f+bX4Hwj91YEmnjLVH5KluPG1i37HcgOMzYjmqlQZIbDjxLFx/wVFFo4sER+ZB8R6kxCxkGnrLvIeOyS2MGqayYvbRveRZgDEFoarGbriKTO5fsvHowRnZ0CbFC2cFAsBsiVB7sfZsT/76oa3GHqP0wiHx3I2/e2GOyTE3GFWKk5Z7sFcfcTWK8bUBhm864t7njDivghyV5hZKsw9VrEUnCryozuUSs5XEAzUwyqbbJsqd4geA1maNskk2dDNbAQCyijWdyD786xYkzY23q8njdCLbOs+J0L1U2TBcY0uUDKfyg3hngnK1g+ZWYY6fYnw0Rma6xVSZHEPzeHUi3ocuuOPjhP5Zb7Ei8WIBYHgiRnOXkUxUQbuwpop/Xqog1XOarps5NZzX0zUxOvQ+aG3Ia1UyUSH/CkMBGpUy3yrzhQjJhG1oq8oI3cu1VVatrTrwvKosApVA5Rlh8YyRTt02vveR92JkAw5CedkgDHPWSYTGjkzA3BggYUjbKJkqnIs6JmscAkDnIu6YHIbR7kKhWYHGJy/Lm0RuxMGbTVsQZgxPxUG4ps7cg5b3gfZl950CARJvEQczmrvu7WTN3eTNbQ7DRUE+I0zo60Iz77uNfAROa7PEfNFdxql5YMbH3Gftq+7BAsLXUQGMThFiteoH1z8MvhcWF4zuhbl9f+3zRAtpSKVzUTyDxQ97HRhmcyATK3H3C46a45hVMCJQyUG4o/QUFceE2bJ739qqrSIt2zGSKVvlERXAdDUKzq9OnEeq56FGRDoh1BFQmQV1dILgK5/OVUbpKeLu5RJUuDDn6HiK1naFZG68zn1KP++nyADExkMrjnQDBNVkPTbhVtm+uS3gCX/8hedBLj1VizdkPpqsxhYskszk/Kg2IcBcFl7fKCm6QPeiAz/4YAdA7mmdm+5FuRdtLrAIgRXzBQp+W3TFAFPvMDHKTHMwyRjYecgNhiMKkHaqZHyvsuhEQrmqv0kIU3MvJFNRNHrtqBYPUQEOUcGYrkSYrsh8VA0Jh1svFCHwwp/nogtkA/e8Fw5zcVvCAKpDZXOXisl/TF4nC+DgtNzIxPIQ2tCO8Why00Kqse1uXkAecl/RcCTWv/6+jiWPoeJ7TFHJAtY2oslgXZD0gbTeUuSFbQCMj6ZIZi4UpMnstkNx23AN4BBXuh1HQOciWwVXtgjJlDFd9cJHy0DnkhvP8EwjQNflC7IQqrQvO2WE2RyjN7tO0XHOiGe1F7JgVM0IhQlxZIMKRdcpl3k/sgtqY0+OMV9S1JaEA9WzyPvy3no65rx1wSWWhT33Qp7je2q0z7vQkCoaABiclu1WH3MXKBu4FXdpVIIqRr4kB6iaEbK90sKQAYDyyno2yc4Ms+Md7N3rw7KAlSfd/ot2ZA2MdFJjuuoeUZ7KvTLvR3YuNcw6uFfGXkxU8VKQnJ8vyfezJRcyLFui4ACguR0qNgWsqIU/6xKaO4zRcW8b7zTU4NIEfNkm9J/LsXefp+08z6qxJ3PkGxL+c1F0IjS33LyUrQh5L8J01XmRqRdpqBMgHQKl8axhPBgfITdbckqld7GyoTqqBEatz7d6muoJJi7CflvCAAo+zNncfRJHuPpVZwAAvQsFth9O3bNA8hBqSEhvWPVmoooltm6ToQyws84E+VIDZuEq2pG1qpMZB6gZJkEK6QICKHTYyzcQXQcl9WPOZZMChJoie1Ra25VD9RhLU5XZ+FiKdMzWu4pzxvBkhMSMfb5s6gwMRDYdyqLsW3vNTUFRAcDaR6agmlEclSd3crSJuAjzCXVKgBdqLDruy9HxNIB5d6+EC3GdUghuaJP1BKgK61I0ZGLPxSil0Wm3qKmiAYD+ORe+ksGZ+fPgynXDW/xNLYcucO1LM3AaIZ6aMZc1ilUXjyzbMWaLMRp7GtM0+zTXLp7VwYI/Op4gKpxnNjwRIapC77NQSO9FuXY6N3UqYBQNbaky8K9Dc9tdl7JJQXgVgCAyPYU1WyY7l3nPLcqAyy36Ybvh6cx+ng1rTNYjtK8ZEIXx1NUD4gSBcmlszVF2Upsr1MR/91Jt32cjlj6iECNldCyxNVd795tr2ZC/necjG+YGXC4R0PuG0dgNT98Pud0puXtVzauPQeBQDuVQDuVVKQxGdcB/LyRE9F4i2iCiR/d9/teJ6EkieoyI/pX3+XcQ0TNE9BQRfYX3+Veaz54hom+/oye8T+5az6boJjYmu3dfit7FGqMTonuzAZsaDVN/sRYhnobQ2mRaWyuuSsnEtF08vErJq9aGrR3R3IR6KMOTcgk0Qdu6FtapVJlAc9XaHh8hjI+4upr2Botn4/2mjsmGqVrbVeA1zZZS9J7PUXbEsmsMaglRdDQJL0lfP/ELiEcDiOcQz5yF7IcLAWDntS2Agea2QlaNRzXTUFgVWL/zhVjmK9OEtpvj/jkxyzVsNlkPaz+AEEHEMdA/N8fefeKKqgegoaDmLmPjLR5qcJOCPMR+jwYAOpfdBkyE0Wln6idTRt6N0LniMuPpNQeGmNyzCE7IepNFR0KCtm7HQNrVmymbcQB1jnPZxkddMbkQV1xwAGCISqB9zSDc+oSy434bFeJ16HWsGpIQ1/CpDaN6+TkpZPZQix6EXHM/vvUfz/Y/Iz7og9E776DLs9UUkRc26z4/A1W1rQMr2wnAHO7Py3N2rpTi7auTWDF6FwoLme5eAs5/mVfQW0nYW8Ejcw+2PfkccddWfs3F3PwwseaBblsYqO7QrgD8MKSJ3o/qB0T0Tgil15tMGci6+fx1AL4GwOsBHAfwy0T0kPnZ9wH4IxA2lg8S0c++EJL3pcpdq2yiysFxy2aEdFRh6Sl5EPKFGFVGFpKa7EueU83BgtncKlB2YwfX3Od9U832oeCYUMZhcnW6jgBl1LrmHgpdDHXR4NhAUs2aVrZJCicnGgIi9J93C6SGIfxQ1XwpsSEcjsgqGkD2G1UONae5Gr9Opmy7sXMMdC7XmC+a/ZvTGNyjeQWgscN2/NOVGKm3CKnSam3JhKbjyu5jdCI15+Bi637cPxuGRXr9cxInWXrChb12H24HMO+1D/t5AULZdtdXFasaBM2tEo0NlxgYPSChQbuIMiPz6mjiiQfHApDMNC8h55F3IwlVsR6Pg8W9ygjZkG04tjLURnqvNbfZ0PWQHYfSCsW5KLHxMQOFzoWJwTJJNMN8VDqWe8yyI5CACvzwqA9TTieSXNeQoULfu5fc+VcNsufDkcxv04PFJ5MKzUui7ZqXAFSMaqlt5twwaBg2hdrUOpXmbzo2xpyBDpP3PAFynyezsNBzyeslqspUDZXmDjD8CnmIur8tN7safADQP1fbc70OMv4ShXHnwmjM/H7TANCXvwYp+5ibbRSl8y4AP2E+f46InoEUzAPAMwYJDFO/+C4Ah8rmToqP6EonYt3XWZhkb5n4cjxnm6AHZIGeLhD6Z039hElo26S7ieX7CU+bjDTgA11wmjs1mjtu0Y0KVzMAmLh7wUFBaTx3Fn2dUGDdEwOzlcRCYFXJKMw04J8C0L6cI+3FtkjTFhQaFNDOV03Q/c1OYEH7YAd9iHsXZKVuXJvi0h/oBTVJ46PugP3z9XWoLSC0iKumwmMrTNbiAPKq10TOXRZe9T40V7LzWrdiRh6cVuaE7JxEBSPb21fL4Y2DY8LFL1tC/2xl9iX1UG5OOSg6rdoZZvf2ke3JeJR+ZnDGAE9qsZgdRFg8qyDP1Ha5EyqFr0vnniPxYLXmhSO32M4XIySTOvBEZstkcy7JVO5jvcfzlqDBfG9hdJzQNB2asrEAOWzxcUJSyOl5N1q/BFyP6Gpfq1DHhGzo8jDJnndTmHmOh6IB89WO5IjaznrQGiLAFMx6NSqag/SFCehcleONj4ZLm0YLVMEAwHLXWBlfMcHO769bo6N/ztWsARJNuDNCqG5FIHf78hCALyGi74ZkjP+u6SR8AmHbdOWUBK7nmnwbXia5e5VNBaQDuTFnK2J5xubBi+d8XcI0mXnV2Ca5OLhHUUhyA9nEL0SBqYdTZVFIiLgvdBOSfDJamxWm5hh1LAuqLgJVI0SjcQyUCdnFXb/TEF6ci1fhgxJ8KXqxPWczAuRdYPp2Z9FLPYpI9wJj4VMu05qdvSbjOrJoP1t+ssTefe7WanqFf5oEVsVQdFNUrQi++pktugnhiOzi2hjUQUipe040YpTLBpzGmBxvoe2BCkDOu5OantCb4Mgt9lUK1O0IvfNygbZeL/fF1htkjtIB0Lla22T1fCFCaytC57yMY2ZoZzSM19qqsXtfjNxNjYRj7fkw8h5ZBVo1hf4mN8qs7ADZnkPXqTGj17JOyBJU5j1ClUbWA6UalqsNMEZO5ghZmyMNEbvtmzsOwaeKRhWrsDkwmjtaAxVhdNy/ThKm0mJeAOg+s4uqL9YL5RWocsg8bpq6l56L/07XUvfMNEJOOt9AsMfcR8iq11m+lD+qTOtEPOT2r4kXwwmArxLtsvP761h+PHw+6pTuoJJxQyr2hz1uLquGWFjlPTcgJt4vCYBlAG8H8AUAftJwRb4i5K5VNgCQLxol43kJKsk0ZFr2Fyh9+DWU4Fx070Hw7imfQBBw/GSALDxUe4WEBo6decWIeTcKHmKfsmP3AaPwtAJcq+Y9Dqs0d9BpqkMvTcNQirwDBIEWP+m8Ay30A1yIKnn+mhlMjOroEqqWu5V8RaNFkEufFAuWtOak6+KIRSeyntdkNbb5Ih3fgqlC7z0rVik9J1V21UOnEeUlJifdWH3PRI1IXUBnSzHqJGQ8yHYLDO4R5VCn4kkM7pGxNbcYwzOE1ENdpRPGwuO7AIDhQ300dkqU5lzaF1RBu/F0L9WAmb/ZchSQkoLFO1DllcwZ877jRkvG5rqY82js1oGyjEq2KMB4LtuqIVN0XX5NvwdCD5IY6D1vaIgKRt6L7WJOLP+yPVcU6S/uo2MRip5jxOhdkDltXZQPKC+topEDEDhOrEczOSnhM/+Z858RDatqfZmGKwNiVe9S6z0dkIPO2HGpeQWogPGQf1S4LlfnVUA6ChiEp5YuhEj1lywMvBjPZnN/88gDyAUA/90Uv/++YVRZxS04JW/x+R2Xu1bZ1Klz+7MRB/UNQEhNA3bhB/nOJFU9D6NqhJ7Qfup6+zqmYGFPpqHLDjCoZswX3JNdp8DouLxffEYGNltNg337ceV0FIbwgvYEEIWjFft1S7wu3/PRwkDAABYaLjavSsavncn7sS30nC+mQUK1fTkHMVslAwD5UibFkQBmqwnKJqE0lm4IdNhXg9JvIHvkWRCZxfnpCxh/0f3BuSVexXk8LTFfzoK5XHh2jmSk4c8M8bTA0hOyQis9/+7DLimV7TnYcHuzQvvs0O3r9y7IONcW7WdVOw0s8iAk2gDQcAln4WaLbA4rmdYoWrFVLlVDvODWplfo2PZCWezqZPS9a1Eh/HvWMGpKuNW/F6PCGVRlTEgmtYOdJ+JZ6GKfzBijY5HLpbRd0h2Q/MzKx5xWnp3oB8+MRhG0jUJUsc3HAKLs6szLK5rjqhFSxxSMfXQ8EjZ2I8uP1zZ/qeOdrpFlY5gtE1IvxBjncPmfWjz74WmPL83LS6bukt+21Af3bF6K/AyAdwL4NQMAyABsQjglf4yI/jUEIPAggN+H3GkPEtG9ECXzNQD+/Ms1uLtW2TC5xHPeS4RAcs/LB3jWzHwhFk/DeB2KovGpNerYFds1d6qbVsRbdJBSqnsFl3b7RmTBCHmPgrDbdC29bnwgRwIJAKmnOFw9Qx0cT1E7go5yT3EdE7peFDcb14jnjI5ZZOv1RWy/ccGGU/J+jHRUY268RLVU25fNgm5Qb8pX1tx13GUqrc3KMTmzs8YzY40nI69Icm0Fu58nFmk6rnHlC9wtfOz3ShTdGM1r8sO9+8Wybps4fjKtrKIBgHRnCjCj6ohnMz4l2+u5yfl44cCrM0Q7npuTiWaMrrg4YdRsAEeFjmdwJgqS8vHcoLsUQcWSk0tNfmxiWIZ1UVUggt4zybTG7ETieStsPZE4lyJX9WAsYWbfJblp7kJPkjskex/OliI0d2trCCXTWgouzViHp8QrU1bptY8VGJxObcgxGZfIl50nY/Of2l/G3B9hgbHvoado7jhaJL2ffQXj53CKvig7P/zVulba+310IsJ8SQhEAaD/rBzXBz2o1zNblvDj5Kg7lpKrAkDpsbvfjrxIz+aWQkQ/DuBLIeG2CwC+E8B7AbzXwKFzAF9vvJzHiOgnIYn/EsC3MnNl9vNtAH4RQAzgvcz82HUHu0Ny1yobqh0z7PCkFNqpK50NJbShD54UPZJdsDmiwGJNphz0XYlnjk5FRRcM8Zg4eOjmC3GQXG3u1I4sEpqYdmOvU+d5tbYZjT1Ho5+O6rAPSc6mh4xTMvN+jIahi6myKIDbSlW+KBmdCwDYecOCOXdZuPSh1v44ttraFLP6VupsKbYLZ9mI0LpWYrqWmN/L/hefkUVrcDoNGshNVmM0jQLrPb5lFQ0AXPhDMVYecXkC9Si2X+fM0p6HzEu3J0GzMs0fVB0ZS3O7sEoTkAV3+TGXu0oumcRZ2wsPTTw+lWYD0/tXkO3JxepcTjFbon2Fji4HlY7F6Jkuu/usTj3AB5l+Og2F60bIBg4wQOzCR9MVyfdoAXJUCkJxP6W/Gi5RIcfS/jRRDmR7JdKRDG7zjW2UTSAy16J7scb4WIT+s+5GXHp6ZnNvs7UmqGTEBh1HBkzBkd+RzEMPEiEuGbsPOA99ukK2aBOApaYBXFhtbBzqo78rJ9L+hER9Jm88Ye9JQLxRmSuZSwVOBMrU41Wbrrl5beyY4Zp7ej/x6ksVBqG6Q6WNzPy1N/nqL95k++8G8N03+PznIQTHL7vctcoGcFZ453Il4QTzjM8XIqncNuzEjYHAIAsvXpzMnIWqZIi6SOQLSRCi8FE/HMmi4N/oQVX3jliXfv0HU0g1X6cuBt8/XwIVO4QXAUjJwmPni+HNnRiG6aItn+e9CKNTjr23tWWsaS/0Nl2O0fQQZMKZpUAEk0A2z/l8MUbrWmk9J+LQmlX0lj7Yk7UY/efFSrbnqzBvENobrk5o/va1gGpm5RHZ7+C0u42pcvkhIKx7sTVORskMH1rEfuk+6zyX6NImynuPOSXTErO7bhgrfWeA6tiy3b5qmpop431q8l1BVPkCwpwNRHErMEXCshw0T/N7JqUjAOR6xPieddEFUJOts0mmEo5V0MpkNRaAhLlH5wvSQE/DYL6XAQD9c4U5Bze36x9x8xqPCsxXm2AKr3PVVOi1qfQ3107O09WmjY7FAOIgT9O9VGP3fu8ZmwC9C/IDjgmt7dq+B4yiycXqan/oOcy/7AFrdDnKIvlbtkKlPz7qUSItAGXHC7HNVPkj2NftigAE7t46+rta2dhVgCQerAtga6sK2jpzJBaRDYGVLjQj76VTps/pVbYjB2GtObDW/UI4jrR+x1j+rbC3BlXSaUbramZLdB3tRp0QuhdlcShM2EotVt02oMiJHLpnZNKD1vIzXpNyVzFJYWNlkD6qhPwx1gkCbyrvx0ESOyph8xCTI4k5jtt+797Ufk81gmK/7deG1qqOCXCejF882D/rJidfSJFeA2ByMbNjsnKPj4sySMfhtdDwY3TJxFCiCMm5q5ZEFADqptcb6HXHMFtN0D0/N/v1u8aZXRSuD03nihgKSg65H2hStk3I1FuAexfcuU3WSdpWKzmmB4EvuqJodE7qRGDPum06kWS5Rfbt1vsYkgnpoLYdWkUZAZ1Lcl9pu4d4JO/nq03ERW3DpFVGSBBZSHJaCuedLvCRQbbFNuwnxay9i04D+CAchSGrQmUSdvPWBS+B0m5ZL3PnLdKNavcPODdk9RebwX1aeOfrF3UWfVEwTVOVUnYQ8K75ZKy3J4TqUNnchUIuBBHnNeLcLWJMYXhJK/jJhhRyxNMKleZiarHatD6kbEXXgQW0fW06DaG38VxixjbMZBYeLYbbv5DXqdTCaDtd9c6Gp1xMvGyFCsn3LGxbXXPlF57hoO5HxuRQPNG8DpSoIuGKnnfrkDsGVfuQe2zg2Vqo6IEj/N+ryCKgiLywEFHZkVceL+w5LzyXB5DXfMHzkAgYPbjkeLUuFhgfdwl8ob0vreLrPD+SvFzkLQhZaj0amRzCbF3CdDNDkrnzGvN9LYu6Y07mALCgnrF6gVpAmTrnK2iJoPenhlh1Ox/1qNe/sR16o2VbxtP0aqKigjE1FP+qdPS6tTYMpNljX25u5u4ZmZWYrzatJ5svxkhHZPMocS5cgk3j5VfNCHXsABvChMEWTUY1o7kbPgt1Sva+pkpABEG/m1aE4YOiJfqf2MT5d7lWFckfFNe894vO01RiTQBY+FSB0YkUC8YYWTgLZKZg91Nfu2wVDSAIu/kSkIx03nBHhAHUdzFD2N2rbA7lUA7lUD7N8jIXdb6i5e5VNhz2q6eSwSapXWUSMtM8SzKVBLuiouKZQTeZiuh8uYGyE1srUePfNnnrgQlmi5F0KFSrtaOIOBcSykZheGfq8TTZTopdD2VUhd5Lc4etFwACtj7HvADQuX8PAND6P10coY7JG/v+hlWMbFhaq1mr4n0Pxa/dmK5EQa1DnKtH4z4DufCe36QNCJmE58ZIXfiUAjkirDxe2FBK6wIwOePM163Xy4HbV9y5FB1HOzRfFBZiPXb3conmJedWWFRZ3yVKyuUOookxbWPCfK1tvT7bisGL6fuQ82RaBzBo9R59JuXGnnvdulYh93jsbIJa0Y+Z5BU1T1K2Qog+1WxZmdUaT0fK8i33uIIXBmcy6c9TahguBnGM9lXZIVUMKmrEU9lRuSAXcLYiczxbipB3BMEGyP1I7HI8VSrdOtVzyXtRAGBo7MkxNALAEVmot0q9D7Lvw+Kf/LY1ADUW7tu1n40eXbZ18VWnxsJTERY+ZYAEH3wW7Q/CsY0fXcXMtOc+8RsFZisJxoalI1+SfczWZGxrH8MdEebDMNpdKcShi84p2aQmEAmqK9M8BVBFhNIwDXQNTU3VMUWh89qF1Iz4cGKfQqSeuboEQEIqTGHeQWP6gCxM/kOmoRQ/2VknCCriqtTta3iKkO0C6Zt3AQCt/75g9hui3VTivAYVbvVU2LYfqpImXeZYDWDqdRa1BYkegqdsIQiVoXYhsaohi39jR6HgbrP139vD4KE+Fh+RBP3iIwCGY/CyaV9wph/0Q0nHQDrkYL4bgxoNo8BGx2Mcfb+DKdPVLdQn1gLocnnvUew+KBpw8ZMTp2gAGz7TPAUQwuWBsMI9ymtEuatzytMYZYvQusY3/L2G+zSUmkyEg8/vTupzwfk9l6JCwlDkUbosPTFxQI2KMVtvIp7KBeqdLxBVjHRHNO/kZBvt54dOccaEqpOhSkU75IupJPl9Vo15eB5Vg6yRFOdyz6hi1fCua3lQo07IKtLpqhhhikDbj8BUg2b3AZmjd7xVELq/9bSrs+LFClTI8Y//BgDUaP3+M/IlRVbRAMDuGxZx+Q/LXBz71UTQfObenZ+SQR795TtUzelJfejZ3H1SpWEPGI4IURXWoiiEuTbJdAsYyISp2If35r3IwoTrGAGPE3s5F1UkQbveKOxD79+P8VxyRqpL/ISnim8NVs1wwVZkV+8/qwcQIuGIhUHXvq+cpatStvYVvHoLamFaadt+JmYx0TnMexQgqjpXpNGbFuR1TQJ88QnRCLuv7WPxcWfuL/7qM4DmKJoN1EddTF7nQueua5BK6ln65wAAax8eg6MI0WXDftBqItoeYvCFZ+w2fkvu3YfbBiAhJ+f3sL+RzBciYMEpnI7xnJsbU/MXQF1j63NF4ceFAEkcHNigpsxc1hkh9khEdTvN+UQVrHKwLODeteSE7PXMlxtIJpX1FrJd07I6kfPtnB8jX2kjMQAAYkY0LVEsOpc070X2961rtRRiKgNFU7wuO/ZExmnZkxlo7CIoGvUjC63NCkUnst9H+1B7qmSyt4ph8PFrxzB4bhFYMucxNiwOFxUYU6L9O0+DlhbtPi5/xTFbv6OKBgCufb4MsFqWc2+cz8wxcUeFQcj5rl1y715lAwCTIx6Of9O1G25cGoCmOeb3CikY1ZHpvinfz1YzxLPaWqzDkyniwi3ijd0aIIf48mtm4kIs/dyrfvZ7twt9jXtv2Z49BRR5TcLKpqviBqQDY5W5EODSk8LWO173lIfP7GsSyApu0ISv7RbpNVbzZd5zye547pLdWS5hHxuCnDHSszWmpmCxtVmhtQl0nhTE1/bbjoiiMYvM4qN7iC552do61K7FYsPCa8sGIR3VFq5dNiiA8CbjCrsPZlj7sCSCIxMSuvbVbhVp7HqexWJYc6SFtco+DYgFr+tFPOfA4xPCVqBzya2U7Y9fQH3MkMvVNYYP9G27CY5lzP78RqXjouudE4CBeokcEUYnQp697mU3/mRcBZ1EJ8dcvC4qatStCM0N8WSorMFpZEN702NtxLMa+YoolyivTWjLhGsLbbnhlIWGxgCvQ6YerwLGq16R6UQUjdZVle0Ice7qtbQcwBpOLPetPlPqwTV+SmJc197CiAoCNowyZKC5SVjxuqqO3/GgZY/Ie4ThPYzhPTLOB+69grMfdSwYANB87sZdRX1G9tuRQ4DAXSoEZym2tmr0nnHJgvyoxHK1XiLvREgnDm3W2C4xPpY6az+C5ICU9M/2sdH6iVBZ+KwAdRoSCtaQEIRf6U3lPtJBdmNP9yFmoooReSGsZMoAs2XDzXsxyg5ZJVN0CHv3pdh7uwT7l38jDnrTzxfJhnMAUUJlkyxVT5UZJmPzfdlBUCOkdCOrv2u8ic0dYGkBbDpeLn1sC9NTC2heNgrh0ga48DytLAVSGcz8NcdkzC2HaErmbI2ELAcmR5xi0K6Vz3+VaML1jxjKHQ/h5nuYqmg0FKjfJUO3oHcuuR8P72nZOQBc/iSZyPizZ64AUYTo6g4AYPx5klDQa6nIrLGcFtobIelpnQJ5GtkWCXlfWhD4NEmagwHEk5s2jbIw915j112MZFQ4DjuOkS84dvD5QowGwdIIAaaAV6HVHaHR8Zkx0nHtWlQXsDkPQIpBE490NJ0Ki7Qqr2xQoWpENoxGtdzDWkycdyJUGTAxgLOoEOWr57X0WISiA0yOG+Tf85qXc8XGPk1R0QGa1whHv9LRY6T3uHxd+UwPvh7Qwk7Z1x0qtAFQvbx0Na9ouXuVTQ2L8eeIkK+0UZpCRy0SHN1jbuSzQDp1XGp5J0XVdIsLVWLpceTBnb1FLM4d5LPohHDeOpEH0r8H/eZp8RwBjLmxVyPvu6JNXXiUTVlDPX6LXSanOKtMmIRVdl9fgXoFmk+JFTw5KlxQukj4ikbnjapwjOkY6F42vVuWoqAGqXlpjGiWi5JRKQqMPkfqIuJpjfbZXW9CGBQrFYN5+nsdM1dOydhdtSPAXLeqQZh54UglSF3/iFssorxGwyinyZEEee96j695zTs3rz4pM6wLqmR0HJXh+IpzRueCu7i8soDNL1iyDAdK0+P351FFAwCjkzL3yufV2nSUQIAoyf2w58lRua7JpJYF2yzGzWsyDi00jWdlQJY6OZahjsmGGpOp5FD0Fq4SAscuj8mxuScMOEHDwfNFnVcx4NTjzgYc1GuVDbLwaJ2zokM2v1a0KWBKT2YctDwgFmNADYWiI8ps3fYn4iAsXralpmdwSsa59uXCNDCv3ByUz3jMnOyAG1pjoySld0ruJIPAq1HuXmVTcchf1oktaqxOgdE9NbpnzfsMALvCTESEZOqSu5YtwORp4kJIBjWZmvdCC44jskVlikayfGsNEpSRp2CI2XGl9TUBq15TjXha29BKY6cKiT8ptPajSizWa291i2j3I00sPCcP1uBMHLRK2N+wDBCPYWZYY1obTtEAwMIzU1TNBNk1qcqz+ZHVJbvN8HXOfM8XYyRHesgePScfJCG9CQBMzyzaj8pWGEZKPcu+ziI0dl34smgTupdKpAMXpyz6GcbHEntuyYQDZF3zmmMHLnpCELrwrAGENCNUzcx6azK3sa1ZKVqErTe0cPQ3Ja+w+QVyzpuv12pgoLkl1CgqviKPTb2ihl3zboKoCltX+PVQ6dgtzho+zLbEreU0RtVMLOqxbKeYrrviWZd7Key8+kIMzPoRotKhyUAOVUileBsaAk5mCApSG7uS09EizzivJSfjVfTXMWFuUomCEmSrkJM5o7Xlro0+S6pQWpsSqvXBLT7oRcEW49fKAMfn14CIcfK4XJvLjxwFYvO8zgl17JSMoh81N+kzTd+u1IdotLtQ2Fmkm2+M0L4CDO4zVuEm0LoS2Qene9HcfObBqjLpORPkVuDyHa3tyiCFXHJUw1JKSWI7bZrEqmuzzBYUIMeSMJUmWhXJFBT/pYRKiTVnNeLcfTczHo2vMNIRY/mjMtaiC6toAKD/fCW9RMzYtfWuhlsAYHCPUwj5AjCMY6w+IspFLeloc9ccLAW6jqus7jaDJHzPdNfEgmdljsxTb4opfWVRtl0eovfRK3L+hkhz7w0CHlBFHOdsK98BUTSAMxLGR2NEhWNRAIDtz6/Qe9owDo+Axaed1i+6Unzre6HN7RrDk46tYXIUePbPLNvfy2S47eeLLuSp4bTMGBzKNBx0gfXaIVBlcmQevLq5bYgwd2ZATKC5SfDPC9Rp23KTTdeNB2RQj42tGapWEigZzasBRpGUjGuf77ywKAfaV933cc42/5SOa0zWEwtlH56K0b1YOe8vi6SpoHmfTAVcYM+V5PnR+7pOCEyubbXmhHz0nq9opiuxGYOcz/A+E1q9INc8Pz1HcjXDlavCthnPyV4XqoC4Csk3/efLR4fejtQg5DdKgN4lctcqmzolXPu8kJ9s9RF5bWPwc31QKPgLiPWsVuN+rjOhkHFhHMDVRqhS8fu5+7BnFYUGly1C+2oYR49zl5jOhlUQPhgfE8p+27OFZXt/fH4yt73ByPsOSWcTwj4ceh/9emBdmwXV70/T/uCz4NxDPajyALDzrtfJPpX1WmHW3jZQpuUihCTNVxroPOvRlUwm4LUlDF+zbMYsFrTORzYoLboKgA2T+rxYsyWgbJvr3K1x5Lcje4LqwUw9zxDMaF+RWM7odNsqGsD0Acr3AT7yoAwnQFk1rrkQkv3Mq7uhWlBYS0+psWPQbZtygDqLRMkYqRsJyPC+1WmE8YlmsC8AaGw55Tk+5s5rukpW6QHA6AQAkO1XwxHQ2vRQkbVcQ52j8dEEyYwtmKG5WwXPSzKrweTQZvN+JD1mgnN3MyXhZddOPKqkhshvl+E/Q8MzAOAABsk4ZKmuNgxD99yzFMzhmltynZoeWGR4PLEGYr6AOyaHAIG7UDgClp5wq6a/uFYpITVEhvb7IoQd+6510aVgAVbjRQsalbFWZb4QWYvQHjPScZFNkgLyUPvEmxwp9Yc5djsKGKh1MfALSfe3/w3768hfDUF0TGsAVY4AMDma2mZsCkHVBKq2Bm5/8Fm3T1/RmFBY8ab7zJwYy3rbaxf83BXA5Gl4uW/zArQ9QHVqzV4HhRBraI6POgZo2XcdcK7pYjI5bvizHlTkk/k6Bmbrbl66z8QGBSU/HB2PgeNxAN0+8esT+7rKpF+KLkrFXI0Sbwj+a1MX4yscf7FNpiFfmybHNSS0+EyOqKiRXNyyv+GuASn0W8H9WnZSNHargAm5dS0PFNB01UNJzgU2Pzot+8h2CNPXz5A9K9sTSx5Gw2/tTfFaNEyWzDgwruZ9CZlph9vChD8dXU1YrxXnHNAy2WfN09QByMYoGkVBxjMYAIO8jwqhq/HNFSopgGa3PE8mHbN9ZlRhaah4Pwz7pQozDos670bxr3k6rsPiQJN011h81RRGAb3ZZysUFNc1txnzJXcjKyW5LvB+WCqdSNGho42XUIGGE1qXC+QLcVDIB3gcU80I6aiyiV219DTRqt6Y78n4YRe/aRTgWKGXn3AWbzJwr6+9eSFAP/Wel9eD+4xyugosfPByoGC4qkCGX4wfOgMAGJ80i1bNaGyXaJ3bdSdXVeA1k9PxlFx5zxEUvRSZsd7jK6Lh6lOyClNRWa4sQJB2RZts22rJsTQMw7B4YZVbaxHnQPuigx4rL5f2lYlyx2IASJhlfNILCRol4wMiqmaEvTMGETV0CXTARG3IeYNli9DaZssQIf1cnOFCteTEFp/xECXMKI+bcOEg5L6vGmFtlB5DZXLEIxE9EgWFtxyJoin7BprcB+Krrsam/5wMqnfeeVVV5qDRNvfF6rHqjuVPMq+R92KrJKjaf1+yGYfZ35QtAa7svw6867IlCkbzLNkesPtaRnPDKKFeWEKQjsRL8xkXxie93GTpzdNxhOdwx4QOizrvRiEWpQFIy9tk4kFSDQRWrbhkJmy6ajXpYqGkg/OFCI0dzzIjQ5VirVZHB1M1CGXDKxAl+acx4qIXW8QXINZzY1h7FCm1FFmaXUeGJt5v9RyVYS/4gKiyQwAIU2M1N7fEYvUJLHce9FZkGKSQF+ob3EdYfsKhlDB0EFKuzHFPyRM7PtO9jpg0HXpP8c4e0OuCZvJZfmrJIqCKnoxpti7We/5AFwtPDXHlCx1FTZ0AnY0wHLX5Rq16l4S/zygQ7bpDK1RXEU4T896n2/FJMtV40HBWawvWsgdE0YyPxHZ/qoz8MH37qgOaRKWAAXQBnK5KL6Hputs+G9eYHBMl0T0XKpd83eW5qNL6FUWf1ZiuuxOZLVPgGlDobINqgQaPzNTG4wgrHwdUW7SulaAq7KYJIs+LM8bUSHMyGsb1FEoWoXfeoNPaoqx8b0g8eLa/07yQSjIFCpMTiqcAImDnTaLoo2kMXp1jftQou+daSCbOs+RY7gd7rMz3AmvsfC6jcdXkG0shNl147g65NEYYd7dnc/ee+aEcyqEcyqdZKkQH+vdqFSL6Izf77q71bADnZnMkqCy/OC/Kw8R9MmNrDWovDkXvKI9T5hX/RaXrsc4RBZXq2ZAtck0p4LWZmdJ8qHUcF4J+mi2GNDcBqICBykR36oTQ2HMdGquGIIBs066OeDUaFgHCXjfj9TisaC8EhjpZc1bx6sdra7Uvvu9p8NyF3aKlRXC/i9FrHNQZzJYQMh3kSD55wX1VlqCeeDQql7+wbeYJ6F4qg7zDlS/yvJpYOpWqJ2JJShfdoevMebDtDbaFnoCHLvRIQqPChU+SmXynBbCdS/KDbFNiN4PXyIE0pOnzogESMk0nzjr3cxSAeJl53x0vnot13z/rNuo96wEnTIgxX8js8bTQONuRCzy9xz3SPoFr1RTPwPdo6tjNQd4Hxqdryy3WPUsAGK1rykNE4MhRPHFM4VzOFVTht9QWLxqQotA4ry1nYDqukY5hiUDLdixoO7PL2NZ5mRql5QS95wtsv1ZuPKqA0eka0cwgAY8bqP0Tjh5jtl4jWnchyGrm5iZuejnDc23Ec6BzCXbfC5+au5bmXoj9doRBKD770Wj/EsD7bvTFXa1sUi0o6xDKdhjPzUZsUVtCReMh0brCAKAKoWwSlp6ubd3OfEGQatpVsnuxDvrDALBhrnk/Qt6LbDghKoFo5hWokfyXmPU8HQuTbgBoaJAdazrlALwQ5wywS6hGFePoBwqku7LD6Ym2rUsBDLdaEcar5wuEY78tKLDxqQ7673/Gfqe5Glow5Jifc8KOCZDFQ8ONAJBc3AJzDZiwD04dBQOYrrp8QuYBzoanEltV76OlZA61jsldm7zv+pAAYdx+fCQMk6VjUSZBN8yxY55WGLoPrsg2x7YJW7ZXIu8nrpcPS82RVtWXTQmBTtZC1KM2BlOUU98rHvRrRfpPjxFNnSKfnZA5tgSxGcJCxm5qm53NlxL0n6+x85BW1Os5mW33MUY3txmdy2Tv8/5515fGDd69LJtkecYAxyOorOCyPVsFGVXyWvnQqGBL/wRo/ZnLQcazGhy5/WWDCrsPpjYvOlsBWpcjjF8rJ9T5gCiZymMR9xVNmlZI0wr5XPZXFxGSCy5c3NxyaLjOxTnGxxtoGAUez++Mp8G4c3U2RPReAF8NYIOZ37Dvu78D4H8FsMbMm0REAL4XwB8FMAHwDcz8EbPt1wP4x+an/5yZf+R2h3azL+5aZUP72HSTqavsT+YcwIPHx6WTp7W6zHZaid+6pl6RbFC2CFXT1cRUGVlUTjKrULbigKustVk5GvlGZOLVLvY9X4xtTibvCo2HxsTrRBLKmvMpmyRsvGZh0IVPGXrbV5yiAWAYp2sMTyo/jixiukjXKbD+Qbf69375CdRF4ar8U0M5/1rJ0UQl26Zi8h6IZxWy54TvjA3EuXzjfXabyZGGbeol8wf32uXjrUK2gAVV4EUI5Z6uuIS7zgkAIBZFkyqgjMRgUGVj2wAboyEuJG/Q2Haad/jggvXSdCFUFNjwZIJsGDYEm65Ebi4V0W1SLb0LhmvPA3P0n3MaIB7NwDFZJQMAs9XUMhnUCbnCw5UGULP1svQc+ufkGNuvjdA7F85tnToPWZWQdhWtY63a9yD9Hh3N4qdMK3SjaDmhgOkcrKAXLwlfs6XDiee10NUo9J0lclC2TV3QSoxkzhgfNewQWRQYQC1Dn7f4QeeWtrZqi9zb+9IJ6PkWovvkos42W0CrQnrRbd++YsZVOVQlAGy9QSYpmSlzw/WlCS9N6E72s/lhAO8G8KPBEYhOAfhyAM97H38VgAfNv7cB+H4AbyOiZQDfCeAtkAv1YSL6WWbeuY1x3XSy7lplwxEC+LEPmc17FGDr1RhpeTQm8ZzR3nQWadGOLLIrmUq9gd6kTG7/1XKK6VqM5pZnFfpJ0FmN8XrsKPybjtVA9xUXHoWJWSxsiK+QsJ2OWRdrH7Y9PeFWcGLZp5JZZsMKo2MJ1j48cNs8d9F6IlyEEB1qNDB7/UkUHu0LeXnV1jWDXlqSBXP8+coP5nsjZMMwPvt12ZZErYoqmdzzxMoObC2IgCVqNM2jkkwq7DyYOZZkrz01YCDhU0bvvPu+ShGAIcoWoTZ1NlHOmC3HNqzSvlYBzBie9GhgvAXZVtUbUfi4T/zbulbArv9aC3NZ5n5ulIz2kAEJuKRc0xCsX1XMGJ52II+9BxhrH2Fsv9bNldSi6Fjkr4InVIH6YBImR9cjnipZKh0t8nX3WRwyg8+dV+OL3iecSP+bIjMhyEqeERfKZkzWowBMAfLqZsx2WnAtv6ntuPo/lODCHyJEHxdLogmgaiV2f62rbkzN7RqzRUcqau/F/p3xQlTupGfDzO8nontu8NW/AfD3Afxf3mfvAvCjzMwAPkBEi0R0DMCXAngfM28DABG9D8BXAvjxOzLIfXLXKhuqQwUT1NAYRWN5mrRw0WdqLghjg/ZRpdPeULZKgRT7nlNl2ummU4E+15mDdJIXEhuekQVDrXi1YpXtYL/Yh72lsXHjARnrmVOh38j2PGt4WrkeK6q0PIaAtd/bdfNy/rLsZ7+S6brJSMYFkrF8Pz7RCs57viChitaWWwi1uBIQa3e/h6nSe14qwjtmkVYaF/Ukih5JSMr8XBdOjfMPTmeI526Oih6CAlmqBa6roZnKU+iAKMT9VeoA0NmovH0gUGC+IuEYqGLnTTS3gXQa9guqk8ii79I9cZlnJ+UGJGZM1t285V1CY1DbxVBDVwCsotl7wI13+3+aIN+W+c82koC9WClwdK5k7GFOyYdNxzmw8Fwdthj3bslsUCKelpgvZ8HYrNHUigLkHscEhpvrKpJCa98I8VGBljjW3AMcieeqBt7SU6YGKzde50KGk7+aW09p56H4umvjjkOSk/Vtcr7xtrcrL8KzWSWiD3nv38PM77nVD4joXQAuMvMjFCr6EwDOe+8vmM9u9vntyJmbfXHXKps6CSuDG7tAbsIptsGTsZjLRriIUI3Aq9Gqfn0Q65iQDcLaHcf0S+hcDWnwq1ZkwwutTVMtbuLZUV4biKm3SLILDWl+RiuzNUfD6iHUooCi0ljDpvo8X0zt9vGsRmPbhdbo7EWgvh66ar+PoqCbpU9lb2Hj2rSrdgWAKsIlZ85/UqN/rra97SfrEVrXtKaI0BiwpQtRCn+VeCYWriojhSTv3q8J9HBBbG0KsEMBEaJMnJIBwpBJY096rGg7hdgyILud+rk8vZ80zKpKRr3Lrc+JsPApQtuEqtTyT8ZyTYqlJpgI4+OeYm65uhsNlaphsHdfahfz3YfNRl6eQhUNAKw8ZhTaJFQCfiFllUWBMkmmbgGuE1G8mnNkArKdHKWBp8dT0dh6f8l5u4fGV2qAx1/nKbR04sLHkyNSFGrZGBi2Ky0g9DLxnNE7ZzznJEJU1th9SIyghacnmK817HORTA0Ltc9Irp5yTwEkHkXNqmcQ3SE+TmZ6MZ7NJjO/5aAbE1EbwD+EhNA+k/L8zb64a5WNL3UCTFfF8gQkyZyOvRxNDtDcLR7LT2kZsssdcEoYH3HTOT4GdMQpsN0oVcoWIfYWtXha20UxKmXxjzx+s90HM+QmbJ+O9yHRImPRe0ohmTEiQ0nAFBYdDu5pIC6A7nlZlIpugvZjl73JYKDXRb0lk0FJIoix2FOcD50BjeWEdj5fyqx18bBz5iGzzv/xGqd/xuSzmpGNqwPCdNC5Wltl1LlSeeUgwrTte29l29W+qOU7OGNYDXYpqIfSVgkaLtU8nC2EHVSYrsRBSwRf9JzswhyTkFF6yqn2Xjd2XLEhIH+JnbJZ+BSjfcUdLB0WoLJC2XPgiPHx1OULo5DyKNutQCxKRkX5/KICqE84RbOyNEL9647xMzVtD2yr5Vjmx9Z/ZRE4geXY06JLH2zCEVlDQJuvKW8dRyRGUcM3sEJFZreDa31RmE6w2SjMddm8qBfqzjtkP089TxwAdh9qIBu64ujhvS3T/VN+39w2hqC34imdTpybWjpz/LJhWmiYQ8f7UIYvVRh4OdFo9wO4F4B6NScBfISI3grgIgCPARAnzWcXIaE0//Nfv81xHOZs9guxS9S2rkpMWEkPm9virmuRIDHQO+8W7LIZiaXmTWsyqhAZT2Z4mtB7u0vwTH53zSYje+cLFN3YPojj46nXmEpkdDyxFnZUmpCY+bpzWbjQavugCD+VJoSpZEw8pVd0COPjiUsE54zWZonksbPyfjYHlh3suDq6jHg0QxR7lt1ogupeAQBQXWN8uguga+ZCQh/+outbgte+pARXhHN/TN43L0fong8pXaarEbqm4ViVulBOOqoRz8l6Nrpoq9JXo0BDLIBZTDTFYfZji2s13DZwAwwKPk3rYk2KWzJGP0zmGaY6HjUkotw1jwPkHqLaQWotU/fIYxxopxidcJPhA1MUHZXtuvHuPJgGrAblmlNeXl0vyp9bQwRHlWQhy942VcNvjmZO1bt2ZdPlUKIS6J33qcjNfHjhmnzJnYeGhvcrbN/DrbLIzr8u9LrfZBaCdIq2eFY253KtRFTU2DUFyFSFXhLH0rjNskw3ZA4UQVd0KGDGmC25dgcChSer0O5cCxp62Yo6mfkTAGw5MBGdBfAWg0b7WQDfRkQ/AQEI7DHzZSL6RQD/goh0AfhyAN/xsgwQd7GyqZMwSZhM2caDAUVpadJcb0Lz4FSMqhHZ0EBUMIan3YPWe/s17H5szeYfVj8etupNZrWNX1Ml1p4eu2iZvh+W/BPoXayDvioSWjHhgRkHIYq8FyOZMcbH3E2d7AL9ZwWCxXGE5JmLjuyyCeT3H0FpLExpYd1C87IMPt4ZYe+dD9p9xXltoN3yfnSaJKRiYMnpGBjc7x7i9GqK3llgbCLB2Z4oCbtAl7CKBhBAhMb7OSbMFiO7AOri7j/8ZcstpMkk9DQU9ODnhDpXKjR23AI9W3HXTVFhQS+iGaM18xb7h2M7dqcU1TugkIJlzmhu13Yhtb1uPC90eNqhoxSa3T9f298DCIwHX9E0doHGxwyT9S5j8ysqLPyWaGK16NWT0EV+f1tx23SvZKB035dNCnjb4pwxOZKh+7xLqpXdNMgp+SwRejyLVoso+Ds5IiAZnRtVaqoA5r0IiPwQl0DKO5cLsx9g94Gma3mgdE9bTpkAzpOJKrk3lEeQIy/31xFvWEOizR1GPGerqPxWJLcjAhC4M5qLiH4c4pWsEtEFAN/JzP/xJpv/PAT2/AwE+vyNAMDM20T0zwB80Gz3XQoWeDnkFaVsjDYeAqgAlPtjlrfCi5vv+wAeB/AzzPxttzxW5eLzUREmAauMDKLMPKA1B/BlAKDCde6smsD4XQN0GhJS2P2YhC/WPmaoNHLTgheuzoaUK23s3HezZ0yOUJAob3ktACR27r6LpzXyfmIfnNFxxwMGiHW88NvnvN0TeG0JZBKp9VIb8bTAfFkWjaItcfvifol9D0/20NhzD2beSxDnXhGfUTI2FEVA5yJhIkzu6J2Vvx1Tx8mJ1pq4B3jej+0i5ye9q4bQ5AzuCRckX3w6mbiQf7r4a1hGewHJALypKGu0rs4wPepyGxyFFm3ZInsf7Dxsivy+WA5anetg5RG3QEroxSWt1VPuP+WQfXXTLc5bb+gE3GmNXfmrNPkyRhdKqxqE1O0KRRvoXnLhoeVfaQaKOJ6zY9WGGEv+4h/njj6/vVFdp4iSmfMkFGAyW/fyc16ILaoYnDgvrDT3kY9uK9rOI6dKQDS+cvY9DaqBxk5tcy5TEzXYer27CZrbjEg71WpPIaMgpUbK7a9shfmo+SKhNNvWqRhBalTWiYTW1HCJ72APtTvFDsDMX/sC39/jvWYA33qT7d4L4L13ZFAiZ2/2xStK2Rh5JzNv3uS7G+LFve//GYD3H+Qgfs+QOjHoNCX905CWl3+gGkEehcktjBvfLJphbyJW5eonvOS+kdjzPubLaVDQFnuhnHRcA5Sgd16bnoSM1FFMKOLYLv6bbzIJWo1lj0Io9cIHL4MnTnPVD59G1YiRXTW1M0TIFxs23BGVrrYBkMJJjlz4yPJ+GW8inQLzhX25i12PUZskNOQDLFIvDq+LhC6SfoJ5+3WmI6ZWk5dh3U1zO+w6ajun1t7iD2dNp2NpPzA5Iuaw9obRc4+LMMwDhIniogfg4RGqcw6JN19wlfTdS3KNO5dNsvySqSlacgv07v3utSoamwM04kOpoyJsbQGEZKKuD5IptPTg0PHc5QLVK7c5GxJPTq/b6EQcKGW95zRhX7YFhmwBA7Fr2CbjIMSzGq1LTvtX7QyjM3INVQlMTKBn5fEaRcd5gnHujQ0uhKgKLc7DyEP7GmPhaceuMLxHMf4uLzdfdNxrarCoZEMPbDBHwEfo16kBYa3R7QiD7phn85kSA0T4OwBOM/NfIaIHATzMzD8HAMz8J2/225cngPjyybtg8OLM/AEAihcHEb0ZwBEAv/SZHOChHMqhHMqNhFkAAgf59wqWHwIwB/CF5v1FAP/8ID98Qc+GiP4khO9mHWLrE8Qz69/yhy9NGMAvERED+MEb4MpviAsnoqsA/jcAfxHAl91s50T0TQC+CQCy9pK1fDURqbkDtdojL1ZLhWvBW3ZiSyMPAE3Dr9L4z46Ft31pjnjiLNbxGWcNU+XoaJJpDU4c4opKRu98gfYTUiJdrfdRZzHKjmFANuGEybq2GDCIM62bmDEWHt0BLkhCigFwniMydTHx3hQxAM5M9fusxHSt4yzgjNC9VNvwSW569ahlmszEi1GYuB7EJm43JeSozwvVUnip0thhdC+5nIk2N1PoMwDMDHVNe0PqgcYn3Hd+kafmG/wiTCqdd1I1NASm8FehufefZW10BwhPnbxWK1ySzMqQDQD1sx0kMxPbv+a47eTc5Hqn286TzNecK6YFjc2dyvyVz7XuKJkIBFyh3LNFkxtTY5jEk1RIvjIc2OMPHGddOqqD2qyoZIumU/G5/BznmTmU9RLd9r43jjTMwcj5z1G1PWTdyZb1FoqetEJfedzto7Eb5hplHPJ+ZsJ7/vktfdIBFBpPXEJ1ag1lW064tVFgtpIiMWHDoivQafVM0zFQNoEFj3fO9xiVkzA4J22XUIbe7u3Iq92zAXA/M/85IvpaAGDmCdENqndvIAcJo/0rAH+MmZ+4nREeUN7BzBeJaB3A+4joSWY+SFjsWwD8PDNfuNV5G+X1HgDoLZxkjS/HWqXv8UwJeaUX8+24FWq6EiMbAYN75X0KYPjICqK223663kD3rCw+4zOdICkcF27f9iEzN35jpxRFYzjH4gubqO87YvND+5PY6VBi+tpnBADqTgPxigBMeGMT0YJnF0ym2HvryQB+nMwdySgobOhVdMlwVLnvy7YrvmzsOOQVAJRtQTANv0gACfR0B4ufZLvAti7J53Xq6HGSi1uYP+hWdA1bpBNG3iX0nzWx9JRQdFy4jGNZxBzKLGx4R2z2b3IwdSbcXJM1XdgogMLmPcK874WKTLOzhhfUzb26mnTCqDKvmBdAVFS49vZF+97/rk6kKHNg+rL0n69QZxQs+kXHkbLakKGGc+cImolFpR/e5ECJjo/KifmN+6KSg4JapfUBHMKvd772tnfwZSYlCjX5M6PIOhfkRqCaA2TafLWBZFZj7z43wd0LIR+a/7zZe86PtdSwfaNUATeeMNC+Xgfx7gRlewEAMFtJzfUmu794zoHRk45heeo6l2skymlXh43bNHRYdO9s4EfCaK+2YNJ1khNRC8aCI6L7IZ7OC8pBlM3VT5OiATNfNH83iOinAbwVYQ7mZnjxLwTwJUT0LRBMbkZEI2b+9lsdz0+IckxBMtWXsh0HZI+AKBpdcKcfWEFnx32XDRggYHSvmPTpqLKJUuUo08r9dFRhvpSgc/HG16u47wjKTmrzQ+kkkh4oPk/UVm0rpdO9AvFzrm6GlheBZgN7b5R6mKhkDE9G9vdRCXCOwHqm2hF3AmE+Ju8LPYzmXWbLdF2TqcHn5mh9XM69uQ0sPeqy2mWvgWheWcAEmLH5zlPoXRANMjyZWQLTfIFApddZM0EAZ61iGYdPCBmVXg5m31zXKaFsImArrlpCwggA832+elQgoN5JZkDvUXcs5adzjcoi7N3TQMOcbmOvFu/KKLtLf6xC7+MZcgM03VyKke0Ay0bZKD2KKsx4Lhx7Pqu1j5RLXFmNFMpWzvPtXFVwigfvXXGPe9EiSwgqYxdF4xe1Vg1yDQQbhKqRWKXQ3K2topENGHMPaKHXoHPJmy8P/KFosJ2HDNHmHoDIy5tMBIKshZbd52Wwu3/gDABg8aObmB9fsPvTgt7RcQ+5t0BIld4mdQg9f3sAABGiytX5KJOBbeh2B52RO8iN9pmS7wTwCwBOEdF/AfDFAL7hID88iLL5EBH9VwA/A0+DMfN/f9HDvIUQUQdAxMxD8/rLAXzXvs1uiBcH8Be8/XwDBF9+S0UDhNTh+xfMqhFZWGU2qJD3ksASW/14jf4zkgy9+va+hVwCgriiyvFNAWGnzTqh4LvmVomya6hvHrsK7jSAjhxcw2fjY4atNhakmiqtbFgHCK54XoFXllCuymKv9CGKOqoTAOQeoLJp6Do8JFDRI0t3QxVQexT8sVmg1BJUGn6dPyag/cnMKi8NfZQ9t5Od13rcbDUwOeKS9mXHLf660Gl75MaOeDWW66wIwzj7ua3kfCmwWoVNQc9T/uq5KmOwj/hqbbt9ta9Ku4PuOZeYLhYaVtns3RNh8dl9IJK8xvk/4T7rf9UV7P6qQPWiuSyASsra3BWOLn+8pT/3ediiuLXp1X6ZMfh1NVWD0DCow+laEqD5qqaASrQFeTbGdTU4MjcKnZaw08pjTkMpNQwATI/JNdXQVFQIFZBt01xyENabLUaYL7mQICdyLyrjtz57ClUenOmid6HGxITFJn9kPSiUzpTeyQOfNHfZevBtw8yh90udRla57EdCckSGGskp1jshdxL6/JkSZn4fEX0EwNshE/Q3bwHoCuQgyqYPgRn7NAgM4I4qG0hy/6dNGCwB8GPM/AtE9M0AwMw/gJvgxV+SkNcdsxWhahKqZsgTpsV/g318ZVHJVtEAwMqjM8xWM+sB2IVXq7FL13dEFdbE8Kp1rpR2OwBAWYLGwPRhge1ojYVfXe0/UHVCKDyLlSoGepllXqYKADsanOlqhOYO24c5ngu81/dkqszRhORLRnF6C3DVcgooKgxDgrEgOxuMbOSgzVUaoTzesbxiHAHTNa8XfCwLn6WvGbnQSXOPMTxNaHheY3ujsrm08ZEEHLv8lXpbfnV5ncCGMKerUdBOQMdcuFSb5JjMNp2L8jtlYGhsTtHYBIb3yQaN3RLjY87taxgYuK4nguqL0P+4vO95igYA2ptmvGM33mzEAZln6efGRnJtVh91lpHeO6ocAm89IuvN7OfQA4Ttmk377mwknGs6dqokX+fnbBafmVuGgNS0Dq9aprulsk97yK2yRVCW0Xgu/GPalwkQhm6tHxucidHxeOuyoUDIc+/aKFME4HJ3Wt1fZXL9tYxAc1R6T9SxEH8q+0OVEroXCzMPcdCHZ3gyBUchB96dkVdvGI2IPn/fRxo+OU1Ep/0SlJvJCyobZn7pC/qLEGZ+FsCbbvD5D3ivb4oX97b5YQj99oElLhjptHb9a3JJKqvns/TkFKNTTSx/4Ir9TXF80VpJmtBW6GbZFAuTPJoLfRijwjVOA4DpaoLuBc9ES7Str9zovXMVym6C+aK7VLsPugVj6Sndj3y2d5+MZeFZhf0ask8TmlHW3njuFqmZVygY57L45styfM4YVBDSgaF+P86oE0bvOZ8Oh21Ih2oOqvJHJ1PhodNFIxLFUmlSfgvAwOUMfPhtMmcsP+ni/BxRANpQeKyf6C06hKhy7/3FT5Wo/7wPXl9g9dieO+aPrVgPQMkdfdoVVTQAMDqeYrZMLmGfM/IeBePxId/Dnz+K5sh5pXpP+Au6X2NTNkXJa8hz8Wl54feY8eHChZePAUlOSUNw42Mu9wIYrjAvjAYiFB0Kwmh5n7Bw1i24qmAAgApzf3RCglX9vY7FFcBKfivzWKT9QmVVNH4BpbBOm33bLIERVZ5GubQ2a3Ds5jQbVJgth8tbMqnR3crN8WMbNm/s1Ni7r2FzgckcwJwxXb7DORsGilepsoEAsG4mDOAPvdAODoJGOwng30FicwDwmxDX6cLNf/XKFx8RpuEXRRMVvRhFJwryKItPDFAvmFBBJ7NEloBD6eQezb6vUFqbVfDeT+R2zxoLUesgliT8pWE1AJisxraeZHCvhgHkuytfbHI5u9pyAGhuAnPjqWg+Q9FeVEsNgc+ey7HHtsxCr9I3yiSq9AGW/XSuEqarEVKvwRkTgQxxp4a29PhMUvXud8PsXIJdOIo2ENXO4pwvkO03M1+gIMG+v97BtiPwHEOfTHHeiwAOG2pxBExOum2OHN9FZVbg5MekS9vKx0T5zNc7SCYl9mxtjBRNqnKYmU6Yc5M6iHNTjKsJ/cLR5wGuFsoyHWcUdEktWxTcG6pk9JzyBelm6RO+qvKNKgmhqoGkoIPhvVo/FYbJ2Cgjv24GCD1oX9E0tnNU7dR6/42rYxSLzZCipg6JYaOJC4XWaahU01FtaI7MM6j5Ee/8/d47rU2tx5G/88UoZJJoSyjYeigRoblbYb7gohXNLY8ktBMDMAaUUSp+mJFqN97tz8Edk1erZ8PM77zdfRwkjPZDAH4MwJ8x7/+i+eymvaZfFUJkOaqU/t/vyZKNaotsSYZzwwcmonFkfyEcH02CRPrCsy7UUbYjG7KbLsdB0eXolKzCPl9X2Wug8CqvVcEA0tgMcAtXsSCKpv9ceHq62NSpPNC6mFTNCMmU7UJddID5MkM75i49Km2kVUlFFWO8noStk73q6uYuo0pdLFy3Gx/3wn4DF/ZQSn4fBaaLNQBkm66VA5NYoPp8Znsc9KRp7DGmy4TILAqNXQ563QzuEzJUnzjTDzOtPbiF6D+tWADUwuN7QCRKRuXa57Ww97Bcm9UPRRY1CEi+Yb4Qdrz07wEr5pj2PDwF43tBml/QzqR+m2Jf1EjKBpVFl+nCq9d96/WRdBn1cvh+XlKJKZ3HHSoajhBY9skkDo4zO9pxIA9PGuY+pkIauQXtI+bsKRVz/2lYrxUq3vbVHO2rQNmRG0WfG0uiOpQmea5VgiLI3DPs577yfoT5olcN64kWeFsjYpECuHvnDpnVnyVFnU0I+vcdkDv7NwH8ADPPbvlDHEzZrDHzD3nvf5iI/tZLGegrSeqYrJJR8ZPNmcefVSw1kQ1LDM7ISq9JU6Z9uRJNmk/YIqCAsAlT2RYrTMML6YSRDSrXsGm9E4R5yjZh4VlHSpiNauw+GNnmV+2r8XVoGSWUBCRZXmUUUOIUHQcAyBcZjW33XWOvBicuDwLIApZOjTLZt5hGpqWBPqgcyYKuYby8r/US8r5sUqBsip6hjreoJ7fvuJBQkiqXfCHMIdQR0L1c2+85IlQpsPeA+b7JGN4LLD6u6DbDiXdZxtT/qQ5Gp4CFx11Car7esWCMvE9W0QDCAwcAbY8ku7kTsg5M1zzlsSh/9VpFhVxvDTHlfUJzu7b0Nlq7pEoGcJQ3gFj984UI/bNyb+7e7/W66Uu/ma3Xh/e0C0+Gysavq9r/GSDEmFHO6FxxtWeckGsxkEaBp0klkMzrIHScDUrbjnpuQlq2j5LxrH1G5SrojttAY6/CbMl0HuUwnKmw7d2H9JkjLD5dhy0T/HtV7xHfuzPPe1xz6HVNGLzl7rUg3HibUr/60Wg/CqEU+3fm/Z8H8J/gnJGbykGUzRYR/UW47m1fC2DrJQzyFSdB7DslZ+WVjMlR51OrdeZ3kfQtRgtVnWmC2igOT8koxYgqGR+9FnkPHEchh1Vzu8boWBJYfUtP1fbBlP4sYW6obBFCUkQ3trId5mjSgdSaLD/h9j9ZdTQm7Q0OFgHlRQvJCV34R7d1MFI5vsKNqUIA225viCejyocjF9JhA4X1Lc/mNqNheLo4ImGdNovG5hsJdbPGF77tSQDA737gtVbRAMDKowVaF4coTUO3qpmgda3EpXcu2m38Is3RaUY8jZAOzaJkrrmSigJA75zbvmwS0mEIOGhfdSEgwNQGdRSxJaFBfwHMRrDKBJDQrOZ91PPZ/By3ivoe4t69kfXcojxULgqM6F7w7hWvjky9JUsaesU07WtGdtsqi2yrgLJlWJg9dByVbAELUV4Hxptt7+CnlWq3+EtNj0PbUSXtDfQ+rlPC4IynSAyoxFcE/vMW2RoaeW+ZuNs6DsLO53gowacjS1OlYJna7C4oYL4N+WxAowF4AzO/znv/a0T0+EF+eBBl8z9DtNi/gczX7+B2UGCvEPEveZ1SUGzm+KQ01BTeIMk0xOxTxcGir9aihs4m62H/9HTskt5USWjAVi/HYR4pHZVYerrEzBBl1ikFi39jrw6K+zgyyiBAJXnnXQOdSz481ORizrokTGOvjZFp4BXPGXEOtK+4/NV8KXVWJgsqS2XnYWnD7BNMxjOgc9kp8mzkGAq2H05QJx7FPQMNM5Q4l/PoGMYBjglFJ3IMwgSAXN7qy77gUQDAb/7fgjNpTYCVxybI+6bK/KLs2G/2NjwRegJ+oSOZMeVLZrBLAOWExHh5zS0E9VfTdfEg9Fpmw9Ao0eugqMayIdddvbnII90EXA5wcFp+OD5doXs2DhLlhVcbtN9zqRpA2TUJ811CayNkDZgciW34ST1hVTL+eAHJxUjPGnnfuibX3EdSBl02rVKKvO/Dvk5+nyedM5drDL0NjoDeeQ7AMY3tsPNq0PJbc1ml+71PHKreLyCKBnCKKB2JgvGf8Tslr9acjScfIaK3G7owENHbAHzoBX4D4GBotHMA/vjtje8VKOyqzffX2MwXHUxXJejXUgLDE4T+85oUZzT22EKmq5VEKNnXVAM4z6a55Sxz2S9huhKjpeESlgU2MYlOJsJ8yV0mjoDO5QJTA222lmDt/u5veevXB2nMvDFQtBoh23PKIl9tY76Q2EWvfXUuSXGD3KlaidmHsRzZUM4YBdDcTmRhWnbjaew4CzwbMUZHE5t7ygayK3+uNenNMZAOGJW3YBF71CoRcPVtZKHNv/z7b8D670VYMGPrPT9HNM7RKGUudz9nCVUGbH7e9ShBAGhdDo0KhVzPvEU0KhzrwHxRihGn6+43VLo6IQFeuO6iUSGV6mXgKbLN+cS5LJKa5NeQ0Pi0u180n6NzoZLthMi3fNHMwXPe2D1PZnJEjjE3jAjdCxWiKuyoWTVcqEwVfOeiS1BVbXejSR8iDn4LhN5GWDAqf3UuiWWxt/dlTLYmTPYvfxef9q2ofXPBwO798oPFZyS/5zNltLYqXHmrjHn5CbY750iYKvwi2eaO27nffPB2hJlQvkqVDRF9AjLjKYDfIaLnzfszAJ48yD5uqmyI6O8z878ion+HEHQIAGDmv/GSRn0oh3Ioh3KXyqs4jPbVt7uDW3k2SlFzIBfp1SYcu1i/hrQKj/+MIxdaiEoJh+h9oiEiDZcl0xpR7izwdFIj73k1B11C7/kQUqQGjkKix0cNDHMNOPbb84Ajyg9FtDfEDWtuy/40mR1YjV6SVD2ytd8XOFitRInGeygWG0hGBeqGCdMZr6F9VeIb6UUx7yevFfM9GZcYe5QgyUzQdpqj2XmNwI01McwEtLZdnU8dS4fE0kymFjRqkpzJ5XSyISPOa3s+mrvxn9ejv8PomBbX26/vYOmxPevpoAa4mWBgevNUmXQObS1KbCvPEzQ/4vUsMKKhnbItuY7YEG/myxWivTgI/cyXgbWPmcLE05Et7ATEq8mGdYDyigpG24SylOdOhcmEpbzz27ufkO16HkTDS8Dv+F6L1C7NVr3vd/0co0D99X6brkqYr3vBr6PxGAnMs6Ah22ygYV8X22p5ode61cD0eMuD2Ic1M8ksbPvMieQRbTdUAL3nXYhhdEJcH801xjMA5MbDBAzud/tLB3Kt8mXZfuOthNWP2q8ts8L9PyUX6Nk/tRB4SemEA1ShX2jr53ZvR17NORsT4bJi+CtvDO+7idxU2TDz/9+8nDDzT+070AsiD14NojdunUocfHzcC9dUDnefDcMwz+qjEsbyw2HTtQRNQ+w5W4qDxO3ip7xOneZe00VdE+d+pfjGmx0kq3WNA0Viq9OPhT1PtLgv70kPD1UyEndmTO6R4H5zY2YVDQA0ro6w9eYlGyrQQjgtVE0vOkUDANuvaQqqSpOpPcJsKbZhkXRgkEMa/mgBuw9EaF0LnWP7MBMFc+s30OLItOD26HiiOSPTfBczIq+L5vpvXwOnsa2KHz7UR9Ug7BnoeNUCWotT5LmcY/p4W5S6lxuI5y4cFefA7IEpqm2Zi4dfLxjYJ58WhEDzcoIVjyutfU1CNzZkaJSKf/186Z0vgpiBshH4RKh+SDQZA8mY0DIdx6frDrSgYdrOBZNn7Mp5TI7J51QROhdhm9oBQDpkS5sUFYzZqosVW5i2pwxa19xqnF7eAzcS1C258KN7RKFraI8qCVO3TW5HuNLCvkYcO0Oqc2mO4Sl339s589Zmv7eQFg77zBYrj1W4/CWyzepHAbCjLmpu5ojHOZ79Uwt2+4mHHOxe9sNmovSUkXx65EWtqbeUV6uyUSGiPw4p8DwOYAMSRnsCwOtf6LcHAQh8B4CfOsBnry7x4LqA3MgaF05mYYGXIsEyT7m0tqvAwm56dOnqdeiD53sa+vAo8y8gtTNLT7nBBO14C/HCWhuuHW7VigO0Wd5zbZrjXBSNX8Uf544/rehnKFsROs/JU7r15iUAwPC0o+RJxw5JNPi8o2E+xRif2kI3ngFVx3Fc6T4sGqgShWQryVnQZmFBLVl6HKod9TxHhhBROatIFkUynkt6bQRupKgbchsXqx3UaYzBvQ6inn/JENUnBR5WHJ8j/lgfTXOsKA/RXFVTxqf3xfitspLf87AwRzz9yClU7RorH3I/mq5EVkFq/kzzQMm4QtmJbf5Nz9+KeT085aHLPChyNmT0PuAZNCuxqZuS940dKYpVib2cQ+ua3Csz4V9FOhAWB/86KV0LAIyPm/bS5h4vOhEauxWaF5yrRvMwd0HzEsVxh1Dwz41qxviIQ1EW++qBgJBIdHy84RUQ6z7c93v3REgn7r6mirH4SbYKXbrr1jj1S8ZTNnnO7nl3kEt/cAFNw+I1X3L7Xn6qtCg7QPJNja05dl5zvdd7O/LZUGcDaVD5dgC/zMyfR0TvhNRevqDcKmfzVRAeshNE9L97X/UB3JmM2WdQFEGmooWEgCQj1z4+x3RNnmpVMloECgD5gmcFxhQkVnUR8WttlK5cH471j7qHYLbi0F1UsQkfsd1Ha6NAVLonL++7y6YJXEXCcSQhEz8ZnO3klsOqbEXI+zHyNy2ZcyUgClFQsQcAKNpRsCA7mLenEOfeos3XLxjZQOiAACnOSyeuXbEuKFFXq7jJ+72cR+OyhGs4jUFFhaorlqYqmtmaWMQaJtp+h2i2ZidH/UTPFn0ufKiBsuPgshwJn5nO3fgoBV0wo7MtnPiCi3j+sWP2s94nE3ttyiZhcL83z3syJ0d+3y3irasOfqUFiqrI1ar3FYxfKJwNSpStOGCf2Hm9awfRuuw8BarlfCyxpVk7dXGV47jX2R5j9wH3Qf95ubdVMSbjCumeu0dpc1deNNTdzzC7dzmghIlnbEOYY8Ppd1VbbKHG4uNh5CDvkOUfKzoR6hhBm4Jkxti7xzEgVJmLBDS3OUDWta/MkS9lljFAz//K2xwNQe70YlDLVHRjRDkHHvR8xXlZvtK7LWHcMYAAEb0XkkfZYOY3mM/+fwD+GIAcwKcAfCMz75rvvgPAXwZQAfgbzPyL5vOvBPC9EDqF/4OZv+cFDl0w8xYRRUQUMfOvEdG/PciYb+XZXILka/44gA97nw8B/C8H2fkrWYLq4gVC5ng1sfiMLBCL7/fClGWJ2ZvO2LeFV6lep6Gy0Riv3w/DR7Skgxx1Jg/FbCW1hWuAeExRUBhXoU4Jc6UF4RAZ19qqhebEu4db13Jkn5TKw+K+I9h7wD1w+rD69QlLT80Qj9wCOXzAad54LqEhn0BxtkLBGDIvT9HaliJL9bw4IqtoAKD/3NTMkVF+nRh1QmhfkjnnOEJ2zVyMKAKNpmCzwFWLGepGjDrRvEAqiCmj1IsWYfuLcpAxsaunRNE0TdgpKmWsWk9RNQSurnU9qAXurC0A0gGw8SsnsGjQZflCWDMzXwYaO+HcrzxWBYuTKhg5ZwNhXne9Zogd3DjbKzBfSpENHLMFlWwLF6s2B2wC+YJjL9Bw2u4Xi4Lg0oST3u8ZRXOv7sXkI7uXDUt0Soi965TuzRBtu4eiPrqKaGeA6pgr0vJzGS4/qUg6YO81NdK960NfgIsW+PdV1XBtnHV/mvurWqKglDuvTghUMrKB3LdKm6N5IqqA4alQuTV2YBGkk7XIIt80pKueGZPsX1k09rcKf6lyh3M2Pwzg3ZAiS5X3AfgOZi6J6F9CIlD/gIheB+BrIKGu4wB+mYgeMr/5PggbzAUAHySin2XmW9XN7BJRF9L65b8Q0QaA8S22t3KrnM0jAB4xfWXGzFwBABHFABo3+92rRTiSAkdArKSiQ1j5uMxZ8qwpEfcanqHdRvNpWbVGb5LAtyqseM6YLzqLV3nXNN7dujqzllJja4664bU2MB5Iy4TeopIhDcyMxzKo9lFwyEKhNQCqPDRM1b04t4oGAIqFDLNFsg+SEhsqiSWAQNGM7umASpe3sEV33iLX2BUgg2wgFqMfnhEF5bwTVIzEdC3NFzMUHQcoqBPC4tNTpFtmB1EENo3V6maKKIlQtbXGKBaWAGU4rgmTtVgaqEGK8HqfyCw8ONuTRcYP7yRT5zVGlYQDLQw3lvNumYp/jmS+FIq98oRwbalHPFuXOTz2m27/6TBslgbAAira1yqM1xPbFG3YjtHaYnSMok2vjpBeBWanXV7h0pc4ZZGMyBa6AiFNzviMCTWWboHtfyyz1nxjj8HwWlPooj5xIcrpugf8WEmxsD1CveAMj/yeNUvhVPQTVF4htIY6hyfcvbr6EYJ6yNmgRtmKgnCzDwGvGhT0JZr3BbLv89pN1xxDw9EPmHzKuluK5otRELLu7MvD5AuJzVEtjCprJNRZFEQCQHLNX46I151SNsz8fiK6Z99nv+S9/QCAP21evwvATzDzHMBzRPQMpFcYADxjSJBhWre8C8CtlM27AMwgDsdfALCA61vB3FAOkrP5JUirZTVzWuazLzrIAV7JopbWoqEriS57xAjM4BUX2B28YTkInwDeg6qNltiFsuKCkW3LIuK75NMjTWmmpgy9RGhvVh4NCAXsvXUWgSpXxa8WqdDXizS8qvfs4h7qo8763H7QxK69JlZ+ISLHwOBBt6DMliK0tmsXYmyH9QdVJgwEtsmVsajb10K+Lw0VJeMKg3tTCDxfFrV0zFh82vyQGfHu1Lap5mZif5svZqgaLq6VTIU5QdszVA2Td9GphCxtXcNlVceiaDrGeqcaaG3MLWJq6w1CrKnV9VEheQ9VyJrD0eutimZk2vc1NiP0z7qQYzyvg5qgKiWLsgOkBXX7WonBGfVszP1kvJ/igUVEOWP7NU7B9J91cz+4D2Z7/R3Aqbnn+rL4UuSBP7yiRG2P7Xv0jR238E+OJth6o6D7AGDhE5tAI0XVl3tXCmi90OmsRjxzIUFAilDDY4aILmJgbOp7Grt1WAOzKS3AlcYGMEW9tbwfnZJGfCrqbfjzG+fXMwVosh9AUE9Wp2QLT+UDx35Q7yusvlPyInM2q0TkI4HfYzoNH1T+ZwD/1bw+AVE+KhfMZwBwft/nb7vVTpnZ92J+5EWM50DKpsnM1p9m5hER3dnM2WdAohJY/r0NAEDda4aKJoqAvoOHDd6wHPw2mdaoE3I3Y8nmvfl5JQuNolh8dJZaVq2rJtwRR8j7KRrbbkWP+045aYhIq58bwxrTlSjgfepc8VpC9+SY0+OiQHoXarOfECarXp0WHSoMN+8Ln5lCeHXxsN0xM50DmP2HkO4qc/MASJisvVFjfMQ92AvPzhDveHG8NAabsFqdRja/VHRjRAXbECXHhKIbWxjrfCESKh5z+tILJrJhsToTxaGKvf/sFJtvalu+sHwBKFuMxo56WUDCrh1CNhSFpnPQ2KuQjoDWpiK+ImSDyiWXFYHnLejJzPC7QRbUshVZFueoYCRzDsJwV9/iLmz7SoiC7J2Vv6NTvsLQ1w0UXWD+oNxHCx9oomj7FfuiaLTyX+aaMF2Vydp6o9mLYYOYnVpEPPdDw3FAvKleydADuuQ9x4kXzxkcUZCPKr182Oh4jM4VB4mPKgAV2+ZpQFiQefT3DOR/Q268+UoTRTe20YPmVoHxiYZVMjZUapS/HyIElMLJGHhae036HUvnU6N00sG+qu/bED64stlk5re8lGMQ0T+C5NX/y0v5/U32+VvM/A4iGiKsuyQAzMz9m/zUykGUzZiIPl+b4xDRmwFMX+A3r3iJJyUwkdOIJvtOp9/F5D7n1SQzg87ywmo+ZQyAgMm5TihEHQEexLRGPCvBsRdPrhn5YsO8RtC7RvfT8JKhmswFDGro3I59P71fYkiaKI3n4hXtpwXRh1EZrIMwmZeDKVriten5RiWjveF+v1+0Q6MqgOY1OXD3OacQabrv4S0qsKn/qVqJrXdKprVptmaQcpNavEkz9d2LhemoahLtiXirGtqRWiph8QWAja9pgtMKjQ3Zf9liJFOvORsZtJ05te6VMgAs1Aaq69eLTFdii0abrMUom2QT8e0N3rfAJsIAYXI0cSGKxvdkb0SW6ct03SkQH31WdOW6NT4kB7TX2+xj3o/Q3gyZKwBHZHnP/5iDidDYcIZrnXnMFYbyJzLtlLMcSPfmGJ4WzZz3gMVnPMqnJPR6ADFYlI6mcyVc/Os43J4YWDjrwBXprub0ZJtsd45kEtvnqOgnyIYVqtQpv4bHpF61hG7HojK90LR6U+0N91w1rzpjaPCQR3Z3m/JyE3GaTsVfDeAPm/5fAHARwClvs5PmM9zi80CY+R3m70uejIMom78F4KeI6BLkMT8K4M+91AO+YqSuw9exu/lmpxcBwLrVpVnMNCFKFQfQ42QurMy6YLt+I+4Qir4CgPlyI0gix16tSL6QIM5rRzFDhDivg1CWz7bbuTBDcbSP9LLTENPVxCqVsi2hCUVcaQ8WXTAbu0oBI9urZalQZFWimkRNZhx4LxxRENtW4IB6I2UnCVB8VNaguVM83G6g6jZRmVAS1SGvW96L7VjjubBXa2ikakaoY5egL5vS/EsXpPmC5GN0UW5diYTYU5tkjQitTQ7IJ6l2Yy+6kiNSC7toE/K+N0cFo2yTDW1WTbnmeh8MTxJADkCh47Q9ZJKw0VrVIHSusIV+KzeaNsYDQgWTL3ivVys09mIkPgR9ytYDbgxqNDwm87wnGvHUr7gFvXl+174ulzugokaxJEaQhnn9Bmr5UhPLT8g+50vCPr6fk0+fBT3P7iVlVaVgLoAQFq1jVXRavtQEVYxkLBdPATYK2c57Qtrpt3Der+wA1/kWcCHpeMZo7NXWaGltyDmqkvHpnm5HmIGqvoEFcYfEIMv+PoA/yMw+V/XPAvgxIvrXEIDAgwB+H7KeP0hE90KUzNdAWJxvtv8YwGPM/JqXMr6DcKN9kIheA+Bh89FTzHzn/MpXgkQRylOrKLvyZCq02PdefCuToxCBpH3iNSTAkakJ8bydsuU0T4BWakUoW5GrsNe4sbcIzRYTdA3VuyoarazmiJBd3MX8tIT6oryWsJ35bd4lFD3YJDpHgJBnemNoenDgxPUGAUzleRoWJvr1Rmohahiufe16mHiUVzbpT7khcGy7UGE8nGK+uiifx4TRcW+uSliLXIEFkyOpmwOCR2pKUnNkGahlcV5+UlboKoswPJ1hZpzW1hajs1F5LNOqNFS5RJgvkPU2pusSllMGicYuSViR3PGqpqcQWOdI/nYvOvJVe+5ekj/ZF+rJhlWAVNxvEMQ5sPdamZveJ2PUEZCNNTREQai1ahAmR7OApTmd1micdYmQct1FQjTPqIo7GUtCXT1wjsR7n3vjI3bhtagKcx72nvKhzX79Uc3IFxI7B9r+I5k5jsBk6CYrKmoM7msHhlfguSl/njmcnoffFtv3zjkmtC/LhZscbQBo2N5IiWdY3p7cuTobIvpxAF8Kye1cAPCdEPRZA8D7SOb5A8z8zcz8GBH9JCTxXwL4Vg/w9W0AfhECfX4vMz92s2Myc0VET5k20M+/2DEfpFNnG8DfBnCGmf8KET1IRA8z88+92IMdyqEcyqHczfIicjYvsB/+2ht8/B9vsf13A/juG3z+8wB+/kUcegnAY0T0+/Agz8z8gmTNB+3U+WEAWp51EcIe8FmjbMrTguMdnfBaPeehW++nxDSEpvUjjhHZi5sBqD3PKLIUK7CsvoBBQOW1tfiiCgCz9WzyToQ6Awan5VJ1rlTimWw7K2/ymjUbpihbURB2yQaMohd6YlHpkrXEYc2NIvRiz5Np7FY21g2EHU3Vql98VkIPeS8J2u/Gozk4SxCN5fu60wA6DZApUqVZjsm9i3b72WJkw1w65olJYkel9NHRsQlTgmuYpYzXmq9YerpE0YpQ9GTuxkcSNHdqzzOQGhu9HqOThPkKo05k/53nCXXDjSMZGwSc8XSKjtR/6H1SdE2LgUjnTf62Nr36lXHtILdpZNt0y/kK6ES/ny3FmBwJwQbxbF+L7edcXUv/vOcZMSMqvRCduT9Sr4ar/eQ19/1SF1TUGJ9u27lhL0RZ9GKUrQiNbddagBEyQfhQ5tTU0fghqKJLQSH14jMeF1tbwCC2+Hm3sl4NAMTTEnUrQZS7zzqXc9ufp+inYSsN4z3WDfehMFmY8gRvqrS2Z+tzTNv3VAAwCiTxvaHbkTtcZ/OZkn/yUn94EGVzPzP/OSL6WgBg5gkRvepnDABqE7rR8JkfKgoStSRhCNdjxlRZT02DqUaEuhGGwvb34lBUjOYfdP9atbz9sCwaS0+XiCrHNDBdEcSVbWZlHuj5isSq45yDuH/elQVC4dBRCXQvcgB3Lr3FChDCSYXhWkU68h50r9dP1YgQ5RwkWJvbBbIrsoJnVyB8Zxo2m5WgWRkQi1JZY3pcAY1tjI66GiV/cdLEuyLg/DYMcu6Gtj8JlavmjYpWhGTu8l/dCwVGJ1IbFpmsRZgvuZqiom8UTaQLmFwni64jWeiDxD27vENrw6CcPDBGY48D8Ejej+1CqCEjHZ/f9ljO24SwvDxNnbprNTwhOR4VheIDLt8TcKt5YaP2pWkQypweb0k7CqO461SognSMynaQm/YbVSphNA3VVSkFz48aJ7V3nyRTx9oRVcDg3swq6s6VClWTbG6QU0KRJGhsGmCACaspUhEIw5HJUB4ozeVUTQlP++wXrc3KGhZ+4XTVIOQ9uq6zp867f/1uS1x1xKtWmPk3XupvD6JsciJqwdhfRHQ/gPmtf/LqkGimFrgUbDYfdae198X32NfJvEYyB5qXDexytYlkWqHwaGOieW2Zcq21593QqmRUCdkK/Jk0EFO58GURWpcjWzndvqoLrBu3D04oWhGKrks0a8Ggryx92HPnShUoEvXmfOoPH9jACWHeduOrMwKVYcM0VTRyghT+TWOgqlB3HSxrcsIh55XWRMff8togWxCDKUDVuhmt8SEOGbFlsXRghiqLkA5LlM3YnisnwM49CjiAUPWYuU4mhLlXAKueEpshFT35rDZrdDy7cctg5SMrumJN9y5Edn9lw3UDLTpiSOh5lm1C+2pl6Wmolt44fgGun8vrek3wMlNMqov8wiW5V0f3uhoq/37MFzK0RnOM73V5mqoZ2TxjnZHUd6lHZGrDfFg3k8cHV6oCZbuv+WKEVGmUYmlCmFivVIAlWnSa92PEObD4qfB+n685hZgO3T0XT0vrHQMCINDj2u0nte2no/d397w885OjaZA/igoGVb4X6dcrhfD+25FXe1toIno7pJnmawFkkFzP+E5Bn78TwC8AOEVE/wXAFwP4hpc82leKxBHq80KQlJ2/hCovEK8JbJhXFtB/cge0Yzi5RmPUD55GPJQHuD2colh3CMBoLkl5XcSpZuS95IYoFvF62CbUZ0sRRg+WoNwgYS7LX3XthcKjdkWggGvKBmd1qfLKTE2I78l0L3oPqVFUCifunZOHL2jQFpMNlVlknbfIxZ430Tq/Fzy0dTMD1XWwEHCSiHcDYHKma0OCgKvXCVBEZnf6kPuFiL41SyUDMVk0GSDFrj1TppaOKqG2MV9n4xrbr41c7QmLwlCosjYciyeaFNfP7e6RjD3PphavSxfcsg3MXjdFdEHiL81NobfR88lGFah20OmyRbYiHjDQYSILc/cbjwFyX6Vee3Afrt46u2s2Mp5JMwNiQvd50YbzpQaqZuRqSxLC5EzfMQJ0ogDQYlm5PVM8mdR2wSA282/EIjVrRT0KDZG2Urf3kXcty5Yr1Cy6QO98bWuWlLLHb/uhNEVywgmSYW6VzH4hFkPMgjtWY/TO5Rjc4/jgJsfcWFob7rdxzrZJoX9utysMelnRaJ8meTcEtfZTAN4C4C8BeOiWvzByEDTa+4joIxCmTwLwN5l58wV+9sqXeQ4YA5vzAlGzAR6a2tXJFNTtgEcORxo9dRY4fgQAUKz3UHST4GEDQg6l1sbcoFpEYVxXc2Aegu03l1h43F2G7oUKeT9sI+0jbsoWYXzCfZcOZL/NbQ62UdGCRFUyjU1Z3ZuPi6KdP3QMVTNyIZ2UUMUuZKh/Y61dicjmnwBgfryPxtVxWJMRRUDsxjM96RXInlZONHnfP19dN2af/8tfnOqYEFeujqbOpCp9YDiwolK8lcm6UdyJIOf8XNzCp9gyVpdtEyZTKPSUQJVDj9UpkA6Bjqk8oNJ0yjRDUiU1OWlqT06OkT7qjBBZPMlZ+5koGvVuVdFo+wWFx4+8fkGtzdrBg4kAcBAu8wlaQYRixXkyRc/do1HFiMaO+sgvWgTEI8qGFUrTfVPh31XDj38iEDb8ZPZrzxDoXixAJWN4xtRPKUrNKLT5IgUFnAvPGiJQD5FXe4zfShmk51sn0fWKxkMmqjFUeIwEN1M06VAUn7JgVBmhc7kIvOY7Ja/2MBoAMPMzRBQbRNsPEdFHIUi4W8qtWJ9fw8xPEtHnm4+UcOs0EZ0CsL2/oc6rSohApu8JveY+4OxFW2vDeQ7ezkFae0MEMooGANKNIYCehTPHuXsAACCZlFbRAGJlRXPngfi90LNrCaZrwNpHPRhoFSouv+hvuiaLo3KRachrctRblBNXyR2VbBK7LvAfXdnG9PXH3Vyw81yo1mZwJlbfiWUfnnWXdxPbxK1xVQYSb3uNtBZcmGx2tIPd+z0ySjMt2jp5shYLS7VHoOgvOEw+JJlQNWO7WKfjGtsPxW7xT4HJccb6h2VfwhgcLhjJ1DE7NzdFYahCni2JIvB53nR+VLKBMxQmx+SYyVCOMb/UQWsGm0dJDDRbw5g+2wEArH1U3ui5l60Ye/clAf1QVLh7IR1WqFqRtbTTkaPGr070UbViZLsyOAVFQNnEixp5Pwkq6X1lo2E4C3Yw133/thpGS+Y1qGSkJleSDoGil7rWEsr558GRJ2teweUeI504xWkNCL+FuXd8pZCpk/i67wAYg8mxOUQFo7FbIhvK70bHkyAc2TvvxjUzLdYDSH8nvmMEnL7cKTTaZ1AmRJQB+BgR/SuIXjiQVr6VZ/O3AXwTpFHOjWSFiB5h5q97UUN9hUjdaQL3iItAZQ2cPIb63AX7PXlFntRsgK9ugo5IMF5DaLZSnSSZqkiZOiHrCQBhcVk6YaQTx4WGyNSM+AlL70GKc8ljKN2LLoR+8R4nwNwYbZoQnazLMZc/WSHdy5HsOpaE0ZtPelQdJn+hCd1GFOSfAATKQq3wdGCK61op4t0J6p5TMPmqe713n+FEM++jObBwzuUlkmnYHC6Z1a7xmlGAsxVV4oyoAsbrOp8xqrZjPJ6tyX423+jF7QcI2gakY5dnITZ5G3P4xh5juuoYrZOxqTsyCL1kzpguO9LG9mXxMvMl49lsR6iabkEer8cBOzZHwOonnNJXY2XgoyDnbsFPx0IQqyHUXslB4WTZjC3whGoxPGYr5n7KpTjWLxj261oUOakhvan5XeeyC7n6Xk2dEsAIGLz9HFDRM2SpWs/STxDP66Bos3fBueuzlXDpqVPx/tUzybVVgIZUlcdNlU5MiCeVvU8VrOMDVzKPQUAVzeikfL/8uCPibGyXmBxNLVeaGDju2bhTLQaYPyuUzddBlMu3Qcg4TwH4Uwf54a1Yn7/J/H3nzbYhol+62XevBpkfFaXRPLeD+sKlQMH4eQgu5AHUJHdpEub+fROV7JAw2h7axJ0Tz1rTYjV9qBY/ORdSwIbjcPJ7rY2PJKjTsBdJc8u12OVEILh+2C2eAcuf9Kr2K8be65dkLNMaRSeyLRLiuXRr9CGis5XEKjfad56aR4l3XWac0xhUmbCL4WbbeUgG3NxhM+bSzF2EZFKjp6EgZhS9xKLyqHAp1KKbYL4QBUWmdeLQY2VHFgUNybUvKVLPjVcYAeR17wJjuuLgvEpzr0ptfFRIR1WhN7dqFF2y6LU6IbSv1RiccTxyANA2AID2BqPoki36BMIK+bVHcsNa7bjU/AU7nTJMLzkAQpFfZ0BrQ9FzMdoblfUafINASElD5Bvt8wz8a6xSdLy8xVYdLLhAGMbMBqVFhUXzGvlCAiwowEMUgj8mHy6v5+QKnyn4C0gotdxXPqCGiNLN6H0ST0JABBMFecfhqQhReQOD20zJ9mszW3ybzDnwejgmgPnO9bHx5LMA+vxmAP+DmQcA/umL+eFBijpTAH8NwB8wH/06gB9k5oKZv/xFDvQVI5wQmh6nGCXhVHDlrKJoeREAMFttBds41BJjuuZ+r33Vs7NSxzB/YN0qGbXo9LfNTdESkdfqGPBQWuaQmviPSlNLYhapeUehqrKd0qrr/pJxgdFpN+7JWmKSsQa+PWdZQLxF0SfN1IXa7xXfPrvnoM3zUpqYGUWr3Q19BdHcKm2w2reudR6yQWkt4tlKYudmZJit1VNoDNh6bADQMKE433ugGki83kS9C96JMUs/mn3IfV2gl5+qMT7quqAWXUIdk61JYjJs23rtzPEt0zFJGFChyckMGB0jLD1t+tNo+MugqqisUbUSLD4jq95sNZPjmfxVNhSFaJGCGm5rO/ocOxeD69kJGhtjFMstOy/x1MHAuSFK16f1L5uuDsWHYwNAPK0CIs7RSeNB+cjIbhx46L4oFNsyYBSioDTsFee4LqFB7EJvra3aggbkuJWdMzumE258zW1G3g/ry7IBW+Uaz4CO12Oq6LrrrtcnYIW+Q1LXr3pl88cA/Bsiej+EVfoXmPlAzTQPgkb7fgg//L8377/OfPb/fgkDfcUI1YzZPUv2fWPDwzxUFSiKQA2Xd5k9dCSgr/HzCkVH8g5+vLn96GUUp8TEjuYVZktuRQza5yqqx9zXc2Mp6uIxaUVIvaZT7Y0S07XEFm5Ghfzrfsot4n5PlaKXIpmxLYzU5LdPwbHf2Fp6prTW/nQ1RmNQo/u817VxmgszNoC6L4uZ0pt0L5UYHU+sVVilkg+K9lF+aA4g78RIR1VAe6I5rWwUMgXbJmINXRQ06ey28ZPOVEktiqLFOldE0bh6CoHiqvU8WxJ+LV3E47l4lOprxXNG92JtkYSAKBq/lsNvEBbljNZW2GivueXyLOmsQtWIbOinymSxV48LHIIB1GvwSVFdkSIH89q6JO5Zum3g+mtt8SI8+vx4VjlqpszlxQABQwSh3VaMyZrL4yjztm+k+Dkgad0QW1bp5naFvBcFEPvGHlvCWKmlImdYDOsAjVdlwvnXO+fuQz8vqq0GNN/lX1dAFHNjj61h4F8zG3pjtu99D/EONdcEg171YTRm/kbjgHwVgK8F8H1E9D5mfkF9cBBl8wXM/Cbv/a8S0SMvcayvHPEeksalAXDyCHDR4R+p0UB1//Eb/NA03fJizlVGSCZsk6wAMPz845bxeHK0ifFxL48wZBu/1lCHPvRlS3IC+qD0z1WIp5W1xtWD6p9zPVo4CinU/Z4qGqZIzaKVTuTBD0/IvayakVkolJuK0fZaGCRX9wIAQNFvCIjAW5j6z+fWwi56CahgO466ESHK6wDSuvuA1yBs5sJbMnfegmOUjKPVB/IFRpY7T6DouBh7OpFFqnNVPQ+BkVeL7tx8oUog4wop5iSM18e54dDa8NFgbBfUsmmYp41nVaemO6gusGbYmkfRGhL16qiSxLsdT8n7CkgZ2W6BzDnk1sIvehnieeXCYKaFdtnN7DGLhdTeb3FeB+3Fs1EtysBLkvte0vC07MfWPCUUXPPx0SQoIKV9t9hsOUbDg7ersaPHiHNC7oX08m6EOHfKtHPZQPRXnCfj97LhiJCOrjecfNm7J7ItN6h2Oan4BtxnvuK8k/JZAEYDMxdE9H9DTqcF4E/gAM7HQZRNRUT3M/OnAICI7oP0sX51C7mCwGiti/Rjz4IyLVZJkb/uFOKJQfYsNFA1IrvwzJbEwvMr98dHI4xOyv765yrUMWFyVMxyv0YltXQwJsfRjFF04wBxRpULHWWDMlgUtM5iP1uuhlbigjE66m1vYLf7+5jYYzGAmm2IZ3hKVoyOabbW2syRXdi121dLXVBdo1hwLkc8q4OYvL9IAVINrspGPKw4YCxOPW/ED+toPZEi2LrnZB+DB03cfkboPxui8IAQTdY/5xR1ZPoOKQO2WrK+BV2nYVhOKGKM52NqRppmsU+mNYYnvYT0UO4JXZA5RtASgUqBG5cmmZ53Q4h7NigDtgZt8qZdTtMRkFzYBIzHXXedZV8nDXCc2G0BoOxmtivsfsi6etp+wj+Z1LYxXedyEYTNes/LYr/7oFvsBw94+aY9oHMZHvUODOuzvG/s1hZ1B0gLDKr2wa9vEIJTg2C+lAYItNlijKhwLBOzJQqaDFYNQjpxXqfC3f1nNp3sO553n7ws8lkAECCir4Kw/n8pJKXyfwD4swf57UGUzd8F8GtE9CzksTkD4BtfykAP5VAO5VDuann1uzZ/CZKr+aumzfSB5ZbKxvQveBOk/4HfYuCzgq5GE45MhPzz7refEzOqRozKWIXJuEDVyDBfDDH+GtedrEYWIQUAe2dipFMXv49yRucSB7+tmiHqxnYsVGJOI4qw8V39qGRke4W3jTPF814s/eW1RXVMaOxWtjePHMO1wGUCEFOQV+h4LaSzywPUvSbYIPWq9g1uGc9Yq7IIiPweMyas4yHA/ERuVAoIwrfw1aOZrpt9NvXcyXo1ANB9XlpUK7VPlcl+NC+1+LTzagBH6Nn02iFXzQg3YxCx4TJvH40B23DRbCkGcdh+uXm1dGG4SMI1GrrS0KvPwdXadCfe/ORVcK9tj1ca71HnLr06RLXu8oyTU217D1qL3QwuJtduWiVf8Apv9ZS8voG158koNDqeekCZirH8pDz607UUgwcI6Z7sqGtYG/Q+rhqEOnHeJvUJzR0OCiWl3YXbHnCgB40iaCO/qAzRYe2rBYpeYj3BzhVBWep+1GtRsEUyE4CAZUbAvtypD5zT+hqFmN/B4s5Xu2dzE7ZpK0T0u8z8hTf67pbKxvQv+Fpm/jcAPn4bY3zFSZ06mGUA0QQABprP76FckdxE2UklPq2h/0qe6cmqV9nfBmKD3ErNAxz0J+HwM7+Gwe/8WLYJZTu2aC6NfWtFNxA+GHUSIR1WKL36ArDf3E1COzZGvq/ojlPCfDG24bl0BHQ/tWf7ztSmgyZnTtFWjThA6vgPo4ZCNAxIFcsi4IcvPLQYRwDI0fMUHQoW/6LHqNYlhLO3DkSbmQ2dKfRYlZLMk3s9XYmDuVKDwA/dlA1H+1I2Cck0ZFUGEQpdsKaCstNmalKV78JskQkLVbafTriwUC2M1e09r9dPWSM9J6jFelVii9rSO5lWiOYVyo5oJ1rrgaoauw87lgCd585GCSpdOLRqRsG5VxkhyjlgHM9GtQMGGFqk1rZOlizG82WPF89rhT5fiLD4ZDhXQREkAa1t16m0TgmT9ThgwPZh15ag1jblAxaedblChcdT4Yf9Knsti06Eou3QdJon8+HPfk61aEdWUSvgQxu1dTbKwAO5U0ScjM8KNNoLyY35g3CwMNpvE9G7Ia6T37/gI3dgYJ8xiXIOFnCOyFVqb8hqmG7K6U7Xl1G2opC9txNZXq+pIRcozOLXvSw3up+H0QW06Ea2E6OMow4eCNfS2Ptpve81Oc+oTgicOBRPPKsRFxwQJgYV+YZixM/btDY86/qy0QSFLoiibErPowkWa6No9LPhEaU78ZPH4QPW2NuH8GG3TTYExh4uo848j25TxjK43yzuc2k/HXvW+eLTXmW4Qbj5vGtKZQMIQ7bPTUaVgABU8ZUtk2tiHbexor32wWUjCpL6Ow+6eWoqk7bntbU2Zha1mIxy1M0U09cek+3mFaarmb1XylaEbI8CmG/VCNtSd66ayv+IEFU1Yp9an+ABAmR7zb1UrRhVShackI5r1DEFCnJ/q3PAKYUqBRB5yp0kN2kLJRX15d9nnqJR7j5lV4hyubf9PEredzB48XYjSynjKxpArl025CAvFJUOtJNMhIpHn0mq3Tk1dmvMlmKLsKtjKS5Vg+qO5XAYt0YwfHbITSfrIMrmc83f79q3wz90GwP6jAsx7EPNRMi2nEmsbYsHn+soaooOWUuMqjCZ2bxmblqvFqX7vFsBJ8eaQfGanwRWVJavXJqbpS3AUwtMwxpiHdcBzJpKRmSrnSXckFgutBnmyw3AO/58ybVa7lwqkG27qKj1aDzEWdlvWt6uOo0CUlDdj4/sqhrOqi9bpmOmmY6yK/+0VXIycR4KAOy8qUTvqCi80bk+0uPuukSDhjmm237xk97iZLuRyt/2tRD+PzqRIO86aLVSyPvIKard50WbAlp+VSqBl+ndB9py2PLUkUELmqlffHKIOnPIvdm6zLHS9lMZC9uyMXqy3QockSggs7/Zolfns1vfkFIGcHVWGpLjBAFAoHltjryfWk9IFU1jxxBg9hLxepyDLGP1uMZ8L1LvXzWMLBHsTQojVcnEXkC+MXD3tXqLtr3EZshGUDUjw0ptlIcFJvjjc9dfARGB4ebXJA1cXyQl1I3ymwz+NuSzgRvtpcpBiDjf+ekYyKdbmMIY9fxIB81PuVqb2f2rwfb+omNvWM9IWfrkDMmuqwEY3e8IGcVClN83N6YoFhpWyfi5EkBi0X6ldzwXJly70JkHoWm4zmbrrSCklXclbr30hHhl+WJDxlv5xXtOs6WDApxESDxGgLqZoeo41FEQy29GiIoQiZfMGIPTbp+je2vLXq1hFq3qj3JpKqZeIQiYHmHUHdPe+KiLsXG/RD7K0DzrxpLtAisXrkesARLKHNwTo301tOZVmjs1qIwsfY1ayXNTaFhnjvFABiDK1PdkonltvQFVGqpkiIXlWe+VskVYeNpTlltDUK8lbMs65mZkWzVHFSPdc6EijoVtIEAzevdhPK8tNx4xQCCLcuSIEOXOOo9njHRnbnNuo9MyCbNFrWli9M46AymqGMNTWdjCvEHuugHB/T9blr+9857y75IQ0MPlbkZaAsDhwt/Yq8WIU448djk4ezgvTKeKIvWKhHW+dNu8n4T9oyj01lRRF+0ImRfatDmzl4GI87MAIPBCclPX7SAMAiuQNgPvgEzVbwH4LmbeumPD+wyJLhqcENJRifykS77mXjdKjinoaZH35bv2VfckZJf2UJtmVNxMgp4xzfMD1D1jlXcyUMk2T6TKRj2DfEHoabSYTy03TbRTyWhuTJEvy2Ih3Ssrl3w2HsXgfhfXb3pJ6DqLsPzxgX0fTQzHWcNlrWtP0RQLGYp9Dx3VLgxHNSzPGSCKRulbALFc50th4SXguiCOT5iQWE/GOLzglPTSoyGIAgiL8abLkdTGmLWbGGhf5eBaFR0HAFCl0vUAENPlEKww9jpjLpxVC9eD+I5KCxzRhLvORZxzYJQsfWIP0Y7DYc8eWHNjUvEezVj78HiULUU3CpgC6sQtwBqmAwAwkMxcaKlqRig6se2aWmcRyoUMkyNybesYmC+4/RZtwszrHTM24VCfJslvl1A15JjTldDT0fdVU7xXvV51IuzcxYIZ+yYsXYyOH9jnebDLs8X76nKcUvHANAtu3uaLEZrbzluRPkfXKxpAiqA5osBz8q+Rr+xvT+5cUScRvRfAVwPYYOY3mM+WIemOewCcBfBnmXnHNLv8XgB/FMAEwDdoGoSIvh7APza7/efM/CMvcNy/DuA/M/POTTa5KVfmQcJoPwHg/XBka3/BnNCXHeC3L0qI6CyAIaSOp2Tmt+z7/oaTRkRnAPw0pDwxBfDvmPkHbnms2rvhSkbZSeyiGhch8iWZVqhTsotOc7O0ldgA0Dy7A26m9sYvmyacct4t6ra/SyT8aJZAcMZBnFxrPPRhmi1GaO7WgfJyXS49WhobdghDAQAwPu4USdcUk0ZD96Rr8h8QRZMvuO3LxvUPx2wpxlTWTbQ2DS+XMQwXn4iQ98KwoB8qyfZCYsx4ZhK8O25Va12RH89WZXtdXOtEkvmad6kTsX79nIiPNtrvNfafL1B044COxx9bblBsszX9G6OxDax+woAnhkVQVBjPaoxOpgGvVjao0NgQBaOKZvSmo/b7sklBzqW1VQUcZpxQ0FLC70ukSlXJUPN+7LqIQhbF4Sm596JC7om8L5M9XSZkI+eh7+f9qlNgdDy2zc40z6XKuDFwHgcg1zvvkS1gtfNnhj49wpgecS0wsl1YRQOY0KpXwOu3l1YpvLYTU0PG2tzWxL/k/fQaz5YjZAO2/XPknAgt45WWnTgIfwYehoIMGuFvbX3UnaoqZIDvHEDghyG9ZX7U++zbAfwKM38PEX27ef8PINX+D5p/b4MwwLzNKKfvhPSlYQAfJqKfvYUiAYAjAD5o2s68F8AvMrvgIDM/erMfHkTZHGPmf+a9/+dE9OcO8LuXKu+8Rb+cG04ahOb6C5l5TkRdAI+aSbt0s4NwBIyPuS6VwYJfy6KmMVuqGHHFFpJcdhK0P+YYossz64iHMxTLRgmQgS9rxKCRovB6b1TNyMaiQRQohoap7t99wFmZk7UIMOGi/vkyLABtam7Hjb+9EeYqAKdkGhcH7iQBcDNF3fGLA8MQgl2w1aJNySoanZt07DyqqiHhK/KGEO0DyseT68ky25e80J5X5Olbo2VTqvt972blydImpeuYDBpMEUpS2NfwoM55l+w+Xc8T+U4VZGPbbo7+2dp6uVuvT7H0dBnkaahiL6RTI5mUqEyx5fB+0Q6ld22odug3BYoE7SRasVUI01Whzxkfd15k/yw7WHvk2opTyZitJDYMOFknzFbIIv+iShZyfzH2Wx6oku49J3GyuhljvpSie1nGONV2ykEC3inoqBKlr+G0xrbJ5WkUMQLaV4ThAXA5O/++DcABRuH6eZXmdu3aTWQAyCnjKJf7o/+83HizpdgqGsDQ5xhqJJXEY8SuGjHQUC8oVAh3lJDzTmENmN9PRPfs+/hdkGJLAPgRSNHlPzCf/6hRCh8gokUiOma2fR8zbwMAEb0PwFcC+PFbHPcfE9E/AfDlkHrLdxPRTwL4j1r4fzM5iLL5JSL6GgA/ad7/aQC/eLONiehnD7DPbWb+hgNst19uOGnMfNnbpoED9FeoM4e8mbRDSKbGdTUkwV4TJwBof1SKCsp7BEAwW2sAaw27eKWjCs0Le3b7fLllCTbzXhI0CgMLNLh9xZnnZTcOKFuCuHkzbCWczBjgGysY/W3ncmnbHwAA5V5YrdVA1XS3QdlOxALzm735w43EIlW0kVq6VnlyaAnOTWRydsR4B3vGK/FqTfL1Aslz8kHXi/k3dypM1mPM+3odgKIN9J83+5rUAapNFaMPx61jsjkVwPGrAY5poHdOjjk+RsHYFVU4PC37bW8Il5eyMezvkVI1CFUjtaFP/Uz3WbaEi02VjAJF1IstW1FwbxRtUVTam0iYlUsXtvP57UzNit4X6v34c1G0XcJfPWE1Qsq2hNyUNaBOI8wXXN2KJuxVIVcNsooGcLD//lnPOPDORXMmep9M12Bza4AoPYWUy/YmvGrmLhsy8m7kFJLZdedyWAek0jsrnvt0XSZAm6j53HkqVSO2NXSAeKf5Qhzki+6cHNizWSWiD3nv38PM73mB3xzx1sIrEC8EAE4AOO9td8F8drPPbynMzER0xRyjBLAE4L8ZjrS/f7PfHUTZ/BUAfwvAfzLvYwBjIvqr5rj9fdu/FrfmySEA33eT7xii3BjCLL1/cm82OZdNQ7f/AeABAH/vRl4NEX0TpEcP0t6SRTXFhVjB7avK9uosSQBAxWhcHICmxkRvNoCZoHlkO0HF+FbT7KSLGfjdFAUF5264/jNielLhNZk66qY0KsKFWcNkfmFitpvbfiLuh2aBZkYyKZFddiE97jRtZ01NGCuUmlNC2YgCssnSRe1szH7qsS/7PWEAYHTaQ6atFaBBgsjwl1UtRtWqkQwNQGKlQPNiahehdFxbZb93r8mJmAWTSqdoAENZUjoC1JnmD7yx+GGZ4SlCMnFKpmyLotFFu39WesDofGuOp29yN2U7Cmh/lN9OvUDtsOnnOXzEliWv9MSvt6JKOOSUWkWII9222aBGnNeISmOB+3UvSwITtgWlsYTM1HOZL0ToXAmh/r1PDVA3DW3NsEC+3LDM0sPT6vUbsEMzrBsqm0D7mpvbOhFF6ivAOiarAOqEULRdAS7H+xin1Vtntz9EQKRgiwYZiigzVyyQ5aBGzgttKxu0fx+3r7nz9/v8aIGz3//Gb789PnKQZfKAcnDPZnN/GuFFHUYUwh2HIxDR34SwCGxCqGr+nuFKiwA8DeClKxtm7r3QNvvkHzHzb9xqAyL6pzf56h3MfJGI1gG8j4ieZOb3H+SgzHwewBuJ6DiAnyGi/8bMV/dt8x4A7wGA1rFTrIto/ymzmGgfmrEkDPUmbJ3fE1/JJNEnD0iswEcCKekmAFStJKjYrxIv4Wusb1VM06MtZHslEqNsOEts7xdALM46Desf2tcq19d+N0e+6JMTxqKIPCsv3Rq7vIx2I9XeO1qv4y16VZMs6mm6QsgX3RxOT8kCu/CoU27ZwB1rdMIolTXTwfGSjE1DY5OTFeLlOdiEW2hLNMnqx6+vj4lKyVM0vaAq7QvjAC4ElI4BECxfWTbkkIZ/VxRJ77y3kByNrIVtW0YoS/TVUnrYXPYYr72iwny5YfZh0Gi1WORqrCgEWg0DrW+yhZSZXFu14nPD0dbz0HYBj10tnnFmKPDjnO3109xK0+vyOfPAD53LJeo0svT5AECzErExhPLVDuJJhZ3XyDllA6lZ0ZwdVWFotL2hSsgpeI6cN1N0CFR6Xm7iyGMBoH1ljnwps0n9OhFvy58LH0KvuTV/+RwdF549AFg4F3r2yZyDkpb2tQrNTfeMRrMK8yOCUkkHhWmyp6zaYWDEN+xuW15eNNpVjfSYMJkyC1+ENDlTOWk+uwgXdtPPf/0FjrEM4E/u79LMzDURffWtfnjHsX3M/JP+eyJqv9A23ucXzd8NSML/rfs2udmk+fu4BOBRAF/yYsd+KIdyKIfysglDQgMH+ffS5GcBfL15/fUA/i/v879EIm8HsGfCbb8I4MuJaImIliB5mJumSACAmb+Tmc8R0ToRndZ/5rsnbvXbO+gfhkJEXwRxs7oAThPRmyDkbd9yk+07ACJmHprXX46wkBSQSfs2IvoJCDBgz2jxkwC2mHn6/7D333G2ZVl5IPit466/YV8879NWZmWWN1RRGAkJ1xQtYYSQAdFSa0YGmelBZqahhXoktQwCaUYICSEQIECMSkVLUJgSVQVUUSYrs0z6fN7GCx/X32N2/7H22nvteCYj33uVZFbm/v3iFxH33nPOPufuvey3vmUf2nsB/MiLzbGxakMQMxEa1wo0LrKPPz7QQeOZq/6DWQaTxejfz+Z4VLJXU1vnGIWJI0znMocME69G4sD1jdJ1FqxLRbdy46msXF1LPJKOluI2scWn2+ImfW/FTea5FkLHnIt65KzxeGvokXAAypbEsHc0i1PrW0OH195oPaHD1rofJug8nTqvrrbJv8WjqW0BtS2CucT3M1raweq8HGNS1GFifg6Ln40AmAA5lqvCQelSyefm+5CC2pFtrNW8pkIiBLQv8f+9IzHicYiMqxJg/UGLsNrg3MHIgi/aF/gampuueXXiwp5k815Fh+8tyg36h71XGeUGycQX1EZTg3RUhR0zlacif0voiyrYvkh+vhp2LWiq8bz3KnuKZy4d+qLLosGe38xp9jCNrYpP7dqhsgKSyN3baG+K0XzkUGfcotXndopG2Opc5iivmch7+gDQXC6RtyMHdkiHBt0n14DCtlc4No94XCIWz8XOUc5b1IHpDNBUsYkqpeDZ6FymbjPOvyuAgPZV4T+0z1PtO/H+81YS5HtkLUoRt26NcKfD3KVTEdF/BHsli0R0EYwq+4cAfomIvg/AOXg25l8FI3hfAKN4vxcAjDHrRPTDAD5tP/f3BCxwi+v+DwD+GYADYM/pKICnATz0YnO+qbIhouPGmDMvdoJbjB8B8EfBCgLGmM8R0ftu8fm9AD7A6GYkAH7eGPMhIvqL9vgfx00eGjhP9E9tjJIA/BNjzBduNbl4DHTPWeqOLELz+VWYhm1l/Iln2ds9xDQi/fvngqr9KgFqqgisaCccO7cx4+GeGHGOIBwmDAXj/W2bCLYw60mFeExIt9nFH++xrr0tVsvbcdCrhgqDvJu4zTO0/W0aqkdNY1kxApQGIArqaEwSB+EIXbwmwq22IedowRweo/37qkup8bmI8QIAUEDrbyIvjGrr/L/AnScLfC+sZOw1VSJ2PE9BMWZNhehEiG8f8WGryaxXtHkH2PPZEr0jUgcDYMbX+ER5CLsez3OYp6Wye5zk92wN7U+qN2uWumeG8679g1lAxVPbImBiAgWhkYNRYYIeMBL+0wAGrTxAHPKTxPlkJsNk1gvZeML8Y27uKTkgyvzT/FAlPFrFhNr61K1hiiJEAMb7GB5WWy9QxWlQ9KjzHS73JTmVjHMolc0Ncn7IuL5JQMgM0bw6Qb7UcbB6WXOOlywOUYYT+93katnpdSFrRn+fusYpb0dIB5XbJ8mwQllPXBh2OpNhqFpx7Gy0BgCrX6kW5v8fd2fcpTqbWxBi/qEbfNYA+Es3Oc+/A0OYdzv+PoB3AfgtY8ybiehrAPyp3Rx4K8/mlwG8lYg+bIy57gZ2M4wxFyhswXvT4Kcx5jSYYXrn6z+u/r7hQzPG/CaAR17K3Kg0qL1wTU6AanUdlNqE/0wXMAbDo5zkj8cViqYiNiRguJSCS3pYqcTj0vGHta4WDEO9xjBSykv0LQw2HlcwMTkrVRKcmw9waiwdck2N2ySDkilPrCKTfIBs1qxfBQKicXWMeNuXeYuSKbselqQZp4tGFJKCNoHOmSHGe3gXH/idEqAUW8ftB4zQ0dhHUVkIrYI3a6EhaLG8LZ4MwcQ7EGuquFALlKbl/dJJ/u0jSSDEahtA/4hcGFh5U4zKNllLhhQUk0a51Ifw/y2L29EMAUJlAwBzHz3HOS5LX4QaMLnXUxhtneB7aVsUfDI0qOKAGSgYVULcT0clotceTHfUHcEBlpKRhT87wkgKrPnOxQKNy/xdrz3KNyVkkgyvV8nxq2NsnWy4PBcL1CYa65pZwtcACUhCAAJVGhoF8ZSRbw72PayQDEpX6Jq3KICt5y15PUTq+WtbtKHKEMcTlUczofKqr7GiEcDAcA+hueKRpFVCmMzE7v3O+QrJQBVhb+fItvl/YVMQcEM8Ma6olS+Ouzbufsr+ZR+5MWaNiCIiiowxv01E/3w3B95K2URE9HcA3EdEf2Pnm8aYf/Yi575gQ2nGthH9frC79YoY0Th3ZJNmaIWzVCM3LLW7WIEGaJ33sKLePS2Q8YiVxNKwa0spKoD+McaGBlxo7Rh5k1DaBP1gb4rmSmgRauEvykisdxHSWmAlA0WnYkM9NCncb81zVtbjwJOZzHJdhw41XXtrG7OnrGWcEdJegcXP2/BeI8baQzuQb5UXBPHEJpNVaAgA5p/0H9eU/FJgKYKpdVXdS25QNCOH8gKAtG8cr1Ztg1/rnOXfq+8okG7EDg02zYwrKgQ8RY4oGXc9CbFMKtQnFRpPKm9mMoXZ54uCNk/UMLDgUPfM7D3u7PwZT03AryXfq2anGC8BqUVoCVJLo/9CehVg76cGrtJfFA3gPVvxFEaLMRqrJWobVqAeriOeGgz2hx6l1KkUdQq8yNapCYpW4hrLxRMOY4myosogG4Tos7IRtlMOKGAAjBfDddM/SI4zbzq7gz2gYqOlavv/YbgdBQAMDgGti17ZaGQcwAZMMvKMDqM9CYrDqrj5sp+bwLolmrB5MsPgAJBd4JPPPWsQZMNvdxh8qQECL8fYtLWMHwPwc0R0DYqg+VbjVsrmT4DbfSYAXioiDQD+Irja/yA4if8buIkr9wcyqgpm2ycTKIpAcxauPJnAzLRRX+E8RdlIkM95z6DzwgAmiRD3+f18sYXJXOoE5qQbBXmPaFq5RS+EgD3b1TPKeeOKRZn1wtqR4T7+fKwEWVR6Kvn6ygQmIme1RVusFCUkaLIEZdNvsnhUoGj43jzjOQLmyAnOeAynaACgeWYLVT3DxHo620dTbr8r9RapFwCAR4iFFO0IKt1vVMGv61V0qMlEhM4lW6g3G6N5rUDTOqSjPcx9tfoOFdKcUQWyy7y8s005F//WCk2js7KzK5xTKNRkM//srn0F8+XlMzy/xhVCbSNklQ7QclIULMpoVIEqg4m1/lfeEiGfK5HbWqTuxwj9g5En8gR7KlJ0uvdTvKfbz/qweu8NXhEO9oX0PqPF2LWs3rzX5ofUrdXX4Io8u2dLxJPKeeOAZb2wLs60GyOeGNV5M0fRjAN28YkKu8VTzjvqXEhR99D9jTcXaJ5NAk9GPysTcQ5K1pkoYh0qq2+WqFYV6rNGjmkAACZdcjVaYuCIJyf7R4bmWGusVVj83AjbJ67DNt3huKPk/ytlvB/cBemvg9lkZnB9bv2G46bKxhjzLIB/RESfN8b82kuZjW269qPGmO9+Kce9rMOYoN4FAMz6Jv8uS5j98+717NnLQByjvMpSLj7GgLhiic35aFphPBu5Qsisz7FiCRMkAwm7seVJFTD3vORkwkXvoKBWxmlLUg8NtabKIBr4/0XRAKwoi0bsGHCncxn3KVGV5CYG5p/V+aUxorHl1KpLiwGbjF+pMNjn51zbMpiqDVRJuYSSe5q8sayxB6AL/uobZUAzIzUXTJuvAQIlFxN2/clX31GAUpXQf87feyH0cOprbl+u3DPPrEGQnV1BMGxuBnmB8ohvlpMNvJLxc1fFwDmH0WQdiGecW4+mtlXgzB+PIK7Q8Xsu48zz+7HvY/58s6fKoL9K61rhAKwbD7Lwm/+iut8L3uOurSVYf6hhj7VhMV0jlRiUdse3LxCqmJuOAUDzss0pLvHx2cYU07nMGQ0m5pCZY9FoxjDkQ5zTTowqpqAYuawRqpLvbdolrL5VVycTxnsrdJ+zhbh1hlMLt5owIQTAlW2fg0kHFTNk2/Vh4lDRFA2GaktvKU1gKkOzYBvyXHeti3yQjjbctfEq9mysXP+vlpy5ArMU7HrsBo32cSL6ZwAkuf9RMGph62YH2KZrR4koM8ZMb/a5P9BBBER2cZYlqNnw4TQA9MRzqKYWQBARKI4dlKR44TTih+93n117Y5ORR3ZxTroRar3KJYfLLA04rlwHSLAno7sh9g+mHNe1izIqjCMMBXxSWcJlVRYjXR24WD3SBIPjXYeUkzzSZMF7ZttHvbAmA3TOlq6eQsKFgxMz7jNVGgXz17F4ybdooRAVcA3EalshDUk6MBgtRkG/m4A6XsXxqeKCTREoUuTZO6k8CaVosgsZigYwPcz3nl3KMNmbo3vaL/N44klQnRVPkljKgdgLrOm9DBAZHGKvbrAkSsTPVyO0ooItf+MSDdzvRsbWsQxAgeP3MNJx7f88hC58Z1aqTJCUr1KgdzAJQlN5m7D8LnYH2pcqtM6xt1M2Ewz31xx3WJUQBvvJ1cM0r3FR6eZJnrwh9iIcN1ktwdYJnzyqsoiZk4WnzRg0VnNnNAHsZbgOsyWH1SoVetbotDIjNC/EQbGFzqc1VrguRoAn4pFklqstGRvXWRdghZ4MK2wftcZQnYtGXasBAkwKkEJC1tZVjVHl6YrkHtoqVD484N11zWxwx+NLoL9ermHlekVEM7eS/zcbu1E2/w5ctyIwuj8N4KcA/LEXOe40uPHaryBsuvZiuZ6XZxgD6ni+DdNpgZpsBtJkgnJlDVFmQ1FFjmo6RXKSs+SmWcPaW+dcXiKeGMAw0yzAFpWEyQCAKo/WoiJs+yyCT2hCWtdKVAk5oVPUCe2LuRO42eoAVSN1bM3RkC3gkQUzVBnh4rcXkF1N11Ic/Fi4wtsXQ5yG7oNy/htYkGmE1lh1W6hSoH3RE5W6anyV5G4th0WT6TAML9W2PCornhrEI2Wxqm6K01lenoP9/DsZAxsPKeWyESG65j2Z0Un7TFILUT42wuJHGhDNLV6Io0LZGoZhMyKU++cdGu3a2xr28/x2VO5IYA+BzeMx5l4IObiGSyonM0cuBDSZB1pnEmx99hDfDzhkI6NKiGlZlGIv6ggavEUlMPeszRUOSkwW+cGLwNR9jmZfKFGq8Ozl9/jtnnf5GSUj+9r+BLXtyiHpXDdSpUx7hzNn+OxkQzDEz08Le200yX05Fmirs6biRNp7rlsu+agAhvvhiD6TIfOiTbv+HoZ7PHiif9Qqpb6d3wp/bwL+oIJbDtQ2+X9RUgB7VUuf6aN/2LuBOqyW77+effy2hsGXQxitD+ALlkdNy/W/+mIH7kbZnDTG/HH1//9GRE/s4rhT9ieCz/m8cpzILAXatnnVvg7SZUVf22wgnp9FteXNIlE0ALD+JlY0YoVP20BU+ZwAapah2MqgZOjXWFuQT7IpbahoosJp007kNnlzlRd9uuWr2ONr2zBduzHy0ikaALjwdRGwmTl6GIARQNqqg6JMl+S7JteMp17BiNAQ4SDcVzqvVATwU/bsXPjGMONw6yofV9YpEGCTmQiYidA5581ciaf3DvImL1ROqHnBX9jE7GWM7vHHmpwEJIi5jzQCoRiPK0S5Qf2sKiXIfX6qPMQ3feq7xXovEHenaH+86e5F32ttnRFhGnAxno1c6E9oZ+RZZNsWaq1zRkqoiQDUtP16SD3O5gm+3uIXSqdk4kmF5tUphvtsvdbEOEUDAFvHErQuAZsP+uvNfyF2IUZBnYnBJKFA8U7yDiHR8PaYUKVAFXtU5HiOPGOD/SXKlOlm/J6Q9SP5vqIJzD1jsH1URQCu+rbXeSsCGX/vJgaGx1SSvx/DRL4ux0RhHnC0mCAZGWze65WMXsMbD/iWHNvHCfs+WWJkywrEu7ob48sAjfaf7c9LHrtRNiMieq8x5ncBgIjeA04Qvdh4yhjzn/QLRPTttzHHL8kwSYzpXgsXjQn53jaq1GMra5/ecH/HJ46h9/CiazSVt7kWRMg76xvcq0OLiIZKA7R2kGRS4T0DYaOVcMZkJmY6GrsR6hf7oLIEjYSeN0G10HFcaqOjM9i8J8XW/faEbb7W3O/5TVVb9wLVJAyfldzQxKem/GfUjQxO8Pm6T6u20FGY8ynq3iJ196gM351hw8Fej1oSYbJ9XPXQ0f1bUqiktMFo0YMZpnN8js7nbbvoB3Ms/W4CWdYSctT8WfXTivvGQZql3TQLnLjrl3f3o97alUS99vquvTVBzd57PLG1PXYliEek4dyN1dK3I7ZoLekxk4yZv02HWgZHFdBkdorG5xqoW13ZP5g6FFrRioOGdlJrVFq9mYw5bHXylxXha8u4dTCeJQCemqhxTRiarcexbrjd+E16uwysNyeAj9R6Ii4ETPz9ydg+TgCFnVaTkUHXwr4cm7PyiDfuSdxaGBy03+22RWkaYO4pXl/u8/dHrnlcfdV22xW7IQ7Dodp7LBsGl746Qb7AF0tXd6Av72S8ipWNzdl8z+021NyNsvmLAH6GiMR83oCnRLjV+NsA/tMuXvsDGYZsFTVY8eSdxC2EeFIhf8R7MuNFTqpLUjueck2Fyy8Yw6EjEk/H1l9YeCUZj0xym89ILLoCYvIV0DaX0X7GKzvE5Bq7RXnlSAYBYPOeBNEUOPBR/j8qY0y6kfNEapuVs9AAhYyy6La5Z5nccPMej44rFJtv+xSfX8JleZOQt7yVLZa+eEK1dduoSudpVFFq76Cvw5ARFXAVWEU9rCWJJ2EPluY1g6nNE03ngMayP8+BD3PLZVGWdQv7rT2vytBzpfglz6WAIte+cQzYpHZyhs1uUTIzZ2z4yiqv5bd5RQOwlR5PQ2VtIv+MBocNsm1vTg/21ew8+Xy9I1GgpF34btZrq2Ti8yw6lwMggDWLR6LBGfPPjFzItGj53I2MgAPPMj+IgpAaHO+12Y6tkuu7yjxyQaMxNb1kzLlHXYPVPWPcOqXS7htJn7Ws59EWpUMY7jOo6jYEOiFEZRxA6qvUM1HLGtJGUNHwr5f6byH3VM9eFA0AFHtfmWnnl3t8yXM2xpjPAXiUiLr2/+1bfZ6IvgFc5X+QiH5MvdUF01G/IgYZg3xGu9TkrOx4XHGveEvzYmKCQehOZz3fEz2aVDAKAppthwlSnZMgwyzQonSmsymiwjjG6fqlbZgscT1mokmOfMFb18P9dUR55UI3S5/l8NrGfb4wbdolJ6i2jzHRpBbg/UOEaGKFxHKFwd7IUcokY1Y2mmKmsWoc6miwLwqK+yShq4s6559WpKQWxTa0EFyxLFMlBE3kQ5JFM2RKBoD+YStwpgyHlQZfs89yUl6Eru9g6SdYO6UUjXgyUk+VpkCjjq237LXHGzS+0HDKJRmx55eqCGuivKTamqUuEu/BKmRtja/pUuPIYOXtQP2qDT1th2i2gx/po3e86aDhJgY6p2PkHVZ6tQ2gqfJhWlGIJ6OT8pESnotfGGG4z4NE8iaHtSTUt/N8kmcqFQN6PPXKIR0a9prsrQ72Jqj1qgAKXdTJ1Tw1r0lPKP5//hkuxBTKqP4B7lWkCzfjKUD2q5zM895J+pYRITWYfY58CwUD1JRXU8WEznmvDIs6kOti4DVVfGwR3g4BOiV0n0zRe8QDTe7WoLvXPO0PanxJczZyslsqGTUuA/gMgG8B8Jh6vQfGZr8+Xh+vj9fHa298eRR1fklzNi9pWE/oc0T08/b8R2zNzitqGBW6qm2FFpl08IyCkIZxnf+i0gSonyrjltFi0Za1KLAwYeASpoLKElYBIeRMet4bqLIE0YRNunyhiTLzVDnJsGRrXjfOIsLs83z8yqM1lHWf5+hcqBBPPNElezV+ar1DEaIiDLe0LnrqDk7q+mstPcYfXH3EZ+3TgcHMKQ9gqGr+5qUCXSerk4lPzkZTwGS+g2NNRQ9He7g3jIbIZop9wCGM7ffUuMY3lqyrTPZY3WwcM/KsprqgPuCREVs2+Sw5mXhikLcJM6dVIlq1Fo5s0YouwgS8xVzFhLkn4b77jYcN6lcjJPZRVRnT4yx+nl2n3nF2+5znmPsW1QBAX7eOEYDNS2yizzzlt6/kZiTcWWWeHw0Axradtc6Haa9maoPk8vwF3q49YhP5Z503iaHpyuivEvK9gLqEdOTDwuPZCLVtf7KpDaNKsSnA32esnk2VAJUAMgx7HO3z9t+Ym9LJs06HBnmDgvotzVSRjAFa8euuSoDued9aZNqhoNFca7lC56LN/Q0LPI+7NF7lysYY85Jqa/T4krE+g9uL/hMAGYDjRPQmcH3Ot3wJr7nroZXFeC5CmZHLDbiwiKra15BcQEgVLZR2o0TRTpH2WComRCgbiWf4DWpQQhhy0p+CigpVgy9qsgRlK3GJzTLj2LjE2uNpFfRUiQqDtTc2MVqU/znUNPcMz2Uyl2C4FDuF0b5okLcIkxn/+Z1z1GwFk1kK4iv1NWGy9p9pX1D9Xgwr0OFev3On3cidMx7zhhe002SWPyOdJaskbDud9n3egModlDB2Ws2rXqGkV1QoWVqjx175mb0LmOxjYEhVi3DtTZpGmGP8WpG3L1WOZkaAFkJ22bbMBrrIVMOWDXEoSNbTzLORJS7lIWG3iWWniHJmV8hVzmzhKzz7+KUz/CUf+nW5RunCsVvHo4AMde6ZnBFcdqkIHLtUykG35m5aBStkp4b83wCDLUbzccBaXdQ9/1lUhBx3nUsVyowck4aJuFhXGtJVGR+z8KQAYyJkPU86OuQSJzQsnqNKwtCuKD1ZDyZio08odwTxKIq9zHjNSSi8SghjaXVdcNM8CQULI4RQQuneVHc6Xu1oNCK6F8A/APAGAA6baYw58WLHvqiysf1o/ibYQ/nz9mL3G2P+64sc+kPgfjQfsZN5goiO3+qAl3OY2BeOSRxZW0U6wZ03CclYeTJg7yaaeAWUbXiBW3RFeKiVZYWCkCPWljkxQWWJsttwYIW8y9JgOi8Q1gqmYrQRYJFVSeRaEay9ka1hDf899MEYw72W5mZqUN+onBAc7uW+9PUN/xy41bOfa6GsX7HEOrY2p2jGGM9HbmNGpXFFj4A/lyB9pKOi0PGIZSntoutrxhKNyucN0oH6HlQBqXwnmp0gyn3r36JOmL+y5ZSMadWBVh3TPfyMsuW+UzQAsHkiQbbtrXo5X1fBsHV7APm+x0sq99GOA4oVwOtmISwV7yQZspe2MyEt3y3AORz5bjbv5ROubLP2yRbGmP9ACwJXq2Jy4IvFp7iVgVxbWBikkdx1gxjMkSrhm0x2eMwxIbNGlkQBxKjJtsM9ArAnoY+vb3pCWTleF3Xq/FMy5rbP8kxmn2NlpeHJcY4g98ngC5uTaQiykv/vnOXPi8dniDBzrnDziCc+n1NmBBgP0zaJZYmuhXv2roxXubIB11j+IJjV/2vAzPu7ekC78Wx+Cpx7ebf9/xIYUfZiyiY3xmztYH1+RT1qCR1pC04Glb6vSOtKjqoWgaQY0Aqg0V4Wss2LQwwPNn1Yx+4hx41VGN8JszKorYxR2f410aRg2nV7bN6JGQ1n626KZoTeYcL8M5YxICWsP5Agntq2vX90BSsrM8DEnj+rcOUrIhz8mO0bMsu0Iq6bpmHa/v5BT7BY1rynIUNqWyQ5rrs+Ap71uTQUhMUAtu7F4qTKh9J4Piwo6mtKiBdw0JHxvJ9HPA2VjSiZ4WJIl+Mp9w223rTkCvdq1wZO0QDAZH8HeStG77AlQT1kgQbP+Ll3z3tFIz1/ElsLI8pGF8FSaZy1T6VheLaiTdFhMGm1oEOFUeF70ABAT9WZRG/awsaggfZvWmXTN2hc86Gx0VLqihBEKUoolwzXKekiVH1dUQoe9GJs22UK7lFIQ0cLPkQGCGDA/y9CnZTns/LGBO3LfP5plxWH65JKCLzpwT5GIUpN0swpASTY+RCDH2RdVSkFIcHJfBhuLWvsve/5ggqjAqgr5Sf7uL7BYXTHBpEza4YMcxN9fVvjFSUBb2s0jDEfJiKy3Tp/iIgeA/C/vtiBuy3q/E4i+i4AMMYMaYcGucl4koj+JIDYekN/FcDHd3HcyzKoUiEZAmqbqplZYTCZi4N+NNHEN9Wqkgh526/A3j0txGPj8jFJLwdVBsYWvFFlkKxqfnZCNOCdkS+yVB8viCvA7YLFE5nMAZ3znjZENsjSt3LwOotKVCCsLXMcP7uYYekxfy9yD9K6uLZtkAxLR2aZDCv0DyTeym7a+LaKGlaZb21gIqu47KObdglT+FBRfc0EoS5hQpgqoUcFAois5u/Sm1GaViUKOq2bxNW2TMDFlW0WgSKYLLVAxjhFmlsPQhRj8zIFLa1rW5UjyQR8GEU4s6YzCQb7IrQv8+uuvbO996oVoUxDCHHa93mFhS/a/Jzi3NK9bCaWbaD/dtYgdQD1D3m3K+uV2D6WOY+iv5/Q3y+Fh/aeVCM54QcDgMaaVSTiddXJUdsAvK6c9wFbo0SE/n4dQvX3JTxx8t2I4SYoye0jfNy6bam1+EQV9i2aiwACBgfsC4LsdC0Q+DzOaAELfd0CosqAwho96TYf66DaDUJruXL0OlmvQjIqXV0R4EPpwz1c2ybDNU+zkY6dbAm3O8h8WaDRJkQUAXieiP4y2Plov8gxAHanbKZE5Dg/iOgkgMmtDwEA/BUAf9d+9j+C243+8G4m9bKMKoz/lxl5IWCVj3gjlBsk4xJFQ9OQRI7cT0JQEj+fzGeO2h0A4m31uJII/WNt1Db5/cG+DFR5jikTE8Zz5MAJs89XATR4PBuhsWKw+TOH3SnXvtqbdAtftO6/XdPJtEIyLNAuveU72pO6ey2zyBWnAsA44t7vcnyVcPJehIl4JDo+ry3eyRyhHJFXRlbJlEqh1FQT1Cg3iHIvJLeOJa6aXSDNheOY4xyBeDvJoETZiJBteqMgGeQomzfuQip8cc2mD/s1r6galt4UgyNNp9inHQueGHvYt4l8aCoq+LsPFGcDDoBhJFdzSgRgFBowpWdRBlhpjd40QuMJAV80kIwM6tYQGljFtPwu743MPemFbzw2zoscLYqg5M+K4hZmaBcysocPl2JQiYAQdf0N/r7a5/i3rHmu0C+D3GfAIUbAdNZg8Qm1xxRIROqymnYtFA1g7vkCI5tHSSYGZRqG0QIm8dwAA3LKRiDUnkaJw2SiRPr7Epg4Cc6n8z3DPbEn9bTGiXAdTrt6Enc4Xv2ezfcDaIKdhx8G8LXYXd3lrpTNDwH4EIDDRPRzAN4D3yHzpsMYMwQrm7+7m4m83EOjbFwi2cXamXKEgja4nkKmZ/tiSLzYJ79VDFqhy6ZLTZdjkdCbWNBRYQJrPBkZdAZhV1CqvKdR32SG6e2T9pojwqEPJv5+TIWo9GGVZFhgtFRz1erTbsKV3SIkCIDx3FqdczkG+xNXdFmlnNQXQsdpm1BmoTeiE9Nus1vrOx3aanE7umdMQBMvlrE8g+6FMkAGamJOwNa5iAc5KpGMSkdKCiBQNKL8dQvuaFqhc5rzZVUtZkJHC+zoH2NI3MSitBorxuZd7P+rFYZLUagg1PymHQShoaal6NEKZjKnwApWqPUPKO/hyQZayz7kCbCgBIDNh9jwoNx/frhPPZu+Wkc2hdi6LG3Go6AFwXjO5kesHJXvRBeGau+2czG3n7Ns5R2/fgHviQyUJzT/RQTEnID3FuorrLjEg59/KkfRjIKwKSWeEaCKuWWCb8ZnPW37dfeOEOrrPlwseUDp6iqvyYinxq2zoslrWO/DsgZndDRX756GeLUDBIwx0kK6j13oAT12U9T5GzYm9y7wVvp+Y8zqixwGInobgL8D4Ji+jjHmkZsd83IOMsYX6JWGw1eKoTdvRi60RqVB3rk+cKt7k0eFcaiVbJOF13SBLdSN+zIwKA+Ye3aKyVwSILxal71imth+N56wMEysA0zWuP/j/tpUAfVrLF1Ge+uoCMil6VUrQu9wjKHtPFjbrBCPfU5IGqdJ3irvxJjMEtKh3BeH3mQ+yZjRbDv7jIilLMKhd8gvrfkn/Vxbl0YYHGo4IVWlxN6a9BlJCSOVk9l8sMKex8JwRn1FdSLN/PdS1hMu0JOErjGYziQYK9LF1rUSmfUqL38lK/5HvuEsAKD/I/eirwhUt04S2he9JzWeZ0UjMG0gzFuI0m1dlXxZxPkAG1oSwStKZuMByfX4c7UvemkkVDQXvk4pAKVo0p7/eyeabNohzJzKHYkpwN+1KMeARBa8vnS+LO/wutr7Seuhk1c0AC9fE5E7n7SdWHhKhfGUJyMhR4E/VymhvlqgbiXJcG+CrFcF3k9RI+Q2mjDew5QzI9vxISpYAYniFwofB9Kw0QC5TxOzsSDgjPGcZ9moUmaJkHKFrB8WuI4VNdMdj7ukbIjorwP4n+wZvwAW/PsB/AKABXCe/U8bY6ZEVAPwMwDeCmANwHcaY87e5nX3APgBXI9G+9oXO3Y3aDRpC/3fbvDarcbPAfhfwA+iepHPvuzDxOQsZAmXaRdb53AAhj5PVTy/fSkPKsWpNNxsCtygDETYUnxfc8+qcM24cm2hTUQYL2TOhV99JAYqL3SiEuic98oo7ucoZjIHPrjyrgbiKTCaZ1ejsV45DjeAN1w89Ypx2o4Q1zxqCYbDBcL2W8wRGmu+mRsZW32t6GXSgResEv5y3UgjDn0IVY8wM7QusYIY7a0jyo2zsuOJQdb3iWktoDYfFJgX/8rWJ4imJSYLPgw2UpT3jRW+5s7GWDJadq5X3s3Hjw6WeOcjL+BTj93LH3gfEA98yFC+A0EpJiPjwlOArQNJgNYVq6hbhNbVEvVV/q7rq0C6PsT2g3PBvXkUF4Uccle4H05L5V1WH05glEmeKQWTbflj8zaHzLRnsXUiDbxIQFn3FNLdxFOguez/rz9lQRarHmVZtlK3B1zPHsl/XasCdFrjElsr0nRvtBi2SojHBpv3ps4TKZrEPXIieT9ECRZNoO8jx4hyX5MDKEh3SwAOrDxEocyerhz8GeDvYLjXK169903E+05C2TrfeEfD3B3PhogOgsNYbzDGjIjol8DNLr8RwI8YY36BiH4cwPcB+Ff294Yx5h4i+hMA/hGA77zNy/8cgF8E8E1gKrM/C2DllkfYcVNlQ0R1cGxukYjm4AMEXXD3zRcbK8aYX9nNJP6gxkC1G9ZeyrQbBU3NnGtvw221LRMoK1eoacMxxWwdG/fXg5CA5B3IwCkagC2otFfg6rst1HkIIGJlBniItdTWjA42QaXB9gMefpttG8TWAB3Pcs2QIGuy7Qq1LV9nIXQ2MlpXCwz2J26uolSmKuWXqByMNNsSi5bnVwXwXV0XVFtjRTnaZxWECTtKRiXH+sXCJuMRUkf/myVC7Pn8V5XGSKV3/LGGnZ+dezt2tU8AW9NU+e6MG/fEqFJgqMgtn/it+5G5z/Pv2ib/phKcxN53Y8tWlIxmca5f3EK+wK5Pus4Ct27BIcP9GeKJp3xZeLIKkHriuUneAgDGj46AFf6us01Oqksb6Z1jtEjOrEsHOxq7ia1jQm/cvT8xGC0mDmU4OJCgaBCWrLIpWzzpui2cparC9smWU/CArz8CvJKZzNlwWxnW5Ui4Tjz3KEdQVBmVBvV1r+gnsN5MIvOVe+DfVQxkqg4nGTF4xIXK6oxe27qPr9+84lmsTcLFwr4dAxtjEuLLbvK8b2vcvTBaAqBBRDlYTl8B50/+pH3/p8EpkH8F7q75Q/b1XwbwLy2a7HZms2CM+Uki+n5jzEcBfJSIPv2iR+HWns3/DOCvATgAdslkx20D+Je7OPcPEtG/BfBhKECBMea2qA7u9qgiBLH3K+/zAiUeEOaeNahvhAgVgVYmo8oSPtqcwLRENJiimPUWt2af1UnXeFShVLDKoY3Hty/47711ufAQ1tKgqsXYeCMLsPX3TDD7qRo238qTobTCIyfP4IV//QDfl91cUj8x7XJsvneUz00FoXHN3/dkLkEy3gFtNt4CpgpBHiJvEWpblZtfFROQRS5fJYpXQln9w3UkYxPkNrK+8RTzCuVmL43WFQWuGJcOhmriCKO93pOhAogLf654UgWhULmHrWP+tWwbyL5o4bxLYZGjCBXNUtA7om6eOPnvCg1TwtyzI9egrn5xCzTOkV3aBMDsBPGwxHC/v0gArJC8nL2l3uEEM6ol99o3jBGdaQRFDOm2z58B8NZ5FfKi9Y8A/SMR9n5aKd8aQb7MyQyhfalwSn7zZBo8MxOzQF97E5v1dRvSa1308+uc867FZCFDMixdR1cAKJSHKYg9Eej1de7W2r7iN6HkfQC4UKqg6drneOqSK2ysMIGsBvkkY+POUdsuUdQijC0MPe0bbKuyQykmBoD5ZwyKmg8plhkC1g/NknCnI8gV33osEtFn1P8/YYz5CQAwxlwion8C4DwY/P4bYBm9aYyRB3oR3ik4COCCPbYgoi1wqO1F0yE3GLIArhDRN4HpyeZ3c+Ct2kL/KIAfJaK/Yoz5F7cxqe8F8AC4u4hLX+M2eXVeH6+P18fr4zU0Vo0xb7vRGzbS9H4AxwFsgusev/5lmtfftx0A/iaAfwGOdP213Ry4G4DAvyCih3F9QuhnXuTQtxtj7n+Rz/yBDalABmDDSgbxgF/Y90nLwLzMwfveiVbQfKzKIiA3SIYqhFAUruivd7wR9P2oKTc8QIEBaC5zbYiuVDcpoUg9W3LRJGx/hRQyRMj/8BayL3JA++RXnsXnP/AgwMTF6FzgegZB66R9g97hyPUNGS0wB9T8s7aXyh4LNVWx/bJGnsXZeoC6jfO0E7n4fDxlBmrNhpz2cgy7vFSiHEFzsZ0ejYRFhPoF4NyMjKLjzcpkmKNzJsdknuMteSsOvK6iGaG2UboaKMmRLDzFxtjW8TSkDsr5R+dNdIJevAbXCEw8QvuRuedGiIc54iGff3ywi8aptYBvbf3BmgthUWWC3JdAvDvn/b1vH/OeW/2JBqYzoQde1n0IU1vnjZXw3I6RXIUVx3OJr4faqCxC7XpPEPDrQYeX6+s5yqYAWCIkwwITy7lWxYRpJ3GhuaIRwcQe5ZgOrFejAjc38mpkDs1rjLxz+Ud7a7UNQSJWoIoc0s+VKdjzj2091vrDkks0QEWor1qE2RWVpzEGZDxTepnyXL03hrs37k4Y7Q8DOGOMWQEAIvrPYJTwLBEl1rs5BK6Bgf19GMBFIkoAzICBArczNmx7gS0wg4D0OHvRsRuAwA8C+GqwsvlVAN8A4HfB6IZbjY8T0RuMMU/tZiIv9zARXO4i7RvMf57QvshSoaxHaFz1iKfGco7pbOKgz1I1rpOKZccTU7IQM0FzJsm5uLa50gkxJQ49iZywrwslvzQpM7YY7OC+DVx+dkmaUWLt3x1FC77NMlW8wTsXcjfXqPACbObUNLh+LSOM9ijizFnOWThhZR+DoIim7cjVfch54qlB2lP9P5qJrz0xxkPL7bMps7CvSTw1DjUVUMVUFdKtsaP/ESXTP+jvZ9oF2pd5bjUb9pzsyEnJ2AmemM5YQaggw07BgHMIVAIzZ/hzkxkKqtRX3tRAlTaw8EV+ceP+DBv37w/uTecRxADQiW0YX9i58jU55n7f0+zXNvlHQrJbJyLXiRIIWzGIohkc8q/pxmTDJb5GUBPV9XD+KOe15sgr7S9dQ1QluqqekHdSJH1f+KpzQKl9PVe5PF3MCnhDR19P8iSt5QpFgxy6Tkg3dXi6tlG6dSyUMvLdm1hqeez9jSNUmXH8apMZoHPR5x+nXdWyOkFQgKq/wzsadwkgAA6fvctSiY0A/CEw0/5vA/g2MCLtzwL4oP38r9j/P2Hf/++3ma8B2Jt5yy5eu27sps7m2wA8CuBxY8z3EtFeAD+7i+PeBeAJIjoDztkQAPNKgT4DPsnYuZAz8aD1VJKhtajtup7O7igSjMnBmwEg2RjCZKqTZWJ71wvCq0YgC9FN+7xBNKFhVBiX4/BFbfzeBED/LV46TX9+L45dyrF1wgIKpiEfVdovA2s2mpRoLJcYHLRzGhUwWYztY95cG+4F4pF8njebWLZREbbXbS7nKBu+AC7tX9+iKG/HLodTZdyt0W8yTpBrT6q5XCC2c44HBYwVakUjAxkTFNNuH02dpe96n9hTjRYTWwPkn+1o0X8vIpA3HlVsEe0ceIGfRTII4/PNq1wLIs3P4onBcIk8s7IV3ALuAMLkvYlYqMVKcMVjb71XCWHtzSXQsswC9Ry9r86Bs5yf04oG4O6vJkKgzISOZmdr+9nnDIoaobCKbLxovQs71d6hyJGfAh72rMEjmlKIz5E4hRFPDKJp5YAgab9EPC6Qd/ihCCIw1rVqSreMbb1RZvOZRY0CElNWXCFaT+dQRcmIkeKKWG1NzOAAoX3BoHnFMm/khj1scVZToHfYe02y7uX/vAskqo/RXRt3QdkYYz5JRL8M4LNgoqfHAfwEGDH8C0T09+1rP2kP+UkA/4GIXgCwDkauvaRBRO8G8BUA9hDR31BvdQHsitBnN8pmZIypiKiwDdSugV2yFxsvVwzxtsfMaaUwVEhMKPLzrkXSiNegSRlVoaBJrNVY543WPjfCZLHmkDCMQJOKbQoEg0CGC4fusucUSOmKQfcD3hzNLBJLPBcAyLZy13kx6eWY7Kk5Rdc/yN6AFE8ODzcZFWTDYOsPRkH4pHu+BIhcWCwec9I3mvjiy2haOQ+srMdIt/xzHC3VAiRftl0hhm+/UNQitC/mmMzzfBvLfB9S9AoAZcMvy6IeY+paF4cFld2zniJeRq4ElihQUTIiuGhqPzM/RfsxDeoARnuBhvIeRgsU1NUAcKzUrnGc8jASDcetsWLRHkiVemWz9mae0KEDHNFY7zcxfW4GrUv+eCirWhijA69QIo7E5xVlN9hLqG36Sn0AGM8zw4R+JqWDY8s9Kfjyul/j8bBk3j4VLtVFkPHYGmq2FicZlcz5p0ZUhgi4bFChr4pIi6av0zERI9f6BzQq1F8vbzNiVK+Hhiq+nH+6xGTWe25Fg5GJrjOoAr5k26yENTgi3fKcaGn/LmgIe7mXABC45TDG/CCYEFOP02Dy452fHQP49ju8ZAampUkAaDD4NtghedGxG2XzGSKaBfBvwIiHPtgdu+Egos8aY95iSdpu+ZndTPBLNeKpcVZGPC4CASdeBpXhIttZtEm5LfqsKqAC4m2WbsMTs45fCQBSxd+lPSgASDfH6N3TDWpDdLhBlI4g2gRK2rqkWKZbil/LKhod+0/7JiAJHS1GrkVAtsnCa/55RfnS88qjrLOiKRuKdXrHZ/qHvcCediIkY+OEVpWQ45Tj+yiR9vIAzhxNCpcLAIDxgv9bhyKlcFR/L2RCWv/YRz8ZvdTwfUqSIbDxsJ/L/EfqmHZ8mERo7QcWw1Pb2FHAqcNfN3htcMQgez7si2II6NvQVnMZ2D6uhPm+ARY6A6z3PR1DsXeKLZt/2/cb1iuxSEERipWFpceTkHyyEhQV2CPN2yGcWBQN4GHW/YM3hv83l6coWknQvyfbLtyzL2uxUzAAQ9L5t7KkVKSmaHILATnfzOnSwuWtF2sfgaa80d9r2jcYz0UOCl3bZniyeLG1bTY02he9ARJPPDFpOjKOj02enQwxCmQdVAlfbyf7wV0Zd/NcL+NQMOd/fyvZfquxG4DA/93++eNE9CEAXWPM529xyINEdKv3CZyg+oMdysKoajE3glKWWhV7Vx/g2H8ysJQdWyxhaGTzH40MrtUwgNrKBOlWhLzLQlOHmtyxSmD2D8XYPumPv+cX/U5w9ByqhbVWNKO9NaT90imrohYBNaBuBXNUGIZq24LTwb4M9Q3jPKlkZMMTNpGbbeUoVfMzqZ/JtlUyt51gsI/DclVCqG1XqkkaW8sSeqPKBBBYzmVFwf1P5xtuY2uC02mXAsvVhVQUD6xmTJZw12g/n7t7ipirTGoxMhYcc1/g+ay+m7/Pe44zQdfwtw+H7NV2lepwysy5Mig8vfZ2//cb33YaeBvw5MdPAuAktLRSAICt97AmrDW8oj3eWceVNb5QOU7Q+YL/nrcsVFd7RunQeyFljSH8cm+AT2aXQMBWvOcJm0MR5ogxk7NqT6a+XrgQrImJaX4k0lSa4DtL+nwPgZGmFI1JRYn4BzqejSGED1QxqWrThvKExkYzMuhRNBlc0LnoF8TmPYkLabr6IFXD1VBEo3mTEE+MU1ZaUZd15mYTpvPmVVY0HoCxI0Z5u+Pu5Wz+IMeQiP4xgIdwtxkEAFexelQ+T0TvM8Z87CYff2AXp7w7NKp3MExCrhpaktulyg0IigbwSWYXOqsq0KQAptbDmU6BWg3FApuc4hmJB1TVYqTrLGiMtQB7J73JrBXN4mcJm/c2nLCur06Qz2RIrMJK+gCVFYYHeGfkrYh/hHNqI/TTp50IvUOxayolwkUX4+kx3FdDWSOs/zGWaJ1fr6FoIFBAXONgw36FwbRNrsraxED3nC+mE29Q8krJkAlNJYwGhEy+WkDW1xmRJBxVMhyBImmKfBaQ/SMG2RZfc7zIsXhRNtv38rNZ/FNsmNWnNdSTHKe/aF2ZPRUaV/y1xGuZPaNzBX4ey+8CABMIkCc/fhJ1W09dJcD05BhkP0BXeW/mCt708elx9/eej6QAQpYCzRLg2joob0WUTDzhHJZuMJYqL0+UjPCw9Q8kmD1VOZr+1lVWHjo0poewY+hQcpVFnhoIAIxxSka+M61EMtWAsEoJ6aByQBier6+Zqm+UGC0kN0TIAaxo+ET2/kccIhOEH4CAY0+ANvp59o/Za60BnQvG5ZQmc8z/NzwgbuJd1BCvfmUjDALfjLvFICCDiITa4Cl4JWEA3FDZ3K6L9QcxxIor6xHKLHLJTKqYhLO5bCk7VoYwUYRopHzvqgISKWe2IQTZeA5ppuLRM4piZdFbrytviTD7tD+tdDZ09Of76ijrhLZVNhK60FZm3iDbOAqYzEeB1+ao360nU9TItSEGuFnbtOuXwfK38wH5pkWAvTPH4V+NHHJIaOkdI3OdYGJyuQpBwznvQzi4hHAzIhSt2KF9xnPMMu3aWCvLVUgURUFGBQsUyVkY4uS7LpTc/3slrr3VGhHbQPsP+wrWqIyxp9VHf+ql9cXfPewYBCQEF9C2KOVtImYhkJH0gbJVoXWWv+fTZ0+iM/aht8Gb+KFUAxsW7JRINpVw3Sbgi03HfOwUjQr1DA8ANQtSNTEDDoThYDIbhoOk2BTwuYHxvPymANY9e6pCMihdFX3RiJAOyjAvmWv334bPVMhW90BiJUHOKJp2MqdoAODaOw2ACAd/W4XWGlHYgI08CKBKCN3zpVM+giKUNghaqeo5yFpoLXPDPgnFxWMOvck+6B3zx432MCJRQnllBhQtzz1X27hLng3w5aBsviQMAjK+FdyZczdtBV41g0rjkuh5OwYZL+DTfgVDQP3aUH2+dNYd8iLobW/mLSxKbz5tIVahcmhd9mGw7rNjLL9nHq1lyV4zNFhi1+nQYLRAGFk+sO1Hpzj8X6KgFXHQp94KcVEyIsR13xfxiuQ5xNPKCY79/7GOMiVc+iP8+cO/SkiGJbq2gdhkPgEVXtBw22NPlS/CWZiYq5QQ2RYNbr4LKk+wxR0ZNx603sh8gtF+Psfs07brp4QS24S84VtSjxfl+/L3L4oGAN73XY8BAH7/6jH32vkPH/XX3gAaavOLVexbGVPAfCBV7bpfTeNyFIAC+Dj+TZZmpnFNrQX1Z9rj76lo+Dcn877JmRgQcszsqSpozNZYNYGy0ahEyWUUFggw+6xBOqgw+4I95Q2gzcO93gjqnOUFJB1k+ZwqlxZRoGyigtFp01n+TDIyAZJs9mnJg6p85aByXq3MXRium6tl4EVKSGyw5Nd9NjA3BSyIxyYKpHXVBOHPtO/RjN0z9pnYqSUFvy8OqO55dKfjyyCMdvcZBNQ4DWYB+LJSNnpEJYMFBLGVrdm8ylQVbeZq54yEv90ndrm1swrpqLyCRlpVNYYNxz0+x2RvG7MvTF3tSDI2YZLU5iS2H/XB7N4hX5xXtADzVo+3HazxnOY/679a3a8mHbFwl7lKjxfxRJL1AfKFFu77d9YTSSPmiZpjQZSMuJFbobjRAvbraRjGi3KO9cvzyDsxattlgMi7/FUEk1nFXy/Qfpx3eVHn56jDJ+kI2LxPCbmcE+EAUD04wJ97yPfn+6mnbHPZ5zzRW3PDX9f1xbFhuTIlp2gAL7xEyXS/lamVLz3BSIL6CgW8Wc0VWw+j5ld2CwysUKtfTBFPQqu8aAGL778AADj/+4fxtq/xbu7vf/p+tC5EmDmjE/tK+Cvh2VhhGSDU/0D4nNYfBvY8Tk6oc+6MHHQ465mgcFmQZIL0q20U7LGKvTWbIB5XQbh5OpM4UIDQBgn6rHW1RH+/ipESr0F53usPJKhteEU9WIoxc3bq6G/kXtuqbkqHybaP2ZIBQdWNDEZ7/PvjuVD5ANwWG2BvMSp8bY3r/Gq3uS44vqNh8AqkJH7J40YMAn99NwfuRtkMwfUyOznO/uptTPQVNYT63aHSrFIom4kjUeQXDDBQAXCrZIb3LLiX8k6M5lVLUlhUIGVlFe3UJ14jQlmLMe1ykiMqTFCkOJmNUF+rPDdUyRto74f9Z6QPPABM5g1wpoPosJ9vtppcx/YrveqTQYFkgBCWqhTj6MgMosK4cF3e5OZu2qLWQIp4xAKitqGEgOInm87EaF71ijKaGuTtGGsP82dMAphMEVk+W3cCwRUw2stJ75aypRK/M6oZWRHh33zuvag9xa7C9ECJbEORfk4ZPqy54ZKxcUi/FCxoBNAg8GBhb9i+Nge6HEKlQVztLqO/P0LV1MScGhUoE/HXF0UDAP/bt/8CAODv/tp3qHvy9SCNVe4FJKGiznkPNDARK5r1B9T3lPo1sOdxiw60310Vk+MNA6w3J7B2sJevIeWjvSmaV6bOc5l0I6AbOUby6UwSdPqsWwNEK5io9HUxeZObpa0/4EVQ54Iq6LVTz7Zy+xtAZZDbdUuFQVIYnP96Pn/7HO8TURRljVBf96wKkgsMuPCURxoWcXKfnZkz6sW7NF7tno0x5r/aP7dgGQR2O3ajbH7F/nzZDalyTgY2H7LtFQqN1ULr9YE0BWp2pRqDwRv2uOZqeYe9ldEefj/bLhEVVVBxLZs07Rco08h35ow4Ti2bUFBkOpatwxFRYRz9CgCMvsImds+yhE4BtL38cta7oMXyZoZ4alQ3ykYAtZYk7uCwBzDonIhsXhGw0ophcMDv4vbFievLM/M0Z2TzOR+6Gy5FTuBvf8UIydU64pGNtV9RSeB7BVygrt8xrqdLMVOCKkLrOS/Qk4EPK9ZWYiQDuI6Z8dDWwdjTFY0daDclqACusRm8bYTkjJ/77LMKkWUVePB8uu5PxNsxiqZR9RoUoK2276mwdXEJ/593fwAA8Lc/wuUK3XNhqEyGQOgj1dNGw+jZE7Pfc5ePW/p0KN2kTTcANFQrZFEyOsQ52BuhteyNpMHBWgBeWPycl9ZUAc2V0iEvJ3MpyoycJ9Y7HCHrsZKRce0tieti2ljJUVvxBtN0wX5QeU5FM3F7brg3wepbDEyXr9d7I9B8NnN9bcSjcrVVpa9TAjjXJUCLrMf5v5oiYO1cKtA7ZPfs6C5qiFepsiGif4FbzH43zsduoM8//RLn9fp4fbw+Xh+vjxuMV7FnIwzU7wFTl/2i/f/bweCxFx236mfzS8aY7yCiL+AGGu2VRDtzO4NK4zwaYIdXM7Gv92zcw3o0w/s4fmUSwsa9/tFFU6B9uUS27Tt7GiLEE0mqZ0GtTW3Dp7+kTfTsC/ya5DYmqq6mUtZgWef2t/KNHPo3KSazCa69nf/vnGVCQal5mHY5fq0t+PrKxMFzh/ssqWVbKGL491DFu3Xb584FKWSV2DxxHxmJzduuo+3TnMyobPdM8XQA5u/K5/g8tVPsNXTO+SW2fULyQcDx95zDMy8ccO/RWNU+nUkA49FZAKOvYoWMQxSGzdbeXGL2Kdu1dCtsIzHpRgARBoofbf636nD8WvYZuuR7k9kFapvW2t5HGC8YRAM+f9muEI0iB8UumkwvM52z3sIMe6U//DPMHtJEWFOTbTF6UPIUZUqYdiJHYZO3Y1eXJEwUS5/lc0rRbP+I98rK1KMI4x0RomknDsACg712HUg414Sw9M6FEpN5/53WNviERd3OZ1Rh84QnPhXqHgE/DPfDeTUAlx0Mj3SQDEKYeWnPt/ZQhvmnc8cjBwDzX4hQ2+L/N09GIOPrmoSHrnnFz1muLUPYOlzO0e69rFdisOSvo0NvdzxepcpGnA4i+r8BeK+0MrBN2n5nN+e4lWfz/fb3N9/JJF/JQ5L0AECbfaDBgt/U7GNpMMiCtvoYPrTffXb9/oTjwVbozL5wfWxXiigBINvM3cZsXh5hcMhrD0nUa0DBtmpj7GLNNlUwe6pAWY8cfcy0m2DaJcw+a6/VqzCZicLEbTtCy8K466vToLLbQZhVqG4yS04YNdYqTDr+eNmgwtKbjCsko8olcMtajNoVnzWPBiUmB7pIB9IlM0M8BrqP8Y1tcf0j+odVUv2kl7hnP3YUtGAV02qMyUKF+ooPBUnYBGBUV+OaBwxIqO/qV/nvov184oo/G2scTuxb5uPWcoWN+ylgIdDhzK3jPs8EsGLIWz606NoMN1RCXyHRJBeV9O38+zWY2ATX00ZBc6VAVIaElWVKNySGlHWkw2rbxxXqUPKS9nuNJwxP37iX5zJztgoAB1VqedyUcJTqfD0qB6LIMFqM0D3LF1h7iB+GIPeaV0K2g8XPh+e60fwnCxk27vHN1zbvSx36jsqQs0+AGkO/TdG45vdP62rlFKi/Jv+edqKgXmtgFZqcX7Np39EweNUqGzXmwKAA2Xlt+9qLjlv1s7lif79q6mZe0tBo1A3hqQgtnFzixgtNlLUIm/dIrQmvmMUvsLIaL6TItj1Uk2tJvNWnK6u3bTGno6HpGzQvDjFUCijOPUxTlEz7okJ8Tfzf47kIte3K5RuyrQLZVlgDoS3W0VLNHQdwnL2oR0Gh4NyzU4wtgWX7/BhtKEBBREGTq80TCZrXKpe7qS33YWqpo59JtidBQy2p9dDeS9E0kNKXKjXANivbxvkE2RZQXxEqFN+tUkZZ90IsnnKRo8C+p50Im2+oEI0tvHYYoaHIJYWXa2TpYcZ7IlQ14/JH9TX2dgSYEOUhfY4UAYqSmS7ZuqxlqQ8KvQHhLRsv+Tlkm+QKM5vLBnmb0D3rBe5gb+xzSVIoGwvsvHJrQTwcbajoXIMvrvRrRxQNAFx7G7/fuKoQbgr5ptcTAPQPxHbO/JnRYoSiDqw/YPMcQ6b90Z5FMrle0sreqFJiL9W+Lq0LFp60jNoPZAHMWxryaYbvqAD2PO6VWP9gHHSWrW/6OhsTI+ikO1UKRcANktPV0Po7GYRXdRhNxj8E8DgR/Tb4lt4H3wX0luNWYbQebp0Q6t7svdsdRHQWTD1YAih2Ng8iIgLwo+Be20MA32OM+SwRvQnc/rRrj/3fjTG/iFsMmhagLbWKahlM00vcsl1ztOVFM0YyLJ01lvYLVGkUcHitP+g3+cwZ6wVMRRDoQjh7OQWxzef8dac2kb/4BCvAeGuErTcuonnVe2G9Yw0nPIT6X0J4oJDos8oooJrZOs7XEoE12JegtVxgaolbBeHUfZ6fDeUlRgfbAXfb9tHIoXeSoWUAkPDf3JxrgwwAK29pIu0Zxyrs+rof8GZu3ChQTvj60WaK7JItyhxwQl/qUKLCPj+7KpNR2KlRYOLGfy3Bs8hny4BBGgSU790Cnpvhe3+Uqye3nmGPdrzPIFvT6C6ev9QyZX0AfUWaupxgtORPj8qi26ynFU2AsuGLBZtXuaBQikgbaxUaax7NN+0Qekf9DQjwQysMee7poMK0E9agAL7ehOdbYbjk7yfbBnonBAJ/fc2SVjBlxjT+mlE7KuAQbfGUDaREKYSuCpM5j9gK9dwW9jrvxIbpHM8eAbX1AlsneV9Fefhdyndd3/TX0IoHANqXysDQK1TNlPaKdGtugAtIk4lxz0KjK+90vNqVjTHmp4jo1wC80770A8YYV5ZMRA8ZY5680bG38mw69uAfBve3/g9gTfbdAPbf7Li7ML7GGHOzdqXfAOBe+/NOsIJ5J1jx/BljzPNEdADAY0T068aYzVteSYWaTD1FVWMpZdLIUaUDTBkDhIytm/fUMfCphKDewimUVTaxJ/Md1FdVfmhcuLqVeFwFCKDumTGiSYF4y8dW2hd8WGnr3lYwF0FEBTU+anPUV6eosggTi4arb1RI+yW2j/H1W8sFmme20JTCtrxE1a6HdUW2Zw0ArD3Icx28k+fX/gRrAqNyDYP9/tmVKVDOk2tlXGZMbZNe4evnCwXKaYZoJKRZxnkAaZ8VjY6Z65bIkrsQ9FhjYjBteyaI4R5Cuh1h6Z1sXm98eD+mXaB6B1+glhW4b24NX/MAk2F8eO1BPL28lyv7eTJBywHxmOR31mMYsi/KBDpn/fx6xyS0aJ/FHmDP45VDApqYw4C6aFA33RsucUhPMwM0rxUOkQUgUDBF0/d/mbaZ5VgYBABgcDByeQsxFloXFIxdKQrmCiPXwqB9wYSKZsprZ2TpZvIWsSLWpANK0Mv6EQblssb1U+5+ybKrK32xea9/+BLyCuiYyCtEUTTbRywU+moF3do5KgwSJehl3wC+hkfYBmrbnOMU0tud7RvuaLzKlQ0AWOXywZu8/R9wk942u4E+f4sx5lH1/78ios8B+F9f2hTvyng/gJ+xjX9+n4hmiWi/MeY5+YAx5jIRXQOwB9wy9cbDGKDuPYqq4f+OJgVqiol4vMjv6ToCrWhkA89aj6aKgebFgbM6u8/3XViNKoOilTr2gt7hFOnQoHXJ7/R4e+zpQeZbGC9d3yqwqbnNiBxhJpXM1ZVtsOcQjXNEY8/rVrRTREWF2Resp2Svo5VLtD3C8KSVUjGhp5iBh29iSdv5hJew8VQlZK+VmHQ9O286YP4yvWEJXth1n0kwnQHqq/7dnezKAheWkEj3KQ4XmzoLo5W3eCc7zj3jwGQBoHv6WP4020ZxHSjfMAAK27JgkOGzG00cafL5jrXW8Mzj99xQwQBe6Ai8ub5pKXPsfCUsI0K5cS2E2+553JOTAuy1xWPjwjUAMNyTujqqul1XmhlA1g3ADBd1C37onWCtoPMu9Y3KFQiL0pixbRkkBDjc69e0Zrgu67CdNvn/wUFCWed6FgAuhyc5rXQYFiMbAkBhqEozAiSWB00sfal1qq/yl736SCOofXGQae2pKjqcZGQwniNWMvJRpZccl5+0umhGQb2YZpjuHSHMnDZ3V8nI+DJQNi8ybvrUdqNsBkT03eDubwbAdwEY3PqQ2x4GwG8QMxf+a2PMT+x4/yAAVUWCi/Y1FxkmoneAey+c2nlyIvoLAP4CANQTL6Dy/TOeigaAsbtC8htAyF4LAE2FcBI+r2zNK4xoMEHVtpXwKn8Tj7idQe8wv9ZYtZtfExoqihAqKjQuDzFZEIXDyK9s0+/EeFxgvMTCX8g/h/v58+0zOSrV2I1j+7FjDojHBapmBpooOpn9XuqIopGkOwA0H28EmyYeGxfOo5IRXgMLskiHJkC2SXJbQhRVwkLZCVQDRxoKAIkSHsK71buf85HNK2MMDjactZu3uHuqhOwAIL/ahNlnq+vHEeILTZR7rPba5u/gv/zO293na7GqzahYKEfKyYvHQGWXRX+/tYS3+P+JrVIXD2G8EKLsJISka236hyJIbYw8k84Ff4zurLl1khDniizWNJ1ns/ZGm4OzAY3mtQpbx/yamntOijr9+coaOSSWtMTQLZADYW1fF9ThtBMjKkMFEygWcVjkJQLqmyUmlmZJFI/w3E1mIsyc8lZG+5JtsX5Y5fvUWnDnV3NsrvicTDo0Qb5M6JrECKpiQv+gXyeTBePCm7MvhDVkd22YV38YbRfjpne4G2XzJ8F5kh+1J/o9+9qXYrzXGHOJiJYA/CYRPXMLdunrBhHtB7txf9YYcx0xhFVePwEAM839xtSlGtlWTSvizMl8FiiY1iXV46UWoXc0DUgjs42pY7xNr/Vgogg0tAI9idw1pvO8a7tnlMTRFt/WFJXKHYmSEShr2ssRjXMUHf6MVPqLkpnOZihrnsJ/cKSNKqGgaRXABXLyu37F2w79kx1sndDs1/xb6PbTUw2UDS9QhU9MIKtlPcJgnyL2/IoK84/HbpNNrdISwUpVyNxMpXECKMpZeAhtfNaPMZkhJ4g272uGYIGUkA1M0DDsxEOX3N9XfpN7/hUjhYZQg8oQMQWLRRBQQ5XwZ0RgCUuDMApTuQNNZk0gLfQAYN3ywDVWwtCVhAULZbGvvck/m9Z5q9wUXLuxIhOuK0JPoH8wQpyHRaHZdhmwAgBeyUy7OwQhhVX1gpjTinLSiRz9ioARGte0EVSif4TXr+QoNWSeKvYiZES513RiMIjXGINzKZJrEWRZ+4pnPNBN5SSEJ8wWVRahTMnxn03nQkUDALPPK3SbyqmO9uyKHH9X4241T3s1jls+RSKKAfxlY8z7X47JGGMu2d/XiOgD4K5zWtlcQtgl9JB9DbaL6H8D8HeNMb+/m+vli2zBbx+zSXO7/uobFYqm72DZvMI7TBKFVBl0z0x93mRYBCSAxuZ+qoY3Szce6gTXmHvSZ2Ir1RNEwln5om3jrHjFAA6LVbXUgQ/WHulg7pkhprM6vq0sY0suKiFAaQUgHREBYHDCN94TRaPRaXnHC8UoZ6EqYZm5918G/vGeoDWy3vTzj4eSNuvz8x3ukWcZJrH1vWqrHmBl0rpSOQGZ9SqU9Sioal+5FxDj6hu/9jH82m96jIk4j5qRIfhbOLHsvRbNMF8SOWCACmvlBuVQJdJ30P9XGTnEFgCsvFmFmgAUNWDpcZaow31ZALNdfUuFqCC0zvtjhosR2udG9vwxlt9mjZFtqP4rcEL1yler8Og4wv7f8fcaTz2z87QbBbknE/G9JCqGEZXwfGOGwSE7q+sl/9i6NEFUGHRP81xHe2so6hQo0vE8OaNDQBLaK9XfjShs6dxZXzchy0ZlEE+Vos6torE5nagwoMqgd0gow32edfYUGzh1xQ2nSwfu6vjy92xu0pHoRZSNMaYkovfe/flcP4ioBSAyxvTs338EwN/b8bFfAfCXiegXwMCALWPMFSLKAHwAnM/55V1eMKCTMeQ30mhPjNYVg/ZFfm7Siyaz9Snjfa3AQpnOpMi2csR9llJV3Raa3efDUTr+27paYGo9lnhScoHpOu/qYt4eIyCdmBCPvTIrWxmoMFh/2J97/aGmox6hyiPaAE9AqK+fjIGtEx7l01gvMZq3iV6rRIr7OONP55owsXE1IvVVDlUtfa33GOIfWIbtCIMLH2db4Ph/3uRns8gH6jYGq4+o+R3IsfjJNAjTyVw1OSLA0Nnh3ghzz/H3cumrMqRbHu0Vj4D/8es+6T7/gQ+/E0gN6stKGTQ8fNkQEFcabMGvSc6mtgmM54BEPBlpWqZCTZoMMh3wnIWQczQfBQltMsDSZz1zc95kRbNxnz9h/ygwneXvsn2Wv5PZU/z/9uEYS4956b95T90VtAoKjpRnpalzBP7tuMLIAhwsdb/rAmrvXTw0Xfyp11BZs83bMgklhs3Vhges563yTXIt/puVunheQvkjYJBkFNY4jdu87qTsAPCGlMwH8GFpgIuvXavnSN6/cU4nHVYOcUmFQTw1Tj40r90lIk7cvTCa7Z78bwE8DFZhfw7As+DK/mMAzgL4DmPMxs1QvLd53f8M4CcB/NpNokfvutmxu/EPHyeiXwHwn6ByNcaY/3wbc73V2AvgA/xckAD4eWPMh4joL9rr/TiAXwU/sBfAD+177bHfAcZ7LxDR99jXvscY88TNLlbFnqY/266wcZ+vZ9D8XACAmEBFhfE+L+BHS97sFjp2scppUmLjkRn3/mSGAt6lWOVHUHEiMt/DEpPDSAqeujZEVU+dshFoqNv4UchxZWLu3d475M8R5cxw4OaugA5FE+g1Y09nD69oACCfL9A+pbsx8u/1/3bQv6acl8Vz/BBPf9ssAODQR6Y4820RRHsmmzHiMSsZAKhfTtE/DHRPX2/ZVgmHd0Sgrr/Bwl2/mV+YB3B12T/n//GRJ1AawuNr3Ie5tmphtt5xQ/zQNiaWGZuyCtVqinQrRC/onE1tSwkl4vyUC31RqGyEBl+QUc3VEuPZyDMPNOQ79Jc78+cNZj/Kf/eP8u/OC7E9nwkguq2rFVbf1HKhPg1SkXCiZpsAVM3PgGn2tcLQa0QUqPZktGeR2fU7mQuVa309lDciTCV3ogspNeOyeBWjBZ/n0p6VhCjH6npRwd1bAa+kRJnpcKSex3hWPHoK6oZGC1FgyLSuVa6XjzSF8yCXuxT7Mribns2PAviQMebbrLHdBPB3AHzYGPMPiehvAfhbAH4AN0fx3s74/4Hl7o8R0X8C8FPGmGd3c+BulE0dwBqAr1WvGQB3VdkYY04DePQGr/+4+tsA+Es3+MzPAvjZuzmf18fr4/Xx+rjr4y4oG0vx/z4A3wMAxpgpgCkRvR/AV9uP/TSAj4CVzc1QvFfwEocx5rcA/Jadw3fZvy8A+DcAftYYc1Oq7N0QcX7vi33mVTmUhbdxX4yFp70Z135qFZNDs0i3PDqmbNccv1nvmEV+2ULK8R724XXtlyR7AaB3JHaIpWRkMFrMHH1LMuC4cqnQaFSxRwMAlQUx9I56s0/nOLTlC3hmAN3QS3s+AnUVK5JKbtKVP6AKZQyh6PN1xauR3h8A92Bx59tviwfn+dl0z8VYeXOE6RKvudPfQWi/kHArBADJSVss2vc5HvFqgB0J6Fn+vfkQz797cBtfc+h5PNfzlZOPvOGy+/t0n5EByx+xXpdjW+bf9a9axXCcIW7y3KoiRvOS8qQyYOHJnSzKYe2Lbqa2eQ8FSfmiTkGeaWgbfbVUGObSVyov8dAIrfYE+TdZmv61FjpfSNFQoaKt495tFOtdQmZRAe/RJMyILSM70UP5VDfwVAb7ydH75A1blGpH/bwN1R3x15s5p9bNUmzbg/tj0v71klO3+NZQ6iph8IUOI+n6qfEC0LrscyWS4NfrWLdykLCYfB/xxHB9leT8jMGkGweQZt0mQ3s1UWkwWohVTq4Kckv6nu5kEF5SGG2RiD6j/v8Jhc49Dm7F/FNE9CiAx8D0YnuVArkKjhYBu0DxvpRBRAsA/hSAPw3gcXCr6PeCW0R/9c2O201b6EPgJjnvsS/9DoDvN8ZcvJ2JvpKG1Dcc+VX26WnkJV26OcLoIIe2autTR1gJwHGLBd04jcFkPvOvG2DtIZtsr/tGVtkmYeGpnAvYwEnerRMZumf52hKykWuP52NQ6cMHQNhnXeouJB5OlQnqBxorIQIpGXE1vw5pTOYrD4izcZbmglV2C0DjQ91AiAZV+VbJUMoTv/zNFVpP1jC1ArH9Ah8oIa368x2svXeC2U/55zlQENThSS8F3vnAaQDAZz5xn3vtg597k/t73/5NbIyauPYU9xSIDw0RPd3iPgtgIsvhO4aY7fK9DMcZKkPInmNNW18LwQyzz5cos7CPy8o3jFF/kj+fvnsdk8fmnWI2lxvY6gDNKzZ8agj19cqds7KyWocZZ573f28mdUT3jbC5zHE+ioCyCfSbYY5Ih4gGBxRZJ6lzG6B+zzaqio+dnuoANePaEUR5yCMn8HLd+K6sR2jZolkBXQxVZ0wTATOnPMw8Kv0eEANI50E0Ei6eGPSORMG91DcMepYTr+VtBjtfg1LVDEkDM/luqAqNAFFSOrFPCkY/bRFMrJWUgrSXofLp749twe3dh469BODB6k4GFTUScOHkXzHGfJKIfhQcMnPDGGNsCcldHRa4dT8Y9fs/KOX2izuU4w0n/WLjpwD8PJhKGmCN9lMAvu72pvvKGFQZ1K+y2Se5lt4jvr9yrPjHtk6ysBErKh1UMDE5RBXARW6SsJQNKs2/ekeBbNNfu38g9XULOVPOTOaFT8vG/Wf9Jq9SH8NOB8wJJUJG6hxksxR1QlN5MtlWgaKRBfDbxqpxUOir7wZmn4mAZ1j7jOwjyFs+aW0UX0T7Al9fLM75z6boHTMo9rKybD1ZQ20TqH3KekZXC6w9kLgCRQBY/GgGiScMba3KeA//H20nMJHfI5/5xH1OURWrc6jXgPx+zpEtP8eeTGq5zPBcC6mijxm9cwAYwkBBnavnOhwYhs8/NVbste1jW3vEz/Whw1cc/vHU2gLe901P4DeffYCfy+IU3cf9uSX5PJ5XFnQBFA3fMKy+bpxyTbcIk0/PY94i3sbzTGkjAAZRWmIYFA3bRMwOqoDKeg/xW/iN0RV+Ic0pEOxlnRWVrMOOhXPXthQPWyPDeMEL8N7hGDXbgnu0h9A5r6DBPeFEU83RFLw+6zE7gAYIzD+dY+uE5U6zNTkNyz5epayQNEVOc1kljcCKRnscjdXSKRxhKBCkIhDynckzFC/XJB6GjzYzJYiXKntFX+uujLuXs7kI4KIxRtAwvwxWNssSHrNlIFINeFMU722MHzPG/PaN3riFcgSwO2WzxxjzU+r/f09Ef+0lTO4VO6Z7vHkvdC4A4/nj6fWhE3H7yyzCaAloW99OqqlloRd1Tp5KIpgqn4BtXg3RTIJW0spt2o0DC1F33XRFdEbOHbYPiHODvB25uqCyGaO+Wbp2Cv1DtUAo7P19AKiwqUgZaxtAbYPvef6ZKa68O0NbFRrWNo17Jr3jhhmnn+WHM/c0K/DJohfCBz/qaYqHB5sYz0aYWvRRbK104Ruragb1azyXz//q/ahp+hMh61zx5057ft7xGOjdn4NinmunnmMwqKF6hj2HKg5p5sXy1pDXKiVH4VLbNnhufALt8/bzAH775Jvcpln8XIXW5RGGe3k+wjumw0cm8JyYIbtj2ZO3jtdgIi/cBGYt4UMThzU6omhkbfSO+4cTne6iqlVIt/wB0/s8/UHrMTaYhEEAgKPNAYDBPtv2234fRRNO0QDA/LPWs1BhxNFiHLACAN7rjqeM6JIoQJRzh1YJhWmFLCMICVsD0HkfRAFgQVBngiAr69dD/tOhUWE/Cgk1zfU1ZO5eTOjpBBQ5dzjuhq9hjLlKRBeI6H6bnP9D4J4yT4FDWf/Q/v6gPeSGKN7bvPZvE9HD4J42dfX6z7zYsbtRNmtE9KcA/Ef7/3eBAQOv6mEiCtiIk7FfULUttmqkKh5gj0FivqMloHXFC4Kt49xyYKSswvEckEiVvIpNV1m4kGWBCzIOsPFtK/9E0ci1qQJvBrWxpl1ylqIcNzgYds509zkyQS2HbOCDH1PtFkrj+ogsvz1DVPjiwoUnKMhbtC4Rsu0KrUsjeyw/s7TnBfjwoFfqm7aOR55JPAKGB/y9GEVgRVUI5a1SIO9UyDYUWm/Tv7/9ED/Y1pytQzGE9Hmf68r6/J21rtowaAQ01krkLRUqSoCu7dkTjytk255rrcwIXZXHMBFQNGKnUOobFdYfiDwSam+JeBgFeaG8FbmWAfUNbv8tXmpcsVAWUkwgZG4QwRvkG5RR1Lrg72P0Rn4GyVm+/8kC0FShqmknQlEjbJ6wLOALrOyk0j7rA50zipPvHtagOl+oB5Ok+l5A3QulUzQAK/F4UrmOtEAYwksHFXspqs4KRK6uprFSISqNQ3WWGaGsR8F8dL5PvEJRfsxjF7n9pg2C1NpCQRGr4p/TFEB3PO5eYOuvAPg5i0Q7DUaIRQB+iYi+D8A5MEoXuDmK9yUPIvpBcF7mDfa83wDgdwHcFWXz58A5mx+x//8e7mCyr5RBBkiGVqiMSkznMuQ2HhwVxi1OgN378Zwv8qxbVauTpfHEoGlDAr2D5Go/rrtuGR47XIoC9mKG+yph0+SkrhY63MvDvm8Vh1Bv5F2DhS+Eyddrb/aulFDs19dVsZ/aWK6viPWE9n4aOPvNCRYfV+GVo6xsAaB12YZT9vpr1BSVTt5JEBUGQxVu0QrXJEB9hTA4xvNpXlQGgJV1Yx/d5JCfnCdhb6W0uozsW9HH2FQtmkBTmUWuDYM1IqKSq9w95JWQJ559oUoJmQozVXEcFPvKcStvCr1CGY3LMarUW86bjQjx2HsMAj3WFrdW5CaCLVS014cUwfL/7bNRYClrHjbxZCTcaYjv1+VgbEhHH1M0PS3T3k/ypISXr7ZdosyUcCfsqNjn70LADXmLeypJWFgUpeRedg4pmNY0N5qhemCVju6pU6Z+7U473HLbFeZOmbWgsJ6P7GVRNskoVDi69UR9vcR4Pg5CgHdr3K0sii3ruFHY6g/d4LM3RPHe5vg2MGr4cWPM9xLRXuwSCbwbNNo5AN9yZ/N7ZY54ZF3xRox4XCK2ZJXTThIURuZNsrF3/l/ozm9EjwH4IkNRKPEEWPy8JRpcYS9DQlnrb2gGNPnO8lIJfBMDnQt8fP9AhCrx9QlipfeP+M8LHQrgCS5dnc+OxR7lBlFRebBDxfmcouVvbuEJ37ZAgA5S6yH3oWt3NF9Wc7nAYH8SEDa2Lo1x9V18g3JeqS0BvMBwdU8KiqKBClQCcQmX1B6eBDpf9B+Yf4aZCvTz1eCKxtURykaCouPBFdlW6ah/8k6KrROq/YNVGrMvsMA0GWH1kcTXjOytYLZ8JX48DddIPGUPQBLfzgNS9Do7izKp8JQtkWEhOe1e/yzSAecI5fiyFiK5krFBlPs8xMTeS6pAiLOnlDd6wLJ5qxzKtEMho/YgFJ71TU+CKutClEgJfhaS65nMWA9XPJCdZK2lQTowjgoI4BognZOROcmzGC0QmiuSiyqxfcQ/fHlm+vlqNF5ts3LJ+3hcoXW5coXIuh/UHQ0VunsVj5ExpiKiwrK2XEOYD7rpeE2j0aQhWJQbkDGYdvzjiKfGscqKINYMwLJ4AWDmTOnCQ4BXMkd+3YevSIUUdM5E2JdFqIlXI96TScKqZ7GCtQBtXstRPO9dGWn2BfjNLOii1hWDZGwcqidKDbLtCpT7awwO1l2IMG8QNh/w5xP6/9ZVb2FWCbl4u2z+5rLf1e3LhaPXifISk/kMc8/x+5snEtyMJ9ZEIRKL8wB+w+ZNCvqZHP6VCFFROGu2SjhUpenkRdkDwHS2hnhSOg8XAJJRCWOLastGjOZqibUHPaownoT9TagCBsdtD6CnUsD4Ntr9IwbR1FexM68aYJQhoav84IERTgAAZudJREFUkwFAsYKlVwgYsOMxr9Vs23sXojR2jnjCAljgyeJJirXevAaXNwO4zXOZEWL3rChANcrQpKom8Tm3ZGx8TyXA9VAa7PfrsmiQ6ycU5fz9uoLXOrexcJ6KNfbqa9cbYoAv/hUYeH0VTtEAQm1UYuN+vy+FEgdgIk4Jf7au8LyFnLZoJgHjRTy+ixri7jtLL/f4jGUv+DdgyHUfwCd2c+BrFo1mKAwfxaMSk8M2SWoFeVMYmQVlptrzamQO91+PXaV7bRPonvcBZDIhlUfZiIP/AU/MufpoLaiNgOHeJGJhitJKh/76ujvjcC8fIwovmoBJFe10RguEzkXjcj7DPTHfl5pOTdUIVRmhe9qH/4Z7ge45BiEAQNoPlYwLBwpKaGKcogFYgNZXp7j6jmbwmvu79LkwMiGxJcf9jUvY1je4QZ0wZntr2gtETd8fTwzybua80ChntgbpOwQA64946b/+sMxJeUab5BBQUt0+/5hi9VYtAJqXbWhTebrJACiUgqmpMJ94IgIv3j4SobapQ0MS3rMHmBDOrKl2qoSfnyiMdFihqFHwrNuX/D+iZCZzoYcp33vvsPX+jMxFQbDBxsxoMcGeJ1RtWhahvuaJMItGHORFNEu0GBzi9cVTDl1Lrx+qDDeiU/kT3agu63EYTzwnUVbNq+E15HlIbgqwubtrE+Qzil9QH3aXFMRLrLN5xQ1Le/MPbJ+wHyeiDwHoGmM+v5vjX7toNANMZi1p4OUxNu/ziWSh2Oie9ysjb0Zuc0iepGF7b0STEvs/PkBpmZRNRC6eL++LtSyCUbylKokQTSts3svaIRmGCKZ4LGEBBVZQSdHRfIyBZ47hc8ZhEebM2RJbx1iIdGx7aSn+jIqwLsckwMqj3oScsY0axCLunBdeLT6PCG4Jk5kopPeIxyWqLHI5oNVHWcnoEEah0Hna0i+aQOdsiMiKCtUWm+QZ8Hz7h6xXpdo/lJk62DAgwBVCZuRQcACw8VAXVAJrb7QfJ6CqV6DCnvcyMylLaKe2bYDIsw0nY2DrHpVvW+B7bp32X2ipCiNbF9kDkWeXtxnoIc9UGsVpOv5oR8pD5+Zq2wawejMdVBguxgFRpuY5k+9TqHUm3STwlkUpyVprXjMY7FfrhDjnoYVn62oVFCcDfr2XGbczcOFaE4anahYRuH3cK27dVC7K2aCRvUcls2qLghbDURjC5R7kPl2+Tt2jzskMDvlF6LvQ3uUWA0AAmni1DVu786sA3mj/P/tSjn/totEUid+1t7IAdNT2dkNLn3VgB1Jlyr3is20rsCelUzQAUFseoGx5KTA45BXZpBth5uwk4D/LOzFaVy002faPcZvErk2tYHQ4wtUKqDH7gufUMsRdCReetkpmIUFRD61/nVcQvq0q4wtvPAh0ztF13FPi2VSx7Qef6fnpIlL+W5QMT8r/mQ4N0iGwrajmpSZi5nmraG0YMW9HQcfTzXtZMOlQEhlfKyStAfT1Nk8mTtgvfj5H/1CK/iE+wbTNyDiyhZFUAa2LMUrF2VVmDP2We09VDmjd1ueULcVGcSrxnprN9YnnWmU2TGhvvbZZBd+LKDVdHDlcIrcWo9KvDyr4e9TeQkMVJQrRpTwLMTDESywaFKzxss7ElxpuHABirKesGa31GpAul/Ka1KbJ8A3mFNOyAWZO8yTG8zFMFNbp5IoNQIhqfciYkG1en+8Txc0MA/7wou6jBeLlSz7HUIhGu5vj1ezZ2PFZInq7MebTL/XAl4pGMwA+ji8DNFpZg6stcT0zdFvcps/RFHUAdYXsiQgzp6fOmirrCaJphcTS24ii2brHC9hgI84kQbhFJ9e37rWv28LGhScoYLeddjipK0qmyjhENv+MgBuiwAvKLAR5uFeFCCoPqaaKBZFYz5ITmrfKqX8oDoRdmbEy1lXxlWLXlVCWCKntI/a6apMJKzIADPdEmMwiEOhS1wKEHp30Stl4wN/LeD6EvLYuh3F7wNetxNNQYK68mZVVrHJxs8/6Z531Kwe9BXid1LbCZnDa65LzNC77baU9kbIBILKhTfe+QWmNeVkjGkKfDn3oqH8EmBzzB2fnai63l05ZgYinkkwMhksU1M0Afk2X8LkKgK+R7ajapwpOEW4fFaVh7zsKDTCZu9S9mJgLj2XuRT0OnlVLCjaVAqJphdEiPztBS0pYb7gUoVDfXSL9lDb9awJZB4C0X2DzZC0ocNZGkF4zQkcjXmLnEneblUZTO/sR3fawCMBX+XgngO8monNgYmYCOz2P3Pqw1zga7fXx+nh9vD5ezkFfgsjcyzVszuYvgGt4XvLYDRrtp8Hos037/xyAf2qM+XO3c8FX0pjMeTOjfT4M4xQtTzjZWGbrJlgoRBAzJbJdOPNZjvuWjQTDpSSApmY6j6G8mvF8zE2pHLW+ASKD9llbHzDLXoT3HMK+I4D3agBYWHSE7jk23cZzNtTU1dayv77U54wXbchqnTCvSElnn8sxmU+c1bfyPjZn5z7NL2TbJqCBFy9M2j/zs/IWvliJmuqnsernNG1TwJigi1d71kvSeQqd48o2GaE2neX/6ythQ7F0wJ8XqLGDFKukuU4cX/lqA6BEYqvyk0HYWTLtI+DVKhtcZ+Oar9X4teks34MwEwj8uqyx15pM/L0DO3rIxH4dxmMgrRcwp32BiKyxyZz3OgBgUiPEYx+Onc5wbkmQbsnIFnY2BaUoLphqO6BCeh2bv5RcXf9gDEP+e0wHhgtk27p9RZjjAYDmtdL937w8xnSB3RUBkehCag1WiMf8o9k3NMOB1PEIWS4AzD3nwQoD218nVxQ0GiGq19Tm8RiNNXP3PBo1Xs1hNJuz+f8aY954O8fvJoz2iCgae8ENInrz7VzslTSiKXDiv3BM4dpbmkG4QwR7Y9m/1rlY+oKwYQUYn8idzPFK1YlxMt5Vn8yQTwI3CYO9ClY54TCdJMazTUJ9lTyqyC54Cf/kXZ67bl+89gbVp/2Ssa/5mENNwYMZTEDIuzzXxlXCeNFg6TPyGaYZEYBD0YwQjyvXF2T2M1mwYfImWTiwfy2qQqFAOrdQWnCC3fNRHio/HfaQkJz0rU9GBv1DnrbERMDgiP88vb0P82mfwBnvsd0khQ/LXlOHzbSiiUecU/OK3CsagOtsqFLhIzttKQ6MRxz3l//7J0qgWaD9BX44ZY3JJ0WIacEKcMJc15oArKz09dLHfeM+E4VQeF1NL89b+s8I1Fgj2UaLhLkX/MLXNSyiRGTN19dNwGQOA4DCHFGZRY4YdqwYloHrKV+al8fcXdYaE6M9KWpbJcZKwej9NJnj34L4lHuWPUiV4RoiIz2qCpfXBHx76KKuWLc1MCb2ey3rcVgwu1t9bNxF8KoGCNjxJc3ZREQ0Z4zZAAAimt/lca/oobnIOhdLjBZjVzvTtQ3ApFagaIYcUEItE3AxpeTizYBKyMIK2Nlw84X1I0BqSQAdSEGUScrWaKWFeQ4PFa2p18AJZCkyBFjRbDzg5zL35hWsPL/gkVz7K8SDCCI5HcrM1pJEJdA7qDbtlgm8jeGir2mRoRPL8ttRspRhMWJtswrOp3NbksvSVe661kmSw62DHto2XlDe6oUwKS4N4uTZFu0QtVc2eL69Y3xA3I+Qbfn5OiGvnr1mimhe4ffknjvPx6hSn6vIemEORxLS4gHU1y3iS5BTfQAjf8x4Icz31JVlL2ACDUcGeaVfpXzfLsfTN6htV0gUNLx/QKHmsnCu43nCaDEO1i1z/ql1rvJrwpisySx1HU7vRBO19cIR0AIIWCZEEYtCmDlVocrIQezl/FIDU2Xk2AoAX98zsutz9oUcZS1yx2luNm0YAZwXbV9Viq4T7t07Ga9mz8aOL13OBsA/BfAJ25UN4Hqb//12Z/pKGVUWYbTEklqYa+efYomdjEtUqV9ghq63goCwLe1k1tcEDC0/lLaUZLQvF/bzgiajYFM7lgL1WpQr69ZH7wB48sagZa/aPNe+Nkd6OcPcm1fca7WDA+RnbWfQnNBcBoYW7p1buhKNxqkpCGrWK1E0Iw+d3qE8CDu432Ker+4EqgWmMCAM9sbhMWCFHNRSbHLITJRMcXgMAjA6Y60Eg4BXTMJkWll1VX2UiQjjWQqg5ke/8xQ+/8RxAEDjihQW2uvZ70Zzuc09tSP8WvNhPSoQfFfxhHvdS4gTYLYKYR3vH2ZEmLbeqwwo7fNoLpsglNs/SD4UaF/XITkynj5ncIBremRdlXVCOSGM9vKXt3XMksgqCheGDiMY2ujS7AP19Spg0aAqVDQmJky7sYL8A6Ml/+ANhR6uqweSvZZREAquEn6eQq8DAL3D6nz2ZQFGrD6Sspc7EDg5eQaHBV5bep9qqpxSre87Hq9+ZfNHb/fA3QAEfsb2Kfha+9IfM8Y8dbsXfCUO6TGejNVOVotClEp9zUvRZDDF9knemZPZCJM5H7IYL1VoXIp8LQmFNQO6fYCJgcFeX5k/sYKxrPnPzzzvN5nUQuj2ydOOn2veZmG1/SgLtK968DngQWBqNd/jlw85RQOwJTzthP1EdE4pGTPDs1ilwz2JvQd+Px1YShWosSMUFJWeSn4iVCvWI1t7KMZ0xqB7yj8PnWeZLBp07f2LsC9aKlypGKCpBKYzBslQ1bq0vKchQlwgr+vfykmOn3vHTwIA/v75b3aKBgCG99k6qid9QL9/0q+R+c/yXeveQOO9BvHY5l4EnWbvXdoS66p6Ia4EmBAyGXmBnwxDJFSZEpD6kBJVKndXhOHS1tUSm/fELn9F5Q5hXjKDs9St3IjLLwg35hzCFeW2kyRVhjbA4olxykWiALqZmc7NJRMOLzrvhCgo4BTPeScztJxjsE/qxvScjfMaxePToUZNE7Rz3cX5XVYyePUXdQIOMAYiWoJifd7N2FU4zCqXLysFYyJv/ZiIG0HpGK8eUrwZD72y2bzf707ZCOMlvxGmKmFb2+K6HADI2wkvYrvmZcGP9vpVGNDKbxJGe4COovjXiiZvsUU2sD1n8tkK+Sxw5NCqn4vCKY/7GeZOEUb2ulKBrrtJCt0LwJXn6RBBLF17G3kTQdhO8lSa0bd3ROUCDtkQ5aa1pJt8X9sn+Xf9mvIWLWhhcIj/LxsGJjIwtoUAtlJPQQ84JSN1LNMOw9VF0Ky9mT/857/qtwEAP/30O52iAYAnL+9DvDRCMfJS5t6jV3F1TlWaXuk4JZNZK1mKHfMZr2h4PvxbQpN5kzCej5BYoR+V7IXKfI0Ne3k6Hv6tKXmKBoXehSqOzXq+9cNgP3eHbV0Nj5WcioQrxUvcWTBapQZGQdory37gClhHPFdJrE+7ERdaSm8gm28Tj3syF5KGTroMWpE+R/HUBGEwn6vkE4iS0kAZTfsk9T6SK4pyA0PkuAH5/sOC6Zbq6KLpp4oWUAAYn2C3rnZOxbDvZBjzUpqnvSIHEX0LONp1AMyLdhTA0wAeerFjX/W5l9seFJJnahQLwArIVThLbULThhyO1zCZ9eGvdGCQDoD6Ki/03rGQPFKzOMsmcnUtVlBri7ClgIX1DYMyw3VWnr82/5ZGbXQlQvvbLuOHT37Qff7/OP/17u+5T/LG2fM5RbqowhnpdoF0G25T5N0U47kYvUP++lGuCBSFK00V9wWEioZzYP2Dui2A4hYrCWlP5ahIVXAPWLCWDaWI6wrVt80PM+0rAa+ap0ld1PqjfMzXv/NzOFzfwF+a+xwA4C99xefwrU9/pzs2t62qpR8OADx/fi86s7Z9wm/Poguv/LJngc2TIeKqaBi0LmrkHwJSV8CzR4y7rCx06EqzGM2c4XnL8+jvj2xS3j8rAY8Jh5gI4MaawaQbuXVDJXsiIvDztveiAQ6XmQgo6xY5dylU3KMltvZlLo4Bfd2Hjus9z67BfHkm8GRyVUslSkY8nrxBiCceICF703UALUOQgdzn9lFeA63LVQBKMLpVgR3iWQJhq2eqYGmB+Jz9FjlFA4RItTser25dAwA/DOBdAH7LGPNmIvoaMIXZi47XrLKh0m+YtFeiVF6NIV54QuBYNGKMFhMnYCUZ27rqecG0sph9zmAyQ87K156Aq/5WsXCTeKFd2+AciE7+TjuEwSELTV6jIP+xvZBj/rNpwHl17uwe/Jmz/xMA4OF7Wes9+/s2PHSU59C2Vt1wKUHWrxwSKR5HiMcl1h5SrAdzcJtElJxQqQCKUdo+O53DGVlUU2X3dvtMhNZyhVVLiSO9RJzVa+BCPwBQNtWNVYRkU8FhR2G4isodvYMSoH+8wh999xPutWY0xU9tPQgA+OBlzmmufoj5fjpgBbX5tqmb1OynawBYQacDg+3jhLl3WD6cdwDTZxeBkueRbVnkl/K2NNdbZfNX2nPVrSPEyxShWmYU8PcBFhBiX9KUP3mbgEGYU9GWvygBDWeOpopLzX5nugRAh+mcMWMF8mSWUNswziCbe54LYEMgjfJSZzgEJ8WYVQokQ88SXbee8EQBaTRYpLbNITnHdUfkFA0AXHtrhNblECCRTEJOQr4uufckBDfpRoHHkQyB2U/VXPPDuzle7WE0ALkxZo2IIiKKbDO1f76bA3dTZ/NXAPysoNG+XAZVxhMb1iLbwpYXvCyI0R7egSbizeoo75ctO61s+m2DtTeqjWGT1BJbj3J4i4akZ42fC3fG5L8lLzHYR+5YUTQAMH4z76ai7yV60dhBZd/0XssznzoGwFvDVcYdHzfus7Q4UxZaQpgIAIODNScQp90QZr30WZYW/UNeqo80zJW4X4wADoDQmBMP6MDv8fXW72PW51zlnYIDKh2Wouve18KlaPHz0kzJrXMRnriXXZHvPPIYfuyxr/HHXuSQc0t9fuPRAsh57sePL2Pz0wcDeG88ArY/ytbDeB/fS33Nz7G+4tkQZG4S+x/v4dyY8N3FY1ba0jFTrpNtem01OJC4vI6cVzp2VvEO4WUU+i9h71zyNMmYm/tpdBrA3HMA53NGi36NJyPjmML1kHuqbRjuhOlaGhAaaybguds+5hkMopyVrGt+ltq8j73etBPZOiC1j1T4UCsZGfEI2D7Bf0vOUYNBdOhZ2l7oTqKiiOobJYZ7YwdZlzlLWxAAOHXdk7iNYQC8ysNoADaJqA3gY+DmbdfAqLQXHbvxbPYC+DQRfRbAvwPw67YZz6t6VKlPQMZTxugLX1k6qFy/Df4sf0b3QgGAK1/p/842fUhMFMYNXWbDm0SSyrLZ+zY0U18LLd/JHPDW9z6Hz5xnM+u+/WxVP/vYUT8/9S1GOdD6TMM1HKsSg7TnN2jzqu1aOCugBybi7B9g6ZNMTKA8REktfMG7YvGowMzzLGV6xxsAyOWgJjO8sX2DsdBLG89H6J4vWMnYoRVNYJlPgWzqa44EiRdQwOgUJbEy1UKGvtuj8H7sw3/Uzn+HcrDnH+7n444f9wVWmw+WWPqUJfo8QIFHlw5CuiMgRN1Nu7wudPO3vOlDQ+IZO9qjjFFX6dBeb3+MZOzrcqKpGD78fzwNBbLkBQGPhJNi4bLGiki6x2Y9XmcStpq2KACxSLHv9azSZO89DNsBYR+jKAc65zygpX+A0Llk3FqVnKn2Ag15dJ2sA1dkSxw9kOsNbBO4+af99cVAk+sP9kXX5bTctWLfKkJCfaLEAaBzMQ+APHdtvOolJ94PYAzgrwP4bgAzAP7ebg7cDRrt/0VE/28AfwTMifYvieiXAPykMeauKPw/iFElwPobeLHNnLIejnWxh3sSxLnx/F+G3Xjtavf3x3AFEXNT0MEc/VXWMs2zCWbOequoqJNLIlMBdC9UwAV+b/tohLwFTOdsSG4utFbf8dbnAQB//IEn3Gu//N/f7f6ubVCgnMaz/Lv7qM+Mbn1xwYVHZMNJWGu4hwJl0D8QBWGwfZ/k3Z/0vHszONJyilo8GNeDRYUnAe6iubVAaF71ry2/LXG5AQCIJhrB5D9XNODaYAP8e6clD4QKZ/6pHUCKX1vC2CqAuCEehcojtIG8w68vPMzPbHvCJ1w/O+sUDeCT7cKYEBWhZ7XxsEHjKgXPb3gAqAsLNQHty9cTV2r248msRyuWGUAVYbzXKqUxIcr9ZzvnfFhMFKbjQLPMCoIYE54zqVOqUmUUgQtWW1fI9U7a83nLtdcWRRsFtV5VQlz8qPKemvNusI9Z0qWGqnvBwEQKvixM1mrtasUgYTVRKo31KmBhrvW41YO+vkDUAa+sNDFn0SCnvHTYW8AsNeVR5q2wHcLdGncTIEBEMYDPALhkjPlmIjoO4BcALIB7zfxpY8yUiGrgts1vBZMof+dLZWxW46hCI/+0ncdXA/jIix24q2ol68lctT8FgDkAv0xE/8dLn+vr4/Xx+nh9vDYHmd397HJ8PxgJJuMfAfgRY8w9ADYAfJ99/fsAbNjXf8R+7nbHLxHRDxCPBhH9CwD/YDcH7iZn8/0A/gyAVQD/FsD/YozJiSgC8DyA/+cdTPwPbJgY2PcJNl2iwtcDAEDvUIoypZDDbC5CwzZTK5oRJvNAfYV1dXKujv6xDItP8DmKBtcw+Cp/cuGI9TeV6F4gbB9VidCGh8xWtr7mvW/nNXR1xDGmTz73Nvf57gVlmduQyuCEN8O+5c2fw8eXjwFgr4bvkd8bLhHXxSirTcfmBRoqVuLmPXW0L+ZIbCJ/cKRlz+PnXzR86M/ENuw48e8BCHruGIX2kvvWaDRtcWuqGw9V979NjMBr0ozJkjMrWgrMsEGOdcFRyltPZP0Li+g+5ONGM8/GwVwEiqwt3tEerq0BgKpRYnAciPuxvSfyXg18LyGNLDSRyuFMDSazUYB+iqZA84LlyVs0MIkJcoK6XUHe9kATk7CnIO0XapvX0+YPVX+a2oZ97jZCXtQ5hyKeTfOaQe8oBaEmDoPJ54nrsXq8R5rLvK+kUj8ZM+JMI8IG+/0amn2hQFmLXFPAvBkFrBu83qIAkaaRboXlpdNeblCQaj86ULnE5jWFbsvZmwG+dO0F7ibrs+2g/E3gAvu/YUkyvxbAn7Qf+WkAPwTgX4FDXz9kX/9lcHSKbjMd8k6wsvo4GFPzc/BdnG85dpOzmQcXcp7TL9o+1N/8Eif6ihlpzzghlgxZ6kwtx5n0mpeQRVHnniJSNd9cKTFzOg6KvqpU9Z6f+K6SALD5YOWUSLoRY/URpjYBuHWwri2p2Za/Vx/2iYzlXgfdeZYq/dOzALySGd/PYa5HjnKG9FhrHf/tWQ95T8eE1uWw8LNsMsU84FFxklh2zcussmlfzDFeSDBe4BNMu4QyC1FUAQ+a3b/De3zYLb0aYkejKe34PyyMdDmZm4TNgjqkDTjqeUE69U4oGHZi0Lyk+M0UMKOcrUAVBQCI4jd9hjmxr4vwjwoOcwmYwz0DCdVMo+Desk1WAsJX52C86p6i0sN/RwsRKw8VhtQ5k5kzNl+hlERTtQw3BAcxl2p4oV4CfMM8gLux6nxHf1+EziXfc6ZKCaN538eoqBM6500Q9tJKU/dP4pvksJUgvuKJQd6IgwZtC1/0D76ss6KRsNl0htBY9dBpV6/TDY0cd+8x16yJAVHbCnOZYmDovI2mrOlcDOmY49w4Vgs95zsZBATkn3c4/jnY0JedvQBg0xgju+ciADHxDsIG7o0xBRFt2c+rKqRdjxxcDNIAF3WeMcbsikRuNzmbH7zFe0/f7L1X+qDKwy57R1laSvGhoZAAMe3buLpCzsB4b2gyS8GmlgZfl77KtpnuEaapsEeG80h7xNdSAm/9XVMM1+fc/+OVJqIxz61zhq8pcXwAePioL///7xfuRXK6EeRNhks7BJy6luvIqGo98pkKG1bhTmZStK6aAMZatIC8axPEU8L0oD/h3373rwIA/us1T5VUnfDHPv34MST9sFHXZI/ws4XJf1baypOxq1ULvERZr1v3C2eLvdmCAkUzPMjvS+dNAIgV20D64BZwecYZG9MZFlLiOU1meQ6S8B8fzBFvJTCperjqT1EyMqKCPQHJ1TjFrKlRlF6ePW35vywjclGLYBKfG4nHxiXl46lBPDGYOW3v0V46V3QuO9kIyHivqnOxsk35bA7IGhCat04jxTi3Y1weZbQnQW2zdJ06o8IgKnwPmbLO+RK5dzHEhvvYYpt2uMZGgAeNVQNDQPd84eY7XPI3UN+oUN/wnIOT+ZDRQJ5jwLweKBpy+atkxKAYjVTT9Em6gdsdj12JZQDAomVukfETxpifAABr5F8zxjxm8yUv5/g0gA8CeDuARXB76D9ujPn2FzvwNVtnA+M3QlEnDA6E/dnrK17wFXUuSJtKWGYjQm3D81TNP52jtj4J+NRGSzVMFxSD8QwL5BwZmpdih8DKtthCk1DMeC+QXcqAB1iKjlfY5J99hjfptAMMjlTYc4/Php7dmEdlTb/8iywppX4FCD0P8WQ011rRZJoXGVW9Qvt5D40ez5PrfpmMw88WbYNoK8UPfP0H3WvL+QzePseO8E8/+U48sH8ZTz9+jOe17SHdfL9e0ewcWlAAvihRK5vBfoTCPlF/V4S8BeSz/D3sP8mG3LUv+sKn9EEfFxpuN4A3T1E/ZVsZ1Pl6o73++gArGRkmMb4GyXo13VP+u0rGRnXWZOSfrJvhHhb4iUKzdVQDMAnnDFTRbXO1dFZ+bSN3XpZJCON5X29lCIjyyoW1JrMxqjREryUj4xSIeBDu2Vb83UsiXVBgmSXIzFsUsEB3zk9Q1mJnlJTgglzt/WR9T0dTpYSi5QluDQEz57y2MMTXlhbgZRpfx2ytw2y1daC1XKB3KLHnt+dRS0szb2hUX32DEYGpIiWNCt8GXivZOx0vwbNZNca87SbvvQfAtxDRN4K9iy6AHwUwS0SJ9W4OARCOhEsADgO4SEQJGEF2u92Wv88YI0rwCoD3E9Gfljc1afPO8ZpVNlUtwvLbLeqnVSHpkdu4Lu6tFmrZ9DHg2oZxfGr8QWAyVwtQRf1DMeI+LyxzcAy6whJ/4RnCtAs0LK3HYB8Ln/qKDdk12f2vXmBt1NziUIoOg2XrEbY+xdI/e9MmAK9kAA/BBlhA6hBVc9OeXwny8d4Kkc2dlF3ecIM3eQmYnvHaioodnoF9DP/4g9/qXotyoLyHYVrx8008//xxzFr03eBQCBXunOKHrHmqnNKnECZOBpgsqLBRq0S8kaBUXGlRK0e1YYswbdvu9mn+nnun92K0X8FfI2B8tuuKMXHcKxqAOc36Rw3SLRX+MkDzjH94QqnDz4Y/1z/C/88+Z4IaHSoNqgYFnT51jqpz3ioGBSHWXVCbNmdY22DtVdZj10uJXy/QP+jnZmKvlZvXStTXgWTA5+gfyhjt5loiW49DGVzSe4avxeFTF1IaG0zbEepWIY7n+bq1LcUivd+3xE4HDHuW0FV9o8JkJnI9fOqbBpNu7JRjlbAinsyyiMrboTEoBaSCYGstF/b6/nkPl0LlomHzZQ3oXAy/GzE+pbBUswzclWHMXamzMcb8bQB/G3BIsP+HMea7LVnyt4ERaX8W7IEAwK/Y/z9h3//vt1u+ohSNfu0/qH8/DOAtNzr2NatsygxOSDWuMpW8KJcys5QwwsNUAXUVEqltVyjrEeqWM231EZYW7vgaC8zmVQs3frbhakXE/ZcWBPE0zH8wv1SoIKjwNQCDg8DksLesi+dZydSVggF8jLv/yBTp1QxNRbRZNryVPjxcwaQGpfUOTGIQ1cL49cxbfa3K5hOs5GZeUEn+KVxRq1jx8fP+BtoXPJS1vsmWoty/QIe1AtLMxlR5gSIKybSUEGxULsSIPfYkqdSv2OS0IpQUz4r/BvIZ37eosZwxRNZOvX+U71Fg6QBQv+YFkIQy506wlbL9hYWw10+LMP/5bfSP8RcswmzvY35CvSNekQuMWcI547koIM8cz0XonpmgrHslovn8dHtx8UgcyIXA3rwkwe3ruomdrnlpXy5RZh5WX9SZtDOxNUrCSCDtNgCpw+Hz1jZKNFdKpxi3TtiePsLJtidG0YRvOW6A2naJwta+mESIPO39xISq4ck7TRwqv/F8zN6Zev5Bj6XcKhr79cdT36yusVpiPK/uQ2DT9nnUN3cf+3qx8SVmEPgBAL9ARH8fwOMAhPjvJwH8ByJ6AcA6gD/xJZzDTd3A16yyoQKY/xyvqtFeywulF2cRdgKMc6Mqv3nx9Q/V3GfHC3Dx2NqmXdhqYUnsebgnDjD+URHmUJIBb9qGQjHpry/bBLLNFMOD2iojx3OVbfImGTzIJ80usZYbHLZhkE3CaJ/yDrIKMAST+tcoUkncWZb0x2ZYoD7ZUxWK4E1b1AkzXA6EoskILT1nE3uWARGe4iWaOEzaw/jNHk984hcAihmbj7BoL5MYRFMvLBufb7g5yEgGYREkEHp+nXPG84dJTs46iUmfm8ylPX8NXYAqSmb9oj1grsD8457WaO8n2EJon/ExzbLtF1neTVHfLF1jOiBEujkSTOWFrj3slVNXhZ3yVhQksqdti6a038N4LkYVK4ViODSW9f0xqfpbWA0EqSjN1wRF6RoMrvpjOud8Aq1oxKht5K5QunM+R1mPXM+cssZ7UPZUVHIdjoQOJc3t8iXt0GtprBUoalFQyKqfk9D6aCMm6/nco66PIgM01koM94SF3LDn0wr5jsddroc3xnwEtsbFGHMawDtu8JkxuDXMyzFueoOvWWVTZT4WDzB/mXSvBIClT2mEmBVydiNUGQXklXkDgLLAZQOLG99YNVh7UHUIVEng2rpFDonFZTeXKCTxsmQM3jZC1csQdRTaq14g+gxLwckjbDXHl71QEmsTYIg0TSNAwY9b+/oYj/1One969onVdT7v+pMs8Wvgviru3psUeAN8D16ZpP2Q2l4UyU4FIwqX8wQyb/4tnkdvBiCF9pJcT31VeSt91Zkz5nybKJlkzD+OqHNkMJ5VOQCrR3W4prauLP8pKyJHjPn4AufGTrBknH+cJy5Khiehw2wVks0RRoe8xhrPxtcl4cX6z9scPtICXRfgjlSzMRjfDgK4Hr4ruZhUtb3Wx0cFC2KBFo86EaYd/+xcbkVB3GsbPidT26pgInJV+SBg82TdFTE2V0pMO5HbG+UkhN9LZ005nkqDqDBuH7SWC1QpBYwFyaRyeVKHtNMpu1RxvtnIhBiUZQ2Ye055yDVyDdt6hyIko9DDvivDhGvry3TclHbhNatsgFBA1ld9JffcUwgs87wVsdU1DmGmIjADOhVwIpLht7zAJ7NRcL6yYdA+ZzfRVYvmsSGQKuU6GFnoUcFIm847OZRVB7B4aICnz3FPgXqLpcHxrz8DAHj6iaOggpAM/AXLukHVtArTegJLR3wOb21L9bRfqWN1pe4hvHsniK54azwqGP7s+oO0w2fATa3Us7uBwCwa5GhwoimjvGLl3QmTsIk53Cjnbp2NAyUlVmvqdaM7DvCoLVEOo8WQKHJwQEJC6thIUaX04DpcAgwYiCfAdJbP27wUoXnNoHlNmIenqK2OAupmU1Mhr24dRTPGULUFNwREwipuq+Eb66X9HaLJxOjxuQTaIVw93N4h3uw6alhosgMAEAUoq8Z6iUk3Rv+An29ty/cfMjv43gBumCbPun8wQv9gTZHb8jlF+eXtCFVKTtnFY6C+njtlUcWEaFohmobSOHLKjhCXBtkmv1A2E/QPZu4+k7FBMvZ1VpUltxVl0z/MaFKNdlx9JPRksk3+e/RWvslyyAcvffQuislXP9PXi42bqujXrrIhn9eorwJ7Puvj6P3D9UDoFPWIGzFZq0vyDQIXLmu2oMzK5HgM1K96IaFrJXjxkwsJFA0KuMhqWwadc2NMFrz7s/Eo8HV7Gd3VLzP89uNvcO+NB6wxT33uOL+wUAVcaJN5u7n7yrOa85I9jioUPX+tuGTAgkCCYwsOENCEE9yayVrDlSVsoZTraJ/B8ID/f/6LxlHAVxnT8Cfa+1G2UTz1imhgz6FbOWd95flJesIu99FChNbVylGkRJYDT1il5fvaqazcPCLYhmx2LkrRACyMyxqhe8Y/z/GSt2CmszE6pwcoG95rnHZVi3GDAG6c9Q2yXhUwFWtLfmfCOp74WrEqCRmi04FVtJp7LPI5gyivAMROsfH1K8ckLSElgV8DfK99W+PTPceKRhc+a6t97rkRimaCUinbZGggRJoC545tw8KiEaNsxH5Prk1RNHxnTxNTkO+QXJV89xLOlVGlto22uv/mMgJuNgk9i0ISEk8620Sx9CXgqgHuWlHnK3i87tnsHCbyAIHuYwZVFmO4Nw3el4007RJGR1X/mi2mwRdOLQCIFb9XY61CPI0cNDRoGjUTsdBzG4PPK59JBxXGi5mzCNcfjPDQg2dwus874cmzB7D3d/z3OZkllCmj5QBOYI/fMEJlWaGjkd2USsGYaYzli4zjTrZiYKZEPPDnzFSVvQzJVdTetIH8U3NBKHAy72+weYXnrdFlODgCrrBpPP9FfklCRxUoUDQBE3CXAuYBUXSaWh/Gv563rFelFF1R960exEgQCxZgxaZh6ACw+bD1LC7FyNtAYRF6BYDm+dj1YhG0VlXzSkBaNQAcYlx/uO08vagwLrEOWGt9Eoa82FIXODKvIU21n4w9maVeV1SxQNbetwASAA7pzpwtw4Zj1wqn2KTFhld8FbdsSP28otygfdnnWFD6a7SuMnegeGd5lw8MOncWxqHhimaEcj51z6u2XQUoPBNlMDGhtin1BwZFM8ZoLxs/ooS0sjMxHH/bdImPm3kycc+ntlW5fVXUKCgDmHbCfFnrOY27v3sagqov+zja657NzpFtGez/GC+itF8yjNJag6OF2LPNglsITOZDqo76GifbZYwXgfZ5/jsdGqTD0ntGDR++ybYrDo2o9RvlqqZifxy0y40KYPlnjrn/73luhMl86TZpfd2gdyhy4ZG59yxjBsCVga+E75wh4Azvwua1Cpe/tmIlA4a8pmuJF4iWCt73qrf3+5APuz3yjc+6v59eXUL+wizKrmWB7gJz+3rAk/MAAHN0iOyL3trfPs7EpwIJ1tBgIKwQ33wra4l6lydnnuygsexDQQIg0GGw8R7vkc4+59mtAe/x6EQytyLmv4f7gOli6cASw2MVIqWEm+f57+aygvceiNG9UNi58/tSCxKVJggpJqMKJopcaEzCuKVqoxArxVLWgFEtckKwvhEqHg2jFRaF3kFVjDj1z6J9hQWtCHsTU9jrxYZ3qNTIDo9aTKeV7Wjr0WPJsHTnm8wnKFPy6LDZGHnLtzioEqDsEvIWi5ysV3HYzyqLvBXBEILanXRYIW8LKSmfm/zlUV8vFZAhwnTGszsk/QT1DePCrt3zRbDvuP5JKXFVDiDrxCE2NVT9TobBSynq/LIbdxlIfmeDiM4S0ReI6Ikd1bPyPhHRjxHRC0T0eSJ6i3rvQ0S0SUT/9eWd9evj9fH6eH28+CAYkNndz6t4vKqgz19jjLkZZ883ALjX/rwTTDL3TvvePwbQBPA/7/ZCtQ02XfJODBORe0yN9RLxqMJ4kR/PeJ6QboeJ73jiq6/jMSeTxRuazEQBgqy5UnrIZ0qIc3jae5vjGKkCtFghrpIhh1vk3P3D7GpIeKJ/mNB4j39cRRlhs99E/aKqOleEg+sPRKhfjgLql4CwEGzZafqa6VKOvQ2PIX161SdseudmgNQg7vEJu/duYHOtBezjk859tInxQshiMFogl8jtnrdULPIscxNUbNe7E5TP2jhXwrmiti0QFXCCgyNTWIhb1Lk/ivZkqgS+4r8AyjhsuEWtHI1nebLjJVsbdMV6gfYx6iR6PPEV/lQxesqBJUCgyqBu8yLijYowSQdhdfrO7wEIQzvy2Z3dOwG21HUYSkKHQUfVdZ9cqyytjHS77J4tghAgh/jCthrpyHtWybAEiBzYwRCHqaRNR9FglKJjJkiY4kbDs0cLHjjTPV9wK3YJuxluKhdZz0e65rr6oUnYcyoZ8rOS86fgEJvc/2ieGQhuRDorMGkHRogtUMR+jzqiccfj1a1IdjP+0M3eeCUqm1uN9wP4GVv9+vtENEtE+40xV4wxH34pPEFVSsg7arGOVOHe5R5GBztuo0Q7wi/JkMM/jsfpWo68E6N/0D9OKvzC3zquYNJtIFe1JEVzR5dKcC4o6fO1m7aHioSXKtvhUKrOh4dLJEWMhZbPcm89tsdtpCgHNu+JnLAvLQxauktKAlzXIwAI+s0cPuKV2dV1jksU63xCQsgoMHxiHrNXvGCW3I08u/oK1yHJGC2GdRsyJwBY+HiG3pHMpRxF8Go2BUccCh9em33eI7LiqQnqM8j4uol4wmEuEeKjB8eoP6vqWE4JnZG/RjJSaLdrFabtsKWwSTwljNzzVK0znTMpMwrCiCJIhdQS4GuJgG+s2FotBbvXykoQWfJ3FRMaq17BTOb9cY6PzV5++2iCsga0rqi6lwQY27l3LhdBCG/jPpbEM2fV+ZXwn7ZD3rqoYGXrEJwxAPLM3GvNBLVtuH46JrJKy4Ycs21Wbm4vJmwcCnQ9GXHPKSnUnHaA9mUTwJfLmicW1a3Ho5z3oQOeEAMgbkScesfjy1zZGGPWb/beK03ZGAC/QUQGwL8W4jk1HHupHcJseuWlXoiqkGCvftkX3q2+jXfAxjvY1Jl5PAtjrYZj3mJhjhdTRhbZuLl4BS07q2ToYdJSA6KFezzxx1QpEBWEhpXvkzkK05MRsPRNF3Dq2QNuLs0sx5lnGQqd9GJdQuOUklTRi2KQTQ7wZhupFgX7D65j+Sn2Xr7pfY8BAF7os9a4ClY06WZo3YsnJ2zWAjturgDLb/cWszQgk1j9aDFyAgawRZ4WddU74lFwgPUEah4MAYSKS4pwXWvjsQm8mp2tCgBgqLo71l6ow8S+oLbKfFsBfV7xFKOCq8sl6Z/v6HbZXNkJVbb5HMkbjAwLXJ1XKhjlJs8C4LbFAGASCqDQgVckHoDyHAb7CANLdNm5JPlIqWNhQ0AErEfm+e9CFzn291vvrdT3VzlvaDwXBR1gAc6BybPuMJjSrZMy822u+bpw59H35IyWuZiJQ+2ejSchT1qsup3KGKliTDEAJBepQSLTGa7l8jU5FaoEru7mrg0TPr/X2nilKZv3GmMuEdESgN8komeMMR+7Wycnor8A4C8AQNr20rbxRearW//qo+611ffl6Mxx5rj62iEGp2Yw95SFYUbcRrdjE8NVLcK1NyvhO2Y4r1hngBeEIpgkOdw7XgWewczzxB0qpcMigIPfcca9/3P3fAAA8JtHWBk8NjiOT68dwckHmI/m0kcOB9BhKrlHjkCNqQTyGU8DMnffGnrDOo7PbQIAxkWKH3vgF4AH+P1Pj47j/1x+BM8+7p9NcoPOmjVL61dm3IlUhxFnn1OovWmoEJrXKteXBgBGiva9dZk9QQ1NTibeYhbh0LocKivxEOR9ETpRHgqjokFB6Oq61gFghSOGgUDYt4/x+WZfqEJ0GTHiTRNKJoPC9ZwRxgntxU27vhWyPDNBKmqQCsA1N+IJynlE+NbXK3s+fn+4x4eFAYTAAgigxX8vM2dLVDEFCkYbJMLlJ8osGRusPRShZWmQpJ4qKNZVlxzP2zDXyL8XTXZ8VhUHVxErVwlJzj4XPovJDHvrotzLlDCZCcEiUQFVB8TEoJEKL4qyTvvWcLB7NLP1TK5ttAKt3PH4MvdsbjVeUcrGGHPJ/r5GRB8AUy9oZSPspTI0s+luzv8TAH4CAGYa+03jLEuX8YMHMNybYvnrdFcsv+kGp7jQwqin1TnnP7t9JObKdyvcqhqAITDWm9UK43jqiRoBcLGlIcw9YWPfESPMphame993PAcA+MUTHwYA/PjWPXhT/RwOJjz3f7/5Hlz4Pf9IkgIMSVUWOQy5/4WluHXcB6K//d7H3d/fMvM4rhZdXMg5kfFPHvs6VNMY6cgr2qbyI5tCKLrXS5bNk35zVimHZkTA5i0KaFEcn1wqsXgVrrRhId0kS3sOgnQSa7i2XTkSRYBDndNO2M5A1wS5nE/L/64yH6OXZyZhToYXcxEqACy/LQoUR/e0Pa7u5zBYqrlKec067OajilklpKaVTBUTKtVjRuYhQ7wnfg5+zTbWQkteoPzyemLrqITcE+DiY/EQhP1CFHCVEJorpRPQ6w/anM89ypBQ7RrkutqDTwchFVFR9zD2KgHSkXGGhFyfhK3hYMQtte3lBMKsczBamZmIlb9cv6xZ9KL9+Fg12ZPvQELpZT3CpBu5cKb21u5smNeVzSthEFELQGSM6dm//wiAv7fjY78C4C8T0S+AgQFbxpiXHEKTMT486/7evI+QLLOUK7pMez98gZVM6yIvtvYlL6nGC6oKXDaQqq6OpmBuF/DGF4EhfE2j/b6if/6J2G36aTvC5v1+cd/XXsZ9jau49yPf417bt+AVxcqn9wVklRIOKZp2UZOaDwCTGlQzBfZ1fXzq91ZO4G2LHJ38y099F+6bX8EnTh1372eXMmchSjfEzgW/aYZLvuCOqpAmJB4D4znvQbSuVkHeokpsL3llecvzzJsUFniO2ULX4Q1NMz/YF6F9qUKpivvSfhjaCZqn1f2c5bpUKs/GAg50gjjKmQ0a8MACCUVNZkM6GSkmFTDEzti/If6+tAKNSs9LFuXcnkDfT3O5QusyS9DJQuYo+Ple9X1GDIBQYcTB4cgxOcg9rz58YxolIW6VZ9S5UGKw338Zs88brD0K1JeV965CnsO9Hjjjzp/6ex3Z9grDffa+JxwSlRxLc5k9D/k/G3AdjiiLaZcVmrwvSX7n7dpwre9hEyFve5h72DfJekc1fy9FkzDcx/erWRPuaBi8rmxeIWMvgA9wd1MkAH7eGPMhIvqLAGCM+XEAvwrgGwG8AGAI4HvlYCL6HXDwp01EF8F9F379ZheLjpY49428MtMeBQnxmSfD6mSAPZLtI/xCVLAVqkkKs01vCad9ppiRMZ739Cwbj/Iqr1/ic9U2mQRQNhGHmcjxtP38x97j6P9lrD3bcHPbqWSqLEzuS65J2i/HY0K+6K3Z58/zbj9zjsNycS/BJ7DorFSTGHcNOV9UhElzbWmzBe3zDtN2iLKqMkKVhTUeZIBE5c9k00+7cAWUMmrbVZD0TcbGeQxtm5fQ89EWrInZuhbh0T8cfjbt2xyXetyOuw5KUdmpSghJnkV9g7nJxCqvYtgqe38+XSwroamdTc2kNbJ4UjLHaGqcogGAdFsbP7yWNSKtqJEDL8ha3XhQ3duGulHDAl97gRpBuHnS1hgpdNv+3/UPj8Na5IR71gM27iP37MSA0IZUQDpqgOFeUqFRExglWyesAlbP0sArF8c2bt8fz1HQu8e1Ogh4+uwaj5kTTry24RI3VpOeUPr7uePxGq6zecUoG8tY+ugNXv9x9bcB8JducvxXvpTrDQc1SJRLetQ3L4qA5ddTZdGYKLSApx0KhJ4OWw338zkbtsVA/4T/IDULRNdqyG0DssUvVq46G+DwBBVAuuWFRrYVQpEBb5lV6Y6QGaxiUYs6UT1hqppBtVHD8z1bmm8ZnmPLpEwGiFROJpoS8ra35gHuMSObtsx4U4vym3Z5o4pwSS0k1RX3CZFjqq6hnuO0472k+hqAKOywWNsoMO3yxUQ4SOhkPB8FLMaltaQFBBBPWNFIeKS2BdTXC1x7izfpk5FXAkCYdxNBJ88ynoZMCWNio0I3dysznyeScFrrqv9y4rFxOQEJJQas4OrZ1NdylLUYoyWWsM2rU0xnxAAyQfM+mXfNehLNlRKX3ucnlvZZsMcSdoptcbE1DBxhpZ27Mx50SLPnlVuVMKefQJXXHo6Qdwy2BLVuufrkekCYixNjS/JbeYvQWK2wdTxUMrowNy78PkiHHDKV+VPJ3kkhiLKKv1f9bGUkYxN4yFERfoe6dOBOx6u8huaOxitG2bzcY6YzxNe/77MAgN/52bcCCOsxtDXqmQDgPhePQ8uvrPvjRXlN3s47JwWQW6iwGaTY/7u+1/vaG2zVuUpKA0DZ5AXeuhAFLMqiXHQnwrKh8hi2X0t9zX/exHAxnEq1TAYAZAbpWhJYjI1rXtjUN4zb+AAAClF8VUyufw/ADNdl5r0TRyWjkFMBc69NOFdK+Qg4Qt6T66W9AnknccpIqs3j/MbWcu8wf3Hds4rPbKPAZI6Xfd3WnQi8HNhhxVr0UC4C1iawB2/wrlr9TM0JSEHOpXa+owUKjIRE8nYKMaY9kagwgael7x2AUzICspjMpy78GBU+bCf30VirEKsw2/yT/rxrb67QuOTrXJjORXms9nVBsQEheGMyB0wsQgxgNJeGbFMFZFsURAd0OLK5wh6pBsLoMF77SokyJSw8bWvhWhH6B/z1Z05X7CFbzy1vstEjBogweEvOJp74BnAAe7GyryczxCSrKr80eyoHrMKuX7kJed5LHQZA+dp1bV6zygYA/vvZ+/iP9/Yw3qqj9YxsZmvd7CgGDP5PvZUlAkX+T7d5k4mYyMcJ6le9FLvyXu/15F3+VG3dI6g4bGVrQWxRYoDygQ+VmQi2L4hXMkXDC00qLVuxfb+2Rigb+l4itC552HbDMi5DhRBqW8Ypg2y7RJVGTpmQYSvacWjVbDLWMTKHRZv8Gf/3+sPA4ufCRG+i4uk67LF9lCWFTopLfgPwymnzHiXAc6/IGtdyFM3YCfD+Acsfp66nmZ0kPKfrevRn65YCSPjCgDC8aGJ+DkGn1Guen2uwLwpCPXwt/xxayyFcGeDvU8gzdZ4rbzLHnn7OfJxFp+3j3j1rbw7zQ6IMBGnnvn8gqB8azzIYQiuPgBizBWyfjAKBPZ33gnX26QjTtgeUAOzhSb1PtUMSbR+OuYbH3k46rNC8FjJVJ8MwP2dibyQJtFkMwu75Iuz0uc93EY0KIF3zvHXNa0yTI8+3fttZ4Z3jdYDAa3JsDZpobnnJ0DidOsvKpNdXDQuaBRBvwr8noQFhBwasMjjHUiZST1nIO0dLNrxxmVDVwuuR8eGLKuWqeRc+sGgprZyKpkJQNcKEZt7hYx332ZR/ZKMJGqhz0VZoZ2z9iYdAlUXpkKDRiNsrtLWgg+9qOpXz+k3VO3Q9A/bWvf799TeQq21JlBEZ54ZbN9sQh6C0eke84m5d8RJEhIP02xnPU5Bj2DrBD1Vzz43nlJIb87w1F5YWgqJoFj+W2f8NJ6mVUA7qYKySkWc5mQXGi5EDFJiI5yh5jTILueK2LMRa7qHMCMnEuNoW/T1JEzHt2QlLNgCMl0qMl4D5z3tPunPePzut5GT096l7abMAF2XUWGcAw2C/V2ZAaJDNPu2Pb1/mhydIPROrwlKothJqGhrVV2Wcyxou+nBvlfjvcifHniFC97x312tX+6gaKfpH2DKsb5TOCKkSBqLUFFDDxP6ZXvqaGeCx6x7P7Y3Xlc3r4/Xx+nh9vD6+5ON1ZfMaHCoGUD+bor7ui9jSbdh+6vz/zoZgLkGsQgZlw+d5prMGqDzQIB57yv5oQqhSg+ZllWjdhocoS78Y20W0SpmdVs7VvLojTFbx3HSlfVlXOaCKvQ2x9suMkIx8i4PuuSlMGnkG4QEwVrQmUWm7P0pRqFTnW4tSGpDF1jpvLZcMQVWJ2HTgn99wv+XRWpMwou/ECTAyT4bktWqKCVjnfiYznPNoX7S5nWloWbeuGgz2kgMQyDPVSX9dM1TUuaBS6mriEVv07nyXDRcjqlAae3h8PvFGhUInGfO9ui6X8h2qhLPmIxNv0NPmh55ZMubr68JU3RZZezWb91uIs2JxFq8G4CLjnd5MlRKmqcwLzIqh7j/rGQellvCmMCyk27axnvXu0x5/P5JzAUJPhUr2SnVRLOC35WSGkW2VChWO5lVCTfJ29pkV9bAtRjLhcgOdsyqbfnHoUC5VJiiQrVLCYH/k1o0GCt3RMAiYum93ENFhAD8DRvAaAD9hjPlRIpoH8IsAjgE4C+A7jDEbxBDfHwUjeYcAvscY89k7nshLHK9ZZUM5oX6WF59Qw0gBG+DBAACHpQLwgBSKqc+YyOdfhBFA6m5MBGjOmXRAyO2mbFwLQzWaFBIA06Ab/5nRIlC0DbIttVlKf9y0y3Bdh/oxLOwF6VRfZ4inCPDJfILm5QmGB2p2rlxp7eC/JQuAiQgmQ5jOKMoXRmo7xNi0HQV1MNMuBUKIypAWtnuKfwslC+BzLELzosNUU5UEnz1lUNTC8EdZwSlGKTTtqzJgrSioAEZ7PHwcJuRaK+vcWE8XoTbW/L31DsWM4LLKQcOsAVY0zatQx3JRpM5D1Ta9hphabjEJmXbPc02ShOZ6Ry36ToUaNSx9uN//XV8lTOaAlit5ji1fmv3PXlZT90ixKuDXtqAQNagBADbvvR6K3LlYMYEULLJtWgVUPTpn4tCUsX9m9c3KhVzTPhsSMj+qQg5BUXqZbhTY9bnDxiq3IxC03nhxNrjfmlpvJiFUKQWINF37dCOC1NsbBjB3BSBQAPibxpjPElEHwGNE9JsAvgfAh40x/5CI/haAvwXgB3BrAuOXbbxmlQ0AjI7yjhsvxZh7KsLsC37n6MZTmyfDx0QVAuXh+qc8wwufFQJuisTRORVDlgxz1r8mBWqAhS03QqtVkvw8z1AINK5y/LkUXq01FobiCUm9Qve8R3pNFjJ3/uGSrfAX0FqNgqSzeExa0XbOGUf4KPUz0lZ6sI9zYRKLj3KuhxG4b1SYIA8BhIlvMsZpp2zbYNqlIMkc5z4pLtcWcsZ44lFJ8n+qPMC8w0JtJx3OyMKZhXdNe2m9Q4pU0z6z7eP2euMQot6ygld7a9NO5AAHnQt8grwlvYVCNJyg9nTtiTSAAzyAABCKm5AtQXc0FTJSxyhAYYJ/2mbYsEZFCqIR8M9Y149R4de+eM76+v19fgOIAVJfU8zTKQFWwNc3+f2lx3hzbNzfDBRh75id5xw/y9a5GDNn/P33rZJqLXuI/WhReegW6ZdteZ45PQb7YucxGmIDxnXTrYefve1xl9BotpD9iv27R0RPgzki3w/gq+3HfhrAR8DK5v24CYHxHU/mJYzXrLKhAiBFSRNNge0jvNo2vnaEg78YYesEP57eA3aDTHlBd05xdbYgXuobDLkUIQUAeccvqsbVMHxQ1lSCfosVjd5YtXUvzKmwxWVKMIhXBADV4hQwQPM565mk7FHJ+fM2kEN1GbWWsMBY6xtVuJkMhzBEEO2EeDvqFis/OYRXobQhkmRQwsTk4MW1bYMyY94qwLMKa5jspBN5zjTDtCUAJ6CnHU9uWV/LUV8DBgesVKAwsS2CuFLhFC1wmysV+vsi5wnVtnn+EspxLAliGFBYF+IEsX02UY6gkygV3EBPf5fJJGyZoJ/d9jHWItKpNBmbgH28rEXIW2E9l2YoALwg/L/a+/Igy87qvt+59239Xm+zL2iGkdCGECA5kqyUAFtCphw7BaYcS8E2IIe4yq6wKA6ODTiEICgTJzaO42BC2Y5sGYissFMOYNACCJBEtCBpJEYzWhjNoplR9/T06+633HtP/jjfud+5PT1bv/dmerq/X9Wr995dv+8u3/nO9jsHr3LkoHNy8qEXZEKiz5GGb48YPrQ5o4lVDzNmN1BRu6+bvBv3DGggByD3UMkw51MHMRUjCev7O+6Y/p2jDBh5vuu3b3Zx5DxpcNxlJMbUpUJm4726LENSpzxiUJ/r+n53vJiQVr1mpFGR1ixXmpVjNjdXUJrzjNHD+2SypffNtqNn9NlnQ0TbAFwO4D4AG4wA2Q8xswF9JDDuBStW2JTnGGNP+VFh8jo/qtQeHcKLl6DwkqugAZCXnLMv/txaYPYlPvO7+qKZqZvBL1fJ3fPbGZdZmA7uaiJRTqgoKWpIaa3IEFDbJQfXWaiaF1KtV1MSX4GjUstDmRVK1a7mqbgl2oKeU00rVuBYX9X6BzsFQdheVSq0l0lmlcoE0FpDEpGW+fWWnysZivIE16jLqB/MChT9uaCBXLfSLGNmkxkMIj+7ru+XcFZloAZEG1BzjmosGsm0EAeWNS1xSYSPRt6lNWERaDvTmwopy2Jtza+A5AZZYlBbElvC7aOCsNJaOICYODujkR8Eh7wQB7ygAbymMfMSoyV2geYmNae6NpvQ67Tqo8rUB6htiToiPOaXQdBcJ46pkDNEmQjC8Z3mYSGgNOfKQtfkntros+aWWn7v1fymEYgb7yXnZ/F+FemHFWherdLJj75XST1yJcfV1xTlwmZoIsXsujjXZLtDVNBGay/2xfQlOHlhs3ZeAclPzWfBJ6JhAJ8DcDMzH3HsK+40zI49f8lgxQobO9s8/4anAACPffOCfBmlwLZffCb/P5uUcegfznH7AhgCZs7xx0ga/oGs73GzfPeetVZ7e3VWlpdaTTda10NfurQmM2TrW7CcU1Mb3Cz/oD/3mseTvJYOsbRPhQPH4oC3AyDIv6zqZ1Ahx7H4KHR5eUYCJdRJmpWA8Z3d3MRD7lsZcmWhNYMh15b0f9cV1gKE8yoZ8jVhstg78NWcpuaN6ZdWct8TYGhcjOnGTgAkGZYLWpz1G6g2ZUNwmZALwu6oDNCWgBWRX68TB2vWs/4dyqRejnVGW9r7uM0Ff5fuY82Icdtfj6QuGrWWpZ68MM65zoYOyTp9jvJcnJpvfHvUCxlilpBrt/30FipMItTUG5tCbXHHBC+wtLVb8Mn4bRuujd0Rf/60GuVlpDP3vKhmxlHxmVeNpz3uykK7a6jfSU18X1ZDnt3gJXulmUl5dxtcYoIR2mMEjuXBSivyLFlNDOQTRm3CcW/gUxE2h5j5imOtJKIyRNB8mpk/7xa/oOYxItoEQPXQngiM+4UVK2yy4Qyz18gI+8MntqG2p1zwq5z35l1IMpdDkMhDPLvJDAomyietZ6gdMoMtRNB0TdSa2vI5cgJBE/3y6C6/b4Ek0LmR1LE7/BM1h6RuW0ZrVZzPkGc2St6OtbcrdYccT8gdbaZ4d9ibx4Z3q8/FrWYZzHUQoxSYPqecm7q0oJfWihcyS286SstiklNTGEeEiqmZklYkAVYTFxv7OZ9NZ7EkHiplS1KTYAk1Z83X+lTQ6GAdt4s+jm4jQpT4onfaRjvYt0cpDxLIB18VZlExcCTSRFhTV7YY5YS8z4rGviw3/el620b7HCRDhGQozvcvzzJKs/7aWZ9F7rdyi1rjsZjgtGuZI7pUgUBFh70NZFBYQaMCPepYwSgmVECoggCfcDs/KEYnI+1VRTOaQn05qYlYmz7HC4+kLhq5Nfna50bbN71V18cFk1/T5QOpb7QyhTyfqjpVjMxr7JM+xC2vtfUFDCDrXUty0WV/BeAJZv4Ts+rLAN4O4GPu+0tmed8IjBeLFStsAKBzxJtkKkeA6FqZ9m0bF5tPydlLnnlSwnyqhkJdI88AIHb0+9Y/UKibYpzGyoSrZaFzc4VlNzYRPsDR2dUqaAD/Es+YImCVKb9P3AIq8xLebEZ6Z1R8Qmoy7A4TunX/P8qQ07UD0vYo4dzcYZ2w2m+OPVszuaTHfMCck99+/6JZJxmiPKw67qKYtc5ynXQA1eUqjNpjEuaspqvW6iJrtM5c26Y+CceeeYCdljHs/EpHXhoVOOaqk4zqZJHtoHrYmOPc8e0MP614f5lqBVpbSPritxXhYvi9zHUD5D4kQ1SYjNj6NtUpH22ms3h9DilzJjszbtpESHaTDaslKl8a4MOCtW+UFQMMLKuD7FzUCLKyu76mfIKasQBHe1TzflI9hoUVCElNynbYSLHKdIppN6TlgsbtMrw/w8TFRqs071h7vBhi3m1EMolSLXGPZaLtEX0QNgCuAfBWAI8S0cNu2fshQubviegdAJ4DcINbd0wC49OJFStsiBiVgzIq1Q4B0c96SVGL5W2+76Hz82XVQ37UUvp+G/ZZavpZr2bs64tQe9HYwl0+ip0N29ny7GbnyGzIi7jqoaLRv7EvE7ObG4Q0y9zOEm20W9yRAah2WI7XHo3RPKcYoAD4AbtbR14mAJBITZvDAzgKGI3mmTfpi7sAJ1yoimlJDXMzgvsa2S116zV/ptL0A2ZrlYSyWvbdZMjnP6UVuZ42DLtQPMv1Sfm3NOIq1zhiRwXkBsXqYUap5We5tQmJYNL26H6WidqyEeTRTJGPtIuNJqDmOnu/C4XznkvF9GMG5MpUkpez0Gg7y5hdKHmdMWK36+QWQn1/8bnQawT4/JS89HEqwQxqplRNWO+XXDdronSzfhPVVSAQ1cqZRtMrz2T5pKXszGmtVf75bplJkA3CUNh7SyyTifFdrlRHKsEVG+/vFNpi27f+QX8x4laGuXUuQMNNNspNQ5ibMUoz8uAcuWgU6EtWCvclz4aZv4uj3rwcr19ge8YxCIxPJ1assOGWHwGnX9UGJhr46QueBQA88ID4bkZ+4h/+2Y1mUBmWh3b0fBmpk7tXF2bQ6rS0L581a1mn8fAv7sdV657L/39lx6XoNitYc5/faGR3F13nJFdfiZISxp2iFpUXx5rzJqXa4RTTL/G3enwn55rQ+C7Ju5m1RaiM6aqxL8vt89J2mX3rAF9qzdO8WF5eNWXlJKbObJhWI2mbM+NVmixFrjreZ6OOerXjq88krcpx9Ji1iaLWlVWcYNFxEnClhE3zDGfd3Fo5hnWSU+ZDXpV3Tc0oHBdJRIUHjjDqKnPOrosLmohqOtYnZPOo4pY0dew5P8g19ie5H0z7UT1sgwTMQGpKk2v7Dr3Sn+vNv3k3bv3+awAAa38gz4/m16RVR5KpgSm55qJtKw6KUcooTxdzoayfTs3Dtj3xXAbKNKPVDegzNgzcPzgqaNQHlZVQLPvcLt7H9irkggYQbaTbiArXKqsUTdtgzk1jAAoTAcoYSd1H0gHA7Pp50R29ggHuT57NWYmVK2xKjM46efN+/bL7AQB37LgcAFDaPIuhbw/7GXQNqB721S5TAONbvIMnet0k0ntX5dT0alKY2mYKrBlh1H2dVxV+YfPjONQdxld2XJov23RnCRVXq0QHntohaeusqylvZ3zzK1+WZzi3oQNAUvft0AFw4w+8c+DAT/mpduWICIv6C/4ESSP2A3qJHB+Y/Fe7t/pLOC7myaRlAiJvsmEix/0m28xsEq61fP/Iz9Y5kkFRNQGtNWP9GvMFXVb1ybTEIgxtNGCUohCiXpnmXOsTk5e/lhoKax3HUdcPqHFbklFVKJVakgekJZrVxGXDozVcHvA+F+sbIHN+DROOupbDzD9IdrBUhmtN4vyl3/KCBgAaB6SPtuyz1crm1kRihrO5Yca5roJGkyQBmTSpD0cnJ/beR8bXoUJYNZkoKYaED+9N0B6Li5GM9t4qt1+ivkIJs7Z9kPYt3D+dLBXaXxj9jg7iUGtEX9EHzeZsxYoVNgEBAQGnHYEbbWWivlqmyJ/f9Wqk20eBi8QROPTtYXHeqkaeFRkBSkdiNLevQv3iwwCA9N5VAHwWdFYmNDfbVHDvTJ1bD8QAfv3CBwAAn9kp0Y0j94hBujrFqBzxpoCZTXLSkjFrRImnTZl4eYTh3ShYcBNTMbHboILPhFJgeHcLz1/ntZn2Vu98Ou822W/ikqrbHxjfmebJcHGHAfbRR3G7qLUxiflD7evlGZlh5tfOmWu0ZAKTmJY01LqzOsWah1xkW0no/VXDjFsA2EcUdUZFw9IZcGVS+lc2mk9s/BBZBUgj7y8rz3Ahn0I1jKlzvfnE+kQq0yk6I3EhD6ZloqvUz6F+NKAYHFJz1C8ju53GQEU/gfS5aJ6ijHMzXmdYckUKGo0yamdFp/fXP/o6bIL12WSgbobGXlnQHSkVorkAz4QAAKu3S7v0fmXVCDC+otyn59rWHiaUja8pj2TTrxIVzMppjVBqFatx1g4ZdoEKoTNays1qgGhR8xNwC2H2RmvQJE7VFLlEBW62uMP59VJznfqXKkcYlWnGrGO9XvVknwQEc78CBM5KrFhhE88R6AEZ8ZKG44A6KDG1rbXCk2YduUxAVnNZ2q6SZesRETKR8z8UBAxM1BR52hQmIHlyFLd/x/nxfmYKlW+M5ftEHUZSiwqRNxxToU5KpcmYeLmzL0fCmbXqx9K2tEKF6o6acGhrrhy8fCjPFD9yvuy39QvOeT0MNDdZ/44IGjXbJTXC3FoqDGy1CV8MjiLJWtcsfSFD5DwCSwkT82TRKtBZl4AcvU1lIkZzq14LZ6s3+R/lGR+w0NwigkYpaLKqDIJaNkFNPJrvxG3Zfuxp6UyUZChNOSJSAJ3xCqa3lr1JKOffUhOUsiJkeV8S41fQUHcrYGxwRf0gF30hLKSnVUehEnWKA6T88RF/OjCryUxq2vtNLX2NCkR15GcxkIyU8vs4s1GDGPz+o88af4sm1ur+Jck7KU/7UHeOi1RG9rnTdlnB2RmhPGReo9tsUicbExxHhJGfpJ7ENCK0DXdZbSJFWovyewMUr1t7PEb1cOp9SF3GzIZSIWpOJyFR7seSb52obf6OsVX3CZymJ95omWLFChs7O9WILA1H7o4xumNA6iLCKgdLSE3SpubYFBIvJ/yLM76ri6nzykX7txvcS64YmkaMlf7vWIHcUek+bMGuUguI3TN66Go3MA2LEbv81FAuaLRNxMC0yxzXmaFNaFMBI+2OgIkKDr5a/g8dKJJNZmUCGDiyZV5GthN+PkzXt9f6kKpHhA5Hnf5pRYSRvtjdNVLUqr5X60X742gUmQqbKBFtJOcK2ye1d3ItqSSTBD12aU6Er94XjqWIljqC67tb4HKEzmqnxQ1FktDqatwMu1o5mlio/VHUDqfAYWDyfH+jk4Z/LrSQnQ2rnd1gQpVdYELJOM3jdorOqOught5OaCRhlNeP8RdJvkZdbRpbrTPu+PujfVY/UqFgHHy9GYvYUucMSUKm5lMB8mzkNEhOk8ivdYmQ1vwkRdul1C9JlQrkq6qh2Mg/+46mVQmJt3Q+lHqSUkqBcpIW7lVrTSlnFTiyzVFRne/X6zXTZ06f29aqCMN7ukfxp/UODma0lYhsKMtflJmt8tCVN4kEKAFoT3pVIhkuajSA0JZYRzXgSskCmN1QQnnGF+GizM+ugSLFRk6VMqohqFyYLZdaWvrArW9FoLVtDD3s1a5uwztKVRhoeC7HVHhpJ1/TkiNN+hA2G2wwsicRU5ghxmytigrEm4XIuhIKwQjlphQUU0fx7HrRZLQNaU0GOp97UkLU9iZLjk1ocgkoT/ltoy6jW/fsA2lFC2hpY+RLB/GsJHkdOoMdmkilXe4aTV08jINXMDZ+V9tGqB5OUWnK9t06FXJFNMrORuLNrotzU5veQ70+Q0qTk0c3FStzNvYWE6ridor2an9fps4rYXxXgokLjFM79tfDMhcoS0JeAqIlCZCJYxBIhiQSzdLbjDx/9MBntYPOqC/73K1H6NajPJgCAEqzRbMXAGgp5bRKSCu+DHOlKfduyNDvzDdrWdomkGhX7VFHUupMZHNrioI/r5ZbJyT1Um4S09DumU2u+m5bTHZKqTNqCsdpbpfVnKxQtVF4PYERAgRWKpqvkJe9sqcCXNREZ8ILmPpuf2k6LoHTUsSUZ72ZTXNnDr5aHuzG/mK1x/KsNxtESTHKR2e1Mxv9IGOpZXQmPLfB7PP0UJ7lPvKc7uNf/NJsgsmLpHHlmQzTW81s+qka2muzPL+jO8yoThDWPyz768x0xlZpNGWNKRUTmNLtUAaUbHJgLOYJHWS0wqnOWMszrtyAeecKs+zMH5tJ9tXrXGppYqNbr1nq7lhR6sKlNVqsA3QaUW7fn1st/pbJS9ypqoyRnRGawkKEypT4R/LcE3YzcXdNVKCr/0wHSxUyXBItbHiPy4avRAVfmoZIW02mutdHNaZj0jGb2HjoUvOKKuPAav2mXLCVZoraZTIkQtWagg+/3G+w/n5pmDISZDGhNJvm5lBlPbcJsJbDLnYJmZ1xnwO0UKa9jQirNFmuCfzkQzXuboNQPZzl10tNhpHxE7VNCfCsIlqaar2xm4go9LgVo4mmFco1/UIkG4mwqh420XfGl5Y0iubxnhBCn1cgmETIOND2YWiEbE7NoUmZB8nVspf/yhOmfo/OmMyy1VY/9TIqUNnbuumUidZgnakcEcacVqR0HipkEseVVt8r/7vDxYJfSlNvZ5mdsTIa++X/9Dlyi20uzrr/51/K6S3ye9/VRriuSzD6BPLzAX62Xj+QYmaTf/k4Uj+RM9fVyeWjuL67PCDVPlprRCBphndac9nlWmdlzvs+ahOuvIIyBIxSISBBBaOtzVI/4P1HaUmW5Zn7LlBj5Bm11UuhNM9sLIJG/VG589ydJ6lTgYtL/Wravvq+rGDmKc+pacvM4Fu+feVpOZEKmQNXiu3UCgj7u73emXVflAtQe9Hf17SidDT+WgC+YBwAjO7w7dDQevUTAcW8FMqknoxlGbC8dDFE0Ngy2JT5ZF4NEVcfDyBBCbkPRicfTZ2EcV52Ij+HCYTIYuFCsz4i68ujzGlPxlws/k/3n+AY1/16eywNVwdEcGXVqOBP6gcYAAfNZgUipdw8VmpGRV+BZp7rxCcR2hYVPmnVlQYwJglb0bDsJqtKwcJUzN8oN7N8cM6qEZJalPt3NFlTafJtkiLgBU1ee4cI1f2+IMve149j1Y4kFzKUij9JZ8ONfVwQfuM75fe+a/3LNvJkKX/RRp+VjPr6gWJiYU6C6ZoR59+Sda9+D9VoDl/lTUbj91fyPjX2S/VHpYrPSl6zSYakLMDsOh/5hsgLsrn1IrS08mVWAmY2RDkrd2Of5rs44VMT7je91mreU99QpZkh7phkQlJNya9n8qzPTJJ7ZStv2tl92w18jX2dwvpSUz3SjHRkCNPn+ogCW08mbsnnyEVGo2hGSKtudr6acmp9ZSy2dDI2uqzqBFPNmPGsoOmMllBqZYWIMStYFTmB69qyMDLoY5ipQFczms8Nk37F4q8wIzgZjVZNsZYnLjZm68q0VIzN6yJ1HG2SvqtqQjVt7g55rZQyuBwtp5XawIZE8qOsZmMFjc1z6gnMQbNZiaDUCRm4ATEuho5aP4aGzNr1c+tNbZes+OLoYGcFjKVvSeo+ikZNMTpDjLvFAYcjYPQ545R2hIW1vdP5smRVHS9cKVPgtAYcelUpp3iJ02LBq25dZu+1yaKpbmx75I7PmF1X9AfUJn2xs86I+CzyiodTfnDLr41hI0gawNzmFNXnKvl2tUk/qE2fI5pQXkeFgHUPSOOnX9aQMG/n40qqol3MrZdN04oMxhpU0a0DiHzwRbcRgVLOGbCHdwvJ5/BeHaEo93UAYnJMzew+JwTVSGiW+2P5yIQih/LrJNfIr1+ozHUy7IhdN1QK1DNpWSY0KhDaYzJYju6wJRYYccv4keZ8G225b72WFrXDPgy8uakEYuQD7Hy26Sgp8rIBovnpREwTXq2Zq8Ce0EwLpJrz12tUnvX36QQF8BM+S5JqaXw00MBq7NY3mpUIsUnATYaKdYGsKTvuAGDf3zQSzUu11L4RcWJlR6MRr9DoCCI6CCGrGwTWAjh0wq3OfoR+Li+shH4uto8vZeZ1J97s2CCir7nznwwOMfPP93K+pYYVK2wGCSL64fFqUSwXhH4uL6yEfq6EPi5VRCfeJCAgICAgoDcEYRMQEBAQMHAEYTMYfOrEmywLhH4uL6yEfq6EPi5JBJ9NQEBAQMDAETSbgICAgICBIwibgICAgICBIwibRYKIbieih93nWSJ6eIFtthDRXUS0nYgeJ6L3nMr+SwG99tOtfxcRPenW/dFpa/wpoA/380NEtMcc4xdOawdOAv24l26bf0dETEQnmzNyWtGHe3kLEf3I7f8NItp8WjuwXMHM4dPjB8AfA/jgAss3Afgp93sEwA4Al5zs/kvts5h+ArgWwDcBVN3/9We6HwPq54cAvPdMt32QfXTLtgD4OiQheu2Z7seA7uWo2e7dAD55pvuxHD5Bs+kRREQAbgDw2fnrmHkfMz/ofk8DeALAS052/6WEHvr52wA+xsxtt/7A6Wnx4tDr/Twb0GMfPw7g36PAJrg0sdh+MrOh0UUDZ0FfzwYEYdM7XgvgBWZ+6ngbEdE2AJcDuG8x+y8BLLafFwJ4LRHdR0T3ENGVg21mz+jlfr7TmV/+mohWDbCNvWJRfSSiNwHYw8yPDLyF/cGi7yURfZSIdgP4NQAfHGQjVwpWLBHnyYCIvglg4wKrPsDMX3K/34ITaCVENAzgcwBunjdrOqn9B40B97MEYDWAqwFcCeDvieg8djaK04kB9/MvANwCmQXfAjHf/Kt+tPtUMKg+ElEdwPsBvKGf7V0sBv1uMvMHAHyAiN4H4J0A/mNfGr6ScabteGfzBzKQvgDgnONsU4bYuH9nMfsvhU8v/QTwNQDXmv+7AKw7030axP0022wD8NiZ7k8/+wjglQAOAHjWfRIAPwGw8Uz3acD3cutSvZdn2yeY0XrD9QCeZObnF1rpbMZ/BeAJZv6TU91/CaGXfn4REiQAIroQQAVLl1l40f0kok3m75sBPDawVvaGRfWRmR9l5vXMvI2ZtwF4HuJg3386Gr0I9HIvLzB/3wTgyYG1cgUhCJve8C8xT00nos1E9A/u7zUA3grgumOExB61/xJFL/38awDnEdFjAP43gLezmzIuQfTSzz8iokeJ6EcQ4fpvT1urTw29PrNnC3rp58eI6DF3L98A4Kjw74BTR6CrCQgICAgYOIJmExAQEBAwcARhExAQEBAwcARhExAQEBAwcARhExAQEBAwcARhExAQsGzh2BwOuGjIk9n+BkPO+ZlBt28lIQibgCULImq6781E9H96OM7NLgO+H2262IXJPkREL+vHMc2x/5KILlnEfpfZ8GQieiMR/X4/23YW41YAP38yG7r8mvcBuIaZXwHg5sE1a+UhhD4HLAkQUYmZk3nLmsw83IdjPwvgCmbuOZnUDeIlZv7IIvc/qp99aNNNkP69s5/HXS5w3GdfZeZL3f+XAfgfANYBmAXwm8z8JEn5ix3M/JdnrLHLGEGzCSiAiK50ZJI1Imo4c8KlC2z3NrfdI0R0m1u2jYjudMu/RURbT7D8ViL6JBHdB0mKPJeIvu+SIz9izrVNzSBEdBMRfZ6IvkZET5Gpj0NEf0FEP3Rt/k9u2bsBbAZwFxHd5Za9wZ3nQSK6w/Fjze/fZUT0A9fmLxDRKqc93Azgt/VY8/ZpEtHH3fm/RUTr3PK7iehPieiHAN5DRK93mtGjzsxTNdtdcbw2uvvzPXfd7yeiMQAfBnCj07hudNfoz0/i2v+ZO9bTRPQvTuExOdvxKQDvYuZ/AuC9AD7hll8I4EIiutfd+5PSiAJOEmeaLyd8lt4HwEcA/FfI7O99C6x/BaT+x1r3f7X7/gqEIQAQEsovnmD5rQC+CiB2/78M4G3u978B0HS/t8HxUwG4CcDTAMYA1CB1VbbMa0cM4G4Ar3L/nzVtXQvg2wAa7v/vYeF6Jz8C8DPu94cB/Kn7/SEco24NhITz19zvDwL4c/f7bgCfcL9rAHYDuND9/1sICaRud8Wx2gih+nkawJVu+SiEA+wmPZe5Rnru4137OyATzksA7DzTz90An2f7/AwDmAPwsPk84dZ9FcAXIJxp57r7NH6m279cPkGzCVgIHwbwc5CBb6HKmtcBuIOdWYqZJ9zyfwpAnaq3AXjNCZbDHSd1v6+Bpxi57Tjt+xYzTzFzC8B2AC91y28gogcBPAQRiAv5P652y+8lqeD4drM/AMBpC+PMfI9b9DcAXnec9igyALe733+HYj91+UUAnmHmHcc59rHaeBGAfcz8ACB1V/jEJrnjXfsvMnPGzNsBbDhx95YFIgCHmfky83m5W/c8gC8zc5eZn4FMqC445pECTgmhxEDAQlgDmQGWITPxmQGea/6xT8aJ2Da/UwAlIjoXYhK5kpkniehWSNvngwD8IzO/ZTGNPUXYvpzKNVywjUT0yr60ysNeR+rzsZckWMolPENEv8LMdxARQTTgRyCksW8B8L9ISl5fCNEkA/qAoNkELIT/CeA/APg0gP+8wPo7AfwKEa0BACJa7ZZ/D0KACEjRqe+cYPl83Dtvu1PBKGRAnyKiDQD+mVk3DSn9CwA/AHANEZ3v2t4gYaPOwcxTACaJ6LVu0VsB3IMTIwKgvo9fBfDdBbb5MYBtev5jHPtYbfwxgE3kCtAR0QgRleb1bz5O9tovSxDRZwF8H8BFRPQ8Eb0Dch3eQUSPAHgcwuwMSLmBF4loO4C7APwuM794Jtq9HBE0m4ACiOhtALrM/BkiigF8j4iuY+Y7dRtmfpyIPgrgHiJKIWarmwC8CzIr/F0ABwH8htvlWMvn4z0APkNEvwfgS8fYZkEw8yNE9BCEDn43RHApPgXga0S0l5mvJYne+qw65gH8AcRkYvF2AJ8kCZl++jhttpgBcBUR/QGk9suNC7SzRUS/AeAOJygeAPDJ4iZ8cKE2MvMOIroRwH8noiGI7+F6yMD4+87k9ofzTnmy135Z4jga7FHOfxbHze+4T0CfEUKfAwL6BOoxVJuIHgXwRucvCAhYVghmtICAJQAi+kcAjwZBE7BcETSbgICAgICBI2g2AQEBAQEDRxA2AcsCLlN+zjnJez1WgWvsFPa7kYh2EtFXe21DQMByQxA2AcsJu5j5sj4c5zIACwobF0G2IJj5dgD/ug/nDwhYdgjCJmDZwWk5Tzr+rx1E9Gkiut5xXj1FRFe57RqOm+x+x1X2JiKq4GiusQ8R0W1EdC+A24hoHRF9jogecJ9rzmiHAwLOAgRhE7BccT6APwZwsfv8KoSq5b0A3u+2+QCAO5n5KgDXAvgvENaEDwK43VGZKM3MJQCud3kb/w3Ax5n5SgC/DCCwBAcEnAAhqTNgueIZZn4UAIjocQifGrtclm1umzcAeCMRvdf9rwHYeozjfZmZ59zv6wFcIkwnAIBRIhpm5ma/OxEQsFwQhE3AcoXl/crM/wz+uScAv8zMP7Y7EtFPL3A8y20WAbjaEYEGBAScBIIZLWAl4+sA3uXIGEFEl7vlx+MaA4BvQGhg4Pa7bFANDAhYLgjCJmAl4xaIj+ZHztR2i1t+F8RM9rDjIpuPdwO4whUk2w7gt05PcwMCzl4EBoGAZQGaV/r3DLbjZyHF1f75mWxHQMBSQ9BsApYLUgBj/UjqXCycFvQJAJNnqg0BAUsVQbMJCAgICBg4gmYTEBAQEDBwBGETEBAQEDBwBGETEBAQEDBwBGETEBAQEDBwBGETEBAQEDBw/H+rdy+yu1l+8gAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rds.where(rds!=rds.rio.nodata).plot();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f84f86f13c8>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "merged.where(merged!=merged.rio.nodata).plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/pad_box.ipynb b/0.18.0/.doctrees/nbsphinx/examples/pad_box.ipynb
deleted file mode 100644
index 85a07939..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/pad_box.ipynb
+++ /dev/null
@@ -1,649 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Pad Box"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
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-       "<title>Show/Hide data repr</title>\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<title>Show/Hide attributes</title>\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-053c0e9a-c4a5-4eb1-a6a9-5927f728b70e' class='xr-array-in' type='checkbox' ><label for='section-053c0e9a-c4a5-4eb1-a6a9-5927f728b70e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan 687.0 687.0 ... 491.0 504.0 504.0 515.0 469.0 485.0</span></div><pre class='xr-array-data'>array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-d1b518c1-3206-403c-88a0-bf2caa89205a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1b518c1-3206-403c-88a0-bf2caa89205a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.05e+06 5.05e+06 ... 5.004e+06</div><input id='attrs-5988f292-8b05-4239-8369-28f590630fca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5988f292-8b05-4239-8369-28f590630fca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d26e778d-54c2-4b2d-9bf3-2d70de3fa945' class='xr-var-data-in' type='checkbox'><label for='data-d26e778d-54c2-4b2d-9bf3-2d70de3fa945' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([5049992.781974, 5049761.125615, 5049529.469257, 5049297.812899,\n",
-       "       5049066.156541, 5048834.500182, 5048602.843824, 5048371.187466,\n",
-       "       5048139.531108, 5047907.874749, 5047676.218391, 5047444.562033,\n",
-       "       5047212.905674, 5046981.249316, 5046749.592958, 5046517.9366  ,\n",
-       "       5046286.280241, 5046054.623883, 5045822.967525, 5045591.311167,\n",
-       "       5045359.654808, 5045127.99845 , 5044896.342092, 5044664.685734,\n",
-       "       5044433.029375, 5044201.373017, 5043969.716659, 5043738.0603  ,\n",
-       "       5043506.403942, 5043274.747584, 5043043.091226, 5042811.434867,\n",
-       "       5042579.778509, 5042348.122151, 5042116.465793, 5041884.809434,\n",
-       "       5041653.153076, 5041421.496718, 5041189.84036 , 5040958.184001,\n",
-       "       5040726.527643, 5040494.871285, 5040263.214927, 5040031.558568,\n",
-       "       5039799.90221 , 5039568.245852, 5039336.589493, 5039104.933135,\n",
-       "       5038873.276777, 5038641.620419, 5038409.96406 , 5038178.307702,\n",
-       "       5037946.651344, 5037714.994986, 5037483.338627, 5037251.682269,\n",
-       "       5037020.025911, 5036788.369553, 5036556.713194, 5036325.056836,\n",
-       "       5036093.400478, 5035861.74412 , 5035630.087761, 5035398.431403,\n",
-       "       5035166.775045, 5034935.118686, 5034703.462328, 5034471.80597 ,\n",
-       "       5034240.149612, 5034008.493253, 5033776.836895, 5033545.180537,\n",
-       "       5033313.524179, 5033081.86782 , 5032850.211462, 5032618.555104,\n",
-       "       5032386.898746, 5032155.242387, 5031923.586029, 5031691.929671,\n",
-       "       5031460.273313, 5031228.616954, 5030996.960596, 5030765.304238,\n",
-       "       5030533.647879, 5030301.991521, 5030070.335163, 5029838.678805,\n",
-       "       5029607.022446, 5029375.366088, 5029143.70973 , 5028912.053372,\n",
-       "       5028680.397013, 5028448.740655, 5028217.084297, 5027985.427939,\n",
-       "       5027753.77158 , 5027522.115222, 5027290.458864, 5027058.802506,\n",
-       "       5026827.146147, 5026595.489789, 5026363.833431, 5026132.177072,\n",
-       "       5025900.520714, 5025668.864356, 5025437.207998, 5025205.551639,\n",
-       "       5024973.895281, 5024742.238923, 5024510.582565, 5024278.926206,\n",
-       "       5024047.269848, 5023815.61349 , 5023583.957132, 5023352.300773,\n",
-       "       5023120.644415, 5022888.988057, 5022657.331698, 5022425.67534 ,\n",
-       "       5022194.018982, 5021962.362624, 5021730.706265, 5021499.049907,\n",
-       "       5021267.393549, 5021035.737191, 5020804.080832, 5020572.424474,\n",
-       "       5020340.768116, 5020109.111758, 5019877.455399, 5019645.799041,\n",
-       "       5019414.142683, 5019182.486325, 5018950.829966, 5018719.173608,\n",
-       "       5018487.51725 , 5018255.860891, 5018024.204533, 5017792.548175,\n",
-       "       5017560.891817, 5017329.235458, 5017097.5791  , 5016865.922742,\n",
-       "       5016634.266384, 5016402.610025, 5016170.953667, 5015939.297309,\n",
-       "       5015707.640951, 5015475.984592, 5015244.328234, 5015012.671876,\n",
-       "       5014781.015518, 5014549.359159, 5014317.702801, 5014086.046443,\n",
-       "       5013854.390084, 5013622.733726, 5013391.077368, 5013159.42101 ,\n",
-       "       5012927.764651, 5012696.108293, 5012464.451935, 5012232.795577,\n",
-       "       5012001.139218, 5011769.48286 , 5011537.826502, 5011306.170144,\n",
-       "       5011074.513785, 5010842.857427, 5010611.201069, 5010379.544711,\n",
-       "       5010147.888352, 5009916.231994, 5009684.575636, 5009452.919277,\n",
-       "       5009221.262919, 5008989.606561, 5008757.950203, 5008526.293844,\n",
-       "       5008294.637486, 5008062.981128, 5007831.32477 , 5007599.668411,\n",
-       "       5007368.012053, 5007136.355695, 5006904.699337, 5006673.042978,\n",
-       "       5006441.38662 , 5006209.730262, 5005978.073904, 5005746.417545,\n",
-       "       5005514.761187, 5005283.104829, 5005051.44847 , 5004819.792112,\n",
-       "       5004588.135754, 5004356.479396, 5004124.823037, 5003893.166679])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.274e+06 ... -7.228e+06</div><input id='attrs-67d14f91-6c70-4cb8-9949-f13bd02d3656' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67d14f91-6c70-4cb8-9949-f13bd02d3656' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd9c7350-99e3-4f2a-9b3a-5b4f07d56c37' class='xr-var-data-in' type='checkbox'><label for='data-fd9c7350-99e3-4f2a-9b3a-5b4f07d56c37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([-7273893.821307, -7273662.164949, -7273430.508591, -7273198.852232,\n",
-       "       -7272967.195874, -7272735.539516, -7272503.883158, -7272272.226799,\n",
-       "       -7272040.570441, -7271808.914083, -7271577.257725, -7271345.601366,\n",
-       "       -7271113.945008, -7270882.28865 , -7270650.632291, -7270418.975933,\n",
-       "       -7270187.319575, -7269955.663217, -7269724.006858, -7269492.3505  ,\n",
-       "       -7269260.694142, -7269029.037784, -7268797.381425, -7268565.725067,\n",
-       "       -7268334.068709, -7268102.412351, -7267870.755992, -7267639.099634,\n",
-       "       -7267407.443276, -7267175.786918, -7266944.130559, -7266712.474201,\n",
-       "       -7266480.817843, -7266249.161484, -7266017.505126, -7265785.848768,\n",
-       "       -7265554.19241 , -7265322.536051, -7265090.879693, -7264859.223335,\n",
-       "       -7264627.566977, -7264395.910618, -7264164.25426 , -7263932.597902,\n",
-       "       -7263700.941544, -7263469.285185, -7263237.628827, -7263005.972469,\n",
-       "       -7262774.31611 , -7262542.659752, -7262311.003394, -7262079.347036,\n",
-       "       -7261847.690677, -7261616.034319, -7261384.377961, -7261152.721603,\n",
-       "       -7260921.065244, -7260689.408886, -7260457.752528, -7260226.09617 ,\n",
-       "       -7259994.439811, -7259762.783453, -7259531.127095, -7259299.470737,\n",
-       "       -7259067.814378, -7258836.15802 , -7258604.501662, -7258372.845303,\n",
-       "       -7258141.188945, -7257909.532587, -7257677.876229, -7257446.21987 ,\n",
-       "       -7257214.563512, -7256982.907154, -7256751.250796, -7256519.594437,\n",
-       "       -7256287.938079, -7256056.281721, -7255824.625363, -7255592.969004,\n",
-       "       -7255361.312646, -7255129.656288, -7254897.99993 , -7254666.343571,\n",
-       "       -7254434.687213, -7254203.030855, -7253971.374496, -7253739.718138,\n",
-       "       -7253508.06178 , -7253276.405422, -7253044.749063, -7252813.092705,\n",
-       "       -7252581.436347, -7252349.779989, -7252118.12363 , -7251886.467272,\n",
-       "       -7251654.810914, -7251423.154556, -7251191.498197, -7250959.841839,\n",
-       "       -7250728.185481, -7250496.529122, -7250264.872764, -7250033.216406,\n",
-       "       -7249801.560048, -7249569.903689, -7249338.247331, -7249106.590973,\n",
-       "       -7248874.934615, -7248643.278256, -7248411.621898, -7248179.96554 ,\n",
-       "       -7247948.309182, -7247716.652823, -7247484.996465, -7247253.340107,\n",
-       "       -7247021.683749, -7246790.02739 , -7246558.371032, -7246326.714674,\n",
-       "       -7246095.058315, -7245863.401957, -7245631.745599, -7245400.089241,\n",
-       "       -7245168.432882, -7244936.776524, -7244705.120166, -7244473.463808,\n",
-       "       -7244241.807449, -7244010.151091, -7243778.494733, -7243546.838375,\n",
-       "       -7243315.182016, -7243083.525658, -7242851.8693  , -7242620.212942,\n",
-       "       -7242388.556583, -7242156.900225, -7241925.243867, -7241693.587508,\n",
-       "       -7241461.93115 , -7241230.274792, -7240998.618434, -7240766.962075,\n",
-       "       -7240535.305717, -7240303.649359, -7240071.993001, -7239840.336642,\n",
-       "       -7239608.680284, -7239377.023926, -7239145.367568, -7238913.711209,\n",
-       "       -7238682.054851, -7238450.398493, -7238218.742135, -7237987.085776,\n",
-       "       -7237755.429418, -7237523.77306 , -7237292.116701, -7237060.460343,\n",
-       "       -7236828.803985, -7236597.147627, -7236365.491268, -7236133.83491 ,\n",
-       "       -7235902.178552, -7235670.522194, -7235438.865835, -7235207.209477,\n",
-       "       -7234975.553119, -7234743.896761, -7234512.240402, -7234280.584044,\n",
-       "       -7234048.927686, -7233817.271327, -7233585.614969, -7233353.958611,\n",
-       "       -7233122.302253, -7232890.645894, -7232658.989536, -7232427.333178,\n",
-       "       -7232195.67682 , -7231964.020461, -7231732.364103, -7231500.707745,\n",
-       "       -7231269.051387, -7231037.395028, -7230805.73867 , -7230574.082312,\n",
-       "       -7230342.425954, -7230110.769595, -7229879.113237, -7229647.456879,\n",
-       "       -7229415.80052 , -7229184.144162, -7228952.487804, -7228720.831446,\n",
-       "       -7228489.175087, -7228257.518729, -7228025.862371, -7227794.206013])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c5764eaa-c59c-4881-8426-5defd01b3eea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5764eaa-c59c-4881-8426-5defd01b3eea' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>+a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +units=m +x_0=0 +y_0=0</dd><dt><span>res :</span></dt><dd>[231.65635826 231.65635826]</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodata :</span></dt><dd>-28672.0</dd><dt><span>transform :</span></dt><dd>[ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e+00\n",
-       " -2.31656358e+02  5.05010861e+06  0.00000000e+00  0.00000000e+00\n",
-       "  1.00000000e+00]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fcb90621438>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pad using a bounding box\n",
-    "\n",
-    "See docs for `rio.pad_box`:\n",
-    "\n",
-    "  - [DataArray.pad_box](../rioxarray.rst#rioxarray.raster_array.RasterArray.pad_box)\n",
-    "  - [Dataset.pad_box](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.pad_box)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xdsc = xds.rio.pad_box(\n",
-    "    minx=-7.3e+06,\n",
-    "    miny=4.99e+06,\n",
-    "    maxx=-7.2e+06,\n",
-    "    maxy=5.06e+06,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xdsc.values[0, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fcb8ed54ba8>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xdsc.plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/read-locks.ipynb b/0.18.0/.doctrees/nbsphinx/examples/read-locks.ipynb
deleted file mode 100644
index ac44f228..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/read-locks.ipynb
+++ /dev/null
@@ -1,229 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reading COGs in Parallel\n",
-    "\n",
-    "Cloud Optimized Geotiffs (COGs) can be internally chunked, which makes it possible to read them in parallel from multiple threads. However, the libraries `rioxarray` builds on, `rasterio` and `GDAL`, require some care to be used safely from multiple threads within a single process. By default, [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio) will acquire a per-process lock when reading a chunk of a COG.\n",
-    "\n",
-    "If you're using `rioxarray` with [Dask](http://docs.dask.org/) through the `chunks` keyword, you can also specify the `lock=False` keyword to ensure that reading *and* operating on your data happen in parallel.\n",
-    "\n",
-    "Note: Also see [Reading and Writing with Dask](dask_read_write.ipynb)\n",
-    "\n",
-    "## Scheduler Choice\n",
-    "\n",
-    "Dask has [several schedulers](https://docs.dask.org/en/latest/scheduling.html) which run computations in parallel. Which scheduler is best depends on a variety of factors, including whether your computation holds Python's Global Interpreter Lock, whether how much data needs to be moved around, and whether you need more than one machine's computational power. This section about read-locks only applies if you have more than one thread in a process. This will happen with Dask's [local threaded scheduler](https://docs.dask.org/en/latest/scheduling.html#local-threads) and its [distributed scheduler](https://distributed.dask.org/en/latest/) when configured to use more than one thread per worker.\n",
-    "\n",
-    "By default, `xarray` objects will use the local `threaded` scheduler."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reading without Locks\n",
-    "\n",
-    "To read a COG without any locks, you'd specify `lock=False`. This tells `rioxarray` to open a new `rasterio.DatasetReader` in each thread, rather than trying to share one amongst multiple threads."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "\n",
-    "url = (\n",
-    "    \"https://naipeuwest.blob.core.windows.net/naip/v002/md/2013/md_100cm_2013/\"\n",
-    "    \"39076/m_3907617_ne_18_1_20130924.tif\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.4 s, sys: 361 ms, total: 2.76 s\n",
-      "Wall time: 3.32 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, lock=False, chunks=(4, \"auto\", -1))\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: these timings are from a VM in the same Azure data center that's hosting the COG. Running this locally will give different times.\n",
-    "\n",
-    "## Chunking\n",
-    "\n",
-    "For maximum read performance, the chunking pattern you request should align with the internal chunking of the COG. Typically this means reading the data in a \"row major\" format: your chunks should be as wide as possible along the columns. We did that above with the chunks of `(4, \"auto\", -1)`. The `-1` says \"include all the columns\", and the `\"auto\"` will make the chunking along the rows as large as possible while staying in a reasonable limit (specified in `dask.config.get(\"array.chunk-size\")`).\n",
-    "\n",
-    "If we flipped that, and instead read as much of the rows as possible, we'll see slower performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 8.58 s, sys: 1.08 s, total: 9.66 s\n",
-      "Wall time: 11.2 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, lock=False, chunks=(1, -1, \"auto\"))\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That said, reading is typically just the first step in a larger computation. You'd want to consider what chunking is best for your whole computation. See https://docs.dask.org/en/latest/array-chunks.html for more on choosing chunks."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Caching Considerations\n",
-    "\n",
-    "Specifying `lock=False` will disable some internal caching done by xarray or rasterio. For example, the first and second reads here are roughly the same, since nothing is cached."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.49 s, sys: 392 ms, total: 2.88 s\n",
-      "Wall time: 3.25 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, lock=False, chunks=(4, \"auto\", -1))\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.48 s, sys: 292 ms, total: 2.78 s\n",
-      "Wall time: 2.97 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By default and when a lock is passed in, the initial read is slower (since some threads are waiting around for a lock)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.15 s, sys: 284 ms, total: 2.44 s\n",
-      "Wall time: 5.03 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, chunks=(4, \"auto\", -1))  # use the default locking\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But thanks to caching, subsequent reads are much faster."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 223 ms, sys: 64.9 ms, total: 288 ms\n",
-      "Wall time: 200 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you're repeatedly reading subsets of the data, using the default lock or `lock=some_lock_object` to benefit from the caching."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/reproject.ipynb b/0.18.0/.doctrees/nbsphinx/examples/reproject.ipynb
deleted file mode 100644
index 16da3f3e..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/reproject.ipynb
+++ /dev/null
@@ -1,1177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reproject\n",
-    "\n",
-    "To re-project with dask, see [odc-geo](https://odc-geo.readthedocs.io/) & [pyresample](https://pyresample.readthedocs.io)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "import rasterio\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataset(\"../../test/test_data/input/PLANET_SCOPE_3D.nc\", decode_coords=\"all\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
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-       "Data variables:\n",
-       "    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368\n",
-       "    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-45eb2830-a807-4070-8270-eafb894091e9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45eb2830-a807-4070-8270-eafb894091e9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-866810c8-7763-4ad0-884c-659a75f5c365' class='xr-section-summary-in' type='checkbox'  checked><label for='section-866810c8-7763-4ad0-884c-659a75f5c365' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-28b007a0-1d57-44a2-9044-da130e8cfa2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28b007a0-1d57-44a2-9044-da130e8cfa2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0ff534a-88e7-4944-ba89-b5645ea219f9' class='xr-var-data-in' type='checkbox'><label for='data-c0ff534a-88e7-4944-ba89-b5645ea219f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.663e+05 4.663e+05 ... 4.663e+05</div><input id='attrs-34ea2c4f-000a-4ce2-b62e-a8dfbcc906f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34ea2c4f-000a-4ce2-b62e-a8dfbcc906f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53a4c842-cfce-4d59-b370-8f002ab69088' class='xr-var-data-in' type='checkbox'><label for='data-53a4c842-cfce-4d59-b370-8f002ab69088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5, 466285.5,\n",
-       "       466288.5, 466291.5, 466294.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-12-19T10:27:29.687763 2016-...</div><input id='attrs-85843e76-c4c1-44ee-b8fe-9c6f0b2c934a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85843e76-c4c1-44ee-b8fe-9c6f0b2c934a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08a858f1-899e-4560-a155-b91589db4ab6' class='xr-var-data-in' type='checkbox'><label for='data-08a858f1-899e-4560-a155-b91589db4ab6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-12-19T10:27:29.687763000&#x27;, &#x27;2016-12-29T12:52:42.347451000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.085e+06 8.085e+06 ... 8.085e+06</div><input id='attrs-d2563e2f-3d5c-430b-b264-1aa3c57398b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2563e2f-3d5c-430b-b264-1aa3c57398b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fbadacc-5d51-49a4-9648-0a1ab3420bc4' class='xr-var-data-in' type='checkbox'><label for='data-3fbadacc-5d51-49a4-9648-0a1ab3420bc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,\n",
-       "       8084680.5, 8084677.5, 8084674.5, 8084671.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba0f4c5e-f47a-4f11-988d-a3e5128f44bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba0f4c5e-f47a-4f11-988d-a3e5128f44bc' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a28b5fc7-3647-4072-a0fb-b8aad6f664ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a28b5fc7-3647-4072-a0fb-b8aad6f664ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b7f31d-2d0a-48f5-8f1c-db719140f271' class='xr-var-data-in' type='checkbox'><label for='data-e9b7f31d-2d0a-48f5-8f1c-db719140f271' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd><dt><span>nodata :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[[6.611017, 5.580979, 0.399607, 2.052803, 5.479985, 4.760219,\n",
-       "         5.077927, 5.574792, 0.726683, 5.170288],\n",
-       "        [4.535516, 0.088263, 4.222302, 0.289199, 3.478147, 3.227945,\n",
-       "         2.736443, 2.821799, 1.04221 , 1.099616],\n",
-       "        [0.071364, 4.393267, 0.496907, 2.311926, 1.19123 , 5.984189,\n",
-       "         5.266977, 1.146988, 3.219185, 0.982011],\n",
-       "        [5.818552, 5.176065, 4.891903, 4.557147, 4.706706, 4.670835,\n",
-       "         2.344188, 0.493237, 2.707488, 4.15662 ],\n",
-       "        [0.78458 , 3.838047, 0.803847, 1.588861, 5.002692, 2.565792,\n",
-       "         5.195116, 2.642173, 1.47148 , 6.854931],\n",
-       "        [4.407593, 6.734126, 3.702884, 4.682068, 0.498895, 2.931409,\n",
-       "         5.713952, 6.388658, 6.618634, 3.267176],\n",
-       "        [6.175152, 2.331721, 6.829246, 1.650656, 5.530157, 0.167316,\n",
-       "         6.406627, 4.559968, 0.083601, 1.231478],\n",
-       "        [2.438085, 1.593681, 5.902827, 2.124327, 1.870033, 4.519462,\n",
-       "         4.845505, 6.214475, 2.237778, 4.178488],\n",
-       "        [5.855639, 5.824333, 6.006212, 5.43411 , 3.393885, 6.183958,\n",
-       "         1.284061, 0.028555, 6.726706, 1.516993],\n",
-       "        [0.844103, 5.61329 , 6.978045, 1.183779, 2.539376, 1.268038,\n",
-       "         1.27574 , 5.101129, 2.020225, 2.081746]],\n",
-       "\n",
-       "       [[6.242907, 6.394763, 1.302492, 2.895077, 0.537664, 5.012127,\n",
-       "         4.380069, 1.624007, 5.56236 , 4.303491],\n",
-       "        [6.745937, 3.653031, 5.116965, 1.835256, 3.514732, 6.523679,\n",
-       "         0.17986 , 2.444248, 5.169099, 0.584623],\n",
-       "        [2.119518, 0.479052, 4.274714, 6.019049, 6.346329, 2.858441,\n",
-       "         3.976887, 4.218218, 4.320734, 3.941107],\n",
-       "        [2.974649, 6.67996 , 3.16875 , 3.507162, 2.909711, 1.359386,\n",
-       "         4.023015, 0.258809, 2.007625, 5.744746],\n",
-       "        [2.572982, 1.250802, 4.652006, 6.310958, 3.266477, 3.216613,\n",
-       "         3.47315 , 1.713415, 5.9575  , 2.729016],\n",
-       "        [2.743341, 4.203021, 1.306215, 2.840115, 5.351451, 2.774677,\n",
-       "         2.621753, 5.288077, 0.722618, 4.086008],\n",
-       "        [3.168672, 4.031863, 5.775087, 3.487762, 6.034522, 4.721887,\n",
-       "         5.093018, 0.386024, 2.278799, 1.536351],\n",
-       "        [4.548959, 4.797754, 6.371152, 3.69425 , 6.855343, 1.987757,\n",
-       "         4.749654, 1.750539, 1.804795, 4.966536],\n",
-       "        [1.78139 , 1.544275, 1.750026, 3.373274, 2.604254, 5.894015,\n",
-       "         3.217723, 5.637478, 2.900347, 2.097813],\n",
-       "        [1.844174, 6.994962, 2.504717, 5.292132, 0.184411, 4.870834,\n",
-       "         1.888442, 3.491315, 5.055704, 3.368395]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-488de4a2-274f-4896-a2c7-8eaaf18ca6d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-488de4a2-274f-4896-a2c7-8eaaf18ca6d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b28137b-0d86-4df6-82ed-3dc2a3937270' class='xr-var-data-in' type='checkbox'><label for='data-8b28137b-0d86-4df6-82ed-3dc2a3937270' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd><dt><span>nodata :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[[ 7.920639, 66.150832, 30.096116, 30.437197, 57.794734, 11.538647,\n",
-       "         14.426782, 35.593171, 53.784858,  0.449093],\n",
-       "        [23.804111, 67.910347, 18.694533, 30.41474 , 68.117674, 44.906057,\n",
-       "         62.311842, 37.485047, 57.134336,  7.52572 ],\n",
-       "        [36.653481, 39.596833, 61.07603 , 56.883093, 29.635613, 64.114699,\n",
-       "         42.341689, 54.724789, 31.872344, 11.282554],\n",
-       "        [32.502176, 28.090549, 58.398927, 41.224019, 34.804777, 32.184565,\n",
-       "         59.392327,  9.155824, 52.967172, 67.409236],\n",
-       "        [18.79468 ,  8.543429, 35.834698,  3.596245, 30.252802, 41.549499,\n",
-       "         23.060248,  7.267762, 27.374099,  0.684024],\n",
-       "        [ 9.93227 , 44.509446, 22.01927 , 28.514121, 36.715233, 15.03939 ,\n",
-       "          2.43399 ,  0.636075, 34.43023 , 37.024545],\n",
-       "        [28.874823,  1.514255, 34.210822, 10.49793 , 54.025491, 64.294026,\n",
-       "         36.212813, 17.766628, 45.295952, 10.349576],\n",
-       "        [58.961924, 47.334638, 64.844646, 37.634131,  7.815025, 35.139303,\n",
-       "         18.501505, 35.01185 , 27.761908, 13.240655],\n",
-       "        [49.544668, 57.716538, 27.389778, 11.604377, 24.826367, 15.449456,\n",
-       "         26.482386, 42.855739, 10.4958  , 59.267182],\n",
-       "        [37.094093, 43.294246, 33.240747, 16.85513 , 54.705119, 14.633291,\n",
-       "         35.138742, 50.101683, 57.495953, 52.795405]],\n",
-       "\n",
-       "       [[26.137058, 16.448086,  4.503539, 33.351036, 20.32524 , 63.369743,\n",
-       "         11.531512, 38.629561, 59.821441, 11.547508],\n",
-       "        [ 6.438471, 28.948907,  9.949052, 23.234921, 65.539507,  9.822554,\n",
-       "         55.754023, 51.590388, 57.047098,  4.929671],\n",
-       "        [32.360472, 17.939979, 52.889505, 69.181176, 39.2923  , 56.442225,\n",
-       "          5.699603, 21.092554,  8.93472 , 23.810367],\n",
-       "        [ 3.715695, 30.653733, 44.540496, 48.578544, 24.032477, 30.339109,\n",
-       "         37.376636, 58.787274, 49.308994, 59.510765],\n",
-       "        [37.991912, 50.023013, 63.711135, 26.546118, 60.561058, 36.098302,\n",
-       "         10.725673, 40.51609 , 47.479255, 42.710909],\n",
-       "        [15.32887 , 15.878984, 30.914778, 25.902812,  3.815428, 35.530366,\n",
-       "         48.426293, 44.280075,  4.468083, 18.00032 ],\n",
-       "        [58.286993, 20.397714, 63.124   , 69.117495,  1.32436 , 29.024715,\n",
-       "         31.601531, 34.307982,  7.990292, 36.159696],\n",
-       "        [34.674335, 62.344993,  5.714717,  2.161448, 66.714977, 52.443751,\n",
-       "         12.791859, 63.707491, 13.697063,  9.394474],\n",
-       "        [16.766346, 26.260123, 40.68158 , 18.695085, 64.812126, 23.151592,\n",
-       "         50.597642, 61.308205, 31.517123, 23.469797],\n",
-       "        [44.670989, 17.533084, 39.034907, 32.676726, 53.275139, 48.731172,\n",
-       "         12.958856, 21.760335, 27.292202, 18.409063]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79eaaf20-0b46-43be-b9cf-7f1abeb0b6f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79eaaf20-0b46-43be-b9cf-7f1abeb0b6f9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 ...\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
-       "  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 ...\n",
-       "    green        (time, y, x) float64 ..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f8f13620880>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.green.where(xds.green!=xds.green.rio.nodata).isel(time=1).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.reproject()](../rioxarray.rst#rioxarray.raster_array.RasterArray.reproject)\n",
-    "- Dataset: [rio.reproject()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.reproject)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds_lonlat = xds.rio.reproject(\"EPSG:4326\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * x            (x) float64 -51.32 -51.32 -51.32 ... -51.32 -51.32 -51.32\n",
-       "  * y            (y) float64 -17.32 -17.32 -17.32 ... -17.32 -17.32 -17.32\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
-       "    spatial_ref  int64 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368\n",
-       "    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f82895e0-94c5-41e7-965a-f309f40a772c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f82895e0-94c5-41e7-965a-f309f40a772c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7fbb1a9c-ea36-44de-b015-b0bf3be03674' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fbb1a9c-ea36-44de-b015-b0bf3be03674' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-51.32 -51.32 ... -51.32 -51.32</div><input id='attrs-38e090bd-85be-426b-ade8-c39502fa16a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38e090bd-85be-426b-ade8-c39502fa16a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-defa9d2d-d31c-431a-82e3-3ec3e7b18ea0' class='xr-var-data-in' type='checkbox'><label for='data-defa9d2d-d31c-431a-82e3-3ec3e7b18ea0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-51.317454, -51.317426, -51.317399, -51.317371, -51.317343, -51.317316,\n",
-       "       -51.317288, -51.31726 , -51.317233, -51.317205])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-17.32 -17.32 ... -17.32 -17.32</div><input id='attrs-e72e0d56-f2e8-4f20-9c4d-f081f99576c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e72e0d56-f2e8-4f20-9c4d-f081f99576c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31d9d357-a70b-4b4f-8217-ab20870c7c7f' class='xr-var-data-in' type='checkbox'><label for='data-31d9d357-a70b-4b4f-8217-ab20870c7c7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-17.322876, -17.322903, -17.322931, -17.322959, -17.322986, -17.323014,\n",
-       "       -17.323042, -17.323069, -17.323097, -17.323125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-12-19T10:27:29.687763 2016-...</div><input id='attrs-0206b871-06ba-474f-8f44-3a47010cb630' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0206b871-06ba-474f-8f44-3a47010cb630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe148f3-6ccd-418b-8a5b-234a95472c42' class='xr-var-data-in' type='checkbox'><label for='data-dfe148f3-6ccd-418b-8a5b-234a95472c42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-12-19T10:27:29.687763000&#x27;, &#x27;2016-12-29T12:52:42.347451000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-15f96bc1-395f-4770-af20-9603b114ad1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15f96bc1-395f-4770-af20-9603b114ad1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42902900-c1fd-4910-a111-c16b9cc4a195' class='xr-var-data-in' type='checkbox'><label for='data-42902900-c1fd-4910-a111-c16b9cc4a195' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>-51.31746780720891 2.7678699822269306e-05 0.0 -17.322861663733065 0.0 -2.7678699822269306e-05</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b678e340-da44-426a-a4f7-d951b9ff6f77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b678e340-da44-426a-a4f7-d951b9ff6f77' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.611 5.581 0.3996 ... 5.056 3.368</div><input id='attrs-7c8b5103-9e66-4954-95db-974807557881' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c8b5103-9e66-4954-95db-974807557881' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51ed04e1-5d6e-4b55-9838-56991d4657a9' class='xr-var-data-in' type='checkbox'><label for='data-51ed04e1-5d6e-4b55-9838-56991d4657a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd></dl></div><div class='xr-var-data'><pre>array([[[6.61101706, 5.58097901, 0.39960727, 2.05280345, 5.47998484,\n",
-       "         4.76021916, 5.07792715, 5.57479217, 0.72668295, 5.17028805],\n",
-       "        [4.53551623, 0.08826297, 4.22230213, 0.28919903, 3.47814709,\n",
-       "         3.22794508, 2.73644333, 2.82179869, 1.04221025, 1.09961647],\n",
-       "        [0.07136352, 4.39326719, 0.49690677, 2.31192634, 1.19123023,\n",
-       "         5.98418893, 5.26697738, 1.14698827, 3.21918509, 0.98201077],\n",
-       "        [5.81855223, 5.17606488, 4.89190312, 4.55714657, 4.70670585,\n",
-       "         4.67083516, 2.34418779, 0.4932369 , 2.70748782, 4.15661977],\n",
-       "        [0.78457995, 3.83804728, 0.80384702, 1.58886148, 5.00269192,\n",
-       "         2.56579153, 5.19511563, 2.6421726 , 1.47148022, 6.85493052],\n",
-       "        [4.40759261, 6.73412648, 3.70288412, 4.68206812, 0.49889505,\n",
-       "         2.93140894, 5.71395224, 6.38865828, 6.61863439, 3.26717611],\n",
-       "        [6.17515163, 2.33172093, 6.82924574, 1.65065578, 5.53015689,\n",
-       "         0.16731611, 6.40662729, 4.55996818, 0.08360072, 1.23147801],\n",
-       "        [2.43808458, 1.59368058, 5.90282702, 2.12432736, 1.8700329 ,\n",
-       "         4.51946233, 4.84550488, 6.21447495, 2.23777817, 4.17848811],\n",
-       "        [5.85563919, 5.82433331, 6.00621159, 5.43410984, 3.39388501,\n",
-       "         6.18395763, 1.28406097, 0.02855524, 6.72670605, 1.51699291],\n",
-       "        [0.84410255, 5.61328959, 6.97804534, 1.18377926, 2.53937552,\n",
-       "         1.26803824, 1.27574049, 5.10112907, 2.02022521, 2.08174614]],\n",
-       "...\n",
-       "       [[6.24290664, 6.39476317, 1.30249235, 2.89507681, 0.5376643 ,\n",
-       "         5.01212706, 4.38006939, 1.62400749, 5.56236028, 4.30349107],\n",
-       "        [6.74593668, 3.65303136, 5.11696455, 1.83525557, 3.51473159,\n",
-       "         6.52367921, 0.17986011, 2.44424775, 5.16909939, 0.58462293],\n",
-       "        [2.11951807, 0.47905249, 4.27471358, 6.01904917, 6.34632929,\n",
-       "         2.85844128, 3.97688665, 4.21821752, 4.32073426, 3.9411069 ],\n",
-       "        [2.97464873, 6.67996018, 3.16875018, 3.50716191, 2.9097115 ,\n",
-       "         1.35938603, 4.0230148 , 0.2588095 , 2.00762528, 5.74474576],\n",
-       "        [2.57298212, 1.25080228, 4.65200582, 6.31095753, 3.26647688,\n",
-       "         3.21661254, 3.4731501 , 1.71341466, 5.95750029, 2.72901564],\n",
-       "        [2.74334064, 4.20302139, 1.30621538, 2.84011532, 5.35145147,\n",
-       "         2.77467654, 2.6217532 , 5.28807657, 0.72261773, 4.08600786],\n",
-       "        [3.1686723 , 4.0318634 , 5.77508699, 3.48776179, 6.0345218 ,\n",
-       "         4.72188719, 5.09301838, 0.38602444, 2.27879875, 1.5363515 ],\n",
-       "        [4.54895876, 4.79775447, 6.3711522 , 3.69424969, 6.85534296,\n",
-       "         1.98775739, 4.74965416, 1.75053883, 1.80479493, 4.96653593],\n",
-       "        [1.78138972, 1.54427475, 1.75002571, 3.37327355, 2.60425433,\n",
-       "         5.89401538, 3.2177233 , 5.63747771, 2.90034677, 2.09781306],\n",
-       "        [1.84417405, 6.99496234, 2.50471667, 5.29213205, 0.1844106 ,\n",
-       "         4.87083388, 1.88844217, 3.49131529, 5.05570379, 3.36839469]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.921 66.15 30.1 ... 27.29 18.41</div><input id='attrs-2abda620-506f-4da9-b5d1-28f48d5c9e82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2abda620-506f-4da9-b5d1-28f48d5c9e82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0858356e-9a30-4af8-afde-aefabaf6422f' class='xr-var-data-in' type='checkbox'><label for='data-0858356e-9a30-4af8-afde-aefabaf6422f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd></dl></div><div class='xr-var-data'><pre>array([[[ 7.92063876, 66.15083163, 30.09611622, 30.43719706,\n",
-       "         57.79473407, 11.53864677, 14.42678195, 35.5931706 ,\n",
-       "         53.78485769,  0.44909335],\n",
-       "        [23.80411143, 67.91034696, 18.6945325 , 30.41473977,\n",
-       "         68.11767437, 44.90605663, 62.31184244, 37.48504719,\n",
-       "         57.13433649,  7.52571983],\n",
-       "        [36.65348061, 39.59683262, 61.07602968, 56.88309328,\n",
-       "         29.63561283, 64.11469949, 42.34168918, 54.7247894 ,\n",
-       "         31.87234425, 11.28255401],\n",
-       "        [32.50217644, 28.0905485 , 58.39892665, 41.22401876,\n",
-       "         34.80477692, 32.18456541, 59.39232697,  9.15582412,\n",
-       "         52.96717208, 67.40923645],\n",
-       "        [18.79468009,  8.54342894, 35.83469773,  3.59624497,\n",
-       "         30.25280185, 41.54949902, 23.06024792,  7.26776162,\n",
-       "         27.37409939,  0.68402392],\n",
-       "        [ 9.93226996, 44.50944555, 22.01927024, 28.5141208 ,\n",
-       "         36.71523344, 15.03939035,  2.43398964,  0.63607503,\n",
-       "         34.43022974, 37.02454468],\n",
-       "        [28.87482271,  1.51425484, 34.21082199, 10.49792965,\n",
-       "         54.02549098, 64.29402555, 36.21281265, 17.76662845,\n",
-       "...\n",
-       "         24.03247674, 30.33910879, 37.37663568, 58.78727396,\n",
-       "         49.30899357, 59.51076479],\n",
-       "        [37.99191205, 50.0230132 , 63.71113469, 26.54611832,\n",
-       "         60.5610581 , 36.09830221, 10.72567322, 40.51609048,\n",
-       "         47.47925466, 42.71090946],\n",
-       "        [15.32887012, 15.8789836 , 30.91477844, 25.90281227,\n",
-       "          3.81542846, 35.53036591, 48.42629307, 44.28007476,\n",
-       "          4.46808288, 18.00031979],\n",
-       "        [58.28699332, 20.39771424, 63.12400017, 69.11749464,\n",
-       "          1.32435965, 29.02471509, 31.60153124, 34.30798249,\n",
-       "          7.99029205, 36.15969559],\n",
-       "        [34.67433533, 62.34499312,  5.71471651,  2.16144808,\n",
-       "         66.71497747, 52.4437509 , 12.79185872, 63.70749094,\n",
-       "         13.69706259,  9.39447357],\n",
-       "        [16.76634605, 26.26012306, 40.68158023, 18.69508482,\n",
-       "         64.81212607, 23.15159177, 50.59764238, 61.30820521,\n",
-       "         31.51712269, 23.46979658],\n",
-       "        [44.67098863, 17.53308399, 39.03490717, 32.67672611,\n",
-       "         53.27513876, 48.73117231, 12.95885589, 21.760335  ,\n",
-       "         27.29220175, 18.40906318]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5f85efc-8e6c-4119-a4b9-c904d3b06bac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d5f85efc-8e6c-4119-a4b9-c904d3b06bac' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * x            (x) float64 -51.32 -51.32 -51.32 ... -51.32 -51.32 -51.32\n",
-       "  * y            (y) float64 -17.32 -17.32 -17.32 ... -17.32 -17.32 -17.32\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
-       "    spatial_ref  int64 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368\n",
-       "    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_lonlat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f8f134b63a0>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds_lonlat.green.where(xds_lonlat.green!=xds_lonlat.green.rio.nodata).isel(time=1).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject to UTM\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- [rio.estimate_utm_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.estimate_utm_crs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CRS.from_epsg(32722)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_utm = xds.rio.reproject(xds.rio.estimate_utm_crs())\n",
-    "xds_utm.rio.crs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject Large Rasters with Virtual Warping\n",
-    "\n",
-    "Using [WarpedVRT](https://rasterio.readthedocs.io/en/latest/topics/virtual-warping.html) enables re-projection from disk and reduces the amount\n",
-    "of memory required with the re-projection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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khlcTChT6ijABF4GLqvrJ+vXP4A3HqogcqL2NA8Dadfsfue74w8Dlevvhm2y//piLIhIAPWDrxQY1MxxvIPTfUoHAyseErXuE/HCF3Vhg4TFY+KJj7skR6+9s03m2oPfIEMKAq+9fYOFRqGLI5kEFpAAUio6QLQjRyJFcGrBz3zyNZoBNK8ykQALD9HCbxrkhVSfBbo2oFtqYrEQDg5n4uhEDLyDLX8BVAEwzosvVXs3IVwpzo7H4MnCjMZkZkhluJxShegUoZFW9KiIXROQuVX0K+Hrg8frxPcAP1X9/rj7k54F/LyL/EDiIJ8E/paqViAxF5L3AJ4HvBv7xdcd8D/Bx4DuAX9OX6Ck+MxxvIHSetbQvVVQxFAuORicjHQaMjliqqEOQQvd8yeRoi+YlwcUB4iBdEvKesvLZisbVlCtf1SIaOQYnDM0rwnTBIq6LGigblioyREDZSCgTQ3qoTTCpcAfmCDdGaBRg0tJzD4FP2HDNBmaSQVV9SU3HLjSJIctvmp77UlBrvqLjboWZVzLD7YbTryxZ5Cb474F/JyIR8Dzwx/GJTT8tIt8LnAf+WwBVfUxEfhpvWErg+1R1d8X2Z4D/HWgAv1Q/wBPv/1ZEnsV7Gn/wpQY0MxxvEGTf8m6CqXL1fULrgqH3BOSXuujJHN2yDI/DyZ/c5sk/3+Yt/3BA2Uu48PuaoCAliION+y32dIsDH5uyeW9C7zkIps6HtCYV0VaOyUuqZoQGgi0cNlOS1QkqgoaGcqGF7acUC00wIKXiQoPJK6SokJpU3zUerpkgaeaJ9HH6FU/+r6TRuBVmXskMrxS+DI7jpc+l+nngZjzITduaquoPAj94k+0PA/fdZHtKbXheLm57Oq6IWBH5nIj8Qv367SLyCRH5vIg8LCIP3uSYIyLy63Wxy2Mi8ueve+9visil+vjPi8g33+5reD2geXZA+3KFnQqj4xXZPMgDfWRiMff3WXzUr/AXPxHy7N+I2TndJBxA56ySzzuy/SXZckW67Dj7LQnhBBYf3iYcOzrnpthpiYst6f4mLjTYSUkVWxpXxmhgfHV55Wp9K6HoBp70zivCjQkusmT7W3syJVCHrbIc12kiquSHepRLndfyNn5ZCE4c/5LHDDO8PAiVmpd8vFHxangcfx5fsNKtX/994G+p6i/Vk/7fBz54wzEl8JdV9bMi0gE+IyK/qqqP1+//I1X9B6/C2F832Hr7PHlXqJpKtH9C+HyHaW7RZkXxVJe1b0lZ/eoedgx6oUXRElwIm+9Qlu/cYJxFOCdMr7aRHKbLhqqXEA0Lim5E/3hANFJalwuirSk4R3LJ61JpM6aYSzBZRbYQ05wUNM8OGJ3qkc2FRIOSYJgTbY/9YOvwqE/P9Z6KpAXRpT7lYvuajlUYIGmOxiFSZ0+9plCfTvxi6cIzr2SGlwPfAfCNaxheCrfVcIjIYeBb8G7TX6o3K9eMSI9rzP4e6vzk3RzloYg8gc81fvzGfX8v4NL3v28vOc7FFcXlFo0cytUmh35TufT7KpJnGuQ9h5SgIQxPOMQJ0Y5h+7PLVDGIggWCiXiSvHJc+kCbIIVkU8nbQpIY1ApSAmWFRiGUjnBrgsYh8VZGvtggGBeYUsnbhioyhDeZa70mlg9juSSkXGp5zyYJcN0Ek5eIMUheoI34phlXmvh6jVcjVLUn4/JlYMaVzHAzqAq52pfe8Q2K2+1x/DDwV4Hr4xN/AfgVEfkH+FDZ+17sBLVuyjvwmQC7+HMi8t3Aw3jPZPsmx/1JfHk9Cc2v+AJeD2hdUqrIZ0EhICsp40mDfZ+A7Tstcwe2kEfmSY+VSFzRbKVMJjFhVFI836FcLCAzBEOLKSBbqMjuKHhuX4u5J5WiLWze7w3LdCWke6ZL79kJLm6SLoXYTFl9tyUcCJ2LjsZ6gYst4agknfc/DjO6LuPp+qJT56haEcHOlGwxoWzGBJOAsmlpXCo9JwK3TtMtSi/e+CoYjlsVL75gnxuI/5slAlxvTHbTkqtnz7yyg53hdQ/3CnEcr0fcNsMhIh8C1lT1MyLyweve+jPAX1TVnxWR78Qz+t9wi3O0gZ8F/oKq1hVm/Cjwd/Br8L8D/C/Af3fjsXX15Y8BdGXhDS1T2dgo6d8RYgqY+0KAzS1SQftixuCOBuMn5tH3T5lvT2lEBYNpwtuPXGRSRlwMKsbDhNbjEaPjFa4UNHZ0e1OGg5DxYUt2R4ZOLNKscFFIPxAamxGb94XgYLpPibeEyWFH2TKohCTbFY1zO8TdkORq3Ynw+orv3epzY7yeVWhJ1iZkSw1cZLC5w4xTXy8SWNTdXCxRKsdLZAa+Yrjeq7mVgdjdtmsQbvZ6d9/ra1leIIXvdOaVvMnhyfFZqOorwVcB/3XNYyRAV0R+AvhWPO8B8B+Af3Gzg0UkxBuNf6eq/3F3u6quXrfPPwd+4fYM//WDaFAAoZ9wmoI4WPn8kKoV0rqkBM8p25Mm24dCRtsBJodHTkVU2zEaOpKLIVUMdmKoGg7TLBle6KJWyU6kaGlo7hszvdRGBZpXoGgZumcdq+/xtR9BKgRDIepD60pOuJMyumsekysmL71HoHqtp4dzEIWkh7vEq2OkdGgQEq9NfTZWpV6d9zo595czGe/ixd7bff/LDTtdjxuPvfFzbmZUbqbVdaMhkcprgO31LbnumJlX8maCvKHJ75fCbTMcqvoDwA8A1B7HX1HVP1rzFV8DfBT4OuBLWsbVAlv/EnhCVf/hDe8d2NVoAf4b4Iu36RJeF8i++d1kc5bhSQdOCMcw/3SFmeRIXrLYzyh7MVffHyGFofscJFuO8VqT/lsqovWAsqmIA3copfeJBtPlBkXPIfM5rjB0FsdUn5pn5ZwDgaIFeccwOiqYXAkmQrqomFzonqsYHY5oWSEYVdjUK+1erym1l1UVBYQDP06cYvrFXlaVneS+CRX4LoTXFQfuhoyAF0zINxqWWxmU343BuB4vmPBvLGq8bp+b/b0Z9rS8rEWK8kv2vd4rgZkheSNjRo6/8vgTwI/Upe0pNQ8hIgeBf6Gq34z3Vr4LeFREPl8f99dV9ReBvy8ib8f/b84Cf+pVHf2rjOTKBLUtqo4SbAcUHUcVC9mBDkXb0n5yC51POPZ/VQTjks37G7TPjti8v0e8aknvyJBhgEZKq5mzc2+IFMLi5wybb4+JhkL5bIwGsHWf4CKlalag0Dof0LoE40NgMyFZVxBItipGByOa6yVVArqvQ7g99ROrrfkIESgddpj512GA2gA7zDyvUUuTAC80NrutcMPAk/M3/PZuNBp7p7jxeRjs6Vx9pXgxT+elcL0h+RKDU1W4dvIlApI3NSTXhf80iXFffPIrupYZXn1Ur1wB4OsOr4rhUNWP4j0MVPW3gXfdZJ/LwDdft89N77qqftftGufrETv3dMi7Qus5X/0dTA3xdobJHTYVXLcBwHQpYHJfRLKh9O/sEA6hisBuhT48NTVMz3egWxJuhYwPCiaHsqnkBzx5DnDwxAbDNKaTZFxuzJOvRjSuQDhSorEyPGxpXRVaV3I23hqz+HjB1qmA9pWA5uWpJ8lrbkOy3E981kBeIFFItq9NfGWANqJrHsd1hkOmvqfIrvdyqxDUrYzGHuoV/cv1Pq7f92Yhqa9YMv5mISwRcPj/nQhmnF3rvvglA7s2fkkz7J0ncZ0EySoky6meef4rGtcMtxeKUOibt776zXtlbwJc/ivvw5Q+dCQOooEQDiBdDD1HYCDvtUCVsiFUEUz2C401iPrK+LDgFgviczHZkZyolVOdb5EvVEhXUKuEA4sGjmA9pJiruHxuEdMqGZ3rIaLEm0LZAhcJwxNgChgfFqpmwMLnlO3TIXkXxocszdUOK58Cu1OT5VG4lxXl2hEuCYi2pmgcvvBCd1fVu4R63V0QI2hRfsnk/eVM4jeGt26FF7S9vYmx2t1+42ffbJubb2O2Rzf9DDW1pH1egJW95lraSqBSb2xfLBnAOcxgurfPjV7JLLz1+sCMHJ/hNcN0n6KhokYRJwQjQSrBXnakPUtjq6JoCumiwZQQDaBs1Meu1JNsasmOZpjQUVWG8PiIahijYsBB2XaYtZjFt60xSmNCWzGexujUG6KyCTaF3ZT0YAJSCeVCRf8uQ2MVlh4rmSxZwnEtdLgbbgKyI3PYtMJF9Y+orjZXAaMxUrprjaF2q86dg6pCCs8JaMM3mNLAeI/E3SQ19zoZ+BfDjbzFS+37Yq/3tlmLa8eYwRSpHHZz+KU92G/8rLoDo+z2Rtm9B8Z8yTXsGZwXucbd8+/xJNaCNbg4RD/32Ite5wyvPBSZhapmeG1QLRRQGMJeRjGI0GlAtqisdi2isPSpbdbet4QLhblnMtbfHtO64sjmDTbzRX0L790gNI5xHmGNY2OnzdtOXuCRp49CqKgTmBquXpknbmdkaUhZWBp9IXLC+I4SMzVIhec/jucEzzboPBHRPVeRbBZUsWXpkRFmkuOSgMGdXVAlGjqCUYELDeG690KkqijnmmjkK7SlrPkPEd9kylo/QUah79/hwOQlKgKVelHFSr5Uaff6NOAXMR43C0HdKsX2VmGqF3gZtaHb9QJu5p28YP8bEgGuH7dUDsXtkecvCOE5BW6euQXgOo09zoTA+lCfCMVCQviOe3FJgB1nyCSbeSWvEmbk+AyvOvq/eIr5nw9pfPsqvTjlidEh7KkR5Zk2LvGT+DN/fAmTQxUr6VKEyTxxPT5kCMYwOgzDz+3DTr1USdGrsGPDo6t3EI0FOxXKjqICOMjGETIMCPuGbEExFbTOBrgQTA75nBKsNQgmYHPId2s6tkrKVki1EJMu+q+UiiClY+fOBsm2w+Qx+VwEAtF27o1ApbgoQGOL7U89ob2b1quKZBWuHVG0QkRBigq1xmdkpby45wEvMCC3MhA3S7N9Ka9Ew8C32oVroaVdb0lvbmz2zs0tpOV3jU7tcWgSUbVj7DjHRQEYMP2b1MvUx+56eHseiyo4R3zZlz+ZSbaXySXvuhcpKtKDHeLNlHQlIf7Pn775uGb4iqDKLB13hlcfq+cXCD4wJZ80vDxzJYQf75AfcdipMPeUUHSE6Os3KH95id6Zgq27Q8YHAkwGKER9oWxC2VJEIRhYNPBpteKg6CguVlzskKkFAY2U/HAOmaUCFEvVcmjiwCrl2GIyARWivmBzy+CYJRoq2XxdJZ14Rd7xoRhnoYoM431NTAlx3+HCGDv1BX/BzhTGKdqIcI3QK+yWnueQwivuElrUQtFJsGmF2phgd+KsbcfeCv0l8HLqP14SVYXUoSONI//ZRrzR261jUf0Sr+b6c3+JN7M79uv+ilPylRZSKsEgpVrqgFPsribYCwZeG6+q7ntirR/Pbpq0MbhOAw0M2VKDyUpIslOx9kCbKhGifQ9RtITWqmNwzHDwf/7YS9+HGW4JT47PJEdmeJVhR4YqSxi3AvL1LpHzhHf7EkSDsq63MGx/cgldgMm+ABcr6TIoPm12N7yEgGTiI0AbBhd6zkKtopGCVaRTYKwy153QHyW4uMJtxt6YNCqwCpn/IVQtX++Ri8FOheaqeAHGhFoDy1+DyaDRVyb7BZtB1vCFhM0rGcEwwzVC1BgkAJlk2KJCo8D3EVEoezGSO1xo0EAwhaOKDUGle6tuUS+kuDdB7nocu5PoTXBjbcjNCO6XIuI1CmrDVtaG4roxBHYv3VgD6yvjd3u5q+J6Te89hMG1tGSROgxXF08WJRATXR3i2gkaWuzG8LqLqL0TgKqiWuq88P06s23vfjiHiwNcI8CFhvknhoyPtLwxX3VUoWBzL20z91zFxp96CFP4DpGNjZLVd0Uc/p9mxuTLwYwcn+FVR7VQQmpoLUzJWwXVxSamgqwn9E+GxFvKdJ8XI7QluAiibSGfU8KhkM85qgSCsSAVaAguVMqmFzl04rO0ysJQzCkutziFze15zFRwiTc4LlKiKyEuhqrh0GYFTjAjizjBpuIbQDX9+U0m2BxsBlUDxk0h3lHCEaiBcKKkyzG2F5KsTtEkQIapn+TKCgKDmTiqTowLDMV8iBrBpo502X9dm+uA8Wq9UtUr90gx4wy11kvAZwU3iwq9WBHfi2K3zqT2MKR0vs4kCesQmkWcw7UbEBhcYPZ6m7jQYNMSFEzpUCsUh+aQ3BGu9vcyrSgrn4lWGw7Tn+x5ILv95XHOcyH2mmcDYCb5C65RDdfOWRssMylwjYBkI0WKitEBCwYaG45o6AiHJTYtMaOczhPeiJedmLIdcvjXR2z9dw9hShge9d+pmSG5NRR5JRs5ve4wMxyvQzz7w++l/YQhXVbGay2CvmX+KQjHDjWGhScqNt5qyeedDxsBLlbypXqmFOvDUbXHkWwYqhhwYHOpV5Jgp0q6KEhlqRrGexIVuIZC6TkQDZV8pWRX/jZsFBRpgGsIGghVE6ZHFDsyBJOaS+lAtCMgUDXUf2auBJkileJCIZ23xOuCHedo7Ffv4NNTq0aIBobxgZCysbvyNwQTmH9iiJSOqhF6DqQRgELZCgjrjC3KerW9u+JW9UV2N5DNtzQY13MIu5xBXZAoVQW2VhAufN2JNn12WNWOcQ3fpyRcn/hCR2MIr44hDHBxSNWOCAYpaoR0JSZfWCZeT8GIL5bM8uvG58ditkfXss3CALX6wtCWtZ4Yv7EifTe1eXfsWU64obhmhJSO9pWK1tkRWMFMa2J91wOqeZayFRCMCtQI809MQMDmDfR71nl233vRhZylxSFb/RYn//DnX4Fv/5sHM49jhlcVYd8wXVGqXklzcUK11qVMBBcYghSifokpvFqtizxxXbWVE6eucmFjjkITpFFhNkKqpmNyUJHKGwGpDFFfCIdKa9WT2uIEUwka1KGmHGwqlG23F6YSqxirFIMIKQ0oaKsCUUitrymZcyRrFg0gGvr6k2DkJdzVQpkIUhnCqaN5tcQ1AqRwPnwSW5wVsvmAsmGoQijagqgfUzhRXAhFL8YFQpBWkIOUjmwxIZhWiFOvxLs98fxDI/J/Q4vkJZIW3kilBZqEe6EeGU33ak6k9AS8JrH3XIoKstyH1LT0vUPGKSJhbUDsXiGjVAHh1WE96YaQFpjdwr6yQkJLeLUPQDDJaA0y3xRrvkF0ZfACsv+m3IeIH6NTHwLbzUbLCx8yE7l23HVcywvSnbPcj8kY2s/0ce3ItwGGF+ynjYjpoRbZnKE6GGILpbHh73E4dpgfnmdpRVh/T8jC4QnrF+a59P3vY3KoQgrBlEL7rLDyT39veiUKuBk5PsOriagP4gTRgIlrIT2leQW65wryriWYFLggpn0Bdu5SzIkxscCh1g5XdrrY1YB8zqABEDvs0GclFe2KcGzpnisxhXoCfaAULaGwEG0birbiIqVsOTSqY/eFoDiqzIIKdmwoO5XvPpgJJhfCoZAuO9KVCpML2TwEUyHaFsKR4gKpiXEhjS1u0WJzEBcTjR0uENI5Q5DWXkkd4hL1Ia4ygWiqTFb8teQuIOiENVcjuFgoOoHvjb6vTbCT+Va37Yhs3nMmjSspVSPATkvvMVSKSUskjvZW5xoGdVgn9eGgwHpOYpj69wODRKGv9A58eGrPiyk9Ge0aXlVYrPWy8vMt7NYIGdfS83Hke6+nPhMqGl+TpP8SeZOaB9Ek9hlc+KwunMM1I18zElg0CtE48BX707zmW7jGoxhzjfNRLw0jVYXdHKHN+Jow5e44KsXmDmdt3ctFyDuG5pUMZ0PyrqW1WhB+1JL+4kGOoRRth00tLoLWZUfegfP/4/uI+jC4u4S44s4//pnb98N5XUFesdaxr0fMDMfrDJf+2vuoYkgPlJjUQOCI1izi/ARpC0WKinjbixk2rlqmBy1uO+a3x6cB0EMFYTun3EyQ1B8bDoRkPaR90ZGsZUwPJFSh4AKfBaWmflhFQ98ESjIDFjRwUBokdKgI5aLDboV1cSKUHUex6JC4Qp3gJgF2bDCZz+oCwaZQxUKZyF5cHYXGRoELhHI+8EKMgZDsVNi8zloy4AKIRhCOFZs6kvUp6XKDqmG8h6TeCyublrJhfZptEaKhoehagtQRjkqypRiTO8Kho4oCqthiigryAm1EvmlU4M+pgSHop2hgMKM61VXV12uEAW6uVdduXFMFlkmGtmo5kLwA59BWgt3lcGrpeN0t9AusN0STbK8/O85zJ3uTeO1J7KX9XmcAzPbIGxfn0HaDshP7XvGRvZZ5lV0nJLkL4wsDTeolYWRX3fj6VOaiJF4do9IimwtwATTWC8KtCeFatfeZUjXIewHxVkHzbJ/u4wZN/LSyc3eb8SEY3F0hSUVnbsKZn3wrxSCuOTI4+Vc+cZt+Sa8tFGZZVTO8eoi31cuMdA1Vr6L7xYhDv7RGsdwmGGbXSFnjDUnVgGoaMH9kh8FTC8ihKWUlFNsxArTOWUYnC4JxSN5T9DJM9yeM9xkvUxLD5I4C0yhxpYHCIIXBjn3RX7FcYholve6ErAiYDBJkElAt5VAYgp2AsuGgEoLLMcHYZ1A1ryqDO3ZTfsFkQj7vw1Y2hbJpsCnk3Yhg6vmGeHBtxetCoX0pw05Kxoeb3ug0hMlKQNlsoQbi7RKpCefJvphwXFElQhUZbObIewGtcyPMMKXY16V5ZkCx1CSfj5FSiS8PyPd1sHVIyqQlZJ4D0N0Q1zT3E/XupBoGaGhRgWq+6f8f4OtSrA8V2azYK1Q0w+meh4AxaBxQLDQRpxTtAJs5QhH/eSJQAJH3BnYzrHb7hOwR4jdT1y0dNi1xkcVk5QvrSnYfUQhlhes2qBohrhliR5m/xuuyr/Z4oaIiuTomXjOeBxmmLyDkJS8ZHYwYnBCWHoVgGHrV5omj6iaM9xuqxCG5QEM5ubDBox8/Bd0K13TYTsGz/+i9uLYX1UTBDgJO/r8/fnt/ZK8CVGUWqprh1cPiFyesv6OFKeDUj6VINgTnmO6P6a75+Pn2AyssPFWw+q7QS6ZPLP1+E3t44jM5MoOdyxEgGzTAKum+imjbMNlnatLVf17RU9rLY77m8HP82rnTTNebBH1D+wLkXaHsGGhA9vFFskVHUHrS2+xEmExoXfYTYLqkRAPZmwDGh4SyqWigOIRyzmFywVQQjoTWFUcV+b7oVSREI6XzzIB8qUneDShjYXgkBo29RzF0jA76Cbt/h+9kWLQM4sCUPmU0nQ+whTL3xR1Gp3p0ntxGowBtxoRbE88zlA5bKXaUkx7pYSclVRIgscVMcjQKMNPiWpZS4KU7dsM9av2kWrYjRBWTVr4FrnO4MECtoZxrYkrns63CYI9/QH1dhp2W5HMx00VLOFWKTkCynmJGOcVSk+hyf887AdAkwrViKB1mOKFcaBGu9l+YIVaHvWxWULVignH6pcYj90bBDFPSlQbh2Gd9BWnxAoOwy3NImoPEaMN67wpekDjQv3ee0WGfhDE4amlc9fcOVezWmNaVJqYyVBFwyfI5d5Rm3ydjBFOhaFmMg9bFkKIN2VJF1al4+l8+QPPZiGzREW+aN2z21qwAcIZXBfKuezn3TU1cqCw8VodFAku12CbZ8CRrudQhyJT+HXWtQ1N9iuxORGGVYC6HxGFEKdcTOutCseg1roKxkHdh/6cKpkuWvC00SmEw1+bh+AjpJCJet1QRLD6eUoWGzTSm6DZwMQRjQ9n0FeVSCi5Wxge9hxFvCUXHh7viHcjbPoQkpWBKMIUPUc0/DlXk+5ub4lqILNkoGJ3oYgqlsZoCCfFOgckqJgcS0nlLFdWfcV0EoLlWEkw9uTv3xSFXPrjI4OgC6aKycd8Sh34rxaQV+aEW6ZzFVDD3yCaIkDw/QVsJZTchXB1cK9qrpeFdMwYDVTMi3Bjh6lBWlXiexMWWqul/QjbLwAgutj6zC8gXG7WKcVl7Dsr4QERjwzBdtKSLhkHXX8/8k01Gh9pIBdGJmIUvjjCTgnIuwcWWdDEkbwmdSw2Sczve+xDZk2QXpz7BIAqQynleZpJD5a5lkFlfU1L2EoJJhZmW2FF2jfu4Tgtr18tJD7QZH4jongkI64WLOKXqtZgs+8JMqWD5kZRgc3yNhLeGcKJkGeQ9X9PT+2zskzlK8ZzVQIi3lWjgmC4bgqlldKKE3DA5mSPjgLKlXP6r76OxqlSxD2W+EQh3ZdY6doZXCRe+sUfrorLw+JRwe4prx+TzCcmlAXbLkR2eY3QoYnhMmB4qIfTNnQ4d2mJaBGxvt3HOk9nlKEEjZbJfMVOLSxztSzA8YhgcDXCBMLxD2f+JCtGA/pUVOJHSvAytNcfwSEzW8ynBZUtxjWthpMp6Yl0ygxSCrVOCVQAD08hPJiYXGqteeDEceWn2xkZJlRjSnqFKvFcDoDYiGioB0D/ZxIV+YkGE5tVaMHEhIhhX7JyKiEZK99kxdmdCsdLBZpUP9xhf02IzoYqVSx9IsCksfz4nSJXJiqHqJJSdiLLh+6arFYp9Xey0qEOBjnKxRd4LaaxOCfpTiiUfKlTnsNOCohsj6kl/SSxFr0MVCzZTtu+MMSUk247GWka6klAmhumiQS30T0S4SMl7DkQJxobBHYbxnTnznwrpns0xecXodI+i5UN0ZUNY/GJGfHHnWsW6c97Bi0LKxQbpQuSN8GZBsNsHxblrIa3K152Y3GeOmbx6YQGiCBqFe6T96J4Vdk4HZHOANFisjSuAyQpspoAQTCG6PPDV8w7/F5guGMZHlCpWTCEkW9Bc84ZneMRStD3vJU5prDumK4bkakC6v0Qyi3ZKqiJgmihLX6jYvCck3lbO/NBDmNz/n8uO4/T3ffK2/Sa/csw6AM7wKmHpCyXpgsVmnnzMlps0zmz7DJo5H+IYHY4pW4rJDHQKXGa5utnDBhUiipsGROuBnzgjBQPlYsnSx0L6p4Si7Yj6htZaRbZoufJVguvlLKwMSK/2SJeFqmEpWl5CPd4WijkHofNZVKmhc8bzEdt3ea2rKlGCsaDhLn+hEPoiQBcI4QjivuIs9E+EOIsPMRVKsqmEY1/bUUVC3hFcIERDJe8FZEd8w6jmmT7Tfb6SvbXmY+LpSoOwFRJuTSkWGgTOceDXt9h8YB5TKipC0VaqSPxrK3TPl2y+te0n+VTpphVVYkkPRYTjiPbZEcVCC1M4RJW8FxFvVJStgLJpCYcFag3hdurj/nHA4HjiQ4ABVCGYCoqWMjxmSTabAEyXPdcjJbQvKP27FI0dwY6l7DqqwznBpYTOxYqiY8kWOmzfaT3nlcPSo6mfnGsNKnHXSPlyvkHRDLCpIxoUSOW8AawJ9r2QVxx6WZes8mnBNxGGlCyHOELLivE+i50qSSUv2E+NbyQWTpSi7TXMxqcXaD2zRS3SBECQeaMYbwuty450wZD1oLGlRAMlmEC6KPRPGezUKy+jEPS9/E2l7HnKV99jMDn0T0OyJgxPl4jzodFn/+F7iQaGdKVk7vGAdBGO/q3X1ivx6bgzj2OGVwFRv/BVvXWr1cbz/ofo5lq4yLJzqlGT4iArKa7wmU7zvTFpEZBvJyBKcSSj6IfIfA4bMcu/HTI+ILhAaV4xJNuOYOpYeBwufx20n4jYns5D7JgeKrHPBjSvKvHAh5SCiWF43Pcrd5Gy80CBTC2SQ3RoTLqdUIrFtSpKheaZkO5Zx85dQu+sD4O4QPyCNAKcl7dwARQtIV30nkIwgfYVR9GEuafHlK0Qmxqi7Zydty6QzgvTRU+mNzY8cVu2I65+1TwLT2W4OMAYQ7JdMTwUUDUg7ylzTzvspMS2Lc2nN0kuNzCTbG9iHbx1mbzjaw+mB1o0rk4871F6/mHz3jl2a7minZBorHSey3FxQD4XUrR8ksEud1TFnsdBYHREcU2HZIJrOua+EJD3wE5AA6Fc8MkH7c80qBLYvM9nMM0947POOhcrTO6IVkeeb1Gzl3WlSYyGlnBtiCmaXihS5IVNocIA3c2YcordHPkGUjeIJF6PfLnljfaiEG8q8cDROZdeS+2txRPz7gLRwGBKwVzvudThqnCi2KyW5RehfakibxtcIDS2HMNDlrKjSOELRtMVSNYgXarTmyvD0iPK5r1e7WA3+2981JGsBqSHCl+NPw2oIkUKg1RKeqDi6X/9Lih8vRECd/7JV1fE8ZXUqhKRs8AQr4VQquoDIrIA/BRwHN8J9TtVdbve/weA7633/x9U9Vfq7e8C/negAfwi8OdVVUUkBv4NvsHeJvAHVPXsi43ptvtSImJF5HMi8gv167eLyCdE5PMi8rCIPHiL475JRJ4SkWdF5Puv274gIr8qIs/Uf+dv9zW8Ghj+wfcSbox88Vlo2Xxw2YcOmjGmP8GOMmyhFB2l7FS47RjZitDCsPn8PNPnuph2gTQqtDSEfQMbMY1Vw/Co5yE6Z31+fTR0pHOWjT8wQbo5ra9bg3b9wzc+lrz1DsfOKeMbNx2sK8EBbZWYnYBw26DzBcW5Fq3nQuJ1g6QGyQz5nLL+TiHehK27LP3jlvEBw/CoYbrkJwg1PnMK47kQAA2gaAgLj42QrCK+OiK5MqHs+MZPVSQUTS/QaLOKfKFB2QpYfDwlvthneKzB+jva9O8IyBb8BN59HuYf7eNiS/uJTdxcy6fcxn7NVK50Sed9wkDWE8897Gv4cE4gTBf8PagiH2ozpR9j/84OW3c3WH9rSDbvSX8NoOgq2YJjcqhicrCiWizoHBrgWhXHfk6xhdeDCsaCHRlsP6D5SIPBfTnifD/5xrovsnMx7JwM2Dkdodep3rpek42v3o9rhphxCtZgN0d+Uq+crxPZTb/d5UDq4j6p3F6q7h65vsttBF7SPrrcp2gaumcd809NCVJlfDAmX2nVyQKW7GCX0SFL0VbizXqRU9eCaDNGw4B4u8AUkM37ZInxPsvwDiHeqby3ue0IRkLVUPr3lNip50PU1ooH68LwsCHeEV+vEyhqfQg0n3OYkcUOfBjW5t4zSRcFKbwum0QOAsUOLU//s3fz9I8+yNM/+iDP/fu339bf8i4c5iUfXwa+VlXfrqoP1K+/H/iIqp4GPlK/RkTuAf4gcC/wTcA/FZFdC/aj+Fbdp+vHN9XbvxfYVtVTwD8C/t5LDebV8Dj+PPAE0K1f/33gb6nqL4nIN9evP3j9AfWF/m/A7wMuAp8WkZ9X1ce5dsN+qDYo3w/8tVfhOm4r+ncY5j7nM5TirGTxs1tgBJlkDO9bpnVxQhn7CQqrmJFFQy8NIpXglgrM1RjXrSBQ8oMF8aWQKoZkEzoXSoqWoX0hZfO+BhoI+TQkOpuwvhGjvZJwPWDpEWX1vb74T61l/fdlMAyJ7hvSFqX1Uz2ufsBRLJaYjcgLKy4pR3+1oGhbpguG/mk49BsVwbRi9YGYolMT5vO+7qP3tOdAqthXvQcTr6xbtDwXIkWFiyzOBKT7m7jA15vEO36iqyIhmw9pXE2pGhF5I2Dn5DJFx5Pnec/hGkrQN2RzwsXfN09zTYnnFon63kCWywlVYjCFIjV9o9Z3RRweDkjnW0QjV6f7Kt1zStYVpiteHyxIYectiusUtJ+KqEKoEsUF6qvtA0WM54IO/fWKQ+UW1VyT8f52nW7tlYnbd/QZHUigMHTOOZoXJ4hrMFm2BOO6MZfAs989j8lg8TGl90SfpU9tQl7g5lpI6ZC8YHTvMmXDMPfw6rUv1m5GFfiqeCOUy10QvDJxUfrak3G6Z2RQZe6xHRBherhN1vWZUfH2blW6EK2O6UUGOw1YemQIIjz5fcsc/XCFC4TRQct02euWBRPPbYQTR/sqbN4b0lhXhseEsqVUnQrTKpkcFUwqzD0pZD1h8fGCwbGAYIIPYcbC+JDDTgyNq8L4sPOKCEkFainmK8qOQLfAXokpVwpk5D04DBA4WvNTytLw9D9/NyYpcYWF3HDnn/7UK/p79hG72xqq+jauzZs/jm/N/dfq7T+pqhlwRkSeBR6svZauqn4cQET+DfDtwC/Vx/zN+lw/A/wTERHVW8tN31bDISKHgW8BfhD4S/Vm5ZoR6QGXb3Log8Czqvp8fZ6fxF/c49z6hr2h0TvjmB6f8xPVtNgLR0xOLdK4mjI61mR0zIebkvmU1DUgrrxi7XKGuZJQtSriKyH5QkVy1TI9UtK4FNBYd6y/LWD+Kcf6O5sM7qyQUmAnIr8jZW5uzPZql/JQxpX5gHDHF6aV945gtYnJhdFOg/hCRH63YEcCY0PVdtiR4Y6fm1K2Q8rYp9eiMFm2JP26+tv5boLts1A1hOZGyWi/pWpAuuQn13AAwdQT7NlKk7JhCIcVLhSS1Yx4x6/OhodjbzwV8l6EnTqaZ0ds3b3owxYF2KkQbxmSDa+btfBUiZRK48qY6cFWXUPiiwnBFxYGqVLUuljxjjI8ZhjEhipWbObVf13o+RtTCmkALnEc+1mhbFQUTcPaQxXRYoqrDEFYUVUCZ1tc+JYl2he9ovB0n5DNK1W3hMhRlH4xKMOArXsM23d1SLYgXQI1Sj7nsFNDcMeIbLXJzmlL60rC9umEzkW/GIiGFXHpzz//qatsv3s/85+++iXfsfzIPOlihAo+4UCVarHtVX7BiyfWxHa2v83quyKyBaWxKhz6jaEXkYwC1h7skeworUspSdMwONWmf6JLsgbbdxryDmAg7zribUPVUt+dMjE+DVyhfwryuYrGFUsxJ8jVGFGf2ts/BZ2zyuq7Qjrnay+jUrQSWhcNZQNc6HXXUJCpIT1cQODQ1Hph5JYjXAtBoVguoDQErYLpJMJlFkqBzRhb+Oy+p3/s3UjkOP3HXrnK9pfJcSyJyMPXvf4xVf2xG/ZR4MMiosA/q9/fp6pXAFT1iois1PseAq6vqrxYbyvq5zdu3z3mQn2uUkT6wCKwcatB326P44eBvwp0rtv2F4BfEZF/gF8HvO8mx+1dSI2LwHvq57e6YS+AiPxJvFtGQvMrv4JXARd/4H00ryrdZ1PPCNaG3vWaNM/uUC602LjfUjaVpXvXcSpY60if76D7MlxhSI6PqLYTskM5ZhSQL3jiVS2sPSC067uZbDmq85by3UOq59qUPSErAlqLEyaX25g6S6rsQhyXMKnFCp1QNqDqlIRzKcV2AsD8Y0KVWPKOj10DLD3iCemsa+pUW98TPZh6me7tO31nP2d9RbtU3tMIUmX+iSkutoSjivHBkOERQ3g0IBoozfXSy3yv5WSLIdOlAHGKyRu0L/iUznwO2hc8tzA66gn66aKlaAnZXJdszlex6zwsPjpGKqV5JmV8ct6HOyaO0aEAZyFb2C2887pbLvZJAC5UisM5wdWItXcK4RjyLmBguTdikodsX+nSeSpkfMRRxcL6u3ymV9H24ROcYOOK6SDBxhWUvld8/66K3nNCsglFU2h/0jFZNhQXOoQx8P5trmbzmByinYztO9sc/JVNssM92k9sodbQPj9l7YMH2LkT5p/w4x8dFYIJTA46Dn/EG4jz37ZMOILemYLmJEdV0SRmfKrLlfdZpFTu+Pl0Lw13870rzD09Jtn2ntjZb20gJbV0DaSLvpanbKpXVwayeYdrOortgHTZLxLUKlXb0TxvyRYU4ooqrqASZGqxE4OK0LqiFC2IB8p0xRDtqK8hKoXpMjRWfVqvCwwyMZRLBQSKTgLEiQ913V1gmyXVKKQqDFoabKOkkgBSQzVXQquAYfiKGg2vjvuyQlEb14WfboWvUtXL9Vz3qyLy5IvsezNrpS+y/cWOuSVum+EQkQ8Ba6r6GRH54HVv/RngL6rqz4rIdwL/EviGGw+/ySlf9EK+ZGdvlX8MoCsLX9axrzaCKXQuFZhxLW1RVmgcMT3QJNoOGB9OaKxCPidsf2aZsqWwnHnStTLIMKBcjWChpDk/Jd/selnzCBqrytJqTUQ2hPEBYXpXxj3LGzwHVBdaZOsRruFIrlhaV5TREdCkIjAO7u4z2mnA1HrC1xjKrIlxcMfP56w+EODCiGxByBaUYCRk80L3rCdU0+WIaKckGGac+1CPaAdwPmwVbQuNy16PKho5op0Sk1eE21Mmx7uIg3SfY9Ko2PeblrxjKZpC0UzqnH6oEsP4UJPmFWXuuZKN+wK23+awQ0PrsjA+rAyPClWiTA76dFFTCsd/bgcNLWU7RJoBiCfwq8TzMJOj3iMIV0OCsedidCK+oDGE5Jm41snyRiOf90Zm/eF9mFI49MWKjbd6Q5PPO1TYyyazi1OcM36VHzjM+YQqVganHa0LltFhKNqKBuCiABcKwVSREiaPzGMimHu2Il1KOPBrW7hOwnh/yHRpifaFKcH6kMVHHeGkxeigYbpPiU71fehkGnLpgzHLD8cUbWVyyNG+5An3wdtWyDuGMoHeUzD3XMalr2lw/D8MGd67RDyo2LmzxdrXFJz8Ccf8YzE7d3mDmC16Of/JAc8DOYe/p52KYDNgcsiT/VJ6XTObWh+mShQyizRLmr2U8XoTTX3a8mSf57PCsSfYTemFMouWf100/f0P6v+LGQVooEgp6ELOJI8BcLn18jm1cbLPNaDhPRkUqlHojfkrCC858spQyKp6uf67JiL/CR+RWRWRA/Xi+QCwVu9+EThy3eGH8VGdi/XzG7dff8xFEQnwkaCtFxvT7fQ4vgr4r2seIwG6IvITwLfieQ+A/wD8i5sce6uLh1vfsDcslh+5LtXSGly7gVQV8VZOthBhCiWYKuEYdt4Cy5+F7bsT8uWS4EqEVP7HI8czJtsNmjWZGIwBlPGKId5RopFj/SFH5wsxGystqkoIprX8emUoOsqgCfl8iUwt/XM92ucsnC4xmaGxptjMMD5eooFjshJRRTBd8XUl4VxGOohong2Ze3yAhpZkLduTwAiHXlRRDcR93w1QKiXZLLBpiR1mvjK7KDGZI+sFzD0ORTtk616ldVFoX6mwqWN0MCCb9wVk4NN4Jwd8Nlhj3cuptFYdc89VXHko4NBvFJhSufQ1EVVDee4P9LjjP40om5Yq9tk3LvDeSTiGxsWg1geD6ZESKWp14dyvaVzkZedN7j0QnO+wGA2EzjlH55kBg2Pz2BTGh6BqKuFYSA9kJEmBqiCijIcJxVJJtBZgCh+3t6lQdisQ6CeGZE0omz6rqOg4oh3D5lt8MaNUHZpndxgc7/pxrwcEwPhIk8F3Dsmf7nL0wznrqz2CibK45rj8/8hY/UaDjSqMCkUr5qk/u8KdPz6gakWsvbMJopi8ormqVPNNJsuG9uWK5lrJkf/LMDwS0lgvKRsBosoo8mnI4MNIUglVq6L7WIgLIFvw97JqOao2NC5ZWldgss+QrkCwFlPYmN6q1zfLFn24Ktmp6B8L6FysGBy3dRq3537E1d0tnf/MZM0w3e9FNstxgNuXIYAJHDKOqEwIouQHir2QWdgqcCpebucVxSsjOSIiLcCo6rB+/vuBvw38PPA9wA/Vf3+uPuTngX8vIv8QOIgnwT+lqpWIDEXkvcAnge8G/vF1x3wP8HHgO4BfezF+A26j4VDVHwB+AKD2OP6Kqv5REXkC+Bo8N/F1wDM3OfzTwGkRuQO4hM8S+MP1e7e6YW9IPP0vHuDuHx5dk7UANDQQGIZHE1woOAtlS9i5t0RaJePNhHyxJFlIqTbb5AsVRU9greFlRhaUcCS+q6r6cE3RgvUH8TpUX7PF6pU5wrUQKf2qzmSQ7StpnguQypIfz9DUkj6QEgJl1iCbF7JFR3LF98BwoaNsKzYVgqGl+3CTzsWCxtk1Nt+zjDilebXAzUWoERrrSrJdsfaAD1W1rlTYtEJUyRZjmI8Z7w/Iu76Qrmx6bat83q9MJ4fAhZbeWWXpCyNkWnD1axZoXXWYUmEV0gVD3hEWH8u9+m2pHP51R3x1RHqgTeuirwMZHA0496E2eU9J1gym9PcpHNWGrfQKv9miQ0ohOTAmm4Y4DWlcClh4sqJ5JeXZ70wg8ER8vO2vUQ1MD7WxU59p5cLr/uGjEHpTjCiVM2hlCNs50dMhoztK4qUpxxa3cQiTImSah0wORVTPt8FA56z3IKrY3/fLHwio/kib+CloX6qIL+5w7jv207yqZM92OflTA8xoyuELwuieRdrP9Gl/epHRMUfQjzn+czusvSfh0EcrJCuYnOrQ2HS0L2UE/ZS5ZywmLWmtVkwXrU9SGHoFY7U+iwlqD6CjFAdyWk/5lsFuaik6UDaUuaf8okcqIRh6I5h3dhc4wtzTStEUJgchW6gQ5wUxR/stNleGhy2tK86nby94NQK13ttBId72BiV1gjuQYtdiXB6hscMVAm2HNkvEKEFU4VQQoMwCNDe3JV33Faoc3wf8J/GZbwHw71X1l0Xk08BPi8j3AueB/xZAVR8TkZ/G88El8H2qutvS7M9wLR33l+oH+KjPv62J9C38fPuieC3qOP4E8CO1S5RS8xAichD4F6r6zTVB8+eAXwEs8K9U9bH6+B/iJjfsjYo7fyxDVPfUSfP9Ha6+JyFdcuz7VN3TYurbrzYvBJQtS7aorHzMMj7UITtYEQws5XyJFIZg5NVdowFMV5QUYf5px8YpoXHFMj1UsXO564unjk+wX2ihFrLFimDH0thQtu5TGIQ09o+ZrjVJrgZIpEwPVL6JkPOE4sbbPF8QDfyE0L5S0Di7A6osfnKd9NgctnDkcwHrbzfEW0LnQsnKw2Bzx+oDEaYIaK76jJvtOy2tS57QTpf8Cnb+ScfVo8rSZwzpktC+XNE/HjBZbpNsOw78lzUmJ+a5+t4QcT7s5ywUbUswcYwPBJRN0DsXaK45wrGy9k5LOIDWRSjqMJMLAauENQnfOaPYQghGQrxjCB/tsLjuUAOdZ7ZZf/cclz8QY6eCi+HwRwvG+0PU+HH3T4Q01h15W3CxIxwYsjunsBNhRdm50iXasESVkC8GVG+dElxKaB3JOLuxgKsMD93xPFcmPYajhm+y1SuY5j7EFI6E4nCOphazETM9XNC6FJAenWO63wEGNY5zf90QfHI/vTMVkxVD3p6nsemYf7oim4fRHR0WHpsSro8Y3rtI55mBz2qLfTOtaG1EerhLFXllZhf5jKf25RKpoLGh9E95yZl8uQQV8p7nMuItnwZedOoOkbV0f+ec0j+pZItCtlix8AVDFQqDU9RJDJalR0tGh3yPlmik2FRJ5w02VzrnHcOj3kusGoqdCtMVHyKzqVBt+jCiKX241mS+TshOIspuRVEaZBT4bKzblPj0SmVV1QlCb7vJ9k3g629xzA/iE5Ju3P4wcN9Ntqd8mfPoq2I4VPWjeA8DVf1tfKHJjftcBr75ute/iC9SuXG/W96wNyJsf7qXMjk9scDwcEjrsnLwN6asvqfF+LCjc8Zgc8jma7mHHLbfAuEQtFlRJrU67dDQWBWm+5T5JwvUhHTOVwyPWMIhZHNKctWSnk5ptDImg4Qg2I0TG3pPeRI13hLSfSXT7QYYKLp+wrSLGcGTTQ7/+pTVdzcQB50LfkLK5oQrDwUcYJ7GpRGoEm1nTA42aKzn2LSBWjj7rRE4ofu8DzsUHWVaCYOOxUXK6IgPaWngJ5XRQcsd/6c3ruJiokFFywrTRcNkxeDesUTv8R0O/UaLyx+Ima747KKNxNI942tYio4SbwkutIQjn+U1PlbhLluSVaF8+4h79q/x6BNHKeYdZj5j5w6h6vvY99K7N7n8/BI7uUHnc6Kz85gCTv5szuZbEjqXHFt3R+QdP1lFfZjsU9qXlGgMx37RceEbBDcMkV7B5IvzBBbKYylhXMI0JDif4AIYProIAuVCyeeuHma02kaaJcHQUCQWd2JK44tNTn7j83zxi8d4x/3Pc2Z7kfwTC7z9Tz3CRz59H4ufFaYrfjVePt8mf/uU6b6EIx8pyeYsc49skh7pMT5gWHii1kBbaBGMKy59/TyLjxc0zve9SnBofeFg4av7i5bQXHVs3ht6WZnCfw+zBSW5GFI1vMflYkUHwnReqZqObM5SLeb0ng0ZHheK+YrWeYvJLP2TiuBbEydrlmRLGRwN2HlHwR0/5UgXQ9L9hpXPTrn63gYugGRLyXpC2Pd1IMHUhwydhWAoBBM/aefzghpFQ6XarWbIjO/z4gTbLXDV7bEeM3XcGW4fnIPAsv2ORV/bkAiiynN/1uDKDLHKTqvub1F4/SXXrjAjS2NVaD8dMX37hOixJi7yHkrzsjA4HtC+6GisF4z317pALYdLBM0sRWRZWBoyPh+TL/r03O178UVY+0sIHI1nY9IVnyvfPi9k/SaHf2PK2Q8lSKUc+k1fVJbdaZl/yjE+YAiHJasPzYFA+7JPp7WTkmjg6zXynhBvC9tvK5FmSfx8gs18SKf9jCey83moYoVeQbZg6J+I6J4v2L4HIKRzIaMKY4JMCUcVGodEW1OO/59TNt/eY+PdDkY+RLTv4YL1t4VM7sywm3XjJ6PQKUnvybGBw1WGR586QrwakB3K4XKCGuieN8w/UzBZ3sdiCJMVwQ1i2heVKvTqveJg51SAC/015C0lXYKqVbH+9sBLx/dq8rZb4ArfLGvxEaHvEvb/TsnlrwqI+sLkgDd6LnKEvYyy9KTuvt+IMKWj+8yY8//VHO/79kf4L5+5FzuX88j5w2g/InrHkF/72P2EY79Cj75zlbUv7OPA265y8coCi4/B2e9QzAi2714m3oR9D08JrwxIj8+Tdy02VeafLmlc8PyUmRaUcw3CQU44gOGDHZJNb0DynieXq1iJt3yoLz1c0Hs09OnedZpr2fJthV0MpF42JO/5OqF407B9n5KsG4quX9TEO9A/rcSbgu1brj7k/2etS8qlD9ZZXHgJm7zjjYaLfNhUCgFRNBBfJxOqb6GMNyThSBi9pcBuRLiVDGMUV5hXNJtqF7Oe4zPcNux890O0L+Wc/VBIOPS1Dhr4H5QrDdHFCFMK1VtGFDZGtgOfpZN4+Yp0yf9wmp9pUrTBhYqo1FW5SjSoGB6NyeZ9W1cNlWTVMm2XuAsttlcC9GgGuSFeD8mWK9xyDtOAaC0iXXGYAuae9NXjh39tSroUUXYqDn3ES0vEWxlFJ2TjbcLCF5XpSrRXSb3x1sAbBdPyRqMrtC8ovTM58XaIGq/Ea0qlTISrH6z4znd/gt/5wfcwOGopJwnp/orttrD5Pug85tV97aRg8TMjr2Dbjr18ubVIVbH0qU3ELbJ9t7J9r7J9ryUcKEwt1XxJo5cSBBXDqx00FFwaYIYWA2RHM0zo0H0VwbmEcFxrPBmvMZUv+B7vo8P+vk9TIV/0E5ZJxaegqg+XNJ8LyBaUdJ+iVok3LK0vJmzf42VfJvtg+fOOjftCOmeVrffkzD8c0lxzrL/DEp5rU3SUk788xU4LhifaUDqqBD73L97KXZ8d8NT3tnyHx7mcqrSEA1/lvvbtU9jq0rhzh83fOAD7HFv3gt0JcIliU5h/OsdMS/JDPaaLAaZU8o6h98yIYqGJTUvUOHAKRtg53SLZdOQdYXTEFzuGA0O0LUwOV5jUIElF/35onAtJ91cM780hNyw/bFj9Km9MBidAQ6XzVMh0xRtxU3r9qXTFT/qNNW9cbSaEA8jnfRvh9nmlbMDkgNc1yxZ9Fp8aJZz4GpFd6X41MD5e4TJDMPGp5NlSidkJccs5Whq0Euzg9jRbUqCceRwz3A5s3g+DEzHiHMUdKb3ehO21DkURYjdDinnH/LFtVIWxQJEbwpHFZAYXO46+7yLPPXmQbN6nbkoJyYavas56Qt4OKJu+OjcaGNJORbrPpyRWiwUiPuPEJiVpqLSejrCpZXBnSdFxvvtfJWzfpxz5cMGlDzSYHi6R3NRpqJYqafjQSqfCFoa848MA2ZxSNZX2WUPrUsrG/Q2ffbQm7JwIGR8CDCw+6lh9r49tLzwc8PBPvYv2zojuF1KufMM+sgXBtSuSc74YTU+OWaNDNGjTuZgRrk+QNEOsReMA14wIUp/9JQ7yxYpsBaINS9ETpiRI6MAqwWaICz3JLA4O/Krh4ndVVIOIfKFiVAUMjwZ1bQsc/4WM6VLI9t2WxnlhdFTRZukF7SILgRJf9fsHEyXZgMEJQ3FySkZEuk9JlqfoWoeypeycNFQNZXxXgQSO9rdf5cKFRYINw/hUQbgecOH3N7DTJpMjJVe+eo5g6gUCd97SoXHJULaUsmNpXrJMDlZop0TWE+Jtw6SdEFmYP7HFYNjEOWH+NxO653KCSQVGKNoB3efGVM2QyT6fAm4KpYoNtvApxlk3JNmuyOb8PRUnHPytiu3ThvERn8XUWBdG84ZgKyCcQDExVPhQ6vZd1heirtW1NROf8gsQ9YV0yXsHUgqNvs+YstNrq/XGqtJcd0yWDFXsDcXkYF0PEmudxaVopEjhvQup8IWsu/ph3ZKgH6BGCS5FVIn/PJvdvt/3LFQ1w21BMBHSlZJkecpiZ8yliwtIoJTt2ju4HLAVzLH/2CY72y2oxLv5BqJty/lPHCYQH8vWwhfRAaCw/JkBV766x/BkhUYKaokvhxQ9f+5GN0VVyNaaRBcs9JRszqc2RpuW/HAOpWHuiwErnx6y+mDX9whvVDTOB0T9nKsPRbTPKfOP+4yetXcGzD9bkncCL3EeQLKpXPy6JqDEO8LoeIWGSvv5gNHJksGxADtVOmehsekwuVd2zQ/2sIUSbRtkyyCllygphjHDE47O84bNexN6z1ua59Q3aZpkSFrQ+/yUYLLI6KCl3Aj2qtPbZyxqfc8HKQST+z4hJ/5Dn82390g2Uk7/3YJz39706cVDr6e1+HjJeJ/l8lcle3LoedeHRZh6JVdpl5jAkR2p+1jsC2idt2SHcqQfEW9a0gMF6U4CJwsa50PyOSXeFqqNkKrpuFAuEnczXDvn/gNrfDE7RuusJXvXGAYRd959gacfO8xkxTI+rD52HyjtM5bRMU+ex+di8kVHur/k1E/kpCsx6cVFyveVxFcC4r4S9XPsMKNYahKOSvp3tvak2xceLyhbFtcOiAYVeccS7/j40M6pgKLlvYT+cZ90gArts4bxIfXV2RMhXVSv72WgcTaibChVwxuN3TTYfM6PXZz3Enpn6pTj2PMl4rw+2uiYD5UWHUvrkmPjpDcC4bYXNgwHBvAFhz4k5sny3Wy/MlGKuQrJDXbqz1/MOU+Ml8KJP3WbxA91Fqqa4Tbg6R99EDtSoi1LnrXY2uhg53xYY/5xYXzYN0oicEyyCIDmZbO3QgpHvrZj9f0O7VuSDSEYK1UszD09YXqw5VVkr1omR0uWvqCkc0I+BziYbjTB+Krd8XH/N1iNKFYKwo2Q5tOx1xmaKoOTbYK0/uEPAjSA/h0h6f4Skwcc/C3Pexz+9YJgVMDRgHAE+z5TcOkDIflCiZ0YLxFhINy25D0vPjfd52XebaoEE0f/REKQxlz9Kt+3ITgwplht+ErgNUPrUkjZ9FlXRdcxPG4x5QInfmqAGU7AOcp9PZrnB0T9BulSxP5PTDHTgmK+QdEJqKKA6ZESNXDoNwrKtlfcXXugTe9Myb5P54S/VrD+9oC553I27o9q7kGZf8JXxa+/p0LaJTIICQYGN7K4lYyoWZBvJ2igTA44bD9A92WkNkRKgzYqZGqJN6GxClvvKCGpmFsYs7PWodXI2Nce8ehzh1n6rGH7LUo1iLCDgOdXl8AJ40Pqs+giR5AUpPsFo8JCb0zr4CZnz6wQrQWc+z6l2FGCPjTOh/SedbQuZ9i6v0jRDojXU9AI1Gc+jQ9GRCOf3jw45pV/g9TRP+a79JWtemJu1OHPbsF0JQIDZmr2CiSrhoPKE+d512eahQPv/ZZdnwkYbfjaoWDsazeqyKsZB1Ookt3vuRAOvHrB2oPes5DccyXWGFzo/y8IVB3nkwi6Stj3vx+bQ3QuoGwq2XJJ2Ldo6HyL5Oz2eQTKrJHTDLcDoaPqKS6u0MpgL0W4xHsD/TstwcivhlvPRgwqQVIvz7D4qF+NTZcMeU/oPmHqnt1Ka9VhM4dLLFcftOT7SiQzmIlh55RfjUnd3tmOvNaUGVlfFZ1UNO7eYXy2R2NVGJ7wrWaTTbxSa2gIpobOOR+mWnp0Su9syNZdcPZDnuBunNvh3Lcvk/eU5lVhcCykfR76DUOy7uVH4nVLMPW91UeHhajvUykn+4XttwS4yIfVwn0jgkfaTKLES3cXQtQHm6nvK95QqnbF3CMhCJz/UI/m5S7jI8Id/+4qqBKmBaZocfFr2zQ2lOFRaF8Em0Pjkq9FWX0gYnpUaJ0xLDxZMjrg28+qFbIFGObhnrHunFffd8OCnRqcC9HYUXYh3DZUuSWfBhz4qG/aNDnoPR3OJRQdh0aK2QlI1g0rnx3x9J/wGWZklsHzcxy5Z5VKhSeePwilMF0WqrmCoFVQSl0B7XzGkh1aqp5ijJIN/cJio+iwuRnROj6keShnnEbYq61aVsSLDwbb3mi4yPeUH97R2qvH8N8rIUiFwVFL0VXsFDbuCyk6fnIW51u8aqhIw3+Zypbi5guC9Yhs2fMdJvc1GpP96r/XhVAecmjgkNL4Akf1agQuhCzxDcNQ0BBa5w1l06d5a+Cz72wq2NST7lWiqEDnnJAtWIpWnY479X1hxNVFiI06DFZBtGWpGuplTaa+oPN2YuZxzPCKo3EuYnqoRDNL40LI6JTXgKpKS1UJybqlc8HhAghHIYP3TDm0b5sr5QFaF6grpmHnbug+57vNRf2S8cGQrBf6NMmtAMV30mus+f2LjmF4yuIa3stoPRN4KYuDEWnsQxLBRFn4vM94GR8wbL7NoKGy72NeOqNMYHA8Yf0BaF3wWVzTA8r02Jyv5MXrFh368Cbjk3N0z7m9grG1d0WUTb/aXvxiRTh2TDcCWpczRocjOhcK+icidloteNsIhhHBW8a8ZWWVK//4FFtvMWQrFVJ3HezfU2EyoWo5kg2LlHDh2/bTuuLonpkSbE/oPd/kyjdUSFwxvqugOtMmHPlqdymF9nO+uvnCN8LiZ8A5Yf3tIWXLpwdroNiJMN1fV4kD0ZZv3ASWvOdoXhXCkZ9gt+7xq+Bg5GPteU8J+3UKqPhJ8Pw3tul+EYYnK+aO7tBNMtaHLSZbTWRiEYXx8ZLG2RCbh6RLStkTgkyIN4TRnQWoEP9Oh0QhGCvNTV9Qee6b20xLoXPGMDmgJJuCTR3lnGXjwXnCidK6lDFdDhkf8Kt3U0LrisMFQt6WuiWsr1HJln39TnLZt3LVSEmuBGQrgp0YooGQSYgL/MSfrBmfdWW8d2Fyv7JXq0hm0MR7YjaT2jhJ/dwXW0bbPgtLA6+sq8arKdvSd5QcHfNGLNkQyhZMjxRIboguWYJaiqRqKNlKRdCv29saqNqOcMfg5hxVWzn9525f50BlZjhmuA2YHirBQfu5kNGJEpMaqqtNDv6mMjhmifrKdKl2pR24wnLu+RVYKol2QjqXSt9n+tmAyX4vHVJFIdNlQ9GG5UdKsq5l6z6YHqqYnKqYezjiwG/2STa6pItCMPXeSrrgUxXT9YQjH/GaQq0zfUQXPFGZGYKJMv+ZdTbfs0zvbA7A+EpMuqwc/O0STMiFb7Ac/XDBZCVg4XPbUHhuoLkB8UYO1nsNar1MSeNJpXF5zHSpRzCtWPx8HxVhqZ9RtLpEj7fYuRsmlfDoE6fRd3jy/8DHK858hyCRQ5ol3d9JmO4PqGIv3957viDvWN8bu5PQuprRONeguj8j22jQGNWFi1uePJ3uc7QuGOzI0j/lJxgpauXVEqJtYXRPjkwt2qiwWwFVAvsermhe8k2fJgdithYt+f4CMwyYf1wYnPRNipItIaj5JxfB+MEJlRP0+YSTP5lx8esWGTWVsukQ41fLyZqhexbmv7DJ+W9dBGDhs9b3tTha0X4m5Mg3n+XJyVHmnhQa247t0wHj4xXBwNB7GtJlz6N1z5asvz0kmPhwlIrQv8PX1bgQ4q1duRWzp9/kgmv6UEXLeyd4OgE7Mr69q/MTYzj0QoNl23kl5EatP9Y35MZnmSmAMahVzMTu9X2xqZ/k86bz72XGexPGexb5nDfapv5/DE55gUW1MN3nU4KD7cCPT2B0xGH2p5TDkNbztdzJkqOKvbhklQgqSjC6PdlUu1CE0r15yfE375W9jnHmhx6idTYgueoJ4njdEm8Yom3D6gOGeFuxmdJccyw8kRFOlGAjRJolhEq6qORtw3glqLvNQTT0pPm+T46Ze9pRxQZT+Q5sGnnCdnRUGZ7sULT85LR9vyOdl1o+RMHChd8vhKOS9fcs0DuTocYrqlaJsP6+ZbpnUoJxQbiV4gJon8f37RY49VNjknM7dS9qr+4rFZhMCYYZwfaU5c9PsKlvGasGpgdaiFOKTki+0GD1q3rkCwn7PzYknCrtc0K04VenKn7Ffun9Ie1nQsILEW4cMj7idbnGRxw7byt8D/GWIKXDDlOC7Smty0q+nXjJlamPp6uB3jPQe9pnQQUToZwv/cT7lDD3lHDiZ3Youkp0JUTjimA9pHPWk8CrD1i27utQJRZbKEuPVoRrPrU6mxPa5+DYL03IO+pXyQbGh5QDPxMz/9GExlVhcjAhmPoeJcd+0aGBsvRZn1I6WTHkyy26ZxzJpviMosCvtkcnS87/8nEaVw3jw3D1PYbx/SlB3xCMhM2vzRjd4etndk4HdZqwUjaVfK4O6+R+Je+l76Fo++9n0WWvkM/kvgI8mAgmAww01gytcwHN85Z4y8t/iPoK7c4zlnAouMg3tFLxnxGOvbaXnfgwlp360JiKrwUxqRBteP0tm3kuLFt0lA3PUxQdJV12YKDq+hCZSxzaKqkS39N8eqhCHLQ+0cRMLZN7UrK7p7hW7fU4oepWSGEoe9Utfp2vHBzyko83KmaG4zWAr2L2k3fzXEC2XHkp6kAJ7hySzQvN9ZJwVJF3A2zmlUc1tcwvDX1hVeJjv3FfmXu+oHFljKlguj/B1NLsRcsrkeLAxhUnf2rAZMkQDX2vaDv2ceTOxQpTCOHSlIO/AXkvIBwr4wMR3QslrqEc+O0B4UR906CiYv3dXcqGsvLwCDMtOfhbQ9KlhMmJeXqfW0MDQ9GNiUbOt8Od5kjpCDZGNNcdZSIMDweMDwRMlwxX3xuyczqiaMOlD4S+Yhlorld0znrD0FgXXORXmyufzWhfgNaZgHy5xAVw+Ncqjv68sP5W70jv3O1nQkkLLykiil1Oka/epugqxVLJxntLxgeFyX4h2QA78l5I0fU9ODbeOUeyLrQuwtLHvKSIN7QQTIXpknDpgxFFw7D6HkOxUqDWh+rEwfB4g/ZFb6DGB5WFL/rjg9STyINjhnRZ2Xio5OLXBix8zvpiQgOdSxWX35/gQl/YOd3v4/y9pyyNiwGHP9wnSGvNpjtGNFoZ0d0Dpqe8zpgdWbIFbxCyRcfOPV5bTANluqKMj1ZUDfXe58RP/tk8VJFfyefzyvCkI11xnh9TrytVNnxP9T25mdKnHjev+tDW6GSJS3w1d7ztDVkw8d/3YOK9Pd/F0XNc8bbxBHfd6bXo+Mw1qTwPMTngDZDJ/eKBQnDNeuIv/BRWdL3HUjUdo6MO1/NS6eZKAk5wjQqsQiFos3rFGzd9CdSHql7q8UbFzHC8Bpjsd5T3jsgWHW/70BOYtp/YqqYy7SeYDMqG4eLXW7butr7l6qESQsf2aterqB4SwrEna4uWJV1p0ljLCQclZSw+U3TJx9ipBLmQsPpQD7VQNr2XgfowlguE/Z/MOfBvYqKdEjVCsl0STH2F9Ol/O2Vwqk0VCRoZLn5Dj/aViny54tk/0GLr3jYyLYh2cprn+mAt4+MdyqYlSJX1d7TZeecyozvnwAjtZ/pgYHiHY3jMhyRMBtFQqRI/Ka+/o8l0wWsTLX98m97ZiumyEm8ryYZw7ptDth4scBE0z4a0LyrJ1QlV5FerW/f75kyuGYEI+35tlbt+bEI5DBle7fiq4qGleSYkX6oo7p7Qf6uX3zj+n1OSdeXIz6+RLgnDk47td5Zs3adEO0L/Tt97fXK8IEh9WGf7LUK8KUSrnhsJh8LoKIwOGXbuUjYfqLCZsPMW2LzX0j9piPu+oK1qeI+wXCwYnPRpx0uPFrTOT1j5TEHREnrPKd1nIVn33tzB304ZH2/7xlUTIV9tMtlostCaEF+ICLaCWoZcqSKtaxsEk3kOIdmQPYFLcTBd8VpSVawUPV/BHkx8t0PXqsiXvHFO1v13uJh3FF0oW/iMpsg/pIRwy9K4ZPd6l5Qt9bVGBlyddaV1+/TJQUc+54i3LLaWR5fa6xBlz3hUnao2LlqHzGRv9tLY4dqVf8/6+oxgPfK6bUYx7QLqbpm+F8rt/43vchxvVsMx4zheZTz3Pz9EdGRE2o/54EOP8czOMvuX+6wHDj3fJFgPmRxQsvmAZL0upGv4tNzu52OmB5TsRIZdjRgeMXQuONbfKez/hFAai53WneYWvAz61tdkPpX1bED7il+llYk3HKYSMjXkbdh4R0jjivdifC8PiwbQvKo8+web2Ikw9zTsnEwom9A/HhBuKTYXuuczqm5MthAjqmzf1WLp05uU803KVoCLApKtgvO/L6RzcJnemYLxYUUOpuSbMcHEsu/TGUU3oGg7GqveE7IpVLFBVBnv8/0rtt9WQeTY95GA3jMZaw9E9O+tmHtWuPD7ezTWlPYF5fB/mbL9libpO7ssPTwAfG+Lu/75lLV3d9i+39+LdB9o6Ei+0CTaURqbjsHxBBfB2vuXybtK5zlD0TFMTuZMGwaMElwNKZKK/rsrGk96iZLJIS95bnJIT2fo1GLT+ifmID81xeWW5O4xw80WakPyniNYnlJmAbIZEW0LybYjWUuRrKBxucKFbdQKUb8kmwsYH7Bs3puw/PkJ4dDSPxGRnSzYtzzg0mP7kKZn4cOB7E2S2aLD1JLwpoB0xXlPNJVa6kZxMQSpEKQWF/iMqnjLMD4kNNaE7tmK9XcaknWhedWnwk6XIe9A+6JStL1HaArf/6SKaw+lrbUygtakuzcIpvBehM2kJtLxac+h1xNzUV2rEjsovacgqbcWUtZhO/A1OaPaa8FL6+DEp90mDia+34o0KkzoOPlHPvcq/NJn5PgMryA6Z4Wd+RgTV3z0iTt9/v5GGxlbkpEnj6WCuecKhocC0kXQTum9g1pOu/NITNH0EzwKy59VJouWeOBnieZayXh/6Iu1ppbkcsj43pTJwYj9v6N0zqds3tsg2VCyORgeh0MfLRkeCpiuCNFACYew/lDF+LBw5FeV5pkB6++dZ+GxCelCi86lis4l2LrbT1wrvzNk584mVdJg6WOrIEKwNaaKu6RLcGUxIupD3oONt4aUzcprLA18Bs7geMT4kJdTnxxytYCe0FyF8x9apEp8Dr/vZebDQFI4DnxkjX2fjL3HtS40NyvCQYVLrE89FqjaEdt3JwxOwql/tcbKw8LkQJtsf0lj/5B0GpEu+74ckwNeoTfv+R9967IyPqQUXW+QER/OKrrKyocjyoaw/Mktsv1trrw38p0Xx4JuhuCE6R05ZhBgJ4bKBpipZZR2wXhvIxwJVdlE2xVHft2xdZchnRPC/Q1c2CQcVTRWUyYHG+S9gOFhy+AtJYufsZhJweh0g50PpERRydpaj7hvWHjCMV3wRHze89+TZMOQLfqQT7riV+XNNZ89heBDmspe5lfS9w2TsqRux5vCzmmLSt2oKfAhq+ZV3ZP+r3xWsPd0mpBs+n3Uav3Xh/fU1BXfsa/v2E2XtRlo7sl1HL7pVmFwhvp1nUIbOFQEKcxeEoOLamOTeINIq0KMbze7W3Qo2yFV8iq4G3hyvJqR4zO8UigbMP9wiKzG2M3Q9wUYBUTblvYFZenRFLWwcyKkqntgR60cE1e16w6D0yW9M163qLlakHWEeOAoE0PYLzC5w+ZK0RGCTgH3DUGFZNXQfXpAlViqxK9sVz7jJcXPfpvQWq1YfKJk5bc36J8GM/FpuMNDFslyVn5zDZOWrHw2o0yEi9+g5F1l5y6l6iYsfXKDZLOgWmyTHp1DrSVbCFh6pPKZN5EPU0wOOsxijukUfkIGNh8sKVtK0fZ5/1XDUSyUrL9LWPpC4es27pj61eV2SHO1wDUCUKXsxJQtQzRS+scCznxbyMUPJsRDR/tqyeqDDarIr4Kf+d4VXGA48RNrHPiIZXq+Q9LIfZVxS8kXKsbfMGJ8T0aZeKOKQDAyxO3MazJVfjVvSmhfLMlWWpz5Q0rrkjL/mA8BLXzBy1k0n4murbCnluZFQ+Oipfd4wMmfHfsOePszzNQy3uf7V/gwUYWdOqZLAflcTLKeUyb+Mw/8umH5UzuYrGDxs9u0Ptug+WttonNecn2839+LXY2teNOT4vGmIRwaH67a8RNusu4LSeNtXz9Rxd77mC6rr5Kfd1SxDytm8563wIE4z3G4UMg7nlBX40n1cOg9mWze11Dsyoe0LnnvxqbiU8RXBTvxz3XXMYt8h0bEG9VoR0iuWhpXLWHfQOC9CQLdC2t576X2oqf+/2MGAbId+qSS3CCpwWaCmb56U96MHJ/hFYH76reTrihFV3DLOdVCyeSpORYeFe74TzuMjgjTpZB9nxj6rJKezxypzrf86qn+nsXrAcMjvorcpiWm9EV6eU+4+lATtULrkp/45HyDdBjTeCpm/yczpPQy1UULxvsN23daDnyiItyxpPMWFWHtfUu4CGwu3PUvJ3TPlWw/sII2Y85/S4/B8YiNtwrifAgjHBjs5ojpsTnstMQOUq8Tta+FzRSbOaJhLa/R9KGHpJGjTlCrFAsVf+TBT9C8c4fWBUNzeYzpFAQ7AWW34uLXBUQ7QuPzTeaO7xD1DS4yBBs+x9Xklc8y2++L7sKBj62vv82ycX9IOFTKpq9cjreFK+9v8dz3rJB3he6zhvzpLkXPGys7NhQXWyz8dkSy4Q112VTKnqMqLa3jfd/tb+L5i9UHQ0aHIpZ+K2J4B2RzQveM1krDfvXce1pY+ryXT5kccvBA34sMjnJ6TwnG+iyh+adSjv7ymO7ZkgtfHzE6GFA0hGgrpegG7Jw2DE46omGF5CXFcouz377gPdGGcOg3M+aehsa6b3gEXrdseNxP6tmiJ5DDoTdA8Y4SDxw29RxH0XZkK76Wp5iryHte+0lq4cZg7CXv21cremfKusNiwfIjJcEEwgk1h+Y9tdYlbxSige95XzZ8S9y8q747pXqPRUoIRnXKHBD2686UZa2eIN6g4IDcYPsBwXqIHVrfNjb0PWJ2w27hwGdumcx/D6Tw5yobyqm/9IlX5beub3JyfBaqehVRJZbjPz9l9cEmcx+PMRV0zheEgwKXBBz9hT5YoexESOX5Ayn9D8PtRLi6QGq3faYLYO1dLfI56J/2Am42FeLLA3DKsf+cYyY5T/+/Fjn86yPM1JO/3Sd32LxvHjfyHeuCUcWxXywwRYXdGtOca7Ly8YxiuYWUjub5Ac3nK8qlNsd/dp3JHXNsvtN4GZEMGusKYYBNK64+1KZ7rkHnqR0ufMsiwQiGJxyNVSGY+pTNeM0i+xTZiEg2DeV9Y3577SSD7SbcWRBkIa40uMXCTwhHx0yDpm/S86lFDv/WlPDq0P86I99er/dcyua9CYuPekJ/524oI0f7ohDvOBaeyBkcT2hdybj0tRHNy0LW84T8yZ/sk600qWLDxn3C3HM+wy1bELrP+8y1zfsNeRXzX73/8/z02rsZtg3xhqVsKltvVaqWo3ExIN5RwrFDrRDvCMufm+Aiy6Wv8dX17XOGadZFg5LR6R7TFaHdzJjutAk3xxQLTQDaFyCcqPccuxHJ6pT0iHLsPwpVbKg6CUE/o3MuoblWkqxNGJzugMLwmFfJBZ9o0DlPTUwb0mWfhOEqGHZgnFtfN1Gn+YZ9SzgWxPnaiHR/6bdvW4IJNNccRUMIB45gKgSTijKxXu6m4RtfAWTzQt5VnzW1c01lOBwI0bBusFRRp996wxCOvPfgd/YZWy7w3ocaqOYguerVBXYryfNF318+2OVqao5FrV9o7fYgR66lpb9a0DewYXgpzAzHq4i8F9Acl8w/Xfqq2FGFCw1lJ/RtTmOLHeQ+hLDlf2zxhpeLtqkhHHlvJd5WFh6bcOlrWuT7PAmJeKG3YtGx87ZF5h7ZxAxTAPZ/XFFr6mWQA2uJt4Xu+ZKdk/4rUDYtUb9CGxGj4y2KZpvu2QyXBBQrTZJLQ4K1AdOTi0Q7Oft+p0GyXZJ3DOm8oX/vPGqEA781QANff5B3lNGJCqm8rIjWYYvqcEo1jQgPjzn1rnUu7MwxKUJOHV2jnyZMsoj7D1/iyrjL6uoc8mSb5L4+01FMtR2RzYVs3LfCgQ9fxUXB/83enwfbnmV3feBn7/2bz3zufO+b38s5syqzKmtSlVBJaEISCIMkBIHBgFtutx0Q7m6M1W7btB2OwDQYbNN2NwYHYBshkFEIkAqpJKGpqlRDVmblPLx883DHMw+/ce/+Y/3uzaSUWVUqVVamRK6IG+/dc87v3N8ZfnvttdZ3YHRvQudqShUphqekX19FFpMJXLb3shAWG3sFVWzoveBYrikW25Zyorn8Jzrc84HrvPDyDipKqWJxkFMltK47UJJAyljzT/gQzZtGuC2BY+dflcS3p9z4/j5l4pidhjKWhXSx6VhuRgzvFUb79q/NKZs+7kVFuLdgfr5J86bD3elz7gsDsq0W3jTn8D0JysHq00vMTIQbAS7+bwHKOazvM7nYwEsdyUFJMMrJ1hImZ0XWRZfiumf9uvoySNtyIZUSVp18l/KOiGqiILllKFr1gmukStO57N5VLUw4aghmdr4T4i3ApIb2tRwvFXKdtxR9s+lZ6u+VqOn6U3HuS1e0zN48IBMVZ10BTvSpdCEujt7AoXNBACorqgMmk7lLMJIFuQpBp3J7tC/PuVyXv2uNeKIAJ6q5J0npmxK/uyuKrxbvJo5vYujSMbo3EZOg6zl5x8N6tQDcrMJZBZ50D5P9HFQgInNLaN0u8SeVLPCjHDPPiQ8Txj1o3hCP7fD2GIym7MSg6i+tZ2h/6QB8D4oS20nQ05TtfzXi6NEO/txx92MhOoe1pxRpP6FoCIFudH9I77kQAKfbxJcPhTMwyeg9MePqj26w9Zs5ratzXv3hFt5SYYoGrRcGWK+Bsgq90KBl6FolFTZUqGGAmmlKH545jNG5xsYV3/L4k/zcL3yAolPx1O0LON+R3PCEKHi3SbI9o7gVsvujGeWBwp9vsPLkiP6XJthY4KfRoWJ6T8U9f3/B5GKDcFTijVKKXkx0dYBrRDReyrCtiGs/2K531I5XP32W3i1Yrvvs/FqKso6s52OWljIxzLc0y1MVTgmBTmQxHGWi2f3WvlSBVb04L6UtFx05ln1N+5pluaI5eiQRnaUQ2iphvq5Z++IMM8ugrPCmOc7TbP/anDvf1qCMDdOzHXpPHIJn8OYFWT+sZc8VRaKIxjA9lYhhVg2WKBvSYmvdBJQs6OFQPNxVTfxLVxRlS4bQZiGExrwnbaNsVeYIx7BYb6EoE0FEWSVVhDWQdWV3n3dC+R4XsniXTtF9yWE9amSUaGHlHXVi1apKWdxdDCwhmNbe7FYqrWP/FiEuiiKvbUq1bf3ad6OUSiTekxmLyeR8rVdrVRVakFWBRTUqLv3b3xw01XG8W3H8DkIpZYAvALedcz+glPpJ4L767i4wcs49+mXH3Af85OtuugD85865v6mU+suIb3mNKOf/UdvMvqPj8N/7CK0bJf7SUcTi8gfgL+ry2coFNjub4M0tXlqJEqiFYOLQmcOkJWViQCmm97TpXMkxWUDehv3HQ2Y/1MefGPrPOnpPZRAbVF7iwtoCNPapGtLaQSn8hcMpcRXEKdK+zDjCkSM+tGz/6oyb392hc8UyuugzeGCL1aczMHJBnP8/jrCBR9GP8GeKzquW1jMH8vc8Tf95y/B+jT+D5Zqj8apfS5XLjrhoO/x+yoX1I15+/hQ/88XHUA0r7OJC0bwB0UCeo/WqwV7vkHxsQOf/22LvQ4a0D6MHOxSJomgrlmuiy7T6OY0ZL+l9fobtJMwutCkjxezUOl7qiPdC/MGC5A7kK4rTn4DBA8Ie3/xsQXBzKO/PrgatcMZQxl2UM+RtR3JHMfvognIScPu7QC8tZqko+hWtV2Qz0H+hIG9pZjuG0f2O5g2otGKxIf3/vG0oG4rBQ01s0CTvKFo3LI1bKf5gwalfknbX8P4Q247BOfQix0s88pZPfFQxPSUDdael5VY0ZNEEWUSX64r+SxVlKEx2XYiyr1M15DpGFvJj4cSlzAFUqUQAc6HpvABeZkm7iqwvmxjriT1uMHMEU8tsx4jE+nFLKYBsRSqDMhYZGH8un+UxFDxde61qKJqOYKIoI3lc1hHJewB/LgnIeYCWllbRFlkcXUgiyjuy+cpbr7WmlBMocZmoWiDymzvOdQ4q+27i+J3EXwBeANoAzrk/dnyHUuqvA+MvP8A59xLwaP0YA9wGfvp1D/kbzrm/9tad8jc+goljueoRH5XoUlOFIr6WNw26dKhA1/1iRRkagpm4nDXuipRI2dAEI2i8OqRYbzI5Y9i6taQKA0zmGH8ww9sNadxSxAcFqqqgcLjIZ36+hUkd8Y0xqrSUrRCUwmSWZAnLDY+i5RjdKwNQGyjaVyy3vqtD93JFFSim50TxNu17LDY6NG9nzLZDEcVrK9a/UDC6xye4b5V4dwFAYzdjvh2DFcMek4EpRDZ7esHhoopAweUvnqZ5V96TvOtqDoEsdMP7tPhf9GSXuhkKyTC8tMr2Lx1SdmJW/9oNnvmZB9j6dIVyjuSVAeP3rqJLaD17QOvZJUcf2WB8Cbovgyoto4d7FG3FmX9hmZ72OP2zhxx+cAV/WjB+3wZVAN2X5ujJElUVdJ8b030OXOCRrkeUccLs/hw99WRAq0UFoIygdcOSdQzBrKJxV6MrfZLUnOfIeqJ8G0yhbAgqKe/J0DpvxCgX039+jp7lrDyXovKSqhFgQw8zL8i3Q3Tp6jmGw587wpGg7mZbYiQVTmwtOJgxvD8mGjh05UgLzfScDLvjfVVrV0lrtAplJ18mDj0SfarluqJzxWJy2Rgo68g6mpWnBZhQtgI6qagyT84GzE4rTA7hUD6/xl1hnJvMsVgTI6Z0TTgiugCvBGfUScVSRlItLDbrmUVYy55MxH0Q9RrowGlJgKqG7jpPqiLRQ5Pjy1aFXmps/M2db8C7supfdyilTgHfD/zXwP/1y+5TwI8A3/FVnub3A686566/JSf5TYrZjiYcOQb3+7RuWfxxji48sp5HFSrCYUXekY8j62nm25pwIBedPxdrVRRMHuqT9mTecfvbmjLAXHV4t0Oq0ynzMmLtyVKsVLUjX0nwp4LaKfoJ3ihlsR3hFMy3NNare8s1AsVp8R2fnZL7hvcb/Ak4Y+m/UDK8x6f3csHdj8SsPCeeFss1DzT0XsqpIs3wgRbTM4pwKL4hqpRe9WJTFruy6XCBpfmKT9nwRX9rVSqw7gtQheI5cfT+CjPXeDsLvC81sQHcvtOnXY3ov5Rz+7tXxZfk/7TGZn+Bso4b35ug3r9BdAjtG6+15vpPDcm6fTovzzDznN3vMzx+8TovZffhpY79j67gzx3L9ZDpqeP3t4XJW6x9aYl/ewRadq3R7oL1ZURj12d8SUT5ypalihXhkSZvasb3QjjwiQ6F7Z71ZGdcRdK+ie/Kblz4KSJDM992RANF+3rF0UMNqqhJOLJ4Kz7DewzdV8Uno/PKDKcVLSfOgzqvZPENDStPLaiaAfMdaWkttkKpAkJF2pDk3bwBwcwSTGvTqZ6heTsnb3tyzJow/oumwltYso4M2720ZpQvZWZWtnywDpNblms+VSTVVBWJpI78q0j2K3QhLn7OiOjj8EEhBzolyC+9BJ1LhW0DSWj+zDE/Jcm+8qVNFQwl2Sor31lTQOWDVwiiSxdiwWxDR9FxmEUNzy3fWlHDLw/Hu62q30n8TeA/BlpvcN+3AnvOuVe+ynP8KPATX3bbf6iU+lNIC+z/5pwbfvlBSqkfA34MICL5bZ72Nzbsxx4lGtQ9WyXzjLLlYz25AIpYYXLN6KK0dURwD/KWIpiKoVHn5QXpesxiVQuc1wi80ptrYcq2StzMx3UsedsnGS5wvsEfp1Sxj84rzDSlWGngLSzLVWkvHLeObChaQ6tPL/EHC9KtJoMHQ7qvFCw2PHq/WBHdXdBzCZOzXt2SUOw/rjn3LxYM74tZ+8whBx9eZbGlSNct2f0Z3rUIkyr8hXhH56sVKq4IbwSUDU6kuNtXRCIlHpSML3jY0OGPjPgzXG2QPbxAX48Jbvu8+J+2ue+vzti5VpCd7jJ8tE/vCwfgHJufCVhsSLtovmmwQYPmyyV3P96naMDg4SZrnx3QfiLi1U/dS2sgrZzel0Zc+6E+Wc/iTxxlLImvimD3QzE6j9n61TE6K3BayzyJDsvVEGVVrcYLi3MFZeJhUsXsfMnsPHhTQzBS+DPZiTslA+zkwJJ2NdOL9gRKmvYd1hjKhlimTs/IwDvvWca2tsNttqh86FzLqQJN0Qyxvuzw5++JCceOyofmnYrlqifnta5I9tzJoDlvKqrAkLcV0ZFlfCHAnzl85wjmwtSvIlEZ0IW0i7xU4S8s0bBisRNhUkfW1TKTSC3hWDPb0aJSPITKyKYnb2qUkyQ1PWVEFLGUSqd93bJck02KM4pkzzLbEac+1VC0X3XkTZFPD8auTlziQX78PEElVZzTUm0UDamowiN1MkPc/quf/iZf9e8Ox7+uUEr9ALDvnHtCKfXxN3jIH+e3JoQvf44A+EPAj7/u5v8J+K+QpP5fAX8d+LNffqxz7m8Dfxugrfrut/8KvnHhfE0wtTUMsd45HSyZ3tOiSOo+bVMTH8oFgHrdELDgRH10uSJ+AyghPnkL4S04z0Fq2Dgz4ODlVQ4e8zEPrrL9qxOqyAOlcL4Rk6WuE79lK2JV3cuwsJr1z0+Zn04wC2kuDx4MmZ6zFA2fYAJZ11AmTeLdjPXPLrj7bR32P6C59PcPufvta1gf9j+2eqKoqiyo3YhkTxaBvCMLhTcxBDc8vAXMz1i6zytWvzhBVRVlOyJdC0/et8ZNQdQoB1NiNt+3yzQNmb3Q49b3Rqw9lRHdmRJeF9TR/rdtsvqlKcpGXP+Dhvv/uwPQGtuK2Pz0hHQt5ugRn6s/tMLOr6bc/K6QxabsRBcbfbyFENpsANGRqAk73xEMNP7UcffjHbY/OZAZj3MERwu2f2XJ7EKLvQ9qqtDhDT2KjYIqqND1t64qFbnTNRNeFr+iAaNL4tERjGTDkK7K52oDeQ/zjmJxtgTjCHblPfPnjvm2Il21HL3f0HvG0L5Rsux7zLc03twxPSOziLn1SFcU8aG0oUb3QbIrci7WyNzAKalwoyPZsAwveSeILJPK4mzmUhVNTxv8mVQfZfzaomiNvNDGnuib5S1N3hJJEaflnAHSnqb3SsHwkk9yVxjnAM3bAvHVVT3/s5IQei+VFA0t7PJQrGVN6nD1Z2R9GfR7YyEyRkeAdVJh5DIz5Jh1/jaEe1tXnbc23sqK46PAH1JKfR8QAW2l1P/mnPuTSikP+CPA+7/Kc/wB4IvOub3jG17/f6XU/wz8i2/8qX/jw+SO+aahdaukChWLMw1U5ehcTrGByF2Y3HLw3pDWLREenG+IHlBye4H1NY29krxtyDqa+MiS9Wt7zLmmWimYLkM6F4aMVxK86xHz0w3CUQEOsr4kgCqqvTc2xLdBF5beSxl6UeAtLXkvYnYqqH0aNHnXAULiytYUyW2HssImTu4oZvf18OeisJocVEQHOeMLMY27UkEcI4BUASiHt9By0Zew+kVoXV/iQkPaS4hvT5k/EpPsiSx8OLFUvgg66twx/6ebhD94QPisw19IX91pTXpxhfjaiGhs0aM58WRJ69UNbCdhfqZBfDelij0W6x7WSJVz5Y/WEGgjbOd0yxLdFe7C+lPCwp9eEEfG5EXF7LQMZQ8+JLOR9vUGrWcPWVzss+xpmtdhviNqr/PEiMNeXMLEp33ZUDSlXz++JIt6uiKMbTOXnbI/k9ZQFTqCkQyunYLeUwaTU8NVHeHYogtdC1Uasq7ioOtja4FB6yvyniXdcGR9LaS+vqKKLd0XFZML7kTOvPMqJ23E5YYjb2u8pQyoq1CSU7bm0LnMvpSV23Xt/069MCont89O+YSjY9ST/Jv2ZTDvzxw2UNz9sMfaU9XJQH2ppYoC2SCBJBtvAYMHPKIjx3xLEY5AFzLDqAJppx572ivriIYVVaCZbRuSfZmHqEw2Ylv/7Te72qhfx+/hiuMtgxo4537cOXfKOXcOaTf9snPuT9Z3fyfwonPu1ld5mt9SlSiltl73678FPPsNOuW3LPKORxlpWrdKFqsejeszikTjjGJ+KjqZbRQtQzBxDO4zFImi/0ImfgYOzKIgvj7C5JbmXRl8bn42Jd7V6FKhjMM5hdEOm3qsPOOIBlI9KFeTu2auls62xHcMrWvSOphvBeTrDWZbHpPzAgHOuiJTUUXSK56elSF4uh6yON1i9emCxZZjfM4jXVWc+sQBzWcP8A5nciEPKjY+P0fnMLtQUTYc0YHoKEVHgsxp7BaYrCJbCYkOU2wSkPYVi3WNNXD0iOKef/dF1r5UEh0oNn5jQPQ/9Oi8PGdwv2F4f8TyTItwkIG1tJ8+ZPbQGum5HtufHPDqD7fI2pqiE9TvA6QblqJnUb0c2yppPjBErWTgoHnT0XkFZqcM7Rspm59SrH/GML5HiGv3/j2Rlg/GjvE5Q77TIesaFtuKMlZ0XoFw4ETpdaYxuyGqlEXZW4qkR3QkVaQ/FQ5FFct3pPIV7auWcKho7lY07lriQ+nxl5FwHZSVnf7gIWherxFEFrK+SIOkmxXpWi1maGtHPYdIvQeOyflaiVfJDGB6VqqQxXbNBUIqwzICfwKtq5rwwBDta1SpyHqWMhHyXbZiqWLRrZrvwGLLkW5UTC9Ypucti+1jOK7MHrKeEC69pWJ8wTA7K5I5eUcSQBVJ+8l6klhNVr9nGsJa4yxdkZmJM3L/satlMLPMtmRWqCqBQGc1CswG39RL/SQEVaW/6s/XGkopo5R6Uin1L+rf+0qpTyqlXqn/7b3usT+ulLqslHpJKfU9r7v9/UqpZ+r7/vt6zoxSKlRK/WR9+2eVUue+2vm8XZIjv2VuoZTaVkr93Ot+T4DvAv7plx37V+sX/zTw7cB/9Faf7O8k9v+DbyE8KmhdXxDdWZAclIzva9O5PCc+yNGFLOplokm7soO0ITRv5ehSJCHKpo/KS2wzIjrI8acV4dhy9Q8GzM+VVKHj7NYRUVCQl+KuFh8WLNcCcDDbCSlDfTJcDAeataeKmtQF+99ecPhISJkoOldy+i9m9J93cH5B90VFMJQdn0mlXeYMzDc9zv3zBd7SER06bvzgGihFudqk/9k9nKeYn4oJZpbOc+JrbjIwuaV7uSCYwHLNxynYf8zn9re3uPldTfyZ+EdEA2nBvfATDxAeZjTvVNjYxxnF9HyDYAxHHyiZb3jc+J6WDK6tZbFmuPmdPumpFmc/kbH6qT3imxPCm0NaNzLiXS3w06mPd+QzvtnBWYVqlEzPKfKW9NnLyNC+PGXliQH9ZwXuujjdpH11weR8rRl2JmR8QZO3Hc6D6VnF+D6R7vBnwqrXmbCpF1uOvGuZn3JkXUe6XQqnI6jlxgMY3i/Vx+iCYXRJk/WkOgxmjiKRymB0rxDeZmch3pU5RDhUJzBUp6BsW1xUQavEtmWY4PVT3JklrllRbOTYRiUkRyeD+WAsC3J6Oidbr5g8VDC9UJ1AYJ0R7aiyKbt+nQn6Ku9IS8tp0JmmcVPLe+xgfrasNx+CGis6YixWxuBP9UnFgpMKI9mVjU3RgCqWIXfRUCfiiZ7wWaXyiRWT8zLDWKwYiqZsOIqWYn7asdywLDYd8zPl23DV1y/LffWf30Yco1OP4z8Bfsk5dw/wS/XvKKUeRNbXh4DvBf7HGpkK0ub/MeCe+ud769v/HDB0zl0C/gbw33y1k/mmEACdc78C/Mrrfv933uAxd4Dve93vC2DlDR73b78V5/hWxeanRqisgMqSne4yPeWx9sUpepFD08dLLZPTMsD05+Jk5k+hig3hzZm42M0LipUG852QMlI0dksOH/Kk37ta4vcLDj+5w+KRJXbpoVKNP5gRHFQU/YQiVqJ6O5UFUDnFbNsjbyuSOw5nAvKO7ELHy4Dm3ZLGbk7yP3vsPy6Lwvi+iviOkd75pmFyEVaeLhnf6+g9KyX54IPrTM9odJFw+p/v4+KAKvGxXowNNNGREy+OsiLaDVBZweB9K2JRWsHyvUuygxAbWja+bY/JU9tEA8vkQozJ6z76lRF3P77KYtux9mmPg4+UmGbJre9fp3O1Il1VJHckGVnfJ++us/qUonEnZ3xRBAfju4bF6RJOLaUffjfCn9bD6VWYnVXoUo5vXlPkHXjkB17k1b9zH6OLTYq2JV+rULW0RXt7ylx3CAYa6zl6zyvyNsSHMNsRTkHZK1G+pVwaWLGQiwx464rIgORdEffLalguWoyf/LkgkJwS21x/qiiashMXmLYozZ5ATpslLtciApgaCCsoNf7TDZZbFfgOlZSiEdat8CaGxaaj6pR4zQJ1FOJ8h8o1rlmRhhYqhU41tl2iUkNR+4kHQ1NzNqygnHJFuioVCZ6jccNjsWXJ+6Ie4I8Flux8hzdXtXulomgJSGO5puoEJdfOcl0StIgZKvFsciKZ4k8U0QDSVUXly3fXW8j7V0XSXrOB497/y1ts2PQV4hvVqnoTdOoPAh+v///3kfX1L9W3/yPnXAZcVUpdBj6olLoGtJ1zn6mf8x8Afxj4RH3MX66f66eAv6WUUs69eWp7lzn+Fsdyq0F8Z0a61SQ8SulcgWt/sM2ZX1hg5gVlIiiTztWcrOvR2LU0by7xRkvKTky2GhB4mrJhCIcl8wcCVr4wJdnsidWnVdhKMztfct/OPosi4ObNFfY+0mH16QU2kFnB/LQhq02cvIVIUDgFg0ctyW2D9WRmATK0XK4FHD2k66rEEgzFjzocO+bbmrM/l3Llh9voHFaenTGbJBw9bEgeP2Q4bLI7X2PtyTneOKVIEtaeSikbRvD1uUXlJYvzXZSV4ejeRxzhKzEbnyuYbXns3drBbldkHU3aF2XbtN9i/TdHdK4V2NCnjJw49i0M4cgx2zKkq5bwSOYoVe1Cd/C4ZTgLcEZYxsdltr4SU26UuM2MKvJPLE2Pd986h/lph8kUT/7yfcRNxfRRcddTmcY1Shr9JavNOYutkGXLJ9irPeAXkLclaczPlfL4QoQhWRrMXBOMZPjvLSQZ6AKCpSLvSgsqHErbJtO1/p+DbFVQaE4hciHtCr3QVK1KlGOXRhJDTT5TUw+T1rMqp/AmGrfQolhby3w4T0QCnVO4htjj4oBFjdirwPYKvLDCJSVVZlBzj3y9RM+McD/aFdbT+FN5D/2ZpmhKNVIZkfs45l4cM9H9iSYciOtgGQsiKutxQvjL1kt0pknu6BOBRGr9qSqQ77Iw2qU6zV+3zbRabn+7wqG+1sSxqpT6wut+/9s1sOf18Tf5rejUDefcXQDn3F2lVC22wg7weiXHW/VtRf3/L7/9+Jib9XOVSqkxsmk/fLOTfjdxvIUx+RMflkpiEaGs4/DRJq2bJc1bjsVmSDQoCEYFpu8xPhfQf2GBKi16UeB8g05LGldz5udbWE8xOWOIDxyqqmhfz7BeyOCso1j44Fsu767RSDK8pMSGAXsfTIiOHJMLAgX1p+IzYTLhU6QrIlkdHYgcRTiylIlitmmYn5Ihsi6hcUOTrgppK+1JwhneF3LhJyeoopLzeWZB80rE5eYK9/2vQ1BLUIpiNaGxV4kwX6jJVxuYLEKnBVnXYDLHbMegc0sVOuIbU5arPfKeJTjSpCuyiHsLhVMOG/tE+0v6lWO27Yto4Lp4tJeJLFg2hN6LwlLOW4poX+xYWzct+x+UxWTli4bpWTAjj6qlSO4aUQTOxH1v7wO+eGw7qdTm27DyXEbeDkVWI3FU2rCYhty1CmsV22eOGK3E5JlP+BsJ4/ss/lSRXPfEYrWGwjoNK89mZH2P+aYhHAr3waTH/X5HMJaF13rCiSgbFn9ucMicIBoIU7pqiAimKhTkBhc43Fx4NYQVzhrxv2hX4DnKFnj7fv0axMPbaYfKFbZSkGtZiA2SoDyLqlNtufDwmzmV9XCeOCjaxKLq9qhrlhRG5juCCKzNmgpJVGXLUTYsZVzDZUcyq2jcgckF0bQ6TvZV4PCHRixrS3lPqpZFpzXfyLfEBx5VDFUk4p8nicKC0nWSfhvja0xbh865x9/szq8BnfpbDnmTU3mz27/SMW8a7yaOtzKOGbMbIZ1nB1RBF29eknXEVNpbGFzDo3knJ+t5KOvQWUm5Uk9MLSzXA4pEkxyUrD5dEu7PAQj251QPRLi5h24VGK+imAekV2PsmZQygjP/fMDgfT22Pp0zPeMLLFIJ+sd6siPTuaKKFN3LhSxsnmJ4yRf/81RhlnIq0ZHoHE3PQ+O2oGiqZsB8OyTZyykTQ3J5wIWfrkmMGw2CgyVZV+YS4VFBOMjQ8wyc4+Ajq1QBTM8pLv3EkBvf18OfweyeDr3nJjjVliSXyjlHQ8f4gubgsQbJviUclZjCMW8p8XD3BMWz+qWKxZqmjBTzLfHYqCLZbc/uL9FTD1Uqjj5SoGYeaNHDWp6qRGV1rphteZz+hXn9tyrmW4ZwoLj9baHoQVUQ7ykWj2dUk4BlqYnaGXeurKJq57nxh1O0Z9HDhHTD0rkC0zNCLKwCRd7xasa2ZXJW5i7pqqtnQSL9Ua4U2JFgZnWhyPq1tL6G2WlJ6t5UWl42kF23TsVvQzmoIo1tVARDTRo6/AMhK6KhjCwYcHElrGuj0NpBpbCxE8e9Wk3WX11SFgYTWWxlaPSWzEexoFw9i9sqxM211LikoowqSqdQucZMNTZy5OvlSctLVYLSWmy5es4jc4u8Ky2sok4wZiFV7uxcJeeqJXmgHd7AZ75jxR0QeU+OXQGVVehUwduJanKcVH2/w3hDdCqwp5TaqquNLWC/fvwt4PTrjj8F3KlvP/UGt7/+mFs14rUDDL7SSb2bON7CiAZi9RmOSpZnOsy2DX5X075e4bQiGOW4QGNmOTrz0bNckkYljN/paYFiBmOHqbWripUEsywZ3dtgco+lcd1jcUpRtgqa/QWup+CFNrqEqz/SQ5WK+VZAsudIe5p0BbJ+BZ0CdRRQ9CqCsYe3rPDmBVjL6jLC/3ROuhoxOeudKKua1FF2S7yXPRFdPJrT3Ztw/Yc2WPtSwfi9q3SeOiA73cP6ivEDLXpPHLK40GPwcCKKpVGLxl3L5KLAW81Cc/i+rqjBPpsS3B5z/Y9usP5kQTRSJLfmpJsJZSS73vkpUZ+t7jVkq5bu847ulYz5ZoD1FYP7ZQ4zeKzEGxs6l2GxKQtmVWpst4BCY+IS77aPzuQ+M9P4c0X7hshq7H6kcYJo82eywHkLOHqvoMOUhfDZBBvIQDmdCoIKp9C5wTpFZ2fEYMcHz3LwWCBooVTaUpMzQhhUlQyFJxfA+e7EkKjsVNI+0qA2UuwowClLtOcRDoAVRdFw2EQ8NlQmlrYmVRT9ErRDZQb/0JMFeabl/Z6LORdOINJ66UmbS4GdS1J1oZXZiFXopETXTnqlNbhKMZ8lYo7UzbGFwS49vEZB6YBUg3F4rYLSM8L3KBUqsLiFEYXkTCqLYxFEMeiWSqLoiqKtKhRVS4b8ZuBTtV6bKQGU/QK1NODXzoylkaSRlLiFh6o0F/7SZ96W6/44vhEzDufcj1Pz2OqK4/9eUxr+38CfBv5K/e/P1If8M+AfKqX+W2AbGYJ/zjlXKaWmSqkPA58F/hTwP7zumD8NfAb4IQQB+27F8XaFt6ioIoHd+vMSk3t4qcObW/xFSRV72EDLrlwrgtAIKqZnqIJjsx1LGSucUVSRAedQpSGYWc5+omK+4ZP1NGVDsZiFmFsR/ecde99R4O/7dK45WjdyhveFBBNpVfkTTVkG2MiiCtEOyvo+/mCBTQL8SYYNDHnbkBxaqokimFiKhubcP3U4r2Sx5uF0h/jWlPjAcfiIz/oXcybvWaN5bUZ4c0niHCjRvsp6irUvZux9IOTutwucWOWacKCYXHKsP1GhCku+0xFcv3MkNxaMHmphjaKKhORVxiKH3ritaT6laN7J0blIWZglLC5azELRf9IwO10PT0NHvioChLMzWngIvkX70LrqWGwpTv/CgnQ95OghQ3LH1bIV0LixoLrYwHqK0QOyuC8u5bIwz70T32uK2hM7sNiFR/slj2HRA8+hQoc9lZIfiraXDaWNYzLIVwW1JMQ/8fUI9zyRALcKFzjsVJKPykW3q2wodCYzERaSUG0gA+QqcOiFwZvVvIt6N2/91zw3dC4ExiqxENcDE8+iUgOew0wNVd1NtzOfbBBA3brSeT2vaFW4QmRtlC+LvfYsrulwpaacBKioAiXDcLSDuMItjXA8AmSYX6+tztSVg1fflliUcRjtKLtKWmGhwwQWpZw8fy9HOeAwPJl9uIWHKmoDqLc53mIC4F8B/rFS6s8BN4Aflr/pnlNK/WPgeaAE/gPnXFUf8+8Dfw+IkaH4J+rb/y7wv9aD9AGCyvqK8W7ieKviw+/BzHLioqKKPdKVQExmSsdiw0NZkcUoI4W3dDW5yqeIa1mShfSZlYX4yKIqR7phiAbyHUi7Gn9WicdE4DNfCBA/GMHeRyu6qzNGRZv5jsfyB1K8fxWy/5GK3tMyzERBVRl0Aa3rEA4KXOhjxjKbQIX4c8v++zy8FHY+OSW/v0XW8wiHJf7C4i0qbvxAH13CmZ85wPmG6FohV0wt6+4aEcmNOeEgwN+bcPa6o1xtces7GyxPFczvKwlv+TSuz9DTFNuK8OcR09M+WVecDsvEEe8rovlxglAEI/GxLlqG+abP0cOKjc9XTB4vaD8bsthSlE2LDaD9KkwCw3JNJMHznsXbEzOnwSMWbwl3/qOC3j8MSXYdw4cd/WfEOe/lPxsT3xQJdQf4Y03RLgjigswpvAOfsucIDj3yzQJ9EOC0EPzCI5kvZKHFiwuKToEehjRuO7zUsVyVgbH1a0XXUtG6YoRBnitst6T1bMD8jFQVZqEpOxUWMDON84TA5y2UeHUHssBaLRBYYfFL1eI8TuxWqzqJJDc9slVL1RHIqosq9NSTmUkmVZULHDqTQX7RcQRDSXzO11jjcMoRRiVVqeUzt6CDChMXYosMrHRnDCYJxTjEnxhBSLXLkzkMpYZKSfWgHUqBF5b4fkVVaVTT4QclxljS1Edrh+kvyRcBrqo5TIGs0noh7cZz/9nbXG3wjScAvh6d6pw7QnT83uhx/zWCwPry278APPwGt6fUiedrjXcTx1sUZigKsa7ycEZTBaIamnYNy1VBy5QxNfpHdp+6dDT2S7x5hQ00ac+nqmUZ6Pt4S2FhzzcjgqnFKYhvThifXyHdEmmKounR2p4yOmjidzKap4eMn1pl/oA4uaWrimTXEUyF2e0voPtKRrA/wzZCKCtst0GVeMS7C079qs/wUsSN7+9IuyoHZT106bj+vSHeUohzd75rDVXB1if32P+2DSYX4dI/OITSgm8ws5xivYXOK8qGx/oXC6Z7nsiJnIO7v6/D9icrdFrSfSVncs7n4H2KYCQMZ39e0by5pEgaJAeWowcN8aF4jHupJITD92oaz0YkhyXJIRy8x8NbCgGy+xIMHnE4H7y5EAwXO47N3xQdp/D5FllbWOreDA4/KLvl1d/0OPxIAaUC4wh7Ke4oJssNfjOn3HDogY/OFPGVAL+2RNWVzC9m25qsUJTG4O8FmAwW24iD3VIW/ehAkfUhOhA4cL6doyY+7WckCZmFENnKjiCndFRhE4UrNCo15JETWQ0D1HtL6wPKUbYqVK7R3ZxWI2Xqmqhc448MRdtRNSuUJ60oSpljOQ+cX7fAcmmBOV/ReUkgsrZGMVEpsJo89bCpB0baTn6UU+YGZRxKOY5GTaqDCOWJaVSxYk+8w7VvRdPJOFr9Ob6pyEuPyC+prKLQBmssoV/inMKvq5vlPEQHFVVm0CtCdK2mvihOr1a87eF4e2csb3G8mzjeopg90KdxeSI6UQqS3ZzZqQBloXPNMt80sgtEXNLyrmOxqQhHPp2r+oRBm3UVzTtiBHQsBJe3IN6XFkXYCGlfK0j7AWXTka9UTHdbmKkhej7g6EyMO5WxuT7m6Kl1WjdEHsSfOWzoiK87wrsTbCNEZQXLiyuoUhjH44dFS6togX3PlOkgJr7lkexD69lDpqc2KBoQD0o6r+bsv7/B8kKf5KBi/VNDbCNkfqZB3tC0ry7xD2YsLvbwJwXKwsanBlz9oysUrYrWNc3gfT38hWWxqnGeYvUpy3K1hpVaKBs+wVSYwSaH+bbMXkQiHkDRvlaRXJ9z59s7bH6uYP8xn6ynam0v4T34h+pkt37wqAz7h/crWtehCsWEKdnzKBM4/FAJvsVvycJljCXop+TTgGIc4o0NVWJxnqNoO8qWonFDWmvLFY23gNbLPjr3BR1VS4kEQ7mvTGB21p0sNKqC5nOhLPwOZmfqAbBx+IceVayxVmGaBdXMew1JZCR56MJICzIH16xQxqLiEqUcy0WICitYGooV2UioUBZZPykpRiHBUJP3LESgokoqlBhU5ZOtCNig6FjcsRCXJ0oFAFgI2xnGWHy/wmhLZTVlqVHrSzy/IvRL5ssAZzVVobGFVBs6KpmNY8KkIAoKKqsoraYsDc7B0gYo5agqTSPOSPoFR4MmflyAUxRzXxJcUklCewfEu1pV78ZvKwZ/9iOY3FE92CWYVJhM+vdOqbrn/poQ4HLd4s0V5riloGB8rtYnAhp7soiL45rMPTpXLF4qekp6nqE6Ae1rlvm2JrnrkbfkMYf/1hyXe/zAg8/wq7cuYTLF4EHxZZjcU9G85hENS8p+g+V6bZdaQTisOHooIj6yRKOKcFgwudNk/2Oy2IzPa5rXxMmw/2LFfN1jeI+PqsCf5MSjJbe/Z428A+tPlKw8uw+eYfLICofv0TRveDT2Ku780Iogzxaa1o2C/fcFeKmhe7lkcsYj7Wm6rxYyw/A0ZWwwuWN8SUQBbehYdkQYsHFLs/3pJd5wiSoqdn5hQHqqRf/FirwlFUbeFsjr4px4mce3PJH+aEu7zksdZ382ZfhAzPQMhCOFXmrUXJNcD8l6Iqi3uL9mIytHMFIsE8i2SoJ2Rj6MWGYeyd1av2tPYMJVeCyrIb4cIhPiaF/WFJmQPmdn3Ym3RziU6iPa11SRIl8vTxZ7lKOaixy99M8sZEbQUb4D49ArBZ4vg+0i91Da4nmWqgyglwuCClDa4XkV2TyQuYpCUFXKoX0rZkS5pmxXLHwRakQDxonDHhoCC4WCSkv7qNCEnRQdWMpSFvqiMnja0o4yWlHGncMuXlDRSDLGowSbGXRY4XkVo0EDZZyg0rQgubS2WKtRypEVHotRDNpR5h5uaYSwGMnsjPAdUHGgvlGoqndkvJs43oKoAsg7mujQYXsGlMiDl0mtvTNyZB35UplckZ/K8fYDUOKa5i8c7as5gwdDqkCRdRROK5q3X9vCRAcZ3miJDX0RJTTQf7HALC2Hj4TMTmm8Z5vo90z5+VcfQD/fZPXpisH9hvLRGWvNJcura1hPsVwPyTqaZL8kGBcsNkOqULSi+s9qdOFRNCHc8yhjhzHiXOfPHCa1NO9UHPUC2jcqdj/cwGQN/KlAx6enDIePbIjyb1cG1a2bBbc/7mN9kRPf+IJldClg51fm3PqOBrNtQ94WBvH0tEf/uSV51wgxcVWLTEdbUTZEhFAvNN7CMT4X0VuWKGPQiwxvVhBfn7M802HwQCBw1RziWz7eHBq7luWqZvPTE6YXmrSuzDh6pCUy5qsV+Yb0+h2QrtSS8NYQ7huyTUEulU2HN9e4lYx84eONRMdjuanIuxZlNWVDYKb+tPaLCGHlacfknPAdnIb5GeEv6Fxet3I1MbAF+Y6o/5qBDM29pSJvW2xkxXY3UKikgqknEFrjqDKDLTTOKuJ2KgPl0qCUww1CbLtAD33cWkaJASeIpXSrwvQytLHk0wBKfTJP0YVUz1iETLiSUu3FuKy2aPUcLteosCJf+mTDCL+dk+Y+zimiuGSwiDHKobWl2o2ZlgnGgc6gSjzUUzHeRk3es4qiJS3Dysn8RgWWZaGlDWYsxrPoqKAqDbZSaOO48Cee+mZf8m8c71Yc78ZvJ4KZo6plsbHSFokGtrbslJaCP5fdYuUrrO9jUkXeq5ifrklsJhBiWCgubGXiSJeCz1//QokqLcVKg9mpEGvAy2QRR0G65kTgziqiJ1tsPJETDKekqxHLHVC54cMb1/i1bI1oPyPdCEVUblGRt32G9xqyvvAE9r7FER75hANBLZV9RzCBaCzKtYfv9Vl5tqT3SknjpSNazzjSsz2Co5TJfS2iw4Lo+pDpw2sUTYGglonh7M8uufEHYqoQ9h/XbH6mZPBwIpIm24rmDcdiW7H9GxmTCzFOSd9+vkON2YdoXxJyMBFhPpxjcrFB94UJLvQwWYULxJxo61NTbn5nS0ABkUBCdQlbvzyg7MWkPcXejzaFE+ILJ8DrZmyenXD3S5sUF5dEL8TkfSGbqUzjry9xzRx7s4G5EhMvZWZUNCBdqwUGOw5bs9DTVXeiLZV1ha+w3BGIbNkWddfGbU3Z4ESSwxmHmnqsPKVYbL7G5VCVItr3yNsWVWiYSbXgjT3hOzTq71s7pywMthbUc4MQf6Zwy0AqmNQTaC0I9yCuqHJDWfiohcFbKuJdQd55c0XWt7hA/qZ5oUEyguk5S3jknSTIdEvhTaSFV0wC7DSm6pYshzHBvkcwUrQmols1ufAakzwYalQJyV0ROwxHjiLxTuRwAIquoK/MTFM1Lbabo43FWoXNzNuloP5bw33jh+PvpHg3cXyD4/Zf+hZaNyzhxGJ9feLR7M8dwcziTwWiqyoYn/MIpmDyWqzuyJBulgRjjfUVo/sd3qwenBeKvC04//mmR3QIZcNjtqMJpg4zcZSxkVaGk5ZF60VD53pJeGdG2YspWrIb9m5E/OzR+7HvyVlsJ1z4JxPylRizKMRZ7oZIcRcbBSYqseOY5QZE+4qV5yvmG0b80L1aerurmZ3SLPvrzE7JbMafCW8huj1hfv8qZaRYezLFpCXTcwmT8zFF0+FCy5l/agl3Z4zP9VEWWtccuhKF1uG9IY3dimVfko4/rbkNFtrXKkaXRHQRwHqKtK8ouhF3PxLRvC2wWnlPAryFSHdnWrFch9YtS9mNKNriW2FDy+JcgYoqzN0QdzNh92qCdtD9pYjBIxYbW8xMEpb3VJPs4SU2sVQthzc2eKmgtvyJLOTOCFQ23awwc0MwEZ5G0RRWtSprgcBU49cQWn8C4diS9bTME3yH9UWaxuTCLUE78q6rB+cOq4UMWPQrgb5WApt1+xFqqqjWpH0jpE5BcwW7Hs6HolOJ58eeR+5bWJjjjhjFakHR1ZipEY5FBQQWZxWNO47lmqJ5Q6NzcCNh+VtfmPpoaFzxKRuOYBRgcmm5lrHIv5cNV1fiToQUlRP/8Fo92Fs6Oldy7nxrSBUJyjC6ayibQup0gcUOAzLfQ9VzoHv/zBNvx2X/xvFuxfFufK3RvGWxnshghyNHMBGF0WBUt4ACTRVowlFB865ickY+gmAknh3tq4bDR4W7ER/U85ACJveUEFfosU/e1cxPJxTJ8YWqWK5oWrcq5uuG9hUY+YbFhxb4i5jmKw5/f8rh90Q4v6L32BH7+x3MoU/Rrdj9aIetXx5I77oVUCbivxFeDzCZT9oXK8+iKagwk9VeCQdOZgYbCn8iM4RTvzznlT8tZLgzn7CgFI2XB7j7+1SRaFVlbdk9t65K22W2Zdh7vyQNXUDzbkl0Z0q22Txx1lt9YoiqHM7TqLxk8mBflF2HYthTtKB5q2JyzjC8NzwBHigrnhArzxeYzFK0DCY1VKEib2nmGzHLdUW2alGVov2sz+SioWpa9EKz8xslh4/4LLaQlVSJSjBasXwgRe+GKAPBkSZbr5iHmmAkPhjOA9ss8Q99ioalAsrS0LgljnY6FxMneC2B5B3RbKpCdcKG9maa5YYMzqUagvBQCyoqdsR7msWDKUVgwHfosUe8K5IlRV+Uer2lJwKAiaWKZa6kgXhXoQpDvlmSb5ZEt32s7ygbDtuoUHMPb64oWxaaBca3lAsPoorRAxpViECjDaS1FowVzVsyjzN3TZ04a+HCujrM2zKDG188llWR3F5F8t4GU0XRgLypyO4LhZuSqxPhQix4M4Ue+YRHsNipJVGa75h6o453K45342sML3Wk3XrnuHRE+wsO39fGnvFoXy9xRjSnerkluTXHm0eMLgU09gU5VTTEpW12X07ZEEmM7tkRepyg70SU3ZLFhhLL2LBuZ2gIRzA5I7IlqJpMOwyZXIS1zyiUFklslWuOnllD7aQkl8ZM77Ywmehf2TAgOFxQPBLSe0laHYt10amykbQoGnc8ZmcVjZuOaGwpDsWQZ/2JlOBgjo08gqOEfKXi8GGf9aKNUxCOCrxRyuximyo81p9yBDNpvWQrju6LklCsp7BJIK2mrkfj5SMwGopSNJmMpnV5QtUImG016FwtCIY5ztNk7YiiLYvw5Jyie9ly6l8OUVkOShE6RyMOsElA3g1x6x7xviMcaZZrAk5o3NS1ECQcPCrS72XLwVqGGgQiabG9wJVaOBC5JtuxeAe+qOxa0JHCVRAeBuJFciMgGgBO/C+sL6KSImboyFvqRH1xclFEJfOOxQWOshYGXO5U6FSSuOhqSYtnfqHAuxsSHUhLCSfvp1PCa8j6jiqWygUFBJZKQ/NFg84duqPQM4NtlWQ9S+cVef2z0z46r5FoI010JWK55ujs1kmuC83rUkmPLxiUg/bVisNHNf5UGPdVpEhuK5abrmbWQ+eqxRSOxm2HKeS1C+lTrh9wjO4V6fiyWWGWmmMjK2XldVeB+MXgFP5YWniqfIct1O+0PPYNjHcTxzc4yljRfTVDlZa8G1A1BbvfuFsx2/Jo7paEI8dizWNwX4f1J1PaNwRFZDKoEtltdZ8ImJ92tB4eMDhoS/spkd6ystLqgJoxbGWxRYl/edF2tF+BySV5rHIOF/rs/Ipl90OGsmUJL8fMOyE0K/y5wzYj9HhBudIk2bcEk4qsY9Clo3NZbGqzruHwYzkmLpn6Mf2fy2hdzrn13V323xdx6hNTVFFRdCzNKx7zsxXLmx7B1JJFGv/OGJNZqsgQjqD/vAhh+YMFrRsN/MMZw8dW8GclZpJiyor2kQ/WQlXhmjFOKfQ8RRUV03MxZQP8aYmZZizPtJifUgQTmQ30XnR0nx7Jgqw1y3Nd/FmJN5gzOZ/QupGycn2ETQKO3tPGayo2PzNl+ECT9vWMxUYgBMQmlC1bt4hqnkDNmm6tzpnPIkEnpYHwLRriBW+D4+G58CO8uSPvyoDZnyiCsTupiIKpoLBE30xMmGwsUFrvToitQQBVoyK55VEmx/bBCn/PFw0qhD0d7YsXyPGmoug4TKqptD3hX+ilJm9BMJa5BdpBIXIha08uKBse7WuOom2I9jIAdFpSdkNmOwGtmxmzbfE6n56RyqLyIW9pui/WfA8f2jdKDh/2cAbWvlhRRorFmkaX9flmoirgzY9tcV9jwrua5e4tBAwQjMS2drlTiTBjqVCVxltIhX/+L75eFPZtjnd5HO/G1xrjP/lh8qZisRGQtxT+3DF4MMabQTBTxAOLLhzdlxeMLyXoAkYXQtrXczrXSsbnPYq2tDB0KQvD4HZX8PTH+paV7EzLhFq9VLgHeUdRNEWYcONzFXc/aggvTVjcauI8jfM0g/s9krswV5rsXAZTD2/gyTD6tk8wE7e8zsszqtgn60gvf1G3SZanS7wDn6phcA3HnY8lrD/pcernh0zu6+ACD6c1F/9RxuR8DM4QHeXEVwYCatea0UUff+YYPWTpvhIw2zas/2ZKuhaQd3oEEyskwV6Ctz9h9EhXkGV9RZFAsudY/1RO0U+YnNMEI0DDjT/UB2RXmrziaF9zNK+Il/nsUo/mCwPiayNwjut/dIOi7bB+jD4bkbVl8YqOHPsfbJG34eBbDDiLSnJaX4zIVsDmoo2kaukMZxXLOxG2X4hPRUu2mN5MFGatL315fyIAien5Y20qEW+sIkgOLDYQK1hnZKhexVbEAEsFo0Ak0AFv6Wje9PDmjnRNdtlVKIN+p+Q7Ed8Vjsti2+GPZUGN7wqoonkHZtseRQOWpyq824ayIckjGGlmF4QDcfBYQutmKcZf4/LEQdEH/GFKb5hSNQKSvYKs75FWovDrLV9rDTZvWcYXNGCI9wUxljc1s1OKdN0S7WuxqI2k9VaFtXBmPRsJRiKwudiSGcgxDFjmQUrscxsWGzjm5yzuHQHB/dfjXR7Hu/E1haqg94r4aqQr4kaWd5yI0gHe0qLqnVY0qrCewWnIeh7Jbk77OjjjY5YOL3MkdzRFTxG3UhZ7DcxcY7dTitQjGPoyJC5EjloXMvjM25a9DxqqyBL9bJviHKi8xHmard9YkK6FFA2DPgqZn67ER2HsEexO5bzuzihbIWVimG9pihZUD85w1xo0rnostmUWYCNLMFYM7gtI+h6Tc5oi7tK6mXPn94U47eg/Z4lfPeLwY5uUMWx8Zsz2Jw8ouwnxkSCqNn7jCPJCmPWZZXjJZ+fKiGKlweLeFY4eUZTbOWrkYxaywN/4wTW8uUigWx/8/RnNmxHpqqL/gqX10pCj96+wON2i8eqQ5ktDaXVVFpxj4/MZ+4+HLFdh+9dnXPkjTaIDqYBufUdC444j3vfqAbZH1oeVL2pG92l0BTrzKRNH2akIjxRlW4uBUukDkviTfRFXDAdIJdgQZnh8VFE0NItNWeyPkWaqrP04BhplNUXLEe4K/8Tk0LgjPtplQxJQ2RDzJBmSi9x457IMq8sYGjekAtWFfN+adytuf5umcRsWZ0vwLJNHKmG5Tzx0rkhuGapI4OTLvqFsKFRpSFcUZ//5AFVU8h76Ht5wgecci40V1r6UMtsJKGORPdn87IyDxxqsPynyM7Ntn2DsZE7WFu5K99WC6SlP5nM7kvyyVTF80qmqocjupIVlffHyMLU0i/Vk/mN9J26H+h24Sr8DT+kbFW954qhtC78A3HbO/YBS6ieB++q7u8DIOffoGxx3DZgiOI7yWLNeKdUHfhI4B1wDfsQ5N3xLX8TXGMtVzfhCSDiSHnQwdpil6CGtPil9Wts2REdQJFKS61KG29OzIa2bGf7Mo4wgDxS9yzl5J8AedFi5KUijozCkdUOMeWwgKBVVSUtEZ4JwyTtAt+DoAx5rv2ko1pukKwGN6zOCiaF1UzF8QET5glbOMvG4+f1rbH5+iSosRcunaGqCiSMcwShpUPZL7E6Jm/mYiaH9ssfmrx6QbbeZbQdENU+jaIWYFLqXLe1njshP9RhfgpVnHeP72oSjEl3UyqihZn6xK3LuCqK9JRuDHIoSf2+CSRPigxZZEQqiKBNPjWPo8N7jAeEY7v7+NYKJwF6DcUnRT/AXgvYqH+7Tujwj3Uo4ekiS7fxcyfmfzgl3Z6iiIjposf2rY4puROO2zB+qSJH1xCM8W7Gkm472SwZvIT7gykJwaJhfEOZy8oKcYxnLvClviQJs0ZR5QDRwtG6UjM/74jPupHo61iSbnhbTrGRP3kddKPKWQ1eKU/9qRtEOMLmhmikWW7KwWl+kQbCK9lUIp5Z44Chj0TErmubku+k02Mgxf3yJGgbE1wX15s8dRUNmEM3rkDvZhDgP4gNL++Upep7iAv+knUlegCfP3XviAJeEdOYF2UqEPys5eqTB6tNLipYPStG6XZD2PLwMGr/pWK5qZtvSvrJejZTzINozFE2BEpdtkYg3mXB/bCTItXIqWlf+VInfS8tSBZp7/89vn9Pfm8a7rarfUfwFxCu3DeCc+2PHdyil/jow/grHfrtz7stdqI69dv+KUuo/qX//S9/YU/7tx/X/8iOoShAx89OO8FBRtGXXhHEUDY2qNKZwLNd8WXjmDpw7sUX1d6cEqwHRyDFfN2AdnatCUiuaitatiov/JGPv8YTeC47JedmZArRekb+32HRUmznrqxP431cpIzCTnGRZkm4mZG2RulalcEzKY4VTB3nbR1WOKtIUjVriREP3JUhXfMrYZ3mmwKSK0XtKqniNlWdzuq/MKROf3Q+FeAvZPbe/dABKobOKtacszaszDh9rUzR80hVF99WKMoLGZZEmSV5ZgnNo7xhba9GjOeGgSdGUPrw3V8x2NCaFdM0Q7zmadyvKSDN4QAa6ecfj6KEQk0PzpsWfWdKthCrUrDxfcPSgz/mftsy2fMI9BdZx6l8ecvDhFZyW3Xy+nRPeDMjWKpxniPYkUY8fKlCFJrllsIaaxS0S4P5MFuGsIy3KztVSNgqhoog1RaKYb3noypEHSto0dXtGlyKtXrTk/9HAka1A0aswc83w/gbJfinzgQ1N2SzxexlKORiHeHOZcc03NK2bFbMtTdnQNO44GncLqljg3+GBwbsucyGUoP5mp+Q72n9OOEZOy5ylfTUn3J2Dp7GNCL3IUHXCsI2Q5WZMMCpwnsIsS3RakvYN44s+RQNmpyMat1JmpyM6l+cUDcOyr1ls6Fo1weHPQAcOXcp7tlxTFG3AipKCNxM4MqsZpAbv0BdIrwfpjnwWOlP4A/NGl+TbHurdiuPrizfxyj2+TwE/AnzHb/Npf5A39tp9W6NoCtvVZGLKk3cc4UgRjBRloZlcEpZ0OJQBXykjALwFBCPH6lMTlud76NIRjEt07gj356SrXZI9sVD1Ukve9dn8zJTZuYagUGaCHFquyAUZjBWLFc3o8+tsDgrSviHbSNCFZb7uic/zQnbMZmqwucZpx/yxJdaPad6ymFzaFdYTBI+rE0jZcOCJVtH6pwzKWW5+t8fqFz36Xzzi9CdDVFGx+7Eei0sroCC+PUPZCL0savdBiA4dZaiofIXzDWUzIBgvyE91CfbnoCwuCVnuNFl5Ykg0bjM+67HckCsx7wjTOO0rkgNJJs6TdkcwLrG+6IApC7pyDC8FdK4X7D/qYzLw5iUrT8648+19dn7xCIqSlS9N0MuCYNqn8YmKV38so9tZMO+GpHdjbGKFXKfks+u95Ej7mpVnBUDgz8TR0HoIAiishS0LQR0tVz2yFcDKHKZoQeOOuC5aT+Fljv5zU6bnG0zOGXHnA6qGpUg8xhd8wqEVJWUntqTlKMQfymvNOxDvCxCifaMimIgsShVqnFL4ixJvLrt8Vcp7M74oLa/2FU3/S0OyjQZ5y2fl6Rmj+5ss13zaVxeorMJFPqq0LM62CQ9TgklBFRuyrke8D9bT9J4ZMXhvD7OE9qtzSf5lSLYSEQ1y4n3ZKM13ohMNNy+DspBEEkzkOyeQZNmtFy2wEx8zN5T9kqp2KIz6KdndhGCgCb/S1vPtCicbs9+r8VZXHH+T3+qVexzfCuw55155k2Md8AtKKQf8/17nw/tmXrv/Wiilfgz4MYCI5Ot/BV9jhEORGCkTYblaT+REVAXBRLSG3EpFFQUnHs/RgSbriZbRoGoTDSt0LkS+YFyw3GlRxApdKZq3S/xpcbKLCQcFPeuRN7UMDCuHXsL8g0vUICRbL9n7kM/5f3IEzrE420FXYJ1cpI3bwC3F7KyqbVmFl+GljrQjxMXlGiw3LckdTd6plU/3fXrPibBheJTR/9ycF368y3xrjfjA0X96wtYv7vPyv7uGySHe67H562MGj/XRBXRfybGBJtpbsthJOHx/j87VDJRisR5QRoboYMnwwRbJfsnex3p0rxRkK06c787IbEWECC2tF8cM7u/TvC6L5vRMwMYXSgb3eaR9Rd7yWW5A3vUJpgJ1vdGOqcKYsl8QH/bof24fVTmoLJ0XxxT9mMYzEaNLooHUujBmNo0oAyvOeMaw9zFL5zlNGSr8ytX6UiWVLxVV3jG0btZuhLHIyFRzRbbiBNV2YDBLeU+tB50XxlSNgNbVOZPzbVxoUTV3oWyIL3v78px0PSbrerTOzzikSVmFwsnIAQWzTY/ksJJkNBf492xL03x1TtEKWX26YnzekPektXf6kxXJqwfgGYqmof/cgqId0L6aoipLuhqhS4c3K7ChwZ+WlM0AnYuxWOPWknQtJJhIizCYWYJxyexsQpFoKh+CScWdj8asPVkQDjPmW4ZkT9pgVcNnciEWVKDjhOTpz8Ua1+QKFyqqfoEZiJBh1bako4jkrsyH1v/Wp9/y6/vrincrjt9+fA1euX8c+Imv8BQfdc7dqRPDJ5VSLzrnfu1r/ft1ovnbAG3Vf8s/wqxvSdcdwcDgTzXeXIbl3lLQUVQKLyil72wcyWWRzG7cll1VFYouU7JXER3JbrFMNMt1Rf8lWQBQCm8wp2pH5F2P6LCg9cqSO9/RIxw6pucU5kqM7VlUo6QKDJP7uzJ8tQ5/LmZMJnM0b+c1gzkiXRWjo85VzcGjmuhQkVwt8VKFsoZo4EjX4NQv5XjzkrsfbTB4yOPSP5xy+w9scP4nciZnRd23+4qHnqe0riq6Vwriq0Py7TbBzNJ9aYYqLeP72iwfaAqprYTqjubaj2yw9ZmMOx8N6b1kRCvrQR8U3P1IQHLXMd+B8EjTf6Eia0vCnF9q03+xYrojCKFw6Bif8yjqRWfjMyMWW13KpJYZKSBfsfgDTbDrMbwfnN6g98IUZS1qkRGUlsbdgOYtw/4HYHatgzeTmYUupefujwyLbZG5t0Mx6womlagDLCSRe7MK1ReIboEo+VaRon1ZMz+FQIdHirwLwbQjYpaezK2aVzzSFZFFz3pCqhvf2yDtiW7U/uUVus+LirBJpd0ZDSrSnpAsR/cEZJ2A5i3H4FHL4L0d6KYM8ojuZUvnk2NUfuzDIS3K5M6S/cebbHxqjHKO5U5DZMpjjbdUWF9TJtIWsl2P+G6K8zThUc2h6flMTxuKB8TnRZfgTxw20PRfrPCnokyw9uRS+EYadr8lYe2pnLwtraxw5OR9CiHr1ckkU+jUw58Jb4mRR3Qk7UH7Tob3vJs4vq54Q6/c2vbQA/4I8P43O9g5d6f+d18p9dPAB4Ff4829dt+2eOW/+7BUAgryfoXzHcGRIdqvzXXqirWZZIwyD+92yHKzwptrOBIUjmjzWPyFpUwMzlM0bi5QTmxTk13x9VZljFkWJHczJucj2nlF+1pFFajablOGuOP3VJQdy/S0of+itKysJycyOWsoI5E5QUMVy8738AeXuOsJ8x0HSGto9SmL9WHjc07sZUcLtj6jGN4bszzVYuMLC8wsY/1qxvixdbzDGXsfX2frk7vgezjfI9ifo/MYG3qYIiOYVhw+5hEeSZ/daVlU735LSHQE+4+LbEnrqqN1M2d6OsAZSbKdKzmqcowu1ZpLWtG+mhKMNKN7AoqGovdKifM8Vp7LcL44+mXHHImlIrmjmT+2xA2l+htf8mjf8Ek3VlCVeJxQf57tV0VAsXslZbkaML6ghcVc1pBo9Vovu2gavIWVuUUhrca1wyVV4uOMRlnxSymahioymNQRDS3FUIvCgIK0b1h/smCx5lH5injfI+s6Bg/KZ1c2LTa0dF70GD1Sce6nS6xfAy0qh5dpspZmet6idxaMWg06LxpGD5f0PxWy/qkBkwc6lN0IvSwxswybBKisQi8Lei9m6LzE+QZ/VlIkHv6sYrYTUiSqloZXbP3GFBt7mElO1fDJeuKFka4ASrhFlYZyQ1EmHo3diqLtEx6mzHciFusij9O8ack6hrQnlRlaWoFVKEgqf1p7hPhSjQAnrd4q+iZd4F9vvJs4fvvxZl659d3fCbzonLv1RscqpRqAds5N6/9/N/Bf1ncf++N+udfu2xbeXFG2LbQL0TjyBKEi7F7ZiftDw7iVwCiQx9byGlWgiAYOkwmLdtn3qALwl5rhvQEbnxlTNQNUYfHGGXqRgVYQ+0SDiunZmMZuzuhCiA3Fc3p6zopA3VoKNGQXWl+QaV8MpXovzljsxAxWPJSVC7ZIPZr3jZkdNoifMqA0u9/iWHtC4bQj7/ocPbxKfGjpXk4xi5zBw21WPzMF36PzpUOKrTYbv7yLS2TeoZxjcbZNcnUkUE6l0Lnl7M+m7H0gpnm7wPqanV8csPutfean3Um74vCDJfPtUBBkkaV51RAMM25+d0taaevCTF6uxoRDR7qqaF2X9zI+sOjSUjZ9uq9mjHQo7RwU04dyfM9SBJLg4z24++GYxSlhKeu8T/sKdK5mjC5FtKeg6tlT5wrMtwxFU9pHWUeTdRVlJANeMY6C8CAViRQliXF2OqSIVW2wBGlfxA71gSM+Khlf9DGZI+0rFuuaYOwoWu6EfBgeGrKVivXPKqwxdF5dsP0vp4weW6N5c4l3NAej8eYJ07MRyR1Nda4iM7D69JKNT+fgadCIIVbTZ7ma0H4hw0xSin5ClXh485KyHZGuhyxX5HszuSgt+2MhxHgP5mcSTObQsSwhaa/2YR9AFSuW65ZgookORHerjGtaPJEoJLTqGdQ+gn5LBH0WH0rbb7kumlreHJxW5JETVWSPWnpFAB3n/tO31+nvTeNdAuBbEj/Kl7WplFLbwN9xzn0fsAH8tMzP8YB/6Jz7l/VD39Br9+2MKhFbz8r6lGsFSjuG7/Xwh5rWNRmGlh0r5D3j0EtBBoUDRTR0BFNLOCwomh5xVhLdWTC5t4UuHPOzTcJhgQsMNjAEaQ7W4Yz4kHdenDK91CQaWfKuYXpfgd/KCZ5qYK42aN6xpF25KOc7AhWdPpgzudlAWRnOL7bFe1w1LVnm4TdzUDHxviXeh4PHRdqh+5JH844QrZxRqMqx+rlDQd2kOViLf3sEwNH7enReTfF3x4LUKUp2v3OT9vWSKtIcPGY4/XNHHH6gT/+5Gdl6g+WGo2hXdJ/zqCLwpobsgSXb/0fA6B7D2lMZOq/Y/FyOKh2DB0ImFy3hkWZ+2pHckYFrMMoI9yr2P9Rm7ak5i+2Y+Y7sXE0KauZRAN7EUHQtoxXZGqpCUfaE4zCMfZbrEc2bgvbBxYSTingvY3hvQtFxLDNNFQowoveSJKysrUkOSpzRwgCrHDY0lKGsvJNLIq2y8fmC6RmPg8fkEqzCGg7bkYVz+oi4AFJ7mhcNUY+NBiXxrSmqtLjIp/vMABv6splwjtnpiMWGQL/LF9qsvuhqj5IS24o4+GCP4cMOVSgu/eMZNgnQ8wxvmlElHovNkDKSRDg/JdLwtlHhDeQ8w4FidsZShQZ/Dq1bJU5LBbZcE35FFYgGlxL1kJoNL+20rGNENRoBODgtmyddQjCpFaNDuU/aXLWoZc3aBzCpzA7dcS56h8a7qKrfYbzeK7f+/d95g8fcAb6v/v8V4L1v8lxv6rX7dsSrf+3DMsMAaJe1+YyjAqrUZ7mmifbBnxiq2JxcLOFYcPImlz75YiOgcTuVPnfskezlBBNDeJRSdEN0YTHzAhd4qLzEHy6xgWH0UAtrBL2jKkFKqYOErOeIBorRRc36F3Omp3zWv2jJmxp/5tO6MuPm97RYnipQcYUflhR7MawvcTcS5luK5m3LYl1jGwUq1wwetbRuOJIbE3COshvjLTLB+Uc+qrLYZoxeZKw8OULNUwCCcQFas/bUnCrymJ3y6L9Qcu0Pr3D2ZwYcvb/Hck0Wg/iOBw7mp0Riwl6LGF0SA6Yq0kzubTO+oDnzz49oJ4ai5REOHO1rcPRex9anFphpRtFPhCDoaRrXZ+x9oE1YW7A2bmmSPZ8igdGDkqCqpoVWCZmGZS3XPpLFbnbB0r4GzRcH3Pr+daKBWNWm69B92VKGiuWapvtKSTgsKRsGb65wniZfbTA9FeAvnLg4Rpa8o7l9r5hIFS1L1RCp9nTFUPRLCCuiZk7qFEo7XKExQ82Zn1+isxKVFpQrTbzDKXgGnRVgHXsfX6s9yOW7kHcUw/tgttOXCm3DcepDt5keddn6ByF6vMAFPs4Y0q2mJLp6k3ysRMBqBrmhXCvwDn2sB43bmu6rJWWkWayKEq71RSon26gwM40/lWSg6udM9nKqUPglKJ+sIxDivCkbkLwrn384kk2Kt5TbsFJd2EASqg0c1q8FH4N3+Mr8Dj+930l81ZytlPoPlVK9b8bJ/G4MZwQyWXZLGPvoOxGeX+EyTdUQaGvrVkXjjijJKitM4DIWkpguHfFBQeO2LLLW1+jCstgI8GclyjrMssIphaps/TcNZTs6wef7C0t8WNJ7qUAVisZNWP2Sw5864gPH6GKAqdtiwwdg+JDl5X8vZHm6QDdK3NIQfbqJqhTBk02c51hsW6KjEm/pCHc9wn0hXs22PfY/0oeyQucVex9fB+co+gm2HZNuJ1z7oXUm97YpNzoUmx3MIsclIeZoxvR0ePK6z/3UPgcf6qEcZKviIVK0ZaFpvyLIrtO/lAnr/HMzdC6JLO9K0op3F6w+U7DYllmSzmG5EeGUwhsu6V5J0aXl9nd0pPILoPdSxfoTGaN7YfbxOd5aijOi8WT2AvpPePSeMVStitkZmJ7RhHuG5vU5aNGUyjuSNNLtgt1vq2jsl2x8bkG8t5Qqbl4x34moWhHj8wHBzBIdFQRTx84vS0KqQof3/qF4fmcCJ07vTfE7Gd5BQPVqk86TAc1nQvqbY5o3EWBEw6fYaIuMTBLiQp/RI31e/rFVJudh7YkxaV9x9NGc7HQucwAlirTRQyNu7Pc5/9cc/qzEBT6qqnC+bFB04cgbmtatDKdh7YsODkOoFGYgqrnRwKFKmJ7yCCYVuhQuShlDej7DhdUJy1vnUuGVsWLv8QinhfPRvJ3JEDyt9baW0Lph8WdCoi0TRRUprBHgSNF22MBSdSpcWHuhW8U9f/6zb9+F/zWEcl/953drfC0VxybweaXUF4H/Bfh5534vq7D89sIFTqwzlYOwQgUV6TgEoP2Sx2LLEYw1WU8WNpNDOJQBqrKQXJ3gYh9KC0Z2qUUrIBpK0qByeOMltsbRH8s+qMrHpBa74rFc06Q9jT93nP7FnPl2wGLzeE8gTGhvaRk8IKz1VGmKyLK6PebwZhcz10zuL+m8IOerM4XJFLMdn+W6It49lnzXjO6HCz8xZPrQKv68onm3wnYSvHFG0Y+IdhdUcZvdjyg6L8ds/OZYzrmsGHxonZUnjuS0ipLZg6tM7pFfy4Yl2jXoCjY+PabshOQdD+trVp6ruPqHG7RflZaHzuHGdyf4M4iOHEXDcfSoSFmkXU3LWihKzMyglwX9FyOWK4YycRw8qtn5dcXaU5ajoimD1obDZmIMNLpfER5pEZO8OGcxC+g+EVB0QoxvGLxHoLTpZonKNJf+UUYVGvSiQKc5hx9tsfpMRue5Ic4ovDSiaGiCqWJ0L+RrDtNeYm7GTHdbMusqQWWG6IqHSUVGpfdSSbS/xHka/cs+w0dg/1tWqULFygspe49HJLsRg4eh/xyorSXmlYTlVoOz/+yI+YWOfPq6Im9qmrdz9C8FHD4aY4Mlla9R7VAqmKyiSnxMWtK+IQN8k0O6oon3hAtiA4c1iryjmN2fo+aG6TlDdCiaUsoBS4NKKmy7JI006q5HMJYKqHmrxIaKwUMJOhfEVNESNQCBrMv3IJg6dCEmYnlbsViX68RpoFToVOxrk9u/C+YH/ybPOJxz/0+l1H+GDKj/DPC3lFL/GPi7zrlX3+oTfKeHC2TQjdXoZkFVicIowOS+ksY1D5TwOrQCtZT5RHxUkdxeUHUivP0J2ZkeurQUDY8q0oQDaT+oSvpgOi3k79R6S2ae1y0s2X0VLfmpQoH5qhKyPqimIjqUAW18IG2xMnFQKobjBuGBoWg5TLtgsV0P0Xsl1dTABwYsD1qEgxAvdfSeHtF71qCKktazB1z+sxuc+qUcPU3BWsLZEhcGBBNF4zZEI0e62SC6MyU91SU6qqAoyc70yHqeCP7dqQfG90+YNhOCOwFlJyTYmxLcKsEYDn9gHacc44tCDPMWkPVEqmN+2tK6apifckwvWM7/TA5lJS2caQpKEd9dEB4ZVp4qcEZhQ4/hvQFFy2Jjh5lKEz25KV4jiw1HfNsQvNCEbUfeFeZ8a5Jz6R+m5N2AO9/qidFRy8ebl+RrMdZL2Pn5A8p+g3SnxeD+gPRjU7wvtlisB+RrJd7EYPZjzv/TsQhPPtRCOXEwtB5UMax+KWO57hMODdbXVJFH81aO80Qb7MZ3hZStisWWwj81p/1zHpOXE4IhDO/38dIGjZdEir5qRaj1mLzjoSpP2Np5hWv5pGuhkAI9jfUU8e4Cb5wSAGmvR5HIgFpVwFwxP1exCBUmrLDGYic+6Uq9dXYKVWi8KKVYiH+59RBtrxK8tGLele9mmcgGwAaysFaBzEd0rddVNGufGeMo6ioDQBWaYKzxZrWT5js5juc7v0fja5pxOOecUmoX2AVKoAf8lFLqk865//itPMF3clz9Kx+RRUrVF8DSE7aodgS7Ps6DrOtY/2JB0QxOyvesK+Q1lMI7nJ0cr3JLtm0wuUMXYgOLBVWUON8TyQclfhSUFSariAZQxj7+VK5fXTha15YUnYDpKY/lusIpRdaF5YbsDuWPgdsLybYLdFRhvIrKgE0kEdpuyeHdDn4rY7EdsP75ueyOs0x64b5H+wrMTgUEo4jJxSbjC5pTvzStE5X0tfcej2i3OrWjX8ninhXG53yyFcg7lqoljnvhkz1MW5zzTFpCZXFRiEozei+XxPuG+LAkvjNj91t7ZH1ZaONdgz9x7PyK5eA9HuMLIR3VIbg9Fjl2QI8XaGD0/nVh5o9KooEl7WvSXk7zBcPoQUe6Zum+oFjddTjtWK5pvJnItOcNxWI7RlXSetn+9ZKjh32mpzw6VyzB0ZLFqQbjh/tERyXTHZ/x/SVnu1MOP2jJX2hz8R+VDO8zdF7NqRIRROw/NyVbiZie8ll7ds7Bow2yvk8wFQdJk5ZUkYfOKpZbEb2Xc4pmQLFeYRY+2SzEG8058y8rVGklWdafD3mBM5q0Z044ESg4eqSJv3BMT2uSPeHMWANZp0ljt2B0Scy8vIUg3LKerIAurnCRLOA6qPjWDzzPrzx7H9HNQLgtc03yfIO0LwS+YCKtqGQ/Z7nq07q2ZLEd4S0ti1rFoIzl63iigJvUMw1fvDjEkLz+ypaC7qsixfZfe4eiqV4f/yYnDqXUn0dgr4fA3wH+onOuUEpp4BWEGf5vZJiFwikZrKpC4TINnpjeOA+sJ65ok9M+ZQzJXUc8qDC3LGXDYHKfYDQH5/CmOWaWER94OKMw0xRnDMra15KGc7VWsxYE0+EC1YlYeTolW41rCROBwM43PGZnBB7ZfzHDeYqiETA7azFLRRnIfIZSY2ea3PrQqIh2PbJ1WNkZcjRoYl5oivBc4qMXxWsvvihZ/cKE4cNtbnxPG1OIAOHlP96gcVMY0sv1iDM/O+TFf79F46qHPzf0Xs5ZbsjigFOYmcEtDEXb0rghQ1e9yMFoVF5QrrfRuSXveAQzzeihDrNTIorYviby873LKVnPJ+9bvKXm4D0R7a5P67la5qzurDZuZ6SrATaQHbay0Hg5YLHpaF7zwMLgsYruM1rEAhOZSS02hWm/WDcsVyXJO8+gKtkhjy8ErH96ire0jM8HKAdH77MQWdLSI0t9lILgYE71SCR8i+GCfKOFDQyzbZ/peaiiBr1XcvJW7W8Raso4RFeO5XrA/vsU538mZePzDp5Q3P594B34LE818ccFSilox1BagW0rhQ0NyX5BMDdis6tqLxcnsOzlau3j0oIyUWTdkDI+JqUqUcuNZPH2Dn0ZUnuOKtD8ypfuxxt5ZKvCXSq0o7inxFyNKBtyfOOuZXw+xF84RvcmRKOK8TmfKqJ2lZTkZH2pTKpIPlsbOFSlpD2V6dpSV9G461iufVMv8687jnknvxfja6k4VoE/4py7/vobnXO2Zof/GxvOk0GuqU17dK7QC1E2tc7hp1JWZysQH9Rqtr6ijMQrs4wMAYBSmNFCnlQr/EkhSaOS2YDyAOcEsTRPT3bSFCXe4YxytUkwEBZvthIy3wroXEsZvCfkzCcW5L2AW7/f0HvO0bwNk/OKsi84eKcdqtDQLEiej8j6Dm+iObzbIbzjk3cE3RTsTaXacQ6XhOAce9/SEZHBR5bwXJPFD42JKs18LcB/NWZxIeflvxjReMYTf+y0dvcLBTo735EWXvsVTTi2zLfBn5XYJGB8KSEaVOjcEl8dEh7F6HmGCzzSfu9kcVt9Oifr+czXDeGReGqEE5G9QKkTFVeyHH93TNZfxWnFym/uMz29QdFyuIsLUqcoJwGd5z2qWMyhtj6dc/v3RZhU0botIoNVYGRR9SA+Esc6PXeMHunTeWlCu2GYnDNAxdrGmMEkwQHJvqJqBKw9vUTlltm9PcJRQdbza4VcRboCo8qnaL0GN7X+SSeIaABFJyDcW1A1Ay781IIrP9wh7RqSKyPS0x2CQUrRj6g2E8qGrofP4gNjljJDKJpSgVaB8CbmW7WfRwPyrvyxsmkpm5LgdS6D6mOzKJ0pyAxVu6JcKWivztHKMb7WpbIK27GYuUjwRMOKKtQseyJJPznjkbclSZQNQUsF49q/xMh8zfqOaE/XPCip0r2FVCE4UZ3+XRHfgNNUSkUI8TlE1uufcs79F19JJVwp9ePAn0OUxf+8c+7n69vfD/w9IAZ+DvgLdTcpBP4BQsg+Av6Yc+7aVzqvr4qqcs7951+eNF533wtf7fjfqzH74Q/ReQW6L0AwVARDTXwgnstY6cU3bjmCqeygioYiObCi3xMqmjdTgnH+WhXhHHiG8M4MbzBH5yWqtJIkilKUY2fL106gXsQBvIMpk4sNdGHxFhXTMxrvaMm9f3eAdzgluTyg87IibytGl/SJYq+NLCrXOOXwbofkHUf7VYgOFWEnRVXQvjgiPVVw5UfXmDzYI9/uoPKSw/d1iQ8t0UFOMYrQj42ZTyNOdceEUUHeq/AOfdZ/VhRzL/29A3Z+4UAEAceKyb0VVdNy+pMVW79ySDSsaNy1HD0cYb1azXWQEV0fgu+hR3OOHl/lzrf3MEtJONGRI+t6kjQmglqLRhWNWwsGD4TS0sty+QFwjuZzBzReGQhB0UF0pDj334P/XII3MZQfH4ssvYajhyL8ObSvW45tTY95CrqUxJV1FPN1gykc2XpCdJjTfz6n85Jh+tk1nNXoazFFAxY7CWaWc/SehHgvJW/7RHuCYNr+9RJvKeS5Y1vdsimnXQWSPKwRGZb5Oblj/4Mddn61oHUrY/zIisBUS4s/yZlvelhPndj0ztfFa2O+Jaz2Y3jr7JTCeU7IqKHclu3k8jq7JXolp+qWIt/+Os8LG4reGlaxfLELn+wTHWr8gSHaN6w842jeLeUcavUE8RZx+AuZ5ehCZGFsIEZP3lJg1yaVVle85+hcsfhzR+t2hT9zJIcV/uKbcIH/DuNrQVR9jaiqDPgO59x7gUeB71VKfZjXVMLvAX6p/h2l1IMIT+4h4HuB/7G2tgD4nxD9vnvqn++tb/9zwNA5dwn4G8B/89VO6p2s9PKOjuiolIvB1wRzQ9YWIbusrU52iGWsSPYt3gK81OJPSkyq8ScFqrKYo9lrT+gc5AXKGNklO4fzDVUnxjuYvPaY43hd4sAzdJ8bodIC31rO/MxEhuhAdrrH9T9naX1K2g551+HPFco4kt6c/OU2ZWJpvyqLVhVCY9dSPt3CvH/Et+68yueDMxwdrTO4zxBuGNotj7KhKBuK/Q+GNNanFIVhpT9jVgQsxxHxnmHn15ZUoSbthdz5nnWigXhkLLdk4N96xTC+YEguVySvDhnes07vcsFiK6T96hydC2JrsZOQ3JrTf3rE9FKbKlC4I5hvK+IDgRnntd7X+JzHwWMtwiMk4RqDbcfoici2Ly71ie4u0POU/gsVh48Y7vy+BtmKxbYq7CLAbhXc/RaPcz8z5fa3t4iGFYP7fBY7luY1WH22IhiXpH2PrK2Jx8LlCEYO62v8aYH1AnZ+PWVyPaZ5O+fGH/CZnDPc+u4GO79oUaUl2lty9N4mi3V5L8ORqNwWDen3B0OYn3LYxGJmmuSuVCLjC4bBAzGdK5a8Zch2ZF7SuF0wu9CqVY3Fv12X0L4OpoDVZwr2PuAz35EWUXgsnVIv4uFAviPs+uT9Ch3Wsy9lavc9h5nXboTTWlyzFAZ8OLT0Xi5ZrHl4mSTaMtEnvvV5W1qjIhci7ofHrZwqdrIQWUliqhJVZg/FfFNRNCFve1gfpuc8zv4X71BRwy+PbwCqqkawHi8Ufv3jeHOV8B8E/pFzLgOuKqUuAx+s/Y3azrnPACil/gHwh4FP1Mf85fq5fgoBQKmvhJ59N3F8naGcwxtmqKLCnW4RTCDrGDrXSqpIUYaaYCrJBQfxnflJZVE1w9r34A0+l6qCqsJ2G6i8xCxybK8pBjqeOUEMHct34BzTB/qY1JJcHUmVojUu8Mg2GphFyb3/rynFuqPo+CzWPLIOeO9dst2e8EqziV6KmOL6F3NGFwMO3g/R+TGzu02ebuyglexQm7drNIwng9N0Rcmc5wsdygeWjGcxYViwszNgd7DB4XtiNn91QE8rgkGKXogYXutWm73HDTs/u1u/mQqsZecTe2SnuozP+XSeloXetSKaLw+5851rLLYc0ZHAmht7lrXPT5jc12K+Ic6AwVj0mty0RuV0RBVZL3J5X7VmfN4nugu7v3+d9c9NWKy1hTOQWDAOpUAvDc0bcPC+Js3bltEFvxarVGR9SFcNq0+LGZcNZGGMBhaTSUKcnI9rE6+S7isVi+2Ie//ugJf/zAr+yBANMvQix4Y+y1VF3hdXRW9Ry+1vpkxbfq0Wq1CZqn3m5e2SRd4xOatp3bTEw4rptsfkXMRyTQy4pueEpOgcHD2kifdh8JBHFQl0XBfqpOrwp4pwJJ9nulHhGhWN7hLnIMt8KBXhnoe3rLWlehpdOPEfaWtptWnFcsWjChXpSu11XwqKL+9y0rZxWjzVUe6kDaecQHKPNdNsw5LVK9Mxudb5Ylugyt9FENevraJYVUp94XW//+3XKYEDHJvhPQFcAv4/zrnPKqXeTCV8B3i9+fqt+rai/v+X3358zM36uUql1BhYQebabxjvJo6vM4Jbo5P/J5eFm1ADRChXW9hIoJTB0fJkt3scOpDWy3EoWxvo+B4U5ckMwfnmtXaVtVDbgB4vtHIiPq1nD3FxgAs8xvd3yDqKjV8/BOtYbEcsHk1Ye0JQUeN7IN/O2IlTLt9dg1ZB/wsh/S+NsbFH55piueEz32+QbMy5fnuFsJETnZ8ymbdZeRZG9xjylsPkkG+U5IB3NyK+qZhetEzbJb0rogxcdSJp1bVDwskSlRccPuSRb+Vc+ZObbHy+pPHy0QmyzB+nKBtQdWLMcI4zmuFjK5SJSLtwqIgPLZOzmjJqYz3pzccHr9mmbv9GzsGjAdY3eKOFJFvANiK2fmGXfKfL5i/uAVC0OpgUscU9VVHlGqUdgw+U6KlH52XF+KHy5LMyqSEYKyZnRRo8HEM0KOuhs0cwzCgTRd5STC4khOMKfyrIu43P1wv10QIXeNjEJ1u12NDhwopRU6EqhZv7gqZzgiLyclmIsxVL45boPE3WHdGBYnSPGITZwDE/pWjcguk5aTeijhfq2tciFNHA4nSOHft4tRy7SWFy0cH2Ejf18Q588l2fYrUk2PMIl+Kj4gxkPY0/dQRziy4dZaxONhPjiyJ14nyBgx8LXKlK2mCSIGoJEfva7EZniqoGaygnToUuOP5+V7jcgGcF8uu/87zF3yy+xlbU4bG76ZuFc64CHlVKdREppoe/0p99o6f4Crd/pWPeNN5NHF9HmEvn37haqMM7nOLiALXM37C9pEdzIfchAm4gyYNMZKdVaeXYwJf21fFzOCfPUZSgNeVKA+9gKo+rHCrN6LwIZSuEQlRTF2ua/vMZ3mjB4XsTqsihJj636eGFFdU4YHg/5O0O7RslzZdHBA+sAh7Lsonu5ORLn8q3VCuWcFDSUj7+wnLwqIcKLJ0vhMzOOOY12snf95l92xz1SoMyjmnerQgP58JULkrKlkPNDUXHYgNFdrqLWZZkKyHhYUY4dui0wIUBelHQvJlx+J5IoLoZjC+J29/0rCIYyQB5dL9j8zOW4T2GyVmf1acLzDQDrcGVzB5eQ1Ww7LdZ/fwRi3tXSF46ZPVLGfOtgKKlCA+FJFhtZqixT3SgGd9jT3b+/lCDU8xPW1aeUrRuCDR5sR1RxLVp0zBj9ckpd7+1jZc68qapWzpN4v2cMjYcPt6jfU0kOFqvarzvO2QwbGC1kY84eA2Oo4YBRVu8uL2ZZnauIto3BEPNYsvifEu0K0KVVWwZPVzhzQzeHPypwLOtL1IryS1NtupIno8EyeTVQpGbOX5ciKNgU1H6juC2T3DgoQuZO3gLEZFc/VLO7ocC+i8qvEVF5XtMH5YEVTYrdKqxoUUXivBQ11WEQlmpFI8RWtaXZIInVYY3rfW12hUqqtCeRWmHVg4dFygFVaU4/6NPf6Mv57cm3DceVeWcGymlfgWZTbyZSvgt4PTrDjsF3KlvP/UGt7/+mFu1cnkHGHylc3mHy4T9Lg3nUIvstyaXN0g2xwkEXksiHENvs/zNE5S1eIczXBhIcilKpu9ZRy0y/L0Jd75vi+TVAVuf3GN2KgDr2PxXB4KI0Q7tW8pRwPpnFI3biubtivjOEirLqX+xz4X/fQ9/pLFzH2cV1UHE+qcVZcPgzy2HjwjL+eLpfdJvm9Y+1Q4XVYRHinI/pmw5sp6IC+5/oM3ex3rgHJf+l338sQzATWYJd2ccvK9BMMzxBnNM5iRxalBZjjOK1S+Jf7v14OxPD+heLgV8UAvjeTNNEYsuVzhxzHY80p0mzpOveBFrBvd5NHYLQaPNK/A9qkiGxv0XSrw5lJ0KlxrMUrO8PxV/65HBTDX5ZkF1z0L69huK3Q/FpGshTkPnWoouAa3Q05TeSwWD+wxHDyuiQclyzefo4QhvWVFGiis/Yhif98k7cHi7g/EsflKgUoMrNcazeEGJC6y0qypF0REr2axvSTcqvLki2vMomw4sxLuaxg2PsluSrYiHyvQ8TC9YZqctix1LGTvyrqN9rUJXUK4XtLoLHtq6y6X1Q5xFHCzXS/KViip2mAzCiVi97j0eSAURyNcwHljyrYJyOxOYbr9A5zI0z1atKPwm8lN0hLSJqwfjqcLMNWamBX6cWAgsQZITRgWeVxFGBc4ptLYY87sETXUc7mv4+SqhlFqrKw2UUjG1sjivqYTDv64S/s+AH1VKhUqp88gQ/HN1W2uqlPpw7b76p77smOPn+iHgl7+aOsi7FcfXEbaToCdLqSrK11BPX0u8PlEAuDiELD+5/bhtddK+eqN4XQWislyqEK1ovTQCpZg9uMLWrw5Jz3SJboxY+c39k/bWpb9/wN7H15idivCWCtxrLQcbGE7cm40mGCmKnsKlhv6zivbVBSjF+EKMN5dB7tUnd7Cxw20VbP2Cx+Gj/3/2/jtI9iy77wM/1/xs+iz7vOvXdrp7vMHMYAYDDGEFEFwBpEjQxHKpDS6lJVdkrEStNoIRWkZwFUtS3OVSEVhSIgVRdCAFEIQ3hBmM75numbavu59/r3xV2p+/9+4fN6vqdU/3GGIGmG70iaioqsz8pfll5j33nPM1ejFE9sZKOl9oGbU8g70808cJGD7r0KXj7oc09Q93IajIV1L6L/o+vSqXSW/PsO2E/QcjpuehfRvWPzFidrlH+8UxwnS59UcELrKk1wN06UhuT9l6zxL9F61vFRhHfarP4IkdBp/zlrTZfUuMLwWszWtaL+4zum8VYeHEJ+fcjloEcw8BHa8JTNsQbGjqniW+HVIu+VbV/EJDuKMYX1C0Ni16VNCqLAcPtlj+5BwTSdq3/S59djJg+Nycqt1i4wOxh7wC2UdmNHdSwl1NpfzOXPYq3CyAGOoshMgS3AmwAd4saiywoaAaWJqOxUYeSlucapCZwiZ+mxvteaBDPfAkOlFKXGRRrRp7N2bjI+B0g5CO6TjhuXqdYi9BFpJgPaOeL6BcwsOPEb4iUDVHMNubf9ZgphJqgXP+ejnR3uwqdtjIz41QDhqBKCWi8X7iDn+OZbMQsEygGViCtEJrS9NIrBVYKwgCg7WCsz/25a/3K/rtEd+cPHcC+CeLOYcE/qVz7t8JIT7Fa6iEO+eeWSh7PIsna/+lRasL4C9yDMf9xcUPwD8CfmoxSN/Ho7K+aryVOL7BkI8/RJN4xzQAFwhsN0btTo9uc28b6t5E8Foh8vIrjnt1EvmqcYiuagwetg3t53yVGd84ACmxba+Eanup77X/+22qH14jmDmGn98Fa7n2J9fpvWS9Lop1YB2nf22EU4Jbf6TH5KJj+YuGrfd1Wf5SDsSoCkBiKofc9WTFZEPQuWOZnvG7ymLZUSx7n/XuDV9deC/qDuVggQa6FjC/XBFMYXpG0n/J0HrBz+VEoIkP/H2EE0d2uk22IsmXBzSJQJYOk1g6Ny3t63NMJyLdcvS/uMPe+1e5/dEBF//1xKv3ZiWmE1N1FUvPFMjRnPzikFM/v0mz2kU0lvXPVZR9zeykpPNURL7m0IUnx1nlcO2GMhGosaZaNqhS07meM7vYZXxR0X+5YfROz1BTtWPtM97oykUByZ4l3XGM7tPkKOo4wA1qGhl49r6yKG1J+3MODtrIkcamlnpoCQ4kthGUQw+jjvY8AbEcWoKpoIoktl+zsjoh++0V76C37D8P/dUp8ywC4Tx0WkH79ISiCLB3Uky/odiPEZXAdhuoFd2lOfN5TD0Q1H2BHiuanqF1zasR7D2ucLlDpA1iL8QmFln6NpVN8MnisMlvPOHQhX64rafeg6Zue5RY3YWq7215AawVJNGidQZUjT76+40U34yn7Jz7EvCO17j8dVXCnXN/E/ibr3H554GvmI845wq+QXuKtxLHNximHaGnJRg/vCZQfqG9J1692L9e8nBJ9IrE8er7+LoSyOHc456YvG2J7pd2/D92ISFyiDCaeC7ImV/YZe/dS9z5vlWaFpQXSvYfbrH0lDmSrXBCYEPN8AVDMFnY2Saw+YHEzz2N71mb2FH3DXuPaJrUMXnEImfi6IvjCWSOoi8JTndIro8I7445eGCVYOIHo8n1kNnlmvbqnI2HYsreGqu/tUW91GJ+QpJsO3ovZejdGa0rYLsJ1/5oh3AsqNDIxqGmBUjJ6m9nHLx7hWhiCGaarff16F2rSV+q0DtT+tsTzKDF/ntXifc92koWNTKrkFmN1R1WP18wvi+hteHYewSCuaAcWsQkIN6V5GdqcFBcLtjMWqw8VZJuCqan/Veq6njFV5k3YCFfT8hWJOmupXPTMDsrcLUkuekH4XkcQKsG4di/3Uc0gnAkMaVHP1UnvOe26TWIXHlIa+IHxuWKQXZqpIDdnS6cMqR3lG/frdZYJ7BO4LYSaBlsbMmyCLsXkexK6iqg/wLsvN8Qd0rkFztMT4Ycuhu62KAu5ITKMg8ShLa4RnLm9B7jPCZdGbNz4AEhFF5FWc4kpmegFn7ILzzRNNqTRAeQ7Hk5krrjpVDCkaRccphGIQQ0SlLXCmsFSjmSqHq9r+Rb8QcQbyWObzDUuAAl/MDaGJyWr0gIr642vlrcmzRcFCDKY0mP1z321YnikB19qJnlHN0v7+LSiHqYEN4ZI+cFthUjrD3SuXKhpv/8jOxUyviiQu4FXmhuVpJfHJLcHFMPYqLNKZ2Rl8fYeVeHauColgyElnM/LRaaQ4KDRwXlSuNnAp0K9lLCkcfj22DR2xZe7M62I+QkR2dQn/dCdkTe6Gq2n9K6EhLMLdNHV2i/POHkb+SIosbFvkJxSUh2OmXwnOPgIY8g6tyYUy+1UHkNVjP4/A7NUptoXdHatjQtSXl2QHR3Ao2h6YQMvuy5L7YVHwk1isZzSlCSpc8XIEE2Paq2RM8lsvFVlAgtziwMiWq49se0BygYb3zUeT5gckGiKo+hHV32Hh9VV2ESiLf9opmfbJDtmjA0NJVCSi846ba8wrJowLYdGIFZrqCSuGAhMV759pAeaVwmMR2DHml0JjCJR6HJmWbSdAj6BcYBtZ/ZuGlCUHskE8D0rHepDK50yNe9VpcwflahDgLErRCZgx46TOqlXTb21r3vR+kX9ygTSOOJfk3bYQuJqAV6LlCF54z4ITpM2n721CwkTUyygNsCUjqaRqG19V5YRpIV4Vf9Ln1bxhuvSPq6463h+DcYYrFwO+EZ4jQWGyjM0C8QToqvqA5enURs+pVmyfcmjVdc/uoEIsRXVBhUta8sjB/4AshpTnh7xOTRZY/kKmtE1TB+dMnPZrISdTCnc2XE4ErD/f9wj5O/M2f6yBI3v1eBENz4/hCbBIvH9POKcCToPasRylIMFHUqKJYELjHofoVLDfZ2ip4J6nfOUG8b05wqsZEfZIvG+apHSqyG/vNAfMxKDjcD6o5j+wdKWjfn5CfbiLzyraayYfvDq2RnOrRfHJOtSdY+Zzj1WzOckuTrkff2rg23/ugadz7aAqBO/RzAaoELlFfw3fPJ6MgIyTlQ/nW7SGO6MbbtIc6zE4pwZkl2vRdFuWJQ2yHRncD7x685ZC5gMfgVtSR7V065ZNn4CGx8JxTLlvyEoUl9b79Y9x7jspDYUtHcSbGlotxLsKOFumzkSHYELrR0VmfQSDDCcxlmmnBbE21pei9CuC+JNjz3I9rzrZ/uhRH6RAaBxRqFUw49UdjIIUs/4K76ltYtgS6gc81Dd4OxIN7UXsK8AdNtMJFj9kAFAoLpQiJk4U5olfNzjcR5ld8I9EwweFoyeFaw8pQl3fLvv1MegmsCiA68TpUTeBWDdkMQNkhpkdIybGW+6jCScz/+xptvCPu1f96o8VbF8Q2E+a53YhvnzZXmFS5Q1J2QYFJ69M6Ch/HqePXiL7Pi635MJ+9JFEJ4SOuCZe5v4F7BIi/X2oTbc1+F1A2dF8dUp/v+cQtD76ldbDtCFDV7719j8PSE5LaX85ie8zvg4ZkDnvsrPdJrgpd/vMvgOVj6/AFppCj7AXUbOl/wFYw0ftEQmcKNNHKRYIL3HFDc6GFWCuR2RLopGF+CJklYmpXsvKePqqBu4Qlp2mIbyZlfK7nz4Rh5N2bzOxJMDE4vky8p2rcrVj4/olxJcVqy8mRJMK2oBhEqa0h2KoSx7LxvwPycQRaCzk3oPzvFtALUvEaUDWiFiTXVWkrV0/S+sIWwjmqtS7A99Uk1r3BRiKhq1j/p25KHG4DWVsLeo37jUK82yEKi54JgKinOl2AE8ZcT5ucbes9oxo/VyEKjc0E4hekl46GnPbMQ8pPI9QJXKVzNQt7Ds7jnp0F3ama3upAYZKkIFgTHursAISSQbjqCzFEs+YQc7Ujm+QBZCcT5AjMNiEaSdANMKKm7EO/B6hO+qpqd9NL5xUBy4jd2GT+6RDEQzE4LTNtXBTI02FOGplCosUYtiIlIX3XJuThmBDjvG25CR7amjipOWUEw88q503N+yG8SC9oilaPKQnTU0FSKW3upX4CTNw534xXxJq44vuWJY4EG+Dxwxzn3Q0KIfwE8sLi6D4ycc29/1TFn8KJb6/h9/U865/7e4rq/AfwFYNHE5792zv3Ct/hl+Fh8ENS88pVGEhBtzaBuaJbanrAHUDevP9f4OlpYgPeshiO2OUp5xVhrfZKKQp9AXoXoim4dvOJ+RF4R3vJ8hvpEl/13LzO5KGnd6bLyiW3mDyzRem6HwbPgZJf8NMyeXGLpJuy9q/EmTxcFs1NDhi8YBldqbOD70jo36FJT9RTBSHopEwvl0DHZ6ECrwRmBOjNn2omJthWdmxX56bZfpIaSsg9iprGRpX0lwEkP4bSrJXMihIG9hzVLzzbEt8Yghf/tHMFUg3OEOznz8210Zpid7JDsW87+gsWEkoMHFP3nIdjxQ/niwhCr/fNPbk2JthWunUBt/G0OvcKFQFT1wiXPIZrat/6259ggJRzB7JwlvaHJ1y3xjmdIh7d9S2V+viG+q5lcNrSWMrKijdOC2TnfkpHtmtaXE6qOwwWglgymUp7XIB2m7duBMrC4WhKs5qin2xTLlnK9ofN8QDBRvhLMvLCjbCAaWfIlD2/VM78wB7cjTORFNotlgVzsOfTCGyYYFQT9ACcFnZsVphOT3i0oewmdGzCKNYNnobWp2X/YVzUrT5VsfEfkZUQWycDEvk0lrN88sPDeaNoOG/jWV2AFqliQNdueEIl2yMgQhA1hq8E6QRg1iK6jqjTt9OvfaH27hOCbMxz/do3fj4rjLwPPAV0A59wfP7xCCPG3gfFrHNMAf9U59wUhRAd4YuH98ezi+r/rnPt/fYuf91dEsJv5+UZeQaA9K1lKmuU2elxgWiFSSoSUuLrxLaGpH0Y7Kfxin5dekrv56rsoYewxKkvJ4wXN2MV8xS6E+haJ5bDScc77WBzCdA+9GYwl2Jgw3IDhZx3Ncge0Ir024fYPn+DUb4youp4vcO4XMnbfnqJmEtl446d0LCg7Ep17QlyyMUeUBidanPr1GRvf2aOJ4cRncvYejql6mvn9FbbQOCcId71bHMD22wPPEA5h7XM1dz+ksZUACfsPRKjK4Q5CbOAIM0kwhyb2x44eX6L74hQ5yZGjOaN3rpLs1MT7FaKy7D8UevHF/QYtBGd+ceJbXWnE5HKHzrW5tzAdeWixOBRADANcpH37anEeCQPQElHWuCjwHh+BRlXOq/COBNma50+UQ2+ohfDEumhb+dlO25DNIi+ZEVuSm179Vu4q5qcNvXNjqkZR5KH3Fk8b5M0YGzhUrjGJQ54sSH+rTTkEPRc0WmJiL9NvA+8qaQPv3929WZFuC+brASaCcCIpliGoPZ+mdcfRvVkR3Z0wvzRYSJ6HNIlndMtaoQpFvuYtfvN1zzbPV0HViv5LDfN1xfY7fVJ3wjPPZeURU/m6/10OHTZy2JZBxg3OSG+nHCtMKDCpxfQbzynSFiFh2JlTNRolLfuTlChqWO7NaH/f1W/+l/n3I95KHP9hIYQ4DfwgHhr2X7zqOgH8OPCxVx+3IKsc6rBMhRDP4fVUnn31bX+/ovnYu7w+1ex4FmFaMbJscFLQ9GJMrAmmFfUgRo9KXCAR1T1D78Ui9bWShm3HyFkBgcZJiQ21H3D3Uj/ALQ0ohaiaYyb5PXHEND80fQJQkmaQHhlH6d0paMXmR4ec+pV9hLWsf2pCa7OF3p+z/psZB+/t4rTFGcks0cR7kvTqCNOJcYFCzkqQgux0i2DmCCYwPxHRvd4wPaNxL4bUj3t9tuEzIe3bBcF+xvmfHjN9ZIn9BxRb7wq8RpPzC2HZXzgU4nvpTcsRXIfui1Nwjv4T28evDeh/ae9It2t+/xCdObbfA/GeJt7OELWhWen43fT1jGs/2sEGcPl/Ko50v5xSuFhjWgHB7nEbUOQlIl9UiYd+71oSzAzzNUk0drTuwsEjEO16IED/inezS3b8a4n3Qo++WnZII+i9bJmekZgEXOiYTBPSVkkU1+TTADMOYKEh1bS894W+nqBqryBbd/GOhc5XD3ruwQdlX9J/qabqajpXRsi6xeiiT8Lrn25Ib4xBCPLTHaqupm4PkLWjtd0gGkc4EQu1ZlDzEnciIpw6QNDatFQdQZMImlj536kfdIcTj7RrEl9BWA2m49t3LvBDfTcKcYnhzPldbt1eokoFIvefSxUZTKkIkpq7d4eErYpq4n3OGSm2ZY82b8DE4d6qOH4v8d/jjZ46r3Hdh4Et59yLX+0OhBDn8Tjme53p/zMhxJ/Bt8D+6qEO/auO+0/xEsLEpP8hz/2V9+e88qnVEhfH6GlJ1QuJdzyyisaL3JlIoacVsmpgWuHiCJT5ugmC00dX6Xx5GxdoP9NQAtMKsLFGOIfKGppujMprXHNPkohCn5iE8AtiGPjfh0lKyqOkUZzpeY6HdZz4la2jykQUDd3nfBsICxf+meDGD0S4doOeSpY/6YmEapwftXNsIAhmhv7Et+fy5RBVWZaeyZGVYXy9xdb7IVuTdK57smR5uk+2IlEVDF40COsris61nKv/u8R7MpwqKOMAQsvSEzOEMbg4wCnlPUm0wnRj1P4cFweYdoSwjvlpXwHkK4Jw5D/eemtMs9IlX0/oXIXVT+15ZNVqF709QVjn5zXz8jgJhwEuANON2fxAm/VPzVDTEpGVRHcda5sgjGN6f5/Vzy0WzlggG0eyA92r3i98fjIkX5ZEI7+jn5+QZKcNLjUgHTbTzMchTi8cHwOLib3cBlN9BImNDyzxPsR7kvlJ7zvSpP7xnILVz8/987MWGwWUgwBpYPDMHBso6mGKMI5oNyeINDZU6Fm1AHr4CrdYTcnWNNl6i3jXEY38AKOJvSS/ibzBEvjqKph7d8BDnwwkmJYh6Beeb2okTa6RHQPCcXe3z/LahMZI8jIgCAzZPCJIak9+DA1SOkRgkeOAJnmDr7xv4OH314pvWeJYmDxtO+eeEEJ89DVu8p8A/+xr3Ecb+NfAX3HOLbTF+R+A/xb/Uf1vgb8N/O9ffexCYfInAbpi+Hv/BDoID0qcWiwA59sEE0O5nPh2UqyIN70CrpgXfiaBl8woLgyJr3ohRKfk0eAa4XeyTiua5TbB5pj2M4vRjblnwZ+W5CfbyNoiaoOalrhYI/PKt1f8C34FNNfGEXLqH8Mloa9OrL9dfHeKSyPPSRnl0BjyCwOS6yP/uAvkVnxjhJ6tUCVe7nr6tmXaV/3w+DDZpC/uvwIirPcC71a4iMETGYMn8LvdCwOyU8uYwCecJoWdt0sGzzt04ZieTwjHkmLZwihEDSvMTHP1TwzQc2/heuJXNv1jGYvM60VyVah5Rbo35+JVAVKy//aBT/ZxgMw0wlhaL49IbypfUUiJ3hqz/dF1lr84ASGOhSejEJxjfrFP69qYk79R+/c00AtCZegl8bWi88IBNvKABScEsqhoBn6oG2yPseGA9i1DseJnH2VX0rsK2UrA/LRv19Vri3ZZqTxyCkhuaNJNP7twyhFODJMzAUHuVW8PbYBlDeHcIYvGw63rBmksnS/nfrcmBOoQXLEwtTpEtaGkT/5JgCwawnFFenNCudZmfjKgc3VG1zicltS9iGwtIN2s2XlHRBMf274ewa21g9BSj45Rg7JVEycV2SRGhoZZHiGEo9pMaZYqVpcmbN4dIJRDb4TUgSMoBHXPIivBpb96r9DrGyveqjj+w+KDwA8LIX4AiIGuEOJ/cc79xEJI64/hHadeM4QQAT5p/FPn3L85vNw5t3XPbf5/wL/7Vr2Aw8h/5L3YQJDWlqalcVL4xW+oyYeSwZWScGfuIbqSoy+li/RihzpDWEez2vW79cMv8j0tpWDTj3peSfpz3gXQCoKsQWUNNtRevts437bKF4vOYWWxEEB0gTquJBYzGcxiN20sQgj0lu/Zuzgg2vecEjtoYyKFymuqQUx1oqbzbMipX90nP9VB1L69U57o4JQg2px5Fr31cu7iMOFphQu1T1h1gwsD4o0ZkZY0nYiDhyLKMxXBpu+vm9DvaoMZ5Kf8Vs1MA2S7pnKC4bv22NruYeJ1Tv3yvp89zAtcK/YGV4tzadOU6eUOqnLsPdoCCyufK8hPJqQ3rF9chWD+wBLRXsnKpw+OduleENEvlCIrifb9RgHncK3Yy8s0NWp/fnSORVEjF+feLLdxgUSPFy6NQhBt+c1EZ3OMWWrTvtrgAkX75Zp6KSXYybj73UNkDe1Nw/i8onvTMD2F96zvSuKx993oXauYnwgIZo7etYZ4O/fJEy9HciS5bx0uCf3GpLFHszGs9eiwXgrOYZIAF0jCu35PJvNm8bmBJhLsvqPLymcP2H1Hj3jfIgxsfCCi7jratwTZuqPuW5ITM8oyoNvOMVYy2Vw0GLQljBuqSrG0PGU8TRDCEQUNnJxTzCI2ry0hK4ksBHXf83lM5ZOnab1B0VSH8Vbi+MbDOffXgb8OsKg4/ppz7icWV38P8Lxz7vZrHbuYf/wj4Dnn3N951XUnDnXogR8Fnv7mP/tXRjDz8MnsRESyWVINQppYIhtHNHE0iUK1wiMklJISUTXHsiRJAFnhZUnCgPJEx7cNbnppEPEaEF7wsxBnLYQBwc7c77K1wrRChLGYQQs1K4+ql6Odv7W+hbNQo0W+ag5iLU4HVOtDottjRF4hipryVA+d1QT7mRcCjANE5oloo7f1CTKLDTXFyZR4M8OkIcI4yvU24c7cL1L+BFCvtFDzGicCpHMUp9rEGzNcoCiWAoKZwKU1zoVMLnoToUOXuGBfoUpBuWxofTFhdsGwvdul99mYqgsHb+8z/Mw2BL7l0gwHNC3F7qOa7jVLOLM0iSTdNszXFC4KaL00QRSLSinQxFu5P3fOYeOQrQ/2WP8dy/7jfZY+t0txYUi0nfnqaoFoo6qPKkmU9IN0a31SqY2/P4ufSZU1mIbxQ8sAtG4XzE/H9J/a88nXOY/QCjUnf31/sUFw5MMVpmcUrQ1LODWk18b+vVtUktHtY2j2kTyMcxBExwlCq+PnFkdHxM/8TJdy4L/y/S/sIIvGJxzrgRZImJ3rULUlgysFm+9LmN7fI1sX7L3b0nkhQFUQbAqvqhs74g1FJlsQW6pY009zOudLJkXEbJJQ7CWIUrJXaoK4oZiHFISIvRCWKkRqcE7Q/5Jg7x0CjEJUi9fYfm1u0xsiHG/qxPEHRQD8E7yqTSWEOCmEOITVfhD408DHhBBPLn5+YHHdfyeE+LIQ4kvAdwH/l2/1k9Vz/wGO9hvqbuA9FXLr4aQ9gUkk04stitXo+KCqPhqGy4PZsXx6URFtTAlGxxBD2/5KQuBhCOsQRXW08DstsZE+4jKAlxOx7ejYXxv8IiLxsxJjj2G9zmG7CaKoiW4eMH5s6ejy6NaBT3bGcvCeVfYfSnjwH+wzfL5eoGcc5VpK2VVsfGeP7Xcm3Pn4EuOL4dFj2jQEKcnWIupehJwXVOsdEIKDR/vc/libfMnLog9/NqX3IjRtiwlB5dC+7Vj5ot/dJncVdQt6zymSpxPKAZz51QnDz+4ctfs8Ospbo6oSpIFov0LnltElzdon9th8f8ejpxbkSOoGdTCHqmZ+acDkgQ5rvztGzAuWPrXF9KEh8Y3RUdVwtCAr5Vt56vhrc5g0qBtE5mcMRzwbpRDG0Xt2hKwM/S/s+IW+8gq9XtXYUC+n0Bi2PrKCzh3TS37uU/TVkZPjkTLA4XsLR0kCJY8l/K19RetQFCU0BtONvXf7yND/8oFPFFV9VI06LZF5TbJREM4s134kwgbQvjZDGAi3NdlJy/y8oYmhXDU4CfmZBtWvGK5MOD/c586NJfJac3Gwf7Rwtu5I5G5IGDZI7WAceJvYjQimmmhbMbkILnDIwvNhbGy57ye++LW/nN/G8U2yjv22jN8XAqBz7jfx1oaH//+517jNXeAHFn9/Al7TXATn3J/+VjzHrxZOCqSx1G1NPvQWlsHckexZ5mve8yKcOoqBIphpL3uxWGRei7MhisonA8Bp5RFUXyOEsUcJQDuHnkpMGvjLzKKldZgg4Oj3Edz0MORi0bMWF4X0n9iiXu+htyeg1JHpVNUWFMuC7GKf6SlNMHfMTmiqriefOQVV1yN9imVwcoAqoHOrQnQcOrcI6yjO9jGxrwaCGVQ9R3ZfDUYg54poVxDuK+qeo3MTshPCAwwyaG1Y6pZ3G9S5YOV3tijPDIhGvpqynRg1K6k7ita2oe5oulcmNO2Qnbdr+lc85+XkL2/5WdI958CmEShB6+rolQNx6e1OizM94uv71Os9gq3JUXJ1cYTMCpySVKf7vs2zgO46JTzB0FoINPnZHrOTit6XGlTxqt1zoP3CXdWEd0Y0Kx1vPTyQxJvQfeHAVztSfmXCWMjdHBFOD68//P/e6nKxcTCJRnRiklsT6mFKkyqESchXA/pPjxbkUo3en7P/0RbJln9/b31vD1VCvuZwiaG9Mqca9RCVoHVxTDcuaYclW9MOz7x4mqBboaTjmTsncHMNEvIVRzCWFC/0QDsvtCt8kjcOipO1vzxX2NWKahwQ7ive8PEGTgxfK96SHPka4b7j8SOhOmGgHAjSHUPZE1QtT6aqW17lM5pYbCCP3fm+1n0r+TWhua8O0Ri/oDQWPS5wgYfqekhpcLyIAKZzTyVzuABZeyR0aNMAFwXeDMo75fjFMQlZ+nLGuf9th72HAppEULcFowe8nIQNFwZAifMyGm1vO1p3BMFBgYkkrRf2qNuaybmQgwc05dAxO2cxaxXh3YDuygyWS29V+uAMs1oxegDmpw356ZpixbH3mCCcOsKZpW4Lnv+/9zm4P+TFv7BO04+Rld/pq8Jx84cc2Xszppc66GlJ96qldadANJbt71zlzg+dxHaTo5mOnC2cGSs/YC/ODdh9zxJoSeflKU1L0ax00ZPiFYi4ZinBDNtsf2QNWRp2P+CVcKkbX3lY66s2IUiu7nPql7Zf+b4cfjaq2iPfFoAGvTOle62gbnkf7/mFztH75T8sr9wQHD7mURVl7ZHY5tFm5RCqbS3BXobMa6b396k7msn5gPjWmO5Lcz+nmRY4IShP9ai7jtmlhupkTf5AwfRyg+s06APN/E6HcCSgX3OiM+VgnnDroE8/zRGBV/ddTuc0mUZ2akQtsInFpB5abCMvM6LnAlkJVCaJtjSi8InC5QpRv46g5xss3sySI28ljq8Rnnzn0U963jC4UmNCQZMKetcrWlvG+0vXjuigJtxfVA+LL/hXk0U/5AZ8I8/FJZHvWxvjh7MLW1lZ+raHbSdHC5Le8VyNV7Qw7gk1znGBojrdx3YT6vUed75/DZGV6L0ZWEfnlqVu+4QZzAVV12FCj+YxwwYXW2xkKR7KmZ637L6ri9OC3Q94C+S6LYgOvFe4DR1yJ6T12D7TSYKzguTkjGovxhWKasmglktEoWg6nmi48THDre+F8cMNQdQwOw9nfrVCTwqPdBKCvYcDWisZ7k7C9IxC1IbhZ7cJ9uaUJzosfWkGDj9Ibozv/y+QY7aXsvHxNTY/4FnQ1360z53v7hPvlJ7PEGoINPVal43vPUETK0wS0N5o2H5Xi8Hzc4rzA1wa4Q4H7AsY9OTxFV/ZHL2B7pXvwWGyNr5CMZFi6ZmG1S9UhGNfNWSXl147aRxGWR0BEw6TjO2mHgbu3FGFKbKS2eU+VsPuowGtLT9Il1kNSpJdGrDz3i63vscTL7vPazCC4FYEFuLroRcpTAyzCw1uptmc+iG4tZLrN1cIkprTwxHPP32G7vKcM2sHuMjipKMeNNjAa3NF+5Ig8wtnOPEVrJ5IXOxFGsOJ5Px/86lv6LvxbRfu6/x5g8ZbWlVfI2TZYEOFSfypKnsKEwm6NwwmlgRzi84sQdZgtSQ/mWB1SvepnSPr169XZuSI+Pc6IYzFlRXE0RGqxwUKogC5aH3JQ9goHPfo7xVFPOx/L4arwjjPiHaO+dk2J39zQYlZ9PbDicGGElEu9IgqD6ONdwX1UB55Y9tcE1SC9t2G5O4cbIsmlbQ2LfuPCJrU0rmqaFIoP72E7jpM6HBXYmLJUWPS7iukBZN6a9HkRkB+qiEcFNS7CSJ25KsB8c2aze9eY/BiRTh2HNzo4FZLmv2Y/GyPYFyRnYzpfmmHnQ+vMXyhYvTokHBiSK8eeFRRP2V8vycvnvjVbQ7etUKyJVj77IS6F4HzNsAIwfxU7PWseprxhYCVJ+cszw3CuCOo9dEMREpcGJAvSVTRIr01PWbwH1YBYcCRlIwxmG6Luquo2pKy5/kboz8juPhTFttvIQ9mx+9hoI9IpU6KV9gUNytdZG0w7ZBga8HTaMU4LXFSkK35nb3KLU0/oRyG3hlxw1L2BckO5Kswub8B7ag7HmLbPJgRJxXNPMLl/j7yIqCehVBJ4pWcs8MDrtxYR+eS7Eqfaau7aOYDRqBzwfBpR9kTxPuetzI7fSit4y2H2zdhdu4NvKIuQvA6vfY3SbyVOL5KmI++k7q9IGFZBw6isaFJJKqwmNj/tpFATBwoaF8ZeWgnvGKH+PUkj6+WNJySRxWKKMrjxUNKnBLYNPRMbvBkQS397MM5qN1XSK8DNMOW96GwlqafUgwkncPd+GIXG44q1j4nkLV/vZNzmvyERRjpF/PtxFvMBhLhYL6uqdtdVGlxSjA7JVEFxDuC1pZ//kVfEMwEgysNsq4JxiXTi22ayAv7zU4LbGBxsSW+IjjxKcP8ZErZk9QdKPrw8p9d5cTvNkS3Rqzelax+wrH33hX2HnMkVz1arbtY9JM9g6z9vCRbCzDJEslGwcFDKftvc3SueYZ950ZO+7Zk6/1dOrf8fR9CcftP7lKd7DK6FNG73nhEkhTIWUF5bsjoUkjvasXocsjgSkXV0xRDwf57LPf9k5jRu/qsfHrPI64ac2wPLASEAfl6TLai6L1csfPuALlaEL2Y0rQa9IwjNJhXQnavkK3xQ3mvjSZrw8HDHZY+s3NU0YjMIsIAEwqSXa9Uu/XukJOftMzXlZd/0QKdwey8xaSWeDmn2EkIJgLWK6pxxGwSIjPpFQQ2HZNLLdS5guWTIyZZzLXPnYGuoVnyXiVYQbCvsYGjdVNiAyi7vv2YrUu61w1rny0Y3ReRrS90zgZeofdNEW/8/Pe68VbiWJ0VkwAAgb9JREFU+CpR9TVFT5HsGawWlF1FNDELhrA4Ym2ntzOqfkR4UOCk9LyC15AC+Vpxr7GTi0PPtzhEU0WhX8wb30f3PfXFzjXUOCFwgUYYg5yV1CttgoMc0wpRo+aY57FYCJ2W6FFO0/PyIcJYVj6zf4S6wjm239OlvWm8N3hlqdu+YghOzikHAW4/JtpXtO460m2LCQWqdrRuZWQnE8JxQ69WyMYxOadJN0qQkN51lMvedtfLXyhvJ/vFA5pBAsT0fqsh3J5z40eHXH2PgNJy7t8ZgnnDxgcS9FxQdZXXBFgkxKUn9lj61CLxLbg029+xTLEsKIeKy//jDs/9lSHJjuT6D6cMnoNkW1K3YeNjK5z4NT+POPErE7Y+ukp6N4XGcvWPd1GZIBzDqV/aZvbQ0Hu+70z9uVQeSLD6mYyVSUV+MkEVlmCuENpx90MJZ39u35Pz4BUkS5xX3t17yH8Vlz9fEO5HVDaGyNG6siBY3pv4pVgYiekjoqjTivzikOkZTbJnsZ2Fx8jh4yze/4P7JU3bsfSU5dqPhNi0Yekzmt7VgvHFmGAiwQrqrkZYgTQCe6UFq42fV3QbTv6cpUkU0wsaexCyNR2ixwqnFx4euTeRMrEj2hfMz1rmZy02cPReUERjQ7LnSG7PKFdS0l3D8JmS2x9rIRyc/Ruf/L18bb9t4o2Mmvpa8Vbi+CqRbBbIKvKVRWmJJgZVOsKJ1/QRxqGKhnI5Ibk5pjjdJbo7Y/7gMq3ndo7v6HCBeFXVcW8VAUBZ4QKN7cSeyTzJMb0WLlI4IdD7c99LDxROCU9EW7CgiTVi5qGVth2iRwX1wFN77Yku+iD37YzwuBoq19uowlAOQsJRhelEqL3a28y2E/I1QdPSnPq1A6b3dZmdUswfKBHbKbIQhGPJ8pcb0lvzI9lxnCejRXsVTTugiQXR2DJ8riTYnuIi7QfyM4POjW+XNY5ov6ZZSlCTisFzju13pQSzEB6foMoAN9HsvF2y8hQsf6n2C/Ok9CZM8wXhzkmqMwOCncyjyZSmHBzrKu29d4VgDPuPwNpnLaNLChND1bXEe4Ls4oDk1pTyTJ+mJdh7W5u9d1nAEowVVR+wlvR25jkrWpGdbrP7mEbnkJ9uk61orIbV39oiX1rDlZK1Jyqf5O9RDDj6XEhJsd6ie91iA8H+Y13vURE47vsnY58kGnsMbqhqnFIQ6mPyJ1Cc7WMDwcpnDrz8Sm0wg9R/RpwjvzBgetZb+Drp2FlQb4M9TThzBPsZ4VrIylOOfKgYmxhpoW452jcFwmmE8dDzG98P3Ze9rLuoBeGeov+i5eBBz8WRtSDeEeRrMD9rSG8pihVLMJZUXdh6j0JngmHY9ei8tsQEMTr3arpvmngrcfzhDFF7sx1hHMHMzzAAROMQjUVlFSKviCuPyY+v7oEQpLdmr3ufR3wO693inJLYdoKNNbJZ6CUJ4bkfcUQ9jNHzGpVVNMMWNlKookGUxu8qx5mX/47UEfRSlAbbDsnXI1ThcBry5ZDucwdHhK962KJuK1TeEO0VmJaXy1ZScuOPreLeNeHc3xyz8dEeorHo3DK5H6gla78rmJ2RLD1TE0yaI7FD246RmeecyDAg2J6SHA7n7zmnLtSowjA9FyMbFtBdic58b15lFatP5tSpRv1sh9ZGxeb75dFAMd4pMLH2O+pD1eBAg3UE+zkv/cSA9k3P4k+3HeOO4PS/b9h+Z0DTNsQbitlJxZmf3WTze9a48C/2jgbUVDWySvwg14DoVejrMeEEL/rnnH/seYltRew9orEhTNcs00uS1U87VAVbH1tj/z018e0Q0ZQLCK07bk85rySw97aEg0ctZ37JcvCAJl919F9wnPhUQ9OJCDaLY5jtYnYlihLXio+G37YVUywHzE9I5msDgrmjbvmdfHtvRr3eY3Q5pOot2PUBdK5KbARYX006IdCZ5eb3KlQB5kRJ9HIMOCb3WVq3JU0C9XINyrHfVpAYWs9GVF3H3tsEzbBBZopGO2ZnHbISyEJQ9R3BVC4EGSGYCfovGaKDGqsFwmqKnm9pmog3R7g3Nmrqa8VbieN1wn3gcUTREO14zH7T0oQHBTbSqIlvuRwypUW1aEktJCvELH/lnUl53J44FAfsJt7TIw2wkUbWBicgP9NF1o5opqlXUsLdDBdqmn58VLFYLVG18dpTh/pUh387h+lE6HFO58WaepDglGB6JmT/HUN6L2fIokHPKsK9BiclSNCjEllUZJcGVH2HfKFDPaxIdnx77OD+gGBlRl1oEIqlp2vS62NMe/FNN9ZrPR3KnBz28BcSKIeLu1NiQWJUBHNLnUovq3FgfLstFMhSYiJFOKrQhWH30ZjT/z5DzTzKSc4KRB28AvbsAkV+ukO0W3D5p/aphynBfsbWB4d0rzl2Hg8QBi7+65pwe+ST53qP1U9P2PjYCpNLlrXPQPtWTrA9Zf0zcOfDCQ/8P6ZsfDxBVY6lT3tlXhMrZC/xGwvAhI5o35s4bX2XILkeUj+UIa2gf8Wy/a6IM1uv4lwoxf6DydGu9O6HFE2/Id7QjC87Vj4zY/Jgn+zEKr2n9/1rXUCHXRQyvdyl+9yIOx9fIjvh9at6L1vGFyV1R9DEMHzOD8f3HompW6BKL+niNNiPjpjtp+i9AP20RZQV6Y0Ja58ZsP39BfHzCSZx1Os1FBITSqqhRR0E6HMzZMdRXW8zf6SEuUaPJbpdYyqJ6FXYWYCJHeGupu5bTOwh3cFUEI2gTiQxUCwHOOErwmDuWP0Hb3A01b3xVsXxhy9kbTxSae7lGlTmTYP0Qqbi6CcMXjnLONxd3wOPPBIsDANsGmJSjajt4nE8TNYpSXY6Qs8t0eaEeqmFLAz1MKGJFTo3VD2NziwqX/S1I4VznrlsuiFyx0MrZeHNnUReIVshNlT0X8zZfSyh7oaEtfGMa61wkcSkoZebuNhGF5beS5AvC/S0olMarv/YKtEI3MstlIJgbkivjXzlMMoWYnnyaGG0Cza1TRe+G4FEVsYzySuLSRTFUFN1BK0Ng4kE4bj26K3a+RlBbslPxOi5YXbWooqE7s3AD77dolo7MrPyrzV9cYFu0opgd4bpxEwvekOj1S/UpC8fcPNHVzn5OxanBcHGBBcG6MzRuiOZnoPe8x7eGtwdo/MEpGD5ycz3qxfzIRtI6m5ItqoJx14pdv5ICY1AjTT5+Qqx5xPqzjsEZ3+18kPxQ/HHRfKQDcR7hmxb0zyYESrrFYEnmuf/T32S24q1JypsO2Z6PuXgAe+kJ2vQc9h675BmpSTYDOldtdSp5xXNT1uWv+AfqllqY2IvzOkem5LVijBayMZrizg75+6HW1y6FZCd7bL7mCBpVWTrIa5lUHGDVZL2dxxQvrCEWaoJBFTX2ySXJgTKMA0TahnCOEQPS7iVwKkCpSx1I8GALIV3fOx4Q6m6JXAyIt1umK/7luLsvobBP/mmf5X/wOLNPON4i8fxOiGaY6b2kZxEXvkkcC/B73BnDcfw18O/DyPQEIXM7uux9Z6O992uvP6VKGv0tESW3vo03syo1tpeyBC8RHttCXYz0js54X5B09LMz7W913niJdf1yA9Cq/UOphseLeDFil/A6k5A92aDrLyqrFvIrsuiQs0qgp053ecO0JlB1nDupzcRxlJ3Atq3HJ1bDeFEcOlfTVGlH+hiXkk0c1FIdaJLdrZFtdaiWEuoeiFVP6RcTggmFTaQTE8FlF1BNHbMTiriPb+QBfs5epzTujYlPChpX50iG8f5f1dRtwX5sjrmJjh3zIo/lAAJtBf3M5bxo0seRh07ymXLnY9qbBpx9md3mFyImZyNQSvycx3KoZ/NtO5aT65c3Nfpn9vyBlg7M2ZnYi/iuEAwWS3QhePg7YZiyaG2QoJ2hThZIDKFrAStG4ruyxDfGB1JwR+ho5yj93JOnXr5FecE1SzEZd6U22mLUzC6FCxapp5kuvSMIV831F2I9gRipjn5iYayK9h9nyF7e04wlZR9SbYesfHBFrPzluz+EvFkh6bQpFHNbC/FVYp6GvmkEig2PqhpuoaiCHCpQcwVrc+lMArZvT6ElRJKSTkPUWcynBPM5jGD3hwCz7tpSoVJLHauafY8AVXlEp0JZC2OhAtN4qjbgo0PaUYPQPmu+ZtvpX0T8zjeShyvEeryRd9uupewdfj3q+QfxKvZuq8K20upl9s0vZhovybdtew9kmBirw9k2hEu9Iq7es+3uLw9qfQtnUARjkrQEjkvEUVDfHfqCWLOoeZelkLkFfUgQc9qgt1swfHwb+/+Q5Hf1fUU+UqAjQJse6EvZaxHgdUeeRXeGR/5bph2SPryPibyrZT+iwY5zsiXfVtIlNXROanXurhYE27PSG/OF94lHp2z/1DAzuMBBw+2mZ4NEQ4vr9EVdO40jC5F1J2A/cf7ZGe7zC90KNYSnJaozLfVTv/8NsMvHhzrcR0uwPcwpqkbRNmQ3Tek+8KYvUcTzvyKof+cIN4WR+dj+MQeyX6DaUdUHcX6pzNsAJNzkmalc8yxWBAnzSAl2amPNgPJtQNUYel+aZfTvyIQQO8lcNdbNNOA1i1FMJYsP12z+vnp8WfoEObceIZ+sDWhfbMgnDjkzRhq3zZEO+I7AbKGYig4eKRLtirp3LLsPC5xkaV6KKN8PKNzVXFwf0Dnjkc9pU8lFCdqJpcsZVfiNAzv2ye+HlGuWLCQVwHp1ZDuygxRSVQp2Hl3j7rjWP68Qr+YgnQEI8nkbRXBRHDy0g5KW5KVDFdK0rgim0VIZdm5NfBy8IlBSIdcqvzrcBDuKpafdL5FJkDNFdWyoWk5xg8YqrXauwFuJNz/f/zc7/3L+20Ub2lV/WEL5xa4+Ne+7tXxyuThjtniUnqL06xi+tCQfChJ9iydWwYbKqq+Jt4uKZZjdGFo2qHXxNIxwajwTOfDHfYChisAAo0sDXUnwMTKC9R1Q4JRgY0DnIoQjSU7ldK6nYFISG/NKVdSgklF04uOhBsPZyS2FSFnBU0/Re/NKE/3ufORiLO/bDl43KKHBeNLLUaX11n/dIHK6qPjbKj8LjxUFGs9wv2KYOqVcQ8eCFA5EPvBqM79uZqflDgJyZ7H94/uC2gSmJ3ShBPf786XOujcEc4MaV57rso4889byWORP9Mck+uqmvTKLraXsvrZCXuPdymGAhtBuRwzfk+HdNuw+5gkOuhw+pd2mTw84MwvHRxXG4fvcRR6SZPdKVVvafFYvgqN7oyZPbzExgckyY5gcsnraeksoHXH0iTec0ROPDTbJd7jY+s7l1j7xMGRtljT0qx++oCVL2hGD7TZ+k6DmnvBxnTLMfzyhLsf6dEkoHJBOBaoIsA+XtA0ivlJx9KXIb06QlZLzB6s6C/PKKqAvWGIyxXl00souTj+VEl5pUtzrkbc6aKmkv4Vy+S8JFifs29Tepf2aa4MqNYa3nH5BsXFgI1JF+cE+TRCJIbpLAErKPcTRGyI2yXFRsszxfcVyZ4kP90gjDfpGrzYsPewplyxRNsKWQryszVUPhGG6288X/GvGo63jJz+MMa90Nkj7+/XkQ+5FykFHDO3jcX2vIptOGq8lLj0C4oz3jlvdiYm3m98hSGgSQPCaY0NNcIsvDSaexII0PQThMMz16c189MB4QRkElD1Qy/c5xztq15FLt7yxwWTCln5pCXHGeXpPtHGxMtwLGxoZ2cT+rtTTCJZetqg9+Y8+Pf9gB5TUpxqc/2HQu7/R1Ov6us8wqzpLCTWu4q6lZAtS3ThUIVvSbTvWIqBpOoIbAAm8X36yTmFzh116kX1TAR1xw9LZeN5Fk0iKLsDZmck65/SXnSwMceMafAtwygku28JaRzxtX2QkpVP77H14SXyFcGNHxLc9y9ynBSsf0YzvqCZ3d/3LP/DWMymXCte2PT69mNya0J5ooueVX6uA7Sf2eWkW2Z8QZNsCWbnvLxG/xfmVMPYK+weki07EflqiKrg7vcMGVypGd0XcPKXt6nXO2RrEVVX0H4xoElh+EJD+7l99t67Qjl0RAcCkwhk49Fdo1LjxiEP/NQBWNj+4LK3ry0CRk0HrIDAIoygGTTYXKGngs7PtxnfByuf0uw95mi6hs0Pg6iAzRSXWNKwpro45b6lXQ7KlM1RlzILiNIaUymSVokQMM+8YZXcCyjGC5h3YMEpzKMzmESUa4ZyDfYaSfsmIKAaWlyrIdgIqVca2lcC5mffXM0PwTenohBCnAH+Z2Adn4p+0jn394QQQ+BfAOeB68CPHzqhCiH+OvDnAQP8n51zv7y4/F3APwYS4BeAv+ycc0KIaPEY7wL2gD/unLv+1Z7Xm+vd+iaFtxJ9ZdI4vPzV8RXcDOnJVi70fAwbKpyShHsZ4U7une9iQd1W1C1FslOjM0O4lxNvzFCVpRiGNJ3geFG8h9AGoEc5eneGU5CdjFGlo25JyuXQcyMWbSfRWPIzHWRt2Hu8y93vbLHxoS7lMIBAE+5lYKxXsE397rr/xR3mDywTjGvq1mLAbyyiqBF1w+b7Qy78TMH40SXytQVPJFSYSB71/bNlic4d85OCqi/ITlvG93mf7boN1cDRJI7ZeUO27r25TeTlTGxsqQeGZtjgNIwfbKg6nlXcf9GgigUfQkovJriYd+x85zrZ+S42Euw9HHLtT62z8fFVbvzIMnVL0H/JculfNew8nnBwf0TrxX107mi9NHnlG6qkZ+XX92ha3fO5KNYSsstDkIL84pDZScX8lLdPjbckg2cktz7e9ezuODi6z723xbTu5ARzL8Gx/2CA1bD9oRXufGdC0feDbV1A56YjW/GildI4LwhYw/yUI9nxDPjgWozMvdPhwdv7TO7zHuVNy7F8auxh5HNNvKVY/rQmGPs5gw0gHAmmZ/05Vf2KeEODdsjlkmQ5487NJYoi4OrBkO1JGyEceiMiCBpkYFHKUuQh+kB7q9uW31q7bo3eCXGRo9qLQTiccDjlOPsrht5VQ+clL2KodkP6L0D7hYD5GcPl//wzvOnimzPjaPD22A8B7wf+khDiYeC/An7dOXcZ+PXF/yyu+xPAI8D3Af9ACHEoNfw/4O20Ly9+vm9x+Z8HDpxz9wF/F/h/fq0n9VbF8arQF8+/4v/DSuNrVRz3Xu8iPz8QpUHnNS5QlGst9LQm2iuRnYCqqwinlvnJkHDq/aTnJyLi3Zpw3KBKQ3m6R7Q5O95dVw0uDo4WtNbNDBtp6o7GaokJBHHWYHsp+XqKbBzpzQnZua7f6WuIckfr5tzLktSG6duW6XxpG6Rk4+PrpDse5dR6ec7yQU52eYn0pX2/WFc165+pufudKWd+dXrkKFd3vKR41dfkS97gSjZgYoeYC/Rc0KSOYtl5lFVsEbFBKEsTBjQrlmAnIBwJWjcV+ZpfjGQFyR1NseJoUokqoU5b9J+1uECiDubUa112H0vp3mzYfZv3Skm3HHVHUPb9EFbngt7zXv48GkUMnpsxv39INFnAhheaYl5jakGsWxhosUBCjR4dsveYoEkcneuSncdWkAaaGOJd379v2g4zE3RuWd+GvFVj2wkuUkRjx+RiSrWAyi4917D7iGZ62dC6rjGxIN2yjFcE5cD7gNffNaBJIBo5imWBiR1bH7SE+4LoQJBsQ76ySLjK4WKHjS0HkxS0xWmLmQRkJwSnfqvEaYGeN2y9N6W14Rg9WOC2YobfscndG0vYxsOh18/sU9aaKGjYemkZF1miizPyLKLbyTi420OUEhJLuBGgHpr6FpbyyreiFAgEcqpwyifV7XdIhIHBFYMNFFZD2Ydy2bH26TenqpN4jbb2NxoL07qNxd9TIcRzwCngR4CPLm72T/C2Ff/l4vJ/7pwrgWtCiJeA9wohrgNd59ynAIQQ/zPwR4FfXBzzNxb39dPA3xdCCOde/wW8lTi+zjhKGvdoPR2FUrhAHA09RVkdIWgOjZfyJY1uKeL9CqcEnasz5mfbyMbRuTJien8fJ3w7qWl7hz/ZCOrllCb2ekKqNIjKIgO/41bzCqck6Y0JphVSdz3Ut1EBCAjGlXfnO6jovyxwGnYf0dz4oS79K5ZwYuk8s3f0uk786qZfLBevw7Yi0msjzLCF2p9juwnjC5po35Gd8pBZ/xqFH4Qnfm5htaAcAMJR9RwmtZ4rMvOLh9r1SrD12RKkQ2jrcfwzr1Vkl2vCOyHl6dpDXMeaZAuf+MYWlEAWNTsfWkNVjmIVyoGmXLbYbkPz9op6O+HyT2VUg4jk2gH1Whc9zum97EEPred3jzkwdXMMdjgUiXQOyop6vYfKa5z0TOnOy4pi2YETLH2hoU4l+fKiEswEJoFcSlpblq2PrNB/ufI6Vtdr7n7IcxbMpYydKKXqWcJdP89obRjm64pwzIIj5DCRQOcwPwl13xAeSKpAEE4ErbuW6TmJVZBMHWZYE2wFuL5BAO3nQ2YPVpz/+Tlqf47ppwjrGD3QpupBtgb6pYRy1bC93+XUuT3yWtONS67fXEEohwwsLrK0ljOKl7uEF6Yc3O0hWzU29NyhKjWw61tWLrQ0id8cuH6Nuhkia0l+psbEfuOiM4sNlN8U7FpMIhldFnR/X77Fv4/x9VcUy0KIz9/z/086537ytW4ohDgPvAP4DLB26ITqnNsQQqwubnYKuNeo/fbisnrx96svPzzm1uK+GiHEGFgCdl/vSb+VOL5GfEWVcS9PY4HwsUngGdP3OPC5KMCkAbLyZk6d6znCOZwQKO1L+/a1KaYVMnmwT7xfEzeOph1iEokNQsJxRZMGyMZhYkmTBISTBll5y8+mG9N0AmTtpTt01jC54MlqTglkFaAzg40V4bRmdioi2XFkJwXZiqS1UeGSkNt/pM+Zn9kiu7xEMG0Itqc0S23235aC69DaNKSZ5yLkq37X3aSaU79+wPxCB9H4yqxu4RcQ5XfJwgryNYvTjvY1TdX3kuzhyM8xGIWQGNRWRLIpGD3SIDs17XZBlVastApmeUQuEkaPCsJdxX6oGaiU9Oac9kbDre/WCOswy17+QgSW9HfaFEu+xxxt++H0wf0x6V5A+5nFd0EIb61b3tMOXMw2nPbWv9P7ewQzy+Riws57LK7dUKxIVCZIdh1FX/kKZ8dDUa32Q3/hYHRJonO4+6GIZNux/2BI67bj4IMl8m5McaoG6agSSfu2woSC9oZhfMHfp7C+fYcDaTwLvjzlqx8TKeYnJXXboTPB/vtqTp3ap1pXFLVmepAye6REjgKcrH0ilAK7aHXWLUfrtmB62bB2dp9ZEXFnY0Dcrtjf6tJeypiPE4LQ85OKIsAsVxTzEDWXMIugYxC1RJZeLl8YgRUQjgXlqiG+FiGsPyfBvsZcKAijmpudFid+05Buemn81maK/vUnvtlf22+L+DpnHLvOuXd/zfsSog38a+CvOOcmQrxulfZaV7ivcvlXO+Z141ueOBb9tc8Dd5xzPySE+BfAA4ur+8DIOff21zju+4C/ByjgHzrn/tbi8tcdCv1e49VtKli0qqKF4Y6xx2Q+545E57wPhieG6VFOtdpGNBYbKepuSHLtAHlPUhEmpFj1ojyzU5p0x6AnHlZrgxBZWoJZjY0UOvNqqHouKIde6lvUXsiwOtElvjOlHqZkJyJat3L6V+ZUgwg9b8A6Rg+kDL88Qc5K5Jr/Mkf73gZWlgYai6zxg/ETmuFBxZ0fWPXD6oFPcHvvgtVPDogPDHXbYUPH9H7DC29P6H5OE8wcvWslLSmYnTxc+DzZS1jv8Ge1b13Z2FH1JO1bgvkpMEZ5bSYFwcAja+pa45xgMo+pS42IDHIrIt713h7Rfk1+qsWtPyJQOYvevcCmlvP/VBJv7DN6W9/reWUNGMPwuQy9O3slD2QhgQ4c+ZXYWFOsxOw/6KXGm0hRDAV6uaAeRSRb3pFQ1n5haCJobXrdreAgx4R95qcFxZph6QuS2XlL/4pgdkowuc8ipCM4NydWlvluSrTtz5eJhHf/23WkO4bJeU255FCFIN7xfijtF0KcAJ2z2L0LsrMNnaU5/TinHZTcnfXoxiUH8wT5TLTwQbeI2pCfbtG5XXLwYEzntmHwg1vMq5Dsbhs1LGknJXFYM89DVlfHzIqIItPIuUIZEGbRdmw5sAKnHSaycGi85LxkSPc5zey8RZWC9U8ZslVFcX9FXfsqxYQepm2SFk0i37S712+W5IgQIsAnjX/qnPs3i4u3hBAnFtXGCWB7cflt4Mw9h58G7i4uP/0al997zG0hhAZ6wP5Xe06/H+/ZXwaeA1+NOuf++OEVQoi/DYxffcAi2fx/gY/jX9TnhBD/1jn3LMdDob8lhPivFv//l9+KJ+6U9JagC1FBURpvnCSlF58TwivL1gZRZMhDN7d5jQ0k0d3jwWu10kI03spUzyqSW1NsGpDcbbzkSNFQnPLGOCprMLFGZTVyYTman+6AgLqjES4h2J4Rbk0pT3QXbR5fWZStiOTOHNMNGV1K6L9c+ME30LqdUz6ckm5Zei/45yac48QnpqAky09NEVXDqV8paLoxxWpE2VX0XyzY/ECbg0cU7kRBHNfkoxgmAU7B4PkMldfIyjA72UIYmJ8UOOEX2Pp0RVVL3xfHf6Gqrk8WshE0HUvTAjeNQDrMRGO7DUJbGAdeXK/bYLcDlr54wMZHh0zPW8KR7/UHE0e8Kxk/7JC1Y36hy+CpxV5i4f2td2cLU6sFtFkrP8A+nB0lIdP7OoSjhqKvyNctwdTPG7ILNZ2kwt3w5y7Zqdl5R+SrpymYRJJePfDueW1BsuUo1mH33QYiy+hy6O12WwZXaIpCg7a+DZcLiiEEGVgFOMHe2zThyHH613w1ND0bEkwEVsHgRcPokmL+QIXaC9AjRd6NeLFcodsqmMxjqqlXAjj7vOe1oLyfOEDd0kQHgrsfhdY8ZX6rg2sbwrChGxfc3uujlGN/kuJutkgvTUg/6RtJs3N+VmUih+zUuEYiDgJsv0aMAkTj5fKFg2jPq+Puvm1RaY4SPw9RMD0nGF+SnPvFKXr2JsbnfHNQVQL4R8Bzzrm/c89V/xb4s8DfWvz+2Xsu/1+FEH8HOIkfgn/WOWeEEFMhxPvxra4/A/x/XnVfnwL+Y+A3vtp8A77FiUMIcRr4QeBvAv/Fq64TwI8DH3uNQ98LvOScu7q47T/HD3Ce5fWHQr/neDUElzDAtEJEbY/E+4AF0sgt2Lz2KIkcVSGN9b7jiwXKRRrhvJKuaPzuzwmBmha4yCcm0woJD0pMqr273YKVbTqRv23hB646szSxQrVj5LxEVgukEVB3I+LtHDkv2Ht7F9ng21O7DSoXqHnF8lPWo6DS4Oj5y6LBBd5vvFlqoycFepQRhZLpKc2VvxDSehGCscCegSSqaFoKeTdg+FxF0/JkRj0piPcTslWJiRYw1FMljANI7LHsNiAMBBNBccLghIO2IYgb6izAJhYRWORO6FsgoUPOfdWy/YEBq09ktO9GpHdyqmFIfDdj9EiHiz9do0cl0UblE3pRH3uBL3SlDomDTkqEbaiGCcG0Qs4Kuk/tMHrXKlVPYFdKpics+vkUBGQv9xi8AFVXcPBQ5GcVifNqrlYh7JDdRzR1xw/jXWxQsUG/HFOcq4i7JZEDrS3VC10u/Oyc+SmNbBpMKCiGks6thpvfJ1n9jGD3HTA7F9KkjnRDcOJTNTf+I0Ew17TuOooVjQscpmWJA0MY+IW/HZXsBG3mmy1ufb/gwZfFEb8l2SrYfH8bnXkb1+KFHm6tIumU3L+yw5eePg+thlYvp65DWvePmN7qkgpo321QlfJoLQN7j8bUJypcpyHtFrirEaKBuufbpN2XHSb2M6nRJUm+4qhWG9RIE0x9lbb7eJulf/gm0qa6N755BL8PAn8a+LIQ4snFZf81PmH8SyHEnwduAj8G4Jx7RgjxL/FrZQP8JefcoT/1X+QYjvuLix/wiemnFoP0fTwq66vGt7ri+O+B/yvQeY3rPgxsOedefI3rjoY1i7gNvG/x9+sNhb4p4Q6TAOAW3gc2DfxAdrRIHPeyya1n4yKlRzw13sbVSXmEyrG9BJU1Xs0VvG+GtZ4H0VgvP9JYrzeVN9g0BOtlSdSkwIWaqh/SeWHkGdsOb96UBOhJgWlHzE/FpBsl2akUcTIh2TekN+dk51peRE5LRNEgqgY9K47goi5QXrZbCUw/9Uz0BUM62J4RnovpPh2y9tmMq380wVSS/Tt9RCM4+SV/u7qjibbH1MOUui3o3jRM7wMxqJDSEZzIKPPAVyitBrEdsfJUzY0/aVCbEYNnYXxJIWyEjvxiqO5GR2qrTjlc5CiXof+SQ+/PiSLvIZIPNenViuHndv15rRtsGi1UeANcrP1u+15r1TT0cF5rydYC+vv5kVR5/6k9tv7CEq2nY/SCa7jylCDZmHP3Ix2yx3LYi2CppP2FhOk7CjIiJpclNmkIRoriZI3aD7CJwoZAJSnzgPSZmNO/MsYFM9TBnLAbImvvU9+6W3L9BxMGT0PVwct3dC1OOFQhGd0XEC9Pmb5HM9n3lRkSBl9WyKYNDrYHA2YPVt6/W1tEq2bv3cssPbEHoZcuGbxQky9rOi9qppcbqCX5dspTe2dJ1uY0jaS40vMWHgcpuusY3wfzUx4+vPS04eAB5c2e2hXlZortSYpTnrlOr6bOFVZpuld9FZXsOnQWeSOnZa/W3L6RMf3Qay0Lb6L4JiQO59wneO0ZBMB3v84xfxO/WX/15Z8H3vYalxcsEs/XG9+yxCGE+CFg2zn3hBDio69xk/8E+Gevd/hrXPYNvQ1CiP8Uj1km5muL/Kv7LtzzSAuETVWjxsZLWrxW5Xav/Ii1vi2wWJzEQtBQOoecVd617/AwKfzcxDlk1WClBgkyb3yiChT6wMuPFKe7RJszpHGMHxlgNbQ2Kup2ADIkmFRUvRBV+QrocOAOgJaowhHfmeEihelGqKz2kNOi9jtywKYBMqsXKC1xLOGhJP3nZ75P7hz9KwnldkS860j2DWVP0Xl+zMHb+9g0pEkU8YFlekYRjB21jdAzgZoIOOkROjiBrLwWmFSO6L4Ju6sxcjfgzK81RDve6+LW9w2wsSK9K5mfMcQbmqblcMov/OVAU/U13ZsF80sDooOKqhsQb2eerb1IFKKqvWdH6WdFR4leOJpBSmuzolxNCXfyhX+74/I/nTJ+oEP/mTEmDZlcShAmQVbgrKB1bsL8dof5SYfLNO7yHJsHYATNiYbkxZiqb3GhhXMFp346pvv0BJiAtWT3L1Hc36KJBEHmgRN6UnDpX1bc/a4+85OOlS965NbstKJYcSRbgu7PtRl8ecLuu2Oiked6TM85lp9yTM9K5pcrkl6BXjJkWUT4XMr0PEgzZPDUAfnJFjiYnxLUHUe8pYn2YHLZ+A5erWh2YrT1jo1OAwJU4eca3Zf8x2JwxbKbCIpxhCok9vk23V1BseKoQoXu1MiNgPbdimBSI4uG2aU2TgrmlW9N3f1oB/UmI4vfG98sAuC3a3wrK44PAj8shPgBIAa6Qoj/xTn3E4sBzB/DMxVfK15vwAOvPxR6RSwgbT8J0BXDr/0W3qtmC8e6QofD1NdHMRxf9yrxQwleP+nVSadZeGlUxqOfsgobh7hQggA99bLt2Tk/vxh/YMDSlzPqlqJ9PaPuR5jIQ1+bNKboKQbPzRAOymUvo64qiyxqVGFACS9FIoVnjRc1SHHUzhGBol5Jj6TVkccJUU0LL42x0mH1d/d8MswqyvUOyVYBEjrXC2ZnU0wAqgadOcyFguSZhCZ19L5rk3K/i7uT0L6laRLY+FDEfX93wuYHeqgzjvYNweb7Je2bXXBw5uf32f7ggINHDWufFNSJ8yZFnQiZVSRb3hiqPNNn7xFNfkKS3pVEB12gSzhx9J4ZeUfEwsuLo/xsSs4LL/IYKvS0AuO8R/e215WS04L2rZB6mLD7aIyJINk1xAeO6TRgZiTD8wfEQcP+tEUxjgg7FUI4qo2Wl9KQDpUYLv53DXK+ewz7lZK9RwJWnygxkURWFmEs8ws9ko05TsDJ3zW0r4ww7Yj2bY0NJLPToSemLlpu40veNVEAm3+kJtgMEYUin8RQSIgtxZrh/M80FEteCDPZyJifbZFuOOqpoBzC9ILl5G/B+II/h+FcUPUt+qak7At6L0DVB7knWHp6xub72wgLJz5Zc+u7NabjPelZwJXVRNN+KqB/tSaYNehJAXVDuhEgS/97ejZidlqy9v9+czj9vV58LavoN3J8yxKHc+6vA38dYFFx/DXn3E8srv4e4Hnn3O3XPprPAZeFEBeAO/ie259cXPd6Q6Hf6xM+Nsy5NxZDVKQAeXy97aVHswGRLxbbe+xZga+8r0MbTyE8TLf0bm62HWO1xMYKqwQm0ZhIEkwb6o6me90T/pLt8sgfWlYWVVom5yKiiaVph4S7c6Jdr3ml5jXCOcKduVfB1ZKmEyEzzwBHSV8hOYec5ISL4al/cccVh+dzeJn0aq3N+HxEORSEY8fKp+dee0AI0q2SYsnLjoweUIhbMfVjM5Ry3L22jOzU2F6D2Q4wCRSnK6785yE6nOPuJv41lfgWTUuw+dEhZR86LymayKFqyC500ZlB73uuAEpy8+MhpuO9SZyE8WUwkcOlBoTnxgye2gdjj1qJAKJu0AdeLwrAdEOqk12cEMxPBKh6IXrYAlXAze9VBFOINxT5Gcdo3GJlOGWlO+NOqUnjiuk8RpWC8HRG71916H9x5D8Lh2ZTWnH34ytUXYesLAeXQ7o3G8qVmJ3HNc13dNFzmJ7WhKM2k3MRxZIgnDiEgdaOYXZ/j9lpgVOLGVLLEt0KKdcbVKvBFIr0tubEpwp23h6y9R5F03Zsva/DiU86gqkh2SrZem9KMAFVSmanYHaxofuiJlv39q7ZuufV1B1B1QEbOA4ebGE1rH2+YPfR2G8QjMJ0DHXPgHCk12LKAehZjcxqX8GKgN1HE2woKJb8+az6b95FFfhGeBxvyPiDQsL9CV7VphJCnMTDbn9gQUL5z4BfxsNx/0fn3DOLm77mUOj3ErMffz/d50c0gwSrJDaUhKMSNc6PfZ7rBXRTKeaXh5Q9SbpVIxtHsJBTr9c7BPu5F8t7tWfHvUnFOURZL1znlFeSDZXffWoBxvlKwTqi0hwNd9U4x8UB8XbOwcMdqrZA5w7hHHpWMb/YJZgaTCxxgUSPSmwnQk0K5KwkqA99PPQr5DRsJ/btm4UCL+oQerzwDClrrAophgHRxIKQDJ7LjvzQ9aTg7ncNaN+1zE8rwomHi5bTCDtWyPUSWyrQXm6jf8WS74cIA+UQulct8xPQuWWZr0uqgUPPBPmpBnVVM70AS0/7b2E50OB67D0SYZKUeBfmbXDaMT/fEO4o0vvHTPZa7D/kkT2jy8tc+OdbXo+rOTwHvp3nUi9B7xY/u49HdK8bTOhlzKN9mNzvj2lasPQSVI9UmFnArmzTSktWlyZs3hwiEkNQQfrzXfpf2vMzl7I6gv82A0+UqweWO9+Z4N4+hahi+vwQncHa5y3ZiiSYO5pEEeQOsQODpycI59h/rLd4HguZD+HVZ52CoFsSxzX1lQHn/vU2Gx9fxYRg3znFNZL2Z1t0n93FJgE3frDHqd8uqDua2QnF5D546O+P2PzIErL2UO3WHTwoYF1QLhn0XCJrR/9ly97DMXUboreNyF7ugXLoqKHZjZldNLRuKPYfSehd04zPB4zvd5z+jYbxxYAzv5qx9b7UQ8Df5PGWA+DvMZxzv4lHPx3+/+de4zZ3gR+45/9fwAtxvfp2e7zOUOg/NLovjJlf6NLEkjr1pjdqRWPiFstPzqCxCGsXMughxUBR9QTTsxHhGNqbAfFOhZ7VngwIx7IVhwge5Y4WYsDLiEQhomzQeXVk+3qE0FqYIrk08velFlpVQiAaS//KnJd/LMVqGH5ZUSctgswSTCr01PdXZVlDUeGUwkWBn6cckhUDTdOLMYm3cZVF7RfWw+d46FTY8Z4Kcl7Sfb6mWmlRtSPfgtAKGm82ZbXfoa8+UWISbwN7cH/E6AGH2IwIFtDT9i2LiQRVz+9o411H/4UZ6VbkJd8DqDv+NkjH/LQlOpDsPySId3x/PNksyU45RA3Vit/pisiiQoNcMkwnHvpZLRmSOwpVwd77VuncKgm3pou2lT+PTRKgci9FL+qG1bpD3dWMLwZMH6nQuwGiV+EOfKJpfmyPM3HJTtwm1A3jccp0kiASQ/vJmOELNcmdudf3aox//6qGZpCy/c4UG0L7ZcXssRJVK6rPL2FOG1a+IJicVQyf9yx1nRvCSc3kQsyt7+vhNKw+UVO3Pe+D0CIKRbVeI2caGsV0M0Z1/Ps3O+145ANXeeq5c+huxfTxkufu7zP4kiJ45wHy10KSzZr0Rs3ykwEHjw8ZPl/iZEyxDJ2blt23C5p+TbCvsYFDGtj4DokZVqT9nNmdLq7XEN8OKc9b4i1FuWRpUki3YOfxEBODU57AKivH/iMJVc9x/r95k6Kp7o23Ko43d+Qn2+jcMjvhpT3KvheAi0aOg4faVF1P+spXBNkpgwsNS08oZOrhu6NLiqQbYwNYfnLmKw5xnABEY45lLO6JI9YyvBKhdRjWel+Qw3kLIBb3qbKS+38yo1lqeY6CEJSnekwvtOg9c3DkU2HbkYf2KoFphTglkULQ9BPUuEAfZNg09FyU8lWoMQDj/IzAOjAN4caEQebFDZ0QNCttstWQYOa9toNxgbARJlZ0bjesfKHw/txJ4L0gTreQ+5ay5xfitU+OPIFyLaHsC2wIohGYnvEQ3NDihMRGXgepteWZ3E3icD3rfSO6JU2pcUDzcptkLMjONRBYsguWcEszfL6hbmvCu97RUeTeSyTYMbhIgxIcPLZM0Zc4BfmaQ440zUqF3IlgWGMcjKcpK605/TSnMoq0XTK/2UVWYvH6a3/fWvn3L6+oVzsI61j/9JTZ2ZS7H4PeYE5eBmRnDKIShBPD7KREz5sj8ujm+1OaFIpVQ7SnOHggoIk9877zpYCdDzTeB2PZq9V2n9Oc+sUtwCsLF0bz5z7wCV7OlvnEZx9GSIcqQf9vfVQ2Jj/VIr7r9cZ6L80xsSbet6TbjskF7z0utzU28nybnXeAOJUjraC81sEt1UhtKU5XqJ2Qpu21suoHCvJpSjBzJDsweN6R3s0RNkbWjv33volX1HvireH4H5JYeqZg44N+99y0HVUPOjcgGlnmJyXZKYNaLmlKxehBSf9Zf1x2ygKSZNsxP5PSGS/sVA/bXHBcPSzc3A79NYBj/serI9BezpxjWRNR1Ec7faRAj/Kj+zCJxIQeqisa51tSxnm9rNCz2OO7U2gMemfK+O0r9J7cOfKMOLrfQ06Kc8jsGA12+Pxl5mXeTS9GNt6pzkk/kM9PtpCVJbk5xrYixGJ2ImcltpuQbGQ07ZDBlZr5Cc32+/tH9z++7LCDiiBucLXCWlCdmrqS4CCawPSMom5D65agWPUcj1pExHcC6paj6RnUxRyRB6SdkvlOitOw91DAmV/YPVYAOEzs2hM4y5NddO4IAm9tKs7OUddaqLih7gniVrU4JYIXbqwTxA1xUjHbaaEaL7VhFQT7mQc+zEtfVS5cGKu2xGro3qiQA0P29IDwQOD6nqeBMJz8bU/KHD3Ypns1R1ZQnbSIQUWTx+hMsPJUzc3vVchGIlsNSlmCsCHfaDN8wb8vt35knQs/M8H+wjL/+P9wgv7SjKVL++ze7bH/mKDzssS0QpIbU8/fyfx7pA9yBntzrv/oMrqAumdxw8oT/WYKVUjcRkw4kdiHZ9jtBJsaUJ60YAPQE4WbJUwfLaFQLD2hqP7sAVdu91EzyeV/PCLcHHxzvrDfzuF4bSTmmyT+0CeO8Z96PyYUpLuGfEnTvWbZfYcgmAjfj25B/6WScKoJJop8NUWGXiNI1Y79h8FGlrojEEaQ7ljMsIXMa4SUfng+LzzJLw3ITsTeKlWAnpRHC7uTCzLeJD9+co3x1YoQqNzrSgF+0BsGCGMYPzKg95RH7aRX9ijfs3rEFhblgtPQWKQQxHeniLLBRRqnFL2ndhk/vkzrdoGall4pFo4/8PcKOgoBjcUmgWe1zxfw3cPEFkCdCsq+Jpg6iqUhVgv6V+ZHHuhyVlAvtQj2M1QaUiwpiqGkfdeSL0lsy5PJyjIAB4MTExormS8LbK7Z/Ahc/Jc10caU4nSXrXeH4AQ6kwyfr7j+xwSiklQ7KXoiaeqI2HhV2d41L3VPYxHGHBMEqxqz1CZf9sKQ5UCQr3oobD1sGLQK5tJhrSAIDNkkhkYSxTX5833iXBDvgg1h+emS4mSHaDf3xM52jEk1xUDSpAJhHOF+zv1/00A9Jb8woOpp2jfnR5poNlRYLdh6b4ulZysGV+DGnxI0yzXqdsjspEYsFdTzGHYiWM/JJjHRniK5eYBrxcwvGG60ulQ9i94WdE6VbO579rfKvRDjwQMJK787Q5QVZtA69qAPNZ1bjp13Ww8wqCQ6bWiMIL2mmF60lKuG4KUW7kQNxvusJ1uSqueIdwSTRyuoJcFY0SQC89vLnH+qYnJOMb/U5fz/7Q9Bm4q3Zhxv6tCFI5r4XbMHX8P6pwx1SxLvL3y/JyUmlMQjyfCFkvGFyLcQlgS9Fx2zc4pkS7D0bEE5CKj6EaIbEt2dUS4n6CSgXIrQuSHZ9lyJchDQxIogazCBRJULtvk9Ut6vhgCLoj4erlfeN7x7ZXKsXttv0blRkJ/voyqLHpVej0mIo52/CzSmE6P35zTDFr2ndrGdmM2PDDnx73f9PGbho34k4b44TtQNwpjjqsk5RNUweOqA9jDFJIr49uQVO62mn/rWnZK4KDhixZtY075TMTkfUwwlK0/mFMsJRd4hPTehCRTjScqplRGRNhyIFqZUbLw/4vy/GjFfCzj3M76CGD/cZ+PPlwTGL+gAM91Cd2rMXkQwlnReGLP/jgG9qzn6IMNFgW/hVTWzsymTC5Jy6Ohc9Z4W+lpK+sCENKw52O5AIzGdmiCpMYGiyENM4ohGgnwVdAbhnlfeFY2l6cbocU6xEhONLUtfmntpk0AhckN5us/sVEDdFnReNBD4+VWxEtG5WdHalOw9EjI7Y3EzS7ylGT/U0P+yxtYSu14TtirqnYRgKulcd0daW8ltRXapovVSSHbScOe5NezC61vPBHoGK5/yYo/Ncofx5ZTBM5bbH+9x4hOZF8lsG2RgUNrS1AqMIF/zjHM196ZMeRFStxzhRNCkUK3XNC1Nci1k7Yma6Sno3KlRpaHq+aUm3jmuYN/M8RaP400eqvSmTU2iwIHOLcHUIBt3pC1VLifYUBKNDPlKSLJvSJ4tEI1l6/1d+i9Yj3p5ICKYe90oWVlMx4sKHiaNw2FZ01KowhKOKqph6LWlWl4zybRCL5t+2MYSHmXlpbb98NwsBtaHVrMYB31PcpSVIbmVYdoRxbqX0o72CoTxi74LNbJs2PjYCr3rNXpfYJKAE7+553fkzh3ZnIrGkl8YEG/6RQ844nFg3UIN2L+oYGtC9vgy0Zby7Y/cQzH1OF8saCGyqJjfNyC9NsHEivjulO71kMGTnm+x/KWQYiCpr/ehD25g2X3hBO2bDve4Iyi8A97tH1qjWHb0X4xR45xg5s9VK/WL0nSW0FrJSKOK/IspxYrj1g8NPfx0XND0E/TOFNtOyC/2mJyX1B1vODR+AIKZoD5dUu+lFHkIynmPCeEIw4b5doJrL2YoZxwyF5z/Ge9PUi23kJUhX49oN5a6JQmnFlE0VKstLw9jwUS+/RbvW2yskbWhGsbkS4qD+zXCwuyspXVbkp3wwoYox+QDOdG1mHLV0IxTHvzJPTa+e4W6A7d+cIXTv7RPkzrUWFO/Y4Z2UM9C0qsh/ZcsyU6BntWM3zakSSTCOqqu4M7HegQzuPYXBSvDHfRBB2sFdRZ4YEdg0VONqBXmYsFIRwQTkDVem2tFcPKXFdmaJNmxyNpS9QR5oZieC1AltO9Yrv1IysU3N33DxyGi8k0af6gTx+zH30848dVFMDXUHS9mmK8EpNsV+bLXOdKF9RpRqSSYGaL9kqYVYEL/JUl2a4qhprXliA48f0LUlmoQUvYVqvI7bGEhvZ2R3CkX/hmBn0UEnpRn2hFVPyS5O6deStGTEhtq7wehJTZeSKEsbh9MK6YXWqjKEu94W1iZVV5SA6jbktadgmItQc8NsjRMLiYI6+c5wajAtiOm52IGkwKTBjjlFxMTKXYf90J+3eua7tW518aa+cXZRdonVilp+jF6WtK6W1KupGRrAZ2bpScyNjU7H15j+bMHYB2tF/aw3cTDjYWgfafyXJDKsvuY50qUQ8fJ32mYndKsfO4AUdQsPRkgqoadDyyz/PkDfw4WzyXeyRHPdNHv2WWaRbidiPCFmP0P59izhgs/03D7oyEnfzvz0OXdGXe/f52qC+d+7oC7H+wjL8wRRmJrSeNC5HaEO1FgZ168j3ZDMwuYjUJIDUmnJDcxrpYEE8X+Y13qtq8656diui/PsJEm3m8It+aeU7PnWfHVSot8RVMsCVQpaB5OCTJHfOA3FzaAJvUCkU5CdCCZnzbcf2mDl7eWKdcb0usBZ355THaxT/+lmslZTee2JT/XoX0LshMSnmtTrDcII+hdtdSpIHs4pm7HdG5akr2G7bd7zsr8lFdJto1g74urSAOm60hPT8n2U0SmKE4Yol1FlStOfcIxPi8plxx1V3j+yRlF+65FWMfu2yJWv1iw90hM8XBO+mTCxkcs9//Fz/6BfNf/IOKtiuNNGtGooW55L4R439CkkiYWqMqRrYbEBw3ZiqboK4LQY/HrVKJzTbiXs/3eHr3rNflyQBML4pEhWwuJRg0i8NDRcOqJeliHND5JGCVxWhDtFWAcTS9Czyps5Lkc5WqKVQKrYoSDqh8SZA1WSVTu5dLrXgwCWneKY+hsIKnX2z6h3Z2RbpSYWGNCyehiQLxviSbWuxDmDTZUiNoyeGbiNauEQJYNpuWd9PTcAcLLfIQKUUqvBSMEoqjZ/cAqS0+OKJe9gVTZ16RbJemWb1k0LU20K1n60vT4pAcaOS0IF7pd4d0aM0i59R8N6V+xzE9JVp50xJsZ6cuFr7ikOIIKr/yu9wcXjWXngyvsvr8BKxClpX5uiZUvOJK9hlsfC1DXEpSFvUciLvzMmPxEi/1HUlS50IMCXvwzPfrPwkGc4vo1rlC4boOcaOSdGLtSe82yWnqUV9sgtEVrg9AOV/ld9+yM4PSvzzGpRjYOjEMfZGghsGlI1T1kflus9g5/TnlWtg28pevkvD/vqgSNoHUbDt5XIuaak/ftcHfS9S2QSnoHRiBb1rQ3agYvltz+aEz7pmTp6Yx4lLD3NoHMJcmWJFuBtc/N2H5Xm3DsfeCjhXizMF7NVmdQlRHVek3QqlBWUr/YRZ4qECMvixPtQTgKyZccya5DlZ6+3rvW0KQezh7OnAca7GSM7w9wuWZ2n6/Q/lDFW4njzRk2EKjSYiJF2ff+zlSemW0XZ2bwzITZhQ6qtMQbGQdv6zI/EZJKQed2Q5NIolGDbCui/RrV0qjKK9iGo4qm7X2nbSRRs4Ym8YS/YNZgEs9kVkVD0wnJVheaU3i586oboCrn5UNKg5Ce9OWUPHru4P9XReMXrcVtq+UUJ8AGkqojCafeUa7sSYQNCZRENBbTClDz+kgePj+ZoDOLk4Le1cqr3i68PcQi8VVLKdHmFARUSynB1HgW9I3GL5iNAdcg82PTKZQAK73Y4qKMd0rRDBKEdeTnanQRoAroPbnzSpSXxfNbDst/rdj+wBLz75uhakWvkzF6eYhpG/rPTLBpyH3/64z8dAcnBdF+6WdNmSHdhp3HNcWZGmpBeKDI1r0EvHMgkga1FeGkI94VzNccy6fHjKYJsudQyiI/1SN7pyV5IcJpOPOrE7be1/Uot3mNnlZ+trTg5Yzub7H3mKD3ok8y+ZpPHE0K5YkaNdac+JRlcp8g2ZRIA9mqRWeSwdKMWRJRGcXsIOW+/8mgp1OqYUI50LQ2aw4uh+jcQ22TfcvO21uc+Pc79L6M1yWrDU5KilNtZmc86379M4Y7H9a07kK+6hOAifx8b3I2ZPiC4+ofl7Ba03ky8RL4tZcpccoht/3tg6m/v/0HNd2bFtk4dG4RVrLzvgGydjijsL0GGb4GcvBNHG9VHG/SEI3DBhJhHKrwi3ATS5zw+kvjSwnZSpdoYjGxpB7GRBPPKnZK+BbX3FJ1FXVLoqqA9MaEcr1NvFtQd0M/64gV4b6XJdGNwZWWpqWxgaBJFh7dlSPZqTm4PyScucWuWKALsIHChAnxbnUkhqglWCWp2wvIrQqOZieHkuxI740grCNbldgAWhseOtukCll7vSvSABMrZOXnPFb54w4XaxtK5mcjkl3vKBhMK+qVFoNnZ5RLMbOTGlWCrC3lcsh8zRMkTQjLTze0rk09ckxLsLDxPR6O2b3ZYLVg/wHF0mcdq5/cPe4L3zvjcc4r+ArB9R9bIz9b+zbZ1BtbjUwLWQnO/1vPRVELPkowqVHzGlnWmESjs5pigZ5KbgTk52pM5Fj7rOHuj9eYTKP2tSdP1oKT33eTWwd9dm4NSJYzio0W0Z6ifEdO8ELK8Hnvz16sJNRtmFyIiQ8M4ahGTAtQkqafIBsYPg1mAYoLR44m9cKBgy9qJhfh9vc4grFk+ema0X0BLnCUQ4fLIx47dZcvPHmJpSclwe6Y8duGhFNL2ZUUfUnvek0xULRvw+iy4vQv+7YgyjsZ1kstgr2554jYgHRTeM4SkJ10RLuCqudo2o7b3y1xyjC5P2Dlk96PJN5zzE55uHHrjqPqeekQHIRj6F0v0EWE1TD40ginFKJJmJ4OCPcl6t0j8pe7XPpr9zqkvsnD4WePb9L4Q504wl/2H+TRn/4AVvsWlQk8czw7EaEqh2wc4UGFSfXCvtW3s4KZpWpL0o2SJomIR4Z4K6cZJH5hbgfoee1lQqKAuh/TpIpg3mC1T051S3oL1y1LdFAzPxESzH17a3zRe3tbJYhnDTgohwHh2LeS/BxCIms/wPe6U1B1NcHc4KRAzxua1OtfpVsOVTl04VslwgosPok45ZC1ZfN9Ea0Nr4uEgPElwdrnG4TzLS4nvXObUx5aWg0iLyFy1aPOVG1xuaD/ovFJMVWkt+aYNMRpSb4WUnYks3PWq96uKdp3DaqGzq2appeg92ZH709+YUC0myOqhps/NPSLW9cnBZcYonZJuZtgS8nFnytwWngk2KKtFWxPjxj5dUex/1CEiTyBzmnoPxlQ9mH3UYmZgEgMzUrtDaQmAS996TSiEdz3bwte/o/bXPo3JVZLxndiVOkoe16GY//hgMGLBlk7or0SdZBh0wiZlcja0n15RtWPAF8BOiWYn/AEu2js6L0kqLp6YXClvNbTWCIrQb4f88T0PMFygdMpLlAEM8voYkDvugcg3PwBiSzApIaVz6hj7xEAKTGxQgxSbKhINwTZCUe93BDsauJdz+KXRqAzqE9VOCMgU8xOS1QF4/t8pTR43tAkkmTHE/uchKonGF/w56NzwyPmbKrQhWF2NkQ2UH+pT/oVdm1v/nir4niTR/+nXokr3/vzH6BqS8K5I5z6IbcN/GJpQkE0seQrmiB3ZCdjyq7ngZTLMTozRDsZ5XJCsFcdOfT9/9t782DJrvu+7/M7d+/97cu8WYEZbCQIEQsBSoy4yZQpanFkpeRElv7QEjuWS0nsyGKcilOOVaJccSxbZdmhKUaKtVASJVqyRJESKUEbARAESEBYBxhg9re/3rvvek7+OD2DwWBAYIAZzgzmfqq6Xvftvt3n3dfvnnt+y/ercvstOvNPtn4v1I4rvIHBG9ikaNIUaqcLq3wrMJ4Tpp7LSesOqrAndO0rnKQgj1zyigJRuGPNeMG3uln9nKzhErSzsyf4ykaOcV8yqXJHuTWDSjTGsWGTwlfMPJ2jMoMz1uQVh7BtTw7asaKK2rcrlNGCT9AtOP3NLktfynHGOSoW0pYPAoUreL3c+n07gso1Gti5ySFrGGYeE+rHY9x+gvEcdp0uyBvBWRXh9ffN03o+5cQHHVrP1Jl6LsYdgbq7S/Jsk2wmR3Vc1As+Cy9q8org7dhy26nNEShFvLuJ304Y7KngjjSbd7gYZa1sVQKqgKwGed2ABrfr4Gw6pC1N5bRvmz3HQvW0cPo9EdWTMFyyVXJG7OQerceMF0NaR3KCdkYeOgx3RTT6iT15KzlbrOANMrKaDYdqgTyy6gTjWas3Vjul6e9RbNwlBNsQDoX+benZ8I77ZJWFP98km62RTDk0judsvNMjXigwylC0CrwNjzw654sswuDGJu5Y0zlYwRvbpta8apDY6nhlDcEbWt8MlQrZwAXHYHxNPG+7+N2xIGPYuEuhcpj/iqb2Qo/Nu1rMPh7bixcBd2doP1cbtm8NWfpSyvEPuYSbthv/uqOsqrq+mPnFVzYo9b//XqTAxmyFicCgvXKtnczxRjlGhCJQuBP7TB16tA/5tikwEHp7lTUw+k6h9YRL0rKyHaM5l6Bnyy616zJeNLgDGwYYLDmoAryh/ewzyrVGbNlvESnSmoPK7Qm/CB3cMyuOfoqp+7jjgs4NAdWNAndkn1OFsa8d2xWQ189IXet3rn2FcW3SdDzjUD+ZIYXBHWUkMx7e0IaRFh8q0IGQtgK8XopxbdNksJOgA4d40brnbb7DXpEvPpSwfndA67C9Ah+s1Gm8MLYn1u0RkuSc/MgCYmDtXQHaL+jdILRvC3j7O4/w5AMHkH1Dqn6BerpJ82hBuJ2STHmgNWG74OSHp89Klo8XI4J2TrDaY/8Lhu7tM/T3OIRbhv4+6wuiUuuNrlKhCKF21MEbGtyhQ9qA2qmCdtWhcbRguOigPfC7hnAnRWU2LFh7rkOyWGe45DGeFcLtKv7m0IZstKaouEhucBJNMmPl7SWHvGZP2FlN6Dahsgp+24or1k7Z16ZLIAOH3X/UB6VImx5JQ0gaVkzSOA55ZAgODRl3XevI2Ixwu2OGB5rkkbJhKQ2D3bbsWAzoRo4phCRw8A87eKkhj4Rw1cG4Vg03qxuMZ9CZMF4uQAvhKcX6PYrKqsfMk0PyikceOazdG2BUhbnHcoYLDv194CQeez+XsXOz/5aXUL8Q5YqjhPqnHnzZ4yq2nLd6KrYn2on4oJcWjJcqaFco/Ijm0ZzBkks8K3gDGOxy2f25Ar8bk0e2/Ld9yCWrO9ROGLyBxjiKcMfYOKlg8w6unF1xgM1bjGdd3MTg94uz1ledG1zqpwqc2AA+Tmz9EmqrOe64QHuKIrDhNr+vySqu7SeZspLoKDl7dSi5obqeozKbcDeOLVs2Yv8pnNiQV60vRDITYBxI6g5O6uHvxPgbNuk+HdRxUk2wNWbmKYdkJsDfScmqoe3Or7uozIfQwx0ZOvemtKYHFLFPFGQEXs5jR3ZDq8A5WWHsglowrHyuTbJUx4kNJvDII9u9nEy5uGMbkisiRdGMUKMUd2yormqahwcYp07Y0eSBMFpQZ5V6K+sab2TYuFMRbgtiDK3nC+KWmuhoQdjR7Nwc4iRQ2chZ/5YZsrqQVScVUcNJJZarILHJ/TxySJvupPjBELY1eSwM9tgVUFHVjHYpiorG7SmKewfsmery/IsL3PSJLpIVDG6aYu1dztkyXW9gPTCKWgHPNXAK2L41pLpeEAYOw3mHnXdoRFt1g7P2aMogIxfjatyuItrSjGdtCGq0IDh9GC3bFYgUQt4oULFCZcJ4XlPUC3oHKvg9TdJUtpR429C52XD6WxyksMehv1eonZazFWzXFaWsesmrUftNO5mcWYXL3W9H0pwASKd8Ct9WEAU9jTe2/7XR5hmPAnvyzUOhuqbtP1tqJdKDtpDWhaBjT36qsFeJqjBoTygCF9EGN7HCf0YJKrdJ7KnnrJzJ5u0ejWMKb+QQbqY4IhS+rZBKWiGV0zF51ZaOGlcRro9AQzYV4qb6rNBeEdiJKmvYSagIFP0Vl9aRFARUosmrDtu3uvhdiLY1wdqA498xTWXNsHO7IdxQOKlDrd7AHWu6ez2cJZfqekE65bN+l0fQ8Rgt2k5sL8qo+HbCyH57ntZjAypNQ7jafUVjldexJbvpdERlNeGGp0a075imt8cjr8Lez+xQ1ANrr7vkUHhCemedvCKAonsjgKH1LICwfbtdeSw8aBgtCnmoGM1PQofzBq8vrN+j2HV/Tn+Py+Y7vIkwpkGw0utp04epAL+d2DLmwlYZubG2pd1j+3f0+5r6i4rOzfZ3yWsF/o5VvM12Ik4/0mDpRU1RD20xhTspjcbK1qczOW7PQQ0dKmtC9bSmdnLMyfdVqJ1UDFcEmhniaRh6Z/W8nBi7ujrhUPiQNuzKebRgu8PDbaHwDSoTwm1h7ClYTMh7HsGGS2XV2u7G0w6tw1YupXNjFUSjfZsPMbkQtK1BlDrPluZ6QOClIpW3IOXEcQkxD//12UVCMNmW/M270a5QOxFTBA6q0JhC0K6tqPIGGj/XDHb5kytCO1FU1zXuUJNXFWlVEfQ02rWTRFax2ije2OZH0rpzNoeS1h1qJ2OWv2T7PpIpl3jOel/ELYfaKUPQyclrHirTZDWrCKy90PYQZNpKoBib+7AJWxeVTrrrgelnY7SjUEnBzu0VaqsFSw/GtgS5l3Hk+6fIZjMq6y7NZycnOw+Gi4rRkqB9za77NZ0bXPuZqXUNrKwJvYMFkjmcOjrL8hcF17dGU0wFZ4Udje+eVaBVoxRdDWzYKCuIVyY5pQzmvpqz9q3TJFNWvl371oCqd0CINq3cTBFBuKHYen9MWE3JOyFO26N9C0RrsPoB6wcfrrmYlRjzbITKJpO4Y/sv4iVbmBCuucw8WaADRbg2Omv0ZYw1RFKZobKZE0+7YKC3164e/A64A4e0ZfA7Qr6voNkY0clauGOH6qqgMs3OTT7FQmxLsnOF9F3csTD9iCGpG8ZzCu2F5DXDzm2TIoDYoRjbzysCQ7Rpiz9QhvGc1VxLJ3mOPILqaaF/Q4HkgpPAeFEjhVB9MMIbGpsXiiBpCPNf7qJ9B7c9Io+q1I45DPYVFK0C2fIIdqy/+K6fvf7CVMArnT/fQpQTx2Um+MOHz04iZw529sE7KSIHxIag8oY3cfazcthubMNURWTFD/2BbSJ0kokKbWZluI0DfjuhCCK7kpixHe9GCYVnq2ncWJ9dMgf9wkpM1FwKXwCHoG1LYo0reN1skvcoSKZ83FFB4QpZRZEsC60j2dkci5MUOKOM2cdHNmZvgMihCOzkEp7yiGesOVO0keJvj1h7zxTFYsKNH7d2qeOZKu1boHqwQ3sQYto+xjPQ9Zn5mmI8i10Z3D1LZSMnWWkSnOySt0K8OIO8YHhoBtHG9mu0DW4/o5Zpgq7LcNE5O4n09wPGMFyBcMuG2eJpReUUDPcX0PEZ54qDv5hx+lt9pp/JOfERjSQOjWccBveNJleR0DgC8ZT9a6p3dHGO1immcqJNQx4KiGK4p0b9mR2Ma73l42mXtCGM54XKabuaMR6Em1AIFKH1FfeGhjBKybWylWMdRX8loPHC2ObUElsy7Qwc3KENc8UtQftC95YCCgiWhjSjhK0TLYKpmGQnwhk4+F17jEaLgjuwK6sihNpJg9+zFXdJwyGrOARtmH42sT1H3ZS05eMkmp1brKyOaMibAWqcYyKfxYdSens9gk3bp5RNafLIoXdIM/MN+l+7qihDVSWXGu8Lj+Cd8zj58N0YsZVThS+4Y1v6qjJD2rAnP+2JfS62oZqsqgi3M4qKizO2ISSV2BNoUbHhJZXZqintCSo1uAObC7HdycY2deWGtGlXFFnDwxvmdrLqZqRNj52brZRF60hG2nBwRzZ57vVzsqnQuhd6Nv9ypidl72fHthM+dFGjlOH+Bv6Wwe8a9v+ysHNLRPcQoKF+qE33eJP5B21z4sxTNvfjxhleOyZvBkimcTtjjO9SNCO8VdvynM/UqL7YRYcu8WKFInRwRjlpw8WJNe5YMdwlJNMaXGN9L7qK0aJGO7bSxzjg9hXsHZG1A5LZgMWHYvz1Abv+cIrCF4rAsPwbPknDIejlOIkhryhqawW9v2giAWRjj6xqCyYqL4yJF4KJZHmKjjy8kaZ9s4vfs656yja8U4TgjG3fSFaFlc+1eebtDWpLAyrP+cw+vA3GsPmuGdyxofG0hxFoHi3o3KjweobRsqBdWwklfkF6skp+pI6zX6O7NfyJ7Ls3MGeNyqJJqbcOoHfAXpCE2y71Y5qlB1M7OY1ztu+usvzFHmnLx7hC84UM7dsVc+eGkPGcoDKbG1r4iy16t07b/NC6Q9jWmCPqG/8PdlVQalWVXGaCz760KjlD8uG7UakGbFXVmVxG4dtSUndYWN9ybctJ80nHumjbr2FEyBoOKnup3Fe0Iau7hNsZ2pnIeE+Uee19IQ+t9IhxrNZVZcOgMnui1I4QTzl4I4NK7SThjHLSxWBSWmrLdX3BVhIVhnw2ZPtWl+oxxfRX24gxeItTFPMZZuTQ7VZQiZC0FMMVw8LDGV57bFV5HUUeVQnbfXq3TpHUbYLaG1SprGe4Ixs8l8w23Q2XA9K9toFyNGctVZPZgspJx5bXxkIyWyDNlFgCnKURWit06iCrEV4sGKfAa8foaoA70oTbBfGshxNrQg293S5BVzNaOGPxag2/nFSorGkKX+jvj6idSMibEd56DxULlVMjlschm7fbkKSTQtYw1I9D0C1Y/WaHqSdh6+4p/Dboky3qJ6xQ5nBXxMwTA4wjxHMhaU1ReIIztt4rc1/L2XinzXXoTBCB/j6NyoTKaaFxLKe/yyVt2vCj37W+39kMZA1s8YVvSOt2It18R8DcYwliDLN/bS0B3LiwzZQTteTV905T2dBsLxu8niBakc3X2LrdIdy2zY6D3Yrlf3l9hqmgrKp6U4iIA3wFOGWM+chk2z8EfhzIgT8wxvzkefvcBPzGOZsOAP+7MebnROT/AH4U2Jw8979ObGbfUgSffRiA2rkbv/Memxfp5mR152XJtyI4U16qz4aZ8kBR3YonOQzbeMbkvnaEtOngDTXas5OIE2uSKZswlwKCTkHStJ4K2lHUT6T09vmTSrCA+UcSxLHhrrThMv3kmI27K7SeN0hucFMrQZFXDKfe38Lv2mT+eH7yeSMFw4D9vzu2J6XUljSLMcS7mxSRQ9JUjOam6O0XUDD1jKbwhP5un7mHbEe6ldQQ4inFeE4YIiQzGicRvI5idGts/TXqII5hz+IOp7wmxZrVp3LXfJyRsPuLI9zOmGSxhr85JNyCZDrA7+SM5z3bpf1ihhsX+AOX/m4X40IRCtNPpbijwqrfTq40t2+vM9u3Qozat5LzKxsup7+1xWCvJtxUdG80SOEChvatVs6jmMqResLGUoXe92jmftmQzIQEW2NUqqmsFyTTLs0XczbudOndqNC1HEmU1ZOa9E04ic1HdPe5OImheto2darc/g2c2E4g2rX9GqKFrTvAb0M84xHf6FM/npMcqFN/po0OPHToEc9HNF/MGSw7NJ4Xhrus+dWxbw8JJpNG1rTWvtc15YrjTfETwNNAA0BE3gd8N3C7MSYRkfnzdzDGPAvcMXm9A5wCPnPOS/61Meb/uszjvuoI/8tLyqI+EH/kHqvUW3FJ68r2b7iCcSFrOITtHJVrxoshKjG4sV2h5BWbO/EGGuPaCQOxP11f8PoF/RWftKaorlq9Kq+f0TkY4Y5tuGPmqRztW29x7QqDXQ7tmyo0XtQ448KW2BYGJy7Y+9nUmjkp2Hlbg6xmiA4HqNyWt3o7I/JmhEpzxBjyVsRw2ad9K2djxXmjIFx32H67EK1bK1/jWrkYE9jAn8ohbdkKNOMZ8pkUcQym76HqGZ5XkPZ9Tm03yUY+YVshWwFB2ybni8DBze3vg1JWMiYzjOc9solNcF5V+N0UlTvEs5BVDcGOMFzyCNuK7oGI+omCwbKD1zds39HEH2g6Nzok0xX89sTWNigYNYTGkz79O2Iq9QRjIIl9blle55knd9N4zmH5FxKMkyNphq6GOImteCs8j7SmiJdynEaKWgvRUxnS82BSqqt9u1LNGpAXQudW20xYRIqpJzlbAYWx4SYEvJ4NZQ2WFbVTBYNll8p2Yd0cs4K84lE50ad7c5M8tKGuyprNzYwWrR4a8JIf/PWKeWtXVV3WAKSIrADfAXzinM1/H/iYMSYBMMZsvMbbfAA4Yow5dnlGee0S/v6XkS89hveFR2j+yoN4X3gESbWNXbtiDXRaAUE7owgVKrU5D3ekyUOxXe5bqfVlKKxPSFpT5JFD2Cnwh5r2oQDtCuOFwIaockNlu0AKQzzlkEx5DFZ8Rot2NeGNrASKN7TyFPGMR+dQhaLqgVJMP9Fjzx/FTD1b4HdtnsX4Lm5vovKrNZvfVGG4bFdQTmI1nSiEtKVRiZXIcFLb/GY8h7weEM/5xLOC37V9B85Mghfm6KGtKHKPheSpY1VvD1epP2lDRmlL4/cM3tjgDlJM4OL17KWyM8pJpl28gSbsaMK2of5Ml/V7qmy+w6MIbRUUBiqbOe6woHpaTxSUDY3jKa3nxiR1azucz2Yk0za0KEMXSRx6N+WoTZ9KkBKPfQ4tr/PM8UVULMw8mZBPV8FVGN8jWajgdq1cfWUjY/NdBaqa4fs5wY5Chi5+R9n+jrqVZh8ta+LFgnjOVkdJIWjPMJ4XxnPCeNEe36wx8bQvIOjC1HMZWVXhxgbtCod/oMp4VxVnlKEDl2grQxWQTBnSlm1iDDcVedUQL9kJ49Dfu34k1C+IeR2314GIfFJENkTkiXO2TYvIH4vIc5OfU+c891EReV5EnhWRD52z/U4R+evJc/9WRCZi1xKIyG9Mtj8kIvtea0yXO3P1c8BPYkvPz3AIeM9kgH8mIne/xnt8P/Dr5237cRF5fHJAL2hgLCI/JiJfEZGvZFwfrmMA8sBjVH/7Iaqffgj1Z1/F/9zDdA6EqNwwXghwxtZkyu9b8ymVFjiJYTRve0Oaz48I2pPKLF+obFr/kd5umy8JugUqtX0IRtmVRmUjZ/HBguYRe6U9WHbZvP1MSMshnhU27qxw+j11Tn2gyWgpoHpiRNISGseskZHxHLpvm+bY98zR32usLlWETdLGwtSTgjMSGkcNteOG2smUdC6iqHhkNZe0qtCOldPwp2K0FtJ2SO2Ih7/t0HoG5j4foDJh/tGCaMPQfF4TbSiSltDdrxjsrTDeVUfFOTJOGS2H+J2cnVtdxjOKIhCOfu80KoepwzZnUlm3RmBZ1WH7toCd26zmWbRlq4pOvq9C74BtpiMTUIbqcRcTFahY8JoJooXO47M4J0OO/OVeDvwSeD2FvzXC3RqA1pjQxeul5M0A4ypG8/b3ck6EFM/UyWoGt28njaRlw1RGwO8oKscdjG8ndpVZqZNkypA2DME2tA4blv7SKiKowlac9XbbUunOTUJ/xWH2UWGw6FJUPJxhSnCyS7SpyRu2B6n3tpTxrgKvL9YlMJbX+Ka+9RFjXvP2Ovkl4NvP2/ZTwBeNMQeBL04eIyK3Ys+Zt032+YVJ1Abg3wM/Bhyc3M685w8DbWPMjcC/Bn72tQZ02SYOEfkIsGGMeeS8p1xgCrgX+F+A3zwz813gPXzgu4DfOmfzvwduwIayVoF/daF9jTEfN8bcZYy5y3tF6vn6Yvr/fYDwv3yZyu88hPeFR8irDn4nsxLsowyVG4KeFWnUrmK0FJDWrOOhyiGeC/AGBqNguOjhJJpodWz7AYCs5qAy6xteXS8YLtvk+s5NtuTYiBXzc2KrDOv3Ck5+oE7r+YL6kT5GCac+0GLjTiGe0yCGvK4pGjlpU09EJ4X5r2qaR8ZMP9EjWOszmncpIpfePpfBijBe1GTTOUGQ4R4NqRxz0S7Mf1WjPWxCuycM5x1GC0L3gCLasKWoQcdgHMEb5OiKRzFVwe8XdA/4FL6thOrts/7l3UMFq99iLVg77x/Tvivj9AcLRkuGIoC0ahsyd251qR8zFJFtpms85RHu7zP3gVPIwEWHhmzkoTJb4uv1hBs+tYNKCvZ/at06MirrfVKELlnDR/uK9fuabN1hm0izuRzEVk25o4lfOrZaSweQNjTDfTnkgvYNRajPdv6HW0K4o0nrwvrdCieBwrNNnGnL/g1F216VsFPgJoaNO0NOf2CG0YEpgm5B5YTDeFcOmZqUkBtmH3LQ/hX8wl8tnGlW/Xq31/U25s+BnfM2fzfwy5P7vwx8zznbP2WMSYwxLwLPA/eIyBLQMMY8YIwxwP933j5n3uvTwAde7Zx8hsuZ4/hm4LtE5MNACDRE5FeAk8DvTAb/ZRHRwCwvJbvP5W8Cjxpj1s9sOPe+iPxH4Pcv4+/wliT4g4fP3tdA6N5KOh3RPVjDG2nilrJyHBWr/ira0DiRUfgKb5ijkoLBviqt50YMdkdEm9Yt0Y1tqMPvwuKDfdq31Bgu234BlRu696SYVOEPHJYeiCfttVaqJatBEWlQoCsFpApvxyVvaEigsmZjxmqUkU2FFAsV3LFh7Z4Ad2xj+c5YYOySnZrCMeDGnJ3w3NjgH7dWrioz5FXBSWG4JFa/qbBmTFL4hNs57kgznnUnUiQa41nZ83BL0B2H4YGM4e0ppuvjb9sTZVHTqNj2VFRe7BGdtBd6yVSTeNbQe1vGlJtz8tFlHAGTCrv+0FA50bF+5I7Dxr3TzD+4Y021emPr9V5YoU3JDWv3+dROGPb9lzEnf6JAVivUjkHtdM5gl4PXt/7fKrdJahGhcsJOoGnrJdmRdKZAew7GUcRzGu0bsoWC6mGf4aJi9rHMqhOMbP/JaM4hbdiqrHBHM1qwRRSt5wu8gUsRQPPFgvoz25z4jln2/rPrt5oKsGGo1+dbNSsi5+rNf9wY8/HXsd+CMWYVwBizek6ueBdwrj7Sycm2bHL//O1n9jkxea9cRLrADLD1ah9+2SYOY8xHgY8CiMh7gX9sjPkBEfl7wPuB+0XkEDbP+2oD/DucF6YSkaUzBwz4W8ATr9ir5KLQX3sKl0n1AhBhRR0raylFoBgse1ZZtWqvPqMdB79XkDZ98sjG+KsnY8QYkumA+UesR3nYLmyXdAD9PYoDv2RYv9ujejrGa48Z7amjkoKNO6v25Bxo8AwydGwjoAGnr4g2hcbRFCfR6MjF6yWkrSq9vQ5Oag2RpIDKqjDcrSlCcEcCGryhLSf2+gVJy4Zf4hnF0l/2Wf2WOv0DBdUTDsOVgtZTyvYnHPSR3CeZtpOKrmjbA9IRdv3xDuvfPEXSdZFmgnvY+r97fUhQeH1h4a92kKKgqAc8/9+F3PzzGxz92/NIomivNYj6QvWUIasL0fqY3qEGnYMOuz/fZe6RLmQ5uhGhemMkSUl3tRBt2LjTs2KFK8L2HQGVrzjMrRqGy4I/UDgxzD9gx5dXhNZhK6LoZAZ/oFl9t62cMgJ7f9fQPSB0D9nj7nYdZGTLndOGkNVsFZ4qrBBnVof6icKGuZoKf2DYfKew+48y1u4T5r4Cfiene9vUWRO06xnhdYeitowxd13Sj34l5uts/3r7vCpX4k/8SeCTk0RPCvyQMcaIyDLwCWPMhwFEpAJ8G/Dfn7f/vxSRO7C/2NELPF9yCTgj6ugA08DGP3g3ft8QdjXRplWkjVsO9RMpWd3FzzV5xSPcGLPztjp5VJkYXkG4bePrReiw+OUxhac4+aEpgm1DHkWMlm3z2u59W2x0a7Z7/XCFpS/ZxL3K7Mk/bblUTmWs3dcg3Hnpci6Z1lRPKOLZicrtJDSkPQi3rcRHVrW2phi7Cjn9njpp0+YShnsLjG8d+PyOsqKALsTLOZIL4apLHhmiTUP/YJPaaoGTOORHawz2GJb/siCrKfpGMfeolYo3vssLfzvCRDkv/ouIuJ3hNxPU07WJ/Imd7Pr7KnhDzd5Pb5EuN/HaY+J9U9aHxRjyekDactm+1SWeL3AHimwmx2u7DPfnJDMO+WxKPOsSbAvDXdMUoaEIzVnJfDGQxA7Vk4Cx5k2n/yvbZxPsKJzEhplGuwvQCuVB+yaHYAeaxzK6+xVZ06A9B79jJ7wigJnHDKMFj2BLkTYMvX0B0ZbNT5XwkhnZ5WH9zEX0JAx1psjoJLD7nNetAKcn21cusP3cfU6KiAs0eWVo7GV8QyYOY8z9wP2T+ynwAxd4zWngw+c8HsEr1QqMMX/3co2z5NWZ/3cvDz1EwOhH76MIFNHpIelUiDPKQYTqaoZRwvZtNnyVV4SZJxLGcx7GgfW7PVQCUVsTrcWsvTvC6zqcODqL10jJE4dwCMe/zcO4hsYRxfTTCZUTQ/o31Cki22yocmwPQWAYLxmylq3mcSZX360jVg3YyQx5YEtOW4dHdG6q2FJgDcm8jf9LrChaOXR8e+INAGVAC8l8wcJf2T6RaNucveLGQNAWentd/J5N5u/cGtA45jCac/D39mn85xrTj/bAxIz2txjNTZLUuSHo5lSO9cAYRjdOk9Uc8opD94A3WSV59PYFdG6ykvru0Fa/uV3XTgiF7RhXfoG3N2E872FiB2/bxRkLWc0KZvb3F7hDhfYNkgnVU8JgjybsKFQ2sbCdsaq8RtkCA8exx2f1Ppci1Hh9xXjOMJ6DuUc1TmaoH+4y2ttg4RErW+P3rVLygZ968JVfoOuN1x+qeqP8HvBDwMcmP3/3nO2/JiL/N7CMTYJ/2RhTiEhfRO4FHgJ+EPj5897rAeBvA38ySSW8KuWisuQNM/MfrW+JgbMSKvq+d+B3U5KZgNYRa7Nb+EIy5ZJHQtK0V+8SwmDZAULCdZuY9boeYjyc0CrR+j1hvC+jsmG/pse+s2kNg0aAgnjKlsOOWpq8kYEW3A2f+lGoreZox5YcS6HAKPIQ+vsjkpaVSBEN3qZLtphhQgOpoogM6UyBVHIcx6ADofpkyHAJWs/nRGsjnDggmfbII9vzEM9ZJzx3xETh12H73gz3+Tr1YzHpQo3OgQBVwMyjbeLlOl43RWWFrZjyXQZLLllD0J4iaBtqp3PW3l1h+qkclSnatwp5TROuOeR1jZqPYSdAVwvmpgaMU49UXNSZSUXbarQ8hNox21MSzyhGu3P6viLamHT5d+0KItywYap4IQfHUDQNwY6PE0M6n6PDSehwpOjtV0w9W9B+ewuVw9q9DlnNEG4q4vmC5q9egS/jVcilEjkUkV8H3ovNh5wE/hl2wvhNEflh4DjwfQDGmCdF5DeBp7AN1v/AGHOmoebvYyu0IuAPJzeAXwT+k4g8j11pfP9rjamcOEouKfLAY8BL6sDdH7j3rPNgb79QWbUS5r39UATQX7GKvZX1jHB9RDJfYf0uHz8T4oMJ4dGA1Q8n1B4PyA6N0JshWQOidVshNbjN1p1WnwzJKzD7eEHcVKjUUFkdYgKHtBlZmRVf0V+xnuNZzYaK8qa2KoMOqJFDuCFoz2HpczBccJj/qzb5jMHtxLa6qRmhAyvT4o2EvCrWG7xhcMfWLW/nNoPquTgpeFsDjn3PHOOVArenGC1Ms/SlMW7PSnkYzyFvhFbccmRIG1ZSf+MOOxXv3Oxajam6xhkp0pZBhxpHaby5MUGQsbnRwNnyETEUtYLKCZd4VuOEQvMwjBeE9u02cW87xO3ka6Xi7eSXzGqCLdt5LonCRJqkZf9u/rpLXrNKuo0j4CSardsdECs3gjHMfE0YLcLSX3zDv3JXL5do4jDG/J1XeeoDr/L6nwZ++gLbvwK87QLbYyYTz+ulnDhKLivNX3kpbFH9bftz8H3vYvcXM1Smad8UEfQKuvs9/LaDvzmmfswl7BSshj71o4as7jG4PcGMXagU4BckaUDrWVj8Jc3WbRFZHRpHrAtio2M70LPpEK+b4I5tI2PYMSQtIZvouBShIdh0iHcXiKvZ9WeauCmowjr0zX25h2Q53lqfYqqCqQW4O0P0UkTccqhs5owWPXSgCXYcG+J62wD1XBUQm2epBlTWDYhjpT0KiOd83Im9rE08u2jf2siKgbxhPTHSJjSfKdi5xaF6wmFwS2rDH7ki69upOc1Dai+4OIltwmNbES9ojGOITihEG4Y3Jexb2eL4+jTzfxjQO2BDXr2bi7OriKKqSTNBohwZ+bjb7kS2REhvHKPHLjpxyUOx5dauQSVWBmbvZ0e0b6ogGmq/9dA39gt21VKKHJaUXFLOPblM/xWkH7qL2ccTuzJR0Do8JJ4LmX9UE3Qypp/MOP7ttslt6Y41Tq5PkU/lOImD5IaFh/uMdkVIju0Gb7qEOymSaYa7q5NtwnDFSnGkLSsAWNQ0mTK4Wx5FTTNcsB7w818e4PRj0IZiqsL226tMPT223hMTCXfjWDVgI6ASRbgFw12glCHZneB4mqnPBIyXKsw9tMPgYJOsovB7mmhthPYdnCRDRHDHBVLYk79x7a1/sEAyob/bOuoN3x5Dqphb6jKMfbLUJXzY/m5WNgSiDUPY0azOCUHHIW1a4Urlak59ZRkvFTb+Rozs+MQLBuNpJFfksxmq7yIG6o+GpC37fpU1Q/+Aga2Aua8JvRugv9/QOGKPZ1Y3zH45t02kqWG8+NY9UV40BngLS46UE0fJFcf/vC1jP/NvZu57B9oTas91wVV0bmmw5/ft/dW1Jdx5w8qfZqg0JZ71iVbHqMxKoLhjQ7id4XZixsu1if+IIq/Y0MzgQA7KoLUQrLs23DIQal8TWn9t1XvHK3Wcimc73w/6LP7ZlvUhcRQoIdxK2bk5ontI4YygflTo3lSwcHCLtaMzuD2HuUcN/T2K2ccS4qUa1Rd6VvEX0NUQEQFtGO9vkVcU8awwOGAd/by+UASCmc7o3wFGC2GUEY8iOv2IfL3C9OPCYK8duzY2we0kcPpbwesIWU2jUiFtGHTmoBzs73oiIF3I8Wop6rkKWcOgxi4YG9bKI2xH/VZO+5DtHaluKnoHIK9o3JEw9VzKzs0BTiyc+KCD320gBSz/xXWsTXUBSiOnkpJvIPLAY0S8VJTS+Bqc+Oi7SacMBz7dRwoNSkGukcIw2FthNOeQ1+xVtzty6BxsITlnu9izum1yUyOF8Q3GNSBWoLD5Yo4Ta/o3WeG+rGpF+wpf8EaACFv3zaIdmH5mbENNMfh9ob9PEy/a/pP1F2bx+gq/I2QVmDpsVXVrpxIrVR/5bH9Ti6wmLN6/gwk9VKrp3uoRzxoqx21iO6vbyisdO6AMTs8h+KoPM+A+XyPKbI4mmBRMqtTKoQ93WS2qbE9C64GA/gGbJI+O+KTTmqxhxR9xDN7jVfKaLV8GMMow2KNoPafJKmK9V2KDyoVo0yC54PUVeRVe+F6H+QcNnYPQelYYLoM7hMrvlGGql1FOHCUlV5aVn3mpHNgA6m03gyO0bwpJG2J7ODIh3yfs3ObiDe0JUSUwXsnx2g5FVWNqBeRC42mPwodoSxNPORhlO6OLYNLDMWsT+X5fY5TtJXHH1vdkOG8rtOI9GjOVER0JqKwZ0rrNUagEqus52hOCdmGteCdKvmck6zffNcX8l7bYvi1gcEcMHY+8qsh2pczO9Ugyl956jdbXPOsL7xiW/jKliBzcUcGpb/WpHbc+4dmiIZ/LqD3jwy1DiqM1ikgIt2wpdGXNkDUElsboxEF1PEaHEtxNn2IhwT8WoH0hq2u6NyjSpiZat7a21ZOG9s2QzWW2Aq1V4M/EJM0axjNsf1PBrj8RenudV/nLXacYzrplvhUpJ46SaxL9xDMAzDz20rbDn7gLCiE87ZFHBrNnjN4MkcI2BRJoSBVO36F2SjOaV/h9TbiVcvpbKlZnqqUxriFadWwD4U6KCRzqxxN2bglJWh61NSsbj1hP77Sl8buKXX/SIV6s4g5zpJiYanWtc6GOPLbfUbX9H9jVwca7ZxnuMrh+jpnVZA0HUwjtbhVzKmLvn+YUYYETa/x2Stb08fo5xhFm/lpz+m9oZr7sMtaCyjySOwfo41VkZczAC9GBxus49PdBPptBx0dyQYca/6RPOlPgnQ5IFnO8LRevb8tpERjszwnXbd6lelJIRh7VU4ate6zboVuDbCYHMcRNj8Gey9u0cO1RJsdLSq4JDv3IV172+Miv3cHM14TW8wmSa1SuGa5UyEND5wbF8l+OSVsevf0h6ZTVtDKBQRIh3DGEbY1KClAKtztm6jlh7V0h8YyVaq+cUsRzQusZiHZyiopHdLxL75Yp6s/3kDQnXawzXPIZzSnETFYITUMeaZIFg7/lkPQDdu3eZvWZeZqHFc1jgjeI6e8OCDqayrEeecMaOcnE1wSq3PCrhiKE8axHZU3o1SsYzwoTqtR6n+cNbUtwRw5u3zZNqsza4UanXcYrOU4tw9Qy4oGH03NQuZW0T1uaeF+GX8lIOwHjvUAhuGHGeMklWHXRB0e03+4y81iphvsKyomjpOTa44b/9msve5x+8E7EGPyBYeZpTdryGM47DFcEdaBPshPh7TioHJpHUvztmM6tdeuprqF9MwQdq/+ktPVu97r2JNzd71J7rks6X8M4wmh3ncrRHnnFYTyjSKdsj0laVWzsLhAtuH3F/L2rnHp6gfjTC+x7IUUVGmeY0buhhhSgfWHnjikaL4zZuKtO2LbqwoNll+bRlPZBj3DHkEdWdj6va/TAw1Q0KlagwR0LRmyPiRNbq9u0LgxXtHWE3AmQVBDXwFKCFkOeK8zIRXoeaSH4rYRsK8IZKLy5AmfbKh9nnQA3EaY/+cAV+RtftRigeOuuwsqJo+S6wfvCI2c73M/Q+R/fTbyUI6tVqqtWJ37pgTGn3x2RNX2iVaG2qhksKxovWD/3rArxtHVaxMDMEzHOIKWo+oznfdKaMJpz8QYVOjd4qNTmJzbvCGyDnRF2f85KnHdXl1h5IbdNeFMu4U7G9u11ou0Cd1iw/bYA8942e2Y3OP7FmxjshZUvWmMp0TZfMloSsqpBFRCddhgvaptUjyFoK9yRYbxgRR9VCknTFgzUjimMo/C7huaLKUWoGM2HdA6BrmsrcQKY2GPxdxWb73AoIkP4+Qa10zaH09vn2nLgkvMwYMqJo6TkLcniz32JxXMeF+99J5Jq6sc1/T22k3rnFkW8kqGiHD1yUQPbNR1uKOonNMPlgNpxTVFx6e2bXOWP4Ni3B0Sbtpdi/e6AcNuw8BdbJMsNRgtW6bZ22pawqsyWDfd3B0Tbdlv3gI87NjR/ocrJqYPk92iKqiaZ9mwnvCP4AzspBDtik/cRRGvKKgSPraBjPCM0n9eEOwXDRZesZic9b2jwhtacyyjo3OghudXR8tsKyYXpZybOgNoQbhv8viHaKti52cOJDdqFqWfLmeOClKGqkpLrA+f+RwFoPmAlQl/42fsIt4TFP3Gorhbs3OJS2dD43Rx3lNO+ucJ4RiiCCoVvPTAwMNxTEGw5pA2onTCM54S5R3ogghMXeCOHpOEQbRWM5ly6ByFraMJ1RX+3a8toNw1FKKzf5RNtGsJNwT2m0K49mfvtmLTlEm04dlUxL3hDcBJr9xp0z5z0wR9ogu2Y8WwNv2/IQ6iuF/jtlPFCwM6ddtLwBraqDAW1VUP7oCJoQ6SgcdyKGXb3uxQhzD8Ss/HOiOCzD1/wWF7XlFVVJSXXLwf+yctj93P3g3rHLejIY7gSEc9a46R0SnCHVlIkbRqcsZX1MA4gsOv+IQDGVbg7Q5y6hxMauvs88ho4Y/tcUZn4fzcyiiiw9zVkDcFvQ3+fZrQspC/4iLE2e8MVM5GLt+XFZ0JRWUVoHUlJplyC7ZTxUsTUMwOMQPuWGltvc3FSl7QBWVPj9oXxLqvdFW4p4llIbxkRbwWoJ638etJQRFuaaFtIpj2W/6xzmUVgr2HKFUdJSckZ9GNPA1Cd3F782H1IZrWiRFsb19kncrZvc1n5oxEqzpBcgwiIEO9u4vUz8sh2tNsENkSbkFeFdMoQPR8SzxeYig1bmdGkwfGUgzuyApFBx/aFRBuK4YpGx4Lf18TT1vFPPCGPrOmWaMN4xkGKCJVpGscTRksh4wVD3ihQY0U6V6CqGWwH5BWbP2n8eUQRWZXdtG69SsJtq9HVfKJj7W1LLkw5cZSUlLwa+3/q5auStZ94N4UvRBuG7dsivFFI7WSK200o6gFeLyWveETrMePpCsm0bWCM52xuJJwbk08pKkFOPPYxmwGN5xTjBUPWsOErKWC8AH7HlthKYcthtSuE25rhks1z7NziUj+h8Xz7OgTGsy71k4n1gG8Lc48oaidjevtCNt7lE3QU2jFU1u1qynbeG2qnNfUXh0hWoNIA8gL91OErcMSvAYyB4q0rwVJOHCUll5jFf2O73KvnbOv83fuIIgfRBuO4eMMcAH9oULldrWjPkNUNztM1Gsdt17czZygqhrQByVKO03GpnoLxHCCgPSgiCLaF0Z6C0y1h6kkr9X7GydDrC50bXRpHNf1dDn7f0D4Y4sSGoA3DRUXnYAUnBVNJyWMr3phMWUtc0SC50LnRQUyV+uEubi8mn6ld0HO0ZEK54njjiIgDfAU4ZYz5yGTbPwR+HGs08gfGmJ+8wH5HgT5QAPkZX14RmQZ+A9iHtY79b4wx7cv9e5SUvBla/+nlq5L8A3fid2LCLZdk2mP3F2LW7o1wx5DWwY01owUhXUlxgwL/xphsFKA7LvE0TD+tGc/aCq7KlmbtPnCmEsxqSDxr8y0AEtgciPYN22+3KxUQGi/YsFg8Z/MyWc2QAzJ0UamVU0+mNOM9BVOPulRPQ/1kTnS8y/CGFklT4SSG2jf6QF5LlBPHm+IngKeBBoCIvA/4buB2Y0wiIvNfZ9/3GWO2ztv2U8AXjTEfE5Gfmjz+J5dh3CUllw33i4+gsc6JHrD9I/cxXtQUjQIZOaRNaxYeHA9oHdasf9AhfDGgumNdBv1uYfMYGtoHFcyOyRMHcW3uI562awG/DeNFqB2fJPGbhsK3K5XxvCGfycAIKswxO9bjwx1ZzS4pBHfHZbgCaKit2gR+5cUe4zunaP5WaRH76pi3dFWVupxvLiIrwHcAnzhn898HPmaMSQCMMRsX2vfr8N3AL0/u/zLwPW9ymCUlV5yZTzzADf/oQQ796MMc/IkHrYqtsUnzzk1C86sBOjAkTQi3BVUYGi/G9PbaSixZCxFloJUydTgBYLyrIGvYiSCt28qtyinrvT5cAe0ZyO0pQI+tT7kzFsJtg1HWlteJrXyJyoThvIuu+JjQZfqpwZU8XFc/BozRr3m7VrncK46fA34SqJ+z7RDwHhH5aSAG/rEx5kKF4Ab4IxExwP9jjPn4ZPuCMWYVwBiz+horlpKSa5Ib/tErr+Zf+Nh95HOafGHMybkK3sBn8cGEtXcFREMoNkIq64bBLkMRGSQVxisZzsBBCkHldiWRTRe4jRTneARK4YwE7VuPd79jcxuibUJc5TZsFW4ZKpsFOnAgcPC2Rrx1U7+XiFJy5OIRkY8AG8aYR0Tkved95hRwL3A31nD9gDGvCAh+szHm9GRi+GMRecYY8+cX8fk/BvwYQEjlTfwmJSVXBwfOq97a/uH76O/xWfliHxVnxMt1wpN9BoeaFKHt5aAQwk1FMmUFEZNpjWRCkSlMq4CgwFnIkWMV4nmN9hXBNtYAyjfUjisqW5poMyWruRSeYv3ugJWf+eqVOQjXCsaALieON8I3A98lIh8GQqAhIr8CnAR+ZzJRfFlENDALbJ67szHm9OTnhoh8BrgH+HNgXUSWJquNJeCCoa7JCuXjAA2ZfusGG0uuW2Z+8aWJRAOh3AIK3GHB9BOK0bxCe4rRkibcUiQ3jW0IJXbxTgVoD8xIkfsuZioHx6CHHllNmH7SMFhROKnBSTVZ1aVyrIdkBbs7AeU/1OugTI5fPMaYjwIfBZisOP6xMeYHROTvAe8H7heRQ4APvCwBLiJVQBlj+pP7fwP455Onfw/4IeBjk5+/e7l+h5KSa4kzjYn+4/afqgWc+KfvBteQTGtMx8drK/KawR3aFYk3FAb7Crxmgutqxrmi8B28oaJ2UlM/mWCU4PZT8mYECpxBeiV/zWsGU644LimfBD4pIk8AKfBDxhgjIsvAJ4wxHwYWgM+IyJkx/pox5nOT/T+GDW/9MHAc+L5v+G9QUnKNsPunv/Syx4c/cReSOKgCJAN3aAg2HcxODWcIFQeyhhUvLHyBwuB1E4yrcAYpovXZCark61EaOb1pjDH3A/dP7qfAD1zgNaeBD0/uvwC841Xeaxv4wGUaaknJW5rzza42/od3IwZqRw211Zx42mHznTC4QZOvOVQ3PJykQLsKdzQmWayXXcOvh1LksKSk5K3K/C+8fEWiP3IPM49bM6usapDcEM+HRKeGGKXw+mmZ33gdGMCUkiMlJSXXA+Hvf5nwnMeD73sXlbWUvB7grffOer2XvAbm0hk5ici3A/8GcLDh/I9dkjd+E5QTR0lJyatS+62Hzt5/614/Xx7MJQhVTSSb/h3wbdiK1IdF5PeMMU+96Td/E1zWzvGSkpKS6xajX/v22twDPG+MeWGSH/4UVj3jinJdrDj6tLe+YD597EqP4zxmOa8M+SrmWhnrtTJOKMd6ObhU49z7Zt+gT/vzXzCfnn0dLw1F5NyKhY+fo5IBsAs4cc7jk8C73uz43izXxcRhjJm70mM4HxH5yhnF36uda2Ws18o4oRzr5eBqGqcx5tsv0VtdSLn+itcnlKGqkpKSkquXk8Ducx6vAKev0FjOUk4cJSUlJVcvDwMHRWS/iPjA92PVM64o10Wo6irl46/9kquGa2Ws18o4oRzr5eBaGefrxhiTi8iPA5/HluN+0hjz5BUeFvJKUdqSkpKSkpJXpwxVlZSUlJRcFOXEUVJSUlJyUZQTxyVGRL5PRJ4UES0id52z/dtE5BER+evJz/dfYN/fm6gGX+h994nIWES+Nrn9h6txnJPnPyoiz4vIsyLyoTczzjc6VhH5nIg8NtnvP0w6cM9/30t6TC/nWCevu2TH9WLHKSIVEfkDEXlmst8FZS+uhmP6esc6ee0l/a5eNxhjytslvAG3ADdh1YDvOmf7NwHLk/tvA06dt99/Dfwa8MSrvO++V3vuKhvnrcBjQADsB44Azjd6rEBj8lOA3wa+/3If08s81kt6XC92nEAFeN/kvg/8BfA3r8ZjehFjveTf1evlVlZVXWKMMU8DTLxEzt1+rtfmk9iO0cAYk4hIDfifsVa3v3mNj/O7gU8ZYxLgRRF5Hiub8MCrvP6yjNUY05tsd7Enj29IFchlHOslPa5vYJwj4E8nr0lF5FFsT8Fl5zKO9ZJ/V68XylDVleF7ga9OvrAA/yfwr4DRa+y3X0S+KiJ/JiLvuawjtLyRcV5IImHX5Rneyzh/rIjI57HWwn3g06+y3zf6mMIbG+uVOK6vGCeAiLSA7wS++Cr7XRXHFF5zrFfqu3rNU6443gAi8gVg8QJP/VNjzNe1shWR24CfxdrhIiJ3ADcaY/4nEdn3dXZdBfYYY7ZF5E7gP4vIbedcrV4t43xDEgmXcqxnP9SYD4lICPwq1q74j8/b9aKP6RUc60Uf18sxThFxgV8H/q2xhmvnc9Uc09cx1qtSzuNaoJw43gDGmA++kf1EZAX4DPCDxpgjk833AXeKyFHs32NeRO43xrz3vM9MgGRy/xEROQIcAl5u6XaFx8kblEi4xGM9931jEfk9bFjij8977qKP6ZUaK2/guF6mcX4ceM4Y83Ov8plX0zH9umPlKpXzuCa40kmWt+qNVybyWthE3Pd+nX328epJ5zkmiTvgAHAKmL4Kx3kbL084vsAlSji+3rECNWBpct8FfgP48W/UMb1MY70sx/Vi/v7Av8Am79XXeb8rfkwvYqyX7bv6Vr9d8QG81W7A38JeySTAOvD5yfb/DRgCXzvnNn/evi87IQPfBfzzyf3vxSYAHwMeBb7zahzn5PE/xVaoPMsFqlku91iBBazGz+OTY/bzgHu5j+nlHOulPq5vYJwr2DDO0+ds/5Gr9Ji+rrFeju/q9XIrJUdKSkpKSi6KsqqqpKSkpOSiKCeOkpKSkpKLopw4SkpKSkouinLiKCkpKSm5KMqJo6SkpKTkoignjpKSkpKSi6KcOEpKSkpKLopy4ii5rhCRu0XkcREJRaQ68Wt425UeV0nJtUTZAFhy3SEi/wIIgQg4aYz5mSs8pJKSa4py4ii57hARHyvzEQPvNsYUV3hIJSXXFGWoquR6ZBorLljHrjxKSkougnLFUXLdMZEu/xRWEXXJGPPjV3hIJSXXFKUfR8l1hYj8IJAbY35NRBzgSyLyfmPMn1zpsZWUXCuUK46SkpKSkouizHGUlJSUlFwU5cRRUlJSUnJRlBNHSUlJSclFUU4cJSUlJSUXRTlxlJSUlJRcFOXEUVJSUlJyUZQTR0lJSUnJRfH/A5WhfRIBjMFqAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "url = (\n",
