Interactive_Variogram_Calculation_Modeling.ipynb

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    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Variogram Calculation and Modeling Demonstration\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### The Interactive Workflow\n",
    "\n",
    "Here's an interactive workflow for calculating directional experimental variograms in 2D. \n",
    "\n",
    "* setting the variogram calculation parameters for identifying spatial data pairs \n",
    "\n",
    "This approach is essential for quantifying spatial continuity with sparsely sampled, irregular spatial data.\n",
    "\n",
    "I have more comprehensive workflows for variogram calculation:\n",
    "\n",
    "* [Experimental Variogram Calculation in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_variogram_calculation.ipynb)\n",
    "\n",
    "* [Determination of Major and Minor Spatial Continuity Directions in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_spatial_continuity_directions.ipynb)\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Variogram Calculation Parameters\n",
    "\n",
    "The variogram calculation parameters include:\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth (isotropic variograms are calculated with an azimuth tolerance of to 90.0)\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance, usually set to the minimum data spacing\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance, commonly set to 50% of unit lag distanceonal smoothing\n",
    "\n",
    "* **number of lags** - set based on the spatial extent of the dataset, we can typically calculate reliable variograms up to 1/2 the extent of the dataset\n",
    "\n",
    "* **bandwidth** is the maximum offset allowable from the lag vector \n",
    "\n",
    "\n",
    "#### Variogram Modeling\n",
    "\n",
    "Spatial continuity can be modeled with nested, positive definate variogram structures:\n",
    "\n",
    "\\begin{equation}\n",
    "\\Gamma_x(\\bf{h}) = \\sum_{i=1}^{nst} \\gamma_i(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "where $\\Gamma_x(\\bf{h})$ is the nested variogram model resulting from the summation of $nst$ nested variograms  $\\gamma_i(\\bf{h})$.\n",
    "\n",
    "The types of structure commonly applied include:\n",
    "\n",
    "* spherical\n",
    "\n",
    "* exponential\n",
    "\n",
    "* gaussian\n",
    "\n",
    "* nugget\n",
    "\n",
    "Other less common models include:\n",
    "\n",
    "* hole effect\n",
    "\n",
    "* dampenned hole effect\n",
    "\n",
    "* power law\n",
    "\n",
    "these will not be covered here.\n",
    "\n",
    "Each one of these variogram structures, $\\gamma_i(\\bf{h})$, is based on a geometric anisotropy model parameterized by the orientation and range in the major and minor directions.  In 2D this is simply an azimuth and ranges, $azi$, $a_{maj}$ and $a_{min}$. Note, the range in the minor direction (orthogonal to the major direction).\n",
    "\n",
    "The geometric anisotropy model assumes that the range in all off-diagonal directions is based on an ellipse with the major and minor axes alligned with and set to the major and minor for the variogram.\n",
    "\n",
    "\\begin{equation}\n",
    "\\bf{h}_i = \\sqrt{\\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2 + \\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2}  \n",
    "\\end{equation}\n",
    "\n",
    "Therefore, if we know the major direction, range in major and minor directions, we may completely describe each nested componnent of the complete spatial continuity of the variable of interest, $i = 1,\\dots,nst$.\n",
    "\n",
    "Some comments on modeling nested variograms:\n",
    "\n",
    "* we can capture nugget, short and long range continuity structures\n",
    "\n",
    "* we rely on the geometric anisotropy model, so all structures must inform the same level of contribution (porportion of the sill) in all directions.\n",
    "\n",
    "* the geometric anisotropy model is based on azimuth of the major direction of continuity, range in the major direction and range in the minor direction (orthogonal to the major direction).  The range is interpolated between the major and minor azimuths with a ellipse model\n",
    "\n",
    "* we can vary the type of variogram, direction or azimuth of the major direction, and major and minor ranges by structure\n",
    "\n",
    "In this workflow we will explore methods to:\n",
    "\n",
    "1. detect directionality from a spatial dataset\n",
    "2. calculate the directional variograms in the major and minor directions \n",
    "3. build a consistent 2D model fit to the major and minor directions\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\pm27995\\Anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "C:\\Users\\pm27995\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll\n",
      "C:\\Users\\pm27995\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.XWYDX2IKJW2NMTWSFYNGFUWKQU3LYTCZ.gfortran-win_amd64.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    }
   ],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"c:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Facies</th>\n",
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       "       X      Y   Porosity        Perm           AI  Facies\n",
       "0  150.0  909.0  13.133413  485.231659  3848.226472     1.0\n",
       "1  690.0    9.0   3.235632  138.238177  5673.521586     0.0\n",
       "2  825.0  775.0   9.012557  317.338989  4370.886936     1.0\n",
       "3  530.0  759.0  11.138872  414.252249  4003.754472     1.0\n",
       "4  775.0   75.0   9.233079  350.924567  4134.272337     1.0"
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     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "data = 2\n",
    "\n",
    "if data == 1:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_MV_biased.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df['Porosity'] = df['Porosity']*100.0\n",
    "    df = df.iloc[:,1:]                                      # remove first column\n",
    "elif data == 2:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v3.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df = df.iloc[:,1:] \n",