-    "    \"https://storage.googleapis.com/\"\n",
-    "    \"gcp-public-data-landsat/LC08/01/047/027/\"\n",
-    "    \"LC08_L1TP_047027_20130421_20170310_01_T1/\"\n",
-    "    \"LC08_L1TP_047027_20130421_20170310_01_T1_B4.TIF\"\n",
-    ")\n",
-    "env = rasterio.Env(\n",
-    "    GDAL_DISABLE_READDIR_ON_OPEN=\"EMPTY_DIR\",\n",
-    "    CPL_VSIL_CURL_USE_HEAD=False,\n",
-    "    CPL_VSIL_CURL_ALLOWED_EXTENSIONS=\"TIF\",\n",
-    ")\n",
-    "with env:\n",
-    "    with rasterio.open(url) as src:\n",
-    "        with rasterio.vrt.WarpedVRT(src, crs=\"EPSG:4326\") as vrt:\n",
-    "            rds = rioxarray.open_rasterio(vrt)\n",
-    "            rds.sel(band=1).plot.imshow()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/reproject_match.ipynb b/0.18.0/.doctrees/nbsphinx/examples/reproject_match.ipynb
deleted file mode 100644
index 81ba1cf2..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/reproject_match.ipynb
+++ /dev/null
@@ -1,283 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reproject Match (For Raster Calculations/Stacking)\n",
-    "\n",
-    "`rio.reproject_match` will reproject to match the resolution, projection, and region of another raster.\n",
-    "\n",
-    "This is useful for raster caclulations and stacking rasters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_raster(raster):\n",
-    "    print(\n",
-    "        f\"shape: {raster.rio.shape}\\n\"\n",
-    "        f\"resolution: {raster.rio.resolution()}\\n\"\n",
-    "        f\"bounds: {raster.rio.bounds()}\\n\"\n",
-    "        f\"sum: {raster.sum().item()}\\n\"\n",
-    "        f\"CRS: {raster.rio.crs}\\n\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")\n",
-    "xds_match = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY_MATCH.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(ncols=2, figsize=(12,4))\n",
-    "xds.plot(ax=axes[0])\n",
-    "xds_match.plot(ax=axes[1]) \n",
-    "plt.draw()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original Raster:\n",
-      "----------------\n",
-      "\n",
-      "shape: (200, 200)\n",
-      "resolution: (231.6563582639561, -231.65635826375018)\n",
-      "bounds: (-7274009.649486291, 5003777.3385, -7227678.3778335, 5050108.61015275)\n",
-      "sum: 23209796.0\n",
-      "CRS: PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"unknown\",SPHEROID[\"unknown\",6371007.181,0]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]\n",
-      "\n",
-      "Raster to Match:\n",
-      "----------------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.65122672362685, -386.65122672362685)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 4903477.0\n",
-      "CRS: EPSG:32615\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Original Raster:\\n----------------\\n\")\n",
-    "print_raster(xds)\n",
-    "print(\"Raster to Match:\\n----------------\\n\")\n",
-    "print_raster(xds_match)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject Match\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.reproject_match()](../rioxarray.rst#rioxarray.raster_array.RasterArray.reproject_match)\n",
-    "- Dataset: [rio.reproject_match()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.reproject_match)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds_repr_match = xds.rio.reproject_match(xds_match)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reprojected Raster:\n",
-      "-------------------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.6512267236268, -386.6512267236231)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 4930593.0\n",
-      "CRS: EPSG:32615\n",
-      "\n",
-      "Raster to Match:\n",
-      "----------------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.65122672362685, -386.65122672362685)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 4903477.0\n",
-      "CRS: EPSG:32615\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Reprojected Raster:\\n-------------------\\n\")\n",
-    "print_raster(xds_repr_match)\n",
-    "print(\"Raster to Match:\\n----------------\\n\")\n",
-    "print_raster(xds_match)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Raster Calculations\n",
-    "\n",
-    "Now that the rasters have the same projection, resolution, and extents,\n",
-    "you can do raster calculations.\n",
-    "\n",
-    "It is recommended to use ``assign_coords`` to make the coordinates the exact same\n",
-    "due to tiny differences in the coordinate values due to floating precision ([issue 298](https://github.com/corteva/rioxarray/issues/298))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds_repr_match = xds_repr_match.assign_coords({\n",
-    "    \"x\": xds_match.x,\n",
-    "    \"y\": xds_match.y,\n",
-    "})\n",
-    "xds_sum = xds_repr_match + xds_match"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sum Raster:\n",
-      "-----------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.6512267236268, -386.6512267236231)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 9814687.0\n",
-      "CRS: EPSG:32615\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Sum Raster:\\n-----------\\n\")\n",
-    "print_raster(xds_sum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x288 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(ncols=3, figsize=(16,4))\n",
-    "\n",
-    "xds_repr_match.plot(ax=axes[0])\n",
-    "xds_match.plot(ax=axes[1]) \n",
-    "xds_sum.plot(ax=axes[2]) \n",
-    "\n",
-    "plt.draw()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/resampling.ipynb b/0.18.0/.doctrees/nbsphinx/examples/resampling.ipynb
deleted file mode 100644
index 249e4a7e..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/resampling.ipynb
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Resampling\n",
-    "\n",
-    "This example demonstrates how to reproduce `rasterio`'s resampling example [here](https://rasterio.readthedocs.io/en/latest/topics/resampling.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rasterio.enums import Resampling\n",
-    "\n",
-    "import rioxarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset\n",
-    "\n",
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "  \n",
-    "Notes:\n",
-    "\n",
-    " - `masked=True` will convert from integer to `float64` and fill with `NaN`. If this behavior is not desired, you can skip this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    masked=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Resampling\n",
-    "\n",
-    "API Reference for `rio.reproject`:\n",
-    "\n",
-    "  - [DataArray.reproject](../rioxarray.rst#rioxarray.raster_array.RasterArray.reproject)\n",
-    "  - [Dataset.reproject](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.reproject)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "upscale_factor = 2\n",
-    "new_width = xds.rio.width * upscale_factor\n",
-    "new_height = xds.rio.height * upscale_factor\n",
-    "\n",
-    "xds_upsampled = xds.rio.reproject(\n",
-    "    xds.rio.crs, \n",
-    "    shape=(new_height, new_width), \n",
-    "    resampling=Resampling.bilinear,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 245, 574)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 490, 1148)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_upsampled.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3.0, -3.0)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds.rio.resolution()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1.5, -1.5)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_upsampled.rio.resolution()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/examples/transform_bounds.ipynb b/0.18.0/.doctrees/nbsphinx/examples/transform_bounds.ipynb
deleted file mode 100644
index 33d25f0d..00000000
--- a/0.18.0/.doctrees/nbsphinx/examples/transform_bounds.ipynb
+++ /dev/null
@@ -1,369 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Transform Bounds\n",
-    "\n",
-    "The [rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds)\n",
-    "method allows you to correctly estimate\n",
-    "the bounds of your raster in a different CRS without\n",
-    "needing to re-project it. If you simply calculate the bounds\n",
-    "by transforming the bounds, there are often situations when\n",
-    "this is incorrect due to nonlinear transformations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import pyproj\n",
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "from shapely.geometry import box\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")\n",
-    "transformer = pyproj.Transformer.from_crs(xds.rio.crs, \"EPSG:4326\", always_xy=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Original Raster & Bounds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f324f4456c0>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "xds.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *box(*xds.rio.bounds()).exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Determine bounds of re-projected raster\n",
-    "\n",
-    "The [rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds) method allows you to safely convert a bounding box into another projection taking into account the effects of nonlinear transformations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "reprojected_raster = xds.rio.reproject(\"EPSG:4326\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Boundary calculated from the re-projected raster (inefficient)\n",
-    "\n",
-    "This is the benchmark. However, this method is computationally\n",
-    "inefficient. So, if you don't need to re-project, [rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds) is a more efficent method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "reprojected_raster_box = box(*reprojected_raster.rio.bounds())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f324735e4d0>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *reprojected_raster_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Boundary calculated from original corners (incorrect)\n",
-    "\n",
-    "Directly transforming the corners is an incorrect method to calculate the new boundary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "transform_box = box(*transformer.transform(*xds.rio.bounds()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3245a3bee0>]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *transform_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Boundary calculates using transform_bounds\n",
-    "\n",
-    "[rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds) is both computationally efficient and a correct method for calculating the bounds of your raster in the new projection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "transform_bounds_box = box(*xds.rio.transform_bounds(\"EPSG:4326\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f32459316c0>]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *transform_bounds_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As seen below, this is equivalent to the [Transformer.transform_bounds](https://pyproj4.github.io/pyproj/stable/api/transformer.html#pyproj.transformer.Transformer.transform_bounds) method in pyproj:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "pyproj_transform_bounds_box = box(*transformer.transform_bounds(*xds.rio.bounds()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f324580d120>]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *transform_bounds_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Coordinate Reference System Management"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "xarray \"... is particularly tailored to working with netCDF files, which were the source of xarray’s data model...\" (http://xarray.pydata.org).\n",
-    "\n",
-    "For netCDF files, the GIS community uses CF conventions (http://cfconventions.org/).\n",
-    "\n",
-    "Additionally, GDAL also supports these attributes:\n",
-    "\n",
-    "- spatial_ref (Well Known Text)\n",
-    "- GeoTransform (GeoTransform array)\n",
-    "\n",
-    "References:\n",
-    "\n",
-    "- Esri: https://pro.arcgis.com/en/pro-app/latest/help/data/multidimensional/spatial-reference-for-netcdf-data.htm\n",
-    "- GDAL: https://gdal.org/drivers/raster/netcdf.html#georeference\n",
-    "- pyproj: https://pyproj4.github.io/pyproj/stable/build_crs_cf.html\n",
-    "\n",
-    "Operations on xarray objects can cause data loss. Due to this, rioxarray writes and expects the spatial reference information to exist in the coordinates."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Accessing the CRS object"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you have opened a dataset and the Coordinate Reference System (CRS) can be determined, you can access it via the `rio.crs` accessor.\n",
-    "\n",
-    "#### Search order for the CRS (DataArray and Dataset):\n",
-    "1. Look in attributes (`attrs`) of your data array for the `grid_mapping` coordinate name.\n",
-    "   Inside the `grid_mapping` coordinate first look for `spatial_ref` then `crs_wkt` and lastly the CF grid mapping attributes.\n",
-    "   This is in line with the Climate and Forecast (CF) conventions for storing the CRS as well as GDAL netCDF conventions.\n",
-    "2. Look in the `crs` attribute and load in the CRS from there. This is for backwards compatibility with `xarray.open_rasterio`, which is deprecated since version 0.20.0. We recommend using `rioxarray.open_rasterio` instead.\n",
-    "\n",
-    "The value for the `crs` is anything accepted by `rasterio.crs.CRS.from_user_input()`\n",
-    "\n",
-    "#### Search order for the CRS for Dataset:\n",
-    "If the CRS is not found using the search methods above, it also searches the `data_vars` and uses the\n",
-    "first valid CRS found.\n",
-    "\n",
-    "#### decode_coords=\"all\"\n",
-    "\n",
-    "If you use one of xarray's open methods such as ``xarray.open_dataset`` to load netCDF files\n",
-    "with the default engine, it is recommended to use `decode_coords=\"all\"`. This will load the grid mapping\n",
-    "variable into coordinates for compatibility with rioxarray.\n",
-    "\n",
-    "#### API Documentation\n",
-    "\n",
-    "- [rio.write_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_crs)\n",
-    "- [rio.crs](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.crs)\n",
-    "- [rio.estimate_utm_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.estimate_utm_crs)\n",
-    "- [rio.set_spatial_dims()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.set_spatial_dims)\n",
-    "- [rio.write_coordinate_system()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_coordinate_system)\n",
-    "- [rio.write_transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_transform)\n",
-    "- [rio.transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray  # activate the rio accessor\n",
-    "import xarray\n",
-    "from affine import Affine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = xarray.open_dataset(\"../../test/test_data/input/PLANET_SCOPE_3D.nc\", decode_coords=\"all\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'units': 'DN', 'nodata': 0.0}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.green.attrs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
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-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    spatial_ref:  PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'spatial_ref'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ed98a4ac-15a8-4b42-a4d5-b41d08429b39' class='xr-array-in' type='checkbox' checked><label for='section-ed98a4ac-15a8-4b42-a4d5-b41d08429b39' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-13a73705-474f-4849-a2b6-31c4c1cc60d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13a73705-474f-4849-a2b6-31c4c1cc60d7' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ff9e3017-55fa-4eb5-af42-af1cc7f27bd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff9e3017-55fa-4eb5-af42-af1cc7f27bd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a4c305b-e31f-4faa-a83f-8aeec07cc0ee' class='xr-var-data-in' type='checkbox'><label for='data-7a4c305b-e31f-4faa-a83f-8aeec07cc0ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c56ca51-d50d-408e-8550-3fa40536ce7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c56ca51-d50d-408e-8550-3fa40536ce7d' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'spatial_ref' ()>\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    spatial_ref:  PROJCS[\"WGS 84 / UTM zone 22S\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1..."
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.green.spatial_ref"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CRS.from_epsg(32722)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.green.rio.crs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Setting the CRS\n",
-    "\n",
-    "Use the `rio.write_crs` method to set the CRS on your `xarray.Dataset` or `xarray.DataArray`.\n",
-    "This modifies the `xarray.Dataset` or `xarray.DataArray` and sets the CRS in a CF compliant manner.\n",
-    "\n",
-    "- [rio.write_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_crs)\n",
-    "- [rio.crs](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.crs)\n",
-    "\n",
-    "**Note:** It is recommended to use `rio.write_crs()` if you want the CRS to persist on the Dataset/DataArray and to write the CRS CF compliant metadata. Calling only `rio.set_crs()` CRS storage method is lossy and will not modify the Dataset/DataArray metadata."
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-   "execution_count": 6,
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-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;spatial_ref&#x27; ()&gt;\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    crs_wkt:                      GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;...\n",
-       "    semi_major_axis:              6378137.0\n",
-       "    semi_minor_axis:              6356752.314245179\n",
-       "    inverse_flattening:           298.257223563\n",
-       "    reference_ellipsoid_name:     WGS 84\n",
-       "    longitude_of_prime_meridian:  0.0\n",
-       "    prime_meridian_name:          Greenwich\n",
-       "    geographic_crs_name:          WGS 84\n",
-       "    grid_mapping_name:            latitude_longitude\n",
-       "    spatial_ref:                  GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'spatial_ref'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8edcf942-accb-4c39-88d9-d91a01903b83' class='xr-array-in' type='checkbox' checked><label for='section-8edcf942-accb-4c39-88d9-d91a01903b83' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-91b27a9b-d677-4786-8c5e-8208bbc943d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91b27a9b-d677-4786-8c5e-8208bbc943d0' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ed33fcd3-3802-4a97-9594-5e9199c8ad93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed33fcd3-3802-4a97-9594-5e9199c8ad93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c29d6c2-7c47-4a94-a90a-5de80d8ce90b' class='xr-var-data-in' type='checkbox'><label for='data-6c29d6c2-7c47-4a94-a90a-5de80d8ce90b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e504052f-18ed-4e21-98d1-667acb5bfc9e' class='xr-section-summary-in' type='checkbox'  ><label for='section-e504052f-18ed-4e21-98d1-667acb5bfc9e' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'spatial_ref' ()>\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    crs_wkt:                      GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"...\n",
-       "    semi_major_axis:              6378137.0\n",
-       "    semi_minor_axis:              6356752.314245179\n",
-       "    inverse_flattening:           298.257223563\n",
-       "    reference_ellipsoid_name:     WGS 84\n",
-       "    longitude_of_prime_meridian:  0.0\n",
-       "    prime_meridian_name:          Greenwich\n",
-       "    geographic_crs_name:          WGS 84\n",
-       "    grid_mapping_name:            latitude_longitude\n",
-       "    spatial_ref:                  GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"..."
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xda = xarray.DataArray(1)\n",
-    "xda.rio.write_crs(4326, inplace=True)\n",
-    "xda.spatial_ref"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CRS.from_epsg(4326)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xda.rio.crs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Spatial dimensions\n",
-    "\n",
-    "Only 1-dimensional X and Y dimensions are supported.\n",
-    "\n",
-    "The expected X/Y dimension names searched for in the `coords` are:\n",
-    "\n",
-    "- x | y\n",
-    "- longitude | latitude\n",
-    "- Coordinates (`coords`) with the CF attributes in `attrs`:\n",
-    "    - axis: X | Y\n",
-    "    - standard_name: longitude | latitude or projection_x_coordinate | projection_y_coordinate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Option 1: Write the CF attributes for non-standard dimension names\n",
-    "\n",
-    "If you don't want to rename your dimensions/coordinates,\n",
-    "you can write the CF attributes so the coordinates can be found.\n",
-    "\n",
-    "- [rio.set_spatial_dims()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.set_spatial_dims)\n",
-    "- [rio.write_coordinate_system()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_coordinate_system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds.rio.write_crs(\n",
-    "    4326\n",
-    "    inplace=True,\n",
-    ").rio.set_spatial_dims(\n",
-    "    x_dim=\"lon\",\n",
-    "    y_dim=\"lat\"\n",
-    "    inplace=True,\n",
-    ").rio.write_coordinate_system(inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Option 2: Rename your coordinates\n",
-    "\n",
-    "[xarray.Dataset.rename](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.rename.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rds.rename(lon=longitude, lat=latitude) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Setting the transform of the dataset\n",
-    "\n",
-    "The transform can be calculated from the coordinates of your data.\n",
-    "This method is useful if your netCDF file does not have coordinates present.\n",
-    "Use the `rio.write_transform` method to set the transform on your `xarray.Dataset` or `xarray.DataArray`.\n",
-    "\n",
-    "- [rio.write_transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_transform)\n",
-    "- [rio.transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'466266.0 3.0 0.0 8084700.0 0.0 -3.0'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transform = Affine(3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0)\n",
-    "xda.rio.write_transform(transform, inplace=True)\n",
-    "xda.spatial_ref.GeoTransform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Affine(3.0, 0.0, 466266.0,\n",
-       "       0.0, -3.0, 8084700.0)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xda.rio.transform()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/getting_started/manage_information_loss.ipynb b/0.18.0/.doctrees/nbsphinx/getting_started/manage_information_loss.ipynb
deleted file mode 100644
index aeb02d4e..00000000
--- a/0.18.0/.doctrees/nbsphinx/getting_started/manage_information_loss.ipynb
+++ /dev/null
@@ -1,240 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Managing Information Loss with xarray operations\n",
-    "\n",
-    "Sometimes, you can lose important information from your dataset when performing operations.\n",
-    "You will likely want to keep track of the attributes, `nodata`, and `CRS`.\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)\n",
-    "- [rio.write_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_crs)\n",
-    "- [rio.write_transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_transform)\n",
-    "- [rio.update_attrs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.update_attrs)\n",
-    "- [rio.update_encoding()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.update_encoding)\n",
-    "- [rio.crs](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.crs)\n",
-    "- [rio.nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.nodata)\n",
-    "- [rio.encoded_nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.encoded_nodata)\n",
-    "- [rio.write_nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.write_nodata)\n",
-    "- [rio.transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform)\n",
-    "\n",
-    "Note that `write_transform` is only needed if you are not saving the x,y coordinates. It is for\n",
-    "GDAL to  be able to read in the transform without needing the original coordinates and is useful\n",
-    "if you read in the file with `parse_coordinates=False`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "import xarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/input/PLANET_SCOPE_3D.nc\",\n",
-    "    variable=[\"green\"],\n",
-    "    mask_and_scale=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice the original data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'nodata': 0, 'units': ('DN', 'DN')},\n",
-       " {'dtype': 'float64',\n",
-       "  'grid_mapping': 'spatial_ref',\n",
-       "  'scale_factor': 1.0,\n",
-       "  'add_offset': 0.0,\n",
-       "  '_FillValue': nan,\n",
-       "  'source': 'netcdf:../../test/test_data/input/PLANET_SCOPE_3D.nc:green'},\n",
-       " CRS.from_epsg(32722),\n",
-       " nan)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.green.attrs, rds.green.encoding, rds.green.rio.crs, rds.green.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice how information is lost in the operation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({}, {}, CRS.from_epsg(32722), None)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_ds = rds.green + rds.green\n",
-    "new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To preserve attributes, xarray has [set_options](http://xarray.pydata.org/en/stable/generated/xarray.set_options.html#xarray-set-options) with `keep_attrs=True`. However, it does not preserve the encoding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'nodata': 0, 'units': ('DN', 'DN')}, {}, CRS.from_epsg(32722), 0.0)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with xarray.set_options(keep_attrs=True):\n",
-    "    new_ds = rds.green + rds.green\n",
-    "new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another solution is to save the original attributes and then copy them over\n",
-    "once the operation is complete:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'nodata': 0, 'units': ('DN', 'DN')},\n",
-       " {'grid_mapping': 'spatial_ref',\n",
-       "  'dtype': 'float64',\n",
-       "  'scale_factor': 1.0,\n",
-       "  'add_offset': 0.0,\n",
-       "  '_FillValue': nan,\n",
-       "  'source': 'netcdf:../../test/test_data/input/PLANET_SCOPE_3D.nc:green'},\n",
-       " CRS.from_epsg(32722),\n",
-       " nan)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_ds = rds.green + rds.green\n",
-    "new_ds.rio.write_crs(rds.green.rio.crs, inplace=True)\n",
-    "new_ds.rio.update_attrs(rds.green.attrs, inplace=True)\n",
-    "new_ds.rio.update_encoding(rds.green.encoding, inplace=True)\n",
-    "new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_ds.rio.to_raster(\"combination_keep_attrs.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"green\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": false, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info combination_keep_attrs.tif"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/.doctrees/nbsphinx/getting_started/nodata_management.ipynb b/0.18.0/.doctrees/nbsphinx/getting_started/nodata_management.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Nodata Management"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you have opened a dataset and the nodata value can be determined, you can access it via the `rio.nodata` or `rio.encoded_nodata` accessors.\n",
-    "\n",
-    "If your dataset's nodata value cannot be determined, you can use the `rio.write_nodata` method.\n",
-    "\n",
-    "### Search order for nodata (DataArray only):\n",
-    "1. Check if DataArray values are masked. If they are masked, return `NaN`. If the DataArray is masked, the original nodata value can be retreived from `rio.encoded_nodata`.\n",
-    "2. Look in attributes (`attrs`) of your data array for the `_FillValue` then `missing_value` then `fill_value` and finally `nodata`.\n",
-    "3. Look in the `nodatavals` attribute. This is for backwards compatibility with `xarray.open_rasterio`. We recommend using `rioxarray.open_rasterio` instead.\n",
-    "\n",
-    "### API Documentation\n",
-    "\n",
-    "- [rio.write_nodata()](../rioxarray.rst#rioxarray.raster_array.RasterArray.write_nodata)\n",
-    "- [rio.nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.nodata)\n",
-    "- [rio.encoded_nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.encoded_nodata)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "import xarray\n",
-    "\n",
-    "file_path = \"../../test/test_data/input/tmmx_20190121.nc\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example of loading unmaksed data\n",
-    "\n",
-    "In this case, the nodata value is in the attributes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataset(file_path, mask_and_scale=False) # performs mask_and_scale by default\n",
-    "rds = rioxarray.open_rasterio(file_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata:\n",
-      "- xarray.open_dataset: 32767\n",
-      "- rioxarray.open_rasterio: 32767\n",
-      "\n",
-      "encoded_nodata:\n",
-      "- xarray.open_dataset: None\n",
-      "- rioxarray.open_rasterio: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.nodata}\")\n",
-    "print(\"\\nencoded_nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.encoded_nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.encoded_nodata}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "attributes:\n",
-      "\n",
-      "- xarray.open_dataset:\n",
-      "    {'_FillValue': 32767, 'units': 'K', 'description': 'Daily Maximum Temperature', 'long_name': 'tmmx', 'standard_name': 'tmmx', 'missing_value': 32767, 'dimensions': 'lon lat time', 'grid_mapping': 'crs', 'coordinate_system': 'WGS84,EPSG:4326', 'scale_factor': 0.1, 'add_offset': 220.0, '_Unsigned': 'true'}\n",
-      "\n",
-      "- rioxarray.open_rasterio:\n",
-      "    {'add_offset': 220.0, 'coordinates': 'day', 'coordinate_system': 'WGS84,EPSG:4326', 'description': 'Daily Maximum Temperature', 'dimensions': 'lon lat time', 'long_name': 'tmmx', 'missing_value': 32767, 'scale_factor': 0.1, 'standard_name': 'tmmx', 'units': 'K', '_FillValue': 32767.0, '_Unsigned': 'true'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"attributes:\")\n",
-    "print(f\"\\n- xarray.open_dataset:\\n    {xds.air_temperature.attrs}\")\n",
-    "print(f\"\\n- rioxarray.open_rasterio:\\n    {rds.air_temperature.attrs}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example of data loaded in with mask_and_scale=True\n",
-    "\n",
-    "When the dataset is opened with `mask_and_scale=True` with `rioxarray.open_rasterio` or `xarray.open_dataset`, the\n",
-    "nodata metadata is written to the encoding attribute. Then, when the dataset is written using\n",
-    "`to_netcdf` or `rio.to_raster` the data is decoded and it writes the original nodata value to the raster.\n",
-    "\n",
-    "When this happens, `rio.nodata` returns `numpy.nan` and `rio.encoded_nodata` contains the original value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/snowal/miniconda/envs/midas/lib/python3.10/site-packages/rioxarray/_io.py:618: SerializationWarning: variable 'air_temperature' has _Unsigned attribute but is not of integer type. Ignoring attribute.\n",
-      "  rioda = open_rasterio(\n"
-     ]
-    }
-   ],
-   "source": [
-    "xds = xarray.open_dataset(file_path) # performs mask_and_scale by default\n",
-    "rds = rioxarray.open_rasterio(file_path, mask_and_scale=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata:\n",
-      "- xarray.open_dataset: nan\n",
-      "- rioxarray.open_rasterio: nan\n",
-      "\n",
-      "encoded_nodata:\n",
-      "- xarray.open_dataset: 32767.0\n",
-      "- rioxarray.open_rasterio: 32767.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.nodata}\")\n",
-    "print(\"\\nencoded_nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.encoded_nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.encoded_nodata}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "attributes:\n",
-      "\n",
-      "- xarray.open_dataset:\n",
-      "    {'units': 'K', 'description': 'Daily Maximum Temperature', 'long_name': 'tmmx', 'standard_name': 'tmmx', 'dimensions': 'lon lat time', 'grid_mapping': 'crs', 'coordinate_system': 'WGS84,EPSG:4326'}\n",
-      "\n",
-      "- rioxarray.open_rasterio:\n",
-      "    {'coordinates': 'day', 'coordinate_system': 'WGS84,EPSG:4326', 'description': 'Daily Maximum Temperature', 'dimensions': 'lon lat time', 'long_name': 'tmmx', 'standard_name': 'tmmx', 'units': 'K'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"attributes:\")\n",
-    "print(f\"\\n- xarray.open_dataset:\\n    {xds.air_temperature.attrs}\")\n",
-    "print(f\"\\n- rioxarray.open_rasterio:\\n    {rds.air_temperature.attrs}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "encoding:\n",
-      "\n",
-      "- xarray.open_dataset:\n",
-      "    {'zlib': True, 'shuffle': True, 'complevel': 5, 'fletcher32': False, 'contiguous': False, 'chunksizes': (585, 1386), 'source': '/home/snowal/scripts/rioxarray/test/test_data/input/tmmx_20190121.nc', 'original_shape': (585, 1386), 'dtype': dtype('uint16'), '_Unsigned': 'true', 'missing_value': 32767, '_FillValue': 32767, 'scale_factor': 0.1, 'add_offset': 220.0, 'coordinates': 'day'}\n",
-      "\n",
-      "- rioxarray.open_rasterio:\n",
-      "    {'_Unsigned': 'true', 'dtype': 'uint16', 'grid_mapping': 'crs', 'scale_factor': 0.1, 'add_offset': 220.0, '_FillValue': 32767.0, 'missing_value': 32767, 'source': 'netcdf:../../test/test_data/input/tmmx_20190121.nc:air_temperature', 'rasterio_dtype': 'uint16'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"encoding:\")\n",
-    "print(f\"\\n- xarray.open_dataset:\\n    {xds.air_temperature.encoding}\")\n",
-    "print(f\"\\n- rioxarray.open_rasterio:\\n    {rds.air_temperature.encoding}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Manually masking data\n",
-    "\n",
-    "If you use `xarray.where` to mask you data, then you need to ensure that the\n",
-    "attributes stored on the DataArray reflect the correct values.\n",
-    "[rio.write_nodata()](../rioxarray.rst#rioxarray.raster_array.RasterArray.write_nodata) can help ensure that the nodata attributes are written correctly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata: 32767.0\n",
-      "encoded_nodata: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "xds = xarray.open_dataset(file_path, mask_and_scale=False) # performs mask_and_scale by default\n",
-    "raster = xds.air_temperature \n",
-    "raster = raster.where(raster != raster.rio.nodata)\n",
-    "# nodata does not reflect the data has been masked\n",
-    "print(f\"nodata: {raster.rio.nodata}\")\n",
-    "print(f\"encoded_nodata: {raster.rio.encoded_nodata}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata: nan\n",
-      "encoded_nodata: 32767.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# update nodata value to show the data has been masked\n",
-    "raster.rio.write_nodata(raster.rio.nodata, encoded=True, inplace=True)\n",
-    "print(f\"nodata: {raster.rio.nodata}\")\n",
-    "print(f\"encoded_nodata: {raster.rio.encoded_nodata}\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
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-
-
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" data-content_root="../">
-<head>
-  <meta charset="utf-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Overview: module code &mdash; rioxarray 0.18.0 documentation</title>
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-    <script src="../_static/js/theme.js"></script>
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-</head>
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-            rioxarray
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-             
-  <h1>Source code for rioxarray._io</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">Credits:</span>
-
-<span class="sd">This file was adopted from: https://github.com/pydata/xarray # noqa</span>
-<span class="sd">Source file: https://github.com/pydata/xarray/blob/1d7bcbdc75b6d556c04e2c7d7a042e4379e15303/xarray/backends/rasterio_.py # noqa</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="c1"># pylint: disable=too-many-lines</span>
-<span class="kn">import</span> <span class="nn">contextlib</span>
-<span class="kn">import</span> <span class="nn">importlib.metadata</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">re</span>
-<span class="kn">import</span> <span class="nn">threading</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
-<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Hashable</span><span class="p">,</span> <span class="n">Iterable</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">rasterio</span>
-<span class="kn">from</span> <span class="nn">numpy.typing</span> <span class="kn">import</span> <span class="n">NDArray</span>
-<span class="kn">from</span> <span class="nn">packaging</span> <span class="kn">import</span> <span class="n">version</span>
-<span class="kn">from</span> <span class="nn">rasterio.errors</span> <span class="kn">import</span> <span class="n">NotGeoreferencedWarning</span>
-<span class="kn">from</span> <span class="nn">rasterio.vrt</span> <span class="kn">import</span> <span class="n">WarpedVRT</span>
-<span class="kn">from</span> <span class="nn">xarray</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">IndexVariable</span>
-<span class="kn">from</span> <span class="nn">xarray.backends.common</span> <span class="kn">import</span> <span class="n">BackendArray</span>
-<span class="kn">from</span> <span class="nn">xarray.backends.file_manager</span> <span class="kn">import</span> <span class="n">CachingFileManager</span><span class="p">,</span> <span class="n">FileManager</span>
-<span class="kn">from</span> <span class="nn">xarray.backends.locks</span> <span class="kn">import</span> <span class="n">SerializableLock</span>
-<span class="kn">from</span> <span class="nn">xarray.coding</span> <span class="kn">import</span> <span class="n">times</span><span class="p">,</span> <span class="n">variables</span>
-<span class="kn">from</span> <span class="nn">xarray.core</span> <span class="kn">import</span> <span class="n">indexing</span>
-<span class="kn">from</span> <span class="nn">xarray.core.dataarray</span> <span class="kn">import</span> <span class="n">DataArray</span>
-<span class="kn">from</span> <span class="nn">xarray.core.dtypes</span> <span class="kn">import</span> <span class="n">maybe_promote</span>
-<span class="kn">from</span> <span class="nn">xarray.core.utils</span> <span class="kn">import</span> <span class="n">is_scalar</span>
-<span class="kn">from</span> <span class="nn">xarray.core.variable</span> <span class="kn">import</span> <span class="n">as_variable</span>
-
-<span class="kn">from</span> <span class="nn">rioxarray.exceptions</span> <span class="kn">import</span> <span class="n">RioXarrayError</span>
-<span class="kn">from</span> <span class="nn">rioxarray.rioxarray</span> <span class="kn">import</span> <span class="n">_generate_spatial_coords</span>
-
-<span class="n">FILL_VALUE_NAMES</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">,</span> <span class="s2">&quot;missing_value&quot;</span><span class="p">,</span> <span class="s2">&quot;fill_value&quot;</span><span class="p">,</span> <span class="s2">&quot;nodata&quot;</span><span class="p">)</span>
-<span class="n">UNWANTED_RIO_ATTRS</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">,</span> <span class="s2">&quot;is_tiled&quot;</span><span class="p">,</span> <span class="s2">&quot;res&quot;</span><span class="p">)</span>
-<span class="c1"># TODO: should this be GDAL_LOCK instead?</span>
-<span class="n">RASTERIO_LOCK</span> <span class="o">=</span> <span class="n">SerializableLock</span><span class="p">()</span>
-<span class="n">NO_LOCK</span> <span class="o">=</span> <span class="n">contextlib</span><span class="o">.</span><span class="n">nullcontext</span><span class="p">()</span>
-
-
-<span class="k">def</span> <span class="nf">_ensure_warped_vrt</span><span class="p">(</span><span class="n">riods</span><span class="p">,</span> <span class="n">vrt_params</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Ensuire the dataset is represented as a warped vrt</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">vrt_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">riods</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">riods</span><span class="p">,</span> <span class="n">SingleBandDatasetReader</span><span class="p">):</span>
-        <span class="n">riods</span><span class="o">.</span><span class="n">_create_vrt</span><span class="p">(</span><span class="n">vrt_params</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">riods</span> <span class="o">=</span> <span class="n">WarpedVRT</span><span class="p">(</span><span class="n">riods</span><span class="p">,</span> <span class="o">**</span><span class="n">vrt_params</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">riods</span>
-
-
-<span class="k">class</span> <span class="nc">SingleBandDatasetReader</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Hack to have a DatasetReader behave like it only has one band</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">riods</span><span class="p">,</span> <span class="n">bidx</span><span class="p">,</span> <span class="n">vrt_params</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_riods</span> <span class="o">=</span> <span class="n">riods</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span> <span class="o">=</span> <span class="n">bidx</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_vrt_params</span> <span class="o">=</span> <span class="n">vrt_params</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_create_vrt</span><span class="p">(</span><span class="n">vrt_params</span><span class="o">=</span><span class="n">vrt_params</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">__name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
-        <span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="p">,</span> <span class="n">__name</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_create_vrt</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vrt_params</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">vrt_params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="p">,</span> <span class="n">WarpedVRT</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_riods</span> <span class="o">=</span> <span class="n">WarpedVRT</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="p">,</span> <span class="o">**</span><span class="n">vrt_params</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_vrt_params</span> <span class="o">=</span> <span class="n">vrt_params</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">name</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        str: name of the dataset. Usually the path.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="p">,</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">vrt</span><span class="o">.</span><span class="n">WarpedVRT</span><span class="p">):</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">src_dataset</span><span class="o">.</span><span class="n">name</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">name</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">count</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        int: band count</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="mi">1</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">nodata</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Nodata value for the band</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">nodatavals</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span><span class="p">]</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">offsets</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Offset value for the band</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">offsets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span><span class="p">]]</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">scales</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Scale value for the band</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">scales</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span><span class="p">]]</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">units</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Unit for the band</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">units</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span><span class="p">]]</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">descriptions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Description for the band</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">descriptions</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span><span class="p">]]</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">dtypes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        dtype for the band</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">dtypes</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span><span class="p">]]</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">indexes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        indexes for the band</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">indexes</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span><span class="p">]]</span>
-
-    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indexes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>  <span class="c1"># pylint: disable=unused-argument</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        read data for the band</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isscalar</span><span class="p">(</span><span class="n">indexes</span><span class="p">):</span>
-            <span class="n">indexes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span> <span class="o">+</span> <span class="mi">1</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">indexes</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">indexes</span><span class="o">=</span><span class="n">indexes</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">tags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bidx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>  <span class="c1"># pylint: disable=unused-argument</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        read tags for the band</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_riods</span><span class="o">.</span><span class="n">tags</span><span class="p">(</span><span class="n">bidx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_bidx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-
-<span class="n">RasterioReader</span> <span class="o">=</span> <span class="n">Union</span><span class="p">[</span>
-    <span class="n">rasterio</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">DatasetReader</span><span class="p">,</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">vrt</span><span class="o">.</span><span class="n">WarpedVRT</span><span class="p">,</span> <span class="n">SingleBandDatasetReader</span>
-<span class="p">]</span>
-
-
-<span class="k">try</span><span class="p">:</span>
-    <span class="n">_DASK_GTE_018</span> <span class="o">=</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">importlib</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">version</span><span class="p">(</span><span class="s2">&quot;dask&quot;</span><span class="p">))</span> <span class="o">&gt;=</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span>
-        <span class="s2">&quot;0.18.0&quot;</span>
-    <span class="p">)</span>
-<span class="k">except</span> <span class="n">importlib</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">PackageNotFoundError</span><span class="p">:</span>
-    <span class="n">_DASK_GTE_018</span> <span class="o">=</span> <span class="kc">False</span>
-
-
-<span class="k">def</span> <span class="nf">_get_unsigned_dtype</span><span class="p">(</span><span class="n">unsigned</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Based on: https://github.com/pydata/xarray/blob/abe1e613a96b000ae603c53d135828df532b952e/xarray/coding/variables.py#L306-L334</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">dtype</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">unsigned</span> <span class="ow">is</span> <span class="kc">True</span> <span class="ow">and</span> <span class="n">dtype</span><span class="o">.</span><span class="n">kind</span> <span class="o">==</span> <span class="s2">&quot;i&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;u</span><span class="si">{</span><span class="n">dtype</span><span class="o">.</span><span class="n">itemsize</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">unsigned</span> <span class="ow">is</span> <span class="kc">False</span> <span class="ow">and</span> <span class="n">dtype</span><span class="o">.</span><span class="n">kind</span> <span class="o">==</span> <span class="s2">&quot;u&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;i</span><span class="si">{</span><span class="n">dtype</span><span class="o">.</span><span class="n">itemsize</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="kc">None</span>
-
-
-<span class="k">class</span> <span class="nc">FileHandleLocal</span><span class="p">(</span><span class="n">threading</span><span class="o">.</span><span class="n">local</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This contains the thread local ThreadURIManager</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>  <span class="c1"># pylint: disable=super-init-not-called</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">thread_manager</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># Initialises in each thread</span>
-
-
-<span class="k">class</span> <span class="nc">ThreadURIManager</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This handles opening &amp; closing file handles in each thread.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">opener</span><span class="p">,</span>
-        <span class="o">*</span><span class="n">args</span><span class="p">,</span>
-        <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span>
-        <span class="n">kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_opener</span> <span class="o">=</span> <span class="n">opener</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_args</span> <span class="o">=</span> <span class="n">args</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_mode</span> <span class="o">=</span> <span class="n">mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">if</span> <span class="n">kwargs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">dict</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_file_handle</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">file_handle</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        File handle returned by the opener.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_file_handle</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_file_handle</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_file_handle</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_opener</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">_args</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_mode</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_file_handle</span>
-
-    <span class="k">def</span> <span class="nf">close</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Close file handle.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_file_handle</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_file_handle</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_file_handle</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span>
-
-    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">type_</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">traceback</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-
-<span class="k">class</span> <span class="nc">URIManager</span><span class="p">(</span><span class="n">FileManager</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    The URI manager is used for lockless reading</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">opener</span><span class="p">,</span>
-        <span class="o">*</span><span class="n">args</span><span class="p">,</span>
-        <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span>
-        <span class="n">kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_opener</span> <span class="o">=</span> <span class="n">opener</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_args</span> <span class="o">=</span> <span class="n">args</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_mode</span> <span class="o">=</span> <span class="n">mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">if</span> <span class="n">kwargs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">dict</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_local</span> <span class="o">=</span> <span class="n">FileHandleLocal</span><span class="p">()</span>
-
-    <span class="k">def</span> <span class="nf">acquire</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">needs_lock</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_local</span><span class="o">.</span><span class="n">thread_manager</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_local</span><span class="o">.</span><span class="n">thread_manager</span> <span class="o">=</span> <span class="n">ThreadURIManager</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_opener</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">_args</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_mode</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span>
-            <span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_local</span><span class="o">.</span><span class="n">thread_manager</span><span class="o">.</span><span class="n">file_handle</span>
-
-    <span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
-    <span class="k">def</span> <span class="nf">acquire_context</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">needs_lock</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquire</span><span class="p">(</span><span class="n">needs_lock</span><span class="o">=</span><span class="n">needs_lock</span><span class="p">)</span>
-        <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">needs_lock</span><span class="o">=</span><span class="n">needs_lock</span><span class="p">)</span>
-            <span class="k">raise</span>
-
-    <span class="k">def</span> <span class="nf">close</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">needs_lock</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_local</span><span class="o">.</span><span class="n">thread_manager</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_local</span><span class="o">.</span><span class="n">thread_manager</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_local</span><span class="o">.</span><span class="n">thread_manager</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">needs_lock</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;State for pickling.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_opener</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mode</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Restore from a pickle.&quot;&quot;&quot;</span>
-        <span class="n">opener</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">mode</span><span class="p">,</span> <span class="n">kwargs</span> <span class="o">=</span> <span class="n">state</span>
-        <span class="bp">self</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">opener</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="n">kwargs</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">RasterioArrayWrapper</span><span class="p">(</span><span class="n">BackendArray</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;A wrapper around rasterio dataset objects&quot;&quot;&quot;</span>
-
-    <span class="c1"># pylint: disable=too-many-instance-attributes</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">manager</span><span class="p">,</span>
-        <span class="n">lock</span><span class="p">,</span>
-        <span class="n">name</span><span class="p">,</span>
-        <span class="n">vrt_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">masked</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">mask_and_scale</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">unsigned</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">manager</span> <span class="o">=</span> <span class="n">manager</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lock</span> <span class="o">=</span> <span class="n">lock</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">masked</span> <span class="o">=</span> <span class="n">masked</span> <span class="ow">or</span> <span class="n">mask_and_scale</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mask_and_scale</span> <span class="o">=</span> <span class="n">mask_and_scale</span>
-
-        <span class="c1"># cannot save riods as an attribute: this would break pickleability</span>
-        <span class="n">riods</span> <span class="o">=</span> <span class="n">_ensure_warped_vrt</span><span class="p">(</span><span class="n">manager</span><span class="o">.</span><span class="n">acquire</span><span class="p">(),</span> <span class="n">vrt_params</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">vrt_params</span> <span class="o">=</span> <span class="n">vrt_params</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="n">riods</span><span class="o">.</span><span class="n">height</span><span class="p">,</span> <span class="n">riods</span><span class="o">.</span><span class="n">width</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_unsigned_dtype</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_fill_value</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">nodata</span>
-        <span class="n">dtypes</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">dtypes</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">numpy</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">dtypes</span><span class="p">)</span> <span class="o">==</span> <span class="n">dtypes</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;All bands should have the same dtype&quot;</span><span class="p">)</span>
-
-        <span class="n">dtype</span> <span class="o">=</span> <span class="n">_rasterio_to_numpy_dtype</span><span class="p">(</span><span class="n">dtypes</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">mask_and_scale</span> <span class="ow">and</span> <span class="n">unsigned</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_unsigned_dtype</span> <span class="o">=</span> <span class="n">_get_unsigned_dtype</span><span class="p">(</span>
-                <span class="n">unsigned</span><span class="o">=</span><span class="n">unsigned</span><span class="p">,</span>
-                <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unsigned_dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fill_value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_fill_value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unsigned_dtype</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_fill_value</span><span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unsigned_dtype</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-                    <span class="sa">f</span><span class="s2">&quot;variable </span><span class="si">{</span><span class="n">name</span><span class="si">!r}</span><span class="s2"> has _Unsigned attribute but is not &quot;</span>
-                    <span class="s2">&quot;of integer type. Ignoring attribute.&quot;</span><span class="p">,</span>
-                    <span class="n">variables</span><span class="o">.</span><span class="n">SerializationWarning</span><span class="p">,</span>
-                    <span class="n">stacklevel</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-                <span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">masked</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fill_value</span> <span class="o">=</span> <span class="n">maybe_promote</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="n">dtype</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Data type of the array</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">fill_value</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Fill value of the array</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fill_value</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Shape of the array</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span>
-
-    <span class="k">def</span> <span class="nf">_get_indexer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get indexer for rasterio array.</span>
-
-<span class="sd">        Parameter</span>
-<span class="sd">        ---------</span>
-<span class="sd">        key: tuple of int</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        band_key: an indexer for the 1st dimension</span>
-<span class="sd">        window: two tuples. Each consists of (start, stop).</span>
-<span class="sd">        squeeze_axis: axes to be squeezed</span>
-<span class="sd">        np_ind: indexer for loaded numpy array</span>
-
-<span class="sd">        See also</span>
-<span class="sd">        --------</span>
-<span class="sd">        indexing.decompose_indexer</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">RioXarrayError</span><span class="p">(</span><span class="s2">&quot;rasterio datasets should always be 3D&quot;</span><span class="p">)</span>
-
-        <span class="c1"># bands cannot be windowed but they can be listed</span>
-        <span class="n">band_key</span> <span class="o">=</span> <span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">np_inds</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="c1"># bands (axis=0) cannot be windowed but they can be listed</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">band_key</span><span class="p">,</span> <span class="nb">slice</span><span class="p">):</span>
-            <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span> <span class="o">=</span> <span class="n">band_key</span><span class="o">.</span><span class="n">indices</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">band_key</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="p">)</span>
-        <span class="c1"># be sure we give out a list</span>
-        <span class="n">band_key</span> <span class="o">=</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">band_key</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">band_key</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>  <span class="c1"># if band_key is not a scalar</span>
-            <span class="n">np_inds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span>
-
-        <span class="c1"># but other dims can only be windowed</span>
-        <span class="n">window</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">squeeze_axis</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">iii</span><span class="p">,</span> <span class="p">(</span><span class="n">ikey</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:])):</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ikey</span><span class="p">,</span> <span class="nb">slice</span><span class="p">):</span>
-                <span class="c1"># step is always positive. see indexing.decompose_indexer</span>
-                <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span> <span class="o">=</span> <span class="n">ikey</span><span class="o">.</span><span class="n">indices</span><span class="p">(</span><span class="n">size</span><span class="p">)</span>
-                <span class="n">np_inds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">step</span><span class="p">))</span>
-            <span class="k">elif</span> <span class="n">is_scalar</span><span class="p">(</span><span class="n">ikey</span><span class="p">):</span>
-                <span class="c1"># windowed operations will always return an array</span>
-                <span class="c1"># we will have to squeeze it later</span>
-                <span class="n">squeeze_axis</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="mi">2</span> <span class="o">-</span> <span class="n">iii</span><span class="p">))</span>
-                <span class="n">start</span> <span class="o">=</span> <span class="n">ikey</span>
-                <span class="n">stop</span> <span class="o">=</span> <span class="n">ikey</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">start</span><span class="p">,</span> <span class="n">stop</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ikey</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">ikey</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
-                <span class="n">np_inds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ikey</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span>
-            <span class="n">window</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-            <span class="c1"># do outer-style indexing</span>
-            <span class="n">np_inds</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ix_</span><span class="p">(</span><span class="o">*</span><span class="n">np_inds</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:])</span>
-
-        <span class="k">return</span> <span class="n">band_key</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">window</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">squeeze_axis</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">np_inds</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_getitem</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
-        <span class="n">band_key</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">squeeze_axis</span><span class="p">,</span> <span class="n">np_inds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_indexer</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">band_key</span> <span class="ow">or</span> <span class="nb">any</span><span class="p">(</span><span class="n">start</span> <span class="o">==</span> <span class="n">stop</span> <span class="k">for</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span> <span class="ow">in</span> <span class="n">window</span><span class="p">):</span>
-            <span class="c1"># no need to do IO</span>
-            <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">band_key</span><span class="p">),)</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">stop</span> <span class="o">-</span> <span class="n">start</span> <span class="k">for</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span> <span class="ow">in</span> <span class="n">window</span><span class="p">)</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">lock</span><span class="p">:</span>
-                <span class="n">riods</span> <span class="o">=</span> <span class="n">_ensure_warped_vrt</span><span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">manager</span><span class="o">.</span><span class="n">acquire</span><span class="p">(</span><span class="n">needs_lock</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">vrt_params</span>
-                <span class="p">)</span>
-                <span class="n">out</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">band_key</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">masked</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">masked</span><span class="p">)</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unsigned_dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_unsigned_dtype</span><span class="p">)</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">masked</span><span class="p">:</span>
-                    <span class="n">out</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">filled</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">fill_value</span><span class="p">)</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_and_scale</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">band_key</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">):</span>
-                        <span class="n">out</span> <span class="o">=</span> <span class="p">(</span>
-                            <span class="n">out</span> <span class="o">*</span> <span class="n">riods</span><span class="o">.</span><span class="n">scales</span><span class="p">[</span><span class="n">band_key</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
-                            <span class="o">+</span> <span class="n">riods</span><span class="o">.</span><span class="n">offsets</span><span class="p">[</span><span class="n">band_key</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
-                        <span class="p">)</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="k">for</span> <span class="n">iii</span><span class="p">,</span> <span class="n">band_iii</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">band_key</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-                            <span class="n">out</span><span class="p">[</span><span class="n">iii</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                                <span class="n">out</span><span class="p">[</span><span class="n">iii</span><span class="p">]</span> <span class="o">*</span> <span class="n">riods</span><span class="o">.</span><span class="n">scales</span><span class="p">[</span><span class="n">band_iii</span><span class="p">]</span>
-                                <span class="o">+</span> <span class="n">riods</span><span class="o">.</span><span class="n">offsets</span><span class="p">[</span><span class="n">band_iii</span><span class="p">]</span>
-                            <span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">squeeze_axis</span><span class="p">:</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">squeeze_axis</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">out</span><span class="p">[</span><span class="n">np_inds</span><span class="p">]</span>
-
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">indexing</span><span class="o">.</span><span class="n">explicit_indexing_adapter</span><span class="p">(</span>
-            <span class="n">key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">indexing</span><span class="o">.</span><span class="n">IndexingSupport</span><span class="o">.</span><span class="n">OUTER</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem</span>
-        <span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_parse_envi</span><span class="p">(</span><span class="n">meta</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Parse ENVI metadata into Python data structures.</span>
-
-<span class="sd">    See the link for information on the ENVI header file format:</span>
-<span class="sd">    http://www.harrisgeospatial.com/docs/enviheaderfiles.html</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    meta : dict</span>
-<span class="sd">        Dictionary of keys and str values to parse, as returned by the rasterio</span>
-<span class="sd">        tags(ns=&#39;ENVI&#39;) call.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    parsed_meta : dict</span>
-<span class="sd">        Dictionary containing the original keys and the parsed values</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">parsevec</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">fromstring</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">default</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">value</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-    <span class="n">parse</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">parsevec</span><span class="p">,</span> <span class="s2">&quot;fwhm&quot;</span><span class="p">:</span> <span class="n">parsevec</span><span class="p">}</span>
-    <span class="n">parsed_meta</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">parse</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">default</span><span class="p">)(</span><span class="n">value</span><span class="p">)</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">meta</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-    <span class="k">return</span> <span class="n">parsed_meta</span>
-
-
-<span class="k">def</span> <span class="nf">_rasterio_to_numpy_dtype</span><span class="p">(</span><span class="n">dtypes</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Numpy dtype from first entry of rasterio dataset.dtypes&quot;&quot;&quot;</span>
-    <span class="c1"># rasterio has some special dtype names (complex_int16 -&gt; numpy.complex64)</span>
-    <span class="k">if</span> <span class="n">dtypes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;complex_int16&quot;</span><span class="p">:</span>
-        <span class="n">dtype</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="s2">&quot;complex64&quot;</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">dtype</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">dtypes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="k">return</span> <span class="n">dtype</span>
-
-
-<span class="k">def</span> <span class="nf">_to_numeric</span><span class="p">(</span><span class="n">value</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Convert the value to a number</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">value</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
-    <span class="k">except</span> <span class="p">(</span><span class="ne">TypeError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">):</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">value</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
-        <span class="k">except</span> <span class="p">(</span><span class="ne">TypeError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">):</span>
-            <span class="k">pass</span>
-    <span class="k">return</span> <span class="n">value</span>
-
-
-<span class="k">def</span> <span class="nf">_parse_tag</span><span class="p">(</span><span class="o">*</span><span class="p">,</span> <span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
-    <span class="c1"># NC_GLOBAL is appended to tags with netcdf driver and is not really needed</span>
-    <span class="n">key</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;NC_GLOBAL#&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">value</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;{&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">value</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">&quot;}&quot;</span><span class="p">):</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">new_val</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">fromstring</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
-            <span class="c1"># pylint: disable=len-as-condition</span>
-            <span class="n">value</span> <span class="o">=</span> <span class="n">new_val</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_val</span><span class="p">)</span> <span class="k">else</span> <span class="n">_to_numeric</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
-        <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
-            <span class="n">value</span> <span class="o">=</span> <span class="n">_to_numeric</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">value</span> <span class="o">=</span> <span class="n">_to_numeric</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span>
-
-
-<span class="k">def</span> <span class="nf">_parse_tags</span><span class="p">(</span><span class="n">tags</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-    <span class="n">parsed_tags</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">tags</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">_parse_tag</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
-        <span class="n">parsed_tags</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-    <span class="k">return</span> <span class="n">parsed_tags</span>
-
-
-<span class="n">NETCDF_DTYPE_MAP</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="mi">0</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span>  <span class="c1"># NC_NAT</span>
-    <span class="mi">1</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">byte</span><span class="p">,</span>  <span class="c1"># NC_BYTE</span>
-    <span class="mi">2</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">char</span><span class="p">,</span>  <span class="c1"># NC_CHAR</span>
-    <span class="mi">3</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">short</span><span class="p">,</span>  <span class="c1"># NC_SHORT</span>
-    <span class="mi">4</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">int_</span><span class="p">,</span>  <span class="c1"># NC_INT, NC_LONG</span>
-    <span class="mi">5</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>  <span class="c1"># NC_FLOAT</span>
-    <span class="mi">6</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">double</span><span class="p">,</span>  <span class="c1"># NC_DOUBLE</span>
-    <span class="mi">7</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ubyte</span><span class="p">,</span>  <span class="c1"># NC_UBYTE</span>
-    <span class="mi">8</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ushort</span><span class="p">,</span>  <span class="c1"># NC_USHORT</span>
-    <span class="mi">9</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">uint</span><span class="p">,</span>  <span class="c1"># NC_UINT</span>
-    <span class="mi">10</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">int64</span><span class="p">,</span>  <span class="c1"># NC_INT64</span>
-    <span class="mi">11</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">,</span>  <span class="c1"># NC_UINT64</span>
-    <span class="mi">12</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span>  <span class="c1"># NC_STRING</span>
-<span class="p">}</span>
-
-
-<span class="k">def</span> <span class="nf">_load_netcdf_attrs</span><span class="p">(</span><span class="o">*</span><span class="p">,</span> <span class="n">tags</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">data_array</span><span class="p">:</span> <span class="n">DataArray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Loads the netCDF attributes into the data array</span>
-
-<span class="sd">    Attributes stored in this format:</span>
-<span class="sd">    - variable_name#attr_name: attr_value</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">tags</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">_parse_tag</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
-        <span class="n">key_split</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;#&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">key_split</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">continue</span>
-        <span class="n">variable_name</span><span class="p">,</span> <span class="n">attr_name</span> <span class="o">=</span> <span class="n">key_split</span>
-        <span class="k">if</span> <span class="n">variable_name</span> <span class="ow">in</span> <span class="n">data_array</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
-            <span class="n">data_array</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">attr_name</span><span class="p">:</span> <span class="n">value</span><span class="p">})</span>
-
-
-<span class="k">def</span> <span class="nf">_parse_netcdf_attr_array</span><span class="p">(</span><span class="n">attr</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">NDArray</span><span class="p">,</span> <span class="nb">str</span><span class="p">],</span> <span class="o">*</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">NDArray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Expected format: &#39;{2,6}&#39; or &#39;[2. 6.]&#39;</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">value</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">NDArray</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">]</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">attr</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">attr</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;{&quot;</span><span class="p">):</span>
-            <span class="n">value</span> <span class="o">=</span> <span class="n">attr</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">value</span> <span class="o">=</span> <span class="n">attr</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;[]&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
-    <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">attr</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">):</span>
-        <span class="n">value</span> <span class="o">=</span> <span class="p">[</span><span class="n">attr</span><span class="p">]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">value</span> <span class="o">=</span> <span class="n">attr</span>
-    <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_load_netcdf_1d_coords</span><span class="p">(</span><span class="n">tags</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Dimension information:</span>
-<span class="sd">        - NETCDF_DIM_EXTRA: &#39;{time}&#39; (comma separated list of dim names)</span>
-<span class="sd">        - NETCDF_DIM_time_DEF: &#39;{2,6}&#39; or &#39;[2. 6.]&#39; (dim size, dim dtype)</span>
-<span class="sd">        - NETCDF_DIM_time_VALUES: &#39;{0,872712.659688}&#39; (comma separated list of data) or [     0.       872712.659688]</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">dim_names</span> <span class="o">=</span> <span class="n">tags</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;NETCDF_DIM_EXTRA&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">dim_names</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">{}</span>
-    <span class="n">dim_names</span> <span class="o">=</span> <span class="n">_parse_netcdf_attr_array</span><span class="p">(</span><span class="n">dim_names</span><span class="p">)</span>
-    <span class="n">coords</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">dim_name</span> <span class="ow">in</span> <span class="n">dim_names</span><span class="p">:</span>
-        <span class="n">dim_def</span> <span class="o">=</span> <span class="n">tags</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;NETCDF_DIM_</span><span class="si">{</span><span class="n">dim_name</span><span class="si">}</span><span class="s2">_DEF&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">dim_def</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">continue</span>
-        <span class="c1"># pylint: disable=unused-variable</span>
-        <span class="n">dim_size</span><span class="p">,</span> <span class="n">dim_dtype</span> <span class="o">=</span> <span class="n">_parse_netcdf_attr_array</span><span class="p">(</span><span class="n">dim_def</span><span class="p">)</span>
-        <span class="n">dim_dtype</span> <span class="o">=</span> <span class="n">NETCDF_DTYPE_MAP</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">dim_dtype</span><span class="p">)),</span> <span class="nb">object</span><span class="p">)</span>
-        <span class="n">dim_values</span> <span class="o">=</span> <span class="n">_parse_netcdf_attr_array</span><span class="p">(</span><span class="n">tags</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;NETCDF_DIM_</span><span class="si">{</span><span class="n">dim_name</span><span class="si">}</span><span class="s2">_VALUES&quot;</span><span class="p">])</span>
-        <span class="n">coords</span><span class="p">[</span><span class="n">dim_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">IndexVariable</span><span class="p">(</span><span class="n">dim_name</span><span class="p">,</span> <span class="n">dim_values</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">coords</span>
-
-
-<span class="k">def</span> <span class="nf">build_subdataset_filter</span><span class="p">(</span>
-    <span class="n">group_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]]],</span>
-    <span class="n">variable_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]]],</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Example::</span>
-<span class="sd">        &#39;HDF4_EOS:EOS_GRID:&quot;./modis/MOD09GQ.A2017290.h11v04.006.NRT.hdf&quot;:</span>
-<span class="sd">        MODIS_Grid_2D:sur_refl_b01_1&#39;</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    group_names: str or list or tuple</span>
-<span class="sd">        Name or names of netCDF groups to filter by.</span>
-
-<span class="sd">    variable_names: str or list or tuple</span>
-<span class="sd">        Name or names of netCDF variables to filter by.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    re.SRE_Pattern: output of re.compile()</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">variable_query</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;\w+&quot;</span>
-    <span class="k">if</span> <span class="n">variable_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">variable_names</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
-            <span class="n">variable_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">variable_names</span><span class="p">]</span>
-        <span class="n">variable_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">re</span><span class="o">.</span><span class="n">escape</span><span class="p">(</span><span class="n">variable_name</span><span class="p">)</span> <span class="k">for</span> <span class="n">variable_name</span> <span class="ow">in</span> <span class="n">variable_names</span><span class="p">]</span>
-        <span class="n">variable_query</span> <span class="o">=</span> <span class="sa">rf</span><span class="s2">&quot;(?:</span><span class="si">{</span><span class="s1">&#39;|&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">variable_names</span><span class="p">)</span><span class="si">}</span><span class="s2">)&quot;</span>
-    <span class="k">if</span> <span class="n">group_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">group_names</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
-            <span class="n">group_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">group_names</span><span class="p">]</span>
-        <span class="n">group_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">re</span><span class="o">.</span><span class="n">escape</span><span class="p">(</span><span class="n">group_name</span><span class="p">)</span> <span class="k">for</span> <span class="n">group_name</span> <span class="ow">in</span> <span class="n">group_names</span><span class="p">]</span>
-        <span class="n">group_query</span> <span class="o">=</span> <span class="sa">rf</span><span class="s2">&quot;(?:</span><span class="si">{</span><span class="s1">&#39;|&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">group_names</span><span class="p">)</span><span class="si">}</span><span class="s2">)&quot;</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="sa">r</span><span class="s2">&quot;.*(?:\:/|\:)(/+)?&quot;</span><span class="p">,</span> <span class="n">variable_query</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;$&quot;</span><span class="p">]))</span>
-    <span class="k">return</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
-        <span class="sa">r</span><span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-            <span class="p">[</span><span class="sa">r</span><span class="s2">&quot;.*(?:\:/|\:)(/+)?&quot;</span><span class="p">,</span> <span class="n">group_query</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;[:/](/+)?&quot;</span><span class="p">,</span> <span class="n">variable_query</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;$&quot;</span><span class="p">]</span>
-        <span class="p">)</span>
-    <span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_get_rasterio_attrs</span><span class="p">(</span><span class="n">riods</span><span class="p">:</span> <span class="n">RasterioReader</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Get rasterio specific attributes</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># pylint: disable=too-many-branches</span>
-    <span class="c1"># Add rasterio attributes</span>
-    <span class="n">attrs</span> <span class="o">=</span> <span class="n">_parse_tags</span><span class="p">({</span><span class="o">**</span><span class="n">riods</span><span class="o">.</span><span class="n">tags</span><span class="p">(),</span> <span class="o">**</span><span class="n">riods</span><span class="o">.</span><span class="n">tags</span><span class="p">(</span><span class="mi">1</span><span class="p">)})</span>
-    <span class="c1"># remove attributes with informaiton</span>
-    <span class="c1"># that should be added by GDAL/rasterio</span>
-    <span class="k">for</span> <span class="n">unwanted_attr</span> <span class="ow">in</span> <span class="n">FILL_VALUE_NAMES</span> <span class="o">+</span> <span class="n">UNWANTED_RIO_ATTRS</span><span class="p">:</span>
-        <span class="n">attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">unwanted_attr</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">riods</span><span class="o">.</span><span class="n">nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="c1"># The nodata values for the raster bands</span>
-        <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">nodata</span>
-    <span class="c1"># The scale values for the raster bands</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">scales</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;scales&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">scales</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-            <span class="s2">&quot;Offsets differ across bands. The &#39;scale_factor&#39; attribute will &quot;</span>
-            <span class="s2">&quot;not be added. See the &#39;scales&#39; attribute.&quot;</span>
-        <span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;scale_factor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">scales</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="c1"># The offset values for the raster bands</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">offsets</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;offsets&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">offsets</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-            <span class="s2">&quot;Offsets differ across bands. The &#39;add_offset&#39; attribute will &quot;</span>
-            <span class="s2">&quot;not be added. See the &#39;offsets&#39; attribute.&quot;</span>
-        <span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;add_offset&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">offsets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">descriptions</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">descriptions</span><span class="p">))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;long_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">descriptions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Descriptions for each dataset band</span>
-            <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;long_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">descriptions</span>
-    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">units</span><span class="p">):</span>
-        <span class="c1"># A list of units string for each dataset band</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">units</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;units&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">units</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;units&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">units</span>
-
-    <span class="k">return</span> <span class="n">attrs</span>
-
-
-<span class="k">def</span> <span class="nf">_decode_datetime_cf</span><span class="p">(</span>
-    <span class="n">data_array</span><span class="p">:</span> <span class="n">DataArray</span><span class="p">,</span>
-    <span class="n">decode_times</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">decode_timedelta</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">],</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataArray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Decide the datetime based on CF conventions</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">decode_timedelta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">decode_timedelta</span> <span class="o">=</span> <span class="n">decode_times</span>
-
-    <span class="k">for</span> <span class="n">coord</span> <span class="ow">in</span> <span class="n">data_array</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
-        <span class="n">time_var</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="n">decode_times</span> <span class="ow">and</span> <span class="s2">&quot;since&quot;</span> <span class="ow">in</span> <span class="n">data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;units&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">):</span>
-            <span class="n">time_var</span> <span class="o">=</span> <span class="n">times</span><span class="o">.</span><span class="n">CFDatetimeCoder</span><span class="p">(</span><span class="n">use_cftime</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span>
-                <span class="n">as_variable</span><span class="p">(</span><span class="n">data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]),</span> <span class="n">name</span><span class="o">=</span><span class="n">coord</span>
-            <span class="p">)</span>
-        <span class="k">elif</span> <span class="p">(</span>
-            <span class="n">decode_timedelta</span>
-            <span class="ow">and</span> <span class="n">data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;units&quot;</span><span class="p">)</span> <span class="ow">in</span> <span class="n">times</span><span class="o">.</span><span class="n">TIME_UNITS</span>
-        <span class="p">):</span>
-            <span class="n">time_var</span> <span class="o">=</span> <span class="n">times</span><span class="o">.</span><span class="n">CFTimedeltaCoder</span><span class="p">()</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span>
-                <span class="n">as_variable</span><span class="p">(</span><span class="n">data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]),</span> <span class="n">name</span><span class="o">=</span><span class="n">coord</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="n">time_var</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">dimensions</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">attributes</span><span class="p">,</span> <span class="n">encoding</span> <span class="o">=</span> <span class="n">variables</span><span class="o">.</span><span class="n">unpack_for_decoding</span><span class="p">(</span>
-                <span class="n">time_var</span>
-            <span class="p">)</span>
-            <span class="n">data_array</span> <span class="o">=</span> <span class="n">data_array</span><span class="o">.</span><span class="n">assign_coords</span><span class="p">(</span>
-                <span class="p">{</span>
-                    <span class="n">coord</span><span class="p">:</span> <span class="n">IndexVariable</span><span class="p">(</span>
-                        <span class="n">dims</span><span class="o">=</span><span class="n">dimensions</span><span class="p">,</span>
-                        <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span>
-                        <span class="n">attrs</span><span class="o">=</span><span class="n">attributes</span><span class="p">,</span>
-                        <span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="p">}</span>
-            <span class="p">)</span>
-    <span class="k">return</span> <span class="n">data_array</span>
-
-
-<span class="k">def</span> <span class="nf">_parse_driver_tags</span><span class="p">(</span>
-    <span class="n">riods</span><span class="p">:</span> <span class="n">RasterioReader</span><span class="p">,</span>
-    <span class="n">attrs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span>
-    <span class="n">coords</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-    <span class="c1"># Parse extra metadata from tags, if supported</span>
-    <span class="n">parsers</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;ENVI&quot;</span><span class="p">:</span> <span class="n">_parse_envi</span><span class="p">}</span>
-
-    <span class="n">driver</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">driver</span>
-    <span class="k">if</span> <span class="n">driver</span> <span class="ow">in</span> <span class="n">parsers</span><span class="p">:</span>
-        <span class="n">meta</span> <span class="o">=</span> <span class="n">parsers</span><span class="p">[</span><span class="n">driver</span><span class="p">](</span><span class="n">riods</span><span class="o">.</span><span class="n">tags</span><span class="p">(</span><span class="n">ns</span><span class="o">=</span><span class="n">driver</span><span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">meta</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="c1"># Add values as coordinates if they match the band count,</span>
-            <span class="c1"># as attributes otherwise</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="o">==</span> <span class="n">riods</span><span class="o">.</span><span class="n">count</span><span class="p">:</span>
-                <span class="n">coords</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;band&quot;</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">attrs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-
-
-<span class="k">def</span> <span class="nf">_pop_global_netcdf_attrs_from_vars</span><span class="p">(</span><span class="n">dataset_to_clean</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
-    <span class="c1"># remove GLOBAL netCDF attributes from dataset variables</span>
-    <span class="k">for</span> <span class="n">coord</span> <span class="ow">in</span> <span class="n">dataset_to_clean</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">variable</span> <span class="ow">in</span> <span class="n">dataset_to_clean</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
-            <span class="n">dataset_to_clean</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span> <span class="o">=</span> <span class="p">{</span>
-                <span class="n">attr</span><span class="p">:</span> <span class="n">value</span>
-                <span class="k">for</span> <span class="n">attr</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">dataset_to_clean</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-                <span class="k">if</span> <span class="n">attr</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dataset_to_clean</span><span class="o">.</span><span class="n">attrs</span>
-                <span class="ow">and</span> <span class="ow">not</span> <span class="n">attr</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">coord</span><span class="si">}</span><span class="s2">#&quot;</span><span class="p">)</span>
-            <span class="p">}</span>
-    <span class="k">return</span> <span class="n">dataset_to_clean</span>
-
-
-<span class="k">def</span> <span class="nf">_subdataset_groups_to_dataset</span><span class="p">(</span>
-    <span class="o">*</span><span class="p">,</span> <span class="n">dim_groups</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">]],</span> <span class="n">global_tags</span><span class="p">:</span> <span class="nb">dict</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">Dataset</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="n">Dataset</span><span class="p">]]:</span>
-    <span class="k">if</span> <span class="n">dim_groups</span><span class="p">:</span>
-        <span class="n">dataset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Dataset</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="n">Dataset</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">dim_group</span> <span class="ow">in</span> <span class="n">dim_groups</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-            <span class="n">dataset_group</span> <span class="o">=</span> <span class="n">_pop_global_netcdf_attrs_from_vars</span><span class="p">(</span>
-                <span class="n">Dataset</span><span class="p">(</span><span class="n">dim_group</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">global_tags</span><span class="p">)</span>
-            <span class="p">)</span>
-
-            <span class="k">def</span> <span class="nf">_ds_close</span><span class="p">():</span>
-                <span class="c1"># pylint: disable=cell-var-from-loop</span>
-                <span class="k">for</span> <span class="n">data_var</span> <span class="ow">in</span> <span class="n">dim_group</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-                    <span class="n">data_var</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-            <span class="n">dataset_group</span><span class="o">.</span><span class="n">set_close</span><span class="p">(</span><span class="n">_ds_close</span><span class="p">)</span>
-            <span class="n">dataset</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dataset_group</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">attrs</span><span class="o">=</span><span class="n">global_tags</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">dataset</span>
-
-
-<span class="k">def</span> <span class="nf">_load_subdatasets</span><span class="p">(</span>
-    <span class="n">riods</span><span class="p">:</span> <span class="n">RasterioReader</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">group</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]]],</span>
-    <span class="n">variable</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]]],</span>
-    <span class="n">parse_coordinates</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">chunks</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">dict</span><span class="p">]],</span>
-    <span class="n">cache</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">],</span>
-    <span class="n">lock</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
-    <span class="n">masked</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">mask_and_scale</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">decode_times</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">decode_timedelta</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">],</span>
-    <span class="o">**</span><span class="n">open_kwargs</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">Dataset</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="n">Dataset</span><span class="p">]]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Load in rasterio subdatasets</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">dim_groups</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">]]</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
-    <span class="n">subdataset_filter</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">if</span> <span class="nb">any</span><span class="p">((</span><span class="n">group</span><span class="p">,</span> <span class="n">variable</span><span class="p">)):</span>
-        <span class="n">subdataset_filter</span> <span class="o">=</span> <span class="n">build_subdataset_filter</span><span class="p">(</span><span class="n">group</span><span class="p">,</span> <span class="n">variable</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">subdataset</span> <span class="ow">in</span> <span class="n">riods</span><span class="o">.</span><span class="n">subdatasets</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">subdataset_filter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">subdataset_filter</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">subdataset</span><span class="p">):</span>
-            <span class="k">continue</span>
-        <span class="k">with</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">subdataset</span><span class="p">)</span> <span class="k">as</span> <span class="n">rds</span><span class="p">:</span>
-            <span class="n">shape</span> <span class="o">=</span> <span class="n">rds</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">rioda</span><span class="p">:</span> <span class="n">DataArray</span> <span class="o">=</span> <span class="n">open_rasterio</span><span class="p">(</span>  <span class="c1"># type: ignore</span>
-            <span class="n">subdataset</span><span class="p">,</span>
-            <span class="n">parse_coordinates</span><span class="o">=</span><span class="n">shape</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dim_groups</span> <span class="ow">and</span> <span class="n">parse_coordinates</span><span class="p">,</span>
-            <span class="n">chunks</span><span class="o">=</span><span class="n">chunks</span><span class="p">,</span>
-            <span class="n">cache</span><span class="o">=</span><span class="n">cache</span><span class="p">,</span>
-            <span class="n">lock</span><span class="o">=</span><span class="n">lock</span><span class="p">,</span>
-            <span class="n">masked</span><span class="o">=</span><span class="n">masked</span><span class="p">,</span>
-            <span class="n">mask_and_scale</span><span class="o">=</span><span class="n">mask_and_scale</span><span class="p">,</span>
-            <span class="n">default_name</span><span class="o">=</span><span class="n">subdataset</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">,</span> <span class="s2">&quot;_&quot;</span><span class="p">),</span>
-            <span class="n">decode_times</span><span class="o">=</span><span class="n">decode_times</span><span class="p">,</span>
-            <span class="n">decode_timedelta</span><span class="o">=</span><span class="n">decode_timedelta</span><span class="p">,</span>
-            <span class="o">**</span><span class="n">open_kwargs</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">dim_groups</span><span class="p">[</span><span class="n">shape</span><span class="p">][</span><span class="n">rioda</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">rioda</span>
-    <span class="k">return</span> <span class="n">_subdataset_groups_to_dataset</span><span class="p">(</span>
-        <span class="n">dim_groups</span><span class="o">=</span><span class="n">dim_groups</span><span class="p">,</span> <span class="n">global_tags</span><span class="o">=</span><span class="n">_parse_tags</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">tags</span><span class="p">())</span>
-    <span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_load_bands_as_variables</span><span class="p">(</span>
-    <span class="n">riods</span><span class="p">:</span> <span class="n">RasterioReader</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">parse_coordinates</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">chunks</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">dict</span><span class="p">]],</span>
-    <span class="n">cache</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">],</span>
-    <span class="n">lock</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
-    <span class="n">masked</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">mask_and_scale</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">decode_times</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">decode_timedelta</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">],</span>
-    <span class="n">vrt_params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">],</span>
-    <span class="o">**</span><span class="n">open_kwargs</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">Dataset</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="n">Dataset</span><span class="p">]]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Load in rasterio bands as variables</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">global_tags</span> <span class="o">=</span> <span class="n">_parse_tags</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">tags</span><span class="p">())</span>
-    <span class="n">data_vars</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">band</span> <span class="ow">in</span> <span class="n">riods</span><span class="o">.</span><span class="n">indexes</span><span class="p">:</span>
-        <span class="n">band_riods</span> <span class="o">=</span> <span class="n">SingleBandDatasetReader</span><span class="p">(</span>
-            <span class="n">riods</span><span class="o">=</span><span class="n">riods</span><span class="p">,</span>
-            <span class="n">bidx</span><span class="o">=</span><span class="n">band</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span>
-            <span class="n">vrt_params</span><span class="o">=</span><span class="n">vrt_params</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">band_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;band_</span><span class="si">{</span><span class="n">band</span><span class="si">}</span><span class="s2">&quot;</span>
-        <span class="n">data_vars</span><span class="p">[</span><span class="n">band_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">open_rasterio</span><span class="p">(</span>  <span class="c1"># type: ignore</span>
-                <span class="n">band_riods</span><span class="p">,</span>
-                <span class="n">parse_coordinates</span><span class="o">=</span><span class="n">band</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">parse_coordinates</span><span class="p">,</span>
-                <span class="n">chunks</span><span class="o">=</span><span class="n">chunks</span><span class="p">,</span>
-                <span class="n">cache</span><span class="o">=</span><span class="n">cache</span><span class="p">,</span>
-                <span class="n">lock</span><span class="o">=</span><span class="n">lock</span><span class="p">,</span>
-                <span class="n">masked</span><span class="o">=</span><span class="n">masked</span><span class="p">,</span>
-                <span class="n">mask_and_scale</span><span class="o">=</span><span class="n">mask_and_scale</span><span class="p">,</span>
-                <span class="n">default_name</span><span class="o">=</span><span class="n">band_name</span><span class="p">,</span>
-                <span class="n">decode_times</span><span class="o">=</span><span class="n">decode_times</span><span class="p">,</span>
-                <span class="n">decode_timedelta</span><span class="o">=</span><span class="n">decode_timedelta</span><span class="p">,</span>
-                <span class="o">**</span><span class="n">open_kwargs</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>  <span class="c1"># type: ignore</span>
-            <span class="o">.</span><span class="n">drop_vars</span><span class="p">(</span><span class="s2">&quot;band&quot;</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
-        <span class="p">)</span>
-    <span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">global_tags</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_ds_close</span><span class="p">():</span>
-        <span class="k">for</span> <span class="n">data_var</span> <span class="ow">in</span> <span class="n">data_vars</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-            <span class="n">data_var</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-    <span class="n">dataset</span><span class="o">.</span><span class="n">set_close</span><span class="p">(</span><span class="n">_ds_close</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">dataset</span>
-
-
-<span class="k">def</span> <span class="nf">_prepare_dask</span><span class="p">(</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">result</span><span class="p">:</span> <span class="n">DataArray</span><span class="p">,</span>
-    <span class="n">riods</span><span class="p">:</span> <span class="n">RasterioReader</span><span class="p">,</span>
-    <span class="n">filename</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">PathLike</span><span class="p">],</span>
-    <span class="n">chunks</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">dict</span><span class="p">],</span>
-    <span class="n">bidx</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataArray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Prepare the data for dask computations</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># pylint: disable=import-outside-toplevel</span>
-    <span class="kn">from</span> <span class="nn">dask.base</span> <span class="kn">import</span> <span class="n">tokenize</span>
-
-    <span class="c1"># augment the token with the file modification time</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">mtime</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">getmtime</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
-    <span class="k">except</span> <span class="p">(</span><span class="ne">TypeError</span><span class="p">,</span> <span class="ne">OSError</span><span class="p">):</span>
-        <span class="c1"># the filename is probably an s3 bucket rather than a regular file</span>
-        <span class="n">mtime</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">if</span> <span class="n">chunks</span> <span class="ow">in</span> <span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="s2">&quot;auto&quot;</span><span class="p">):</span>
-        <span class="kn">from</span> <span class="nn">dask.array.core</span> <span class="kn">import</span> <span class="n">normalize_chunks</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">_DASK_GTE_018</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Automatic chunking requires dask &gt;= 0.18.0&quot;</span><span class="p">)</span>
-        <span class="n">block_shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="n">riods</span><span class="o">.</span><span class="n">block_shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">chunks</span> <span class="o">=</span> <span class="n">normalize_chunks</span><span class="p">(</span>
-            <span class="n">chunks</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;auto&quot;</span><span class="p">,</span> <span class="s2">&quot;auto&quot;</span><span class="p">),</span>
-            <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="n">riods</span><span class="o">.</span><span class="n">height</span><span class="p">,</span> <span class="n">riods</span><span class="o">.</span><span class="n">width</span><span class="p">),</span>
-            <span class="n">dtype</span><span class="o">=</span><span class="n">_rasterio_to_numpy_dtype</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">dtypes</span><span class="p">),</span>
-            <span class="n">previous_chunks</span><span class="o">=</span><span class="n">block_shape</span><span class="p">,</span>
-        <span class="p">)</span>
-    <span class="n">token_filename</span> <span class="o">=</span> <span class="n">filename</span>
-    <span class="k">if</span> <span class="n">bidx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">token_filename</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">filename</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="n">bidx</span><span class="si">}</span><span class="s2">&quot;</span>
-    <span class="n">token</span> <span class="o">=</span> <span class="n">tokenize</span><span class="p">(</span><span class="n">token_filename</span><span class="p">,</span> <span class="n">mtime</span><span class="p">,</span> <span class="n">chunks</span><span class="p">)</span>
-    <span class="n">name_prefix</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;open_rasterio-</span><span class="si">{</span><span class="n">token</span><span class="si">}</span><span class="s2">&quot;</span>
-    <span class="k">return</span> <span class="n">result</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="n">chunks</span><span class="p">,</span> <span class="n">name_prefix</span><span class="o">=</span><span class="n">name_prefix</span><span class="p">,</span> <span class="n">token</span><span class="o">=</span><span class="n">token</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_handle_encoding</span><span class="p">(</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">result</span><span class="p">:</span> <span class="n">DataArray</span><span class="p">,</span>
-    <span class="n">mask_and_scale</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">masked</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">da_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Hashable</span><span class="p">],</span>
-    <span class="n">unsigned</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">bool</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Make sure encoding handled properly</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="s2">&quot;grid_mapping&quot;</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">:</span>
-        <span class="n">variables</span><span class="o">.</span><span class="n">pop_to</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">,</span> <span class="s2">&quot;grid_mapping&quot;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">da_name</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">mask_and_scale</span><span class="p">:</span>
-        <span class="k">if</span> <span class="s2">&quot;scale_factor&quot;</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">:</span>
-            <span class="n">variables</span><span class="o">.</span><span class="n">pop_to</span><span class="p">(</span>
-                <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">,</span> <span class="s2">&quot;scale_factor&quot;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">da_name</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="s2">&quot;scales&quot;</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">:</span>
-            <span class="n">variables</span><span class="o">.</span><span class="n">pop_to</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">,</span> <span class="s2">&quot;scales&quot;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">da_name</span><span class="p">)</span>
-        <span class="k">if</span> <span class="s2">&quot;add_offset&quot;</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">:</span>
-            <span class="n">variables</span><span class="o">.</span><span class="n">pop_to</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">,</span> <span class="s2">&quot;add_offset&quot;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">da_name</span><span class="p">)</span>
-        <span class="k">if</span> <span class="s2">&quot;offsets&quot;</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">:</span>
-            <span class="n">variables</span><span class="o">.</span><span class="n">pop_to</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">,</span> <span class="s2">&quot;offsets&quot;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">da_name</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">masked</span><span class="p">:</span>
-        <span class="k">if</span> <span class="s2">&quot;_FillValue&quot;</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">:</span>
-            <span class="n">variables</span><span class="o">.</span><span class="n">pop_to</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">,</span> <span class="s2">&quot;_FillValue&quot;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">da_name</span><span class="p">)</span>
-        <span class="k">if</span> <span class="s2">&quot;missing_value&quot;</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">:</span>
-            <span class="n">variables</span><span class="o">.</span><span class="n">pop_to</span><span class="p">(</span>
-                <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">,</span> <span class="s2">&quot;missing_value&quot;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">da_name</span>
-            <span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">mask_and_scale</span> <span class="ow">and</span> <span class="n">unsigned</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="s2">&quot;_FillValue&quot;</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">:</span>
-        <span class="n">unsigned_dtype</span> <span class="o">=</span> <span class="n">_get_unsigned_dtype</span><span class="p">(</span>
-            <span class="n">unsigned</span><span class="o">=</span><span class="n">unsigned</span><span class="p">,</span>
-            <span class="n">dtype</span><span class="o">=</span><span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">],</span>
-        <span class="p">)</span>
-        <span class="k">if</span> <span class="n">unsigned_dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">unsigned_dtype</span><span class="o">.</span><span class="n">type</span><span class="p">(</span>
-                <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">]</span>
-            <span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_single_band_open</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">bidx</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Open file as if it only has a single band</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">SingleBandDatasetReader</span><span class="p">(</span>
-        <span class="n">riods</span><span class="o">=</span><span class="n">rasterio</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span>
-        <span class="n">bidx</span><span class="o">=</span><span class="n">bidx</span><span class="p">,</span>
-    <span class="p">)</span>
-
-
-<div class="viewcode-block" id="open_rasterio">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.open_rasterio">[docs]</a>
-<span class="k">def</span> <span class="nf">open_rasterio</span><span class="p">(</span>
-    <span class="n">filename</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span>
-        <span class="nb">str</span><span class="p">,</span>
-        <span class="n">os</span><span class="o">.</span><span class="n">PathLike</span><span class="p">,</span>
-        <span class="n">rasterio</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">DatasetReader</span><span class="p">,</span>
-        <span class="n">rasterio</span><span class="o">.</span><span class="n">vrt</span><span class="o">.</span><span class="n">WarpedVRT</span><span class="p">,</span>
-        <span class="n">SingleBandDatasetReader</span><span class="p">,</span>
-    <span class="p">],</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">parse_coordinates</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">chunks</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">dict</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">cache</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">lock</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">masked</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-    <span class="n">mask_and_scale</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-    <span class="n">variable</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">group</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">default_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">decode_times</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-    <span class="n">decode_timedelta</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">band_as_variable</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-    <span class="o">**</span><span class="n">open_kwargs</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="n">Dataset</span><span class="p">]]:</span>
-    <span class="c1"># pylint: disable=too-many-statements,too-many-locals,too-many-branches</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Open a file with rasterio (experimental).</span>
-
-<span class="sd">    This should work with any file that rasterio can open (most often:</span>
-<span class="sd">    geoTIFF). The x and y coordinates are generated automatically from the</span>
-<span class="sd">    file&#39;s geoinformation and refer to the center of the pixel.</span>
-
-<span class="sd">    .. versionadded:: 0.13 band_as_variable</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    filename: str, rasterio.io.DatasetReader, or rasterio.vrt.WarpedVRT</span>
-<span class="sd">        Path to the file to open. Or already open rasterio dataset.</span>
-<span class="sd">    parse_coordinates: bool, optional</span>
-<span class="sd">        Whether to parse the x and y coordinates out of the file&#39;s</span>
-<span class="sd">        ``transform`` attribute or not. The default is to automatically</span>
-<span class="sd">        parse the coordinates only if they are rectilinear (1D).</span>
-<span class="sd">        It can be useful to set ``parse_coordinates=False``</span>
-<span class="sd">        if your files are very large or if you don&#39;t need the coordinates.</span>
-<span class="sd">    chunks: int, tuple or dict, optional</span>
-<span class="sd">        Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or</span>
-<span class="sd">        ``{&#39;x&#39;: 5, &#39;y&#39;: 5}``. If chunks is provided, it used to load the new</span>
-<span class="sd">        DataArray into a dask array. Chunks can also be set to</span>
-<span class="sd">        ``True`` or ``&quot;auto&quot;`` to choose sensible chunk sizes according to</span>
-<span class="sd">        ``dask.config.get(&quot;array.chunk-size&quot;)``.</span>
-<span class="sd">    cache: bool, optional</span>
-<span class="sd">        If True, cache data loaded from the underlying datastore in memory as</span>
-<span class="sd">        NumPy arrays when accessed to avoid reading from the underlying data-</span>
-<span class="sd">        store multiple times. Defaults to True unless you specify the `chunks`</span>
-<span class="sd">        argument to use dask, in which case it defaults to False.</span>
-<span class="sd">    lock: bool or dask.utils.SerializableLock, optional</span>
-
-<span class="sd">        If chunks is provided, this argument is used to ensure that only one</span>
-<span class="sd">        thread per process is reading from a rasterio file object at a time.</span>
-
-<span class="sd">        By default and when a lock instance is provided,</span>
-<span class="sd">        a :class:`xarray.backends.CachingFileManager` is used to cache File objects.</span>
-<span class="sd">        Since rasterio also caches some data, this will make repeated reads from the</span>
-<span class="sd">        same object fast.</span>
-
-<span class="sd">        When ``lock=False``, no lock is used, allowing for completely parallel reads</span>
-<span class="sd">        from multiple threads or processes. However, a new file handle is opened on</span>
-<span class="sd">        each request.</span>
-
-<span class="sd">    masked: bool, optional</span>
-<span class="sd">        If True, read the mask and set values to NaN. Defaults to False.</span>
-<span class="sd">    mask_and_scale: bool, default=False</span>
-<span class="sd">        Lazily scale (using the `scales` and `offsets` from rasterio) and mask.</span>
-<span class="sd">        If the _Unsigned attribute is present treat integer arrays as unsigned.</span>
-<span class="sd">    variable: str or list or tuple, optional</span>
-<span class="sd">        Variable name or names to use to filter loading.</span>
-<span class="sd">    group: str or list or tuple, optional</span>
-<span class="sd">        Group name or names to use to filter loading.</span>
-<span class="sd">    default_name: str, optional</span>
-<span class="sd">        The name of the data array if none exists. Default is None.</span>
-<span class="sd">    decode_times: bool, default=True</span>
-<span class="sd">        If True, decode times encoded in the standard NetCDF datetime format</span>
-<span class="sd">        into datetime objects. Otherwise, leave them encoded as numbers.</span>
-<span class="sd">    decode_timedelta: bool, optional</span>
-<span class="sd">        If True, decode variables and coordinates with time units in</span>
-<span class="sd">        {“days”, “hours”, “minutes”, “seconds”, “milliseconds”, “microseconds”}</span>
-<span class="sd">        into timedelta objects. If False, leave them encoded as numbers.</span>
-<span class="sd">        If None (default), assume the same value of decode_time.</span>
-<span class="sd">    band_as_variable: bool, default=False</span>
-<span class="sd">        If True, will load bands in a raster to separate variables.</span>
-<span class="sd">    **open_kwargs: kwargs, optional</span>
-<span class="sd">        Optional keyword arguments to pass into :func:`rasterio.open`.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    :obj:`xarray.Dataset` | :obj:`xarray.DataArray` | list[:obj:`xarray.Dataset`]:</span>
-<span class="sd">        The newly created dataset(s).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">parse_coordinates</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="n">parse_coordinates</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">parse_coordinates</span>
-    <span class="n">masked</span> <span class="o">=</span> <span class="n">masked</span> <span class="ow">or</span> <span class="n">mask_and_scale</span>
-    <span class="n">vrt_params</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="n">file_opener</span> <span class="o">=</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">open</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">SingleBandDatasetReader</span><span class="p">):</span>
-        <span class="n">file_opener</span> <span class="o">=</span> <span class="n">_single_band_open</span>
-        <span class="n">open_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">bidx</span><span class="o">=</span><span class="n">filename</span><span class="o">.</span><span class="n">_bidx</span><span class="p">)</span>
-        <span class="n">vrt_params</span> <span class="o">=</span> <span class="n">filename</span><span class="o">.</span><span class="n">_vrt_params</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="p">(</span><span class="n">rasterio</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">DatasetReader</span><span class="p">,</span> <span class="n">SingleBandDatasetReader</span><span class="p">)):</span>
-        <span class="n">filename</span> <span class="o">=</span> <span class="n">filename</span><span class="o">.</span><span class="n">name</span>
-    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">vrt</span><span class="o">.</span><span class="n">WarpedVRT</span><span class="p">):</span>
-        <span class="n">vrt</span> <span class="o">=</span> <span class="n">filename</span>
-        <span class="n">filename</span> <span class="o">=</span> <span class="n">vrt</span><span class="o">.</span><span class="n">src_dataset</span><span class="o">.</span><span class="n">name</span>
-        <span class="n">vrt_params</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="s2">&quot;src_crs&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">src_crs</span><span class="o">.</span><span class="n">to_string</span><span class="p">()</span> <span class="k">if</span> <span class="n">vrt</span><span class="o">.</span><span class="n">src_crs</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
-            <span class="s2">&quot;crs&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">dst_crs</span><span class="o">.</span><span class="n">to_string</span><span class="p">()</span> <span class="k">if</span> <span class="n">vrt</span><span class="o">.</span><span class="n">dst_crs</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
-            <span class="s2">&quot;resampling&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">resampling</span><span class="p">,</span>
-            <span class="s2">&quot;tolerance&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">tolerance</span><span class="p">,</span>
-            <span class="s2">&quot;src_nodata&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">src_nodata</span><span class="p">,</span>
-            <span class="s2">&quot;nodata&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">dst_nodata</span><span class="p">,</span>
-            <span class="s2">&quot;width&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">dst_width</span><span class="p">,</span>
-            <span class="s2">&quot;height&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">dst_height</span><span class="p">,</span>
-            <span class="s2">&quot;src_transform&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">src_transform</span><span class="p">,</span>
-            <span class="s2">&quot;transform&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">dst_transform</span><span class="p">,</span>
-            <span class="s2">&quot;dtype&quot;</span><span class="p">:</span> <span class="n">vrt</span><span class="o">.</span><span class="n">working_dtype</span><span class="p">,</span>
-            <span class="o">**</span><span class="n">vrt</span><span class="o">.</span><span class="n">warp_extras</span><span class="p">,</span>
-        <span class="p">}</span>
-
-    <span class="k">if</span> <span class="n">lock</span> <span class="ow">in</span> <span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
-        <span class="n">lock</span> <span class="o">=</span> <span class="n">RASTERIO_LOCK</span>
-    <span class="k">elif</span> <span class="n">lock</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-        <span class="n">lock</span> <span class="o">=</span> <span class="n">NO_LOCK</span>
-
-    <span class="c1"># ensure default for sharing is False</span>
-    <span class="c1"># ref https://github.com/mapbox/rasterio/issues/1504</span>
-    <span class="n">open_kwargs</span><span class="p">[</span><span class="s2">&quot;sharing&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">open_kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sharing&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
-
-    <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">(</span><span class="n">record</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">rio_warnings</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">lock</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">NO_LOCK</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">PathLike</span><span class="p">)):</span>
-            <span class="n">manager</span><span class="p">:</span> <span class="n">FileManager</span> <span class="o">=</span> <span class="n">CachingFileManager</span><span class="p">(</span>
-                <span class="n">file_opener</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">lock</span><span class="o">=</span><span class="n">lock</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="n">open_kwargs</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">manager</span> <span class="o">=</span> <span class="n">URIManager</span><span class="p">(</span><span class="n">file_opener</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="n">open_kwargs</span><span class="p">)</span>
-        <span class="n">riods</span> <span class="o">=</span> <span class="n">manager</span><span class="o">.</span><span class="n">acquire</span><span class="p">()</span>
-        <span class="n">captured_warnings</span> <span class="o">=</span> <span class="n">rio_warnings</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-    <span class="c1"># raise the NotGeoreferencedWarning if applicable</span>
-    <span class="k">for</span> <span class="n">rio_warning</span> <span class="ow">in</span> <span class="n">captured_warnings</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">riods</span><span class="o">.</span><span class="n">subdatasets</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span>
-            <span class="n">rio_warning</span><span class="o">.</span><span class="n">message</span><span class="p">,</span> <span class="n">NotGeoreferencedWarning</span>
-        <span class="p">):</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">rio_warning</span><span class="o">.</span><span class="n">message</span><span class="p">),</span> <span class="nb">type</span><span class="p">(</span><span class="n">rio_warning</span><span class="o">.</span><span class="n">message</span><span class="p">))</span>  <span class="c1"># type: ignore</span>
-
-    <span class="c1"># open the subdatasets if they exist</span>
-    <span class="k">if</span> <span class="n">riods</span><span class="o">.</span><span class="n">subdatasets</span><span class="p">:</span>
-        <span class="n">subdataset_result</span> <span class="o">=</span> <span class="n">_load_subdatasets</span><span class="p">(</span>
-            <span class="n">riods</span><span class="o">=</span><span class="n">riods</span><span class="p">,</span>
-            <span class="n">group</span><span class="o">=</span><span class="n">group</span><span class="p">,</span>
-            <span class="n">variable</span><span class="o">=</span><span class="n">variable</span><span class="p">,</span>
-            <span class="n">parse_coordinates</span><span class="o">=</span><span class="n">parse_coordinates</span><span class="p">,</span>
-            <span class="n">chunks</span><span class="o">=</span><span class="n">chunks</span><span class="p">,</span>
-            <span class="n">cache</span><span class="o">=</span><span class="n">cache</span><span class="p">,</span>
-            <span class="n">lock</span><span class="o">=</span><span class="n">lock</span><span class="p">,</span>
-            <span class="n">masked</span><span class="o">=</span><span class="n">masked</span><span class="p">,</span>
-            <span class="n">mask_and_scale</span><span class="o">=</span><span class="n">mask_and_scale</span><span class="p">,</span>
-            <span class="n">decode_times</span><span class="o">=</span><span class="n">decode_times</span><span class="p">,</span>
-            <span class="n">decode_timedelta</span><span class="o">=</span><span class="n">decode_timedelta</span><span class="p">,</span>
-            <span class="o">**</span><span class="n">open_kwargs</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">manager</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">subdataset_result</span>
-
-    <span class="k">if</span> <span class="n">band_as_variable</span><span class="p">:</span>
-        <span class="n">dataset_result</span> <span class="o">=</span> <span class="n">_load_bands_as_variables</span><span class="p">(</span>
-            <span class="n">riods</span><span class="o">=</span><span class="n">riods</span><span class="p">,</span>
-            <span class="n">parse_coordinates</span><span class="o">=</span><span class="n">parse_coordinates</span><span class="p">,</span>
-            <span class="n">chunks</span><span class="o">=</span><span class="n">chunks</span><span class="p">,</span>
-            <span class="n">cache</span><span class="o">=</span><span class="n">cache</span><span class="p">,</span>
-            <span class="n">lock</span><span class="o">=</span><span class="n">lock</span><span class="p">,</span>
-            <span class="n">masked</span><span class="o">=</span><span class="n">masked</span><span class="p">,</span>
-            <span class="n">mask_and_scale</span><span class="o">=</span><span class="n">mask_and_scale</span><span class="p">,</span>
-            <span class="n">decode_times</span><span class="o">=</span><span class="n">decode_times</span><span class="p">,</span>
-            <span class="n">decode_timedelta</span><span class="o">=</span><span class="n">decode_timedelta</span><span class="p">,</span>
-            <span class="n">vrt_params</span><span class="o">=</span><span class="n">vrt_params</span><span class="p">,</span>
-            <span class="o">**</span><span class="n">open_kwargs</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">manager</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">dataset_result</span>
-
-    <span class="k">if</span> <span class="n">cache</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">cache</span> <span class="o">=</span> <span class="n">chunks</span> <span class="ow">is</span> <span class="kc">None</span>
-
-    <span class="n">riods</span> <span class="o">=</span> <span class="n">_ensure_warped_vrt</span><span class="p">(</span><span class="n">riods</span><span class="p">,</span> <span class="n">vrt_params</span><span class="p">)</span>
-
-    <span class="c1"># Get bands</span>
-    <span class="k">if</span> <span class="n">riods</span><span class="o">.</span><span class="n">count</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unknown dims&quot;</span><span class="p">)</span>
-
-    <span class="c1"># parse tags &amp; load alternate coords</span>
-    <span class="n">attrs</span> <span class="o">=</span> <span class="n">_get_rasterio_attrs</span><span class="p">(</span><span class="n">riods</span><span class="o">=</span><span class="n">riods</span><span class="p">)</span>
-    <span class="n">coords</span> <span class="o">=</span> <span class="n">_load_netcdf_1d_coords</span><span class="p">(</span><span class="n">attrs</span><span class="p">)</span>
-    <span class="n">_parse_driver_tags</span><span class="p">(</span><span class="n">riods</span><span class="o">=</span><span class="n">riods</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">attrs</span><span class="p">,</span> <span class="n">coords</span><span class="o">=</span><span class="n">coords</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">coord</span> <span class="ow">in</span> <span class="n">coords</span><span class="p">:</span>
-        <span class="k">if</span> <span class="sa">f</span><span class="s2">&quot;NETCDF_DIM_</span><span class="si">{</span><span class="n">coord</span><span class="si">}</span><span class="s2">&quot;</span> <span class="ow">in</span> <span class="n">attrs</span><span class="p">:</span>
-            <span class="n">coord_name</span> <span class="o">=</span> <span class="n">coord</span>
-            <span class="n">attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;NETCDF_DIM_</span><span class="si">{</span><span class="n">coord</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-            <span class="k">break</span>
-        <span class="k">if</span> <span class="sa">f</span><span class="s2">&quot;NETCDF_DIM_</span><span class="si">{</span><span class="n">coord</span><span class="si">}</span><span class="s2">_VALUES&quot;</span> <span class="ow">in</span> <span class="n">attrs</span><span class="p">:</span>
-            <span class="n">coord_name</span> <span class="o">=</span> <span class="n">coord</span>
-            <span class="n">attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;NETCDF_DIM_</span><span class="si">{</span><span class="n">coord</span><span class="si">}</span><span class="s2">_VALUES&quot;</span><span class="p">)</span>
-            <span class="n">attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;NETCDF_DIM_</span><span class="si">{</span><span class="n">coord</span><span class="si">}</span><span class="s2">_DEF&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-            <span class="n">attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;NETCDF_DIM_EXTRA&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-            <span class="k">break</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">coord_name</span> <span class="o">=</span> <span class="s2">&quot;band&quot;</span>
-        <span class="n">coords</span><span class="p">[</span><span class="n">coord_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">indexes</span><span class="p">)</span>
-
-    <span class="n">has_gcps</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">gcps</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">has_gcps</span><span class="p">:</span>
-        <span class="n">parse_coordinates</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="c1"># Get geospatial coordinates</span>
-    <span class="k">if</span> <span class="n">parse_coordinates</span><span class="p">:</span>
-        <span class="n">coords</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
-            <span class="n">_generate_spatial_coords</span><span class="p">(</span>
-                <span class="n">affine</span><span class="o">=</span><span class="n">riods</span><span class="o">.</span><span class="n">transform</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">riods</span><span class="o">.</span><span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="n">riods</span><span class="o">.</span><span class="n">height</span>
-            <span class="p">)</span>
-        <span class="p">)</span>
-
-    <span class="n">unsigned</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="n">encoding</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">if</span> <span class="n">mask_and_scale</span> <span class="ow">and</span> <span class="s2">&quot;_Unsigned&quot;</span> <span class="ow">in</span> <span class="n">attrs</span><span class="p">:</span>
-        <span class="n">unsigned</span> <span class="o">=</span> <span class="n">variables</span><span class="o">.</span><span class="n">pop_to</span><span class="p">(</span><span class="n">attrs</span><span class="p">,</span> <span class="n">encoding</span><span class="p">,</span> <span class="s2">&quot;_Unsigned&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;true&quot;</span>
-
-    <span class="k">if</span> <span class="n">masked</span><span class="p">:</span>
-        <span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_rasterio_to_numpy_dtype</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">dtypes</span><span class="p">))</span>
-
-    <span class="n">da_name</span> <span class="o">=</span> <span class="n">attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;NETCDF_VARNAME&quot;</span><span class="p">,</span> <span class="n">default_name</span><span class="p">)</span>
-    <span class="n">data</span><span class="p">:</span> <span class="n">Any</span> <span class="o">=</span> <span class="n">indexing</span><span class="o">.</span><span class="n">LazilyOuterIndexedArray</span><span class="p">(</span>
-        <span class="n">RasterioArrayWrapper</span><span class="p">(</span>
-            <span class="n">manager</span><span class="o">=</span><span class="n">manager</span><span class="p">,</span>
-            <span class="n">lock</span><span class="o">=</span><span class="n">lock</span><span class="p">,</span>
-            <span class="n">name</span><span class="o">=</span><span class="n">da_name</span><span class="p">,</span>
-            <span class="n">vrt_params</span><span class="o">=</span><span class="n">vrt_params</span><span class="p">,</span>
-            <span class="n">masked</span><span class="o">=</span><span class="n">masked</span><span class="p">,</span>
-            <span class="n">mask_and_scale</span><span class="o">=</span><span class="n">mask_and_scale</span><span class="p">,</span>
-            <span class="n">unsigned</span><span class="o">=</span><span class="n">unsigned</span><span class="p">,</span>
-        <span class="p">)</span>
-    <span class="p">)</span>
-
-    <span class="c1"># this lets you write arrays loaded with rasterio</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">indexing</span><span class="o">.</span><span class="n">CopyOnWriteArray</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">cache</span> <span class="ow">and</span> <span class="n">chunks</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">indexing</span><span class="o">.</span><span class="n">MemoryCachedArray</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-
-    <span class="n">result</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span>
-        <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">(</span><span class="n">coord_name</span><span class="p">,</span> <span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">),</span> <span class="n">coords</span><span class="o">=</span><span class="n">coords</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">attrs</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">da_name</span>
-    <span class="p">)</span>
-    <span class="n">result</span><span class="o">.</span><span class="n">encoding</span> <span class="o">=</span> <span class="n">encoding</span>
-
-    <span class="c1"># update attributes from NetCDF attributes</span>
-    <span class="n">_load_netcdf_attrs</span><span class="p">(</span><span class="n">tags</span><span class="o">=</span><span class="n">riods</span><span class="o">.</span><span class="n">tags</span><span class="p">(),</span> <span class="n">data_array</span><span class="o">=</span><span class="n">result</span><span class="p">)</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="n">_decode_datetime_cf</span><span class="p">(</span>
-        <span class="n">result</span><span class="p">,</span> <span class="n">decode_times</span><span class="o">=</span><span class="n">decode_times</span><span class="p">,</span> <span class="n">decode_timedelta</span><span class="o">=</span><span class="n">decode_timedelta</span>
-    <span class="p">)</span>
-
-    <span class="c1"># make sure the _FillValue is correct dtype</span>
-    <span class="k">if</span> <span class="s2">&quot;_FillValue&quot;</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">:</span>
-        <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">])</span>
-
-    <span class="c1"># handle encoding</span>
-    <span class="n">_handle_encoding</span><span class="p">(</span>
-        <span class="n">result</span><span class="o">=</span><span class="n">result</span><span class="p">,</span>
-        <span class="n">mask_and_scale</span><span class="o">=</span><span class="n">mask_and_scale</span><span class="p">,</span>
-        <span class="n">masked</span><span class="o">=</span><span class="n">masked</span><span class="p">,</span>
-        <span class="n">da_name</span><span class="o">=</span><span class="n">da_name</span><span class="p">,</span>
-        <span class="n">unsigned</span><span class="o">=</span><span class="n">unsigned</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="c1"># Affine transformation matrix (always available)</span>
-    <span class="c1"># This describes coefficients mapping pixel coordinates to CRS</span>
-    <span class="c1"># For serialization store as tuple of 6 floats, the last row being</span>
-    <span class="c1"># always (0, 0, 1) per definition (see</span>
-    <span class="c1"># https://github.com/sgillies/affine)</span>
-    <span class="n">result</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_transform</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">transform</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">rio_crs</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">crs</span> <span class="ow">or</span> <span class="n">result</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span>
-    <span class="k">if</span> <span class="n">rio_crs</span><span class="p">:</span>
-        <span class="n">result</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_crs</span><span class="p">(</span><span class="n">rio_crs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">has_gcps</span><span class="p">:</span>
-        <span class="n">result</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_gcps</span><span class="p">(</span><span class="o">*</span><span class="n">riods</span><span class="o">.</span><span class="n">gcps</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">chunks</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">result</span> <span class="o">=</span> <span class="n">_prepare_dask</span><span class="p">(</span>
-            <span class="n">result</span><span class="o">=</span><span class="n">result</span><span class="p">,</span>
-            <span class="n">riods</span><span class="o">=</span><span class="n">riods</span><span class="p">,</span>
-            <span class="n">filename</span><span class="o">=</span><span class="n">filename</span><span class="p">,</span>
-            <span class="n">chunks</span><span class="o">=</span><span class="n">chunks</span><span class="p">,</span>
-            <span class="n">bidx</span><span class="o">=</span><span class="n">open_kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;bidx&quot;</span><span class="p">),</span>
-        <span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;preferred_chunks&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="n">result</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span><span class="p">:</span> <span class="n">riods</span><span class="o">.</span><span class="n">block_shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-            <span class="n">result</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">:</span> <span class="n">riods</span><span class="o">.</span><span class="n">block_shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
-            <span class="n">coord_name</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
-        <span class="p">}</span>
-
-    <span class="c1"># add file path to encoding</span>
-    <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;source&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">riods</span><span class="o">.</span><span class="n">name</span>
-    <span class="n">result</span><span class="o">.</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;rasterio_dtype&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">riods</span><span class="o">.</span><span class="n">dtypes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="c1"># remove duplicate coordinate information</span>
-    <span class="k">for</span> <span class="n">coord</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
-        <span class="n">result</span><span class="o">.</span><span class="n">attrs</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="n">attr</span><span class="p">:</span> <span class="n">value</span>
-            <span class="k">for</span> <span class="n">attr</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">attr</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">coord</span><span class="si">}</span><span class="s2">#&quot;</span><span class="p">)</span>
-        <span class="p">}</span>
-    <span class="c1"># remove duplicate tags</span>
-    <span class="k">if</span> <span class="n">result</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
-        <span class="n">result</span><span class="o">.</span><span class="n">attrs</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="n">attr</span><span class="p">:</span> <span class="n">value</span>
-            <span class="k">for</span> <span class="n">attr</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">attr</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">result</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">#&quot;</span><span class="p">)</span>
-        <span class="p">}</span>
-    <span class="c1"># Make the file closeable</span>
-    <span class="n">result</span><span class="o">.</span><span class="n">set_close</span><span class="p">(</span><span class="n">manager</span><span class="o">.</span><span class="n">close</span><span class="p">)</span>
-    <span class="n">result</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_manager</span> <span class="o">=</span> <span class="n">manager</span>
-    <span class="k">return</span> <span class="n">result</span></div>
-
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2019-2023, Corteva Agriscience™.</p>
-  </div>
-
-  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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-          SphinxRtdTheme.Navigation.enable(true);
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diff --git a/0.18.0/_modules/rioxarray/_options.html b/0.18.0/_modules/rioxarray/_options.html
deleted file mode 100644
index f94472c5..00000000
--- a/0.18.0/_modules/rioxarray/_options.html
+++ /dev/null
@@ -1,205 +0,0 @@
-
-
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" data-content_root="../../">
-<head>
-  <meta charset="utf-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>rioxarray._options &mdash; rioxarray 0.18.0 documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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-      <li class="breadcrumb-item active">rioxarray._options</li>
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-             
-  <h1>Source code for rioxarray._options</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">This file contains global options for rioxarray</span>
-
-<span class="sd">Credits:</span>
-
-<span class="sd">This file was adopted from: https://github.com/pydata/xarray # noqa</span>
-<span class="sd">Source file: https://github.com/pydata/xarray/blob/2ab0666c1fcc493b1e0ebc7db14500c427f8804e/xarray/core/options.py  # noqa</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
-
-<span class="n">EXPORT_GRID_MAPPING</span> <span class="o">=</span> <span class="s2">&quot;export_grid_mapping&quot;</span>
-<span class="n">SKIP_MISSING_SPATIAL_DIMS</span> <span class="o">=</span> <span class="s2">&quot;skip_missing_spatial_dims&quot;</span>
-
-<span class="n">OPTIONS</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">EXPORT_GRID_MAPPING</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-    <span class="n">SKIP_MISSING_SPATIAL_DIMS</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-<span class="p">}</span>
-<span class="n">OPTION_NAMES</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">OPTIONS</span><span class="p">)</span>
-
-<span class="n">VALIDATORS</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">EXPORT_GRID_MAPPING</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">choice</span><span class="p">:</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">choice</span><span class="p">,</span> <span class="nb">bool</span><span class="p">),</span>
-<span class="p">}</span>
-
-
-<span class="k">def</span> <span class="nf">get_option</span><span class="p">(</span><span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Get the global rioxarray option.</span>
-
-<span class="sd">    .. versionadded:: 0.3.0</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    key: str</span>
-<span class="sd">        The name of the option.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    Any: the value of the option.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">OPTIONS</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="set_options">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.set_options">[docs]</a>
-<span class="k">class</span> <span class="nc">set_options</span><span class="p">:</span>  <span class="c1"># pylint: disable=invalid-name</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Set the global rioxarray option.</span>
-
-<span class="sd">    .. versionadded:: 0.3.0</span>
-<span class="sd">    .. versionadded:: 0.7.0 skip_missing_spatial_dims</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    export_grid_mapping: bool, default=True</span>
-<span class="sd">        If True, this option will export the full Climate and Forecasts (CF)</span>
-<span class="sd">        grid mapping attributes for the CRS. This is useful if you are exporting</span>
-<span class="sd">        your file to netCDF using :meth:`xarray.Dataset.to_netcdf()`. When disabled,</span>
-<span class="sd">        only the ``crs_wkt`` and ``spatial_ref`` attributes will be written and the</span>
-<span class="sd">        program will be faster due to not needing to use</span>
-<span class="sd">        :meth:`pyproj.CRS.to_cf() &lt;pyproj.crs.CRS.to_cf&gt;`.</span>
-<span class="sd">    skip_missing_spatial_dims: bool, default=False</span>
-<span class="sd">        If True, it will not perform spatial operations on variables</span>
-<span class="sd">        within a :class:`xarray.Dataset` if the spatial dimensions</span>
-<span class="sd">        are not found.</span>
-
-
-<span class="sd">    Usage as a context manager::</span>
-
-<span class="sd">        with rioxarray.set_options(export_grid_mapping=False):</span>
-<span class="sd">            rds = rioxarray.open_rasterio(...)</span>
-
-<span class="sd">    Usage for global settings::</span>
-
-<span class="sd">        rioxarray.set_options(export_grid_mapping=False)</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">old</span> <span class="o">=</span> <span class="n">OPTIONS</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">OPTIONS</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="sa">f</span><span class="s2">&quot;argument name </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2"> is not in the set of valid options &quot;</span>
-                    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">OPTION_NAMES</span><span class="si">}</span><span class="s2">.&quot;</span>
-                <span class="p">)</span>
-            <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">VALIDATORS</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">VALIDATORS</span><span class="p">[</span><span class="n">key</span><span class="p">](</span><span class="n">value</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;option </span><span class="si">{</span><span class="n">key</span><span class="si">!r}</span><span class="s2"> gave an invalid value: </span><span class="si">{</span><span class="n">value</span><span class="si">!r}</span><span class="s2">.&quot;</span><span class="p">)</span>
-            <span class="n">OPTIONS</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-
-    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span>
-
-    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">traceback</span><span class="p">):</span>
-        <span class="k">global</span> <span class="n">OPTIONS</span>  <span class="c1"># pylint: disable=global-statement</span>
-        <span class="n">OPTIONS</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">old</span></div>
-
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2019-2023, Corteva Agriscience™.</p>
-  </div>
-
-  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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diff --git a/0.18.0/_modules/rioxarray/_show_versions.html b/0.18.0/_modules/rioxarray/_show_versions.html
deleted file mode 100644
index 55a6216d..00000000
--- a/0.18.0/_modules/rioxarray/_show_versions.html
+++ /dev/null
@@ -1,214 +0,0 @@
-
-
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" data-content_root="../../">
-<head>
-  <meta charset="utf-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>rioxarray._show_versions &mdash; rioxarray 0.18.0 documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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-    <script src="../../_static/js/theme.js"></script>
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-</head>
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-  <div class="wy-grid-for-nav">
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-            rioxarray
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-<div role="search">
-  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
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-              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../readme.html">rioxarray README</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../getting_started/getting_started.html">Getting Started</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../examples/examples.html">Usage Examples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../modules.html">rioxarray</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../authors.html">Contributors ✨</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../history.html">History</a></li>
-</ul>
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-
-    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
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-        <div class="rst-content">
-          <div role="navigation" aria-label="Page navigation">
-  <ul class="wy-breadcrumbs">
-      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Module code</a></li>
-      <li class="breadcrumb-item active">rioxarray._show_versions</li>
-      <li class="wy-breadcrumbs-aside">
-      </li>
-  </ul>
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-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-           <div itemprop="articleBody">
-             
-  <h1>Source code for rioxarray._show_versions</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Utility methods to print system info for debugging</span>
-
-<span class="sd">adapted from :func:`sklearn.utils._show_versions`</span>
-<span class="sd">which was adapted from :func:`pandas.show_versions`</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="c1"># pylint: disable=import-outside-toplevel</span>
-<span class="kn">import</span> <span class="nn">importlib.metadata</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">platform</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-
-
-<span class="k">def</span> <span class="nf">_get_sys_info</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;System information</span>
-<span class="sd">    Return</span>
-<span class="sd">    ------</span>
-<span class="sd">    sys_info : dict</span>
-<span class="sd">        system and Python version information</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">blob</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="p">(</span><span class="s2">&quot;python&quot;</span><span class="p">,</span> <span class="n">sys</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot; &quot;</span><span class="p">)),</span>
-        <span class="p">(</span><span class="s2">&quot;executable&quot;</span><span class="p">,</span> <span class="n">sys</span><span class="o">.</span><span class="n">executable</span><span class="p">),</span>
-        <span class="p">(</span><span class="s2">&quot;machine&quot;</span><span class="p">,</span> <span class="n">platform</span><span class="o">.</span><span class="n">platform</span><span class="p">()),</span>
-    <span class="p">]</span>
-
-    <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="n">blob</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_get_main_info</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the main dependency information to hightlight.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    proj_info: dict</span>
-<span class="sd">        system GDAL information</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="kn">import</span> <span class="nn">rasterio</span>
-
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">proj_data</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">pathsep</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">rasterio</span><span class="o">.</span><span class="n">_env</span><span class="o">.</span><span class="n">get_proj_data_search_paths</span><span class="p">())</span>
-    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-        <span class="n">proj_data</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">gdal_data</span> <span class="o">=</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">_env</span><span class="o">.</span><span class="n">get_gdal_data</span><span class="p">()</span>
-    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-        <span class="n">gdal_data</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="n">blob</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="p">(</span><span class="s2">&quot;rasterio&quot;</span><span class="p">,</span> <span class="n">importlib</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">version</span><span class="p">(</span><span class="s2">&quot;rasterio&quot;</span><span class="p">)),</span>
-        <span class="p">(</span><span class="s2">&quot;xarray&quot;</span><span class="p">,</span> <span class="n">importlib</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">version</span><span class="p">(</span><span class="s2">&quot;xarray&quot;</span><span class="p">)),</span>
-        <span class="p">(</span><span class="s2">&quot;GDAL&quot;</span><span class="p">,</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">__gdal_version__</span><span class="p">),</span>
-        <span class="p">(</span><span class="s2">&quot;GEOS&quot;</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">rasterio</span><span class="p">,</span> <span class="s2">&quot;__geos_version__&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)),</span>
-        <span class="p">(</span><span class="s2">&quot;PROJ&quot;</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">rasterio</span><span class="p">,</span> <span class="s2">&quot;__proj_version__&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)),</span>
-        <span class="p">(</span><span class="s2">&quot;PROJ DATA&quot;</span><span class="p">,</span> <span class="n">proj_data</span><span class="p">),</span>
-        <span class="p">(</span><span class="s2">&quot;GDAL DATA&quot;</span><span class="p">,</span> <span class="n">gdal_data</span><span class="p">),</span>
-    <span class="p">]</span>
-
-    <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="n">blob</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_get_deps_info</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Overview of the installed version of dependencies</span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    deps_info: dict</span>
-<span class="sd">        version information on relevant Python libraries</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">deps</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;scipy&quot;</span><span class="p">,</span> <span class="s2">&quot;pyproj&quot;</span><span class="p">]</span>
-
-    <span class="k">def</span> <span class="nf">get_version</span><span class="p">(</span><span class="n">module</span><span class="p">):</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">importlib</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">version</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
-        <span class="k">except</span> <span class="n">importlib</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">PackageNotFoundError</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-
-    <span class="k">return</span> <span class="p">{</span><span class="n">dep</span><span class="p">:</span> <span class="n">get_version</span><span class="p">(</span><span class="n">dep</span><span class="p">)</span> <span class="k">for</span> <span class="n">dep</span> <span class="ow">in</span> <span class="n">deps</span><span class="p">}</span>
-
-
-<span class="k">def</span> <span class="nf">_print_info_dict</span><span class="p">(</span><span class="n">info_dict</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Print the information dictionary&quot;&quot;&quot;</span>
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">stat</span> <span class="ow">in</span> <span class="n">info_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">key</span><span class="si">:</span><span class="s2">&gt;10</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">stat</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="show_versions">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.show_versions">[docs]</a>
-<span class="k">def</span> <span class="nf">show_versions</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    .. versionadded:: 0.0.26</span>
-
-<span class="sd">    Print useful debugging information</span>
-
-<span class="sd">    Example</span>
-<span class="sd">    -------</span>
-<span class="sd">    &gt; python -c &quot;import rioxarray; rioxarray.show_versions()&quot;</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;rioxarray (</span><span class="si">{</span><span class="n">importlib</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">version</span><span class="p">(</span><span class="s1">&#39;rioxarray&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">) deps:&quot;</span><span class="p">)</span>
-    <span class="n">_print_info_dict</span><span class="p">(</span><span class="n">_get_main_info</span><span class="p">())</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Other python deps:&quot;</span><span class="p">)</span>
-    <span class="n">_print_info_dict</span><span class="p">(</span><span class="n">_get_deps_info</span><span class="p">())</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">System:&quot;</span><span class="p">)</span>
-    <span class="n">_print_info_dict</span><span class="p">(</span><span class="n">_get_sys_info</span><span class="p">())</span></div>
-
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diff --git a/0.18.0/_modules/rioxarray/exceptions.html b/0.18.0/_modules/rioxarray/exceptions.html
deleted file mode 100644
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-  <h1>Source code for rioxarray.exceptions</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">This contains exceptions for rioxarray.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-
-<div class="viewcode-block" id="RioXarrayError">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.exceptions.RioXarrayError">[docs]</a>
-<span class="k">class</span> <span class="nc">RioXarrayError</span><span class="p">(</span><span class="ne">RuntimeError</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is the base exception for errors in the rioxarray extension.&quot;&quot;&quot;</span></div>
-
-
-
-<div class="viewcode-block" id="NoDataInBounds">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.exceptions.NoDataInBounds">[docs]</a>
-<span class="k">class</span> <span class="nc">NoDataInBounds</span><span class="p">(</span><span class="n">RioXarrayError</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is for when there are no data in the bounds for clipping a raster.&quot;&quot;&quot;</span></div>
-
-
-
-<div class="viewcode-block" id="SingleVariableDataset">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.exceptions.SingleVariableDataset">[docs]</a>
-<span class="k">class</span> <span class="nc">SingleVariableDataset</span><span class="p">(</span><span class="n">RioXarrayError</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is for when you have a dataset with a single variable.&quot;&quot;&quot;</span></div>
-
-
-
-<div class="viewcode-block" id="DimensionError">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.exceptions.DimensionError">[docs]</a>
-<span class="k">class</span> <span class="nc">DimensionError</span><span class="p">(</span><span class="n">RioXarrayError</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is raised when there are more dimensions than is supported by the method&quot;&quot;&quot;</span></div>
-
-
-
-<div class="viewcode-block" id="MissingSpatialDimensionError">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.exceptions.MissingSpatialDimensionError">[docs]</a>
-<span class="k">class</span> <span class="nc">MissingSpatialDimensionError</span><span class="p">(</span><span class="n">DimensionError</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is raised when the dimension cannot be found&quot;&quot;&quot;</span></div>
-
-
-
-<div class="viewcode-block" id="TooManyDimensions">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.exceptions.TooManyDimensions">[docs]</a>
-<span class="k">class</span> <span class="nc">TooManyDimensions</span><span class="p">(</span><span class="n">DimensionError</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is raised when there are more dimensions than is supported by the method&quot;&quot;&quot;</span></div>
-
-
-
-<div class="viewcode-block" id="InvalidDimensionOrder">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.exceptions.InvalidDimensionOrder">[docs]</a>
-<span class="k">class</span> <span class="nc">InvalidDimensionOrder</span><span class="p">(</span><span class="n">DimensionError</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is raised when there the dimensions are not ordered correctly.&quot;&quot;&quot;</span></div>
-
-
-
-<div class="viewcode-block" id="OneDimensionalRaster">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.exceptions.OneDimensionalRaster">[docs]</a>
-<span class="k">class</span> <span class="nc">OneDimensionalRaster</span><span class="p">(</span><span class="n">DimensionError</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is an error when you have a 1 dimensional raster.&quot;&quot;&quot;</span></div>
-
-
-
-<div class="viewcode-block" id="DimensionMissingCoordinateError">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.exceptions.DimensionMissingCoordinateError">[docs]</a>
-<span class="k">class</span> <span class="nc">DimensionMissingCoordinateError</span><span class="p">(</span><span class="n">RioXarrayError</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is raised when the dimension does not have the supporting coordinate.&quot;&quot;&quot;</span></div>
-
-
-
-<div class="viewcode-block" id="MissingCRS">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.exceptions.MissingCRS">[docs]</a>
-<span class="k">class</span> <span class="nc">MissingCRS</span><span class="p">(</span><span class="n">RioXarrayError</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Missing the CRS in the dataset.&quot;&quot;&quot;</span></div>
-
-</pre></div>
-
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diff --git a/0.18.0/_modules/rioxarray/merge.html b/0.18.0/_modules/rioxarray/merge.html
deleted file mode 100644
index 8988f602..00000000
--- a/0.18.0/_modules/rioxarray/merge.html
+++ /dev/null
@@ -1,369 +0,0 @@
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-             
-  <h1>Source code for rioxarray.merge</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">This module allows you to merge xarray Datasets/DataArrays</span>
-<span class="sd">geospatially with the `rasterio.merge` module.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Sequence</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">from</span> <span class="nn">rasterio.crs</span> <span class="kn">import</span> <span class="n">CRS</span>
-<span class="kn">from</span> <span class="nn">rasterio.io</span> <span class="kn">import</span> <span class="n">MemoryFile</span>
-<span class="kn">from</span> <span class="nn">rasterio.merge</span> <span class="kn">import</span> <span class="n">merge</span> <span class="k">as</span> <span class="n">_rio_merge</span>
-<span class="kn">from</span> <span class="nn">xarray</span> <span class="kn">import</span> <span class="n">DataArray</span><span class="p">,</span> <span class="n">Dataset</span>
-
-<span class="kn">from</span> <span class="nn">rioxarray._io</span> <span class="kn">import</span> <span class="n">open_rasterio</span>
-<span class="kn">from</span> <span class="nn">rioxarray.rioxarray</span> <span class="kn">import</span> <span class="n">_get_nonspatial_coords</span><span class="p">,</span> <span class="n">_make_coords</span>
-
-
-<span class="k">class</span> <span class="nc">RasterioDatasetDuck</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This class is to provide the attributes and methods necessary</span>
-<span class="sd">    to make the :func:`rasterio.merge.merge` function think that</span>
-<span class="sd">    the :obj:`xarray.DataArray` is a :obj:`rasterio.io.DatasetReader`.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># pylint: disable=too-many-instance-attributes</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xds</span><span class="p">:</span> <span class="n">DataArray</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_xds</span> <span class="o">=</span> <span class="n">xds</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">crs</span> <span class="o">=</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bounds</span> <span class="o">=</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">bounds</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">count</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">xds</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">xds</span><span class="o">.</span><span class="n">name</span>
-        <span class="k">if</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">encoded_nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">nodatavals</span> <span class="o">=</span> <span class="p">[</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">encoded_nodata</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">nodatavals</span> <span class="o">=</span> <span class="p">[</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">nodata</span><span class="p">]</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">resolution</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">res</span> <span class="o">=</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">res</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">abs</span><span class="p">(</span><span class="n">res</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">profile</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="s2">&quot;crs&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span>
-            <span class="s2">&quot;nodata&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodatavals</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-        <span class="p">}</span>
-        <span class="n">valid_scale_factor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_xds</span><span class="o">.</span><span class="n">encoding</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;scale_factor&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">any</span><span class="p">(</span>
-            <span class="n">scale</span> <span class="o">!=</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">scale</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_xds</span><span class="o">.</span><span class="n">encoding</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;scales&quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,))</span>
-        <span class="p">)</span>
-        <span class="n">valid_offset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_xds</span><span class="o">.</span><span class="n">encoding</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;add_offset&quot;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">any</span><span class="p">(</span>
-            <span class="n">offset</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">offset</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_xds</span><span class="o">.</span><span class="n">encoding</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;offsets&quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,))</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_mask_and_scale</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">encoded_nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-            <span class="ow">or</span> <span class="n">valid_scale_factor</span>
-            <span class="ow">or</span> <span class="n">valid_offset</span>
-            <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_xds</span><span class="o">.</span><span class="n">encoding</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;_Unsigned&quot;</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-        <span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">colormap</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        colormap is only used for writing to a file.</span>
-<span class="sd">        This never happens with rioxarray merge.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># pylint: disable=unused-argument</span>
-        <span class="k">return</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">MaskedArray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        This method is meant to be used by the rasterio.merge.merge function.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">with</span> <span class="n">MemoryFile</span><span class="p">()</span> <span class="k">as</span> <span class="n">memfile</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">to_raster</span><span class="p">(</span><span class="n">memfile</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
-            <span class="k">with</span> <span class="n">memfile</span><span class="o">.</span><span class="n">open</span><span class="p">()</span> <span class="k">as</span> <span class="n">dataset</span><span class="p">:</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mask_and_scale</span><span class="p">:</span>
-                    <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;masked&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="n">out</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mask_and_scale</span><span class="p">:</span>
-                    <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_xds</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-                    <span class="k">for</span> <span class="n">iii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">):</span>
-                        <span class="n">out</span><span class="p">[</span><span class="n">iii</span><span class="p">]</span> <span class="o">=</span> <span class="n">out</span><span class="p">[</span><span class="n">iii</span><span class="p">]</span> <span class="o">*</span> <span class="n">dataset</span><span class="o">.</span><span class="n">scales</span><span class="p">[</span><span class="n">iii</span><span class="p">]</span> <span class="o">+</span> <span class="n">dataset</span><span class="o">.</span><span class="n">offsets</span><span class="p">[</span><span class="n">iii</span><span class="p">]</span>
-                <span class="k">return</span> <span class="n">out</span>
-
-
-<div class="viewcode-block" id="merge_arrays">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.merge.merge_arrays">[docs]</a>
-<span class="k">def</span> <span class="nf">merge_arrays</span><span class="p">(</span>
-    <span class="n">dataarrays</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">DataArray</span><span class="p">],</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">bounds</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">res</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">nodata</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">precision</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">method</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">crs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">CRS</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">parse_coordinates</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataArray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Merge data arrays geospatially.</span>
-
-<span class="sd">    Uses :func:`rasterio.merge.merge`</span>
-
-<span class="sd">    .. versionadded:: 0.2 crs</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataarrays: list[xarray.DataArray]</span>
-<span class="sd">        List of multiple xarray.DataArray with all geo attributes.</span>
-<span class="sd">        The first one is assumed to have the same</span>
-<span class="sd">        CRS, dtype, and dimensions as the others in the array.</span>
-<span class="sd">    bounds: tuple, optional</span>
-<span class="sd">        Bounds of the output image (left, bottom, right, top).</span>
-<span class="sd">        If not set, bounds are determined from bounds of input DataArrays.</span>
-<span class="sd">    res: tuple, optional</span>
-<span class="sd">        Output resolution in units of coordinate reference system.</span>
-<span class="sd">        If not set, the resolution of the first DataArray is used.</span>
-<span class="sd">        If a single value is passed, output pixels will be square.</span>
-<span class="sd">    nodata: float, optional</span>
-<span class="sd">        nodata value to use in output file.</span>
-<span class="sd">        If not set, uses the nodata value in the first input DataArray.</span>
-<span class="sd">    precision: float, optional</span>
-<span class="sd">        Number of decimal points of precision when computing inverse transform.</span>
-<span class="sd">    method: str or callable, optional</span>
-<span class="sd">        See :func:`rasterio.merge.merge` for details.</span>
-<span class="sd">    crs: rasterio.crs.CRS, optional</span>
-<span class="sd">        Output CRS. If not set, the CRS of the first DataArray is used.</span>
-<span class="sd">    parse_coordinates: bool, optional</span>
-<span class="sd">        If False, it will disable loading spatial coordinates.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    :obj:`xarray.DataArray`:</span>
-<span class="sd">        The geospatially merged data.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">input_kwargs</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;bounds&quot;</span><span class="p">:</span> <span class="n">bounds</span><span class="p">,</span>
-        <span class="s2">&quot;res&quot;</span><span class="p">:</span> <span class="n">res</span><span class="p">,</span>
-        <span class="s2">&quot;nodata&quot;</span><span class="p">:</span> <span class="n">nodata</span><span class="p">,</span>
-        <span class="s2">&quot;precision&quot;</span><span class="p">:</span> <span class="n">precision</span><span class="p">,</span>
-        <span class="s2">&quot;method&quot;</span><span class="p">:</span> <span class="n">method</span><span class="p">,</span>
-    <span class="p">}</span>
-
-    <span class="k">if</span> <span class="n">crs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">crs</span> <span class="o">=</span> <span class="n">dataarrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span>
-    <span class="k">if</span> <span class="n">res</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">res_val</span><span class="p">)</span> <span class="k">for</span> <span class="n">res_val</span> <span class="ow">in</span> <span class="n">dataarrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">resolution</span><span class="p">())</span>
-
-    <span class="c1"># prepare the duck arrays</span>
-    <span class="n">rioduckarrays</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">dataarray</span> <span class="ow">in</span> <span class="n">dataarrays</span><span class="p">:</span>
-        <span class="n">da_res</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">res_val</span><span class="p">)</span> <span class="k">for</span> <span class="n">res_val</span> <span class="ow">in</span> <span class="n">dataarray</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">resolution</span><span class="p">())</span>
-        <span class="k">if</span> <span class="n">da_res</span> <span class="o">!=</span> <span class="n">res</span> <span class="ow">or</span> <span class="n">dataarray</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span> <span class="o">!=</span> <span class="n">crs</span><span class="p">:</span>
-            <span class="n">rioduckarrays</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                <span class="n">RasterioDatasetDuck</span><span class="p">(</span>
-                    <span class="n">dataarray</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">reproject</span><span class="p">(</span><span class="n">dst_crs</span><span class="o">=</span><span class="n">crs</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="n">res</span><span class="p">)</span>
-                <span class="p">)</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">rioduckarrays</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">RasterioDatasetDuck</span><span class="p">(</span><span class="n">dataarray</span><span class="p">))</span>
-
-    <span class="c1"># use rasterio to merge</span>
-    <span class="c1"># generate merged data array</span>
-    <span class="n">representative_array</span> <span class="o">=</span> <span class="n">rioduckarrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">_xds</span>
-    <span class="k">with</span> <span class="n">MemoryFile</span><span class="p">()</span> <span class="k">as</span> <span class="n">memfile</span><span class="p">:</span>
-        <span class="n">_rio_merge</span><span class="p">(</span>
-            <span class="n">rioduckarrays</span><span class="p">,</span>
-            <span class="o">**</span><span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">val</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">input_kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">val</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">},</span>
-            <span class="n">dst_path</span><span class="o">=</span><span class="n">memfile</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="k">with</span> <span class="n">open_rasterio</span><span class="p">(</span>  <span class="c1"># type: ignore</span>
-            <span class="n">memfile</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
-            <span class="n">parse_coordinates</span><span class="o">=</span><span class="n">parse_coordinates</span><span class="p">,</span>
-            <span class="n">mask_and_scale</span><span class="o">=</span><span class="n">rioduckarrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">_mask_and_scale</span><span class="p">,</span>
-        <span class="p">)</span> <span class="k">as</span> <span class="n">xda</span><span class="p">:</span>
-            <span class="n">xda</span> <span class="o">=</span> <span class="n">xda</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
-        <span class="n">xda</span><span class="o">.</span><span class="n">coords</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
-            <span class="p">{</span>
-                <span class="n">coord</span><span class="p">:</span> <span class="n">value</span>
-                <span class="k">for</span> <span class="n">coord</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">_get_nonspatial_coords</span><span class="p">(</span><span class="n">representative_array</span><span class="p">)</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-                <span class="k">if</span> <span class="n">coord</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">xda</span><span class="o">.</span><span class="n">coords</span>
-            <span class="p">}</span>
-        <span class="p">)</span>
-    <span class="k">return</span> <span class="n">xda</span>  <span class="c1"># type: ignore</span></div>
-
-
-
-<div class="viewcode-block" id="merge_datasets">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.merge.merge_datasets">[docs]</a>
-<span class="k">def</span> <span class="nf">merge_datasets</span><span class="p">(</span>
-    <span class="n">datasets</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Dataset</span><span class="p">],</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">bounds</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">res</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">nodata</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">precision</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">method</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">crs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">CRS</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Merge datasets geospatially.</span>
-
-<span class="sd">    Uses :func:`rasterio.merge.merge`</span>
-
-<span class="sd">    .. versionadded:: 0.2 crs</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    datasets: list[xarray.Dataset]</span>
-<span class="sd">        List of multiple xarray.Dataset with all geo attributes.</span>
-<span class="sd">        The first one is assumed to have the same</span>
-<span class="sd">        CRS, dtype, dimensions, and data_vars as the others in the array.</span>
-<span class="sd">    bounds: tuple, optional</span>
-<span class="sd">        Bounds of the output image (left, bottom, right, top).</span>
-<span class="sd">        If not set, bounds are determined from bounds of input Dataset.</span>
-<span class="sd">    res: tuple, optional</span>
-<span class="sd">        Output resolution in units of coordinate reference system.</span>
-<span class="sd">        If not set, the resolution of the first Dataset is used.</span>
-<span class="sd">        If a single value is passed, output pixels will be square.</span>
-<span class="sd">    nodata: float, optional</span>
-<span class="sd">        nodata value to use in output file.</span>
-<span class="sd">        If not set, uses the nodata value in the first input Dataset.</span>
-<span class="sd">    precision: float, optional</span>
-<span class="sd">        Number of decimal points of precision when computing inverse transform.</span>
-<span class="sd">    method: str or callable, optional</span>
-<span class="sd">        See rasterio docs.</span>
-<span class="sd">    crs: rasterio.crs.CRS, optional</span>
-<span class="sd">        Output CRS. If not set, the CRS of the first DataArray is used.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    :obj:`xarray.Dataset`:</span>
-<span class="sd">        The geospatially merged data.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">representative_ds</span> <span class="o">=</span> <span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">merged_data</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">data_var</span> <span class="ow">in</span> <span class="n">representative_ds</span><span class="o">.</span><span class="n">data_vars</span><span class="p">:</span>
-        <span class="n">merged_data</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span> <span class="o">=</span> <span class="n">merge_arrays</span><span class="p">(</span>
-            <span class="p">[</span><span class="n">dataset</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span> <span class="k">for</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="n">datasets</span><span class="p">],</span>
-            <span class="n">bounds</span><span class="o">=</span><span class="n">bounds</span><span class="p">,</span>
-            <span class="n">res</span><span class="o">=</span><span class="n">res</span><span class="p">,</span>
-            <span class="n">nodata</span><span class="o">=</span><span class="n">nodata</span><span class="p">,</span>
-            <span class="n">precision</span><span class="o">=</span><span class="n">precision</span><span class="p">,</span>
-            <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">,</span>
-            <span class="n">crs</span><span class="o">=</span><span class="n">crs</span><span class="p">,</span>
-            <span class="n">parse_coordinates</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="p">)</span>
-    <span class="n">data_var</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">representative_ds</span><span class="o">.</span><span class="n">data_vars</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">xds</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span>
-        <span class="n">merged_data</span><span class="p">,</span>
-        <span class="n">coords</span><span class="o">=</span><span class="n">_make_coords</span><span class="p">(</span>
-            <span class="n">src_data_array</span><span class="o">=</span><span class="n">merged_data</span><span class="p">[</span><span class="n">data_var</span><span class="p">],</span>
-            <span class="n">dst_affine</span><span class="o">=</span><span class="n">merged_data</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">transform</span><span class="p">(),</span>
-            <span class="n">dst_width</span><span class="o">=</span><span class="n">merged_data</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
-            <span class="n">dst_height</span><span class="o">=</span><span class="n">merged_data</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span>
-            <span class="n">force_generate</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="p">),</span>
-        <span class="n">attrs</span><span class="o">=</span><span class="n">representative_ds</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_crs</span><span class="p">(</span><span class="n">merged_data</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">xds</span></div>
-
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
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deleted file mode 100644
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-             
-  <h1>Source code for rioxarray.raster_array</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">This module is an extension for xarray to provide rasterio capabilities</span>
-<span class="sd">to xarray dataarrays.</span>
-
-<span class="sd">Credits: The `reproject` functionality was adopted from https://github.com/opendatacube/datacube-core # noqa: E501</span>
-<span class="sd">Source file:</span>
-<span class="sd">- https://github.com/opendatacube/datacube-core/blob/084c84d78cb6e1326c7fbbe79c5b5d0bef37c078/datacube/api/geo_xarray.py  # noqa: E501</span>
-<span class="sd">datacube is licensed under the Apache License, Version 2.0:</span>
-<span class="sd">- https://github.com/opendatacube/datacube-core/blob/1d345f08a10a13c316f81100936b0ad8b1a374eb/LICENSE  # noqa: E501</span>
-
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Hashable</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">Mapping</span>
-<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Literal</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">rasterio</span>
-<span class="kn">import</span> <span class="nn">rasterio.mask</span>
-<span class="kn">import</span> <span class="nn">rasterio.warp</span>
-<span class="kn">import</span> <span class="nn">xarray</span>
-<span class="kn">from</span> <span class="nn">affine</span> <span class="kn">import</span> <span class="n">Affine</span>
-<span class="kn">from</span> <span class="nn">rasterio.dtypes</span> <span class="kn">import</span> <span class="n">dtype_rev</span>
-<span class="kn">from</span> <span class="nn">rasterio.enums</span> <span class="kn">import</span> <span class="n">Resampling</span>
-<span class="kn">from</span> <span class="nn">rasterio.features</span> <span class="kn">import</span> <span class="n">geometry_mask</span>
-<span class="kn">from</span> <span class="nn">xarray.backends.file_manager</span> <span class="kn">import</span> <span class="n">FileManager</span>
-<span class="kn">from</span> <span class="nn">xarray.core.dtypes</span> <span class="kn">import</span> <span class="n">get_fill_value</span>
-
-<span class="kn">from</span> <span class="nn">rioxarray._io</span> <span class="kn">import</span> <span class="n">FILL_VALUE_NAMES</span><span class="p">,</span> <span class="n">UNWANTED_RIO_ATTRS</span>
-<span class="kn">from</span> <span class="nn">rioxarray.crs</span> <span class="kn">import</span> <span class="n">crs_from_user_input</span>
-<span class="kn">from</span> <span class="nn">rioxarray.exceptions</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">MissingCRS</span><span class="p">,</span>
-    <span class="n">NoDataInBounds</span><span class="p">,</span>
-    <span class="n">OneDimensionalRaster</span><span class="p">,</span>
-    <span class="n">RioXarrayError</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="kn">from</span> <span class="nn">rioxarray.raster_writer</span> <span class="kn">import</span> <span class="n">RasterioWriter</span><span class="p">,</span> <span class="n">_ensure_nodata_dtype</span>
-<span class="kn">from</span> <span class="nn">rioxarray.rioxarray</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">XRasterBase</span><span class="p">,</span>
-    <span class="n">_get_data_var_message</span><span class="p">,</span>
-    <span class="n">_make_coords</span><span class="p">,</span>
-    <span class="n">_order_bounds</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># DTYPE TO NODATA MAP</span>
-<span class="c1"># Based on: https://github.com/OSGeo/gdal/blob/</span>
-<span class="c1"># dee861e7c91c2da7ef8ff849947713e4d9bd115c/</span>
-<span class="c1"># swig/python/gdal-utils/osgeo_utils/gdal_calc.py#L61</span>
-<span class="c1"># And: https://github.com/rasterio/rasterio/blob/</span>
-<span class="c1"># 9e643c3f563a679aa5400d9b1a263df97b34f9e0/rasterio/dtypes.py#L99-L112</span>
-<span class="n">_NODATA_DTYPE_MAP</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="mi">1</span><span class="p">:</span> <span class="mi">255</span><span class="p">,</span>  <span class="c1"># GDT_Byte</span>
-    <span class="mi">2</span><span class="p">:</span> <span class="mi">65535</span><span class="p">,</span>  <span class="c1"># GDT_UInt16</span>
-    <span class="mi">3</span><span class="p">:</span> <span class="o">-</span><span class="mi">32768</span><span class="p">,</span>  <span class="c1"># GDT_Int16</span>
-    <span class="mi">4</span><span class="p">:</span> <span class="mi">4294967295</span><span class="p">,</span>  <span class="c1"># GDT_UInt32</span>
-    <span class="mi">5</span><span class="p">:</span> <span class="o">-</span><span class="mi">2147483648</span><span class="p">,</span>  <span class="c1"># GDT_Int32</span>
-    <span class="mi">6</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>  <span class="c1"># GDT_Float32</span>
-    <span class="mi">7</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>  <span class="c1"># GDT_Float64</span>
-    <span class="mi">8</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>  <span class="c1"># GDT_CInt16</span>
-    <span class="mi">9</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>  <span class="c1"># GDT_CInt32</span>
-    <span class="mi">10</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>  <span class="c1"># GDT_CFloat32</span>
-    <span class="mi">11</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>  <span class="c1"># GDT_CFloat64</span>
-    <span class="mi">12</span><span class="p">:</span> <span class="mi">18446744073709551615</span><span class="p">,</span>  <span class="c1"># GDT_UInt64</span>
-    <span class="mi">13</span><span class="p">:</span> <span class="o">-</span><span class="mi">9223372036854775808</span><span class="p">,</span>  <span class="c1"># GDT_Int64</span>
-    <span class="mi">14</span><span class="p">:</span> <span class="o">-</span><span class="mi">128</span><span class="p">,</span>  <span class="c1"># GDT_Int8</span>
-<span class="p">}</span>
-
-
-<span class="k">def</span> <span class="nf">_generate_attrs</span><span class="p">(</span>
-    <span class="o">*</span><span class="p">,</span> <span class="n">src_data_array</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">dst_nodata</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
-    <span class="c1"># add original attributes</span>
-    <span class="n">new_attrs</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">src_data_array</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-    <span class="c1"># remove all nodata information</span>
-    <span class="k">for</span> <span class="n">unwanted_attr</span> <span class="ow">in</span> <span class="n">FILL_VALUE_NAMES</span> <span class="o">+</span> <span class="n">UNWANTED_RIO_ATTRS</span><span class="p">:</span>
-        <span class="n">new_attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">unwanted_attr</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-
-    <span class="c1"># add nodata information</span>
-    <span class="n">fill_value</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">nodata</span>
-        <span class="k">if</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-        <span class="k">else</span> <span class="n">dst_nodata</span>
-    <span class="p">)</span>
-    <span class="k">if</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">encoded_nodata</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">fill_value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">new_attrs</span><span class="p">[</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fill_value</span>
-
-    <span class="k">return</span> <span class="n">new_attrs</span>
-
-
-<span class="k">def</span> <span class="nf">_add_attrs_proj</span><span class="p">(</span>
-    <span class="o">*</span><span class="p">,</span> <span class="n">new_data_array</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">src_data_array</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Make sure attributes and projection correct&quot;&quot;&quot;</span>
-    <span class="c1"># make sure dimension information is preserved</span>
-    <span class="k">if</span> <span class="n">new_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_x_dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">new_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_x_dim</span> <span class="o">=</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span>
-    <span class="k">if</span> <span class="n">new_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_y_dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">new_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_y_dim</span> <span class="o">=</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span>
-
-    <span class="c1"># make sure attributes preserved</span>
-    <span class="n">new_attrs</span> <span class="o">=</span> <span class="n">_generate_attrs</span><span class="p">(</span><span class="n">src_data_array</span><span class="o">=</span><span class="n">src_data_array</span><span class="p">,</span> <span class="n">dst_nodata</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-    <span class="c1"># remove fill value if it already exists in the encoding</span>
-    <span class="c1"># this is for data arrays pulling the encoding from a</span>
-    <span class="c1"># source data array instead of being generated anew.</span>
-    <span class="k">if</span> <span class="s2">&quot;_FillValue&quot;</span> <span class="ow">in</span> <span class="n">new_data_array</span><span class="o">.</span><span class="n">encoding</span><span class="p">:</span>
-        <span class="n">new_attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-
-    <span class="n">new_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_attrs</span><span class="p">(</span><span class="n">new_attrs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="c1"># make sure projection added</span>
-    <span class="n">new_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_grid_mapping</span><span class="p">(</span><span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">grid_mapping</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">new_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_crs</span><span class="p">(</span><span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">new_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_coordinate_system</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">new_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_transform</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="c1"># make sure encoding added</span>
-    <span class="n">new_data_array</span><span class="o">.</span><span class="n">encoding</span> <span class="o">=</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">encoding</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="k">return</span> <span class="n">new_data_array</span>
-
-
-<span class="k">def</span> <span class="nf">_make_dst_affine</span><span class="p">(</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">src_data_array</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span>
-    <span class="n">src_crs</span><span class="p">:</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">CRS</span><span class="p">,</span>
-    <span class="n">dst_crs</span><span class="p">:</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">CRS</span><span class="p">,</span>
-    <span class="n">dst_resolution</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">dst_shape</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Determine the affine of the new projected `xarray.DataArray`&quot;&quot;&quot;</span>
-    <span class="n">src_bounds</span> <span class="o">=</span> <span class="p">()</span>
-    <span class="k">if</span> <span class="p">(</span>
-        <span class="s2">&quot;gcps&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span>
-        <span class="ow">and</span> <span class="s2">&quot;rpcs&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span>
-        <span class="ow">and</span> <span class="s2">&quot;src_geoloc_array&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span>
-    <span class="p">):</span>
-        <span class="n">src_bounds</span> <span class="o">=</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">bounds</span><span class="p">()</span>
-    <span class="n">src_height</span><span class="p">,</span> <span class="n">src_width</span> <span class="o">=</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">shape</span>
-    <span class="n">dst_height</span><span class="p">,</span> <span class="n">dst_width</span> <span class="o">=</span> <span class="n">dst_shape</span> <span class="k">if</span> <span class="n">dst_shape</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-    <span class="c1"># pylint: disable=isinstance-second-argument-not-valid-type</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dst_resolution</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">):</span>
-        <span class="n">dst_resolution</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">res_val</span><span class="p">)</span> <span class="k">for</span> <span class="n">res_val</span> <span class="ow">in</span> <span class="n">dst_resolution</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
-    <span class="k">elif</span> <span class="n">dst_resolution</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">dst_resolution</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">dst_resolution</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
-
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="p">(</span>
-        <span class="p">(</span><span class="s2">&quot;resolution&quot;</span><span class="p">,</span> <span class="n">dst_resolution</span><span class="p">),</span>
-        <span class="p">(</span><span class="s2">&quot;dst_height&quot;</span><span class="p">,</span> <span class="n">dst_height</span><span class="p">),</span>
-        <span class="p">(</span><span class="s2">&quot;dst_width&quot;</span><span class="p">,</span> <span class="n">dst_width</span><span class="p">),</span>
-    <span class="p">):</span>
-        <span class="k">if</span> <span class="n">value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">kwargs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-    <span class="n">dst_affine</span><span class="p">,</span> <span class="n">dst_width</span><span class="p">,</span> <span class="n">dst_height</span> <span class="o">=</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">warp</span><span class="o">.</span><span class="n">calculate_default_transform</span><span class="p">(</span>
-        <span class="n">src_crs</span><span class="p">,</span>
-        <span class="n">dst_crs</span><span class="p">,</span>
-        <span class="n">src_width</span><span class="p">,</span>
-        <span class="n">src_height</span><span class="p">,</span>
-        <span class="o">*</span><span class="n">src_bounds</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">dst_affine</span><span class="p">,</span> <span class="n">dst_width</span><span class="p">,</span> <span class="n">dst_height</span>
-
-
-<span class="k">def</span> <span class="nf">_clip_from_disk</span><span class="p">(</span>
-    <span class="n">xds</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">geometries</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">,</span>
-    <span class="n">all_touched</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">drop</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">invert</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    clip from disk if the file object is available</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">out_image</span><span class="p">,</span> <span class="n">out_transform</span> <span class="o">=</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">mask</span><span class="o">.</span><span class="n">mask</span><span class="p">(</span>
-            <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_manager</span><span class="o">.</span><span class="n">acquire</span><span class="p">(),</span>
-            <span class="n">geometries</span><span class="p">,</span>
-            <span class="n">all_touched</span><span class="o">=</span><span class="n">all_touched</span><span class="p">,</span>
-            <span class="n">invert</span><span class="o">=</span><span class="n">invert</span><span class="p">,</span>
-            <span class="n">crop</span><span class="o">=</span><span class="n">drop</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="k">if</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">encoded_nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span>
-            <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">encoded_nodata</span>
-        <span class="p">):</span>
-            <span class="n">out_image</span> <span class="o">=</span> <span class="n">out_image</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-            <span class="n">out_image</span><span class="p">[</span><span class="n">out_image</span> <span class="o">==</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">encoded_nodata</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nan</span>
-
-        <span class="n">height</span><span class="p">,</span> <span class="n">width</span> <span class="o">=</span> <span class="n">out_image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span>
-        <span class="n">cropped_ds</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">(</span>
-            <span class="n">name</span><span class="o">=</span><span class="n">xds</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
-            <span class="n">data</span><span class="o">=</span><span class="n">out_image</span><span class="p">,</span>
-            <span class="n">coords</span><span class="o">=</span><span class="n">_make_coords</span><span class="p">(</span>
-                <span class="n">src_data_array</span><span class="o">=</span><span class="n">xds</span><span class="p">,</span>
-                <span class="n">dst_affine</span><span class="o">=</span><span class="n">out_transform</span><span class="p">,</span>
-                <span class="n">dst_width</span><span class="o">=</span><span class="n">width</span><span class="p">,</span>
-                <span class="n">dst_height</span><span class="o">=</span><span class="n">height</span><span class="p">,</span>
-            <span class="p">),</span>
-            <span class="n">dims</span><span class="o">=</span><span class="n">xds</span><span class="o">.</span><span class="n">dims</span><span class="p">,</span>
-            <span class="n">attrs</span><span class="o">=</span><span class="n">xds</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">cropped_ds</span><span class="o">.</span><span class="n">encoding</span> <span class="o">=</span> <span class="n">xds</span><span class="o">.</span><span class="n">encoding</span>
-        <span class="k">return</span> <span class="n">cropped_ds</span>
-    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">None</span>
-
-
-<span class="k">def</span> <span class="nf">_clip_xarray</span><span class="p">(</span>
-    <span class="n">xds</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">geometries</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">,</span>
-    <span class="n">all_touched</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">drop</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">invert</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    clip the xarray DataArray</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">clip_mask_arr</span> <span class="o">=</span> <span class="n">geometry_mask</span><span class="p">(</span>
-        <span class="n">geometries</span><span class="o">=</span><span class="n">geometries</span><span class="p">,</span>
-        <span class="n">out_shape</span><span class="o">=</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">height</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">width</span><span class="p">)),</span>
-        <span class="n">transform</span><span class="o">=</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-        <span class="n">invert</span><span class="o">=</span><span class="ow">not</span> <span class="n">invert</span><span class="p">,</span>
-        <span class="n">all_touched</span><span class="o">=</span><span class="n">all_touched</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">clip_mask_xray</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">(</span>
-        <span class="n">clip_mask_arr</span><span class="p">,</span>
-        <span class="n">dims</span><span class="o">=</span><span class="p">(</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">),</span>
-    <span class="p">)</span>
-    <span class="n">cropped_ds</span> <span class="o">=</span> <span class="n">xds</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">clip_mask_xray</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">drop</span><span class="p">:</span>
-        <span class="n">cropped_ds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span>
-            <span class="n">x_dim</span><span class="o">=</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span>
-        <span class="n">cropped_ds</span> <span class="o">=</span> <span class="n">cropped_ds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">isel_window</span><span class="p">(</span>
-            <span class="n">rasterio</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">get_data_window</span><span class="p">(</span>
-                <span class="n">numpy</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">masked_array</span><span class="p">(</span><span class="n">clip_mask_arr</span><span class="p">,</span> <span class="o">~</span><span class="n">clip_mask_arr</span><span class="p">)</span>
-            <span class="p">)</span>
-        <span class="p">)</span>
-    <span class="k">if</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">nodata</span><span class="p">):</span>
-        <span class="n">cropped_ds</span> <span class="o">=</span> <span class="n">cropped_ds</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">nodata</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">cropped_ds</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">xds</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="RasterArray">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray">[docs]</a>
-<span class="nd">@xarray</span><span class="o">.</span><span class="n">register_dataarray_accessor</span><span class="p">(</span><span class="s2">&quot;rio&quot;</span><span class="p">)</span>
-<span class="k">class</span> <span class="nc">RasterArray</span><span class="p">(</span><span class="n">XRasterBase</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is the GIS extension for :obj:`xarray.DataArray`&quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xarray_obj</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">xarray_obj</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span>
-        <span class="c1"># properties</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_manager</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span>
-            <span class="n">FileManager</span>
-        <span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># https://github.com/corteva/rioxarray/issues/254</span>
-
-<div class="viewcode-block" id="RasterArray.set_nodata">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray.set_nodata">[docs]</a>
-    <span class="k">def</span> <span class="nf">set_nodata</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">input_nodata</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="o">*</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Set the nodata value for the DataArray without modifying</span>
-<span class="sd">        the data array.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        input_nodata: Optional[float]</span>
-<span class="sd">            Valid nodata for dtype.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is True.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.DataArray`:</span>
-<span class="sd">            Dataset with nodata attribute set.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">obj</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
-        <span class="n">obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_nodata</span> <span class="o">=</span> <span class="n">input_nodata</span>
-        <span class="k">return</span> <span class="n">obj</span></div>
-
-
-<div class="viewcode-block" id="RasterArray.write_nodata">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray.write_nodata">[docs]</a>
-    <span class="k">def</span> <span class="nf">write_nodata</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">input_nodata</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="o">*</span><span class="p">,</span> <span class="n">encoded</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Write the nodata to the DataArray in a CF compliant manner.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        input_nodata: Optional[float]</span>
-<span class="sd">            Nodata value for the DataArray.</span>
-<span class="sd">            If input_nodata is None, it will remove the _FillValue attribute.</span>
-<span class="sd">        encoded: bool, optional</span>
-<span class="sd">            If True, it will write the nodata value in the encoding and remove</span>
-<span class="sd">            the fill value from the attributes. This is useful for masking</span>
-<span class="sd">            with nodata. Default is False.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing DataArray. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.DataArray`:</span>
-<span class="sd">            Modified DataArray with CF compliant nodata information.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        To write the nodata value if it is missing:</span>
-
-<span class="sd">        &gt;&gt;&gt; raster.rio.write_nodata(-9999, inplace=True)</span>
-
-<span class="sd">        To write the nodata value on a copy:</span>
-
-<span class="sd">        &gt;&gt;&gt; raster = raster.rio.write_nodata(-9999)</span>
-
-<span class="sd">        To mask with nodata:</span>
-
-<span class="sd">        &gt;&gt;&gt; nodata = raster.rio.nodata</span>
-<span class="sd">        &gt;&gt;&gt; raster = raster.where(raster != nodata)</span>
-<span class="sd">        &gt;&gt;&gt; raster.rio.write_nodata(nodata, encoded=True, inplace=True)</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">data_obj</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
-        <span class="n">input_nodata</span> <span class="o">=</span> <span class="kc">False</span> <span class="k">if</span> <span class="n">input_nodata</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">input_nodata</span>
-        <span class="k">if</span> <span class="n">input_nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span><span class="p">:</span>
-            <span class="n">input_nodata</span> <span class="o">=</span> <span class="n">_ensure_nodata_dtype</span><span class="p">(</span>
-                <span class="n">original_nodata</span><span class="o">=</span><span class="n">input_nodata</span><span class="p">,</span> <span class="n">new_dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dtype</span>
-            <span class="p">)</span>
-            <span class="k">if</span> <span class="n">encoded</span><span class="p">:</span>
-                <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">update_encoding</span><span class="p">({</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">:</span> <span class="n">input_nodata</span><span class="p">},</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">update_attrs</span><span class="p">({</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">:</span> <span class="n">input_nodata</span><span class="p">},</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">input_nodata</span> <span class="ow">is</span> <span class="kc">False</span> <span class="ow">or</span> <span class="n">encoded</span><span class="p">:</span>
-            <span class="n">new_attrs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">data_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-            <span class="n">new_attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-            <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_attrs</span><span class="p">(</span><span class="n">new_attrs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">input_nodata</span> <span class="ow">is</span> <span class="kc">False</span> <span class="ow">and</span> <span class="n">encoded</span><span class="p">:</span>
-            <span class="n">new_encoding</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">data_obj</span><span class="o">.</span><span class="n">encoding</span><span class="p">)</span>
-            <span class="n">new_encoding</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-            <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_encoding</span><span class="p">(</span><span class="n">new_encoding</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">encoded</span><span class="p">:</span>
-            <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_nodata</span><span class="p">(</span><span class="n">input_nodata</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">data_obj</span></div>
-
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">encoded_nodata</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the encoded nodata value for the dataset if encoded.&quot;&quot;&quot;</span>
-        <span class="n">encoded_nodata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">encoding</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">encoded_nodata</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="n">_ensure_nodata_dtype</span><span class="p">(</span>
-            <span class="n">original_nodata</span><span class="o">=</span><span class="n">encoded_nodata</span><span class="p">,</span> <span class="n">new_dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dtype</span>
-        <span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">nodata</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the nodata value for the dataset.&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span> <span class="ow">is</span> <span class="kc">False</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoded_nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span> <span class="o">=</span> <span class="n">get_fill_value</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
-                <span class="s2">&quot;_FillValue&quot;</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
-                    <span class="s2">&quot;missing_value&quot;</span><span class="p">,</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;fill_value&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;nodata&quot;</span><span class="p">)),</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-
-        <span class="c1"># look in places used by `xarray.open_rasterio`</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_manager</span><span class="o">.</span><span class="n">acquire</span><span class="p">()</span><span class="o">.</span><span class="n">nodata</span>  <span class="c1"># type: ignore</span>
-            <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-                <span class="k">try</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-                <span class="k">except</span> <span class="p">(</span><span class="ne">KeyError</span><span class="p">,</span> <span class="ne">IndexError</span><span class="p">):</span>
-                    <span class="k">pass</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="k">return</span> <span class="kc">None</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span> <span class="o">=</span> <span class="n">_ensure_nodata_dtype</span><span class="p">(</span>
-            <span class="n">original_nodata</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span><span class="p">,</span> <span class="n">new_dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dtype</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nodata</span>
-
-<div class="viewcode-block" id="RasterArray.reproject">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray.reproject">[docs]</a>
-    <span class="k">def</span> <span class="nf">reproject</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">dst_crs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">resolution</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">shape</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">transform</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Affine</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">resampling</span><span class="p">:</span> <span class="n">Resampling</span> <span class="o">=</span> <span class="n">Resampling</span><span class="o">.</span><span class="n">nearest</span><span class="p">,</span>
-        <span class="n">nodata</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Reproject :obj:`xarray.DataArray` objects</span>
-
-<span class="sd">        Powered by :func:`rasterio.warp.reproject`</span>
-
-<span class="sd">        .. note:: Only 2D/3D arrays with dimensions &#39;x&#39;/&#39;y&#39; are currently supported.</span>
-<span class="sd">            Requires either a grid mapping variable with &#39;spatial_ref&#39; or</span>
-<span class="sd">            a &#39;crs&#39; attribute to be set containing a valid CRS.</span>
-<span class="sd">            If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.</span>
-
-<span class="sd">        .. note:: To re-project with dask, see</span>
-<span class="sd">            `odc-geo &lt;https://odc-geo.readthedocs.io/&gt;`__ &amp;</span>
-<span class="sd">            `pyresample &lt;https://pyresample.readthedocs.io/&gt;`__.</span>
-
-<span class="sd">        .. versionadded:: 0.0.27 shape</span>
-<span class="sd">        .. versionadded:: 0.0.28 transform</span>
-<span class="sd">        .. versionadded:: 0.5.0 nodata, kwargs</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        dst_crs: str</span>
-<span class="sd">            OGC WKT string or Proj.4 string.</span>
-<span class="sd">        resolution: float or tuple(float, float), optional</span>
-<span class="sd">            Size of a destination pixel in destination projection units</span>
-<span class="sd">            (e.g. degrees or metres).</span>
-<span class="sd">        shape: tuple(int, int), optional</span>
-<span class="sd">            Shape of the destination in pixels (dst_height, dst_width). Cannot be used</span>
-<span class="sd">            together with resolution.</span>
-<span class="sd">        transform: Affine, optional</span>
-<span class="sd">            The destination transform.</span>
-<span class="sd">        resampling: rasterio.enums.Resampling, optional</span>
-<span class="sd">            See :func:`rasterio.warp.reproject` for more details.</span>
-<span class="sd">        nodata: float, optional</span>
-<span class="sd">            The nodata value used to initialize the destination;</span>
-<span class="sd">            it will remain in all areas not covered by the reprojected source.</span>
-<span class="sd">            Defaults to the nodata value of the source image if none provided</span>
-<span class="sd">            and exists or attempts to find an appropriate value by dtype.</span>
-<span class="sd">        **kwargs: dict</span>
-<span class="sd">            Additional keyword arguments to pass into :func:`rasterio.warp.reproject`.</span>
-<span class="sd">            To override:</span>
-<span class="sd">            - src_transform: `rio.write_transform`</span>
-<span class="sd">            - src_crs: `rio.write_crs`</span>
-<span class="sd">            - src_nodata: `rio.write_nodata`</span>
-
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.DataArray`:</span>
-<span class="sd">            The reprojected DataArray.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">resolution</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="p">(</span><span class="n">shape</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="n">RioXarrayError</span><span class="p">(</span><span class="s2">&quot;resolution cannot be used with shape or transform.&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">MissingCRS</span><span class="p">(</span>
-                <span class="s2">&quot;CRS not found. Please set the CRS with &#39;rio.write_crs()&#39;.&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-        <span class="n">gcps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_gcps</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">gcps</span><span class="p">:</span>
-            <span class="n">kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;gcps&quot;</span><span class="p">,</span> <span class="n">gcps</span><span class="p">)</span>
-
-        <span class="n">use_affine</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="s2">&quot;gcps&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span>
-            <span class="ow">and</span> <span class="s2">&quot;rpcs&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span>
-            <span class="ow">and</span> <span class="s2">&quot;src_geoloc_array&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span>
-        <span class="p">)</span>
-        <span class="n">src_affine</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">use_affine</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">transform</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">dst_affine</span><span class="p">,</span> <span class="n">dst_width</span><span class="p">,</span> <span class="n">dst_height</span> <span class="o">=</span> <span class="n">_make_dst_affine</span><span class="p">(</span>
-                <span class="n">src_data_array</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span>
-                <span class="n">src_crs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span>
-                <span class="n">dst_crs</span><span class="o">=</span><span class="n">dst_crs</span><span class="p">,</span>
-                <span class="n">dst_resolution</span><span class="o">=</span><span class="n">resolution</span><span class="p">,</span>
-                <span class="n">dst_shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span>
-                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">dst_affine</span> <span class="o">=</span> <span class="n">transform</span>
-            <span class="k">if</span> <span class="n">shape</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">dst_height</span><span class="p">,</span> <span class="n">dst_width</span> <span class="o">=</span> <span class="n">shape</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dst_height</span><span class="p">,</span> <span class="n">dst_width</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="n">dst_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_dst_data</span><span class="p">(</span><span class="n">dst_height</span><span class="o">=</span><span class="n">dst_height</span><span class="p">,</span> <span class="n">dst_width</span><span class="o">=</span><span class="n">dst_width</span><span class="p">)</span>
-
-        <span class="n">dst_nodata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dst_nodata</span><span class="p">(</span><span class="n">nodata</span><span class="p">)</span>
-        <span class="n">rasterio</span><span class="o">.</span><span class="n">warp</span><span class="o">.</span><span class="n">reproject</span><span class="p">(</span>
-            <span class="n">source</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-            <span class="n">destination</span><span class="o">=</span><span class="n">dst_data</span><span class="p">,</span>
-            <span class="n">src_transform</span><span class="o">=</span><span class="n">src_affine</span><span class="p">,</span>
-            <span class="n">src_crs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span>
-            <span class="n">src_nodata</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">nodata</span><span class="p">,</span>
-            <span class="n">dst_transform</span><span class="o">=</span><span class="n">dst_affine</span><span class="p">,</span>
-            <span class="n">dst_crs</span><span class="o">=</span><span class="n">dst_crs</span><span class="p">,</span>
-            <span class="n">dst_nodata</span><span class="o">=</span><span class="n">dst_nodata</span><span class="p">,</span>
-            <span class="n">resampling</span><span class="o">=</span><span class="n">resampling</span><span class="p">,</span>
-            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="c1"># add necessary attributes</span>
-        <span class="n">new_attrs</span> <span class="o">=</span> <span class="n">_generate_attrs</span><span class="p">(</span><span class="n">src_data_array</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span> <span class="n">dst_nodata</span><span class="o">=</span><span class="n">dst_nodata</span><span class="p">)</span>
-        <span class="c1"># make sure dimensions with coordinates renamed to x,y</span>
-        <span class="n">dst_dims</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Hashable</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dims</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">:</span>
-                <span class="n">dst_dims</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;x&quot;</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">:</span>
-                <span class="n">dst_dims</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dst_dims</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
-        <span class="n">xda</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">(</span>
-            <span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
-            <span class="n">data</span><span class="o">=</span><span class="n">dst_data</span><span class="p">,</span>
-            <span class="n">coords</span><span class="o">=</span><span class="n">_make_coords</span><span class="p">(</span>
-                <span class="n">src_data_array</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span>
-                <span class="n">dst_affine</span><span class="o">=</span><span class="n">dst_affine</span><span class="p">,</span>
-                <span class="n">dst_width</span><span class="o">=</span><span class="n">dst_width</span><span class="p">,</span>
-                <span class="n">dst_height</span><span class="o">=</span><span class="n">dst_height</span><span class="p">,</span>
-                <span class="n">force_generate</span><span class="o">=</span><span class="ow">not</span> <span class="n">use_affine</span><span class="p">,</span>
-            <span class="p">),</span>
-            <span class="n">dims</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">dst_dims</span><span class="p">),</span>
-            <span class="n">attrs</span><span class="o">=</span><span class="n">new_attrs</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">xda</span><span class="o">.</span><span class="n">encoding</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">encoding</span>
-        <span class="n">xda</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_transform</span><span class="p">(</span><span class="n">dst_affine</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">xda</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_crs</span><span class="p">(</span><span class="n">dst_crs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">xda</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_coordinate_system</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">xda</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_get_dst_nodata</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodata</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]:</span>
-        <span class="n">default_nodata</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">_NODATA_DTYPE_MAP</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">dtype_rev</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodata</span> <span class="ow">is</span> <span class="kc">None</span>
-            <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodata</span>
-        <span class="p">)</span>
-        <span class="n">dst_nodata</span> <span class="o">=</span> <span class="n">default_nodata</span> <span class="k">if</span> <span class="n">nodata</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nodata</span>
-        <span class="k">return</span> <span class="n">dst_nodata</span>
-
-    <span class="k">def</span> <span class="nf">_create_dst_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">dst_height</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">dst_width</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-        <span class="n">extra_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_dimensions</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">extra_dim</span><span class="p">:</span>
-            <span class="n">dst_data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
-                <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">extra_dim</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">dst_height</span><span class="p">,</span> <span class="n">dst_width</span><span class="p">),</span>
-                <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">type</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">dst_data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dst_height</span><span class="p">,</span> <span class="n">dst_width</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">type</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">dst_data</span>
-
-<div class="viewcode-block" id="RasterArray.reproject_match">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray.reproject_match">[docs]</a>
-    <span class="k">def</span> <span class="nf">reproject_match</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">match_data_array</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">],</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">resampling</span><span class="p">:</span> <span class="n">Resampling</span> <span class="o">=</span> <span class="n">Resampling</span><span class="o">.</span><span class="n">nearest</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">reproject_kwargs</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Reproject a DataArray object to match the resolution, projection,</span>
-<span class="sd">        and region of another DataArray.</span>
-
-<span class="sd">        Powered by :func:`rasterio.warp.reproject`</span>
-
-<span class="sd">        .. note:: Only 2D/3D arrays with dimensions &#39;x&#39;/&#39;y&#39; are currently supported.</span>
-<span class="sd">            Requires either a grid mapping variable with &#39;spatial_ref&#39; or</span>
-<span class="sd">            a &#39;crs&#39; attribute to be set containing a valid CRS.</span>
-<span class="sd">            If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.</span>
-
-<span class="sd">        .. versionadded:: 0.9 reproject_kwargs</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        match_data_array:  :obj:`xarray.DataArray` | :obj:`xarray.Dataset`</span>
-<span class="sd">            DataArray of the target resolution and projection.</span>
-<span class="sd">        resampling: rasterio.enums.Resampling, optional</span>
-<span class="sd">            See :func:`rasterio.warp.reproject` for more details.</span>
-<span class="sd">        **reproject_kwargs:</span>
-<span class="sd">            Other options to pass to :meth:`rioxarray.raster_array.RasterArray.reproject`</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        --------</span>
-<span class="sd">        :obj:`xarray.DataArray`:</span>
-<span class="sd">            Contains the data from the src_data_array, reprojected to match</span>
-<span class="sd">            match_data_array.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">reprojected_data_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reproject</span><span class="p">(</span>
-            <span class="n">match_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span>
-            <span class="n">transform</span><span class="o">=</span><span class="n">match_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-            <span class="n">shape</span><span class="o">=</span><span class="n">match_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
-            <span class="n">resampling</span><span class="o">=</span><span class="n">resampling</span><span class="p">,</span>
-            <span class="o">**</span><span class="n">reproject_kwargs</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="c1"># hack to resolve: https://github.com/corteva/rioxarray/issues/298</span>
-        <span class="c1"># may be resolved in the future by flexible indexes:</span>
-        <span class="c1"># https://github.com/pydata/xarray/pull/4489#issuecomment-831809607</span>
-        <span class="n">x_attrs</span> <span class="o">=</span> <span class="n">reprojected_data_array</span><span class="p">[</span><span class="n">reprojected_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">y_attrs</span> <span class="o">=</span> <span class="n">reprojected_data_array</span><span class="p">[</span><span class="n">reprojected_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="c1"># ensure coords the same</span>
-        <span class="n">reprojected_data_array</span> <span class="o">=</span> <span class="n">reprojected_data_array</span><span class="o">.</span><span class="n">assign_coords</span><span class="p">(</span>
-            <span class="p">{</span>
-                <span class="n">reprojected_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">:</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span>
-                    <span class="n">match_data_array</span><span class="p">[</span><span class="n">match_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-                <span class="p">),</span>
-                <span class="n">reprojected_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span><span class="p">:</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span>
-                    <span class="n">match_data_array</span><span class="p">[</span><span class="n">match_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-                <span class="p">),</span>
-            <span class="p">}</span>
-        <span class="p">)</span>
-        <span class="c1"># ensure attributes copied</span>
-        <span class="n">reprojected_data_array</span><span class="p">[</span><span class="n">reprojected_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span> <span class="o">=</span> <span class="n">x_attrs</span>
-        <span class="n">reprojected_data_array</span><span class="p">[</span><span class="n">reprojected_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span> <span class="o">=</span> <span class="n">y_attrs</span>
-        <span class="k">return</span> <span class="n">reprojected_data_array</span></div>
-
-
-<div class="viewcode-block" id="RasterArray.pad_xy">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray.pad_xy">[docs]</a>
-    <span class="k">def</span> <span class="nf">pad_xy</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">minx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">miny</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxy</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">constant_values</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span>
-            <span class="nb">float</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">Mapping</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]],</span> <span class="kc">None</span>
-        <span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Pad the array to x,y bounds.</span>
-
-<span class="sd">        .. versionadded:: 0.0.29</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        minx: float</span>
-<span class="sd">            Minimum bound for x coordinate.</span>
-<span class="sd">        miny: float</span>
-<span class="sd">            Minimum bound for y coordinate.</span>
-<span class="sd">        maxx: float</span>
-<span class="sd">            Maximum bound for x coordinate.</span>
-<span class="sd">        maxy: float</span>
-<span class="sd">            Maximum bound for y coordinate.</span>
-<span class="sd">        constant_values: scalar, tuple or mapping of hashable to tuple</span>
-<span class="sd">            The value used for padding. If None, nodata will be used if it is</span>
-<span class="sd">            set, and numpy.nan otherwise.</span>
-
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.DataArray`:</span>
-<span class="sd">            The padded object.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># pylint: disable=too-many-locals</span>
-        <span class="n">left</span><span class="p">,</span> <span class="n">bottom</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">top</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_internal_bounds</span><span class="p">()</span>
-        <span class="n">resolution_x</span><span class="p">,</span> <span class="n">resolution_y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resolution</span><span class="p">()</span>
-        <span class="n">y_before</span> <span class="o">=</span> <span class="n">y_after</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">x_before</span> <span class="o">=</span> <span class="n">x_after</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">y_coord</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">]</span>
-        <span class="n">x_coord</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">top</span> <span class="o">-</span> <span class="n">resolution_y</span> <span class="o">&lt;</span> <span class="n">maxy</span><span class="p">:</span>
-            <span class="n">new_y_coord</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">bottom</span><span class="p">,</span> <span class="n">maxy</span><span class="p">,</span> <span class="o">-</span><span class="n">resolution_y</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">y_before</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_y_coord</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_coord</span><span class="p">)</span>
-            <span class="n">y_coord</span> <span class="o">=</span> <span class="n">new_y_coord</span>
-            <span class="n">top</span> <span class="o">=</span> <span class="n">y_coord</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">bottom</span> <span class="o">+</span> <span class="n">resolution_y</span> <span class="o">&gt;</span> <span class="n">miny</span><span class="p">:</span>
-            <span class="n">new_y_coord</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">top</span><span class="p">,</span> <span class="n">miny</span><span class="p">,</span> <span class="n">resolution_y</span><span class="p">)</span>
-            <span class="n">y_after</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_y_coord</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_coord</span><span class="p">)</span>
-            <span class="n">y_coord</span> <span class="o">=</span> <span class="n">new_y_coord</span>
-            <span class="n">bottom</span> <span class="o">=</span> <span class="n">y_coord</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">left</span> <span class="o">-</span> <span class="n">resolution_x</span> <span class="o">&gt;</span> <span class="n">minx</span><span class="p">:</span>
-            <span class="n">new_x_coord</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">minx</span><span class="p">,</span> <span class="o">-</span><span class="n">resolution_x</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">x_before</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_x_coord</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_coord</span><span class="p">)</span>
-            <span class="n">x_coord</span> <span class="o">=</span> <span class="n">new_x_coord</span>
-            <span class="n">left</span> <span class="o">=</span> <span class="n">x_coord</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">right</span> <span class="o">+</span> <span class="n">resolution_x</span> <span class="o">&lt;</span> <span class="n">maxx</span><span class="p">:</span>
-            <span class="n">new_x_coord</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">maxx</span><span class="p">,</span> <span class="n">resolution_x</span><span class="p">)</span>
-            <span class="n">x_after</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_x_coord</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_coord</span><span class="p">)</span>
-            <span class="n">x_coord</span> <span class="o">=</span> <span class="n">new_x_coord</span>
-            <span class="n">right</span> <span class="o">=</span> <span class="n">x_coord</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">constant_values</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">constant_values</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nan</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodata</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodata</span>
-
-        <span class="n">superset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span>
-            <span class="n">pad_width</span><span class="o">=</span><span class="p">{</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">:</span> <span class="p">(</span><span class="n">x_before</span><span class="p">,</span> <span class="n">x_after</span><span class="p">),</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">:</span> <span class="p">(</span><span class="n">y_before</span><span class="p">,</span> <span class="n">y_after</span><span class="p">),</span>
-            <span class="p">},</span>
-            <span class="n">constant_values</span><span class="o">=</span><span class="n">constant_values</span><span class="p">,</span>  <span class="c1"># type: ignore</span>
-        <span class="p">)</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span><span class="n">x_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">superset</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">]</span> <span class="o">=</span> <span class="n">x_coord</span>
-        <span class="n">superset</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_coord</span>
-        <span class="n">superset</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_transform</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">superset</span></div>
-
-
-<div class="viewcode-block" id="RasterArray.pad_box">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray.pad_box">[docs]</a>
-    <span class="k">def</span> <span class="nf">pad_box</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">minx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">miny</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxy</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">constant_values</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span>
-            <span class="nb">float</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">Mapping</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]],</span> <span class="kc">None</span>
-        <span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Pad the :obj:`xarray.DataArray` to a bounding box</span>
-
-<span class="sd">        .. versionadded:: 0.0.29</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        minx: float</span>
-<span class="sd">            Minimum bound for x coordinate.</span>
-<span class="sd">        miny: float</span>
-<span class="sd">            Minimum bound for y coordinate.</span>
-<span class="sd">        maxx: float</span>
-<span class="sd">            Maximum bound for x coordinate.</span>
-<span class="sd">        maxy: float</span>
-<span class="sd">            Maximum bound for y coordinate.</span>
-<span class="sd">        constant_values: scalar, tuple or mapping of hashable to tuple</span>
-<span class="sd">            The value used for padding. If None, nodata will be used if it is</span>
-<span class="sd">            set, and numpy.nan otherwise.</span>
-
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.DataArray`:</span>
-<span class="sd">            The padded object.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">resolution_x</span><span class="p">,</span> <span class="n">resolution_y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resolution</span><span class="p">()</span>
-
-        <span class="n">pad_minx</span> <span class="o">=</span> <span class="n">minx</span> <span class="o">-</span> <span class="nb">abs</span><span class="p">(</span><span class="n">resolution_x</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span>
-        <span class="n">pad_miny</span> <span class="o">=</span> <span class="n">miny</span> <span class="o">-</span> <span class="nb">abs</span><span class="p">(</span><span class="n">resolution_y</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span>
-        <span class="n">pad_maxx</span> <span class="o">=</span> <span class="n">maxx</span> <span class="o">+</span> <span class="nb">abs</span><span class="p">(</span><span class="n">resolution_x</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span>
-        <span class="n">pad_maxy</span> <span class="o">=</span> <span class="n">maxy</span> <span class="o">+</span> <span class="nb">abs</span><span class="p">(</span><span class="n">resolution_y</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span>
-
-        <span class="n">pd_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad_xy</span><span class="p">(</span>
-            <span class="n">minx</span><span class="o">=</span><span class="n">pad_minx</span><span class="p">,</span>
-            <span class="n">miny</span><span class="o">=</span><span class="n">pad_miny</span><span class="p">,</span>
-            <span class="n">maxx</span><span class="o">=</span><span class="n">pad_maxx</span><span class="p">,</span>
-            <span class="n">maxy</span><span class="o">=</span><span class="n">pad_maxy</span><span class="p">,</span>
-            <span class="n">constant_values</span><span class="o">=</span><span class="n">constant_values</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="c1"># make sure correct attributes preserved &amp; projection added</span>
-        <span class="n">_add_attrs_proj</span><span class="p">(</span><span class="n">new_data_array</span><span class="o">=</span><span class="n">pd_array</span><span class="p">,</span> <span class="n">src_data_array</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">pd_array</span></div>
-
-
-<div class="viewcode-block" id="RasterArray.clip_box">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray.clip_box">[docs]</a>
-    <span class="k">def</span> <span class="nf">clip_box</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">minx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">miny</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxy</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">auto_expand</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">bool</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="n">auto_expand_limit</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
-        <span class="n">crs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">allow_one_dimensional_raster</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Clip the :obj:`xarray.DataArray` by a bounding box.</span>
-
-<span class="sd">        .. versionadded:: 0.12 crs</span>
-<span class="sd">        .. versionadded:: 0.16 allow_one_dimensional_raster</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        minx: float</span>
-<span class="sd">            Minimum bound for x coordinate.</span>
-<span class="sd">        miny: float</span>
-<span class="sd">            Minimum bound for y coordinate.</span>
-<span class="sd">        maxx: float</span>
-<span class="sd">            Maximum bound for x coordinate.</span>
-<span class="sd">        maxy: float</span>
-<span class="sd">            Maximum bound for y coordinate.</span>
-<span class="sd">        auto_expand: Union[bool, int]</span>
-<span class="sd">            If True, it will expand clip search if only 1D raster found with clip.</span>
-<span class="sd">        auto_expand_limit: int</span>
-<span class="sd">            maximum number of times the clip will be retried before raising</span>
-<span class="sd">            an exception.</span>
-<span class="sd">        crs: :obj:`rasterio.crs.CRS`, optional</span>
-<span class="sd">            The CRS of the bounding box. Default is to assume it is the same</span>
-<span class="sd">            as the dataset.</span>
-<span class="sd">        allow_one_dimensional_raster: bool, optional</span>
-<span class="sd">            If True, allow clipping to/from a one dimensional raster.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        xarray.DataArray:</span>
-<span class="sd">            The clipped object.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">allow_one_dimensional_raster</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">width</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">height</span> <span class="o">==</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="n">OneDimensionalRaster</span><span class="p">(</span>
-                <span class="s2">&quot;At least one of the raster x,y coordinates has only one point.&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">. &quot;</span>
-                <span class="s2">&quot;Set allow_one_dimensional_raster=True to disable this error.&quot;</span>
-            <span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">crs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">MissingCRS</span><span class="p">(</span>
-                <span class="s2">&quot;CRS not found. Please set the CRS with &#39;rio.write_crs()&#39;.&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-
-        <span class="n">crs</span> <span class="o">=</span> <span class="n">crs_from_user_input</span><span class="p">(</span><span class="n">crs</span><span class="p">)</span> <span class="k">if</span> <span class="n">crs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span> <span class="o">!=</span> <span class="n">crs</span><span class="p">:</span>
-            <span class="n">minx</span><span class="p">,</span> <span class="n">miny</span><span class="p">,</span> <span class="n">maxx</span><span class="p">,</span> <span class="n">maxy</span> <span class="o">=</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">warp</span><span class="o">.</span><span class="n">transform_bounds</span><span class="p">(</span>
-                <span class="n">src_crs</span><span class="o">=</span><span class="n">crs</span><span class="p">,</span>
-                <span class="n">dst_crs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span>
-                <span class="n">left</span><span class="o">=</span><span class="n">minx</span><span class="p">,</span>
-                <span class="n">bottom</span><span class="o">=</span><span class="n">miny</span><span class="p">,</span>
-                <span class="n">right</span><span class="o">=</span><span class="n">maxx</span><span class="p">,</span>
-                <span class="n">top</span><span class="o">=</span><span class="n">maxy</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="k">if</span> <span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">crs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-                <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">is_geographic</span>  <span class="c1"># pylint: disable=no-member</span>
-                <span class="ow">and</span> <span class="n">minx</span> <span class="o">&gt;</span> <span class="n">maxx</span>
-            <span class="p">):</span>
-                <span class="k">raise</span> <span class="n">RioXarrayError</span><span class="p">(</span>
-                    <span class="s2">&quot;Transformed bounds crossed the antimeridian. &quot;</span>
-                    <span class="s2">&quot;Please transform your bounds manually using &quot;</span>
-                    <span class="s2">&quot;rasterio.warp.transform_bounds and clip using &quot;</span>
-                    <span class="s2">&quot;the bounding box(es) desired.&quot;</span>
-                <span class="p">)</span>
-
-        <span class="n">resolution_x</span><span class="p">,</span> <span class="n">resolution_y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resolution</span><span class="p">()</span>
-        <span class="c1"># make sure that if the coordinates are</span>
-        <span class="c1"># in reverse order that it still works</span>
-        <span class="n">left</span><span class="p">,</span> <span class="n">bottom</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">top</span> <span class="o">=</span> <span class="n">_order_bounds</span><span class="p">(</span>
-            <span class="n">minx</span><span class="o">=</span><span class="n">minx</span><span class="p">,</span>
-            <span class="n">miny</span><span class="o">=</span><span class="n">miny</span><span class="p">,</span>
-            <span class="n">maxx</span><span class="o">=</span><span class="n">maxx</span><span class="p">,</span>
-            <span class="n">maxy</span><span class="o">=</span><span class="n">maxy</span><span class="p">,</span>
-            <span class="n">resolution_x</span><span class="o">=</span><span class="n">resolution_x</span><span class="p">,</span>
-            <span class="n">resolution_y</span><span class="o">=</span><span class="n">resolution_y</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="c1"># pull the data out</span>
-        <span class="n">window_error</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">window</span> <span class="o">=</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">from_bounds</span><span class="p">(</span>
-                <span class="n">left</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">left</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
-                <span class="n">bottom</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">bottom</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
-                <span class="n">right</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">right</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
-                <span class="n">top</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">top</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
-                <span class="n">transform</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-            <span class="p">)</span>
-            <span class="n">cl_array</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">isel_window</span><span class="p">(</span><span class="n">window</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
-        <span class="k">except</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">errors</span><span class="o">.</span><span class="n">WindowError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
-            <span class="n">window_error</span> <span class="o">=</span> <span class="n">err</span>
-
-        <span class="c1"># check that the window has data in it</span>
-        <span class="k">if</span> <span class="n">window_error</span> <span class="ow">or</span> <span class="n">cl_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">width</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">cl_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">height</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">auto_expand</span> <span class="ow">and</span> <span class="n">auto_expand</span> <span class="o">&lt;</span> <span class="n">auto_expand_limit</span><span class="p">:</span>
-                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_box</span><span class="p">(</span>
-                    <span class="n">minx</span><span class="o">=</span><span class="n">minx</span> <span class="o">-</span> <span class="nb">abs</span><span class="p">(</span><span class="n">resolution_x</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">,</span>
-                    <span class="n">miny</span><span class="o">=</span><span class="n">miny</span> <span class="o">-</span> <span class="nb">abs</span><span class="p">(</span><span class="n">resolution_y</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">,</span>
-                    <span class="n">maxx</span><span class="o">=</span><span class="n">maxx</span> <span class="o">+</span> <span class="nb">abs</span><span class="p">(</span><span class="n">resolution_x</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">,</span>
-                    <span class="n">maxy</span><span class="o">=</span><span class="n">maxy</span> <span class="o">+</span> <span class="nb">abs</span><span class="p">(</span><span class="n">resolution_y</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">,</span>
-                    <span class="n">auto_expand</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">auto_expand</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
-                    <span class="n">auto_expand_limit</span><span class="o">=</span><span class="n">auto_expand_limit</span><span class="p">,</span>
-                <span class="p">)</span>
-            <span class="k">if</span> <span class="n">window_error</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="n">window_error</span>
-            <span class="k">if</span> <span class="n">cl_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">width</span> <span class="o">&lt;</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">cl_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">height</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="n">NoDataInBounds</span><span class="p">(</span>
-                    <span class="sa">f</span><span class="s2">&quot;No data found in bounds.</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-                <span class="p">)</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">allow_one_dimensional_raster</span> <span class="ow">and</span> <span class="p">(</span>
-                <span class="n">cl_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">width</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">cl_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">height</span> <span class="o">==</span> <span class="mi">1</span>
-            <span class="p">):</span>
-                <span class="k">raise</span> <span class="n">OneDimensionalRaster</span><span class="p">(</span>
-                    <span class="s2">&quot;At least one of the clipped raster x,y coordinates&quot;</span>
-                    <span class="s2">&quot; has only one point.&quot;</span>
-                    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">. &quot;</span>
-                    <span class="s2">&quot;Set allow_one_dimensional_raster=True to disable this error.&quot;</span>
-                <span class="p">)</span>
-
-        <span class="c1"># make sure correct attributes preserved &amp; projection added</span>
-        <span class="n">_add_attrs_proj</span><span class="p">(</span><span class="n">new_data_array</span><span class="o">=</span><span class="n">cl_array</span><span class="p">,</span> <span class="n">src_data_array</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">cl_array</span></div>
-
-
-<div class="viewcode-block" id="RasterArray.clip">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray.clip">[docs]</a>
-    <span class="k">def</span> <span class="nf">clip</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">geometries</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">,</span>
-        <span class="n">crs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">all_touched</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="n">drop</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-        <span class="n">invert</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="n">from_disk</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Crops a :obj:`xarray.DataArray` by geojson like geometry dicts.</span>
-
-<span class="sd">        Powered by `rasterio.features.geometry_mask`.</span>
-
-<span class="sd">        Examples:</span>
-
-<span class="sd">            &gt;&gt;&gt; geometry = &#39;&#39;&#39; {&quot;type&quot;: &quot;Polygon&quot;,</span>
-<span class="sd">            ...                 &quot;coordinates&quot;: [</span>
-<span class="sd">            ...                 [[-94.07955380199459, 41.69085871273774],</span>
-<span class="sd">            ...                 [-94.06082436942204, 41.69103313774798],</span>
-<span class="sd">            ...                 [-94.06063203899649, 41.67932439500822],</span>
-<span class="sd">            ...                 [-94.07935807746362, 41.679150041277325],</span>
-<span class="sd">            ...                 [-94.07955380199459, 41.69085871273774]]]}&#39;&#39;&#39;</span>
-<span class="sd">            &gt;&gt;&gt; cropping_geometries = [geojson.loads(geometry)]</span>
-<span class="sd">            &gt;&gt;&gt; xds = xarray.open_rasterio(&#39;cool_raster.tif&#39;)</span>
-<span class="sd">            &gt;&gt;&gt; cropped = xds.rio.clip(geometries=cropping_geometries, crs=4326)</span>
-
-
-<span class="sd">        .. versionadded:: 0.2 from_disk</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        geometries: Iterable</span>
-<span class="sd">            A list of geojson geometry dicts or objects with __geo_interface__ with</span>
-<span class="sd">            if you have rasterio 1.2+.</span>
-<span class="sd">        crs: :obj:`rasterio.crs.CRS`, optional</span>
-<span class="sd">            The CRS of the input geometries. Default is to assume it is the same</span>
-<span class="sd">            as the dataset.</span>
-<span class="sd">        all_touched : bool, optional</span>
-<span class="sd">            If True, all pixels touched by geometries will be burned in.  If</span>
-<span class="sd">            false, only pixels whose center is within the polygon or that</span>
-<span class="sd">            are selected by Bresenham&#39;s line algorithm will be burned in.</span>
-<span class="sd">        drop: bool, optional</span>
-<span class="sd">            If True, drop the data outside of the extent of the mask geoemtries</span>
-<span class="sd">            Otherwise, it will return the same raster with the data masked.</span>
-<span class="sd">            Default is True.</span>
-<span class="sd">        invert: boolean, optional</span>
-<span class="sd">            If False, pixels that do not overlap shapes will be set as nodata.</span>
-<span class="sd">            Otherwise, pixels that overlap the shapes will be set as nodata.</span>
-<span class="sd">            False by default.</span>
-<span class="sd">        from_disk: boolean, optional</span>
-<span class="sd">            If True, it will clip from disk using rasterio.mask.mask if possible.</span>
-<span class="sd">            This is beneficial when the size of the data is larger than memory.</span>
-<span class="sd">            Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.DataArray`:</span>
-<span class="sd">            The clipped object.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">MissingCRS</span><span class="p">(</span>
-                <span class="s2">&quot;CRS not found. Please set the CRS with &#39;rio.write_crs()&#39;.&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-        <span class="n">crs</span> <span class="o">=</span> <span class="n">crs_from_user_input</span><span class="p">(</span><span class="n">crs</span><span class="p">)</span> <span class="k">if</span> <span class="n">crs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span> <span class="o">!=</span> <span class="n">crs</span><span class="p">:</span>
-            <span class="n">geometries</span> <span class="o">=</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">warp</span><span class="o">.</span><span class="n">transform_geom</span><span class="p">(</span><span class="n">crs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span> <span class="n">geometries</span><span class="p">)</span>
-        <span class="n">cropped_ds</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="n">from_disk</span><span class="p">:</span>
-            <span class="n">cropped_ds</span> <span class="o">=</span> <span class="n">_clip_from_disk</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span>
-                <span class="n">geometries</span><span class="o">=</span><span class="n">geometries</span><span class="p">,</span>
-                <span class="n">all_touched</span><span class="o">=</span><span class="n">all_touched</span><span class="p">,</span>
-                <span class="n">drop</span><span class="o">=</span><span class="n">drop</span><span class="p">,</span>
-                <span class="n">invert</span><span class="o">=</span><span class="n">invert</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="n">cropped_ds</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">cropped_ds</span> <span class="o">=</span> <span class="n">_clip_xarray</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span>
-                <span class="n">geometries</span><span class="o">=</span><span class="n">geometries</span><span class="p">,</span>
-                <span class="n">all_touched</span><span class="o">=</span><span class="n">all_touched</span><span class="p">,</span>
-                <span class="n">drop</span><span class="o">=</span><span class="n">drop</span><span class="p">,</span>
-                <span class="n">invert</span><span class="o">=</span><span class="n">invert</span><span class="p">,</span>
-            <span class="p">)</span>
-
-        <span class="k">if</span> <span class="p">(</span>
-            <span class="n">cropped_ds</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">]</span><span class="o">.</span><span class="n">size</span> <span class="o">&lt;</span> <span class="mi">1</span>
-            <span class="ow">or</span> <span class="n">cropped_ds</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">]</span><span class="o">.</span><span class="n">size</span> <span class="o">&lt;</span> <span class="mi">1</span>
-        <span class="p">):</span>
-            <span class="k">raise</span> <span class="n">NoDataInBounds</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;No data found in bounds.</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-
-        <span class="c1"># make sure correct attributes preserved &amp; projection added</span>
-        <span class="n">_add_attrs_proj</span><span class="p">(</span><span class="n">new_data_array</span><span class="o">=</span><span class="n">cropped_ds</span><span class="p">,</span> <span class="n">src_data_array</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">cropped_ds</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_interpolate_na</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">src_data</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">method</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="s2">&quot;cubic&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;nearest&quot;</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        This method uses scipy.interpolate.griddata to interpolate missing data.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        src_data: Any</span>
-<span class="sd">            Input data array.</span>
-<span class="sd">        method: {&#39;linear&#39;, &#39;nearest&#39;, &#39;cubic&#39;}, optional</span>
-<span class="sd">            The method to use for interpolation in `scipy.interpolate.griddata`.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :class:`numpy.ndarray`:</span>
-<span class="sd">            An interpolated :class:`numpy.ndarray`.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="p">(</span>  <span class="c1"># pylint: disable=import-outside-toplevel,import-error</span>
-                <span class="n">griddata</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">except</span> <span class="ne">ModuleNotFoundError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ModuleNotFoundError</span><span class="p">(</span>
-                <span class="s2">&quot;scipy is not found. Use rioxarray[interp] to install.&quot;</span>
-            <span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
-
-        <span class="n">src_data_flat</span> <span class="o">=</span> <span class="n">src_data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">data_isnan</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodata</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
-        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
-            <span class="n">data_isnan</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">data_isnan</span><span class="p">:</span>
-            <span class="n">data_bool</span> <span class="o">=</span> <span class="n">src_data_flat</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodata</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">data_bool</span> <span class="o">=</span> <span class="o">~</span><span class="n">numpy</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">src_data_flat</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">data_bool</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
-            <span class="k">return</span> <span class="n">src_data</span>
-
-        <span class="n">x_coords</span><span class="p">,</span> <span class="n">y_coords</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-        <span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">griddata</span><span class="p">(</span>
-            <span class="n">points</span><span class="o">=</span><span class="p">(</span><span class="n">x_coords</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="n">data_bool</span><span class="p">],</span> <span class="n">y_coords</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="n">data_bool</span><span class="p">]),</span>
-            <span class="n">values</span><span class="o">=</span><span class="n">src_data_flat</span><span class="p">[</span><span class="n">data_bool</span><span class="p">],</span>
-            <span class="n">xi</span><span class="o">=</span><span class="p">(</span><span class="n">x_coords</span><span class="p">,</span> <span class="n">y_coords</span><span class="p">),</span>
-            <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">,</span>
-            <span class="n">fill_value</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">nodata</span><span class="p">,</span>
-        <span class="p">)</span>
-
-<div class="viewcode-block" id="RasterArray.interpolate_na">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray.interpolate_na">[docs]</a>
-    <span class="k">def</span> <span class="nf">interpolate_na</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="s2">&quot;cubic&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;nearest&quot;</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        This method uses scipy.interpolate.griddata to interpolate missing data.</span>
-
-<span class="sd">        .. warning:: scipy is an optional dependency.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        method: {&#39;linear&#39;, &#39;nearest&#39;, &#39;cubic&#39;}, optional</span>
-<span class="sd">            The method to use for interpolation in `scipy.interpolate.griddata`.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.DataArray`:</span>
-<span class="sd">            An interpolated :obj:`xarray.DataArray` object.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodata</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">RioXarrayError</span><span class="p">(</span>
-                <span class="s2">&quot;nodata not found. Please set the nodata with &#39;rio.write_nodata()&#39;.&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-
-        <span class="n">extra_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_dimensions</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">extra_dim</span><span class="p">:</span>
-            <span class="n">interp_data</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">sub_xds</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">extra_dim</span><span class="p">):</span>
-                <span class="n">interp_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_interpolate_na</span><span class="p">(</span>
-                        <span class="n">sub_xds</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="n">extra_dim</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="n">method</span>
-                    <span class="p">)</span>
-                <span class="p">)</span>
-            <span class="n">interp_data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">interp_data</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">interp_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_interpolate_na</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
-        <span class="n">interp_array</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">(</span>
-            <span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
-            <span class="n">data</span><span class="o">=</span><span class="n">interp_data</span><span class="p">,</span>
-            <span class="n">coords</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">,</span>
-            <span class="n">dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dims</span><span class="p">,</span>
-            <span class="n">attrs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">interp_array</span><span class="o">.</span><span class="n">encoding</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">encoding</span>
-
-        <span class="c1"># make sure correct attributes preserved &amp; projection added</span>
-        <span class="n">_add_attrs_proj</span><span class="p">(</span><span class="n">new_data_array</span><span class="o">=</span><span class="n">interp_array</span><span class="p">,</span> <span class="n">src_data_array</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">interp_array</span></div>
-
-
-<div class="viewcode-block" id="RasterArray.to_raster">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_array.RasterArray.to_raster">[docs]</a>
-    <span class="k">def</span> <span class="nf">to_raster</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">raster_path</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">PathLike</span><span class="p">],</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">driver</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">dtype</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dtype</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">tags</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">windowed</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="n">recalc_transform</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-        <span class="n">lock</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">compute</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">profile_kwargs</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Export the DataArray to a raster file.</span>
-
-<span class="sd">        ..versionadded:: 0.2 lock</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        raster_path: Union[str, os.PathLike]</span>
-<span class="sd">            The path to output the raster to.</span>
-<span class="sd">        driver: str, optional</span>
-<span class="sd">            The name of the GDAL/rasterio driver to use to export the raster.</span>
-<span class="sd">            Default is &quot;GTiff&quot; if rasterio &lt; 1.2 otherwise it will autodetect.</span>
-<span class="sd">        dtype: str, optional</span>
-<span class="sd">            The data type to write the raster to. Default is the datasets dtype.</span>
-<span class="sd">        tags: dict, optional</span>
-<span class="sd">            A dictionary of tags to write to the raster.</span>
-<span class="sd">        windowed: bool, optional</span>
-<span class="sd">            If True, it will write using the windows of the output raster.</span>
-<span class="sd">            This is useful for loading data in chunks when writing. Does not</span>
-<span class="sd">            do anything when writing with dask.</span>
-<span class="sd">            Default is False.</span>
-<span class="sd">        recalc_transform: bool, optional</span>
-<span class="sd">            If False, it will write the raster with the cached transform from</span>
-<span class="sd">            the dataarray rather than recalculating it.</span>
-<span class="sd">            Default is True.</span>
-<span class="sd">        lock: boolean or Lock, optional</span>
-<span class="sd">            Lock to use to write data using dask.</span>
-<span class="sd">            If not supplied, it will use a single process for writing.</span>
-<span class="sd">        compute: bool, optional</span>
-<span class="sd">            If True and data is a dask array, then compute and save</span>
-<span class="sd">            the data immediately. If False, return a dask Delayed object.</span>
-<span class="sd">            Call &quot;.compute()&quot; on the Delayed object to compute the result</span>
-<span class="sd">            later. Call ``dask.compute(delayed1, delayed2)`` to save</span>
-<span class="sd">            multiple delayed files at once. Default is True.</span>
-<span class="sd">        **profile_kwargs</span>
-<span class="sd">            Additional keyword arguments to pass into writing the raster. The</span>
-<span class="sd">            nodata, transform, crs, count, width, and height attributes</span>
-<span class="sd">            are ignored.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`dask.Delayed`:</span>
-<span class="sd">            If the data array is a dask array and compute</span>
-<span class="sd">            is True. Otherwise None is returned.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">driver</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">extension</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">raster_path</span><span class="p">)</span><span class="o">.</span><span class="n">suffix</span>
-            <span class="c1"># https://github.com/rasterio/rasterio/pull/2008</span>
-            <span class="k">if</span> <span class="n">extension</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;.tif&quot;</span><span class="p">,</span> <span class="s2">&quot;.tiff&quot;</span><span class="p">):</span>
-                <span class="n">driver</span> <span class="o">=</span> <span class="s2">&quot;GTiff&quot;</span>
-
-        <span class="c1"># get the output profile from the rasterio object</span>
-        <span class="c1"># if opened with xarray.open_rasterio()</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">out_profile</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_manager</span><span class="o">.</span><span class="n">acquire</span><span class="p">()</span><span class="o">.</span><span class="n">profile</span>  <span class="c1"># type: ignore</span>
-        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-            <span class="n">out_profile</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">out_profile</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">profile_kwargs</span><span class="p">)</span>
-
-        <span class="c1"># filter out the generated attributes</span>
-        <span class="n">out_profile</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="n">key</span><span class="p">:</span> <span class="n">value</span>
-            <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">out_profile</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-            <span class="k">if</span> <span class="n">key</span>
-            <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span>
-                <span class="s2">&quot;driver&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;height&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;width&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;crs&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;transform&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;nodata&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;count&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;dtype&quot;</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="p">}</span>
-        <span class="n">rio_nodata</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">encoded_nodata</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoded_nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodata</span>
-        <span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">RasterioWriter</span><span class="p">(</span><span class="n">raster_path</span><span class="o">=</span><span class="n">raster_path</span><span class="p">)</span><span class="o">.</span><span class="n">to_raster</span><span class="p">(</span>
-            <span class="n">xarray_dataarray</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span>
-            <span class="n">tags</span><span class="o">=</span><span class="n">tags</span><span class="p">,</span>
-            <span class="n">driver</span><span class="o">=</span><span class="n">driver</span><span class="p">,</span>
-            <span class="n">height</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">height</span><span class="p">),</span>
-            <span class="n">width</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">width</span><span class="p">),</span>
-            <span class="n">count</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">),</span>
-            <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
-            <span class="n">crs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span>
-            <span class="n">transform</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="n">recalc_transform</span><span class="p">),</span>
-            <span class="n">gcps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">get_gcps</span><span class="p">(),</span>
-            <span class="n">nodata</span><span class="o">=</span><span class="n">rio_nodata</span><span class="p">,</span>
-            <span class="n">windowed</span><span class="o">=</span><span class="n">windowed</span><span class="p">,</span>
-            <span class="n">lock</span><span class="o">=</span><span class="n">lock</span><span class="p">,</span>
-            <span class="n">compute</span><span class="o">=</span><span class="n">compute</span><span class="p">,</span>
-            <span class="o">**</span><span class="n">out_profile</span><span class="p">,</span>
-        <span class="p">)</span></div>
-</div>
-
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
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-    <p>&#169; Copyright 2019-2023, Corteva Agriscience™.</p>
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diff --git a/0.18.0/_modules/rioxarray/raster_dataset.html b/0.18.0/_modules/rioxarray/raster_dataset.html
deleted file mode 100644
index d4691d3b..00000000
--- a/0.18.0/_modules/rioxarray/raster_dataset.html
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-             
-  <h1>Source code for rioxarray.raster_dataset</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">This module is an extension for xarray to provide rasterio capabilities</span>
-<span class="sd">to xarray datasets.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">Mapping</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Literal</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
-<span class="kn">from</span> <span class="nn">uuid</span> <span class="kn">import</span> <span class="n">uuid4</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">rasterio.crs</span>
-<span class="kn">import</span> <span class="nn">xarray</span>
-<span class="kn">from</span> <span class="nn">affine</span> <span class="kn">import</span> <span class="n">Affine</span>
-<span class="kn">from</span> <span class="nn">rasterio.enums</span> <span class="kn">import</span> <span class="n">Resampling</span>
-
-<span class="kn">from</span> <span class="nn">rioxarray._options</span> <span class="kn">import</span> <span class="n">SKIP_MISSING_SPATIAL_DIMS</span><span class="p">,</span> <span class="n">get_option</span>
-<span class="kn">from</span> <span class="nn">rioxarray.exceptions</span> <span class="kn">import</span> <span class="n">MissingSpatialDimensionError</span><span class="p">,</span> <span class="n">RioXarrayError</span>
-<span class="kn">from</span> <span class="nn">rioxarray.rioxarray</span> <span class="kn">import</span> <span class="n">XRasterBase</span><span class="p">,</span> <span class="n">_get_spatial_dims</span>
-
-
-<div class="viewcode-block" id="RasterDataset">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_dataset.RasterDataset">[docs]</a>
-<span class="nd">@xarray</span><span class="o">.</span><span class="n">register_dataset_accessor</span><span class="p">(</span><span class="s2">&quot;rio&quot;</span><span class="p">)</span>
-<span class="k">class</span> <span class="nc">RasterDataset</span><span class="p">(</span><span class="n">XRasterBase</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is the GIS extension for :class:`xarray.Dataset`&quot;&quot;&quot;</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">vars</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;list: Returns non-coordinate varibles&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">data_vars</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">crs</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="n">rasterio</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">CRS</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;:obj:`rasterio.crs.CRS`:</span>
-<span class="sd">        Retrieve projection from `xarray.Dataset`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span> <span class="ow">is</span> <span class="kc">False</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">crs</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span>
-        <span class="c1"># ensure all the CRS of the variables are the same</span>
-        <span class="n">crs_list</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vars</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">crs_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span><span class="p">)</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">crs</span> <span class="o">=</span> <span class="n">crs_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="n">crs</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="n">crs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span> <span class="o">=</span> <span class="kc">False</span>
-            <span class="k">return</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">crs_i</span> <span class="o">==</span> <span class="n">crs</span> <span class="k">for</span> <span class="n">crs_i</span> <span class="ow">in</span> <span class="n">crs_list</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span> <span class="o">=</span> <span class="n">crs</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">RioXarrayError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;CRS in DataArrays differ in the Dataset: </span><span class="si">{</span><span class="n">crs_list</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span>
-
-<div class="viewcode-block" id="RasterDataset.reproject">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_dataset.RasterDataset.reproject">[docs]</a>
-    <span class="k">def</span> <span class="nf">reproject</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">dst_crs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">resolution</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">shape</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">transform</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Affine</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">resampling</span><span class="p">:</span> <span class="n">Resampling</span> <span class="o">=</span> <span class="n">Resampling</span><span class="o">.</span><span class="n">nearest</span><span class="p">,</span>
-        <span class="n">nodata</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Reproject :class:`xarray.Dataset` objects</span>
-
-<span class="sd">        .. note:: Only 2D/3D arrays with dimensions &#39;x&#39;/&#39;y&#39; are currently supported.</span>
-<span class="sd">            Others are appended as is.</span>
-<span class="sd">            Requires either a grid mapping variable with &#39;spatial_ref&#39; or</span>
-<span class="sd">            a &#39;crs&#39; attribute to be set containing a valid CRS.</span>
-<span class="sd">            If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.</span>
-
-<span class="sd">        .. note:: To re-project with dask, see</span>
-<span class="sd">            `odc-geo &lt;https://odc-geo.readthedocs.io/&gt;`__ &amp;</span>
-<span class="sd">            `pyresample &lt;https://pyresample.readthedocs.io/&gt;`__.</span>
-
-<span class="sd">        .. versionadded:: 0.0.27 shape</span>
-<span class="sd">        .. versionadded:: 0.0.28 transform</span>
-<span class="sd">        .. versionadded:: 0.5.0 nodata, kwargs</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        dst_crs: str</span>
-<span class="sd">            OGC WKT string or Proj.4 string.</span>
-<span class="sd">        resolution: float or tuple(float, float), optional</span>
-<span class="sd">            Size of a destination pixel in destination projection units</span>
-<span class="sd">            (e.g. degrees or metres).</span>
-<span class="sd">        shape: tuple(int, int), optional</span>
-<span class="sd">            Shape of the destination in pixels (dst_height, dst_width). Cannot be used</span>
-<span class="sd">            together with resolution.</span>
-<span class="sd">        transform: Affine, optional</span>
-<span class="sd">            The destination transform.</span>
-<span class="sd">        resampling: rasterio.enums.Resampling, optional</span>
-<span class="sd">            See :func:`rasterio.warp.reproject` for more details.</span>
-<span class="sd">        nodata: float, optional</span>
-<span class="sd">            The nodata value used to initialize the destination;</span>
-<span class="sd">            it will remain in all areas not covered by the reprojected source.</span>
-<span class="sd">            Defaults to the nodata value of the source image if none provided</span>
-<span class="sd">            and exists or attempts to find an appropriate value by dtype.</span>
-<span class="sd">        **kwargs: dict</span>
-<span class="sd">            Additional keyword arguments to pass into :func:`rasterio.warp.reproject`.</span>
-<span class="sd">            To override:</span>
-<span class="sd">            - src_transform: `rio.write_transform`</span>
-<span class="sd">            - src_crs: `rio.write_crs`</span>
-<span class="sd">            - src_nodata: `rio.write_nodata`</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`xarray.Dataset`:</span>
-<span class="sd">            The reprojected Dataset.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">resampled_dataset</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">attrs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vars</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span> <span class="o">=</span> <span class="n">_get_spatial_dims</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">)</span>
-                <span class="n">resampled_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span><span class="n">x_dim</span><span class="o">=</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">reproject</span><span class="p">(</span>
-                        <span class="n">dst_crs</span><span class="p">,</span>
-                        <span class="n">resolution</span><span class="o">=</span><span class="n">resolution</span><span class="p">,</span>
-                        <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span>
-                        <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
-                        <span class="n">resampling</span><span class="o">=</span><span class="n">resampling</span><span class="p">,</span>
-                        <span class="n">nodata</span><span class="o">=</span><span class="n">nodata</span><span class="p">,</span>
-                        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="p">)</span>
-            <span class="k">except</span> <span class="n">MissingSpatialDimensionError</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">get_option</span><span class="p">(</span>
-                    <span class="n">SKIP_MISSING_SPATIAL_DIMS</span>
-                <span class="p">):</span>
-                    <span class="k">raise</span>
-                <span class="n">resampled_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">resampled_dataset</span></div>
-
-
-<div class="viewcode-block" id="RasterDataset.reproject_match">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_dataset.RasterDataset.reproject_match">[docs]</a>
-    <span class="k">def</span> <span class="nf">reproject_match</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">match_data_array</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">],</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">resampling</span><span class="p">:</span> <span class="n">Resampling</span> <span class="o">=</span> <span class="n">Resampling</span><span class="o">.</span><span class="n">nearest</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">reproject_kwargs</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Reproject a Dataset object to match the resolution, projection,</span>
-<span class="sd">        and region of another DataArray.</span>
-
-<span class="sd">        .. note:: Only 2D/3D arrays with dimensions &#39;x&#39;/&#39;y&#39; are currently supported.</span>
-<span class="sd">            Others are appended as is.</span>
-<span class="sd">            Requires either a grid mapping variable with &#39;spatial_ref&#39; or</span>
-<span class="sd">            a &#39;crs&#39; attribute to be set containing a valid CRS.</span>
-<span class="sd">            If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.</span>
-
-<span class="sd">        .. versionadded:: 0.9 reproject_kwargs</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        match_data_array: :obj:`xarray.DataArray` | :obj:`xarray.Dataset`</span>
-<span class="sd">            Dataset with the target resolution and projection.</span>
-<span class="sd">        resampling: rasterio.enums.Resampling, optional</span>
-<span class="sd">            See :func:`rasterio.warp.reproject` for more details.</span>
-<span class="sd">        **reproject_kwargs:</span>
-<span class="sd">            Other options to pass to :meth:`rioxarray.raster_dataset.RasterDataset.reproject`</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        --------</span>
-<span class="sd">        :obj:`xarray.Dataset`:</span>
-<span class="sd">            Contains the data from the src_data_array,</span>
-<span class="sd">            reprojected to match match_data_array.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">resampled_dataset</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">attrs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vars</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span> <span class="o">=</span> <span class="n">_get_spatial_dims</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">)</span>
-                <span class="n">resampled_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span><span class="n">x_dim</span><span class="o">=</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">reproject_match</span><span class="p">(</span>
-                        <span class="n">match_data_array</span><span class="p">,</span> <span class="n">resampling</span><span class="o">=</span><span class="n">resampling</span><span class="p">,</span> <span class="o">**</span><span class="n">reproject_kwargs</span>
-                    <span class="p">)</span>
-                <span class="p">)</span>
-            <span class="k">except</span> <span class="n">MissingSpatialDimensionError</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">get_option</span><span class="p">(</span>
-                    <span class="n">SKIP_MISSING_SPATIAL_DIMS</span>
-                <span class="p">):</span>
-                    <span class="k">raise</span>
-                <span class="n">resampled_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">resampled_dataset</span></div>
-
-
-<div class="viewcode-block" id="RasterDataset.pad_box">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_dataset.RasterDataset.pad_box">[docs]</a>
-    <span class="k">def</span> <span class="nf">pad_box</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">minx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">miny</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxy</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">constant_values</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span>
-            <span class="nb">float</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">Mapping</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]],</span> <span class="kc">None</span>
-        <span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Pad the :class:`xarray.Dataset` to a bounding box.</span>
-
-<span class="sd">        .. warning:: Only works if all variables in the dataset have the</span>
-<span class="sd">                     same coordinates.</span>
-
-<span class="sd">        .. warning:: Pads variables that have dimensions &#39;x&#39;/&#39;y&#39;. Others are appended as is.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        minx: float</span>
-<span class="sd">            Minimum bound for x coordinate.</span>
-<span class="sd">        miny: float</span>
-<span class="sd">            Minimum bound for y coordinate.</span>
-<span class="sd">        maxx: float</span>
-<span class="sd">            Maximum bound for x coordinate.</span>
-<span class="sd">        maxy: float</span>
-<span class="sd">            Maximum bound for y coordinate.</span>
-<span class="sd">        constant_values: scalar, tuple or mapping of hashable to tuple</span>
-<span class="sd">            The value used for padding. If None, nodata will be used if it is</span>
-<span class="sd">            set, and numpy.nan otherwise.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset`:</span>
-<span class="sd">            The padded object.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">padded_dataset</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">attrs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vars</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span> <span class="o">=</span> <span class="n">_get_spatial_dims</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">)</span>
-                <span class="n">padded_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span><span class="n">x_dim</span><span class="o">=</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">pad_box</span><span class="p">(</span>
-                        <span class="n">minx</span><span class="p">,</span> <span class="n">miny</span><span class="p">,</span> <span class="n">maxx</span><span class="p">,</span> <span class="n">maxy</span><span class="p">,</span> <span class="n">constant_values</span><span class="o">=</span><span class="n">constant_values</span>
-                    <span class="p">)</span>
-                <span class="p">)</span>
-            <span class="k">except</span> <span class="n">MissingSpatialDimensionError</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">get_option</span><span class="p">(</span>
-                    <span class="n">SKIP_MISSING_SPATIAL_DIMS</span>
-                <span class="p">):</span>
-                    <span class="k">raise</span>
-                <span class="n">padded_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">padded_dataset</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span>
-            <span class="n">x_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="RasterDataset.clip_box">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_dataset.RasterDataset.clip_box">[docs]</a>
-    <span class="k">def</span> <span class="nf">clip_box</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">minx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">miny</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxy</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">auto_expand</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">bool</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="n">auto_expand_limit</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
-        <span class="n">crs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">allow_one_dimensional_raster</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Clip the :class:`xarray.Dataset` by a bounding box in dimensions &#39;x&#39;/&#39;y&#39;.</span>
-
-<span class="sd">        .. warning:: Clips variables that have dimensions &#39;x&#39;/&#39;y&#39;. Others are appended as is.</span>
-
-<span class="sd">        .. versionadded:: 0.12 crs</span>
-<span class="sd">        .. versionadded:: 0.16 allow_one_dimensional_raster</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        minx: float</span>
-<span class="sd">            Minimum bound for x coordinate.</span>
-<span class="sd">        miny: float</span>
-<span class="sd">            Minimum bound for y coordinate.</span>
-<span class="sd">        maxx: float</span>
-<span class="sd">            Maximum bound for x coordinate.</span>
-<span class="sd">        maxy: float</span>
-<span class="sd">            Maximum bound for y coordinate.</span>
-<span class="sd">        auto_expand: bool</span>
-<span class="sd">            If True, it will expand clip search if only 1D raster found with clip.</span>
-<span class="sd">        auto_expand_limit: int</span>
-<span class="sd">            maximum number of times the clip will be retried before raising</span>
-<span class="sd">            an exception.</span>
-<span class="sd">        crs: :obj:`rasterio.crs.CRS`, optional</span>
-<span class="sd">            The CRS of the bounding box. Default is to assume it is the same</span>
-<span class="sd">            as the dataset.</span>
-<span class="sd">        allow_one_dimensional_raster: bool, optional</span>
-<span class="sd">            If True, allow clipping to/from a one dimensional raster.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        Dataset:</span>
-<span class="sd">            The clipped object.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">clipped_dataset</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">attrs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vars</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span> <span class="o">=</span> <span class="n">_get_spatial_dims</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">)</span>
-                <span class="n">clipped_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span><span class="n">x_dim</span><span class="o">=</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">clip_box</span><span class="p">(</span>
-                        <span class="n">minx</span><span class="p">,</span>
-                        <span class="n">miny</span><span class="p">,</span>
-                        <span class="n">maxx</span><span class="p">,</span>
-                        <span class="n">maxy</span><span class="p">,</span>
-                        <span class="n">auto_expand</span><span class="o">=</span><span class="n">auto_expand</span><span class="p">,</span>
-                        <span class="n">auto_expand_limit</span><span class="o">=</span><span class="n">auto_expand_limit</span><span class="p">,</span>
-                        <span class="n">crs</span><span class="o">=</span><span class="n">crs</span><span class="p">,</span>
-                        <span class="n">allow_one_dimensional_raster</span><span class="o">=</span><span class="n">allow_one_dimensional_raster</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="p">)</span>
-            <span class="k">except</span> <span class="n">MissingSpatialDimensionError</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">get_option</span><span class="p">(</span>
-                    <span class="n">SKIP_MISSING_SPATIAL_DIMS</span>
-                <span class="p">):</span>
-                    <span class="k">raise</span>
-                <span class="n">clipped_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">clipped_dataset</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span>
-            <span class="n">x_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="RasterDataset.clip">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_dataset.RasterDataset.clip">[docs]</a>
-    <span class="k">def</span> <span class="nf">clip</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">geometries</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">,</span>
-        <span class="n">crs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">all_touched</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="n">drop</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-        <span class="n">invert</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="n">from_disk</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Crops a :class:`xarray.Dataset` by geojson like geometry dicts in dimensions &#39;x&#39;/&#39;y&#39;.</span>
-
-<span class="sd">        .. warning:: Clips variables that have dimensions &#39;x&#39;/&#39;y&#39;. Others are appended as is.</span>
-
-<span class="sd">        Powered by `rasterio.features.geometry_mask`.</span>
-
-<span class="sd">        Examples:</span>
-
-<span class="sd">            &gt;&gt;&gt; geometry = &#39;&#39;&#39; {&quot;type&quot;: &quot;Polygon&quot;,</span>
-<span class="sd">            ...                 &quot;coordinates&quot;: [</span>
-<span class="sd">            ...                 [[-94.07955380199459, 41.69085871273774],</span>
-<span class="sd">            ...                 [-94.06082436942204, 41.69103313774798],</span>
-<span class="sd">            ...                 [-94.06063203899649, 41.67932439500822],</span>
-<span class="sd">            ...                 [-94.07935807746362, 41.679150041277325],</span>
-<span class="sd">            ...                 [-94.07955380199459, 41.69085871273774]]]}&#39;&#39;&#39;</span>
-<span class="sd">            &gt;&gt;&gt; cropping_geometries = [geojson.loads(geometry)]</span>
-<span class="sd">            &gt;&gt;&gt; xds = xarray.open_rasterio(&#39;cool_raster.tif&#39;)</span>
-<span class="sd">            &gt;&gt;&gt; cropped = xds.rio.clip(geometries=cropping_geometries, crs=4326)</span>
-
-
-<span class="sd">        .. versionadded:: 0.2 from_disk</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        geometries: list</span>
-<span class="sd">            A list of geojson geometry dicts.</span>
-<span class="sd">        crs: :obj:`rasterio.crs.CRS`, optional</span>
-<span class="sd">            The CRS of the input geometries. Default is to assume it is the same</span>
-<span class="sd">            as the dataset.</span>
-<span class="sd">        all_touched : boolean, optional</span>
-<span class="sd">            If True, all pixels touched by geometries will be burned in.  If</span>
-<span class="sd">            false, only pixels whose center is within the polygon or that</span>
-<span class="sd">            are selected by Bresenham&#39;s line algorithm will be burned in.</span>
-<span class="sd">        drop: bool, optional</span>
-<span class="sd">            If True, drop the data outside of the extent of the mask geometries</span>
-<span class="sd">            Otherwise, it will return the same raster with the data masked.</span>
-<span class="sd">            Default is True.</span>
-<span class="sd">        invert: boolean, optional</span>
-<span class="sd">            If False, pixels that do not overlap shapes will be set as nodata.</span>
-<span class="sd">            Otherwise, pixels that overlap the shapes will be set as nodata.</span>
-<span class="sd">            False by default.</span>
-<span class="sd">        from_disk: boolean, optional</span>
-<span class="sd">            If True, it will clip from disk using rasterio.mask.mask if possible.</span>
-<span class="sd">            This is beneficial when the size of the data is larger than memory.</span>
-<span class="sd">            Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset`:</span>
-<span class="sd">            The clipped object.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">clipped_dataset</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">attrs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vars</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span> <span class="o">=</span> <span class="n">_get_spatial_dims</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">)</span>
-                <span class="n">clipped_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span><span class="n">x_dim</span><span class="o">=</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span>
-                        <span class="n">geometries</span><span class="p">,</span>
-                        <span class="n">crs</span><span class="o">=</span><span class="n">crs</span><span class="p">,</span>
-                        <span class="n">all_touched</span><span class="o">=</span><span class="n">all_touched</span><span class="p">,</span>
-                        <span class="n">drop</span><span class="o">=</span><span class="n">drop</span><span class="p">,</span>
-                        <span class="n">invert</span><span class="o">=</span><span class="n">invert</span><span class="p">,</span>
-                        <span class="n">from_disk</span><span class="o">=</span><span class="n">from_disk</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="p">)</span>
-            <span class="k">except</span> <span class="n">MissingSpatialDimensionError</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">get_option</span><span class="p">(</span>
-                    <span class="n">SKIP_MISSING_SPATIAL_DIMS</span>
-                <span class="p">):</span>
-                    <span class="k">raise</span>
-                <span class="n">clipped_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">clipped_dataset</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span>
-            <span class="n">x_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="RasterDataset.interpolate_na">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_dataset.RasterDataset.interpolate_na">[docs]</a>
-    <span class="k">def</span> <span class="nf">interpolate_na</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="s2">&quot;cubic&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;nearest&quot;</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        This method uses `scipy.interpolate.griddata` to interpolate missing data.</span>
-
-<span class="sd">        .. warning:: scipy is an optional dependency.</span>
-
-<span class="sd">        .. warning:: Interpolates variables that have dimensions &#39;x&#39;/&#39;y&#39;. Others are appended as is.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        method: {&#39;linear&#39;, &#39;nearest&#39;, &#39;cubic&#39;}, optional</span>
-<span class="sd">            The method to use for interpolation in `scipy.interpolate.griddata`.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.DataArray`:</span>
-<span class="sd">             The interpolated object.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">interpolated_dataset</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">attrs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vars</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span> <span class="o">=</span> <span class="n">_get_spatial_dims</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">)</span>
-                <span class="n">interpolated_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span><span class="n">x_dim</span><span class="o">=</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-                    <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">interpolate_na</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">)</span>
-                <span class="p">)</span>
-            <span class="k">except</span> <span class="n">MissingSpatialDimensionError</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">get_option</span><span class="p">(</span>
-                    <span class="n">SKIP_MISSING_SPATIAL_DIMS</span>
-                <span class="p">):</span>
-                    <span class="k">raise</span>
-                <span class="n">interpolated_dataset</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">interpolated_dataset</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span>
-            <span class="n">x_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="RasterDataset.to_raster">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.raster_dataset.RasterDataset.to_raster">[docs]</a>
-    <span class="k">def</span> <span class="nf">to_raster</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">raster_path</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">PathLike</span><span class="p">],</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">driver</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">dtype</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dtype</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">tags</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">windowed</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="n">recalc_transform</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-        <span class="n">lock</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">compute</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">profile_kwargs</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Export the Dataset to a raster file. Only works with 2D data.</span>
-
-<span class="sd">        ..versionadded:: 0.2 lock</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        raster_path: str</span>
-<span class="sd">            The path to output the raster to.</span>
-<span class="sd">        driver: str, optional</span>
-<span class="sd">            The name of the GDAL/rasterio driver to use to export the raster.</span>
-<span class="sd">            Default is &quot;GTiff&quot; if rasterio &lt; 1.2 otherwise it will autodetect.</span>
-<span class="sd">        dtype: str, optional</span>
-<span class="sd">            The data type to write the raster to. Default is the datasets dtype.</span>
-<span class="sd">        tags: dict, optional</span>
-<span class="sd">            A dictionary of tags to write to the raster.</span>
-<span class="sd">        windowed: bool, optional</span>
-<span class="sd">            If True, it will write using the windows of the output raster.</span>
-<span class="sd">            This is useful for loading data in chunks when writing. Does not</span>
-<span class="sd">            do anything when writing with dask.</span>
-<span class="sd">            Default is False.</span>
-<span class="sd">        recalc_transform: bool, optional</span>
-<span class="sd">            If False, it will write the raster with the cached transform from</span>
-<span class="sd">            the dataset rather than recalculating it.</span>
-<span class="sd">            Default is True.</span>
-<span class="sd">        lock: boolean or Lock, optional</span>
-<span class="sd">            Lock to use to write data using dask.</span>
-<span class="sd">            If not supplied, it will use a single process for writing.</span>
-<span class="sd">        compute: bool, optional</span>
-<span class="sd">            If True and data is a dask array, then compute and save</span>
-<span class="sd">            the data immediately. If False, return a dask Delayed object.</span>
-<span class="sd">            Call &quot;.compute()&quot; on the Delayed object to compute the result</span>
-<span class="sd">            later. Call ``dask.compute(delayed1, delayed2)`` to save</span>
-<span class="sd">            multiple delayed files at once. Default is True.</span>
-<span class="sd">        **profile_kwargs</span>
-<span class="sd">            Additional keyword arguments to pass into writing the raster. The</span>
-<span class="sd">            nodata, transform, crs, count, width, and height attributes</span>
-<span class="sd">            are ignored.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`dask.Delayed`:</span>
-<span class="sd">            If the data array is a dask array and compute</span>
-<span class="sd">            is True. Otherwise None is returned.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># pylint: disable=too-many-locals</span>
-        <span class="n">variable_dim</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;band_</span><span class="si">{</span><span class="n">uuid4</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span>
-        <span class="n">data_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">to_array</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="n">variable_dim</span><span class="p">)</span>
-        <span class="c1"># ensure raster metadata preserved</span>
-        <span class="n">attr_scales</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">attr_offsets</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">attr_nodatavals</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">encoded_scales</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">encoded_offsets</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">encoded_nodatavals</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">band_tags</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">long_name</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">data_var</span> <span class="ow">in</span> <span class="n">data_array</span><span class="p">[</span><span class="n">variable_dim</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">encoded_scales</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;scale_factor&quot;</span><span class="p">])</span>
-            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-                <span class="n">attr_scales</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;scale_factor&quot;</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">encoded_offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;add_offset&quot;</span><span class="p">])</span>
-            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-                <span class="n">attr_offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;add_offset&quot;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">))</span>
-            <span class="n">long_name</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;long_name&quot;</span><span class="p">,</span> <span class="n">data_var</span><span class="p">))</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">encoded_nodata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">encoded_nodatavals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">encoded_nodata</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">attr_nodatavals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">nodata</span><span class="p">)</span>
-            <span class="n">band_tags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">data_var</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span>
-        <span class="k">if</span> <span class="n">encoded_scales</span><span class="p">:</span>
-            <span class="n">data_array</span><span class="o">.</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;scales&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">encoded_scales</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">data_array</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;scales&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">attr_scales</span>
-        <span class="k">if</span> <span class="n">encoded_offsets</span><span class="p">:</span>
-            <span class="n">data_array</span><span class="o">.</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;offsets&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">encoded_offsets</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">data_array</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;offsets&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">attr_offsets</span>
-        <span class="n">data_array</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;band_tags&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">band_tags</span>
-        <span class="n">data_array</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;long_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">long_name</span>
-
-        <span class="n">use_encoded_nodatavals</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">encoded_nodatavals</span><span class="p">)</span>
-        <span class="n">nodatavals</span> <span class="o">=</span> <span class="n">encoded_nodatavals</span> <span class="k">if</span> <span class="n">use_encoded_nodatavals</span> <span class="k">else</span> <span class="n">attr_nodatavals</span>
-        <span class="n">nodata</span> <span class="o">=</span> <span class="n">nodatavals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="p">(</span>
-            <span class="nb">all</span><span class="p">(</span><span class="n">nodataval</span> <span class="o">==</span> <span class="n">nodata</span> <span class="k">for</span> <span class="n">nodataval</span> <span class="ow">in</span> <span class="n">nodatavals</span><span class="p">)</span>
-            <span class="ow">or</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">nodatavals</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
-        <span class="p">):</span>
-            <span class="n">data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_nodata</span><span class="p">(</span>
-                <span class="n">nodata</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">encoded</span><span class="o">=</span><span class="n">use_encoded_nodatavals</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">RioXarrayError</span><span class="p">(</span>
-                <span class="s2">&quot;All nodata values must be the same when exporting to raster. &quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;Current values: </span><span class="si">{</span><span class="n">attr_nodatavals</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_crs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="c1"># write it to a raster</span>
-        <span class="k">return</span> <span class="n">data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span>
-            <span class="n">x_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span>
-            <span class="n">y_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span>
-            <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="p">)</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">to_raster</span><span class="p">(</span>
-            <span class="n">raster_path</span><span class="o">=</span><span class="n">raster_path</span><span class="p">,</span>
-            <span class="n">driver</span><span class="o">=</span><span class="n">driver</span><span class="p">,</span>
-            <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
-            <span class="n">tags</span><span class="o">=</span><span class="n">tags</span><span class="p">,</span>
-            <span class="n">windowed</span><span class="o">=</span><span class="n">windowed</span><span class="p">,</span>
-            <span class="n">recalc_transform</span><span class="o">=</span><span class="n">recalc_transform</span><span class="p">,</span>
-            <span class="n">lock</span><span class="o">=</span><span class="n">lock</span><span class="p">,</span>
-            <span class="n">compute</span><span class="o">=</span><span class="n">compute</span><span class="p">,</span>
-            <span class="o">**</span><span class="n">profile_kwargs</span><span class="p">,</span>
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-  <h1>Source code for rioxarray.rioxarray</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">This module is an extension for xarray to provide rasterio capabilities</span>
-<span class="sd">to xarray datasets/dataarrays.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1"># pylint: disable=too-many-lines</span>
-<span class="kn">import</span> <span class="nn">json</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Hashable</span><span class="p">,</span> <span class="n">Iterable</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Literal</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">pyproj</span>
-<span class="kn">import</span> <span class="nn">rasterio.warp</span>
-<span class="kn">import</span> <span class="nn">rasterio.windows</span>
-<span class="kn">import</span> <span class="nn">xarray</span>
-<span class="kn">from</span> <span class="nn">affine</span> <span class="kn">import</span> <span class="n">Affine</span>
-<span class="kn">from</span> <span class="nn">pyproj.aoi</span> <span class="kn">import</span> <span class="n">AreaOfInterest</span>
-<span class="kn">from</span> <span class="nn">pyproj.database</span> <span class="kn">import</span> <span class="n">query_utm_crs_info</span>
-<span class="kn">from</span> <span class="nn">rasterio.control</span> <span class="kn">import</span> <span class="n">GroundControlPoint</span>
-<span class="kn">from</span> <span class="nn">rasterio.crs</span> <span class="kn">import</span> <span class="n">CRS</span>
-
-<span class="kn">from</span> <span class="nn">rioxarray._options</span> <span class="kn">import</span> <span class="n">EXPORT_GRID_MAPPING</span><span class="p">,</span> <span class="n">get_option</span>
-<span class="kn">from</span> <span class="nn">rioxarray.crs</span> <span class="kn">import</span> <span class="n">crs_from_user_input</span>
-<span class="kn">from</span> <span class="nn">rioxarray.exceptions</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">DimensionError</span><span class="p">,</span>
-    <span class="n">DimensionMissingCoordinateError</span><span class="p">,</span>
-    <span class="n">InvalidDimensionOrder</span><span class="p">,</span>
-    <span class="n">MissingCRS</span><span class="p">,</span>
-    <span class="n">MissingSpatialDimensionError</span><span class="p">,</span>
-    <span class="n">NoDataInBounds</span><span class="p">,</span>
-    <span class="n">OneDimensionalRaster</span><span class="p">,</span>
-    <span class="n">RioXarrayError</span><span class="p">,</span>
-    <span class="n">TooManyDimensions</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">DEFAULT_GRID_MAP</span> <span class="o">=</span> <span class="s2">&quot;spatial_ref&quot;</span>
-
-
-<span class="k">def</span> <span class="nf">_affine_has_rotation</span><span class="p">(</span><span class="n">affine</span><span class="p">:</span> <span class="n">Affine</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Determine if the affine has rotation.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    affine: :obj:`affine.Affine`</span>
-<span class="sd">        The affine of the grid.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    bool</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">affine</span><span class="o">.</span><span class="n">b</span> <span class="o">==</span> <span class="n">affine</span><span class="o">.</span><span class="n">d</span> <span class="o">!=</span> <span class="mi">0</span>
-
-
-<span class="k">def</span> <span class="nf">_resolution</span><span class="p">(</span><span class="n">affine</span><span class="p">:</span> <span class="n">Affine</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Determine if the resolution of the affine.</span>
-<span class="sd">    If it has rotation, the sign of the resolution is lost.</span>
-
-<span class="sd">    Based on: https://github.com/mapbox/rasterio/blob/6185a4e4ad72b5669066d2d5004bf46d94a6d298/rasterio/_base.pyx#L943-L951</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    affine: :obj:`affine.Affine`</span>
-<span class="sd">        The affine of the grid.</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    --------</span>
-<span class="sd">    x_resolution: float</span>
-<span class="sd">        The X resolution of the affine.</span>
-<span class="sd">    y_resolution: float</span>
-<span class="sd">        The Y resolution of the affine.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">_affine_has_rotation</span><span class="p">(</span><span class="n">affine</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">affine</span><span class="o">.</span><span class="n">a</span><span class="p">,</span> <span class="n">affine</span><span class="o">.</span><span class="n">e</span>
-    <span class="k">return</span> <span class="p">(</span>
-        <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">affine</span><span class="o">.</span><span class="n">a</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">affine</span><span class="o">.</span><span class="n">d</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
-        <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">affine</span><span class="o">.</span><span class="n">b</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">affine</span><span class="o">.</span><span class="n">e</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
-    <span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">affine_to_coords</span><span class="p">(</span>
-    <span class="n">affine</span><span class="p">:</span> <span class="n">Affine</span><span class="p">,</span> <span class="n">width</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">height</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">x_dim</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">y_dim</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;y&quot;</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate 1d pixel centered coordinates from affine.</span>
-
-<span class="sd">    Based on code from the xarray rasterio backend.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    affine: :obj:`affine.Affine`</span>
-<span class="sd">        The affine of the grid.</span>
-<span class="sd">    width: int</span>
-<span class="sd">        The width of the grid.</span>
-<span class="sd">    height: int</span>
-<span class="sd">        The height of the grid.</span>
-<span class="sd">    x_dim: str, optional</span>
-<span class="sd">        The name of the X dimension. Default is &#39;x&#39;.</span>
-<span class="sd">    y_dim: str, optional</span>
-<span class="sd">        The name of the Y dimension. Default is &#39;y&#39;.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    dict: x and y coordinate arrays.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">transform</span> <span class="o">=</span> <span class="n">affine</span> <span class="o">*</span> <span class="n">affine</span><span class="o">.</span><span class="n">translation</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">affine</span><span class="o">.</span><span class="n">is_rectilinear</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">_affine_has_rotation</span><span class="p">(</span><span class="n">affine</span><span class="p">):</span>
-        <span class="n">x_coords</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">transform</span> <span class="o">*</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">width</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">width</span><span class="p">))</span>
-        <span class="n">_</span><span class="p">,</span> <span class="n">y_coords</span> <span class="o">=</span> <span class="n">transform</span> <span class="o">*</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">height</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">height</span><span class="p">))</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">x_coords</span><span class="p">,</span> <span class="n">y_coords</span> <span class="o">=</span> <span class="n">transform</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span>
-            <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">width</span><span class="p">),</span>
-            <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">height</span><span class="p">),</span>
-        <span class="p">)</span>
-    <span class="k">return</span> <span class="p">{</span><span class="n">y_dim</span><span class="p">:</span> <span class="n">y_coords</span><span class="p">,</span> <span class="n">x_dim</span><span class="p">:</span> <span class="n">x_coords</span><span class="p">}</span>
-
-
-<span class="k">def</span> <span class="nf">_generate_spatial_coords</span><span class="p">(</span>
-    <span class="o">*</span><span class="p">,</span> <span class="n">affine</span><span class="p">:</span> <span class="n">Affine</span><span class="p">,</span> <span class="n">width</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">height</span><span class="p">:</span> <span class="nb">int</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;get spatial coords in new transform&quot;&quot;&quot;</span>
-    <span class="n">new_spatial_coords</span> <span class="o">=</span> <span class="n">affine_to_coords</span><span class="p">(</span><span class="n">affine</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">new_spatial_coords</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;x&quot;</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">IndexVariable</span><span class="p">(</span><span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">new_spatial_coords</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">]),</span>
-            <span class="s2">&quot;y&quot;</span><span class="p">:</span> <span class="n">xarray</span><span class="o">.</span><span class="n">IndexVariable</span><span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="n">new_spatial_coords</span><span class="p">[</span><span class="s2">&quot;y&quot;</span><span class="p">]),</span>
-        <span class="p">}</span>
-    <span class="k">return</span> <span class="p">{</span>
-        <span class="s2">&quot;xc&quot;</span><span class="p">:</span> <span class="p">((</span><span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">),</span> <span class="n">new_spatial_coords</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">]),</span>
-        <span class="s2">&quot;yc&quot;</span><span class="p">:</span> <span class="p">((</span><span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">),</span> <span class="n">new_spatial_coords</span><span class="p">[</span><span class="s2">&quot;y&quot;</span><span class="p">]),</span>
-    <span class="p">}</span>
-
-
-<span class="k">def</span> <span class="nf">_get_nonspatial_coords</span><span class="p">(</span>
-    <span class="n">src_data_array</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">]</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Variable</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">IndexVariable</span><span class="p">]]:</span>
-    <span class="n">coords</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Variable</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">IndexVariable</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">coord</span> <span class="ow">in</span> <span class="nb">set</span><span class="p">(</span><span class="n">src_data_array</span><span class="o">.</span><span class="n">coords</span><span class="p">)</span> <span class="o">-</span> <span class="p">{</span>
-        <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span>
-        <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span>
-        <span class="n">DEFAULT_GRID_MAP</span><span class="p">,</span>
-        <span class="s2">&quot;xc&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;yc&quot;</span><span class="p">,</span>
-    <span class="p">}:</span>
-        <span class="k">if</span> <span class="n">src_data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">coords</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">IndexVariable</span><span class="p">(</span>
-                <span class="n">src_data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span><span class="p">,</span>
-                <span class="n">src_data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-                <span class="n">src_data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">coords</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span>
-                <span class="n">src_data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span><span class="p">,</span>
-                <span class="n">src_data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-                <span class="n">src_data_array</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span>
-            <span class="p">)</span>
-    <span class="k">return</span> <span class="n">coords</span>
-
-
-<span class="k">def</span> <span class="nf">_make_coords</span><span class="p">(</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">src_data_array</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">],</span>
-    <span class="n">dst_affine</span><span class="p">:</span> <span class="n">Affine</span><span class="p">,</span>
-    <span class="n">dst_width</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-    <span class="n">dst_height</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-    <span class="n">force_generate</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate the coordinates of the new projected `xarray.DataArray`&quot;&quot;&quot;</span>
-    <span class="n">coords</span> <span class="o">=</span> <span class="n">_get_nonspatial_coords</span><span class="p">(</span><span class="n">src_data_array</span><span class="p">)</span>
-    <span class="k">if</span> <span class="p">(</span>
-        <span class="n">force_generate</span>
-        <span class="ow">or</span> <span class="p">(</span>
-            <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span> <span class="ow">in</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">coords</span>
-            <span class="ow">and</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span> <span class="ow">in</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">coords</span>
-        <span class="p">)</span>
-        <span class="ow">or</span> <span class="p">(</span><span class="s2">&quot;xc&quot;</span> <span class="ow">in</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">coords</span> <span class="ow">and</span> <span class="s2">&quot;yc&quot;</span> <span class="ow">in</span> <span class="n">src_data_array</span><span class="o">.</span><span class="n">coords</span><span class="p">)</span>
-    <span class="p">):</span>
-        <span class="n">new_coords</span> <span class="o">=</span> <span class="n">_generate_spatial_coords</span><span class="p">(</span>
-            <span class="n">affine</span><span class="o">=</span><span class="n">dst_affine</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">dst_width</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="n">dst_height</span>
-        <span class="p">)</span>
-        <span class="n">new_coords</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">coords</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">new_coords</span>
-    <span class="k">return</span> <span class="n">coords</span>
-
-
-<span class="k">def</span> <span class="nf">_get_data_var_message</span><span class="p">(</span><span class="n">obj</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Get message for named data variables.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot; Data variable: </span><span class="si">{</span><span class="n">obj</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">if</span> <span class="n">obj</span><span class="o">.</span><span class="n">name</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
-    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-        <span class="k">return</span> <span class="s2">&quot;&quot;</span>
-
-
-<span class="k">def</span> <span class="nf">_get_spatial_dims</span><span class="p">(</span>
-    <span class="n">obj</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">],</span> <span class="o">*</span><span class="p">,</span> <span class="n">var</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Hashable</span><span class="p">]</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Retrieve the spatial dimensions of the dataset</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span>
-    <span class="k">except</span> <span class="n">MissingSpatialDimensionError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span>
-                <span class="n">x_dim</span><span class="o">=</span><span class="n">obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="n">obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-            <span class="p">)</span>
-            <span class="k">return</span> <span class="n">obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">y_dim</span>
-        <span class="k">except</span> <span class="n">MissingSpatialDimensionError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">err</span> <span class="kn">from</span> <span class="kc">None</span>
-
-
-<span class="k">def</span> <span class="nf">_has_spatial_dims</span><span class="p">(</span>
-    <span class="n">obj</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">],</span> <span class="o">*</span><span class="p">,</span> <span class="n">var</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Hashable</span><span class="p">]</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Check to see if the variable in the Dataset has spatial dimensions</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="c1"># pylint: disable=pointless-statement</span>
-        <span class="n">_get_spatial_dims</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">)</span>
-    <span class="k">except</span> <span class="n">MissingSpatialDimensionError</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">False</span>
-    <span class="k">return</span> <span class="kc">True</span>
-
-
-<span class="k">def</span> <span class="nf">_order_bounds</span><span class="p">(</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">minx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-    <span class="n">miny</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-    <span class="n">maxx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-    <span class="n">maxy</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-    <span class="n">resolution_x</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-    <span class="n">resolution_y</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Make sure that the bounds are in the correct order</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">resolution_y</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">top</span> <span class="o">=</span> <span class="n">maxy</span>
-        <span class="n">bottom</span> <span class="o">=</span> <span class="n">miny</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">top</span> <span class="o">=</span> <span class="n">miny</span>
-        <span class="n">bottom</span> <span class="o">=</span> <span class="n">maxy</span>
-    <span class="k">if</span> <span class="n">resolution_x</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">left</span> <span class="o">=</span> <span class="n">maxx</span>
-        <span class="n">right</span> <span class="o">=</span> <span class="n">minx</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">left</span> <span class="o">=</span> <span class="n">minx</span>
-        <span class="n">right</span> <span class="o">=</span> <span class="n">maxx</span>
-
-    <span class="k">return</span> <span class="n">left</span><span class="p">,</span> <span class="n">bottom</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">top</span>
-
-
-<div class="viewcode-block" id="XRasterBase">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase">[docs]</a>
-<span class="k">class</span> <span class="nc">XRasterBase</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is the base class for the GIS extensions for xarray&quot;&quot;&quot;</span>
-
-    <span class="c1"># pylint: disable=too-many-instance-attributes</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xarray_obj</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">]):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">]</span> <span class="o">=</span> <span class="n">xarray_obj</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_x_dim</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Hashable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_y_dim</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Hashable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="c1"># Determine the spatial dimensions of the `xarray.DataArray`</span>
-        <span class="k">if</span> <span class="s2">&quot;x&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dims</span> <span class="ow">and</span> <span class="s2">&quot;y&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dims</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_x_dim</span> <span class="o">=</span> <span class="s2">&quot;x&quot;</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_y_dim</span> <span class="o">=</span> <span class="s2">&quot;y&quot;</span>
-        <span class="k">elif</span> <span class="s2">&quot;longitude&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dims</span> <span class="ow">and</span> <span class="s2">&quot;latitude&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dims</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_x_dim</span> <span class="o">=</span> <span class="s2">&quot;longitude&quot;</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_y_dim</span> <span class="o">=</span> <span class="s2">&quot;latitude&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># look for coordinates with CF attributes</span>
-            <span class="k">for</span> <span class="n">coord</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
-                <span class="c1"># make sure to only look in 1D coordinates</span>
-                <span class="c1"># that has the same dimension name as the coordinate</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span> <span class="o">!=</span> <span class="p">(</span><span class="n">coord</span><span class="p">,):</span>
-                    <span class="k">continue</span>
-                <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;axis&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;X&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;standard_name&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-                    <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;projection_x_coordinate&quot;</span><span class="p">)</span>
-                <span class="p">):</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_x_dim</span> <span class="o">=</span> <span class="n">coord</span>
-                <span class="k">elif</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;axis&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;Y&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">coord</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;standard_name&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-                    <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;projection_y_coordinate&quot;</span><span class="p">)</span>
-                <span class="p">):</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_y_dim</span> <span class="o">=</span> <span class="n">coord</span>
-
-        <span class="c1"># properties</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_count</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_height</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_width</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">rasterio</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">CRS</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">Literal</span><span class="p">[</span><span class="kc">False</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_gcps</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">GroundControlPoint</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">crs</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="n">rasterio</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">CRS</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;:obj:`rasterio.crs.CRS`:</span>
-<span class="sd">        Retrieve projection from :obj:`xarray.Dataset` | :obj:`xarray.DataArray`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span> <span class="ow">is</span> <span class="kc">False</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span>
-
-        <span class="c1"># look in wkt attributes to avoid using</span>
-        <span class="c1"># pyproj CRS if possible for performance</span>
-        <span class="k">for</span> <span class="n">crs_attr</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;spatial_ref&quot;</span><span class="p">,</span> <span class="s2">&quot;crs_wkt&quot;</span><span class="p">):</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_set_crs</span><span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">grid_mapping</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="n">crs_attr</span><span class="p">],</span>
-                    <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span>
-            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-                <span class="k">pass</span>
-
-        <span class="c1"># look in grid_mapping</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_set_crs</span><span class="p">(</span>
-                <span class="n">pyproj</span><span class="o">.</span><span class="n">CRS</span><span class="o">.</span><span class="n">from_cf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">grid_mapping</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="p">),</span>
-                <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">except</span> <span class="p">(</span><span class="ne">KeyError</span><span class="p">,</span> <span class="n">pyproj</span><span class="o">.</span><span class="n">exceptions</span><span class="o">.</span><span class="n">CRSError</span><span class="p">):</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="c1"># look in attrs for &#39;crs&#39;</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_set_crs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;crs&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span> <span class="o">=</span> <span class="kc">False</span>
-                <span class="k">return</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span>
-
-    <span class="k">def</span> <span class="nf">_get_obj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Get the object to modify.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        inplace: bool</span>
-<span class="sd">            If True, returns self.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span>
-        <span class="n">obj_copy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="c1"># preserve attribute information</span>
-        <span class="n">obj_copy</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_x_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_x_dim</span>
-        <span class="n">obj_copy</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_y_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_y_dim</span>
-        <span class="n">obj_copy</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_width</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_width</span>
-        <span class="n">obj_copy</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_height</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_height</span>
-        <span class="n">obj_copy</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_crs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_crs</span>
-        <span class="n">obj_copy</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_gcps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gcps</span>
-        <span class="k">return</span> <span class="n">obj_copy</span>
-
-<div class="viewcode-block" id="XRasterBase.set_crs">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.set_crs">[docs]</a>
-    <span class="k">def</span> <span class="nf">set_crs</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">input_crs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Set the CRS value for the Dataset/DataArray without modifying</span>
-<span class="sd">        the dataset/data array.</span>
-
-<span class="sd">        .. deprecated:: 0.15.8</span>
-<span class="sd">            It is recommended to use `rio.write_crs()` instead. This</span>
-<span class="sd">        method will likely be removed in a future release.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        input_crs: object</span>
-<span class="sd">            Anything accepted by `rasterio.crs.CRS.from_user_input`.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            Dataset with crs attribute.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-            <span class="s2">&quot;It is recommended to use &#39;rio.write_crs()&#39; instead. &#39;rio.set_crs()&#39; will likely&quot;</span>
-            <span class="s2">&quot;be removed in a future release.&quot;</span><span class="p">,</span>
-            <span class="ne">FutureWarning</span><span class="p">,</span>
-            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_crs</span><span class="p">(</span><span class="n">input_crs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_set_crs</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">input_crs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Set the CRS value for the Dataset/DataArray without modifying</span>
-<span class="sd">        the dataset/data array.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        input_crs: object</span>
-<span class="sd">            Anything accepted by `rasterio.crs.CRS.from_user_input`.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        xarray.Dataset | xarray.DataArray</span>
-<span class="sd">            Dataset with crs attribute.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">crs</span> <span class="o">=</span> <span class="n">crs_from_user_input</span><span class="p">(</span><span class="n">input_crs</span><span class="p">)</span>
-        <span class="n">obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>
-        <span class="n">obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_crs</span> <span class="o">=</span> <span class="n">crs</span>
-        <span class="k">return</span> <span class="n">obj</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">grid_mapping</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        str: The CF grid_mapping attribute. &#39;spatial_ref&#39; is the default.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">grid_mapping</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">encoding</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
-            <span class="s2">&quot;grid_mapping&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;grid_mapping&quot;</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="k">if</span> <span class="n">grid_mapping</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">grid_mapping</span>
-        <span class="n">grid_mapping</span> <span class="o">=</span> <span class="n">DEFAULT_GRID_MAP</span>
-        <span class="c1"># search the dataset for the grid mapping name</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span> <span class="s2">&quot;data_vars&quot;</span><span class="p">):</span>
-            <span class="n">grid_mappings</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-            <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">data_vars</span><span class="p">:</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">_has_spatial_dims</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">):</span>
-                    <span class="k">continue</span>
-                <span class="n">var_grid_mapping</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">encoding</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
-                    <span class="s2">&quot;grid_mapping&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;grid_mapping&quot;</span><span class="p">)</span>
-                <span class="p">)</span>
-                <span class="k">if</span> <span class="n">var_grid_mapping</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">grid_mapping</span> <span class="o">=</span> <span class="n">var_grid_mapping</span>
-                    <span class="n">grid_mappings</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">grid_mapping</span><span class="p">)</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_mappings</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="n">RioXarrayError</span><span class="p">(</span><span class="s2">&quot;Multiple grid mappings exist.&quot;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">grid_mapping</span>
-
-<div class="viewcode-block" id="XRasterBase.write_grid_mapping">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_grid_mapping">[docs]</a>
-    <span class="k">def</span> <span class="nf">write_grid_mapping</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">grid_mapping_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">DEFAULT_GRID_MAP</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Write the CF grid_mapping attribute to the encoding.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        grid_mapping_name: str, optional</span>
-<span class="sd">            Name of the grid_mapping coordinate.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            Modified dataset with CF compliant CRS information.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">data_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data_obj</span><span class="p">,</span> <span class="s2">&quot;data_vars&quot;</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">data_vars</span><span class="p">:</span>
-                <span class="k">try</span><span class="p">:</span>
-                    <span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span> <span class="o">=</span> <span class="n">_get_spatial_dims</span><span class="p">(</span><span class="n">data_obj</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">)</span>
-                <span class="k">except</span> <span class="n">MissingSpatialDimensionError</span><span class="p">:</span>
-                    <span class="k">continue</span>
-                <span class="c1"># remove grid_mapping from attributes if it exists</span>
-                <span class="c1"># and update the grid_mapping in encoding</span>
-                <span class="n">new_attrs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">data_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-                <span class="n">new_attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;grid_mapping&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-                <span class="n">data_obj</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">update_encoding</span><span class="p">(</span>
-                    <span class="p">{</span><span class="s2">&quot;grid_mapping&quot;</span><span class="p">:</span> <span class="n">grid_mapping_name</span><span class="p">},</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-                <span class="p">)</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_attrs</span><span class="p">(</span><span class="n">new_attrs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span>
-                    <span class="n">x_dim</span><span class="o">=</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-                <span class="p">)</span>
-        <span class="c1"># remove grid_mapping from attributes if it exists</span>
-        <span class="c1"># and update the grid_mapping in encoding</span>
-        <span class="n">new_attrs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">data_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-        <span class="n">new_attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;grid_mapping&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">update_encoding</span><span class="p">(</span>
-            <span class="p">{</span><span class="s2">&quot;grid_mapping&quot;</span><span class="p">:</span> <span class="n">grid_mapping_name</span><span class="p">},</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_attrs</span><span class="p">(</span><span class="n">new_attrs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.write_crs">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_crs">[docs]</a>
-    <span class="k">def</span> <span class="nf">write_crs</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">input_crs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">grid_mapping_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Write the CRS to the dataset in a CF compliant manner.</span>
-
-<span class="sd">        .. warning:: The grid_mapping attribute is written to the encoding.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        input_crs: Any</span>
-<span class="sd">            Anything accepted by `rasterio.crs.CRS.from_user_input`.</span>
-<span class="sd">        grid_mapping_name: str, optional</span>
-<span class="sd">            Name of the grid_mapping coordinate to store the CRS information in.</span>
-<span class="sd">            Default is the grid_mapping name of the dataset.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            Modified dataset with CF compliant CRS information.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        Write the CRS of the current `xarray` object:</span>
-
-<span class="sd">        &gt;&gt;&gt; raster.rio.write_crs(&quot;epsg:4326&quot;, inplace=True)</span>
-
-<span class="sd">        Write the CRS on a copy:</span>
-
-<span class="sd">        &gt;&gt;&gt; raster = raster.rio.write_crs(&quot;epsg:4326&quot;)</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">input_crs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">data_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_crs</span><span class="p">(</span><span class="n">input_crs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">data_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>
-
-        <span class="c1"># get original transform</span>
-        <span class="n">transform</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_transform</span><span class="p">()</span>
-        <span class="c1"># remove old grid maping coordinate if exists</span>
-        <span class="n">grid_mapping_name</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">grid_mapping</span> <span class="k">if</span> <span class="n">grid_mapping_name</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">grid_mapping_name</span>
-        <span class="p">)</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="k">del</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span>
-        <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-            <span class="k">pass</span>
-
-        <span class="k">if</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">MissingCRS</span><span class="p">(</span>
-                <span class="s2">&quot;CRS not found. Please set the CRS with &#39;rio.write_crs()&#39;.&quot;</span>
-            <span class="p">)</span>
-        <span class="c1"># add grid mapping coordinate</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Variable</span><span class="p">((),</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="n">grid_map_attrs</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">if</span> <span class="n">get_option</span><span class="p">(</span><span class="n">EXPORT_GRID_MAPPING</span><span class="p">):</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">grid_map_attrs</span> <span class="o">=</span> <span class="n">pyproj</span><span class="o">.</span><span class="n">CRS</span><span class="o">.</span><span class="n">from_user_input</span><span class="p">(</span><span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span><span class="p">)</span><span class="o">.</span><span class="n">to_cf</span><span class="p">()</span>
-            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-                <span class="k">pass</span>
-        <span class="c1"># spatial_ref is for compatibility with GDAL</span>
-        <span class="n">crs_wkt</span> <span class="o">=</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">to_wkt</span><span class="p">()</span>
-        <span class="n">grid_map_attrs</span><span class="p">[</span><span class="s2">&quot;spatial_ref&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crs_wkt</span>
-        <span class="n">grid_map_attrs</span><span class="p">[</span><span class="s2">&quot;crs_wkt&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crs_wkt</span>
-        <span class="k">if</span> <span class="n">transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">grid_map_attrs</span><span class="p">[</span><span class="s2">&quot;GeoTransform&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-                <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">item</span><span class="p">)</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">transform</span><span class="o">.</span><span class="n">to_gdal</span><span class="p">()]</span>
-            <span class="p">)</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_attrs</span><span class="p">(</span><span class="n">grid_map_attrs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="c1"># remove old crs if exists</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;crs&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_grid_mapping</span><span class="p">(</span>
-            <span class="n">grid_mapping_name</span><span class="o">=</span><span class="n">grid_mapping_name</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.estimate_utm_crs">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.estimate_utm_crs">[docs]</a>
-    <span class="k">def</span> <span class="nf">estimate_utm_crs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">datum_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;WGS 84&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">CRS</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the estimated UTM CRS based on the bounds of the dataset.</span>
-
-<span class="sd">        .. versionadded:: 0.2</span>
-
-<span class="sd">        .. note:: Requires pyproj 3+</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        datum_name : str, optional</span>
-<span class="sd">            The name of the datum to use in the query. Default is WGS 84.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        rasterio.crs.CRS</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;crs must be set to estimate UTM CRS.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># ensure using geographic coordinates</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">is_geographic</span><span class="p">:</span>  <span class="c1"># pylint: disable=no-member</span>
-            <span class="n">minx</span><span class="p">,</span> <span class="n">miny</span><span class="p">,</span> <span class="n">maxx</span><span class="p">,</span> <span class="n">maxy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounds</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">minx</span><span class="p">,</span> <span class="n">miny</span><span class="p">,</span> <span class="n">maxx</span><span class="p">,</span> <span class="n">maxy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform_bounds</span><span class="p">(</span><span class="s2">&quot;EPSG:4326&quot;</span><span class="p">,</span> <span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="n">x_center</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">([</span><span class="n">minx</span><span class="p">,</span> <span class="n">maxx</span><span class="p">])</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
-        <span class="n">y_center</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">([</span><span class="n">miny</span><span class="p">,</span> <span class="n">maxy</span><span class="p">])</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
-
-        <span class="n">utm_crs_list</span> <span class="o">=</span> <span class="n">query_utm_crs_info</span><span class="p">(</span>
-            <span class="n">datum_name</span><span class="o">=</span><span class="n">datum_name</span><span class="p">,</span>
-            <span class="n">area_of_interest</span><span class="o">=</span><span class="n">AreaOfInterest</span><span class="p">(</span>
-                <span class="n">west_lon_degree</span><span class="o">=</span><span class="n">x_center</span><span class="p">,</span>
-                <span class="n">south_lat_degree</span><span class="o">=</span><span class="n">y_center</span><span class="p">,</span>
-                <span class="n">east_lon_degree</span><span class="o">=</span><span class="n">x_center</span><span class="p">,</span>
-                <span class="n">north_lat_degree</span><span class="o">=</span><span class="n">y_center</span><span class="p">,</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">CRS</span><span class="o">.</span><span class="n">from_epsg</span><span class="p">(</span><span class="n">utm_crs_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">code</span><span class="p">)</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Unable to determine UTM CRS&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_cached_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Affine</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Get the transform from:</span>
-<span class="sd">        1. The GeoTransform metatada property in the grid mapping</span>
-<span class="sd">        2. The transform attribute.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="c1"># look in grid_mapping</span>
-            <span class="n">transform</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">fromstring</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">grid_mapping</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;GeoTransform&quot;</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot; &quot;</span>
-            <span class="p">)</span>
-            <span class="c1"># Calling .tolist() to assure the arguments are Python float and JSON serializable</span>
-            <span class="k">return</span> <span class="n">Affine</span><span class="o">.</span><span class="n">from_gdal</span><span class="p">(</span><span class="o">*</span><span class="n">transform</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
-
-        <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">Affine</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;transform&quot;</span><span class="p">][:</span><span class="mi">6</span><span class="p">])</span>
-            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-                <span class="k">pass</span>
-        <span class="k">return</span> <span class="kc">None</span>
-
-<div class="viewcode-block" id="XRasterBase.write_transform">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_transform">[docs]</a>
-    <span class="k">def</span> <span class="nf">write_transform</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">transform</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Affine</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">grid_mapping_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        .. versionadded:: 0.0.30</span>
-
-<span class="sd">        Write the GeoTransform to the dataset where GDAL can read it in.</span>
-
-<span class="sd">        https://gdal.org/drivers/raster/netcdf.html#georeference</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        transform: affine.Affine, optional</span>
-<span class="sd">            The transform of the dataset. If not provided, it will be calculated.</span>
-<span class="sd">        grid_mapping_name: str, optional</span>
-<span class="sd">            Name of the grid_mapping coordinate to store the transform information in.</span>
-<span class="sd">            Default is the grid_mapping name of the dataset.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            Modified dataset with Geo Transform written.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">data_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>
-        <span class="c1"># delete the old attribute to prevent confusion</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;transform&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-        <span class="n">grid_mapping_name</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">grid_mapping</span> <span class="k">if</span> <span class="n">grid_mapping_name</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">grid_mapping_name</span>
-        <span class="p">)</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">grid_map_attrs</span> <span class="o">=</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-            <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Variable</span><span class="p">((),</span> <span class="mi">0</span><span class="p">)</span>
-            <span class="n">grid_map_attrs</span> <span class="o">=</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">grid_map_attrs</span><span class="p">[</span><span class="s2">&quot;GeoTransform&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-            <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">item</span><span class="p">)</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">transform</span><span class="o">.</span><span class="n">to_gdal</span><span class="p">()]</span>
-        <span class="p">)</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_attrs</span><span class="p">(</span><span class="n">grid_map_attrs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_grid_mapping</span><span class="p">(</span>
-            <span class="n">grid_mapping_name</span><span class="o">=</span><span class="n">grid_mapping_name</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.transform">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.transform">[docs]</a>
-    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">recalc</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Affine</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        recalc: bool, optional</span>
-<span class="sd">            If True, it will re-calculate the transform instead of using</span>
-<span class="sd">            the cached transform.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`affine.Affine`:</span>
-<span class="sd">            The affine of the :obj:`xarray.Dataset` | :obj:`xarray.DataArray`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">transform</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_transform</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">transform</span> <span class="ow">and</span> <span class="p">(</span>
-            <span class="ow">not</span> <span class="n">transform</span><span class="o">.</span><span class="n">is_rectilinear</span> <span class="ow">or</span> <span class="n">_affine_has_rotation</span><span class="p">(</span><span class="n">transform</span><span class="p">)</span>
-        <span class="p">):</span>
-            <span class="k">if</span> <span class="n">recalc</span><span class="p">:</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-                    <span class="s2">&quot;Transform that is non-rectilinear or with rotation found. &quot;</span>
-                    <span class="s2">&quot;Unable to recalculate.&quot;</span>
-                <span class="p">)</span>
-            <span class="k">return</span> <span class="n">transform</span>
-
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">src_left</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">src_top</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unordered_bounds</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="n">recalc</span><span class="p">)</span>
-            <span class="n">src_resolution_x</span><span class="p">,</span> <span class="n">src_resolution_y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resolution</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="n">recalc</span><span class="p">)</span>
-        <span class="k">except</span> <span class="p">(</span><span class="n">DimensionMissingCoordinateError</span><span class="p">,</span> <span class="n">DimensionError</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">Affine</span><span class="o">.</span><span class="n">identity</span><span class="p">()</span> <span class="k">if</span> <span class="n">transform</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">transform</span>
-        <span class="k">return</span> <span class="n">Affine</span><span class="o">.</span><span class="n">translation</span><span class="p">(</span><span class="n">src_left</span><span class="p">,</span> <span class="n">src_top</span><span class="p">)</span> <span class="o">*</span> <span class="n">Affine</span><span class="o">.</span><span class="n">scale</span><span class="p">(</span>
-            <span class="n">src_resolution_x</span><span class="p">,</span> <span class="n">src_resolution_y</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.write_coordinate_system">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_coordinate_system">[docs]</a>
-    <span class="k">def</span> <span class="nf">write_coordinate_system</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Write the coordinate system CF metadata.</span>
-
-<span class="sd">        .. versionadded:: 0.0.30</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            The dataset with the CF coordinate system attributes added.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">data_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>
-        <span class="c1"># add metadata to x,y coordinates</span>
-        <span class="n">is_projected</span> <span class="o">=</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span> <span class="ow">and</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">is_projected</span>
-        <span class="n">is_geographic</span> <span class="o">=</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span> <span class="ow">and</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">is_geographic</span>
-        <span class="n">x_coord_attrs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-        <span class="n">x_coord_attrs</span><span class="p">[</span><span class="s2">&quot;axis&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;X&quot;</span>
-        <span class="n">y_coord_attrs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-        <span class="n">y_coord_attrs</span><span class="p">[</span><span class="s2">&quot;axis&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;Y&quot;</span>
-        <span class="k">if</span> <span class="n">is_projected</span><span class="p">:</span>
-            <span class="n">units</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span> <span class="s2">&quot;linear_units_factor&quot;</span><span class="p">):</span>
-                <span class="n">unit_factor</span> <span class="o">=</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">crs</span><span class="o">.</span><span class="n">linear_units_factor</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-                <span class="k">if</span> <span class="n">unit_factor</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="n">units</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">unit_factor</span><span class="si">}</span><span class="s2"> metre&quot;</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">units</span> <span class="o">=</span> <span class="s2">&quot;metre&quot;</span>
-            <span class="c1"># X metadata</span>
-            <span class="n">x_coord_attrs</span><span class="p">[</span><span class="s2">&quot;long_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;x coordinate of projection&quot;</span>
-            <span class="n">x_coord_attrs</span><span class="p">[</span><span class="s2">&quot;standard_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;projection_x_coordinate&quot;</span>
-            <span class="k">if</span> <span class="n">units</span><span class="p">:</span>
-                <span class="n">x_coord_attrs</span><span class="p">[</span><span class="s2">&quot;units&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">units</span>
-            <span class="c1"># Y metadata</span>
-            <span class="n">y_coord_attrs</span><span class="p">[</span><span class="s2">&quot;long_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;y coordinate of projection&quot;</span>
-            <span class="n">y_coord_attrs</span><span class="p">[</span><span class="s2">&quot;standard_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;projection_y_coordinate&quot;</span>
-            <span class="k">if</span> <span class="n">units</span><span class="p">:</span>
-                <span class="n">y_coord_attrs</span><span class="p">[</span><span class="s2">&quot;units&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">units</span>
-        <span class="k">elif</span> <span class="n">is_geographic</span><span class="p">:</span>
-            <span class="c1"># X metadata</span>
-            <span class="n">x_coord_attrs</span><span class="p">[</span><span class="s2">&quot;long_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;longitude&quot;</span>
-            <span class="n">x_coord_attrs</span><span class="p">[</span><span class="s2">&quot;standard_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;longitude&quot;</span>
-            <span class="n">x_coord_attrs</span><span class="p">[</span><span class="s2">&quot;units&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;degrees_east&quot;</span>
-            <span class="c1"># Y metadata</span>
-            <span class="n">y_coord_attrs</span><span class="p">[</span><span class="s2">&quot;long_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;latitude&quot;</span>
-            <span class="n">y_coord_attrs</span><span class="p">[</span><span class="s2">&quot;standard_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;latitude&quot;</span>
-            <span class="n">y_coord_attrs</span><span class="p">[</span><span class="s2">&quot;units&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;degrees_north&quot;</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span> <span class="o">=</span> <span class="n">y_coord_attrs</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span> <span class="o">=</span> <span class="n">x_coord_attrs</span>
-        <span class="k">return</span> <span class="n">data_obj</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.set_attrs">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.set_attrs">[docs]</a>
-    <span class="k">def</span> <span class="nf">set_attrs</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">new_attrs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Set the attributes of the dataset/dataarray and reset</span>
-<span class="sd">        rioxarray properties to re-search for them.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        new_attrs: dict</span>
-<span class="sd">            A dictionary of new attributes.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            Modified dataset with new attributes.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">data_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>
-        <span class="c1"># set the attributes</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">attrs</span> <span class="o">=</span> <span class="n">new_attrs</span>
-        <span class="c1"># reset rioxarray properties depending</span>
-        <span class="c1"># on attributes to be generated</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_nodata</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_crs</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="n">data_obj</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.update_attrs">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.update_attrs">[docs]</a>
-    <span class="k">def</span> <span class="nf">update_attrs</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">new_attrs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Update the attributes of the dataset/dataarray and reset</span>
-<span class="sd">        rioxarray properties to re-search for them.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        new_attrs: dict</span>
-<span class="sd">            A dictionary of new attributes to update with.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            Modified dataset with updated attributes.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">data_attrs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
-        <span class="n">data_attrs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="o">**</span><span class="n">new_attrs</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">set_attrs</span><span class="p">(</span><span class="n">data_attrs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.set_encoding">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.set_encoding">[docs]</a>
-    <span class="k">def</span> <span class="nf">set_encoding</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">new_encoding</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Set the encoding of the dataset/dataarray and reset</span>
-<span class="sd">        rioxarray properties to re-search for them.</span>
-
-<span class="sd">        .. versionadded:: 0.4</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        new_encoding: dict</span>
-<span class="sd">            A dictionary for encoding.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            Modified dataset with new attributes.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">data_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>
-        <span class="c1"># set the attributes</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">encoding</span> <span class="o">=</span> <span class="n">new_encoding</span>
-        <span class="c1"># reset rioxarray properties depending</span>
-        <span class="c1"># on attributes to be generated</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_nodata</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_crs</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="n">data_obj</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.update_encoding">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.update_encoding">[docs]</a>
-    <span class="k">def</span> <span class="nf">update_encoding</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">new_encoding</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Update the encoding of the dataset/dataarray and reset</span>
-<span class="sd">        rioxarray properties to re-search for them.</span>
-
-<span class="sd">        .. versionadded:: 0.4</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        new_encoding: dict</span>
-<span class="sd">            A dictionary with encoding values to update with.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            Modified dataset with updated attributes.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">data_encoding</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">encoding</span><span class="p">)</span>
-        <span class="n">data_encoding</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="o">**</span><span class="n">new_encoding</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">set_encoding</span><span class="p">(</span><span class="n">data_encoding</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.set_spatial_dims">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.set_spatial_dims">[docs]</a>
-    <span class="k">def</span> <span class="nf">set_spatial_dims</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">x_dim</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">y_dim</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        This sets the spatial dimensions of the dataset.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x_dim: str</span>
-<span class="sd">            The name of the x dimension.</span>
-<span class="sd">        y_dim: str</span>
-<span class="sd">            The name of the y dimension.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will modify the dataframe in place.</span>
-<span class="sd">            Otherwise it will return a modified copy.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            Dataset with spatial dimensions set.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">data_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">x_dim</span> <span class="ow">in</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">dims</span><span class="p">:</span>
-            <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_x_dim</span> <span class="o">=</span> <span class="n">x_dim</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">MissingSpatialDimensionError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;x dimension (</span><span class="si">{</span><span class="n">x_dim</span><span class="si">}</span><span class="s2">) not found.</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="n">data_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="n">y_dim</span> <span class="ow">in</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">dims</span><span class="p">:</span>
-            <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">_y_dim</span> <span class="o">=</span> <span class="n">y_dim</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">MissingSpatialDimensionError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;y dimension (</span><span class="si">{</span><span class="n">y_dim</span><span class="si">}</span><span class="s2">) not found.</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="n">data_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-        <span class="k">return</span> <span class="n">data_obj</span></div>
-
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">x_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Hashable</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Hashable: The dimension for the X-axis.&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_x_dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_x_dim</span>
-        <span class="k">raise</span> <span class="n">MissingSpatialDimensionError</span><span class="p">(</span>
-            <span class="s2">&quot;x dimension not found. &#39;rio.set_spatial_dims()&#39; or &quot;</span>
-            <span class="s2">&quot;using &#39;rename()&#39; to change the dimension name to &#39;x&#39; can address this.&quot;</span>
-            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-        <span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">y_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Hashable</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Hashable: The dimension for the Y-axis.&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_y_dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_y_dim</span>
-        <span class="k">raise</span> <span class="n">MissingSpatialDimensionError</span><span class="p">(</span>
-            <span class="s2">&quot;y dimension not found. &#39;rio.set_spatial_dims()&#39; or &quot;</span>
-            <span class="s2">&quot;using &#39;rename()&#39; to change the dimension name to &#39;y&#39; can address this.&quot;</span>
-            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-        <span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">width</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;int: Returns the width of the dataset (x dimension size)&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_width</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_width</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">]</span><span class="o">.</span><span class="n">size</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_width</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">height</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;int: Returns the height of the dataset (y dimension size)&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_height</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_height</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_height</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">]</span><span class="o">.</span><span class="n">size</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_height</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;tuple(int, int): Returns the shape (height, width)&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">height</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">width</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_check_dimensions</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        This function validates that the dimensions 2D/3D and</span>
-<span class="sd">        they are are in the proper order.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        str or None: Name extra dimension.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">extra_dims</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dims</span><span class="p">))</span> <span class="o">-</span> <span class="p">{</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">})</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">extra_dims</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">TooManyDimensions</span><span class="p">(</span>
-                <span class="s2">&quot;Only 2D and 3D data arrays supported.&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="n">extra_dims</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dims</span> <span class="o">!=</span> <span class="p">(</span><span class="n">extra_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">):</span>
-            <span class="n">dim_info</span><span class="p">:</span> <span class="nb">tuple</span> <span class="o">=</span> <span class="p">(</span><span class="n">extra_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">)</span>
-            <span class="k">raise</span> <span class="n">InvalidDimensionOrder</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;Invalid dimension order. Expected order: </span><span class="si">{</span><span class="n">dim_info</span><span class="si">}</span><span class="s2">. &quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;You can use `DataArray.transpose</span><span class="si">{</span><span class="n">dim_info</span><span class="si">}</span><span class="s2">`&quot;</span>
-                <span class="s2">&quot; to reorder your dimensions.&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">extra_dims</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">dims</span> <span class="o">!=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">):</span>
-            <span class="n">dim_info</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">)</span>
-            <span class="k">raise</span> <span class="n">InvalidDimensionOrder</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;Invalid dimension order. Expected order: </span><span class="si">{</span><span class="n">dim_info</span><span class="si">}</span><span class="s2">. &quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;You can use `DataArray.transpose</span><span class="si">{</span><span class="n">dim_info</span><span class="si">}</span><span class="s2">`&quot;</span>
-                <span class="s2">&quot; to reorder your dimensions.&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="n">extra_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">if</span> <span class="n">extra_dims</span> <span class="k">else</span> <span class="kc">None</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">count</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;int: Returns the band count (z dimension size)&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_count</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_count</span>
-        <span class="n">extra_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_dimensions</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_count</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">if</span> <span class="n">extra_dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="n">extra_dim</span><span class="p">]</span><span class="o">.</span><span class="n">size</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_count</span>
-
-    <span class="k">def</span> <span class="nf">_internal_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Determine the internal bounds of the `xarray.DataArray`&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">DimensionMissingCoordinateError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="si">}</span><span class="s2"> missing coordinates.&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">DimensionMissingCoordinateError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="si">}</span><span class="s2"> missing coordinates.&quot;</span><span class="p">)</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">left</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">right</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-            <span class="n">top</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">bottom</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">NoDataInBounds</span><span class="p">(</span>
-                <span class="s2">&quot;Unable to determine bounds from coordinates.&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="n">left</span><span class="p">,</span> <span class="n">bottom</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">top</span>
-
-<div class="viewcode-block" id="XRasterBase.resolution">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.resolution">[docs]</a>
-    <span class="k">def</span> <span class="nf">resolution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">recalc</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Determine if the resolution of the grid.</span>
-<span class="sd">        If the transformation has rotation, the sign of the resolution is lost.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        recalc: bool, optional</span>
-<span class="sd">            Will force the resolution to be recalculated instead of using the</span>
-<span class="sd">            transform attribute.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        x_resolution, y_resolution: float</span>
-<span class="sd">            The resolution of the `xarray.DataArray` | `xarray.Dataset`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">transform</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_transform</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="p">(</span>
-            <span class="ow">not</span> <span class="n">recalc</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">width</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">height</span> <span class="o">==</span> <span class="mi">1</span>
-        <span class="p">)</span> <span class="ow">and</span> <span class="n">transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">_resolution</span><span class="p">(</span><span class="n">transform</span><span class="p">)</span>
-
-        <span class="c1"># if the coordinates of the spatial dimensions are missing</span>
-        <span class="c1"># use the cached transform resolution</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">left</span><span class="p">,</span> <span class="n">bottom</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">top</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_internal_bounds</span><span class="p">()</span>
-        <span class="k">except</span> <span class="n">DimensionMissingCoordinateError</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">transform</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">raise</span>
-            <span class="k">return</span> <span class="n">_resolution</span><span class="p">(</span><span class="n">transform</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">width</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">height</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">OneDimensionalRaster</span><span class="p">(</span>
-                <span class="s2">&quot;Only 1 dimenional array found. Cannot calculate the resolution.&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_get_data_var_message</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
-
-        <span class="n">resolution_x</span> <span class="o">=</span> <span class="p">(</span><span class="n">right</span> <span class="o">-</span> <span class="n">left</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">width</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">resolution_y</span> <span class="o">=</span> <span class="p">(</span><span class="n">bottom</span> <span class="o">-</span> <span class="n">top</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">height</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">resolution_x</span><span class="p">,</span> <span class="n">resolution_y</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_unordered_bounds</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">recalc</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Unordered bounds.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        recalc: bool, optional</span>
-<span class="sd">            Will force the bounds to be recalculated instead of using the</span>
-<span class="sd">            transform attribute.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        left, bottom, right, top: float</span>
-<span class="sd">            Outermost coordinates of the `xarray.DataArray` | `xarray.Dataset`.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">resolution_x</span><span class="p">,</span> <span class="n">resolution_y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resolution</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="n">recalc</span><span class="p">)</span>
-
-        <span class="k">try</span><span class="p">:</span>
-            <span class="c1"># attempt to get bounds from xarray coordinate values</span>
-            <span class="n">left</span><span class="p">,</span> <span class="n">bottom</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">top</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_internal_bounds</span><span class="p">()</span>
-            <span class="n">left</span> <span class="o">-=</span> <span class="n">resolution_x</span> <span class="o">/</span> <span class="mf">2.0</span>
-            <span class="n">right</span> <span class="o">+=</span> <span class="n">resolution_x</span> <span class="o">/</span> <span class="mf">2.0</span>
-            <span class="n">top</span> <span class="o">-=</span> <span class="n">resolution_y</span> <span class="o">/</span> <span class="mf">2.0</span>
-            <span class="n">bottom</span> <span class="o">+=</span> <span class="n">resolution_y</span> <span class="o">/</span> <span class="mf">2.0</span>
-        <span class="k">except</span> <span class="n">DimensionMissingCoordinateError</span> <span class="k">as</span> <span class="n">error</span><span class="p">:</span>
-            <span class="n">transform</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_transform</span><span class="p">()</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">transform</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="n">RioXarrayError</span><span class="p">(</span><span class="s2">&quot;Transform not able to be determined.&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">error</span>
-            <span class="n">left</span> <span class="o">=</span> <span class="n">transform</span><span class="o">.</span><span class="n">c</span>
-            <span class="n">top</span> <span class="o">=</span> <span class="n">transform</span><span class="o">.</span><span class="n">f</span>
-            <span class="n">right</span> <span class="o">=</span> <span class="n">left</span> <span class="o">+</span> <span class="n">resolution_x</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">width</span>
-            <span class="n">bottom</span> <span class="o">=</span> <span class="n">top</span> <span class="o">+</span> <span class="n">resolution_y</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">height</span>
-
-        <span class="k">return</span> <span class="n">left</span><span class="p">,</span> <span class="n">bottom</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">top</span>
-
-<div class="viewcode-block" id="XRasterBase.bounds">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.bounds">[docs]</a>
-    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">recalc</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        recalc: bool, optional</span>
-<span class="sd">            Will force the bounds to be recalculated instead of using the</span>
-<span class="sd">            transform attribute.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        left, bottom, right, top: float</span>
-<span class="sd">            Outermost coordinates of the `xarray.DataArray` | `xarray.Dataset`.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">minx</span><span class="p">,</span> <span class="n">miny</span><span class="p">,</span> <span class="n">maxx</span><span class="p">,</span> <span class="n">maxy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unordered_bounds</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="n">recalc</span><span class="p">)</span>
-        <span class="n">resolution_x</span><span class="p">,</span> <span class="n">resolution_y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resolution</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="n">recalc</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">_order_bounds</span><span class="p">(</span>
-            <span class="n">minx</span><span class="o">=</span><span class="n">minx</span><span class="p">,</span>
-            <span class="n">miny</span><span class="o">=</span><span class="n">miny</span><span class="p">,</span>
-            <span class="n">maxx</span><span class="o">=</span><span class="n">maxx</span><span class="p">,</span>
-            <span class="n">maxy</span><span class="o">=</span><span class="n">maxy</span><span class="p">,</span>
-            <span class="n">resolution_x</span><span class="o">=</span><span class="n">resolution_x</span><span class="p">,</span>
-            <span class="n">resolution_y</span><span class="o">=</span><span class="n">resolution_y</span><span class="p">,</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.isel_window">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.isel_window">[docs]</a>
-    <span class="k">def</span> <span class="nf">isel_window</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">window</span><span class="p">:</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">Window</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">pad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Use a rasterio.windows.Window to select a subset of the data.</span>
-
-<span class="sd">        .. versionadded:: 0.6.0 pad</span>
-
-<span class="sd">        .. warning:: Float indices are converted to integers.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        window: :class:`rasterio.windows.Window`</span>
-<span class="sd">            The window of the dataset to read.</span>
-<span class="sd">        pad: bool, default=False</span>
-<span class="sd">            Set to True to expand returned DataArray to dimensions of the window</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            The data in the window.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="p">(</span><span class="n">row_start</span><span class="p">,</span> <span class="n">row_stop</span><span class="p">),</span> <span class="p">(</span><span class="n">col_start</span><span class="p">,</span> <span class="n">col_stop</span><span class="p">)</span> <span class="o">=</span> <span class="n">window</span><span class="o">.</span><span class="n">toranges</span><span class="p">()</span>
-        <span class="n">row_start</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">row_start</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">row_start</span><span class="p">)</span>
-        <span class="n">row_stop</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">row_stop</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">row_stop</span><span class="p">)</span>
-        <span class="n">col_start</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">col_start</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">col_start</span><span class="p">)</span>
-        <span class="n">col_stop</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">col_stop</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">col_stop</span><span class="p">)</span>
-        <span class="n">row_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">row_start</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">row_stop</span><span class="p">))</span>
-        <span class="n">col_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">col_start</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">col_stop</span><span class="p">))</span>
-        <span class="n">array_subset</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">isel</span><span class="p">({</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">:</span> <span class="n">row_slice</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">:</span> <span class="n">col_slice</span><span class="p">})</span>
-            <span class="o">.</span><span class="n">copy</span><span class="p">()</span>  <span class="c1"># this is to prevent sharing coordinates with the original dataset</span>
-            <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span><span class="n">x_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_transform</span><span class="p">(</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">rasterio</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span>
-                    <span class="n">rasterio</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">Window</span><span class="o">.</span><span class="n">from_slices</span><span class="p">(</span>
-                        <span class="n">rows</span><span class="o">=</span><span class="n">row_slice</span><span class="p">,</span>
-                        <span class="n">cols</span><span class="o">=</span><span class="n">col_slice</span><span class="p">,</span>
-                        <span class="n">width</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">width</span><span class="p">,</span>
-                        <span class="n">height</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">height</span><span class="p">,</span>
-                    <span class="p">),</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-                <span class="p">),</span>
-                <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="p">)</span>
-        <span class="k">if</span> <span class="n">pad</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">array_subset</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">pad_box</span><span class="p">(</span>
-                <span class="o">*</span><span class="n">rasterio</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">bounds</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
-            <span class="p">)</span>
-        <span class="k">return</span> <span class="n">array_subset</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.slice_xy">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.slice_xy">[docs]</a>
-    <span class="k">def</span> <span class="nf">slice_xy</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">minx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">miny</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxx</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-        <span class="n">maxy</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Slice the array by x,y bounds.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        minx: float</span>
-<span class="sd">            Minimum bound for x coordinate.</span>
-<span class="sd">        miny: float</span>
-<span class="sd">            Minimum bound for y coordinate.</span>
-<span class="sd">        maxx: float</span>
-<span class="sd">            Maximum bound for x coordinate.</span>
-<span class="sd">        maxy: float</span>
-<span class="sd">            Maximum bound for y coordinate.</span>
-
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            The data in the slice.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">left</span><span class="p">,</span> <span class="n">bottom</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">top</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_internal_bounds</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">top</span> <span class="o">&gt;</span> <span class="n">bottom</span><span class="p">:</span>
-            <span class="n">y_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">maxy</span><span class="p">,</span> <span class="n">miny</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">y_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">miny</span><span class="p">,</span> <span class="n">maxy</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">left</span> <span class="o">&gt;</span> <span class="n">right</span><span class="p">:</span>
-            <span class="n">x_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">maxx</span><span class="p">,</span> <span class="n">minx</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">x_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">minx</span><span class="p">,</span> <span class="n">maxx</span><span class="p">)</span>
-
-        <span class="n">subset</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">sel</span><span class="p">({</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">:</span> <span class="n">x_slice</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">:</span> <span class="n">y_slice</span><span class="p">})</span>
-            <span class="o">.</span><span class="n">copy</span><span class="p">()</span>  <span class="c1"># this is to prevent sharing coordinates with the original dataset</span>
-            <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_spatial_dims</span><span class="p">(</span><span class="n">x_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_dim</span><span class="p">,</span> <span class="n">y_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_dim</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_transform</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="n">subset</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.transform_bounds">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.transform_bounds">[docs]</a>
-    <span class="k">def</span> <span class="nf">transform_bounds</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">dst_crs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">densify_pts</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">21</span><span class="p">,</span> <span class="n">recalc</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Transform bounds from src_crs to dst_crs.</span>
-
-<span class="sd">        Optionally densifying the edges (to account for nonlinear transformations</span>
-<span class="sd">        along these edges) and extracting the outermost bounds.</span>
-
-<span class="sd">        Note: this does not account for the antimeridian.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        dst_crs: str, :obj:`rasterio.crs.CRS`, or dict</span>
-<span class="sd">            Target coordinate reference system.</span>
-<span class="sd">        densify_pts: uint, optional</span>
-<span class="sd">            Number of points to add to each edge to account for nonlinear</span>
-<span class="sd">            edges produced by the transform process.  Large numbers will produce</span>
-<span class="sd">            worse performance.  Default: 21 (gdal default).</span>
-<span class="sd">        recalc: bool, optional</span>
-<span class="sd">            Will force the bounds to be recalculated instead of using the transform</span>
-<span class="sd">            attribute.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        left, bottom, right, top: float</span>
-<span class="sd">            Outermost coordinates in target coordinate reference system.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">rasterio</span><span class="o">.</span><span class="n">warp</span><span class="o">.</span><span class="n">transform_bounds</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span> <span class="n">dst_crs</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">bounds</span><span class="p">(</span><span class="n">recalc</span><span class="o">=</span><span class="n">recalc</span><span class="p">),</span> <span class="n">densify_pts</span><span class="o">=</span><span class="n">densify_pts</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.write_gcps">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_gcps">[docs]</a>
-    <span class="k">def</span> <span class="nf">write_gcps</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">gcps</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">GroundControlPoint</span><span class="p">],</span>
-        <span class="n">gcp_crs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
-        <span class="o">*</span><span class="p">,</span>
-        <span class="n">grid_mapping_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xarray</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">xarray</span><span class="o">.</span><span class="n">DataArray</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Write the GroundControlPoints to the dataset.</span>
-
-<span class="sd">        https://rasterio.readthedocs.io/en/latest/topics/georeferencing.html#ground-control-points</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        gcp: list of :obj:`rasterio.control.GroundControlPoint`</span>
-<span class="sd">            The Ground Control Points to integrate to the dataset.</span>
-<span class="sd">        gcp_crs: str, :obj:`rasterio.crs.CRS`, or dict</span>
-<span class="sd">            Coordinate reference system for the GCPs.</span>
-<span class="sd">        grid_mapping_name: str, optional</span>
-<span class="sd">            Name of the grid_mapping coordinate to store the GCPs information in.</span>
-<span class="sd">            Default is the grid_mapping name of the dataset.</span>
-<span class="sd">        inplace: bool, optional</span>
-<span class="sd">            If True, it will write to the existing dataset. Default is False.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        :obj:`xarray.Dataset` | :obj:`xarray.DataArray`:</span>
-<span class="sd">            Modified dataset with Ground Control Points written.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">grid_mapping_name</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">grid_mapping</span> <span class="k">if</span> <span class="n">grid_mapping_name</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">grid_mapping_name</span>
-        <span class="p">)</span>
-        <span class="n">data_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obj</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">gcp_crs</span><span class="p">:</span>
-            <span class="n">data_obj</span> <span class="o">=</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_crs</span><span class="p">(</span>
-                <span class="n">gcp_crs</span><span class="p">,</span> <span class="n">grid_mapping_name</span><span class="o">=</span><span class="n">grid_mapping_name</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span>
-            <span class="p">)</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">grid_map_attrs</span> <span class="o">=</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-            <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">Variable</span><span class="p">((),</span> <span class="mi">0</span><span class="p">)</span>
-            <span class="n">grid_map_attrs</span> <span class="o">=</span> <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">geojson_gcps</span> <span class="o">=</span> <span class="n">_convert_gcps_to_geojson</span><span class="p">(</span><span class="n">gcps</span><span class="p">)</span>
-        <span class="n">grid_map_attrs</span><span class="p">[</span><span class="s2">&quot;gcps&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">geojson_gcps</span><span class="p">)</span>
-        <span class="n">data_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="n">grid_mapping_name</span><span class="p">]</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">set_attrs</span><span class="p">(</span><span class="n">grid_map_attrs</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_gcps</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">gcps</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">data_obj</span></div>
-
-
-<div class="viewcode-block" id="XRasterBase.get_gcps">
-<a class="viewcode-back" href="../../rioxarray.html#rioxarray.rioxarray.XRasterBase.get_gcps">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_gcps</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">GroundControlPoint</span><span class="p">]]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Get the GroundControlPoints from the dataset.</span>
-
-<span class="sd">        https://rasterio.readthedocs.io/en/latest/topics/georeferencing.html#ground-control-points</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        list of :obj:`rasterio.control.GroundControlPoint` or None</span>
-<span class="sd">            The Ground Control Points from the dataset or None if not applicable</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gcps</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gcps</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">geojson_gcps</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_obj</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">grid_mapping</span><span class="p">]</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;gcps&quot;</span><span class="p">])</span>
-        <span class="k">except</span> <span class="p">(</span><span class="ne">KeyError</span><span class="p">,</span> <span class="ne">AttributeError</span><span class="p">):</span>
-            <span class="k">return</span> <span class="kc">None</span>
-
-        <span class="k">def</span> <span class="nf">_parse_gcp</span><span class="p">(</span><span class="n">gcp</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">GroundControlPoint</span><span class="p">:</span>
-            <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="o">*</span><span class="n">z</span> <span class="o">=</span> <span class="n">gcp</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">][</span><span class="s2">&quot;coordinates&quot;</span><span class="p">]</span>
-            <span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">z</span> <span class="k">else</span> <span class="kc">None</span>
-            <span class="k">return</span> <span class="n">GroundControlPoint</span><span class="p">(</span>
-                <span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span>
-                <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
-                <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">,</span>
-                <span class="n">row</span><span class="o">=</span><span class="n">gcp</span><span class="p">[</span><span class="s2">&quot;properties&quot;</span><span class="p">][</span><span class="s2">&quot;row&quot;</span><span class="p">],</span>
-                <span class="n">col</span><span class="o">=</span><span class="n">gcp</span><span class="p">[</span><span class="s2">&quot;properties&quot;</span><span class="p">][</span><span class="s2">&quot;col&quot;</span><span class="p">],</span>
-                <span class="nb">id</span><span class="o">=</span><span class="n">gcp</span><span class="p">[</span><span class="s2">&quot;properties&quot;</span><span class="p">][</span><span class="s2">&quot;id&quot;</span><span class="p">],</span>
-                <span class="n">info</span><span class="o">=</span><span class="n">gcp</span><span class="p">[</span><span class="s2">&quot;properties&quot;</span><span class="p">][</span><span class="s2">&quot;info&quot;</span><span class="p">],</span>
-            <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_gcps</span> <span class="o">=</span> <span class="p">[</span><span class="n">_parse_gcp</span><span class="p">(</span><span class="n">gcp</span><span class="p">)</span> <span class="k">for</span> <span class="n">gcp</span> <span class="ow">in</span> <span class="n">geojson_gcps</span><span class="p">[</span><span class="s2">&quot;features&quot;</span><span class="p">]]</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gcps</span></div>
-</div>
-
-
-
-<span class="k">def</span> <span class="nf">_convert_gcps_to_geojson</span><span class="p">(</span>
-    <span class="n">gcps</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">GroundControlPoint</span><span class="p">],</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Convert GCPs to geojson.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    gcps: The list of GroundControlPoint instances.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    A FeatureCollection dict.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">_gcp_coordinates</span><span class="p">(</span><span class="n">gcp</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">gcp</span><span class="o">.</span><span class="n">z</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">[</span><span class="n">gcp</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">gcp</span><span class="o">.</span><span class="n">y</span><span class="p">]</span>
-        <span class="k">return</span> <span class="p">[</span><span class="n">gcp</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">gcp</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="n">gcp</span><span class="o">.</span><span class="n">z</span><span class="p">]</span>
-
-    <span class="n">features</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="p">{</span>
-            <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;Feature&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;properties&quot;</span><span class="p">:</span> <span class="p">{</span>
-                <span class="s2">&quot;id&quot;</span><span class="p">:</span> <span class="n">gcp</span><span class="o">.</span><span class="n">id</span><span class="p">,</span>
-                <span class="s2">&quot;info&quot;</span><span class="p">:</span> <span class="n">gcp</span><span class="o">.</span><span class="n">info</span><span class="p">,</span>
-                <span class="s2">&quot;row&quot;</span><span class="p">:</span> <span class="n">gcp</span><span class="o">.</span><span class="n">row</span><span class="p">,</span>
-                <span class="s2">&quot;col&quot;</span><span class="p">:</span> <span class="n">gcp</span><span class="o">.</span><span class="n">col</span><span class="p">,</span>
-            <span class="p">},</span>
-            <span class="s2">&quot;geometry&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;Point&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="n">_gcp_coordinates</span><span class="p">(</span><span class="n">gcp</span><span class="p">)},</span>
-        <span class="p">}</span>
-        <span class="k">for</span> <span class="n">gcp</span> <span class="ow">in</span> <span class="n">gcps</span>
-    <span class="p">]</span>
-    <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;FeatureCollection&quot;</span><span class="p">,</span> <span class="s2">&quot;features&quot;</span><span class="p">:</span> <span class="n">features</span><span class="p">}</span>
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2019-2023, Corteva Agriscience™.</p>
-  </div>
-
-  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
-   
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-</footer>
-        </div>
-      </div>
-    </section>
-  </div>
-  <script>
-      jQuery(function () {
-          SphinxRtdTheme.Navigation.enable(true);
-      });
-  </script> 
-
-</body>
-</html>
\ No newline at end of file
diff --git a/0.18.0/_sources/authors.rst.txt b/0.18.0/_sources/authors.rst.txt
deleted file mode 100644
index e122f914..00000000
--- a/0.18.0/_sources/authors.rst.txt
+++ /dev/null
@@ -1 +0,0 @@
-.. include:: ../AUTHORS.rst
diff --git a/0.18.0/_sources/contributing.rst.txt b/0.18.0/_sources/contributing.rst.txt
deleted file mode 100644
index e582053e..00000000
--- a/0.18.0/_sources/contributing.rst.txt
+++ /dev/null
@@ -1 +0,0 @@
-.. include:: ../CONTRIBUTING.rst
diff --git a/0.18.0/_sources/examples/COG.ipynb.txt b/0.18.0/_sources/examples/COG.ipynb.txt
deleted file mode 100644
index 55b0265b..00000000
--- a/0.18.0/_sources/examples/COG.ipynb.txt
+++ /dev/null
@@ -1,137 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Cloud Optimized GeoTiff (COG)\n",
-    "\n",
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from https://openaerialmap.org/\n",
-    "cog_url = (\n",
-    "    \"https://oin-hotosm.s3.amazonaws.com/\"\n",
-    "    \"5d7dad0becaf880008a9bc88/0/5d7dad0becaf880008a9bc89.tif\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rioxarray.open_rasterio(cog_url, masked=True, overview_level=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray (band: 3, y: 312, x: 688)>\n",
-       "[643968 values with dtype=float64]\n",
-       "Coordinates:\n",
-       "  * band         (band) int64 1 2 3\n",
-       "  * y            (y) float64 4.34e+06 4.34e+06 4.34e+06 ... 4.339e+06 4.339e+06\n",
-       "  * x            (x) float64 -1.333e+07 -1.333e+07 ... -1.333e+07 -1.333e+07\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    transform:     (1.194328566955879, 0.0, -13334019.180693429, 0.0, -1.1943...\n",
-       "    scales:        (1.0, 1.0, 1.0)\n",
-       "    offsets:       (0.0, 0.0, 0.0)\n",
-       "    grid_mapping:  spatial_ref"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fa30cc3aeb8>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rds.astype(\"int\").plot.imshow(rgb=\"band\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
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diff --git a/0.18.0/_sources/examples/clip_box.ipynb.txt b/0.18.0/_sources/examples/clip_box.ipynb.txt
deleted file mode 100644
index 7543b350..00000000
--- a/0.18.0/_sources/examples/clip_box.ipynb.txt
+++ /dev/null
@@ -1,717 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Clip Box"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
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-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 200, x: 200)&gt;\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-30c63a87-57cb-472f-837d-4f925d7c1c64' class='xr-array-in' type='checkbox' checked><label for='section-30c63a87-57cb-472f-837d-4f925d7c1c64' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan 687.0 687.0 ... 491.0 504.0 504.0 515.0 469.0 485.0</span></div><div class='xr-array-data'><pre>array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-af2aad65-513d-46c2-8e63-fda2854beb0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af2aad65-513d-46c2-8e63-fda2854beb0c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.05e+06 5.05e+06 ... 5.004e+06</div><input id='attrs-30d9748d-4a6f-413c-9e83-c0ff31dcab89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30d9748d-4a6f-413c-9e83-c0ff31dcab89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00a56479-fbb4-4223-92f1-b83428597e50' class='xr-var-data-in' type='checkbox'><label for='data-00a56479-fbb4-4223-92f1-b83428597e50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([5049992.781974, 5049761.125615, 5049529.469257, 5049297.812899,\n",
-       "       5049066.156541, 5048834.500182, 5048602.843824, 5048371.187466,\n",
-       "       5048139.531108, 5047907.874749, 5047676.218391, 5047444.562033,\n",
-       "       5047212.905674, 5046981.249316, 5046749.592958, 5046517.9366  ,\n",
-       "       5046286.280241, 5046054.623883, 5045822.967525, 5045591.311167,\n",
-       "       5045359.654808, 5045127.99845 , 5044896.342092, 5044664.685734,\n",
-       "       5044433.029375, 5044201.373017, 5043969.716659, 5043738.0603  ,\n",
-       "       5043506.403942, 5043274.747584, 5043043.091226, 5042811.434867,\n",
-       "       5042579.778509, 5042348.122151, 5042116.465793, 5041884.809434,\n",
-       "       5041653.153076, 5041421.496718, 5041189.84036 , 5040958.184001,\n",
-       "       5040726.527643, 5040494.871285, 5040263.214927, 5040031.558568,\n",
-       "       5039799.90221 , 5039568.245852, 5039336.589493, 5039104.933135,\n",
-       "       5038873.276777, 5038641.620419, 5038409.96406 , 5038178.307702,\n",
-       "       5037946.651344, 5037714.994986, 5037483.338627, 5037251.682269,\n",
-       "       5037020.025911, 5036788.369553, 5036556.713194, 5036325.056836,\n",
-       "       5036093.400478, 5035861.74412 , 5035630.087761, 5035398.431403,\n",
-       "       5035166.775045, 5034935.118686, 5034703.462328, 5034471.80597 ,\n",
-       "       5034240.149612, 5034008.493253, 5033776.836895, 5033545.180537,\n",
-       "       5033313.524179, 5033081.86782 , 5032850.211462, 5032618.555104,\n",
-       "       5032386.898746, 5032155.242387, 5031923.586029, 5031691.929671,\n",
-       "       5031460.273313, 5031228.616954, 5030996.960596, 5030765.304238,\n",
-       "       5030533.647879, 5030301.991521, 5030070.335163, 5029838.678805,\n",
-       "       5029607.022446, 5029375.366088, 5029143.70973 , 5028912.053372,\n",
-       "       5028680.397013, 5028448.740655, 5028217.084297, 5027985.427939,\n",
-       "       5027753.77158 , 5027522.115222, 5027290.458864, 5027058.802506,\n",
-       "       5026827.146147, 5026595.489789, 5026363.833431, 5026132.177072,\n",
-       "       5025900.520714, 5025668.864356, 5025437.207998, 5025205.551639,\n",
-       "       5024973.895281, 5024742.238923, 5024510.582565, 5024278.926206,\n",
-       "       5024047.269848, 5023815.61349 , 5023583.957132, 5023352.300773,\n",
-       "       5023120.644415, 5022888.988057, 5022657.331698, 5022425.67534 ,\n",
-       "       5022194.018982, 5021962.362624, 5021730.706265, 5021499.049907,\n",
-       "       5021267.393549, 5021035.737191, 5020804.080832, 5020572.424474,\n",
-       "       5020340.768116, 5020109.111758, 5019877.455399, 5019645.799041,\n",
-       "       5019414.142683, 5019182.486325, 5018950.829966, 5018719.173608,\n",
-       "       5018487.51725 , 5018255.860891, 5018024.204533, 5017792.548175,\n",
-       "       5017560.891817, 5017329.235458, 5017097.5791  , 5016865.922742,\n",
-       "       5016634.266384, 5016402.610025, 5016170.953667, 5015939.297309,\n",
-       "       5015707.640951, 5015475.984592, 5015244.328234, 5015012.671876,\n",
-       "       5014781.015518, 5014549.359159, 5014317.702801, 5014086.046443,\n",
-       "       5013854.390084, 5013622.733726, 5013391.077368, 5013159.42101 ,\n",
-       "       5012927.764651, 5012696.108293, 5012464.451935, 5012232.795577,\n",
-       "       5012001.139218, 5011769.48286 , 5011537.826502, 5011306.170144,\n",
-       "       5011074.513785, 5010842.857427, 5010611.201069, 5010379.544711,\n",
-       "       5010147.888352, 5009916.231994, 5009684.575636, 5009452.919277,\n",
-       "       5009221.262919, 5008989.606561, 5008757.950203, 5008526.293844,\n",
-       "       5008294.637486, 5008062.981128, 5007831.32477 , 5007599.668411,\n",
-       "       5007368.012053, 5007136.355695, 5006904.699337, 5006673.042978,\n",
-       "       5006441.38662 , 5006209.730262, 5005978.073904, 5005746.417545,\n",
-       "       5005514.761187, 5005283.104829, 5005051.44847 , 5004819.792112,\n",
-       "       5004588.135754, 5004356.479396, 5004124.823037, 5003893.166679])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.274e+06 ... -7.228e+06</div><input id='attrs-ee56b91c-468c-4d4d-ad20-772a210aedbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee56b91c-468c-4d4d-ad20-772a210aedbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a47375a5-e337-405a-8fc2-ef4176e4ba9b' class='xr-var-data-in' type='checkbox'><label for='data-a47375a5-e337-405a-8fc2-ef4176e4ba9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([-7273893.821307, -7273662.164949, -7273430.508591, -7273198.852232,\n",
-       "       -7272967.195874, -7272735.539516, -7272503.883158, -7272272.226799,\n",
-       "       -7272040.570441, -7271808.914083, -7271577.257725, -7271345.601366,\n",
-       "       -7271113.945008, -7270882.28865 , -7270650.632291, -7270418.975933,\n",
-       "       -7270187.319575, -7269955.663217, -7269724.006858, -7269492.3505  ,\n",
-       "       -7269260.694142, -7269029.037784, -7268797.381425, -7268565.725067,\n",
-       "       -7268334.068709, -7268102.412351, -7267870.755992, -7267639.099634,\n",
-       "       -7267407.443276, -7267175.786918, -7266944.130559, -7266712.474201,\n",
-       "       -7266480.817843, -7266249.161484, -7266017.505126, -7265785.848768,\n",
-       "       -7265554.19241 , -7265322.536051, -7265090.879693, -7264859.223335,\n",
-       "       -7264627.566977, -7264395.910618, -7264164.25426 , -7263932.597902,\n",
-       "       -7263700.941544, -7263469.285185, -7263237.628827, -7263005.972469,\n",
-       "       -7262774.31611 , -7262542.659752, -7262311.003394, -7262079.347036,\n",
-       "       -7261847.690677, -7261616.034319, -7261384.377961, -7261152.721603,\n",
-       "       -7260921.065244, -7260689.408886, -7260457.752528, -7260226.09617 ,\n",
-       "       -7259994.439811, -7259762.783453, -7259531.127095, -7259299.470737,\n",
-       "       -7259067.814378, -7258836.15802 , -7258604.501662, -7258372.845303,\n",
-       "       -7258141.188945, -7257909.532587, -7257677.876229, -7257446.21987 ,\n",
-       "       -7257214.563512, -7256982.907154, -7256751.250796, -7256519.594437,\n",
-       "       -7256287.938079, -7256056.281721, -7255824.625363, -7255592.969004,\n",
-       "       -7255361.312646, -7255129.656288, -7254897.99993 , -7254666.343571,\n",
-       "       -7254434.687213, -7254203.030855, -7253971.374496, -7253739.718138,\n",
-       "       -7253508.06178 , -7253276.405422, -7253044.749063, -7252813.092705,\n",
-       "       -7252581.436347, -7252349.779989, -7252118.12363 , -7251886.467272,\n",
-       "       -7251654.810914, -7251423.154556, -7251191.498197, -7250959.841839,\n",
-       "       -7250728.185481, -7250496.529122, -7250264.872764, -7250033.216406,\n",
-       "       -7249801.560048, -7249569.903689, -7249338.247331, -7249106.590973,\n",
-       "       -7248874.934615, -7248643.278256, -7248411.621898, -7248179.96554 ,\n",
-       "       -7247948.309182, -7247716.652823, -7247484.996465, -7247253.340107,\n",
-       "       -7247021.683749, -7246790.02739 , -7246558.371032, -7246326.714674,\n",
-       "       -7246095.058315, -7245863.401957, -7245631.745599, -7245400.089241,\n",
-       "       -7245168.432882, -7244936.776524, -7244705.120166, -7244473.463808,\n",
-       "       -7244241.807449, -7244010.151091, -7243778.494733, -7243546.838375,\n",
-       "       -7243315.182016, -7243083.525658, -7242851.8693  , -7242620.212942,\n",
-       "       -7242388.556583, -7242156.900225, -7241925.243867, -7241693.587508,\n",
-       "       -7241461.93115 , -7241230.274792, -7240998.618434, -7240766.962075,\n",
-       "       -7240535.305717, -7240303.649359, -7240071.993001, -7239840.336642,\n",
-       "       -7239608.680284, -7239377.023926, -7239145.367568, -7238913.711209,\n",
-       "       -7238682.054851, -7238450.398493, -7238218.742135, -7237987.085776,\n",
-       "       -7237755.429418, -7237523.77306 , -7237292.116701, -7237060.460343,\n",
-       "       -7236828.803985, -7236597.147627, -7236365.491268, -7236133.83491 ,\n",
-       "       -7235902.178552, -7235670.522194, -7235438.865835, -7235207.209477,\n",
-       "       -7234975.553119, -7234743.896761, -7234512.240402, -7234280.584044,\n",
-       "       -7234048.927686, -7233817.271327, -7233585.614969, -7233353.958611,\n",
-       "       -7233122.302253, -7232890.645894, -7232658.989536, -7232427.333178,\n",
-       "       -7232195.67682 , -7231964.020461, -7231732.364103, -7231500.707745,\n",
-       "       -7231269.051387, -7231037.395028, -7230805.73867 , -7230574.082312,\n",
-       "       -7230342.425954, -7230110.769595, -7229879.113237, -7229647.456879,\n",
-       "       -7229415.80052 , -7229184.144162, -7228952.487804, -7228720.831446,\n",
-       "       -7228489.175087, -7228257.518729, -7228025.862371, -7227794.206013])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-385e9570-f830-4782-83c0-4339e8e097a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-385e9570-f830-4782-83c0-4339e8e097a0' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>+a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +units=m +x_0=0 +y_0=0</dd><dt><span>res :</span></dt><dd>[231.65635826 231.65635826]</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodata :</span></dt><dd>-28672.0</dd><dt><span>transform :</span></dt><dd>[ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e+00\n",
-       " -2.31656358e+02  5.05010861e+06  0.00000000e+00  0.00000000e+00\n",
-       "  1.00000000e+00]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f6aa6241360>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clip using a bounding box\n",
-    "\n",
-    "See docs for `rio.clip_box`:\n",
-    "\n",
-    "  - [DataArray.clip_box](../rioxarray.rst#rioxarray.raster_array.RasterArray.clip_box)\n",
-    "  - [Dataset.clip_box](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.clip_box)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xdsc = xds.rio.clip_box(\n",
-    "    minx=-7272967.1958741,\n",
-    "    miny=5048602.84382404,\n",
-    "    maxx=-7272503.88315758,\n",
-    "    maxy=5049066.15654056,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f6a9e1c64a0>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xdsc.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also clip using bounds in a CRS different from the dataset if you pass in the `crs` kwarg (requires rioxarray 0.12+):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xdscn = xds.rio.clip_box(\n",
-    "    minx=-93.1558,\n",
-    "    miny=45.403,\n",
-    "    maxx=-93.1557,\n",
-    "    maxy=45.4065,\n",
-    "    crs=\"EPSG:4326\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f6a9c8815a0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xdscn.plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/clip_geom.ipynb.txt b/0.18.0/_sources/examples/clip_geom.ipynb.txt
deleted file mode 100644
index f6b17bc1..00000000
--- a/0.18.0/_sources/examples/clip_geom.ipynb.txt
+++ /dev/null
@@ -1,339 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Clip\n",
-    "\n",
-    "API Reference for `rio.clip`:\n",
-    "\n",
-    "  - [DataArray.clip](../rioxarray.rst#rioxarray.raster_array.RasterArray.clip)\n",
-    "  - [Dataset.clip](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.clip)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset\n",
-    "\n",
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "  \n",
-    "Notes:\n",
-    "\n",
-    " - `masked=True` will convert from integer to `float64` and fill with `NaN`. If this behavior is not desired, you can skip this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    masked=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc957fb1ac0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clip using a geometry\n",
-    "\n",
-    "By default, it assumes that the CRS of the geometry is the same as the CRS\n",
-    "of the dataset. If it is different, make sure to pass in the CRS of the geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometries = [\n",
-    "    {\n",
-    "        'type': 'Polygon',\n",
-    "        'coordinates': [[\n",
-    "            [425499.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615331.540546387]\n",
-    "        ]]\n",
-    "    }\n",
-    "]\n",
-    "clipped = xds.rio.clip(geometries)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc957829d90>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clipped.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clipped.rio.to_raster(\"clipped.tif\", compress='LZMA', tiled=True, dtype=\"int32\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clip using a GeoDataFrame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas\n",
-    "from shapely.geometry import box"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geodf = geopandas.GeoDataFrame(\n",
-    "    geometry=[\n",
-    "        box(425499.18381405267, 4615331.540546387, 425526.18381405267, 4615478.540546387)\n",
-    "    ],\n",
-    "    crs=\"EPSG:26915\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clipped = xds.rio.clip(geodf.geometry.values, geodf.crs, drop=False, invert=True)\n",
-    "# Note: If you have rasterio < 1.2 you will need convert the geometries to dict-like objects if the projection\n",
-    "# of the geometries differ from the raster. For example:\n",
-    "#\n",
-    "# from shapely.geometry import mapping\n",
-    "#  geometries = geodf.geometry.apply(mapping)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc9572aa100>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clipped.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clipped.rio.to_raster(\"clipped_invert.tif\", compress='LZMA', tiled=True, dtype=\"int32\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clipping larger rasters\n",
-    "\n",
-    "Note: Loading from disk will likely only work directly after opening a raster with [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "\n",
-    "The clip operation needs the full raster loaded with the default method.\n",
-    "This can be an issue if you don't have enough memory (RAM) on you machine.\n",
-    "If this is something you have run into, it is recommended to use the\n",
-    "`from_disk=True` option. This option uses [rasterio.mask.mask](https://rasterio.readthedocs.io/en/latest/topics/masking-by-shapefile.html) when loading the data if possible.\n",
-    "\n",
-    "But be careful, these two methods, as they use different core functions, can have **different outputs**: small discrepencies may appear on the borders (1 pixel added or removed on some borders, see issue [#310](https://github.com/corteva/rioxarray/issues/310))\n",
-    "\n",
-    "Alternatively, you can also use `rio.clip_box` followed by `rio.clip` for a more\n",
-    "consistent memory efficient clip operation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometries = [\n",
-    "    {\n",
-    "        'type': 'Polygon',\n",
-    "        'coordinates': [[\n",
-    "            [425499.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615331.540546387]\n",
-    "        ]]\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "clipped = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    masked=True,\n",
-    ").rio.clip(geometries, from_disk=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc9571db850>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clipped.plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/convert_to_raster.ipynb.txt b/0.18.0/_sources/examples/convert_to_raster.ipynb.txt
deleted file mode 100644
index b7b70d58..00000000
--- a/0.18.0/_sources/examples/convert_to_raster.ipynb.txt
+++ /dev/null
@@ -1,677 +0,0 @@
-{
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-   "source": [
-    "# Example - Convert dataset to raster (GeoTiff)\n",
-    "\n",
-    "Often, it is desirable to take a variable (band) out of your dataset and export it to a raster.\n",
-    "This is possible with the `rio.to_raster()`method. It does most of the work for you so you don't\n",
-    "have to.\n",
-    "\n",
-    "Note: The `rio.to_raster()` method only works on a 2-dimensional or 3-dimensional `xarray.DataArray` or a 2-dimensional `xarray.Dataset`.\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_array.RasterArray.to_raster)\n",
-    "- Dataset: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
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-       "\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * time         (time) object 2016-12-19 10:27:29.687763 2016-12-29 12:52:42...\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06\n",
-       "    spatial_ref  int32 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 ...\n",
-       "    green        (time, y, x) float64 ...\n",
-       "Attributes:\n",
-       "    coordinates:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c445223f-de4e-4b1e-8098-cbee34b218b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c445223f-de4e-4b1e-8098-cbee34b218b7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-28147fcc-df3f-4a34-95e9-c553e0ce5379' class='xr-section-summary-in' type='checkbox'  checked><label for='section-28147fcc-df3f-4a34-95e9-c553e0ce5379' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2016-12-19 10:27:29.687763 2016-...</div><input id='attrs-e088320f-bca6-467e-8cb8-e8986a40cb2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e088320f-bca6-467e-8cb8-e8986a40cb2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-059f464e-7159-4921-a87b-f7cd4f581362' class='xr-var-data-in' type='checkbox'><label for='data-059f464e-7159-4921-a87b-f7cd4f581362' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(2016, 12, 19, 10, 27, 29, 687763, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2016, 12, 29, 12, 52, 42, 347451, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.663e+05 4.663e+05 ... 4.663e+05</div><input id='attrs-b437c71c-6f09-48b3-ad9a-90d13232f2ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b437c71c-6f09-48b3-ad9a-90d13232f2ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57e77136-7143-44d9-b7cf-73fbe2ee23a9' class='xr-var-data-in' type='checkbox'><label for='data-57e77136-7143-44d9-b7cf-73fbe2ee23a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5, 466285.5,\n",
-       "       466288.5, 466291.5, 466294.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.085e+06 8.085e+06 ... 8.085e+06</div><input id='attrs-4c2f981f-ffc5-4814-8a22-ebf2f86a7aef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c2f981f-ffc5-4814-8a22-ebf2f86a7aef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1df8c2f-d47d-47d7-91ab-beeb0f27b13a' class='xr-var-data-in' type='checkbox'><label for='data-f1df8c2f-d47d-47d7-91ab-beeb0f27b13a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,\n",
-       "       8084680.5, 8084677.5, 8084674.5, 8084671.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-da9d5c75-1701-48fa-ba98-c71fc592eebc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da9d5c75-1701-48fa-ba98-c71fc592eebc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ea59b80-4a53-40ca-a226-83a68cd832d4' class='xr-var-data-in' type='checkbox'><label for='data-2ea59b80-4a53-40ca-a226-83a68cd832d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 22S</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-51.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>10000000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>466266.0 3.0 0.0 8084700.0 0.0 -3.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9028421c-78d8-4324-8c53-b922b911a026' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9028421c-78d8-4324-8c53-b922b911a026' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fa584176-aba1-477b-a671-19b6df4dcaae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa584176-aba1-477b-a671-19b6df4dcaae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca424900-74f9-432d-806d-13c47b426ed2' class='xr-var-data-in' type='checkbox'><label for='data-ca424900-74f9-432d-806d-13c47b426ed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>NETCDF_DIM_EXTRA :</span></dt><dd>{time}</dd><dt><span>NETCDF_DIM_time_DEF :</span></dt><dd>[2. 6.]</dd><dt><span>NETCDF_DIM_time_VALUES :</span></dt><dd>[     0.       872712.659688]</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>(&#x27;DN&#x27;, &#x27;DN&#x27;)</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31195140-ecc1-4c84-9a7c-8a4b5cac2d66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31195140-ecc1-4c84-9a7c-8a4b5cac2d66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c047550-03f6-40db-8ad2-26cd7cc23c76' class='xr-var-data-in' type='checkbox'><label for='data-7c047550-03f6-40db-8ad2-26cd7cc23c76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>NETCDF_DIM_EXTRA :</span></dt><dd>{time}</dd><dt><span>NETCDF_DIM_time_DEF :</span></dt><dd>[2. 6.]</dd><dt><span>NETCDF_DIM_time_VALUES :</span></dt><dd>[     0.       872712.659688]</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>(&#x27;DN&#x27;, &#x27;DN&#x27;)</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[200 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3faed2e-e71f-483f-b9bb-c4f9d96230fe' class='xr-section-summary-in' type='checkbox'  ><label for='section-b3faed2e-e71f-483f-b9bb-c4f9d96230fe' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-464f11fb-83f1-46e4-8aea-e2df9c3d5021' class='xr-index-data-in' type='checkbox'/><label for='index-464f11fb-83f1-46e4-8aea-e2df9c3d5021' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2016-12-19 10:27:29.687763, 2016-12-29 12:52:42.347451],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=2,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ab3c0dc-2aab-409d-b42b-cd0f4e962bf8' class='xr-index-data-in' type='checkbox'/><label for='index-9ab3c0dc-2aab-409d-b42b-cd0f4e962bf8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5,\n",
-       "              466285.5, 466288.5, 466291.5, 466294.5],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a08603e-8475-4cf3-917c-37933b01c87c' class='xr-index-data-in' type='checkbox'/><label for='index-8a08603e-8475-4cf3-917c-37933b01c87c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,\n",
-       "              8084680.5, 8084677.5, 8084674.5, 8084671.5],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81c7d954-b5ff-4a73-a2e0-f373fd0ae1d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81c7d954-b5ff-4a73-a2e0-f373fd0ae1d9' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coordinates :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * time         (time) object 2016-12-19 10:27:29.687763 2016-12-29 12:52:42...\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06\n",
-       "    spatial_ref  int32 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 ...\n",
-       "    green        (time, y, x) float64 ...\n",
-       "Attributes:\n",
-       "    coordinates:  spatial_ref"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/input/PLANET_SCOPE_3D.nc\",\n",
-    ")\n",
-    "rds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Converting Dataset to raster\n",
-    "\n",
-    "Dataset: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# note how one time slice was selected on export to make the dataset 2D\n",
-    "rds.isel(time=0).rio.to_raster(\"planet_scope.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"blue\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": false, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info planet_scope.tif"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Converting DataArray to raster\n",
-    "\n",
-    "DataArray: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_array.RasterArray.to_raster)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# note how selecting one variable allowed for multiple time steps in a single raster\n",
-    "rds.green.rio.to_raster(\"planet_scope_green.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"green\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": false, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info planet_scope_green.tif"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Converting DataArray to raster in a different format\n",
-    "Example here, an ER Mapper grid.\n",
-    "Look at gdal for possible formats that you can use to write to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# you will get a two file raster: the .ers file with the metdata and the data with no extension\n",
-    "rds.blue.rio.to_raster(\"planet_scope_green.ers\", driver=\"ERS\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Change the compression of the raster and explicitly make it a Geotiff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds.blue.rio.to_raster(\"planet_scope_green_LZW_compression.tif\", driver=\"GTiff\", compress=\"LZW\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Change the basic datatype of the raster (in this example, also saving space going to 32 bit)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('float64')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.blue.dtype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds.blue.astype('float32').rio.to_raster(\"planet_scope_green_LZW_compression.tif\", driver=\"GTiff\", compress=\"LZW\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Memory efficient raster writing\n",
-    "\n",
-    "Useful for reading and writing larger rasters to disk.\n",
-    "\n",
-    "Note: This will increase the time it takes to generate the raster.\n",
-    "\n",
-    "Also see:\n",
-    "\n",
-    "- [Reading and Writing with Dask](dask_read_write.ipynb)\n",
-    "- [Reading COGs in Parallel](read-locks.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/input/PLANET_SCOPE_3D.nc\",\n",
-    "    lock=False,  # disable internal caching\n",
-    "    cache=False,  # don't keep data loaded in memory. pull from disk every time\n",
-    ")\n",
-    "\n",
-    "rds.green.rio.to_raster(\n",
-    "    \"planet_scope_tiled.tif\",\n",
-    "    tiled=True,  # GDAL: By default striped TIFF files are created. This option can be used to force creation of tiled TIFF files.\n",
-    "    windowed=True,  # rioxarray: read & write one window at a time\n",
-    ") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"blockxsize\": 256, \"blockysize\": 256, \"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"green\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": true, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info planet_scope_tiled.tif"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/dask_read_write.ipynb.txt b/0.18.0/_sources/examples/dask_read_write.ipynb.txt
deleted file mode 100644
index 4773066d..00000000
--- a/0.18.0/_sources/examples/dask_read_write.ipynb.txt
+++ /dev/null
@@ -1,137 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reading and Writing with Dask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import multiprocessing\n",
-    "# Linux/OSX:\n",
-    "import multiprocessing.popen_spawn_posix\n",
-    "# Windows:\n",
-    "# import multiprocessing.popen_spawn_win32\n",
-    "import threading\n",
-    "\n",
-    "from dask.distributed import Client, LocalCluster, Lock\n",
-    "\n",
-    "import rioxarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tips for using dask locks:\n",
-    "- Be careful about what lock you use for your process. It is required to have a lock for each worker, so the more fine-grained the better.\n",
-    "- The reading and writing processes need the same type of lock. They don't have to share the same lock, but they do nead a lock of the same type.\n",
-    "\n",
-    "See docs for:\n",
-    "\n",
-    "- [Reading COGs in Parallel](read-locks.ipynb)\n",
-    "- [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "- DataArray: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_array.RasterArray.to_raster)\n",
-    "- Dataset: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### No distributed computing example\n",
-    "Note: Without a lock provided, `to_raster` does not use dask to write to disk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    chunks=True,\n",
-    ")\n",
-    "xds.rio.to_raster(\"simple_write.tif\", tiled=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multithreaded example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    chunks=True,\n",
-    "    lock=False,\n",
-    "    # lock=threading.Lock(), # when too many file handles open\n",
-    "xds.rio.to_raster(\n",
-    "    \"dask_thread.tif\", tiled=True, lock=threading.Lock(),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multiple worker example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with LocalCluster() as cluster, Client(cluster) as client:\n",
-    "    xds = rioxarray.open_rasterio(\n",
-    "        \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "        chunks=True,\n",
-    "        lock=False,\n",
-    "        # lock=Lock(\"rio-read\", client=client), # when too many file handles open\n",
-    "    )\n",
-    "    xds.rio.to_raster(\n",
-    "        \"dask_multiworker.tif\",\n",
-    "        tiled=True,\n",
-    "        lock=Lock(\"rio\", client=client),\n",
-    "    )"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/examples.rst.txt b/0.18.0/_sources/examples/examples.rst.txt
deleted file mode 100644
index 9afe8367..00000000
--- a/0.18.0/_sources/examples/examples.rst.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-.. _usage_examples:
-
-Usage Examples
-==============
-
-This page contains links to a collection of examples of how to use rioxarray.
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Notebooks:
-
-   resampling.ipynb
-   convert_to_raster.ipynb
-   clip_geom.ipynb
-   clip_box.ipynb
-   pad_box.ipynb
-   read-locks.ipynb
-   reproject.ipynb
-   reproject_match.ipynb
-   merge.ipynb
-   interpolate_na.ipynb
-   transform_bounds.ipynb
-   COG.ipynb
-   dask_read_write.ipynb
-   Example - Zonal Statistics <https://corteva.github.io/geocube/html/examples/zonal_statistics.html>
-
-
-.. toctree::
-   :maxdepth: 1
-   :caption: stackexchange:
-
-   Extracting data within geometry (shape) <https://gis.stackexchange.com/a/328320/144357>
-   Converting NetCDF dataset array to GeoTiff <https://gis.stackexchange.com/a/329141/144357>
-   How do I add projection to this NetCDF file? (Satellite) <https://gis.stackexchange.com/a/345697/144357>
-   Create a new raster TIFF file which is masked based on the GeoJSON file <https://gis.stackexchange.com/a/346899/144357>
-   How to mask NetCDF time series data from a shapefile in Python? <https://gis.stackexchange.com/a/354798/144357>
-   Extract data from raster at a point <https://gis.stackexchange.com/a/358058/144357>
-   Convert raster to CSV with lat, lon, and value columns <https://gis.stackexchange.com/a/358057/144357>
diff --git a/0.18.0/_sources/examples/interpolate_na.ipynb.txt b/0.18.0/_sources/examples/interpolate_na.ipynb.txt
deleted file mode 100644
index 1b30f330..00000000
--- a/0.18.0/_sources/examples/interpolate_na.ipynb.txt
+++ /dev/null
@@ -1,216 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Interpolate Missing Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"MODIS_ARRAY.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f3bf197f978>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.isel(x=slice(0, 20), y=slice(0, 20)).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fill missing with interpolate_na\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.interpolate_na()](../rioxarray.rst#rioxarray.raster_array.RasterArray.interpolate_na)\n",
-    "- Dataset: [rio.interpolate_na()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.interpolate_na)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filled = xds.rio.interpolate_na()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[673., 558., 687., ..., 656., 656., 554.],\n",
-       "       [673., 558., 558., ..., 694., 694., 642.],\n",
-       "       [673., 558., 558., ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y            (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x            (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    transform:     (231.6563582639561, 0.0, -7274009.649486291, 0.0, -231.656...\n",
-       "    _FillValue:    -28672.0\n",
-       "    grid_mapping:  spatial_ref"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filled"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f3bf1865860>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "filled.isel(x=slice(0, 20), y=slice(0, 20)).plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/merge.ipynb.txt b/0.18.0/_sources/examples/merge.ipynb.txt
deleted file mode 100644
index 08f07723..00000000
--- a/0.18.0/_sources/examples/merge.ipynb.txt
+++ /dev/null
@@ -1,130 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Merge"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "from rioxarray.merge import merge_arrays\n",
-    "# Note: You can merge datasets with the merge_datasets method\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset\n",
-    "\n",
-    "API reference:\n",
-    "\n",
-    "- [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "- [rioxarray.merge.merge_arrays](../rioxarray.rst#rioxarray.merge.merge_arrays)\n",
-    "- [rioxarray.merge.merge_datasets](../rioxarray.rst#rioxarray.merge.merge_datasets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dem_test = \"../../test/test_data/input/MODIS_ARRAY.nc\"\n",
-    "rds = rioxarray.open_rasterio(dem_test)\n",
-    "arrays = [\n",
-    "    rds.isel(x=slice(100), y=slice(100)),\n",
-    "    rds.isel(x=slice(100, 200), y=slice(100, 200)),\n",
-    "    rds.isel(x=slice(100), y=slice(100, 200)),\n",
-    "    rds.isel(x=slice(100, 200), y=slice(100)),\n",
-    "]\n",
-    "merged = merge_arrays(arrays)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rds.where(rds!=rds.rio.nodata).plot();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f84f86f13c8>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "merged.where(merged!=merged.rio.nodata).plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/pad_box.ipynb.txt b/0.18.0/_sources/examples/pad_box.ipynb.txt
deleted file mode 100644
index 85a07939..00000000
--- a/0.18.0/_sources/examples/pad_box.ipynb.txt
+++ /dev/null
@@ -1,649 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Pad Box"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<title>Show/Hide attributes</title>\n",
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-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-053c0e9a-c4a5-4eb1-a6a9-5927f728b70e' class='xr-array-in' type='checkbox' ><label for='section-053c0e9a-c4a5-4eb1-a6a9-5927f728b70e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan 687.0 687.0 ... 491.0 504.0 504.0 515.0 469.0 485.0</span></div><pre class='xr-array-data'>array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-d1b518c1-3206-403c-88a0-bf2caa89205a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1b518c1-3206-403c-88a0-bf2caa89205a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.05e+06 5.05e+06 ... 5.004e+06</div><input id='attrs-5988f292-8b05-4239-8369-28f590630fca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5988f292-8b05-4239-8369-28f590630fca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d26e778d-54c2-4b2d-9bf3-2d70de3fa945' class='xr-var-data-in' type='checkbox'><label for='data-d26e778d-54c2-4b2d-9bf3-2d70de3fa945' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([5049992.781974, 5049761.125615, 5049529.469257, 5049297.812899,\n",
-       "       5049066.156541, 5048834.500182, 5048602.843824, 5048371.187466,\n",
-       "       5048139.531108, 5047907.874749, 5047676.218391, 5047444.562033,\n",
-       "       5047212.905674, 5046981.249316, 5046749.592958, 5046517.9366  ,\n",
-       "       5046286.280241, 5046054.623883, 5045822.967525, 5045591.311167,\n",
-       "       5045359.654808, 5045127.99845 , 5044896.342092, 5044664.685734,\n",
-       "       5044433.029375, 5044201.373017, 5043969.716659, 5043738.0603  ,\n",
-       "       5043506.403942, 5043274.747584, 5043043.091226, 5042811.434867,\n",
-       "       5042579.778509, 5042348.122151, 5042116.465793, 5041884.809434,\n",
-       "       5041653.153076, 5041421.496718, 5041189.84036 , 5040958.184001,\n",
-       "       5040726.527643, 5040494.871285, 5040263.214927, 5040031.558568,\n",
-       "       5039799.90221 , 5039568.245852, 5039336.589493, 5039104.933135,\n",
-       "       5038873.276777, 5038641.620419, 5038409.96406 , 5038178.307702,\n",
-       "       5037946.651344, 5037714.994986, 5037483.338627, 5037251.682269,\n",
-       "       5037020.025911, 5036788.369553, 5036556.713194, 5036325.056836,\n",
-       "       5036093.400478, 5035861.74412 , 5035630.087761, 5035398.431403,\n",
-       "       5035166.775045, 5034935.118686, 5034703.462328, 5034471.80597 ,\n",
-       "       5034240.149612, 5034008.493253, 5033776.836895, 5033545.180537,\n",
-       "       5033313.524179, 5033081.86782 , 5032850.211462, 5032618.555104,\n",
-       "       5032386.898746, 5032155.242387, 5031923.586029, 5031691.929671,\n",
-       "       5031460.273313, 5031228.616954, 5030996.960596, 5030765.304238,\n",
-       "       5030533.647879, 5030301.991521, 5030070.335163, 5029838.678805,\n",
-       "       5029607.022446, 5029375.366088, 5029143.70973 , 5028912.053372,\n",
-       "       5028680.397013, 5028448.740655, 5028217.084297, 5027985.427939,\n",
-       "       5027753.77158 , 5027522.115222, 5027290.458864, 5027058.802506,\n",
-       "       5026827.146147, 5026595.489789, 5026363.833431, 5026132.177072,\n",
-       "       5025900.520714, 5025668.864356, 5025437.207998, 5025205.551639,\n",
-       "       5024973.895281, 5024742.238923, 5024510.582565, 5024278.926206,\n",
-       "       5024047.269848, 5023815.61349 , 5023583.957132, 5023352.300773,\n",
-       "       5023120.644415, 5022888.988057, 5022657.331698, 5022425.67534 ,\n",
-       "       5022194.018982, 5021962.362624, 5021730.706265, 5021499.049907,\n",
-       "       5021267.393549, 5021035.737191, 5020804.080832, 5020572.424474,\n",
-       "       5020340.768116, 5020109.111758, 5019877.455399, 5019645.799041,\n",
-       "       5019414.142683, 5019182.486325, 5018950.829966, 5018719.173608,\n",
-       "       5018487.51725 , 5018255.860891, 5018024.204533, 5017792.548175,\n",
-       "       5017560.891817, 5017329.235458, 5017097.5791  , 5016865.922742,\n",
-       "       5016634.266384, 5016402.610025, 5016170.953667, 5015939.297309,\n",
-       "       5015707.640951, 5015475.984592, 5015244.328234, 5015012.671876,\n",
-       "       5014781.015518, 5014549.359159, 5014317.702801, 5014086.046443,\n",
-       "       5013854.390084, 5013622.733726, 5013391.077368, 5013159.42101 ,\n",
-       "       5012927.764651, 5012696.108293, 5012464.451935, 5012232.795577,\n",
-       "       5012001.139218, 5011769.48286 , 5011537.826502, 5011306.170144,\n",
-       "       5011074.513785, 5010842.857427, 5010611.201069, 5010379.544711,\n",
-       "       5010147.888352, 5009916.231994, 5009684.575636, 5009452.919277,\n",
-       "       5009221.262919, 5008989.606561, 5008757.950203, 5008526.293844,\n",
-       "       5008294.637486, 5008062.981128, 5007831.32477 , 5007599.668411,\n",
-       "       5007368.012053, 5007136.355695, 5006904.699337, 5006673.042978,\n",
-       "       5006441.38662 , 5006209.730262, 5005978.073904, 5005746.417545,\n",
-       "       5005514.761187, 5005283.104829, 5005051.44847 , 5004819.792112,\n",
-       "       5004588.135754, 5004356.479396, 5004124.823037, 5003893.166679])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.274e+06 ... -7.228e+06</div><input id='attrs-67d14f91-6c70-4cb8-9949-f13bd02d3656' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67d14f91-6c70-4cb8-9949-f13bd02d3656' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd9c7350-99e3-4f2a-9b3a-5b4f07d56c37' class='xr-var-data-in' type='checkbox'><label for='data-fd9c7350-99e3-4f2a-9b3a-5b4f07d56c37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([-7273893.821307, -7273662.164949, -7273430.508591, -7273198.852232,\n",
-       "       -7272967.195874, -7272735.539516, -7272503.883158, -7272272.226799,\n",
-       "       -7272040.570441, -7271808.914083, -7271577.257725, -7271345.601366,\n",
-       "       -7271113.945008, -7270882.28865 , -7270650.632291, -7270418.975933,\n",
-       "       -7270187.319575, -7269955.663217, -7269724.006858, -7269492.3505  ,\n",
-       "       -7269260.694142, -7269029.037784, -7268797.381425, -7268565.725067,\n",
-       "       -7268334.068709, -7268102.412351, -7267870.755992, -7267639.099634,\n",
-       "       -7267407.443276, -7267175.786918, -7266944.130559, -7266712.474201,\n",
-       "       -7266480.817843, -7266249.161484, -7266017.505126, -7265785.848768,\n",
-       "       -7265554.19241 , -7265322.536051, -7265090.879693, -7264859.223335,\n",
-       "       -7264627.566977, -7264395.910618, -7264164.25426 , -7263932.597902,\n",
-       "       -7263700.941544, -7263469.285185, -7263237.628827, -7263005.972469,\n",
-       "       -7262774.31611 , -7262542.659752, -7262311.003394, -7262079.347036,\n",
-       "       -7261847.690677, -7261616.034319, -7261384.377961, -7261152.721603,\n",
-       "       -7260921.065244, -7260689.408886, -7260457.752528, -7260226.09617 ,\n",
-       "       -7259994.439811, -7259762.783453, -7259531.127095, -7259299.470737,\n",
-       "       -7259067.814378, -7258836.15802 , -7258604.501662, -7258372.845303,\n",
-       "       -7258141.188945, -7257909.532587, -7257677.876229, -7257446.21987 ,\n",
-       "       -7257214.563512, -7256982.907154, -7256751.250796, -7256519.594437,\n",
-       "       -7256287.938079, -7256056.281721, -7255824.625363, -7255592.969004,\n",
-       "       -7255361.312646, -7255129.656288, -7254897.99993 , -7254666.343571,\n",
-       "       -7254434.687213, -7254203.030855, -7253971.374496, -7253739.718138,\n",
-       "       -7253508.06178 , -7253276.405422, -7253044.749063, -7252813.092705,\n",
-       "       -7252581.436347, -7252349.779989, -7252118.12363 , -7251886.467272,\n",
-       "       -7251654.810914, -7251423.154556, -7251191.498197, -7250959.841839,\n",
-       "       -7250728.185481, -7250496.529122, -7250264.872764, -7250033.216406,\n",
-       "       -7249801.560048, -7249569.903689, -7249338.247331, -7249106.590973,\n",
-       "       -7248874.934615, -7248643.278256, -7248411.621898, -7248179.96554 ,\n",
-       "       -7247948.309182, -7247716.652823, -7247484.996465, -7247253.340107,\n",
-       "       -7247021.683749, -7246790.02739 , -7246558.371032, -7246326.714674,\n",
-       "       -7246095.058315, -7245863.401957, -7245631.745599, -7245400.089241,\n",
-       "       -7245168.432882, -7244936.776524, -7244705.120166, -7244473.463808,\n",
-       "       -7244241.807449, -7244010.151091, -7243778.494733, -7243546.838375,\n",
-       "       -7243315.182016, -7243083.525658, -7242851.8693  , -7242620.212942,\n",
-       "       -7242388.556583, -7242156.900225, -7241925.243867, -7241693.587508,\n",
-       "       -7241461.93115 , -7241230.274792, -7240998.618434, -7240766.962075,\n",
-       "       -7240535.305717, -7240303.649359, -7240071.993001, -7239840.336642,\n",
-       "       -7239608.680284, -7239377.023926, -7239145.367568, -7238913.711209,\n",
-       "       -7238682.054851, -7238450.398493, -7238218.742135, -7237987.085776,\n",
-       "       -7237755.429418, -7237523.77306 , -7237292.116701, -7237060.460343,\n",
-       "       -7236828.803985, -7236597.147627, -7236365.491268, -7236133.83491 ,\n",
-       "       -7235902.178552, -7235670.522194, -7235438.865835, -7235207.209477,\n",
-       "       -7234975.553119, -7234743.896761, -7234512.240402, -7234280.584044,\n",
-       "       -7234048.927686, -7233817.271327, -7233585.614969, -7233353.958611,\n",
-       "       -7233122.302253, -7232890.645894, -7232658.989536, -7232427.333178,\n",
-       "       -7232195.67682 , -7231964.020461, -7231732.364103, -7231500.707745,\n",
-       "       -7231269.051387, -7231037.395028, -7230805.73867 , -7230574.082312,\n",
-       "       -7230342.425954, -7230110.769595, -7229879.113237, -7229647.456879,\n",
-       "       -7229415.80052 , -7229184.144162, -7228952.487804, -7228720.831446,\n",
-       "       -7228489.175087, -7228257.518729, -7228025.862371, -7227794.206013])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c5764eaa-c59c-4881-8426-5defd01b3eea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5764eaa-c59c-4881-8426-5defd01b3eea' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>+a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +units=m +x_0=0 +y_0=0</dd><dt><span>res :</span></dt><dd>[231.65635826 231.65635826]</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodata :</span></dt><dd>-28672.0</dd><dt><span>transform :</span></dt><dd>[ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e+00\n",
-       " -2.31656358e+02  5.05010861e+06  0.00000000e+00  0.00000000e+00\n",
-       "  1.00000000e+00]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fcb90621438>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pad using a bounding box\n",
-    "\n",
-    "See docs for `rio.pad_box`:\n",
-    "\n",
-    "  - [DataArray.pad_box](../rioxarray.rst#rioxarray.raster_array.RasterArray.pad_box)\n",
-    "  - [Dataset.pad_box](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.pad_box)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xdsc = xds.rio.pad_box(\n",
-    "    minx=-7.3e+06,\n",
-    "    miny=4.99e+06,\n",
-    "    maxx=-7.2e+06,\n",
-    "    maxy=5.06e+06,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xdsc.values[0, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fcb8ed54ba8>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xdsc.plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/read-locks.ipynb.txt b/0.18.0/_sources/examples/read-locks.ipynb.txt
deleted file mode 100644
index ac44f228..00000000
--- a/0.18.0/_sources/examples/read-locks.ipynb.txt
+++ /dev/null
@@ -1,229 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reading COGs in Parallel\n",
-    "\n",
-    "Cloud Optimized Geotiffs (COGs) can be internally chunked, which makes it possible to read them in parallel from multiple threads. However, the libraries `rioxarray` builds on, `rasterio` and `GDAL`, require some care to be used safely from multiple threads within a single process. By default, [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio) will acquire a per-process lock when reading a chunk of a COG.\n",
-    "\n",
-    "If you're using `rioxarray` with [Dask](http://docs.dask.org/) through the `chunks` keyword, you can also specify the `lock=False` keyword to ensure that reading *and* operating on your data happen in parallel.\n",
-    "\n",
-    "Note: Also see [Reading and Writing with Dask](dask_read_write.ipynb)\n",
-    "\n",
-    "## Scheduler Choice\n",
-    "\n",
-    "Dask has [several schedulers](https://docs.dask.org/en/latest/scheduling.html) which run computations in parallel. Which scheduler is best depends on a variety of factors, including whether your computation holds Python's Global Interpreter Lock, whether how much data needs to be moved around, and whether you need more than one machine's computational power. This section about read-locks only applies if you have more than one thread in a process. This will happen with Dask's [local threaded scheduler](https://docs.dask.org/en/latest/scheduling.html#local-threads) and its [distributed scheduler](https://distributed.dask.org/en/latest/) when configured to use more than one thread per worker.\n",
-    "\n",
-    "By default, `xarray` objects will use the local `threaded` scheduler."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reading without Locks\n",
-    "\n",
-    "To read a COG without any locks, you'd specify `lock=False`. This tells `rioxarray` to open a new `rasterio.DatasetReader` in each thread, rather than trying to share one amongst multiple threads."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "\n",
-    "url = (\n",
-    "    \"https://naipeuwest.blob.core.windows.net/naip/v002/md/2013/md_100cm_2013/\"\n",
-    "    \"39076/m_3907617_ne_18_1_20130924.tif\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.4 s, sys: 361 ms, total: 2.76 s\n",
-      "Wall time: 3.32 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, lock=False, chunks=(4, \"auto\", -1))\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: these timings are from a VM in the same Azure data center that's hosting the COG. Running this locally will give different times.\n",
-    "\n",
-    "## Chunking\n",
-    "\n",
-    "For maximum read performance, the chunking pattern you request should align with the internal chunking of the COG. Typically this means reading the data in a \"row major\" format: your chunks should be as wide as possible along the columns. We did that above with the chunks of `(4, \"auto\", -1)`. The `-1` says \"include all the columns\", and the `\"auto\"` will make the chunking along the rows as large as possible while staying in a reasonable limit (specified in `dask.config.get(\"array.chunk-size\")`).\n",
-    "\n",
-    "If we flipped that, and instead read as much of the rows as possible, we'll see slower performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 8.58 s, sys: 1.08 s, total: 9.66 s\n",
-      "Wall time: 11.2 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, lock=False, chunks=(1, -1, \"auto\"))\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That said, reading is typically just the first step in a larger computation. You'd want to consider what chunking is best for your whole computation. See https://docs.dask.org/en/latest/array-chunks.html for more on choosing chunks."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Caching Considerations\n",
-    "\n",
-    "Specifying `lock=False` will disable some internal caching done by xarray or rasterio. For example, the first and second reads here are roughly the same, since nothing is cached."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.49 s, sys: 392 ms, total: 2.88 s\n",
-      "Wall time: 3.25 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, lock=False, chunks=(4, \"auto\", -1))\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.48 s, sys: 292 ms, total: 2.78 s\n",
-      "Wall time: 2.97 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By default and when a lock is passed in, the initial read is slower (since some threads are waiting around for a lock)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.15 s, sys: 284 ms, total: 2.44 s\n",
-      "Wall time: 5.03 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, chunks=(4, \"auto\", -1))  # use the default locking\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But thanks to caching, subsequent reads are much faster."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 223 ms, sys: 64.9 ms, total: 288 ms\n",
-      "Wall time: 200 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you're repeatedly reading subsets of the data, using the default lock or `lock=some_lock_object` to benefit from the caching."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/reproject.ipynb.txt b/0.18.0/_sources/examples/reproject.ipynb.txt
deleted file mode 100644
index 16da3f3e..00000000
--- a/0.18.0/_sources/examples/reproject.ipynb.txt
+++ /dev/null
@@ -1,1177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reproject\n",
-    "\n",
-    "To re-project with dask, see [odc-geo](https://odc-geo.readthedocs.io/) & [pyresample](https://pyresample.readthedocs.io)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "import rasterio\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataset(\"../../test/test_data/input/PLANET_SCOPE_3D.nc\", decode_coords=\"all\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
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-       "Data variables:\n",
-       "    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368\n",
-       "    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-45eb2830-a807-4070-8270-eafb894091e9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45eb2830-a807-4070-8270-eafb894091e9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-866810c8-7763-4ad0-884c-659a75f5c365' class='xr-section-summary-in' type='checkbox'  checked><label for='section-866810c8-7763-4ad0-884c-659a75f5c365' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-28b007a0-1d57-44a2-9044-da130e8cfa2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28b007a0-1d57-44a2-9044-da130e8cfa2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0ff534a-88e7-4944-ba89-b5645ea219f9' class='xr-var-data-in' type='checkbox'><label for='data-c0ff534a-88e7-4944-ba89-b5645ea219f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.663e+05 4.663e+05 ... 4.663e+05</div><input id='attrs-34ea2c4f-000a-4ce2-b62e-a8dfbcc906f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34ea2c4f-000a-4ce2-b62e-a8dfbcc906f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53a4c842-cfce-4d59-b370-8f002ab69088' class='xr-var-data-in' type='checkbox'><label for='data-53a4c842-cfce-4d59-b370-8f002ab69088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5, 466285.5,\n",
-       "       466288.5, 466291.5, 466294.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-12-19T10:27:29.687763 2016-...</div><input id='attrs-85843e76-c4c1-44ee-b8fe-9c6f0b2c934a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85843e76-c4c1-44ee-b8fe-9c6f0b2c934a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08a858f1-899e-4560-a155-b91589db4ab6' class='xr-var-data-in' type='checkbox'><label for='data-08a858f1-899e-4560-a155-b91589db4ab6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-12-19T10:27:29.687763000&#x27;, &#x27;2016-12-29T12:52:42.347451000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.085e+06 8.085e+06 ... 8.085e+06</div><input id='attrs-d2563e2f-3d5c-430b-b264-1aa3c57398b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2563e2f-3d5c-430b-b264-1aa3c57398b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fbadacc-5d51-49a4-9648-0a1ab3420bc4' class='xr-var-data-in' type='checkbox'><label for='data-3fbadacc-5d51-49a4-9648-0a1ab3420bc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,\n",
-       "       8084680.5, 8084677.5, 8084674.5, 8084671.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba0f4c5e-f47a-4f11-988d-a3e5128f44bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba0f4c5e-f47a-4f11-988d-a3e5128f44bc' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a28b5fc7-3647-4072-a0fb-b8aad6f664ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a28b5fc7-3647-4072-a0fb-b8aad6f664ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b7f31d-2d0a-48f5-8f1c-db719140f271' class='xr-var-data-in' type='checkbox'><label for='data-e9b7f31d-2d0a-48f5-8f1c-db719140f271' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd><dt><span>nodata :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[[6.611017, 5.580979, 0.399607, 2.052803, 5.479985, 4.760219,\n",
-       "         5.077927, 5.574792, 0.726683, 5.170288],\n",
-       "        [4.535516, 0.088263, 4.222302, 0.289199, 3.478147, 3.227945,\n",
-       "         2.736443, 2.821799, 1.04221 , 1.099616],\n",
-       "        [0.071364, 4.393267, 0.496907, 2.311926, 1.19123 , 5.984189,\n",
-       "         5.266977, 1.146988, 3.219185, 0.982011],\n",
-       "        [5.818552, 5.176065, 4.891903, 4.557147, 4.706706, 4.670835,\n",
-       "         2.344188, 0.493237, 2.707488, 4.15662 ],\n",
-       "        [0.78458 , 3.838047, 0.803847, 1.588861, 5.002692, 2.565792,\n",
-       "         5.195116, 2.642173, 1.47148 , 6.854931],\n",
-       "        [4.407593, 6.734126, 3.702884, 4.682068, 0.498895, 2.931409,\n",
-       "         5.713952, 6.388658, 6.618634, 3.267176],\n",
-       "        [6.175152, 2.331721, 6.829246, 1.650656, 5.530157, 0.167316,\n",
-       "         6.406627, 4.559968, 0.083601, 1.231478],\n",
-       "        [2.438085, 1.593681, 5.902827, 2.124327, 1.870033, 4.519462,\n",
-       "         4.845505, 6.214475, 2.237778, 4.178488],\n",
-       "        [5.855639, 5.824333, 6.006212, 5.43411 , 3.393885, 6.183958,\n",
-       "         1.284061, 0.028555, 6.726706, 1.516993],\n",
-       "        [0.844103, 5.61329 , 6.978045, 1.183779, 2.539376, 1.268038,\n",
-       "         1.27574 , 5.101129, 2.020225, 2.081746]],\n",
-       "\n",
-       "       [[6.242907, 6.394763, 1.302492, 2.895077, 0.537664, 5.012127,\n",
-       "         4.380069, 1.624007, 5.56236 , 4.303491],\n",
-       "        [6.745937, 3.653031, 5.116965, 1.835256, 3.514732, 6.523679,\n",
-       "         0.17986 , 2.444248, 5.169099, 0.584623],\n",
-       "        [2.119518, 0.479052, 4.274714, 6.019049, 6.346329, 2.858441,\n",
-       "         3.976887, 4.218218, 4.320734, 3.941107],\n",
-       "        [2.974649, 6.67996 , 3.16875 , 3.507162, 2.909711, 1.359386,\n",
-       "         4.023015, 0.258809, 2.007625, 5.744746],\n",
-       "        [2.572982, 1.250802, 4.652006, 6.310958, 3.266477, 3.216613,\n",
-       "         3.47315 , 1.713415, 5.9575  , 2.729016],\n",
-       "        [2.743341, 4.203021, 1.306215, 2.840115, 5.351451, 2.774677,\n",
-       "         2.621753, 5.288077, 0.722618, 4.086008],\n",
-       "        [3.168672, 4.031863, 5.775087, 3.487762, 6.034522, 4.721887,\n",
-       "         5.093018, 0.386024, 2.278799, 1.536351],\n",
-       "        [4.548959, 4.797754, 6.371152, 3.69425 , 6.855343, 1.987757,\n",
-       "         4.749654, 1.750539, 1.804795, 4.966536],\n",
-       "        [1.78139 , 1.544275, 1.750026, 3.373274, 2.604254, 5.894015,\n",
-       "         3.217723, 5.637478, 2.900347, 2.097813],\n",
-       "        [1.844174, 6.994962, 2.504717, 5.292132, 0.184411, 4.870834,\n",
-       "         1.888442, 3.491315, 5.055704, 3.368395]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-488de4a2-274f-4896-a2c7-8eaaf18ca6d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-488de4a2-274f-4896-a2c7-8eaaf18ca6d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b28137b-0d86-4df6-82ed-3dc2a3937270' class='xr-var-data-in' type='checkbox'><label for='data-8b28137b-0d86-4df6-82ed-3dc2a3937270' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd><dt><span>nodata :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[[ 7.920639, 66.150832, 30.096116, 30.437197, 57.794734, 11.538647,\n",
-       "         14.426782, 35.593171, 53.784858,  0.449093],\n",
-       "        [23.804111, 67.910347, 18.694533, 30.41474 , 68.117674, 44.906057,\n",
-       "         62.311842, 37.485047, 57.134336,  7.52572 ],\n",
-       "        [36.653481, 39.596833, 61.07603 , 56.883093, 29.635613, 64.114699,\n",
-       "         42.341689, 54.724789, 31.872344, 11.282554],\n",
-       "        [32.502176, 28.090549, 58.398927, 41.224019, 34.804777, 32.184565,\n",
-       "         59.392327,  9.155824, 52.967172, 67.409236],\n",
-       "        [18.79468 ,  8.543429, 35.834698,  3.596245, 30.252802, 41.549499,\n",
-       "         23.060248,  7.267762, 27.374099,  0.684024],\n",
-       "        [ 9.93227 , 44.509446, 22.01927 , 28.514121, 36.715233, 15.03939 ,\n",
-       "          2.43399 ,  0.636075, 34.43023 , 37.024545],\n",
-       "        [28.874823,  1.514255, 34.210822, 10.49793 , 54.025491, 64.294026,\n",
-       "         36.212813, 17.766628, 45.295952, 10.349576],\n",
-       "        [58.961924, 47.334638, 64.844646, 37.634131,  7.815025, 35.139303,\n",
-       "         18.501505, 35.01185 , 27.761908, 13.240655],\n",
-       "        [49.544668, 57.716538, 27.389778, 11.604377, 24.826367, 15.449456,\n",
-       "         26.482386, 42.855739, 10.4958  , 59.267182],\n",
-       "        [37.094093, 43.294246, 33.240747, 16.85513 , 54.705119, 14.633291,\n",
-       "         35.138742, 50.101683, 57.495953, 52.795405]],\n",
-       "\n",
-       "       [[26.137058, 16.448086,  4.503539, 33.351036, 20.32524 , 63.369743,\n",
-       "         11.531512, 38.629561, 59.821441, 11.547508],\n",
-       "        [ 6.438471, 28.948907,  9.949052, 23.234921, 65.539507,  9.822554,\n",
-       "         55.754023, 51.590388, 57.047098,  4.929671],\n",
-       "        [32.360472, 17.939979, 52.889505, 69.181176, 39.2923  , 56.442225,\n",
-       "          5.699603, 21.092554,  8.93472 , 23.810367],\n",
-       "        [ 3.715695, 30.653733, 44.540496, 48.578544, 24.032477, 30.339109,\n",
-       "         37.376636, 58.787274, 49.308994, 59.510765],\n",
-       "        [37.991912, 50.023013, 63.711135, 26.546118, 60.561058, 36.098302,\n",
-       "         10.725673, 40.51609 , 47.479255, 42.710909],\n",
-       "        [15.32887 , 15.878984, 30.914778, 25.902812,  3.815428, 35.530366,\n",
-       "         48.426293, 44.280075,  4.468083, 18.00032 ],\n",
-       "        [58.286993, 20.397714, 63.124   , 69.117495,  1.32436 , 29.024715,\n",
-       "         31.601531, 34.307982,  7.990292, 36.159696],\n",
-       "        [34.674335, 62.344993,  5.714717,  2.161448, 66.714977, 52.443751,\n",
-       "         12.791859, 63.707491, 13.697063,  9.394474],\n",
-       "        [16.766346, 26.260123, 40.68158 , 18.695085, 64.812126, 23.151592,\n",