    "elif data == 3:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/12_sample_data.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s) \n",
    "    df['Porosity'] = df['Porosity']*100.0\n",
    "    df = df.iloc[:,1:] \n",
    "elif data == 4:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v5_sand_only.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s) \n",
    "    df = df.iloc[:,1:] \n",
    "else:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v1.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df = df.iloc[:,1:] \n",
    "    \n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features:\n",
    "\n",
    "* **X** - x coordinate in meters\n",
    "* **Y** - y coordinate in meters\n",
    "* **Porosity** - rock porosity averaged over a specific rock unit from a vertical well\n",
    "* **Perm** - rock permeability averaged (scaled up) over a specific rock unit from a vertical well \n",
    "* **AI** - acoustic impedance from a seismic cube assigned at a specific rock unit and at the location of a vertical well \n",
    "* **facies** - facies, 0 - shale, 1 - sandstone\n",
    "\n",
    "Concerning facies:\n",
    "\n",
    "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit.   \n",
    "\n",
    "You are welcome to repeat this workflow on a by-facies basis.  The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n",
    "\n",
    "```p\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index()  # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "```\n",
    "\n",
    "Let's look at summary statistics for all facies combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>270.0</td>\n",
       "      <td>472.537037</td>\n",
       "      <td>288.917266</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>475.000000</td>\n",
       "      <td>745.000000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>270.0</td>\n",
       "      <td>522.622222</td>\n",
       "      <td>277.643599</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>769.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>270.0</td>\n",
       "      <td>10.778590</td>\n",
       "      <td>3.665004</td>\n",
       "      <td>3.135247</td>\n",
       "      <td>7.909297</td>\n",
       "      <td>10.557808</td>\n",
       "      <td>13.119702</td>\n",
       "      <td>21.599413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>270.0</td>\n",
       "      <td>406.286106</td>\n",
       "      <td>147.891654</td>\n",
       "      <td>138.238177</td>\n",
       "      <td>309.663433</td>\n",
       "      <td>408.150631</td>\n",
       "      <td>495.549617</td>\n",
       "      <td>798.263353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>270.0</td>\n",
       "      <td>4292.475630</td>\n",
       "      <td>433.786043</td>\n",
       "      <td>3630.239427</td>\n",
       "      <td>3981.691959</td>\n",
       "      <td>4192.107297</td>\n",
       "      <td>4491.224552</td>\n",
       "      <td>5701.203128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>270.0</td>\n",
       "      <td>0.785185</td>\n",
       "      <td>0.411456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "X         270.0   472.537037  288.917266     0.000000   225.000000   \n",
       "Y         270.0   522.622222  277.643599     9.000000   309.000000   \n",
       "Porosity  270.0    10.778590    3.665004     3.135247     7.909297   \n",
       "Perm      270.0   406.286106  147.891654   138.238177   309.663433   \n",
       "AI        270.0  4292.475630  433.786043  3630.239427  3981.691959   \n",
       "Facies    270.0     0.785185    0.411456     0.000000     1.000000   \n",
       "\n",
       "                  50%          75%          max  \n",
       "X          475.000000   745.000000   990.000000  \n",
       "Y          525.000000   769.000000   999.000000  \n",
       "Porosity    10.557808    13.119702    21.599413  \n",
       "Perm       408.150631   495.549617   798.263353  \n",
       "AI        4192.107297  4491.224552  5701.203128  \n",
       "Facies       1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                               # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Model Parameters\n",
    "\n",
    "See the the following model parameters:\n",
    "\n",
    "* **xmin**, **xmax**, **ymin** and **ymax** - extents of the dataset for plotting\n",
    "* **feature** and **feature_units** - feature of interest and associated units\n",
    "* **vmin** and **vmax** - minimum and maximum of the feature of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0                                # spatial extents in x and y\n",
    "ymin = 0.0; ymax = 1000.0\n",
    "feature = 'Porosity'; feature_units = 'percentage'         # name and units of the feature of interest\n",
    "vmin = 0.0; vmax = 25.0                                  # min and max of the feature of interest\n",
    "cmap = plt.cm.inferno                                    # set the color map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's transform the feature to standard normal (mean = 0.0, standard deviation = 1.0, Gaussian shape). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['N' + feature], tvPor, tnsPor = geostats.nscore(df, feature) # nscore transform for all facies porosity "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the updated DataFrame to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Facies</th>\n",
       "      <th>NPorosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.0</td>\n",
       "      <td>909.0</td>\n",
       "      <td>13.133413</td>\n",
       "      <td>485.231659</td>\n",
       "      <td>3848.226472</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.674490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>690.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.235632</td>\n",
       "      <td>138.238177</td>\n",
       "      <td>5673.521586</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.539185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>825.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>9.012557</td>\n",
       "      <td>317.338989</td>\n",