-       "         50.597642, 61.308205, 31.517123, 23.469797],\n",
-       "        [44.670989, 17.533084, 39.034907, 32.676726, 53.275139, 48.731172,\n",
-       "         12.958856, 21.760335, 27.292202, 18.409063]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79eaaf20-0b46-43be-b9cf-7f1abeb0b6f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79eaaf20-0b46-43be-b9cf-7f1abeb0b6f9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 ...\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
-       "  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 ...\n",
-       "    green        (time, y, x) float64 ..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f8f13620880>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.green.where(xds.green!=xds.green.rio.nodata).isel(time=1).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.reproject()](../rioxarray.rst#rioxarray.raster_array.RasterArray.reproject)\n",
-    "- Dataset: [rio.reproject()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.reproject)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds_lonlat = xds.rio.reproject(\"EPSG:4326\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * x            (x) float64 -51.32 -51.32 -51.32 ... -51.32 -51.32 -51.32\n",
-       "  * y            (y) float64 -17.32 -17.32 -17.32 ... -17.32 -17.32 -17.32\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
-       "    spatial_ref  int64 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368\n",
-       "    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f82895e0-94c5-41e7-965a-f309f40a772c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f82895e0-94c5-41e7-965a-f309f40a772c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7fbb1a9c-ea36-44de-b015-b0bf3be03674' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fbb1a9c-ea36-44de-b015-b0bf3be03674' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-51.32 -51.32 ... -51.32 -51.32</div><input id='attrs-38e090bd-85be-426b-ade8-c39502fa16a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38e090bd-85be-426b-ade8-c39502fa16a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-defa9d2d-d31c-431a-82e3-3ec3e7b18ea0' class='xr-var-data-in' type='checkbox'><label for='data-defa9d2d-d31c-431a-82e3-3ec3e7b18ea0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-51.317454, -51.317426, -51.317399, -51.317371, -51.317343, -51.317316,\n",
-       "       -51.317288, -51.31726 , -51.317233, -51.317205])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-17.32 -17.32 ... -17.32 -17.32</div><input id='attrs-e72e0d56-f2e8-4f20-9c4d-f081f99576c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e72e0d56-f2e8-4f20-9c4d-f081f99576c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31d9d357-a70b-4b4f-8217-ab20870c7c7f' class='xr-var-data-in' type='checkbox'><label for='data-31d9d357-a70b-4b4f-8217-ab20870c7c7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-17.322876, -17.322903, -17.322931, -17.322959, -17.322986, -17.323014,\n",
-       "       -17.323042, -17.323069, -17.323097, -17.323125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-12-19T10:27:29.687763 2016-...</div><input id='attrs-0206b871-06ba-474f-8f44-3a47010cb630' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0206b871-06ba-474f-8f44-3a47010cb630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe148f3-6ccd-418b-8a5b-234a95472c42' class='xr-var-data-in' type='checkbox'><label for='data-dfe148f3-6ccd-418b-8a5b-234a95472c42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-12-19T10:27:29.687763000&#x27;, &#x27;2016-12-29T12:52:42.347451000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-15f96bc1-395f-4770-af20-9603b114ad1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15f96bc1-395f-4770-af20-9603b114ad1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42902900-c1fd-4910-a111-c16b9cc4a195' class='xr-var-data-in' type='checkbox'><label for='data-42902900-c1fd-4910-a111-c16b9cc4a195' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>-51.31746780720891 2.7678699822269306e-05 0.0 -17.322861663733065 0.0 -2.7678699822269306e-05</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b678e340-da44-426a-a4f7-d951b9ff6f77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b678e340-da44-426a-a4f7-d951b9ff6f77' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.611 5.581 0.3996 ... 5.056 3.368</div><input id='attrs-7c8b5103-9e66-4954-95db-974807557881' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c8b5103-9e66-4954-95db-974807557881' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51ed04e1-5d6e-4b55-9838-56991d4657a9' class='xr-var-data-in' type='checkbox'><label for='data-51ed04e1-5d6e-4b55-9838-56991d4657a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd></dl></div><div class='xr-var-data'><pre>array([[[6.61101706, 5.58097901, 0.39960727, 2.05280345, 5.47998484,\n",
-       "         4.76021916, 5.07792715, 5.57479217, 0.72668295, 5.17028805],\n",
-       "        [4.53551623, 0.08826297, 4.22230213, 0.28919903, 3.47814709,\n",
-       "         3.22794508, 2.73644333, 2.82179869, 1.04221025, 1.09961647],\n",
-       "        [0.07136352, 4.39326719, 0.49690677, 2.31192634, 1.19123023,\n",
-       "         5.98418893, 5.26697738, 1.14698827, 3.21918509, 0.98201077],\n",
-       "        [5.81855223, 5.17606488, 4.89190312, 4.55714657, 4.70670585,\n",
-       "         4.67083516, 2.34418779, 0.4932369 , 2.70748782, 4.15661977],\n",
-       "        [0.78457995, 3.83804728, 0.80384702, 1.58886148, 5.00269192,\n",
-       "         2.56579153, 5.19511563, 2.6421726 , 1.47148022, 6.85493052],\n",
-       "        [4.40759261, 6.73412648, 3.70288412, 4.68206812, 0.49889505,\n",
-       "         2.93140894, 5.71395224, 6.38865828, 6.61863439, 3.26717611],\n",
-       "        [6.17515163, 2.33172093, 6.82924574, 1.65065578, 5.53015689,\n",
-       "         0.16731611, 6.40662729, 4.55996818, 0.08360072, 1.23147801],\n",
-       "        [2.43808458, 1.59368058, 5.90282702, 2.12432736, 1.8700329 ,\n",
-       "         4.51946233, 4.84550488, 6.21447495, 2.23777817, 4.17848811],\n",
-       "        [5.85563919, 5.82433331, 6.00621159, 5.43410984, 3.39388501,\n",
-       "         6.18395763, 1.28406097, 0.02855524, 6.72670605, 1.51699291],\n",
-       "        [0.84410255, 5.61328959, 6.97804534, 1.18377926, 2.53937552,\n",
-       "         1.26803824, 1.27574049, 5.10112907, 2.02022521, 2.08174614]],\n",
-       "...\n",
-       "       [[6.24290664, 6.39476317, 1.30249235, 2.89507681, 0.5376643 ,\n",
-       "         5.01212706, 4.38006939, 1.62400749, 5.56236028, 4.30349107],\n",
-       "        [6.74593668, 3.65303136, 5.11696455, 1.83525557, 3.51473159,\n",
-       "         6.52367921, 0.17986011, 2.44424775, 5.16909939, 0.58462293],\n",
-       "        [2.11951807, 0.47905249, 4.27471358, 6.01904917, 6.34632929,\n",
-       "         2.85844128, 3.97688665, 4.21821752, 4.32073426, 3.9411069 ],\n",
-       "        [2.97464873, 6.67996018, 3.16875018, 3.50716191, 2.9097115 ,\n",
-       "         1.35938603, 4.0230148 , 0.2588095 , 2.00762528, 5.74474576],\n",
-       "        [2.57298212, 1.25080228, 4.65200582, 6.31095753, 3.26647688,\n",
-       "         3.21661254, 3.4731501 , 1.71341466, 5.95750029, 2.72901564],\n",
-       "        [2.74334064, 4.20302139, 1.30621538, 2.84011532, 5.35145147,\n",
-       "         2.77467654, 2.6217532 , 5.28807657, 0.72261773, 4.08600786],\n",
-       "        [3.1686723 , 4.0318634 , 5.77508699, 3.48776179, 6.0345218 ,\n",
-       "         4.72188719, 5.09301838, 0.38602444, 2.27879875, 1.5363515 ],\n",
-       "        [4.54895876, 4.79775447, 6.3711522 , 3.69424969, 6.85534296,\n",
-       "         1.98775739, 4.74965416, 1.75053883, 1.80479493, 4.96653593],\n",
-       "        [1.78138972, 1.54427475, 1.75002571, 3.37327355, 2.60425433,\n",
-       "         5.89401538, 3.2177233 , 5.63747771, 2.90034677, 2.09781306],\n",
-       "        [1.84417405, 6.99496234, 2.50471667, 5.29213205, 0.1844106 ,\n",
-       "         4.87083388, 1.88844217, 3.49131529, 5.05570379, 3.36839469]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.921 66.15 30.1 ... 27.29 18.41</div><input id='attrs-2abda620-506f-4da9-b5d1-28f48d5c9e82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2abda620-506f-4da9-b5d1-28f48d5c9e82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0858356e-9a30-4af8-afde-aefabaf6422f' class='xr-var-data-in' type='checkbox'><label for='data-0858356e-9a30-4af8-afde-aefabaf6422f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd></dl></div><div class='xr-var-data'><pre>array([[[ 7.92063876, 66.15083163, 30.09611622, 30.43719706,\n",
-       "         57.79473407, 11.53864677, 14.42678195, 35.5931706 ,\n",
-       "         53.78485769,  0.44909335],\n",
-       "        [23.80411143, 67.91034696, 18.6945325 , 30.41473977,\n",
-       "         68.11767437, 44.90605663, 62.31184244, 37.48504719,\n",
-       "         57.13433649,  7.52571983],\n",
-       "        [36.65348061, 39.59683262, 61.07602968, 56.88309328,\n",
-       "         29.63561283, 64.11469949, 42.34168918, 54.7247894 ,\n",
-       "         31.87234425, 11.28255401],\n",
-       "        [32.50217644, 28.0905485 , 58.39892665, 41.22401876,\n",
-       "         34.80477692, 32.18456541, 59.39232697,  9.15582412,\n",
-       "         52.96717208, 67.40923645],\n",
-       "        [18.79468009,  8.54342894, 35.83469773,  3.59624497,\n",
-       "         30.25280185, 41.54949902, 23.06024792,  7.26776162,\n",
-       "         27.37409939,  0.68402392],\n",
-       "        [ 9.93226996, 44.50944555, 22.01927024, 28.5141208 ,\n",
-       "         36.71523344, 15.03939035,  2.43398964,  0.63607503,\n",
-       "         34.43022974, 37.02454468],\n",
-       "        [28.87482271,  1.51425484, 34.21082199, 10.49792965,\n",
-       "         54.02549098, 64.29402555, 36.21281265, 17.76662845,\n",
-       "...\n",
-       "         24.03247674, 30.33910879, 37.37663568, 58.78727396,\n",
-       "         49.30899357, 59.51076479],\n",
-       "        [37.99191205, 50.0230132 , 63.71113469, 26.54611832,\n",
-       "         60.5610581 , 36.09830221, 10.72567322, 40.51609048,\n",
-       "         47.47925466, 42.71090946],\n",
-       "        [15.32887012, 15.8789836 , 30.91477844, 25.90281227,\n",
-       "          3.81542846, 35.53036591, 48.42629307, 44.28007476,\n",
-       "          4.46808288, 18.00031979],\n",
-       "        [58.28699332, 20.39771424, 63.12400017, 69.11749464,\n",
-       "          1.32435965, 29.02471509, 31.60153124, 34.30798249,\n",
-       "          7.99029205, 36.15969559],\n",
-       "        [34.67433533, 62.34499312,  5.71471651,  2.16144808,\n",
-       "         66.71497747, 52.4437509 , 12.79185872, 63.70749094,\n",
-       "         13.69706259,  9.39447357],\n",
-       "        [16.76634605, 26.26012306, 40.68158023, 18.69508482,\n",
-       "         64.81212607, 23.15159177, 50.59764238, 61.30820521,\n",
-       "         31.51712269, 23.46979658],\n",
-       "        [44.67098863, 17.53308399, 39.03490717, 32.67672611,\n",
-       "         53.27513876, 48.73117231, 12.95885589, 21.760335  ,\n",
-       "         27.29220175, 18.40906318]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5f85efc-8e6c-4119-a4b9-c904d3b06bac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d5f85efc-8e6c-4119-a4b9-c904d3b06bac' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * x            (x) float64 -51.32 -51.32 -51.32 ... -51.32 -51.32 -51.32\n",
-       "  * y            (y) float64 -17.32 -17.32 -17.32 ... -17.32 -17.32 -17.32\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
-       "    spatial_ref  int64 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368\n",
-       "    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_lonlat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f8f134b63a0>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ZTrmkA9AhbXB0DfXpNTv9pBp567V8ZuhfuEF+31N7rv1ubmnNnVr8O836ef/DmXPMrKofn+6/11r2ztz0yfmfnfDxaDMbUk1a9eaFTxU6rtOFzb7+/VzSWv+VSr9prK01nnw1l3SmnLbCygB10+35pbml9euft2a6sNb5yRHH5pLOSyetkUs6AOd/4c+5pfXHr2daGaMm/j7uvP9UG8ecuUt4anTv1PNW6/FaplkfGskLH+ecaxrGEsvvi1Q9eeHjnHNNwoClNZ09rHG88HHOuSayFK/5OOecy5FhLFlJfh7jhY9zzjUJAz7xmo9zzrm8eZ+Pc865XBl4s5tzzrn8rRyNbl74OOdc0zCMJd7s5pxzLk9m8MnKUfZ44eOcc81DLEGNzkRNeOHjnHNNwoClXvNxzjmXN6/5OOecy5XhhY9zzrkGWGpe+DjnnMvRUsQi2jc6GzXhhY9zzjURr/k455zLlff5OOecawCxxNo1OhM14YWPc841ibCSqRc+q7zV5i2i113TcknrP8f0zSUdgI2md88lnXWn5JIMALdc8ovc0jrlwONzS2vnm8fnkk6XDzbIJR2AmZ+sn1ta7abNzC2tWvFmN+ecc7kyE5+Yj3ZzzjmXozDgwJvdnHPO5coHHDjnnMvZyjTgoGFXIamLpAclvRr/luxllDRE0suSpkganhZe0r6SnpX0Qvy7dyLM1yRNkDRJ0qWJ/atLuj2m8ZSkvnW8dOeca7UlptQtC0nrSbpL0kuSJkvaLet9uRYaWYQOB8aYWX9gTHy+HEntgSuBA4ABwFBJA1LCzwEONrNtgGHATTGursBlwGAz2wroLmlwDHM8MM/MNgMuBy6p9cU651y1DPGJdUjdMvo18ICZbQlsB0wmw325VhpZ+BwKjIyPRwKHlThnZ2CKmU01s0XAbTFc2fBm9pyZvRX3TwI6SVod2AR4xczejsceAg4vEdddwGBJK8d4RufcSqMw4CBtSyNpHeALwPUAZrbIzN4l2325JhpZ+HQ3s5kA8e+GJc7pBUxPPJ8R92UNfzjwnJl9DEwBtpTUV1IHwovapzgdM1sMvAd0bf2lOedc7RnpTW6x2a2bpHGJ7cSiqDYB3gZ+L+k5SddJWots99WaqOuAA0kPAZ8pceicrFGU2JdpHT9JWxGaz/YDMLN5kk4GbgeWAv8kvAEtSie+iScCdGq/dpasOOdczWQccDDHzAZVON4B2AH4jpk9JenX1LGJrVwG6sbM9il3TNIsST3MbKakHsDsEqfNYFntBKA3UGhSKxteUm/gbuAYM3stkZ97gHviOScCS4rSmRFrResCc8tc0whgBMC6HbuvJAvaOueagRm1Gmo9A5hhZk/F53cRCp8s9+WaaGSz2yjCgADi37+WOOcZoL+kfpI6AkfFcGXDS1oP+Btwtpk9kYxM0obx7/rAKcB1JeI6AnjYzLxgcc61MWJphi2Nmf0XmC5pi7hrMPAi2e7LNdHI3/lcDNwh6XjgDeBIAEk9gevM7EAzWyzpVGA00B64wcwmVQoPnApsBpwr6dy4bz8zmw38WtJ2cd/5ZvZKfHw9cJOkKYQaz1F1umbnnGs1AxZlH82W5jvAzfGL/VTgOEKFpNR9teYaVviY2TuE0rZ4/1vAgYnn9wH3tSD8hcCFZdIcWmb/Qur4IjvnXC0YqtlicmY2HijVL7TCfbUefIYD55xrIj63m3POuVwZsNTndnPOOZcv+Xo+zjnn8uU1H+ecc7nzxeScc841hK/n45xzLldhPR/v83HOOZcrX8nUAR9v2JEpp/TNJa3PPL0k/aQa6X3DjFzSua/3HbmkA7DJA6fnlta3bnki/aQaeXLfPukn1cDcfTfNJR2Ad5/Jb0L5WYfXbdLmFV1TfRRhwIHXfJxzzuUoLCbnAw6cc87lLOOSCm2eFz7OOdckwpIK3uzmnHMuZ97n45xzLldhVmtvdnPOOZezVWJuN0mjKh2P5prZsbXJjnPOuXIMsXjpqjHa7bPACRWOC7iydtlxzjlXyaoyw8E5ZvZopRMk/ayG+XHOOVfGKjPazcxSf4Ke5RznnHO1sUoNOJC0OXAGsHEyjJntXad8OeecKxJGu60CNZ+EO4GrgWuB/CYZc845t5xVpc+nYLGZXVXXnDjnnKvIYNUY7SapS3x4j6RTgLuBjwvHzWxuHfPmnHMuyVadZrdnCYVt4WrPSBwzYJN6ZMo559yKarmYnKRpwHxCV8piMxsUKxy3A32BacBXzWxeTRIskjbarV/MZCczW5g8JqlTPTLknHOuvBrXfPYyszmJ58OBMWZ2saTh8flZtUywIOuYvX9m3Oecc65OCovJpW1VOBQYGR+PBA6rMstlpfX5fAboBawhaXuWNb+tA6xZr0w555xbUZheJ1OdoZukcYnnI8xsxArRwd8lGXBNPN7dzGYCmNlMSXVb6jWtz2d/4FigN/BLlhU+7wM/qlemnHPOlZaxz2eOmQ1KOWd3M3srFjAPSnqp+txll9bnM1LSTcBQM7s5pzw555wrxWrX52Nmb8W/syXdDewMzJLUI9Z6egCza5JYCan1NzNbCvxvvTLgnHMum1r1+UhaS9LahcfAfsBEYBQwLJ42DPhrfa4k+49MH5T0Q8IQvA8KO1f13/msPvMjNr34hVzSmvb9bXJJB+DdtzbOJZ1953dJP6lGujzVMbe0bpm2V25pLTwvnwlHzt6zbvegFfxl1sDc0vpujztzS+vYa2oTT41qPt2BuyVBKAduMbMHJD0D3CHpeOAN4MhaJFZK1sLnW/HvtxP7qvqdT9bx5JKGAL8G2gPXmdnFlcJL2he4GOgILALOMLOHY5ivAefEuP5mZmfG/ccClwFvxmR/a2bXtfbanHOuHmo1t5uZTQW2K7H/HWBw1QlkkGnYhJn1K7FV+wPTwnjy/sCY+Hw5ktoT1gs6ABgADJU0ICX8HOBgM9uGUG28KcbVlVDADDazrYDukpIv8u1mNjBuXvA459qkJdYudWsGmXIpaTVJp0m6K26nSlqtyrSzjCffGZhiZlPNbBFwWwxXNryZPVfoSAMmAZ0krU6opb1iZm/HYw8Bh1d5Dc45lxuzuv/OJzdZi8irgB2B38Vtx7ivGsuNJwdKjSfvBUxPPJ8R92UNfzjwnJl9DEwBtpTUV1IHQmHVJ3mupAmxcO1TIi4AJJ0oaZykcYuWn/TBOefqzkypWzPI2uezk5kl2wcflvR8WiBJDwGfKXHonIzplnoVLVNAaSvgEsIoDmJ/0MmEfqKlhBkaCk2H9wC3mtnHkk4i1KRKrlUUf4g1AmDd9t0y5cU552qjeWo2abIWPkskbWpmrwFI2oQM6/qY2T7ljknKMp58BsvXTnoDhSa1suEl9SbMwH1MIc8xP/cQChoknVi4htjJVnAtodByzrk2p1lqNmmyNrudATwiaaykR4GHgR9UmXaW8eTPAP0l9ZPUETgqhisbXtJ6wN+As83siWRkhakiJK0PnAJcF5/3SJx2CDC5mgtzzrl6yGFut9xkqvmY2RhJ/YEtCE1hL8V+lGpcTInx5JJ6EoZUH2hmiyWdCowmDI++wcwmVQoPnApsBpwr6dy4bz8zmw38WlKh+fB8M3slPj5N0iHAYmAuYUoh55xrWwyWNEnhkiZrsxuEQQZ9Y5jtJGFmf2htwuXGk8eRagcmnt8H3NeC8BcCF5ZJc2iZ/WcDZ2fNu3PONYKx8jS7ZSp84vxumwLjWdbXY0CrCx/nnHMt1TzNammy1nwGAQPMzEd3OedcA60sd+GsAw4mUnrItHPOuRytar/z6Qa8KOlp4NOBBmZ2SF1y5ZxzbgVmsCTbYnJtXtbC57x6ZsI551w2K0uzW9ah1o9WOi7pSTPbrTZZcs45V06zNKulaclQ60o61Sge55xzZRjN06eTplaFz0pSEXTOubat0TdbSV/JcNrC+BvNsmpV+DjnnKs3axPNbtcSpjOrlJEvUGJygKRaFT4NfzWcc25VYEsbfru938y+VekESX9MiyTrYnJrSWoXH28u6ZCixeS+mSUe55xz1TFL3+qbvn2jFudkrfn8A/h8nA16DDAO+BpwdExoYsZ4VioL+3TipXM+m0ta22w5NZd0AH6y8T25pPOtX303l3QAfvLD1C9iNXPm/V/PLa0trp6fSzr/t+YBuaQD0LvH3NzSuuj5/K4Lnqw6hrYwt5ukL1Q6bmb/yBJP1sJHZvZhnEH6N2Z2qaTnMoZ1zjlXCwY0vs/njBL7DNiOsOZa+yyRZC58JO1GqOkc38KwzjnnaqTRPzI1s4OTzyXtQVideiZhSZtMshYgpxOWHLjbzCbFlUwfyZqIc865WlBbGHAAgKTBwLmEWs/PzezBloRvyQwHj0paKz6fCpzWwrw655yrVg1rPpLaE/rw3zSzL0nqAtxOWLttGvBVM5tXFOYgQk3nPeCc4hWjs8o62m03SS8Sl5eWtJ2k37UmQeecc61kNZ/V+rvE+3o0HBhjZv0Jg8uGlwhzD6FvZzFwlqRRyS1rwlmb3X4F7A+MAjCz59NGPDjnnKuDGtV8JPUGDgIuAr4fdx8K7BkfjwTGAmcVBd2rFulnHjRgZtOl5UrUJeXOdc45Vy+ZajbdJI1LPB9hZiOKzvkVcCawdmJfdzObCWBmMyVtWBxxcqJpSRvEfW9ny/syWQuf6ZI+B5ikjoT+nskpYZxzztVatprPHDMbVO6gpC8Bs83sWUl7tiR5hVrITwkj2wS0k7SY8DOc87PGk3VVopOAbwO9gBnAwPjcOedcXgxYqvQt3e7AIZKmAbcBe8cpcWZJ6gEQ/84uEfb0GH4nM+tqZusDuwC7S/pe1kvJVPiY2RwzO9rMupvZhmb2DTN7J2sizjnnaqMW0+uY2dlm1tvM+gJHAQ/HKXFGAcPiacMIE4gWOwYYamavJ+KbCnwjHssk62i3zSWNkTQxPt9W0o+zJuKcc65GLMPWehcD+0p6Fdg3Pi+2mpnNWSFbod9ntRLnl5S12e1awo9MP4mJTCCUls455/JkSt9aEp3ZWDP7Unz8jpkNNrP+8W+pifYWVYiu0rHlZB1wsKaZPV002m1x1kScc87Vhhq9mhxsJ+n9EvtFC1a1zlr4zJG0KbFCJ+kIwjw+zjnn8lJ9s1r1WTDLNHFomqyFz7eBEcCWkt4EXicup+Cccy4vmUeztXmphU+c++dkM9snzu3WzszyWUTEOefc8hpc85H0bzPbodpzUgsfM1siacf4+IOWZdM551xNNb7P57OSJlQ4LmDdtEiyNrs9FyeMuxP4tAAysz9nDO+cc65abWMxuS0znJM6/VrWodZdgHeAvYGD4/aljGFLktRF0oOSXo1/1y9z3hBJL0uaIml4WnhJO0saH7fnJX05EWZHSS/EuK6I00QgaXVJt8f9T0nqW821OedcvcjSt3oys/9k2GakxZN1hoPjSmzfqvIaUqfujv1NVwIHAAOAoZIGpISfCAwys4HAEOAaSYUa3lXAiUD/uA2J+48H5pnZZsDlwCVVXptzztVHfX9kmptMzW6Sriix+z1gnJmVmn4hiyxTd+8MTIlTNyDpthjuxXLhzezDRPhOLBse3gNYx8yejM//ABwG3B/jOi+GuQv4rSSZNXrBWuecW14b+J1PTWTt8+lEaOe7Mz4/HJgEHC9pLzM7vRVpp07dTZjIdHri+QzCBHYVw0vaBbgB2Bj4ppktllSYFDUZV6/idOK57wFdgRWmkJB0IqH2xOqd1qPfnUtbdtWtNL9d71zSAfjG576bSzqd8nnpALh+h+3yS+zC/JJa9MsFuaSz1vz8+hk6f3Ve+kk1oi+Wuu3Ux6u1iqjxfT41kbXw2QzY28wWA0i6Cvg7Ye6fF8oFkvQQ8JkSh87JmG6pVzm13Dezp4CtJH0WGCnp/pS4MqcT18QYAbD2Or1Xku8gzrmm0Iaa1SR9hdBFsSHhHirAzGydLOGzFj69gLUITW3Exz3jMOyPywUys30qZHyWpB6x1lJu6u4ZQJ/E897AW/FxangzmyzpA2DrGFey+pCMq5DOjNg/tC5Qak4j55xrrDZS+ACXAgebWavWdss62u1SYLyk30u6EXgO+EX80elDrUmYbFN3PwP0l9QvLmJ3VAxXNnw8t0N8vDGwBTAtNtHNl7RrHOV2TCLNZFxHEKYXbztvsXPORY0e7ZYwq7UFD2Ss+ZjZ9ZLuIwwAEPAjMyvUGs5oZdoXA3dIOh54AzgSQFJP4DozOzD2v5wKjAbaAzeY2aRK4YE9gOGSPgGWAqckpv8+GbgRWIMw0OD+uP964CZJUwg1Hp+x2znXNuXYV5pinKTbgb8An7aAZf39Z9bRbgIGA5uY2fmSNpK0s5k93YoMFzL4ToyzeP9bwIGJ5/cB97Ug/E3ATWXSHEdogivev5BlhZdzzrVJOdds0qwDfAjsl9hnQO0KH+B3hPJ2b+B8YD7wJ2CnzNl0zjlXvTYy2s3MjqsmfNY+n13M7NvAwpjoPKBjNQk755xrhTbyI9NqV7jOWvh8EmcbKPxgcwPaUsujc86tItrQgIOqVrjOWvhcAdwNbCjpIuBx4Octy6dzzrmqtZGaD3GF66J9mVe4zjra7WZJzxI6+AUcVs0QO+ecc61goLbT5lTVCtcVCx9JXRJPZwO3Jo+Zmf8Q0znn8tR2RruVWuH6G1kDp9V8niVcqoCNgHnx8XqE39b0a3l+nXPOtVZbGWodJ3xu9QrXFft8zKyfmW1C+JHnwWbWzcy6Etby8YXknHNuFSWpu6TrgbvMbL6kAfFH/5lkHXCwU/yxJwBmdj/wxRbm1TnnXLVqMOBAUidJT8cFNydJ+lncn2mRz+hGQsWkZ3z+CnB61svIWvjMkfRjSX0lbSzpHMLKps455/ISBxykbRl8TFipYDtgIDBE0q5kWOQzoZuZ3UH82U1c9SB1+eyCrIXPUGADwnDru+PjoVkTcc45VyM1qPlYUFgMarW4GWFhzZFx/0jCgpvlfCCpK8tGu+3KspUPUmUdaj0XyGeFMeeccyWJzAMOukkal3g+Iq5FtiyuMHHAs4T12q40s6ckZVnks+D7hBUBNpX0BKFSckTWa0kban2emZ1X7TnOOedqJFvhM8fMBlWMxmwJMFDSesDdklaYdLmcWHB9MW5bEMrFl83sk6xxpNV8TpD0fqU8EKZTOC9rgs4551qpDtPnmNm7ksYCQ8iwSGcMs0TSoWZ2OTCp1Dlp0vp8rgXWrrB1juc455zLQ21Gu20QazxIWgPYB3iJbIt8Fjwh6beSPi9ph8KW9TIq1nzM7GdZI3LOOVd/NZpepwcwMjaftQPuMLN7JT1J6UU6S/lc/Ht+Yp8Rlt5JlXU9H1fC+hu9z+FXjM4lrT9ecFAu6QD8fthvckln19Xz+/jtf8V2uaW1xn+zDiKt3pvv9c4lnSVr5vezenX9MLe0ZgzOcW2cv9Qonhq8FXEG6u1L7C+5SGeZOPaqJg9e+DjnXLPId9bqiiR9v8Tu94BnzWx8Wvj8vqI555yrWhtaz2cQcBLQK24nAnsC10o6My1wpsKn2hXrnHPO1UjbWc+nK7CDmf3AzH5AKIw2AL4AHJsWOGvNp6oV65xzztVGG6r5bAQsSjz/BNjYzD4iTN9TUdY+nzXN7Glpuc65zCvWOeecqwEjzqTWJtwC/EtSYTj2wcCtcYmFF9MCZy18qlqxzjnnXPUUt7bAzC6QdB+wByFbJ5lZYUqfo9PCZy18qlqxzjnnXI20kdFuAGb2LGF+uBbLOrFoVSvWOeecq422spJptdImFi01jptC34+Z/b865Mk551w5q0LhQ5i/DcKspTsR5v2B0LH0j3plyjnnXAlWs+l1Gi7T3G6S/k4Yzz0/Pj8PuLPuuXPOObe8VaTmU1A8nnsR0LfmuXHOOVfRKtHnk3AT8LSkuwnl7peBP9QtV84550pbSQqfTDMcmNlFwHHAPOBd4Dgz+3k1CUvqIulBSa/Gv+uXOW+IpJclTZE0PC28pJ0ljY/b85K+nAizo6QXYlxXKI6ckHSspLcT4U6o5tqcc65e2tAMB1XJOrfbRsAc4O64vRP3VWM4MMbM+gNj4vPidNsDVwIHAAOAoZIGpISfCAwys4GElfmukVSo4V1FmPyuf9yGJJK73cwGxu26Kq/NOedqL8u8bitT4QP8Dbg3bmOAqcD9VaZ9KDAyPh4JHFbinJ2BKWY21cwWAbfFcGXDm9mHZlaY+qcTy2Zl6AGsY2ZPmpkRmg1Lpemcc22SCKPd0rZmkLXZbRsz2zZu/QmFwuNVpt3dzGbG+GcCG5Y4pxcwPfF8RtxXMbykXSRNAl4gTPmwOIabUSYugMMlTZB0l6Q+5TIt6URJ4ySNWzBvUbnTnHOuPlaxms9yzOzfhN/9VCTpIUkTS2yHpoUtRFEq+Qz5e8rMtop5PFtSp5S47gH6mtm2wEMsq1GVinuEmQ0ys0Gd1++YegHOOVdLMkvdmkGm0W5FMx20A3YA3k4LZ2b7VIhzlqQeZjYzNonNLnHaDCBZC+kNvBUfp4Y3s8mSPgC2jnEl1xz+NK64dGzBtcAladfmnHO5a6KaTZqsNZ+1E9vqhD6grLWXckYBw+LjYcBfS5zzDNBfUj9JHQlrCI2qFD6e2yE+3pgwO8O02DQ3X9KucZTbMYkwPRJpHgJMrvLanHOuLlaW0W5Zf+fzopktN6OBpCOpbpaDi4E7JB0PvAEcGePtCVxnZgea2WJJpwKjgfbADWY2qVJ4wvTewyV9Qlj54hQzmxOPnQzcCKxBGDBRGDRxmqRDCGsUzSXDKnzOOdcIzTKgII0sQ/ugpH+b2Q5p+1Y1q/fpY72+/71c0rruy9fkkg7AZbuVbS2tqSXvzMslHYA/vD42t7TOevOA3NJ663v9cknn57fm9+uDs795Ym5pdZjzQW5pjX7x58+a2aBq4lirWx/b6qD0e84zf/hB1WnVW9qs1gcABwK9JF2ROLQOvpKpc87lq4ma1dKk9fm8BYwDFhIWDCpso4D965s155xzK6jBUGtJfSQ9ImmypEmSvhv3Z5p5phbSZrV+Hnhe0s2JH24655xrAFGzms9i4Adm9m9JawPPSnqQ0N89xswujtOZDQfOqkmKRdKa3e4ws68Cz0krXnL8XYxzzrm81OB3PHH0b+FH+vMlTSb86P5QYM942khgLI0ofIDvxr9fqkfizjnnWiD7YnLdJI1LPB9hZiNKnSipL7A98BRFM8dIKjXzTE2kNbvNjA9PMbPlSj9Jl1CnEtE551xpGQufOVlGu0nqDPwJON3M3o8T/eci649M9y2xL7/xpM4554Iaze0maTVCwXOzmf057p5V+NF9hZlnaqJi4SPpZEkvAFvESTcL2+vAhHplyjnnXGm1mOEgzvJyPTDZzP5f4lCWmWdqIq3P5xbCLAD/x/Lr7cw3s7n1ypRzzrkSjJoMOAB2B74JvCBpfNz3I8rPHFNzaX0+7wHvAUMBYudTJ6CzpM5m9ka9Muacc25FtRhqbWaPU3qmf4DB1aeQLutKpgdLehV4HXgUmEb1i8k555xrgVVuMTngQmBX4BUz60coGZ+oW66cc86tyCzb1gSyFj6fxDVv2klqZ2aPAAPrly3nnHOlrGpLKrwbx4P/A7hZ0mx8YlHnnMtfkxQuabLWfA4FPgK+BzwAvAYcXK9MOeecK22VqvmYWXLRi5F1yotzzrlKDFjSJKVLirSJRedTupInwMxsnbrkyjnnXEnNUrNJk/Y7n7XzyohzzrkMmmQ0W5qsAw6cc861AatEzcc551wb0oKJQ9s6L3yq0HG+0efhJbmkdfr0k3JJB2D+ZQtzSafnX/rlkg7A3KWP5pbWxOu2zi2tjS6dkks6/12cX/fu9H3WyC2ttWbklxYvVh9FWMl05Sh9vPBxzrkmolVhtJtzzrk2xJvdnHPO5a955m5L44WPc841ER/t5pxzLn9e83HOOZcra571etJ44eOcc81kqdd8nHPO5cx/5+Occy5/K0nhk3U9n5qT1EXSg5JejX/XL3PeEEkvS5oiaXhaeEk7Sxoft+clfTkR5iJJ0yUtKEpjdUm3xzSektS3TpftnHOtZ8DSDFsTaFjhAwwHxphZf2BMfL4cSe2BK4EDgAHAUEkDUsJPBAaZ2UBgCHCNpEIN7x5g5xJ5OR6YZ2abAZcDl1R/ec45V1vCkKVvzaCRhc+hLFuYbiRwWIlzdgammNlUM1sE3BbDlQ1vZh+aWWGJ704kfg9sZv8ys5kpebkLGCxJrbgm55yrr6VL07cm0MjCp3uhIIh/NyxxTi9geuL5jLivYnhJu0iaBLwAnJQojMr5NJ147ntA1xZfkXPO1VMNm90k3SBptqSJiX2ZukNqoa6Fj6SHJE0ssR2aHjpEUWJfap3SzJ4ys62AnYCzJXWqVTqSTpQ0TtK4TxZ9UOoU55yrmxo2u91I6JpISu0OqZW6jnYzs33KHZM0S1IPM5spqQcwu8RpM4A+iee9gbfi49TwZjZZ0gfA1sC4ClktpDMj9g+tC8wtc00jgBEAa6/XuzkaV51zK48a9emY2T9KDK46FNgzPh4JjAXOqkmCRRrZ7DYKGBYfDwP+WuKcZ4D+kvpJ6ggcFcOVDR/P7RAfbwxsAUxrQV6OAB42a5JeO+fcKiROLJq2QbdCC03cTsyYQJbukJpo5O98LgbukHQ88AZwJICknsB1ZnagmS2WdCowGmgP3GBmkyqFB/YAhkv6hND6eYqZzYlxXwp8HVhT0oyYznnA9cBNkqYQajxH1fnanXOu5YysNZ85ZjaozrmpSsMKHzN7BxhcYv9bwIGJ5/cB97Ug/E3ATWXSPBM4s8T+hSwrvJxzrs2q82JyWbpDaqKRzW7OOedaKluzW2tl6Q6pCZ9exznnmoVRs4lFJd1KGFzQLXZD/JTy3Rk154WPc841jdqtZGpmQ8scWqE7ox688KnC4m5Lmfs/C9JPrIH1buicSzoA72/TPpd0Zg/KJx2AX80qO+q/5tYbOj39pBpZcGaPXNI5f/Njc0kHoGOOP+9ec86S/BKrlZVkIK4XPs4510y88HHOOZcrM1jShLW1Erzwcc65ZuI1H+ecc7mq4Wi3RvPCxznnmonXfJxzzuXOCx/nnHO58gEHzjnnGsJrPs4553LnhY9zzrl8mY92c845lzMDs6WNzkVNeOHjnHPNxGs+zjnncuWj3ZxzzjWEDzhwzjmXN1vqfT7OOedyVbvF5BrNCx/nnGsWPrGoc865vBlgPuDAOedcrszAf+fjnHMub+bNbs4553K3ktR8ZCvJyIlGkPQ28J8qo+kGzKlBdhqp2a/B8994zX4NWfK/sZltUE0ikh6IaaWZY2ZDqkmr3rzwaTBJ48xsUKPzUY1mvwbPf+M1+zU0e/4boV2jM+Ccc27V44WPc8653Hnh03gjGp2BGmj2a/D8N16zX0Oz5z933ufjnHMud17zcc45lzsvfJxzzuXOC58qSOoi6UFJr8a/65c5b4iklyVNkTQ8S3hJZ8fzX5a0f4k4R0mamHh+rKS3JY2P2wlNeA2rS7o9hnlKUt+2mH9JD0h6XtIkSVdLah/3N817UOEa2vx7IGlNSX+T9FLM/8WJ81v8HrSx/Lf49W9aZuZbKzfgUmB4fDwcuKTEOe2B14BNgI7A88CASuGBAfG81YF+MXz7RJxfAW4BJib2HQv8tsmv4RTg6vj4KOD2tph/YJ34V8CfgKOa7T2ocA1t/j0A1gT2iud0BB4DDmjte9DG8t/i179Zt4ZnoJk34GWgR3zcA3i5xDm7AaMTz88Gzq4UPnlOfD4a2C0+7gw8Hj/YtSh82tI1JM/pQPjFuNpa/hP7VgPuAb7WbO9BhWtoqvcg7v818D+tfQ/aWP5b/Po36+bNbtXpbmYzAeLfDUuc0wuYnng+I+6rFL5SmAuAXwIflkjrcEkTJN0lqU8TXsOnYcxsMfAe0LUN5h9Jo4HZwHzgrsR5zfIelLuGpnkP4jWsBxwMjEnsbul70Jby35rXvyn5xKIpJD0EfKbEoXOyRlFiX9r49pJhJA0ENjOz75VoC74HuNXMPpZ0EjAS2Bua6hrKhWkz+f/0gdn+kjoBNxNe5wdpkvfg0welr6Fp3gNJHYBbgSvMbGrcXfI9aKL8tyadpuSFTwoz26fcMUmzJPUws5mSehC+RRabASS/ffUG3oqPy4UvF2Y3YEdJ0wjv3YaSxprZnmb2TuL8a4FLmu0aEmFmxH/MdYG5bSz/nzKzhZJGAYcCDzbRe1D2Gmiu92AE8KqZ/SpxPSXfg2bJP2Ve/3J5b2be7FadUcCw+HgY8NcS5zwD9JfUT1JHQifiqJTwo4Cj4siXfkB/4Gkzu8rMeppZX2AP4JV40yZ+6AsOASY32zUUxXUE8LDFxu+2kn9JnQuvdbw5HAi8FJ83xXtQ6Rpogvcg5vtCwo359GQirXwP2kz+ad3r35wa3enUzBuhLXYM8Gr82yXu7wnclzjvQOAVwmiXc9LCx2PnxPNfJo6EKUq7L8t31v8fMIkwuuYRYMsmvIZOwJ3AFMI/6SZtLf9Ad8KNaEJ8vX8DdGim9yDlGprhPehNaIqaDIyP2wmtfQ/aWP5b/Po36+bT6zjnnMudN7s555zLnRc+zjnncueFj3POudx54eOccy53Xvg455Yj6TxJb2rZ5JwHljnvBkmzlZgcNu6/IM4wMF7S3yX1jPu7SnpE0gJJv02cv3YirfGS5kj6VTxWdqJNScMUJvN8VdKwxP5+8dxXY9iOcb8kXRHjmiBph0SYFk8aWuH1e0DSu5LuTX+1V11e+DjnSrnczAbG7b4y59wIDCmx/zIz29bMBgL3Aj+J+xcC5wI/TJ5sZvMTaQ0E/gP8OR4+HphnZpsBlxN/NCqpC/BTYBdgZ+CniYLhkpj//sC8GAfAAYTf2vQHTgSuinG1B66MxwcAQyUNiGGGA2NiXGPi8zSXAd/McN4qzQsfVzeSFtQhzkMK30wlHZa4SbQkjrGSBrXw/JclHVLiWN/ib/4rM0k/Sjx9BvgjMEBSt8JOM3s/cc5axOlhzOwDM3ucUAiVi78/YW60x+KuQwlT5ECYf26wJAH7E2aVmGtm8wizMwyJx/Zm2Vx1I4HDEnH9wYJ/AevFH6XuDEwxs6lmtgi4LZ5bnP6ncUlqL+kySc/EWtT/Jq5/DGG+PFeBFz6uqZjZKDMrrH9yGOGbah6ONrNR6ae1XvwG3lacGm+qNxQ1NX1a+JjZR4QfXi4uDizpIknTgaNZVvPJYihhGYHCDxDLTbRZbtLOrsC78dzkfiqEac2koccD75nZTsBOwP/EWQxcRl74uLqLbe2XSZoo6QVJX4v794y1irsUFta6OX5zRdKBcd/jsZ3+3rj/WEm/lfQ5wvQpl8V+gk2TNRpJ3RTmj0PSGpJuizfT24E1EnnbT9KTkv4t6U5JnTNcz44KC7E9CXw7sb/kt2FJ7ST9TmHhsHsl3SfpiHhsmqSfSHocOLJcfmKaj0p6VtJoLZse5zRJL8b0bquQ57ViQfKMpOfiazYx1ug+kPRR3M4iNEftDrxLKFymSvq8wqJna8SwN1d6jczsHDPrQ5i09NS01zThKMJkm59mvVT0rdjfmrgq2Q84RtJ44ClCodc/JYxL8MLH5eErwEBgO2AfQoFRmINre8L8VgMIC3XtrjDT8jWE6Uj2ADYojtDM/kmYB+uM2FfwWoX0TwY+NLNtgYuAHSEUUMCPgX3MbAdgHPD9DNfze+A0M9utaH+5b8NfIUwltA1wAmFy1aSF8TofKpUfSasRpsA5wsx2BG6I1wGhD2L7eG0nVcjzOYR5wnYC9iIsaLYL4fXvamZrANsCR5rZLEIhMBrYFXgTGG9mw4GP4ut9dIbXCcKCgYdnOVHSdoRpfp5N7P50ck4tP9FmuUk75xCa0zoU7adCmNRJQ2P6yUlDBXwn0VfVz8z+nuU6XeCFj8vDHoRp7pfEG9ujhJszhMlGZ5jZUsIcV32BLYGpZvZ6POdWqvMFQt8EZjaBMKcZhBvrAOCJ+A12GLBxpYgkrQusZ2aPxl03JQ6X+za8B3CnmS01s/8S5hxLuj0lP1sAWwMPxv0/Jtwgiddys6RvUKL5qyhvw2P4sYQ5xDYiLCZ3raQXCHOKDYg32WeA44CrgRlmlrkPI/bbFBzCsklL0wxlxfe63ESbo4H9JK0fmwX3Iyz2ZoTX94gYpniiz2NiTXxXwheFmbRu0tDRwMnxiwGSNpe0VsbrdPiSCi4fpZo1Cj5OPF5C+ExWOr+SxSz7QtWp6FipZhQROq2HtiANlYmrcOw7ZjZ6uZ3SQSlxflApP5K2ASaVqGkBHEQoXA8BzpW0VaK/ozhvh5vZy0VxnwfMItRK2xEGA1xKqKkuIqyBs7qkYwg1s06JsLcCexIKsAmSzjWz64GLJW0BLCWMXDspEWYasA7QUdJhwH5m9mI8/FVCM1/S9cBNkqYQajxHAZjZXEkXEAoOgPPNrLD0wFnAbQozRz8X4wC4L8Y/hbCQ4XExrsWSTiUUKO2BG8xsUgxzMXCHpOOBN4Aj4/7rCF+U/h2bit9m2WCExwhfoDpLmgEcX/yZcPis1r7VbwMWxL9fYdk/9gaEG9JnCDeuexPn/5awDPIahA7gvnH/zYXzSCyTTGiKOi4R/jrg5Pj4dGBafPx94Lr4eGtCITUo5uUNwuJ2EJqiNi9xHWOBQYnnE4A94uNLiDNzE4bv/gVYLT7fnDDa60jCkON2hBml5xKa0ACmAd3i45L5AToSbpiF5ZVXA7aK8fVN7JtFqJWVei9+Hl/fwmTC28e/lwM/iI+PC7cEg1Dj6pB4LX8VH88rXF8i7k+vwTffsm7e7ObycDfhhv088DBwpoXmp5IsjKI6BXggdsTPIoxyKnYbcEbsQN8U+AWhKeSfQLfEeVcRvoVOAM4krqliZm8TCrNb47F/Eb6xpjkOuDIOOPgosf864EXCt+GJhH6rDsCfCP0KhX1PlbqecvmxMPz3COASSc8Tmic/RyjM/xibzJ4j/Lbl3TJ5voBlNZSJ8TnA74Bhkv5FKOgKtbA9gfGSniP02fw67h8R46g44MC5NL6kgmuTJHU2swWxSeNKwoqPlzcoL2OBH5rZuCriKFxPV0Lht3ulAriZxKa0QWY2p9F5cc3Daz6urfqf2Dk+iTDC6ZoG5mUucKNK/Mi0Be6N1/MYcMHKUPAoDGEfT6hRLW1wdlyT8ZqPcysRSccB3y3a/YSZfbvU+c41ihc+zjnncufNbs4553LnhY9zzrnceeHjnHMud174OOecy93/B4e6kosxAiZXAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds_lonlat.green.where(xds_lonlat.green!=xds_lonlat.green.rio.nodata).isel(time=1).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject to UTM\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- [rio.estimate_utm_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.estimate_utm_crs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CRS.from_epsg(32722)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_utm = xds.rio.reproject(xds.rio.estimate_utm_crs())\n",
-    "xds_utm.rio.crs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject Large Rasters with Virtual Warping\n",
-    "\n",
-    "Using [WarpedVRT](https://rasterio.readthedocs.io/en/latest/topics/virtual-warping.html) enables re-projection from disk and reduces the amount\n",
-    "of memory required with the re-projection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "url = (\n",
-    "    \"https://storage.googleapis.com/\"\n",
-    "    \"gcp-public-data-landsat/LC08/01/047/027/\"\n",
-    "    \"LC08_L1TP_047027_20130421_20170310_01_T1/\"\n",
-    "    \"LC08_L1TP_047027_20130421_20170310_01_T1_B4.TIF\"\n",
-    ")\n",
-    "env = rasterio.Env(\n",
-    "    GDAL_DISABLE_READDIR_ON_OPEN=\"EMPTY_DIR\",\n",
-    "    CPL_VSIL_CURL_USE_HEAD=False,\n",
-    "    CPL_VSIL_CURL_ALLOWED_EXTENSIONS=\"TIF\",\n",
-    ")\n",
-    "with env:\n",
-    "    with rasterio.open(url) as src:\n",
-    "        with rasterio.vrt.WarpedVRT(src, crs=\"EPSG:4326\") as vrt:\n",
-    "            rds = rioxarray.open_rasterio(vrt)\n",
-    "            rds.sel(band=1).plot.imshow()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/reproject_match.ipynb.txt b/0.18.0/_sources/examples/reproject_match.ipynb.txt
deleted file mode 100644
index 81ba1cf2..00000000
--- a/0.18.0/_sources/examples/reproject_match.ipynb.txt
+++ /dev/null
@@ -1,283 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reproject Match (For Raster Calculations/Stacking)\n",
-    "\n",
-    "`rio.reproject_match` will reproject to match the resolution, projection, and region of another raster.\n",
-    "\n",
-    "This is useful for raster caclulations and stacking rasters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_raster(raster):\n",
-    "    print(\n",
-    "        f\"shape: {raster.rio.shape}\\n\"\n",
-    "        f\"resolution: {raster.rio.resolution()}\\n\"\n",
-    "        f\"bounds: {raster.rio.bounds()}\\n\"\n",
-    "        f\"sum: {raster.sum().item()}\\n\"\n",
-    "        f\"CRS: {raster.rio.crs}\\n\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")\n",
-    "xds_match = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY_MATCH.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(ncols=2, figsize=(12,4))\n",
-    "xds.plot(ax=axes[0])\n",
-    "xds_match.plot(ax=axes[1]) \n",
-    "plt.draw()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original Raster:\n",
-      "----------------\n",
-      "\n",
-      "shape: (200, 200)\n",
-      "resolution: (231.6563582639561, -231.65635826375018)\n",
-      "bounds: (-7274009.649486291, 5003777.3385, -7227678.3778335, 5050108.61015275)\n",
-      "sum: 23209796.0\n",
-      "CRS: PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"unknown\",SPHEROID[\"unknown\",6371007.181,0]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]\n",
-      "\n",
-      "Raster to Match:\n",
-      "----------------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.65122672362685, -386.65122672362685)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 4903477.0\n",
-      "CRS: EPSG:32615\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Original Raster:\\n----------------\\n\")\n",
-    "print_raster(xds)\n",
-    "print(\"Raster to Match:\\n----------------\\n\")\n",
-    "print_raster(xds_match)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject Match\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.reproject_match()](../rioxarray.rst#rioxarray.raster_array.RasterArray.reproject_match)\n",
-    "- Dataset: [rio.reproject_match()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.reproject_match)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds_repr_match = xds.rio.reproject_match(xds_match)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reprojected Raster:\n",
-      "-------------------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.6512267236268, -386.6512267236231)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 4930593.0\n",
-      "CRS: EPSG:32615\n",
-      "\n",
-      "Raster to Match:\n",
-      "----------------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.65122672362685, -386.65122672362685)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 4903477.0\n",
-      "CRS: EPSG:32615\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Reprojected Raster:\\n-------------------\\n\")\n",
-    "print_raster(xds_repr_match)\n",
-    "print(\"Raster to Match:\\n----------------\\n\")\n",
-    "print_raster(xds_match)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Raster Calculations\n",
-    "\n",
-    "Now that the rasters have the same projection, resolution, and extents,\n",
-    "you can do raster calculations.\n",
-    "\n",
-    "It is recommended to use ``assign_coords`` to make the coordinates the exact same\n",
-    "due to tiny differences in the coordinate values due to floating precision ([issue 298](https://github.com/corteva/rioxarray/issues/298))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds_repr_match = xds_repr_match.assign_coords({\n",
-    "    \"x\": xds_match.x,\n",
-    "    \"y\": xds_match.y,\n",
-    "})\n",
-    "xds_sum = xds_repr_match + xds_match"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sum Raster:\n",
-      "-----------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.6512267236268, -386.6512267236231)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 9814687.0\n",
-      "CRS: EPSG:32615\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Sum Raster:\\n-----------\\n\")\n",
-    "print_raster(xds_sum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x288 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(ncols=3, figsize=(16,4))\n",
-    "\n",
-    "xds_repr_match.plot(ax=axes[0])\n",
-    "xds_match.plot(ax=axes[1]) \n",
-    "xds_sum.plot(ax=axes[2]) \n",
-    "\n",
-    "plt.draw()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/resampling.ipynb.txt b/0.18.0/_sources/examples/resampling.ipynb.txt
deleted file mode 100644
index 249e4a7e..00000000
--- a/0.18.0/_sources/examples/resampling.ipynb.txt
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Resampling\n",
-    "\n",
-    "This example demonstrates how to reproduce `rasterio`'s resampling example [here](https://rasterio.readthedocs.io/en/latest/topics/resampling.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rasterio.enums import Resampling\n",
-    "\n",
-    "import rioxarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset\n",
-    "\n",
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "  \n",
-    "Notes:\n",
-    "\n",
-    " - `masked=True` will convert from integer to `float64` and fill with `NaN`. If this behavior is not desired, you can skip this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    masked=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Resampling\n",
-    "\n",
-    "API Reference for `rio.reproject`:\n",
-    "\n",
-    "  - [DataArray.reproject](../rioxarray.rst#rioxarray.raster_array.RasterArray.reproject)\n",
-    "  - [Dataset.reproject](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.reproject)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "upscale_factor = 2\n",
-    "new_width = xds.rio.width * upscale_factor\n",
-    "new_height = xds.rio.height * upscale_factor\n",
-    "\n",
-    "xds_upsampled = xds.rio.reproject(\n",
-    "    xds.rio.crs, \n",
-    "    shape=(new_height, new_width), \n",
-    "    resampling=Resampling.bilinear,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 245, 574)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 490, 1148)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_upsampled.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3.0, -3.0)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds.rio.resolution()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1.5, -1.5)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_upsampled.rio.resolution()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/examples/transform_bounds.ipynb.txt b/0.18.0/_sources/examples/transform_bounds.ipynb.txt
deleted file mode 100644
index 33d25f0d..00000000
--- a/0.18.0/_sources/examples/transform_bounds.ipynb.txt
+++ /dev/null
@@ -1,369 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Transform Bounds\n",
-    "\n",
-    "The [rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds)\n",
-    "method allows you to correctly estimate\n",
-    "the bounds of your raster in a different CRS without\n",
-    "needing to re-project it. If you simply calculate the bounds\n",
-    "by transforming the bounds, there are often situations when\n",
-    "this is incorrect due to nonlinear transformations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import pyproj\n",
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "from shapely.geometry import box\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")\n",
-    "transformer = pyproj.Transformer.from_crs(xds.rio.crs, \"EPSG:4326\", always_xy=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Original Raster & Bounds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f324f4456c0>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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5Jj3++ON0zjnnHPHjc3BwcHBwOJSoUlVPRLyubRyvcGU+DgNSqRQlk0maJKLL/+bLNB5LUPcLJqQM7xgXy1bapIZg4hTDKmntUL5TRkZFldaW2NmcIYnPQH/bOX4eFuvIUbMVwZycRrLL6VlApU8Bva6n32pqGg7RU3jCHDgi19ir8nzl58pQdXxxSByHbW9aRYCK3UAUh/aCodPaiMiE3A5Ey+Vpmg3NUGihMI492y0jysSgPFk+Rf1DrF5iDQJYu7her6z3r5idpNmOvqb6GmAKBaJ1RN36eMD45DuggJbbJXtupb6mURyv2q2ErBANV8DKSD20BbaL/RVYRItrGR0xnydfSZnlsobGKHfIey/bZ1bQOpvJBdOnUvgQ+VJiKhP3ndXcKA1Ny44qr4+pRL9os2YbW3bJB0Bqruw7uD/0/RdS/d5OE7NkUmuCwB5iWlrr3QBMh6EPgiECcI0wTR2ZkLotAEzS5DxzPGCIAEzVaoas3KBtas7OAtFheSC439Oz5QnQrKeWGQSz095j6l0xQ5qRxDHYZ4wSr+e5f9YCRO25ND360/+XUkSUJKLJyUlqbT08Ocv4XdrxyuxDkn029+Rdh7W9RwtOgu7g4ODg4ODgQMf49NmxgM6XChQIBSm8UWa9kd+MRzPzZLSqU1BzHI1rJkdHzZohshGtT0VUKrKEEFsLtkOp5owQWAMtPLZQ20/sVtoAZGQoETIYIo3sIiNERcp+ap5cEdGYxxGv1gjk25EWK7cbmpTvtZg5PImME/k5zivOT2JngbKzZJs045GNysiy64W0eO/faxiMqWXSQ6uuzzDnRtseIC27rLaPc4xUes1CgGnKd5rb32aRlXDMcj1E/2CIgOhIlZeHWF6yEugMYOGg48J+dF/U7Cj6EtYH66JT+evRPuvAdvP5YiF3eFQ6+NaCZkP+cdOgcpe5ByOjJW6/oUMyfWa5kLk8ps/yrpAybplQ6Kn4dsj0mwVbtyqqhNeHVm6Kxe9gJAPMTGh2zqfOjT53moUDQ5TrkcvFd5vXHIsawZSi/0eH5XswRGCU8GoTOpBUp0ySoc0JZJEUgOxSaPXAnkoNj2aIkI3qWe2jes6x5imYay6wBootuJacbaYE4/hci/qrAR+FWZ9UaFd6roZkFWYsK82vhWXJj/KvrhNa7xtuUOTg4ODg4PAmQZVqVHGDohnhBkWHGeFX91DYP0UUkFFG6qx+8w9LuqZYM6AzhOxcPr9CSxQblMsFJflg074teD+RCflxVpm8aXM3u3pD+jjrRDJqOUUcFVrlip7yF4IZXaFNdsWcyubJzJLfx/aYV0RngH6P85mey6wFsxWaCdPQWT1I1U/sLIjP955Zp5KQmRPfxftgzUzbamXYlwfdZBiJibfKOnvISCIfMoFYo6Ai0JkYonyH/BxRONi2Mtsf4FyB4cG5ynX5xPcAWBFkFjWcoyIyCjky5r4AvUl6tmR0wADFWZdTVmweNFceNBm8errfnDewLYEC2zbMwDYWW83y4PP8rMEq9Rp6tMaZkeibyPCqZ2ya18hYzTI2+tyUlC9Q+3o+52XZ9mwXs1Ql3Gfmc5xLj0+aryiZC82sgtlN7JIMlfZ0AvC8wDVKcB/FfWytMLhd2ojTaqbUdtEOMNdgAwOKuUIqPRjv8RPlAYHRVlY/DcyQ1qGBDSXFnCM7Ldch+zoYIvRJ7A8WCtCfgc3MdfqslQQ0asgMrrNzMmUfTLHuEzhXYB6r2rLjCMExRfuG0xQ5ODg4ODg4OJBjig4/PI/I8yh7imQDUgNsVsgDbkRgeA0qDREYImR96KgFkaZmiBA5Qgtj98NkQlhlVzXa6vN7nVWmHXM5ytaRHiJA66WjIshMn2kQmCkYIGpXYzBDDe1iQNNRN+yT+43vQlaLjMARQYIdAQodvN5ueVxT88yJz3fU2weGCOyAbfOgZJXAEKXPkgUfrXaIPZDq2SoceULj0wZdhIm6SwnJCKH0BKJ9lOuw37ebk4OoHxl60OTgFZ9rx2oArAX6LpiUotK1ZeabDcR2cPYcnL3zYjFKDcj1ul6SnS2UkrRFe5oZJGQDtZsN4zyFJs33vpIuw8BlUFhTFBg3Dcn3G03R5ILmBq6dr9Tbg0zAen+GEZ98lNZy8r6x1wZmhPx5aoF5xTkpZcHa8X2gNG0wccXy0MxZvy9mQMD4RsbQbqWVUZmHYMXyfP/huZLuV+upU6QZajxXkJ3ZuoVNKhfqOiDybXhSXivNdurnEhiiyKjsK2V2qobWMdcl2Un9XLXu6px9Z0t5TNML6YxUnHOcU2QKIiPOGlN6uK/MezBEQM1r1IYdCVRqNaocZNL5wa7/RoYbFDk4ODg4OLxJUKWGggmvaxvHK9yg6DBj+J1zaSKaoJadJtodO8mEWjZA4H8Q2WE+Wkcn0ATY4ptMQtQzlHjkjuidmaO6NT13Y0SkHPFahikvs0SAeu0g848uhImIEMxMbBhW1hw1oVkqItLeHmiH9h/RNv8A9CTW3l+5OYO5yswy5xsMkXbeRXFGaDvAECHay/ZyhMmRJNiO0rSkQTgqowRE9/M50YZSnwkVs30m7AeTo/VX1h8H7JqHfckFoanRdeUSQzJqrvsb8bXTLt8xyZZZt2H1xGvIssG5Uh4v6QEZPYIhAlq3NdcoaXlCdHdWLhdWLAx0MBVokbjv7TYNq7FPkz+H1CrWEHXJFENoq/LMwCHjULMYtj5fvtrAuKT7TdvQP6G3mpoNt27zXmvYStzfoT9Df9TaHTBFyO7SzIzuQ3iO6HpyOivTwi4nPwYTimtdbMFxqNUbihvL91MDIbEcNEFwoI/tZTdxpT2EJgkMlmaEwqPmAViOmxMFhqigCkAndlfF59bHKY3jMQ1OQ7PIq08uqp+vatj8H9/O/UoRwNp/rH4fMWM7QU1RjnpUPn4Jl2MWblDk4ODg4ODwJkHlEGSfHez6b2S4QdFhRi1k2IXJhSaiaWHH1DRHkogg60U3WePSjWjXbEd7XwTYl8MWH2U2oJ49IiPGii0kCZ8h8zkiJ53ZBJZiao7KCmPfk+he3q6SCoAxQvYK2IAMMy6ZXln4FX4vlilS26uqgrJgiDRjlW/n87ud3YrjcMzljCtoDeLw8DHrwW9F+64AYIhsdg48c6ZptbTOYnwJMxJ8SlEAVXujgJ2DnkJH2QXlRI1zq/tC6zZpEgPtEKJxQDNA2mkXLFrrNhMKTy40dGWDZxWid9ajDZ8t2ZPwiOwDgHZT1s/V+KBcAZqM9ICkMXCPgG2MD8njD+6VdEp2fhu3m9kPZhXBwOFesYVoVd0+sEPliL+hxhmYIayjj9EyrX55f2pPJvS72F5mjJiFQ80+XXgWfQX9E8wy2gdmVGeH6oxBzIPommmaBUSWm92OYg9xXzQ+p5RmkRmrlh2yNlm+C0V45fKJ7WbDXoUd8YMo9MwnhLUtDftV2bNxZlGtfxFIQV4PTD76SIL9nHa8q34P4ZpBbwUfr0JS7IqyS6VorvM3kvafrkc8GqjUrDn3QW3jQPD444/TN77xDVq9ejUNDg7SfffdR+973/vEMuvWraP/9b/+Fz322GNUrVbp1FNPpf/zf/4PDQwMEJEp+L5ixQq65557KJfL0Tvf+U764Q9/SHPm1DWa4+PjdN1119Evf/lLIiK64oor6LbbbqO2trb9bqsbFB1mJHaWqRIu22meYNrcWe3rueI4//DhgYzU0ICcQbA/TKBk9Q8ojOfKLL4F5T+TkM+mxuOFH9plf/PUWwyGtBlddK98QMz0Q4gfXFtWgH+3bLkM3ixoc7S/dbvcIAY3gJ5awoDAllngqaiiMjDE/nEekS4LYbXdviraWo6Jt1TzE8XZiA7lJfSPfWq+LKYbHpeicJR8CKofkIbSBaOmsbbwK2YCZsuLhR8s+8Or+oAVpSubBQyGgPZ1shNOnGQOXpdQ6V6tRbDcl/hcoS/rASd+kDA1Eh6WKtj0CbJ8gP2B5R/CkCrw6s8qB0Gsl9ciXnO+MKiyU0s1tFMO1IFKyLPnDtPTEJnb/o0fzjE5FYdBkTX4K3HZj6IckGVmsTUHH0psSCYGYJpLi9Ux+MG5t4kKmJEt4nu5ni6zgUQIvGJqsqymDWGQCWDaH+cMfRftQWAAYDAU2T3Fr9gQn49203kwGAKqYX6+hdX0O7/HecDgE32loTgrnndRTJ9zsNonfxLbX6r/rweYGAhHxs3r1KXmoZJ4UjrhagPN1m0cqIU9iqjB2/GKTCZDy5Yto4997GOi1ijw6quv0tve9jb6xCc+QTfddBMlk0lat24dRSL1gOizn/0s3X///XTvvfdSZ2cnff7zn6fLL7+cVq9eTX6/6Rcf/vCHaefOnfTggw8SEdGnPvUp+shHPkL333//frfVDYocHBwcHBzeJDgaQuvLLruMLrvsshm/v+GGG+g973kP3XrrrfazhQsX2v8nJydp5cqVdPfdd9O73vUuIiJatWoVzZ07lx555BG69NJLad26dfTggw/SM888Q+eeey4REf34xz+m5cuX0/r16+mkk07ar7a6QdERAlJ3EZlUQ81Tz6N7ZeHIhjId/D7HgszWrTxdpNKzbUq6EqXq1FabRsofW4YIUz9c7BTRECJAMD62KCj2C2EoR2rZHkWzF+T32L+eykF7UIoC5Q80M4SyAHZqSLEisUFzfoJs85/tMdvTZT0wTTl+maE1Ek+YkFpPc2gLg+nRY8tOZjz2mo3tfruZk/MrAiPTJxvZysVCZzIfBPQUAVLQwSBB8KmjYvQpAAwV2AtMW1lhMmN8iaQVwPSAJQlk5UnHtcG1RCkVzXg1TNVyO3a9y+Sbt27l6URmbOpFUyUbEFTi+krMzL3mF7Ty/s1xFVvlYy41T07hYjqjXqpCsp7Tpx3BmOD+9Mq4z3BsOGY5HYwyFdZk0Q9mGOUwuA+oaSy0DQxRgPsp9o/nSBHsILdVTx8htT4yat6HwKjkJWOF+xHCZF0w1SZSMGYyKozt5TR1Ps7QlOxbgUl1U6g+4Z8yVFiRS7BYQXhMPufAsgK2OLNKGQdTPxPAqMf3mHZm+hp/GrVUAMwQ0JFgSvRS8zr+e0MRgSkFQwT4Ko2M/5FAlTyq6Afl69jGoUK1WqX/+3//L33hC1+gSy+9lP7whz/QggUL6Itf/KKdYlu9ejWVSiW65JJL7Hr9/f102mmn0VNPPUWXXnopPf3005RMJu2AiIjovPPOo2QySU899dR+D4qceaODg4ODg4PDASOVSom/QqHw2ispDA8PUzqdpq9//ev07ne/mx566CF6//vfT1deeSU99thjREQ0NDREoVCI2tvbxbq9vb00NDRkl+np6WnYfk9Pj11mf+CYosOM4FSZgoWyLTIJoJAhIr6ZUmdtKj1Dp3+m5sO4ToldJxWjwZGoLeyKQouKVWgoMwJNkI72WZMUHTGhTo7bBZ0KIkREphCCavNGRNhlpD0Xmy8HgXg9ojWfawsBjVKLMoGz55cjbJ7+z50n9Sz1oqYGiZ1mveSr8qYPbd1r/6/0tonvOtj0b3KhvM1gqGffq3RjMDalhOkzFWYXdVCZb5MXxZZK4M1BZK7Fv4ltsiaCr2hWgKlhtt/QaLEhLQIyL2A7tR2E1uCgz4C9gP6mylYBLTtMZxs9Vd4bo6exxorTv+N7ZFo1XqOjLLTeYY4n3ysF2RCKo8zJxEKz3WKbPCybzGDPGwu5WbQMpq0S8eqFkJm1ghA6NAnNHx+rYoDRT+vMrNxHhQXE0LBhO7hfwbBaRheicGiYlM4r1y23A6YX7dcMEVg4a0rJTFJkHEWHzRfpft3nsH2zPBgiILFpwhxfq+lTMND0KtJIsxZRxVZbJC2T6zZ9xD6/YF2izrNmkmdKJtDQWsXpukCwbriPoO+K/YbLxOD+ukwyRB0vN3824VmW6fUolD3youtqzfwd7DaIiObOnSs+/8d//Ee68cYbD2xbVXOe/uzP/ow+97nPERHRGWecQU899RTdcccd9I53vGPGdWu1Gnle/RxO/3+mZV4LblDk4ODg4ODwJkHlEEyfYf0dO3ZQa2s9ISIcDs+0yozo6uqiQCBAp5xyivh8yZIl9Lvf/Y6IiPr6+qhYLNL4+Lhgi4aHh+n888+3y+zZo0ofENHevXupt7d3v9vjBkWHGaVkkIrhYD0bgqFNCpGVUlP2+zr6bjDWi8kU3PpyKkJS90CgUG36OVJeAUTP2J5dXo28QynZMGT26MgRWSfAxAkyIsS8vTUyZMT2yO0HmVGqF5JsrsHSGVaIMAGYNfpfkfqZ+G7xtiETK7CdGaKAnyp95iatROXtpBkim87PbWrfwOnGZVXkMiGZk7qWho3vuKgobAQAHGtyq1muZbPUPHhbjFNg5UST4gqGKDtHHrvW/OjnJ9iGfDsyHc3nUZVhFJowTFBqvnlQ4pri+FPzzXFGRs32puZxX1YGgWANky9PmOVONA/h8Lhpf5nPV2ynquFA8rhg5Ed8baGH8xQhBpYA+hkwU4FCzVpBgKFBgVPbr/glPCGZGNzHOHcw+gQDhPXB1CIbUltBaONArTMDk9Sy3TQQz51iCwpOy2wsvIbYFqLA11QzKulZzGS1yONu2SmveXSX+cLjvgWGyMKPPoB6PKw9nCPTOvXzUT+noKMDUHhaZyRqk9iGLDT9vFC/iIF8za4DjZm+76xm764uIiLqKrCeKth84GHtR4KNWb5HAodyUNTa2ioGRa8HoVCI3vrWt9L69evF5xs2bKB58+YREdFZZ51FwWCQHn74YbrqqquIiGhwcJDWrl1rxdnLly+nyclJ+v3vf0/nnHMOERE9++yzNDk5aQdO+wM3KHJwcHBwcHA4bEin07Rp0yb7fsuWLbRmzRrq6OiggYEB+vu//3v64Ac/SG9/+9vp4osvpgcffJDuv/9++u1vf0tERMlkkj7xiU/Q5z//eers7KSOjg5asWIFLV261GajLVmyhN797nfTNddcQz/60Y+IyKTkX3755fstsiZyg6LDjlLUo1LEsyaJuqAkoDOUkD1RVXPmOntMa28QcWptUmOhVvWeIzjNpMAkEqhnS0jfJBgNAoiA0v3m87ZN8gBh1qbbobMx4Imjj1szaoAtTMsReTUqtVNag+RVZQOsKSWzwPBjscwQY3pRV5QoSOw0YXyhzRxbdES2GZlw0JNohgiFTT2O5vNcZBSRZZl1F40Gl+Z1euFSIqJSK5svvrDZbNdjxmPjTiIiypy/iJpBX3NonAodZsf6Wic3m+MOpJEJGeL1TGjdvo6N8XzyXMP3CEC2GkpZxFivFts6Jff37E7xvtrdJt5XYqzFUvdOQ6YkmH5+xfUCKwFWKGiNVatUiiLNTG4DDE90RLF+MZXFhazOMU1Z8GZhHFmVGjp8bhmnGUrF2M+V7xiy22xpmIDU5IBpgX4GzBD8gOA5NZOBYecaSe/lZxv2QD+/gindRwOiXWXlPwSmqxpSfmSqLAhYVGgZ9XmBFqqsfJqg+2nwS+PzkOv2rMEsMgGDKkPQPrtr8lrhGTw1INtaUl5nRxrVmkfVg6xEe6DrP/fcc3TxxRfb99dffz0REV199dV055130vvf/36644476JZbbqHrrruOTjrpJPr3f/93etvb3mbX+c53vkOBQICuuuoqa9545513Wo8iIqKf//zndN1119kstSuuuIJuv/32A2qrGxQ5ODg4ODi8SXAop8/2FxdddBHVlE2Cxsc//nH6+Mc/PuP3kUiEbrvtNrrttttmXKajo4NWrVp1QG3TcIOiw4xyxKNy1KNiiznViGDCk8o7Rs0tIxq3Wh0PXiraq6a5hgZRLrJHtE6kHFXMjuoJ2I/vNSoWIrILqYwdncWG7BG7Pz2XzodhfUYYQcXsNBaOrDZtJ+z66yUcZOhoi6jukM0IZaRPVJxZimpPGxERjZ2e5HZWbHvQRjBEWg8BhgilRwBociLsT4T3GsjwS8/m76FxYJ1JSOlMAmlVHLfbpNJNnGk0D4iqh94qL/qsZ5H1xu3ay+U+FkldSIy9XAI5ZtHSqszGOKdz8UOwEjd0Smau3A7OIVANqCy9PeYAfeNKZBSSVJlvSKbz+SJM3/QZcWVqHut3lAs09DnWG0jpbOC5FeTDgZaLqH4f6zIWuI/AtuVnB8S+0a/A6IBlQCFYtEn7DKHsBphUj5ez/kjISgvK5wRKy0RY4wQm2LavQ/apqblSZwVfpO41rA8b4HI6nDkYyJgFix3y2mqGCGWGam3yxtf3rdYETZzAurNxaLOI36vC1YoZ0v5FJZa9gOnSmWHRvXwc/HxBGaZCO1GhnfUzm2VbtRu49VbrkJmE2T7ZNhSQJmoyQ+Bw1OEGRYcZ0fEqFbJVmpojK3Hj4YWq0RBz4qFVr+ou6W8IA/VUAB7OmHLA4MivUvq1+Rke4vWbs/lgCIM0bdKGh1NRlcOoG++Jj+1gCPuL8jQC6n8VW1AOBbWO1HbVYA20OwZBoNXDXP0agyFdswo/QpiqwWAI1wMYP80MgqxpJT9U8fAMpSpWnA5G2doo8A+hpv4x+ME1w8ASD+Zcd4C3LdvStkn+MNWnWlisyj/aET4XLS8bPwQMhoCd/49ZrvMFmUatp5vGTpE8f4squRIcM+JzryR/yJBmDVTi5ngiY2b9gvphxA90x0tSKB3YPSreU0yJdrPSWoB4MJRbZAaBoUlzPuODZn/5djmwAKYGpJVBkJuB65nrqN+TdtCBxAnYFKDsB9+/tio7j+ds5XSMv2DC2ikF2HY6DoMSVIufwVHO1kIryedKtpeNNPk+w7kIps3ryOkxbjevz30pwbUZMzx91rpZ3sDtG02nxpRqvttsANPVfi5f4injzZpPDfghD8DHHPT5eXkMhnDcOE9WLM/IdcjtagF2hme5+56WfTf2R5N0kD19NhE1Blv2uZCqB5TaWLNhgKlqG+a6+dC4SeFx8TXVvJnLMB1OVMhHlYO0KDwKnpNHDG5Q5ODg4ODg8CZB7RBoimpHYzR3hOAGRYcZ1YBH1YBH8UE2K4NAmIMORJSI+PycKm+ru8NGX6WWWnqdX1A9HkDkWEwGxHtb9kOVA0DkWk+3bm7SBoAhssJuNV2Ge0YzNJYpYvq/dQdqR3A0hlIXWI0j36oqi6BhC22iJAQErsw8pdljLMwzLdFRNe2hhNuIQCOq9EbVnj9E5vX2YCqj0Gb+AfNTU6VUNAuny1UgsoRRJ4rigiGy+4OxJp+s2DCnVfPUaeE8E6oWk/LYwBABqQH5GMD+YRlg26mqzzeU62CGaOrENtoXEpvldJhvt5lPKC+YRUTTGKKoNGOshrmSOk+nVWZ1iO8rEXkcmLKFkaFtJze7mOTj0Cn5aBefBxitmqQGJBaowsSyBqhlGqwgO4JX2Ra7HgurY3sh6paJDGCYwQZa48ySbI8tsjsOdqN5TN+6Td6wYCeBnufltfenmeXrMgdS85r36UoEBW0lo4M+WT+nVfE+PQtMDVsCqN9cMEQTi9RzkJ0yWnbK5yvsIfA5AIaIioZCiz23xbTvXSeYj/l5MT0xBawdTBs105jpkyxTgftVOa4E2XklOs8cnTIfDvuGGxQ5ODg4ODi8SXA0hNbHEtyg6HCjVjN/nkwVRTSOOXBERnXNgXkFEwPtD6IWiF0BH6d3Q0NTUgViyzGYxckIVOtSEA3Z5nM7QlZUKJfTZmiIVLGVEEfM0HNo8zmwIRAnJ3aZiLSkRMnQSOj1dYo92gvDQzBEdnnWBVSUtgmaJlsYkoukosSGZpKmH7ctsKr0T9ApaOM+q7PycGzyNqyo7UwuCIrlcc206H5sSXNdBFAv8aJtF+R2WrfKk1xMmv0H4UrAqfX5WZIeyfTzcpnmfUmL5sEQYXuBbexGG1Ki/IgUVmdPMYwSLAsSOwq8f+VVoAA2Nj5U5VfePu8u19nIEhDV07XLMa+eQKB+E1p2ynOIQsj4tJ6mjbYwo5mQDBGuDe4HaH6wPiw4rPkiSpNMoOC0YmZ5O0FmnvPdRrRUZ5bMcvHd5sB8yiZCM0TQDCFpAO0NsEYFhVeDljmSDJFPlRXxW8E5GF7Ww+2SFIo2sQ1IL1XLwKGPh5nhje6Udg5Wl8av42d3i68n3i51al2/ijQ840rqHIMZst+zqBvMUGSYj4HtAIJSOnfEUan5qDKTSG2/t3GIGvMGhCsI6+Dg4ODg4OBAjik67Kj5PKr5PPIXYfvPn3vyVWdJQaMDFsKz2SFmA35Oh64gtd6WA0DxSo7copIhArC9XIc0qNMRGSJbZLvZjCmdhTYmi49q1gTReJkDtfgQa3JU+vrUXNaNlCXb0FDeQKfgq8gd7UhugvYHC8rlLHMGbQRrujTTpsuTlFqm3TqebBOi74YSLWAB/fJ7bZjZAPV1PTLlDEaW6OiU84qyPeh82XQqnOPkFrYKUMUwwQwBOLfpxe28f7M8WD0wRGAN8JoY5FIP3Bfi25m6QokYZeZoGSKlJUJpiHyPyZgCQwSMn8zLKzYF7ENiF3Q6upyK0s3xdSkpg7/pjFs4pZlJpRFC2R3F0umyO+j30LhpJhJmiTimyIRcH0xTrrt5qRL0xehwc8YFWZaRkaI4Dn+ejTqZGYJmrsg6uWAaWXNS24Q+HGFtI54/yC7DuUffsOVHEmDnauI49b2D5wmeG7jHbPapukfQp6cWm5ul9Y+GldzxZ7IGVuAd0s6h5VdSp1acVsEi+arp7+nZfP8wo5rcar4PDRsK6NUPmW2AIbL7Yoao0M7v04336JFAlTyqHiQfUtUP0uMIblDk4ODg4ODwJoHTFO0bblB0mOEvVClA9egS/h01ZobqGgLzPbLBbDYVR0Iw5EMkBwQmTaRX7DBaAcz1I1LU2SfWH0X5HOU5qwumklgONv4AmCy0D+VLtJYB3i6ANoeEr5E1o0PkpxggZNHYiIo3O7qUxAfxRZNivei/y4l+ROL188IZRYo9QAmO0FRZrFdsVbqfaeyOLsoJbYo+Zn9RnpOKLijNX4MVKytbHgDMEFCQwS0lXzXHNDXHtAMMEfQVUa6SkZ0nCzmOniobHBuS5wp9odCGV6PhgUYK7QZDFNkt6RJtskitUpNU7jAUjS+LVEzeT7ehTcC0oe/lurgPKw2QLgmjWQoLFeyWFUEVll2KonsrVGxV7JLapGVOsC+wg8yO4Zpq0z4wJQFm+7T5aZDvf6sBRD9m/6HUPHMtcO7xPRhPj2mw2B4uNQPtH2uEUJKlnJSdMt9p+gSyUYtxaQaJ+xjXBllsuF+xHJgfZHdpXyaca7QLDC6eQ7qcCqAZNvu5kpe98hloh0x7kgsnxPfptXwTGdsiqsR5ufU+yxDF/nszv/JK3LZan/EBy/ebSrGzHzPL49xlWF9WbJdtynfXqJA+8ozLodEUHb9MkdMUOTg4ODg4ODiQY4oOPzwZTdasDT/CW+XgHJJz8RWORsqdJvRKbJUhZiVuPoe/UUWV77DLKVdl7c5aVWUFwApAZ2HdmnPNWREA0baO1LTGQvtzWFZFhd6VoNzf1FzWREzwds8y/0T/QzJDWpOgGShovBAp2/XU+dN6G2wXEXslTJRrkct46tgCynjZMkA6yMU2g/VtE9UZmPC4yl5j9DxrwuzUiYb5aXthlF95gSkjZqh1mO/BEOWVGzCyYlDwUvcR6GnCzFSh2G/f45IB8vaY/Vdnm+hcM0TlBabuwcRicyLaNphUIssQMaAhsuupjERdKgewbtN4z9ca7s7wsimyrxJYzuhedbxq+9O9iaCpg+M0+rNmVqvqdoS2Da9gPqCZ8VQE3r7OnBvrdcVMSr6Hs8FYW4iyG2Atg+Om02TnmHMY285ZWDgkFICOmxu1EjSdrchu49ZPSGkRA4Xm5wjMqdUA8rnG/WOds5VWENfEPv9Qnqco2UDsB/5hMznma9Z24gSz/tvOeUl8/ruNshhyrY2ZuJLZf/9j+KZK0d9zdXcPtvbyGk+c1kZERIPvNNuY9ah5mFm3cj6Xhbny2d33SJDalA7wSMBoig5u+utg138jww2KHBwcHBwc3iSoHoIyH05o7fC6AUdrwDpHV5oXMkX2WDWu2AdoE0Ls+Ao5hWKAMGeP7BIbqaoItKaywzTjo5kc7A96knrD5FuwGsie0/5LM0FrBSo2slTtUD22ZRV0MRxBtzc/b/EhuSHtxtzg68QRrp569ysWouSvL4C2+pReBNcYeiztfoyMGugz4GeSUN43besMRTOxhBmhl6Xope1RjmiR6cS1wKp9UnSkrwWuaUK5/0KPps8N0L2aGSjOIvMNspERZ4/5xgw7kVo+T6xXVMzaxEmGzah7RHFxzrbm+50Jlslh0hCMUbwgGZ/IcI5f+YOq+X70DLMi+i68ver1uaYxHHytUfjUr7QhmiEC42rvK+sPJpeLqX6qGSJoB5ENCU1NaKKo1jNtj+8w16jYac5xgH2HwEj5uIZZqU1qiXCNsP3oXtYeocYamGwwssjexHmB2zovl++Qy4UnePmKzEINqGuFYsjwNcLy+rkAgBkCQucYlvLFvcbbKrWlzXzRzll3GZn+FduFbF2zg9hTG8lrbxPLDF5qtgVXfzBEwN634D/WTXWYcx7eIenziROIvMzxy7gcq3CDosOMXLefstOmZKIjoPJ5KmK3+aHzcuYmLSwwxSy9qvxRxvRYvsvcWBg8YSpgag5Po5Xk4ACmbhi84OGiC7jaHwKe2imqtOTGH3rerHqoa3GxFqJaYTWvV696LX9UgmzOVuHnCATo7a/w9ABPW2R61GBGtTusUpmRKqyraOM81wvx0j5R4B+Nmr8+EEQqd4jPFaYArHge5SK28lQAl2zAgz/KXobxV8w/Y+ea9GEMhuCY1rbWDIZ8u1XOr0r7BvCDhxIMKB2DKQuUPMHnWpwf4DIkE4vNxcBgyJeTI9m9l58g3tu+x0CJFl36JaSqjSPl2W4H4n8ULy5gCkb+ANbFueZ9fLf85Yy9aBTm1VnmHsNgaOoEM8hE0oC1TAjLKWSier/P8yZatsl0f0yVIvhBtXU9EE0MynODcxxKyXOanSXV3z6e7q3ysUeGTefz2HSxhrIzED7PMoMhPC+KnaYv4PlTFzLzYK8kEz2wIQxatFC6PIOdBO7zTJe01IDpIgY3KHpszWX53oHxqS47ZIOnmnwO4PuqSnEP/8Kom/eezYMpnh6j4fD0w6PIiPm88yXZZzJvW2yLYaPfTs2v8at5f8IC4wK69Q9zmp0KimxRWoKjPA5yQut9ww2KHBwcHBwc3iSoks/5FO0DblB0mFH1m7/oKBco3CTzqYt9LeI9ilgi9TWYlWaM4TETyWRmmeXsgB+vSFtmVkKbKCLyAhOjmRxEQ3UBKC+nBaP8as0o+XtdfqCBceHvQb83CLB5v5YmVyJlG8FypBLncifFFmZBWOgKhgjC18mF5kAmzzPKxo7H1DQkHyemkrSAFstBaI0ItRKqTw1gWbBVukhuMCPZga6nebppZNy8tpvpm1rY3Jbta4xgOTfXfB4ZZIaGGaJaSU0JwvwwaF4LJ88S35ei0igPolmwBmC4sr0y3C6ogrLbLzMH2PO8YpSUaFRPwWqGqG7AJ5cLTMnzFN8tNzw1X3oVgE3Uae6BrDk/oU1czwPTfHvM+c6cOVssj75aVKaOGT6NseE6QwSg3xSDSJjgaa5WsG6S0QCQSg9gijLHU556Wj08ITsTpsEqUTlPhQLQAX5ugOUAoeOp6V/0a1t2BGJ2LK+mmdGHUeAVfRxp54APBaf5/gXDBpNYazILdjLFpo9hMEZsGYCSNryfEO8fz0dc+2yvXA4MHs5j+0tc/ojvzWy/+TyxXbYbxZzRHpy/6etG9pp1+t69Q6wbnC8fZuVN/GxX44/w+LT/VbkSh6MPNyhycHBwcHB4k6BS86iio+HXsY3jFW5QdJiRGKxQOVyxc/cQPGIOPTUgL0Ga56sTW837YI7FuZwCXOQUfAiRER0j6sbcf80nIzydJo5IDFExoiBdKkIXotX3QkVNl1uhtSrLAV0IImgIOHUEHRltLrLF5wDaAWYN7QhNqpTcUznVtsWEkJH15sRlTVY4Bafk8WmGCLCmefz59ONGKnti0HwJfYMu2hvZzUxPni/ayLj4nkpcRmCpSWX3s04ltnVCLodivn4+R34ViraYi1kNKNG50hyVuA8SvyJqzysxPXRWQM/zqrAr9HH8igK3YO8ArfeK7JXvdcFYROsAGCJ9HJpdiO+UnbjWaZi2kbcafUlsL66TFtdD1GzeZyTRRuk59f4SGzb7gh4MgBYGrNlM5T2yfabfgtGxjAgzG5G98hgqEXNOYd5qGSJGdpbpkLivwDxB64T94rFQwXMCpqbQGEEQbosWm+91IkahjZfD8weL83FAJ6YLLkODpMuD4BqC2bWGoSibwufJWoMwE57uNx9ERnn/fHzWZJZPI55vYK56VjfXTAFW41SqUWqu+b/7kl3yHFTkNbDMEMC7gG5SF4Jt2V6hREE9aI4AKocg+6xyHE+fOfNGBwcHBwcHBwdyTNFhh1epiXl5GNChzAY0Cen5JhJKbOXPwUTU5Nw6jMMQ8YCBgSYAkR/WQzZaGdF0AenhzefuwWih6KgudeBTEZ4tW6CYIVsuIyU1FgD2iwgQrAgivfB4hdsjt4tIUetetPZn7zmSdUg8b8K15BazYGoeay0Us2QZtRnYYehr8sbZn6LDdYYISG4yFwfRfWgvmxMOKmqkS/r+T50iBStgywK9JgINrd1mvgjofG95DLl5beI9srR0BlRQ6Vpg74B0aWQqIlpPcDZXMCXFO6VW01kzs8zx4hyCddMFb8EQlVrwyn2Js+OSm832oaOrRMz2oWep9wEuYcGHUWKmZ/Q00yn7njDp2GCIgBGUM+G+ApYh1y0Wa2AL/VP1/5G9WUwExLK6P+mAWh+DzvQLjZq+U2NjSfQh3JflmOn3uR7u/7wdXe4iupfLdkSbx73oC/lWZJPJ7DJsFwaiOMdW88ddoKEsCa+HzEP7/IngecMFl5Fiz9sFS4fjKLDTRr2EDLLM+J7g98icRB/TZpNggKIjMgNUa7a02et0o87MEnOwmR3cQZhum9Nv+tfgC0w7+/mZW8CxmI/BEKH8DuAv1RqMRo8EqjUfVQ8y+6zqss8cHBwcHBwcjnW46bN9ww2KDjMqIR9Vwj4aOZ11JpwIk1rI2gH2pokOyQgtsUtFFUVsT2oZGnyC+BU+PNExNnmz5QWkiRo+r2t7mOHhRIqyMmer+yYhKuL3JbTPvCIjC68NJRRSsuEwk4TWAdkp/qJcLt+tjEgUo4Psn44/mOOHUSIYIqB1O7JdeL+2zAIaZF6g+QBS8yVLU0wSTbG2p+sFwwghugd8IxPcOG57QpavqCYMi6VNDVu2KfotqTQLaQ5Bo9LLRjM55Zj8vuUPQ+J9LW7ogMnTpMkj2Dy8+sryXIAhAsBaZvrYY4b7RHqubPbYW7g0xUazHDIQ2zbK4y2x3g0Zh1rPFhmThW/RF6EX2/wXHWL7Fur3wOpjYDiqiLgQs6WlaZdNs0hoCwC2DNvSRWojY2ZngXGmWpgh8Qol8VoNsrGljxnmHtn/cW7Co2Y70BpphgieWLb9YHyYMdn7FqmnAhMU2yOXx3HA0wnMV7YH+zXLTc01+0vswnMK2Zrw9/LE5zgOsCs+xdjieYbnhtVOsuYJujftm6RLtWiGCNutH4dp39TC+nqhnaafFwdMBwvsMe+H9piOBmYI/Qrn0M+veMbbNqln35FGlQ5eKH10j+DwwmmKHBwcHBwcHBzIMUWHHaNLfTQ2zfcETEvXC+a1MXsLEZUcyev3mJO3ZUOU8yuQ7TaRELLIdCYRIi09xVxQGiBdCFYDRUyhDYjtae6HgkgTkSw0FTr7A/oUMFVRROI1uR19vDpSRJZQsVWWP9E6l4ZyK3wdpga0fke+nc5ClBKqZMB/bzarFJWBTlqmoYz/2Sly32BmVLFavR4F+KIqvyKgwO7F8c1T8ossFxntNlqbqZMNo2L1G0UZjYdS5gt44wDI0AHAEAH5dizHfTphttv7pDTV0v5NOaUXg2YqNmTYkPSAYU/AEAGFDrTfvDa4sPN7HeXqkhFhxWpCU0XUqLEDU4vyEu3rmzOckRGzc+i2LEPEqLI3lcdaoir7HmVmS5ZPM8PhUcmuwb8MyLGjdEi1O20tmlijyF0L9ync1W2ZHt6v1QJmwAqyLxJc21nrGJlgJlY9twJw4vdk2Q7tezT9nBM1Mj71rNiafF+Bdojbr7RW+rk2NQ//ScfsQIZ1eOX6MVWG5cPaV1BsCzcRGjX0t4hydp/qN42tBol8uSP/E3xozBuPXz7FDYocHBwcHBzeJDg0ZT7coMjhdSK5sUa1SD3y1IwEtCxwetW+JohcdOaQdt0toZaZTn5RRIe/yMzLaHN/DGRdhNLNmaEKSisxQxVSUT4iSmiVoPPAPYTIsR4Rmi/yHfJ49JR3Tn2P/SBCtevpbDV1/GCe4oOqgKYndwgfmdgesyNdaBKutLHh+vGDGbLb1AyRytgoLVtIRI16E7iWA4EtrAFi7VKtg1NzuM3emHFJr8w12THoQyh8CiD7rdbXRc0AdqMSbB4BA9l+IxwZX6x0KigGiiKgPfK4Epv4C1u3ymw43c+f90sdGDD7t9L2F30ILCD6WklF7pqFrer3QfmqGSPNVgRy07QrzIjCSRnA/dO2yVx7sH2BXaNiuVrCnMNKa3PvpTL7kYWZcYH/ERDdyxlRikkCMwTg/oOvWXqANUHj7D90qqFUQpvNAnjOIGsU2WCxEakNQjaZreenGOiCzWojXo6zzlQmJJ5j9VqInniPe0M78zcUmFZ9tMGpXzFEDb5sYMSgqSphPSLNw3plsPO8a/4VjSrtEM4Jnl266HW+iyivyF+How83KDpCAN2c75APN21eiNRXVJ+21ab5ps13ynRPIDLGNHS7vGH9ukxGXpmmYVDG7YBpGr5HCjpucAggo4PmDi+yDT5M1zRgHof06mBaVl7X9Db2r1Nr9TSZHkjYEg9q4IEipEDHOjndEEjJ93vPMl4E+rq0bJfvUwt5cLWHKPnfg2bXahBUq/APCZeXqJ04T3yfmcM/RPxDg8FQdNuEWI54O5juIjWAK883v8ylFvNDGuKpGf/QuFiuOtcs55XM9qYWJ8X3MFvEYCD5qjk39dR4Mx2XniUF0hX5u2yvRWwXDALN+/iQPIdZFv9imgHTXwAEqpk5SpiuZte0SSbaO8m2CyGePYSgGrA9g08njgcDnugYftTYIHC81pB4gB/vKNfmbduk3E+5P5b7zcH5U+qGRJvD2khSTQtH1aC9N6TeQ5Suds+bwWCo3MoFWHlc7d8TFsu3blH9foec9sOAFM+HBtuFGqZ+ZTts4gIXtkZfw2DFipMb7msd9MCSpCaOr94eFt8P6PPJ2+PLgzR5TCtOLEEgYbYDu4jp05XBtJxq1GVlMnP0s1xds365vK/UOBA/EqiSR9WDrEp7sOu/keEGRQ4ODg4ODm8SuOmzfcMNig4zCu0e5WMeFWA6hkKsKD6pimSCrraMTkhGUjq9ODKGwoVsvDeu6GZrEmc+r08JSIG2NWOEWRtS8D35inTSEkd6jeUvzILhKWnGCMYIETC2B2FkZByRX3MBNEozADqSLsblceV4WgOiR2simZQ0w/hiRXMwbHmBXHOGqGPdtLIIU/KigCGymGtCxMy8hGi7ZsOCUyq8HucwtsWs5+WZnZtrGCOUZgBDBOR7TFhcPMGsl1xvqJKh5a1iObCQ8WHdNwxGTjedodhm3sN0EX0J8E3IZuvioBC9ZtXnVfX00X1bT41iei/K1xRTOAAYokyvLLWhmSU9pYqkgHqpDfMKg0YwArkuz7Yx1yO3gWlklNtIbGvOCBV7pK2CV5HzPuUYynlUeT/yJMHiQs8P6TI+9nOIf7mIaRoMEQuJO1/Ekmz1sbfM2+PnSEj9+DFLWZ+alO2AJQbudwvF4Pp5uy07uPxHTDJRejquPh2PB5NkpDSjBMasBIYIl4MPZ3yZOU5fjhnrLsOKFvp4u1vMioFsfdrVPrPb5KFVQ3rq0xz7+BnMAO9hg0/uV2Hj+UjJLWVKFo4CVeSwT7hBkYODg4ODw5sEh8a80TFFDq8TXtX8gW2EiBSRlk4f1qnviJb1nDuiepiygVFBNILUcwDfo9wHGJmwShcFCwBTs8iETk/mMiTQOvnRTm4fyo1AA9WG1FuxmcYUf35bYfkI0sFRNkFrB6DDgQaL4pIh0tqIkCo2mumBpkk2w6bkckSa7ZaRcNeLnK7OkWvbwxupVpC6JF97m9l2q7nY6ZPb1bGyOJX1VTBbDGzYKRcrc9TO5AIYImBwuTlZ0MygDIcW5Q6drxgi/hqaGVxLzdw0RsTmFbo2CJ2hOwO01qIiZSt140Cc67xcLsx9Lr5bbig0YsQcqZNlw6BPm6lkAvRhwSzaB7GwFPUCYB2LKDUBNqJQv+6tW+VKLZuVYlbpvorJkGgjWK3QuGQKcvPlRYCeCYB+C0yIZohwbXENcAyZAWZuS2Z7ia3YrmSIUEYIz4eqKhzbcK0125mRDapbbbBWh8scWVE/M0bQXELr4+F5FfOL48Rzxa8IlkCWi/x2mPPXst1ctLElQbF+ms+DL89sbb8U8fvWyarF+Z4q+XpUcd68vEb+iEqM2GbuS4jc47tJtAFaPZR8OtKo1jyqHqx540Gu/0bG8Tvcc3BwcHBwcHA4ADim6DAjkK5RsFKz2VllZkJ0NI0UeJgPgnHJt6tUUtY56CgbUW/7Ro4IYYqWlMxQkSM9lBGB5sdXkJEvNDxIrUUJCpuFhsAyD7ZBapisKRxHS4gE0c4GawKraZJaHm09YLUDSOnl7BVok/qeMScmOGF2nJttTjjMLu3+OOLWrAWAiHjWk4aGycw1EWTr45vEctMzzrykCcuzS2eLZXBsCK6gAwOQrl2r8TWAzmRu3/RDpVyXzDgCQwRMzTW3c57rymrDPiCmzAm1rg3sQkBpfDSjkultrhFCtA/mR6fY28wf4yTQUDJGa4nAEOVnmQ2FJsGIQavBrCGvhgK96Lvoc9C5ZbuV/QO/hd6vKJPybEmY6duy3200bfPlJFuYn63YuZAs9KzNSsts/BnfbTpioT3A+2Z9yomcSZeSbcM5s9lV6rmCrNT4oNkfnietO2QfrCh9lrb2wHFHxlX5obx8j2tS3w7S9OR9ivfIRvNK6APNLT5wjcBYYb/4HPsNpcxxTSw25xPPKdwT0UHWnS0xJy7+jGSGKlHZ/OksUTBYEa/FApswljjzb6fUJ0LPCOPJ+C6zz0y/uTHC4+VGg9YjgOohmD5z5o0ODg4ODg4OxzyqNR9VDzJ77GDXfyPDDYoOM8pxj0rTPEagBYBfBrQrujxFph9z++a9nUtXNiihSWgCzHudFQJ/EzAjKMyKLA+YqgXynAUSNREpNDcACtCCqamE0TBkgcisk0Iba3bAUCWQXUJiORsxcwCJ7DZEpoGCZIYQ/QP4PjYkGSIA5w37m5rDx8WfI3LXppM9/y1pmJZH1pnvS1ywk40UKVi/hfJLTJaZLbnSpQrDwjuKz3VoizG3qanyHeXTF4r32V4TWeYUw1FWUW1Z2vk0ZPBY7yXFAvhKsu9BK5TrrPvziP2ALeFTYBkiyDNwzrmvgikKatkNswZgRcHWhcckbQc/JWiwNBsBfdnUHGYLpqRmCMh1SqNAraGCN03LTlWEeJpHVusWScX40+YgwWBohijfxd5RKVnOBsdc5FIsxMcAfRTOjd3vNtOmsSWctbXNfK77APovNHt+eTtQfIjvO9wGYFxVMWKdKdj2qmSW0MdrAWSjaaZJ+prpTmc1gYohwnPFDwalJjWV0BhB84j7P9OH50tzo0T4SNnj+W8pdIuO8nOLmfXJi0xn9rZHybeQmcoRPtlRZox2yW2g2DfaOt3YlYho9DR5sQJ5P1V9R15XVCGPKgfpM3Sg6z/++OP0jW98g1avXk2Dg4N033330fve976my/7N3/wN/cu//At95zvfoc9+9rP280KhQCtWrKB77rmHcrkcvfOd76Qf/vCHNGfOHLvM+Pg4XXfddfTLX/6SiIiuuOIKuu2226itrW2/23r8DvccHBwcHBwcjjoymQwtW7aMbr/99n0u95//+Z/07LPPUn9/f8N3n/3sZ+m+++6je++9l373u99ROp2myy+/nCrTLFA+/OEP05o1a+jBBx+kBx98kNasWUMf+chHDqitjik6zAhmahSaFvXpEgrQc2gdg2YnwQQB0GHAfh9AdhicnBExgqGCXgMMCtpT6TAhZo4LyEZGlXZA+4AwwwRGCdG31iYgcrVMl7XP94v2ATbLjper+SRjptkQbfcPDZH+HvuPjpoPUIg2zYVnu1dLsYa3ZRcfEGsXStqil5cLhyl/qolU4N1kv1MZMijNAFTbDauQeYvUIGmXb+imcM0LbUqPwocM/xNAu3IXla6qzHIKOPTWPaOq/Mrfc2bP+OIQt4+Xx/GpoNGWYOFr27JDLo/iwdoDCqxmlQvC+lD0l13g8Wo9q2qSIQI0ywHGSrMHKOEC1NTTMLrXrGDj+2mLhwdNfyloZqiTN+JJ368y7iuV7QmGaGpAmilNnmA+737evIIhAuqFTHEs8j3YMrBrgPb3wn0RTOtCyTLDEE7Rdj17P/rFe8BmpXn7ZhRwz4Bxwv1aCnFGIfzP8JyyWbucHdoj2T/dJ20xV1XiBZpK217OhsNxtv7UXMed/49HvhcN1QnFUCUaEPuMqnMf4eLVNvO2DDZM9vdCq48K+SPPSxyN6bPLLruMLrvssn0us2vXLvrMZz5Dv/rVr+hP//RPxXeTk5O0cuVKuvvuu+ld73oXERGtWrWK5s6dS4888ghdeumltG7dOnrwwQfpmWeeoXPPPZeIiH784x/T8uXLaf369XTSSSftV1vdoOgwoxLwxECooYaZHgzhZlYi15LUA9opjwybu+nBUWwYPxzmBYMkXY6jwtW48QOFh2kV02v8kPCUMHpqnnyIY6oG0wR2++F93zz63sIPo25nkac2arzb5KumXaFJNS2R45IYfqRVy4GKfrh3Pzsh3ns7BmX7ZhoMJeoXJJApidfM7GjTYygkpRAzOirPoa46jykGvR1dyqGFxbj4gYjzjz3MBxu214Jriy/MizZlxGAoNWB+UXQdO0w3wfxQ7wc/sHowBFhjUH9z8T2mSID4sOxbVvitfnf14AaDuAq/YsopwoPIYA5iX12JnX/U+AczOFmfu87PMTeuV8OPszw4nHtMB1sxuRImzzQYAsbeby5Sccz0ndAwC7C3yuVgXQHg2gF20KHGZLp8CITayS2cuq6uib5fQylzcf0581ro4L6ijtMGTVGI39XgjPsA9maDNl4MZrQ6YNBToGFtIZKQQRWmgvE8bF+v6gMWeYqWLRTmPFq0Az+I3WfqX/U2QZogEwDqK9T/1dfjSKBCBz791WwbRESplAwmw+EwhcPhxhVeA9VqlT7ykY/Q3//939Opp57a8P3q1aupVCrRJZdcYj/r7++n0047jZ566im69NJL6emnn6ZkMmkHRERE5513HiWTSXrqqaf2e1Dkps8cHBwcHBwcDhhz586lZDJp/2655ZbXtZ1/+qd/okAgQNddd13T74eGhigUClF7u/Rq6+3tpaGhIbtMT09Pw7o9PT12mf2BY4oOM0qtRMVpMzrhCfNaZPGpT6XQQowK2l1HJYgsNDOESAi0MiI8lA0JpZoXpLXW9RxRxfeodHE2Y0OEB2FkdETRzyyURFRdLwegWAR+CxGsTrnHFI5Ova9hyog3D/bEV+ZpiRF5IottQbEdRK7hMak89bbyNFlVCjtnAoq7Ums9zzw7SzJADaVbVJSuK44DEKtbQ0xQ+dtgGGn2DUYouhfFdsFKQCAty4g0TG3ALJGnFMAsYQoBmFgEw0Fqup3oCDNPbCQKg8w688P7UQyRTrkPM9sH24RCC8S2KupX03/arkKzrjjOmQTpo0vN9pKv8lTRkOy7tr0Z07dK7RGq8XeZfsXygXFRCQN2G3wtJxeymSB/PXGS6m/KKBAMEdD5ktxuEEyunpVT0/RWgKyYH0xx4r5EX7VmsHxfg7kJjZtzUebSMmCIAH0fomyJ3Z9isAB9bUuKwcJUMO4RFMC11iC2IDS/593Y6Xi88j3Rso0L3YINLJvjnDjRMMDJjYahK3SH7TPJShD4VU/Hoo36/teC61xX/VhLgYNjbF4PDuX02Y4dO6i1tT6F/HpYotWrV9P3vvc9ev7558l7jelWjVqtJtZptr5e5rXgmCIHBwcHB4c3CVAQ9mD/iIhaW1vF3+sZFD3xxBM0PDxMAwMDFAgEKBAI0LZt2+jzn/88zZ8/n4iI+vr6qFgs0vj4uFh3eHiYent77TJ79uzRm6e9e/faZfYHjik6QsDcd67LvELPAKM8MERWkMyRj6fM2TrWK/VuVTEqHCFmeuWlzcwyr3GWzMBYT6fsIuL162KcOWmWZsWDecUQMSZYlKuPr6G8B2/P6lkUUwNWwVeRhWRje+R5SM03BwSBdmKHibhLCdZcvSS1Qva8cbHV6qi5IF7ALG9LbPgVs3aiUbh6GXPixt/SZb9DtKsd8DXjseMKc64G/pPFuKyvQDowkOuQGiEwS/GhitoP67BiSpTO1xLMCIqZah1Gah4XE54Au8fMFmRpVtdhXrXRKFCzrKRZEZogf3NZloVmCWzKPPQjNilArqcLvYb5eWmNDPPy1bKUfD6Sr0o7BwCFeb0y21S01FW6YIjssXPTtf1BaKIi9gmGCEgtlDYE1dmSIepsNxer+ttu2bas7Pf2HHE31fcnGCIwzpWAvLZgUjVjiwQHsJGhCckAoTSNLeAMZjismejmjJXej30u8P1fYmsQ29eUvUKDNUlaJxV4Yrmg0hICEyea5waeP2CYphaYTpTr8lm9FowwAX0f4b7Bs9sW9Y5AdO9xW+rPbM2avhnxkY98xIqngUsvvZQ+8pGP0Mc+9jEiIjrrrLMoGAzSww8/TFdddRUREQ0ODtLatWvp1ltvJSKi5cuX0+TkJP3+97+nc845h4iInn32WZqcnKTzzz9/v9vjBkUODg4ODg5vEtTIo+pBCq1rB7h+Op2mTZvq1QC2bNlCa9asoY6ODhoYGKDOzk6xfDAYpL6+PiuOTiaT9IlPfII+//nPU2dnJ3V0dNCKFSto6dKldkC1ZMkSeve7303XXHMN/ehHPyIiok996lN0+eWX77fImsgNig47SgmTOYa0TcyBI20TjBGyJBDtI8Js2SEjQ7AKdm5eBRoBTsH1sXZoasBsr+U8mdOffdpEoDAca9lR4nagUKrUTtTNGeUO0/2mC1lGh5uLbDG0Lz4os8SqNqri1OGMLE+CbLesYrygJcpgvzCpK0LrZBoQeGmr+TzPVFiHFOhV+jrM92y+5/MrtiJtNAWVBcYvw2PNUWYAWiLzWo549lxp5kIX69x7oWlbrWKW3/Ze83mESw8kOHVdpw9Dg4CCr8hmBKOCdGp/wXwOTZG2LwCDotk7nbFjI2Bkl6sgG31UPxd15qHOZrNFiZnBgmGg1t80ZJPpDEV1XGA7oSsJ8astnpyQ5wsFOrXNRDAt77VKzFzQ9Oz6BdHsGEo4AGCIgHEuN1HoUMfQLdkpnzrm8n+Z+xOHHh2V9w+gWUlk/NnMP8XS6T4JBgZ9Dvdvyw5FIetrogvetstOq7NVwQbqvqR1dWCS0HcqiiGqG4M2ZyvRN23pF6TNc8FblNWYWGy0WjgfWsNky4kU6ucEOk9cA1hX4JmkLTCgebPHYmeWsHy1QeN1JDB9+utgtnEgeO655+jiiy+276+//noiIrr66qvpzjvv3K9tfOc736FAIEBXXXWVNW+88847yT+Nzf/5z39O1113nc1Su+KKK17TG0nDDYocHBwcHBwcDhsuuugiqr1GEst0bN26teGzSCRCt912G912220zrtfR0UGrVq16PU20cIOiw4zoXqL8NM8hMBulRPOIL6D8ggD4/YBJwXswRmBypgZkxAaGaGJNN2/ffN71ooyKa9ZbA2U2sD/+nkNZy2hBe4SyHyEZcbawORpYDKCepcPMUx7mcDKELbKpG77PzFLlPSbMa+tmw+jUmOkJbOJsMtYGwXGtuKiX989MmM3OMfRJZNCcGP+4uViTFy8W+0NmVr3Arvk8PeDVM3MmzSsYmNQixUTsMaxBy1bzPjNbrgcGxzIffInAEAEwyLRFMVFiok1eM82oaFYBzJH1VWHfIs14wfAS0L5J0DiFxyUtke+UfXF6uYzp7QSgY4vmFdtyEmuT+LxotiGYln1QM0DIOAI7C/YhvlMJU9RDe2pAikanF8Bt3SH9hwDNbGqGCNmn4TUhfm/WH7nUHHPyd+aiaN8h3H+aWWnwwGHYQsdgIspyeTBElvVjWFPEXtO+xHbp44PCtdqXSZ+HhsxKlAVRlBjeZ3ulaWxFMVgA2BhkKIJKQ8YX/NlQDig+WOL9mOUmTjAPBLA2ug+i3Mf0Pg7mFHoj3B/5Dvks0L5i2DYYI3gk4Vz5yq+ttzscqNY8quqHwevYxvEKNyhycHBwcHB4k6BCPqocZOL5wa7/RsYxPSi68cYb6aabbhKfTTdzaobHHnuMrr/+enrppZeov7+fvvCFL9CnP/3ppsvee++99KEPfYj+7M/+jP7zP//zdbXRn69RwKvVvV6U+6ktzGrLcWAuXEa/Ae0QW0JExYUQmRHJ/JlxGI2HjbACDBHQvYbn1pn5qLH3DfxI7PaZSUFWTcPcPiHCQ/vlt9EtMgSr+5/I5ZDVhiKfOO50vzxRyJyChiP55DbVHM686jbaIY+daavtMd6PuQAFLmeCciiIYEuLjD/J1JwW3g+J9hRbTPuQWYJoLzg57VgQ9fGpiu/ic9Rn3oMhAuI7zSsygxBxBpQ2ocCu3IjytVsw9CNwJU/Nbx5lawTT8j3aj9fgDJk/oZSiJ9Rbjz1fonsME5Prk147YAeQEYSgEywi7gEwRED+AtPgyjZzrTpf4Hby+apn9TCbwMwZGCKgdb104a1GJOsxelqc15fHBZaHqO4VBaCki71fcE3krqjE7F1it1kOLETHr805ashcBKtQ0tdc9l+cU/RP6MrAnMzEKFnXZd48HOKBfI/y4JrBVwx9GJqqsrq/dHkRMFRVlemIa9ag91L3BNobZl0P2pFTPmyjp8qbABlkPu77+nmEdlkH8mkMGI5B64BQdqeMY0KWJj8bbJUAPke4RtUgUbW5bdNhhWOK9o1jelBERHTqqafSI488Yt/7VQr1dGzZsoXe85730DXXXEOrVq2iJ598kq699lrq7u6mD3zgA2LZbdu20YoVK+jCCy88bG13cHBwcHBweOPgmB8UBQIB6uvr269l77jjDhoYGKDvfve7RGRS+J577jn65je/KQZFlUqF/sf/+B9000030RNPPEETExMH3c6qyuRBJNng2wPbkZKM4LQPkPUzYtZg+NOSqpnMGm1C1x/leno7fqXlAZPiV8yRX2mckC1GnjkwZK9ZR1kVTflY91Lyo4AsaymWKW0CfEU4ktMRY/K/jd9QLSuPt3rSABHVfVJCe6a4fZzp1GZEBMiCQfsyfTKCje1F5E3iNaDkJ2C+gjmiQpI/0745E+a1fR0fAxgRvvbarTyovFZ0FItrrl2Dx06Rls1gLQLImlHaImQ8hpRuxa/c1XGNdJRumR7uAzgX2V5zjsGG2uyvOByq5XY1pjv9EtVrq9FJkiECCklPvCZ2y74cHzQnILI7Iz4vt0v2Y2KRfK8ZogZvMGosQgsmGMyuRkXuou5ADx8yZuN0wVjch1he1xLUXk7QbaEvpmfz/ZDSfUu95+cNGB48d6zTNV9TFFKuHwf8gJgd3C3px0rMMDXpeVwPUGmYslyVofNlqeWxTvRgVZTfkc76A4OlMw4B3NfJjbIvTM3XFab53uT9Fdo8y/6BgdUI4VFjXbP5fVV+HgDrNP3UHwWboir5qHqQ018Hu/4bGcf8oGjjxo3U399P4XCYzj33XPra175GCxcubLrs008/LQrKERmTqJUrV1KpVKJg0DxJvvzlL1N3dzd94hOfoCeeeOKg2hfM1ShYqzWkjkI8q1NsQQMDmCazUwxx83SanCcZsQjuTEZ4VYt4H9tt7lS/Mn/LzJM/NBByW/EtTBv5vRUV8kMVg6HYumEiIqr0GLVwlatcl+PmnOryItkepP6TOL7YpBRgJ9eOmy92Gk8DnJ1a0TwtfVyY1T/JQml8H+LpuLw53lx3XLQfP16J3bJMgi2iyjvCQxyDIvzYoEzL9GVt+u+InPrEMeL7sjzlFGYBZmK3VF0G0vI9ynwA+S7zg4MpEmujMFsuFx6T+2soT6AG5hhU6cKu9QE8pnp56rNFXksgy5XhERBEJuQ0gp2C7amnPxMR5ZT5bHWzOWGBPA8c9srtAeFx2beDY3LgXOyWP4Co0A5Exiv8KvePgUIgW7XXAIaaELfbDGVPvga5CRDfI9jR1wC2BOhf9ar1arpKle9psEWY0kVtdao5r9YwAJbvdVBEqmyIFkyHx83FwyAIyMwxgyGb+t6Cgax5j8GQ3c5E84QLAH0n36nOizqf7RukpUB4nfFhqMw1coIy2y1Eh0u8PfM+wM/bUqJeRgSDdVuCiQe4ya1qSlMNiNFW+4zTvgu1WoPdxJFApeZR5SCnvw52/TcyjulB0bnnnkt33XUXnXjiibRnzx66+eab6fzzz6eXXnqpwQyKyBSM03bfvb29VC6XaWRkhGbNmkVPPvkkrVy5ktasWbPf7SgUClQo1G9CXTnYwcHBwcHB4Y2PY3pQdNlll9n/ly5dSsuXL6dFixbRz372M2sOpaELw8E7wfM8mpqaor/6q7+iH//4x9TV1dVs9aa45ZZbGgTfQHCyQqF8hfwRRXurCA5RuTY9AzMEoHRCiFnq1ALeD38/9YIZDPpicju5HjO1kdhqomnLEKk0ZDttV4bAWEVqHA0hKgdDRMzc+HeOmOUWmsEnhOAzpWMHmeACRd2yQ4aw1TiX7+g0AurasNm+L9kqliOeTps8Zw4RNZbMAHVtU3D59Gs6H7Q7IjikydupLyYbICwHyzN9XQguIXSeOt/kunsbzTlv28Ap4sxMRHdn5TEHEc7zvnaNmmNaLAf0MD+0hSh5/62bmU3g6L7ElxpTEYhcEbVjP5himTHtG+1hgTT6QoBZhTozxIJT9XRB2nShVW4X7AhewyNqPVX4FceL8wumDPCVzPu957WJz/VyaCdYmtSAaX/r9oo4vunsC84lhPp6atJOfyNGUoQLpkTr07I8XatYtkyfPHkwb6xvR053AblOea50MdyWHWo6He1h5qmm2o8+pBmr+E5zA6CvaDPHQpe5b5EgMrlQHk9iJ4TM+2a29P3ZMGtjS8Jwu/Yo5okZImphRnnC3GvlmKGqwBDV+7i0KvAXag1MLxijbDfS/s0XgbK8f3RRXp8ucJzwUbVy5BkXJ7TeN47pQZFGPB6npUuX0saNG5t+39fX15CZNjw8TIFAgDo7O+mll16irVu30nvf+177fZWdjAOBAK1fv54WLVrUsN0vfvGLYhCWSqVo7ty5h+KQHBwcHBwcDhlqNZ+tcn8w2zhecVwNigqFAq1bt27GjLHly5fT/fffLz576KGH6Oyzz6ZgMEgnn3wy/fGPfxTff+lLX6KpqSn63ve+N+NAJxwOz1gd2COZDouITKfYapRjJjxBdK0BhgiMRe4ZwxDFlR7CCix5M+kFJmIKcjkQpJkGlPlakdPAsVyh3XSV+C5l/69QYoYIWiIIMINZsx+wGdq0DKZpOO7gpFnAv0UWcvU62sw/EXO+J083jB4i56k5UtRrI2EQUErzYbVErUoPoETTYIwCHCVCFJ3v8Br0DEDqDLPT6IvmnEPg3L5WTq+WW8yx+AqsDYJ2iFm8kYtNv2vZWeRj5GK7LDDGK7RAOGabAqzMICt+eQwoVwBYmwIIpGfoG4iEobGA4BmoMEsRGeXvFbln9wcBttIzQ8fVsla2D1omAH0Yr5NcHDjMpxmiXKuNYqZr93vN+W55kc+nISNppJ3ZWL6XOpgpgiEgUZ0NQGkVCKlxzm1ihUq51oJ9sAc1GP2x9i6uCh77VMJBvlOVv4GdgSQdLVMEhkgXIMU5wbmvlwkx2wdjAgE4GKL6Btj+QNku6CK/8d3qGipbCW2AOH6i2T/S2sEQWdFyFu01r7Fhs73EdnkCJt4+j4iI2v5g6MdCf1J871eJJyhbBBSSntWFoSCxNtbU2wDbBH0oRPS4X2wJkWKtIZHE4ejjmB4UrVixgt773vfSwMAADQ8P080330ypVIquvvpqIjIMzq5du+iuu+4iIqJPf/rTdPvtt9P1119P11xzDT399NO0cuVKuueee4jI2IifdtppYh9tbW1ERA2fOzg4ODg4HGuokEeVgywIe7Drv5FxTA+Kdu7cSR/60IdoZGSEuru76bzzzqNnnnmG5s0z0cHg4CBt377dLr9gwQJ64IEH6HOf+xz94Ac/oP7+fvr+97/f4FF0KFFo81M+UhcMzMQqYM4ekQ+Ka8I0UM+1d71oopPWTUZctOc8E4aDcQFqKmIF8wMg6ob2x6aaq+UioyaULCfMBmMvcTYYa36IX8EQAZlZ3H4/Mph4v8w+QK+hDQn9YE1YS1TuYkO9DpndApM6q19B8hg0QDaCRySt5vWZiUMBW5ynqiL+/CoCR9QXyNevmTXoRCbdhpBok86IAUMEjC+RAhWwWDDIRMo7stfAwGiGJcvSozAzHVpLZNupsmF0mrZmiKxWSGklkEYNJsqm0jNwbitK36KNDaNjcn8xZkvATCW2yXTqUpLPh2WIzGvb5hnsKzii3/E+ZeZ4mZlSn3jUWHv4+DKBEchwpmRkomqzzfQ5KOv+wudc2yqgYLFdT5ka6sKvYG7CbEaa6zYdXRtzgqmCpUWI7RJCfMpmKhwLgJG2liGsp+p8SXZ8X1E+F3KzZJ/FemDALBPFq6GPaY0SzmuB2TrodvD8wn2MJNuKOt8ouZGaZ9JCW3ayzo0zx7J/YnL//YroDmVknwspW4zIRM3qE2Mjmt3C/cBMJTNC2ijTluPh5W2GK3lU9B35aahq7eA1QTO4ahwXOKYHRffee+8+v29Wffcd73gHPf/88/u9j/2t4Ovg4ODg4OBwbOOYHhQdCwhNVSlcrFIpinIc0CBwwdOsjD7AEKXmScYF2VmItMAQAZ1rTYgI7xpoYjQzBb8hZJdphgjI9rCGaKgs1rMom889juhyJ5lITBfEbCgRkW7OPpSURgJ+SdQS4uNi3yEEqvx1lKM3eImg6CIiSWuOx5oLrR1CxO3jyB66EsusKTYDbEd1Gjtk98UsWHyYWTC+RGCnKogo+w3Vg0xC6Cly3ZJxQUYNWAAwPYG0/D7CjMbUADIDZZuRdQWNQ4avEbYHzyhtHhlUfdOycax1wjkvJcRi9jwAmjmyfk28XnxXc4YoPJLjV/P51EKzQniCMyhnyXsEpVkABMN1g07z2vqiedeiGCK7/5EZjEqp3n/hrQSU1TkgvkboX11r5Y1YU0wQoFkGsAvQEGl9qy2wzMC1qDEDEUrLwrU4J+jfYC20T1HbJvYdYjPUYEpSLJWoaY/OqEJGZEP7+L6BBgvaJjBEQIzvHfg0peZBY6UYHTaTRVmQoupjqXn79upCli2ujy0qHME9WPeDwndgu3GNoDGqMGuY2GWucSnB5ywjWbWpOWYFXMP4cIVihSNf56N6CITWB7v+GxluUOTg4ODg4PAmQZU8qh6kJuhg138jww2KjhAQmQRZwwOH63r2AbKZTJTR/oqJktNzDU3Q8Yy0Eij1t5m1EEl2SZEBohrMxUOr4KnIzmcddjn671S+SMzQJHaqyfiA7Dr+PGfybOOsNtYeFdrkchOL5c3Uvl5uNtdlvp9cKI8nyd479aKpcKI2nyPCtO0pyEg83yH3A80HWIxiB2uYQuw1UmJtVcqcj1w/3JjZT2kLOxl7RLE9cOEm0aaQKo2S5kgRjIuNbpFsBmYIDtCsGSJmq5BJpEs2QJ/V8Yr0fkHf0C7pukSCduK1GiHloaJZAM2y6eAxdaqJnLtmSQon8G+dYv329ZJaqqmSDmCIgHQ/XNL5GsEtughvLa/pcemyKlMPGIYoAkavIO8FYDqLogvB4v4Co4isx7aNkhmqaJ8ydYwl5TdkHbGz0gU8M0sdE7yjoNVT2jfruxOXDA0A5jS5VTIWmhnySqq8h9IOWtdv3r4+nnrpC8nQhKxzN7SMsm9qhshTfRnHj7JA1pdJa140w8aMEJjmut6O711m7PMdjT+RePYkRovcdj+vaz5HkdrJheyxxvdHAKeU+1muw0fh3JFnXJyj9b7hBkWHGbWAR9WA1yBihfkhSg0ghVWnvLetM0/batIIGitxTh9uUzXDlAmaNl3Ug53oSKXp59pELrFV0eYskKy2y1oVGAQB2S5Oa+apndSC5invQxfIdgcneFqRH/IRnjop8MNbp49D5GgNB/lFmzDaqSf8/vJycfZ2a+VBjg+CUPvDyKnIe8z2rOX/tKoqMK7z2NNKC5nRdjxHCjxA02JRtKVeYoTbAvErnzOkvgez8r02J9SDGABVvS3UW6Q32+WxHUwbKME0znF2jlyvt3/CLM8HjsEQ0LnGDJYKPVzGg0vQTNpaZLJyPAYnGAzZ9nGWtb8oxfz1OoLmVYtD/So9HvvBYCqkfqDLUa/h/tDWEjh3xSTX+FMCYwyGMFBFf0NiAYIiXa5jakG97MT0tuq2YxCla5bZY1DT2XowFB7jH3oug4Fp/vAeM/IstZlrUmyXQUu9TA7uIz4+7qNIBsB9r6frMBjCgBhCdm1FoM+/NqeMqqlPDJQxzabPG4xWMcVtp7x8sCKoUCEppQ6RUWkwWzfY5QCqo3nwANiaaNXG6VKHow83KHJwcHBwcHiTwGmK9g03KDrMCGTKFCyVrcgW0MUobVTKrENgyjA0mQE5dZBXUYhmB1AeQJsPJjfLkBZ0N0oc5DhC1WZi6bmSzgCtbLfDaeUllVrcwAypaAlReomjfDBErVuoKRA5I4K0r1aMzPR9DpEhpg3M94UOnp7kHt++FuyONDC0YmQWmms2B1NFOL+RiZplcMAM6XUy/UpsDlNBFn/aqJkX0+UxwITY9Zk9w7FZhiMshdtg6WxaOC+HVPMcMy5gosJcYLWojPdSXF85zj6aFdW3dLTbvdjM+/nu5pIz/HnyZZ5G4w/AEAF7zzRh/+RJpo91PcespJoGw3RZnSGS+9d9vwEgFdVzXTNDevpteumYPJNelt1jaLNG2yYWVuP+wf2nmQv089FTYZxpPg8o8bpOoIgoOwOfKjyrGSIr7lfPk0BWMce8Wr6PCyoH9/1jGE7JaXqIjjF9Z/eDqUp+3ugp05IVaDdn7WJ7ivxq3pfjAT4e+RzDdjGVjTIouAcaLDoSqqzRNOuFIpt3FtqkUaWGT/UBy3DyM2l6wWNf5shPQ1XpEJT5OI41RcfvcM/BwcHBwcHB4QDgmKLDjErA18ASETUa54XGZehXajfRSGjKhPmpeawlUmU/at4MqewwF0Sx0FYZAU0vWUBUL2xZtoUQeTNZKTxEai+i/IYUYV4fwmgIyhHpTSxmIzSO8GJ7mN2YIfCwhS+jaBeEmrA2aC4Shoi52GaWD4/J5SA2hvgWaekAIvZgjvczA/vgK9Ys84CI0JYs4M9xLorqnCOat1G8YoogAkfkWS/FINvgt2wAie3Y8h9Kv1HlZiQGq2J5y3TxsU6ewMtHOOV/gXnf9jKf46Rsb3SQU95/YfpGmqviJF+WvgboOzD2hNgXDBGQHpDXLCYrvlCErQd0AVtYG9j9tcn10PfAoqCPQxyM9kTGuLwH0r6nkbaaIcKyALQvYJdat5qdTSySHQnlY5JbJEOkURfZy7bb/anVGkTm+nuknjOjAusNMFqwDPBBtB9E6rvcEPokigLrRI5QqizaXVDCZTA5dUNDA7/ajrYuyPRzKZdJ1mJxaRbcc5p50oVyJ07E89G8tm2UTLTd7/TLNYOOy77n+8eP4t5aN8X9rDZavy+jWhh/BFA7BNlnteOYKXKDIgcHBwcHhzcJqrVDMH3mss8cXi+qEZ+wtLdZFZjzZyYk2ydFN3oOXmcMaY2Bjpb9nBobVHPmmiGy5QFUxr0uF6LTlBF54thwHIiY08wCaC1A+3qZgouCk9rU0bIoiiECEIXhOMFQ6dT7YIq1R9zTO9bJ9qAMAHQoSO3XkSl0K2iHTA3m6Br6JrVuvSCkbDuyxqBDacxkMq/QDuEYcO6hp7LMFBJnwCzxfqA3QVSNiBbtwvuR07mdEbPi8nNfISKip59ZQkR1hgjoZEPC6C6TildOms5UiZiGRvcalmD3xW1ivciE0q0NsJVADpl9fM1VH8/Mlu9btsnt4HiQGahNI8EQ6QwlXDdrRcDXAazI9Oc/DDnB/ADI9gTzOF1/REQ0slQyRFo3NsnZZWA6oF3TjJA2ykzsVPeNynLVpqxgiOJDFfW5vI8rIZ94xX2I73VpGZjBWoapKG0hgLK6NzSDZdlWD8fD7/3yPe4/ZLvhvsTzNTVPH7fcjrYu0M/F6efRPmuU3hLPLhzj+FJVPmaj1Dk2FJ321VlbhzcOXteg6Ne//jX9+te/puHhYapWZUf413/910PSMAcHBwcHB4dDC5d9tm8c8KDopptuoi9/+ct09tln06xZs8jzjl8a7VACEYz2E2qY6weDEm9+XsEQIXsCQOSkmRetLUC2GUzodCSKciJoJ7aLOXpEWnrOHpFVMG1CufaN5jXfwYaFQWRmNdf22IjQJ18tM6MynGaKMOO75YKRCWUGt3VKvA9PGjEVDAGxPzBDsSFJoRXa2aofx1+rl50Axk8K8TK8TptsKzLv4oOSLQSrhmMZOwnFgM17XCucizAfCiJYfB7fXRJthAcWIlrLfPArvKLe9da1op1P/H/LiKiue0Bx0GKrOQdgiIDsLJmVMzW7+YNT6zs81QeL7XwC2vl76z/EWh82tdT6upypNGP1Kzj/KCKq2VXdh9D3wWbUmT/z6ivXl7FtVVmkqQGz0cyAYWISW3HDyH2VWuX7mTRC2Hc5wX1kwrQtOoxjU8xnLzx1JNMKaIZInwMwL5bhQRLYXtnHddkf/TyohnS5D/ke51Yz1Lo8j/YlqvuVNdft4HhadpjvtVksMj5jyhRSS2SmM8H62YRzBEB7B4AhAvBMhsEktGnhyRqFckfeqMhNn+0bBzwouuOOO+jOO++kj3zkI4ejPQ4ODg4ODg4ORwUHPCgqFot0/vnnH462HJfwqjXyqrWGSBAotClfEBW5ac8VzOVPzTYj9dbtcFGGbgT+G6zd4UKSYBOy3cqdmIP7yKjUndT3z94a7HwdRZZNTbITAXaChbtzoV1lmaAI4qA5ESgf4tPahKp81ZEiNBJaK6Gdb8NcCgMRb2hS7qjYZRiiQtK0AwxAbE+Bj4Pbwdb9tgCmKsvi1YgCab64cMEd46wqRN0d8phQsBUMEFi9NDymUAIBSVsz+Or4LItn3gf5mCsqKrfuxsgs4q/3nAu2yyzwyO9PIyKinmfNAknecct2c058GUOfhcvmWk8sNVQOHHtHzmyuCwOig82jS13ANq9ZyJLUhYF5C02aVzBEAO4RMEp1N3P0Cdk+9HGtd8E9Y/VkxTqDAOdpna0EhsgeizTxbmA8Q3zsugQJsiaBli3NmVkADBFQaDfLJ3aiGLD5PJBVWqKwzCrTGqD4LmkCVYnJ/aAYqtY0TtdREjVqdnS5EZ1RiWuN8wW2xd7XftknwPToLNG2jZpilm89eTvTxCLORtsEFrdRH4aSSUPnmHPRsQ4bwf0FvRMznMo5HSyX2P8RhKt9tm8c8MTgJz/5Sfq3f/u3w9EWBwcHBwcHh8MITJ8d7N/xigNmivL5PP3Lv/wLPfLII3T66adTMCiH5t/+9rcPWeOOB9R8HtV8XoOWqBRvzhDVszzMe+ggbN2sNrl9aIYCXGgWTAbYgiAzRogEbcFGjmJatje34LWOt6o2WqaP6/t0m/eznuTJf+1XpNiK2LCkyiJjZr/wqgEaIkgViWvmrPv3RiRQjSnLbGis2kwICjanGkZRVuWBwwxRcJekLbJLDA0RyJgLkunX7a27EyP7afxkycZZjQv0GWOymC3qYSFTsMwLBjOqBpmq34RsNbj1+jnjB+dMO+vq51jfU+xRs8OEsmOnmjS39peYgqmBYuK3nFWWWmSWA0O090JzbqJtprMWi2a5yPMx2hegG4FHFjKr/Hn2ruEivb5Jv1jearWYgeteYxoIPU94Uu4HDBH0N9rdGYxRjL/PtzePFWveNC2NOpeTzDCEJhSTEla+PeOa8eHluFvlu9TyEzrrlPtMQDK4uS5envVTYIiAYEoypeW4bie3PyX7nFeR76NKk1eNmvsr12+MlOq1CLG+PB7c3w0O23z8yCYNq3a0bJfPj/RsSS1hPVvPDswR9GH8PrVI7heFfNH3iux8P3yOWa7rD/VlwRABi35hOtrmDySJqJGVwv2sHden39dh9btwJOA0RfvGAQ+KXnzxRTrjjDOIiGjtWinMdKLrRlRCHlXCHlWDUnCZ6VdTHBUsb17xcNNTJl1r5fSTnu7KdfMP0oQ0NdMpwG2vKuGkunR46CJtHCirlODhs+TDKbqXp+/U4EZvXw+GML2IAQJEtJjWwmCoLjDn6cD5RrEaGeanoa4Iv8eoG0fPMlM9sT0QgMv957vMiQ/uMu8xGALGTo6IdgaRZtvi2XOMKQA8aK2RHB7QbMA3cYK5qDhXGvah6cnpLvu9slmwA2qUOskrOwUWj4cgnufBji8v+07Pk3vN9oIoF2K2N3ViKx+fac8kl3DB8ejBUPBlVLLlDavnvh3c4IeQfzTyJ5jtVMbMtTjp1J1ivVc2mpz8yKBZsXOtKv2wtz7lQTRtelKJkXXf1GjZgYq+8vPMrPqIHNM5gJ7mDWTwysJoc2rtVB/sBiqqYkR8J4vj+T6z5qKzzKtX4ZR69NM+uX5wCgkPPKDkAV++S0YTDSVO1CAkulf+kgcHzQCgFuYpXh4MpefLUi3o8xgMYeAfY6F2yZaQ8cTx2XbxeURQFd9tOsuUKjfUcA3V8wWFmwEEDvZ4pJ8odb5kztfghWY9Oxiq1ZMvIiPmnPh5GhmDISCrTEMTg3KfsT1yYBdIlyhSUkpzh6OOAx4U/eY3vzkc7XBwcHBwcHA4zHBM0b5xUOaNO3fuJM/zaPbs2a+98JsUXlWmjSKCgSAQIryKmv3RpochxQhFxyBsluuBIbLvx+R7RHJ6WkpHWqkBVRiR2wfzRUBHzJjSgRAyytNmNsU4iqkQndrsE8tB5AqGSEd6mCryMytSajUNtBHmFhMKgiECpgbMBsFAgZWBwDR1Zp9oB6CF8nkWsvrzRBUOllGoFMA+rOFdBccKewbz3gqh+ZSDpauLafk9B+9WsD0hzRhxznFOrAFeCaJwPua9hj2rYSqRo/9SlzkQsJqpBbK0TPFCQ19WNhhXxFK/OQD/GmbrYDiIornq6QJWxLIJfByZc2Su/PyThoiIaOMLpk5IJWYW7HxObjDXiSliyRABOG4kAWDKCO+BBrGrej81t86y6OK06JctzyjGtlMKsS3TybOzJTWz6FdiXDBL6Id5nh4Dw4EyNrrPJXbJjprplw8WMMtgbNCHIjvlnKNXUCdTfV7ql54C+hyCIcr0ShNXlFIB9PNLi5JRzgNTwVq8r1PyJ+ejT5j3uD5grto2yKlULT+Y+xCXdpmWKJLYIRu1+x2GIYpwYeaCfMRQx/oyb1sZ5bL4PDxq7pvxk2NUy1WJ/puOKNygaN84YKF1tVqlL3/5y5RMJmnevHk0MDBAbW1t9JWvfKXByNHBwcHBwcHB4VjBATNFN9xwA61cuZK+/vWv0wUXXEC1Wo2efPJJuvHGGymfz9NXv/rVw9HOYxalmEelaaLCkpyCt4xR94smesh1mw80MwRTRKCYVBoBZgl0yq2OenVh2BKnjWvdRc8fZHSU75SmhYi86iLfmtg+GCJfWW632Cq7nC4wifR0RMgQluoU5NC4oSOQKg+GCPsvLuN0cTBivD1tvudXKfaIZDXL0VhGpX5NwfjYdXjRmaLaEKcVBzk6zXF6t2U8StLEEfAlUHrBU9uX5yg8aBgdaIO8kukTlYShajRDlO8Oc/uleHfsbebAInFzrqvrDD3h43YlnzPrlblPo3QCrh0Ez7immT7WIikdjW+rESfNfqsRymx/aZb4vmWDtJWASDe1SPWJSRm99v5esibRPVK/UY5r2whOzVdJAtPZodiwvL9Q8LTMDKhOTBg/VbKEsCWAlkYXEQbzo5lKsBL1Nsn3IdbaTZwgv2jdLp8bYMnwGpyU97k3MiE3HIbI0bzmFxh1u9bkQegMcT4YImDPcttyIiJqe7m5prLIprXxYclo2ZI2SrcKbREYIitc52ZDE4lSN/o5B3PWYnuIl4eerr7M0LlRsU5RkmQNxYFLCbMNJL1ojV+hU/kPHGHU6OBT6o+Ck8ARwwEPin72s5/RT37yE7riiivsZ8uWLaPZs2fTtdde6wZFDg4ODg4Ob1C46bN944AHRWNjY3TyySc3fH7yySfT2NjYIWnU8YgiRywoKAm0bZLRa9vj2+QCZc6WWjZPfFzP4jCAdqHBnC2iLOeVRT2ysYBgilmBkIl2wBAhwwqApkmzIaFURbSnwBEYQgudUo/Cs7Ai0BEysmBCGwZN+xf2EhHR5AkyetNZcrrgY/t6ExH705I9mDpBUnfQOiHizLfJBuU7pUaEqG4iaNsMg0ukzhfAfnFGXU4VjtwiNTUoqAoNDBicGGfi1PzM7rE2iDhLzEub7dQ4uq+0mddqGNtBurRpvDXuY3atxNdg7Hxzzj0WilTWS4YownoXnCMcP1g9m96dhuUAHxhOCwqushYDOpnhXxttYhvKeHByjy7gilT88HjzPoNMIs20aWZI3xuZHi5ky6nXtmwKl8YITZZsmRcwRBWkoDNLN3Eis2ExyRABOCZrCKlKj0xcYPpprSzb1vW4YoYLKitUlfNIDMr70K/6HBgi35h8IFX7jHjJN24uSmVWh/henzOdBQaWBPfH5MlV3t++s8G0hlLfd+hTYHr1fmF7gYxInHewsriHcJ1CKdMJwRABNmuO15+a65v2nXmFLgzGuTDEhbGkttLQWahoS2S8SpGjkJJ/NPD444/TN77xDVq9ejUNDg7SfffdR+973/uIiKhUKtGXvvQleuCBB2jz5s2UTCbpXe96F33961+n/v5+u41CoUArVqyge+65h3K5HL3zne+kH/7whzRnzhy7zPj4OF133XX0y1/+koiIrrjiCrrtttuora1tv9t6wJqiZcuW0e23397w+e23307Lli070M05ODg4ODg4HCEcDfPGTCYz49ghm83S888/T//wD/9Azz//PP3Hf/wHbdiwQcxGERF99rOfpfvuu4/uvfde+t3vfkfpdJouv/xyqlTqkceHP/xhWrNmDT344IP04IMP0po1aw64JNkBM0W33nor/emf/ik98sgjtHz5cvI8j5566inasWMHPfDAAwe6ueMelahH5ahn57RR6LXzRSMeCGwelCvU1GxtzKSpRDaa8Dy9TBqT1AvNmvUKbVJ/gcgH0Qn8SKJ7TKSI+W1kRIBVALSWxzJE+JzvDWhxwBQhYqy307wiooXfkGZ4oM9J7DL/gCECSknWNrRx5MfNCCoGDoVm7XYVQwR/FZSEgMZD3+vaADE8YV5hXkm1usZAZwLVWSep/YGXUiBrdl5kRgfsHzQzuGZtGw2dEBzlHTAzBM1QNWJOro+ZoEoMRXihj2D2gqNtr2pes93MEHF2WRXFNP/I55hLVIAJQqSsM43AEKHP4ZwhQw/+TVYXwuc6uoffQ08D4ovPT+c6syHoPJDpmO+R13bWE7I9wSlVqkYZdcKAM7aXDUSZIYKJ5BSXsohy6Rt45QT3pCnIbc4PSI+a3RdKJieQliwWzp028svMUwWiFUPUuoYzALmP4b7BJQjAg0pp5VDmB/0516NMR5kBTjJTVE1KxrQ4nzs4a3hKrAW0xqDMEPuVXmZqtrzvu57HuQdDA2bYHKfOltWlVtBnI+NKl9Mq75GKJI7t/Zlj76C+Z+T9n+uRuh4Yo+qs3Pg0ryH4DBVRGoi9oJJpZIGa5VAMVz878ay05Xm8Rq3ikcDRmD677LLL6LLLLmv6XTKZpIcfflh8dtttt9E555xD27dvp4GBAZqcnKSVK1fS3XffTe9617uIiGjVqlU0d+5ceuSRR+jSSy+ldevW0YMPPkjPPPMMnXvuuURE9OMf/5iWL19O69evp5NOOmm/2nrATNE73vEO2rBhA73//e+niYkJGhsboyuvvJLWr19PF1544YFuzsHBwcHBweEYRCqVEn+FwqExo5ycnCTP8+y01+rVq6lUKtEll1xil+nv76fTTjuNnnrqKSIievrppymZTNoBERHReeedR8lk0i6zP3hdPkX9/f1OUL2fCKZrFCrXqO1laaHqGxyVCzJDVOuUphep08ycvs68AWxECI8aZFNhe/D94cguNGY6rc6AyPVGuL3s6QI/EY4UYyPMEPEceI0jKu07gigJDBUiPa15yPTJ9cITSiOwy9AT1T6paRhbLLtsYrfKbmtp7jScWiwjYZRygP7H+i3xq/ZLQcZXHsVdUWBzmiYktrfadB0wNcj4SS0Aq8AZfbxr6CXADOEa+idYKxQyx45yG9gumKZKWKZ1wXsF1yDLbAGYG+snhEvN6+FKJNhQGgyP1dgMSs1OdJhZRtbXjJ4WEccFd2Z4PcGTB8yQ9i9CH9cMUdrYFlF4xOyndatk4FBKRxfERfRfUDoVMGVwW07Ng6ZI9kVokUontNmMorGTJTPUulm8pdRC+b4c5/uXu2EtyPdnq2QwPJ/cd93B3QCsHI5ZF6QNj0vmJctFhkdPN+9R2iX5R05n40zESqvpFJYAUFleYIRw7XTh2GKLf4b2qnuCNUm41hkuZBueYH8gXXyVnzt4vqHIqm0XGPGq7CMRJW/VjIzuC/Wiv8hCnZZxqJyodXFp60WlfImI94nSMFVd6Lhca3B7PxI4lEzR3Llzxef/+I//SDfeeONBbTufz9P//t//mz784Q9Ta6uhSIeGhigUClF7u/x97O3tpaGhIbtMT09Pw/Z6enrsMvuD/RoUvfjii3TaaaeRz+ejF198cZ/Lnn766fu98zcD2lePkC+QpWoLl4nQgyGeCqFWWT8DgyENPGQaDP7KNfU5bx7lQ1Dtngc/WvyHwRCA6TWIeout5uEZHpOjBX+rHFzplH9Uig9zKiwM93Q16/iQnFfA+QJy/ebXpGWnTvHXDxrzWsbgRlVGx2AI0xHFVlkvSz/UdYovgMFQi6oxZZaV1wCAcDo2bI4h0ysfosnNLAYfVypxTIPxYAiDD9gR2BRgmBVm5Q8JvoeAGD88GNTgBwHC6GyPTING+jcGMRgwt242J2FkmZl3KvMUBsTE5ShbBIxjqsN8HuBTDANClLTBYA3XAEaD2G90BMcjp2qtSZ56zuupEAx0yxFsjwMAHkThutjzWJB9h4hoz9my48b4WavF3i1b5fv0XD14wfswH5N5V1hsGpl8xjQSJo9+G4DLwVB0b/Mf6FyXuXij6nEcnuDEjbltvF2dmMF9LSgPSE9rTSlzV1zL5GYMUGVygTa9LCtbhnS/2V58SNYFtNPX1gJErqcHUX3PqhqLw6aPFjr5fPK9gGm8yKhZPsO11ErquUFUH2RrsTpg7yOYpqq+gGesPSbuXzX/wQ1MXi9qNY9qBzkowvo7duywAxcionD44OwGSqUS/eVf/iVVq1X64Q9/uB/tqInyYs1KjellXgv7NSg644wz7CjsjDPOIM/zqKa1L9yg6aInBwcHBwcHhzcOquQdtE8R1m9tbRWDooNBqVSiq666irZs2UKPPvqo2G5fXx8Vi0UaHx8XbNHw8DCdf/75dpk9e/Y0bHfv3r3U29u73+3Yr0HRli1bqLu72/7vcADI5Yj8HvmyuebfM0OUXShpwYCNOrjzqkFoJdy8U9fNCA1sKuoMer564UjQ4yaSBEMEIMopctV5REcQdtvl1H7CyixNm8nZcgPbxsXnuUWd4j2mUhCB4vh15XQAkV6+Qx5Hg3BapdMjLR3nEfsBIxcblttvBkSCto3MxET2yvn2xBbJjnk5VUvENoqZnxhPk4EhQtmKnIxQYQAa5KlVTLHiOYhSEEjJx2u9gGrziNaybSxyH/5LniYLmvaFh017wBAFciyqx6WF0BSHyacwMYSyCFLEC6YKTI6eOoZRIkwvMR1mherMcMWGMfUsVqd0P6YhzXuk3mOqGZ9PnyLW5V40K6BhC8DypdflPMAQWabyuYhYXu+nwMwmprMBPY0Ga4D5/5dtHDhSDg/LbIBqSBlYtkomzMfldELcVYOTXKB1wFBDYIjaNinBOBIXfM2fU2BRIZgGQ6RhxflqO3jOJLfKExWckO/Rd0P8eSDLTBg/3yAgD7E4vxKUD5Jwqn6ewRihLbbEUEKz47KtsWH5zIvsqTPB0ZJihd+kwIBo48aN9Jvf/IY6O+Xz/6yzzqJgMEgPP/wwXXXVVURENDg4SGvXrqVbb72ViIiWL19Ok5OT9Pvf/57OOeccIiJ69tlnaXJy0g6c9gf7NSiaN2+e/X/btm10/vnnUyAgVy2Xy/TUU0+JZR0cHBwcHBzeODga2WfpdJo2bdpk32/ZsoXWrFlDHR0d1N/fT3/+539Ozz//PP3Xf/0XVSoVqwHq6OigUChEyWSSPvGJT9DnP/956uzspI6ODlqxYgUtXbrUZqMtWbKE3v3ud9M111xDP/rRj4iI6FOf+hRdfvnl+515RvQ6hNYXX3wxDQ4ONgiaJicn6eKLL3bTZzMBdeH8MqrID7SJ9xDllcNKLAqBYkXO2QPQP2Du3xZQVGmhVcWogCECCh2SCQL8eXldkZaKwqw27dnzxOdasKzLesR3mgVKfYYqDQ5K6ibXxdE8WIOYFFxCB5Pv0JoFjownuFl8GupsiGyXTwktARixwSxO64VqPq8hjV8bVELjA8GuLtnicSkUryCZo1rMXAuU56jw+mDtNCsHsSuO3c8GfxV+D8Eo+lJVMUBYD8eKKBu2CdD8gO2IDqFPmvc4h0hHh+ki9g8mBn0L5wWRNiJw6ESg74D+C9cMTBL6AkT8EGyjj+MemJqDvsnb4S6mWZ56H+HtKR1MJexRfIj1TpxKjnMO5Lq0QFnuA3or+76LWbZJFKuV3wdhiImCsqyFCY9LyqrYIkVvc38tGZPIjgnxvtzBlhR8/5faufCqMhMMpuR2iu3mJHesM/svsKmr1c2oPolzjOeQPqeJ3fB5aM4E2e2oe0wfP5gwtA/PyUBGmtECKJSL5xGep5EJxXjNwHQREWVVKRP0Q5Q8gS0ImFgkJACpE1vIl/eI9i3TPeQ4lJqi/cVzzz1HF198sX1//fXXExHR1VdfTTfeeKM1WzzjjDPEer/5zW/ooosuIiKi73znOxQIBOiqq66y5o133nkn+af9pv785z+n6667zmapXXHFFU29kfaFAx4UzSRaGh0dpXg83mQNBwcHBwcHhzcrLrrooqY6ZGBf3wGRSIRuu+02uu2222ZcpqOjg1atWvW62gjs96DoyiuvJCIjpv7rv/5roTKvVCr04osvHtC83ZsOnGVWnmts9MsJE/rpAqlaK9SQyeDzmn5eVqJ/sAlYDgN7n2JCUMiyvp7aLvQcUWQsySw3QGeX5FlrBL2IZogQTeF4QrsmiIioMNDB7WT2BFlyvB4YohJrGWA8aM8HLwiGyB4Hswi2aCkyobLNU57B9jSUE1CFeqfrCZDZBlNAQDNDviJrhFCwtSi/B0ME+Kc4g6arzXzPDA4ydgAYUUJvos0gs73moGzxTr5kSGX3Khxt87UCO6cZoo5XzD/I+poaYLNHlsVZ00Mu6jmTLgSMFTQ7YKSg24EeB9lnBXP4FJ4Ak4MDl+0EY2RZGvW8hY4nsUuVY6jIBbXRok4vJ6prUXQZHF1QGCwXtjm5xKzXsoENOLn/hjJSS6izNK29Qp85eE9lOKKETHirzEkv90gxrLbksAWlM9KIEBpCtAf3Y0Edr7VTeA2mWt9vmiHC/u3X0DIyM21tJlR2XICZbDBGgSl58cCIpxaadD79PGrQaE17vuHZqbPFtM2I1hli+dig6YjZPpxz81qM+6joUw/yIwBX+2zf2O9BUTJpeN9arUYtLS0UjdYtREOhEJ133nl0zTXXHPoWOjg4ODg4OBwSHI3ps2MJ+z0o+ulPf0pERPPnz6e///u/pxiXn3DYP5QHusX79GwZAiKCbOhrKrDSWiJEhp7SBlUUAwRUFRMFDQEivXyHKvMBw70iMphkJIisODBFRWYnkPmTGjBdDHoSy+SMyUgue7I5P9AcgJnSGg0UeGxgiNBe+BQpQ0BdIFYXpPQrRgiaEV3GoKQ0JNMzW9s2G81AscUcc2RUHqM/zZlAnPHjy7DpYZwjSH61GqO8WT+7oE1sB8UyoeHR5yDLHjWI0q0OLS9ZsBBnBiJzCSwhGCJkQLVvNCe1xNekxMeXYU0FSjDU2RCZPYY+lWZtT6ETbCMzSts5Wywsjwf6Gms2yX2xxLP0KO2gTSJRHBTb0axhdETpZiw7Y97DB8oa/uHF59lzWve9MseW7VWsAbNUYKsqismNb5GFU1t3KBaK7ytfWbFripENqoLMsVf2yuXbzUnB8yEzYJ7buEY6sxD9G/dfeIyzUZk5wd60T5r2MQqqAq+aWS6B8Y3Jz9s2SeqrHJMeXNajiu/PgNI6+nNm/SpnaIKVBeKD5qZJ7OTtcbZdg75sGmtYVfpOGMTiWazZ9aAquTK61JxzsH7wOCtHiZwC942HA+buPvrRj9KuXbsaPt+4cSNt3br1ULTJwcHBwcHB4TCgdgiKwTqmaBr++q//mj7+8Y/T4sWLxefPPvss/eQnP6Hf/va3h6ptxwWqnUmqBuJWQwRorUqD/4nVSciITDtPB3LsrwGvmXBz7Y+OZhAZVWbwgEF7AoopGTvJRG5gD8AYhZRjNfYHhkhrpQqdhkqyfkAqAoZ2qB7tI3I27xO7WGugynpobZX9nIlN6F0aGDd1Xv0qCwfnF5qs6VqiyJg5WaGhKX7lL5hVs9oh9oDCqw5JwBDl+sHCmtd0nyzyq6PuoNJpQGuDEiYWKBvCDJP1Awo0Z+PAYoAhChRkpmFipzlusJ7IgMx2s0aINUbQb5VaJUNEPkTraA+/gixQWqEGTyBIo/g1yh5S1mFYeVjBKVtnGMKF3WYs5ZqzHP5irUFXovutzrbEOUS/m5qNTDyttdHlaiQjOZMPFxDbzaVglB4t1x8Vx2BLUvCr1RLlZKFW2442s2O4g+Ncg/XA5/p5phnWqvLyQfvBFOE5klpgOi+ef/b5wc8ny+qhzBBbw4RHmIVVWiN4egFaNxaYkg9eZKlB71aO+hr0YXBCBwPqU2wdnnV4NmldGPp1IN+YnXgkUKMG27vXtY3jFQfMFP3hD3+gCy64oOHz8847j9asWXMo2uTg4ODg4ODgcMRxwEyR53k0NTXV8Pnk5KTzKGoCX75EvkCREPVHNpo5/8ha6VkxecF88R5ReYAXiwwiA8mIZcAQlVT2GjRCqLOlWQAd1YAh0kyS9drhSGbsJLmfne9i741B1KEyn8f2aHZCvtUMjdWpJKQ3TFlJ1jSTpmueIaLUjI/WboWUw7b2bYJ2ohCTxwvNFLJ9UEOKqM4QWWjLCryHWy7fJ9WEtFjOzpYHnVE+KDg3mgHSLB/8UXQhV/gJ2UwhxQrU2QRuLkflyDILTpV5f6wR4nMLJmh8PrQWvEHefJD7RiDL57ZNsg5ay1Tj98gwxPdVlQWndWIa+S7eDmuNELm37JRsJtjFCLORyNqz30fQJz2K7TEnEW7aWCbCJQ1x7gGdlZnYrbIZpxRDyQxLcrd0wE8vkHYn+j4uJg3DEmXdWmaBzDYD8wGm0/ZnFG7m97ogtK4fZ7NYQXbq7DL4KvH+UHg1mIaGySxXikuGyTJXvP16bUJ2smd2s+1VdBaxWyp0S4YMfRWA1ghsLABfIwDtttvJVq2LuH52JHaw5q+Pbxiv+bMWWZ3AdFYtUDjynEuVPPIOUZmP4xEHPCi68MIL6ZZbbqF77rnHmiZVKhW65ZZb6G1ve9shb+CxjuruIap6EQrt2E1ERJWi+XX3dxsb81qnyeprfWWciIi8cfMDW0sblWl18YBZntOyY/xa6mkR+9GCaTzcbEori2P1IMm2006zsRkip5fjNb2YHypFORgCIC4EdYwf1HJMLoepFaChLAlEwKowq54mS+xSDz31cEGpi5ZtUuxcaFdlDVA0tUWKk2171I+aX005RXdMNjwMqxFOl2bDTv0grjHlj2m07Dzzqw1ROhCQv4uNxnK827CyCdAlH/SUgYdj5GOvqEFVps9co5Yd5j36UjUgC68ifXxsCQzw0DDePA9eILovtqkplizSwMXHVGyT7/G9nT7j04BpwnoRYPOaP8WcON9OM3UU4YLwMJPEeQvZe0ROISPVOtetRcBVe61RvgY/mBq4j7TgWJeZiW6dUCvywJT7EK5RYrsZARbYZBE/3rYIKffH7LxWsR0M8LQVB+5PCzWfguk89Eg7gFX3hx5E2OdHhwzKMBBuuL9UX0VRYVS5x4C2ZYccoIdSulyQtkRQ14Wn0ZCqrwdDGjjeUtRXt4jgBAY8U1LzpWFmdpY8FkznAlbcjrIygyWKFmco7XMY4bLP9o0DHhTdeuut9Pa3v51OOukkuvDCC4mI6IknnqBUKkWPPvroIW+gg4ODg4ODw6FBteaR53yKZsQBD4pOOeUUevHFF+n222+nF154gaLRKH30ox+lz3zmM9TR0XE42nhcoMYRgS/CZmhT7EjHhWK9hKHHwRABvvVbzT/9psovGKJSgtmGcnP61aYTM2AxDwMxMDozFmzkAGbsLBORJV+WXSWxk8sccKTcML2lzNEQfWdmy+WCKbn/yJia3lMGaWHFIIEhCo9IWiXysmHmCifOIqJ6RAtBKSLUil9GsDqSrRcH5eWUALvQ30rhPczq6eKaMGbzKxZrTkK81wxRmWdKWndIWkGfC0+xDjrqRhkPPzNFVljN0Tai4dRcvoYcfIPxyfYwK4jSKMxI6SnJ5KtgF+XUpxVOo/xHDlM25j1YADtdx7OQcU5uxVRlHrUh+fCqMkCn7By2BJhjrkNwrWRR6ywDRMWYMpIMEabCNEMU3YvzV7/2KCZrlxmRTG2dPWTxthJS+xR7aJmdTjlNBvsD3Oc+XMuMLJGii/cCYJIwTYcUd5RYASoq7Xym2REwUvq5o9lIFB3GclPzuJixtgRh9gRMki6/k9wsGaEGETz6OB+nT7UD5xnM0YwMETfLr5IJiOpT+8BrMUTox2C9Yntl/4sPlvh7H1WOgnmjw75xwIMiIqL+/n762te+dqjb4uDg4ODg4HAYUasdguyz4zj97HUNip544gn60Y9+RJs3b6Zf/OIXNHv2bLr77rtpwYIFTlek4Hk+8nw+8k5eaD7YymEw67FqRRNG19jM0ENxO44cPWaIgOAwQikTDaNMhzVXZCAyCmRZt9InhYiIaH0FmX5dTMjIJbTXdJEce092/0HZ8EO8q5ipktISYX2wBkEmxLTQOdun2A7uocnNksmpm8vJnFbfkBEj5E7tlxvm5lmBKYqScuSJsiIQqNv9KM1EkRm62LCJ9sASERH5x2SYW01K4XS+z7AAE4t0aRfZVIh2s90c1eckS4BzrqNmW44AJSIieJWiYOhJxk5EX+P2ch/I9pv99Kw2r5PzcQ6aR7VggFBeAxoeMDpg98Ak1dvRdHMNIlqI+ME8ZWfJ9gamWNS725zfKHcJpL0HVFkTiPRt6r4q59H9B1UiooI+56fJhZwCPtHcUsNqiZiZgeUFmEqUfAFLVZndysuZaxGaMBsCQ2TBjAhMGFEeyK/7gGKKtJDbGl3Gm2voGiwHkHIPzSKsQTiVHaxIqSUotqeZpKgqo4G+DaCPwF4CzJoWlDckjjQUvNbPj+bHCdQZZL5eITDfzEBPlCk0ZbYJdlCL6VsUo5vvkteuoTRQHPfj0RlZOE3RvnHA3N2///u/06WXXkrRaJSef/55KhQM1z41NeXYIwcHBwcHB4djFgfMFN188810xx130Ec/+lG699577efnn38+ffnLXz6kjTsuMNBHFIjXM5DmmDC3um2nWMwyRHgP7dEeE3Z7vSa/uCHrDFoCGAVCI1NExhDPuauIS2uJEKEFWWtgtTM+VY5EG5VpDQ4H2Ugbz/RyhKxYAZ1xhLTuArML2jAt22Pa27GBU+8nzY4CEyp1+aw5ph3IylHHic9hcqktDADN5kBXAgRTZv/VaJD8EyYzqNoimaFil3w/uVCeS2zRxxqe5DaZ7g2GSBvjwVDTGnzqYrydYAnBMJnPMz1YjrNGuXl+PoX5brmfkdNVhiEzNhUly8C1RYo8WEidjQY2AKVbLNOD7DIU6eUSLEhXznVA82TexwalPq3YzrqZMbAy3E5mOTI9sqQGgCygrj9KtlEXSU5N01BZQ0i4LGSQxg89FWfuoRwIL2dtEJi1q9gCy+ZzWxKlk4v2stbGnktVzkIXTtVlfaCTynXKg44PSo2O1hJZXRqyWBUTpZkbMER2fX7+lMFkqaxYoGWnFCHmO5v/FNlUfR/sHpgpSypzS2RijiuhHbfHao/4+Yb7HQyRPR5lMhlK1b/XDFF6jlm242VZRBelUZCqDw1fncnFq0fVypFnXBxTtG8c8KBo/fr19Pa3v73h89bWVpqYmDgUbXJwcHBwcHA4DHDZZ/vGAQ+KZs2aRZs2baL58+eLz3/3u9/RwoULD1W7jhsUehJUCCUoss34EFV3mqwozQxpr5taSUZ0MPorK1NB3TcRTVu7+qiO9s12A2puvqLs8RGRtW3goqUoD8CRJbQMKuHJGg4iKteZQpFRaHU4AkQJCE660Vls8Lrp2CDPB7JdJk9tN8fD7YEvC16RnWa9dlRkjAgVjJbNGVLHpb2AwA4RTSvjwaaMlRZJpYyfaE5CZFyGy5FRNkOMgdkxx9CCTCVWM0JfAoYIuhI0EZmIhaRkiACwbjjXyG5DxIr3sd3yoAud4m1dh8WH3sJFNXOdiObN5yHlzQMmK8N6MZTCAMMUGeVrl4B+RXpBIXsnNY8ziKQvIcXYjDE2XBPbKbSp9iu2ovsF1vNBg4W+wadBsyLBXI0sMcrLTM2VBZCj3AZoZmLD7IUEjZpiJG2xW2aGtG+X1uaA8dD9WAPMFRDlc6xZC7tddSPDBwhlM3yWoYHIz7zCKwoMjT4+Xe5DZ7VBV6MZXf0emZeaxQPApuJ5h/Vxz/hVtp1lkvm5q/3LpmxG5j7OM1+asSVcsgimosiIVcyS1Wvxfe1VG01zHY4+DlhT9Dd/8zf0d3/3d/Tss8+S53m0e/du+vnPf04rVqyga6+99nC00cHBwcHBweEQANlnB/t3vOKAmaIvfOELNDk5SRdffDHl83l6+9vfTuFwmFasWEGf+cxnDkcbj2lEdkxSIVCnP7xA81NeUyVSfB1t4n2+K9p0vXqpBI7au+X2wykZioS2mjIjhRN6iKjOEOk5fWw3MiKpm7pztrwrwBBVVDPhTA0PHNj8Q5tUUBEtGKb4oMruQdmTDBchHZA7yvJx1x1wzflE1GYLRaqbGQwRoEtH6PMX2zrJ22N2qFCmWhjaAfPZ+MlSS6SZGzBEeLJofQiAa4CoHSwBrhW+T/epIpgc9Ye5sGu2R57j8Kjcj9baWEYoLT8HM2TB7YdTdEN5EwbYj471rDPr44zJgmR2rB6FiTawGXDYxrWLqPZb7yrod/h0oggrmKk0+8mgmHFDSYcpWQoCxUTbNpkN5LtC9TZGpa8O2DFbCkL55YANRAFU23bVv7TfT3jY0GmlDtPfbcYc/LaYTatZ3zHzfWS8eSkYv2LhAFwjP2tsdGHV9BylLdTleliLozWHGrrQLfzM/PyYAfOELDCbGTjDrzDuATBzYMS047W/oDPEJIWdni2PF35pxVavoRAxCiaDjQNDFN+jXLYTsp/r0iPVkG9Gn7nDCTOoOVhN0SFqzBsQrysl/6tf/SrdcMMN9PLLL1O1WqVTTjmFEonEa6/o4ODg4ODg4PAGxesaFBERxWIxOvvssw9lW45L5AeSlA/VB4zh4RG5ADNEHjubemFpWpM/0fgUaSdY7VFTisuMJR0JxtYOmuXmGqGIjyOnfLuMALXuoqHmEUdNhWRzRiobRfFQuaHYsImSwGQhA8m6HvNr4lUZ0QWVzwqyXaAhyHI9opIak+tCizMFRu2b4BvD2Tq8PRxPYrvMTPJyHLry9aq21hmrQqe5dondZpvwNUF0X7E6Kz72GYpBQntQ5GsKPVShXUXZ7CkVYkanrLLCJk5UurEwIlbtKizX067CYF6mZnO7OeCFDxAYoortStBzyOw51NGzDFEM7IVZC7ocFJvEcoldVbE+AIZIZyqGVN+DXifKDBP0ZgCYO1v4ljO9oDFCxF8JebZNVjcFtk85Vjdqh3DuauLYtFcNrn10t0zPDI5xQehuw0JazY1yZEeWGnyMdGYivKvgFt6QTcqaHTAv2G50VLGZmjRU/kDWD4nPHe6ryBi3r6V5kVVc00IS5xzfoI8xCzpD7Tksn2OvoJZt8v7Vfm0oWGv3PyH77PTMT/RXtHGm/qcz2Kw2EMxRsfl9f6Tgss/2jf0aFF155ZV05513UmtrK1155ZX7XDaRSNCpp55Kn/70pymZTB6SRh4PCO/mfOY5bMa4S1YLxGCoskiZDiogvVpPd9kSBjxVo03bpt5ithvZi3If5hc006+nEMz6SJnV9C4etjBPxEMTD49WTisHDY8fTD2th+VshXRUxFCDPS0Ub7QSYME3T3vhodsARYFj6qT+8PaJ44gNyZFCYI+ZNtOGjKXWcN3wUf3AtG5nQ86KFEx7qnSItQcoQiyu7AFOkANXTAdpQXNWT6OF1VTMuDx3xST3laKcCoLo3Zos8rnFD058jxwMwQyy0ibPoQYGVyjui74MAbgWnWLqBD+4sWFdKoOnjPkHF4NC/EhhsIjpQQzm7A+xeq5jQKErrmPKzKvUTQztMSH1Xqsz+YcQZoyhcbUvDkpKLSHx3gqhMT1bYgFzAmJeXi8ZFPuHeSvuTwB9xA5CVMKAnq6bGpDTShALI8jSfTzTZ/ZXU+MAXYIGQKHYsCpurIMYtMtfRICg+m4C02tykBkfVAWgO+Xx6EEQ7sEg95X9+a2HmWnLTpYSIOgJy0H/TBADSP+RH1zUqGFM+7q2cbxivwZFyWSSPH4AvtZAp1Ao0B133EFPPvkk/fKXvzz4Fjo4ODg4ODgcEjimaN/Yr0HRT3/606b/z4SXX36Z3vrWt77+Vh1HqER8VA77yNdt5neCazYTEZEX4vCVX4unzCUiIn+Wbf6TKNwqTd7y7ZLW1vQ4RKnpOeYVjAyiXTBEVbWenu5CRIoSEaB+dfkORISgtSFw1BFrULEaepoO9xgEqaD1031qOzm5YnSvEi+q1GK7H56mAEM0NVeGtvHdpn3RERNphnZOiO8r7eb6eVVmc5L1uSqbdgyhL+9LR+FATRnSYQoQpopFFXcE1XSWFtFmuzVDZF4T2+S5SC3m9OS8+bx1sxK5q6eBNtxEXwJDZO0fmEUIpfl4lQpTT3FYuwYl8AYD5sd0W5tsX4TZFkwdT81RRntT8p4AywEWw05x4TqB5YF4mqdmwUJoe4hQqmytKurHooX6pj+CeQjs5OlyZoKrCcVCBdik1R8Q6wNgiKphLgs0Q2FiPQ2uTRdx7Nkesx8UJdWC6pbtkmmZWCyZltQJ8toGDYFK8UH+AF3AJ1/B0oEh0oVbMV2Ge6ahsO1rCLjBThbaZbkRIN8GNpfbncWULlhA7jthT7wGszV7X2BZQD97g9kZ2qjuF4c3Nl63poiIaOfOneR5Hs2eLUufn3TSSfTUU08dVMMcHBwcHBwcDjHc/Nk+ccCDomq1SjfffDN961vfonTahEMtLS30+c9/nm644Qby+Xzk9/tp2bJlh7yxxyKCU2UKFcrWJKx45iLxPVLbKxwJ4hWp55WwidQKbZIhAjCnnu1iLYK6opPzWKzLpRwQTUN8Gt9da7pdMEQaYEEQcelCtNoETc+vYz+hSRmGF1Ski9TdUEayD4ggwxOcOqwLREKvg1RlfM3raZEtGCIgNGi0X1U2YKz5URJjH7eKYpIh2CWfTJPWxWW12aFOC0Z0DZsDzVyAIcr1qP1HcM7N/sEQAYntnNrfwftH+Y6Q3A/E620bJUNk998l+0hElViwKe+vwbRrrZDeD6wFwPiALQULaFPy0Q5OjbaaJTQDRBH3wQZdnmKuosqOIrJhD9VQyoXbWE5KdTv2Fdxj6L1KT7v4PjvXrI/7VrMNOEgQJeV4835XVIkODbMZsvpNA6Oly4L4VbkLMDkdrxgNYq7bnJzUCcygTJrXxA65HzwfwLSAZcGrx8V4YWRabtAM4h+5HQBiZ60/CzNjZQvSqu9je2ShXTB+8SFp+jqdIQJgv2C1fNwfoaUDGpJUdLfWBWBr1GDdcERwCKbP9kt8dYzigAdFN9xwA61cuZK+/vWv0wUXXEC1Wo2efPJJuvHGGymfz9NXv/rVw9FOBwcHBwcHB4fDigMeFP3sZz+jn/zkJ3TFFVfYz5YtW0azZ8+ma6+91g2KFMoRP5XD/ka7e4ADhch2E+qUO00kWY7z3Djm0pHwg+iXAz0wRHZ/KPLJGUNBFTHqVH5sV3+uC0XC/K2+H46UY8xsZaGjUftT9vo6eqoGkMKPLJvm6ax1xklG2Mha0VlyVi/C+h0wbdA2QfOReNWcd6/IBokxqaGohZSGi5k8ZAkRNUa7DWnOQalbsFGp1iQos0QbNfNiuvioZmBKLdzGHqMLmWQGyTdijgkaIl0mQzNN2sASBWr1tdOspNaBlFHCgrsA+lCA+2RFymss+1KKyuXQt1DGBCyGjzVP0CD5lB6l4sd+mke1iNyxfmxS6nl8bOIY3GYMT6tddbFXrj/ObUQBZZS54KywblO42auYbUycFBfbRp+ID8MwUmre8KrPOfoMmF5kcwFgHbGcZT6Y1YiO8YJ4nvBroUNeTJttVpbnvu0Vboe6dg0sCJ/yKJsg2sxAPu4sF+mNjmhrEdWH1HPHmsDyvZHcLDVQM5XEAfA8qinmuG4Pgezd+nq61IfO7IXOEuyfto7ALAGu9dGeejoUjtTOvHEaxsbG6OSTT274/OSTT6axsbEmazg4ODg4ODi8EeCyz/aNAx4ULVu2jG6//Xb6/ve/Lz6//fbbnY6oCQK5CgWmlfBA9ggiq+CwpAeCIyb8zfUYwQdYCG34VeQIB9F0rlfut8RsQGJQRWIqewwRHXx6oNUB4J2joyXtV9LgU9Iwny73B82SLVMQkOZsYNbAulRUVplmtrA+Im6dhRYdVvqQQUXL2AK8kilqKMCr2ZBpLBG+m+qVJ0OX+UBhVA3oIho9b+R68BPKKEurakjpt0ZU5tAiZlQKfI45yPYrNtFqiBh5ZRoZUR4z2R7Z4GIC3lQqE5A3i/2C+YJmw3pQWa2Q3I+NtBllZjO1d9D4YnnNUIQY8CttVnTYiEXQFwNp08BqxLA+uSWzzHqFCuVQHgKanyj8fzy7DFG9jAQYV814xPcwY4FsMmaU/Mx2VVVx2rofkWw7ssWgEcJ9gmxTMER4r1kz/VzRpq9ot9VbcbMsm8iLI5sVfVfrvBruR8UQwbvKvm8FI8a7sZ5Z6jnYKkve1DMxuUgwFxPWDBGAPmm1lkrj4yvXLDOEbYAdx7MUbcO5Q4Yd7hsYX+IaQM9UDfmOik+Rw75xwIOiW2+9lf70T/+UHnnkEVq+fDl5nkdPPfUU7dixgx544IHD0UYHBwcHBweHQ4Gad/BCaccU1fGOd7yDNmzYQD/4wQ/olVdeoVqtRldeeSVde+211N+/bzfmNyU884d55dCoFGx4BRn6pc6QlA/m2PFqo23F1ET26owdnXEk6YDsLM6uUg7R2odFuytrRigywloclERQ8+uIYOv6DemQCyAS9ikmzRYn5bn+8IiJ6gsdEKzI7aAUBiLc+G52FB4ryP0Vlb+Rcqout5rz42NNArJ3yoppm84AaCdn6C4QnZej8nMwNOWEfA0xY4RoXGuAxpeZtrf0GbYrvc0sEOyXfcuXksIPXQakbYOKulW0jr4RU15QQHq2eXwUud1F6woul9M6M3tNeTnoOQLKg0ozQA36NHUPZHsVQzQG4YzcL57nba8Yyg2FfMF+5ntkXyiyHs0r+y0jgv4ZmpCMj2aS8nBQhnYQPj1lzbQq7UxBU61gVJt/D6f6YquhdKBJ0gxReNxcS2hvrK5NSvbqx55QDK3Sm83EEOu+RDNIKu1+WvVzSH6P5xmeG359r/FpBwOlM8GsRgt+RKwJbPBL8+njrff9hpJI+hLpYr5oM7NkKNcD+IpV8oqvcWIOA5ymaN84oEFRqVSiSy65hH70ox85QfV+ohr0iXTYQq8RXEZelTXQ8ou6mq6vfyj0jajFtu0bzC9NYEL+MqUXtYj3ddrcvEaGzS80TCMxGNIp7ABSXKtKkI1BFR46+GELqAdCZIwrj/eYkcJ04TJR/QcWD/P2dWZasdhm2mfPQ0VXA5dP6WCKTepY0B2YkE/1aoSnOeJyqqkhhTkCQ7/GVGoMhlIDct/pBSxuHcSxiK+pYMrQ2SmC8ARvD+NX3kWul6dU4uYcYjAE1FrZMDNtjiGyVR5LiLfbuXPfpo8Q6afmm+OI7Wk+ZQPARNIrQxxsPseUBVDgEhOobYZ07PoB8P4xkFXTZBgA4AdeT91iMIQfuOiI+Qf3DqbnkhvVoHHUDIq8FtMHs/Pk6BMWCqgT5qvUKDgpBbxW1B3GMSrxvLp/7dQJdxVbWxD13vJqWq04baqF6oOB4LgZNcAqIj0gR7wYjMFQs2WrDIowTTQ111wU/Vwph6XY3UI9b2DrALTsUANtDKr4eLVBaLpfDhobU+35fCMY5HbjvGkbCdtMJfzWA5ZgRj6P9HXE+sXWgO2Xdh84JDX1iIEuAsOQEu5rq4ly1EdVXX/I4ajjgAZFwWCQ1q5da0t+ODg4ODg4OBxDqJEzb9wHDnj67KMf/aj1KXJ4bVR9HlX9nhUCB9Mc1c9pF8vBrBCwJoWqgGOxVS4HxgYI7TZzL9UYlw+IBHi/iqnZAZNCs1yZmRIr6MxLUzMArAjM41CqAWUEKiE9vYRoWzJSxQ6enoLZGovRYainBaGpRTKlOaKM9RBJd7yYEp/7spLmqIYlBV5VDFEpad6Xos0jOESQoMS9aj1lHQBDFNupWTTzWuBLr6vTA5hmy8xWkWiLOeapnZL1a1/b3GgT0NF5rgNTCeY9pucQfYMh0n3P9gW+NGCAgIQywsR+bPs5cM70ymub3KqnFVRUz/cM7BC0cSGuBSJ3za62/9HcE75xWbckf0K3eN/AiqrYzz/NrE+bHyJRAVOBtm2Y7uLuqkXf1hIjL4XAYCbB2EZGWfzN/bzM/TTby2VAuDmFpNw/2pNXRW4zKhmgKm8Dy7ZhqtGynNzeXGfzaTV8DtYQU8HaxDE1l4+PXQ4iTJz71dSr/vFtmLKCYwlvV5vF1st3qPJEVcVmJuX5QOHYyFjV9i8865AgAOipUNiL2KQadQzT+1lDUsURgMs+2zcOeFBULBbpJz/5CT388MN09tlnUzwuf6y+/e1vH7LGOTg4ODg4OBxiHMdMz8HigAdFa9eupbe85S1ERLRhwwbxnZtWa4SvUjORBEcTsO0HEwHBtI6AYAqHKAXRM4TNOpUzsnWciIhqnEaMSKgcUeLTHZJJ8digDlPbFQiKOUIFM6SZG13ME5EYtAwQlGqGKtcvRaxaMGr312BsKLeT6ZcNSOyUzJFvSoacMGG07WeGqJiU2ynPYPQHIM02xyRDdKQeRYMJaVtnzkGRCR0tRtUiUkTTFSWERuFWoDQuw/nokNlwvktuB6yEZSn41CF1Hp9Dj6FT1HX5gpl0Za3buXQCG25mepszYwAE2UC+G69m/TDbnHX9kfvOlNl+oVMeN1jM9Bxz7XTqfihlDjg8bJghzRCll/WJ99CM6LR5pFJPNwYF41tULFljSrl4S9G90KjADFR+Dy3S1FxzcXANLTPLwv9cBydsoOBsrvnzw26Xu3e6H+al/ByZaM7qoaQKAJYD7cVxhqSsrX5t+bRABwerkGCK15sw78EQATb5AKU00s0TMjQrA6NPexyd8rpExiBsN+/BzKBP5/m4Ubqj0Kafcx5FWeNmn4lK8N8wwFDP5lLDsxPKf6Jq9c3xm/n444/TN77xDVq9ejUNDg7SfffdR+973/vs97VajW666Sb6l3/5FxofH6dzzz2XfvCDH9Cpp55qlykUCrRixQq65557KJfL0Tvf+U764Q9/SHPmzLHLjI+P03XXXUe//OUviYjoiiuuoNtuu43a2tr2u60HTN795je/mfHv0UcfPdDNHRRuvPFG8jxP/PX19e1znccee4zOOussikQitHDhQrrjjjvE9//xH/9BZ599NrW1tVE8HqczzjiD7r777sN5GA4ODg4ODkcEmD472L8DQSaTsR6HzXDrrbfSt7/9bbr99tvpv//7v6mvr4/+5E/+hKampuwyn/3sZ+m+++6je++9l373u99ROp2myy+/nCrTfAA//OEP05o1a+jBBx+kBx98kNasWUMf+chHDqitB8wUTceOHTvI8zwxUjvSOPXUU+mRRx6x7/3+mfUVW7Zsofe85z10zTXX0KpVq+jJJ5+ka6+9lrq7u+kDH/gAERF1dHTQDTfcQCeffDKFQiH6r//6L/rYxz5GPT09dOmllx5w+7K9QcpEgtY2voFxAVHD1xXZJpgLR7YFCq+CaYqt2Sm2U55najX4mSEpdaBwJYn1MQyusbam1C7pCVtmAMG51zydNDwm30+cIDULNrOJX1t3cCqwSmn3VJFUrcmIDTdPBwfAEIV3SQYMJxbMWTUu29dgUheFhqO5rgSmeLlu9XWlRkEmInBtbdQL4zl1CD7FoAAozYKsNLsPvnSx3Sqzbkoup7UONmuGL5XWFnW+YhqG6BnZXfUyGdIOAsZ5YVX4FRlG2L8+tygsqxkzMENA61ZzoNDXjZ5qrl37Ri6FUWquF8ErGC6kUVcS5kJMLVJZZaqPeeoe1Aam0zOZSqwlAkOT4zI70LJk+utaM3NMEL2wRgfmhyhDoYrTIjMv28NaoE7cf7KNtvxGW/NjqRtkSkaoZYvR9FU5SxOFmBODZsM5VTak0czQvIIZQnvANiIbLTw2Qyo/H39syLyWWH0BlhPQz4EG00bF1OnsMjBE0PXUn2dYHwy8eY97pXW7OUBrvDjt+YNxAHRe+pkIXRgADRyhOG7wDcIKHQWh9WWXXUaXXXZZ803VavTd736XbrjhBrryyiuJyJQT6+3tpX/7t3+jv/mbv6HJyUlauXIl3X333fSud72LiIhWrVpFc+fOpUceeYQuvfRSWrduHT344IP0zDPP0LnnnktERD/+8Y9p+fLltH79ejrppJP2q60HzBSVy2X6h3/4B0omkzR//nyaN28eJZNJ+tKXvkSlUum1N3CIEQgEqK+vz/51d3fPuOwdd9xBAwMD9N3vfpeWLFlCn/zkJ+njH/84ffOb37TLXHTRRfT+97+flixZQosWLaK/+7u/o9NPP51+97vfHYnDcXBwcHBwOCaQSqXEX6EwQ8S3D2zZsoWGhobokksusZ+Fw2F6xzveQU899RQREa1evdpaAgH9/f102mmn2WWefvppSiaTdkBERHTeeedRMpm0y+wPDpgp+sxnPkP33Xcf3XrrrbR8+XLbmBtvvJFGRkYapqMONzZu3Ej9/f0UDofp3HPPpa997Wu0cOHCpss+/fTT4qQSEV166aW0cuVKKpVKFAxKfUmtVqNHH32U1q9fT//0T/80YxsKhYLoDKlUnbWohDyqhD3KxpoXQNReFzUVretCsrE/7BDvy/PNpL3NLuHXakAyPJGdMhQrdpgUJ48j2CKbuZXVHD0iXDAosSE58EUBV2gAgAbfE1Xg0hYHhQkbMp9egxnC+vFBs5yvqKL6ospKi5rzUVHaKlu+A7tXpm068wgRPjQPKGMwPbPERqPwXFHSA2SdAfle1s5MythE67WKPeaYAls4qldeMBFmblBks9CKPmS+LzFp2Lqd95dFFC33o1ky7asEJkmbJE6cqKJ6KeGhlm3MosxiFkWdF12KZmqA+9owsyGcGRRlE8l0v9m/1hLVmTo2MAzp45H7R19HHwRDpA1MbamOqK/h/sC5BfuU3AxNnPkcZok2Q05lR9lrwPcZ7gtokOptEG/rTIrNXpPLg0FFSQpkr9VYMwgfLhR6xbnRpojoQ/he68L80vqpzowx9PnSXj84T2Bgkflot89MDtpZ345isFS5DvicWYZJ3c/xQZXxqJimlq11TWKuh73blJFlSDG1ABgiFKG2y7PWrZiEWWijtuzIwKOGE/K6tkE0d+5c8ek//uM/0o033nhAWxoaMrRhb680Lu7t7aVt27bZZUKhELW3tzcsg/WHhoaop0dVtyainp4eu8z+4IAHRffccw/de++9ggo7/fTTaWBggP7yL//yiA6Kzj33XLrrrrvoxBNPpD179tDNN99M559/Pr300kvU2dnZsPzQ0FDTE18ul2lkZIRmzTI1jiYnJ2n27NlUKBTI7/fTD3/4Q/qTP/mTGdtxyy230E033XRoD87BwcHBweFQ4xBOn+3YsYNaW+vT0+FweIYVXhs6UatWq71m8pZeptny+7Od6TjgQVEkEqH58+c3fD5//nwKhUKNKxxGTB+YLV26lJYvX06LFi2in/3sZ3T99dc3XafZideft7S00Jo1ayidTtOvf/1ruv7662nhwoV00UUXNd3mF7/4RbG/VCplR9CViCmvgDl36DNieziCVFlWdadmjl5ZK+PlmImKcKfLS1t/6B4QHev57vwcme7hKytmBzeJOj+tm2SaiVeS2832Sb2GzXzS2WnqOCNKlxKaMCFhqUWtaDfM7AdfL+hGQoNSS1SLG41UNcR+R6qgKwrR1jhyR1HRfHtzd2e7XXVP5XrqH9gsK/WgSQ+oaL/bnBwvxRlGRWY2onztoqyJmWI38U6zfGQXmA+zHehEwDJOLlC+PdxFoGUCQwTk25ABJTVAeeU9o49HZ/5MzWWmJYt2mVecOzBEYEfAIqCvoY9ov6PWrSi8aT6PqjIjLSrTEHowMEiA9t7RJSqQXTYTGrxtKjXLKObbzSsYQxTzBUIpXCNkP7GuhM+11QS1SzduMB5gaKBd0oxJfEg5MnO7Wl5l/zHW0iGDr4iyOHCyHpD3GRgWlILR2seydSuXfcAW0c3La22/hwt5RfY1MGyhlDxeuKTb/eoC1ug70K3x1z64l4fl+cU9g/OLYq2agdcZYyjoS9T4bIjtlefer7RENaWpA0MEBKex6g3lXI4xtLa2ikHR6wGSo4aGhiwxQUQ0PDxsSYy+vj4qFos0Pj4u2KLh4WE6//zz7TJ79uxp2P7evXsbyJB94YAHRX/7t39LX/nKV+inP/2pHRUWCgX66le/Sp/5zGf2uS7S5A4Ef/Inf0LRaHS/lo3H47R06VLauHFj0+/7+voaaLTh4WEKBAKCWfL5fHTCCScQEdEZZ5xB69ato1tuuWXGQVE4HD6oEbKDg4ODg8MRwRvM0XrBggXU19dHDz/8MJ155plEZPwQH3vsMStbOeussygYDNLDDz9MV111FRERDQ4O0tq1a+nWW28lIqLly5fT5OQk/f73v6dzzjmHiIieffZZmpyctAOn/cEBD4r+8Ic/0K9//WuaM2cOLVu2jIiIXnjhBSoWi/TOd77TqseJTHr7dEz3JdgfeJ5HGzdunFEjpFEoFGjdunV04YUXNv1++fLldP/994vPHnroITr77LMb9ETTUavVXpeAjMi4E5djRK3rK+pzZnQy0v0UUUZ0B0xneAXWSWRPkMWGdA0lFIYEKtGA2K79PKAceZWeBExTrs8MSFHHJ8BMUY2ZmMiojOKhYYCGQkeciLLAvIAhKrbJsB7z8ZZR0rWLRjPcDj4OlXWIzyuqFpqu1VZhDQdqVMGRt9gmFqPcXMlOJNfW+0tIebukZ6tzzQxRcLc8RmSPZeewlqXDXLsaX2JvVA60u15U7F+79LyBNw7cgQGdmaN1DGCerBaImz81h/VizCLojCDUagPj07JDRr2ZPtYG7WnUXxHVmaw4WFPuo7FB6TGFOmOAZT3sfphtQRYZ603AwtrCsAz0KWR+2b4awqvsw9hesbXed1pUHTmdWVQvjGraFpoyx2gLJXO/1K7fkQnZ1rz6Hlo6aIKCU/JienneDzPBxS6T3uVnb53xk825Q58Fg1NWeiudMQl9F2A9nawmjz+PyoxFbMfqyPgXp3Wb7MuxIWa+2809Ah0ZMibBROlroYsua28s3efg14RibMltzUU9KEA9nSHGsysyInfiY6ao0Guelai3GGQWDNdaM49HDTXv4KvcH+D66XSaNm3aZN9v2bKF1qxZQx0dHTQwMECf/exn6Wtf+xotXryYFi9eTF/72tcoFovRhz/8YSIiSiaT9IlPfII+//nPU2dnJ3V0dNCKFSto6dKlNhttyZIl9O53v5uuueYa+tGPfkRERJ/61Kfo8ssv3+/MM6LXMShqa2uz6euAFlvtCzOJoZqhpaVln9+vWLGC3vve99LAwAANDw/TzTffTKlUiq6++moiMtNau3btorvuuouIiD796U/T7bffTtdffz1dc8019PTTT9PKlSvpnnvusdu85ZZb6Oyzz6ZFixZRsVikBx54gO666y7653/+5/0+xumIjNYomq1Z2hspntGd5o7J95tjjL6ihGA8FYkf9/RJ5pfSplcj23OMBZR+NgzkhwpoWT0Y0oMNPCwjampEU8IeF15F4VR/rvlgCD+oVryKQQ3T04G0XK/QIQtSanEiBNr4ofRPmjkQT03/VVTKfSnRvGvre1mXshhdqgZXc/kHOmu217KOU/xx/ifqy2IwhKmU8CQPdHeZY8xxt9ep9JE9LMzkOYqa35y7ruetCtcckxq4auFnVP1waSEyjEJz3apUzLCi/3mziV3m8ykudAuRuU6txw/X2BI2ABwn3g9ezeeJHbJdflUhHD+MmML1lIi+3MJ9m48jPVcOMvF5gDeDHzYYnwZzMg3etl8NaPR73HNetT6dpe0NdGkSbfCX75BB19RsFYTw1B7sDVDqBQPY5GYubIwEjALMUdmuAIWRAyjFYpbL9Zr9Npgz4hCVUBuDGr8SMuvjxfd1Owf5fWyPOX6UP8F5g/C59aVRs2CZBxTzzfMNzx0/Bjloph/Tj7waT+cVk9if3L8tzaHarRNAdIketDPAfYU8osQQW1eoZ4dPPSMRuJXYNkWLtxvu37hHody+S/QcL3juuefo4osvtu8hN7n66qvpzjvvpC984QuUy+Xo2muvteaNDz30kBgDfOc736FAIEBXXXWVNW+88847hQ3Pz3/+c7ruuutsQtUVV1wxozfSTDjgQdFPf/rTA13F4uqrr97vqTAior/6q7/a53zlzp076UMf+hCNjIxQd3c3nXfeefTMM8/QvHnziMjQa9u3b7fLL1iwgB544AH63Oc+Rz/4wQ+ov7+fvv/974tBXiaToWuvvZZ27txJ0WiUTj75ZFq1ahV98IMffB1H7ODg4ODg8MZBrWaTHQ9qGweCiy66yOpBm8HzPLrxxhv3mbkWiUTotttuo9tuu23GZTo6OmjVqlUH1jjdltq+WurwupBKpSiZTNIkEV399htoMpSwAujYBjO3UYtylLttt1x5jhGapU8yYjIIArXgMMzTWb4CR2TJ5iL3DKdp+21BSsnURHcZIXV+lsm1LatCqIhI0Y5gyoTh+W45uEVUhJIP/pwMyay4lqcoEHVlu+W4PKpMIaN7JFUdGJP53ijwWuM02BpPC8LkEtDHpSP78LjZz+YrzXQDGKLEM2oQj8iaI/v8tCRHRPW6pICOVvE5ynoUOuW56nxhhjRmRp5LPfhVQdnYsC6ZIt+nBuQ0U6FNrl/ioKz7eckQIRq37SnKdunyJDqijqsurtPHkSTQ/uhmuWBYTakuNmLJ9GzzeaZPTecxQ4fpPn2NdRYyxLgN6d3q/XQLApTZ0NvE1KFOHcc5tqaK3J1DaSXCR4FhfhzrsjWY9oVwGQyxbQafw8Ck6be4n227u819oi1AtIhYi+mtsBlTiWUwRMz8WiE4mCDZ57LMSsLY07KBDJTZ0fcn2gVWUt9DKG2DaTlMVQH63tD3EPoshPK6iDD2G8xUbX8OqGMLZFjEjuK8fbKRup/r6eeRC0vUkUrT89fdTCkiSpLJfD5Y4fJMwO/SnNtuIl808tor7APVXJ52/s9/PKztPVo46pOcmzZtol/96leUyxmnVTdGc3BwcHBwOEyApuhg/45THFSZj4PB6OgoffCDH6RHH31UCKo/+clPUltbG33rW986Wk07pAhvGaGwP2v5xuqIqW3gsbDbS/Iom7/PzjNhuWaI7Fw4GJYeaBTMKxggzMnDnDDO8+G2jMCwGXwitT7NJRCwP8zdQxOhU1cnTjZ0AiJCCLJtNMXCcbTXagFUSj4iQ5g+6gg2OsQaolROfA5mCKi0Si2RFlZjP1rkaNPGtxjKJ99tIqf+JyAgMfuZXMArcPPqwk5erNrIAti2zsAQAaUEtEPMnkGWpU0fk/LcNTBEe5QRnYr2wRDpIqXQ/qQH0GDzsvcMFrmGYX/ALMEM0Xe9WKh5jQ/K5eLKkBPGekD7Y9vMP9AGFHhHfI7BEAGTC+X5SnDFmwDrVaBv0X6cGhB241WnTo8uYSuEaUE1dFVWO8fd05b74H6MlH2tYWnZyZrC3WbF0WVSN1ljLVC93I5KB+f7YnKRYTBx7usshOnY0TGV2q6K3mrLDLBr+FwzKzgurG9Lq/BzAPd9MckFr1EaRpWeKSkGt9EwtLkVgDUiVTJT3HNgovGq+3qEJUy4lkjVz3azuH+vPE70iULSb5dt2Y5jlScnxMJqvKYHJAsDmwJbCqb3zaEjOlZx1Jiiz33ucxQIBGj79u0Ui9VNYT74wQ/Sgw8+eLSa5eDg4ODgcNzCqx2av+MV+8UUdXR00IYNG6irq4s+/vGP0/e+973XzAx7LTz00EP0q1/9qqGY7OLFi62193GBUoWoWqZaVjIethKsmttFthg6XXy7dJybOiEuvocRWCCnTRXlpUVEmZ5vtAa6lAG0QIg4kZac4ewVmLZpzYBmYDSzhGwyzX7oqDyQkWxCQ/mOQlm8VpPSXREMkdYmYP/QDOjMqeGzzPloe9VEeWC0kOrc9WKZtyuLlAog/ZqjUxu9VtSrYg06XpLvoW8AMr0qQ4mjbrB/ADQwYBW1iWEwjTRm1qOMy/22bDWvI+eY7QbHmSniCLkY4mufklF8QHXpBoYI7YTPHve5CL9GX1JiI2aIan3SjX5ioaGMMrPNe30Ncf61hgNA9O8r7rvvohAtkEe2IOtVQtN0K9rcUzOduN96f280cCjDA4YI0Bo6aGlyXSgLZL4PjzMLMTci9peZpctf4FgkMxRWthHxV00nhfau0IrCtub7uobIvIdmKZSRrAdQ4ftDnwdYeWjku5pboCCD08+nCdYY+t7Be7Cx1YRaDswuSwIz/DMTZ1YR7I82owTAzgZytXrxX9Y/lufKtid2y2PUpVJguzKxiLVw/ebz0I4QBTNH1vCYiN5wPkVvNOwXU1QsFm09r5/97GeUz+dfY43XRiaTEQwRMDIy4owQHRwcHBwcHI449ospWr58Ob3vfe+js846i2q1Gl133XUzptb/67/+637t+O1vfzvddddd9JWvfIWITEpetVqlb3zjG8LP4FhHbSpNtWlueZ6PNULtnNLDppC1pAl1InvNgBOmi6V2OUBs2cSmhexH4k+b5Uts0lZoZ3t/jugQAWo/HkTNiILy7cpHZY5ZD5EnIkVEnqGp5kVFs31yOzpqQpYaNBMwm7TZLDxf75uUDBmy9Wq2fIfaTw6MDpu/MUMEbQe1Sx0KtCFgiIDYFpPCVI3wdlhrlJrH55WrnthCmcF6NGqPUQfHKqqCJkgvp5khrVHSvkM6S8qWetjFOjI+ByiyGxs2y9mIl68lGCKgnJR9JbxHPiZCE9ifbJ9msLSxYGgrCzfKqMiqTkBIXtPh87vE+1LSHG90UDJekQmV6ajNKhVDBDYBXjTo2wXWw+x9izmwUjtndppEUGp93KM0+wtpQ0hogUrMWIAhAhLrx8T7qVMkG5bpa64zAWOE14nFKjNRnUJoZwptZjmUTAEzDE0hYP2+WExUbGXGBx5PvP3whCwwW1FeTgWVxYY+juW1Z48tJ8LM7PiZ3Ee38nNvhokIq1Xk3cHfCffjdDaPqDGrLMJFf6sjsv3QMuU6FdPc6tkCy5rJhW4LzzINMER2+VGzfNcL5hqkFsaobQZ287DiKJg3HkvYr0HRqlWr6Dvf+Q69+uqr5HkeTU5OHjRb9I1vfIMuuugieu6556hYLNIXvvAFeumll2hsbIyefPLJg9q2g4ODg4ODQxO46bN9Yr8GRb29vfT1r3+diIwB4t133920Cv2B4JRTTqEXX3yR/vmf/5n8fj9lMhm68sor6W//9m9FUbhjHp7XUGSViKg2NmFeK5y9NUuW7witZ70FZ+RUhkyY758v3cPLPUbIgmg438b+HswaILsLA3tERAEOZJHFBi0DIrH2jdKRVgM2/IDNWlGRK6JwDV2OBMv5MvJz6+fEAIMGjQ8Kw8LJG1orRMp2O9zcjvWKvRg1g3tf3oS2YIgARLqxvebEoHQFEJ6sUVFFTVVdw1KRAFqLg0w27SoMwH0c5TjqbZPXWkfjYAdxzcECAGCIvKBylt6gzkFctlf7/SR2m/XRV0KKlbQMkQZ8iEqmHZUB6XQfUplLYIgAXTwULsvIPtOlc4BSGOyp2e+WD+CCme0tOMHce1s2mudQ3+P19dteNcdmfXT43rYZdszGjS+R0oCOteoc75BMaHjU9OuxU6O8XfO5zRrTBYoDzLjyEzyxg7PK+NjjQ2Z/sd1yP/keLtszzuV1+L6x5Tr8MrsMruO4D9AuZDhChwX/pFBa67a4XRX0QWYnz1IiIS6Ym+81n7du4Ow7FKJlt3aU4YmMa6ZObi6cks893DvQDOL4cLyaIYKjuFcmCvL9GszteySgi7ti38jIi++UN34wW6XgMV4Q9njEAafkb9my5aB3WiqV6JJLLqEf/ehHdNNNNx309t7Q8HlEPh8RD368GD/0lPDaW7OBiIiqRfOwqvAD3UOaco0fTpuMwZ3/NFnLZXSpeWr6lImanT7j6a5SDKm+TJe3yKeJNgDENJlfCbnTs9lSAM8JfrWV11XVaf2DDiF1lQXdwRGeFsQAMmi6ZmaBGfTBrK6sqt4XOuX0Ymqe/OFH+1pQeZ0f0lrAnlmYFO9RW0qfH51iPD1dvqEMAP9OVlASZJK3kdVCTH7gc1p3QH2vMz102jIGlEgjxgN/coE8V1OL1DSSGgyFdpgfSPwgFOeacx7iMiWFXjOF0rpZbtemHOebT9GQxz84JZ6q9CuB82IZBGXmmF/CTI8a2M5QagKwZowwz7R52ualrITVk/Mx+DMrLDjBlNoZvd+ocpHVjYGBV601mB0iGJiaHeBlVQo6l+3Yc56ZD0rsMtuKbzP9vcL9OTvL9OMIT8ngWmZmcco4DwowBRodhXiXaxvyMSKoweCkGjbfTy6UCR3VEIIgDG74uNgaJDpirlXd+sMA01E2iKlIAXZVJYro8h+YLovtgG+C/F7bPkRZCF03UDSvmNICYIaJwCKUksGPnZ7n52JqHvd1HnSWeNCJdmN/tSCRp8ryhMdUvTkr+oYNg+wDCfWsyfbvf1WHwwLHFO0Trysl/7HHHqP3vve9dMIJJ9DixYvpiiuuoCeeeGK/1w8Gg7R27VrymjAoDg4ODg4ODocJtUP0d5zigJmiVatW0cc+9jG68sor6brrrqNarUZPPfWULc6GqravhY9+9KO0cuVKOy13vMJLxMnz17nvWgun1HPmncdC68peo5D0oRBsmVPEmTkKLDIugrWYiShHzzLqT6R72xRZO33EkSAHNRBO23ZVYa5GYjldpFOn2NeYQYlzEVFER4igEXmhTAFYixAzQdWoiTx92SK/8nZRwJLNK0Fz7/wLRGV8PMNm/dmPN6edEzsrTT+vKeZq+2VSyalLUeSlxtcyAomdiJTN++A0PW1ZOefH9zSvGg9xqBYEQ8gJATVYB5RMsVMBU/IYi23yNs7MMu//f/b+PM6yqjwXx99TZ6xzaq7uqup5boYGZBRBAlEmZ6Mx3KhJjPFnUIwCwhejJhGjtoGrQMSBq0FBCeBN1Ku5omG4giKo2NACTdNAz1N1dc115vH3x3qfdep9TlVBQXcDzX4+n/7s3ufss/fae6+9a73Pet7njajsb2QNMUIjKqIfsNNkuRU2TG+KKru21DE/c+5FhOvaxdNXaLcXybOgGsaEOlVc0dIsAydTyRiYQurPGwz7dPejy9zvO58hVkCvU7aHUuw7YdLo1gs6Y53aqmzKw44hwtWEMBaoRkLe5JAZRNz73FwSQus5dG6yJoeFOe4HzCpUaaqv4xmdAqUEhz2vJRPENrK4yOn32hcwnQQmmcuRsGh/QovtgvEFM8Xw7GjIMkSeuSImma+fT6mnDP0iZlbpOkNIDjYwqyRjTBMgwASBGSq22euUnWtNNtNL7HlH0nq+OuPbVKpPjaKQMPYZH3Wfg3UCMOXX83vXqPQiO/c5WYDNU24BXnzMelD0hS98Qa6++mq59NJL/WcXX3yxXHPNNfK5z33uOQ+KisWi/Nu//ZvcddddcvLJJ0sqZYUL11xzzWybFiBAgAABAgSYCUH22YyY9aBoy5Yt8ta3vrXh87e97W3yqU996jnv5/HHH5cTTzxRRESeeuqp2Tbj5YNUUiSSlGKflsfYl7bfq8Yo3NUhIiLVMTuBDYYIGD7eMkReRKupwE2UsopSCYjIEGFFPEPjli1UisELL0nEWyDhNbQIiDSTgzaijI7ZLMXwgAvTa20aPWm5ETBEwM5z9TijLgprKpJYVssD8Py+UDkRNjLMzbWbI3UYzBBHuIhg27Zx+nv9/8wQQUgNPZcXyeouUFQ01Q97Ar3GFK37aF6Xrdstk4NU4IkFXNrEtie5094zCL+h0cmttPutlUBJuUWnMkTMFoBFRLsT22zaudcQob0LLf22+b2gB7RETZtrR8sDesH0cvD1jethUDKjoZiosqTos96WAUfT3YMxwrPUYClAKdWTGQEIfqdDExEro8tdm+Y8hjI4ygIqU5Dsd+ee7Yvp57ZvAGNLVbS7W/d7lD1Q12OoFeMWYGaAAiViAND+gJmJWEeBermeKLa3ZXpwjX25Ebo8YN2g6ZuueDIAmwvcq84n3XmMLyENZL9urxYBeC/g+cV1R5/PLiWjxXTYHD+5z7Znsg1Gbo4ysKqrGl1lGSI+h5EjbaA/vsy1se+3+s6bG5F4/tBX2joQjtSveEfryVi0aJHcc889snLlSvP5PffcI4sWLZrmV434xS9+MdtDBwgQIECAAAFeCAKh9YyY9aDosssuk4997GOyfv16Of300yUUCsn9998vN910k/zrv/7rc97PdOVCMpmMfPSjH33OJpAvdRTnpqQYa/GRVqnXUTrVqK1YGH9oxKyHly8VEZGJY1x0jegX2SxgOJoHNeNHfz6+SCMlakczZUVzkU4A8+beJE13VEq5iAqRIVLyk/vVUkCjpMQux4SFkG2X04ZqNlm1293vEDFEoyu1AOsReuAW277OX9uoLD5sWQhfHkV/jvZBNzIdmAXOLLfHbdtoHxGc5+SUfzAZ0DswuMTJdBl+KBQJvQMb5I0vs9egXgTUgW0R4qPIatPUdI2Wi532jdb6qNvv+FHumvbcj3N2S84cZJ1ZYsugWfcFXQFNvR9fYSPncJvNUmu7z2ovYIQIsO5r4CTVduh1h9ll0ZOOYODs77igLkppQM8Tp9IU2V7X/kheZGKRXnOSgmSWkDlqhztI8x8c5ZFQdgtZmyjvUdbnqkIM58Riy/5V9N5CJ4YsrRX/aU+unIJmSM1LfT9VRrnDrTVrFlvJ3xJNdVdTSmT2cbsYGdJt4R5ESdvjs1F1gb4JgEXB9x1PkdZH2Zm27W6dNV1sRzGy0vUNPAuZBdSHx7X8kP6s8wm3xLPo93NEk8/US2g3R7FbZKwx8wqwvqzS7A62+4/VoLK7LKWJssj3JcBLCLPOPvvwhz8st99+uzz22GNyySWXyMUXXyyPP/64fP/735cLL7zwOe/n5ptvllwu1/B5LpeT7373u7NtVoAAAQIECBAgwAvC85rQfMc73iHveMc7ntcBx8fHpVarSa1Wk4mJCUkk6oKBSqUid9xxh/T09Mywh5cXQpWahCpVqUW04GqrXnKNUKApKB1ntUP5OdaMEIZ+iHLhZeM9a9RfxGc8haA1siwBihdivzB9ZF8h7C8CVsCbnME0kbw4nrRMF7YvLnQaqKZS1ZwXMIqITs9r/n3aLjX3gS4HLEhcSzqgVAXABSPhV9K5CREvjmfLl5SJjWjZbNuD7DKwMoisoRmZrHeBLgmaF7S5wZcoZxsLjxsAkSjgDTH9MS0rBiBKr3vJuHV43BTVU6nobok077PHmX+PlmQoWW+YxIhlI+JP77MHLrFjZ2jG9YE3Kd2hhn6RrVYEBYaofas9QfTFfSdbhgiADgXPCLOAYPlwzzKL4GljY8NMn/W+QpbdxOKmBtaPWSgwRL7Nek/AsLKPkT8mFXZlfx82/Ox60n0ABhqFXQE+dzBEQA7FblXCCObGm0VC5+Z1WVY7iPcOdFhg2arMLNHp4rnE+6ZKf4Hatur3+uzgevv3GCyv9Dn0Op4WtNcts326n4RmRBbwvbuwXIwZBXChzZz8bDED7I0ddZsKr1ONV+4zpe6pWfpDhZAcAE3RAWnJSxOHXOXV0dEhoVBIQqGQrF69uuH7UCh0+Bs6BggQIECAAAFecjjkg6Jf/OIXUqvV5PWvf7384Ac/kK6uuugjFovJkiVLZP78+Ye6WQcNpbaolOL10AFz7NCLQJ8BZ+cmLdCICBADeji2Apj79oyPMk410qH4DBtymGXvG7+fsmWOih3qK6Rao+Q+F+UkdmsWmRZorabi2g4XOpe6rT4kO8+F52CMkDHU87DNThtZ7bZD9g3KAiAiH1/apO2wbAiQXqjba2QI5gzFVhEZQ5sBpihqk/6kebBmzhseQ8wMRCaZ1XIB166N9oNK0ob/WS3yWbKXypcVABANIzpHRk7EGuV6pFX34tku3S6qfahjk1vHuYG94HNLDBFDtJkYItYOVWHepGF+s7uXYyf26v7dcZofc6E1GCGfrdeFdtrdRkjDBIYIbB1YBc6YYp3J0HFCG7jv95/iVhP9mlGlzwz7MC24Ny0Ty9zFR38CA9u6BUywOzcUq02SVxUzOKwdYoaIs9jmPOYuVpbYLDCZYBU5446Py4VToR3C84Q+wWwntE14MWV6lbXT9xHr4MA4VfUVCNYy7MuIuM+7nnTLrOpw8Pyl54Phcd8XrRTT/z6kXRV9CO+zSBpMs/b9p/T9i8un28VJS4T3X+uOWv25U4axRG1Af/Ssl62xW9f6aQZt2wb3wcRxRQk1vQiK5SAlf0Yc8kHRWWedJSKuXMjixYsDV+sAAQIECBDgUCHIPpsRh94kQbFkyRL51a9+Jf/rf/0v2bJli/zHf/yHLFiwQL73ve/JsmXL5IwzznixmnZAke8KSy4R9n4XHEll5lkrV44MfXSt2WKIftn5FnP5cKzFdhWt98QRqO/UYIooqobWKZwnp9wJy35UlSlihqjiayvp77JuP56dKNjjwdG642m3//2vcqEc3GERAbfurJrfw5ekzhDZ04STt/dnIhYmtQvnWzPboZ1Azzr7w8Hj6joY1C5r32xZr2rcXnToowD2oIH+BMwQmJ4a3H1VRwEWAoD3EqJxrh8VG7PnwnoCaB6aB+zFiwwT9ZKni4u6fHCsjlv2InukNYUaW27PH9lk/l6qLqR9C3nJZCmzq2JfW8iYYni3ZI36O6Ej0cs+coz6KylDBPYQ17GonlxzHnXUFVgikUmsmi7z5H8VOtcJy9BrRnc7eqH9Cdv2in38vYYNbejaaNm6fPfMGYjMENUz8RzQd7iGHzOu6IPoGziOr7NHuhmvKdLzQW0ysJ7QICKLFg7bvp2koxtdZdfRZ8N0j/w9hrbPayXd71t26Me+0K39HRiwkmo2uRhzfNKzg/4R0kxeXCPsq22HZhQ227qJFftYePa6dVdEOl4En6IAM+N535FisShbt26VFStWSCQy+9384Ac/kL/8y7+U9773vfLwww9LQctdTExMyNq1a+WOO+54vk17SQJCRdDUEMOyvT2EiACXdAB88UudjmrSSurllqj+TsuDoLSCVpeHoJCVcpg2Y0TSbj8hTKtpmQ5Mm1VU4Im0VQyGvFg3DIG4TveVqDSEngcK2ubm4HO3xNRP55OuHYVOLaDZEza/R/kNiJQL7XY/HnTeYbrePtWe6OHEkB3gTK7S3bLTDobqL3B3T7K99q1Y1MERjo0XPV6ymGItdNi2ojgm/gCxESWmnSCaxR8A7lN8DZL9drAT3TtmN6jR78P2WtR6u1171aC0qgPxgePpvQDhtk+hJ7G+FkvFQJ5tF0Lajpbd9qYW22x7OF0btxJTLXjm2je54+S7bTN5Gq7QqVPDpfq0GdsFdJ/eb9Z3b7VGlQv/G23S9H99LsaW6bQuHbPzSQ0yUja44NIlLOot0LkkycYgUrAi+gjdA9gv5LrccarTTIdhUIXna3JhZBGRVtzLGGwOrHVHWQtTF9T81Q/odX/dG2D9oVOaE3Yd5T2AZk2X94Mdmg5nsTOeCT+wQTvH8V5tHAzhWuP5hKTBlzrqsu8k/x7QxysxSlYc4xWJFqd+vx9UBEzRjJh1Sn42m5UPfOADkkwmZc2aNbJjhxuKf+xjH5tVHbPPf/7zcsMNN8i3vvUtiUbrI4PTTz9dHn744dk2K0CAAAECBAjwLICj9Qv9d7hi1hTPJz/5SfnDH/4g9957r7zhDW/wn59zzjnymc98Rv7+7//+Oe1n06ZNcuaZZzZ83tbWJqOjo7Nt1ksWxdaQFJpDDYZxTNNy2jbo6kiepsl0iemxpoKNNGIjlrUot1mWgktJIHUeDA8Q3+coGpgwVtqadV0tBNpsiFrsQnkCFXBrQ2FO5w3/IsqS5FxICIYI4JITC3/sfp/tdQNnCEAhggUrkO3VCFbZEphZemM1PT1mhso0/cARUKsWmC2rSDrfpe3X6K+pUpPMQluHgo8FUze8SGArgKibywMUNGU+MWTF5DgXMCbRDPUhmhLhPlVjEa8SMbmeuGlPF5giPU4tpeeny+Jcd89iWrIGDBEwutydMMS8PIWD47ZR2ZIQTVly3873kLi4RQ34uLSNAmwIpi+wxFQXhOqYbgQTwKwC+rBIvd+hf42usgfdP+4opFi3ew67fgR3RJvAAFuGOU8og1q2DA6YFGBiIc9/E/R3sIOIEosBRqhh2lrbE1NGGpYbAJhb3EvuUwCmmXj/iVFliLR9vH8Oy1mYDkYHU5m4Nx1PWSaK731Y+xgnktRtGTBd5tbrJq8qsN5W3w+mODHF3769bM7Fl2SJ410KttktUOIE7wEUra3EQ1J9MZLbA6ZoRsyaKfo//+f/yFe/+lU544wzjEj66KOPls2bNz/n/cybN0+eeeaZhs/vv/9+Wb58+WybFSBAgAABAgQI8IIwa6Zo//79U5orZjKZWWWSXXjhhXLxxRfLt7/9bQmFQrJnzx558MEH5fLLL5d/+qd/mm2zXrII59xF5rl7RsgLqN16aq8LdaDPCJUwh2/3k+t10Xtylwt7swtcFI/IiAWUMGtEZIr9iwoo4/tdhFtNaSHWgouKiu0Il9yi1IooSZmjFhQ/1SKlmhbe9aRqKFSbMHykO8Fw0e0vdr5TLe7fr3RCQdsTc/vde7pbX/BLt59ChxZw1K4WG0cI6BbQBKQXWEYHLAEXwgQQMXI6OJghhmcfaqEGgTQAFgvRri9BQoJraIdwz8AQAV4XhXTrLnsOYW80OTMzlJ1DbOCYbRci3rHj3fMdH3UHjA84wQsYIt/ueY4hQgmYiUVumVlo29HxpG1H2w7LEJVa7GsoooVYmSmqkcGof2aiIbPuy5t0U6FY0mB5qwMlwljAjusO40ARkQkqSNp0vGPVRjKuA7XcpUyRMjXNA1YXlevR50gFyA3sWMXqUVDst2TJuIa2MkNTt/Cw7w3PrNC1LOrzjEK3rHWsp+rbz1koHiIN0v5j3b1t2aOMT5tldpA44QkTejwzfdYqAHYU7ZvRHtsH8F5AYok3i0TBWGKGwRBxcgJYn0K7yNzHKMFAgTwJMEa4xmATve0K2HntCtVovV9Wq7PmJV44AqZoRsx6UHTKKafIT3/6U/noRz8qIuIHQt/61rfktNNOe877ueKKK2RsbExe97rXST6flzPPPFPi8bhcfvnl8nd/93ezbVaAAAECBAgQ4FlwIDRBgaZoEr74xS/KG97wBnniiSekXC7Lv/7rv8qGDRvkwQcflPvuu29W+/rCF74gn/70p+WJJ56QarUqRx99tLS0tDz7D19GiBRqEgnVJhVCtNEvIsVCp4s2EkM2swamjMjeqkaQ/WJpgImVTrsAHQui5siEC0+QSlsLN5n1yCBXy9TU9owLnUpzbAmGfDcEMm5RUdNJsCLQw7Tu0MiwFSnx9inq+RMn0I+pBwHm1of2udTl2C7HVPWss9eLr0+mT83jlCFC6n9SC15i/j49320HTYI3QESaLelIfMovaRaYmSq2hQRBJqJrMD2c9YUMRKBI0X+obI8JlC1B0xClxSmrJUJlRMCu+e2VIULpCSCmzBCzCIUe17egZQLbVkrZ/eIaJfeoTmXcNhS2FIV2+9qJjduLX9TvsYRxZsse1bfR9QFbUdVMLaxzaQuwgGBjux8nu4msvR7ZXtvOQmfIa2vSpziqB2qyxM+tcAoanfGlriNBi5OeF9Kl6q6ImUwO2Db5khLaN5qHLOPjn0OlLRLDU2d3spbHF/kNoT2cbWnbFSMWEn2MLS6Qjj6+2O5veI1bzlmv2bL02vHPhv4sA/9e6utg70qkufJsLH6G4sctVD5Fn+uyMr3RcbtfX+5ENZ+pfVXJKruHDLhITlnrdjLeVP1Wdq4tlg0gk9CvN4ckn3sRmKIAM2LWd+T000+XX//615LNZmXFihVy5513Sm9vrzz44INy0kknzboByWRSTj75ZHn1q1992A2IAgQIECBAgJcU4Gj9Qv8dpnhePkXHHnus3HzzzS/owPl8Xq6//nr5xS9+IQMDA1Kt2gjncEnLjxRqEpkU7nifoqydSweDxBqiSB6GYDYqQWQVVb8jzqpClktBs8LiVNQzPE7z5MpApZe2aHvc9pk+LUxbrZkl2ASYxcF0suNpzbSg0hQwbYPXzuh3F5nDD/2xndTvfryeoeEOrM1UxiySdaFdC7LwdJmbG9XvkZ2ixx20/SvfpKwCEpqgRdAnAkaJiIRZ5zPZl4X1FwW9JpWczUJhZqhCDFDcWt34TEFE1WARxpYqyzCh956KjELXhb4GrRGW0OxUNKoHQwREMiVt39SvB7wP48P2niWTNgsvuZeKo6p3VmaxO3GwiHU2UbfTvgyGCH0JGVjYDn2+gVlTdgVGnjW6P+2bwQa4HTews+hT5HFViYrkjncdonk9GFS3hO8Ysq0yxDLte43V+HRusPEozgV6s9wce05giJgVzPSF9XsqJ6K7h68RmFDPECmGj7bHadluVv37BfuD3xCbx8I/yEO/Lna4389Zb/soG5eimDKQ1GcB97Lzac34Ui8gaDSh22E9HzNMXtejGZtgilBOxDcblWoy9YwyMD5pZaVr4ciUx2TvIzBGYO1ZzxjNViVKJWwOCQJN0YyY9aAoHA7L3r17G8TWQ0ND0tPTI5XKczOj+pu/+Ru566675F3vepe8+tWvDsp9BAgQIECAAAFeVMx6UFRjd1tFoVCQWCw25XdT4ac//anccccd8trXvna2TXh5gq+b91DRKBlZJ+QjBA0RWIOJRTbshd8GM0VhikC4PEexx0Xr8AtCFhsA3Qc0CawzQWTcmoHWSbNWIDkCEaZjZLi5gjEaX6Esh7IpC38cMb+D0REYKGivwBDBWyc+ptlxbYje0A7LMCGyAdPVul2ZMNV2wAkbrA9cplHAEhlI8LKZrPNhN2LO+IOeBO7c48vstWzbqueWm1q3AeAetO1UpocyfTyrRvAFVaEzU00Elk3kqssMUZXuPZd+QUZj6xZ3gihvgnsBhii9NGV+B0YN7GHdT0g/V3Yv22OZJN9OOl/PxNFlSPbb68rMEPR8AEf0KEoqIpLY4KiL1D6rLfPbKpswusYypqGS3We2z/4ukqbny9qNSWqPazNYQDBEAJhjr43T43Kfysyz9AZr6Vp3WUYZfaTUavsE3gus7cmQNqnrcbfE88gCXZTXSGjpDDBT0Ch2PeHag/NuKE+izYLeB1lnuTnog3qcFtsueAZNLIYWS/czbjMya2GR8cX23Pn5BlOL5xHvBrwX+N2Jfl5sbZJY9tBrigKh9cx4zoOir3zlKyLiss3+7d/+zeh/KpWK/PKXv5QjjzzyOR94wYIF0tra+uwbBggQIECAAAEODILpsxnxnAdF1157rYg4puiGG26Q8KT6R7FYTJYuXSo33HDDcz7wl7/8ZfnEJz4hN9xwgyxZsmQWTX55IZyvSqRW9VE6M0QAIi5oizAPDT+g6cAaA8/saGQWG7W6jmK3i3RHVoPVc8vOTba2GAvpUnss01TotIyVL8xI7EV93l3bozqYeQ/YdiP6Sgy4EBnMVVV3V0K9sBR8kOB07Za4DmDI4JuESBnZKNAi4Lqi1hlYHJ+FohEjzgt6ATBK2B+ykUTqug5ErcDEQvuYdW2w36d2O51KZmGztsF6q3g91rD1P8mR79DoUe77ueuUaRmizL39NlWoGqM6WgncRHyPYmwavSt7mF9iGeGUap1iqkPb80eWdTzujdtERCR97Sq3XGjbPbbCtRf168AGwCPKZxxZoqnBM4cZu1Q/vK1UOwT/GNUSsS4GDNHIkfZzsCnhYr2NQPOwvcY7zyUmhhii6IRdn642GZiS9s2W0fTb5eqal8ltBOBbhOePva3ge4Tnrve3yhBBu5cjjyjI4/Qa4bjQfQHdT1D2HGmHOHMQhWHR1xODZV2675EBiGeKtUhlbUdJNZfwosLvc6ryQB8Ck4R1MET+PJvsshIP+WuLZx4MJlhjsHRgmbFEfTwcE5mGh7FGeUqUy2W58sor5d///d+lv79f5s2bJ3/9138t//AP/yBNqu2s1Wry2c9+Vr75zW/KyMiInHrqqfK1r31N1qxZ4/dTKBTk8ssvl9tuu01yuZycffbZ8vWvf10WLlx4QNv7nAdFW7duFRGR173udfLDH/5QOjs7X9CBTz75ZMnn87J8+XJJJpOm/pmIyPDw8DS/fJmhUhOp1OomiQoW6XGqPoCCsEVKY27Z7V5iTVTdHoMpFHDF9BhSb8eW2T9oGAwBmGpBajtegqjOjf0PHqdPur5U8ccC012tO+wgKpxWGrxdTSF1ymXva9xAAH/gcl2Oe8YAAAMCAC9jbI/BEFJvw3H9Y0DTZlEVXjcp1V3G1I0KWPHyBi1c1r/rJarejT+8WE5OoYaRpTRZ8WeLTn34Pyj6BwCDIQwAMUWKqRH8YYMRH/6g8B8aDIY89BxiwwU9Zx0cdNvBijcSVDTvt9NKlcTM1H6K0sf3nub2n1vgPj/1OOdY/7t1bjAkWtUnrNcOInYeaCAdGn/4WXTsB+De6NQOWDEYSgwWdem2iw67ke/4Ue7dxdexXsJi6oFGcm/N/4Hjcx88RqdvaW4lRoOg2JjdJ/5woh/y9NPYck0coGkwwB9Of8aiezwnyX3288QT9l7HB+18XSXljov3Dk8pevuFAdcALv/RvDtr1gtztW/MgXkrT/drAsYqTX/XoARleFBmp15CR7R9Zjd+6ipt8zh8skKYpiW5oK5Q0FMN1wc5HVvsYAjXBOeOUkN+EMVlZ3Qd78imikiEBvaHBAdg+mw2TNFVV10lN9xwg9x8882yZs0a+f3vfy/vf//7pb29XS6++GIREbn66qvlmmuukZtuuklWr14tn//85+Xcc8+VTZs2+RmlSy65RP7rv/5Lbr/9dunu7pbLLrtM3vKWt8i6desMSfNCMWtN0S9+8YsDcuB3v/vdsnv3blm7dq309vYGQusAAQIECBDgYOMQT589+OCD8va3v13e/OY3i4jI0qVL5bbbbpPf//73ble1mlx33XXy6U9/Wt75zneKiMjNN98svb29cuutt8qFF14oY2NjcuONN8r3vvc9Oeecc0RE5JZbbpFFixbJ3XffLeeff/4LPKE6nldK/q5du+QnP/mJ7NixQ4pFO9S95pprntM+HnjgAXnwwQflVa961fNpwssG2b6IZJrrl5mnu4ptMGO0YQULCjGFAeM9L94DM0SMUVjFreUOF6GNHJGYcr8QMCJyAEPkD6uHiU64yLL/NC38ikAQbAgxVyjRECq5882h/Ih+P36kLe6JyBCFHMEQgR1BSm1M6fa4RtxIOQargjICQKrftRvTDzh/ZoCKZJEVoXT6VL+9b2Csmio1b7BZJjNDMD+4V/Ehy57l+hLmGCyeRUSJdGecI+4VGKQlP7VRf3TCimWrUbff6Lj7PL3UGnKChYAhaFOJzlXvAVgJsHgjK8O6f/d5dontO+vvPsK10+/HfC3xUbf0jIxecpR2mA5ghvx6vz3/xC7XOUrdbr4NDJH/Xg1Ls/NQxBjTle77bp3e5HIsk6cjkRoO5F+ltNd+169jo9aMECaB08GzYT4lXNs6Yu8FTxn6yssKNknFuYGhgcg3o2amYOV6lCkCQwQkBlyfDallyvgKd02ZVQxRIgmYIb+u0/Lo02Dp8DyyABzTaWB4KvZ14d8zmP7CeeAJ89Nl+uoNkwMJT6fFJuz3eCZi6WpdQK1tRqmQsdVum+ReZTYhrIb4W9/VmPKDdADv1GJr6MWZSjvEg6IzzjhDbrjhBnnqqadk9erV8oc//EHuv/9+ue6660TEzUL19/fLeeed538Tj8flrLPOkgceeEAuvPBCWbdunZRKJbPN/Pnz5ZhjjpEHHnjgxR0U3XPPPfK2t71Nli1bJps2bZJjjjlGtm3bJrVaTU488cTnvJ8jjzxScrncs28YIECAAAECBHjJYXzcjvbj8bjE43YE+4lPfELGxsbkyCOPlHA4LJVKRb7whS/Iu9/9bhER6e93plS9vb3md729vbJ9+3a/TSwWa5Dt9Pb2+t8fKMx6UPTJT35SLrvsMvnnf/5naW1tlR/84AfS09Mj733ve+UNb3jDc97Pv/zLv8hll10mX/jCF+TYY49t0BS1tbXNtmkvSTRVbCrx3jMpvVnNxDo3aeQzMrU1PAoWIk3bsxAhaGw0vTpjGSKAxb9s4hbOQcRrI7ZsH2mZdlKUrnqZJmKskJY9cqyLLIdf60K1jt+5B2b0JNfOUNQd97gVTrP2zP9yGYws2I6mwQTZ1OMJ1eiHyqoRGjA/8xEqUpwbCrz6VH275LRuMEsoVYHzrYZDIipIhsibWTsIltOLEtoWK5IFwPx4kSeJUr0IVNdRNBhAqjwKTqKkS67XPlsoGREu2+NBE8VJAJyCPLbUfg+xeexxLSrag/bLlNv59TF7nSaoNATuAUwYm1UbhL7RuckFVWW1EABDFMq76xLbPSoiItkjnfo2rCwoGCJgshGnSKMAGwzSxKKIFz4DQ290Hatpq4rk7Rl4hgjWDoDXn0DUTpKI9GIs3R57H5qaqcRFwjm07HY3FwzR6Aq695NSzd12eh7HO91GgoTjKUrRb91uRTkF1RqCYYb2ByiTLo0LyNbLk6iGSBmflr2WieLUf04yQJ/25pN6eaAx8rYPeuu5BI9PpEDCi5aeKcebJK+FhfEOGl9uzwEFnYGuJ/VcdGyAe+Gfh8nStReBKTqQKfmLFlnx1mc+8xm58sorzWff//735ZZbbpFbb71V1qxZI+vXr5dLLrlE5s+fL+973/vq+yQJTa1We1ZZzXPZZraY9aBo48aNctttt7kfRyKSy+WkpaVF/vmf/1ne/va3y4c//OHntB8MoM4++2zzOU7yuZpABggQIECAAAEOPXbu3GkIDGaJRET+v//v/5O///u/lz//8z8XEVcRY/v27fLFL35R3ve+90lfnzPtQmYaMDAw4Nmjvr4+KRaLMjIyYtiigYEBOf300w/oOc16UJRKpaRQcOHF/PnzZfPmzT5tbnBw8Dnv50AJtl/qqDW5f3Wti2pnMi766Pst6SH2ueh3YrljWOLD9nufJo0yIFma2y8rc6OanollLoJtTIW17eToGBFMcp8tEhoizUIN8+2qW8mqaRqyRsZP1ynSsmqnznHRfOxxlzay4o+2iYjIoz86ym2nDGrrTls4EuZriNYmFrn9dTylmolupAq77bs2uXbDah/gLB6fzebFCLpAyQnKqkFJCmSchItVXy4iQoaZ0PZk25Bd5j7H9gBnqeBeVenpBAsAILsMKLdaBiSSdQds3eqWhS53cXwhV7rl0JfFla2ExoiztLrVUG9sWXTK/eA8seQsLs42A2vioavM+iHC7nzK9amwnh+W+QXu5dy82VUzBUMEDB/lzh+6HBh5sr4M2g+gdUf9uo8vJc2NlvtAFlSDwaQStuhnzCqAweA2cCTPOq+8MqC+CLFqj+rmjjOzffwcsNYxMYxSL+4AMJHF+wYMEYw9i63W7BV9HIwUrjW0Uj61ns7z2RginAey3rzlALJUqSvFR/Q9qUx4SD0+YODJWcG47vlJRZSHj0FWqX6JfQy63yb3imkL9J+hGumi9F7h3CtRkQoVxn25oa2t7VlndbLZrE+9B8LhsC/ttWzZMunr65O77rpLTjjhBBERKRaLct9998lVV10lIiInnXSSRKNRueuuu+SCCy4QEZG9e/fK448/LldfffUBPadZD4pe85rXyK9//Ws5+uij5c1vfrNcdtll8thjj8kPf/hDec1rXjPjbx999FE55phjpKmpSc4666xnPdaGDRvkiCOOkEjkeenBAwQIECBAgACTcYiF1m9961vlC1/4gixevFjWrFkjjzzyiFxzzTXyN3/zNyLips0uueQSWbt2raxatUpWrVola9eulWQyKe95z3tERKS9vV0+8IEPyGWXXSbd3d3S1dUll19+uRx77LE+G+1AYdajjWuuuUbSaedCdeWVV0o6nZbvf//7snLlSm/wOB1OOOEE6e/vl7lz5864HXDaaafJ+vXrZfny5c++8UsUpZaQFJMhz3B0Papz5rtcuAovmOZ+Kzpv3udCiGKHRmAl0uxMUwix0mozi+pRu+pH6I4jO4wjU18uIwrvDYhvsGMbkoEhghmi349GVQv6RkREZM8mJzhBvD30bScKSulT5s/Ta3zceuvOkmlHU9lG7O2bbVoO2h9X1iVHjBEidp8BpafXRNp/mMvBBylBflK1ppD3XEGGHgCtiy9YigwdytTxkeQ0DBH2jwy6tu32XJEZFB1zeo9ym2NEwAwB6QX2mhU1wGvZoxl9pGcrTJPJB0znJVVst+cXpRIWzAyB3QOj1K5lT6CTaaKMq/3Huz5ejbpl9+Nug5EjYrp0FDxfR84w4tIP7GGDFz8MBPe/riSdv3Gf4dqh/2AJ7d7YcmUR9tldRmwiXANDlCEfOjChQLaHSrBwMeI2WxYIfQvPJbJX+Y8aF9UFMwSAKS61qm9S2vqncdZbNM3aSGJss0RdKZjZ5XbC1DK1T5kxeFohM7OC9rslaynRx71nWMyeJ/o8GC5XqFavZV5NGmN43vU3yhK27rJFrDE7wL5aTZPZoZe5pui54Prrr5d//Md/lIsuukgGBgZk/vz5cuGFF8o//dM/+W2uuOIKyeVyctFFF3nzxjvvvNNUvbj22mslEonIBRdc4M0bb7rppgPqUSTyPAZFkwcoyWRSvv71rz/n39ZqNfnHf/xHSSaTz76xSEO6f4AAAQIECBDg5YPW1la57rrrfAr+VAiFQnLllVc2iLQnI5FIyPXXXy/XX3/9gW/kJDzvealisSgDAwN+XhBYvHjxtL8588wzZdOmTc/5GKeddpo0Nzc/+4YvYTQVRcLhOtMBXwrMzSNy9HoQvZxgiBiI2Lh8R2TE7agWs7+rRYg5gqMzygNoqQdEdswQ+d+h9IRGVuzTElF5C1Qu6RNt2F281YmFlqqf0dhy9YjR9iBDA0B7WEsBrVTzPrfMLNCsH3Xurun5jC+12XdZ1SqFc9iPW/oyHjkwUG4dWWZAUpm7ipYTAGMVTZOIZBKgyUFWGqJx6DDq0Zb1yWG9B3RdYb0W4Yyeq0bz5WZ1G69Bz2HvzfiSqDm3IksA9HDey8YXvbV9AN/78yOWY+RVlh0ItWhI/Ixm3amepEpZaUnNqEUxULiJo09ke+Bpo78ndgTeWQB7AoFNRUQfJh0HGCKfCab6uqET9HxSWkA3UZKJP9Yfb1PN36hbZUaieb89NrNWKLMxnU8NGCI8p2VlqxxzMUmbo6c+oaVToFECuLwH+3glhqYO+fN6r8Ho4F7AiR7vITyn4XxZz8vdHHZDD3Ohawru81SUN6bZsTh/MERAnZkCk233x1m87MgN1pXd0psHlZXVIsAtO2uS3GvZN68J1J+GfHai/ZzfMfi8pM9fJN3I9B8yvNDps8MYsx4UPfXUU/KBD3xAHnjgAfP5c8kau/fee2fdwJc72rYXpRarD2BYGI3UdaDUZqfLABY4N1Gl8hrprqoJ96S2bHejgMIcN0cBI7K6WSMEi0ob00val65QlL1pod3O2/7rS7ntR/YvV2ycUnt32vXYmJYBSenLWEXKhbmu3RjMpRfYKSEIKLOLHPvoTeFUCD18lKbwE1vftkNPQNM5IZJGWjtSi2HMiD8C+KOA6cNKIizRMTtAzfWooJkMOWE8GQYdX8EfPrddyy4Iot01wBQq4Eu2KCrN9p6XE67NxXatA9Vha4f5c99G9alI+F2iP0BhmlLEYIj/EIWK+rsudz1a1rEthFvmdIDaTFNLEMtzjTP/vc668xRUmNa5wryfnqtO/XsMsvC9HwwpFs53wu3hdFKKT7mRVWq33bfQ1Fy+m47BU6KIHvCHFaaNOqDL6BQjBl0YDPn9d7ll9+Na648GqBX/HNjfceIA126DbQFqA4YpeYArvmMw5PevNdOwLLbTCFbhS13Qew2DoTSZOSK1HrXVfIkNNX9Mz2fzW9tOPItY8jOBwRDQtbFeN68+GCIbBeo3uJewnsCAlW0QomN2/ZDjEGuKXm6Y9aDo/e9/v0QiEfm///f/yrx584LyHAECBAgQIECAwwKzHhStX79e1q1bJ0ceeeTBaM/hBx2VI6Li6L5KQj82/vO7mabqfaik5TQwjamL8LgL77PLO0SkbgIJRNMUAdK0nt9u1IXdEysd58u0OAsmmQ6GSSRMFIHUbhvOgyECmCHiVGgI18F4IRqDqVtCU3Fjo+5zROpdT1NkO2GvZyVhGSJMl3HEPPl3MGUEkLYPQzhE55iWCRftvqIZdw+Rws9lOpoK2neS9hrku+06i+ibh+xxuG/h2vHUBDND3giv2doegHEZOcYep+vehO7XrYMhys4zm0lmgVvGnQa/gSFqED7LzN9nFus9f9q2E0AfT6uYGSLo8WXEnvS5uanuVrccTtc1kOVed9/HlO3qu9NedJiDMltQ1TIyYIhYPF4lYz8wGSgYy+cChsi3ecgyPukFtl2ccp/cpyav+tyFqbxPbNw1AH2mEsdzQEx3NEzrFCRTUgEsAzDdiOO2b1GG1guydWqY5KcoeQNw38V7AZXr0WfjWkYIfQBTw7AmATvaskvb0YzSOnVGF9O72LcvwkslRJiZRP/HOwhtrEZCEnsRijocaqH1yw2zHhQdffTRs/IjChAgQIAAAQK8RBBMn82IWQ+KrrrqKrniiitk7dq1h3V5jgMNaIeQYl6luXkID1lwiDToSMaxB5ExGx6Hci7iqzUjJKUU1P0ujImO6Zx6m7tfLBBu2C+xCumFKmBeYfe/8vs2TGKmqNRuRbDMEOV649oeF6GBiYLORjTqSijrAQ2CL3dSdstMnzsOGCJonyIaiXnhpV5/aJgqpOnioq6ImP35tET0eJoWHgn5tH2kQ3vDvgTSoGEcByGxvUh1uwSUMLHXvthVP5Zrg20jxLOsk2DNj9BUd5FS0suW8JLcPNWHbVaxr2r2fTFNaCa0uZ2PufYPnmaZrpXLnJI6+wtXEoD7CIwPgUjarrdv1+idTCQHTrHrx568xf3nZLfY8MAKEamb6xU67X7HXmvD9HizbfeyVldtdO+Qa2AlH5HWx2x/HiO3ENYrQdODJViEKgTYJDr3fUfXWXcydz2lureAlXTrmT4IpG0fSqgJLBIXoA2Chg6mpaFpCkxH0vbaNDDexBDVqFxQmUwYYY4Y8+aOblnQz6FNLDUTi/ksCck+OUCb37rLPhSjK1EYWs+DGDguygzbCZF6IViIzsFCMfuHe4jnBctkv2WMCx0iRbr/hwIBUzQzZj0oglFSUJ4jQIAAAQIECHA4YdaDoldKeY4DhVJbRIrxSIOxX4XSpsEiAGyU57PNlAkKFZBfrYVV4emktWfK3U6MwJolaJLAXEWHXbRcI23AxAor8GCGaM7DLtoZXeXCILAhiUEXNoEhiigjheg/VNFssfnud9A6+aVGXyhbwIBeZ0KZK2gFGrJq9k+fKi8iku3TbDxllIbf6UL51v/W66fRHTNJ9cKSYGdqnnGBhgbRfdt2FNnVzD4qpYKI0xfT1D6B7DN/zpTFxuxBYtiWPMA1YkDjBF0FCtDW9+u+SEObM+bWkfmEFGMwReOrqE/8xXbXnqK7homIYxe2PK7ioblu++a9lK5N2qCOrZxObb/f543zra4MAEOU0LR4aDmKK9yBQvqDUL+lxkoVu/5AcZlZn3tv1B+TU7ljY7YNzEDwOhgisAywSYAhIBDlzD/qC2lNHU/PdyfZsVmz0ZTVSPVbhoezxxihMunQKDsM7xN+r0A7BIaI+yCeazA9sQl7j8E0QYMIM1gAeh70BTC/uW533s9WxgQMUf2AbgH9HDLJuMRLZRIDxsa0fM/TS7VtLllRWndiVkB/3wnDSLeenV+TRPpFoFyC6bMZMetB0XMpzxEgQIAAAQIEeAkiGBTNiOc0KJpcs+zRRx+dcdvjjjvugDTscEFTqSpNTVWvlYGtfNiXs9ClrsOoL75fzRi1kF5TjlIcoB2CPxFZnftIjkzLqjzX326j49wcO8m9/0QtvLrRHj4+RpGesiDZPrc/6GlalCnirBXWLCCSLKj/SqELogt73AhFzt7cTpdzHrWClFKbO59im+3q+/7M7qg0qqUxTnWR9aI73PFRdiAxbBsC08lyIuSj7wiZAHr2D1oemlkO56xHFPRMKAOALBewDGBoWCcxvtieG1gzr/3RKBiMCZgimBiinAYw79euoQMnqf5CM3RazrEVWpsqrr1zU+6ap4uWDtl1v9MQoUdxVltyH+leiB0EKzCykoxC9RZXUm771Da34ZZtjiFq1fuAbLbM8ZaKqmb0grSqp84osRLjej0ed6lPMJcUqdUZIso8ys53y7iyBGDzYBgJvyGUl+GMpWbKXWHmA75EMGPkorpgiCKayQijTGRRIcOxwe+MzFGFGOsKZYWyCWKdxdNnQJ/3oprRsiZo4FTs3/1wwS8oOw3tJeYX1xsMEd438BtrKAyryHUrI04MHKNu8KkMG8qIJEI+ww2MJthpvHMmltp9wU8L5W2QQedNUbVfRidCEsnMzNwFOPR4ToOi448/Xvr7+6Wnp0eOP/54CYVCUqs1DhUDTVGAAAECBAjw0kUgtJ4Zz2lQtHXrVl/EdevWrQe1QYcbQsWaNIVqPmMInQmRDvyCEL0nBmz6SqiCCE8jupKG/3nK+uqirD+O6FhLADaDGJvUHhtVt21y6/te60LV1D44QbsFtDXwD0GEB3fiXLdjjsZf5cQEi/6Pag7abHSepxrBISrFAIaIM6xi5LcErZLfj54fvIEQ6c67TRkt1QzsPs99v+gOjXhV59OmETZ0PiE9PvYTLopUo5aZYcajqajRrR4L2WYRciXPd5O3jOq0cI1HjlKNj7YlN899DxYvDM2QRr0oeFpqRrvUZ4X0MFHK9gJDBJz57nVm/Tf9S836jnuW2Har71AzvThZrwG20RfnjNt2wXMKKHbY/TXvsZlXDFyH0H7LYDUP0LNAq2AV0OfKzfUNwWTiHLHO+wJzB9XKNgAAtJJJREFUk+u25wDnZGaKuMwNl1gpq2N1xyb1zFHtTcczeli61lzgNdtrGeDWbZa2g9avfnzywILjPb1XkA0Kp/dihxaMzSGLzbarYyP7spEvWsYWYObrktbsuuSgZYiAZmJ0Mz1cPkSv/7M4dZcmXX8wPan+Ojts2qzPD3RhbfQnEu+MSNluX0mIxClb8ZAgmD6bEc9pULRkyZIp/x8gQIAAAQIECHC44DkNin7yk5885x2+7W1ve96NOZwBV1SMsOGzExsif6AiaW9KFGrlKCxOWcvXSpsLa5kBqpFHDdfR8j5KYFYm3HEKvS6LreMZx/SkF2gkqKxEg8Msed+AIQImFrouB50L5tdrJ9kqnpkhe15dD9uu2jxIrtA5y6rgPOLDZGyCWmfDTnRR6nYNWP1tZZJUcwXmrtipWXQa+SKrpl4DrtbgFtxEjtX+c+jIKsgCUyanFZl0FXNsYM9ZGqXH1Lsl4e5dyyOO7YK/EPbHmTfIYBpdTVG+snFwTa4e5a7J36yxdQ2/88RptkFPtZjV5Ij9GvWoAPQVsHKsR+MoHQxR25/sMZ/vXu8ssRP71d2dCr8m99vrPkbnW2lz1y2jEX1il+uEYG1Yd4K+OeftO0VEZMdvFsnJr7Piut88dISIiKR2uja3b2WnaWJgiGVo3m8p0VKr7ed8z4aPccu5j1jPnLrXlfscBU/BzsWH7fPONcmg5YmPqJM1tEUgqLVANRgWzpYttkOwpvc6bt8LYJRS/e7ep+dRCmWInlvtI8NHRrRdunvdLRig9m3qzK21Cvn6tlAWb4X8lMaXkl5NmRs8Q7m59e3zncTekdt23NlaeR0ZmGM8Z6wTC+cbGcNDgoApmhHPaVD0J3/yJ2adNUWT658FmiKLanOTe0FQJ8KgpJJUMesw8agY1GRInUqDoOxKW3kSf2CT/e5pw7RbiP7wlFu0cnrJTishBb3Y5vLL8TLDYAgoqKARpor4Qwa6HC+X3nvs73JU2LLQpee5VY+3yF6H2KCa0eWmfgoxGIpkYHLpPm8oREmDwtxi9+bC+fkq30mtOF/FNKB9ubPpZjhXf1HjDwrAfxhQqDXZbwdqKGWCKdahY3Rghr8zMbvfxCYdDGlXQLkKD21its9+DGEyUGon24eyO+63/nCGO58n3AC7ON/d1NiI/QMCszoUP222Omw/GMKUCO4I/jjAxJKLmY6f7vr8+IBzWwztmbqwLM4zOWDPC8VEq0m6bru4VIxtr9BUDAZDwGf/7Hb//0//7AL7Uz0UKqVjmgz9FgLe1h2UIo+pTh0MDR9J/S1q+/3cRyhFXvsjlnnqr34ASgWNMX3PxYBzve4aJfe6m4vpMBiTii5bd7j3CwZDEDQDiVG+J9R3tFkYvJW0L3dqGR4MhoDWnVTeiKdm1YzVp8nr81vS9wAE5hFd7niDa0+Lc5Hw7yv0TUzlJoZrvn+yFUaFjBdjFK82kQQAA1QIt9u3lqS1SBsdAgSaopkxtaEJoVqt+n933nmnHH/88fKzn/1MRkdHZWxsTO644w458cQT5ec///nBbm+AAAECBAgQIMBBwax9ii655BK54YYb5IwzzvCfnX/++ZJMJuVv//ZvZePGjTP8+pWHSKYi0VLFMxkACrYCoTxFDBMaxqKMSlzDEmXoMkc7ZTJS+cEQgX7OzXXbx3RKBuUwqhEWr9qyHxWdPgJTwmZpiOwSNC3AKbQssAQj0/2EPc/c6RQ5b3MhI2L6FhusN0zNIIIFw4NUeUxzFVsd28GFa9k8LrPImlVymjqzGWAn4qP1/WTm29CxZZeLpgud7mzaN1q3t1KnFYVne5RlUMYFjElETQbDOU0X3kvGdKvYFJLa3grbB2UH2lFEWPf3lGVQwLZB/B7fHzaf13TzMKYaECHrYVH+oKHsCE0fIBqHKD9zsp7vVntdIC727cvZHTXcK8o5CI/r1EpS+7RG/EiZ5tIR4yvd/sd29YiIyNrTfiQiIp+8911+m7btUwuoAS643FSybeTCy/UpRJTjsfvreWjq0NwXJ0U79tt+zVO5LObP9LrfIwUdjHFmgROns0nlnD/QdL/uPqnHxXsEfR7T2ZhGBJMGtq9EU1ADJ7r2tW+u6fm49wMsSoBiN/2QpvPKysDj/ZjtdeuDJ2of0KnUiWO1/Zu0TJBOgTVNsi7AuwzLvCXn/RQYrDNwbpgGj4/ZtrXu1mMvjEo4R4z2oUAwfTYjZj0o2rx5s7S3tzd83t7eLtu2bTsQbQoQIECAAAECHAQE02czY9aDolNOOUUuueQSueWWW2TePCd87O/vl8suu0xe/epXH/AGvtwRyVYkUqpHhQ0MUYHCaTBEccs6ZFc7MQ6ElCOr7K2DvqNlj+o/lCFiUW+4gBRzt38uDBsfscq/XK/Vc3Q8UzD7AwpU+LVKgRzMHBGJIdJY+C3VLKiQc+AU93nrNqSv6/5UIIm0V2g1mI1IaAFclHhAOQ8AZRJYS5Gda88HBSOB1p02AgeTVomHvBbIi0n32GvYssUpgqsJvWcoftlpo8TMQm1jpztWfLNlTFq32zfR+HKIvd36stc6gcSTz8y3bc1TkeGtECu5BYwFARgFhsmMEpPtrB0aOsE1oOMJFYxrZOyL8Cq8LkX7TqbXXvOuu9HXpr63SFPHEuaM8VFlA/qgq9HfZ1x7Ki1aPDinLMWYNdUraXmWYqeyJe2Wvfzcd/9cRESS0ljwFRoWCO8h7IWgF4xlbELLb2hfwTJcsExOz8Pu2NEJ24b0YtsXKmDrlBkNTyNNKSqDzCn6YIiALDSByAch/Qz6f6HL9tn4iD1wOaHnpWze6HLdXg9fN0nE7/X47k+JZ4gAlEPKLnY3CeaUAPZX0eMOrXHvoa6Nrl1giIAuLVocH3Ofj66w542iwaVJCSMoKAygzQw8h5x4gf4OdjrTM+s/uwcWAVM0I56TpmgybrzxRhkYGJAlS5bIypUrZeXKlbJ48WLZu3ev3HjjjQejjQECBAgQIECAAAcdsx6yrlq1Sv7whz/I3XffLU8++aTUajU5+uij5ZxzzjFZaAEcwum8hCP1sCs0qkxQs4uKa3G6Bc0uTA+Nue2ya+aZr4ePUE3PsGqJNGrueGbmLIYmKvQYG3XbI/JL7nEMVmahpXieLbV/fIlliBoyNDT479isEbRmHDVphIzyG8U2mNNp+zSyRsp/QwpwC7QQbr+JQaXKyGkdkTRQJq0Tijwi0ob2odBq999EvyslYRxY9RoXpAMjgy++1+aMN8EIcr6ju1B6Ye9pqodSZqZtnfv92Ap7zPQiSjFfYWmLbb90HmKhbmWaBrXAbLcW691vYyDP2ilgUggmCKn6nN3Vf5Zdb3na3UNYAzQPWV1Xus/qVkaO0GtOiZWsSxtbRoVw9VEBWwOmCHqvKskzqs2UHk+mjZxSHUnr8dKOXayFaw3tZPYqud/2D5SFAcAY+Yw5Aj9frDUaX0ZmpJzFqv3WmxyGwCSrRcA22zcBXCvPBtJ+W3bNnEWMbDeUBfI2Cttcg4bW2JsB402wLk2k45rz6NTHe7brU+h2xx/Rgq+4D6Orrd0C9EAwRAXY1iFrX7fSPFB/p6X63W+ZZau31S09O0jFXjPEWsXHqhInE8lDgoApmhGzGhSVy2VJJBKyfv16Oe+88+S88847WO0KECBAgAABAhxghKTBxP157eNwxawGRZFIRJYsWRJ4ET0PhEYoVERBV2JeSsiq0CW8bkZXojCpHaLPecyFevluFxlBS4S59nqxURu5VdnEbIXNvsLvoxrtJHe58DxLTBIK23qWxEqQpGUXZeGQhgLZM/FxmMK5z2NjZV3a8/DtJ41Ersdqh7BfZMXAE6diN5POTS5kzc9xj0LLDnc9YU/o/Y70+GCIgNHlkYZMtPg+x/LV4pqBoyUTIuMubIXRHMDFPVkrhKwp1Fv13jXjLkpu3qFGfXqtEpotBjYgNgotjz133CtE7cjCqir5hzIXiHxHj1ZtTl5LMGQ1W27Ith8+QUCu1y3zc9U/KA4GRnVmWkQVmiP4K0FPVqO3VJXYSJxnsYf0cfvU40qZJWYxo8QS5HvsecRGlYHS65HcV/Nak7Zt9liZXs3+hM8NPV/ILkO5GTwH0BYBzLxGyZ+LC6yGKbsMDBEwcDKZRfbTOmWR8nMGpOfbdib3WX8ysITDR2o2q17zzALdnnQ5kcLMVAPeT/49Bf2bfg+GCOje4DrvyJHKupIpojcQVe1XoY2MTPV2zn3E/m1LLwh7hsh7F6mGDToyn81ImbFFylhl76ZouiKRQvC39KWGWWuK/uEf/kE++clPyvDw8LNvHCBAgAABAgR46aB2gP4dppi1pugrX/mKPPPMMzJ//nxZsmSJpFKWYXj44YcPWOMOB4TGMxIKT+pBmlVWS1rKotLi1isxjbyUTUBhUsy5I1usqn5CYIiA4aNsBNW+lVyLi4hQ7a3nwopciqHUadtbbLM/mLPeMWHhMUcvjB3rsuWS/dbXZGKp00gg4gVDBIDpAoPGJS/AEMXG7XmNLbPtQ2Se0QKS0AYVxV1XdhZue9qxOyirklvguCJooIDxJaqJ0p9HsnV9E9pc6HQpLIlBe4z9J6oHkzrb5rssIwQU51vBRbhZ9VgFzXIbVVZwt7qha6Yc/IKgDUL06++tdkMwQIie/XGpREuNNDp8L0odysI1c8kGt6icodTVUy7VqPVVQ2azsSedfi7f59oRG2I3Zz0PdAm9nDE4UesS55dUZijXY5vjix/DSVtpwCb4y+j1ik64hif73XpuLvarnjlDVWnWUwCDC3fiiSVUXoL8tZjRQV/xBVBbpy5cCpSoQCwKIcPbyn+u7NfEcvf7+DBlVZKLNzNDcGmuv4emZlTgnA3NHpjiCDE0bZRN1qDNSyGTUPu23mOv/aFsuPQipaTgAaTlS8ZWxMzvua9y3wbbAzBzBLTsrjSw6mUqXMw6pULb1FxDXqsAgCVrcFQ/RAhS8mfGrAdFXPLjxcSVV14pn/3sZ81nvb290t/fP+1v7rvvPvn4xz8uGzZskPnz58sVV1whH/rQh/z33/rWt+S73/2uPP744yIictJJJ8natWufv91A1fbAWsK96as6tYJaW6VW+xcoMWjfLmx8N7rSvRwyNvu6QTjYMNgZdH8RC12tehwyldRyF6j5hVpHbPrWttX9BW5SSwEMhoCWnVYEPLbKDp5xPmzE1yDsplIZEFRX9aVdUPNJVKaPpt1bd3ypaz8GQ8mtY7rU4+jgp9qSMOseTZjucO0ZOopM705159vyYF0IW6N07cw8e0+RRl3RwVByQCn9GNK33ffRva7tpW4dDBX1hZ/TvwzanzD9g5crBkNcfgDHASBi9+3Sl3yzvqx9hXIY36ldQXTcfd5zqpsLGbnHqVJhk1B9tWtQXMuSrO50I4jXHflLERG5Z+goERHZuK9X94c/LnaaCojkpl5HuQSYNpatFllat9nznVhKf/h1/FzRQc/cRzAw0fIqepkhRI+N1/dX4erzPVY03jxo25Ic0FpiJRqA0iAIg4+IvwduHc8JbBKAzALUKnPrXFIitZMNPe33uGYw2oQtQcvOmmkPgClWPE+5OWotkLIDe35P8YCCEx8wPY8+CKsNf51DOL+aWQdGV9lOw6axeC/UN3ALDAZ5MDS+WMt/9E8alKENKBVCAwJ+h7GoHQPn+Li1mih0RqSYf5HT8wM0YNZ35DOf+czBaMfzxpo1a+Tuu+/26+FweNptt27dKm9605vkgx/8oNxyyy3y61//Wi666CKZO3eu/Omf/qmIiNx7773y7ne/W04//XRJJBJy9dVXy3nnnScbNmyQBQsWHPTzCRAgQIAAAQ4aguyzGfG8h6nr1q2TjRs3SigUkqOPPlpOOOGEA9mu54xIJCJ9fX3PvqGI3HDDDbJ48WK57rrrRETkqKOOkt///vfypS99yQ+K/v3f/9385lvf+pb853/+p9xzzz3yV3/1V7NvYDwuEqlP7VSb7TQPmJZ4AQViHZ2Qn2O344KLzBCxoViHTptBlJrcpXMsSttjuggCbKS8Yx1FSicWQTipxoS7bcgZHtc5G5gZdjlGKN9DimsCUpk9QhCE23IlINliIy5UbdJyKE162Ka8TuGgwK1aD3Q8Y9vlD0OMUJOaaWZXwLFQp0MW2Ecje7ylLVonMUQQKEOEmxzQ1Ps2GEViqkTPJQ1DTbNLHwQj6m97Uu0K1OguMWi3DNuZyYb2QCAKtD3hqI9awkbX+0+0dTHAEHmjPS1rEFrp+sy+hxxDFNZbXDlaT6zs7l0+4/b/8IibLlycdMddmnLM0ZOPrBSRZ2eGGqbrUN1+1H6P68BCV7AdsBjg8gxgiAA8A5iGDKOgbbreZ7JzXT9DYeMEPXdhEhA3ETOSGMjq0q1PLG+1bSaWAUwHrClKdnNp36aMJ8WC2V77QckStV5kD1YMDE9mgU2YQMFUMEQA3gdYon2Av3e65HvDDDYYInyO5x7TiwCY4sHj3PPHDBmXDWFmiYXqOG6+E+ab3Cfq/2eWsKkCVk0TCMJTM0TAxGK33r6l/vxzHz9kOIwHNS8Usx4UDQwMyJ//+Z/LvffeKx0dHVKr1WRsbExe97rXye233y5z5849GO2cFk8//bTMnz9f4vG4nHrqqbJ27VpZvnz5lNs++OCDDTYC559/vtx4441SKpUkGo02/CabzUqpVJKurq6G74BCoSCFQn2wMD4+Pu22AQIECBAgQICXJmY9KProRz8q4+PjsmHDBjnqKKcReOKJJ+R973uffOxjH5PbbrvtgDdyOpx66qny3e9+V1avXi379u2Tz3/+83L66afLhg0bpLu7u2H7/v5+6e3tNZ/19vZKuVyWwcFBX7ZkMv7+7/9eFixYIOecc8607fjiF7/YoG1ilOa5cD9EJoo1Cpk4tbycnFq0l6RSC627bCQXG7KMTlPGrUNDwyn64ZwyVc2uS4Ahah60zAq0PB4Viqz0/Jr3uIi40A3GyJbCgHmkP75qmfI9zfq9FRtn57n9tGwtaTts162nNqtx4nDR7LeqBWNDlAKbn2dDaGaIYGAIJB9RhmhSpAVGwdsKaATpi+iqQSeiai4pwsZ+0AhVtSlgOjwLgfTkcRvuRSjKjVAJl4kjnAA8uddRIZkFzdpOFd+rPgRdEkJwoNTvwvBan7sHJaTm71T7iLl6z8Zt3/o/vzrFrMfBTpAOBWwHi3HBBFX10UjPY6GrWxY6p9bRgCHiMilc5JcLw6YXoq+75WRhbOtOuy82jhxboWxeyTI2oZq7VtAUDR1rnyeIvGHzMLbUft/5FB2XzVJVm4NSE6WU/T1bZjQU6aXv8byivWBHmBlqYIKYBcXX+nliFGyq2y8zR+j7OE77ZkuLtuy2nWRiEdlc0LPQ0B467+R+m2aPZ3WyjUOMivyCAQZDlF5gD1LQcjMQ8Hc8Y38fH634GYJDiUBoPTNmnZL/85//XL7xjW/4AZGIyNFHHy1f+9rX5Gc/+9kBbdyz4Y1vfKP86Z/+qRx77LFyzjnnyE9/+lMREbn55pun/Q27btfUAXkqN+6rr75abrvtNvnhD38oicT000Gf/OQnZWxszP/buXPntNsGCBAgQIAALxqClPwZMWumqFqtTjnNFI1GpVqtTvGLQ4dUKiXHHnusPP3001N+39fX15CZNjAwIJFIpIFZ+tKXviRr166Vu+++W4477rgZjxuPxyUej0/5XS0RkVok2sAQIaUeQIFWZoZSu234ilTgiSXuHjQwRKq9QVZbdMDRELUmLZeRVQYlouvarmKXHfS1baWwmTUAY5oFRtYCdWbIAYUtsYQmqNzqfgcmx7dfGaJiR8ycL9iXzGJH3VS1MC4iWUY5GTHLxF5b4TW9wokzxpZTJE9dGHqeqBZnRfo2WAhkQonUi1UijR92AMC+093Oux5R3ZT+tEhsFBgJb2RJ5QJwLRBNIzMHUW6mL6y/0zIfmqWF6Hp0tQov2MwRJSk06s/Psd8vX7PbrO+9a5FZL+emfgbq7db2UtdC30JzYGaJ88PvEJlHyNAQpVr8/rkUBxkHsokjMHyUapC0mDBnbE3O2iuTNmXoeNum1A5is6g4bvN+XAT3vCQpYTa9ABmAuv0gZRCOww5h6rgWDBEyAxsie5/VZT/m0ivMnqH8DWwO2FyyeYAZYNfO9GJ3nmz1Mbmwslt3n0N/AzSV7HuFWUywibi1SH9HujyX5mjZ6z7He6RKfwknZ83FR5RtVpYcz0lI+1mxc2qGCOh4mmxH6BocagRM0cyYNVP0+te/Xi6++GLZs2eP/2z37t1y6aWXytlnn31AGzdbFAoF2bhx45TTYCIip512mtx1113mszvvvFNOPvlkM9D7n//zf8rnPvc5+fnPfy4nn3zyQW1zgAABAgQIEOClgVkzRV/96lfl7W9/uyxdulQWLVokoVBIduzYIccee6zccsstB6ON0+Lyyy+Xt771rbJ48WIZGBiQz3/+8zI+Pi7ve9/7RMRNa+3evVu++93viojIhz70IfnqV78qH//4x+WDH/ygPPjgg3LjjTcaHdTVV18t//iP/yi33nqrLF261DNLLS0t0tLS0tiIZ0GpKymlWErGl5K5IObWVc8BhgiRUnLv1AwRIiswOd7vRwsl1igDAqUmgGqzXR9Z02raA3RusM5i1WbbVTiLqzRHs0EqyKygSE8ZIvgzwURy6Dh3/M4nnQYJDJE/DvkWIbIDkJVXpKyc7ids+zKU5cMMEZf/QJYP2AJE1GAj8H3n2/eI/E+XXNB/mm07R59giBgwI0RfyM61fYGN+/jasp4FQESb2mv1JWA8wGjl5tj97V/ljyQiIm96/ToREfnZXTZAYHkZe8Q0rBMLh2uLwqzs8cMsBjNmYMgq2al9iABkucH4EyUqgP0nUOaULsu6n55HHA2R7Ys16JAGT9SyHWW91jvsvrJaBqNle07b4PrAvpMd8wGvqUKHbXOI2K69f0xZk6rnmvcr3V5PCcxOUwkFl5EBafcHDQ+uVcQSqV7XlYP+TS8KSqYwQwTAzwxZqmBy27a488/1ugMiy44ZNzBAYEthnMnfA9zHmAVMz3cnivJIXHQY71NcN98emEiOlL1nGc4Fv5lYqA84MmT1XnZstkxnYtjezMnFrcPlF2F2JUjJnxGzHhQtWrRIHn74YbnrrrvkySeflFqtJkcfffSMQuSDhV27dsm73/1uGRwclLlz58prXvMa+c1vfiNLliwREZG9e/fKjh07/PbLli2TO+64Qy699FL52te+JvPnz5evfOUrPh1fROTrX/+6FItFede73mWO9ZnPfEauvPLKQ3JeAQIECBAgwMFAMH02M0I1KI0DHDCMj49Le3u7jInIX531DzIWa5H0QnWw1kAkRxlIqb3uNrTsciFLdNhO7of0NuX7XLbUdM6xsTEX4oTTmm2WsOPe0SMs28VZH6l+crjWbC0wNpFhF1KWu2zWVjUCN2B46EydVRHSaGv4GCpAqxFesxZwxflxOZF8J7Mmdv8R8u4B29A8rOU7utyB0ovtduXVLgQObdcMq7B9LBKDNjOr5/V1fU24yd6MnQ9Yrc2yH466c5ljTVSKbfbeDB5H5zrfNX7Ob13f4Uw4nDs0NNMxK9AUdT7l+tbusxx1EtWsLZS9gJ7kTf/jN2Y/P7rnVLOe2EfMjLIQYGS8f5AyQXU2036PQq9gBcD24R5GyCGcM6MqxJBFie1I7teipV3KFpC7Mb/Yc1q6Al43YIhGVtcPnF6ibdUSJy3bLDXR+bQ6qms2VM8626jho22/55IkzBDBm6nQ674AQwTMeVj7pV5T7/bdQVmtqg/DPWLdVUP5D9abEfuGax0fU/aEGF2+tuzfBN0bAKYMfQYaKi7PAcYLnlbM/OD9gOeUC2izRorPi7NtQ7V6/+V3ZZlKADUwoVTaBe8+fF6NNEl7MS0//n+fk3ERaReRsbExaWuzvmEHCvi7dNzfrJVwbGYfuWdDpZiXR7/9qefc3t27d8snPvEJ+dnPfia5XE5Wr14tN954o5x00kki4hKePvvZz8o3v/lNGRkZkVNPPVW+9rWvyZo1a/w+CoWCXH755XLbbbdJLpeTs88+W77+9a/LwoULX9C5MGatKRJxpTLe+ta3ysqVK2XVqlXytre9TX71q18d0IYFCBAgQIAAAQ4wDnH22cjIiLz2ta+VaDQqP/vZz+SJJ56QL3/5y9LR0eG3ufrqq+Waa66Rr371q/LQQw9JX1+fnHvuuTIxUfcrueSSS+RHP/qR3H777XL//fdLOp2Wt7zlLVKpHFjh+qynz2655RZ5//vfL+985zvlYx/7mNRqNXnggQfk7LPPlptuukne8573HNAGvtxRag1LMR6WmHrYjKzWzCMNIsAQNSCMDAetPdbHEaYNk1u3WWYJuhP48owc126+B3uASM8flnx84AiNyLE019EK9QwoKkg55ML7qtZ4A3PkCzn69ul/9OdgiPz3YbhAY/7eHgesSMse+zt2/oZeZUIL7BbIgxMMEVDqciF0y2b7aLBuZ/in9ZIv7BUzZ7uNELe8q0NERBbe68LUre/CubjtIqNuB8igAUOU2OMOmlbiqU2dcFlXAe0CMo3io9rGo8kd+S3uC1yC/n22T7zjuPUiIlLRm/PIkIvA4oPkV0T6rfAaJ6YoDLmLHYppJDyobuhjU1t3s08R/IZ8xO0dqzXCLtjPK1RXq3mYC3Oqhkijf2QkgSVhPUvaEnyy9YPuuB336fdL6t+1PhPWY9a07fbYcBMfPN49t8i4Yyd6zgCssiOzIq7FbqH9SfXb5xLg54TZNdYOsSYnRu8D9n4CQ5QYnloLA4aIi6Zy9leZzpNrNua6lXGG7ow0UZyByAwy7nFR+wBn74G54gxDYDLTle+AblH3NWTPHQxjQ/059ZoCQ4TsNSzLzSEp5qcvS3XQcIg1RVdddZUsWrRIvvOd7/jPli5dWt9VrSbXXXedfPrTn5Z3vvOdIuJsdXp7e+XWW2+VCy+8UMbGxuTGG2+U733ve16qc8stt8iiRYvk7rvvlvPPP/8FnlAdsx4UfeELX5Crr75aLr30Uv/ZxRdfLNdcc4187nOfCwZF0wCDoe6N9i3U8oRTlxYWdoiISHTMzv9UWhy/G1UjPlSZB5Cam59reWCqo9pQBHRCCx/iDxFeMrk5OrWi6eWRDOZm1P4/NjVlzIMhf5wltr0sGuY/JkC+0x6Hp8V4EMVlDfASxUsfg6HSkTQno39VymnXbh4MxYft5mUql5CdV387YEDVtt21BQLeYo97W265QFPjn4lom3RKcoWduwilrWAbgyGgIU26w66PrnEn3bbA/aV53UJnUfHUhJ2rOe7oPWZ9S9rm4O+7Vwd+9N7GVEviLNd3s3ktHpxUMb2W+0jupsGbnlb3Bjt3w8kBXE6hQn9gR1fqYIfS2CHe5QFstseeQGrAHn/3H9EAeKELMFIt7i9m6c1uWRxKSetjampK0zJjy+wx+I8tpsn8tJWeEga0pVZKuV/uIuTKE26ky4OZjBpYomhtSft7jKbBEjvsc4KCp0D7dnqO9Fr54sI8bZae+a8higkDXF7E20hoH+ZpNi5mDOPNlHZViJS50Cy/H2B+CfDUF/qUHxz5grOY29VpyLZwQ7kODIIAHgwh2QQBGr6H+SMPxl/O4OoNU9nT/OQnP5Hzzz9f/uzP/kzuu+8+WbBggVx00UXywQ9+UERcTdL+/n5TbSIej8tZZ50lDzzwgFx44YWybt06KZVKZpv58+fLMcccIw888MABHRTNevpsy5Yt8ta3vrXh87e97W2ydevWA9KoAAECBAgQIMCBB4TWL/SfiEu8am9v9/+++MUvNhxvy5Yt8o1vfENWrVol//3f/y0f+tCH5GMf+5jPCkeG91TVJvBdf3+/xGIx6ezsnHabA4XnlX12zz33yMqVK83n99xzjyxatGiaX71yEclUJFqqyOI77Ig6lLPhfnRUU1YXuOkplKnI9lGoRrp4jrJ9hKNmkP57/dnQGhe9gFYfXa0C7VG37H5CTRZLoHzd9mPL3f7atlkrAADtznehoKv7HPQ1MNnsUKSxYCYEoj4ipPMDQ8Smdb6wo04tMT1f6NLUaXxA8w7JbmW6NDJt/rmL0Jl1aChS2lVnHUJRd4w9b3HL1AZ374rKErQ8Y3eGaanE024+augMRy90/M7e8wyVD8iusKHpqUduMeu/f3C1Wf/xH443633zRkVEZCTnLtLAE85SILzQXYOmjRrea3NjyiZmX+2+72hzSzBEVb0osae0bIir+9pgSdChImQwQXwP97/RhfuJDW4/0dMcDVJY52g+sHy1Pe77MZ3GS+7VqeIa2BMIWbUdPCNMzFc7eb2ORtzD0bTaPZOj+9yBQk0iFe1XaWU8WQzODBGmy/x2YIjQBu3+iZXu/VCtug2Km/Xk4m6DppKmheutTxCDySVfUOYDgP1CagCMMPUpYtPArLRvtiVgkKqP9wozv/xemG7aamKxmlPS9UqM6PeL1OLAkpke3o6BCui27LYsIPoY2sUsZIgLRvN6rX5tiyiDo5eKbQ18eRqYiBKDlJ4XNr9PDFdfnIKwB3D6bOfOnUZoPZWJcbValZNPPlnWrl0rIiInnHCCbNiwQb7xjW+YIutTVZuYqtLEbLeZLWY9KLrsssvkYx/7mKxfv15OP/10CYVCcv/998tNN90k//qv/3pAGxcgQIAAAQIEeGmira3tWbPP5s2bJ0cffbT57KijjpIf/OAHIuIqTYg4Nmiy8fLAwIBnj/r6+qRYLMrIyIhhiwYGBuT0008/IOcCzHpQ9OEPf1j6+vrky1/+svzv//2/RcSd4Pe//315+9vffkAbdzggMZCVQiTUYLg3cdxcsx4u2BBrbIXV4mAOPJpx2yFSg9EfkNqnlvRqcsgRYHKfHl9Fo7FR2970fEcPgElCtINip4UuFXxmSdTaYY8DhgUMDgTTOA9EylxYEseDPiRJ2qHYmDu/cjOYMNt+RKYwWus/zX3e8aQe50kX6ufs5ZdSioTgZIrestO2HxqGroejMrHUfVfudSwaGCIInuO/U72S2h0MHamlR0bsMebcB0GF21+WCp/m5+q5jbvf15psuAeGCAxUedC9PBIavJWOcMzHvqesdiia0+M85RgiaIYQCedOVUGLhrUZKudRfUpZDb2EzK41a7FNLvQ6RNVz1izSehxKOG8ecrTdmW9eLyIid2060jVjjrvObY/YdjQP2j6Z76K+5QXW7voh9R5p22DkIAwvPOQYqq5B7E+kSZkN2AkwGwaGEpocsGyAL36rZFz4RLtBbq/eg5IVGgN4LsFA4flt3UVM0ZhlTDL6vOS76T2k1gFxZWhgFdK6g0pTkCYxN8c+eFxuB4wwmBlOye/a6B70seUqxqeCsii1gr6E39eaiOHdN3XmERgi1u8g5d63i5hqLqBbnGTWyeyzf06Q6ADLDLURQEICtG/8rio3h6TMSvtDgFCt5i1eXsg+nite+9rXyqZNm8xnTz31lPcTXLZsmfT19cldd90lJ5xwgoiIFItFue++++Sqq64SEZGTTjpJotGo3HXXXXLBBReIiPMhfPzxx+Xqq69+QefCmPWgSETkHe94h7zjHe84oA0JECBAgAABAhxkHOLss0svvVROP/10Wbt2rVxwwQXyu9/9Tr75zW/KN7/5TRFx02aXXHKJrF27VlatWiWrVq2StWvXSjKZ9Ilb7e3t8oEPfEAuu+wy6e7ulq6uLrn88st9MfgDiVkPih566CGpVqty6qnW0O23v/2thMPhoFYYodjdLMVYPcQodNgwGmnC4eLMGTfIykD2F7JZWna5JRgiABFPWSNLpNIi7djPsev3iGY4hZcN75jRKrZZiwGgoWgnMUJ4qDCHz2ZyyAYpaTYLCuNWNLU+MWqz49IL47ofe9xe70PoGji6yrYjPoKlu95dT7rj7NWSHWCI/PajljGaWFaTDgRBm9xvOjfaVKHCHMtoLLhvwqxnF7j+gb5QVLuEMOlVQsqiVVVnkhhw2z96xxGubWzoyeUu9tsPohP2WsASYOIId6CQGli2Jtx6JuN+X33ShcBg++LEeLGOhMscIHMotVNtIVSz8VR+uYiItKgJPbjSX6w4XkTqL6s5f1DN1h7HfGW1dES2Z+bMp1qDxkktEXJu2brL9amxZW5/YMq8seikMiTI+PP6EmIBpmOIJpbZm9S0RbVrevOiY3ZHxdXWaiO1zjLI7dssU1Jstdcg02fTuaBtAusBhgjo2kSaHHoPgSHibC4ADBEYGM/E1KCNwnOtGYqaJcasHqMhS5XLCFXs52w1wKaM0FjhfTddOaFotjYpA08/owy/+rvMLVFE2n/dZLdjrdGhxqF2tD7llFPkRz/6kXzyk5+Uf/7nf5Zly5bJddddJ+9973v9NldccYXkcjm56KKLvHnjnXfeKa2tdf+Pa6+9ViKRiFxwwQXevPGmm26ScPjA2hrMOvvsIx/5iOzcubPh8927d8tHPvKRA9KoAAECBAgQIMDhgbe85S3y2GOPST6fl40bN/p0fCAUCsmVV14pe/fulXw+L/fdd58cc8wxZptEIiHXX3+9DA0NSTablf/6r/86KMlds2aKnnjiCTnxxBMbPj/hhBPkiSeeOCCNOpxQbg5LOV4fyUbyNuKBTxDmvqHd8b9XbQ28McAQpVR+gQh1bJnqVIbhkUF6FNWmRTTKqZIfiLf/pyiGI0IwQ/538B3RAJMZIrTbM0mIligyQ5YatAXMHGUW2Aa37LJiCxyXC3ZyxLjgl3n6HlksWqjzlJg5/tDx7vvu9erTRJFzanfIG3Omdud0n/YeRifsyYAZAkapOC3uBcpuZOdTxmGEM2Z0SdcMeoxSq9sgNkJGm6N2+/E19uanOrWIqWqJok+TRxY0Fdp8bygIQ84hd/FLKcps0j7TttN9H9ZnIjaurCkyi1T30UZeOth/uTls9gdWc/hI7XN6mfK9epys+5z9k8CqZueGzX5yWswVjFi4Wmc64sO0DyrBwvqpBi8bYoZTO+01yh1rGaLINnftC5odmaSsLDBEYHZGlzt6Az4/YLnglYN717rV0pFjKy29xtllDDwn0PpkevWe7USaGjEwUWiMXDsy88gXjLLmoKH0WWOcaaTrKPzarKVd8H6BOS1+D4aIz4u9vyZrxcB++RIqynqjHzIjGbVEcMO9955I8iLVEDvE02cvN8yaKYrH47Jv376Gz/fu3SuRyPOSKAUIECBAgAABDgEOpE/R4YhZj2LOPfdc+eQnPyk//vGPpb3dTZ6Ojo7Kpz71KTn33HMPeANf7ojkKhKpVCScc5FTsdPRAKVmlByYOgpBtgWcncEGwAMGYMdZ/C6p2RsTmlFTpkiWwYwK9gudBgowRrTkQr20BEXMSXLapePiOGDImNlJa3tLbcrQPKbtIWZr4AQrfmoeYh2LPaGmkv2eNQTQJvU+5Na3vcWd4JxHKFtHs/bA1KX21CPbXK9tU3zUhoilVrdP3PPsnKkZIkSWngHZr1H4UndOyV32d+yVk6fMOp95h+Mgc0p3A++dEIVITb90zzf0J0nqe6wjA2MGliSc1+K+vsyB3lstDeNLxfhixqQ9Uq1AOWmfgRDpxvYfP7VODGjeE9bjuHVoPkb1GYSWCvoaLpUxWUPCTCHYAjANyDrzlUr0P8h0a9lmWSwAjA7A2iFkO/oiwHqNfXYp9gd2jPbns+L0Wvb+1gpj8j2u78bHkZ1FjAo5cAO4puhLcPoG+wbNILJVmUFjXyFGhaz5cf64fqwjy8y36y27SEuk7cX7AG7VuH+4X1iPj1V84VfWReLe493IjNF07yR4uYULtYasvAAvPmY9KPryl78sZ555pixZssSnz61fv156e3vle9/73gFv4Msd4XxVwpWKVJpR36pilkX9Q8nV4PEyqqcRu6X/gzntS8q+tHkwhMEOUn3nPOreAon9djoKf7iHj3ZvUwyGAJ5WK1G6KqZUWne6/YPeRnsxvYcpF4Cr1w8fZc8nMWi/97Xbpnm34OXXVLZWBuIF3ppOn7IXEtNluM4wufT71ZfmZGE3113jl2hSxfCYMkCFbyxTu91fvv7XJM2xAdTbAvwggQYnqV12nVPkMTAN6xKGftkVepzH7Q+6nnQHgP0D9wU25Gzud38lKpr6Xm61fyBjY+7AsVF3EUut7nhjy+0InwWrHc+461fTwdXgcW7/qJuV69V7PIZ0bD1PvVf8rIT9PXRLGAE2DFgmORhMV8Uen2OABbcE/MFEXTq+F0iAgFUG9oPnlMtX4Np76wqddi/QtYrSQLljM03hzreDLk5194MFfX6yczG9re2ka5QYtVOnnPCAwQ2GKLgXSPUvtE89LR/TsiL8LGBAjen2QofdAGVROMWezw/3A++j5H5Mu7mWji+udxrcQ98G6gtcYgVTgej3mCZGIFVsizTIBA4JgumzGTHrQdGCBQvk0UcflX//93+XP/zhD9Lc3Czvf//75d3vfrdEo9Fn30GAAAECBAgQ4EXBoc4+e7nheYmAUqmU/O3f/u2BbsthiWJrRIrxqGcsYHoFhghA6mqpGVbyFPlY3WVD1NK+1UU2LNrl6bXF/20ZITbh4giv4xkbqiKa52kzTOthyoeN9DiNldmG5IALfctP23mybJ/9HUf7KFeQ2lsz+4V5W1NU2Qmd2gmVbLsyC9y0AZgtXP/RI+1xolTFO9Vfp+WRvou0YS4gmSS7hJY9br2pqFMKJbcvlGbpfMp9P7pcT/ZZ/N38FA4Z/dXTonU3fipHpzhH7T1Y9BNM6apBZsKyexAgI4UdYJax2BHX9qhtQtZOYUR0Krmm02hgUVHNfugoW4oG58VTKTifzDLXd9qe0KBMT8vbUCxWdkXFzZ5h02aBUaoR21miZyySEQmF7W/8dAsxOp4xKqH/2WkoZnYYOGcwGSjE2iDaxXT5gFuHnQPQupPMCv29g4nqzJ0LDBGA5xt2EXjeUJgaiI2TeeQ8+1yD4YJY3jPgnmnS7RLWZNFPexGznqCpKmayUf4HQMIKmGcwREAFthF7KzJyBBvgknhcryGuTWqvvRYo2VROYlYg0N6+lBHcnQABAgQIEOCVgmD6bEYEg6KDjKZyTZomlWOA4LqwyEVODYyJRstgHwCkCwNswx8fQQiptv2jYpZtO2zOKRgiFhwjaufPgbatbj+Dr3JsQCRDG+jPimp1D60CM1JRKhMyvoQYIhRM1p/5oosgJSA41dOCJgDlDpCSi+vmrx+dVpzKF0AM3KY1VsEmoD1t25XRU1PJaLr+ezBEfE+rVOoADBHgRfSD7mT6X52c8nu/7oty6roejvUJ9fRntwGYHpQXQTuqMSv6B1hUHE3bCBj7LbXFzPF8MWFlguKDluYcPs5SMMPWjqSh76FYMdiOfKdtV9c6O23PpRuSe6ZOn/e6He3DZWKG4iQsn6zvQaHUcS1siuesLtSlPoAm6sdc0NVrBPUxADuHews2As8NUu+5b7TsJrNVYvUKndPo0vTWTiwiNq6G83FLFvWDqc3NcQ2eu95SZhBsJ4a0/FAMei/LEAFc7oNZ0goxR0hEiakBKPQ7OK9yYmqGCIhp+jyE4tA4TWaikv1Ttwn3JJa219hbUgy4l1Wp3b7bJk89vVjTUIfz9NcLxaxT8gMECBAgQIAAAQ5HBEzRQUaxPSKFeERSe1wENbraZn1keu24tG2HHcKXNIUWERSnsDcru9Ck+o15D7iwt5JE0VCNKIkFwPbQdSCCA6BpQrQPVmF0lTJEGjFy6YQwsSRsz18m07ScpqdmFsiU8KUkKLJGeYOxpW6D1l1UDLTTsh+snUC797/Knnf7ZnscsCGtWnoCET0iysnZfsgiQ6QI4zm/L804xLWGDcDgqywzxFksZSq9wloXpFu3btM2kus9GKQmlGih6DunJRbSC0kLNWC3q8Rox3qrYdLoI2hldELEAoyscQ0HKzF0rO4Gv0uo7qusmUB7YObovkeGEcqCSBP6qFsFkzO2kpihbkuhpbbYTlvRRxLMEbL3oOvBfS21hPxziPueHJhay8Kmpgy2mPDnpKQa+g5sG6JkihqeRlsEFFTzV1D9CrOXzDDhucT5ZKgYMe4R0s6ZaUj1w2Rx6jgbfR5sHzKzvHYSjFjMHjdOJWLGl1lWEAwRAOYJrC3el+hzSbXSiJDmC8j0NTJpuLYNFhR0TfkeZBbaB5dZsUi24nV3hxS1WoOp5vPax2GKWTNFf/3Xfy2//OUvD0ZbAgQIECBAgAAHEYF548yYNVM0MTEh5513nixatEje//73y/ve9z5ZsGCaMD+Ax8BJlg2YLpJMz7eRSkO2CbLUVLOD0ghgfsAQAfF9yhylbGiaWWgZq4LOobdvK2j7yDep1bUr1a8FWBdQ9hyiKXpYmBnirJPis5hKdjwD3xCNLHV3yIDq3qgFJbvVE0eDM9bDcNZaZr5dr8bccUaOcuut26HHmbpd0BRVw/UovBLjc7XXMJKz68wQsd6p7mPk1scX0/715+1PEzunmX9oY2LQdrLRVTba5kwo3MOcmkC27KKG0eroCndxwbTMedR12vTCqC7dAXCvUbYkVLW6mJSaUlZs1/SsCorxgj2Mkj/S8HH0u5SNwls2K3sKLRZ5fkFb5MuskH4tPlpt6Ffsj8PFcLM90Fm5dZgX4hrD6wb9kzU1zcOkCaLsMs+wEBMK/RieN36PQJsDZms681aAy2Ak99l2cd8vdEam/B4MGGfX8nuiecD2WVyv9i3uRGCACCacWZoSZYAKsTz1rFZ9j47q2hTeX6zNY5au7K+lXmvSxOEeo5+FSy/yiCIQWs+IWTNFP/jBD2T37t3yd3/3d/If//EfsnTpUnnjG98o//mf/yml0otc/jdAgAABAgQIEOB54nlpirq7u+Xiiy+Wiy++WB555BH59re/LX/5l38pLS0t8hd/8Rdy0UUXyapVqw50W1+WGFvWJKOppkYfE2IgEPXDj8jrSHTpbf41GmnfoloiFM9MaAkJ1f5ExtwPmCEaW2nZiYYIr93th7Nn2K15bBWFCpphBydozp4rtrolWA+wBr60hUaiXU/C6VuzekiTFOPiqr2U2aGbo0As1hFhe70P6Xa6lHFKL3TnyRE/2Aowe9VJlwPnimNxFhei8PHFJCShS5jcb88VDtKFDm0DMSgtO+w6X6vmARukjBxpj5/vcktmAVJ7pvZsAdAe/I5Zhf0nUDYYeWx1bLL7Q/ZOmko0+PIbY8gkJJ0LSZz4OM177OuNWVl/PeGNQ88k7iNKQ0x+VrjgMvo1WAM4sxeW2p3GtruLBV+vKNy2w9AAaQajshJgmqDhKxO5iPcFiB72yEF7Yv49YdvtGRH9eHwJnZeyZ94/iBknen+g4KrXJMLXSY9b1sLLfO9S+4iqIiYpVESRXhS+ttsjqw5lP8rUJyN0b7kYsi9gq4im1StsRVyS++maEgPMz0+xxX6Pd0frbs089pltTRLPH3pZb6jaqI96Pvs4XPGCss/27t0rd955p9x5550SDoflTW96k2zYsEGOPvpoufbaaw9UGwMECBAgQIAABwK1A/TvMMWsh6mlUkl+8pOfyHe+8x2588475bjjjpNLL71U3vve90prq6MDbr/9dvnwhz8sl1566QFv8MsNxY6aFFrqPahlx9S6kHLKLpu1FpLXP3CSQgj7Ue+bsh26lzocxYT6U9ket+TaSzHOkCKGCHP30ELkuuxxwRCh0CVYBESIdfbELRE1VYk0AUME1Gukuf22bXfhWL6T9DBtHLHb9qPArD+fOapRGnafd220x+14yoXCha6Iae/+M22I3PmQ+yI2XmvwzWGWDF4oHvBYgtaAomYwRECzuu7i3OABFaGMJNYyTBAzxRlPnEGI6BmO18UOt57Q4sK4twBYBOwHNcKYFW3irB3yddn7x2i3OmCPuQsCjc8Eaak8y6e3DuwFmB8UhAUbC9YA3xc73PFSO/k+KZsSt2wnWBtcd5FGXQnuIZ5fXINoQuu1bbHVQvEcFjp1O/L7KmgbsB/oWeBYjecDztq+xpoyrNDopPZy+huxGKSRaqXsV2RZphcow6Obo49CA4UMxFKL7cysFeIaZsmBivk8qVm6xW5309jTKz4+s++Sv166rFDmJor+AlygFgwR0PlUvTNn5lv6Ce73AHux8fM2ukxZaHXfroUbn/0ALz5mPSiaN2+eVKtVefe73y2/+93v5Pjjj2/Y5vzzz5eOjo4D0LyXP5b8LC8d0bAMnOhGP0zhMw2JwRDQqlWefUVmmB7WbGpwodM+gZwOziZsmE7y6cb68sr0soDaLTGdh3RwGOolBvUljcEOPeSYWsHvfCFVopyHjrY/bNldo+/tCwmiW6Ce0q+puG3u/Jv71fBPB0M9v8fvVDSpg0BYFqCKOIo35jtcuzp+706QheSlZGhSGQr7HQYD/GIOsdhW12Ej4AdN+oeXB3pM5/N0XaFNBxU6aEKqvTd91MNkFtv9hE5xo43aQ1Z5nVfBNQY/GJTwHzievuLBEL7H9EF9YGwHQwAKwvqBNQYifA8wENH9Q9iKz9PLkY/tvmh5zN0o3K/ESP2PlEjjH16gGgs1nLM/AyrY7EuNPJIy58DTtxDpl5L8B9buv9BpBdM+9Z9MIWGm2PmMfdFwYVQeeGN/Ca1yHyNTU3/NtZksCEcqfhNMFbunnoSAAJzhB0Md2il0gJ+b604QBVrzNAji9xwGmQBMNQFcb7w3YfYIQTo6AcqUICFERCQ+Yq9pOWHflQ22H2F87pYIjjDlGMtUG6wSDgWC2mczY9aDomuvvVb+7M/+TBKJxLTbdHZ2ytatW19QwwIECBAgQIAABxiBT9GMmPWg6C//8i8PRjsOe4DxyakZGwq6tm0nITEilCRSTu1+im1kLkZTJoiAIEgEOJXXsxMdU0d0jcVF3TLar8ehnuOZH41cQetXmT1BJK3NaWBX9HsITGNUiBUM0ciR9nw6T3BzPPuf7jb7z81z1zecwXlOHQlXtdgo6PSJBfY6Q+zLU1TZOfXrx9eE0515ChQRZFg/56lNGNzxMVncyiL4fLfdDxcT5iK7qQVWdZ7vtsdroWkmsAaFLvsx+kBZRfRsuAk2xZeUWKpsXVpLNYy5z3EdmEWtUZ8pk50DjPmwHY7T+nRY92tFvojcG9hbWscUUGK4Vjc11LZ5o82c3RfuAYu3EzR9wyn9XEbDPycag+La4Fp7wbYWjAXLFaGSLOn5nCJv2wugcCreU/weCHn2k94nJPJPkJVAmaaauIDsxHLHpMOsEdPXAEwsAWbzwNKg/ApMIGHiyO0BE4ztIlR2aHIB25w+4x3PuJcTChNjn/kumpKchi9AsklL/2GsUj4MEDhaBwgQIECAAK8QBNNnMyMYFB1k5OfEJBeP+8gL6HrCRR0RlH6I0hw/AtJp5qkBFvUWOmyBxGwviYApSgZa9tiQsdABc0XVMnD0TOnh/L03qyNmiPUgEBEDrNngqGvg9W6H0T0ukgNDBMQXOMVqaZujEUIlt8OkarWyWlYF5VMANlSLU/mA2IS7T9Ac+TIilUaGB6fA6cv+3k3HtFBUnqJClJlee/OxP0TzDcUuR3U7fcrBEJUX5U07c1vbTLsgQgdYOM1MVBsVJ4ZtRF5ZEC4Fs+R/uFoqj65fJiIizXvtvQD7wX0Mpo9A5xPTJC0ok4TjeuaH+h7Ey2D4IOYHiq1q9qhJDelFIX9PWasCfVRF70lyn2rViB2E8B/XlIW+LOrGHx+Ix2E6imK1PoFBmZtKQZmeXtcpx5baa1u2eu96AdtpTEqZqY4SkwW2pEq2Db4gazO/v5TBaQubdZ9Y0WM7C94HrKtrSAmHfidmrQ0A7B/3vEzP/8QiOu6kr9GvBo9z19Rr61RXVS967T7H84Hnj9+1sA0QEYlnX4Tyo4F544wICsIGCBAgQIAAAQJIwBQdMiTJth4MkQeNvJkBSgxZ2iGScVHt+AoX+oEhQvYFslXyPZqFtVsZJGggdPdcUBEMkW+WrmZ63TKqGp8CsQWVuN1P+9OklaAsi3yX/R7z7UAJupRRtxx/lTvfs456yn2g5TiKSn09smeh+902KzRBVIf9p/bY47AFgS/ZEYemyd2n7FzSaHW4ZTRTZyIasmunyVQCO9CMVHcq8Aod1tAaZevaXZvatFgt7gmnyBc0w65Nrz0zLeUUZSTut4IusBo4XiRrT8AXA9XtmC1BxDz8Jza//N9ffaOIiHx+x1tEpM4QAdnVlqFp2mAzKdMr7LPS9TCnfdt25HtVq5RXVoZNHfW6Nw9NryMREckQyxqdmGSuqtfC2xpw1iWYE0q9B8PhM++073A2ZarfnfPoSu0DHfp7vRQNzIl+XtZ+i0KorLtiNGQM6msG2aJgtHz5i2kK3AL83gIzA0aINZBFKsfBNhMR/3tdh/YnBDPIqRkhgA1N8TscJ9NneYFGxrvmNWWs9+KMQVhSANM9p7BzqETrxqCHEsH02cwIBkUBAgQIECDAKwVB9tmMCAZFBxm1JvxTbQ4iuuYGXsGgedBGz+GsZYpGj7AhIEdIYIiAIpnEIZsKhWVLLWpWiMhFgyCOfnK99mHgCBn+RSgm2rpzZoYI7APm3TPz9PMOLWra4dYXL7TioyIZIuXTqjHajOwZ93mCMqBSA+w1YiPFqEai0E6wLwrrdkrJxgw5tv1HFhkwsZg8YxZSBuIoDPjstRtf4dYTA6Qjm0PaI0eaSaUZmgdE27rdmLvJDXoWYoZgngiWDaVmEDEPnWB38MGzfiEiIjdvPFVE6gwRsGGPEzWFexw9Uc7ZMHnVEpfa2N9J1Nle1wAwRDHyyEFGWKndMkT187K7Q+YhIn1kD0WyyDB020HPhutQC9U/A3NSIpYqQYwPNDUN2hwqM8PGkZl57lzjmpHH+jJf6FWZTs5IhH5susLT1aj2CSpRA5bSF8fV3/tyIii4rExXUT2nwFQl9+s1JO8saIMKqsVr8GFqw1KzxWDAWQRrydlm9ve4bsw8sXkst4sL28JfCeWTaqGQJEj3CAaWtXKp3Xa9SN3YG/TiWMsLEhufRsx1EBEwRTMj0BQFCBAgQIAAAQJIwBQddFQjIZOZwTbyAJgkZE8IzfVXkppNssyFcvA3QQSETAgwQYlBN96dWOrWU7vs/oqcnUGRl3feJRaENQOp7fZ7+LAgkpxuzr/ebvt7ZImFNCOp5V1OBPS5FT8221294w1mvfO3Vh8z9w9UOJayWqLqB4Xzg7Ntqc1dZzBEEwtt+6G5wHlMZolSFHVyJh2iK3hTpRfYmAQMkd++4nYQhZ8OGChyvI5kLBsBhsi3I0EeLePkCpwmZgWO1XBRp+yz4Ve5/b3h1D+IiMgiDes/0unWP3K6W/7Jxv9hfldKU/HesG3n0zuccK21w3X+0C86REQEATcYsNgmtxxdQZlNulrW80/tIk0Q9GVtU8eC0L3k9YBgcyZnbJEkRtq32muLe5KeZ93JfT/3qYlukRiyOwSTgVIQcP/Gc+Q9r7R7o09Bg8daP2SV+b6R0Guze2pWEExomLJHOdsLztfIbk1oH4WWCe8JMNLM4JRI7wZmCID2CKU0wppV57NyKdsNrB+uDztnM0M0vsQ9A6k96jVEDty1ED28k8BaNC4Ai2sFzR3eLWm9Nvnlh54dMgiyz2ZEMCgKECBAgAABXiEIps9mRjAoOsgIVd2/qPrcVEhL5N13NZqI5FyYAc0RnKnBTLADbkrdURGJMZPT8ZRGnOpoDb0La2PYU4c9c2rEjMA3BR497NSL9mQWqtPukPqHkDfPeLc7cNfDbkcckW7f5sRJf7Xt/yciIsesspTXpt9oJtMSex4tOr8PhghFSFEDKpzH0t2XoTU2dPU1lPS0WJORHKifL6JhoB7N2t/kqN5UVVdbtrr/gGkafJUW2yR/E/8i0iUykoBKki5eVbNmRqloZo4yhFAzDM7aVMgSfS+9zO3//NPWm++TSmF9Z8ylBP54z3Hm+8Gfu8J0SDAE8zR6snZGPbGOh8D2uSXYz/Flrr2drx5wX7/aLYqbVDimjFpsjFg90kz52m9UkwrXk/VxJcrcmqxPAxMBLU9Taeq/Et6xXb+OkZYImj6BbolILGY4wMTU90/nrJfU1yKkPsgFqTkbLkm1F/FewnsHBVXBaHc+razn/KYp28+MdKHdLZHVFinYdkLX5WuxkR6vQA78rKWCvxhYvyicvZX5AUMEDJykz94eHN+2P1KoNWTMAWCrkCGHjDawe7h2fl+6747fuU6RXiQSSdOLN8CLjmBQdJARLtUk3FTzZSTwEIcL7mHnETcKIOKlgJcgXl4pNYXzL2G8bPVlMHQsvSTIiA9/7BuKa+IPufYIFgliEIQlp3tn+vDHQdcXksD6BPu2Kaft29qXBaEeGU7a0ciTv1tq1jEtgZc6SkWMrHY7wiAQf4wSQ3Z/mQXuBYU/iDhvLljb87AdJaYXTioDwMUvvd2Bu2kwjAT4FcvTbvN/7do4vBrug25RItuChh1VWWBMUwC0Pf8BwDQR7iGbNqa2u/NYv8rNY/2PxetEROQr615n97vLOm6maD8jr0LFVre/ZcvcX+LRh9zgiYuNYtA2fp8byef7qKTEENlXaMo9yorweSKZAIVu8QcxnIdQ261j4IvK85PbFRu1I66MltFAGn+Fng9fwkT7WUOkret+YKq3HtPqSMGHZUSuW49D5T+A1m1ifodCsXiP4A84Pp8OuHYYDGF/FZhG6qAM031lMlsdX2rT2X1BWxTf1YAC98Sn/usS1x7Xg80g2cogmuaR8NQBwPhyt2SLDjYmnZwoUmin6W4Vx/ui0jp4SoyojYearZbIvgGY82hVOvIvQsmPas39e6H7OEwRDIoCBAgQIECAVwoCTdGMCAZFBxnleEjKiZCPJhDpVCOIPl2kUGi3tC4iP/wOUQlj7x/ZdVjLQ0DMjM60nVk/RyQFQzxEnEBaxa4oxcBTDmCiTjrDmSz+fsciERFZPW/AbLdp3RKz3lBgViPK1O/dCSCqr0Y0Yp2wEWBSC9XiOoHuh5AV5VLS89Wqv4CIm1LySfjd/ZhliMI5FyK3P+2WE8uaxVM5SDtuxxI0un6t0TVPNSIlvG0HMUQKZogapljAhhUh1hfTnummABsKV4It1N+znULovbakylfuOd+s87ScZ2x0f9l5dn9giIDRo1yE3fM7d4Lp+W5/k6cqRUSiGXsBmNHiKVqwf3g20JcApNVDrJuigp1gZSux+rRRVMszpDV1HgwOngfckzrjq23Vz5nh8NNoCi5NklMrC0zHgVGCKB6WFjg+pnCLyjSzSSvKjQBsXeGngHG+xN7xtB6zKHh+WzURA9cH97R1tz6XnhGzS576hPAZ95qfAUyPgxmCjADtzPRQmaWNtv1gurn9mb6mae0TfNt01ygUy6JysIRA6y63czbKDfDSQDAoChAgQIAAAV4hCMkBEFofkJa8NBEMig4yRo5okpFUk7Rvtr0QIj2Uj0BBUhZ/QjjIoj1EqCIaIXW6EDS0wEUh6UHHsCS3uf23b7PRL1J8YXyHyLRtp2630y3Gl2gBVY10i50q7FZGiB+uV5/0tFn/0yPXm/X//H+nmfX4iKbaUtCU77Drba+yDmpjj7vJfwhHOXqDiDk7F2ZwdP1UGMpajL7fWtohMmHFRZnF7kLg+k3WC4GV82nTJBeoKCMxpnoQsFvAvpPVQDNBUXmBdBEkgvfHxfFIkD2dfsW3ixijrieYydLj/0w1PcrEhCn1n00TIVQutbrtuo+x93C84A48vK1DROoMEcCGhflOa0PBGo2RYzSdvV/ZArq3WS2mmgBpqc1t2WNvFIt3q5PWUbIBUT5YsJDqufK9bl9NeWjs7L7AnECLUyFROxhaXz6Eitr6e6y7hakizpWZ4aKylqm9qv0ZdDuY+6ilYpBW7p8L63DhmRcwUhVKiU/up5IpfVaH1aR6m7adEFJr4gWE2MT8sEUIszUswAYT1KwFaiNZe37xCVzv0JTtB/MNTGaiWOcEth/sFCe3sFlrnPRnpZRra1Opkb09JAgcrWdEYN4YIECAAAECBAggAVN00NHzUEni8ZKf20aqKDCx0IV4iFy4QGlebfGbB120UU5q4VeN1hP7tUTBdhd1p5e60HPOerc/RI4oFIkIE5EqtATDx7v9t+1E6iqlvjYjM0ejcCoAe8YpG0VEpD9nBTC/fepks479A6WUWZXMcpui+rYT/iAiIg/sWyoidYYIQKSV7bGMV5gyXTnLhi36ESmOrnTXsUXn/SMoP7LYNjTb08gQeT0HUr6hCyvY7YDMArteC8/MvLB5I+/PM1RITKSQh3UbaCczVshsArw9gaKcsu0E2+f1LhT91vQ6DD/mUujb1lgBS/sm1xA+Hy6hwbYRKCWDArDVZncBMurSEE5rRK5aq4SVtUnrrqnZU99uvU4+K61Y8ynhzPDg3iR36vOppVdqqoFDFiiuZYSyJsGqwfIC/RMMCXRqMAQMT2MBkJ1HrAVni9aQJWazusAUQb81sQQ2B3b/dc0P9oN7DybJ3QOk9uO950upKBELU8bGory203Y8U9b9K/OURYkWt56nFP36c+mWbOLIeh9cF1wnZk0NK0tzRhnKKk0OTG3kCWZount2qPFi+xR98YtflE996lNy8cUXy3XXXSciIrVaTT772c/KN7/5TRkZGZFTTz1Vvva1r8maNWv87wqFglx++eVy2223SS6Xk7PPPlu+/vWvy8KFC1/YyRACpihAgAABAgR4paB2gP49Dzz00EPyzW9+U447znqZXX311XLNNdfIV7/6VXnooYekr69Pzj33XJmYqJu1XXLJJfKjH/1Ibr/9drn//vslnU7LW97yFqlUKnyYF4SAKTrIgKgtkrXhc7HThYiY8wegQYDfR/OgRlrKGCX3uw7QvkU1DaSbqEa1LIBGQGFvjgZ/JLe/0aN0PlwZn+iI29+g9tXkXrdML1bvDSpCGh+36/3HWIZo34Rbb+tyIXF6S4f5HgxR/gir4TluiTMOWZpybMJPN60x30eVPYG/SJEys6DbwWPCJpRgU0rkwyTKFIEhyndHdOkOABO6CkX2Io3RJWuJsiutLinaTzQD2lqcWr4IFqLMzAllKD2bdqiheC9YBBQBpWs1sZzKlijrkdxtd8SeVhUt5gudDXs+le+ybF+Evsc1xvlBx4Nonk0VIasLFbWYJ11HZJ2BlWnRzCdkmwF8/ZABBf+ZXHeTZ3iE7jFnb7VvpewmYnCSg1OXhEHpl9iE+dqXhgHwPgDiWuSWs6rSqu1p3e2+B1ON9wGy2jybqcxP6w7SOIr93h93bJrSNp7NVJ8i9UXC+6fUbLP2gO7HbWeoJCxDBO1QsR0aKfd7MEBcWqdAJV2YjcQzgcLXYDlRiHdyVmyFdFasYwQbBrTumvqPNRijTG9Y8rlDz0uEajUJvUBN0PP5fTqdlve+973yrW99Sz7/+c/7z2u1mlx33XXy6U9/Wt75zneKiMjNN98svb29cuutt8qFF14oY2NjcuONN8r3vvc9Oeecc0RE5JZbbpFFixbJ3XffLeeff/6Ux3w+CJiiAAECBAgQIMCsMT4+bv4VCtPXdfvIRz4ib37zm/2gBti6dav09/fLeeed5z+Lx+Ny1llnyQMPPCAiIuvWrZNSqWS2mT9/vhxzzDF+mwOFgCk6yKiF3Dz8xBJLJ0wsbvLfi9QdcDEPDS2Bz3whh1doAxD5wZeHI8rEoGWodp/lwuWIZmMUo6h9MHX74Qfk20dR/fBr3AfZYSs8ye93tEaTltNo3WpDOGTpAMcssday/2/nKtfOLe7ADeyLlilpiO6pffy7MmmYSu1ugxHvLeSoN2Q+cZkC/L7Uplk1xZAUF9iDfvK0O8z6/x2wVHF1ud3nxkeWiohIJI2MJdvGwlzXxrD683DGSp0NdEuvGaKnm6N+ZobGjqDIVpkhKWu2HDFE2QV2+1CZsuTIUTt6lIbfe9zFBkuKJSJxaJzAEOE84C+U1wzL8Jj7ohZlSsyughli4DpCH4PMpMa+Vo8dWUvUsYWK7Ra1TI9qYNB2ZGdBnwRfH2gIwaC0b3HXlPs1NDQAs37QJOJ3OA50U2BssGR2s4lKWbBzdLNnttx2yO7KafYsMqwqMfRRfT7K2J9qsRI2c6vu8G+Pn+1znZkZIGSzgSHC5/D4wnXIki8RtEUoPAstErSZrIObfJ85i7GRIQIbJrqsM4uTt8e9zvS+yP5EVWlgOp/XPkRk0aJF5uPPfOYzcuWVVzZsfvvtt8vDDz8sDz30UMN3/f3uge/t7TWf9/b2yvbt2/02sVhMOjs7G7bB7w8UgkHRQUaluUnK8aZ6Cvz8qV+8MLrzLxF9aaGaNFLgyyPuQYPtPqaDuja6PxTxYfeXDtNoQK7H/cUpdts/ZE3t7g96SdxbAH/4YBgIoSVoZ4ha89p/Y7v17XGkOy4GQ0DHkzr40v1lFmtK8EqbA7ttxL2dqvqWKz1u5xW5DljDS53+wPuBBWh8bVaxnVL3tYJ8y9O2LAhedBDzQiDKvy+31KRpzL01P/GGH5vv9pXcOZzS6R7smzecKiIiR85zKlQMhqI0FYm2Y+AYzsxM6PJLG+BK6jwoysxzy4ZBRYTWdRoMU56lDteH5q2wKfYDj9uCen4QpMiOayc6wV3kxGbXd3Av0c5cr20/gMEQgOk8b05J02Ztm23fw1QNri8GAjD2xLMEGwe0JzJphrd1p31+WDyb6bGv1KQmSOCPd3xEn9MRnIOm8nfZ2n/YvqnkPoCAGVYAuOdsAok/yBjUsLDY9wE9Dvo7mx0CKFWDckOxCfviat3hHrxKXKe1MM2sDx4G6DzthrI7EZ0Ww3ReWUXJfmCvP2vfbjuDN5dEu7VsUiVqp+V4ipSF2XGd9kztUzPWhVprctIzxQkL3qKCvsfzgYEXTD+59EhTuW5XEH8RynwcyOmznTt3Sltbff48Ho83bLtz5065+OKL5c4775REgh1jJ+2TSrLUarWGzxjPZZvZIpg+CxAgQIAAAQLMGm1tbebfVIOidevWycDAgJx00kkSiUQkEonIfffdJ1/5ylckEol4hogZn4GBAf9dX1+fFItFGRkZmXabA4WAKTrI2H98WIZbwlJJqamYTkchUkQKLsBRCYTDmCIBQ5Qc4Lxntyh0uk5ZJQO69EJNC00r7bzAhb+hvW7k3g1GRwf9zfuV6u2zUXRiv0Z02i4IEavPuHA8qeQAhKcshI4NuxMc+52jYGLHj5rvGxgiuj5gD1h0nByl9hB7wqZ6lTYq6Hm8FXxHt9qIxrMuPEU06Tb8zx//ifkOjERlpXPiCz/tGv300y5nvEMNMjOaUcrlKlo3284AMahvExn5cYkWMB2FbhLDpty5h0fULDJFqcQp1/DqiFarH7ftaFGR/8QW9zLKUfkO9OH8NteZfPX6ZZYhApqVJU0v0ch6zDI16NvJrfamIu0d4HuTXuyWHU/pfqlUBQTUVWVVwBDVN3ALsKStO+p9hstagLHAPQFDBIAhqiTUJqBMBn8jriOlF9hzrIUtvZcccPvF8xXJuHUUKPZmknpuDf2ViAnsD8D0FvbjhdC6ROp+ooRUe9ve+JjdX3qeTnHqaeAe4DmFOBnTW7iuRS17klAmrNDmdgDGzF9v9PEOt0OUS2EmHqaU6JuQJ4AhqrefpvF66tfPv5vBTPqiuG7Zumvq/oWpwwKxVMWWJimGXwRe4gVkj5l9PEecffbZ8thjj5nP3v/+98uRRx4pn/jEJ2T58uXS19cnd911l5xwwgkiIlIsFuW+++6Tq666SkRETjrpJIlGo3LXXXfJBRdcICIie/fulccff1yuvvrqF3gyFsGgKECAAAECBHil4BA7Wre2tsoxxxxjPkulUtLd3e0/v+SSS2Tt2rWyatUqWbVqlaxdu1aSyaS85z3vERGR9vZ2+cAHPiCXXXaZdHd3S1dXl1x++eVy7LHHNgi3XyiCQdFBRiVZlUqqKs39KrpTJgVRByIyRE5NVbtMkEg0rnP8SFVNDLroe/A4m2vq9w9DPUSwWgIhtsltjzRlFiIWNOUVmgNOgwZzlNQon5kbMCs4XxgVFhZZXUj5acsMJYgZ8ueDlOXjXIOQ1p60+mwvCMfxs4s0FVl1MxVdQo/SFJ86bbb9JFv8dHS9rSLa/kz9vuAaDR07tVAaDBHQogwRShUkRt06dCC49lzGgpmkEDUd9xhRMDNLYIiASjPYM+0sc+kAUf2+ZKNZCEr9ZqSJArunkipp3odlzLQTfQYMEYBSMkBigBgzEul3Lneddvwxl+rfIFJWPUzXo65h6aWuMyOCB3rX2RObWGzZQhgUitTFs0iNj2ZJfKuft21VzU3CMj7lZrue66aC0FQ6wpu64mNo4GEMSKav2blUoJX6SsseCKNV+6OHh/YH1yySs+tAsc22t15A1rUjPqImjmohAqZsbLmdXsHxs3PVwFP7BITU9XJHal5LAnYItL1hKiwH9D0AjRUSUpgBy3epFgraK+o7k9PwvRYN5BIYWn3+wW7BaBf7BngWgJ/fVzKuuOIKyeVyctFFF3nzxjvvvFNaW+tTDddee61EIhG54IILvHnjTTfdJOHwgRWuB4OiAAECBAgQ4BWCF9vRWkTk3nvvtfsLheTKK6+cMnMNSCQScv3118v111//wg7+LAgGRQcZHRuaRJqbfEYNMzgAoo84FS5FdosvNUDZCumFcfP7PHzxdDOUBWCjPwCprIjUOEL1acuU6g4TO0SIzVRCgS3xYaAXG3UahOwCnn9XlqTbbo9oKnOUa4DPdlNkFtV0e80W6yP9TMym89Si9vtQE5nHddjKk0vbHQuxYcIyRUC4WI+u27UWLqJdZK7xtUBUi5RdZhmgH8N2bI6Ie+jLUND2YIjK7ZQyr2UvPEtWtKFr86OWbWT2D/ccWTZh6hOs/2rdrpqKxNQ6D6Tiw4qg1OY2iE7YdpVIlwZmCBjepTvqdJ216xHNINK3W++DNguuZatNZay02Iex1Ob6aELTzPMd9UiUWUA+J9ayDR1j2aY2yqIqpVD+gtiyFjC1liHKd+pzqk3yDJDXuejzkLb7i9K6t/ZQoAwOzg99C+V++Dw9k6No3W7TP8GEgSGqRtSaYwe0VShAq7o2KhGD9x0yBFFAFu/DWoMthbZHGW3WBjUPuR+AaYL2COD75t/PtUaGFiwhMu2Y0cWAoXnI3Ry8WxuOFRWpvsDBxfNCUBB2RgTZZwECBAgQIECAABIwRQcd+bkiuUmGgTm1ckFUDPT8zkYumEMHQhohIassS34o8DMS0pVwxIhsCkR6Q0fZ/VTJnA4+Hr7sAObRyWwNDBNrpIDMyU6vUZ1wGzS1UumLhIvkmn7vaIHCcVbfEd5jI25oF/z+tZAsSj0IFVdN9bkLks/bkLCrLWPWB4ctLTG8wdEuCByT++x+S8lQg0bGt7lgl7gnzAyx1mA6ZghsXd1bRpd0z6DhmVACJUT+Pex7lBgkTVDaLut6E3t8+PdgifOAjiPfQR4xRLYx+xAfJh0MtBp6HmAx0o84OtF7Vy23DBHADBFHtyHNAIuMur6WW2jvPRiiycaG3vxQn0No7Uot9rkCEsT85uaQ/sF7IanmLW4ZEQZ7TUXT9iLy/sEcgdWAdjCnTBGyQ3FPG4oIU5FjX2BW2T+U+QCTg8KteE+MrnCdJqSUCDRGYKrwfqrgWSEmLjauDI8yT9g/Mrtwfnj/IJsMvkfsuxQpwBdJC8p28gnb1Wq0zuywCSjYJCw7nyLNnt5L6EAnFiorqK+2UOXF0RWFqo3P3vPZx+GKYFAUIECAAAECvFIQTJ/NiGBQdJBRTloGIaEmwGB8Op/QL0h3Aq2Bn0snLRG0PGAVmC3wGgR1X60X91Tfjw4b0QGVZvd9y3aNvPptKIMsGURi3r9HN/NFPNU+v/VUl8UFnmfOQsfMbNzu7JQTKcsYLXvDVvf9+iW6f523z9iGVhIaISa1LALpY3oWW93J0Jit71Hd71o0qEvvhtzrQtamvVZngvNiHcHkrLwqsWRhctl+NlYBLERUySswJSh3wRqexLDNvEHWGtqR2gaXYfs71khELVnmgf2iRAUAxiY3x3q/AHBtB0r20tfdinUJFhIROc4bTte4jkUtNJvcrcWRB5R9GHANTe1xP4wPaihOpStqccoAa3MHKCfd59le+zr0rtLYXbR+Xs3DFV3qOVIZDmZ64e/jH7gGRkKfc2VgK+QzhuermQqweuYoZH/P7YTPT3q+vQYofFrQPlKjgqmsl0kMo7CsW0cB2/QC97z4DMgJe3ywJiW9DjhfMF3ok4lh1SApk4NssiYtn4Ilo8kzXXodKtAaui8qyYi2E35O+hwrQwe2M9etz5B2hdh4vV+mFyHFTo9JuqbB46bWDkEfmTvJXsxKNiqhcaLGDgUOsU/Ryw2BpihAgAABAgQIEEACpuiQoBaqM0RzH7ZamfQiF61y9FzW7AzUz0HWBNgAAAVKMa+NOX+s+wisn3Qkw7aN9ewLaAXgxqpZXd0UCev3rdvdAQrdlqoaeZVbntu73Z2nio1+8cjRZrt8xgpxNv/BuT1LNzKR7PkWuqgAZ5o0UZ2WTgmr4VN5wrYvXLFO3SheGiYnaziOc9YRtGEiUxRoJSaG2bhcn7t22fn2867HlfkJWcYnqgFmhLVIpC8Jk+YoQ/uP0z1H/Smv/6LoD9cG9z7Vr67DbYjebR8pdrjtObNyOiYKQJ/H8aAhYobIn8cYjuOO27bV3vN8j+1TRdUGtW5xDak0W10ZPHfYsZiLqMbSNV/7K0QsFGtX6syQBZgcX48OtbtIQxTNEDtH2hN/zaA3K2F7dy5gaAAUYMU954woLmyLa5uep9d4u2WI2BeJNSadT7n3XDmJ7DJ7PN+XwXARAxTOqy8RtES6BHuXGCqa7+FDhCWnjLMvlGdztetwbTa8D/Pdjdc+uc/uA/cCmbMNbvr6eWib65flnheBHZqEA1n77HBEMCg6yKiknHlj2zoIpUHVk50/9MFFK4zMLYFg0H0f1T8I+ANUauV0bvtwNw8pPV1ExWZ9qVOfhr2+/0PnXxL2+PgdXtr5OUpH60t0+Ci3nzVHuWmwLWn3Rtiwzf2F7v2VfTkVVIxb0cuBsiYw7MsfrQLttNugKUcvNxoE1Yru+327OkVEJDKm22t6ejhjfx8bgUBUpgTSwePHu9Fm6Xduv5OnKwtd9mIm95LAmEwUZYEOjPe6uYqux+3XvlCpYKpu6j9AWKKvwCDT74dFq9ZtoC7g1u18qj2m/Wgwhz6BPyQ8QMc0gd+9XmouLgyMHqNTUChCrNOLZS3BgrFmcof7HsaiXJqiGrcDEEw9+3UtCjp8jDsAzg8DGTYm9FMwXvxbv/71aS0dCLba54qnvTA9gz+g/Nz5ArDhqafJ2WASiRLt2yrmHACU/8GgrUKDgfoAD4M79zn+kKP9GKS17LGp8VKx7Ur16yBPhdSYYoStAdBgRqntRpmSsk4/VrRsCO4hRMpcVqXW5B5AXLf4KOr/6PtLp0RzvQmznT8+Deq8+aO+B4o99UinfUNEz9GtQ1yOd15ZlyhKjecJInZ+DlNP1a9NMjtNNeeDiUBTNCOC6bMAAQIECBAgQAAJmKKDjt4HaxKP1ySadhGRL0ioTAuEyzFKrY2NqyBahdIcZSeGdLvRqVmJlh16HGUZoll7fER6XuyqEZkXjFIggGgHUXNmnms30q/9dhpg7fvuUvP5SqXTC12uHYgEIRZGuiqEo52vdRy1zqTI3gxcKR1at+p5b3WhXXLAtX/P6zXNWhkiCLWjQ66rhymaQ4Toizva2TNJrLGC7ePetMmsbxzskdIzHe63bW4nEypa7exTtekGpzqvLXHzYLHH7fTOuM4Ytm/We5WxS0ahzcYyoydZtizR5k6ytsGFqkjRx7Vls0cWQiN1Hn0DBVULlGLP6cQ8bYD9Q6yb7XPL4hzti2qkmV2qIlpi8cAQ+fV9VGxURcNtO911L1DpCTBEYDlY+B7JIZ1cDf2077O1QqVaP+8wMUFgGHNxTHe7daS+M3PEbn1lYignFthz4FT5lr2WpQDT4qeNaFqvwYagQu3BLBaY6KJNsfcMEawD9D2C4xa6dHosaoXLsDMAC4f3EBizirKbpZT7AEybF5wXweLpe0qbje2AaFbfWy06zUYC6pBtviSGrSUAkgUwZQv5QUSn5RMjNZ+oAKNbfkfiWE0lYgmpOHVDWZ5y/bofUtTEi8Vf0D4OUwSDogABAgQIEOAVgkBTNDOCQdFBRmy0LPFYWUqtEHNqNKELCCLDOWh03C1BKj1KJ3CUC8EmNAIQ9yK9GcwT5uLZTBEmat5mHxGaRrp5Taln0XCuh+fmKTpSVgDniXZAUA5AY4A01+bXDprvyxXX7tG0C9sTu+wJgBkCho9U5mmPLRjJ7AgD0VuZ2JKiiiF7m+0F2DjYY9YntreLaJHZ8IQ7aNsqF26ODulO+9zBO+9z5wLhJbNSSAfGsm0H60twj+y9B8AQVTapmEGvAUThKETLxX19GQ1ki9OkutcS6aVgRoiF2p51Q9kRS/JJKOWubfMmdwHyPVQAdq+yfBTNcjo57mmmB5oPa+TnLRL0xPA92ALWHuFFD2E4X9/p+pBIo26Ef8tCagAsA2tm2H4B9gNAfNhSDNWY/f34EmXRtim7kWJNjk3hDxGDFc1ZpgsMEYTRsC8AgwOdDZgenL/XvbXaVPfmQWV0qbyJT+jQ/YKdQXu9NknvFRgfaCHB/gHYPlzA+5AF326J+4f2oIuHizV/7XNa4BXboDQKgHfO5BIhrm26CiNMEE0xkeqLq7kOMAWCQVGAAAECBAjwSkFNDoDQ+oC05CWJl/Wg6Morr5TPfvaz5rPe3l7p7++f9jf33XeffPzjH5cNGzbI/Pnz5YorrpAPfehD/vsNGzbIP/3TP8m6detk+/btcu2118oll1zyvNtYbglLaVI6KkcyiT2O2sktcOE6IipOA0V0jsiGdSfNAy7kACOVXmBvLeauEeWMLaMyITAWxBw8tEeqr+CinP53mv2Gop7Jfvu0QP/i59VREkNN4rKLVKNQdu3uTtn87bF1TuCCKAwR3ehKzVZRtqVCZT8SQzbl3qd5c7q8AmaQwKLFlrnqH7YOiOVhLV8gdd0SkF3vaLaOvW4d58xZaLgmCedv6Yv3AtA7AMyc4Ny6H3CUyMRipUagk6IotEj3sESmjlxCouNpayQInQcX24RuIzvXtRfsA5gVb+twlLv4iU1UJHWzPc8yMWgoi+DNJJUlRNo7sxzoK/FRZY6I2Sq2Tl0KA8D5sqZrcgZVnEwU0Tb8tnm/ZXK4LA8zSWwi6LPWVCvUPGj3By1Pff9Tl6sYX2ILrqb22mwyXKu8XpPWPWVzHsDIamtp0b6N2kMMTFHfJxFi18Aigrny5rPehFL355I8ZUhT+uPKmINh8oy7AtlrsXHLaOE96u+dLlCCBn0rrhpOFOLFs9Kyp9agBfKlWLxlBJ1jCW3SfcMKA+zXdug3Q5LKvwijiyD7bEa87LPP1qxZI3v37vX/HnvssWm33bp1q7zpTW+SP/qjP5JHHnlEPvWpT8nHPvYx+cEPfuC3yWazsnz5cvmXf/kX6evrOxSnECBAgAABAgR4CeBlzRSJiEQikec8eLnhhhtk8eLFct1114mIyFFHHSW///3v5Utf+pL86Z/+qYiInHLKKXLKKaeIiMjf//3fv+D2NRVrEp7ENYIZAgZP7jTrI692E9Dtj3hBhAWYHNjfK7WTnxM13yOrhbUyKWUvwDghUkPxT4AZFR/16/58SYYyIlm3XugMTW5GHTr87nmzE7Zs3jTftDcZc+HV1k2u/EdkwoWOVNfVM0yAL65IbE3BXtZ6aYrllj6Zt8CFcfuecMKbN5+5znz/TNrRO+AewRBFR+uRMa4N9BLJvfbYKJORVEZo3yk2FkkRuwZGBkwRomMArASYkonFbh1Gk4DXxqBwJWVVMTMVp+KlYKY8a0GMC3vusBYp22fvSfwZeMa49eYBt4T2p0TtY2YMDBHYhsSoRtypkFkic9P/bv90JTdwXOhU3MJnVIK1mJR1B8YVBpJsoJkY0WwwZSa4/EeDTon8clhjk9FrmOlzF791NxWA7UaBVLcONhLsRKORpv09mzim5xHDXOFriWtuC6rGJqbOUETGIfpG63bbHp+Npn2gQs83G39yAVdcP8/GdqoeTZtdZy01my1J15+0XhVLiElubv14YB4B9EP25wIr7bM+fUFZ1V2ByRyvSrzwIlRWrUqDB9nz2sdhipc9U/T000/L/PnzZdmyZfLnf/7nsmXLlmm3ffDBB+W8884zn51//vny+9//Xkql5694KxQKMj4+bv4FCBAgQIAALzUg++yF/jtc8bJmik499VT57ne/K6tXr5Z9+/bJ5z//eTn99NNlw4YN0t3d3bB9f3+/9Pb2ms96e3ulXC7L4OCgzJs373m144tf/GKDtonR/Phusz78x0vM+uCZblDW2ukonOrr3TKz2YUdnU8oA6PD2KJGxa3q0QJX34ETKENHGZ8oZYUBYAfY1Rfz4RPL1PmWmJj2p5FlIvq9LvX7BRdsNdv/+8ofmfW7FjtmZl3GmfQ8NOTojhVH7hERkd33LnLtpUjcO/DGVRMRstqhUnvN/K5ztTN0msg6lmJZ56iIiOTLLnz7ypG3uw2PdIuHcq49/7XvOBER2fSIvU8RcgyfnJEUV+8oRJttO21kCMD3x+9jmpIJYEZ8toqeU66LmCZ3ybwujKNr6DqwZMYntWdqJspncdH2iJ7BvnF07bPkSE8CDxiAs+DAGNWLg9rvx5e643Y8Yxki324wO+q03b7d6l4imbIu3Xp6oaVRmJmCU7jPupt0H5GRx/5iABii6XRhYC5QYLV+TLd9dq7VwAANvkeKuj+Z7UtwvoY2iZkhZlSb90/NXGE5tEadrKnPocgxFx/m9iOr1Wd75ex2TZzhh99Di6jvpSqeCco07Hhq6vuB7D5oEMEmIuu2oOwOe3Y1lScXv7XvYGSVITvNF3RO138r4ryO3DEtY1mJN/k+e0gRaIpmxMt6UPTGN77R///YY4+V0047TVasWCE333yzfPzjH5/yN6EQvUj15vLns8EnP/lJc7zx8XFZtMj9UW/eMSbFSEnyR7npIpT32HcuF9OyLysMhnw76U61bre/H1+s0036oOIPGWzrRQdFeX4J6h9y/C692H6PKvR4ejvXayquvhhgvljUP0SrL3jK/P77y+8REZEbxlaKiMjxCcefL4i4v5A3jb5WRER2/nqR+V0Ef8+QMk9TK2gPPq9FrEtbatmY2fzPVj1i1t/W7tb7y+4tvrPkBtFfWneuO28tFxLN2Rdh49RY/eWQ6bV9aHSF/YOIewKxK/5AeoO7NAmGicf1tgk0uGGRLsT6AE8nRalsCKYkfEmFDntgCI59EgB1Xa791pDyn7JL/AGDISnfW4j66yUwRNvllvtObjLfA21EEqN+IJDpcQ9D3XZi5me+idLiJwuveTCEQYdfRu2+G6YCaUoP0z5A89DUUzVcDgjbRbL2eMlBu3/cU0wXZclaAwNWDITRPvyhR/keYHwlDezp+M9WDxADd38+mJ7HoIXq8kV1ShMDe25/SO9peoHbYQsG+tpMiKMBTqfnQZoPREL1tvt6koX6dyIi+W7aF/UbJNdUtD8i+YTF+gFeGnhZD4oYqVRKjj32WHn66aen/L6vr68hM21gYEAikciUzNJzRTwel3h8muJZAQIECBAgwEsFAVM0Iw6rQVGhUJCNGzfKH/3RH035/WmnnSb/9V//ZT6788475eSTT5Zo9OAU5ssvaJN8rB46j67WVNB97njlNi3YmlHa/BnHEKV22eijZbcNvfLd9tYxqxDO2nUfvejYDREmIltEVIiKcvNcpBjSQrJd610kiwgUYtXRI2w7V7c4deHqZjf4XHXvX5vv+7otg7P/IafERCQN6pkFouWkFb/yedXUQLHa7q5TX5vVdf16/3IRETl5jhN6/90T73bt7HLq5wc3LzPbx3aj4KRbRyQN+r11Z/2lgKgV4k6ci2frdIlrm++0jA+qz3NqOKJ2TJnw1AnuOcSjPNXoI1z9HZggIE+MUKZPo+zdiGzt8cBksZiWp0zYlNIzPvDlw1QnHouQ/Z5L2iCKb1ahOptBQlQMJilBgvFCG7EE3fZ+McAAoA9OZtqQ4u1NA5V58VOJdM2S+9zJp/a4m1Hodv2qiQS20TRfU90/jDDp9ZRZBBG+ZR2BwWPs+6FKU5zJPXa93q/dCaKMDwB7hqFXufXEPmKaSEaZVZUCm8r69uj54Nrm5tp7AoE2ptOQFIC+k9xnp6zweSxjTWtxL9FHfWFr3Z6NFj2bOyn5ASJySHDRb73xI7NiBRJvE0tV1uc12xcWydJDeygQDIpmxMtaaH355ZfLfffdJ1u3bpXf/va38q53vUvGx8flfe97n4i4aa2/+qu/8tt/6EMfku3bt8vHP/5x2bhxo3z729+WG2+8US6//HK/TbFYlPXr18v69eulWCzK7t27Zf369fLMM88c8vMLECBAgAABAhw6vKyZol27dsm73/1uGRwclLlz58prXvMa+c1vfiNLljhx7N69e2XHjh1++2XLlskdd9whl156qXzta1+T+fPny1e+8hWfji8ismfPHjnhhBP8+pe+9CX50pe+JGeddZbce++9s2/jOVEZbolKdEKjfTIJbN9gIwUW5ULrM77YfYGoBPoGzI0jUkV6KHQaYF4KXXa/voyIzu2PvMqGO4ndapqm+2se0lTjENqF89DUY2W8bv2l0wg15afWawxtajbnyVYAiNwQ2fL14lTQcD5klqU5Vkvx9A5r17B1uxN4oyTHg+IUqtBEQJvE7cJxcf0nGwxylF7Xfbgl0rdhaMcC5GosZJacBo3oOTJNBIooOEFCZgAMEZvQ+eKd2hdaKN2bz4u1E2Cm0IcQOacXTf17XFNvq0BdBIwPWIUG0S70YsRy4F6AIcJ1AYMHY0C0l0toMCvJYuPJDBx0XShEyronrKPsBBgiIDrOjK+lgLjcR1nPBQaVrIUZOcq2PT5CF1VPBYwL68GY1RtdQUV4qbzIvPvtTa0LmFV4rqwK7uHI6pBZZ0abmWZun2eOeq3oP7VHGTpiV8eW2wOwvgdbe0sROh76BtjcyWn4zMSyNg9JLLBLQVFgsFnoj9AtRSdEojk59AhS8mfEy3pQdPvtt8/4/U033dTw2VlnnSUPP/zwtL9ZunSpF18HCBAgQIAAhxOCgrAz42U9KHo5oZwiE7RdYCbsdlHSAnkdBukykAnE0T9n8mTn2eM397vfpZfbH4aSLoJtGnBhM1Lb5zxuC0NCQ4FsFKTiR8dshOYziyjFFQDjgoitIbtMAQaIIxNEWz5FV1P0qyOu/U9PLNADUZZM2pq7NVGKfZMWuIVuADoW/3tEeRolVmKhejkJvZdI5eZMFtxbRKfYB5fXqFHmEpd88fdezyExhA3doq6BcIiPlLVdroENJSa0nXlN9Y9RFlyF9B8wZQTjBYYI2TRgxhJatHTgRCtowfEaGJkmvhdm1d9rH81T2ZR8yLKffF29QSBS/4klgLYLCCuTBn1PZdJ9mVzyQ6TxHiWG3M4rWuIn1+MuYrLfNb7YDuZX+0B06oKtAMpQICts95n25KBJ8vo1Ko2CPod7xuwYrgnuHdCQyaiMT73QtPZFfZ7BkA0do/obLQM0hhrFGbu/MDElbCfBKeuwU0DGJoxNx5ahMKzd3lt44LqgnXhvZW1mJa6PLxSdDHkNEN7B6LfcB3wbwcCSZs+/8ybdOmbOArz4CAZFAQIECBAgwCsFgdB6RgSDooOM15/2qKQ7E/KrW04yn7MhHuscOIKADoMzmVgTgHXPwBBDVTjFhVo4fEnLVtQy7hNoBjAvPnS0jUjZg8YfN6lZNjtRFFTMkhkhrOM8K83kpTNuozCwIf53voAkojZ6SGE2qYVio0MamdN1RqkJRM4JMrP0JTagpSJdTzUc8r/1keQgWCTse2p/nxIxNqyP4JIPDd435am3QxujE26DUqs7dzBLsQliREoz6zomFtnO2LbN/h5MVKHTHQcMEcBFglmbAaEHtFQlsBNk2Jc52oqxElvdhQd70FDmRK8Dss2YtYwM2fUwXUfW9zSVaw06Kf9b6hdghvz3yqAUutznnnnVS8UZcr4UypBmp1K2WtcGe/yhE9z3zbtBLetCfwYGyGvhiORoKB9CBqEweSxQGQ2UsmDPHRw3NqYMFv2l4QxD+H1B38ZmsJw917LXUTlg77o3al/X8iPp+bb97VvUSBW6PdVoIXMTbC9Y1Pzc+m/xrvUsWwLZoG4dWjm8y2AeincxezR1bNYHrCkkbQVKXTsUqNamT72czT4OUwTkXYAAAQIECBAggARM0UHHr3askJGxlMgZbjI+P+bCh9STLmIEQ8QaGwARVsPnuu7nvikKxudgXLAfjO9LeY3q+23YvvcMqz0qtZE+ZdhqF+pZWsoQoSSCft+QQaRAVpnXTOn5Q0MEZggMmc/ugb9Iwm4fH9LojBg1tCulVVZQHLV5mCIdZMfAl2gMWg/NnlGLfmg/wP6EanX9B44J5sg74KLUChXjZG0P+5kMH+OWc/7glpx5FKEgk7NhxpfY8LqhMGqrjYmYcRpdSUyJBri4Js0D7oNyUt3UlS1Jz7edlX1chPQ37CxdbLXf8+/BEAEte2y7y8Se+qwzvQ+sX0M5FVx/+DVxAVDXNtvW1L6pC7QC6K/Nw8pskNcUmAqf1Uh9or4/9THTZMrW7ardOcG2EdomPO9cKoX7fYgifuhgvNcW/YVgggHvnfEV9vkHil32+nRsdNshEzNJujLourI90CzJlBhf5G5q+zZLKSErMDkAjRCxr9mpPbjQR7w31Wj9O7xr2na4YzFbmO6LmH14vdKQZZ3Rz0otbsNKLCSJfOBT9FJDMCgKECBAgAABXjE4AIMiCQZFAZ4nCuNxyUs9dG3e4qJozJHXkNkzxr90aKi34zU4djvO4ii2037g97PdhclNdOeRJQLkejTbZY+yIIigqJ2IHDG/DrYk16PfI3KlelfMOCF691lren4RysbzdbF0v7gu0Ap5h2sNwGLkN9S6y2oL0G6wIIgCUa9INKPJe5DAIbelHmmWtO1N3qPGHpOzuSYWWjaBo/mxVXb74aPBzGjbKEMH9azQNxDtc32uicU2Kk3ttd8ziwHXYDA57Fkztpyzyoh16CRmC+7Gej180VMFswLMEM35pbpBl2vme2Y7UIwVYGYI9xoO2Hkt2gqfJH8du6zHTiVm65+JiIwttcfCNcK1hLcUtC7c30ot9hqwnisz367neyq6dOtdj4Z1P269dQcVmCVmi5Huo2ul+wFTAmapeRjtc+uZeZa5ApjRBjMEtOyxNxX16fC8giECEsRscTYa17fDcw3GKDvHaqDQx9BX+X7Wj+P207aj/jDH+93LpNrsTjK92NFkiRGwyGDl4Vekv6Pag/i81BKSUnjm+3NQEDBFMyIYFB0iJLa5Bwl/ACFcjKo4FA87Bg94eNnoj1/wTFdjsIRpuWIH3gZ6HOxXp50w6EJqejVqB0OAF7Hqx/zyzqmtP37fVLKWA0ktOdcwHearhtNxcD46nvQCbxp44A+3/yOkLztUMW/b7jasIeUZf0B1YJHvso9AU8UaAGJQxJQ5jpOZX79OYf3jmdqHIrp6SErdRdoxzhm2CegDmAos6dQjxKwAjDSBGr1Y4ySkZkE3KoJDSNyyyzWUiwkDKHmAgrdIqcc99+2gNHIunotpLdwbDHAxoC+TiB8Gfb7P0SCpPjC1A1wA1w+DMVxfX+yT3n6Y3vC/T5HNxKTBLZuX8oDRT0XqMRva9iyDodEjaJqrYrfHYAiIj9jUcgb+YBf13vpq8HoYvvawdYAdA0/NIhDA+wv3BsFY1KfuuyWE0AAPZhDkYKCOKScGBjcQM6OdVRrQ57rY58Ku4l6iL7JFAQaz1XCoQeReSdoHhKe9MUivv9vsVDwGlJEXw7gxwLMiGBQFCBAgQIAArxRUa/KCp78O4+yzYFB0kBHfGZVEKirNg/T5iF0v03QYpo0aUvenYYYAXyRUBdKhso1iUCjWp/xT346quVpJIzxM2fDUBhflBMAQ1VkSt8yp0V65xX2BVF0AkSL2C4E2pjT89GAN7XRLpOQmhq2lPtiSgjJByT2OlsnOdyGhnw7DdUBztB0+IkXkrTQDImFz/ZD9r1EriuVyAVaYOnJUjXPn2Lhts10HTQ/46L+Fom56YRUp3btjs0bJGh0zvV9Bs5UlA0MEoIwHwAwORPM5TWv2Ynw9DBgcfzyU6RjE9rY9SEsHJhY6FsAbEiprw2VIADBEYCvr+3XXE32G2ZD4qKVviu119gH3rG2HLeaLqbuJJTR1SFOeXEYGbCGQGHS/B6OMRAER1wYwUTACZCNKtvTAVCHA7w2YlLItATC6aubyGZiWll36vb837vPGPmp/z32oSuxnYtT9AFPPvnwRCr+SyW2JxPpcUDc2Qexpm53O94V2a3WjzfycDvMbHDNOz2Utgn1pej+ZOJoCw6UXYXBRq7p/L3QfhymClPwAAQIECBAgQAAJmKKDjvyikuRaS5LvcSFd5xNuHNrxjA21ys12fDq6Yupb4yMsCjA4Mux40kUpdYbGLZ/NRI2FzYiyERkXO+z3fi5e2+PLb2gkyloJTp0vN08dKcESwKfK6hKp+mAH8D0XgmzboToZnc8vdMdMe1Cc0bMS0FaoPoBTo9m0cnKkjfTo5kFN2aWCrigdkukjkT3ps1CQFWUlwD6wWNe3gXQUvh4RmCuNSMEUcfoz2AWcK7c734G0Zl2fa772n0dJB4YoHdE8l26I6O/QN1F0GGANFpghgPvU+DJtJ1LuyZ4itcuusyYL1gSwAmjdaQ9QSqlYt9xoPMmGm1ywFNoX3xZK4S/71HBlEfl5IRE+rCLqx3dL/5zS4wTdFe4V92NfIgbtYf0ZFZIGC4j3DTR9vj3UfqSr+/YTe5oYshSRNyhVdgUMEdCzzr2gRo5ImnYAE0vterHT3evUdk3h32r3l6akh9Q+y8Dl5tTbj3tdN6bUItnTlPvI9CH13q17zaA+l/GxmsQLLwZTdGiF1l/84hflhz/8oTz55JPS3Nwsp59+ulx11VVyxBFHTNpdTT772c/KN7/5TRkZGZFTTz1Vvva1r8maNWv8NoVCQS6//HK57bbbJJfLydlnny1f//rXZeHChS/sXAgBUxQgQIAAAQK8UlCtHZh/zxH33XeffOQjH5Hf/OY3ctddd0m5XJbzzjtPMpl6tHT11VfLNddcI1/96lfloYcekr6+Pjn33HNlYmLCb3PJJZfIj370I7n99tvl/vvvl3Q6LW95y1ukUqlMddjnjYApOsgI5cISitTDS8zFjy924cPI6x21suD7bnw6ttzdkokjOdXGfd+6GSyC+xipsyhPgUgPUThQarURUnP/1NkfYH58SrsySWCIODJDJAvmxBeIRV+mZwdaJaA6p2i2Sz6lmh+UM4EGyqex6hKRNY5HGg2UKUiMqNaIzNpwPETyiLCnK5/CGoXJlgCxcRjCaVZJRiPIMHQh7p4iQgTDg8yc1F7SslDJhIIyGWDH0NZoDpl+NvMIDBGKkoINyMzXkBW2ApSpxKxGlTJymLVI7nftRFq3j4CVOfLsRMqyfvXzFNMetpVoKCmj5w02IrPAfo2+17JDN6e+CsaN2RD/e72n40utACWiWqNIvla3alBU4lr4NAW2ze4TzyUD/XH/q+3+mnJuf837LKOKZYnuWesOu/8c6apgQJlVXViDlhFFdn0JC/s9NIV+f9o3YTrJOqx8p2p8tBn8PQri1o9vOwVYmNZdJbuftFsfX+4uBGwoypT5BWYI6Ps1vtf3gLYbdgv83kj263HDyEprqhd0JvNVzniLZN2x0/qcIUMVbF0LnnOw0s0hKYdehJT8Q4yf//znZv073/mO9PT0yLp16+TMM8+UWq0m1113nXz605+Wd77znSIicvPNN0tvb6/ceuutcuGFF8rY2JjceOON8r3vfU/OOeccERG55ZZbZNGiRXL33XfL+eeff8DaGzBFAQIECBAgwCsFmD57of9EZHx83PwrFArPcnCRsTEXDXV1ubnZrVu3Sn9/v5x33nl+m3g8LmeddZY88MADIiKybt06KZVKZpv58+fLMccc47c5UAiYooOMti1NUplkJgdmCEg85iKfoaPdOkeaYIg8aJUjUTBE2QUuqvEFIYdIj0IsAPsheTahwy0xxw6WgrNpQqhxiHIl1LMQgXoTSbR/s20IWA3OFqmg3AnKlcCETSNfLs8BeH0KzqfN6k+gq8F+G7J0KHLm7L+eh4sNbBiYIb4GXkOk2oSEth1ZU97EsWq3Tw7aTgH9GRgiLMHcFFtt9O0ZIuxevwYDkplH0ap2FTAyKOjqyxXstxEzdDL10hDsy6T6mo6Zo2K+9t5TSx8ZmGPinqT26O+QqUj6OF/MV8GZnAAK5Obm4rq6zyPEcjSV62VemIVKDFMGkmY3Qh8GdgpLsIR+3zkygSStTmYB6ce0jel51HcUXKIEzzvMFtkPDOcDJhsMD+4ls5e4Zp4NpeK56AtgxDqeoQdHTyeS0wzAhO2zbNKYXuRuumdFKZsNRqVghureQXoeVEyZTSHBEAGVST5KeOeVk7YESr2orDLEyhShpEt2rjsnaObgFzZZ0xYhD6RDgpocAE2RWyxaZFNRP/OZz8iVV145/c9qNfn4xz8uZ5xxhhxzjKtj1N/v0kJ7e63xWW9vr2zfvt1vE4vFpLOzs2Eb/P5AIRgUBQgQIECAAAFmjZ07d0pbW91jIx6Pz7C1yN/93d/Jo48+Kvfff3/DdyGaSqzVag2fMZ7LNrNFMCg6RFh5wdNm/fG7V5l1MERL37zVfJ4tu/B28A6nsPd+RrrMLLQdopyykUdyNxf9dMu8ZpX46J6KmSJjCKwCIjtEcIjWoeNgn5E4RaJjvcQQ7bft7t7gdpBe4Lqkj86gqdDjgRGCyzOzAWin9wghjQUYLuwH2WfYzp+3kitR+EXpk9LxjIsofSbKpOwj6EvqbeHsMP3PNHomRJI+uyUD7yVEqDYzBmwEZ6Eh22VCC8JWKEpu8IihjCFmH8EiIpOP9Vq8v7rbswOXbvAsADxl9J0K9oOdpj07Su7nAGfVsc8RrpvXzZAOBQyRbz8ygsh/avK++JrXy/AoI5S02r+kupyPrHY3Dz44zYN2O1ybZmKeKgl7UcCOgSFC5iFKkqDtE4ssMwpwlmmY/HLgbu7vJS4J2JFn8cbyru6KUqttP54V6O+q9OyAYfIMFr1foDkCCh2a6UX3FuvYHxgwZr6yvZZGBPOU6w57VsyfC7lt4zmuhfV5g3RPf8a6qrpLfq0hW/CQ4ABmn7W1tZlB0Uz46Ec/Kj/5yU/kl7/8pckY6+tz9GV/f7/Mm1c37BoYGPDsUV9fnxSLRRkZGTFs0cDAgJx++ukv7FwIwaDoICN3alaybSH5/calIiKS2O0ePk6FX/4O59RXrmpNobJ9SLPz6EVPtv+VpHsCE4NTy8QwGII9P6cve/MzTxXrDxvKW9h1flmFydQNf8BbdrBAtKK/10FJp5rSYUqnTw3QdJDGqcMYREI4iZc3/sD7Sul6WC/QVsF0y06aGtF24/yxH7YkmFjo7gtEzs376xcA1a/9taxYYS8GJzgGBNH4QxojISf+8HoDTK2YjrIbOA6uMSqkY+nF5Rh06TWYboqTB0M4vrc1ICE2LAJwDzFNxkLmyYMKkbpRHgZD/Adb8PdOz4+NTZsoGGVj1MayC/Z7LkeC0hIQnvP2k8+b+399Wgz33v7xRVV2gFPC+Vrh2Hn9I+8F3LiEmA4fxfZ2AMdtZ8NKBg+GeIDcRANcP2DX5IJ8F0377dckg2nMYTlwKHROLbQGOGW/QuVB8DwCeG9hGp0TLPhZ4vOdWIztdfA6SWienmePDWkB3uWo5YdjcyJDaq8dKIbzVWl6MabPqlVpeLE/r308N9RqNfnoRz8qP/rRj+Tee++VZcuWme+XLVsmfX19ctddd8kJJ5wgIiLFYlHuu+8+ueqqq0RE5KSTTpJoNCp33XWXXHDBBSIisnfvXnn88cfl6quvfmHnQggGRQECBAgQIMArBYfYp+gjH/mI3HrrrfLjH/9YWltbvQaovb1dmpubJRQKySWXXCJr166VVatWyapVq2Tt2rWSTCblPe95j9/2Ax/4gFx22WXS3d0tXV1dcvnll8uxxx7rs9EOFIJB0UFGcSIqRakLXSFwbHqd472Xdtgc2YiqSbc+aX3/41mK0tuI7s5RKi4JL7kgJRvcIVoH/Y7ptQZBJqVVMzMEMAsBgCHy7aLIEQwRgCjMF8hFO6epbo0IEEC5EFgEgPFBQU4wZ/gcYl5fNNRPoej3qNCuEfZkczd/TjBrC9tUdEw1Irr3rELOrtf3zeJQtwS7gJRfiMXrQmf9AWY+SCTfUI1ezx2MCNgQTKkifZnvPU8LFNpomgyCbi0dUb+Wbh0pyuNLQHnZ/aPIKdLI+XqALYEYmNvDUzpgvsCU8TQfKqPXr5f9vtwc8tce02V8L/2+muq/mXxMIDfHXiuwC2D9eIqGp2S8CJzkFFz1vUZWEsyM4Fx9WY7C1NfQi4vpsQMz1AD0NZq6RZ/yfSFtbzrEyn57pMYnrGUJH4fBLA2Y42aYv9IUbGzCHXdC/yQaKwLdVUu/a+vwkTTdSu9AJBRwkWAwq55dPvyz8UVE5Bvf+IaIiPzxH/+x+fw73/mO/PVf/7WIiFxxxRWSy+Xkoosu8uaNd955p7S21jvwtddeK5FIRC644AJv3njTTTdJOEwvpheIYFAUIECAAAECvFJwiJmi2nPYNhQKyZVXXjlj5loikZDrr79err/++ud87OeDYFB0kBEbjEosH/XFLpv+2FI4ibANIX/7yEqzHh+0o+BycmotDBBRMzLoMCabDE5eQqPjU3TrxqEi0qjTYF1Hdj6lO6fcgTofmTrvGboNL9jWyHps6cyFIhsEoUUbUSdG3XELbe6DtOr32HwSACviGSISmte8ZmLK0/ARNaf6mzZCjwQxNhgb0gB58EtDV1t3VrVtymop6xBL22uQ7wyZcwArhmuAewfxOFgH3HPWf5SIRWwiMgCMF5eQwLXEcdGeorYfaeJgjhDNIz3a3wswNawvsaSqZ8oAZkNrTVbz9GwCcO7jXEy5fXtFct2uccxwxMbcRcl3a6IAlUzhdbaO8GJw3e2ICqShCeJr4QF9GonLYa6I5x/GldC1QYtX348VozPLFibtC5e2YBsGLnTKOi8YIXpzxoztZPlO+x7JEwPMDBEzYNyH8QyC9ezY7I6H+wJTzr7fuRfq5PPhc+152F6LcB6WDq7NESqqG03bc8O1jWTKEisRhXgoUK3JtBTbrPZxeCIwbwwQIECAAAECBJCAKTpkmDhOQ7BhF8KcumqbiIg89JBNzW/dYcep2T6KuFpslNK20lEh5XudCIh1H2xSxhEdZ3WxwV3Lm12o+uq5283n//WUM94qpV143v1b+0MU1SypkSAXzkzPR+kKt85RfvOQ3R6sAHQhYIgmFtgu3PGMzV7r2Iy0dreeJV0KZ2x5RotSkMHWYD9gVyL5KfRTNXxn28yGf8iU8enJYD5U/4DoH9E0s2TQLnDaMlLaYa+A42A7aJNYNwUmCUyQZ/Wwqv9hPQaOh+1gIIrjsJEgrmk9S4e1SFbbw30H5wX2oW27azDM8ljfw1ojZojy3fZ8oFvDXtq31yP9VL8W/UWbiHCJj7KJo1viucO5MZOCcx081rbtHR+8V0REbnrwDBERmfMb+4CDlcS1YD1ZpEEjpL/LTx3pNylzEp0gbY/2f9YActYqn1c4B9sGdDZiUYghKlLqPjNEsDLAeZaI8WJGHChoFjcYIgA6Hyz5/lVjU/AGYNXyxBg1ZOppBlzSlj4Bsj1RacpXRH7XeIiDiVqtKrXadNTjc9/H4YpgUBQgQIAAAQK8UlCrvfDprxeqSXoJIxgUHWQU55Sk2FqSvzjehgP/8ZTzY4jMd6KZ5l86Exmv+1C2ID4KDYB+rr/vWGTFMk1nOsFF5dcuJGpWQzvODhlbam85M0ulM62Y5k3zN4iIyKCa3IAhAub9P7e/2DhF0IrEoAtls32WSWINABvu1Yswus/ZD6WctOfBbEDfb6yhzMCJVjACzRDYnOQ+26BySlkHsCgRm0WEiBveJCJ1nx9c0wZTRWTi0Lkh6wnRNn6H8htog9+//h5MC9a9UZ2eqtfI6O/ZY2c6hgveUCjG60u7oFgu+QSBZeMixMgyA6tXz/6y9wraJIDvJfoKWAivixtx+wXT5LVKbdAqqdaDMr24kCyKKgPsJQRGoBYONfRvLmjaVLK/5ZIrzCZMLLJtS+12yz/50L0iUmeI/PcD9nkGOwawzgrZmD67je45GxMyQ1Rstz8Akwz/ItabcZ9vCnNWnl2Hdgh9gn2bWva4B67QHp6y/Q1Gn8QQYb/N+3E8YmUJxfbpryfrrBquTcNfU3subBKa7ROR9CskBe1lhGBQFCBAgAABArxSUDsAQuuAKQrwfNHckZdke1h+uPlVIiJSeUKNc45waWJgiLw3CiIvFFRUF2JEIZFxzbJ6wjFCySNH3X6VIQISozZzKT2fKCHSAuR63BJb/cXqh0RE5NZnTjY/a73PaaIQecbGbaiYmWe7FGdigGlpHlLPj6NcQ1p26gYUOMGRFqwD/Fs4gwvXrWWnozN2vd4yQ4XFNk1v+fdsu4aPjuv+3XrHM1p+AK7OVO4AzFW40Mi2gdmB3gH7RAkRRJ/+npLuA95K2A80LyhFUuxybet+RNks3U9RLT3ANnqnaN09HHixf58tpr+PaXYXrmWUmKUwZTB5VlP7EjIWwYBNLnzp2mNZibFllj3kjCx4x4BtYU1WntyQOaOKMxvZmyux364j2w99kLOGROqZSKyt8X5FyoygYKnX8DBDtNB6M7HXzX9/4UwREYFbWT37TDU6ykil9rglymiwYzMwvsxem64n7LmhD/pyG5Qtx1pDXwhWWb4oZeOxU7b3zIpA+0Qsqj7neF8wm5gYtFlaeC6LbeorNMhZufZ9wc9oQwkeYoGik9zlwQ6i7WVy1ca9xT1knRMYXDjY412W7XuR8pyq1RnSGZ8jDmNNUZB9FiBAgAABAgQIIAFTdNARWt8qoWSLj1Ixty37HQWUVx0GXHvZK8VrWhJuZN5UoDn5PziGqClhPm5khtAe6EbAQsy1xyk/6WiE7//qbPfBWU67FLuz3ewHkRiiJnaQRSRZpnYhowoMEaL/iSVu+85N7jy9/oSCdRTCBFr22Ahv/wnuAuJ6jq+0Ec3iH7kDgoFLE7PFDFG9lppbh6vy5Mge2hWccwjBtt7L+DiyxuDrY7VE2De0OoUOMevFuarXKmrEOaxs4WL3PbyqfOYN1RIDQwUdVXqRW4Ihiurn0BKBFYilsX/KBNT911AEVffTvgXFct31iIy5htWi2kc6HLU0sVh1JHpvK8RCgCHKzrX3Jq71tnC9mBmC9xTAzBB7T6GQbEMmlmcD3fHjY1qnr9jIHtQ/cAs4nDPT4bVD+Jh+ntpHPkAUrsInCAxpWZkh9M8MsQ7soN22jbLCuOYZ9q9sCJiT6ITbka/rh+eatD/8nD5bzTO8L+Dmzk7anKlVI60StElwyPeZlPp5ocMeL6EFduGMHSPtFN9PFJiNj1bqGXW6zPS6a896TTCorJfEc8w1AOf/qiTtBdr4UCCYPpsRwaDoEIFNBFFGo9SuNK+OOSpqghjbH9F1+7LkQrAN1emRbq2bdWx2D93YcveXp0FoqYMH/KHDH0SYJkZ+5hqG6S7f/k5+ydn9Yn9hfW8Mvsa+QJpa3F/U6NNu5IDBEJ8XBnETC9wJMU3O5RB4EBQf1nYOuz/I+90sprfx5/PyZQn0MOP6x4zLG2CwNzn1lxlpFo/zH3Wk1MOOAIMovERL3WWz3+SesGmbPy4X5dVrj2uI6Szc29Ree1w/Xad9AwNKtAMCbW8tgEukzWnb4Q6E1OOkTmH6wVCXTk02u3VcF5hOtuy1Fw5/kABcNwDCbRl1i5GVtlNjMITzh+0EAg4uv5DtpZIbZCEQmZQ2Hi7olF6bXkz6443q9ih5kpk3DRmvv2vbQefeRinmNODFNQYgIuf3AACh8nRAyjxQabbV6zEYAvB81PsWDebQNzDNmLADBT6/MpIDdAnxvAdNc7GxJw8eMVWFKepIjt+f+r7V6+inist636jv5bsjkux3jR9fan1DRlbabf29pOez3t9VKrDb7a8WCUmN3ueHArVqVWovcPosSMkPECBAgAABArz8ETBFMyIYFB1kNJXdv8xiSn2d56gY3IDCiJ1nKpNJI0+bhTV65zRrAAxRVqlesAVIIfeFJWmqhc0eucAkIj1EXDyFgf3BLA0hcSivkdkcF/I2r7fzhDBPQ4THzAxKPHhjP4oQR87ITzqaiIzYqI4p7dbdLtT1ougUGfppVFeh6cwGs8tIo10AIkNE02BYsj12msyn0uutx/b1gqU6RYLSCxrV1jhaxjTYGO2nhKjY3nMwRF7ojT7EBVlHrSAcpS0wTQBWxKdh6z0bO9LNTe4/2X3Qdz/OM6T7VTZU05HRPi4eOp35JfoY0tEh0GYWAveqeT+dmG4GE0dmhlJ7pq5yHC5UpNBl+xUKlHZsdhd9eBWlaXsDSrfElB2Qo75Tfy4xPY0pVrdfsF0wbSzYWW1p3TXzHyueJipqeRw8b2BQsEyQmWEkO7Xg2aMJfRxspGUlMX2Oe44+BEwnYvYMEZqvh8U0IMr8sGAaZVkAsI54lvD+gsUH2jW5bEpmnmMFMcWHqdFCu2srs31csJmn8iazb5VA1vuSQzAoChAgQIAAAV4pqNYao87ZImCKAjxfZI4oSrqtKLHdGippKn5x2DJDyZ32VhTbKCV1PxvcuSUiRy7ouv9VLrpJ9VuGiH9fj36wHxv1RMiGP9NnI+XmQbs96zNyvbS/La7BJdWxtG637W6m1FtEpiNHuN9BwzCxmHQgT7vrWZijkaAW8yy1uPOLD7v1nvWWIaqfF5tD2u8RWUIEDfFyqCoSITbNmzdGbZQMhgj6EES/2FcRUT+9bxr0IlX7O18GBLoT7RNeEN1kP+cCsFhCLOvLhsAsUYmTorJpOL9cl02VHzlamxd3v2t9xn2BIr0xmE2GLUOE9zN0JXWzSRZ4W3E/AIYIjBk0VS3KBlZQqoHuOcqD+P1TX4/vsULASnudNgRDBAweQ69SIqcKXViqdmYIx9Sm0fZeyIxrQ4zl6FH2Bz2/I5YtC2G2shZZFSR7oTRfQ7ueHCQ7hSyZOnbYwrc1Mmlk8L0EE4N7w8wvtIJYxketXQIL2ZvIQqBApWNgHwENFvpImNhUPr6ISIw0bWC/oG9sMIRE0kqS2q6YbPUQKTTaPhx01GrS0EGf1z4OTwTcXYAAAQIECBAggARM0UFHtD8msYk6uxJ6wuktUCmBtS7Qj4AZwvfIHALDAyBTCCwDWAlk4Iyt0NRXSkfmFFhEqtDWsBkbUl3bVatUIOM8METIykJGVHKPahQ0BT6517YDhWMB1iwU28F4uc8nFtouy4Vk566zEd/EIru+9zXEyGm6e9tGMe0EENEntbxCZp4971rTZPsAG3XWSE/CxW+h5YE5I7ZHZhy0Rrj3+B56MqSgo/Aq2AQwPmBQwCDh98ySQZ+CaD05YC0GKhH7PXQeXAy0dSv6rEb5mpGI40NThOuD68FGgw2R9hiXzpg6HTypWWyccRSldWZFJpfxEBGJTlhNERiigVPqKZbM3PB6occyALEhd/HRnzx7qMv4qFtOl1WV67XrbU/ZDVBmB5hsHyDSWNgUx4EuhrWDFdJ3odeDISoleX9WC8ilWZDaD8Bs0qfSk4NIXZfnlsW2qeN3NsUEM4ZSMWwdwHYVuA5e60TZrE3Fmtd1oV+CNYZuCuBjwarDH1uvBQwyayGRaujQ8xK1ak1qL3D6rHYYM0XBoChAgAABAgR4paBWlRc+fRak5Ad4nqikqlJuqUokTeZtihDbcmBaGhlE2vfAIHkjMJU7eB2KokxMBzKSALAKMA7kuXkwRGA9ENVAx4FMpPR8mrOP23azfoMZoo5nNBrXdsT7M+b7PWd3iIhI51OWIcL1gh8TtBqpvVObwHU8Y9f3vs4+zK1PasQK3xg1uQMbkaQCnJ4dwX2ZRCp4TxmNXrEN/Hi4NMnoqy0j0fE7pQ20LdCDwdwwr3oU3ANoirz/jmZZZefWS5C4RupC7w1KuoCRAjOE/WagC/v/t3fnUVFc2R/Av83STdMNGERBBUV0UFHZxPG4IJiEUZKDmEQxjIkhojFjPC45JopxFE1ijv6cIY7b6JlxHWdJXCcS1xEUxSgijRHZbBEUUUQjoiLNcn9/QFWoplswdtNI7uccjnbVq6pXt7q7br969arhH5XeOELCAJZCS5b6uvQuNf2764T3hHD3WpPHIcik5YVWBqG8cGyEz4I46KW9tP4C/f4tVXq//lUl0rgL5W0eNmy44b1f61Af2IqeP48Cqd+SKPZNafj3QR+9vjgNn/vahn5WwjG0bRgYU2ghEogtLg27pP94DoVeS5Od3p1z+i1EwmMwbJ5IB2cU11f+9JObcEfgE5f6N6Hw8F7hGOrf7UXi3WcN9SuTtgSLd14JLQ16zZb6Y27p39lZ5aTX8qN3V64wQKLQiiO+dxrqK3zfCa2L+n259ONRrZT93EIkzNLr89ZkIEuxdctwnyL9llrWtnBSZGYvPaw/c1k/arglXX+gvSZPdYaknJXeh1Y8IQqXUPRGA67Se4J5k8tterfwC8mP8CVhWyXcsqrXnA5pUmT7WC8pEuotnBP0Pvj6z9Fy1OklRTXSilY+rt+QU1X9im0qhTOudD26hsXsnxhOivRV6XUkVz+WfgSUDeupbjh5KPU6Qto+lt7CLGt0frXVGxlZvERR2XCiaIiRcPKXP5CenJ0qpUmRQrglXbgc9lgvKaqUrk/ZcOJTVDYM6KeXFAkdke0akillpXQ58db7x9KkyL5hfm3Dt7ldQz2Eeqn0YqZ/SUS4TChvWI9wua664QRaIwzQJyRR1dLyYkfyhulPGvZPODJKvWeq6Y/WXKP3ZrTXGUmKqoUxFKTLCyNe21VaoVp/pGm9yzE2eiMlC597gTC0hvC5lOt/HvV+pFg/0kti9D/PQt0a3qdW1Xon9ar6gyqMiP2sgwXW6ZXXCaOyNxwL/aRIf7BFhU6aFNVU6V9+bnjP6H9/CNur1Nu+/mU9vdEThM9gbZ00KRLLVz89KdLfn2o0SoqM/KDVf3/pH0NFlfBDE03q1KFK+mOwNfDls6fjpMjMjn71f5auwovpkpnWu89M62Xtl8bSFWDMhPjy2VNxUmQGQhb9oJlyjDHGmHCuaI0WmBpUP/eA1jWwwDPbWgknRWZQUVE/aJCHhevBGGPsxVFRUQEnJ6fmC/4Ccrkcbm5uOHXre5Osz83NDXK5vPmCLxgZteeLgxZSV1eHmzdvwsHBATKZZXvVPXjwAB4eHrh+/TocHR2bX4A1i2NqHhxX8+C4mp6pY0pEqKioQNeuXWFlZb7b9J88eQKdzvBjbJ6VXC6HnZ1d8wVfMNxSZAZWVlZwd3e3dDUkHB0d+QvRxDim5sFxNQ+Oq+mZMqbmaiFqzM7Orl0mMqbEI1ozxhhjjIGTIsYYY4wxAJwUtXsKhQJLliyBQqFovjBrEY6peXBczYPjanoc0/aLO1ozxhhjjIFbihhjjDHGAHBSxBhjjDEGgJMixhhjjDEAnBS90JKTkyGTyQz+paWlGVymuroa8+fPx8CBA6FSqdC1a1dMnjwZN2/eFMtcu3bN6Hq//fbb1to9izFXXAVnzpzByy+/DJVKhQ4dOiA0NBSVlZUG1tp+mDOmoaGhTdb59ttvt8ZuWZy536tA/cCC4eHhkMlk2Ldvnxn3pu0wZ1ynT5+OXr16QalUolOnToiMjEROTk5r7BZrAe5o/QLT6XS4d++eZNof//hHHDt2DFevXjU4mnZ5eTnGjx+PadOmwc/PDz/99BPmzJmDmpoanD9/HgBQW1uLO3fuSJbbtGkTVq5ciVu3bkGtVptvp9oAc8UVqE+IxowZg7i4OEREREAulyMzMxMRERHt+k4Wc8Y0NDQU3t7eWLZsmThNqVS2ymB4lmbOuAoSEhJw9OhRHDx4EHv37sW4cePMtTtthjnjumnTJvTt2xfdu3fHvXv3EB8fD41Gg4KCAlhbW5t931gziLUbOp2OOnfuTMuWLXum5c6dO0cAqLCw0GgZf39/mjJlyvNW8YVkyrgOGTKEFi1aZOoqvnBMGdOQkBCaPXu2iWv4YjL1d4BGoyF3d3cqKSkhALR3714T1vbFYc7v1szMTAJAV65ced5qMhPgy2ftyH//+1+UlZUhJibmmZYrLy+HTCZDhw4dDM5PT0+HRqNBbGzs81fyBWSquJaWluLs2bPo3Lkzhg0bBldXV4SEhODUqVOmr3QbZ+r36s6dO+Hi4oL+/ftj3rx54kOZf21MGdfHjx8jOjoaa9euhZubm2kr+oIx13fro0ePsGXLFvTs2RMeHvwI8TbB0lkZM53w8HAKDw9/pmUqKytp0KBBNGnSJKNl/vCHP1C/fv2et3ovLFPF9cyZMwSAnJ2dafPmzXThwgWaM2cOyeVyysvLM3W12zRTvlc3bdpER48epR9//JH+9a9/kaenJ7366qumrO4Lw5Rx/eCDDyg2NlZ8jV9xS5Gpv1vXrVtHKpWKAFDfvn25lagN4aSoDVqyZAkBeOpfWlqaZJnr16+TlZUV7dq1q8Xb0el0FBkZSQEBAVReXm6wzOPHj8nJyYlWrVr1XPvUFlg6rqdPnyYAFBcXJyk/cOBAWrBgwfPtnIVYOqaGnD9/ngBQenr6L9qntsDScd2/fz/17t2bKioqxGntISmydFwF9+/fp7y8PDpx4gRFRERQYGAgVVZWPvf+sefHHa3boLKyMpSVlT21jKenp+Rpx59//jnWrFmD4uJi2NraNruN6upqREVF4erVqzh+/Dg6duxosNyOHTsQGxuL4uJidOrU6dl2pI2xdFwLCgrg5eWFHTt24J133hGnT5w4ETY2Nti5c+cv2CvLsnRMDSEiKBQK7NixAxMnTmzZjrQxlo7rnDlz8Je//AVWVj/3sKitrYWVlRWCg4ORnJz87DvVBlg6robodDq89NJL+Nvf/obo6OiW7QgzGxtLV4A15eLiAhcXlxaXJyJs2bIFkydPfqYPbX5+PpKSkp76of373/+OsWPHvvAJEWD5uHp6eqJr167Izc2VTM/Ly0N4eHiL69WWWDqmhmRlZaG6uhpdunRpcb3aGkvHdcGCBZg6dapk2sCBA5GQkICIiIgW16utsXRcn7adqqqqFteLmZHF2qiYyRw7dowA0OXLlw3O79OnD+3Zs4eIiKqrq2ns2LHk7u5OGo2GSkpKxL+qqirJcvn5+SSTyejgwYNm34e2yBxxTUhIIEdHR/r2228pPz+fFi1aRHZ2dr+aPgWmjumVK1do6dKllJaWRgUFBZSYmEh9+/algIAAqqmpabX9sjRzfQc0hnZw+exZmTquWq2Wli9fTufPn6fCwkJKTU2lyMhIcnZ2ptu3b7fafjHjOClqB6Kjo2nYsGFG5wOgLVu2EBFRQUGB0WvpSUlJkuXi4uLI3d2damtrzVj7tstccf3qq6/I3d2d7O3taejQoZSSkmLGvWhbTB3ToqIiGjlyJDk7O5NcLqdevXrRrFmz6O7du62wN22Hud6r+uv4tSVFpo5rcXExhYeHU+fOncnW1pbc3d3p97//PeXk5LTC3rCW4D5FjDHGGGPgx3wwxhhjjAHgpIgxxhhjDAAnRYwxxhhjADgpYowxxhgDwEkRY4wxxhgATooYY4wxxgBwUsQYY4wxBoCTIsYYY4wxAJwUMdbuyGQy7Nu3DwBw7do1yGQyaDQai9bpedy6dQthYWFQqVTo0KFDq28/JiYG48aNM+s22sNxau9OnjyJiIgIdO3aVfIZexZEhFWrVsHb2xsKhQIeHh5Yvny56SvLfjF+ICxj7ZiHhwdKSkqe6SGYLeHp6Yk5c+Zgzpw5Jl2vIQkJCSgpKYFGo4GTk5PZt6dv9erVMOXA/zExMbh//77kpGqu48RM59GjR/Dz88P777+Pt9566xetY/bs2Thy5AhWrVqFgQMHory8HGVlZSauKXsenBQx9gKqrq5u0VO7ra2t4ebm1go1Mh+tVotBgwbhN7/5jUnXq9PpIJfLmy3XGolYezhO7V14eDjCw8ONztfpdFi0aBF27tyJ+/fvY8CAAVixYgVCQ0MBANnZ2diwYQMuXbqEPn36tFKt2bPiy2eMNXLnzh24ublJmrTPnj0LuVyOI0eOGF3uxo0bePvtt+Hs7AyVSoWgoCCcPXtWnL9hwwb06tULcrkcffr0wY4dOyTLFxUVITIyEmq1Go6OjoiKisLt27fF+fHx8fD398fmzZvh5eUFhUIBIkJ+fj5GjhwJOzs7+Pj44OjRo5L16l+WSU5Ohkwmw//+9z8EBQXB3t4ew4YNQ25urriMVqtFZGQkXF1doVarMXjwYBw7dkycHxoaisLCQsydOxcymQwymUycl5qaipEjR0KpVMLDwwOzZs3Co0ePnhrzp8XG09MTu3fvxvbt2yGTyRATE2NwHcIlrqVLl6Jz585wdHTE9OnTodPpJPWeOXMmPv74Y7i4uCAsLAwAcOLECfz2t7+FQqFAly5dsGDBAtTU1DRZt4CIsHLlSnh5eUGpVMLPzw+7du2S1CcrKwuvv/46HB0d4eDggODgYGi1WsTHx2Pbtm3Yv3+/GLvk5GSDl8+aq1doaChmzZqFTz/9FM7OznBzc0N8fPxTY83M5/3338fp06fx73//GxcvXsSECRMwZswY5OfnAwC+++47eHl54cCBA+jZsyc8PT0xdepU3Lt3z8I1ZxKWfBotY21RYmIi2draUlpaGlVUVFDv3r1p9uzZRstXVFSQl5cXBQcHU0pKCuXn59N//vMfSk1NJSKiPXv2kK2tLa1bt45yc3PpT3/6E1lbW9Px48eJiKiuro4CAgJoxIgRdP78efrhhx8oMDCQQkJCxG0sWbKEVCoVjR49mi5cuECZmZlUU1NDAwYMoNDQUMrIyKATJ05QQECA5GnmwpO7MzIyiIgoKSmJANCQIUMoOTmZsrKyKDg4WPIkcI1GQ3/961/p4sWLlJeXR5999hnZ2dlRYWEhERHdvXuX3N3dadmyZVRSUkIlJSVERHTx4kVSq9WUkJBAeXl5dPr0aQoICKCYmBijsWsuNqWlpTRmzBiKioqikpISun//vsH1vPfee6RWq2nixIl06dIlOnDgAHXq1IkWLlwolgkJCSG1Wk2ffPIJ5eTkUHZ2Nt24cYPs7e1pxowZlJ2dTXv37iUXFxdasmSJZN2RkZHi64ULF1Lfvn3p0KFDpNVqacuWLaRQKCg5OZmIiG7cuEHOzs705ptvUlpaGuXm5tLmzZspJyeHKioqKCoqisaMGSPGrqqqqslxakm9QkJCyNHRkeLj4ykvL4+2bdtGMpmMjhw5YjTezDQaf8aIiK5cuUIymYyKi4sl5V555RWKi4sjIqLp06eTQqGgIUOG0MmTJykpKYn8/f1p1KhRrVl11gxOihgzYMaMGeTt7U2TJk2iAQMGUGVlpdGyGzduJAcHB7p7967B+cOGDaNp06ZJpk2YMIFee+01IiI6cuQIWVtbU1FRkTg/KyuLANC5c+eIqD4psrW1pdLSUrHM4cOHydramq5fvy5OO3jwYIuSomPHjonLJCYmEoCn7qOPjw+tWbNGfN2jRw9KSEiQlHn33Xfpgw8+kExLSUkhKysro+tuLjZERJGRkfTee+8ZrRtRfeLi7OxMjx49Eqdt2LCB1Go11dbWElF9EuHv7y9ZbuHChdSnTx+qq6sTp61bt06yXOOk6OHDh2RnZycmvILY2FiKjo4mIqK4uDjq2bMn6XQ6o3VtnGQRNT1OLalXSEgIjRgxQrKewYMH0/z5843GiZmGflL0zTffEABSqVSSPxsbG4qKiiIiomnTphEAys3NFZdLT08nAJSTk9Pau8CM4MtnjBmwatUq1NTU4JtvvsHOnTthZ2dntKxGo0FAQACcnZ0Nzs/Ozsbw4cMl04YPH47s7GxxvoeHBzw8PMT5Pj4+6NChg1gGAHr06IFOnTpJ1tu9e3e4u7uL04YOHdqi/fP19RX/36VLFwBAaWkpgPoOpZ9++qlYB7VajZycHBQVFT11nenp6di6dSvUarX4N3r0aNTV1aGgoMDgMs3F5ln4+fnB3t5efD106FA8fPgQ169fF6cFBQU12f7QoUMllwCHDx+Ohw8f4saNG022cfnyZTx58gRhYWGS/dy+fTu0Wi2A+vdDcHBwi/p8GdPSejU+jkD9sRSOI2s9dXV1sLa2Rnp6OjQajfiXnZ2N1atXA6g/NjY2NvD29haX69evHwA0+9lirYc7WjNmwNWrV3Hz5k3U1dWhsLCwycmnMaVS2ez6Gp/cgPp+KcK0xv83VgYAVCpVk/nNbceYxidsYZm6ujoAwCeffILDhw9j1apV6N27N5RKJcaPHy/pn2NIXV0dpk+fjlmzZjWZ1717d6PLPS02ptBcDA1t31C9gJ9jlJiYiG7duknmKRQKAC17PzSnpfXST7xkMplYR9Z6AgICUFtbi9LSUgQHBxssM3z4cNTU1ECr1aJXr14AgLy8PAD1P3hY28AtRYzp0el0mDRpEiZOnIgvvvgCsbGxkk7P+nx9faHRaIx2mOzXrx9OnTolmZaamir+SvTx8UFRUZGkRePy5csoLy8XyxgiLHfz5k1x2pkzZ1q0j0+TkpKCmJgYvPHGGxg4cCDc3Nxw7do1SRm5XI7a2lrJtMDAQGRlZaF3795N/ozd5dVcbJ5FZmYmKisrxdc//PAD1Gq1pCVNn4+PD1JTUyUJZmpqKhwcHJokPUJ5hUKBoqKiJvsotPT5+voiJSUF1dXVBrdpKHbPWy9mfg8fPhRbgACgoKAAGo0GRUVF8Pb2xqRJkzB58mTs2bMHBQUFSEtLw4oVK/D9998DAF599VUEBgZiypQpyMjIQHp6OqZPn46wsDBJ6xGzMAtdtmOszZo3bx55enpSeXk51dbW0siRI+n11183Wr6qqoq8vb0pODiYTp06RVqtlnbt2iX2O9m7dy/Z2trShg0bKC8vT+xMnJSUREQ/d7QODg6m9PR0Onv2LA0aNKhJR2s/Pz/Jdmtra8nHx4deeeUV0mg0dPLkSRo0aFCL+hT99NNP4noyMjIIABUUFBAR0bhx48jf358yMjJIo9FQREQEOTg4SDqbh4WF0dixY+nGjRt0584dIiLKzMwkpVJJM2bMoIyMDMrLy6P9+/fTzJkzjcauudgQtbxPkVqtpujoaMrKyqLvv/+eXF1dacGCBWKZkJCQJh3mhQ7NH330EWVnZ9O+ffua7Wj92WefUceOHWnr1q105coVunDhAq1du5a2bt1KRERlZWXUsWNHsaN1Xl4ebd++Xew38uWXX1L37t0pJyeH7ty5QzqdzmhH66fVy9D+tCRW7JcRPjv6f0K8dTodLV68mDw9PcnW1pbc3NzojTfeoIsXL4rrKC4upjfffJPUajW5urpSTEyM0b6IzDI4KWKskaSkJLKxsaGUlBRxWmFhITk5OdH69euNLnft2jV66623yNHRkezt7SkoKIjOnj0rzl+/fj15eXmRra0teXt70/bt2yXLFxYW0tixY0mlUpGDgwNNmDCBbt26Jc43lBQREeXm5tKIESNILpeTt7c3HTp06LmTooKCAho1ahQplUry8PCgtWvXNjkBnzlzhnx9fUmhUFDj31bnzp2jsLAwUqvVpFKpyNfXl7788kujcWtJbFqaFEVGRtLixYupY8eOpFaraerUqfTkyROxjKEkgogoOTmZBg8eTHK5nNzc3Gj+/PlUXV3dZN2Curo6Wr16NfXp04dsbW2pU6dONHr0aDpx4oRYJjMzk373u9+Rvb09OTg4UHBwMGm1WiKqv6NOiBEASkpKanKcWlIvTooYMz0ZkQmHamWMMQswNEq0qURHR8Pa2hr/+Mc/TL5uxljbwn2KGGPMgJqaGly+fBlnzpxB//79LV0dxlgr4KSIMcYMuHTpEoKCgtC/f398+OGHlq4OY6wV8OUzxhhjjDFwSxFjjDHGGABOihhjjYSGhooPKm38cNK2LiYmRqy3OTpbM8Z+HTgpYoxJTJs2DSUlJRgwYIDZtpGcnAyZTIb79++bZH2rV69GSUmJSdbFGPv14sd8MMYk7O3t4ebmZulqAKgfXdzYaNiNOTk5wcnJqRVqxBhrz7iliDFmlNCic/jwYQQEBECpVOLll19GaWkpDh48iH79+sHR0RHR0dF4/PixuBwRYeXKlfDy8oJSqYSfnx927doFALh27RpGjRoFAHjppZcgk8kQExMDoP7y3cyZM/Hxxx/DxcUFYWFhAOofe/Laa69BrVbD1dUV7777LsrKylo3GIyxdo+TIsZYs+Lj47F27Vqkpqbi+vXriIqKwtdff41//vOfSExMxNGjR7FmzRqx/KJFi7BlyxZs2LABWVlZmDt3Lt555x2cOHECHh4e2L17NwAgNzcXJSUl4pPEAWDbtm2wsbHB6dOnsXHjRpSUlCAkJAT+/v44f/48Dh06hNu3byMqKqrV48AYa9/48hljrFlffPEFhg8fDgCIjY1FXFwctFotvLy8AADjx49HUlIS5s+fj0ePHuHPf/4zjh8/jqFDhwIAvLy8cOrUKWzcuBEhISFwdnYGAHTu3BkdOnSQbKt3795YuXKl+Hrx4sUIDAzE8uXLxWmbN2+Gh4cH8vLy+GGajDGT4aSIMdYsX19f8f+urq6wt7cXEyJh2rlz5wDUX+p68uSJeOlLoNPpEBAQ0Oy2goKCJK/T09ORlJQEtVrdpKxWq+WkiDFmMpwUMcaaZWtrK/5fJpNJXgvT6urqAED8NzExEd26dZOUUygUzW5LpVJJXtfV1SEiIgIrVqxoUrZLly4t2wHGGGsBTooYYybl4+MDhUKBoqIihISEGCwj3FFWW1vb7PoCAwOxe/dueHp6wsaGv7IYY+bDHa0ZYybl4OCAefPmYe7cudi2bRu0Wi0yMjKwbt06bNu2DQDQo0cPyGQyHDhwAHfu3MHDhw+Nru+jjz7CvXv3EB0djXPnzuHq1as4cuQIpkyZ0qKkijHGWoqTIsaYyX3++edYvHgxvvrqK/Tr1w+jR4/Gd999h549ewIAunXrhqVLl2LBggVwdXXFzJkzja6ra9euOH36NGprazF69GgMGDAAs2fPhpOTE6ys+CuMMWY6/EBYxpgoNDQU/v7++Prrry1dlV9EJpNh7969GDdunKWrwhh7AfHPLMaYxPr166FWq/Hjjz9auiot9uGHHxq8O40xxp4FtxQxxkTFxcWorKwEAHTv3r1Fj9hoC0pLS/HgwQMA9Xek6d/BxhhjLcFJEWOMMcYY+PIZY4wxxhgATooYY4wxxgBwUsQYY4wxBoCTIsYYY4wxAJwUMcYYY4wB4KSIMcYYYwwAJ0WMMcYYYwA4KWKMMcYYA8BJEWOMMcYYAOD/Aau4lfi9wb4VAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "xds.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *box(*xds.rio.bounds()).exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Determine bounds of re-projected raster\n",
-    "\n",
-    "The [rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds) method allows you to safely convert a bounding box into another projection taking into account the effects of nonlinear transformations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "reprojected_raster = xds.rio.reproject(\"EPSG:4326\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Boundary calculated from the re-projected raster (inefficient)\n",
-    "\n",
-    "This is the benchmark. However, this method is computationally\n",
-    "inefficient. So, if you don't need to re-project, [rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds) is a more efficent method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "reprojected_raster_box = box(*reprojected_raster.rio.bounds())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f324735e4d0>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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E0Y+wOIfVi6QMvam7L0ApK3aRWCyGSCSCKIBwY2OBAPS4E38NAPCsbZD7ohNczx9bZ8h20sNAAXKOgFXNQupv0kVM6jxyfsDocMp0eioo/+QWAIABwa1C62IZTIpp9YQNZrt14tUabN2tltfjpIsB2xHXGjH3uBHlsi01wxGqExUS1QaynZ4cyOuRyj5IQGsUDcQOrZDaE2ZyqBkbZkCV5U2/o+UJKgg0CL1KpkiF4oiYk2UK/M3ur4/800odg91+B7dwIcCG0PvkRb7pYkXGpCeFxie02XmKiQP+zQmxnbOQK3KfemoeLbZt3e2Tpw1yDOODGIrWOfqnLBAfIAp52jnUjCiT1y7lBcp6kksNVKoS8NeL/Rn34TAYMbe8CMwJKetqbrQkh+acI1GpyCfKjLgN0xGzWx4GLcWhpWw57nlyIRUdAxV5PsXRhKlZ99ymj8l+G3G3bjbE5NgCwKo//BwEQXwFTU1ARQViACIAotEowuHdY4KS/3+p7pMBvX6cfdCILbs11j0JaXwIgiAIoh+RFzf35tVTXn31VUydOhU1NTVgjOHpp5/ersyaNWswbdo0RCIRhEIhjB8/Hps2bZLHM5kMLr/8cpSVlSEQCGDatGnYvHlzQRttbW2YMWMGIpEIIpEIZsyYgfb29h7FShMfgiAIguhH2OCwevHalYlPIpHAoYceinvvvXeHxz///HN897vfxYgRI7Bs2TJ88MEHuPnmmwsMEmfNmoW///3vWLx4MV5//XXE43GccsopsCzXiXr69OlYtWoVlixZgiVLlmDVqlWYMWNGj2LdayY+8+bNA2MMs2bNkvvOP/98MMYKXuPHj+92m4sXLwZjDN///ve3O3bfffdh6NCh8Hq9GDNmDF577bU+6AVBEARBfPOYMmUK5syZg9NPP32Hx2+66SZ873vfw/z583H44Ydjv/32w8knn4yKigoA4hbgww8/jLvuugsnnHACDj/8cDz++ONYvXo1XnrpJQBixWjJkiV46KGHMGHCBEyYMAELFy7Es88+i7Vr13Y71r1i4rN8+XI8+OCDOOSQQ7Y7NnnyZNTX18vX888/3602N27ciNmzZ+Ooo47a7thTTz2FWbNm4aabbsL777+Po446ClOmTClYciMIgiCIfZG+utUVi8UKXplMZtfisW0899xzGD58OCZNmoSKigqMGzeu4HbYihUrkMvlcNJJJ8l9NTU1GDVqFN58800AwFtvvYVIJIJx48bJMuPHj0ckEpFlusMef6orHo/jnHPOwcKFCzFnzpztjns8HlRVVfWoTcuycM455+C2227Da6+9tt39v7vvvhsXXnghLrroIgDAPffcgxdeeAF//OMfMW/evF3uS55/LXVN3qYMEQJQLWWjbbgz3LYrIlZyQDYMeFvFe6ODu4LaGJfCYDXjCk6VnCui1eMoSF+RDbrbeSGqqgLZIsecThMC1bwYWOkkfA3XFRoE5p2mtTRHyhHmJqoZuCqCTxczKcAN1LsCZK05jo5DxCyecVeYq1gcuZAq42UcyDgCPDXHC5yn83GAOU7OEALcfIqL8EaOZJnYny5iUqxtG0yKtW0DKP1QCKY9bTmgk44/Xe5BttjrjK0FNS3KsYyFyHoxcNEhGiIbXKfBnDO25assdAzKmzBa0B2TQmYDLQc7QudWFAjIMyUMmtBSw9MK6Il8LK4hJCDcvgFhdhl3jBjtInGdAWH0mDeNVHJAyhmDVJmKyHoRq+lTYWtCJA4A3hZTmkjm/O7nKBdyxeKemCvkzrqm1CKtxTZxbT3tvEAYfegVwrTzg9+TyJkg9ib66qmuLyfivuWWW3Drrbf2uL3GxkbE43H8+te/xpw5c/Cb3/wGS5Yswemnn45XXnkFxxxzDBoaGmAYBoqLiwvqVlZWoqFBPCjU0NAgV4g6U1FRIct0hz2+4vPTn/4UJ598Mk444YQdHl+2bBkqKiowfPhwXHzxxWhsbPzKNm+//XaUl5fjwgsv3O5YNpvFihUrCmaVAHDSSSd1OWPMZDLbzX4JgiAIYm/D7oMXIDLJR6NR+brhhht2LR5btHjqqafi5z//OQ477DBcf/31OOWUU3D//fd3WZdzXpDfbEe5zr5c5qvYoys+ixcvxsqVK7F8+fIdHp8yZQp++MMfora2FuvXr8fNN9+M4447DitWrIDH49lhnTfeeAMPP/wwVq1atcPjzc3NsCwLlZWVBfs7zyp3xLx583Dbbbd1r2MEQRAEsY8TDof75HH2srIyaJqGgzolawWAkSNH4vXXXwcAVFVVIZvNoq2trWDVp7GxEUceeaQss23btu3ab2pq2u7/9K7YYys+dXV1uPLKK/H444/vNO39WWedhZNPPhmjRo3C1KlT8c9//hOffvopnnvuuR2W7+jowI9//GMsXLgQZWVlOyyT58uzw6+aMd5www0FM9+6urqv6CFBEARBfP305omu/KsvMQwD3/rWt7YTIH/66aeora0FAIwZMwa6rmPp0qXyeH19PT788EM58ZkwYQKi0SjeffddWeadd95BNBqVZbrDHlvxWbFiBRobGzFmzBi5z7IsvPrqq7j33nuRyWSgqmpBnerqatTW1mLdunU7bPPzzz/Hhg0bMHXqVLkvv8SmaRrWrl2LQYMGQVXV7VZ3Ghsbu5wxejyena4ydcU/NwgdxJFn3oWizx3zvE5pc1NlKvS4O+HSk1wa5RkxE5mIuES66WpG1KzIdA44ZoghJuvmM5ZzBpmkNF2sIl0qto0OoUXJ60oCW01YUv+hyGzknpibwDJTpBRkAc8b3Smmm008PkBBaLMY64YTKpH/3mTDItElAFi6Bj3FC0zwwptEkPEaDeliN2u41DlFOWw1H2sWasYVxCg5x2iwkzYoWc6gJ92xVNOO0WAsg44DwrCdrOVa0kYu6GSob0pCyYg4MtVhWB5FjnM+uSfv9FHM+VSpf+oYpEr9jekFStbYTmzO2Jc6eqY2IF0i9mlxdwyZBamTMjtlYbc1hsgXttOuq+sJ1Fuw8tnVvQDPiv2+FkvqvkrfawU4hxUSDZpBHYrhjom+iTsxcqm/6qzpSlSprvFj3M30bvoY7O9ExRi8HoHl/F4Z/+O7YavAu49cBYIg9jwWRy+zs/e8Tjwex2effSbfr1+/HqtWrUJJSQkGDx6Ma665BmeddRaOPvpoHHvssViyZAmeeeYZLFu2DAAQiURw4YUX4uqrr0ZpaSlKSkowe/ZsjB49WkphRo4cicmTJ+Piiy/GAw88AAC45JJLcMopp+DAAw/sdqx7bOJz/PHHY/Xq1QX7Zs6ciREjRuC6667bbtIDAC0tLairq0N1dfUO2xwxYsR2bf7iF79AR0cHfve732HQoEEwDANjxozB0qVLcdppp8lyS5cuxamnntoHPSMIgiCIbxbvvfcejj32WPn+qqvED6HzzjsPixYtwmmnnYb7778f8+bNwxVXXIEDDzwQ//d//4fvfve7ss6CBQugaRrOPPNMpFIpHH/88Vi0aFHBfOCJJ57AFVdcIXW606ZN26l30M7YYxOfUCiEUaNGFewLBAIoLS3FqFGjEI/Hceutt+KMM85AdXU1NmzYgBtvvBFlZWUFE5Zzzz0XAwYMwLx58+D1erdrs6ioCAAK9l911VWYMWMGxo4diwkTJuDBBx/Epk2b8JOf/GT3dZggCIIgvgY6C5R3tX5PmThxIr4qA9YFF1yACy64YKfHvV4v/vCHP+APf/jDTsuUlJTg8ccf34UIXfb44+w7Q1VVrF69Go8++ija29tRXV2NY489Fk899RRCoZAst2nTJihKz6RKZ511FlpaWnD77bejvr4eo0aNwvPPPy/vNRIEQRDEvooNBgvdf8ppR/X7M5SkdBfpKknpV3H8cfOQKjPke2ZzoJOwWs2K+Xaiwp2XWl6hdwEAo8OWGpF88lBZznDfa2kn0WeFInUyXAX0hKvN4Ux4xgBC8/HluqKQG1+6SJHeMbkJHcBHYhIa3AQUfZ4W8a1vcuOpKka6QohB2odp0BJAeKPIvKqlLOSCoo+mX0W6aPuEp55OfQ1tSEHJCo1PvDYgk3BCcTVBAJAsyydBZVIvZesM2TCQczxq9A5AS7l1yt8XJjupKi9SJaJ+phjIFonjakokQwVEss78tVByHMlKx7unzdXD6EmhZcoFHS2W5mpoLMP1I9KSgJbKJzZl0NJOl7KAv0kIq3IBV2Nla0C8RpFjlPd/8jdb8LRknLoWmGmDa44WKGQgWel+3jJh9zOST0Kq5LjUjXX2O8qG3YFlpqtD4hqkbqz0I9Gx2CC37Ee/IW8fggCwR5KUrvy4EsFeJCmNd9g44qBt/TZJ6V674kMQBEEQRM+xuXj1pn5/hiY+BEEQBNGPsHp5q6s3dfcF9rhzM0EQBEEQxNcFrfjsAV7+l2v7PW7G3TDiHNlgPh8Vh+n4tHTW2egpSL1KJqLIXE9a2gZX3Nl53odHS7q6/ECDJb2DkhUaTC9Q9JkwmkmXaUiXiDrMYtAdzYeW6tQuc7UyeoJLXYhVH0TrCFHE9APNo4WWp2adhY5vuTlejA4hUAlvZEKv0skkwjacviYtcMfzxvIKHQwABBrdsuraTWBMlFEGDJN+O1rSgp4Sopnofl6ppSlZ3QErKMyP1JQJrjC0jfA7Y8ik1075e+3yHKEVLfBXCNdQK6AjWS0CSZUqIjcZAF8Ll7ojrjKENolzp8pU6fejZgt1W54ol3GBu55MzHJ1WUaH0G/lyWtu1CwKyOd5M32At01sJ8tV+DeLcU4ODAAA9FheI6RK/ZaS4+DOwwCcdfJkynFYzvk6BiowHS1Uycc2TG/nfoh/LYNB78jXBcC59I3KBYCRNwv/qjW/Iq0PQXzd0IpP19DEhyAIgiD6ETZnsPmuT156U3dfgCY+BEEQBNGPoBWfriGND0EQBEEQ3xjIx2cX6Y2Pz5c58sy7ZL4mrkLmubIMBm+70I+YHkV6wgCAr9nNWZULOvmg3F0AB7Kd/FryOhJvuw3LYFLbkQ0xmTtLzQKR9TknDiY1NKlSd36sJzkU09EahRXk/E48rVzG7W23pD7IVl3/GlsX5/K0iUC5zpAJq7JOno6BhQuReY8ef6MtPWgyEQZPTJzQ9Ln9LPk4I7eNxg6pbeKGhtiBEXks9HkHlC3Cb8jcrxpaW9I9YUqY6ZjVJbC9Ir74QA+SFeI8Sk7oWOR5HK2LmgaMhOhsukhBpggyb5gIwt00nXELb7Sl1ihVpkDJur5DejKfs82Cp1UIfXIRA8lyEVNooyv+sfwq1LQzrop7fQEgVaFD7eTpxJ3h5cyNAwCK17r1dccjyf/BFiAjxrT1pANkXjhmQ+YGUyxxPfJEh9vwtLifGSMm/v3P3aT3Ib6B7AEfn399OKjXPj7HjaojHx+CIAiCIPZ+eC81Pryfa3zoVhdBEARBEN8YaMWHIAiCIPoRJG7uGpr4EARBEEQ/wuIKLL7rN3Ssfq78pYnPXsCb/3O13J4w/S4pGtXSXIqEMxEFqmNCZ2uAmnaN7vLb2bDqJppkAHcM9BjnwlAPQnSrpThMvyN2buPSTC9ZwdB0uHjjbYY041NyrsA452cwOlwzQ1sT7VgGk+LdnF+B7hgoMoXJ+NQ0kAupyBSrTlyQwupMRIUp/A8LEo523m4brsBwDPS4BpkstewjEywnzpELa7JOtqhYinyNDgu5AJMib21AAD5nfLSNje75IiHALzJxZks9SJWKWHMBBn+jawKYT5CaC6qybmwwQ6pcnFyPA+FNrtFhLuD+gmK2myA1E1FkGcsL+OJiW81yaZiYC2hIlouBKvo8DTWcF7PbUJyEtmZARbJCXDsjbiMTVuFtEQaGesJN9OprtZEJuwaZrFnst3U3IamaBTKG6Jc5bhDCH4pC8YEMgWPcBLTpF8plf4o+y0mTxOK1QN2JIhbfNjeB7ME3LJBC7tV3ktCZIIg9A018CIIgCKIfYYPB7oWE10b/XvKhiQ9BEARB9CNI49M19FQXQRAEQRDfGGjFZy/jrb+4ep/vnn4nmLPiGNhmyv22xpApEpeOq0BofQIAoJg6MsVCW8FswBNzdUB5bYua4cgUuboL08tgOUkvmeXqekwfoDintDzutpLlMmllRy1D8FtC/6H8TyksZ382qMgEq2rKBtcdrYpfkfGLdhmyTjJMxQZUR/di6662h9lAzimjpiD1RUYHpObJ90U7WIcYg8ThA5B2NETpIibNBW2dwYjZiA8Qx/wbO8C2tTgnV5AbVgUAaD/Ah9JVQkjk3RoHIE6uJRXYzhhylck+eFtM2E7/whsV2cf8WGUiihzDfOy5AJPjDACWo7FSM5DXonhNEiFN1E2XGlAz4lpahuJqwOJZmQhVj+Vga4YskyliUDPiJFwT2ixA6HfyhpKZCJMJUD3tttQhqRkujSK5wtA2Rmh5LC9wRHkdAGDpupHAOHHBrDYDgQYV4ZfXimumaTjgP6LdNb+pRXiViEu1gNgRwgxx3Ll3451HrwJBEH1P78XNdKuLIAiCIIh9BKHx6UWS0n5+q4smPgRBEATRj7ChwCJx804hjQ9BEARBEN8YKEnpLtKXSUq74vDLFgAQ2htfixBnRD5sA0sJcUbs0Arp9aPmuEzKGR+gSV8Vod8Qc9xsmIHZQKbYPYfi5PVULDfRqa1BJk5lnfbrSS69WCyDSS2PnrCRqHK8X3yAJ+qcO8plklOuQJYHhJYk7x1jq+L8gNDG6Cm33c5pYzIRcY7iD6Nu/JsbATufIdWDxOEDRRxeBZrjIWR6FRgxE20jhN6kYnkCSlp0pHlMEbxtTnLRYgXZkGgqn1wzH7uRcHRLaRumo+WxDCBQLwRQ/v9sgVVdCgDo2F9kMc35OumCHC2PHufQnP5xFYjXOL48HAhusZ0yFvQOEV+yyisTw/q2pcEs26mrIF3ukXWZUyafXDZPNqzA7qQpyo9zopLB057f515XAPJ82aAity0PQ0IMLSLrOFoOc8t7GxlKP3KEYIwhNkicsGMox8gxGwAAa94fIsvrUSY/X+H1HMv/RHofop+yB5KULl51EPwh9asr7IRkh4UfHfYxJSklCIIgCGLvx4ZCPj5dQLe6CIIgCIL4xkArPgRBEATRj7A4g8V7YWDYi7r7AjTx2ct5/z43p9HR034LAOg4sBjpIrFYlylyy3IF8Lbl3wg9DiD0JXlNiZbmTq4oN8dWJiKqqElAcXxdMkXCV0bUgczDxRmDnhAiEV+z6xPELI7wBvE+WalJn5otJ9kIrhOxFn1uIV2sSL2Q5WHSk8YKMtjOd82/zYanVQSidWSgbHUSShWFHVcdIHZwKTxtQlPi2QzAcAQ0uZzUOTGbS72LHrfAFYbwBlGn9aAAgluEqKXk4ySyYVFfCTLoCRFIslrkF8sT2SDKmz4FesLJjxZS0TFQ1G0fNgTVr4oLkA0qSFQDPif0VIWrk8pEmBx/APA3ucvK+dxlpl+V/Qitj8PWxYBaAQ2pcnE+rjCp3fFvy8HyiTeKyZEqVWU+ts7tKjnAdNZ5PVHA1yKC4iqT+dxMH0Mm5OiObNd/KK0xhDY47ekMxR/l2+TQMjZsXZH9y68l22VZfLSxBgAw7JDNWP+BEAnZhvv5UnLA0aeKz/ar/+8aEATRO6xePtVl0a0ugiAIgiCI/gGt+BAEQRBEP8LmCuxeODfb/fxhb5r4EARBEEQ/gm51dQ1NfPYhXv2Hq3/Y/7d3AwD89QyedkcnYzDp3QNA6jT0JAdzfG7SxQqY7Wo+9ARHcKt7jkzEyUHVyqU/EACYfvGvEXPzT5l+BVrS1fnEa4TGxNfKAUcvUvSBJn1jskFxbtXxi+msn8t73ABA02EqvK0+Uf9zHf5N20T5hiaZmyq0rRkt3x8JAFByA2Vd/dMt8P9ni3MCjvjYQaI/2xJQtjaDlxU5Y1UCM+BqYoz2rOxTIij2MxNI1oh+lK0CEtUiyMDWHFIVYju41XJzlw1UsOE01yBJyUDqp5SMq/HxtrnjqWSBbFDUT1UAxWsdfZLF3Wtp24AiLpiaMIHyfD42DtUZfi1lIVEj9qfKGIwokC4RxyyPm4PNv83NiRZoFJorcT5ITZGS5TLPW87PpFaIa4DRnvdFYvLzYWsMtgnXK4gDSZH6DJ4NHvlZ2/rpILDivP5KeCAB4jPHbBHHEf+1QOYu++D3rr6NIAiir6CJD0EQBEH0I2z07sks+6uL7NPQxIcgCIIg+hG9NzDs38890cSHIAiCIPoRFldg9ULc3Ju6+wL9u3cEQRAEQRCd2GsmPvPmzQNjDLNmzZL7zj//fDDGCl7jx4/vsp2//e1vGDt2LIqKihAIBHDYYYfhscceKyhz6623btduVVXV7ujWbuOza67CZ9dcBVsVSS/1uCNMtdyXluHQMhxcBRJVChJVCnJBYRjHNfGyvAxcFeJXWwP8TTb8TTbUHGB5xUuxOHzNNnzNNmxdiFq5wqCmuTyX6VdEokwuRLLMhnwpOSHUZVycO1XCkCphCGzNylfNqzH4mjl8zRyRzzn0DvGqH68BoaB4AYBpAqYJPmwgSlZ3oGR1B+KDfdCbE9CbE0BJEXhxSLxKwvC0ZuFpzYIbGuAxYPs8sH0ehNbFwBVh+hjdz4AV0GAFNDFeTAivmQUE6hgCdQypMiZMHLkQOWdDDNkQQ8tBKjJhBZmwgvAmG8E6jmAdR+V7JspXm1CzgJoV4wAmXqkKyLaMOIeeEC9/PdC+v4L2/RUky1UwzsE4h+0zoEaTUKNJQGHw12fhr89CyXKYXgbTyxAd5oWa4VAzHIopBM3ZYo5sMUemOgcrYMMK2EiVOQlJLQ4lY8Po4DA6ONQsh+lXYPoVcJXJa+drtZGJMGQiDJwBuYCCXECBlnE/X0bCBleAZAVDsoIhU8TgaQU8rYC/HjCDNsygjVwQYDkmXhbgaxQvWweaxjA0jWGwdCBdLl4H/moBRtwiXgRBdB8brNev/sxeMfFZvnw5HnzwQRxyyCHbHZs8eTLq6+vl6/nnn++yrZKSEtx0001466238J///AczZ87EzJkz8cILLxSUO/jggwvaXb16dZ/2iSAIgiD2BPlbXb159ZRXX30VU6dORU1NDRhjePrpp3da9tJLLwVjDPfcc0/B/kwmg8svvxxlZWUIBAKYNm0aNm/eXFCmra0NM2bMQCQSQSQSwYwZM9De3t6jWPf4xCcej+Occ87BwoULUVxcvN1xj8eDqqoq+SopKemyvYkTJ+K0007DyJEjMWzYMFx55ZU45JBD8PrrrxeU0zStoN3y8vI+7RdBEARBfFNIJBI49NBDce+993ZZ7umnn8Y777yDmpqa7Y7NmjULf//737F48WK8/vrriMfjOOWUU2BZliwzffp0rFq1CkuWLMGSJUuwatUqzJgxo0ex7vGJz09/+lOcfPLJOOGEE3Z4fNmyZaioqMDw4cNx8cUXo7Gxsdttc87x8ssvY+3atTj66KMLjq1btw41NTUYOnQofvSjH+GLL77osq1MJoNYLFbwIgiCIIi9jbyBYW9ePWXKlCmYM2cOTj/99J2W2bJlC372s5/hiSeegK7rBcei0Sgefvhh3HXXXTjhhBNw+OGH4/HHH8fq1avx0ksvAQDWrFmDJUuW4KGHHsKECRMwYcIELFy4EM8++yzWrl3b7Vj36FNdixcvxsqVK7F8+fIdHp8yZQp++MMfora2FuvXr8fNN9+M4447DitWrIDH49lpu9FoFAMGDEAmk4Gqqrjvvvtw4oknyuPjxo3Do48+iuHDh2Pbtm2YM2cOjjzySHz00UcoLS3dYZvz5s3Dbbfd1rsO7wY+/K1r8nbIzxcg55jhWR43waYREy9AmPXZGpPHtDSXCUwVkyNZ5pgQtlgwOkSZVKkizeqYxcHV/DkYLE/exA7wtTjuDwzS1E9LcxiOGZ7lVaDkODIl4lhigOEm+yzSkA2J/UpO6EcAwGgHot+qBgBE/hUHd2b+yrY2xMYJc0I1w5EcUgQA8C//wr07XRyB7RHaoPoJfpR+4kUmIvqXKnPvYXvaOVoPdD9PaWfxz2h3E4jmdS6iH+746wnRX0D0ObxRGCEaTUlER0YQ3GLLMcwnjQWEtig/bvkxzPkAj5NkVk9yxAeImCKfJWCFhKFjLqSDa6K8vz6NbNjvtM9kIMwGoAp9EgCon+loGy3GzdviHAfAdQY1azvxKPKaKSaHp9WUsUbWu8luO19XxRT7syEFRtyGEXfHwegQ7bbtr8nx0Q7ogP1xSLTFgPR3RYVcyv0zlKo1YW0IAADCXzBp+nj0qb+lBKYE0U1szmD3xsfHqfvlH/gej6fL/3u7bNO2MWPGDFxzzTU4+OCDtzu+YsUK5HI5nHTSSXJfTU0NRo0ahTfffBOTJk3CW2+9hUgkgnHjxsky48ePRyQSwZtvvokDDzywW7HssYlPXV0drrzySrz44ovwer07LHPWWWfJ7VGjRmHs2LGora3Fc8891+WsMhQKYdWqVYjH43j55Zdx1VVXYb/99sPEiRMBiAlVntGjR2PChAkYNmwYHnnkEVx11VU7bPOGG24oOBaLxTBo0KCedJkgCIIg9hm+/H/cLbfcgltvvXWX2vrNb34DTdNwxRVX7PB4Q0MDDMPYTvJSWVmJhoYGWaaiomK7uhUVFbJMd9hjE58VK1agsbERY8aMkfssy8Krr76Ke++9V67WdKa6uhq1tbVYt25dl20rioL9998fAHDYYYdhzZo1mDdvnpz4fJlAIIDRo0d32W5vZroEQRAE8XVh9zJXV97AsK6uDuFwWO7f1f8DV6xYgd/97ndYuXIlGOvZShTnvKDOjup/ucxXscc0PscffzxWr16NVatWydfYsWNxzjnnYNWqVdtNegCgpaUFdXV1qK6u7tG5OOfIZDI7PZ7JZLBmzZoet0sQBEEQexv57Oy9eQFAOBwueO3qxOe1115DY2MjBg8eDE3ToGkaNm7ciKuvvhpDhgwBAFRVVSGbzaKtra2gbmNjIyorK2WZbdu2bdd+U1OTLNMd9tiKTygUwqhRowr2BQIBlJaWYtSoUYjH47j11ltxxhlnoLq6Ghs2bMCNN96IsrIynHbaabLOueeeiwEDBmDevHkAhBZn7NixGDZsGLLZLJ5//nk8+uij+OMf/yjrzJ49G1OnTsXgwYPR2NiIOXPmIBaL4bzzzvt6Or+b+M8CV+8z9qK7YetucsmkXB1kgAJ4m8Q7W2VQM0JIkQsoUrfBVQbLmXt6ohzJCvFF8Lba0FKiPOOQmiA1JxJaAoBiujoSJedqgrgCmD6RQBMAfI05mH5VhhXYZjt1bPi2ikyaRQCUdZtErLkcmDMhTh3kTlJtDcg62h31kCHIFLsf6/y5vW1A64GaTA7qae2kSQJg6e6vhdBG3qmvjkanzBXiqRmR3FWew/mloVhAfIDIvKkX6chEFPgbxViFtliIDRIxmn5AS+XHx43D0Bl8jTnZLuOO9iqWRmag+NWVKtdgOpogr4fB1yLaN30aogeI8uUrgNaD3P74twGRteLcRZ+byPmcJLMe93ePZTB57Y12C7YhjuWCbpm8T5DoNJdJSaVOK+T+WGnbX1wDrgHhT0Ub9hchBOtFWS3NgRVCt7T1uwr0OHPGhiOw1dFSGWKsASBRoWHMJQucMeNY/ucd35ImCGLvY8aMGds9wDRp0iTMmDEDM2fOBACMGTMGuq5j6dKlOPPMMwEA9fX1+PDDDzF//nwAwIQJExCNRvHuu+/i29/+NgDgnXfeQTQaxZFHHtntePbalBWqqmL16tV49NFH0d7ejurqahx77LF46qmnEAqFZLlNmzZBUdw/zolEApdddhk2b94Mn8+HESNG4PHHHy/QC23evBlnn302mpubUV5ejvHjx+Ptt99GbW3t19pHgiAIguhrLDBYvTAh3JW68Xgcn332mXy/fv16rFq1CiUlJRg8ePB2Dw7puo6qqiopSI5EIrjwwgtx9dVXo7S0FCUlJZg9ezZGjx4tJ00jR47E5MmTcfHFF+OBBx4AAFxyySU45ZRTui1sBvayic+yZcvkts/n28508KvqAMCcOXMwZ86cLussXrx4V8IjCIIgiL2ezrerdrV+T3nvvfdw7LHHyvf5h4HOO+88LFq0qFttLFiwAJqm4cwzz0QqlcLxxx+PRYsWFUhfnnjiCVxxxRXy6a9p06Z9pXfQl9mrJj4EQRAEQfQOC7u2atO5fk+ZOHEiOOdfXdBhw4YN2+3zer34wx/+gD/84Q87rVdSUoLHH398FyJ0oYlPP+W9h67Cwde7OY78jh4s4zwpaAp5BXIhSE1M0WcWVGFDA8XkUveSKnVn/+kSBYF658NtcwQahN9LLqjCExX6DZbjyAXFDJ1xIFXm1mcWoDt+L41jDRStE3V8TVlkioWOxtYVcF3UUT/eANhOu4oifXy8b34C30Ch84mOKkUmIspngx7pkRNotABLxKonRb4x2/HA4arQtQBALsCkdoUrTO63dSat25Ws0PDkyeunhAeSqJvzMaFdgdApgSlIlzj9yADeNnEszRgyRXDqA5liR3PjByKGGAN/g4m8AU5e3wMIPZMtZESwvAq0pOnEDXBVtN/4bUCtTCLXIQqWfqhKHQ5nkFoey8Og5NzxyWP5FKhZ8T64MQVb73T9bO7Eqkkdl+kX+btyPua0ZSOwTZRLVoocbgDgbe+sFeMINIgP25BnbDFeANIVXkSHqM4Yun5H+c8rAPg7OA77maP3sYCVf3S1bQRBEF8FTXwIgiAIoh+xJ2517UvQxIcgCIIg+hG7mmi0c/3+TP/uHUEQBEEQRCdoxacf89GvXe3D8LlCE+HbBvgbXf+aZLmC8CY3L5Oac7Ueelzsbz3Yg9IjhR14y5tVYI7Bj7fNhuX4WVkGA7OExiNdocFwNCXRwQqK17k+QQBgxMT74FaORJX4CGYiXpkzzNeUg7pWePewSAhWlRAmKR0ZKCnH2KUjDjsgTq5YnfQpnbKfpEoVNH8n3zcbJe/osBx9jGIBwa0iDiPu6n1SpQw5kSoKplcBd74hnnZIDyC4wwdbYwhuccU/bu4yBs4AzcmJlg0yqVdRcq7OKf8vILQ7pqOTyRSrCNal5bHo/q7IhXU6f16/yFXAaBcnyNRmYKY1GGGhocmGAlK71RlbY7ItS2dSv8MZkAk72qSkJn2eCuoaCpijFbJ1BWqGS3+nzjncPG1cxmh5mPTlsQzhIQUAsWGdBDwcMlajgyMXcNppsaWuytaZ1CRZOsO4GXfjncfI14cg8nAw2L0QN/Ne1N0XoIkPQRAEQfQj6FZX1/Tv3hEEQRAEQXSCVnwIgiAIoh9hcwab7/rtqt7U3RegiQ9BEARB9COsXmZn703dfQGa+HxD+PRGIXQ++IYFyIYUKWTVkxzpYiFWNuIctpOLU83asDXx4S/7wAJ38r+ZPkjTQiXLC4TF+SSVeopLQ0E1A8QGi/2R9SZMvwLTL44xC/DERP34AAYj7oibP9wC+ITglYcDgJMEtOOgEpkIVc1xxAbnzfAgRdZqGshGxDZXGEKrRYe0tDDDy5vp2ZqbYFXLcKiO8aAR5zAcwXH7cPdXT6YY0DvEticK6E6sppchGxJx6Akb/nqh3hUmgR6kSp0x6ZTUtKPWbTcxmKPqTfdYfty0NIeaFAlLba+Ook9FVtMtR/vhwpCsEP1jJuCvF3v1mAe2BwhsNWRf86JpNc2lgL2zIWMmzKTovbN4OlmlwxMTfbAMhmynpKX5+uJz4J4j51fAHYd5NculaaTRwZHNi5VjHLFaEV82JAwNZbuOptvSAa+TwNX0uYLpdLEbn54Qx8afc5c4X4bjjb/OBkEQxM6giQ9BEARB9CPoVlfX0MSHIAiCIPoRNhTYvbhd1Zu6+wI08SEIgiCIfoTFGaxerNr0pu6+AE18vmF8NE9ofY45eT4AkeiyM/nklIBrOJgqUxF7vwoAUHZoI5KfVQAAjA4bofUJAEBiUEDW87Sa0uRQ1xlK3nTEJ+kMcsOq0HSoa1iXNwjU44C32TEb9PvAtzUBABjnUDtEodQhQWgZ17jO1+TGmteR2EFX7+Nr7qSdCTPYGpCqFO+r37bg25IUMeiK1BGZfg0dg4RupvRDG1lnDGy9U3JQD6DkRHlvWydBDGPIFhvybbJSha3l+8mQDTnbCqCmnK5uZmg+RLQV2gj4G8UYeNpyaBwnxEr+bRZsx2AxssGGksvHwaQJYyasIlUmyigWoLe5ZopaisvEpLbBZOJUX4sbuxHn6Bikyvd5s8bQJo6co8niCuBtc8woO2l9AltEZ9LlHqff7rFgvSWT3YoGxTmSFYprJmkCoTrRKW9duyzafliZND/MhpgcfzXnNudvsgEOqU3jCsO3LrhbHl/+JzI2JAiiEJr4EARBEEQ/gjQ+XUMTH4IgCILoR/BeZmfn5NxMEARBEATRP6AVn28o/37uWrk9boariWC20H1wBqkrUUyO8BfO0ucHZShdJwxtYsODiO0fdOpx6HGnruYuk3rbLCQPLAcA+Nc2QWtNoOgz8bFL1GgyUaWa5cgFhf7D3r8UPo8oky0NIFPq+NHoQDIsyjPL9ZHRkpBaENPvanwS1QxGTGwrlvDwCWwR75tHqxi8Oe/FoyG6n0fGnKrIt8XgaXHrp0vEtr/BPUe6REFgm2uIk9camT4Go4MjVeokPy1zk2/auhMzhH+NzzmHlubwbRMmNtkiDzKOH5GviYHlPXNyHJtPM53x9EBNi3EyokBosxj/ZIWCbFiG5CRgdTRC600oWff3Tl4H5G0xYXp1OYbeZnGccTeBK7M5EpWa3C5aJ7Q9XGVQ0u4YhOps2W7Or0hfoHSRIvukJyD1T2oWaB0prrE5pgKqm5tVJjX1xLib5NUEMhE3YSlXxOcVEDqkvMdSuljBEZeK5LwrH3AT9hJEf8cCg9WLRKO9qbsvQBMfgiAIguhH2Lx3Oh2bf3WZfRm61UUQBEEQxDcGWvEhCIIgiH6E3Utxc2/q7gvQxIfAO48Jr5OjT/2t3McA2KZYKvU3WdA7hK6kY5AHarvw7gls0ZGqcLQZXoZchdDoBOpNGDGh+bB1hlSF+JilS6sR3JyB/5NGAICaKYMZEHXa99fQfkDeR0ZF8dpSAEB0P3e5NvIFR6DBEYwwJjUfppchJXNRAclBjreNYYOvE7qV5GALwS9UeNpFHe8Gjnit8B5Klbl+O9kiyHYDWwHTK7a1FFD8qVj/ZTaH4vgdKSaXucfsTtomZgvvoLxGRU1Drq9marMoq24FAOQer0R4QydRi5P7LFOkwr+NO/Ep0l/JMhREVjhaKM3NH6bmuPTFCdRbsDVVxm56gbKPTTc27o5bPvdZNqhDTzrnq3A9h3IJBt3xHNITgJEQ4x+vUqRvj79OCJb8G0WSs3R1AICIMed3c2zpSQ7d0TbZOmT/LC8D65TzLV0sBioXgOxT5+vta7ER3iSucaokn2dOxKUlLKhZsZ2JeKXv07hz74bpxLFiIel9iP6NDQa7Fzqd3tTdF6CJD0EQBEH0I8i5uWv693oWQRAEQRBEJ2jFhyAIgiD6EaTx6Rqa+BCSV//fNXL7yDPvgp7Im8cwpEuEViZUl0F6qDC0idUayAobH+gJSL+W6H56QR6ovPYlGwayIS8CRSLvl5q2pd7E08bhaXNOZwLtwx19UQOkh03Oz2AZ4gupZoBsmHU6h/iXK4Bvs9B9RNa7vjHh1xWYHldHomY4TJ/ixO7uZ5zJXFimF4BzKFHt5udiFpP5okyf0P/k8W9z+80shrZR7nv/VidH1joDvodFYMF4VB5vOTwi+xQbacLTIDrlr3fHUDGBTIl7vmSVCDDyOYPmxBofoMDXbKNjoDifEQdig0Vb3lZbamW8LSbSxeK6ZiNAh98dT6lNygK2I72yVYA7Oc08UY5ElTiQKgkhsM2U7TLeKU9YmsPX3ElflPf0KdVknjbTw6QnEwCpNQrXWUgXq7Je3hMpUamg7QBxssh6x7OnyDl5sSJj5Ir7uWA2pNfTIVctwH/uJp0P0X+x0cuUFf1c49O/p3UEQRAEQRCdoBUfgiAIguhH8F4+1cX7+YoPTXwIgiAIoh9B2dm7hm51EQRBEATxjYFWfIgd8ub/XC23v/ODO2Hr4hdApkiH5XWEtjkO5vwyyEaAQIMQmhoJjmzAESFnObS0a1QHDuSCzrGcm3jSE7XBnAQxeocFNSNEt1wTRnuAEMrKHyKMwRN1kox6gOBmsZsrDN42Vylb8Ykw1cuU+4UpYH1GHkuXCXc8xgE97hjilRtICu011DQQ2CrOUfSFhbbh4uti6wB39LreVkB1+udvNJEL5MW4HAZsnPKdlQCAF/45FtwRCVeszEGLCtPC6EERxGvc3x95IXHgMw3pctFu+0EczBFWB+sYVEdMnaoEPO1OEtRyILRJ7NcTHJaHwXDMDU0f4Gtxrk3Mkslgmw7TpbhcyQHcUUcrWQZbd85RIQTVogyDr0WMU7JSlcJvX4vlJAt1ErR6meyrp9WSSWs7Pyiixy0pLgdzz2F6GRTTMVIsVaFm8qaRgNcRv7cfUPhrlCtuwlrFBJjlGBiGFbQe5IyrlyO8Lp/JFBh1jUhe6mknQ0Oi/0FPdXUNTXwIgiAIoh9Bt7q6pn9P6wiCIAiCIDqx10x85s2bB8YYZs2aJfedf/75YIwVvMaPH99lO3/7298wduxYFBUVIRAI4LDDDsNjjz22Xbn77rsPQ4cOhdfrxZgxY/Daa6/1dZcIgiAI4msnn6urN6/+zF5xq2v58uV48MEHccghh2x3bPLkyfjzn/8s3xuGsV2ZzpSUlOCmm27CiBEjYBgGnn32WcycORMVFRWYNGkSAOCpp57CrFmzcN999+E73/kOHnjgAUyZMgUff/wxBg8e3Led6we88dfZGH/OXQCEnkJ3EkKaXhVGh6Oz8bkJL9MlDEFHG+OJ2dJ8T09ypIsV6E6iS8tgUieSKlVhO5e2+FMLnnYh+khU69LA0NNqgquOiV21Bs3J7clsSA0SZ4B/a9rZbyNb6hPnjmXBNQ/gmNsZm9uQC5eLtqo0mewytMlCulR1+gQkK/KxKwhvFHGLPuSTiTJYIadMIp9kVeiGmA2899sjAAC1mxJQsk5HOIflF521NYaOQ4TuKFycBH+rWNS3AG+T0yeVwXSMImMjXGNDJQdEvhAxxQYpUgvla7FhehmMXF57ZEsjwGSF+5UPNHCZBJZrkLoevcNJqgrA8gKZMtsZfwWZiPitxEwgWS3K+JoZvG052a4SUJEqc8YwoMLbkhXtNidgO/1OVfvldfW1WMg6ui/OxGcpH3ceT2sO8YGibumHtjQjtAyG6DAGPSbem0EgUyr6XbaSo+odsR1c34HPzxSmkUWfcqlHUnMc3/mh+Gwzm+P1/5sNgtjXoVtdXbPHV3zi8TjOOeccLFy4EMXFxdsd93g8qKqqkq+SkpIdtOIyceJEnHbaaRg5ciSGDRuGK6+8Eocccghef/11Webuu+/GhRdeiIsuuggjR47EPffcg0GDBuGPf/xjn/ePIAiCIL5O8hOf3rz6M92a+BxxxBE9eo0ZMwZbtmzpVgA//elPcfLJJ+OEE07Y4fFly5ahoqICw4cPx8UXX4zGxsZud45zjpdffhlr167F0UcfDQDIZrNYsWIFTjrppIKyJ510Et58882dtpXJZBCLxQpeBEEQBEEAr776KqZOnYqamhowxvD000/LY7lcDtdddx1Gjx6NQCCAmpoanHvuudi6dWtBG5lMBpdffjnKysoQCAQwbdo0bN68uaBMW1sbZsyYgUgkgkgkghkzZqC9vb1HsXbrVteqVatw9dVXIxgMfmVZzjl+/etfI5PJfGXZxYsXY+XKlVi+fPkOj0+ZMgU//OEPUVtbi/Xr1+Pmm2/GcccdhxUrVsDj8ey03Wg0igEDBiCTyUBVVdx333048cQTAQDNzc2wLAuVlZUFdSorK9HQ0LDTNufNm4fbbrvtK/tEEARBEHuSPXGrK5FI4NBDD8XMmTNxxhlnFBxLJpNYuXIlbr75Zhx66KFoa2vDrFmzMG3aNLz33nuy3KxZs/DMM89g8eLFKC0txdVXX41TTjkFK1asgKqK2+fTp0/H5s2bsWTJEgDAJZdcghkzZuCZZ57pdqyMc86/qpCiKGhoaEBFRUW3Gg2FQvjggw+w33777bRMXV0dxo4dixdffBGHHnooAHGb6rDDDsM999yzwzr19fWora3F4sWLcfrpp++0bdu28cUXXyAej+Pll1/Gr371Kzz99NOYOHEitm7digEDBuDNN9/EhAkTZJ077rgDjz32GD755JMdtpnJZAomc7FYDIMGDUIUQLixESgv72JE+hcTpt8lt2ODxaKhlgIsR6MTrOcwYo6AgwFf/g7lvWPy2h9A+O/EakVblgFENgh9R87nJgQFAF+T0P6YfgU5vyifiTCoQkYCo8OWiTCNqAnTL74swS9iaB5bJJNn+httGRfXgExo+8VP3ulngSfKwSwRuK0z6bfTuT+mh8HfZMm+GTETiuV+vfR28fmx/DpsJ9lq02gPMLEdAJCIe6B/5miS4m6CTVsXSWABIDM+jqHlLQCAtV/UoHi543ekuMk9FRPwNpswHU8hPW4BzteccSA22NXJWV5nrGIcnqiIPVGlSb1Pzg/Yzm8MNeUmL02XQ+pqGAdKP8oV6IfyXjyedgu+OlGQZdxkpamhxchGOpV39Ei5gCK1YrYOeGLb/3lSctxNiDtEgZ4EvG2OT1GHJRPZamlX2xTclMa2b/tkG/mxLV1jCa0ZgMCWNFpH+gGQtw/RhzQ1ARUViAGIQPwwD4fDu+VUsVgMkUgEJz5/KfRA13rYrsglslj6vQd2OVbGGP7+97/j+9///k7LLF++HN/+9rexceNGDB48GNFoFOXl5Xjsscdw1llnAQC2bt2KQYMG4fnnn8ekSZOwZs0aHHTQQXj77bcxbtw4AMDbb7+NCRMm4JNPPsGBBx7Yrfi6teKzfv16lPfgP/aPP/4YNTU1XZZZsWIFGhsbMWbMGLnPsiy8+uqruPfee+VqTWeqq6tRW1uLdevWddm2oijYf//9AQCHHXYY1qxZg3nz5mHixIkoKyuDqqrbre40NjZutwrUGY/H0+UqE0EQBEH0J74s6ejL/wej0SgYYygqKgIg5gS5XK5AhlJTU4NRo0bhzTffxKRJk/DWW28hEonISQ8AjB8/HpFIBG+++Wa3Jz7d0vjU1taCse4vfQ0aNGi7ScuXOf7447F69WqsWrVKvsaOHYtzzjkHq1at2mH9lpYW1NXVobq6utuxAOL2W361xjAMjBkzBkuXLi0os3TpUhx55JE9apcgCIIg9jY4evdIe36dddCgQVJLE4lEMG/evD6JL51O4/rrr8f06dPlilJDQwMMw9juIafOMpSd3XmqqKjoUqryZXbpcfb29na8++67aGxshG3bBcfOPffcbrURCoUwatSogn2BQAClpaUYNWoU4vE4br31Vpxxxhmorq7Ghg0bcOONN6KsrAynnXZawfkGDBggL8i8efMwduxYDBs2DNlsFs8//zweffTRgie2rrrqKsyYMQNjx47FhAkT8OCDD2LTpk34yU9+sivDQRAEQRB7DX2l8amrqyu41dUXqz25XA4/+tGPYNs27rvvvq8szzkvWHjZ0SLMl8t8FT2e+DzzzDM455xzkEgkEAqFtguouxOfr0JVVaxevRqPPvoo2tvbUV1djWOPPRZPPfUUQqGQLLdp0yYoirtwlUgkcNlll2Hz5s3w+XwYMWIEHn/8cXnPEADOOusstLS04Pbbb0d9fT1GjRqF559/HrW1tX0Se3/nrb+4ebwOu1zkPGI2ULEiCQDQPu+k1A/40XGouIVoepnUwwCAlrSlL4+/Pg1/vdhveVSp/8iEFPhahDYkF1Bk3ifLcNvyN9nSX8bWGUr+tUGeY/OPhgIAkuXiV4SvWUzUA3UJdAwNAMjn93I0RQH38xyvcLctgyG0WZxQS9tQsqI81xhyTl4y08Pgcfxs9LYUrKAHiqNryRV5kCkVghrboyDh6GG4CqQ/KQIAeNsBXQwhmAmZiyxdwmDldTYfBLE1IR4yKEkBwS2i/UxEReM4d3CrX1NhRE3nHAzxAeJ+v7/RRM75+viaOSJfCHGU0diB1CDhcxPbD1JDwxVAc2IyA0B8P0e7FTRhrBJBeaIcscE6DMfbyNNqSp+dZKWGbFiMffhTd9lczdrSwwkA1LQ4YcdAVWqVfK1c6n28bRYsjxjnZLkCb7so720FciHXb6l1hIbqt8Q1sLyK1J21jfTBv020G2jIIVPkeCFlbSimk4eu1IC/WfTvqNPulLG99nfy9iG+mYTD4T7VI+VyOZx55plYv349/vWvfxW0XVVVhWw2i7a2toJVn8bGRnk3pqqqCtu2bduu3aampi6lKl+mxxOfq6++GhdccAHmzp0Lv9/f0+pdsmzZMrnt8/nwwgsv9KgOAMyZMwdz5sz5ynqXXXYZLrvssp6GSBAEQRB7NXujgWF+0rNu3Tq88sorKC0tLTg+ZswY6LqOpUuX4swzzwQgHmj68MMPMX/+fADAhAkTEI1G8e677+Lb3/42AOCdd95BNBrtkVSlxxOfLVu24IorrujzSQ9BEARBEL1nT0x84vE4PvvsM/l+/fr1WLVqFUpKSlBTU4Mf/OAHWLlyJZ599llYliU1OSUlJTAMA5FIBBdeeCGuvvpqlJaWoqSkBLNnz8bo0aOlz9/IkSMxefJkXHzxxXjggQcAiMfZTznllG4Lm4FdmPhMmjQJ7733XpePqhMEQRAE8c3hvffew7HHHivfX3XVVQCA8847D7feeiv+8Y9/ABBPWnfmlVdewcSJEwEACxYsgKZpOPPMM5FKpXD88cdj0aJFBQ87PfHEE7jiiivk01/Tpk3Dvffe26NYu+Xjkw8YEPfSbr/9dsycOROjR4+GrusFZadNm9ajAPZV8n4J30Qfnx0x4ey7EP4sDgBQ6lvcA6EA2g8vAwCoGeG/0vnHRF6nwywu820BjvcMAK0jB64LbYeStZCNuN4U2bCYt6dLXS2HZQDlHzi6lba0LBvbP4hsJ68exeTQ0uLkyTIFWSFvgREFvO3uV6JjoIip+FNL7rM8DIprSYNEpdtu9SvNbrmQD7mw+H5kSjQY7U4bKoPpdT2I8loX08/Ane+3koXMB5YNM5kXy/Rx6B1MxqrHnfFKcXic9m2dIRtUZP/UjA3bGUPTx2ROLyNugzn90NKu3qrpUFXqrTo/9xkf5G4b7UDqiBQAIPimD2oG8HTYzjEL3LmWyXIVLYeKOIY8a0ptFADknOtnetyTdAxWkHVu+5d+ZEvfJQAy7lwACG0W7TCbw9YY0kVuG6GtolPM5MiGxID6mrOIDxSaJM5EDjkAUFNWoa+Uk8Ors8cUOJd6qZdfuQEE0SP2gI/Pd//xU2iBXRcim4kMXp/237s11j1Jt1Z8dmRCdPvtt2+3jzEGy7K2208QBEEQxNcD5wy8F7e6elN3X6BbE58vP7JOEARBEMTeSd6Ppzf1+zM9zs7+6KOP7jAPVzabxaOPPtonQREEQRAEQewOejzxmTlzJqLR6Hb7Ozo6MHPmzD4JiiAIgiCIXSP/VFdvXv2ZHj/VtTOHxM2bNyMSifRJUMS+x1tPuqaGUwZdCeRvj6bSwtAOgOVTYRkMpk98frRkJ129zmRiS8XiMrmokrNhBpyPqaVBSwsNWaZIl+W9rTbUjCN25UCqXJRXMzr0re0AACPqg+ljyIYcYW8HEK/JC36FER4gEmNmIqKMHucI1OfV1wzc+ZnQWdhsGQyRDWKHHsuBe4XKWmmLQ0tlASa+E8wGckHRp2xIkUJdLcPh3+YmXs2LdLU0R7rETdqqOjpto90VQKtZwNdiObFaUuxteRkS1QxGLJ/sU5EGiHanb3yqQpGJRoP17v7iT20Zn6fdRi4o4vA0M5m8lCuAsl4k/eQagIwrCE5UawhvEALzcNyCnshXAoxtHaKvJQF4twlxdLbIAyMqygfrgEyxCDZTrBYkhu2chDWPrTEw2xUre1s6HWRAsE64L9qGKkXkuSCD4YjC85+zPJkiVwQeaLBkQ1qHWOUef85dePuJq0EQezOk8emabk98Dj/8cDDGwBjD8ccfD01zq1qWhfXr12Py5Mm7JUiCIAiCIIi+oNsTn/yTXatWrcKkSZMQDAblMcMwMGTIEJxxxhl9HiBBEARBEN1nb3Ru3pvo9sTnlltugWVZqK2txaRJk3qcIZ0gCIIgiN0P3erqmh5pfFRVxU9+8hOsWbNmd8VD9AP+Wfc7uT1l8CzocaG7iA/UoWYhtRaWl0HJuTofNe7aJtiOxiRTrMvyqsWRqBH6DyXHXU2QyWWi0GzQ1bOoGQNcFcnuuMZgxGyki4SmgyuA5XVjzjqJO5UsoDsPLTIOaCkuy3tbc05Z92vDVTfBJgCYAaFnUXRH7+bE7mnogLmf2GdrkDod08fQMdA1AZWmg1kgUC80JqZfgWed5cbtGP7ZGqA4MpT4QF0aBfq3MlheIOucm2uAknPGVQdyIXFA72AymahlAHrCdsaTyfJggCcqTmLpKpJVooKaEnocQIxjuhQwNkHGHh9oyPHJkynW4N0g2mKmhfhgkSRWy9hIDPTL8cjrdPSEjYxzvWwV0J3Ph5qGNFvMhhhyfobidSLgXEiF6XM0SW0mTL8YW2ZzhD8Xeh/Lp4FZoq1sUaEBa940kiuQiW/1BEfHMPEBSZUrOOLSBbCEvAkf3PNzEASxb9Hjp7pGjx6NL774YnfEQhAEQRBEL+G9fKKrv6/49Hjic8cdd2D27Nl49tlnUV9fj1gsVvAiCIIgCGLPwQFw3ovXnu7AbqbHj7Pnn9yaNm1awWPt+cfcKWUFQRAEQRB7Kz2e+Lzyyiu7Iw6in/LPTfdgykE3AgBKF38Kns1BGTnMLbBJGMiw6gq5K35gidTNMJvDDAidRy6kygSnppdJDUb7oTmwnNj2bYHrvQPIxJTZIIOa5fDEHA0HYzA6+XAGt4gJu5q2kS4VX4tAQxYZRwNi60Cq3NGLmJB+Nlqaw1PvrHQqCmyfU8bigGnLNdXEsCLEakU/1BTga3Z0QUwkCAUAyyhMomo7+hhmA60HipjCm2x42oUGJlWuY9u38glcAd82MTjpcg4t4ep3sp1yDOpxwNvCZJ3EdxJinOr8KF7jJPRscZOD5vwKUs6lKfrUlhqkTESBt825Rhag5rj03PFviALOjyLbp4Mrjv6q2EDyQJGwlivCryl/Djc+G7YzDpwBnmj+HByWweR4ZIrcZKJ6kiO6n+60JTyaRP9U5JzPjhEzkS4VZYwOC/kL49uaQrZEfEZsjwItlddrKdLzKRNWkKhxx9CfBgznczRxynws++e1IIi9CRsMjFJW7JQeT3yOOeaY3REHQRAEQRB9AD3V1TU9nvgAQHt7Ox5++GGsWbMGjDEcdNBBuOCCC8i5mSAIgiD2MDZnYOTjs1N6LG5+7733MGzYMCxYsACtra1obm7G3XffjWHDhmHlypW7I0aCIAiCIIg+gXHOeyTgPuqoo7D//vtj4cKFMm2FaZq46KKL8MUXX+DVV1/dLYHubcRiMUQiEUQBhBsbgfLyPR3SPsHk8Exg6EDxpq4BPCtyNDFFARtQJctlK4VviuXToGTz/jI2kpWOHsNgUlOSKVLQUeueo3ylKJ8LMJnXKRdkSFYCuqP/sDXAFlYz4BoQ/sLxz8lx+LcKIx/ji0akDhbiDq4IL6A8eT2OkrNhOhoVT6sJzzbhFaPERR4qOyL8abYe466GWl7A2yK2tRSH6ngZaUnb1fkwyJxmRsxG24GO/xAD0hWivKel8FcZc+QpnjZAT3JXB6MASl5SZELmHEuXu7m7ckUW/FvEOUo+tpAqU502ufBeAuBrMsFsce6Wgw142hw/oCQHsyF9lcA51IzYNn0KPO15j51OaW4MBVrS8QfyKtAS7kMR+Zxmao7DVvP5xoRGCwCMtizMkKOlytlgNmA53j2JCk1qm/SELXN4FYxTzgbXlYL3ctvpgm0o8nNna0xqhTxRV18l3ltSewQOvP632dudj/iG09QEVFQgBiACIBqNIhwOf1WtXSL//9LBT10D1e/Z5XasZAYfnfXb3RrrnqTHt7ree++9gkkPAGiahmuvvRZjx47t0+AIgiAIgugZpPHpmh7f6gqHw9i0adN2++vq6hAKhfokKIIgCIIgiN1Bjyc+Z511Fi688EI89dRTqKurw+bNm7F48WJcdNFFOPvss3dHjARBEARBdJP8ik9vXv2ZHt/quvPOO8EYw7nnngvTdHLq6Dr+67/+C7/+9a/7PECif7Ek9mdMnPwbAACr2h/G6x/JY3zrNrGha9CbhAjGHLc/LK+Yn1tQYMTEZ87yqTCiQjvibVEQ2uTkkMq6eg01p0kdSS6hwtJV6b/jb+Sw8vnAIkBHrVM/zaDHnXxbg8tl3qdcQJG6FTVrQ3F0Ie37u1+hjoEGStaK8oF1OXBNAcuKeEs/yiJV0SknVyqv67GkNsfWmeMxA6RLNeQC+T8+ipvfzA/h0QNAzYgcWQAKPIksr9AHBRpEw7FaBSzt9LvFlu16PgcS1flzqIATR7pYQUrY7YDZ7h/AQL0Ny5vXAWWRLhP9yYYY0iVMjq2vCfA3mrJPiWrDGTfIcdMSltTTMMv1akqVusm9fM2W1CN1zomWC+vyuliGCm+rCdP5jIQ3ZcEVJttNVomg8nnIACBXVJgbjTsaLS1lQ0uJ8Veyrp9TbJAic5fFBxoFtraWoUqvIVtj+M4P7pTH3vgr6X2IPQM91dU1PZ74GIaB3/3ud5g3bx4+//xzcM6x//77w+/37474CIIgCIIg+oxd8vEBAL/fj9GjR/dlLARBEARB9JJ8zq3e1O/P9Hjik0gk8Otf/xovv/wyGhsbYduFj4tS5naCIAiC2HOIiU9vnurqw2D2Qno88bnooovw73//GzNmzEB1dXVBolKCIAiCIIi9mR5PfP75z3/iueeew3e+853dEQ/xDWDZkuvk9tGn/hYAoGQ5vG9/KnZmc2AeYb7lX9OIjsNcY8O8WJkrQKpciGYjy+uRG1QCAEhWeWSCTQDQ444AP24hWM/QMUgIWW2NIbRRKFa1Ck2a/QW32LA8oowZ0KSgNlmuIlkuyhR/ZkvRbdFnphTpWoaCbMhJilkWgN4Qg1UUEO9bM+COGV+iSoOlMycud1wYd80FORPJPwGRkFVzxMmZYsD2iBMym8n6egLIBfLlgfAmLs/n38bhbRf9SBcryPldcXS+fnAzR7rYSV5qijoAoOYAxTFYjNUaUIW3I3zNHKH1wqwxWeODnnRNC8Ehk31yhXVKvspl8lKuA96tSWc3R7pSaAQDDVyOeTasymvJbEijSHDRDwAIbLPANSYTvebND/PXLB+7qrplwADLyI85QybsGEV2MGQion5oYwpKTvx59LZBGjfmr0O+3XxsAGAFGLRU/riNo74vPtuvPX0NCOLrhHx8uqbHE5/i4mKUlJTsjlgIgiAIguglHAUPH+5S/f5Mj318fvWrX+GXv/wlksnk7oiHIAiCIIheQD4+XdPjFZ+77roLn3/+OSorKzFkyBDoul5wnBKVEgRBEASxt9Ljic/3v//93RAG8U3l1f/n6h+OO1EYYHLGwJzHCixdkZoPrjNpSKeYQKpM/CrpOG2ATEyppQBvqyivZjmyYfER15I2wDmCW5yEpzmOTLGjx+mwYTjJSz0tWRlPpsRALiAWRfUkR2Cbm0gzr/GwdYb8j6PYIAXBetG+3pyAVRKA5dOdcoqsE9ySg+3oXiwPk9oVzoD4ALHNTHeMQltsJCvEfjUD+BvECWP724h8KvZnw4Dq6E9sD6BmuNQL5XyKNPVTs65eJV30pV91ncwCbSchay4IeNrFfn+TJdvhChAbJnQ5lvPbx9MqgvbWu8IltcgnE7IqGROJQQGnHxzxWrGtJW1AcWPJGz12/tFpxC0kncSp3nYboU05N2yTY8NUcZ31qIJgndjPLCAXcuJVGVib215eq2R63UStmQhDoEFc40yJATUjrqWn3TXGNP0KUqUK9ISokw0zGFEneWrcvUHAFSa1X0eddqdMOPvWX64GQex26F5Xl/R44nPLLbd0q9yTTz6JadOmIRAIdKv8vHnzcOONN+LKK6/EPffcAwA4//zz8cgjjxSUGzduHN5+++2dtrNw4UI8+uij+PDDDwEAY8aMwdy5c/Htb39blrn11ltx2223FdSrrKxEQ0NDt2IlCIIgiL2W3t6u6ue3unqs8ekul156KbZt29atssuXL8eDDz6IQw45ZLtjkydPRn19vXw9//zzXba1bNkynH322XjllVfw1ltvYfDgwTjppJOwZcuWgnIHH3xwQburV6/ufucIgiAIgtgn2WXn5q+Cd9MBKR6P45xzzsHChQsxZ86c7Y57PB5UVVXtoOaOeeKJJwreL1y4EH/961/x8ssv49xzz5X7NU3rUbsEQRAEsS9Azs1ds9smPt3lpz/9KU4++WSccMIJO5z4LFu2DBUVFSgqKsIxxxyDO+64AxUVFd1uP5lMIpfLbfcI/rp161BTUwOPx4Nx48Zh7ty52G+//XbaTiaTQSaTke9jsVi3YyC6x7+WXg8AOHrab6X/jgobRkOHLGONENcx52fwNwrdRSakIFUpjmcjwncFALJBRWp/siEF3jYLipOsMlOsQnF0NKbX1WNYPk1+67Wkm0jT0pn0oLE8TOo/GHdXhYu+MKWXDTc0qC1xsIjQwWTKfNJXBxDaIABIVLm+M1qSgztvuQroTreT5QqyYbHtawISA8S2UpJBbH+PrK9knOSj7a73TZ58jJkIk1ogy+t6BaXKmNStWDqTvj9qlhe00XZAPl4V3hbX6ye0MS31P2aRD7YmFpM7J2Y1ohqYJep0DHL/9ATq3QSk3GDwREVQtgo5nqZfKdDQ5PVZaobD8iooXyH2p4sBM5AfT8jkolqKS52UGEdxPsvDwLX8+AG5oCLbzY9fslxBZKOTXDVpw+9KvWB0cCim83lJWEiXOY3pTOq0mM2hOOP43dPvhOKMQWd9G0H0JeTj0zW77VZXd1i8eDFWrlyJefPm7fD4lClT8MQTT+Bf//oX7rrrLixfvhzHHXdcwQTkq7j++usxYMAAnHDCCXLfuHHj8Oijj+KFF17AwoUL0dDQgCOPPBItLS07bWfevHmIRCLyNWjQoO53lCAIgiCIvYI9tuJTV1eHK6+8Ei+++CK8Xu8Oy5x11llye9SoURg7dixqa2vx3HPP4fTTT//Kc8yfPx9PPvkkli1bVnCOKVOmyO3Ro0djwoQJGDZsGB555BFcddVVO2zrhhtuKDgWi8Vo8kMQBEHsfXDWO4EyrfjsHlasWIHGxkaMGTMGmqZB0zT8+9//xu9//3tomgbLsrarU11djdraWqxbt+4r27/zzjsxd+5cvPjiizsUTXcmEAhg9OjRXbbr8XgQDocLXgRBEASxt5HX+PTm1VNeffVVTJ06FTU1NWCM4emnn/5STBy33norampq4PP5MHHiRHz00UcFZTKZDC6//HKUlZUhEAhg2rRp2Lx5c0GZtrY2zJgxQ959mTFjBtrb23sU625b8amtrd3O3LAzxx9//HZPUs2cORMjRozAddddB1VVt6vT0tKCuro6VFdXd3nu3/72t5gzZw5eeOEFjB079itjzWQyWLNmDY466qivLEvsfl79h6t9mDhlPtIDxCTT93kLwqvEk4KxwyplGTXLEdrovGGAb5sQdujtKXQME3U1R5OT/0YrWS49ehgHjHYx0ba8Ckyv+LXja85BcbRGzKdKXY7pZfBvEbdbs0VGXkaERI37ddJjBtSc5eqFUhYsr/hMmz5Fal2MGJc5wEwPkxofywOky0QZ3zYm9T6xA2xYARFraSSJVlP0wbPBA6NdlAlttpGoVGD63TH1N+U9hCzpKZNLKXJ/NsRg+kRZbxtHusjJhdVoSf0NAJR+JMY2Wen21dYAZnHoW4S4igd9SAwJyeNKJ08i1fEyMjpcn6FsWIGTOkvoYZxrpVpcjrl/S1J6IiUGeKT3TnBzBpZXRccgccwT5TIPGuBqmGJDGEJ1Tl+DTOp9/E02so7Xj611ysHV6QevJ8qFXgziuJrl8voB4nqK+prUOTHb1f4oFgfLbf8/yXfPuBOv/9/s7fYTxL5IIpHAoYceipkzZ+KMM87Y7vj8+fNx9913Y9GiRRg+fDjmzJmDE088EWvXrkUoJP5ezJo1C8888wwWL16M0tJSXH311TjllFOwYsUKOSeYPn06Nm/ejCVLlgAALrnkEsyYMQPPPPNMt2Pt8cSnrq4OjDEMHDgQAPDuu+/iL3/5Cw466CBccsklslzeR2dnhEIhjBo1qmBfIBBAaWkpRo0ahXg8jltvvRVnnHEGqqursWHDBtx4440oKyvDaaedJuuce+65GDBggNQJzZ8/HzfffDP+8pe/YMiQIdKbJxgMIhgMAgBmz56NqVOnYvDgwWhsbMScOXMQi8Vw3nnn9XQ4CIIgCGLvYg8YGE6ZMqVARlLQHOe45557cNNNN0mZyiOPPILKykr85S9/waWXXopoNIqHH34Yjz32mNTkPv744xg0aBBeeuklTJo0CWvWrMGSJUvw9ttvY9y4cQDEk9sTJkzA2rVrceCBB3Yr1h7f6po+fTpeeeUVAEBDQwNOPPFEvPvuu7jxxhtx++2397S5naKqKlavXo1TTz0Vw4cPx3nnnYfhw4fjrbfekrNDANi0aRPq6+vl+/vuuw/ZbBY/+MEPUF1dLV933nmnLLN582acffbZOPDAA3H66afDMAy8/fbbqK2t7bP4CYIgCGJP0Fe5umKxWMGrJw8WdWb9+vVoaGjASSedJPd5PB4cc8wxePPNNwEI+UsulysoU1NTg1GjRskyb731FiKRiJz0AMD48eMRiURkme7Q4xWfDz/8ULog/8///A9GjRqFN954Ay+++CJ+8pOf4Je//GVPm5QsW7ZMbvt8Przwwgs9qgMAGzZs+Mo6ixcv7mFkBEEQBLEP0QdePF9+gOeWW27Brbfe2uN28ndeKisrC/ZXVlZi48aNsoxhGCguLt6uTL5+Q0PDDu1sKioqepR5occTn1wuB49HeIe89NJLmDZtGgBgxIgRBSsvBNEXLPvntXL72+ff7WowAGhpRwPTYoM5egquMXjWiS9Abkg5wp9GAQDZUiF40WPiFwvjXunRkwso0mNHS1hIVAnFSabII/NQhessqf1RTI62kaI9f1OhCL/snU6WCCoDN8RXjCtM5g3LBpnUkFg6g+VocfwNHFYm7xUEeJvcbdsRwQQ2KUg5+pSOxlL4UmK/3gHpw9MxSIGacT1sIutNmXuLK0xqm4rX5pCoFjEpWQ7YokyyjMnzZYpVhDc6v/4GKyj5VPTXv82E6VflmNm6guiYKmc8O2lsbEBPumOU7eSTk4mI7UQ1EFmfTxTm+g9lilQkKxz9k8+A7qQAMzq4PEfrSB/0BEfxmqQYK7+GTET0KVWqSp2Nt5UhUZm/fkDOeTYhXa5Ac8ZNT7jxdfZA0pO2jJVZzufO6SJXIDVCStbVMPm3WcgFxfjYOoNuijFQ0zYsj2jL9DKMP+cuGdObT1EeL2Lvoq6uruBBnvz//bsKY4VPi3HOt9v3Zb5cZkflu9NOZ3p8q+vggw/G/fffj9deew1Lly7F5MmTAQBbt25FaWlpT5sjCIIgCKIP6atbXV9+knlXJz75LAlfXpVpbGyUq0BVVVXIZrNoa2vrssyOUmE1NTVtt5rUFT2e+PzmN7/BAw88gIkTJ+Lss8/GoYceCgD4xz/+UZAIlCAIgiCIPQDvg1cfMnToUFRVVWHp0qVyXzabxb///W8ceeSRAERCcV3XC8rU19fjww8/lGUmTJiAaDSKd999V5Z55513EI1GZZnu0ONbXRMnTkRzczNisVjBvbhLLrkEfr+/i5oEQRAEQfRH4vE4PvvsM/l+/fr1WLVqFUpKSjB48GDMmjULc+fOxQEHHIADDjgAc+fOhd/vx/Tp0wEAkUgEF154Ia6++mqUlpaipKQEs2fPxujRo+VTXiNHjsTkyZNx8cUX44EHHgAg5h6nnHJKt5/oAnbRx4dzjhUrVuDzzz/H9OnTEQqFYBgGTXwIgiAIYo/DUGBGtUv1e8Z7772HY489Vr7PZzo477zzsGjRIlx77bVIpVK47LLL0NbWhnHjxuHFF18seEp7wYIF0DQNZ555JlKpFI4//ngsWrSowNfviSeewBVXXCGf/po2bRruvffenvWOdzeNusPGjRsxefJkbNq0CZlMBp9++in2228/zJo1C+l0Gvfff3+PAthXicViiEQiiAIINzYC5eV7OqRvBIfOWgBACHe1VF646opnFQvwr2kUbxhDcngZAMA2FGmMJ8s6wtdcUJWC38hnCbCcaC86whX1dXZwtzXA55zTaM8hF+5k1Gk74tYvxH1qKyJcAS2/DssjvryZIhUZxyAw53OrctUVMasZyOXmxCAOXu6Ispvde+zMBKxSoWD21BmoWCGcAqNDNZh+yCSZ4Tpbxm9rDOnSvDJXmPMBQMfgTh3kQGS9k4A045oIZkMMvmYxhrkAg6/RMXfkwvixY4Don2UAetJtztcsxkpP2LANR7BtKMhEXMF1XqiupTj0uCjPNYZMxGmz0xBraVcY7WuxEVzfAdsrCmRKDSl0j9XqnRLRCpE4IAwa82JvT8z9TGSDijRGtHUgtNn9XCUqVdlXrrjiZ2YCzPkT6m2z3WSrGoPpiJjTpa6iILQpBy0l2s2FtMJEsk4xSl7az2hqAioqEAMQARCNRneb83/+/6VBf7wVim/HqaC6g51Ko+6/bt2tse5JeqzxufLKKzF27Fi0tbXB53P/ap922ml4+eWX+zQ4giAIgiCIvqTHt7pef/11vPHGGzAMo2B/bW0ttmzZ0meBEQRBEASxC+wB5+Z9iR5PfGzb3mEC0c2bNxfcqyMIgiAIYg9A2dm7pMcTnxNPPBH33HMPHnzwQQDCTCgej+OWW27B9773vT4PkCA688E9P5fbYy4Rep90iYrSFUJTkxoQkjob6Ko0i2Mc0BMm0mVipVJN27Cc5JKKyRFoyAIAMsUeaGkxsQ/WpaGkhEgkW+qVOiAA8hcRV5nUi+R1JwCgdwShNyegxtJiB2NS4+Npt6SmBQC2HuXWs8qEZifwsQGPY2ehxxg8m8T9eq5AJhMFgJTP0Q1VWEhWaE77HMxi0Bxzw2xAgScqxCSZIib0QwD8ja72R48zaXgIANH9HA1MO0NKyKRQ+pEtk3OGv0jLskrWghkyoCfz5oQMWsZJ0Okk9ZSxO9fD1oUmRo5bfjwVIBsWfTJiljxfqlqRBo2mj3XSQtlIDgzA2yg65d/YgfZRReIcGqAnRX09wWVy1nxcgDARtFVX8yS1Ru0coTWtAID0oAiKHN1RJqKBa0C8WvRDSwN5IajQODlaINs9t6fdhpaynX4zsLijCdqWQqZcXEyWs6WcdPw5d0EVH0e88b9kakgQfU2PJz4LFizAsccei4MOOgjpdBrTp0/HunXrUFZWhieffHJ3xEgQBEEQRDfhXLx6U78/0+OJT01NDVatWoUnn3wSK1euhG3buPDCC3HOOecUiJ0JgiAIgtgDkManS3bJx8fn8+GCCy7ABRdc0NfxEARBEATRG0jj0yW7NPF57LHH8MADD+CLL77AW2+9hdraWixYsAD77bcfTj311L6OkSB2yIoHXb3PlKHCLMu3fjO4Y3bFQkGZNDLnU5Cq8MhEo2rKhh4T+h0zqEGLClGFkTWRHCxE+qZPgbdVaDlS5Rq0pNBp6AnXfMX0usZaapaLZJ8AUhUeaG1JmaSUZU2oKceTxqvKvyuWh6F8pdg2OizkHSbahwGZErFfS7k+NloGUqNj64B/i2jT38DBnVC4IjyBLMfGw7/NjTdUZ6L5EBFTplhB1nkegdmA7RGxG20MzHl+wVaFNxIgtDiBBjFm8cE+FC/b4PZdU8GZyMez5WgDgXrmjBVknzp7ojFbXAMASO2nyljVtKtNSpYryAXd8oEGy9mvQnXKcJUJnY4hzhHbv0gkgQUQaHD7nS5WYOv5cXZ/zjLujq2v1ZYeQvEBGtoPK3XK28iEXR1WJqJIz51EDaA5nkVqCjADbh/z/kfJcga/k3CW2cLHCQAC9TkYbeJzxxUxpi4ixqGPz5Pjtv6cG0AQRO/psY/PH//4R1x11VWYMmUK2tra5BNexcXFuOeee/o6PoIgCIIgegDjvX/1Z3o88fnDH/6AhQsX4qabboKmuQtGY8eOxerVq/s0OIIgCIIgeshelqR0b6PHE5/169fj8MMP326/x+NBIpHok6AIgiAIgiB2Bz3W+AwdOhSrVq1CbW1twf5//vOfOOigg/osMILoCf9cfzcAYHLEFdzzjji8/xKrkMahw6Ft3AarqQUAoA4ZhFyVyEGjR7NoPlyIXbxtNiyPK0ZpGSkSPIW2WMj5xe8Ey6vAdvQ0tg6ZD0pLcoCJur7GDMySAPSGmIjF5zqdaykLluO/E692tSelawBvoxCv1GzIwfaJA/HaABJVjm9MErCdnysKh9T1pMoZAvXiZ5rlYWC2q+3RE5b0M7IMhtIPnYAZk95DjceYYLoj5mn3IudoVXJBwCuGDEacy9xSxW9uBfLu7dwGsjlocWEEVPypjo6Bzjg0d9LT2JC5sLQ0B3P8ljpG5jBq/80AgA/XDUT1K6JQzs+gOF5GgUYLyXLR2bL/xAuet238VghJxw/H9AOpKnEsOpzBv0XEEaznCH8ufphxhSFdLq5rNqzCiItOKSaHmfd2yrn6zkxYkdd46/E2ABssK8qFP1VkvjOuAmh3hoQBnpjjCZR0x5+rDPEasZ2oMmB0iDH0RLnUkKmdcspVPW8guEl4Jn3n6bvI14foHiRu7pIeT3yuueYa/PSnP0U6nQbnHO+++y6efPJJzJs3Dw899NDuiJEgCIIgiO5Cj7N3SY8nPjNnzoRpmrj22muRTCYxffp0DBgwAL/73e/wox/9aHfESBAEQRAE0Sf0aOJjmiaeeOIJTJ06FRdffDGam5th2zYqKip2V3wEQRAEQfQEWvHpEsZ5z8yp/X4/1qxZs53G55tGLBZDJBJBFEC4sREoL9/TIRFfYsrgWe4bQwdSadit7QAAO5OGNmwoAKDpmCrpW8MVwDbyubcgNS16AgjUi0Jq2paPe9qGm+spXaIgtEnoXDyNCcB2tRqpQWFsnmHK99X/m9fHCN0H4NyWd36KtI5wnzsIbAHSzsfL1oDQRlfLsyOvHltjUCwOy8h76XBoSSf2rFsuU6QjWSHO0zbKgtEqNDRKDkgPE2ZBmseNOfJSQObXMmImvBscAU4qDXNQGWxD1N/2La/UxGRDkDm2AKD407y+CEhUis5yFUg7nkX5/GQA4GuxpYYpG1SQDTvbYcjrVfmeGO9sxPFI0pn0WVKzHFuPEvvNiJtYuXiVBl+zKJMLFuYoy/kdbVKLJa99LuheC64A4ECiRpTLBSDrKxbgbRbbtubm/fK12LDynj4ViszD1T6CQ4s7nkNbgGC96bSpFPgGla4WAxiv9UFP2IgOEeP2we9dHytiL6apCaioQAxABEA0GkU4HN4tp8r/vzTozl9B8Xl3uR07lUbd7Jt3a6x7kh4/1TVu3Di8//77uyMWgiAIgiCI3UqPNT6XXXYZrr76amzevBljxoxBIBAoOH7IIYf0WXAEQRAEQfQQeqqrS3o88TnrrLMAAFdccYXcxxgD5xyMMenkTBAEQRDE109v3Zf7u3Nzjyc+69ev3x1xEARBEATRF5C4uUt6PPH5pouaiX2Hf266B8cfN0++54zBNgYBALzLPwM0IXz1RDk6Bgq5m5oFfE2O8NWvyASRzHR/BdkeBYpjMsdMDsXZr2aBbER8pTzbOFg6B6tI3Ar2bk0g8E4RACB2RAabvy+ErCVveBCqE+pYW2fIOMaGzM1XimSNOD8ApIdkoSWFMNrXzKUYNzpUkUaDgBA+BxzBM7M5EtWaPAfrtCibN08MrlelsDhVCahN4hzeBg+8Lfm/glwaB1oexRVvewxodc1onTgYABBo4Gg7VQhy/a8EZaJRPQ7EBufjcJOtZsOQSUdtDQjWuwJsT7vouK3qSJU7CWbTQLJWdGLLeVmEX3Zvt/tabSlOjw1W4XHGJLBZQ+mHmfzoIDFA9C8bZMgUO/F1uONixF3RM7OBbMhd+jc6OIJbnHEwxOcKADqGuoJmLcVh+pz9A1XZV9MPREeI/tl+C/k/wYwz5ALigreMUqRI2jKALRNF/7gGWIYCyy/OMeSR30BtFhfw86uvAkEQ3aPHE59//OMfO9zPGIPX68X++++PoUOH9jowgiAIgiCIvqbHE5/vf//7UtPTmc46n+9+97t4+umnUVxc3GeBEgRBEATx1TD0UuPTZ5HsnfT4cfalS5fiW9/6FpYuXYpoNIpoNIqlS5fi29/+Np599lm8+uqraGlpwezZs3dHvARBEARBELtMjw0MR40ahQcffBBHHnlkwf433ngDl1xyCT766CO89NJLuOCCC7Bp06Y+DXZvggwM9z1OOGYuskVCE2FrDHpc6ESYyZGqFPtNr/tbxxO1pYkdAJlU01YZ1LSb2DJvQGgbDCwnynibUmBZE9kyoc9Il+pQTHHMiJpoO1AkyVQzrhYkFwSCW53EnYOY1IV0JlUOGI4WJbCFu7qXrFtGcbaLPhMblleRCT4zxQyao6fhnX72dH56NV3uGgnqcci49YT7p8LbmoPns0anghOoJhaQ248aLMs1H6JI/Y7eARR/7hoi5vU+lu6aHCoWR8uhznk6xRT+VIG3TewPf55EwwRH96ICuRBQ/Il7ofLanI7BbjJZxXT3+5sLzR7TJa5hpdFJ56M5MTEuNDuAMDwEANXR8lheBsvjtNvothuvUmA7+5kFab6oZiETwELhUmMV+ZTJX+hGB5fJXE0vQ2KAG5MeE/0FhE4qf53iozPYMON6EHshe8DAsPbXd0Dx9sLAMJ3Gxutv6rcGhj2+1fX555/vcCDC4TC++OILAMABBxyA5ubm3kdHEARBEETPoKe6uqTHt7rGjBmDa665Bk1NTXJfU1MTrr32WnzrW98CAKxbtw4DBw7suygJgiAIgiD6gB5PfB5++GGsX78eAwcOxP77748DDjgAAwcOxIYNG/DQQw8BAOLxOG6++eYetTtv3jwwxjBr1iy57/zzzwdjrOA1fvz4LttZuHAhjjrqKBQXF6O4uBgnnHAC3n333e3K3XfffRg6dCi8Xi/GjBmD1157rUfxEgRBEMReCe+DVz+mx7e6DjzwQKxZswYvvPACPv30U3DOMWLECJx44olQFDGP+v73v9+jNpcvX44HH3xwh+kuJk+ejD//+c/yvWEYXba1bNkynH322TjyyCPh9Xoxf/58nHTSSfjoo48wYIC4Wf7UU09h1qxZuO+++/Cd73wHDzzwAKZMmYKPP/4YgwcP7rJ9Yt/lpX/fKLcP+fkCqBlVvteTjv9Kmks9BwCp5dFjWShZoQnKFXkK2uWOGEXJcni3CpGIGfHCLPPB9IvvhJ5yE1VaXgUln6QBAK0jvYiOF9sD/q7L5JSR9bb0Fuqc5DO4WfjYAEA2zKS2J1TnmvNkgwoYB5JVQkCi5Di0vAyn3hb+RF+C2RxcEfF5Wt1zGvHOehjI5KPxGgOejaJ9HgkiUxGAp6HDqW+idaT4nqoZwL8NTp+yUHKONipjobxZBJWq9MpEoFwBwuvEtuVxk7NmSl19TaosgFCd44XjePb4LtwKANgWCyH0t7AzJjY05/qxHIcZcK93fECnxKOOliiw1U0Sa8TcfutJjlSp4pwbCNRzmTxVS3J4onkNlI1EhWjA6ABM55L4G22ZMNb0MoSdz5rpZVAdTRjv9AhOJsyQcBbMQ+uByGdu+cA2E+liJ/GqzxVBVb1g4Ki/3SnGzWB486mrQXxzIefmrunxxAcQj65PnjwZEydOhMfjAWO7/vBbPB7HOeecg4ULF2LOnDnbHfd4PKiqqup2e0888UTB+4ULF+Kvf/0rXn75ZZx77rkAgLvvvhsXXnghLrroIgDAPffcgxdeeAF//OMfMW/evO3aJAiCIAiif9DjW122beNXv/oVBgwYgGAwKFNY3HzzzXj44Yd7HMBPf/pTnHzyyTjhhBN2eHzZsmWoqKjA8OHDcfHFF6OxsbFH7SeTSeRyOZSUlAAAstksVqxYgZNOOqmg3EknnYQ333yzx/ETBEEQxF4F3erqkh5PfObMmYNFixZh/vz5BbedRo8eLTU+3WXx4sVYuXLlTldZpkyZgieeeAL/+te/cNddd2H58uU47rjjkMns4DnfnXD99ddjwIABcmLV3NwMy7JQWVlZUK6yshINDQ07bSeTySAWixW8CIIgCGKvgyY+XdLjW12PPvooHnzwQRx//PH4yU9+Ivcfcsgh+OSTT7rdTl1dHa688kq8+OKL8O7EbyCfCR4Q/kFjx45FbW0tnnvuOZx++ulfeY758+fjySefxLJly7Y7x5dvz+Vdp3fGvHnzcNttt33lOYl9g/8s+DmOuHQBAKHr8USFIEPJcaRLxNfC22JK/YcZ1JENic+Q5WHQE0IDYkRNqJZTN23BCjj+PIksLK+GbND9bZENi8aMDgX5r142BByxXx0AYOulYbSvFBPyyuU2Sj4RgppEpQaPozmxdQYtKbaTmiq9eNr3U6F0ysGlx0XbAgbDmadraUjfmWwnVwpP1P3sM+56zRhxFGieYrVCXxLcYiN2aIUoExNxZqqdEzKG8CYRTJSpMjdYJuL+ufFETWSK3Pf5c1gGoKWcWBRIzyHYgOUTm0Wf2dATotFESEOmmCHxvBDF6BNbkXW0QMEOG5mIiDdezWDERX3/NgvBzaJ++wGqzHFmeQFvqy3HIK+3yoZcDZiSE9egs6eRkuVO/1SknN9TmRKOsved4bBdfVKiBlJUpFiAf6vY1tKubohZgMdxA0mXAD5n29aBbEiFr1XEnipRYXlE/WzQ9WeyPEx+tlc+8HMQ3zxI49M1PV7x2bJlC/bff//t9tu2jVwu1+12VqxYgcbGRowZMwaapkHTNPz73//G73//e2iaBsuytqtTXV2N2tparFu37ivbv/POOzF37ly8+OKLBaLpsrIyqKq63epOY2PjdqtAnbnhhhukU3U0GkVdXV23+0oQBEEQxN5Bj1d8Dj74YLz22mvbZWn/3//9Xxx++OHdbuf444/H6tWrC/bNnDkTI0aMwHXXXQdVVber09LSgrq6OlRXV3fZ9m9/+1vMmTMHL7zwAsaOHVtwzDAMjBkzBkuXLsVpp50m9y9duhSnnnrqTtv0eDzweDw7PU4QBEEQewWcFdqx70r9fkyPV3xuueUW/OxnP8NvfvMb2LaNv/3tb7j44osxd+5c/PKXv+x2O6FQCKNGjSp4BQIBlJaWYtSoUYjH45g9ezbeeustbNiwAcuWLcPUqVNRVlZWMGE599xzccMNN8j38+fPxy9+8Qv86U9/wpAhQ9DQ0ICGhgbE43FZ5qqrrsJDDz2EP/3pT1izZg1+/vOfY9OmTQW37giCIAhin+Rr1viYpolf/OIXGDp0KHw+H/bbbz/cfvvtsG3XFoJzjltvvRU1NTXw+XyYOHEiPvroo4J2MpkMLr/8cpSVlSEQCGDatGnYvHnzroxAl/R4xWfq1Kl46qmnMHfuXDDG8Mtf/hJHHHEEnnnmGZx44ol9Fpiqqli9ejUeffRRtLe3o7q6GsceeyyeeuophEJSvIBNmzZJ/yBAGBNms1n84Ac/KGjvlltuwa233gpAaIdaWlpw++23o76+HqNGjcLzzz+/3SoW0b/prH/4zg+EB4qtK1LvY/oVaClHW6MxGB1if6zYkB4qXGVQHY2HtyOLTJkQomhJBZlizdWueJnM5cRsSG1GqM7GlvvErePGsYCa/4PDxTkBILglh1xQrID6GnPSn8fbYktdSHiDhXiN2J8pBjJFQueTf59yJG56nMn8Y4EGjthQcY6kDyhZ4/6RyuuIuMKkB42tM/iaRYBq1gYz87oXG0rWhhnQtqtf8X5O+h/ZHhVqUtwO7xgakF46RqfnBHIB1yuImYCSf46BAb56pz9FCnTnuqhZwNfEkahxYnyjBH4nj1a6SIHl5F5LDLGQyjl9rVJlHi5wwMxrh74w0T5U9IHZkOOkpd3cWcwG4oOA6rdEn9SkhVxIXASjw4a/Ie9HxGBrTm43zdUEBTcz+ZnwNZnI5b2FmJsTzVYZbN39m5bvmxETXkr5vGumz72WSg5IVKoyxjyHzloAT7to991FV4Egdge/+c1vcP/99+ORRx7BwQcfjPfeew8zZ85EJBLBlVdeCUAsStx9991YtGgRhg8fjjlz5uDEE0/E2rVr5f/ps2bNwjPPPIPFixejtLQUV199NU455RSsWLFih3eBdpVd8vGZNGkSJk2a1GdB5Fm2bJnc9vl8eOGFF3pUBwA2bNjQrXNddtlluOyyy3oQHUEQBEHs/Xzd4ua33noLp556Kk4++WQAwJAhQ/Dkk0/ivffeAyBWe+655x7cdNNN8sGkRx55BJWVlfjLX/6CSy+9FNFoFA8//DAee+wx+RT2448/jkGDBuGll17q0zlHj291EQRBEASxF/M13+r67ne/i5dffhmffvopAOCDDz7A66+/ju9973sAgPXr16OhoaHAP8/j8eCYY46R/nkrVqxALpcrKFNTU4NRo0b1ucdet1Z8iouLu+3O3Nra2quACIIgCILY83zZr25nD/lcd911iEajGDFiBFRVhWVZuOOOO3D22WcDgHyKekf+eRs3bpRlDMNAcXHxdmW68tjbFbo18bnnnnvkdktLC+bMmYNJkyZhwoQJAMQy1wsvvNDjxKQEQRAEQfQxvbzVlV/xGTRoUMHuzlrZzjz11FN4/PHH8Ze//AUHH3wwVq1ahVmzZqGmpgbnnXeeLNdT/7zulukp3Zr4dA78jDPOwO23346f/exnct8VV1yBe++9Fy+99BJ+/nMyzCL2Pd7462y5fczU3wJwjOcc8aket2CreUFyFh2DhGu5lrTBbPFXIlfkgdEiMog2jQ3BNoCSNSKLqJlVZMLTXFBF24HiLnNwE4NiifpDnsmCO+dQcjYaDxeKZKNDlefIhnUprFYCDIqT5JLZKoJbhHBYsTSYXvcPhZYUyULzBLY5wtwsh9dZoOUKk4JrJcelsDpdrMpEpZYONI3N/zVVcMCToq8sayFb6kXSSdDpazJh+UT/TL+CRKVXnrflO2J7/LT/4JX3DhZxJBQUrWXOOAOMi3Okypm8Ge+v58gFRJmy1WnZFyWkwNQYIl+IGFtGKVKwHdwEBLcIpXS6VEOy2lX9SgEwB4wOcb6mQzToIs8q1IwwDAScZKKOgD3QwEXSVed6aIksAPFZyEY0eNsc0XVGge4IvP1bkvK8bSOD8pplwypyfid5qZ9JM0qr0w/qTIm7rZiQQvP8+7y4PdRky8+FrQOpMib7mQ2J7cMuXyBNLT+eS3+n+zW9dV926tbV1SEcdp1Od2bpcs011+D666/Hj370IwAik8PGjRsxb948nHfeeTLfZkNDQ4EdTWf/vKqqKmSzWbS1tRWs+jQ2NuLII4/sRWe2p8canxdeeAGTJ0/ebv+kSZPw0ksv9UlQBEEQBEHsWcLhcMFrZxOfZDJZ8HQ1IJ7Mzj/OPnToUFRVVWHp0qXyeDabxb///W85qRkzZgx0XS8oU19fjw8//LDPJz49fqqrtLQUf//733HNNdcU7H/66adRWlraZ4ERBEEQBLEL9NGKT3eZOnUq7rjjDgwePBgHH3ww3n//fdx999244IILAIhbXLNmzcLcuXNxwAEH4IADDsDcuXPh9/sxffp0AEAkEsGFF16Iq6++GqWlpSgpKcHs2bMxevTonSYx31V6PPG57bbbcOGFF2LZsmVS4/P2229jyZIlPU5SShAEQRBE3/J1P87+hz/8ATfffDMuu+wyNDY2oqamBpdeemmBqfG1116LVCqFyy67DG1tbRg3bhxefPHFAl++BQsWQNM0nHnmmUilUjj++OOxaNGiPvXwAQDGOe/x8Lzzzjv4/e9/jzVr1oBzjoMOOghXXHEFxo0b16fB7c3EYjFEIhFEAYQbG4Hy8j0dEtHHjL3obplYtGM/GwNfdr8qeW2Gp82C7eguPK1ZeZxZHG0j/PK9v8kCc7Q8XGWAI9VgJoetu7qNWK34LdIxPoVwWGSdHFrUirXPCZPDUB2XGhA94epeUqUMpfmkphUaGOdSs2MZrimgluRS3+Jts2D6HRPBDrsgoWo+Jq5A6oVsDQhvEg15G1OyrBkyoCZNZEpFYLlO7RjtrsGf5WFIF+U7XqhfyScyDW3iSFQ5upcA4GsU+5UsUL7SfcJEiQrdTGJkGaK1mkzQmQu6mh0ACNSLeKNDdZmU1Qy65w1uEgaPAOBvcMczFwSKPxVBJStUqffxtnFkwq4ep7N2yhO14W0ROiuuMZiOzomrTJoT5gIKuOKOZ/6XtbfdzU2YCyhyPLgqjBjz+JtsaIm8IaSCbEgcs1WgabxzYTngaXIML7cBniiXbWWcz7NtAKvvJJ3P10JTE1BRgRiACIBoNFqgm+lL8v8vDbtxLtSdJP/uDlY6jc/n3rhbY92T7JKB4bhx4/DEE0/0dSwEQRAEQRC7lW6Jm7/8LP9X0dHRsUvBEARBEATRS75mA8N9jW5NfIqLi9HY2NjtRgcMGIAvvvhil4MiCIIgCGLXyGt8evPqz3TrVhfnHA899BCCweBXFwaQy+V6FRRB7A2895Cb1PHwny6AmhafazVlwmgT+7muwPSLr1GmxICnzdV4lH4QQ/RAIdzLhhQozteCcUBLOjoNnUHNiL8yiSoNRkxs+1f60OF45tT9bRjyT5Ey7mo2PK05OPk1UfyJhcQAcU+/eG0S0WE+JIV1BvSY61vDOOCJ5b1mOACxrWRs+JyYMiW6q0nxM+hJsW16mUyQySwvjHahaVKTJiyv+6ckVarIxJ8YqMC/zUnUarjaJiPGwSzxpu1w9++FretynJSc21fTy1A3SWgNWA4AxLYZFP3Ln0/JQeqytCTQMUiIc6Lj09B9Qu9jbQigbJU8pdTcZEMMOUfOwBnQME70NbTR1QFZOoPtAXyNTiLUEib/k8hGFNiGIdvNJ2rVOyy33+05ZCMiJsujSH8m06MgXez+Ds2PfzbMENos2jF9DKaPQTFFOSVrw/SIGFPlQOl7bsLT/C920w8kKxytl0ckdc1vH/irBQAAbzPwwe9I70N8c+jWxGfw4MFYuHBhtxutqqqCruu7HBRBEARBEL2gn6/a9IZuTXy6m/GcIAiCIIg9zNfs47OvQdnZCYIgCIL4xrBLj7MTxDeN9//75zj6VJHDSyZIclBz7s+jTLG4xWu058A1BUUftosDto3E/kIokgsoULNuXiw1LbQ1pe/H0XZIEQAgsI0j8r9C2MMVDr1B6FOyERW+bSJXlenXYHnFbxfLoyBV5uTH8vrgidmIfC7eZ4oYIhtMGaMec7QuXlXmBrN1Bj1jOfWZzO+kpTm0hOifHrdlX41oDnASBwqfGhu5gOi7t42DNYtyintaZENM+tNYXiZ1R+GPdHQME28yJTZC60XcuRAQHcac8RS6GwBQrU6anqzQLSUGivrBDQryzmTxgYAZcHKZNXkw6LAmAECzJ4vGSuGxVLnEA4+jq0qVMunXo2QBnyiOTDGgOj5BmRJxPiMu6vhaLMQHaE6/3VxgetyGlnI6awvNV558zjY1bcvxt7wKDGec/dtcP6jGwz1oOdjJ67aZIxtiSJXm88epbg6xepHvCwBMD+B3xj+vzwKAZKXi+g5xwNPuhKcDR1wq9D5qjmP5n1xtG7Fv8nUbGO5r0MSHIAiCIPoTdKurS+hWF0EQBEEQ3xhoxYcgCIIg+hF0q6trdmni89prr+GBBx7A559/jr/+9a8YMGAAHnvsMQwdOhTf/e53+zpGgtgrePX/XSO3jzzrLgCAmuXCnwaAmrGhOLIOLZ4Fy7oCF5bOIfhJCwAgWx2W2g7OmPSRiY6MQMnl9SYKMmGh5Qg0WMg625bBwByPFz2WQ7JKaFXiA1wNSSbCoCdVqeeoeDchjyUG+mCWC6+Zlh8lkIkJHdGA5zR5DsXkUHJOzio/k7m6fC02LGcbto5cwNU6JaoVqecJ1Zky55iW5NLDRktzWSZTxBDeKAardbiKwSMaxNiYGqoPE07x7Rkvtrw1EIDI2+VvcK9FZL2o2zbcyaOV7z5z82dZByTlbjOhY8P7A51xYyiud/Z7OFIVnfQ3IgUYuAaZ/6vzsr9/m/BEahue190wZCLiWHiDBcsj9qtpW+ZmY6YNlnb8egKuzYetMan9MaI5JMuED1N0iActR4j+DXjZzacGCP2UEXO1RHm9D+B6NekpyJxjppfB056PQ+R3E9sMHUNF+dAGMb4AkChmGH6H0Pt8ehN5++yz0K2uLunxra7/+7//w6RJk+Dz+fD+++8jkxF/ZTo6OjB37tw+D5AgCIIgiB5AKSu6pMcTnzlz5uD+++/HwoULC0wKjzzySKxcubJPgyMIgiAIguhLenyra+3atTj66KO32x8Oh9He3t4XMREEQRAEsYuQxqdrerziU11djc8++2y7/a+//jr222+/PgmKIAiCIIhdhG51dUmPV3wuvfRSXHnllfjTn/4Exhi2bt2Kt956C7Nnz8Yvf/nL3REjQex1vPnU1XL7mKnC2ND0KlByQpTKNQUsK4StAABdA2JxsWloMCNCyMoVBq47QtlNKdhO0snAFhuWr1PizzKxnfMzNB0hlKjt384AEGZ3oQ88YBPbZPlocxBFK8Wt6HitD3pCxME44GsUujz/7zRorUJIbIV9sJ04MqUGfK2iH8kyFb4WS7YrDfEUBqPDMTz0KQhvcstsOplBjQhlsG+lD2raqWKJ5JgAkKgGUmWOmPqgDpw5YAUA4L/XHIOW9ytkW/5G57wJDi2d33bFvaFNNkIXb5bvt2wdLM9X+Tcv6p1nLRQAVpmTZDZpwHIMEE2FYciU9QCACl+HbOfd/zsE/7+9M4+vojr//2eWO3dL7s2+kZCw7yqCRXABZJOqxeXnVitirdWvKyLVorZiVXBBwGpdsCq4fcFvK61WRaEKFnHBIBUBEWSHhOy5+zZzfn+cuWdyBQKBhGB43q/XfWXuzDlnnnNucnPmzGc+j7PGOk9S4M1jSkAN88+jvrcEZuNjEs1UxZg09NCg+fl+R701NnGnDEetGYfOoCNpQKnAVZMcTwmF/zHPJwGRDL6dvluHrDPU9uHntjeKZuHapwvhuRoyxGcJAA1d+bazhiFYkBTiA859/HgkJ5n4FdDqAcXcPuXWOfD+EMeK9+4CQXQkWjzxueuuu9DY2IiRI0ciEong7LPPht1ux9SpU3HLLbe0RYwEQRAEQRwu9FRXsxzR4+wPP/ww7r33XmzYsAGGYaBv375IS0tr7dgIgiAIgmghpPFpniM2MHS5XBg8eHBrxkIQBEEQBNGmHNbE5+KLLz7sBt96660jDoYgfoqseIcbG44Y/5jYZ2gK5BCAhKnvCIa4zgeAFIoi3tkDAIily5BMUz+7bOlI4hk22AKmXkiR4DA1N9EMGa5qrj2xLddE0k+AwfUMX3WtvD4ChzcCf5fkn7el9zBUCcEiLrTR7RLQhWuNbEEmzmeoEqJeXkdigL+Ea3EyN8VFO+FcFWm7ub7IXRlEItOBhJOX8663IXhmMuko4NnK6zT0ZFDDpsYkBsTT+WWl4bPjmVcvAABoQUA3JT42PxDN4NuSLonEpIYiC92M5meoeK8zItlmYF7AXWFdrmat4/0IFQChpKmfm8Hv4WXStstYv74zAGBjRgxG1DQE7B2DZHCjR1cVgy3A+xNLlyEZDN4tXMPk7+xCPJnYNM6QMJPGSgagmKaFcacMzUwMa3gVVJ/Cx99VbcUZzpGQu9YUJzEVNlMrFM2ywVnLz13fU4GkW2aD7gqGzPU+0UY0hw+QEjWQSOOffTBPQbDU1GsNiCNjFf+85QSQcJjj7OM6H17X0vhoPoZwrioSmK55ngwNfzLQra5mOayJj9frFduMMSxevBher1es+JSXl6OhoaFFEySCIAiCIFofutXVPIc18Xn55ZfF9t13343LLrsMzz33HBTFtNHXddx0003weDxtEyVBEARBEEQr0GIfn5deeglTp04Vkx4AUBQFU6ZMwUsvvdSqwREEQRAE0ULIx6dZWixuTiQS2LhxI3r16pWyf+PGjTAM4yC1CKLjs/x9y+9kzJkPA7IMSGYy0pwMsS01+MFMOU8sTUaC5xmFLShBNhOQ2utigKn5iXg1MPMSxVljJb9M25NAYxnXoTAFaOjGxSa5r8pQIgaiGbxcLF0SSTwTTkkkSHVX6nBUR0XM0WzeVsIhiZicNcxKqGqTEHfzQOw+A8EiXt5pk+DY44NezG+JZ26OI5LLhSjuPVzbAwBqWALry72MsCkNhtP8vogq0E29ib0esNckx6OJbxCAyhFcJ5P2vQ2yzi+8AoUS3BWM65UAeLda30H1PWWwJt9w2j4+PrrbgO4yzPGQodXxtoyAQ1wJ2mslMebhLAmOeibGubGrBmc1r5P9rQ41ZGquggk0duUdUSKMjzsALcAQ85qBMCb8dwKdJKhmUlRJB8I5fDwlBkRyTG1SrgzvNi668RfLiOQAnu3J8TEQKOO6ruTvDQCooQRqe6ui3cz1irmtwG4mOA3lyAgV8fLOfXzck+dm5jVt3C1BCzDYwrztU2+cI87z1V+ngDiOIY1Ps7R44nPttdfi17/+NbZs2YLTTz8dAPD555/jkUcewbXXXtvqARIEQRAEcfhIENc6R1y/I9PiW12zZs3C73//e8yZMwdnn302zj77bMyZMwd33XUXHn/88SMOZObMmZAkCZMnTxb7Jk2aBEmSUl7JydbBWL9+PS655BKUlZVBkiTMnTt3vzLTp0/fr92CgoIjjp0gCIIgiJ8GLV7xkWUZd911F+666y74fPxRyqMVNa9evRrz5s3DSSedtN+xc889N0VcrWlas22FQiF07doVl156Ke644+CPX/br1w/Lli0T75tqlgiCIAjiJwvd6mqWIzYwBI5+wgMAgUAAV111FV544QU89NBD+x232+0tWo057bTTcNpppwEAfv/73x+0nKqqtMpDtBlLV94LABhfZk6+NQWGm/u3RPsWQjK1EpmbwgDj23V9XEJ3YWsEMrZx/5W07QE09uRaDmdNAkrEzOnkUqEFeV337gi0qoA4fyLDhVA+F+owCVBNnYajNgbd9JrR6mPQzdxgsQwVapC3a/dJyFrLhShSXIdht1kd68zjqO2rIj6Iny/8aRoc+dkwzG+TcK4ENWiWZ4Crki+c+/rGIVeY4iGPgayvFbMfgG5ez6hhBlcV16EYmrXgnnBISPuex1Fw7i5U/LuElx9Sj555ezE6awMAYNbGMYj9N4PXtwFK2Ao9cxMfg2ChjORit6MG8JeaocpAXrmZd8sjCa2Ls8YQ2qZgoRmTedBVpYv7AqECuxWvU4Jkyo0auksiDjkBMU5yFMKHKeECageYMdVaMXt26KjvyfutxID0nRD6sIRLhmHGqNsl8RlHM5xI28tPHs6VYZgfn9Tk/oWz1oBkWAv+jgZeVzIYmKktMxRA8xsIZ/FycpyJx5yHTJwt4vhyAel9jjfocfbmafGtri5duqBr164HfbWUm2++Geeddx5Gjx59wOPLly9HXl4eevbsieuvvx5VVVUtPseB2Lx5M4qKitClSxdcccUV2Lp1a7Plo9EofD5fyosgCIIgiJ8WLV7xaarBAYB4PI6vv/4aS5Yswe9+97sWtbVw4UKsWbMGq1evPuDx8ePH49JLL0VpaSm2bduGP/zhDzjnnHNQXl4Ou91+wDqHw5AhQ/DKK6+gZ8+e2LdvHx566CEMGzYM69evR3Z29gHrzJw5Ew888MARn5MgCIIgjgl0q6tZWjzxuf322w+4/y9/+Qu++uqrw25n165duP322/Hhhx/C4XAcsMzll18utvv374/BgwejtLQU77777lG5RI8fP15sDxgwAEOHDkW3bt2wYMECTJly4GXbadOmpRzz+XwoKSk54hgIgiAIos3o4JOXo+GoND5NGT9+PKZNm5YiRG6O8vJyVFVVYdCgQWKfruv45JNP8PTTTyMaje4nOC4sLERpaSk2b97cWmEDANxuNwYMGNBsu3a7/ahWmYgTk/e38zxH43veDT2H62M0X1zoegAg2Mkptl17+U+mQvj1GJqCzK+58ENPc0B3c9GGVhuBvzgdABDL1GBo3EfHsasBan0QGRu4zqOhb5rQqGgNcdh8MdFuJIe3lb41AEPjf2/xNAXxLK7FUSIJSDHunxMpcAttkuYDogYXeegaEMmSoPn4saxNOgKFvC01yhDz8nLZX9oQzeT9swUgNDBpuy3vHTViwBbg5wvnaEJno4YZHHW8nR1fFSPRyRTHVKTj06198R97b2vQs02tUq0i9DeSAfjKkvocQDbzURka7wsAKDUS4k6rfDK+hFNCOJvXdVanegtFvTIau/Egs75j4lhjdwmuSl4mY4uBxi58/F01TGhxEo4mudncksjZFs0EdPOrJlSgIHOjmfMrjZdPnsNQrBjdlQlEvTwOJvGYASDnvxFEsi2NVmMXXiatgsHeyNvRNSAhvtok4dsjQ+L6MDPnmK+sSc43DcjYzPcPP+8xrHjX8rAiiOOdVpv4/O1vf0NWVtZhlx81ahTWrVuXsu/aa69F7969cffddx/wKava2lrs2rULhYWFRx1vU6LRKDZu3IizzjqrVdslCIIgiGMNiZubp8UTn4EDB0Jq8ngAYwyVlZWorq7GM888c9jtpKeno3///in73G43srOz0b9/fwQCAUyfPh2XXHIJCgsLsX37dtxzzz3IycnBRRddJOpMnDgRnTp1wsyZMwEAsVgMGzZsENt79uzB2rVrkZaWhu7duwMApk6digsuuACdO3dGVVUVHnroIfh8PlxzzTUtHQ6CIAiCOL4gjU+ztHjiM2HChJSJjyzLyM3NxYgRI9C7d+9marYMRVGwbt06vPLKK2hoaEBhYSFGjhyJRYsWIT09XZTbuXMnZNlagt27dy8GDhwo3s+aNQuzZs3C8OHDsXz5cgDA7t27ceWVV6Kmpga5ubk4/fTT8fnnn6O0tLTV4icIgiAI4vijxROf6dOnt0EYnOTEBACcTic++OCDFtUBgLKyMjDW/HR14cKFRxIeQRAEQRz3tMetrj179uDuu+/G+++/j3A4jJ49e+LFF18UOl7GGB544AHMmzcP9fX1GDJkCP7yl7+gX79+oo1oNIqpU6fif//3fxEOhzFq1Cg888wzKC4uPvLOHIAWT3wURUFFRQXy8vJS9tfW1iIvLw+6rrdacATRUXj/+0fF9jmjHoGvi5lcVJLgqOd/M7Ywg7uCC491hyKExIk0GwwbF0arjVGoDby84bRBNgXQ4SwZWRU842WswAPVF4Fk/i26K+JQg1w5K0fiMOz8z76ujxNpFXx/oNQt4mOKhPqe/EnLeDqQtYErgRMuBQ3drNVVdQOvE/cAesIS3+ZeswtVK8p4jDZrdVjzW8kwHXUGIqYxXiRLbpIcSIZu4/EpUYikmpLOhdIA4KqQECjhdS8btQr/9+EwOJKJRm2W4DdZL9mWzTRVVMNMLOVLDLCb5n0xj4yYRxL7XfushsJ5ZoDVQOb3UTR0t55EjWfwcv5OMtQoL1fy7wgqT+Nl6ntZYxboJCHY01QxR2QULTeTq25n8JXK5jhb4ms1AAQLeBlZh0hqCqQKsO11MRg2fr6EQ0LCHPd9pzng2sfMcZYgm6eOpUvC/DDuBtKbCMyTBoZxFxesa35eP3u9AX+x9S9DMkzTQ51h5NhHzf7ZsPplMjRsd47xra76+nqcccYZGDlyJN5//33k5eXhhx9+QEZGhijz2GOPYfbs2Zg/fz569uyJhx56CGPGjMGmTZvEXZzJkyfjnXfewcKFC5GdnY0777wT559/PsrLy1s1u0KLJz4HW02JRqOHTCdBEARBEETbcqxXfB599FGUlJSkPNVdVlYmthljmDt3Lu69915hRbNgwQLk5+fjjTfewA033IDGxka8+OKLePXVV4Wh8WuvvYaSkhIsW7YM48aNO/IO/YjDnvj8+c9/BgBIkoS//vWvSEtLE8eSj6G3psaHIAiCIIj248cZCg5m6/L2229j3LhxuPTSS7FixQp06tQJN910E66//noAwLZt21BZWYmxY8emtDV8+HCsWrUKN9xwA8rLyxGPx1PKFBUVoX///li1alX7THzmzOF+JIwxPPfccynLTpqmoaysDM8991yrBUYQBEEQxBHQSre6fmzSe//99x9Q57t161Y8++yzmDJlCu655x58+eWXuO2222C32zFx4kRUVnJTq/z8/JR6+fn52LFjBwCgsrISmqYhMzNzvzLJ+q3FYU98tm3bBgAYOXIk3nrrrf2CIwji8Pjo37/HyHFcExHzqKjrwy8iJB0wFH67OGudD1KC6y4ihWkIdOb6jfQdgOKLAgDkcFx8QcU9Eur78FXY9N0xJLwOMPPpSzmqI+Hif+oquIYHALQAQ6CI73fUGk10NkDcXNCVYzwZJsB1RAo/NaJZIk8n0CsA+WtrBXjbx2XQ/HzbUAHvNks/ojVykcm2yySoDaYBYnYc2h7eb88PDLF0c78dIqFqsJih6NQKAIAdgL+Se4bZJB3dT9uJncv4E5lMARwncyGRv9EJqZZfnTr3SoiZOZWZLKXogJSI2dc4g2oOgqPOQNRjaXMyvzP1Vk4JO8c6wLqagqFtbihB05ywmsHewBsO5WuwN/AiwU4QY2vYANs+y1CwoQf/6dxn6Z8kA9CbmNknY5UMQIkyBDqZRoYBwFHH4wp0dgoTwqjX+iB1BxDNkMS5mdkl3Q44a8w+uSQE8y2DxST2RgMJpwwlapjtWoaQzloreWk0XRNaMXujgdN/NduMT8d//tGyNEZEK9FKE59du3alJCM/mImvYRgYPHgwZsyYAYDb3qxfvx7PPvssJk6cKMo1fSIc4AspP963XyiHUaaltDhJ6ccff0yTHoIgCILo4Hg8npTXwSY+hYWF6Nu3b8q+Pn36YOfOnQCAgoICANhv5aaqqkqsAhUUFCAWi6G+vv6gZVqLw1rxmTJlCh588EG43e6D5rJKMnv27FYJjCAIgiCIlnOsxc1nnHEGNm3alLLv+++/F954Xbp0QUFBAZYuXSp89mKxGFasWIFHH+Wr34MGDYLNZsPSpUtx2WWXAQAqKirw7bff4rHHHjvyzhyAw5r4fP3114jH+fOVa9asafVlJ4IgCIIgWolj/Dj7HXfcgWHDhmHGjBm47LLL8OWXX2LevHmYN28eAH6La/LkyZgxYwZ69OiBHj16YMaMGXC5XPjlL38JAPB6vbjuuutw5513Ijs7G1lZWZg6dSoGDBggnvJqLSR2KLc/4oD4fD54vV40AvBUVQG5ue0dEvETZdQIfl9c9UfFPn93j9BWRDIV6HZT1xFkiLv5trsyAUPl26F8RXjWRL2AvcHSiThrrT9xNWQgmsHvcDPZSnipaxJ00/tFiTOR5NJZrcNu6nL8JZrQm8gJIJJpXQCF84CEm7elBiWk7TRjyQBUU0PTeHoErm95UPYGwF3JAw42iR0AgkVm3RwDTDETZsZl2GtMrYoGsD4BAEBsnws2vwytkddJnOYHY7yc3ZZAeBNP3OqslISmRfMbqDiTj8Hgod+L8xY4GvGvTQMAAK7VTqTvsoRAwUJZ9DvutsaWKYCjhm9Hsi3/HUm3Eo0q1scK3Qk4BtcBABp2e2Gvsh4SSWqmsr+1zuuoSSBYaDPbl2CvZwiZnkKenYbw5QnlymKbn9P8LCIMapi319hVhS1gJUhNxsVkwFHPyyhxhnC2pYDQfAxKjB+Lu2XEmuiekgldbUH+OQPc98m1z2pXDfPt8nl34ISluhrIy4MPgBdAY2Njim6mNUn+Xzp54gwomuPQFQ6CHovgv6/c06JY//Wvf2HatGnYvHkzunTpgilTpoinugDLwPD5559PMTBsmroqEongd7/7Hd54440UA8Mfi6yPlhZrfH7961/D7/fvtz8YDOLXv/51qwRFEARBEMSRITF21K+Wcv7552PdunWIRCLYuHFjyqQH4Ks+06dPR0VFBSKRCFasWLFfvk6Hw4GnnnoKtbW1CIVCeOedd1p90gMcwcRnwYIFCIfD++0Ph8N45ZVXWiUogiAIgiCOENYKrw7MYT/O7vP5wBgDYwx+vx8Oh7WMpus63nvvvf3SWBAEQRAEQRxPHPbEJyMjA5IkQZIk9OzZc7/jkiThgQceaNXgCOJE4N/L7xHbZ100CwCgxJjl38IAVxUXwSScktD7BDqpQkfiqGUI5/D9TAFChYCjlh+LeiTYGw1Rv6kvjGTqQuIZ1n6DAbqZfSaaISOeponyabt4LrFIjk3kvpITgL0RcO/hxwxNRuXPTF1Kvo7sr7l4xfuZA65qfhL3nggknW/bfDZEs3j5hm6WV5B7p4xQEb/0NNw6kl9XShiIbzF9g9INSAlLWxP125GRExDjKZu5sxIu7mPD42XI/4K3u6O8B+xm7rM9DhkFZp4qzRcTOasi2arQSdX3khApTCB9E48lba+BuOlz5Ky2xs0WtDQ7sTSeZwsAlBjQsIvrjuSYpbOJnRQUMdfIbuR9xSsEim3i8064AFcVUPQfq6yvu4t/Nox7JiU/p6RGSwsYQgeWvlNHsFAR5W0hQ5RJmH5JuiaJuu5Kfjzm4XXiLklohwxFEl5NWtAAU2Rx7rhLhAeF/0pg8G9mi75+8Srl8mpr2iNJ6U+Jw574fPzxx2CM4ZxzzsHf//53ZGVliWOapqG0tBRFRUVtEiRBEARBEIfJMX6q66fGYU98hg8fDoA7OJeUlECWWywPIgiCIAiijaEVn+ZpcXb2pCFRKBTCzp07EYvFUo6fdNJJrRMZQRAEQRBEK9PiiU91dTWuvfZavP/++wc8ruv6AfcTBHFo/rN4KgDgnDGPQI7yvyUlogqdhW6XoIZ4WabwXFoAECywPHXSdzPU95LgL+OXbflfMjireEFJN1DXl4sw1DBDoJiv3CZcEF44dp+lF0rmmwKA2pOBur5cVOSoaXKMMTjrdJFXS44zFH/Mg0y4bfAX89iVKLDvNN5ugW5Z34ezFcgJHmvOujhi3qbeNknPIVX41EgJyx/GvZsfT+p3vOUa7PWZYkxMyY3wEgKAhEOGexdvQHeqiHv416BzXxQxD2+osUxDjEtxkLlZR/XJitk3wLVDRdS80x/JlWEzJUU2S1qEeBqQMH1uJB2ImFl+bH4gfbMiYk7G7frcLfRWugNCi2MLMJi2RJB0IJQnQfPzsdM1GWqYmWNr5TgL50oilkCRAs2f9EJiwq+HyRJsQdMnKkMR+iBbiMFdwX/vYh4Z0QwJqikpUqIMMTMPmK4BrirTKyjMYJg2ZrrT0lvZApbuyF1pQDZ9qYZd/gRWLboTRBtCt7qapcX3qyZPnoz6+np8/vnncDqdWLJkCRYsWIAePXrg7bffbosYCYIgCII4TJK3uo7m1ZFp8YrPRx99hH/+85847bTTIMsySktLMWbMGHg8HsycORPnnXdeW8RJEARBEARx1LR4xScYDAq/nqysLFRXVwMABgwYgDVr1rRudARBEARBtAwyMGyWFk98evXqJbKwnnLKKXj++eexZ88ePPfccygsLGz1AAmCIAiCaBl0m+vgtPhW1+TJk1FRUQEAuP/++zFu3Di8/vrr0DQN8+fPb+34COKE5KOlvxfbwy94HJqfC06jGTJspqDVFrTM6UK5Mpy1VnJLm09C2k7TxK4xLsz4wCR4t5lC5wRD2m6+u3qgXYiHw1myMD/UNQmOen7uglVAOIeLbkMFQFQIdiWEc1R4dvLzSzpDqLtpesggxLVRrwTPNh5HbT8Fnh18v7NWRySTtxvNVKEmjfX8CTj5gjLkqI7KIZYzXvLLWdcAWwgidjnBhGg3+zsG1uTSLmqKppU4QyyTx8dUSYhu5WgCwSKuSJZ1hlAnczwKZDj5Vx6UOBDzAO495vuYlaAzOR7mMFufhR9wmok7nTWGlejT4EaVQKqxpBJvYiCZKQkRsRI1zQlNEbktZCCSYQnBbUFeLpIpIW76O9rreUJSAIhnS8hdy10EdbsMQ0sKlSUhLpcMiP1KjAuYDfMUsg4hdA4WARFTAJ+xSYa7wjSjDEpgUlKNDSG+jqXJSK/jv3fMJmHY5U+I8336fyR0Jo4tLZ74XHXVVWJ74MCB2L59O7777jt07twZOTk5rRocQRAEQRAthDH+Opr6HZgWT3x+jMvlwqmnntoasRAEQRAEcZSQgWHzHNbEZ8qUw8+tMnv27CMOhiAIgiAIoi05rInP119/fViNSZJ06EIEQbSIFe/8Tmz3+eMcKKZ5nxJVkDBlL2oYaCwztR9BbgSoxE3NTdwQGo7G7k7RVsIuCUO7zM0JBPP410FjXwMw/5Sz10pC18MUnnAT4NqMvHJdbNsClnFpqMAmtCuSAQTzTI2JB8hez8+n22WhaYm7ZUgGL89kIJ5mmio6bYilWSKdpGmhq9pAKI/vj+YC0WzAs5XXtzcaUMO84bo+NlE3qXkBAPdeGYbN1BRlNNXj2MDMb0Q1BGRs4NtawECgiBcyZG7QF0/nx6IqEPeY566ThAlhsr8AYGiAWtekr+ZQRbIBNWSaDuYBia68g8p2J9K3W+0kTQATTgkJFxAPJwOWkbaXN6ZrVvJaV7V1ue4rk2Azk4nKCcDfWRPHkolTmQroplYsniaJ5KRKVIccMxDOtZn9SwBZPBh7fZMEq14JMQ+v79mhwxY0DRDTLf1RwinDV+YQ/UhqmNL36DjtWn6xzBTgq79SAtNWgQwMm+WwJj4ff/xxW8dBEARBEEQrIBkQFxZHWr8jc9QaH4IgCIIgjiNoxadZKMU6QRAEQRAnDLTiQxA/ITb+6Q78bBLXREQyrWSUCReQMHUstiCQcCuAmcwUNhnxNK63cFYnEM7lf/ZqlAnPlqBXRczUpKTtkEWCzrjLSqRpqNY55DjQ0M1MnOoA0vbIIklm8jgABIuBeLrpk6NbnjLerQmhMUk4ZAQK+TVYHNzHhiMh7k5tDwDCObJ476oE7I1NLk8lS+viqGUIFEuifrCMnzDcCXDuMXVLkpVc1BaymomlAQl3sjMyFDMZbMOABNRGBbaA2W4McFSbfjh2QDN1SA39E5CdXPDjWucQmilbyIo15pGQzNWqNQC2tVx/ZWhc/wMAmg8wklocheuckslPdQcQzjWTmfq5TgsA4mkKQrlJDyfA3mBqkHyGSBirhiztkL3BQCSLj79kWJ83k2TYdCZiZzZJ1MnYrKO+l2LWt8YtniYj4UpqzSxtmRrVhZYqaFfFZ+zrbCVI1XzA6Vdxf5/PXydvn6OBnupqHpr4EARBEERHgnx8moVudREEQRAEccJAKz4EQRAE0YGgW13Nc9ys+MycOROSJGHy5Mli36RJkyBJUsrr9NNPb7ad9evX45JLLkFZWRkkScLcuXMPWO6ZZ55Bly5d4HA4MGjQIPznP/9pxd4QRNvx5fwp+HL+FHwz5w64K3W4K3VoPqDg8zgKPo8ja2MUtoCOeJrCX+kKDE2CoUmIZKmIeiREPRKC+RL8nfhLd/BcUEoUyPhBh6MacFRz7xdDtfQgWj1/yVFw7YcEKBFAt0uQ41xLw2Sud9HtfLu41z4U99oHZ6kfNScDNScDDd1U+Iv4K5wlQwswaAEGu4+JPhmqFZNu2c/AUPl7XQMiOUCgkwQ1zEReKH+xCn+ximiGJGJXQ4BnkwLPJgWKN4ZY3zB/ZRuIp3GfH0MBIpn8Zdh4nipXFYOu8WOGAgzrvxm6y4CUAH/pvI9MBiJ5BpQIHw/PBhWO7xxwfMf1PeEc/oq7JDir43BWx+HdpsNRy+CoZTA0wO7jL81nndsWZAgVSAgVSDBsPK7kOez1XA9kaFwXFeikItCJf1AJN3+F84GGXhIaekmo7acg5gViXiCSxTVCuoNrpuwNBuwNBhx1BhIOCQmHBN0hIZqhiDEMZytiHAxNQtZGHVkbdThrDDCJ66WiHglKlEGJMkS9Mhq6qmjoqqK+u4qGbvwVzgUSDv5SwxAxxdN5Tq9YmowBU+ag3+/nHNs/rI4EZWdvluNi4rN69WrMmzcPJ5100n7Hzj33XFRUVIjXe++912xboVAIXbt2xSOPPIKCgoIDllm0aBEmT56Me++9F19//TXOOussjB8/Hjt37myV/hAEQRAEcXzS7hOfQCCAq666Ci+88AIyMzP3O26321FQUCBeWVlZzbZ32mmn4fHHH8cVV1wBu91+wDKzZ8/Gddddh9/85jfo06cP5s6di5KSEjz77LOt0ieCIAiCaC+St7qO5tWRafeJz80334zzzjsPo0ePPuDx5cuXIy8vDz179sT111+PqqqqozpfLBZDeXk5xo4dm7J/7NixWLVq1VG1TRAEQRDtTvKprqN5dWDaVdy8cOFCrFmzBqtXrz7g8fHjx+PSSy9FaWkptm3bhj/84Q8455xzUF5eftDVnENRU1MDXdeRn5+fsj8/Px+VlZUHrReNRhGNRsV7n893ROcniNbk079NBQCcesMcxLymr4ppN28LmTmXIjpg7mvsahfeMY56S7vj78Lg3s2NVvzFCpiZZinR5Bsimm1tu/cAtiD/ckw4JEQzLK8dW9Aq1+VnOzGr698AAH/afQG2/JsbBHl2Wbm9wBg0n5nkymDwl/KcTtEMrjECuIdN0kvHXWHlEnPv4z/3nMNj0eoU6A7Tt6ZWEvWDRYDuMgeh2g6Wxs8vG5LIzxVPs650lShQeQYvf8fIJbg1g98GH7/p54BLR7i7madMMZCZxTss/zsbgWJeP3MTQ0MGDzKaa8C1m19jGjagoTsXLBkq18sAgBIGwrnWkDhr+U85xoRnEDM1VUnfIlnnnyHAtTWS8EUCYulWW8k+RbMY3LvMnFzpTfoaAcLZpo+SB7CbecUMFZAMy5tJd1ieRzFZgiOZC86AyPkWyZSFHkvzG9DMPGG6JgmvILWJX1JyrACeJyyJ5gcCnYEur88EAGy7ahoIorVot4nPrl27cPvtt+PDDz+Ew+E4YJnLL79cbPfv3x+DBw9GaWkp3n33XVx88cVHdf4fJ1RljDWbZHXmzJl44IEHjuqcBEEQBNHW0FNdzdNut7rKy8tRVVWFQYMGQVVVqKqKFStW4M9//jNUVYWu6/vVKSwsRGlpKTZv3nzE583JyYGiKPut7lRVVe23CtSUadOmobGxUbx27dp1xDEQBEEQRJtBT3U1S7ut+IwaNQrr1q1L2Xfttdeid+/euPvuu6Eoyn51amtrsWvXLhQWFh7xeTVNw6BBg7B06VJcdNFFYv/SpUsxYcKEg9az2+1HfHuNIAiCII4VtOLTPO028UlPT0f//v1T9rndbmRnZ6N///4IBAKYPn06LrnkEhQWFmL79u245557kJOTkzJhmThxIjp16oSZM/m94Fgshg0bNojtPXv2YO3atUhLS0P37t0BAFOmTMHVV1+NwYMHY+jQoZg3bx527tyJG2+88Rj1niAIgiCI9uC4dW5WFAXr1q3DK6+8goaGBhQWFmLkyJFYtGgR0tMt5d7OnTshy9Ydu71792LgwIHi/axZszBr1iwMHz4cy5cvB8C1Q7W1tfjTn/6EiooK9O/fH++99x5KS0uPWf8IojVZ8/wdYvusi2aBSRDJOhWbhHga/xtR4gy2IN+vhhmUGL+0CxbLCJt3eu01VjJSMG6QBwBMYbDXm8k5E0DUy7clBkSzGZRSLvKNAYgFeKXoh51xJaYAAOIDg9C78PNFsxUoYSv+9N18hddRayUvhQQhyi74ynqwwF+sIeGy9HgJB5C+xYxRBtzmXWwtoKOutyLilYJ8DBJuA1LA3B+VrKScCoTw21CBtK28zLzt58F93VsAgC0VeVCrbEIIDgB1p/ByXh3wbOf985VJQuzNZCuxqOaHuI2g6EC4M9/2/MBgb+Tbul0CM7/SbGEDqhl3zMuT0Uq6Oe4GoPl4Y7YgQ8Jpft4xnrwVACJNBNO55ZaA2r3H2lZkQDUF6WoYIrFoMqmsv8wsF7bE8M5qhlhSjCwBhiqJMUxuN30ySIky+MzP3r1HEuORvssqYyiAs9EUTCcAvUqGGuT6z5NvmwNnHS/7+WtTQBwCg/HX0dTvwBxXE5/kxAQAnE4nPvjggxbVAYCysjKww3gU76abbsJNN93U0hAJgiAI4vjmaHU6HXve0/4+PgRBEARBEMeK42rFhyAIgiCIo0PCUYqbWy2S4xOJHc59IWI/fD4fvF4vGgF4qqqA3NxD1iGIY0nPh3iSR+/W1D9xQ+Ffa/ZGQ3w5BgplBIZx0Y3LFYX+ZQYAwLPNENqRcK4kNBm+UglxUwcUz9QBBowcvB4A0CXpvgdg/kfDYa+xFpbj6bx+/in7ULmOi4qyvrViC2dLcJhajnCOZS5Y8HkUqj8myvl68pMH8yUk3JZ5IsC1MADgqgDCeXxbdzIoEVOfFLc0N1IT1wzJ4C8AwgQRALLWA5EsXjeSBeguqy0pYfWp+KQK7PyBnzD3c0XodAAglm7qqkJA/QB+EnutVcDmN40ZTXSNl0+4JEQzk/t4vLEMU4MTk5C+gx8zmiRxZRKEASWTUw0DEy4zHg+Q841pcBkzEEvnFRq7Wv/y0ncy6JoERwMvFyyQhWmlGmYI55qmjDLE5wRAmBbaGw1EvbxM+u6EOB7KVaDbrPOwJg/wJj8PLci4gWKTz8fRwN/E0qxx++x/78RxT3U1kJcHHwAvgMbGRng8njY5VfL/0hmjpkNVD+yPdzgkEhF8+u/pRxzrzJkzcc899+D2228XicIZY3jggQcwb9481NfXY8iQIfjLX/6Cfv36iXrRaBRTp07F//7v/yIcDmPUqFF45plnUFxcfMR9ORB0q4sgCIIgiFbhYEnHH3vsMcyePRtPP/00Vq9ejYKCAowZMwZ+v1+UmTx5MhYvXoyFCxdi5cqVCAQCOP/88w/o63c00MSHIAiCIDoQ7ZWk9GBJxxljmDt3Lu69915cfPHF6N+/PxYsWIBQKIQ33ngDAF8Je/HFF/HEE09g9OjRGDhwIF577TWsW7cOy5Yta41hEdDEhyAIgiA6Eq3k3Ozz+VJeTfNVHoiDJR3ftm0bKisrU5KD2+12DB8+XCQHLy8vRzweTylTVFSE/v37t3oCcRI3E0QH5fv7uLfP8J8/JvbZAnFUDOVCj0iWLPQVzmqgaCE3tAlnO5D85gvnyFDM7zpXJUPFCL7kbKtX4NrLdRq6U4ajSsLab/nSdt2VP2C3nwttMnvUIbYjBwAQOs0y7jkr/wf8vYH7cTWG3bBZq90IFJt+NBGIc+8eaUfabm6yo4abXI4yrmlJ6nqa6nQaB0ehVnLxi6RDaG6Svj2AeWVrWNssaU2jS9BMX51gISCb8qJYlg6oDLZG/tUZ9zAYZlbYXRsKoMRNLVCGBNWMXW9i+J5wAc5KHkgkzxAnjGUAusb3u/ZZ/VPDTCR9jWZw/xtnlanRqgPsPlN/UyjDVWXV85fwMpoPcNaYCUSzZKG/SdtjWP5FqizUrJoPUCPJweFjGTK1PI46Bi3A20o4JThr+PlibgnOOr7fFrBuSRiahEAhrxvOVhD1WLqeZPLYmMfSB0lxCC+jSIYEXQO0gDVukWxeML1JgtuBN83B189YHlZE61JSUpLy/v7778f06dMPWLa5pOPJFFEHSg6+Y8cOUUbTtJSVomSZ5hKIHwk08SEIgiCIDoTEGKSjeG4pWXfXrl0p4uaDpW06nKTjQMuTgx9umZZCt7oIgiAIoiNhtMILgMfjSXkdbOJzqKTjyZWe5pKDFxQUIBaLob6+/qBlWgua+BAEQRBEByK54nM0r5aQTDq+du1a8Ro8eDCuuuoqrF27Fl27dkVBQQGWLl0q6sRiMaxYsQLDhg0DAAwaNAg2my2lTEVFBb799ltRprWgW10E0cFZ8d5dYnvI1bMRLOMaCfcOBfYGvl+3Aw3dTd2KG8hfY4owqqx2DFWCdwMvEx4aREDjWiHXHgnOaoZQAV+OXv9pNzCVf3FqDRIcpmbEsdaJhJtvf+jpDfzA3zT1cYlmN9G3hLjOAwBi2TpqiyyfG9XF45O3OWELAAnzEo7JPKcYAChVGuQuXCDDNqchfScvE84Fkqe0+Zvks9Itz5uECwh2NrUk7gTkeh4IsxtQ0+JINPBxcFRJSLYWLmIwbKbuJVNC3AoX0WxTA9MgiydmtDpZ7JejEqJZ/ICrCrCF+LaUAPydretTOQ54dpianQwZ0Qx+TA0Casj05YkYcJp6HyYDgU5czCPpgHuf5acTzrLymMXdVp6vpBbK3mgg4ZThqOftxtJlkX9LjkuQE3x/1KMIn6JYugrNb+Zj80pw1pq/Bz5D5IgzVOt8toCVt0sLAO59utmmjFC+hEgTuUdSCxR3y1AiyTiAQdfPEX1tmrOOOHYcKuk4wB9VnzFjBnr06IEePXpgxowZcLlc+OUvfwkA8Hq9uO6663DnnXciOzsbWVlZmDp1KgYMGLCfWPpooYkPQRAEQXQkjsNcXXfddRfC4TBuuukmYWD44YcfpiQdnzNnDlRVxWWXXSYMDOfPnw9FUZppueXQxIcgCIIgOhKMidW5I65/lPw4gbgkSZg+ffpBnwoDAIfDgaeeegpPPfXUUZ+/OUjjQxAEQRDECQPl6jpCKFcX8VPn1BvmCM2NrlneKo5aBsXMfWWvtzQhvjIbItnJ/Za3DQDIOoOhmr4+DisXlhxHikdP8lIr7gaCxaaOpYkbvb1WQtxa+Raam3ChDmb65Uiq9ZVlc8WRl+FHd28NAODLd/rDZT440jRXlr8MwqvGvdvS0ABAwmF672RbjrVyzNIeBbolIEXNxjQDUkSGs4K/150/yoVl9tvmBxz1vLFk3i0ACJQAcQ/vhxqQhRYqt381KvZYgpaM/3JdTsEqn7j6TqRxnVHcw4/FPArkOD8WyZChmroXw2ZpaDw7Eki4rIGIpvNtJf6jr33zLVMg8nElYw/nmnnJ9FQPpKSPj2d7FAmnYpax6jZ21aCbTzan7TEgJ6xjSb+kcLYCm+nLpPkNMVaxdAmRLEl4IKmWBRTArPeuagOhPKt/hnkP479PHkdan3bI1TV82B+OOlfXilUPtmms7Qnd6iIIgiCIjsRxcKvreIZudREEQRAEccJAKz4EQRAE0YFomrrlSOt3ZGjiQxAEQRAdCbrV1Sw08SGIE5Q1z9+BHjMt8zenaVbIZEskGs1QIJtCZ2e1gViabO4HbGYCSX83U6zrt0S8ybY8O3WEcpp4cJhXkjEPUNhvHwAgzxXAllqeyBQ9gPBOLqa0+WToplgYbl0IjItLa1DVaCmg9/yQi4qoaWmfxSCbiULlmGV6Z6+3hMfxdCBuGu45qxmUGP+StzdIiHNPRshxQDPN9xy1KsK5ybh5DIYpunXUQgiDk2Z9ADfvSzhN0XQWhIgcAJQw36+GAMng23XleZAyuco7p1xGipGK+U9IierQnaoQYCccgIqk8SCDLWhdpiuxJoJfxRSdaxACYzXCkLCb46QDNjP5qKFKwozQWavDsFmff8Jl/V6oISCSaZ47qiHQiZ9P0gHPTt6WvdGAHjGF0QagNXChvKFZsTWNmcmAo5qfzO5SYaiaWHmIZAOymfTVsANhU28byZGF4FrX+OcMAD3/9iC87/FBX/3SFBBEU2jiQxAEQRAdiePQwPB4giY+BEEQBNGBaK3s7B0VmvgQBEEQREeCND7NQhMfgjiB2TzNMnor+8sTAID0FYBumvoxRYJuSjLq+klC78EkIJLHBRjpP8hw1jDETN2FvdH60gxnKcI40FAB9z5ex1kHVNkKAAAFwzcjGOCimZwPHJAzTBM7LxDvbyYZ9WuQTd3Knn2ZYHG+LTfYAG8CzNT12OtkxLz8fBID0gdyY0P/mhwRUyzbgHMvr1/fB1BK+TniFS5krTONCe3WGMlxBvdes66Xj0FS6yLHIW4LBAolKE1MHe0NZqJQSRLaE6ZCaGjsDUDDAK57URsVwDQzDBZKyPqOj1OgLE3ocuJuGXGXBCVq6nTCDHG3LD6PWNKcMMYQdyVNB2URqy3MhHGgoUqQTeNIJcJE3VCehMzNlmmloyYBW4AfS7hkYVDIFEvPBUmCd6suyiQTy2oBAzB1YJFMBWqEa73iTlkYLNobDSvRaoSJ9hMuBc4aXWiqHLUS4qZOKtAjLk6d8V+bSIqq2yWE8vh+fbsbjd35OfreOwfuCl6G9D4EQBMfgiAIguhYMIgHCY64fgeGJj4EQRAE0YEgjU/zkHMzQRAEQRAnDLTiQxAEAGD7zXcCAAavmQ1HPV8nVyLWennRf4D6XlaWyrTd/LpJ8xtwVcURzeDCmEimJHQX8XQglrTckbjvCt9vPW/7zYoeyNqS1LFIwrtFjgLedJ6NsqCgCt9/2oXHVGMlX4x5GVxbbKKOrgGK6fcSzTaQ4eD1M4btwu76DACAUetEqBMvowZlOD9J4+fLBoLmfiUMyDGuETE0y8k2fQf3qvF15e+DhVyrA3BNUcSUEhkqoPlgjg+zkqIyIOq1rjfVNK5Xse1W4d7Dv44TTqC+p6l7CQNq0Gzf4NqjZP8STkvvIycYIpmm3qeJbZLEANUsk3BIcFVzLU4wTxE+Q/BIIsFp2l5DJD6NZvJ4QnmSiF03vX/Sd1srAppfh25680SyJHi283MwCYh6eTDhHAmxdN6eHLdijHkURM3crM5qSRgvMZlrmOwN5sBLXPcDANlf2Kz66YCryorFXm9qm5ilQZLjVuLVn02ajS/nnwA6H4ajFDe3WiTHJTTxIQiCIIiOBD3V1Sx0q4sgCIIgiBMGWvEhCIIgiI6EAWEjccT1OzA08SEIIoWvXrQ0EKf+zxzkv7tDvA8UlfKNi2vEvtg/chD12uHdyvUqlafbEDZ1IfZ6wMVTckHSrZxVNr+EYCn/dtVtDJEs04MmCnS/YjMA4P+6LhPnmFnbE/KZfPl980ddYZgaDyUiwdCAWEbSX4bBWewHAPTOqsefu/4fAODzcGf8w34qAGDNnm5CvyPHuYaHx8Q1LqJfpkeOs9aAZHreMAVgsgT3Hl4/4bQ0N0wCHNXmtgqh6zGaePcknJYXkq4Baat4AjHdwXOLAYCkNcntxXheLIB73sQTEqJe6z+ad7vpAxTUYdh4RwwFQqcTzrJyWTnqGRKOZJ90+Er5179k8D4BgBoxEMq3tD2uKib8iHxdJKTtNMcqaCBYwD+EWLqKiNDpALF0vr+urwT3HogxiGSbQUuAzdQ/yXGrf7E0wLCZOiWZvxJm7rSMzQZcpgdUsFCG5jPzl8UkNHQ39VAhqzyTgDRTh5RwSgjl8jLeHQkMv+Bx3r9cpcP6+tBTXc1DEx+CIAiC6EiQxqdZSONDEARBEMQJw3Ez8Zk5cyYkScLkyZPFvkmTJkGSpJTX6aeffsi2/v73v6Nv376w2+3o27cvFi9enHJ8+vTp+7VbUFDQ2l0iCIIgiGNPcsXnaF4dmOPiVtfq1asxb948nHTSSfsdO/fcc/Hyyy+L95qmNdvWZ599hssvvxwPPvggLrroIixevBiXXXYZVq5ciSFDhohy/fr1w7JlloZAUZQDNUcQJzRrnrVyeY0c9yg8O7kQZefGbMsvpi+De5cEpnKdiHcLQziHb8fTgIRpu6P5LD0HUwCt3vR+KUzAFkz61jD89z89AACPZmzHyxuGivOXZPOEV0wGbGYOKN3B/XqaCjlDfn5CNdvALT9cBgAIxOzYV88NhZT8MLCDi0GUiOUDE8qVECi0cnUltT+RsAzvdq4vkWMMut3y6FEiksjrxRRAjZh9rTWaeNXIiHMpD5x11j+UhFMSecUAIJplbUtmuqyEC/CbPkjRDBmSYWll7PVAbV/+Fe7dKkO3mTnO0nFAdJsEZh5TYgpcVabGSpPg2RY2Y9DgruAntwUUxDwSmMTbTep7AK4dCufybcOydgJTIT57915AC5j6oB6As5Lv13xAyLzO1PxWXVuAIZxr/t6k89+jJJFMWeis1BBEOTnGvYoAMxecmZNL8zEEC8zretkaz0CBCls4qQ/iebySbX4zx/pd/8lDt7qapd1XfAKBAK666iq88MILyMzM3O+43W5HQUGBeGVlZR2gFYu5c+dizJgxmDZtGnr37o1p06Zh1KhRmDt3bko5VVVT2s3NzW3NbhEEQRAEcRzS7hOfm2++Geeddx5Gjx59wOPLly9HXl4eevbsieuvvx5VVVXNtvfZZ59h7NixKfvGjRuHVatWpezbvHkzioqK0KVLF1xxxRXYunVrs+1Go1H4fL6UF0EQBEEcdxit8OrAtOvEZ+HChVizZg1mzpx5wOPjx4/H66+/jo8++ghPPPEEVq9ejXPOOQfRaPSgbVZWViI/Pz9lX35+PiorK8X7IUOG4JVXXsEHH3yAF154AZWVlRg2bBhqa2sP2u7MmTPh9XrFq6SkpIW9JQiCIIi2J/k4+9G8OjLtpvHZtWsXbr/9dnz44YdwOBwHLHP55ZeL7f79+2Pw4MEoLS3Fu+++i4svvvigbUtSqnMTYyxl3/jx48X2gAEDMHToUHTr1g0LFizAlCkH9nWYNm1ayjGfz0eTH4IgCIL4idFuE5/y8nJUVVVh0KBBYp+u6/jkk0/w9NNPIxqN7ic4LiwsRGlpKTZv3nzQdgsKClJWdwCgqqpqv1WgprjdbgwYMKDZdu12O+x2+6G6RRAdlo8/uBtdnnrCfMeEqNS9gy8c+4vNBJOSZeon6xDL5vF0IMTzgcIWACJFXHGqpMfR2Iv/rRf+x0BeOb9ImR8bA2YKZ7VGYLcrTcSim2LhhItBiUiQo7yO7mBAlKuSv20sg+HkilhJBqQAP4cStha64+mWGNcWgkj6CfBkqwAXwYbyeB17IxPmegBPAKqafU2aFAIAUyTYaxLJoYIaNkW7LkkIfjUfEyLpaIYEp3kX395owFdmxeisFptgMo8HAOJuLu4GgNoBsqgvG9xYEbBMEQHAFmbQzfGMuSXRDgD4y3gFd0UM4VxeSIkwOOoYwtmKOLfDTBpaebqEZCZLW0CyEsN6rPgMG0+MCgDpP1jnUiIM7t2mwaXPQOUw65gaMMc8ygXMrMk9iaQw3lVtIJphGR0mf7/UKIQQO5QnWYJ3Zv0+GjYgZorwYQAO04dTdwInT56D/87tIAJnEjc3S7vd6ho1ahTWrVuHtWvXitfgwYNx1VVXYe3atQd8yqq2tha7du1CYWHhQdsdOnQoli5dmrLvww8/xLBhww5Sg+t3Nm7c2Gy7BEEQBPGTwGBH/+rAtNuKT3p6Ovr375+yz+12Izs7G/3790cgEMD06dNxySWXoLCwENu3b8c999yDnJwcXHTRRaLOxIkT0alTJ6ETuv3223H22Wfj0UcfxYQJE/DPf/4Ty5Ytw8qVK0WdqVOn4oILLkDnzp1RVVWFhx56CD6fD9dcc82x6TxBEARBEO3CceHjcyAURcG6devwyiuvoKGhAYWFhRg5ciQWLVqE9HTLqGLnzp2QZWvhatiwYVi4cCHuu+8+/OEPf0C3bt2waNGiFA+f3bt348orr0RNTQ1yc3Nx+umn4/PPP0dpaekx7SNBEARBtDp0q6tZJMY6eA/bCJ/PB6/Xi0YAnqoqgHyAiBOMsudn8Q1nAo4fLP2bvcEqYyhAwpLmQE7qYTJMPQ4ApjJoDfziJe5hQrPhqJWgmW1JjCf1BIBwPk9GCgBaowTDxo0IAdNEMMi3o1lNtEZRS5ejBQDNz+vrGkTSUDXCIOmmmWGedatdb/LshbOGwV9saXnsDUBjT15HjknwbLPK2hsNcwys8uFcCe4Kyzgwaf4Xzpbg3sfbiaVJUE2TvWimVddZwxDKlcDMy9Wm+hf3XgY5YY1VUrcUyrf6x8fLqps0D0wmNE3GZ6+3xkO3S+J4LN3SBQU7AdE8XdRL+4GPl2eHgajHNKNsopcK5ktQzDgcdUy0GSiUES7k25IuIW2X2d4e3pl4Gm83lCeJxKSsyYMqdp8hPrNwriKS16phBjWSHE8Znh08cEdFAIaL/yIFSlxIOHlbTEnVd/lLZKx/pJX0PtXVQF4efAC8ABobG+HxeFqn7R+R/L80uuttUOUj16QmjCiWbf1zm8banhy3Kz4EQRAEQRwBtOLTLO1uYEgQBEEQBHGsoBUfgiAIguhIGAxJu4Ejr99xoYkPQRBHxPYbport3g/Mgd00PpcYEHdZ5RSe/5In7TTlGUoMkBP8TcJl6XfkGLgREHii0GR53QaR0JNJAAx+IJrBYNgtjZBkcG8egPu+JJNT2oKAo55ra1K8YSKWV0zcbfnRAEDMlDYwGWJt3FCArO90hHO4mMRQAXutJPot9En1lud/PF2CZL61+YFAEW/MVcWE1kiJWn1NOIFAZzMJZxwiOWfMI0HzNRlbBqGJMVQJ9kZe0F+iwGZ6BakeKTmccDWxN7OFGGwhM/FqnCHm4f1J380gmf/0dJuEYCH36UmSHB/JAFy7FDHOSaIeGYqp3zFUrl0Sx7zWGCQTqibSAJvf6qu90Ty3XYZhs+q69hlCwxTJkISfkL+TDJup6YIB6C5rrJIDmraHoXog/wVzlGTBUc/HiakQsWo1OgybhFAu/5foqAMG/ZYnMC2f9xP09mEGfx1N/Q4M3eoiCIIgCOKImTlzJk477TSkp6cjLy8PF154ITZt2pRShjGG6dOno6ioCE6nEyNGjMD69etTykSjUdx6663IycmB2+3GL37xC+zevbvV46WJD0EQBEF0JJLi5qN5tYAVK1bg5ptvxueff46lS5cikUhg7NixCAaDosxjjz2G2bNn4+mnn8bq1atRUFCAMWPGwO/3izKTJ0/G4sWLsXDhQqxcuRKBQADnn38+dF0/0GmPGLrVRRAEQRAdiWOs8VmyZEnK+5dffhl5eXkoLy/H2WefDcYY5s6di3vvvVfk2VywYAHy8/Pxxhtv4IYbbkBjYyNefPFFvPrqqxg9ejQA4LXXXkNJSQmWLVuGcePGHXl/fgRNfAiCOGq+u/8ODJjKNRH+7jqyvub6D//oIAre4EY4Oy8wIMX4InP6FkVoNhybAZ/pHRousL6wbX45VWdjfherEUAN8G1D41qOpIYm5gX0Ui4q0r5zAqZ2iClAPI2fu6luBgDUsLUdyzVzRYUh4otmWseDRRK0gIT0XdyUJpyrQjU9hEK5EmwhJuJK5u6SY0x42CQcEtL28mCdNXEEC3mAUa+EaIZ1nqSWxl4HuKp4+bhbTomXyU30TGEglM/HXNe4RxDAdVK2gDUGST0TU4C4y/ROcklcTwXAFmSwme1rfgNpe2QxDk21UWm7gJohfBDlsAxboyzGSo6b+bl2MjHO9kaGkC0Zk6WrUkNWfACQSMZhcD+j5HgCEpwVUbOvKqQE35+1SUcov8m/sRA/h7+njvyV/CSGauUsYwpgC/LxTDAZSjhVy5L0+/F3tol+/+ya2SLn2Jrnf4J6n6PA5/OlvD/cnJWNjdwwKisrCwCwbds2VFZWYuzYsSltDR8+HKtWrcINN9yA8vJyxOPxlDJFRUXo378/Vq1a1aoTH7rVRRAEQRAdiVa61VVSUgKv1yteydRQzZ+aYcqUKTjzzDNFWqpk4vAfJwvPz88XxyorK6FpGjIzMw9aprWgFR+CIAiC6EgwHKWBIf+xa9euFOfmw1ntueWWW/DNN9+k5MdMIjVx3Qb4JOnH+/YL5TDKtBRa8SEIgiAIYj88Hk/K61ATn1tvvRVvv/02Pv74YxQXF4v9BQUFALDfyk1VVZVYBSooKEAsFkN9ff1By7QWtOJDEESrsG6WpX/o/dafAABxvx31PUyPmO8U9LnwewBAqK8NG9eUAQACJUDyEtNeJ4u8W2oYiLv5djxdFIFkAHqWWSbE9TDJHFJqEGBVTn6+nTpi6da1nb+UXzU6ai2Nj93HIMfMvFHFMmKmZia9kcGI8PLuvRA5oADu95P0rlGilo7FWcuPweyO5jNzctklJOxJrxqGmKk1Sjg1oR2RE5bWRbcDrn3mtgYwM9dXwsH9dxz1zHzf1CPHyvsFCCskyPHU7SRSE2mLGmGw+628VkmfoFiaDDXMRD4rACKHmC1koPBjuUkcTPQ7OQZxlyTygTFFgqvK1D/ZJDFmaoO1KqHErHxZks4gGVYfXVUJxNP5v6uEXYZub+otxOs46nQknDwm9l9FfJZKjKX4DwULrH97/gH8g83YbCBYKAuvKJsPYpzjaRLS9vABG37B4+J34T//+B2OW45xygrGGG699VYsXrwYy5cvR5cuXVKOd+nSBQUFBVi6dCkGDhwIAIjFYlixYgUeffRRAMCgQYNgs9mwdOlSXHbZZQCAiooKfPvtt3jssceOvC8HgCY+BEEQBNGRMAwIN80jrn/43HzzzXjjjTfwz3/+E+np6WJlx+v1wul0QpIkTJ48GTNmzECPHj3Qo0cPzJgxAy6XC7/85S9F2euuuw533nknsrOzkZWVhalTp2LAgAHiKa/WgiY+BEEQBNGROMYrPs8++ywAYMSIESn7X375ZUyaNAkAcNdddyEcDuOmm25CfX09hgwZgg8//BDp6emi/Jw5c6CqKi677DKEw2GMGjUK8+fPh6IoaE1o4kMQBEEQxBHDDmOiJEkSpk+fjunTpx+0jMPhwFNPPYWnnnqqFaPbH5r4EARBEERH4hiv+PzUoIkPQRCtzncX/xEAcPK//oBgLvflyOxfg+2NfLtv9j4YTq4j0KoVJNymYNfNkDAFzaiVhCDXvReImIJmrREpprS6EwiUmpqE9AS8a7gpYKBYEaLUhm6WaDruAuwNZl0bN+0DAEctQyifi2aDRTwhKADEnRDCYXsDF+0ix/zqbPKUrZzgotzk/qSIWQsyqLoh9ke1ZDZSCONANWKJjxNuCGEuN160TmLYLGNENWIJlyUGuCvNBJ+aFROTJJF01LA1NQS0hMNMBmRTbJxwWp1KOHkiWc8OS6SdJJYmW4lXAwx6E6F1MkFrKE+GYcaiBnksAGBvNBA3BdNqlAuzefuAI54USUtwVyaEsBuSBD055MxMCAvLjBDgYy8Sm0qWGWLCIVkJTrOt/mVs1pH7X75fCevQ/LJ1vibIcdkyb9SZSGw6+uyHseyTe/crf1xA2dmbhR5nJwiCIAjihIFWfAiCIAiiA8GYAcaO/Kmuo6n7U4AmPgRBEATRkWDs6G5XkcaHIAjiyPjv+Q8C5/PtsvmP4oafrQAALPjudDhyuQugsjUdSsw0vXMDWSdXi/qBT3IBcI1I3nYu1PCVqTDMm/TSyHqoAK4uWwcAeHPzQDjquCDHUW9lIk3bIwktCWAZ+BmqBH+xIval7eL7bUEGz3buelc1yAGHaSarhhlc+xKIp/E6cZck9CahHAkO05CPydwYEOAalmTiTslgYOaTuZEsCfZGJs7NnFaC1GTyUXu9pQMyVCCeZhkdJhzWOEczeXsAkF+eENofAPCVqqJPyf1MBlzVfDxj6YoYG8lIGkpytIYm+qZGPeWfaXIMlLhlcqjErOO6Bvxm0vsAgCc/H43O/7CCirvMQWAMhqmrsQUZ10+BG0sqUQPRDB67YZOE0aSsMyvpaBOxRjxdgWwmL9V8DGkB/iGHs61CTJWgBs0x88pgclIvxcskP0s1bAi9lr3RMkaMZFv/MsO5Gk6awhPzhgoZtvxuCoifBjTxIQiCIIiOBDtKcTOt+BAEQRAE8ZPBMFLzkrSUDq7xoae6CIIgCII4YZDY4VguEvvh8/ng9XrRCMBTVQXk5rZ3SATxk6HvfXNEok/A8qPRnUDMY+5Tra8mNdDEJ6YWwj/HUIBwk8TNaf3qgCXc8MdZYwgvnaZoQQN1fWRx3qzvTJ8bFQgUWedJ3833h3Ik+HtyvVDJEr4vqZtxVxgiFn8nGYrpxWNvSNXyqGHz3D5DJE7VHUCwiO9PeHXY6kzNTBQiqSYMy3MoUMKTsjLVGodkgk5DtZKUen+Iw17HTYsieU74S3gFXQMcdVaCU7ufX9Un7BISLktjlRz/pI9R+i7r6l83PYicpj4IACABoTx+DjlhjaHU5G6LZ6ch9DjJmJNxBwtlUTfpqQRwvx85xs8tGVyLBPD+Jz2I7A2G8CnSNeuzNjQpNeFpk/9yyTh0TULUy9846gzoDgmaOSaSzhD1KmZfreyuCbcCQ23iZ2R6Kml+BluI113xzo+Sl1ZXA3l58AHwAmhsbITH40FbkPy/NCrtl1Al7dAVDkKCxfDvwBttGmt7Qre6CIIgCKIDwQwD7ChuddHj7ARBEARB/HQgcXOzkMaHIAiCIIgTBlrxIQjimLPhoTvQ/c2H+Jttbmi9GgEAwToXnLu4WCXmARzV+3vvyAkrb5ekc68ZZ415bGMmND/X46ghA6E8fm3nrLGW7hu6y4hm8fc2v4xYk1xYuWu5nsPf2SYumHUnYK/ieo895xjQ6mTkfc01LkwCGrryr9GEG0im5Ip5rFxfWiMTOaBC+bLw5ZHjgHuPWT6giFxdPPZkp4GEi2+m7eKx6KaWJ+G0tC7uSkvPFMpXsXt0GgDA0BjStplx+Jo86CMDkQxeXg0zkV9LjstCm8QUwLvV0ubYQgacNZY3kn03/8z0DBfqe/GgkrEC/MGgpB+R35CF/1BTdCfg687PnfeF9TnYQgaUiCF0PUktDQC4qnU4q3kc0QybiE8yGGIes08RJvYbqiR0WHGXBHuDYZVRTI2UJiHulhDJNHVWEcu/KOrVhEZI8zGRC023S1DD/EDcLYHJ/ITDL3gccRffXrXozv07fSwwmiQ0OxI6+IoPTXwIgiAIoiPBGICjeZy9Y0986FYXQRAEQRAnDLTiQxAEQRAdCGYwsKO41dXRXW5o4kMQRLuw5bL7xHbZX54AANgCstBm2OskoUlRYlY9KWH53ESyAa0RCOfw945ay5tFYoBnB9fiJFwyfJ1Nv5gY4NpjnkSG0GxkbLFENrmrGxAu5jqZaKMK2ZS2RGIyPNsMxN3WYrmvP6/n2WCDs5r/w7CFrH8cgSJZxO+oY6JPkSxJ5NuK9IrA8wV/42hgQscTzpOE3sfuNxCVZaEFChYB2et5Y0yWEPPy/e4KwFGV9NuxdDaRLMDuk8w4DIRzeB9i6ZLwH7KFGJhketMEDBiqNfaxdAVqgA8EUyQkMt18nA0Drn08Dl+ZDCUsuo5QFz7+ukOBEuHtJvOhAVzLpAaS2iTufwTwXF28cXPcGgzL60mTYNjMHF4qEDNzhtnCTMRqqBIcDfzc8TRF6JbUcBO9VZaMaAbfr0TMn0n/JBlCv6M00V5JDCK/lxKz/IHiTkn4+8gJQI7zA2deMgsZkQD+hWMMM3B0t7o69uPsdKuLIAiCIIgTBlrxaQ1qato7AoL4SZPl54/8qEFrRUOO8atnIHXFRwkD8eQTN3bA1sQB2h6GyNDtiFoZthOyDCXUJEt3clMCbOYKhSdqXdo74iFo0eT5VEjmik80CKRHjJQHZiLmU2TpIRscZkZ2WxN3YlvI6pMasVZ8oiEJCbNdhy+C9DDvrD3CoJv9doSsFR9nxEBUk8XlqhYEMiLWio8txFccXBEmth1hhoS5wpHQAc3sqyNiwBHmDRkKRNwAEDNXNDSz7eSKh84kOGP8g2CKBDXOg5QMA7JZXwnKYvUEAGx+XkYOWis+7iYrQlICUM2nvewhQDNXWZxRHXKcIR7hSzWyzlIyzifHXzckxBTeD1uTPjAZsEfN1T6bIj5vJlu/U1FNRsxc7Uuu9CSzvkOCyAavNDWpNoA4ks7UTKwEJiQJUvIza/J7BwnIiAZxrKFbXc1DE5/WoG/f9o6AIH7SrGnvAA7EusMst7hNo/hpcFx+gCcwdKurWWjic4QkZ8S+do6DIAiCOP5J/q84FqspCcSPyrg5gfihC/2EoYnPEeL3+wEAJe0cB0EQBPHTwe/3w+v1tknbmqahoKAAKyvfO+q2CgoKoGlHnuj0eIaysx8hhmFg7969SE9PhyRJh67QAfD5fCgpKcGuXbs6ZMbeg3Gi9hs4cftO/T6x+g20fd8ZY/D7/SgqKoIst91zRZFIBLFY7NAFD4GmaXA4HK0Q0fEHrfgcIbIso7i4uL3DaBc8Hs8J96UInLj9Bk7cvlO/Tzzasu9ttdLTFIfD0WEnLK0FPc5OEARBEMQJA018CIIgCII4YaCJD3HY2O123H///bDb7e0dyjHlRO03cOL2nfp9YvUbOLH7fqJB4maCIAiCIE4YaMWHIAiCIIgTBpr4EARBEARxwkATH4IgCIIgThho4kMQBEEQxAkDTXwIwZo1azBmzBhkZGQgOzsbv/3tbxEIBMTx2tpanHvuuSgqKoLdbkdJSQluueUW+HzNZyyLRqO49dZbkZOTA7fbjV/84hfYvXt3W3fnsDlUv//73//iyiuvRElJCZxOJ/r06YMnn3zykO1WVlbi6quvRkFBAdxuN0499VT87W9/a8uutJi26jsAfPbZZzjnnHPgdruRkZGBESNGIBwOH7riMaAt+w1wl97x48dDkiT84x//aIMeHDlt0fe6ujrceuut6NWrF1wuFzp37ozbbrsNjY2Nbd2dw6atPvPj/fuN2B+a+BAAgL1792L06NHo3r07vvjiCyxZsgTr16/HpEmTRBlZljFhwgS8/fbb+P777zF//nwsW7YMN954Y7NtT548GYsXL8bChQuxcuVKBAIBnH/++dB1vY17dWgOp9/l5eXIzc3Fa6+9hvXr1+Pee+/FtGnT8PTTTzfb9tVXX41Nmzbh7bffxrp163DxxRfj8ssvx9dff93GvTo82rLvn332Gc4991yMHTsWX375JVavXo1bbrmlTa36D5e27HeSuXPnHpepbNqq73v37sXevXsxa9YsrFu3DvPnz8eSJUtw3XXXHYNeHZq2/MyP5+834iAwgmCMPf/88ywvL4/pui72ff311wwA27x580HrPfnkk6y4uPigxxsaGpjNZmMLFy4U+/bs2cNkWWZLlixpneCPgiPt90033cRGjhzZbNtut5u98sorKfuysrLYX//616MLupVoy74PGTKE3Xfffa0Wa2vSlv1mjLG1a9ey4uJiVlFRwQCwxYsXt0bYrUJb970pb775JtM0jcXj8SOOt7Voq34f799vxIFp/8sv4rggGo1C07SUK3Kn0wkAWLly5QHr7N27F2+99RaGDx9+0HbLy8sRj8cxduxYsa+oqAj9+/fHqlWrWin6I+dI+g0AjY2NyMrKarbtM888E4sWLUJdXR0Mw8DChQsRjUYxYsSIVon9aGmrvldVVeGLL75AXl4ehg0bhvz8fAwfPrzZNo8lbfmZh0IhXHnllXj66adRUFDQOgG3Im3Z9wPV8Xg8UNX2TwnZVv0+3r/fiANDEx8CAHDOOeegsrISjz/+OGKxGOrr63HPPfcAACoqKlLKXnnllXC5XOjUqRM8Hg/++te/HrTdyspKaJqGzMzMlP35+fmorKxs/Y60kJb0O8lnn32GN998EzfccEOzbS9atAiJRALZ2dmw2+244YYbsHjxYnTr1q3V+3EktFXft27dCgCYPn06rr/+eixZsgSnnnoqRo0ahc2bN7d+R1pIW37md9xxB4YNG4YJEya0etytQVv2vSm1tbV48MEHW1SnLWmrfh/v32/EgaGJTwdn+vTpkCSp2ddXX32Ffv36YcGCBXjiiSfgcrlQUFCArl27Ij8/H4qipLQ5Z84crFmzBv/4xz/www8/YMqUKS2OizHWphqItug3AKxfvx4TJkzAH//4R4wZM6bZGO677z7U19dj2bJl+OqrrzBlyhRceumlWLduXVt1G0D7990wDADADTfcgGuvvRYDBw7EnDlz0KtXL7z00ksdtt9vv/02PvroI8ydO7fN+ngw2rvvTfH5fDjvvPPQt29f3H///a3d1RSOp343pa2/34ijg1JWdHBqampQU1PTbJmysjI4HA7xft++fXC73ZAkCR6PBwsXLsSll156wLorV67EWWedhb1796KwsHC/4x999BFGjRqFurq6lKuik08+GRdeeCEeeOCBI+xZ87RFvzds2ICRI0fiN7/5DR5++OFm2/7hhx/QvXt3fPvtt+jXr5/YnxRYPvfcc0fYs0PT3n3ftm0bunbtildffRW/+tWvxP7LL78cqqri9ddfP8KeNU9793vy5Mn485//nHI7Rdd1yLKMs846C8uXLz+yjh0G7d33JH6/H+PGjYPL5cK//vWvlPO1Be3d7/b6fiOOkvaVGBHHMy+++CJzuVysvr7+oGU++eQTBoBt27btgMeT4r9FixaJfXv37j2uxX8H6ve3337L8vLy2O9+97vDauObb75hANiGDRtS9o8dO5Zdf/31rRluq9IafTcMgxUVFe0nbj7llFPYtGnTWjPcVqM1+l1RUcHWrVuX8gLAnnzySbZ169Y2ivzoaY2+M8ZYY2MjO/3009nw4cNZMBhsg0hbl9bo90/x+41gjCY+hOCpp55i5eXlbNOmTezpp59mTqeTPfnkk+L4u+++y1566SW2bt06tm3bNvbuu++yfv36sTPOOEOU2b17N+vVqxf74osvxL4bb7yRFRcXs2XLlrE1a9awc845h5188skskUgc0/4djEP1+9tvv2W5ubnsqquuYhUVFeJVVVUlyvy437FYjHXv3p2dddZZ7IsvvmBbtmxhs2bNYpIksXffffeY9/FgtEXfGWNszpw5zOPxsP/7v/9jmzdvZvfddx9zOBxsy5Ytx7R/B6Ot+v1jcJw91cVY2/Td5/OxIUOGsAEDBrAtW7ak1OvIf+eMHf/fb8T+0MSHEFx99dUsKyuLaZrGTjrppP0exf7oo4/Y0KFDmdfrZQ6Hg/Xo0YPdfffdKVdM27ZtYwDYxx9/LPaFw2F2yy23sKysLOZ0Otn555/Pdu7ceYx6dWgO1e/777+fAdjvVVpaKsocqN/ff/89u/jii1leXh5zuVwHbLu9aau+M8bYzJkzWXFxMXO5XGzo0KHsP//5zzHo0eHRlv1uyvE48WmLvn/88ccHrNPcavCxpq0+8+P9+43YH9L4EARBEARxwkBPdREEQRAEccJAEx+CIAiCIE4YaOJDEARBEMQJA018CIIgCII4YaCJD0EQBEEQJww08SEIgiAI4oSBJj4EQRAEQZww0MSHOKEZMWIEJk+e3KHOO2nSJFx44YVH1UZZWZlI8tjQ0HDQcvPnz0dGRsZRnYs4OJMmTRKfwz/+8Y/2DocgOgQ08SGIduCtt97Cgw8+KN6XlZW1S1bv5vjTn/6EiooKeL3e9g6lw7N8+fIDTjKffPJJVFRUtE9QBNFBUds7AII4EcnKymrvEA5Jeno6CgoK2jsMAEA8HofNZmvvMI45Xq+XJp4E0crQig9BNKG+vh4TJ05EZmYmXC4Xxo8fj82bN4vjyVs7H3zwAfr06YO0tDSce+65KVfliUQCt912GzIyMpCdnY27774b11xzTcrtp6a3ukaMGIEdO3bgjjvuELc1AGD69Ok45ZRTUuKbO3cuysrKxHtd1zFlyhRxrrvuugs/zkLDGMNjjz2Grl27wul04uSTT8bf/va3Ixqf+fPno3PnznC5XLjoootQW1u7X5l33nkHgwYNgsPhQNeuXfHAAw8gkUiI49999x3OPPNMOBwO9O3bF8uWLUu5lbN9+3ZIkoQ333wTI0aMgMPhwGuvvQYAePnll9GnTx84HA707t0bzzzzTMq59+zZg8svvxyZmZnIzs7GhAkTsH37dnF8+fLl+NnPfga3242MjAycccYZ2LFjx2H1/VD9mj17NgYMGAC3242SkhLcdNNNCAQC4viOHTtwwQUXIDMzE263G/369cN7772H7du3Y+TIkQCAzMxMSJKESZMmHVZMBEG0HJr4EEQTJk2ahK+++gpvv/02PvvsMzDG8POf/xzxeFyUCYVCmDVrFl599VV88skn2LlzJ6ZOnSqOP/roo3j99dfx8ssv49NPP4XP52tWn/HWW2+huLhY3Fpqya2NJ554Ai+99BJefPFFrFy5EnV1dVi8eHFKmfvuuw8vv/wynn32Waxfvx533HEHfvWrX2HFihWHPzAAvvjiC/z617/GTTfdhLVr12LkyJF46KGHUsp88MEH+NWvfoXbbrsNGzZswPPPP4/58+fj4YcfBgAYhoELL7wQLpcLX3zxBebNm4d77733gOe7++67cdttt2Hjxo0YN24cXnjhBdx77714+OGHsXHjRsyYMQN/+MMfsGDBAgD8cxk5ciTS0tLwySefYOXKlWJiGovFkEgkcOGFF2L48OH45ptv8Nlnn+G3v/2tmGg2x6H6BQCyLOPPf/4zvv32WyxYsAAfffQR7rrrLnH85ptvRjQaxSeffIJ169bh0UcfRVpaGkpKSvD3v/8dALBp0yZUVFTgySefbNFnQxBEC2jXFKkE0c4MHz6c3X777Ywxnk0dAPv000/F8ZqaGuZ0Otmbb77JGGPs5ZdfZgDYli1bRJm//OUvLD8/X7zPz89njz/+uHifSCRY586d2YQJEw54XsYYKy0tZXPmzEmJ7f7772cnn3xyyr45c+akZIsuLCxkjzzyiHgfj8dZcXGxOFcgEGAOh4OtWrUqpZ3rrruOXXnllQcdlwPFc+WVV7Jzzz03Zd/ll1/OvF6veH/WWWexGTNmpJR59dVXWWFhIWOMsffff5+pqsoqKirE8aVLl6ZkMU9mwJ47d25KOyUlJeyNN95I2ffggw+yoUOHMsYYe/HFF1mvXr2YYRjieDQaZU6nk33wwQestraWAWDLly8/aL8PxqH6dSDefPNNlp2dLd4PGDCATZ8+/YBlk9nN6+vrD3gcx2GWd4L4qUIaH4Iw2bhxI1RVxZAhQ8S+7Oxs9OrVCxs3bhT7XC4XunXrJt4XFhaiqqoKANDY2Ih9+/bhZz/7mTiuKAoGDRoEwzBaNd7GxkZUVFRg6NChYp+qqhg8eLC43bVhwwZEIhGMGTMmpW4sFsPAgQNbdL6NGzfioosuStk3dOhQLFmyRLwvLy/H6tWrU1ZCdF1HJBJBKBTCpk2bUFJSkqIdajpWTRk8eLDYrq6uxq5du3Ddddfh+uuvF/sTiYTQwJSXl2PLli1IT09PaScSieCHH37A2LFjMWnSJIwbNw5jxozB6NGjcdlll6GwsPCQfT9Uv1wuFz7++GPMmDEDGzZsgM/nQyKRQCQSQTAYhNvtxm233Yb/+Z//wYcffojRo0fjkksuwUknnXTIcxME0brQxIcgTNiPtDFN9ze9HfJjka0kSfvV/fHtk4O13RyyLO9Xr+ktt8MhOdl699130alTp5Rjdru9RW0dTh8Mw8ADDzyAiy++eL9jDodjv7FsDrfbndIuALzwwgspE1OATyyTZQYNGoTXX399v7Zyc3MBcI3QbbfdhiVLlmDRokW47777sHTpUpx++ulH1a8dO3bg5z//OW688UY8+OCDyMrKwsqVK3HdddeJz+w3v/kNxo0bh3fffRcffvghZs6ciSeeeAK33nrrYY0HQRCtA018CMKkb9++SCQS+OKLLzBs2DAAQG1tLb7//nv06dPnsNrwer3Iz8/Hl19+ibPOOgsAXxn4+uuv9xMqN0XTNOi6nrIvNzcXlZWVKZOFtWvXppyrsLAQn3/+Oc4++2wAfAWkvLwcp556quiT3W7Hzp07MXz48MPqw8Ho27cvPv/885R9P35/6qmnYtOmTejevfsB2+jduzd27tyJffv2IT8/HwCwevXqQ547Pz8fnTp1wtatW3HVVVcdsMypp56KRYsWIS8vDx6P56BtDRw4EAMHDsS0adMwdOhQvPHGG4ec+ByqX1999RUSiQSeeOIJyDKXTr755pv7lSspKcGNN96IG2+8EdOmTcMLL7yAW2+9FZqmAcB+vwMEQbQ+NPEhCJMePXpgwoQJuP766/H8888jPT0dv//979GpUydMmDDhsNu59dZbMXPmTHTv3h29e/fGU089hfr6+mZXOsrKyvDJJ5/giiuugN1uR05ODkaMGIHq6mo89thj+H//7/9hyZIleP/991P+qd9+++145JFH0KNHD/Tp0wezZ89O8YJJT0/H1KlTcccdd8AwDJx55pnw+XxYtWoV0tLScM011xx2v2677TYMGzYMjz32GC688EJ8+OGHKbe5AOCPf/wjzj//fJSUlODSSy+FLMv45ptvsG7dOjz00EMYM2YMunXrhmuuuQaPPfYY/H6/EDcfaiVo+vTpuO222+DxeDB+/HhEo1F89dVXqK+vx5QpU3DVVVfh8ccfx4QJE/CnP/0JxcXF2LlzJ9566y387ne/Qzwex7x58/CLX/wCRUVF2LRpE77//ntMnDjxkH0/VL+6deuGRCKBp556ChdccAE+/fRTPPfccyltTJ48GePHj0fPnj1RX1+Pjz76SEyoS0tLIUkS/vWvf+HnP/85nE4n0tLSDvuzIQiiBbSbuoggjgN+LDKuq6tjV199NfN6vczpdLJx48ax77//Xhx/+eWXU8S8jDG2ePFi1vRPKR6Ps1tuuYV5PB6WmZnJ7r77bnbppZeyK6644qDn/eyzz9hJJ53E7HZ7SlvPPvssKykpYW63m02cOJE9/PDDKeLmeDzObr/9dubxeFhGRgabMmUKmzhxYoqQ2jAM9uSTT7JevXoxm83GcnNz2bhx49iKFSsOOi4HEjczxgXExcXFzOl0sgsuuIDNmjVrv/FYsmQJGzZsGHM6nczj8bCf/exnbN68eeL4xo0b2RlnnME0TWO9e/dm77zzDgPAlixZwhizxM1ff/31fud//fXX2SmnnMI0TWOZmZns7LPPZm+99ZY4XlFRwSZOnMhycnKY3W5nXbt2Zddffz1rbGxklZWV7MILL2SFhYVM0zRWWlrK/vjHPzJd1w86Di3p1+zZs1lhYaH4vXnllVdSBMu33HIL69atG7Pb7Sw3N5ddffXVrKamRtT/05/+xAoKCpgkSeyaa65JOTdI3EwQrYbE2BGIDwiCOGwMw0CfPn1w2WWXpbg1H8+UlZVh8uTJxySdx6effoozzzwTW7ZsSRGNExaSJGHx4sVHnYqEIAjy8SGIVmfHjh144YUX8P3332PdunX4n//5H2zbtg2//OUv2zu0FnH33XcjLS0NjY2Nrdru4sWLsXTpUmzfvh3Lli3Db3/7W5xxxhk06TkAN954I93yIohWhlZ8CKKV2bVrF6644gp8++23YIyhf//+eOSRR4QA+afAjh07xNNIXbt2FYLd1uCVV17Bgw8+iF27diEnJwejR4/GE088gezs7FY7R0vp16/fQR2cn3/++YMKqtuaqqoq+Hw+ANw2oemTbgRBHBk08SEI4oSn6UTvx+Tn5+/nDUQQxE8XmvgQBEEQBHHCQBofgiAIgiBOGGjiQxAEQRDECQNNfAiCIAiCOGGgiQ9BEARBECcMNPEhCIIgCOKEgSY+BEEQBEGcMNDEhyAIgiCIEwaa+BAEQRAEccLw/wEGKnbVTQeUzAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *reprojected_raster_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Boundary calculated from original corners (incorrect)\n",
-    "\n",
-    "Directly transforming the corners is an incorrect method to calculate the new boundary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "transform_box = box(*transformer.transform(*xds.rio.bounds()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3245a3bee0>]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *transform_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Boundary calculates using transform_bounds\n",
-    "\n",
-    "[rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds) is both computationally efficient and a correct method for calculating the bounds of your raster in the new projection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "transform_bounds_box = box(*xds.rio.transform_bounds(\"EPSG:4326\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f32459316c0>]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *transform_bounds_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As seen below, this is equivalent to the [Transformer.transform_bounds](https://pyproj4.github.io/pyproj/stable/api/transformer.html#pyproj.transformer.Transformer.transform_bounds) method in pyproj:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "pyproj_transform_bounds_box = box(*transformer.transform_bounds(*xds.rio.bounds()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f324580d120>]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *transform_bounds_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/getting_started/crs_management.ipynb.txt b/0.18.0/_sources/getting_started/crs_management.ipynb.txt
deleted file mode 100644
index 54511d46..00000000
--- a/0.18.0/_sources/getting_started/crs_management.ipynb.txt
+++ /dev/null
@@ -1,1108 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Coordinate Reference System Management"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "xarray \"... is particularly tailored to working with netCDF files, which were the source of xarray’s data model...\" (http://xarray.pydata.org).\n",
-    "\n",
-    "For netCDF files, the GIS community uses CF conventions (http://cfconventions.org/).\n",
-    "\n",
-    "Additionally, GDAL also supports these attributes:\n",
-    "\n",
-    "- spatial_ref (Well Known Text)\n",
-    "- GeoTransform (GeoTransform array)\n",
-    "\n",
-    "References:\n",
-    "\n",
-    "- Esri: https://pro.arcgis.com/en/pro-app/latest/help/data/multidimensional/spatial-reference-for-netcdf-data.htm\n",
-    "- GDAL: https://gdal.org/drivers/raster/netcdf.html#georeference\n",
-    "- pyproj: https://pyproj4.github.io/pyproj/stable/build_crs_cf.html\n",
-    "\n",
-    "Operations on xarray objects can cause data loss. Due to this, rioxarray writes and expects the spatial reference information to exist in the coordinates."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Accessing the CRS object"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you have opened a dataset and the Coordinate Reference System (CRS) can be determined, you can access it via the `rio.crs` accessor.\n",
-    "\n",
-    "#### Search order for the CRS (DataArray and Dataset):\n",
-    "1. Look in attributes (`attrs`) of your data array for the `grid_mapping` coordinate name.\n",
-    "   Inside the `grid_mapping` coordinate first look for `spatial_ref` then `crs_wkt` and lastly the CF grid mapping attributes.\n",
-    "   This is in line with the Climate and Forecast (CF) conventions for storing the CRS as well as GDAL netCDF conventions.\n",
-    "2. Look in the `crs` attribute and load in the CRS from there. This is for backwards compatibility with `xarray.open_rasterio`, which is deprecated since version 0.20.0. We recommend using `rioxarray.open_rasterio` instead.\n",
-    "\n",
-    "The value for the `crs` is anything accepted by `rasterio.crs.CRS.from_user_input()`\n",
-    "\n",
-    "#### Search order for the CRS for Dataset:\n",
-    "If the CRS is not found using the search methods above, it also searches the `data_vars` and uses the\n",
-    "first valid CRS found.\n",
-    "\n",
-    "#### decode_coords=\"all\"\n",
-    "\n",
-    "If you use one of xarray's open methods such as ``xarray.open_dataset`` to load netCDF files\n",
-    "with the default engine, it is recommended to use `decode_coords=\"all\"`. This will load the grid mapping\n",
-    "variable into coordinates for compatibility with rioxarray.\n",
-    "\n",
-    "#### API Documentation\n",
-    "\n",
-    "- [rio.write_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_crs)\n",
-    "- [rio.crs](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.crs)\n",
-    "- [rio.estimate_utm_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.estimate_utm_crs)\n",
-    "- [rio.set_spatial_dims()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.set_spatial_dims)\n",
-    "- [rio.write_coordinate_system()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_coordinate_system)\n",
-    "- [rio.write_transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_transform)\n",
-    "- [rio.transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray  # activate the rio accessor\n",
-    "import xarray\n",
-    "from affine import Affine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = xarray.open_dataset(\"../../test/test_data/input/PLANET_SCOPE_3D.nc\", decode_coords=\"all\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'units': 'DN', 'nodata': 0.0}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.green.attrs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;spatial_ref&#x27; ()&gt;\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    spatial_ref:  PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'spatial_ref'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ed98a4ac-15a8-4b42-a4d5-b41d08429b39' class='xr-array-in' type='checkbox' checked><label for='section-ed98a4ac-15a8-4b42-a4d5-b41d08429b39' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-13a73705-474f-4849-a2b6-31c4c1cc60d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13a73705-474f-4849-a2b6-31c4c1cc60d7' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ff9e3017-55fa-4eb5-af42-af1cc7f27bd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff9e3017-55fa-4eb5-af42-af1cc7f27bd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a4c305b-e31f-4faa-a83f-8aeec07cc0ee' class='xr-var-data-in' type='checkbox'><label for='data-7a4c305b-e31f-4faa-a83f-8aeec07cc0ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c56ca51-d50d-408e-8550-3fa40536ce7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c56ca51-d50d-408e-8550-3fa40536ce7d' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'spatial_ref' ()>\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    spatial_ref:  PROJCS[\"WGS 84 / UTM zone 22S\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1..."
-      ]
-     },
-     "execution_count": 4,
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-    }
-   ],
-   "source": [
-    "rds.green.spatial_ref"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CRS.from_epsg(32722)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.green.rio.crs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Setting the CRS\n",
-    "\n",
-    "Use the `rio.write_crs` method to set the CRS on your `xarray.Dataset` or `xarray.DataArray`.\n",
-    "This modifies the `xarray.Dataset` or `xarray.DataArray` and sets the CRS in a CF compliant manner.\n",
-    "\n",
-    "- [rio.write_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_crs)\n",
-    "- [rio.crs](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.crs)\n",
-    "\n",
-    "**Note:** It is recommended to use `rio.write_crs()` if you want the CRS to persist on the Dataset/DataArray and to write the CRS CF compliant metadata. Calling only `rio.set_crs()` CRS storage method is lossy and will not modify the Dataset/DataArray metadata."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;spatial_ref&#x27; ()&gt;\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    crs_wkt:                      GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;...\n",
-       "    semi_major_axis:              6378137.0\n",
-       "    semi_minor_axis:              6356752.314245179\n",
-       "    inverse_flattening:           298.257223563\n",
-       "    reference_ellipsoid_name:     WGS 84\n",
-       "    longitude_of_prime_meridian:  0.0\n",
-       "    prime_meridian_name:          Greenwich\n",
-       "    geographic_crs_name:          WGS 84\n",
-       "    grid_mapping_name:            latitude_longitude\n",
-       "    spatial_ref:                  GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'spatial_ref'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8edcf942-accb-4c39-88d9-d91a01903b83' class='xr-array-in' type='checkbox' checked><label for='section-8edcf942-accb-4c39-88d9-d91a01903b83' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-91b27a9b-d677-4786-8c5e-8208bbc943d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91b27a9b-d677-4786-8c5e-8208bbc943d0' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ed33fcd3-3802-4a97-9594-5e9199c8ad93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed33fcd3-3802-4a97-9594-5e9199c8ad93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c29d6c2-7c47-4a94-a90a-5de80d8ce90b' class='xr-var-data-in' type='checkbox'><label for='data-6c29d6c2-7c47-4a94-a90a-5de80d8ce90b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e504052f-18ed-4e21-98d1-667acb5bfc9e' class='xr-section-summary-in' type='checkbox'  ><label for='section-e504052f-18ed-4e21-98d1-667acb5bfc9e' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'spatial_ref' ()>\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    crs_wkt:                      GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"...\n",
-       "    semi_major_axis:              6378137.0\n",
-       "    semi_minor_axis:              6356752.314245179\n",
-       "    inverse_flattening:           298.257223563\n",
-       "    reference_ellipsoid_name:     WGS 84\n",
-       "    longitude_of_prime_meridian:  0.0\n",
-       "    prime_meridian_name:          Greenwich\n",
-       "    geographic_crs_name:          WGS 84\n",
-       "    grid_mapping_name:            latitude_longitude\n",
-       "    spatial_ref:                  GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"..."
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xda = xarray.DataArray(1)\n",
-    "xda.rio.write_crs(4326, inplace=True)\n",
-    "xda.spatial_ref"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CRS.from_epsg(4326)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xda.rio.crs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Spatial dimensions\n",
-    "\n",
-    "Only 1-dimensional X and Y dimensions are supported.\n",
-    "\n",
-    "The expected X/Y dimension names searched for in the `coords` are:\n",
-    "\n",
-    "- x | y\n",
-    "- longitude | latitude\n",
-    "- Coordinates (`coords`) with the CF attributes in `attrs`:\n",
-    "    - axis: X | Y\n",
-    "    - standard_name: longitude | latitude or projection_x_coordinate | projection_y_coordinate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Option 1: Write the CF attributes for non-standard dimension names\n",
-    "\n",
-    "If you don't want to rename your dimensions/coordinates,\n",
-    "you can write the CF attributes so the coordinates can be found.\n",
-    "\n",
-    "- [rio.set_spatial_dims()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.set_spatial_dims)\n",
-    "- [rio.write_coordinate_system()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_coordinate_system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds.rio.write_crs(\n",
-    "    4326\n",
-    "    inplace=True,\n",
-    ").rio.set_spatial_dims(\n",
-    "    x_dim=\"lon\",\n",
-    "    y_dim=\"lat\"\n",
-    "    inplace=True,\n",
-    ").rio.write_coordinate_system(inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Option 2: Rename your coordinates\n",
-    "\n",
-    "[xarray.Dataset.rename](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.rename.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rds.rename(lon=longitude, lat=latitude) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Setting the transform of the dataset\n",
-    "\n",
-    "The transform can be calculated from the coordinates of your data.\n",
-    "This method is useful if your netCDF file does not have coordinates present.\n",
-    "Use the `rio.write_transform` method to set the transform on your `xarray.Dataset` or `xarray.DataArray`.\n",
-    "\n",
-    "- [rio.write_transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_transform)\n",
-    "- [rio.transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'466266.0 3.0 0.0 8084700.0 0.0 -3.0'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transform = Affine(3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0)\n",
-    "xda.rio.write_transform(transform, inplace=True)\n",
-    "xda.spatial_ref.GeoTransform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Affine(3.0, 0.0, 466266.0,\n",
-       "       0.0, -3.0, 8084700.0)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xda.rio.transform()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/getting_started/getting_started.rst.txt b/0.18.0/_sources/getting_started/getting_started.rst.txt
deleted file mode 100644
index 8f36f7f3..00000000
--- a/0.18.0/_sources/getting_started/getting_started.rst.txt
+++ /dev/null
@@ -1,84 +0,0 @@
-.. _getting_started:
-
-Getting Started
-================
-
-Welcome! This page aims to help you gain a foundational understanding of rioxarray.
-
-rio accessor
--------------
-
-rioxarray `extends xarray <https://docs.xarray.dev/en/stable/internals/extending-xarray.html>`__
-with the `rio` accessor. The `rio` accessor is activated by importing rioxarray like so:
-
-.. code-block:: python
-
-    import rioxarray
-
-
-You can learn how to `clip`, `merge`, and `reproject` rasters in the :ref:`usage_examples`
-section of the documentation. Need to export to a raster (GeoTiff)? There is an example for
-that as well.
-
-
-Reading Files
--------------
-
-xarray
-~~~~~~~
-
-Since `rioxarray` is an extension of `xarray`, you can load in files using the standard
-`xarray` open methods. If you use one of xarray's open methods such as ``xarray.open_dataset``
-to load netCDF files with the default engine, it is recommended to use `decode_coords="all"`.
-This will load the grid mapping variable into coordinates for compatibility with rioxarray.
-
-.. code-block:: python
-
-    import xarray
-
-    xds = xarray.open_dataset("file.nc", decode_coords="all")
-
-
-rioxarray
-~~~~~~~~~~
-
-rioxarray 0.4+ enables passing `engine="rasterio"` to ``xarray.open_dataset``
-and ``xarray.open_mfdataset`` for xarray 0.18+. This uses
-:func:`rioxarray.open_rasterio` as the backend and always returns an ``xarray.Dataset``.
-
-.. code-block:: python
-
-    import xarray
-
-    xds = xarray.open_dataset("my.tif", engine="rasterio")
-
-You can also use :func:`rioxarray.open_rasterio`. This objects returned depend on
-your input file type.
-
-.. code-block:: python
-
-    import rioxarray
-
-    xds = rioxarray.open_rasterio("my.tif")
-
-
-Why use :func:`rioxarray.open_rasterio` instead of `xarray.open_rasterio`?
-
-1. It supports multidimensional datasets such as netCDF.
-2. It stores the CRS as a WKT, which is the recommended format (`PROJ FAQ <https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems>`__).
-3. It loads in the CRS, transform, and nodata metadata in standard CF & GDAL locations.
-4. It supports masking and scaling data with the `masked` and `mask_and_scale` kwargs.
-5. It adds the coordinate axis CF metadata.
-6. It loads raster metadata into the attributes.
-7. `xarray.open_rasterio` is deprecated (since v0.20.0)
-
-
-Introductory Information
---------------------------
-
-.. toctree::
-   :maxdepth: 1
-
-   crs_management.ipynb
-   nodata_management.ipynb
-   manage_information_loss.ipynb
diff --git a/0.18.0/_sources/getting_started/manage_information_loss.ipynb.txt b/0.18.0/_sources/getting_started/manage_information_loss.ipynb.txt
deleted file mode 100644
index aeb02d4e..00000000
--- a/0.18.0/_sources/getting_started/manage_information_loss.ipynb.txt
+++ /dev/null
@@ -1,240 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Managing Information Loss with xarray operations\n",
-    "\n",
-    "Sometimes, you can lose important information from your dataset when performing operations.\n",
-    "You will likely want to keep track of the attributes, `nodata`, and `CRS`.\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)\n",
-    "- [rio.write_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_crs)\n",
-    "- [rio.write_transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_transform)\n",
-    "- [rio.update_attrs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.update_attrs)\n",
-    "- [rio.update_encoding()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.update_encoding)\n",
-    "- [rio.crs](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.crs)\n",
-    "- [rio.nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.nodata)\n",
-    "- [rio.encoded_nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.encoded_nodata)\n",
-    "- [rio.write_nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.write_nodata)\n",
-    "- [rio.transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform)\n",
-    "\n",
-    "Note that `write_transform` is only needed if you are not saving the x,y coordinates. It is for\n",
-    "GDAL to  be able to read in the transform without needing the original coordinates and is useful\n",
-    "if you read in the file with `parse_coordinates=False`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "import xarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/input/PLANET_SCOPE_3D.nc\",\n",
-    "    variable=[\"green\"],\n",
-    "    mask_and_scale=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice the original data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'nodata': 0, 'units': ('DN', 'DN')},\n",
-       " {'dtype': 'float64',\n",
-       "  'grid_mapping': 'spatial_ref',\n",
-       "  'scale_factor': 1.0,\n",
-       "  'add_offset': 0.0,\n",
-       "  '_FillValue': nan,\n",
-       "  'source': 'netcdf:../../test/test_data/input/PLANET_SCOPE_3D.nc:green'},\n",
-       " CRS.from_epsg(32722),\n",
-       " nan)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.green.attrs, rds.green.encoding, rds.green.rio.crs, rds.green.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice how information is lost in the operation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({}, {}, CRS.from_epsg(32722), None)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_ds = rds.green + rds.green\n",
-    "new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To preserve attributes, xarray has [set_options](http://xarray.pydata.org/en/stable/generated/xarray.set_options.html#xarray-set-options) with `keep_attrs=True`. However, it does not preserve the encoding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'nodata': 0, 'units': ('DN', 'DN')}, {}, CRS.from_epsg(32722), 0.0)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with xarray.set_options(keep_attrs=True):\n",
-    "    new_ds = rds.green + rds.green\n",
-    "new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another solution is to save the original attributes and then copy them over\n",
-    "once the operation is complete:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'nodata': 0, 'units': ('DN', 'DN')},\n",
-       " {'grid_mapping': 'spatial_ref',\n",
-       "  'dtype': 'float64',\n",
-       "  'scale_factor': 1.0,\n",
-       "  'add_offset': 0.0,\n",
-       "  '_FillValue': nan,\n",
-       "  'source': 'netcdf:../../test/test_data/input/PLANET_SCOPE_3D.nc:green'},\n",
-       " CRS.from_epsg(32722),\n",
-       " nan)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_ds = rds.green + rds.green\n",
-    "new_ds.rio.write_crs(rds.green.rio.crs, inplace=True)\n",
-    "new_ds.rio.update_attrs(rds.green.attrs, inplace=True)\n",
-    "new_ds.rio.update_encoding(rds.green.encoding, inplace=True)\n",
-    "new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_ds.rio.to_raster(\"combination_keep_attrs.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"green\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": false, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info combination_keep_attrs.tif"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/getting_started/nodata_management.ipynb.txt b/0.18.0/_sources/getting_started/nodata_management.ipynb.txt
deleted file mode 100644
index 1c60169c..00000000
--- a/0.18.0/_sources/getting_started/nodata_management.ipynb.txt
+++ /dev/null
@@ -1,301 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Nodata Management"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you have opened a dataset and the nodata value can be determined, you can access it via the `rio.nodata` or `rio.encoded_nodata` accessors.\n",
-    "\n",
-    "If your dataset's nodata value cannot be determined, you can use the `rio.write_nodata` method.\n",
-    "\n",
-    "### Search order for nodata (DataArray only):\n",
-    "1. Check if DataArray values are masked. If they are masked, return `NaN`. If the DataArray is masked, the original nodata value can be retreived from `rio.encoded_nodata`.\n",
-    "2. Look in attributes (`attrs`) of your data array for the `_FillValue` then `missing_value` then `fill_value` and finally `nodata`.\n",
-    "3. Look in the `nodatavals` attribute. This is for backwards compatibility with `xarray.open_rasterio`. We recommend using `rioxarray.open_rasterio` instead.\n",
-    "\n",
-    "### API Documentation\n",
-    "\n",
-    "- [rio.write_nodata()](../rioxarray.rst#rioxarray.raster_array.RasterArray.write_nodata)\n",
-    "- [rio.nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.nodata)\n",
-    "- [rio.encoded_nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.encoded_nodata)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "import xarray\n",
-    "\n",
-    "file_path = \"../../test/test_data/input/tmmx_20190121.nc\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example of loading unmaksed data\n",
-    "\n",
-    "In this case, the nodata value is in the attributes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataset(file_path, mask_and_scale=False) # performs mask_and_scale by default\n",
-    "rds = rioxarray.open_rasterio(file_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata:\n",
-      "- xarray.open_dataset: 32767\n",
-      "- rioxarray.open_rasterio: 32767\n",
-      "\n",
-      "encoded_nodata:\n",
-      "- xarray.open_dataset: None\n",
-      "- rioxarray.open_rasterio: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.nodata}\")\n",
-    "print(\"\\nencoded_nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.encoded_nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.encoded_nodata}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "attributes:\n",
-      "\n",
-      "- xarray.open_dataset:\n",
-      "    {'_FillValue': 32767, 'units': 'K', 'description': 'Daily Maximum Temperature', 'long_name': 'tmmx', 'standard_name': 'tmmx', 'missing_value': 32767, 'dimensions': 'lon lat time', 'grid_mapping': 'crs', 'coordinate_system': 'WGS84,EPSG:4326', 'scale_factor': 0.1, 'add_offset': 220.0, '_Unsigned': 'true'}\n",
-      "\n",
-      "- rioxarray.open_rasterio:\n",
-      "    {'add_offset': 220.0, 'coordinates': 'day', 'coordinate_system': 'WGS84,EPSG:4326', 'description': 'Daily Maximum Temperature', 'dimensions': 'lon lat time', 'long_name': 'tmmx', 'missing_value': 32767, 'scale_factor': 0.1, 'standard_name': 'tmmx', 'units': 'K', '_FillValue': 32767.0, '_Unsigned': 'true'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"attributes:\")\n",
-    "print(f\"\\n- xarray.open_dataset:\\n    {xds.air_temperature.attrs}\")\n",
-    "print(f\"\\n- rioxarray.open_rasterio:\\n    {rds.air_temperature.attrs}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example of data loaded in with mask_and_scale=True\n",
-    "\n",
-    "When the dataset is opened with `mask_and_scale=True` with `rioxarray.open_rasterio` or `xarray.open_dataset`, the\n",
-    "nodata metadata is written to the encoding attribute. Then, when the dataset is written using\n",
-    "`to_netcdf` or `rio.to_raster` the data is decoded and it writes the original nodata value to the raster.\n",
-    "\n",
-    "When this happens, `rio.nodata` returns `numpy.nan` and `rio.encoded_nodata` contains the original value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/snowal/miniconda/envs/midas/lib/python3.10/site-packages/rioxarray/_io.py:618: SerializationWarning: variable 'air_temperature' has _Unsigned attribute but is not of integer type. Ignoring attribute.\n",
-      "  rioda = open_rasterio(\n"
-     ]
-    }
-   ],
-   "source": [
-    "xds = xarray.open_dataset(file_path) # performs mask_and_scale by default\n",
-    "rds = rioxarray.open_rasterio(file_path, mask_and_scale=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata:\n",
-      "- xarray.open_dataset: nan\n",
-      "- rioxarray.open_rasterio: nan\n",
-      "\n",
-      "encoded_nodata:\n",
-      "- xarray.open_dataset: 32767.0\n",
-      "- rioxarray.open_rasterio: 32767.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.nodata}\")\n",
-    "print(\"\\nencoded_nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.encoded_nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.encoded_nodata}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "attributes:\n",
-      "\n",
-      "- xarray.open_dataset:\n",
-      "    {'units': 'K', 'description': 'Daily Maximum Temperature', 'long_name': 'tmmx', 'standard_name': 'tmmx', 'dimensions': 'lon lat time', 'grid_mapping': 'crs', 'coordinate_system': 'WGS84,EPSG:4326'}\n",
-      "\n",
-      "- rioxarray.open_rasterio:\n",
-      "    {'coordinates': 'day', 'coordinate_system': 'WGS84,EPSG:4326', 'description': 'Daily Maximum Temperature', 'dimensions': 'lon lat time', 'long_name': 'tmmx', 'standard_name': 'tmmx', 'units': 'K'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"attributes:\")\n",
-    "print(f\"\\n- xarray.open_dataset:\\n    {xds.air_temperature.attrs}\")\n",
-    "print(f\"\\n- rioxarray.open_rasterio:\\n    {rds.air_temperature.attrs}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "encoding:\n",
-      "\n",
-      "- xarray.open_dataset:\n",
-      "    {'zlib': True, 'shuffle': True, 'complevel': 5, 'fletcher32': False, 'contiguous': False, 'chunksizes': (585, 1386), 'source': '/home/snowal/scripts/rioxarray/test/test_data/input/tmmx_20190121.nc', 'original_shape': (585, 1386), 'dtype': dtype('uint16'), '_Unsigned': 'true', 'missing_value': 32767, '_FillValue': 32767, 'scale_factor': 0.1, 'add_offset': 220.0, 'coordinates': 'day'}\n",
-      "\n",
-      "- rioxarray.open_rasterio:\n",
-      "    {'_Unsigned': 'true', 'dtype': 'uint16', 'grid_mapping': 'crs', 'scale_factor': 0.1, 'add_offset': 220.0, '_FillValue': 32767.0, 'missing_value': 32767, 'source': 'netcdf:../../test/test_data/input/tmmx_20190121.nc:air_temperature', 'rasterio_dtype': 'uint16'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"encoding:\")\n",
-    "print(f\"\\n- xarray.open_dataset:\\n    {xds.air_temperature.encoding}\")\n",
-    "print(f\"\\n- rioxarray.open_rasterio:\\n    {rds.air_temperature.encoding}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Manually masking data\n",
-    "\n",
-    "If you use `xarray.where` to mask you data, then you need to ensure that the\n",
-    "attributes stored on the DataArray reflect the correct values.\n",
-    "[rio.write_nodata()](../rioxarray.rst#rioxarray.raster_array.RasterArray.write_nodata) can help ensure that the nodata attributes are written correctly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata: 32767.0\n",
-      "encoded_nodata: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "xds = xarray.open_dataset(file_path, mask_and_scale=False) # performs mask_and_scale by default\n",
-    "raster = xds.air_temperature \n",
-    "raster = raster.where(raster != raster.rio.nodata)\n",
-    "# nodata does not reflect the data has been masked\n",
-    "print(f\"nodata: {raster.rio.nodata}\")\n",
-    "print(f\"encoded_nodata: {raster.rio.encoded_nodata}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata: nan\n",
-      "encoded_nodata: 32767.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# update nodata value to show the data has been masked\n",
-    "raster.rio.write_nodata(raster.rio.nodata, encoded=True, inplace=True)\n",
-    "print(f\"nodata: {raster.rio.nodata}\")\n",
-    "print(f\"encoded_nodata: {raster.rio.encoded_nodata}\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/_sources/history.rst.txt b/0.18.0/_sources/history.rst.txt
deleted file mode 100644
index 3726d504..00000000
--- a/0.18.0/_sources/history.rst.txt
+++ /dev/null
@@ -1,414 +0,0 @@
-History
-=======
-
-0.18.0
-------
-- ENH:reproject: Support geolocation arrays (pull #822)
-- REF: Add positional arguments requirements (pull #806)
-- BUG:merge: Fix merging masked and scaled data (issue #814)
-- BUG: Fix chunk arguments for normalize_chunks (pull #820)
-- BUG: Squeeze when using interpolate_na with extra dim (pull #810)
-* BUG: Properly handle encoding/decoding scales and offsets by (pull #821)
-- DOC: Clearer coordinate docstring for `open_rasterio` (pull #811)
-
-0.17.0
-------
-- REF:reproject: Make NaN default float nodata & update integer defaults
-
-0.16.0
-------
-- ENH: Add `allow_one_dimensional_raster` option to `rio.clip_box` (issue #708)
-- MNT: recommend `rio.write_crs`` & deprecate `rio.set_crs` (pull #793)
-
-0.15.7
-------
-- BUG: Remove grid_mapping from attrs when writing (pull #783)
-- BUG: Ensure gcp_crs exists before writing in `rio.write_gcps` (issue #646)
-
-0.15.6
-------
-- BUG: Raise OverflowError when nodata data type conversion is unsafe (pull #782)
-- BUG: Support writing GCPs to netCDF (issue #778)
-- BUG: Fix reading dask chunks when band_as_variable=True (issue #761)
-- REF:merge: Use merge path & rio.to_raster (pull #781)
-
-0.15.5
-------
-- BUG:reproject: Allow rotated rasters (issue #746)
-
-0.15.4
-------
-- BUG:reproject_match: Remove setting spatial dims on output resampled dataset (issue #768)
-
-0.15.3
-------
-- BUG:merge: Use `rasterio.io.MemoryFile`` for reading (pull #765)
-- BUG:merge: Add simple defaults for unused payload/colormap (pull #766)
-
-0.15.2
-------
-- BUG: Add decode_coords kwarg to backend entrypoint (pull #763)
-- BUG: Avoid DeprecationWarning: xr.Dataset.drop() -> xr.Dataset.drop_vars() (pull #740)
-
-0.15.1
--------
-- DEP: Support Python 3.10-3.12 (pull #723)
-- DEP: rasterio 1.3+, pyproj 3.3+ (pull #725, #727)
-- DEP: xarray 2022.3.0+ & numpy 1.23+ (pull #728)
-- ENH: Robust handling of GCPs without `z` component (issue #731)
-
-0.15.0
-------
-- BUG: Fix setting spatial dims internally during propagation (pull #682)
-- ENH: Pass on on-disk chunk sizes as preferred chunk sizes to the xarray backend (pull #678)
-- MNT: add __all__ to top level module (issue #680)
-
-0.14.1
-------
-- BUG: Fix :mod:`rioxarray.merge` CRS check (pull #655)
-- BUG: Remove tags with metadata added by rasterio in :func:`rioxarray.open_rasterio` (issue #666)
-
-0.14.0
-------
-- DEP: Drop Python 3.8 support (issue #582)
-- DEP: pin rasterio>=1.2 (pull #642)
-- BUG: Fix WarpedVRT in :func:`rioxarray.open_rasterio` when band_as_variable=True (issue #644)
-- BUG: Fix usage of `encode_cf_variable` in `rio.to_raster` (pull #652)
-
-0.13.4
-------
-- DEP: pin numpy>=1.21 (pull #636)
-
-0.13.3
-------
-- BUG: Handle data type error in `rio.reproject` (issue #618)
-
-0.13.2
-------
-- BUG:dataset: Fix writing tags for bands (issue #615)
-- BUG:dataset: prevent overwriting long_name attribute (pull #616)
-
-0.13.1
-------
-- BUG: Fix closing files manually (pull #607)
-- BUG: Add GDAL 3.6 driver auto-select fix (pull #606)
-
-0.13.0
--------
-- ENH: Added band_as_variable option to open_rasterio (pull #600)
-
-0.12.4
-------
-- ENH: Added band_as_variable option to open_rasterio (issue #296)
-- BUG: Pass warp_extras dictionary to raster.vrt.WarpedVRT (issue #598)
-
-0.12.3
-------
-- BUG: Handle CF CRS export errors in `rio.write_crs` (discussion #591)
-
-0.12.2
-------
-- BUG: Fix `mask_and_scale` data load after `.sel` (issue #580)
-
-0.12.1
-------
-- BUG: Handle `_Unsigned` and load in all attributes (pull #575)
-
-0.12.0
--------
-- ENH: Allow passing in bounds of different CRS in `rio.clip_box` (pull #563)
-
-0.11.2
-------
-- BUG: Fix reading file handle with dask (issue #550)
-- BUG: Fix reading cint16 files with dask (issue #542)
-- BUG: Ensure `rio.bounds` ordered correctly (issue #545)
-- BUG: Allow reading from `io.BytesIO` (issue #549)
-
-0.11.1
-------
-- BUG: Fix WarpedVRT param cache in :func:`rioxarray.open_rasterio` (issue #515)
-- BUG: Always generate coordinates in `rio.reproject` when GCPS|RPCS present (issue #517)
-
-0.11.0
-------
-- TYPE: Add more type hints (issue #373)
-- ENH: Add additional GDAL information to :func:`rioxarray.show_versions` (pull #513)
-
-0.10.3
-------
-- BUG: Remove xarray crs attribute in rio.write_crs (issue #488)
-
-0.10.2
--------
-- BUG: Lazy load colormap through _manager.acquire() in merge (issue #479)
-
-0.10.1
--------
-- DEP: pin rasterio>=1.1.1 (pull #471)
-- BUG: Corrected bounds and transform args to float (pull #475)
-
-0.10.0
--------
-- DEP: Drop Python 3.7 support (issue #451)
-- ENH: Add GCPs reading and writing (issue #376)
-
-0.9.1
-------
-- BUG: Force coordinates to be exactly the same in `rio.reproject_match` (issue #298)
-
-0.9.0
-------
-- ENH: Allow additional kwargs to pass from reproject_match() -> reproject() (pull #436)
-
-0.8.0
-------
-- DEP: Make scipy an optional dependency (issue #413)
-- BUG: Return cached transform when axis data missing (pull #419)
-- BUG: Fix negative indexes in `rio.isel_window` (issue #421)
-
-0.7.1
-------
-- BUG: Handle transforms with rotation (pull #401)
-
-0.7.0
-------
-- BUG: `rio.clip` and `rio.clip_box` skip non-geospatial arrays in datasets when clipping (pull #392)
-- ENH: Add option for users to skip variables without spatial dimensions (pull #395)
-
-0.6.1
-------
-- BUG: Fix indexing error when `mask_and_scale=True` was combined with band dim chunking (issue #387, pull #388)
-
-0.6.0
-------
-- ENH: Add pad option to `rio.isel_window` (issue #381; pull #383)
-- BUG: Fix negative start in row or col window offsets in `rio.isel_window` (issue #381; pull #383)
-
-0.5.0
-------
-- ENH: Allow passing in kwargs to `rio.reproject` (issue #369; pull #370)
-- ENH: Allow nodata override and provide default nodata based on dtype in `rio.reproject` (pull #370)
-- ENH: Add support for passing in gcps to rio.reproject (issue #339; pull #370)
-- BUG: Remove duplicate acquire in open_rasterio (pull #364)
-- BUG: Fix exporting dataset to raster with non-standard dimensions (issue #372)
-
-0.4.3
-------
-- BUG: support GDAL CInt16, rasterio complex_int16 (pull #353)
-- TST: Fix merge tests for rasterio 1.2.5+ (issue #358)
-
-0.4.2
-------
-- BUG: Improve WarpedVRT support for gcps (pull #351)
-
-0.4.1
-------
-- BUG: pass kwargs with lock=False (issue #344)
-- BUG: Close file handle with lock=False (pull #346)
-
-0.4.0
-------
-- DEP: Python 3.7+ (issue #215)
-- DEP: xarray 0.17+ (needed for issue #282)
-- REF: Store `grid_mapping` in `encoding` instead of `attrs` (issue #282)
-- ENH: enable `engine="rasterio"` via xarray backend API (issue #197 pull #281)
-- ENH: Generate 2D coordinates for non-rectilinear sources (issue #290)
-- ENH: Add `encoded` kwarg to `rio.write_nodata` (discussions #313)
-- ENH: Added `decode_times` and `decode_timedelta` kwargs to `rioxarray.open_rasterio` (issue #316)
-- BUG: Use float32 for smaller dtypes when masking (discussions #302)
-- BUG: Return correct transform in `rio.transform` with non-rectilinear transform (discussions #280)
-- BUG: Update to handle WindowError in rasterio 1.2.2 (issue #286)
-- BUG: Don't generate x,y coords in `rio` methods if not previously there (pull #294)
-- BUG: Preserve original data type for writing to disk (issue #305)
-- BUG: handle lock=True in open_rasterio (issue #273)
-
-0.3.1
-------
-- BUG: Compatibility changes with xarray 0.17 (issue #254)
-- BUG: Raise informative error in interpolate_na if missing nodata (#250)
-
-0.3.0
-------
-- REF: Reduce pyproj.CRS internal usage for speed (issue #241)
-- ENH: Add `rioxarray.set_options` to disable exporting CRS CF grid mapping (issue #241)
-- BUG: Handle merging 2D DataArray (discussion #244)
-
-0.2.0
-------
-- ENH: Added `rio.estimate_utm_crs` (issue #181)
-- ENH: Add support for merging datasets with different CRS (issue #173)
-- ENH: Add support for using dask in `rio.to_raster` (issue #9, pull #219, pull #223)
-- ENH: Use the list version of `transform_geom` with rasterio 1.2+ (issue #180)
-- ENH: Support driver autodetection with rasterio 1.2+ (issue #180)
-- ENH: Allow multithreaded, lockless reads with `rioxarray.open_rasterio` (issue #214)
-- ENH: Add support to clip from disk (issue #115)
-- BUG: Allow `rio.write_crs` when spatial dimensions not found (pull #186)
-- BUG: Update to support rasterio 1.2+ merge (issue #180)
-
-0.1.1
-------
-- BUG: Check all CRS are the same in the dataset in crs() method
-
-0.1.0
-------
-- BUG: Ensure transform correct in rio.clip without coords (pull #165)
-- BUG: Ensure the nodata value matches the dtype (pull #166)
-- Raise deprecation exception in add_spatial_ref and add_xy_grid_meta (pull #168)
-
-0.0.31
-------
-- Deprecate add_spatial_ref and fix warning for add_xy_grid_meta (pull #158)
-
-0.0.30
-------
-- BUG: Fix assigning fill value in `rio.pad_box` (pull #140)
-- ENH: Add `rio.write_transform` to store cache in GDAL location (issue #129 & #139)
-- ENH: Use rasterio windows for `rio.clip_box` (issue #142)
-- BUG: Add support for negative indexes in rio.isel_window (pull #145)
-- BUG: Write transform based on window in rio.isel_window (pull #145)
-- ENH: Add `rio.count`, `rio.slice_xy()`, `rio.bounds()`, `rio.resolution()`, `rio.transform_bounds()` to Dataset level
-- ENH: Add `rio.write_coordinate_system()` (issue #147)
-- ENH: Search CF coordinate metadata to find coordinates (issue #147)
-- ENH: Default `rio.clip` to assume geometry has CRS of dataset (pull #150)
-- ENH: Add `rio.grid_mapping` and `rio.write_grid_mapping` & preserve original grid mapping (pull #151)
-
-0.0.29
--------
-- BUG: Remove unnecessary memory copies in reproject method (pull #136)
-- BUG: Fix order of axis in `rio.isel_window` (pull #133)
-- BUG: Allow clipping with disjoint geometries (issue #132)
-- BUG: Remove automatically setting tiled=True for windowed writing (pull #134)
-- ENH: Add `rio.pad_box` (pull #138)
-
-0.0.28
--------
-- rio.reproject: change input kwarg dst_affine_width_height -> shape & transform (#125)
-- ENH: Use pyproj.CRS to read/write CF parameters (issue #124)
-
-0.0.27
-------
-- ENH: Added optional `shape` argument to `rio.reproject` (pull #116)
-- Fix ``RasterioDeprecationWarning`` (pull #117)
-- BUG: Make rio.shape order same as rasterio dataset shape (height, width) (pull #121)
-- Fix open_rasterio() for WarpedVRT with specified src_crs (pydata/xarray/pull/4104 & pull #120)
-- BUG: Use internal reprojection as engine for resampling window in merge (pull #123)
-
-0.0.26
-------
-- ENH: Added :func:`rioxarray.show_versions` (issue #106)
-
-0.0.25
-------
-- BUG: Use recalc=True when using transform internally & ensure stable when coordinates unavailable. (issue #97)
-
-0.0.24
-------
-- ENH: Add variable names to error messages for clarity (pull #99)
-- BUG: Use assign_coords in _decode_datetime_cf (issue #101)
-
-0.0.23
-------
-- BUG: Fix 'rio.set_spatial_dims' so information saved with 'rio' accesors (issue #94)
-- ENH: Make 'rio.isel_window' available for datasets (pull #95)
-
-0.0.22
--------
-- ENH: Use pyproj.CRS internally to manage GDAL 2/3 transition (issue #92)
-- ENH: Add MissingCRS exceptions for 'rio.clip' and 'rio.reproject' (pull #93)
-
-0.0.21
--------
-- ENH: Added to_raster method for Datasets (issue #76)
-
-0.0.20
-------
-- BUG: ensure band_key is list when iterating over bands for mask and scale (pull #87)
-
-0.0.19
--------
-- Add support for writing scales & offsets to raster (pull #79)
-- Don't write standard raster metadata to raster tags (issue #78)
-
-0.0.18
-------
-- Fixed windowed writing to require tiled output raster (pull #66)
-- Write data array attributes using `rio.to_raster` (issue #64)
-- Write variable name to descriptions if possible in `rio.to_raster` (issue #64)
-- Add `mask_and_scale` option to `rioxarray.open_rasterio()` (issue #67)
-- Hide NotGeoreferencedWarning warning when subdatasets are present using open_rasterio (issue #65)
-- Add support for loading in 1D variables in `xarray.open_rasterio()` (issue #43)
-- Load in netCDF metadata on the variable level (pull #73)
-- Add rioxarray.merge module (issue #46)
-
-0.0.17
-------
-- Renamed `descriptions` to `long_name` when opening with `open_rasterio()` (pull #63)
-- Make `units` & `long_name` scalar if they exist in rasterio attributes (pull #63)
-
-0.0.16
-------
--  Add support for netcdf/hdf groups with different shapes (pull #62)
-
-0.0.15
-------
-- Added `variable` and `group` kwargs to `rioxarray.open_rasterio()` to allow filtering of subdatasets (pull #57)
-- Added `default_name` kwarg to `rioxarray.open_rasterio()` for backup when the original does not exist (pull #59)
-- Added `recalc_transform` kwarg to `rio.to_raster()` (pull #56)
-
-0.0.14
-------
-- Added `windowed` kwarg to `rio.to_raster()` to write to raster using windowed writing (pull #54)
-- Added add `rio.isel_window()` to allow selection using a rasterio.windows.Window (pull #54)
-
-0.0.13
-------
-- Improve CRS searching for xarray.Dataset & use default grid mapping name (pull #51)
-
-0.0.12
-------
-- Use `xarray.open_rasterio()` for `rioxarray.open_rasterio()` with xarray<0.12.3 (pull #40)
-
-0.0.11
-------
-- Added `open_kwargs` to pass into `rasterio.open()` when using `rioxarray.open_rasterio()` (pull #48)
-- Added example opening Cloud Optimized GeoTiff (issue #45)
-
-0.0.10
-------
-- Add support for opening netcdf/hdf files with `rioxarray.open_rasterio` (issue #32)
-- Added support for custom CRS with wkt attribute for datacube CRS support (issue #35)
-- Added `rio.set_nodata()`, `rio.write_nodata()`, `rio.set_attrs()`, `rio.update_attrs()` (issue #37)
-
-0.0.9
------
-- Add `rioxarray.open_rasterio` (issue #7)
-
-0.0.8
------
-- Fix setting nodata in _add_attrs_proj (pull #30)
-
-0.0.7
------
-- Add option to do an inverted clip (pull #29)
-
-0.0.6
------
-- Add support for scalar coordinates in reproject (issue #15)
-- Updated writing encoding for FutureWarning (issue #18)
-- Use input raster profile for defaults to write output raster profile if opened with `xarray.open_rasterio` (issue #19)
-- Preserve None nodata if opened with `xarray.open_rasterio` (issue #20)
-- Added `drop` argument for `clip()` (issue #25)
-- Fix order of `CRS` for reprojecting geometries in `clip()` (pull #24)
-- Added `set_spatial_dims()` method for datasets when dimensions not found (issue #27)
-
-0.0.5
------
-- Find nodata and nodatavals in 'nodata' property (pull #12)
-- Added 'encoded_nodata' property to DataArray (pull #12)
-- Write the raster with encoded_nodata instead of NaN for nodata (pull #12)
-- Added methods to set and write CRS (issue #5)
-
-0.0.4
-------
-- Added ability to export data array to raster (pull #8)
diff --git a/0.18.0/_sources/index.rst.txt b/0.18.0/_sources/index.rst.txt
deleted file mode 100644
index 348ebbd4..00000000
--- a/0.18.0/_sources/index.rst.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-Welcome to rioxarray's documentation!
-======================================
-
-GitHub: http://github.com/corteva/rioxarray
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Contents:
-
-   readme
-   installation
-   getting_started/getting_started
-   examples/examples
-   modules
-   contributing
-   authors
-   history
-
-Indices and tables
-==================
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/0.18.0/_sources/installation.rst.txt b/0.18.0/_sources/installation.rst.txt
deleted file mode 100644
index 35ad3724..00000000
--- a/0.18.0/_sources/installation.rst.txt
+++ /dev/null
@@ -1,62 +0,0 @@
-.. highlight:: shell
-
-============
-Installation
-============
-
-
-Stable release
---------------
-
-1. Use pip to install package from `PyPI <https://pypi.org/project/rioxarray/>`__:
-
-  .. code-block:: bash
-
-      pip install rioxarray
-
-
-2. Use `conda <https://conda.io/en/latest/>`__ with the `conda-forge <https://conda-forge.org/>`__ channel:
-
-  .. code-block:: bash
-
-      conda config --prepend channels conda-forge
-      conda config --set channel_priority strict
-      conda create -n rioxarray_env rioxarray
-      conda activate rioxarray_env
-
-  - `rioxarray` `conda-forge repository <http://github.com/conda-forge/rioxarray-feedstock>`__
-
-  .. note::
-      "... we recommend always installing your packages inside a
-      new environment instead of the base environment from
-      anaconda/miniconda. Using envs make it easier to
-      debug problems with packages and ensure the stability
-      of your root env."
-        -- https://conda-forge.org/docs/user/tipsandtricks.html
-
-  .. warning::
-      Avoid using `pip install` with a conda environment. If you encounter
-      a python package that isn't in conda-forge, consider submitting a
-      recipe: https://github.com/conda-forge/staged-recipes/
-
-
-From source
------------
-
-The source for rioxarray can be installed from the `GitHub repo`_.
-
-.. code-block:: bash
-
-    python -m pip install git+git://github.com/corteva/rioxarray.git#egg=rioxarray
-
-
-To install for local development:
-
-.. code-block:: bash
-
-    git clone git@github.com:corteva/rioxarray.git
-    cd rioxarray
-    python -m pip install -e .[dev]
-
-
-.. _GitHub repo: https://github.com/corteva/rioxarray
diff --git a/0.18.0/_sources/modules.rst.txt b/0.18.0/_sources/modules.rst.txt
deleted file mode 100644
index 5af24d17..00000000
--- a/0.18.0/_sources/modules.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rioxarray
-=========
-
-.. toctree::
-   :maxdepth: 4
-
-   rioxarray
diff --git a/0.18.0/_sources/readme.rst.txt b/0.18.0/_sources/readme.rst.txt
deleted file mode 100644
index 72a33558..00000000
--- a/0.18.0/_sources/readme.rst.txt
+++ /dev/null
@@ -1 +0,0 @@
-.. include:: ../README.rst
diff --git a/0.18.0/_sources/rioxarray.rst.txt b/0.18.0/_sources/rioxarray.rst.txt
deleted file mode 100644
index b32f8658..00000000
--- a/0.18.0/_sources/rioxarray.rst.txt
+++ /dev/null
@@ -1,63 +0,0 @@
-rioxarray package
-=================
-
-rioxarray.open_rasterio
------------------------
-
-.. autofunction:: rioxarray.open_rasterio
-
-
-rioxarray.merge module
-----------------------
-
-.. autofunction:: rioxarray.merge.merge_arrays
-
-.. autofunction:: rioxarray.merge.merge_datasets
-
-
-rioxarray.set_options
------------------------
-
-.. autoclass:: rioxarray.set_options
-
-
-rioxarray.show_versions
------------------------
-
-.. autofunction:: rioxarray.show_versions
-
-
-rioxarray `rio` accessors
---------------------------
-
-rioxarray `extends xarray <http://xarray.pydata.org/en/stable/internals/extending-xarray.html>`__
-with the `rio` accessor. The `rio` accessor is activated by importing rioxarray like so:
-
-.. code-block:: python
-
-    import rioxarray
-
-
-.. autoclass:: rioxarray.rioxarray.XRasterBase
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-.. autoclass:: rioxarray.raster_array.RasterArray
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-.. autoclass:: rioxarray.raster_dataset.RasterDataset
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-rioxarray.exceptions module
----------------------------
-
-.. automodule:: rioxarray.exceptions
-    :members:
-    :undoc-members:
-    :show-inheritance:
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diff --git a/0.18.0/_static/doctools.js b/0.18.0/_static/doctools.js
deleted file mode 100644
index 0398ebb9..00000000
--- a/0.18.0/_static/doctools.js
+++ /dev/null
@@ -1,149 +0,0 @@
-/*
- * Base JavaScript utilities for all Sphinx HTML documentation.
- */
-"use strict";
-
-const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
-  "TEXTAREA",
-  "INPUT",
-  "SELECT",
-  "BUTTON",
-]);
-
-const _ready = (callback) => {
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-    callback();
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-    document.addEventListener("DOMContentLoaded", callback);
-  }
-};
-
-/**
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- */
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-    Documentation.initDomainIndexTable();
-    Documentation.initOnKeyListeners();
-  },
-
-  /**
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-
-  // gettext and ngettext don't access this so that the functions
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-    );
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-  },
-
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-  /**
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-
-  initOnKeyListeners: () => {
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-
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-      // some keyboard layouts may need Shift to get /
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diff --git a/0.18.0/_static/documentation_options.js b/0.18.0/_static/documentation_options.js
deleted file mode 100644
index 7c4f999a..00000000
--- a/0.18.0/_static/documentation_options.js
+++ /dev/null
@@ -1,13 +0,0 @@
-const DOCUMENTATION_OPTIONS = {
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-    LANGUAGE: 'en',
-    COLLAPSE_INDEX: false,
-    BUILDER: 'html',
-    FILE_SUFFIX: '.html',
-    LINK_SUFFIX: '.html',
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diff --git a/0.18.0/_static/jquery.js b/0.18.0/_static/jquery.js
deleted file mode 100644
index c4c6022f..00000000
--- a/0.18.0/_static/jquery.js
+++ /dev/null
@@ -1,2 +0,0 @@
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n=i+o,r}),window.cancelAnimationFrame||(window.cancelAnimationFrame=function(n){clearTimeout(n)})}()}).call(window)},function(n,e){n.exports=jQuery},function(n,e,t){}]);
\ No newline at end of file
diff --git a/0.18.0/_static/js/versions.js b/0.18.0/_static/js/versions.js
deleted file mode 100644
index 818bc996..00000000
--- a/0.18.0/_static/js/versions.js
+++ /dev/null
@@ -1,224 +0,0 @@
-const themeFlyoutDisplay = "hidden";
-const themeVersionSelector = "True";
-const themeLanguageSelector = "True";
-
-if (themeFlyoutDisplay === "attached") {
-  function renderLanguages(config) {
-    if (!config.projects.translations.length) {
-      return "";
-    }
-
-    const languagesHTML = `
-      <dl>
-        <dt>Languages</dt>
-        ${config.projects.translations
-          .map(
-            (translation) => `
-        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
-          <a href="${translation.urls.documentation}">${translation.language.code}</a>
-        </dd>
-        `,
-          )
-          .join("\n")}
-      </dl>
-    `;
-    return languagesHTML;
-  }
-
-  function renderVersions(config) {
-    if (!config.versions.active.length) {
-      return "";
-    }
-    const versionsHTML = `
-      <dl>
-        <dt>Versions</dt>
-        ${config.versions.active
-          .map(
-            (version) => `
-        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
-          <a href="${version.urls.documentation}">${version.slug}</a>
-        </dd>
-        `,
-          )
-          .join("\n")}
-      </dl>
-    `;
-    return versionsHTML;
-  }
-
-  function renderDownloads(config) {
-    if (!Object.keys(config.versions.current.downloads).length) {
-      return "";
-    }
-    const downloadsNameDisplay = {
-      pdf: "PDF",
-      epub: "Epub",
-      htmlzip: "HTML",
-    };
-
-    const downloadsHTML = `
-      <dl>
-        <dt>Downloads</dt>
-        ${Object.entries(config.versions.current.downloads)
-          .map(
-            ([name, url]) => `
-          <dd>
-            <a href="${url}">${downloadsNameDisplay[name]}</a>
-          </dd>
-        `,
-          )
-          .join("\n")}
-      </dl>
-    `;
-    return downloadsHTML;
-  }
-
-  document.addEventListener("readthedocs-addons-data-ready", function (event) {
-    const config = event.detail.data();
-
-    const flyout = `
-      <div class="rst-versions" data-toggle="rst-versions" role="note">
-        <span class="rst-current-version" data-toggle="rst-current-version">
-          <span class="fa fa-book"> Read the Docs</span>
-          v: ${config.versions.current.slug}
-          <span class="fa fa-caret-down"></span>
-        </span>
-        <div class="rst-other-versions">
-          <div class="injected">
-            ${renderLanguages(config)}
-            ${renderVersions(config)}
-            ${renderDownloads(config)}
-            <dl>
-              <dt>On Read the Docs</dt>
-              <dd>
-                <a href="${config.projects.current.urls.home}">Project Home</a>
-              </dd>
-              <dd>
-                <a href="${config.projects.current.urls.builds}">Builds</a>
-              </dd>
-              <dd>
-                <a href="${config.projects.current.urls.downloads}">Downloads</a>
-              </dd>
-            </dl>
-            <dl>
-              <dt>Search</dt>
-              <dd>
-                <form id="flyout-search-form">
-                  <input
-                    class="wy-form"
-                    type="text"
-                    name="q"
-                    aria-label="Search docs"
-                    placeholder="Search docs"
-                    />
-                </form>
-              </dd>
-            </dl>
-            <hr />
-            <small>
-              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
-            </small>
-          </div>
-        </div>
-    `;
-
-    // Inject the generated flyout into the body HTML element.
-    document.body.insertAdjacentHTML("beforeend", flyout);
-
-    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
-    document
-      .querySelector("#flyout-search-form")
-      .addEventListener("focusin", () => {
-        const event = new CustomEvent("readthedocs-search-show");
-        document.dispatchEvent(event);
-      });
-  })
-}
-
-if (themeLanguageSelector || themeVersionSelector) {
-  function onSelectorSwitch(event) {
-    const option = event.target.selectedIndex;
-    const item = event.target.options[option];
-    window.location.href = item.dataset.url;
-  }
-
-  document.addEventListener("readthedocs-addons-data-ready", function (event) {
-    const config = event.detail.data();
-
-    const versionSwitch = document.querySelector(
-      "div.switch-menus > div.version-switch",
-    );
-    if (themeVersionSelector) {
-      let versions = config.versions.active;
-      if (config.versions.current.hidden || config.versions.current.type === "external") {
-        versions.unshift(config.versions.current);
-      }
-      const versionSelect = `
-    <select>
-      ${versions
-        .map(
-          (version) => `
-        <option
-  value="${version.slug}"
-  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
-              data-url="${version.urls.documentation}">
-              ${version.slug}
-          </option>`,
-        )
-        .join("\n")}
-    </select>
-  `;
-
-      versionSwitch.innerHTML = versionSelect;
-      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
-    }
-
-    const languageSwitch = document.querySelector(
-      "div.switch-menus > div.language-switch",
-    );
-
-    if (themeLanguageSelector) {
-      if (config.projects.translations.length) {
-        // Add the current language to the options on the selector
-        let languages = config.projects.translations.concat(
-          config.projects.current,
-        );
-        languages = languages.sort((a, b) =>
-          a.language.name.localeCompare(b.language.name),
-        );
-
-        const languageSelect = `
-      <select>
-        ${languages
-          .map(
-            (language) => `
-              <option
-                  value="${language.language.code}"
-                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
-                  data-url="${language.urls.documentation}">
-                  ${language.language.name}
-              </option>`,
-          )
-          .join("\n")}
-       </select>
-    `;
-
-        languageSwitch.innerHTML = languageSelect;
-        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
-      }
-      else {
-        languageSwitch.remove();
-      }
-    }
-  });
-}
-
-document.addEventListener("readthedocs-addons-data-ready", function (event) {
-  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
-  document
-    .querySelector("[role='search'] input")
-    .addEventListener("focusin", () => {
-      const event = new CustomEvent("readthedocs-search-show");
-      document.dispatchEvent(event);
-    });
-});
\ No newline at end of file
diff --git a/0.18.0/_static/language_data.js b/0.18.0/_static/language_data.js
deleted file mode 100644
index c7fe6c6f..00000000
--- a/0.18.0/_static/language_data.js
+++ /dev/null
@@ -1,192 +0,0 @@
-/*
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- */
-
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
-
-
-/* Non-minified version is copied as a separate JS file, if available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
-  var step2list = {
-    ational: 'ate',
-    tional: 'tion',
-    enci: 'ence',
-    anci: 'ance',
-    izer: 'ize',
-    bli: 'ble',
-    alli: 'al',
-    entli: 'ent',
-    eli: 'e',
-    ousli: 'ous',
-    ization: 'ize',
-    ation: 'ate',
-    ator: 'ate',
-    alism: 'al',
-    iveness: 'ive',
-    fulness: 'ful',
-    ousness: 'ous',
-    aliti: 'al',
-    iviti: 'ive',
-    biliti: 'ble',
-    logi: 'log'
-  };
-
-  var step3list = {
-    icate: 'ic',
-    ative: '',
-    alize: 'al',
-    iciti: 'ic',
-    ical: 'ic',
-    ful: '',
-    ness: ''
-  };
-
-  var c = "[^aeiou]";          // consonant
-  var v = "[aeiouy]";          // vowel
-  var C = c + "[^aeiouy]*";    // consonant sequence
-  var V = v + "[aeiou]*";      // vowel sequence
-
-  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
-  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
-  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
-  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
-
-  this.stemWord = function (w) {
-    var stem;
-    var suffix;
-    var firstch;
-    var origword = w;
-
-    if (w.length < 3)
-      return w;
-
-    var re;
-    var re2;
-    var re3;
-    var re4;
-
-    firstch = w.substr(0,1);
-    if (firstch == "y")
-      w = firstch.toUpperCase() + w.substr(1);
-
-    // Step 1a
-    re = /^(.+?)(ss|i)es$/;
-    re2 = /^(.+?)([^s])s$/;
-
-    if (re.test(w))
-      w = w.replace(re,"$1$2");
-    else if (re2.test(w))
-      w = w.replace(re2,"$1$2");
-
-    // Step 1b
-    re = /^(.+?)eed$/;
-    re2 = /^(.+?)(ed|ing)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      re = new RegExp(mgr0);
-      if (re.test(fp[1])) {
-        re = /.$/;
-        w = w.replace(re,"");
-      }
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1];
-      re2 = new RegExp(s_v);
-      if (re2.test(stem)) {
-        w = stem;
-        re2 = /(at|bl|iz)$/;
-        re3 = new RegExp("([^aeiouylsz])\\1$");
-        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-        if (re2.test(w))
-          w = w + "e";
-        else if (re3.test(w)) {
-          re = /.$/;
-          w = w.replace(re,"");
-        }
-        else if (re4.test(w))
-          w = w + "e";
-      }
-    }
-
-    // Step 1c
-    re = /^(.+?)y$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(s_v);
-      if (re.test(stem))
-        w = stem + "i";
-    }
-
-    // Step 2
-    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step2list[suffix];
-    }
-
-    // Step 3
-    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step3list[suffix];
-    }
-
-    // Step 4
-    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
-    re2 = /^(.+?)(s|t)(ion)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      if (re.test(stem))
-        w = stem;
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1] + fp[2];
-      re2 = new RegExp(mgr1);
-      if (re2.test(stem))
-        w = stem;
-    }
-
-    // Step 5
-    re = /^(.+?)e$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      re2 = new RegExp(meq1);
-      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
-        w = stem;
-    }
-    re = /ll$/;
-    re2 = new RegExp(mgr1);
-    if (re.test(w) && re2.test(w)) {
-      re = /.$/;
-      w = w.replace(re,"");
-    }
-
-    // and turn initial Y back to y
-    if (firstch == "y")
-      w = firstch.toLowerCase() + w.substr(1);
-    return w;
-  }
-}
-
diff --git a/0.18.0/_static/minus.png b/0.18.0/_static/minus.png
deleted file mode 100644
index d96755fd..00000000
Binary files a/0.18.0/_static/minus.png and /dev/null differ
diff --git a/0.18.0/_static/nbsphinx-broken-thumbnail.svg b/0.18.0/_static/nbsphinx-broken-thumbnail.svg
deleted file mode 100644
index 4919ca88..00000000
--- a/0.18.0/_static/nbsphinx-broken-thumbnail.svg
+++ /dev/null
@@ -1,9 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
-  <style>
-    svg { fill: lightcoral; }
-    @media (prefers-color-scheme: dark) {
-      svg { fill: crimson; }
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diff --git a/0.18.0/_static/searchtools.js b/0.18.0/_static/searchtools.js
deleted file mode 100644
index 2c774d17..00000000
--- a/0.18.0/_static/searchtools.js
+++ /dev/null
@@ -1,632 +0,0 @@
-/*
- * Sphinx JavaScript utilities for the full-text search.
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
-  var Scorer = {
-    // Implement the following function to further tweak the score for each result
-    // The function takes a result array [docname, title, anchor, descr, score, filename]
-    // and returns the new score.
-    /*
-    score: result => {
-      const [docname, title, anchor, descr, score, filename, kind] = result
-      return score
-    },
-    */
-
-    // query matches the full name of an object
-    objNameMatch: 11,
-    // or matches in the last dotted part of the object name
-    objPartialMatch: 6,
-    // Additive scores depending on the priority of the object
-    objPrio: {
-      0: 15, // used to be importantResults
-      1: 5, // used to be objectResults
-      2: -5, // used to be unimportantResults
-    },
-    //  Used when the priority is not in the mapping.
-    objPrioDefault: 0,
-
-    // query found in title
-    title: 15,
-    partialTitle: 7,
-    // query found in terms
-    term: 5,
-    partialTerm: 2,
-  };
-}
-
-// Global search result kind enum, used by themes to style search results.
-class SearchResultKind {
-    static get index() { return  "index"; }
-    static get object() { return "object"; }
-    static get text() { return "text"; }
-    static get title() { return "title"; }
-}
-
-const _removeChildren = (element) => {
-  while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
-  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, searchTerms, highlightTerms) => {
-  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
-  const contentRoot = document.documentElement.dataset.content_root;
-
-  const [docName, title, anchor, descr, score, _filename, kind] = item;
-
-  let listItem = document.createElement("li");
-  // Add a class representing the item's type:
-  // can be used by a theme's CSS selector for styling
-  // See SearchResultKind for the class names.
-  listItem.classList.add(`kind-${kind}`);
-  let requestUrl;
-  let linkUrl;
-  if (docBuilder === "dirhtml") {
-    // dirhtml builder
-    let dirname = docName + "/";
-    if (dirname.match(/\/index\/$/))
-      dirname = dirname.substring(0, dirname.length - 6);
-    else if (dirname === "index/") dirname = "";
-    requestUrl = contentRoot + dirname;
-    linkUrl = requestUrl;
-  } else {
-    // normal html builders
-    requestUrl = contentRoot + docName + docFileSuffix;
-    linkUrl = docName + docLinkSuffix;
-  }
-  let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + anchor;
-  linkEl.dataset.score = score;
-  linkEl.innerHTML = title;
-  if (descr) {
-    listItem.appendChild(document.createElement("span")).innerHTML =
-      " (" + descr + ")";
-    // highlight search terms in the description
-    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
-      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
-  }
-  else if (showSearchSummary)
-    fetch(requestUrl)
-      .then((responseData) => responseData.text())
-      .then((data) => {
-        if (data)
-          listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, anchor)
-          );
-        // highlight search terms in the summary
-        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
-          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
-      });
-  Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
-  Search.stopPulse();
-  Search.title.innerText = _("Search Results");
-  if (!resultCount)
-    Search.status.innerText = Documentation.gettext(
-      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
-    );
-  else
-    Search.status.innerText = Documentation.ngettext(
-      "Search finished, found one page matching the search query.",
-      "Search finished, found ${resultCount} pages matching the search query.",
-      resultCount,
-    ).replace('${resultCount}', resultCount);
-};
-const _displayNextItem = (
-  results,
-  resultCount,
-  searchTerms,
-  highlightTerms,
-) => {
-  // results left, load the summary and display it
-  // this is intended to be dynamic (don't sub resultsCount)
-  if (results.length) {
-    _displayItem(results.pop(), searchTerms, highlightTerms);
-    setTimeout(
-      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
-      5
-    );
-  }
-  // search finished, update title and status message
-  else _finishSearch(resultCount);
-};
-// Helper function used by query() to order search results.
-// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
-// Order the results by score (in opposite order of appearance, since the
-// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
-const _orderResultsByScoreThenName = (a, b) => {
-  const leftScore = a[4];
-  const rightScore = b[4];
-  if (leftScore === rightScore) {
-    // same score: sort alphabetically
-    const leftTitle = a[1].toLowerCase();
-    const rightTitle = b[1].toLowerCase();
-    if (leftTitle === rightTitle) return 0;
-    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-  }
-  return leftScore > rightScore ? 1 : -1;
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
-  var splitQuery = (query) => query
-      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
-      .filter(term => term)  // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
-  _index: null,
-  _queued_query: null,
-  _pulse_status: -1,
-
-  htmlToText: (htmlString, anchor) => {
-    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    for (const removalQuery of [".headerlink", "script", "style"]) {
-      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
-    }
-    if (anchor) {
-      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
-      if (anchorContent) return anchorContent.textContent;
-
-      console.warn(
-        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
-      );
-    }
-
-    // if anchor not specified or not found, fall back to main content
-    const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent) return docContent.textContent;
-
-    console.warn(
-      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
-    );
-    return "";
-  },
-
-  init: () => {
-    const query = new URLSearchParams(window.location.search).get("q");
-    document
-      .querySelectorAll('input[name="q"]')
-      .forEach((el) => (el.value = query));
-    if (query) Search.performSearch(query);
-  },
-
-  loadIndex: (url) =>
-    (document.body.appendChild(document.createElement("script")).src = url),
-
-  setIndex: (index) => {
-    Search._index = index;
-    if (Search._queued_query !== null) {
-      const query = Search._queued_query;
-      Search._queued_query = null;
-      Search.query(query);
-    }
-  },
-
-  hasIndex: () => Search._index !== null,
-
-  deferQuery: (query) => (Search._queued_query = query),
-
-  stopPulse: () => (Search._pulse_status = -1),
-
-  startPulse: () => {
-    if (Search._pulse_status >= 0) return;
-
-    const pulse = () => {
-      Search._pulse_status = (Search._pulse_status + 1) % 4;
-      Search.dots.innerText = ".".repeat(Search._pulse_status);
-      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
-    };
-    pulse();
-  },
-
-  /**
-   * perform a search for something (or wait until index is loaded)
-   */
-  performSearch: (query) => {
-    // create the required interface elements
-    const searchText = document.createElement("h2");
-    searchText.textContent = _("Searching");
-    const searchSummary = document.createElement("p");
-    searchSummary.classList.add("search-summary");
-    searchSummary.innerText = "";
-    const searchList = document.createElement("ul");
-    searchList.setAttribute("role", "list");
-    searchList.classList.add("search");
-
-    const out = document.getElementById("search-results");
-    Search.title = out.appendChild(searchText);
-    Search.dots = Search.title.appendChild(document.createElement("span"));
-    Search.status = out.appendChild(searchSummary);
-    Search.output = out.appendChild(searchList);
-
-    const searchProgress = document.getElementById("search-progress");
-    // Some themes don't use the search progress node
-    if (searchProgress) {
-      searchProgress.innerText = _("Preparing search...");
-    }
-    Search.startPulse();
-
-    // index already loaded, the browser was quick!
-    if (Search.hasIndex()) Search.query(query);
-    else Search.deferQuery(query);
-  },
-
-  _parseQuery: (query) => {
-    // stem the search terms and add them to the correct list
-    const stemmer = new Stemmer();
-    const searchTerms = new Set();
-    const excludedTerms = new Set();
-    const highlightTerms = new Set();
-    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
-    splitQuery(query.trim()).forEach((queryTerm) => {
-      const queryTermLower = queryTerm.toLowerCase();
-
-      // maybe skip this "word"
-      // stopwords array is from language_data.js
-      if (
-        stopwords.indexOf(queryTermLower) !== -1 ||
-        queryTerm.match(/^\d+$/)
-      )
-        return;
-
-      // stem the word
-      let word = stemmer.stemWord(queryTermLower);
-      // select the correct list
-      if (word[0] === "-") excludedTerms.add(word.substr(1));
-      else {
-        searchTerms.add(word);
-        highlightTerms.add(queryTermLower);
-      }
-    });
-
-    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
-      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
-    }
-
-    // console.debug("SEARCH: searching for:");
-    // console.info("required: ", [...searchTerms]);
-    // console.info("excluded: ", [...excludedTerms]);
-
-    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-    const allTitles = Search._index.alltitles;
-    const indexEntries = Search._index.indexentries;
-
-    // Collect multiple result groups to be sorted separately and then ordered.
-    // Each is an array of [docname, title, anchor, descr, score, filename, kind].
-    const normalResults = [];
-    const nonMainIndexResults = [];
-
-    _removeChildren(document.getElementById("search-progress"));
-
-    const queryLower = query.toLowerCase().trim();
-    for (const [title, foundTitles] of Object.entries(allTitles)) {
-      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
-        for (const [file, id] of foundTitles) {
-          const score = Math.round(Scorer.title * queryLower.length / title.length);
-          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
-          normalResults.push([
-            docNames[file],
-            titles[file] !== title ? `${titles[file]} > ${title}` : title,
-            id !== null ? "#" + id : "",
-            null,
-            score + boost,
-            filenames[file],
-            SearchResultKind.title,
-          ]);
-        }
-      }
-    }
-
-    // search for explicit entries in index directives
-    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
-      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
-        for (const [file, id, isMain] of foundEntries) {
-          const score = Math.round(100 * queryLower.length / entry.length);
-          const result = [
-            docNames[file],
-            titles[file],
-            id ? "#" + id : "",
-            null,
-            score,
-            filenames[file],
-            SearchResultKind.index,
-          ];
-          if (isMain) {
-            normalResults.push(result);
-          } else {
-            nonMainIndexResults.push(result);
-          }
-        }
-      }
-    }
-
-    // lookup as object
-    objectTerms.forEach((term) =>
-      normalResults.push(...Search.performObjectSearch(term, objectTerms))
-    );
-
-    // lookup as search terms in fulltext
-    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
-    // let the scorer override scores with a custom scoring function
-    if (Scorer.score) {
-      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
-      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
-    }
-
-    // Sort each group of results by score and then alphabetically by name.
-    normalResults.sort(_orderResultsByScoreThenName);
-    nonMainIndexResults.sort(_orderResultsByScoreThenName);
-
-    // Combine the result groups in (reverse) order.
-    // Non-main index entries are typically arbitrary cross-references,
-    // so display them after other results.
-    let results = [...nonMainIndexResults, ...normalResults];
-
-    // remove duplicate search results
-    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
-    let seen = new Set();
-    results = results.reverse().reduce((acc, result) => {
-      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
-      if (!seen.has(resultStr)) {
-        acc.push(result);
-        seen.add(resultStr);
-      }
-      return acc;
-    }, []);
-
-    return results.reverse();
-  },
-
-  query: (query) => {
-    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
-    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
-
-    // for debugging
-    //Search.lastresults = results.slice();  // a copy
-    // console.info("search results:", Search.lastresults);
-
-    // print the results
-    _displayNextItem(results, results.length, searchTerms, highlightTerms);
-  },
-
-  /**
-   * search for object names
-   */
-  performObjectSearch: (object, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const objects = Search._index.objects;
-    const objNames = Search._index.objnames;
-    const titles = Search._index.titles;
-
-    const results = [];
-
-    const objectSearchCallback = (prefix, match) => {
-      const name = match[4]
-      const fullname = (prefix ? prefix + "." : "") + name;
-      const fullnameLower = fullname.toLowerCase();
-      if (fullnameLower.indexOf(object) < 0) return;
-
-      let score = 0;
-      const parts = fullnameLower.split(".");
-
-      // check for different match types: exact matches of full name or
-      // "last name" (i.e. last dotted part)
-      if (fullnameLower === object || parts.slice(-1)[0] === object)
-        score += Scorer.objNameMatch;
-      else if (parts.slice(-1)[0].indexOf(object) > -1)
-        score += Scorer.objPartialMatch; // matches in last name
-
-      const objName = objNames[match[1]][2];
-      const title = titles[match[0]];
-
-      // If more than one term searched for, we require other words to be
-      // found in the name/title/description
-      const otherTerms = new Set(objectTerms);
-      otherTerms.delete(object);
-      if (otherTerms.size > 0) {
-        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
-        if (
-          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
-        )
-          return;
-      }
-
-      let anchor = match[3];
-      if (anchor === "") anchor = fullname;
-      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
-      const descr = objName + _(", in ") + title;
-
-      // add custom score for some objects according to scorer
-      if (Scorer.objPrio.hasOwnProperty(match[2]))
-        score += Scorer.objPrio[match[2]];
-      else score += Scorer.objPrioDefault;
-
-      results.push([
-        docNames[match[0]],
-        fullname,
-        "#" + anchor,
-        descr,
-        score,
-        filenames[match[0]],
-        SearchResultKind.object,
-      ]);
-    };
-    Object.keys(objects).forEach((prefix) =>
-      objects[prefix].forEach((array) =>
-        objectSearchCallback(prefix, array)
-      )
-    );
-    return results;
-  },
-
-  /**
-   * search for full-text terms in the index
-   */
-  performTermsSearch: (searchTerms, excludedTerms) => {
-    // prepare search
-    const terms = Search._index.terms;
-    const titleTerms = Search._index.titleterms;
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-
-    const scoreMap = new Map();
-    const fileMap = new Map();
-
-    // perform the search on the required terms
-    searchTerms.forEach((word) => {
-      const files = [];
-      const arr = [
-        { files: terms[word], score: Scorer.term },
-        { files: titleTerms[word], score: Scorer.title },
-      ];
-      // add support for partial matches
-      if (word.length > 2) {
-        const escapedWord = _escapeRegExp(word);
-        if (!terms.hasOwnProperty(word)) {
-          Object.keys(terms).forEach((term) => {
-            if (term.match(escapedWord))
-              arr.push({ files: terms[term], score: Scorer.partialTerm });
-          });
-        }
-        if (!titleTerms.hasOwnProperty(word)) {
-          Object.keys(titleTerms).forEach((term) => {
-            if (term.match(escapedWord))
-              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
-          });
-        }
-      }
-
-      // no match but word was a required one
-      if (arr.every((record) => record.files === undefined)) return;
-
-      // found search word in contents
-      arr.forEach((record) => {
-        if (record.files === undefined) return;
-
-        let recordFiles = record.files;
-        if (recordFiles.length === undefined) recordFiles = [recordFiles];
-        files.push(...recordFiles);
-
-        // set score for the word in each file
-        recordFiles.forEach((file) => {
-          if (!scoreMap.has(file)) scoreMap.set(file, {});
-          scoreMap.get(file)[word] = record.score;
-        });
-      });
-
-      // create the mapping
-      files.forEach((file) => {
-        if (!fileMap.has(file)) fileMap.set(file, [word]);
-        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
-      });
-    });
-
-    // now check if the files don't contain excluded terms
-    const results = [];
-    for (const [file, wordList] of fileMap) {
-      // check if all requirements are matched
-
-      // as search terms with length < 3 are discarded
-      const filteredTermCount = [...searchTerms].filter(
-        (term) => term.length > 2
-      ).length;
-      if (
-        wordList.length !== searchTerms.size &&
-        wordList.length !== filteredTermCount
-      )
-        continue;
-
-      // ensure that none of the excluded terms is in the search result
-      if (
-        [...excludedTerms].some(
-          (term) =>
-            terms[term] === file ||
-            titleTerms[term] === file ||
-            (terms[term] || []).includes(file) ||
-            (titleTerms[term] || []).includes(file)
-        )
-      )
-        break;
-
-      // select one (max) score for the file.
-      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
-      // add result to the result list
-      results.push([
-        docNames[file],
-        titles[file],
-        "",
-        null,
-        score,
-        filenames[file],
-        SearchResultKind.text,
-      ]);
-    }
-    return results;
-  },
-
-  /**
-   * helper function to return a node containing the
-   * search summary for a given text. keywords is a list
-   * of stemmed words.
-   */
-  makeSearchSummary: (htmlText, keywords, anchor) => {
-    const text = Search.htmlToText(htmlText, anchor);
-    if (text === "") return null;
-
-    const textLower = text.toLowerCase();
-    const actualStartPosition = [...keywords]
-      .map((k) => textLower.indexOf(k.toLowerCase()))
-      .filter((i) => i > -1)
-      .slice(-1)[0];
-    const startWithContext = Math.max(actualStartPosition - 120, 0);
-
-    const top = startWithContext === 0 ? "" : "...";
-    const tail = startWithContext + 240 < text.length ? "..." : "";
-
-    let summary = document.createElement("p");
-    summary.classList.add("context");
-    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
-
-    return summary;
-  },
-};
-
-_ready(Search.init);
diff --git a/0.18.0/_static/sphinx_highlight.js b/0.18.0/_static/sphinx_highlight.js
deleted file mode 100644
index 8a96c69a..00000000
--- a/0.18.0/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,154 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0 &&
-      !parent.classList.contains(className) &&
-      !parent.classList.contains("nohighlight")
-    ) {
-      let span;
-
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
-
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      const rest = document.createTextNode(val.substr(pos + text.length));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
-          rest,
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-      /* There may be more occurrences of search term in this node. So call this
-       * function recursively on the remaining fragment.
-       */
-      _highlight(rest, addItems, text, className);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
-      }
-    }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
-  }
-};
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-
-  /**
-   * highlight the search words provided in localstorage in the text
-   */
-  highlightSearchWords: () => {
-    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
-
-    // get and clear terms from localstorage
-    const url = new URL(window.location);
-    const highlight =
-        localStorage.getItem("sphinx_highlight_terms")
-        || url.searchParams.get("highlight")
-        || "";
-    localStorage.removeItem("sphinx_highlight_terms")
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
-
-    // get individual terms from highlight string
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">' +
-            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
-            _("Hide Search Matches") +
-            "</a></p>"
-        )
-    );
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    localStorage.removeItem("sphinx_highlight_terms")
-  },
-
-  initEscapeListener: () => {
-    // only install a listener if it is really needed
-    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
-    document.addEventListener("keydown", (event) => {
-      // bail for input elements
-      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
-      // bail with special keys
-      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
-      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
-        SphinxHighlight.hideSearchWords();
-        event.preventDefault();
-      }
-    });
-  },
-};
-
-_ready(() => {
-  /* Do not call highlightSearchWords() when we are on the search page.
-   * It will highlight words from the *previous* search query.
-   */
-  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
-  SphinxHighlight.initEscapeListener();
-});
diff --git a/0.18.0/authors.html b/0.18.0/authors.html
deleted file mode 100644
index fe2220cb..00000000
--- a/0.18.0/authors.html
+++ /dev/null
@@ -1,181 +0,0 @@
-
-
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" data-content_root="./">
-<head>
-  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Contributors ✨ &mdash; rioxarray 0.18.0 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
-
-  
-      <script src="_static/jquery.js?v=5d32c60e"></script>
-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=ba2e7b09"></script>
-      <script src="_static/doctools.js?v=9bcbadda"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-    <script src="_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="genindex.html" />
-    <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="History" href="history.html" />
-    <link rel="prev" title="Contributing" href="contributing.html" /> 
-</head>
-
-<body class="wy-body-for-nav"> 
-  <div class="wy-grid-for-nav">
-    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
-      <div class="wy-side-scroll">
-        <div class="wy-side-nav-search" >
-
-          
-          
-          <a href="index.html" class="icon icon-home">
-            rioxarray
-          </a>
-<div role="search">
-  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
-    <input type="hidden" name="check_keywords" value="yes" />
-    <input type="hidden" name="area" value="default" />
-  </form>
-</div>
-        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="readme.html">rioxarray README</a></li>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started.html">Getting Started</a></li>
-<li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Usage Examples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="modules.html">rioxarray</a></li>
-<li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Contributors ✨</a></li>
-<li class="toctree-l1"><a class="reference internal" href="history.html">History</a></li>
-</ul>
-
-        </div>
-      </div>
-    </nav>
-
-    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
-          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="index.html">rioxarray</a>
-      </nav>
-
-      <div class="wy-nav-content">
-        <div class="rst-content">
-          <div role="navigation" aria-label="Page navigation">
-  <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
-      <li class="breadcrumb-item active">Contributors ✨</li>
-      <li class="wy-breadcrumbs-aside">
-            <a href="_sources/authors.rst.txt" rel="nofollow"> View page source</a>
-      </li>
-  </ul>
-  <hr/>
-</div>
-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-           <div itemprop="articleBody">
-             
-  <section id="contributors">
-<h1>Contributors ✨<a class="headerlink" href="#contributors" title="Link to this heading"></a></h1>
-<p>Thanks goes to these wonderful people (<a class="reference external" href="https://allcontributors.org/docs/en/emoji-key">emoji key</a>):</p>
-<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
-<!-- prettier-ignore-start -->
-<!-- markdownlint-disable -->
-<table>
-  <tr>
-    <td align="center"><a href="https://www.linkedin.com/in/alan-snow-55bb8726/"><img src="https://avatars3.githubusercontent.com/u/8699967?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Alan D. Snow</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=snowman2" title="Code">💻</a> <a href="#ideas-snowman2" title="Ideas, Planning, & Feedback">🤔</a> <a href="#question-snowman2" title="Answering Questions">💬</a> <a href="https://github.com/corteva/rioxarray/issues?q=author%3Asnowman2" title="Bug reports">🐛</a> <a href="https://github.com/corteva/rioxarray/commits?author=snowman2" title="Documentation">📖</a> <a href="#example-snowman2" title="Examples">💡</a> <a href="#maintenance-snowman2" title="Maintenance">🚧</a> <a href="https://github.com/corteva/rioxarray/pulls?q=is%3Apr+reviewed-by%3Asnowman2" title="Reviewed Pull Requests">👀</a> <a href="https://github.com/corteva/rioxarray/commits?author=snowman2" title="Tests">⚠️</a></td>
-    <td align="center"><a href="https://github.com/alfredoahds"><img src="https://avatars2.githubusercontent.com/u/16520854?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Alfredo Delos Santos</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=alfredoahds" title="Code">💻</a> <a href="#ideas-alfredoahds" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/corteva/rioxarray/pulls?q=is%3Apr+reviewed-by%3Aalfredoahds" title="Reviewed Pull Requests">👀</a></td>
-    <td align="center"><a href="https://github.com/djhoese"><img src="https://avatars3.githubusercontent.com/u/1828519?v=4?s=100" width="100px;" alt=""/><br /><sub><b>David Hoese</b></sub></a><br /><a href="#ideas-djhoese" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/corteva/rioxarray/pulls?q=is%3Apr+reviewed-by%3Adjhoese" title="Reviewed Pull Requests">👀</a> <a href="https://github.com/corteva/rioxarray/commits?author=djhoese" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=djhoese" title="Tests">⚠️</a></td>
-    <td align="center"><a href="https://github.com/justingruca"><img src="https://avatars2.githubusercontent.com/u/32334907?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Justin Gruca</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/pulls?q=is%3Apr+reviewed-by%3Ajustingruca" title="Reviewed Pull Requests">👀</a></td>
-    <td align="center"><a href="https://remotepixel.ca"><img src="https://avatars2.githubusercontent.com/u/10407788?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Vincent Sarago</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=vincentsarago" title="Documentation">📖</a> <a href="https://github.com/corteva/rioxarray/commits?author=vincentsarago" title="Tests">⚠️</a></td>
-    <td align="center"><a href="http://about.me/rich.signell"><img src="https://avatars1.githubusercontent.com/u/1872600?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Rich Signell</b></sub></a><br /><a href="#ideas-rsignell-usgs" title="Ideas, Planning, & Feedback">🤔</a></td>
-    <td align="center"><a href="https://github.com/pmallas"><img src="https://avatars0.githubusercontent.com/u/6051395?v=4?s=100" width="100px;" alt=""/><br /><sub><b>pmallas</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=pmallas" title="Code">💻</a> <a href="#ideas-pmallas" title="Ideas, Planning, & Feedback">🤔</a></td>
-  </tr>
-  <tr>
-    <td align="center"><a href="https://github.com/davidbrochart"><img src="https://avatars2.githubusercontent.com/u/4711805?v=4?s=100" width="100px;" alt=""/><br /><sub><b>David Brochart</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=davidbrochart" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=davidbrochart" title="Tests">⚠️</a> <a href="#ideas-davidbrochart" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/corteva/rioxarray/commits?author=davidbrochart" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://github.com/cheginit"><img src="https://avatars2.githubusercontent.com/u/13016644?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Taher Chegini</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=cheginit" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/issues?q=author%3Acheginit" title="Bug reports">🐛</a></td>
-    <td align="center"><a href="http://joehamman.com"><img src="https://avatars3.githubusercontent.com/u/2443309?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Joe Hamman</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=jhamman" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/issues?q=author%3Ajhamman" title="Bug reports">🐛</a></td>
-    <td align="center"><a href="https://tomaugspurger.github.io/"><img src="https://avatars.githubusercontent.com/u/1312546?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Tom Augspurger</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=TomAugspurger" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/issues?q=author%3ATomAugspurger" title="Bug reports">🐛</a> <a href="#ideas-TomAugspurger" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/corteva/rioxarray/commits?author=TomAugspurger" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://github.com/RichardScottOZ"><img src="https://avatars.githubusercontent.com/u/72196131?v=4?s=100" width="100px;" alt=""/><br /><sub><b>RichardScottOZ</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=RichardScottOZ" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://sites.google.com/view/raybellwaves/home"><img src="https://avatars.githubusercontent.com/u/17162724?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Ray Bell</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=raybellwaves" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://bopen.eu/"><img src="https://avatars.githubusercontent.com/u/226037?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Alessandro Amici</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=alexamici" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=alexamici" title="Documentation">📖</a> <a href="https://github.com/corteva/rioxarray/commits?author=alexamici" title="Tests">⚠️</a></td>
-  </tr>
-  <tr>
-    <td align="center"><a href="https://github.com/remi-braun"><img src="https://avatars.githubusercontent.com/u/67311115?v=4?s=100" width="100px;" alt=""/><br /><sub><b>remi-braun</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=remi-braun" title="Documentation">📖</a></td>
-    <td align="center"><a href="http://scottyhq.github.io/"><img src="https://avatars.githubusercontent.com/u/3924836?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Scott Henderson</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/issues?q=author%3Ascottyhq" title="Bug reports">🐛</a> <a href="https://github.com/corteva/rioxarray/commits?author=scottyhq" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=scottyhq" title="Tests">⚠️</a></td>
-    <td align="center"><a href="http://andrewannex.com/"><img src="https://avatars.githubusercontent.com/u/2126916?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Andrew Annex</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=AndrewAnnex" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=AndrewAnnex" title="Documentation">📖</a> <a href="https://github.com/corteva/rioxarray/commits?author=AndrewAnnex" title="Tests">⚠️</a></td>
-    <td align="center"><a href="https://github.com/fbunt"><img src="https://avatars.githubusercontent.com/u/7178572?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Fred Bunt</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/issues?q=author%3Afbunt" title="Bug reports">🐛</a> <a href="https://github.com/corteva/rioxarray/commits?author=fbunt" title="Tests">⚠️</a> <a href="https://github.com/corteva/rioxarray/commits?author=fbunt" title="Code">💻</a></td>
-    <td align="center"><a href="https://github.com/MarkusZehner"><img src="https://avatars.githubusercontent.com/u/56972144?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Markus Zehner</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/issues?q=author%3AMarkusZehner" title="Bug reports">🐛</a> <a href="https://github.com/corteva/rioxarray/commits?author=MarkusZehner" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=MarkusZehner" title="Tests">⚠️</a> <a href="#ideas-MarkusZehner" title="Ideas, Planning, & Feedback">🤔</a></td>
-    <td align="center"><a href="http://www.four43.com/"><img src="https://avatars.githubusercontent.com/u/517006?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Seth Miller</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=four43" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=four43" title="Documentation">📖</a> <a href="https://github.com/corteva/rioxarray/commits?author=four43" title="Tests">⚠️</a></td>
-    <td align="center"><a href="https://github.com/mraspaud"><img src="https://avatars.githubusercontent.com/u/167802?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Martin Raspaud</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=mraspaud" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=mraspaud" title="Tests">⚠️</a> <a href="https://github.com/corteva/rioxarray/commits?author=mraspaud" title="Documentation">📖</a> <a href="#ideas-mraspaud" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/corteva/rioxarray/issues?q=author%3Amraspaud" title="Bug reports">🐛</a></td>
-  </tr>
-  <tr>
-    <td align="center"><a href="https://cordmaur.medium.com/"><img src="https://avatars.githubusercontent.com/u/19617404?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Mauricio Cordeiro</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/issues?q=author%3Acordmaur" title="Bug reports">🐛</a> <a href="https://github.com/corteva/rioxarray/commits?author=cordmaur" title="Code">💻</a></td>
-    <td align="center"><a href="https://github.com/GCBallesteros"><img src="https://avatars.githubusercontent.com/u/6859287?v=4?s=100" width="100px;" alt=""/><br /><sub><b>GBallesteros</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/issues?q=author%3AGCBallesteros" title="Bug reports">🐛</a> <a href="https://github.com/corteva/rioxarray/commits?author=GCBallesteros" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=GCBallesteros" title="Tests">⚠️</a></td>
-    <td align="center"><a href="https://github.com/apiwat-chantawibul"><img src="https://avatars.githubusercontent.com/u/8740786?v=4?s=100" width="100px;" alt=""/><br /><sub><b>apiwat-chantawibul</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=apiwat-chantawibul" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://mwtoews.github.io/"><img src="https://avatars.githubusercontent.com/u/895458?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Mike Taves</b></sub></a><br /><a href="#maintenance-mwtoews" title="Maintenance">🚧</a></td>
-    <td align="center"><a href="https://github.com/Zionett"><img src="https://avatars.githubusercontent.com/u/9539198?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Sangzi Liang</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=Zionett" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://github.com/jonasViehweger"><img src="https://avatars.githubusercontent.com/u/53001455?v=4?s=100" width="100px;" alt=""/><br /><sub><b>jonasViehweger</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=jonasViehweger" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/issues?q=author%3AjonasViehweger" title="Bug reports">🐛</a></td>
-    <td align="center"><a href="https://github.com/chbrandt"><img src="https://avatars.githubusercontent.com/u/482051?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Carlos H Brandt</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=chbrandt" title="Documentation">📖</a></td>
-  </tr>
-  <tr>
-    <td align="center"><a href="https://github.com/JessicaS11"><img src="https://avatars.githubusercontent.com/u/11756442?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Jessica Scheick</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=JessicaS11" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://www.linkedin.com/in/micheleclaus/"><img src="https://avatars.githubusercontent.com/u/31700619?v=4?s=100" width="100px;" alt=""/><br /><sub><b>clausmichele</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/pulls?q=is%3Apr+reviewed-by%3Aclausmichele" title="Reviewed Pull Requests">👀</a></td>
-    <td align="center"><a href="https://github.com/caldwellst"><img src="https://avatars.githubusercontent.com/u/8086547?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Seth Caldwell</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/issues?q=author%3Acaldwellst" title="Bug reports">🐛</a> <a href="https://github.com/corteva/rioxarray/commits?author=caldwellst" title="Code">💻</a></td>
-    <td align="center"><a href="https://github.com/stefank0"><img src="https://avatars.githubusercontent.com/u/32932217?v=4?s=100" width="100px;" alt=""/><br /><sub><b>stefank0</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=stefank0" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://itcarroll.github.io/"><img src="https://avatars.githubusercontent.com/u/3383837?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Ian Carroll</b></sub></a><br /><a href="#maintenance-itcarroll" title="Maintenance">🚧</a></td>
-    <td align="center"><a href="https://github.com/yvonnefroehlich"><img src="https://avatars.githubusercontent.com/u/94163266?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Yvonne Fröhlich</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=yvonnefroehlich" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://github.com/Kirill888"><img src="https://avatars.githubusercontent.com/u/1428024?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Kirill Kouzoubov</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=Kirill888" title="Code">💻</a></td>
-  </tr>
-  <tr>
-    <td align="center"><a href="https://github.com/xaviernogueira"><img src="https://avatars.githubusercontent.com/u/58796351?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Xavier Nogueira</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/issues?q=author%3Axaviernogueira" title="Bug reports">🐛</a></td>
-    <td align="center"><a href="https://github.com/valanm22"><img src="https://avatars.githubusercontent.com/u/84893551?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Valan Baptist Mathuranayagam</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=valanm22" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://github.com/peterm790"><img src="https://avatars.githubusercontent.com/u/52179978?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Peter Marsh</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=peterm790" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=peterm790" title="Tests">⚠️</a></td>
-    <td align="center"><a href="https://github.com/dluks"><img src="https://avatars.githubusercontent.com/u/4911680?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Daniel Lusk</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=dluks" title="Code">💻</a> <a href="https://github.com/corteva/rioxarray/commits?author=dluks" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://github.com/DahnJ"><img src="https://avatars.githubusercontent.com/u/18722560?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Daniel Jahn (dahn)</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/commits?author=DahnJ" title="Documentation">📖</a></td>
-    <td align="center"><a href="https://phofl.github.io/"><img src="https://avatars.githubusercontent.com/u/61934744?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Patrick Hoefler</b></sub></a><br /><a href="https://github.com/corteva/rioxarray/issues?q=author%3Aphofl" title="Bug reports">🐛</a></td>
-  </tr>
-</table>
-
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-<li class="toctree-l1"><a class="reference internal" href="readme.html">rioxarray README</a></li>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started.html">Getting Started</a></li>
-<li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Usage Examples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="modules.html">rioxarray</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Contributing</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#types-of-contributions">Types of Contributions</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#report-bugs">Report Bugs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fix-bugs">Fix Bugs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#implement-features">Implement Features</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#write-documentation">Write Documentation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#submit-feedback">Submit Feedback</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="#get-started">Get Started!</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#running-tests-with-docker">Running tests with docker</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#pull-request-guidelines">Pull Request Guidelines</a></li>
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-             
-  <section id="contributing">
-<h1>Contributing<a class="headerlink" href="#contributing" title="Link to this heading"></a></h1>
-<p>Contributions are welcome, and they are greatly appreciated! Every little bit
-helps, and credit will always be given.</p>
-<p>You can contribute in many ways:</p>
-<section id="types-of-contributions">
-<h2>Types of Contributions<a class="headerlink" href="#types-of-contributions" title="Link to this heading"></a></h2>
-<section id="report-bugs">
-<h3>Report Bugs<a class="headerlink" href="#report-bugs" title="Link to this heading"></a></h3>
-<p>Report bugs at <a class="reference external" href="https://github.com/corteva/rioxarray/issues">https://github.com/corteva/rioxarray/issues</a>.</p>
-<p>If you are reporting a bug, please include:</p>
-<ul class="simple">
-<li><p>Your operating system name and version.</p></li>
-<li><p>Any details about your local setup that might be helpful in troubleshooting.</p></li>
-<li><p>Detailed steps to reproduce the bug.</p></li>
-</ul>
-</section>
-<section id="fix-bugs">
-<h3>Fix Bugs<a class="headerlink" href="#fix-bugs" title="Link to this heading"></a></h3>
-<p>Look through the GitHub issues for bugs. Anything tagged with “bug” and “help
-wanted” is open to whoever wants to implement it.</p>
-</section>
-<section id="implement-features">
-<h3>Implement Features<a class="headerlink" href="#implement-features" title="Link to this heading"></a></h3>
-<p>Look through the GitHub issues for features. Anything tagged with “enhancement”
-and “help wanted” is open to whoever wants to implement it.</p>
-</section>
-<section id="write-documentation">
-<h3>Write Documentation<a class="headerlink" href="#write-documentation" title="Link to this heading"></a></h3>
-<p>rioxarray could always use more documentation, whether as part of the
-official rioxarray docs, in docstrings, or even on the web in blog posts,
-articles, and such.</p>
-</section>
-<section id="submit-feedback">
-<h3>Submit Feedback<a class="headerlink" href="#submit-feedback" title="Link to this heading"></a></h3>
-<p>The best way to send feedback is to file an issue at <a class="reference external" href="https://github.com/corteva/rioxarray/issues">https://github.com/corteva/rioxarray/issues</a>.</p>
-<p>If you are proposing a feature:</p>
-<ul class="simple">
-<li><p>Explain in detail how it would work.</p></li>
-<li><p>Keep the scope as narrow as possible, to make it easier to implement.</p></li>
-<li><p>Remember that this is a volunteer-driven project, and that contributions
-are welcome :)</p></li>
-</ul>
-</section>
-</section>
-<section id="get-started">
-<h2>Get Started!<a class="headerlink" href="#get-started" title="Link to this heading"></a></h2>
-<p>Ready to contribute? Here’s how to set up <cite>rioxarray</cite> for local development.</p>
-<ol class="arabic">
-<li><p>Fork the <cite>rioxarray</cite> repo on GitHub.</p></li>
-<li><p>Clone your fork locally:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>git@github.com:your_name_here/rioxarray.git
-</pre></div>
-</div>
-</li>
-<li><p>Create a python virtual environment</p></li>
-</ol>
-<p>Using conda:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>rioxarray/
-$<span class="w"> </span>conda<span class="w"> </span>env<span class="w"> </span>create
-$<span class="w"> </span>conda<span class="w"> </span>activate<span class="w"> </span>rioxarray
-</pre></div>
-</div>
-<p>Using python:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>rioxarray/
-$<span class="w"> </span>python<span class="w"> </span>-m<span class="w"> </span>venv<span class="w"> </span>venv
-$<span class="w"> </span>.<span class="w"> </span>venv/bin/activate
-</pre></div>
-</div>
-<ol class="arabic" start="4">
-<li><p>Install your local copy into a virtualenv:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span><span class="s2">&quot;.[all]&quot;</span>
-$<span class="w"> </span>python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements/dev.txt
-</pre></div>
-</div>
-</li>
-<li><p>Setup pre-commit hooks:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pre-commit<span class="w"> </span>install
-</pre></div>
-</div>
-</li>
-<li><p>Create a branch for local development:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>git<span class="w"> </span>checkout<span class="w"> </span>-b<span class="w"> </span>name-of-your-bugfix-or-feature
-</pre></div>
-</div>
-<p>Now you can make your changes locally.</p>
-</li>
-<li><p>When you’re done making changes, check that the tests pass:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pytest
-</pre></div>
-</div>
-</li>
-<li><p>Commit your changes and push your branch to GitHub (this should trigger pre-commit checks):</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>git<span class="w"> </span>add<span class="w"> </span>.
-$<span class="w"> </span>git<span class="w"> </span>commit<span class="w"> </span>-m<span class="w"> </span><span class="s2">&quot;Your detailed description of your changes.&quot;</span>
-$<span class="w"> </span>git<span class="w"> </span>push<span class="w"> </span>origin<span class="w"> </span>name-of-your-bugfix-or-feature
-</pre></div>
-</div>
-</li>
-<li><p>Submit a pull request through the GitHub website.</p></li>
-</ol>
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-<section id="running-tests-with-docker">
-<h2>Running tests with docker<a class="headerlink" href="#running-tests-with-docker" title="Link to this heading"></a></h2>
-<p>This assumes you have cloned the rioxarray repository and are in the base folder.</p>
-<ol class="arabic simple">
-<li><p>Build the docker image</p></li>
-</ol>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span>build<span class="w"> </span>-t<span class="w"> </span>rioxarray<span class="w"> </span>.
-</pre></div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Run the tests</p></li>
-</ol>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span>run<span class="w"> </span>--rm<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>-v<span class="w"> </span><span class="nv">$PWD</span>/test/:/app/test<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>-t<span class="w"> </span>rioxarray<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span><span class="s1">&#39;source /venv/bin/activate &amp;&amp; python -m pytest&#39;</span>
-</pre></div>
-</div>
-</section>
-<section id="pull-request-guidelines">
-<h2>Pull Request Guidelines<a class="headerlink" href="#pull-request-guidelines" title="Link to this heading"></a></h2>
-<p>Before you submit a pull request, check that it meets these guidelines:</p>
-<ol class="arabic simple">
-<li><p>The pull request should include tests.</p></li>
-<li><p>If the pull request adds functionality, the docs should be updated. Put
-your new functionality into a function with a docstring, and add the
-feature to the list in README.rst.</p></li>
-<li><p>The pull request should work for Python 3.10-3.12.</p></li>
-</ol>
-</section>
-<section id="tips">
-<h2>Tips<a class="headerlink" href="#tips" title="Link to this heading"></a></h2>
-<p>To run a subset of tests:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pytest<span class="w"> </span>test/unit/test_show_versions.py::test_get_main_info
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diff --git a/0.18.0/examples/COG.html b/0.18.0/examples/COG.html
deleted file mode 100644
index b3dc7a8a..00000000
--- a/0.18.0/examples/COG.html
+++ /dev/null
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-<li class="toctree-l1 current"><a class="reference internal" href="examples.html">Usage Examples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="resampling.html">Example - Resampling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="convert_to_raster.html">Example - Convert dataset to raster (GeoTiff)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="clip_geom.html">Example - Clip</a></li>
-<li class="toctree-l2"><a class="reference internal" href="clip_box.html">Example - Clip Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pad_box.html">Example - Pad Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="read-locks.html">Example - Reading COGs in Parallel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="reproject.html">Example - Reproject</a></li>
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-<li class="toctree-l2"><a class="reference external" href="https://corteva.github.io/geocube/html/examples/zonal_statistics.html">Example - Zonal Statistics</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/328320/144357">Extracting data within geometry (shape)</a></li>
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-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/346899/144357">Create a new raster TIFF file which is masked based on the GeoJSON file</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/354798/144357">How to mask NetCDF time series data from a shapefile in Python?</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/358058/144357">Extract data from raster at a point</a></li>
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-            <a href="../_sources/examples/COG.ipynb.txt" rel="nofollow"> View page source</a>
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-           <div itemprop="articleBody">
-             
-  <section id="Example---Cloud-Optimized-GeoTiff-(COG)">
-<h1>Example - Cloud Optimized GeoTiff (COG)<a class="headerlink" href="#Example---Cloud-Optimized-GeoTiff-(COG)" title="Link to this heading"></a></h1>
-<p>See docs for <a class="reference internal" href="../rioxarray.html#rioxarray-open-rasterio"><span class="std std-ref">rioxarray.open_rasterio</span></a></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray
-
-%matplotlib inline
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># from https://openaerialmap.org/
-cog_url = (
-    &quot;https://oin-hotosm.s3.amazonaws.com/&quot;
-    &quot;5d7dad0becaf880008a9bc88/0/5d7dad0becaf880008a9bc89.tif&quot;
-)
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds = rioxarray.open_rasterio(cog_url, masked=True, overview_level=4)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;xarray.DataArray (band: 3, y: 312, x: 688)&gt;
-[643968 values with dtype=float64]
-Coordinates:
-  * band         (band) int64 1 2 3
-  * y            (y) float64 4.34e+06 4.34e+06 4.34e+06 ... 4.339e+06 4.339e+06
-  * x            (x) float64 -1.333e+07 -1.333e+07 ... -1.333e+07 -1.333e+07
-    spatial_ref  int64 0
-Attributes:
-    transform:     (1.194328566955879, 0.0, -13334019.180693429, 0.0, -1.1943...
-    scales:        (1.0, 1.0, 1.0)
-    offsets:       (0.0, 0.0, 0.0)
-    grid_mapping:  spatial_ref
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds.astype(&quot;int&quot;).plot.imshow(rgb=&quot;band&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
-</pre></div></div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.image.AxesImage at 0x7fa30cc3aeb8&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_COG_5_2.png" src="../_images/examples_COG_5_2.png" />
-</div>
-</div>
-</section>
-
-
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-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="transform_bounds.html" class="btn btn-neutral float-left" title="Example - Transform Bounds" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="dask_read_write.html" class="btn btn-neutral float-right" title="Example - Reading and Writing with Dask" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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diff --git a/0.18.0/examples/COG.ipynb b/0.18.0/examples/COG.ipynb
deleted file mode 100644
index 55b0265b..00000000
--- a/0.18.0/examples/COG.ipynb
+++ /dev/null
@@ -1,137 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Cloud Optimized GeoTiff (COG)\n",
-    "\n",
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from https://openaerialmap.org/\n",
-    "cog_url = (\n",
-    "    \"https://oin-hotosm.s3.amazonaws.com/\"\n",
-    "    \"5d7dad0becaf880008a9bc88/0/5d7dad0becaf880008a9bc89.tif\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rioxarray.open_rasterio(cog_url, masked=True, overview_level=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray (band: 3, y: 312, x: 688)>\n",
-       "[643968 values with dtype=float64]\n",
-       "Coordinates:\n",
-       "  * band         (band) int64 1 2 3\n",
-       "  * y            (y) float64 4.34e+06 4.34e+06 4.34e+06 ... 4.339e+06 4.339e+06\n",
-       "  * x            (x) float64 -1.333e+07 -1.333e+07 ... -1.333e+07 -1.333e+07\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    transform:     (1.194328566955879, 0.0, -13334019.180693429, 0.0, -1.1943...\n",
-       "    scales:        (1.0, 1.0, 1.0)\n",
-       "    offsets:       (0.0, 0.0, 0.0)\n",
-       "    grid_mapping:  spatial_ref"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fa30cc3aeb8>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rds.astype(\"int\").plot.imshow(rgb=\"band\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
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diff --git a/0.18.0/examples/clip_box.html b/0.18.0/examples/clip_box.html
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-%matplotlib inline
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-    is_tiled:   0
-    nodata:     -28672.0
-    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-30c63a87-57cb-472f-837d-4f925d7c1c64' class='xr-array-in' type='checkbox' checked><label for='section-30c63a87-57cb-472f-837d-4f925d7c1c64' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan 687.0 687.0 ... 491.0 504.0 504.0 515.0 469.0 485.0</span></div><div class='xr-array-data'><pre>array([[ nan,  nan,  nan, ..., 656., 656., 554.],
-       [ nan,  nan,  nan, ..., 694., 694., 642.],
-       [ nan,  nan,  nan, ..., 456., 575., 642.],
-       ...,
-       [993., 817., 817., ..., 471., 479., 498.],
-       [893., 893., 816., ..., 479., 479., 469.],
-       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-af2aad65-513d-46c2-8e63-fda2854beb0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af2aad65-513d-46c2-8e63-fda2854beb0c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.05e+06 5.05e+06 ... 5.004e+06</div><input id='attrs-30d9748d-4a6f-413c-9e83-c0ff31dcab89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30d9748d-4a6f-413c-9e83-c0ff31dcab89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00a56479-fbb4-4223-92f1-b83428597e50' class='xr-var-data-in' type='checkbox'><label for='data-00a56479-fbb4-4223-92f1-b83428597e50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([5049992.781974, 5049761.125615, 5049529.469257, 5049297.812899,
-       5049066.156541, 5048834.500182, 5048602.843824, 5048371.187466,
-       5048139.531108, 5047907.874749, 5047676.218391, 5047444.562033,
-       5047212.905674, 5046981.249316, 5046749.592958, 5046517.9366  ,
-       5046286.280241, 5046054.623883, 5045822.967525, 5045591.311167,
-       5045359.654808, 5045127.99845 , 5044896.342092, 5044664.685734,
-       5044433.029375, 5044201.373017, 5043969.716659, 5043738.0603  ,
-       5043506.403942, 5043274.747584, 5043043.091226, 5042811.434867,
-       5042579.778509, 5042348.122151, 5042116.465793, 5041884.809434,
-       5041653.153076, 5041421.496718, 5041189.84036 , 5040958.184001,
-       5040726.527643, 5040494.871285, 5040263.214927, 5040031.558568,
-       5039799.90221 , 5039568.245852, 5039336.589493, 5039104.933135,
-       5038873.276777, 5038641.620419, 5038409.96406 , 5038178.307702,
-       5037946.651344, 5037714.994986, 5037483.338627, 5037251.682269,
-       5037020.025911, 5036788.369553, 5036556.713194, 5036325.056836,
-       5036093.400478, 5035861.74412 , 5035630.087761, 5035398.431403,
-       5035166.775045, 5034935.118686, 5034703.462328, 5034471.80597 ,
-       5034240.149612, 5034008.493253, 5033776.836895, 5033545.180537,
-       5033313.524179, 5033081.86782 , 5032850.211462, 5032618.555104,
-       5032386.898746, 5032155.242387, 5031923.586029, 5031691.929671,
-       5031460.273313, 5031228.616954, 5030996.960596, 5030765.304238,
-       5030533.647879, 5030301.991521, 5030070.335163, 5029838.678805,
-       5029607.022446, 5029375.366088, 5029143.70973 , 5028912.053372,
-       5028680.397013, 5028448.740655, 5028217.084297, 5027985.427939,
-       5027753.77158 , 5027522.115222, 5027290.458864, 5027058.802506,
-       5026827.146147, 5026595.489789, 5026363.833431, 5026132.177072,
-       5025900.520714, 5025668.864356, 5025437.207998, 5025205.551639,
-       5024973.895281, 5024742.238923, 5024510.582565, 5024278.926206,
-       5024047.269848, 5023815.61349 , 5023583.957132, 5023352.300773,
-       5023120.644415, 5022888.988057, 5022657.331698, 5022425.67534 ,
-       5022194.018982, 5021962.362624, 5021730.706265, 5021499.049907,
-       5021267.393549, 5021035.737191, 5020804.080832, 5020572.424474,
-       5020340.768116, 5020109.111758, 5019877.455399, 5019645.799041,
-       5019414.142683, 5019182.486325, 5018950.829966, 5018719.173608,
-       5018487.51725 , 5018255.860891, 5018024.204533, 5017792.548175,
-       5017560.891817, 5017329.235458, 5017097.5791  , 5016865.922742,
-       5016634.266384, 5016402.610025, 5016170.953667, 5015939.297309,
-       5015707.640951, 5015475.984592, 5015244.328234, 5015012.671876,
-       5014781.015518, 5014549.359159, 5014317.702801, 5014086.046443,
-       5013854.390084, 5013622.733726, 5013391.077368, 5013159.42101 ,
-       5012927.764651, 5012696.108293, 5012464.451935, 5012232.795577,
-       5012001.139218, 5011769.48286 , 5011537.826502, 5011306.170144,
-       5011074.513785, 5010842.857427, 5010611.201069, 5010379.544711,
-       5010147.888352, 5009916.231994, 5009684.575636, 5009452.919277,
-       5009221.262919, 5008989.606561, 5008757.950203, 5008526.293844,
-       5008294.637486, 5008062.981128, 5007831.32477 , 5007599.668411,
-       5007368.012053, 5007136.355695, 5006904.699337, 5006673.042978,
-       5006441.38662 , 5006209.730262, 5005978.073904, 5005746.417545,
-       5005514.761187, 5005283.104829, 5005051.44847 , 5004819.792112,
-       5004588.135754, 5004356.479396, 5004124.823037, 5003893.166679])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.274e+06 ... -7.228e+06</div><input id='attrs-ee56b91c-468c-4d4d-ad20-772a210aedbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee56b91c-468c-4d4d-ad20-772a210aedbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a47375a5-e337-405a-8fc2-ef4176e4ba9b' class='xr-var-data-in' type='checkbox'><label for='data-a47375a5-e337-405a-8fc2-ef4176e4ba9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([-7273893.821307, -7273662.164949, -7273430.508591, -7273198.852232,
-       -7272967.195874, -7272735.539516, -7272503.883158, -7272272.226799,
-       -7272040.570441, -7271808.914083, -7271577.257725, -7271345.601366,
-       -7271113.945008, -7270882.28865 , -7270650.632291, -7270418.975933,
-       -7270187.319575, -7269955.663217, -7269724.006858, -7269492.3505  ,
-       -7269260.694142, -7269029.037784, -7268797.381425, -7268565.725067,
-       -7268334.068709, -7268102.412351, -7267870.755992, -7267639.099634,
-       -7267407.443276, -7267175.786918, -7266944.130559, -7266712.474201,
-       -7266480.817843, -7266249.161484, -7266017.505126, -7265785.848768,
-       -7265554.19241 , -7265322.536051, -7265090.879693, -7264859.223335,
-       -7264627.566977, -7264395.910618, -7264164.25426 , -7263932.597902,
-       -7263700.941544, -7263469.285185, -7263237.628827, -7263005.972469,
-       -7262774.31611 , -7262542.659752, -7262311.003394, -7262079.347036,
-       -7261847.690677, -7261616.034319, -7261384.377961, -7261152.721603,
-       -7260921.065244, -7260689.408886, -7260457.752528, -7260226.09617 ,
-       -7259994.439811, -7259762.783453, -7259531.127095, -7259299.470737,
-       -7259067.814378, -7258836.15802 , -7258604.501662, -7258372.845303,
-       -7258141.188945, -7257909.532587, -7257677.876229, -7257446.21987 ,
-       -7257214.563512, -7256982.907154, -7256751.250796, -7256519.594437,
-       -7256287.938079, -7256056.281721, -7255824.625363, -7255592.969004,
-       -7255361.312646, -7255129.656288, -7254897.99993 , -7254666.343571,
-       -7254434.687213, -7254203.030855, -7253971.374496, -7253739.718138,
-       -7253508.06178 , -7253276.405422, -7253044.749063, -7252813.092705,
-       -7252581.436347, -7252349.779989, -7252118.12363 , -7251886.467272,
-       -7251654.810914, -7251423.154556, -7251191.498197, -7250959.841839,
-       -7250728.185481, -7250496.529122, -7250264.872764, -7250033.216406,
-       -7249801.560048, -7249569.903689, -7249338.247331, -7249106.590973,
-       -7248874.934615, -7248643.278256, -7248411.621898, -7248179.96554 ,
-       -7247948.309182, -7247716.652823, -7247484.996465, -7247253.340107,
-       -7247021.683749, -7246790.02739 , -7246558.371032, -7246326.714674,
-       -7246095.058315, -7245863.401957, -7245631.745599, -7245400.089241,
-       -7245168.432882, -7244936.776524, -7244705.120166, -7244473.463808,
-       -7244241.807449, -7244010.151091, -7243778.494733, -7243546.838375,
-       -7243315.182016, -7243083.525658, -7242851.8693  , -7242620.212942,
-       -7242388.556583, -7242156.900225, -7241925.243867, -7241693.587508,
-       -7241461.93115 , -7241230.274792, -7240998.618434, -7240766.962075,
-       -7240535.305717, -7240303.649359, -7240071.993001, -7239840.336642,
-       -7239608.680284, -7239377.023926, -7239145.367568, -7238913.711209,
-       -7238682.054851, -7238450.398493, -7238218.742135, -7237987.085776,
-       -7237755.429418, -7237523.77306 , -7237292.116701, -7237060.460343,
-       -7236828.803985, -7236597.147627, -7236365.491268, -7236133.83491 ,
-       -7235902.178552, -7235670.522194, -7235438.865835, -7235207.209477,
-       -7234975.553119, -7234743.896761, -7234512.240402, -7234280.584044,
-       -7234048.927686, -7233817.271327, -7233585.614969, -7233353.958611,
-       -7233122.302253, -7232890.645894, -7232658.989536, -7232427.333178,
-       -7232195.67682 , -7231964.020461, -7231732.364103, -7231500.707745,
-       -7231269.051387, -7231037.395028, -7230805.73867 , -7230574.082312,
-       -7230342.425954, -7230110.769595, -7229879.113237, -7229647.456879,
-       -7229415.80052 , -7229184.144162, -7228952.487804, -7228720.831446,
-       -7228489.175087, -7228257.518729, -7228025.862371, -7227794.206013])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-385e9570-f830-4782-83c0-4339e8e097a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-385e9570-f830-4782-83c0-4339e8e097a0' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>+a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +units=m +x_0=0 +y_0=0</dd><dt><span>res :</span></dt><dd>[231.65635826 231.65635826]</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodata :</span></dt><dd>-28672.0</dd><dt><span>transform :</span></dt><dd>[ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e+00
- -2.31656358e+02  5.05010861e+06  0.00000000e+00  0.00000000e+00
-  1.00000000e+00]</dd></dl></div></li></ul></div></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds.plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7f6aa6241360&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_clip_box_5_1.png" src="../_images/examples_clip_box_5_1.png" />
-</div>
-</div>
-</section>
-<section id="Clip-using-a-bounding-box">
-<h2>Clip using a bounding box<a class="headerlink" href="#Clip-using-a-bounding-box" title="Link to this heading"></a></h2>
-<p>See docs for <code class="docutils literal notranslate"><span class="pre">rio.clip_box</span></code>:</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.clip_box"><span class="std std-ref">DataArray.clip_box</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.clip_box"><span class="std std-ref">Dataset.clip_box</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xdsc = xds.rio.clip_box(
-    minx=-7272967.1958741,
-    miny=5048602.84382404,
-    maxx=-7272503.88315758,
-    maxy=5049066.15654056,
-)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xdsc.plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7f6a9e1c64a0&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_clip_box_8_1.png" src="../_images/examples_clip_box_8_1.png" />
-</div>
-</div>
-<p>You can also clip using bounds in a CRS different from the dataset if you pass in the <code class="docutils literal notranslate"><span class="pre">crs</span></code> kwarg (requires rioxarray 0.12+):</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xdscn = xds.rio.clip_box(
-    minx=-93.1558,
-    miny=45.403,
-    maxx=-93.1557,
-    maxy=45.4065,
-    crs=&quot;EPSG:4326&quot;,
-)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xdscn.plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7f6a9c8815a0&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_clip_box_11_1.png" src="../_images/examples_clip_box_11_1.png" />
-</div>
-</div>
-</section>
-</section>
-
-
-           </div>
-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="clip_geom.html" class="btn btn-neutral float-left" title="Example - Clip" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="pad_box.html" class="btn btn-neutral float-right" title="Example - Pad Box" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
-    </div>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2019-2023, Corteva Agriscience™.</p>
-  </div>
-
-  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
-   
-
-</footer>
-        </div>
-      </div>
-    </section>
-  </div>
-  <script>
-      jQuery(function () {
-          SphinxRtdTheme.Navigation.enable(true);
-      });
-  </script> 
-
-</body>
-</html>
\ No newline at end of file
diff --git a/0.18.0/examples/clip_box.ipynb b/0.18.0/examples/clip_box.ipynb
deleted file mode 100644
index 7543b350..00000000
--- a/0.18.0/examples/clip_box.ipynb
+++ /dev/null
@@ -1,717 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Clip Box"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  float: right;\n",
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-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 200, x: 200)&gt;\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-30c63a87-57cb-472f-837d-4f925d7c1c64' class='xr-array-in' type='checkbox' checked><label for='section-30c63a87-57cb-472f-837d-4f925d7c1c64' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan 687.0 687.0 ... 491.0 504.0 504.0 515.0 469.0 485.0</span></div><div class='xr-array-data'><pre>array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-af2aad65-513d-46c2-8e63-fda2854beb0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af2aad65-513d-46c2-8e63-fda2854beb0c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.05e+06 5.05e+06 ... 5.004e+06</div><input id='attrs-30d9748d-4a6f-413c-9e83-c0ff31dcab89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30d9748d-4a6f-413c-9e83-c0ff31dcab89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00a56479-fbb4-4223-92f1-b83428597e50' class='xr-var-data-in' type='checkbox'><label for='data-00a56479-fbb4-4223-92f1-b83428597e50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([5049992.781974, 5049761.125615, 5049529.469257, 5049297.812899,\n",
-       "       5049066.156541, 5048834.500182, 5048602.843824, 5048371.187466,\n",
-       "       5048139.531108, 5047907.874749, 5047676.218391, 5047444.562033,\n",
-       "       5047212.905674, 5046981.249316, 5046749.592958, 5046517.9366  ,\n",
-       "       5046286.280241, 5046054.623883, 5045822.967525, 5045591.311167,\n",
-       "       5045359.654808, 5045127.99845 , 5044896.342092, 5044664.685734,\n",
-       "       5044433.029375, 5044201.373017, 5043969.716659, 5043738.0603  ,\n",
-       "       5043506.403942, 5043274.747584, 5043043.091226, 5042811.434867,\n",
-       "       5042579.778509, 5042348.122151, 5042116.465793, 5041884.809434,\n",
-       "       5041653.153076, 5041421.496718, 5041189.84036 , 5040958.184001,\n",
-       "       5040726.527643, 5040494.871285, 5040263.214927, 5040031.558568,\n",
-       "       5039799.90221 , 5039568.245852, 5039336.589493, 5039104.933135,\n",
-       "       5038873.276777, 5038641.620419, 5038409.96406 , 5038178.307702,\n",
-       "       5037946.651344, 5037714.994986, 5037483.338627, 5037251.682269,\n",
-       "       5037020.025911, 5036788.369553, 5036556.713194, 5036325.056836,\n",
-       "       5036093.400478, 5035861.74412 , 5035630.087761, 5035398.431403,\n",
-       "       5035166.775045, 5034935.118686, 5034703.462328, 5034471.80597 ,\n",
-       "       5034240.149612, 5034008.493253, 5033776.836895, 5033545.180537,\n",
-       "       5033313.524179, 5033081.86782 , 5032850.211462, 5032618.555104,\n",
-       "       5032386.898746, 5032155.242387, 5031923.586029, 5031691.929671,\n",
-       "       5031460.273313, 5031228.616954, 5030996.960596, 5030765.304238,\n",
-       "       5030533.647879, 5030301.991521, 5030070.335163, 5029838.678805,\n",
-       "       5029607.022446, 5029375.366088, 5029143.70973 , 5028912.053372,\n",
-       "       5028680.397013, 5028448.740655, 5028217.084297, 5027985.427939,\n",
-       "       5027753.77158 , 5027522.115222, 5027290.458864, 5027058.802506,\n",
-       "       5026827.146147, 5026595.489789, 5026363.833431, 5026132.177072,\n",
-       "       5025900.520714, 5025668.864356, 5025437.207998, 5025205.551639,\n",
-       "       5024973.895281, 5024742.238923, 5024510.582565, 5024278.926206,\n",
-       "       5024047.269848, 5023815.61349 , 5023583.957132, 5023352.300773,\n",
-       "       5023120.644415, 5022888.988057, 5022657.331698, 5022425.67534 ,\n",
-       "       5022194.018982, 5021962.362624, 5021730.706265, 5021499.049907,\n",
-       "       5021267.393549, 5021035.737191, 5020804.080832, 5020572.424474,\n",
-       "       5020340.768116, 5020109.111758, 5019877.455399, 5019645.799041,\n",
-       "       5019414.142683, 5019182.486325, 5018950.829966, 5018719.173608,\n",
-       "       5018487.51725 , 5018255.860891, 5018024.204533, 5017792.548175,\n",
-       "       5017560.891817, 5017329.235458, 5017097.5791  , 5016865.922742,\n",
-       "       5016634.266384, 5016402.610025, 5016170.953667, 5015939.297309,\n",
-       "       5015707.640951, 5015475.984592, 5015244.328234, 5015012.671876,\n",
-       "       5014781.015518, 5014549.359159, 5014317.702801, 5014086.046443,\n",
-       "       5013854.390084, 5013622.733726, 5013391.077368, 5013159.42101 ,\n",
-       "       5012927.764651, 5012696.108293, 5012464.451935, 5012232.795577,\n",
-       "       5012001.139218, 5011769.48286 , 5011537.826502, 5011306.170144,\n",
-       "       5011074.513785, 5010842.857427, 5010611.201069, 5010379.544711,\n",
-       "       5010147.888352, 5009916.231994, 5009684.575636, 5009452.919277,\n",
-       "       5009221.262919, 5008989.606561, 5008757.950203, 5008526.293844,\n",
-       "       5008294.637486, 5008062.981128, 5007831.32477 , 5007599.668411,\n",
-       "       5007368.012053, 5007136.355695, 5006904.699337, 5006673.042978,\n",
-       "       5006441.38662 , 5006209.730262, 5005978.073904, 5005746.417545,\n",
-       "       5005514.761187, 5005283.104829, 5005051.44847 , 5004819.792112,\n",
-       "       5004588.135754, 5004356.479396, 5004124.823037, 5003893.166679])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.274e+06 ... -7.228e+06</div><input id='attrs-ee56b91c-468c-4d4d-ad20-772a210aedbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee56b91c-468c-4d4d-ad20-772a210aedbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a47375a5-e337-405a-8fc2-ef4176e4ba9b' class='xr-var-data-in' type='checkbox'><label for='data-a47375a5-e337-405a-8fc2-ef4176e4ba9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([-7273893.821307, -7273662.164949, -7273430.508591, -7273198.852232,\n",
-       "       -7272967.195874, -7272735.539516, -7272503.883158, -7272272.226799,\n",
-       "       -7272040.570441, -7271808.914083, -7271577.257725, -7271345.601366,\n",
-       "       -7271113.945008, -7270882.28865 , -7270650.632291, -7270418.975933,\n",
-       "       -7270187.319575, -7269955.663217, -7269724.006858, -7269492.3505  ,\n",
-       "       -7269260.694142, -7269029.037784, -7268797.381425, -7268565.725067,\n",
-       "       -7268334.068709, -7268102.412351, -7267870.755992, -7267639.099634,\n",
-       "       -7267407.443276, -7267175.786918, -7266944.130559, -7266712.474201,\n",
-       "       -7266480.817843, -7266249.161484, -7266017.505126, -7265785.848768,\n",
-       "       -7265554.19241 , -7265322.536051, -7265090.879693, -7264859.223335,\n",
-       "       -7264627.566977, -7264395.910618, -7264164.25426 , -7263932.597902,\n",
-       "       -7263700.941544, -7263469.285185, -7263237.628827, -7263005.972469,\n",
-       "       -7262774.31611 , -7262542.659752, -7262311.003394, -7262079.347036,\n",
-       "       -7261847.690677, -7261616.034319, -7261384.377961, -7261152.721603,\n",
-       "       -7260921.065244, -7260689.408886, -7260457.752528, -7260226.09617 ,\n",
-       "       -7259994.439811, -7259762.783453, -7259531.127095, -7259299.470737,\n",
-       "       -7259067.814378, -7258836.15802 , -7258604.501662, -7258372.845303,\n",
-       "       -7258141.188945, -7257909.532587, -7257677.876229, -7257446.21987 ,\n",
-       "       -7257214.563512, -7256982.907154, -7256751.250796, -7256519.594437,\n",
-       "       -7256287.938079, -7256056.281721, -7255824.625363, -7255592.969004,\n",
-       "       -7255361.312646, -7255129.656288, -7254897.99993 , -7254666.343571,\n",
-       "       -7254434.687213, -7254203.030855, -7253971.374496, -7253739.718138,\n",
-       "       -7253508.06178 , -7253276.405422, -7253044.749063, -7252813.092705,\n",
-       "       -7252581.436347, -7252349.779989, -7252118.12363 , -7251886.467272,\n",
-       "       -7251654.810914, -7251423.154556, -7251191.498197, -7250959.841839,\n",
-       "       -7250728.185481, -7250496.529122, -7250264.872764, -7250033.216406,\n",
-       "       -7249801.560048, -7249569.903689, -7249338.247331, -7249106.590973,\n",
-       "       -7248874.934615, -7248643.278256, -7248411.621898, -7248179.96554 ,\n",
-       "       -7247948.309182, -7247716.652823, -7247484.996465, -7247253.340107,\n",
-       "       -7247021.683749, -7246790.02739 , -7246558.371032, -7246326.714674,\n",
-       "       -7246095.058315, -7245863.401957, -7245631.745599, -7245400.089241,\n",
-       "       -7245168.432882, -7244936.776524, -7244705.120166, -7244473.463808,\n",
-       "       -7244241.807449, -7244010.151091, -7243778.494733, -7243546.838375,\n",
-       "       -7243315.182016, -7243083.525658, -7242851.8693  , -7242620.212942,\n",
-       "       -7242388.556583, -7242156.900225, -7241925.243867, -7241693.587508,\n",
-       "       -7241461.93115 , -7241230.274792, -7240998.618434, -7240766.962075,\n",
-       "       -7240535.305717, -7240303.649359, -7240071.993001, -7239840.336642,\n",
-       "       -7239608.680284, -7239377.023926, -7239145.367568, -7238913.711209,\n",
-       "       -7238682.054851, -7238450.398493, -7238218.742135, -7237987.085776,\n",
-       "       -7237755.429418, -7237523.77306 , -7237292.116701, -7237060.460343,\n",
-       "       -7236828.803985, -7236597.147627, -7236365.491268, -7236133.83491 ,\n",
-       "       -7235902.178552, -7235670.522194, -7235438.865835, -7235207.209477,\n",
-       "       -7234975.553119, -7234743.896761, -7234512.240402, -7234280.584044,\n",
-       "       -7234048.927686, -7233817.271327, -7233585.614969, -7233353.958611,\n",
-       "       -7233122.302253, -7232890.645894, -7232658.989536, -7232427.333178,\n",
-       "       -7232195.67682 , -7231964.020461, -7231732.364103, -7231500.707745,\n",
-       "       -7231269.051387, -7231037.395028, -7230805.73867 , -7230574.082312,\n",
-       "       -7230342.425954, -7230110.769595, -7229879.113237, -7229647.456879,\n",
-       "       -7229415.80052 , -7229184.144162, -7228952.487804, -7228720.831446,\n",
-       "       -7228489.175087, -7228257.518729, -7228025.862371, -7227794.206013])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-385e9570-f830-4782-83c0-4339e8e097a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-385e9570-f830-4782-83c0-4339e8e097a0' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>+a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +units=m +x_0=0 +y_0=0</dd><dt><span>res :</span></dt><dd>[231.65635826 231.65635826]</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodata :</span></dt><dd>-28672.0</dd><dt><span>transform :</span></dt><dd>[ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e+00\n",
-       " -2.31656358e+02  5.05010861e+06  0.00000000e+00  0.00000000e+00\n",
-       "  1.00000000e+00]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f6aa6241360>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clip using a bounding box\n",
-    "\n",
-    "See docs for `rio.clip_box`:\n",
-    "\n",
-    "  - [DataArray.clip_box](../rioxarray.rst#rioxarray.raster_array.RasterArray.clip_box)\n",
-    "  - [Dataset.clip_box](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.clip_box)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xdsc = xds.rio.clip_box(\n",
-    "    minx=-7272967.1958741,\n",
-    "    miny=5048602.84382404,\n",
-    "    maxx=-7272503.88315758,\n",
-    "    maxy=5049066.15654056,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f6a9e1c64a0>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xdsc.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also clip using bounds in a CRS different from the dataset if you pass in the `crs` kwarg (requires rioxarray 0.12+):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xdscn = xds.rio.clip_box(\n",
-    "    minx=-93.1558,\n",
-    "    miny=45.403,\n",
-    "    maxx=-93.1557,\n",
-    "    maxy=45.4065,\n",
-    "    crs=\"EPSG:4326\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f6a9c8815a0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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diff --git a/0.18.0/examples/clip_geom.html b/0.18.0/examples/clip_geom.html
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-              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../readme.html">rioxarray README</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Example - Clip</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Load-in-xarray-dataset">Load in xarray dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Clip-using-a-geometry">Clip using a geometry</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Clip-using-a-GeoDataFrame">Clip using a GeoDataFrame</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Clipping-larger-rasters">Clipping larger rasters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="clip_box.html">Example - Clip Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pad_box.html">Example - Pad Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="read-locks.html">Example - Reading COGs in Parallel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="reproject.html">Example - Reproject</a></li>
-<li class="toctree-l2"><a class="reference internal" href="reproject_match.html">Example - Reproject Match (For Raster Calculations/Stacking)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="merge.html">Example - Merge</a></li>
-<li class="toctree-l2"><a class="reference internal" href="interpolate_na.html">Example - Interpolate Missing Data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="transform_bounds.html">Example - Transform Bounds</a></li>
-<li class="toctree-l2"><a class="reference internal" href="COG.html">Example - Cloud Optimized GeoTiff (COG)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dask_read_write.html">Example - Reading and Writing with Dask</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://corteva.github.io/geocube/html/examples/zonal_statistics.html">Example - Zonal Statistics</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/328320/144357">Extracting data within geometry (shape)</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/329141/144357">Converting NetCDF dataset array to GeoTiff</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/345697/144357">How do I add projection to this NetCDF file? (Satellite)</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/346899/144357">Create a new raster TIFF file which is masked based on the GeoJSON file</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/354798/144357">How to mask NetCDF time series data from a shapefile in Python?</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/358058/144357">Extract data from raster at a point</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/358057/144357">Convert raster to CSV with lat, lon, and value columns</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../modules.html">rioxarray</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../contributing.html">Contributing</a></li>
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-      <li class="breadcrumb-item active">Example - Clip</li>
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-            <a href="../_sources/examples/clip_geom.ipynb.txt" rel="nofollow"> View page source</a>
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-             
-  <section id="Example---Clip">
-<h1>Example - Clip<a class="headerlink" href="#Example---Clip" title="Link to this heading"></a></h1>
-<p>API Reference for <code class="docutils literal notranslate"><span class="pre">rio.clip</span></code>:</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.clip"><span class="std std-ref">DataArray.clip</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.clip"><span class="std std-ref">Dataset.clip</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray
-
-%matplotlib inline
-</pre></div>
-</div>
-</div>
-<section id="Load-in-xarray-dataset">
-<h2>Load in xarray dataset<a class="headerlink" href="#Load-in-xarray-dataset" title="Link to this heading"></a></h2>
-<p>See docs for <a class="reference internal" href="../rioxarray.html#rioxarray-open-rasterio"><span class="std std-ref">rioxarray.open_rasterio</span></a></p>
-<p>Notes:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">masked=True</span></code> will convert from integer to <code class="docutils literal notranslate"><span class="pre">float64</span></code> and fill with <code class="docutils literal notranslate"><span class="pre">NaN</span></code>. If this behavior is not desired, you can skip this.</p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = rioxarray.open_rasterio(
-    &quot;../../test/test_data/compare/small_dem_3m_merged.tif&quot;,
-    masked=True,
-)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds.plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7fc957fb1ac0&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_clip_geom_4_1.png" src="../_images/examples_clip_geom_4_1.png" />
-</div>
-</div>
-</section>
-<section id="Clip-using-a-geometry">
-<h2>Clip using a geometry<a class="headerlink" href="#Clip-using-a-geometry" title="Link to this heading"></a></h2>
-<p>By default, it assumes that the CRS of the geometry is the same as the CRS of the dataset. If it is different, make sure to pass in the CRS of the geometry.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>geometries = [
-    {
-        &#39;type&#39;: &#39;Polygon&#39;,
-        &#39;coordinates&#39;: [[
-            [425499.18381405267, 4615331.540546387],
-            [425499.18381405267, 4615478.540546387],
-            [425526.18381405267, 4615478.540546387],
-            [425526.18381405267, 4615331.540546387],
-            [425499.18381405267, 4615331.540546387]
-        ]]
-    }
-]
-clipped = xds.rio.clip(geometries)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>clipped.plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7fc957829d90&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_clip_geom_7_1.png" src="../_images/examples_clip_geom_7_1.png" />
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>clipped.rio.to_raster(&quot;clipped.tif&quot;, compress=&#39;LZMA&#39;, tiled=True, dtype=&quot;int32&quot;)
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Clip-using-a-GeoDataFrame">
-<h2>Clip using a GeoDataFrame<a class="headerlink" href="#Clip-using-a-GeoDataFrame" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import geopandas
-from shapely.geometry import box
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>geodf = geopandas.GeoDataFrame(
-    geometry=[
-        box(425499.18381405267, 4615331.540546387, 425526.18381405267, 4615478.540546387)
-    ],
-    crs=&quot;EPSG:26915&quot;
-)
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>clipped = xds.rio.clip(geodf.geometry.values, geodf.crs, drop=False, invert=True)
-# Note: If you have rasterio &lt; 1.2 you will need convert the geometries to dict-like objects if the projection
-# of the geometries differ from the raster. For example:
-#
-# from shapely.geometry import mapping
-#  geometries = geodf.geometry.apply(mapping)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>clipped.plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7fc9572aa100&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_clip_geom_13_1.png" src="../_images/examples_clip_geom_13_1.png" />
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>clipped.rio.to_raster(&quot;clipped_invert.tif&quot;, compress=&#39;LZMA&#39;, tiled=True, dtype=&quot;int32&quot;)
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Clipping-larger-rasters">
-<h2>Clipping larger rasters<a class="headerlink" href="#Clipping-larger-rasters" title="Link to this heading"></a></h2>
-<p>Note: Loading from disk will likely only work directly after opening a raster with <a class="reference internal" href="../rioxarray.html#rioxarray-open-rasterio"><span class="std std-ref">rioxarray.open_rasterio</span></a></p>
-<p>The clip operation needs the full raster loaded with the default method. This can be an issue if you don’t have enough memory (RAM) on you machine. If this is something you have run into, it is recommended to use the <code class="docutils literal notranslate"><span class="pre">from_disk=True</span></code> option. This option uses <a class="reference external" href="https://rasterio.readthedocs.io/en/latest/topics/masking-by-shapefile.html">rasterio.mask.mask</a> when loading the data if possible.</p>
-<p>But be careful, these two methods, as they use different core functions, can have <strong>different outputs</strong>: small discrepencies may appear on the borders (1 pixel added or removed on some borders, see issue <a class="reference external" href="https://github.com/corteva/rioxarray/issues/310">#310</a>)</p>
-<p>Alternatively, you can also use <code class="docutils literal notranslate"><span class="pre">rio.clip_box</span></code> followed by <code class="docutils literal notranslate"><span class="pre">rio.clip</span></code> for a more consistent memory efficient clip operation.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>geometries = [
-    {
-        &#39;type&#39;: &#39;Polygon&#39;,
-        &#39;coordinates&#39;: [[
-            [425499.18381405267, 4615331.540546387],
-            [425499.18381405267, 4615478.540546387],
-            [425526.18381405267, 4615478.540546387],
-            [425526.18381405267, 4615331.540546387],
-            [425499.18381405267, 4615331.540546387]
-        ]]
-    }
-]
-
-clipped = rioxarray.open_rasterio(
-    &quot;../../test/test_data/compare/small_dem_3m_merged.tif&quot;,
-    masked=True,
-).rio.clip(geometries, from_disk=True)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>clipped.plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7fc9571db850&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_clip_geom_17_1.png" src="../_images/examples_clip_geom_17_1.png" />
-</div>
-</div>
-</section>
-</section>
-
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-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="convert_to_raster.html" class="btn btn-neutral float-left" title="Example - Convert dataset to raster (GeoTiff)" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/0.18.0/examples/clip_geom.ipynb b/0.18.0/examples/clip_geom.ipynb
deleted file mode 100644
index f6b17bc1..00000000
--- a/0.18.0/examples/clip_geom.ipynb
+++ /dev/null
@@ -1,339 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Clip\n",
-    "\n",
-    "API Reference for `rio.clip`:\n",
-    "\n",
-    "  - [DataArray.clip](../rioxarray.rst#rioxarray.raster_array.RasterArray.clip)\n",
-    "  - [Dataset.clip](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.clip)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset\n",
-    "\n",
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "  \n",
-    "Notes:\n",
-    "\n",
-    " - `masked=True` will convert from integer to `float64` and fill with `NaN`. If this behavior is not desired, you can skip this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    masked=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc957fb1ac0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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lvR65eLThtguXuLhKJfW3MXB5XHG8XTFG4Wg1MoR0/mYcuHez5lJYpxSHywO6CSU3QX24ac5hUAXRpJWo5SKY49nD8hhctJNpBerDlRYsWHBjoDCeXz44+4QQRIl52SyiaKyB+83i2plkJKToJBFlvRo5GkZuXW04GlJI7zb7DwJKRFiHkVUmhEEiqsI4BmIUxk1ABi3Z0IigojUsVUnRSC4xTc1cNUGesc89sFyERTtYsOCGIyv55xZnnhB6lIggzH9gO5L9RbLmICFFKA1D5MKw5eJqy1HIGkLqfVMimC4O20IIl2TFqIHjcWA7BsIqEjWHogaSX2GUYv/RfO05YoDi285fkt+gEfzBfBEsPoQFC244hPEc/yM8V4QgkvMRqM7mGF3imAXsZEdzCJGjYeQBR5c5CluOQtIQSihrSE7mlURWoaarH8eB42HFvawJQ0z5CmIaQa6dtEmRR0W6K6AdMUB1JudjeuexWlQU9fgFCxbcGCSn8vn9R3guCCFIMu3EmPwHCkUQRxXGzYCOgqxq6GYQZTVE1kMyGR2FkQtZQ1iTitptNRQyWEsk5pOPh4HLqy1H623xWdhSX0dJYalKKpmh5tsg65udFxyvLUgNR805Ezqk5DnsnpbEtAULbhhSHsJCCDctokpy+HasbX4FjTlhbJ1NRdmMlDQETf4BidlPMDKgjJkQNhpYS+RC2BKcM/d4WHFp2HI0jOhampiDJizVBL+Zq6wong2lUiuj2nH2JrlUxeA0hMhCCAsW3GDERUO4uTHGmqEM1NW61qQyCbE4lI0UhhAZQtIAAspaxqQ5AGsZuaCBVdYcBiIjgaiBy8OKi8OKC+tt0RoM2xCQkMNSI9X8k8teWNG7UqLbe6gcQVjRPR00aTakMc7x4mTBgpsei4Zwk6PPESkEQEkRYLUe2cRVIoIQkaCJDESTnwBNpCCVFAxrGdMrbIkaiCpcjisuDWsuDFvGGFCV4nTeDoHLG9NMXJZzlGwuSjWSUMtbqDkLiKJbqXkTq0QGMljI7LV6igsWLDgESi5pc05x5gnBoCqMMbBejTnSqJJDyLWBtsc1ekgkkcIq+waGrCFcCNtCAHXbhoHIRofGdLQKIx8ZbuHjmyNuWW0IohyPA/esjrh3tebSsCZkoogxlBpJcQzEMRPDGAqppUilXFhvHUvRvTBEQvEjXK8numDBgjksJqMzAtlhXzefgX3270E0VSaSFFE0SEyvTAZr2XJRNmmbRi7puhDH5bDi1tUGSKGpkHIXxhjSaxWa+kjbcUi5CyFVY40SiLgWndnfLJDKZeRXyCGyIcTZ8hwLFiy4PlCE49LX9vzhmuk+IvI4EXmze31MRP5XEbldRF4lIu/O7w9253yTiLxHRH5VRL74iq95FfMMolzIwrxui4UMbg3HXAybajrKZHBh2LIOIxeHDfdfHXNx2KTXasuF1bbkONj3C6uUEX203rIaRoaVldzOHd9CMmdBTUYzx7eENNZ6NbIaUqmNBQsWXH+kxLRw0Oss4pppCKr6q8ATAHJHoP9CavrwPODVqvoCEXle/v6NIvLppG4+n0FqD/czIvJY1xHoRIhok7nc7KNqBQbzFQRnhwkoQ9YYjBTWUgnDzEmrOHIUtoy57/LK5TAcjwOrYeRIhaPVNmU8D8mktZEBERhHZZPHjKPVuU61lrR8oSbQhUQEJUltwYIFNwTn2al8vWjsqcCvqep/Bp4OvCRvfwnw5fnz04EfU9XLqvobwHuAJ1/JRXaZjCAlqF0JBpLp6EhGBvx78jFcDBsuhC23DBtuGdLnCyFlO18ckgZxtErvt6w33LLacHG14eI6lchYr9JrtYpJWxhqfSUZWjIIIedMhMgqvxYsWHD9oSqMGg56nQQRuSgibxCRt4jI20XkW/P2WSuKiKxF5CUi8jYReaeIfJMb64l5+3tE5IUiafkrIhdE5KV5++tF5FH75nS9COGZwI/mz5+kqu8DyO8Pz9sfAbzXnXNn3tZARJ4jIm8UkTduP3aP237CylnIoah+LKcZNJ9zF7asKQyiSQksWkNyNBsJ9K+Lw4ZbVxsecHSZW1eb5mVEYaRQ+jAMmkmhltawbOoUERVLmOywEMKCBTcMETnodQAuA1+gqo8nWVOeJiJPoVpRHgO8On+H1Arzgqp+FvBE4H92Av5FwHOAx+TX0/L2ZwMfVtVHA98BfNu+CV1zp7KIHAFfBnzTSYfObJtIeVV9MfBigFse/ck6ZBNKzBVO58xFFnHU1DZyMKE/uLJVxcGcLYLmVA5UUrgYktHHCMMQh8B2HdjGgVVOfIsIx+OqzNET0EaG0pRHcmJbGLL/QNq8CVmcygsW3DAkp/LpiE1Nmat356/r/FKSteTz8/aXAK8BvjHvu5+IrIBbgGPgYyJyB3Cbqr4WQER+kGR5eWUe61vyWC8HvltERHW+qcP1iDL6b4FfVtXfzd9/V0TuUNX35Rt5f95+J/Ap7rxHAr+9b+Cj1cjDH3BX6ZC21VDCT8cobOLAOKbPQaxTWk1kG3Mo6L3jURLQAwT3nEYNbHRVyGItI0cyclE2Rbe6EDZsYnqMkU7Qa8hhrLl09ipw73jEratjjuOKS9sVH98ccWlYs4mBS8dr4jrWLm1SS2yYlnBhcSgvWHDDYE7lA/FQEXmj+/7ivKAtyP7VNwGPBv6xqr5eRBorioiYFeXlJAH/PuBW4H9T1Q+JyJNI8tPgrSvF8qKqWxH5KPAQ4ANzE74ehPDnqOYigB8HngW8IL//K7f9R0Tk20lO5ccAb9g38FEY+dT7f5htHDiOK6IKl8YVl8YV927XyDZRKATGWCuhhs68FDXlIdgq3xLQoqQklEBk1Bo5YNrCWkaiBshJa2hIxBEiGx1ZUfMaAFY6JJ/AuCZqKM7io9UWtis2uUFPCMl/sBrGZDLKxwXRydwXLFhwfTEerqV/QFWftO+AHDTzBBF5EPAKEfnMPYc/GRhJ8vHBwC+IyM+w37pykOXFcE0JQURuBf4b4H92m18AvExEng38Fskuhqq+XUReBryD1L3yuSdFGB3Jlkde/AiX44qNDmzjwMe2F7lnewRQBOgmhNRzeQsjofEdRE32vpFUlC5q+rwvG9FMSGtGogQCUvqsrsOYEuRyTwUjhEEimzgwWBVWIx37ca3geBySD0GzySpU7WAlCyEsWHCjca0ylVX1IyLyGpLtf5cV5c8DP6WqG+D9IvJLwJOAXyBZVAzeumKWlzuzqemBwId2zeOaOpVV9R5VfYiqftRt+6CqPlVVH5PfP+T2PV9V/6CqPk5VX3nodXohGUS5mPMEVsOYV9hJuFqWsheuK0k9DUaVUq/I4ENO09hpxW6r/nWOOupfUMkgRSRtuHU45pZwXHIZLG+hzDUkV5QloXntYAiRo+yPWC1d0xYsuGFIi7mTXydBRB6WNQNE5BbgC4F3Ua0o0FpRfgv4Akm4H/AU4F3ZvHSXiDwlRxd9Na3lxcZ6BvCzu/wHcMYzlSU7fm2VHXLV0kvjGkj9DMYYs9moOpbts8HOt/dxzx90IBKJjAhHsuWYVe6bCQEhIMnWbxFHLqltzB7tzZAyHSPJ7xGH9D5kElCVQmDrYSxkYf0ZFixYcGOQitud2jr6DuAl2Y8QgJep6k+IyGuZsaIA/xj4fuBXSKag71fVt+Z9Xwf8AMnZ/Mr8Avg+4IdE5D0kzeCZ+yZ0tgmB5NRN4aHKvfGIW3TDuJJijtlmG71uVhACMWjqdJZNPCVMrDB7NbmdFEuckthiKqGqMccjpVIYph00CW7pJDY6EFXY6MAmDEQNbENgPYwMQYu/w7SDVdYOFpPRggU3Fkr6d3sqYyVh/jkz2z9Iyt3qt99NJYd+3xuBif9BVS/tOmcOZ5wQlIuyZcOKW4fL6Q81wOUc9bMKkZVmLUHS8aUcRA7nLNqB9yNkP8zgTDODxBShRCpwNxCyriAMRI5Z5YR1YR22EGFthfIkl7LITutNGHIE08A2DGw1cDSE5KweIhAIpSJrNhWFxVS0YMGNhurJC8WzjDNPCGvZcutwGYCLYcMmRlYhmYyOwsg2BrbZ1DKqpEJx4eBqGBMMooxK7o8grCWt+IdMDqYlEKpvIYhy5HwRF2XLRlbFl7BRy1lIvoIYxfVqiIUMVvdh3gsWLDgNHJx0diZxpgkhiHJrOGatIyOBAeUuLnLb6hLbo4G7thdYhYEQlaPVFhkHRqtf5Mwvx+OKo1zuuhnffbdSFse64khSOOsRMPbniBI05rBSy11I9YyG/H2QlPVsBLLRwDYM3LrasDm6zOUh/VkuDMnhfOvqmKMwloioBQsW3Bgoi4ZwZmAlJ1ImcA3TDJ4EhrE6oZ0wv2XYpHNSR+QcTZQyla389aihmI4GBCT9OFL2ch5Ik/mpEf6ZDCzPIeRIpoCVxEhzXIXkRN7EofZyEOXisGEtkY2GEra6YMGCG4OlQc5NCu1WzPcLl7knHLGOK9ZZEFv8/iCxBNla+Yehi9oxwWzC3MpUWH8ELKKpEf7VqbzuFu89GZQchFwXqZCG9WNAS49oI6ttDNwybBhQHra6l8u6vjYPc8GCBSdCEZYGOTcpRJRRQxaqafVtNnvTEIBcnjqWshRW6sKa2lwcNs0f2TSNQTRpByWLOWQTUSyrhJA1Bk8KJsy9VuBX9qZyVi0mF9CzF8p6SPex1cCtw4ZACmUNu0OIFyxYcI2hwOaUahndjDjTd6aaTDMRSXH+Grg1HHO3XARozEarEJt8g77AnGEgaQPpPTZCHch5D/NhZ4Ooy0mI7fY9mHRty47k2y/cw4cu3wrArcNxudcFCxbcKMi57odwpgnBhOOY7fEWAgqUxDJb7R/lCJ2Yi+B5jWBOyJrj1z4DJey0h2kJIyEXwptGAw1Oq/DXG4p2kMhgJakU9lEYeeD6Xi6Nay6PKx6+Pk4/xHPcvm/BgpsdCgdlIZ9VnGlCkJkaTSZ0PYsHUe6/vszxuGKrgRWRbQxln4eZcAxD7qDWrwrSaj0wutO90PeltJv5mf8iayLNdVGOhkQmRyH5Fh524S5uW93LhbDhclz8Bwta/LePy6XyVSnNPuw92vfut+jNjlF375v77puSB8kNywMMAUJI26LW+Yy2mHJzyHW+uOcedLtNx4xjPixO5+/O15ltPQ455r5g0RBuUkjOCvbr8Y0O3DMesY2p2J1VFYXkK7CqqH2gQMiZwR7WCyF9rtfx1/QkYN/TeO0PcdeqwiKNSr5C1mQuDqmZzkOGSzxwuLecf55D3hacEkSmgnwXTIDPnWvvvomIfTYyGIZEBKlhByrp35YYGdh46q4TKqmIJN3+tKA9wZ0yVGXREG5e1IziiHAprrknHnHveMRGU8bxKowpbDML2luGDZczKWw1cBxXHI8rjocVtw6bJjsZ4FKO6um3Q/YTSNISegKYaAhCcT6vGRklOcM3MqQy2iFwy3CcqqWqcNvqErcN95akO1uVrGU8tdT5BecE+7SDuWM9mtX0zDk9GZgwD0PVCoaADgIhoCGkxdaoECOMisSYrjPQkswqiR/7p2FaQjOvboUvQarQz4Ec6TBFQrdyvwbaQXIqn99/f2eaEOyPM5LKQGx04O7txZwQFonZD3AhWOmIXN00jGzjkIkhpNIRYTsZf3RlLGC+McZcbfRd5qJ+7JDnZ8ls6/x5LSO3DBt3bFqVjKdYR2XBgoPJoNcKvInIk8F6SIQAIJGUnJ+qe4lk0rJ/GiFpFMQApm8319MrFujXWjtIkHOtpZ9pQoBKCJfjeqdtb6sD9wuXWedexCsZU72jba5wamakGYw5Vc3M/aNWkoiE6ijunNhgZS7aktq7mmusZSTmMNgLkjSatSOpIJFRh1lNZcEnMIJUIXsl2kF/jNcEelORM/HsJIMQ0FWohGBzGgVhRMeszw+dxuF9EYCEkPwIQfC24FlhP+dX6PadNpJTefEh3LS4J14A0ip6E61ncSilsC0nwPwIF2TDhbBNtYcGYTPD9lFDKYFtNYtGhmZlMGoowt3bFKOmnsgDcSL8jTz8Kn/IiWtk0rH5rXMpDUtaixrY0GZXL1gwC+8XuBrB6MlhFxkEqWSwStt0yJ8BxizgiShDIoWUwpPHBZFQ/Q9BQENrNmJG2Hf3c9L+a4ElU/kmhtnUL8oWAsQoXBi2hBggpiY1dtxaRm4djrkQkq/gsq6zaWm6eh/z6t8aaltYq+0z7SCZcqZOZTsOKMeMKo3ZqWgVKEFG7iV1XLMeCpAb8hCdoXXBAofsxG2slLscyt5h7O3tcyvsngig1QykJYO4HojrRAqQWsDaL12ISdh7RsjjEJNjWsaIinY+i4CEuNMUdLCJyOZ/Cv9+lkzlmxipu1jEgjGt2f1KUqc08x2sZeSBq3uAtCK/EDZEDem4MDZaQqsFiDML1Wii5pj7sFpI+QtDyXm4EDfcGjJhlWilutIbUOJiMlrgoCJ5mdJJO9MSnOMVqALXk0bvjPXHSnC+AxdJZOahTAa6EnSdmj0BSEzkE+zfiioykohAs+APITmnZUzkoIk4RDWZjdApKbj76Z3Iavd7jTHnSzwvONuEIMr9wuWyir8nHoEGLobkkDXzivUlGHIhuYuy4bKsi9ZwL+vZsFOPOTKA/bkH5buLMEo/5lou2wgtSOSBOd/Al+KAFOF0zCrXOzq/q5MFV4EAGiUJx0O0BMO+0NTi3J0hAx9NtMp+g5WgQyDaZwHZKqKhuiNGTeGlUZLAHwQxp3IeXyQULUFCQFULqUlw/oIdQt+O2YlTWEupwiaeX0I403eW+iGMpdfAWsYi/FMP48vcOlxmLWNyKudjvWN2HVJ0j9UrstX64IrRefRO3XKsyz+wc+38gTomUAveZe0l5OtfCBsuyoYjSa03bw2XuZg7wtWSGjVaasGCYjLyIaF03yVMX3bM7Cu0ZGBmoqwRNGQwpFdcB+JaiKvkW9CVEAf7XE1LBKq5aahEU0xIQapfob8nklZgmoH/vPM+/f2eArTprnifeypfFJE3iMhbROTtIvKtefvtIvIqEXl3fn+wO+ezReS1+fi3iaQ6PSLyxPz9PSLywtxbGRG5ICIvzdtfLyKP2jena6oh5AbS30tq7abAXwJ+FXgp8CjgN4E/o6ofzsd/E/BsUnzBX1XVn943fiCt9i/Kho+Mt3JruNys1q0xTa04mhrqDKJc0hRZtJKR+60ul9pGF1wP5LnwUa8h9ElpngxKLSMJjW8ikcSWja4aIrN5Qqra+qDh4wB8LN6SimnF1C70FHN4FpwHZMFZVt9QM4WhLvn6pDCGqe+g8Rt0JqKhCnFdD8WBHI+SZhDXQlwL45EgEcKWLIyFsI2oQBBBoiRtYRVSHkIEceGmSYpFdMwxg6qoxJTY5u5DXPS1zEViT5zLA0wjy68Kp5ipfBn4AlW9W0TWwC+KyCuB/wF4taq+QESeBzwP+EYRWQE/DHyVqr5FRB4CWHz6i4DnAK8DfhJ4Gqmv8rOBD6vqo0XkmcC3AX9214Sutcnou4CfUtVniMgRcCvwN5m/2U8nNYD+DOCTgZ8Rkceq6s42YZr/MEFiMhNls2PaVruUlQJ1koQtJCFcnb7tH3h32Yl8fHYQ+3DSOeyzNaa+CoEj2ZaKralAn6SWmyibYlrKvRk0aRobHXj+2780FfMbLpfSGveMF4g5Ttr6NkcCmxh25i9MmgJJ9V30GpIR2zYOjAjHcZX8LDmXo4yBsgojgygrGXN+RWQdYpOV7cuDb3Ro5t3PN2rNxQCK073uT42GLscVl8Y1d28ucBwHjseBS9s1YwwcjwPjGHK125R1qu5vL+5+VYVxDIzbQNwEdBuQTUCOBdkKw2UhbEC2EMb8vgWJ6bNEkJygSw6gqYO7fRaKH9L2sIXhMqwuKcOxMlxWhkuRsNVkhhnTZ6Ii24isAjKaTT4Pak5ms9WrzvsJ5rZ5MghSMo/nyECHpAWYZjAeZY0gz0Mi6Cr97YatRRIJOoAOARky0WzJvoSYMp9xMRTjiORQVo2xhq2eZBLrzTqnFH10mmGnqqrA3fnrOr8UeDrw+Xn7S4DXAN8IfBHwVlV9Sz7/gwAicgdwm6q+Nn//QeDLSYTwdOBb8lgvB75bRCRfe4JrZjISkduAPwF8X578sap+JE/wJfmwl+SJk7f/mKpeVtXfAN4DPPmk65gAW0uNzrkYNmXFHYopSIvQteN7odLDIoNGC0O16CJHBr1TuY8msmN3zj+bvQKRi3LMg4Z7Uk5CycK2XglTUxUkgb527Tm9BhOcprLL3DTm6o37Vj3pGViIbfvu78Ne1uozRU+1Yb9N/wnXRW7ub9FfY8xVbYdu3Athy4Vhy1pqcl/I/bI1a36hEfZTMjjp2gVShbkJe9uu7rN910AWgBBXEIcsJFd5WyYDDRTi0JCPC9ns4swvNdwzC1PJQjuvstWbWvrEsnIPu0xFHRlY1nFwUUVGDEGSz2CdicFMRSHNW4d6Lxokb8vvZQxnKrJXCLkchkzMRzIMSAiJIKS7v/5VzE/2LE5L1J2eyShNXQYReTPwfuBVqvp64JNU9X0A+f3h+fDHAioiPy0ivywifyNvfwRwpxv2zrzN9r03j7UFPgo8ZNd8rqWG8AeA3wO+X0QeD7wJ+Hq6mxURu9lHkNQdg7+pWQi1cf1RXvlfknXJXIYqIAcia9mW7dFtjxrSinZGMxhEJ8K9JJnZdezfW1fCwpPBrtDTdI1EWBtdcb/O7DUSU3c2O05TUT3TEzc6JDMXyX8SYyKw6tC2Wk5tFdhy7a5wn+VwzAnEQ1ZGJniNyNI28510kVN5n5HBJdrifX4su/ZGh9y0KG2zvtRRhTGkvBIjorny5v092BFFlosWolC3UwQQRSU7Td3K34hBJMcOZDKQdNNF6Mes9Jj2AO78/B6iEYgQV4pEiFthiMmhqkO+iNUHCiGHdWbhSUxOZpgSwS705SlcTaLGZ1AIKfsMVkIcSO/rTHKS7q8SQTLp6ErQmEgs7U+fdRVS8tqYTUhss0YTkKipuK93gEeLMMoJbG7upklA0iZkGHK0Eqe69L2CEvQPFZE3uu8vVtUX+wOyBeQJ2bz+ChH5zD3jrYA/Dvxh4B7g1SLyJuBjM8f2P+25fbMXuFZYAZ8L/C+q+noR+S6SeWgXDpq4iDyHZCvjYZ+85mIO0bwoWx4QLvHB8X58JN7KRTnmrngL0AonXw3VTBO+ef1kBa0yyTgeHUH0K3cP648wpyEUorJuapmw1jISVEvLzmr2UoJmIadpnpt8HyYk1zKyYQUSGcSS52o/BmstSja92L3uclLPk0Jo3g2TqrHd2N7Bbvddz63ht8nMNZRnnp6V5CRBu49ASHaGlMQnYzKv7YkS8/di2oHmz7LnvHSQ+y5KURNEijaAIwEjDNMQjAysr4pqEpppvGpOEjIZxCRcJYJEIQwQoxJiMnUl81ISrPZZB6lhnUYKfeTRHPokNNM4TPtwZJAiiSRHFQm6Tu+JDBI52L/iRAbaagmr5ENQI4isAcgwwJD9HmHIpi+FIYWqaohp4Kj5/oAYKynQkoH/Pkcc9wUpyujg8jEfUNUnHTaufkREXkOy/f+uiNyRF8x3kLQHSIvkn1PVDwCIyE+SZOwPA490wz0S+G13zqcAd2YfxAOBD+2ax7WMMroTuDOrQJDsV59Lvlkoti9/s5/izvc3VaCqL1bVJ6nqkx54+8CakYuy5aJsuJ9suV+4zFFngrAoIBPoUVubuq0458waAD4j2ZOBCaC5chTHMzb7ktfQr9IzYdmcTViaOeVItqVZj0UsGaKmsh33xKNyT2aGKqG25uh297UvUmlO3fVq8ElOtaF7jk1P60lFWW3G3LWyT/tieW10YB3SM7kY2qKEkdoASfIriM5Udk6rbU8OxZTUmJWkPbe7/d5U5I9TpmYjXUFct6ajKjiTRhFXWciWY+ZNR2Z+aaJ2zNTjTSfexALt92JWcWRgSWer0FxHh9CYsWKZU5qvaQXlnoPbJuK+Z+3ARSLNmo6GRBhiUU9DqPeYw1MLGewwfzXH3EdYYtohr5MgIg/LmgEicgvwhcC7gB8HnpUPexbwr/LnnwY+W0RuzcL9TwLvyBaXu0TkKTm66KvdOX6sZwA/u8t/ANdQQ1DV3xGR94rI41T1V4GnAu/Ir2cBL6C92R8HfkREvp3kVH4M8IZ91xDgKNvfAS5lgbh2jtqLsikCNGrgWIdisrHVtn33TXF6WOYyVMG+z1F11NnED01gGzU47WBkREp01EhgI0NxLttxACGX7rDucXY/6zAyxrAjGXVqRrIyGVcCOyf0q37/3uVVmP8AaIiMoiVUu+9omk3GhbAlErJHp/aj3gUzAZnlwQjAhL6gKUAhOweUlPGu+VW0BHMc7PNndhqDmYKst5GdbrXevLnRPKka076kJSRSkDFrDENrOpKQisATIyJJMMuYblYHsCT3Gm3U/OGwB1NMRD4k1Nn6LdfAtINYzFqZ6IZ0vtpzEm21hEHQUdFVXlwNQsjmJwkBgqJBkaCJCLzMGkcIIBYZNYDav73obsgTgzcx7cu5uAqcYtfCO4CXiMhAevovU9WfEJHXAi8TkWcDvwV8JYCqfjjLx/9A+sX8pKr+mzzW1wE/ANxCcia/Mm//PuCHROQ9JM3gmfsmdK2jjP4X4J/nCKNfB/4i+cZnbvbtIvIyEmFsgefuizCCZO+9KJumJLRpC3fpLcken4VBstGnfqj3xKNmnDHbooNzus4VkRuIZeUfiEUo1RXxrrBT8wfsVjUnSW3OaUz2MQw6L/iq01vK/iKks5/FC1iIB5OA3+7NTD0aMpgxF6Vqs3FiKvLXtsgraxZE9pfU471DuvbMtkq3o07bos45jk0biLEu7Y0UClHEUMkgSs68zZYiE3oeM1qDelKwl5lUujmVaQ9GWvk1ZiJZ5c+BtKrOpiMdUqIXQyIGiSnEU0IiCYJWn0J6cO6ikufqyEBkQga4Vb1pB5UMjBzy3LI5rNx/NhcVgjDfiZmeYr5eecZ+gi5O1BrnDEm4SwzVv5D+0K3GYz8zI5e4e/F2JVBONcrorcDnzGz/IGkBPXfOD5NMRP32N5LC+/vtl8gy9hBcU0JQ1TcDcza0XTf7fOD5V3KNtIIO3Bo2XJTIJQ08IFxmTbIrQ62Ieok1EeGSrovZKBJKBJJPSgNKzkIglhXcQM0rMDIwM45pKxbhFKlO5WPSueTVvScTuw7k1UfWEuwaI0MO19wyyLpoNgYLNR26VTkkIXshbBljuraRktcOehOUf28dzjJZHXlbv31P49e2oHPO5CEn48WsyYVcBi1IhJj2b+LAmMRcqTdl4+wKAJhsC5GPHx+xGmI2IeUdJpg0awE+Csm0g21AR0FGgW16lzETw0hafZuD2MbzGkJoX5AFeyA7qN1lo6Tw1XFKHsX2RBKuISg6KDKkFXc4FmSMaXUdpdrgc8w/QSlWglyCWlSnWoF3JJekMxP+2X+wFsajwHjknclVU5Ast03LUU1/V1QY0m1CyKGqm0Dyh68YSnRTTKGm22wW2o4Qx5pbYYJ9TP/yS0c2jyCUtVfUpE0Mp2cdXxrk3OSI2Zs3oFyUSNSRjYTyj2jTrcytguhA5LKuSlx8u8rNTmj1K+t8vhOCfRbyvvLUqUhd2OmM3uhQnMhgIaEn5w9Us5EWQQvwwNXHi29hiCsQ75g1m31LIrs1BU8M0+ex8547Z3LN0na+AHL57yz8CfkaAYKmfIxEpMq2z0/IprJdGGNgNWTHY9ZUhLx6zctY1aQjgAkzQceQpJd7Scymm604TcG9DJ0foTpa83UHLSYlMx8haQERlKolDEkWDuTP0UwfUhb6Gs0Mk5zJdklVEDWzUdYijBQGYKuZFEJrMiplrH0IqRTNZFxXzaD4CDoNIT1WQSWZi+yfhAYpEseczBrN0RyKWiQa8nmazVoDMKbbs1pIw5BMSXaeR+MoJ2kTp5WHoNLk3Jw3nGlC8P/ujBSAFI/uBO5FtlzSVSP8BlXWYeRWjstx1cGZrNMbXYFssUghE4QbZzYa80rfvnv07T17P8LQrXTN9m9aQg+7li+TYclcfRx/kMg98ahEIwUxE0wiGYvT7+FNOHPO48bUZOTijitJaTOmpUF8aRAt93wh154yvWnISXn1mpLJLDZ/9CCayLALk+3NakOIKR+B6mTu70yzUC8aQ0yrXInkFTVFgkukvJuG4MNIC0F4X4K9ghZ/gvdZJPdBG/JaHNEDyKqSgQ0uAjK6yCNCGnPUHJGTNAHMoe6IQQdBtmbWdGQw1LyGxgHcEUTMfoNCBOX+Os1Iqefm+atkR7mNZ7kVMSDZ5yCD1gGINKRgD8h8A/siiPK9ySnWH1qqnd6kGFX4SLw12/ZTDP86i+BN7l/QdDzTGuFza85buIfkT7gU12Wl/9HxFu4eLwKWwLadNVMMEgmqjCLZRzF1SjdF6jKBmIYxN95xLmkx57iaFs9L1U+tmJ/BBK7P6rXQTHT/ij4/qfSm4cSIImgFsCddy0y2yCFzAJuGZiRmWtFIaEx97gKEvAoMjCWCyJBMS64/hZuzdOa1HqYRWP5B8hvklesoqa6/dyynx4JoFtSSScHeYyWXwuueGLDVdB4s2PzSgiaiqZSDQo4gJq6NO8xJm97DRhDNZsh04zBScgHIzuZCBJ4YsLk5AvBJaU0kk4twsuQ6R1YWGWWEYK/kM9BMaOnv4kN84yCJAAZJDXSE7BNJzzyF0kolLM3vJTM7VBPSXP9n227nnwJO04dwM+JME4KS+ihDThIbBza6aqKBAC7monF3xYtJ6ORidyZU1jLywOHeUnb6omwbQXhJ16Dz5hQ7zoScfQbc9zhLKBuvEeR5XJQN7x0fksw87jzLwr4nXuCSrrNfZP6HGTUwZEf7Js9nHbZsxsFpCmGWGMwx2+cs7NMo6mczD2npTmcF/CyD3IjA7s3ue6MDF8Om+EJCtnWPSCqh3Pg52s5xMZe7mD4HYRMHtuPAJgbGGNhsh5LBDNSEM7PfePOPCbgsVHQwKa9EkaI9qJLq6WTfgmidrncuF25y4xftAc2JbbUaqJnMwwDjUQ6i8WGd5peQgI6KbJQQUomLpLJE2IZsTXLEkDikRuUEkvN4qKv18grknAFKqQr12sFA41QOY7KwRpdOQNYSsOeXiTKuhDAKbLWSkKZ2mxpCnqtjUslmscYkFGok0XXCQgg3KQaJ/L7VR8uq32sFfdOakcDtw93N9rWMPCDc20ay0K64LZqozx3YFXrmG+l4+HDSZH6SJqM6akhaS6BEQZUVvUmQAHfFi6ldqN1nEwWUzFlhiFyQyD3bC6U2UJpzaPIn0rx2/7hPcp5NtSEtZTAujytWYWTlhjd/wQZIroKxbLdntNGBS7rmclxzKa6b+Tf5DN6Hkp/jNg4lWuzSuOLydpVDbnP0VYhcOErZ3KrCNoTk9HR5ByXcdLR8hGQ+is6PQEyaQxH8+ZjkY5BqSjIrnll6ImlVP5rpxRFy8UlICTG18+KqeghUslldEsNIJJt38vYxrbolKmGTTVPZLi9jdS4nB3n6bPkLngziKjTZxn2IacmpsIBJMxdp0haCJdgNQlDN5JXmnKKP0udxLcgmRxhFSRXiTUuwhUATPRQS0zThszt+p9eAKJYGOTcxUnx+tUeX5DPTELJgTkIzlhVoyTvINulDcgT6nsl9dM6ubf35aV5Vg5lLbnvY6i5XSE+KIxbg9uHjbMIwacfpxzcT10PXd2WBWk0/lqtQ5tITnZuHEVeaR3ucr23kS0z0hLPNAv1y/qlFQi54N5bGRXaNu+JF7t5eLGGklklu1/B+iYcc3U1A+Z3j27h3TFFjt60uEUS5Zdhw//UxF4ctl8baL9sShlJ7VGHMWkNSRtL3GOu7kYVGafISdKxmpWgmGE8UkSzgKkkUgU816SSh7DSK2H3OpBC2lPMtUVqHdD+yqitzDUKMiqyFMCrxKMComWgyEZjbQLUQgjepNRpCFs46UwRPTBuKTstx4zT/DMx0lr+k+0/qkisSkJWaNC/ZRtiMKcpo0swnhzP5CCPvS/DHn2L+geEU8xBuOpxpQpAcY9+0yMt+gCTocokGicnMYE5JW3BLElAD496VMkyjh6z+UDuhPWkTed+oUiv2ODJa58sn4a3Fvt6ThiWkpe+hjUqaRP9UO70JxTGvpuYcx8lMVX0P3udixOTf++tawpnt86v4ueirIJHLcd30hLh1qE5+7wMqQpxQtACA21aXmjapALetL7EKI9s4cDlHIG3z+dHeEbYxsI2hfDaS2GooZDFm89J2DIUoWpIIKcTSktjy9oYkiuYgRehb+QoNLnopqwEqZsqyY5zgLWYiymo6ObgTCYhdV5PQFUcsRgwWHSUuW7FETEkd34jAfA2pmqsSNtmkJfV4O6eMhV3DyEMzgWj5HjZK2Crh8kjYxBQ6u02vFHpqhBBpfARzQt77E4CSt3DKhKAK23PcIOdME4LSlkkuq+BcNGbMJhUTKtZZbddqdw4jYWL79+ed7KCtZidgohHM9VQwOWoOaIOtwG28FGLqiGBOOyn2apMktkS0uVVyGLLvpC7mxoPLVXjsKql9UlLbhbDhApudpqq5v/UDJPKAoTU7RRVWqxEr/w21y5VpHNucyLaNA1bqwsp4R81koaGQhoUbjvnzJr+PUYgxlHcF4hhajSLXIFI17cI0iEQQybZPIZAmssmHu3bag7kJShazT57L3z0hVFIgRxulZ5mDnSqE7GBOXyf/PDoi8MdUsmmvIyrtHI2sRgjjkMp7bzSTWi31LaMim4jEmLQdJ+hl1KpBjLH+tiHbrrSajU7RdLSYjG5SjBr4WEzRQL3fwPcuSN8rWfi8gTGHl/aC2cM8CrMNc7ooIhvTb7Ncgn78XvC18z/5R+c1gn1moLkS3f76+5zTu/bvM4/tymie2z43xlyiXHJs786o7rWWojl22c7++H3Ocu+3CEbEoqjobAvFJHOU6PaZw5rsLCZbNbKrIEXcZF8AKrUoXsiKrDeB5/OwfVD9q4EaqGRy0U2xmKjcgllVChH4NU2z6hdHBsJejaA4zY1kimlKZgkimGltADZZU1ppkt9jNhuNgbBR5EiziSmTgILEmD/nV7Sxyw222sH8T/WKsfgQbmJEUthp+a5tvaF0TKAPPwV4QLjEWrZcimvuirdwJFuOdVXs1zbenOPUCKUtFpdXwRZ2uSfW0dcg8vfSY84UM7p7POmHafNLtvhVEah9uWtvIkrnTX0j3ik+N78e0Y/j77XTFvw1dwn7VJk2NN/9uf2zTMfUBYEvTljnt5sE0nymRLHveU9zo2pOwRVhslynrsj3KaMH7C8WIefL8O+T4x0B7DzWkYazOJXQXNumYlPMUVTO/BVX2U9iN2HspslXUu4vO9BltIQ6J/Qju30Hp242WgjhpsQgkYcMd7NmbOzq/vOGJOA/Hi+w0VUpbhekOqDvHi/WZLMuhn90Zoq2zEIo/7BSKYp8/EQLqCtNSMTRdwMz520KmZ3/sV2UDSOBj25v3dn9zD8XmI94sudjyWB3by8Ws1hvHuvrAm2aZ1GdyZscBupJI9UlGtIcfGiqCXOXk5DGC0Xwe03Cspl9NrK/p74UsT1LIwLfaa34EEgNf47jqvErVD+DFHOR+RUizsmczT8WvmrvJXQVkm9BnfAwG33+7BPdyn7abd4W7809s1Daa8zsP8C6OX+q0xIKDpCJRgKVFdy5HYF1kcXtdQIphC77DzQkU5NKzi8vobRpUtI7mE850mhxKt/EMDIYzBErKVM5qnCcK4NCykVY68hah0ZopxyEe7gnXmhMSd4PUVC6ddWy1NbtzKPYrfO1TECXRjA5d6IITyKRIfVDyGME0RIhVMhLA+uwZc22cfima2bi6U0yjhTMvr7RdTluJLDVgW02a102P0sW+NvY1j5qxhadjSyagyWoWXRRlJju1ZGCCf5UjmLg1uGYCxK5e3uRj21vmS2vMZL8ABud71JlDmQv+IHGV1D315c9KxP45mD2EUjjbCRSdjKrJblRhLxmx7LZ0S2OtLWtk30E9XvxAeD8Bj4iyfwGRhw48tCOWGCeHLLQ9otfv8J3h9ShTtBKGgLzx3Xb6n3o3vH6SKn5a05UtVPVEJJrYiGEmxK7/iybbFbZaPIPzJkUoAoWq6fvcwT2mTD8Kno0g28Hf83j7KOo8xtq2QVcBJALh40qk1INkGv+zCyn/Dp5X+irmc98DsNKqgN2cDbyEqGTHa9QCae2Lm2zsz0x2Dh9L+Qgyq3hmPuvLhVqHkk1iawMxwNX9xbifeDqXu6/ulTG8k7qEeHDm/tx73jEx7YXuTSuOY7DZKVvjmGgEIA5iBMBVOEOtGGnXiMwwe8EfhydEd+sZDORRmGUVvADukomonApFbebOn+ZOpG9sPf7/Pd9zmTYKXgl/684lZ12UPcdsOjuNBof2VSc3p7QypxrFFJyPDu/gVJ9A5r8CEANn22uf7pmoor0uzmvONOEALVq5q6mKkARfpFpD+RDsM+BOgefNFZNGENDOrUkduvgBSYk4B2rvQ/Arge7SnbrrBlqdKvlk0ws/eo7aVIp/HQUJUCpCnshbLuMZNNE0lh3jRf52PYid48XUrezYSz7bh0uc/vw8dIFD1JuwsNWd7n7qRrfmM1svxOOuScecb/trXxkcwt3bS9yz/aI4zhwabtKpS1cGKnPN0jZwPlZOBOPRQmVbeU9Cfc5gQ95ZY8JO5nkInihT4DtLenY4TiZQXDCsnzetmSwkxC8UO2IwuYEVGGZ24Gmebt/P7kwXf3O1LmcP8MOk9LMfZQ5aJpfjhYueQlhVMKxIwIXaVTIbNTqPM5EIL0D2dc5yvdzulrCoiHcpNj/R55E2+z5Q/aF6Ay+HPTOqJo5p6Yjg1ICO5tjLN4f2RHJM+No7YX6nEPVbPd+rpPznGZwqL3dx+4Hk2RZe6nnKz5/yUx41q/CBPjtqzUfGu7Pr9/7UC7HVSGPEeFxF363mN/M3HZBkq9jba1EM4lelGOOWZW8hHRM6q+8HTYp9DSbDlVTnZ6tpuqn4xhKXoGt+FEhjukGTNibuUfNtKPUvAKl5BaUUti2n5kVvYM5VM3xClTnqdllskAvztdsCsKX3W5CTrOQdd9RSrhqyT/I10jbahiTNj8/nagAs0K/OYDSIrSJKnIaQdrn8iByw5zxKGlYslGGSyPheGxzEizktBf6VhLbE0JUiGOdv4VbzyTXXQ2UxWR000KgaAd9SGg1w0z/eLUBPY1hNObIhugMMObUtNr9g3Ujc9ebc9xaOQXvJLXwVTP5jC6ayddEqvaEVjPYhbmopUNgoZmx/Fua1wzM3g4UEggqEFL91VGUoLVnwaiBW8NxU8Mo9YuGQZVbjz7Eh7b34+7xQjEbRQ385vFDedTRB8rKf8S6xoXc7CgRnu0bJDJo5EHDPbUYYYiEUTnKKbD3X6dEt2JW2Nq9psJrY17yWnJZsflb/+KcI5BOkvp7USMFe7mSE1B8AQXZ/GK9lXXQpmT09hYtBAImsKUwhgaquaYxHaXrhq0g27Tqli1NDkIhDieok7aSxxargtrN18tfn6RGJjCh1lSyufVEZclond9DRk1zzaYhIBXQO0omWBkDMkQ4grBJuQZawk6NRevnmnWtKWzJk8QYU6ns00CnjJw3nGlCAGbJoM9H8OUsYhYqqfT0yJhijnLCV5jVEvrYeavKOQevGRiMAEzwjfYvqYznBL5EKI7kjmi6cEt/zR6HhHOm42TybpqB317HrOG2UQMxh2fmuyOqsB5q9rT1ge7zPC6GDR/bXmzu4Q8cvb+Y9ax66SC5UZE7P9ASYF/oz2tyfVZpLYvjNMJcD2JXqKgJurohbdQs5KU/Z0Zg5KCrdKkRisT1GkFPQEox6wi5HlDIZFJ+QlnYboXhsqAbCJskiEtJrjxPKzRngjnaqtk0m2Ja6m5A0rZUf4iGCKwtqN8uSsovKAQg1RxWyCjNb3WJpDXk68RBUAnpvG1AxphLCuT+DZ3JyOaqZRGlNSfBsMraxCnhtKKMROQi8PPABZIsfrmq/l0RuR14KfAo4DeBP6OqH3bnfSqps+S3qOr/kbc9kdpC8yeBr1dVFZELwA8CTwQ+CPxZVf3NXXM6896ROTJIZpqapeqFxzpHBqX3bY4WGhNBiNXr3//j8c1e/Ptc4Tor9uab1Kd5SrOvtMF04Zd997L+8yFawSENb04610hi7tx9ZrgU4Bsbc4+d46OhPAE154tOzG1zSXcpUquOZ82OVuJKb4uWBjlDiIQQGYZIGGLqjxBIReFCllr5swzpcxLGml/UbYOiK50kbXn04aP1lYWwRRW5jOVmNe2Ptc953ElSmXs/cS79nKBqClrHb8w/WfMJI6XDWzVT1Rf9926uceg0DqE2zVkH4pC6qll11+LoDtQy3bl3Qy3bHcAdo8VsJKdoMqo1sE56HYDLwBeo6uOBJwBPE5GnAM8DXq2qjwFenb97fAe1Z7LhRcBzSL3oHwM8LW9/NvBhVX10Pu/b9k3ozBOCrSbtc1ThWIcS019DLudNR0eyxfcmqP2Ap70NmnO7fW3V0WouKiUVOgLwwn4+sarVBrwWMe4455B9aUwzp1XNqbkm4hL75v8hnbRKsppLQw5NXRvpkjKH12HbEUtb+dVgf5ei4Xltr7zXcValdpNpSNaHQVlJEv6D1FaaAgQjAbfSndxecEJfMimIW6lbM3l/bj+GF7JOOE7CM810NKdp7Fqr+JV7NkXZ3AqH2FyFdq79/TpisZe/B//ehL16J3JHJpPz7WuAxo9hc1u5xjkm8KUe6zWEog1YWYtidtPWdHRK8MPue508jqqq3p2/rvNLgacDL8nbXwJ8uZ0jIl9O6k//drftDuA2VX2tpnK2P+jO8WO9HHiqyO4YsTNPCIZeMzi2xuszt9iXMhjyitIcmn0Wsm/6EpwGYRpFPweYrurnCKAPAy3RUI4E7FWucaCvwIjBv/z5u8pkzMfyn6wpePjVumkJg3u+/TGQ/SAuq7i//qjS/N02uRZRS5iSw3VTNzyD1xCGEIuWMISYy0eQO6nZu0k7wJGHEUAhhc5Usk9LyKdXgVhWzzOagQ/J9GGZnTCePKqe1ALtHE2Whm5b877nBooW0X4vn/sXM8cxNUmpE/Tq5lI0gCDEVSKFpg/0HkzMXqdoMrIkxJNeh0BEBhF5M/B+4FWq+nrgk1T1fela+j7g4fnY+wHfCHxrN8wjgDvd9zvzNtv33jzWFvgo8JBd8znzPgRoHcijmVyQppidZeHOJVCltpQpuSoSm0icckynEewrTdGXg/DRP74k977+y7NjmmCmrTzqtx2CWZLUliBKZNF9iKgoHdIkJp+LG8p8G+ao3zf3S7rORf/q389qUBnWMrIOI2MMmdyrOc+bjVYSk4BRYQzKakjtNQkQiaiGKvDLhBUhrVBVc9exHHWkWnsS4KJznD+4hQn6QPIlzKy0y6OwMWIW4ipJsEkmEqaCVfNC2n82/0Gpk6SU6CWJmKsivU+Ed72X9K7pacT8eEybkfaefWis/26COmUTU4nJ5hBqjSXLSi7bhnw/mvoxSA4EmAj/4LZL/RueBprs85PxUBF5o/v+YlV9cTuejsATRORBwCtE5DP3jPetwHeo6t3dIn9uQnrAvgmuKSGIyG8Cd5F++ltVfdI+h4mIfBPJ5jUCf1VVf/qka/RkYLWLmr4Inc07ZdeGUiNokFw9VJMjN/gEMboIIOaL3JXxmZKB9Uj2PZMD2uQr1IeWYpx2hbn2ppqrJQX/7Px3CzOdu1aPk3wtHkMOAfXVXK0ndBLmqa5U3w60zi0J6oEa5bXB1ZDKYa4bhuJD8ESQej2b+clIOUUShSAoAdHUXAbA/L0AJY5dQZC8PZuNVIqzFwsTdYJ4YmKBFMYqTBzV3txSTpG67SDLh7h3oYSzZvlavvjvOY6hvKcWm+l+9/olbL75o+y4j4bk/OmZAGwiqoJEbYlJJDf5cWNktkt/q5iEfzOwTktZnJIPAa4o7PQDqvqkQw5U1Y+IyGtItv/fFZE7VPV92Rz0/nzYHwGeISL/CHgQEEXkEvAvgEe64R4J/Hb+fCfwKcCdIrICHgh8aNc8rofJ6E+p6hPcg5l1mIjIpwPPBD6D9FD+iYjsL9qTUW3fVTsYs5lhzA5eX/iuh29abyvQfUL/ajF2wraaiNqVuDcp2fukP/QJwnoOlqGbxqxC0UxtzbFaw0/98T3seVWz2vRffu+fSdsqiRk+Ot5aS4bMPA+//ZIe5UZArTlvHcZsAlS8iW8VkpN5FbJvI8TiS6jmaS2mIzMjVSlHcSYnxzMpXj9oeW9MNEzNMdhQpiXQmogaDaHXFtihcfjtYkK2e7nex4QdxwS3P6/KfVRRNTFJY35q5nSli3A33/LZPyvxzzD7FITsV8jv1vpzkNQG1BzLro9ycTqfEk7LhyAiD8uaASJyC/CFwLuAHweelQ97FvCv0nX181T1Uar6KOA7gX+gqt+dzUp3ichTsn/gq+2cbqxnAD+runt2N8Jk9HTg8/PnlwCvIdnFng78mKpeBn5DRN4DPBl47b7BTDOwzxsdXNVSq0dkvwzKEs0vGFLntW22U6eoGCunHYq+bhm6eUXaO5VnQk29BhBKw5up07fOL9kS7NjZ+83n9tffpRnMVlYtDuMp8WziwHZCSJLv36280WKaKa0tXVLZBdqVfqo5pVyUDZdYs5Yt21z8rjyvbNY7Ln+7LUjgAcO9dT4Cl+Iq/Y3y/C7Ihrv0lva+Sc7jo5BLfYQ8/5jmezwOsM7/eMuyI2ThkxK2zOyQMpTzu63YnUYg+RklMx6E47wyNbMKNKYTtX24/ekPUknATDDiTpdsKsq+jGrLl3YFbUJ0RS4KRyE0jVXbsMQ3cdqBxHpvRJJ5aMcKv0Db25jsNo1JKTkPatnQ2R+QFvUKo2TzliIipnjlXIV8E8VjLWjIPRIiiMQkiGWoDuV8s6Yt3Fco0pQ4v4+4A3hJXvgG4GWq+hMi8lrgZSLybOC3gK88YKyvo4advpIahfR9wA9lefoh0qJ7J641ISjwbyV56f5ptp81DhMReXg+9hHA69y53jFSICLPIYVX8UmPcNU3XXiiL1HhHZWD+03YMT7zIIU5poBxH23U2r61dSS7lbtpInMoGb9M7fK+cqjHviinQ3wQtn++3HYr7M1c5Ofkt0+K5uVoHatY6uefSlZk84y7bqBWep2rpPrJ6w+X40vEkQtbrfWT6vcUybUqyX92HSMrM1MlegmJFFRLeeuymBQlSo2HB6l2dbLdPEtzNaO82bYDSNBMIFWQF/ORbZshBf9T8FqCmU3s8dk+yyWooUP1HA/vS8DmA9Yiwt2nXdy923xD/twdutN81G/fQSKN49qIz57FQOk5XXwDg+TnoSVru/hAFIr/xiZmfwt/0bBjMleB0xpJVd8KfM7M9g8CTz3h3G/pvr8RmPgfVPUShxEKcO0J4Y+p6m9nof8qEXnXnmN3/czaDYlUXgzwhz77gtYaRd6HIMyZiMqKfwYp8SkCQyrwpuZDGGZLQ+9Drx308GM10T7sSo3L+w+MMPJEcVJiWrqu5UL4ip+hIQkjhV47qNfUCVn2JcF9GKpv/Wn3Zn8zI+uLspn8vbxvpXcqb6hOZLt3my9EQibeuVcUJQRFx2SWCCE3a8k+A08GRhB4x2g2rxRyyE7nsr8zrdimckxnIjJhXoigP8abJbyJaRf8HMRpHORxegKx+blL7Bzazc9vOxHiTjGnuaarGSkkZ7oWh7O/nUaTwmkikAjASmafJpRzXcvomvoQVPW38/v7gVeQTEC/mx0ldA4Tc34YvGNkB7qVNtVnYJqBhSt680htolNDPg2WVTvpobyDDLwfAGqo6Nxq266ZjvOF7/rVu63Od2gbxf6/3/l7UkE+H7bpr+3J4JAmPJMIrB3XHdBGwPchpsc6lNLgRz5p0J/TmbnWsi29o6fJafMJat7RLJnEQmiT1yQTBJIT1MpKW5v3+qohqb0tXP1xhmyyMX9Cidd3L9HptqIZKNPIJJWTSaF7n8zN3UNzXv8zOEQm7jvGXdtCdqsvQ0oGdPWDSOPb0JzBnfweNRy1+Ask+xXc91PDzr9L9zqDuGaEICL3E5EH2Gfgi4BfYYfDJG9/pohcEJFPI2XbveGk63jtoISZurj1ufaJsDvDtu1hXAUJTM0cU+F+2OP0dff9tvljd9j/O1LoETotYdc8ymd210uam9vQEUEgHqRBWSmLYpLr5lZ8Lmb+yfuPZsxQRt6BGsFUSmVYcIBUbaYhB7zwpyEBL/Tr9zlnc4eOJBpi8N+hCnRHBIUgZkhBtN1Xs4dpfQ/7sGd/sUB1RFDmK91r7tb9HJudM2P48b3gL589KYgT/FTHspGDOaWdg9k7lk/fqXx6eQg3G66lyeiTSHG1dp0fUdWfEpH/wIzDRFXfLiIvI9Xo2ALPzTG6O2G/u7rSr3VwLPy0tfdL40fw2BeBNGbbs3039CahuRIVPeY0gtPEIF1/Atry1/XZVI3G+gE0TWKYIa28rfQYJuRIH7cyn5E6o4Zc+G7cSxopoXBVNLRRhbXkv43GJkjAnM9BIvfkbnh9pFTSSJKQjpkE+vwkrxHYZ80EEavBCMjhkMjuCJJeCJjwM5u8E36ltg/UH7K2pxazTmceEjNblJj+6bVPNNlIZ7Iq9+BMMG4e3rR1NWic4DZdyeY3C/Oywd29JjORIpodyNSwXxWpz3QgVU9FZs1Hp+dUJlXHPae4ZoSgqr8OPH5m+06Hiao+H3j+lVynzeKttvC6bT4qJwkVnfUNBIkEjcWi7/fvii460U6fV71X048BZuzxZVWcG+10Nv10TKvtxB2tN5tQWOf4nji/nT9hDnOO8ercn4a2To7VXL4iC54oofgMfIkSKysSdV1MTCmnYX+UctU6WtORFI2BEt0C6bM2hu4ZeDOOQagSVFrhVASjkYVL8LLHZ0MJVCdyc632+GJC2meu0B2fHTEUApoR/M28bYxD5KIbqyeFdG2pyXIhH2D/xFTQoZKBDSZ50v7PXSO6Min0juXTcirPkO95wvnIVEac4Gn9A72QTKvN1oxiBdJ6pIS1qWnGF7Pz17RM5H31hNJ53Ur2hOXcPsf0rgilSYXWGdOT+Qps7nPox485hLaU8EZO/BH5ZEGYyfqWmjDWd6MbJXAchzLOugvJtVLYbeHA6b2ETIihZF9NYVoB0FQ+na2C2ghqaQX0LswI2sbUo91h0u7rfQoaZUImB2PX8fk2ey2haA7aHtcMaUQ2N3ZHDIWIIJl1GmKtmkLJj+h1loB7SO19pdzHkP5u4+xU7xOW8tc3MWoHsupITlblupq1rOA5W3p1zBqRpPMGUchaAoAFjnsyGCSyibXnQd+3wLSJmJvQA2x1mLXJB2kTuBrC2uFHKM0/NAm7dRjxpbPnSMHMQ8149qx6s1Hnr7Bjt3FIcf0aCJpIKa3mkxSxfAF7nqMKUSRlEXe+A3+f/+ne38cfvPj+UhxwowP3xKNCSCUySdpieACXXDXUbQltjazCyDamqrMlY1kiMQgXh1Rgb5SQk9Nas9G+laBmqaaxc+S6ZbQJvn0CSYUmMc3uz8s/yavhRhDpjFl8RpPwDuvGCe2O7wW45SM0JiN3itdk/Lk7SaknA3ePmonGSoOn/Ijc4wElZnVFIqnnRNbgSt6Z17RGN6kARGdvm2uzebVYCOHmRRX8boWoVUs4pF7QXIkLQzl/x6pytgWm369hdgW/rwPb3BziDJEUk1HWFka3et83dtsZrYabQpvNPHueBbTne6g+k3kntvfroLt9NQCPuPBhV6SwmotSIqEWs1votIjLcd2F+kqjdQWJrCGFpWbn80qE41zmYgi5s9oYitkIs0XPzLNoC7NmlUoGk6foBXCvKcR2U7cerkLelb3YxVfFuTuTAd28d/Py5/v3k447GO6mPDGUngqdtqRKCT+1HWICv5S00EwKknMYUt+GpAhqNoW5kiT3GWfXYXwIzjwhGPbV5Y/IxNRQ93U+B2d+GKQ6IftkNKg1+uewq4vZ6IRxKMfWSKZUJrrzUzjT1ElJbcns0t2jm4cnzj7P4CRnuCVz2fuhungpzCetttNfL3Wmi/MmII0lIMDuYS0jx9mZnIhhVRPmiAQR1qT6RpBIAeOuAKswsNKYC+LVaCNzAItotuNn5yU5vyBHE6k4W7UAfrutXGEi3YuD2cmosvKWqgmUQ4wEHCk4JWyKzrS0kwz2ycjeVHWl2GVakul2+1ObtlR8CtF8B5UIvPlIIiVpjeR0qqQgmdLN3nXaYafnFOeCEPraP7tg9fh39kaesz3TrnbTODkDWCMb2m5d7fViMz9LzLKUrCvREjxsHs1KOQtNqCGbNCv1fSv/K3N011yFXBk2r8pHaYW4Fa4rc9aWkHpsdGCdNZzyTJ1TGbWwVbgU1zxguFRMS+ZHWrtkNtMS1qS+eKM5LDMprEJkFSPbEMvzUQVCIEatK/6QzyOXWyjFj/LnoMX+rULJ8MUdeyJ/OpuMdKRQSMA2mPnHC1X3eZ8WMCGD0xZuJ2gVzS4jTenmb9pPcPvtmZZnWzWHpuCgkQKgUWFI5qdTW9RnojqvOBeEcBJ6MwPMd1qb2z/mzOUUZmnOz/5YPSACv0Vv34daFsPKQcz1QWhW+AQXg5/Gq2adSja7GvD0EVm+ZtFBHdVow1Vt2/Ra+Trisshn5mTmpzZM1qRBdipr6tN8a7hcyGATV8V3cM8ohViAkhk9quY+0BA0PZdVTMXuVuYHCckcEKMSQmWOGEMW6EoI+TlZBFJe7ZdVfyaKEkqZBdjkmH3oSKFZ6XsLprff+3F7IpgjgRlBLTv2TaKDZuZ7kLbYm4zyuwY3hBP+KfLIjW8EEEkmI9FCGJLNRhoyKdjfJTug5aAJHoqFEG5a7Cv3bOWtm+OvcEVuWgVu9Yv6OP94UF5BdUbXH5NvtjN77Y4U5lCurWmOTZ6EN7t0ppo509Nkzo5k/HF91FF9TUt1mJZgGkzNE5m/L++YbyPBuo5xzvG8yf4Vq5WU+lp0WlT++5kJqSSszYSgDqKMZjISqaYjpBPCXfSRI4KJycjePTHsgIWAGinYit/3RC7jmYClfp4zD+36mV2RaPMmL+227zh2st89C08KkO/PHNpZ4VWpJjrzFWjWyIwEkhNZUm8K3OSMYQrTnRIWk9HNDetnsNnxhyomHtr3g8aeO0eqCWjTxb6XEMpcIC8iTUTNWka2Eyv/YfAlq9P10w332o/lJZCd6j6HodEIzH9wSvq0Lw8SUUYJqTGOzYtUiK62ykyRUKYt7TIXGUwrMG3OtAMbz8igdyobSvay1raa1jRnK6FsU2kT1Mx0VEZ0kUTlexE+bp/TFq7IhK1O1hspuD+x/Xkb34QJV5veLnOR1vH7a5ZjPLxgPwRzwr+/lCeDbBYq/gMjBXdfZY1RNIX8NdLce4l3EFIlyxFSZdV6zqlgIYSbE+nvHnNUSqqKmYTFtslUDkz7HJjjcW5132sRk8S17PS8IJvqpAQsUyYZG7TYu20eVhV0Fcay0rZ5HEJSwTm5oyOFXkswX0LteOLuRZTL+OiiUMb2pqJdZiOvNVj4KXHFKozFfLTJ8f7mJF7rlsFFclmexu1HH2/Gfu+l2/nkix9mE1epPlFulmMEcU+8wFpGLsX0s33/8W2JRBzRbWLWVgoJt+QwiEIYGRFu6Up0+wztlTMZQcirT5yJyK88PTnklxW4y5uLvdwcplL4Yn71HquWIXa+/XlDZxMvc3KK7KFCqyeLvO0kx7Nd/0oczuUZQMo9CM5klN9FSeGjeX9cgYwpdFwFwhaMpeI6+QcstDYYqTgiSSGsux7yVUDpHv75wpkmhEPgV8++Ac4h8OTRINcPHgiMrja6VRb1TVuikVLxDXR1dmZW9/vLXsvEbLMPfY7BZqaW+y6H80m+BF8jyMY3Z7J1MCuOddMeCFzSNRsd+MDx/XnEhY80Y26ysJ97BkYyMecgrGUs5iKg1dYKEc7/DUtZCztKa56CaQl+3wRFgMpUQCjQ5Sc0AtZW/DO2fm/qacxARgyxKiN18EoGZdUM0zpHvXagzGoTPRlMzENXIVs9GZiGUB6f9xM4M5MKhEhr9nKmo34ipgkIAqfYQ3lyL4uGcPMi2dlrG8wUiaLuX071A9TEr5NX437FPjk+/8vbMDUblUzYrBEgbfnpIMoqF8RIoZHX/9e1W/hnX4ecbEbyRf96rMN2dvuowqW4LnkQdaz0fB9ydHdJJEzJbTlCaSY81UpWWNJZP2Y0/8DO+43lWtZes5TsQBhjqE1aOrOR+Q0a/4F93hOBMvu4vLm7217CUHEmIulIweRiqArinBN5cu2eDHrhP0cGV/lTnTwm6d7tXkxzmiGG4ivwJiI7r+yTIq0bP0fngL/PWKKMblbM2IlzHDtQTEUWajq3Ig9Exh02/bqS7whByaRAWVH6zmreVGOkYLZyIxBPBlFT0b0207e9ZlQpGbhzmPYNqIX8+h7T0EYDma8jCebQ5hpwsqYw7CGHdK1QeiGbH8GE+GxGtsz3lR4RNnHFpbjm3nHNLcOGjTm06RLScrJcvWfT2uo87W+3CiNbDe5vWZ3LKrlxTnFu1ucwcSofaEqYE85zn4tzmUY+tqfmA4ww1B/Ur/ZNO5ghg71E0M/xCuAFfnEeexLoFvoTjYXu2O7VaBeeLBxRaGGP08ENWMNdN5ymq+W6I2mIteSxdcoys0yJEIKyH9pOYoamgF2nHQz9a8bH0Oc31H6+sf2OEVT7q/LRN9YsBq48R8DGgtqus2/tWeZtTmnxTtYambWv9He9T20+7/KFWEmR5BQ2P8Ph/7IsyqhEFDmSquGvtQfGJoehbrKT2kxNPkS26bksOQQ15H7LIZaoI+uVAJSqqPdFwFyJCdqbkJIpijYDGRpzU1Oqwp9r14YT534tyKAOXl/qSML7Urw5rTU1ufLXQtfzWcq7BlLUkdNETk3S6RW8ToCIXBSRN4jIW0Tk7SLyrXn77SLyKhF5d35/cN7+34jIm0Tkbfn9C9xYT8zb3yMiL8y9lcntBF6at79eRB61b05nmhDmMDhSgPqPvl/tn9x+sj3eTAtFoDtysL4J9draRM/0VUdtnh6+v8GEFJDJcV7IH2IC69H3G2j2Sb3XOVKYhPIeKDHsniw66KTQ15Ir0ZGBHXv3eCEJ/Tiwzb2gj+OKrdWXylrVNu/flDF2lyj392xVUH157GuGObNOhhfs3h/gBX9PHJOxvGClFbTA1Kwzd27/glZAd6+J4O9MRLLjnub+NBOtoiGG9jOZOBisX4I03dbuG/bc8OQBnIjLwBeo6uOBJwBPE5GnAM8DXq2qjwFenb8DfAD471X1s0i9ZH7IjfUiUmvhx+TX0/L2ZwMfVtVHA98BfNu+CZ0bQqhF03RCCkWAy3S132POd2ARQ0fZSWrfp1U767WrE1kdSdQV6RyKY9aRQl9R9bTRxOOzWyOYkIJzKENtam+fJ/dm2k82+yQT1fQadv+1pWZNZLOCdpfjinvHI47jKpGBhvTKgv84rsr3TSGCUMjBvvek4O+xEsH0mWn2iNZ329GudHeaX/ZhxsxTxug0AROgE6dx/9mjN+F0xNAL+r3YRRQzRLDzenmeYezug91zb8etWoJ2WoH28zgtnJKGoAl356/r/FLg6cBL8vaXAF+ej/+P1oUSeDtwMWsAdwC3qeprVVWBH7RzurFeDjzVtIc5nHEfQotBcn6/6yTeRPUQi73cn9PnEjRjFiIx/0PbIyFZ5kPbSEedHyMveXYRQd8wZySdX8JG8cX69v+q55r4HIL0fPI11UdL6cQ8M39dbxrb/S/BBLpvO2o5CzDv/I2OCJpENNos6W0cilM4iEKspEX2D2wxM1gs+QpJm5hqXQejW617u3x6b8fcKaB3wPwIzaFKLYVhn51ZRJVTbyXs4W+pv5/mdqXb1msJ/vyOwJooLKnHqAhiBe1s7HzvUnooa30+QVKl09N8IFeukO+EiAzAm4BHA/9YVV8vIp+kqu8DUNX35Z70Pb4C+I+qellEHkFqQWy4E3hE/vwI4L15rK2IfBR4CEnbmODME8JgUTyFDEzwhyLEfdtGnxuQUIXDxHcgM98VBhGiDhzJmMo+59VvLXdR6wn5InBJK9GmWY2PoU+lmJNjdMXYCOPeDzCnndg1ymeZFtjzBOUTvwIhR+Xb3PN2lWY177WIdA3fo9juXwjUMFQj4UuaIoNGDdwbjyYr9N+693buuPjRVHtITP+vYbFe+I8qxTyUtIHqHwiiKdHMm7ucX6Q8h/wstxo4HttxgGIiSjWO8ismiaYqEHP565g+l+gTlfk+yRlznFls6UIrCJUaX+AFaXeOSjW39LKvrJT9tv5afscB89z1fWKGchqBn4dKpxG478UX0k+4n0sfe2H3k7UFYiIOkdnTrw42+cPwUBF5o/v+YlV9cTNc6gr5BBF5EKnD5GeeNKiIfAbJ9PNFtmnHTE/aN8GZJ4Re4E2jjCoZHIlFutTs4Zgjfnyk0VzM+j6YllCQicObeeZqJ832G1DJVValaDo+Qqh1NlftJ+q0oqs9m1Q62s23rPyj+141hZBJbNeKuThiw5h9KlXYzvlmvE9kE1fcG4+aktu+PEcql60EVYL9vQoxtM72uf4N0Um+klsgTivUrCXka2/zOJ4MxphIYoyhmIMaUlAyMTA11aSJFsHR2Mhxx89gp3K173gpMjC9RyardFFqddQ54ThjWjrYzDXzE5nY+23bDtFUtILJ82OSryE+CcA/y46Igv8ZmpZwSrgCE+AHVPVJhxyoqh8RkdeQbP+/KyJ3ZO3gDuD95doijwReAXy1qv5a3nwn8Eg33COB33b7PgW4U0RWwAOBD+2ax7nxIXh4Z29NLstRSHmVbvC+B/++c+wdzuhyPRelM2e6mQi/QgpV2JUmPyoNcVjM/Vx9o97G7+fZaxh2XOswjpNV9JzjdbriniZ+2fO1yClv8rknk8EcHnrh7klvBnMkbzRpBSO+33Mogtz8CD7buGoUoXkdjwPHMb22MZT3bQwdGaSxVJMZpsj0QgbOQG0CbJSpD8CteicmJUhCe/aJOC2gI585n4FpJf2r9zUU2bznp67Svvefbd67ztupGew4r9xfnmfji/H7T8DcvE8de/4WV+JDEJGHZc0AEbkF+ELgXcCPk5zG5Pd/lY95EPBvgG9S1V8q00nmpbtE5CnZP/DVdk431jOAn81+hlmcqCGIyF8B/rmqfvjkW7z+KCaj8jmt9L0wXstYI37MtGShmc1YJuwqmaxl3BnF42sK9YmRvbnGm5E2nd26r99TjJQl9XQ3rIpoYL7fgyHMzBGc9pM17Ll7KWM4wrCyG+l7r/3UXg9JQA+lGF0x+cxIhW1MJS+i1G5ohRSpGoF3JHvBb8fb/QBVW+iv51b+Rhrm1zBNwH8GinbQXoDpP37thNoVrr6LOcVfKo/l96kz+fjbbe7UNkjVErQ/Yc88/HtzoT3Hz5mN5h7bdICO+Pw2d8xJ824wZxa7eXAH8JLsRwjAy1T1J0TktcDLROTZwG8BX5mP/yskX8M3i8g3521fpKrvB74O+AHgFuCV+QXwfcAPich7SJrBM/dN6BCT0e8D/oOI/DLwz4Cf3scw1xM2ierslYnTuAgxYjEZ+fZUQzYw7qpr1GNXr2R/ft/QflffAyOU+XLPsalT1K+q7bhQsnkrMfjQ2n29nXtTWHDzriUp5h3uNcqo+g/MNGf3HG05CnmFH7JJqDUXWTZx6qswJIdwiLmxTd6nfS5BJQMjhvIcqOaiPmR3zgw2xhwBFSsh2DWjI4PyngWWemFVpKe95pbP+8lgQgQzmKya8+UaO3wnkHtzUs6pnL+Wn3a//6TVd0ccDRnsIBWBCWnOzmvf/pyh7JLbm+2njSuKGtsDVX0r8Dkz2z8IPHVm+98H/v6Osd4ITPwPqnqJSign4kQJqKp/mxTX+n3A1wDvFpF/ICJ/8JALiMggIv9RRH4if59Nusj7viknUPyqiHzxIeM3q3CzmeNyBiSydkIyhYa2tu7eeXzQdQnMhYJOahNdgVWut6mfFFVkgnWup8EcLDva50hMj9FZ8ir7u+S7XeOYaauPDtpEJ9C75aJF/Picg20+z/Zt8nne9j/rS9B5s9HYvUzb2IyD256eafEXeGGP+27beo3AbZ+sencIk8nmXqExqT5nMupDTzsb/CRZrXN47xRw0r1sHjvm6M+ZJYPejzCnOXXagN8mhwh3gRKKmue7039xNVBqAMFJrzOIg6RV1gh+J7+2wIOBl4vIPzrg9K8H3um+zyZdiMink9SZzyA5Vv5JVqVOxK6V/eAEVim25oWi9P6E/UlrZYV7YPbwHBlEbYmkt9PXFW6YCPs5lJXyVbqDdgn/SYmHRhvQ1mfQ+SPauVVncrX7D5NjAO4d12y11iYyv4FpDz7JzIR8L/hV60p/bp+RycZyFGaIYE5bqKTQaQIzmDNxnBQxU092h3XCOI2jzefyvSOlPsLJb5Nuu0fjFpl59QQx+b7nvsoYzQWZfZZTJ7eebN7qSEA9OZxaYhrtnPe9ziBOlCIi8ldF5E3APwJ+CfgsVf064ImkWNh95z4S+BLge93mpzOTdJG3/5iqXlbV3wDeAzz5hNmdNP3iD/ArY08KPuMY2kziQzJwrcdAiQKiJlHVY9rmLnZcH1208xonkEI/hr+23bvHXFmNOfRZyj4qyZe6mM6jJpWVshH53dptbtzzSMfVekQpecxnGA8lyawmodV3cwBbQl9yBE/JwJzFJuzt83a0khd1vyrE6LQEqD4EbyZyJqTymalT1LbNfa4b6/tkVeu+S+yJIH2XqI1A6jWHEs4Z5+c3udYhr5n5T7SDOSI4BIcI1V6Tsp++a106S0T3AQ3x7nmdRRziQ3go8D+o6n/2G1U1isiXnnDudwJ/A3iA27Yr6eIRwOvccT65okBEnkNK0eaTPnlVbNIGM0NYOOQgkbVEjhhZZ43gmMCgypgDtiNbjnXoTEfzIZQW5ZJKPEciu4vjAcXuTU6A29UprMxfQ3LqhhHzJTSx8yVz2ZmXNLCJEPL9XJBabbT0cyaSOqrNJ5kN2d7vHcupCuiY+h6QiGDVZWivQ2QlY1Pq2q5rjmGfYWxj2Xu9Z+Ge7RGsUqXSjYZcjFBLVrGVprAoISODufLdVsm0zMcRgSqFNNK+9O6dyel73aZREkE4U0AVtq1JqYk6mlsxXsFq0oSr2OcgiRD8T9MTTTeYCiUvQUXSElBo+gb465wowG27tl/7+c6Sgbjzdmgtk2gsWu1gztme/CRCLmOX2pLEfIBV/zvNJfsZFfaH4ERCUNW/s2ffO3fty2TxflV9k4h8/gFzmfsJTh59Tux4McDjPvuintRiEmqy1jprBWjkOHvWRtHcY7cV6o25aY9z1pt/6upfSlRNOiYJ7egFVOcvgN2F7Er27Q7MOpgJJ9Zr8tVW67bkSLbKp9vok9dqlm+QyIWwLRpWMx/19ZjSszBfQCQllJV7Q1JlUxdFtCXkH2Ys2lVU4XhclWNs3mbemX1u3TM0E5wX+tGRgZGFfffbCxlkSacWXjq3IvREQLfdJVz584SZ4/dAxV1zRhOxP6mNm4SmYv2GZyOO3NiHmID66+5dhe/6133AarpkJ7P/vvtrFaIIysk3dAX4RCaE+4A/BnyZiPx3wEXgNhH5YXYnXVgChcEnV+zELkFdTT9a3pOJKJkTjzIpDOryFebi7p1YKRE/naXtEL+CZe9OE+lq32QrGQEuw7mLUOqPmdjtLbGL8WBfR8iextFFGNXy1NNff0CTtuV8Bz35eDJIoaLp/fJYfQn+WICjMGIJd5UUqM5javLZRCPovtu850JGTfDv0wigmonQdpzktO3MRfmESYYtu8mBEwRbEX4mfMuKuArIdiXt5pGPt01lCFU0SI048uRwgMzcJ1o9EZ04Tik1weT+G6FfBp8eZ/dXH0/WEpSSxWzbT8uGc5bNQYfgmiWmqeo3qeojVfVRJGfxz6rq/8iOpIu8/Zm5WNOnkSKb3rD/GlJsxvYyNHb0TAZrSa1W1yhB6vZyjPiENl/2ogo8P+5GWz61OZSaPc6p6mFO1Sb/4CrRr5jn/A1zJqKawNYWpfPbm6qned9aYspQljjRDkrIKbVsRdEM7Fkgk+cBloxXE85K9rDWwnW9I9n7CebueeJcztqECX5zJJu/IJpJiZqZ7MmgMQn1ULk6QdELcYOZW1wlz53nsmPV7Aiq8R1kX4PXcBo4nutfe29l/rHM2JV2fO7mcKVI15Jqthrya58J7GpwjqOMbkTpihcwk3Shqm8XkZcB7yBFMj031/k4ESPhxIJuiQBywpQoMRfIGtHkT8i5DOX4A8NPd82nfq4/DF++eQ5Nxy9LUMtaAifMZ5oMttvMNLvqd1qCNxuVvASxqqixIQ7TDnoHvGkGPkrIZxDbPMe8lCvbJWs+TjuJKjWb2DmSzQ+g3TOd02zq6r+SQeMjYEbw02oWB2HP33fi4PXmonL+js/NOLtUCnedmWG8ooG6xjG7pL50A7ivO++yWtVmD7K5nWj6mdvW+Ax2HGOn+/GFUyx/fb41hOtCCKr6GuA1+fNs0kXe93zg+Vcy9tiZcXpiMMGwRglISURLeqVyROSS5MYu3R+6D0v1iCRTzi6zjF/9H2q6qWPLRMAedJ7zI6TvTmOaUQabDnKSBPiAluuPHan4zmj+84QMukiqqLUUdZ9/ENW6xfXNa5JW0uQRqCtiN0MuTdOcniRow1JnycCRxgTdeCcKBZ0SQL/NjpvdfgK8H2LX9ZtjO/MKmn0K+f799YsftgzQjdeNvwv7VuUnPr85E9EBx5vfAKA4mfuEtfuKhRBuTiguisZMOjPaQhtTb6vQpB0EgUFTP12cgB/cSnjfqj6NWQvq9R3K2nnU404aMx3Tagnef9AeQ5exfJjWlM6zVXjVEgICRAYEK2PR1jpqGwTB1PFetYNQksm8+QZgG9vQ0zm/QDlWp6ai3gcQO2FuiapVk2i1hDmzUMzVTE+SWGWVepUoBFEGZOcKn/6YPcQhO8bxZCCAucwk/eGbcNpCIB0xNHPtrmVmenUHTnwKe7Sfk/wPfdlrrykUQvOkoCTzUSaFU/Mp64k/jTONM00IhkgoP7DeuWmrSf+DCHkFfCRC1GQXH22MzhltmJSRpo1M6stV+NwDO39fBvDOe2uih9qQ0/4+kWT0SrVXx6nze8+/uJDDUgGG/A8vVRytxeu8uahNUmtJwRLliqlIQ/URdIJ/zHPtfQG7/AJQQ4u9M7g5zpz0qk1OQk8kvVZQy1sDIldfQr9fnevMNur2WVv+XrtMN053zXrdujE5XHuzUdUWyvUttqJRJ6bXmTjK3XxnF/f9Bkca+7Qdbx7aGW3kv3dWP3M0n6q3dCGEmxOKUCqGSkgrdZ0Pt9wgXCT5EYBkIkGLwSkdEwhX8MsptZFYYXH7vWba+wW8ScT+9c0KeGc2it1ytK6Id891m8t6z4WWQjUXWUhuCpVNZjVC6oNA57qwktfmhF7LyDpsXXVTycSnjDEloh3HVUk2s9yDfi7mdP745ogLq+2sD+BE7cA9oHJM1u4swWzMjmOlCn/TCGyFXEgBkJDmYH0Rio8hKIw7pLWXSnPwJSPYIVj9Zyc4/SXm7O9zArUeZxeU1r6fCkmRDUhppW/RP+IFazemvWZutVxyj9mpHLjvPvw5RdC7CCtnBms0A3duMSGd4rL+KtZ1ZwZnmhCgzeKNBNYuKWsfBiSRg0TWKEdmNupj6nc4iJsqqzlefsyx//s6sO2CEUTU2lCm1vBvJfM+Ikir8Ro5dIhpasg5B6nLXKiRV6pEX/PJTEYTk9ycKcsXiJuajDxRGSHtOsbfx5x24JPMmmPdyn+0rGOq0E8kMEMG+bIaBZFidLgyOEFnAnyfIOkFPlDqF7X7/LJ4z3VntqXhlEb16YR6Md0YaTlSaHwdc5fxRNO/z82pe0a79tuftvpCsr6zS6PoWUw53Sijc4xrFnZ6vTEXbmkr5EnpBqS81gTWCEFgnbOaT4Jvn1n7LXQF82Z+rfv6KZ+EPoS1D7OdPWc2X6ItiOf7J5Se0D6U1EUTeVhmcm2OU81GfcitD/s0v0EV8DUaaR3GhhD6l88FsJBS+16fU+1fYKGkhQwaMxGoNcDJZFCFUF0el+0e+/6EVxNu2AvG3oSyx6Ri2+cIZRfmxk3lL/Ic+uzhHXNt3psLtF9nhXFHQu0J8/PeK9Q9xx1y/fsCPfB1BnHmNQSYd6CelKU7lHz+9LZGieRuas4Ecgh8tJHF61szF9gfNWTlKPZdy85v8yxyZ7WZsU1Q+vLZe+efx276SUjSDlIY6u5ft/kVfESVDzO1+ftkuvk5p1BSEa0OXre/bMvv62FkMw6zGkNLHJ15yTQBI45YfwdqS1y7cP6NOINK/scuOz47bWCHkJjdR93XC9qJWWmfoDlJCHltIH8ujuCZeZqVqSy4Z1b0RZvYIfRnhfEe0jvpXrxPYR8areI0MafNnCOcGw0B9ucOrNFKAhkrBtYysJbAIDmD2dnd+1W+/95oBmaL33H9OTLwYZvNsa5MBFTNJxaBW1f3/b4ehxbF89frNaqT2odC3wVOmlW/TyrzphzvXPak5ZMNm1V9HjuIMoTY2PXnMo0nDmQ3vpmKvGnIeiWbkG9bZJqJKRGG74OwU/izgxxm0P8M/Hl91dI04ZlBJgJ2/mLe2dxrI6Yl2HW9puDvYfJ5bjr7TEXuHif3M/P85seX+QY+h87jvmAH2Z8HDeFcEYKhtsWMrrF9f0xgkJByExDWTjOA+eQtG9OuYW05SwSOzBfE2ztXaauITq+nhQSKPX4m+xlaf4F97kmhOYbd5qd95OortbY9jnO7zNiai8o1VCYr+tpCtCtP4YihnZeWv29jTtdWi/D+gyQDnanI73P1iXqhqyXr1PbL/D96lXnhP9dzwL4zYx6aOa4cH51Zh+l5DbzA34GdpqkTBNxknleBnSaoHUSw91qy4/O1xDkmhHNhMvLmorJyb1bz6bP5DbymMEgoDdetzlHKXN7962p6JmeVeUCIpC5fJ2ZNk7qrmbC3OZ5kotrnTDbUQnexOGbNvDQ2Qjx3ZFOZkGWZp6Sqp7vmZqG0VgPJMrHNibztfQjuVRzJnZbjfQXlKXaXnito19QaglkyKOWsozitIF+g/EPOGkBMRdGUHKXi+4rqDDlEEnloK9Cmq3BOFhizAlgn5OKFs8oOIeq3We4BYM5lbzKS/OBUpZmn+jHdPXhr0cEr8Z7o5ojJz/t6CfkDIXCuo4zOpYbgC9vt7gwmOcjUEtGu7Jdn5Rp8clYa9+RrN8ddxVLrkMih/lhPBj43IPVzmCbV7fI9mCnI+hf4BjhWw2lESt9orxX04/QajTf1NMRQCFubY23cIaRn6SuWzucazAjyGTIogj1rCOqK2Wl0VU7z56a/QLeqn1sBF5zwp68mG51dlfufwezPqCODyfgzxMPcfewgoWb+3fXn5iPaakYTAuvIco4Ed2HXP4nG9HYacM/jpNdJEJGLIvIGEXmLiLxdRL41b7/irpIi8kQReVve90KRJNBybbiX5u2vF5FH7ZvTuSGE0vAdK2S3LfbwVPZaag5Ch3XXmG2fMPed1lKvhdSFbS1jYzbqk9DKOeVYZSVJrK5DWyNoXwLbHBmkEg9teKyP9PE2/NJ2k5wbkFf1JsCtIN2c/2F0q/jLccWluOae8Si94hF3jxe4dzzK4+brUttX9u0ugbYRjuteVovPhSLoJWsV23EoPQxElFvWmzLHviTFibBVsAn+IvAFRkFGSdrCmF5GFsUcVL5LETqeJIq5J9brFaFIK6x22ub9qnxmRV3+VBZe5eB9AZ6kzJcg3TZRkFGbeftXGWOuaN5YrzO5j3473TFqJjHN/R5sDupeHUn6+5whJInpXsImvU4NPXntep2My8AXqOrjgScATxORp3B1XSVfROoT85j8elre/mzgw6r6aOA7gG/bN6EzTQh9fHhvvzf/QXvMzGqVyHhABbPQmaOqKcq+T01XkzGkHmtJYe32Oo/qC9HZMfwxRiJtFrC0Dt++tAR1tW8vH5JajtmxwrdKphsdSnvLTS5V4cmgn9dsFjL1uqrziWj9uW1p6/mqpz3aP/MOr6T9g3ZC31cNZRTmoosmWgGdAO/2TVbws0Jfm++z42q9sRNXp7tIoRNkfejpLq1nzqdhT9OT2ITQJp91IkilG78Qxgn/VuvfQlsyOy2cEiFowt356zq/lCvsKpnbCNymqq/N7Y5/sDvHxno58FTTHuZwpgnBw/dDDuZIxvVA6I6PKDH/12OXELTr+Jcvmz051mkKPk7fR/JYdJI5pv25ZT5OYPnuaYP73Nxbtwr3ZTSKjd+Rw1a9ecev4ucc1/Xc1Aozk0IMpbPZcVw1c/BRRXNCe8zEYcXsoC52jQwsHHVO8F/aVlfYRDuY+YfZVEhtVrDSCjgV5xg27aD1E8wK4DmhsEtA+OMmQl93kkuz7SR44vDXcaRAdy+FiLwGsCvqaIfJbCL0YeY68/d5kj+kfw79z6pqO4kYwnjIgzoMV2AyeqiIvNG9njMZS2QQkTeT+sK8SlVfT9dVEvBdJd/rTreuko/In/vtzTmqugU+Cjxk172deaeyz0FoavMXYUlxmoZZx2jkkqbi171Inyu4dhKsF7PPcB4cKVh5C8sOLg5W93s9qaDeLuKYQ3Esn2THl9A5naf332YOp1aflqW80WpqmoSVuvdq/5/6EYCSeVyu6QuazT0LtPg++oXjpDnOjmeqvaDxx6n0asX0OOeX6G3ts0Tgjp1bYfsV8xwJ7L+Bdrweomm65mD2glrze79P3XiNZuIuM/kZBrfP3fNJq+neVGYD2LzniECYbke1kIGMevoawmH4gKo+ae9QqcT/E0TkQcArROQz9xw+9wPWPdv3nTOLM08IHr6PspljzASzFlvVpnyEZCaSrClMn09veulhhdxSpdMhO5cVM1UOOZIo5n9hTTOeTAxBRjYMkMtmpJ7INWqnXksnNn3rNzyH6cq+LXvhS2JYxJEPIx0bYR7acaQ+nxTBNBTzkdUsmkQW7SBW00IsIsrm1fQ3cORo44TuecRG8O/xHUgqemB2jb6PQvEdKM6XAOrOSRdqzUV2bmMW6ce9j7iaMbwQrMRTCj80gt+2N0I65u5qflAjK9JzaWR0rxkYl+ZH16yenelpVsPqv0s97sQ1ml3H+z+2p6Qh6Cmbn2xY1Y+IyGtItv8r7Sp5Z/7cb/fn3CkiK+CBwId2zePcmIw8eht/70w2f4GZjSCVwoYUXegJoK9f5DuqNWNiZaijMwu1K/myz5mZzMGcGs3M/2hPSjDzJqi+tIWPBDIyKDkN1GqkJY8AL9Tb3gVp/GpOMmewJwPrcDZJRGu0g2lTm23n3/Ak4Ftf9hCxcNJ2ux9fRFNNtyKR2Kt19Cvzhih2rfj77zPEsAu7iOTE65QBZnwgs+dreW98HmUOOhHqMtmmk88Tp7RzPtujNkf01DRkc2LyfCcaVDOvHY/C9jtTkWyTlnBqmNNw5l4nQEQeljUDROQW4AuBd3GFXSWzWekuEXlK9g98dXeOjfUMUufKTwwNAaaOX49GE8hLpFG1EESvCcw1lfHoewBM9hNLaYoyv86nkOYlk4J4vRlpDn1tJE8G5R6yTu0zn21VbkKcUHsp9CRgJTgG0eY+fOa0b3nZN7rpE9E8rOsZVu4ihplOZ3Vp2Ap5EBIR+hBTtePyqrIebxs0n6yoP8jO6SBahzJN4TRW/M09ipuqE4ATR6yb0xz2zStdQ5Cokx4F5Scq7lpaTUXS/bspK3XcPrX8DUqVXLW+xlLPm/MzNGPK9D4mmkH3t/VzK9pOEx11uoRwin//O4CX5EihALxMVX9CRF7LlXeV/DrgB4BbgFfmF8D3AT8kIu8haQbP3Dehc0cI0EbslLaS6E5NIX3O7yboDsiIGTW5lud6JSST0ZCIKWsUaxlZy8gGR1gSCRry8QKEkgjmW1iaoD2pOJ4X1HWeMvkHZKSQPktDJr0vwcjDiMWOsf1VI+hCSztHcmM+EkpyWR+NVDQCagJbH21U7u0KiskZKajskigcRgpXgrk/1wkayq7IIptLs/2k6Lid95NNRzLdXhzndr5WYexX7LPmIv9Z3Tm98Nd6n7MO5H7uB5qMqpaSQ1dHLaRwajiloVT1rcDnzGy/4q6SqvpGYOJ/UNVLZEI5BOeKEBpn5C7zi62UM0EUUxE1GbV3hvrELciagSOOJCBDI4TXrmdfyI7vi7JhI0PON0jCeKMDhDz3vLq6HFdp1UxtZVnuUVNl1n3wwrqeV8+vDWSEKG3jnr7cBAC5JPeW+lzNxFNyHLJQNwKYMxuNThNQUY6GLZs4FE1nHAMhxCbbmJCWTsfbAZF0fTHzWs5vMDTagW2YgYimZitBa+8DBe8/qINKUSq8H6F3BtfBd1xT3W4nZMXvs9j7XkBqJ4jLZ22OKefE7tjuuMKHxXzmNKpgE5Qq8afriXrv5XlosjgFKddI391cXG7GxLRykpD19+cu7YnK7tUq4Ms2mYzCNiLjKRn+dxD1ecG5IoR9GHVaTM6TQd1msfctCbRj1SY8c/tTldGYbfSpz4A1o1/L2Ia6Sl6t57mYP8BaWkbN413lj3CfVhHNiUhdnZft7rvNEwDFrfyn/Y13aQZ9sTkJ8/9AfWRQCjXNzwgpHdDM1zLmY3rNofcf7IKvdDq/gqUTALn/ljdd0H12wnhi7ulWzJOEMTfW1IeRP8cq3It5Zeb8ep46wqmHNFqPpHE15OQ6oXUo6+5VefMo8soc15LTTEYyc0/9tsnYO7Y3857524lC2GrdHk8vysgT+HnENXMqn2Za9j5YdA/MVxWdg/clzEUYzZ+TzEMGMxdFhGNdMVfqwQv5km+Qo5/MmWx+BnNElzINna+hX8X79/R5aiqac+rO9TYuK3r6/fM9DXoysEzkuYgi70i27yItOSfzlTIM6R77ukQTjaV7vtVXOu8H2AsV2qihusK17d5Rat+9hNxl826v4961E4jumF7zmIy1I6fIr8T788rtBTdtb5ZSxeoaNfOLOjtfaZ5Du38vsfbPgjrO7PPwx8+MO5lb1JwtrYWcZLR8itOT4v18d73OIq5llNFppmXPwju79lUZHWekhIWbjqoc52J2860sWyLwaWnHOjhNItf36TSGWia7lrWw+kdHubyGmZBKk5r8a+ozoZt76oR9mltr+5/ey3R/X1G0JYJKDm04aZhWJnVk4E1H6rb3Bensev69JwFfmTR9T+cl01H6PAmy0XpcQTlWpxthfgk8J8hmktOay1yJIPCCrZurBvfK37FLB0mmGSsBLfWc8tkdi6Tj1e1DkoPZm1rK5T352TFOm/HE1RBY2aYTMphoW7vunSm5NAJ/sq2bU94mzneQPp9irKge+DqDuGaEcFpp2VdyzTlSiGXfbsO7P2tX/kE1JdWX+Q42mRh8Bq/Np+RE5F+IOZatjHPREPx3pp3VdvVPqPOetrBM9yaN0Pf5BfuFvjTnTjQD7YT/LjMRrTPY+hkElN5BPJn7jKbh4Z/HQf/+/On5BN/boFmh0x170qqvEwJ7j525bckJeHMEUFw5/WdmPoeWGCbT7Aij8Vt04ajV5q9l34QAuueyc/8hf6AZgVrH00oKVpJiTHMMIym8NL+nz0YEmkxGN2HY6c2Ia5qHcEpp2f2Yz7FU8I9+KHmP2laWh68ERtVCBj7/wGr6zJ/ji8jlKCMshj/MhqrWnINtLlORPjeE0c3bSllYPkOg/Q5my58S0ZVgzixkY6f97bZZTaDXFDoy8DBzkV+p2zmNgC8Cu3Y3s2vZcSKaicVO8sfP+D+oY9rxdWP3YLzpyPbPvmbMVDu0BtmxP13OVvHSznfuT7rrz3ylf36pJrGdAkxboVyOnxvOP6tDLj+jXczv14m/pS16l7OSi1bAVDs4oFbZQfDayQmvs4hrSgiqOqrqE0iZc0++yrTsfswXq+qTVPVJD7x9dUXlo33Yac090Ikm4FE0Am3LRNeXVDJQS/iydppxYu5JGcax2d/3NQ75GJ9pbZjULaIX4NNcgbS9rvDtezNOp1G058xnHe8jAz+mOoEfpM2wniuN7f/teke0Nx0ZRJgSwMw4dVtaGjc9lIvg8f4Df5K7ngmkbgV8ojD0q26/+rbdTjNIZp668veawTXBPmHZX3cPKZQ/wQmE5p/vyeTinOJj1QqKdrAF2SYyCJkUmNMOFg3hIFyXKKP7mJZ9MA5p9zgH0w52hZs2x3oh66qEeiLw+9v5RaxQsxFZLXVR/QgpTj/lI0SJuQZTTGGyWpvd+L7K7f3slx6zJpmZMXqTza73WTJgSga2ojftoCczjzRO8hNElZpPojS9l01zqNoB+Owm1VyoIQ1X/Qtldd/6BOoEpoKr+XUpiCtvYcd4mOAnQsjvE7LphahdR3JE08y4uzSQMo84f9wcZn8qvclqH+bMWnm7P1+6+fTPYd5U15mw5u7TlfCWqCXUtNEOYjxVH8K1KF1xs+CaEYKIPAzYZDKwtOxvo6ZSv4BpWvaPiMi3A59MTsveew0oJhi/6pwrPW1hp6mzWRbG1NpDm1y2wRq8QBXoo5V86JLQUrXPVTpnZmVuSVz7MEhk0GQyWjMWnS0U72n6vI2p2f02DvXm6aOKpnM4iRz8sXWcKTnW6CedaAbb3LPAxvJhppAa2FjYrY2R5t6Ovx3T/P2KPwXAaA4xFVSVYxlY5bDVTUx6nyWolfOikJyu1fls3dLiNmCNb2QUGJNwNyFfyMDKOliugklrlbY8gxP8kFavYVu/A8QAkjN4yeeWOkIZxUmaLjbRUloHrVup7yGCicN4RvCWUFMFRcv9asjH9yY3lF3RTpDMX+0FKMRW5usIsJ+jv6/6nDtHNVT/h3cgbxUZI2GryPGIjCOyjbA9RUI4o6v/Q3AtNYTTTMveCR+1A62WMCKsG6KY/oh9dNFc7sEcGUR8tzEpx/hzAEJOThtznoHXJAbqrRkpbHJmc8j/EkzwIoGY6wtESdVFPdkc0lrz0OPmCKTXFOZCV9NzmRJJrdM07f3Qz6nJJYAkeEUbciAk4T8UId9FKBUtoY5hwkuV1ORGBWJqfuP9AE0kzNyq3x0n/YpfnQljzCtjFyNnBJOVv6ql2Ao61ktckcCZIQNRN84cGfTakBsr8UIaQEaqtuBgx5QieTYNuxfVCSnMEtG++/Rk0M/bSlOURj71+4QMRk0awin6EM6qOegQXDNCOM207NPAIFLKXwdqhjJUUogqe/0JUMkAskMWKxg3b16qJCLl+DkEUYbs5B6y8C/2do2sA2wixXwUC8nMmHXm5n1CKKo/33+fG/MkB7IPLzUzUfEf7JEC69XI5U36SZYaRv2cImwZygI1xkAcXXtMKOYjsnbRO6jNdCS9qSif367E65iTVas3Z9gr87wvTSWxFZgmQRuziVtFG1RkryDbJ1AnWkQZdMcY2gp4YtICFAoxQNVq5kjBhKVK/TwxtZ2Ag3wKfVip+Qmi8xvEmLWx/H5ahAALIdy8aP8ygVYtPMSnEHUqCKGai2ptoNBoBgCXdD0hgibKSOv3gTgbgZRGDbm15jbVMOoEupXIDiIMpT9AIoUhLz2LKam5t/2r/UO2pytN/Qn7HMiWZWwE4D/Xqqzt9byJqUlM6ySEqkCEzTab0MbQagbu/KQlOK3Dcghyr+TZ1d5Jq9ZOc/BEUMhgxSRRzK/kcx5YK0g95ARNYZdwO2kV3ms9nfbgOTgJcitw7XgrTklhttrq3M/JP+9mXrqXtPwc95FB2DoyUPee1Mj5Qa8QwmIyuqlhMf5zwt9MRkMWspaLYH4DX9AuOYelCP60P7/7DOUu+azUMfLJa2pNaWr4qjcvDblYnI+QSvWRQhb4Y/FzeMdrpJqOKikMzVzmktgONSkdgt5M5OfYdzOb0w5Cf1/eTLTnH5o3J0WS6cda+HoysNtvylLg9plANuG016HcaQd+HPfuNQQdqLkA1BVysdM7U5cnhUIAve+gQ0NI7vpTYus2nDBmWtW7SdEK/HqMI4X+/KvALiJotTDF+xEaf0FU0JYMLAcBtQij5Fg+LZxqobybDGeaEPrfoM9BiL3A3eHc9Y3jixbQmI1Cox302+1znOyrWoGVZjDzEtoWlDMTkWkJgcCGlMkHyTm9YSBkLQFqEx1vNtrlxLauZunz/m5sPXa1vvSmIqhk4BPQVhIRUVbZqWzkMBflNIQ49VP6Fb46zQEpkUo1soj6fY4Msk1GO+1glgj8WZ15yPsQSlN5s/8P+eUFPySfgX2x1XWvKdg5J2goPfqIFz//PiqnHUvn95npyIQ/mRSAvjrqFaGZV+tj2OUYL/4C//wtgigmf4Eo1Vy0zb6CbSYATwaLD+EgnGlC2Ic5W3XqbzYTgVTMQzPOZJf01SSgac1Ujs6M1I+70eQoNsIZNYDE4lhOfQYiiLPT55WiJwWgVEoFiunIFiv7BH3tStaHxe7+1z03Xp+F7Lf3moEJ7OJIpjUZ9ZrMqit2553JHr1ZqPoE3LNTaCRyfjcySA5hKdpB2u/mn/c3UT8u7NEcyipZYA4U+3+pFyT1usQq8IsSEFpSKCGa+SXuc50Y88LIaw17MCE7r5Gon3JrKjINpvEzOEV18meam3t3/TnfyKRelL8/l6mMUvIMimN5m0NLLdT0tBPSdtzHecOZJwTrPna16J3JI1MHcUMUTlPoTUXl+IlfQTIZWfXTVMLCzEZGCmv/D2iGFGpJ7YEoSUtIZS/kPplID4osmvWztFqB1w7mfAdtdFE1hXkEUTSXwNbAfDXTaOGp1Os2ROEEve+m1pBBZ36BSiaeXAwmzM08MvTnpFcpG4Hbb1MyEsAJY7uPXSTQYa9v4Wqwa6zCVFRN4QTtwPv1PRoNwAvoLOhNY5rzcZh2IJEk5E0jcOdI7MjAvts/ilP0IZR5n1OceUIw9A7l3mQ0X+CuCqZRvQ8hbbMqpqYdWM6BHW/X6f0KRi5JM7AexSE1x9EhhZdmArDw00GUgbEcs9YxaxeRUQJBY5l08lFUB3NQK2eRyGauZMRJfoQ+z6Df3tr62xpF/j2ZflrfwUpiMRsBTQ8DjyFENtvqE1EgZqdxzCv6ElI6IQGqZhCh8StYApmZivL+nRDNK3Ypph5vMirXUifcmZETms1F+U9ThH0mAN01biaHxG0CYXfDnFnkG5/1S+zINPbRQI2moGkOdZiqJdg1+jHUF93zD8buwbbb/bnJ+Ogofz+mHXhzEVEJmzERQPYZlM9RYRxhu01mo1PEedYQrmnpipsBh96gdT4rOQe0pqKNropTeKNDc5wJ/b5iapvjkJLeSu9iV/oCchlvrBBe7be8ljFvrzWMLoTt6T2gfc/kqg3GVzemJaadKvb9473af9hXed5eQbJHZu09b2ZVfcXoV89ZcPcr9Vkfhl/wN4J+etxBuQAzzvBE5nm7ZSdHqkkounMtxDTGRAhjttcNp/jb0gNfJ0BEPkVE/p2IvDO3CPj6vP3xIvJaEXmbiPxrEbktb1+LyEvy9neKyDe5sZ6Yt79HRF6YeyuT+y+/NG9/vYg8at+czryGcKi5aJdTeQ5NAppb/U98DFqFvWFuJe5t+D4JLrXNzEtICa5An+L9CmM2OKH1fr/jCT928P0sOL/4r//M/3nyinVGGzgI3lSU/QdJK0rmJIuqulqYFjFxQczNzxFU0SKMHKweiX2G8p0hQBg4sc3gwZNm4si/D9gCf11Vf1lEHgC8SUReBXwv8A2q+nMi8peA/x34ZlIS7wVV/SwRuRV4h4j8qKr+JvAi4DnA64CfJJUJeiXwbODDqvpoEXkmqVrEn901oXOrIfjCcHM36f+mx9a3mHnnMLj+Ay7MtCELDTvJYNTQmI7S9upknktWu5KifQsWAJPV9STiqI/394jaCFwf2dNoH90+7weYnxOHr5h70xLdHNScyjYPnWpW5i8Ys79ttYLVAOF0RJ3QPp99r5Ogqu9T1V/On+8C3kmq8Pw44OfzYa8CvsJOAe4nIivgFuAY+FiuCXebqr5WVRX4Qdq2Ai/Jn18OPNW0hzmcW0Lo0Sd7GbxJ57TQ9E3oo5Vw1VFdlvNGh6bhjoevzWR9FBYs2IdJyOmBpqTZSKQ5QXeQ+WfPLivzPWtaqo7kWqSuzS0ozmOnHTTkMMZEBsOQtIRTNRnpYS94qJXqz6/n7Boym3I+B3g98CvAl+VdX0kt+vly4OPA+0hlf/4PVf0QiUTudMP51gGlrYCqboGPAg/ZNY8zbTLa1y/3EOwjAZ93sEtrOAnRk4EL+zR/gqEIfCF7IPt5fsLw9oKrQHUey25B3e/rD9vzT6k4nL2TOH/uk9ROxMw/Oe3NRj6DOjpisNIUCqVI4BzMVDRkU9GeQnxXgysQOx9Q1SedOJ7I/YF/AfyvqvqxbCZ6oYj8HVLRz+N86JOBkVT888HAL4jIzzAfl2az3LdvgjNNCIYSm8+0Vs4g025pu/oo+yS0cmxT0C40oaa9GSiN0X33PRQQNnHFOjuFE2FEauYSzZ/P92Cocz99R++CMwwfQXQA+rDV5p9L1Gprrwkuuy+tzOcgHDi3WR+CmYpM2Je+Bl5bOKFY3XaE9bpoBZPqq/cFB5q/DoWIrElk8M9V9V8CqOq7gC/K+x8LfEk+/M8DP6WqG+D9IvJLwJOAXyC1CzD41gHWVuDObGp6IPChXfM510tP/1v2pLDLfORRqpl2FU0rGcxHFM2RQZlD/iVF9ZFJNbHNymhX30Ldf5rlJxZ8AqER0O3v/qoU7LmEsm5TE3Lq3k0wFwFd9smkFIYopTRF1QycdtA5k8v3MUIcYQgp/HWQJOVO8Z/PpNHRjteJ4yRb/vcB71TVb3fbH57fA/C3ge/Ju34L+AJJuB/wFOBdufPkXSLylDzmV9O2FXhW/vwM4Gezn2EW50JDMKQEsORMrlpDW+V07u80SORSTOlfPu+gjy7qcw8GtDH9NHPR2oQekpN7o0N5jRJYh22JQBpUc3G7Gm1kJNFEJy3EsGAXvNN4TgvonMqTJDel1RKo381sJHac5mgjn+HtTEuFFJRS7jsl5WVScPkZtq+B9UZ2PQ4SQdQkNLsnRkW2Ixwfp4vdchFd34fwpxNwilFGfwz4KuBtudUwwN8EHiMiz83f/yXw/fnzP86ff4X0dL8/V5UG+DrgB0jO5lfmFyTC+SEReQ9JM3jmvgmdeUIogl/ipMDd0GTBnIx9wvbERjfofp+EzmRAl9TV9B5cCKqd481Si5awYIK5tZ6P0Cnbpv6D2eQ0wWX45h35uwQpIaiN4V8dGWj3fW6e0r3scjPmo6oluKiiLrQ0aQZj8hmsV+jKKgteA/Oqwk4/zZUOpfqL7BZQ3zVz/N3k/jEz+94ITFoUq+qlXefM4cwTQo/UsL7+wQKmJcxF76TjTFAPua7A6Dqb7GtcPyKTGkFWzXQXrBKqFbkDMhkYQVAcy70zedEOFsxhLru3jwbqNQfphH3B3BrKHStkE0y0A/O7pE5rYmscL9x7MsjvalnKWaMopTuaa9fy1j7PoJiLIJuJFC6sExkERwh2zCmSw3mOCD8XhNCXrYC2F4KZjAYJRFW8qPUaRm/+Gdnd0OZqYESRymq0+2LREvpy29KYjBYs2IWJoJohg51E4MZQt2qfbJsjBSG1Ic1kIKITvwBUE5V6ArDtImjQdl//k59ZZ6Xy1mNLBs5XcU3+1SyEcPNjLsIoUJ3JaxmyljAyIKXZ/SG4Fivz0ZuLoHyOrkTFlVQnXbBgDnM+hQZ7TE7+fHEd1BpSyCShItm0I1mYJ7LYqWB3gn9iLirHJaKoZbGd2cheIoUMNITGgawp3f/UUG7tnOLcEILBHMpBUsipb4wDbYRRoC2T3Xc1S993Zy/vwpUSyNj1R4jd9wULrglmyUCn5hVnsy+F7TJBiCMDVckaAq1juYfblgrhqav0mpkhgO6QvCXkNALbET1aVzIw1buoHwrhFCW46rlukHOujNLDVS4Fept/qjwad+6v10s/jF547zq+78DmK6se4jC2Ut/3pdz3gvOPg1ewO1bvTc2gHq7Q3SSLOXb7izPYne7MRDsV3lx+Ytf1yzxjTCaiQSoZSPqciCZvd2akU4Ee+DqDODcawr7m7QBjLhY3SGBNYES5pKneyVoio47lB3qsK5DASGQgpMg7lBFyQbpUljr51EJxLqO5xSW1C1pfTtrnMQTRrB2MfqqFYEL2KWxI1yFs2cTVYjpa0CCZW4RSrvoQBMmrfFoTUSSZaPoIJEumd+3pdBQ0pDLjcUgLcRlBB5AVaBDiSnNfC2oegq191K32m4uZ+Wlmn5mLtsmRrEe5TlGg0RBUpIytSip9fko4zyaja6Yh7CnteruIvEpE3p3fH+zO+aZcpvVXReSLr+a6QZS1WC6Cxe4rEWXUyEZHNhrZ5Ege73zeZ+opfRAIpc5/imhqw10b0483T8l8I5/BCf9mu9dQnF8Bpt3GFnwCw3IJ9hWum1vxX4HZY25c63nQ+AiyySc1CWqdyk3tok47KHWLRmrNItvns5P7DOUhMY2KFFKoY7rjTlOhzj6Tg15nENdSQ9hV2vVrgFer6gtE5HnA84BvFJFPJyVNfAapVsfPiMhjVXXcMf4s+gqnkUQGGx1ZS1rfW7OcIa+Oatnp2rQ+kDuREVNDGxPIWlf5aYVP0Q58TgFA0MDIjnaejiA8KSSCyaGzGjMBVYdzXMxFCxz+/Uv/+o2ewpmByN89nYHOpqw/CNdMQ9hT2vXp1HKsL6Et0/pjqnpZVX8DeA+pmNNe7ArHjKQuaaMqYyaFSNIM0ueU7JgSykJJbOtbcpZeBA7WyMY+A0VT6DUGDxP2ffXSNI69R6d9aDO+vS8O5wULbhxOq/z1zYjr4kPoSrt+Uq69gaq+z+p2kMjide40X8LVj/UcUiMIHv7J+6c/atICNhqJInl1r2wySZyEQZRBIyMDAW1MQJbEZj0S/Uqe3O94kLjT3j9ILGRgAt5CZ4fcMc3GD4zZ55DGXbBgwY3DeY4yuuaEMFPadeehM9smT15VXwy8GOBxn32x7I8qHEkqHzGU0DkT/CljMdUTillzMC1CsqNYkhNZcv0hmY/8KeYd52SujXMpjuaYwiwI3RhmPiqawYy5qJCFkQKABIbsC1mwYMENwhmOIDoE15QQ5kq7Ar8rIndk7eAO4P15u5VpNfgSrnth+b1W3A4siTIRwYhyJJLNRRQyuK8YJKZSFUYKgLW+XJNzHkIlloHWb+A1g/SqZJBMWBTNZCBrOQsWLLhhSIlp55cRrmWUkTBT2pW2HOuzaMu0PjM3hf404DHAGw69nvkSrCx1KvmQSz8Ax6psUI5Vi2YwzkQBFT9CbnbvUUw8bolgeQHmG1jLyFrG4kswv0J55fFbM5HtT/vWsi3j2OeQzUmLD2HBghuMeODrDOJaagi7Sru+AHiZiDybVN/7KwFU9e0i8jLgHaQIpedeaYTRJV1lJ/GYSlNDUw7b2G+TY+XMkbvO5ae92chi5pJgD2y6RYGt4MdyXJ2qkcMoIcdND/k6Yyl5bdc34ljLyJFsWcuWQZSLkpokjZJ7J0hg0JhbbZ6b9JEFC84czrOGcM0kywmlXZ+645znA8+/0muNCMFVGU2NbbJ9P7sQRoQjjRPNoBljUrN3ij4HYDa3wI5TSy4zAsoRSC4iCVwEEcogWkgBKCYosrYz6vqqM7IXLFhwH7H4EM4OasG49D0ygIyMKklzEDMlWb2Teu5gTuJcznpsxpXiL9gHMzHFHMYakbSqp40M6hPN2n2xNVeZw1og6vrQR7FgwYJrgvNdy+jcEEIpU63UPHtwfRBqZdEUWVT/qHFnScbkEB5ET0w8NAE+5ISy1O4mh5GqhZfqpH/CggULzhjOscno3Eim4lSm9iQe1foRS2pbiTmcQ9EUPBkUk85V4KS+Cb0TeX6MGVNW187T+jwvWLDgBsDKbBzwOgl7yvs8XkReKyJvE5F/LSK3uXM+O+97e95/MW9/Yv7+HhF5YQ7qIQfpvDRvf33OCduJcyVZTOBvGNhkw4v3GZgwtZ7Iu/wJu9CXmpidgyeYUoaizWru0XRcsznml5HBmIluwYIFNxi+F8O+18mw8j7/FfAU4Lm5hM/3As9T1c8CXgH87wAisgJ+GPhaVf0M4POhtHZ5ESlh9zH59bS8/dnAh1X10cB3AN+2b0LnSsKYVlC+I1Vb6DQCe/fEYJ+v9JqGXkvw+0qOwS7toOu5PKowlp7KIX2e0WoWLFhwnaEHvk4aZnd5n8cBP58PexXwFfnzFwFvVdW35HM+qKpjzue6TVVfq6oK/CBtSSArFfRy4KmmPczhTBPCrrvyxFBW2s5/4DUG0xZ6HFJiuow5cz0PrxlMuqBNeiQ4EsCTgRHbYjJasOBGolRePeEFPFRE3uhez9k5Zlve51eAL8u7vpKasPtYQEXkp0Xkl0Xkb+TtjyAl9hp82Z9HAO8FUNUt8FHgIbvmccadypqjg6rw9oI8aooygtQ4M2otVFfCUqGsvotG0SSsxRLyCbSRSV3k0UaHJiw1um5rcxVPjQw2OrAGNgxc1jUbXbGWbd6X+h8YGfjzFixYcJ2hXEnS2QdU9UknHTRT3ucvAS8Ukb9DStg9zoeugD8O/GHgHuDVIvIm4GM7ZgoHlgQynHFCSNE9I9OCbyOppIQJ7FQSIrard/9Y9oSBnuQwho6IdmgXjXlpR8STNcwxjcCTgTnHFyxYcGMg6Kkmps2V91HVd5HMQ4jIY4EvyYffCfycqn4g7/tJ4HNJfoVHumF92R8rCXRn9kE8EPjQrvmc6aWmid0+MqiYX/A2+WrK8RFJ3qTkj+kxSEoaC+6sOXgy6J3Ae53RTpswEogIx7pyZLAialg0hAULbiROyam8q7yPVYAWkQD8beB78q6fBj5bRG7Nwv1PAu/I1aPvEpGn5DG/mrYkkJUKegbws9nPMIszryEMJ8T1R1fwzpuCbGVet7Xk4RF2JKXti/rx+7y5yEpY+3yEVJTP5hVKLoWFzdr2K3V4L1iw4Brg9DSEXeV9HiMiz83f/yXw/emy+mER+XbgP5CkxE+q6r/Jx30d8APALcAr8wsS4fyQiLyHpBk8c9+EzjwhGCzT2DC6UhaeFPw+8xnY5/3ju8zhjFh8EGHSL2Hn+WWu02OjJqNWyk5ucw5MM1hIYcGCG4gr8yHsH2p/eZ/v2nHOD5NMRP32NwKfObP9Erle3CE4F4Qw8Q1keFKY27fvOCtBHQkl8xiSYC+O4j3+hblSF1ZFde6clBE9Zg0hprIbtL6GBQsW3HjkCKJziTNPCL1m0KP6EWrpCg/THvaRB9RM42j9D6A4s/fVOfL9modS4rrrrOZMWoHO91HKelsuxeI/WLDgxuHgpLMziTNNCOLMQMnJe1gETm/a8aTgkbSBmY5oZX/VHpIvo221uYsoUkMdGjIoc9HQkI9/X7BgwQ2GshDCzQ4TvEcycqwDOLPOIUjH1hLUNiamNVjvZGDo9JGBkFtyCmsjCy/AXQe0i7JhlJCqn+ay3Bv60hVS+iZ7n0ExHS3ksGDBjcX5tRidbUIQqD0MrqKK6CGkYav5uVSOI8kZI5kwrCJqY3rKpBJyTSMri50ykJPJa+73tZDBggU3J5YGOZ8AsES2ORRS8N+BY10xINn8U7Ogo3M6p7bKkSMZOZKRS7kwto3jHcy9pjBHBIeU1FiwYME1xEIINy9Kg3ultL+MLpv3JGfxFV0nw5uVLAoJ2VIep7a9EYbcB8E6oV1WyWSQ5tnnJMC8VrCQwYIFNxiqMJ5fm9GZJwSPPpwzrcWv/o/XO4rNFwDZ1KMCsk0rfq15CdXfkMjKuqANJFJYSyKCMc/PJ6p5p/IcGSxmowULbjAWDeHmRC1dUbUEj1GFILu1BJ9TsMtcZNqAkcGAFpJZy0hQTe0tCSkiCco8gtQSF6lX8ljaY4ZsoopI6dtg58SujPdCBgsW3EQ4x4RwzSSMiPwzEXm/iPyK23a7iLxKRN6d3x/s9n1T7urzqyLyxYdeJ+UhuBIPrgy1f09NZ1wVUzP75JX73mtkZ7CRwSDJJ7BmZJDImpG1bDkyge/6HvgeyWtGLoZNOs7nJdjY+RxzPi9YsOAmgwJRD3udQVzLJecPULv2GJ4HvFpVHwO8On8ndwl6JvAZ+Zx/IiIHl/XsBXofPdSbkowM1rIvpS2P3QnmQgyihRSO3Mp/EG1MREUzcCTiNQebv5GC5SV4UmhCYRcsWHADkfyCB73OIK6ZyUhVf36mf+fTSW3fIHXxeQ3wjXn7j6nqZeA3ciGmJwOvvZJr7ktO82ajxvSzw0/r6w15zWAtkSPGFO4qsNaxlKQ2X8Mxq9nyFB/Xo5SDQO39HF3P5CYr2ZW+3pS8BDmoFPeCBQuuEZRz7VS+3tLlk3KpVvL7w/P20tUnw3f8ORGjMw+V/gHFhCSl49gchmI6mqp4ZbVufoBciyiIltX82pWjGFwSmofv2HYppgY4vT/Awkyt4F4pge1afsbcPW1cWmguWHDjcHo9lW863CxO5YO7+uQ2dM8BuOMRg2uHWfsiW3N6cI5jjYySM5JzHSEvkqflsMnn685opcH7IbIzeNMTUplHam7j+yMbLMTUSmNbdJOHkcGCBQtuMM6osD8E11tD+N3cEJr8/v683br6GHzHnwaq+mJVfZKqPunBt1utHxPCoRHG1psYTs5KntMQelxJxVErXV3LVgcu6VFuiRmaY/Zd3zvMIWsKCzEsWHCDcKB2cEZJ43oTgu/e8yzarj7PFJELIvJpwGOAN1zp4HOrbvvcO569OcgL4znB7E1Gc2icwb6nciYCI4ZjHRhzG8y2gU7bK8GPYyaq+5JPsWDBglOCAjEe9jqDuGYmIxH5UZID+aEicifwd4EXAC8TkWcDv0Vu3KCqbxeRlwHvALbAc1X15BAgaicxbybyMGIYXNcy71wGGuNUJBQnsjcVXWmETyGDYjoaOKL6E/qVP9S6Rrs6tC1YsOAmwBld/R+CayZ1VPXPqeodqrpW1Ueq6vep6gdV9amq+pj8/iF3/PNV9Q+q6uNU9ZX7xr4SjIRmBW7oNQO/vTqIu6gk50xOY1syme5dxVsq2rEOxY8wue5c4tzcvPO1FixYcCOQS1cc8joBIvIpIvLvROSdIvJ2Efn6vP3xIvJaEXmbiPxrEbmtO+9TReRuEfkGt+2J+fj3iMgLc29lstXlpXn762ciPxuc+2WomYpCSRDLQl1ctJAlnvlX3p5yDIwMTv4j14Sz6bE+2sjCTXuEXntpxl6IYMGCGwoF1XjQ6wBsgb+uqv8V8BTguTkn63uB56nqZwGvAP737rzvoPZMNryIFGzzmPyyHLBnAx9W1Ufn875t34TOFSGsc68CKyZXCsr5JLC88vchpD2MACyk1EpOeM1gRNiULmbpNTS+gFr51HIJ7NWTgTme53AoES1YsOA64ZQylVX1far6y/nzXcA7SeH2jwN+Ph/2KuAr7BwR+XLg14G3u213ALep6mtVVYEfBL487346KecL4OXAU017mMO5IoRdsJV1OCCSqJzjs42dmciQhLqUd9hda6ivUtprBm2pi+m10jGLY3nBgpsCh0cZPVRE3uhez9k1ZDblfA7weuBXgC/Lu76SHIEpIvcjJfJ+a3f6I0iRmgafx1VyvFR1C3wUeMiuedwseQhXhVRWRFz9omzTJ6Y+BVQy8FVKDebY9YJ8mIv4cVqEaQOW8zCWsarQH51D2cYu73s42JfBTvcx7Re9mI0WLLiBUL2SCKIPqOqTTjpIRO4P/Avgf1XVj4nIXwJeKCJ/hxSBeZwP/VbgO1T17m6Rvy+P6+AcLzjjhDCHvo9x6MxF/t0wl3hmZiP7PJeD4KOEUnax9xGEZl9vIvLo/Q2p1pG4chvWu/ng8k4LFiy4VjjFKCMRWZPI4J+r6r9Mw+u7gC/K+x8LfEk+/I8AzxCRfwQ8CIgicimf/0g3rM/jshyvO0VkBTwQ+BA7cKYJQXHmmj7DuFtJ77LDl/pBe7KR+6iiqh24vIfue93ebvM1i2x83wNhH0JuuLNgwYIbBUXHgyLiT0S25X8f8E5V/Xa3/eGq+n4RCcDfBr4HQFU/zx3zLcDdqvrd+ftdIvIUksnpq4H/Kx9quV+vBZ4B/Gz2M8ziTBOCocn83eWc7eoSgctwLgI69VQYchVUI4MglFVB7ExD5XMhlikpmGlqzoFtJTNOQhBlVJaQ0wULbiSs/PXp4I8BXwW8TUTenLf9TeAxIvLc/P1fAt9/wFhfR6owfQspAsmikL4P+KFcMPRDpKrSO3GmCUFhYqPvMecTGHMtI6hRQINERmRi4Q+SzxMpAnnMfoRKRK0fwpLkTqMyqfcj+IY+CxYsuEE4JS1dVX+RnfWW+a4Tzv2W7vsbgc+cOe4SOQH4EJxpQqDrNtbj0KiciMBMV7WiIQAj874EX500fb/6InRDLsc96pBMSdkf4v0iSwjqggU3DgroGW1+cwjONCEocKzDbIvJWn9o3pFco36cqUcDyFToDtlktBEp/vmaizA0pqpdJTSAJkS1jI11TKvzjVg1VuvHkE1eMm+SWrBgwXWCnm8/3pkmhNPCyLT4nWGfgWZfvaF9WsKuqCWbyy4sZLBgwY3HaTmVb0bIHofzTQ8R+T3gP9/AKTwU+MANvP4+LHO7ctys84JlblcLP7ffr6oPuy+DichP5TEPwQdUtW8jfFPjTBPCjYaIvPGQxJMbgWVuV46bdV6wzO1qcTPP7WbEErKyYMGCBQuAhRAWLFiwYEHGQgj3DS++0RPYg2VuV46bdV6wzO1qcTPP7abD4kNYsGDBggXAoiEsWLBgwYKMhRAWLFiwYAHwCUwIIjKIyH8UkZ/I3/9/IvIuEXmriLxCRB6Utz9KRO4VkTfn1/fk7beKyL/J57xdRF7gxv4aEfk9d87/5PY9S0TenV/PuhZzy/teIyK/6vY9PG/f2WP1esxNRB7gtr1ZRD4gIt95PZ9b3vfZkvrWvl1SL9qLefsV96a9HnO7GX5vJzy3U/+9ncIzu2a/tXMLVf2EfAF/DfgR4Cfy9y8CVvnztwHflj8/CviVmfNvBf5U/nwE/ALw3+bvXwN898w5t5Pa390OPDh/fvBpzy3vew3wpJntfxn4nvz5mcBLr/fcuvHeBPyJ6/zcVsBbgcfn7w8Bhvz5DcAfJRUde6X7m16v5zY7N26O39u+5/YaTvn3dhrzula/tfP6+oTUEETkkaSmE99r21T132pqMQfwOtqGExOo6j2q+u/y52Pgl086B/hi4FWq+iFV/TCpX2qTyXgaczsBu3qsXve5ichjgIeThNs+nPbcvgh4q6q+JR/3QVUd5ep6016Xud0kv7fZuZ0wh6t6bqc9r9P8rZ1nfEISAvCdwN+AnaVD/xK1njjAp2XV9edE5PP6g7Pq+t8Dr3abvyKrti8XkU/J20p/0wzf+/RazO37szr8zWb6YHeP1es9N4A/R1ox+lC36/HcHguoiPy0iPyyiPwNd50r7U17veZWcAN/byfN7TR/b6c5Lzjd39q5xSccIYjIlwLvV9U37dj/t4At8M/zpvcBn6qqn0NWYUXkNnf8CvhR4IWq+ut5878GHqWqnw38DHWFtLe/6SnP7S+o6mcBn5dfX3XCHK7n3AzPJD07w/V6bivgjwN/Ib//aRF56gnXuV7Pbdfc7Pgb+XvbN7dT+72d9jPLOJXf2nnHJxwhkLoUfZmI/CbwY8AXiMgPQ3ImAV9K+nErgKpeVtUP5s9vAn6NtCIxvBh4t6p+p23IKuvl/PX/Bp6YP1t/U4PvfXqqc1PV/5Lf7yLZYZ/cz0HaHqvXbW75nMeT7MHlH/31em55vJ9T1Q+o6j3ATwKfm7ef1Jv2mj63PXMz3LDf2765nfLv7VSf2Sn/1s439jkYzvsL+Hyqw+ppwDuAh3XHPIzqOPsDwH8Bbs/f/z6pwXXozrnDff7TwOvy59uB3yA5qx6cP99+2nMjrZgemrevSbbbr83fn0vr5HvZ9Zyb2/8C4Ftv0HN7MMkGf2t+Vj8DfEne9x+Ap1Cdyv/ddX5u++Z2o39vs3PjGv7e7uszu5a/tfP4uuETuKE33/7Y3kOyHb45v+xH/BXA24G35B/df5+3P5KkSr7TnfM/5X3/0J3z74A/5K75l/K13gP8xWs0t/uRIiremvd/F1U4XwT+nzzmG4A/cD3n5sb4df9crudzy/v+x3ytXwH+kdv+pLzt14DvpmbzX5fntmtu3AS/tz1zu2a/t/v697yWv7Xz+FpKVyxYsGDBAuAT04ewYMGCBQtmsBDCggULFiwAFkJYsGDBggUZCyEsWLBgwQJgIYQFCxYsWJCxEMKCBQsWLAAWQliwYMGCBRkLISw4NxCRP5yLlV0UkftJqo3/mTd6XgsWnBUsiWkLzhVE5O+TsmNvAe5U1X94g6e0YMGZwUIIC84VROSIVI/oEvBf68n1+hcsWJCxmIwWnDfcDtwfeABJU1iwYMGBWDSEBecKIvLjpJLJn0aqaPlXbvCUFiw4M1jd6AksWHBaEJGvBraq+iMiMgD/XkS+QFV/9kbPbcGCs4BFQ1iwYMGCBcDiQ1iwYMGCBf9ve3VMAAAAgDBo/VP7GANKcEIAoBICACcEACohAHBCAKASAgA3E7HLE0/yKg0AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clip using a geometry\n",
-    "\n",
-    "By default, it assumes that the CRS of the geometry is the same as the CRS\n",
-    "of the dataset. If it is different, make sure to pass in the CRS of the geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometries = [\n",
-    "    {\n",
-    "        'type': 'Polygon',\n",
-    "        'coordinates': [[\n",
-    "            [425499.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615331.540546387]\n",
-    "        ]]\n",
-    "    }\n",
-    "]\n",
-    "clipped = xds.rio.clip(geometries)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc957829d90>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clipped.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clipped.rio.to_raster(\"clipped.tif\", compress='LZMA', tiled=True, dtype=\"int32\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clip using a GeoDataFrame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas\n",
-    "from shapely.geometry import box"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geodf = geopandas.GeoDataFrame(\n",
-    "    geometry=[\n",
-    "        box(425499.18381405267, 4615331.540546387, 425526.18381405267, 4615478.540546387)\n",
-    "    ],\n",
-    "    crs=\"EPSG:26915\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clipped = xds.rio.clip(geodf.geometry.values, geodf.crs, drop=False, invert=True)\n",
-    "# Note: If you have rasterio < 1.2 you will need convert the geometries to dict-like objects if the projection\n",
-    "# of the geometries differ from the raster. For example:\n",
-    "#\n",
-    "# from shapely.geometry import mapping\n",
-    "#  geometries = geodf.geometry.apply(mapping)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc9572aa100>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clipped.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clipped.rio.to_raster(\"clipped_invert.tif\", compress='LZMA', tiled=True, dtype=\"int32\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clipping larger rasters\n",
-    "\n",
-    "Note: Loading from disk will likely only work directly after opening a raster with [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "\n",
-    "The clip operation needs the full raster loaded with the default method.\n",
-    "This can be an issue if you don't have enough memory (RAM) on you machine.\n",
-    "If this is something you have run into, it is recommended to use the\n",
-    "`from_disk=True` option. This option uses [rasterio.mask.mask](https://rasterio.readthedocs.io/en/latest/topics/masking-by-shapefile.html) when loading the data if possible.\n",
-    "\n",
-    "But be careful, these two methods, as they use different core functions, can have **different outputs**: small discrepencies may appear on the borders (1 pixel added or removed on some borders, see issue [#310](https://github.com/corteva/rioxarray/issues/310))\n",
-    "\n",
-    "Alternatively, you can also use `rio.clip_box` followed by `rio.clip` for a more\n",
-    "consistent memory efficient clip operation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometries = [\n",
-    "    {\n",
-    "        'type': 'Polygon',\n",
-    "        'coordinates': [[\n",
-    "            [425499.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615478.540546387],\n",
-    "            [425526.18381405267, 4615331.540546387],\n",
-    "            [425499.18381405267, 4615331.540546387]\n",
-    "        ]]\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "clipped = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    masked=True,\n",
-    ").rio.clip(geometries, from_disk=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fc9571db850>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clipped.plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/examples/convert_to_raster.html b/0.18.0/examples/convert_to_raster.html
deleted file mode 100644
index d22dfcdb..00000000
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-  <section id="Example---Convert-dataset-to-raster-(GeoTiff)">
-<h1>Example - Convert dataset to raster (GeoTiff)<a class="headerlink" href="#Example---Convert-dataset-to-raster-(GeoTiff)" title="Link to this heading"></a></h1>
-<p>Often, it is desirable to take a variable (band) out of your dataset and export it to a raster. This is possible with the <code class="docutils literal notranslate"><span class="pre">rio.to_raster()</span></code>method. It does most of the work for you so you don’t have to.</p>
-<p>Note: The <code class="docutils literal notranslate"><span class="pre">rio.to_raster()</span></code> method only works on a 2-dimensional or 3-dimensional <code class="docutils literal notranslate"><span class="pre">xarray.DataArray</span></code> or a 2-dimensional <code class="docutils literal notranslate"><span class="pre">xarray.Dataset</span></code>.</p>
-<p>API Reference:</p>
-<ul class="simple">
-<li><p>DataArray: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.to_raster"><span class="std std-ref">rio.to_raster()</span></a></p></li>
-<li><p>Dataset: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.to_raster"><span class="std std-ref">rio.to_raster()</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray
-</pre></div>
-</div>
-</div>
-<p>See docs for <a class="reference internal" href="../rioxarray.html#rioxarray-open-rasterio"><span class="std std-ref">rioxarray.open_rasterio</span></a></p>
-<div class="nbinput docutils container">
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-</pre></div>
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds = rioxarray.open_rasterio(
-    &quot;../../test/test_data/input/PLANET_SCOPE_3D.nc&quot;,
-)
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-</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;
-Dimensions:      (time: 2, x: 10, y: 10)
-Coordinates:
-  * time         (time) object 2016-12-19 10:27:29.687763 2016-12-29 12:52:42...
-  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05
-  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06
-    spatial_ref  int32 0
-Data variables:
-    blue         (time, y, x) float64 ...
-    green        (time, y, x) float64 ...
-Attributes:
-    coordinates:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c445223f-de4e-4b1e-8098-cbee34b218b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c445223f-de4e-4b1e-8098-cbee34b218b7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-28147fcc-df3f-4a34-95e9-c553e0ce5379' class='xr-section-summary-in' type='checkbox'  checked><label for='section-28147fcc-df3f-4a34-95e9-c553e0ce5379' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2016-12-19 10:27:29.687763 2016-...</div><input id='attrs-e088320f-bca6-467e-8cb8-e8986a40cb2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e088320f-bca6-467e-8cb8-e8986a40cb2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-059f464e-7159-4921-a87b-f7cd4f581362' class='xr-var-data-in' type='checkbox'><label for='data-059f464e-7159-4921-a87b-f7cd4f581362' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(2016, 12, 19, 10, 27, 29, 687763, has_year_zero=True),
-       cftime.DatetimeProlepticGregorian(2016, 12, 29, 12, 52, 42, 347451, has_year_zero=True)],
-      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.663e+05 4.663e+05 ... 4.663e+05</div><input id='attrs-b437c71c-6f09-48b3-ad9a-90d13232f2ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b437c71c-6f09-48b3-ad9a-90d13232f2ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57e77136-7143-44d9-b7cf-73fbe2ee23a9' class='xr-var-data-in' type='checkbox'><label for='data-57e77136-7143-44d9-b7cf-73fbe2ee23a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5, 466285.5,
-       466288.5, 466291.5, 466294.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.085e+06 8.085e+06 ... 8.085e+06</div><input id='attrs-4c2f981f-ffc5-4814-8a22-ebf2f86a7aef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c2f981f-ffc5-4814-8a22-ebf2f86a7aef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1df8c2f-d47d-47d7-91ab-beeb0f27b13a' class='xr-var-data-in' type='checkbox'><label for='data-f1df8c2f-d47d-47d7-91ab-beeb0f27b13a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,
-       8084680.5, 8084677.5, 8084674.5, 8084671.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-da9d5c75-1701-48fa-ba98-c71fc592eebc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da9d5c75-1701-48fa-ba98-c71fc592eebc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ea59b80-4a53-40ca-a226-83a68cd832d4' class='xr-var-data-in' type='checkbox'><label for='data-2ea59b80-4a53-40ca-a226-83a68cd832d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 22S</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-51.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>10000000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>466266.0 3.0 0.0 8084700.0 0.0 -3.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9028421c-78d8-4324-8c53-b922b911a026' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9028421c-78d8-4324-8c53-b922b911a026' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fa584176-aba1-477b-a671-19b6df4dcaae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa584176-aba1-477b-a671-19b6df4dcaae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca424900-74f9-432d-806d-13c47b426ed2' class='xr-var-data-in' type='checkbox'><label for='data-ca424900-74f9-432d-806d-13c47b426ed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>NETCDF_DIM_EXTRA :</span></dt><dd>{time}</dd><dt><span>NETCDF_DIM_time_DEF :</span></dt><dd>[2. 6.]</dd><dt><span>NETCDF_DIM_time_VALUES :</span></dt><dd>[     0.       872712.659688]</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>(&#x27;DN&#x27;, &#x27;DN&#x27;)</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31195140-ecc1-4c84-9a7c-8a4b5cac2d66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31195140-ecc1-4c84-9a7c-8a4b5cac2d66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c047550-03f6-40db-8ad2-26cd7cc23c76' class='xr-var-data-in' type='checkbox'><label for='data-7c047550-03f6-40db-8ad2-26cd7cc23c76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>NETCDF_DIM_EXTRA :</span></dt><dd>{time}</dd><dt><span>NETCDF_DIM_time_DEF :</span></dt><dd>[2. 6.]</dd><dt><span>NETCDF_DIM_time_VALUES :</span></dt><dd>[     0.       872712.659688]</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>(&#x27;DN&#x27;, &#x27;DN&#x27;)</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[200 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3faed2e-e71f-483f-b9bb-c4f9d96230fe' class='xr-section-summary-in' type='checkbox'  ><label for='section-b3faed2e-e71f-483f-b9bb-c4f9d96230fe' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-464f11fb-83f1-46e4-8aea-e2df9c3d5021' class='xr-index-data-in' type='checkbox'/><label for='index-464f11fb-83f1-46e4-8aea-e2df9c3d5021' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2016-12-19 10:27:29.687763, 2016-12-29 12:52:42.347451],
-            dtype=&#x27;object&#x27;,
-            length=2,
-            calendar=&#x27;proleptic_gregorian&#x27;,
-            freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ab3c0dc-2aab-409d-b42b-cd0f4e962bf8' class='xr-index-data-in' type='checkbox'/><label for='index-9ab3c0dc-2aab-409d-b42b-cd0f4e962bf8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5,
-              466285.5, 466288.5, 466291.5, 466294.5],
-             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a08603e-8475-4cf3-917c-37933b01c87c' class='xr-index-data-in' type='checkbox'/><label for='index-8a08603e-8475-4cf3-917c-37933b01c87c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,
-              8084680.5, 8084677.5, 8084674.5, 8084671.5],
-             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81c7d954-b5ff-4a73-a2e0-f373fd0ae1d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81c7d954-b5ff-4a73-a2e0-f373fd0ae1d9' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coordinates :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div></div>
-</div>
-<section id="Converting-Dataset-to-raster">
-<h2>Converting Dataset to raster<a class="headerlink" href="#Converting-Dataset-to-raster" title="Link to this heading"></a></h2>
-<p>Dataset: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.to_raster"><span class="std std-ref">rio.to_raster()</span></a></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># note how one time slice was selected on export to make the dataset 2D
-rds.isel(time=0).rio.to_raster(&quot;planet_scope.tif&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!rio info planet_scope.tif
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#34;bounds&#34;: [466266.0, 8084670.0, 466296.0, 8084700.0], &#34;colorinterp&#34;: [&#34;gray&#34;, &#34;undefined&#34;], &#34;count&#34;: 2, &#34;crs&#34;: &#34;EPSG:32722&#34;, &#34;descriptions&#34;: [&#34;blue&#34;, &#34;green&#34;], &#34;driver&#34;: &#34;GTiff&#34;, &#34;dtype&#34;: &#34;float64&#34;, &#34;height&#34;: 10, &#34;indexes&#34;: [1, 2], &#34;interleave&#34;: &#34;pixel&#34;, &#34;lnglat&#34;: [-51.31732641226951, -17.322997474192466], &#34;mask_flags&#34;: [[&#34;nodata&#34;], [&#34;nodata&#34;]], &#34;nodata&#34;: NaN, &#34;res&#34;: [3.0, 3.0], &#34;shape&#34;: [10, 10], &#34;tiled&#34;: false, &#34;transform&#34;: [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], &#34;units&#34;: [null, null], &#34;width&#34;: 10}
-</pre></div></div>
-</div>
-</section>
-<section id="Converting-DataArray-to-raster">
-<h2>Converting DataArray to raster<a class="headerlink" href="#Converting-DataArray-to-raster" title="Link to this heading"></a></h2>
-<p>DataArray: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.to_raster"><span class="std std-ref">rio.to_raster()</span></a></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># note how selecting one variable allowed for multiple time steps in a single raster
-rds.green.rio.to_raster(&quot;planet_scope_green.tif&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!rio info planet_scope_green.tif
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#34;bounds&#34;: [466266.0, 8084670.0, 466296.0, 8084700.0], &#34;colorinterp&#34;: [&#34;gray&#34;, &#34;undefined&#34;], &#34;count&#34;: 2, &#34;crs&#34;: &#34;EPSG:32722&#34;, &#34;descriptions&#34;: [&#34;green&#34;, &#34;green&#34;], &#34;driver&#34;: &#34;GTiff&#34;, &#34;dtype&#34;: &#34;float64&#34;, &#34;height&#34;: 10, &#34;indexes&#34;: [1, 2], &#34;interleave&#34;: &#34;pixel&#34;, &#34;lnglat&#34;: [-51.31732641226951, -17.322997474192466], &#34;mask_flags&#34;: [[&#34;nodata&#34;], [&#34;nodata&#34;]], &#34;nodata&#34;: NaN, &#34;res&#34;: [3.0, 3.0], &#34;shape&#34;: [10, 10], &#34;tiled&#34;: false, &#34;transform&#34;: [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], &#34;units&#34;: [null, null], &#34;width&#34;: 10}
-</pre></div></div>
-</div>
-</section>
-<section id="Converting-DataArray-to-raster-in-a-different-format">
-<h2>Converting DataArray to raster in a different format<a class="headerlink" href="#Converting-DataArray-to-raster-in-a-different-format" title="Link to this heading"></a></h2>
-<p>Example here, an ER Mapper grid. Look at gdal for possible formats that you can use to write to.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># you will get a two file raster: the .ers file with the metdata and the data with no extension
-rds.blue.rio.to_raster(&quot;planet_scope_green.ers&quot;, driver=&quot;ERS&quot;)
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Change-the-compression-of-the-raster-and-explicitly-make-it-a-Geotiff">
-<h2>Change the compression of the raster and explicitly make it a Geotiff<a class="headerlink" href="#Change-the-compression-of-the-raster-and-explicitly-make-it-a-Geotiff" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds.blue.rio.to_raster(&quot;planet_scope_green_LZW_compression.tif&quot;, driver=&quot;GTiff&quot;, compress=&quot;LZW&quot;)
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Change-the-basic-datatype-of-the-raster-(in-this-example,-also-saving-space-going-to-32-bit)">
-<h2>Change the basic datatype of the raster (in this example, also saving space going to 32 bit)<a class="headerlink" href="#Change-the-basic-datatype-of-the-raster-(in-this-example,-also-saving-space-going-to-32-bit)" title="Link to this heading"></a></h2>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds.blue.dtype
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-dtype(&#39;float64&#39;)
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds.blue.astype(&#39;float32&#39;).rio.to_raster(&quot;planet_scope_green_LZW_compression.tif&quot;, driver=&quot;GTiff&quot;, compress=&quot;LZW&quot;)
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Memory-efficient-raster-writing">
-<h2>Memory efficient raster writing<a class="headerlink" href="#Memory-efficient-raster-writing" title="Link to this heading"></a></h2>
-<p>Useful for reading and writing larger rasters to disk.</p>
-<p>Note: This will increase the time it takes to generate the raster.</p>
-<p>Also see:</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="dask_read_write.html"><span class="doc">Reading and Writing with Dask</span></a></p></li>
-<li><p><a class="reference internal" href="read-locks.html"><span class="doc">Reading COGs in Parallel</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds = rioxarray.open_rasterio(
-    &quot;../../test/test_data/input/PLANET_SCOPE_3D.nc&quot;,
-    lock=False,  # disable internal caching
-    cache=False,  # don&#39;t keep data loaded in memory. pull from disk every time
-)
-
-rds.green.rio.to_raster(
-    &quot;planet_scope_tiled.tif&quot;,
-    tiled=True,  # GDAL: By default striped TIFF files are created. This option can be used to force creation of tiled TIFF files.
-    windowed=True,  # rioxarray: read &amp; write one window at a time
-)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!rio info planet_scope_tiled.tif
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
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-{&#34;blockxsize&#34;: 256, &#34;blockysize&#34;: 256, &#34;bounds&#34;: [466266.0, 8084670.0, 466296.0, 8084700.0], &#34;colorinterp&#34;: [&#34;gray&#34;, &#34;undefined&#34;], &#34;count&#34;: 2, &#34;crs&#34;: &#34;EPSG:32722&#34;, &#34;descriptions&#34;: [&#34;green&#34;, &#34;green&#34;], &#34;driver&#34;: &#34;GTiff&#34;, &#34;dtype&#34;: &#34;float64&#34;, &#34;height&#34;: 10, &#34;indexes&#34;: [1, 2], &#34;interleave&#34;: &#34;pixel&#34;, &#34;lnglat&#34;: [-51.31732641226951, -17.322997474192466], &#34;mask_flags&#34;: [[&#34;nodata&#34;], [&#34;nodata&#34;]], &#34;nodata&#34;: NaN, &#34;res&#34;: [3.0, 3.0], &#34;shape&#34;: [10, 10], &#34;tiled&#34;: true, &#34;transform&#34;: [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], &#34;units&#34;: [null, null], &#34;width&#34;: 10}
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-
-
-           </div>
-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="resampling.html" class="btn btn-neutral float-left" title="Example - Resampling" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="clip_geom.html" class="btn btn-neutral float-right" title="Example - Clip" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
-    </div>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2019-2023, Corteva Agriscience™.</p>
-  </div>
-
-  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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\ No newline at end of file
diff --git a/0.18.0/examples/convert_to_raster.ipynb b/0.18.0/examples/convert_to_raster.ipynb
deleted file mode 100644
index b7b70d58..00000000
--- a/0.18.0/examples/convert_to_raster.ipynb
+++ /dev/null
@@ -1,677 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Convert dataset to raster (GeoTiff)\n",
-    "\n",
-    "Often, it is desirable to take a variable (band) out of your dataset and export it to a raster.\n",
-    "This is possible with the `rio.to_raster()`method. It does most of the work for you so you don't\n",
-    "have to.\n",
-    "\n",
-    "Note: The `rio.to_raster()` method only works on a 2-dimensional or 3-dimensional `xarray.DataArray` or a 2-dimensional `xarray.Dataset`.\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_array.RasterArray.to_raster)\n",
-    "- Dataset: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)"
-   ]
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-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * time         (time) object 2016-12-19 10:27:29.687763 2016-12-29 12:52:42...\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06\n",
-       "    spatial_ref  int32 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 ...\n",
-       "    green        (time, y, x) float64 ...\n",
-       "Attributes:\n",
-       "    coordinates:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c445223f-de4e-4b1e-8098-cbee34b218b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c445223f-de4e-4b1e-8098-cbee34b218b7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-28147fcc-df3f-4a34-95e9-c553e0ce5379' class='xr-section-summary-in' type='checkbox'  checked><label for='section-28147fcc-df3f-4a34-95e9-c553e0ce5379' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2016-12-19 10:27:29.687763 2016-...</div><input id='attrs-e088320f-bca6-467e-8cb8-e8986a40cb2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e088320f-bca6-467e-8cb8-e8986a40cb2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-059f464e-7159-4921-a87b-f7cd4f581362' class='xr-var-data-in' type='checkbox'><label for='data-059f464e-7159-4921-a87b-f7cd4f581362' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(2016, 12, 19, 10, 27, 29, 687763, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2016, 12, 29, 12, 52, 42, 347451, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.663e+05 4.663e+05 ... 4.663e+05</div><input id='attrs-b437c71c-6f09-48b3-ad9a-90d13232f2ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b437c71c-6f09-48b3-ad9a-90d13232f2ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57e77136-7143-44d9-b7cf-73fbe2ee23a9' class='xr-var-data-in' type='checkbox'><label for='data-57e77136-7143-44d9-b7cf-73fbe2ee23a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5, 466285.5,\n",
-       "       466288.5, 466291.5, 466294.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.085e+06 8.085e+06 ... 8.085e+06</div><input id='attrs-4c2f981f-ffc5-4814-8a22-ebf2f86a7aef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c2f981f-ffc5-4814-8a22-ebf2f86a7aef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1df8c2f-d47d-47d7-91ab-beeb0f27b13a' class='xr-var-data-in' type='checkbox'><label for='data-f1df8c2f-d47d-47d7-91ab-beeb0f27b13a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,\n",
-       "       8084680.5, 8084677.5, 8084674.5, 8084671.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-da9d5c75-1701-48fa-ba98-c71fc592eebc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da9d5c75-1701-48fa-ba98-c71fc592eebc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ea59b80-4a53-40ca-a226-83a68cd832d4' class='xr-var-data-in' type='checkbox'><label for='data-2ea59b80-4a53-40ca-a226-83a68cd832d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 22S</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-51.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>10000000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>466266.0 3.0 0.0 8084700.0 0.0 -3.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9028421c-78d8-4324-8c53-b922b911a026' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9028421c-78d8-4324-8c53-b922b911a026' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fa584176-aba1-477b-a671-19b6df4dcaae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa584176-aba1-477b-a671-19b6df4dcaae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca424900-74f9-432d-806d-13c47b426ed2' class='xr-var-data-in' type='checkbox'><label for='data-ca424900-74f9-432d-806d-13c47b426ed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>NETCDF_DIM_EXTRA :</span></dt><dd>{time}</dd><dt><span>NETCDF_DIM_time_DEF :</span></dt><dd>[2. 6.]</dd><dt><span>NETCDF_DIM_time_VALUES :</span></dt><dd>[     0.       872712.659688]</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>(&#x27;DN&#x27;, &#x27;DN&#x27;)</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31195140-ecc1-4c84-9a7c-8a4b5cac2d66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31195140-ecc1-4c84-9a7c-8a4b5cac2d66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c047550-03f6-40db-8ad2-26cd7cc23c76' class='xr-var-data-in' type='checkbox'><label for='data-7c047550-03f6-40db-8ad2-26cd7cc23c76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>NETCDF_DIM_EXTRA :</span></dt><dd>{time}</dd><dt><span>NETCDF_DIM_time_DEF :</span></dt><dd>[2. 6.]</dd><dt><span>NETCDF_DIM_time_VALUES :</span></dt><dd>[     0.       872712.659688]</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>(&#x27;DN&#x27;, &#x27;DN&#x27;)</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[200 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3faed2e-e71f-483f-b9bb-c4f9d96230fe' class='xr-section-summary-in' type='checkbox'  ><label for='section-b3faed2e-e71f-483f-b9bb-c4f9d96230fe' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-464f11fb-83f1-46e4-8aea-e2df9c3d5021' class='xr-index-data-in' type='checkbox'/><label for='index-464f11fb-83f1-46e4-8aea-e2df9c3d5021' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2016-12-19 10:27:29.687763, 2016-12-29 12:52:42.347451],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=2,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ab3c0dc-2aab-409d-b42b-cd0f4e962bf8' class='xr-index-data-in' type='checkbox'/><label for='index-9ab3c0dc-2aab-409d-b42b-cd0f4e962bf8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5,\n",
-       "              466285.5, 466288.5, 466291.5, 466294.5],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a08603e-8475-4cf3-917c-37933b01c87c' class='xr-index-data-in' type='checkbox'/><label for='index-8a08603e-8475-4cf3-917c-37933b01c87c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,\n",
-       "              8084680.5, 8084677.5, 8084674.5, 8084671.5],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81c7d954-b5ff-4a73-a2e0-f373fd0ae1d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81c7d954-b5ff-4a73-a2e0-f373fd0ae1d9' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coordinates :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * time         (time) object 2016-12-19 10:27:29.687763 2016-12-29 12:52:42...\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06\n",
-       "    spatial_ref  int32 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 ...\n",
-       "    green        (time, y, x) float64 ...\n",
-       "Attributes:\n",
-       "    coordinates:  spatial_ref"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/input/PLANET_SCOPE_3D.nc\",\n",
-    ")\n",
-    "rds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Converting Dataset to raster\n",
-    "\n",
-    "Dataset: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# note how one time slice was selected on export to make the dataset 2D\n",
-    "rds.isel(time=0).rio.to_raster(\"planet_scope.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"blue\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": false, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info planet_scope.tif"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Converting DataArray to raster\n",
-    "\n",
-    "DataArray: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_array.RasterArray.to_raster)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# note how selecting one variable allowed for multiple time steps in a single raster\n",
-    "rds.green.rio.to_raster(\"planet_scope_green.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"green\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": false, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info planet_scope_green.tif"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Converting DataArray to raster in a different format\n",
-    "Example here, an ER Mapper grid.\n",
-    "Look at gdal for possible formats that you can use to write to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# you will get a two file raster: the .ers file with the metdata and the data with no extension\n",
-    "rds.blue.rio.to_raster(\"planet_scope_green.ers\", driver=\"ERS\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Change the compression of the raster and explicitly make it a Geotiff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds.blue.rio.to_raster(\"planet_scope_green_LZW_compression.tif\", driver=\"GTiff\", compress=\"LZW\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Change the basic datatype of the raster (in this example, also saving space going to 32 bit)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('float64')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.blue.dtype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds.blue.astype('float32').rio.to_raster(\"planet_scope_green_LZW_compression.tif\", driver=\"GTiff\", compress=\"LZW\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Memory efficient raster writing\n",
-    "\n",
-    "Useful for reading and writing larger rasters to disk.\n",
-    "\n",
-    "Note: This will increase the time it takes to generate the raster.\n",
-    "\n",
-    "Also see:\n",
-    "\n",
-    "- [Reading and Writing with Dask](dask_read_write.ipynb)\n",
-    "- [Reading COGs in Parallel](read-locks.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/input/PLANET_SCOPE_3D.nc\",\n",
-    "    lock=False,  # disable internal caching\n",
-    "    cache=False,  # don't keep data loaded in memory. pull from disk every time\n",
-    ")\n",
-    "\n",
-    "rds.green.rio.to_raster(\n",
-    "    \"planet_scope_tiled.tif\",\n",
-    "    tiled=True,  # GDAL: By default striped TIFF files are created. This option can be used to force creation of tiled TIFF files.\n",
-    "    windowed=True,  # rioxarray: read & write one window at a time\n",
-    ") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"blockxsize\": 256, \"blockysize\": 256, \"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"green\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": true, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info planet_scope_tiled.tif"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/examples/dask_read_write.html b/0.18.0/examples/dask_read_write.html
deleted file mode 100644
index 9d3b7900..00000000
--- a/0.18.0/examples/dask_read_write.html
+++ /dev/null
@@ -1,230 +0,0 @@
-
-
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" data-content_root="../">
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-  <title>Example - Reading and Writing with Dask &mdash; rioxarray 0.18.0 documentation</title>
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-<li class="toctree-l1"><a class="reference internal" href="../readme.html">rioxarray README</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="examples.html">Usage Examples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="resampling.html">Example - Resampling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="convert_to_raster.html">Example - Convert dataset to raster (GeoTiff)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="clip_geom.html">Example - Clip</a></li>
-<li class="toctree-l2"><a class="reference internal" href="clip_box.html">Example - Clip Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pad_box.html">Example - Pad Box</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="reproject.html">Example - Reproject</a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Example - Reading and Writing with Dask</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#No-distributed-computing-example">No distributed computing example</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference external" href="https://corteva.github.io/geocube/html/examples/zonal_statistics.html">Example - Zonal Statistics</a></li>
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-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/354798/144357">How to mask NetCDF time series data from a shapefile in Python?</a></li>
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-           <div itemprop="articleBody">
-             
-  <section id="Example---Reading-and-Writing-with-Dask">
-<h1>Example - Reading and Writing with Dask<a class="headerlink" href="#Example---Reading-and-Writing-with-Dask" title="Link to this heading"></a></h1>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import multiprocessing
-# Linux/OSX:
-import multiprocessing.popen_spawn_posix
-# Windows:
-# import multiprocessing.popen_spawn_win32
-import threading
-
-from dask.distributed import Client, LocalCluster, Lock
-
-import rioxarray
-</pre></div>
-</div>
-</div>
-<p>Tips for using dask locks: - Be careful about what lock you use for your process. It is required to have a lock for each worker, so the more fine-grained the better. - The reading and writing processes need the same type of lock. They don’t have to share the same lock, but they do nead a lock of the same type.</p>
-<p>See docs for:</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="read-locks.html"><span class="doc">Reading COGs in Parallel</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray-open-rasterio"><span class="std std-ref">rioxarray.open_rasterio</span></a></p></li>
-<li><p>DataArray: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.to_raster"><span class="std std-ref">rio.to_raster()</span></a></p></li>
-<li><p>Dataset: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.to_raster"><span class="std std-ref">rio.to_raster()</span></a></p></li>
-</ul>
-<section id="No-distributed-computing-example">
-<h2>No distributed computing example<a class="headerlink" href="#No-distributed-computing-example" title="Link to this heading"></a></h2>
-<p>Note: Without a lock provided, <code class="docutils literal notranslate"><span class="pre">to_raster</span></code> does not use dask to write to disk.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = rioxarray.open_rasterio(
-    &quot;../../test/test_data/compare/small_dem_3m_merged.tif&quot;,
-    chunks=True,
-)
-xds.rio.to_raster(&quot;simple_write.tif&quot;, tiled=True)
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Multithreaded-example">
-<h2>Multithreaded example<a class="headerlink" href="#Multithreaded-example" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = rioxarray.open_rasterio(
-    &quot;../../test/test_data/compare/small_dem_3m_merged.tif&quot;,
-    chunks=True,
-    lock=False,
-    # lock=threading.Lock(), # when too many file handles open
-xds.rio.to_raster(
-    &quot;dask_thread.tif&quot;, tiled=True, lock=threading.Lock(),
-)
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Multiple-worker-example">
-<h2>Multiple worker example<a class="headerlink" href="#Multiple-worker-example" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>with LocalCluster() as cluster, Client(cluster) as client:
-    xds = rioxarray.open_rasterio(
-        &quot;../../test/test_data/compare/small_dem_3m_merged.tif&quot;,
-        chunks=True,
-        lock=False,
-        # lock=Lock(&quot;rio-read&quot;, client=client), # when too many file handles open
-    )
-    xds.rio.to_raster(
-        &quot;dask_multiworker.tif&quot;,
-        tiled=True,
-        lock=Lock(&quot;rio&quot;, client=client),
-    )
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-
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-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="COG.html" class="btn btn-neutral float-left" title="Example - Cloud Optimized GeoTiff (COG)" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/0.18.0/examples/dask_read_write.ipynb b/0.18.0/examples/dask_read_write.ipynb
deleted file mode 100644
index 4773066d..00000000
--- a/0.18.0/examples/dask_read_write.ipynb
+++ /dev/null
@@ -1,137 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reading and Writing with Dask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import multiprocessing\n",
-    "# Linux/OSX:\n",
-    "import multiprocessing.popen_spawn_posix\n",
-    "# Windows:\n",
-    "# import multiprocessing.popen_spawn_win32\n",
-    "import threading\n",
-    "\n",
-    "from dask.distributed import Client, LocalCluster, Lock\n",
-    "\n",
-    "import rioxarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tips for using dask locks:\n",
-    "- Be careful about what lock you use for your process. It is required to have a lock for each worker, so the more fine-grained the better.\n",
-    "- The reading and writing processes need the same type of lock. They don't have to share the same lock, but they do nead a lock of the same type.\n",
-    "\n",
-    "See docs for:\n",
-    "\n",
-    "- [Reading COGs in Parallel](read-locks.ipynb)\n",
-    "- [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "- DataArray: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_array.RasterArray.to_raster)\n",
-    "- Dataset: [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### No distributed computing example\n",
-    "Note: Without a lock provided, `to_raster` does not use dask to write to disk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    chunks=True,\n",
-    ")\n",
-    "xds.rio.to_raster(\"simple_write.tif\", tiled=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multithreaded example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    chunks=True,\n",
-    "    lock=False,\n",
-    "    # lock=threading.Lock(), # when too many file handles open\n",
-    "xds.rio.to_raster(\n",
-    "    \"dask_thread.tif\", tiled=True, lock=threading.Lock(),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multiple worker example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with LocalCluster() as cluster, Client(cluster) as client:\n",
-    "    xds = rioxarray.open_rasterio(\n",
-    "        \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "        chunks=True,\n",
-    "        lock=False,\n",
-    "        # lock=Lock(\"rio-read\", client=client), # when too many file handles open\n",
-    "    )\n",
-    "    xds.rio.to_raster(\n",
-    "        \"dask_multiworker.tif\",\n",
-    "        tiled=True,\n",
-    "        lock=Lock(\"rio\", client=client),\n",
-    "    )"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/examples/examples.html b/0.18.0/examples/examples.html
deleted file mode 100644
index 546cb8ab..00000000
--- a/0.18.0/examples/examples.html
+++ /dev/null
@@ -1,174 +0,0 @@
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-<li class="toctree-l1 current"><a class="current reference internal" href="#">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="resampling.html">Example - Resampling</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="clip_box.html">Example - Clip Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pad_box.html">Example - Pad Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="read-locks.html">Example - Reading COGs in Parallel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="reproject.html">Example - Reproject</a></li>
-<li class="toctree-l2"><a class="reference internal" href="reproject_match.html">Example - Reproject Match (For Raster Calculations/Stacking)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="merge.html">Example - Merge</a></li>
-<li class="toctree-l2"><a class="reference internal" href="interpolate_na.html">Example - Interpolate Missing Data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="transform_bounds.html">Example - Transform Bounds</a></li>
-<li class="toctree-l2"><a class="reference internal" href="COG.html">Example - Cloud Optimized GeoTiff (COG)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dask_read_write.html">Example - Reading and Writing with Dask</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://corteva.github.io/geocube/html/examples/zonal_statistics.html">Example - Zonal Statistics</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/328320/144357">Extracting data within geometry (shape)</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/329141/144357">Converting NetCDF dataset array to GeoTiff</a></li>
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-<span id="id1"></span><h1>Usage Examples<a class="headerlink" href="#usage-examples" title="Link to this heading"></a></h1>
-<p>This page contains links to a collection of examples of how to use rioxarray.</p>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="resampling.html">Example - Resampling</a></li>
-<li class="toctree-l1"><a class="reference internal" href="convert_to_raster.html">Example - Convert dataset to raster (GeoTiff)</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="pad_box.html">Example - Pad Box</a></li>
-<li class="toctree-l1"><a class="reference internal" href="read-locks.html">Example - Reading COGs in Parallel</a></li>
-<li class="toctree-l1"><a class="reference internal" href="reproject.html">Example - Reproject</a></li>
-<li class="toctree-l1"><a class="reference internal" href="reproject_match.html">Example - Reproject Match (For Raster Calculations/Stacking)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="merge.html">Example - Merge</a></li>
-<li class="toctree-l1"><a class="reference internal" href="interpolate_na.html">Example - Interpolate Missing Data</a></li>
-<li class="toctree-l1"><a class="reference internal" href="transform_bounds.html">Example - Transform Bounds</a></li>
-<li class="toctree-l1"><a class="reference internal" href="COG.html">Example - Cloud Optimized GeoTiff (COG)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dask_read_write.html">Example - Reading and Writing with Dask</a></li>
-<li class="toctree-l1"><a class="reference external" href="https://corteva.github.io/geocube/html/examples/zonal_statistics.html">Example - Zonal Statistics</a></li>
-</ul>
-</div>
-<div class="toctree-wrapper compound">
-<p class="caption" role="heading"><span class="caption-text">stackexchange:</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference external" href="https://gis.stackexchange.com/a/328320/144357">Extracting data within geometry (shape)</a></li>
-<li class="toctree-l1"><a class="reference external" href="https://gis.stackexchange.com/a/329141/144357">Converting NetCDF dataset array to GeoTiff</a></li>
-<li class="toctree-l1"><a class="reference external" href="https://gis.stackexchange.com/a/345697/144357">How do I add projection to this NetCDF file? (Satellite)</a></li>
-<li class="toctree-l1"><a class="reference external" href="https://gis.stackexchange.com/a/346899/144357">Create a new raster TIFF file which is masked based on the GeoJSON file</a></li>
-<li class="toctree-l1"><a class="reference external" href="https://gis.stackexchange.com/a/354798/144357">How to mask NetCDF time series data from a shapefile in Python?</a></li>
-<li class="toctree-l1"><a class="reference external" href="https://gis.stackexchange.com/a/358058/144357">Extract data from raster at a point</a></li>
-<li class="toctree-l1"><a class="reference external" href="https://gis.stackexchange.com/a/358057/144357">Convert raster to CSV with lat, lon, and value columns</a></li>
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-</section>
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-<li class="toctree-l2"><a class="reference internal" href="resampling.html">Example - Resampling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="convert_to_raster.html">Example - Convert dataset to raster (GeoTiff)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="clip_geom.html">Example - Clip</a></li>
-<li class="toctree-l2"><a class="reference internal" href="clip_box.html">Example - Clip Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pad_box.html">Example - Pad Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="read-locks.html">Example - Reading COGs in Parallel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="reproject.html">Example - Reproject</a></li>
-<li class="toctree-l2"><a class="reference internal" href="reproject_match.html">Example - Reproject Match (For Raster Calculations/Stacking)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="merge.html">Example - Merge</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Example - Interpolate Missing Data</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Load-in-xarray-dataset">Load in xarray dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Fill-missing-with-interpolate_na">Fill missing with interpolate_na</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="transform_bounds.html">Example - Transform Bounds</a></li>
-<li class="toctree-l2"><a class="reference internal" href="COG.html">Example - Cloud Optimized GeoTiff (COG)</a></li>
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-<li class="toctree-l2"><a class="reference external" href="https://corteva.github.io/geocube/html/examples/zonal_statistics.html">Example - Zonal Statistics</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/328320/144357">Extracting data within geometry (shape)</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/329141/144357">Converting NetCDF dataset array to GeoTiff</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/345697/144357">How do I add projection to this NetCDF file? (Satellite)</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/346899/144357">Create a new raster TIFF file which is masked based on the GeoJSON file</a></li>
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-  <section id="Example---Interpolate-Missing-Data">
-<h1>Example - Interpolate Missing Data<a class="headerlink" href="#Example---Interpolate-Missing-Data" title="Link to this heading"></a></h1>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray # for the extension to load
-import xarray
-
-%matplotlib inline
-</pre></div>
-</div>
-</div>
-<section id="Load-in-xarray-dataset">
-<h2>Load in xarray dataset<a class="headerlink" href="#Load-in-xarray-dataset" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = xarray.open_dataarray(&quot;MODIS_ARRAY.nc&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
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-&lt;xarray.DataArray (y: 200, x: 200)&gt;
-array([[ nan,  nan,  nan, ..., 656., 656., 554.],
-       [ nan,  nan,  nan, ..., 694., 694., 642.],
-       [ nan,  nan,  nan, ..., 456., 575., 642.],
-       ...,
-       [993., 817., 817., ..., 471., 479., 498.],
-       [893., 893., 816., ..., 479., 479., 469.],
-       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)
-Coordinates:
-  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06
-  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06
-Attributes:
-    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...
-    res:        [231.65635826 231.65635826]
-    is_tiled:   0
-    nodata:     -28672.0
-    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e...
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds.isel(x=slice(0, 20), y=slice(0, 20)).plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7f3bf197f978&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_interpolate_na_5_1.png" src="../_images/examples_interpolate_na_5_1.png" />
-</div>
-</div>
-</section>
-<section id="Fill-missing-with-interpolate_na">
-<h2>Fill missing with interpolate_na<a class="headerlink" href="#Fill-missing-with-interpolate_na" title="Link to this heading"></a></h2>
-<p>API Reference:</p>
-<ul class="simple">
-<li><p>DataArray: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.interpolate_na"><span class="std std-ref">rio.interpolate_na()</span></a></p></li>
-<li><p>Dataset: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.interpolate_na"><span class="std std-ref">rio.interpolate_na()</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>filled = xds.rio.interpolate_na()
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>filled
-</pre></div>
-</div>
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-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;xarray.DataArray (y: 200, x: 200)&gt;
-array([[673., 558., 687., ..., 656., 656., 554.],
-       [673., 558., 558., ..., 694., 694., 642.],
-       [673., 558., 558., ..., 456., 575., 642.],
-       ...,
-       [993., 817., 817., ..., 471., 479., 498.],
-       [893., 893., 816., ..., 479., 479., 469.],
-       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)
-Coordinates:
-  * y            (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06
-  * x            (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06
-    spatial_ref  int64 0
-Attributes:
-    transform:     (231.6563582639561, 0.0, -7274009.649486291, 0.0, -231.656...
-    _FillValue:    -28672.0
-    grid_mapping:  spatial_ref
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>filled.isel(x=slice(0, 20), y=slice(0, 20)).plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
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-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7f3bf1865860&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_interpolate_na_9_1.png" src="../_images/examples_interpolate_na_9_1.png" />
-</div>
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diff --git a/0.18.0/examples/interpolate_na.ipynb b/0.18.0/examples/interpolate_na.ipynb
deleted file mode 100644
index 1b30f330..00000000
--- a/0.18.0/examples/interpolate_na.ipynb
+++ /dev/null
@@ -1,216 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Interpolate Missing Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"MODIS_ARRAY.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f3bf197f978>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.isel(x=slice(0, 20), y=slice(0, 20)).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fill missing with interpolate_na\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.interpolate_na()](../rioxarray.rst#rioxarray.raster_array.RasterArray.interpolate_na)\n",
-    "- Dataset: [rio.interpolate_na()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.interpolate_na)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filled = xds.rio.interpolate_na()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[673., 558., 687., ..., 656., 656., 554.],\n",
-       "       [673., 558., 558., ..., 694., 694., 642.],\n",
-       "       [673., 558., 558., ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y            (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x            (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    transform:     (231.6563582639561, 0.0, -7274009.649486291, 0.0, -231.656...\n",
-       "    _FillValue:    -28672.0\n",
-       "    grid_mapping:  spatial_ref"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filled"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f3bf1865860>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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diff --git a/0.18.0/examples/merge.html b/0.18.0/examples/merge.html
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-  <title>Example - Merge &mdash; rioxarray 0.18.0 documentation</title>
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-              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../readme.html">rioxarray README</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="convert_to_raster.html">Example - Convert dataset to raster (GeoTiff)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="clip_geom.html">Example - Clip</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="pad_box.html">Example - Pad Box</a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Example - Merge</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Load-in-xarray-dataset">Load in xarray dataset</a></li>
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-  <section id="Example---Merge">
-<h1>Example - Merge<a class="headerlink" href="#Example---Merge" title="Link to this heading"></a></h1>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray # for the extension to load
-import xarray
-from rioxarray.merge import merge_arrays
-# Note: You can merge datasets with the merge_datasets method
-
-%matplotlib inline
-</pre></div>
-</div>
-</div>
-<section id="Load-in-xarray-dataset">
-<h2>Load in xarray dataset<a class="headerlink" href="#Load-in-xarray-dataset" title="Link to this heading"></a></h2>
-<p>API reference:</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray-open-rasterio"><span class="std std-ref">rioxarray.open_rasterio</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.merge.merge_arrays"><span class="std std-ref">rioxarray.merge.merge_arrays</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.merge.merge_datasets"><span class="std std-ref">rioxarray.merge.merge_datasets</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>dem_test = &quot;../../test/test_data/input/MODIS_ARRAY.nc&quot;
-rds = rioxarray.open_rasterio(dem_test)
-arrays = [
-    rds.isel(x=slice(100), y=slice(100)),
-    rds.isel(x=slice(100, 200), y=slice(100, 200)),
-    rds.isel(x=slice(100), y=slice(100, 200)),
-    rds.isel(x=slice(100, 200), y=slice(100)),
-]
-merged = merge_arrays(arrays)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds.where(rds!=rds.rio.nodata).plot();
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_merge_4_0.png" src="../_images/examples_merge_4_0.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>merged.where(merged!=merged.rio.nodata).plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7f84f86f13c8&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_merge_5_1.png" src="../_images/examples_merge_5_1.png" />
-</div>
-</div>
-</section>
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diff --git a/0.18.0/examples/merge.ipynb b/0.18.0/examples/merge.ipynb
deleted file mode 100644
index 08f07723..00000000
--- a/0.18.0/examples/merge.ipynb
+++ /dev/null
@@ -1,130 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Merge"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "from rioxarray.merge import merge_arrays\n",
-    "# Note: You can merge datasets with the merge_datasets method\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset\n",
-    "\n",
-    "API reference:\n",
-    "\n",
-    "- [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "- [rioxarray.merge.merge_arrays](../rioxarray.rst#rioxarray.merge.merge_arrays)\n",
-    "- [rioxarray.merge.merge_datasets](../rioxarray.rst#rioxarray.merge.merge_datasets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dem_test = \"../../test/test_data/input/MODIS_ARRAY.nc\"\n",
-    "rds = rioxarray.open_rasterio(dem_test)\n",
-    "arrays = [\n",
-    "    rds.isel(x=slice(100), y=slice(100)),\n",
-    "    rds.isel(x=slice(100, 200), y=slice(100, 200)),\n",
-    "    rds.isel(x=slice(100), y=slice(100, 200)),\n",
-    "    rds.isel(x=slice(100, 200), y=slice(100)),\n",
-    "]\n",
-    "merged = merge_arrays(arrays)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rds.where(rds!=rds.rio.nodata).plot();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f84f86f13c8>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="convert_to_raster.html">Example - Convert dataset to raster (GeoTiff)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="clip_box.html">Example - Clip Box</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Example - Pad Box</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Load-in-xarray-dataset">Load in xarray dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Pad-using-a-bounding-box">Pad using a bounding box</a></li>
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-<h1>Example - Pad Box<a class="headerlink" href="#Example---Pad-Box" title="Link to this heading"></a></h1>
-<div class="nbinput nblast docutils container">
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray # for the extension to load
-import xarray
-
-%matplotlib inline
-</pre></div>
-</div>
-</div>
-<section id="Load-in-xarray-dataset">
-<h2>Load in xarray dataset<a class="headerlink" href="#Load-in-xarray-dataset" title="Link to this heading"></a></h2>
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = xarray.open_dataarray(&quot;../../test/test_data/input/MODIS_ARRAY.nc&quot;)
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-       [ nan,  nan,  nan, ..., 456., 575., 642.],
-       ...,
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-       5012001.139218, 5011769.48286 , 5011537.826502, 5011306.170144,
-       5011074.513785, 5010842.857427, 5010611.201069, 5010379.544711,
-       5010147.888352, 5009916.231994, 5009684.575636, 5009452.919277,
-       5009221.262919, 5008989.606561, 5008757.950203, 5008526.293844,
-       5008294.637486, 5008062.981128, 5007831.32477 , 5007599.668411,
-       5007368.012053, 5007136.355695, 5006904.699337, 5006673.042978,
-       5006441.38662 , 5006209.730262, 5005978.073904, 5005746.417545,
-       5005514.761187, 5005283.104829, 5005051.44847 , 5004819.792112,
-       5004588.135754, 5004356.479396, 5004124.823037, 5003893.166679])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.274e+06 ... -7.228e+06</div><input id='attrs-67d14f91-6c70-4cb8-9949-f13bd02d3656' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67d14f91-6c70-4cb8-9949-f13bd02d3656' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd9c7350-99e3-4f2a-9b3a-5b4f07d56c37' class='xr-var-data-in' type='checkbox'><label for='data-fd9c7350-99e3-4f2a-9b3a-5b4f07d56c37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([-7273893.821307, -7273662.164949, -7273430.508591, -7273198.852232,
-       -7272967.195874, -7272735.539516, -7272503.883158, -7272272.226799,
-       -7272040.570441, -7271808.914083, -7271577.257725, -7271345.601366,
-       -7271113.945008, -7270882.28865 , -7270650.632291, -7270418.975933,
-       -7270187.319575, -7269955.663217, -7269724.006858, -7269492.3505  ,
-       -7269260.694142, -7269029.037784, -7268797.381425, -7268565.725067,
-       -7268334.068709, -7268102.412351, -7267870.755992, -7267639.099634,
-       -7267407.443276, -7267175.786918, -7266944.130559, -7266712.474201,
-       -7266480.817843, -7266249.161484, -7266017.505126, -7265785.848768,
-       -7265554.19241 , -7265322.536051, -7265090.879693, -7264859.223335,
-       -7264627.566977, -7264395.910618, -7264164.25426 , -7263932.597902,
-       -7263700.941544, -7263469.285185, -7263237.628827, -7263005.972469,
-       -7262774.31611 , -7262542.659752, -7262311.003394, -7262079.347036,
-       -7261847.690677, -7261616.034319, -7261384.377961, -7261152.721603,
-       -7260921.065244, -7260689.408886, -7260457.752528, -7260226.09617 ,
-       -7259994.439811, -7259762.783453, -7259531.127095, -7259299.470737,
-       -7259067.814378, -7258836.15802 , -7258604.501662, -7258372.845303,
-       -7258141.188945, -7257909.532587, -7257677.876229, -7257446.21987 ,
-       -7257214.563512, -7256982.907154, -7256751.250796, -7256519.594437,
-       -7256287.938079, -7256056.281721, -7255824.625363, -7255592.969004,
-       -7255361.312646, -7255129.656288, -7254897.99993 , -7254666.343571,
-       -7254434.687213, -7254203.030855, -7253971.374496, -7253739.718138,
-       -7253508.06178 , -7253276.405422, -7253044.749063, -7252813.092705,
-       -7252581.436347, -7252349.779989, -7252118.12363 , -7251886.467272,
-       -7251654.810914, -7251423.154556, -7251191.498197, -7250959.841839,
-       -7250728.185481, -7250496.529122, -7250264.872764, -7250033.216406,
-       -7249801.560048, -7249569.903689, -7249338.247331, -7249106.590973,
-       -7248874.934615, -7248643.278256, -7248411.621898, -7248179.96554 ,
-       -7247948.309182, -7247716.652823, -7247484.996465, -7247253.340107,
-       -7247021.683749, -7246790.02739 , -7246558.371032, -7246326.714674,
-       -7246095.058315, -7245863.401957, -7245631.745599, -7245400.089241,
-       -7245168.432882, -7244936.776524, -7244705.120166, -7244473.463808,
-       -7244241.807449, -7244010.151091, -7243778.494733, -7243546.838375,
-       -7243315.182016, -7243083.525658, -7242851.8693  , -7242620.212942,
-       -7242388.556583, -7242156.900225, -7241925.243867, -7241693.587508,
-       -7241461.93115 , -7241230.274792, -7240998.618434, -7240766.962075,
-       -7240535.305717, -7240303.649359, -7240071.993001, -7239840.336642,
-       -7239608.680284, -7239377.023926, -7239145.367568, -7238913.711209,
-       -7238682.054851, -7238450.398493, -7238218.742135, -7237987.085776,
-       -7237755.429418, -7237523.77306 , -7237292.116701, -7237060.460343,
-       -7236828.803985, -7236597.147627, -7236365.491268, -7236133.83491 ,
-       -7235902.178552, -7235670.522194, -7235438.865835, -7235207.209477,
-       -7234975.553119, -7234743.896761, -7234512.240402, -7234280.584044,
-       -7234048.927686, -7233817.271327, -7233585.614969, -7233353.958611,
-       -7233122.302253, -7232890.645894, -7232658.989536, -7232427.333178,
-       -7232195.67682 , -7231964.020461, -7231732.364103, -7231500.707745,
-       -7231269.051387, -7231037.395028, -7230805.73867 , -7230574.082312,
-       -7230342.425954, -7230110.769595, -7229879.113237, -7229647.456879,
-       -7229415.80052 , -7229184.144162, -7228952.487804, -7228720.831446,
-       -7228489.175087, -7228257.518729, -7228025.862371, -7227794.206013])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c5764eaa-c59c-4881-8426-5defd01b3eea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5764eaa-c59c-4881-8426-5defd01b3eea' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>+a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +units=m +x_0=0 +y_0=0</dd><dt><span>res :</span></dt><dd>[231.65635826 231.65635826]</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodata :</span></dt><dd>-28672.0</dd><dt><span>transform :</span></dt><dd>[ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e+00
- -2.31656358e+02  5.05010861e+06  0.00000000e+00  0.00000000e+00
-  1.00000000e+00]</dd></dl></div></li></ul></div></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds.plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7fcb90621438&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_pad_box_5_1.png" src="../_images/examples_pad_box_5_1.png" />
-</div>
-</div>
-</section>
-<section id="Pad-using-a-bounding-box">
-<h2>Pad using a bounding box<a class="headerlink" href="#Pad-using-a-bounding-box" title="Link to this heading"></a></h2>
-<p>See docs for <code class="docutils literal notranslate"><span class="pre">rio.pad_box</span></code>:</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.pad_box"><span class="std std-ref">DataArray.pad_box</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.pad_box"><span class="std std-ref">Dataset.pad_box</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xdsc = xds.rio.pad_box(
-    minx=-7.3e+06,
-    miny=4.99e+06,
-    maxx=-7.2e+06,
-    maxy=5.06e+06,
-)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xdsc.values[0, 0]
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-nan
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xdsc.plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7fcb8ed54ba8&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_pad_box_9_1.png" src="../_images/examples_pad_box_9_1.png" />
-</div>
-</div>
-</section>
-</section>
-
-
-           </div>
-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="clip_box.html" class="btn btn-neutral float-left" title="Example - Clip Box" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="read-locks.html" class="btn btn-neutral float-right" title="Example - Reading COGs in Parallel" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
-    </div>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2019-2023, Corteva Agriscience™.</p>
-  </div>
-
-  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
-   
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-</footer>
-        </div>
-      </div>
-    </section>
-  </div>
-  <script>
-      jQuery(function () {
-          SphinxRtdTheme.Navigation.enable(true);
-      });
-  </script> 
-
-</body>
-</html>
\ No newline at end of file
diff --git a/0.18.0/examples/pad_box.ipynb b/0.18.0/examples/pad_box.ipynb
deleted file mode 100644
index 85a07939..00000000
--- a/0.18.0/examples/pad_box.ipynb
+++ /dev/null
@@ -1,649 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Pad Box"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")"
-   ]
-  },
-  {
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-   "execution_count": 3,
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-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-053c0e9a-c4a5-4eb1-a6a9-5927f728b70e' class='xr-array-in' type='checkbox' ><label for='section-053c0e9a-c4a5-4eb1-a6a9-5927f728b70e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan 687.0 687.0 ... 491.0 504.0 504.0 515.0 469.0 485.0</span></div><pre class='xr-array-data'>array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-d1b518c1-3206-403c-88a0-bf2caa89205a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1b518c1-3206-403c-88a0-bf2caa89205a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.05e+06 5.05e+06 ... 5.004e+06</div><input id='attrs-5988f292-8b05-4239-8369-28f590630fca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5988f292-8b05-4239-8369-28f590630fca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d26e778d-54c2-4b2d-9bf3-2d70de3fa945' class='xr-var-data-in' type='checkbox'><label for='data-d26e778d-54c2-4b2d-9bf3-2d70de3fa945' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([5049992.781974, 5049761.125615, 5049529.469257, 5049297.812899,\n",
-       "       5049066.156541, 5048834.500182, 5048602.843824, 5048371.187466,\n",
-       "       5048139.531108, 5047907.874749, 5047676.218391, 5047444.562033,\n",
-       "       5047212.905674, 5046981.249316, 5046749.592958, 5046517.9366  ,\n",
-       "       5046286.280241, 5046054.623883, 5045822.967525, 5045591.311167,\n",
-       "       5045359.654808, 5045127.99845 , 5044896.342092, 5044664.685734,\n",
-       "       5044433.029375, 5044201.373017, 5043969.716659, 5043738.0603  ,\n",
-       "       5043506.403942, 5043274.747584, 5043043.091226, 5042811.434867,\n",
-       "       5042579.778509, 5042348.122151, 5042116.465793, 5041884.809434,\n",
-       "       5041653.153076, 5041421.496718, 5041189.84036 , 5040958.184001,\n",
-       "       5040726.527643, 5040494.871285, 5040263.214927, 5040031.558568,\n",
-       "       5039799.90221 , 5039568.245852, 5039336.589493, 5039104.933135,\n",
-       "       5038873.276777, 5038641.620419, 5038409.96406 , 5038178.307702,\n",
-       "       5037946.651344, 5037714.994986, 5037483.338627, 5037251.682269,\n",
-       "       5037020.025911, 5036788.369553, 5036556.713194, 5036325.056836,\n",
-       "       5036093.400478, 5035861.74412 , 5035630.087761, 5035398.431403,\n",
-       "       5035166.775045, 5034935.118686, 5034703.462328, 5034471.80597 ,\n",
-       "       5034240.149612, 5034008.493253, 5033776.836895, 5033545.180537,\n",
-       "       5033313.524179, 5033081.86782 , 5032850.211462, 5032618.555104,\n",
-       "       5032386.898746, 5032155.242387, 5031923.586029, 5031691.929671,\n",
-       "       5031460.273313, 5031228.616954, 5030996.960596, 5030765.304238,\n",
-       "       5030533.647879, 5030301.991521, 5030070.335163, 5029838.678805,\n",
-       "       5029607.022446, 5029375.366088, 5029143.70973 , 5028912.053372,\n",
-       "       5028680.397013, 5028448.740655, 5028217.084297, 5027985.427939,\n",
-       "       5027753.77158 , 5027522.115222, 5027290.458864, 5027058.802506,\n",
-       "       5026827.146147, 5026595.489789, 5026363.833431, 5026132.177072,\n",
-       "       5025900.520714, 5025668.864356, 5025437.207998, 5025205.551639,\n",
-       "       5024973.895281, 5024742.238923, 5024510.582565, 5024278.926206,\n",
-       "       5024047.269848, 5023815.61349 , 5023583.957132, 5023352.300773,\n",
-       "       5023120.644415, 5022888.988057, 5022657.331698, 5022425.67534 ,\n",
-       "       5022194.018982, 5021962.362624, 5021730.706265, 5021499.049907,\n",
-       "       5021267.393549, 5021035.737191, 5020804.080832, 5020572.424474,\n",
-       "       5020340.768116, 5020109.111758, 5019877.455399, 5019645.799041,\n",
-       "       5019414.142683, 5019182.486325, 5018950.829966, 5018719.173608,\n",
-       "       5018487.51725 , 5018255.860891, 5018024.204533, 5017792.548175,\n",
-       "       5017560.891817, 5017329.235458, 5017097.5791  , 5016865.922742,\n",
-       "       5016634.266384, 5016402.610025, 5016170.953667, 5015939.297309,\n",
-       "       5015707.640951, 5015475.984592, 5015244.328234, 5015012.671876,\n",
-       "       5014781.015518, 5014549.359159, 5014317.702801, 5014086.046443,\n",
-       "       5013854.390084, 5013622.733726, 5013391.077368, 5013159.42101 ,\n",
-       "       5012927.764651, 5012696.108293, 5012464.451935, 5012232.795577,\n",
-       "       5012001.139218, 5011769.48286 , 5011537.826502, 5011306.170144,\n",
-       "       5011074.513785, 5010842.857427, 5010611.201069, 5010379.544711,\n",
-       "       5010147.888352, 5009916.231994, 5009684.575636, 5009452.919277,\n",
-       "       5009221.262919, 5008989.606561, 5008757.950203, 5008526.293844,\n",
-       "       5008294.637486, 5008062.981128, 5007831.32477 , 5007599.668411,\n",
-       "       5007368.012053, 5007136.355695, 5006904.699337, 5006673.042978,\n",
-       "       5006441.38662 , 5006209.730262, 5005978.073904, 5005746.417545,\n",
-       "       5005514.761187, 5005283.104829, 5005051.44847 , 5004819.792112,\n",
-       "       5004588.135754, 5004356.479396, 5004124.823037, 5003893.166679])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.274e+06 ... -7.228e+06</div><input id='attrs-67d14f91-6c70-4cb8-9949-f13bd02d3656' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67d14f91-6c70-4cb8-9949-f13bd02d3656' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd9c7350-99e3-4f2a-9b3a-5b4f07d56c37' class='xr-var-data-in' type='checkbox'><label for='data-fd9c7350-99e3-4f2a-9b3a-5b4f07d56c37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([-7273893.821307, -7273662.164949, -7273430.508591, -7273198.852232,\n",
-       "       -7272967.195874, -7272735.539516, -7272503.883158, -7272272.226799,\n",
-       "       -7272040.570441, -7271808.914083, -7271577.257725, -7271345.601366,\n",
-       "       -7271113.945008, -7270882.28865 , -7270650.632291, -7270418.975933,\n",
-       "       -7270187.319575, -7269955.663217, -7269724.006858, -7269492.3505  ,\n",
-       "       -7269260.694142, -7269029.037784, -7268797.381425, -7268565.725067,\n",
-       "       -7268334.068709, -7268102.412351, -7267870.755992, -7267639.099634,\n",
-       "       -7267407.443276, -7267175.786918, -7266944.130559, -7266712.474201,\n",
-       "       -7266480.817843, -7266249.161484, -7266017.505126, -7265785.848768,\n",
-       "       -7265554.19241 , -7265322.536051, -7265090.879693, -7264859.223335,\n",
-       "       -7264627.566977, -7264395.910618, -7264164.25426 , -7263932.597902,\n",
-       "       -7263700.941544, -7263469.285185, -7263237.628827, -7263005.972469,\n",
-       "       -7262774.31611 , -7262542.659752, -7262311.003394, -7262079.347036,\n",
-       "       -7261847.690677, -7261616.034319, -7261384.377961, -7261152.721603,\n",
-       "       -7260921.065244, -7260689.408886, -7260457.752528, -7260226.09617 ,\n",
-       "       -7259994.439811, -7259762.783453, -7259531.127095, -7259299.470737,\n",
-       "       -7259067.814378, -7258836.15802 , -7258604.501662, -7258372.845303,\n",
-       "       -7258141.188945, -7257909.532587, -7257677.876229, -7257446.21987 ,\n",
-       "       -7257214.563512, -7256982.907154, -7256751.250796, -7256519.594437,\n",
-       "       -7256287.938079, -7256056.281721, -7255824.625363, -7255592.969004,\n",
-       "       -7255361.312646, -7255129.656288, -7254897.99993 , -7254666.343571,\n",
-       "       -7254434.687213, -7254203.030855, -7253971.374496, -7253739.718138,\n",
-       "       -7253508.06178 , -7253276.405422, -7253044.749063, -7252813.092705,\n",
-       "       -7252581.436347, -7252349.779989, -7252118.12363 , -7251886.467272,\n",
-       "       -7251654.810914, -7251423.154556, -7251191.498197, -7250959.841839,\n",
-       "       -7250728.185481, -7250496.529122, -7250264.872764, -7250033.216406,\n",
-       "       -7249801.560048, -7249569.903689, -7249338.247331, -7249106.590973,\n",
-       "       -7248874.934615, -7248643.278256, -7248411.621898, -7248179.96554 ,\n",
-       "       -7247948.309182, -7247716.652823, -7247484.996465, -7247253.340107,\n",
-       "       -7247021.683749, -7246790.02739 , -7246558.371032, -7246326.714674,\n",
-       "       -7246095.058315, -7245863.401957, -7245631.745599, -7245400.089241,\n",
-       "       -7245168.432882, -7244936.776524, -7244705.120166, -7244473.463808,\n",
-       "       -7244241.807449, -7244010.151091, -7243778.494733, -7243546.838375,\n",
-       "       -7243315.182016, -7243083.525658, -7242851.8693  , -7242620.212942,\n",
-       "       -7242388.556583, -7242156.900225, -7241925.243867, -7241693.587508,\n",
-       "       -7241461.93115 , -7241230.274792, -7240998.618434, -7240766.962075,\n",
-       "       -7240535.305717, -7240303.649359, -7240071.993001, -7239840.336642,\n",
-       "       -7239608.680284, -7239377.023926, -7239145.367568, -7238913.711209,\n",
-       "       -7238682.054851, -7238450.398493, -7238218.742135, -7237987.085776,\n",
-       "       -7237755.429418, -7237523.77306 , -7237292.116701, -7237060.460343,\n",
-       "       -7236828.803985, -7236597.147627, -7236365.491268, -7236133.83491 ,\n",
-       "       -7235902.178552, -7235670.522194, -7235438.865835, -7235207.209477,\n",
-       "       -7234975.553119, -7234743.896761, -7234512.240402, -7234280.584044,\n",
-       "       -7234048.927686, -7233817.271327, -7233585.614969, -7233353.958611,\n",
-       "       -7233122.302253, -7232890.645894, -7232658.989536, -7232427.333178,\n",
-       "       -7232195.67682 , -7231964.020461, -7231732.364103, -7231500.707745,\n",
-       "       -7231269.051387, -7231037.395028, -7230805.73867 , -7230574.082312,\n",
-       "       -7230342.425954, -7230110.769595, -7229879.113237, -7229647.456879,\n",
-       "       -7229415.80052 , -7229184.144162, -7228952.487804, -7228720.831446,\n",
-       "       -7228489.175087, -7228257.518729, -7228025.862371, -7227794.206013])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c5764eaa-c59c-4881-8426-5defd01b3eea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5764eaa-c59c-4881-8426-5defd01b3eea' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>+a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +units=m +x_0=0 +y_0=0</dd><dt><span>res :</span></dt><dd>[231.65635826 231.65635826]</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodata :</span></dt><dd>-28672.0</dd><dt><span>transform :</span></dt><dd>[ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e+00\n",
-       " -2.31656358e+02  5.05010861e+06  0.00000000e+00  0.00000000e+00\n",
-       "  1.00000000e+00]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (y: 200, x: 200)>\n",
-       "array([[ nan,  nan,  nan, ..., 656., 656., 554.],\n",
-       "       [ nan,  nan,  nan, ..., 694., 694., 642.],\n",
-       "       [ nan,  nan,  nan, ..., 456., 575., 642.],\n",
-       "       ...,\n",
-       "       [993., 817., 817., ..., 471., 479., 498.],\n",
-       "       [893., 893., 816., ..., 479., 479., 469.],\n",
-       "       [816., 816., 832., ..., 515., 469., 485.]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 5.05e+06 5.05e+06 5.05e+06 ... 5.004e+06 5.004e+06\n",
-       "  * x        (x) float64 -7.274e+06 -7.274e+06 ... -7.228e+06 -7.228e+06\n",
-       "Attributes:\n",
-       "    crs:        +a=6371007.181 +b=6371007.181 +lon_0=0 +no_defs +proj=sinu +u...\n",
-       "    res:        [231.65635826 231.65635826]\n",
-       "    is_tiled:   0\n",
-       "    nodata:     -28672.0\n",
-       "    transform:  [ 2.31656358e+02  0.00000000e+00 -7.27400965e+06  0.00000000e..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fcb90621438>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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YTP72PmcepEXMuMWk1xm7nSSgB8QWhqoNEKbi2cUojEI2ilgXRgXuBwCTkxnSuXdpSBCz67NdPeOAz4CR84wCeteNEzx1hxwDmq3zJpIsqN6WAWlgdIEfXjKeRACsvMSD7d4oUJ5eQr51CMwLjD94Nnr2ZG5dVxVvxsYG/6rlBPlRg6rvmX4xVI7RtQ75d8WKZNGwW0s08XLIn51lYJ9bGCEZZrhlYLlNLmp0r3oXhwgAADg6z+etP+9fUH7kOeHKuHaCq1zJ0bQV8sPaPpu1bsoGOk9BxiDdn2N+mjnK4SOegyYVsPwq/05cY/yeNWbrfsuZmV8rxVBxMNV+PnqEx15NRSBSFJQtVvj4fMW7vuoOCx4AaO/FAkcSFUQjnvcJ7X2D8engnEAIZEGiA8DuwuHrJQ4fDaRQYIm0Dr3AFCGfWMGVzgyqnkJ718973VHOQp6ts7AO96VOgcy6ruoWAUEAX/aqPHvVJQyuN87lRA2nm5YcXnGW2XEr/u2SAdAshMCDTXtPZfc8LotESDSLh5FEAAj1tuJnDVNeAS8AhPKtw3teP/S/fqtTuZJHn1UVWwTHc9xDAQB4ASB0PIPmYabQygNiK/VuxALAUyjovhVoYQk8YFQt5bj10QsYXKvYApADxBqS+LdFkxAXkGoMB/BcJoRxGTVNm2w6mmWKc9buxBWRzk2UymaI0/eKod/5nGcfBDWJ7si7DgNbdZuitFFJtxPq7DU+1c6a52JpTE5lyCbGuaSS0mB0ViG1GnGxaotlbD55NmKNOTSR2zuc2ggAG1+agbRBdZIl5vRkG0kVBy05XZFcUV3V8wfHp7OoJqJ/0zPIqqt4rsLMpq63XKiJi6uc71eexVoM4/N+I4oVAADDy94XD8C9006Q269bgWZuC5Jqu066W3OYTCGZ2THXGtW6D67U3QTz5QStQ+/S4ftZN9VcR9r4+HQKVXl31uiMcu4icdtVdl31rvO7c4HkjBmk+OlFUw/fQ3vPv5fartuIFKJY0HyV3FyWA68xAz6BIYxBjM7n7vt8pDHdVOhu2wwq694Ut5FJEWn+rd0CdS9zCQmS9dPf0u5zPjbcExDsWhyfSl3h4OFjgUuwZdC7opx1DviEBUDWjUHrGCB8mA58P8gAqBYxgQeMiBna4aMZBtc1xmd4BeVHxuaYW7fIhkIyi1MQ05l2C77JfAGXMDwJzOmM00Ml913cB8LMR2d5SiUw19mO8+ybnFMYhTFNThAmJ3xdQPc2u6N08BudkNO0O3tNJGDmKxkGV0rUPd4ErSPNpnZPgq8c7AsDfoA3tUnb4Ki9ZFLEboH9pzuAAdp7ktpnhc9cXDpNxCiKpcRdq8kpmuPhZeZg4v6Zbsa560BsUZgEGF4ucPgoS21hlOLSaB8Y3P5QkMW1Q5Gf+zjzB4DeDX+CIcL4vOeK6cyg7Cv0bvqIaLbtA0TTi8swKTnBW/XYteXqDmzqrzD+up1EKaFJyeeEWTCuWMoWDYaBa1UD3W2bcTQk1D3/W1Uxg5b32LQ4ECpuQOcODOI/XCAZZJEFqbYSWwgZZTI/vkcCF2hhMLjqUzzn6xlU4BbtX5mDGu3qWOpuChgTXy+Io/Vu1qwYiTxtDAbXKpda2t8Crv5IUCjYeMu96hGKIL11+h0s2db+0AeAQktXCujeLhmY++YOIqJfBfCTAG6HbXGJ6O8D+Hmw4/D/Ncb8I/v9PwbwM2BstX9gjPmk/f6DAH4NQAecePML5k0A4h4qIZCUBsMrNeq2QjZusPISL9ByKUGTkws0pceCpqRNxMjauxVq68fWKUVZGHK+vHuTEOokDqrNNhFlfXS2jctwESYlm9kkNnXP8pq6yymoolHplDC84hmXaFKhtl2spE4LNYqcAAD4uqrxWUziBgrz/OuuH7tJgN4NjWLZXt8+xtFF/pyUQGvfuPHP1hJkAXMINdPObsN1Afbw+Exmn8H7bkO/cj6Ki9CGl1nVW3nRa+4HT3WjdNiNL/q/qx6h7vr3KwJPBHV7t0brtnc8jx9n68YxN2OQB3UAyTRIjwGQzi3DW+XnKPuKtW0j94srWZuckI+MC5I2FmJE1lp7zweldcIWkMB7JCULl8kpmzJacuW1qxxvx/GObMJrzFVDEweTQwsvTOfMpoYrb63VIynC/S3//E2L3PMYxfPbDtKH02mD9hZLofYWgMagWeHFRI2tmLfV09rWatT232xiWNGy/ImC/QTwOk/ncQHZyvPx8wqRBtr7wOjHeBP1/5QXuyhiADC8rN2z3pFa+42SAZr7IwMAZty/AuAT8gURfQQMrfN+Y0xhC2dBRO8F8DcAvA/AaQC/T0RPGmMaMP7azwL4DFgI/DiA373XjR8qISCUTVkb1nkcXO1Y0zUpTJz2qQizJcLwks3/DgKZTU4RbokrCpMglA06CyNo72u09z0zVFWc89y0WdsNC9WSwmvAOqVIGyYDzNdSlyoozF/S8SJ8FgDdGyWyQeKKv1yhks3K2P/oFP0/6UUaZxjkls01uMYctLU9w9YPDqKaislJf8PhVX1HFo2QaH0SKO7sNphuJFFqoLwTfnZmhqKtixtm/2nPyVSQdshzQm5OVGWQHx7LRQ92qUkI139kBcNLFmag5noOP6cmKmZrujnmjwyRH/J4BAbi6AKvD9KsYfpUSrZEIhdW17tlqGY8G5l7o7zFR7XN8LFMsFhWSKc60tznq+TcOemM17Gs8bLD2Tmhdj0+TWhbYPR8wgH8cP0WSyoqvJP6C+DODJvudgOdEPKRd/Gkh8GikP0xYslUrvfY/dT1e0lqIABbiBcoqOLeDMkQ0LvF95ucjFlVaF0L81/tW+n/Y1Psf27TKQPDy77mBmDr+36QAe5blYAx5lNEdPHY138PwP9kjCnsOZKi9zEAv2m/f52IXgHwrEVeHhpjPg0ARPQJAH8N305CgBogO6oxX2NNLbEbIinMHT5S5+OH9yceXeSVFeY2i79XClUM2RzogJGpYy6IGJzOoLPTYGbvoRNmdLI5m1acHWQSoE7JMV05JvAASclaeBiHCKkaJO6Z7QhQ9oHZh70GzPn0TP1rBkuveudqfmmbx3Vi2X23+rUah4/6pdIOCopCv28yq1H1MzTWWhF2Ol/2E2KUx8tpHenINdK/zJJKlXyCyRJMT3fQDeIIIG8NcU1CrH0b5ZlwkwG6qzC4yi9o9328Lnaf4TnKjoDeLe3808WSQmdXoXeVxzG38A/ijursahw8mqD0U8NuRfc8BuWAnGBr2gxDUVohU/eA/NBnO4UYRvlYQ6fkgs7lgNBkyllspOGwjACrfOQeSLA9FlenP7+97zOqRACIwOPqbYP2vtRwKIxPh++J3S1SJAgA/VcO0AxZq6CyATWNq5kwbVvBPPDcebaROSu6acWYTaHgdvc8BiQo75kP+j/TOe8BsYq6f9hnF+FHmevvf24Tqy/E+0NndN+YvydCcy+UvZjWiegLweePvxEeWkBPAvjLRPRLAOYAftEY83kwvtpngvMEc62yfx///p70UAkBkxLK5cz588PgajqLkTtDxiGbUkzi2NSMff4AIuArwOP3AMwQSAcFShYWIg+KnMq+ijZXWDp/8LgVRFLxKVWyAcZLVvoUU9KxVSPuFAmCAxwMTr7mtWkpIAK8qyW9sm0Hk6A5uYKm45dGKACkuGrl66zxUR1WUzOTDa2V6Xri4hEyviVbdTp4jTU4ep2rd5onz0OVNaZn/VhDTV72mzC2+UoCncYVzvlBhaOLzLR1xpr30UUeV3vXYHSBkAUB0GxqsPQCRxBHTw7R2q9R2+foXhPB6cfT39IW2IQtvBBMD4a1aREqaWFQDD12UDqx78U+R+vAa+a1FRgSkE8KPlcUjKrv4zdyHIh99mSAwRULB1IZlIPEMVky/F9uUzmLlSRiuuNTCtXAV8APrvGcdq7zF1TWTgDwDQgmSZ0FMD3LbqBwz4V7pLNr72sDyOJ2iwABg1ctazoCtZv7Z0mCwjbAWpSfYIy29aKJwPIAm2xhXbb63kmDb5k4MPyWhcDON9ATJQWwAuDDAL4HwG8R0aN4Y8y1N/r+TW/y0JBRnDOcjw2qroo08tCdAePNaD4m2r6fL2GizpQ/BlHs/k4oYrjpLDY9AQPSBsWS33E6A8an+fPyKzyw+XoWXTv0W2ZjE9UiRDDUYEEgBVq6w1ZKaClIwRFgA9Ut7/sV5h/m/pfDxBWQFctZFEjr3ihBxkTMX1IqqTKYr6dOwNbt5FiAG1j7WpAdNGwhf+41EFmm+fI1TL7/sejZ0qDCNJnVKFbzaC6XXiuQjsWNlyOZVVh5kTmnwDAfPOWDHvmhz6zp7jToXhr5a32WlSi94VX9pptFGmzk2msBaPlAI2MXKRcjSWcaVSdxW7NpWavReCwcqSMoh2QD8CEn9OuONONTOYWlzW7DcC2qyis6dUJIp9pnZqWsiQsTTucG41PK++q7dwZb177speX8zDDaM9mRrYOwLjLVGOfvB1gI6TyIW9n7ihtTJxSNfXxaMbqvpdUXtIuPyXjFyuneZhTfLHCVJSV8fEHzvh2dD/CEgrhX5l/52yID/EUsgW+ErgH4Nzaw+zki0gDW8caYa9fs38e/vyc9dEKg7hDKQcrAZ4eBvzmQ/sVSwpq51dIlqyF0A4lGXQwV2vtNpJGGFbAuW0Ogc+s7BW/dUi4IXQ4och/NNrI7xgfy4GUAkAUM3afi6eh+kkXB2Sp+d+mE0A/AuvOJRlIY9Czz05vL2Hv/knMLlMME2VijWLbjstft3rCM1mYhCZ5P+8Bj+wh1LF7Q+EwCGK+95lZ7TcdewtHGGg6+izW4bKJx83v8kjz12RpVP0F7m394+Bhrol3rJ05njRMAAJDtzwBj0PTYEpic4/Pl2fh5ArfWrTnUfmAW5Cyx1E3v71LtFnCSYTGOLqgoGJsUNttGMloMx3wyG3+ZWtRKYXbHK5/TmcbcwiMnc2ZcorknJRfPicbvgN6GPrhJhXehcGyK3Dqcryi0D7RTUNKZ5hx+O4TRObZiBKV048sVjs5nznWWTmqUq17zd/E1wfe36yMuXAwt2swWXtq5tus5ZPxhjKAashAK3TidbX7P5TDB+IxyqL/FCmH4mr+vBLzFkpivshtt6ovZHSggANQBWvDbJf3WLYFvhH4HwA8D+CMiehJADmAHjLn2G0T0y+DA8BMAPmeMaYhoREQfBvBZAH8LwD97s5s8VEJA1axhjc5y7raYhPmITXTZEJxHT46RGkWRhpfODLqWkekEriIW8IEuYf5sYZhoMxRLSRRUa+9rD3IGCUj6cevMWyqdPYPWoYdLzsY6xoEvjcXw98y/GCZoHfF4m1xFaYlchcvMX+YCAPafWbLPzgxFhIv0J3DVlXMNzBFpdfOVxDG0uqXcZp1tpJHba/kVZixhY53peoK2FSyDF3adAACAaz+cYO0574cWDXzvvV6NGwSZUtneNGriIv7ppscvp71XOWEGMCNcfd7HRtItG5jpBm6OaYBr0G5h9tiaqxru3cgwX6FjufM+xpFN2BqYrfp1prMg0E/eDdm0ODArufnUMBMTN8hsjd1DUtOias4YO154JgqFqvhe0h9AlUB+WCMb8+B23t9F3QaUfRf96xqTUwrD1/xCXHl57oLx8402qDZIbLYS2SC6UWGnliCbiwhJbXDwuLdoZ2vk6gAAOIgIwLuHJlZvPflpfpDun7NrcPr+M25NAmy9+bkyUcC8aRELuQB3aLbhA/Ati78oa/o4YOA3SvfTErC90X8IHDu4BuCfAPhVAL9KRF8FUAL429YqeJ6IfgvAC+DU0Z+zmUEAB5N/DZwi+rt4k6Aw8JAJAUOsufZuNGwW23VSLCku0rFol60jTherAn+ky6owHsQL4M1VLqVuI5skzsIwijdruACjAp591sbC/HVDMaSwzryPd3i1BhrjM24IQEau7L9YjrlAahFLnVthoDA+59EgO7vWHRC4kGarCdpBRg9jykgA2gYOxRJaTtDZrp2lQYaFmM6YAYXZNNRwkF1SWo/OW4Yg6bAgdG/7OofiwxsR5MPac8b+Lo2uKfEHIM7bdzUalvmPngwitpb6r3lNX23toH7klGf+HVZTdctqtftHaE6tuvObtsdWNjQAACAASURBVK35sNaauCMkqaVcQhwTAAtUSUgQTBvAF0eFPSuyMbyrqB1bolUfgCZXJ5DO2K0oiQrT9YQD45W/PjXGuXNCrRwAhpcr+wx+bje/5Oc1GVco1tswFL/npi0pqraoy747fk5fWzM+lQBIojhAf0vj4LFgj02BwTXbMS0hdPa0+wxYAVCyNtT9wusofuRxALymo1qSmi3+UBhPTgbQJEtA3QtcRXMRyvz5Xt3d/iJkQGig3vzEt3ItY376DQ79zTc4/5cA/NJdvv8CgGfu/MUb00MlBGDAO5TY3yiaQGe3iVoRGiVBN867VrV3MQAMeSwuFcG9qbsSsOPNGGq3YYGNUVJ/YDXlToxtTg0j/UtdwHyF7qh81Cmhf503bWXdL6LhyblRlbLywdix9RSKptRYK0OwXQxxwZRk8IhwCMcoVo5YIKKRSfDSWTEETE/4JSSuAgejQLzhwiKivadj7U7GBHjNP3SbDC/5ySmXMmTbAKyvf36KOerkNN8vm8TvQtxoasv6ApRCevkWkHvrQLeD3gzvPYX5eor+1cJeN4aPAFjwSR+A3k0W4AJs5rJ+2rz+6m7g+rPDGlzzzzbdJBe1Yz+2z5ap+iwAZE50yumhco9sajjYK5lJBzp6dp0SsiPtOsqxkAB6WzwggfVOxvy5WG8jqbRz9zU5IYVyqZtZzZhQwniVzTRyRZWlcRg+QmHyhaRriqAz5NFyO9esk77bcVbZ/od858eDH2TVff2TLLRlrVbB84bFYtWQGX/bJlTWvRgvLAQRfLv0DruDvin0cAmBN6GQ0QH3v4z8YaK7ZVfci5psMZdCd2AFLabmDckJgDeg23+luOfxd5MMCOXxvNZvQXq4hACxeyYpNZLSa5iS2y8uGIFtIOcfLZHMGjQS8NU+DtC0FeqOimoMSPteqNkszlNPCg5MOZ+5jQ1IpaVOYwarMy7okt6sEogdnfOBt7oTWwth/MHBDtg3ufSKiYrXeEzGCUBV6AjVU9JTq0GwFMjfg5pj6bTG1jIEvu07KPiKNTRJk42rXAVyee2Fyj3z0utllB9eLnmt3RAwfmLF485crzA57bN3GOO+dgKpd2XMwX8VcOU8c24gnhzCfJNjDnOL8Ln/Hntcs8bt4ZhNlK0k7kThA1KdG0KRh/0PxN8v8aLwPOeOs++/tRe78Oouj6cdFPapymBm8fzFIpD31rlt8/8DSOf2TunGkMxrFOtt5/4rlxNkY3LB2qRkrK22dY02bQWd+Gwthj8xLsWTtEH7IN4LOiO3rqnhDKKo6UxHYfQEq/DDP9/B1Y/5vhTpf8b+zMEnvXsuBHxcerVyFehLlwosXQJyWw3+6k+vOisA4LTXYgVIxzJvuC9kAOj75A56N+mhEgKkje9dWhsY69JpcuJsjLn3r6rGuCyVZG6zTWwFZLnacmaxqmMoAaPICQCAC6Fah9pvcAvZIAInnXGP3dDqmAU4Jq7zU9DTl3u7+s3e3jeOYYKA3e+wfwDoPcbonp3/y9vDOiH3+3x0vJGHQT6qHYORKtiQmYe557M1FeVqJ6Uwf/+dMP2kiJvXADEomUBYL73K4xmdVVh7oXLaYOcaML3gd/ru+/jG0qg+HzE0gsB/FMsMaCYCsn+jRnvLc1aX5TP0jvh6tQc1tVwgIRQbXScg05lhtNXAZxym5qYzHaWLiqANQdlaAdhqZ7tBGeA8ucCkZKPlPm5liFB34lRm0sYBvAnjysYCGMhrXILWRxdy7o9QizspAZkE3Vt8QWoMqNJIbE/kesnCh6/xHM9XFMoeZxQBvB7J+BhCk3F3MWHy5UBFgevWId9DlCWjyKfEWtLHUpvD9OGv/fwGAI2lRz3q2/irq67UqelpLL3Ec7n0aoXu519D9/P2REUwJ9cxt20+z/xxhflaiomtyi9tVtF8g8e28WXcN3qHU0S/KfRQCQEg0P4zcsEsQHGWTS5+cKBRhNpWFvctXETTs5XGhfZWgaUw7TLMTNBzn1cNsFZoKPZri88YYIYRLn7RBsMgl06DBwHQZP5ao3OE/ADIPsibpfNvlux14+wjoaTUoCrIbrLPFWrb3LzE3qsFzIJOaK7QKcioqDuIXRxBhW7T8ky5tR9nQW1+9hBHTw6x/BwHZpefAzCawKxahNILwwiKOJsA2SjOBGkdabSsYBmfTnDyUz6dk27tQp/ZiFI860dO4uAJlkzLX596AQA4C0AEPhCnFQNxRasqNVTp6zTKLEHdIXS2zV1/LxaLWIPplDGqwm5qIVZS2PNCVaxJh7DVKy9OfYC+MZhvtpHM+AUNrlZQjUG2z5M/PdtF98rIC7SE0PRyNBlz7XI54+BuWEVfxM/RtMgpL0nJa0YEnlioHtWUK55FwM3WWTmSjKDjGXGiaBw8znP0l55lYKD/+LKvEzHLDaji+5/+Y0AepvO5VwBSQFAkefDMMm785zwXp/4g5ewq6ad8jgd58vfvU5WYjM8QmocAJ/yhEgKGPASzUQTVxLn04uLRNojqQKtyRr4M0yAlRTMfad6owWY0gTtHGHzU9k/FfWZDhpkU7I4KA9XHKdSemjaizSOZNoNfF405zkwiw4iM7nPjNUOhunOskC5gdJVtyemghO0mlzksBxRluPRu+gY4dY9hKISWXzzCwdNDLL/g1ePlP3gFkE5q7Rb0SW/uy1zI3PVt5ohYYuEzAMDGFycwSkHdsNXOnTbU3ghH33fBnRO29jx4qmsD4zb/fPne/txiSQFLXhD0rJXZvj2z/wLQGrvfyYI4qTiBwKdNwiUeAJxrnwTgd6EAcMByLuGA/w0hM0zqMazK1RbSaeO06/zAtr60LRh7Vyco17pIbeCXjIGa1aiWvQlXDpT7fWdbc4GXVJy32UpxY095jA6N0wCtA0TFaGnQa7qz06DqKXdcHcuiEuafP8sC+yvbp3D0+jKwYp9jYqu2r0tCRI3uf3qZn2WFs8Bu/NgpAJyBJwIAALa/mwfYrPKzt67m9p6476QXlsCDRToDpid4FXd2fNvC1tYRaFaieIRBc0gr2y3MwiOv50jm2ml4o7MeemK+otA60I6RVz0V5fwnFWvGZVDtGPZmZRiJYIySLhmsHRXg5tftuLlNNmUICXFlrXyN0R8nmwFTD5Eirc9YQNvExytMP+w1EFIx8P7tpPD+7bxk94Vzpc0Nsksas3WZ5wYdm3zT+9oO9r73BJZflCa7BstfPYTaChy0OpZ61XLLpSHWLUI21i6ttW5R5IpLJw0Onsix8UX2/Srr2tj+Sb+7WweBJr4c10xIwZ74kgHbHF1qPgoTWUgMNAj0gn7Q3a9cgz5lwZe0xujxoYMVNwmPOZxfVXuspsFljimIVWUUObhzEYD9GwFK56SJOp9NT3m/k6o0dEehfZs1f6o1TKace2l2qotkrlGuMdNXpbYuGut2rARa3TNxcfEAcIib7n4NMFkPitemLACkLqTuKiSlzySTtGmn0Bhet6KwiMXT+tfsq9n+kIGqCLhthZQB2juEtaAL3OQvPQGA63DKAWF0kcc6ukh4/JGbuPRnYbEs0H797l3Qor7Ob4M4MPytz0K/9Z8gJGLtqrOrMXjFO6PLk+wrlHzvsqeQTbVjlK29GpNTmdeOFaJy9ahYqzbIpjETD6uAdYaICWiwKR1WdlIdnwPjtcLsWPBKNQYqcMWkMwMY49AVy0GCukeO+Vc9wuGjGQ4/zM7k1T9Oot6zxTI5twRgq6zb5CAzmtwiY9rjdQ9RjYOU/a9/2mrfO/vAii08a6VY+fIuZuf4c/vGBGrrNkwVWCZ5BmQ8mOI9rMlVHR9cTAvjhHdeAtMTnmFLg5UrH2UJtfklC30RBJtDi0wEgLi0XL/foO9tb8v/eHSx4+YA8P75dMrjz1+5CSgFdYurjybfxQ5reZcSJJ3wY6F7Owbr0xlQZspBYZdDcq4umX/x8QNs+czalolb5tw68C8jHVce48kkKJc82myxlKBFDOUhZFSQgtpjOIuwEj6baFcHkFRwPnWAi8zSACwvmzEqqQiV/KhB01LOHUTaQqTb9VL2FJocmNrYr6pYKMpzrTyvUPWA6WkbhL8icR9fxBjChVQ9oL3N55z8cS6Jzy76eFD9ygBhzFYKxvha96dQYBEYfgBJlcDgesOY+mtdl9svxUdjqzn0LwHZzEPllr0MTdtvemoQuJV8doNQUvqsiKoXZ7zolDdKKCRC2NukQJTp0zrUKIe+GEyYgaBzissiROs05AVakzMypdDB+xrQoEL7JdYapycZK0U2bygAABtgbOIxZhOgf8Ni56+oqIaivTWBmpfM/IUqZkzj79hAMtPoXgpaOmkDSqTNmd0wg56dK8/83aW6CrDvrWkR5oFbTYD9Nr/kN7EqNVpWaExPpCgHd1pI7e3g2YL6itxWWQvzl3E0FgMnKQ161/zLNWtL2PmeFVfRLHAZYX8EEQAAMD7Lc9+9bTN2dvzEl8ME6Tx+dp0Spif5vaZTzYzUMsn2No9DCtiSeR2B/E1P5dAJOZdZOmMfvV2+aFKGRBcBaxK7JmxxmLg1i2WZV1aexULNj0xUb1K3yGUQyZxVPS/Uqi4hm+qg8txE0NZkWEiLAK96LGQ2XX8IE4HR1V1fk3B0TmHjR6+7Y0VjYdNfCRDljA/YS42AgOvdT2oWdQIPFpExTrOpe4nL4tEZML6o0b9kP+cAjC/4giKkMx/UC4G4YLgqU8CxksKgHMQaj1G+s5Fkhzg8opbN+ggYPxnjsYSGEngTjVAjmWnnHmjtNzFgHcXasWpYw9t+1jO3/pfaWHqdF/zRhaD9IXBHIxeANey5RW/o3PYCAACWXpmhaafIt7nax/nf11fcOaP3enW3XE6QnuDNmH/1MpDGMAMAMLvgK3vrTgzLnQWasM4VWge+EK7qEvpbNbIj72+rhjkmp1L3bOnURJlO7W2PNlkNCHU7wdJrNhGgrdC0c2fd8NwmLt2y6hB2n+ng5J+w33rne/iZd94ngFFAe5chCoRCAZvYFHhxH5b91B137yR89olnmuIGy3fZDDRZgqaduiy0upthtpm5d+l9+5Wb15DIAPOhgqp9dg/IZ3lRzdq5uDLTubVG7fVbBxwzkOKxpNTs85fOmx1WZAobquKsLeMEZVoYdHbNHcCMwug7O+xyDJMawmQHCbIDwOTpEpOrGxAJd/b0Hm48dxJIbOyvIOjEM3/JRhMhEiKXvh26nxXD7yY9VEKAfZ4KO+9X6N4Ejh61WtQO0Lmp3ILuX7eLwi74JmfM/8h3b/+drSXo7DUBDC0HzOSzQAO4zmA2oObbNRoXDOZ7sbtFAmySWRLlk2eEJuhTm5T+2HzDV+MKZWOD1T/jzVb14QQAAAyvNIzlbl1a0sJP3AYAcHTRM+pyCRglCdafY6Yvmqfasdp9lgF9j+Wj++0o+Dq4HEi7Jct9x3Y32vz8kInXXe/nHvzZTX5+CwB3+AwHjcPezyqErh6y+SLCe3Iygap81TQA7H13g8HLFsFyDCy/7MdX9bmeI1Tm2nsao7O+Ont6Enjtr6+63/Nk+POLZe+6E7dQbhUBQa6MutYFsNfU+N/IM7b3LIDb/hxICFTYwG5RQWddh90z27QWg81Ca+3O0XTSiPlL3AawDL422P5ub7WoEuje8seT0rj4RjbRmG6mLuV3dC5B/3rjraVcWQgVn3atg65pIN47sq65Q59vfykxhzCbKhQAs7XECefppsLo0cCCu5ajPF8gvcXv/+atk0gKcu+FGiBpYtC4cH+F2Xpvl/QiO+jBoroLbH+XDXAqYP05/t75eIsgPzv4F0DQbONOLCCGcuC/ZRELxoqsgbBfa5geKiR5/nWH0L0V+2mT0gck81ETmcGTUwzN7DDzDZ8fji8M4nVvG5RD5YDiXCAwTBs9BrMbaaOW0UlvAADofv41mDKIdo99Df7+x97rr1v69ojReYLcWcUpIsVaC73XgorR6RRmYwWj96zaMbPGKfORH9Uu2wXwUB4hbsx8Bai79j33NU78qQ/wCFOZBZYUjEH3Jvskxue7TgAAtg9DeSzQX8bdpMKsl9a2d4W474K6AdI+K2blpVi4t3dK6Fwx87ekWynI4iLpTGFyph1dC+AG7kKTU/65ZuvkhBEAjM8AALl+AUYBnZ0gS03z+5M5mpxMkc6NC2K3D5pov6RzDUM++6cYKsb4j549QPFNLeaUGFEN10CEsOjhHhpdAISrqwpIJ753QzoDmtvef6kKMYfsWHf5PbWDJIHR6dQpbmUAMfF2yAALS+BBo2QOrLwoKWmeszUZIbMAXEKqYi1e/LmhiVj1KWKMYRA3KckhIAoVS8ppUO6e9nJGkQuOAbzZQsA4o3zzeYB94iGiqWzSsEDteBvBuL8B/yumdM9CQIvQAoDpycw1qRGmJIEzaTHY/fxr/pqhAAhSZasPPBoVU7X2bNHd66zRI0m4BkBAyfaO0JzbcO9BUi3FxWROekRRgAVABEdhp3F62uLLPCGZKPZwAsw3/Xj6ryQ2K4V/OD6dAKeTKMX1zB/5/sVNTujeNo5ZVIUoC8EQwr9tXn8oCEImmM6Chu8dckFRgNfM8iulE5rpdXaWm74NTg870XqtexlaB02ErNnZLiPBMFsPstYKTi8en7cB533C7H1z5K95/J38yDg3UneHtXxx96RzEzVXKobs+pGOfJV14/mK4bjeJCnjhArn8gskaJRcMQwVMhtnEwHV8H6VimEHXVWLgsNCphNo/tnEuD0jlpq4PI+nq36jZECoFrARDxZ5bV3HRUc22GoU4603bYpSNedrFOVst/cMihW/wELo2XRuIvdKNuViJtG4iyU2ecUs7tyoUC4lUYEQEJTftxWyceMCeqIZSYBNrJdQ8w8Zb9hMAwhRRgmrL7JWmR55bXH7g0tR85zBFf/30aOE3i1g6fM3gEEfZpd94aaxVapKwTzpc/AnZ9uMbGmZf+eydRnZ883GihMAAFBfPIFqkCG32m5ykyWPPsfckarGwQgAnPlUdcm1v2QffssiVrLV0oQIECXQve5TNAWyoOoqVF0ubCp8WQLaO8DkbODaCgwECYZLBtnhhQT5yAdOAaun2sfLxszoO7YpzGyVLJ6+z6snzTEXAFh+xWr89t3Vp1eRHMUYx00rru3gfwNhfiIAvzuhooI+o1gA1EObwjkEklu+RmD4Ot93cJUFvM4VmtynkEaxFWMcI61b3AA+LTR3LmsYBJGa4+syVFKI3W5Knoth3ENrtO74BItswi61g6dFWSBUgzjVOht7q0bcupOzQeyrDubpNP97v+Ai3HMZLIrFHjR6MwkfolkCsSbysJEIgLdKR4/GkyECQIhUvNgP3zOMPjsB8ABSqJ2/FQqzoQAWAPeiEAMIiGFB7kZOAHwrkIn3TNibG2ABcM+fq/j4XbGmouvHn9u3H+RNSotisQeNTMJafbGSIJ0G+ds2X1xM33TOEL2S9imbWMCyiiWFli3Y0hkBhCj1DfDYPE2LULeC6mPi/yQQVQ0Sl4YJsMuhNdIBXo3mCl67lpTFhA83j6rjht8RwFqPxzOzrob2Lpv5IfDa/hOBumyfIZvFFsDqiz51ECPP1cQKwDlWpyYX+ncA6mWjCvVqD+neBNg/BAbsb6F5hfLciktLrAY8pvkmuzzKx/tYemmEm9/nBYpOgd7t2Le+836BOuBsnxBGQgWyR/LahZFM7ecQ+yhk2OJSE998ZxfOHQKwFTA5kbjriaUQegC6t3yWmao5E0g009k6N/SZbfrz84nG9BRr8P3LseZfbvoUR2qkCEtSQjVmm/5B5qsUaTF0LPuRNOfRj+3UJhOFta8A4ufsbNegJm4BCaLA9WUt3bEEfiUmFWj7ucLgqk0Z7bIlEbqQ2O1p3O8k+CyUzoDKBp6TGQAF7H+ABbCaJTDrBYqT1r37esfBUeuc30EZWGU6D11nGvvfadC6ZZMaagbkW3r9PvmBLBksLIEHkjhlk7NkwqIfVcYB23Ru3MYRLHTJpghxTiTA6ispFYyiqDI1HxlXmSsoj9LkRcrthZEkFWejzJdjuIkomGyAxnopdEpoHcamNPcUtuf2WACIeQ/EvQYmm3FDcVVxut50wzOQ9a9ox+CWf+9lmMK7j9TKMsywj/F7fEoojHFAZtlRifTr1/yhugZZXlae49/c+L6unSegv1VHfu2b3x8IgIQ7q7l+BgKuF212FvQAB8GLoCmNy/YKwO1U5d0A6ZyPSWFdb4t/kO+wX+HoPXwjcc2FuEEAu/6yqWdkoQ8cYIFcDv39koIZ4fCSP2nwWgBsH7jKyqUcqjLO/ZTv225tF/0WDS2Mps1MNGT+OvFzUA6ByXntsHf6lwiAcV3goAhGBTArCcVzWUgwPWzNyQoHwMVmSen7bmQTjWwCB2BXdxPOfrKXTFydiq2xWE0xuFJh72leeNQA4/Maam4zs07blOQXfTn8fNPuxU1bMzH3c5O0PYNPL3eRFEDPdtelBlh6tfCtUVfvnx9/ERh+wIgaBhyreoS6GyMy5mPjsmiSykRmbNXnil9h1HWbsPKyRSNtDIol5TTzconQv64jaGYATlMvhgrlQDmNSNWAmgeFL8T/Sy2fzSbaNWsRYuvCamIzEwWtk9IAxgfSVGNw8jMVsgO+4OxM12U6ARZ7qIr9ocUS4dSfclbO5FwPw0+94o5JEJiWLKjbd5xxYwJ4U7eDzmvp9V0YIwhyGjh30sXUZ+sshfMgAWh0LnVVtGH2Cs+hzeMOoB7KoYcABmK/8ORErOlnE2byUfeuiUcylXTdMKie70xcc5r8sEY5TD2MtuGaCamirdtsxU03gsC98g1TJOtkGBQlhbnuw5cnUDMvYOdnAgGYK+gccYFUP3NNYIqVFMMrGvtPSgWtPJM99xgCaXvPoHeD3DofXvWQ0H7w/s+6TVFfa8HZEpRZPt84waUa/tuh9lbGwbAAXhmTGFcy1zDKXy8/anDwROaUrfka0LmhMHmaH6j3GWb+TYBKK8wfALKsQZbZvthFCl0ppNe8xdve9dlJvesFJqdbaFnBmhT3r05g0VTmASOTeITGdOYDTWlhojTKyWnu/OS0FGmEfiimMiKzuO6QC0B2bLcn8Y2m8wZ1J4mwfDo7jYcLbilrCnuzulhOvFDpczm9mNs65cwkcSfVbWJ0R7thhSEJ4mP3phcAACyCKfdZtsOHzj3z1Bmw+XnPlQe//yJ0Vfmq3sxCCz/N7h9VG4ezz5+BZN4gf50jnCZIF63f/ygAYHqCVXHBug/hpWsfh3WC0gWqRbBWccrrzGr+gqnkGGvCAiCTBB9iQS5CwLUTtGmMScUuidael4ijJ5acVSMMSrJyRmdT5KMYI3+25lsdivCRYrTBNYtFFQTxh697zpyM5zAJRcx/vs6acH7EqcGuoGmtBWjjrBJ5huFlvsfe0wqDy/Hc6sxblCIcpAuaTqRKN0h9DmAhll/lORABaFKKkHNhJNkhCL5q42ApkkIzbETl3UbU+J7cs7UEaWEwOWmrwXMVKSYuYP55b8Z1drUrEjv8oSnoCj+senSC+U4H6Fhr9Dr/pmuT0qjxWW4AsPsM/y6d28K1QAl4O2QAVPcJO4iIfhXATwK4bYx55tixXwTwPwPYMMbs2O/+MYCfAdAA+AfGmE/a7z8I32P43wH4BduX+A3poRIC1MClaoapheWAotxgca10AjiBpPDN5QHvzimWuaBIMi/SObtz5PrNaobZRoL2bqBFhX7PucZkMwlaD8YAWoa4kExcJLKJnauq4uCajFmYaFhlOzvjOSsZvqaAsOWjBuNTKTa+6LGU6PXrrLUDMFWcMkGtFubvO4sqgF8I++h2tm02yQozssl3n3HHHMSyVEEXJkJTrbu+9zHgmX8ZWC51Dy7rg+MjGm2bvppOG+w/kTv3FsMeBGPXPH+Dq/54kyGKf9QdgrZ1Aqo0mK8mzj3Q3W4AYzA6G8AxBIzSVdFakjTbkA90tis4viy5/Dd47gvL/AXDH0Su1WG9kUR57dAGo/M+rnP4uMHGlwz2nvZzNfKJWq7oy2ERWcEWxo8MedgMtuzIQVpI8aBfZ0mMNFt4KyAkWScm5f4DVW5daQ3vEe+SNZhuqih+AvIQ5XKeFHLyb7Qb1/D/THHth60V8pU+2oCDzSANdG75MbX3NObLHgxPXFvF8H67buh+9hP4NQC/AuAT0R2IzgH4LwBcCb57L4C/AeB9AE4D+H0ietI2m//nAH4WwGfAQuDH8SbN5h8qIRAy55BJigBwOCZSEBUif1aEiQ28hcKge7sBjE+9FEujsR2ZshmniGrpjzvVzi8KAKMLvv8t4PFqBJ/mjmeQTdgR36tlrFbbNBmXwecWw0WnhGTWBAw4ceMQ2visj57S1Rt8nePMv+8nI51USCd8fHKmE6W3FktsEnV2PYOSoi2AtcPw/DBtcXBFY7qp0LPMU+AURPOuBsSuFftzYWjiRz46nyMp/BxVA0SFd6Q5rVFcDE0gaAEWUserUgGgd7sJroFIsIQM3iRAk3jtu70HZLO4X4NOlQuEZ4dsYs7P8gIkYzDd9PNW9slZfElhnAsGgBMAh4/78e79l1OUezz/+e00QsMUGAqZKx57HLMI00uTElh6Xcdd6oIlmR/VSGY1ilXmzjI2p8x0VBRENwn3S3b9ohUnVYTFYGGA3gEe2jVgFFt6ss9WXrI1JKW10pZynP0DVkDqboL9J5M73o2/D3HML9R/zd3PfTtkcP8qho0xnyKii3c59L8A+EcA/u/gu48B+E1jTAHgdSJ6BcCzRHQJwNAY82kAIKJPAPhr+LYSAoln+K0DoLRuAdf4wmqYdSve3KRjxi9VvICtckzIIQ+K1uiRIwm9WzHccdNRzkzu7Mgmt0GtUtssjIB5Ge/iEP+/VGJKDMCIRq1ZMKjaBqJ3bDe05cydn8w1WnveRUSXrgP6zuwOd1ypqPtWCFnsMqukmYn2hUVC6Nf0mwAAIABJREFU4uoyCQsfcVnoTGG6qdDZlpoIQuvIuLJ9gWoWSuasEYqQkKydg8dye5+YUXV2OKAvgXBm8p75A7Hp3zpkjHuBzU4coqa/aBgrkrUk7kJh/mKN7X6HwtKrhK51uYimnE74nVQrbRgiTE4HAlMYsYmhDNKpxuGjmWOyB0/ZcQd+cBEAALD2vBU005g5hwVaTa4iJp/OPGPUKQtEiWkZAvL9ErXN4EpmLEllffFz+00TChsgwHcKBE029W7Q6QkuNnPV1wauix7AMA9JYTC4bC3NVEHVGgdPsnKy9PIUxYZFVG3YMhcFw3VdE8tyIIkDAVTEeqCo3Eccub+AJbBORF8IPn/cGPPxe/2AiH4KwHVjzHMUW2FnwJq+0DX7XWX/Pv79PemhEgJCOgVm66ypARxczCZBDKAEKGiFuPqSlJx637Q0jpmc4CkSdMjejbh7FsAbOwmYTTLTjlmpmpmyCiACDp7IXfVjNjmWGaSsBhww63RuoGwJsqE4j/3oYgtJBfSvMrOo+im6z98IJsMAgz60zfunNOUMnsSrQ+bJC6AJP9D+d3NZpYPRkDkLMmWu/pTG+d/hA3VbReBes1Wv6ZM26N1sgkxGRm4NrZ2669M2RVM8umCrmA8oSuUVSGxx+UmcxxXYHTWYrSUR9HVI8kyOYSac/hsKjbBgrLUfFzElcyuwrRBYetWge9PfLBtVoLpBPfBFXJPTmY9HKUQplPlB45jy4aN8Y8G7UhWgz3gBsLYyhv4jj1SXWXhr17Ix4flxqcu5gknhMKikmCtMMjCKnICWpjSC62QUsbISFKyFWr3ECyS2JRDnle1cl4+P9d6WuFtQWV/2yBeIBZYrABw82UI+8kWXo0c6jpG3Dg3ae0Eqaipjsm7I0qaB2/vXLQuVbn+SVHcqQt8IGUN/EUtgxxjzobd6MhF1AfwPAH70bofvNpx7fH9PeqiEgEnYRdC5xT5HAetq77HZKXnnZIDBVc9I67bymo2dMuk/rFYTjM4TBh/2AYTppzdcEGpwtULVT9wGmZzOgoYdTOPTqdNIVW1dO/Zw7wYHBLVbwIzfIoFAqg2mJ/xrqnqEyenUBwBLg85OjfT5S/x5XgCrPp2zObmKZDyHSgJNaDxF8wgHfklrTM73AfTtXLAJHzLDUHPa/ss1TEO4/Ff5c/uGQt/64MWvK5u1v8VN34XpZmONpCBnCQgzFWEswrp1cGyDiwvdXscVf4nb6MgPMKohsC0QJRjqNO/Q3RPsYRmPCHhV+qY6AK8h0j710CG/jv1aaroZxme8EAgTEkIsnfyAx7z/hO2tYCuZ6w0vVMI6q/r/2YACHGSJS+0MzmlaYdMY+6jBu6vb3kevamBwNYS2tfMRpq2u+OdwvYuPCdLQImxy5eZfGLBcN53HyRlVly0RuU5nu4aqNA5sTQs1sVVhEt/QRtVszcsctPc1qh5FlfDzFQ9rzSnD5ATN/Uro4cDwOwYb8RiARwCIFXAWwJeI6Fmwhh/AJOIsgC37/dm7fH9PeqiEgKp8gCidGedvBCRrRoKlQeYOOPNC/k6nDVRlMDrvN8Dgw9s4+PKGvS6w/pW45V86184/Sg1rR3LvqmNx1x1oHTC4riNc+7pDTvNP5yYytctBgnRuMDnluVV6AAxf45QYkyikr1z3IG1toHzshOslzBZNB+0bbDsn+2McfuQJd62k1DYFlj+PzxO7Bmz6ZjYBjh7zmyu7lWFwif+enOHzxKpJCt6g/aATV9NWzp9sEsJ8WTnGJEw33JR1x2/udBpr5hLsDmMOvZsNWvtB3+A1/94kSyfqBTE36Mz9F/tPJY65eGEl2jRFQf6kMGjvacfgXK+BwGobnffZLZLCCgDDqzoqsBPBHsJYtA6A1pctMuqBwc6PNVj6jywhxecvmrcw3+PtSV0zotoAtT9etymqnE5Kg+mJHP0rPmhT97MoZhGOV+7nsocURf9OT3ByhMyNCBthzMVAASp01XDqbe9GZa8DHDze9tDWwTvr7Oq4L3PKriRZG/NVXr8uttRj61Fce+19g6TwtT9hI523R+9cj2FjzJ8DcGWG1t//IWPMDhH9WwC/QUS/DA4MPwHgc8aYhohGRPRhAJ8F8LcA/LM3u9dDJQTIWNdJFQd/mpycAEgtkFyTM+OVVEsJ7jE+DTD5GGd09FqlEwAAsPHlmlv12cCw+FRJsIQm3gy1o8L0BEUB0k4A9cy+WX8smWmUw9Qt6PFpj5MDsDa59KeXg8sTzMYKyAbQ9EoXyaxCsco7pOqyX7h6jH2ro7MDtA79hikHKZIyKA4KcvcTiyffu243+kk4AQAAvWscW5F9kMqGt8FpMoiCnU2LK5WPLsaMIqSwojep+D9hyuJekF4MACJjl2qNzq05Zie979yoWAOsO34t7D9li4d+gG/aXO5h7TnPuNiF4IOVYlkOX/KZVrrtmebuM70IW6gVVDNPN61Aks5cEw5iZ/5SqLpAf4vvrVNg9f9rRwIyKWKU1qalIqaclB4muXu7uUNApHOveUtiwXwziP8EriLVGJjUWyy1XUdhtlHV9RYsNZw8EQrNUDMnDbT2tYt1SQP60TnvGm3vGSjprHcMlI9rPMIsrzjeUSwTaivkdMbKiSh7OmUXkQiN5D7FBDgwfH/MCiL6VwB+CBw7uAbgnxhj/o+73teY54notwC8AMbT+zmbGQQAfw8+RfR38SZBYeAdFgJWeo3Auaz1cZ8YsZ3zTwH8BIApgL9jjPlScDwB8AVwcOQn3+54jkM8p0X8OSqk+Tan43716cljJxxnMNP7pV09fBQWPAFxXODbncrhvY/rB1xNvV8Vw8aYn36T4xePff4lAL90l/O+AOCZ49/fi74ZU/wRKXC4C30UbMo8AeB7wTmu3xsc/wUALwJ4k6USk05Z83CIiML8gyAnaUTBWkNea739d73afjjtYP3P/XnymyRw2RSrWVQtmQR+6WyiAUoxuCqdR2KYa5UQqiRxmvnOB2x2hgTMxnHdwdLnb8BM/fj0U+fRtBLkt2wBGBHK5Zbz3araF+gAXJVrVKC9Cy6OdcFkM6BYOhYgPfAQ3SDbTD3C4wn98P57Q3F2yd57bRtHgRCo4+Kx9l7cKtO1+9SBZg7vgsgm3GtAitNMYiFAepL9E/usgThLpBoAeGqM5rJPjy2WPHxCf4vfce+GzZTZ4vSyesVrzweP+b/FCnCJBpbCugNVxX0shKQY0TcjslW8Qe1AUviEA3FfusAwsftL3tv4TBJZTLLmJFun7nLOvssWSnw3Mx4HIZlrdLa8adZ0c4wv8DsU19LUOizWXmCXjW8AFIwNPh4i1kZSxu7a7rbB0su+8HB0UQpifPBfak6yibcmhfJRkGlUIMLrCostgbhg7u3QomL4/tDHAHzCVrR9hoiWieiUMeYGEZ0F8FfA0u4fvpWLkbY9AlqyiW0GhWVwKvAFUuVb+dW9JEKKbAc4B61f5wT97hY7jhOblTG54BkHNR4WIp1pmNRnwFBtMLhaofsil0Q2m0PoPEHds2Bq1iyebvrUU0NAV/K+5wZLX90HrnGwwwAwZQll8/qTwxkSACa3hTPzGrONnmcWOaG/pZ0boLS9EmQTp3PbAD2AWhDYY4DTMJvcwzOT9r17AaC1b9DfipufC2nrMptb+Ijuba5nmJzxmzAsHnOpfoHFRrVn5E1LXDni2mM449D1FzYAah8IDpEwLA4uCtgeAOjXekjn1ne87bGf+Nn4XWd7XuiWG15qSaFUe7+x//L3UjeRTjV05rOl5ss29iJ8g1joAhx7kYpmd/8jj+mUjXVUWyKpy2GQW3CugCBwLO/NCVR/fqi4IIt9/Pz8BZpukOl0tuMYazXglqprL/hrtA7iWBaPgz/PrZsqfL6Vr/vAdOvFLTTnNlB3+YE7tyvM1zKk1v1V9X0ry9m6QjbxPYSXLDZTKFwFsyt6JhPHNe4HLRrNvzkZAP+BiAyAf3mXvNgzAK4GnyWv9QaA/xVcJDHAPYiIfhZcIYdWexn5QYNEqnIDHBYGXQt8ij3POWZrCfIxcPQIf84AjJ5jgBtlu1TNNlnb7F+qWQCEaIqVv7Zb/HZBtvZrFgAWkye5tgP96AnnejoevMxGrOkIzjsA6F4LyRpn/JjbO1BLgWE0neHw2bNRmmZaeHA8UAylXPXJYrj443XX51wLeqpoaXWXM0pG38+BaHq5h+WvG8f4Olu+KYvOEq4CtQ1SiidO2DmxhXVTg7JPGL5mfbUZcQMR+6ic3eUZW34UNwIimwQnhXA6Z+ya6YYwHIrcB+WAe946bdc2gWkFdmkZ1AVkU4MmtwWCllTVYPvD3tEfHtMpF3sdWVz84ZUGOqeIGVc9X33urB6xSAtETVZUHVpoJhJuk5P8YGFDI1WbqFBP4DUAn3E1uBpYsbVP8zQkAHc2PmMFTO8aLwTSJsoUKtZbSOcah4/6Ce5fi/GCwv3m1lzIIzVc3w4RjK0XbfLKoIfkYIq6y8UZ87XMvm9y15OYgEmswmcNh+mGQu+GRiqYTzpuaCPWT3WfegsLGQNUeiEE3ox+wBizRUSbAH6PiL5mjPlUcPyuea1EJBgaXySiH7rXDaxg+TgADJbOGgfpnFDc6jCguptEIGUACwBhhLPPrKG374/lR8aNdPxID9m4cQEyiStIpW42blCspOhdD9LvAqoePYG6lznXUzZVDD8c4qjsaoe5kh1WSF73ef+0ugy0Wzh8P+fzq9pgdFa536saMCUibZN03LkpdPWUQ4ZpEJfOfJXuaL5x9J0lOl9h9b+9B6x81Ucz60ELqrBNZDLGSdr5CGevDa6VGJ3NUVr3SrlE3NBcyjJSn1EEcDVuNjYRkJmqA/fOsbnWGaFuI0K/bDoMHgbANT1316oQQWCkc2Dw1aBgy+I3+QYuCocXW2jZx20datcQBQC2/mqDwVdylDYjd2clQb4PrFohIDAFIsiSgjGoQpTUMAsmxNJXlYFpvKXYs7UhYU8MBz8BzkJLvTxG3WEBEBbLNS1yz8/ppKlj1u0D7QQAn2BQBAF2eQe9rWC+gqC/ZOfsP2kB4g4BqMAlM+VUTSng6l/hwR784AUAwPKf7aA47bFdpFBwfNo/o7znbMa1Ha3IdRjsdSKoxtcpSOWyuKruX4roX6hO4IGld1QIGGO27L+3iei3ATwLIBQCb5Tv+l8B+Cki+gkAbQBDIvp1Y8zfvOf9UnKb5jgja1rKpZ/lRw3KQRppLutf0Ri+wv7PWx8eohNgAZlgswomS9gZTKfkvgeA9m6Num8hKJ6/xY3TbfN0cQNNTvFxnXDmkAiTfKSjjJqkaPD/s/emsZZl13nYt/cZ7/jmmqt65qAmRTGUKdmKEDuWDVlIYgFCAvmPHUWAIUOG/SeALOSHAAMCbAQIEMSBFQExbAWRZdmBYCWWbA2BQsVRk6BJimqSPbG7umt+9cY7n2nv/Fh77b32repuNvu1WGrVBgr17n33nXvGNXzrW9+yO1tod8kIcxs/s0BMCkCFG7stXdu8KNQ2I+VlJ1QHGCG1nLBGexbgoa4I588qoP9K7p0Kp/DtKGzk+ONCu8gAi/NMGyzQDoJRZgPEYxaLY8oCvBZQE8MR69ovdLwqivKoe5qPk/7nY+WCrGTg9ESTUf8eyVoP3wxYdLNReCdw+qTG5utrdaPa4MaPhvfGf+UuTv5vqprrigwTiwmWJ6RhI/e3lee+jmsovQPRu+L2QfYFdIVC4Vhgy700Yld1JdWReJRpPscDPQR0bphiSvDJzteC52CJBgBYXqRryhCLbojNxM+Bbm0ET602NaqtEJ3blO5FRlb52ePRqZMnhhjdNFi4Gs3iL52LGjBzllkR9SaG6qoNjf6BdEAKJtPe6HMg6KnJWjmJkuDwzmqdoXbQd2x9YE5AKTUAoK21U/fzXwbw99c+9usA/rZS6ldABeFTa+0dAD/r/sFlAv/tuzkAgLD5YmLQ9DS6UqErYx0dbiqarOn56NZ6BwAAOy+uPI5tstih2FRBtUH3nR3JwukODe62sMK5oG2h5sDyo1RBY3647KaUN7pJFRoR4anOAqPcK3mqDoANchTLXY3y2PqHLKmIBikj/y4P7fr1Fm1DGsauFxyDblxHtAsKB/sW+Sywf7pMo7008Lo7VgNLN5tAt5Tu+yJnQjpNDAGUpxbTa8rPMwYIXuFazfx8CpuE+ghnJ7Kb1KTwUNxyV0ey0bzPjQAQlQFa95nBLfo77rguDpYoDoDp0/SB4qSNhrXzkHg2plRg1xh/lV6PhAMAgP6B218xUzqf2UiErpW1l1mgye6+6PjyKcMzsUEDyJhx9L+uMQWQeqp1E+DyGfUl+EHyHdWDZE1g87XKdwRnbgQpi7IxhCKLqG1PgdXxkor0eVab4ff9fev7XyZPJBgIXad8SlTbWlwb7gwHQm2Iu3m7nK4/0619DQT0vkmUPzddRpphw1uNOw9JNAdheiWD1bFG1Fmss6SIfifXB5kJnAfwa67bLQXwy9baf6uU+ikAsNb+Akjl7kcAvAaiiP7EWXxx0lhkSxPmB9RUTOQsYeulJWZXS2y/cNf/TXNp00eh7AAAitTbMkRkrAmkxf9sEAFguZtieFOENClPhqK/H73ZoR2mqDbDqT95LtxIWy/zdui906dpXzZeZ2aME6lzEAOrQCZVMB4r0YCU1GQU620H2eQWqlHIJk7i95KFSS1Gb0hZCuuhCWVs1IU7u5LBpCHahg6RfJe6qN85mLYXc/rTymL7pYAjW62iYj0zSGSBrxko6C68lkaJnZvMyCfPN9i9GJod0l/e8YaWRcmk/AE7AACYXcqw2lY+mk1qi3qkov2RrKjpb1xAOQtZHN8T0tByfwBAkTE736QBNl8N0YUPKsS+NQLvh6KaBUNJ84sB2wcIypRwEJRCM1ARHFSPFTauB0OYidnTqnH3xyAWBuS/530JjXVUP8mFKqlsgGQHIBuzSMXUbbuHmGbMTs0Z/d6BgU3COc0nHVbbsbni4G54WKMrEg//FscGp08XvtaUVgCqMHj+7NZjOOgdl7X2dQCfesj7vyB+tgB++l2283sAfu9b+lKlvDAWENgdzShBM9ARTr/5jQnMhkt5B7kXYANi1gQXeqWh7x100WtZwBtedxEVU/i2CMZheAgAFruJp0JOngoNLgBw9wdcreCEpaVpIHrlInvGy5l5owzR36Qao02EeqclmYPxGyxMxw8WbWdwTxHbQgx+sUpBOcE5hmj4+62iLlc5vYtlFIh1BGhWmswIx2W9/2pDRYXVdaqel50WwZUUAatGGrAx795qYHElfOb8pRN0zjKmv0zF/Z2vkFOozg2QLlqcelonNWOx0V65yV0VC8fVrsmPC7lNkJcCAo3XK2fmKprq1vZUdG/Iuk9/36DeSCJ4xSTBKeqOoEAOXLjYPH2Kqb8x3GOdk5CUTyDOOKUDKI5qdP3MZ8vFvTmazTKWijCxoKFeBEjPZLGzy2bGyY24yJ3xd3H8cvZB74CppA7i2dRx53ifIE0f0WuF8oTHvyZIFwbloRC3GyQA3LPqjL2Ey5QJ+3v0SZzZejxj+BFbqrPIZq2XeZaa+PnMeKZBOq2cXg4tximlgWI2BhvnjdfDE9z2tdehWW4nEY9/dpWso9SzaUcFGtFpyYYfoIEvQDAozQY5gPEb8bGxETAZPWj8kHclzTtgA9oMgGrbgifvbb1I4yjZeejOYn4ujUcwim7K8sSiywI2z5+bXxLw1SSk71J6maPUSsxuyA+CZLdVQFcEnfr81EYzAYpTi+W28k6kOLHRrIHJ007AT+oaCaO899wh9P+24wkpG18/BTQZf173P93D6Ufp2ux+UXsWF0B4drURT+iSRXS/mHzAxyEMv8wamLHFk9S8s0RcEAYIBswnnWf7sEHk6374vKapaKJ2K2FKFlQLGWrsAKxGFAmniyT6ntWFge+Cl6uYhAy4GSaxTHhlhbF39x/DU73YIfbv1ejfA9oB3ST83HjxvykNDwqS2C4DGSTh/pIzQsYa1WY8O9ufF3duvXPfVBEteHDzwb/5dhaxgz4w7aA/tvWhcgI0TyBWSeSVC32ZZqtEPm0xeYIsMBfL6lHq01WfxmYUkci5uHI4RdunqIXT5GxhkU+6MMji3ACxSJnCxuuhlyGfGZw8p/1QkP695AH2AguhAVQk7XIVSVM0g1D4rTctiqPwu+LUwKYBZwfIsGRLZ+TXjJx20tX8AFlNhpbhqFocu24cA0M4gWYkHsI2zhiSxnG73efrjRijNhoY3jH+91YrdBlw+qz7fWkxfQrY/Dqzjeg7e3don8b/coDZVWDj66HgUZ0b+CJ8PVbeAQCkkwQAfSG6Wh7HDWZLMYuZm8H4WumGrjdDJfVYoTwyXmaCey+k8WfpCYCiZHYU4+sNTp4RswbGpPd/+HxsmDmazidrtSrRF7L+HkCCbrq2GNwNvTM2VUFKOtNRZqZaIK2Mhz/pO1s/1rJy0IyfY+EyUanQ2UXT/AoUpx1WW0FSRMJyTG89+YgL1BYKm6+GwAdYu1fXsiA6XrcPxsZZysLCHoZ7LYLN3sd63Cz2CC6rlZjcpUJU1FosLoTcMBqS4iJoGWFJI5CsbKSBwg6Ai59s/CWbSA4otzrWeCmPDGYX0yhK2nrZ+AeG9PFttK22pxCLecFLV7f9uAaQTYgrv/2NsP3Fbugk7e/b6OFk3aBYVCvAGPzZQLcLDqLaILZIDHOEyN84XSEOlqyjDMpIrTyyKJyOjdWKVEzdw3zw3QqmNPiz3/cSAOAPXvi4dwAAsPNig96tKVo36KYrU/Tut7j9FwKvXzZ/za5ZJEuNbOqMhbvmc6G4PnpT9JKUCtk0LjT37wUoA0A00lQ3BHFJu5C7oGJ8nU5S7SdxIertOPhk5s8Zr9OndJhlXcdGnwviw5viXhF9MExY8GJ3d+MZw7q16HLtJaHbnlP1FGwl1VpfqNa1iYIqL+MtyxYmGGTqSQjsJ9WRjDXfxyZTmDwhyAtlgI7YSMtgSx6bMnEXPcmTKxx/UrC2XtVeKYBJEkzpjxoj3+d6DAc9Yosvh8lUhOtHEVGhIicAkAOQnGPVWc+fzmc2iq6KCU3Hkg9kNg/FTtVRiuu7FZPAbgBIdnjr1RYrJ/BmMhUZ5eLU+AeH5R34wfE9EGsP3uC2pNGJ4tz1qdsm1T5mlzIS4nJwR/8uebBqKwv7aoklw+v4o2GcI0fCDF0N7ljo1nqHpgxw9NHUGzLd0D4Xrt6Q1IRZ8/B0myg0Ax0UKRUAFeoiP/RnXgQA/P5vUmmptwB2vrZAPXZdpbdow+npCpOP0c5NL8eRs2ygUu6atSVgegbYAlTNQYBCeRgPuV+eCwPdbUJ0x3RJRj+bh4E4zDJrC+k8yQBKnaBawJOTaxrzax2G1/lGof8a0duwHul3BdAOXaH0RKG3H3cJL84nHkbhzJGNP2/D78swnHcoknL2n/PdxhK2lCwh7c8JEAIhyVDj35mUjHR5HEfnVgOjG7SvTIxgiNFPixOPqQxSuKlOCt5xtgiQAwBCw1o2I8Mvn/GzWI/ZQY/gerdLLI0t8DZ47yOy1kkH64qQ77bYAfCaXYoPlh2A/77knW9mqY4JkAOI9u/sqNfvebED+FaX6cU7my7f+djXJePfTQBOQmBAbGAB4PTpR4hRsnboEb35IUuqiALxcKWHvZbd6g9bkhkHxONC/ySsx+ygR2wpSzID641i1WbgtPOKhqa0wPSywvgtZsRYUaAy6HZSn34u9hxDxcFB5WGAM2i7CsudBD3Gfi1Fv6ljOVilUG2F0241MLjTYOn6AHz6bML/6/NTZZMbF+aKCVNIFfLTYHTq3T6qjdQbrv69ihgxLJ3dS902QraRzho/0aQ8Silq3A77UxwH2CKfWcy4iJ67yHktY2HGi02AbGLRCUOirNC50cC971O+D+B3vvAJnPu8xobb4OitCnpeo2jpXJ58cgtdDhx8OobPePXuxAaG+xNWwuHpJkhNVJvU6bo8F/5GtaHZjVhXIQPQDckTtBG8FjKtpKYIlhk+jG3Pr4X7hYvGfC545ccxHbXedOfgDbHvAiJZnKfvqJwMxvBmB93FYyC7IuD+nAUMboUqeNcPNxoNA7LR3wIxRBN3I9P/fC6VJQfg78tE+cZG2j79v/mqbIRYOxcWOHmG/mDztTAPgGE0pm3f/WyC7W9Yv3GrSZ5EdmCXx2HjcjLf+1nWKrSPncCjtawibJChmUboA1kdUmTdUlrPmRxHuQz7pEvjjWvX08gWxj886YrUD0dvxfQOvheYOjq/4Ohqe8DFf19FGioymurvk8cqj2h7XMSMHjDBimDntfcFyp0NC3w5Q9tsFkhnDUzBGLMrft6jEC27RZZw8XGydOm8xVy05qcrYj9xDeD4Y+T0PCyigN5R6FMwSZjo1FoVNUox7Y9rBvnUIqmNPx6uDciM+sL/ZzFwozKPnh9g62un3inAALZMMXGzEbqcJp31Ngncr+sU5ZeELKlbHJ22fcLSEycYV2930KdhqIxNiP669xXX8HRN+4YxgBxAPjUR60Y3Fn0HybAOFC+rXGQtju/0GYX8RBjbQhRej6WBp76L1a74/Ylg5ixJtJDvt+UuwVXDm7IPQHQgu2eBs2Gema26cL16Ins0vQLLSz1BRY4hmXQVj4+0KdWp5Nzk0VshGptdJi/BtaxkBUCF/bEKmDwjmFoTulb1Nn1+/7MKu1/2v440lJ75l6d4/cc2IoeSLWzE8pL35VlKxj+Ggx615W4qkyWABeaXRMTZBd5wPo2zgt0XKRKXEf1yj05NeUKMBlmw2/ymwE89M4Z+YH0T2Rm6/5mAD/Tu28jA+27Ui7HcMDcN1SPCn9n4U4ZisXiSwONyf+UdAAAU92Y4/MyWj3a4wYYb4LJbwQEAwNHHSmK5cBFtpLDaSnxkl00ck4MjuB5w8qxG7/6DaTvJZqvo3MoQlr+yAAAgAElEQVSiutVulKeQxdCVRc71FGuhxdSvc//+PmyW+C7Y6UfG6AqFU0ex7XpAb3OJuqZjzL7eJ2cbKWWGiDqpgdWzS3RHdC4++jxxBV96lSrD5Z0UO0JLqH+fok+f9ThjL6+fXKMbTRTNcvexhERkVpfOgXROv+vdpwyEi9WcaQ5uOsLAkI5j4WZdq05hcCue85BNrZcv0Y3Fajeku57OKox0736wktmdU9gihenRhZ89SY6WsxPVUZbdd9AWaQnFkuI2CQHO4HaF6dVw3/tzJmymlPXmhkTZyb7ztQ53fpA+s/tl+HObVBblQY1kTvv/+o8RJ3khmFzDOzLyJ2fECrfL8w+nlr7X9bgm8IguhlGWu9qnnOkqbhzhQmYujH7vqIsi0lLI4nKUDtBDISNzvqlZSRIgWGTrZaEkKcf6NZQa9/bDWL2ul0Tsn3oUxj0mNTkA2bWb1EFfqBnnaHsagzfo6Tn8DKmZTa8FaYxsHpgdk09fiIw0Q2c8ii9ZAd0gaMDwNhh6UB05Ct85agP7JzTqMaPEwUcngf1jExU0XRQZK+Ui/ez+DLbIYAq6LZvdAUyWYPJUoPLWPzhF9wrRdZpLFZKvjFE6W6brmF3TlbR/fE/MP0sW9smPUqf4q394FV3fYOeL4Y+WO9o7LoYfGGJK5x3aQeKhPT5+v9zP06vB+Mo+hHxqMXpBBBo7iYeITEZwVSMSmUTAGb37dK+sSDcQ2YS6tuV1YtkEAJhfcmMq3T3eDDSKkw7lzZDaqCqGRVTVorkUKtPy2JSxmJ8PrLZmrZ8BiAXw5pcK0ZjI2wi/P31SI1uE+1p1FpuvWO9oaRqgwdXfcmJ8AkIdukzx9n9Cxr88AKowVhvbL7ee9QQQlFUcVjj+2INZ4vtdj53AI7asDga3EZr3XQbsfbXCco8eTjb+ckB4vSGipkRFWGo0kML1C7AsLd+0574cnoDVTuYLraqzDgax/u97+w10K7qSx+EyMOzENFGrKfWX+G9+XHuNl7anUY8T1J/acseqAB0XLxOB+Td9HRlKr3C5Eo6qEsbUPvgg5xOS5QCo6ccbzcZED7oeanS5En9Px1HcIdjBZglU06EbUmTGDmC1RxEkwx1H/zF5nHJQw3xj5JvJNr5IAnVMKbSa9H743M0vKB9RA4C+3sPlP3MLb33ton9v9Eqo97SliuYp56d0Ts5/IRjX3r1Q+eTGJ3awHAVLwy8bEPNJi7aXRN3mx88H59q7EyJrZeh4vCCbs2mlkME2MrA5tTh5NrwxfovubXZY6bxDdhruUXXgZl8WnB7nWD21HUkzJCvrobi507y692f9t2Pz63GmXQ+U1+dpBhomQSRHna4sTp8MHc9dHjLn8shGTKf+3Qr1Vo5q48F63t3vo2YJOZVM9mI0wwS6tlHGWe2ErOSsSAyP+wQe4VVvKM/PBkgoCwA2P/dmeLNtsfrUE/5lIzpTTRY7AYkhsvGXxaVsUsPkbl7tTuYbYgDKMHTUcNPBZAoVt+fbuEjdOzQkNyBu+t79Gvkr1NHUPH0ep8+G/nt+iGQDzNbLKyRiuMv02eAReeC2FP5a7ahoH3KBg/eOqHnLa7lr5R0AAIzfCA0WXZmiHSShDnG7gk008vs8OFZDzZawzvB0mzlMkcCkjDtnVLxkR9tTOPpzNZQLSbuXyQGU993mWtpX5oN3BfVY+CZOQ7RQlnrOJsD+717Gpiv01hsx57/aBorj+NzvfK2LjAYbfjpe+uD8XND6VzbQMvPTBtVWhtxh812moVqL/r0Wt38wQ9e3UedwLbqVGRY6+QEy3LZ1sMjnRLBSCd6+q3cN7zjV0UwhEdcpO11BH4WHwlzYhT6eoLsYmkzkfe41g4Zc1AZOP2aQnT4I4QAhu5b3VVcoH4zw9ri21PXIcbC2lHHCjPnE9VO454PrEKoDpldjp8OF/vFbBos97YvQjP97SrWi7XPX/Pq0ufezzqpPQCn1TwCwhP4n3Hv/PYD/HEAN4JsAfsJae+J+97MAfhI0uvfvWGv/nXv/Mwgzhn8DwN918jxvuz5UTsBqap4qjyyagcLOVymMSl93LaHyXPT7KF8lazL7FAGrnJonlfUCb0lto8lh+YSGmXNkURxWMEUw+hyx9xyEpFsLGuziIvxJ5x8s+k56gJmNxEad4Zbhrco7AABoNnKsNoOCohxAz0s6gNmTA6g24OK+mUcYn+KECtj0AYqwJMxAjiNE8+gsUjdhrd7MvQNtS2Iubb5KFiw7nANaw2bOkJQZdKrR9blHIqEGP1bMNAqLvYQGy4Cae0Z/lHsGTX5KD7+EKdJlyLJ0R7CWZ6okdNw91+FrNZ0vZivtfKNDtZH47HF1js7hxd8P28/EyEW+B7iQ3r/fYX4u9cNipv0EvUOLgZtCl92bIbsHrK4FHY3bPxiMeDoTE+i6WK5i/oSDzNpg+MZfCUPZi1MLCyFBzsZ2EaC25TlR8N/JsHE0g9kIAUH95J6XUmnGKTrRYMmQ3fRyuFd3v6TAGWU+MWh7OoJNJUuqK1Q0F6Ia070hdZ+We6Ej+8ILDq8/FyL2alNH0OtgDeevN1wtaNZhY9b5c2nyeAg9FF3zsw7arQXasxsq808B/CMAvyTe+20AP2utbZVS/xCkrPwzSqnvAvDjAJ4HcAnA7yilPuKGzf9j0JCtF0BO4IfxLsPmP1ROQHUUnWw62QB95zD80lrYnQAcTj6xHcEAgHiAxAAKWKIEcjt8flRFqeXyfElDZljxUSn0DzrRjq8iNUiTa6gudO1yBCfnABeiyzW/dQpzIURrR8/RJRuK4R6ywckmwOS58KCvtjR6RybUJfoxda7LqePYD/9wEWj/fqyHw5BHOu8weSoDzV8jY8PR3uarS8BaJCdOrTNPYcvU/229maMrAj6TLqlTmmW4u8Lh+nwqQSZn6LReTEIOYOCiXWWA3n7lGSyHnyBBOO6m1Q3h6uwouUbA15sdwMxNtCgONMbXA3SWVCais3aZ8pIQAI2y7N9vMXmCMwF3P7lsoXl2E7q2OPpYMPzj18O5nzwdfm4HFu0AsJnLuMZkFJUWRX/R7MRjNqWeTnEcDPLiQorD7ya2FQBs/NEBUGToxm4es4J3AAAZ/WQVoC2Amtvi74wZNsoCc0dNLU5M1AHdO6BRorWYI5zUFsrQ69lVGlDEi6NzeX6TOnQGy7oaF3olFdpkKmpogwndzmatYfMs11nBQdbazymlnlx777fEyxdAc1YAGsv7K9baCsAbSqnXAHxWKXUdwNha+wcAoJT6JQA/ij9NTiCdt9j+/D7MqIwdgNbAONB1Jp/Yjv9uacIc4tYCrRWvKcLkiGR5vozYMszG6N1zaXuiUY8zFEfB0ibj4DQY6uBux2JqsNzRkS7K4K4YLTkio7m8RIZ9dNO47cR0wpZnBjseO9MV6zHp/TDVkR9qP80r53MAt/2Y+trlYWgLQFTD/r7B/Hx44DZep2NNjh0m5SJ/W6Ywmfb1i2aYQDfWQ202UWiGiaf7VRvaSQLQZkiLX4fCc04GnR3u+PUlDj7V93o69QbQ9iwKR7U0KZDaIHudT8nR8DkoTjtkM6B3wAwcjXzShaKi57RLXDvMtu0d0IQ5VjrVjUVa2QhOuve94cL278bY9uh6+Hl2lb6j8DTRAs0QqJ6jc7vxQommL5uxyAHIRjSTKSx36WQdfrfbiuv+Xl3dRFJJiDOJBOM4ip8KgkM9CppRSWVpeIuod7Si3jK7lGBwN1CHdQegsxDjuiMnceHzjhq9TzdetVOiGSaevVQeNpg7WmnTVx7yA4i2LaEugKVUXODlvsbPUjCWJrXxPILJWiPRt7neY01gVyn1RfH6Fx8ybved1n8D4F+4ny+DnAIvHsvbuJ/X33/H9aFyAjAGWCyhF8v4/fEQi6dDFpCuHFtGwEPr3cRSm8Sk6oGO3UDFM0hWLWwi8EpjUW8W7mdEswN4O4UognERD3AsjjfD1JXlM4SFcIEsqSiLkNx2IODirIgawT0C4296lOXw8erWor+P6CGTiydKsWEu77si7X0gndGbarn2ULE2fT9H10t9v0a6NG4IjWMuLQxlX+7UD281bgKcK7CmlNkxRGETgk9WTu9p/8dL2KxDsU/bb3sW6VIMrVGO/eQObXi3jQrVxlEaJd99uRMK3Yu9hCAu5yj7+3bN8KXU7OdqAEljHTlBaN48RORNLm5MS6qYDdQM6boVXyz97+U2qrFG/yBuUgSCANuT/6aCVQrFfsD1TC6aFJ30hnZjGfMayE4rTK+Rx6xHwOZrQnoljbMEgAIJlhgf3I2NsknizysLbFwPRfXshH72KqInFdJF4p+jZpwid1Bc54KKQijzdr0ge5GsTASxcvbR3w/PVXkvFM0mH3nHseXvadlv3QkcWGu/99v5DqXUfwegBfC/81sP25V3eP8d14fLCfAyBkjCTbG6FmQFbKrQ+rF4pMapOhtRNNMqqHzqbk2Yy49CFHjndhEVDxPBda83UiR1yDSgFJLaRJCMVG8c3FyhuTBGdidY7uVu6o1926cUOxczgYEAcRQn3IVLn+dITPsu1rh4lq5sFO1brSLslAvGXU5Uz3aQRqwq1RqoKmQutl94tk83SKGMjRxoPUr8vtLMXeXT+67UMEnAydtSuYHroQejK4Kx7N3VJEjHw0NmCr0DG4mmKRO6Zpsh1SA4Im36CvVYnKPGou0rD9F1JV1vdqjTKwpQoXDO+zm/kGB4pyPMW0TKXaEwuOuyjpMu0g7ioUHS8NdCgrve7VCcJkglVXdpfcZYTAwKoYxbj8hTXf3dYGjLGyf+53Z7ANUYNFsUnDBcKQfL1Fsltr9B26y2SM02nmwWngU+zuFtVgN0dR35eXEfyX21SqHeKn3RN50HYoXqDGZXS9SjIDbHeljrToiXn9bHSrsr6+ZBu87ifTpGNv7mXaQx3sv6oAXklFJ/A1Qw/ouiwPt2Y3lvup/X33/H9Se/5/k9rHVdFCnH/Hi98ypOYpiIG3Uer8DIebu1fu7YAfxpXJIyCpADkGt2NW7kOst5wGe9rKWawLfy79tZSqkfBvAzAP4La60UwP51AD+ulCqUUk8BeA7AF9xo3qlS6vsVjXT86wD+9bt9z4czE9Aa7dVdtEMKm5iHLyEfmZpbHdMC5TBwZSjF96waLib3EvEZ8bc9jbang6wCF6dEhLjaTDF0uu6cBXA7vdUK+a0TVNe23fcZqkG4v62HCs0InkFjNUDKn2IfSsGdTxENnU+X1Hwmu15l0xyn1VxT6N9/sKdC151n/KhadE/3nTTA1OG8u5uwicLskjhXLTyM0QwIilmcz8I5UBCKrIoa54QyZ7ICtl+i8LnLNabXcqwc0tc7tBjsd0K6miN6jvw1qg3lIZrlOaoxsGxIcaKoRqLC9/EAd9ognyP6f3grKMfykgyfdA23zqddRB+W/Rr5lP729ONuDOkrCYwOg+N5tgKvrlBYXMgj6edsaVBcD9XW9lwg0jOZgbOqdE5sGoYtrSbIsxL7pyzeNiP295TsA5BNdMai3kj9OeA5H+kqaGil03CydGMwebofZcUR3CUCOKsQCR5yBiAhTZso9O/QhVtcKAAUfkBRWr0rQvItLoXujNhBSql/DuDPg2oHNwH8HIgNVAD4bTem9wVr7U9Za7+mlPpVAF8HwUQ/7ZhBAPC3ECiiv4l3KQoDH1In0F4jvuPsshgZWcfpqUTKOO1k/rsSUE8njD0AGH4ovNQBvEAY4BgptfEPh+4AWOudQD3QMDkwucaD6Tsy4kfhgVh8bM+n221PR5FjPrFoRrHT0m0o0ikb9wwwlzsRRr846XwRDYgnsLEB3HydUuh6lEZj/JJZBZun0HP6vRkUwMDVP1oDtaqxeCrAb6tN7eEa3ueFK17qlui3vG/UGW08vs3ieYyHb73aoulpNCM6d/PzKcpj4+GcpKYeAb4esysK1Q7NUAaAwVsKpgj7kc4dI8k5hWZA/HW+T5oh8dr58+xoeweCfz83gZqYaT/uk46XOqj596utBIvzcZGZHQzTWgdvBF7++IbUXrbQrYCa3P2RiR6U/kv3w++3hlCNwfxa358bK6C2ZpSg7WkUR+RFbKpIBkEQJCTlk+cHS2PcDFXE2tl8TWgV9YkE4JsqTzrvAAAgWbYwvRS6Du8N7tR+PkIzzmLJdOFojYN5mBWXVGtDZBxb7fCTbnxsRsQHJhCw0ziL9R5qAu+yHfvXHvL2//oOn/95AD//kPe/COAT7+W7P1xOIE1gdjd9BiCj3ahApyiSChr/rqty6QpRhfY3mjLEFFrXQmfqIOPbvH3uUjz6qBtq/2oL3YXO4uUOMWD8kA/3oFU7hOkmdTy/oB7Sg8u0Ud0Cw1s2ooXKzAUgoTSmK3oHNxMPoJi10BUaurZRYa08apDfJcua3wXpAXHkv2qhVm0EJCrX/by81AfQ96qiSW0jo8EFV2YkSaVV+rxT5kxjp8d1iaankVahvjK82WB2OfOR3WJPo9oKPRHN2DkAzYaFrpNnOykyvlHB1gZcu7fvWCeiCF+c2og0UI+TgG0vY0MpxyfScbuIVdQB+Nh6hxbTy6GGAATKMiCG0UjtIRH59m8vfSYGgMTfUiVkKUiyg/eRu5trp7DbZcpJosC/ls8PBw1G3CfpMszd0B0weSr3DnRwt0NXBkFBmyk0aYriwBWEXWbAzDFgTaBuSg8U1wq6UoeObPcnPZcprLaTqCGzKxTqkXpgEhmfd3n93s+yeCwb8egta6FXNQBqBCtfDEWv0x940v+cVgZpBZR3GLYokS47NEK+gSN92QELBAkFNv7sHHzH7YoGq/C6+UMavTvad0r277HhC7sti9JNT6MZhgIjNyJJJybpoYO7XWTgOfuRLfhyDKpNFap+2D+TK6g2HiTDDoAOUMX/ZwnQdTDDgN0uLgdNFpYXAMjo9cQ4RV+8do1tzPvnHgVlY4VVMmKhiN3lGtm0ReuGo88uZ7ApcPwkR4QgyQx3rtOFQiUa6zizsG6XmhG9Z3gYyurhowdZr6cZUvQ5uqn99toiTC9rBuTg+TjbvkL/XudlIpQhWWrZ2CdhwqEYDsTMGDa+G7fpXp09FXpApOGrN3L0ZhXmTwUIqCu1hy9NTgQIn0G43hZJf7VK6CW17Nis31a1qZGxnImbqJb6LI4IBdzMVo8TJDWw+c34fq/2gqPKpuGeS5atDyYAKlLz9/rPO6dYjXV0fw9v1FhcyCJoSjcWqpNZl+y3eOcazre8bNx/+id1fbicQN3A3LiN/MZtdHWDZI/olXZnA+OXjqGOnWbNbA7z3DWPXfenSzTnAm1MV0Z05HaEbzoIYp1ZQFmC9Rj6aktj9lwLVTtmgpt/yykqtdKb0FwGN6PALY5S2KnkjtMuI//hLfHwOAfCtMvRm4S9RDMLEuUhH364pfFJRPTdu3EaPUymzKGMiR5Qm6aUDQBYPDH00BYQj+n0rA63OX74ZIOTjP5Ua4Eknp88v6AxukE/Z7OOJCbcr/O5wdHHdeDOWzLkTOlkDf5kwTg4vx/2MZ2LTMBQlsKGsO0Dq+9aQt8kHKE8IJkJPwR+1kGZQDFteyqaSbz5GsGCTAeWWvwAZZmZGDMqab296w57ctfClDmQKAzfIi9VbRXoSh248anC4olx6AAeBAcACCllYbXShfEGQFl3/t1i48u1rdU2BUM8jtXfR+Jatr3QANYMgdEN43suWDpDyruzXAgdcIp0Wnvjv76UpQAJoIBouZv4e33yJF3wxcWwL7398LdJbf3cDnlsZ7Eej5d8RJetG+iygJ26VtHFEmo4gJ0Fvp1++Tpw6TwAoDk3QjNMo4eAFz8ETDNbXCjQFeoBuhpH6kefabHxdaF4eLNDPY7HUcriV9tT0YzbbELbLY9s9Ble3OjExr84IKtbfp2YYNVHLqIrdYAmMoUuCdAX/88d0FaHYeMAUF0ao7g3jznlWgNJ2J/lFdF4JxxAOwDGN4SMgNtvqY8jjYZJFJIu9AGYnLpQJ1eDyFhSAYtzzqGmVMSWtZ6Nb1qvgNr2HdzDlNElzUDmQq7JgGwKDG7Ra9W6oS5ul9h5LK447vyVObIXQ3BARi00LnU5OQCvXOscAMtsM414JuY19A5CgEEG20XcLsqVwoJQCs1OiPybUbhHdWeh50GCRDZDAZRB5NMOrRsUwzTZrpA4HqJlnX6P/7Vw0MNbDVRrMX3C6T5xbcw5mmpTRY1hG687AbulzEiDgixLd/DxmlQ/6AAESWC9FgEE4w/EDiCbkkPirvcuVxjcaR6YivZ+lz3DwvB3cn3onIDSGupjTwPXb/leAVvXsEc1FPcOKAXlHAAAZPtTACPP+ElqceOmGumidQwD9+eWdPABMixy1ml+P8VyD9j7smBKdLFolWwmWu6R0WKtHoZuFheEsUxD56ZurSvoBWBZ3z3C8vlL4STYEOkrw0NyArylW4tWREP1MPXDbYp7tCPJkRgwshHgntWFAU6eEZCPqEeUh9RgxUZSdySQJg2BVZK1o9CViTei2dzg6CNJMMoZsLhkce4/0PZIgTJ+6NJlUAotD8iQs6NcbZGBljpIfH545ZPgwBcX6TvTKX1HdXuA3goep08de4nhONndDAB7X679cQPEIDt9Oo1kQHTjYJfOIpt2vrbUlRrZLEggd5fH6HoJ8hPaOS6Gg9VpG4N6nEads9IJMJzki9zuuq9/luGgtDJQrUXmsPhsCjSjLEiIsyaWYOws9kJUX5xaZAsbnBk7dgmlRv02LsNJkwd+x+cjXYbubd1YD1nmU4XZpTSC1UYi+Fi5KX0R622QnKlwHK/HcNCjtooMePIyQRdXLsK8GTqolWgeU2UBe+8A6jyBvQwF+c5UpcKA+JqagBKBv8osIFtYZIugFQTt6I6yUCVu8KSmgijLLrCBkk1BNgUqF+RwEWxxjr5z+5UO2WmN9CTgLrPPXBEt8w4f50JeoaEr4ydLAYiMOEet2YQMmOllSE4WMKNg+Ovd8PPp004zyL3WFbDxJj1syx1yABJ/TVcmDKRxjmm146h6CwvdAfNzfD4TdP2goLnao+0cfLfAhSeI5KGzecDxlXV1Aff1xanFcjcopKZzR5l1jKm0slhuB7Gx/h3KyuotlwkcaXRlMJTzc0mktmo1sPtHwRlzEDGRrLRKSIzMSdgQIOmLURugJdI0SrxTUIYCgtWOu59qarqTjYiSksksNoamlu7vBncCdCizAJMpwCJShJU1hmbkRP7cc9COUyQCJgWA0c2Q3q52YlNisnjKX82S0AwNss4RO4NEIVl0/j5lkoYkLOSiY1g6gNmVBNtfDwJyxVGLxYXMawlR4BGejbOch31W7KDv5PpQOQGTJqgujFC+eQxz83Zk+CXObRt6MLi42bpCqbyeATtPIhGxbNIiFdEN85/5Zt98pSIxK/fAJUsDOYNmfj6FyWIt+PIwjOqzKVEVJXyUrIDtV0SXbmdx+jyR49OlQTPQXskzqWi6lBEP/Gon9U5HrR0n4/TJSaiI2iyB6hx84LSLjj9CO8yzWsvD1p077aO/0cIA1vqoNV0ZqCagps0wRbWho74FkwY2Tzugh7V1CEj/NjOnwv5SBzD9PLppsdwJtEeWM2ZnM79AYnnsaMtDg2aoPJvIpAr9+waTJ4LOEgD0XeG3v0+jRCtRQ5BGcO8Pa6eCGrSGpCHNlhZixg6mV7W/7r19SwJ0Tn9ftTZy1CSmFzOR1FokLa8xL3YyAMmSS0MIxHBcPmk9S0dXhlQ5nTJnl5OhlvskacV8TBxds4GVAVLbU1E/DRAKtCz7wGy6ZBEXwtdncU+vauj2weOlDwNHH8893TatbOQkbKIAa8/U+AOUBTx2Ao/Y0nWH0unuqDQ+NNuFKEJv01O92u1FnwksEuvHSwKI5qbm1++jevacN/4cAfHflgdkvbUYmQgE1gxL6eZCyM0mIXOoBkzpo8+xfC5vL503mF0L+73YS10RztFcK0sPtjBWUuyNDag8pv7100ABrVoa7uKoeTyNSRpudgCwNopG+TxwEdAkCqud1J+bmVNK5ci6mFif4QBA4TT/ZLStDJCK2RCjm+LArKV5AGtdqGw4t182mF8IU9uaoYJJlO+psMqpt/K1c9/vlTMVQXlMt01XwOyiwtarbj6Ag9SY5aJag66XYvM1skar3Zy+z9VG8qmYajc10VjQtq8jzn3hZwALVsv+HM12z5+XRAgf2oKcoZRvbkvlj13SVgEgWXaRgNzsiss4JFNtmEQZrVxMWfXNjg05DoZvkhoPYCXKBgipd2j8fULf2/lz5vfpcti/8sh6GC6MymRWlkKyAgZixkczDNedr0+kMnpG6zFF9BFbpkiwepIi5GJfjGDqOqoVFAHAXn3kPJSxvlgkcetmEHBtPyDlRTfU5eoOdNVhtRUsVTSGj1kWLPTlIit+qBc9jUwM4+jvt1jupb4hTDf0b/hNMTBcaNo3owzpyvqGKy56yi7I9fty67XWR8fL3QTFxGD4lpgytaxJaRWAGZOR4Q7T4e0Ws0upj6K6LNQb9FrnpU0U6kHiKavcfco1k3wWK0+y2BkPW8+mDMGFz8hio+qA6eWgczS4Sw4g8MGJssjR5mqL9GfYuCYVZWCcmySVxfCWiQbEF6d2rZNX4Pm1Re8wHkBUHgYcP1t16ArtIYwuJyOcVJaMqbUR9z+O/Kl4z4bLR9gOLundpnQmO3K05r0+Rd1CJjlZdaE7Pg91F4CK4BFE2Uuw2At1Aj+4XfpYUWMgie7Eq5SWRx3qUUzVLE6tFzqkXhAVHP7UROyoLidNrNGb4T6UdTcpKV2c2Oi6AqT7FNRg42vmISRr/WuZUT1MyO/bXY9rAo/oKm5PgCuu8HuLuGLsALpnLj3wed3FmGaXKx/5coFt+bELaPsa5f0KiwtkyXiQPRv10c0mpOwGqDdTtL2AOacri/GbHZJlEN3ijKN3QLOAecGZv2wAACAASURBVKwgAF/0k3AUp9v9gw5NXyFbxDOQ6YDCj12pRQesRrqy6N+tafBGbZDeO40Kv8248MaJoY3xW04ttCOoRwlmkSm0Lzoz/a8ZJn4+AjfocAS8EHMT2PgDQUK53rDAAMiPiW3SDAKGmy1Co9ngHjmAbG6QgTqT2Uiw8BzPQwboGts0xoOTmgxdMQldy3ytlrsJ2lII8c0oQ6HMTZEekHC2yarzHHjqtiVxu1Qwr1Rr1xrT3D120iA/FtuqOjSj3EfHJtd+FCcAtMMcyapD40aiqtYiqU00pjSfmTCq1EF+MquYXqMTaVKF3iHVvSSUNb+QRo1pau0WW20nKE5IjVcZ64OQ4rRDPdRIaoVaQFP0nstCKovBndofG8OH0vBbrfwciLcLtqsNhdWWwugmPTcMy/lsRil/jqVDO6tloWAes4Me8XVrP3q57gDeC2VM8vqB4AB4ySIZQA5ALhnFAHgAwvA8brfW9dLXl4QOvpXVDOP91fU7b1+vwQDSgDx0+yKqlQNygOAA3m4FDX33+eN3PrbBvXhf5EhD4MFzrdcM2PpK1j7P0a7fn1n08kExvVX8un0IVv92i9k/fltVvK11CKMd5tHrh9Gao+2/S2OUzwC+zbXOuKmH73zsyVr26FlPb7f9d7jvmBr8tuuPIUz/ECQCHzInoMiw670hsq+8DpVzx1WG+ruuIlk4ut1Gga7QPipcbVFaLOUa2JDNrmiM3+w8S2JxoYwarTKvzeMKqWWCZphENFAlhtLkkzaK2LhZaF2Cl51O0lgvwwBQsRHAA8NE/HdZAMZ6vHp6lQzawE0i6x3UyG8GtbluawhlDJqNgNMkKxMV/tYfRJuFPgmCpRzss8HnJHxWYtTcFMe00uGbju/9nCsOrhTGr8fUWCCmeI7f7IIukxv+w7La/NBL2MFkcY2B9HocXOQan0oXhadLg+kVwUaZ0j3BRUabIJp/oFri5reOSVMP436QfNJGEh08AY1Hc2YzIL3pYMuigBkGOMSkBWyS+s8C5AB4lOl6fwfDk5Ltky6Mn9o2uNNENYCRy+5OngtOZfKsKGqfAoM7EDpIcFLSbndPjKfCAjTvQnVrvQoPqSewg662sogWutpMoJsgLbLaUtEEvq5Q/ncmjR2A74JePDzQ+iAmigFwHcOPawKP1FLGMR6UQv3pZ8L71qIrEnTuAUrnDboiR7UZc5Q5XV3sas9YAYDTJxJkjraYTy10bTG4HcMHXRlHj37CEgvKucWMBxkR6dYiP23EZ4LVqkcJzY8VaW1x0kWy2LoJo/SsApCoCLceiFGU+Z0JzKiEdcyprv+QW0Dc112uAS01/h3bRDByZAFPt6H47fn3zvjzAJWu5GNX3gEAwPAtGnXph5HntA2ue2y+2kUZFNdFSjFWsSs13q6JkyeAyW0UE+thj9VWAmXjMY7lvdZnElbTPcbRN0OIUqOmdxCuY/nKPdhR339f6xwtn7vs3hTduTDsaHG17+9BH5C4nUtUPOQegJ+xCwjIRHRsG2H0mULqoUhQr8L2S9QEudzLMHlWITulDQ1dlzbfx12hYNLgmNVYoTy2UTZNirbh80DIWDng4gFHuo3ZOv17DZpR6p3m4C6x3ng70sC3PWJ95W8zWF5+nzwGdmhn2jT2IUgFPlROwCriakdUNgCwQPnWKdodwr7bQUZwB0OHHT1ri13Ryetg8mQB7wAAUUC28WvJwZaTqtq+QttPfI3Bz/JdSNndsH2TamTTDq3gR8PKoTcUnTIGq9aaeWymUG0mPsPIZsDwm6de8tn0KfKzeXCAXZFEsIN8SDia40xGdZYeTpkNCbjEangj7Hnxwig3I4vuHEWhp+cAfZD76J8pmuws6DyFn5c7SXSu2FFHssJFgH/aUiFdxg1tUAqNlx4m1hMPmaEu3JApaBfZdl7KOvYuylAtoS9m3erWIHuT1DzNLqVGPBo0XXbQVYd24LD8vRFURwd08lH6DJ/nwX4L1YaMrit1dOxdrqBrGynY5jMTCsJOnqTHytKWjGS1LXSj0tBJXm1obL4Un6sI6lFA7yhMVjOZwuJcEimqSnqqF1b0w4qAjddDxb8ZpZ5CzCtddP5aNgNiS/F9LwvQTBXNJWGiH2oOXOjn2QWD/TYy1mclIAc8zgQeuSWnSHHBCgCyfbJS2QHhCstz22h7OlaDHGive7MMzcRoxsDwjrjR/UB593qoI4xY1ybiM4fRiPF+Rj+rkEmYVMGmgVWRrAySxsZzbmWXqGv1l5BQb19Eo3echW7YUJETaEUGEGuz6+i96XmWHZBFw/jGL9YmbfEDN79Axd25KMWYXGRAB7Qvk2ec0a1ojGUinO7mq6IT1LGN5KARlpQASHFVaveojoq/rNvU9hyMZXm/XdQpxhC2hY6KucfPhfNUsjKrgHx6+yvPIEtnNUyZYfnxi/S5qsNyN/f3StvTyE9VRIfk4IGN5uBeuH91Z5A4yMUUlOEon7XSNhnW6XoJukx52DKbG5hERY5rfWSq/N4uA6CF01XEAvMNWC47iu4z4QC4AM80Tl3TvS0j+Hoc6MKUHWov7SAdAEDXLp/aCHJih5VPO6SLIInR9MlB8jEVJwarrTC72iTUtMaBzlnBQxaAMY+dwKO1FP2zSiE/DCEkjz+cfE+w7s1A+chFdTF+Wd5/kK89fCtYpsXFMmqKkbgvF0il0S8PWt/YwxELp+cUTZqovV61Ftp3N1La7NkXBytU2wUgvr/aCiMbB7cb5EdBPdVnAIIB1I5Lr2tjMh0VvXk7srjaFSEKbntuuIs7He2Q/gE0djFdhIgeAI4/1WJ0gRzR7M0xskvhuuhJ4b4zfH7zFWE0/OAcx4gSdRAAmF1OUQ9DMZKZSJLJokx4f31gORv7KCsT9wGPLfQ6Toq2x8Hf5ktTmDzIGK/O0TlmeWbVJqTeaZgF1MFqRY7Bbc8XtS05t4dJOwChT4ShJZsiqgmU9yvU48xnDuwAimMn3DZKKUsICSXt61AEFyLr4vuXAxYvYPg2NX42/km49VBMwn3N2ZWXET+Iu4+7UjuVU9fU6GsRcv/C9Y8o2nxqZE/FJMyl8PPA34UM8Z6XxdtTl/4ErQ+VE7Aq4KDV+QHKb4ZegdUzu9FnpTHwkbm4nluvkOVIT+j/2TNBSIwiKvr7cn+JZqMI9MhBjDf27zVRZ2dSkbKiN0DuBi2dFtDqXC+CZuoh4aJb36Aspt5084w72RQUPE42aWBTjVR0AJsyRzcIBcAIKy41DZMX8E66sphcC9ucPWW8GirDBdzFq+swbCVdAlDA8ryLGgeddwAAYMct6lmO8nrYl/wE2Ln5YPEYIEhu8mSC/r04+uVVHhuoVnsZCY4qK0eNNHnocKYdICcnI39dGR89szFn468sUXf5Xml7ChuvCid2OIUa9Ui9k/e51H7al+4sstMAediEuot1a73jzcR9mFTGa0cpCygoTziwWkHXIZpNVhbZceVrOrNrdBLYqeQzi9H1ELjozmJ6NY+n4BUqUn2V9/+KBtthdEM45aFiDoCvDcwuBScmt12cGgqunGFWNtR4/NcJuIkNeCaaD/l88WeZUNGWbjqc21/OcNiBNn2NXEB0viZzxgJywOM+gUdymYTglGzWor4Sim5yyLdNVKQpXo/pdzzwHADy24RxmH4BW6aRZn95YwIzclHsICee9oq58hyV0t1Rb5BMBDcJcaTDBVbVWpT7S9Tb9BDTtK0uFB1dBD55JqhJlqL4aHKN7a9O/Gu9cPzrIkRKRjiAZiP3kry8lAlwkjLw+vcAOQCWUQAo0qu24oYuXm0PmF8W6fuowfRmcJ5bLyYP/I1s8lluE7eeMwllgf49G12rZhAKv2zsh6LwvdyOi9RzMclr4zpHhIIFM2s9YYALrXwuktpGwcLWH51CHweq0urZvbBPvIQhTXgOgpBOYKouF0z5+HUT4CYAgKVRjF7ordRoBomf8mZyjXYjx+K84/snxJv339NXWAnt/rmD9aRciZTF7gr6zuVOnBnw666kTI/316Sk9to4Rlh5EKak8f4Da9CnDXUc3xTXcSGZjb0gUWyE81ZtapRHnL3ynIkHoSKAmit5XKY/H+IaSXbf+16PncAjtqy7GVqLdpB6Y5c0MRMhXXYwmfLGoDxofeclAJTXj2FLV7wzBm0pcOEbZHC9vr4m/SAvfLWyEQ7L9ES+yVebGuWJiZwKTeRym+MMwafPcUoLAPNLwcAPXX+CnoYnkIu+ADmAeiN8vi0efABWWwmWZM/QO3C6NS6Q2vyGRj2K4S2Z8uensaBbshKG6JgsTu+um1u8S5/naNCkVMRlXN+kFC1KzF32T6xnWeO3GmpMOx87KV61g6lWe/x/guII2P0jVzSfNn6iG+27wexKFunO5JMOxT4ZfnYAs09d8L9vSxURAXqHXcTdt6mKpMPlXAgJm/XuW9TjJEw9Axmr6VW693RD90Q9ppO93FbIZyGjXdfFMRkwu5T4ITBcR2EnWUxChA7Q9a5HKuqJqIfwjYfL8xbL80HqPD+BdwAA/PznfG0MpTTOjZBEXzoRQTbsuqWaEl/j1bb27B+eYcDOubffoh0kEYwXGWMuLkfPYSAorDe+fftLnVlhWCn1TwD8ZwD2rbWfcO9tA/gXAJ4EcB3Af2WtPXa/+1kAPwmgA/B3rLX/zr3/GYQZw78B4O9a+875ygfqBJRS1wFM3Y621trvXfu9AvA/AvgRAAsA/7W19ktKqRLA50BDllMA/8pa+3Pv9n0mV5hfzJBPTWyIDRkbxgRVZ5F01lM320GK/leC4mj7xDkkzqg2231ACZqnBmyRoRHa512pPdYJpSKDXbhu3pNnQ1S22NOAgz3GN9q4p6CMI0WApCXWFxv/4tYkHCQAW2Y095ePnQuAziF6Q8oRYKa8A+Bzk81DBtIVBMPIgeZaGFqAGFRAgIjYIPVvk/GXfQMyemtLBd3F2cDOS22Y2Zsox85hxghxxQtBCa2Hym+Tj5Uppey4ijB7HePrxmeFh89n2Hq1jeoAqrMCmjBIFy06x9+fPkNWuxXXRpnARmKCQCQb3gsSJMtd7SPg+SWSuR5fZ/CbuolZUE21Fqud1MNZi3MKq53QRcuD39lA8r4wZMbOc/QG4T2mTFBtZRjecR3wjlUUF16D49QdOWOGhYojVytiNEwD/bvU0Q2Qcwfi+zYqCjtHKHH78sgEWfEcgBId3nWoT43faqnQK54Dq0L9zY+HFU17XZEARew85Hk6s3V2mcA/BfCPAPySeO/vAfhda+0/UEr9Pff6Z5RS3wXgxwE8D+ASgN9RSn3EDZv/xwD+JoAXQE7gh/Euw+b/ODKBv2CtPXib3/0VAM+5f98HOoDvA1AB+E+ttTOlVAbg/1VK/aa19oV3/CZFxmvRj6lrjBlyam3FcAsA6H/5hv+5ffI8pdEulTYpGfXy5qn/TL3d88Jw9SiNB1dbolD274Zwth0mUddshMuW8UjCdEXU1YcZfv7bwZ02GtCtagEP9Qp0InNp+yk1tcghOHJ3NUVwzP7gyNA7NRtHTlVA2LA6b5Cdxo1ZAFCfo/1J38gwFJhyedxhcS5BxUJgCdD0gfFbLjJfmEhWgR2WpC2aRHnMHgj6Q0BoKhu96QztRRXtO7O8ptdou/190rrhxrt1jfquUOiKzEN4/B5vk/jq1ht/Jgh4xk9PR/dG0w8OZON165Q6XeE+U1HvCHPu+b5gyW95Lpp+KPRy5sjBQdsn6IgbxEymUW0E3j0XatlRdoXyDgAIjt07KcTDjRiT5/tkuQdfuwHIGTH1lj7PtFt6nU8t6qEOjsJtenAn7mPgNboeMt3lucIPlqFtIVpdkfgeIICyuXojieoRZ7IsYM+IHWSt/ZxS6sm1t/8qgD/vfv5nAH4PwM+493/FWlsBeEMp9RqAz7qge2yt/QMAUEr9EoAfxSPgBN5p/VUAv+TSlReUUptKqYvW2jsAODHN3L939blWE9MkaQJnuX+vjTBAKqpaH0GrZQWUBbCi8LYeZ1AmsBQ4ylhdCbmvnP6kLKLmo/FrtNus8wIAiwsh79dN3MHKcI9seMpPaq/nHv7QGU5rkS5a5HdCHcAOSj8JjAuFTDm1mUJb6EgkrQ3ok8eEl0LNU45rBIDZNcEU2mugJg6iqBW6nkXXcxDWNEGz06C8lbljo4iUnfDpUw5zd4ZMtcEBAK5rtA2jLleMT4srL+GF6VWFdBGMf9snB8DGdHydNPj5fHMNYexqA21fR53XLP3BWRNPBJM4umTQeApiFnR3ZL+I6qx3vqstEjwrQiyBfGJ8hqlbFfP2t4hO6RvVEoJ+ONKvNjQGd0URVSuMvjmBcTBmNm1QbxdeqXR6jWdPuyJ3Gfc9tCXQvx9TSNOVjRyTSZQ3zCZVaPqhsc8mawqmnN3asD1oIhelS4vWCeaxE1GWqJ2AkKwWEK1UF+V7uX8/HL+U7uDGSTl/QI7xlHOw3//6lp3ArlLqi+L1L1prf/Fd/ua8s4Ww1t5RSnEHzWVQpM/rpnuvcT+vv/+O64N2AhbAbymlLID/5SEHfRnADfGad/qOUioB8B8APAvgf7bWfv5hX6CU+pug9AfZaCv6Xf9eHE2vp4FqGeMai+++8u5H9KdlvYvL1XV886fTB4u+f1rWus7S+lrXuElXb/PBPwVLFtofth5o9HzU17cOBx2sw+HvYz3M89h3eP8d1wftBH7AWnvbebDfVkq9ZK39nPj92+60w7e+Rym1CeDXlFKfsNa++MCHybH8IgD0Ll61bR8Yv+wiPRfRZXNiC3CE0LtxSgUvx6BZPEvAp6TnlfeDg+h6aSTT0KWC7TGIM4blhR7y0xapywRsngb9fVCabrK4iad/vwvDy09q1Jsh6qk2E8oSRGqcHc5D8ZdHaLrXvulMRKRdqTwVcbmjokHry6sU/W68GDIPLsgBwOyyw2z33NjB27RvjPMvrnRItitYhx2rw4BX7H7VZVFb3J1MxdBSgINqDZMGAp6dzQEoeD2ffGpjzf0TivJHN0SUd0F7WMLPh2Dp6XstDZK5I2S0RcdqvV24bTiKqCEDz5kk9wtw1sZNer5LN0+irKB2GkYjQYGNdJ4MwYl0bC31j7jrxwXcUoymXAnm0+BOC5Npr5UPAGrVInFZar07QLLocPwxOqZ8Qo1XTAxQXVzn6e9zhhCyL6sDBNQMFFQroMHU6Tjx39+tUG/lntFjUoKo5LmQ/SacbUoGz+xS0KHaeDMO4NLKRpT8/v0O5UF4RvWqQ3We9EqySeMm0LlnYk3QT2bd73t9sOyge4yMKKUuAmBFzJsArorPXQFw271/5SHvv+P6QJ2Atfa2+39fKfVrAD4LKvjyeruDkds4UUr9HqjA8YATkEu3RHurNjR6+y16NylXXV0aoffS3fDBPIfNE8w+SpZLd+QAiiPKtW2iUW+RsdOViRwAG2WehFRyB6dIR1VnPC8/WfIELvYw9HDI8XrpLNzw1TZxuSWm2ZbaG67kdBGYSQA6VwRuhmtDdMQDIymWh590TuOqM4SLFKNvZN4BMuefjX9xChSnCvYWHc/y3JpA3L0Elev2sonF7pc0+MlgBynx295+eGoYf+dGvaUbOMLTtvg4hrfo9fRagmQVM5VMChx93DFejgmbXrqi+/AGfYfUburfrTx8p1xdpR25a91YzK4GB6wbi7QKjXq6tsiWJp7wJYw6/8wQDs13jrXuJT1VsltW2+SEp0KHKVuEZq62R/f3xuvkjK3rgs3cvaM6A6TaH9vyfIbltvYsIBopF2oHbS8emcr7mHiZ8BAUAUD/XkdS5C6YyBYW468dAq2bHfHkNpJVh4SNvNtH3m5bksBg/17YpslUdG5kps40Zf6+dBmE+4Z323gKID97Hd93aVRP8Pehg4iLkzPKNiw+6GaxXwfwNwD8A/f/vxbv/7JS6n8AFYafA/AFa22nlJoqpb4fwOcB/HUA/9O7fckH5gSUUgMA2lo7dT//ZQB/f+1jvw7gbyulfgVUED51Xm8PQOMcQA/ADwH4h+/2nUllMX6zRpdr9F89gO25kYh/8DKZpSvUzj/76FbUpWtSoBDNJe0wDdhzorDYSzxtkaN67kheXRy6AqCjo1YGyUohm1CUstpz0YlrgmmGSSQTrVqLZpz6e2nB8wXEjIDePdEB3FlAqagPwKZJFFHJphg2OsUxb2MAe3WF4QtiqpoNWPdqBwBUpNxpdTASxRG9ZlpotROOhRxA3BC02lZRk1chsgw2rpNrIfKuNoMDbEbA3pc6TK8JldKN0KOgm5ieutqmSHUgwggq7obu7OHnxS8LJ6GxQZ3ks8t5JIlRnCqgspHhlkwu3doICuIMRhaupVGHoqyFC6bVRu5HVypD0XHvKGyvy5QnIGx/g04qZ3gmUSiOan8PK62hAawuEF2nOGphkiySipB1IX9/M2afE0ZvXA2D6g8WmRiCIymt/bsVmnMjTz/me87r9iQx66ty16YRt528L/iekddTQkfNUPvzaxX13TABQnUW9UaOhVDbXR9AAwAHPyhuzP8DZ7LOqllMKfXPQUXgXaXUTQA/BzL+v6qU+kkAbwH4L+k77deUUr8K4OsAWgA/7ZATAPhbCBTR38S7FIWBDzYTOA+Ccfh7ftla+2+VUj8FANbaXwBRmH4EwGsgiuhPuL+9COCfubqABvCr1tr/692+UFUNitf2AWthDo6gMsf13xgD1mLxBBV3k5VB2xeCXApYnOP6Mxl6juzbforB3dY3b5X7S6imw8zRBZOVgU2Uj+oY0zz5GDVJZQvqCWAjn847kh7gAdt5TOHMZyZ6cHt3V0gmoa2TjX83DrCLVDBtezoWs+sDozcWWO3R03Xp9ztAZTh9yn3AsiyEOxUmGCT/neIuYfZOM6R93P2SChID7jaUTUvyQe87XRxZ3J1cSyPjUhwDs2v8xcD970lg3PCZdKGiJjXdML+dXg/uuP8Fs0rq22/9P28SfOZkRFAA1XNBSuT0aTqWoSutpQsLk0QKHdEyKRVzZQHy8OPZWt8EfASbLh1N1AudqSj6Hd1s0btN1/rwU3RQLIJGNORwLP27K5w+0/MQGhm6PnpHspM89DBwcZwLwyaLnXVSExPJ02MXBum88w10zUBF9N5mwO/HzKnw3Y79FXoFkVQCorOxUykPyQFwoXixp9C/H5h9JlXR1LLRWwbpXDR3ThrkE3rN3dNc1E4q65vl6Mtxduvs2EF/7W1+9Rff5vM/D+DnH/L+FwF84r189wfmBKy1rwP41EPe/wXxswXw0w/5zFcBfPq9fymApoVdOKPJ3Yc9B1dw1GSBwVuB5jF9dgBlA4MgFXK7HF3wwzZ7chhrBQ0TNH2FzmHy8/MZ+vfjCEoaZXYSHO2y4ZSGJJ0LWQMHWaiq9f9LHaCuTKLIv9okXrqETPY/M8TmN10kmStk0xa7X3UwVS/B4fNrTCQTHtCkcvixgDgAYPtr4eNSelk2bmVzi8FdcSwNySUw6wYAslmYHVs4Xf/Rdfr/4LMtsuPEs3Pq3PpmJQBe8oCNPwD6PvcRXRmUlUHvayL6r2rYC2Fy/cnTBeaOP+HPmTvG9eE0SW0j/Rm+rrIbfXUOyBxjhpkzko0lHbxugfNfoJRrtVd4BwCETJAj6+Vugt5Bh+LYGbqrJZLaYn5R0jADz74tVZR1Db5ZoR2kftZCUjlWk7s8yljk85gN1PXisYyRFAOA1W5838wuK68pVW+udQsbCibMMLyGDbLj8yvA4GZwApKpBFBgEaAhmgHeXhVNk7fFvGJ33Tj7Pnkmx/wSkN+gjW+9bPEmzmapM8oEvpPrO00RPdvVdbATAqyV1lBbjtZZVbAbQ5T3CQfveimarRBJj16bw6Yaycw1iO0OvKZ/NreoxjrC1XVt/A3JQlbTKwEKUSbwu/NpzH1fXHADvYWB0V2QDC7vV7Ba+ShHn5KzYmjL5im6frj5k2WLthdmI6y2FLAVovNkBe8AAKD/xilMmaNymcHkiQzZTPDFs/BgAsH5xVK8iDpb365jlzF/CZlYrTC6RX+82kzQ32/Rd+Wu5V6KtlQ4+KyA5jZE4909V0Q94W3R/9LRyEJpfv0+Ydat2Nk8nLv9P0d6Us0G7V/vjkJxHKuURoVrbjZkJ7E0UMb6OdL3/yONZqtD40hq488pzC7rIEAHiuy5mY0dAAAMX6aOtul3BQc1vxAzrpa7YcbvyXOu/iAOrTwM3bXj6x2SyqDcD45FtcanBPWYBrGHITEN2n4SqdVWAj5KaqprSay9LQPF+fjTLfrX0yjyl+fKaqpx8H3GDlJCPuVJB3MgCBiF8p3FAHx/STVWUeDROzL++eElNYh6hwa7f7jE5Ok+znRZPJaNeCSX4OzbI7IWtutgL2779/OXbwNJgu4uWZ/kSapNt+co/NW18UJcVivkMyM0VhTSOcNHFKkpA2y9yph/fDN6toSzPTLykkuykZSx0PPwmh0AQA6s7SVeUbHeykknXnSO2gTYflkYw8MV9MppzpQsJe2KsPdNNA6yOLWoRbGLR6jKzEKKjnVFXGwEAvuCMVzmjJM8siwMd9SkNA4bP/hsC5WJQu4r4dhblk8SGfjwtvHnnEcp5tfvI1oO+0fTorsWhhXk82D8eUmue9IQHMRcf84kG5cBFKct3vgxDU4dnnr2Nt549SIufC5sb/ObXaRvP9hvPcfj+OPBKG2/SNvo3wgZanGY4uj5nvtbB+/IHo/UonNP8PCGgkloGAsA9G+7mtU5+vv8uCayA5cCEoJ+fNd8P4FVAaqrRwlMoqImx65QMB0dWz1WOPiM7HpUWJ03GL/iGvxKYhyx9hB3PkeEhUl4rrK5IcVVd3/YJHYAbS+wmbJlLLzHS6qqWhW0oAY36YaV2fnZLPVBF4b/WNaHywkoBWgNdB1UvxdgIQDqK6/A1E57XSuoJAGsewBeex3JJz7qP3v4yb4vBmYLQ5mAG2rRmQ5cWgAAIABJREFU9DW6PIs0YPzEKlDkL6c3zS5nlDK6e1a31qtWAsFQMuxj8gTZwdxjwchSzJ8ae+YSzwGodkImM3kiGFFlgdH1zlMBGfaaPx2a3Uymo/2XWC/j+RH7ooUfrFKcxnIA2dxi6Ybx+ME54vmUOLEy1AjGDzo3j02fEZG3cAD5jRxtD6iv0rHnt3JU5xuMXw+3bVIF8T4f9SouXDRAInR7niNiwPwKZUHzc2zcw/5KxoxuKVK2HsiO5wefPpkDaPHUs8Q8O/w/r2CMIDGijI2KsSYDppfdZDl3fzFz6t73jzC8ZTB4k7KDrp9icbEIommpwvyi8lTO/j41q508Q9uziqJur91TpDh9OhQnTK5JiZN1jKxF76DxwQxAUbmfiNcRPGQEhCrZQl2u0L+ReG0hIBYV7N23roBLrzmK51Gg6coinwgZCK2QLgwmT7ggpaRmNC8prQDrdlU5dlpxJOixJsiG8DEMBeS7uBTSW9nJ/L7X40zgEVtaQ40o17ajAVSfwiZVVejuH0LnDlJpG5i6RvoMVUf/f/beNNa25DoP+6r2cPYZ73zvm/tNPbHJ7iZFipNGS7IjWQBlQxIUxEnsKDECKFYSBIkkZzCCQIGABA4EBIhNxzIkwIqsKKIlwLQkihBFyxSnHsRmz/2GfvOd75nP2VPlx6pVteq+180eHq3m6y7g4t57hj3WXmvVWt/6PtNqYPe7llzeO5obd3Pni0QNzOkeAFC1R8+oMpSPZIPE7frtrQp1rJwxKDOFzrXCGcJ0Z4y6mTj2Tz2hiHFqi9h1qnDtp0rw06a2Ehz/YhjRdK6FuGfJQ3/lR2l9LhEzM8GqXSdA55on2HPdt6K42d4McfjJJEyT8NJcF4QoYfSTUcToyCNfpOk2Pkq/4xmw/4gw+vsaestH/tNz9poktkh/eorVLzTBN4ejdkdJ0J+E6R+lUB1dduigrQ837efpbV0dKlxOgIMzEZZeCTlqJusi57+kXCpjvgy0L8XoP0nQ7BiUeuBRx4roEYTD5evK4jcMJ116kQqx81X6ABsyqTOx+EqFSqQZb3zSP75Fz/LnTO1rR2M0BrULZpx6mnByw5OpC0gOC84bRddPGmEZzPB5OVZRpgzhRZc952yX/tclMDkKR1AXT4g3SOptT9Z80Xx0n0CRjRSa2/6+seoaf7dxUDrnAdAqZP3rI4xO+mWTTA8VR+9iY+N3WG/bnca95QQiDXRayI90kWwKOsRWE9HyIuq+B7izAwCAvce9A2AOFS1ymWgIyt+SohuOlDuMROGHxaY85iItlHe1e/haOzZV0vcNS9HWAKZnJ2xROQcAAFd/RAMHadChW7R1EAVB0GJw0VWSwkW5N/z8MPNDy9wwsm5RBjA9Em53aQhDDJaAQvtWjSpTgWGZL2jAGpzuq3blxaL3x+nhK0XNoXXV79hEFJVPz/uQ0hSKQVtY+kIzMFbRrIYuDLLLgiGu8PWP6gSd9IX/gKPdElEvR+dLLXcu8lwbe4TQkYX22aJ2KSzu/OVrkQ4sJFXWJISxYcMk6Znl0MLuHpzVWH2mcsY/mtdo3coxOWL7TebGOQAA6J+O0b4OHDzsN7L8TORSZYwCmou0JuCj+aJLaU0eJiJ65jryKLXZkvLNefYXOzmiffDpTZ4/XE8qW8DSCwaD+8SK+ZaXzyzaGsrQedP+gclpUdwdRW6brU3avuR1mq7Grp5wcH8aHAMA7D/kqdcHZxSOfKXC1MKveTXytse3v0/g38m4p5yAiSPkG12YSKHY6KBOPAat8bV993d09jSG7ycDMVvUKDoey97cqZHtE1c64NPPTZFmbh8id1NCNJvZDXlZPl+IqCPYTtDs2giqqqCmTPcYo17pOq6h6X0LODifoP+g3WCH9rX0b32k09jzhs7EBDPk2sPclz78Z8Q8HZ+l7fWeF/KSOqwplJmP4HjEEw8jPZz+YvhdlIcymIMzdMyyy7dOIIqRBtNVX8TOl+jh7H7Dyk4+XGD9z2LwNOXUmaQWyC6K9mMH/WTZSjIEUc+nBXt/6qNDLtDKVdLWd8Vo2HOP5rY3wc4CjkQl7LW5U3lZQ4ueYY7/eEb8RnTi9Gt8nwAYLOZo/gVZzWyPUoeMCirbUSD0w70SLGofzyj9cu53BFFh27h5MFskR83d4c0tZvy0EfqeIdnS1+DWH9vVDxf6Exu5u1SmovvHY3BGASpUhounBj0Lw3HsoGIFuX8+dnNhfFyspgfU97L0HP3P+sL7D9oeiVQj2/HNX0XLBxA8pMxo1TS4/gMxihXaWbJzCA33NsZ76KB32DCKOidNHBEFcaYBQ1HV/CPnoYW2K0A3sGx6BxBPrBaAMT4FohTyjjdUnRul52DPvSoV5WQNUQHb16YrQmC+BtovWUcUKZhmimKlDV3UATnWwfkYOgcWXtRo36qhq8jC+oiioHFQu4iGt8sjnhksvUj7q1Lg4Lx2hoslIDsXBDxzLMRNIopMOTKerYAedBtk14mvBRQd5ci7OMcNhELjugRQUfpLHiMbFco/e7qCfEEhXwKam34bxz7vpRuNArJ9b/AaL9vW00I4ZK6jzOYOFrz1YzPAFjPjS02MjwPt6+QAFi75A4tnNTY/HD4OVdMbfOlI+RqNTxr079dYe8L+f8TXabL92rGV8ipTF0Dv5QiD+yvoRdrw9LEpul9t3tYfAiCAf9Z3KMwvv0B/mEihtLj9Rr/CzCLFAnoQ0endODBBD4GuqHbhSfCUW93UsXK9LJIQj8EAtegKhlhhqsp3O5NOAL2edxVgAG3vyeSIQZ0Z0pcuKPqX8OM6oZW5qiltOTqhXIBSJ7RCV7VfgahaMOCKa88OAADKjTsoIr3VcQ84gW9ZIVFK/RdKqaVv9bl3wlCifa9sRcENYgfgPwuMRG4wnphgef6txmG90sNoH6nOBQgHYEex0sbrDUZ58DjMiX54BJrA1gHwKDvhZ3WOoCO4aOP2cxe7kw4ACNkb5Wfc9u/Mgu2P9RCyI194/XM7vOJ2DuC1RjN7zbfYAQTHI1YWjUMroMP7N4eeGHYAckiE0WEBE+kAAKD71SbezAjkIBHWfwA4B/BaQ0bvQBiZA2EhXzoAwKeU3PtRSPsgaaf9dw79LT7CDkC+v/INsf1EBbWn0YlD+z8Uwt5GEHkXKYLu5fFGVgJHAHxNKfUkgF8D8IffSqnmL2uYSKFY4PygcqmJaFaTILjl3DGRgkGYG0yHXvhaz2sYYXTTgV/21REQTT20TRmiluYVQb6YQJfGMZhm1wcwaeyEXvS8QLHiUxKToyT6znno9SepVrD/gO96zHvKRfSD00SQJif86ISCnls00GaN8YZ2UV88Iycg+X6aO8ZBAcdHdNA5ymgOifdffl6Q6Vlo6cTi1QsBWUysgXL54QZBGiX9MgCMbIpE54QdZ/WrxRcJkeM0Yx1Vh1gBXBAOgNM/3BSYJEAzQ/9DG/b7Bs1nms7ox1NKlyWiXHTYAejKOzTOmUtDufuoOBFtsP0RILtl8+iD0AEc/8IIwzMt10dhIqB7MULRJcPf2AdaougunQ2nf+QKQDrq1WemmIiVR9GiHL1kLJXb42I2p39IgMa4c00mhlJN9lTHGzEawzpI75WZco17rS0WZqL/l1+gLt/mji3sHiPBINkVHOWAsrdyvkzPTjyyKZ7EYPEl5fUSDBwij66DQveKX6mUGVDITvNd4RRsO4SDZecKvWcTDB/1KLO7Nd4V6SBjzP+glPofQdw/fwfA/2l5K/6pMebCt/sA38yoY4XpSoRGP5y846M0G+RDpAvjlIp0ZQIURp166UldGVQNHTyMkrSVIxXuIma6iXjoDWedxtBzmv3FSgtV6ikr4klFhk8KiiiFxZfp+9uPNVBlPrruXq0RzY1r1ycH4A9teEJT8VpEjO1rvoWeinl+X+tP0Ad3HvURaTI2WLjgC9d1Q2i99hgH7q9XbPdvNBl2wwiRtu8CBqhYXachlDAVTT8OiWnvU3OLNhzviWXLTJxsFBESqCFU2x7yFfG+LTpyzj+aGxQdhYWLogApSNy0Bd3L5i7AG5c6Ulh6Fu7e77/fILulHTV0nRJNxeo3yMsMz5CHdE628FKXAKB+ZA98mw6u97DwnH8cOffPK7Y69fxBADBbub3eIh1AbrEFfP0ZBiyDB6NFuqSlCMIr7GMdK6/F0FNIph4GPFvUaAwEr5BNBXITG0D3MxLXpo6Bmhdphoxz54r9NyKxHr7WycSgaKpAyEY2iMUzQG3786hjoHfFswfnXeFQQMFR95qtLU1KvIy7MAzuGm3EX+Z4QzUBY4xRSt0CcAtEWLQE4HeUUp8zxvx3384DfCtjtqRRpb7N3EV2ImUioYsAk4FZyOF+hbLD4hw5YqVQNYUIeYChD9eg8SiHKmvUTfq+SWNU7Ri15X2pUsq98jI+yuuAzliXBrsfaGG6yv9TtLz0AlnO+VKMyXrkDHnnmkHRVpgv+M8DCI5RdifPF8MGl2yXDDw38wBA56qgWjbk2CYb/onKe9ptM5r5yN9o5QjRAMKK13EoX5mMfEpCVYeoGexhtW55Q5/cFCosvACNvFMyGyuYHyGcZ93Q2Hpc0lJSgVs62M712tE9cIGd04gd28ksm9ckvNMoimZ5Pi28qC3hHg1eOcxtN7ouqJtapp9WPuHZbK9f8ljdE3+oAFRuRdk/o4OU3dILBSFq7FRh2KrMf8/FsbSs42OSPqP83wAV2afLUZBmLDPPD6TLkAOqe71GlSrXOW80NQGyUE+d0ndWnmVAhEY69GR5E2rRQNPW8es4XJ2yM+L5YDQFY0x9IRFo8YzOm9NT6Yjm2YwlM0sSE+LVLHeAMzWLZAV+2+PdsBJQSv08iMZ0B8D/DeC/NcYUSikN4GUA7xgnYCKKWHiJKqMImdMuWgqxEESvQRG/rBuk+94QloKszU1G+7AyqVdjk3IeqqpQ9ZpE7Qug6NFTmi8z1K+GqeGE6aMZUQAz5fTuByh6lDDJE78XYbJh6SZyg2y/dsZpskG6s9m+vwYkGSnrIzKpTb+615ggL8JsWbsHRlfGNVMBfluMvGAFKC4qypzvfAnIdgVSKiIDm4zFfRCNaXxPZDeyLkg+ECCjtHyz74y/aWdAO0O+Rtco3Rw5BwAAB2djpAMfBfP2eq/6aylpoB1QYF2kVTpRQHUAeJ/JRHsczccTWtU4dSyWnWz7E8r2a3dvDu6nDW4PyCukKzMsf4ZrQ5RSTGyPxepzRFnN++aua9ZWuG3YIj6n1upYIZ4fWmFGCqkNfrhmxcFGOgifEYAib/n97MATIbqal7j/MrUVz0g+kq/J4kvkROR8iQoEaVVSBrQw3CYj3ej/7mU4Jt8qodUyaw5MV4gCw+lip1R8ZjiriS3raCN8Zu/GeFekgwCsAvibxphX5YvGmFop9ePfnsN6a4P428OIx71XeV739s0CdUM7gXGOqqcbZPxa1yaYHLeNZiZcQuucHk4n2lIbNLZnqNusP1ASva49hKIbIZp7aoOypTE8qbD8gpcm3HsoRpRb+b+/to3t7QVgbref1rj5CY3jX7S87YvU3u+EXwzRM4+Oe2KwqnF7EY+x+ZwPlwIlgId/VkYhnofGPe8qF6Gp2qeE6Hj8g5Dt+iiSdkpU0jyiPHQCbPwnqyFtBV/vaG7Qf3wdjQPaYGNr7BwAAMyPdlG0IwxPWvK+E7a28II/9t4V7wBYcyG2WH52ArK4qirjomNVGYKxCvoCmc5hSm2Z8iKxdgHtFDh5/Xgf++MmOp+zTmBk0NzyKZ7pegLOD7Gz4nqEMtRnIZvb5H7ZWPs6l7HyjSo4Rya7m66ooJgvReoBn45SYqWw/YEYnRsWzdUjg+4EfRSC1ef4CKHCuKdi4QLXIDyaro4EyihRQWprvhymDauGX+2uPRMqAmaKHAE/x9k+pYNd93dBXfI8zOvXzt/ceDc4AWPM//Q67z1/dw/nbY7aTioFJ/4N0AM1X4oChS8992IjdaxRdPzMGJ5vu0haVQbxsHDoHxNR+328I3l4FfSYZmyxStaWqadJjDtykft8Cehe8e37PHHXf4KSo6muUENhd5OqXum1FOtP+HPhc2D1q8bAIJ5UjoQtntQYHYt9VNqy+VOR/apTT2FttHUodjLnPYUcPuWR7ZogZcOdz7kwRszp4mixJU+XeEhYzCMWegpSPKfRNwFXTXpQBgZ6vt6GMh6mWNiI2zUV3VCBKlqjXztyN8CnA5hTJl+IMT6i0blBrzuFMHvudVujSkKoZTLyeeuVb9r6j+CkkVoCc9tdPPoIWfYMQPYHfpmSDisMTlultonB6KjC6Cg3NNlzEgI7kj+nuWsNPK9SMuUoJgCaVxKhloxrQCmMjspUoD8v5lHie8M1JQYsDE7R9/YeoddXn65D3YglDShgfMy+wCALR3VtqUX4uQIZY0n1XadAaYORZEDf5dRh2VQOtj1ZT5AOa8f269XM7Op4LQoQbE5UxmYGDndHv63xbnAC30lDGT/JqlT5h9M6BY7eVWEQzyqUTUkHoB0plUyl1JHCfDl1FL4AEA1EJBJrjE530Dig98dHUqjaCKehMFtSrii9+DKxivLSdLao0dw2OPgNL7C2+wM+BFr5pl3G2mc3zmvEkxKdykeK07XEnWuVajR3vDGYaZL14+/XMRVs+SHn6F3mf2WEOF9SqKbKOwlr/Cth6BtCtE0XxhmH1laF/unYda8y6odTG1VKOWheHcTjClVTIz3wzjoeF6had1ZNYz6lVsunr1o3/bWLhznGp1rO4eZdWzSfeWSU0T7Foku69xIWWTbhCu/MXbNwgQ2TDgOLygTw2CoBpo9P0Xyai+5NV6fKDqpA8HzzYwaAwdKzgrlzZtyqa7rKBoy+zw6VmUZd6sN+fbIeQVW+wQ4A9t7nz6tj1/U856kjtwoAEgHHjgLyRYPVp/2zIcEB3JHesnOhbAJLL5eY2jx9PKdGwgBiKwxoVBhgrJwTYJSRpzPx6Z7WdoXRkRgmioNzlvWEyVrkyehs0MAEcnkvXAW/1aHMuycd9J03uIDocrnU+q8COT3fHDO0vOScj5ROgLtTJdonX2+5HD6nkDji1KUJotd4atAdhypmqvaReXZAjKWDc9ZQThVO/F7sU1Cmhq58eiCelJiuN1x3at6LqZOTH14FwPi+he6rBcZHY9fIVSdUzHVNWh2FKg2jd1mQdA+hzRcnE9sdakfvkglw/zLKN5FC72oVILUkDh2wEM2Ko9AK8bRyZHoAAgfARVMp5anzGt2LVI+pGxERkQ3JEYxOU759blEzzW0T6Nw2d2pM1nVouMXx5V0EKY7WLWvAheGfL4kitTU2o2Mi2n62ifamT93xGB2JcfCIpxlXhY1ij4hrMxLzyJao2jdYrlQHVNOzJZt/Z4No74lsOJOrwe61wn7Ost92/fwFfOQ+FiuH5W8KkjbOtDBz7DY5FF7xLj9XoGzpIP2nYt/9W0dEje1FiuzK1N7u4SmFzDYqpgNjhZjof6lEx0N2q5ctmsPyOawacMFAa+cuWu53CzroO2W4jt2KUkJSzKVoaZciUpVB0b09MUhdqRYhZB8GEyukBz66zFea2H8gBcACFTnmS3GAuGnf8A6DdQk80dbhgiqRjB39kmgwqoFsi5766UaGWgEFi4G0NYYnI0wsVUPjoEY08zUHFpThukjRjTBfVEgsVl+XlELi44lnhC46zPPOkSU/tMMTfqosP+uPtX19ivGJprtmdaIcDXdzj3KzU5HzP3i4xtoT4bI82xbKaam/L1UWE+abC3nGIF+IMRNkYe2tCqldhd34XnLIj/7oZQDA6P+4HyNB/Nc/p9C55lces2VyAIXo25N5cXaG7Vtcj9GUb7YpEjaIbPz3H+Jagt9W55o3OFIy9OqP8KpUFM2H/u/D6J68q7BwoXDkewDd66pxaJ9zHw3LekzRpXm18RW7TFOheJJRtrfGbo/pxVeeE+koEflz6oxhonWikO2UyCz6Z7IRIx3WwWqhbCgUdvU9WwOyHepkBmhe1pFPH2WCDspoBKtnVZFD4BVvlZIT5BVsnVBDIMO601HYMzFbvHuG+72VwDttGACVL9rKpaesEQAEEc1Fvrhz3Yq4cCGOf5c1Rf2KoXve+POIZ7WTlzRaYbaSuu/vPBoBtTcGugK6V7yTiEYFyoXUdSDf/FgTUQ5Mlyk0b+55bQOAHoQo9+mAvKMRNTyKBIaWvdznUC4pNHe9yI0yRHsgKR6SsTd4nIN26mmalvCs3CSb8NrXp5huZG5FNT5CKA0uTlapCgzHwcMMu6Ff6d4cOq8wX/HpnKmgNm5u0z4PC4bwaNtjvflx+v70eIWPPvoKvvrE/fSB7wOisU998T1g1Fg8NS7NAlgcewy0b1oH2lZo36qQ7dC9znaAZG+CwcPUQM/n5lE1Koi2WzeNM0R8rDvvZ+pndrJ+/6lAwxYdSv3ISLx/Nrmt21pKpEraiSgHWpv+/+w5W1zf8ai3qp24Z8BpJnB9ZasO0ELN6xRFsBjRdDUO9zczOLg/cTWZsqVIo0Dz+yFqq2wBI58BhS58TwEQQl9hgxS+lnUCLF6s3WeMVkgmBpMN4SQOdSrryvcJ5YeEb97WuItOQCn1XwP4T+1WnwH1ZbUA/AsApwFcBvDTxph9+/lfAvCzACoAP2+M+cO3st97ygnUqcL4GNPLikaWng7EXuQSFcag0RcpnMoErerRMEe5mGH/wSz4Lue1lYFzAAAsfXKJWx+3kNAJAB06GT2vXG/A9HgLqjIYPORhiunAODjcbJF6HhjpkA5qNPoeJ84dxTzat0qMj8buONnY5wKrHoscv6SnqBrKHp9lirSFV8ffEyk0dr0Dmx7JABMqYOnK55KjOeG12Snc968sgdfQ11fqJELC2rCnm/b47LF3Ite7AZBTUTWcju7++Qh1AkwEKdvTf/wgUvd5+t2wSmSqAhUvj9w5EmTjL1lBs2t9R/GR7JEh7D2/j3ytg8nR1EXedUIYeYmckgVIzo3PHrMeabuB9EC7dBNLUsoxXVWOeC4ZHxK84RjEhMVg9/7cYLoaO9TX+FiMsqmwbp1A1bZ62rYhT9U1BufazvECIQ0LG//5kk0bVWFfAaedeKWrCwTNWroyLrovmwpz+Ki/jsMOdV4VpKKPIJ560ECVKKs3YAOzBwxaNz0rqompCdHTblOQxKmq9A7X+i2Nu1gTUEodB/DzAN5njJnahtyfAfA+AJ83xvyKUuoXAfwigF9QSr3Pvv8IgGMA/lgp9YAQnH/D4+4BZr8TxyH2i+gQH9C7aRzO1UusO4DbOWrWGni3jnwtJB+ShfQ7DUavuM9vv3uvnezdAW7n//lWvFPvuGHe4M8bGzGAplIqBq0AbgD4FIBft+//OoCfsH9/CsBvGWPmxphLAF4B8N1v5RTuqZUAjJ9EN7/PT7ZorLD0onGyhwDBxhiHHE+Jl57VvKK8cpDPcpEioFTwmEjERTStUQkMMgvTd676O9++UQZpproRYf8DFF3ufXKOxa82cPBdtD+V1Hj03CW88o8fAuAjK24CyntUABzeR9tWpUJzy1+C+VKMeHaoD8D4tIGqERQ7i7ZCo1+746sjBaTaFcR5VcR5+dHJDPHMBE6DI32jEVATGyJxRfumQFbNKofZNpHGdMNbUFUCUWl8CmFeB3UdPof+af9aOgDSb1rs+3qYRuCIT1JTDE+Jk1eE/HFdrInC0otTp96WXetDzQqk12kpMXloDdGkwuSo30mAquLivz2l4ckYC0LfefdHZ9CXmkHkxSsALtS7lEYd8gaNTgGjUxobXwtXbnwz5wsKneulW5kcnEuCa2YiirZ3H6dcSGbrE+1r/vi6r/p8zHwlRTypnAwpAJQiLccwWo62sz2DsqnQuemtOBeXAbi6EENcO6/SoTMgobltUKVhB3k8M24bjUHlVN1mKxrJyGBw1l8f2am+/IJB2fD1kSoFJNWLpMZ4u+Mwad3rjFWl1NfF/582xnya/zHGXFdK/e8AroC6Rf7IGPNHSqkNY8xN+5mbSinWRz0O4Mtie9fsa2963FtOAGR4KD1iEI1p0hz5iiVz26Sl+PBsOxBlqVMNFMbp9gKAsupUel5heKYZGLeGWE4GqBwArU3CtsvOVJMolIknXitbCoNPMBBbo/jhPtJvUsL03Pdexjc+8zBAHGjoXiU8NqMnkpHB8KR2vO3TFeJIYU3hyZqF5InccdVQfrmtWRjHv593tcv/RjmR2UlitWRYYNIjZ6gLBKIrMvdapSGrJNMwpHt+rV92/RMYTwp0LxWYL1NkXLSjwEGVLY3GfuV6ODgHv/IcGa7+mSSk8CjoR+blZWGWDawTSGHnaT+y9NIU0aRANKHtz4730LywG/AR7T3ccKkYVZugtsJQ2O4VP48Gp72Ty55uIl8Io93KlkOqxu2UG3LbjtxQpMdmS7Hv59ivLWLodqcJ+Pkg06TZXoGqxcAFjXhSYm45iepIIe/GLsVUNjV1gFvUWTK2DkBEuXdyAHwMrS1CQrn6FsN89xkZVkPVyiGvHJzbbl+yo+69vwa0ccicbEejdVPMRWOgjCddrBI6Vu+48JcxdowxH36tNy1T86cAnAFwAOD/VUr9rdfZ3p1ymm8pOXVPOQETkQNIRgbL31DoXPPKVs1bHoHS3CyQL8Yu181dooFB63pCNTIujBai1zin7+T3WLmJ6W/5FtnXmXWTxVuMncDHj+zjxovrLJ6F3V+7D20Yf2w1PXjdq4U7Vl16w7JwIQ/230gVpmuC8G2RcuLOiNjLwKiOvKORiaK50QpRbpAMBf96K/bYeWOCNJoufPTPDoALhuOjcUjZUNdI+jNHw8HGf3Tcn0/eI/F4AGjYldv8UM2Dx+Gieb5gDZSAVjrDD8pRqwpYuESfmy+ooCt1+/Em6qSJlW/Si/sPpth/8Gjg2GSemh2zLGjC+Iax7R8ssPRlz6TiKCoUAAAgAElEQVTZOKAfXlX2z2q0BCmqZFtlBzA+4V+Tgi2Tdeay8u/Pex72rAuaa450zf6SPRB1LLtoFYpugnjkG+pkjSGxrxciTRgfEm6fiHnn+0osiGCzRtlUDu3EZHFyhd3Yr9w8ZmoHvvcmkrKoBnqmUaf2fg+B+QLQvWZrBg2FvCekL2MEjW3yHr7tcfcKwz8M4JIxZhsAlFK/C+ATADaVUkftKuAoAA5drgEQpXWcAKWP3vS4p5wACcgA3auFFbkmgxFPbARq5xtr3bqvRSEMNN6fwKRCeSsWDiE3KBsKykIZkxFNXEnEpUvj0ie+WYbemwMYfchbjfw3N3D6eoH+WVtIzkO+lmRUBdGfnldoblYYH7ewzGkJk0YYnPbhzWQDiKb8eXoIOBLUZSjT19osUDV9Y00yuj0pW3Qilx6qU1KX8gUxT0fM+2Aa7aioEY1LGGtsymYKZUzQpDe4L3GRsaMdttuersa2h8Ff2+nq7SI2+4+J7vBOAbxC1yIeh0v/1i3CsrMoTDQ3mKx7vWg+Dy7qA2HB1mgyNpEwKNHMR7t1rLD7wQpo207irMDwBwrgMqX+pAMASK2OAw82WEwLcVhDYfElmneldTCzVRuN20MdnqCmQx4MD5WgAW4MdJ9ZjZ0hj+YGOq9d7ScZVYhmJYouXRRGaEWy10bY/Jntl0htqrRsqIB8jxxKiJ6S6Vk2/gyBdc1xFtM/PqZcirV1k5ydW5EqQCXA8KT/n+c9/1/0gFhQiN+VcRcLw6A00MeUUi1QOuiHAHwdwBjE3fYr9vfv2c//PoDfVEr9Q1Bh+H4AX30rO76nnEA0N1i4SMZcpnaYCrmwwtQuypZkYqIBycSiQSlL0Hl1ivkqRa1VoiwiiDs0VfDAMrSytDlzhqU56N22Qe8zPnxLLTKGI30ASPuFU4qKhwXmaw0H+Rwdp+PgpqzJyRahNGw6Z+9hHaQBelcqQCmX3olmlOdlBJCJFMFTOQLLIiR97xCn640AWZUOakQwroZQNjQ6Nq88X47R3BT5f9tQxwysAFBmEXIngRg2avUueypgHoUwJOzY2PizQVGsv7yco/OEd4bpEJhuAE0RbU9XVNAXAHg9ZqelICLyWMIWG2TwZcReJ94J7H6QDujEMeJj2Bu1kL+0gPZ1/32IKFQykLpVFGfOFG2XndB4Q6FxIKNhYLZMHeXymlQOtsrnJGCeok8hmlTEayXSfrL4H81sADWt3O98IayA6ypEJKXjGiPRnFa2fJ+B0YQkGh2TKD3Rx9AhBJ+cD03R1LX8fOX0kskBeMU6dw/s5tIBOUdZD0n6vtksuVsaw8BdWwkYY76ilPodAE+CmJqfAvBpAB0Av62U+lmQo/gp+/lnLYLoOfv5n3sryCDgHnMCXImPZmVgeDgql9BPAI5SllcBrPOr6tqtGqLBFJOzi45/BAASwW8jVxwAkBzMMDzfC7DtctnMzoCLywy5a18XrKVtwT9jHYDMLScj47n3ayq6MRV0ekAGZfllQb0w9Ea9ysgBVE3BYnroM6OT3pDmXY14ZpwxqWMVoKiSceUgn/xbz63xt/nm2Yo/dqkDyw1p8r4oE9I3R1JJSxG6hKGH8QTYf78/luUvZMi7frnP9MVjWy5r7B9qDJNpnDu8Nj5lkL4c8tIbBYxsiqa1CQzOCCN7ZIyV7hh7I99+XW7k6Nv6zpE/slG8LdqzsQKAukMOQKanai5oglZwRSeEXbIDADwcdSTkPmX+v7WZo2zHgX5COijdta8akTP8AEF36beIcALlPqKK5u0tXKwsooyJEulzknpC3tdkRJKWjBZqDAjGyau+xoACgM41HxhIpttkahyPFVQIMWVnzfOgjml/h7ud78q4m/7EmH8A4B8cenkOWhXc6fO/DOCX3+5+7y0nYEfdiEggQ0Q2deSXrADlluOxbZ3v05PP4u+mmcKpVQFobM+R9C0JVS8JUibuu4EMXoTBOf/98//Cz1DXJr8gul6FA5huNJCMKudEyoYGGkC26zuZ42ntdAzGR1Jk+8atPOKpXWbbAl7aL1AJURiGfqYDUcTrxBgfofRSHSs0BrUQj6HoklNIqjYBSkQXxqV7+Brky35bkpgv76kg0nOpAeXvk2Tg5LTN9Chtt3dBEZePI6yjB3rpGdr/zsfpfp4/QwQ2kz85GbKh2mYlmRZYeLUKGtq2PuL//sCHLwIfBp790jkAQOsmEQDy6H+SPFSj6Vc/Z7p7uLlLO6pmMbrP+PvcF2gWXk0k4nfVAGpOD9mvucIxELBfrj1tc/TcKT4jUkEZ+Wd7pUslmkgR3QbbzUP9MPGIziEInoQDMAkbd39BZ4sRuMFb1UQG2LIpKaaTkB3YcpQtKip3r/kJcXA+dqk5198gelCagiCvaCmXNi2bKnCgVUbcRcyc27pFDsAX3u/cJ/Jmh8KbQge9Y8c95QRMrJAvxK6oWYncs9QA5uKiSwHVNdS8BHK7Ishzp1ZVrnQ8bQFo1VA3IiR7VuTbRkzDcz7ElA5g9UmFg/ubzohmO3MUCyli60jiEaCqGpNjNGOLtqYf5mTZD2dZ3tUYnoic2AY/9LLJR47JkQaqhsLe3yRr0/3DBsomAsdA8DybvioN8o5yXZUmAnqveqU2Xj3xAxhPPBHffNmyc4r0kTRc2R4hRJjDhYcj/lKh5KeJFEanDNI+7XO2SrledgKD++narP6tV2n7eQNZXODiN23ov1ajeVMSstHvxUsyF+2PY/NjAGCCSPHZL51DZhWs6hjIz82g7AfULbLQhYCbfCk/4/5e+wLpNcquZNkVLKN6/puNfzSnGokUXknEqoiNP/MUjY7FWLxQuwaq9i0y6of7O3goG0TIlGid6mCuwxhn/PmeSeOeCmGmOlFIxrUDQNDxerhvtl9huhLfEbEEkAOgDdnzn1KahxFXAAIOqvni7dd0dNruaxfoXvXd2vMl4seaHONl1d3K4bxHG/GOG6omdbAq06hS7YpYqibyOC5YNrYnMFpDT8Uasq4BrgUI5ao61S5fDnhe8nJBUB2s+mhv+0Mai4Jgm5WYHM3tkQxVptCxToCX4DIqK5rKCmoA82XtHgxAUPzayL9sKCdnCJCITd4T7JQ/RV8oDiwi56MFTn5WOyQH0w87hs9MwUTK5cIZneSideaomfqaiO8stqpu1qDpMoz0mPiLHZcu6UHnfLhRVHSV2Puj/7bC1ndZWcAB0Plh3xShqwhr7RFGubem1/7spOsY5lRSQJ+wH6bm9s8LtMsIqNo12pfpHl+8fA7dmU8hjR+ni1KPbXqrWyE+EEZvoIBvthyTpnMA9nSiOTCxVMuNXXKQzJHfOCAkl0xrcP8C4CPO2TL/VgH8dfFCjXhcua7ZsqmRjKuw7iUABrxSrETqUWpQkPFWLljJu6lzAACw9VEDQOP4n4gUUVOHwjTKF3/rWKF3pXJOgVFdTHed3AGxQ4V4gS6yK7a8qxDN4AKhsqEwPO2/N10jhBinpKoUKNuem6mxf3dWAnSQd29Tf1njnnICpFtqSB3KeMObjEgYPtvyFT1VVS4aQlEG2rVmWShYM6eKjKjq0Gi3b/h0Tu/FGTY/uYz2JlctCULJudFkYjBdUZhavpzBYzlO/ksdSBoGOrQ208DGn6NgybvPqwg6L4Mor90DffT/yVAlCtf/Kn3+5GcV4kmFnhVWmS/HUKU3ACSf6CmRHaFe7kVwdGECAjKnnQBCHvGqYv9hhdlyjOlR2sbi81alTDBHFk0vbTlb5fvlz58dAAB837//BADgy7dOu9eufP4+v+99oCkeSo4ivSSiCprcuIFJ6gU0b+igGEzfo9/Kdvo2t8RcEH8mQ7pPZdO/OV/24i/s2Pl7ixfqQLCmuWMCJyBRYpwrL20BePFFg2RcY/EVu7k7QEAnGz446V6mCcSKd7RNUavRKnACuiS0UL5In4mnJkD2LD7PKUBRDxvXbhXIxz6yzZOtnSpYdXFqZ7zu5306Nq9ZqJaCM2ULaN8yQRovGXl0We+SvSb20OKS3ucFm9SceNvjPSfwzhy6MoDxCJp01+btc9EMVogZPRVPfZvCh6pHT7LjJBd5a0a9ALb+UBlEQ9rGfKODxVdyh32PZyYsjtmc9+AxnywdnvBNP2UbMN/lcYnjXTqe5Sf9rZJ6AcmUjC4fJ3Psc+Qe741RrLTxwK/ZyD3RxKOyRAYinpLAjUczmaCgqA9RaejCBNek6EZosHarvUQ3vt86ztSgyEp0nqKnr8zoOzINkEyBgweE8SngNHnrh8f4Tx75knvvnz33cfrjJU/b0BLqWk6XgDUlEq+JC3ijwsa/9xMEq77+NFWQs20V8Mq0ti2eXxxf1SsxtsYmu5YgmodRbGlXDaufuoorXz6JD/+gXxZ++WsPon2V9r1wibbNxXG6Pn4/zW3y/kzxDITXae/9wNpTPi9OtRnlIJbp0AQNkYzsYeRVY7+kFR7HQYsxolkdpE3zhdgVg7lzm9FA7VsVRkdFrk/RHOTrvfdQjMa+d6Dj9QgLl3PXgczn2hF9HzLdMzgt+xEs2d+af3+2FDoFgOQ1AVpd6dL3BjilOvuYcxPj3RjvpYPutdFufevPvDcAhMLfwO249nfzWP3U1dd9nx3Ae+NbDwnHfUeO95zAO2sYrYjr3d4Yh1NvxY4Bkl4wwFhU2azxn5z3wG3WG2jdmkOVNZRYmpadxKMutELViJD3qJKqSxN0wM4XNbLd2nOnVDSxNz7vPzMV2O/5sgEudaFP+uNNd+LbKIRZkDwel4jHCDHcYtUyPbUAXRpXeyhapHwm0xASRRVNKXpr7IsITfD35AsRWrf8KobTb7vvt3xAMWBSwcL5YuYiNdcda3fHAipVW6A+FoRSV6nxT/7ie9B4jlZl+bEK6b5gLM0Jay+5k+KZcfDbBBQFMpqJsfRM2THYWoK6EfYVQBHFAY/RUY26JVlFJVSXD8TvXzqA//mnfgsA8N//658W5+Sbmpo7XpBnvqDQveJRRkbTKmDvIXGfEj8H1p6ykF177+pIYSbuaaNfkeypk1BVQf/FdCNB62bu0j3zngZ62tGc5wtxIE+Z2dWhjP515Zu7ihYpie095E1K96roFreHnvYL+xtAbVDYeatKg7g0uPLv0fY7r9JzwlF81VDI9izySOhfAB5mm4oFfdghTGI3C5fEi3djmPfQQe+4oWqDZFQhHtui68AbejUTE2A4ApIEaDAhucH4fWtOeazo+g7a6VqKdFA5SCa32vPDk4xKVIn2cpKaimH8cDC0UxbMZG5Vl8Zx4QDA9BMW1XGZLGcCoCMCS055MISzaKWIciMkFJtBXwIjOMYnPXpJFl7ZMLLhY92F8TGfT+5cmztxnIXnCYpRLPk6xGRdO0M8+MQUsUXNRFPl6JkB4OB+RhaJ/XeNE1YpFyqoWqH9kje08djXSBrbEeIxnMxjNLHNXHZzZfMQBFUYEIAaxcYfniK+5I998UUBk7SONbg+ojwUDSKULSOajlQAgRycr9G/to7/9eOfAQD80hd+EgDQezXM+/OQ/SbaXgPZc0LpK4Ym0/fWv3ao63dJbFvo6rLxl/Wa8YZ2Or1GK4yPNwLk0upfeCuqapJxZDj0fClBlSqXvhqe1EiHZPx5bH0odtKbze0CjW0fyOQr9oMi3VS2YvfMTTZi7HzIwPRof8MPAK0XU0c/LZvSVEU/stkumnuEVTqklWlDEAd2r5cYnrDP7PQuhu/vrQTeWUNV5s4OwDYvYWhDN2v8Jw9QCG5ihf37/aXQOZz4eDqooCrBMDqvMF9Og16Bxr7g2Ldyk4uv0Gv8vbnoC6jFg0MC4XCT6cQ/STBfjLH1Efq/e5mIsBiznfcoPyqNXbY9dzDGyRFLxtZhqgb6PRH5VCkf2b3KDXKc+yVsv8v9WpW0zkVKltdW7YudAuD5bYqlCo0L3sB2X6VtDM767ukzn3wVL7xyzH1GzUTvxqUYMJ7/HyA0TCSQStBh5L/7wQqLz1mVtX7IFDvvaUApjAV/0PIfZ+CLzdfQFV1b1E3M4uaTIwqzFQM9pu1XnRp6qh1ktWwRzUO+ZA3rAjnw/+U3fgYAcQHLdEbaJyQL58GrRDnnnQ5rFJ3I9VVw5/n6k2Ej3uiUv75V4lFd0aEgN+9GQZF4vGHnAa9ITQjf7V6tMF/295Q1tcvMHs+0xsFZT9jHFBpc9J4c9drLAMGzJ6e6iMchHLey29t9JMXy84XjWQKA5Wc0Gn36/+CchjK+L6MQ/SOtm3yM4Tlzd76radlnLx1WGK/7/VR3k0X0PSfwzhqq9gVadTACmmSQTcOeZpPgGao/wuSRo+57ew/GyPaMQyAsvnL7spFXAgCQHhTugWndmGJ8wlv1OxWSB0IOkR88RiosXihRZdrROOS9GHlPYfFFu69hjfmCDgt2HY22hbtmO3nQyemgnmK1MV9Uzkg0d2vMu/77/OAw62M8qxFPPRyvakRo3PTVUj2uMD/WQzJmVa/UGeneExH65/x+RyftNs55S3j5i/dBrdB3GzsR5is1sm0fzUpZwbJJBp8LxbxaufX9/l50Xo5dU1lzl1ZEI8uk2d6ssf+gCrqO5Yqsf8ansAAy2EUbgR4zANRN0ZkrkEGc5opH9vhHDZjIBPuTzrq1XUJXIdEaF0MPk5rxPJIrg8EZgQLjlKe9r9GcYLz799vC8+U6KDSztrQ0WtyNK0ftiucppqsavcu0g91H6GIwkqp1M+xuXv1GuK07Hf98JcX+eS9Kc/BA4tBQqgo5rbhAP/GPqXP+JiIxJHZsfp/0O+/qoN9kbB0Nb1+ys77t8Z4TeGcOtc/94mFEUPCSdKWFqqFxcJ6x8nQnV58hazZbSZAy4kUzFt5HSbKTcmCbxBwdxMigdW2CiXAMUeHhbGz8O9ckKZz/e7ak0RjULpWR9kuk/RDDLSO86XrDfQ+gJXyZ6aARaenFHDNLvNa5MkMHooagVcD7fnA2RmurdmmhxuYIppE4Coh4MA845iVWfXBWkeJUy3YON2wee0BOsHklRtoHsm2mJECgrsXXh41LlFPzFMNj867Gwftq6JmFIU40moIUjXlrppamYbamUTcMoqltvNul1QHXInQR0lhI+UIAyNdtX8km9zeE0TPz+szW/TGkB8o1fLU2DYqOQu+yN4TjjcinqWRHbkR0HDwXeEUgAwiZxvBNW37usAMAgK0P0/vNWwJxJJBIcj4BwOiYrYHZdNF0VaPMgL2HbAplQvQbHIUDXv9YDn426oQ6gnkaMkX1yrOWofWhNIDDskaFZIzVJbD2lHcuo+N0jO1bNdUIDnyfgIlCxcBcGHquZzATanK3iOQM3nMC77hRVVB9n/IxLW8Jq07D0dOWrQjxpHLRSzIqUSc64LjZe9g/fAuXRLEyr1F0ZION3Z2AIhZLfr+5zd2vPk2OKepP0f/AKlq3fP51eLrpHmqmeGYnBBUS1NWpCigf+mdoXwx/HB+J0d4skYMeGC429l6m66KKCtPjnYDbaHCfdoW0eGI7fjmNtbSEbMevjLY/1EIyNI6lUosAMD9GD3jUtOmOeQR9kCC9bpu9xpTDZxy9Lu31sw9SPA1FRRhOa0TNW16LYrEKGEmhgOp7+sBL1IHVfYyI3Pov0ApwdsQg3ZWFVjp+7sVIRwBGguxvM3ZC6PQFW2i2KxM9B6qmb0Jq3aJGJW5Oa+7WaO76wnreVRje509A1nqcMbfXPRnXyLshhh4I8fLpqMZkXZL7AcOzDBW+vedCGv4qJbpmydCqS7jicpRT4BILQ90T6R63grTGtmhH0IWI5m26yfFQKaCxV6J/jp4rXYT3ku81G3YgpBAHgM5136sC+IZJIFxFSIlPgBrT4rlx10ICHd7OUHgvHfQth1LqMog3sQJQHhZVUEopAL8K4McATAD8bWPMk0qpkwB+A8AREJL508aYX/2WOzRwhSeTJagbZD1Moh0lLkDUDUBY2T84n2HsU9UBXjwQrN+ZYr7cRbbjDXE0Kx3uPprVQTGud2kGPS8R9X2OoHPVp0f697eDY+HipGxGM2LSZjs56lRjbgvT2X6NZFRhcJr2394s0brUR4sbZooKdScL+yKsZgAA7D5Mxzr+KB1f58/JQhuRyx4f9deuSoBqWTklrCr1FBPJzRTFSokqtw/6NAKUcRFzMiIHIHOyvB3A0yxzIbc5N8g7vvN7sqaQDDTWP0rh6P7njyLvAfV30w4aaYkHlnbxgw99EQDw+d2H8fzmBnXy0tUIqKV5hcG/06HBfEGJZi+ge9kf3/A0p8jstVgD1p6qXV7fRJTOks1IUoxosu5TU9wN3NqyHeP2HKXhL1uefz/vEGvmTDScjY9rlxdnJ96+KpBewoATl45yVNWdqyZ0ADnNnamlfSjatKqTz4hcAfP8YUbOqkH9H+58FVxTIY+D+/3F59RNQIuivKNiBzA4ZdFCt2ofdSvLoSUMMD83AILOYoA6i3XpyRrvJpz5PSfwxsYPGmN2XuO9HwXxYN8P4KMA/i/7uwTw31iH0AXwhFLqc8aY5153T1oBGT2hddNH43peojEvPaulpYWWEDjpAGTBafFSiToCWtdsNVUp9F4eufSQqg3KduKonocnEyQTg/Z1/wRGg5lv019uY7Z+u7RRS3L/KE/FoCor1r5PUbaeFdAzz3tUdhLossbiK3ZlYfcjjb4eTDE5Z61HpDAUTJOTx8kqdf/cW74o94W41laFec+zPSZj4veRDxL/qQug90LsiNqyHXr3MFsnI2pY5L73HBUCTEZGYvtDHpITFb7DeL4CqPMjbH6NEsVRBlTvGwOlpaYep3hyv4VTLdre6fYuXnjq/B0NP+CNASOAsgNLXWGPl9MLbCybWyEiZe0pT6oH0ConmhmXdgCAyVriIMCZmFeuySv3ViTbmiCzee/hWbLWMq+f7deu8ZCN+YKl3+ZU1mTDz2nJmFplsMpg9P/4uEKVWZlHwNWIuGaSTMImR6MAqDDlIoOj2PIEsVFkmG62Qzd759FmANt0qCK5shO0FPHUYLakyPjzR4W/kM5VVwZlSwdQZ8lYOjylsHDRfHt6Wd5zAm97fArAbxhjDIAvK6UWWUUHAOtqDpVSz4P0M1/fCdhRHF3wlBAAjJ2tnD8HQjZEAGgJxInku0l3yZjrMf2uO1lQH4imRFs9PEmvNXfsQymJuESrviprNG9MMF9hR0BInPRA6vCWmK2TUWaa68lR+nznUoFaCN5Q7jhyncLRrETdSqHm3hDNjnprwA6gEFrpraeawWSOZsalpVRFiJuxLa4nExMgjWRBMxkRDxAX8KI5dW43RGQci4eaeWmGDxIEpHVzhvHxposOizapvXHqCQCKWy2YI7abdqYRXW2hWrNeZUD34F/+m4+4zzciH3Wybq9MYUUzoLbTYnTURo6WkGy+xIgwex1XPOKJjw8ImTJHJzQY1snph67Qm5ZKYP1zClEhSA5Ny60Edj9gazyWh6i1VQfayksvcZ+A317VUA4Zw9TnUkoxMKL2dUaB5d0IugoNf2DwOcAX0Xh2UGFu6U4Oi+PMFzQWLnjvz6mc4UlRTxJzwW1fHGNrm5iA65jmnazHSNqUsklCR6Pjfp7MV4xL0y2+EsKf7+p4zwl8y2EA/JEi2sV/LIWV7TgOQLZXsliyKz8ppU4D+CCAr9xpB0qpvwvg7wJAlvRgssQ5gFoIwM+X08Dwt68Ljv2GxvA+ejrZAXDkbRKNZGsIoy20bpIjirXbR75MT1PvkrAEMkLq56hFbYKNv+Tg17MCZZc+w01dbPzzxRRVQ7v00PhUB3WsAtw0ACeOXrZiZDc9BnR0rov+WcmmSr85Wk8uNFE1vaFjvh2G9lWZxviIIKT7RI3lpyJnDHLhTJIRbV8iM1RlnGHQBVFeMz1wOoqsQDrt6+CBVlgkThTSsQmEVM4+ct39ffNzpK5XTkUVXAxVhQgW2Bo0F7PrmD7DqxzXuGUZKlV1CN1jZ6U0RgCw97BdKWyHKRhOb5Uiwt193F+b9hWR07bQ1uY2H3AmiOiA0XGNqAj7DNJBFTSAAd74571DqQoVNlBxWko6sHnXkxVyEbq5JYOTCqNTNH+5BiahxaqmqJuHLrwHYkfOq6wIlKvnXD4jfTo3fXOblPWUqajGfok61a6wrkogXwodAAAsvizQRqJmN127S2bPvJcOeiPjk8aYG0qpdQCfU0q9YIz5onj/Tgs0d1mVUh0A/x+A/8oYM7jDZ2Edy6cBoNc7YYrVNganbbHUbp1FuJ3o9U2rPcxi1rVxRpyUo8qAvMo0BDKoSX/vP9IN9rH07Eh8xl9WTssUq1YOUvQcAJTeqRuJa+7ZfbSLpRcmyBdl/lREkpYUj1NZnI9nBScAGNtUAgDnACRaqOh6Y8XC7JxeWPrUDeB/WwskFuXDuPxU5M5bGSqmcuQ+WaNrLIuX8lxlFAyQkW/frJ3hSofEACsbmLbvB3hK/NhfeQL/+nO+rMSLLdl8F/zNnDH2XMtWyMypXUFYFDwLg2oiCqjiutUpFeYZQQMA2x8UKRMAZQNYf4os3eRIGsARdz5UQ5f0f/tKiNvvvDpFnUbY/LANEgYIhOeZDO3mD4g030zj6L/x5xrlnik07+mgtmE0nUssekR0Bc/HYwgUcLiRiutb7etz6NKgd5G8x3SjgTJTgYObLSu3+uHiuFzFyXvDjpSVxrI9EzZU1sY5qLKpgEJ0sWsKgtgBDU9QfwnX8RYvUOCRCe4kCbG+q+M9J/D6wxhzw/7eUkp9BsB3A5BO4DXFkpVSCcgB/HNjzO++kf2pqnYdvWycp2skvM6dq51rudMCSAAoYzA70g6WoflC4trbo9GcGqTsOHigHWwfIH2C3Eb40Zyay+I9etrKZZuKYdBEpBDNvJOp2ilUabD3fp+y2Xuk5bo/Ve0RRoAnzpL7j2dwGsW6IBlB1jZm414+QJVe9WoLJjIO457tKLeUX/8rFGVHv7AJyyOgYWkAACAASURBVMiPq1/yt+fM7x4gX/XQV6as3nlUHN+xAqtfIWvPKSc+VknqBRDEcLKhsfQSPe3Xvz9F0vfom2gK/I0f8QvAz3z+o0BikG0KI930ME+jgKiWRXZ6jWsCjQNgtgTEHPnb4rdMmUjYZjKmY2YiuemyDgqZygDrT9r3VjSKlncA+w/QRkf32Wu1WKFz2S8hFi9UGJyMsP6Et8oH5zPXKMeoJCVWIrJ7mWGy7GSMsoVtS9HMqmV87ryikU1lcg5VDStqk3JKLBSdmRyzK1UBC+V90d/kbHmlwl3XtNKieyGN/Kzj5x1DtDnA4ePhwSlW1rJQdViPYEJF+Qwnk9oh4FRpEOXG2QYuxt+NcS/QRtwdrNQdhlKqbYu6UEq1AfxVAN889LHfB/AfKRofA9A3xty0qKF/CuB5Y8w/fKP7rKPwdCTrIEAOIDhGcw+48bs02AG81jjzuwfB/1Kz4N0+JA8T4B0Aj3wxzEUvvfxtyE2/Q4dcjdxpsAN4rcEO4J06lHljP29oW1QT/R2l1AtKqeeVUh9XSi0rpT6nlHrZ/l4Sn/8lpdQrSqkXlVJ/7a2ew7fzSd4A8Bmy54gB/KYx5g+UUv85ABhj/hGAz4Lgoa+AIKJ/x373kwD+QwDPKKWetq/9fWPMZ19vhyZSyHsR0kGN/Qd8U47krwEARAqqrDE74qPv6brPVTD3OkDpDDWvsP/ognttvqACXpJIFGFREwqhWKNwlnLiAsu9O0GdJW4lwDhqF5XpkAPGRCTQPTzht6ELT2sBhCinsgUMW1HAXc+rAAAolkt0LkgJQfq996+O+9dEznv1VbqIF3+SchMnvpDj0k9q8NImPohcnnd2rEB2I8HILh56F02QDqhjylVztLv3PosN/3F6YRnArU1/nf/Go0+jMgpP7RIvRWPHYtJ9tgvRIwPMLd22SmvUOwmSfghdkoXhRl9Eb4qK4FLcXa4EmPOe4YqtnQqzRS+GzqmQkVjLXvrPDBb/lP7mVUD3lchuzwR49vatGjuP0xzUeYhQ40SppBgBROPamDj1ZTQv5wivbmT6R6ZjUjt/50vhyifbC0NbNmCMyZdduqU4Nk7FTFd8MV06AK63SApoXZLkKOBXELzSkLUVeRwAyVrmXRU0v01XdLDKbG/VTlCHFdM8wu0uhe93v1nsVwH8gTHmJ5VSKYh55O8D+Lwx5leUUr8I4BcB/IJS6n0AfgbAIwCOAfhjpdQDb0Vs/tvmBIwxFwE8dofX/5H42wD4uTt85s9w53rBGxr7D0RYed5fi85zO5ifWETS92iFqtNw/D/D0xaJYxu0Zmt+LcoQfS7yAcDwVOQQJPHUYLqaOhqFeFwCWrnGNMAWS3fJENcZWd3hff4JkTl0aSQA3wkshU6kk2BIID9wqiLxkuIhAfQ3CuWI9ssOoCHpGQSUcGL1fItluja9VyNsf1AjX6cn7OJPK3ReiYntFEB8ztdC1IhyD72LwkHKwqPNcR88QsffOz7AD554GS8NfUfWo++74f6+OKKK8OYXrINyxG30O/v+HUxmKaIWHVtdRmhdF04nBVaePUzIFsILpcjMwXkVFGPLTAV1jIkVQGmLdML17xUO9cQU7c4cxV+3TJy7bXSfSdAU0W7/jPew0tBN123Kho1/TOR6PNKzQ1TP9QKjPj6qHM1G0bTNbnZkV+gaM84eID1leS4kM+q/k4xut2hSKlRCTuuYHJc0zrL/Y7YCtG/4XDwXduU8lmytnN7h+xHNDfWHcE3JGIdEYvinXIFJB6Arg+lKJGo+dVC7kOf0tsddcgJKqR6A7wPwtwHAGJMDyJVSnwLwA/Zjvw7gCwB+AYSs/C1jzBzAJaXUK6B0+5+/2X3fU2t6ZhE99VkKS1g4HgCSgymmxyk6b+zljmgNgOPeCdTD7Gvz5ZRetzd795EIVeYFPtIDhZXnCsevX6cR+mdT9C77QjMAt+/ZcgRV+QgICHVUGTfO+VZVmwD/3NwOESHxlKJtGZXNl2uf57OhYmvFOqEVoPkHvcC4BV241virhA78xo/XaD/bQG7tdOcV+iJH5dnLXex+D1mzxa/SNR0LqN7knH86P/rQRQDA1//8Affa7/3F4+7vI0cPsD9tYes5og2NTkygn29T8QZEwDb57gkWe3Quk1mK2iikL5EHzHbDIvbiyxWqNKRQ3v7RGbJn6fPJx/cwf2LZOUxzo4l+F2jdtLlno5Dt1W6bNUtSi5XSwsv+74M4g35gioNNWqooDVQtYNQKaxDS+HP0H08AKLFtA2TnB6hr+m5+oQs0jGMb1UXIs8QwXCkIVGUabduMx8X2iVDyMhpYuODhuLryzwAHJjLnLZFJ0dxgeEoH55LtGwwtX1Tb+3J7vAaV6HlgYRe+N6oOnTM7D1nQZYfTGBjkgk6ai94O+luFTmF0NLKNfHc3ga+AN4MOWlVKfV38/+lDaMmzALYB/DOl1GMAngDwXwLYsJB52FQ5R0zHAXxZfJ+RlW963FNOQBc1sltjh0gZPup1GiPBz9M/R0aAo45kXMNECpM1ASG1zTN1rIIHp7UJDO8DUpEiHx1LPO66IOoHFl1nWuf5otAtTvzyOBkTZ4oTxbY4bZ7EZabQEpF/2i9RNtMAptjcMQ4yeuvjwOILGniBvMLUXoKi7fPURpByda7S/jlCW34ywfC0QblBTqz9bAONA6DxVbuSuFVi96E4aHxa/VMOAQ0mRwVaZM1AD2IYIez99T9/wDmQcmcJWQMoHqT02+ZLFPknlusHL7WRCBqH6UfHgFEYC0ho/VIXyPx1BYDmtm3Espdt91F/rI+cvOmgCBd2V/B9f/1pfO7Fh+joV3P0nvLb5oLjbFlEnCVQNj2HfrZnnNNL+grzry1j2SKQZstELcGFa3Ym7LDLZig8r2qgttF29CF6Y3qTXkgKFRjcKiMHwvOwa2Gvjb7gKWqmmK2IpqmTERpWynO6ptC9IinHmTNI6AUIGHI6pG5gWRhefr5A/6zlFrI9BU3LZlsn5CgkVUVrM8xUlE0dROjNnco5Au5IZuQYEPIByaAnGdE1dnDlDnVG86qOnxW5r7s13gTqaOcwY8KhEQP4EIC/Z4z5ilLqV0Gpn9fc9R1ee0vrknvKCZhYI1+j2cG0CgDhkaP89hQAL1+rVGO6DnSu0f/sAACahGXm6ZdHJ23UYg1P61aILmH0iHQ6eS8KIiopEOOac+xLqg5poqPCoOho19dQtSJkB14zYXSiETysG18GgBoHgkysse/FtZdfyHHz4yk6ooGpcWDcNRmeMcRg+iJdnKXn6cTnq944Hv9T3yE2Od5youH5gkIkslCqAOqGQbZF73/jsw+iIWkI7Cbrbb/tZOiPO5oBwwcLqIiOtZsVGI8bqF+gSLuOwu5ujlQlNLBOlKNSaAwMXpqdReeK/TyAPzn3uHsIVv+iRvvGFJMNOh7m5ZFpEBOsNIhxtWvZOPtnGkQ4GPHx0G9Og5ko7DE47ACGZwSp4MUe6kaNpO+/kD/ga1XtJyiQ4Y5hAI6+AgDGR6x8qL0fZQvOAQDA8os2Eg80l6MAdQP4VWqUE8KGV8i6IDEhTulIR8kjSG3awMxF60oFNQouADOip8puh0YzuohSWCokgjO398C4czHhyiCgqng74+7WBK4BuGaMYTjc74CcwCY30CqljgLYEp+/I7LyzY57ywloOIbLWAhWN/oUBXAXLEARNucUp+tA+6Z/QPtnYoda4ELXzNbk4xECGoI6DScYTzwpHF/HHqLHDoD3rWrQJBUTPu8pF1nx98bHQ5EXHvHUBFh0frCOf1EIhFTG8bhvfiSFLn3T0srTofh6+7pCOqjRvh4KkydDb1gnx30YdiAa0eqUYJ2TY6LTVBC8qDqEPNYJUHRrpPuicC5WWINH6MK2l+hYaqOQvOxrKekIllbYpvM00NytULRFyiMGelYzIZrVSAeei6hKFXoiT240UDYjZ+iz/Rp7D2m35J9tVIgmOqg7FG3tqKGzfVKQ41VdVJOxZDI3IOzUlp3LPIe0CFbaV/15TD9A1yC+TOc/XwFa4pFnneiDs5ZVdoWcEHfWpiOge0lwVp0nzybrUXIQuZ/XYuhdrSApy+tEIZrXTjwJCFNRyZi0r6XKHZRyfQHN7Zr0rPv+XlSZDo5H1pNkmi8dGsvzZOHgOnTUrPkcNMcJOVRJxfF2x91qFjPG3FJKXVVKPWiMeRHAD4EYEp4D8B8D+BX7+/fsV34fwG8qpf4hqDB8P4CvvpV931NOgBq9KkTTCvlSisLmG3UZTppobjBb8s1jGZFNBkUyXva2toHhceWw67ftswq/O1nXARsmIWKEEWhRMU8aA+JSt+9bg84t8EXPYOWZsOi29UG/9GAq5WxPNBGJCe943e3KYeNrwOUfj7H6lEgT3EdOEADaN2xaYMPvoyEoLYpuDF0aTFZD40/7JaObbVskyekKrWuhYDjglcIAm7ri7cQU3VfWxyj7lv4ihXZlC2jt+u86um3r3HVFXa0eFaJQxL7buk4UUpEuqaMoaCLk720/Hq6ieDRvRKgTH2keNDWimY+wGZ0jI1TpYI2GaIAifBXvu2gBncs6RMEIniKO/DltZxSdr0tV2qhUfqdseXqUja/QQTFvVWNQoUqF0VU41KFL96LpJB1J04LTm+zAXku0nRsxJd2EZDwdW2cgNQ2qxM/dvEuqba7hL/ddymWmQ14jTX0I0hFIivFsr8JsOQpSWXdt3N1N/j0A/9wigy6C0JIawG8rpX4WwBUAPwUAxphnlVK/DXISJYCfeyvIIOAecwKoDaJphaoZIZpViCzJWt6Ng4aroqVsbpf+Z1rb12pTlw6galBhb/UbliBr23ILWQOy975WQIfMKwMpw2cioHuVvj86plHHfsXBUe3olP880xIATMompPMOTUJdGOiy9kXumuoFZduf3MrTnp6aC9xMr8DnIWGn8oFrbZYYH40DorH2dbJMtz7WCorMDI10RpYhu9f8Z2SBWlVAVMEVMyfngO43/QeWX6gxWdPB9ZVF9eatKapmjLLri+ppv3IUHEU3Qf+soPm2xnzxFTJkJlXYeTT2cMeNGqbvO2+jPJwjUU4RMxc83YpB0Fwcbvbi/6MZoI3QSuiF1yIZU/2JP181QmRNPDPQhc9zz5kGRKTjFi+I1dsxyw4rcvR5V4UMreMwss0OPHkfzws27hXoWnAtYb5gNSLs9bmNZLAySMbGUXIABF+VOX8+JoCuxXRFobXNtY4Kg1P+4ueicY6vkURHNQ5ql6+PZjXaN2rX2yL1ON7uuJu0EcaYpwHcqW7wQ6/x+V8G8Mtvd7/3lBMwkUK+kEAXBsoY5F1/elFuHEshG0jJKCkn1cKlKkhzyBXCqT+0NNRiaSxz8szmycaGVwG82jCxLzgCPmqUhq21VaB82Yf+LIIC+IeM0R7tmwbxzDiUhU4M0kENVfh9jI9nLtVVNBUOHvLbY5rn9i0fRNSxcvlcfihbok7SuVE6mgtdkNwmACy9VOLgbPya4F6jQ2QM5ZllNKwCPvmTv6+hy9IJxdcxpVwkbTA7YQDIFxuI5hXiiT+XeFrB2D6NqhmhtVNh92FLpZGxNm0I5x2fsRoMzyWAEfWgUwY6V96p2W5YIxy87OqNx4CKBHy3RsCoGs185JsOFKC8MT88ojkZRoZxcqrDrVi3qCbDo3uViqyRu1YqQJnxkGSAJoar6cQz4zUtAKdhMT7q52XZVE7PQRd0f10PRUZ05S6yt0FYtnt7gAR4egnulM524BwAwBQjdDz7D7IAjkhPRcql8fhzTKpYtuKguTESqeK3Pb4Ni4t/1+OecgJUALLef1phftIWx6yBbTHDp20/lzJ/EilB+qpWt/aAfnpXfIJSmbClvmpGwf+AJ5TbeawRYLthiBueIzJ2JokQh5dqUpMN+g47Ij0HoHzkPl1R6F4zrqYwWYvovMThNESPQ50q9C76NNZkA+i9apwmMYufs/F3aS1GbcyNcwAAGTamC7713S33mnu/8rUWZUJCNsorG1eoy/ZJtIcZWH30KRqaBE1zNDcoeqlbtemCGvMaO9677z3qrfLe+/mYxEriQDlECjcyLT8hWGIF1XPrhk3R2ZUhc/GUwvA3RLqKI3eGYQ5OadcoR1w/MscOgoUK2KekvKhjun5syJNJjbKhgmvdue7/YeM/XwrJA/m+D0/a1ZLh8wz1kKerCtPVGGtPi96aVCPbtai5VKNsRkHeXbKOciDAq6QopxQsay2o2pBAj8jPSwGfdOjhsumwDlbyrVuMorDXJlIBq6jRQHNrjkLoesuI/a5F7yac69+p455yAiZWmC/GaN+Y4eABX0DkLsfeFX/3i5Z2k1YWVps7OfS8wtEvkeWuWjGMVi5fDAB67qNLNli8uqhjDZ3XOLifrHY8CREl0YyXt6JILYph0+UI40No3zoKm7sWLlfon7bKYVamkpvKdBn2FZgY2H7MP0ALF+xx2Aiye4V5Z5h5lY2ML7bKDstoVrlzjscldh7zYTAvx0uBlpKRcdkCupdDhIwuhbym4mtA2x+dsKsQQfNdpeLLhgrBzhikyqmEAcD+Iz2oCtj9gP24AuqshrIkbq0bxMzJKYrGwADas1fGM6B/XtRzVv5/9t401rIsKxP79pnuueMb472Yp5yzsrMqqQlc5QIK4caAgJYNdMvYjYW71RKttmVbBvzHkm0k5LaQG1kyXRI24AYDthsDNt00U1FQXUNWVmYNOcWQMb94L9787nTm7R9rr73XvhFJVWa8aqKC2FIo3rvv3jPdc9bwrW99iwxg9y0xtFw0XHVvukwnntBoyXis7TWVA3R04BMG3Pm5n1sHGjD+LB43mCyHnsCb1AHi75O7m/NB5GWXbKz4Xuvc0RgLOq9WhKlLA9ldb7ymR8Dd73VCstUWdtQ+zNIyDK2Dc86hymE7QekCjbJLTCFWaY1EhgTAqs46QUDtGd/EnKfE/Mcn3U3opuYdrgTFO+wTeGDXQ+UE7ne1t4qv/aZHC4ArNPOSEeFf9yWF0u61ZmmYf53X7NB3Wfe4198fuPUQ6I89lE7gzvspOrU69sZe8TBtYIY+VlDUlhyYaDqvUXcE9W1jjLrrQrTxSZdl5IMAc1dzTx+o7IforhsevxniwlGK1WIR0b/EVot7sJDmLznNGa1olN7S6yYDWIpQpT5kIouX3JHaJLTj3WeA/jV1lzYLw0FNaIZ+J/L4ZIcy/SwzAGgX1cYTbYuTB0Zbnht75i6aLMjURMpe4I3p3HuCokaJiyvtGt7sUHvx3O09FtlIfPnLJUYnY4xO0gaKHtFVVeO6X7s3Q9RC06ZOqE+Czz0WheYd02RWd4UEyeXIwVuGUMBwX5OYTMBcutZe430vnHHIztvJiusADmp3f6iKrqmEWNqi45VVOvlacPbH0FrVVt49Xqek2im5+R4bzsQ/UiZb3gM8mpFf4wZLXm76mpBv1sDcW3QQ2WIIHfjNZqWY/sUqu67+pZDs3U0qAByMKDOhKnUQK0OjXDDWyqeIHuZ6lAk8YKtuAXtPBG5whZyx2nGF4CoFkAq6XaAw91ZhU9A6jSzuHe1nngPYf5wsmveAzEUeviuZNftPmNdN1+zSK8qTzC36xOhg498khPcvvsHMpsCDjhLD15+sCryzcf0HqiEDwdEmF54XjdMYnQw9I1QnZLylFEITOayZcXk2HgenzX7Fzc9SywDNFODmKDa23JwF+DAYDyzZfdqdS7bo88O7a35xEHDNV2HhG7LNF8iJhKLgP/+mu9bJqLE8dYAKs619f1KahKrsPOA195hI+KZuAwhMncb+XaM2CAjfI1PRtRtPHA4+Og3kZ92Hk2stSyCICzLsDO9EucZkRXnNX4C7p2u4gihA+0hmpBpUA+ugDs6wMTfnHfiBER87N2/pkLra+dirNPSuVZe7gYVjUEWD6TJdO6Ywc41ishKgEt8dD7SXfSLc3wEA8ajC3mP0Ae6gl8GJvGdYG4ihtf6t2gyf53PB4SxDy/1mXw+VE2giIF+gb6V3XUYpJJLGQmntDboRZFRETS2GeSFGU5bzKep2hMmKoZexjo3EyYUDyBZDGtZh1TM1EGj0rtINmM+TwXVG1td9B5wD4HOaLgUYXKO7PFsw0fJAGha3f+4vyJZN1L2jsCjE9OYvlMgXI/uAbH6MnvyFF+mF5EB7So/ssOQYSRr0bc5OPFAsu8GdsvFEo+gpr0NaNsUNjUOROLisoSR7xBgqjNFPN/1BK/GY3s+UTHb+gcSLRcHw9ndoADUi04Ubjf1JWPEInu5M3aY+ATuUpkWvFfN0DtyJzDTVukUOXg6HB2Y0/EN3H4YZEKeGnvoWvcj3V77gDDQA5C2a1cyRdDFHtQtmHkVT0zDWYdYYeyuhLCqykr6pj3EtaHQihFbuO4zHmhrvev7ge3se5sfOndr+3lnLUCyRoeYgSjZoyiJ1mNE/2W0vO5q5D4FFHgFg4YLDisbHnQdh1p9k7Ml7au9ciPa2PjzjL9ajwvADtlq7Dc7/P1Pc+ZaOF7HxF9XecK/1b9Y2Wo4mDaBdAS9fcHcQF0UliyKfcy3sZUdhvCroZzllGlwQTfYU0i3lWB7mRuQIthxQ5C/HIG4/K+aw3tLmNaFsKmiUVERWKAd0nO11hWxZY+UL/B5q9+fCdtUJEGYNMqNlNP+FxEtpy44ytEn3WtD4D6uSsEXtitI8wlA6Jal7xFkFq0FGU43RSScfoANgfNq9X31wBP2iw4ayI2b6lclu2BDJyF86gHBKcJ1zsM4BANQnoBoRAZvD5qajcEqQAv8+Ol8DnQq9r9DFqVskmsbGRRo8gAqls8PN67a/v/hl2jgPSpGUYdk9y9ebpZ+Zkml7CzQxehYu+ZInvNi4y0EuUhkXGpgdJF8ngRU0zGZmJsxKL3TWMpqGZ5z89EiM1n6NTBh+STDITQd+S0T+UeaeQdVo6oEwFzc5qCxkCogpYwAqM/TJI0SEkl1EmU1yWBLSYj1yAg/o6t+sMV0OLfd/YDTxmetcdXyNFJZ48LRKzM3I6WwuONiqhtXL4eXz34HYiFfZugQb+dh0i0ojW8JR6lriNRBmnIjBmq09jd2n3bEsvLCJzYtLjllzrEE4DsAWzbJ+DBc+qIHhCfEw7WsvOudxh7KuILFk/t9KI9R+k1Nrr/G2J2EzhslkV6vs1WA8uHvCdf1kS25bvRs+Ds4zE/jaVj2fRVW36XiHZw17ZBQg2XfHyw+wFtdeNgZ2btPf+Jz7F0M0sYNBkqEPDzEGzRFzumMYONzZPAIwdZ/JlnwoKRWRMNcPJG0TyjnjJqbztvDRSKN10CASFNrRccFiSvxjzRYVpsuhd9+SJpa4zwV0xwqcUoRN9hEMz3fQ2qmscCIAr6ucHSQb6rnLDZpEWYYQb585/E2ibHcy4PcnTJcDzF8qbX9HutN42kUyYAEIcpVD7fO+/+y+66XxqDD8oK0mDjBdaVklxMXXyJJGWe0Nndfq7qgBgIfVuzF9GpNVAY+EPjTSW6vM+7mwq7yHzXYli9eCUkSDDoUC4KAUb/SfuKnvfLxEvJZg4YVN+1rrxBjlVTPEplTobAATQ4stjWyALIy1BFUvGdaoOoGjmM4YdYUZbaSQjlcOrZGGjDueAWBsZh3w9Q3zGS74HkE/bPyrUxkUgOkV4701PN0dO7xGOJGB6O/QgUI2rzxK7pkfvYwvv3IOANC+zQ1LZn/mu5FaRwuvzcCILQdPqQredxXmNMuWoTqAutNZxXZ0ioqzMtptEqA216OzoT1IcnRCOUjLvC6hJaWdjMX4OPUk8H1Vpwp1rjBdpS9v/yydq5wVoZq7h7XIYEh2G6c7jdc1rxrfAfAAJ0eNBqYr7sJrhZmZwXzR+DooD9JsIrqeLHMBAMNTYnviOKsOsPV8bB1kPCa1UtuxvUT3lnxOpWRFLe7v+12PCsMP6OIZolEmJ1u7H9nYp9vOukXjAgeP0ROTzwc2Xc0XFLKVBu1bximMACif8yxlonUIjFddJ25uDFbdcu+fu+hufuZyywlchZicVfbIiBy8lwzNtz9zAXgGKEwo/vLaSesAAIoci76v5y5rFlFGiqEcxU2OmEzHYO3xmIydB5/OQBpB7SSDc9EH0LRo3kIxR/sbXKbrIXH8fFljYM6fjXDVFc1nQlFU1UAxpxGJwe9l10XmbFyZFbLzQwSi/9qHfgkA8N9f/37rAABg8iRdw+BVF1WOHnP3yOIXzYAeQXzKVjXCzGD7XCg2586TrWSUOhYBQzykTIcNcTTxayh1rOyshHyBDKWFriof9uuu19h7PLT1EVXPGNmaFEGZUnkvrSsPNisJimSnMyvux0sGRmGurdHnrLkQDB9Z+4lygslsNK+U1xjGmeas0ihvY3yU+17kMTOkqbwMiWEzKdcxe9+F5eEaf7ejb8A2/w2vh84J6ICMQlDDwxDl4n6AcOKcwN5T7qmRUEi2YjpojVOIxwShlKboV/YiurnM/c034nRVNAZJ+eA9hekRoC+knKUDKLsUwYyN5n8536CcB06f3LLvKQSVJxslWLisMDX75Y5TOf2KZRcA6jSNJ/CwWhmdlx148BOzLqRC5PC0wJpPCubSXoCq487r4DGN9I7IrkyxekzTIlG3NXSgoY1UNPZjT1mTjT9TMIs+MbrYAGy/QG/+e9/+pwCAX3n9w9YBAMCra0cRrkxRTd3T/8SZdawvCM91u2+Nf2LwcG6iKuecA6DjMedpILayo5AtBoiMMQ5qytr4eLWBb6RIHC+Wx+DoepbNRftxEt/jYzTNTmZaVVtZzJ5hN86qZhvRmlhDC9ZXY7qdbWPclI6VC6rFIKAGLp7NYOo5nKHmC77YXT4gsgLPmQgL7cE5rhZGG2DnIQkSUn6FqapciwhKbTMs1s/iIMLKRYgx2VIGpuqSwlp2ntKg1jWBxd7HUniUCTx4S4mO15Ef1uhAuY5GdVuuNQAAIABJREFUptV1TOp8roV83sE48VhbZka6FWB41hc9k6qgfHPbh9gYUBlBda+5z6a7GnWCu6Iit2/6v2OK2Op2gN6/v4b/7rHfse//H65/j/154XN0Qx/5khALE2l5fFAhPoAV0yoHMbKFEMOTbv9BKYS/WEtI8MU9ITBNNZbRCSn/LLR3auWkfAvYYjEARGOirtZt4SBTwbI6oIsZj4ThFUNlmNK78176zPd8+Es4le7iJxe+BAD4yX/rS/ih13/UfrY04y55HgEAXLy+iv68kcn+03kM4JxS8iaw95jPgKnaGt2bPsVTShgArls8G5ARlxCMVBOZu+LONSiB0bHABg/xGAZ7o99ZY4cNY3tbIx8E9r5RNUXubITKnss6AYJ9dADUqWEy3fId6nSFomP+bqyirlENnawqpEPXrEV6UtqL/EvRb8HGnzOEsq0Q5q4wzs+lnVhW+8VlPs+DM2aE51rjFaO1lKQ2S84YliMjVUNZIt/zo66yDgDwmUP3tbR+J0NlHtj1cDkB0A0QD0lJlJdWdEOw8FjVDjFdjqzh4yJcE5EMQ9H3ufzzF7QtDIeFHzlbHXiBterIGdPWLmHssuhX9BXGJw2Fc1t5+PrBUonFL8Ye6+Da1SP4j67+JwCA554gb/TmZw3McYaOoWeioMlKhGTUWGZImAUIsxrb7xENbguwaSw7HylpYBVKzbWTNYKpYZk05pnrXQmsw9h6b2AdAGAMlIaFMACg7siuH4VoT9AGpz7souqZ2Q0RMDrX4G9+2yv2tU5Q4H/bfwYA8Dtr1N219S9Jd6MPchx7HyjsAc2/2AJAjjMeaxycU1j4kNGl+BBQvLkM1Ib6aQbWy+xEaiE1pj4iMz0pEc5ZGRu7OlGeHAIgWFjaV8EsewoY+1i4jJTZOEvaZ1AIrSHznUmqtISbbJBhjFg+r9Da1TZQWrhIPRU+gUJkdXMEJTG/v4mBaOJUR1OTOeaCQCFJAq0DgpasFpRS1gEAwJ33BxbS5AyM6yOzOl1NTLRchpLyQeAZ52hCo09Hp3D465vfBzxcTkBpSkObVmBG4fn0zukRejJoCIVLjbsbTu0QmvD+7b8hblhRnMwXjOHnL1/xzAB3HDTJi37mlHV81FH72AEAQPYC3eE8CJ4/40kWd1yU/8bnzwJwGUOT0ISq3SdNZ3JBxoSFvgBgfKJlDVUx8OmoK1+kp3h0UrAvJB1QkV4/F5oB/76XGcPxT1fYedKpiJb9e3ygkfCKuuvvEuutunS9pPJm91qAV56g0P1HT7+EX3jpO91nb1IFvSvev/veCijp2M+d28Deiyc8GmQ4BQ7+jLx6dtQI2W27Y0w3XdMbHxtjy9kRqr2wHlSYkTPlCV+8nyZU9vsYG8bOeFV5ncvJvukfkddKCzZWRNks1wGiTGO6pDy2EEDaTADVC6bL7h6Pptoqz8rF59Ta1YgnGrWVrlZob2tPB+rgrMPig5Kcnx0KE5u6gtlf0Q9MH4N4jkSNQxp/XuEUODhPP8uaFhMBJIQq5c2ToaFBG+eQ7taYrIaW2svHzPLvAHD5rivx7tYjOOgBW03kJGxZiiEoNeJxY/XOAXpguFgq9egB4Pa/Tf9z52J84Aw5gLs9v6abl4uJ/BCODMSQbvuRYr4AvP+jF/CF6xSWPHmMotA3XzrjnQevoAS6X2jbQSxNpBEP3YPTWadz4Gwm3SYBudFxsgpRrj2jLhuQlr5CDiCcVpi7SE//8Fwb1oorivj4gePiocxqssUAg+v02R3jiEpR2PYi2QJICtczwcworwtX0vsUOTn58Kv/wLGifuGP/6Y5/hmjbbY/OUafO3fONYjsPVNj5fNGoO648jKgeMzZkzsEmaUVA3df8PdRdhzE0TU0xGTYoEqVaQI08OQkwOhYaIkAOqTrYedNdM3gFGEoue4EOGYSNyHWLXIQkckekiFtk+GXoqs88gI3Ed6tUmqOz06yu7ehDUqgf80RGUbHFfq3tL1XqRZn3st9H8qxneR9kIwaQLlsO6g0xmY4zuAtF/Fz4MT752JxbM/Zr7sxZZQhKzm+s3+z9Agch7I0gEdw0IO1vA5g4K7Ue3bd5QA+ethH9OAudgBvu2aCxq/VFMMO4K/DkpPR7rVmxxdyV639/MLdEfnDsoL6L/+77OAGfKcDOAfwTbO+yQ73XuuhenKrNrDzbIC5yyYNN8WoyZEIYamdPo4mTFLihqNjIWxXz0IBdYLA/tFWG52rEeauCunnVFkGiaqAwY0GuEF/OzgToOwCxYJjFcmU8UPvvwgA+Peedrj2//Un32Z/bu0qL3PIDJ4+eK9jB+1/dclivezIGKOfHFFepD46HniY/tHPUWgWDR0mND7dtYaLYR87CKX2HUDdAfaXFDrr7rWNDxhxMVOEDHJJK3Tvq9qwM5UB+n8W/qDtuJcWX5thUf2LFWQmOg/bDMOIYmUPKPv0+tJzdM0OctrgztV5mwUAjmnDRjmofDhq9zmN9rryrt/kOJCytLUCemt3C65JSeV83lGI6wRQjUK2Sp8JMoWgdO/tX3MYP2czViPIyGkwjZN1gLjZron9jLWYA7q3lR1gdOTLRouqx1lQ4DUsNpGizlrRGyA1ocZHSXqdGwEHN4ibb7n+5rJKDSrJdOIaAUf87Z3Gk3ZuDWmug9x/KmYzyEyCYSeGmpJR49XwmMnW2nPbL7v+7IPDWocJBymlQgBfAHBLa/39SqlFAL8J4CyAqwB+RGu9a977MwB+AiQb9Y+01n/wbvf7UDmBeKRx9DMlgsrxmQFgeDJGHStf42chQNsMmak6AfJFIN00OvnXUozO0lO4/IpC1SYONgDq2mwpm1bvvK/G4IbCwRlRAGs7amFj+gM++sHXAQDrU8JKPnfBTZEb3BBGzOCY4/Pujv2BF76Ef71xFgA5AMBBKJMVRbx+cYNL7NcOTTcP1N7jKXo3S0SmgDs+3TXbccdftR2EpUMDn+XubwC8mQdasG+kQWZ2kDROUnKCH2z5vw7hORgpvsa9G1VXFLF3lS2uWtVIY7R3vrKMwXsc/jH3ZugdC1M2pXGYHqHeAABo2jXG54BwZKZnFco5ALhZDjLy14GoERQa+XzgsVGCAujcMDpSyxo6MgSBHYV8wVckLXuOYKAjMqqssNrau1sZcyLmA7R2zXU3Ha1VShg9O4HOHY3hGeVBJjpw2k5VqqifZEjPSGeDonbuzI0yYgBJhs74mLuH5i9VqFuBHZZUdgKvy57ut8BjCEnmUWV0m2RA4DW6iWSK54V07vjsq7JL39s3SkEUwGGzg/5TAK8D4ArjTwP4Y631zymlftr8/lNKqWcB/G0A7wENmf8jpdSTj2YMm6U0EE3IGhRGA4hnyXLUVaUk58tdsp3NGnNvhV4zCXcYV20e7WeYK7nG3jONNe7xboit591AjNFpnxvfOqCf159zQPnGsI/BIj3to7co1Gfjnz1FkfrzZ6gydra7g//vzffYz8aZQnfNbyirOxQOAI6lxNCYHepinEDvZolsKUK2RBsoBoRdS1aLpxPEEr+Pu8whXvc5dkGhZn6n/zmKtZj/20T+Xh/FLqy6JDNPhucFXTXS6NwS+j+iIF/PN1CN8grf1R863mRkXmejHFQUqXMR314D7m8qAu/ckj0yzqznZOmO4pyC2rFXpksBGXWRSUlMfu6K+yBnlh0xelQrWCoud7+yBAogNJtA0+MWXxcZ4NEA/VtO7rmJFaaLTkK8ShX617UXuUtnJqXL6SQp8mYGTphrlO3QG1yz9FV34euUHABH/sWcQnvLUUxtv8HADz7suYfUc8OOvbXv18qkthVnw1I6on/Tt4dhqW0Huzzm+1qG/XYYSyl1EsD3gWYG/+fm5R8E8B3m518B8EkAP2Ve/w2tdQ7gilLqEoAPAfjMu9n3Q+cEdAAMz5AV46YmrXzhrnhkUnbBZIB22UM+r7yHTWre3/r2BNFQoYi5+uXvPx4S60Maop1vLTDZWbC/Z5sdBBkdW/+K4Zivuv09d8ZRI/7kxhOI3mp7kMxkZcbwiH3ZCVKCq17ONdg1jjCfi9Fd1x7dr+oC5cBAOYVCccJt8Ge+7fcBAP/vnefta81599nXXz6LaOSYTwCQHzE6MOPAK/rynFwb+Zu7TxqiSER7+0+xdoI52Up5DmBygv7Ok8IAIBTdxfEz+8DanA0CijkyHpxp5PN0DIzxZydKhPsRdCwurviRjT+voKLImWEg6zClRIHwl/NvmetiFDarVmCvQWezQZhpW4wNC40w15h7y5wj9wMIWYXZ7mOlXRbSv9kYcoSBl4xjl7pOkrlDsJG2EM30SITWXm0niwWVRlA5+eY6pZGOtRg7CgCToxRJFX3qEeCCc3uLBs8ziaCOFSYr7gTS3QbprtPkyhf9DmZ5Ha2Sr6jpZYvKQmPRlMgQ/PewcA5AHuv9LgV4yqX3uf4nAP8ViNnMa1VrfRsAtNa3lVJMTj8B4LPifTfNa+9qPVROQAcKVZsaasbH/fmr6aaLSKuUGl24C7jaDdDadToui6+XaO2QNeKMYLpiBscv0ZuCOTKUJRJ0boWWEZPsU0TDkEK2CiS3EuBp2l62SeHx/Bv08BR9YHy6wZHHHQB6dXcRjQmVyq+SBZP8exmpc+QvtYiqDqx0A0AjFXsXHYU0W1R2UEuU+e+tehrBfoyf+h7XnLZRzuGDC9cAAL/y6ofx9LENvP7yWTquA5/Bka2ygN3dq5lp2+dmJ+kExsfgG+FI/NwolF2gnKfv4NhjhPnf+apr3IifcfjG5KANvFAgvWwkq1Pa33TV7R8g489LR9r1UJgsYHDZfVdRpl1vSE1MLDuv+QgZYjkdqy808RmWGItmPp57rRXNtuasREcK2aLrF9EKCMrGwjP5fIgm9tlE0VRbw84Rt722DX33kpEDEP0XIMq0VBXtX89Rt0IbLNSgRj+ZLSQjJwvRxApV1wkzagXMXXNWXCvaN48SrePwLqVUCRe1doDuRoXhSV/CHXBBhOy0J9o3/ZzuEilEzqMOKjdOVjq/+15fv4roslLqC+L3T2itPwEASqnvB3BHa/2SUuo7vo5t3esE3rU3eqicQNUFNj4You42iIbKPlAWVxW2qe44jLG1q63eEL0RyBfI6HORb3TS4IsjDX0ig7pNlnjpDYViALQ3Ha1N1UC6aaCnDqWxzSXyEp19ggQknJPsBNj/PFnl5H3EQWTjD7guZIAMl9S26eyZ7YuHJFttEBhsvh7QgzB+n7NM8RXnRVQ1E0mby/CPf+eH7GtBCdSPU8U0vNjBxYvnMG8K4eOTPqWyf9ldZJbQsM5Y+XRapYF8ScAf3RrhboRaaAkF3RLNrmnuMpPfem/RdzF8axXTY4ImGADZ1YFt8sI55wAA0vwZndGI9wWMo4HOFXfxWNqCro357k/T7/MXtNdjoGqNpq28oTSyBtK/bgy2oFrKgT3sAAByAHUaerMsWrsVRifcsenQecvOnRrpDhCNaRujkwkVnu1ULROhCyMlWUq1obBaaCTTKHoB0pKhFdpva1+okh5zU9XiMdFDGYJJdxvkc4GdoZDuaeSD0DqtJiIHmc8bGnHPD9K4MY2Lyd2NyuzfXW+ewgaYZ5mddUUZXv+m/91w1sINa7Kr+LDWO8gEtrTWH3ibv30EwA8opb4XQApgoJT6ZwA2lFLHTBZwDABXpG4CkK1vJwGs4V2uh8oJ6ACouw3a6yQZzEa/Tow0A+uUNEAqUvvWQYM6DZAaTaGt59v+NlvOkHXWFZI325brzmms7CiW+Drpr/iGW1WOwzw+AeSnXCRaXSTjnwrDDzgMdfR8gXg9QUd85XXbRbWTUw10rFGbaFpHGkHLx0fn3u+49nuvkPOZuySKuwVssxxHveFFdwK9G47tke65yCqf84W9rDiZ7Lht3IPOTkKL8Y11u7FQGY6YDcSGTWOavqT8tMxE4gOgnHNzI9obCbFIzKGPztA5MnMLANI7wmkZSG7hPEUPB19Z8mctdBUWv3yA0Vn6gtnIrL7kDmh42jlYZvpYzHoh8ETfsoUAgysUidSpEa8Tne5yQh1H8JbcYHosKi5+mtd5bgDg0zV7azXqxDHPqpTE5iLTY8EdyCyrDnAfgSlc79bobNZo7dINsX/ezFRgzaIjIaqOGJ2pgdZBjcpw93XEAnTmfEKFpu1E53ToO6VsMaRsRlz/ekZ+3UKNiu5ZdkDtrRrZojgPc0n4Pkz3vv7w/S9dh1QT0Fr/DICfAQCTCfyXWusfU0r9YwB/F8DPmf85Pf9dAL+ulPp5UGH4CQCff7f7f6icQDQFFr8UYLrqjDevoPInF4WlFp2edFOMTrbse61csdEhkcVNwGGbkyOhR08LKh+jj8b0MLUFq0Qmc8kekOzFmJyQUYyyOjDJHt2842doo8kt8j7jUyad31OYHhXRdNIAWkHHQpkzEMW7ebLAZ+fI0L069EnvYUEGYo6YrKg6ZsavOGYduq5iadTCnP4mi7XQ7iHkv7Pxr+YM3m3YNzrSCApnxNpfbttj4BWNxdAXc51lptS/pp2+Dtd8TFIVjWj4Tjx0+5CNbWz8d26aDyxUWHzZyYusfoY8d++Kw+bqnphwNYiR7tV2YA/gM4/8Obn0//ZzzmkMBHxSdgOvgFn0DLvNfA/ZQogmFIZeE8STjNxnYvEzdzEzc4yH0jCrzQ5eEqNH+9dcgaZqh2jtlnaWdv96iToN7MyCukWBCD9TQU0UUobAtDk1i8f3/Ci/vV2hagVeg5zMbusW7LPHwQU712LgBx9KA+3tGpMjfoMoK7ZKR3l/6xuuHfRzAH5LKfUTAK4D+GEA0Fq/qpT6LQCvgbTxfvLdMoOAh8wJNJHDe6crsNO2AGDl85KxY4yPuUGbRHmia2UbFuuLR/AerMkKsRy2n3Hvl/o2rR3D5DC745ueHQVnJbzGH5iiGSYI+oJ9k1YIvkDWKX+eosxwzRkLjs4AopKqIgAETbN7dIQsc0/Q4sC1CW/t0HZ3XiVL3ALp2ttz7ygveqZzcNBZPPINv68twy/Sf0HBODQfN/3PkfpwDlCCfcO1hHRLRPcjMUkspHoOG/8oo39WYG6qkc0LjNn4N2l8WzsiUi7IQbChHr28RLWX82SxFl+mA2fjTwch4aIG0d4U05POk2Tz4V3FV46Wy57rAmZjK3s6pmIIC7ST/Qbupjky1h+L5iv5+aCi4j9TMKf9AEXfXTuL3QsqcGvXYf6t/QY6ULYLFwrYeyy1Rq+zWaPoB/bZqHOfpsyTwPjzqtYIKm2fg+5GhSZWXrNYlDe2BieZT3w/sVNoYr9IX7fo38IFkVG2lB1kMzwZIJre3Ux6KOuQh8porT8JYgFBa70N4Lve5n0/C2IS3fd6qJyADpzxSrccnr/wGrxItuwGFKVkdytlFgPfqENRAcrRFIn7LbdXtzV618zNvW7YFSaVb2Li8fMNGFTEfOh/mCCZFMDyyTFev0ba0WmXntJz33MFAPD6K2egKoVo7HZYpxpNxzgyEzmvnN61f9/ed9SgZjPF1mbqqI6rOYLbLnoNKj+SKsXAe4CHfYhrdw9DxnBQPCbDKgfBA06ZUocEGfG2u1dDL2vgCE9KW/DnANe8xEZ7uuwLnI2PM7QhPivkDJKhm8gFUKE4zIFinrbbuRWgc0ejc4eVLAu0tqaeFKhuiZm7gxRVJ8REjBfVCghYpZZpxYVGMmrQ3vHZPS0xmYvwah/+aGJlgwjLQDIfaRsKpy38KuWxXto7NfJBiNFxOUXOzX/QM3pIAA2S4Ws9OhFgdKJlHWg8pG2yUyp7AZpYWScUZkC6U1oj3oQKQdHYWcO8AuuEFMJaI9mjF+pOhNGJxJ5nlGlbYJ8u0Txlru01MQ3s4UCNs/St5/3InyU+pu+nk6wn5EVW/uyQzJ7+2p303wzroXICUPQQplvAkS86nHZ0KvWMQZUGCEqNOiYjIkdHVl2KKrioXLfMDb7uHl7J9aboRNnUtmorT6unta/Rv5YhX3KeZfe9wHevEttmVCf405eftX/LxuTFLn/pHL2w1HhaQfmieehGIhNZcFlEGDSohm5fYU2FaqZOhqYozA+UNahSGVXSOkXR146wPGrw5+P0++JXjZFXZOB5QhXr7EsqY1g45zA2n5cjIZORyJQY/jaGb7oUoLve2C7VwGhEsUopw3+zToQXj8lkeEg6AICMZN1SGFxx1zNbcSlRMR+i/9YYddtlWcVAjCrV8GiZycjN8eWCrYx8ZwuVYa5tDaKJfMVRns8rcX4dOOpoUDYAQrR33BuSUWOVSRkaYZoqQOc6Mv0Jg2vkAGRDpTRwCxemqDoRauEEo4m2AnBMew3NIKeqHaJuhza4SrcLVG03iUyHVCguFltIdnJbCwkLYgwxLMmriR1Ey9eAlVBJXM9JaXPGwO9XVzuoVr4B7cLAoWcCfxXr4XIC73DpGWiwHNz7fY8W7iKlzV67e+m9P1q0ZqWPHy23kh1fw0pSRgE/q3sg10Pw1T5UToDZQYOXNJokxGQ19v7GUU4xUJieEUNk9knznjVnACAU+jft7QZhwZ2bjT9RaS6giNRGLbRdfk88bpAtJzaN3nkmwHueuYK3RhSmvHr1OFb/3EVX+bxCHROFFSD2SvbsFI2Rmg6mBmYS0b8uQmzcpKaHaD8E5mqEY7fNREgr8OKCaOt9uyg/v+BBYPmiO8HObdPMJkb34cQUuE14wuJXzVlrTXr1UDYDAJzOC0DXXcpNcBYidfSh3etl10BRwi6QSqw5TpPBcdoPUNYhezYAYO85Ch3bt0KUPaAytNkKQOd6aAeiMIWyaTkPx3MZADJQO8/1LDwWVNqyagADceQ+fk/wBnP3AyTDxtPV5+JvE/kNgKqhaFlClsxGAqg2NXe19qdx3amsw+F5GlJfR4n50TzboLfmCrmo3T6661T0ZEirHMTmOCWdWFuKatUJUC/G9nq1DhqPGquDBDpUaO1xs45G1QkxXaXMlDMEmYnoEFbfqFhx6encqxENjjGQWN1SqFrK65kp+n5RvntBNqkcnuVWzTc/HvRQOYF4CBz7FDWJ6MCl0NOl0FMvTA408kVfNyXdJqYNLzZ6vetUCI1NJ6UO6EGxOPNBQzivuK+CUjQGHQu92atBBWz86ln7++MXpsgXa/vwpDsaw5OBxXoXPrKBOQC3x07+oH9FAVfo6ejcabD28YaMP4gfHm9HzlAZ3Xc3kNyc73tcDeH5733T/vz61grKS/OoB0ZaegAsHB0Cr5Jqmz4zQfJVB5EcnIMV7KOJbP4DJmUB9t5P1jsd0MHpV/tobzhcm9lDMvrLjrisY/6Ck8wGHEzkNRKFDueeHAWK5doypSZnGwTCOXau08+dDcGFPx5icKMyx05/5+g0qLVXH4mmDXQQWJyf61G1mJkQCoNft4BpK7DGKd31HYKUJWbpjOEJ0ekqpKd7txvULWWNsA6Vn3EYmELVktblqMRx0ZgxrI7SGU1qu718MUIdK0fZnA9Rdt08gyYC6oFC2SUTkgxpMD0b8bIbQCt4DWjxpEHZYzE92rZyu0e6UwsWU4BizsGy0SiyRfRiQOw8W1/R3MQnnKvoneH7xNKojxxS1qrxTprFHtj1DXUCSqmrAIYgaZtqtllCKaUA/BMA3wtgAuDHtdZfNH/7XwFwJ91zX8/+glqjtVuh7Ic0fNx81+2dGuG0QbZMp5stKsQHfsEzzF23ZZg5rZZkRBGNZPR0NmtHjYsVwhJO2ZKLWaKxJRQMmGhCESM7pdEpssocYY1OKbQ/4hRDqzrA3qiD9KboMhVCWTtPB0jXAk+CIfQzbBpCLgxrsVJite3A/te3XEFgeG0OiDXCIW1w8MQu9ra7wFHa6MKfdZAt+V3LLPI2XVIYXJeRK1EEZYdmOshRv2lC9YhqET3TeMZFaUvbVD7sVKWkTy+NfhPBbxoKBb0XgOqWaL9JB8vzotPbxmGaQ5XF0zB3Hb2qITaLLZJDQTUaqcHd2XFzw1A89rtRZ78HwI9O5XtnZc8jISEBuDqKNwFux0XHjZF34Olcg6uVl8VQluJTGuOpc0LRpAaUskVurSjSZs2sqk2sMduJHJHUhKSxTpccYWJwvaKRrpw5aBq2wzORecqf7X/I/Zkf0YSuFW8/hssSOnc0potOt4hpr/wMMJ3UFqFDQxAw36MM/u5nKejDlI34K1v/JjKB79Rab73N3/5dUKPDEwA+DOB/Mf8DwC8D+J8B/OrXuyMdAGXfiERNRUPQ2hDTE317AwczEWQ08aPY9p3Sbmd0whgE87xFmcb+OUEn7QGl4MJXHZ97DhDMxPo6HSNfzBFyYyYycZfp5FSNqAqx1HXVzf2XjtgbPCiBvccDa4RrQxflaVhc+PQKunAyzwBw6rT7OtZ3qBBS7dAGFfwO4skri5i/7QwmZ0h87dJN6qPgNV12hoc/w5HY0r9OMDydAKE7F8DvnpZ1Gc4Q5i86hkxYaI9frrTjfYc5RepsXKfPZEjfFDx8080sp2VFU8E+utOg6PmjCXXkpBn4nIu+T8XkVSfKy4TYwDFsAdC+2PC2N++eCy0dg2TIRBlJVLSFjlW+GInPu6gYAA7ORKhbQPe24O1HQGaOvb9WeVnI7pNkIeeuiu0Lo1z0fF2noCInyOyuJgSgnNLrdidC6wBWyloHxpmY6D05IKdjn8WIgjam+EZTknvnBrCiDwtd8f1k6bO5P8I0KOk5tIQDRYXvewn+3fd65ATue/0ggF/VWmsAn1VKzXObtNb6U0qps+9oa9rx8tM119Cz9QG6M3c/RKHB3MuJn8ZpSqc5GsuWY/swVS0/iu7eJqfBNz9z2KXRDXP3mSYGgkqhbexuvqB8RDIAVr7vBi6/edweSycpceVNooxGw1C2AFhnwV2zbLBzp0+HoASmQor62IkdbLxG0f73fewlAMClEVnzdZADiPf8aJgzH1ZHZXpmZxPY+KAz9KzJD1CkNl1iNWq6AAAgAElEQVQO7IMPmOYxw4IZnnasJMBEzi1X/wB8h8LNfXZEYqa9LGBWkhoAJmIaVetSCh26Rr0mcfLRcrucWQUVdZMyzl/OTOfqbPqa/ICBisxboqkmQyghq4pYR3wtABp/CAA6Uh5l1MsiOGIWkfb4qMLYCLT1bzHUyTx8ctBs+BxTyn0XsnlqdMwEN7U8PzebOlsIvIl1AMFrfK37RG6z90mdwBuXyQwtyfcPC+2CiYWQBO/M8xrmvo4Qw6lSOG4qmrykZlJQ+XWhwnSN2znLtxo0EWzfwKGuR07gay4N4F8ppTSAf8qCSWKdgB3HAsCp4d2+n522v0pT13e+44x9betjJfoLBBY3H59gfHkOC6+ZNDKgcXx9gwU3rQB3XhBGMXO0R45o2ECxwWA8eHiu8SLpuYs0j8AOAwFw4keu2L//2uO/DQD4w9NkpF8an8OL26fx2NOkC3Hrk6c8iqWqaUYBs3FUDZRzrh1/4cltDCcpzi3QU5FVMX7h6d8Anqa/vzg9h9/beB5vvuyuTXSPITAto2dXJzQ0R8Jh8xd8w2+PrSGDKhuRpkLet7tGmZOkcEa5P6yc3uc7EY6o+e9sDILSNxJVW3kQzF0S0SBHwA6bqb4HZ2l785cav9CrqPgshdCicWU1/7nDXFIpi4EbqcjXjEkDs1O1yk5wV+Zkg5idxmyP/j454uBNwB/aDpAD6F1338Xc1RpNqDzDLwMF1rpiJxNlGtvvccPduR/E6/4Wu8wWDVwzdX8L8pn3iqbDJiCnx9Da/AX/WuRzlN2y061jhXzOrw9xXUuHNC7TjrMsCCKy/Soj49DNM5qYfgw7flLUqe5rPaoJfF3rI1rrNSOB+odKqTe01p8Sf79XheYduVal1N8H8PcBII0GaF/dRfbMcUxWY2x8t5wW4h6G8WUiimtx9v1r7r0Hp0OLwTaxYSgYJ5CZB6ltjGRYOIExANTEpRUWXjHYakDF3sIwWZ78kQsAgN88/8cAgF/cfxzvS6/hREQW65f3PoIbn3baUFEFYm2ICBZa2d9Z9bJ7zgGdP/zEy/bnH5h7GevVADdKAsr/x5e+G00RIp46B9gRLrfDQnir7qvZe0wIoMUEMbDhK7vKkyfwRPpiZeWjAQdvyOEhMtLmoiNHj62Dxop/AQTZFX3l4eqyp8HWFLru/yZxGDBfM4briIHjmts2PhB4Bn3wlvlc6o5hvNKynbFSxdIej5AMYWhIGv8mVGiExj8vm5VsuiJ1tuDu2fY29xnQ78x249cj0wciReny+cBG1Cy+xo6xiRQ6m7U1nDvPmJrC48LBC1lu3q/MeOOx+77DnGA2Zno1ERBPtXXwvH/F3dknAprKZnbH0I4ciCSdjA5cQ2eYmf4ds22tgEwMH+LvgCHhOg2QDwILy8ns5n7XI3bQ11ha6zXz/x2l1G+DBh9IJ3Dfangmu/gEAPQHJ3V2ip7ovScVog2yPtWA5I0nl8j4d2/STdC75SxItiS6PoUhC43xtw93ix5IfpCh6aacHnMdvIuvhPZhLHoB9p5yN92TvQ082V7HE5/8cfva0SVnwDdfPOqJrHFaX3XM06L849GxRjNX4ejA4Syf3jyPDyxTgvUPX/s7eHJxE5+5fM7+PbmV2MyBpzfJYe6TFWWhFtUYR2gMeZjROEZ+ALvrjYeLN5GTSeD/7TD1jj86M8zoPTJNl3LC46MBerca1KJpKB75EIU3VCZ1x8z7VbXIBEyhWRYGg5LURQFXUGZIJZ/3ZR24SY0L4bPYslb0fUnHFtROtyconQw1n1Nngw62u0YNhSy1TOcqz5NmM8jBR+NTge3a5nPeeu7eciYsOMjXqH+jxviY+zLmL2psvxdINwScJqC7yapPmABMV64516mR0Z4cNeedE7TH+H1ngyJ1/j0ZE+FCduoHorOei7s2OxSwI2kGBfZ7jSYzDY7cZd1y51J1FCZHQ/v+w1n6ERz0ly2lVBdAoLUemp//HQD/7czbfhfAP1RK/QaoILzPQxTezSrmFK59b0yDXUQhdO5Vw60XZxsWwMFpeiGoKGqT4lqMMVYdM9Zv0X02W1QW0th9L9196S3aVmuPxKts1FJoAMrqGP36pz5iZZ55bb/Ztsc2a/ybxC/qcvrJYxzDTKFcdtHfxev0FF65RvBSOIzwGSzbqE5H2pv9iobOXxZLZTRMEafDtYuez3ppEmXlOayMMWPkMw9jMYDl5PNqHTSepkuUaRth9wzuLY9HRnw6pGiUH+rRKf+98cjUUMTlttpOuFvriKEQvhbpLmn3cBTbhDBdtW57sv+CIZbZYS88YpEzD/45KDS6a0Li+0AGJWT9JEOoailbtOZ7dfcZcW67Ph00yH02kmR07T1m6LGCbXTsL9zFI3hGWZG2ZAjsPqnstWPHLgMcTyxPA5NVJSA+7QUL++eNYxTXUsMZfTuzoaRtZQvKqwPIa8wZJEO1OiTNJM5yJis0cIZncsjP3tfSeOQEvsZaBfDbxAJFBODXtdb/Uin1DwBAa/2LAH4fRA+9BAJc/mP+sFLq/wCNVltWSt0E8N9orX/p69kxz6Dt3PSpY7GIAHTgR4xFX3kFUQm/TI65bbbXFUbnndVSnQrBnRZKM5hl+auNbcQBKM1WFRDvu4c52feLzYCLZJp4BvqBMfjCuEVCk79paTS7LVwcmi4soxjKc3GVhjf4PSgUyp6LfgHS+OcHqU7oYePrUAzoAeKHPjbUPcsXl5pnsc++Aui6slNItwEE/kSo1m6FYuAzYxgCyBYDT7yvNpEnF3/DnBwAp/mtfSDdqXDnW1wIHE2dcQacoaBrQf/ztQwLvykuU+Ts5TnWiatDMCzUXXdfTphpizlzo5WnMiuuTbpdWhmG6UqMznqBYo4DE211eORxt0zk3dmscetj7sDiERnckOGT0PSrGIfNzoqP3Tp1Cc0NndNpItK8Ykrn9nMByr7GPrN7jZZVKGW9Ra2HgyCun5RdGnq/f843/rLXI6zccxBP3CjKMDc05w7X34iuyt+rvL6ACSRERhlU/ncoKdb3vb750aBvnBPQWr8F4L33eP0Xxc8awE++zef/zjvd51x/gu/52Bfx5//s/QB8PrmM3uRg87plbkATkFnqpfm/if3B5vkHx4gBlIZSqccxjv2Fm+W6/axpMBLFSACoOybtvxF4qpxs9PlYdUCCdLxYL58HxTexiWQUPwBMYzIPQqIRb0dehNW+44xAuqvtAwkAUELeF4RZy/kJ7S1Nhq/lsiTAZ8V4SpCCzcKYt6TXAm5/8bBC2Y+sk+DGorC8d3Q5PEVf3OCq0PvZrZAv0G2cGt58Z/3eESM0ZSslGz5TuBw/61Kb9ErLGi5mMsXmeKdLynPeEdeFxDnLyD2otJeZyHMHyPDb1wuNfDG2EFpQOfiJz6O93SAUcNHiq2672y80aN9yPH3qqBUZnnmdWUWAX7TPF4DcMHYAYtdIaqtqgGRfedm0hNU6m5TBSQKEhKN6t2vUscLS6/SGshtgdNztf+6txmWUDTGFOPKv2soqwgL0rIa5g9TqhLI+fq7zOUXigKJ+MX+5BIwjTW+/jbjUu1iP+gQewPUnV58EPjpEtk93f/eNGGFhooHZJiP48rTA3Y1VqiZDzO/XAMosQrrurMvtj2q01w2t0Mzqbe04RgvBL4bLbpqdihmdIoZ8dACjy+6Mf9UWMEJt1C/N31vbNNPYnVuA7i3Xcdo2Cp4YwVBhSdSODXRyUNuIs25R1K4DYl7o0Mn08rFLyqGdXyuw153ngOUv0c8MWUQCr5Uy1AdnyErIYijj54BzGnuPC8NaOufSvlOi6oTWsI6OG2kNsT+IyJthJtmXIN+bmi7squt6EyRMpkNnWDh76Nxp7PmPjwYeZEH7ctemuyEMsKB2AkahUzB+yo4yM33VzGcMW+gozU7YfsGvP/B9yswn+/0DXv9DNk9FcGnUZY2j6gIHjwWeIS0W3fHPvx6gMIEOkwk4I5qsuObKJqGo/+BU6HoQFHUPd+5wpM/FbS7kMqxDb69bPgW0TqkZzZtMdtRNPQsqIN52kh6dO9SpzNc3vS/u4cx65AQerHVQpBDQPbpvxG/7XsB3Cg/9+hr3qjTkwN246Wzz2aHOaX3AFjuAt1uzcN07XVJl9pt9dTb9a2Wb1sySGSmAu/iAs0PfJQkA8J3oA7e0BupvfjzooXICulI2A2i/Fdt0VMd3t4p7FLPAb3SROCfLDtvi7LUOAnHVWHRuumKw2jWFpuXvT2mHxTYxSSVYLNRQGGXmUHUErbHtsxnKPn3WagMV9I+NNlP0+jdNW35CY/sYVlGNoc4pZu4oaOVG8wHUUGVHcRa8XfewDk/eW1Z7/wlTB3mWI3WHCQPUAKQDxwBi6uTwtPM43dtC6jhhBo2hji4qr5C5f54uqtRmyhbceUQZHbfUipGRL2cBy59KzO+aGCoiYvaauYzx52uZzwPZcmDZRDqgY+TiaZ3A6yDeN/0IfA51omzxvOwG3vfEMxskHMbS2wCQrdTIVoDFLzv4sS8kO+5lPEdHxbn0KLrmTKG9Q+yl8TGXaQB+oDT/uvt8b40uHtNndeg7ADtDQhyGpNo2CRXMJ8uudtVE7ruc1aDSSmFw3XmU1voIjZH0Hp3uIt2tbYbYRMRCawmWlg7dNb31nXPAS3ddnne3HmUCD+ZKr8ZId1xzTHwAMy+Vfp8dlGILgyLaZacQFkAxr20BSEcExTBEEuQKTazRWZPcdzgqJ+v1m4lnTUxCV1ys7qzPwD0NHZvsrK1TUWNoTHPMhjAkU6diOrhWQMeBEyMbA5mQFwhqM62Km824G9c8fDyYJTSGrLtRE1VPFN/isbt+k2PuQW9tK5QDNzkMIKYUL66btISoWCkyinyOMPWeGRgeFr4R6q5rjFeVLRzzNZXFXtnzUKVmlq6Bf8IpGT+7vTVNTU4CEiJn6LBmwElZRBmdq60p8XcoCo1Sr4cdp2uo851YlGl7n9h9CWcsHcDeU4Z5JQTh2AEA1Lw4a/ibWKGI+bgAaP/8k6G2lFOG6bijOj4wA4dMEBQP6fthTB/wjbqqyYHLZjvAQUz5HDGNGgF5TRclq8Ac04hhOF/5NsodLZvrInXH3Rwyk1WN9hrvmlhhfCyw9018aBRRPHICD9pShUJ6NbYSDS1BR6xEpB9NZorGXBQW77Hc9oH2OoDDifmb+O7jsUJpHpb2HT/alGJmAEjpULv3TJeBqqeR7IubuHafKwZEa7TZiSYjzMyTdIeocGxY88UInbUck+Mtcx6ms5I3X9ODmbPB0ArFnOiNIEarm9/aCzwefzFQnnFQtZ/hDy67n1kegSM0lluQkXYhip/zlzWqlh/B1Q2sw+IGtpHoLJEGXFU0D9lSYLWvRVSnNHBINre1t925DU+GxKgxRlvSUQFyAJ11iM9Ss5Wsc7T2nOUujPYOZ32D69RTwdnF8IzIWkzGJOm7k2Pu53RLIV8Aurf4ldDoCZnfzG5lj0su9HT43mZWmCxmA8DeE3dTNvs3G+rkgWEaFY0nmSExectuC905pXuNzRrjETl4O/S98TW22BklcoDSwFFM21tOcbSYi5Atu5PTAdDaFcFGpNDEymMIyd6Newn7vaul4Sm/3s9SSp0C6aQdBYWcn9Ba/xOl1CKA3wRwFsBVAD+itd41n/kZAD8Bqnz9I631H7ybfT9UTkAnGtMzJbKVEAuvBZi/5O5oqcW+95h/2qqBZ9TlgzT/hjKGmn5n4y3hHgnXaGVE3OQDKIZkR1NySDLK4+IuHad/U7XXKbWtWXdmm4wUZw7Mtx5cd8ybfCmx25+smI5eJhG1lIfnc4YhHWD/mrZCZXYiVsHFT4LZOPoPSsfnp4Ym7UEcgC9xoLSLfpMDmoMrceWwdPUG3jeLioU5fJZI7g+ZL/tmJvGMLMXU0D5Zl0hmNcOTdw+FPzhn9pfN6EYZgyizm8LM7gWA/o3SnDfLevu1FWZRSe68nGonC8eU2fnd0XICG4vo2Q5i5Rd2ix5RniVLjRlmgLvGsv9FVe7e50xT7n901D03HBik20LJNFaAMbzpHv195SV6OHaf6njP1fCsOc4FupbdayHmrrjzHxnn0d1wVOTpsshoBfMq2a+hZ2ii46Ohza60osDCTv+bqTu8+6UBfWg1gQrAf6G1/qJSqg/gJaXUHwL4cQB/rLX+OaXUTwP4aQA/pZR6FsDfBvAeAMcB/JFS6sl3M3D+oXICaBSUkYcICuDgNN0Fux+f4sRvBtg/T6c7fNrcuGZQTP9y4IlQpbtOh4QNSNl3X3Z73U+D65bA5M2AGnnDt3ackVWVaVoRDyxnEQDQLBeABjoXTCQfm+zDbL/sASXEQBwTOTLdL91t/JtcUyrOBiLM/KYhK6Fgbh2CohrUJtWPxjV0qCwNs3WgUSek6wI4lUrAUQpzw/AJC4re4ikbFIIsOCpLt0uk28D4uHlalY9ls4FsBCwgDWFns8HoaGAzh5aRB2dIwnZFs8NWfr3HGkimupbwht6oiuZJyO8yyvVdRXG+dgdnybrzUJ0o056abd0KUHaV1xwnO5IBZ6A2P2RE7aa08/YGBQp8HzHNtS/0gqYic2ntaUxWlZ8Nd0TfgLkHWFwPoO+PRdxmJTy08pldnfXCbNN5OdUA/Zule/+oxMF5OuCw1KgEZMPG/+in+bUGVUdZBhff1511s73QDb2Xs0IAWIgpMjM/RscTRFOnQNq7TUEQf29V65CcgMahFYZNk+xt8/NQKfU6SEftB0H9UgDwK6Ah9D9lXv8NrXUO4IpS6hJIkeEz73TfD5UTCHNg7iLdKLsfd097+pU2tp+F9/CxAwDArDvvgWTjPznREOd6271fYpU2tTT3VTFPUYvtmjVRKWumBJUPF9Wp3xGcXqaNc9TGaXLNlLuIsGgjNWQpn7xYkpdhljCj6Jr3yRCBdASyFrLyxcJzUPlC5B2vVhSFcedvtqRccbgxkInQr6nagW2cC0qNzmbjSTFbBwC6btFEY3xMPKSBi0Y760T7Y0VTgKJnjgg5wuei4r00YiREoiNyClwEr1PqGuZh7Ow8pCrqLKMsGTaeoJ2cqka05MBzIqmYAawDZRvLeDZxLgICdgCAi8zHJ0RWVcLOCObvQFJU65Yr8HKNSYquxRN9l9w192roUHk9D6ohBzV/SdwsCoimZrJYSt+pLASPTqX2u2cYickARz+tDI7vcHs6D+loXBpSy/qDJklqK1XeEMTGTqC9U2NyJLSZX9lWXvaWbh9a9P4NqQkY9eQXAHwOwCqrKGitbxsdNoAcxGfFx1h88x2vh8oJ8Hr8Ry4CAL76R0/Y11QNnP2+K/b3SRVj6/dPAjD1gjYwPunu4KrrbpTOLcldJwVFa3hietgYgmBddX4Y6pQiSoldS02W/VUTFW+6fS+9Wrk5BpqOj422DqnwKg0TlHuIGMdm56NDwsD59XgMbxh8EwHzl0oLVSjzfy1GLELMD1YaNrvg37m4qxrShGHoTTWEWXPh1mkK0baGZxJb2+DPAz4EIR0zNdlpL+uRuDRj+ZKlohVsUb8ckOGUwoEI3N/ZoUt4StYPVEPzCmQRUsobh7n26in8GQmHOUaaQtUJ7H3R2aix+2RotYDaW/Q3vo94gHyduoPPB874K62JlWTePzylPOfOkKUcfRkWomitjTH1MH/33q6Zvlb23f7rVmAnkfFITs5kdODf85wh5PNmspi5hvx/lVJtRTaoyfGwyajBdIk+y45KFqHzOQUd0o1VJ3QvycwFykluSOG++15fvxNYVkp9Qfz+iXuoKkMp1QPwfwP4z7TWB+rtZ3ff6w/vyiM9VE6g6TWYfGSML7x+Fumt2MPtz/+ty6gaQ3+r6OaaHBMPq2Bd1J0G6ZbPd+bhHmXHx4p1wJEJH4R5v4AdPHErVic1wXDvOqf1tXmvRrYQ2ohyfJQopxLPlS30YUGiZLIztOw5mKd3gzF982dNRpaNi6qB4cnYQjY86ITHAJIIm4NA6pigJYZ0dKCQiNS8Tlxj3XQpQHdd2+izCamhiaUTqpSK5AzLzGZJ7ADYiIa5j6GXXapBcORv5+kKI5wPlC0OW6PITibwCQMBN9iJEUg+6wT2nHl1bzcWwuK/y2OU9wFF+6HdRjzRtkEKgIeJV21/mzze0eL+jRFoY0Ot/EKtLGDzkg7AavPMSIEnBuvPjKPlRr5ZMgQHCfmCDwfx4lqBjOCHJ51RrzqUwUrosolcD4XXKX5aAQg96Gp0zG23mCcYlqnArX2fKdW9TefA85p1eFhOQL8TJ7A1O1lxdimlYpAD+DWt9T83L2/wjBWl1DEAfBXuW3yT10PlBHStUBxQNJAcAMF3Uph0dp6wi8jk/VfeINpFS0jlcqcvAIRTga3ykA6pWy+KhaysyOMlbdot1TJnGmaamavODgBwD9dYDEdJ9t1nwgxIZjjUsvmoGFDNgaGvsqdQdtzvQQNPljcZGn68MRCy+MbnrUOn/qkMj54NYTx1Pwel9qSA0x0yZkw/DUv4XaqarhMbNn6dnUQ+R3RQhmCyRV+FlCM9OcdYh67TWJuovGfqFgdnAk/rpbWr0dr15YtbewJWMtuXEXGduHoMR9E824HOxb2XIR6O/uV1A2DmVZtrZ7Yp5wu09h37h6Nevg9VY6AnYc8kt16bIEAaU9YTAhx9ks9NNX5hWXZx04f9CLqJzfUVMtkMxwBGfiR1dTjehlzSUFcpybNL5k4ypO0NEXkOAArorTfYeVpkYeIZy+d9Km7ZDSi44azqllRQvI+lARySlLQZtftLAF7XWv+8+NPvAvi7AH7O/P874vVfV0r9PKgw/ASAz7+bfT9UTkBVCslmjHQLCL7DVRDTkJ6wz738uH2tteWsiZVphtAiN/dJ1fabs8KconKLtRo+vYweZXQ5OW6atrp0Qy+87IPK3dsNwUfGOHBDkYyqJPsoLMgwpHtmIPggxOikX5gGnCEtO7By0ACRGWQPAmCkGJhdMRMkhSWgKzEQPDA1DzZoMhLSQP+Gm0tbthWSkTNk2QJR/qSaY9V2/Rt1QtdT0lW9oSIzA8OZAWMj9NBIcpj9t/Y0osxFhekOMUr4ePhzUtlUNp5ZdklgotNKe0N0GHaS37c3UOha7SAMYyyTfTr4bCnypnrxz97ozEYjNMe4e0qhs+7fF3yNaPuGkcUjFGsqYjPcxpkjf1903STUZqJkwbLxhO940pfIjOJxY4OJ2MBC2YK7vzPZGS2K77zkd6s0Ofn5y7W9HlxUP/r5wjsWPsaVL0rhvgbTI6Ywz3OJR0LosdGIxnTtD54aAF/E4azDqwl8BMB/COArSqlXzGv/Ncj4/5ZS6icAXAfww7Rb/apS6rcAvAZiFv3ku2EGAQ+ZE+A1fD4Hdrr48BNXAQAvvki1gQBA9zp3RIqHvedupsHj+6g+uehFnLJYxTejhGdksbD3fev40JFr9vffu/AcylGCpc+5N/VvuBnGjMWzmFZY+FmHHRoyddBIuldjeMJ9dfOXtM0c5i9T38BEDucQEEz3dmPxXzp2ilbZ8EbZTKai3UNVJ8orclYdgwuz4UzJGEkZ7SZ0BVrGieVA8KrjjH6642cpTWIMPtsvwIwkFIcXuQBzukzbkMVR1ThqIOsSMRygQ1/8jnSSFAZmktjkSOhF7pwZyJqD7AMJM+dD564Z6uO6odpWftTe2hMwC7jL1anP8vFt/Q23r7/19z6JX/7MRwEAy5+l+4f7A+qWgeGYkGAjfT4231gFtUY8FJF7rLw6EMOc8njCaQPVcKecMbRjSZd1Nw47AK5xNBH88ZG5/z3mC7AOAKDonSnGfK2aZEZugx2agXn8KXfazjPm+2myMlPVv++lD5Md9Be4N84PAN/1Np/5WQA/e7/7fiidwNstdgBvt+ZP7f+lf//rvGbHGcrO1kfLX+wAeKnKN8CHh0k//IsdwAO5NKAPr0/gr2w9VE5ARxrFkRI/9j6Cxv7PCy8AAKLjE7Q/1XOwQwq09tyIxhq+Awg+tov606Q50d7UHj66f9aMjRSZQvkxh6987/FXsVX28HsXnrOvHfuTCMmBiAgBpFsUwk3M4HCZJs+Oa4zH2hbqAKDquK+No9Ojn3UVyDvf4vCJ5IAi+c6G20HVDV20HSmjl0O/c3GNi7I69B1AHSsgcPizVq5YWScK42PKbjvdpaIwR+Ik3a0sfMIDX2TxdDYLaVqw092UpkxFUnSD2tF5AaoBMFRG+L27lswbl6yRoHTRbphTlzNnDFFGzWw875fxetlLINUtubArC5DSATCnPiilxo+YZS0iWZbN5g7hH/oHLgsAgO4dOkc5Q1hCWdOlgGoKUvpZyHVwFsDzCwDKaLlQzJmj/O4D4bw4Q2L4J6j8/oneWoV8LvTpxfK7Ze2rigkJ1JMgz4GO797n53Wdm3Pwa213M7gYwj3UdUgdw3+V66FyAkHUoLM4xT+//F7Urw2ApwjYb3+qR0U7tuUNdQDzTRMdhBi9toDO0/REswMAqPOxiRVGx2XrpyuiTVeAEMCPPfkiAODXLxEBoP9nBHi29rUddA0A42O0U6m7H1ROvmDnmQC9G/ASw0pMeCq7yhuQoWqgdyPDzY87w5+fdlWy8/87fW7n2Zb5PDB/qbYNNmGhAe3YIGHuOzitKI1n/DYe08Nor51QgSwGMPNe6fdsSaFYrLH0smHERCTjzM44zABo111dDMgZsbFIdun8YuEkQoFzNwlQB64eE4+1xwdnY7x/zsEAEnNPhjWKfuhBXHKuL+PoXKcBfFJAaiQY+jeYsO7j0HTOPsyiGm0zgaLnuO7sAKw4X+MXO//gZz+GY5A1gQaqbNBdM3TJfuSxawDX+QwAi6/RcfH31bQCQNQkbM3IHFveU4hF0dcyi/i/SHk4fZ0qRJk/PSzdEt3EiUIxiCw8BJDDmW3s8+jIwsDK5jBVaehIedpFYaHt9WLYiesXyYFGMtSYGBzRSfoAAA+OSURBVAG9hTcO0XA/0g56wNY4hHpxgKprNFI2iXuYLZOOkCzgaQU0qenKNJO3si+R8Q9EI5Vn/EHGv1FOvkAroHpjgN/8cwPbffs+kn/lOn6CQqNKA48JoUPl6dQnI20HfeuANGUW3jQiWYnyplFxI1Nvzd18my+0bWfoweP0udO/bYqWPWB0TNYPyAFw5lGlCtNl5RmcdMcNyVEBdalyV24TKoS5towYKfSVz1OUXhwxWU+hkOyEGJ3ma2Gw4Jlh5VyoHp0iB8BSEI0Z+MPKqBylMl1X5/T+ubfoZIKqQbRvBPQAFPMJhqdjF9VafRqOorkLurHnUgncmsXUpOGXRfXOpvaxdk1ifa19Q/ctaLseHKQcA0sazOGpgAyseKuUkXAD3Q3RIASqfmS/x/FRLl67zw+uCjyfG/b48xHx5uOhowTr0JcUkfedGwgvmGh9ZanFzDaSzWI68TOH/vVaDHRSyIW2T7pTo04D+90A/nXL50MLDQWlBkqN8SpdR87UOTiwc5zNfckB1PE/F+n2YSytD40d9Fe5Hi4nYBYzZJi2Wc5plHNAbRg6yWaEWjSDcY+A19DF8sCKphLtnycL0ogoFTCF1NgxeKJ/MeeJknHbvRxkEmVAaO71rW81N3aP8uP4Yts6AD4mpYGh6RTlSEo2yrDhp+MOgJ0Em2amW/uOL5LWxMTNPzBwA2c07JQcndEdr4SnWgckS8HF3jpxUgZ1CyiX3LCPzlroGTVm9bAT4NnOVkvnNsle88AdHZHz5oc5mpriNBeKQxouwgXAzo0MOg5QLJqspx1Qo5yRl+4ZmWpuWOLz4ZXu1cAesPu4eyyqrrsveMCPpB9OVgWl0xSkI1EsDfMaxYAnFtF/3PiVDwIr3ewuEv03MLLQcrpYWLjvh8+ZISpvkA6c1LNcoZSwaFOjF/eDAHRvWDkSE3nbax0p1KkLHvi4WIKhaikrGEjnYbI1wcSSGVfdIuqwlNVQtRPXUzUQV7X3XWVLxuivlzg469gTu4/Te/ia8T3H9222EKB3q7xLX+hQ1qNM4AFbmh6G8Wm6GeJjZJkjAPmuC72rnp8BACQfILFpXvOXS0xWI4uBT47QfNNIRLOSPWQlCwZM1dNedBllLHFt/p4FUMs52q+4NKXsOmzUzue1MhE+Q2f3oxltadc9FLK+0L9VEaQjJI6zhcATjPOYThG8+kM8Io19xoYnKxT58zHUqTNAQQWERYSAHUltuPP8bEZAvC/eX9IYQf57nbCmPB8M/cfGtYmIaskRn53GZa7R/tM9bH5A4+hf8LEptPZqJCNDWe0oj+seZdpjPEUZYewMGfF3yNenzXIVlm3iTxLrrvkNIWFeI19038v++QjzlyvsPCFweDENS3Yqc1e0lfrOqLGqSnkeMzGDpMxE/+bdBklG08XAjY8sOwHKTmDrJwAQTXz4hg7MQSu1mU0BUPZadpR1aADugmekfAoUZSP5wIjrGaiHKbQAOWSGbKuOQtWJLLQj51mMj8UIcwc95XOBcwBwvSky05DOTrKi7m9p6PoBLlx/neuhcgJNW2P0ngLJrQR4aoRixxn+zg13qoVpDJNSDfHEwUXM/QeAzffG6K77g0niiUt/g8ovuHEUOD7qHn4p8cCR43RVfOattu1q7V/jz7gHMppU2H2KDi4eNxieFtHnxRT5cmP56WVPo7WjsPIKfZ4jubEcKCImY6maYByWvVANEMmmo5DSbH74eRgPG6947GSl3cxht300btta0Wf5OkcZN0yZv3NXKm+nNrRSLtwWQNENLH48XSQ8f/dZs6uWRv9SgBGpgSDZJ/zdcue1iVzNNWFHy/UZNmJs/Gl2BNC7Zbpfk8Cr1TCVVEb+rTVHMKjn6MRkw9TWc+KRE9lWvgjki8o6nGjsZ2NVm5ydhDT3nnFvWPk8HRh3IDehQjSpLazHUh6ysU5qPIWm0auYN3Iltb4ni0kWZ5ORpmsCFxRwhlp2FVp7jb1ebLADUYfIxSjRJqGshrPE0AQIvGTmy13NHJi0t7RfVFbkRFp7ohAuajVVd2Zs3rtdGo8Kww/aUoUiBwBAvdYDk0hsi7yJUNNNZWbV0u+so8O4ejHnuPpVF9h/THmSxXIknmooypZFNB0ozF02Wimm0MjGvzJaQp01+r3s+YNQWI5YRmXFXGz55sOT9JXJXoIjL7kHZHiKfr79rcLpHakweB12f4CLbjt3aoyPuYdCB1yHMLBTRxk+vTl308fA0Xq25Ip67TuUGbgiI2VYjK1TB7HoCB4orxBt+wuENn7njqtP1BG9Zjt1TYG+f4WxYBog45QyyQFwvcMWTc1+qo7ytGq4bsPH17ndeHBFPGWIRkS8mTu+eEg7YuN/54OEAUrDLX/OV5xhSrZDpNvue60TloVw1wJwg3QAYHDBHQezz7gOAfi8etVQxCy7iqVuUwhyAHKSmmpckyCzqLiGAFAx2mL8HBSMODjSVhzP7kOwn5qQZDNkDULWilRjsg1h/LkmVKXkyDlrsPpDYlvM6ALIoTStwKtXHNp6RBF9sJYOCeqJRoGPRXOnKQcKFcknsFOozTjIQqTWcgJTLNoH0h2iRcqiXjxqrNFsWgGq1A3a5iYwlkOWzU+AcwB29oFSaK07Qfy175rHwoXKGn9VU72CdeC7t7XnlOYv0c+3v9PdnP03IvsADK5SB23njlSzdMaGO6ZD+z912TKuzhnA3occ9DH/eScF3V1306qyRWpg4kygapP88+SIYyIhEJIJK+RIeFJXEwHj1cCqvPLoSZZ5qFPSRuJrzTAV1x6SUYOwEE1KijML93etnIqoVkQblpPCZDScG4PUvV14f49GXInUqPttDM+5SrLU8w8zZ+gOnjJ6NqPAHLtGtqishDIrYEpZB8n2aRmHkQo4SjqAYhAhyhqPwSMdHi8rPLgcUwc234YNO1qGgxytmc4rJDxcWFYlMkCGFKWOUijg12RIE+7sXIrCyJfws8pQoDhmO4VOmb+LjM4raFdE7ZWZgHQAkqJ7P0sD0I8ygQdrqRqIRgEZqtCn2EmcnKmF8u/TFaGt3/g3NERAY9U2mSoXUJGOWQ0MKXBEFZa+IdABMLgmipFGaCtdcxhUtdDBxgcpZKxTYOv5yEothLU/CKTsULSb7vqQ09xrgdm+xuSIjzenu42N0oo+YeIc0TEeLqEIqa1TdYHp8Rqta4l9H++7iRSGJ52+T2sXgAKOvEgHP3ysS3RYU0OpWhSNT404bp2QkeRietkBELiie9kNoGptFVV7N6gPobfGlkNZLB0g6KwW0bDtzmXGqKbvR+r1kFSFsteJrpH7+73GZVY9I0i4mnhSEHVMgQYb6nzOQVGDC3SRxscN1JUZOGfqjlGODaXzg7fSPUeXHR2LoPT/396dhchRRWEc/3/jqNGoEEmCKw5KRhHFjYAokRhExAc3MCiigsEHIXEjCmFwwSiooD65IOiDuEaE6INECYqKS4iKJmMSFY3LBE00JmAeolGPD3VHa5bM9HR3dVd3fT8o0t11a+Ye7qRP3dvVp/jvjW909dKev0bWLYJspjR8gjT8HYr8cs2Ib0vv+ntEMbjR+4evksp/npS/3/Pw78kX98vXHRr+gDk/w83+BrNLT//p1X8zxJ492Zt+vjR8fkl2nz+B+D/ev3uyE8ThWV3TvqwX4ZmAmVmReneX+0y7Gz4YVnTBJU7DJP0CfF/Qj58J/Dppq85XhTirECM4zokcExGzJm+2d5JWpd9di18j4oJGfl9RuioJFEnSx5PVA+8GVYizCjGC47TaTFxRzczMupqTgJlZhTkJ1G7M/UC7VBXirEKM4DitBv5MwMyswjwTMDOrMCcBM7MKcxJIJL0k6bO0fZe72XO+zdGS3pa0UdIXkm6ayvFl0Gicaf8SSV+mfQ+2rve1a8J43i1pS+5nXNjaCGrTjPFMbZZKCkm1XvfeUk0Yz+WS1qXj35R0RGsjKLGI8DZqAx4C7hzn9cOB09Pjg4GvgBNrPb5sWz1xAucCq4H90/PZ7Y6joDjvBpa2u+9Fx5leOxp4g+yLljPbHUdB43lIrt2NwBPtjqMsm2cCo0gSsBB4YfS+iPgpIj5Nj38HNgJH1np8mTQQ5w3A/RHxR9q/rTU9rk+j49kpGozzEeB2RpRdLKd644yIXB1gptMBsbaKk8BY84CtEfH1RI0k9QGnAWvqOb4E6o2zH5gnaY2kdyTNLbSXjWtkPBenJYSnJc0Y98DyqCtOSRcBWyLi86I72CR1j6ek+yT9CFwF3FlgHztKpQrISVoNHDbOroGIeDU9vpJJzuIlHQS8Atw86gyjpuOLVnCcvcAM4ExgLrBC0rGR5tmtVHCcjwPLyc4Yl5MtQVzXjH5PVVFxSjoQGADOb2Z/61X0/8+IGAAGJC0DFgN3NaXjna7d61Fl2sje4LYCR03QZl+y9dNb6zm+DFsjcQKrgPm5598As9odUxHjmWvTBwy2O55mxwmcDGwDvkvbX8APwGHtjqng8TymzOPZ6s3LQSOdB2yKiKHxdqb1yKeAjRHx8FSPL5FG4lwJLEjt+oH9KG+lyrrjlHR47umlwGBhvWxcXXFGxPqImB0RfRHRBwyRfbD6cys6XYdGxnNO7ulFwKbCetlhnARGuoJRU01JR0h6PT09G7gaWLCXSwfHHF9SjcT5NHCspEHgReDaSKdXJdRInA9KWi9pHdkVUbe0rNdT1+jfbadoJM77JQ2m8TwfGHOZbFW5bISZWYV5JmBmVmFOAmZmFeYkYGZWYU4CZmYV5iRgZh0lfYN7W7pCrZb2CyVtSEXlni+6f53GVweZWUeRdA6wC3gmIk6apO0cYAWwICJ2SJodJa931WqeCZhZR4mId4Hf8q9JOk7SKkmfSHpP0glp1/XAoxGxIx3rBDCKk4B1JUlzU/G3aZKmp6WACc8araM9CSyJiDOApcBj6fV+oF/S+5I+knRB23pYUpUqIGfVERFrJb0G3AscADwbEWUu/WB1SgXjzgJezipHALB/+rcXmAPMB44C3pN0UkTsbHU/y8pJwLrZPcBaYDfZjUSsO/UAOyPi1HH2DQEfRcQeYLOkL8mSwtpWdrDMvBxk3exQ4CCyu0xNa3NfrCCRlYveLOlyyArJSTol7V5JVvsJZbfO7Ae+bUtHS8pJwLrZk8AdwHPAA23uizWJpBeAD4HjJQ1JWkR2o5hFkj4HvgAuTs3fALZL2gC8DdwWEdvb0e+y8iWi1pUkXQNcEhGXSdoH+ABYFhFvtblrZqXiJGBmVmFeDjIzqzAnATOzCnMSMDOrMCcBM7MKcxIwM6swJwEzswpzEjAzq7B/AbQosGp8iZYeAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pad using a bounding box\n",
-    "\n",
-    "See docs for `rio.pad_box`:\n",
-    "\n",
-    "  - [DataArray.pad_box](../rioxarray.rst#rioxarray.raster_array.RasterArray.pad_box)\n",
-    "  - [Dataset.pad_box](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.pad_box)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xdsc = xds.rio.pad_box(\n",
-    "    minx=-7.3e+06,\n",
-    "    miny=4.99e+06,\n",
-    "    maxx=-7.2e+06,\n",
-    "    maxy=5.06e+06,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xdsc.values[0, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fcb8ed54ba8>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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diff --git a/0.18.0/examples/read-locks.html b/0.18.0/examples/read-locks.html
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-  <title>Example - Reading COGs in Parallel &mdash; rioxarray 0.18.0 documentation</title>
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-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="convert_to_raster.html">Example - Convert dataset to raster (GeoTiff)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="pad_box.html">Example - Pad Box</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Example - Reading COGs in Parallel</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Scheduler-Choice">Scheduler Choice</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Reading-without-Locks">Reading without Locks</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Chunking">Chunking</a></li>
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-      <li class="breadcrumb-item active">Example - Reading COGs in Parallel</li>
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-             
-  <section id="Example---Reading-COGs-in-Parallel">
-<h1>Example - Reading COGs in Parallel<a class="headerlink" href="#Example---Reading-COGs-in-Parallel" title="Link to this heading"></a></h1>
-<p>Cloud Optimized Geotiffs (COGs) can be internally chunked, which makes it possible to read them in parallel from multiple threads. However, the libraries <code class="docutils literal notranslate"><span class="pre">rioxarray</span></code> builds on, <code class="docutils literal notranslate"><span class="pre">rasterio</span></code> and <code class="docutils literal notranslate"><span class="pre">GDAL</span></code>, require some care to be used safely from multiple threads within a single process. By default, <a class="reference internal" href="../rioxarray.html#rioxarray-open-rasterio"><span class="std std-ref">rioxarray.open_rasterio</span></a> will acquire a per-process lock when reading a chunk of a COG.</p>
-<p>If you’re using <code class="docutils literal notranslate"><span class="pre">rioxarray</span></code> with <a class="reference external" href="http://docs.dask.org/">Dask</a> through the <code class="docutils literal notranslate"><span class="pre">chunks</span></code> keyword, you can also specify the <code class="docutils literal notranslate"><span class="pre">lock=False</span></code> keyword to ensure that reading <em>and</em> operating on your data happen in parallel.</p>
-<p>Note: Also see <a class="reference internal" href="dask_read_write.html"><span class="doc">Reading and Writing with Dask</span></a></p>
-<section id="Scheduler-Choice">
-<h2>Scheduler Choice<a class="headerlink" href="#Scheduler-Choice" title="Link to this heading"></a></h2>
-<p>Dask has <a class="reference external" href="https://docs.dask.org/en/latest/scheduling.html">several schedulers</a> which run computations in parallel. Which scheduler is best depends on a variety of factors, including whether your computation holds Python’s Global Interpreter Lock, whether how much data needs to be moved around, and whether you need more than one machine’s computational power. This section about read-locks only applies if you have more than one thread in a process. This will happen with Dask’s <a class="reference external" href="https://docs.dask.org/en/latest/scheduling.html#local-threads">local threaded
-scheduler</a> and its <a class="reference external" href="https://distributed.dask.org/en/latest/">distributed scheduler</a> when configured to use more than one thread per worker.</p>
-<p>By default, <code class="docutils literal notranslate"><span class="pre">xarray</span></code> objects will use the local <code class="docutils literal notranslate"><span class="pre">threaded</span></code> scheduler.</p>
-</section>
-<section id="Reading-without-Locks">
-<h2>Reading without Locks<a class="headerlink" href="#Reading-without-Locks" title="Link to this heading"></a></h2>
-<p>To read a COG without any locks, you’d specify <code class="docutils literal notranslate"><span class="pre">lock=False</span></code>. This tells <code class="docutils literal notranslate"><span class="pre">rioxarray</span></code> to open a new <code class="docutils literal notranslate"><span class="pre">rasterio.DatasetReader</span></code> in each thread, rather than trying to share one amongst multiple threads.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray
-
-url = (
-    &quot;https://naipeuwest.blob.core.windows.net/naip/v002/md/2013/md_100cm_2013/&quot;
-    &quot;39076/m_3907617_ne_18_1_20130924.tif&quot;
-)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ds = rioxarray.open_rasterio(url, lock=False, chunks=(4, &quot;auto&quot;, -1))
-%time _ = ds.mean().compute()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CPU times: user 2.4 s, sys: 361 ms, total: 2.76 s
-Wall time: 3.32 s
-</pre></div></div>
-</div>
-<p>Note: these timings are from a VM in the same Azure data center that’s hosting the COG. Running this locally will give different times.</p>
-</section>
-<section id="Chunking">
-<h2>Chunking<a class="headerlink" href="#Chunking" title="Link to this heading"></a></h2>
-<p>For maximum read performance, the chunking pattern you request should align with the internal chunking of the COG. Typically this means reading the data in a “row major” format: your chunks should be as wide as possible along the columns. We did that above with the chunks of <code class="docutils literal notranslate"><span class="pre">(4,</span> <span class="pre">&quot;auto&quot;,</span> <span class="pre">-1)</span></code>. The <code class="docutils literal notranslate"><span class="pre">-1</span></code> says “include all the columns”, and the <code class="docutils literal notranslate"><span class="pre">&quot;auto&quot;</span></code> will make the chunking along the rows as large as possible while staying in a reasonable limit (specified in
-<code class="docutils literal notranslate"><span class="pre">dask.config.get(&quot;array.chunk-size&quot;)</span></code>).</p>
-<p>If we flipped that, and instead read as much of the rows as possible, we’ll see slower performance.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ds = rioxarray.open_rasterio(url, lock=False, chunks=(1, -1, &quot;auto&quot;))
-%time _ = ds.mean().compute()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CPU times: user 8.58 s, sys: 1.08 s, total: 9.66 s
-Wall time: 11.2 s
-</pre></div></div>
-</div>
-<p>That said, reading is typically just the first step in a larger computation. You’d want to consider what chunking is best for your whole computation. See <a class="reference external" href="https://docs.dask.org/en/latest/array-chunks.html">https://docs.dask.org/en/latest/array-chunks.html</a> for more on choosing chunks.</p>
-</section>
-<section id="Caching-Considerations">
-<h2>Caching Considerations<a class="headerlink" href="#Caching-Considerations" title="Link to this heading"></a></h2>
-<p>Specifying <code class="docutils literal notranslate"><span class="pre">lock=False</span></code> will disable some internal caching done by xarray or rasterio. For example, the first and second reads here are roughly the same, since nothing is cached.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ds = rioxarray.open_rasterio(url, lock=False, chunks=(4, &quot;auto&quot;, -1))
-%time _ = ds.mean().compute()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CPU times: user 2.49 s, sys: 392 ms, total: 2.88 s
-Wall time: 3.25 s
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%time _ = ds.mean().compute()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CPU times: user 2.48 s, sys: 292 ms, total: 2.78 s
-Wall time: 2.97 s
-</pre></div></div>
-</div>
-<p>By default and when a lock is passed in, the initial read is slower (since some threads are waiting around for a lock).</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ds = rioxarray.open_rasterio(url, chunks=(4, &quot;auto&quot;, -1))  # use the default locking
-%time _ = ds.mean().compute()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CPU times: user 2.15 s, sys: 284 ms, total: 2.44 s
-Wall time: 5.03 s
-</pre></div></div>
-</div>
-<p>But thanks to caching, subsequent reads are much faster.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%time _ = ds.mean().compute()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CPU times: user 223 ms, sys: 64.9 ms, total: 288 ms
-Wall time: 200 ms
-</pre></div></div>
-</div>
-<p>If you’re repeatedly reading subsets of the data, using the default lock or <code class="docutils literal notranslate"><span class="pre">lock=some_lock_object</span></code> to benefit from the caching.</p>
-</section>
-</section>
-
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diff --git a/0.18.0/examples/read-locks.ipynb b/0.18.0/examples/read-locks.ipynb
deleted file mode 100644
index ac44f228..00000000
--- a/0.18.0/examples/read-locks.ipynb
+++ /dev/null
@@ -1,229 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reading COGs in Parallel\n",
-    "\n",
-    "Cloud Optimized Geotiffs (COGs) can be internally chunked, which makes it possible to read them in parallel from multiple threads. However, the libraries `rioxarray` builds on, `rasterio` and `GDAL`, require some care to be used safely from multiple threads within a single process. By default, [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio) will acquire a per-process lock when reading a chunk of a COG.\n",
-    "\n",
-    "If you're using `rioxarray` with [Dask](http://docs.dask.org/) through the `chunks` keyword, you can also specify the `lock=False` keyword to ensure that reading *and* operating on your data happen in parallel.\n",
-    "\n",
-    "Note: Also see [Reading and Writing with Dask](dask_read_write.ipynb)\n",
-    "\n",
-    "## Scheduler Choice\n",
-    "\n",
-    "Dask has [several schedulers](https://docs.dask.org/en/latest/scheduling.html) which run computations in parallel. Which scheduler is best depends on a variety of factors, including whether your computation holds Python's Global Interpreter Lock, whether how much data needs to be moved around, and whether you need more than one machine's computational power. This section about read-locks only applies if you have more than one thread in a process. This will happen with Dask's [local threaded scheduler](https://docs.dask.org/en/latest/scheduling.html#local-threads) and its [distributed scheduler](https://distributed.dask.org/en/latest/) when configured to use more than one thread per worker.\n",
-    "\n",
-    "By default, `xarray` objects will use the local `threaded` scheduler."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reading without Locks\n",
-    "\n",
-    "To read a COG without any locks, you'd specify `lock=False`. This tells `rioxarray` to open a new `rasterio.DatasetReader` in each thread, rather than trying to share one amongst multiple threads."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "\n",
-    "url = (\n",
-    "    \"https://naipeuwest.blob.core.windows.net/naip/v002/md/2013/md_100cm_2013/\"\n",
-    "    \"39076/m_3907617_ne_18_1_20130924.tif\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.4 s, sys: 361 ms, total: 2.76 s\n",
-      "Wall time: 3.32 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, lock=False, chunks=(4, \"auto\", -1))\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: these timings are from a VM in the same Azure data center that's hosting the COG. Running this locally will give different times.\n",
-    "\n",
-    "## Chunking\n",
-    "\n",
-    "For maximum read performance, the chunking pattern you request should align with the internal chunking of the COG. Typically this means reading the data in a \"row major\" format: your chunks should be as wide as possible along the columns. We did that above with the chunks of `(4, \"auto\", -1)`. The `-1` says \"include all the columns\", and the `\"auto\"` will make the chunking along the rows as large as possible while staying in a reasonable limit (specified in `dask.config.get(\"array.chunk-size\")`).\n",
-    "\n",
-    "If we flipped that, and instead read as much of the rows as possible, we'll see slower performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 8.58 s, sys: 1.08 s, total: 9.66 s\n",
-      "Wall time: 11.2 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, lock=False, chunks=(1, -1, \"auto\"))\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That said, reading is typically just the first step in a larger computation. You'd want to consider what chunking is best for your whole computation. See https://docs.dask.org/en/latest/array-chunks.html for more on choosing chunks."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Caching Considerations\n",
-    "\n",
-    "Specifying `lock=False` will disable some internal caching done by xarray or rasterio. For example, the first and second reads here are roughly the same, since nothing is cached."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.49 s, sys: 392 ms, total: 2.88 s\n",
-      "Wall time: 3.25 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, lock=False, chunks=(4, \"auto\", -1))\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.48 s, sys: 292 ms, total: 2.78 s\n",
-      "Wall time: 2.97 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By default and when a lock is passed in, the initial read is slower (since some threads are waiting around for a lock)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.15 s, sys: 284 ms, total: 2.44 s\n",
-      "Wall time: 5.03 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = rioxarray.open_rasterio(url, chunks=(4, \"auto\", -1))  # use the default locking\n",
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But thanks to caching, subsequent reads are much faster."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 223 ms, sys: 64.9 ms, total: 288 ms\n",
-      "Wall time: 200 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time _ = ds.mean().compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you're repeatedly reading subsets of the data, using the default lock or `lock=some_lock_object` to benefit from the caching."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/examples/reproject.html b/0.18.0/examples/reproject.html
deleted file mode 100644
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-  <section id="Example---Reproject">
-<h1>Example - Reproject<a class="headerlink" href="#Example---Reproject" title="Link to this heading"></a></h1>
-<p>To re-project with dask, see <a class="reference external" href="https://odc-geo.readthedocs.io/">odc-geo</a> &amp; <a class="reference external" href="https://pyresample.readthedocs.io">pyresample</a>.</p>
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray # for the extension to load
-import xarray
-import rasterio
-
-%matplotlib inline
-</pre></div>
-</div>
-</div>
-<section id="Load-in-xarray-dataset">
-<h2>Load in xarray dataset<a class="headerlink" href="#Load-in-xarray-dataset" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
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-Data variables:
-    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368
-    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-45eb2830-a807-4070-8270-eafb894091e9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45eb2830-a807-4070-8270-eafb894091e9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-866810c8-7763-4ad0-884c-659a75f5c365' class='xr-section-summary-in' type='checkbox'  checked><label for='section-866810c8-7763-4ad0-884c-659a75f5c365' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-28b007a0-1d57-44a2-9044-da130e8cfa2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28b007a0-1d57-44a2-9044-da130e8cfa2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0ff534a-88e7-4944-ba89-b5645ea219f9' class='xr-var-data-in' type='checkbox'><label for='data-c0ff534a-88e7-4944-ba89-b5645ea219f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.663e+05 4.663e+05 ... 4.663e+05</div><input id='attrs-34ea2c4f-000a-4ce2-b62e-a8dfbcc906f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34ea2c4f-000a-4ce2-b62e-a8dfbcc906f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53a4c842-cfce-4d59-b370-8f002ab69088' class='xr-var-data-in' type='checkbox'><label for='data-53a4c842-cfce-4d59-b370-8f002ab69088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5, 466285.5,
-       466288.5, 466291.5, 466294.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-12-19T10:27:29.687763 2016-...</div><input id='attrs-85843e76-c4c1-44ee-b8fe-9c6f0b2c934a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85843e76-c4c1-44ee-b8fe-9c6f0b2c934a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08a858f1-899e-4560-a155-b91589db4ab6' class='xr-var-data-in' type='checkbox'><label for='data-08a858f1-899e-4560-a155-b91589db4ab6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-12-19T10:27:29.687763000&#x27;, &#x27;2016-12-29T12:52:42.347451000&#x27;],
-      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.085e+06 8.085e+06 ... 8.085e+06</div><input id='attrs-d2563e2f-3d5c-430b-b264-1aa3c57398b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2563e2f-3d5c-430b-b264-1aa3c57398b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fbadacc-5d51-49a4-9648-0a1ab3420bc4' class='xr-var-data-in' type='checkbox'><label for='data-3fbadacc-5d51-49a4-9648-0a1ab3420bc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,
-       8084680.5, 8084677.5, 8084674.5, 8084671.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba0f4c5e-f47a-4f11-988d-a3e5128f44bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba0f4c5e-f47a-4f11-988d-a3e5128f44bc' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a28b5fc7-3647-4072-a0fb-b8aad6f664ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a28b5fc7-3647-4072-a0fb-b8aad6f664ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b7f31d-2d0a-48f5-8f1c-db719140f271' class='xr-var-data-in' type='checkbox'><label for='data-e9b7f31d-2d0a-48f5-8f1c-db719140f271' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd><dt><span>nodata :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[[6.611017, 5.580979, 0.399607, 2.052803, 5.479985, 4.760219,
-         5.077927, 5.574792, 0.726683, 5.170288],
-        [4.535516, 0.088263, 4.222302, 0.289199, 3.478147, 3.227945,
-         2.736443, 2.821799, 1.04221 , 1.099616],
-        [0.071364, 4.393267, 0.496907, 2.311926, 1.19123 , 5.984189,
-         5.266977, 1.146988, 3.219185, 0.982011],
-        [5.818552, 5.176065, 4.891903, 4.557147, 4.706706, 4.670835,
-         2.344188, 0.493237, 2.707488, 4.15662 ],
-        [0.78458 , 3.838047, 0.803847, 1.588861, 5.002692, 2.565792,
-         5.195116, 2.642173, 1.47148 , 6.854931],
-        [4.407593, 6.734126, 3.702884, 4.682068, 0.498895, 2.931409,
-         5.713952, 6.388658, 6.618634, 3.267176],
-        [6.175152, 2.331721, 6.829246, 1.650656, 5.530157, 0.167316,
-         6.406627, 4.559968, 0.083601, 1.231478],
-        [2.438085, 1.593681, 5.902827, 2.124327, 1.870033, 4.519462,
-         4.845505, 6.214475, 2.237778, 4.178488],
-        [5.855639, 5.824333, 6.006212, 5.43411 , 3.393885, 6.183958,
-         1.284061, 0.028555, 6.726706, 1.516993],
-        [0.844103, 5.61329 , 6.978045, 1.183779, 2.539376, 1.268038,
-         1.27574 , 5.101129, 2.020225, 2.081746]],
-
-       [[6.242907, 6.394763, 1.302492, 2.895077, 0.537664, 5.012127,
-         4.380069, 1.624007, 5.56236 , 4.303491],
-        [6.745937, 3.653031, 5.116965, 1.835256, 3.514732, 6.523679,
-         0.17986 , 2.444248, 5.169099, 0.584623],
-        [2.119518, 0.479052, 4.274714, 6.019049, 6.346329, 2.858441,
-         3.976887, 4.218218, 4.320734, 3.941107],
-        [2.974649, 6.67996 , 3.16875 , 3.507162, 2.909711, 1.359386,
-         4.023015, 0.258809, 2.007625, 5.744746],
-        [2.572982, 1.250802, 4.652006, 6.310958, 3.266477, 3.216613,
-         3.47315 , 1.713415, 5.9575  , 2.729016],
-        [2.743341, 4.203021, 1.306215, 2.840115, 5.351451, 2.774677,
-         2.621753, 5.288077, 0.722618, 4.086008],
-        [3.168672, 4.031863, 5.775087, 3.487762, 6.034522, 4.721887,
-         5.093018, 0.386024, 2.278799, 1.536351],
-        [4.548959, 4.797754, 6.371152, 3.69425 , 6.855343, 1.987757,
-         4.749654, 1.750539, 1.804795, 4.966536],
-        [1.78139 , 1.544275, 1.750026, 3.373274, 2.604254, 5.894015,
-         3.217723, 5.637478, 2.900347, 2.097813],
-        [1.844174, 6.994962, 2.504717, 5.292132, 0.184411, 4.870834,
-         1.888442, 3.491315, 5.055704, 3.368395]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-488de4a2-274f-4896-a2c7-8eaaf18ca6d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-488de4a2-274f-4896-a2c7-8eaaf18ca6d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b28137b-0d86-4df6-82ed-3dc2a3937270' class='xr-var-data-in' type='checkbox'><label for='data-8b28137b-0d86-4df6-82ed-3dc2a3937270' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd><dt><span>nodata :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[[ 7.920639, 66.150832, 30.096116, 30.437197, 57.794734, 11.538647,
-         14.426782, 35.593171, 53.784858,  0.449093],
-        [23.804111, 67.910347, 18.694533, 30.41474 , 68.117674, 44.906057,
-         62.311842, 37.485047, 57.134336,  7.52572 ],
-        [36.653481, 39.596833, 61.07603 , 56.883093, 29.635613, 64.114699,
-         42.341689, 54.724789, 31.872344, 11.282554],
-        [32.502176, 28.090549, 58.398927, 41.224019, 34.804777, 32.184565,
-         59.392327,  9.155824, 52.967172, 67.409236],
-        [18.79468 ,  8.543429, 35.834698,  3.596245, 30.252802, 41.549499,
-         23.060248,  7.267762, 27.374099,  0.684024],
-        [ 9.93227 , 44.509446, 22.01927 , 28.514121, 36.715233, 15.03939 ,
-          2.43399 ,  0.636075, 34.43023 , 37.024545],
-        [28.874823,  1.514255, 34.210822, 10.49793 , 54.025491, 64.294026,
-         36.212813, 17.766628, 45.295952, 10.349576],
-        [58.961924, 47.334638, 64.844646, 37.634131,  7.815025, 35.139303,
-         18.501505, 35.01185 , 27.761908, 13.240655],
-        [49.544668, 57.716538, 27.389778, 11.604377, 24.826367, 15.449456,
-         26.482386, 42.855739, 10.4958  , 59.267182],
-        [37.094093, 43.294246, 33.240747, 16.85513 , 54.705119, 14.633291,
-         35.138742, 50.101683, 57.495953, 52.795405]],
-
-       [[26.137058, 16.448086,  4.503539, 33.351036, 20.32524 , 63.369743,
-         11.531512, 38.629561, 59.821441, 11.547508],
-        [ 6.438471, 28.948907,  9.949052, 23.234921, 65.539507,  9.822554,
-         55.754023, 51.590388, 57.047098,  4.929671],
-        [32.360472, 17.939979, 52.889505, 69.181176, 39.2923  , 56.442225,
-          5.699603, 21.092554,  8.93472 , 23.810367],
-        [ 3.715695, 30.653733, 44.540496, 48.578544, 24.032477, 30.339109,
-         37.376636, 58.787274, 49.308994, 59.510765],
-        [37.991912, 50.023013, 63.711135, 26.546118, 60.561058, 36.098302,
-         10.725673, 40.51609 , 47.479255, 42.710909],
-        [15.32887 , 15.878984, 30.914778, 25.902812,  3.815428, 35.530366,
-         48.426293, 44.280075,  4.468083, 18.00032 ],
-        [58.286993, 20.397714, 63.124   , 69.117495,  1.32436 , 29.024715,
-         31.601531, 34.307982,  7.990292, 36.159696],
-        [34.674335, 62.344993,  5.714717,  2.161448, 66.714977, 52.443751,
-         12.791859, 63.707491, 13.697063,  9.394474],
-        [16.766346, 26.260123, 40.68158 , 18.695085, 64.812126, 23.151592,
-         50.597642, 61.308205, 31.517123, 23.469797],
-        [44.670989, 17.533084, 39.034907, 32.676726, 53.275139, 48.731172,
-         12.958856, 21.760335, 27.292202, 18.409063]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79eaaf20-0b46-43be-b9cf-7f1abeb0b6f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79eaaf20-0b46-43be-b9cf-7f1abeb0b6f9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds.green.where(xds.green!=xds.green.rio.nodata).isel(time=1).plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7f8f13620880&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_reproject_5_1.png" src="../_images/examples_reproject_5_1.png" />
-</div>
-</div>
-</section>
-<section id="Reproject">
-<h2>Reproject<a class="headerlink" href="#Reproject" title="Link to this heading"></a></h2>
-<p>API Reference:</p>
-<ul class="simple">
-<li><p>DataArray: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.reproject"><span class="std std-ref">rio.reproject()</span></a></p></li>
-<li><p>Dataset: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.reproject"><span class="std std-ref">rio.reproject()</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds_lonlat = xds.rio.reproject(&quot;EPSG:4326&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds_lonlat
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
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-</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;
-Dimensions:      (time: 2, x: 10, y: 10)
-Coordinates:
-  * x            (x) float64 -51.32 -51.32 -51.32 ... -51.32 -51.32 -51.32
-  * y            (y) float64 -17.32 -17.32 -17.32 ... -17.32 -17.32 -17.32
-  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...
-    spatial_ref  int64 0
-Data variables:
-    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368
-    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f82895e0-94c5-41e7-965a-f309f40a772c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f82895e0-94c5-41e7-965a-f309f40a772c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7fbb1a9c-ea36-44de-b015-b0bf3be03674' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fbb1a9c-ea36-44de-b015-b0bf3be03674' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-51.32 -51.32 ... -51.32 -51.32</div><input id='attrs-38e090bd-85be-426b-ade8-c39502fa16a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38e090bd-85be-426b-ade8-c39502fa16a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-defa9d2d-d31c-431a-82e3-3ec3e7b18ea0' class='xr-var-data-in' type='checkbox'><label for='data-defa9d2d-d31c-431a-82e3-3ec3e7b18ea0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-51.317454, -51.317426, -51.317399, -51.317371, -51.317343, -51.317316,
-       -51.317288, -51.31726 , -51.317233, -51.317205])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-17.32 -17.32 ... -17.32 -17.32</div><input id='attrs-e72e0d56-f2e8-4f20-9c4d-f081f99576c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e72e0d56-f2e8-4f20-9c4d-f081f99576c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31d9d357-a70b-4b4f-8217-ab20870c7c7f' class='xr-var-data-in' type='checkbox'><label for='data-31d9d357-a70b-4b4f-8217-ab20870c7c7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-17.322876, -17.322903, -17.322931, -17.322959, -17.322986, -17.323014,
-       -17.323042, -17.323069, -17.323097, -17.323125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-12-19T10:27:29.687763 2016-...</div><input id='attrs-0206b871-06ba-474f-8f44-3a47010cb630' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0206b871-06ba-474f-8f44-3a47010cb630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe148f3-6ccd-418b-8a5b-234a95472c42' class='xr-var-data-in' type='checkbox'><label for='data-dfe148f3-6ccd-418b-8a5b-234a95472c42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-12-19T10:27:29.687763000&#x27;, &#x27;2016-12-29T12:52:42.347451000&#x27;],
-      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-15f96bc1-395f-4770-af20-9603b114ad1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15f96bc1-395f-4770-af20-9603b114ad1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42902900-c1fd-4910-a111-c16b9cc4a195' class='xr-var-data-in' type='checkbox'><label for='data-42902900-c1fd-4910-a111-c16b9cc4a195' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>-51.31746780720891 2.7678699822269306e-05 0.0 -17.322861663733065 0.0 -2.7678699822269306e-05</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b678e340-da44-426a-a4f7-d951b9ff6f77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b678e340-da44-426a-a4f7-d951b9ff6f77' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.611 5.581 0.3996 ... 5.056 3.368</div><input id='attrs-7c8b5103-9e66-4954-95db-974807557881' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c8b5103-9e66-4954-95db-974807557881' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51ed04e1-5d6e-4b55-9838-56991d4657a9' class='xr-var-data-in' type='checkbox'><label for='data-51ed04e1-5d6e-4b55-9838-56991d4657a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd></dl></div><div class='xr-var-data'><pre>array([[[6.61101706, 5.58097901, 0.39960727, 2.05280345, 5.47998484,
-         4.76021916, 5.07792715, 5.57479217, 0.72668295, 5.17028805],
-        [4.53551623, 0.08826297, 4.22230213, 0.28919903, 3.47814709,
-         3.22794508, 2.73644333, 2.82179869, 1.04221025, 1.09961647],
-        [0.07136352, 4.39326719, 0.49690677, 2.31192634, 1.19123023,
-         5.98418893, 5.26697738, 1.14698827, 3.21918509, 0.98201077],
-        [5.81855223, 5.17606488, 4.89190312, 4.55714657, 4.70670585,
-         4.67083516, 2.34418779, 0.4932369 , 2.70748782, 4.15661977],
-        [0.78457995, 3.83804728, 0.80384702, 1.58886148, 5.00269192,
-         2.56579153, 5.19511563, 2.6421726 , 1.47148022, 6.85493052],
-        [4.40759261, 6.73412648, 3.70288412, 4.68206812, 0.49889505,
-         2.93140894, 5.71395224, 6.38865828, 6.61863439, 3.26717611],
-        [6.17515163, 2.33172093, 6.82924574, 1.65065578, 5.53015689,
-         0.16731611, 6.40662729, 4.55996818, 0.08360072, 1.23147801],
-        [2.43808458, 1.59368058, 5.90282702, 2.12432736, 1.8700329 ,
-         4.51946233, 4.84550488, 6.21447495, 2.23777817, 4.17848811],
-        [5.85563919, 5.82433331, 6.00621159, 5.43410984, 3.39388501,
-         6.18395763, 1.28406097, 0.02855524, 6.72670605, 1.51699291],
-        [0.84410255, 5.61328959, 6.97804534, 1.18377926, 2.53937552,
-         1.26803824, 1.27574049, 5.10112907, 2.02022521, 2.08174614]],
-...
-       [[6.24290664, 6.39476317, 1.30249235, 2.89507681, 0.5376643 ,
-         5.01212706, 4.38006939, 1.62400749, 5.56236028, 4.30349107],
-        [6.74593668, 3.65303136, 5.11696455, 1.83525557, 3.51473159,
-         6.52367921, 0.17986011, 2.44424775, 5.16909939, 0.58462293],
-        [2.11951807, 0.47905249, 4.27471358, 6.01904917, 6.34632929,
-         2.85844128, 3.97688665, 4.21821752, 4.32073426, 3.9411069 ],
-        [2.97464873, 6.67996018, 3.16875018, 3.50716191, 2.9097115 ,
-         1.35938603, 4.0230148 , 0.2588095 , 2.00762528, 5.74474576],
-        [2.57298212, 1.25080228, 4.65200582, 6.31095753, 3.26647688,
-         3.21661254, 3.4731501 , 1.71341466, 5.95750029, 2.72901564],
-        [2.74334064, 4.20302139, 1.30621538, 2.84011532, 5.35145147,
-         2.77467654, 2.6217532 , 5.28807657, 0.72261773, 4.08600786],
-        [3.1686723 , 4.0318634 , 5.77508699, 3.48776179, 6.0345218 ,
-         4.72188719, 5.09301838, 0.38602444, 2.27879875, 1.5363515 ],
-        [4.54895876, 4.79775447, 6.3711522 , 3.69424969, 6.85534296,
-         1.98775739, 4.74965416, 1.75053883, 1.80479493, 4.96653593],
-        [1.78138972, 1.54427475, 1.75002571, 3.37327355, 2.60425433,
-         5.89401538, 3.2177233 , 5.63747771, 2.90034677, 2.09781306],
-        [1.84417405, 6.99496234, 2.50471667, 5.29213205, 0.1844106 ,
-         4.87083388, 1.88844217, 3.49131529, 5.05570379, 3.36839469]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.921 66.15 30.1 ... 27.29 18.41</div><input id='attrs-2abda620-506f-4da9-b5d1-28f48d5c9e82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2abda620-506f-4da9-b5d1-28f48d5c9e82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0858356e-9a30-4af8-afde-aefabaf6422f' class='xr-var-data-in' type='checkbox'><label for='data-0858356e-9a30-4af8-afde-aefabaf6422f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd></dl></div><div class='xr-var-data'><pre>array([[[ 7.92063876, 66.15083163, 30.09611622, 30.43719706,
-         57.79473407, 11.53864677, 14.42678195, 35.5931706 ,
-         53.78485769,  0.44909335],
-        [23.80411143, 67.91034696, 18.6945325 , 30.41473977,
-         68.11767437, 44.90605663, 62.31184244, 37.48504719,
-         57.13433649,  7.52571983],
-        [36.65348061, 39.59683262, 61.07602968, 56.88309328,
-         29.63561283, 64.11469949, 42.34168918, 54.7247894 ,
-         31.87234425, 11.28255401],
-        [32.50217644, 28.0905485 , 58.39892665, 41.22401876,
-         34.80477692, 32.18456541, 59.39232697,  9.15582412,
-         52.96717208, 67.40923645],
-        [18.79468009,  8.54342894, 35.83469773,  3.59624497,
-         30.25280185, 41.54949902, 23.06024792,  7.26776162,
-         27.37409939,  0.68402392],
-        [ 9.93226996, 44.50944555, 22.01927024, 28.5141208 ,
-         36.71523344, 15.03939035,  2.43398964,  0.63607503,
-         34.43022974, 37.02454468],
-        [28.87482271,  1.51425484, 34.21082199, 10.49792965,
-         54.02549098, 64.29402555, 36.21281265, 17.76662845,
-...
-         24.03247674, 30.33910879, 37.37663568, 58.78727396,
-         49.30899357, 59.51076479],
-        [37.99191205, 50.0230132 , 63.71113469, 26.54611832,
-         60.5610581 , 36.09830221, 10.72567322, 40.51609048,
-         47.47925466, 42.71090946],
-        [15.32887012, 15.8789836 , 30.91477844, 25.90281227,
-          3.81542846, 35.53036591, 48.42629307, 44.28007476,
-          4.46808288, 18.00031979],
-        [58.28699332, 20.39771424, 63.12400017, 69.11749464,
-          1.32435965, 29.02471509, 31.60153124, 34.30798249,
-          7.99029205, 36.15969559],
-        [34.67433533, 62.34499312,  5.71471651,  2.16144808,
-         66.71497747, 52.4437509 , 12.79185872, 63.70749094,
-         13.69706259,  9.39447357],
-        [16.76634605, 26.26012306, 40.68158023, 18.69508482,
-         64.81212607, 23.15159177, 50.59764238, 61.30820521,
-         31.51712269, 23.46979658],
-        [44.67098863, 17.53308399, 39.03490717, 32.67672611,
-         53.27513876, 48.73117231, 12.95885589, 21.760335  ,
-         27.29220175, 18.40906318]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5f85efc-8e6c-4119-a4b9-c904d3b06bac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d5f85efc-8e6c-4119-a4b9-c904d3b06bac' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div></div>
-</div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds_lonlat.green.where(xds_lonlat.green!=xds_lonlat.green.rio.nodata).isel(time=1).plot()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7f8f134b63a0&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_reproject_9_1.png" src="../_images/examples_reproject_9_1.png" />
-</div>
-</div>
-</section>
-<section id="Reproject-to-UTM">
-<h2>Reproject to UTM<a class="headerlink" href="#Reproject-to-UTM" title="Link to this heading"></a></h2>
-<p>API Reference:</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.estimate_utm_crs"><span class="std std-ref">rio.estimate_utm_crs()</span></a></p></li>
-</ul>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds_utm = xds.rio.reproject(xds.rio.estimate_utm_crs())
-xds_utm.rio.crs
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CRS.from_epsg(32722)
-</pre></div></div>
-</div>
-</section>
-<section id="Reproject-Large-Rasters-with-Virtual-Warping">
-<h2>Reproject Large Rasters with Virtual Warping<a class="headerlink" href="#Reproject-Large-Rasters-with-Virtual-Warping" title="Link to this heading"></a></h2>
-<p>Using <a class="reference external" href="https://rasterio.readthedocs.io/en/latest/topics/virtual-warping.html">WarpedVRT</a> enables re-projection from disk and reduces the amount of memory required with the re-projection.</p>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>url = (
-    &quot;https://storage.googleapis.com/&quot;
-    &quot;gcp-public-data-landsat/LC08/01/047/027/&quot;
-    &quot;LC08_L1TP_047027_20130421_20170310_01_T1/&quot;
-    &quot;LC08_L1TP_047027_20130421_20170310_01_T1_B4.TIF&quot;
-)
-env = rasterio.Env(
-    GDAL_DISABLE_READDIR_ON_OPEN=&quot;EMPTY_DIR&quot;,
-    CPL_VSIL_CURL_USE_HEAD=False,
-    CPL_VSIL_CURL_ALLOWED_EXTENSIONS=&quot;TIF&quot;,
-)
-with env:
-    with rasterio.open(url) as src:
-        with rasterio.vrt.WarpedVRT(src, crs=&quot;EPSG:4326&quot;) as vrt:
-            rds = rioxarray.open_rasterio(vrt)
-            rds.sel(band=1).plot.imshow()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
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-<div class="output_area docutils container">
-<img alt="../_images/examples_reproject_13_0.png" src="../_images/examples_reproject_13_0.png" />
-</div>
-</div>
-</section>
-</section>
-
-
-           </div>
-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="read-locks.html" class="btn btn-neutral float-left" title="Example - Reading COGs in Parallel" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="reproject_match.html" class="btn btn-neutral float-right" title="Example - Reproject Match (For Raster Calculations/Stacking)" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
-    </div>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2019-2023, Corteva Agriscience™.</p>
-  </div>
-
-  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
-   
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\ No newline at end of file
diff --git a/0.18.0/examples/reproject.ipynb b/0.18.0/examples/reproject.ipynb
deleted file mode 100644
index 16da3f3e..00000000
--- a/0.18.0/examples/reproject.ipynb
+++ /dev/null
@@ -1,1177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reproject\n",
-    "\n",
-    "To re-project with dask, see [odc-geo](https://odc-geo.readthedocs.io/) & [pyresample](https://pyresample.readthedocs.io)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "import rasterio\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataset(\"../../test/test_data/input/PLANET_SCOPE_3D.nc\", decode_coords=\"all\")"
-   ]
-  },
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-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
-       "  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368\n",
-       "    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-45eb2830-a807-4070-8270-eafb894091e9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45eb2830-a807-4070-8270-eafb894091e9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-866810c8-7763-4ad0-884c-659a75f5c365' class='xr-section-summary-in' type='checkbox'  checked><label for='section-866810c8-7763-4ad0-884c-659a75f5c365' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-28b007a0-1d57-44a2-9044-da130e8cfa2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28b007a0-1d57-44a2-9044-da130e8cfa2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0ff534a-88e7-4944-ba89-b5645ea219f9' class='xr-var-data-in' type='checkbox'><label for='data-c0ff534a-88e7-4944-ba89-b5645ea219f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.663e+05 4.663e+05 ... 4.663e+05</div><input id='attrs-34ea2c4f-000a-4ce2-b62e-a8dfbcc906f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34ea2c4f-000a-4ce2-b62e-a8dfbcc906f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53a4c842-cfce-4d59-b370-8f002ab69088' class='xr-var-data-in' type='checkbox'><label for='data-53a4c842-cfce-4d59-b370-8f002ab69088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([466267.5, 466270.5, 466273.5, 466276.5, 466279.5, 466282.5, 466285.5,\n",
-       "       466288.5, 466291.5, 466294.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-12-19T10:27:29.687763 2016-...</div><input id='attrs-85843e76-c4c1-44ee-b8fe-9c6f0b2c934a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85843e76-c4c1-44ee-b8fe-9c6f0b2c934a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08a858f1-899e-4560-a155-b91589db4ab6' class='xr-var-data-in' type='checkbox'><label for='data-08a858f1-899e-4560-a155-b91589db4ab6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-12-19T10:27:29.687763000&#x27;, &#x27;2016-12-29T12:52:42.347451000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.085e+06 8.085e+06 ... 8.085e+06</div><input id='attrs-d2563e2f-3d5c-430b-b264-1aa3c57398b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2563e2f-3d5c-430b-b264-1aa3c57398b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fbadacc-5d51-49a4-9648-0a1ab3420bc4' class='xr-var-data-in' type='checkbox'><label for='data-3fbadacc-5d51-49a4-9648-0a1ab3420bc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([8084698.5, 8084695.5, 8084692.5, 8084689.5, 8084686.5, 8084683.5,\n",
-       "       8084680.5, 8084677.5, 8084674.5, 8084671.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba0f4c5e-f47a-4f11-988d-a3e5128f44bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba0f4c5e-f47a-4f11-988d-a3e5128f44bc' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a28b5fc7-3647-4072-a0fb-b8aad6f664ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a28b5fc7-3647-4072-a0fb-b8aad6f664ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b7f31d-2d0a-48f5-8f1c-db719140f271' class='xr-var-data-in' type='checkbox'><label for='data-e9b7f31d-2d0a-48f5-8f1c-db719140f271' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd><dt><span>nodata :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[[6.611017, 5.580979, 0.399607, 2.052803, 5.479985, 4.760219,\n",
-       "         5.077927, 5.574792, 0.726683, 5.170288],\n",
-       "        [4.535516, 0.088263, 4.222302, 0.289199, 3.478147, 3.227945,\n",
-       "         2.736443, 2.821799, 1.04221 , 1.099616],\n",
-       "        [0.071364, 4.393267, 0.496907, 2.311926, 1.19123 , 5.984189,\n",
-       "         5.266977, 1.146988, 3.219185, 0.982011],\n",
-       "        [5.818552, 5.176065, 4.891903, 4.557147, 4.706706, 4.670835,\n",
-       "         2.344188, 0.493237, 2.707488, 4.15662 ],\n",
-       "        [0.78458 , 3.838047, 0.803847, 1.588861, 5.002692, 2.565792,\n",
-       "         5.195116, 2.642173, 1.47148 , 6.854931],\n",
-       "        [4.407593, 6.734126, 3.702884, 4.682068, 0.498895, 2.931409,\n",
-       "         5.713952, 6.388658, 6.618634, 3.267176],\n",
-       "        [6.175152, 2.331721, 6.829246, 1.650656, 5.530157, 0.167316,\n",
-       "         6.406627, 4.559968, 0.083601, 1.231478],\n",
-       "        [2.438085, 1.593681, 5.902827, 2.124327, 1.870033, 4.519462,\n",
-       "         4.845505, 6.214475, 2.237778, 4.178488],\n",
-       "        [5.855639, 5.824333, 6.006212, 5.43411 , 3.393885, 6.183958,\n",
-       "         1.284061, 0.028555, 6.726706, 1.516993],\n",
-       "        [0.844103, 5.61329 , 6.978045, 1.183779, 2.539376, 1.268038,\n",
-       "         1.27574 , 5.101129, 2.020225, 2.081746]],\n",
-       "\n",
-       "       [[6.242907, 6.394763, 1.302492, 2.895077, 0.537664, 5.012127,\n",
-       "         4.380069, 1.624007, 5.56236 , 4.303491],\n",
-       "        [6.745937, 3.653031, 5.116965, 1.835256, 3.514732, 6.523679,\n",
-       "         0.17986 , 2.444248, 5.169099, 0.584623],\n",
-       "        [2.119518, 0.479052, 4.274714, 6.019049, 6.346329, 2.858441,\n",
-       "         3.976887, 4.218218, 4.320734, 3.941107],\n",
-       "        [2.974649, 6.67996 , 3.16875 , 3.507162, 2.909711, 1.359386,\n",
-       "         4.023015, 0.258809, 2.007625, 5.744746],\n",
-       "        [2.572982, 1.250802, 4.652006, 6.310958, 3.266477, 3.216613,\n",
-       "         3.47315 , 1.713415, 5.9575  , 2.729016],\n",
-       "        [2.743341, 4.203021, 1.306215, 2.840115, 5.351451, 2.774677,\n",
-       "         2.621753, 5.288077, 0.722618, 4.086008],\n",
-       "        [3.168672, 4.031863, 5.775087, 3.487762, 6.034522, 4.721887,\n",
-       "         5.093018, 0.386024, 2.278799, 1.536351],\n",
-       "        [4.548959, 4.797754, 6.371152, 3.69425 , 6.855343, 1.987757,\n",
-       "         4.749654, 1.750539, 1.804795, 4.966536],\n",
-       "        [1.78139 , 1.544275, 1.750026, 3.373274, 2.604254, 5.894015,\n",
-       "         3.217723, 5.637478, 2.900347, 2.097813],\n",
-       "        [1.844174, 6.994962, 2.504717, 5.292132, 0.184411, 4.870834,\n",
-       "         1.888442, 3.491315, 5.055704, 3.368395]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-488de4a2-274f-4896-a2c7-8eaaf18ca6d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-488de4a2-274f-4896-a2c7-8eaaf18ca6d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b28137b-0d86-4df6-82ed-3dc2a3937270' class='xr-var-data-in' type='checkbox'><label for='data-8b28137b-0d86-4df6-82ed-3dc2a3937270' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd><dt><span>nodata :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[[ 7.920639, 66.150832, 30.096116, 30.437197, 57.794734, 11.538647,\n",
-       "         14.426782, 35.593171, 53.784858,  0.449093],\n",
-       "        [23.804111, 67.910347, 18.694533, 30.41474 , 68.117674, 44.906057,\n",
-       "         62.311842, 37.485047, 57.134336,  7.52572 ],\n",
-       "        [36.653481, 39.596833, 61.07603 , 56.883093, 29.635613, 64.114699,\n",
-       "         42.341689, 54.724789, 31.872344, 11.282554],\n",
-       "        [32.502176, 28.090549, 58.398927, 41.224019, 34.804777, 32.184565,\n",
-       "         59.392327,  9.155824, 52.967172, 67.409236],\n",
-       "        [18.79468 ,  8.543429, 35.834698,  3.596245, 30.252802, 41.549499,\n",
-       "         23.060248,  7.267762, 27.374099,  0.684024],\n",
-       "        [ 9.93227 , 44.509446, 22.01927 , 28.514121, 36.715233, 15.03939 ,\n",
-       "          2.43399 ,  0.636075, 34.43023 , 37.024545],\n",
-       "        [28.874823,  1.514255, 34.210822, 10.49793 , 54.025491, 64.294026,\n",
-       "         36.212813, 17.766628, 45.295952, 10.349576],\n",
-       "        [58.961924, 47.334638, 64.844646, 37.634131,  7.815025, 35.139303,\n",
-       "         18.501505, 35.01185 , 27.761908, 13.240655],\n",
-       "        [49.544668, 57.716538, 27.389778, 11.604377, 24.826367, 15.449456,\n",
-       "         26.482386, 42.855739, 10.4958  , 59.267182],\n",
-       "        [37.094093, 43.294246, 33.240747, 16.85513 , 54.705119, 14.633291,\n",
-       "         35.138742, 50.101683, 57.495953, 52.795405]],\n",
-       "\n",
-       "       [[26.137058, 16.448086,  4.503539, 33.351036, 20.32524 , 63.369743,\n",
-       "         11.531512, 38.629561, 59.821441, 11.547508],\n",
-       "        [ 6.438471, 28.948907,  9.949052, 23.234921, 65.539507,  9.822554,\n",
-       "         55.754023, 51.590388, 57.047098,  4.929671],\n",
-       "        [32.360472, 17.939979, 52.889505, 69.181176, 39.2923  , 56.442225,\n",
-       "          5.699603, 21.092554,  8.93472 , 23.810367],\n",
-       "        [ 3.715695, 30.653733, 44.540496, 48.578544, 24.032477, 30.339109,\n",
-       "         37.376636, 58.787274, 49.308994, 59.510765],\n",
-       "        [37.991912, 50.023013, 63.711135, 26.546118, 60.561058, 36.098302,\n",
-       "         10.725673, 40.51609 , 47.479255, 42.710909],\n",
-       "        [15.32887 , 15.878984, 30.914778, 25.902812,  3.815428, 35.530366,\n",
-       "         48.426293, 44.280075,  4.468083, 18.00032 ],\n",
-       "        [58.286993, 20.397714, 63.124   , 69.117495,  1.32436 , 29.024715,\n",
-       "         31.601531, 34.307982,  7.990292, 36.159696],\n",
-       "        [34.674335, 62.344993,  5.714717,  2.161448, 66.714977, 52.443751,\n",
-       "         12.791859, 63.707491, 13.697063,  9.394474],\n",
-       "        [16.766346, 26.260123, 40.68158 , 18.695085, 64.812126, 23.151592,\n",
-       "         50.597642, 61.308205, 31.517123, 23.469797],\n",
-       "        [44.670989, 17.533084, 39.034907, 32.676726, 53.275139, 48.731172,\n",
-       "         12.958856, 21.760335, 27.292202, 18.409063]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79eaaf20-0b46-43be-b9cf-7f1abeb0b6f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79eaaf20-0b46-43be-b9cf-7f1abeb0b6f9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 ...\n",
-       "  * x            (x) float64 4.663e+05 4.663e+05 ... 4.663e+05 4.663e+05\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
-       "  * y            (y) float64 8.085e+06 8.085e+06 ... 8.085e+06 8.085e+06\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 ...\n",
-       "    green        (time, y, x) float64 ..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f8f13620880>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds.green.where(xds.green!=xds.green.rio.nodata).isel(time=1).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.reproject()](../rioxarray.rst#rioxarray.raster_array.RasterArray.reproject)\n",
-    "- Dataset: [rio.reproject()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.reproject)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds_lonlat = xds.rio.reproject(\"EPSG:4326\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * x            (x) float64 -51.32 -51.32 -51.32 ... -51.32 -51.32 -51.32\n",
-       "  * y            (y) float64 -17.32 -17.32 -17.32 ... -17.32 -17.32 -17.32\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
-       "    spatial_ref  int64 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368\n",
-       "    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f82895e0-94c5-41e7-965a-f309f40a772c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f82895e0-94c5-41e7-965a-f309f40a772c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7fbb1a9c-ea36-44de-b015-b0bf3be03674' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fbb1a9c-ea36-44de-b015-b0bf3be03674' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-51.32 -51.32 ... -51.32 -51.32</div><input id='attrs-38e090bd-85be-426b-ade8-c39502fa16a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38e090bd-85be-426b-ade8-c39502fa16a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-defa9d2d-d31c-431a-82e3-3ec3e7b18ea0' class='xr-var-data-in' type='checkbox'><label for='data-defa9d2d-d31c-431a-82e3-3ec3e7b18ea0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-51.317454, -51.317426, -51.317399, -51.317371, -51.317343, -51.317316,\n",
-       "       -51.317288, -51.31726 , -51.317233, -51.317205])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-17.32 -17.32 ... -17.32 -17.32</div><input id='attrs-e72e0d56-f2e8-4f20-9c4d-f081f99576c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e72e0d56-f2e8-4f20-9c4d-f081f99576c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31d9d357-a70b-4b4f-8217-ab20870c7c7f' class='xr-var-data-in' type='checkbox'><label for='data-31d9d357-a70b-4b4f-8217-ab20870c7c7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-17.322876, -17.322903, -17.322931, -17.322959, -17.322986, -17.323014,\n",
-       "       -17.323042, -17.323069, -17.323097, -17.323125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-12-19T10:27:29.687763 2016-...</div><input id='attrs-0206b871-06ba-474f-8f44-3a47010cb630' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0206b871-06ba-474f-8f44-3a47010cb630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe148f3-6ccd-418b-8a5b-234a95472c42' class='xr-var-data-in' type='checkbox'><label for='data-dfe148f3-6ccd-418b-8a5b-234a95472c42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-12-19T10:27:29.687763000&#x27;, &#x27;2016-12-29T12:52:42.347451000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-15f96bc1-395f-4770-af20-9603b114ad1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15f96bc1-395f-4770-af20-9603b114ad1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42902900-c1fd-4910-a111-c16b9cc4a195' class='xr-var-data-in' type='checkbox'><label for='data-42902900-c1fd-4910-a111-c16b9cc4a195' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>-51.31746780720891 2.7678699822269306e-05 0.0 -17.322861663733065 0.0 -2.7678699822269306e-05</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b678e340-da44-426a-a4f7-d951b9ff6f77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b678e340-da44-426a-a4f7-d951b9ff6f77' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.611 5.581 0.3996 ... 5.056 3.368</div><input id='attrs-7c8b5103-9e66-4954-95db-974807557881' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c8b5103-9e66-4954-95db-974807557881' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51ed04e1-5d6e-4b55-9838-56991d4657a9' class='xr-var-data-in' type='checkbox'><label for='data-51ed04e1-5d6e-4b55-9838-56991d4657a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd></dl></div><div class='xr-var-data'><pre>array([[[6.61101706, 5.58097901, 0.39960727, 2.05280345, 5.47998484,\n",
-       "         4.76021916, 5.07792715, 5.57479217, 0.72668295, 5.17028805],\n",
-       "        [4.53551623, 0.08826297, 4.22230213, 0.28919903, 3.47814709,\n",
-       "         3.22794508, 2.73644333, 2.82179869, 1.04221025, 1.09961647],\n",
-       "        [0.07136352, 4.39326719, 0.49690677, 2.31192634, 1.19123023,\n",
-       "         5.98418893, 5.26697738, 1.14698827, 3.21918509, 0.98201077],\n",
-       "        [5.81855223, 5.17606488, 4.89190312, 4.55714657, 4.70670585,\n",
-       "         4.67083516, 2.34418779, 0.4932369 , 2.70748782, 4.15661977],\n",
-       "        [0.78457995, 3.83804728, 0.80384702, 1.58886148, 5.00269192,\n",
-       "         2.56579153, 5.19511563, 2.6421726 , 1.47148022, 6.85493052],\n",
-       "        [4.40759261, 6.73412648, 3.70288412, 4.68206812, 0.49889505,\n",
-       "         2.93140894, 5.71395224, 6.38865828, 6.61863439, 3.26717611],\n",
-       "        [6.17515163, 2.33172093, 6.82924574, 1.65065578, 5.53015689,\n",
-       "         0.16731611, 6.40662729, 4.55996818, 0.08360072, 1.23147801],\n",
-       "        [2.43808458, 1.59368058, 5.90282702, 2.12432736, 1.8700329 ,\n",
-       "         4.51946233, 4.84550488, 6.21447495, 2.23777817, 4.17848811],\n",
-       "        [5.85563919, 5.82433331, 6.00621159, 5.43410984, 3.39388501,\n",
-       "         6.18395763, 1.28406097, 0.02855524, 6.72670605, 1.51699291],\n",
-       "        [0.84410255, 5.61328959, 6.97804534, 1.18377926, 2.53937552,\n",
-       "         1.26803824, 1.27574049, 5.10112907, 2.02022521, 2.08174614]],\n",
-       "...\n",
-       "       [[6.24290664, 6.39476317, 1.30249235, 2.89507681, 0.5376643 ,\n",
-       "         5.01212706, 4.38006939, 1.62400749, 5.56236028, 4.30349107],\n",
-       "        [6.74593668, 3.65303136, 5.11696455, 1.83525557, 3.51473159,\n",
-       "         6.52367921, 0.17986011, 2.44424775, 5.16909939, 0.58462293],\n",
-       "        [2.11951807, 0.47905249, 4.27471358, 6.01904917, 6.34632929,\n",
-       "         2.85844128, 3.97688665, 4.21821752, 4.32073426, 3.9411069 ],\n",
-       "        [2.97464873, 6.67996018, 3.16875018, 3.50716191, 2.9097115 ,\n",
-       "         1.35938603, 4.0230148 , 0.2588095 , 2.00762528, 5.74474576],\n",
-       "        [2.57298212, 1.25080228, 4.65200582, 6.31095753, 3.26647688,\n",
-       "         3.21661254, 3.4731501 , 1.71341466, 5.95750029, 2.72901564],\n",
-       "        [2.74334064, 4.20302139, 1.30621538, 2.84011532, 5.35145147,\n",
-       "         2.77467654, 2.6217532 , 5.28807657, 0.72261773, 4.08600786],\n",
-       "        [3.1686723 , 4.0318634 , 5.77508699, 3.48776179, 6.0345218 ,\n",
-       "         4.72188719, 5.09301838, 0.38602444, 2.27879875, 1.5363515 ],\n",
-       "        [4.54895876, 4.79775447, 6.3711522 , 3.69424969, 6.85534296,\n",
-       "         1.98775739, 4.74965416, 1.75053883, 1.80479493, 4.96653593],\n",
-       "        [1.78138972, 1.54427475, 1.75002571, 3.37327355, 2.60425433,\n",
-       "         5.89401538, 3.2177233 , 5.63747771, 2.90034677, 2.09781306],\n",
-       "        [1.84417405, 6.99496234, 2.50471667, 5.29213205, 0.1844106 ,\n",
-       "         4.87083388, 1.88844217, 3.49131529, 5.05570379, 3.36839469]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.921 66.15 30.1 ... 27.29 18.41</div><input id='attrs-2abda620-506f-4da9-b5d1-28f48d5c9e82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2abda620-506f-4da9-b5d1-28f48d5c9e82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0858356e-9a30-4af8-afde-aefabaf6422f' class='xr-var-data-in' type='checkbox'><label for='data-0858356e-9a30-4af8-afde-aefabaf6422f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DN</dd></dl></div><div class='xr-var-data'><pre>array([[[ 7.92063876, 66.15083163, 30.09611622, 30.43719706,\n",
-       "         57.79473407, 11.53864677, 14.42678195, 35.5931706 ,\n",
-       "         53.78485769,  0.44909335],\n",
-       "        [23.80411143, 67.91034696, 18.6945325 , 30.41473977,\n",
-       "         68.11767437, 44.90605663, 62.31184244, 37.48504719,\n",
-       "         57.13433649,  7.52571983],\n",
-       "        [36.65348061, 39.59683262, 61.07602968, 56.88309328,\n",
-       "         29.63561283, 64.11469949, 42.34168918, 54.7247894 ,\n",
-       "         31.87234425, 11.28255401],\n",
-       "        [32.50217644, 28.0905485 , 58.39892665, 41.22401876,\n",
-       "         34.80477692, 32.18456541, 59.39232697,  9.15582412,\n",
-       "         52.96717208, 67.40923645],\n",
-       "        [18.79468009,  8.54342894, 35.83469773,  3.59624497,\n",
-       "         30.25280185, 41.54949902, 23.06024792,  7.26776162,\n",
-       "         27.37409939,  0.68402392],\n",
-       "        [ 9.93226996, 44.50944555, 22.01927024, 28.5141208 ,\n",
-       "         36.71523344, 15.03939035,  2.43398964,  0.63607503,\n",
-       "         34.43022974, 37.02454468],\n",
-       "        [28.87482271,  1.51425484, 34.21082199, 10.49792965,\n",
-       "         54.02549098, 64.29402555, 36.21281265, 17.76662845,\n",
-       "...\n",
-       "         24.03247674, 30.33910879, 37.37663568, 58.78727396,\n",
-       "         49.30899357, 59.51076479],\n",
-       "        [37.99191205, 50.0230132 , 63.71113469, 26.54611832,\n",
-       "         60.5610581 , 36.09830221, 10.72567322, 40.51609048,\n",
-       "         47.47925466, 42.71090946],\n",
-       "        [15.32887012, 15.8789836 , 30.91477844, 25.90281227,\n",
-       "          3.81542846, 35.53036591, 48.42629307, 44.28007476,\n",
-       "          4.46808288, 18.00031979],\n",
-       "        [58.28699332, 20.39771424, 63.12400017, 69.11749464,\n",
-       "          1.32435965, 29.02471509, 31.60153124, 34.30798249,\n",
-       "          7.99029205, 36.15969559],\n",
-       "        [34.67433533, 62.34499312,  5.71471651,  2.16144808,\n",
-       "         66.71497747, 52.4437509 , 12.79185872, 63.70749094,\n",
-       "         13.69706259,  9.39447357],\n",
-       "        [16.76634605, 26.26012306, 40.68158023, 18.69508482,\n",
-       "         64.81212607, 23.15159177, 50.59764238, 61.30820521,\n",
-       "         31.51712269, 23.46979658],\n",
-       "        [44.67098863, 17.53308399, 39.03490717, 32.67672611,\n",
-       "         53.27513876, 48.73117231, 12.95885589, 21.760335  ,\n",
-       "         27.29220175, 18.40906318]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5f85efc-8e6c-4119-a4b9-c904d3b06bac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d5f85efc-8e6c-4119-a4b9-c904d3b06bac' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 2, x: 10, y: 10)\n",
-       "Coordinates:\n",
-       "  * x            (x) float64 -51.32 -51.32 -51.32 ... -51.32 -51.32 -51.32\n",
-       "  * y            (y) float64 -17.32 -17.32 -17.32 ... -17.32 -17.32 -17.32\n",
-       "  * time         (time) datetime64[ns] 2016-12-19T10:27:29.687763 2016-12-29T...\n",
-       "    spatial_ref  int64 0\n",
-       "Data variables:\n",
-       "    blue         (time, y, x) float64 6.611 5.581 0.3996 ... 3.491 5.056 3.368\n",
-       "    green        (time, y, x) float64 7.921 66.15 30.1 ... 21.76 27.29 18.41"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_lonlat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f8f134b63a0>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xds_lonlat.green.where(xds_lonlat.green!=xds_lonlat.green.rio.nodata).isel(time=1).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject to UTM\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- [rio.estimate_utm_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.estimate_utm_crs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CRS.from_epsg(32722)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_utm = xds.rio.reproject(xds.rio.estimate_utm_crs())\n",
-    "xds_utm.rio.crs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject Large Rasters with Virtual Warping\n",
-    "\n",
-    "Using [WarpedVRT](https://rasterio.readthedocs.io/en/latest/topics/virtual-warping.html) enables re-projection from disk and reduces the amount\n",
-    "of memory required with the re-projection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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khlcTChT6ijABF4GLqvrJ+vXP4A3HqogcqL2NA8Dadfsfue74w8Dlevvhm2y//piLIhIAPWDrxQY1MxxvIPTfUoHAyseErXuE/HCF3Vhg4TFY+KJj7skR6+9s03m2oPfIEMKAq+9fYOFRqGLI5kEFpAAUio6QLQjRyJFcGrBz3zyNZoBNK8ykQALD9HCbxrkhVSfBbo2oFtqYrEQDg5n4uhEDLyDLX8BVAEwzosvVXs3IVwpzo7H4MnCjMZkZkhluJxShegUoZFW9KiIXROQuVX0K+Hrg8frxPcAP1X9/rj7k54F/LyL/EDiIJ8E/paqViAxF5L3AJ4HvBv7xdcd8D/Bx4DuAX9OX6Ck+MxxvIHSetbQvVVQxFAuORicjHQaMjliqqEOQQvd8yeRoi+YlwcUB4iBdEvKesvLZisbVlCtf1SIaOQYnDM0rwnTBIq6LGigblioyREDZSCgTQ3qoTTCpcAfmCDdGaBRg0tJzD4FP2HDNBmaSQVV9SU3HLjSJIctvmp77UlBrvqLjboWZVzLD7YbTryxZ5Cb474F/JyIR8Dzwx/GJTT8tIt8LnAf+WwBVfUxEfhpvWErg+1R1d8X2Z4D/HWgAv1Q/wBPv/1ZEnsV7Gn/wpQY0MxxvEGTf8m6CqXL1fULrgqH3BOSXuujJHN2yDI/DyZ/c5sk/3+Yt/3BA2Uu48PuaoCAliION+y32dIsDH5uyeW9C7zkIps6HtCYV0VaOyUuqZoQGgi0cNlOS1QkqgoaGcqGF7acUC00wIKXiQoPJK6SokJpU3zUerpkgaeaJ9HH6FU/+r6TRuBVmXskMrxS+DI7jpc+l+nngZjzITduaquoPAj94k+0PA/fdZHtKbXheLm57Oq6IWBH5nIj8Qv367SLyCRH5vIg8LCIP3uSYIyLy63Wxy2Mi8ueve+9visil+vjPi8g33+5reD2geXZA+3KFnQqj4xXZPMgDfWRiMff3WXzUr/AXPxHy7N+I2TndJBxA56ySzzuy/SXZckW67Dj7LQnhBBYf3iYcOzrnpthpiYst6f4mLjTYSUkVWxpXxmhgfHV55Wp9K6HoBp70zivCjQkusmT7W3syJVCHrbIc12kiquSHepRLndfyNn5ZCE4c/5LHDDO8PAiVmpd8vFHxangcfx5fsNKtX/994G+p6i/Vk/7fBz54wzEl8JdV9bMi0gE+IyK/qqqP1+//I1X9B6/C2F832Hr7PHlXqJpKtH9C+HyHaW7RZkXxVJe1b0lZ/eoedgx6oUXRElwIm+9Qlu/cYJxFOCdMr7aRHKbLhqqXEA0Lim5E/3hANFJalwuirSk4R3LJ61JpM6aYSzBZRbYQ05wUNM8OGJ3qkc2FRIOSYJgTbY/9YOvwqE/P9Z6KpAXRpT7lYvuajlUYIGmOxiFSZ0+9plCfTvxi6cIzr2SGlwPfAfCNaxheCrfVcIjIYeBb8G7TX6o3K9eMSI9rzP4e6vzk3RzloYg8gc81fvzGfX8v4NL3v28vOc7FFcXlFo0cytUmh35TufT7KpJnGuQ9h5SgIQxPOMQJ0Y5h+7PLVDGIggWCiXiSvHJc+kCbIIVkU8nbQpIY1ApSAmWFRiGUjnBrgsYh8VZGvtggGBeYUsnbhioyhDeZa70mlg9juSSkXGp5zyYJcN0Ek5eIMUheoI34phlXmvh6jVcjVLUn4/JlYMaVzHAzqAq52pfe8Q2K2+1x/DDwV4Hr4xN/AfgVEfkH+FDZ+17sBLVuyjvwmQC7+HMi8t3Aw3jPZPsmx/1JfHk9Cc2v+AJeD2hdUqrIZ0EhICsp40mDfZ+A7Tstcwe2kEfmSY+VSFzRbKVMJjFhVFI836FcLCAzBEOLKSBbqMjuKHhuX4u5J5WiLWze7w3LdCWke6ZL79kJLm6SLoXYTFl9tyUcCJ2LjsZ6gYst4agknfc/DjO6LuPp+qJT56haEcHOlGwxoWzGBJOAsmlpXCo9JwK3TtMtSi/e+CoYjlsVL75gnxuI/5slAlxvTHbTkqtnz7yyg53hdQ/3CnEcr0fcNsMhIh8C1lT1MyLyweve+jPAX1TVnxWR78Qz+t9wi3O0gZ8F/oKq1hVm/Cjwd/Br8L8D/C/Af3fjsXX15Y8BdGXhDS1T2dgo6d8RYgqY+0KAzS1SQftixuCOBuMn5tH3T5lvT2lEBYNpwtuPXGRSRlwMKsbDhNbjEaPjFa4UNHZ0e1OGg5DxYUt2R4ZOLNKscFFIPxAamxGb94XgYLpPibeEyWFH2TKohCTbFY1zO8TdkORq3Ynw+orv3epzY7yeVWhJ1iZkSw1cZLC5w4xTXy8SWNTdXCxRKsdLZAa+Yrjeq7mVgdjdtmsQbvZ6d9/ra1leIIXvdOaVvMnhyfFZqOorwVcB/3XNYyRAV0R+AvhWPO8B8B+Af3Gzg0UkxBuNf6eq/3F3u6quXrfPPwd+4fYM//WDaFAAoZ9wmoI4WPn8kKoV0rqkBM8p25Mm24dCRtsBJodHTkVU2zEaOpKLIVUMdmKoGg7TLBle6KJWyU6kaGlo7hszvdRGBZpXoGgZumcdq+/xtR9BKgRDIepD60pOuJMyumsekysmL71HoHqtp4dzEIWkh7vEq2OkdGgQEq9NfTZWpV6d9zo595czGe/ixd7bff/LDTtdjxuPvfFzbmZUbqbVdaMhkcprgO31LbnumJlX8maCvKHJ75fCbTMcqvoDwA8A1B7HX1HVP1rzFV8DfBT4OuBLWsbVAlv/EnhCVf/hDe8d2NVoAf4b4Iu36RJeF8i++d1kc5bhSQdOCMcw/3SFmeRIXrLYzyh7MVffHyGFofscJFuO8VqT/lsqovWAsqmIA3copfeJBtPlBkXPIfM5rjB0FsdUn5pn5ZwDgaIFeccwOiqYXAkmQrqomFzonqsYHY5oWSEYVdjUK+1erym1l1UVBYQDP06cYvrFXlaVneS+CRX4LoTXFQfuhoyAF0zINxqWWxmU343BuB4vmPBvLGq8bp+b/b0Z9rS8rEWK8kv2vd4rgZkheSNjRo6/8vgTwI/Upe0pNQ8hIgeBf6Gq34z3Vr4LeFREPl8f99dV9ReBvy8ib8f/b84Cf+pVHf2rjOTKBLUtqo4SbAcUHUcVC9mBDkXb0n5yC51POPZ/VQTjks37G7TPjti8v0e8aknvyJBhgEZKq5mzc2+IFMLi5wybb4+JhkL5bIwGsHWf4CKlalag0Dof0LoE40NgMyFZVxBItipGByOa6yVVArqvQ7g99ROrrfkIESgddpj512GA2gA7zDyvUUuTAC80NrutcMPAk/M3/PZuNBp7p7jxeRjs6Vx9pXgxT+elcL0h+RKDU1W4dvIlApI3NSTXhf80iXFffPIrupYZXn1Ur1wB4OsOr4rhUNWP4j0MVPW3gXfdZJ/LwDdft89N77qqftftGufrETv3dMi7Qus5X/0dTA3xdobJHTYVXLcBwHQpYHJfRLKh9O/sEA6hisBuhT48NTVMz3egWxJuhYwPCiaHsqnkBzx5DnDwxAbDNKaTZFxuzJOvRjSuQDhSorEyPGxpXRVaV3I23hqz+HjB1qmA9pWA5uWpJ8lrbkOy3E981kBeIFFItq9NfGWANqJrHsd1hkOmvqfIrvdyqxDUrYzGHuoV/cv1Pq7f92Yhqa9YMv5mISwRcPj/nQhmnF3rvvglA7s2fkkz7J0ncZ0EySoky6meef4rGtcMtxeKUOibt776zXtlbwJc/ivvw5Q+dCQOooEQDiBdDD1HYCDvtUCVsiFUEUz2C401iPrK+LDgFgviczHZkZyolVOdb5EvVEhXUKuEA4sGjmA9pJiruHxuEdMqGZ3rIaLEm0LZAhcJwxNgChgfFqpmwMLnlO3TIXkXxocszdUOK58Cu1OT5VG4lxXl2hEuCYi2pmgcvvBCd1fVu4R63V0QI2hRfsnk/eVM4jeGt26FF7S9vYmx2t1+42ffbJubb2O2Rzf9DDW1pH1egJW95lraSqBSb2xfLBnAOcxgurfPjV7JLLz1+sCMHJ/hNcN0n6KhokYRJwQjQSrBXnakPUtjq6JoCumiwZQQDaBs1Meu1JNsasmOZpjQUVWG8PiIahijYsBB2XaYtZjFt60xSmNCWzGexujUG6KyCTaF3ZT0YAJSCeVCRf8uQ2MVlh4rmSxZwnEtdLgbbgKyI3PYtMJF9Y+orjZXAaMxUrprjaF2q86dg6pCCs8JaMM3mNLAeI/E3SQ19zoZ+BfDjbzFS+37Yq/3tlmLa8eYwRSpHHZz+KU92G/8rLoDo+z2Rtm9B8Z8yTXsGZwXucbd8+/xJNaCNbg4RD/32Ite5wyvPBSZhapmeG1QLRRQGMJeRjGI0GlAtqisdi2isPSpbdbet4QLhblnMtbfHtO64sjmDTbzRX0L790gNI5xHmGNY2OnzdtOXuCRp49CqKgTmBquXpknbmdkaUhZWBp9IXLC+I4SMzVIhec/jucEzzboPBHRPVeRbBZUsWXpkRFmkuOSgMGdXVAlGjqCUYELDeG690KkqijnmmjkK7SlrPkPEd9kylo/QUah79/hwOQlKgKVelHFSr5Uaff6NOAXMR43C0HdKsX2VmGqF3gZtaHb9QJu5p28YP8bEgGuH7dUDsXtkecvCOE5BW6euQXgOo09zoTA+lCfCMVCQviOe3FJgB1nyCSbeSWvEmbk+AyvOvq/eIr5nw9pfPsqvTjlidEh7KkR5Zk2LvGT+DN/fAmTQxUr6VKEyTxxPT5kCMYwOgzDz+3DTr1USdGrsGPDo6t3EI0FOxXKjqICOMjGETIMCPuGbEExFbTOBrgQTA75nBKsNQgmYHPId2s6tkrKVki1EJMu+q+UiiClY+fOBsm2w+Qx+VwEAtF27o1ApbgoQGOL7U89ob2b1quKZBWuHVG0QkRBigq1xmdkpby45wEvMCC3MhA3S7N9Ka9Ew8C32oVroaVdb0lvbmz2zs0tpOV3jU7tcWgSUbVj7DjHRQEYMP2b1MvUx+56eHseiyo4R3zZlz+ZSbaXySXvuhcpKtKDHeLNlHQlIf7Pn775uGb4iqDKLB13hlcfq+cXCD4wJZ80vDxzJYQf75AfcdipMPeUUHSE6Os3KH95id6Zgq27Q8YHAkwGKER9oWxC2VJEIRhYNPBpteKg6CguVlzskKkFAY2U/HAOmaUCFEvVcmjiwCrl2GIyARWivmBzy+CYJRoq2XxdJZ14Rd7xoRhnoYoM431NTAlx3+HCGDv1BX/BzhTGKdqIcI3QK+yWnueQwivuElrUQtFJsGmF2phgd+KsbcfeCv0l8HLqP14SVYXUoSONI//ZRrzR261jUf0Sr+b6c3+JN7M79uv+ilPylRZSKsEgpVrqgFPsribYCwZeG6+q7ntirR/Pbpq0MbhOAw0M2VKDyUpIslOx9kCbKhGifQ9RtITWqmNwzHDwf/7YS9+HGW4JT47PJEdmeJVhR4YqSxi3AvL1LpHzhHf7EkSDsq63MGx/cgldgMm+ABcr6TIoPm12N7yEgGTiI0AbBhd6zkKtopGCVaRTYKwy153QHyW4uMJtxt6YNCqwCpn/IVQtX++Ri8FOheaqeAHGhFoDy1+DyaDRVyb7BZtB1vCFhM0rGcEwwzVC1BgkAJlk2KJCo8D3EVEoezGSO1xo0EAwhaOKDUGle6tuUS+kuDdB7nocu5PoTXBjbcjNCO6XIuI1CmrDVtaG4roxBHYv3VgD6yvjd3u5q+J6Te89hMG1tGSROgxXF08WJRATXR3i2gkaWuzG8LqLqL0TgKqiWuq88P06s23vfjiHiwNcI8CFhvknhoyPtLwxX3VUoWBzL20z91zFxp96CFP4DpGNjZLVd0Uc/p9mxuTLwYwcn+FVR7VQQmpoLUzJWwXVxSamgqwn9E+GxFvKdJ8XI7QluAiibSGfU8KhkM85qgSCsSAVaAguVMqmFzl04rO0ysJQzCkutziFze15zFRwiTc4LlKiKyEuhqrh0GYFTjAjizjBpuIbQDX9+U0m2BxsBlUDxk0h3lHCEaiBcKKkyzG2F5KsTtEkQIapn+TKCgKDmTiqTowLDMV8iBrBpo502X9dm+uA8Wq9UtUr90gx4wy11kvAZwU3iwq9WBHfi2K3zqT2MKR0vs4kCesQmkWcw7UbEBhcYPZ6m7jQYNMSFEzpUCsUh+aQ3BGu9vcyrSgrn4lWGw7Tn+x5ILv95XHOcyH2mmcDYCb5C65RDdfOWRssMylwjYBkI0WKitEBCwYaG45o6AiHJTYtMaOczhPeiJedmLIdcvjXR2z9dw9hShge9d+pmSG5NRR5JRs5ve4wMxyvQzz7w++l/YQhXVbGay2CvmX+KQjHDjWGhScqNt5qyeedDxsBLlbypXqmFOvDUbXHkWwYqhhwYHOpV5Jgp0q6KEhlqRrGexIVuIZC6TkQDZV8pWRX/jZsFBRpgGsIGghVE6ZHFDsyBJOaS+lAtCMgUDXUf2auBJkileJCIZ23xOuCHedo7Ffv4NNTq0aIBobxgZCysbvyNwQTmH9iiJSOqhF6DqQRgELZCgjrjC3KerW9u+JW9UV2N5DNtzQY13MIu5xBXZAoVQW2VhAufN2JNn12WNWOcQ3fpyRcn/hCR2MIr44hDHBxSNWOCAYpaoR0JSZfWCZeT8GIL5bM8uvG58ditkfXss3CALX6wtCWtZ4Yv7EifTe1eXfsWU64obhmhJSO9pWK1tkRWMFMa2J91wOqeZayFRCMCtQI809MQMDmDfR71nl233vRhZylxSFb/RYn//DnX4Fv/5sHM49jhlcVYd8wXVGqXklzcUK11qVMBBcYghSifokpvFqtizxxXbWVE6eucmFjjkITpFFhNkKqpmNyUJHKGwGpDFFfCIdKa9WT2uIEUwka1KGmHGwqlG23F6YSqxirFIMIKQ0oaKsCUUitrymZcyRrFg0gGvr6k2DkJdzVQpkIUhnCqaN5tcQ1AqRwPnwSW5wVsvmAsmGoQijagqgfUzhRXAhFL8YFQpBWkIOUjmwxIZhWiFOvxLs98fxDI/J/Q4vkJZIW3kilBZqEe6EeGU33ak6k9AS8JrH3XIoKstyH1LT0vUPGKSJhbUDsXiGjVAHh1WE96YaQFpjdwr6yQkJLeLUPQDDJaA0y3xRrvkF0ZfACsv+m3IeIH6NTHwLbzUbLCx8yE7l23HVcywvSnbPcj8kY2s/0ce3ItwGGF+ynjYjpoRbZnKE6GGILpbHh73E4dpgfnmdpRVh/T8jC4QnrF+a59P3vY3KoQgrBlEL7rLDyT39veiUKuBk5PsOriagP4gTRgIlrIT2leQW65wryriWYFLggpn0Bdu5SzIkxscCh1g5XdrrY1YB8zqABEDvs0GclFe2KcGzpnisxhXoCfaAULaGwEG0birbiIqVsOTSqY/eFoDiqzIIKdmwoO5XvPpgJJhfCoZAuO9KVCpML2TwEUyHaFsKR4gKpiXEhjS1u0WJzEBcTjR0uENI5Q5DWXkkd4hL1Ia4ygWiqTFb8teQuIOiENVcjuFgoOoHvjb6vTbCT+Va37Yhs3nMmjSspVSPATkvvMVSKSUskjvZW5xoGdVgn9eGgwHpOYpj69wODRKGv9A58eGrPiyk9Ge0aXlVYrPWy8vMt7NYIGdfS83Hke6+nPhMqGl+TpP8SeZOaB9Ek9hlc+KwunMM1I18zElg0CtE48BX707zmW7jGoxhzjfNRLw0jVYXdHKHN+Jow5e44KsXmDmdt3ctFyDuG5pUMZ0PyrqW1WhB+1JL+4kGOoRRth00tLoLWZUfegfP/4/uI+jC4u4S44s4//pnb98N5XUFesdaxr0fMDMfrDJf+2vuoYkgPlJjUQOCI1izi/ARpC0WKinjbixk2rlqmBy1uO+a3x6cB0EMFYTun3EyQ1B8bDoRkPaR90ZGsZUwPJFSh4AKfBaWmflhFQ98ESjIDFjRwUBokdKgI5aLDboV1cSKUHUex6JC4Qp3gJgF2bDCZz+oCwaZQxUKZyF5cHYXGRoELhHI+8EKMgZDsVNi8zloy4AKIRhCOFZs6kvUp6XKDqmG8h6TeCyublrJhfZptEaKhoehagtQRjkqypRiTO8Kho4oCqthiigryAm1EvmlU4M+pgSHop2hgMKM61VXV12uEAW6uVdduXFMFlkmGtmo5kLwA59BWgt3lcGrpeN0t9AusN0STbK8/O85zJ3uTeO1J7KX9XmcAzPbIGxfn0HaDshP7XvGRvZZ5lV0nJLkL4wsDTeolYWRX3fj6VOaiJF4do9IimwtwATTWC8KtCeFatfeZUjXIewHxVkHzbJ/u4wZN/LSyc3eb8SEY3F0hSUVnbsKZn3wrxSCuOTI4+Vc+cZt+Sa8tFGZZVTO8eoi31cuMdA1Vr6L7xYhDv7RGsdwmGGbXSFnjDUnVgGoaMH9kh8FTC8ihKWUlFNsxArTOWUYnC4JxSN5T9DJM9yeM9xkvUxLD5I4C0yhxpYHCIIXBjn3RX7FcYholve6ErAiYDBJkElAt5VAYgp2AsuGgEoLLMcHYZ1A1ryqDO3ZTfsFkQj7vw1Y2hbJpsCnk3Yhg6vmGeHBtxetCoX0pw05Kxoeb3ug0hMlKQNlsoQbi7RKpCefJvphwXFElQhUZbObIewGtcyPMMKXY16V5ZkCx1CSfj5FSiS8PyPd1sHVIyqQlZJ4D0N0Q1zT3E/XupBoGaGhRgWq+6f8f4OtSrA8V2azYK1Q0w+meh4AxaBxQLDQRpxTtAJs5QhH/eSJQAJH3BnYzrHb7hOwR4jdT1y0dNi1xkcVk5QvrSnYfUQhlhes2qBohrhliR5m/xuuyr/Z4oaIiuTomXjOeBxmmLyDkJS8ZHYwYnBCWHoVgGHrV5omj6iaM9xuqxCG5QEM5ubDBox8/Bd0K13TYTsGz/+i9uLYX1UTBDgJO/r8/fnt/ZK8CVGUWqprh1cPiFyesv6OFKeDUj6VINgTnmO6P6a75+Pn2AyssPFWw+q7QS6ZPLP1+E3t44jM5MoOdyxEgGzTAKum+imjbMNlnatLVf17RU9rLY77m8HP82rnTTNebBH1D+wLkXaHsGGhA9vFFskVHUHrS2+xEmExoXfYTYLqkRAPZmwDGh4SyqWigOIRyzmFywVQQjoTWFUcV+b7oVSREI6XzzIB8qUneDShjYXgkBo29RzF0jA76Cbt/h+9kWLQM4sCUPmU0nQ+whTL3xR1Gp3p0ntxGowBtxoRbE88zlA5bKXaUkx7pYSclVRIgscVMcjQKMNPiWpZS4KU7dsM9av2kWrYjRBWTVr4FrnO4MECtoZxrYkrns63CYI9/QH1dhp2W5HMx00VLOFWKTkCynmJGOcVSk+hyf887AdAkwrViKB1mOKFcaBGu9l+YIVaHvWxWULVignH6pcYj90bBDFPSlQbh2Gd9BWnxAoOwy3NImoPEaMN67wpekDjQv3ee0WGfhDE4amlc9fcOVezWmNaVJqYyVBFwyfI5d5Rm3ydjBFOhaFmMg9bFkKIN2VJF1al4+l8+QPPZiGzREW+aN2z21qwAcIZXBfKuezn3TU1cqCw8VodFAku12CbZ8CRrudQhyJT+HXWtQ1N9iuxORGGVYC6HxGFEKdcTOutCseg1roKxkHdh/6cKpkuWvC00SmEw1+bh+AjpJCJet1QRLD6eUoWGzTSm6DZwMQRjQ9n0FeVSCi5Wxge9hxFvCUXHh7viHcjbPoQkpWBKMIUPUc0/DlXk+5ub4lqILNkoGJ3oYgqlsZoCCfFOgckqJgcS0nlLFdWfcV0EoLlWEkw9uTv3xSFXPrjI4OgC6aKycd8Sh34rxaQV+aEW6ZzFVDD3yCaIkDw/QVsJZTchXB1cK9qrpeFdMwYDVTMi3Bjh6lBWlXiexMWWqul/QjbLwAgutj6zC8gXG7WKcVl7Dsr4QERjwzBdtKSLhkHXX8/8k01Gh9pIBdGJmIUvjjCTgnIuwcWWdDEkbwmdSw2Sczve+xDZk2QXpz7BIAqQynleZpJD5a5lkFlfU1L2EoJJhZmW2FF2jfu4Tgtr18tJD7QZH4jongkI64WLOKXqtZgs+8JMqWD5kZRgc3yNhLeGcKJkGeQ9X9PT+2zskzlK8ZzVQIi3lWjgmC4bgqlldKKE3DA5mSPjgLKlXP6r76OxqlSxD2W+EQh3ZdY6doZXCRe+sUfrorLw+JRwe4prx+TzCcmlAXbLkR2eY3QoYnhMmB4qIfTNnQ4d2mJaBGxvt3HOk9nlKEEjZbJfMVOLSxztSzA8YhgcDXCBMLxD2f+JCtGA/pUVOJHSvAytNcfwSEzW8ynBZUtxjWthpMp6Yl0ygxSCrVOCVQAD08hPJiYXGqteeDEceWn2xkZJlRjSnqFKvFcDoDYiGioB0D/ZxIV+YkGE5tVaMHEhIhhX7JyKiEZK99kxdmdCsdLBZpUP9xhf02IzoYqVSx9IsCksfz4nSJXJiqHqJJSdiLLh+6arFYp9Xey0qEOBjnKxRd4LaaxOCfpTiiUfKlTnsNOCohsj6kl/SSxFr0MVCzZTtu+MMSUk247GWka6klAmhumiQS30T0S4SMl7DkQJxobBHYbxnTnznwrpns0xecXodI+i5UN0ZUNY/GJGfHHnWsW6c97Bi0LKxQbpQuSN8GZBsNsHxblrIa3K152Y3GeOmbx6YQGiCBqFe6T96J4Vdk4HZHOANFisjSuAyQpspoAQTCG6PPDV8w7/F5guGMZHlCpWTCEkW9Bc84ZneMRStD3vJU5prDumK4bkakC6v0Qyi3ZKqiJgmihLX6jYvCck3lbO/NBDmNz/n8uO4/T3ffK2/Sa/csw6AM7wKmHpCyXpgsVmnnzMlps0zmz7DJo5H+IYHY4pW4rJDHQKXGa5utnDBhUiipsGROuBnzgjBQPlYsnSx0L6p4Si7Yj6htZaRbZoufJVguvlLKwMSK/2SJeFqmEpWl5CPd4WijkHofNZVKmhc8bzEdt3ea2rKlGCsaDhLn+hEPoiQBcI4QjivuIs9E+EOIsPMRVKsqmEY1/bUUVC3hFcIERDJe8FZEd8w6jmmT7Tfb6SvbXmY+LpSoOwFRJuTSkWGgTOceDXt9h8YB5TKipC0VaqSPxrK3TPl2y+te0n+VTpphVVYkkPRYTjiPbZEcVCC1M4RJW8FxFvVJStgLJpCYcFag3hdurj/nHA4HjiQ4ABVCGYCoqWMjxmSTabAEyXPdcjJbQvKP27FI0dwY6l7DqqwznBpYTOxYqiY8kWOmzfaT3nlcPSo6mfnGsNKnHXSPlyvkHRDLCpIxoUSOW8AawJ9r2QVxx6WZes8mnBNxGGlCyHOELLivE+i50qSSUv2E+NbyQWTpSi7TXMxqcXaD2zRS3SBECQeaMYbwuty450wZD1oLGlRAMlmEC6KPRPGezUKy+jEPS9/E2l7HnKV99jMDn0T0OyJgxPl4jzodFn/+F7iQaGdKVk7vGAdBGO/q3X1ivx6bgzj2OGVwFRv/BVvXWr1cbz/ofo5lq4yLJzqlGT4iArKa7wmU7zvTFpEZBvJyBKcSSj6IfIfA4bMcu/HTI+ILhAaV4xJNuOYOpYeBwufx20n4jYns5D7JgeKrHPBjSvKvHAh5SCiWF43Pcrd5Gy80CBTC2SQ3RoTLqdUIrFtSpKheaZkO5Zx85dQu+sD4O4QPyCNAKcl7dwARQtIV30nkIwgfYVR9GEuafHlK0Qmxqi7Zydty6QzgvTRU+mNzY8cVu2I65+1TwLT2W4OMAYQ7JdMTwUUDUg7ylzTzvspMS2Lc2nN0kuNzCTbG9iHbx1mbzjaw+mB1o0rk4871F6/mHz3jl2a7minZBorHSey3FxQD4XUrR8ksEud1TFnsdBYHREcU2HZIJrOua+EJD3wE5AA6Fc8MkH7c80qBLYvM9nMM0947POOhcrTO6IVkeeb1Gzl3WlSYyGlnBtiCmaXihS5IVNocIA3c2YcordHPkGUjeIJF6PfLnljfaiEG8q8cDROZdeS+2txRPz7gLRwGBKwVzvudThqnCi2KyW5RehfakibxtcIDS2HMNDlrKjSOELRtMVSNYgXarTmyvD0iPK5r1e7WA3+2981JGsBqSHCl+NPw2oIkUKg1RKeqDi6X/9Lih8vRECd/7JV1fE8ZXUqhKRs8AQr4VQquoDIrIA/BRwHN8J9TtVdbve/weA7633/x9U9Vfq7e8C/negAfwi8OdVVUUkBv4NvsHeJvAHVPXsi43ptvtSImJF5HMi8gv167eLyCdE5PMi8rCIPHiL475JRJ4SkWdF5Puv274gIr8qIs/Uf+dv9zW8Ghj+wfcSbox88Vlo2Xxw2YcOmjGmP8GOMmyhFB2l7FS47RjZitDCsPn8PNPnuph2gTQqtDSEfQMbMY1Vw/Co5yE6Z31+fTR0pHOWjT8wQbo5ra9bg3b9wzc+lrz1DsfOKeMbNx2sK8EBbZWYnYBw26DzBcW5Fq3nQuJ1g6QGyQz5nLL+TiHehK27LP3jlvEBw/CoYbrkJwg1PnMK47kQAA2gaAgLj42QrCK+OiK5MqHs+MZPVSQUTS/QaLOKfKFB2QpYfDwlvthneKzB+jva9O8IyBb8BN59HuYf7eNiS/uJTdxcy6fcxn7NVK50Sed9wkDWE8897Gv4cE4gTBf8PagiH2ozpR9j/84OW3c3WH9rSDbvSX8NoOgq2YJjcqhicrCiWizoHBrgWhXHfk6xhdeDCsaCHRlsP6D5SIPBfTnifD/5xrovsnMx7JwM2Dkdodep3rpek42v3o9rhphxCtZgN0d+Uq+crxPZTb/d5UDq4j6p3F6q7h65vsttBF7SPrrcp2gaumcd809NCVJlfDAmX2nVyQKW7GCX0SFL0VbizXqRU9eCaDNGw4B4u8AUkM37ZInxPsvwDiHeqby3ue0IRkLVUPr3lNip50PU1ooH68LwsCHeEV+vEyhqfQg0n3OYkcUOfBjW5t4zSRcFKbwum0QOAsUOLU//s3fz9I8+yNM/+iDP/fu339bf8i4c5iUfXwa+VlXfrqoP1K+/H/iIqp4GPlK/RkTuAf4gcC/wTcA/FZFdC/aj+Fbdp+vHN9XbvxfYVtVTwD8C/t5LDebV8Dj+PPAE0K1f/33gb6nqL4nIN9evP3j9AfWF/m/A7wMuAp8WkZ9X1ce5dsN+qDYo3w/8tVfhOm4r+ncY5j7nM5TirGTxs1tgBJlkDO9bpnVxQhn7CQqrmJFFQy8NIpXglgrM1RjXrSBQ8oMF8aWQKoZkEzoXSoqWoX0hZfO+BhoI+TQkOpuwvhGjvZJwPWDpEWX1vb74T61l/fdlMAyJ7hvSFqX1Uz2ufsBRLJaYjcgLKy4pR3+1oGhbpguG/mk49BsVwbRi9YGYolMT5vO+7qP3tOdAqthXvQcTr6xbtDwXIkWFiyzOBKT7m7jA15vEO36iqyIhmw9pXE2pGhF5I2Dn5DJFx5Pnec/hGkrQN2RzwsXfN09zTYnnFon63kCWywlVYjCFIjV9o9Z3RRweDkjnW0QjV6f7Kt1zStYVpiteHyxIYectiusUtJ+KqEKoEsUF6qvtA0WM54IO/fWKQ+UW1VyT8f52nW7tlYnbd/QZHUigMHTOOZoXJ4hrMFm2BOO6MZfAs989j8lg8TGl90SfpU9tQl7g5lpI6ZC8YHTvMmXDMPfw6rUv1m5GFfiqeCOUy10QvDJxUfrak3G6Z2RQZe6xHRBherhN1vWZUfH2blW6EK2O6UUGOw1YemQIIjz5fcsc/XCFC4TRQct02euWBRPPbYQTR/sqbN4b0lhXhseEsqVUnQrTKpkcFUwqzD0pZD1h8fGCwbGAYIIPYcbC+JDDTgyNq8L4sPOKCEkFainmK8qOQLfAXokpVwpk5D04DBA4WvNTytLw9D9/NyYpcYWF3HDnn/7UK/p79hG72xqq+jauzZs/jm/N/dfq7T+pqhlwRkSeBR6svZauqn4cQET+DfDtwC/Vx/zN+lw/A/wTERHVW8tN31bDISKHgW8BfhD4S/Vm5ZoR6QGXb3Log8Czqvp8fZ6fxF/c49z6hr2h0TvjmB6f8xPVtNgLR0xOLdK4mjI61mR0zIebkvmU1DUgrrxi7XKGuZJQtSriKyH5QkVy1TI9UtK4FNBYd6y/LWD+Kcf6O5sM7qyQUmAnIr8jZW5uzPZql/JQxpX5gHDHF6aV945gtYnJhdFOg/hCRH63YEcCY0PVdtiR4Y6fm1K2Q8rYp9eiMFm2JP26+tv5boLts1A1hOZGyWi/pWpAuuQn13AAwdQT7NlKk7JhCIcVLhSS1Yx4x6/OhodjbzwV8l6EnTqaZ0ds3b3owxYF2KkQbxmSDa+btfBUiZRK48qY6cFWXUPiiwnBFxYGqVLUuljxjjI8ZhjEhipWbObVf13o+RtTCmkALnEc+1mhbFQUTcPaQxXRYoqrDEFYUVUCZ1tc+JYl2he9ovB0n5DNK1W3hMhRlH4xKMOArXsM23d1SLYgXQI1Sj7nsFNDcMeIbLXJzmlL60rC9umEzkW/GIiGFXHpzz//qatsv3s/85+++iXfsfzIPOlihAo+4UCVarHtVX7BiyfWxHa2v83quyKyBaWxKhz6jaEXkYwC1h7skeworUspSdMwONWmf6JLsgbbdxryDmAg7zribUPVUt+dMjE+DVyhfwryuYrGFUsxJ8jVGFGf2ts/BZ2zyuq7Qjrnay+jUrQSWhcNZQNc6HXXUJCpIT1cQODQ1Hph5JYjXAtBoVguoDQErYLpJMJlFkqBzRhb+Oy+p3/s3UjkOP3HXrnK9pfJcSyJyMPXvf4xVf2xG/ZR4MMiosA/q9/fp6pXAFT1iois1PseAq6vqrxYbyvq5zdu3z3mQn2uUkT6wCKwcatB326P44eBvwp0rtv2F4BfEZF/gF8HvO8mx+1dSI2LwHvq57e6YS+AiPxJvFtGQvMrv4JXARd/4H00ryrdZ1PPCNaG3vWaNM/uUC602LjfUjaVpXvXcSpY60if76D7MlxhSI6PqLYTskM5ZhSQL3jiVS2sPSC067uZbDmq85by3UOq59qUPSErAlqLEyaX25g6S6rsQhyXMKnFCp1QNqDqlIRzKcV2AsD8Y0KVWPKOj10DLD3iCemsa+pUW98TPZh6me7tO31nP2d9RbtU3tMIUmX+iSkutoSjivHBkOERQ3g0IBoozfXSy3yv5WSLIdOlAHGKyRu0L/iUznwO2hc8tzA66gn66aKlaAnZXJdszlex6zwsPjpGKqV5JmV8ct6HOyaO0aEAZyFb2C2887pbLvZJAC5UisM5wdWItXcK4RjyLmBguTdikodsX+nSeSpkfMRRxcL6u3ymV9H24ROcYOOK6SDBxhWUvld8/66K3nNCsglFU2h/0jFZNhQXOoQx8P5trmbzmByinYztO9sc/JVNssM92k9sodbQPj9l7YMH2LkT5p/w4x8dFYIJTA46Dn/EG4jz37ZMOILemYLmJEdV0SRmfKrLlfdZpFTu+Pl0Lw13870rzD09Jtn2ntjZb20gJbV0DaSLvpanbKpXVwayeYdrOortgHTZLxLUKlXb0TxvyRYU4ooqrqASZGqxE4OK0LqiFC2IB8p0xRDtqK8hKoXpMjRWfVqvCwwyMZRLBQSKTgLEiQ913V1gmyXVKKQqDFoabKOkkgBSQzVXQquAYfiKGg2vjvuyQlEb14WfboWvUtXL9Vz3qyLy5IvsezNrpS+y/cWOuSVum+EQkQ8Ba6r6GRH54HVv/RngL6rqz4rIdwL/EviGGw+/ySlf9EK+ZGdvlX8MoCsLX9axrzaCKXQuFZhxLW1RVmgcMT3QJNoOGB9OaKxCPidsf2aZsqWwnHnStTLIMKBcjWChpDk/Jd/selnzCBqrytJqTUQ2hPEBYXpXxj3LGzwHVBdaZOsRruFIrlhaV5TREdCkIjAO7u4z2mnA1HrC1xjKrIlxcMfP56w+EODCiGxByBaUYCRk80L3rCdU0+WIaKckGGac+1CPaAdwPmwVbQuNy16PKho5op0Sk1eE21Mmx7uIg3SfY9Ko2PeblrxjKZpC0UzqnH6oEsP4UJPmFWXuuZKN+wK23+awQ0PrsjA+rAyPClWiTA76dFFTCsd/bgcNLWU7RJoBiCfwq8TzMJOj3iMIV0OCsedidCK+oDGE5Jm41snyRiOf90Zm/eF9mFI49MWKjbd6Q5PPO1TYyyazi1OcM36VHzjM+YQqVganHa0LltFhKNqKBuCiABcKwVSREiaPzGMimHu2Il1KOPBrW7hOwnh/yHRpifaFKcH6kMVHHeGkxeigYbpPiU71fehkGnLpgzHLD8cUbWVyyNG+5An3wdtWyDuGMoHeUzD3XMalr2lw/D8MGd67RDyo2LmzxdrXFJz8Ccf8YzE7d3mDmC16Of/JAc8DOYe/p52KYDNgcsiT/VJ6XTObWh+mShQyizRLmr2U8XoTTX3a8mSf57PCsSfYTemFMouWf100/f0P6v+LGQVooEgp6ELOJI8BcLn18jm1cbLPNaDhPRkUqlHojfkrCC858spQyKp6uf67JiL/CR+RWRWRA/Xi+QCwVu9+EThy3eGH8VGdi/XzG7dff8xFEQnwkaCtFxvT7fQ4vgr4r2seIwG6IvITwLfieQ+A/wD8i5sce6uLh1vfsDcslh+5LtXSGly7gVQV8VZOthBhCiWYKuEYdt4Cy5+F7bsT8uWS4EqEVP7HI8czJtsNmjWZGIwBlPGKId5RopFj/SFH5wsxGystqkoIprX8emUoOsqgCfl8iUwt/XM92ucsnC4xmaGxptjMMD5eooFjshJRRTBd8XUl4VxGOohong2Ze3yAhpZkLduTwAiHXlRRDcR93w1QKiXZLLBpiR1mvjK7KDGZI+sFzD0ORTtk616ldVFoX6mwqWN0MCCb9wVk4NN4Jwd8Nlhj3cuptFYdc89VXHko4NBvFJhSufQ1EVVDee4P9LjjP40om5Yq9tk3LvDeSTiGxsWg1geD6ZESKWp14dyvaVzkZedN7j0QnO+wGA2EzjlH55kBg2Pz2BTGh6BqKuFYSA9kJEmBqiCijIcJxVJJtBZgCh+3t6lQdisQ6CeGZE0omz6rqOg4oh3D5lt8MaNUHZpndxgc7/pxrwcEwPhIk8F3Dsmf7nL0wznrqz2CibK45rj8/8hY/UaDjSqMCkUr5qk/u8KdPz6gakWsvbMJopi8ormqVPNNJsuG9uWK5lrJkf/LMDwS0lgvKRsBosoo8mnI4MNIUglVq6L7WIgLIFvw97JqOao2NC5ZWldgss+QrkCwFlPYmN6q1zfLFn24Ktmp6B8L6FysGBy3dRq3537E1d0tnf/MZM0w3e9FNstxgNuXIYAJHDKOqEwIouQHir2QWdgqcCpebucVxSsjOSIiLcCo6rB+/vuBvw38PPA9wA/Vf3+uPuTngX8vIv8QOIgnwT+lqpWIDEXkvcAnge8G/vF1x3wP8HHgO4BfezF+A26j4VDVHwB+AKD2OP6Kqv5REXkC+Bo8N/F1wDM3OfzTwGkRuQO4hM8S+MP1e7e6YW9IPP0vHuDuHx5dk7UANDQQGIZHE1woOAtlS9i5t0RaJePNhHyxJFlIqTbb5AsVRU9greFlRhaUcCS+q6r6cE3RgvUH8TpUX7PF6pU5wrUQKf2qzmSQ7StpnguQypIfz9DUkj6QEgJl1iCbF7JFR3LF98BwoaNsKzYVgqGl+3CTzsWCxtk1Nt+zjDilebXAzUWoERrrSrJdsfaAD1W1rlTYtEJUyRZjmI8Z7w/Iu76Qrmx6bat83q9MJ4fAhZbeWWXpCyNkWnD1axZoXXWYUmEV0gVD3hEWH8u9+m2pHP51R3x1RHqgTeuirwMZHA0496E2eU9J1gym9PcpHNWGrfQKv9miQ0ohOTAmm4Y4DWlcClh4sqJ5JeXZ70wg8ER8vO2vUQ1MD7WxU59p5cLr/uGjEHpTjCiVM2hlCNs50dMhoztK4qUpxxa3cQiTImSah0wORVTPt8FA56z3IKrY3/fLHwio/kib+CloX6qIL+5w7jv207yqZM92OflTA8xoyuELwuieRdrP9Gl/epHRMUfQjzn+czusvSfh0EcrJCuYnOrQ2HS0L2UE/ZS5ZywmLWmtVkwXrU9SGHoFY7U+iwlqD6CjFAdyWk/5lsFuaik6UDaUuaf8okcqIRh6I5h3dhc4wtzTStEUJgchW6gQ5wUxR/stNleGhy2tK86nby94NQK13ttBId72BiV1gjuQYtdiXB6hscMVAm2HNkvEKEFU4VQQoMwCNDe3JV33Faoc3wf8J/GZbwHw71X1l0Xk08BPi8j3AueB/xZAVR8TkZ/G88El8H2qutvS7M9wLR33l+oH+KjPv62J9C38fPuieC3qOP4E8CO1S5RS8xAichD4F6r6zTVB8+eAXwEs8K9U9bH6+B/iJjfsjYo7fyxDVPfUSfP9Ha6+JyFdcuz7VN3TYurbrzYvBJQtS7aorHzMMj7UITtYEQws5XyJFIZg5NVdowFMV5QUYf5px8YpoXHFMj1UsXO564unjk+wX2ihFrLFimDH0thQtu5TGIQ09o+ZrjVJrgZIpEwPVL6JkPOE4sbbPF8QDfyE0L5S0Di7A6osfnKd9NgctnDkcwHrbzfEW0LnQsnKw2Bzx+oDEaYIaK76jJvtOy2tS57QTpf8Cnb+ScfVo8rSZwzpktC+XNE/HjBZbpNsOw78lzUmJ+a5+t4QcT7s5ywUbUswcYwPBJRN0DsXaK45wrGy9k5LOIDWRSjqMJMLAauENQnfOaPYQghGQrxjCB/tsLjuUAOdZ7ZZf/cclz8QY6eCi+HwRwvG+0PU+HH3T4Q01h15W3CxIxwYsjunsBNhRdm50iXasESVkC8GVG+dElxKaB3JOLuxgKsMD93xPFcmPYajhm+y1SuY5j7EFI6E4nCOphazETM9XNC6FJAenWO63wEGNY5zf90QfHI/vTMVkxVD3p6nsemYf7oim4fRHR0WHpsSro8Y3rtI55mBz2qLfTOtaG1EerhLFXllZhf5jKf25RKpoLGh9E95yZl8uQQV8p7nMuItnwZedOoOkbV0f+ec0j+pZItCtlix8AVDFQqDU9RJDJalR0tGh3yPlmik2FRJ5w02VzrnHcOj3kusGoqdCtMVHyKzqVBt+jCiKX241mS+TshOIspuRVEaZBT4bKzblPj0SmVV1QlCb7vJ9k3g629xzA/iE5Ju3P4wcN9Ntqd8mfPoq2I4VPWjeA8DVf1tfKHJjftcBr75ute/iC9SuXG/W96wNyJsf7qXMjk9scDwcEjrsnLwN6asvqfF+LCjc8Zgc8jma7mHHLbfAuEQtFlRJrU67dDQWBWm+5T5JwvUhHTOVwyPWMIhZHNKctWSnk5ptDImg4Qg2I0TG3pPeRI13hLSfSXT7QYYKLp+wrSLGcGTTQ7/+pTVdzcQB50LfkLK5oQrDwUcYJ7GpRGoEm1nTA42aKzn2LSBWjj7rRE4ofu8DzsUHWVaCYOOxUXK6IgPaWngJ5XRQcsd/6c3ruJiokFFywrTRcNkxeDesUTv8R0O/UaLyx+Ima747KKNxNI942tYio4SbwkutIQjn+U1PlbhLluSVaF8+4h79q/x6BNHKeYdZj5j5w6h6vvY99K7N7n8/BI7uUHnc6Kz85gCTv5szuZbEjqXHFt3R+QdP1lFfZjsU9qXlGgMx37RceEbBDcMkV7B5IvzBBbKYylhXMI0JDif4AIYProIAuVCyeeuHma02kaaJcHQUCQWd2JK44tNTn7j83zxi8d4x/3Pc2Z7kfwTC7z9Tz3CRz59H4ufFaYrfjVePt8mf/uU6b6EIx8pyeYsc49skh7pMT5gWHii1kBbaBGMKy59/TyLjxc0zve9SnBofeFg4av7i5bQXHVs3ht6WZnCfw+zBSW5GFI1vMflYkUHwnReqZqObM5SLeb0ng0ZHheK+YrWeYvJLP2TiuBbEydrlmRLGRwN2HlHwR0/5UgXQ9L9hpXPTrn63gYugGRLyXpC2Pd1IMHUhwydhWAoBBM/aefzghpFQ6XarWbIjO/z4gTbLXDV7bEeM3XcGW4fnIPAsv2ORV/bkAiiynN/1uDKDLHKTqvub1F4/SXXrjAjS2NVaD8dMX37hOixJi7yHkrzsjA4HtC+6GisF4z317pALYdLBM0sRWRZWBoyPh+TL/r03O178UVY+0sIHI1nY9IVnyvfPi9k/SaHf2PK2Q8lSKUc+k1fVJbdaZl/yjE+YAiHJasPzYFA+7JPp7WTkmjg6zXynhBvC9tvK5FmSfx8gs18SKf9jCey83moYoVeQbZg6J+I6J4v2L4HIKRzIaMKY4JMCUcVGodEW1OO/59TNt/eY+PdDkY+RLTv4YL1t4VM7sywm3XjJ6PQKUnvybGBw1WGR586QrwakB3K4XKCGuieN8w/UzBZ3sdiCJMVwQ1i2heVKvTqveJg51SAC/015C0lXYKqVbH+9sBLx/dq8rZb4ArfLGvxEaHvEvb/TsnlrwqI+sLkgDd6LnKEvYyy9KTuvt+IMKWj+8yY8//VHO/79kf4L5+5FzuX88j5w2g/InrHkF/72P2EY79Cj75zlbUv7OPA265y8coCi4/B2e9QzAi2714m3oR9D08JrwxIj8+Tdy02VeafLmlc8PyUmRaUcw3CQU44gOGDHZJNb0DynieXq1iJt3yoLz1c0Hs09OnedZpr2fJthV0MpF42JO/5OqF407B9n5KsG4quX9TEO9A/rcSbgu1brj7k/2etS8qlD9ZZXHgJm7zjjYaLfNhUCgFRNBBfJxOqb6GMNyThSBi9pcBuRLiVDGMUV5hXNJtqF7Oe4zPcNux890O0L+Wc/VBIOPS1Dhr4H5QrDdHFCFMK1VtGFDZGtgOfpZN4+Yp0yf9wmp9pUrTBhYqo1FW5SjSoGB6NyeZ9W1cNlWTVMm2XuAsttlcC9GgGuSFeD8mWK9xyDtOAaC0iXXGYAuae9NXjh39tSroUUXYqDn3ES0vEWxlFJ2TjbcLCF5XpSrRXSb3x1sAbBdPyRqMrtC8ovTM58XaIGq/Ea0qlTISrH6z4znd/gt/5wfcwOGopJwnp/orttrD5Pug85tV97aRg8TMjr2Dbjr18ubVIVbH0qU3ELbJ9t7J9r7J9ryUcKEwt1XxJo5cSBBXDqx00FFwaYIYWA2RHM0zo0H0VwbmEcFxrPBmvMZUv+B7vo8P+vk9TIV/0E5ZJxaegqg+XNJ8LyBaUdJ+iVok3LK0vJmzf42VfJvtg+fOOjftCOmeVrffkzD8c0lxzrL/DEp5rU3SUk788xU4LhifaUDqqBD73L97KXZ8d8NT3tnyHx7mcqrSEA1/lvvbtU9jq0rhzh83fOAD7HFv3gt0JcIliU5h/OsdMS/JDPaaLAaZU8o6h98yIYqGJTUvUOHAKRtg53SLZdOQdYXTEFzuGA0O0LUwOV5jUIElF/35onAtJ91cM780hNyw/bFj9Km9MBidAQ6XzVMh0xRtxU3r9qXTFT/qNNW9cbSaEA8jnfRvh9nmlbMDkgNc1yxZ9Fp8aJZz4GpFd6X41MD5e4TJDMPGp5NlSidkJccs5Whq0Euzg9jRbUqCceRwz3A5s3g+DEzHiHMUdKb3ehO21DkURYjdDinnH/LFtVIWxQJEbwpHFZAYXO46+7yLPPXmQbN6nbkoJyYavas56Qt4OKJu+OjcaGNJORbrPpyRWiwUiPuPEJiVpqLSejrCpZXBnSdFxvvtfJWzfpxz5cMGlDzSYHi6R3NRpqJYqafjQSqfCFoa848MA2ZxSNZX2WUPrUsrG/Q2ffbQm7JwIGR8CDCw+6lh9r49tLzwc8PBPvYv2zojuF1KufMM+sgXBtSuSc74YTU+OWaNDNGjTuZgRrk+QNEOsReMA14wIUp/9JQ7yxYpsBaINS9ETpiRI6MAqwWaICz3JLA4O/Krh4ndVVIOIfKFiVAUMjwZ1bQsc/4WM6VLI9t2WxnlhdFTRZukF7SILgRJf9fsHEyXZgMEJQ3FySkZEuk9JlqfoWoeypeycNFQNZXxXgQSO9rdf5cKFRYINw/hUQbgecOH3N7DTJpMjJVe+eo5g6gUCd97SoXHJULaUsmNpXrJMDlZop0TWE+Jtw6SdEFmYP7HFYNjEOWH+NxO653KCSQVGKNoB3efGVM2QyT6fAm4KpYoNtvApxlk3JNmuyOb8PRUnHPytiu3ThvERn8XUWBdG84ZgKyCcQDExVPhQ6vZd1heirtW1NROf8gsQ9YV0yXsHUgqNvs+YstNrq/XGqtJcd0yWDFXsDcXkYF0PEmudxaVopEjhvQup8IWsu/ph3ZKgH6BGCS5FVIn/PJvdvt/3LFQ1w21BMBHSlZJkecpiZ8yliwtIoJTt2ju4HLAVzLH/2CY72y2oxLv5BqJty/lPHCYQH8vWwhfRAaCw/JkBV766x/BkhUYKaokvhxQ9f+5GN0VVyNaaRBcs9JRszqc2RpuW/HAOpWHuiwErnx6y+mDX9whvVDTOB0T9nKsPRbTPKfOP+4yetXcGzD9bkncCL3EeQLKpXPy6JqDEO8LoeIWGSvv5gNHJksGxADtVOmehsekwuVd2zQ/2sIUSbRtkyyCllygphjHDE47O84bNexN6z1ua59Q3aZpkSFrQ+/yUYLLI6KCl3Aj2qtPbZyxqfc8HKQST+z4hJ/5Dn82390g2Uk7/3YJz39706cVDr6e1+HjJeJ/l8lcle3LoedeHRZh6JVdpl5jAkR2p+1jsC2idt2SHcqQfEW9a0gMF6U4CJwsa50PyOSXeFqqNkKrpuFAuEnczXDvn/gNrfDE7RuusJXvXGAYRd959gacfO8xkxTI+rD52HyjtM5bRMU+ex+di8kVHur/k1E/kpCsx6cVFyveVxFcC4r4S9XPsMKNYahKOSvp3tvak2xceLyhbFtcOiAYVeccS7/j40M6pgKLlvYT+cZ90gArts4bxIfXV2RMhXVSv72WgcTaibChVwxuN3TTYfM6PXZz3Enpn6pTj2PMl4rw+2uiYD5UWHUvrkmPjpDcC4bYXNgwHBvAFhz4k5sny3Wy/MlGKuQrJDXbqz1/MOU+Ml8KJP3WbxA91Fqqa4Tbg6R99EDtSoi1LnrXY2uhg53xYY/5xYXzYN0oicEyyCIDmZbO3QgpHvrZj9f0O7VuSDSEYK1UszD09YXqw5VVkr1omR0uWvqCkc0I+BziYbjTB+Krd8XH/N1iNKFYKwo2Q5tOx1xmaKoOTbYK0/uEPAjSA/h0h6f4Skwcc/C3Pexz+9YJgVMDRgHAE+z5TcOkDIflCiZ0YLxFhINy25D0vPjfd52XebaoEE0f/REKQxlz9Kt+3ITgwplht+ErgNUPrUkjZ9FlXRdcxPG4x5QInfmqAGU7AOcp9PZrnB0T9BulSxP5PTDHTgmK+QdEJqKKA6ZESNXDoNwrKtlfcXXugTe9Myb5P54S/VrD+9oC553I27o9q7kGZf8JXxa+/p0LaJTIICQYGN7K4lYyoWZBvJ2igTA44bD9A92WkNkRKgzYqZGqJN6GxClvvKCGpmFsYs7PWodXI2Nce8ehzh1n6rGH7LUo1iLCDgOdXl8AJ40Pqs+giR5AUpPsFo8JCb0zr4CZnz6wQrQWc+z6l2FGCPjTOh/SedbQuZ9i6v0jRDojXU9AI1Gc+jQ9GRCOf3jw45pV/g9TRP+a79JWtemJu1OHPbsF0JQIDZmr2CiSrhoPKE+d512eahQPv/ZZdnwkYbfjaoWDsazeqyKsZB1Ookt3vuRAOvHrB2oPes5DccyXWGFzo/y8IVB3nkwi6Stj3vx+bQ3QuoGwq2XJJ2Ldo6HyL5Oz2eQTKrJHTDLcDoaPqKS6u0MpgL0W4xHsD/TstwcivhlvPRgwqQVIvz7D4qF+NTZcMeU/oPmHqnt1Ka9VhM4dLLFcftOT7SiQzmIlh55RfjUnd3tmOvNaUGVlfFZ1UNO7eYXy2R2NVGJ7wrWaTTbxSa2gIpobOOR+mWnp0Su9syNZdcPZDnuBunNvh3Lcvk/eU5lVhcCykfR76DUOy7uVH4nVLMPW91UeHhajvUykn+4XttwS4yIfVwn0jgkfaTKLES3cXQtQHm6nvK95QqnbF3CMhCJz/UI/m5S7jI8Id/+4qqBKmBaZocfFr2zQ2lOFRaF8Em0Pjkq9FWX0gYnpUaJ0xLDxZMjrg28+qFbIFGObhnrHunFffd8OCnRqcC9HYUXYh3DZUuSWfBhz4qG/aNDnoPR3OJRQdh0aK2QlI1g0rnx3x9J/wGWZklsHzcxy5Z5VKhSeePwilMF0WqrmCoFVQSl0B7XzGkh1aqp5ijJIN/cJio+iwuRnROj6keShnnEbYq61aVsSLDwbb3mi4yPeUH97R2qvH8N8rIUiFwVFL0VXsFDbuCyk6fnIW51u8aqhIw3+Zypbi5guC9Yhs2fMdJvc1GpP96r/XhVAecmjgkNL4Akf1agQuhCzxDcNQ0BBa5w1l06d5a+Cz72wq2NST7lWiqEDnnJAtWIpWnY479X1hxNVFiI06DFZBtGWpGuplTaa+oPN2YuZxzPCKo3EuYnqoRDNL40LI6JTXgKpKS1UJybqlc8HhAghHIYP3TDm0b5sr5QFaF6grpmHnbug+57vNRf2S8cGQrBf6NMmtAMV30mus+f2LjmF4yuIa3stoPRN4KYuDEWnsQxLBRFn4vM94GR8wbL7NoKGy72NeOqNMYHA8Yf0BaF3wWVzTA8r02Jyv5MXrFh368Cbjk3N0z7m9grG1d0WUTb/aXvxiRTh2TDcCWpczRocjOhcK+icidloteNsIhhHBW8a8ZWWVK//4FFtvMWQrFVJ3HezfU2EyoWo5kg2LlHDh2/bTuuLonpkSbE/oPd/kyjdUSFwxvqugOtMmHPlqdymF9nO+uvnCN8LiZ8A5Yf3tIWXLpwdroNiJMN1fV4kD0ZZv3ASWvOdoXhXCkZ9gt+7xq+Bg5GPteU8J+3UKqPhJ8Pw3tul+EYYnK+aO7tBNMtaHLSZbTWRiEYXx8ZLG2RCbh6RLStkTgkyIN4TRnQWoEP9Oh0QhGCvNTV9Qee6b20xLoXPGMDmgJJuCTR3lnGXjwXnCidK6lDFdDhkf8Kt3U0LrisMFQt6WuiWsr1HJln39TnLZt3LVSEmuBGQrgp0YooGQSYgL/MSfrBmfdWW8d2Fyv7JXq0hm0MR7YjaT2jhJ/dwXW0bbPgtLA6+sq8arKdvSd5QcHfNGLNkQyhZMjxRIboguWYJaiqRqKNlKRdCv29saqNqOcMfg5hxVWzn9525f50BlZjhmuA2YHirBQfu5kNGJEpMaqqtNDv6mMjhmifrKdKl2pR24wnLu+RVYKol2QjqXSt9n+tmAyX4vHVJFIdNlQ9GG5UdKsq5l6z6YHqqYnKqYezjiwG/2STa6pItCMPXeSrrgUxXT9YQjH/GaQq0zfUQXPFGZGYKJMv+ZdTbfs0zvbA7A+EpMuqwc/O0STMiFb7Ac/XDBZCVg4XPbUHhuoLkB8UYO1nsNar1MSeNJpXF5zHSpRzCtWPx8HxVhqZ9RtLpEj7fYuRsmlfDoE6fRd3jy/8DHK858hyCRQ5ol3d9JmO4PqGIv3957viDvWN8bu5PQuprRONeguj8j22jQGNWFi1uePJ3uc7QuGOzI0j/lJxgpauXVEqJtYXRPjkwt2qiwWwFVAvsermhe8k2fJgdithYt+f4CMwyYf1wYnPRNipItIaj5JxfB+MEJlRP0+YSTP5lx8esWGTWVsukQ41fLyZqhexbmv7DJ+W9dBGDhs9b3tTha0X4m5Mg3n+XJyVHmnhQa247t0wHj4xXBwNB7GtJlz6N1z5asvz0kmPhwlIrQv8PX1bgQ4q1duRWzp9/kgmv6UEXLeyd4OgE7Mr69q/MTYzj0QoNl23kl5EatP9Y35MZnmSmAMahVzMTu9X2xqZ/k86bz72XGexPGexb5nDfapv5/DE55gUW1MN3nU4KD7cCPT2B0xGH2p5TDkNbztdzJkqOKvbhklQgqSjC6PdlUu1CE0r15yfE375W9jnHmhx6idTYgueoJ4njdEm8Yom3D6gOGeFuxmdJccyw8kRFOlGAjRJolhEq6qORtw3glqLvNQTT0pPm+T46Ze9pRxQZT+Q5sGnnCdnRUGZ7sULT85LR9vyOdl1o+RMHChd8vhKOS9fcs0DuTocYrqlaJsP6+ZbpnUoJxQbiV4gJon8f37RY49VNjknM7dS9qr+4rFZhMCYYZwfaU5c9PsKlvGasGpgdaiFOKTki+0GD1q3rkCwn7PzYknCrtc0K04VenKn7Ffun9Ie1nQsILEW4cMj7idbnGRxw7byt8D/GWIKXDDlOC7Smty0q+nXjJlamPp6uB3jPQe9pnQQUToZwv/cT7lDD3lHDiZ3Youkp0JUTjimA9pHPWk8CrD1i27utQJRZbKEuPVoRrPrU6mxPa5+DYL03IO+pXyQbGh5QDPxMz/9GExlVhcjAhmPoeJcd+0aGBsvRZn1I6WTHkyy26ZxzJpviMosCvtkcnS87/8nEaVw3jw3D1PYbx/SlB3xCMhM2vzRjd4etndk4HdZqwUjaVfK4O6+R+Je+l76Fo++9n0WWvkM/kvgI8mAgmAww01gytcwHN85Z4y8t/iPoK7c4zlnAouMg3tFLxnxGOvbaXnfgwlp360JiKrwUxqRBteP0tm3kuLFt0lA3PUxQdJV12YKDq+hCZSxzaKqkS39N8eqhCHLQ+0cRMLZN7UrK7p7hW7fU4oepWSGEoe9Utfp2vHBzyko83KmaG4zWAr2L2k3fzXEC2XHkp6kAJ7hySzQvN9ZJwVJF3A2zmlUc1tcwvDX1hVeJjv3FfmXu+oHFljKlguj/B1NLsRcsrkeLAxhUnf2rAZMkQDX2vaDv2ceTOxQpTCOHSlIO/AXkvIBwr4wMR3QslrqEc+O0B4UR906CiYv3dXcqGsvLwCDMtOfhbQ9KlhMmJeXqfW0MDQ9GNiUbOt8Od5kjpCDZGNNcdZSIMDweMDwRMlwxX3xuyczqiaMOlD4S+Yhlorld0znrD0FgXXORXmyufzWhfgNaZgHy5xAVw+Ncqjv68sP5W70jv3O1nQkkLLykiil1Oka/epugqxVLJxntLxgeFyX4h2QA78l5I0fU9ODbeOUeyLrQuwtLHvKSIN7QQTIXpknDpgxFFw7D6HkOxUqDWh+rEwfB4g/ZFb6DGB5WFL/rjg9STyINjhnRZ2Xio5OLXBix8zvpiQgOdSxWX35/gQl/YOd3v4/y9pyyNiwGHP9wnSGvNpjtGNFoZ0d0Dpqe8zpgdWbIFbxCyRcfOPV5bTANluqKMj1ZUDfXe58RP/tk8VJFfyefzyvCkI11xnh9TrytVNnxP9T25mdKnHjev+tDW6GSJS3w1d7ztDVkw8d/3YOK9Pd/F0XNc8bbxBHfd6bXo+Mw1qTwPMTngDZDJ/eKBQnDNeuIv/BRWdL3HUjUdo6MO1/NS6eZKAk5wjQqsQiFos3rFGzd9CdSHql7q8UbFzHC8Bpjsd5T3jsgWHW/70BOYtp/YqqYy7SeYDMqG4eLXW7butr7l6qESQsf2aterqB4SwrEna4uWJV1p0ljLCQclZSw+U3TJx9ipBLmQsPpQD7VQNr2XgfowlguE/Z/MOfBvYqKdEjVCsl0STH2F9Ol/O2Vwqk0VCRoZLn5Dj/aViny54tk/0GLr3jYyLYh2cprn+mAt4+MdyqYlSJX1d7TZeecyozvnwAjtZ/pgYHiHY3jMhyRMBtFQqRI/Ka+/o8l0wWsTLX98m97ZiumyEm8ryYZw7ptDth4scBE0z4a0LyrJ1QlV5FerW/f75kyuGYEI+35tlbt+bEI5DBle7fiq4qGleSYkX6oo7p7Qf6uX3zj+n1OSdeXIz6+RLgnDk47td5Zs3adEO0L/Tt97fXK8IEh9WGf7LUK8KUSrnhsJh8LoKIwOGXbuUjYfqLCZsPMW2LzX0j9piPu+oK1qeI+wXCwYnPRpx0uPFrTOT1j5TEHREnrPKd1nIVn33tzB304ZH2/7xlUTIV9tMtlostCaEF+ICLaCWoZcqSKtaxsEk3kOIdmQPYFLcTBd8VpSVawUPV/BHkx8t0PXqsiXvHFO1v13uJh3FF0oW/iMpsg/pIRwy9K4ZPd6l5Qt9bVGBlyddaV1+/TJQUc+54i3LLaWR5fa6xBlz3hUnao2LlqHzGRv9tLY4dqVf8/6+oxgPfK6bUYx7QLqbpm+F8rt/43vchxvVsMx4zheZTz3Pz9EdGRE2o/54EOP8czOMvuX+6wHDj3fJFgPmRxQsvmAZL0upGv4tNzu52OmB5TsRIZdjRgeMXQuONbfKez/hFAai53WneYWvAz61tdkPpX1bED7il+llYk3HKYSMjXkbdh4R0jjivdifC8PiwbQvKo8+web2Ikw9zTsnEwom9A/HhBuKTYXuuczqm5MthAjqmzf1WLp05uU803KVoCLApKtgvO/L6RzcJnemYLxYUUOpuSbMcHEsu/TGUU3oGg7GqveE7IpVLFBVBnv8/0rtt9WQeTY95GA3jMZaw9E9O+tmHtWuPD7ezTWlPYF5fB/mbL9libpO7ssPTwAfG+Lu/75lLV3d9i+39+LdB9o6Ei+0CTaURqbjsHxBBfB2vuXybtK5zlD0TFMTuZMGwaMElwNKZKK/rsrGk96iZLJIS95bnJIT2fo1GLT+ifmID81xeWW5O4xw80WakPyniNYnlJmAbIZEW0LybYjWUuRrKBxucKFbdQKUb8kmwsYH7Bs3puw/PkJ4dDSPxGRnSzYtzzg0mP7kKZn4cOB7E2S2aLD1JLwpoB0xXlPNJVa6kZxMQSpEKQWF/iMqnjLMD4kNNaE7tmK9XcaknWhedWnwk6XIe9A+6JStL1HaArf/6SKaw+lrbUygtakuzcIpvBehM2kJtLxac+h1xNzUV2rEjsovacgqbcWUtZhO/A1OaPaa8FL6+DEp90mDia+34o0KkzoOPlHPvcq/NJn5PgMryA6Z4Wd+RgTV3z0iTt9/v5GGxlbkpEnj6WCuecKhocC0kXQTum9g1pOu/NITNH0EzwKy59VJouWeOBnieZayXh/6Iu1ppbkcsj43pTJwYj9v6N0zqds3tsg2VCyORgeh0MfLRkeCpiuCNFACYew/lDF+LBw5FeV5pkB6++dZ+GxCelCi86lis4l2LrbT1wrvzNk584mVdJg6WOrIEKwNaaKu6RLcGUxIupD3oONt4aUzcprLA18Bs7geMT4kJdTnxxytYCe0FyF8x9apEp8Dr/vZebDQFI4DnxkjX2fjL3HtS40NyvCQYVLrE89FqjaEdt3JwxOwql/tcbKw8LkQJtsf0lj/5B0GpEu+74ckwNeoTfv+R9967IyPqQUXW+QER/OKrrKyocjyoaw/Mktsv1trrw38p0Xx4JuhuCE6R05ZhBgJ4bKBpipZZR2wXhvIxwJVdlE2xVHft2xdZchnRPC/Q1c2CQcVTRWUyYHG+S9gOFhy+AtJYufsZhJweh0g50PpERRydpaj7hvWHjCMV3wRHze89+TZMOQLfqQT7riV+XNNZ89heBDmspe5lfS9w2TsqRux5vCzmmLSt2oKfAhq+ZV3ZP+r3xWsPd0mpBs+n3Uav3Xh/fU1BXfsa/v2E2XtRlo7sl1HL7pVmFwhvp1nUIbOFQEKcxeEoOLamOTeINIq0KMbze7W3Qo2yFV8iq4G3hyvJqR4zO8UigbMP9wiKzG2M3Q9wUYBUTblvYFZenRFLWwcyKkqntgR60cE1e16w6D0yW9M163qLlakHWEeOAoE0PYLzC5w+ZK0RGCTgH3DUGFZNXQfXpAlViqxK9sVz7jJcXPfpvQWq1YfKJk5bc36J8GM/FpuMNDFslyVn5zDZOWrHw2o0yEi9+g5F1l5y6l6iYsfXKDZLOgWmyTHp1DrSVbCFh6pPKZN5EPU0wOOsxijukUfkIGNh8sKVtK0fZ5/1XDUSyUrL9LWPpC4es27pj61eV2SHO1wDUCUKXsxJQtQzRS+scCznxbyMUPJsRDR/tqyeqDDarIr4Kf+d4VXGA48RNrHPiIZXq+Q9LIfZVxS8kXKsbfMGJ8T0aZeKOKQDAyxO3MazJVfjVvSmhfLMlWWpz5Q0rrkjL/mA8BLXzBy1k0n4murbCnluZFQ+Oipfd4wMmfHfsOePszzNQy3uf7V/gwUYWdOqZLAflcTLKeUyb+Mw/8umH5UzuYrGDxs9u0Ptug+WttonNecn2839+LXY2teNOT4vGmIRwaH67a8RNusu4LSeNtXz9Rxd77mC6rr5Kfd1SxDytm8563wIE4z3G4UMg7nlBX40n1cOg9mWze11Dsyoe0LnnvxqbiU8RXBTvxz3XXMYt8h0bEG9VoR0iuWhpXLWHfQOC9CQLdC2t576X2oqf+/2MGAbId+qSS3CCpwWaCmb56U96MHJ/hFYH76reTrihFV3DLOdVCyeSpORYeFe74TzuMjgjTpZB9nxj6rJKezxypzrf86qn+nsXrAcMjvorcpiWm9EV6eU+4+lATtULrkp/45HyDdBjTeCpm/yczpPQy1UULxvsN23daDnyiItyxpPMWFWHtfUu4CGwu3PUvJ3TPlWw/sII2Y85/S4/B8YiNtwrifAgjHBjs5ojpsTnstMQOUq8Tta+FzRSbOaJhLa/R9KGHpJGjTlCrFAsVf+TBT9C8c4fWBUNzeYzpFAQ7AWW34uLXBUQ7QuPzTeaO7xD1DS4yBBs+x9Xklc8y2++L7sKBj62vv82ycX9IOFTKpq9cjreFK+9v8dz3rJB3he6zhvzpLkXPGys7NhQXWyz8dkSy4Q112VTKnqMqLa3jfd/tb+L5i9UHQ0aHIpZ+K2J4B2RzQveM1krDfvXce1pY+ryXT5kccvBA34sMjnJ6TwnG+iyh+adSjv7ymO7ZkgtfHzE6GFA0hGgrpegG7Jw2DE46omGF5CXFcouz377gPdGGcOg3M+aehsa6b3gEXrdseNxP6tmiJ5DDoTdA8Y4SDxw29RxH0XZkK76Wp5iryHte+0lq4cZg7CXv21cremfKusNiwfIjJcEEwgk1h+Y9tdYlbxSige95XzZ8S9y8q747pXqPRUoIRnXKHBD2686UZa2eIN6g4IDcYPsBwXqIHVrfNjb0PWJ2w27hwGdumcx/D6Tw5yobyqm/9IlX5beub3JyfBaqehVRJZbjPz9l9cEmcx+PMRV0zheEgwKXBBz9hT5YoexESOX5Ayn9D8PtRLi6QGq3faYLYO1dLfI56J/2Am42FeLLA3DKsf+cYyY5T/+/Fjn86yPM1JO/3Sd32LxvHjfyHeuCUcWxXywwRYXdGtOca7Ly8YxiuYWUjub5Ac3nK8qlNsd/dp3JHXNsvtN4GZEMGusKYYBNK64+1KZ7rkHnqR0ufMsiwQiGJxyNVSGY+pTNeM0i+xTZiEg2DeV9Y3577SSD7SbcWRBkIa40uMXCTwhHx0yDpm/S86lFDv/WlPDq0P86I99er/dcyua9CYuPekJ/524oI0f7ohDvOBaeyBkcT2hdybj0tRHNy0LW84T8yZ/sk600qWLDxn3C3HM+wy1bELrP+8y1zfsNeRXzX73/8/z02rsZtg3xhqVsKltvVaqWo3ExIN5RwrFDrRDvCMufm+Aiy6Wv8dX17XOGadZFg5LR6R7TFaHdzJjutAk3xxQLTQDaFyCcqPccuxHJ6pT0iHLsPwpVbKg6CUE/o3MuoblWkqxNGJzugMLwmFfJBZ9o0DlPTUwb0mWfhOEqGHZgnFtfN1Gn+YZ9SzgWxPnaiHR/6bdvW4IJNNccRUMIB45gKgSTijKxXu6m4RtfAWTzQt5VnzW1c01lOBwI0bBusFRRp996wxCOvPfgd/YZWy7w3ocaqOYguerVBXYryfNF318+2OVqao5FrV9o7fYgR66lpb9a0DewYXgpzAzHq4i8F9Acl8w/Xfqq2FGFCw1lJ/RtTmOLHeQ+hLDlf2zxhpeLtqkhHHlvJd5WFh6bcOlrWuT7PAmJeKG3YtGx87ZF5h7ZxAxTAPZ/XFFr6mWQA2uJt4Xu+ZKdk/4rUDYtUb9CGxGj4y2KZpvu2QyXBBQrTZJLQ4K1AdOTi0Q7Oft+p0GyXZJ3DOm8oX/vPGqEA781QANff5B3lNGJCqm8rIjWYYvqcEo1jQgPjzn1rnUu7MwxKUJOHV2jnyZMsoj7D1/iyrjL6uoc8mSb5L4+01FMtR2RzYVs3LfCgQ9fxUXB/83enwfbnmV3feBn7/2bz3zufO+b38s5syqzKmtSlVBJaEISCIMkBIHBgFtutx0Q7m6M1W7btB2OwDQYbNN2NwYHYBshkFEIkAqpJKGpqlRDVmblPLx883DHMw+/ce/+Y/3uzaSUWVUqVVamRK6IG+/dc87v3N8ZfnvttdZ3YHRvQudqShUphqekX19FFpMJXLb3shAWG3sFVWzoveBYrikW25Zyorn8Jzrc84HrvPDyDipKqWJxkFMltK47UJJAyljzT/gQzZtGuC2BY+dflcS3p9z4/j5l4pidhjKWhXSx6VhuRgzvFUb79q/NKZs+7kVFuLdgfr5J86bD3elz7gsDsq0W3jTn8D0JysHq00vMTIQbAS7+bwHKOazvM7nYwEsdyUFJMMrJ1hImZ0XWRZfiumf9uvoySNtyIZUSVp18l/KOiGqiILllKFr1gmukStO57N5VLUw4aghmdr4T4i3ApIb2tRwvFXKdtxR9s+lZ6u+VqOn6U3HuS1e0zN48IBMVZ10BTvSpdCEujt7AoXNBACorqgMmk7lLMJIFuQpBp3J7tC/PuVyXv2uNeKIAJ6q5J0npmxK/uyuKrxbvJo5vYujSMbo3EZOg6zl5x8N6tQDcrMJZBZ50D5P9HFQgInNLaN0u8SeVLPCjHDPPiQ8Txj1o3hCP7fD2GIym7MSg6i+tZ2h/6QB8D4oS20nQ05TtfzXi6NEO/txx92MhOoe1pxRpP6FoCIFudH9I77kQAKfbxJcPhTMwyeg9MePqj26w9Zs5ratzXv3hFt5SYYoGrRcGWK+Bsgq90KBl6FolFTZUqGGAmmlKH545jNG5xsYV3/L4k/zcL3yAolPx1O0LON+R3PCEKHi3SbI9o7gVsvujGeWBwp9vsPLkiP6XJthY4KfRoWJ6T8U9f3/B5GKDcFTijVKKXkx0dYBrRDReyrCtiGs/2K531I5XP32W3i1Yrvvs/FqKso6s52OWljIxzLc0y1MVTgmBTmQxHGWi2f3WvlSBVb04L6UtFx05ln1N+5pluaI5eiQRnaUQ2iphvq5Z++IMM8ugrPCmOc7TbP/anDvf1qCMDdOzHXpPHIJn8OYFWT+sZc8VRaKIxjA9lYhhVg2WKBvSYmvdBJQs6OFQPNxVTfxLVxRlS4bQZiGExrwnbaNsVeYIx7BYb6EoE0FEWSVVhDWQdWV3n3dC+R4XsniXTtF9yWE9amSUaGHlHXVi1apKWdxdDCwhmNbe7FYqrWP/FiEuiiKvbUq1bf3ad6OUSiTekxmLyeR8rVdrVRVakFWBRTUqLv3b3xw01XG8W3H8DkIpZYAvALedcz+glPpJ4L767i4wcs49+mXH3Af85OtuugD85865v6mU+suIb3mNKOf/UdvMvqPj8N/7CK0bJf7SUcTi8gfgL+ry2coFNjub4M0tXlqJEqiFYOLQmcOkJWViQCmm97TpXMkxWUDehv3HQ2Y/1MefGPrPOnpPZRAbVF7iwtoCNPapGtLaQSn8hcMpcRXEKdK+zDjCkSM+tGz/6oyb392hc8UyuugzeGCL1aczMHJBnP8/jrCBR9GP8GeKzquW1jMH8vc8Tf95y/B+jT+D5Zqj8apfS5XLjrhoO/x+yoX1I15+/hQ/88XHUA0r7OJC0bwB0UCeo/WqwV7vkHxsQOf/22LvQ4a0D6MHOxSJomgrlmuiy7T6OY0ZL+l9fobtJMwutCkjxezUOl7qiPdC/MGC5A7kK4rTn4DBA8Ie3/xsQXBzKO/PrgatcMZQxl2UM+RtR3JHMfvognIScPu7QC8tZqko+hWtV2Qz0H+hIG9pZjuG0f2O5g2otGKxIf3/vG0oG4rBQ01s0CTvKFo3LI1bKf5gwalfknbX8P4Q247BOfQix0s88pZPfFQxPSUDdael5VY0ZNEEWUSX64r+SxVlKEx2XYiyr1M15DpGFvJj4cSlzAFUqUQAc6HpvABeZkm7iqwvmxjriT1uMHMEU8tsx4jE+nFLKYBsRSqDMhYZGH8un+UxFDxde61qKJqOYKIoI3lc1hHJewB/LgnIeYCWllbRFlkcXUgiyjuy+cpbr7WmlBMocZmoWiDymzvOdQ4q+27i+J3EXwBeANoAzrk/dnyHUuqvA+MvP8A59xLwaP0YA9wGfvp1D/kbzrm/9tad8jc+goljueoRH5XoUlOFIr6WNw26dKhA1/1iRRkagpm4nDXuipRI2dAEI2i8OqRYbzI5Y9i6taQKA0zmGH8ww9sNadxSxAcFqqqgcLjIZ36+hUkd8Y0xqrSUrRCUwmSWZAnLDY+i5RjdKwNQGyjaVyy3vqtD93JFFSim50TxNu17LDY6NG9nzLZDEcVrK9a/UDC6xye4b5V4dwFAYzdjvh2DFcMek4EpRDZ7esHhoopAweUvnqZ5V96TvOtqDoEsdMP7tPhf9GSXuhkKyTC8tMr2Lx1SdmJW/9oNnvmZB9j6dIVyjuSVAeP3rqJLaD17QOvZJUcf2WB8Cbovgyoto4d7FG3FmX9hmZ72OP2zhxx+cAV/WjB+3wZVAN2X5ujJElUVdJ8b030OXOCRrkeUccLs/hw99WRAq0UFoIygdcOSdQzBrKJxV6MrfZLUnOfIeqJ8G0yhbAgqKe/J0DpvxCgX039+jp7lrDyXovKSqhFgQw8zL8i3Q3Tp6jmGw587wpGg7mZbYiQVTmwtOJgxvD8mGjh05UgLzfScDLvjfVVrV0lrtAplJ18mDj0SfarluqJzxWJy2Rgo68g6mpWnBZhQtgI6qagyT84GzE4rTA7hUD6/xl1hnJvMsVgTI6Z0TTgiugCvBGfUScVSRlItLDbrmUVYy55MxH0Q9RrowGlJgKqG7jpPqiLRQ5Pjy1aFXmps/M2db8C7supfdyilTgHfD/zXwP/1y+5TwI8A3/FVnub3A686566/JSf5TYrZjiYcOQb3+7RuWfxxji48sp5HFSrCYUXekY8j62nm25pwIBedPxdrVRRMHuqT9mTecfvbmjLAXHV4t0Oq0ynzMmLtyVKsVLUjX0nwp4LaKfoJ3ihlsR3hFMy3NNare8s1AsVp8R2fnZL7hvcb/Ak4Y+m/UDK8x6f3csHdj8SsPCeeFss1DzT0XsqpIs3wgRbTM4pwKL4hqpRe9WJTFruy6XCBpfmKT9nwRX9rVSqw7gtQheI5cfT+CjPXeDsLvC81sQHcvtOnXY3ov5Rz+7tXxZfk/7TGZn+Bso4b35ug3r9BdAjtG6+15vpPDcm6fTovzzDznN3vMzx+8TovZffhpY79j67gzx3L9ZDpqeP3t4XJW6x9aYl/ewRadq3R7oL1ZURj12d8SUT5ypalihXhkSZvasb3QjjwiQ6F7Z71ZGdcRdK+ie/Kblz4KSJDM992RANF+3rF0UMNqqhJOLJ4Kz7DewzdV8Uno/PKDKcVLSfOgzqvZPENDStPLaiaAfMdaWkttkKpAkJF2pDk3bwBwcwSTGvTqZ6heTsnb3tyzJow/oumwltYso4M2720ZpQvZWZWtnywDpNblms+VSTVVBWJpI78q0j2K3QhLn7OiOjj8EEhBzolyC+9BJ1LhW0DSWj+zDE/Jcm+8qVNFQwl2Sor31lTQOWDVwiiSxdiwWxDR9FxmEUNzy3fWlHDLw/Hu62q30n8TeA/BlpvcN+3AnvOuVe+ynP8KPATX3bbf6iU+lNIC+z/5pwbfvlBSqkfA34MICL5bZ72Nzbsxx4lGtQ9WyXzjLLlYz25AIpYYXLN6KK0dURwD/KWIpiKoVHn5QXpesxiVQuc1wi80ptrYcq2StzMx3UsedsnGS5wvsEfp1Sxj84rzDSlWGngLSzLVWkvHLeObChaQ6tPL/EHC9KtJoMHQ7qvFCw2PHq/WBHdXdBzCZOzXt2SUOw/rjn3LxYM74tZ+8whBx9eZbGlSNct2f0Z3rUIkyr8hXhH56sVKq4IbwSUDU6kuNtXRCIlHpSML3jY0OGPjPgzXG2QPbxAX48Jbvu8+J+2ue+vzti5VpCd7jJ8tE/vCwfgHJufCVhsSLtovmmwQYPmyyV3P96naMDg4SZrnx3QfiLi1U/dS2sgrZzel0Zc+6E+Wc/iTxxlLImvimD3QzE6j9n61TE6K3BayzyJDsvVEGVVrcYLi3MFZeJhUsXsfMnsPHhTQzBS+DPZiTslA+zkwJJ2NdOL9gRKmvYd1hjKhlimTs/IwDvvWca2tsNttqh86FzLqQJN0Qyxvuzw5++JCceOyofmnYrlqifnta5I9tzJoDlvKqrAkLcV0ZFlfCHAnzl85wjmwtSvIlEZ0IW0i7xU4S8s0bBisRNhUkfW1TKTSC3hWDPb0aJSPITKyKYnb2qUkyQ1PWVEFLGUSqd93bJck02KM4pkzzLbEac+1VC0X3XkTZFPD8auTlziQX78PEElVZzTUm0UDamowiN1MkPc/quf/iZf9e8Ox7+uUEr9ALDvnHtCKfXxN3jIH+e3JoQvf44A+EPAj7/u5v8J+K+QpP5fAX8d+LNffqxz7m8Dfxugrfrut/8KvnHhfE0wtTUMsd45HSyZ3tOiSOo+bVMTH8oFgHrdELDgRH10uSJ+AyghPnkL4S04z0Fq2Dgz4ODlVQ4e8zEPrrL9qxOqyAOlcL4Rk6WuE79lK2JV3cuwsJr1z0+Zn04wC2kuDx4MmZ6zFA2fYAJZ11AmTeLdjPXPLrj7bR32P6C59PcPufvta1gf9j+2eqKoqiyo3YhkTxaBvCMLhTcxBDc8vAXMz1i6zytWvzhBVRVlOyJdC0/et8ZNQdQoB1NiNt+3yzQNmb3Q49b3Rqw9lRHdmRJeF9TR/rdtsvqlKcpGXP+Dhvv/uwPQGtuK2Pz0hHQt5ugRn6s/tMLOr6bc/K6QxabsRBcbfbyFENpsANGRqAk73xEMNP7UcffjHbY/OZAZj3MERwu2f2XJ7EKLvQ9qqtDhDT2KjYIqqND1t64qFbnTNRNeFr+iAaNL4tERjGTDkK7K52oDeQ/zjmJxtgTjCHblPfPnjvm2Il21HL3f0HvG0L5Rsux7zLc03twxPSOziLn1SFcU8aG0oUb3QbIrci7WyNzAKalwoyPZsAwveSeILJPK4mzmUhVNTxv8mVQfZfzaomiNvNDGnuib5S1N3hJJEaflnAHSnqb3SsHwkk9yVxjnAM3bAvHVVT3/s5IQei+VFA0t7PJQrGVN6nD1Z2R9GfR7YyEyRkeAdVJh5DIz5Jh1/jaEe1tXnbc23sqK46PAH1JKfR8QAW2l1P/mnPuTSikP+CPA+7/Kc/wB4IvOub3jG17/f6XU/wz8i2/8qX/jw+SO+aahdaukChWLMw1U5ehcTrGByF2Y3HLw3pDWLREenG+IHlBye4H1NY29krxtyDqa+MiS9Wt7zLmmWimYLkM6F4aMVxK86xHz0w3CUQEOsr4kgCqqvTc2xLdBF5beSxl6UeAtLXkvYnYqqH0aNHnXAULiytYUyW2HssImTu4oZvf18OeisJocVEQHOeMLMY27UkEcI4BUASiHt9By0Zew+kVoXV/iQkPaS4hvT5k/EpPsiSx8OLFUvgg66twx/6ebhD94QPisw19IX91pTXpxhfjaiGhs0aM58WRJ69UNbCdhfqZBfDelij0W6x7WSJVz5Y/WEGgjbOd0yxLdFe7C+lPCwp9eEEfG5EXF7LQMZQ8+JLOR9vUGrWcPWVzss+xpmtdhviNqr/PEiMNeXMLEp33ZUDSlXz++JIt6uiKMbTOXnbI/k9ZQFTqCkQyunYLeUwaTU8NVHeHYogtdC1Uasq7ioOtja4FB6yvyniXdcGR9LaS+vqKKLd0XFZML7kTOvPMqJ23E5YYjb2u8pQyoq1CSU7bm0LnMvpSV23Xt/069MCont89O+YSjY9ST/Jv2ZTDvzxw2UNz9sMfaU9XJQH2ppYoC2SCBJBtvAYMHPKIjx3xLEY5AFzLDqAJppx572ivriIYVVaCZbRuSfZmHqEw2Ylv/7Te72qhfx+/hiuMtgxo4537cOXfKOXcOaTf9snPuT9Z3fyfwonPu1ld5mt9SlSiltl73678FPPsNOuW3LPKORxlpWrdKFqsejeszikTjjGJ+KjqZbRQtQzBxDO4zFImi/0ImfgYOzKIgvj7C5JbmXRl8bn42Jd7V6FKhjMM5hdEOm3qsPOOIBlI9KFeTu2auls62xHcMrWvSOphvBeTrDWZbHpPzAgHOuiJTUUXSK56elSF4uh6yON1i9emCxZZjfM4jXVWc+sQBzWcP8A5nciEPKjY+P0fnMLtQUTYc0YHoKEVHgsxp7BaYrCJbCYkOU2wSkPYVi3WNNXD0iOKef/dF1r5UEh0oNn5jQPQ/9Oi8PGdwv2F4f8TyTItwkIG1tJ8+ZPbQGum5HtufHPDqD7fI2pqiE9TvA6QblqJnUb0c2yppPjBErWTgoHnT0XkFZqcM7Rspm59SrH/GML5HiGv3/j2Rlg/GjvE5Q77TIesaFtuKMlZ0XoFw4ETpdaYxuyGqlEXZW4qkR3QkVaQ/FQ5FFct3pPIV7auWcKho7lY07lriQ+nxl5FwHZSVnf7gIWherxFEFrK+SIOkmxXpWi1maGtHPYdIvQeOyflaiVfJDGB6VqqQxXbNBUIqwzICfwKtq5rwwBDta1SpyHqWMhHyXbZiqWLRrZrvwGLLkW5UTC9Ypucti+1jOK7MHrKeEC69pWJ8wTA7K5I5eUcSQBVJ+8l6klhNVr9nGsJa4yxdkZmJM3L/satlMLPMtmRWqCqBQGc1CswG39RL/SQEVaW/6s/XGkopo5R6Uin1L+rf+0qpTyqlXqn/7b3usT+ulLqslHpJKfU9r7v9/UqpZ+r7/vt6zoxSKlRK/WR9+2eVUue+2vm8XZIjv2VuoZTaVkr93Ot+T4DvAv7plx37V+sX/zTw7cB/9Faf7O8k9v+DbyE8KmhdXxDdWZAclIzva9O5PCc+yNGFLOplokm7soO0ITRv5ehSJCHKpo/KS2wzIjrI8acV4dhy9Q8GzM+VVKHj7NYRUVCQl+KuFh8WLNcCcDDbCSlDfTJcDAeataeKmtQF+99ecPhISJkoOldy+i9m9J93cH5B90VFMJQdn0mlXeYMzDc9zv3zBd7SER06bvzgGihFudqk/9k9nKeYn4oJZpbOc+JrbjIwuaV7uSCYwHLNxynYf8zn9re3uPldTfyZ+EdEA2nBvfATDxAeZjTvVNjYxxnF9HyDYAxHHyiZb3jc+J6WDK6tZbFmuPmdPumpFmc/kbH6qT3imxPCm0NaNzLiXS3w06mPd+QzvtnBWYVqlEzPKfKW9NnLyNC+PGXliQH9ZwXuujjdpH11weR8rRl2JmR8QZO3Hc6D6VnF+D6R7vBnwqrXmbCpF1uOvGuZn3JkXUe6XQqnI6jlxgMY3i/Vx+iCYXRJk/WkOgxmjiKRymB0rxDeZmch3pU5RDhUJzBUp6BsW1xUQavEtmWY4PVT3JklrllRbOTYRiUkRyeD+WAsC3J6Oidbr5g8VDC9UJ1AYJ0R7aiyKbt+nQn6Ku9IS8tp0JmmcVPLe+xgfrasNx+CGis6YixWxuBP9UnFgpMKI9mVjU3RgCqWIXfRUCfiiZ7wWaXyiRWT8zLDWKwYiqZsOIqWYn7asdywLDYd8zPl23DV1y/LffWf30Yco1OP4z8Bfsk5dw/wS/XvKKUeRNbXh4DvBf7HGpkK0ub/MeCe+ud769v/HDB0zl0C/gbw33y1k/mmEACdc78C/Mrrfv933uAxd4Dve93vC2DlDR73b78V5/hWxeanRqisgMqSne4yPeWx9sUpepFD08dLLZPTMsD05+Jk5k+hig3hzZm42M0LipUG852QMlI0dksOH/Kk37ta4vcLDj+5w+KRJXbpoVKNP5gRHFQU/YQiVqJ6O5UFUDnFbNsjbyuSOw5nAvKO7ELHy4Dm3ZLGbk7yP3vsPy6Lwvi+iviOkd75pmFyEVaeLhnf6+g9KyX54IPrTM9odJFw+p/v4+KAKvGxXowNNNGREy+OsiLaDVBZweB9K2JRWsHyvUuygxAbWja+bY/JU9tEA8vkQozJ6z76lRF3P77KYtux9mmPg4+UmGbJre9fp3O1Il1VJHckGVnfJ++us/qUonEnZ3xRBAfju4bF6RJOLaUffjfCn9bD6VWYnVXoUo5vXlPkHXjkB17k1b9zH6OLTYq2JV+rULW0RXt7ylx3CAYa6zl6zyvyNsSHMNsRTkHZK1G+pVwaWLGQiwx464rIgORdEffLalguWoyf/LkgkJwS21x/qiiashMXmLYozZ5ATpslLtciApgaCCsoNf7TDZZbFfgOlZSiEdat8CaGxaaj6pR4zQJ1FOJ8h8o1rlmRhhYqhU41tl2iUkNR+4kHQ1NzNqygnHJFuioVCZ6jccNjsWXJ+6Ie4I8Flux8hzdXtXulomgJSGO5puoEJdfOcl0StIgZKvFsciKZ4k8U0QDSVUXly3fXW8j7V0XSXrOB497/y1ts2PQV4hvVqnoTdOoPAh+v///3kfX1L9W3/yPnXAZcVUpdBj6olLoGtJ1zn6mf8x8Afxj4RH3MX66f66eAv6WUUs69eWp7lzn+Fsdyq0F8Z0a61SQ8SulcgWt/sM2ZX1hg5gVlIiiTztWcrOvR2LU0by7xRkvKTky2GhB4mrJhCIcl8wcCVr4wJdnsidWnVdhKMztfct/OPosi4ObNFfY+0mH16QU2kFnB/LQhq02cvIVIUDgFg0ctyW2D9WRmATK0XK4FHD2k66rEEgzFjzocO+bbmrM/l3Llh9voHFaenTGbJBw9bEgeP2Q4bLI7X2PtyTneOKVIEtaeSikbRvD1uUXlJYvzXZSV4ejeRxzhKzEbnyuYbXns3drBbldkHU3aF2XbtN9i/TdHdK4V2NCnjJw49i0M4cgx2zKkq5bwSOYoVe1Cd/C4ZTgLcEZYxsdltr4SU26UuM2MKvJPLE2Pd986h/lph8kUT/7yfcRNxfRRcddTmcY1Shr9JavNOYutkGXLJ9irPeAXkLclaczPlfL4QoQhWRrMXBOMZPjvLSQZ6AKCpSLvSgsqHErbJtO1/p+DbFVQaE4hciHtCr3QVK1KlGOXRhJDTT5TUw+T1rMqp/AmGrfQolhby3w4T0QCnVO4htjj4oBFjdirwPYKvLDCJSVVZlBzj3y9RM+McD/aFdbT+FN5D/2ZpmhKNVIZkfs45l4cM9H9iSYciOtgGQsiKutxQvjL1kt0pknu6BOBRGr9qSqQ77Iw2qU6zV+3zbRabn+7wqG+1sSxqpT6wut+/9s1sOf18Tf5rejUDefcXQDn3F2lVC22wg7weiXHW/VtRf3/L7/9+Jib9XOVSqkxsmk/fLOTfjdxvIUx+RMflkpiEaGs4/DRJq2bJc1bjsVmSDQoCEYFpu8xPhfQf2GBKi16UeB8g05LGldz5udbWE8xOWOIDxyqqmhfz7BeyOCso1j44Fsu767RSDK8pMSGAXsfTIiOHJMLAgX1p+IzYTLhU6QrIlkdHYgcRTiylIlitmmYn5Ihsi6hcUOTrgppK+1JwhneF3LhJyeoopLzeWZB80rE5eYK9/2vQ1BLUIpiNaGxV4kwX6jJVxuYLEKnBVnXYDLHbMegc0sVOuIbU5arPfKeJTjSpCuyiHsLhVMOG/tE+0v6lWO27Yto4Lp4tJeJLFg2hN6LwlLOW4poX+xYWzct+x+UxWTli4bpWTAjj6qlSO4aUQTOxH1v7wO+eGw7qdTm27DyXEbeDkVWI3FU2rCYhty1CmsV22eOGK3E5JlP+BsJ4/ss/lSRXPfEYrWGwjoNK89mZH2P+aYhHAr3waTH/X5HMJaF13rCiSgbFn9ucMicIBoIU7pqiAimKhTkBhc43Fx4NYQVzhrxv2hX4DnKFnj7fv0axMPbaYfKFbZSkGtZiA2SoDyLqlNtufDwmzmV9XCeOCjaxKLq9qhrlhRG5juCCKzNmgpJVGXLUTYsZVzDZUcyq2jcgckF0bQ6TvZV4PCHRixrS3lPqpZFpzXfyLfEBx5VDFUk4p8nicKC0nWSfhvja0xbh865x9/szq8BnfpbDnmTU3mz27/SMW8a7yaOtzKOGbMbIZ1nB1RBF29eknXEVNpbGFzDo3knJ+t5KOvQWUm5Uk9MLSzXA4pEkxyUrD5dEu7PAQj251QPRLi5h24VGK+imAekV2PsmZQygjP/fMDgfT22Pp0zPeMLLFIJ+sd6siPTuaKKFN3LhSxsnmJ4yRf/81RhlnIq0ZHoHE3PQ+O2oGiqZsB8OyTZyykTQ3J5wIWfrkmMGw2CgyVZV+YS4VFBOMjQ8wyc4+Ajq1QBTM8pLv3EkBvf18OfweyeDr3nJjjVliSXyjlHQ8f4gubgsQbJviUclZjCMW8p8XD3BMWz+qWKxZqmjBTzLfHYqCLZbc/uL9FTD1Uqjj5SoGYeaNHDWp6qRGV1rphteZz+hXn9tyrmW4ZwoLj9baHoQVUQ7ykWj2dUk4BlqYnaGXeurKJq57nxh1O0Z9HDhHTD0rkC0zNCLKwCRd7xasa2ZXJW5i7pqqtnQSL9Ua4U2JFgZnWhyPq1tL6G2WlJ6t5UWl42kF23TsVvQzmoIo1tVARDTRo6/AMhK6KhjCwYcHElrGuj0NpBpbCxE8e9Wk3WX11SFgYTWWxlaPSWzEexoFw9i9sqxM211LikoowqSqdQucZMNTZy5OvlSctLVYLSWmy5es4jc4u8Ky2sok4wZiFV7uxcJeeqJXmgHd7AZ75jxR0QeU+OXQGVVehUwduJanKcVH2/w3hDdCqwp5TaqquNLWC/fvwt4PTrjj8F3KlvP/UGt7/+mFs14rUDDL7SSb2bON7CiAZi9RmOSpZnOsy2DX5X075e4bQiGOW4QGNmOTrz0bNckkYljN/paYFiBmOHqbWripUEsywZ3dtgco+lcd1jcUpRtgqa/QWup+CFNrqEqz/SQ5WK+VZAsudIe5p0BbJ+BZ0CdRRQ9CqCsYe3rPDmBVjL6jLC/3ROuhoxOeudKKua1FF2S7yXPRFdPJrT3Ztw/Yc2WPtSwfi9q3SeOiA73cP6ivEDLXpPHLK40GPwcCKKpVGLxl3L5KLAW81Cc/i+rqjBPpsS3B5z/Y9usP5kQTRSJLfmpJsJZSS73vkpUZ+t7jVkq5bu847ulYz5ZoD1FYP7ZQ4zeKzEGxs6l2GxKQtmVWpst4BCY+IS77aPzuQ+M9P4c0X7hshq7H6kcYJo82eywHkLOHqvoMOUhfDZBBvIQDmdCoIKp9C5wTpFZ2fEYMcHz3LwWCBooVTaUpMzQhhUlQyFJxfA+e7EkKjsVNI+0qA2UuwowClLtOcRDoAVRdFw2EQ8NlQmlrYmVRT9ErRDZQb/0JMFeabl/Z6LORdOINJ66UmbS4GdS1J1oZXZiFXopETXTnqlNbhKMZ8lYo7UzbGFwS49vEZB6YBUg3F4rYLSM8L3KBUqsLiFEYXkTCqLYxFEMeiWSqLoiqKtKhRVS4b8ZuBTtV6bKQGU/QK1NODXzoylkaSRlLiFh6o0F/7SZ96W6/44vhEzDufcj1Pz2OqK4/9eUxr+38CfBv5K/e/P1If8M+AfKqX+W2AbGYJ/zjlXKaWmSqkPA58F/hTwP7zumD8NfAb4IQQB+27F8XaFt6ioIoHd+vMSk3t4qcObW/xFSRV72EDLrlwrgtAIKqZnqIJjsx1LGSucUVSRAedQpSGYWc5+omK+4ZP1NGVDsZiFmFsR/ecde99R4O/7dK45WjdyhveFBBNpVfkTTVkG2MiiCtEOyvo+/mCBTQL8SYYNDHnbkBxaqokimFiKhubcP3U4r2Sx5uF0h/jWlPjAcfiIz/oXcybvWaN5bUZ4c0niHCjRvsp6irUvZux9IOTutwucWOWacKCYXHKsP1GhCku+0xFcv3MkNxaMHmphjaKKhORVxiKH3ritaT6laN7J0blIWZglLC5azELRf9IwO10PT0NHvioChLMzWngIvkX70LrqWGwpTv/CgnQ95OghQ3LH1bIV0LixoLrYwHqK0QOyuC8u5bIwz70T32uK2hM7sNiFR/slj2HRA8+hQoc9lZIfiraXDaWNYzLIVwW1JMQ/8fUI9zyRALcKFzjsVJKPykW3q2wodCYzERaSUG0gA+QqcOiFwZvVvIt6N2/91zw3dC4ExiqxENcDE8+iUgOew0wNVd1NtzOfbBBA3brSeT2vaFW4QmRtlC+LvfYsrulwpaacBKioAiXDcLSDuMItjXA8AmSYX6+tztSVg1fflliUcRjtKLtKWmGhwwQWpZw8fy9HOeAwPJl9uIWHKmoDqLc53mIC4F8B/rFS6s8BN4Aflr/pnlNK/WPgeaAE/gPnXFUf8+8Dfw+IkaH4J+rb/y7wv9aD9AGCyvqK8W7ieKviw+/BzHLioqKKPdKVQExmSsdiw0NZkcUoI4W3dDW5yqeIa1mShfSZlYX4yKIqR7phiAbyHUi7Gn9WicdE4DNfCBA/GMHeRyu6qzNGRZv5jsfyB1K8fxWy/5GK3tMyzERBVRl0Aa3rEA4KXOhjxjKbQIX4c8v++zy8FHY+OSW/v0XW8wiHJf7C4i0qbvxAH13CmZ85wPmG6FohV0wt6+4aEcmNOeEgwN+bcPa6o1xtces7GyxPFczvKwlv+TSuz9DTFNuK8OcR09M+WVecDsvEEe8rovlxglAEI/GxLlqG+abP0cOKjc9XTB4vaD8bsthSlE2LDaD9KkwCw3JNJMHznsXbEzOnwSMWbwl3/qOC3j8MSXYdw4cd/WfEOe/lPxsT3xQJdQf4Y03RLgjigswpvAOfsucIDj3yzQJ9EOC0EPzCI5kvZKHFiwuKToEehjRuO7zUsVyVgbH1a0XXUtG6YoRBnitst6T1bMD8jFQVZqEpOxUWMDON84TA5y2UeHUHssBaLRBYYfFL1eI8TuxWqzqJJDc9slVL1RHIqosq9NSTmUkmVZULHDqTQX7RcQRDSXzO11jjcMoRRiVVqeUzt6CDChMXYosMrHRnDCYJxTjEnxhBSLXLkzkMpYZKSfWgHUqBF5b4fkVVaVTT4QclxljS1Edrh+kvyRcBrqo5TIGs0noh7cZz/9nbXG3wjScAvh6d6pw7QnT83uhx/zWCwPry278APPwGt6fUiedrjXcTx1sUZigKsa7ycEZTBaIamnYNy1VBy5QxNfpHdp+6dDT2S7x5hQ00ac+nqmUZ6Pt4S2FhzzcjgqnFKYhvThifXyHdEmmKounR2p4yOmjidzKap4eMn1pl/oA4uaWrimTXEUyF2e0voPtKRrA/wzZCKCtst0GVeMS7C079qs/wUsSN7+9IuyoHZT106bj+vSHeUohzd75rDVXB1if32P+2DSYX4dI/OITSgm8ws5xivYXOK8qGx/oXC6Z7nsiJnIO7v6/D9icrdFrSfSVncs7n4H2KYCQMZ39e0by5pEgaJAeWowcN8aF4jHupJITD92oaz0YkhyXJIRy8x8NbCgGy+xIMHnE4H7y5EAwXO47N3xQdp/D5FllbWOreDA4/KLvl1d/0OPxIAaUC4wh7Ke4oJssNfjOn3HDogY/OFPGVAL+2RNWVzC9m25qsUJTG4O8FmAwW24iD3VIW/ehAkfUhOhA4cL6doyY+7WckCZmFENnKjiCndFRhE4UrNCo15JETWQ0D1HtL6wPKUbYqVK7R3ZxWI2Xqmqhc448MRdtRNSuUJ60oSpljOQ+cX7fAcmmBOV/ReUkgsrZGMVEpsJo89bCpB0baTn6UU+YGZRxKOY5GTaqDCOWJaVSxYk+8w7VvRdPJOFr9Ob6pyEuPyC+prKLQBmssoV/inMKvq5vlPEQHFVVm0CtCdK2mvihOr1a87eF4e2csb3G8mzjeopg90KdxeSI6UQqS3ZzZqQBloXPNMt80sgtEXNLyrmOxqQhHPp2r+oRBm3UVzTtiBHQsBJe3IN6XFkXYCGlfK0j7AWXTka9UTHdbmKkhej7g6EyMO5WxuT7m6Kl1WjdEHsSfOWzoiK87wrsTbCNEZQXLiyuoUhjH44dFS6togX3PlOkgJr7lkexD69lDpqc2KBoQD0o6r+bsv7/B8kKf5KBi/VNDbCNkfqZB3tC0ry7xD2YsLvbwJwXKwsanBlz9oysUrYrWNc3gfT38hWWxqnGeYvUpy3K1hpVaKBs+wVSYwSaH+bbMXkQiHkDRvlaRXJ9z59s7bH6uYP8xn6ynam0v4T34h+pkt37wqAz7h/crWtehCsWEKdnzKBM4/FAJvsVvycJljCXop+TTgGIc4o0NVWJxnqNoO8qWonFDWmvLFY23gNbLPjr3BR1VS4kEQ7mvTGB21p0sNKqC5nOhLPwOZmfqAbBx+IceVayxVmGaBdXMew1JZCR56MJICzIH16xQxqLiEqUcy0WICitYGooV2UioUBZZPykpRiHBUJP3LESgokoqlBhU5ZOtCNig6FjcsRCXJ0oFAFgI2xnGWHy/wmhLZTVlqVHrSzy/IvRL5ssAZzVVobGFVBs6KpmNY8KkIAoKKqsoraYsDc7B0gYo5agqTSPOSPoFR4MmflyAUxRzXxJcUklCewfEu1pV78ZvKwZ/9iOY3FE92CWYVJhM+vdOqbrn/poQ4HLd4s0V5riloGB8rtYnAhp7soiL45rMPTpXLF4qekp6nqE6Ae1rlvm2JrnrkbfkMYf/1hyXe/zAg8/wq7cuYTLF4EHxZZjcU9G85hENS8p+g+V6bZdaQTisOHooIj6yRKOKcFgwudNk/2Oy2IzPa5rXxMmw/2LFfN1jeI+PqsCf5MSjJbe/Z428A+tPlKw8uw+eYfLICofv0TRveDT2Ku780Iogzxaa1o2C/fcFeKmhe7lkcsYj7Wm6rxYyw/A0ZWwwuWN8SUQBbehYdkQYsHFLs/3pJd5wiSoqdn5hQHqqRf/FirwlFUbeFsjr4px4mce3PJH+aEu7zksdZ382ZfhAzPQMhCOFXmrUXJNcD8l6Iqi3uL9mIytHMFIsE8i2SoJ2Rj6MWGYeyd1av2tPYMJVeCyrIb4cIhPiaF/WFJmQPmdn3Ym3RziU6iPa11SRIl8vTxZ7lKOaixy99M8sZEbQUb4D49ArBZ4vg+0i91Da4nmWqgyglwuCClDa4XkV2TyQuYpCUFXKoX0rZkS5pmxXLHwRakQDxonDHhoCC4WCSkv7qNCEnRQdWMpSFvqiMnja0o4yWlHGncMuXlDRSDLGowSbGXRY4XkVo0EDZZyg0rQgubS2WKtRypEVHotRDNpR5h5uaYSwGMnsjPAdUHGgvlGoqndkvJs43oKoAsg7mujQYXsGlMiDl0mtvTNyZB35UplckZ/K8fYDUOKa5i8c7as5gwdDqkCRdRROK5q3X9vCRAcZ3miJDX0RJTTQf7HALC2Hj4TMTmm8Z5vo90z5+VcfQD/fZPXpisH9hvLRGWvNJcura1hPsVwPyTqaZL8kGBcsNkOqULSi+s9qdOFRNCHc8yhjhzHiXOfPHCa1NO9UHPUC2jcqdj/cwGQN/KlAx6enDIePbIjyb1cG1a2bBbc/7mN9kRPf+IJldClg51fm3PqOBrNtQ94WBvH0tEf/uSV51wgxcVWLTEdbUTZEhFAvNN7CMT4X0VuWKGPQiwxvVhBfn7M802HwQCBw1RziWz7eHBq7luWqZvPTE6YXmrSuzDh6pCUy5qsV+Yb0+h2QrtSS8NYQ7huyTUEulU2HN9e4lYx84eONRMdjuanIuxZlNWVDYKb+tPaLCGHlacfknPAdnIb5GeEv6Fxet3I1MbAF+Y6o/5qBDM29pSJvW2xkxXY3UKikgqknEFrjqDKDLTTOKuJ2KgPl0qCUww1CbLtAD33cWkaJASeIpXSrwvQytLHk0wBKfTJP0YVUz1iETLiSUu3FuKy2aPUcLteosCJf+mTDCL+dk+Y+zimiuGSwiDHKobWl2o2ZlgnGgc6gSjzUUzHeRk3es4qiJS3Dysn8RgWWZaGlDWYsxrPoqKAqDbZSaOO48Cee+mZf8m8c71Yc78ZvJ4KZo6plsbHSFokGtrbslJaCP5fdYuUrrO9jUkXeq5ifrklsJhBiWCgubGXiSJeCz1//QokqLcVKg9mpEGvAy2QRR0G65kTgziqiJ1tsPJETDKekqxHLHVC54cMb1/i1bI1oPyPdCEVUblGRt32G9xqyvvAE9r7FER75hANBLZV9RzCBaCzKtYfv9Vl5tqT3SknjpSNazzjSsz2Co5TJfS2iw4Lo+pDpw2sUTYGglonh7M8uufEHYqoQ9h/XbH6mZPBwIpIm24rmDcdiW7H9GxmTCzFOSd9+vkON2YdoXxJyMBFhPpxjcrFB94UJLvQwWYULxJxo61NTbn5nS0ABkUBCdQlbvzyg7MWkPcXejzaFE+ILJ8DrZmyenXD3S5sUF5dEL8TkfSGbqUzjry9xzRx7s4G5EhMvZWZUNCBdqwUGOw5bs9DTVXeiLZV1ha+w3BGIbNkWddfGbU3Z4ESSwxmHmnqsPKVYbL7G5VCVItr3yNsWVWiYSbXgjT3hOzTq71s7pywMthbUc4MQf6Zwy0AqmNQTaC0I9yCuqHJDWfiohcFbKuJdQd55c0XWt7hA/qZ5oUEyguk5S3jknSTIdEvhTaSFV0wC7DSm6pYshzHBvkcwUrQmols1ufAakzwYalQJyV0ROwxHjiLxTuRwAIquoK/MTFM1Lbabo43FWoXNzNuloP5bw33jh+PvpHg3cXyD4/Zf+hZaNyzhxGJ9feLR7M8dwcziTwWiqyoYn/MIpmDyWqzuyJBulgRjjfUVo/sd3qwenBeKvC04//mmR3QIZcNjtqMJpg4zcZSxkVaGk5ZF60VD53pJeGdG2YspWrIb9m5E/OzR+7HvyVlsJ1z4JxPylRizKMRZ7oZIcRcbBSYqseOY5QZE+4qV5yvmG0b80L1aerurmZ3SLPvrzE7JbMafCW8huj1hfv8qZaRYezLFpCXTcwmT8zFF0+FCy5l/agl3Z4zP9VEWWtccuhKF1uG9IY3dimVfko4/rbkNFtrXKkaXRHQRwHqKtK8ouhF3PxLRvC2wWnlPAryFSHdnWrFch9YtS9mNKNriW2FDy+JcgYoqzN0QdzNh92qCdtD9pYjBIxYbW8xMEpb3VJPs4SU2sVQthzc2eKmgtvyJLOTOCFQ23awwc0MwEZ5G0RRWtSprgcBU49cQWn8C4diS9bTME3yH9UWaxuTCLUE78q6rB+cOq4UMWPQrgb5WApt1+xFqqqjWpH0jpE5BcwW7Hs6HolOJ58eeR+5bWJjjjhjFakHR1ZipEY5FBQQWZxWNO47lmqJ5Q6NzcCNh+VtfmPpoaFzxKRuOYBRgcmm5lrHIv5cNV1fiToQUlRP/8Fo92Fs6Oldy7nxrSBUJyjC6ayibQup0gcUOAzLfQ9VzoHv/zBNvx2X/xvFuxfFufK3RvGWxnshghyNHMBGF0WBUt4ACTRVowlFB865ickY+gmAknh3tq4bDR4W7ER/U85ACJveUEFfosU/e1cxPJxTJ8YWqWK5oWrcq5uuG9hUY+YbFhxb4i5jmKw5/f8rh90Q4v6L32BH7+x3MoU/Rrdj9aIetXx5I77oVUCbivxFeDzCZT9oXK8+iKagwk9VeCQdOZgYbCn8iM4RTvzznlT8tZLgzn7CgFI2XB7j7+1SRaFVlbdk9t65K22W2Zdh7vyQNXUDzbkl0Z0q22Txx1lt9YoiqHM7TqLxk8mBflF2HYthTtKB5q2JyzjC8NzwBHigrnhArzxeYzFK0DCY1VKEib2nmGzHLdUW2alGVov2sz+SioWpa9EKz8xslh4/4LLaQlVSJSjBasXwgRe+GKAPBkSZbr5iHmmAkPhjOA9ss8Q99ioalAsrS0LgljnY6FxMneC2B5B3RbKpCdcKG9maa5YYMzqUagvBQCyoqdsR7msWDKUVgwHfosUe8K5IlRV+Uer2lJwKAiaWKZa6kgXhXoQpDvlmSb5ZEt32s7ygbDtuoUHMPb64oWxaaBca3lAsPoorRAxpViECjDaS1FowVzVsyjzN3TZ04a+HCujrM2zKDG188llWR3F5F8t4GU0XRgLypyO4LhZuSqxPhQix4M4Ue+YRHsNipJVGa75h6o453K45342sML3Wk3XrnuHRE+wsO39fGnvFoXy9xRjSnerkluTXHm0eMLgU09gU5VTTEpW12X07ZEEmM7tkRepyg70SU3ZLFhhLL2LBuZ2gIRzA5I7IlqJpMOwyZXIS1zyiUFklslWuOnllD7aQkl8ZM77Ywmehf2TAgOFxQPBLSe0laHYt10amykbQoGnc8ZmcVjZuOaGwpDsWQZ/2JlOBgjo08gqOEfKXi8GGf9aKNUxCOCrxRyuximyo81p9yBDNpvWQrju6LklCsp7BJIK2mrkfj5SMwGopSNJmMpnV5QtUImG016FwtCIY5ztNk7YiiLYvw5Jyie9ly6l8OUVkOShE6RyMOsElA3g1x6x7xviMcaZZrAk5o3NS1ECQcPCrS72XLwVqGGgQiabG9wJVaOBC5JtuxeAe+qOxa0JHCVRAeBuJFciMgGgBO/C+sL6KSImboyFvqRH1xclFEJfOOxQWOshYGXO5U6FSSuOhqSYtnfqHAuxsSHUhLCSfvp1PCa8j6jiqWygUFBJZKQ/NFg84duqPQM4NtlWQ9S+cVef2z0z46r5FoI010JWK55ujs1kmuC83rUkmPLxiUg/bVisNHNf5UGPdVpEhuK5abrmbWQ+eqxRSOxm2HKeS1C+lTrh9wjO4V6fiyWWGWmmMjK2XldVeB+MXgFP5YWniqfIct1O+0PPYNjHcTxzc4yljRfTVDlZa8G1A1BbvfuFsx2/Jo7paEI8dizWNwX4f1J1PaNwRFZDKoEtltdZ8ImJ92tB4eMDhoS/spkd6ystLqgJoxbGWxRYl/edF2tF+BySV5rHIOF/rs/Ipl90OGsmUJL8fMOyE0K/y5wzYj9HhBudIk2bcEk4qsY9Clo3NZbGqzruHwYzkmLpn6Mf2fy2hdzrn13V323xdx6hNTVFFRdCzNKx7zsxXLmx7B1JJFGv/OGJNZqsgQjqD/vAhh+YMFrRsN/MMZw8dW8GclZpJiyor2kQ/WQlXhmjFOKfQ8RRUV03MxZQP8aYmZZizPtJifUgQTmQ30XnR0nx7Jgqw1y3Nd/FmJN5gzOZ/QupGycn2ETQKO3tPGayo2PzNl+ECT9vWMxUYgBMQmlC1bt4hqnkDNmm6tzpnPIkEnpYHwLRriBW+D4+G58CO8uSPvyoDZnyiCsTupiIKpoLBE30xMmGwsUFrvToitQQBVoyK55VEmx/bBCn/PFw0qhD0d7YsXyPGmoug4TKqptD3hX+ilJm9BMJa5BdpBIXIha08uKBse7WuOom2I9jIAdFpSdkNmOwGtmxmzbfE6n56RyqLyIW9pui/WfA8f2jdKDh/2cAbWvlhRRorFmkaX9flmoirgzY9tcV9jwrua5e4tBAwQjMS2drlTiTBjqVCVxltIhX/+L75eFPZtjnd5HO/G1xrjP/lh8qZisRGQtxT+3DF4MMabQTBTxAOLLhzdlxeMLyXoAkYXQtrXczrXSsbnPYq2tDB0KQvD4HZX8PTH+paV7EzLhFq9VLgHeUdRNEWYcONzFXc/aggvTVjcauI8jfM0g/s9krswV5rsXAZTD2/gyTD6tk8wE7e8zsszqtgn60gvf1G3SZanS7wDn6phcA3HnY8lrD/pcernh0zu6+ACD6c1F/9RxuR8DM4QHeXEVwYCatea0UUff+YYPWTpvhIw2zas/2ZKuhaQd3oEEyskwV6Ctz9h9EhXkGV9RZFAsudY/1RO0U+YnNMEI0DDjT/UB2RXmrziaF9zNK+Il/nsUo/mCwPiayNwjut/dIOi7bB+jD4bkbVl8YqOHPsfbJG34eBbDDiLSnJaX4zIVsDmoo2kaukMZxXLOxG2X4hPRUu2mN5MFGatL315fyIAien5Y20qEW+sIkgOLDYQK1hnZKhexVbEAEsFo0Ak0AFv6Wje9PDmjnRNdtlVKIN+p+Q7Ed8Vjsti2+GPZUGN7wqoonkHZtseRQOWpyq824ayIckjGGlmF4QDcfBYQutmKcZf4/LEQdEH/GFKb5hSNQKSvYKs75FWovDrLV9rDTZvWcYXNGCI9wUxljc1s1OKdN0S7WuxqI2k9VaFtXBmPRsJRiKwudiSGcgxDFjmQUrscxsWGzjm5yzuHQHB/dfjXR7Hu/E1haqg94r4aqQr4kaWd5yI0gHe0qLqnVY0qrCewWnIeh7Jbk77OjjjY5YOL3MkdzRFTxG3UhZ7DcxcY7dTitQjGPoyJC5EjloXMvjM25a9DxqqyBL9bJviHKi8xHmard9YkK6FFA2DPgqZn67ER2HsEexO5bzuzihbIWVimG9pihZUD85w1xo0rnostmUWYCNLMFYM7gtI+h6Tc5oi7tK6mXPn94U47eg/Z4lfPeLwY5uUMWx8Zsz2Jw8ouwnxkSCqNn7jCPJCmPWZZXjJZ+fKiGKlweLeFY4eUZTbOWrkYxaywN/4wTW8uUigWx/8/RnNmxHpqqL/gqX10pCj96+wON2i8eqQ5ktDaXVVFpxj4/MZ+4+HLFdh+9dnXPkjTaIDqYBufUdC444j3vfqAbZH1oeVL2pG92l0BTrzKRNH2akIjxRlW4uBUukDkviTfRFXDAdIJdgQZnh8VFE0NItNWeyPkWaqrP04BhplNUXLEe4K/8Tk0LgjPtplQxJQ2RDzJBmSi9x457IMq8sYGjekAtWFfN+adytuf5umcRsWZ0vwLJNHKmG5Tzx0rkhuGapI4OTLvqFsKFRpSFcUZ//5AFVU8h76Ht5wgecci40V1r6UMtsJKGORPdn87IyDxxqsPynyM7Ntn2DsZE7WFu5K99WC6SlP5nM7kvyyVTF80qmqocjupIVlffHyMLU0i/Vk/mN9J26H+h24Sr8DT+kbFW954qhtC78A3HbO/YBS6ieB++q7u8DIOffoGxx3DZgiOI7yWLNeKdUHfhI4B1wDfsQ5N3xLX8TXGMtVzfhCSDiSHnQwdpil6CGtPil9Wts2REdQJFKS61KG29OzIa2bGf7Mo4wgDxS9yzl5J8AedFi5KUijozCkdUOMeWwgKBVVSUtEZ4JwyTtAt+DoAx5rv2ko1pukKwGN6zOCiaF1UzF8QET5glbOMvG4+f1rbH5+iSosRcunaGqCiSMcwShpUPZL7E6Jm/mYiaH9ssfmrx6QbbeZbQdENU+jaIWYFLqXLe1njshP9RhfgpVnHeP72oSjEl3UyqihZn6xK3LuCqK9JRuDHIoSf2+CSRPigxZZEQqiKBNPjWPo8N7jAeEY7v7+NYKJwF6DcUnRT/AXgvYqH+7Tujwj3Uo4ekiS7fxcyfmfzgl3Z6iiIjposf2rY4puROO2zB+qSJH1xCM8W7Gkm472SwZvIT7gykJwaJhfEOZy8oKcYxnLvClviQJs0ZR5QDRwtG6UjM/74jPupHo61iSbnhbTrGRP3kddKPKWQ1eKU/9qRtEOMLmhmikWW7KwWl+kQbCK9lUIp5Z44Chj0TErmubku+k02Mgxf3yJGgbE1wX15s8dRUNmEM3rkDvZhDgP4gNL++Upep7iAv+knUlegCfP3XviAJeEdOYF2UqEPys5eqTB6tNLipYPStG6XZD2PLwMGr/pWK5qZtvSvrJejZTzINozFE2BEpdtkYg3mXB/bCTItXIqWlf+VInfS8tSBZp7/89vn9Pfm8a7rarfUfwFxCu3DeCc+2PHdyil/jow/grHfrtz7stdqI69dv+KUuo/qX//S9/YU/7tx/X/8iOoShAx89OO8FBRtGXXhHEUDY2qNKZwLNd8WXjmDpw7sUX1d6cEqwHRyDFfN2AdnatCUiuaitatiov/JGPv8YTeC47JedmZArRekb+32HRUmznrqxP431cpIzCTnGRZkm4mZG2RulalcEzKY4VTB3nbR1WOKtIUjVriREP3JUhXfMrYZ3mmwKSK0XtKqniNlWdzuq/MKROf3Q+FeAvZPbe/dABKobOKtacszaszDh9rUzR80hVF99WKMoLGZZEmSV5ZgnNo7xhba9GjOeGgSdGUPrw3V8x2NCaFdM0Q7zmadyvKSDN4QAa6ecfj6KEQk0PzpsWfWdKthCrUrDxfcPSgz/mftsy2fMI9BdZx6l8ecvDhFZyW3Xy+nRPeDMjWKpxniPYkUY8fKlCFJrllsIaaxS0S4P5MFuGsIy3KztVSNgqhoog1RaKYb3noypEHSto0dXtGlyKtXrTk/9HAka1A0aswc83w/gbJfinzgQ1N2SzxexlKORiHeHOZcc03NK2bFbMtTdnQNO44GncLqljg3+GBwbsucyGUoP5mp+Q72n9OOEZOy5ylfTUn3J2Dp7GNCL3IUHXCsI2Q5WZMMCpwnsIsS3RakvYN44s+RQNmpyMat1JmpyM6l+cUDcOyr1ls6Fo1weHPQAcOXcp7tlxTFG3AipKCNxM4MqsZpAbv0BdIrwfpjnwWOlP4A/NGl+TbHurdiuPrizfxyj2+TwE/AnzHb/Npf5A39tp9W6NoCtvVZGLKk3cc4UgRjBRloZlcEpZ0OJQBXykjALwFBCPH6lMTlud76NIRjEt07gj356SrXZI9sVD1Ukve9dn8zJTZuYagUGaCHFquyAUZjBWLFc3o8+tsDgrSviHbSNCFZb7uic/zQnbMZmqwucZpx/yxJdaPad6ymFzaFdYTBI+rE0jZcOCJVtH6pwzKWW5+t8fqFz36Xzzi9CdDVFGx+7Eei0sroCC+PUPZCL0savdBiA4dZaiofIXzDWUzIBgvyE91CfbnoCwuCVnuNFl5Ykg0bjM+67HckCsx7wjTOO0rkgNJJs6TdkcwLrG+6IApC7pyDC8FdK4X7D/qYzLw5iUrT8648+19dn7xCIqSlS9N0MuCYNqn8YmKV38so9tZMO+GpHdjbGKFXKfks+u95Ej7mpVnBUDgz8TR0HoIAiishS0LQR0tVz2yFcDKHKZoQeOOuC5aT+Fljv5zU6bnG0zOGXHnA6qGpUg8xhd8wqEVJWUntqTlKMQfymvNOxDvCxCifaMimIgsShVqnFL4ixJvLrt8Vcp7M74oLa/2FU3/S0OyjQZ5y2fl6Rmj+5ss13zaVxeorMJFPqq0LM62CQ9TgklBFRuyrke8D9bT9J4ZMXhvD7OE9qtzSf5lSLYSEQ1y4n3ZKM13ohMNNy+DspBEEkzkOyeQZNmtFy2wEx8zN5T9kqp2KIz6KdndhGCgCb/S1vPtCicbs9+r8VZXHH+T3+qVexzfCuw55155k2Md8AtKKQf8/17nw/tmXrv/Wiilfgz4MYCI5Ot/BV9jhEORGCkTYblaT+REVAXBRLSG3EpFFQUnHs/RgSbriZbRoGoTDSt0LkS+YFyw3GlRxApdKZq3S/xpcbKLCQcFPeuRN7UMDCuHXsL8g0vUICRbL9n7kM/5f3IEzrE420FXYJ1cpI3bwC3F7KyqbVmFl+GljrQjxMXlGiw3LckdTd6plU/3fXrPibBheJTR/9ycF368y3xrjfjA0X96wtYv7vPyv7uGySHe67H562MGj/XRBXRfybGBJtpbsthJOHx/j87VDJRisR5QRoboYMnwwRbJfsnex3p0rxRkK06c787IbEWECC2tF8cM7u/TvC6L5vRMwMYXSgb3eaR9Rd7yWW5A3vUJpgJ1vdGOqcKYsl8QH/bof24fVTmoLJ0XxxT9mMYzEaNLooHUujBmNo0oAyvOeMaw9zFL5zlNGSr8ytX6UiWVLxVV3jG0btZuhLHIyFRzRbbiBNV2YDBLeU+tB50XxlSNgNbVOZPzbVxoUTV3oWyIL3v78px0PSbrerTOzzikSVmFwsnIAQWzTY/ksJJkNBf492xL03x1TtEKWX26YnzekPektXf6kxXJqwfgGYqmof/cgqId0L6aoipLuhqhS4c3K7ChwZ+WlM0AnYuxWOPWknQtJJhIizCYWYJxyexsQpFoKh+CScWdj8asPVkQDjPmW4ZkT9pgVcNnciEWVKDjhOTpz8Ua1+QKFyqqfoEZiJBh1bako4jkrsyH1v/Wp9/y6/vrincrjt9+fA1euX8c+Imv8BQfdc7dqRPDJ5VSLzrnfu1r/ft1ovnbAG3Vf8s/wqxvSdcdwcDgTzXeXIbl3lLQUVQKLyil72wcyWWRzG7cll1VFYouU7JXER3JbrFMNMt1Rf8lWQBQCm8wp2pH5F2P6LCg9cqSO9/RIxw6pucU5kqM7VlUo6QKDJP7uzJ8tQ5/LmZMJnM0b+c1gzkiXRWjo85VzcGjmuhQkVwt8VKFsoZo4EjX4NQv5XjzkrsfbTB4yOPSP5xy+w9scP4nciZnRd23+4qHnqe0riq6Vwriq0Py7TbBzNJ9aYYqLeP72iwfaAqprYTqjubaj2yw9ZmMOx8N6b1kRCvrQR8U3P1IQHLXMd+B8EjTf6Eia0vCnF9q03+xYrojCKFw6Bif8yjqRWfjMyMWW13KpJYZKSBfsfgDTbDrMbwfnN6g98IUZS1qkRGUlsbdgOYtw/4HYHatgzeTmYUupefujwyLbZG5t0Mx6womlagDLCSRe7MK1ReIboEo+VaRon1ZMz+FQIdHirwLwbQjYpaezK2aVzzSFZFFz3pCqhvf2yDtiW7U/uUVus+LirBJpd0ZDSrSnpAsR/cEZJ2A5i3H4FHL4L0d6KYM8ojuZUvnk2NUfuzDIS3K5M6S/cebbHxqjHKO5U5DZMpjjbdUWF9TJtIWsl2P+G6K8zThUc2h6flMTxuKB8TnRZfgTxw20PRfrPCnokyw9uRS+EYadr8lYe2pnLwtraxw5OR9CiHr1ckkU+jUw58Jb4mRR3Qk7UH7Tob3vJs4vq54Q6/c2vbQA/4I8P43O9g5d6f+d18p9dPAB4Ff4829dt+2eOW/+7BUAgryfoXzHcGRIdqvzXXqirWZZIwyD+92yHKzwptrOBIUjmjzWPyFpUwMzlM0bi5QTmxTk13x9VZljFkWJHczJucj2nlF+1pFFajablOGuOP3VJQdy/S0of+itKysJycyOWsoI5E5QUMVy8738AeXuOsJ8x0HSGto9SmL9WHjc07sZUcLtj6jGN4bszzVYuMLC8wsY/1qxvixdbzDGXsfX2frk7vgezjfI9ifo/MYG3qYIiOYVhw+5hEeSZ/daVlU735LSHQE+4+LbEnrqqN1M2d6OsAZSbKdKzmqcowu1ZpLWtG+mhKMNKN7AoqGovdKifM8Vp7LcL44+mXHHImlIrmjmT+2xA2l+htf8mjf8Ek3VlCVeJxQf57tV0VAsXslZbkaML6ghcVc1pBo9Vovu2gavIWVuUUhrca1wyVV4uOMRlnxSymahioymNQRDS3FUIvCgIK0b1h/smCx5lH5injfI+s6Bg/KZ1c2LTa0dF70GD1Sce6nS6xfAy0qh5dpspZmet6idxaMWg06LxpGD5f0PxWy/qkBkwc6lN0IvSwxswybBKisQi8Lei9m6LzE+QZ/VlIkHv6sYrYTUiSqloZXbP3GFBt7mElO1fDJeuKFka4ASrhFlYZyQ1EmHo3diqLtEx6mzHciFusij9O8ack6hrQnlRlaWoFVKEgqf1p7hPhSjQAnrd4q+iZd4F9vvJs4fvvxZl659d3fCbzonLv1RscqpRqAds5N6/9/N/Bf1ncf++N+udfu2xbeXFG2LbQL0TjyBKEi7F7ZiftDw7iVwCiQx9byGlWgiAYOkwmLdtn3qALwl5rhvQEbnxlTNQNUYfHGGXqRgVYQ+0SDiunZmMZuzuhCiA3Fc3p6zopA3VoKNGQXWl+QaV8MpXovzljsxAxWPJSVC7ZIPZr3jZkdNoifMqA0u9/iWHtC4bQj7/ocPbxKfGjpXk4xi5zBw21WPzMF36PzpUOKrTYbv7yLS2TeoZxjcbZNcnUkUE6l0Lnl7M+m7H0gpnm7wPqanV8csPutfean3Um74vCDJfPtUBBkkaV51RAMM25+d0taaevCTF6uxoRDR7qqaF2X9zI+sOjSUjZ9uq9mjHQo7RwU04dyfM9SBJLg4z24++GYxSlhKeu8T/sKdK5mjC5FtKeg6tlT5wrMtwxFU9pHWUeTdRVlJANeMY6C8CAViRQliXF2OqSIVW2wBGlfxA71gSM+Khlf9DGZI+0rFuuaYOwoWu6EfBgeGrKVivXPKqwxdF5dsP0vp4weW6N5c4l3NAej8eYJ07MRyR1Nda4iM7D69JKNT+fgadCIIVbTZ7ma0H4hw0xSin5ClXh485KyHZGuhyxX5HszuSgt+2MhxHgP5mcSTObQsSwhaa/2YR9AFSuW65ZgookORHerjGtaPJEoJLTqGdQ+gn5LBH0WH0rbb7kumlreHJxW5JETVWSPWnpFAB3n/tO31+nvTeNdAuBbEj/Kl7WplFLbwN9xzn0fsAH8tMzP8YB/6Jz7l/VD39Br9+2MKhFbz8r6lGsFSjuG7/Xwh5rWNRmGlh0r5D3j0EtBBoUDRTR0BFNLOCwomh5xVhLdWTC5t4UuHPOzTcJhgQsMNjAEaQ7W4Yz4kHdenDK91CQaWfKuYXpfgd/KCZ5qYK42aN6xpF25KOc7AhWdPpgzudlAWRnOL7bFe1w1LVnm4TdzUDHxviXeh4PHRdqh+5JH844QrZxRqMqx+rlDQd2kOViLf3sEwNH7enReTfF3x4LUKUp2v3OT9vWSKtIcPGY4/XNHHH6gT/+5Gdl6g+WGo2hXdJ/zqCLwpobsgSXb/0fA6B7D2lMZOq/Y/FyOKh2DB0ImFy3hkWZ+2pHckYFrMMoI9yr2P9Rm7ak5i+2Y+Y7sXE0KauZRAN7EUHQtoxXZGqpCUfaE4zCMfZbrEc2bgvbBxYSTingvY3hvQtFxLDNNFQowoveSJKysrUkOSpzRwgCrHDY0lKGsvJNLIq2y8fmC6RmPg8fkEqzCGg7bkYVz+oi4AFJ7mhcNUY+NBiXxrSmqtLjIp/vMABv6splwjtnpiMWGQL/LF9qsvuhqj5IS24o4+GCP4cMOVSgu/eMZNgnQ8wxvmlElHovNkDKSRDg/JdLwtlHhDeQ8w4FidsZShQZ/Dq1bJU5LBbZcE35FFYgGlxL1kJoNL+20rGNENRoBODgtmyddQjCpFaNDuU/aXLWoZc3aBzCpzA7dcS56h8a7qKrfYbzeK7f+/d95g8fcAb6v/v8V4L1v8lxv6rX7dsSrf+3DMsMAaJe1+YyjAqrUZ7mmifbBnxiq2JxcLOFYcPImlz75YiOgcTuVPnfskezlBBNDeJRSdEN0YTHzAhd4qLzEHy6xgWH0UAtrBL2jKkFKqYOErOeIBorRRc36F3Omp3zWv2jJmxp/5tO6MuPm97RYnipQcYUflhR7MawvcTcS5luK5m3LYl1jGwUq1wwetbRuOJIbE3COshvjLTLB+Uc+qrLYZoxeZKw8OULNUwCCcQFas/bUnCrymJ3y6L9Qcu0Pr3D2ZwYcvb/Hck0Wg/iOBw7mp0Riwl6LGF0SA6Yq0kzubTO+oDnzz49oJ4ai5REOHO1rcPRex9anFphpRtFPhCDoaRrXZ+x9oE1YW7A2bmmSPZ8igdGDkqCqpoVWCZmGZS3XPpLFbnbB0r4GzRcH3Pr+daKBWNWm69B92VKGiuWapvtKSTgsKRsGb65wniZfbTA9FeAvnLg4Rpa8o7l9r5hIFS1L1RCp9nTFUPRLCCuiZk7qFEo7XKExQ82Zn1+isxKVFpQrTbzDKXgGnRVgHXsfX6s9yOW7kHcUw/tgttOXCm3DcepDt5keddn6ByF6vMAFPs4Y0q2mJLp6k3ysRMBqBrmhXCvwDn2sB43bmu6rJWWkWayKEq71RSon26gwM40/lWSg6udM9nKqUPglKJ+sIxDivCkbkLwrn384kk2Kt5TbsFJd2EASqg0c1q8FH4N3+Mr8Dj+930l81ZytlPoPlVK9b8bJ/G4MZwQyWXZLGPvoOxGeX+EyTdUQaGvrVkXjjijJKitM4DIWkpguHfFBQeO2LLLW1+jCstgI8GclyjrMssIphaps/TcNZTs6wef7C0t8WNJ7qUAVisZNWP2Sw5864gPH6GKAqdtiwwdg+JDl5X8vZHm6QDdK3NIQfbqJqhTBk02c51hsW6KjEm/pCHc9wn0hXs22PfY/0oeyQucVex9fB+co+gm2HZNuJ1z7oXUm97YpNzoUmx3MIsclIeZoxvR0ePK6z/3UPgcf6qEcZKviIVK0ZaFpvyLIrtO/lAnr/HMzdC6JLO9K0op3F6w+U7DYllmSzmG5EeGUwhsu6V5J0aXl9nd0pPILoPdSxfoTGaN7YfbxOd5aijOi8WT2AvpPePSeMVStitkZmJ7RhHuG5vU5aNGUyjuSNNLtgt1vq2jsl2x8bkG8t5Qqbl4x34moWhHj8wHBzBIdFQRTx84vS0KqQof3/qF4fmcCJ07vTfE7Gd5BQPVqk86TAc1nQvqbY5o3EWBEw6fYaIuMTBLiQp/RI31e/rFVJudh7YkxaV9x9NGc7HQucwAlirTRQyNu7Pc5/9cc/qzEBT6qqnC+bFB04cgbmtatDKdh7YsODkOoFGYgqrnRwKFKmJ7yCCYVuhQuShlDej7DhdUJy1vnUuGVsWLv8QinhfPRvJ3JEDyt9baW0Lph8WdCoi0TRRUprBHgSNF22MBSdSpcWHuhW8U9f/6zb9+F/zWEcl/953drfC0VxybweaXUF4H/Bfh5534vq7D89sIFTqwzlYOwQgUV6TgEoP2Sx2LLEYw1WU8WNpNDOJQBqrKQXJ3gYh9KC0Z2qUUrIBpK0qByeOMltsbRH8s+qMrHpBa74rFc06Q9jT93nP7FnPl2wGLzeE8gTGhvaRk8IKz1VGmKyLK6PebwZhcz10zuL+m8IOerM4XJFLMdn+W6It49lnzXjO6HCz8xZPrQKv68onm3wnYSvHFG0Y+IdhdUcZvdjyg6L8ds/OZYzrmsGHxonZUnjuS0ipLZg6tM7pFfy4Yl2jXoCjY+PabshOQdD+trVp6ruPqHG7RflZaHzuHGdyf4M4iOHEXDcfSoSFmkXU3LWihKzMyglwX9FyOWK4YycRw8qtn5dcXaU5ajoimD1obDZmIMNLpfER5pEZO8OGcxC+g+EVB0QoxvGLxHoLTpZonKNJf+UUYVGvSiQKc5hx9tsfpMRue5Ic4ovDSiaGiCqWJ0L+RrDtNeYm7GTHdbMusqQWWG6IqHSUVGpfdSSbS/xHka/cs+w0dg/1tWqULFygspe49HJLsRg4eh/xyorSXmlYTlVoOz/+yI+YWOfPq6Im9qmrdz9C8FHD4aY4Mlla9R7VAqmKyiSnxMWtK+IQN8k0O6oon3hAtiA4c1iryjmN2fo+aG6TlDdCiaUsoBS4NKKmy7JI006q5HMJYKqHmrxIaKwUMJOhfEVNESNQCBrMv3IJg6dCEmYnlbsViX68RpoFToVOxrk9u/C+YH/ybPOJxz/0+l1H+GDKj/DPC3lFL/GPi7zrlX3+oTfKeHC2TQjdXoZkFVicIowOS+ksY1D5TwOrQCtZT5RHxUkdxeUHUivP0J2ZkeurQUDY8q0oQDaT+oSvpgOi3k79R6S2ae1y0s2X0VLfmpQoH5qhKyPqimIjqUAW18IG2xMnFQKobjBuGBoWg5TLtgsV0P0Xsl1dTABwYsD1qEgxAvdfSeHtF71qCKktazB1z+sxuc+qUcPU3BWsLZEhcGBBNF4zZEI0e62SC6MyU91SU6qqAoyc70yHqeCP7dqQfG90+YNhOCOwFlJyTYmxLcKsEYDn9gHacc44tCDPMWkPVEqmN+2tK6apifckwvWM7/TA5lJS2caQpKEd9dEB4ZVp4qcEZhQ4/hvQFFy2Jjh5lKEz25KV4jiw1HfNsQvNCEbUfeFeZ8a5Jz6R+m5N2AO9/qidFRy8ebl+RrMdZL2Pn5A8p+g3SnxeD+gPRjU7wvtlisB+RrJd7EYPZjzv/TsQhPPtRCOXEwtB5UMax+KWO57hMODdbXVJFH81aO80Qb7MZ3hZStisWWwj81p/1zHpOXE4IhDO/38dIGjZdEir5qRaj1mLzjoSpP2Np5hWv5pGuhkAI9jfUU8e4Cb5wSAGmvR5HIgFpVwFwxP1exCBUmrLDGYic+6Uq9dXYKVWi8KKVYiH+59RBtrxK8tGLele9mmcgGwAaysFaBzEd0rddVNGufGeMo6ioDQBWaYKzxZrWT5js5juc7v0fja5pxOOecUmoX2AVKoAf8lFLqk865//itPMF3clz9Kx+RRUrVF8DSE7aodgS7Ps6DrOtY/2JB0QxOyvesK+Q1lMI7nJ0cr3JLtm0wuUMXYgOLBVWUON8TyQclfhSUFSariAZQxj7+VK5fXTha15YUnYDpKY/lusIpRdaF5YbsDuWPgdsLybYLdFRhvIrKgE0kEdpuyeHdDn4rY7EdsP75ueyOs0x64b5H+wrMTgUEo4jJxSbjC5pTvzStE5X0tfcej2i3OrWjX8ninhXG53yyFcg7lqoljnvhkz1MW5zzTFpCZXFRiEozei+XxPuG+LAkvjNj91t7ZH1ZaONdgz9x7PyK5eA9HuMLIR3VIbg9Fjl2QI8XaGD0/nVh5o9KooEl7WvSXk7zBcPoQUe6Zum+oFjddTjtWK5pvJnItOcNxWI7RlXSetn+9ZKjh32mpzw6VyzB0ZLFqQbjh/tERyXTHZ/x/SVnu1MOP2jJX2hz8R+VDO8zdF7NqRIRROw/NyVbiZie8ll7ds7Bow2yvk8wFQdJk5ZUkYfOKpZbEb2Xc4pmQLFeYRY+2SzEG8058y8rVGklWdafD3mBM5q0Z044ESg4eqSJv3BMT2uSPeHMWANZp0ljt2B0Scy8vIUg3LKerIAurnCRLOA6qPjWDzzPrzx7H9HNQLgtc03yfIO0LwS+YCKtqGQ/Z7nq07q2ZLEd4S0ti1rFoIzl63iigJvUMw1fvDjEkLz+ypaC7qsixfZfe4eiqV4f/yYnDqXUn0dgr4fA3wH+onOuUEpp4BWEGf5vZJiFwikZrKpC4TINnpjeOA+sJ65ok9M+ZQzJXUc8qDC3LGXDYHKfYDQH5/CmOWaWER94OKMw0xRnDMra15KGc7VWsxYE0+EC1YlYeTolW41rCROBwM43PGZnBB7ZfzHDeYqiETA7azFLRRnIfIZSY2ea3PrQqIh2PbJ1WNkZcjRoYl5oivBc4qMXxWsvvihZ/cKE4cNtbnxPG1OIAOHlP96gcVMY0sv1iDM/O+TFf79F46qHPzf0Xs5ZbsjigFOYmcEtDEXb0rghQ1e9yMFoVF5QrrfRuSXveAQzzeihDrNTIorYviby873LKVnPJ+9bvKXm4D0R7a5P67la5qzurDZuZ6SrATaQHbay0Hg5YLHpaF7zwMLgsYruM1rEAhOZSS02hWm/WDcsVyXJO8+gKtkhjy8ErH96ire0jM8HKAdH77MQWdLSI0t9lILgYE71SCR8i+GCfKOFDQyzbZ/peaiiBr1XcvJW7W8Raso4RFeO5XrA/vsU538mZePzDp5Q3P594B34LE818ccFSilox1BagW0rhQ0NyX5BMDdis6tqLxcnsOzlau3j0oIyUWTdkDI+JqUqUcuNZPH2Dn0ZUnuOKtD8ypfuxxt5ZKvCXSq0o7inxFyNKBtyfOOuZXw+xF84RvcmRKOK8TmfKqJ2lZTkZH2pTKpIPlsbOFSlpD2V6dpSV9G461iufVMv8687jnknvxfja6k4VoE/4py7/vobnXO2Zof/GxvOk0GuqU17dK7QC1E2tc7hp1JWZysQH9Rqtr6ijMQrs4wMAYBSmNFCnlQr/EkhSaOS2YDyAOcEsTRPT3bSFCXe4YxytUkwEBZvthIy3wroXEsZvCfkzCcW5L2AW7/f0HvO0bwNk/OKsi84eKcdqtDQLEiej8j6Dm+iObzbIbzjk3cE3RTsTaXacQ6XhOAce9/SEZHBR5bwXJPFD42JKs18LcB/NWZxIeflvxjReMYTf+y0dvcLBTo735EWXvsVTTi2zLfBn5XYJGB8KSEaVOjcEl8dEh7F6HmGCzzSfu9kcVt9Oifr+czXDeGReGqEE5G9QKkTFVeyHH93TNZfxWnFym/uMz29QdFyuIsLUqcoJwGd5z2qWMyhtj6dc/v3RZhU0botIoNVYGRR9SA+Esc6PXeMHunTeWlCu2GYnDNAxdrGmMEkwQHJvqJqBKw9vUTlltm9PcJRQdbza4VcRboCo8qnaL0GN7X+SSeIaABFJyDcW1A1Ay781IIrP9wh7RqSKyPS0x2CQUrRj6g2E8qGrofP4gNjljJDKJpSgVaB8CbmW7WfRwPyrvyxsmkpm5LgdS6D6mOzKJ0pyAxVu6JcKWivztHKMb7WpbIK27GYuUjwRMOKKtQseyJJPznjkbclSZQNQUsF49q/xMh8zfqOaE/XPCip0r2FVCE4UZ3+XRHfgNNUSkUI8TlE1uufcs79F19JJVwp9ePAn0OUxf+8c+7n69vfD/w9IAZ+DvgLdTcpBP4BQsg+Av6Yc+7aVzqvr4qqcs7951+eNF533wtf7fjfqzH74Q/ReQW6L0AwVARDTXwgnstY6cU3bjmCqeygioYiObCi3xMqmjdTgnH+WhXhHHiG8M4MbzBH5yWqtJIkilKUY2fL106gXsQBvIMpk4sNdGHxFhXTMxrvaMm9f3eAdzgluTyg87IibytGl/SJYq+NLCrXOOXwbofkHUf7VYgOFWEnRVXQvjgiPVVw5UfXmDzYI9/uoPKSw/d1iQ8t0UFOMYrQj42ZTyNOdceEUUHeq/AOfdZ/VhRzL/29A3Z+4UAEAceKyb0VVdNy+pMVW79ySDSsaNy1HD0cYb1azXWQEV0fgu+hR3OOHl/lzrf3MEtJONGRI+t6kjQmglqLRhWNWwsGD4TS0sty+QFwjuZzBzReGQhB0UF0pDj334P/XII3MZQfH4ssvYajhyL8ObSvW45tTY95CrqUxJV1FPN1gykc2XpCdJjTfz6n85Jh+tk1nNXoazFFAxY7CWaWc/SehHgvJW/7RHuCYNr+9RJvKeS5Y1vdsimnXQWSPKwRGZb5Oblj/4Mddn61oHUrY/zIisBUS4s/yZlvelhPndj0ztfFa2O+Jaz2Y3jr7JTCeU7IqKHclu3k8jq7JXolp+qWIt/+Os8LG4reGlaxfLELn+wTHWr8gSHaN6w842jeLeUcavUE8RZx+AuZ5ehCZGFsIEZP3lJg1yaVVle85+hcsfhzR+t2hT9zJIcV/uKbcIH/DuNrQVR9jaiqDPgO59x7gUeB71VKfZjXVMLvAX6p/h2l1IMIT+4h4HuB/7G2tgD4nxD9vnvqn++tb/9zwNA5dwn4G8B/89VO6p2s9PKOjuiolIvB1wRzQ9YWIbusrU52iGWsSPYt3gK81OJPSkyq8ScFqrKYo9lrT+gc5AXKGNklO4fzDVUnxjuYvPaY43hd4sAzdJ8bodIC31rO/MxEhuhAdrrH9T9naX1K2g551+HPFco4kt6c/OU2ZWJpvyqLVhVCY9dSPt3CvH/Et+68yueDMxwdrTO4zxBuGNotj7KhKBuK/Q+GNNanFIVhpT9jVgQsxxHxnmHn15ZUoSbthdz5nnWigXhkLLdk4N96xTC+YEguVySvDhnes07vcsFiK6T96hydC2JrsZOQ3JrTf3rE9FKbKlC4I5hvK+IDgRnntd7X+JzHwWMtwiMk4RqDbcfoici2Ly71ie4u0POU/gsVh48Y7vy+BtmKxbYq7CLAbhXc/RaPcz8z5fa3t4iGFYP7fBY7luY1WH22IhiXpH2PrK2Jx8LlCEYO62v8aYH1AnZ+PWVyPaZ5O+fGH/CZnDPc+u4GO79oUaUl2lty9N4mi3V5L8ORqNwWDen3B0OYn3LYxGJmmuSuVCLjC4bBAzGdK5a8Zch2ZF7SuF0wu9CqVY3Fv12X0L4OpoDVZwr2PuAz35EWUXgsnVIv4uFAviPs+uT9Ch3Wsy9lavc9h5nXboTTWlyzFAZ8OLT0Xi5ZrHl4mSTaMtEnvvV5W1qjIhci7ofHrZwqdrIQWUliqhJVZg/FfFNRNCFve1gfpuc8zv4X71BRwy+PbwCqqkawHi8Ufv3jeHOV8B8E/pFzLgOuKqUuAx+s/Y3azrnPACil/gHwh4FP1Mf85fq5fgoBQKmvhJ59N3F8naGcwxtmqKLCnW4RTCDrGDrXSqpIUYaaYCrJBQfxnflJZVE1w9r34A0+l6qCqsJ2G6i8xCxybK8pBjqeOUEMHct34BzTB/qY1JJcHUmVojUu8Mg2GphFyb3/rynFuqPo+CzWPLIOeO9dst2e8EqziV6KmOL6F3NGFwMO3g/R+TGzu02ebuyglexQm7drNIwng9N0Rcmc5wsdygeWjGcxYViwszNgd7DB4XtiNn91QE8rgkGKXogYXutWm73HDTs/u1u/mQqsZecTe2SnuozP+XSeloXetSKaLw+5851rLLYc0ZHAmht7lrXPT5jc12K+Ic6AwVj0mty0RuV0RBVZL3J5X7VmfN4nugu7v3+d9c9NWKy1hTOQWDAOpUAvDc0bcPC+Js3bltEFvxarVGR9SFcNq0+LGZcNZGGMBhaTSUKcnI9rE6+S7isVi+2Ie//ugJf/zAr+yBANMvQix4Y+y1VF3hdXRW9Ry+1vpkxbfq0Wq1CZqn3m5e2SRd4xOatp3bTEw4rptsfkXMRyTQy4pueEpOgcHD2kifdh8JBHFQl0XBfqpOrwp4pwJJ9nulHhGhWN7hLnIMt8KBXhnoe3rLWlehpdOPEfaWtptWnFcsWjChXpSu11XwqKL+9y0rZxWjzVUe6kDaecQHKPNdNsw5LVK9Mxudb5Ylugyt9FENevraJYVUp94XW//+3XKYEDHJvhPQFcAv4/zrnPKqXeTCV8B3i9+fqt+rai/v+X3358zM36uUql1BhYQebabxjvJo6vM4Jbo5P/J5eFm1ADRChXW9hIoJTB0fJkt3scOpDWy3EoWxvo+B4U5ckMwfnmtXaVtVDbgB4vtHIiPq1nD3FxgAs8xvd3yDqKjV8/BOtYbEcsHk1Ye0JQUeN7IN/O2IlTLt9dg1ZB/wsh/S+NsbFH55piueEz32+QbMy5fnuFsJETnZ8ymbdZeRZG9xjylsPkkG+U5IB3NyK+qZhetEzbJb0rogxcdSJp1bVDwskSlRccPuSRb+Vc+ZObbHy+pPHy0QmyzB+nKBtQdWLMcI4zmuFjK5SJSLtwqIgPLZOzmjJqYz3pzccHr9mmbv9GzsGjAdY3eKOFJFvANiK2fmGXfKfL5i/uAVC0OpgUscU9VVHlGqUdgw+U6KlH52XF+KHy5LMyqSEYKyZnRRo8HEM0KOuhs0cwzCgTRd5STC4khOMKfyrIu43P1wv10QIXeNjEJ1u12NDhwopRU6EqhZv7gqZzgiLyclmIsxVL45boPE3WHdGBYnSPGITZwDE/pWjcguk5aTeijhfq2tciFNHA4nSOHft4tRy7SWFy0cH2Ejf18Q588l2fYrUk2PMIl+Kj4gxkPY0/dQRziy4dZaxONhPjiyJ14nyBgx8LXKlK2mCSIGoJEfva7EZniqoGaygnToUuOP5+V7jcgGcF8uu/87zF3yy+xlbU4bG76ZuFc64CHlVKdREppoe/0p99o6f4Crd/pWPeNN5NHF9HmEvn37haqMM7nOLiALXM37C9pEdzIfchAm4gyYNMZKdVaeXYwJf21fFzOCfPUZSgNeVKA+9gKo+rHCrN6LwIZSuEQlRTF2ua/vMZ3mjB4XsTqsihJj636eGFFdU4YHg/5O0O7RslzZdHBA+sAh7Lsonu5ORLn8q3VCuWcFDSUj7+wnLwqIcKLJ0vhMzOOOY12snf95l92xz1SoMyjmnerQgP58JULkrKlkPNDUXHYgNFdrqLWZZkKyHhYUY4dui0wIUBelHQvJlx+J5IoLoZjC+J29/0rCIYyQB5dL9j8zOW4T2GyVmf1acLzDQDrcGVzB5eQ1Ww7LdZ/fwRi3tXSF46ZPVLGfOtgKKlCA+FJFhtZqixT3SgGd9jT3b+/lCDU8xPW1aeUrRuCDR5sR1RxLVp0zBj9ckpd7+1jZc68qapWzpN4v2cMjYcPt6jfU0kOFqvarzvO2QwbGC1kY84eA2Oo4YBRVu8uL2ZZnauIto3BEPNYsvifEu0K0KVVWwZPVzhzQzeHPypwLOtL1IryS1NtupIno8EyeTVQpGbOX5ciKNgU1H6juC2T3DgoQuZO3gLEZFc/VLO7ocC+i8qvEVF5XtMH5YEVTYrdKqxoUUXivBQ11WEQlmpFI8RWtaXZIInVYY3rfW12hUqqtCeRWmHVg4dFygFVaU4/6NPf6Mv57cm3DceVeWcGymlfgWZTbyZSvgt4PTrDjsF3KlvP/UGt7/+mFu1cnkHGHylc3mHy4T9Lg3nUIvstyaXN0g2xwkEXksiHENvs/zNE5S1eIczXBhIcilKpu9ZRy0y/L0Jd75vi+TVAVuf3GN2KgDr2PxXB4KI0Q7tW8pRwPpnFI3biubtivjOEirLqX+xz4X/fQ9/pLFzH2cV1UHE+qcVZcPgzy2HjwjL+eLpfdJvm9Y+1Q4XVYRHinI/pmw5sp6IC+5/oM3ex3rgHJf+l338sQzATWYJd2ccvK9BMMzxBnNM5iRxalBZjjOK1S+Jf7v14OxPD+heLgV8UAvjeTNNEYsuVzhxzHY80p0mzpOveBFrBvd5NHYLQaPNK/A9qkiGxv0XSrw5lJ0KlxrMUrO8PxV/65HBTDX5ZkF1z0L69huK3Q/FpGshTkPnWoouAa3Q05TeSwWD+wxHDyuiQclyzefo4QhvWVFGiis/Yhif98k7cHi7g/EsflKgUoMrNcazeEGJC6y0qypF0REr2axvSTcqvLki2vMomw4sxLuaxg2PsluSrYiHyvQ8TC9YZqctix1LGTvyrqN9rUJXUK4XtLoLHtq6y6X1Q5xFHCzXS/KViip2mAzCiVi97j0eSAURyNcwHljyrYJyOxOYbr9A5zI0z1atKPwm8lN0hLSJqwfjqcLMNWamBX6cWAgsQZITRgWeVxFGBc4ptLYY87sETXUc7mv4+SqhlFqrKw2UUjG1sjivqYTDv64S/s+AH1VKhUqp88gQ/HN1W2uqlPpw7b76p77smOPn+iHgl7+aOsi7FcfXEbaToCdLqSrK11BPX0u8PlEAuDiELD+5/bhtddK+eqN4XQWislyqEK1ovTQCpZg9uMLWrw5Jz3SJboxY+c39k/bWpb9/wN7H15idivCWCtxrLQcbGE7cm40mGCmKnsKlhv6zivbVBSjF+EKMN5dB7tUnd7Cxw20VbP2Cx+Gj/3/2/jtI9iy77wM/1/xs+iz7vOvXdrp7vMHMYAYDDGEFEFwBpEjQxHKpDS6lJVdkrEStNoIRWkZwFUtS3OVSEVhSIgVRdCAFEIQ3hBmM75numbavu59/r3xV2p+/9+4fN6vqdU/3GGIGmG70iaioqsz8pfll5j33nPM1ejFE9sZKOl9oGbU8g70808cJGD7r0KXj7oc09Q93IajIV1L6L/o+vSqXSW/PsO2E/QcjpuehfRvWPzFidrlH+8UxwnS59UcELrKk1wN06UhuT9l6zxL9F61vFRhHfarP4IkdBp/zlrTZfUuMLwWszWtaL+4zum8VYeHEJ+fcjloEcw8BHa8JTNsQbGjqniW+HVIu+VbV/EJDuKMYX1C0Ni16VNCqLAcPtlj+5BwTSdq3/S59djJg+Nycqt1i4wOxh7wC2UdmNHdSwl1NpfzOXPYq3CyAGOoshMgS3AmwAd4saiywoaAaWJqOxUYeSlucapCZwiZ+mxvteaBDPfAkOlFKXGRRrRp7N2bjI+B0g5CO6TjhuXqdYi9BFpJgPaOeL6BcwsOPEb4iUDVHMNubf9ZgphJqgXP+ejnR3uwqdtjIz41QDhqBKCWi8X7iDn+OZbMQsEygGViCtEJrS9NIrBVYKwgCg7WCsz/25a/3K/rtEd+cPHcC+CeLOYcE/qVz7t8JIT7Fa6iEO+eeWSh7PIsna/+lRasL4C9yDMf9xcUPwD8CfmoxSN/Ho7K+aryVOL7BkI8/RJN4xzQAFwhsN0btTo9uc28b6t5E8Foh8vIrjnt1EvmqcYiuagwetg3t53yVGd84ACmxba+Eanup77X/+22qH14jmDmGn98Fa7n2J9fpvWS9Lop1YB2nf22EU4Jbf6TH5KJj+YuGrfd1Wf5SDsSoCkBiKofc9WTFZEPQuWOZnvG7ymLZUSx7n/XuDV9deC/qDuVggQa6FjC/XBFMYXpG0n/J0HrBz+VEoIkP/H2EE0d2uk22IsmXBzSJQJYOk1g6Ny3t63NMJyLdcvS/uMPe+1e5/dEBF//1xKv3ZiWmE1N1FUvPFMjRnPzikFM/v0mz2kU0lvXPVZR9zeykpPNURL7m0IUnx1nlcO2GMhGosaZaNqhS07meM7vYZXxR0X+5YfROz1BTtWPtM97oykUByZ4l3XGM7tPkKOo4wA1qGhl49r6yKG1J+3MODtrIkcamlnpoCQ4kthGUQw+jjvY8AbEcWoKpoIoktl+zsjoh++0V76C37D8P/dUp8ywC4Tx0WkH79ISiCLB3Uky/odiPEZXAdhuoFd2lOfN5TD0Q1H2BHiuanqF1zasR7D2ucLlDpA1iL8QmFln6NpVN8MnisMlvPOHQhX64rafeg6Zue5RY3YWq7215AawVJNGidQZUjT76+40U34yn7Jz7EvCO17j8dVXCnXN/E/ibr3H554GvmI845wq+QXuKtxLHNximHaGnJRg/vCZQfqG9J1692L9e8nBJ9IrE8er7+LoSyOHc456YvG2J7pd2/D92ISFyiDCaeC7ImV/YZe/dS9z5vlWaFpQXSvYfbrH0lDmSrXBCYEPN8AVDMFnY2Saw+YHEzz2N71mb2FH3DXuPaJrUMXnEImfi6IvjCWSOoi8JTndIro8I7445eGCVYOIHo8n1kNnlmvbqnI2HYsreGqu/tUW91GJ+QpJsO3ovZejdGa0rYLsJ1/5oh3AsqNDIxqGmBUjJ6m9nHLx7hWhiCGaarff16F2rSV+q0DtT+tsTzKDF/ntXifc92koWNTKrkFmN1R1WP18wvi+hteHYewSCuaAcWsQkIN6V5GdqcFBcLtjMWqw8VZJuCqan/Veq6njFV5k3YCFfT8hWJOmupXPTMDsrcLUkuekH4XkcQKsG4di/3Uc0gnAkMaVHP1UnvOe26TWIXHlIa+IHxuWKQXZqpIDdnS6cMqR3lG/frdZYJ7BO4LYSaBlsbMmyCLsXkexK6iqg/wLsvN8Qd0rkFztMT4Ycuhu62KAu5ITKMg8ShLa4RnLm9B7jPCZdGbNz4AEhFF5FWc4kpmegFn7ILzzRNNqTRAeQ7Hk5krrjpVDCkaRccphGIQQ0SlLXCmsFSjmSqHq9r+Rb8QcQbyWObzDUuAAl/MDaGJyWr0gIr642vlrcmzRcFCDKY0mP1z321YnikB19qJnlHN0v7+LSiHqYEN4ZI+cFthUjrD3SuXKhpv/8jOxUyviiQu4FXmhuVpJfHJLcHFMPYqLNKZ2Rl8fYeVeHauColgyElnM/LRaaQ4KDRwXlSuNnAp0K9lLCkcfj22DR2xZe7M62I+QkR2dQn/dCdkTe6Gq2n9K6EhLMLdNHV2i/POHkb+SIosbFvkJxSUh2OmXwnOPgIY8g6tyYUy+1UHkNVjP4/A7NUptoXdHatjQtSXl2QHR3Ao2h6YQMvuy5L7YVHwk1isZzSlCSpc8XIEE2Paq2RM8lsvFVlAgtziwMiWq49se0BygYb3zUeT5gckGiKo+hHV32Hh9VV2ESiLf9opmfbJDtmjA0NJVCSi846ba8wrJowLYdGIFZrqCSuGAhMV759pAeaVwmMR2DHml0JjCJR6HJmWbSdAj6BcYBtZ/ZuGlCUHskE8D0rHepDK50yNe9VpcwflahDgLErRCZgx46TOqlXTb21r3vR+kX9ygTSOOJfk3bYQuJqAV6LlCF54z4ITpM2n721CwkTUyygNsCUjqaRqG19V5YRpIV4Vf9Ln1bxhuvSPq6463h+DcYYrFwO+EZ4jQWGyjM0C8QToqvqA5enURs+pVmyfcmjVdc/uoEIsRXVBhUta8sjB/4AshpTnh7xOTRZY/kKmtE1TB+dMnPZrISdTCnc2XE4ErD/f9wj5O/M2f6yBI3v1eBENz4/hCbBIvH9POKcCToPasRylIMFHUqKJYELjHofoVLDfZ2ip4J6nfOUG8b05wqsZEfZIvG+apHSqyG/vNAfMxKDjcD6o5j+wdKWjfn5CfbiLzyraayYfvDq2RnOrRfHJOtSdY+Zzj1WzOckuTrkff2rg23/ugadz7aAqBO/RzAaoELlFfw3fPJ6MgIyTlQ/nW7SGO6MbbtIc6zE4pwZkl2vRdFuWJQ2yHRncD7x685ZC5gMfgVtSR7V065ZNn4CGx8JxTLlvyEoUl9b79Y9x7jspDYUtHcSbGlotxLsKOFumzkSHYELrR0VmfQSDDCcxlmmnBbE21pei9CuC+JNjz3I9rzrZ/uhRH6RAaBxRqFUw49UdjIIUs/4K76ltYtgS6gc81Dd4OxIN7UXsK8AdNtMJFj9kAFAoLpQiJk4U5olfNzjcR5ld8I9EwweFoyeFaw8pQl3fLvv1MegmsCiA68TpUTeBWDdkMQNkhpkdIybGW+6jCScz/+xptvCPu1f96o8VbF8Q2E+a53YhvnzZXmFS5Q1J2QYFJ69M6Ch/HqePXiL7Pi635MJ+9JFEJ4SOuCZe5v4F7BIi/X2oTbc1+F1A2dF8dUp/v+cQtD76ldbDtCFDV7719j8PSE5LaX85ie8zvg4ZkDnvsrPdJrgpd/vMvgOVj6/AFppCj7AXUbOl/wFYw0ftEQmcKNNHKRYIL3HFDc6GFWCuR2RLopGF+CJklYmpXsvKePqqBu4Qlp2mIbyZlfK7nz4Rh5N2bzOxJMDE4vky8p2rcrVj4/olxJcVqy8mRJMK2oBhEqa0h2KoSx7LxvwPycQRaCzk3oPzvFtALUvEaUDWiFiTXVWkrV0/S+sIWwjmqtS7A99Uk1r3BRiKhq1j/p25KHG4DWVsLeo37jUK82yEKi54JgKinOl2AE8ZcT5ucbes9oxo/VyEKjc0E4hekl46GnPbMQ8pPI9QJXKVzNQt7Ds7jnp0F3ama3upAYZKkIFgTHursAISSQbjqCzFEs+YQc7Ujm+QBZCcT5AjMNiEaSdANMKKm7EO/B6hO+qpqd9NL5xUBy4jd2GT+6RDEQzE4LTNtXBTI02FOGplCosUYtiIlIX3XJuThmBDjvG25CR7amjipOWUEw88q503N+yG8SC9oilaPKQnTU0FSKW3upX4CTNw534xXxJq44vuWJY4EG+Dxwxzn3Q0KIfwE8sLi6D4ycc29/1TFn8KJb6/h9/U865/7e4rq/AfwFYNHE5792zv3Ct/hl+Fh8ENS88pVGEhBtzaBuaJbanrAHUDevP9f4OlpYgPeshiO2OUp5xVhrfZKKQp9AXoXoim4dvOJ+RF4R3vJ8hvpEl/13LzO5KGnd6bLyiW3mDyzRem6HwbPgZJf8NMyeXGLpJuy9q/EmTxcFs1NDhi8YBldqbOD70jo36FJT9RTBSHopEwvl0DHZ6ECrwRmBOjNn2omJthWdmxX56bZfpIaSsg9iprGRpX0lwEkP4bSrJXMihIG9hzVLzzbEt8Yghf/tHMFUg3OEOznz8210Zpid7JDsW87+gsWEkoMHFP3nIdjxQ/niwhCr/fNPbk2JthWunUBt/G0OvcKFQFT1wiXPIZrat/6259ggJRzB7JwlvaHJ1y3xjmdIh7d9S2V+viG+q5lcNrSWMrKijdOC2TnfkpHtmtaXE6qOwwWglgymUp7XIB2m7duBMrC4WhKs5qin2xTLlnK9ofN8QDBRvhLMvLCjbCAaWfIlD2/VM78wB7cjTORFNotlgVzsOfTCGyYYFQT9ACcFnZsVphOT3i0oewmdGzCKNYNnobWp2X/YVzUrT5VsfEfkZUQWycDEvk0lrN88sPDeaNoOG/jWV2AFqliQNdueEIl2yMgQhA1hq8E6QRg1iK6jqjTt9OvfaH27hOCbMxz/do3fj4rjLwPPAV0A59wfP7xCCPG3gfFrHNMAf9U59wUhRAd4YuH98ezi+r/rnPt/fYuf91dEsJv5+UZeQaA9K1lKmuU2elxgWiFSSoSUuLrxLaGpH0Y7Kfxin5dekrv56rsoYewxKkvJ4wXN2MV8xS6E+haJ5bDScc77WBzCdA+9GYwl2Jgw3IDhZx3Ncge0Ir024fYPn+DUb4youp4vcO4XMnbfnqJmEtl446d0LCg7Ep17QlyyMUeUBidanPr1GRvf2aOJ4cRncvYejql6mvn9FbbQOCcId71bHMD22wPPEA5h7XM1dz+ksZUACfsPRKjK4Q5CbOAIM0kwhyb2x44eX6L74hQ5yZGjOaN3rpLs1MT7FaKy7D8UevHF/QYtBGd+ceJbXWnE5HKHzrW5tzAdeWixOBRADANcpH37anEeCQPQElHWuCjwHh+BRlXOq/COBNma50+UQ2+ohfDEumhb+dlO25DNIi+ZEVuSm179Vu4q5qcNvXNjqkZR5KH3Fk8b5M0YGzhUrjGJQ54sSH+rTTkEPRc0WmJiL9NvA+8qaQPv3929WZFuC+brASaCcCIpliGoPZ+mdcfRvVkR3Z0wvzRYSJ6HNIlndMtaoQpFvuYtfvN1zzbPV0HViv5LDfN1xfY7fVJ3wjPPZeURU/m6/10OHTZy2JZBxg3OSG+nHCtMKDCpxfQbzynSFiFh2JlTNRolLfuTlChqWO7NaH/f1W/+l/n3I95KHP9hIYQ4DfwgHhr2X7zqOgH8OPCxVx+3IKsc6rBMhRDP4fVUnn31bX+/ovnYu7w+1ex4FmFaMbJscFLQ9GJMrAmmFfUgRo9KXCAR1T1D78Ui9bWShm3HyFkBgcZJiQ21H3D3Uj/ALQ0ohaiaYyb5PXHEND80fQJQkmaQHhlH6d0paMXmR4ec+pV9hLWsf2pCa7OF3p+z/psZB+/t4rTFGcks0cR7kvTqCNOJcYFCzkqQgux0i2DmCCYwPxHRvd4wPaNxL4bUj3t9tuEzIe3bBcF+xvmfHjN9ZIn9BxRb7wq8RpPzC2HZXzgU4nvpTcsRXIfui1Nwjv4T28evDeh/ae9It2t+/xCdObbfA/GeJt7OELWhWen43fT1jGs/2sEGcPl/Ko50v5xSuFhjWgHB7nEbUOQlIl9UiYd+71oSzAzzNUk0drTuwsEjEO16IED/inezS3b8a4n3Qo++WnZII+i9bJmekZgEXOiYTBPSVkkU1+TTADMOYKEh1bS894W+nqBqryBbd/GOhc5XD3ruwQdlX9J/qabqajpXRsi6xeiiT8Lrn25Ib4xBCPLTHaqupm4PkLWjtd0gGkc4EQu1ZlDzEnciIpw6QNDatFQdQZMImlj536kfdIcTj7RrEl9BWA2m49t3LvBDfTcKcYnhzPldbt1eokoFIvefSxUZTKkIkpq7d4eErYpq4n3OGSm2ZY82b8DE4d6qOH4v8d/jjZ46r3Hdh4Et59yLX+0OhBDn8Tjme53p/zMhxJ/Bt8D+6qEO/auO+0/xEsLEpP8hz/2V9+e88qnVEhfH6GlJ1QuJdzyyisaL3JlIoacVsmpgWuHiCJT5ugmC00dX6Xx5GxdoP9NQAtMKsLFGOIfKGppujMprXHNPkohCn5iE8AtiGPjfh0lKyqOkUZzpeY6HdZz4la2jykQUDd3nfBsICxf+meDGD0S4doOeSpY/6YmEapwftXNsIAhmhv7Et+fy5RBVWZaeyZGVYXy9xdb7IVuTdK57smR5uk+2IlEVDF40COsris61nKv/u8R7MpwqKOMAQsvSEzOEMbg4wCnlPUm0wnRj1P4cFweYdoSwjvlpXwHkK4Jw5D/eemtMs9IlX0/oXIXVT+15ZNVqF709QVjn5zXz8jgJhwEuANON2fxAm/VPzVDTEpGVRHcda5sgjGN6f5/Vzy0WzlggG0eyA92r3i98fjIkX5ZEI7+jn5+QZKcNLjUgHTbTzMchTi8cHwOLib3cBlN9BImNDyzxPsR7kvlJ7zvSpP7xnILVz8/987MWGwWUgwBpYPDMHBso6mGKMI5oNyeINDZU6Fm1AHr4CrdYTcnWNNl6i3jXEY38AKOJvSS/ibzBEvjqKph7d8BDnwwkmJYh6Beeb2okTa6RHQPCcXe3z/LahMZI8jIgCAzZPCJIak9+DA1SOkRgkeOAJnmDr7xv4OH314pvWeJYmDxtO+eeEEJ89DVu8p8A/+xr3Ecb+NfAX3HOLbTF+R+A/xb/Uf1vgb8N/O9ffexCYfInAbpi+Hv/BDoID0qcWiwA59sEE0O5nPh2UqyIN70CrpgXfiaBl8woLgyJr3ohRKfk0eAa4XeyTiua5TbB5pj2M4vRjblnwZ+W5CfbyNoiaoOalrhYI/PKt1f8C34FNNfGEXLqH8Mloa9OrL9dfHeKSyPPSRnl0BjyCwOS6yP/uAvkVnxjhJ6tUCVe7nr6tmXaV/3w+DDZpC/uvwIirPcC71a4iMETGYMn8LvdCwOyU8uYwCecJoWdt0sGzzt04ZieTwjHkmLZwihEDSvMTHP1TwzQc2/heuJXNv1jGYvM60VyVah5Rbo35+JVAVKy//aBT/ZxgMw0wlhaL49IbypfUUiJ3hqz/dF1lr84ASGOhSejEJxjfrFP69qYk79R+/c00AtCZegl8bWi88IBNvKABScEsqhoBn6oG2yPseGA9i1DseJnH2VX0rsK2UrA/LRv19Vri3ZZqTxyCkhuaNJNP7twyhFODJMzAUHuVW8PbYBlDeHcIYvGw63rBmksnS/nfrcmBOoQXLEwtTpEtaGkT/5JgCwawnFFenNCudZmfjKgc3VG1zicltS9iGwtIN2s2XlHRBMf274ewa21g9BSj45Rg7JVEycV2SRGhoZZHiGEo9pMaZYqVpcmbN4dIJRDb4TUgSMoBHXPIivBpb96r9DrGyveqjj+w+KDwA8LIX4AiIGuEOJ/cc79xEJI64/hHadeM4QQAT5p/FPn3L85vNw5t3XPbf5/wL/7Vr2Aw8h/5L3YQJDWlqalcVL4xW+oyYeSwZWScGfuIbqSoy+li/RihzpDWEez2vW79cMv8j0tpWDTj3peSfpz3gXQCoKsQWUNNtRevts437bKF4vOYWWxEEB0gTquJBYzGcxiN20sQgj0lu/Zuzgg2vecEjtoYyKFymuqQUx1oqbzbMipX90nP9VB1L69U57o4JQg2px5Fr31cu7iMOFphQu1T1h1gwsD4o0ZkZY0nYiDhyLKMxXBpu+vm9DvaoMZ5Kf8Vs1MA2S7pnKC4bv22NruYeJ1Tv3yvp89zAtcK/YGV4tzadOU6eUOqnLsPdoCCyufK8hPJqQ3rF9chWD+wBLRXsnKpw+OduleENEvlCIrifb9RgHncK3Yy8s0NWp/fnSORVEjF+feLLdxgUSPFy6NQhBt+c1EZ3OMWWrTvtrgAkX75Zp6KSXYybj73UNkDe1Nw/i8onvTMD2F96zvSuKx993oXauYnwgIZo7etYZ4O/fJEy9HciS5bx0uCf3GpLFHszGs9eiwXgrOYZIAF0jCu35PJvNm8bmBJhLsvqPLymcP2H1Hj3jfIgxsfCCi7jratwTZuqPuW5ITM8oyoNvOMVYy2Vw0GLQljBuqSrG0PGU8TRDCEQUNnJxTzCI2ry0hK4ksBHXf83lM5ZOnab1B0VSH8Vbi+MbDOffXgb8OsKg4/ppz7icWV38P8Lxz7vZrHbuYf/wj4Dnn3N951XUnDnXogR8Fnv7mP/tXRjDz8MnsRESyWVINQppYIhtHNHE0iUK1wiMklJISUTXHsiRJAFnhZUnCgPJEx7cNbnppEPEaEF7wsxBnLYQBwc7c77K1wrRChLGYQQs1K4+ql6Odv7W+hbNQo0W+ag5iLU4HVOtDottjRF4hipryVA+d1QT7mRcCjANE5oloo7f1CTKLDTXFyZR4M8OkIcI4yvU24c7cL1L+BFCvtFDzGicCpHMUp9rEGzNcoCiWAoKZwKU1zoVMLnoToUOXuGBfoUpBuWxofTFhdsGwvdul99mYqgsHb+8z/Mw2BL7l0gwHNC3F7qOa7jVLOLM0iSTdNszXFC4KaL00QRSLSinQxFu5P3fOYeOQrQ/2WP8dy/7jfZY+t0txYUi0nfnqaoFoo6qPKkmU9IN0a31SqY2/P4ufSZU1mIbxQ8sAtG4XzE/H9J/a88nXOY/QCjUnf31/sUFw5MMVpmcUrQ1LODWk18b+vVtUktHtY2j2kTyMcxBExwlCq+PnFkdHxM/8TJdy4L/y/S/sIIvGJxzrgRZImJ3rULUlgysFm+9LmN7fI1sX7L3b0nkhQFUQbAqvqhs74g1FJlsQW6pY009zOudLJkXEbJJQ7CWIUrJXaoK4oZiHFISIvRCWKkRqcE7Q/5Jg7x0CjEJUi9fYfm1u0xsiHG/qxPEHRQD8E7yqTSWEOCmEOITVfhD408DHhBBPLn5+YHHdfyeE+LIQ4kvAdwH/l2/1k9Vz/wGO9hvqbuA9FXLr4aQ9gUkk04stitXo+KCqPhqGy4PZsXx6URFtTAlGxxBD2/5KQuBhCOsQRXW08DstsZE+4jKAlxOx7ejYXxv8IiLxsxJjj2G9zmG7CaKoiW4eMH5s6ejy6NaBT3bGcvCeVfYfSnjwH+wzfL5eoGcc5VpK2VVsfGeP7Xcm3Pn4EuOL4dFj2jQEKcnWIupehJwXVOsdEIKDR/vc/libfMnLog9/NqX3IjRtiwlB5dC+7Vj5ot/dJncVdQt6zymSpxPKAZz51QnDz+4ctfs8Ospbo6oSpIFov0LnltElzdon9th8f8ejpxbkSOoGdTCHqmZ+acDkgQ5rvztGzAuWPrXF9KEh8Y3RUdVwtCAr5Vt56vhrc5g0qBtE5mcMRzwbpRDG0Xt2hKwM/S/s+IW+8gq9XtXYUC+n0Bi2PrKCzh3TS37uU/TVkZPjkTLA4XsLR0kCJY8l/K19RetQFCU0BtONvXf7yND/8oFPFFV9VI06LZF5TbJREM4s134kwgbQvjZDGAi3NdlJy/y8oYmhXDU4CfmZBtWvGK5MOD/c586NJfJac3Gwf7Rwtu5I5G5IGDZI7WAceJvYjQimmmhbMbkILnDIwvNhbGy57ye++LW/nN/G8U2yjv22jN8XAqBz7jfx1oaH//+517jNXeAHFn9/Al7TXATn3J/+VjzHrxZOCqSx1G1NPvQWlsHckexZ5mve8yKcOoqBIphpL3uxWGRei7MhisonA8Bp5RFUXyOEsUcJQDuHnkpMGvjLzKKldZgg4Oj3Edz0MORi0bMWF4X0n9iiXu+htyeg1JHpVNUWFMuC7GKf6SlNMHfMTmiqriefOQVV1yN9imVwcoAqoHOrQnQcOrcI6yjO9jGxrwaCGVQ9R3ZfDUYg54poVxDuK+qeo3MTshPCAwwyaG1Y6pZ3G9S5YOV3tijPDIhGvpqynRg1K6k7ita2oe5oulcmNO2Qnbdr+lc85+XkL2/5WdI958CmEShB6+rolQNx6e1OizM94uv71Os9gq3JUXJ1cYTMCpySVKf7vs2zgO46JTzB0FoINPnZHrOTit6XGlTxqt1zoP3CXdWEd0Y0Kx1vPTyQxJvQfeHAVztSfmXCWMjdHBFOD68//P/e6nKxcTCJRnRiklsT6mFKkyqESchXA/pPjxbkUo3en7P/0RbJln9/b31vD1VCvuZwiaG9Mqca9RCVoHVxTDcuaYclW9MOz7x4mqBboaTjmTsncHMNEvIVRzCWFC/0QDsvtCt8kjcOipO1vzxX2NWKahwQ7ive8PEGTgxfK96SHPka4b7j8SOhOmGgHAjSHUPZE1QtT6aqW17lM5pYbCCP3fm+1n0r+TWhua8O0Ri/oDQWPS5wgYfqekhpcLyIAKZzTyVzuABZeyR0aNMAFwXeDMo75fjFMQlZ+nLGuf9th72HAppEULcFowe8nIQNFwZAifMyGm1vO1p3BMFBgYkkrRf2qNuaybmQgwc05dAxO2cxaxXh3YDuygyWS29V+uAMs1oxegDmpw356ZpixbH3mCCcOsKZpW4Lnv+/9zm4P+TFv7BO04+Rld/pq8Jx84cc2Xszppc66GlJ96qldadANJbt71zlzg+dxHaTo5mOnC2cGSs/YC/ODdh9zxJoSeflKU1L0ax00ZPiFYi4ZinBDNtsf2QNWRp2P+CVcKkbX3lY66s2IUiu7nPql7Zf+b4cfjaq2iPfFoAGvTOle62gbnkf7/mFztH75T8sr9wQHD7mURVl7ZHY5tFm5RCqbS3BXobMa6b396k7msn5gPjWmO5Lcz+nmRY4IShP9ai7jtmlhupkTf5AwfRyg+s06APN/E6HcCSgX3OiM+VgnnDroE8/zRGBV/ddTuc0mUZ2akQtsInFpB5abCMvM6LnAlkJVCaJtjSi8InC5QpRv46g5xss3sySI28ljq8Rnnzn0U963jC4UmNCQZMKetcrWlvG+0vXjuigJtxfVA+LL/hXk0U/5AZ8I8/FJZHvWxvjh7MLW1lZ+raHbSdHC5Le8VyNV7Qw7gk1znGBojrdx3YT6vUed75/DZGV6L0ZWEfnlqVu+4QZzAVV12FCj+YxwwYXW2xkKR7KmZ637L6ri9OC3Q94C+S6LYgOvFe4DR1yJ6T12D7TSYKzguTkjGovxhWKasmglktEoWg6nmi48THDre+F8cMNQdQwOw9nfrVCTwqPdBKCvYcDWisZ7k7C9IxC1IbhZ7cJ9uaUJzosfWkGDj9Ibozv/y+QY7aXsvHxNTY/4FnQ1360z53v7hPvlJ7PEGoINPVal43vPUETK0wS0N5o2H5Xi8Hzc4rzA1wa4Q4H7AsY9OTxFV/ZHL2B7pXvwWGyNr5CMZFi6ZmG1S9UhGNfNWSXl147aRxGWR0BEw6TjO2mHgbu3FGFKbKS2eU+VsPuowGtLT9Il1kNSpJdGrDz3i63vscTL7vPazCC4FYEFuLroRcpTAyzCw1uptmc+iG4tZLrN1cIkprTwxHPP32G7vKcM2sHuMjipKMeNNjAa3NF+5Ig8wtnOPEVrJ5IXOxFGsOJ5Px/86lv6LvxbRfu6/x5g8ZbWlVfI2TZYEOFSfypKnsKEwm6NwwmlgRzi84sQdZgtSQ/mWB1SvepnSPr169XZuSI+Pc6IYzFlRXE0RGqxwUKogC5aH3JQ9goHPfo7xVFPOx/L4arwjjPiHaO+dk2J39zQYlZ9PbDicGGElEu9IgqD6ONdwX1UB55Y9tcE1SC9t2G5O4cbIsmlbQ2LfuPCJrU0rmqaFIoP72E7jpM6HBXYmLJUWPS7iukBZN6a9HkRkB+qiEcFNS7CSJ25KsB8c2aze9eY/BiRTh2HNzo4FZLmv2Y/GyPYFyRnYzpfmmHnQ+vMXyhYvTokHBiSK8eeFRRP2V8vycvnvjVbQ7etUKyJVj77IS6F4HzNsAIwfxU7PWseprxhYCVJ+cszw3CuCOo9dEMREpcGJAvSVTRIr01PWbwH1YBYcCRlIwxmG6Luquo2pKy5/kboz8juPhTFttvIQ9mx+9hoI9IpU6KV9gUNytdZG0w7ZBga8HTaMU4LXFSkK35nb3KLU0/oRyG3hlxw1L2BckO5Kswub8B7ag7HmLbPJgRJxXNPMLl/j7yIqCehVBJ4pWcs8MDrtxYR+eS7Eqfaau7aOYDRqBzwfBpR9kTxPuetzI7fSit4y2H2zdhdu4NvKIuQvA6vfY3SbyVOL5KmI++k7q9IGFZBw6isaFJJKqwmNj/tpFATBwoaF8ZeWgnvGKH+PUkj6+WNJySRxWKKMrjxUNKnBLYNPRMbvBkQS397MM5qN1XSK8DNMOW96GwlqafUgwkncPd+GIXG44q1j4nkLV/vZNzmvyERRjpF/PtxFvMBhLhYL6uqdtdVGlxSjA7JVEFxDuC1pZ//kVfEMwEgysNsq4JxiXTi22ayAv7zU4LbGBxsSW+IjjxKcP8ZErZk9QdKPrw8p9d5cTvNkS3Rqzelax+wrH33hX2HnMkVz1arbtY9JM9g6z9vCRbCzDJEslGwcFDKftvc3SueYZ950ZO+7Zk6/1dOrf8fR9CcftP7lKd7DK6FNG73nhEkhTIWUF5bsjoUkjvasXocsjgSkXV0xRDwf57LPf9k5jRu/qsfHrPI64ac2wPLASEAfl6TLai6L1csfPuALlaEL2Y0rQa9IwjNJhXQnavkK3xQ3mvjSZrw8HDHZY+s3NU0YjMIsIAEwqSXa9Uu/XukJOftMzXlZd/0QKdwey8xaSWeDmn2EkIJgLWK6pxxGwSIjPpFQQ2HZNLLdS5guWTIyZZzLXPnYGuoVnyXiVYQbCvsYGjdVNiAyi7vv2YrUu61w1rny0Y3ReRrS90zgZeofdNEW/8/Pe68VbiWJ0VkwAAgb9JREFU+CpR9TVFT5HsGawWlF1FNDELhrA4Ym2ntzOqfkR4UOCk9LyC15AC+Vpxr7GTi0PPtzhEU0WhX8wb30f3PfXFzjXUOCFwgUYYg5yV1CttgoMc0wpRo+aY57FYCJ2W6FFO0/PyIcJYVj6zf4S6wjm239OlvWm8N3hlqdu+YghOzikHAW4/JtpXtO460m2LCQWqdrRuZWQnE8JxQ69WyMYxOadJN0qQkN51lMvedtfLXyhvJ/vFA5pBAsT0fqsh3J5z40eHXH2PgNJy7t8ZgnnDxgcS9FxQdZXXBFgkxKUn9lj61CLxLbg029+xTLEsKIeKy//jDs/9lSHJjuT6D6cMnoNkW1K3YeNjK5z4NT+POPErE7Y+ukp6N4XGcvWPd1GZIBzDqV/aZvbQ0Hu+70z9uVQeSLD6mYyVSUV+MkEVlmCuENpx90MJZ39u35Pz4BUkS5xX3t17yH8Vlz9fEO5HVDaGyNG6siBY3pv4pVgYiekjoqjTivzikOkZTbJnsZ2Fx8jh4yze/4P7JU3bsfSU5dqPhNi0Yekzmt7VgvHFmGAiwQrqrkZYgTQCe6UFq42fV3QbTv6cpUkU0wsaexCyNR2ixwqnFx4euTeRMrEj2hfMz1rmZy02cPReUERjQ7LnSG7PKFdS0l3D8JmS2x9rIRyc/Ruf/L18bb9t4o2Mmvpa8Vbi+CqRbBbIKvKVRWmJJgZVOsKJ1/QRxqGKhnI5Ibk5pjjdJbo7Y/7gMq3ndo7v6HCBeFXVcW8VAUBZ4QKN7cSeyTzJMb0WLlI4IdD7c99LDxROCU9EW7CgiTVi5qGVth2iRwX1wFN77Yku+iD37YzwuBoq19uowlAOQsJRhelEqL3a28y2E/I1QdPSnPq1A6b3dZmdUswfKBHbKbIQhGPJ8pcb0lvzI9lxnCejRXsVTTugiQXR2DJ8riTYnuIi7QfyM4POjW+XNY5ov6ZZSlCTisFzju13pQSzEB6foMoAN9HsvF2y8hQsf6n2C/Ok9CZM8wXhzkmqMwOCncyjyZSmHBzrKu29d4VgDPuPwNpnLaNLChND1bXEe4Ls4oDk1pTyTJ+mJdh7W5u9d1nAEowVVR+wlvR25jkrWpGdbrP7mEbnkJ9uk61orIbV39oiX1rDlZK1Jyqf5O9RDDj6XEhJsd6ie91iA8H+Y13vURE47vsnY58kGnsMbqhqnFIQ6mPyJ1Cc7WMDwcpnDrz8Sm0wg9R/RpwjvzBgetZb+Drp2FlQb4M9TThzBPsZ4VrIylOOfKgYmxhpoW452jcFwmmE8dDzG98P3Ze9rLuoBeGeov+i5eBBz8WRtSDeEeRrMD9rSG8pihVLMJZUXdh6j0JngmHY9ei8tsQEMTr3arpvmngrcfzhDFF7sx1hHMHMzzAAROMQjUVlFSKviCuPyY+v7oEQpLdmr3ufR3wO693inJLYdoKNNbJZ6CUJ4bkfcUQ9jNHzGpVVNMMWNlKookGUxu8qx5mX/47UEfRSlAbbDsnXI1ThcBry5ZDucwdHhK962KJuK1TeEO0VmJaXy1ZScuOPreLeNeHc3xyz8dEeorHo3DK5H6gla78rmJ2RLD1TE0yaI7FD246RmeecyDAg2J6SHA7n7zmnLtSowjA9FyMbFtBdic58b15lFatP5tSpRv1sh9ZGxeb75dFAMd4pMLH2O+pD1eBAg3UE+zkv/cSA9k3P4k+3HeOO4PS/b9h+Z0DTNsQbitlJxZmf3WTze9a48C/2jgbUVDWySvwg14DoVejrMeEEL/rnnH/seYltRew9orEhTNcs00uS1U87VAVbH1tj/z018e0Q0ZQLCK07bk85rySw97aEg0ctZ37JcvCAJl919F9wnPhUQ9OJCDaLY5jtYnYlihLXio+G37YVUywHzE9I5msDgrmjbvmdfHtvRr3eY3Q5pOot2PUBdK5KbARYX006IdCZ5eb3KlQB5kRJ9HIMOCb3WVq3JU0C9XINyrHfVpAYWs9GVF3H3tsEzbBBZopGO2ZnHbISyEJQ9R3BVC4EGSGYCfovGaKDGqsFwmqKnm9pmog3R7g3Nmrqa8VbieN1wn3gcUTREO14zH7T0oQHBTbSqIlvuRwypUW1aEktJCvELH/lnUl53J44FAfsJt7TIw2wkUbWBicgP9NF1o5opqlXUsLdDBdqmn58VLFYLVG18dpTh/pUh387h+lE6HFO58WaepDglGB6JmT/HUN6L2fIokHPKsK9BiclSNCjEllUZJcGVH2HfKFDPaxIdnx77OD+gGBlRl1oEIqlp2vS62NMe/FNN9ZrPR3KnBz28BcSKIeLu1NiQWJUBHNLnUovq3FgfLstFMhSYiJFOKrQhWH30ZjT/z5DzTzKSc4KRB28AvbsAkV+ukO0W3D5p/aphynBfsbWB4d0rzl2Hg8QBi7+65pwe+ST53qP1U9P2PjYCpNLlrXPQPtWTrA9Zf0zcOfDCQ/8P6ZsfDxBVY6lT3tlXhMrZC/xGwvAhI5o35s4bX2XILkeUj+UIa2gf8Wy/a6IM1uv4lwoxf6DydGu9O6HFE2/Id7QjC87Vj4zY/Jgn+zEKr2n9/1rXUCHXRQyvdyl+9yIOx9fIjvh9at6L1vGFyV1R9DEMHzOD8f3HompW6BKL+niNNiPjpjtp+i9AP20RZQV6Y0Ja58ZsP39BfHzCSZx1Os1FBITSqqhRR0E6HMzZMdRXW8zf6SEuUaPJbpdYyqJ6FXYWYCJHeGupu5bTOwh3cFUEI2gTiQxUCwHOOErwmDuWP0Hb3A01b3xVsXxhy9kbTxSae7lGlTmTYP0Qqbi6CcMXjnLONxd3wOPPBIsDANsGmJSjajt4nE8TNYpSXY6Qs8t0eaEeqmFLAz1MKGJFTo3VD2NziwqX/S1I4VznrlsuiFyx0MrZeHNnUReIVshNlT0X8zZfSyh7oaEtfGMa61wkcSkoZebuNhGF5beS5AvC/S0olMarv/YKtEI3MstlIJgbkivjXzlMMoWYnnyaGG0Cza1TRe+G4FEVsYzySuLSRTFUFN1BK0Ng4kE4bj26K3a+RlBbslPxOi5YXbWooqE7s3AD77dolo7MrPyrzV9cYFu0opgd4bpxEwvekOj1S/UpC8fcPNHVzn5OxanBcHGBBcG6MzRuiOZnoPe8x7eGtwdo/MEpGD5ycz3qxfzIRtI6m5ItqoJx14pdv5ICY1AjTT5+Qqx5xPqzjsEZ3+18kPxQ/HHRfKQDcR7hmxb0zyYESrrFYEnmuf/T32S24q1JypsO2Z6PuXgAe+kJ2vQc9h675BmpSTYDOldtdSp5xXNT1uWv+AfqllqY2IvzOkem5LVijBayMZrizg75+6HW1y6FZCd7bL7mCBpVWTrIa5lUHGDVZL2dxxQvrCEWaoJBFTX2ySXJgTKMA0TahnCOEQPS7iVwKkCpSx1I8GALIV3fOx4Q6m6JXAyIt1umK/7luLsvobBP/mmf5X/wOLNPON4i8fxOiGaY6b2kZxEXvkkcC/B73BnDcfw18O/DyPQEIXM7uux9Z6O992uvP6VKGv0tESW3vo03syo1tpeyBC8RHttCXYz0js54X5B09LMz7W913niJdf1yA9Cq/UOphseLeDFil/A6k5A92aDrLyqrFvIrsuiQs0qgp053ecO0JlB1nDupzcRxlJ3Atq3HJ1bDeFEcOlfTVGlH+hiXkk0c1FIdaJLdrZFtdaiWEuoeiFVP6RcTggmFTaQTE8FlF1BNHbMTiriPb+QBfs5epzTujYlPChpX50iG8f5f1dRtwX5sjrmJjh3zIo/lAAJtBf3M5bxo0seRh07ymXLnY9qbBpx9md3mFyImZyNQSvycx3KoZ/NtO5aT65c3Nfpn9vyBlg7M2ZnYi/iuEAwWS3QhePg7YZiyaG2QoJ2hThZIDKFrAStG4ruyxDfGB1JwR+ho5yj93JOnXr5FecE1SzEZd6U22mLUzC6FCxapp5kuvSMIV831F2I9gRipjn5iYayK9h9nyF7e04wlZR9SbYesfHBFrPzluz+EvFkh6bQpFHNbC/FVYp6GvmkEig2PqhpuoaiCHCpQcwVrc+lMArZvT6ElRJKSTkPUWcynBPM5jGD3hwCz7tpSoVJLHauafY8AVXlEp0JZC2OhAtN4qjbgo0PaUYPQPmu+ZtvpX0T8zjeShyvEeryRd9uupewdfj3q+QfxKvZuq8K20upl9s0vZhovybdtew9kmBirw9k2hEu9Iq7es+3uLw9qfQtnUARjkrQEjkvEUVDfHfqCWLOoeZelkLkFfUgQc9qgt1swfHwb+/+Q5Hf1fUU+UqAjQJse6EvZaxHgdUeeRXeGR/5bph2SPryPibyrZT+iwY5zsiXfVtIlNXROanXurhYE27PSG/OF94lHp2z/1DAzuMBBw+2mZ4NEQ4vr9EVdO40jC5F1J2A/cf7ZGe7zC90KNYSnJaozLfVTv/8NsMvHhzrcR0uwPcwpqkbRNmQ3Tek+8KYvUcTzvyKof+cIN4WR+dj+MQeyX6DaUdUHcX6pzNsAJNzkmalc8yxWBAnzSAl2amPNgPJtQNUYel+aZfTvyIQQO8lcNdbNNOA1i1FMJYsP12z+vnp8WfoEObceIZ+sDWhfbMgnDjkzRhq3zZEO+I7AbKGYig4eKRLtirp3LLsPC5xkaV6KKN8PKNzVXFwf0Dnjkc9pU8lFCdqJpcsZVfiNAzv2ye+HlGuWLCQVwHp1ZDuygxRSVQp2Hl3j7rjWP68Qr+YgnQEI8nkbRXBRHDy0g5KW5KVDFdK0rgim0VIZdm5NfBy8IlBSIdcqvzrcBDuKpafdL5FJkDNFdWyoWk5xg8YqrXauwFuJNz/f/zc7/3L+20Ub2lV/WEL5xa4+Ne+7tXxyuThjtniUnqL06xi+tCQfChJ9iydWwYbKqq+Jt4uKZZjdGFo2qHXxNIxwajwTOfDHfYChisAAo0sDXUnwMTKC9R1Q4JRgY0DnIoQjSU7ldK6nYFISG/NKVdSgklF04uOhBsPZyS2FSFnBU0/Re/NKE/3ufORiLO/bDl43KKHBeNLLUaX11n/dIHK6qPjbKj8LjxUFGs9wv2KYOqVcQ8eCFA5EPvBqM79uZqflDgJyZ7H94/uC2gSmJ3ShBPf786XOujcEc4MaV57rso4889byWORP9Mck+uqmvTKLraXsvrZCXuPdymGAhtBuRwzfk+HdNuw+5gkOuhw+pd2mTw84MwvHRxXG4fvcRR6SZPdKVVvafFYvgqN7oyZPbzExgckyY5gcsnraeksoHXH0iTec0ROPDTbJd7jY+s7l1j7xMGRtljT0qx++oCVL2hGD7TZ+k6DmnvBxnTLMfzyhLsf6dEkoHJBOBaoIsA+XtA0ivlJx9KXIb06QlZLzB6s6C/PKKqAvWGIyxXl00souTj+VEl5pUtzrkbc6aKmkv4Vy+S8JFifs29Tepf2aa4MqNYa3nH5BsXFgI1JF+cE+TRCJIbpLAErKPcTRGyI2yXFRsszxfcVyZ4kP90gjDfpGrzYsPewplyxRNsKWQryszVUPhGG6288X/GvGo63jJz+MMa90Nkj7+/XkQ+5FykFHDO3jcX2vIptOGq8lLj0C4oz3jlvdiYm3m98hSGgSQPCaY0NNcIsvDSaexII0PQThMMz16c189MB4QRkElD1Qy/c5xztq15FLt7yxwWTCln5pCXHGeXpPtHGxMtwLGxoZ2cT+rtTTCJZetqg9+Y8+Pf9gB5TUpxqc/2HQu7/R1Ov6us8wqzpLCTWu4q6lZAtS3ThUIVvSbTvWIqBpOoIbAAm8X36yTmFzh116kX1TAR1xw9LZeN5Fk0iKLsDZmck65/SXnSwMceMafAtwygku28JaRzxtX2QkpVP77H14SXyFcGNHxLc9y9ynBSsf0YzvqCZ3d/3LP/DWMymXCte2PT69mNya0J5ooueVX6uA7Sf2eWkW2Z8QZNsCWbnvLxG/xfmVMPYK+weki07EflqiKrg7vcMGVypGd0XcPKXt6nXO2RrEVVX0H4xoElh+EJD+7l99t67Qjl0RAcCkwhk49Fdo1LjxiEP/NQBWNj+4LK3ry0CRk0HrIDAIoygGTTYXKGngs7PtxnfByuf0uw95mi6hs0Pg6iAzRSXWNKwpro45b6lXQ7KlM1RlzILiNIaUymSVokQMM+8YZXcCyjGC5h3YMEpzKMzmESUa4ZyDfYaSfsmIKAaWlyrIdgIqVca2lcC5mffXM0PwTenohBCnAH+Z2Adn4p+0jn394QQQ+BfAOeB68CPHzqhCiH+OvDnAQP8n51zv7y4/F3APwYS4BeAv+ycc0KIaPEY7wL2gD/unLv+1Z7Xm+vd+iaFtxJ9ZdI4vPzV8RXcDOnJVi70fAwbKpyShHsZ4U7une9iQd1W1C1FslOjM0O4lxNvzFCVpRiGNJ3geFG8h9AGoEc5eneGU5CdjFGlo25JyuXQcyMWbSfRWPIzHWRt2Hu8y93vbLHxoS7lMIBAE+5lYKxXsE397rr/xR3mDywTjGvq1mLAbyyiqBF1w+b7Qy78TMH40SXytQVPJFSYSB71/bNlic4d85OCqi/ITlvG93mf7boN1cDRJI7ZeUO27r25TeTlTGxsqQeGZtjgNIwfbKg6nlXcf9GgigUfQkovJriYd+x85zrZ+S42Euw9HHLtT62z8fFVbvzIMnVL0H/JculfNew8nnBwf0TrxX107mi9NHnlG6qkZ+XX92ha3fO5KNYSsstDkIL84pDZScX8lLdPjbckg2cktz7e9ezuODi6z723xbTu5ARzL8Gx/2CA1bD9oRXufGdC0feDbV1A56YjW/GildI4LwhYw/yUI9nxDPjgWozMvdPhwdv7TO7zHuVNy7F8auxh5HNNvKVY/rQmGPs5gw0gHAmmZ/05Vf2KeEODdsjlkmQ5487NJYoi4OrBkO1JGyEceiMiCBpkYFHKUuQh+kB7q9uW31q7bo3eCXGRo9qLQTiccDjlOPsrht5VQ+clL2KodkP6L0D7hYD5GcPl//wzvOnimzPjaPD22A8B7wf+khDiYeC/An7dOXcZ+PXF/yyu+xPAI8D3Af9ACHEoNfw/4O20Ly9+vm9x+Z8HDpxz9wF/F/h/fq0n9VbF8arQF8+/4v/DSuNrVRz3Xu8iPz8QpUHnNS5QlGst9LQm2iuRnYCqqwinlvnJkHDq/aTnJyLi3Zpw3KBKQ3m6R7Q5O95dVw0uDo4WtNbNDBtp6o7GaokJBHHWYHsp+XqKbBzpzQnZua7f6WuIckfr5tzLktSG6duW6XxpG6Rk4+PrpDse5dR6ec7yQU52eYn0pX2/WFc165+pufudKWd+dXrkKFd3vKR41dfkS97gSjZgYoeYC/Rc0KSOYtl5lFVsEbFBKEsTBjQrlmAnIBwJWjcV+ZpfjGQFyR1NseJoUokqoU5b9J+1uECiDubUa112H0vp3mzYfZv3Skm3HHVHUPb9EFbngt7zXv48GkUMnpsxv39INFnAhheaYl5jakGsWxhosUBCjR4dsveYoEkcneuSncdWkAaaGOJd379v2g4zE3RuWd+GvFVj2wkuUkRjx+RiSrWAyi4917D7iGZ62dC6rjGxIN2yjFcE5cD7gNffNaBJIBo5imWBiR1bH7SE+4LoQJBsQ76ySLjK4WKHjS0HkxS0xWmLmQRkJwSnfqvEaYGeN2y9N6W14Rg9WOC2YobfscndG0vYxsOh18/sU9aaKGjYemkZF1miizPyLKLbyTi420OUEhJLuBGgHpr6FpbyyreiFAgEcqpwyifV7XdIhIHBFYMNFFZD2Ydy2bH26TenqpN4jbb2NxoL07qNxd9TIcRzwCngR4CPLm72T/C2Ff/l4vJ/7pwrgWtCiJeA9wohrgNd59ynAIQQ/zPwR4FfXBzzNxb39dPA3xdCCOde/wW8lTi+zjhKGvdoPR2FUrhAHA09RVkdIWgOjZfyJY1uKeL9CqcEnasz5mfbyMbRuTJien8fJ3w7qWl7hz/ZCOrllCb2ekKqNIjKIgO/41bzCqck6Y0JphVSdz3Ut1EBCAjGlXfnO6jovyxwGnYf0dz4oS79K5ZwYuk8s3f0uk786qZfLBevw7Yi0msjzLCF2p9juwnjC5po35Gd8pBZ/xqFH4Qnfm5htaAcAMJR9RwmtZ4rMvOLh9r1SrD12RKkQ2jrcfwzr1Vkl2vCOyHl6dpDXMeaZAuf+MYWlEAWNTsfWkNVjmIVyoGmXLbYbkPz9op6O+HyT2VUg4jk2gH1Whc9zum97EEPred3jzkwdXMMdjgUiXQOyop6vYfKa5z0TOnOy4pi2YETLH2hoU4l+fKiEswEJoFcSlpblq2PrNB/ufI6Vtdr7n7IcxbMpYydKKXqWcJdP89obRjm64pwzIIj5DCRQOcwPwl13xAeSKpAEE4ErbuW6TmJVZBMHWZYE2wFuL5BAO3nQ2YPVpz/+Tlqf47ppwjrGD3QpupBtgb6pYRy1bC93+XUuT3yWtONS67fXEEohwwsLrK0ljOKl7uEF6Yc3O0hWzU29NyhKjWw61tWLrQ0id8cuH6Nuhkia0l+psbEfuOiM4sNlN8U7FpMIhldFnR/X77Fv4/x9VcUy0KIz9/z/086537ytW4ohDgPvAP4DLB26ITqnNsQQqwubnYKuNeo/fbisnrx96svPzzm1uK+GiHEGFgCdl/vSb+VOL5GfEWVcS9PY4HwsUngGdP3OPC5KMCkAbLyZk6d6znCOZwQKO1L+/a1KaYVMnmwT7xfEzeOph1iEokNQsJxRZMGyMZhYkmTBISTBll5y8+mG9N0AmTtpTt01jC54MlqTglkFaAzg40V4bRmdioi2XFkJwXZiqS1UeGSkNt/pM+Zn9kiu7xEMG0Itqc0S23235aC69DaNKSZ5yLkq37X3aSaU79+wPxCB9H4yqxu4RcQ5XfJwgryNYvTjvY1TdX3kuzhyM8xGIWQGNRWRLIpGD3SIDs17XZBlVastApmeUQuEkaPCsJdxX6oGaiU9Oac9kbDre/WCOswy17+QgSW9HfaFEu+xxxt++H0wf0x6V5A+5nFd0EIb61b3tMOXMw2nPbWv9P7ewQzy+Riws57LK7dUKxIVCZIdh1FX/kKZ8dDUa32Q3/hYHRJonO4+6GIZNux/2BI67bj4IMl8m5McaoG6agSSfu2woSC9oZhfMHfp7C+fYcDaTwLvjzlqx8TKeYnJXXboTPB/vtqTp3ap1pXFLVmepAye6REjgKcrH0ilAK7aHXWLUfrtmB62bB2dp9ZEXFnY0Dcrtjf6tJeypiPE4LQ85OKIsAsVxTzEDWXMIugYxC1RJZeLl8YgRUQjgXlqiG+FiGsPyfBvsZcKAijmpudFid+05Buemn81maK/vUnvtlf22+L+DpnHLvOuXd/zfsSog38a+CvOOcmQrxulfZaV7ivcvlXO+Z141ueOBb9tc8Dd5xzPySE+BfAA4ur+8DIOff21zju+4C/ByjgHzrn/tbi8tcdCv1e49VtKli0qqKF4Y6xx2Q+545E57wPhieG6VFOtdpGNBYbKepuSHLtAHlPUhEmpFj1ojyzU5p0x6AnHlZrgxBZWoJZjY0UOvNqqHouKIde6lvUXsiwOtElvjOlHqZkJyJat3L6V+ZUgwg9b8A6Rg+kDL88Qc5K5Jr/Mkf73gZWlgYai6zxg/ETmuFBxZ0fWPXD6oFPcHvvgtVPDogPDHXbYUPH9H7DC29P6H5OE8wcvWslLSmYnTxc+DzZS1jv8Ge1b13Z2FH1JO1bgvkpMEZ5bSYFwcAja+pa45xgMo+pS42IDHIrIt713h7Rfk1+qsWtPyJQOYvevcCmlvP/VBJv7DN6W9/reWUNGMPwuQy9O3slD2QhgQ4c+ZXYWFOsxOw/6KXGm0hRDAV6uaAeRSRb3pFQ1n5haCJobXrdreAgx4R95qcFxZph6QuS2XlL/4pgdkowuc8ipCM4NydWlvluSrTtz5eJhHf/23WkO4bJeU255FCFIN7xfijtF0KcAJ2z2L0LsrMNnaU5/TinHZTcnfXoxiUH8wT5TLTwQbeI2pCfbtG5XXLwYEzntmHwg1vMq5Dsbhs1LGknJXFYM89DVlfHzIqIItPIuUIZEGbRdmw5sAKnHSaycGi85LxkSPc5zey8RZWC9U8ZslVFcX9FXfsqxYQepm2SFk0i37S712+W5IgQIsAnjX/qnPs3i4u3hBAnFtXGCWB7cflt4Mw9h58G7i4uP/0al997zG0hhAZ6wP5Xe06/H+/ZXwaeA1+NOuf++OEVQoi/DYxffcAi2fx/gY/jX9TnhBD/1jn3LMdDob8lhPivFv//l9+KJ+6U9JagC1FBURpvnCSlF58TwivL1gZRZMhDN7d5jQ0k0d3jwWu10kI03spUzyqSW1NsGpDcbbzkSNFQnPLGOCprMLFGZTVyYTman+6AgLqjES4h2J4Rbk0pT3QXbR5fWZStiOTOHNMNGV1K6L9c+ME30LqdUz6ckm5Zei/45yac48QnpqAky09NEVXDqV8paLoxxWpE2VX0XyzY/ECbg0cU7kRBHNfkoxgmAU7B4PkMldfIyjA72UIYmJ8UOOEX2Pp0RVVL3xfHf6Gqrk8WshE0HUvTAjeNQDrMRGO7DUJbGAdeXK/bYLcDlr54wMZHh0zPW8KR7/UHE0e8Kxk/7JC1Y36hy+CpxV5i4f2td2cLU6sFtFkrP8A+nB0lIdP7OoSjhqKvyNctwdTPG7ILNZ2kwt3w5y7Zqdl5R+SrpymYRJJePfDueW1BsuUo1mH33QYiy+hy6O12WwZXaIpCg7a+DZcLiiEEGVgFOMHe2zThyHH613w1ND0bEkwEVsHgRcPokmL+QIXaC9AjRd6NeLFcodsqmMxjqqlXAjj7vOe1oLyfOEDd0kQHgrsfhdY8ZX6rg2sbwrChGxfc3uujlGN/kuJutkgvTUg/6RtJs3N+VmUih+zUuEYiDgJsv0aMAkTj5fKFg2jPq+Puvm1RaY4SPw9RMD0nGF+SnPvFKXr2JsbnfHNQVQL4R8Bzzrm/c89V/xb4s8DfWvz+2Xsu/1+FEH8HOIkfgn/WOWeEEFMhxPvxra4/A/x/XnVfnwL+Y+A3vtp8A77FiUMIcRr4QeBvAv/Fq64TwI8DH3uNQ98LvOScu7q47T/HD3Ce5fWHQr/neDUElzDAtEJEbY/E+4AF0sgt2Lz2KIkcVSGN9b7jiwXKRRrhvJKuaPzuzwmBmha4yCcm0woJD0pMqr273YKVbTqRv23hB646szSxQrVj5LxEVgukEVB3I+LtHDkv2Ht7F9ng21O7DSoXqHnF8lPWo6DS4Oj5y6LBBd5vvFlqoycFepQRhZLpKc2VvxDSehGCscCegSSqaFoKeTdg+FxF0/JkRj0piPcTslWJiRYw1FMljANI7LHsNiAMBBNBccLghIO2IYgb6izAJhYRWORO6FsgoUPOfdWy/YEBq09ktO9GpHdyqmFIfDdj9EiHiz9do0cl0UblE3pRH3uBL3SlDomDTkqEbaiGCcG0Qs4Kuk/tMHrXKlVPYFdKpics+vkUBGQv9xi8AFVXcPBQ5GcVifNqrlYh7JDdRzR1xw/jXWxQsUG/HFOcq4i7JZEDrS3VC10u/Oyc+SmNbBpMKCiGks6thpvfJ1n9jGD3HTA7F9KkjnRDcOJTNTf+I0Ew17TuOooVjQscpmWJA0MY+IW/HZXsBG3mmy1ufb/gwZfFEb8l2SrYfH8bnXkb1+KFHm6tIumU3L+yw5eePg+thlYvp65DWvePmN7qkgpo321QlfJoLQN7j8bUJypcpyHtFrirEaKBuufbpN2XHSb2M6nRJUm+4qhWG9RIE0x9lbb7eJulf/gm0qa6N755BL8PAn8a+LIQ4snFZf81PmH8SyHEnwduAj8G4Jx7RgjxL/FrZQP8JefcoT/1X+QYjvuLix/wiemnFoP0fTwq66vGt7ri+O+B/yvQeY3rPgxsOedefI3rjoY1i7gNvG/x9+sNhb4p4Q6TAOAW3gc2DfxAdrRIHPeyya1n4yKlRzw13sbVSXmEyrG9BJU1Xs0VvG+GtZ4H0VgvP9JYrzeVN9g0BOtlSdSkwIWaqh/SeWHkGdsOb96UBOhJgWlHzE/FpBsl2akUcTIh2TekN+dk51peRE5LRNEgqgY9K47goi5QXrZbCUw/9Uz0BUM62J4RnovpPh2y9tmMq380wVSS/Tt9RCM4+SV/u7qjibbH1MOUui3o3jRM7wMxqJDSEZzIKPPAVyitBrEdsfJUzY0/aVCbEYNnYXxJIWyEjvxiqO5GR2qrTjlc5CiXof+SQ+/PiSLvIZIPNenViuHndv15rRtsGi1UeANcrP1u+15r1TT0cF5rydYC+vv5kVR5/6k9tv7CEq2nY/SCa7jylCDZmHP3Ix2yx3LYi2CppP2FhOk7CjIiJpclNmkIRoriZI3aD7CJwoZAJSnzgPSZmNO/MsYFM9TBnLAbImvvU9+6W3L9BxMGT0PVwct3dC1OOFQhGd0XEC9Pmb5HM9n3lRkSBl9WyKYNDrYHA2YPVt6/W1tEq2bv3cssPbEHoZcuGbxQky9rOi9qppcbqCX5dspTe2dJ1uY0jaS40vMWHgcpuusY3wfzUx4+vPS04eAB5c2e2hXlZortSYpTnrlOr6bOFVZpuld9FZXsOnQWeSOnZa/W3L6RMf3Qay0Lb6L4JiQO59wneO0ZBMB3v84xfxO/WX/15Z8H3vYalxcsEs/XG9+yxCGE+CFg2zn3hBDio69xk/8E+Gevd/hrXPYNvQ1CiP8Uj1km5muL/Kv7LtzzSAuETVWjxsZLWrxW5Xav/Ii1vi2wWJzEQtBQOoecVd617/AwKfzcxDlk1WClBgkyb3yiChT6wMuPFKe7RJszpHGMHxlgNbQ2Kup2ADIkmFRUvRBV+QrocOAOgJaowhHfmeEihelGqKz2kNOi9jtywKYBMqsXKC1xLOGhJP3nZ75P7hz9KwnldkS860j2DWVP0Xl+zMHb+9g0pEkU8YFlekYRjB21jdAzgZoIOOkROjiBrLwWmFSO6L4Ju6sxcjfgzK81RDve6+LW9w2wsSK9K5mfMcQbmqblcMov/OVAU/U13ZsF80sDooOKqhsQb2eerb1IFKKqvWdH6WdFR4leOJpBSmuzolxNCXfyhX+74/I/nTJ+oEP/mTEmDZlcShAmQVbgrKB1bsL8dof5SYfLNO7yHJsHYATNiYbkxZiqb3GhhXMFp346pvv0BJiAtWT3L1Hc36KJBEHmgRN6UnDpX1bc/a4+85OOlS965NbstKJYcSRbgu7PtRl8ecLuu2Oiked6TM85lp9yTM9K5pcrkl6BXjJkWUT4XMr0PEgzZPDUAfnJFjiYnxLUHUe8pYn2YHLZ+A5erWh2YrT1jo1OAwJU4eca3Zf8x2JwxbKbCIpxhCok9vk23V1BseKoQoXu1MiNgPbdimBSI4uG2aU2TgrmlW9N3f1oB/UmI4vfG98sAuC3a3wrK44PAj8shPgBIAa6Qoj/xTn3E4sBzB/DMxVfK15vwAOvPxR6RSwgbT8J0BXDr/0W3qtmC8e6QofD1NdHMRxf9yrxQwleP+nVSadZeGlUxqOfsgobh7hQggA99bLt2Tk/vxh/YMDSlzPqlqJ9PaPuR5jIQ1+bNKboKQbPzRAOymUvo64qiyxqVGFACS9FIoVnjRc1SHHUzhGBol5Jj6TVkccJUU0LL42x0mH1d/d8MswqyvUOyVYBEjrXC2ZnU0wAqgadOcyFguSZhCZ19L5rk3K/i7uT0L6laRLY+FDEfX93wuYHeqgzjvYNweb7Je2bXXBw5uf32f7ggINHDWufFNSJ8yZFnQiZVSRb3hiqPNNn7xFNfkKS3pVEB12gSzhx9J4ZeUfEwsuLo/xsSs4LL/IYKvS0AuO8R/e215WS04L2rZB6mLD7aIyJINk1xAeO6TRgZiTD8wfEQcP+tEUxjgg7FUI4qo2Wl9KQDpUYLv53DXK+ewz7lZK9RwJWnygxkURWFmEs8ws9ko05TsDJ3zW0r4ww7Yj2bY0NJLPToSemLlpu40veNVEAm3+kJtgMEYUin8RQSIgtxZrh/M80FEteCDPZyJifbZFuOOqpoBzC9ILl5G/B+II/h+FcUPUt+qak7At6L0DVB7knWHp6xub72wgLJz5Zc+u7NabjPelZwJXVRNN+KqB/tSaYNehJAXVDuhEgS/97ejZidlqy9v9+czj9vV58LavoN3J8yxKHc+6vA38dYFFx/DXn3E8srv4e4Hnn3O3XPprPAZeFEBeAO/ie259cXPd6Q6Hf6xM+Nsy5NxZDVKQAeXy97aVHswGRLxbbe+xZga+8r0MbTyE8TLf0bm62HWO1xMYKqwQm0ZhIEkwb6o6me90T/pLt8sgfWlYWVVom5yKiiaVph4S7c6Jdr3ml5jXCOcKduVfB1ZKmEyEzzwBHSV8hOYec5ISL4al/cccVh+dzeJn0aq3N+HxEORSEY8fKp+dee0AI0q2SYsnLjoweUIhbMfVjM5Ry3L22jOzU2F6D2Q4wCRSnK6785yE6nOPuJv41lfgWTUuw+dEhZR86LymayKFqyC500ZlB73uuAEpy8+MhpuO9SZyE8WUwkcOlBoTnxgye2gdjj1qJAKJu0AdeLwrAdEOqk12cEMxPBKh6IXrYAlXAze9VBFOINxT5Gcdo3GJlOGWlO+NOqUnjiuk8RpWC8HRG71916H9x5D8Lh2ZTWnH34ytUXYesLAeXQ7o3G8qVmJ3HNc13dNFzmJ7WhKM2k3MRxZIgnDiEgdaOYXZ/j9lpgVOLGVLLEt0KKdcbVKvBFIr0tubEpwp23h6y9R5F03Zsva/DiU86gqkh2SrZem9KMAFVSmanYHaxofuiJlv39q7ZuufV1B1B1QEbOA4ebGE1rH2+YPfR2G8QjMJ0DHXPgHCk12LKAehZjcxqX8GKgN1HE2woKJb8+az6b95FFfhGeBxvyPiDQsL9CV7VphJCnMTDbn9gQUL5z4BfxsNx/0fn3DOLm77mUOj3ErMffz/d50c0gwSrJDaUhKMSNc6PfZ7rBXRTKeaXh5Q9SbpVIxtHsJBTr9c7BPu5F8t7tWfHvUnFOURZL1znlFeSDZXffWoBxvlKwTqi0hwNd9U4x8UB8XbOwcMdqrZA5w7hHHpWMb/YJZgaTCxxgUSPSmwnQk0K5KwkqA99PPQr5DRsJ/btm4UCL+oQerzwDClrrAophgHRxIKQDJ7LjvzQ9aTg7ncNaN+1zE8rwomHi5bTCDtWyPUSWyrQXm6jf8WS74cIA+UQulct8xPQuWWZr0uqgUPPBPmpBnVVM70AS0/7b2E50OB67D0SYZKUeBfmbXDaMT/fEO4o0vvHTPZa7D/kkT2jy8tc+OdbXo+rOTwHvp3nUi9B7xY/u49HdK8bTOhlzKN9mNzvj2lasPQSVI9UmFnArmzTSktWlyZs3hwiEkNQQfrzXfpf2vMzl7I6gv82A0+UqweWO9+Z4N4+hahi+vwQncHa5y3ZiiSYO5pEEeQOsQODpycI59h/rLd4HguZD+HVZ52CoFsSxzX1lQHn/vU2Gx9fxYRg3znFNZL2Z1t0n93FJgE3frDHqd8uqDua2QnF5D546O+P2PzIErL2UO3WHTwoYF1QLhn0XCJrR/9ly97DMXUboreNyF7ugXLoqKHZjZldNLRuKPYfSehd04zPB4zvd5z+jYbxxYAzv5qx9b7UQ8Df5PGWA+DvMZxzv4lHPx3+/+de4zZ3gR+45/9fwAtxvfp2e7zOUOg/NLovjJlf6NLEkjr1pjdqRWPiFstPzqCxCGsXMughxUBR9QTTsxHhGNqbAfFOhZ7VngwIx7IVhwge5Y4WYsDLiEQhomzQeXVk+3qE0FqYIrk08velFlpVQiAaS//KnJd/LMVqGH5ZUSctgswSTCr01PdXZVlDUeGUwkWBn6cckhUDTdOLMYm3cZVF7RfWw+d46FTY8Z4Kcl7Sfb6mWmlRtSPfgtAKGm82ZbXfoa8+UWISbwN7cH/E6AGH2IwIFtDT9i2LiQRVz+9o411H/4UZ6VbkJd8DqDv+NkjH/LQlOpDsPySId3x/PNksyU45RA3Vit/pisiiQoNcMkwnHvpZLRmSOwpVwd77VuncKgm3pou2lT+PTRKgci9FL+qG1bpD3dWMLwZMH6nQuwGiV+EOfKJpfmyPM3HJTtwm1A3jccp0kiASQ/vJmOELNcmdudf3aox//6qGZpCy/c4UG0L7ZcXssRJVK6rPL2FOG1a+IJicVQyf9yx1nRvCSc3kQsyt7+vhNKw+UVO3Pe+D0CIKRbVeI2caGsV0M0Z1/Ps3O+145ANXeeq5c+huxfTxkufu7zP4kiJ45wHy10KSzZr0Rs3ykwEHjw8ZPl/iZEyxDJ2blt23C5p+TbCvsYFDGtj4DokZVqT9nNmdLq7XEN8OKc9b4i1FuWRpUki3YOfxEBODU57AKivH/iMJVc9x/r95k6Kp7o23Ko43d+Qn2+jcMjvhpT3KvheAi0aOg4faVF1P+spXBNkpgwsNS08oZOrhu6NLiqQbYwNYfnLmKw5xnABEY45lLO6JI9YyvBKhdRjWel+Qw3kLIBb3qbKS+38yo1lqeY6CEJSnekwvtOg9c3DkU2HbkYf2KoFphTglkULQ9BPUuEAfZNg09FyU8lWoMQDj/IzAOjAN4caEQebFDZ0QNCttstWQYOa9toNxgbARJlZ0bjesfKHw/txJ4L0gTreQ+5ay5xfitU+OPIFyLaHsC2wIohGYnvEQ3NDihMRGXgepteWZ3E3icD3rfSO6JU2pcUDzcptkLMjONRBYsguWcEszfL6hbmvCu97RUeTeSyTYMbhIgxIcPLZM0Zc4BfmaQ440zUqF3IlgWGMcjKcpK605/TSnMoq0XTK/2UVWYvH6a3/fWvn3L6+oVzsI61j/9JTZ2ZS7H4PeYE5eBmRnDKIShBPD7KREz5sj8ujm+1OaFIpVQ7SnOHggoIk9877zpYCdDzTeB2PZq9V2n9Oc+sUtwCsLF0bz5z7wCV7OlvnEZx9GSIcqQf9vfVQ2Jj/VIr7r9cZ6L80xsSbet6TbjskF7z0utzU28nybnXeAOJUjraC81sEt1UhtKU5XqJ2Qpu21suoHCvJpSjBzJDsweN6R3s0RNkbWjv33volX1HvireH4H5JYeqZg44N+99y0HVUPOjcgGlnmJyXZKYNaLmlKxehBSf9Zf1x2ygKSZNsxP5PSGS/sVA/bXHBcPSzc3A79NYBj/serI9BezpxjWRNR1Ec7faRAj/Kj+zCJxIQeqisa51tSxnm9rNCz2OO7U2gMemfK+O0r9J7cOfKMOLrfQ06Kc8jsGA12+Pxl5mXeTS9GNt6pzkk/kM9PtpCVJbk5xrYixGJ2ImcltpuQbGQ07ZDBlZr5Cc32+/tH9z++7LCDiiBucLXCWlCdmrqS4CCawPSMom5D65agWPUcj1pExHcC6paj6RnUxRyRB6SdkvlOitOw91DAmV/YPVYAOEzs2hM4y5NddO4IAm9tKs7OUddaqLih7gniVrU4JYIXbqwTxA1xUjHbaaEaL7VhFQT7mQc+zEtfVS5cGKu2xGro3qiQA0P29IDwQOD6nqeBMJz8bU/KHD3Ypns1R1ZQnbSIQUWTx+hMsPJUzc3vVchGIlsNSlmCsCHfaDN8wb8vt35knQs/M8H+wjL/+P9wgv7SjKVL++ze7bH/mKDzssS0QpIbU8/fyfx7pA9yBntzrv/oMrqAumdxw8oT/WYKVUjcRkw4kdiHZ9jtBJsaUJ60YAPQE4WbJUwfLaFQLD2hqP7sAVdu91EzyeV/PCLcHHxzvrDfzuF4bSTmmyT+0CeO8Z96PyYUpLuGfEnTvWbZfYcgmAjfj25B/6WScKoJJop8NUWGXiNI1Y79h8FGlrojEEaQ7ljMsIXMa4SUfng+LzzJLw3ITsTeKlWAnpRHC7uTCzLeJD9+co3x1YoQqNzrSgF+0BsGCGMYPzKg95RH7aRX9ijfs3rEFhblgtPQWKQQxHeniLLBRRqnFL2ndhk/vkzrdoGall4pFo4/8PcKOgoBjcUmgWe1zxfw3cPEFkCdCsq+Jpg6iqUhVgv6V+ZHHuhyVlAvtQj2M1QaUiwpiqGkfdeSL0lsy5PJyjIAB4MTExormS8LbK7Z/Ahc/Jc10caU4nSXrXeH4AQ6kwyfr7j+xwSiklQ7KXoiaeqI2HhV2d41L3VPYxHGHBMEqxqz1CZf9sKQ5UCQr3oobD1sGLQK5tJhrSAIDNkkhkYSxTX5833iXBDvgg1h+emS4mSHaDf3xM52jEk1xUDSpAJhHOF+zv1/00A9Jb8woOpp2jfnR5poNlRYLdh6b4ulZysGV+DGnxI0yzXqdsjspEYsFdTzGHYiWM/JJjHRniK5eYBrxcwvGG60ulQ9i94WdE6VbO579rfKvRDjwQMJK787Q5QVZtA69qAPNZ1bjp13Ww8wqCQ6bWiMIL2mmF60lKuG4KUW7kQNxvusJ1uSqueIdwSTRyuoJcFY0SQC89vLnH+qYnJOMb/U5fz/7Q9Bm4q3Zhxv6tCFI5r4XbMHX8P6pwx1SxLvL3y/JyUmlMQjyfCFkvGFyLcQlgS9Fx2zc4pkS7D0bEE5CKj6EaIbEt2dUS4n6CSgXIrQuSHZ9lyJchDQxIogazCBRJULtvk9Ut6vhgCLoj4erlfeN7x7ZXKsXttv0blRkJ/voyqLHpVej0mIo52/CzSmE6P35zTDFr2ndrGdmM2PDDnx73f9PGbho34k4b44TtQNwpjjqsk5RNUweOqA9jDFJIr49uQVO62mn/rWnZK4KDhixZtY075TMTkfUwwlK0/mFMsJRd4hPTehCRTjScqplRGRNhyIFqZUbLw/4vy/GjFfCzj3M76CGD/cZ+PPlwTGL+gAM91Cd2rMXkQwlnReGLP/jgG9qzn6IMNFgW/hVTWzsymTC5Jy6Ohc9Z4W+lpK+sCENKw52O5AIzGdmiCpMYGiyENM4ohGgnwVdAbhnlfeFY2l6cbocU6xEhONLUtfmntpk0AhckN5us/sVEDdFnReNBD4+VWxEtG5WdHalOw9EjI7Y3EzS7ylGT/U0P+yxtYSu14TtirqnYRgKulcd0daW8ltRXapovVSSHbScOe5NezC61vPBHoGK5/yYo/Ncofx5ZTBM5bbH+9x4hOZF8lsG2RgUNrS1AqMIF/zjHM196ZMeRFStxzhRNCkUK3XNC1Nci1k7Yma6Sno3KlRpaHq+aUm3jmuYN/M8RaP400eqvSmTU2iwIHOLcHUIBt3pC1VLifYUBKNDPlKSLJvSJ4tEI1l6/1d+i9Yj3p5ICKYe90oWVlMx4sKHiaNw2FZ01KowhKOKqph6LWlWl4zybRCL5t+2MYSHmXlpbb98NwsBtaHVrMYB31PcpSVIbmVYdoRxbqX0o72CoTxi74LNbJs2PjYCr3rNXpfYJKAE7+553fkzh3ZnIrGkl8YEG/6RQ844nFg3UIN2L+oYGtC9vgy0Zby7Y/cQzH1OF8saCGyqJjfNyC9NsHEivjulO71kMGTnm+x/KWQYiCpr/ehD25g2X3hBO2bDve4Iyi8A97tH1qjWHb0X4xR45xg5s9VK/WL0nSW0FrJSKOK/IspxYrj1g8NPfx0XND0E/TOFNtOyC/2mJyX1B1vODR+AIKZoD5dUu+lFHkIynmPCeEIw4b5doJrL2YoZxwyF5z/Ge9PUi23kJUhX49oN5a6JQmnFlE0VKstLw9jwUS+/RbvW2yskbWhGsbkS4qD+zXCwuyspXVbkp3wwoYox+QDOdG1mHLV0IxTHvzJPTa+e4W6A7d+cIXTv7RPkzrUWFO/Y4Z2UM9C0qsh/ZcsyU6BntWM3zakSSTCOqqu4M7HegQzuPYXBSvDHfRBB2sFdRZ4YEdg0VONqBXmYsFIRwQTkDVem2tFcPKXFdmaJNmxyNpS9QR5oZieC1AltO9Yrv1IysU3N33DxyGi8k0af6gTx+zH30848dVFMDXUHS9mmK8EpNsV+bLXOdKF9RpRqSSYGaL9kqYVYEL/JUl2a4qhprXliA48f0LUlmoQUvYVqvI7bGEhvZ2R3CkX/hmBn0UEnpRn2hFVPyS5O6deStGTEhtq7wehJTZeSKEsbh9MK6YXWqjKEu94W1iZVV5SA6jbktadgmItQc8NsjRMLiYI6+c5wajAtiOm52IGkwKTBjjlFxMTKXYf90J+3eua7tW518aa+cXZRdonVilp+jF6WtK6W1KupGRrAZ2bpScyNjU7H15j+bMHYB2tF/aw3cTDjYWgfafyXJDKsvuY50qUQ8fJ32mYndKsfO4AUdQsPRkgqoadDyyz/PkDfw4WzyXeyRHPdNHv2WWaRbidiPCFmP0P59izhgs/03D7oyEnfzvz0OXdGXe/f52qC+d+7oC7H+wjL8wRRmJrSeNC5HaEO1FgZ168j3ZDMwuYjUJIDUmnJDcxrpYEE8X+Y13qtq8656diui/PsJEm3m8It+aeU7PnWfHVSot8RVMsCVQpaB5OCTJHfOA3FzaAJvUCkU5CdCCZnzbcf2mDl7eWKdcb0usBZ355THaxT/+lmslZTee2JT/XoX0LshMSnmtTrDcII+hdtdSpIHs4pm7HdG5akr2G7bd7zsr8lFdJto1g74urSAOm60hPT8n2U0SmKE4Yol1FlStOfcIxPi8plxx1V3j+yRlF+65FWMfu2yJWv1iw90hM8XBO+mTCxkcs9//Fz/6BfNf/IOKtiuNNGtGooW55L4R439CkkiYWqMqRrYbEBw3ZiqboK4LQY/HrVKJzTbiXs/3eHr3rNflyQBML4pEhWwuJRg0i8NDRcOqJeliHND5JGCVxWhDtFWAcTS9Czyps5Lkc5WqKVQKrYoSDqh8SZA1WSVTu5dLrXgwCWneKY+hsIKnX2z6h3Z2RbpSYWGNCyehiQLxviSbWuxDmDTZUiNoyeGbiNauEQJYNpuWd9PTcAcLLfIQKUUqvBSMEoqjZ/cAqS0+OKJe9gVTZ16RbJemWb1k0LU20K1n60vT4pAcaOS0IF7pd4d0aM0i59R8N6V+xzE9JVp50xJsZ6cuFr7ikOIIKr/yu9wcXjWXngyvsvr8BKxClpX5uiZUvOJK9hlsfC1DXEpSFvUciLvzMmPxEi/1HUlS50IMCXvwzPfrPwkGc4vo1rlC4boOcaOSdGLtSe82yWnqUV9sgtEVrg9AOV/ld9+yM4PSvzzGpRjYOjEMfZGghsGlI1T1kflus9g5/TnlWtg28pevkvD/vqgSNoHUbDt5XIuaak/ftcHfS9S2QSnoHRiBb1rQ3agYvltz+aEz7pmTp6Yx4lLD3NoHMJcmWJFuBtc/N2H5Xm3DsfeCjhXizMF7NVmdQlRHVek3QqlBWUr/YRZ4qECMvixPtQTgKyZccya5DlZ6+3rvW0KQezh7OnAca7GSM7w9wuWZ2n6/Q/lDFW4njzRk2EKjSYiJF2ff+zlSemW0XZ2bwzITZhQ6qtMQbGQdv6zI/EZJKQed2Q5NIolGDbCui/RrV0qjKK9iGo4qm7X2nbSRRs4Ym8YS/YNZgEs9kVkVD0wnJVheaU3i586oboCrn5UNKg5Ce9OWUPHru4P9XReMXrcVtq+UUJ8AGkqojCafeUa7sSYQNCZRENBbTClDz+kgePj+ZoDOLk4Le1cqr3i68PcQi8VVLKdHmFARUSynB1HgW9I3GL5iNAdcg82PTKZQAK73Y4qKMd0rRDBKEdeTnanQRoAroPbnzSpSXxfNbDst/rdj+wBLz75uhakWvkzF6eYhpG/rPTLBpyH3/64z8dAcnBdF+6WdNmSHdhp3HNcWZGmpBeKDI1r0EvHMgkga1FeGkI94VzNccy6fHjKYJsudQyiI/1SN7pyV5IcJpOPOrE7be1/Uot3mNnlZ+trTg5Yzub7H3mKD3ok8y+ZpPHE0K5YkaNdac+JRlcp8g2ZRIA9mqRWeSwdKMWRJRGcXsIOW+/8mgp1OqYUI50LQ2aw4uh+jcQ22TfcvO21uc+Pc79L6M1yWrDU5KilNtZmc86379M4Y7H9a07kK+6hOAifx8b3I2ZPiC4+ofl7Ba03ky8RL4tZcpccoht/3tg6m/v/0HNd2bFtk4dG4RVrLzvgGydjijsL0GGb4GcvBNHG9VHG/SEI3DBhJhHKrwi3ATS5zw+kvjSwnZSpdoYjGxpB7GRBPPKnZK+BbX3FJ1FXVLoqqA9MaEcr1NvFtQd0M/64gV4b6XJdGNwZWWpqWxgaBJFh7dlSPZqTm4PyScucWuWKALsIHChAnxbnUkhqglWCWp2wvIrQqOZieHkuxI740grCNbldgAWhseOtukCll7vSvSABMrZOXnPFb54w4XaxtK5mcjkl3vKBhMK+qVFoNnZ5RLMbOTGlWCrC3lcsh8zRMkTQjLTze0rk09ckxLsLDxPR6O2b3ZYLVg/wHF0mcdq5/cPe4L3zvjcc4r+ArB9R9bIz9b+zbZ1BtbjUwLWQnO/1vPRVELPkowqVHzGlnWmESjs5pigZ5KbgTk52pM5Fj7rOHuj9eYTKP2tSdP1oKT33eTWwd9dm4NSJYzio0W0Z6ifEdO8ELK8Hnvz16sJNRtmFyIiQ8M4ahGTAtQkqafIBsYPg1mAYoLR44m9cKBgy9qJhfh9vc4grFk+ema0X0BLnCUQ4fLIx47dZcvPHmJpSclwe6Y8duGhFNL2ZUUfUnvek0xULRvw+iy4vQv+7YgyjsZ1kstgr2554jYgHRTeM4SkJ10RLuCqudo2o7b3y1xyjC5P2Dlk96PJN5zzE55uHHrjqPqeekQHIRj6F0v0EWE1TD40ginFKJJmJ4OCPcl6t0j8pe7XPpr9zqkvsnD4WePb9L4Q504wl/2H+TRn/4AVvsWlQk8czw7EaEqh2wc4UGFSfXCvtW3s4KZpWpL0o2SJomIR4Z4K6cZJH5hbgfoee1lQqKAuh/TpIpg3mC1T051S3oL1y1LdFAzPxESzH17a3zRe3tbJYhnDTgohwHh2LeS/BxCIms/wPe6U1B1NcHc4KRAzxua1OtfpVsOVTl04VslwgosPok45ZC1ZfN9Ea0Nr4uEgPElwdrnG4TzLS4nvXObUx5aWg0iLyFy1aPOVG1xuaD/ovFJMVWkt+aYNMRpSb4WUnYks3PWq96uKdp3DaqGzq2appeg92ZH709+YUC0myOqhps/NPSLW9cnBZcYonZJuZtgS8nFnytwWngk2KKtFWxPjxj5dUex/1CEiTyBzmnoPxlQ9mH3UYmZgEgMzUrtDaQmAS996TSiEdz3bwte/o/bXPo3JVZLxndiVOkoe16GY//hgMGLBlk7or0SdZBh0wiZlcja0n15RtWPAF8BOiWYn/AEu2js6L0kqLp6YXClvNbTWCIrQb4f88T0PMFygdMpLlAEM8voYkDvugcg3PwBiSzApIaVz6hj7xEAKTGxQgxSbKhINwTZCUe93BDsauJdz+KXRqAzqE9VOCMgU8xOS1QF4/t8pTR43tAkkmTHE/uchKonGF/w56NzwyPmbKrQhWF2NkQ2UH+pT/oVdm1v/nir4niTR/+nXokr3/vzH6BqS8K5I5z6IbcN/GJpQkE0seQrmiB3ZCdjyq7ngZTLMTozRDsZ5XJCsFcdOfT9/9t782DJrvu+7/M7d+/97cu8WYEZbCQIEQsBSoy4yZQpanFkpeRElv7QEjuWS0nsyGKcilOOVaJccSxbZdmhKUaKtVASJVqyRJESKUEbARAESEBYBxhg9re/3rvvek7+OD2DwWBAYIAZzgzmfqq6Xvftvt3n3dfvnnt+y/ercvstOvNPtn4v1I4rvIHBG9ikaNIUaqcLq3wrMJ4Tpp7LSesOqrAndO0rnKQgj1zyigJRuGPNeMG3uln9nKzhErSzsyf4ykaOcV8yqXJHuTWDSjTGsWGTwlfMPJ2jMoMz1uQVh7BtTw7asaKK2rcrlNGCT9AtOP3NLktfynHGOSoW0pYPAoUreL3c+n07gso1Gti5ySFrGGYeE+rHY9x+gvEcdp0uyBvBWRXh9ffN03o+5cQHHVrP1Jl6LsYdgbq7S/Jsk2wmR3Vc1As+Cy9q8org7dhy26nNEShFvLuJ304Y7KngjjSbd7gYZa1sVQKqgKwGed2ABrfr4Gw6pC1N5bRvmz3HQvW0cPo9EdWTMFyyVXJG7OQerceMF0NaR3KCdkYeOgx3RTT6iT15KzlbrOANMrKaDYdqgTyy6gTjWas3Vjul6e9RbNwlBNsQDoX+benZ8I77ZJWFP98km62RTDk0judsvNMjXigwylC0CrwNjzw654sswuDGJu5Y0zlYwRvbpta8apDY6nhlDcEbWt8MlQrZwAXHYHxNPG+7+N2xIGPYuEuhcpj/iqb2Qo/Nu1rMPh7bixcBd2doP1cbtm8NWfpSyvEPuYSbthv/uqOsqrq+mPnFVzYo9b//XqTAxmyFicCgvXKtnczxRjlGhCJQuBP7TB16tA/5tikwEHp7lTUw+k6h9YRL0rKyHaM5l6Bnyy616zJeNLgDGwYYLDmoAryh/ewzyrVGbNlvESnSmoPK7Qm/CB3cMyuOfoqp+7jjgs4NAdWNAndkn1OFsa8d2xWQ189IXet3rn2FcW3SdDzjUD+ZIYXBHWUkMx7e0IaRFh8q0IGQtgK8XopxbdNksJOgA4d40brnbb7DXpEvPpSwfndA67C9Ah+s1Gm8MLYn1u0RkuSc/MgCYmDtXQHaL+jdILRvC3j7O4/w5AMHkH1Dqn6BerpJ82hBuJ2STHmgNWG74OSHp89Klo8XI4J2TrDaY/8Lhu7tM/T3OIRbhv4+6wuiUuuNrlKhCKF21MEbGtyhQ9qA2qmCdtWhcbRguOigPfC7hnAnRWU2LFh7rkOyWGe45DGeFcLtKv7m0IZstKaouEhucBJNMmPl7SWHvGZP2FlN6Dahsgp+24or1k7Z16ZLIAOH3X/UB6VImx5JQ0gaVkzSOA55ZAgODRl3XevI2Ixwu2OGB5rkkbJhKQ2D3bbsWAzoRo4phCRw8A87eKkhj4Rw1cG4Vg03qxuMZ9CZMF4uQAvhKcX6PYrKqsfMk0PyikceOazdG2BUhbnHcoYLDv194CQeez+XsXOz/5aXUL8Q5YqjhPqnHnzZ4yq2nLd6KrYn2on4oJcWjJcqaFco/Ijm0ZzBkks8K3gDGOxy2f25Ar8bk0e2/Ld9yCWrO9ROGLyBxjiKcMfYOKlg8w6unF1xgM1bjGdd3MTg94uz1ledG1zqpwqc2AA+Tmz9EmqrOe64QHuKIrDhNr+vySqu7SeZspLoKDl7dSi5obqeozKbcDeOLVs2Yv8pnNiQV60vRDITYBxI6g5O6uHvxPgbNuk+HdRxUk2wNWbmKYdkJsDfScmqoe3Or7uozIfQwx0ZOvemtKYHFLFPFGQEXs5jR3ZDq8A5WWHsglowrHyuTbJUx4kNJvDII9u9nEy5uGMbkisiRdGMUKMUd2yormqahwcYp07Y0eSBMFpQZ5V6K+sab2TYuFMRbgtiDK3nC+KWmuhoQdjR7Nwc4iRQ2chZ/5YZsrqQVScVUcNJJZarILHJ/TxySJvupPjBELY1eSwM9tgVUFHVjHYpiorG7SmKewfsmery/IsL3PSJLpIVDG6aYu1dztkyXW9gPTCKWgHPNXAK2L41pLpeEAYOw3mHnXdoRFt1g7P2aMogIxfjatyuItrSjGdtCGq0IDh9GC3bFYgUQt4oULFCZcJ4XlPUC3oHKvg9TdJUtpR429C52XD6WxyksMehv1eonZazFWzXFaWsesmrUftNO5mcWYXL3W9H0pwASKd8Ct9WEAU9jTe2/7XR5hmPAnvyzUOhuqbtP1tqJdKDtpDWhaBjT36qsFeJqjBoTygCF9EGN7HCf0YJKrdJ7KnnrJzJ5u0ejWMKb+QQbqY4IhS+rZBKWiGV0zF51ZaOGlcRro9AQzYV4qb6rNBeEdiJKmvYSagIFP0Vl9aRFARUosmrDtu3uvhdiLY1wdqA498xTWXNsHO7IdxQOKlDrd7AHWu6ez2cJZfqekE65bN+l0fQ8Rgt2k5sL8qo+HbCyH57ntZjAypNQ7jafUVjldexJbvpdERlNeGGp0a075imt8cjr8Lez+xQ1ANrr7vkUHhCemedvCKAonsjgKH1LICwfbtdeSw8aBgtCnmoGM1PQofzBq8vrN+j2HV/Tn+Py+Y7vIkwpkGw0utp04epAL+d2DLmwlYZubG2pd1j+3f0+5r6i4rOzfZ3yWsF/o5VvM12Ik4/0mDpRU1RD20xhTspjcbK1qczOW7PQQ0dKmtC9bSmdnLMyfdVqJ1UDFcEmhniaRh6Z/W8nBi7ujrhUPiQNuzKebRgu8PDbaHwDSoTwm1h7ClYTMh7HsGGS2XV2u7G0w6tw1YupXNjFUSjfZsPMbkQtK1BlDrPluZ6QOClIpW3IOXEcQkxD//12UVCMNmW/M270a5QOxFTBA6q0JhC0K6tqPIGGj/XDHb5kytCO1FU1zXuUJNXFWlVEfQ02rWTRFax2ije2OZH0rpzNoeS1h1qJ2OWv2T7PpIpl3jOel/ELYfaKUPQyclrHirTZDWrCKy90PYQZNpKoBib+7AJWxeVTrrrgelnY7SjUEnBzu0VaqsFSw/GtgS5l3Hk+6fIZjMq6y7NZycnOw+Gi4rRkqB9za77NZ0bXPuZqXUNrKwJvYMFkjmcOjrL8hcF17dGU0wFZ4Udje+eVaBVoxRdDWzYKCuIVyY5pQzmvpqz9q3TJFNWvl371oCqd0CINq3cTBFBuKHYen9MWE3JOyFO26N9C0RrsPoB6wcfrrmYlRjzbITKJpO4Y/sv4iVbmBCuucw8WaADRbg2Omv0ZYw1RFKZobKZE0+7YKC3164e/A64A4e0ZfA7Qr6voNkY0clauGOH6qqgMs3OTT7FQmxLsnOF9F3csTD9iCGpG8ZzCu2F5DXDzm2TIoDYoRjbzysCQ7Rpiz9QhvGc1VxLJ3mOPILqaaF/Q4HkgpPAeFEjhVB9MMIbGpsXiiBpCPNf7qJ9B7c9Io+q1I45DPYVFK0C2fIIdqy/+K6fvf7CVMArnT/fQpQTx2Um+MOHz04iZw529sE7KSIHxIag8oY3cfazcthubMNURWTFD/2BbSJ0kokKbWZluI0DfjuhCCK7kpixHe9GCYVnq2ncWJ9dMgf9wkpM1FwKXwCHoG1LYo0reN1skvcoSKZ83FFB4QpZRZEsC60j2dkci5MUOKOM2cdHNmZvgMihCOzkEp7yiGesOVO0keJvj1h7zxTFYsKNH7d2qeOZKu1boHqwQ3sQYto+xjPQ9Zn5mmI8i10Z3D1LZSMnWWkSnOySt0K8OIO8YHhoBtHG9mu0DW4/o5Zpgq7LcNE5O4n09wPGMFyBcMuG2eJpReUUDPcX0PEZ54qDv5hx+lt9pp/JOfERjSQOjWccBveNJleR0DgC8ZT9a6p3dHGO1immcqJNQx4KiGK4p0b9mR2Ma73l42mXtCGM54XKabuaMR6Em1AIFKH1FfeGhjBKybWylWMdRX8loPHC2ObUElsy7Qwc3KENc8UtQftC95YCCgiWhjSjhK0TLYKpmGQnwhk4+F17jEaLgjuwK6sihNpJg9+zFXdJwyGrOARtmH42sT1H3ZS05eMkmp1brKyOaMibAWqcYyKfxYdSens9gk3bp5RNafLIoXdIM/MN+l+7qihDVSWXGu8Lj+Cd8zj58N0YsZVThS+4Y1v6qjJD2rAnP+2JfS62oZqsqgi3M4qKizO2ISSV2BNoUbHhJZXZqintCSo1uAObC7HdycY2deWGtGlXFFnDwxvmdrLqZqRNj52brZRF60hG2nBwRzZ57vVzsqnQuhd6Nv9ypidl72fHthM+dFGjlOH+Bv6Wwe8a9v+ysHNLRPcQoKF+qE33eJP5B21z4sxTNvfjxhleOyZvBkimcTtjjO9SNCO8VdvynM/UqL7YRYcu8WKFInRwRjlpw8WJNe5YMdwlJNMaXGN9L7qK0aJGO7bSxzjg9hXsHZG1A5LZgMWHYvz1Abv+cIrCF4rAsPwbPknDIejlOIkhryhqawW9v2giAWRjj6xqCyYqL4yJF4KJZHmKjjy8kaZ9s4vfs656yja8U4TgjG3fSFaFlc+1eebtDWpLAyrP+cw+vA3GsPmuGdyxofG0hxFoHi3o3KjweobRsqBdWwklfkF6skp+pI6zX6O7NfyJ7Ls3MGeNyqJJqbcOoHfAXpCE2y71Y5qlB1M7OY1ztu+usvzFHmnLx7hC84UM7dsVc+eGkPGcoDKbG1r4iy16t07b/NC6Q9jWmCPqG/8PdlVQalWVXGaCz760KjlD8uG7UakGbFXVmVxG4dtSUndYWN9ybctJ80nHumjbr2FEyBoOKnup3Fe0Iau7hNsZ2pnIeE+Uee19IQ+t9IhxrNZVZcOgMnui1I4QTzl4I4NK7SThjHLSxWBSWmrLdX3BVhIVhnw2ZPtWl+oxxfRX24gxeItTFPMZZuTQ7VZQiZC0FMMVw8LDGV57bFV5HUUeVQnbfXq3TpHUbYLaG1SprGe4Ixs8l8w23Q2XA9K9toFyNGctVZPZgspJx5bXxkIyWyDNlFgCnKURWit06iCrEV4sGKfAa8foaoA70oTbBfGshxNrQg293S5BVzNaOGPxag2/nFSorGkKX+jvj6idSMibEd56DxULlVMjlschm7fbkKSTQtYw1I9D0C1Y/WaHqSdh6+4p/Dboky3qJ6xQ5nBXxMwTA4wjxHMhaU1ReIIztt4rc1/L2XinzXXoTBCB/j6NyoTKaaFxLKe/yyVt2vCj37W+39kMZA1s8YVvSOt2It18R8DcYwliDLN/bS0B3LiwzZQTteTV905T2dBsLxu8niBakc3X2LrdIdy2zY6D3Yrlf3l9hqmgrKp6U4iIA3wFOGWM+chk2z8EfhzIgT8wxvzkefvcBPzGOZsOAP+7MebnROT/AH4U2Jw8979ObGbfUgSffRiA2rkbv/Memxfp5mR152XJtyI4U16qz4aZ8kBR3YonOQzbeMbkvnaEtOngDTXas5OIE2uSKZswlwKCTkHStJ4K2lHUT6T09vmTSrCA+UcSxLHhrrThMv3kmI27K7SeN0hucFMrQZFXDKfe38Lv2mT+eH7yeSMFw4D9vzu2J6XUljSLMcS7mxSRQ9JUjOam6O0XUDD1jKbwhP5un7mHbEe6ldQQ4inFeE4YIiQzGicRvI5idGts/TXqII5hz+IOp7wmxZrVp3LXfJyRsPuLI9zOmGSxhr85JNyCZDrA7+SM5z3bpf1ihhsX+AOX/m4X40IRCtNPpbijwqrfTq40t2+vM9u3Qozat5LzKxsup7+1xWCvJtxUdG80SOEChvatVs6jmMqResLGUoXe92jmftmQzIQEW2NUqqmsFyTTLs0XczbudOndqNC1HEmU1ZOa9E04ic1HdPe5OImheto2darc/g2c2E4g2rX9GqKFrTvAb0M84xHf6FM/npMcqFN/po0OPHToEc9HNF/MGSw7NJ4Xhrus+dWxbw8JJpNG1rTWvtc15YrjTfETwNNAA0BE3gd8N3C7MSYRkfnzdzDGPAvcMXm9A5wCPnPOS/61Meb/uszjvuoI/8tLyqI+EH/kHqvUW3FJ68r2b7iCcSFrOITtHJVrxoshKjG4sV2h5BWbO/EGGuPaCQOxP11f8PoF/RWftKaorlq9Kq+f0TkY4Y5tuGPmqRztW29x7QqDXQ7tmyo0XtQ448KW2BYGJy7Y+9nUmjkp2Hlbg6xmiA4HqNyWt3o7I/JmhEpzxBjyVsRw2ad9K2djxXmjIFx32H67EK1bK1/jWrkYE9jAn8ohbdkKNOMZ8pkUcQym76HqGZ5XkPZ9Tm03yUY+YVshWwFB2ybni8DBze3vg1JWMiYzjOc9solNcF5V+N0UlTvEs5BVDcGOMFzyCNuK7oGI+omCwbKD1zds39HEH2g6Nzok0xX89sTWNigYNYTGkz79O2Iq9QRjIIl9blle55knd9N4zmH5FxKMkyNphq6GOImteCs8j7SmiJdynEaKWgvRUxnS82BSqqt9u1LNGpAXQudW20xYRIqpJzlbAYWx4SYEvJ4NZQ2WFbVTBYNll8p2Yd0cs4K84lE50ad7c5M8tKGuyprNzYwWrR4a8JIf/PWKeWtXVV3WAKSIrADfAXzinM1/H/iYMSYBMMZsvMbbfAA4Yow5dnlGee0S/v6XkS89hveFR2j+yoN4X3gESbWNXbtiDXRaAUE7owgVKrU5D3ekyUOxXe5bqfVlKKxPSFpT5JFD2Cnwh5r2oQDtCuOFwIaockNlu0AKQzzlkEx5DFZ8Rot2NeGNrASKN7TyFPGMR+dQhaLqgVJMP9Fjzx/FTD1b4HdtnsX4Lm5vovKrNZvfVGG4bFdQTmI1nSiEtKVRiZXIcFLb/GY8h7weEM/5xLOC37V9B85Mghfm6KGtKHKPheSpY1VvD1epP2lDRmlL4/cM3tjgDlJM4OL17KWyM8pJpl28gSbsaMK2of5Ml/V7qmy+w6MIbRUUBiqbOe6woHpaTxSUDY3jKa3nxiR1azucz2Yk0za0KEMXSRx6N+WoTZ9KkBKPfQ4tr/PM8UVULMw8mZBPV8FVGN8jWajgdq1cfWUjY/NdBaqa4fs5wY5Chi5+R9n+jrqVZh8ta+LFgnjOVkdJIWjPMJ4XxnPCeNEe36wx8bQvIOjC1HMZWVXhxgbtCod/oMp4VxVnlKEDl2grQxWQTBnSlm1iDDcVedUQL9kJ49Dfu34k1C+IeR2314GIfFJENkTkiXO2TYvIH4vIc5OfU+c891EReV5EnhWRD52z/U4R+evJc/9WRCZi1xKIyG9Mtj8kIvtea0yXO3P1c8BPYkvPz3AIeM9kgH8mIne/xnt8P/Dr5237cRF5fHJAL2hgLCI/JiJfEZGvZFwfrmMA8sBjVH/7Iaqffgj1Z1/F/9zDdA6EqNwwXghwxtZkyu9b8ymVFjiJYTRve0Oaz48I2pPKLF+obFr/kd5umy8JugUqtX0IRtmVRmUjZ/HBguYRe6U9WHbZvP1MSMshnhU27qxw+j11Tn2gyWgpoHpiRNISGseskZHxHLpvm+bY98zR32usLlWETdLGwtSTgjMSGkcNteOG2smUdC6iqHhkNZe0qtCOldPwp2K0FtJ2SO2Ih7/t0HoG5j4foDJh/tGCaMPQfF4TbSiSltDdrxjsrTDeVUfFOTJOGS2H+J2cnVtdxjOKIhCOfu80KoepwzZnUlm3RmBZ1WH7toCd26zmWbRlq4pOvq9C74BtpiMTUIbqcRcTFahY8JoJooXO47M4J0OO/OVeDvwSeD2FvzXC3RqA1pjQxeul5M0A4ypG8/b3ck6EFM/UyWoGt28njaRlw1RGwO8oKscdjG8ndpVZqZNkypA2DME2tA4blv7SKiKowlac9XbbUunOTUJ/xWH2UWGw6FJUPJxhSnCyS7SpyRu2B6n3tpTxrgKvL9YlMJbX+Ka+9RFjXvP2Ovkl4NvP2/ZTwBeNMQeBL04eIyK3Ys+Zt032+YVJ1Abg3wM/Bhyc3M685w8DbWPMjcC/Bn72tQZ02SYOEfkIsGGMeeS8p1xgCrgX+F+A3zwz813gPXzgu4DfOmfzvwduwIayVoF/daF9jTEfN8bcZYy5y3tF6vn6Yvr/fYDwv3yZyu88hPeFR8irDn4nsxLsowyVG4KeFWnUrmK0FJDWrOOhyiGeC/AGBqNguOjhJJpodWz7AYCs5qAy6xteXS8YLtvk+s5NtuTYiBXzc2KrDOv3Ck5+oE7r+YL6kT5GCac+0GLjTiGe0yCGvK4pGjlpU09EJ4X5r2qaR8ZMP9EjWOszmncpIpfePpfBijBe1GTTOUGQ4R4NqRxz0S7Mf1WjPWxCuycM5x1GC0L3gCLasKWoQcdgHMEb5OiKRzFVwe8XdA/4FL6thOrts/7l3UMFq99iLVg77x/Tvivj9AcLRkuGIoC0ahsyd251qR8zFJFtpms85RHu7zP3gVPIwEWHhmzkoTJb4uv1hBs+tYNKCvZ/at06MirrfVKELlnDR/uK9fuabN1hm0izuRzEVk25o4lfOrZaSweQNjTDfTnkgvYNRajPdv6HW0K4o0nrwvrdCieBwrNNnGnL/g1F216VsFPgJoaNO0NOf2CG0YEpgm5B5YTDeFcOmZqUkBtmH3LQ/hX8wl8tnGlW/Xq31/U25s+BnfM2fzfwy5P7vwx8zznbP2WMSYwxLwLPA/eIyBLQMMY8YIwxwP933j5n3uvTwAde7Zx8hsuZ4/hm4LtE5MNACDRE5FeAk8DvTAb/ZRHRwCwvJbvP5W8Cjxpj1s9sOPe+iPxH4Pcv4+/wliT4g4fP3tdA6N5KOh3RPVjDG2nilrJyHBWr/ira0DiRUfgKb5ijkoLBviqt50YMdkdEm9Yt0Y1tqMPvwuKDfdq31Bgu234BlRu696SYVOEPHJYeiCfttVaqJatBEWlQoCsFpApvxyVvaEigsmZjxmqUkU2FFAsV3LFh7Z4Ad2xj+c5YYOySnZrCMeDGnJ3w3NjgH7dWrioz5FXBSWG4JFa/qbBmTFL4hNs57kgznnUnUiQa41nZ83BL0B2H4YGM4e0ppuvjb9sTZVHTqNj2VFRe7BGdtBd6yVSTeNbQe1vGlJtz8tFlHAGTCrv+0FA50bF+5I7Dxr3TzD+4Y021emPr9V5YoU3JDWv3+dROGPb9lzEnf6JAVivUjkHtdM5gl4PXt/7fKrdJahGhcsJOoGnrJdmRdKZAew7GUcRzGu0bsoWC6mGf4aJi9rHMqhOMbP/JaM4hbdiqrHBHM1qwRRSt5wu8gUsRQPPFgvoz25z4jln2/rPrt5oKsGGo1+dbNSsi5+rNf9wY8/HXsd+CMWYVwBizek6ueBdwrj7Sycm2bHL//O1n9jkxea9cRLrADLD1ah9+2SYOY8xHgY8CiMh7gX9sjPkBEfl7wPuB+0XkEDbP+2oD/DucF6YSkaUzBwz4W8ATr9ir5KLQX3sKl0n1AhBhRR0raylFoBgse1ZZtWqvPqMdB79XkDZ98sjG+KsnY8QYkumA+UesR3nYLmyXdAD9PYoDv2RYv9ujejrGa48Z7amjkoKNO6v25Bxo8AwydGwjoAGnr4g2hcbRFCfR6MjF6yWkrSq9vQ5Oag2RpIDKqjDcrSlCcEcCGryhLSf2+gVJy4Zf4hnF0l/2Wf2WOv0DBdUTDsOVgtZTyvYnHPSR3CeZtpOKrmjbA9IRdv3xDuvfPEXSdZFmgnvY+r97fUhQeH1h4a92kKKgqAc8/9+F3PzzGxz92/NIomivNYj6QvWUIasL0fqY3qEGnYMOuz/fZe6RLmQ5uhGhemMkSUl3tRBt2LjTs2KFK8L2HQGVrzjMrRqGy4I/UDgxzD9gx5dXhNZhK6LoZAZ/oFl9t62cMgJ7f9fQPSB0D9nj7nYdZGTLndOGkNVsFZ4qrBBnVof6icKGuZoKf2DYfKew+48y1u4T5r4Cfiene9vUWRO06xnhdYeitowxd13Sj34l5uts/3r7vCpX4k/8SeCTk0RPCvyQMcaIyDLwCWPMhwFEpAJ8G/Dfn7f/vxSRO7C/2NELPF9yCTgj6ugA08DGP3g3ft8QdjXRplWkjVsO9RMpWd3FzzV5xSPcGLPztjp5VJkYXkG4bePrReiw+OUxhac4+aEpgm1DHkWMlm3z2u59W2x0a7Z7/XCFpS/ZxL3K7Mk/bblUTmWs3dcg3Hnpci6Z1lRPKOLZicrtJDSkPQi3rcRHVrW2phi7Cjn9njpp0+YShnsLjG8d+PyOsqKALsTLOZIL4apLHhmiTUP/YJPaaoGTOORHawz2GJb/siCrKfpGMfeolYo3vssLfzvCRDkv/ouIuJ3hNxPU07WJ/Imd7Pr7KnhDzd5Pb5EuN/HaY+J9U9aHxRjyekDactm+1SWeL3AHimwmx2u7DPfnJDMO+WxKPOsSbAvDXdMUoaEIzVnJfDGQxA7Vk4Cx5k2n/yvbZxPsKJzEhplGuwvQCuVB+yaHYAeaxzK6+xVZ06A9B79jJ7wigJnHDKMFj2BLkTYMvX0B0ZbNT5XwkhnZ5WH9zEX0JAx1psjoJLD7nNetAKcn21cusP3cfU6KiAs0eWVo7GV8QyYOY8z9wP2T+ynwAxd4zWngw+c8HsEr1QqMMX/3co2z5NWZ/3cvDz1EwOhH76MIFNHpIelUiDPKQYTqaoZRwvZtNnyVV4SZJxLGcx7GgfW7PVQCUVsTrcWsvTvC6zqcODqL10jJE4dwCMe/zcO4hsYRxfTTCZUTQ/o31Cki22yocmwPQWAYLxmylq3mcSZX360jVg3YyQx5YEtOW4dHdG6q2FJgDcm8jf9LrChaOXR8e+INAGVAC8l8wcJf2T6RaNucveLGQNAWentd/J5N5u/cGtA45jCac/D39mn85xrTj/bAxIz2txjNTZLUuSHo5lSO9cAYRjdOk9Uc8opD94A3WSV59PYFdG6ykvru0Fa/uV3XTgiF7RhXfoG3N2E872FiB2/bxRkLWc0KZvb3F7hDhfYNkgnVU8JgjybsKFQ2sbCdsaq8RtkCA8exx2f1Ppci1Hh9xXjOMJ6DuUc1TmaoH+4y2ttg4RErW+P3rVLygZ968JVfoOuN1x+qeqP8HvBDwMcmP3/3nO2/JiL/N7CMTYJ/2RhTiEhfRO4FHgJ+EPj5897rAeBvA38ySSW8KuWisuQNM/MfrW+JgbMSKvq+d+B3U5KZgNYRa7Nb+EIy5ZJHQtK0V+8SwmDZAULCdZuY9boeYjyc0CrR+j1hvC+jsmG/pse+s2kNg0aAgnjKlsOOWpq8kYEW3A2f+lGoreZox5YcS6HAKPIQ+vsjkpaVSBEN3qZLtphhQgOpoogM6UyBVHIcx6ADofpkyHAJWs/nRGsjnDggmfbII9vzEM9ZJzx3xETh12H73gz3+Tr1YzHpQo3OgQBVwMyjbeLlOl43RWWFrZjyXQZLLllD0J4iaBtqp3PW3l1h+qkclSnatwp5TROuOeR1jZqPYSdAVwvmpgaMU49UXNSZSUXbarQ8hNox21MSzyhGu3P6viLamHT5d+0KItywYap4IQfHUDQNwY6PE0M6n6PDSehwpOjtV0w9W9B+ewuVw9q9DlnNEG4q4vmC5q9egS/jVcilEjkUkV8H3ovNh5wE/hl2wvhNEflh4DjwfQDGmCdF5DeBp7AN1v/AGHOmoebvYyu0IuAPJzeAXwT+k4g8j11pfP9rjamcOEouKfLAY8BL6sDdH7j3rPNgb79QWbUS5r39UATQX7GKvZX1jHB9RDJfYf0uHz8T4oMJ4dGA1Q8n1B4PyA6N0JshWQOidVshNbjN1p1WnwzJKzD7eEHcVKjUUFkdYgKHtBlZmRVf0V+xnuNZzYaK8qa2KoMOqJFDuCFoz2HpczBccJj/qzb5jMHtxLa6qRmhAyvT4o2EvCrWG7xhcMfWLW/nNoPquTgpeFsDjn3PHOOVArenGC1Ms/SlMW7PSnkYzyFvhFbccmRIG1ZSf+MOOxXv3Oxajam6xhkp0pZBhxpHaby5MUGQsbnRwNnyETEUtYLKCZd4VuOEQvMwjBeE9u02cW87xO3ka6Xi7eSXzGqCLdt5LonCRJqkZf9u/rpLXrNKuo0j4CSardsdECs3gjHMfE0YLcLSX3zDv3JXL5do4jDG/J1XeeoDr/L6nwZ++gLbvwK87QLbYyYTz+ulnDhKLivNX3kpbFH9bftz8H3vYvcXM1Smad8UEfQKuvs9/LaDvzmmfswl7BSshj71o4as7jG4PcGMXagU4BckaUDrWVj8Jc3WbRFZHRpHrAtio2M70LPpEK+b4I5tI2PYMSQtIZvouBShIdh0iHcXiKvZ9WeauCmowjr0zX25h2Q53lqfYqqCqQW4O0P0UkTccqhs5owWPXSgCXYcG+J62wD1XBUQm2epBlTWDYhjpT0KiOd83Im9rE08u2jf2siKgbxhPTHSJjSfKdi5xaF6wmFwS2rDH7ki69upOc1Dai+4OIltwmNbES9ojGOITihEG4Y3Jexb2eL4+jTzfxjQO2BDXr2bi7OriKKqSTNBohwZ+bjb7kS2REhvHKPHLjpxyUOx5dauQSVWBmbvZ0e0b6ogGmq/9dA39gt21VKKHJaUXFLOPblM/xWkH7qL2ccTuzJR0Do8JJ4LmX9UE3Qypp/MOP7ttslt6Y41Tq5PkU/lOImD5IaFh/uMdkVIju0Gb7qEOymSaYa7q5NtwnDFSnGkLSsAWNQ0mTK4Wx5FTTNcsB7w818e4PRj0IZiqsL226tMPT223hMTCXfjWDVgI6ASRbgFw12glCHZneB4mqnPBIyXKsw9tMPgYJOsovB7mmhthPYdnCRDRHDHBVLYk79x7a1/sEAyob/bOuoN3x5Dqphb6jKMfbLUJXzY/m5WNgSiDUPY0azOCUHHIW1a4Urlak59ZRkvFTb+Rozs+MQLBuNpJFfksxmq7yIG6o+GpC37fpU1Q/+Aga2Aua8JvRugv9/QOGKPZ1Y3zH45t02kqWG8+NY9UV40BngLS46UE0fJFcf/vC1jP/NvZu57B9oTas91wVV0bmmw5/ft/dW1Jdx5w8qfZqg0JZ71iVbHqMxKoLhjQ7id4XZixsu1if+IIq/Y0MzgQA7KoLUQrLs23DIQal8TWn9t1XvHK3Wcimc73w/6LP7ZlvUhcRQoIdxK2bk5ontI4YygflTo3lSwcHCLtaMzuD2HuUcN/T2K2ccS4qUa1Rd6VvEX0NUQEQFtGO9vkVcU8awwOGAd/by+UASCmc7o3wFGC2GUEY8iOv2IfL3C9OPCYK8duzY2we0kcPpbwesIWU2jUiFtGHTmoBzs73oiIF3I8Wop6rkKWcOgxi4YG9bKI2xH/VZO+5DtHaluKnoHIK9o3JEw9VzKzs0BTiyc+KCD320gBSz/xXWsTXUBSiOnkpJvIPLAY0S8VJTS+Bqc+Oi7SacMBz7dRwoNSkGukcIw2FthNOeQ1+xVtzty6BxsITlnu9izum1yUyOF8Q3GNSBWoLD5Yo4Ta/o3WeG+rGpF+wpf8EaACFv3zaIdmH5mbENNMfh9ob9PEy/a/pP1F2bx+gq/I2QVmDpsVXVrpxIrVR/5bH9Ti6wmLN6/gwk9VKrp3uoRzxoqx21iO6vbyisdO6AMTs8h+KoPM+A+XyPKbI4mmBRMqtTKoQ93WS2qbE9C64GA/gGbJI+O+KTTmqxhxR9xDN7jVfKaLV8GMMow2KNoPafJKmK9V2KDyoVo0yC54PUVeRVe+F6H+QcNnYPQelYYLoM7hMrvlGGql1FOHCUlV5aVn3mpHNgA6m03gyO0bwpJG2J7ODIh3yfs3ObiDe0JUSUwXsnx2g5FVWNqBeRC42mPwodoSxNPORhlO6OLYNLDMWsT+X5fY5TtJXHH1vdkOG8rtOI9GjOVER0JqKwZ0rrNUagEqus52hOCdmGteCdKvmck6zffNcX8l7bYvi1gcEcMHY+8qsh2pczO9Ugyl956jdbXPOsL7xiW/jKliBzcUcGpb/WpHbc+4dmiIZ/LqD3jwy1DiqM1ikgIt2wpdGXNkDUElsboxEF1PEaHEtxNn2IhwT8WoH0hq2u6NyjSpiZat7a21ZOG9s2QzWW2Aq1V4M/EJM0axjNsf1PBrj8RenudV/nLXacYzrplvhUpJ46SaxL9xDMAzDz20rbDn7gLCiE87ZFHBrNnjN4MkcI2BRJoSBVO36F2SjOaV/h9TbiVcvpbKlZnqqUxriFadWwD4U6KCRzqxxN2bglJWh61NSsbj1hP77Sl8buKXX/SIV6s4g5zpJiYanWtc6GOPLbfUbX9H9jVwca7ZxnuMrh+jpnVZA0HUwjtbhVzKmLvn+YUYYETa/x2Stb08fo5xhFm/lpz+m9oZr7sMtaCyjySOwfo41VkZczAC9GBxus49PdBPptBx0dyQYca/6RPOlPgnQ5IFnO8LRevb8tpERjszwnXbd6lelJIRh7VU4ate6zboVuDbCYHMcRNj8Gey9u0cO1RJsdLSq4JDv3IV172+Miv3cHM14TW8wmSa1SuGa5UyEND5wbF8l+OSVsevf0h6ZTVtDKBQRIh3DGEbY1KClAKtztm6jlh7V0h8YyVaq+cUsRzQusZiHZyiopHdLxL75Yp6s/3kDQnXawzXPIZzSnETFYITUMeaZIFg7/lkPQDdu3eZvWZeZqHFc1jgjeI6e8OCDqayrEeecMaOcnE1wSq3PCrhiKE8axHZU3o1SsYzwoTqtR6n+cNbUtwRw5u3zZNqsza4UanXcYrOU4tw9Qy4oGH03NQuZW0T1uaeF+GX8lIOwHjvUAhuGHGeMklWHXRB0e03+4y81iphvsKyomjpOTa44b/9msve5x+8E7EGPyBYeZpTdryGM47DFcEdaBPshPh7TioHJpHUvztmM6tdeuprqF9MwQdq/+ktPVu97r2JNzd71J7rks6X8M4wmh3ncrRHnnFYTyjSKdsj0laVWzsLhAtuH3F/L2rnHp6gfjTC+x7IUUVGmeY0buhhhSgfWHnjikaL4zZuKtO2LbqwoNll+bRlPZBj3DHkEdWdj6va/TAw1Q0KlagwR0LRmyPiRNbq9u0LgxXtHWE3AmQVBDXwFKCFkOeK8zIRXoeaSH4rYRsK8IZKLy5AmfbKh9nnQA3EaY/+cAV+RtftRigeOuuwsqJo+S6wfvCI2c73M/Q+R/fTbyUI6tVqqtWJ37pgTGn3x2RNX2iVaG2qhksKxovWD/3rArxtHVaxMDMEzHOIKWo+oznfdKaMJpz8QYVOjd4qNTmJzbvCGyDnRF2f85KnHdXl1h5IbdNeFMu4U7G9u11ou0Cd1iw/bYA8942e2Y3OP7FmxjshZUvWmMp0TZfMloSsqpBFRCddhgvaptUjyFoK9yRYbxgRR9VCknTFgzUjimMo/C7huaLKUWoGM2HdA6BrmsrcQKY2GPxdxWb73AoIkP4+Qa10zaH09vn2nLgkvMwYMqJo6TkLcniz32JxXMeF+99J5Jq6sc1/T22k3rnFkW8kqGiHD1yUQPbNR1uKOonNMPlgNpxTVFx6e2bXOWP4Ni3B0Sbtpdi/e6AcNuw8BdbJMsNRgtW6bZ22pawqsyWDfd3B0Tbdlv3gI87NjR/ocrJqYPk92iKqiaZ9mwnvCP4AzspBDtik/cRRGvKKgSPraBjPCM0n9eEOwXDRZesZic9b2jwhtacyyjo3OghudXR8tsKyYXpZybOgNoQbhv8viHaKti52cOJDdqFqWfLmeOClKGqkpLrA+f+RwFoPmAlQl/42fsIt4TFP3Gorhbs3OJS2dD43Rx3lNO+ucJ4RiiCCoVvPTAwMNxTEGw5pA2onTCM54S5R3ogghMXeCOHpOEQbRWM5ly6ByFraMJ1RX+3a8toNw1FKKzf5RNtGsJNwT2m0K49mfvtmLTlEm04dlUxL3hDcBJr9xp0z5z0wR9ogu2Y8WwNv2/IQ6iuF/jtlPFCwM6ddtLwBraqDAW1VUP7oCJoQ6SgcdyKGXb3uxQhzD8Ss/HOiOCzD1/wWF7XlFVVJSXXLwf+yctj93P3g3rHLejIY7gSEc9a46R0SnCHVlIkbRqcsZX1MA4gsOv+IQDGVbg7Q5y6hxMauvs88ho4Y/tcUZn4fzcyiiiw9zVkDcFvQ3+fZrQspC/4iLE2e8MVM5GLt+XFZ0JRWUVoHUlJplyC7ZTxUsTUMwOMQPuWGltvc3FSl7QBWVPj9oXxLqvdFW4p4llIbxkRbwWoJ638etJQRFuaaFtIpj2W/6xzmUVgr2HKFUdJSckZ9GNPA1Cd3F782H1IZrWiRFsb19kncrZvc1n5oxEqzpBcgwiIEO9u4vUz8sh2tNsENkSbkFeFdMoQPR8SzxeYig1bmdGkwfGUgzuyApFBx/aFRBuK4YpGx4Lf18TT1vFPPCGPrOmWaMN4xkGKCJVpGscTRksh4wVD3ihQY0U6V6CqGWwH5BWbP2n8eUQRWZXdtG69SsJtq9HVfKJj7W1LLkw5cZSUlLwa+3/q5auStZ94N4UvRBuG7dsivFFI7WSK200o6gFeLyWveETrMePpCsm0bWCM52xuJJwbk08pKkFOPPYxmwGN5xTjBUPWsOErKWC8AH7HlthKYcthtSuE25rhks1z7NziUj+h8Xz7OgTGsy71k4n1gG8Lc48oaidjevtCNt7lE3QU2jFU1u1qynbeG2qnNfUXh0hWoNIA8gL91OErcMSvAYyB4q0rwVJOHCUll5jFf2O73KvnbOv83fuIIgfRBuO4eMMcAH9oULldrWjPkNUNztM1Gsdt17czZygqhrQByVKO03GpnoLxHCCgPSgiCLaF0Z6C0y1h6kkr9X7GydDrC50bXRpHNf1dDn7f0D4Y4sSGoA3DRUXnYAUnBVNJyWMr3phMWUtc0SC50LnRQUyV+uEubi8mn6ld0HO0ZEK54njjiIgDfAU4ZYz5yGTbPwR+HGs08gfGmJ+8wH5HgT5QAPkZX14RmQZ+A9iHtY79b4wx7cv9e5SUvBla/+nlq5L8A3fid2LCLZdk2mP3F2LW7o1wx5DWwY01owUhXUlxgwL/xphsFKA7LvE0TD+tGc/aCq7KlmbtPnCmEsxqSDxr8y0AEtgciPYN22+3KxUQGi/YsFg8Z/MyWc2QAzJ0UamVU0+mNOM9BVOPulRPQ/1kTnS8y/CGFklT4SSG2jf6QF5LlBPHm+IngKeBBoCIvA/4buB2Y0wiIvNfZ9/3GWO2ztv2U8AXjTEfE5Gfmjz+J5dh3CUllw33i4+gsc6JHrD9I/cxXtQUjQIZOaRNaxYeHA9oHdasf9AhfDGgumNdBv1uYfMYGtoHFcyOyRMHcW3uI562awG/DeNFqB2fJPGbhsK3K5XxvCGfycAIKswxO9bjwx1ZzS4pBHfHZbgCaKit2gR+5cUe4zunaP5WaRH76pi3dFWVupxvLiIrwHcAnzhn898HPmaMSQCMMRsX2vfr8N3AL0/u/zLwPW9ymCUlV5yZTzzADf/oQQ796MMc/IkHrYqtsUnzzk1C86sBOjAkTQi3BVUYGi/G9PbaSixZCxFloJUydTgBYLyrIGvYiSCt28qtyinrvT5cAe0ZyO0pQI+tT7kzFsJtg1HWlteJrXyJyoThvIuu+JjQZfqpwZU8XFc/BozRr3m7VrncK46fA34SqJ+z7RDwHhH5aSAG/rEx5kKF4Ab4IxExwP9jjPn4ZPuCMWYVwBiz+horlpKSa5Ib/tErr+Zf+Nh95HOafGHMybkK3sBn8cGEtXcFREMoNkIq64bBLkMRGSQVxisZzsBBCkHldiWRTRe4jRTneARK4YwE7VuPd79jcxuibUJc5TZsFW4ZKpsFOnAgcPC2Rrx1U7+XiFJy5OIRkY8AG8aYR0Tkved95hRwL3A31nD9gDGvCAh+szHm9GRi+GMRecYY8+cX8fk/BvwYQEjlTfwmJSVXBwfOq97a/uH76O/xWfliHxVnxMt1wpN9BoeaFKHt5aAQwk1FMmUFEZNpjWRCkSlMq4CgwFnIkWMV4nmN9hXBNtYAyjfUjisqW5poMyWruRSeYv3ugJWf+eqVOQjXCsaALieON8I3A98lIh8GQqAhIr8CnAR+ZzJRfFlENDALbJ67szHm9OTnhoh8BrgH+HNgXUSWJquNJeCCoa7JCuXjAA2ZfusGG0uuW2Z+8aWJRAOh3AIK3GHB9BOK0bxCe4rRkibcUiQ3jW0IJXbxTgVoD8xIkfsuZioHx6CHHllNmH7SMFhROKnBSTVZ1aVyrIdkBbs7AeU/1OugTI5fPMaYjwIfBZisOP6xMeYHROTvAe8H7heRQ4APvCwBLiJVQBlj+pP7fwP455Onfw/4IeBjk5+/e7l+h5KSa4kzjYn+4/afqgWc+KfvBteQTGtMx8drK/KawR3aFYk3FAb7Crxmgutqxrmi8B28oaJ2UlM/mWCU4PZT8mYECpxBeiV/zWsGU644LimfBD4pIk8AKfBDxhgjIsvAJ4wxHwYWgM+IyJkx/pox5nOT/T+GDW/9MHAc+L5v+G9QUnKNsPunv/Syx4c/cReSOKgCJAN3aAg2HcxODWcIFQeyhhUvLHyBwuB1E4yrcAYpovXZCark61EaOb1pjDH3A/dP7qfAD1zgNaeBD0/uvwC841Xeaxv4wGUaaknJW5rzza42/od3IwZqRw211Zx42mHznTC4QZOvOVQ3PJykQLsKdzQmWayXXcOvh1LksKSk5K3K/C+8fEWiP3IPM49bM6usapDcEM+HRKeGGKXw+mmZ33gdGMCUkiMlJSXXA+Hvf5nwnMeD73sXlbWUvB7grffOer2XvAbm0hk5ici3A/8GcLDh/I9dkjd+E5QTR0lJyatS+62Hzt5/614/Xx7MJQhVTSSb/h3wbdiK1IdF5PeMMU+96Td/E1zWzvGSkpKS6xajX/v22twDPG+MeWGSH/4UVj3jinJdrDj6tLe+YD597EqP4zxmOa8M+SrmWhnrtTJOKMd6ObhU49z7Zt+gT/vzXzCfnn0dLw1F5NyKhY+fo5IBsAs4cc7jk8C73uz43izXxcRhjJm70mM4HxH5yhnF36uda2Ws18o4oRzr5eBqGqcx5tsv0VtdSLn+itcnlKGqkpKSkquXk8Ducx6vAKev0FjOUk4cJSUlJVcvDwMHRWS/iPjA92PVM64o10Wo6irl46/9kquGa2Ws18o4oRzr5eBaGefrxhiTi8iPA5/HluN+0hjz5BUeFvJKUdqSkpKSkpJXpwxVlZSUlJRcFOXEUVJSUlJyUZQTxyVGRL5PRJ4UES0id52z/dtE5BER+evJz/dfYN/fm6gGX+h994nIWES+Nrn9h6txnJPnPyoiz4vIsyLyoTczzjc6VhH5nIg8NtnvP0w6cM9/30t6TC/nWCevu2TH9WLHKSIVEfkDEXlmst8FZS+uhmP6esc6ee0l/a5eNxhjytslvAG3ADdh1YDvOmf7NwHLk/tvA06dt99/Dfwa8MSrvO++V3vuKhvnrcBjQADsB44Azjd6rEBj8lOA3wa+/3If08s81kt6XC92nEAFeN/kvg/8BfA3r8ZjehFjveTf1evlVlZVXWKMMU8DTLxEzt1+rtfmk9iO0cAYk4hIDfifsVa3v3mNj/O7gU8ZYxLgRRF5Hiub8MCrvP6yjNUY05tsd7Enj29IFchlHOslPa5vYJwj4E8nr0lF5FFsT8Fl5zKO9ZJ/V68XylDVleF7ga9OvrAA/yfwr4DRa+y3X0S+KiJ/JiLvuawjtLyRcV5IImHX5Rneyzh/rIjI57HWwn3g06+y3zf6mMIbG+uVOK6vGCeAiLSA7wS++Cr7XRXHFF5zrFfqu3rNU6443gAi8gVg8QJP/VNjzNe1shWR24CfxdrhIiJ3ADcaY/4nEdn3dXZdBfYYY7ZF5E7gP4vIbedcrV4t43xDEgmXcqxnP9SYD4lICPwq1q74j8/b9aKP6RUc60Uf18sxThFxgV8H/q2xhmvnc9Uc09cx1qtSzuNaoJw43gDGmA++kf1EZAX4DPCDxpgjk833AXeKyFHs32NeRO43xrz3vM9MgGRy/xEROQIcAl5u6XaFx8kblEi4xGM9931jEfk9bFjij8977qKP6ZUaK2/guF6mcX4ceM4Y83Ov8plX0zH9umPlKpXzuCa40kmWt+qNVybyWthE3Pd+nX328epJ5zkmiTvgAHAKmL4Kx3kbL084vsAlSji+3rECNWBpct8FfgP48W/UMb1MY70sx/Vi/v7Av8Am79XXeb8rfkwvYqyX7bv6Vr9d8QG81W7A38JeySTAOvD5yfb/DRgCXzvnNn/evi87IQPfBfzzyf3vxSYAHwMeBb7zahzn5PE/xVaoPMsFqlku91iBBazGz+OTY/bzgHu5j+nlHOulPq5vYJwr2DDO0+ds/5Gr9Ji+rrFeju/q9XIrJUdKSkpKSi6KsqqqpKSkpOSiKCeOkpKSkpKLopw4SkpKSkouinLiKCkpKSm5KMqJo6SkpKTkoignjpKSkpKSi6KcOEpKSkpKLopy4ii5rhCRu0XkcREJRaQ68Wt425UeV0nJtUTZAFhy3SEi/wIIgQg4aYz5mSs8pJKSa4py4ii57hARHyvzEQPvNsYUV3hIJSXXFGWoquR6ZBorLljHrjxKSkougnLFUXLdMZEu/xRWEXXJGPPjV3hIJSXXFKUfR8l1hYj8IJAbY35NRBzgSyLyfmPMn1zpsZWUXCuUK46SkpKSkouizHGUlJSUlFwU5cRRUlJSUnJRlBNHSUlJSclFUU4cJSUlJSUXRTlxlJSUlJRcFOXEUVJSUlJyUZQTR0lJSUnJRfH/A5WhfRIBjMFqAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "url = (\n",
-    "    \"https://storage.googleapis.com/\"\n",
-    "    \"gcp-public-data-landsat/LC08/01/047/027/\"\n",
-    "    \"LC08_L1TP_047027_20130421_20170310_01_T1/\"\n",
-    "    \"LC08_L1TP_047027_20130421_20170310_01_T1_B4.TIF\"\n",
-    ")\n",
-    "env = rasterio.Env(\n",
-    "    GDAL_DISABLE_READDIR_ON_OPEN=\"EMPTY_DIR\",\n",
-    "    CPL_VSIL_CURL_USE_HEAD=False,\n",
-    "    CPL_VSIL_CURL_ALLOWED_EXTENSIONS=\"TIF\",\n",
-    ")\n",
-    "with env:\n",
-    "    with rasterio.open(url) as src:\n",
-    "        with rasterio.vrt.WarpedVRT(src, crs=\"EPSG:4326\") as vrt:\n",
-    "            rds = rioxarray.open_rasterio(vrt)\n",
-    "            rds.sel(band=1).plot.imshow()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/examples/reproject_match.html b/0.18.0/examples/reproject_match.html
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-
-
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" data-content_root="../">
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-  <title>Example - Reproject Match (For Raster Calculations/Stacking) &mdash; rioxarray 0.18.0 documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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-</head>
-
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-  <div class="wy-grid-for-nav">
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-      <div class="wy-side-scroll">
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-<div role="search">
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-        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../readme.html">rioxarray README</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started.html">Getting Started</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="examples.html">Usage Examples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="resampling.html">Example - Resampling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="convert_to_raster.html">Example - Convert dataset to raster (GeoTiff)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="clip_geom.html">Example - Clip</a></li>
-<li class="toctree-l2"><a class="reference internal" href="clip_box.html">Example - Clip Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pad_box.html">Example - Pad Box</a></li>
-<li class="toctree-l2"><a class="reference internal" href="read-locks.html">Example - Reading COGs in Parallel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="reproject.html">Example - Reproject</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Example - Reproject Match (For Raster Calculations/Stacking)</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Load-in-xarray-datasets">Load in xarray datasets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Reproject-Match">Reproject Match</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Raster-Calculations">Raster Calculations</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="merge.html">Example - Merge</a></li>
-<li class="toctree-l2"><a class="reference internal" href="interpolate_na.html">Example - Interpolate Missing Data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="transform_bounds.html">Example - Transform Bounds</a></li>
-<li class="toctree-l2"><a class="reference internal" href="COG.html">Example - Cloud Optimized GeoTiff (COG)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dask_read_write.html">Example - Reading and Writing with Dask</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://corteva.github.io/geocube/html/examples/zonal_statistics.html">Example - Zonal Statistics</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/328320/144357">Extracting data within geometry (shape)</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/329141/144357">Converting NetCDF dataset array to GeoTiff</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/345697/144357">How do I add projection to this NetCDF file? (Satellite)</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/346899/144357">Create a new raster TIFF file which is masked based on the GeoJSON file</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/354798/144357">How to mask NetCDF time series data from a shapefile in Python?</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/358058/144357">Extract data from raster at a point</a></li>
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-      <li class="breadcrumb-item active">Example - Reproject Match (For Raster Calculations/Stacking)</li>
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-  <section id="Example---Reproject-Match-(For-Raster-Calculations/Stacking)">
-<h1>Example - Reproject Match (For Raster Calculations/Stacking)<a class="headerlink" href="#Example---Reproject-Match-(For-Raster-Calculations/Stacking)" title="Link to this heading"></a></h1>
-<p><code class="docutils literal notranslate"><span class="pre">rio.reproject_match</span></code> will reproject to match the resolution, projection, and region of another raster.</p>
-<p>This is useful for raster caclulations and stacking rasters.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray # for the extension to load
-import xarray
-
-import matplotlib.pyplot as plt
-
-%matplotlib inline
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def print_raster(raster):
-    print(
-        f&quot;shape: {raster.rio.shape}\n&quot;
-        f&quot;resolution: {raster.rio.resolution()}\n&quot;
-        f&quot;bounds: {raster.rio.bounds()}\n&quot;
-        f&quot;sum: {raster.sum().item()}\n&quot;
-        f&quot;CRS: {raster.rio.crs}\n&quot;
-    )
-</pre></div>
-</div>
-</div>
-<section id="Load-in-xarray-datasets">
-<h2>Load in xarray datasets<a class="headerlink" href="#Load-in-xarray-datasets" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = xarray.open_dataarray(&quot;../../test/test_data/input/MODIS_ARRAY.nc&quot;)
-xds_match = xarray.open_dataarray(&quot;../../test/test_data/input/MODIS_ARRAY_MATCH.nc&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, axes = plt.subplots(ncols=2, figsize=(12,4))
-xds.plot(ax=axes[0])
-xds_match.plot(ax=axes[1])
-plt.draw()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_reproject_match_5_0.png" src="../_images/examples_reproject_match_5_0.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;Original Raster:\n----------------\n&quot;)
-print_raster(xds)
-print(&quot;Raster to Match:\n----------------\n&quot;)
-print_raster(xds_match)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Original Raster:
-----------------
-
-shape: (200, 200)
-resolution: (231.6563582639561, -231.65635826375018)
-bounds: (-7274009.649486291, 5003777.3385, -7227678.3778335, 5050108.61015275)
-sum: 23209796.0
-CRS: PROJCS[&#34;unknown&#34;,GEOGCS[&#34;unknown&#34;,DATUM[&#34;unknown&#34;,SPHEROID[&#34;unknown&#34;,6371007.181,0]],PRIMEM[&#34;Greenwich&#34;,0,AUTHORITY[&#34;EPSG&#34;,&#34;8901&#34;]],UNIT[&#34;degree&#34;,0.0174532925199433,AUTHORITY[&#34;EPSG&#34;,&#34;9122&#34;]]],PROJECTION[&#34;Sinusoidal&#34;],PARAMETER[&#34;longitude_of_center&#34;,0],PARAMETER[&#34;false_easting&#34;,0],PARAMETER[&#34;false_northing&#34;,0],UNIT[&#34;metre&#34;,1,AUTHORITY[&#34;EPSG&#34;,&#34;9001&#34;]],AXIS[&#34;Easting&#34;,EAST],AXIS[&#34;Northing&#34;,NORTH]]
-
-Raster to Match:
-----------------
-
-shape: (100, 150)
-resolution: (386.65122672362685, -386.65122672362685)
-bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)
-sum: 4903477.0
-CRS: EPSG:32615
-
-</pre></div></div>
-</div>
-</section>
-<section id="Reproject-Match">
-<h2>Reproject Match<a class="headerlink" href="#Reproject-Match" title="Link to this heading"></a></h2>
-<p>API Reference:</p>
-<ul class="simple">
-<li><p>DataArray: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.reproject_match"><span class="std std-ref">rio.reproject_match()</span></a></p></li>
-<li><p>Dataset: <a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.reproject_match"><span class="std std-ref">rio.reproject_match()</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds_repr_match = xds.rio.reproject_match(xds_match)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;Reprojected Raster:\n-------------------\n&quot;)
-print_raster(xds_repr_match)
-print(&quot;Raster to Match:\n----------------\n&quot;)
-print_raster(xds_match)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Reprojected Raster:
--------------------
-
-shape: (100, 150)
-resolution: (386.6512267236268, -386.6512267236231)
-bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)
-sum: 4930593.0
-CRS: EPSG:32615
-
-Raster to Match:
-----------------
-
-shape: (100, 150)
-resolution: (386.65122672362685, -386.65122672362685)
-bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)
-sum: 4903477.0
-CRS: EPSG:32615
-
-</pre></div></div>
-</div>
-</section>
-<section id="Raster-Calculations">
-<h2>Raster Calculations<a class="headerlink" href="#Raster-Calculations" title="Link to this heading"></a></h2>
-<p>Now that the rasters have the same projection, resolution, and extents, you can do raster calculations.</p>
-<p>It is recommended to use <code class="docutils literal notranslate"><span class="pre">assign_coords</span></code> to make the coordinates the exact same due to tiny differences in the coordinate values due to floating precision (<a class="reference external" href="https://github.com/corteva/rioxarray/issues/298">issue 298</a>).</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds_repr_match = xds_repr_match.assign_coords({
-    &quot;x&quot;: xds_match.x,
-    &quot;y&quot;: xds_match.y,
-})
-xds_sum = xds_repr_match + xds_match
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;Sum Raster:\n-----------\n&quot;)
-print_raster(xds_sum)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Sum Raster:
------------
-
-shape: (100, 150)
-resolution: (386.6512267236268, -386.6512267236231)
-bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)
-sum: 9814687.0
-CRS: EPSG:32615
-
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, axes = plt.subplots(ncols=3, figsize=(16,4))
-
-xds_repr_match.plot(ax=axes[0])
-xds_match.plot(ax=axes[1])
-xds_sum.plot(ax=axes[2])
-
-plt.draw()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_reproject_match_13_0.png" src="../_images/examples_reproject_match_13_0.png" />
-</div>
-</div>
-</section>
-</section>
-
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-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="reproject.html" class="btn btn-neutral float-left" title="Example - Reproject" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/0.18.0/examples/reproject_match.ipynb b/0.18.0/examples/reproject_match.ipynb
deleted file mode 100644
index 81ba1cf2..00000000
--- a/0.18.0/examples/reproject_match.ipynb
+++ /dev/null
@@ -1,283 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Reproject Match (For Raster Calculations/Stacking)\n",
-    "\n",
-    "`rio.reproject_match` will reproject to match the resolution, projection, and region of another raster.\n",
-    "\n",
-    "This is useful for raster caclulations and stacking rasters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_raster(raster):\n",
-    "    print(\n",
-    "        f\"shape: {raster.rio.shape}\\n\"\n",
-    "        f\"resolution: {raster.rio.resolution()}\\n\"\n",
-    "        f\"bounds: {raster.rio.bounds()}\\n\"\n",
-    "        f\"sum: {raster.sum().item()}\\n\"\n",
-    "        f\"CRS: {raster.rio.crs}\\n\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")\n",
-    "xds_match = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY_MATCH.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(ncols=2, figsize=(12,4))\n",
-    "xds.plot(ax=axes[0])\n",
-    "xds_match.plot(ax=axes[1]) \n",
-    "plt.draw()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original Raster:\n",
-      "----------------\n",
-      "\n",
-      "shape: (200, 200)\n",
-      "resolution: (231.6563582639561, -231.65635826375018)\n",
-      "bounds: (-7274009.649486291, 5003777.3385, -7227678.3778335, 5050108.61015275)\n",
-      "sum: 23209796.0\n",
-      "CRS: PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"unknown\",SPHEROID[\"unknown\",6371007.181,0]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]\n",
-      "\n",
-      "Raster to Match:\n",
-      "----------------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.65122672362685, -386.65122672362685)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 4903477.0\n",
-      "CRS: EPSG:32615\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Original Raster:\\n----------------\\n\")\n",
-    "print_raster(xds)\n",
-    "print(\"Raster to Match:\\n----------------\\n\")\n",
-    "print_raster(xds_match)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reproject Match\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- DataArray: [rio.reproject_match()](../rioxarray.rst#rioxarray.raster_array.RasterArray.reproject_match)\n",
-    "- Dataset: [rio.reproject_match()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.reproject_match)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds_repr_match = xds.rio.reproject_match(xds_match)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reprojected Raster:\n",
-      "-------------------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.6512267236268, -386.6512267236231)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 4930593.0\n",
-      "CRS: EPSG:32615\n",
-      "\n",
-      "Raster to Match:\n",
-      "----------------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.65122672362685, -386.65122672362685)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 4903477.0\n",
-      "CRS: EPSG:32615\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Reprojected Raster:\\n-------------------\\n\")\n",
-    "print_raster(xds_repr_match)\n",
-    "print(\"Raster to Match:\\n----------------\\n\")\n",
-    "print_raster(xds_match)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Raster Calculations\n",
-    "\n",
-    "Now that the rasters have the same projection, resolution, and extents,\n",
-    "you can do raster calculations.\n",
-    "\n",
-    "It is recommended to use ``assign_coords`` to make the coordinates the exact same\n",
-    "due to tiny differences in the coordinate values due to floating precision ([issue 298](https://github.com/corteva/rioxarray/issues/298))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds_repr_match = xds_repr_match.assign_coords({\n",
-    "    \"x\": xds_match.x,\n",
-    "    \"y\": xds_match.y,\n",
-    "})\n",
-    "xds_sum = xds_repr_match + xds_match"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sum Raster:\n",
-      "-----------\n",
-      "\n",
-      "shape: (100, 150)\n",
-      "resolution: (386.6512267236268, -386.6512267236231)\n",
-      "bounds: (485124.8828918401, 4990535.635952473, 543122.5669003841, 5029200.758624835)\n",
-      "sum: 9814687.0\n",
-      "CRS: EPSG:32615\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Sum Raster:\\n-----------\\n\")\n",
-    "print_raster(xds_sum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
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-    "\n",
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-    "xds_match.plot(ax=axes[1]) \n",
-    "xds_sum.plot(ax=axes[2]) \n",
-    "\n",
-    "plt.draw()"
-   ]
-  }
- ],
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diff --git a/0.18.0/examples/resampling.html b/0.18.0/examples/resampling.html
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-              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="examples.html">Usage Examples</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Example - Resampling</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Load-in-xarray-dataset">Load in xarray dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Resampling">Resampling</a></li>
-</ul>
-</li>
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-<li class="toctree-l2"><a class="reference internal" href="interpolate_na.html">Example - Interpolate Missing Data</a></li>
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-             
-  <section id="Example---Resampling">
-<h1>Example - Resampling<a class="headerlink" href="#Example---Resampling" title="Link to this heading"></a></h1>
-<p>This example demonstrates how to reproduce <code class="docutils literal notranslate"><span class="pre">rasterio</span></code>’s resampling example <a class="reference external" href="https://rasterio.readthedocs.io/en/latest/topics/resampling.html">here</a>.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from rasterio.enums import Resampling
-
-import rioxarray
-
-%matplotlib inline
-</pre></div>
-</div>
-</div>
-<section id="Load-in-xarray-dataset">
-<h2>Load in xarray dataset<a class="headerlink" href="#Load-in-xarray-dataset" title="Link to this heading"></a></h2>
-<p>See docs for <a class="reference internal" href="../rioxarray.html#rioxarray-open-rasterio"><span class="std std-ref">rioxarray.open_rasterio</span></a></p>
-<p>Notes:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">masked=True</span></code> will convert from integer to <code class="docutils literal notranslate"><span class="pre">float64</span></code> and fill with <code class="docutils literal notranslate"><span class="pre">NaN</span></code>. If this behavior is not desired, you can skip this.</p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = rioxarray.open_rasterio(
-    &quot;../../test/test_data/compare/small_dem_3m_merged.tif&quot;,
-    masked=True,
-)
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Resampling">
-<h2>Resampling<a class="headerlink" href="#Resampling" title="Link to this heading"></a></h2>
-<p>API Reference for <code class="docutils literal notranslate"><span class="pre">rio.reproject</span></code>:</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.reproject"><span class="std std-ref">DataArray.reproject</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.reproject"><span class="std std-ref">Dataset.reproject</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>upscale_factor = 2
-new_width = xds.rio.width * upscale_factor
-new_height = xds.rio.height * upscale_factor
-
-xds_upsampled = xds.rio.reproject(
-    xds.rio.crs,
-    shape=(new_height, new_width),
-    resampling=Resampling.bilinear,
-)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds.shape
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(1, 245, 574)
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds_upsampled.shape
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(1, 490, 1148)
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds.rio.resolution()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(3.0, -3.0)
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds_upsampled.rio.resolution()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(1.5, -1.5)
-</pre></div></div>
-</div>
-</section>
-</section>
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diff --git a/0.18.0/examples/resampling.ipynb b/0.18.0/examples/resampling.ipynb
deleted file mode 100644
index 249e4a7e..00000000
--- a/0.18.0/examples/resampling.ipynb
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Resampling\n",
-    "\n",
-    "This example demonstrates how to reproduce `rasterio`'s resampling example [here](https://rasterio.readthedocs.io/en/latest/topics/resampling.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rasterio.enums import Resampling\n",
-    "\n",
-    "import rioxarray\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load in xarray dataset\n",
-    "\n",
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)\n",
-    "  \n",
-    "Notes:\n",
-    "\n",
-    " - `masked=True` will convert from integer to `float64` and fill with `NaN`. If this behavior is not desired, you can skip this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/compare/small_dem_3m_merged.tif\",\n",
-    "    masked=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Resampling\n",
-    "\n",
-    "API Reference for `rio.reproject`:\n",
-    "\n",
-    "  - [DataArray.reproject](../rioxarray.rst#rioxarray.raster_array.RasterArray.reproject)\n",
-    "  - [Dataset.reproject](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.reproject)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "upscale_factor = 2\n",
-    "new_width = xds.rio.width * upscale_factor\n",
-    "new_height = xds.rio.height * upscale_factor\n",
-    "\n",
-    "xds_upsampled = xds.rio.reproject(\n",
-    "    xds.rio.crs, \n",
-    "    shape=(new_height, new_width), \n",
-    "    resampling=Resampling.bilinear,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 245, 574)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 490, 1148)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_upsampled.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3.0, -3.0)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds.rio.resolution()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1.5, -1.5)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xds_upsampled.rio.resolution()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/examples/transform_bounds.html b/0.18.0/examples/transform_bounds.html
deleted file mode 100644
index 4845c28f..00000000
--- a/0.18.0/examples/transform_bounds.html
+++ /dev/null
@@ -1,388 +0,0 @@
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-  <title>Example - Transform Bounds &mdash; rioxarray 0.18.0 documentation</title>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Example - Transform Bounds</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Original-Raster-&amp;-Bounds">Original Raster &amp; Bounds</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Determine-bounds-of-re-projected-raster">Determine bounds of re-projected raster</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#Boundary-calculated-from-the-re-projected-raster-(inefficient)">Boundary calculated from the re-projected raster (inefficient)</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/328320/144357">Extracting data within geometry (shape)</a></li>
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-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/346899/144357">Create a new raster TIFF file which is masked based on the GeoJSON file</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/354798/144357">How to mask NetCDF time series data from a shapefile in Python?</a></li>
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-<li class="toctree-l2"><a class="reference external" href="https://gis.stackexchange.com/a/358057/144357">Convert raster to CSV with lat, lon, and value columns</a></li>
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-             
-  <section id="Example---Transform-Bounds">
-<h1>Example - Transform Bounds<a class="headerlink" href="#Example---Transform-Bounds" title="Link to this heading"></a></h1>
-<p>The <a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.transform_bounds"><span class="std std-ref">rio.transform_bounds()</span></a> method allows you to correctly estimate the bounds of your raster in a different CRS without needing to re-project it. If you simply calculate the bounds by transforming the bounds, there are often situations when this is incorrect due to nonlinear transformations.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import pyproj
-import rioxarray # for the extension to load
-import xarray
-from shapely.geometry import box
-
-import matplotlib.pyplot as plt
-
-%matplotlib inline
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = xarray.open_dataarray(&quot;../../test/test_data/input/MODIS_ARRAY.nc&quot;)
-transformer = pyproj.Transformer.from_crs(xds.rio.crs, &quot;EPSG:4326&quot;, always_xy=True)
-</pre></div>
-</div>
-</div>
-<section id="Original-Raster-&amp;-Bounds">
-<h2>Original Raster &amp; Bounds<a class="headerlink" href="#Original-Raster-&-Bounds" title="Link to this heading"></a></h2>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = plt.subplot()
-xds.plot(ax=ax)
-ax.plot(
-    *box(*xds.rio.bounds()).exterior.xy,
-    color=&quot;red&quot;,
-    linewidth=3,
-)
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
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-<div class="highlight"><pre>
-[&lt;matplotlib.lines.Line2D at 0x7f324f4456c0&gt;]
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_transform_bounds_4_1.png" src="../_images/examples_transform_bounds_4_1.png" />
-</div>
-</div>
-</section>
-<section id="Determine-bounds-of-re-projected-raster">
-<h2>Determine bounds of re-projected raster<a class="headerlink" href="#Determine-bounds-of-re-projected-raster" title="Link to this heading"></a></h2>
-<p>The <a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.transform_bounds"><span class="std std-ref">rio.transform_bounds()</span></a> method allows you to safely convert a bounding box into another projection taking into account the effects of nonlinear transformations.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>reprojected_raster = xds.rio.reproject(&quot;EPSG:4326&quot;)
-</pre></div>
-</div>
-</div>
-<section id="Boundary-calculated-from-the-re-projected-raster-(inefficient)">
-<h3>Boundary calculated from the re-projected raster (inefficient)<a class="headerlink" href="#Boundary-calculated-from-the-re-projected-raster-(inefficient)" title="Link to this heading"></a></h3>
-<p>This is the benchmark. However, this method is computationally inefficient. So, if you don’t need to re-project, <a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.transform_bounds"><span class="std std-ref">rio.transform_bounds()</span></a> is a more efficent method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>reprojected_raster_box = box(*reprojected_raster.rio.bounds())
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = plt.subplot()
-reprojected_raster.plot(ax=ax)
-ax.plot(
-    *reprojected_raster_box.exterior.xy,
-    color=&quot;red&quot;,
-    linewidth=3,
-)
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
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-<div class="highlight"><pre>
-[&lt;matplotlib.lines.Line2D at 0x7f324735e4d0&gt;]
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
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-<div class="output_area docutils container">
-<img alt="../_images/examples_transform_bounds_9_1.png" src="../_images/examples_transform_bounds_9_1.png" />
-</div>
-</div>
-</section>
-<section id="Boundary-calculated-from-original-corners-(incorrect)">
-<h3>Boundary calculated from original corners (incorrect)<a class="headerlink" href="#Boundary-calculated-from-original-corners-(incorrect)" title="Link to this heading"></a></h3>
-<p>Directly transforming the corners is an incorrect method to calculate the new boundary.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>transform_box = box(*transformer.transform(*xds.rio.bounds()))
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = plt.subplot()
-reprojected_raster.plot(ax=ax)
-ax.plot(
-    *transform_box.exterior.xy,
-    color=&quot;red&quot;,
-    linewidth=3,
-)
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[&lt;matplotlib.lines.Line2D at 0x7f3245a3bee0&gt;]
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_transform_bounds_12_1.png" src="../_images/examples_transform_bounds_12_1.png" />
-</div>
-</div>
-</section>
-<section id="Boundary-calculates-using-transform_bounds">
-<h3>Boundary calculates using transform_bounds<a class="headerlink" href="#Boundary-calculates-using-transform_bounds" title="Link to this heading"></a></h3>
-<p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.transform_bounds"><span class="std std-ref">rio.transform_bounds()</span></a> is both computationally efficient and a correct method for calculating the bounds of your raster in the new projection.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>transform_bounds_box = box(*xds.rio.transform_bounds(&quot;EPSG:4326&quot;))
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = plt.subplot()
-reprojected_raster.plot(ax=ax)
-ax.plot(
-    *transform_bounds_box.exterior.xy,
-    color=&quot;red&quot;,
-    linewidth=3,
-)
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[&lt;matplotlib.lines.Line2D at 0x7f32459316c0&gt;]
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_transform_bounds_15_1.png" src="../_images/examples_transform_bounds_15_1.png" />
-</div>
-</div>
-<p>As seen below, this is equivalent to the <a class="reference external" href="https://pyproj4.github.io/pyproj/stable/api/transformer.html#pyproj.transformer.Transformer.transform_bounds">Transformer.transform_bounds</a> method in pyproj:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>pyproj_transform_bounds_box = box(*transformer.transform_bounds(*xds.rio.bounds()))
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = plt.subplot()
-reprojected_raster.plot(ax=ax)
-ax.plot(
-    *transform_bounds_box.exterior.xy,
-    color=&quot;red&quot;,
-    linewidth=3,
-)
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[&lt;matplotlib.lines.Line2D at 0x7f324580d120&gt;]
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_transform_bounds_18_1.png" src="../_images/examples_transform_bounds_18_1.png" />
-</div>
-</div>
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diff --git a/0.18.0/examples/transform_bounds.ipynb b/0.18.0/examples/transform_bounds.ipynb
deleted file mode 100644
index 33d25f0d..00000000
--- a/0.18.0/examples/transform_bounds.ipynb
+++ /dev/null
@@ -1,369 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example - Transform Bounds\n",
-    "\n",
-    "The [rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds)\n",
-    "method allows you to correctly estimate\n",
-    "the bounds of your raster in a different CRS without\n",
-    "needing to re-project it. If you simply calculate the bounds\n",
-    "by transforming the bounds, there are often situations when\n",
-    "this is incorrect due to nonlinear transformations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import pyproj\n",
-    "import rioxarray # for the extension to load\n",
-    "import xarray\n",
-    "from shapely.geometry import box\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataarray(\"../../test/test_data/input/MODIS_ARRAY.nc\")\n",
-    "transformer = pyproj.Transformer.from_crs(xds.rio.crs, \"EPSG:4326\", always_xy=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Original Raster & Bounds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f324f4456c0>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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5Jj3++ON0zjnnHPHjc3BwcHBwOJSoUlVPRLyubRyvcGU+DgNSqRQlk0maJKLL/+bLNB5LUPcLJqQM7xgXy1bapIZg4hTDKmntUL5TRkZFldaW2NmcIYnPQH/bOX4eFuvIUbMVwZycRrLL6VlApU8Bva6n32pqGg7RU3jCHDgi19ir8nzl58pQdXxxSByHbW9aRYCK3UAUh/aCodPaiMiE3A5Ey+Vpmg3NUGihMI492y0jysSgPFk+Rf1DrF5iDQJYu7her6z3r5idpNmOvqb6GmAKBaJ1RN36eMD45DuggJbbJXtupb6mURyv2q2ErBANV8DKSD20BbaL/RVYRItrGR0xnydfSZnlsobGKHfIey/bZ1bQOpvJBdOnUvgQ+VJiKhP3ndXcKA1Ny44qr4+pRL9os2YbW3bJB0Bqruw7uD/0/RdS/d5OE7NkUmuCwB5iWlrr3QBMh6EPgiECcI0wTR2ZkLotAEzS5DxzPGCIAEzVaoas3KBtas7OAtFheSC439Oz5QnQrKeWGQSz095j6l0xQ5qRxDHYZ4wSr+e5f9YCRO25ND360/+XUkSUJKLJyUlqbT08Ocv4XdrxyuxDkn029+Rdh7W9RwtOgu7g4ODg4ODgQMf49NmxgM6XChQIBSm8UWa9kd+MRzPzZLSqU1BzHI1rJkdHzZohshGtT0VUKrKEEFsLtkOp5owQWAMtPLZQ20/sVtoAZGQoETIYIo3sIiNERcp+ap5cEdGYxxGv1gjk25EWK7cbmpTvtZg5PImME/k5zivOT2JngbKzZJs045GNysiy64W0eO/faxiMqWXSQ6uuzzDnRtseIC27rLaPc4xUes1CgGnKd5rb32aRlXDMcj1E/2CIgOhIlZeHWF6yEugMYOGg48J+dF/U7Cj6EtYH66JT+evRPuvAdvP5YiF3eFQ6+NaCZkP+cdOgcpe5ByOjJW6/oUMyfWa5kLk8ps/yrpAybplQ6Kn4dsj0mwVbtyqqhNeHVm6Kxe9gJAPMTGh2zqfOjT53moUDQ5TrkcvFd5vXHIsawZSi/0eH5XswRGCU8GoTOpBUp0ySoc0JZJEUgOxSaPXAnkoNj2aIkI3qWe2jes6x5imYay6wBootuJacbaYE4/hci/qrAR+FWZ9UaFd6roZkFWYsK82vhWXJj/KvrhNa7xtuUOTg4ODg4PAmQZVqVHGDohnhBkWHGeFX91DYP0UUkFFG6qx+8w9LuqZYM6AzhOxcPr9CSxQblMsFJflg074teD+RCflxVpm8aXM3u3pD+jjrRDJqOUUcFVrlip7yF4IZXaFNdsWcyubJzJLfx/aYV0RngH6P85mey6wFsxWaCdPQWT1I1U/sLIjP955Zp5KQmRPfxftgzUzbamXYlwfdZBiJibfKOnvISCIfMoFYo6Ai0JkYonyH/BxRONi2Mtsf4FyB4cG5ynX5xPcAWBFkFjWcoyIyCjky5r4AvUl6tmR0wADFWZdTVmweNFceNBm8errfnDewLYEC2zbMwDYWW83y4PP8rMEq9Rp6tMaZkeibyPCqZ2ya18hYzTI2+tyUlC9Q+3o+52XZ9mwXs1Ql3Gfmc5xLj0+aryiZC82sgtlN7JIMlfZ0AvC8wDVKcB/FfWytMLhd2ojTaqbUdtEOMNdgAwOKuUIqPRjv8RPlAYHRVlY/DcyQ1qGBDSXFnCM7Ldch+zoYIvRJ7A8WCtCfgc3MdfqslQQ0asgMrrNzMmUfTLHuEzhXYB6r2rLjCMExRfuG0xQ5ODg4ODg4OJBjig4/PI/I8yh7imQDUgNsVsgDbkRgeA0qDREYImR96KgFkaZmiBA5Qgtj98NkQlhlVzXa6vN7nVWmHXM5ytaRHiJA66WjIshMn2kQmCkYIGpXYzBDDe1iQNNRN+yT+43vQlaLjMARQYIdAQodvN5ueVxT88yJz3fU2weGCOyAbfOgZJXAEKXPkgUfrXaIPZDq2SoceULj0wZdhIm6SwnJCKH0BKJ9lOuw37ebk4OoHxl60OTgFZ9rx2oArAX6LpiUotK1ZeabDcR2cPYcnL3zYjFKDcj1ul6SnS2UkrRFe5oZJGQDtZsN4zyFJs33vpIuw8BlUFhTFBg3Dcn3G03R5ILmBq6dr9Tbg0zAen+GEZ98lNZy8r6x1wZmhPx5aoF5xTkpZcHa8X2gNG0wccXy0MxZvy9mQMD4RsbQbqWVUZmHYMXyfP/huZLuV+upU6QZajxXkJ3ZuoVNKhfqOiDybXhSXivNdurnEhiiyKjsK2V2qobWMdcl2Un9XLXu6px9Z0t5TNML6YxUnHOcU2QKIiPOGlN6uK/MezBEQM1r1IYdCVRqNaocZNL5wa7/RoYbFDk4ODg4OLxJUKWGggmvaxvHK9yg6DBj+J1zaSKaoJadJtodO8mEWjZA4H8Q2WE+Wkcn0ATY4ptMQtQzlHjkjuidmaO6NT13Y0SkHPFahikvs0SAeu0g848uhImIEMxMbBhW1hw1oVkqItLeHmiH9h/RNv8A9CTW3l+5OYO5yswy5xsMkXbeRXFGaDvAECHay/ZyhMmRJNiO0rSkQTgqowRE9/M50YZSnwkVs30m7AeTo/VX1h8H7JqHfckFoanRdeUSQzJqrvsb8bXTLt8xyZZZt2H1xGvIssG5Uh4v6QEZPYIhAlq3NdcoaXlCdHdWLhdWLAx0MBVokbjv7TYNq7FPkz+H1CrWEHXJFENoq/LMwCHjULMYtj5fvtrAuKT7TdvQP6G3mpoNt27zXmvYStzfoT9Df9TaHTBFyO7SzIzuQ3iO6HpyOivTwi4nPwYTimtdbMFxqNUbihvL91MDIbEcNEFwoI/tZTdxpT2EJgkMlmaEwqPmAViOmxMFhqigCkAndlfF59bHKY3jMQ1OQ7PIq08uqp+vatj8H9/O/UoRwNp/rH4fMWM7QU1RjnpUPn4Jl2MWblDk4ODg4ODwJkHlEGSfHez6b2S4QdFhRi1k2IXJhSaiaWHH1DRHkogg60U3WePSjWjXbEd7XwTYl8MWH2U2oJ49IiPGii0kCZ8h8zkiJ53ZBJZiao7KCmPfk+he3q6SCoAxQvYK2IAMMy6ZXln4FX4vlilS26uqgrJgiDRjlW/n87ud3YrjcMzljCtoDeLw8DHrwW9F+64AYIhsdg48c6ZptbTOYnwJMxJ8SlEAVXujgJ2DnkJH2QXlRI1zq/tC6zZpEgPtEKJxQDNA2mkXLFrrNhMKTy40dGWDZxWid9ajDZ8t2ZPwiOwDgHZT1s/V+KBcAZqM9ICkMXCPgG2MD8njD+6VdEp2fhu3m9kPZhXBwOFesYVoVd0+sEPliL+hxhmYIayjj9EyrX55f2pPJvS72F5mjJiFQ80+XXgWfQX9E8wy2gdmVGeH6oxBzIPommmaBUSWm92OYg9xXzQ+p5RmkRmrlh2yNlm+C0V45fKJ7WbDXoUd8YMo9MwnhLUtDftV2bNxZlGtfxFIQV4PTD76SIL9nHa8q34P4ZpBbwUfr0JS7IqyS6VorvM3kvafrkc8GqjUrDn3QW3jQPD444/TN77xDVq9ejUNDg7SfffdR+973/vEMuvWraP/9b/+Fz322GNUrVbp1FNPpf/zf/4PDQwMEJEp+L5ixQq65557KJfL0Tvf+U764Q9/SHPm1DWa4+PjdN1119Evf/lLIiK64oor6LbbbqO2trb9bqsbFB1mJHaWqRIu22meYNrcWe3rueI4//DhgYzU0ICcQbA/TKBk9Q8ojOfKLL4F5T+TkM+mxuOFH9plf/PUWwyGtBlddK98QMz0Q4gfXFtWgH+3bLkM3ixoc7S/dbvcIAY3gJ5awoDAllngqaiiMjDE/nEekS4LYbXdviraWo6Jt1TzE8XZiA7lJfSPfWq+LKYbHpeicJR8CKofkIbSBaOmsbbwK2YCZsuLhR8s+8Or+oAVpSubBQyGgPZ1shNOnGQOXpdQ6V6tRbDcl/hcoS/rASd+kDA1Eh6WKtj0CbJ8gP2B5R/CkCrw6s8qB0Gsl9ciXnO+MKiyU0s1tFMO1IFKyLPnDtPTEJnb/o0fzjE5FYdBkTX4K3HZj6IckGVmsTUHH0psSCYGYJpLi9Ux+MG5t4kKmJEt4nu5ni6zgUQIvGJqsqymDWGQCWDaH+cMfRftQWAAYDAU2T3Fr9gQn49203kwGAKqYX6+hdX0O7/HecDgE32loTgrnndRTJ9zsNonfxLbX6r/rweYGAhHxs3r1KXmoZJ4UjrhagPN1m0cqIU9iqjB2/GKTCZDy5Yto4997GOi1ijw6quv0tve9jb6xCc+QTfddBMlk0lat24dRSL1gOizn/0s3X///XTvvfdSZ2cnff7zn6fLL7+cVq9eTX6/6Rcf/vCHaefOnfTggw8SEdGnPvUp+shHPkL333//frfVDYocHBwcHBzeJDgaQuvLLruMLrvsshm/v+GGG+g973kP3XrrrfazhQsX2v8nJydp5cqVdPfdd9O73vUuIiJatWoVzZ07lx555BG69NJLad26dfTggw/SM888Q+eeey4REf34xz+m5cuX0/r16+mkk07ar7a6QdERAlJ3EZlUQ81Tz6N7ZeHIhjId/D7HgszWrTxdpNKzbUq6EqXq1FabRsofW4YIUz9c7BTRECJAMD62KCj2C2EoR2rZHkWzF+T32L+eykF7UIoC5Q80M4SyAHZqSLEisUFzfoJs85/tMdvTZT0wTTl+maE1Ek+YkFpPc2gLg+nRY8tOZjz2mo3tfruZk/MrAiPTJxvZysVCZzIfBPQUAVLQwSBB8KmjYvQpAAwV2AtMW1lhMmN8iaQVwPSAJQlk5UnHtcG1RCkVzXg1TNVyO3a9y+Sbt27l6URmbOpFUyUbEFTi+krMzL3mF7Ty/s1xFVvlYy41T07hYjqjXqpCsp7Tpx3BmOD+9Mq4z3BsOGY5HYwyFdZk0Q9mGOUwuA+oaSy0DQxRgPsp9o/nSBHsILdVTx8htT4yat6HwKjkJWOF+xHCZF0w1SZSMGYyKozt5TR1Ps7QlOxbgUl1U6g+4Z8yVFiRS7BYQXhMPufAsgK2OLNKGQdTPxPAqMf3mHZm+hp/GrVUAMwQ0JFgSvRS8zr+e0MRgSkFQwT4Ko2M/5FAlTyq6Afl69jGoUK1WqX/+3//L33hC1+gSy+9lP7whz/QggUL6Itf/KKdYlu9ejWVSiW65JJL7Hr9/f102mmn0VNPPUWXXnopPf3005RMJu2AiIjovPPOo2QySU899dR+D4qceaODg4ODg4PDASOVSom/QqHw2ispDA8PUzqdpq9//ev07ne/mx566CF6//vfT1deeSU99thjREQ0NDREoVCI2tvbxbq9vb00NDRkl+np6WnYfk9Pj11mf+CYosOM4FSZgoWyLTIJoJAhIr6ZUmdtKj1Dp3+m5sO4ToldJxWjwZGoLeyKQouKVWgoMwJNkI72WZMUHTGhTo7bBZ0KIkREphCCavNGRNhlpD0Xmy8HgXg9ojWfawsBjVKLMoGz55cjbJ7+z50n9Sz1oqYGiZ1mveSr8qYPbd1r/6/0tonvOtj0b3KhvM1gqGffq3RjMDalhOkzFWYXdVCZb5MXxZZK4M1BZK7Fv4ltsiaCr2hWgKlhtt/QaLEhLQIyL2A7tR2E1uCgz4C9gP6mylYBLTtMZxs9Vd4bo6exxorTv+N7ZFo1XqOjLLTeYY4n3ysF2RCKo8zJxEKz3WKbPCybzGDPGwu5WbQMpq0S8eqFkJm1ghA6NAnNHx+rYoDRT+vMrNxHhQXE0LBhO7hfwbBaRheicGiYlM4r1y23A6YX7dcMEVg4a0rJTFJkHEWHzRfpft3nsH2zPBgiILFpwhxfq+lTMND0KtJIsxZRxVZbJC2T6zZ9xD6/YF2izrNmkmdKJtDQWsXpukCwbriPoO+K/YbLxOD+ukwyRB0vN3824VmW6fUolD3youtqzfwd7DaIiObOnSs+/8d//Ee68cYbD2xbVXOe/uzP/ow+97nPERHRGWecQU899RTdcccd9I53vGPGdWu1Gnle/RxO/3+mZV4LblDk4ODg4ODwJkHlEEyfYf0dO3ZQa2s9ISIcDs+0yozo6uqiQCBAp5xyivh8yZIl9Lvf/Y6IiPr6+qhYLNL4+Lhgi4aHh+n888+3y+zZo0ofENHevXupt7d3v9vjBkWHGaVkkIrhYD0bgqFNCpGVUlP2+zr6bjDWi8kU3PpyKkJS90CgUG36OVJeAUTP2J5dXo28QynZMGT26MgRWSfAxAkyIsS8vTUyZMT2yO0HmVGqF5JsrsHSGVaIMAGYNfpfkfqZ+G7xtiETK7CdGaKAnyp95iatROXtpBkim87PbWrfwOnGZVXkMiGZk7qWho3vuKgobAQAHGtyq1muZbPUPHhbjFNg5UST4gqGKDtHHrvW/OjnJ9iGfDsyHc3nUZVhFJowTFBqvnlQ4pri+FPzzXFGRs32puZxX1YGgWANky9PmOVONA/h8Lhpf5nPV2ynquFA8rhg5Ed8baGH8xQhBpYA+hkwU4FCzVpBgKFBgVPbr/glPCGZGNzHOHcw+gQDhPXB1CIbUltBaONArTMDk9Sy3TQQz51iCwpOy2wsvIbYFqLA11QzKulZzGS1yONu2SmveXSX+cLjvgWGyMKPPoB6PKw9nCPTOvXzUT+noKMDUHhaZyRqk9iGLDT9vFC/iIF8za4DjZm+76xm764uIiLqKrCeKth84GHtR4KNWb5HAodyUNTa2ioGRa8HoVCI3vrWt9L69evF5xs2bKB58+YREdFZZ51FwWCQHn74YbrqqquIiGhwcJDWrl1rxdnLly+nyclJ+v3vf0/nnHMOERE9++yzNDk5aQdO+wM3KHJwcHBwcHA4bEin07Rp0yb7fsuWLbRmzRrq6OiggYEB+vu//3v64Ac/SG9/+9vp4osvpgcffJDuv/9++u1vf0tERMlkkj7xiU/Q5z//eers7KSOjg5asWIFLV261GajLVmyhN797nfTNddcQz/60Y+IyKTkX3755fstsiZyg6LDjlLUo1LEsyaJuqAkoDOUkD1RVXPmOntMa28QcWptUmOhVvWeIzjNpMAkEqhnS0jfJBgNAoiA0v3m87ZN8gBh1qbbobMx4Imjj1szaoAtTMsReTUqtVNag+RVZQOsKSWzwPBjscwQY3pRV5QoSOw0YXyhzRxbdES2GZlw0JNohgiFTT2O5vNcZBSRZZl1F40Gl+Z1euFSIqJSK5svvrDZbNdjxmPjTiIiypy/iJpBX3NonAodZsf6Wic3m+MOpJEJGeL1TGjdvo6N8XzyXMP3CEC2GkpZxFivFts6Jff37E7xvtrdJt5XYqzFUvdOQ6YkmH5+xfUCKwFWKGiNVatUiiLNTG4DDE90RLF+MZXFhazOMU1Z8GZhHFmVGjp8bhmnGUrF2M+V7xiy22xpmIDU5IBpgX4GzBD8gOA5NZOBYecaSe/lZxv2QD+/gindRwOiXWXlPwSmqxpSfmSqLAhYVGgZ9XmBFqqsfJqg+2nwS+PzkOv2rMEsMgGDKkPQPrtr8lrhGTw1INtaUl5nRxrVmkfVg6xEe6DrP/fcc3TxxRfb99dffz0REV199dV055130vvf/36644476JZbbqHrrruOTjrpJPr3f/93etvb3mbX+c53vkOBQICuuuoqa9545513Wo8iIqKf//zndN1119kstSuuuIJuv/32A2qrGxQ5ODg4ODi8SXAop8/2FxdddBHVlE2Cxsc//nH6+Mc/PuP3kUiEbrvtNrrttttmXKajo4NWrVp1QG3TcIOiw4xyxKNy1KNiiznViGDCk8o7Rs0tIxq3Wh0PXiraq6a5hgZRLrJHtE6kHFXMjuoJ2I/vNSoWIrILqYwdncWG7BG7Pz2XzodhfUYYQcXsNBaOrDZtJ+z66yUcZOhoi6jukM0IZaRPVJxZimpPGxERjZ2e5HZWbHvQRjBEWg8BhgilRwBociLsT4T3GsjwS8/m76FxYJ1JSOlMAmlVHLfbpNJNnGk0D4iqh94qL/qsZ5H1xu3ay+U+FkldSIy9XAI5ZtHSqszGOKdz8UOwEjd0Smau3A7OIVANqCy9PeYAfeNKZBSSVJlvSKbz+SJM3/QZcWVqHut3lAs09DnWG0jpbOC5FeTDgZaLqH4f6zIWuI/AtuVnB8S+0a/A6IBlQCFYtEn7DKHsBphUj5ez/kjISgvK5wRKy0RY4wQm2LavQ/apqblSZwVfpO41rA8b4HI6nDkYyJgFix3y2mqGCGWGam3yxtf3rdYETZzAurNxaLOI36vC1YoZ0v5FJZa9gOnSmWHRvXwc/HxBGaZCO1GhnfUzm2VbtRu49VbrkJmE2T7ZNhSQJmoyQ+Bw1OEGRYcZ0fEqFbJVmpojK3Hj4YWq0RBz4qFVr+ou6W8IA/VUAB7OmHLA4MivUvq1+Rke4vWbs/lgCIM0bdKGh1NRlcOoG++Jj+1gCPuL8jQC6n8VW1AOBbWO1HbVYA20OwZBoNXDXP0agyFdswo/QpiqwWAI1wMYP80MgqxpJT9U8fAMpSpWnA5G2doo8A+hpv4x+ME1w8ASD+Zcd4C3LdvStkn+MNWnWlisyj/aET4XLS8bPwQMhoCd/49ZrvMFmUatp5vGTpE8f4squRIcM+JzryR/yJBmDVTi5ngiY2b9gvphxA90x0tSKB3YPSreU0yJdrPSWoB4MJRbZAaBoUlzPuODZn/5djmwAKYGpJVBkJuB65nrqN+TdtCBxAnYFKDsB9+/tio7j+ds5XSMv2DC2ikF2HY6DoMSVIufwVHO1kIryedKtpeNNPk+w7kIps3ryOkxbjevz30pwbUZMzx91rpZ3sDtG02nxpRqvttsANPVfi5f4injzZpPDfghD8DHHPT5eXkMhnDcOE9WLM/IdcjtagF2hme5+56WfTf2R5N0kD19NhE1Blv2uZCqB5TaWLNhgKlqG+a6+dC4SeFx8TXVvJnLMB1OVMhHlYO0KDwKnpNHDG5Q5ODg4ODg8CZB7RBoimpHYzR3hOAGRYcZ1YBH1YBH8UE2K4NAmIMORJSI+PycKm+ru8NGX6WWWnqdX1A9HkDkWEwGxHtb9kOVA0DkWk+3bm7SBoAhssJuNV2Ge0YzNJYpYvq/dQdqR3A0hlIXWI0j36oqi6BhC22iJAQErsw8pdljLMwzLdFRNe2hhNuIQCOq9EbVnj9E5vX2YCqj0Gb+AfNTU6VUNAuny1UgsoRRJ4rigiGy+4OxJp+s2DCnVfPUaeE8E6oWk/LYwBABqQH5GMD+YRlg26mqzzeU62CGaOrENtoXEpvldJhvt5lPKC+YRUTTGKKoNGOshrmSOk+nVWZ1iO8rEXkcmLKFkaFtJze7mOTj0Cn5aBefBxitmqQGJBaowsSyBqhlGqwgO4JX2Ra7HgurY3sh6paJDGCYwQZa48ySbI8tsjsOdqN5TN+6Td6wYCeBnufltfenmeXrMgdS85r36UoEBW0lo4M+WT+nVfE+PQtMDVsCqN9cMEQTi9RzkJ0yWnbK5yvsIfA5AIaIioZCiz23xbTvXSeYj/l5MT0xBawdTBs105jpkyxTgftVOa4E2XklOs8cnTIfDvuGGxQ5ODg4ODi8SXA0hNbHEtyg6HCjVjN/nkwVRTSOOXBERnXNgXkFEwPtD6IWiF0BH6d3Q0NTUgViyzGYxckIVOtSEA3Z5nM7QlZUKJfTZmiIVLGVEEfM0HNo8zmwIRAnJ3aZiLSkRMnQSOj1dYo92gvDQzBEdnnWBVSUtgmaJlsYkoukosSGZpKmH7ctsKr0T9ApaOM+q7PycGzyNqyo7UwuCIrlcc206H5sSXNdBFAv8aJtF+R2WrfKk1xMmv0H4UrAqfX5WZIeyfTzcpnmfUmL5sEQYXuBbexGG1Ki/IgUVmdPMYwSLAsSOwq8f+VVoAA2Nj5U5VfePu8u19nIEhDV07XLMa+eQKB+E1p2ynOIQsj4tJ6mjbYwo5mQDBGuDe4HaH6wPiw4rPkiSpNMoOC0YmZ5O0FmnvPdRrRUZ5bMcvHd5sB8yiZCM0TQDCFpAO0NsEYFhVeDljmSDJFPlRXxW8E5GF7Ww+2SFIo2sQ1IL1XLwKGPh5nhje6Udg5Wl8av42d3i68n3i51al2/ijQ840rqHIMZst+zqBvMUGSYj4HtAIJSOnfEUan5qDKTSG2/t3GIGvMGhCsI6+Dg4ODg4OBAjik67Kj5PKr5PPIXYfvPn3vyVWdJQaMDFsKz2SFmA35Oh64gtd6WA0DxSo7copIhArC9XIc0qNMRGSJbZLvZjCmdhTYmi49q1gTReJkDtfgQa3JU+vrUXNaNlCXb0FDeQKfgq8gd7UhugvYHC8rlLHMGbQRrujTTpsuTlFqm3TqebBOi74YSLWAB/fJ7bZjZAPV1PTLlDEaW6OiU84qyPeh82XQqnOPkFrYKUMUwwQwBOLfpxe28f7M8WD0wRGAN8JoY5FIP3Bfi25m6QokYZeZoGSKlJUJpiHyPyZgCQwSMn8zLKzYF7ENiF3Q6upyK0s3xdSkpg7/pjFs4pZlJpRFC2R3F0umyO+j30LhpJhJmiTimyIRcH0xTrrt5qRL0xehwc8YFWZaRkaI4Dn+ejTqZGYJmrsg6uWAaWXNS24Q+HGFtI54/yC7DuUffsOVHEmDnauI49b2D5wmeG7jHbPapukfQp6cWm5ul9Y+GldzxZ7IGVuAd0s6h5VdSp1acVsEi+arp7+nZfP8wo5rcar4PDRsK6NUPmW2AIbL7Yoao0M7v04336JFAlTyqHiQfUtUP0uMIblDk4ODg4ODwJoHTFO0bblB0mOEvVClA9egS/h01ZobqGgLzPbLBbDYVR0Iw5EMkBwQmTaRX7DBaAcz1I1LU2SfWH0X5HOU5qwumklgONv4AmCy0D+VLtJYB3i6ANoeEr5E1o0PkpxggZNHYiIo3O7qUxAfxRZNivei/y4l+ROL188IZRYo9QAmO0FRZrFdsVbqfaeyOLsoJbYo+Zn9RnpOKLijNX4MVKytbHgDMEFCQwS0lXzXHNDXHtAMMEfQVUa6SkZ0nCzmOniobHBuS5wp9odCGV6PhgUYK7QZDFNkt6RJtskitUpNU7jAUjS+LVEzeT7ehTcC0oe/lurgPKw2QLgmjWQoLFeyWFUEVll2KonsrVGxV7JLapGVOsC+wg8yO4Zpq0z4wJQFm+7T5aZDvf6sBRD9m/6HUPHMtcO7xPRhPj2mw2B4uNQPtH2uEUJKlnJSdMt9p+gSyUYtxaQaJ+xjXBllsuF+xHJgfZHdpXyaca7QLDC6eQ7qcCqAZNvu5kpe98hloh0x7kgsnxPfptXwTGdsiqsR5ufU+yxDF/nszv/JK3LZan/EBy/ebSrGzHzPL49xlWF9WbJdtynfXqJA+8ozLodEUHb9MkdMUOTg4ODg4ODiQY4oOPzwZTdasDT/CW+XgHJJz8RWORsqdJvRKbJUhZiVuPoe/UUWV77DLKVdl7c5aVWUFwApAZ2HdmnPNWREA0baO1LTGQvtzWFZFhd6VoNzf1FzWREzwds8y/0T/QzJDWpOgGShovBAp2/XU+dN6G2wXEXslTJRrkct46tgCynjZMkA6yMU2g/VtE9UZmPC4yl5j9DxrwuzUiYb5aXthlF95gSkjZqh1mO/BEOWVGzCyYlDwUvcR6GnCzFSh2G/f45IB8vaY/Vdnm+hcM0TlBabuwcRicyLaNphUIssQMaAhsuupjERdKgewbtN4z9ca7s7wsimyrxJYzuhedbxq+9O9iaCpg+M0+rNmVqvqdoS2Da9gPqCZ8VQE3r7OnBvrdcVMSr6Hs8FYW4iyG2Atg+Om02TnmHMY285ZWDgkFICOmxu1EjSdrchu49ZPSGkRA4Xm5wjMqdUA8rnG/WOds5VWENfEPv9Qnqco2UDsB/5hMznma9Z24gSz/tvOeUl8/ruNshhyrY2ZuJLZf/9j+KZK0d9zdXcPtvbyGk+c1kZERIPvNNuY9ah5mFm3cj6Xhbny2d33SJDalA7wSMBoig5u+utg138jww2KHBwcHBwc3iSoHoIyH05o7fC6AUdrwDpHV5oXMkX2WDWu2AdoE0Ls+Ao5hWKAMGeP7BIbqaoItKaywzTjo5kc7A96knrD5FuwGsie0/5LM0FrBSo2slTtUD22ZRV0MRxBtzc/b/EhuSHtxtzg68QRrp569ysWouSvL4C2+pReBNcYeiztfoyMGugz4GeSUN43besMRTOxhBmhl6Xope1RjmiR6cS1wKp9UnSkrwWuaUK5/0KPps8N0L2aGSjOIvMNspERZ4/5xgw7kVo+T6xXVMzaxEmGzah7RHFxzrbm+50Jlslh0hCMUbwgGZ/IcI5f+YOq+X70DLMi+i68ver1uaYxHHytUfjUr7QhmiEC42rvK+sPJpeLqX6qGSJoB5ENCU1NaKKo1jNtj+8w16jYac5xgH2HwEj5uIZZqU1qiXCNsP3oXtYeocYamGwwssjexHmB2zovl++Qy4UnePmKzEINqGuFYsjwNcLy+rkAgBkCQucYlvLFvcbbKrWlzXzRzll3GZn+FduFbF2zg9hTG8lrbxPLDF5qtgVXfzBEwN634D/WTXWYcx7eIenziROIvMzxy7gcq3CDosOMXLefstOmZKIjoPJ5KmK3+aHzcuYmLSwwxSy9qvxRxvRYvsvcWBg8YSpgag5Po5Xk4ACmbhi84OGiC7jaHwKe2imqtOTGH3rerHqoa3GxFqJaYTWvV696LX9UgmzOVuHnCATo7a/w9ABPW2R61GBGtTusUpmRKqyraOM81wvx0j5R4B+Nmr8+EEQqd4jPFaYArHge5SK28lQAl2zAgz/KXobxV8w/Y+ea9GEMhuCY1rbWDIZ8u1XOr0r7BvCDhxIMKB2DKQuUPMHnWpwf4DIkE4vNxcBgyJeTI9m9l58g3tu+x0CJFl36JaSqjSPl2W4H4n8ULy5gCkb+ANbFueZ9fLf85Yy9aBTm1VnmHsNgaOoEM8hE0oC1TAjLKWSier/P8yZatsl0f0yVIvhBtXU9EE0MynODcxxKyXOanSXV3z6e7q3ysUeGTefz2HSxhrIzED7PMoMhPC+KnaYv4PlTFzLzYK8kEz2wIQxatFC6PIOdBO7zTJe01IDpIgY3KHpszWX53oHxqS47ZIOnmnwO4PuqSnEP/8Kom/eezYMpnh6j4fD0w6PIiPm88yXZZzJvW2yLYaPfTs2v8at5f8IC4wK69Q9zmp0KimxRWoKjPA5yQut9ww2KHBwcHBwc3iSoks/5FO0DblB0mFH1m7/oKBco3CTzqYt9LeI9ilgi9TWYlWaM4TETyWRmmeXsgB+vSFtmVkKbKCLyAhOjmRxEQ3UBKC+nBaP8as0o+XtdfqCBceHvQb83CLB5v5YmVyJlG8FypBLncifFFmZBWOgKhgjC18mF5kAmzzPKxo7H1DQkHyemkrSAFstBaI0ItRKqTw1gWbBVukhuMCPZga6nebppZNy8tpvpm1rY3Jbta4xgOTfXfB4ZZIaGGaJaSU0JwvwwaF4LJ88S35ei0igPolmwBmC4sr0y3C6ogrLbLzMH2PO8YpSUaFRPwWqGqG7AJ5cLTMnzFN8tNzw1X3oVgE3Uae6BrDk/oU1czwPTfHvM+c6cOVssj75aVKaOGT6NseE6QwSg3xSDSJjgaa5WsG6S0QCQSg9gijLHU556Wj08ITsTpsEqUTlPhQLQAX5ugOUAoeOp6V/0a1t2BGJ2LK+mmdGHUeAVfRxp54APBaf5/gXDBpNYazILdjLFpo9hMEZsGYCSNryfEO8fz0dc+2yvXA4MHs5j+0tc/ojvzWy/+TyxXbYbxZzRHpy/6etG9pp1+t69Q6wbnC8fZuVN/GxX44/w+LT/VbkSh6MPNyhycHBwcHB4k6BS86iio+HXsY3jFW5QdJiRGKxQOVyxc/cQPGIOPTUgL0Ga56sTW837YI7FuZwCXOQUfAiRER0j6sbcf80nIzydJo5IDFExoiBdKkIXotX3QkVNl1uhtSrLAV0IImgIOHUEHRltLrLF5wDaAWYN7QhNqpTcUznVtsWEkJH15sRlTVY4Bafk8WmGCLCmefz59ONGKnti0HwJfYMu2hvZzUxPni/ayLj4nkpcRmCpSWX3s04ltnVCLodivn4+R34ViraYi1kNKNG50hyVuA8SvyJqzysxPXRWQM/zqrAr9HH8igK3YO8ArfeK7JXvdcFYROsAGCJ9HJpdiO+UnbjWaZi2kbcafUlsL66TFtdD1GzeZyTRRuk59f4SGzb7gh4MgBYGrNlM5T2yfabfgtGxjAgzG5G98hgqEXNOYd5qGSJGdpbpkLivwDxB64T94rFQwXMCpqbQGEEQbosWm+91IkahjZfD8weL83FAJ6YLLkODpMuD4BqC2bWGoSibwufJWoMwE57uNx9ERnn/fHzWZJZPI55vYK56VjfXTAFW41SqUWqu+b/7kl3yHFTkNbDMEMC7gG5SF4Jt2V6hREE9aI4AKocg+6xyHE+fOfNGBwcHBwcHBwdyTNFhh1epiXl5GNChzAY0Cen5JhJKbOXPwUTU5Nw6jMMQ8YCBgSYAkR/WQzZaGdF0AenhzefuwWih6KgudeBTEZ4tW6CYIVsuIyU1FgD2iwgQrAgivfB4hdsjt4tIUetetPZn7zmSdUg8b8K15BazYGoeay0Us2QZtRnYYehr8sbZn6LDdYYISG4yFwfRfWgvmxMOKmqkS/r+T50iBStgywK9JgINrd1mvgjofG95DLl5beI9srR0BlRQ6Vpg74B0aWQqIlpPcDZXMCXFO6VW01kzs8zx4hyCddMFb8EQlVrwyn2Js+OSm832oaOrRMz2oWep9wEuYcGHUWKmZ/Q00yn7njDp2GCIgBGUM+G+ApYh1y0Wa2AL/VP1/5G9WUwExLK6P+mAWh+DzvQLjZq+U2NjSfQh3JflmOn3uR7u/7wdXe4iupfLdkSbx73oC/lWZJPJ7DJsFwaiOMdW88ddoKEsCa+HzEP7/IngecMFl5Fiz9sFS4fjKLDTRr2EDLLM+J7g98icRB/TZpNggKIjMgNUa7a02et0o87MEnOwmR3cQZhum9Nv+tfgC0w7+/mZW8CxmI/BEKH8DuAv1RqMRo8EqjUfVQ8y+6zqss8cHBwcHBwcjnW46bN9ww2KDjMqIR9Vwj4aOZ11JpwIk1rI2gH2pokOyQgtsUtFFUVsT2oZGnyC+BU+PNExNnmz5QWkiRo+r2t7mOHhRIqyMmer+yYhKuL3JbTPvCIjC68NJRRSsuEwk4TWAdkp/qJcLt+tjEgUo4Psn44/mOOHUSIYIqB1O7JdeL+2zAIaZF6g+QBS8yVLU0wSTbG2p+sFwwghugd8IxPcOG57QpavqCYMi6VNDVu2KfotqTQLaQ5Bo9LLRjM55Zj8vuUPQ+J9LW7ogMnTpMkj2Dy8+sryXIAhAsBaZvrYY4b7RHqubPbYW7g0xUazHDIQ2zbK4y2x3g0Zh1rPFhmThW/RF6EX2/wXHWL7Fur3wOpjYDiqiLgQs6WlaZdNs0hoCwC2DNvSRWojY2ZngXGmWpgh8Qol8VoNsrGljxnmHtn/cW7Co2Y70BpphgieWLb9YHyYMdn7FqmnAhMU2yOXx3HA0wnMV7YH+zXLTc01+0vswnMK2Zrw9/LE5zgOsCs+xdjieYbnhtVOsuYJujftm6RLtWiGCNutH4dp39TC+nqhnaafFwdMBwvsMe+H9piOBmYI/Qrn0M+veMbbNqln35FGlQ5eKH10j+DwwmmKHBwcHBwcHBzIMUWHHaNLfTQ2zfcETEvXC+a1MXsLEZUcyev3mJO3ZUOU8yuQ7TaRELLIdCYRIi09xVxQGiBdCFYDRUyhDYjtae6HgkgTkSw0FTr7A/oUMFVRROI1uR19vDpSRJZQsVWWP9E6l4ZyK3wdpga0fke+nc5ClBKqZMB/bzarFJWBTlqmoYz/2Sly32BmVLFavR4F+KIqvyKgwO7F8c1T8ossFxntNlqbqZMNo2L1G0UZjYdS5gt44wDI0AHAEAH5dizHfTphttv7pDTV0v5NOaUXg2YqNmTYkPSAYU/AEAGFDrTfvDa4sPN7HeXqkhFhxWpCU0XUqLEDU4vyEu3rmzOckRGzc+i2LEPEqLI3lcdaoir7HmVmS5ZPM8PhUcmuwb8MyLGjdEi1O20tmlijyF0L9ync1W2ZHt6v1QJmwAqyLxJc21nrGJlgJlY9twJw4vdk2Q7tezT9nBM1Mj71rNiafF+Bdojbr7RW+rk2NQ//ScfsQIZ1eOX6MVWG5cPaV1BsCzcRGjX0t4hydp/qN42tBol8uSP/E3xozBuPXz7FDYocHBwcHBzeJDg0ZT7coMjhdSK5sUa1SD3y1IwEtCxwetW+JohcdOaQdt0toZaZTn5RRIe/yMzLaHN/DGRdhNLNmaEKSisxQxVSUT4iSmiVoPPAPYTIsR4Rmi/yHfJ49JR3Tn2P/SBCtevpbDV1/GCe4oOqgKYndwgfmdgesyNdaBKutLHh+vGDGbLb1AyRytgoLVtIRI16E7iWA4EtrAFi7VKtg1NzuM3emHFJr8w12THoQyh8CiD7rdbXRc0AdqMSbB4BA9l+IxwZX6x0KigGiiKgPfK4Epv4C1u3ymw43c+f90sdGDD7t9L2F30ILCD6WklF7pqFrer3QfmqGSPNVgRy07QrzIjCSRnA/dO2yVx7sH2BXaNiuVrCnMNKa3PvpTL7kYWZcYH/ERDdyxlRikkCMwTg/oOvWXqANUHj7D90qqFUQpvNAnjOIGsU2WCxEakNQjaZreenGOiCzWojXo6zzlQmJJ5j9VqInniPe0M78zcUmFZ9tMGpXzFEDb5sYMSgqSphPSLNw3plsPO8a/4VjSrtEM4Jnl266HW+iyivyF+How83KDpCAN2c75APN21eiNRXVJ+21ab5ps13ynRPIDLGNHS7vGH9ukxGXpmmYVDG7YBpGr5HCjpucAggo4PmDi+yDT5M1zRgHof06mBaVl7X9Db2r1Nr9TSZHkjYEg9q4IEipEDHOjndEEjJ93vPMl4E+rq0bJfvUwt5cLWHKPnfg2bXahBUq/APCZeXqJ04T3yfmcM/RPxDg8FQdNuEWI54O5juIjWAK883v8ylFvNDGuKpGf/QuFiuOtcs55XM9qYWJ8X3MFvEYCD5qjk39dR4Mx2XniUF0hX5u2yvRWwXDALN+/iQPIdZFv9imgHTXwAEqpk5SpiuZte0SSbaO8m2CyGePYSgGrA9g08njgcDnugYftTYIHC81pB4gB/vKNfmbduk3E+5P5b7zcH5U+qGRJvD2khSTQtH1aC9N6TeQ5Suds+bwWCo3MoFWHlc7d8TFsu3blH9foec9sOAFM+HBtuFGqZ+ZTts4gIXtkZfw2DFipMb7msd9MCSpCaOr94eFt8P6PPJ2+PLgzR5TCtOLEEgYbYDu4jp05XBtJxq1GVlMnP0s1xds365vK/UOBA/EqiSR9WDrEp7sOu/keEGRQ4ODg4ODm8SuOmzfcMNig4zCu0e5WMeFWA6hkKsKD6pimSCrraMTkhGUjq9ODKGwoVsvDeu6GZrEmc+r08JSIG2NWOEWRtS8D35inTSEkd6jeUvzILhKWnGCMYIETC2B2FkZByRX3MBNEozADqSLsblceV4WgOiR2simZQ0w/hiRXMwbHmBXHOGqGPdtLIIU/KigCGymGtCxMy8hGi7ZsOCUyq8HucwtsWs5+WZnZtrGCOUZgBDBOR7TFhcPMGsl1xvqJKh5a1iObCQ8WHdNwxGTjedodhm3sN0EX0J8E3IZuvioBC9ZtXnVfX00X1bT41iei/K1xRTOAAYokyvLLWhmSU9pYqkgHqpDfMKg0YwArkuz7Yx1yO3gWlklNtIbGvOCBV7pK2CV5HzPuUYynlUeT/yJMHiQs8P6TI+9nOIf7mIaRoMEQuJO1/Ekmz1sbfM2+PnSEj9+DFLWZ+alO2AJQbudwvF4Pp5uy07uPxHTDJRejquPh2PB5NkpDSjBMasBIYIl4MPZ3yZOU5fjhnrLsOKFvp4u1vMioFsfdrVPrPb5KFVQ3rq0xz7+BnMAO9hg0/uV2Hj+UjJLWVKFo4CVeSwT7hBkYODg4ODw5sEh8a80TFFDq8TXtX8gW2EiBSRlk4f1qnviJb1nDuiepiygVFBNILUcwDfo9wHGJmwShcFCwBTs8iETk/mMiTQOvnRTm4fyo1AA9WG1FuxmcYUf35bYfkI0sFRNkFrB6DDgQaL4pIh0tqIkCo2mumBpkk2w6bkckSa7ZaRcNeLnK7OkWvbwxupVpC6JF97m9l2q7nY6ZPb1bGyOJX1VTBbDGzYKRcrc9TO5AIYImBwuTlZ0MygDIcW5Q6drxgi/hqaGVxLzdw0RsTmFbo2CJ2hOwO01qIiZSt140Cc67xcLsx9Lr5bbig0YsQcqZNlw6BPm6lkAvRhwSzaB7GwFPUCYB2LKDUBNqJQv+6tW+VKLZuVYlbpvorJkGgjWK3QuGQKcvPlRYCeCYB+C0yIZohwbXENcAyZAWZuS2Z7ia3YrmSIUEYIz4eqKhzbcK0125mRDapbbbBWh8scWVE/M0bQXELr4+F5FfOL48Rzxa8IlkCWi/x2mPPXst1ctLElQbF+ms+DL89sbb8U8fvWyarF+Z4q+XpUcd68vEb+iEqM2GbuS4jc47tJtAFaPZR8OtKo1jyqHqx540Gu/0bG8Tvcc3BwcHBwcHA4ADim6DAjkK5RsFKz2VllZkJ0NI0UeJgPgnHJt6tUUtY56CgbUW/7Ro4IYYqWlMxQkSM9lBGB5sdXkJEvNDxIrUUJCpuFhsAyD7ZBapisKRxHS4gE0c4GawKraZJaHm09YLUDSOnl7BVok/qeMScmOGF2nJttTjjMLu3+OOLWrAWAiHjWk4aGycw1EWTr45vEctMzzrykCcuzS2eLZXBsCK6gAwOQrl2r8TWAzmRu3/RDpVyXzDgCQwRMzTW3c57rymrDPiCmzAm1rg3sQkBpfDSjkultrhFCtA/mR6fY28wf4yTQUDJGa4nAEOVnmQ2FJsGIQavBrCGvhgK96Lvoc9C5ZbuV/QO/hd6vKJPybEmY6duy3200bfPlJFuYn63YuZAs9KzNSsts/BnfbTpioT3A+2Z9yomcSZeSbcM5s9lV6rmCrNT4oNkfnietO2QfrCh9lrb2wHFHxlX5obx8j2tS3w7S9OR9ivfIRvNK6APNLT5wjcBYYb/4HPsNpcxxTSw25xPPKdwT0UHWnS0xJy7+jGSGKlHZ/OksUTBYEa/FApswljjzb6fUJ0LPCOPJ+C6zz0y/uTHC4+VGg9YjgOohmD5z5o0ODg4ODg4OxzyqNR9VDzJ77GDXfyPDDYoOM8pxj0rTPEagBYBfBrQrujxFph9z++a9nUtXNiihSWgCzHudFQJ/EzAjKMyKLA+YqgXynAUSNREpNDcACtCCqamE0TBkgcisk0Iba3bAUCWQXUJiORsxcwCJ7DZEpoGCZIYQ/QP4PjYkGSIA5w37m5rDx8WfI3LXppM9/y1pmJZH1pnvS1ywk40UKVi/hfJLTJaZLbnSpQrDwjuKz3VoizG3qanyHeXTF4r32V4TWeYUw1FWUW1Z2vk0ZPBY7yXFAvhKsu9BK5TrrPvziP2ALeFTYBkiyDNwzrmvgikKatkNswZgRcHWhcckbQc/JWiwNBsBfdnUHGYLpqRmCMh1SqNAraGCN03LTlWEeJpHVusWScX40+YgwWBohijfxd5RKVnOBsdc5FIsxMcAfRTOjd3vNtOmsSWctbXNfK77APovNHt+eTtQfIjvO9wGYFxVMWKdKdj2qmSW0MdrAWSjaaZJ+prpTmc1gYohwnPFDwalJjWV0BhB84j7P9OH50tzo0T4SNnj+W8pdIuO8nOLmfXJi0xn9rZHybeQmcoRPtlRZox2yW2g2DfaOt3YlYho9DR5sQJ5P1V9R15XVCGPKgfpM3Sg6z/++OP0jW98g1avXk2Dg4N033330fve976my/7N3/wN/cu//At95zvfoc9+9rP280KhQCtWrKB77rmHcrkcvfOd76Qf/vCHNGfOHLvM+Pg4XXfddfTLX/6SiIiuuOIKuu2226itrW2/23r8DvccHBwcHBwcjjoymQwtW7aMbr/99n0u95//+Z/07LPPUn9/f8N3n/3sZ+m+++6je++9l373u99ROp2myy+/nCrTLFA+/OEP05o1a+jBBx+kBx98kNasWUMf+chHDqitjik6zAhmahSaFvXpEgrQc2gdg2YnwQQB0GHAfh9AdhicnBExgqGCXgMMCtpT6TAhZo4LyEZGlXZA+4AwwwRGCdG31iYgcrVMl7XP94v2ATbLjper+SRjptkQbfcPDZH+HvuPjpoPUIg2zYVnu1dLsYa3ZRcfEGsXStqil5cLhyl/qolU4N1kv1MZMijNAFTbDauQeYvUIGmXb+imcM0LbUqPwocM/xNAu3IXla6qzHIKOPTWPaOq/Mrfc2bP+OIQt4+Xx/GpoNGWYOFr27JDLo/iwdoDCqxmlQvC+lD0l13g8Wo9q2qSIQI0ywHGSrMHKOEC1NTTMLrXrGDj+2mLhwdNfyloZqiTN+JJ368y7iuV7QmGaGpAmilNnmA+737evIIhAuqFTHEs8j3YMrBrgPb3wn0RTOtCyTLDEE7Rdj17P/rFe8BmpXn7ZhRwz4Bxwv1aCnFGIfzP8JyyWbucHdoj2T/dJ20xV1XiBZpK217OhsNxtv7UXMed/49HvhcN1QnFUCUaEPuMqnMf4eLVNvO2DDZM9vdCq48K+SPPSxyN6bPLLruMLrvssn0us2vXLvrMZz5Dv/rVr+hP//RPxXeTk5O0cuVKuvvuu+ld73oXERGtWrWK5s6dS4888ghdeumltG7dOnrwwQfpmWeeoXPPPZeIiH784x/T8uXLaf369XTSSSftV1vdoOgwoxLwxECooYaZHgzhZlYi15LUA9opjwybu+nBUWwYPxzmBYMkXY6jwtW48QOFh2kV02v8kPCUMHpqnnyIY6oG0wR2++F93zz63sIPo25nkac2arzb5KumXaFJNS2R45IYfqRVy4GKfrh3Pzsh3ns7BmX7ZhoMJeoXJJApidfM7GjTYygkpRAzOirPoa46jykGvR1dyqGFxbj4gYjzjz3MBxu214Jriy/MizZlxGAoNWB+UXQdO0w3wfxQ7wc/sHowBFhjUH9z8T2mSID4sOxbVvitfnf14AaDuAq/YsopwoPIYA5iX12JnX/U+AczOFmfu87PMTeuV8OPszw4nHtMB1sxuRImzzQYAsbeby5Sccz0ndAwC7C3yuVgXQHg2gF20KHGZLp8CITayS2cuq6uib5fQylzcf0581ro4L6ijtMGTVGI39XgjPsA9maDNl4MZrQ6YNBToGFtIZKQQRWmgvE8bF+v6gMWeYqWLRTmPFq0Az+I3WfqX/U2QZogEwDqK9T/1dfjSKBCBz791WwbRESplAwmw+EwhcPhxhVeA9VqlT7ykY/Q3//939Opp57a8P3q1aupVCrRJZdcYj/r7++n0047jZ566im69NJL6emnn6ZkMmkHRERE5513HiWTSXrqqaf2e1Dkps8cHBwcHBwcDhhz586lZDJp/2655ZbXtZ1/+qd/okAgQNddd13T74eGhigUClF7u/Rq6+3tpaGhIbtMT09Pw7o9PT12mf2BY4oOM0qtRMVpMzrhCfNaZPGpT6XQQowK2l1HJYgsNDOESAi0MiI8lA0JpZoXpLXW9RxRxfeodHE2Y0OEB2FkdETRzyyURFRdLwegWAR+CxGsTrnHFI5Ova9hyog3D/bEV+ZpiRF5IottQbEdRK7hMak89bbyNFlVCjtnAoq7Ums9zzw7SzJADaVbVJSuK44DEKtbQ0xQ+dtgGGn2DUYouhfFdsFKQCAty4g0TG3ALJGnFMAsYQoBmFgEw0Fqup3oCDNPbCQKg8w688P7UQyRTrkPM9sH24RCC8S2KupX03/arkKzrjjOmQTpo0vN9pKv8lTRkOy7tr0Z07dK7RGq8XeZfsXygXFRCQN2G3wtJxeymSB/PXGS6m/KKBAMEdD5ktxuEEyunpVT0/RWgKyYH0xx4r5EX7VmsHxfg7kJjZtzUebSMmCIAH0fomyJ3Z9isAB9bUuKwcJUMO4RFMC11iC2IDS/593Y6Xi88j3Rso0L3YINLJvjnDjRMMDJjYahK3SH7TPJShD4VU/Hoo36/teC61xX/VhLgYNjbF4PDuX02Y4dO6i1tT6F/HpYotWrV9P3vvc9ev7558l7jelWjVqtJtZptr5e5rXgmCIHBwcHB4c3CVAQ9mD/iIhaW1vF3+sZFD3xxBM0PDxMAwMDFAgEKBAI0LZt2+jzn/88zZ8/n4iI+vr6qFgs0vj4uFh3eHiYent77TJ79uzRm6e9e/faZfYHjik6QsDcd67LvELPAKM8MERWkMyRj6fM2TrWK/VuVTEqHCFmeuWlzcwyr3GWzMBYT6fsIuL162KcOWmWZsWDecUQMSZYlKuPr6G8B2/P6lkUUwNWwVeRhWRje+R5SM03BwSBdmKHibhLCdZcvSS1Qva8cbHV6qi5IF7ALG9LbPgVs3aiUbh6GXPixt/SZb9DtKsd8DXjseMKc64G/pPFuKyvQDowkOuQGiEwS/GhitoP67BiSpTO1xLMCIqZah1Gah4XE54Au8fMFmRpVtdhXrXRKFCzrKRZEZogf3NZloVmCWzKPPQjNilArqcLvYb5eWmNDPPy1bKUfD6Sr0o7BwCFeb0y21S01FW6YIjssXPTtf1BaKIi9gmGCEgtlDYE1dmSIepsNxer+ttu2bas7Pf2HHE31fcnGCIwzpWAvLZgUjVjiwQHsJGhCckAoTSNLeAMZjismejmjJXej30u8P1fYmsQ29eUvUKDNUlaJxV4Yrmg0hICEyea5waeP2CYphaYTpTr8lm9FowwAX0f4b7Bs9sW9Y5AdO9xW+rPbM2avhnxkY98xIqngUsvvZQ+8pGP0Mc+9jEiIjrrrLMoGAzSww8/TFdddRUREQ0ODtLatWvp1ltvJSKi5cuX0+TkJP3+97+nc845h4iInn32WZqcnKTzzz9/v9vjBkUODg4ODg5vEtTIo+pBCq1rB7h+Op2mTZvq1QC2bNlCa9asoY6ODhoYGKDOzk6xfDAYpL6+PiuOTiaT9IlPfII+//nPU2dnJ3V0dNCKFSto6dKldkC1ZMkSeve7303XXHMN/ehHPyIiok996lN0+eWX77fImsgNig47SgmTOYa0TcyBI20TjBGyJBDtI8Js2SEjQ7AKdm5eBRoBTsH1sXZoasBsr+U8mdOffdpEoDAca9lR4nagUKrUTtTNGeUO0/2mC1lGh5uLbDG0Lz4os8SqNqri1OGMLE+CbLesYrygJcpgvzCpK0LrZBoQeGmr+TzPVFiHFOhV+jrM92y+5/MrtiJtNAWVBcYvw2PNUWYAWiLzWo549lxp5kIX69x7oWlbrWKW3/Ze83mESw8kOHVdpw9Dg4CCr8hmBKOCdGp/wXwOTZG2LwCDotk7nbFjI2Bkl6sgG31UPxd15qHOZrNFiZnBgmGg1t80ZJPpDEV1XGA7oSsJ8astnpyQ5wsFOrXNRDAt77VKzFzQ9Oz6BdHsGEo4AGCIgHEuN1HoUMfQLdkpnzrm8n+Z+xOHHh2V9w+gWUlk/NnMP8XS6T4JBgZ9Dvdvyw5FIetrogvetstOq7NVwQbqvqR1dWCS0HcqiiGqG4M2ZyvRN23pF6TNc8FblNWYWGy0WjgfWsNky4kU6ucEOk9cA1hX4JmkLTCgebPHYmeWsHy1QeN1JDB9+utgtnEgeO655+jiiy+276+//noiIrr66qvpzjvv3K9tfOc736FAIEBXXXWVNW+88847yT+Nzf/5z39O1113nc1Su+KKK17TG0nDDYocHBwcHBwcDhsuuugiqr1GEst0bN26teGzSCRCt912G912220zrtfR0UGrVq16PU20cIOiw4zoXqL8NM8hMBulRPOIL6D8ggD4/YBJwXswRmBypgZkxAaGaGJNN2/ffN71ooyKa9ZbA2U2sD/+nkNZy2hBe4SyHyEZcbawORpYDKCepcPMUx7mcDKELbKpG77PzFLlPSbMa+tmw+jUmOkJbOJsMtYGwXGtuKiX989MmM3OMfRJZNCcGP+4uViTFy8W+0NmVr3Arvk8PeDVM3MmzSsYmNQixUTsMaxBy1bzPjNbrgcGxzIffInAEAEwyLRFMVFiok1eM82oaFYBzJH1VWHfIs14wfAS0L5J0DiFxyUtke+UfXF6uYzp7QSgY4vmFdtyEmuT+LxotiGYln1QM0DIOAI7C/YhvlMJU9RDe2pAikanF8Bt3SH9hwDNbGqGCNmn4TUhfm/WH7nUHHPyd+aiaN8h3H+aWWnwwGHYQsdgIspyeTBElvVjWFPEXtO+xHbp44PCtdqXSZ+HhsxKlAVRlBjeZ3ulaWxFMVgA2BhkKIJKQ8YX/NlQDig+WOL9mOUmTjAPBLA2ug+i3Mf0Pg7mFHoj3B/5Dvks0L5i2DYYI3gk4Vz5yq+ttzscqNY8quqHwevYxvEKNyhycHBwcHB4k6BCPqocZOL5wa7/RsYxPSi68cYb6aabbhKfTTdzaobHHnuMrr/+enrppZeov7+fvvCFL9CnP/3ppsvee++99KEPfYj+7M/+jP7zP//zdbXRn69RwKvVvV6U+6ktzGrLcWAuXEa/Ae0QW0JExYUQmRHJ/JlxGI2HjbACDBHQvYbn1pn5qLH3DfxI7PaZSUFWTcPcPiHCQ/vlt9EtMgSr+5/I5ZDVhiKfOO50vzxRyJyChiP55DbVHM686jbaIY+daavtMd6PuQAFLmeCciiIYEuLjD/J1JwW3g+J9hRbTPuQWYJoLzg57VgQ9fGpiu/ic9Rn3oMhAuI7zSsygxBxBpQ2ocCu3IjytVsw9CNwJU/Nbx5lawTT8j3aj9fgDJk/oZSiJ9Rbjz1fonsME5Prk147YAeQEYSgEywi7gEwRED+AtPgyjZzrTpf4Hby+apn9TCbwMwZGCKgdb104a1GJOsxelqc15fHBZaHqO4VBaCki71fcE3krqjE7F1it1kOLETHr805ashcBKtQ0tdc9l+cU/RP6MrAnMzEKFnXZd48HOKBfI/y4JrBVwx9GJqqsrq/dHkRMFRVlemIa9ag91L3BNobZl0P2pFTPmyjp8qbABlkPu77+nmEdlkH8mkMGI5B64BQdqeMY0KWJj8bbJUAPke4RtUgUbW5bdNhhWOK9o1jelBERHTqqafSI488Yt/7VQr1dGzZsoXe85730DXXXEOrVq2iJ598kq699lrq7u6mD3zgA2LZbdu20YoVK+jCCy88bG13cHBwcHBweOPgmB8UBQIB6uvr269l77jjDhoYGKDvfve7RGRS+J577jn65je/KQZFlUqF/sf/+B9000030RNPPEETExMH3c6qyuRBJNng2wPbkZKM4LQPkPUzYtZg+NOSqpnMGm1C1x/leno7fqXlAZPiV8yRX2mckC1GnjkwZK9ZR1kVTflY91Lyo4AsaymWKW0CfEU4ktMRY/K/jd9QLSuPt3rSABHVfVJCe6a4fZzp1GZEBMiCQfsyfTKCje1F5E3iNaDkJ2C+gjmiQpI/0745E+a1fR0fAxgRvvbarTyovFZ0FItrrl2Dx06Rls1gLQLImlHaImQ8hpRuxa/c1XGNdJRumR7uAzgX2V5zjsGG2uyvOByq5XY1pjv9EtVrq9FJkiECCklPvCZ2y74cHzQnILI7Iz4vt0v2Y2KRfK8ZogZvMGosQgsmGMyuRkXuou5ADx8yZuN0wVjch1he1xLUXk7QbaEvpmfz/ZDSfUu95+cNGB48d6zTNV9TFFKuHwf8gJgd3C3px0rMMDXpeVwPUGmYslyVofNlqeWxTvRgVZTfkc76A4OlMw4B3NfJjbIvTM3XFab53uT9Fdo8y/6BgdUI4VFjXbP5fVV+HgDrNP3UHwWboir5qHqQ018Hu/4bGcf8oGjjxo3U399P4XCYzj33XPra175GCxcubLrs008/LQrKERmTqJUrV1KpVKJg0DxJvvzlL1N3dzd94hOfoCeeeOKg2hfM1ShYqzWkjkI8q1NsQQMDmCazUwxx83SanCcZsQjuTEZ4VYt4H9tt7lS/Mn/LzJM/NBByW/EtTBv5vRUV8kMVg6HYumEiIqr0GLVwlatcl+PmnOryItkepP6TOL7YpBRgJ9eOmy92Gk8DnJ1a0TwtfVyY1T/JQml8H+LpuLw53lx3XLQfP16J3bJMgi2iyjvCQxyDIvzYoEzL9GVt+u+InPrEMeL7sjzlFGYBZmK3VF0G0vI9ynwA+S7zg4MpEmujMFsuFx6T+2soT6AG5hhU6cKu9QE8pnp56rNFXksgy5XhERBEJuQ0gp2C7amnPxMR5ZT5bHWzOWGBPA8c9srtAeFx2beDY3LgXOyWP4Co0A5Exiv8KvePgUIgW7XXAIaaELfbDGVPvga5CRDfI9jR1wC2BOhf9ar1arpKle9psEWY0kVtdao5r9YwAJbvdVBEqmyIFkyHx83FwyAIyMwxgyGb+t6Cgax5j8GQ3c5E84QLAH0n36nOizqf7RukpUB4nfFhqMw1coIy2y1Eh0u8PfM+wM/bUqJeRgSDdVuCiQe4ya1qSlMNiNFW+4zTvgu1WoPdxJFApeZR5SCnvw52/TcyjulB0bnnnkt33XUXnXjiibRnzx66+eab6fzzz6eXXnqpwQyKyBSM03bfvb29VC6XaWRkhGbNmkVPPvkkrVy5ktasWbPf7SgUClQo1G9CXTnYwcHBwcHB4Y2PY3pQdNlll9n/ly5dSsuXL6dFixbRz372M2sOpaELw8E7wfM8mpqaor/6q7+iH//4x9TV1dVs9aa45ZZbGgTfQHCyQqF8hfwRRXurCA5RuTY9AzMEoHRCiFnq1ALeD38/9YIZDPpicju5HjO1kdhqomnLEKk0ZDttV4bAWEVqHA0hKgdDRMzc+HeOmOUWmsEnhOAzpWMHmeACRd2yQ4aw1TiX7+g0AurasNm+L9kqliOeTps8Zw4RNZbMAHVtU3D59Gs6H7Q7IjikydupLyYbICwHyzN9XQguIXSeOt/kunsbzTlv28Ap4sxMRHdn5TEHEc7zvnaNmmNaLAf0MD+0hSh5/62bmU3g6L7ElxpTEYhcEbVjP5himTHtG+1hgTT6QoBZhTozxIJT9XRB2nShVW4X7AhewyNqPVX4FceL8wumDPCVzPu957WJz/VyaCdYmtSAaX/r9oo4vunsC84lhPp6atJOfyNGUoQLpkTr07I8XatYtkyfPHkwb6xvR053AblOea50MdyWHWo6He1h5qmm2o8+pBmr+E5zA6CvaDPHQpe5b5EgMrlQHk9iJ4TM+2a29P3ZMGtjS8Jwu/Yo5okZImphRnnC3GvlmKGqwBDV+7i0KvAXag1MLxijbDfS/s0XgbK8f3RRXp8ucJzwUbVy5BkXJ7TeN47pQZFGPB6npUuX0saNG5t+39fX15CZNjw8TIFAgDo7O+mll16irVu30nvf+177fZWdjAOBAK1fv54WLVrUsN0vfvGLYhCWSqVo7ty5h+KQHBwcHBwcDhlqNZ+tcn8w2zhecVwNigqFAq1bt27GjLHly5fT/fffLz576KGH6Oyzz6ZgMEgnn3wy/fGPfxTff+lLX6KpqSn63ve+N+NAJxwOz1gd2COZDouITKfYapRjJjxBdK0BhgiMRe4ZwxDFlR7CCix5M+kFJmIKcjkQpJkGlPlakdPAsVyh3XSV+C5l/69QYoYIWiIIMINZsx+wGdq0DKZpOO7gpFnAv0UWcvU62sw/EXO+J083jB4i56k5UtRrI2EQUErzYbVErUoPoETTYIwCHCVCFJ3v8Br0DEDqDLPT6IvmnEPg3L5WTq+WW8yx+AqsDYJ2iFm8kYtNv2vZWeRj5GK7LDDGK7RAOGabAqzMICt+eQwoVwBYmwIIpGfoG4iEobGA4BmoMEsRGeXvFbln9wcBttIzQ8fVsla2D1omAH0Yr5NcHDjMpxmiXKuNYqZr93vN+W55kc+nISNppJ3ZWL6XOpgpgiEgUZ0NQGkVCKlxzm1ihUq51oJ9sAc1GP2x9i6uCh77VMJBvlOVv4GdgSQdLVMEhkgXIMU5wbmvlwkx2wdjAgE4GKL6Btj+QNku6CK/8d3qGipbCW2AOH6i2T/S2sEQWdFyFu01r7Fhs73EdnkCJt4+j4iI2v5g6MdCf1J871eJJyhbBBSSntWFoSCxNtbU2wDbBH0oRPS4X2wJkWKtIZHE4ejjmB4UrVixgt773vfSwMAADQ8P080330ypVIquvvpqIjIMzq5du+iuu+4iIqJPf/rTdPvtt9P1119P11xzDT399NO0cuVKuueee4jI2IifdtppYh9tbW1ERA2fOzg4ODg4HGuokEeVgywIe7Drv5FxTA+Kdu7cSR/60IdoZGSEuru76bzzzqNnnnmG5s0z0cHg4CBt377dLr9gwQJ64IEH6HOf+xz94Ac/oP7+fvr+97/f4FF0KFFo81M+UhcMzMQqYM4ekQ+Ka8I0UM+1d71oopPWTUZctOc8E4aDcQFqKmIF8wMg6ob2x6aaq+UioyaULCfMBmMvcTYYa36IX8EQAZlZ3H4/Mph4v8w+QK+hDQn9YE1YS1TuYkO9DpndApM6q19B8hg0QDaCRySt5vWZiUMBW5ynqiL+/CoCR9QXyNevmTXoRCbdhpBok86IAUMEjC+RAhWwWDDIRMo7stfAwGiGJcvSozAzHVpLZNupsmF0mrZmiKxWSGklkEYNJsqm0jNwbitK36KNDaNjcn8xZkvATCW2yXTqUpLPh2WIzGvb5hnsKzii3/E+ZeZ4mZlSn3jUWHv4+DKBEchwpmRkomqzzfQ5KOv+wudc2yqgYLFdT5ka6sKvYG7CbEaa6zYdXRtzgqmCpUWI7RJCfMpmKhwLgJG2liGsp+p8SXZ8X1E+F3KzZJ/FemDALBPFq6GPaY0SzmuB2TrodvD8wn2MJNuKOt8ouZGaZ9JCW3ayzo0zx7J/YnL//YroDmVknwspW4zIRM3qE2Mjmt3C/cBMJTNC2ijTluPh5W2GK3lU9B35aahq7eA1QTO4ahwXOKYHRffee+8+v29Wffcd73gHPf/88/u9j/2t4Ovg4ODg4OBwbOOYHhQdCwhNVSlcrFIpinIc0CBwwdOsjD7AEKXmScYF2VmItMAQAZ1rTYgI7xpoYjQzBb8hZJdphgjI9rCGaKgs1rMom889juhyJ5lITBfEbCgRkW7OPpSURgJ+SdQS4uNi3yEEqvx1lKM3eImg6CIiSWuOx5oLrR1CxO3jyB66EsusKTYDbEd1Gjtk98UsWHyYWTC+RGCnKogo+w3Vg0xC6Cly3ZJxQUYNWAAwPYG0/D7CjMbUADIDZZuRdQWNQ4avEbYHzyhtHhlUfdOycax1wjkvJcRi9jwAmjmyfk28XnxXc4YoPJLjV/P51EKzQniCMyhnyXsEpVkABMN1g07z2vqiedeiGCK7/5EZjEqp3n/hrQSU1TkgvkboX11r5Y1YU0wQoFkGsAvQEGl9qy2wzMC1qDEDEUrLwrU4J+jfYC20T1HbJvYdYjPUYEpSLJWoaY/OqEJGZEP7+L6BBgvaJjBEQIzvHfg0peZBY6UYHTaTRVmQoupjqXn79upCli2ujy0qHME9WPeDwndgu3GNoDGqMGuY2GWucSnB5ywjWbWpOWYFXMP4cIVihSNf56N6CITWB7v+GxluUOTg4ODg4PAmQZU8qh6kJuhg138jww2KjhAQmQRZwwOH63r2AbKZTJTR/oqJktNzDU3Q8Yy0Eij1t5m1EEl2SZEBohrMxUOr4KnIzmcddjn671S+SMzQJHaqyfiA7Dr+PGfybOOsNtYeFdrkchOL5c3Uvl5uNtdlvp9cKI8nyd479aKpcKI2nyPCtO0pyEg83yH3A80HWIxiB2uYQuw1UmJtVcqcj1w/3JjZT2kLOxl7RLE9cOEm0aaQKo2S5kgRjIuNbpFsBmYIDtCsGSJmq5BJpEs2QJ/V8Yr0fkHf0C7pukSCduK1GiHloaJZAM2y6eAxdaqJnLtmSQon8G+dYv329ZJaqqmSDmCIgHQ/XNL5GsEtughvLa/pcemyKlMPGIYoAkavIO8FYDqLogvB4v4Co4isx7aNkhmqaJ8ydYwl5TdkHbGz0gU8M0sdE7yjoNVT2jfruxOXDA0A5jS5VTIWmhnySqq8h9IOWtdv3r4+nnrpC8nQhKxzN7SMsm9qhshTfRnHj7JA1pdJa140w8aMEJjmut6O711m7PMdjT+RePYkRovcdj+vaz5HkdrJheyxxvdHAKeU+1muw0fh3JFnXJyj9b7hBkWHGbWAR9WA1yBihfkhSg0ghVWnvLetM0/batIIGitxTh9uUzXDlAmaNl3Ug53oSKXp59pELrFV0eYskKy2y1oVGAQB2S5Oa+apndSC5invQxfIdgcneFqRH/IRnjop8MNbp49D5GgNB/lFmzDaqSf8/vJycfZ2a+VBjg+CUPvDyKnIe8z2rOX/tKoqMK7z2NNKC5nRdjxHCjxA02JRtKVeYoTbAvErnzOkvgez8r02J9SDGABVvS3UW6Q32+WxHUwbKME0znF2jlyvt3/CLM8HjsEQ0LnGDJYKPVzGg0vQTNpaZLJyPAYnGAzZ9nGWtb8oxfz1OoLmVYtD/So9HvvBYCqkfqDLUa/h/tDWEjh3xSTX+FMCYwyGMFBFf0NiAYIiXa5jakG97MT0tuq2YxCla5bZY1DT2XowFB7jH3oug4Fp/vAeM/IstZlrUmyXQUu9TA7uIz4+7qNIBsB9r6frMBjCgBhCdm1FoM+/NqeMqqlPDJQxzabPG4xWMcVtp7x8sCKoUCEppQ6RUWkwWzfY5QCqo3nwANiaaNXG6VKHow83KHJwcHBwcHiTwGmK9g03KDrMCGTKFCyVrcgW0MUobVTKrENgyjA0mQE5dZBXUYhmB1AeQJsPJjfLkBZ0N0oc5DhC1WZi6bmSzgCtbLfDaeUllVrcwAypaAlReomjfDBErVuoKRA5I4K0r1aMzPR9DpEhpg3M94UOnp7kHt++FuyONDC0YmQWmms2B1NFOL+RiZplcMAM6XUy/UpsDlNBFn/aqJkX0+UxwITY9Zk9w7FZhiMshdtg6WxaOC+HVPMcMy5gosJcYLWojPdSXF85zj6aFdW3dLTbvdjM+/nu5pIz/HnyZZ5G4w/AEAF7zzRh/+RJpo91PcespJoGw3RZnSGS+9d9vwEgFdVzXTNDevpteumYPJNelt1jaLNG2yYWVuP+wf2nmQv089FTYZxpPg8o8bpOoIgoOwOfKjyrGSIr7lfPk0BWMce8Wr6PCyoH9/1jGE7JaXqIjjF9Z/eDqUp+3ugp05IVaDdn7WJ7ivxq3pfjAT4e+RzDdjGVjTIouAcaLDoSqqzRNOuFIpt3FtqkUaWGT/UBy3DyM2l6wWNf5shPQ1XpEJT5OI41RcfvcM/BwcHBwcHB4QDgmKLDjErA18ASETUa54XGZehXajfRSGjKhPmpeawlUmU/at4MqewwF0Sx0FYZAU0vWUBUL2xZtoUQeTNZKTxEai+i/IYUYV4fwmgIyhHpTSxmIzSO8GJ7mN2YIfCwhS+jaBeEmrA2aC4Shoi52GaWD4/J5SA2hvgWaekAIvZgjvczA/vgK9Ys84CI0JYs4M9xLorqnCOat1G8YoogAkfkWS/FINvgt2wAie3Y8h9Kv1HlZiQGq2J5y3TxsU6ewMtHOOV/gXnf9jKf46Rsb3SQU95/YfpGmqviJF+WvgboOzD2hNgXDBGQHpDXLCYrvlCErQd0AVtYG9j9tcn10PfAoqCPQxyM9kTGuLwH0r6nkbaaIcKyALQvYJdat5qdTSySHQnlY5JbJEOkURfZy7bb/anVGkTm+nuknjOjAusNMFqwDPBBtB9E6rvcEPokigLrRI5QqizaXVDCZTA5dUNDA7/ajrYuyPRzKZdJ1mJxaRbcc5p50oVyJ07E89G8tm2UTLTd7/TLNYOOy77n+8eP4t5aN8X9rDZavy+jWhh/BFA7BNlnteOYKXKDIgcHBwcHhzcJqrVDMH3mss8cXi+qEZ+wtLdZFZjzZyYk2ydFN3oOXmcMaY2Bjpb9nBobVHPmmiGy5QFUxr0uF6LTlBF54thwHIiY08wCaC1A+3qZgouCk9rU0bIoiiECEIXhOMFQ6dT7YIq1R9zTO9bJ9qAMAHQoSO3XkSl0K2iHTA3m6Br6JrVuvSCkbDuyxqBDacxkMq/QDuEYcO6hp7LMFBJnwCzxfqA3QVSNiBbtwvuR07mdEbPi8nNfISKip59ZQkR1hgjoZEPC6C6TildOms5UiZiGRvcalmD3xW1ivciE0q0NsJVADpl9fM1VH8/Mlu9btsnt4HiQGahNI8EQ6QwlXDdrRcDXAazI9Oc/DDnB/ADI9gTzOF1/REQ0slQyRFo3NsnZZWA6oF3TjJA2ykzsVPeNynLVpqxgiOJDFfW5vI8rIZ94xX2I73VpGZjBWoapKG0hgLK6NzSDZdlWD8fD7/3yPe4/ZLvhvsTzNTVPH7fcjrYu0M/F6efRPmuU3hLPLhzj+FJVPmaj1Dk2FJ321VlbhzcOXteg6Ne//jX9+te/puHhYapWZUf413/910PSMAcHBwcHB4dDC5d9tm8c8KDopptuoi9/+ct09tln06xZs8jzjl8a7VACEYz2E2qY6weDEm9+XsEQIXsCQOSkmRetLUC2GUzodCSKciJoJ7aLOXpEWnrOHpFVMG1CufaN5jXfwYaFQWRmNdf22IjQJ18tM6MynGaKMOO75YKRCWUGt3VKvA9PGjEVDAGxPzBDsSFJoRXa2aofx1+rl50Axk8K8TK8TptsKzLv4oOSLQSrhmMZOwnFgM17XCucizAfCiJYfB7fXRJthAcWIlrLfPArvKLe9da1op1P/H/LiKiue0Bx0GKrOQdgiIDsLJmVMzW7+YNT6zs81QeL7XwC2vl76z/EWh82tdT6upypNGP1Kzj/KCKq2VXdh9D3wWbUmT/z6ivXl7FtVVmkqQGz0cyAYWISW3HDyH2VWuX7mTRC2Hc5wX1kwrQtOoxjU8xnLzx1JNMKaIZInwMwL5bhQRLYXtnHddkf/TyohnS5D/ke51Yz1Lo8j/YlqvuVNdft4HhadpjvtVksMj5jyhRSS2SmM8H62YRzBEB7B4AhAvBMhsEktGnhyRqFckfeqMhNn+0bBzwouuOOO+jOO++kj3zkI4ejPQ4ODg4ODg4ORwUHPCgqFot0/vnnH462HJfwqjXyqrWGSBAotClfEBW5ac8VzOVPzTYj9dbtcFGGbgT+G6zd4UKSYBOy3cqdmIP7yKjUndT3z94a7HwdRZZNTbITAXaChbtzoV1lmaAI4qA5ESgf4tPahKp81ZEiNBJaK6Gdb8NcCgMRb2hS7qjYZRiiQtK0AwxAbE+Bj4Pbwdb9tgCmKsvi1YgCab64cMEd46wqRN0d8phQsBUMEFi9NDymUAIBSVsz+Or4LItn3gf5mCsqKrfuxsgs4q/3nAu2yyzwyO9PIyKinmfNAknecct2c058GUOfhcvmWk8sNVQOHHtHzmyuCwOig82jS13ANq9ZyJLUhYF5C02aVzBEAO4RMEp1N3P0Cdk+9HGtd8E9Y/VkxTqDAOdpna0EhsgeizTxbmA8Q3zsugQJsiaBli3NmVkADBFQaDfLJ3aiGLD5PJBVWqKwzCrTGqD4LmkCVYnJ/aAYqtY0TtdREjVqdnS5EZ1RiWuN8wW2xd7XftknwPToLNG2jZpilm89eTvTxCLORtsEFrdRH4aSSUPnmHPRsQ4bwf0FvRMznMo5HSyX2P8RhKt9tm8c8MTgJz/5Sfq3f/u3w9EWBwcHBwcHh8MITJ8d7N/xigNmivL5PP3Lv/wLPfLII3T66adTMCiH5t/+9rcPWeOOB9R8HtV8XoOWqBRvzhDVszzMe+ggbN2sNrl9aIYCXGgWTAbYgiAzRogEbcFGjmJatje34LWOt6o2WqaP6/t0m/eznuTJf+1XpNiK2LCkyiJjZr/wqgEaIkgViWvmrPv3RiRQjSnLbGis2kwICjanGkZRVuWBwwxRcJekLbJLDA0RyJgLkunX7a27EyP7afxkycZZjQv0GWOymC3qYSFTsMwLBjOqBpmq34RsNbj1+jnjB+dMO+vq51jfU+xRs8OEsmOnmjS39peYgqmBYuK3nFWWWmSWA0O090JzbqJtprMWi2a5yPMx2hegG4FHFjKr/Hn2ruEivb5Jv1jearWYgeteYxoIPU94Uu4HDBH0N9rdGYxRjL/PtzePFWveNC2NOpeTzDCEJhSTEla+PeOa8eHluFvlu9TyEzrrlPtMQDK4uS5envVTYIiAYEoypeW4bie3PyX7nFeR76NKk1eNmvsr12+MlOq1CLG+PB7c3w0O23z8yCYNq3a0bJfPj/RsSS1hPVvPDswR9GH8PrVI7heFfNH3iux8P3yOWa7rD/VlwRABi35hOtrmDySJqJGVwv2sHden39dh9btwJOA0RfvGAQ+KXnzxRTrjjDOIiGjtWinMdKLrRlRCHlXCHlWDUnCZ6VdTHBUsb17xcNNTJl1r5fSTnu7KdfMP0oQ0NdMpwG2vKuGkunR46CJtHCirlODhs+TDKbqXp+/U4EZvXw+GML2IAQJEtJjWwmCoLjDn6cD5RrEaGeanoa4Iv8eoG0fPMlM9sT0QgMv957vMiQ/uMu8xGALGTo6IdgaRZtvi2XOMKQA8aK2RHB7QbMA3cYK5qDhXGvah6cnpLvu9slmwA2qUOskrOwUWj4cgnufBji8v+07Pk3vN9oIoF2K2N3ViKx+fac8kl3DB8ejBUPBlVLLlDavnvh3c4IeQfzTyJ5jtVMbMtTjp1J1ivVc2mpz8yKBZsXOtKv2wtz7lQTRtelKJkXXf1GjZgYq+8vPMrPqIHNM5gJ7mDWTwysJoc2rtVB/sBiqqYkR8J4vj+T6z5qKzzKtX4ZR69NM+uX5wCgkPPKDkAV++S0YTDSVO1CAkulf+kgcHzQCgFuYpXh4MpefLUi3o8xgMYeAfY6F2yZaQ8cTx2XbxeURQFd9tOsuUKjfUcA3V8wWFmwEEDvZ4pJ8odb5kztfghWY9Oxiq1ZMvIiPmnPh5GhmDISCrTEMTg3KfsT1yYBdIlyhSUkpzh6OOAx4U/eY3vzkc7XBwcHBwcHA4zHBM0b5xUOaNO3fuJM/zaPbs2a+98JsUXlWmjSKCgSAQIryKmv3RpochxQhFxyBsluuBIbLvx+R7RHJ6WkpHWqkBVRiR2wfzRUBHzJjSgRAyytNmNsU4iqkQndrsE8tB5AqGSEd6mCryMytSajUNtBHmFhMKgiECpgbMBsFAgZWBwDR1Zp9oB6CF8nkWsvrzRBUOllGoFMA+rOFdBccKewbz3gqh+ZSDpauLafk9B+9WsD0hzRhxznFOrAFeCaJwPua9hj2rYSqRo/9SlzkQsJqpBbK0TPFCQ19WNhhXxFK/OQD/GmbrYDiIornq6QJWxLIJfByZc2Su/PyThoiIaOMLpk5IJWYW7HxObjDXiSliyRABOG4kAWDKCO+BBrGrej81t86y6OK06JctzyjGtlMKsS3TybOzJTWz6FdiXDBL6Id5nh4Dw4EyNrrPJXbJjprplw8WMMtgbNCHIjvlnKNXUCdTfV7ql54C+hyCIcr0ShNXlFIB9PNLi5JRzgNTwVq8r1PyJ+ejT5j3uD5grto2yKlULT+Y+xCXdpmWKJLYIRu1+x2GIYpwYeaCfMRQx/oyb1sZ5bL4PDxq7pvxk2NUy1WJ/puOKNygaN84YKF1tVqlL3/5y5RMJmnevHk0MDBAbW1t9JWvfKXByNHBwcHBwcHB4VjBATNFN9xwA61cuZK+/vWv0wUXXEC1Wo2efPJJuvHGGymfz9NXv/rVw9HOYxalmEelaaLCkpyCt4xR94smesh1mw80MwRTRKCYVBoBZgl0yq2OenVh2BKnjWvdRc8fZHSU75SmhYi86iLfmtg+GCJfWW632Cq7nC4wifR0RMgQluoU5NC4oSOQKg+GCPsvLuN0cTBivD1tvudXKfaIZDXL0VhGpX5NwfjYdXjRmaLaEKcVBzk6zXF6t2U8StLEEfAlUHrBU9uX5yg8aBgdaIO8kukTlYShajRDlO8Oc/uleHfsbebAInFzrqvrDD3h43YlnzPrlblPo3QCrh0Ez7immT7WIikdjW+rESfNfqsRymx/aZb4vmWDtJWASDe1SPWJSRm99v5esibRPVK/UY5r2whOzVdJAtPZodiwvL9Q8LTMDKhOTBg/VbKEsCWAlkYXEQbzo5lKsBL1Nsn3IdbaTZwgv2jdLp8bYMnwGpyU97k3MiE3HIbI0bzmFxh1u9bkQegMcT4YImDPcttyIiJqe7m5prLIprXxYclo2ZI2SrcKbREYIitc52ZDE4lSN/o5B3PWYnuIl4eerr7M0LlRsU5RkmQNxYFLCbMNJL1ojV+hU/kPHGHU6OBT6o+Ck8ARwwEPin72s5/RT37yE7riiivsZ8uWLaPZs2fTtdde6wZFDg4ODg4Ob1C46bN944AHRWNjY3TyySc3fH7yySfT2NjYIWnU8YgiRywoKAm0bZLRa9vj2+QCZc6WWjZPfFzP4jCAdqHBnC2iLOeVRT2ysYBgilmBkIl2wBAhwwqApkmzIaFURbSnwBEYQgudUo/Cs7Ai0BEysmBCGwZN+xf2EhHR5AkyetNZcrrgY/t6ExH705I9mDpBUnfQOiHizLfJBuU7pUaEqG4iaNsMg0ukzhfAfnFGXU4VjtwiNTUoqAoNDBicGGfi1PzM7rE2iDhLzEub7dQ4uq+0mddqGNtBurRpvDXuY3atxNdg7Hxzzj0WilTWS4YownoXnCMcP1g9m96dhuUAHxhOCwqushYDOpnhXxttYhvKeHByjy7gilT88HjzPoNMIs20aWZI3xuZHi5ky6nXtmwKl8YITZZsmRcwRBWkoDNLN3Eis2ExyRABOCZrCKlKj0xcYPpprSzb1vW4YoYLKitUlfNIDMr70K/6HBgi35h8IFX7jHjJN24uSmVWh/henzOdBQaWBPfH5MlV3t++s8G0hlLfd+hTYHr1fmF7gYxInHewsriHcJ1CKdMJwRABNmuO15+a65v2nXmFLgzGuTDEhbGkttLQWahoS2S8SpGjkJJ/NPD444/TN77xDVq9ejUNDg7SfffdR+973/uIiKhUKtGXvvQleuCBB2jz5s2UTCbpXe96F33961+n/v5+u41CoUArVqyge+65h3K5HL3zne+kH/7whzRnzhy7zPj4OF133XX0y1/+koiIrrjiCrrtttuora1tv9t6wJqiZcuW0e23397w+e23307Lli070M05ODg4ODg4HCEcDfPGTCYz49ghm83S888/T//wD/9Azz//PP3Hf/wHbdiwQcxGERF99rOfpfvuu4/uvfde+t3vfkfpdJouv/xyqlTqkceHP/xhWrNmDT344IP04IMP0po1aw64JNkBM0W33nor/emf/ik98sgjtHz5cvI8j5566inasWMHPfDAAwe6ueMelahH5ahn57RR6LXzRSMeCGwelCvU1GxtzKSpRDaa8Dy9TBqT1AvNmvUKbVJ/gcgH0Qn8SKJ7TKSI+W1kRIBVALSWxzJE+JzvDWhxwBQhYqy307wiooXfkGZ4oM9J7DL/gCECSknWNrRx5MfNCCoGDoVm7XYVQwR/FZSEgMZD3+vaADE8YV5hXkm1usZAZwLVWSep/YGXUiBrdl5kRgfsHzQzuGZtGw2dEBzlHTAzBM1QNWJOro+ZoEoMRXihj2D2gqNtr2pes93MEHF2WRXFNP/I55hLVIAJQqSsM43AEKHP4ZwhQw/+TVYXwuc6uoffQ08D4ovPT+c6syHoPJDpmO+R13bWE7I9wSlVqkYZdcKAM7aXDUSZIYKJ5BSXsohy6Rt45QT3pCnIbc4PSI+a3RdKJieQliwWzp028svMUwWiFUPUuoYzALmP4b7BJQjAg0pp5VDmB/0516NMR5kBTjJTVE1KxrQ4nzs4a3hKrAW0xqDMEPuVXmZqtrzvu57HuQdDA2bYHKfOltWlVtBnI+NKl9Mq75GKJI7t/Zlj76C+Z+T9n+uRuh4Yo+qs3Pg0ryH4DBVRGoi9oJJpZIGa5VAMVz878ay05Xm8Rq3ikcDRmD677LLL6LLLLmv6XTKZpIcfflh8dtttt9E555xD27dvp4GBAZqcnKSVK1fS3XffTe9617uIiGjVqlU0d+5ceuSRR+jSSy+ldevW0YMPPkjPPPMMnXvuuURE9OMf/5iWL19O69evp5NOOmm/2nrATNE73vEO2rBhA73//e+niYkJGhsboyuvvJLWr19PF1544YFuzsHBwcHBweEYRCqVEn+FwqExo5ycnCTP8+y01+rVq6lUKtEll1xil+nv76fTTjuNnnrqKSIievrppymZTNoBERHReeedR8lk0i6zP3hdPkX9/f1OUL2fCKZrFCrXqO1laaHqGxyVCzJDVOuUphep08ycvs68AWxECI8aZFNhe/D94cguNGY6rc6AyPVGuL3s6QI/EY4UYyPMEPEceI0jKu07gigJDBUiPa15yPTJ9cITSiOwy9AT1T6paRhbLLtsYrfKbmtp7jScWiwjYZRygP7H+i3xq/ZLQcZXHsVdUWBzmiYktrfadB0wNcj4SS0Aq8AZfbxr6CXADOEa+idYKxQyx45yG9gumKZKWKZ1wXsF1yDLbAGYG+snhEvN6+FKJNhQGgyP1dgMSs1OdJhZRtbXjJ4WEccFd2Z4PcGTB8yQ9i9CH9cMUdrYFlF4xOyndatk4FBKRxfERfRfUDoVMGVwW07Ng6ZI9kVokUontNmMorGTJTPUulm8pdRC+b4c5/uXu2EtyPdnq2QwPJ/cd93B3QCsHI5ZF6QNj0vmJctFhkdPN+9R2iX5R05n40zESqvpFJYAUFleYIRw7XTh2GKLf4b2qnuCNUm41hkuZBueYH8gXXyVnzt4vqHIqm0XGPGq7CMRJW/VjIzuC/Wiv8hCnZZxqJyodXFp60WlfImI94nSMFVd6Lhca3B7PxI4lEzR3Llzxef/+I//SDfeeONBbTufz9P//t//mz784Q9Ta6uhSIeGhigUClF7u/x97O3tpaGhIbtMT09Pw/Z6enrsMvuD/RoUvfjii3TaaaeRz+ejF198cZ/Lnn766fu98zcD2lePkC+QpWoLl4nQgyGeCqFWWT8DgyENPGQaDP7KNfU5bx7lQ1Dtngc/WvyHwRCA6TWIeout5uEZHpOjBX+rHFzplH9Uig9zKiwM93Q16/iQnFfA+QJy/ebXpGWnTvHXDxrzWsbgRlVGx2AI0xHFVlkvSz/UdYovgMFQi6oxZZaV1wCAcDo2bI4h0ysfosnNLAYfVypxTIPxYAiDD9gR2BRgmBVm5Q8JvoeAGD88GNTgBwHC6GyPTING+jcGMRgwt242J2FkmZl3KvMUBsTE5ShbBIxjqsN8HuBTDANClLTBYA3XAEaD2G90BMcjp2qtSZ56zuupEAx0yxFsjwMAHkThutjzWJB9h4hoz9my48b4WavF3i1b5fv0XD14wfswH5N5V1hsGpl8xjQSJo9+G4DLwVB0b/Mf6FyXuXij6nEcnuDEjbltvF2dmMF9LSgPSE9rTSlzV1zL5GYMUGVygTa9LCtbhnS/2V58SNYFtNPX1gJErqcHUX3PqhqLw6aPFjr5fPK9gGm8yKhZPsO11ErquUFUH2RrsTpg7yOYpqq+gGesPSbuXzX/wQ1MXi9qNY9qBzkowvo7duywAxcionD44OwGSqUS/eVf/iVVq1X64Q9/uB/tqInyYs1KjellXgv7NSg644wz7CjsjDPOIM/zqKa1L9yg6aInBwcHBwcHhzcOquQdtE8R1m9tbRWDooNBqVSiq666irZs2UKPPvqo2G5fXx8Vi0UaHx8XbNHw8DCdf/75dpk9e/Y0bHfv3r3U29u73+3Yr0HRli1bqLu72/7vcADI5Yj8HvmyuebfM0OUXShpwYCNOrjzqkFoJdy8U9fNCA1sKuoMer564UjQ4yaSBEMEIMopctV5REcQdtvl1H7CyixNm8nZcgPbxsXnuUWd4j2mUhCB4vh15XQAkV6+Qx5Hg3BapdMjLR3nEfsBIxcblttvBkSCto3MxET2yvn2xBbJjnk5VUvENoqZnxhPk4EhQtmKnIxQYQAa5KlVTLHiOYhSEEjJx2u9gGrziNaybSxyH/5LniYLmvaFh017wBAFciyqx6WF0BSHyacwMYSyCFLEC6YKTI6eOoZRIkwvMR1mherMcMWGMfUsVqd0P6YhzXuk3mOqGZ9PnyLW5V40K6BhC8DypdflPMAQWabyuYhYXu+nwMwmprMBPY0Ga4D5/5dtHDhSDg/LbIBqSBlYtkomzMfldELcVYOTXKB1wFBDYIjaNinBOBIXfM2fU2BRIZgGQ6RhxflqO3jOJLfKExWckO/Rd0P8eSDLTBg/3yAgD7E4vxKUD5Jwqn6ewRihLbbEUEKz47KtsWH5zIvsqTPB0ZJihd+kwIBo48aN9Jvf/IY6O+Xz/6yzzqJgMEgPP/wwXXXVVURENDg4SGvXrqVbb72ViIiWL19Ok5OT9Pvf/57OOeccIiJ69tlnaXJy0g6c9gf7NSiaN2+e/X/btm10/vnnUyAgVy2Xy/TUU0+JZR0cHBwcHBzeODga2WfpdJo2bdpk32/ZsoXWrFlDHR0d1N/fT3/+539Ozz//PP3Xf/0XVSoVqwHq6OigUChEyWSSPvGJT9DnP/956uzspI6ODlqxYgUtXbrUZqMtWbKE3v3ud9M111xDP/rRj4iI6FOf+hRdfvnl+515RvQ6hNYXX3wxDQ4ONgiaJicn6eKLL3bTZzMBdeH8MqrID7SJ9xDllcNKLAqBYkXO2QPQP2Du3xZQVGmhVcWogCECCh2SCQL8eXldkZaKwqw27dnzxOdasKzLesR3mgVKfYYqDQ5K6ibXxdE8WIOYFFxCB5Pv0JoFjownuFl8GupsiGyXTwktARixwSxO64VqPq8hjV8bVELjA8GuLtnicSkUryCZo1rMXAuU56jw+mDtNCsHsSuO3c8GfxV+D8Eo+lJVMUBYD8eKKBu2CdD8gO2IDqFPmvc4h0hHh+ki9g8mBn0L5wWRNiJw6ESg74D+C9cMTBL6AkT8EGyjj+MemJqDvsnb4S6mWZ56H+HtKR1MJexRfIj1TpxKjnMO5Lq0QFnuA3or+76LWbZJFKuV3wdhiImCsqyFCY9LyqrYIkVvc38tGZPIjgnxvtzBlhR8/5faufCqMhMMpuR2iu3mJHesM/svsKmr1c2oPolzjOeQPqeJ3fB5aM4E2e2oe0wfP5gwtA/PyUBGmtECKJSL5xGep5EJxXjNwHQREWVVKRP0Q5Q8gS0ImFgkJACpE1vIl/eI9i3TPeQ4lJqi/cVzzz1HF198sX1//fXXExHR1VdfTTfeeKM1WzzjjDPEer/5zW/ooosuIiKi73znOxQIBOiqq66y5o133nkn+af9pv785z+n6667zmapXXHFFU29kfaFAx4UzSRaGh0dpXg83mQNBwcHBwcHhzcrLrrooqY6ZGBf3wGRSIRuu+02uu2222ZcpqOjg1atWvW62gjs96DoyiuvJCIjpv7rv/5roTKvVCr04osvHtC83ZsOnGVWnmts9MsJE/rpAqlaK9SQyeDzmn5eVqJ/sAlYDgN7n2JCUMiyvp7aLvQcUWQsySw3QGeX5FlrBL2IZogQTeF4QrsmiIioMNDB7WT2BFlyvB4YohJrGWA8aM8HLwiGyB4Hswi2aCkyobLNU57B9jSUE1CFeqfrCZDZBlNAQDNDviJrhFCwtSi/B0ME+Kc4g6arzXzPDA4ydgAYUUJvos0gs73moGzxTr5kSGX3Khxt87UCO6cZoo5XzD/I+poaYLNHlsVZ00Mu6jmTLgSMFTQ7YKSg24EeB9lnBXP4FJ4Ak4MDl+0EY2RZGvW8hY4nsUuVY6jIBbXRok4vJ6prUXQZHF1QGCwXtjm5xKzXsoENOLn/hjJSS6izNK29Qp85eE9lOKKETHirzEkv90gxrLbksAWlM9KIEBpCtAf3Y0Edr7VTeA2mWt9vmiHC/u3X0DIyM21tJlR2XICZbDBGgSl58cCIpxaadD79PGrQaE17vuHZqbPFtM2I1hli+dig6YjZPpxz81qM+6joUw/yIwBX+2zf2O9BUTJpeN9arUYtLS0UjdYtREOhEJ133nl0zTXXHPoWOjg4ODg4OBwSHI3ps2MJ+z0o+ulPf0pERPPnz6e///u/pxiXn3DYP5QHusX79GwZAiKCbOhrKrDSWiJEhp7SBlUUAwRUFRMFDQEivXyHKvMBw70iMphkJIisODBFRWYnkPmTGjBdDHoSy+SMyUgue7I5P9AcgJnSGg0UeGxgiNBe+BQpQ0BdIFYXpPQrRgiaEV3GoKQ0JNMzW9s2G81AscUcc2RUHqM/zZlAnPHjy7DpYZwjSH61GqO8WT+7oE1sB8UyoeHR5yDLHjWI0q0OLS9ZsBBnBiJzCSwhGCJkQLVvNCe1xNekxMeXYU0FSjDU2RCZPYY+lWZtT6ETbCMzSts5Wywsjwf6Gms2yX2xxLP0KO2gTSJRHBTb0axhdETpZiw7Y97DB8oa/uHF59lzWve9MseW7VWsAbNUYKsqismNb5GFU1t3KBaK7ytfWbFripENqoLMsVf2yuXbzUnB8yEzYJ7buEY6sxD9G/dfeIyzUZk5wd60T5r2MQqqAq+aWS6B8Y3Jz9s2SeqrHJMeXNajiu/PgNI6+nNm/SpnaIKVBeKD5qZJ7OTtcbZdg75sGmtYVfpOGMTiWazZ9aAquTK61JxzsH7wOCtHiZwC942HA+buPvrRj9KuXbsaPt+4cSNt3br1ULTJwcHBwcHB4TCgdgiKwTqmaBr++q//mj7+8Y/T4sWLxefPPvss/eQnP6Hf/va3h6ptxwWqnUmqBuJWQwRorUqD/4nVSciITDtPB3LsrwGvmXBz7Y+OZhAZVWbwgEF7AoopGTvJRG5gD8AYhZRjNfYHhkhrpQqdhkqyfkAqAoZ2qB7tI3I27xO7WGugynpobZX9nIlN6F0aGDd1Xv0qCwfnF5qs6VqiyJg5WaGhKX7lL5hVs9oh9oDCqw5JwBDl+sHCmtd0nyzyq6PuoNJpQGuDEiYWKBvCDJP1Awo0Z+PAYoAhChRkpmFipzlusJ7IgMx2s0aINUbQb5VaJUNEPkTraA+/gixQWqEGTyBIo/g1yh5S1mFYeVjBKVtnGMKF3WYs5ZqzHP5irUFXovutzrbEOUS/m5qNTDyttdHlaiQjOZMPFxDbzaVglB4t1x8Vx2BLUvCr1RLlZKFW2442s2O4g+Ncg/XA5/p5phnWqvLyQfvBFOE5klpgOi+ef/b5wc8ny+qhzBBbw4RHmIVVWiN4egFaNxaYkg9eZKlB71aO+hr0YXBCBwPqU2wdnnV4NmldGPp1IN+YnXgkUKMG27vXtY3jFQfMFP3hD3+gCy64oOHz8847j9asWXMo2uTg4ODg4ODgcMRxwEyR53k0NTXV8Pnk5KTzKGoCX75EvkCREPVHNpo5/8ha6VkxecF88R5ReYAXiwwiA8mIZcAQlVT2GjRCqLOlWQAd1YAh0kyS9drhSGbsJLmfne9i741B1KEyn8f2aHZCvtUMjdWpJKQ3TFlJ1jSTpmueIaLUjI/WboWUw7b2bYJ2ohCTxwvNFLJ9UEOKqM4QWWjLCryHWy7fJ9WEtFjOzpYHnVE+KDg3mgHSLB/8UXQhV/gJ2UwhxQrU2QRuLkflyDILTpV5f6wR4nMLJmh8PrQWvEHefJD7RiDL57ZNsg5ay1Tj98gwxPdVlQWndWIa+S7eDmuNELm37JRsJtjFCLORyNqz30fQJz2K7TEnEW7aWCbCJQ1x7gGdlZnYrbIZpxRDyQxLcrd0wE8vkHYn+j4uJg3DEmXdWmaBzDYD8wGm0/ZnFG7m97ogtK4fZ7NYQXbq7DL4KvH+UHg1mIaGySxXikuGyTJXvP16bUJ2smd2s+1VdBaxWyp0S4YMfRWA1ghsLABfIwDtttvJVq2LuH52JHaw5q+Pbxiv+bMWWZ3AdFYtUDjynEuVPPIOUZmP4xEHPCi68MIL6ZZbbqF77rnHmiZVKhW65ZZb6G1ve9shb+CxjuruIap6EQrt2E1ERJWi+XX3dxsb81qnyeprfWWciIi8cfMDW0sblWl18YBZntOyY/xa6mkR+9GCaTzcbEori2P1IMm2006zsRkip5fjNb2YHypFORgCIC4EdYwf1HJMLoepFaChLAlEwKowq54mS+xSDz31cEGpi5ZtUuxcaFdlDVA0tUWKk2171I+aX005RXdMNjwMqxFOl2bDTv0grjHlj2m07Dzzqw1ROhCQv4uNxnK827CyCdAlH/SUgYdj5GOvqEFVps9co5Yd5j36UjUgC68ifXxsCQzw0DDePA9eILovtqkplizSwMXHVGyT7/G9nT7j04BpwnoRYPOaP8WcON9OM3UU4YLwMJPEeQvZe0ROISPVOtetRcBVe61RvgY/mBq4j7TgWJeZiW6dUCvywJT7EK5RYrsZARbYZBE/3rYIKffH7LxWsR0M8LQVB+5PCzWfguk89Eg7gFX3hx5E2OdHhwzKMBBuuL9UX0VRYVS5x4C2ZYccoIdSulyQtkRQ14Wn0ZCqrwdDGjjeUtRXt4jgBAY8U1LzpWFmdpY8FkznAlbcjrIygyWKFmco7XMY4bLP9o0DHhTdeuut9Pa3v51OOukkuvDCC4mI6IknnqBUKkWPPvroIW+gg4ODg4ODw6FBteaR53yKZsQBD4pOOeUUevHFF+n222+nF154gaLRKH30ox+lz3zmM9TR0XE42nhcoMYRgS/CZmhT7EjHhWK9hKHHwRABvvVbzT/9psovGKJSgtmGcnP61aYTM2AxDwMxMDozFmzkAGbsLBORJV+WXSWxk8sccKTcML2lzNEQfWdmy+WCKbn/yJia3lMGaWHFIIEhCo9IWiXysmHmCifOIqJ6RAtBKSLUil9GsDqSrRcH5eWUALvQ30rhPczq6eKaMGbzKxZrTkK81wxRmWdKWndIWkGfC0+xDjrqRhkPPzNFVljN0Tai4dRcvoYcfIPxyfYwK4jSKMxI6SnJ5KtgF+XUpxVOo/xHDlM25j1YADtdx7OQcU5uxVRlHrUh+fCqMkCn7By2BJhjrkNwrWRR6ywDRMWYMpIMEabCNEMU3YvzV7/2KCZrlxmRTG2dPWTxthJS+xR7aJmdTjlNBvsD3Oc+XMuMLJGii/cCYJIwTYcUd5RYASoq7Xym2REwUvq5o9lIFB3GclPzuJixtgRh9gRMki6/k9wsGaEGETz6OB+nT7UD5xnM0YwMETfLr5IJiOpT+8BrMUTox2C9Yntl/4sPlvh7H1WOgnmjw75xwIMiIqL+/n762te+dqjb4uDg4ODg4HAYUasdguyz4zj97HUNip544gn60Y9+RJs3b6Zf/OIXNHv2bLr77rtpwYIFTlek4Hk+8nw+8k5eaD7YymEw67FqRRNG19jM0ENxO44cPWaIgOAwQikTDaNMhzVXZCAyCmRZt9InhYiIaH0FmX5dTMjIJbTXdJEce092/0HZ8EO8q5ipktISYX2wBkEmxLTQOdun2A7uocnNksmpm8vJnFbfkBEj5E7tlxvm5lmBKYqScuSJsiIQqNv9KM1EkRm62LCJ9sASERH5x2SYW01K4XS+z7AAE4t0aRfZVIh2s90c1eckS4BzrqNmW44AJSIieJWiYOhJxk5EX+P2ch/I9pv99Kw2r5PzcQ6aR7VggFBeAxoeMDpg98Ak1dvRdHMNIlqI+ME8ZWfJ9gamWNS725zfKHcJpL0HVFkTiPRt6r4q59H9B1UiooI+56fJhZwCPtHcUsNqiZiZgeUFmEqUfAFLVZndysuZaxGaMBsCQ2TBjAhMGFEeyK/7gGKKtJDbGl3Gm2voGiwHkHIPzSKsQTiVHaxIqSUotqeZpKgqo4G+DaCPwF4CzJoWlDckjjQUvNbPj+bHCdQZZL5eITDfzEBPlCk0ZbYJdlCL6VsUo5vvkteuoTRQHPfj0RlZOE3RvnHA3N2///u/06WXXkrRaJSef/55KhQM1z41NeXYIwcHBwcHB4djFgfMFN188810xx130Ec/+lG699577efnn38+ffnLXz6kjTsuMNBHFIjXM5DmmDC3um2nWMwyRHgP7dEeE3Z7vSa/uCHrDFoCGAVCI1NExhDPuauIS2uJEKEFWWtgtTM+VY5EG5VpDQ4H2Ugbz/RyhKxYAZ1xhLTuArML2jAt22Pa27GBU+8nzY4CEyp1+aw5ph3IylHHic9hcqktDADN5kBXAgRTZv/VaJD8EyYzqNoimaFil3w/uVCeS2zRxxqe5DaZ7g2GSBvjwVDTGnzqYrydYAnBMJnPMz1YjrNGuXl+PoX5brmfkdNVhiEzNhUly8C1RYo8WEidjQY2AKVbLNOD7DIU6eUSLEhXznVA82TexwalPq3YzrqZMbAy3E5mOTI9sqQGgCygrj9KtlEXSU5N01BZQ0i4LGSQxg89FWfuoRwIL2dtEJi1q9gCy+ZzWxKlk4v2stbGnktVzkIXTtVlfaCTynXKg44PSo2O1hJZXRqyWBUTpZkbMER2fX7+lMFkqaxYoGWnFCHmO5v/FNlUfR/sHpgpSypzS2RijiuhHbfHao/4+Yb7HQyRPR5lMhlK1b/XDFF6jlm242VZRBelUZCqDw1fncnFq0fVypFnXBxTtG8c8KBo/fr19Pa3v73h89bWVpqYmDgUbXJwcHBwcHA4DHDZZ/vGAQ+KZs2aRZs2baL58+eLz3/3u9/RwoULD1W7jhsUehJUCCUoss34EFV3mqwozQxpr5taSUZ0MPorK1NB3TcRTVu7+qiO9s12A2puvqLs8RGRtW3goqUoD8CRJbQMKuHJGg4iKteZQpFRaHU4AkQJCE660Vls8Lrp2CDPB7JdJk9tN8fD7YEvC16RnWa9dlRkjAgVjJbNGVLHpb2AwA4RTSvjwaaMlRZJpYyfaE5CZFyGy5FRNkOMgdkxx9CCTCVWM0JfAoYIuhI0EZmIhaRkiACwbjjXyG5DxIr3sd3yoAud4m1dh8WH3sJFNXOdiObN5yHlzQMmK8N6MZTCAMMUGeVrl4B+RXpBIXsnNY8ziKQvIcXYjDE2XBPbKbSp9iu2ovsF1vNBg4W+wadBsyLBXI0sMcrLTM2VBZCj3AZoZmLD7IUEjZpiJG2xW2aGtG+X1uaA8dD9WAPMFRDlc6xZC7tddSPDBwhlM3yWoYHIz7zCKwoMjT4+Xe5DZ7VBV6MZXf0emZeaxQPApuJ5h/Vxz/hVtp1lkvm5q/3LpmxG5j7OM1+asSVcsgimosiIVcyS1Wvxfe1VG01zHY4+DlhT9Dd/8zf0d3/3d/Tss8+S53m0e/du+vnPf04rVqyga6+99nC00cHBwcHBweEQANlnB/t3vOKAmaIvfOELNDk5SRdffDHl83l6+9vfTuFwmFasWEGf+cxnDkcbj2lEdkxSIVCnP7xA81NeUyVSfB1t4n2+K9p0vXqpBI7au+X2wykZioS2mjIjhRN6iKjOEOk5fWw3MiKpm7pztrwrwBBVVDPhTA0PHNj8Q5tUUBEtGKb4oMruQdmTDBchHZA7yvJx1x1wzflE1GYLRaqbGQwRoEtH6PMX2zrJ22N2qFCmWhjaAfPZ+MlSS6SZGzBEeLJofQiAa4CoHSwBrhW+T/epIpgc9Ye5sGu2R57j8Kjcj9baWEYoLT8HM2TB7YdTdEN5EwbYj471rDPr44zJgmR2rB6FiTawGXDYxrWLqPZb7yrod/h0oggrmKk0+8mgmHFDSYcpWQoCxUTbNpkN5LtC9TZGpa8O2DFbCkL55YANRAFU23bVv7TfT3jY0GmlDtPfbcYc/LaYTatZ3zHzfWS8eSkYv2LhAFwjP2tsdGHV9BylLdTleliLozWHGrrQLfzM/PyYAfOELDCbGTjDrzDuATBzYMS047W/oDPEJIWdni2PF35pxVavoRAxCiaDjQNDFN+jXLYTsp/r0iPVkG9Gn7nDCTOoOVhN0SFqzBsQrysl/6tf/SrdcMMN9PLLL1O1WqVTTjmFEonEa6/o4ODg4ODg4PAGxesaFBERxWIxOvvssw9lW45L5AeSlA/VB4zh4RG5ADNEHjubemFpWpM/0fgUaSdY7VFTisuMJR0JxtYOmuXmGqGIjyOnfLuMALXuoqHmEUdNhWRzRiobRfFQuaHYsImSwGQhA8m6HvNr4lUZ0QWVzwqyXaAhyHI9opIak+tCizMFRu2b4BvD2Tq8PRxPYrvMTPJyHLry9aq21hmrQqe5dondZpvwNUF0X7E6Kz72GYpBQntQ5GsKPVShXUXZ7CkVYkanrLLCJk5UurEwIlbtKizX067CYF6mZnO7OeCFDxAYoortStBzyOw51NGzDFEM7IVZC7ocFJvEcoldVbE+AIZIZyqGVN+DXifKDBP0ZgCYO1v4ljO9oDFCxF8JebZNVjcFtk85Vjdqh3DuauLYtFcNrn10t0zPDI5xQehuw0JazY1yZEeWGnyMdGYivKvgFt6QTcqaHTAv2G50VLGZmjRU/kDWD4nPHe6ryBi3r6V5kVVc00IS5xzfoI8xCzpD7Tksn2OvoJZt8v7Vfm0oWGv3PyH77PTMT/RXtHGm/qcz2Kw2EMxRsfl9f6Tgss/2jf0aFF155ZV05513UmtrK1155ZX7XDaRSNCpp55Kn/70pymZTB6SRh4PCO/mfOY5bMa4S1YLxGCoskiZDiogvVpPd9kSBjxVo03bpt5ithvZi3If5hc006+nEMz6SJnV9C4etjBPxEMTD49WTisHDY8fTD2th+VshXRUxFCDPS0Ub7QSYME3T3vhodsARYFj6qT+8PaJ44gNyZFCYI+ZNtOGjKXWcN3wUf3AtG5nQ86KFEx7qnSItQcoQiyu7AFOkANXTAdpQXNWT6OF1VTMuDx3xST3laKcCoLo3Zos8rnFD058jxwMwQyy0ibPoQYGVyjui74MAbgWnWLqBD+4sWFdKoOnjPkHF4NC/EhhsIjpQQzm7A+xeq5jQKErrmPKzKvUTQztMSH1Xqsz+YcQZoyhcbUvDkpKLSHx3gqhMT1bYgFzAmJeXi8ZFPuHeSvuTwB9xA5CVMKAnq6bGpDTShALI8jSfTzTZ/ZXU+MAXYIGQKHYsCpurIMYtMtfRICg+m4C02tykBkfVAWgO+Xx6EEQ7sEg95X9+a2HmWnLTpYSIOgJy0H/TBADSP+RH1zUqGFM+7q2cbxivwZFyWSSPH4AvtZAp1Ao0B133EFPPvkk/fKXvzz4Fjo4ODg4ODgcEjimaN/Yr0HRT3/606b/z4SXX36Z3vrWt77+Vh1HqER8VA77yNdt5neCazYTEZEX4vCVX4unzCUiIn+Wbf6TKNwqTd7y7ZLW1vQ4RKnpOeYVjAyiXTBEVbWenu5CRIoSEaB+dfkORISgtSFw1BFrULEaepoO9xgEqaD1031qOzm5YnSvEi+q1GK7H56mAEM0NVeGtvHdpn3RERNphnZOiO8r7eb6eVVmc5L1uSqbdgyhL+9LR+FATRnSYQoQpopFFXcE1XSWFtFmuzVDZF4T2+S5SC3m9OS8+bx1sxK5q6eBNtxEXwJDZO0fmEUIpfl4lQpTT3FYuwYl8AYD5sd0W5tsX4TZFkwdT81RRntT8p4AywEWw05x4TqB5YF4mqdmwUJoe4hQqmytKurHooX6pj+CeQjs5OlyZoKrCcVCBdik1R8Q6wNgiKphLgs0Q2FiPQ2uTRdx7Nkesx8UJdWC6pbtkmmZWCyZltQJ8toGDYFK8UH+AF3AJ1/B0oEh0oVbMV2Ge6ahsO1rCLjBThbaZbkRIN8GNpfbncWULlhA7jthT7wGszV7X2BZQD97g9kZ2qjuF4c3Nl63poiIaOfOneR5Hs2eLUufn3TSSfTUU08dVMMcHBwcHBwcDjHc/Nk+ccCDomq1SjfffDN961vfonTahEMtLS30+c9/nm644Qby+Xzk9/tp2bJlh7yxxyKCU2UKFcrWJKx45iLxPVLbKxwJ4hWp55WwidQKbZIhAjCnnu1iLYK6opPzWKzLpRwQTUN8Gt9da7pdMEQaYEEQcelCtNoETc+vYz+hSRmGF1Ski9TdUEayD4ggwxOcOqwLREKvg1RlfM3raZEtGCIgNGi0X1U2YKz5URJjH7eKYpIh2CWfTJPWxWW12aFOC0Z0DZsDzVyAIcr1qP1HcM7N/sEQAYntnNrfwftH+Y6Q3A/E620bJUNk998l+0hElViwKe+vwbRrrZDeD6wFwPiALQULaFPy0Q5OjbaaJTQDRBH3wQZdnmKuosqOIrJhD9VQyoXbWE5KdTv2Fdxj6L1KT7v4PjvXrI/7VrMNOEgQJeV4835XVIkODbMZsvpNA6Oly4L4VbkLMDkdrxgNYq7bnJzUCcygTJrXxA65HzwfwLSAZcGrx8V4YWRabtAM4h+5HQBiZ60/CzNjZQvSqu9je2ShXTB+8SFp+jqdIQJgv2C1fNwfoaUDGpJUdLfWBWBr1GDdcERwCKbP9kt8dYzigAdFN9xwA61cuZK+/vWv0wUXXEC1Wo2efPJJuvHGGymfz9NXv/rVw9FOBwcHBwcHB4fDigMeFP3sZz+jn/zkJ3TFFVfYz5YtW0azZ8+ma6+91g2KFMoRP5XD/ka7e4ADhch2E+qUO00kWY7z3Djm0pHwg+iXAz0wRHZ/KPLJGUNBFTHqVH5sV3+uC0XC/K2+H46UY8xsZaGjUftT9vo6eqoGkMKPLJvm6ax1xklG2Mha0VlyVi/C+h0wbdA2QfOReNWcd6/IBokxqaGohZSGi5k8ZAkRNUa7DWnOQalbsFGp1iQos0QbNfNiuvioZmBKLdzGHqMLmWQGyTdijgkaIl0mQzNN2sASBWr1tdOspNaBlFHCgrsA+lCA+2RFymss+1KKyuXQt1DGBCyGjzVP0CD5lB6l4sd+mke1iNyxfmxS6nl8bOIY3GYMT6tddbFXrj/ObUQBZZS54KywblO42auYbUycFBfbRp+ID8MwUmre8KrPOfoMmF5kcwFgHbGcZT6Y1YiO8YJ4nvBroUNeTJttVpbnvu0Vboe6dg0sCJ/yKJsg2sxAPu4sF+mNjmhrEdWH1HPHmsDyvZHcLDVQM5XEAfA8qinmuG4Pgezd+nq61IfO7IXOEuyfto7ALAGu9dGeejoUjtTOvHEaxsbG6OSTT274/OSTT6axsbEmazg4ODg4ODi8EeCyz/aNAx4ULVu2jG6//Xb6/ve/Lz6//fbbnY6oCQK5CgWmlfBA9ggiq+CwpAeCIyb8zfUYwQdYCG34VeQIB9F0rlfut8RsQGJQRWIqewwRHXx6oNUB4J2joyXtV9LgU9Iwny73B82SLVMQkOZsYNbAulRUVplmtrA+Im6dhRYdVvqQQUXL2AK8kilqKMCr2ZBpLBG+m+qVJ0OX+UBhVA3oIho9b+R68BPKKEurakjpt0ZU5tAiZlQKfI45yPYrNtFqiBh5ZRoZUR4z2R7Z4GIC3lQqE5A3i/2C+YJmw3pQWa2Q3I+NtBllZjO1d9D4YnnNUIQY8CttVnTYiEXQFwNp08BqxLA+uSWzzHqFCuVQHgKanyj8fzy7DFG9jAQYV814xPcwY4FsMmaU/Mx2VVVx2rofkWw7ssWgEcJ9gmxTMER4r1kz/VzRpq9ot9VbcbMsm8iLI5sVfVfrvBruR8UQwbvKvm8FI8a7sZ5Z6jnYKkve1DMxuUgwFxPWDBGAPmm1lkrj4yvXLDOEbYAdx7MUbcO5Q4Yd7hsYX+IaQM9UDfmOik+Rw75xwIOiW2+9lf70T/+UHnnkEVq+fDl5nkdPPfUU7dixgx544IHD0UYHBwcHBweHQ4Gad/BCaccU1fGOd7yDNmzYQD/4wQ/olVdeoVqtRldeeSVde+211N+/bzfmNyU884d55dCoFGx4BRn6pc6QlA/m2PFqo23F1ET26owdnXEk6YDsLM6uUg7R2odFuytrRigywloclERQ8+uIYOv6DemQCyAS9ikmzRYn5bn+8IiJ6gsdEKzI7aAUBiLc+G52FB4ryP0Vlb+Rcqout5rz42NNArJ3yoppm84AaCdn6C4QnZej8nMwNOWEfA0xY4RoXGuAxpeZtrf0GbYrvc0sEOyXfcuXksIPXQakbYOKulW0jr4RU15QQHq2eXwUud1F6woul9M6M3tNeTnoOQLKg0ozQA36NHUPZHsVQzQG4YzcL57nba8Yyg2FfMF+5ntkXyiyHs0r+y0jgv4ZmpCMj2aS8nBQhnYQPj1lzbQq7UxBU61gVJt/D6f6YquhdKBJ0gxReNxcS2hvrK5NSvbqx55QDK3Sm83EEOu+RDNIKu1+WvVzSH6P5xmeG359r/FpBwOlM8GsRgt+RKwJbPBL8+njrff9hpJI+hLpYr5oM7NkKNcD+IpV8oqvcWIOA5ymaN84oEFRqVSiSy65hH70ox85QfV+ohr0iXTYQq8RXEZelTXQ8ou6mq6vfyj0jajFtu0bzC9NYEL+MqUXtYj3ddrcvEaGzS80TCMxGNIp7ABSXKtKkI1BFR46+GELqAdCZIwrj/eYkcJ04TJR/QcWD/P2dWZasdhm2mfPQ0VXA5dP6WCKTepY0B2YkE/1aoSnOeJyqqkhhTkCQ7/GVGoMhlIDct/pBSxuHcSxiK+pYMrQ2SmC8ARvD+NX3kWul6dU4uYcYjAE1FrZMDNtjiGyVR5LiLfbuXPfpo8Q6afmm+OI7Wk+ZQPARNIrQxxsPseUBVDgEhOobYZ07PoB8P4xkFXTZBgA4AdeT91iMIQfuOiI+Qf3DqbnkhvVoHHUDIq8FtMHs/Pk6BMWCqgT5qvUKDgpBbxW1B3GMSrxvLp/7dQJdxVbWxD13vJqWq04baqF6oOB4LgZNcAqIj0gR7wYjMFQs2WrDIowTTQ111wU/Vwph6XY3UI9b2DrALTsUANtDKr4eLVBaLpfDhobU+35fCMY5HbjvGkbCdtMJfzWA5ZgRj6P9HXE+sXWgO2Xdh84JDX1iIEuAsOQEu5rq4ly1EdVXX/I4ajjgAZFwWCQ1q5da0t+ODg4ODg4OBxDqJEzb9wHDnj67KMf/aj1KXJ4bVR9HlX9nhUCB9Mc1c9pF8vBrBCwJoWqgGOxVS4HxgYI7TZzL9UYlw+IBHi/iqnZAZNCs1yZmRIr6MxLUzMArAjM41CqAWUEKiE9vYRoWzJSxQ6enoLZGovRYainBaGpRTKlOaKM9RBJd7yYEp/7spLmqIYlBV5VDFEpad6Xos0jOESQoMS9aj1lHQBDFNupWTTzWuBLr6vTA5hmy8xWkWiLOeapnZL1a1/b3GgT0NF5rgNTCeY9pucQfYMh0n3P9gW+NGCAgIQywsR+bPs5cM70ymub3KqnFVRUz/cM7BC0cSGuBSJ3za62/9HcE75xWbckf0K3eN/AiqrYzz/NrE+bHyJRAVOBtm2Y7uLuqkXf1hIjL4XAYCbB2EZGWfzN/bzM/TTby2VAuDmFpNw/2pNXRW4zKhmgKm8Dy7ZhqtGynNzeXGfzaTV8DtYQU8HaxDE1l4+PXQ4iTJz71dSr/vFtmLKCYwlvV5vF1st3qPJEVcVmJuX5QOHYyFjV9i8865AgAOipUNiL2KQadQzT+1lDUsURgMs+2zcOeFBULBbpJz/5CT388MN09tlnUzwuf6y+/e1vH7LGOTg4ODg4OBxiHMdMz8HigAdFa9eupbe85S1ERLRhwwbxnZtWa4SvUjORBEcTsO0HEwHBtI6AYAqHKAXRM4TNOpUzsnWciIhqnEaMSKgcUeLTHZJJ8digDlPbFQiKOUIFM6SZG13ME5EYtAwQlGqGKtcvRaxaMGr312BsKLeT6ZcNSOyUzJFvSoacMGG07WeGqJiU2ynPYPQHIM02xyRDdKQeRYMJaVtnzkGRCR0tRtUiUkTTFSWERuFWoDQuw/nokNlwvktuB6yEZSn41CF1Hp9Dj6FT1HX5gpl0Za3buXQCG25mepszYwAE2UC+G69m/TDbnHX9kfvOlNl+oVMeN1jM9Bxz7XTqfihlDjg8bJghzRCll/WJ99CM6LR5pFJPNwYF41tULFljSrl4S9G90KjADFR+Dy3S1FxzcXANLTPLwv9cBydsoOBsrvnzw26Xu3e6H+al/ByZaM7qoaQKAJYD7cVxhqSsrX5t+bRABwerkGCK15sw78EQATb5AKU00s0TMjQrA6NPexyd8rpExiBsN+/BzKBP5/m4Ubqj0Kafcx5FWeNmn4lK8N8wwFDP5lLDsxPKf6Jq9c3xm/n444/TN77xDVq9ejUNDg7SfffdR+973/vs97VajW666Sb6l3/5FxofH6dzzz2XfvCDH9Cpp55qlykUCrRixQq65557KJfL0Tvf+U764Q9/SHPmzLHLjI+P03XXXUe//OUviYjoiiuuoNtuu43a2tr2u60HTN795je/mfHv0UcfPdDNHRRuvPFG8jxP/PX19e1znccee4zOOussikQitHDhQrrjjjvE9//xH/9BZ599NrW1tVE8HqczzjiD7r777sN5GA4ODg4ODkcEmD472L8DQSaTsR6HzXDrrbfSt7/9bbr99tvpv//7v6mvr4/+5E/+hKampuwyn/3sZ+m+++6je++9l373u99ROp2myy+/nCrTfAA//OEP05o1a+jBBx+kBx98kNasWUMf+chHDqitB8wUTceOHTvI8zwxUjvSOPXUU+mRRx6x7/3+mfUVW7Zsofe85z10zTXX0KpVq+jJJ5+ka6+9lrq7u+kDH/gAERF1dHTQDTfcQCeffDKFQiH6r//6L/rYxz5GPT09dOmllx5w+7K9QcpEgtY2voFxAVHD1xXZJpgLR7YFCq+CaYqt2Sm2U55najX4mSEpdaBwJYn1MQyusbam1C7pCVtmAMG51zydNDwm30+cIDULNrOJX1t3cCqwSmn3VJFUrcmIDTdPBwfAEIV3SQYMJxbMWTUu29dgUheFhqO5rgSmeLlu9XWlRkEmInBtbdQL4zl1CD7FoAAozYKsNLsPvnSx3Sqzbkoup7UONmuGL5XWFnW+YhqG6BnZXfUyGdIOAsZ5YVX4FRlG2L8+tygsqxkzMENA61ZzoNDXjZ5qrl37Ri6FUWquF8ErGC6kUVcS5kJMLVJZZaqPeeoe1Aam0zOZSqwlAkOT4zI70LJk+utaM3NMEL2wRgfmhyhDoYrTIjMv28NaoE7cf7KNtvxGW/NjqRtkSkaoZYvR9FU5SxOFmBODZsM5VTak0czQvIIZQnvANiIbLTw2Qyo/H39syLyWWH0BlhPQz4EG00bF1OnsMjBE0PXUn2dYHwy8eY97pXW7OUBrvDjt+YNxAHRe+pkIXRgADRyhOG7wDcIKHQWh9WWXXUaXXXZZ803VavTd736XbrjhBrryyiuJyJQT6+3tpX/7t3+jv/mbv6HJyUlauXIl3X333fSud72LiIhWrVpFc+fOpUceeYQuvfRSWrduHT344IP0zDPP0LnnnktERD/+8Y9p+fLltH79ejrppJP2q60HzBSVy2X6h3/4B0omkzR//nyaN28eJZNJ+tKXvkSlUum1N3CIEQgEqK+vz/51d3fPuOwdd9xBAwMD9N3vfpeWLFlCn/zkJ+njH/84ffOb37TLXHTRRfT+97+flixZQosWLaK/+7u/o9NPP51+97vfHYnDcXBwcHBwOCaQSqXEX6EwQ8S3D2zZsoWGhobokksusZ+Fw2F6xzveQU899RQREa1evdpaAgH9/f102mmn2WWefvppSiaTdkBERHTeeedRMpm0y+wPDpgp+sxnPkP33Xcf3XrrrbR8+XLbmBtvvJFGRkYapqMONzZu3Ej9/f0UDofp3HPPpa997Wu0cOHCpss+/fTT4qQSEV166aW0cuVKKpVKFAxKfUmtVqNHH32U1q9fT//0T/80YxsKhYLoDKlUnbWohDyqhD3KxpoXQNReFzUVretCsrE/7BDvy/PNpL3NLuHXakAyPJGdMhQrdpgUJ48j2CKbuZXVHD0iXDAosSE58EUBV2gAgAbfE1Xg0hYHhQkbMp9egxnC+vFBs5yvqKL6ospKi5rzUVHaKlu+A7tXpm068wgRPjQPKGMwPbPERqPwXFHSA2SdAfle1s5MythE67WKPeaYAls4qldeMBFmblBks9CKPmS+LzFp2Lqd95dFFC33o1ky7asEJkmbJE6cqKJ6KeGhlm3MosxiFkWdF12KZmqA+9owsyGcGRRlE8l0v9m/1hLVmTo2MAzp45H7R19HHwRDpA1MbamOqK/h/sC5BfuU3AxNnPkcZok2Q05lR9lrwPcZ7gtokOptEG/rTIrNXpPLg0FFSQpkr9VYMwgfLhR6xbnRpojoQ/he68L80vqpzowx9PnSXj84T2Bgkflot89MDtpZ345isFS5DvicWYZJ3c/xQZXxqJimlq11TWKuh73blJFlSDG1ABgiFKG2y7PWrZiEWWijtuzIwKOGE/K6tkE0d+5c8ek//uM/0o033nhAWxoaMrRhb680Lu7t7aVt27bZZUKhELW3tzcsg/WHhoaop0dVtyainp4eu8z+4IAHRffccw/de++9ggo7/fTTaWBggP7yL//yiA6Kzj33XLrrrrvoxBNPpD179tDNN99M559/Pr300kvU2dnZsPzQ0FDTE18ul2lkZIRmzTI1jiYnJ2n27NlUKBTI7/fTD3/4Q/qTP/mTGdtxyy230E033XRoD87BwcHBweFQ4xBOn+3YsYNaW+vT0+FweIYVXhs6UatWq71m8pZeptny+7Od6TjgQVEkEqH58+c3fD5//nwKhUKNKxxGTB+YLV26lJYvX06LFi2in/3sZ3T99dc3XafZideft7S00Jo1ayidTtOvf/1ruv7662nhwoV00UUXNd3mF7/4RbG/VCplR9CViCmvgDl36DNieziCVFlWdadmjl5ZK+PlmImKcKfLS1t/6B4QHev57vwcme7hKytmBzeJOj+tm2SaiVeS2832Sb2GzXzS2WnqOCNKlxKaMCFhqUWtaDfM7AdfL+hGQoNSS1SLG41UNcR+R6qgKwrR1jhyR1HRfHtzd2e7XXVP5XrqH9gsK/WgSQ+oaL/bnBwvxRlGRWY2onztoqyJmWI38U6zfGQXmA+zHehEwDJOLlC+PdxFoGUCQwTk25ABJTVAeeU9o49HZ/5MzWWmJYt2mVecOzBEYEfAIqCvoY9ov6PWrSi8aT6PqjIjLSrTEHowMEiA9t7RJSqQXTYTGrxtKjXLKObbzSsYQxTzBUIpXCNkP7GuhM+11QS1SzduMB5gaKBd0oxJfEg5MnO7Wl5l/zHW0iGDr4iyOHCyHpD3GRgWlILR2seydSuXfcAW0c3La22/hwt5RfY1MGyhlDxeuKTb/eoC1ug70K3x1z64l4fl+cU9g/OLYq2agdcZYyjoS9T4bIjtlefer7RENaWpA0MEBKex6g3lXI4xtLa2ikHR6wGSo4aGhiwxQUQ0PDxsSYy+vj4qFos0Pj4u2KLh4WE6//zz7TJ79uxp2P7evXsbyJB94YAHRX/7t39LX/nKV+inP/2pHRUWCgX66le/Sp/5zGf2uS7S5A4Ef/Inf0LRaHS/lo3H47R06VLauHFj0+/7+voaaLTh4WEKBAKCWfL5fHTCCScQEdEZZ5xB69ato1tuuWXGQVE4HD6oEbKDg4ODg8MRwRvM0XrBggXU19dHDz/8MJ155plEZPwQH3vsMStbOeussygYDNLDDz9MV111FRERDQ4O0tq1a+nWW28lIqLly5fT5OQk/f73v6dzzjmHiIieffZZmpyctAOn/cEBD4r+8Ic/0K9//WuaM2cOLVu2jIiIXnjhBSoWi/TOd77TqseJTHr7dEz3JdgfeJ5HGzdunFEjpFEoFGjdunV04YUXNv1++fLldP/994vPHnroITr77LMb9ETTUavVXpeAjMi4E5djRK3rK+pzZnQy0v0UUUZ0B0xneAXWSWRPkMWGdA0lFIYEKtGA2K79PKAceZWeBExTrs8MSFHHJ8BMUY2ZmMiojOKhYYCGQkeciLLAvIAhKrbJsB7z8ZZR0rWLRjPcDj4OlXWIzyuqFpqu1VZhDQdqVMGRt9gmFqPcXMlOJNfW+0tIebukZ6tzzQxRcLc8RmSPZeewlqXDXLsaX2JvVA60u15U7F+79LyBNw7cgQGdmaN1DGCerBaImz81h/VizCLojCDUagPj07JDRr2ZPtYG7WnUXxHVmaw4WFPuo7FB6TGFOmOAZT3sfphtQRYZ603AwtrCsAz0KWR+2b4awqvsw9hesbXed1pUHTmdWVQvjGraFpoyx2gLJXO/1K7fkQnZ1rz6Hlo6aIKCU/JienneDzPBxS6T3uVnb53xk825Q58Fg1NWeiudMQl9F2A9nawmjz+PyoxFbMfqyPgXp3Wb7MuxIWa+2809Ah0ZMibBROlroYsua28s3efg14RibMltzUU9KEA9nSHGsysyInfiY6ao0Guelai3GGQWDNdaM49HDTXv4KvcH+D66XSaNm3aZN9v2bKF1qxZQx0dHTQwMECf/exn6Wtf+xotXryYFi9eTF/72tcoFovRhz/8YSIiSiaT9IlPfII+//nPU2dnJ3V0dNCKFSto6dKlNhttyZIl9O53v5uuueYa+tGPfkRERJ/61Kfo8ssv3+/MM6LXMShqa2uz6euAFlvtCzOJoZqhpaVln9+vWLGC3vve99LAwAANDw/TzTffTKlUiq6++moiMtNau3btorvuuouIiD796U/T7bffTtdffz1dc8019PTTT9PKlSvpnnvusdu85ZZb6Oyzz6ZFixZRsVikBx54gO666y7653/+5/0+xumIjNYomq1Z2hspntGd5o7J95tjjL6ihGA8FYkf9/RJ5pfSplcj23OMBZR+NgzkhwpoWT0Y0oMNPCwjampEU8IeF15F4VR/rvlgCD+oVryKQQ3T04G0XK/QIQtSanEiBNr4ofRPmjkQT03/VVTKfSnRvGvre1mXshhdqgZXc/kHOmu217KOU/xx/ifqy2IwhKmU8CQPdHeZY8xxt9ep9JE9LMzkOYqa35y7ruetCtcckxq4auFnVP1waSEyjEJz3apUzLCi/3mziV3m8ykudAuRuU6txw/X2BI2ABwn3g9ezeeJHbJdflUhHD+MmML1lIi+3MJ9m48jPVcOMvF5gDeDHzYYnwZzMg3etl8NaPR73HNetT6dpe0NdGkSbfCX75BB19RsFYTw1B7sDVDqBQPY5GYubIwEjALMUdmuAIWRAyjFYpbL9Zr9Npgz4hCVUBuDGr8SMuvjxfd1Owf5fWyPOX6UP8F5g/C59aVRs2CZBxTzzfMNzx0/Bjloph/Tj7waT+cVk9if3L8tzaHarRNAdIketDPAfYU8osQQW1eoZ4dPPSMRuJXYNkWLtxvu37hHody+S/QcL3juuefo4osvtu8hN7n66qvpzjvvpC984QuUy+Xo2muvteaNDz30kBgDfOc736FAIEBXXXWVNW+88847hQ3Pz3/+c7ruuutsQtUVV1wxozfSTDjgQdFPf/rTA13F4uqrr97vqTAior/6q7/a53zlzp076UMf+hCNjIxQd3c3nXfeefTMM8/QvHnziMjQa9u3b7fLL1iwgB544AH63Oc+Rz/4wQ+ov7+fvv/974tBXiaToWuvvZZ27txJ0WiUTj75ZFq1ahV98IMffB1H7ODg4ODg8MZBrWaTHQ9qGweCiy66yOpBm8HzPLrxxhv3mbkWiUTotttuo9tuu23GZTo6OmjVqlUH1jjdltq+WurwupBKpSiZTNIkEV399htoMpSwAujYBjO3UYtylLttt1x5jhGapU8yYjIIArXgMMzTWb4CR2TJ5iL3DKdp+21BSsnURHcZIXV+lsm1LatCqIhI0Y5gyoTh+W45uEVUhJIP/pwMyay4lqcoEHVlu+W4PKpMIaN7JFUdGJP53ijwWuM02BpPC8LkEtDHpSP78LjZz+YrzXQDGKLEM2oQj8iaI/v8tCRHRPW6pICOVvE5ynoUOuW56nxhhjRmRp5LPfhVQdnYsC6ZIt+nBuQ0U6FNrl/ioKz7eckQIRq37SnKdunyJDqijqsurtPHkSTQ/uhmuWBYTakuNmLJ9GzzeaZPTecxQ4fpPn2NdRYyxLgN6d3q/XQLApTZ0NvE1KFOHcc5tqaK3J1DaSXCR4FhfhzrsjWY9oVwGQyxbQafw8Ck6be4n227u819oi1AtIhYi+mtsBlTiWUwRMz8WiE4mCDZ57LMSsLY07KBDJTZ0fcn2gVWUt9DKG2DaTlMVQH63tD3EPoshPK6iDD2G8xUbX8OqGMLZFjEjuK8fbKRup/r6eeRC0vUkUrT89fdTCkiSpLJfD5Y4fJMwO/SnNtuIl808tor7APVXJ52/s9/PKztPVo46pOcmzZtol/96leUyxmnVTdGc3BwcHBwOEyApuhg/45THFSZj4PB6OgoffCDH6RHH31UCKo/+clPUltbG33rW986Wk07pAhvGaGwP2v5xuqIqW3gsbDbS/Iom7/PzjNhuWaI7Fw4GJYeaBTMKxggzMnDnDDO8+G2jMCwGXwitT7NJRCwP8zdQxOhU1cnTjZ0AiJCCLJtNMXCcbTXagFUSj4iQ5g+6gg2OsQaolROfA5mCKi0Si2RFlZjP1rkaNPGtxjKJ99tIqf+JyAgMfuZXMArcPPqwk5erNrIAti2zsAQAaUEtEPMnkGWpU0fk/LcNTBEe5QRnYr2wRDpIqXQ/qQH0GDzsvcMFrmGYX/ALMEM0Xe9WKh5jQ/K5eLKkBPGekD7Y9vMP9AGFHhHfI7BEAGTC+X5SnDFmwDrVaBv0X6cGhB241WnTo8uYSuEaUE1dFVWO8fd05b74H6MlH2tYWnZyZrC3WbF0WVSN1ljLVC93I5KB+f7YnKRYTBx7usshOnY0TGV2q6K3mrLDLBr+FwzKzgurG9Lq/BzAPd9MckFr1EaRpWeKSkGt9EwtLkVgDUiVTJT3HNgovGq+3qEJUy4lkjVz3azuH+vPE70iULSb5dt2Y5jlScnxMJqvKYHJAsDmwJbCqb3zaEjOlZx1Jiiz33ucxQIBGj79u0Ui9VNYT74wQ/Sgw8+eLSa5eDg4ODgcNzCqx2av+MV+8UUdXR00IYNG6irq4s+/vGP0/e+973XzAx7LTz00EP0q1/9qqGY7OLFi62193GBUoWoWqZaVjIethKsmttFthg6XXy7dJybOiEuvocRWCCnTRXlpUVEmZ5vtAa6lAG0QIg4kZac4ewVmLZpzYBmYDSzhGwyzX7oqDyQkWxCQ/mOQlm8VpPSXREMkdYmYP/QDOjMqeGzzPloe9VEeWC0kOrc9WKZtyuLlAog/ZqjUxu9VtSrYg06XpLvoW8AMr0qQ4mjbrB/ADQwYBW1iWEwjTRm1qOMy/22bDWvI+eY7QbHmSniCLkY4mufklF8QHXpBoYI7YTPHve5CL9GX1JiI2aIan3SjX5ioaGMMrPNe30Ncf61hgNA9O8r7rvvohAtkEe2IOtVQtN0K9rcUzOduN96f280cCjDA4YI0Bo6aGlyXSgLZL4PjzMLMTci9peZpctf4FgkMxRWthHxV00nhfau0IrCtub7uobIvIdmKZSRrAdQ4ftDnwdYeWjku5pboCCD08+nCdYY+t7Be7Cx1YRaDswuSwIz/DMTZ1YR7I82owTAzgZytXrxX9Y/lufKtid2y2PUpVJguzKxiLVw/ebz0I4QBTNH1vCYiN5wPkVvNOwXU1QsFm09r5/97GeUz+dfY43XRiaTEQwRMDIy4owQHRwcHBwcHI449ospWr58Ob3vfe+js846i2q1Gl133XUzptb/67/+637t+O1vfzvddddd9JWvfIWITEpetVqlb3zjG8LP4FhHbSpNtWlueZ6PNULtnNLDppC1pAl1InvNgBOmi6V2OUBs2cSmhexH4k+b5Uts0lZoZ3t/jugQAWo/HkTNiILy7cpHZY5ZD5EnIkVEnqGp5kVFs31yOzpqQpYaNBMwm7TZLDxf75uUDBmy9Wq2fIfaTw6MDpu/MUMEbQe1Sx0KtCFgiIDYFpPCVI3wdlhrlJrH55WrnthCmcF6NGqPUQfHKqqCJkgvp5khrVHSvkM6S8qWetjFOjI+ByiyGxs2y9mIl68lGCKgnJR9JbxHPiZCE9ifbJ9msLSxYGgrCzfKqMiqTkBIXtPh87vE+1LSHG90UDJekQmV6ajNKhVDBDYBXjTo2wXWw+x9izmwUjtndppEUGp93KM0+wtpQ0hogUrMWIAhAhLrx8T7qVMkG5bpa64zAWOE14nFKjNRnUJoZwptZjmUTAEzDE0hYP2+WExUbGXGBx5PvP3whCwwW1FeTgWVxYY+juW1Z48tJ8LM7PiZ3Ee38nNvhokIq1Xk3cHfCffjdDaPqDGrLMJFf6sjsv3QMuU6FdPc6tkCy5rJhW4LzzINMER2+VGzfNcL5hqkFsaobQZ287DiKJg3HkvYr0HRqlWr6Dvf+Q69+uqr5HkeTU5OHjRb9I1vfIMuuugieu6556hYLNIXvvAFeumll2hsbIyefPLJg9q2g4ODg4ODQxO46bN9Yr8GRb29vfT1r3+diIwB4t133920Cv2B4JRTTqEXX3yR/vmf/5n8fj9lMhm68sor6W//9m9FUbhjHp7XUGSViKg2NmFeK5y9NUuW7witZ70FZ+RUhkyY758v3cPLPUbIgmg438b+HswaILsLA3tERAEOZJHFBi0DIrH2jdKRVgM2/IDNWlGRK6JwDV2OBMv5MvJz6+fEAIMGjQ8Kw8LJG1orRMp2O9zcjvWKvRg1g3tf3oS2YIgARLqxvebEoHQFEJ6sUVFFTVVdw1KRAFqLg0w27SoMwH0c5TjqbZPXWkfjYAdxzcECAGCIvKBylt6gzkFctlf7/SR2m/XRV0KKlbQMkQZ8iEqmHZUB6XQfUplLYIgAXTwULsvIPtOlc4BSGOyp2e+WD+CCme0tOMHce1s2mudQ3+P19dteNcdmfXT43rYZdszGjS+R0oCOteoc75BMaHjU9OuxU6O8XfO5zRrTBYoDzLjyEzyxg7PK+NjjQ2Z/sd1yP/keLtszzuV1+L6x5Tr8MrsMruO4D9AuZDhChwX/pFBa67a4XRX0QWYnz1IiIS6Ym+81n7du4Ow7FKJlt3aU4YmMa6ZObi6cks893DvQDOL4cLyaIYKjuFcmCvL9GszteySgi7ti38jIi++UN34wW6XgMV4Q9njEAafkb9my5aB3WiqV6JJLLqEf/ehHdNNNNx309t7Q8HlEPh8RD368GD/0lPDaW7OBiIiqRfOwqvAD3UOaco0fTpuMwZ3/NFnLZXSpeWr6lImanT7j6a5SDKm+TJe3yKeJNgDENJlfCbnTs9lSAM8JfrWV11XVaf2DDiF1lQXdwRGeFsQAMmi6ZmaBGfTBrK6sqt4XOuX0Ymqe/OFH+1pQeZ0f0lrAnlmYFO9RW0qfH51iPD1dvqEMAP9OVlASZJK3kdVCTH7gc1p3QH2vMz102jIGlEgjxgN/coE8V1OL1DSSGgyFdpgfSPwgFOeacx7iMiWFXjOF0rpZbtemHOebT9GQxz84JZ6q9CuB82IZBGXmmF/CTI8a2M5QagKwZowwz7R52ualrITVk/Mx+DMrLDjBlNoZvd+ocpHVjYGBV601mB0iGJiaHeBlVQo6l+3Yc56ZD0rsMtuKbzP9vcL9OTvL9OMIT8ngWmZmcco4DwowBRodhXiXaxvyMSKoweCkGjbfTy6UCR3VEIIgDG74uNgaJDpirlXd+sMA01E2iKlIAXZVJYro8h+YLovtgG+C/F7bPkRZCF03UDSvmNICYIaJwCKUksGPnZ7n52JqHvd1HnSWeNCJdmN/tSCRp8ryhMdUvTkr+oYNg+wDCfWsyfbvf1WHwwLHFO0Trysl/7HHHqP3vve9dMIJJ9DixYvpiiuuoCeeeGK/1w8Gg7R27VrymjAoDg4ODg4ODocJtUP0d5zigJmiVatW0cc+9jG68sor6brrrqNarUZPPfWULc6GqravhY9+9KO0cuVKOy13vMJLxMnz17nvWgun1HPmncdC68peo5D0oRBsmVPEmTkKLDIugrWYiShHzzLqT6R72xRZO33EkSAHNRBO23ZVYa5GYjldpFOn2NeYQYlzEVFER4igEXmhTAFYixAzQdWoiTx92SK/8nZRwJLNK0Fz7/wLRGV8PMNm/dmPN6edEzsrTT+vKeZq+2VSyalLUeSlxtcyAomdiJTN++A0PW1ZOefH9zSvGg9xqBYEQ8gJATVYB5RMsVMBU/IYi23yNs7MMu//f/b+PM6yqjwXx99TZ6xzaq7uqup5boYGZBRBAlEmZ6Mx3KhJjPFnUIwCwhejJhGjtoGrQMSBq0FBCeBN1Ku5omG4giKo2NACTdNAz1N1dc115vH3x3qfdep9TlVBQXcDzX4+n/7s3ufss/fae6+9a73Pet7njajsb2QNMUIjKqIfsNNkuRU2TG+KKru21DE/c+5FhOvaxdNXaLcXybOgGsaEOlVc0dIsAydTyRiYQurPGwz7dPejy9zvO58hVkCvU7aHUuw7YdLo1gs6Y53aqmzKw44hwtWEMBaoRkLe5JAZRNz73FwSQus5dG6yJoeFOe4HzCpUaaqv4xmdAqUEhz2vJRPENrK4yOn32hcwnQQmmcuRsGh/QovtgvEFM8Xw7GjIMkSeuSImma+fT6mnDP0iZlbpOkNIDjYwqyRjTBMgwASBGSq22euUnWtNNtNL7HlH0nq+OuPbVKpPjaKQMPYZH3Wfg3UCMOXX83vXqPQiO/c5WYDNU24BXnzMelD0hS98Qa6++mq59NJL/WcXX3yxXHPNNfK5z33uOQ+KisWi/Nu//ZvcddddcvLJJ0sqZYUL11xzzWybFiBAgAABAgSYCUH22YyY9aBoy5Yt8ta3vrXh87e97W3yqU996jnv5/HHH5cTTzxRRESeeuqp2Tbj5YNUUiSSlGKflsfYl7bfq8Yo3NUhIiLVMTuBDYYIGD7eMkReRKupwE2UsopSCYjIEGFFPEPjli1UisELL0nEWyDhNbQIiDSTgzaijI7ZLMXwgAvTa20aPWm5ETBEwM5z9TijLgprKpJYVssD8Py+UDkRNjLMzbWbI3UYzBBHuIhg27Zx+nv9/8wQQUgNPZcXyeouUFQ01Q97Ar3GFK37aF6Xrdstk4NU4IkFXNrEtie5094zCL+h0cmttPutlUBJuUWnMkTMFoBFRLsT22zaudcQob0LLf22+b2gB7RETZtrR8sDesH0cvD1jethUDKjoZiosqTos96WAUfT3YMxwrPUYClAKdWTGQEIfqdDExEro8tdm+Y8hjI4ygIqU5Dsd+ee7Yvp57ZvAGNLVbS7W/d7lD1Q12OoFeMWYGaAAiViAND+gJmJWEeBermeKLa3ZXpwjX25Ebo8YN2g6ZuueDIAmwvcq84n3XmMLyENZL9urxYBeC/g+cV1R5/PLiWjxXTYHD+5z7Znsg1Gbo4ysKqrGl1lGSI+h5EjbaA/vsy1se+3+s6bG5F4/tBX2joQjtSveEfryVi0aJHcc889snLlSvP5PffcI4sWLZrmV434xS9+MdtDBwgQIECAAAFeCAKh9YyY9aDosssuk4997GOyfv16Of300yUUCsn9998vN910k/zrv/7rc97PdOVCMpmMfPSjH33OJpAvdRTnpqQYa/GRVqnXUTrVqK1YGH9oxKyHly8VEZGJY1x0jegX2SxgOJoHNeNHfz6+SCMlakczZUVzkU4A8+beJE13VEq5iAqRIVLyk/vVUkCjpMQux4SFkG2X04ZqNlm1293vEDFEoyu1AOsReuAW277OX9uoLD5sWQhfHkV/jvZBNzIdmAXOLLfHbdtoHxGc5+SUfzAZ0DswuMTJdBl+KBQJvQMb5I0vs9egXgTUgW0R4qPIatPUdI2Wi532jdb6qNvv+FHumvbcj3N2S84cZJ1ZYsugWfcFXQFNvR9fYSPncJvNUmu7z2ovYIQIsO5r4CTVduh1h9ll0ZOOYODs77igLkppQM8Tp9IU2V7X/kheZGKRXnOSgmSWkDlqhztI8x8c5ZFQdgtZmyjvUdbnqkIM58Riy/5V9N5CJ4YsrRX/aU+unIJmSM1LfT9VRrnDrTVrFlvJ3xJNdVdTSmT2cbsYGdJt4R5ESdvjs1F1gb4JgEXB9x1PkdZH2Zm27W6dNV1sRzGy0vUNPAuZBdSHx7X8kP6s8wm3xLPo93NEk8/US2g3R7FbZKwx8wqwvqzS7A62+4/VoLK7LKWJssj3JcBLCLPOPvvwhz8st99+uzz22GNyySWXyMUXXyyPP/64fP/735cLL7zwOe/n5ptvllwu1/B5LpeT7373u7NtVoAAAQIECBAgwAvC85rQfMc73iHveMc7ntcBx8fHpVarSa1Wk4mJCUkk6oKBSqUid9xxh/T09Mywh5cXQpWahCpVqUW04GqrXnKNUKApKB1ntUP5OdaMEIZ+iHLhZeM9a9RfxGc8haA1siwBihdivzB9ZF8h7C8CVsCbnME0kbw4nrRMF7YvLnQaqKZS1ZwXMIqITs9r/n3aLjX3gS4HLEhcSzqgVAXABSPhV9K5CREvjmfLl5SJjWjZbNuD7DKwMoisoRmZrHeBLgmaF7S5wZcoZxsLjxsAkSjgDTH9MS0rBiBKr3vJuHV43BTVU6nobok077PHmX+PlmQoWW+YxIhlI+JP77MHLrFjZ2jG9YE3Kd2hhn6RrVYEBYaofas9QfTFfSdbhgiADgXPCLOAYPlwzzKL4GljY8NMn/W+QpbdxOKmBtaPWSgwRL7Nek/AsLKPkT8mFXZlfx82/Ox60n0ABhqFXQE+dzBEQA7FblXCCObGm0VC5+Z1WVY7iPcOdFhg2arMLNHp4rnE+6ZKf4Hatur3+uzgevv3GCyv9Dn0Op4WtNcts326n4RmRBbwvbuwXIwZBXChzZz8bDED7I0ddZsKr1ONV+4zpe6pWfpDhZAcAE3RAWnJSxOHXOXV0dEhoVBIQqGQrF69uuH7UCh0+Bs6BggQIECAAAFecjjkg6Jf/OIXUqvV5PWvf7384Ac/kK6uuugjFovJkiVLZP78+Ye6WQcNpbaolOL10AFz7NCLQJ8BZ+cmLdCICBADeji2Apj79oyPMk410qH4DBtymGXvG7+fsmWOih3qK6Rao+Q+F+UkdmsWmRZorabi2g4XOpe6rT4kO8+F52CMkDHU87DNThtZ7bZD9g3KAiAiH1/apO2wbAiQXqjba2QI5gzFVhEZQ5sBpihqk/6kebBmzhseQ8wMRCaZ1XIB166N9oNK0ob/WS3yWbKXypcVABANIzpHRk7EGuV6pFX34tku3S6qfahjk1vHuYG94HNLDBFDtJkYItYOVWHepGF+s7uXYyf26v7dcZofc6E1GCGfrdeFdtrdRkjDBIYIbB1YBc6YYp3J0HFCG7jv95/iVhP9mlGlzwz7MC24Ny0Ty9zFR38CA9u6BUywOzcUq02SVxUzOKwdYoaIs9jmPOYuVpbYLDCZYBU5446Py4VToR3C84Q+wWwntE14MWV6lbXT9xHr4MA4VfUVCNYy7MuIuM+7nnTLrOpw8Pyl54Phcd8XrRTT/z6kXRV9CO+zSBpMs/b9p/T9i8un28VJS4T3X+uOWv25U4axRG1Af/Ssl62xW9f6aQZt2wb3wcRxRQk1vQiK5SAlf0Yc8kHRWWedJSKuXMjixYsDV+sAAQIECBDgUCHIPpsRh94kQbFkyRL51a9+Jf/rf/0v2bJli/zHf/yHLFiwQL73ve/JsmXL5IwzznixmnZAke8KSy4R9n4XHEll5lkrV44MfXSt2WKIftn5FnP5cKzFdhWt98QRqO/UYIooqobWKZwnp9wJy35UlSlihqjiayvp77JuP56dKNjjwdG642m3//2vcqEc3GERAbfurJrfw5ekzhDZ04STt/dnIhYmtQvnWzPboZ1Azzr7w8Hj6joY1C5r32xZr2rcXnToowD2oIH+BMwQmJ4a3H1VRwEWAoD3EqJxrh8VG7PnwnoCaB6aB+zFiwwT9ZKni4u6fHCsjlv2InukNYUaW27PH9lk/l6qLqR9C3nJZCmzq2JfW8iYYni3ZI36O6Ej0cs+coz6KylDBPYQ17GonlxzHnXUFVgikUmsmi7z5H8VOtcJy9BrRnc7eqH9Cdv2in38vYYNbejaaNm6fPfMGYjMENUz8RzQd7iGHzOu6IPoGziOr7NHuhmvKdLzQW0ysJ7QICKLFg7bvp2koxtdZdfRZ8N0j/w9hrbPayXd71t26Me+0K39HRiwkmo2uRhzfNKzg/4R0kxeXCPsq22HZhQ227qJFftYePa6dVdEOl4En6IAM+N535FisShbt26VFStWSCQy+9384Ac/kL/8y7+U9773vfLwww9LQctdTExMyNq1a+WOO+54vk17SQJCRdDUEMOyvT2EiACXdAB88UudjmrSSurllqj+TsuDoLSCVpeHoJCVcpg2Y0TSbj8hTKtpmQ5Mm1VU4Im0VQyGvFg3DIG4TveVqDSEngcK2ubm4HO3xNRP55OuHYVOLaDZEza/R/kNiJQL7XY/HnTeYbrePtWe6OHEkB3gTK7S3bLTDobqL3B3T7K99q1Y1MERjo0XPV6ymGItdNi2ojgm/gCxESWmnSCaxR8A7lN8DZL9drAT3TtmN6jR78P2WtR6u1171aC0qgPxgePpvQDhtk+hJ7G+FkvFQJ5tF0Lajpbd9qYW22x7OF0btxJTLXjm2je54+S7bTN5Gq7QqVPDpfq0GdsFdJ/eb9Z3b7VGlQv/G23S9H99LsaW6bQuHbPzSQ0yUja44NIlLOot0LkkycYgUrAi+gjdA9gv5LrccarTTIdhUIXna3JhZBGRVtzLGGwOrHVHWQtTF9T81Q/odX/dG2D9oVOaE3Yd5T2AZk2X94Mdmg5nsTOeCT+wQTvH8V5tHAzhWuP5hKTBlzrqsu8k/x7QxysxSlYc4xWJFqd+vx9UBEzRjJh1Sn42m5UPfOADkkwmZc2aNbJjhxuKf+xjH5tVHbPPf/7zcsMNN8i3vvUtiUbrI4PTTz9dHn744dk2K0CAAAECBAjwLICj9Qv9d7hi1hTPJz/5SfnDH/4g9957r7zhDW/wn59zzjnymc98Rv7+7//+Oe1n06ZNcuaZZzZ83tbWJqOjo7Nt1ksWxdaQFJpDDYZxTNNy2jbo6kiepsl0iemxpoKNNGIjlrUot1mWgktJIHUeDA8Q3+coGpgwVtqadV0tBNpsiFrsQnkCFXBrQ2FO5w3/IsqS5FxICIYI4JITC3/sfp/tdQNnCEAhggUrkO3VCFbZEphZemM1PT1mhso0/cARUKsWmC2rSDrfpe3X6K+pUpPMQluHgo8FUze8SGArgKibywMUNGU+MWTF5DgXMCbRDPUhmhLhPlVjEa8SMbmeuGlPF5giPU4tpeeny+Jcd89iWrIGDBEwutydMMS8PIWD47ZR2ZIQTVly3873kLi4RQ34uLSNAmwIpi+wxFQXhOqYbgQTwKwC+rBIvd+hf42usgfdP+4opFi3ew67fgR3RJvAAFuGOU8og1q2DA6YFGBiIc9/E/R3sIOIEosBRqhh2lrbE1NGGpYbAJhb3EvuUwCmmXj/iVFliLR9vH8Oy1mYDkYHU5m4Nx1PWSaK731Y+xgnktRtGTBd5tbrJq8qsN5W3w+mODHF3769bM7Fl2SJ410KttktUOIE7wEUra3EQ1J9MZLbA6ZoRsyaKfo//+f/yFe/+lU544wzjEj66KOPls2bNz/n/cybN0+eeeaZhs/vv/9+Wb58+WybFSBAgAABAgQI8IIwa6Zo//79U5orZjKZWWWSXXjhhXLxxRfLt7/9bQmFQrJnzx558MEH5fLLL5d/+qd/mm2zXrII59xF5rl7RsgLqN16aq8LdaDPCJUwh2/3k+t10Xtylwt7swtcFI/IiAWUMGtEZIr9iwoo4/tdhFtNaSHWgouKiu0Il9yi1IooSZmjFhQ/1SKlmhbe9aRqKFSbMHykO8Fw0e0vdr5TLe7fr3RCQdsTc/vde7pbX/BLt59ChxZw1K4WG0cI6BbQBKQXWEYHLAEXwgQQMXI6OJghhmcfaqEGgTQAFgvRri9BQoJraIdwz8AQAV4XhXTrLnsOYW80OTMzlJ1DbOCYbRci3rHj3fMdH3UHjA84wQsYIt/ueY4hQgmYiUVumVlo29HxpG1H2w7LEJVa7GsoooVYmSmqkcGof2aiIbPuy5t0U6FY0mB5qwMlwljAjusO40ARkQkqSNp0vGPVRjKuA7XcpUyRMjXNA1YXlevR50gFyA3sWMXqUVDst2TJuIa2MkNTt/Cw7w3PrNC1LOrzjEK3rHWsp+rbz1koHiIN0v5j3b1t2aOMT5tldpA44QkTejwzfdYqAHYU7ZvRHtsH8F5AYok3i0TBWGKGwRBxcgJYn0K7yNzHKMFAgTwJMEa4xmATve0K2HntCtVovV9Wq7PmJV44AqZoRsx6UHTKKafIT3/6U/noRz8qIuIHQt/61rfktNNOe877ueKKK2RsbExe97rXST6flzPPPFPi8bhcfvnl8nd/93ezbVaAAAECBAgQ4FlwIDRBgaZoEr74xS/KG97wBnniiSekXC7Lv/7rv8qGDRvkwQcflPvuu29W+/rCF74gn/70p+WJJ56QarUqRx99tLS0tDz7D19GiBRqEgnVJhVCtNEvIsVCp4s2EkM2swamjMjeqkaQ/WJpgImVTrsAHQui5siEC0+QSlsLN5n1yCBXy9TU9owLnUpzbAmGfDcEMm5RUdNJsCLQw7Tu0MiwFSnx9inq+RMn0I+pBwHm1of2udTl2C7HVPWss9eLr0+mT83jlCFC6n9SC15i/j49320HTYI3QESaLelIfMovaRaYmSq2hQRBJqJrMD2c9YUMRKBI0X+obI8JlC1B0xClxSmrJUJlRMCu+e2VIULpCSCmzBCzCIUe17egZQLbVkrZ/eIaJfeoTmXcNhS2FIV2+9qJjduLX9TvsYRxZsse1bfR9QFbUdVMLaxzaQuwgGBjux8nu4msvR7ZXtvOQmfIa2vSpziqB2qyxM+tcAoanfGlriNBi5OeF9Kl6q6ImUwO2Db5khLaN5qHLOPjn0OlLRLDU2d3spbHF/kNoT2cbWnbFSMWEn2MLS6Qjj6+2O5veI1bzlmv2bL02vHPhv4sA/9e6utg70qkufJsLH6G4sctVD5Fn+uyMr3RcbtfX+5ENZ+pfVXJKruHDLhITlnrdjLeVP1Wdq4tlg0gk9CvN4ckn3sRmKIAM2LWd+T000+XX//615LNZmXFihVy5513Sm9vrzz44INy0kknzboByWRSTj75ZHn1q1992A2IAgQIECBAgJcU4Gj9Qv8dpnhePkXHHnus3HzzzS/owPl8Xq6//nr5xS9+IQMDA1Kt2gjncEnLjxRqEpkU7nifoqydSweDxBqiSB6GYDYqQWQVVb8jzqpClktBs8LiVNQzPE7z5MpApZe2aHvc9pk+LUxbrZkl2ASYxcF0suNpzbSg0hQwbYPXzuh3F5nDD/2xndTvfryeoeEOrM1UxiySdaFdC7LwdJmbG9XvkZ2ixx20/SvfpKwCEpqgRdAnAkaJiIRZ5zPZl4X1FwW9JpWczUJhZqhCDFDcWt34TEFE1WARxpYqyzCh956KjELXhb4GrRGW0OxUNKoHQwREMiVt39SvB7wP48P2niWTNgsvuZeKo6p3VmaxO3GwiHU2UbfTvgyGCH0JGVjYDn2+gVlTdgVGnjW6P+2bwQa4HTews+hT5HFViYrkjncdonk9GFS3hO8Ysq0yxDLte43V+HRusPEozgV6s9wce05giJgVzPSF9XsqJ6K7h68RmFDPECmGj7bHadluVv37BfuD3xCbx8I/yEO/Lna4389Zb/soG5eimDKQ1GcB97Lzac34Ui8gaDSh22E9HzNMXtejGZtgilBOxDcblWoy9YwyMD5pZaVr4ciUx2TvIzBGYO1ZzxjNViVKJWwOCQJN0YyY9aAoHA7L3r17G8TWQ0ND0tPTI5XKczOj+pu/+Ru566675F3vepe8+tWvDsp9BAgQIECAAAFeVMx6UFRjd1tFoVCQWCw25XdT4ac//anccccd8trXvna2TXh5gq+b91DRKBlZJ+QjBA0RWIOJRTbshd8GM0VhikC4PEexx0Xr8AtCFhsA3Qc0CawzQWTcmoHWSbNWIDkCEaZjZLi5gjEaX6Esh7IpC38cMb+D0REYKGivwBDBWyc+ptlxbYje0A7LMCGyAdPVul2ZMNV2wAkbrA9cplHAEhlI8LKZrPNhN2LO+IOeBO7c48vstWzbqueWm1q3AeAetO1UpocyfTyrRvAFVaEzU00Elk3kqssMUZXuPZd+QUZj6xZ3gihvgnsBhii9NGV+B0YN7GHdT0g/V3Yv22OZJN9OOl/PxNFlSPbb68rMEPR8AEf0KEoqIpLY4KiL1D6rLfPbKpswusYypqGS3We2z/4ukqbny9qNSWqPazNYQDBEAJhjr43T43Kfysyz9AZr6Vp3WUYZfaTUavsE3gus7cmQNqnrcbfE88gCXZTXSGjpDDBT0Ch2PeHag/NuKE+izYLeB1lnuTnog3qcFtsueAZNLIYWS/czbjMya2GR8cX23Pn5BlOL5xHvBrwX+N2Jfl5sbZJY9tBrigKh9cx4zoOir3zlKyLiss3+7d/+zeh/KpWK/PKXv5QjjzzyOR94wYIF0tra+uwbBggQIECAAAEODILpsxnxnAdF1157rYg4puiGG26Q8KT6R7FYTJYuXSo33HDDcz7wl7/8ZfnEJz4hN9xwgyxZsmQWTX55IZyvSqRW9VE6M0QAIi5oizAPDT+g6cAaA8/saGQWG7W6jmK3i3RHVoPVc8vOTba2GAvpUnss01TotIyVL8xI7EV93l3bozqYeQ/YdiP6Sgy4EBnMVVV3V0K9sBR8kOB07Za4DmDI4JuESBnZKNAi4Lqi1hlYHJ+FohEjzgt6ATBK2B+ykUTqug5ErcDEQvuYdW2w36d2O51KZmGztsF6q3g91rD1P8mR79DoUe77ueuUaRmizL39NlWoGqM6WgncRHyPYmwavSt7mF9iGeGUap1iqkPb80eWdTzujdtERCR97Sq3XGjbPbbCtRf168AGwCPKZxxZoqnBM4cZu1Q/vK1UOwT/GNUSsS4GDNHIkfZzsCnhYr2NQPOwvcY7zyUmhhii6IRdn642GZiS9s2W0fTb5eqal8ltBOBbhOePva3ge4Tnrve3yhBBu5cjjyjI4/Qa4bjQfQHdT1D2HGmHOHMQhWHR1xODZV2675EBiGeKtUhlbUdJNZfwosLvc6ryQB8Ck4R1MET+PJvsshIP+WuLZx4MJlhjsHRgmbFEfTwcE5mGh7FGeUqUy2W58sor5d///d+lv79f5s2bJ3/9138t//AP/yBNqu2s1Wry2c9+Vr75zW/KyMiInHrqqfK1r31N1qxZ4/dTKBTk8ssvl9tuu01yuZycffbZ8vWvf10WLlx4QNv7nAdFW7duFRGR173udfLDH/5QOjs7X9CBTz75ZMnn87J8+XJJJpOm/pmIyPDw8DS/fJmhUhOp1OomiQoW6XGqPoCCsEVKY27Z7V5iTVTdHoMpFHDF9BhSb8eW2T9oGAwBmGpBajtegqjOjf0PHqdPur5U8ccC012tO+wgKpxWGrxdTSF1ymXva9xAAH/gcl2Oe8YAAAMCAC9jbI/BEFJvw3H9Y0DTZlEVXjcp1V3G1I0KWPHyBi1c1r/rJarejT+8WE5OoYaRpTRZ8WeLTn34Pyj6BwCDIQwAMUWKqRH8YYMRH/6g8B8aDIY89BxiwwU9Zx0cdNvBijcSVDTvt9NKlcTM1H6K0sf3nub2n1vgPj/1OOdY/7t1bjAkWtUnrNcOInYeaCAdGn/4WXTsB+De6NQOWDEYSgwWdem2iw67ke/4Ue7dxdexXsJi6oFGcm/N/4Hjcx88RqdvaW4lRoOg2JjdJ/5woh/y9NPYck0coGkwwB9Of8aiezwnyX3288QT9l7HB+18XSXljov3Dk8pevuFAdcALv/RvDtr1gtztW/MgXkrT/drAsYqTX/XoARleFBmp15CR7R9Zjd+6ipt8zh8skKYpiW5oK5Q0FMN1wc5HVvsYAjXBOeOUkN+EMVlZ3Qd78imikiEBvaHBAdg+mw2TNFVV10lN9xwg9x8882yZs0a+f3vfy/vf//7pb29XS6++GIREbn66qvlmmuukZtuuklWr14tn//85+Xcc8+VTZs2+RmlSy65RP7rv/5Lbr/9dunu7pbLLrtM3vKWt8i6desMSfNCMWtN0S9+8YsDcuB3v/vdsnv3blm7dq309vYGQusAAQIECBDgYOMQT589+OCD8va3v13e/OY3i4jI0qVL5bbbbpPf//73ble1mlx33XXy6U9/Wt75zneKiMjNN98svb29cuutt8qFF14oY2NjcuONN8r3vvc9Oeecc0RE5JZbbpFFixbJ3XffLeeff/4LPKE6nldK/q5du+QnP/mJ7NixQ4pFO9S95pprntM+HnjgAXnwwQflVa961fNpwssG2b6IZJrrl5mnu4ptMGO0YQULCjGFAeM9L94DM0SMUVjFreUOF6GNHJGYcr8QMCJyAEPkD6uHiU64yLL/NC38ikAQbAgxVyjRECq5882h/Ih+P36kLe6JyBCFHMEQgR1BSm1M6fa4RtxIOQargjICQKrftRvTDzh/ZoCKZJEVoXT6VL+9b2Csmio1b7BZJjNDMD+4V/Ehy57l+hLmGCyeRUSJdGecI+4VGKQlP7VRf3TCimWrUbff6Lj7PL3UGnKChYAhaFOJzlXvAVgJsHgjK8O6f/d5dontO+vvPsK10+/HfC3xUbf0jIxecpR2mA5ghvx6vz3/xC7XOUrdbr4NDJH/Xg1Ls/NQxBjTle77bp3e5HIsk6cjkRoO5F+ltNd+169jo9aMECaB08GzYT4lXNs6Yu8FTxn6yssKNknFuYGhgcg3o2amYOV6lCkCQwQkBlyfDallyvgKd02ZVQxRIgmYIb+u0/Lo02Dp8DyyABzTaWB4KvZ14d8zmP7CeeAJ89Nl+uoNkwMJT6fFJuz3eCZi6WpdQK1tRqmQsdVum+ReZTYhrIb4W9/VmPKDdADv1GJr6MWZSjvEg6IzzjhDbrjhBnnqqadk9erV8oc//EHuv/9+ue6660TEzUL19/fLeeed538Tj8flrLPOkgceeEAuvPBCWbdunZRKJbPN/Pnz5ZhjjpEHHnjgxR0U3XPPPfK2t71Nli1bJps2bZJjjjlGtm3bJrVaTU488cTnvJ8jjzxScrncs28YIECAAAECBHjJYXzcjvbj8bjE43YE+4lPfELGxsbkyCOPlHA4LJVKRb7whS/Iu9/9bhER6e93plS9vb3md729vbJ9+3a/TSwWa5Dt9Pb2+t8fKMx6UPTJT35SLrvsMvnnf/5naW1tlR/84AfS09Mj733ve+UNb3jDc97Pv/zLv8hll10mX/jCF+TYY49t0BS1tbXNtmkvSTRVbCrx3jMpvVnNxDo3aeQzMrU1PAoWIk3bsxAhaGw0vTpjGSKAxb9s4hbOQcRrI7ZsH2mZdlKUrnqZJmKskJY9cqyLLIdf60K1jt+5B2b0JNfOUNQd97gVTrP2zP9yGYws2I6mwQTZ1OMJ1eiHyqoRGjA/8xEqUpwbCrz6VH275LRuMEsoVYHzrYZDIipIhsibWTsIltOLEtoWK5IFwPx4kSeJUr0IVNdRNBhAqjwKTqKkS67XPlsoGREu2+NBE8VJAJyCPLbUfg+xeexxLSrag/bLlNv59TF7nSaoNATuAUwYm1UbhL7RuckFVWW1EABDFMq76xLbPSoiItkjnfo2rCwoGCJgshGnSKMAGwzSxKKIFz4DQ290Hatpq4rk7Rl4hgjWDoDXn0DUTpKI9GIs3R57H5qaqcRFwjm07HY3FwzR6Aq695NSzd12eh7HO91GgoTjKUrRb91uRTkF1RqCYYb2ByiTLo0LyNbLk6iGSBmflr2WieLUf04yQJ/25pN6eaAx8rYPeuu5BI9PpEDCi5aeKcebJK+FhfEOGl9uzwEFnYGuJ/VcdGyAe+Gfh8nStReBKTqQKfmLFlnx1mc+8xm58sorzWff//735ZZbbpFbb71V1qxZI+vXr5dLLrlE5s+fL+973/vq+yQJTa1We1ZZzXPZZraY9aBo48aNctttt7kfRyKSy+WkpaVF/vmf/1ne/va3y4c//OHntB8MoM4++2zzOU7yuZpABggQIECAAAEOPXbu3GkIDGaJRET+v//v/5O///u/lz//8z8XEVcRY/v27fLFL35R3ve+90lfnzPtQmYaMDAw4Nmjvr4+KRaLMjIyYtiigYEBOf300w/oOc16UJRKpaRQcOHF/PnzZfPmzT5tbnBw8Dnv50AJtl/qqDW5f3Wti2pnMi766Pst6SH2ueh3YrljWOLD9nufJo0yIFma2y8rc6OanollLoJtTIW17eToGBFMcp8tEhoizUIN8+2qW8mqaRqyRsZP1ynSsmqnznHRfOxxlzay4o+2iYjIoz86ym2nDGrrTls4EuZriNYmFrn9dTylmolupAq77bs2uXbDah/gLB6fzebFCLpAyQnKqkFJCmSchItVXy4iQoaZ0PZk25Bd5j7H9gBnqeBeVenpBAsAILsMKLdaBiSSdQds3eqWhS53cXwhV7rl0JfFla2ExoiztLrVUG9sWXTK/eA8seQsLs42A2vioavM+iHC7nzK9amwnh+W+QXu5dy82VUzBUMEDB/lzh+6HBh5sr4M2g+gdUf9uo8vJc2NlvtAFlSDwaQStuhnzCqAweA2cCTPOq+8MqC+CLFqj+rmjjOzffwcsNYxMYxSL+4AMJHF+wYMEYw9i63W7BV9HIwUrjW0Uj61ns7z2RginAey3rzlALJUqSvFR/Q9qUx4SD0+YODJWcG47vlJRZSHj0FWqX6JfQy63yb3imkL9J+hGumi9F7h3CtRkQoVxn25oa2t7VlndbLZrE+9B8LhsC/ttWzZMunr65O77rpLTjjhBBERKRaLct9998lVV10lIiInnXSSRKNRueuuu+SCCy4QEZG9e/fK448/LldfffUBPadZD4pe85rXyK9//Ws5+uij5c1vfrNcdtll8thjj8kPf/hDec1rXjPjbx999FE55phjpKmpSc4666xnPdaGDRvkiCOOkEjkeenBAwQIECBAgACTcYiF1m9961vlC1/4gixevFjWrFkjjzzyiFxzzTXyN3/zNyLips0uueQSWbt2raxatUpWrVola9eulWQyKe95z3tERKS9vV0+8IEPyGWXXSbd3d3S1dUll19+uRx77LE+G+1AYdajjWuuuUbSaedCdeWVV0o6nZbvf//7snLlSm/wOB1OOOEE6e/vl7lz5864HXDaaafJ+vXrZfny5c++8UsUpZaQFJMhz3B0Papz5rtcuAovmOZ+Kzpv3udCiGKHRmAl0uxMUwix0mozi+pRu+pH6I4jO4wjU18uIwrvDYhvsGMbkoEhghmi349GVQv6RkREZM8mJzhBvD30bScKSulT5s/Ta3zceuvOkmlHU9lG7O2bbVoO2h9X1iVHjBEidp8BpafXRNp/mMvBBylBflK1ppD3XEGGHgCtiy9YigwdytTxkeQ0DBH2jwy6tu32XJEZFB1zeo9ym2NEwAwB6QX2mhU1wGvZoxl9pGcrTJPJB0znJVVst+cXpRIWzAyB3QOj1K5lT6CTaaKMq/3Huz5ejbpl9+Nug5EjYrp0FDxfR84w4tIP7GGDFz8MBPe/riSdv3Gf4dqh/2AJ7d7YcmUR9tldRmwiXANDlCEfOjChQLaHSrBwMeI2WxYIfQvPJbJX+Y8aF9UFMwSAKS61qm9S2vqncdZbNM3aSGJss0RdKZjZ5XbC1DK1T5kxeFohM7OC9rslaynRx71nWMyeJ/o8GC5XqFavZV5NGmN43vU3yhK27rJFrDE7wL5aTZPZoZe5pui54Prrr5d//Md/lIsuukgGBgZk/vz5cuGFF8o//dM/+W2uuOIKyeVyctFFF3nzxjvvvNNUvbj22mslEonIBRdc4M0bb7rppgPqUSTyPAZFkwcoyWRSvv71rz/n39ZqNfnHf/xHSSaTz76xSEO6f4AAAQIECBDg5YPW1la57rrrfAr+VAiFQnLllVc2iLQnI5FIyPXXXy/XX3/9gW/kJDzvealisSgDAwN+XhBYvHjxtL8588wzZdOmTc/5GKeddpo0Nzc/+4YvYTQVRcLhOtMBXwrMzSNy9HoQvZxgiBiI2Lh8R2TE7agWs7+rRYg5gqMzygNoqQdEdswQ+d+h9IRGVuzTElF5C1Qu6RNt2F281YmFlqqf0dhy9YjR9iBDA0B7WEsBrVTzPrfMLNCsH3Xurun5jC+12XdZ1SqFc9iPW/oyHjkwUG4dWWZAUpm7ipYTAGMVTZOIZBKgyUFWGqJx6DDq0Zb1yWG9B3RdYb0W4Yyeq0bz5WZ1G69Bz2HvzfiSqDm3IksA9HDey8YXvbV9AN/78yOWY+RVlh0ItWhI/Ixm3amepEpZaUnNqEUxULiJo09ke+Bpo78ndgTeWQB7AoFNRUQfJh0HGCKfCab6uqET9HxSWkA3UZKJP9Yfb1PN36hbZUaieb89NrNWKLMxnU8NGCI8p2VlqxxzMUmbo6c+oaVToFECuLwH+3glhqYO+fN6r8Ho4F7AiR7vITyn4XxZz8vdHHZDD3Ohawru81SUN6bZsTh/MERAnZkCk233x1m87MgN1pXd0psHlZXVIsAtO2uS3GvZN68J1J+GfHai/ZzfMfi8pM9fJN3I9B8yvNDps8MYsx4UPfXUU/KBD3xAHnjgAfP5c8kau/fee2fdwJc72rYXpRarD2BYGI3UdaDUZqfLABY4N1Gl8hrprqoJ96S2bHejgMIcN0cBI7K6WSMEi0ob00val65QlL1pod3O2/7rS7ntR/YvV2ycUnt32vXYmJYBSenLWEXKhbmu3RjMpRfYKSEIKLOLHPvoTeFUCD18lKbwE1vftkNPQNM5IZJGWjtSi2HMiD8C+KOA6cNKIizRMTtAzfWooJkMOWE8GQYdX8EfPrddyy4Iot01wBQq4Eu2KCrN9p6XE67NxXatA9Vha4f5c99G9alI+F2iP0BhmlLEYIj/EIWK+rsudz1a1rEthFvmdIDaTFNLEMtzjTP/vc668xRUmNa5wryfnqtO/XsMsvC9HwwpFs53wu3hdFKKT7mRVWq33bfQ1Fy+m47BU6KIHvCHFaaNOqDL6BQjBl0YDPn9d7ll9+Na648GqBX/HNjfceIA126DbQFqA4YpeYArvmMw5PevNdOwLLbTCFbhS13Qew2DoTSZOSK1HrXVfIkNNX9Mz2fzW9tOPItY8jOBwRDQtbFeN68+GCIbBeo3uJewnsCAlW0QomN2/ZDjEGuKXm6Y9aDo/e9/v0QiEfm///f/yrx584LyHAECBAgQIECAwwKzHhStX79e1q1bJ0ceeeTBaM/hBx2VI6Li6L5KQj82/vO7mabqfaik5TQwjamL8LgL77PLO0SkbgIJRNMUAdK0nt9u1IXdEysd58u0OAsmmQ6GSSRMFIHUbhvOgyECmCHiVGgI18F4IRqDqVtCU3Fjo+5zROpdT1NkO2GvZyVhGSJMl3HEPPl3MGUEkLYPQzhE55iWCRftvqIZdw+Rws9lOpoK2neS9hrku+06i+ibh+xxuG/h2vHUBDND3giv2doegHEZOcYep+vehO7XrYMhys4zm0lmgVvGnQa/gSFqED7LzN9nFus9f9q2E0AfT6uYGSLo8WXEnvS5uanuVrccTtc1kOVed9/HlO3qu9NedJiDMltQ1TIyYIhYPF4lYz8wGSgYy+cChsi3ecgyPukFtl2ccp/cpyav+tyFqbxPbNw1AH2mEsdzQEx3NEzrFCRTUgEsAzDdiOO2b1GG1guydWqY5KcoeQNw38V7AZXr0WfjWkYIfQBTw7AmATvaskvb0YzSOnVGF9O72LcvwkslRJiZRP/HOwhtrEZCEnsRijocaqH1yw2zHhQdffTRs/IjChAgQIAAAQK8RBBMn82IWQ+KrrrqKrniiitk7dq1h3V5jgMNaIeQYl6luXkID1lwiDToSMaxB5ExGx6Hci7iqzUjJKUU1P0ujImO6Zx6m7tfLBBu2C+xCumFKmBeYfe/8vs2TGKmqNRuRbDMEOV649oeF6GBiYLORjTqSijrAQ2CL3dSdstMnzsOGCJonyIaiXnhpV5/aJgqpOnioq6ImP35tET0eJoWHgn5tH2kQ3vDvgTSoGEcByGxvUh1uwSUMLHXvthVP5Zrg20jxLOsk2DNj9BUd5FS0suW8JLcPNWHbVaxr2r2fTFNaCa0uZ2PufYPnmaZrpXLnJI6+wtXEoD7CIwPgUjarrdv1+idTCQHTrHrx568xf3nZLfY8MAKEamb6xU67X7HXmvD9HizbfeyVldtdO+Qa2AlH5HWx2x/HiO3ENYrQdODJViEKgTYJDr3fUfXWXcydz2lureAlXTrmT4IpG0fSqgJLBIXoA2Chg6mpaFpCkxH0vbaNDDexBDVqFxQmUwYYY4Y8+aOblnQz6FNLDUTi/ksCck+OUCb37rLPhSjK1EYWs+DGDguygzbCZF6IViIzsFCMfuHe4jnBctkv2WMCx0iRbr/hwIBUzQzZj0oglFSUJ4jQIAAAQIECHA4YdaDoldKeY4DhVJbRIrxSIOxX4XSpsEiAGyU57PNlAkKFZBfrYVV4emktWfK3U6MwJolaJLAXEWHXbRcI23AxAor8GCGaM7DLtoZXeXCILAhiUEXNoEhiigjheg/VNFssfnud9A6+aVGXyhbwIBeZ0KZK2gFGrJq9k+fKi8iku3TbDxllIbf6UL51v/W66fRHTNJ9cKSYGdqnnGBhgbRfdt2FNnVzD4qpYKI0xfT1D6B7DN/zpTFxuxBYtiWPMA1YkDjBF0FCtDW9+u+SEObM+bWkfmEFGMwReOrqE/8xXbXnqK7homIYxe2PK7ioblu++a9lK5N2qCOrZxObb/f543zra4MAEOU0LR4aDmKK9yBQvqDUL+lxkoVu/5AcZlZn3tv1B+TU7ljY7YNzEDwOhgisAywSYAhIBDlzD/qC2lNHU/PdyfZsVmz0ZTVSPVbhoezxxihMunQKDsM7xN+r0A7BIaI+yCeazA9sQl7j8E0QYMIM1gAeh70BTC/uW533s9WxgQMUf2AbgH9HDLJuMRLZRIDxsa0fM/TS7VtLllRWndiVkB/3wnDSLeenV+TRPpFoFyC6bMZMetB0XMpzxEgQIAAAQIEeAkiGBTNiOc0KJpcs+zRRx+dcdvjjjvugDTscEFTqSpNTVWvlYGtfNiXs9ClrsOoL75fzRi1kF5TjlIcoB2CPxFZnftIjkzLqjzX326j49wcO8m9/0QtvLrRHj4+RpGesiDZPrc/6GlalCnirBXWLCCSLKj/SqELogt73AhFzt7cTpdzHrWClFKbO59im+3q+/7M7qg0qqUxTnWR9aI73PFRdiAxbBsC08lyIuSj7wiZAHr2D1oemlkO56xHFPRMKAOALBewDGBoWCcxvtieG1gzr/3RKBiMCZgimBiinAYw79euoQMnqf5CM3RazrEVWpsqrr1zU+6ap4uWDtl1v9MQoUdxVltyH+leiB0EKzCykoxC9RZXUm771Da34ZZtjiFq1fuAbLbM8ZaKqmb0grSqp84osRLjej0ed6lPMJcUqdUZIso8ys53y7iyBGDzYBgJvyGUl+GMpWbKXWHmA75EMGPkorpgiCKayQijTGRRIcOxwe+MzFGFGOsKZYWyCWKdxdNnQJ/3oprRsiZo4FTs3/1wwS8oOw3tJeYX1xsMEd438BtrKAyryHUrI04MHKNu8KkMG8qIJEI+ww2MJthpvHMmltp9wU8L5W2QQedNUbVfRidCEsnMzNwFOPR4ToOi448/Xvr7+6Wnp0eOP/54CYVCUqs1DhUDTVGAAAECBAjw0kUgtJ4Zz2lQtHXrVl/EdevWrQe1QYcbQsWaNIVqPmMInQmRDvyCEL0nBmz6SqiCCE8jupKG/3nK+uqirD+O6FhLADaDGJvUHhtVt21y6/te60LV1D44QbsFtDXwD0GEB3fiXLdjjsZf5cQEi/6Pag7abHSepxrBISrFAIaIM6xi5LcErZLfj54fvIEQ6c67TRkt1QzsPs99v+gOjXhV59OmETZ0PiE9PvYTLopUo5aZYcajqajRrR4L2WYRciXPd5O3jOq0cI1HjlKNj7YlN899DxYvDM2QRr0oeFpqRrvUZ4X0MFHK9gJDBJz57nVm/Tf9S836jnuW2Har71AzvThZrwG20RfnjNt2wXMKKHbY/TXvsZlXDFyH0H7LYDUP0LNAq2AV0OfKzfUNwWTiHLHO+wJzB9XKNgAAtJJJREFUk+u25wDnZGaKuMwNl1gpq2N1xyb1zFHtTcczeli61lzgNdtrGeDWbZa2g9avfnzywILjPb1XkA0Kp/dihxaMzSGLzbarYyP7spEvWsYWYObrktbsuuSgZYiAZmJ0Mz1cPkSv/7M4dZcmXX8wPan+Ojts2qzPD3RhbfQnEu+MSNluX0mIxClb8ZAgmD6bEc9pULRkyZIp/x8gQIAAAQIECHC44DkNin7yk5885x2+7W1ve96NOZwBV1SMsOGzExsif6AiaW9KFGrlKCxOWcvXSpsLa5kBqpFHDdfR8j5KYFYm3HEKvS6LreMZx/SkF2gkqKxEg8Msed+AIQImFrouB50L5tdrJ9kqnpkhe15dD9uu2jxIrtA5y6rgPOLDZGyCWmfDTnRR6nYNWP1tZZJUcwXmrtipWXQa+SKrpl4DrtbgFtxEjtX+c+jIKsgCUyanFZl0FXNsYM9ZGqXH1Lsl4e5dyyOO7YK/EPbHmTfIYBpdTVG+snFwTa4e5a7J36yxdQ2/88RptkFPtZjV5Ij9GvWoAPQVsHKsR+MoHQxR25/sMZ/vXu8ssRP71d2dCr8m99vrPkbnW2lz1y2jEX1il+uEYG1Yd4K+OeftO0VEZMdvFsnJr7Piut88dISIiKR2uja3b2WnaWJgiGVo3m8p0VKr7ed8z4aPccu5j1jPnLrXlfscBU/BzsWH7fPONcmg5YmPqJM1tEUgqLVANRgWzpYttkOwpvc6bt8LYJRS/e7ep+dRCmWInlvtI8NHRrRdunvdLRig9m3qzK21Cvn6tlAWb4X8lMaXkl5NmRs8Q7m59e3zncTekdt23NlaeR0ZmGM8Z6wTC+cbGcNDgoApmhHPaVD0J3/yJ2adNUWT658FmiKLanOTe0FQJ8KgpJJUMesw8agY1GRInUqDoOxKW3kSf2CT/e5pw7RbiP7wlFu0cnrJTishBb3Y5vLL8TLDYAgoqKARpor4Qwa6HC+X3nvs73JU2LLQpee5VY+3yF6H2KCa0eWmfgoxGIpkYHLpPm8oREmDwtxi9+bC+fkq30mtOF/FNKB9ubPpZjhXf1HjDwrAfxhQqDXZbwdqKGWCKdahY3Rghr8zMbvfxCYdDGlXQLkKD21its9+DGEyUGon24eyO+63/nCGO58n3AC7ON/d1NiI/QMCszoUP222Omw/GMKUCO4I/jjAxJKLmY6f7vr8+IBzWwztmbqwLM4zOWDPC8VEq0m6bru4VIxtr9BUDAZDwGf/7Hb//0//7AL7Uz0UKqVjmgz9FgLe1h2UIo+pTh0MDR9J/S1q+/3cRyhFXvsjlnnqr34ASgWNMX3PxYBzve4aJfe6m4vpMBiTii5bd7j3CwZDEDQDiVG+J9R3tFkYvJW0L3dqGR4MhoDWnVTeiKdm1YzVp8nr81vS9wAE5hFd7niDa0+Lc5Hw7yv0TUzlJoZrvn+yFUaFjBdjFK82kQQAA1QIt9u3lqS1SBsdAgSaopkxtaEJoVqt+n933nmnHH/88fKzn/1MRkdHZWxsTO644w458cQT5ec///nBbm+AAAECBAgQIMBBwax9ii655BK54YYb5IwzzvCfnX/++ZJMJuVv//ZvZePGjTP8+pWHSKYi0VLFMxkACrYCoTxFDBMaxqKMSlzDEmXoMkc7ZTJS+cEQgX7OzXXbx3RKBuUwqhEWr9qyHxWdPgJTwmZpiOwSNC3AKbQssAQj0/2EPc/c6RQ5b3MhI2L6FhusN0zNIIIFw4NUeUxzFVsd28GFa9k8LrPImlVymjqzGWAn4qP1/WTm29CxZZeLpgud7mzaN1q3t1KnFYVne5RlUMYFjElETQbDOU0X3kvGdKvYFJLa3grbB2UH2lFEWPf3lGVQwLZB/B7fHzaf13TzMKYaECHrYVH+oKHsCE0fIBqHKD9zsp7vVntdIC727cvZHTXcK8o5CI/r1EpS+7RG/EiZ5tIR4yvd/sd29YiIyNrTfiQiIp+8911+m7btUwuoAS643FSybeTCy/UpRJTjsfvreWjq0NwXJ0U79tt+zVO5LObP9LrfIwUdjHFmgROns0nlnD/QdL/uPqnHxXsEfR7T2ZhGBJMGtq9EU1ADJ7r2tW+u6fm49wMsSoBiN/2QpvPKysDj/ZjtdeuDJ2of0KnUiWO1/Zu0TJBOgTVNsi7AuwzLvCXn/RQYrDNwbpgGj4/ZtrXu1mMvjEo4R4z2oUAwfTYjZj0o2rx5s7S3tzd83t7eLtu2bTsQbQoQIECAAAECHAQE02czY9aDolNOOUUuueQSueWWW2TePCd87O/vl8suu0xe/epXH/AGvtwRyVYkUqpHhQ0MUYHCaTBEccs6ZFc7MQ6ElCOr7K2DvqNlj+o/lCFiUW+4gBRzt38uDBsfscq/XK/Vc3Q8UzD7AwpU+LVKgRzMHBGJIdJY+C3VLKiQc+AU93nrNqSv6/5UIIm0V2g1mI1IaAFclHhAOQ8AZRJYS5Gda88HBSOB1p02AgeTVomHvBbIi0n32GvYssUpgqsJvWcoftlpo8TMQm1jpztWfLNlTFq32zfR+HKIvd36stc6gcSTz8y3bc1TkeGtECu5BYwFARgFhsmMEpPtrB0aOsE1oOMJFYxrZOyL8Cq8LkX7TqbXXvOuu9HXpr63SFPHEuaM8VFlA/qgq9HfZ1x7Ki1aPDinLMWYNdUraXmWYqeyJe2Wvfzcd/9cRESS0ljwFRoWCO8h7IWgF4xlbELLb2hfwTJcsExOz8Pu2NEJ24b0YtsXKmDrlBkNTyNNKSqDzCn6YIiALDSByAch/Qz6f6HL9tn4iD1wOaHnpWze6HLdXg9fN0nE7/X47k+JZ4gAlEPKLnY3CeaUAPZX0eMOrXHvoa6Nrl1giIAuLVocH3Ofj66w542iwaVJCSMoKAygzQw8h5x4gf4OdjrTM+s/uwcWAVM0I56TpmgybrzxRhkYGJAlS5bIypUrZeXKlbJ48WLZu3ev3HjjjQejjQECBAgQIECAAAcdsx6yrlq1Sv7whz/I3XffLU8++aTUajU5+uij5ZxzzjFZaAEcwum8hCP1sCs0qkxQs4uKa3G6Bc0uTA+Nue2ya+aZr4ePUE3PsGqJNGrueGbmLIYmKvQYG3XbI/JL7nEMVmahpXieLbV/fIlliBoyNDT479isEbRmHDVphIzyG8U2mNNp+zSyRsp/QwpwC7QQbr+JQaXKyGkdkTRQJq0Tijwi0ob2odBq999EvyslYRxY9RoXpAMjgy++1+aMN8EIcr6ju1B6Ye9pqodSZqZtnfv92Ap7zPQiSjFfYWmLbb90HmKhbmWaBrXAbLcW691vYyDP2ilgUggmCKn6nN3Vf5Zdb3na3UNYAzQPWV1Xus/qVkaO0GtOiZWsSxtbRoVw9VEBWwOmCHqvKskzqs2UHk+mjZxSHUnr8dKOXayFaw3tZPYqud/2D5SFAcAY+Yw5Aj9frDUaX0ZmpJzFqv3WmxyGwCSrRcA22zcBXCvPBtJ+W3bNnEWMbDeUBfI2Cttcg4bW2JsB402wLk2k45rz6NTHe7brU+h2xx/Rgq+4D6Orrd0C9EAwRAXY1iFrX7fSPFB/p6X63W+ZZau31S09O0jFXjPEWsXHqhInE8lDgoApmhGzGhSVy2VJJBKyfv16Oe+88+S88847WO0KECBAgAABAhxghKTBxP157eNwxawGRZFIRJYsWRJ4ET0PhEYoVERBV2JeSsiq0CW8bkZXojCpHaLPecyFevluFxlBS4S59nqxURu5VdnEbIXNvsLvoxrtJHe58DxLTBIK23qWxEqQpGUXZeGQhgLZM/FxmMK5z2NjZV3a8/DtJ41Ersdqh7BfZMXAE6diN5POTS5kzc9xj0LLDnc9YU/o/Y70+GCIgNHlkYZMtPg+x/LV4pqBoyUTIuMubIXRHMDFPVkrhKwp1Fv13jXjLkpu3qFGfXqtEpotBjYgNgotjz133CtE7cjCqir5hzIXiHxHj1ZtTl5LMGQ1W27Ith8+QUCu1y3zc9U/KA4GRnVmWkQVmiP4K0FPVqO3VJXYSJxnsYf0cfvU40qZJWYxo8QS5HvsecRGlYHS65HcV/Nak7Zt9liZXs3+hM8NPV/ILkO5GTwH0BYBzLxGyZ+LC6yGKbsMDBEwcDKZRfbTOmWR8nMGpOfbdib3WX8ysITDR2o2q17zzALdnnQ5kcLMVAPeT/49Bf2bfg+GCOje4DrvyJHKupIpojcQVe1XoY2MTPV2zn3E/m1LLwh7hsh7F6mGDToyn81ImbFFylhl76ZouiKRQvC39KWGWWuK/uEf/kE++clPyvDw8LNvHCBAgAABAgR46aB2gP4dppi1pugrX/mKPPPMMzJ//nxZsmSJpFKWYXj44YcPWOMOB4TGMxIKT+pBmlVWS1rKotLi1isxjbyUTUBhUsy5I1usqn5CYIiA4aNsBNW+lVyLi4hQ7a3nwopciqHUadtbbLM/mLPeMWHhMUcvjB3rsuWS/dbXZGKp00gg4gVDBIDpAoPGJS/AEMXG7XmNLbPtQ2Se0QKS0AYVxV1XdhZue9qxOyirklvguCJooIDxJaqJ0p9HsnV9E9pc6HQpLIlBe4z9J6oHkzrb5rssIwQU51vBRbhZ9VgFzXIbVVZwt7qha6Yc/IKgDUL06++tdkMwQIie/XGpREuNNDp8L0odysI1c8kGt6icodTVUy7VqPVVQ2azsSedfi7f59oRG2I3Zz0PdAm9nDE4UesS55dUZijXY5vjix/DSVtpwCb4y+j1ik64hif73XpuLvarnjlDVWnWUwCDC3fiiSVUXoL8tZjRQV/xBVBbpy5cCpSoQCwKIcPbyn+u7NfEcvf7+DBlVZKLNzNDcGmuv4emZlTgnA3NHpjiCDE0bZRN1qDNSyGTUPu23mOv/aFsuPQipaTgAaTlS8ZWxMzvua9y3wbbAzBzBLTsrjSw6mUqXMw6pULb1FxDXqsAgCVrcFQ/RAhS8mfGrAdFXPLjxcSVV14pn/3sZ81nvb290t/fP+1v7rvvPvn4xz8uGzZskPnz58sVV1whH/rQh/z33/rWt+S73/2uPP744yIictJJJ8natWufv91A1fbAWsK96as6tYJaW6VW+xcoMWjfLmx8N7rSvRwyNvu6QTjYMNgZdH8RC12tehwyldRyF6j5hVpHbPrWttX9BW5SSwEMhoCWnVYEPLbKDp5xPmzE1yDsplIZEFRX9aVdUPNJVKaPpt1bd3ypaz8GQ8mtY7rU4+jgp9qSMOseTZjucO0ZOopM705159vyYF0IW6N07cw8e0+RRl3RwVByQCn9GNK33ffRva7tpW4dDBX1hZ/TvwzanzD9g5crBkNcfgDHASBi9+3Sl3yzvqx9hXIY36ldQXTcfd5zqpsLGbnHqVJhk1B9tWtQXMuSrO50I4jXHflLERG5Z+goERHZuK9X94c/LnaaCojkpl5HuQSYNpatFllat9nznVhKf/h1/FzRQc/cRzAw0fIqepkhRI+N1/dX4erzPVY03jxo25Ic0FpiJRqA0iAIg4+IvwduHc8JbBKAzALUKnPrXFIitZMNPe33uGYw2oQtQcvOmmkPgClWPE+5OWotkLIDe35P8YCCEx8wPY8+CKsNf51DOL+aWQdGV9lOw6axeC/UN3ALDAZ5MDS+WMt/9E8alKENKBVCAwJ+h7GoHQPn+Li1mih0RqSYf5HT8wM0YNZ35DOf+czBaMfzxpo1a+Tuu+/26+FweNptt27dKm9605vkgx/8oNxyyy3y61//Wi666CKZO3eu/Omf/qmIiNx7773y7ne/W04//XRJJBJy9dVXy3nnnScbNmyQBQsWHPTzCRAgQIAAAQ4aguyzGfG8h6nr1q2TjRs3SigUkqOPPlpOOOGEA9mu54xIJCJ9fX3PvqGI3HDDDbJ48WK57rrrRETkqKOOkt///vfypS99yQ+K/v3f/9385lvf+pb853/+p9xzzz3yV3/1V7NvYDwuEqlP7VSb7TQPmJZ4AQViHZ2Qn2O344KLzBCxoViHTptBlJrcpXMsSttjuggCbKS8Yx1FSicWQTipxoS7bcgZHtc5G5gZdjlGKN9DimsCUpk9QhCE23IlINliIy5UbdJyKE162Ka8TuGgwK1aD3Q8Y9vlD0OMUJOaaWZXwLFQp0MW2Ecje7ylLVonMUQQKEOEmxzQ1Ps2GEViqkTPJQ1DTbNLHwQj6m97Uu0K1OguMWi3DNuZyYb2QCAKtD3hqI9awkbX+0+0dTHAEHmjPS1rEFrp+sy+hxxDFNZbXDlaT6zs7l0+4/b/8IibLlycdMddmnLM0ZOPrBSRZ2eGGqbrUN1+1H6P68BCV7AdsBjg8gxgiAA8A5iGDKOgbbreZ7JzXT9DYeMEPXdhEhA3ETOSGMjq0q1PLG+1bSaWAUwHrClKdnNp36aMJ8WC2V77QckStV5kD1YMDE9mgU2YQMFUMEQA3gdYon2Av3e65HvDDDYYInyO5x7TiwCY4sHj3PPHDBmXDWFmiYXqOG6+E+ab3Cfq/2eWsKkCVk0TCMJTM0TAxGK33r6l/vxzHz9kOIwHNS8Usx4UDQwMyJ//+Z/LvffeKx0dHVKr1WRsbExe97rXye233y5z5849GO2cFk8//bTMnz9f4vG4nHrqqbJ27VpZvnz5lNs++OCDDTYC559/vtx4441SKpUkGo02/CabzUqpVJKurq6G74BCoSCFQn2wMD4+Pu22AQIECBAgQICXJmY9KProRz8q4+PjsmHDBjnqKKcReOKJJ+R973uffOxjH5PbbrvtgDdyOpx66qny3e9+V1avXi379u2Tz3/+83L66afLhg0bpLu7u2H7/v5+6e3tNZ/19vZKuVyWwcFBX7ZkMv7+7/9eFixYIOecc8607fjiF7/YoG1ilOa5cD9EJoo1Cpk4tbycnFq0l6RSC627bCQXG7KMTlPGrUNDwyn64ZwyVc2uS4Ahah60zAq0PB4Viqz0/Jr3uIi40A3GyJbCgHmkP75qmfI9zfq9FRtn57n9tGwtaTts162nNqtx4nDR7LeqBWNDlAKbn2dDaGaIYGAIJB9RhmhSpAVGwdsKaATpi+iqQSeiai4pwsZ+0AhVtSlgOjwLgfTkcRvuRSjKjVAJl4kjnAA8uddRIZkFzdpOFd+rPgRdEkJwoNTvwvBan7sHJaTm71T7iLl6z8Zt3/o/vzrFrMfBTpAOBWwHi3HBBFX10UjPY6GrWxY6p9bRgCHiMilc5JcLw6YXoq+75WRhbOtOuy82jhxboWxeyTI2oZq7VtAUDR1rnyeIvGHzMLbUft/5FB2XzVJVm4NSE6WU/T1bZjQU6aXv8byivWBHmBlqYIKYBcXX+nliFGyq2y8zR+j7OE77ZkuLtuy2nWRiEdlc0LPQ0B467+R+m2aPZ3WyjUOMivyCAQZDlF5gD1LQcjMQ8Hc8Y38fH634GYJDiUBoPTNmnZL/85//XL7xjW/4AZGIyNFHHy1f+9rX5Gc/+9kBbdyz4Y1vfKP86Z/+qRx77LFyzjnnyE9/+lMREbn55pun/Q27btfUAXkqN+6rr75abrvtNvnhD38oicT000Gf/OQnZWxszP/buXPntNsGCBAgQIAALxqClPwZMWumqFqtTjnNFI1GpVqtTvGLQ4dUKiXHHnusPP3001N+39fX15CZNjAwIJFIpIFZ+tKXviRr166Vu+++W4477rgZjxuPxyUej0/5XS0RkVok2sAQIaUeQIFWZoZSu234ilTgiSXuHjQwRKq9QVZbdMDRELUmLZeRVQYlouvarmKXHfS1baWwmTUAY5oFRtYCdWbIAYUtsYQmqNzqfgcmx7dfGaJiR8ycL9iXzGJH3VS1MC4iWUY5GTHLxF5b4TW9wokzxpZTJE9dGHqeqBZnRfo2WAhkQonUi1UijR92AMC+093Oux5R3ZT+tEhsFBgJb2RJ5QJwLRBNIzMHUW6mL6y/0zIfmqWF6Hp0tQov2MwRJSk06s/Psd8vX7PbrO+9a5FZL+emfgbq7db2UtdC30JzYGaJ88PvEJlHyNAQpVr8/rkUBxkHsokjMHyUapC0mDBnbE3O2iuTNmXoeNum1A5is6g4bvN+XAT3vCQpYTa9ABmAuv0gZRCOww5h6rgWDBEyAxsie5/VZT/m0ivMnqH8DWwO2FyyeYAZYNfO9GJ3nmz1Mbmwslt3n0N/AzSV7HuFWUywibi1SH9HujyX5mjZ6z7He6RKfwknZ83FR5RtVpYcz0lI+1mxc2qGCOh4mmxH6BocagRM0cyYNVP0+te/Xi6++GLZs2eP/2z37t1y6aWXytlnn31AGzdbFAoF2bhx45TTYCIip512mtx1113mszvvvFNOPvlkM9D7n//zf8rnPvc5+fnPfy4nn3zyQW1zgAABAgQIEOClgVkzRV/96lfl7W9/uyxdulQWLVokoVBIduzYIccee6zccsstB6ON0+Lyyy+Xt771rbJ48WIZGBiQz3/+8zI+Pi7ve9/7RMRNa+3evVu++93viojIhz70IfnqV78qH//4x+WDH/ygPPjgg3LjjTcaHdTVV18t//iP/yi33nqrLF261DNLLS0t0tLS0tiIZ0GpKymlWErGl5K5IObWVc8BhgiRUnLv1AwRIiswOd7vRwsl1igDAqUmgGqzXR9Z02raA3RusM5i1WbbVTiLqzRHs0EqyKygSE8ZIvgzwURy6Dh3/M4nnQYJDJE/DvkWIbIDkJVXpKyc7ids+zKU5cMMEZf/QJYP2AJE1GAj8H3n2/eI/E+XXNB/mm07R59giBgwI0RfyM61fYGN+/jasp4FQESb2mv1JWA8wGjl5tj97V/ljyQiIm96/ToREfnZXTZAYHkZe8Q0rBMLh2uLwqzs8cMsBjNmYMgq2al9iABkucH4EyUqgP0nUOaULsu6n55HHA2R7Ys16JAGT9SyHWW91jvsvrJaBqNle07b4PrAvpMd8wGvqUKHbXOI2K69f0xZk6rnmvcr3V5PCcxOUwkFl5EBafcHDQ+uVcQSqV7XlYP+TS8KSqYwQwTAzwxZqmBy27a488/1ugMiy44ZNzBAYEthnMnfA9zHmAVMz3cnivJIXHQY71NcN98emEiOlL1nGc4Fv5lYqA84MmT1XnZstkxnYtjezMnFrcPlF2F2JUjJnxGzHhQtWrRIHn74YbnrrrvkySeflFqtJkcfffSMQuSDhV27dsm73/1uGRwclLlz58prXvMa+c1vfiNLliwREZG9e/fKjh07/PbLli2TO+64Qy699FL52te+JvPnz5evfOUrPh1fROTrX/+6FItFede73mWO9ZnPfEauvPLKQ3JeAQIECBAgwMFAMH02M0I1KI0DHDCMj49Le3u7jInIX531DzIWa5H0QnWw1kAkRxlIqb3uNrTsciFLdNhO7of0NuX7XLbUdM6xsTEX4oTTmm2WsOPe0SMs28VZH6l+crjWbC0wNpFhF1KWu2zWVjUCN2B46EydVRHSaGv4GCpAqxFesxZwxflxOZF8J7Mmdv8R8u4B29A8rOU7utyB0ovtduXVLgQObdcMq7B9LBKDNjOr5/V1fU24yd6MnQ9Yrc2yH466c5ljTVSKbfbeDB5H5zrfNX7Ob13f4Uw4nDs0NNMxK9AUdT7l+tbusxx1EtWsLZS9gJ7kTf/jN2Y/P7rnVLOe2EfMjLIQYGS8f5AyQXU2036PQq9gBcD24R5GyCGcM6MqxJBFie1I7teipV3KFpC7Mb/Yc1q6Al43YIhGVtcPnF6ibdUSJy3bLDXR+bQ6qms2VM8626jho22/55IkzBDBm6nQ674AQwTMeVj7pV5T7/bdQVmtqg/DPWLdVUP5D9abEfuGax0fU/aEGF2+tuzfBN0bAKYMfQYaKi7PAcYLnlbM/OD9gOeUC2izRorPi7NtQ7V6/+V3ZZlKADUwoVTaBe8+fF6NNEl7MS0//n+fk3ERaReRsbExaWuzvmEHCvi7dNzfrJVwbGYfuWdDpZiXR7/9qefc3t27d8snPvEJ+dnPfia5XE5Wr14tN954o5x00kki4hKePvvZz8o3v/lNGRkZkVNPPVW+9rWvyZo1a/w+CoWCXH755XLbbbdJLpeTs88+W77+9a/LwoULX9C5MGatKRJxpTLe+ta3ysqVK2XVqlXytre9TX71q18d0IYFCBAgQIAAAQ4wDnH22cjIiLz2ta+VaDQqP/vZz+SJJ56QL3/5y9LR0eG3ufrqq+Waa66Rr371q/LQQw9JX1+fnHvuuTIxUfcrueSSS+RHP/qR3H777XL//fdLOp2Wt7zlLVKpHFjh+qynz2655RZ5//vfL+985zvlYx/7mNRqNXnggQfk7LPPlptuukne8573HNAGvtxRag1LMR6WmHrYjKzWzCMNIsAQNSCMDAetPdbHEaYNk1u3WWYJuhP48owc126+B3uASM8flnx84AiNyLE019EK9QwoKkg55ML7qtZ4A3PkCzn69ul/9OdgiPz3YbhAY/7eHgesSMse+zt2/oZeZUIL7BbIgxMMEVDqciF0y2b7aLBuZ/in9ZIv7BUzZ7uNELe8q0NERBbe68LUre/CubjtIqNuB8igAUOU2OMOmlbiqU2dcFlXAe0CMo3io9rGo8kd+S3uC1yC/n22T7zjuPUiIlLRm/PIkIvA4oPkV0T6rfAaJ6YoDLmLHYppJDyobuhjU1t3s08R/IZ8xO0dqzXCLtjPK1RXq3mYC3Oqhkijf2QkgSVhPUvaEnyy9YPuuB336fdL6t+1PhPWY9a07fbYcBMfPN49t8i4Yyd6zgCssiOzIq7FbqH9SfXb5xLg54TZNdYOsSYnRu8D9n4CQ5QYnloLA4aIi6Zy9leZzpNrNua6lXGG7ow0UZyByAwy7nFR+wBn74G54gxDYDLTle+AblH3NWTPHQxjQ/059ZoCQ4TsNSzLzSEp5qcvS3XQcIg1RVdddZUsWrRIvvOd7/jPli5dWt9VrSbXXXedfPrTn5Z3vvOdIuJsdXp7e+XWW2+VCy+8UMbGxuTGG2+U733ve16qc8stt8iiRYvk7rvvlvPPP/8FnlAdsx4UfeELX5Crr75aLr30Uv/ZxRdfLNdcc4187nOfCwZF0wCDoe6N9i3U8oRTlxYWdoiISHTMzv9UWhy/G1UjPlSZB5Cam59reWCqo9pQBHRCCx/iDxFeMrk5OrWi6eWRDOZm1P4/NjVlzIMhf5wltr0sGuY/JkC+0x6Hp8V4EMVlDfASxUsfg6HSkTQno39VymnXbh4MxYft5mUql5CdV387YEDVtt21BQLeYo97W265QFPjn4lom3RKcoWduwilrWAbgyGgIU26w66PrnEn3bbA/aV53UJnUfHUhJ2rOe7oPWZ9S9rm4O+7Vwd+9N7GVEviLNd3s3ktHpxUMb2W+0jupsGbnlb3Bjt3w8kBXE6hQn9gR1fqYIfS2CHe5QFstseeQGrAHn/3H9EAeKELMFIt7i9m6c1uWRxKSetjampK0zJjy+wx+I8tpsn8tJWeEga0pVZKuV/uIuTKE26ky4OZjBpYomhtSft7jKbBEjvsc4KCp0D7dnqO9Fr54sI8bZae+a8higkDXF7E20hoH+ZpNi5mDOPNlHZViJS50Cy/H2B+CfDUF/qUHxz5grOY29VpyLZwQ7kODIIAHgwh2QQBGr6H+SMPxl/O4OoNU9nT/OQnP5Hzzz9f/uzP/kzuu+8+WbBggVx00UXywQ9+UERcTdL+/n5TbSIej8tZZ50lDzzwgFx44YWybt06KZVKZpv58+fLMcccIw888MABHRTNevpsy5Yt8ta3vrXh87e97W2ydevWA9KoAAECBAgQIMCBB4TWL/SfiEu8am9v9/+++MUvNhxvy5Yt8o1vfENWrVol//3f/y0f+tCH5GMf+5jPCkeG91TVJvBdf3+/xGIx6ezsnHabA4XnlX12zz33yMqVK83n99xzjyxatGiaX71yEclUJFqqyOI77Ig6lLPhfnRUU1YXuOkplKnI9lGoRrp4jrJ9hKNmkP57/dnQGhe9gFYfXa0C7VG37H5CTRZLoHzd9mPL3f7atlkrAADtznehoKv7HPQ1MNnsUKSxYCYEoj4ipPMDQ8Smdb6wo04tMT1f6NLUaXxA8w7JbmW6NDJt/rmL0Jl1aChS2lVnHUJRd4w9b3HL1AZ374rKErQ8Y3eGaanE024+augMRy90/M7e8wyVD8iusKHpqUduMeu/f3C1Wf/xH443633zRkVEZCTnLtLAE85SILzQXYOmjRrea3NjyiZmX+2+72hzSzBEVb0osae0bIir+9pgSdChImQwQXwP97/RhfuJDW4/0dMcDVJY52g+sHy1Pe77MZ3GS+7VqeIa2BMIWbUdPCNMzFc7eb2ORtzD0bTaPZOj+9yBQk0iFe1XaWU8WQzODBGmy/x2YIjQBu3+iZXu/VCtug2Km/Xk4m6DppKmheutTxCDySVfUOYDgP1CagCMMPUpYtPArLRvtiVgkKqP9wozv/xemG7aamKxmlPS9UqM6PeL1OLAkpke3o6BCui27LYsIPoY2sUsZIgLRvN6rX5tiyiDo5eKbQ18eRqYiBKDlJ4XNr9PDFdfnIKwB3D6bOfOnUZoPZWJcbValZNPPlnWrl0rIiInnHCCbNiwQb7xjW+YIutTVZuYqtLEbLeZLWY9KLrsssvkYx/7mKxfv15OP/10CYVCcv/998tNN90k//qv/3pAGxcgQIAAAQIEeGmira3tWbPP5s2bJ0cffbT57KijjpIf/OAHIuIqTYg4Nmiy8fLAwIBnj/r6+qRYLMrIyIhhiwYGBuT0008/IOcCzHpQ9OEPf1j6+vrky1/+svzv//2/RcSd4Pe//315+9vffkAbdzggMZCVQiTUYLg3cdxcsx4u2BBrbIXV4mAOPJpx2yFSg9EfkNqnlvRqcsgRYHKfHl9Fo7FR2970fEcPgElCtINip4UuFXxmSdTaYY8DhgUMDgTTOA9EylxYEseDPiRJ2qHYmDu/cjOYMNt+RKYwWus/zX3e8aQe50kX6ufs5ZdSioTgZIrestO2HxqGroejMrHUfVfudSwaGCIInuO/U72S2h0MHamlR0bsMebcB0GF21+WCp/m5+q5jbvf15psuAeGCAxUedC9PBIavJWOcMzHvqesdiia0+M85RgiaIYQCedOVUGLhrUZKudRfUpZDb2EzK41a7FNLvQ6RNVz1izSehxKOG8ecrTdmW9eLyIid2060jVjjrvObY/YdjQP2j6Z76K+5QXW7voh9R5p22DkIAwvPOQYqq5B7E+kSZkN2AkwGwaGEpocsGyAL36rZFz4RLtBbq/eg5IVGgN4LsFA4flt3UVM0ZhlTDL6vOS76T2k1gFxZWhgFdK6g0pTkCYxN8c+eFxuB4wwmBlOye/a6B70seUqxqeCsii1gr6E39eaiOHdN3XmERgi1u8g5d63i5hqLqBbnGTWyeyzf06Q6ADLDLURQEICtG/8rio3h6TMSvtDgFCt5i1eXsg+nite+9rXyqZNm8xnTz31lPcTXLZsmfT19cldd90lJ5xwgoiIFItFue++++Sqq64SEZGTTjpJotGo3HXXXXLBBReIiPMhfPzxx+Xqq69+QefCmPWgSETkHe94h7zjHe84oA0JECBAgAABAhxkHOLss0svvVROP/10Wbt2rVxwwQXyu9/9Tr75zW/KN7/5TRFx02aXXHKJrF27VlatWiWrVq2StWvXSjKZ9Ilb7e3t8oEPfEAuu+wy6e7ulq6uLrn88st9MfgDiVkPih566CGpVqty6qnW0O23v/2thMPhoFYYodjdLMVYPcQodNgwGmnC4eLMGTfIykD2F7JZWna5JRgiABFPWSNLpNIi7djPsev3iGY4hZcN75jRKrZZiwGgoWgnMUJ4qDCHz2ZyyAYpaTYLCuNWNLU+MWqz49IL47ofe9xe70PoGji6yrYjPoKlu95dT7rj7NWSHWCI/PajljGaWFaTDgRBm9xvOjfaVKHCHMtoLLhvwqxnF7j+gb5QVLuEMOlVQsqiVVVnkhhw2z96xxGubWzoyeUu9tsPohP2WsASYOIId6CQGli2Jtx6JuN+X33ShcBg++LEeLGOhMscIHMotVNtIVSz8VR+uYiItKgJPbjSX6w4XkTqL6s5f1DN1h7HfGW1dES2Z+bMp1qDxkktEXJu2brL9amxZW5/YMq8seikMiTI+PP6EmIBpmOIJpbZm9S0RbVrevOiY3ZHxdXWaiO1zjLI7dssU1Jstdcg02fTuaBtAusBhgjo2kSaHHoPgSHibC4ADBEYGM/E1KCNwnOtGYqaJcasHqMhS5XLCFXs52w1wKaM0FjhfTddOaFotjYpA08/owy/+rvMLVFE2n/dZLdjrdGhxqF2tD7llFPkRz/6kXzyk5+Uf/7nf5Zly5bJddddJ+9973v9NldccYXkcjm56KKLvHnjnXfeKa2tdf+Pa6+9ViKRiFxwwQXevPGmm26ScPjA2hrMOvvsIx/5iOzcubPh8927d8tHPvKRA9KoAAECBAgQIMDhgbe85S3y2GOPST6fl40bN/p0fCAUCsmVV14pe/fulXw+L/fdd58cc8wxZptEIiHXX3+9DA0NSTablf/6r/86KMlds2aKnnjiCTnxxBMbPj/hhBPkiSeeOCCNOpxQbg5LOV4fyUbyNuKBTxDmvqHd8b9XbQ28McAQpVR+gQh1bJnqVIbhkUF6FNWmRTTKqZIfiLf/pyiGI0IwQ/538B3RAJMZIrTbM0mIligyQ5YatAXMHGUW2Aa37LJiCxyXC3ZyxLjgl3n6HlksWqjzlJg5/tDx7vvu9erTRJFzanfIG3Omdud0n/YeRifsyYAZAkapOC3uBcpuZOdTxmGEM2Z0SdcMeoxSq9sgNkJGm6N2+/E19uanOrWIqWqJok+TRxY0Fdp8bygIQ84hd/FLKcps0j7TttN9H9ZnIjaurCkyi1T30UZeOth/uTls9gdWc/hI7XN6mfK9epys+5z9k8CqZueGzX5yWswVjFi4Wmc64sO0DyrBwvqpBi8bYoZTO+01yh1rGaLINnftC5odmaSsLDBEYHZGlzt6Az4/YLnglYN717rV0pFjKy29xtllDDwn0PpkevWe7USaGjEwUWiMXDsy88gXjLLmoKH0WWOcaaTrKPzarKVd8H6BOS1+D4aIz4u9vyZrxcB++RIqynqjHzIjGbVEcMO9955I8iLVEDvE02cvN8yaKYrH47Jv376Gz/fu3SuRyPOSKAUIECBAgAABDgEOpE/R4YhZj2LOPfdc+eQnPyk//vGPpb3dTZ6Ojo7Kpz71KTn33HMPeANf7ojkKhKpVCScc5FTsdPRAKVmlByYOgpBtgWcncEGwAMGYMdZ/C6p2RsTmlFTpkiWwYwK9gudBgowRrTkQr20BEXMSXLapePiOGDImNlJa3tLbcrQPKbtIWZr4AQrfmoeYh2LPaGmkv2eNQTQJvU+5Na3vcWd4JxHKFtHs/bA1KX21CPbXK9tU3zUhoilVrdP3PPsnKkZIkSWngHZr1H4UndOyV32d+yVk6fMOp95h+Mgc0p3A++dEIVITb90zzf0J0nqe6wjA2MGliSc1+K+vsyB3lstDeNLxfhixqQ9Uq1AOWmfgRDpxvYfP7VODGjeE9bjuHVoPkb1GYSWCvoaLpUxWUPCTCHYAjANyDrzlUr0P8h0a9lmWSwAjA7A2iFkO/oiwHqNfXYp9gd2jPbns+L0Wvb+1gpj8j2u78bHkZ1FjAo5cAO4puhLcPoG+wbNILJVmUFjXyFGhaz5cf64fqwjy8y36y27SEuk7cX7AG7VuH+4X1iPj1V84VfWReLe493IjNF07yR4uYULtYasvAAvPmY9KPryl78sZ555pixZssSnz61fv156e3vle9/73gFv4Msd4XxVwpWKVJpR36pilkX9Q8nV4PEyqqcRu6X/gzntS8q+tHkwhMEOUn3nPOreAon9djoKf7iHj3ZvUwyGAJ5WK1G6KqZUWne6/YPeRnsxvYcpF4Cr1w8fZc8nMWi/97Xbpnm34OXXVLZWBuIF3ppOn7IXEtNluM4wufT71ZfmZGE3113jl2hSxfCYMkCFbyxTu91fvv7XJM2xAdTbAvwggQYnqV12nVPkMTAN6xKGftkVepzH7Q+6nnQHgP0D9wU25Gzud38lKpr6Xm61fyBjY+7AsVF3EUut7nhjy+0InwWrHc+461fTwdXgcW7/qJuV69V7PIZ0bD1PvVf8rIT9PXRLGAE2DFgmORhMV8Uen2OABbcE/MFEXTq+F0iAgFUG9oPnlMtX4Np76wqddi/QtYrSQLljM03hzreDLk5194MFfX6yczG9re2ka5QYtVOnnPCAwQ2GKLgXSPUvtE89LR/TsiL8LGBAjen2QofdAGVROMWezw/3A++j5H5Mu7mWji+udxrcQ98G6gtcYgVTgej3mCZGIFVsizTIBA4JgumzGTHrQdGCBQvk0UcflX//93+XP/zhD9Lc3Czvf//75d3vfrdEo9Fn30GAAAECBAgQ4EXBoc4+e7nheYmAUqmU/O3f/u2BbsthiWJrRIrxqGcsYHoFhghA6mqpGVbyFPlY3WVD1NK+1UU2LNrl6bXF/20ZITbh4giv4xkbqiKa52kzTOthyoeN9DiNldmG5IALfctP23mybJ/9HUf7KFeQ2lsz+4V5W1NU2Qmd2gmVbLsyC9y0AZgtXP/RI+1xolTFO9Vfp+WRvou0YS4gmSS7hJY9br2pqFMKJbcvlGbpfMp9P7pcT/ZZ/N38FA4Z/dXTonU3fipHpzhH7T1Y9BNM6apBZsKyexAgI4UdYJax2BHX9qhtQtZOYUR0Krmm02hgUVHNfugoW4oG58VTKTifzDLXd9qe0KBMT8vbUCxWdkXFzZ5h02aBUaoR21miZyySEQmF7W/8dAsxOp4xKqH/2WkoZnYYOGcwGSjE2iDaxXT5gFuHnQPQupPMCv29g4nqzJ0LDBGA5xt2EXjeUJgaiI2TeeQ8+1yD4YJY3jPgnmnS7RLWZNFPexGznqCpKmayUf4HQMIKmGcwREAFthF7KzJyBBvgknhcryGuTWqvvRYo2VROYlYg0N6+lBHcnQABAgQIEOCVgmD6bEYEg6KDjKZyTZomlWOA4LqwyEVODYyJRstgHwCkCwNswx8fQQiptv2jYpZtO2zOKRgiFhwjaufPgbatbj+Dr3JsQCRDG+jPimp1D60CM1JRKhMyvoQYIhRM1p/5oosgJSA41dOCJgDlDpCSi+vmrx+dVpzKF0AM3KY1VsEmoD1t25XRU1PJaLr+ezBEfE+rVOoADBHgRfSD7mT6X52c8nu/7oty6roejvUJ9fRntwGYHpQXQTuqMSv6B1hUHE3bCBj7LbXFzPF8MWFlguKDluYcPs5SMMPWjqSh76FYMdiOfKdtV9c6O23PpRuSe6ZOn/e6He3DZWKG4iQsn6zvQaHUcS1siuesLtSlPoAm6sdc0NVrBPUxADuHews2As8NUu+5b7TsJrNVYvUKndPo0vTWTiwiNq6G83FLFvWDqc3NcQ2eu95SZhBsJ4a0/FAMei/LEAFc7oNZ0goxR0hEiakBKPQ7OK9yYmqGCIhp+jyE4tA4TWaikv1Ttwn3JJa219hbUgy4l1Wp3b7bJk89vVjTUIfz9NcLxaxT8gMECBAgQIAAAQ5HBEzRQUaxPSKFeERSe1wENbraZn1keu24tG2HHcKXNIUWERSnsDcru9Ck+o15D7iwt5JE0VCNKIkFwPbQdSCCA6BpQrQPVmF0lTJEGjFy6YQwsSRsz18m07ScpqdmFsiU8KUkKLJGeYOxpW6D1l1UDLTTsh+snUC797/Knnf7ZnscsCGtWnoCET0iysnZfsgiQ6QI4zm/L804xLWGDcDgqywzxFksZSq9wloXpFu3btM2kus9GKQmlGih6DunJRbSC0kLNWC3q8Rox3qrYdLoI2hldELEAoyscQ0HKzF0rO4Gv0uo7qusmUB7YObovkeGEcqCSBP6qFsFkzO2kpihbkuhpbbYTlvRRxLMEbL3oOvBfS21hPxziPueHJhay8Kmpgy2mPDnpKQa+g5sG6JkihqeRlsEFFTzV1D9CrOXzDDhucT5ZKgYMe4R0s6ZaUj1w2Rx6jgbfR5sHzKzvHYSjFjMHjdOJWLGl1lWEAwRAOYJrC3el+hzSbXSiJDmC8j0NTJpuLYNFhR0TfkeZBbaB5dZsUi24nV3hxS1WoOp5vPax2GKWTNFf/3Xfy2//OUvD0ZbAgQIECBAgAAHEYF548yYNVM0MTEh5513nixatEje//73y/ve9z5ZsGCaMD+Ax8BJlg2YLpJMz7eRSkO2CbLUVLOD0ghgfsAQAfF9yhylbGiaWWgZq4LOobdvK2j7yDep1bUr1a8FWBdQ9hyiKXpYmBnirJPis5hKdjwD3xCNLHV3yIDq3qgFJbvVE0eDM9bDcNZaZr5dr8bccUaOcuut26HHmbpd0BRVw/UovBLjc7XXMJKz68wQsd6p7mPk1scX0/715+1PEzunmX9oY2LQdrLRVTba5kwo3MOcmkC27KKG0eroCndxwbTMedR12vTCqC7dAXCvUbYkVLW6mJSaUlZs1/SsCorxgj2Mkj/S8HH0u5SNwls2K3sKLRZ5fkFb5MuskH4tPlpt6Ffsj8PFcLM90Fm5dZgX4hrD6wb9kzU1zcOkCaLsMs+wEBMK/RieN36PQJsDZms681aAy2Ak99l2cd8vdEam/B4MGGfX8nuiecD2WVyv9i3uRGCACCacWZoSZYAKsTz1rFZ9j47q2hTeX6zNY5au7K+lXmvSxOEeo5+FSy/yiCIQWs+IWTNFP/jBD2T37t3yd3/3d/If//EfsnTpUnnjG98o//mf/yml0otc/jdAgAABAgQIEOB54nlpirq7u+Xiiy+Wiy++WB555BH59re/LX/5l38pLS0t8hd/8Rdy0UUXyapVqw50W1+WGFvWJKOppkYfE2IgEPXDj8jrSHTpbf41GmnfoloiFM9MaAkJ1f5ExtwPmCEaW2nZiYYIr93th7Nn2K15bBWFCpphBydozp4rtrolWA+wBr60hUaiXU/C6VuzekiTFOPiqr2U2aGbo0As1hFhe70P6Xa6lHFKL3TnyRE/2Aowe9VJlwPnimNxFhei8PHFJCShS5jcb88VDtKFDm0DMSgtO+w6X6vmARukjBxpj5/vcktmAVJ7pvZsAdAe/I5Zhf0nUDYYeWx1bLL7Q/ZOmko0+PIbY8gkJJ0LSZz4OM177OuNWVl/PeGNQ88k7iNKQ0x+VrjgMvo1WAM4sxeW2p3GtruLBV+vKNy2w9AAaQajshJgmqDhKxO5iPcFiB72yEF7Yv49YdvtGRH9eHwJnZeyZ94/iBknen+g4KrXJMLXSY9b1sLLfO9S+4iqIiYpVESRXhS+ttsjqw5lP8rUJyN0b7kYsi9gq4im1StsRVyS++maEgPMz0+xxX6Pd0frbs089pltTRLPH3pZb6jaqI96Pvs4XPGCss/27t0rd955p9x5550SDoflTW96k2zYsEGOPvpoufbaaw9UGwMECBAgQIAABwK1A/TvMMWsh6mlUkl+8pOfyHe+8x2588475bjjjpNLL71U3vve90prq6MDbr/9dvnwhz8sl1566QFv8MsNxY6aFFrqPahlx9S6kHLKLpu1FpLXP3CSQgj7Ue+bsh26lzocxYT6U9ket+TaSzHOkCKGCHP30ELkuuxxwRCh0CVYBESIdfbELRE1VYk0AUME1Gukuf22bXfhWL6T9DBtHLHb9qPArD+fOapRGnafd220x+14yoXCha6Iae/+M22I3PmQ+yI2XmvwzWGWDF4oHvBYgtaAomYwRECzuu7i3OABFaGMJNYyTBAzxRlPnEGI6BmO18UOt57Q4sK4twBYBOwHNcKYFW3irB3yddn7x2i3OmCPuQsCjc8Eaak8y6e3DuwFmB8UhAUbC9YA3xc73PFSO/k+KZsSt2wnWBtcd5FGXQnuIZ5fXINoQuu1bbHVQvEcFjp1O/L7KmgbsB/oWeBYjecDztq+xpoyrNDopPZy+huxGKSRaqXsV2RZphcow6Obo49CA4UMxFKL7cysFeIaZsmBivk8qVm6xW5309jTKz4+s++Sv166rFDmJor+AlygFgwR0PlUvTNn5lv6Ce73AHux8fM2ukxZaHXfroUbn/0ALz5mPSiaN2+eVKtVefe73y2/+93v5Pjjj2/Y5vzzz5eOjo4D0LyXP5b8LC8d0bAMnOhGP0zhMw2JwRDQqlWefUVmmB7WbGpwodM+gZwOziZsmE7y6cb68sr0soDaLTGdh3RwGOolBvUljcEOPeSYWsHvfCFVopyHjrY/bNldo+/tCwmiW6Ce0q+puG3u/Jv71fBPB0M9v8fvVDSpg0BYFqCKOIo35jtcuzp+706QheSlZGhSGQr7HQYD/GIOsdhW12Ej4AdN+oeXB3pM5/N0XaFNBxU6aEKqvTd91MNkFtv9hE5xo43aQ1Z5nVfBNQY/GJTwHzievuLBEL7H9EF9YGwHQwAKwvqBNQYifA8wENH9Q9iKz9PLkY/tvmh5zN0o3K/ESP2PlEjjH16gGgs1nLM/AyrY7EuNPJIy58DTtxDpl5L8B9buv9BpBdM+9Z9MIWGm2PmMfdFwYVQeeGN/Ca1yHyNTU3/NtZksCEcqfhNMFbunnoSAAJzhB0Md2il0gJ+b604QBVrzNAji9xwGmQBMNQFcb7w3YfYIQTo6AcqUICFERCQ+Yq9pOWHflQ22H2F87pYIjjDlGMtUG6wSDgWC2mczY9aDomuvvVb+7M/+TBKJxLTbdHZ2ytatW19QwwIECBAgQIAABxiBT9GMmPWg6C//8i8PRjsOe4DxyakZGwq6tm0nITEilCRSTu1+im1kLkZTJoiAIEgEOJXXsxMdU0d0jcVF3TLar8ehnuOZH41cQetXmT1BJK3NaWBX9HsITGNUiBUM0ciR9nw6T3BzPPuf7jb7z81z1zecwXlOHQlXtdgo6PSJBfY6Q+zLU1TZOfXrx9eE0515ChQRZFg/56lNGNzxMVncyiL4fLfdDxcT5iK7qQVWdZ7vtsdroWkmsAaFLvsx+kBZRfRsuAk2xZeUWKpsXVpLNYy5z3EdmEWtUZ8pk50DjPmwHY7T+nRY92tFvojcG9hbWscUUGK4Vjc11LZ5o82c3RfuAYu3EzR9wyn9XEbDPycag+La4Fp7wbYWjAXLFaGSLOn5nCJv2wugcCreU/weCHn2k94nJPJPkJVAmaaauIDsxHLHpMOsEdPXAEwsAWbzwNKg/ApMIGHiyO0BE4ztIlR2aHIB25w+4x3PuJcTChNjn/kumpKchi9AsklL/2GsUj4MEDhaBwgQIECAAK8QBNNnMyMYFB1k5OfEJBeP+8gL6HrCRR0RlH6I0hw/AtJp5qkBFvUWOmyBxGwviYApSgZa9tiQsdABc0XVMnD0TOnh/L03qyNmiPUgEBEDrNngqGvg9W6H0T0ukgNDBMQXOMVqaZujEUIlt8OkarWyWlYF5VMANlSLU/mA2IS7T9Ac+TIilUaGB6fA6cv+3k3HtFBUnqJClJlee/OxP0TzDcUuR3U7fcrBEJUX5U07c1vbTLsgQgdYOM1MVBsVJ4ZtRF5ZEC4Fs+R/uFoqj65fJiIizXvtvQD7wX0Mpo9A5xPTJC0ok4TjeuaH+h7Ey2D4IOYHiq1q9qhJDelFIX9PWasCfVRF70lyn2rViB2E8B/XlIW+LOrGHx+Ix2E6imK1PoFBmZtKQZmeXtcpx5baa1u2eu96AdtpTEqZqY4SkwW2pEq2Db4gazO/v5TBaQubdZ9Y0WM7C94HrKtrSAmHfidmrQ0A7B/3vEzP/8QiOu6kr9GvBo9z19Rr61RXVS967T7H84Hnj9+1sA0QEYlnX4Tyo4F544wICsIGCBAgQIAAAQJIwBQdMiTJth4MkQeNvJkBSgxZ2iGScVHt+AoX+oEhQvYFslXyPZqFtVsZJGggdPdcUBEMkW+WrmZ63TKqGp8CsQWVuN1P+9OklaAsi3yX/R7z7UAJupRRtxx/lTvfs456yn2g5TiKSn09smeh+902KzRBVIf9p/bY47AFgS/ZEYemyd2n7FzSaHW4ZTRTZyIasmunyVQCO9CMVHcq8Aod1tAaZevaXZvatFgt7gmnyBc0w65Nrz0zLeUUZSTut4IusBo4XiRrT8AXA9XtmC1BxDz8Jza//N9ffaOIiHx+x1tEpM4QAdnVlqFp2mAzKdMr7LPS9TCnfdt25HtVq5RXVoZNHfW6Nw9NryMREckQyxqdmGSuqtfC2xpw1iWYE0q9B8PhM++073A2ZarfnfPoSu0DHfp7vRQNzIl+XtZ+i0KorLtiNGQM6msG2aJgtHz5i2kK3AL83gIzA0aINZBFKsfBNhMR/3tdh/YnBDPIqRkhgA1N8TscJ9NneYFGxrvmNWWs9+KMQVhSANM9p7BzqETrxqCHEsH02cwIBkUBAgQIECDAKwVB9tmMCAZFBxm1JvxTbQ4iuuYGXsGgedBGz+GsZYpGj7AhIEdIYIiAIpnEIZsKhWVLLWpWiMhFgyCOfnK99mHgCBn+RSgm2rpzZoYI7APm3TPz9PMOLWra4dYXL7TioyIZIuXTqjHajOwZ93mCMqBSA+w1YiPFqEai0E6wLwrrdkrJxgw5tv1HFhkwsZg8YxZSBuIoDPjstRtf4dYTA6Qjm0PaI0eaSaUZmgdE27rdmLvJDXoWYoZgngiWDaVmEDEPnWB38MGzfiEiIjdvPFVE6gwRsGGPEzWFexw9Uc7ZMHnVEpfa2N9J1Nle1wAwRDHyyEFGWKndMkT187K7Q+YhIn1kD0WyyDB020HPhutQC9U/A3NSIpYqQYwPNDUN2hwqM8PGkZl57lzjmpHH+jJf6FWZTs5IhH5susLT1aj2CSpRA5bSF8fV3/tyIii4rExXUT2nwFQl9+s1JO8saIMKqsVr8GFqw1KzxWDAWQRrydlm9ve4bsw8sXkst4sL28JfCeWTaqGQJEj3CAaWtXKp3Xa9SN3YG/TiWMsLEhufRsx1EBEwRTMj0BQFCBAgQIAAAQJIwBQddFQjIZOZwTbyAJgkZE8IzfVXkppNssyFcvA3QQSETAgwQYlBN96dWOrWU7vs/oqcnUGRl3feJRaENQOp7fZ7+LAgkpxuzr/ebvt7ZImFNCOp5V1OBPS5FT8221294w1mvfO3Vh8z9w9UOJayWqLqB4Xzg7Ntqc1dZzBEEwtt+6G5wHlMZolSFHVyJh2iK3hTpRfYmAQMkd++4nYQhZ8OGChyvI5kLBsBhsi3I0EeLePkCpwmZgWO1XBRp+yz4Ve5/b3h1D+IiMgiDes/0unWP3K6W/7Jxv9hfldKU/HesG3n0zuccK21w3X+0C86REQEATcYsNgmtxxdQZlNulrW80/tIk0Q9GVtU8eC0L3k9YBgcyZnbJEkRtq32muLe5KeZ93JfT/3qYlukRiyOwSTgVIQcP/Gc+Q9r7R7o09Bg8daP2SV+b6R0Guze2pWEExomLJHOdsLztfIbk1oH4WWCe8JMNLM4JRI7wZmCID2CKU0wppV57NyKdsNrB+uDztnM0M0vsQ9A6k96jVEDty1ED28k8BaNC4Ai2sFzR3eLWm9Nvnlh54dMgiyz2ZEMCgKECBAgAABXiEIps9mRjAoOsgIVd2/qPrcVEhL5N13NZqI5FyYAc0RnKnBTLADbkrdURGJMZPT8ZRGnOpoDb0La2PYU4c9c2rEjMA3BR497NSL9mQWqtPukPqHkDfPeLc7cNfDbkcckW7f5sRJf7Xt/yciIsesspTXpt9oJtMSex4tOr8PhghFSFEDKpzH0t2XoTU2dPU1lPS0WJORHKifL6JhoB7N2t/kqN5UVVdbtrr/gGkafJUW2yR/E/8i0iUykoBKki5eVbNmRqloZo4yhFAzDM7aVMgSfS+9zO3//NPWm++TSmF9Z8ylBP54z3Hm+8Gfu8J0SDAE8zR6snZGPbGOh8D2uSXYz/Flrr2drx5wX7/aLYqbVDimjFpsjFg90kz52m9UkwrXk/VxJcrcmqxPAxMBLU9Taeq/Et6xXb+OkZYImj6BbolILGY4wMTU90/nrJfU1yKkPsgFqTkbLkm1F/FewnsHBVXBaHc+razn/KYp28+MdKHdLZHVFinYdkLX5WuxkR6vQA78rKWCvxhYvyicvZX5AUMEDJykz94eHN+2P1KoNWTMAWCrkCGHjDawe7h2fl+6747fuU6RXiQSSdOLN8CLjmBQdJARLtUk3FTzZSTwEIcL7mHnETcKIOKlgJcgXl4pNYXzL2G8bPVlMHQsvSTIiA9/7BuKa+IPufYIFgliEIQlp3tn+vDHQdcXksD6BPu2Kaft29qXBaEeGU7a0ciTv1tq1jEtgZc6SkWMrHY7wiAQf4wSQ3Z/mQXuBYU/iDhvLljb87AdJaYXTioDwMUvvd2Bu2kwjAT4FcvTbvN/7do4vBrug25RItuChh1VWWBMUwC0Pf8BwDQR7iGbNqa2u/NYv8rNY/2PxetEROQr615n97vLOm6maD8jr0LFVre/ZcvcX+LRh9zgiYuNYtA2fp8byef7qKTEENlXaMo9yorweSKZAIVu8QcxnIdQ261j4IvK85PbFRu1I66MltFAGn+Fng9fwkT7WUOkret+YKq3HtPqSMGHZUSuW49D5T+A1m1ifodCsXiP4A84Pp8OuHYYDGF/FZhG6qAM031lMlsdX2rT2X1BWxTf1YAC98Sn/usS1x7Xg80g2cogmuaR8NQBwPhyt2SLDjYmnZwoUmin6W4Vx/ui0jp4SoyojYearZbIvgGY82hVOvIvQsmPas39e6H7OEwRDIoCBAgQIECAVwoCTdGMCAZFBxnleEjKiZCPJhDpVCOIPl2kUGi3tC4iP/wOUQlj7x/ZdVjLQ0DMjM60nVk/RyQFQzxEnEBaxa4oxcBTDmCiTjrDmSz+fsciERFZPW/AbLdp3RKz3lBgViPK1O/dCSCqr0Y0Yp2wEWBSC9XiOoHuh5AV5VLS89Wqv4CIm1LySfjd/ZhliMI5FyK3P+2WE8uaxVM5SDtuxxI0un6t0TVPNSIlvG0HMUQKZogapljAhhUh1hfTnummABsKV4It1N+znULovbakylfuOd+s87ScZ2x0f9l5dn9giIDRo1yE3fM7d4Lp+W5/k6cqRUSiGXsBmNHiKVqwf3g20JcApNVDrJuigp1gZSux+rRRVMszpDV1HgwOngfckzrjq23Vz5nh8NNoCi5NklMrC0zHgVGCKB6WFjg+pnCLyjSzSSvKjQBsXeGngHG+xN7xtB6zKHh+WzURA9cH97R1tz6XnhGzS576hPAZ95qfAUyPgxmCjADtzPRQmaWNtv1gurn9mb6mae0TfNt01ygUy6JysIRA6y63czbKDfDSQDAoChAgQIAAAV4hCMkBEFofkJa8NBEMig4yRo5okpFUk7Rvtr0QIj2Uj0BBUhZ/QjjIoj1EqCIaIXW6EDS0wEUh6UHHsCS3uf23b7PRL1J8YXyHyLRtp2630y3Gl2gBVY10i50q7FZGiB+uV5/0tFn/0yPXm/X//H+nmfX4iKbaUtCU77Drba+yDmpjj7vJfwhHOXqDiDk7F2ZwdP1UGMpajL7fWtohMmHFRZnF7kLg+k3WC4GV82nTJBeoKCMxpnoQsFvAvpPVQDNBUXmBdBEkgvfHxfFIkD2dfsW3ixijrieYydLj/0w1PcrEhCn1n00TIVQutbrtuo+x93C84A48vK1DROoMEcCGhflOa0PBGo2RYzSdvV/ZArq3WS2mmgBpqc1t2WNvFIt3q5PWUbIBUT5YsJDqufK9bl9NeWjs7L7AnECLUyFROxhaXz6Eitr6e6y7hakizpWZ4aKylqm9qv0ZdDuY+6ilYpBW7p8L63DhmRcwUhVKiU/up5IpfVaH1aR6m7adEFJr4gWE2MT8sEUIszUswAYT1KwFaiNZe37xCVzv0JTtB/MNTGaiWOcEth/sFCe3sFlrnPRnpZRra1Opkb09JAgcrWdEYN4YIECAAAECBAggAVN00NHzUEni8ZKf20aqKDCx0IV4iFy4QGlebfGbB120UU5q4VeN1hP7tUTBdhd1p5e60HPOerc/RI4oFIkIE5EqtATDx7v9t+1E6iqlvjYjM0ejcCoAe8YpG0VEpD9nBTC/fepks479A6WUWZXMcpui+rYT/iAiIg/sWyoidYYIQKSV7bGMV5gyXTnLhi36ESmOrnTXsUXn/SMoP7LYNjTb08gQeT0HUr6hCyvY7YDMArteC8/MvLB5I+/PM1RITKSQh3UbaCczVshsArw9gaKcsu0E2+f1LhT91vQ6DD/mUujb1lgBS/sm1xA+Hy6hwbYRKCWDArDVZncBMurSEE5rRK5aq4SVtUnrrqnZU99uvU4+K61Y8ynhzPDg3iR36vOppVdqqoFDFiiuZYSyJsGqwfIC/RMMCXRqMAQMT2MBkJ1HrAVni9aQJWazusAUQb81sQQ2B3b/dc0P9oN7DybJ3QOk9uO950upKBELU8bGory203Y8U9b9K/OURYkWt56nFP36c+mWbOLIeh9cF1wnZk0NK0tzRhnKKk0OTG3kCWZount2qPFi+xR98YtflE996lNy8cUXy3XXXSciIrVaTT772c/KN7/5TRkZGZFTTz1Vvva1r8maNWv87wqFglx++eVy2223SS6Xk7PPPlu+/vWvy8KFC1/YyRACpihAgAABAgR4paB2gP49Dzz00EPyzW9+U447znqZXX311XLNNdfIV7/6VXnooYekr69Pzj33XJmYqJu1XXLJJfKjH/1Ibr/9drn//vslnU7LW97yFqlUKnyYF4SAKTrIgKgtkrXhc7HThYiY8wegQYDfR/OgRlrKGCX3uw7QvkU1DaSbqEa1LIBGQGFvjgZ/JLe/0aN0PlwZn+iI29+g9tXkXrdML1bvDSpCGh+36/3HWIZo34Rbb+tyIXF6S4f5HgxR/gir4TluiTMOWZpybMJPN60x30eVPYG/SJEys6DbwWPCJpRgU0rkwyTKFIEhyndHdOkOABO6CkX2Io3RJWuJsiutLinaTzQD2lqcWr4IFqLMzAllKD2bdqiheC9YBBQBpWs1sZzKlijrkdxtd8SeVhUt5gudDXs+le+ybF+Evsc1xvlBx4Nonk0VIasLFbWYJ11HZJ2BlWnRzCdkmwF8/ZABBf+ZXHeTZ3iE7jFnb7VvpewmYnCSg1OXhEHpl9iE+dqXhgHwPgDiWuSWs6rSqu1p3e2+B1ON9wGy2jybqcxP6w7SOIr93h93bJrSNp7NVJ8i9UXC+6fUbLP2gO7HbWeoJCxDBO1QsR0aKfd7MEBcWqdAJV2YjcQzgcLXYDlRiHdyVmyFdFasYwQbBrTumvqPNRijTG9Y8rlDz0uEajUJvUBN0PP5fTqdlve+973yrW99Sz7/+c/7z2u1mlx33XXy6U9/Wt75zneKiMjNN98svb29cuutt8qFF14oY2NjcuONN8r3vvc9Oeecc0RE5JZbbpFFixbJ3XffLeeff/6Ux3w+CJiiAAECBAgQIMCsMT4+bv4VCtPXdfvIRz4ib37zm/2gBti6dav09/fLeeed5z+Lx+Ny1llnyQMPPCAiIuvWrZNSqWS2mT9/vhxzzDF+mwOFgCk6yKiF3Dz8xBJLJ0wsbvLfi9QdcDEPDS2Bz3whh1doAxD5wZeHI8rEoGWodp/lwuWIZmMUo6h9MHX74Qfk20dR/fBr3AfZYSs8ye93tEaTltNo3WpDOGTpAMcssday/2/nKtfOLe7ADeyLlilpiO6pffy7MmmYSu1ugxHvLeSoN2Q+cZkC/L7Uplk1xZAUF9iDfvK0O8z6/x2wVHF1ud3nxkeWiohIJI2MJdvGwlzXxrD683DGSp0NdEuvGaKnm6N+ZobGjqDIVpkhKWu2HDFE2QV2+1CZsuTIUTt6lIbfe9zFBkuKJSJxaJzAEOE84C+U1wzL8Jj7ohZlSsyughli4DpCH4PMpMa+Vo8dWUvUsYWK7Ra1TI9qYNB2ZGdBnwRfH2gIwaC0b3HXlPs1NDQAs37QJOJ3OA50U2BssGR2s4lKWbBzdLNnttx2yO7KafYsMqwqMfRRfT7K2J9qsRI2c6vu8G+Pn+1znZkZIGSzgSHC5/D4wnXIki8RtEUoPAstErSZrIObfJ85i7GRIQIbJrqsM4uTt8e9zvS+yP5EVWlgOp/XPkRk0aJF5uPPfOYzcuWVVzZsfvvtt8vDDz8sDz30UMN3/f3uge/t7TWf9/b2yvbt2/02sVhMOjs7G7bB7w8UgkHRQUaluUnK8aZ6Cvz8qV+8MLrzLxF9aaGaNFLgyyPuQYPtPqaDuja6PxTxYfeXDtNoQK7H/cUpdts/ZE3t7g96SdxbAH/4YBgIoSVoZ4ha89p/Y7v17XGkOy4GQ0DHkzr40v1lFmtK8EqbA7ttxL2dqvqWKz1u5xW5DljDS53+wPuBBWh8bVaxnVL3tYJ8y9O2LAhedBDzQiDKvy+31KRpzL01P/GGH5vv9pXcOZzS6R7smzecKiIiR85zKlQMhqI0FYm2Y+AYzsxM6PJLG+BK6jwoysxzy4ZBRYTWdRoMU56lDteH5q2wKfYDj9uCen4QpMiOayc6wV3kxGbXd3Av0c5cr20/gMEQgOk8b05J02Ztm23fw1QNri8GAjD2xLMEGwe0JzJphrd1p31+WDyb6bGv1KQmSOCPd3xEn9MRnIOm8nfZ2n/YvqnkPoCAGVYAuOdsAok/yBjUsLDY9wE9Dvo7mx0CKFWDckOxCfviat3hHrxKXKe1MM2sDx4G6DzthrI7EZ0Ww3ReWUXJfmCvP2vfbjuDN5dEu7VsUiVqp+V4ipSF2XGd9kztUzPWhVprctIzxQkL3qKCvsfzgYEXTD+59EhTuW5XEH8RynwcyOmznTt3Sltbff48Ho83bLtz5065+OKL5c4775REgh1jJ+2TSrLUarWGzxjPZZvZIpg+CxAgQIAAAQLMGm1tbebfVIOidevWycDAgJx00kkSiUQkEonIfffdJ1/5ylckEol4hogZn4GBAf9dX1+fFItFGRkZmXabA4WAKTrI2H98WIZbwlJJqamYTkchUkQKLsBRCYTDmCIBQ5Qc4Lxntyh0uk5ZJQO69EJNC00r7bzAhb+hvW7k3g1GRwf9zfuV6u2zUXRiv0Z02i4IEavPuHA8qeQAhKcshI4NuxMc+52jYGLHj5rvGxgiuj5gD1h0nByl9hB7wqZ6lTYq6Hm8FXxHt9qIxrMuPEU06Tb8zx//ifkOjERlpXPiCz/tGv300y5nvEMNMjOaUcrlKlo3284AMahvExn5cYkWMB2FbhLDpty5h0fULDJFqcQp1/DqiFarH7ftaFGR/8QW9zLKUfkO9OH8NteZfPX6ZZYhApqVJU0v0ch6zDI16NvJrfamIu0d4HuTXuyWHU/pfqlUBQTUVWVVwBDVN3ALsKStO+p9hstagLHAPQFDBIAhqiTUJqBMBn8jriOlF9hzrIUtvZcccPvF8xXJuHUUKPZmknpuDf2ViAnsD8D0FvbjhdC6ROp+ooRUe9ve+JjdX3qeTnHqaeAe4DmFOBnTW7iuRS17klAmrNDmdgDGzF9v9PEOt0OUS2EmHqaU6JuQJ4AhqrefpvF66tfPv5vBTPqiuG7Zumvq/oWpwwKxVMWWJimGXwRe4gVkj5l9PEecffbZ8thjj5nP3v/+98uRRx4pn/jEJ2T58uXS19cnd911l5xwwgkiIlIsFuW+++6Tq666SkRETjrpJIlGo3LXXXfJBRdcICIie/fulccff1yuvvrqF3gyFsGgKECAAAECBHil4BA7Wre2tsoxxxxjPkulUtLd3e0/v+SSS2Tt2rWyatUqWbVqlaxdu1aSyaS85z3vERGR9vZ2+cAHPiCXXXaZdHd3S1dXl1x++eVy7LHHNgi3XyiCQdFBRiVZlUqqKs39KrpTJgVRByIyRE5NVbtMkEg0rnP8SFVNDLroe/A4m2vq9w9DPUSwWgIhtsltjzRlFiIWNOUVmgNOgwZzlNQon5kbMCs4XxgVFhZZXUj5acsMJYgZ8ueDlOXjXIOQ1p60+mwvCMfxs4s0FVl1MxVdQo/SFJ86bbb9JFv8dHS9rSLa/kz9vuAaDR07tVAaDBHQogwRShUkRt06dCC49lzGgpmkEDUd9xhRMDNLYIiASjPYM+0sc+kAUf2+ZKNZCEr9ZqSJArunkipp3odlzLQTfQYMEYBSMkBigBgzEul3Lneddvwxl+rfIFJWPUzXo65h6aWuMyOCB3rX2RObWGzZQhgUitTFs0iNj2ZJfKuft21VzU3CMj7lZrue66aC0FQ6wpu64mNo4GEMSKav2blUoJX6SsseCKNV+6OHh/YH1yySs+tAsc22t15A1rUjPqImjmohAqZsbLmdXsHxs3PVwFP7BITU9XJHal5LAnYItL1hKiwH9D0AjRUSUpgBy3epFgraK+o7k9PwvRYN5BIYWn3+wW7BaBf7BngWgJ/fVzKuuOIKyeVyctFFF3nzxjvvvFNaW+tTDddee61EIhG54IILvHnjTTfdJOHwgRWuB4OiAAECBAgQ4BWCF9vRWkTk3nvvtfsLheTKK6+cMnMNSCQScv3118v111//wg7+LAgGRQcZHRuaRJqbfEYNMzgAoo84FS5FdosvNUDZCumFcfP7PHzxdDOUBWCjPwCprIjUOEL1acuU6g4TO0SIzVRCgS3xYaAXG3UahOwCnn9XlqTbbo9oKnOUa4DPdlNkFtV0e80W6yP9TMym89Si9vtQE5nHddjKk0vbHQuxYcIyRUC4WI+u27UWLqJdZK7xtUBUi5RdZhmgH8N2bI6Ie+jLUND2YIjK7ZQyr2UvPEtWtKFr86OWbWT2D/ccWTZh6hOs/2rdrpqKxNQ6D6Tiw4qg1OY2iE7YdpVIlwZmCBjepTvqdJ216xHNINK3W++DNguuZatNZay02Iex1Ob6aELTzPMd9UiUWUA+J9ayDR1j2aY2yqIqpVD+gtiyFjC1liHKd+pzqk3yDJDXuejzkLb7i9K6t/ZQoAwOzg99C+V++Dw9k6No3W7TP8GEgSGqRtSaYwe0VShAq7o2KhGD9x0yBFFAFu/DWoMthbZHGW3WBjUPuR+AaYL2COD75t/PtUaGFiwhMu2Y0cWAoXnI3Ry8WxuOFRWpvsDBxfNCUBB2RgTZZwECBAgQIECAABIwRQcd+bkiuUmGgTm1ckFUDPT8zkYumEMHQhohIassS34o8DMS0pVwxIhsCkR6Q0fZ/VTJnA4+Hr7sAObRyWwNDBNrpIDMyU6vUZ1wGzS1UumLhIvkmn7vaIHCcVbfEd5jI25oF/z+tZAsSj0IFVdN9bkLks/bkLCrLWPWB4ctLTG8wdEuCByT++x+S8lQg0bGt7lgl7gnzAyx1mA6ZghsXd1bRpd0z6DhmVACJUT+Pex7lBgkTVDaLut6E3t8+PdgifOAjiPfQR4xRLYx+xAfJh0MtBp6HmAx0o84OtF7Vy23DBHADBFHtyHNAIuMur6WW2jvPRiiycaG3vxQn0No7Uot9rkCEsT85uaQ/sF7IanmLW4ZEQZ7TUXT9iLy/sEcgdWAdjCnTBGyQ3FPG4oIU5FjX2BW2T+U+QCTg8KteE+MrnCdJqSUCDRGYKrwfqrgWSEmLjauDI8yT9g/Mrtwfnj/IJsMvkfsuxQpwBdJC8p28gnb1Wq0zuywCSjYJCw7nyLNnt5L6EAnFiorqK+2UOXF0RWFqo3P3vPZx+GKYFAUIECAAAECvFIQTJ/NiGBQdJBRTloGIaEmwGB8Op/QL0h3Aq2Bn0snLRG0PGAVmC3wGgR1X60X91Tfjw4b0QGVZvd9y3aNvPptKIMsGURi3r9HN/NFPNU+v/VUl8UFnmfOQsfMbNzu7JQTKcsYLXvDVvf9+iW6f523z9iGVhIaISa1LALpY3oWW93J0Jit71Hd71o0qEvvhtzrQtamvVZngvNiHcHkrLwqsWRhctl+NlYBLERUySswJSh3wRqexLDNvEHWGtqR2gaXYfs71khELVnmgf2iRAUAxiY3x3q/AHBtB0r20tfdinUJFhIROc4bTte4jkUtNJvcrcWRB5R9GHANTe1xP4wPaihOpStqccoAa3MHKCfd59le+zr0rtLYXbR+Xs3DFV3qOVIZDmZ64e/jH7gGRkKfc2VgK+QzhuermQqweuYoZH/P7YTPT3q+vQYofFrQPlKjgqmsl0kMo7CsW0cB2/QC97z4DMgJe3ywJiW9DjhfMF3ok4lh1SApk4NssiYtn4Ilo8kzXXodKtAaui8qyYi2E35O+hwrQwe2M9etz5B2hdh4vV+mFyHFTo9JuqbB46bWDkEfmTvJXsxKNiqhcaLGDgUOsU/Ryw2BpihAgAABAgQIEEACpuiQoBaqM0RzH7ZamfQiF61y9FzW7AzUz0HWBNgAAAVKMa+NOX+s+wisn3Qkw7aN9ewLaAXgxqpZXd0UCev3rdvdAQrdlqoaeZVbntu73Z2nio1+8cjRZrt8xgpxNv/BuT1LNzKR7PkWuqgAZ5o0UZ2WTgmr4VN5wrYvXLFO3SheGiYnaziOc9YRtGEiUxRoJSaG2bhcn7t22fn2867HlfkJWcYnqgFmhLVIpC8Jk+YoQ/uP0z1H/Smv/6LoD9cG9z7Vr67DbYjebR8pdrjtObNyOiYKQJ/H8aAhYobIn8cYjuOO27bV3vN8j+1TRdUGtW5xDak0W10ZPHfYsZiLqMbSNV/7K0QsFGtX6syQBZgcX48OtbtIQxTNEDtH2hN/zaA3K2F7dy5gaAAUYMU954woLmyLa5uep9d4u2WI2BeJNSadT7n3XDmJ7DJ7PN+XwXARAxTOqy8RtES6BHuXGCqa7+FDhCWnjLMvlGdztetwbTa8D/Pdjdc+uc/uA/cCmbMNbvr6eWib65flnheBHZqEA1n77HBEMCg6yKiknHlj2zoIpUHVk50/9MFFK4zMLYFg0H0f1T8I+ANUauV0bvtwNw8pPV1ExWZ9qVOfhr2+/0PnXxL2+PgdXtr5OUpH60t0+Ci3nzVHuWmwLWn3Rtiwzf2F7v2VfTkVVIxb0cuBsiYw7MsfrQLttNugKUcvNxoE1Yru+327OkVEJDKm22t6ejhjfx8bgUBUpgTSwePHu9Fm6Xduv5OnKwtd9mIm95LAmEwUZYEOjPe6uYqux+3XvlCpYKpu6j9AWKKvwCDT74dFq9ZtoC7g1u18qj2m/Wgwhz6BPyQ8QMc0gd+9XmouLgyMHqNTUChCrNOLZS3BgrFmcof7HsaiXJqiGrcDEEw9+3UtCjp8jDsAzg8DGTYm9FMwXvxbv/71aS0dCLba54qnvTA9gz+g/Nz5ArDhqafJ2WASiRLt2yrmHACU/8GgrUKDgfoAD4M79zn+kKP9GKS17LGp8VKx7Ur16yBPhdSYYoStAdBgRqntRpmSsk4/VrRsCO4hRMpcVqXW5B5AXLf4KOr/6PtLp0RzvQmznT8+Deq8+aO+B4o99UinfUNEz9GtQ1yOd15ZlyhKjecJInZ+DlNP1a9NMjtNNeeDiUBTNCOC6bMAAQIECBAgQAAJmKKDjt4HaxKP1ySadhGRL0ioTAuEyzFKrY2NqyBahdIcZSeGdLvRqVmJlh16HGUZoll7fER6XuyqEZkXjFIggGgHUXNmnms30q/9dhpg7fvuUvP5SqXTC12uHYgEIRZGuiqEo52vdRy1zqTI3gxcKR1at+p5b3WhXXLAtX/P6zXNWhkiCLWjQ66rhymaQ4Toizva2TNJrLGC7ePetMmsbxzskdIzHe63bW4nEypa7exTtekGpzqvLXHzYLHH7fTOuM4Ytm/We5WxS0ahzcYyoydZtizR5k6ytsGFqkjRx7Vls0cWQiN1Hn0DBVULlGLP6cQ8bYD9Q6yb7XPL4hzti2qkmV2qIlpi8cAQ+fV9VGxURcNtO911L1DpCTBEYDlY+B7JIZ1cDf2077O1QqVaP+8wMUFgGHNxTHe7daS+M3PEbn1lYignFthz4FT5lr2WpQDT4qeNaFqvwYagQu3BLBaY6KJNsfcMEawD9D2C4xa6dHosaoXLsDMAC4f3EBizirKbpZT7AEybF5wXweLpe0qbje2AaFbfWy06zUYC6pBtviSGrSUAkgUwZQv5QUSn5RMjNZ+oAKNbfkfiWE0lYgmpOHVDWZ5y/bofUtTEi8Vf0D4OUwSDogABAgQIEOAVgkBTNDOCQdFBRmy0LPFYWUqtEHNqNKELCCLDOWh03C1BKj1KJ3CUC8EmNAIQ9yK9GcwT5uLZTBEmat5mHxGaRrp5Taln0XCuh+fmKTpSVgDniXZAUA5AY4A01+bXDprvyxXX7tG0C9sTu+wJgBkCho9U5mmPLRjJ7AgD0VuZ2JKiiiF7m+0F2DjYY9YntreLaJHZ8IQ7aNsqF26ODulO+9zBO+9z5wLhJbNSSAfGsm0H60twj+y9B8AQVTapmEGvAUThKETLxX19GQ1ki9OkutcS6aVgRoiF2p51Q9kRS/JJKOWubfMmdwHyPVQAdq+yfBTNcjo57mmmB5oPa+TnLRL0xPA92ALWHuFFD2E4X9/p+pBIo26Ef8tCagAsA2tm2H4B9gNAfNhSDNWY/f34EmXRtim7kWJNjk3hDxGDFc1ZpgsMEYTRsC8AgwOdDZgenL/XvbXaVPfmQWV0qbyJT+jQ/YKdQXu9NknvFRgfaCHB/gHYPlzA+5AF326J+4f2oIuHizV/7XNa4BXboDQKgHfO5BIhrm26CiNMEE0xkeqLq7kOMAWCQVGAAAECBAjwSkFNDoDQ+oC05CWJl/Wg6Morr5TPfvaz5rPe3l7p7++f9jf33XeffPzjH5cNGzbI/Pnz5YorrpAPfehD/vsNGzbIP/3TP8m6detk+/btcu2118oll1zyvNtYbglLaVI6KkcyiT2O2sktcOE6IipOA0V0jsiGdSfNAy7kACOVXmBvLeauEeWMLaMyITAWxBw8tEeqr+CinP53mv2Gop7Jfvu0QP/i59VREkNN4rKLVKNQdu3uTtn87bF1TuCCKAwR3ehKzVZRtqVCZT8SQzbl3qd5c7q8AmaQwKLFlrnqH7YOiOVhLV8gdd0SkF3vaLaOvW4d58xZaLgmCedv6Yv3AtA7AMyc4Ny6H3CUyMRipUagk6IotEj3sESmjlxCouNpayQInQcX24RuIzvXtRfsA5gVb+twlLv4iU1UJHWzPc8yMWgoi+DNJJUlRNo7sxzoK/FRZY6I2Sq2Tl0KA8D5sqZrcgZVnEwU0Tb8tnm/ZXK4LA8zSWwi6LPWVCvUPGj3By1Pff9Tl6sYX2ILrqb22mwyXKu8XpPWPWVzHsDIamtp0b6N2kMMTFHfJxFi18Aigrny5rPehFL355I8ZUhT+uPKmINh8oy7AtlrsXHLaOE96u+dLlCCBn0rrhpOFOLFs9Kyp9agBfKlWLxlBJ1jCW3SfcMKA+zXdug3Q5LKvwijiyD7bEa87LPP1qxZI3v37vX/HnvssWm33bp1q7zpTW+SP/qjP5JHHnlEPvWpT8nHPvYx+cEPfuC3yWazsnz5cvmXf/kX6evrOxSnECBAgAABAgR4CeBlzRSJiEQikec8eLnhhhtk8eLFct1114mIyFFHHSW///3v5Utf+pL86Z/+qYiInHLKKXLKKaeIiMjf//3fv+D2NRVrEp7ENYIZAgZP7jTrI692E9Dtj3hBhAWYHNjfK7WTnxM13yOrhbUyKWUvwDghUkPxT4AZFR/16/58SYYyIlm3XugMTW5GHTr87nmzE7Zs3jTftDcZc+HV1k2u/EdkwoWOVNfVM0yAL65IbE3BXtZ6aYrllj6Zt8CFcfuecMKbN5+5znz/TNrRO+AewRBFR+uRMa4N9BLJvfbYKJORVEZo3yk2FkkRuwZGBkwRomMArASYkonFbh1Gk4DXxqBwJWVVMTMVp+KlYKY8a0GMC3vusBYp22fvSfwZeMa49eYBt4T2p0TtY2YMDBHYhsSoRtypkFkic9P/bv90JTdwXOhU3MJnVIK1mJR1B8YVBpJsoJkY0WwwZSa4/EeDTon8clhjk9FrmOlzF791NxWA7UaBVLcONhLsRKORpv09mzim5xHDXOFriWtuC6rGJqbOUETGIfpG63bbHp+Npn2gQs83G39yAVdcP8/GdqoeTZtdZy01my1J15+0XhVLiElubv14YB4B9EP25wIr7bM+fUFZ1V2ByRyvSrzwIlRWrUqDB9nz2sdhipc9U/T000/L/PnzZdmyZfLnf/7nsmXLlmm3ffDBB+W8884zn51//vny+9//Xkql5694KxQKMj4+bv4FCBAgQIAALzUg++yF/jtc8bJmik499VT57ne/K6tXr5Z9+/bJ5z//eTn99NNlw4YN0t3d3bB9f3+/9Pb2ms96e3ulXC7L4OCgzJs373m144tf/GKDtonR/Phusz78x0vM+uCZblDW2ukonOrr3TKz2YUdnU8oA6PD2KJGxa3q0QJX34ETKENHGZ8oZYUBYAfY1Rfz4RPL1PmWmJj2p5FlIvq9LvX7BRdsNdv/+8ofmfW7FjtmZl3GmfQ8NOTojhVH7hERkd33LnLtpUjcO/DGVRMRstqhUnvN/K5ztTN0msg6lmJZ56iIiOTLLnz7ypG3uw2PdIuHcq49/7XvOBER2fSIvU8RcgyfnJEUV+8oRJttO21kCMD3x+9jmpIJYEZ8toqeU66LmCZ3ybwujKNr6DqwZMYntWdqJspncdH2iJ7BvnF07bPkSE8CDxiAs+DAGNWLg9rvx5e643Y8Yxki324wO+q03b7d6l4imbIu3Xp6oaVRmJmCU7jPupt0H5GRx/5iABii6XRhYC5QYLV+TLd9dq7VwAANvkeKuj+Z7UtwvoY2iZkhZlSb90/NXGE5tEadrKnPocgxFx/m9iOr1Wd75ex2TZzhh99Di6jvpSqeCco07Hhq6vuB7D5oEMEmIuu2oOwOe3Y1lScXv7XvYGSVITvNF3RO138r4ryO3DEtY1mJN/k+e0gRaIpmxMt6UPTGN77R///YY4+V0047TVasWCE333yzfPzjH5/yN6EQvUj15vLns8EnP/lJc7zx8XFZtMj9UW/eMSbFSEnyR7npIpT32HcuF9OyLysMhnw76U61bre/H1+s0036oOIPGWzrRQdFeX4J6h9y/C692H6PKvR4ejvXayquvhhgvljUP0SrL3jK/P77y+8REZEbxlaKiMjxCcefL4i4v5A3jb5WRER2/nqR+V0Ef8+QMk9TK2gPPq9FrEtbatmY2fzPVj1i1t/W7tb7y+4tvrPkBtFfWneuO28tFxLN2Rdh49RY/eWQ6bV9aHSF/YOIewKxK/5AeoO7NAmGicf1tgk0uGGRLsT6AE8nRalsCKYkfEmFDntgCI59EgB1Xa791pDyn7JL/AGDISnfW4j66yUwRNvllvtObjLfA21EEqN+IJDpcQ9D3XZi5me+idLiJwuveTCEQYdfRu2+G6YCaUoP0z5A89DUUzVcDgjbRbL2eMlBu3/cU0wXZclaAwNWDITRPvyhR/keYHwlDezp+M9WDxADd38+mJ7HoIXq8kV1ShMDe25/SO9peoHbYQsG+tpMiKMBTqfnQZoPREL1tvt6koX6dyIi+W7aF/UbJNdUtD8i+YTF+gFeGnhZD4oYqVRKjj32WHn66aen/L6vr68hM21gYEAikciUzNJzRTwel3h8muJZAQIECBAgwEsFAVM0Iw6rQVGhUJCNGzfKH/3RH035/WmnnSb/9V//ZT6788475eSTT5Zo9OAU5ssvaJN8rB46j67WVNB97njlNi3YmlHa/BnHEKV22eijZbcNvfLd9tYxqxDO2nUfvejYDREmIltEVIiKcvNcpBjSQrJd610kiwgUYtXRI2w7V7c4deHqZjf4XHXvX5vv+7otg7P/IafERCQN6pkFouWkFb/yedXUQLHa7q5TX5vVdf16/3IRETl5jhN6/90T73bt7HLq5wc3LzPbx3aj4KRbRyQN+r11Z/2lgKgV4k6ci2frdIlrm++0jA+qz3NqOKJ2TJnw1AnuOcSjPNXoI1z9HZggIE+MUKZPo+zdiGzt8cBksZiWp0zYlNIzPvDlw1QnHouQ/Z5L2iCKb1ahOptBQlQMJilBgvFCG7EE3fZ+McAAoA9OZtqQ4u1NA5V58VOJdM2S+9zJp/a4m1Hodv2qiQS20TRfU90/jDDp9ZRZBBG+ZR2BwWPs+6FKU5zJPXa93q/dCaKMDwB7hqFXufXEPmKaSEaZVZUCm8r69uj54Nrm5tp7AoE2ptOQFIC+k9xnp6zweSxjTWtxL9FHfWFr3Z6NFj2bOyn5ASJySHDRb73xI7NiBRJvE0tV1uc12xcWydJDeygQDIpmxMtaaH355ZfLfffdJ1u3bpXf/va38q53vUvGx8flfe97n4i4aa2/+qu/8tt/6EMfku3bt8vHP/5x2bhxo3z729+WG2+8US6//HK/TbFYlPXr18v69eulWCzK7t27Zf369fLMM88c8vMLECBAgAABAhw6vKyZol27dsm73/1uGRwclLlz58prXvMa+c1vfiNLljhx7N69e2XHjh1++2XLlskdd9whl156qXzta1+T+fPny1e+8hWfji8ismfPHjnhhBP8+pe+9CX50pe+JGeddZbce++9s2/jOVEZbolKdEKjfTIJbN9gIwUW5ULrM77YfYGoBPoGzI0jUkV6KHQaYF4KXXa/voyIzu2PvMqGO4ndapqm+2se0lTjENqF89DUY2W8bv2l0wg15afWawxtajbnyVYAiNwQ2fL14lTQcD5klqU5Vkvx9A5r17B1uxN4oyTHg+IUqtBEQJvE7cJxcf0nGwxylF7Xfbgl0rdhaMcC5GosZJacBo3oOTJNBIooOEFCZgAMEZvQ+eKd2hdaKN2bz4u1E2Cm0IcQOacXTf17XFNvq0BdBIwPWIUG0S70YsRy4F6AIcJ1AYMHY0C0l0toMCvJYuPJDBx0XShEyronrKPsBBgiIDrOjK+lgLjcR1nPBQaVrIUZOcq2PT5CF1VPBYwL68GY1RtdQUV4qbzIvPvtTa0LmFV4rqwK7uHI6pBZZ0abmWZun2eOeq3oP7VHGTpiV8eW2wOwvgdbe0sROh76BtjcyWn4zMSyNg9JLLBLQVFgsFnoj9AtRSdEojk59AhS8mfEy3pQdPvtt8/4/U033dTw2VlnnSUPP/zwtL9ZunSpF18HCBAgQIAAhxOCgrAz42U9KHo5oZwiE7RdYCbsdlHSAnkdBukykAnE0T9n8mTn2eM397vfpZfbH4aSLoJtGnBhM1Lb5zxuC0NCQ4FsFKTiR8dshOYziyjFFQDjgoitIbtMAQaIIxNEWz5FV1P0qyOu/U9PLNADUZZM2pq7NVGKfZMWuIVuADoW/3tEeRolVmKhejkJvZdI5eZMFtxbRKfYB5fXqFHmEpd88fdezyExhA3doq6BcIiPlLVdroENJSa0nXlN9Y9RFlyF9B8wZQTjBYYI2TRgxhJatHTgRCtowfEaGJkmvhdm1d9rH81T2ZR8yLKffF29QSBS/4klgLYLCCuTBn1PZdJ9mVzyQ6TxHiWG3M4rWuIn1+MuYrLfNb7YDuZX+0B06oKtAMpQICts95n25KBJ8vo1Ko2CPod7xuwYrgnuHdCQyaiMT73QtPZFfZ7BkA0do/obLQM0hhrFGbu/MDElbCfBKeuwU0DGJoxNx5ahMKzd3lt44LqgnXhvZW1mJa6PLxSdDHkNEN7B6LfcB3wbwcCSZs+/8ybdOmbOArz4CAZFAQIECBAgwCsFgdB6RgSDooOM15/2qKQ7E/KrW04yn7MhHuscOIKADoMzmVgTgHXPwBBDVTjFhVo4fEnLVtQy7hNoBjAvPnS0jUjZg8YfN6lZNjtRFFTMkhkhrOM8K83kpTNuozCwIf53voAkojZ6SGE2qYVio0MamdN1RqkJRM4JMrP0JTagpSJdTzUc8r/1keQgWCTse2p/nxIxNqyP4JIPDd435am3QxujE26DUqs7dzBLsQliREoz6zomFtnO2LbN/h5MVKHTHQcMEcBFglmbAaEHtFQlsBNk2Jc52oqxElvdhQd70FDmRK8Dss2YtYwM2fUwXUfW9zSVaw06Kf9b6hdghvz3yqAUutznnnnVS8UZcr4UypBmp1K2WtcGe/yhE9z3zbtBLetCfwYGyGvhiORoKB9CBqEweSxQGQ2UsmDPHRw3NqYMFv2l4QxD+H1B38ZmsJw917LXUTlg77o3al/X8iPp+bb97VvUSBW6PdVoIXMTbC9Y1Pzc+m/xrvUsWwLZoG4dWjm8y2AeincxezR1bNYHrCkkbQVKXTsUqNamT72czT4OUwTkXYAAAQIECBAggARM0UHHr3askJGxlMgZbjI+P+bCh9STLmIEQ8QaGwARVsPnuu7nvikKxudgXLAfjO9LeY3q+23YvvcMqz0qtZE+ZdhqF+pZWsoQoSSCft+QQaRAVpnXTOn5Q0MEZggMmc/ugb9Iwm4fH9LojBg1tCulVVZQHLV5mCIdZMfAl2gMWg/NnlGLfmg/wP6EanX9B44J5sg74KLUChXjZG0P+5kMH+OWc/7glpx5FKEgk7NhxpfY8LqhMGqrjYmYcRpdSUyJBri4Js0D7oNyUt3UlS1Jz7edlX1chPQ37CxdbLXf8+/BEAEte2y7y8Se+qwzvQ+sX0M5FVx/+DVxAVDXNtvW1L6pC7QC6K/Nw8pskNcUmAqf1Uh9or4/9THTZMrW7ardOcG2EdomPO9cKoX7fYgifuhgvNcW/YVgggHvnfEV9vkHil32+nRsdNshEzNJujLourI90CzJlBhf5G5q+zZLKSErMDkAjRCxr9mpPbjQR7w31Wj9O7xr2na4YzFbmO6LmH14vdKQZZ3Rz0otbsNKLCSJfOBT9FJDMCgKECBAgAABXjE4AIMiCQZFAZ4nCuNxyUs9dG3e4qJozJHXkNkzxr90aKi34zU4djvO4ii2037g97PdhclNdOeRJQLkejTbZY+yIIigqJ2IHDG/DrYk16PfI3KlelfMOCF691lren4RysbzdbF0v7gu0Ap5h2sNwGLkN9S6y2oL0G6wIIgCUa9INKPJe5DAIbelHmmWtO1N3qPGHpOzuSYWWjaBo/mxVXb74aPBzGjbKEMH9azQNxDtc32uicU2Kk3ttd8ziwHXYDA57Fkztpyzyoh16CRmC+7Gej180VMFswLMEM35pbpBl2vme2Y7UIwVYGYI9xoO2Hkt2gqfJH8du6zHTiVm65+JiIwttcfCNcK1hLcUtC7c30ot9hqwnisz367neyq6dOtdj4Z1P269dQcVmCVmi5Huo2ul+wFTAmapeRjtc+uZeZa5ApjRBjMEtOyxNxX16fC8giECEsRscTYa17fDcw3GKDvHaqDQx9BX+X7Wj+P207aj/jDH+93LpNrsTjK92NFkiRGwyGDl4Vekv6Pag/i81BKSUnjm+3NQEDBFMyIYFB0iJLa5Bwl/ACFcjKo4FA87Bg94eNnoj1/wTFdjsIRpuWIH3gZ6HOxXp50w6EJqejVqB0OAF7Hqx/zyzqmtP37fVLKWA0ktOdcwHearhtNxcD46nvQCbxp44A+3/yOkLztUMW/b7jasIeUZf0B1YJHvso9AU8UaAGJQxJQ5jpOZX79OYf3jmdqHIrp6SErdRdoxzhm2CegDmAos6dQjxKwAjDSBGr1Y4ySkZkE3KoJDSNyyyzWUiwkDKHmAgrdIqcc99+2gNHIunotpLdwbDHAxoC+TiB8Gfb7P0SCpPjC1A1wA1w+DMVxfX+yT3n6Y3vC/T5HNxKTBLZuX8oDRT0XqMRva9iyDodEjaJqrYrfHYAiIj9jUcgb+YBf13vpq8HoYvvawdYAdA0/NIhDA+wv3BsFY1KfuuyWE0AAPZhDkYKCOKScGBjcQM6OdVRrQ57rY58Ku4l6iL7JFAQaz1XCoQeReSdoHhKe9MUivv9vsVDwGlJEXw7gxwLMiGBQFCBAgQIAArxRUa/KCp78O4+yzYFB0kBHfGZVEKirNg/T5iF0v03QYpo0aUvenYYYAXyRUBdKhso1iUCjWp/xT346quVpJIzxM2fDUBhflBMAQ1VkSt8yp0V65xX2BVF0AkSL2C4E2pjT89GAN7XRLpOQmhq2lPtiSgjJByT2OlsnOdyGhnw7DdUBztB0+IkXkrTQDImFz/ZD9r1EriuVyAVaYOnJUjXPn2Lhts10HTQ/46L+Fom56YRUp3btjs0bJGh0zvV9Bs5UlA0MEoIwHwAwORPM5TWv2Ynw9DBgcfzyU6RjE9rY9SEsHJhY6FsAbEiprw2VIADBEYCvr+3XXE32G2ZD4qKVviu119gH3rG2HLeaLqbuJJTR1SFOeXEYGbCGQGHS/B6OMRAER1wYwUTACZCNKtvTAVCHA7w2YlLItATC6aubyGZiWll36vb837vPGPmp/z32oSuxnYtT9AFPPvnwRCr+SyW2JxPpcUDc2Qexpm53O94V2a3WjzfycDvMbHDNOz2Utgn1pej+ZOJoCw6UXYXBRq7p/L3QfhymClPwAAQIECBAgQAAJmKKDjvyikuRaS5LvcSFd5xNuHNrxjA21ys12fDq6Yupb4yMsCjA4Mux40kUpdYbGLZ/NRI2FzYiyERkXO+z3fi5e2+PLb2gkyloJTp0vN08dKcESwKfK6hKp+mAH8D0XgmzboToZnc8vdMdMe1Cc0bMS0FaoPoBTo9m0cnKkjfTo5kFN2aWCrigdkukjkT3ps1CQFWUlwD6wWNe3gXQUvh4RmCuNSMEUcfoz2AWcK7c734G0Zl2fa772n0dJB4YoHdE8l26I6O/QN1F0GGANFpghgPvU+DJtJ1LuyZ4itcuusyYL1gSwAmjdaQ9QSqlYt9xoPMmGm1ywFNoX3xZK4S/71HBlEfl5IRE+rCLqx3dL/5zS4wTdFe4V92NfIgbtYf0ZFZIGC4j3DTR9vj3UfqSr+/YTe5oYshSRNyhVdgUMEdCzzr2gRo5ImnYAE0vterHT3evUdk3h32r3l6akh9Q+y8Dl5tTbj3tdN6bUItnTlPvI9CH13q17zaA+l/GxmsQLLwZTdGiF1l/84hflhz/8oTz55JPS3Nwsp59+ulx11VVyxBFHTNpdTT772c/KN7/5TRkZGZFTTz1Vvva1r8maNWv8NoVCQS6//HK57bbbJJfLydlnny1f//rXZeHChS/sXAgBUxQgQIAAAQK8UlCtHZh/zxH33XeffOQjH5Hf/OY3ctddd0m5XJbzzjtPMpl6tHT11VfLNddcI1/96lfloYcekr6+Pjn33HNlYmLCb3PJJZfIj370I7n99tvl/vvvl3Q6LW95y1ukUqlMddjnjYApOsgI5cISitTDS8zFjy924cPI6x21suD7bnw6ttzdkokjOdXGfd+6GSyC+xipsyhPgUgPUThQarURUnP/1NkfYH58SrsySWCIODJDJAvmxBeIRV+mZwdaJaA6p2i2Sz6lmh+UM4EGyqex6hKRNY5HGg2UKUiMqNaIzNpwPETyiLCnK5/CGoXJlgCxcRjCaVZJRiPIMHQh7p4iQgTDg8yc1F7SslDJhIIyGWDH0NZoDpl+NvMIDBGKkoINyMzXkBW2ApSpxKxGlTJymLVI7nftRFq3j4CVOfLsRMqyfvXzFNMetpVoKCmj5w02IrPAfo2+17JDN6e+CsaN2RD/e72n40utACWiWqNIvla3alBU4lr4NAW2ze4TzyUD/XH/q+3+mnJuf837LKOKZYnuWesOu/8c6apgQJlVXViDlhFFdn0JC/s9NIV+f9o3YTrJOqx8p2p8tBn8PQri1o9vOwVYmNZdJbuftFsfX+4uBGwoypT5BWYI6Ps1vtf3gLYbdgv83kj263HDyEprqhd0JvNVzniLZN2x0/qcIUMVbF0LnnOw0s0hKYdehJT8Q4yf//znZv073/mO9PT0yLp16+TMM8+UWq0m1113nXz605+Wd77znSIicvPNN0tvb6/ceuutcuGFF8rY2JjceOON8r3vfU/OOeccERG55ZZbZNGiRXL33XfL+eeff8DaGzBFAQIECBAgwCsFmD57of9EZHx83PwrFArPcnCRsTEXDXV1ubnZrVu3Sn9/v5x33nl+m3g8LmeddZY88MADIiKybt06KZVKZpv58+fLMccc47c5UAiYooOMti1NUplkJgdmCEg85iKfoaPdOkeaYIg8aJUjUTBE2QUuqvEFIYdIj0IsAPsheTahwy0xxw6WgrNpQqhxiHIl1LMQgXoTSbR/s20IWA3OFqmg3AnKlcCETSNfLs8BeH0KzqfN6k+gq8F+G7J0KHLm7L+eh4sNbBiYIb4GXkOk2oSEth1ZU97EsWq3Tw7aTgH9GRgiLMHcFFtt9O0ZIuxevwYDkplH0ap2FTAyKOjqyxXstxEzdDL10hDsy6T6mo6Zo2K+9t5TSx8ZmGPinqT26O+QqUj6OF/MV8GZnAAK5Obm4rq6zyPEcjSV62VemIVKDFMGkmY3Qh8GdgpLsIR+3zkygSStTmYB6ce0jel51HcUXKIEzzvMFtkPDOcDJhsMD+4ls5e4Zp4NpeK56AtgxDqeoQdHTyeS0wzAhO2zbNKYXuRuumdFKZsNRqVghureQXoeVEyZTSHBEAGVST5KeOeVk7YESr2orDLEyhShpEt2rjsnaObgFzZZ0xYhD6RDgpocAE2RWyxaZFNRP/OZz8iVV145/c9qNfn4xz8uZ5xxhhxzjKtj1N/v0kJ7e63xWW9vr2zfvt1vE4vFpLOzs2Eb/P5AIRgUBQgQIECAAAFmjZ07d0pbW91jIx6Pz7C1yN/93d/Jo48+Kvfff3/DdyGaSqzVag2fMZ7LNrNFMCg6RFh5wdNm/fG7V5l1MERL37zVfJ4tu/B28A6nsPd+RrrMLLQdopyykUdyNxf9dMu8ZpX46J6KmSJjCKwCIjtEcIjWoeNgn5E4RaJjvcQQ7bft7t7gdpBe4Lqkj86gqdDjgRGCyzOzAWin9wghjQUYLuwH2WfYzp+3kitR+EXpk9LxjIsofSbKpOwj6EvqbeHsMP3PNHomRJI+uyUD7yVEqDYzBmwEZ6Eh22VCC8JWKEpu8IihjCFmH8EiIpOP9Vq8v7rbswOXbvAsADxl9J0K9oOdpj07Su7nAGfVsc8RrpvXzZAOBQyRbz8ygsh/avK++JrXy/AoI5S02r+kupyPrHY3Dz44zYN2O1ybZmKeKgl7UcCOgSFC5iFKkqDtE4ssMwpwlmmY/HLgbu7vJS4J2JFn8cbyru6KUqttP54V6O+q9OyAYfIMFr1foDkCCh2a6UX3FuvYHxgwZr6yvZZGBPOU6w57VsyfC7lt4zmuhfV5g3RPf8a6qrpLfq0hW/CQ4ABmn7W1tZlB0Uz46Ec/Kj/5yU/kl7/8pckY6+tz9GV/f7/Mm1c37BoYGPDsUV9fnxSLRRkZGTFs0cDAgJx++ukv7FwIwaDoICN3alaybSH5/calIiKS2O0ePk6FX/4O59RXrmpNobJ9SLPz6EVPtv+VpHsCE4NTy8QwGII9P6cve/MzTxXrDxvKW9h1flmFydQNf8BbdrBAtKK/10FJp5rSYUqnTw3QdJDGqcMYREI4iZc3/sD7Sul6WC/QVsF0y06aGtF24/yxH7YkmFjo7gtEzs376xcA1a/9taxYYS8GJzgGBNH4QxojISf+8HoDTK2YjrIbOA6uMSqkY+nF5Rh06TWYboqTB0M4vrc1ICE2LAJwDzFNxkLmyYMKkbpRHgZD/Adb8PdOz4+NTZsoGGVj1MayC/Z7LkeC0hIQnvP2k8+b+399Wgz33v7xRVV2gFPC+Vrh2Hn9I+8F3LiEmA4fxfZ2AMdtZ8NKBg+GeIDcRANcP2DX5IJ8F0377dckg2nMYTlwKHROLbQGOGW/QuVB8DwCeG9hGp0TLPhZ4vOdWIztdfA6SWienmePDWkB3uWo5YdjcyJDaq8dKIbzVWl6MabPqlVpeLE/r308N9RqNfnoRz8qP/rRj+Tee++VZcuWme+XLVsmfX19ctddd8kJJ5wgIiLFYlHuu+8+ueqqq0RE5KSTTpJoNCp33XWXXHDBBSIisnfvXnn88cfl6quvfmHnQggGRQECBAgQIMArBYfYp+gjH/mI3HrrrfLjH/9YWltbvQaovb1dmpubJRQKySWXXCJr166VVatWyapVq2Tt2rWSTCblPe95j9/2Ax/4gFx22WXS3d0tXV1dcvnll8uxxx7rs9EOFIJB0UFGcSIqRakLXSFwbHqd472Xdtgc2YiqSbc+aX3/41mK0tuI7s5RKi4JL7kgJRvcIVoH/Y7ptQZBJqVVMzMEMAsBgCHy7aLIEQwRgCjMF8hFO6epbo0IEEC5EFgEgPFBQU4wZ/gcYl5fNNRPoej3qNCuEfZkczd/TjBrC9tUdEw1Irr3rELOrtf3zeJQtwS7gJRfiMXrQmf9AWY+SCTfUI1ezx2MCNgQTKkifZnvPU8LFNpomgyCbi0dUb+Wbh0pyuNLQHnZ/aPIKdLI+XqALYEYmNvDUzpgvsCU8TQfKqPXr5f9vtwc8tce02V8L/2+muq/mXxMIDfHXiuwC2D9eIqGp2S8CJzkFFz1vUZWEsyM4Fx9WY7C1NfQi4vpsQMz1AD0NZq6RZ/yfSFtbzrEyn57pMYnrGUJH4fBLA2Y42aYv9IUbGzCHXdC/yQaKwLdVUu/a+vwkTTdSu9AJBRwkWAwq55dPvyz8UVE5Bvf+IaIiPzxH/+x+fw73/mO/PVf/7WIiFxxxRWSy+Xkoosu8uaNd955p7S21jvwtddeK5FIRC644AJv3njTTTdJOEwvpheIYFAUIECAAAECvFJwiJmi2nPYNhQKyZVXXjlj5loikZDrr79err/++ud87OeDYFB0kBEbjEosH/XFLpv+2FI4ibANIX/7yEqzHh+0o+BycmotDBBRMzLoMCabDE5eQqPjU3TrxqEi0qjTYF1Hdj6lO6fcgTofmTrvGboNL9jWyHps6cyFIhsEoUUbUSdG3XELbe6DtOr32HwSACviGSISmte8ZmLK0/ARNaf6mzZCjwQxNhgb0gB58EtDV1t3VrVtymop6xBL22uQ7wyZcwArhmuAewfxOFgH3HPWf5SIRWwiMgCMF5eQwLXEcdGeorYfaeJgjhDNIz3a3wswNawvsaSqZ8oAZkNrTVbz9GwCcO7jXEy5fXtFct2uccxwxMbcRcl3a6IAlUzhdbaO8GJw3e2ICqShCeJr4QF9GonLYa6I5x/GldC1QYtX348VozPLFibtC5e2YBsGLnTKOi8YIXpzxoztZPlO+x7JEwPMDBEzYNyH8QyC9ezY7I6H+wJTzr7fuRfq5PPhc+152F6LcB6WDq7NESqqG03bc8O1jWTKEisRhXgoUK3JtBTbrPZxeCIwbwwQIECAAAECBJCAKTpkmDhOQ7BhF8KcumqbiIg89JBNzW/dYcep2T6KuFpslNK20lEh5XudCIh1H2xSxhEdZ3WxwV3Lm12o+uq5283n//WUM94qpV143v1b+0MU1SypkSAXzkzPR+kKt85RfvOQ3R6sAHQhYIgmFtgu3PGMzV7r2Iy0dreeJV0KZ2x5RotSkMHWYD9gVyL5KfRTNXxn28yGf8iU8enJYD5U/4DoH9E0s2TQLnDaMlLaYa+A42A7aJNYNwUmCUyQZ/Wwqv9hPQaOh+1gIIrjsJEgrmk9S4e1SFbbw30H5wX2oW27azDM8ljfw1ojZojy3fZ8oFvDXtq31yP9VL8W/UWbiHCJj7KJo1viucO5MZOCcx081rbtHR+8V0REbnrwDBERmfMb+4CDlcS1YD1ZpEEjpL/LTx3pNylzEp0gbY/2f9YActYqn1c4B9sGdDZiUYghKlLqPjNEsDLAeZaI8WJGHChoFjcYIgA6Hyz5/lVjU/AGYNXyxBg1ZOppBlzSlj4Bsj1RacpXRH7XeIiDiVqtKrXadNTjc9/H4YpgUBQgQIAAAQK8UlCrvfDprxeqSXoJIxgUHWQU55Sk2FqSvzjehgP/8ZTzY4jMd6KZ5l86Exmv+1C2ID4KDYB+rr/vWGTFMk1nOsFF5dcuJGpWQzvODhlbam85M0ulM62Y5k3zN4iIyKCa3IAhAub9P7e/2DhF0IrEoAtls32WSWINABvu1Yswus/ZD6WctOfBbEDfb6yhzMCJVjACzRDYnOQ+26BySlkHsCgRm0WEiBveJCJ1nx9c0wZTRWTi0Lkh6wnRNn6H8htog9+//h5MC9a9UZ2eqtfI6O/ZY2c6hgveUCjG60u7oFgu+QSBZeMixMgyA6tXz/6y9wraJIDvJfoKWAivixtx+wXT5LVKbdAqqdaDMr24kCyKKgPsJQRGoBYONfRvLmjaVLK/5ZIrzCZMLLJtS+12yz/50L0iUmeI/PcD9nkGOwawzgrZmD67je45GxMyQ1Rstz8Akwz/ItabcZ9vCnNWnl2Hdgh9gn2bWva4B67QHp6y/Q1Gn8QQYb/N+3E8YmUJxfbpryfrrBquTcNfU3subBKa7ROR9CskBe1lhGBQFCBAgAABArxSUDsAQuuAKQrwfNHckZdke1h+uPlVIiJSeUKNc45waWJgiLw3CiIvFFRUF2JEIZFxzbJ6wjFCySNH3X6VIQISozZzKT2fKCHSAuR63BJb/cXqh0RE5NZnTjY/a73PaaIQecbGbaiYmWe7FGdigGlpHlLPj6NcQ1p26gYUOMGRFqwD/Fs4gwvXrWWnozN2vd4yQ4XFNk1v+fdsu4aPjuv+3XrHM1p+AK7OVO4AzFW40Mi2gdmB3gH7RAkRRJ/+npLuA95K2A80LyhFUuxybet+RNks3U9RLT3ANnqnaN09HHixf58tpr+PaXYXrmWUmKUwZTB5VlP7EjIWwYBNLnzp2mNZibFllj3kjCx4x4BtYU1WntyQOaOKMxvZmyux364j2w99kLOGROqZSKyt8X5FyoygYKnX8DBDtNB6M7HXzX9/4UwREYFbWT37TDU6ykil9rglymiwYzMwvsxem64n7LmhD/pyG5Qtx1pDXwhWWb4oZeOxU7b3zIpA+0Qsqj7neF8wm5gYtFlaeC6LbeorNMhZufZ9wc9oQwkeYoGik9zlwQ6i7WVy1ca9xT1knRMYXDjY412W7XuR8pyq1RnSGZ8jDmNNUZB9FiBAgAABAgQIIAFTdNARWt8qoWSLj1Ixty37HQWUVx0GXHvZK8VrWhJuZN5UoDn5PziGqClhPm5khtAe6EbAQsy1xyk/6WiE7//qbPfBWU67FLuz3ewHkRiiJnaQRSRZpnYhowoMEaL/iSVu+85N7jy9/oSCdRTCBFr22Ahv/wnuAuJ6jq+0Ec3iH7kDgoFLE7PFDFG9lppbh6vy5Mge2hWccwjBtt7L+DiyxuDrY7VE2De0OoUOMevFuarXKmrEOaxs4WL3PbyqfOYN1RIDQwUdVXqRW4Ihiurn0BKBFYilsX/KBNT911AEVffTvgXFct31iIy5htWi2kc6HLU0sVh1JHpvK8RCgCHKzrX3Jq71tnC9mBmC9xTAzBB7T6GQbEMmlmcD3fHjY1qnr9jIHtQ/cAs4nDPT4bVD+Jh+ntpHPkAUrsInCAxpWZkh9M8MsQ7soN22jbLCuOYZ9q9sCJiT6ITbka/rh+eatD/8nD5bzTO8L+Dmzk7anKlVI60StElwyPeZlPp5ocMeL6EFduGMHSPtFN9PFJiNj1bqGXW6zPS6a896TTCorJfEc8w1AOf/qiTtBdr4UCCYPpsRwaDoEIFNBFFGo9SuNK+OOSpqghjbH9F1+7LkQrAN1emRbq2bdWx2D93YcveXp0FoqYMH/KHDH0SYJkZ+5hqG6S7f/k5+ydn9Yn9hfW8Mvsa+QJpa3F/U6NNu5IDBEJ8XBnETC9wJMU3O5RB4EBQf1nYOuz/I+90sprfx5/PyZQn0MOP6x4zLG2CwNzn1lxlpFo/zH3Wk1MOOAIMovERL3WWz3+SesGmbPy4X5dVrj2uI6Szc29Ree1w/Xad9AwNKtAMCbW8tgEukzWnb4Q6E1OOkTmH6wVCXTk02u3VcF5hOtuy1Fw5/kABcNwDCbRl1i5GVtlNjMITzh+0EAg4uv5DtpZIbZCEQmZQ2Hi7olF6bXkz6443q9ih5kpk3DRmvv2vbQefeRinmNODFNQYgIuf3AACh8nRAyjxQabbV6zEYAvB81PsWDebQNzDNmLADBT6/MpIDdAnxvAdNc7GxJw8eMVWFKepIjt+f+r7V6+inist636jv5bsjkux3jR9fan1DRlbabf29pOez3t9VKrDb7a8WCUmN3ueHArVqVWovcPosSMkPECBAgAABArz8ETBFMyIYFB1kNJXdv8xiSn2d56gY3IDCiJ1nKpNJI0+bhTV65zRrAAxRVqlesAVIIfeFJWmqhc0eucAkIj1EXDyFgf3BLA0hcSivkdkcF/I2r7fzhDBPQ4THzAxKPHhjP4oQR87ITzqaiIzYqI4p7dbdLtT1ougUGfppVFeh6cwGs8tIo10AIkNE02BYsj12msyn0uutx/b1gqU6RYLSCxrV1jhaxjTYGO2nhKjY3nMwRF7ojT7EBVlHrSAcpS0wTQBWxKdh6z0bO9LNTe4/2X3Qdz/OM6T7VTZU05HRPi4eOp35JfoY0tEh0GYWAveqeT+dmG4GE0dmhlJ7pq5yHC5UpNBl+xUKlHZsdhd9eBWlaXsDSrfElB2Qo75Tfy4xPY0pVrdfsF0wbSzYWW1p3TXzHyueJipqeRw8b2BQsEyQmWEkO7Xg2aMJfRxspGUlMX2Oe44+BEwnYvYMEZqvh8U0IMr8sGAaZVkAsI54lvD+gsUH2jW5bEpmnmMFMcWHqdFCu2srs31csJmn8iazb5VA1vuSQzAoChAgQIAAAV4pqNYao87ZImCKAjxfZI4oSrqtKLHdGippKn5x2DJDyZ32VhTbKCV1PxvcuSUiRy7ouv9VLrpJ9VuGiH9fj36wHxv1RMiGP9NnI+XmQbs96zNyvbS/La7BJdWxtG637W6m1FtEpiNHuN9BwzCxmHQgT7vrWZijkaAW8yy1uPOLD7v1nvWWIaqfF5tD2u8RWUIEDfFyqCoSITbNmzdGbZQMhgj6EES/2FcRUT+9bxr0IlX7O18GBLoT7RNeEN1kP+cCsFhCLOvLhsAsUYmTorJpOL9cl02VHzlamxd3v2t9xn2BIr0xmE2GLUOE9zN0JXWzSRZ4W3E/AIYIjBk0VS3KBlZQqoHuOcqD+P1TX4/vsULASnudNgRDBAweQ69SIqcKXViqdmYIx9Sm0fZeyIxrQ4zl6FH2Bz2/I5YtC2G2shZZFSR7oTRfQ7ueHCQ7hSyZOnbYwrc1Mmlk8L0EE4N7w8wvtIJYxketXQIL2ZvIQqBApWNgHwENFvpImNhUPr6ISIw0bWC/oG9sMIRE0kqS2q6YbPUQKTTaPhx01GrS0EGf1z4OTwTcXYAAAQIECBAggARM0UFHtD8msYk6uxJ6wuktUCmBtS7Qj4AZwvfIHALDAyBTCCwDWAlk4Iyt0NRXSkfmFFhEqtDWsBkbUl3bVatUIOM8METIykJGVHKPahQ0BT6517YDhWMB1iwU28F4uc8nFtouy4Vk566zEd/EIru+9zXEyGm6e9tGMe0EENEntbxCZp4971rTZPsAG3XWSE/CxW+h5YE5I7ZHZhy0Rrj3+B56MqSgo/Aq2AQwPmBQwCDh98ySQZ+CaD05YC0GKhH7PXQeXAy0dSv6rEb5mpGI40NThOuD68FGgw2R9hiXzpg6HTypWWyccRSldWZFJpfxEBGJTlhNERiigVPqKZbM3PB6occyALEhd/HRnzx7qMv4qFtOl1WV67XrbU/ZDVBmB5hsHyDSWNgUx4EuhrWDFdJ3odeDISoleX9WC8ilWZDaD8Bs0qfSk4NIXZfnlsW2qeN3NsUEM4ZSMWwdwHYVuA5e60TZrE3Fmtd1oV+CNYZuCuBjwarDH1uvBQwyayGRaujQ8xK1ak1qL3D6rHYYM0XBoChAgAABAgR4paBWlRc+fRak5Ad4nqikqlJuqUokTeZtihDbcmBaGhlE2vfAIHkjMJU7eB2KokxMBzKSALAKMA7kuXkwRGA9ENVAx4FMpPR8mrOP23azfoMZoo5nNBrXdsT7M+b7PWd3iIhI51OWIcL1gh8TtBqpvVObwHU8Y9f3vs4+zK1PasQK3xg1uQMbkaQCnJ4dwX2ZRCp4TxmNXrEN/Hi4NMnoqy0j0fE7pQ20LdCDwdwwr3oU3ANoirz/jmZZZefWS5C4RupC7w1KuoCRAjOE/WagC/v/t3fnUVFc2R/Av83STdMNGERBBUV0UFHZxPG4IJiEUZKDmEQxjIkhojFjPC45JopxFE1ijv6cIY7b6JlxHWdJXCcS1xEUxSgijRHZbBEUUUQjoiLNcn9/QFWoplswdtNI7uccjnbVq6pXt7q7br969arhH5XeOELCAJZCS5b6uvQuNf2764T3hHD3WpPHIcik5YVWBqG8cGyEz4I46KW9tP4C/f4tVXq//lUl0rgL5W0eNmy44b1f61Af2IqeP48Cqd+SKPZNafj3QR+9vjgNn/vahn5WwjG0bRgYU2ghEogtLg27pP94DoVeS5Od3p1z+i1EwmMwbJ5IB2cU11f+9JObcEfgE5f6N6Hw8F7hGOrf7UXi3WcN9SuTtgSLd14JLQ16zZb6Y27p39lZ5aTX8qN3V64wQKLQiiO+dxrqK3zfCa2L+n259ONRrZT93EIkzNLr89ZkIEuxdctwnyL9llrWtnBSZGYvPaw/c1k/arglXX+gvSZPdYaknJXeh1Y8IQqXUPRGA67Se4J5k8tterfwC8mP8CVhWyXcsqrXnA5pUmT7WC8pEuotnBP0Pvj6z9Fy1OklRTXSilY+rt+QU1X9im0qhTOudD26hsXsnxhOivRV6XUkVz+WfgSUDeupbjh5KPU6Qto+lt7CLGt0frXVGxlZvERR2XCiaIiRcPKXP5CenJ0qpUmRQrglXbgc9lgvKaqUrk/ZcOJTVDYM6KeXFAkdke0akillpXQ58db7x9KkyL5hfm3Dt7ldQz2Eeqn0YqZ/SUS4TChvWI9wua664QRaIwzQJyRR1dLyYkfyhulPGvZPODJKvWeq6Y/WXKP3ZrTXGUmKqoUxFKTLCyNe21VaoVp/pGm9yzE2eiMlC597gTC0hvC5lOt/HvV+pFg/0kti9D/PQt0a3qdW1Xon9ar6gyqMiP2sgwXW6ZXXCaOyNxwL/aRIf7BFhU6aFNVU6V9+bnjP6H9/CNur1Nu+/mU9vdEThM9gbZ00KRLLVz89KdLfn2o0SoqM/KDVf3/pH0NFlfBDE03q1KFK+mOwNfDls6fjpMjMjn71f5auwovpkpnWu89M62Xtl8bSFWDMhPjy2VNxUmQGQhb9oJlyjDHGmHCuaI0WmBpUP/eA1jWwwDPbWgknRWZQUVE/aJCHhevBGGPsxVFRUQEnJ6fmC/4Ccrkcbm5uOHXre5Osz83NDXK5vPmCLxgZteeLgxZSV1eHmzdvwsHBATKZZXvVPXjwAB4eHrh+/TocHR2bX4A1i2NqHhxX8+C4mp6pY0pEqKioQNeuXWFlZb7b9J88eQKdzvBjbJ6VXC6HnZ1d8wVfMNxSZAZWVlZwd3e3dDUkHB0d+QvRxDim5sFxNQ+Oq+mZMqbmaiFqzM7Orl0mMqbEI1ozxhhjjIGTIsYYY4wxAJwUtXsKhQJLliyBQqFovjBrEY6peXBczYPjanoc0/aLO1ozxhhjjIFbihhjjDHGAHBSxBhjjDEGgJMixhhjjDEAnBS90JKTkyGTyQz+paWlGVymuroa8+fPx8CBA6FSqdC1a1dMnjwZN2/eFMtcu3bN6Hq//fbb1to9izFXXAVnzpzByy+/DJVKhQ4dOiA0NBSVlZUG1tp+mDOmoaGhTdb59ttvt8ZuWZy536tA/cCC4eHhkMlk2Ldvnxn3pu0wZ1ynT5+OXr16QalUolOnToiMjEROTk5r7BZrAe5o/QLT6XS4d++eZNof//hHHDt2DFevXjU4mnZ5eTnGjx+PadOmwc/PDz/99BPmzJmDmpoanD9/HgBQW1uLO3fuSJbbtGkTVq5ciVu3bkGtVptvp9oAc8UVqE+IxowZg7i4OEREREAulyMzMxMRERHt+k4Wc8Y0NDQU3t7eWLZsmThNqVS2ymB4lmbOuAoSEhJw9OhRHDx4EHv37sW4cePMtTtthjnjumnTJvTt2xfdu3fHvXv3EB8fD41Gg4KCAlhbW5t931gziLUbOp2OOnfuTMuWLXum5c6dO0cAqLCw0GgZf39/mjJlyvNW8YVkyrgOGTKEFi1aZOoqvnBMGdOQkBCaPXu2iWv4YjL1d4BGoyF3d3cqKSkhALR3714T1vbFYc7v1szMTAJAV65ced5qMhPgy2ftyH//+1+UlZUhJibmmZYrLy+HTCZDhw4dDM5PT0+HRqNBbGzs81fyBWSquJaWluLs2bPo3Lkzhg0bBldXV4SEhODUqVOmr3QbZ+r36s6dO+Hi4oL+/ftj3rx54kOZf21MGdfHjx8jOjoaa9euhZubm2kr+oIx13fro0ePsGXLFvTs2RMeHvwI8TbB0lkZM53w8HAKDw9/pmUqKytp0KBBNGnSJKNl/vCHP1C/fv2et3ovLFPF9cyZMwSAnJ2dafPmzXThwgWaM2cOyeVyysvLM3W12zRTvlc3bdpER48epR9//JH+9a9/kaenJ7366qumrO4Lw5Rx/eCDDyg2NlZ8jV9xS5Gpv1vXrVtHKpWKAFDfvn25lagN4aSoDVqyZAkBeOpfWlqaZJnr16+TlZUV7dq1q8Xb0el0FBkZSQEBAVReXm6wzOPHj8nJyYlWrVr1XPvUFlg6rqdPnyYAFBcXJyk/cOBAWrBgwfPtnIVYOqaGnD9/ngBQenr6L9qntsDScd2/fz/17t2bKioqxGntISmydFwF9+/fp7y8PDpx4gRFRERQYGAgVVZWPvf+sefHHa3boLKyMpSVlT21jKenp+Rpx59//jnWrFmD4uJi2NraNruN6upqREVF4erVqzh+/Dg6duxosNyOHTsQGxuL4uJidOrU6dl2pI2xdFwLCgrg5eWFHTt24J133hGnT5w4ETY2Nti5c+cv2CvLsnRMDSEiKBQK7NixAxMnTmzZjrQxlo7rnDlz8Je//AVWVj/3sKitrYWVlRWCg4ORnJz87DvVBlg6robodDq89NJL+Nvf/obo6OiW7QgzGxtLV4A15eLiAhcXlxaXJyJs2bIFkydPfqYPbX5+PpKSkp76of373/+OsWPHvvAJEWD5uHp6eqJr167Izc2VTM/Ly0N4eHiL69WWWDqmhmRlZaG6uhpdunRpcb3aGkvHdcGCBZg6dapk2sCBA5GQkICIiIgW16utsXRcn7adqqqqFteLmZHF2qiYyRw7dowA0OXLlw3O79OnD+3Zs4eIiKqrq2ns2LHk7u5OGo2GSkpKxL+qqirJcvn5+SSTyejgwYNm34e2yBxxTUhIIEdHR/r2228pPz+fFi1aRHZ2dr+aPgWmjumVK1do6dKllJaWRgUFBZSYmEh9+/algIAAqqmpabX9sjRzfQc0hnZw+exZmTquWq2Wli9fTufPn6fCwkJKTU2lyMhIcnZ2ptu3b7fafjHjOClqB6Kjo2nYsGFG5wOgLVu2EBFRQUGB0WvpSUlJkuXi4uLI3d2damtrzVj7tstccf3qq6/I3d2d7O3taejQoZSSkmLGvWhbTB3ToqIiGjlyJDk7O5NcLqdevXrRrFmz6O7du62wN22Hud6r+uv4tSVFpo5rcXExhYeHU+fOncnW1pbc3d3p97//PeXk5LTC3rCW4D5FjDHGGGPgx3wwxhhjjAHgpIgxxhhjDAAnRYwxxhhjADgpYowxxhgDwEkRY4wxxhgATooYY4wxxgBwUsQYY4wxBoCTIsYYY4wxAJwUMdbuyGQy7Nu3DwBw7do1yGQyaDQai9bpedy6dQthYWFQqVTo0KFDq28/JiYG48aNM+s22sNxau9OnjyJiIgIdO3aVfIZexZEhFWrVsHb2xsKhQIeHh5Yvny56SvLfjF+ICxj7ZiHhwdKSkqe6SGYLeHp6Yk5c+Zgzpw5Jl2vIQkJCSgpKYFGo4GTk5PZt6dv9erVMOXA/zExMbh//77kpGqu48RM59GjR/Dz88P777+Pt9566xetY/bs2Thy5AhWrVqFgQMHory8HGVlZSauKXsenBQx9gKqrq5u0VO7ra2t4ebm1go1Mh+tVotBgwbhN7/5jUnXq9PpIJfLmy3XGolYezhO7V14eDjCw8ONztfpdFi0aBF27tyJ+/fvY8CAAVixYgVCQ0MBANnZ2diwYQMuXbqEPn36tFKt2bPiy2eMNXLnzh24ublJmrTPnj0LuVyOI0eOGF3uxo0bePvtt+Hs7AyVSoWgoCCcPXtWnL9hwwb06tULcrkcffr0wY4dOyTLFxUVITIyEmq1Go6OjoiKisLt27fF+fHx8fD398fmzZvh5eUFhUIBIkJ+fj5GjhwJOzs7+Pj44OjRo5L16l+WSU5Ohkwmw//+9z8EBQXB3t4ew4YNQ25urriMVqtFZGQkXF1doVarMXjwYBw7dkycHxoaisLCQsydOxcymQwymUycl5qaipEjR0KpVMLDwwOzZs3Co0ePnhrzp8XG09MTu3fvxvbt2yGTyRATE2NwHcIlrqVLl6Jz585wdHTE9OnTodPpJPWeOXMmPv74Y7i4uCAsLAwAcOLECfz2t7+FQqFAly5dsGDBAtTU1DRZt4CIsHLlSnh5eUGpVMLPzw+7du2S1CcrKwuvv/46HB0d4eDggODgYGi1WsTHx2Pbtm3Yv3+/GLvk5GSDl8+aq1doaChmzZqFTz/9FM7OznBzc0N8fPxTY83M5/3338fp06fx73//GxcvXsSECRMwZswY5OfnAwC+++47eHl54cCBA+jZsyc8PT0xdepU3Lt3z8I1ZxKWfBotY21RYmIi2draUlpaGlVUVFDv3r1p9uzZRstXVFSQl5cXBQcHU0pKCuXn59N//vMfSk1NJSKiPXv2kK2tLa1bt45yc3PpT3/6E1lbW9Px48eJiKiuro4CAgJoxIgRdP78efrhhx8oMDCQQkJCxG0sWbKEVCoVjR49mi5cuECZmZlUU1NDAwYMoNDQUMrIyKATJ05QQECA5GnmwpO7MzIyiIgoKSmJANCQIUMoOTmZsrKyKDg4WPIkcI1GQ3/961/p4sWLlJeXR5999hnZ2dlRYWEhERHdvXuX3N3dadmyZVRSUkIlJSVERHTx4kVSq9WUkJBAeXl5dPr0aQoICKCYmBijsWsuNqWlpTRmzBiKioqikpISun//vsH1vPfee6RWq2nixIl06dIlOnDgAHXq1IkWLlwolgkJCSG1Wk2ffPIJ5eTkUHZ2Nt24cYPs7e1pxowZlJ2dTXv37iUXFxdasmSJZN2RkZHi64ULF1Lfvn3p0KFDpNVqacuWLaRQKCg5OZmIiG7cuEHOzs705ptvUlpaGuXm5tLmzZspJyeHKioqKCoqisaMGSPGrqqqqslxakm9QkJCyNHRkeLj4ykvL4+2bdtGMpmMjhw5YjTezDQaf8aIiK5cuUIymYyKi4sl5V555RWKi4sjIqLp06eTQqGgIUOG0MmTJykpKYn8/f1p1KhRrVl11gxOihgzYMaMGeTt7U2TJk2iAQMGUGVlpdGyGzduJAcHB7p7967B+cOGDaNp06ZJpk2YMIFee+01IiI6cuQIWVtbU1FRkTg/KyuLANC5c+eIqD4psrW1pdLSUrHM4cOHydramq5fvy5OO3jwYIuSomPHjonLJCYmEoCn7qOPjw+tWbNGfN2jRw9KSEiQlHn33Xfpgw8+kExLSUkhKysro+tuLjZERJGRkfTee+8ZrRtRfeLi7OxMjx49Eqdt2LCB1Go11dbWElF9EuHv7y9ZbuHChdSnTx+qq6sTp61bt06yXOOk6OHDh2RnZycmvILY2FiKjo4mIqK4uDjq2bMn6XQ6o3VtnGQRNT1OLalXSEgIjRgxQrKewYMH0/z5843GiZmGflL0zTffEABSqVSSPxsbG4qKiiIiomnTphEAys3NFZdLT08nAJSTk9Pau8CM4MtnjBmwatUq1NTU4JtvvsHOnTthZ2dntKxGo0FAQACcnZ0Nzs/Ozsbw4cMl04YPH47s7GxxvoeHBzw8PMT5Pj4+6NChg1gGAHr06IFOnTpJ1tu9e3e4u7uL04YOHdqi/fP19RX/36VLFwBAaWkpgPoOpZ9++qlYB7VajZycHBQVFT11nenp6di6dSvUarX4N3r0aNTV1aGgoMDgMs3F5ln4+fnB3t5efD106FA8fPgQ169fF6cFBQU12f7QoUMllwCHDx+Ohw8f4saNG022cfnyZTx58gRhYWGS/dy+fTu0Wi2A+vdDcHBwi/p8GdPSejU+jkD9sRSOI2s9dXV1sLa2Rnp6OjQajfiXnZ2N1atXA6g/NjY2NvD29haX69evHwA0+9lirYc7WjNmwNWrV3Hz5k3U1dWhsLCwycmnMaVS2ez6Gp/cgPp+KcK0xv83VgYAVCpVk/nNbceYxidsYZm6ujoAwCeffILDhw9j1apV6N27N5RKJcaPHy/pn2NIXV0dpk+fjlmzZjWZ1717d6PLPS02ptBcDA1t31C9gJ9jlJiYiG7duknmKRQKAC17PzSnpfXST7xkMplYR9Z6AgICUFtbi9LSUgQHBxssM3z4cNTU1ECr1aJXr14AgLy8PAD1P3hY28AtRYzp0el0mDRpEiZOnIgvvvgCsbGxkk7P+nx9faHRaIx2mOzXrx9OnTolmZaamir+SvTx8UFRUZGkRePy5csoLy8XyxgiLHfz5k1x2pkzZ1q0j0+TkpKCmJgYvPHGGxg4cCDc3Nxw7do1SRm5XI7a2lrJtMDAQGRlZaF3795N/ozd5dVcbJ5FZmYmKisrxdc//PAD1Gq1pCVNn4+PD1JTUyUJZmpqKhwcHJokPUJ5hUKBoqKiJvsotPT5+voiJSUF1dXVBrdpKHbPWy9mfg8fPhRbgACgoKAAGo0GRUVF8Pb2xqRJkzB58mTs2bMHBQUFSEtLw4oVK/D9998DAF599VUEBgZiypQpyMjIQHp6OqZPn46wsDBJ6xGzMAtdtmOszZo3bx55enpSeXk51dbW0siRI+n11183Wr6qqoq8vb0pODiYTp06RVqtlnbt2iX2O9m7dy/Z2trShg0bKC8vT+xMnJSUREQ/d7QODg6m9PR0Onv2LA0aNKhJR2s/Pz/Jdmtra8nHx4deeeUV0mg0dPLkSRo0aFCL+hT99NNP4noyMjIIABUUFBAR0bhx48jf358yMjJIo9FQREQEOTg4SDqbh4WF0dixY+nGjRt0584dIiLKzMwkpVJJM2bMoIyMDMrLy6P9+/fTzJkzjcauudgQtbxPkVqtpujoaMrKyqLvv/+eXF1dacGCBWKZkJCQJh3mhQ7NH330EWVnZ9O+ffua7Wj92WefUceOHWnr1q105coVunDhAq1du5a2bt1KRERlZWXUsWNHsaN1Xl4ebd++Xew38uWXX1L37t0pJyeH7ty5QzqdzmhH66fVy9D+tCRW7JcRPjv6f0K8dTodLV68mDw9PcnW1pbc3NzojTfeoIsXL4rrKC4upjfffJPUajW5urpSTEyM0b6IzDI4KWKskaSkJLKxsaGUlBRxWmFhITk5OdH69euNLnft2jV66623yNHRkezt7SkoKIjOnj0rzl+/fj15eXmRra0teXt70/bt2yXLFxYW0tixY0mlUpGDgwNNmDCBbt26Jc43lBQREeXm5tKIESNILpeTt7c3HTp06LmTooKCAho1ahQplUry8PCgtWvXNjkBnzlzhnx9fUmhUFDj31bnzp2jsLAwUqvVpFKpyNfXl7788kujcWtJbFqaFEVGRtLixYupY8eOpFaraerUqfTkyROxjKEkgogoOTmZBg8eTHK5nNzc3Gj+/PlUXV3dZN2Curo6Wr16NfXp04dsbW2pU6dONHr0aDpx4oRYJjMzk373u9+Rvb09OTg4UHBwMGm1WiKqv6NOiBEASkpKanKcWlIvTooYMz0ZkQmHamWMMQswNEq0qURHR8Pa2hr/+Mc/TL5uxljbwn2KGGPMgJqaGly+fBlnzpxB//79LV0dxlgr4KSIMcYMuHTpEoKCgtC/f398+OGHlq4OY6wV8OUzxhhjjDFwSxFjjDHGGABOihhjjYSGhooPKm38cNK2LiYmRqy3OTpbM8Z+HTgpYoxJTJs2DSUlJRgwYIDZtpGcnAyZTIb79++bZH2rV69GSUmJSdbFGPv14sd8MMYk7O3t4ebmZulqAKgfXdzYaNiNOTk5wcnJqRVqxBhrz7iliDFmlNCic/jwYQQEBECpVOLll19GaWkpDh48iH79+sHR0RHR0dF4/PixuBwRYeXKlfDy8oJSqYSfnx927doFALh27RpGjRoFAHjppZcgk8kQExMDoP7y3cyZM/Hxxx/DxcUFYWFhAOofe/Laa69BrVbD1dUV7777LsrKylo3GIyxdo+TIsZYs+Lj47F27Vqkpqbi+vXriIqKwtdff41//vOfSExMxNGjR7FmzRqx/KJFi7BlyxZs2LABWVlZmDt3Lt555x2cOHECHh4e2L17NwAgNzcXJSUl4pPEAWDbtm2wsbHB6dOnsXHjRpSUlCAkJAT+/v44f/48Dh06hNu3byMqKqrV48AYa9/48hljrFlffPEFhg8fDgCIjY1FXFwctFotvLy8AADjx49HUlIS5s+fj0ePHuHPf/4zjh8/jqFDhwIAvLy8cOrUKWzcuBEhISFwdnYGAHTu3BkdOnSQbKt3795YuXKl+Hrx4sUIDAzE8uXLxWmbN2+Gh4cH8vLy+GGajDGT4aSIMdYsX19f8f+urq6wt7cXEyJh2rlz5wDUX+p68uSJeOlLoNPpEBAQ0Oy2goKCJK/T09ORlJQEtVrdpKxWq+WkiDFmMpwUMcaaZWtrK/5fJpNJXgvT6urqAED8NzExEd26dZOUUygUzW5LpVJJXtfV1SEiIgIrVqxoUrZLly4t2wHGGGsBTooYYybl4+MDhUKBoqIihISEGCwj3FFWW1vb7PoCAwOxe/dueHp6wsaGv7IYY+bDHa0ZYybl4OCAefPmYe7cudi2bRu0Wi0yMjKwbt06bNu2DQDQo0cPyGQyHDhwAHfu3MHDhw+Nru+jjz7CvXv3EB0djXPnzuHq1as4cuQIpkyZ0qKkijHGWoqTIsaYyX3++edYvHgxvvrqK/Tr1w+jR4/Gd999h549ewIAunXrhqVLl2LBggVwdXXFzJkzja6ra9euOH36NGprazF69GgMGDAAs2fPhpOTE6ys+CuMMWY6/EBYxpgoNDQU/v7++Prrry1dlV9EJpNh7969GDdunKWrwhh7AfHPLMaYxPr166FWq/Hjjz9auiot9uGHHxq8O40xxp4FtxQxxkTFxcWorKwEAHTv3r1Fj9hoC0pLS/HgwQMA9Xek6d/BxhhjLcFJEWOMMcYY+PIZY4wxxhgATooYY4wxxgBwUsQYY4wxBoCTIsYYY4wxAJwUMcYYY4wB4KSIMcYYYwwAJ0WMMcYYYwA4KWKMMcYYA8BJEWOMMcYYAOD/Aau4lfi9wb4VAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "xds.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *box(*xds.rio.bounds()).exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Determine bounds of re-projected raster\n",
-    "\n",
-    "The [rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds) method allows you to safely convert a bounding box into another projection taking into account the effects of nonlinear transformations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "reprojected_raster = xds.rio.reproject(\"EPSG:4326\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Boundary calculated from the re-projected raster (inefficient)\n",
-    "\n",
-    "This is the benchmark. However, this method is computationally\n",
-    "inefficient. So, if you don't need to re-project, [rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds) is a more efficent method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "reprojected_raster_box = box(*reprojected_raster.rio.bounds())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f324735e4d0>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *reprojected_raster_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Boundary calculated from original corners (incorrect)\n",
-    "\n",
-    "Directly transforming the corners is an incorrect method to calculate the new boundary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "transform_box = box(*transformer.transform(*xds.rio.bounds()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3245a3bee0>]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *transform_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Boundary calculates using transform_bounds\n",
-    "\n",
-    "[rio.transform_bounds()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform_bounds) is both computationally efficient and a correct method for calculating the bounds of your raster in the new projection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "transform_bounds_box = box(*xds.rio.transform_bounds(\"EPSG:4326\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f32459316c0>]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *transform_bounds_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As seen below, this is equivalent to the [Transformer.transform_bounds](https://pyproj4.github.io/pyproj/stable/api/transformer.html#pyproj.transformer.Transformer.transform_bounds) method in pyproj:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "pyproj_transform_bounds_box = box(*transformer.transform_bounds(*xds.rio.bounds()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f324580d120>]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot()\n",
-    "reprojected_raster.plot(ax=ax)\n",
-    "ax.plot(\n",
-    "    *transform_bounds_box.exterior.xy,\n",
-    "    color=\"red\",\n",
-    "    linewidth=3,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/genindex.html b/0.18.0/genindex.html
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-  <title>Index &mdash; rioxarray 0.18.0 documentation</title>
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- <a href="#B"><strong>B</strong></a>
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- | <a href="#X"><strong>X</strong></a>
- | <a href="#Y"><strong>Y</strong></a>
- 
-</div>
-<h2 id="B">B</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="rioxarray.html#rioxarray.rioxarray.XRasterBase.bounds">bounds() (rioxarray.rioxarray.XRasterBase method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="C">C</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="rioxarray.html#rioxarray.raster_array.RasterArray.clip">clip() (rioxarray.raster_array.RasterArray method)</a>
-
-      <ul>
-        <li><a href="rioxarray.html#rioxarray.raster_dataset.RasterDataset.clip">(rioxarray.raster_dataset.RasterDataset method)</a>
-</li>
-      </ul></li>
-      <li><a href="rioxarray.html#rioxarray.raster_array.RasterArray.clip_box">clip_box() (rioxarray.raster_array.RasterArray method)</a>
-
-      <ul>
-        <li><a href="rioxarray.html#rioxarray.raster_dataset.RasterDataset.clip_box">(rioxarray.raster_dataset.RasterDataset method)</a>
-</li>
-      </ul></li>
-  </ul></td>
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-      <li><a href="rioxarray.html#rioxarray.raster_dataset.RasterDataset.crs">crs (rioxarray.raster_dataset.RasterDataset property)</a>
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-        <li><a href="rioxarray.html#rioxarray.rioxarray.XRasterBase.crs">(rioxarray.rioxarray.XRasterBase property)</a>
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-      <li><a href="rioxarray.html#rioxarray.raster_array.RasterArray.encoded_nodata">encoded_nodata (rioxarray.raster_array.RasterArray property)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="rioxarray.html#rioxarray.rioxarray.XRasterBase.estimate_utm_crs">estimate_utm_crs() (rioxarray.rioxarray.XRasterBase method)</a>
-</li>
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-      <li><a href="rioxarray.html#rioxarray.rioxarray.XRasterBase.get_gcps">get_gcps() (rioxarray.rioxarray.XRasterBase method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="rioxarray.html#rioxarray.rioxarray.XRasterBase.grid_mapping">grid_mapping (rioxarray.rioxarray.XRasterBase property)</a>
-</li>
-  </ul></td>
-</tr></table>
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-<h2 id="H">H</h2>
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-      <li><a href="rioxarray.html#rioxarray.raster_array.RasterArray.interpolate_na">interpolate_na() (rioxarray.raster_array.RasterArray method)</a>
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-</li>
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-</li>
-  </ul></td>
-</tr></table>
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-<h2 id="M">M</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="rioxarray.html#rioxarray.merge.merge_arrays">merge_arrays() (in module rioxarray.merge)</a>
-</li>
-      <li><a href="rioxarray.html#rioxarray.merge.merge_datasets">merge_datasets() (in module rioxarray.merge)</a>
-</li>
-      <li><a href="rioxarray.html#rioxarray.exceptions.MissingCRS">MissingCRS</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
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-      <li><a href="rioxarray.html#rioxarray.exceptions.OneDimensionalRaster">OneDimensionalRaster</a>
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-</li>
-      <li><a href="rioxarray.html#rioxarray.raster_array.RasterArray.reproject">reproject() (rioxarray.raster_array.RasterArray method)</a>
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-      <ul>
-        <li><a href="rioxarray.html#rioxarray.raster_dataset.RasterDataset.reproject">(rioxarray.raster_dataset.RasterDataset method)</a>
-</li>
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-</li>
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-      <li><a href="rioxarray.html#rioxarray.raster_array.RasterArray.to_raster">to_raster() (rioxarray.raster_array.RasterArray method)</a>
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-      <ul>
-        <li><a href="rioxarray.html#rioxarray.raster_dataset.RasterDataset.to_raster">(rioxarray.raster_dataset.RasterDataset method)</a>
-</li>
-      </ul></li>
-  </ul></td>
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-      <li><a href="rioxarray.html#rioxarray.exceptions.TooManyDimensions">TooManyDimensions</a>
-</li>
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-</li>
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-</li>
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-<h2 id="U">U</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-</li>
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-</tr></table>
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-<h2 id="V">V</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="rioxarray.html#rioxarray.raster_dataset.RasterDataset.vars">vars (rioxarray.raster_dataset.RasterDataset property)</a>
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-<h2 id="W">W</h2>
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-</li>
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-</li>
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-  <td style="width: 33%; vertical-align: top;"><ul>
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-  <section id="Coordinate-Reference-System-Management">
-<h1>Coordinate Reference System Management<a class="headerlink" href="#Coordinate-Reference-System-Management" title="Link to this heading"></a></h1>
-<p>xarray “… is particularly tailored to working with netCDF files, which were the source of xarray’s data model…” (<a class="reference external" href="http://xarray.pydata.org">http://xarray.pydata.org</a>).</p>
-<p>For netCDF files, the GIS community uses CF conventions (<a class="reference external" href="http://cfconventions.org/">http://cfconventions.org/</a>).</p>
-<p>Additionally, GDAL also supports these attributes:</p>
-<ul class="simple">
-<li><p>spatial_ref (Well Known Text)</p></li>
-<li><p>GeoTransform (GeoTransform array)</p></li>
-</ul>
-<p>References:</p>
-<ul class="simple">
-<li><p>Esri: <a class="reference external" href="https://pro.arcgis.com/en/pro-app/latest/help/data/multidimensional/spatial-reference-for-netcdf-data.htm">https://pro.arcgis.com/en/pro-app/latest/help/data/multidimensional/spatial-reference-for-netcdf-data.htm</a></p></li>
-<li><p>GDAL: <a class="reference external" href="https://gdal.org/drivers/raster/netcdf.html#georeference">https://gdal.org/drivers/raster/netcdf.html#georeference</a></p></li>
-<li><p>pyproj: <a class="reference external" href="https://pyproj4.github.io/pyproj/stable/build_crs_cf.html">https://pyproj4.github.io/pyproj/stable/build_crs_cf.html</a></p></li>
-</ul>
-<p>Operations on xarray objects can cause data loss. Due to this, rioxarray writes and expects the spatial reference information to exist in the coordinates.</p>
-<section id="Accessing-the-CRS-object">
-<h2>Accessing the CRS object<a class="headerlink" href="#Accessing-the-CRS-object" title="Link to this heading"></a></h2>
-<p>If you have opened a dataset and the Coordinate Reference System (CRS) can be determined, you can access it via the <code class="docutils literal notranslate"><span class="pre">rio.crs</span></code> accessor.</p>
-<section id="Search-order-for-the-CRS-(DataArray-and-Dataset):">
-<h3>Search order for the CRS (DataArray and Dataset):<a class="headerlink" href="#Search-order-for-the-CRS-(DataArray-and-Dataset):" title="Link to this heading"></a></h3>
-<ol class="arabic simple">
-<li><p>Look in attributes (<code class="docutils literal notranslate"><span class="pre">attrs</span></code>) of your data array for the <code class="docutils literal notranslate"><span class="pre">grid_mapping</span></code> coordinate name. Inside the <code class="docutils literal notranslate"><span class="pre">grid_mapping</span></code> coordinate first look for <code class="docutils literal notranslate"><span class="pre">spatial_ref</span></code> then <code class="docutils literal notranslate"><span class="pre">crs_wkt</span></code> and lastly the CF grid mapping attributes. This is in line with the Climate and Forecast (CF) conventions for storing the CRS as well as GDAL netCDF conventions.</p></li>
-<li><p>Look in the <code class="docutils literal notranslate"><span class="pre">crs</span></code> attribute and load in the CRS from there. This is for backwards compatibility with <code class="docutils literal notranslate"><span class="pre">xarray.open_rasterio</span></code>, which is deprecated since version 0.20.0. We recommend using <code class="docutils literal notranslate"><span class="pre">rioxarray.open_rasterio</span></code> instead.</p></li>
-</ol>
-<p>The value for the <code class="docutils literal notranslate"><span class="pre">crs</span></code> is anything accepted by <code class="docutils literal notranslate"><span class="pre">rasterio.crs.CRS.from_user_input()</span></code></p>
-</section>
-<section id="Search-order-for-the-CRS-for-Dataset:">
-<h3>Search order for the CRS for Dataset:<a class="headerlink" href="#Search-order-for-the-CRS-for-Dataset:" title="Link to this heading"></a></h3>
-<p>If the CRS is not found using the search methods above, it also searches the <code class="docutils literal notranslate"><span class="pre">data_vars</span></code> and uses the first valid CRS found.</p>
-</section>
-<section id="decode_coords=%22all%22">
-<h3>decode_coords=”all”<a class="headerlink" href="#decode_coords=%22all%22" title="Link to this heading"></a></h3>
-<p>If you use one of xarray’s open methods such as <code class="docutils literal notranslate"><span class="pre">xarray.open_dataset</span></code> to load netCDF files with the default engine, it is recommended to use <code class="docutils literal notranslate"><span class="pre">decode_coords=&quot;all&quot;</span></code>. This will load the grid mapping variable into coordinates for compatibility with rioxarray.</p>
-</section>
-<section id="API-Documentation">
-<h3>API Documentation<a class="headerlink" href="#API-Documentation" title="Link to this heading"></a></h3>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_crs"><span class="std std-ref">rio.write_crs()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.crs"><span class="std std-ref">rio.crs</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.estimate_utm_crs"><span class="std std-ref">rio.estimate_utm_crs()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.set_spatial_dims"><span class="std std-ref">rio.set_spatial_dims()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_coordinate_system"><span class="std std-ref">rio.write_coordinate_system()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_transform"><span class="std std-ref">rio.write_transform()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.transform"><span class="std std-ref">rio.transform()</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray  # activate the rio accessor
-import xarray
-from affine import Affine
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-</div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
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-{&#39;units&#39;: &#39;DN&#39;, &#39;nodata&#39;: 0.0}
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-    spatial_ref:  PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'spatial_ref'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ed98a4ac-15a8-4b42-a4d5-b41d08429b39' class='xr-array-in' type='checkbox' checked><label for='section-ed98a4ac-15a8-4b42-a4d5-b41d08429b39' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-13a73705-474f-4849-a2b6-31c4c1cc60d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13a73705-474f-4849-a2b6-31c4c1cc60d7' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ff9e3017-55fa-4eb5-af42-af1cc7f27bd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff9e3017-55fa-4eb5-af42-af1cc7f27bd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a4c305b-e31f-4faa-a83f-8aeec07cc0ee' class='xr-var-data-in' type='checkbox'><label for='data-7a4c305b-e31f-4faa-a83f-8aeec07cc0ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c56ca51-d50d-408e-8550-3fa40536ce7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c56ca51-d50d-408e-8550-3fa40536ce7d' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div></li></ul></div></div></div>
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-</pre></div>
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-CRS.from_epsg(32722)
-</pre></div></div>
-</div>
-</section>
-</section>
-<section id="Setting-the-CRS">
-<h2>Setting the CRS<a class="headerlink" href="#Setting-the-CRS" title="Link to this heading"></a></h2>
-<p>Use the <code class="docutils literal notranslate"><span class="pre">rio.write_crs</span></code> method to set the CRS on your <code class="docutils literal notranslate"><span class="pre">xarray.Dataset</span></code> or <code class="docutils literal notranslate"><span class="pre">xarray.DataArray</span></code>. This modifies the <code class="docutils literal notranslate"><span class="pre">xarray.Dataset</span></code> or <code class="docutils literal notranslate"><span class="pre">xarray.DataArray</span></code> and sets the CRS in a CF compliant manner.</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_crs"><span class="std std-ref">rio.write_crs()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.crs"><span class="std std-ref">rio.crs</span></a></p></li>
-</ul>
-<p><strong>Note:</strong> It is recommended to use <code class="docutils literal notranslate"><span class="pre">rio.write_crs()</span></code> if you want the CRS to persist on the Dataset/DataArray and to write the CRS CF compliant metadata. Calling only <code class="docutils literal notranslate"><span class="pre">rio.set_crs()</span></code> CRS storage method is lossy and will not modify the Dataset/DataArray metadata.</p>
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xda = xarray.DataArray(1)
-xda.rio.write_crs(4326, inplace=True)
-xda.spatial_ref
-</pre></div>
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-</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;spatial_ref&#x27; ()&gt;
-array(0)
-Coordinates:
-    spatial_ref  int64 0
-Attributes:
-    crs_wkt:                      GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;...
-    semi_major_axis:              6378137.0
-    semi_minor_axis:              6356752.314245179
-    inverse_flattening:           298.257223563
-    reference_ellipsoid_name:     WGS 84
-    longitude_of_prime_meridian:  0.0
-    prime_meridian_name:          Greenwich
-    geographic_crs_name:          WGS 84
-    grid_mapping_name:            latitude_longitude
-    spatial_ref:                  GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'spatial_ref'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8edcf942-accb-4c39-88d9-d91a01903b83' class='xr-array-in' type='checkbox' checked><label for='section-8edcf942-accb-4c39-88d9-d91a01903b83' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-91b27a9b-d677-4786-8c5e-8208bbc943d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91b27a9b-d677-4786-8c5e-8208bbc943d0' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ed33fcd3-3802-4a97-9594-5e9199c8ad93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed33fcd3-3802-4a97-9594-5e9199c8ad93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c29d6c2-7c47-4a94-a90a-5de80d8ce90b' class='xr-var-data-in' type='checkbox'><label for='data-6c29d6c2-7c47-4a94-a90a-5de80d8ce90b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e504052f-18ed-4e21-98d1-667acb5bfc9e' class='xr-section-summary-in' type='checkbox'  ><label for='section-e504052f-18ed-4e21-98d1-667acb5bfc9e' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div></li></ul></div></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xda.rio.crs
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CRS.from_epsg(4326)
-</pre></div></div>
-</div>
-</section>
-<section id="Spatial-dimensions">
-<h2>Spatial dimensions<a class="headerlink" href="#Spatial-dimensions" title="Link to this heading"></a></h2>
-<p>Only 1-dimensional X and Y dimensions are supported.</p>
-<p>The expected X/Y dimension names searched for in the <code class="docutils literal notranslate"><span class="pre">coords</span></code> are:</p>
-<ul class="simple">
-<li><p>x | y</p></li>
-<li><p>longitude | latitude</p></li>
-<li><p>Coordinates (<code class="docutils literal notranslate"><span class="pre">coords</span></code>) with the CF attributes in <code class="docutils literal notranslate"><span class="pre">attrs</span></code>:</p>
-<ul>
-<li><p>axis: X | Y</p></li>
-<li><p>standard_name: longitude | latitude or projection_x_coordinate | projection_y_coordinate</p></li>
-</ul>
-</li>
-</ul>
-<p>Option 1: Write the CF attributes for non-standard dimension names</p>
-<p>If you don’t want to rename your dimensions/coordinates, you can write the CF attributes so the coordinates can be found.</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.set_spatial_dims"><span class="std std-ref">rio.set_spatial_dims()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_coordinate_system"><span class="std std-ref">rio.write_coordinate_system()</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds.rio.write_crs(
-    4326
-    inplace=True,
-).rio.set_spatial_dims(
-    x_dim=&quot;lon&quot;,
-    y_dim=&quot;lat&quot;
-    inplace=True,
-).rio.write_coordinate_system(inplace=True)
-</pre></div>
-</div>
-</div>
-<p>Option 2: Rename your coordinates</p>
-<p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.rename.html">xarray.Dataset.rename</a></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds = rds.rename(lon=longitude, lat=latitude)
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Setting-the-transform-of-the-dataset">
-<h2>Setting the transform of the dataset<a class="headerlink" href="#Setting-the-transform-of-the-dataset" title="Link to this heading"></a></h2>
-<p>The transform can be calculated from the coordinates of your data. This method is useful if your netCDF file does not have coordinates present. Use the <code class="docutils literal notranslate"><span class="pre">rio.write_transform</span></code> method to set the transform on your <code class="docutils literal notranslate"><span class="pre">xarray.Dataset</span></code> or <code class="docutils literal notranslate"><span class="pre">xarray.DataArray</span></code>.</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_transform"><span class="std std-ref">rio.write_transform()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.transform"><span class="std std-ref">rio.transform()</span></a></p></li>
-</ul>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>transform = Affine(3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0)
-xda.rio.write_transform(transform, inplace=True)
-xda.spatial_ref.GeoTransform
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&#39;466266.0 3.0 0.0 8084700.0 0.0 -3.0&#39;
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xda.rio.transform()
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Affine(3.0, 0.0, 466266.0,
-       0.0, -3.0, 8084700.0)
-</pre></div></div>
-</div>
-</section>
-</section>
-
-
-           </div>
-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/0.18.0/getting_started/crs_management.ipynb b/0.18.0/getting_started/crs_management.ipynb
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--- a/0.18.0/getting_started/crs_management.ipynb
+++ /dev/null
@@ -1,1108 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Coordinate Reference System Management"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "xarray \"... is particularly tailored to working with netCDF files, which were the source of xarray’s data model...\" (http://xarray.pydata.org).\n",
-    "\n",
-    "For netCDF files, the GIS community uses CF conventions (http://cfconventions.org/).\n",
-    "\n",
-    "Additionally, GDAL also supports these attributes:\n",
-    "\n",
-    "- spatial_ref (Well Known Text)\n",
-    "- GeoTransform (GeoTransform array)\n",
-    "\n",
-    "References:\n",
-    "\n",
-    "- Esri: https://pro.arcgis.com/en/pro-app/latest/help/data/multidimensional/spatial-reference-for-netcdf-data.htm\n",
-    "- GDAL: https://gdal.org/drivers/raster/netcdf.html#georeference\n",
-    "- pyproj: https://pyproj4.github.io/pyproj/stable/build_crs_cf.html\n",
-    "\n",
-    "Operations on xarray objects can cause data loss. Due to this, rioxarray writes and expects the spatial reference information to exist in the coordinates."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Accessing the CRS object"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you have opened a dataset and the Coordinate Reference System (CRS) can be determined, you can access it via the `rio.crs` accessor.\n",
-    "\n",
-    "#### Search order for the CRS (DataArray and Dataset):\n",
-    "1. Look in attributes (`attrs`) of your data array for the `grid_mapping` coordinate name.\n",
-    "   Inside the `grid_mapping` coordinate first look for `spatial_ref` then `crs_wkt` and lastly the CF grid mapping attributes.\n",
-    "   This is in line with the Climate and Forecast (CF) conventions for storing the CRS as well as GDAL netCDF conventions.\n",
-    "2. Look in the `crs` attribute and load in the CRS from there. This is for backwards compatibility with `xarray.open_rasterio`, which is deprecated since version 0.20.0. We recommend using `rioxarray.open_rasterio` instead.\n",
-    "\n",
-    "The value for the `crs` is anything accepted by `rasterio.crs.CRS.from_user_input()`\n",
-    "\n",
-    "#### Search order for the CRS for Dataset:\n",
-    "If the CRS is not found using the search methods above, it also searches the `data_vars` and uses the\n",
-    "first valid CRS found.\n",
-    "\n",
-    "#### decode_coords=\"all\"\n",
-    "\n",
-    "If you use one of xarray's open methods such as ``xarray.open_dataset`` to load netCDF files\n",
-    "with the default engine, it is recommended to use `decode_coords=\"all\"`. This will load the grid mapping\n",
-    "variable into coordinates for compatibility with rioxarray.\n",
-    "\n",
-    "#### API Documentation\n",
-    "\n",
-    "- [rio.write_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_crs)\n",
-    "- [rio.crs](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.crs)\n",
-    "- [rio.estimate_utm_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.estimate_utm_crs)\n",
-    "- [rio.set_spatial_dims()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.set_spatial_dims)\n",
-    "- [rio.write_coordinate_system()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_coordinate_system)\n",
-    "- [rio.write_transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_transform)\n",
-    "- [rio.transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray  # activate the rio accessor\n",
-    "import xarray\n",
-    "from affine import Affine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = xarray.open_dataset(\"../../test/test_data/input/PLANET_SCOPE_3D.nc\", decode_coords=\"all\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'units': 'DN', 'nodata': 0.0}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.green.attrs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;spatial_ref&#x27; ()&gt;\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    spatial_ref:  PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'spatial_ref'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ed98a4ac-15a8-4b42-a4d5-b41d08429b39' class='xr-array-in' type='checkbox' checked><label for='section-ed98a4ac-15a8-4b42-a4d5-b41d08429b39' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-13a73705-474f-4849-a2b6-31c4c1cc60d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13a73705-474f-4849-a2b6-31c4c1cc60d7' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ff9e3017-55fa-4eb5-af42-af1cc7f27bd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff9e3017-55fa-4eb5-af42-af1cc7f27bd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a4c305b-e31f-4faa-a83f-8aeec07cc0ee' class='xr-var-data-in' type='checkbox'><label for='data-7a4c305b-e31f-4faa-a83f-8aeec07cc0ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c56ca51-d50d-408e-8550-3fa40536ce7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c56ca51-d50d-408e-8550-3fa40536ce7d' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 22S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-51],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32722&quot;]]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'spatial_ref' ()>\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    spatial_ref:  PROJCS[\"WGS 84 / UTM zone 22S\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1..."
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
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-   "source": [
-    "rds.green.spatial_ref"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CRS.from_epsg(32722)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.green.rio.crs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Setting the CRS\n",
-    "\n",
-    "Use the `rio.write_crs` method to set the CRS on your `xarray.Dataset` or `xarray.DataArray`.\n",
-    "This modifies the `xarray.Dataset` or `xarray.DataArray` and sets the CRS in a CF compliant manner.\n",
-    "\n",
-    "- [rio.write_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_crs)\n",
-    "- [rio.crs](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.crs)\n",
-    "\n",
-    "**Note:** It is recommended to use `rio.write_crs()` if you want the CRS to persist on the Dataset/DataArray and to write the CRS CF compliant metadata. Calling only `rio.set_crs()` CRS storage method is lossy and will not modify the Dataset/DataArray metadata."
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-  {
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-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
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-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;spatial_ref&#x27; ()&gt;\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    crs_wkt:                      GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;...\n",
-       "    semi_major_axis:              6378137.0\n",
-       "    semi_minor_axis:              6356752.314245179\n",
-       "    inverse_flattening:           298.257223563\n",
-       "    reference_ellipsoid_name:     WGS 84\n",
-       "    longitude_of_prime_meridian:  0.0\n",
-       "    prime_meridian_name:          Greenwich\n",
-       "    geographic_crs_name:          WGS 84\n",
-       "    grid_mapping_name:            latitude_longitude\n",
-       "    spatial_ref:                  GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'spatial_ref'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8edcf942-accb-4c39-88d9-d91a01903b83' class='xr-array-in' type='checkbox' checked><label for='section-8edcf942-accb-4c39-88d9-d91a01903b83' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-91b27a9b-d677-4786-8c5e-8208bbc943d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91b27a9b-d677-4786-8c5e-8208bbc943d0' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ed33fcd3-3802-4a97-9594-5e9199c8ad93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed33fcd3-3802-4a97-9594-5e9199c8ad93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c29d6c2-7c47-4a94-a90a-5de80d8ce90b' class='xr-var-data-in' type='checkbox'><label for='data-6c29d6c2-7c47-4a94-a90a-5de80d8ce90b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e504052f-18ed-4e21-98d1-667acb5bfc9e' class='xr-section-summary-in' type='checkbox'  ><label for='section-e504052f-18ed-4e21-98d1-667acb5bfc9e' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'spatial_ref' ()>\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    crs_wkt:                      GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"...\n",
-       "    semi_major_axis:              6378137.0\n",
-       "    semi_minor_axis:              6356752.314245179\n",
-       "    inverse_flattening:           298.257223563\n",
-       "    reference_ellipsoid_name:     WGS 84\n",
-       "    longitude_of_prime_meridian:  0.0\n",
-       "    prime_meridian_name:          Greenwich\n",
-       "    geographic_crs_name:          WGS 84\n",
-       "    grid_mapping_name:            latitude_longitude\n",
-       "    spatial_ref:                  GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"..."
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xda = xarray.DataArray(1)\n",
-    "xda.rio.write_crs(4326, inplace=True)\n",
-    "xda.spatial_ref"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CRS.from_epsg(4326)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xda.rio.crs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Spatial dimensions\n",
-    "\n",
-    "Only 1-dimensional X and Y dimensions are supported.\n",
-    "\n",
-    "The expected X/Y dimension names searched for in the `coords` are:\n",
-    "\n",
-    "- x | y\n",
-    "- longitude | latitude\n",
-    "- Coordinates (`coords`) with the CF attributes in `attrs`:\n",
-    "    - axis: X | Y\n",
-    "    - standard_name: longitude | latitude or projection_x_coordinate | projection_y_coordinate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Option 1: Write the CF attributes for non-standard dimension names\n",
-    "\n",
-    "If you don't want to rename your dimensions/coordinates,\n",
-    "you can write the CF attributes so the coordinates can be found.\n",
-    "\n",
-    "- [rio.set_spatial_dims()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.set_spatial_dims)\n",
-    "- [rio.write_coordinate_system()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_coordinate_system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds.rio.write_crs(\n",
-    "    4326\n",
-    "    inplace=True,\n",
-    ").rio.set_spatial_dims(\n",
-    "    x_dim=\"lon\",\n",
-    "    y_dim=\"lat\"\n",
-    "    inplace=True,\n",
-    ").rio.write_coordinate_system(inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Option 2: Rename your coordinates\n",
-    "\n",
-    "[xarray.Dataset.rename](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.rename.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rds.rename(lon=longitude, lat=latitude) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Setting the transform of the dataset\n",
-    "\n",
-    "The transform can be calculated from the coordinates of your data.\n",
-    "This method is useful if your netCDF file does not have coordinates present.\n",
-    "Use the `rio.write_transform` method to set the transform on your `xarray.Dataset` or `xarray.DataArray`.\n",
-    "\n",
-    "- [rio.write_transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_transform)\n",
-    "- [rio.transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'466266.0 3.0 0.0 8084700.0 0.0 -3.0'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transform = Affine(3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0)\n",
-    "xda.rio.write_transform(transform, inplace=True)\n",
-    "xda.spatial_ref.GeoTransform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Affine(3.0, 0.0, 466266.0,\n",
-       "       0.0, -3.0, 8084700.0)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xda.rio.transform()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
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- "nbformat_minor": 4
-}
diff --git a/0.18.0/getting_started/getting_started.html b/0.18.0/getting_started/getting_started.html
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-<li class="toctree-l1 current"><a class="current reference internal" href="#">Getting Started</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#rio-accessor">rio accessor</a></li>
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-  <section id="getting-started">
-<span id="id1"></span><h1>Getting Started<a class="headerlink" href="#getting-started" title="Link to this heading"></a></h1>
-<p>Welcome! This page aims to help you gain a foundational understanding of rioxarray.</p>
-<section id="rio-accessor">
-<h2>rio accessor<a class="headerlink" href="#rio-accessor" title="Link to this heading"></a></h2>
-<p>rioxarray <a class="reference external" href="https://docs.xarray.dev/en/stable/internals/extending-xarray.html">extends xarray</a>
-with the <cite>rio</cite> accessor. The <cite>rio</cite> accessor is activated by importing rioxarray like so:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">rioxarray</span>
-</pre></div>
-</div>
-<p>You can learn how to <cite>clip</cite>, <cite>merge</cite>, and <cite>reproject</cite> rasters in the <a class="reference internal" href="../examples/examples.html#usage-examples"><span class="std std-ref">Usage Examples</span></a>
-section of the documentation. Need to export to a raster (GeoTiff)? There is an example for
-that as well.</p>
-</section>
-<section id="reading-files">
-<h2>Reading Files<a class="headerlink" href="#reading-files" title="Link to this heading"></a></h2>
-<section id="xarray">
-<h3>xarray<a class="headerlink" href="#xarray" title="Link to this heading"></a></h3>
-<p>Since <cite>rioxarray</cite> is an extension of <cite>xarray</cite>, you can load in files using the standard
-<cite>xarray</cite> open methods. If you use one of xarray’s open methods such as <code class="docutils literal notranslate"><span class="pre">xarray.open_dataset</span></code>
-to load netCDF files with the default engine, it is recommended to use <cite>decode_coords=”all”</cite>.
-This will load the grid mapping variable into coordinates for compatibility with rioxarray.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">xarray</span>
-
-<span class="n">xds</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s2">&quot;file.nc&quot;</span><span class="p">,</span> <span class="n">decode_coords</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="rioxarray">
-<h3>rioxarray<a class="headerlink" href="#rioxarray" title="Link to this heading"></a></h3>
-<p>rioxarray 0.4+ enables passing <cite>engine=”rasterio”</cite> to <code class="docutils literal notranslate"><span class="pre">xarray.open_dataset</span></code>
-and <code class="docutils literal notranslate"><span class="pre">xarray.open_mfdataset</span></code> for xarray 0.18+. This uses
-<a class="reference internal" href="../rioxarray.html#rioxarray.open_rasterio" title="rioxarray.open_rasterio"><code class="xref py py-func docutils literal notranslate"><span class="pre">rioxarray.open_rasterio()</span></code></a> as the backend and always returns an <code class="docutils literal notranslate"><span class="pre">xarray.Dataset</span></code>.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">xarray</span>
-
-<span class="n">xds</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s2">&quot;my.tif&quot;</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;rasterio&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>You can also use <a class="reference internal" href="../rioxarray.html#rioxarray.open_rasterio" title="rioxarray.open_rasterio"><code class="xref py py-func docutils literal notranslate"><span class="pre">rioxarray.open_rasterio()</span></code></a>. This objects returned depend on
-your input file type.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">rioxarray</span>
-
-<span class="n">xds</span> <span class="o">=</span> <span class="n">rioxarray</span><span class="o">.</span><span class="n">open_rasterio</span><span class="p">(</span><span class="s2">&quot;my.tif&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Why use <a class="reference internal" href="../rioxarray.html#rioxarray.open_rasterio" title="rioxarray.open_rasterio"><code class="xref py py-func docutils literal notranslate"><span class="pre">rioxarray.open_rasterio()</span></code></a> instead of <cite>xarray.open_rasterio</cite>?</p>
-<ol class="arabic simple">
-<li><p>It supports multidimensional datasets such as netCDF.</p></li>
-<li><p>It stores the CRS as a WKT, which is the recommended format (<a class="reference external" href="https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems">PROJ FAQ</a>).</p></li>
-<li><p>It loads in the CRS, transform, and nodata metadata in standard CF &amp; GDAL locations.</p></li>
-<li><p>It supports masking and scaling data with the <cite>masked</cite> and <cite>mask_and_scale</cite> kwargs.</p></li>
-<li><p>It adds the coordinate axis CF metadata.</p></li>
-<li><p>It loads raster metadata into the attributes.</p></li>
-<li><p><cite>xarray.open_rasterio</cite> is deprecated (since v0.20.0)</p></li>
-</ol>
-</section>
-</section>
-<section id="introductory-information">
-<h2>Introductory Information<a class="headerlink" href="#introductory-information" title="Link to this heading"></a></h2>
-<div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="crs_management.html">Coordinate Reference System Management</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="manage_information_loss.html">Managing Information Loss with xarray operations</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="getting_started.html#rio-accessor">rio accessor</a></li>
-<li class="toctree-l2"><a class="reference internal" href="getting_started.html#reading-files">Reading Files</a></li>
-<li class="toctree-l2 current"><a class="reference internal" href="getting_started.html#introductory-information">Introductory Information</a><ul class="current">
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-  <section id="Managing-Information-Loss-with-xarray-operations">
-<h1>Managing Information Loss with xarray operations<a class="headerlink" href="#Managing-Information-Loss-with-xarray-operations" title="Link to this heading"></a></h1>
-<p>Sometimes, you can lose important information from your dataset when performing operations. You will likely want to keep track of the attributes, <code class="docutils literal notranslate"><span class="pre">nodata</span></code>, and <code class="docutils literal notranslate"><span class="pre">CRS</span></code>.</p>
-<p>API Reference:</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_dataset.RasterDataset.to_raster"><span class="std std-ref">rio.to_raster()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_crs"><span class="std std-ref">rio.write_crs()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.write_transform"><span class="std std-ref">rio.write_transform()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.update_attrs"><span class="std std-ref">rio.update_attrs()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.update_encoding"><span class="std std-ref">rio.update_encoding()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.crs"><span class="std std-ref">rio.crs</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.nodata"><span class="std std-ref">rio.nodata</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.encoded_nodata"><span class="std std-ref">rio.encoded_nodata</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.write_nodata"><span class="std std-ref">rio.write_nodata</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.rioxarray.XRasterBase.transform"><span class="std std-ref">rio.transform()</span></a></p></li>
-</ul>
-<p>Note that <code class="docutils literal notranslate"><span class="pre">write_transform</span></code> is only needed if you are not saving the x,y coordinates. It is for GDAL to be able to read in the transform without needing the original coordinates and is useful if you read in the file with <code class="docutils literal notranslate"><span class="pre">parse_coordinates=False</span></code>.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray
-import xarray
-</pre></div>
-</div>
-</div>
-<p>See docs for <a class="reference internal" href="../rioxarray.html#rioxarray-open-rasterio"><span class="std std-ref">rioxarray.open_rasterio</span></a></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds = rioxarray.open_rasterio(
-    &quot;../../test/test_data/input/PLANET_SCOPE_3D.nc&quot;,
-    variable=[&quot;green&quot;],
-    mask_and_scale=True,
-)
-</pre></div>
-</div>
-</div>
-<p>Notice the original data:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rds.green.attrs, rds.green.encoding, rds.green.rio.crs, rds.green.rio.nodata
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-({&#39;nodata&#39;: 0, &#39;units&#39;: (&#39;DN&#39;, &#39;DN&#39;)},
- {&#39;dtype&#39;: &#39;float64&#39;,
-  &#39;grid_mapping&#39;: &#39;spatial_ref&#39;,
-  &#39;scale_factor&#39;: 1.0,
-  &#39;add_offset&#39;: 0.0,
-  &#39;_FillValue&#39;: nan,
-  &#39;source&#39;: &#39;netcdf:../../test/test_data/input/PLANET_SCOPE_3D.nc:green&#39;},
- CRS.from_epsg(32722),
- nan)
-</pre></div></div>
-</div>
-<p>Notice how information is lost in the operation:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>new_ds = rds.green + rds.green
-new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-({}, {}, CRS.from_epsg(32722), None)
-</pre></div></div>
-</div>
-<p>To preserve attributes, xarray has <a class="reference external" href="http://xarray.pydata.org/en/stable/generated/xarray.set_options.html#xarray-set-options">set_options</a> with <code class="docutils literal notranslate"><span class="pre">keep_attrs=True</span></code>. However, it does not preserve the encoding.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>with xarray.set_options(keep_attrs=True):
-    new_ds = rds.green + rds.green
-new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-({&#39;nodata&#39;: 0, &#39;units&#39;: (&#39;DN&#39;, &#39;DN&#39;)}, {}, CRS.from_epsg(32722), 0.0)
-</pre></div></div>
-</div>
-<p>Another solution is to save the original attributes and then copy them over once the operation is complete:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>new_ds = rds.green + rds.green
-new_ds.rio.write_crs(rds.green.rio.crs, inplace=True)
-new_ds.rio.update_attrs(rds.green.attrs, inplace=True)
-new_ds.rio.update_encoding(rds.green.encoding, inplace=True)
-new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-({&#39;nodata&#39;: 0, &#39;units&#39;: (&#39;DN&#39;, &#39;DN&#39;)},
- {&#39;grid_mapping&#39;: &#39;spatial_ref&#39;,
-  &#39;dtype&#39;: &#39;float64&#39;,
-  &#39;scale_factor&#39;: 1.0,
-  &#39;add_offset&#39;: 0.0,
-  &#39;_FillValue&#39;: nan,
-  &#39;source&#39;: &#39;netcdf:../../test/test_data/input/PLANET_SCOPE_3D.nc:green&#39;},
- CRS.from_epsg(32722),
- nan)
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>new_ds.rio.to_raster(&quot;combination_keep_attrs.tif&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!rio info combination_keep_attrs.tif
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#34;bounds&#34;: [466266.0, 8084670.0, 466296.0, 8084700.0], &#34;colorinterp&#34;: [&#34;gray&#34;, &#34;undefined&#34;], &#34;count&#34;: 2, &#34;crs&#34;: &#34;EPSG:32722&#34;, &#34;descriptions&#34;: [&#34;green&#34;, &#34;green&#34;], &#34;driver&#34;: &#34;GTiff&#34;, &#34;dtype&#34;: &#34;float64&#34;, &#34;height&#34;: 10, &#34;indexes&#34;: [1, 2], &#34;interleave&#34;: &#34;pixel&#34;, &#34;lnglat&#34;: [-51.31732641226951, -17.322997474192466], &#34;mask_flags&#34;: [[&#34;nodata&#34;], [&#34;nodata&#34;]], &#34;nodata&#34;: NaN, &#34;res&#34;: [3.0, 3.0], &#34;shape&#34;: [10, 10], &#34;tiled&#34;: false, &#34;transform&#34;: [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], &#34;units&#34;: [null, null], &#34;width&#34;: 10}
-</pre></div></div>
-</div>
-</section>
-
-
-           </div>
-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="nodata_management.html" class="btn btn-neutral float-left" title="Nodata Management" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="../examples/examples.html" class="btn btn-neutral float-right" title="Usage Examples" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
-    </div>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2019-2023, Corteva Agriscience™.</p>
-  </div>
-
-  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
-   
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-          SphinxRtdTheme.Navigation.enable(true);
-      });
-  </script> 
-
-</body>
-</html>
\ No newline at end of file
diff --git a/0.18.0/getting_started/manage_information_loss.ipynb b/0.18.0/getting_started/manage_information_loss.ipynb
deleted file mode 100644
index aeb02d4e..00000000
--- a/0.18.0/getting_started/manage_information_loss.ipynb
+++ /dev/null
@@ -1,240 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Managing Information Loss with xarray operations\n",
-    "\n",
-    "Sometimes, you can lose important information from your dataset when performing operations.\n",
-    "You will likely want to keep track of the attributes, `nodata`, and `CRS`.\n",
-    "\n",
-    "API Reference:\n",
-    "\n",
-    "- [rio.to_raster()](../rioxarray.rst#rioxarray.raster_dataset.RasterDataset.to_raster)\n",
-    "- [rio.write_crs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_crs)\n",
-    "- [rio.write_transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.write_transform)\n",
-    "- [rio.update_attrs()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.update_attrs)\n",
-    "- [rio.update_encoding()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.update_encoding)\n",
-    "- [rio.crs](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.crs)\n",
-    "- [rio.nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.nodata)\n",
-    "- [rio.encoded_nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.encoded_nodata)\n",
-    "- [rio.write_nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.write_nodata)\n",
-    "- [rio.transform()](../rioxarray.rst#rioxarray.rioxarray.XRasterBase.transform)\n",
-    "\n",
-    "Note that `write_transform` is only needed if you are not saving the x,y coordinates. It is for\n",
-    "GDAL to  be able to read in the transform without needing the original coordinates and is useful\n",
-    "if you read in the file with `parse_coordinates=False`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "import xarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See docs for [rioxarray.open_rasterio](../rioxarray.rst#rioxarray-open-rasterio)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rds = rioxarray.open_rasterio(\n",
-    "    \"../../test/test_data/input/PLANET_SCOPE_3D.nc\",\n",
-    "    variable=[\"green\"],\n",
-    "    mask_and_scale=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice the original data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'nodata': 0, 'units': ('DN', 'DN')},\n",
-       " {'dtype': 'float64',\n",
-       "  'grid_mapping': 'spatial_ref',\n",
-       "  'scale_factor': 1.0,\n",
-       "  'add_offset': 0.0,\n",
-       "  '_FillValue': nan,\n",
-       "  'source': 'netcdf:../../test/test_data/input/PLANET_SCOPE_3D.nc:green'},\n",
-       " CRS.from_epsg(32722),\n",
-       " nan)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rds.green.attrs, rds.green.encoding, rds.green.rio.crs, rds.green.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice how information is lost in the operation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({}, {}, CRS.from_epsg(32722), None)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_ds = rds.green + rds.green\n",
-    "new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To preserve attributes, xarray has [set_options](http://xarray.pydata.org/en/stable/generated/xarray.set_options.html#xarray-set-options) with `keep_attrs=True`. However, it does not preserve the encoding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'nodata': 0, 'units': ('DN', 'DN')}, {}, CRS.from_epsg(32722), 0.0)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with xarray.set_options(keep_attrs=True):\n",
-    "    new_ds = rds.green + rds.green\n",
-    "new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another solution is to save the original attributes and then copy them over\n",
-    "once the operation is complete:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'nodata': 0, 'units': ('DN', 'DN')},\n",
-       " {'grid_mapping': 'spatial_ref',\n",
-       "  'dtype': 'float64',\n",
-       "  'scale_factor': 1.0,\n",
-       "  'add_offset': 0.0,\n",
-       "  '_FillValue': nan,\n",
-       "  'source': 'netcdf:../../test/test_data/input/PLANET_SCOPE_3D.nc:green'},\n",
-       " CRS.from_epsg(32722),\n",
-       " nan)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_ds = rds.green + rds.green\n",
-    "new_ds.rio.write_crs(rds.green.rio.crs, inplace=True)\n",
-    "new_ds.rio.update_attrs(rds.green.attrs, inplace=True)\n",
-    "new_ds.rio.update_encoding(rds.green.encoding, inplace=True)\n",
-    "new_ds.attrs, new_ds.encoding, new_ds.rio.crs, new_ds.rio.nodata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_ds.rio.to_raster(\"combination_keep_attrs.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"bounds\": [466266.0, 8084670.0, 466296.0, 8084700.0], \"colorinterp\": [\"gray\", \"undefined\"], \"count\": 2, \"crs\": \"EPSG:32722\", \"descriptions\": [\"green\", \"green\"], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 10, \"indexes\": [1, 2], \"interleave\": \"pixel\", \"lnglat\": [-51.31732641226951, -17.322997474192466], \"mask_flags\": [[\"nodata\"], [\"nodata\"]], \"nodata\": NaN, \"res\": [3.0, 3.0], \"shape\": [10, 10], \"tiled\": false, \"transform\": [3.0, 0.0, 466266.0, 0.0, -3.0, 8084700.0, 0.0, 0.0, 1.0], \"units\": [null, null], \"width\": 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!rio info combination_keep_attrs.tif"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/getting_started/nodata_management.html b/0.18.0/getting_started/nodata_management.html
deleted file mode 100644
index 400804f4..00000000
--- a/0.18.0/getting_started/nodata_management.html
+++ /dev/null
@@ -1,372 +0,0 @@
-
-
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" data-content_root="../">
-<head>
-  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Nodata Management &mdash; rioxarray 0.18.0 documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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-      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script src="../_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="../genindex.html" />
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-    <link rel="next" title="Managing Information Loss with xarray operations" href="manage_information_loss.html" />
-    <link rel="prev" title="Coordinate Reference System Management" href="crs_management.html" /> 
-</head>
-
-<body class="wy-body-for-nav"> 
-  <div class="wy-grid-for-nav">
-    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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-            rioxarray
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-<div role="search">
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-  <section id="Nodata-Management">
-<h1>Nodata Management<a class="headerlink" href="#Nodata-Management" title="Link to this heading"></a></h1>
-<p>If you have opened a dataset and the nodata value can be determined, you can access it via the <code class="docutils literal notranslate"><span class="pre">rio.nodata</span></code> or <code class="docutils literal notranslate"><span class="pre">rio.encoded_nodata</span></code> accessors.</p>
-<p>If your dataset’s nodata value cannot be determined, you can use the <code class="docutils literal notranslate"><span class="pre">rio.write_nodata</span></code> method.</p>
-<section id="Search-order-for-nodata-(DataArray-only):">
-<h2>Search order for nodata (DataArray only):<a class="headerlink" href="#Search-order-for-nodata-(DataArray-only):" title="Link to this heading"></a></h2>
-<ol class="arabic simple">
-<li><p>Check if DataArray values are masked. If they are masked, return <code class="docutils literal notranslate"><span class="pre">NaN</span></code>. If the DataArray is masked, the original nodata value can be retreived from <code class="docutils literal notranslate"><span class="pre">rio.encoded_nodata</span></code>.</p></li>
-<li><p>Look in attributes (<code class="docutils literal notranslate"><span class="pre">attrs</span></code>) of your data array for the <code class="docutils literal notranslate"><span class="pre">_FillValue</span></code> then <code class="docutils literal notranslate"><span class="pre">missing_value</span></code> then <code class="docutils literal notranslate"><span class="pre">fill_value</span></code> and finally <code class="docutils literal notranslate"><span class="pre">nodata</span></code>.</p></li>
-<li><p>Look in the <code class="docutils literal notranslate"><span class="pre">nodatavals</span></code> attribute. This is for backwards compatibility with <code class="docutils literal notranslate"><span class="pre">xarray.open_rasterio</span></code>. We recommend using <code class="docutils literal notranslate"><span class="pre">rioxarray.open_rasterio</span></code> instead.</p></li>
-</ol>
-</section>
-<section id="API-Documentation">
-<h2>API Documentation<a class="headerlink" href="#API-Documentation" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.write_nodata"><span class="std std-ref">rio.write_nodata()</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.nodata"><span class="std std-ref">rio.nodata</span></a></p></li>
-<li><p><a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.encoded_nodata"><span class="std std-ref">rio.encoded_nodata</span></a></p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import rioxarray
-import xarray
-
-file_path = &quot;../../test/test_data/input/tmmx_20190121.nc&quot;
-</pre></div>
-</div>
-</div>
-<section id="Example-of-loading-unmaksed-data">
-<h3>Example of loading unmaksed data<a class="headerlink" href="#Example-of-loading-unmaksed-data" title="Link to this heading"></a></h3>
-<p>In this case, the nodata value is in the attributes.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = xarray.open_dataset(file_path, mask_and_scale=False) # performs mask_and_scale by default
-rds = rioxarray.open_rasterio(file_path)
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;nodata:&quot;)
-print(f&quot;- xarray.open_dataset: {xds.air_temperature.rio.nodata}&quot;)
-print(f&quot;- rioxarray.open_rasterio: {rds.air_temperature.rio.nodata}&quot;)
-print(&quot;\nencoded_nodata:&quot;)
-print(f&quot;- xarray.open_dataset: {xds.air_temperature.rio.encoded_nodata}&quot;)
-print(f&quot;- rioxarray.open_rasterio: {rds.air_temperature.rio.encoded_nodata}&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-nodata:
-- xarray.open_dataset: 32767
-- rioxarray.open_rasterio: 32767
-
-encoded_nodata:
-- xarray.open_dataset: None
-- rioxarray.open_rasterio: None
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;attributes:&quot;)
-print(f&quot;\n- xarray.open_dataset:\n    {xds.air_temperature.attrs}&quot;)
-print(f&quot;\n- rioxarray.open_rasterio:\n    {rds.air_temperature.attrs}&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-attributes:
-
-- xarray.open_dataset:
-    {&#39;_FillValue&#39;: 32767, &#39;units&#39;: &#39;K&#39;, &#39;description&#39;: &#39;Daily Maximum Temperature&#39;, &#39;long_name&#39;: &#39;tmmx&#39;, &#39;standard_name&#39;: &#39;tmmx&#39;, &#39;missing_value&#39;: 32767, &#39;dimensions&#39;: &#39;lon lat time&#39;, &#39;grid_mapping&#39;: &#39;crs&#39;, &#39;coordinate_system&#39;: &#39;WGS84,EPSG:4326&#39;, &#39;scale_factor&#39;: 0.1, &#39;add_offset&#39;: 220.0, &#39;_Unsigned&#39;: &#39;true&#39;}
-
-- rioxarray.open_rasterio:
-    {&#39;add_offset&#39;: 220.0, &#39;coordinates&#39;: &#39;day&#39;, &#39;coordinate_system&#39;: &#39;WGS84,EPSG:4326&#39;, &#39;description&#39;: &#39;Daily Maximum Temperature&#39;, &#39;dimensions&#39;: &#39;lon lat time&#39;, &#39;long_name&#39;: &#39;tmmx&#39;, &#39;missing_value&#39;: 32767, &#39;scale_factor&#39;: 0.1, &#39;standard_name&#39;: &#39;tmmx&#39;, &#39;units&#39;: &#39;K&#39;, &#39;_FillValue&#39;: 32767.0, &#39;_Unsigned&#39;: &#39;true&#39;}
-</pre></div></div>
-</div>
-</section>
-<section id="Example-of-data-loaded-in-with-mask_and_scale=True">
-<h3>Example of data loaded in with mask_and_scale=True<a class="headerlink" href="#Example-of-data-loaded-in-with-mask_and_scale=True" title="Link to this heading"></a></h3>
-<p>When the dataset is opened with <code class="docutils literal notranslate"><span class="pre">mask_and_scale=True</span></code> with <code class="docutils literal notranslate"><span class="pre">rioxarray.open_rasterio</span></code> or <code class="docutils literal notranslate"><span class="pre">xarray.open_dataset</span></code>, the nodata metadata is written to the encoding attribute. Then, when the dataset is written using <code class="docutils literal notranslate"><span class="pre">to_netcdf</span></code> or <code class="docutils literal notranslate"><span class="pre">rio.to_raster</span></code> the data is decoded and it writes the original nodata value to the raster.</p>
-<p>When this happens, <code class="docutils literal notranslate"><span class="pre">rio.nodata</span></code> returns <code class="docutils literal notranslate"><span class="pre">numpy.nan</span></code> and <code class="docutils literal notranslate"><span class="pre">rio.encoded_nodata</span></code> contains the original value.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = xarray.open_dataset(file_path) # performs mask_and_scale by default
-rds = rioxarray.open_rasterio(file_path, mask_and_scale=True)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-/home/snowal/miniconda/envs/midas/lib/python3.10/site-packages/rioxarray/_io.py:618: SerializationWarning: variable &#39;air_temperature&#39; has _Unsigned attribute but is not of integer type. Ignoring attribute.
-  rioda = open_rasterio(
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;nodata:&quot;)
-print(f&quot;- xarray.open_dataset: {xds.air_temperature.rio.nodata}&quot;)
-print(f&quot;- rioxarray.open_rasterio: {rds.air_temperature.rio.nodata}&quot;)
-print(&quot;\nencoded_nodata:&quot;)
-print(f&quot;- xarray.open_dataset: {xds.air_temperature.rio.encoded_nodata}&quot;)
-print(f&quot;- rioxarray.open_rasterio: {rds.air_temperature.rio.encoded_nodata}&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-nodata:
-- xarray.open_dataset: nan
-- rioxarray.open_rasterio: nan
-
-encoded_nodata:
-- xarray.open_dataset: 32767.0
-- rioxarray.open_rasterio: 32767.0
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;attributes:&quot;)
-print(f&quot;\n- xarray.open_dataset:\n    {xds.air_temperature.attrs}&quot;)
-print(f&quot;\n- rioxarray.open_rasterio:\n    {rds.air_temperature.attrs}&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-attributes:
-
-- xarray.open_dataset:
-    {&#39;units&#39;: &#39;K&#39;, &#39;description&#39;: &#39;Daily Maximum Temperature&#39;, &#39;long_name&#39;: &#39;tmmx&#39;, &#39;standard_name&#39;: &#39;tmmx&#39;, &#39;dimensions&#39;: &#39;lon lat time&#39;, &#39;grid_mapping&#39;: &#39;crs&#39;, &#39;coordinate_system&#39;: &#39;WGS84,EPSG:4326&#39;}
-
-- rioxarray.open_rasterio:
-    {&#39;coordinates&#39;: &#39;day&#39;, &#39;coordinate_system&#39;: &#39;WGS84,EPSG:4326&#39;, &#39;description&#39;: &#39;Daily Maximum Temperature&#39;, &#39;dimensions&#39;: &#39;lon lat time&#39;, &#39;long_name&#39;: &#39;tmmx&#39;, &#39;standard_name&#39;: &#39;tmmx&#39;, &#39;units&#39;: &#39;K&#39;}
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;encoding:&quot;)
-print(f&quot;\n- xarray.open_dataset:\n    {xds.air_temperature.encoding}&quot;)
-print(f&quot;\n- rioxarray.open_rasterio:\n    {rds.air_temperature.encoding}&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-encoding:
-
-- xarray.open_dataset:
-    {&#39;zlib&#39;: True, &#39;shuffle&#39;: True, &#39;complevel&#39;: 5, &#39;fletcher32&#39;: False, &#39;contiguous&#39;: False, &#39;chunksizes&#39;: (585, 1386), &#39;source&#39;: &#39;/home/snowal/scripts/rioxarray/test/test_data/input/tmmx_20190121.nc&#39;, &#39;original_shape&#39;: (585, 1386), &#39;dtype&#39;: dtype(&#39;uint16&#39;), &#39;_Unsigned&#39;: &#39;true&#39;, &#39;missing_value&#39;: 32767, &#39;_FillValue&#39;: 32767, &#39;scale_factor&#39;: 0.1, &#39;add_offset&#39;: 220.0, &#39;coordinates&#39;: &#39;day&#39;}
-
-- rioxarray.open_rasterio:
-    {&#39;_Unsigned&#39;: &#39;true&#39;, &#39;dtype&#39;: &#39;uint16&#39;, &#39;grid_mapping&#39;: &#39;crs&#39;, &#39;scale_factor&#39;: 0.1, &#39;add_offset&#39;: 220.0, &#39;_FillValue&#39;: 32767.0, &#39;missing_value&#39;: 32767, &#39;source&#39;: &#39;netcdf:../../test/test_data/input/tmmx_20190121.nc:air_temperature&#39;, &#39;rasterio_dtype&#39;: &#39;uint16&#39;}
-</pre></div></div>
-</div>
-</section>
-<section id="Manually-masking-data">
-<h3>Manually masking data<a class="headerlink" href="#Manually-masking-data" title="Link to this heading"></a></h3>
-<p>If you use <code class="docutils literal notranslate"><span class="pre">xarray.where</span></code> to mask you data, then you need to ensure that the attributes stored on the DataArray reflect the correct values. <a class="reference internal" href="../rioxarray.html#rioxarray.raster_array.RasterArray.write_nodata"><span class="std std-ref">rio.write_nodata()</span></a> can help ensure that the nodata attributes are written correctly.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>xds = xarray.open_dataset(file_path, mask_and_scale=False) # performs mask_and_scale by default
-raster = xds.air_temperature
-raster = raster.where(raster != raster.rio.nodata)
-# nodata does not reflect the data has been masked
-print(f&quot;nodata: {raster.rio.nodata}&quot;)
-print(f&quot;encoded_nodata: {raster.rio.encoded_nodata}&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-nodata: 32767.0
-encoded_nodata: None
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># update nodata value to show the data has been masked
-raster.rio.write_nodata(raster.rio.nodata, encoded=True, inplace=True)
-print(f&quot;nodata: {raster.rio.nodata}&quot;)
-print(f&quot;encoded_nodata: {raster.rio.encoded_nodata}&quot;)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-nodata: nan
-encoded_nodata: 32767.0
-</pre></div></div>
-</div>
-</section>
-</section>
-</section>
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diff --git a/0.18.0/getting_started/nodata_management.ipynb b/0.18.0/getting_started/nodata_management.ipynb
deleted file mode 100644
index 1c60169c..00000000
--- a/0.18.0/getting_started/nodata_management.ipynb
+++ /dev/null
@@ -1,301 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Nodata Management"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you have opened a dataset and the nodata value can be determined, you can access it via the `rio.nodata` or `rio.encoded_nodata` accessors.\n",
-    "\n",
-    "If your dataset's nodata value cannot be determined, you can use the `rio.write_nodata` method.\n",
-    "\n",
-    "### Search order for nodata (DataArray only):\n",
-    "1. Check if DataArray values are masked. If they are masked, return `NaN`. If the DataArray is masked, the original nodata value can be retreived from `rio.encoded_nodata`.\n",
-    "2. Look in attributes (`attrs`) of your data array for the `_FillValue` then `missing_value` then `fill_value` and finally `nodata`.\n",
-    "3. Look in the `nodatavals` attribute. This is for backwards compatibility with `xarray.open_rasterio`. We recommend using `rioxarray.open_rasterio` instead.\n",
-    "\n",
-    "### API Documentation\n",
-    "\n",
-    "- [rio.write_nodata()](../rioxarray.rst#rioxarray.raster_array.RasterArray.write_nodata)\n",
-    "- [rio.nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.nodata)\n",
-    "- [rio.encoded_nodata](../rioxarray.rst#rioxarray.raster_array.RasterArray.encoded_nodata)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rioxarray\n",
-    "import xarray\n",
-    "\n",
-    "file_path = \"../../test/test_data/input/tmmx_20190121.nc\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example of loading unmaksed data\n",
-    "\n",
-    "In this case, the nodata value is in the attributes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xds = xarray.open_dataset(file_path, mask_and_scale=False) # performs mask_and_scale by default\n",
-    "rds = rioxarray.open_rasterio(file_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata:\n",
-      "- xarray.open_dataset: 32767\n",
-      "- rioxarray.open_rasterio: 32767\n",
-      "\n",
-      "encoded_nodata:\n",
-      "- xarray.open_dataset: None\n",
-      "- rioxarray.open_rasterio: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.nodata}\")\n",
-    "print(\"\\nencoded_nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.encoded_nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.encoded_nodata}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "attributes:\n",
-      "\n",
-      "- xarray.open_dataset:\n",
-      "    {'_FillValue': 32767, 'units': 'K', 'description': 'Daily Maximum Temperature', 'long_name': 'tmmx', 'standard_name': 'tmmx', 'missing_value': 32767, 'dimensions': 'lon lat time', 'grid_mapping': 'crs', 'coordinate_system': 'WGS84,EPSG:4326', 'scale_factor': 0.1, 'add_offset': 220.0, '_Unsigned': 'true'}\n",
-      "\n",
-      "- rioxarray.open_rasterio:\n",
-      "    {'add_offset': 220.0, 'coordinates': 'day', 'coordinate_system': 'WGS84,EPSG:4326', 'description': 'Daily Maximum Temperature', 'dimensions': 'lon lat time', 'long_name': 'tmmx', 'missing_value': 32767, 'scale_factor': 0.1, 'standard_name': 'tmmx', 'units': 'K', '_FillValue': 32767.0, '_Unsigned': 'true'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"attributes:\")\n",
-    "print(f\"\\n- xarray.open_dataset:\\n    {xds.air_temperature.attrs}\")\n",
-    "print(f\"\\n- rioxarray.open_rasterio:\\n    {rds.air_temperature.attrs}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example of data loaded in with mask_and_scale=True\n",
-    "\n",
-    "When the dataset is opened with `mask_and_scale=True` with `rioxarray.open_rasterio` or `xarray.open_dataset`, the\n",
-    "nodata metadata is written to the encoding attribute. Then, when the dataset is written using\n",
-    "`to_netcdf` or `rio.to_raster` the data is decoded and it writes the original nodata value to the raster.\n",
-    "\n",
-    "When this happens, `rio.nodata` returns `numpy.nan` and `rio.encoded_nodata` contains the original value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/snowal/miniconda/envs/midas/lib/python3.10/site-packages/rioxarray/_io.py:618: SerializationWarning: variable 'air_temperature' has _Unsigned attribute but is not of integer type. Ignoring attribute.\n",
-      "  rioda = open_rasterio(\n"
-     ]
-    }
-   ],
-   "source": [
-    "xds = xarray.open_dataset(file_path) # performs mask_and_scale by default\n",
-    "rds = rioxarray.open_rasterio(file_path, mask_and_scale=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata:\n",
-      "- xarray.open_dataset: nan\n",
-      "- rioxarray.open_rasterio: nan\n",
-      "\n",
-      "encoded_nodata:\n",
-      "- xarray.open_dataset: 32767.0\n",
-      "- rioxarray.open_rasterio: 32767.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.nodata}\")\n",
-    "print(\"\\nencoded_nodata:\")\n",
-    "print(f\"- xarray.open_dataset: {xds.air_temperature.rio.encoded_nodata}\")\n",
-    "print(f\"- rioxarray.open_rasterio: {rds.air_temperature.rio.encoded_nodata}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "attributes:\n",
-      "\n",
-      "- xarray.open_dataset:\n",
-      "    {'units': 'K', 'description': 'Daily Maximum Temperature', 'long_name': 'tmmx', 'standard_name': 'tmmx', 'dimensions': 'lon lat time', 'grid_mapping': 'crs', 'coordinate_system': 'WGS84,EPSG:4326'}\n",
-      "\n",
-      "- rioxarray.open_rasterio:\n",
-      "    {'coordinates': 'day', 'coordinate_system': 'WGS84,EPSG:4326', 'description': 'Daily Maximum Temperature', 'dimensions': 'lon lat time', 'long_name': 'tmmx', 'standard_name': 'tmmx', 'units': 'K'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"attributes:\")\n",
-    "print(f\"\\n- xarray.open_dataset:\\n    {xds.air_temperature.attrs}\")\n",
-    "print(f\"\\n- rioxarray.open_rasterio:\\n    {rds.air_temperature.attrs}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "encoding:\n",
-      "\n",
-      "- xarray.open_dataset:\n",
-      "    {'zlib': True, 'shuffle': True, 'complevel': 5, 'fletcher32': False, 'contiguous': False, 'chunksizes': (585, 1386), 'source': '/home/snowal/scripts/rioxarray/test/test_data/input/tmmx_20190121.nc', 'original_shape': (585, 1386), 'dtype': dtype('uint16'), '_Unsigned': 'true', 'missing_value': 32767, '_FillValue': 32767, 'scale_factor': 0.1, 'add_offset': 220.0, 'coordinates': 'day'}\n",
-      "\n",
-      "- rioxarray.open_rasterio:\n",
-      "    {'_Unsigned': 'true', 'dtype': 'uint16', 'grid_mapping': 'crs', 'scale_factor': 0.1, 'add_offset': 220.0, '_FillValue': 32767.0, 'missing_value': 32767, 'source': 'netcdf:../../test/test_data/input/tmmx_20190121.nc:air_temperature', 'rasterio_dtype': 'uint16'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"encoding:\")\n",
-    "print(f\"\\n- xarray.open_dataset:\\n    {xds.air_temperature.encoding}\")\n",
-    "print(f\"\\n- rioxarray.open_rasterio:\\n    {rds.air_temperature.encoding}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Manually masking data\n",
-    "\n",
-    "If you use `xarray.where` to mask you data, then you need to ensure that the\n",
-    "attributes stored on the DataArray reflect the correct values.\n",
-    "[rio.write_nodata()](../rioxarray.rst#rioxarray.raster_array.RasterArray.write_nodata) can help ensure that the nodata attributes are written correctly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata: 32767.0\n",
-      "encoded_nodata: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "xds = xarray.open_dataset(file_path, mask_and_scale=False) # performs mask_and_scale by default\n",
-    "raster = xds.air_temperature \n",
-    "raster = raster.where(raster != raster.rio.nodata)\n",
-    "# nodata does not reflect the data has been masked\n",
-    "print(f\"nodata: {raster.rio.nodata}\")\n",
-    "print(f\"encoded_nodata: {raster.rio.encoded_nodata}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nodata: nan\n",
-      "encoded_nodata: 32767.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# update nodata value to show the data has been masked\n",
-    "raster.rio.write_nodata(raster.rio.nodata, encoded=True, inplace=True)\n",
-    "print(f\"nodata: {raster.rio.nodata}\")\n",
-    "print(f\"encoded_nodata: {raster.rio.encoded_nodata}\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/0.18.0/history.html b/0.18.0/history.html
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-<h2>0.18.0<a class="headerlink" href="#id1" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH:reproject: Support geolocation arrays (pull #822)</p></li>
-<li><p>REF: Add positional arguments requirements (pull #806)</p></li>
-<li><p>BUG:merge: Fix merging masked and scaled data (issue #814)</p></li>
-<li><p>BUG: Fix chunk arguments for normalize_chunks (pull #820)</p></li>
-<li><p>BUG: Squeeze when using interpolate_na with extra dim (pull #810)</p></li>
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-<li><p>BUG: Properly handle encoding/decoding scales and offsets by (pull #821)</p></li>
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-</section>
-<section id="id2">
-<h2>0.17.0<a class="headerlink" href="#id2" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>REF:reproject: Make NaN default float nodata &amp; update integer defaults</p></li>
-</ul>
-</section>
-<section id="id3">
-<h2>0.16.0<a class="headerlink" href="#id3" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Add <cite>allow_one_dimensional_raster</cite> option to <cite>rio.clip_box</cite> (issue #708)</p></li>
-<li><p>MNT: recommend <cite>rio.write_crs`</cite> &amp; deprecate <cite>rio.set_crs</cite> (pull #793)</p></li>
-</ul>
-</section>
-<section id="id4">
-<h2>0.15.7<a class="headerlink" href="#id4" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Remove grid_mapping from attrs when writing (pull #783)</p></li>
-<li><p>BUG: Ensure gcp_crs exists before writing in <cite>rio.write_gcps</cite> (issue #646)</p></li>
-</ul>
-</section>
-<section id="id5">
-<h2>0.15.6<a class="headerlink" href="#id5" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Raise OverflowError when nodata data type conversion is unsafe (pull #782)</p></li>
-<li><p>BUG: Support writing GCPs to netCDF (issue #778)</p></li>
-<li><p>BUG: Fix reading dask chunks when band_as_variable=True (issue #761)</p></li>
-<li><p>REF:merge: Use merge path &amp; rio.to_raster (pull #781)</p></li>
-</ul>
-</section>
-<section id="id6">
-<h2>0.15.5<a class="headerlink" href="#id6" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG:reproject: Allow rotated rasters (issue #746)</p></li>
-</ul>
-</section>
-<section id="id7">
-<h2>0.15.4<a class="headerlink" href="#id7" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG:reproject_match: Remove setting spatial dims on output resampled dataset (issue #768)</p></li>
-</ul>
-</section>
-<section id="id8">
-<h2>0.15.3<a class="headerlink" href="#id8" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG:merge: Use <cite>rasterio.io.MemoryFile`</cite> for reading (pull #765)</p></li>
-<li><p>BUG:merge: Add simple defaults for unused payload/colormap (pull #766)</p></li>
-</ul>
-</section>
-<section id="id9">
-<h2>0.15.2<a class="headerlink" href="#id9" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Add decode_coords kwarg to backend entrypoint (pull #763)</p></li>
-<li><p>BUG: Avoid DeprecationWarning: xr.Dataset.drop() -&gt; xr.Dataset.drop_vars() (pull #740)</p></li>
-</ul>
-</section>
-<section id="id10">
-<h2>0.15.1<a class="headerlink" href="#id10" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>DEP: Support Python 3.10-3.12 (pull #723)</p></li>
-<li><p>DEP: rasterio 1.3+, pyproj 3.3+ (pull #725, #727)</p></li>
-<li><p>DEP: xarray 2022.3.0+ &amp; numpy 1.23+ (pull #728)</p></li>
-<li><p>ENH: Robust handling of GCPs without <cite>z</cite> component (issue #731)</p></li>
-</ul>
-</section>
-<section id="id11">
-<h2>0.15.0<a class="headerlink" href="#id11" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Fix setting spatial dims internally during propagation (pull #682)</p></li>
-<li><p>ENH: Pass on on-disk chunk sizes as preferred chunk sizes to the xarray backend (pull #678)</p></li>
-<li><p>MNT: add __all__ to top level module (issue #680)</p></li>
-</ul>
-</section>
-<section id="id12">
-<h2>0.14.1<a class="headerlink" href="#id12" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Fix <code class="xref py py-mod docutils literal notranslate"><span class="pre">rioxarray.merge</span></code> CRS check (pull #655)</p></li>
-<li><p>BUG: Remove tags with metadata added by rasterio in <a class="reference internal" href="rioxarray.html#rioxarray.open_rasterio" title="rioxarray.open_rasterio"><code class="xref py py-func docutils literal notranslate"><span class="pre">rioxarray.open_rasterio()</span></code></a> (issue #666)</p></li>
-</ul>
-</section>
-<section id="id13">
-<h2>0.14.0<a class="headerlink" href="#id13" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>DEP: Drop Python 3.8 support (issue #582)</p></li>
-<li><p>DEP: pin rasterio&gt;=1.2 (pull #642)</p></li>
-<li><p>BUG: Fix WarpedVRT in <a class="reference internal" href="rioxarray.html#rioxarray.open_rasterio" title="rioxarray.open_rasterio"><code class="xref py py-func docutils literal notranslate"><span class="pre">rioxarray.open_rasterio()</span></code></a> when band_as_variable=True (issue #644)</p></li>
-<li><p>BUG: Fix usage of <cite>encode_cf_variable</cite> in <cite>rio.to_raster</cite> (pull #652)</p></li>
-</ul>
-</section>
-<section id="id14">
-<h2>0.13.4<a class="headerlink" href="#id14" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>DEP: pin numpy&gt;=1.21 (pull #636)</p></li>
-</ul>
-</section>
-<section id="id15">
-<h2>0.13.3<a class="headerlink" href="#id15" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Handle data type error in <cite>rio.reproject</cite> (issue #618)</p></li>
-</ul>
-</section>
-<section id="id16">
-<h2>0.13.2<a class="headerlink" href="#id16" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG:dataset: Fix writing tags for bands (issue #615)</p></li>
-<li><p>BUG:dataset: prevent overwriting long_name attribute (pull #616)</p></li>
-</ul>
-</section>
-<section id="id17">
-<h2>0.13.1<a class="headerlink" href="#id17" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Fix closing files manually (pull #607)</p></li>
-<li><p>BUG: Add GDAL 3.6 driver auto-select fix (pull #606)</p></li>
-</ul>
-</section>
-<section id="id18">
-<h2>0.13.0<a class="headerlink" href="#id18" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Added band_as_variable option to open_rasterio (pull #600)</p></li>
-</ul>
-</section>
-<section id="id19">
-<h2>0.12.4<a class="headerlink" href="#id19" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Added band_as_variable option to open_rasterio (issue #296)</p></li>
-<li><p>BUG: Pass warp_extras dictionary to raster.vrt.WarpedVRT (issue #598)</p></li>
-</ul>
-</section>
-<section id="id20">
-<h2>0.12.3<a class="headerlink" href="#id20" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Handle CF CRS export errors in <cite>rio.write_crs</cite> (discussion #591)</p></li>
-</ul>
-</section>
-<section id="id21">
-<h2>0.12.2<a class="headerlink" href="#id21" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Fix <cite>mask_and_scale</cite> data load after <cite>.sel</cite> (issue #580)</p></li>
-</ul>
-</section>
-<section id="id22">
-<h2>0.12.1<a class="headerlink" href="#id22" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Handle <cite>_Unsigned</cite> and load in all attributes (pull #575)</p></li>
-</ul>
-</section>
-<section id="id23">
-<h2>0.12.0<a class="headerlink" href="#id23" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Allow passing in bounds of different CRS in <cite>rio.clip_box</cite> (pull #563)</p></li>
-</ul>
-</section>
-<section id="id24">
-<h2>0.11.2<a class="headerlink" href="#id24" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Fix reading file handle with dask (issue #550)</p></li>
-<li><p>BUG: Fix reading cint16 files with dask (issue #542)</p></li>
-<li><p>BUG: Ensure <cite>rio.bounds</cite> ordered correctly (issue #545)</p></li>
-<li><p>BUG: Allow reading from <cite>io.BytesIO</cite> (issue #549)</p></li>
-</ul>
-</section>
-<section id="id25">
-<h2>0.11.1<a class="headerlink" href="#id25" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Fix WarpedVRT param cache in <a class="reference internal" href="rioxarray.html#rioxarray.open_rasterio" title="rioxarray.open_rasterio"><code class="xref py py-func docutils literal notranslate"><span class="pre">rioxarray.open_rasterio()</span></code></a> (issue #515)</p></li>
-<li><p>BUG: Always generate coordinates in <cite>rio.reproject</cite> when GCPS|RPCS present (issue #517)</p></li>
-</ul>
-</section>
-<section id="id26">
-<h2>0.11.0<a class="headerlink" href="#id26" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>TYPE: Add more type hints (issue #373)</p></li>
-<li><p>ENH: Add additional GDAL information to <a class="reference internal" href="rioxarray.html#rioxarray.show_versions" title="rioxarray.show_versions"><code class="xref py py-func docutils literal notranslate"><span class="pre">rioxarray.show_versions()</span></code></a> (pull #513)</p></li>
-</ul>
-</section>
-<section id="id27">
-<h2>0.10.3<a class="headerlink" href="#id27" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Remove xarray crs attribute in rio.write_crs (issue #488)</p></li>
-</ul>
-</section>
-<section id="id28">
-<h2>0.10.2<a class="headerlink" href="#id28" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Lazy load colormap through _manager.acquire() in merge (issue #479)</p></li>
-</ul>
-</section>
-<section id="id29">
-<h2>0.10.1<a class="headerlink" href="#id29" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>DEP: pin rasterio&gt;=1.1.1 (pull #471)</p></li>
-<li><p>BUG: Corrected bounds and transform args to float (pull #475)</p></li>
-</ul>
-</section>
-<section id="id30">
-<h2>0.10.0<a class="headerlink" href="#id30" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>DEP: Drop Python 3.7 support (issue #451)</p></li>
-<li><p>ENH: Add GCPs reading and writing (issue #376)</p></li>
-</ul>
-</section>
-<section id="id31">
-<h2>0.9.1<a class="headerlink" href="#id31" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Force coordinates to be exactly the same in <cite>rio.reproject_match</cite> (issue #298)</p></li>
-</ul>
-</section>
-<section id="id32">
-<h2>0.9.0<a class="headerlink" href="#id32" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Allow additional kwargs to pass from reproject_match() -&gt; reproject() (pull #436)</p></li>
-</ul>
-</section>
-<section id="id33">
-<h2>0.8.0<a class="headerlink" href="#id33" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>DEP: Make scipy an optional dependency (issue #413)</p></li>
-<li><p>BUG: Return cached transform when axis data missing (pull #419)</p></li>
-<li><p>BUG: Fix negative indexes in <cite>rio.isel_window</cite> (issue #421)</p></li>
-</ul>
-</section>
-<section id="id34">
-<h2>0.7.1<a class="headerlink" href="#id34" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Handle transforms with rotation (pull #401)</p></li>
-</ul>
-</section>
-<section id="id35">
-<h2>0.7.0<a class="headerlink" href="#id35" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: <cite>rio.clip</cite> and <cite>rio.clip_box</cite> skip non-geospatial arrays in datasets when clipping (pull #392)</p></li>
-<li><p>ENH: Add option for users to skip variables without spatial dimensions (pull #395)</p></li>
-</ul>
-</section>
-<section id="id36">
-<h2>0.6.1<a class="headerlink" href="#id36" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Fix indexing error when <cite>mask_and_scale=True</cite> was combined with band dim chunking (issue #387, pull #388)</p></li>
-</ul>
-</section>
-<section id="id37">
-<h2>0.6.0<a class="headerlink" href="#id37" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Add pad option to <cite>rio.isel_window</cite> (issue #381; pull #383)</p></li>
-<li><p>BUG: Fix negative start in row or col window offsets in <cite>rio.isel_window</cite> (issue #381; pull #383)</p></li>
-</ul>
-</section>
-<section id="id38">
-<h2>0.5.0<a class="headerlink" href="#id38" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Allow passing in kwargs to <cite>rio.reproject</cite> (issue #369; pull #370)</p></li>
-<li><p>ENH: Allow nodata override and provide default nodata based on dtype in <cite>rio.reproject</cite> (pull #370)</p></li>
-<li><p>ENH: Add support for passing in gcps to rio.reproject (issue #339; pull #370)</p></li>
-<li><p>BUG: Remove duplicate acquire in open_rasterio (pull #364)</p></li>
-<li><p>BUG: Fix exporting dataset to raster with non-standard dimensions (issue #372)</p></li>
-</ul>
-</section>
-<section id="id39">
-<h2>0.4.3<a class="headerlink" href="#id39" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: support GDAL CInt16, rasterio complex_int16 (pull #353)</p></li>
-<li><p>TST: Fix merge tests for rasterio 1.2.5+ (issue #358)</p></li>
-</ul>
-</section>
-<section id="id40">
-<h2>0.4.2<a class="headerlink" href="#id40" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Improve WarpedVRT support for gcps (pull #351)</p></li>
-</ul>
-</section>
-<section id="id41">
-<h2>0.4.1<a class="headerlink" href="#id41" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: pass kwargs with lock=False (issue #344)</p></li>
-<li><p>BUG: Close file handle with lock=False (pull #346)</p></li>
-</ul>
-</section>
-<section id="id42">
-<h2>0.4.0<a class="headerlink" href="#id42" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>DEP: Python 3.7+ (issue #215)</p></li>
-<li><p>DEP: xarray 0.17+ (needed for issue #282)</p></li>
-<li><p>REF: Store <cite>grid_mapping</cite> in <cite>encoding</cite> instead of <cite>attrs</cite> (issue #282)</p></li>
-<li><p>ENH: enable <cite>engine=”rasterio”</cite> via xarray backend API (issue #197 pull #281)</p></li>
-<li><p>ENH: Generate 2D coordinates for non-rectilinear sources (issue #290)</p></li>
-<li><p>ENH: Add <cite>encoded</cite> kwarg to <cite>rio.write_nodata</cite> (discussions #313)</p></li>
-<li><p>ENH: Added <cite>decode_times</cite> and <cite>decode_timedelta</cite> kwargs to <cite>rioxarray.open_rasterio</cite> (issue #316)</p></li>
-<li><p>BUG: Use float32 for smaller dtypes when masking (discussions #302)</p></li>
-<li><p>BUG: Return correct transform in <cite>rio.transform</cite> with non-rectilinear transform (discussions #280)</p></li>
-<li><p>BUG: Update to handle WindowError in rasterio 1.2.2 (issue #286)</p></li>
-<li><p>BUG: Don’t generate x,y coords in <cite>rio</cite> methods if not previously there (pull #294)</p></li>
-<li><p>BUG: Preserve original data type for writing to disk (issue #305)</p></li>
-<li><p>BUG: handle lock=True in open_rasterio (issue #273)</p></li>
-</ul>
-</section>
-<section id="id43">
-<h2>0.3.1<a class="headerlink" href="#id43" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Compatibility changes with xarray 0.17 (issue #254)</p></li>
-<li><p>BUG: Raise informative error in interpolate_na if missing nodata (#250)</p></li>
-</ul>
-</section>
-<section id="id44">
-<h2>0.3.0<a class="headerlink" href="#id44" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>REF: Reduce pyproj.CRS internal usage for speed (issue #241)</p></li>
-<li><p>ENH: Add <cite>rioxarray.set_options</cite> to disable exporting CRS CF grid mapping (issue #241)</p></li>
-<li><p>BUG: Handle merging 2D DataArray (discussion #244)</p></li>
-</ul>
-</section>
-<section id="id45">
-<h2>0.2.0<a class="headerlink" href="#id45" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Added <cite>rio.estimate_utm_crs</cite> (issue #181)</p></li>
-<li><p>ENH: Add support for merging datasets with different CRS (issue #173)</p></li>
-<li><p>ENH: Add support for using dask in <cite>rio.to_raster</cite> (issue #9, pull #219, pull #223)</p></li>
-<li><p>ENH: Use the list version of <cite>transform_geom</cite> with rasterio 1.2+ (issue #180)</p></li>
-<li><p>ENH: Support driver autodetection with rasterio 1.2+ (issue #180)</p></li>
-<li><p>ENH: Allow multithreaded, lockless reads with <cite>rioxarray.open_rasterio</cite> (issue #214)</p></li>
-<li><p>ENH: Add support to clip from disk (issue #115)</p></li>
-<li><p>BUG: Allow <cite>rio.write_crs</cite> when spatial dimensions not found (pull #186)</p></li>
-<li><p>BUG: Update to support rasterio 1.2+ merge (issue #180)</p></li>
-</ul>
-</section>
-<section id="id46">
-<h2>0.1.1<a class="headerlink" href="#id46" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Check all CRS are the same in the dataset in crs() method</p></li>
-</ul>
-</section>
-<section id="id47">
-<h2>0.1.0<a class="headerlink" href="#id47" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Ensure transform correct in rio.clip without coords (pull #165)</p></li>
-<li><p>BUG: Ensure the nodata value matches the dtype (pull #166)</p></li>
-<li><p>Raise deprecation exception in add_spatial_ref and add_xy_grid_meta (pull #168)</p></li>
-</ul>
-</section>
-<section id="id48">
-<h2>0.0.31<a class="headerlink" href="#id48" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Deprecate add_spatial_ref and fix warning for add_xy_grid_meta (pull #158)</p></li>
-</ul>
-</section>
-<section id="id49">
-<h2>0.0.30<a class="headerlink" href="#id49" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Fix assigning fill value in <cite>rio.pad_box</cite> (pull #140)</p></li>
-<li><p>ENH: Add <cite>rio.write_transform</cite> to store cache in GDAL location (issue #129 &amp; #139)</p></li>
-<li><p>ENH: Use rasterio windows for <cite>rio.clip_box</cite> (issue #142)</p></li>
-<li><p>BUG: Add support for negative indexes in rio.isel_window (pull #145)</p></li>
-<li><p>BUG: Write transform based on window in rio.isel_window (pull #145)</p></li>
-<li><p>ENH: Add <cite>rio.count</cite>, <cite>rio.slice_xy()</cite>, <cite>rio.bounds()</cite>, <cite>rio.resolution()</cite>, <cite>rio.transform_bounds()</cite> to Dataset level</p></li>
-<li><p>ENH: Add <cite>rio.write_coordinate_system()</cite> (issue #147)</p></li>
-<li><p>ENH: Search CF coordinate metadata to find coordinates (issue #147)</p></li>
-<li><p>ENH: Default <cite>rio.clip</cite> to assume geometry has CRS of dataset (pull #150)</p></li>
-<li><p>ENH: Add <cite>rio.grid_mapping</cite> and <cite>rio.write_grid_mapping</cite> &amp; preserve original grid mapping (pull #151)</p></li>
-</ul>
-</section>
-<section id="id50">
-<h2>0.0.29<a class="headerlink" href="#id50" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Remove unnecessary memory copies in reproject method (pull #136)</p></li>
-<li><p>BUG: Fix order of axis in <cite>rio.isel_window</cite> (pull #133)</p></li>
-<li><p>BUG: Allow clipping with disjoint geometries (issue #132)</p></li>
-<li><p>BUG: Remove automatically setting tiled=True for windowed writing (pull #134)</p></li>
-<li><p>ENH: Add <cite>rio.pad_box</cite> (pull #138)</p></li>
-</ul>
-</section>
-<section id="id51">
-<h2>0.0.28<a class="headerlink" href="#id51" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>rio.reproject: change input kwarg dst_affine_width_height -&gt; shape &amp; transform (#125)</p></li>
-<li><p>ENH: Use pyproj.CRS to read/write CF parameters (issue #124)</p></li>
-</ul>
-</section>
-<section id="id52">
-<h2>0.0.27<a class="headerlink" href="#id52" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Added optional <cite>shape</cite> argument to <cite>rio.reproject</cite> (pull #116)</p></li>
-<li><p>Fix <code class="docutils literal notranslate"><span class="pre">RasterioDeprecationWarning</span></code> (pull #117)</p></li>
-<li><p>BUG: Make rio.shape order same as rasterio dataset shape (height, width) (pull #121)</p></li>
-<li><p>Fix open_rasterio() for WarpedVRT with specified src_crs (pydata/xarray/pull/4104 &amp; pull #120)</p></li>
-<li><p>BUG: Use internal reprojection as engine for resampling window in merge (pull #123)</p></li>
-</ul>
-</section>
-<section id="id53">
-<h2>0.0.26<a class="headerlink" href="#id53" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Added <a class="reference internal" href="rioxarray.html#rioxarray.show_versions" title="rioxarray.show_versions"><code class="xref py py-func docutils literal notranslate"><span class="pre">rioxarray.show_versions()</span></code></a> (issue #106)</p></li>
-</ul>
-</section>
-<section id="id54">
-<h2>0.0.25<a class="headerlink" href="#id54" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Use recalc=True when using transform internally &amp; ensure stable when coordinates unavailable. (issue #97)</p></li>
-</ul>
-</section>
-<section id="id55">
-<h2>0.0.24<a class="headerlink" href="#id55" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Add variable names to error messages for clarity (pull #99)</p></li>
-<li><p>BUG: Use assign_coords in _decode_datetime_cf (issue #101)</p></li>
-</ul>
-</section>
-<section id="id56">
-<h2>0.0.23<a class="headerlink" href="#id56" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: Fix ‘rio.set_spatial_dims’ so information saved with ‘rio’ accesors (issue #94)</p></li>
-<li><p>ENH: Make ‘rio.isel_window’ available for datasets (pull #95)</p></li>
-</ul>
-</section>
-<section id="id57">
-<h2>0.0.22<a class="headerlink" href="#id57" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Use pyproj.CRS internally to manage GDAL 2/3 transition (issue #92)</p></li>
-<li><p>ENH: Add MissingCRS exceptions for ‘rio.clip’ and ‘rio.reproject’ (pull #93)</p></li>
-</ul>
-</section>
-<section id="id58">
-<h2>0.0.21<a class="headerlink" href="#id58" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>ENH: Added to_raster method for Datasets (issue #76)</p></li>
-</ul>
-</section>
-<section id="id59">
-<h2>0.0.20<a class="headerlink" href="#id59" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>BUG: ensure band_key is list when iterating over bands for mask and scale (pull #87)</p></li>
-</ul>
-</section>
-<section id="id60">
-<h2>0.0.19<a class="headerlink" href="#id60" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Add support for writing scales &amp; offsets to raster (pull #79)</p></li>
-<li><p>Don’t write standard raster metadata to raster tags (issue #78)</p></li>
-</ul>
-</section>
-<section id="id61">
-<h2>0.0.18<a class="headerlink" href="#id61" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Fixed windowed writing to require tiled output raster (pull #66)</p></li>
-<li><p>Write data array attributes using <cite>rio.to_raster</cite> (issue #64)</p></li>
-<li><p>Write variable name to descriptions if possible in <cite>rio.to_raster</cite> (issue #64)</p></li>
-<li><p>Add <cite>mask_and_scale</cite> option to <cite>rioxarray.open_rasterio()</cite> (issue #67)</p></li>
-<li><p>Hide NotGeoreferencedWarning warning when subdatasets are present using open_rasterio (issue #65)</p></li>
-<li><p>Add support for loading in 1D variables in <cite>xarray.open_rasterio()</cite> (issue #43)</p></li>
-<li><p>Load in netCDF metadata on the variable level (pull #73)</p></li>
-<li><p>Add rioxarray.merge module (issue #46)</p></li>
-</ul>
-</section>
-<section id="id62">
-<h2>0.0.17<a class="headerlink" href="#id62" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Renamed <cite>descriptions</cite> to <cite>long_name</cite> when opening with <cite>open_rasterio()</cite> (pull #63)</p></li>
-<li><p>Make <cite>units</cite> &amp; <cite>long_name</cite> scalar if they exist in rasterio attributes (pull #63)</p></li>
-</ul>
-</section>
-<section id="id63">
-<h2>0.0.16<a class="headerlink" href="#id63" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Add support for netcdf/hdf groups with different shapes (pull #62)</p></li>
-</ul>
-</section>
-<section id="id64">
-<h2>0.0.15<a class="headerlink" href="#id64" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Added <cite>variable</cite> and <cite>group</cite> kwargs to <cite>rioxarray.open_rasterio()</cite> to allow filtering of subdatasets (pull #57)</p></li>
-<li><p>Added <cite>default_name</cite> kwarg to <cite>rioxarray.open_rasterio()</cite> for backup when the original does not exist (pull #59)</p></li>
-<li><p>Added <cite>recalc_transform</cite> kwarg to <cite>rio.to_raster()</cite> (pull #56)</p></li>
-</ul>
-</section>
-<section id="id65">
-<h2>0.0.14<a class="headerlink" href="#id65" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Added <cite>windowed</cite> kwarg to <cite>rio.to_raster()</cite> to write to raster using windowed writing (pull #54)</p></li>
-<li><p>Added add <cite>rio.isel_window()</cite> to allow selection using a rasterio.windows.Window (pull #54)</p></li>
-</ul>
-</section>
-<section id="id66">
-<h2>0.0.13<a class="headerlink" href="#id66" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Improve CRS searching for xarray.Dataset &amp; use default grid mapping name (pull #51)</p></li>
-</ul>
-</section>
-<section id="id67">
-<h2>0.0.12<a class="headerlink" href="#id67" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Use <cite>xarray.open_rasterio()</cite> for <cite>rioxarray.open_rasterio()</cite> with xarray&lt;0.12.3 (pull #40)</p></li>
-</ul>
-</section>
-<section id="id68">
-<h2>0.0.11<a class="headerlink" href="#id68" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Added <cite>open_kwargs</cite> to pass into <cite>rasterio.open()</cite> when using <cite>rioxarray.open_rasterio()</cite> (pull #48)</p></li>
-<li><p>Added example opening Cloud Optimized GeoTiff (issue #45)</p></li>
-</ul>
-</section>
-<section id="id69">
-<h2>0.0.10<a class="headerlink" href="#id69" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Add support for opening netcdf/hdf files with <cite>rioxarray.open_rasterio</cite> (issue #32)</p></li>
-<li><p>Added support for custom CRS with wkt attribute for datacube CRS support (issue #35)</p></li>
-<li><p>Added <cite>rio.set_nodata()</cite>, <cite>rio.write_nodata()</cite>, <cite>rio.set_attrs()</cite>, <cite>rio.update_attrs()</cite> (issue #37)</p></li>
-</ul>
-</section>
-<section id="id70">
-<h2>0.0.9<a class="headerlink" href="#id70" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Add <cite>rioxarray.open_rasterio</cite> (issue #7)</p></li>
-</ul>
-</section>
-<section id="id71">
-<h2>0.0.8<a class="headerlink" href="#id71" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Fix setting nodata in _add_attrs_proj (pull #30)</p></li>
-</ul>
-</section>
-<section id="id72">
-<h2>0.0.7<a class="headerlink" href="#id72" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Add option to do an inverted clip (pull #29)</p></li>
-</ul>
-</section>
-<section id="id73">
-<h2>0.0.6<a class="headerlink" href="#id73" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Add support for scalar coordinates in reproject (issue #15)</p></li>
-<li><p>Updated writing encoding for FutureWarning (issue #18)</p></li>
-<li><p>Use input raster profile for defaults to write output raster profile if opened with <cite>xarray.open_rasterio</cite> (issue #19)</p></li>
-<li><p>Preserve None nodata if opened with <cite>xarray.open_rasterio</cite> (issue #20)</p></li>
-<li><p>Added <cite>drop</cite> argument for <cite>clip()</cite> (issue #25)</p></li>
-<li><p>Fix order of <cite>CRS</cite> for reprojecting geometries in <cite>clip()</cite> (pull #24)</p></li>
-<li><p>Added <cite>set_spatial_dims()</cite> method for datasets when dimensions not found (issue #27)</p></li>
-</ul>
-</section>
-<section id="id74">
-<h2>0.0.5<a class="headerlink" href="#id74" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Find nodata and nodatavals in ‘nodata’ property (pull #12)</p></li>
-<li><p>Added ‘encoded_nodata’ property to DataArray (pull #12)</p></li>
-<li><p>Write the raster with encoded_nodata instead of NaN for nodata (pull #12)</p></li>
-<li><p>Added methods to set and write CRS (issue #5)</p></li>
-</ul>
-</section>
-<section id="id75">
-<h2>0.0.4<a class="headerlink" href="#id75" title="Link to this heading"></a></h2>
-<ul class="simple">
-<li><p>Added ability to export data array to raster (pull #8)</p></li>
-</ul>
-</section>
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-<h1>Installation<a class="headerlink" href="#installation" title="Link to this heading"></a></h1>
-<section id="stable-release">
-<h2>Stable release<a class="headerlink" href="#stable-release" title="Link to this heading"></a></h2>
-<ol class="arabic simple">
-<li><p>Use pip to install package from <a class="reference external" href="https://pypi.org/project/rioxarray/">PyPI</a>:</p></li>
-</ol>
-<blockquote>
-<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>rioxarray
-</pre></div>
-</div>
-</div></blockquote>
-<ol class="arabic simple" start="2">
-<li><p>Use <a class="reference external" href="https://conda.io/en/latest/">conda</a> with the <a class="reference external" href="https://conda-forge.org/">conda-forge</a> channel:</p></li>
-</ol>
-<blockquote>
-<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>config<span class="w"> </span>--prepend<span class="w"> </span>channels<span class="w"> </span>conda-forge
-conda<span class="w"> </span>config<span class="w"> </span>--set<span class="w"> </span>channel_priority<span class="w"> </span>strict
-conda<span class="w"> </span>create<span class="w"> </span>-n<span class="w"> </span>rioxarray_env<span class="w"> </span>rioxarray
-conda<span class="w"> </span>activate<span class="w"> </span>rioxarray_env
-</pre></div>
-</div>
-<ul class="simple">
-<li><p><cite>rioxarray</cite> <a class="reference external" href="http://github.com/conda-forge/rioxarray-feedstock">conda-forge repository</a></p></li>
-</ul>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>“… we recommend always installing your packages inside a
-new environment instead of the base environment from
-anaconda/miniconda. Using envs make it easier to
-debug problems with packages and ensure the stability
-of your root env.”</p>
-<blockquote>
-<div><p>– <a class="reference external" href="https://conda-forge.org/docs/user/tipsandtricks.html">https://conda-forge.org/docs/user/tipsandtricks.html</a></p>
-</div></blockquote>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Avoid using <cite>pip install</cite> with a conda environment. If you encounter
-a python package that isn’t in conda-forge, consider submitting a
-recipe: <a class="reference external" href="https://github.com/conda-forge/staged-recipes/">https://github.com/conda-forge/staged-recipes/</a></p>
-</div>
-</div></blockquote>
-</section>
-<section id="from-source">
-<h2>From source<a class="headerlink" href="#from-source" title="Link to this heading"></a></h2>
-<p>The source for rioxarray can be installed from the <a class="reference external" href="https://github.com/corteva/rioxarray">GitHub repo</a>.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>git+git://github.com/corteva/rioxarray.git#egg<span class="o">=</span>rioxarray
-</pre></div>
-</div>
-<p>To install for local development:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>git@github.com:corteva/rioxarray.git
-<span class="nb">cd</span><span class="w"> </span>rioxarray
-python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.<span class="o">[</span>dev<span class="o">]</span>
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-<h1>rioxarray<a class="headerlink" href="#rioxarray" title="Link to this heading"></a></h1>
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-<li class="toctree-l3"><a class="reference internal" href="rioxarray.html#rioxarray.open_rasterio"><code class="docutils literal notranslate"><span class="pre">open_rasterio()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="rioxarray.html#rioxarray.merge.merge_arrays"><code class="docutils literal notranslate"><span class="pre">merge_arrays()</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="rioxarray.html#rioxarray.merge.merge_datasets"><code class="docutils literal notranslate"><span class="pre">merge_datasets()</span></code></a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="rioxarray.html#rioxarray-rio-accessors">rioxarray <cite>rio</cite> accessors</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase"><code class="docutils literal notranslate"><span class="pre">XRasterBase</span></code></a><ul>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.bounds"><code class="docutils literal notranslate"><span class="pre">XRasterBase.bounds()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.count"><code class="docutils literal notranslate"><span class="pre">XRasterBase.count</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.crs"><code class="docutils literal notranslate"><span class="pre">XRasterBase.crs</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.estimate_utm_crs"><code class="docutils literal notranslate"><span class="pre">XRasterBase.estimate_utm_crs()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.get_gcps"><code class="docutils literal notranslate"><span class="pre">XRasterBase.get_gcps()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.grid_mapping"><code class="docutils literal notranslate"><span class="pre">XRasterBase.grid_mapping</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.height"><code class="docutils literal notranslate"><span class="pre">XRasterBase.height</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.isel_window"><code class="docutils literal notranslate"><span class="pre">XRasterBase.isel_window()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.resolution"><code class="docutils literal notranslate"><span class="pre">XRasterBase.resolution()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.set_attrs"><code class="docutils literal notranslate"><span class="pre">XRasterBase.set_attrs()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.set_crs"><code class="docutils literal notranslate"><span class="pre">XRasterBase.set_crs()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.set_encoding"><code class="docutils literal notranslate"><span class="pre">XRasterBase.set_encoding()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.set_spatial_dims"><code class="docutils literal notranslate"><span class="pre">XRasterBase.set_spatial_dims()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.shape"><code class="docutils literal notranslate"><span class="pre">XRasterBase.shape</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.slice_xy"><code class="docutils literal notranslate"><span class="pre">XRasterBase.slice_xy()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.transform"><code class="docutils literal notranslate"><span class="pre">XRasterBase.transform()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.transform_bounds"><code class="docutils literal notranslate"><span class="pre">XRasterBase.transform_bounds()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.update_attrs"><code class="docutils literal notranslate"><span class="pre">XRasterBase.update_attrs()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.update_encoding"><code class="docutils literal notranslate"><span class="pre">XRasterBase.update_encoding()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.write_coordinate_system"><code class="docutils literal notranslate"><span class="pre">XRasterBase.write_coordinate_system()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.write_crs"><code class="docutils literal notranslate"><span class="pre">XRasterBase.write_crs()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.write_gcps"><code class="docutils literal notranslate"><span class="pre">XRasterBase.write_gcps()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.write_grid_mapping"><code class="docutils literal notranslate"><span class="pre">XRasterBase.write_grid_mapping()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.write_transform"><code class="docutils literal notranslate"><span class="pre">XRasterBase.write_transform()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.x_dim"><code class="docutils literal notranslate"><span class="pre">XRasterBase.x_dim</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.rioxarray.XRasterBase.y_dim"><code class="docutils literal notranslate"><span class="pre">XRasterBase.y_dim</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray"><code class="docutils literal notranslate"><span class="pre">RasterArray</span></code></a><ul>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.clip"><code class="docutils literal notranslate"><span class="pre">RasterArray.clip()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.clip_box"><code class="docutils literal notranslate"><span class="pre">RasterArray.clip_box()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.encoded_nodata"><code class="docutils literal notranslate"><span class="pre">RasterArray.encoded_nodata</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.interpolate_na"><code class="docutils literal notranslate"><span class="pre">RasterArray.interpolate_na()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.nodata"><code class="docutils literal notranslate"><span class="pre">RasterArray.nodata</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.pad_box"><code class="docutils literal notranslate"><span class="pre">RasterArray.pad_box()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.pad_xy"><code class="docutils literal notranslate"><span class="pre">RasterArray.pad_xy()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.reproject"><code class="docutils literal notranslate"><span class="pre">RasterArray.reproject()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.reproject_match"><code class="docutils literal notranslate"><span class="pre">RasterArray.reproject_match()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.set_nodata"><code class="docutils literal notranslate"><span class="pre">RasterArray.set_nodata()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.to_raster"><code class="docutils literal notranslate"><span class="pre">RasterArray.to_raster()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_array.RasterArray.write_nodata"><code class="docutils literal notranslate"><span class="pre">RasterArray.write_nodata()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="rioxarray.html#rioxarray.raster_dataset.RasterDataset"><code class="docutils literal notranslate"><span class="pre">RasterDataset</span></code></a><ul>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_dataset.RasterDataset.clip"><code class="docutils literal notranslate"><span class="pre">RasterDataset.clip()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="rioxarray.html#rioxarray.raster_dataset.RasterDataset.clip_box"><code class="docutils literal notranslate"><span class="pre">RasterDataset.clip_box()</span></code></a></li>
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-<li><p><em>open_rasterio</em>: <a class="reference external" href="https://github.com/pydata/xarray/blob/1d7bcbdc75b6d556c04e2c7d7a042e4379e15303/xarray/backends/rasterio_.py">rasterio_.py</a></p></li>
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-<li><p><a class="reference external" href="https://github.com/pydata/xarray/blob/1d7bcbdc75b6d556c04e2c7d7a042e4379e15303/LICENSE">xarray is licensed</a> under the Apache License, Version 2.0.
-The xarray license is included as <a class="reference external" href="https://github.com/corteva/rioxarray/blob/master/LICENSE_xarray">LICENSE_xarray</a>.</p></li>
-</ul>
-</dd>
-<dt>RasterioWriter dask write functionality was adopted from <a class="reference external" href="https://github.com/dymaxionlabs/dask-rasterio">https://github.com/dymaxionlabs/dask-rasterio</a></dt><dd><ul class="simple">
-<li><p>Source file: <a class="reference external" href="https://github.com/dymaxionlabs/dask-rasterio/blob/8dd7fdece7ad094a41908c0ae6b4fe6ca49cf5e1/dask_rasterio/write.py">write.py</a></p></li>
-</ul>
-</dd>
-</dl>
-<p>This package was originally templated with with <a class="reference external" href="https://github.com/audreyr/cookiecutter">Cookiecutter</a>.</p>
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-<li class="toctree-l1 current"><a class="reference internal" href="modules.html">rioxarray</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">rioxarray package</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#rioxarray-open-rasterio">rioxarray.open_rasterio</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.open_rasterio"><code class="docutils literal notranslate"><span class="pre">open_rasterio()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#rioxarray-merge-module">rioxarray.merge module</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.merge.merge_arrays"><code class="docutils literal notranslate"><span class="pre">merge_arrays()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.merge.merge_datasets"><code class="docutils literal notranslate"><span class="pre">merge_datasets()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#rioxarray-set-options">rioxarray.set_options</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.set_options"><code class="docutils literal notranslate"><span class="pre">set_options</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#rioxarray-show-versions">rioxarray.show_versions</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.show_versions"><code class="docutils literal notranslate"><span class="pre">show_versions()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#rioxarray-rio-accessors">rioxarray <cite>rio</cite> accessors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.rioxarray.XRasterBase"><code class="docutils literal notranslate"><span class="pre">XRasterBase</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.raster_array.RasterArray"><code class="docutils literal notranslate"><span class="pre">RasterArray</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.raster_dataset.RasterDataset"><code class="docutils literal notranslate"><span class="pre">RasterDataset</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#module-rioxarray.exceptions">rioxarray.exceptions module</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.exceptions.DimensionError"><code class="docutils literal notranslate"><span class="pre">DimensionError</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.exceptions.DimensionMissingCoordinateError"><code class="docutils literal notranslate"><span class="pre">DimensionMissingCoordinateError</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.exceptions.InvalidDimensionOrder"><code class="docutils literal notranslate"><span class="pre">InvalidDimensionOrder</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.exceptions.MissingCRS"><code class="docutils literal notranslate"><span class="pre">MissingCRS</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.exceptions.MissingSpatialDimensionError"><code class="docutils literal notranslate"><span class="pre">MissingSpatialDimensionError</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.exceptions.NoDataInBounds"><code class="docutils literal notranslate"><span class="pre">NoDataInBounds</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.exceptions.OneDimensionalRaster"><code class="docutils literal notranslate"><span class="pre">OneDimensionalRaster</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.exceptions.RioXarrayError"><code class="docutils literal notranslate"><span class="pre">RioXarrayError</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="#rioxarray.exceptions.TooManyDimensions"><code class="docutils literal notranslate"><span class="pre">TooManyDimensions</span></code></a></li>
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-  <section id="rioxarray-package">
-<h1>rioxarray package<a class="headerlink" href="#rioxarray-package" title="Link to this heading"></a></h1>
-<section id="rioxarray-open-rasterio">
-<h2>rioxarray.open_rasterio<a class="headerlink" href="#rioxarray-open-rasterio" title="Link to this heading"></a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="rioxarray.open_rasterio">
-<span class="sig-prename descclassname"><span class="pre">rioxarray.</span></span><span class="sig-name descname"><span class="pre">open_rasterio</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">PathLike</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.io.html#rasterio.io.DatasetReader" title="(in rasterio)"><span class="pre">DatasetReader</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.vrt.html#rasterio.vrt.WarpedVRT" title="(in rasterio)"><span class="pre">WarpedVRT</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">SingleBandDatasetReader</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parse_coordinates</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">tuple</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">dict</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lock</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">masked</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_and_scale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">variable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_times</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_timedelta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">band_as_variable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">open_kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="_modules/rioxarray/_io.html#open_rasterio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.open_rasterio" title="Link to this definition"></a></dt>
-<dd><p>Open a file with rasterio (experimental).</p>
-<p>This should work with any file that rasterio can open (most often:
-geoTIFF). The x and y coordinates are generated automatically from the
-file’s geoinformation and refer to the center of the pixel.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.13: </span>band_as_variable</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>filename</strong> (<em>str</em><em>, </em><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.io.html#rasterio.io.DatasetReader" title="(in rasterio)"><em>rasterio.io.DatasetReader</em></a><em>, or </em><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.vrt.html#rasterio.vrt.WarpedVRT" title="(in rasterio)"><em>rasterio.vrt.WarpedVRT</em></a>) – Path to the file to open. Or already open rasterio dataset.</p></li>
-<li><p><strong>parse_coordinates</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to parse the x and y coordinates out of the file’s
-<code class="docutils literal notranslate"><span class="pre">transform</span></code> attribute or not. The default is to automatically
-parse the coordinates only if they are rectilinear (1D).
-It can be useful to set <code class="docutils literal notranslate"><span class="pre">parse_coordinates=False</span></code>
-if your files are very large or if you don’t need the coordinates.</p></li>
-<li><p><strong>chunks</strong> (<em>int</em><em>, </em><em>tuple</em><em> or </em><em>dict</em><em>, </em><em>optional</em>) – Chunk sizes along each dimension, e.g., <code class="docutils literal notranslate"><span class="pre">5</span></code>, <code class="docutils literal notranslate"><span class="pre">(5,</span> <span class="pre">5)</span></code> or
-<code class="docutils literal notranslate"><span class="pre">{'x':</span> <span class="pre">5,</span> <span class="pre">'y':</span> <span class="pre">5}</span></code>. If chunks is provided, it used to load the new
-DataArray into a dask array. Chunks can also be set to
-<code class="docutils literal notranslate"><span class="pre">True</span></code> or <code class="docutils literal notranslate"><span class="pre">&quot;auto&quot;</span></code> to choose sensible chunk sizes according to
-<code class="docutils literal notranslate"><span class="pre">dask.config.get(&quot;array.chunk-size&quot;)</span></code>.</p></li>
-<li><p><strong>cache</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, cache data loaded from the underlying datastore in memory as
-NumPy arrays when accessed to avoid reading from the underlying data-
-store multiple times. Defaults to True unless you specify the <cite>chunks</cite>
-argument to use dask, in which case it defaults to False.</p></li>
-<li><p><strong>lock</strong> (<em>bool</em><em> or </em><em>dask.utils.SerializableLock</em><em>, </em><em>optional</em>) – <p>If chunks is provided, this argument is used to ensure that only one
-thread per process is reading from a rasterio file object at a time.</p>
-<p>By default and when a lock instance is provided,
-a <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.backends.CachingFileManager.html#xarray.backends.CachingFileManager" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">xarray.backends.CachingFileManager</span></code></a> is used to cache File objects.
-Since rasterio also caches some data, this will make repeated reads from the
-same object fast.</p>
-<p>When <code class="docutils literal notranslate"><span class="pre">lock=False</span></code>, no lock is used, allowing for completely parallel reads
-from multiple threads or processes. However, a new file handle is opened on
-each request.</p>
-</p></li>
-<li><p><strong>masked</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, read the mask and set values to NaN. Defaults to False.</p></li>
-<li><p><strong>mask_and_scale</strong> (<em>bool</em><em>, </em><em>default=False</em>) – Lazily scale (using the <cite>scales</cite> and <cite>offsets</cite> from rasterio) and mask.
-If the _Unsigned attribute is present treat integer arrays as unsigned.</p></li>
-<li><p><strong>variable</strong> (<em>str</em><em> or </em><em>list</em><em> or </em><em>tuple</em><em>, </em><em>optional</em>) – Variable name or names to use to filter loading.</p></li>
-<li><p><strong>group</strong> (<em>str</em><em> or </em><em>list</em><em> or </em><em>tuple</em><em>, </em><em>optional</em>) – Group name or names to use to filter loading.</p></li>
-<li><p><strong>default_name</strong> (<em>str</em><em>, </em><em>optional</em>) – The name of the data array if none exists. Default is None.</p></li>
-<li><p><strong>decode_times</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, decode times encoded in the standard NetCDF datetime format
-into datetime objects. Otherwise, leave them encoded as numbers.</p></li>
-<li><p><strong>decode_timedelta</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, decode variables and coordinates with time units in
-{“days”, “hours”, “minutes”, “seconds”, “milliseconds”, “microseconds”}
-into timedelta objects. If False, leave them encoded as numbers.
-If None (default), assume the same value of decode_time.</p></li>
-<li><p><strong>band_as_variable</strong> (<em>bool</em><em>, </em><em>default=False</em>) – If True, will load bands in a raster to separate variables.</p></li>
-<li><p><strong>**open_kwargs</strong> (<em>kwargs</em><em>, </em><em>optional</em>) – Optional keyword arguments to pass into <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.html#rasterio.open" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.open()</span></code></a>.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The newly created dataset(s).</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a> | list[<a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a>]</p>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
-<section id="rioxarray-merge-module">
-<h2>rioxarray.merge module<a class="headerlink" href="#rioxarray-merge-module" title="Link to this heading"></a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="rioxarray.merge.merge_arrays">
-<span class="sig-prename descclassname"><span class="pre">rioxarray.merge.</span></span><span class="sig-name descname"><span class="pre">merge_arrays</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataarrays</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">tuple</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">tuple</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nodata</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Callable</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><span class="pre">CRS</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parse_coordinates</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/merge.html#merge_arrays"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.merge.merge_arrays" title="Link to this definition"></a></dt>
-<dd><p>Merge data arrays geospatially.</p>
-<p>Uses <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.merge.html#rasterio.merge.merge" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.merge.merge()</span></code></a></p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.2: </span>crs</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataarrays</strong> (<em>list</em><em>[</em><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><em>xarray.DataArray</em></a><em>]</em>) – List of multiple xarray.DataArray with all geo attributes.
-The first one is assumed to have the same
-CRS, dtype, and dimensions as the others in the array.</p></li>
-<li><p><strong>bounds</strong> (<em>tuple</em><em>, </em><em>optional</em>) – Bounds of the output image (left, bottom, right, top).
-If not set, bounds are determined from bounds of input DataArrays.</p></li>
-<li><p><strong>res</strong> (<em>tuple</em><em>, </em><em>optional</em>) – Output resolution in units of coordinate reference system.
-If not set, the resolution of the first DataArray is used.
-If a single value is passed, output pixels will be square.</p></li>
-<li><p><strong>nodata</strong> (<em>float</em><em>, </em><em>optional</em>) – nodata value to use in output file.
-If not set, uses the nodata value in the first input DataArray.</p></li>
-<li><p><strong>precision</strong> (<em>float</em><em>, </em><em>optional</em>) – Number of decimal points of precision when computing inverse transform.</p></li>
-<li><p><strong>method</strong> (<em>str</em><em> or </em><em>callable</em><em>, </em><em>optional</em>) – See <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.merge.html#rasterio.merge.merge" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.merge.merge()</span></code></a> for details.</p></li>
-<li><p><strong>crs</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><em>rasterio.crs.CRS</em></a><em>, </em><em>optional</em>) – Output CRS. If not set, the CRS of the first DataArray is used.</p></li>
-<li><p><strong>parse_coordinates</strong> (<em>bool</em><em>, </em><em>optional</em>) – If False, it will disable loading spatial coordinates.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The geospatially merged data.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="rioxarray.merge.merge_datasets">
-<span class="sig-prename descclassname"><span class="pre">rioxarray.merge.</span></span><span class="sig-name descname"><span class="pre">merge_datasets</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">tuple</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">tuple</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nodata</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Callable</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><span class="pre">CRS</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/rioxarray/merge.html#merge_datasets"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.merge.merge_datasets" title="Link to this definition"></a></dt>
-<dd><p>Merge datasets geospatially.</p>
-<p>Uses <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.merge.html#rasterio.merge.merge" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.merge.merge()</span></code></a></p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.2: </span>crs</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>datasets</strong> (<em>list</em><em>[</em><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><em>xarray.Dataset</em></a><em>]</em>) – List of multiple xarray.Dataset with all geo attributes.
-The first one is assumed to have the same
-CRS, dtype, dimensions, and data_vars as the others in the array.</p></li>
-<li><p><strong>bounds</strong> (<em>tuple</em><em>, </em><em>optional</em>) – Bounds of the output image (left, bottom, right, top).
-If not set, bounds are determined from bounds of input Dataset.</p></li>
-<li><p><strong>res</strong> (<em>tuple</em><em>, </em><em>optional</em>) – Output resolution in units of coordinate reference system.
-If not set, the resolution of the first Dataset is used.
-If a single value is passed, output pixels will be square.</p></li>
-<li><p><strong>nodata</strong> (<em>float</em><em>, </em><em>optional</em>) – nodata value to use in output file.
-If not set, uses the nodata value in the first input Dataset.</p></li>
-<li><p><strong>precision</strong> (<em>float</em><em>, </em><em>optional</em>) – Number of decimal points of precision when computing inverse transform.</p></li>
-<li><p><strong>method</strong> (<em>str</em><em> or </em><em>callable</em><em>, </em><em>optional</em>) – See rasterio docs.</p></li>
-<li><p><strong>crs</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><em>rasterio.crs.CRS</em></a><em>, </em><em>optional</em>) – Output CRS. If not set, the CRS of the first DataArray is used.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The geospatially merged data.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
-<section id="rioxarray-set-options">
-<h2>rioxarray.set_options<a class="headerlink" href="#rioxarray-set-options" title="Link to this heading"></a></h2>
-<dl class="py class">
-<dt class="sig sig-object py" id="rioxarray.set_options">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.</span></span><span class="sig-name descname"><span class="pre">set_options</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rioxarray/_options.html#set_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.set_options" title="Link to this definition"></a></dt>
-<dd><p>Set the global rioxarray option.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.3.0.</span></p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.7.0: </span>skip_missing_spatial_dims</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>export_grid_mapping</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, this option will export the full Climate and Forecasts (CF)
-grid mapping attributes for the CRS. This is useful if you are exporting
-your file to netCDF using <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.to_netcdf.html#xarray.Dataset.to_netcdf" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">xarray.Dataset.to_netcdf()</span></code></a>. When disabled,
-only the <code class="docutils literal notranslate"><span class="pre">crs_wkt</span></code> and <code class="docutils literal notranslate"><span class="pre">spatial_ref</span></code> attributes will be written and the
-program will be faster due to not needing to use
-<a class="reference external" href="https://pyproj4.github.io/pyproj/stable/api/crs/crs.html#pyproj.crs.CRS.to_cf" title="(in pyproj v3.7.0)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pyproj.CRS.to_cf()</span></code></a>.</p></li>
-<li><p><strong>skip_missing_spatial_dims</strong> (<em>bool</em><em>, </em><em>default=False</em>) – If True, it will not perform spatial operations on variables
-within a <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> if the spatial dimensions
-are not found.</p></li>
-</ul>
-</dd>
-</dl>
-<p>Usage as a context manager:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">rioxarray</span><span class="o">.</span><span class="n">set_options</span><span class="p">(</span><span class="n">export_grid_mapping</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="n">rds</span> <span class="o">=</span> <span class="n">rioxarray</span><span class="o">.</span><span class="n">open_rasterio</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Usage for global settings:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">rioxarray</span><span class="o">.</span><span class="n">set_options</span><span class="p">(</span><span class="n">export_grid_mapping</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</section>
-<section id="rioxarray-show-versions">
-<h2>rioxarray.show_versions<a class="headerlink" href="#rioxarray-show-versions" title="Link to this heading"></a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="rioxarray.show_versions">
-<span class="sig-prename descclassname"><span class="pre">rioxarray.</span></span><span class="sig-name descname"><span class="pre">show_versions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="_modules/rioxarray/_show_versions.html#show_versions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.show_versions" title="Link to this definition"></a></dt>
-<dd><div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.0.26.</span></p>
-</div>
-<p>Print useful debugging information</p>
-<p class="rubric">Example</p>
-<p>&gt; python -c “import rioxarray; rioxarray.show_versions()”</p>
-</dd></dl>
-
-</section>
-<section id="rioxarray-rio-accessors">
-<h2>rioxarray <cite>rio</cite> accessors<a class="headerlink" href="#rioxarray-rio-accessors" title="Link to this heading"></a></h2>
-<p>rioxarray <a class="reference external" href="http://xarray.pydata.org/en/stable/internals/extending-xarray.html">extends xarray</a>
-with the <cite>rio</cite> accessor. The <cite>rio</cite> accessor is activated by importing rioxarray like so:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">rioxarray</span>
-</pre></div>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.rioxarray.</span></span><span class="sig-name descname"><span class="pre">XRasterBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xarray_obj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase" title="Link to this definition"></a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>This is the base class for the GIS extensions for xarray</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.bounds">
-<span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recalc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.bounds" title="Link to this definition"></a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>recalc</strong> (<em>bool</em><em>, </em><em>optional</em>) – Will force the bounds to be recalculated instead of using the
-transform attribute.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>left, bottom, right, top</strong> – Outermost coordinates of the <cite>xarray.DataArray</cite> | <cite>xarray.Dataset</cite>.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.count">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">count</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.count" title="Link to this definition"></a></dt>
-<dd><p>Returns the band count (z dimension size)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>int</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.crs">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">crs</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><span class="pre">CRS</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.crs" title="Link to this definition"></a></dt>
-<dd><p><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rasterio.crs.CRS</span></code></a>:
-Retrieve projection from <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.estimate_utm_crs">
-<span class="sig-name descname"><span class="pre">estimate_utm_crs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datum_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'WGS</span> <span class="pre">84'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><span class="pre">CRS</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.estimate_utm_crs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.estimate_utm_crs" title="Link to this definition"></a></dt>
-<dd><p>Returns the estimated UTM CRS based on the bounds of the dataset.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.2.</span></p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Requires pyproj 3+</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>datum_name</strong> (<em>str</em><em>, </em><em>optional</em>) – The name of the datum to use in the query. Default is WGS 84.</p>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)">rasterio.crs.CRS</a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.get_gcps">
-<span class="sig-name descname"><span class="pre">get_gcps</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">list</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.control.html#rasterio.control.GroundControlPoint" title="(in rasterio)"><span class="pre">GroundControlPoint</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.get_gcps"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.get_gcps" title="Link to this definition"></a></dt>
-<dd><p>Get the GroundControlPoints from the dataset.</p>
-<p><a class="reference external" href="https://rasterio.readthedocs.io/en/latest/topics/georeferencing.html#ground-control-points">https://rasterio.readthedocs.io/en/latest/topics/georeferencing.html#ground-control-points</a></p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p>The Ground Control Points from the dataset or None if not applicable</p>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>list of <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.control.html#rasterio.control.GroundControlPoint" title="(in rasterio)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rasterio.control.GroundControlPoint</span></code></a> or None</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.grid_mapping">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">grid_mapping</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.grid_mapping" title="Link to this definition"></a></dt>
-<dd><p>The CF grid_mapping attribute. ‘spatial_ref’ is the default.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>str</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.height">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">height</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.height" title="Link to this definition"></a></dt>
-<dd><p>Returns the height of the dataset (y dimension size)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>int</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.isel_window">
-<span class="sig-name descname"><span class="pre">isel_window</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.windows.html#rasterio.windows.Window" title="(in rasterio)"><span class="pre">Window</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.isel_window"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.isel_window" title="Link to this definition"></a></dt>
-<dd><p>Use a rasterio.windows.Window to select a subset of the data.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.6.0: </span>pad</p>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Float indices are converted to integers.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>window</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.windows.html#rasterio.windows.Window" title="(in rasterio)"><code class="xref py py-class docutils literal notranslate"><span class="pre">rasterio.windows.Window</span></code></a>) – The window of the dataset to read.</p></li>
-<li><p><strong>pad</strong> (<em>bool</em><em>, </em><em>default=False</em>) – Set to True to expand returned DataArray to dimensions of the window</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The data in the window.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.resolution">
-<span class="sig-name descname"><span class="pre">resolution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recalc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.resolution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.resolution" title="Link to this definition"></a></dt>
-<dd><p>Determine if the resolution of the grid.
-If the transformation has rotation, the sign of the resolution is lost.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>recalc</strong> (<em>bool</em><em>, </em><em>optional</em>) – Will force the resolution to be recalculated instead of using the
-transform attribute.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>x_resolution, y_resolution</strong> – The resolution of the <cite>xarray.DataArray</cite> | <cite>xarray.Dataset</cite></p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.set_attrs">
-<span class="sig-name descname"><span class="pre">set_attrs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_attrs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.set_attrs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.set_attrs" title="Link to this definition"></a></dt>
-<dd><p>Set the attributes of the dataset/dataarray and reset
-rioxarray properties to re-search for them.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>new_attrs</strong> (<em>dict</em>) – A dictionary of new attributes.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Modified dataset with new attributes.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.set_crs">
-<span class="sig-name descname"><span class="pre">set_crs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.set_crs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.set_crs" title="Link to this definition"></a></dt>
-<dd><p>Set the CRS value for the Dataset/DataArray without modifying
-the dataset/data array.</p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 0.15.8: </span>It is recommended to use <cite>rio.write_crs()</cite> instead. This</p>
-</div>
-<p>method will likely be removed in a future release.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input_crs</strong> (<em>object</em>) – Anything accepted by <cite>rasterio.crs.CRS.from_user_input</cite>.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Dataset with crs attribute.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.set_encoding">
-<span class="sig-name descname"><span class="pre">set_encoding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_encoding</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.set_encoding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.set_encoding" title="Link to this definition"></a></dt>
-<dd><p>Set the encoding of the dataset/dataarray and reset
-rioxarray properties to re-search for them.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.4.</span></p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>new_encoding</strong> (<em>dict</em>) – A dictionary for encoding.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Modified dataset with new attributes.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.set_spatial_dims">
-<span class="sig-name descname"><span class="pre">set_spatial_dims</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.set_spatial_dims"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.set_spatial_dims" title="Link to this definition"></a></dt>
-<dd><p>This sets the spatial dimensions of the dataset.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>x_dim</strong> (<em>str</em>) – The name of the x dimension.</p></li>
-<li><p><strong>y_dim</strong> (<em>str</em>) – The name of the y dimension.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will modify the dataframe in place.
-Otherwise it will return a modified copy.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Dataset with spatial dimensions set.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.shape">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shape</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.shape" title="Link to this definition"></a></dt>
-<dd><p>Returns the shape (height, width)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>tuple(int, int)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.slice_xy">
-<span class="sig-name descname"><span class="pre">slice_xy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">minx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">miny</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.slice_xy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.slice_xy" title="Link to this definition"></a></dt>
-<dd><p>Slice the array by x,y bounds.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>minx</strong> (<em>float</em>) – Minimum bound for x coordinate.</p></li>
-<li><p><strong>miny</strong> (<em>float</em>) – Minimum bound for y coordinate.</p></li>
-<li><p><strong>maxx</strong> (<em>float</em>) – Maximum bound for x coordinate.</p></li>
-<li><p><strong>maxy</strong> (<em>float</em>) – Maximum bound for y coordinate.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The data in the slice.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recalc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Affine</span></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.transform" title="Link to this definition"></a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>recalc</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will re-calculate the transform instead of using
-the cached transform.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The affine of the <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">affine.Affine</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.transform_bounds">
-<span class="sig-name descname"><span class="pre">transform_bounds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">densify_pts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">21</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recalc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.transform_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.transform_bounds" title="Link to this definition"></a></dt>
-<dd><p>Transform bounds from src_crs to dst_crs.</p>
-<p>Optionally densifying the edges (to account for nonlinear transformations
-along these edges) and extracting the outermost bounds.</p>
-<p>Note: this does not account for the antimeridian.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dst_crs</strong> (str, <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rasterio.crs.CRS</span></code></a>, or dict) – Target coordinate reference system.</p></li>
-<li><p><strong>densify_pts</strong> (<em>uint</em><em>, </em><em>optional</em>) – Number of points to add to each edge to account for nonlinear
-edges produced by the transform process.  Large numbers will produce
-worse performance.  Default: 21 (gdal default).</p></li>
-<li><p><strong>recalc</strong> (<em>bool</em><em>, </em><em>optional</em>) – Will force the bounds to be recalculated instead of using the transform
-attribute.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>left, bottom, right, top</strong> – Outermost coordinates in target coordinate reference system.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.update_attrs">
-<span class="sig-name descname"><span class="pre">update_attrs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_attrs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.update_attrs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.update_attrs" title="Link to this definition"></a></dt>
-<dd><p>Update the attributes of the dataset/dataarray and reset
-rioxarray properties to re-search for them.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>new_attrs</strong> (<em>dict</em>) – A dictionary of new attributes to update with.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Modified dataset with updated attributes.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.update_encoding">
-<span class="sig-name descname"><span class="pre">update_encoding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_encoding</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.update_encoding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.update_encoding" title="Link to this definition"></a></dt>
-<dd><p>Update the encoding of the dataset/dataarray and reset
-rioxarray properties to re-search for them.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.4.</span></p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>new_encoding</strong> (<em>dict</em>) – A dictionary with encoding values to update with.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Modified dataset with updated attributes.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.width">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">width</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.width" title="Link to this definition"></a></dt>
-<dd><p>Returns the width of the dataset (x dimension size)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>int</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.write_coordinate_system">
-<span class="sig-name descname"><span class="pre">write_coordinate_system</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.write_coordinate_system"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.write_coordinate_system" title="Link to this definition"></a></dt>
-<dd><p>Write the coordinate system CF metadata.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.0.30.</span></p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is False.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The dataset with the CF coordinate system attributes added.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.write_crs">
-<span class="sig-name descname"><span class="pre">write_crs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_mapping_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.write_crs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.write_crs" title="Link to this definition"></a></dt>
-<dd><p>Write the CRS to the dataset in a CF compliant manner.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>The grid_mapping attribute is written to the encoding.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input_crs</strong> (<em>Any</em>) – Anything accepted by <cite>rasterio.crs.CRS.from_user_input</cite>.</p></li>
-<li><p><strong>grid_mapping_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name of the grid_mapping coordinate to store the CRS information in.
-Default is the grid_mapping name of the dataset.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Modified dataset with CF compliant CRS information.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<p>Write the CRS of the current <cite>xarray</cite> object:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">raster</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_crs</span><span class="p">(</span><span class="s2">&quot;epsg:4326&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Write the CRS on a copy:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">raster</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_crs</span><span class="p">(</span><span class="s2">&quot;epsg:4326&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.write_gcps">
-<span class="sig-name descname"><span class="pre">write_gcps</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gcps</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Iterable</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.control.html#rasterio.control.GroundControlPoint" title="(in rasterio)"><span class="pre">GroundControlPoint</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gcp_crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_mapping_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.write_gcps"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.write_gcps" title="Link to this definition"></a></dt>
-<dd><p>Write the GroundControlPoints to the dataset.</p>
-<p><a class="reference external" href="https://rasterio.readthedocs.io/en/latest/topics/georeferencing.html#ground-control-points">https://rasterio.readthedocs.io/en/latest/topics/georeferencing.html#ground-control-points</a></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>gcp</strong> (list of <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.control.html#rasterio.control.GroundControlPoint" title="(in rasterio)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rasterio.control.GroundControlPoint</span></code></a>) – The Ground Control Points to integrate to the dataset.</p></li>
-<li><p><strong>gcp_crs</strong> (str, <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rasterio.crs.CRS</span></code></a>, or dict) – Coordinate reference system for the GCPs.</p></li>
-<li><p><strong>grid_mapping_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name of the grid_mapping coordinate to store the GCPs information in.
-Default is the grid_mapping name of the dataset.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Modified dataset with Ground Control Points written.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.write_grid_mapping">
-<span class="sig-name descname"><span class="pre">write_grid_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grid_mapping_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'spatial_ref'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.write_grid_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.write_grid_mapping" title="Link to this definition"></a></dt>
-<dd><p>Write the CF grid_mapping attribute to the encoding.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>grid_mapping_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name of the grid_mapping coordinate.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Modified dataset with CF compliant CRS information.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.write_transform">
-<span class="sig-name descname"><span class="pre">write_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Affine</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_mapping_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/rioxarray.html#XRasterBase.write_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.write_transform" title="Link to this definition"></a></dt>
-<dd><div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.0.30.</span></p>
-</div>
-<p>Write the GeoTransform to the dataset where GDAL can read it in.</p>
-<p><a class="reference external" href="https://gdal.org/drivers/raster/netcdf.html#georeference">https://gdal.org/drivers/raster/netcdf.html#georeference</a></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>transform</strong> (<em>affine.Affine</em><em>, </em><em>optional</em>) – The transform of the dataset. If not provided, it will be calculated.</p></li>
-<li><p><strong>grid_mapping_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name of the grid_mapping coordinate to store the transform information in.
-Default is the grid_mapping name of the dataset.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Modified dataset with Geo Transform written.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.x_dim">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">x_dim</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Hashable</span></em><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.x_dim" title="Link to this definition"></a></dt>
-<dd><p>The dimension for the X-axis.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Hashable</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.rioxarray.XRasterBase.y_dim">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">y_dim</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Hashable</span></em><a class="headerlink" href="#rioxarray.rioxarray.XRasterBase.y_dim" title="Link to this definition"></a></dt>
-<dd><p>The dimension for the Y-axis.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Hashable</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.raster_array.</span></span><span class="sig-name descname"><span class="pre">RasterArray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xarray_obj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.rioxarray.XRasterBase" title="rioxarray.rioxarray.XRasterBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">XRasterBase</span></code></a></p>
-<p>This is the GIS extension for <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.clip">
-<span class="sig-name descname"><span class="pre">clip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">geometries</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Iterable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all_touched</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">invert</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">from_disk</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray.clip"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray.clip" title="Link to this definition"></a></dt>
-<dd><p>Crops a <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a> by geojson like geometry dicts.</p>
-<p>Powered by <cite>rasterio.features.geometry_mask</cite>.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">geometry</span> <span class="o">=</span> <span class="s1">&#39;&#39;&#39; {&quot;type&quot;: &quot;Polygon&quot;,</span>
-<span class="gp">... </span><span class="s1">                &quot;coordinates&quot;: [</span>
-<span class="gp">... </span><span class="s1">                [[-94.07955380199459, 41.69085871273774],</span>
-<span class="gp">... </span><span class="s1">                [-94.06082436942204, 41.69103313774798],</span>
-<span class="gp">... </span><span class="s1">                [-94.06063203899649, 41.67932439500822],</span>
-<span class="gp">... </span><span class="s1">                [-94.07935807746362, 41.679150041277325],</span>
-<span class="gp">... </span><span class="s1">                [-94.07955380199459, 41.69085871273774]]]}&#39;&#39;&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">cropping_geometries</span> <span class="o">=</span> <span class="p">[</span><span class="n">geojson</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">geometry</span><span class="p">)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">xds</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">open_rasterio</span><span class="p">(</span><span class="s1">&#39;cool_raster.tif&#39;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">cropped</span> <span class="o">=</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">geometries</span><span class="o">=</span><span class="n">cropping_geometries</span><span class="p">,</span> <span class="n">crs</span><span class="o">=</span><span class="mi">4326</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.2: </span>from_disk</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>geometries</strong> (<em>Iterable</em>) – A list of geojson geometry dicts or objects with __geo_interface__ with
-if you have rasterio 1.2+.</p></li>
-<li><p><strong>crs</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rasterio.crs.CRS</span></code></a>, optional) – The CRS of the input geometries. Default is to assume it is the same
-as the dataset.</p></li>
-<li><p><strong>all_touched</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, all pixels touched by geometries will be burned in.  If
-false, only pixels whose center is within the polygon or that
-are selected by Bresenham’s line algorithm will be burned in.</p></li>
-<li><p><strong>drop</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, drop the data outside of the extent of the mask geoemtries
-Otherwise, it will return the same raster with the data masked.
-Default is True.</p></li>
-<li><p><strong>invert</strong> (<em>boolean</em><em>, </em><em>optional</em>) – If False, pixels that do not overlap shapes will be set as nodata.
-Otherwise, pixels that overlap the shapes will be set as nodata.
-False by default.</p></li>
-<li><p><strong>from_disk</strong> (<em>boolean</em><em>, </em><em>optional</em>) – If True, it will clip from disk using rasterio.mask.mask if possible.
-This is beneficial when the size of the data is larger than memory.
-Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The clipped object.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.clip_box">
-<span class="sig-name descname"><span class="pre">clip_box</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">minx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">miny</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_expand</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_expand_limit</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_one_dimensional_raster</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray.clip_box"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray.clip_box" title="Link to this definition"></a></dt>
-<dd><p>Clip the <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a> by a bounding box.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.12: </span>crs</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.16: </span>allow_one_dimensional_raster</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>minx</strong> (<em>float</em>) – Minimum bound for x coordinate.</p></li>
-<li><p><strong>miny</strong> (<em>float</em>) – Minimum bound for y coordinate.</p></li>
-<li><p><strong>maxx</strong> (<em>float</em>) – Maximum bound for x coordinate.</p></li>
-<li><p><strong>maxy</strong> (<em>float</em>) – Maximum bound for y coordinate.</p></li>
-<li><p><strong>auto_expand</strong> (<em>Union</em><em>[</em><em>bool</em><em>, </em><em>int</em><em>]</em>) – If True, it will expand clip search if only 1D raster found with clip.</p></li>
-<li><p><strong>auto_expand_limit</strong> (<em>int</em>) – maximum number of times the clip will be retried before raising
-an exception.</p></li>
-<li><p><strong>crs</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rasterio.crs.CRS</span></code></a>, optional) – The CRS of the bounding box. Default is to assume it is the same
-as the dataset.</p></li>
-<li><p><strong>allow_one_dimensional_raster</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, allow clipping to/from a one dimensional raster.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The clipped object.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)">xarray.DataArray</a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.encoded_nodata">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">encoded_nodata</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#rioxarray.raster_array.RasterArray.encoded_nodata" title="Link to this definition"></a></dt>
-<dd><p>Return the encoded nodata value for the dataset if encoded.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.interpolate_na">
-<span class="sig-name descname"><span class="pre">interpolate_na</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'linear'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'nearest'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'cubic'</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'nearest'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray.interpolate_na"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray.interpolate_na" title="Link to this definition"></a></dt>
-<dd><p>This method uses scipy.interpolate.griddata to interpolate missing data.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>scipy is an optional dependency.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>method</strong> (<em>{'linear'</em><em>, </em><em>'nearest'</em><em>, </em><em>'cubic'}</em><em>, </em><em>optional</em>) – The method to use for interpolation in <cite>scipy.interpolate.griddata</cite>.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>An interpolated <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a> object.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.nodata">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nodata</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#rioxarray.raster_array.RasterArray.nodata" title="Link to this definition"></a></dt>
-<dd><p>Get the nodata value for the dataset.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.pad_box">
-<span class="sig-name descname"><span class="pre">pad_box</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">minx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">miny</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Mapping</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray.pad_box"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray.pad_box" title="Link to this definition"></a></dt>
-<dd><p>Pad the <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a> to a bounding box</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.0.29.</span></p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>minx</strong> (<em>float</em>) – Minimum bound for x coordinate.</p></li>
-<li><p><strong>miny</strong> (<em>float</em>) – Minimum bound for y coordinate.</p></li>
-<li><p><strong>maxx</strong> (<em>float</em>) – Maximum bound for x coordinate.</p></li>
-<li><p><strong>maxy</strong> (<em>float</em>) – Maximum bound for y coordinate.</p></li>
-<li><p><strong>constant_values</strong> (<em>scalar</em><em>, </em><em>tuple</em><em> or </em><em>mapping</em><em> of </em><em>hashable to tuple</em>) – The value used for padding. If None, nodata will be used if it is
-set, and numpy.nan otherwise.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The padded object.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.pad_xy">
-<span class="sig-name descname"><span class="pre">pad_xy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">minx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">miny</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Mapping</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray.pad_xy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray.pad_xy" title="Link to this definition"></a></dt>
-<dd><p>Pad the array to x,y bounds.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.0.29.</span></p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>minx</strong> (<em>float</em>) – Minimum bound for x coordinate.</p></li>
-<li><p><strong>miny</strong> (<em>float</em>) – Minimum bound for y coordinate.</p></li>
-<li><p><strong>maxx</strong> (<em>float</em>) – Maximum bound for x coordinate.</p></li>
-<li><p><strong>maxy</strong> (<em>float</em>) – Maximum bound for y coordinate.</p></li>
-<li><p><strong>constant_values</strong> (<em>scalar</em><em>, </em><em>tuple</em><em> or </em><em>mapping</em><em> of </em><em>hashable to tuple</em>) – The value used for padding. If None, nodata will be used if it is
-set, and numpy.nan otherwise.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The padded object.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.reproject">
-<span class="sig-name descname"><span class="pre">reproject</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resolution</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Affine</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resampling</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.enums.html#rasterio.enums.Resampling" title="(in rasterio)"><span class="pre">Resampling</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">Resampling.nearest</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nodata</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray.reproject"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray.reproject" title="Link to this definition"></a></dt>
-<dd><p>Reproject <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a> objects</p>
-<p>Powered by <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.warp.html#rasterio.warp.reproject" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.warp.reproject()</span></code></a></p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Only 2D/3D arrays with dimensions ‘x’/’y’ are currently supported.
-Requires either a grid mapping variable with ‘spatial_ref’ or
-a ‘crs’ attribute to be set containing a valid CRS.
-If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.</p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To re-project with dask, see
-<a class="reference external" href="https://odc-geo.readthedocs.io/">odc-geo</a> &amp;
-<a class="reference external" href="https://pyresample.readthedocs.io/">pyresample</a>.</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.0.27: </span>shape</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.0.28: </span>transform</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.5.0: </span>nodata, kwargs</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dst_crs</strong> (<em>str</em>) – OGC WKT string or Proj.4 string.</p></li>
-<li><p><strong>resolution</strong> (<em>float</em><em> or </em><em>tuple</em><em>(</em><em>float</em><em>, </em><em>float</em><em>)</em><em>, </em><em>optional</em>) – Size of a destination pixel in destination projection units
-(e.g. degrees or metres).</p></li>
-<li><p><strong>shape</strong> (<em>tuple</em><em>(</em><em>int</em><em>, </em><em>int</em><em>)</em><em>, </em><em>optional</em>) – Shape of the destination in pixels (dst_height, dst_width). Cannot be used
-together with resolution.</p></li>
-<li><p><strong>transform</strong> (<em>Affine</em><em>, </em><em>optional</em>) – The destination transform.</p></li>
-<li><p><strong>resampling</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.enums.html#rasterio.enums.Resampling" title="(in rasterio)"><em>rasterio.enums.Resampling</em></a><em>, </em><em>optional</em>) – See <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.warp.html#rasterio.warp.reproject" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.warp.reproject()</span></code></a> for more details.</p></li>
-<li><p><strong>nodata</strong> (<em>float</em><em>, </em><em>optional</em>) – The nodata value used to initialize the destination;
-it will remain in all areas not covered by the reprojected source.
-Defaults to the nodata value of the source image if none provided
-and exists or attempts to find an appropriate value by dtype.</p></li>
-<li><p><strong>**kwargs</strong> (<em>dict</em>) – Additional keyword arguments to pass into <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.warp.html#rasterio.warp.reproject" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.warp.reproject()</span></code></a>.
-To override:
-- src_transform: <cite>rio.write_transform</cite>
-- src_crs: <cite>rio.write_crs</cite>
-- src_nodata: <cite>rio.write_nodata</cite></p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The reprojected DataArray.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.reproject_match">
-<span class="sig-name descname"><span class="pre">reproject_match</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">match_data_array</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resampling</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.enums.html#rasterio.enums.Resampling" title="(in rasterio)"><span class="pre">Resampling</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">Resampling.nearest</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">reproject_kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray.reproject_match"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray.reproject_match" title="Link to this definition"></a></dt>
-<dd><p>Reproject a DataArray object to match the resolution, projection,
-and region of another DataArray.</p>
-<p>Powered by <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.warp.html#rasterio.warp.reproject" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.warp.reproject()</span></code></a></p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Only 2D/3D arrays with dimensions ‘x’/’y’ are currently supported.
-Requires either a grid mapping variable with ‘spatial_ref’ or
-a ‘crs’ attribute to be set containing a valid CRS.
-If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.9: </span>reproject_kwargs</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>match_data_array</strong> (<a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a>) – DataArray of the target resolution and projection.</p></li>
-<li><p><strong>resampling</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.enums.html#rasterio.enums.Resampling" title="(in rasterio)"><em>rasterio.enums.Resampling</em></a><em>, </em><em>optional</em>) – See <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.warp.html#rasterio.warp.reproject" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.warp.reproject()</span></code></a> for more details.</p></li>
-<li><p><strong>**reproject_kwargs</strong> – Other options to pass to <a class="reference internal" href="#rioxarray.raster_array.RasterArray.reproject" title="rioxarray.raster_array.RasterArray.reproject"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rioxarray.raster_array.RasterArray.reproject()</span></code></a></p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Contains the data from the src_data_array, reprojected to match
-match_data_array.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.set_nodata">
-<span class="sig-name descname"><span class="pre">set_nodata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_nodata</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray.set_nodata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray.set_nodata" title="Link to this definition"></a></dt>
-<dd><p>Set the nodata value for the DataArray without modifying
-the data array.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input_nodata</strong> (<em>Optional</em><em>[</em><em>float</em><em>]</em>) – Valid nodata for dtype.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing dataset. Default is True.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Dataset with nodata attribute set.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.to_raster">
-<span class="sig-name descname"><span class="pre">to_raster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raster_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">PathLike</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">driver</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">dtype</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">windowed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recalc_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lock</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">profile_kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray.to_raster"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray.to_raster" title="Link to this definition"></a></dt>
-<dd><p>Export the DataArray to a raster file.</p>
-<p>..versionadded:: 0.2 lock</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>raster_path</strong> (<em>Union</em><em>[</em><em>str</em><em>, </em><em>os.PathLike</em><em>]</em>) – The path to output the raster to.</p></li>
-<li><p><strong>driver</strong> (<em>str</em><em>, </em><em>optional</em>) – The name of the GDAL/rasterio driver to use to export the raster.
-Default is “GTiff” if rasterio &lt; 1.2 otherwise it will autodetect.</p></li>
-<li><p><strong>dtype</strong> (<em>str</em><em>, </em><em>optional</em>) – The data type to write the raster to. Default is the datasets dtype.</p></li>
-<li><p><strong>tags</strong> (<em>dict</em><em>, </em><em>optional</em>) – A dictionary of tags to write to the raster.</p></li>
-<li><p><strong>windowed</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write using the windows of the output raster.
-This is useful for loading data in chunks when writing. Does not
-do anything when writing with dask.
-Default is False.</p></li>
-<li><p><strong>recalc_transform</strong> (<em>bool</em><em>, </em><em>optional</em>) – If False, it will write the raster with the cached transform from
-the dataarray rather than recalculating it.
-Default is True.</p></li>
-<li><p><strong>lock</strong> (<em>boolean</em><em> or </em><em>Lock</em><em>, </em><em>optional</em>) – Lock to use to write data using dask.
-If not supplied, it will use a single process for writing.</p></li>
-<li><p><strong>compute</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True and data is a dask array, then compute and save
-the data immediately. If False, return a dask Delayed object.
-Call “.compute()” on the Delayed object to compute the result
-later. Call <code class="docutils literal notranslate"><span class="pre">dask.compute(delayed1,</span> <span class="pre">delayed2)</span></code> to save
-multiple delayed files at once. Default is True.</p></li>
-<li><p><strong>**profile_kwargs</strong> – Additional keyword arguments to pass into writing the raster. The
-nodata, transform, crs, count, width, and height attributes
-are ignored.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>If the data array is a dask array and compute
-is True. Otherwise None is returned.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dask.Delayed</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_array.RasterArray.write_nodata">
-<span class="sig-name descname"><span class="pre">write_nodata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_nodata</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoded</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_array.html#RasterArray.write_nodata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_array.RasterArray.write_nodata" title="Link to this definition"></a></dt>
-<dd><p>Write the nodata to the DataArray in a CF compliant manner.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input_nodata</strong> (<em>Optional</em><em>[</em><em>float</em><em>]</em>) – Nodata value for the DataArray.
-If input_nodata is None, it will remove the _FillValue attribute.</p></li>
-<li><p><strong>encoded</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write the nodata value in the encoding and remove
-the fill value from the attributes. This is useful for masking
-with nodata. Default is False.</p></li>
-<li><p><strong>inplace</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write to the existing DataArray. Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Modified DataArray with CF compliant nodata information.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<p>To write the nodata value if it is missing:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">raster</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_nodata</span><span class="p">(</span><span class="o">-</span><span class="mi">9999</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To write the nodata value on a copy:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">raster</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_nodata</span><span class="p">(</span><span class="o">-</span><span class="mi">9999</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To mask with nodata:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">nodata</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">nodata</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">raster</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">raster</span> <span class="o">!=</span> <span class="n">nodata</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">raster</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_nodata</span><span class="p">(</span><span class="n">nodata</span><span class="p">,</span> <span class="n">encoded</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="rioxarray.raster_dataset.RasterDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.raster_dataset.</span></span><span class="sig-name descname"><span class="pre">RasterDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xarray_obj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rioxarray/raster_dataset.html#RasterDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_dataset.RasterDataset" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.rioxarray.XRasterBase" title="rioxarray.rioxarray.XRasterBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">XRasterBase</span></code></a></p>
-<p>This is the GIS extension for <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a></p>
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_dataset.RasterDataset.clip">
-<span class="sig-name descname"><span class="pre">clip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">geometries</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Iterable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all_touched</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">invert</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">from_disk</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_dataset.html#RasterDataset.clip"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_dataset.RasterDataset.clip" title="Link to this definition"></a></dt>
-<dd><p>Crops a <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> by geojson like geometry dicts in dimensions ‘x’/’y’.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Clips variables that have dimensions ‘x’/’y’. Others are appended as is.</p>
-</div>
-<p>Powered by <cite>rasterio.features.geometry_mask</cite>.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">geometry</span> <span class="o">=</span> <span class="s1">&#39;&#39;&#39; {&quot;type&quot;: &quot;Polygon&quot;,</span>
-<span class="gp">... </span><span class="s1">                &quot;coordinates&quot;: [</span>
-<span class="gp">... </span><span class="s1">                [[-94.07955380199459, 41.69085871273774],</span>
-<span class="gp">... </span><span class="s1">                [-94.06082436942204, 41.69103313774798],</span>
-<span class="gp">... </span><span class="s1">                [-94.06063203899649, 41.67932439500822],</span>
-<span class="gp">... </span><span class="s1">                [-94.07935807746362, 41.679150041277325],</span>
-<span class="gp">... </span><span class="s1">                [-94.07955380199459, 41.69085871273774]]]}&#39;&#39;&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">cropping_geometries</span> <span class="o">=</span> <span class="p">[</span><span class="n">geojson</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">geometry</span><span class="p">)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">xds</span> <span class="o">=</span> <span class="n">xarray</span><span class="o">.</span><span class="n">open_rasterio</span><span class="p">(</span><span class="s1">&#39;cool_raster.tif&#39;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">cropped</span> <span class="o">=</span> <span class="n">xds</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">geometries</span><span class="o">=</span><span class="n">cropping_geometries</span><span class="p">,</span> <span class="n">crs</span><span class="o">=</span><span class="mi">4326</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.2: </span>from_disk</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>geometries</strong> (<em>list</em>) – A list of geojson geometry dicts.</p></li>
-<li><p><strong>crs</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rasterio.crs.CRS</span></code></a>, optional) – The CRS of the input geometries. Default is to assume it is the same
-as the dataset.</p></li>
-<li><p><strong>all_touched</strong> (<em>boolean</em><em>, </em><em>optional</em>) – If True, all pixels touched by geometries will be burned in.  If
-false, only pixels whose center is within the polygon or that
-are selected by Bresenham’s line algorithm will be burned in.</p></li>
-<li><p><strong>drop</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, drop the data outside of the extent of the mask geometries
-Otherwise, it will return the same raster with the data masked.
-Default is True.</p></li>
-<li><p><strong>invert</strong> (<em>boolean</em><em>, </em><em>optional</em>) – If False, pixels that do not overlap shapes will be set as nodata.
-Otherwise, pixels that overlap the shapes will be set as nodata.
-False by default.</p></li>
-<li><p><strong>from_disk</strong> (<em>boolean</em><em>, </em><em>optional</em>) – If True, it will clip from disk using rasterio.mask.mask if possible.
-This is beneficial when the size of the data is larger than memory.
-Default is False.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The clipped object.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_dataset.RasterDataset.clip_box">
-<span class="sig-name descname"><span class="pre">clip_box</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">minx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">miny</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_expand</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_expand_limit</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_one_dimensional_raster</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_dataset.html#RasterDataset.clip_box"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_dataset.RasterDataset.clip_box" title="Link to this definition"></a></dt>
-<dd><p>Clip the <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> by a bounding box in dimensions ‘x’/’y’.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Clips variables that have dimensions ‘x’/’y’. Others are appended as is.</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.12: </span>crs</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.16: </span>allow_one_dimensional_raster</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>minx</strong> (<em>float</em>) – Minimum bound for x coordinate.</p></li>
-<li><p><strong>miny</strong> (<em>float</em>) – Minimum bound for y coordinate.</p></li>
-<li><p><strong>maxx</strong> (<em>float</em>) – Maximum bound for x coordinate.</p></li>
-<li><p><strong>maxy</strong> (<em>float</em>) – Maximum bound for y coordinate.</p></li>
-<li><p><strong>auto_expand</strong> (<em>bool</em>) – If True, it will expand clip search if only 1D raster found with clip.</p></li>
-<li><p><strong>auto_expand_limit</strong> (<em>int</em>) – maximum number of times the clip will be retried before raising
-an exception.</p></li>
-<li><p><strong>crs</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rasterio.crs.CRS</span></code></a>, optional) – The CRS of the bounding box. Default is to assume it is the same
-as the dataset.</p></li>
-<li><p><strong>allow_one_dimensional_raster</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, allow clipping to/from a one dimensional raster.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The clipped object.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Dataset</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.raster_dataset.RasterDataset.crs">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">crs</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><span class="pre">CRS</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#rioxarray.raster_dataset.RasterDataset.crs" title="Link to this definition"></a></dt>
-<dd><p><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.crs.html#rasterio.crs.CRS" title="(in rasterio)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rasterio.crs.CRS</span></code></a>:
-Retrieve projection from <cite>xarray.Dataset</cite></p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_dataset.RasterDataset.interpolate_na">
-<span class="sig-name descname"><span class="pre">interpolate_na</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'linear'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'nearest'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'cubic'</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'nearest'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_dataset.html#RasterDataset.interpolate_na"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_dataset.RasterDataset.interpolate_na" title="Link to this definition"></a></dt>
-<dd><p>This method uses <cite>scipy.interpolate.griddata</cite> to interpolate missing data.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>scipy is an optional dependency.</p>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Interpolates variables that have dimensions ‘x’/’y’. Others are appended as is.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>method</strong> (<em>{'linear'</em><em>, </em><em>'nearest'</em><em>, </em><em>'cubic'}</em><em>, </em><em>optional</em>) – The method to use for interpolation in <cite>scipy.interpolate.griddata</cite>.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The interpolated object.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_dataset.RasterDataset.pad_box">
-<span class="sig-name descname"><span class="pre">pad_box</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">minx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">miny</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Mapping</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_dataset.html#RasterDataset.pad_box"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_dataset.RasterDataset.pad_box" title="Link to this definition"></a></dt>
-<dd><p>Pad the <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> to a bounding box.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Only works if all variables in the dataset have the
-same coordinates.</p>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Pads variables that have dimensions ‘x’/’y’. Others are appended as is.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>minx</strong> (<em>float</em>) – Minimum bound for x coordinate.</p></li>
-<li><p><strong>miny</strong> (<em>float</em>) – Minimum bound for y coordinate.</p></li>
-<li><p><strong>maxx</strong> (<em>float</em>) – Maximum bound for x coordinate.</p></li>
-<li><p><strong>maxy</strong> (<em>float</em>) – Maximum bound for y coordinate.</p></li>
-<li><p><strong>constant_values</strong> (<em>scalar</em><em>, </em><em>tuple</em><em> or </em><em>mapping</em><em> of </em><em>hashable to tuple</em>) – The value used for padding. If None, nodata will be used if it is
-set, and numpy.nan otherwise.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The padded object.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_dataset.RasterDataset.reproject">
-<span class="sig-name descname"><span class="pre">reproject</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_crs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resolution</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Affine</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resampling</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.enums.html#rasterio.enums.Resampling" title="(in rasterio)"><span class="pre">Resampling</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">Resampling.nearest</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nodata</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_dataset.html#RasterDataset.reproject"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_dataset.RasterDataset.reproject" title="Link to this definition"></a></dt>
-<dd><p>Reproject <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a> objects</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Only 2D/3D arrays with dimensions ‘x’/’y’ are currently supported.
-Others are appended as is.
-Requires either a grid mapping variable with ‘spatial_ref’ or
-a ‘crs’ attribute to be set containing a valid CRS.
-If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.</p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To re-project with dask, see
-<a class="reference external" href="https://odc-geo.readthedocs.io/">odc-geo</a> &amp;
-<a class="reference external" href="https://pyresample.readthedocs.io/">pyresample</a>.</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.0.27: </span>shape</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.0.28: </span>transform</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.5.0: </span>nodata, kwargs</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dst_crs</strong> (<em>str</em>) – OGC WKT string or Proj.4 string.</p></li>
-<li><p><strong>resolution</strong> (<em>float</em><em> or </em><em>tuple</em><em>(</em><em>float</em><em>, </em><em>float</em><em>)</em><em>, </em><em>optional</em>) – Size of a destination pixel in destination projection units
-(e.g. degrees or metres).</p></li>
-<li><p><strong>shape</strong> (<em>tuple</em><em>(</em><em>int</em><em>, </em><em>int</em><em>)</em><em>, </em><em>optional</em>) – Shape of the destination in pixels (dst_height, dst_width). Cannot be used
-together with resolution.</p></li>
-<li><p><strong>transform</strong> (<em>Affine</em><em>, </em><em>optional</em>) – The destination transform.</p></li>
-<li><p><strong>resampling</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.enums.html#rasterio.enums.Resampling" title="(in rasterio)"><em>rasterio.enums.Resampling</em></a><em>, </em><em>optional</em>) – See <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.warp.html#rasterio.warp.reproject" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.warp.reproject()</span></code></a> for more details.</p></li>
-<li><p><strong>nodata</strong> (<em>float</em><em>, </em><em>optional</em>) – The nodata value used to initialize the destination;
-it will remain in all areas not covered by the reprojected source.
-Defaults to the nodata value of the source image if none provided
-and exists or attempts to find an appropriate value by dtype.</p></li>
-<li><p><strong>**kwargs</strong> (<em>dict</em>) – Additional keyword arguments to pass into <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.warp.html#rasterio.warp.reproject" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.warp.reproject()</span></code></a>.
-To override:
-- src_transform: <cite>rio.write_transform</cite>
-- src_crs: <cite>rio.write_crs</cite>
-- src_nodata: <cite>rio.write_nodata</cite></p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The reprojected Dataset.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_dataset.RasterDataset.reproject_match">
-<span class="sig-name descname"><span class="pre">reproject_match</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">match_data_array</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><span class="pre">DataArray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resampling</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.enums.html#rasterio.enums.Resampling" title="(in rasterio)"><span class="pre">Resampling</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">Resampling.nearest</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">reproject_kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/rioxarray/raster_dataset.html#RasterDataset.reproject_match"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_dataset.RasterDataset.reproject_match" title="Link to this definition"></a></dt>
-<dd><p>Reproject a Dataset object to match the resolution, projection,
-and region of another DataArray.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Only 2D/3D arrays with dimensions ‘x’/’y’ are currently supported.
-Others are appended as is.
-Requires either a grid mapping variable with ‘spatial_ref’ or
-a ‘crs’ attribute to be set containing a valid CRS.
-If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.</p>
-</div>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.9: </span>reproject_kwargs</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>match_data_array</strong> (<a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html#xarray.DataArray" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></a> | <a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a>) – Dataset with the target resolution and projection.</p></li>
-<li><p><strong>resampling</strong> (<a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.enums.html#rasterio.enums.Resampling" title="(in rasterio)"><em>rasterio.enums.Resampling</em></a><em>, </em><em>optional</em>) – See <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/api/rasterio.warp.html#rasterio.warp.reproject" title="(in rasterio)"><code class="xref py py-func docutils literal notranslate"><span class="pre">rasterio.warp.reproject()</span></code></a> for more details.</p></li>
-<li><p><strong>**reproject_kwargs</strong> – Other options to pass to <a class="reference internal" href="#rioxarray.raster_dataset.RasterDataset.reproject" title="rioxarray.raster_dataset.RasterDataset.reproject"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rioxarray.raster_dataset.RasterDataset.reproject()</span></code></a></p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Contains the data from the src_data_array,
-reprojected to match match_data_array.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray.Dataset" title="(in xarray v2024.10.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.Dataset</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="rioxarray.raster_dataset.RasterDataset.to_raster">
-<span class="sig-name descname"><span class="pre">to_raster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raster_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">PathLike</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">driver</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">dtype</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">windowed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recalc_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lock</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">profile_kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="_modules/rioxarray/raster_dataset.html#RasterDataset.to_raster"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.raster_dataset.RasterDataset.to_raster" title="Link to this definition"></a></dt>
-<dd><p>Export the Dataset to a raster file. Only works with 2D data.</p>
-<p>..versionadded:: 0.2 lock</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>raster_path</strong> (<em>str</em>) – The path to output the raster to.</p></li>
-<li><p><strong>driver</strong> (<em>str</em><em>, </em><em>optional</em>) – The name of the GDAL/rasterio driver to use to export the raster.
-Default is “GTiff” if rasterio &lt; 1.2 otherwise it will autodetect.</p></li>
-<li><p><strong>dtype</strong> (<em>str</em><em>, </em><em>optional</em>) – The data type to write the raster to. Default is the datasets dtype.</p></li>
-<li><p><strong>tags</strong> (<em>dict</em><em>, </em><em>optional</em>) – A dictionary of tags to write to the raster.</p></li>
-<li><p><strong>windowed</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will write using the windows of the output raster.
-This is useful for loading data in chunks when writing. Does not
-do anything when writing with dask.
-Default is False.</p></li>
-<li><p><strong>recalc_transform</strong> (<em>bool</em><em>, </em><em>optional</em>) – If False, it will write the raster with the cached transform from
-the dataset rather than recalculating it.
-Default is True.</p></li>
-<li><p><strong>lock</strong> (<em>boolean</em><em> or </em><em>Lock</em><em>, </em><em>optional</em>) – Lock to use to write data using dask.
-If not supplied, it will use a single process for writing.</p></li>
-<li><p><strong>compute</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True and data is a dask array, then compute and save
-the data immediately. If False, return a dask Delayed object.
-Call “.compute()” on the Delayed object to compute the result
-later. Call <code class="docutils literal notranslate"><span class="pre">dask.compute(delayed1,</span> <span class="pre">delayed2)</span></code> to save
-multiple delayed files at once. Default is True.</p></li>
-<li><p><strong>**profile_kwargs</strong> – Additional keyword arguments to pass into writing the raster. The
-nodata, transform, crs, count, width, and height attributes
-are ignored.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>If the data array is a dask array and compute
-is True. Otherwise None is returned.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dask.Delayed</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="rioxarray.raster_dataset.RasterDataset.vars">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vars</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">list</span></em><a class="headerlink" href="#rioxarray.raster_dataset.RasterDataset.vars" title="Link to this definition"></a></dt>
-<dd><p>Returns non-coordinate varibles</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>list</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-</section>
-<section id="module-rioxarray.exceptions">
-<span id="rioxarray-exceptions-module"></span><h2>rioxarray.exceptions module<a class="headerlink" href="#module-rioxarray.exceptions" title="Link to this heading"></a></h2>
-<p>This contains exceptions for rioxarray.</p>
-<dl class="py exception">
-<dt class="sig sig-object py" id="rioxarray.exceptions.DimensionError">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.exceptions.</span></span><span class="sig-name descname"><span class="pre">DimensionError</span></span><a class="reference internal" href="_modules/rioxarray/exceptions.html#DimensionError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.exceptions.DimensionError" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.exceptions.RioXarrayError" title="rioxarray.exceptions.RioXarrayError"><code class="xref py py-class docutils literal notranslate"><span class="pre">RioXarrayError</span></code></a></p>
-<p>This is raised when there are more dimensions than is supported by the method</p>
-</dd></dl>
-
-<dl class="py exception">
-<dt class="sig sig-object py" id="rioxarray.exceptions.DimensionMissingCoordinateError">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.exceptions.</span></span><span class="sig-name descname"><span class="pre">DimensionMissingCoordinateError</span></span><a class="reference internal" href="_modules/rioxarray/exceptions.html#DimensionMissingCoordinateError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.exceptions.DimensionMissingCoordinateError" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.exceptions.RioXarrayError" title="rioxarray.exceptions.RioXarrayError"><code class="xref py py-class docutils literal notranslate"><span class="pre">RioXarrayError</span></code></a></p>
-<p>This is raised when the dimension does not have the supporting coordinate.</p>
-</dd></dl>
-
-<dl class="py exception">
-<dt class="sig sig-object py" id="rioxarray.exceptions.InvalidDimensionOrder">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.exceptions.</span></span><span class="sig-name descname"><span class="pre">InvalidDimensionOrder</span></span><a class="reference internal" href="_modules/rioxarray/exceptions.html#InvalidDimensionOrder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.exceptions.InvalidDimensionOrder" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.exceptions.DimensionError" title="rioxarray.exceptions.DimensionError"><code class="xref py py-class docutils literal notranslate"><span class="pre">DimensionError</span></code></a></p>
-<p>This is raised when there the dimensions are not ordered correctly.</p>
-</dd></dl>
-
-<dl class="py exception">
-<dt class="sig sig-object py" id="rioxarray.exceptions.MissingCRS">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.exceptions.</span></span><span class="sig-name descname"><span class="pre">MissingCRS</span></span><a class="reference internal" href="_modules/rioxarray/exceptions.html#MissingCRS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.exceptions.MissingCRS" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.exceptions.RioXarrayError" title="rioxarray.exceptions.RioXarrayError"><code class="xref py py-class docutils literal notranslate"><span class="pre">RioXarrayError</span></code></a></p>
-<p>Missing the CRS in the dataset.</p>
-</dd></dl>
-
-<dl class="py exception">
-<dt class="sig sig-object py" id="rioxarray.exceptions.MissingSpatialDimensionError">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.exceptions.</span></span><span class="sig-name descname"><span class="pre">MissingSpatialDimensionError</span></span><a class="reference internal" href="_modules/rioxarray/exceptions.html#MissingSpatialDimensionError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.exceptions.MissingSpatialDimensionError" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.exceptions.DimensionError" title="rioxarray.exceptions.DimensionError"><code class="xref py py-class docutils literal notranslate"><span class="pre">DimensionError</span></code></a></p>
-<p>This is raised when the dimension cannot be found</p>
-</dd></dl>
-
-<dl class="py exception">
-<dt class="sig sig-object py" id="rioxarray.exceptions.NoDataInBounds">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.exceptions.</span></span><span class="sig-name descname"><span class="pre">NoDataInBounds</span></span><a class="reference internal" href="_modules/rioxarray/exceptions.html#NoDataInBounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.exceptions.NoDataInBounds" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.exceptions.RioXarrayError" title="rioxarray.exceptions.RioXarrayError"><code class="xref py py-class docutils literal notranslate"><span class="pre">RioXarrayError</span></code></a></p>
-<p>This is for when there are no data in the bounds for clipping a raster.</p>
-</dd></dl>
-
-<dl class="py exception">
-<dt class="sig sig-object py" id="rioxarray.exceptions.OneDimensionalRaster">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.exceptions.</span></span><span class="sig-name descname"><span class="pre">OneDimensionalRaster</span></span><a class="reference internal" href="_modules/rioxarray/exceptions.html#OneDimensionalRaster"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.exceptions.OneDimensionalRaster" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.exceptions.DimensionError" title="rioxarray.exceptions.DimensionError"><code class="xref py py-class docutils literal notranslate"><span class="pre">DimensionError</span></code></a></p>
-<p>This is an error when you have a 1 dimensional raster.</p>
-</dd></dl>
-
-<dl class="py exception">
-<dt class="sig sig-object py" id="rioxarray.exceptions.RioXarrayError">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.exceptions.</span></span><span class="sig-name descname"><span class="pre">RioXarrayError</span></span><a class="reference internal" href="_modules/rioxarray/exceptions.html#RioXarrayError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.exceptions.RioXarrayError" title="Link to this definition"></a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">RuntimeError</span></code></p>
-<p>This is the base exception for errors in the rioxarray extension.</p>
-</dd></dl>
-
-<dl class="py exception">
-<dt class="sig sig-object py" id="rioxarray.exceptions.SingleVariableDataset">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.exceptions.</span></span><span class="sig-name descname"><span class="pre">SingleVariableDataset</span></span><a class="reference internal" href="_modules/rioxarray/exceptions.html#SingleVariableDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.exceptions.SingleVariableDataset" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.exceptions.RioXarrayError" title="rioxarray.exceptions.RioXarrayError"><code class="xref py py-class docutils literal notranslate"><span class="pre">RioXarrayError</span></code></a></p>
-<p>This is for when you have a dataset with a single variable.</p>
-</dd></dl>
-
-<dl class="py exception">
-<dt class="sig sig-object py" id="rioxarray.exceptions.TooManyDimensions">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">rioxarray.exceptions.</span></span><span class="sig-name descname"><span class="pre">TooManyDimensions</span></span><a class="reference internal" href="_modules/rioxarray/exceptions.html#TooManyDimensions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#rioxarray.exceptions.TooManyDimensions" title="Link to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#rioxarray.exceptions.DimensionError" title="rioxarray.exceptions.DimensionError"><code class="xref py py-class docutils literal notranslate"><span class="pre">DimensionError</span></code></a></p>
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diff --git a/stable b/stable
index 47d04a52..6b2d58c8 120000
--- a/stable
+++ b/stable
@@ -1 +1 @@
-0.18.0
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+0.18.1
\ No newline at end of file