       "      <td>4370.886936</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.375340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>530.0</td>\n",
       "      <td>759.0</td>\n",
       "      <td>11.138872</td>\n",
       "      <td>414.252249</td>\n",
       "      <td>4003.754472</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.097635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>775.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.233079</td>\n",
       "      <td>350.924567</td>\n",
       "      <td>4134.272337</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.306454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X      Y   Porosity        Perm           AI  Facies  NPorosity\n",
       "0  150.0  909.0  13.133413  485.231659  3848.226472     1.0   0.674490\n",
       "1  690.0    9.0   3.235632  138.238177  5673.521586     0.0  -2.539185\n",
       "2  825.0  775.0   9.012557  317.338989  4370.886936     1.0  -0.375340\n",
       "3  530.0  759.0  11.138872  414.252249  4003.754472     1.0   0.097635\n",
       "4  775.0   75.0   9.233079  350.924567  4134.272337     1.0  -0.306454"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                               # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "plt.subplot(121)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df[feature], facecolor='red',bins=np.linspace(vmin,vmax,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black')\n",
    "plt.xlim([vmin,vmax]); plt.ylim([0,1.0])\n",
    "plt.xlabel(feature + '(' + feature_units + ')'); plt.ylabel('Frequency'); plt.title('Porosity')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(122)  \n",
    "plt.hist(df['N'+feature], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Gaussian Transformed ' + feature); plt.ylabel('Frequency'); plt.title('Guassian Transformed ' + feature)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the normal score transform has correctly transformed the feature to standard normal.\n",
    "\n",
    "#### Inspection of Posted Data\n",
    "\n",
    "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n",
    "\n",
    "* This data visualization will also be important to assist with parameter selection for the variogram calculation search template.\n",
    "\n",
    "Let's plot the location maps of the original feature and the normal score transforms of the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y',feature,0,1000,0,1000,vmin,vmax,feature,'X (m)','Y (m)',feature,cmap)\n",
    "\n",
    "plt.subplot(122)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y','N'+feature,0,1000,0,1000,-3,3,'Gaussian Transformed ' + feature,'X (m)','Y (m)','Gaussian Transformed ' + feature,cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see?  Here's my observations:\n",
    "\n",
    "* there is a high degree of spatial agreement between porosity and permeability, this is supported by the high correlation evident in the cross plot.\n",
    "* there are no discontinuities that could suggest that facies represent a distinct change, rather the porosity and permeability seem continuous and the assigned facies are a truncation of their continous behavoir, we doing 'ok' with no facies\n",
    "* suspect a 045 azimuth major direction of continuity (up - right)\n",
    "* there may be cycles in the 135 azimuth \n",
    "* there will not likely be a nugget effect, but there is an hint of some short scale discontinuity?\n",
    "\n",
    "**Do you agree?** If you have a different observations, drop me a line at mpyrcz@austin.utexas.edu and I'll add to this lesson with credit!\n",
    "\n",
    "#### Experimental Variograms\n",
    "\n",
    "We can use the location maps to help determine good variogram calculation parameters. For example:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = azi; atol = 22.5; isill = 1\n",
    "```\n",
    "* **tmin**, **tmax** are trimming limits - set to have no impact, no need to filter the data\n",
    "* **lag_dist**, **lag_tol** are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* **nlag** is number of lags - set to extend just past 50 of the data extent\n",
    "* **bandh** is the horizontal band width - set to have no effect\n",
    "* **azi** is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* **isill** is a boolean to standardize the distribution to a variance of 1 - it has no effect since the previous nscore transform sets the variance to 1.0\n",
    "\n",
    "#### Dashboard for Interactive Variogram Calculation and Modeling\n",
    "\n",
    "Below we make a dashboard with the ipywidgets and matplotlib Python packages for calculating and modeling experimental variograms.\n",
    "\n",
    "* allowing you to calculate and model the variogram of the normal score transformed variogram interactively while changing (and exploring) the search template parameters.\n",
    "\n",
    "* first calculate the isotropic or directional variogram(s)\n",
    "\n",
    "* then fit the same isotropic or directional variogram(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the experimental variogram\n",
    "l = widgets.Text(value='                              Variogram Calculation Interactive Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "lag = widgets.FloatSlider(min = 10, max = 500, value = 10, step = 10, description = 'lag',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "lag.style.handle_color = 'gray'\n",
    "\n",
    "lag_tol = widgets.FloatSlider(min = 5, max = 500, value = 5, step = 10, description = 'lag tolerance',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "lag_tol.style.handle_color = 'gray'\n",
    "\n",
    "nlag = widgets.IntSlider(min = 1, max = 100, value = 100, step = 1, description = 'number of lags',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "nlag.style.handle_color = 'gray'\n",
    "\n",
    "azi = widgets.FloatSlider(min = 0, max = 360, value = 0, step = 5, description = 'azimuth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "azi.style.handle_color = 'gray'\n",
    "\n",
    "azi_tol = widgets.FloatSlider(min = 10, max = 90, value = 10, step = 5, description = 'azimuth tolerance',orientation='vertical',layout=Layout(width='120px', height='200px'))\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "bandwidth = widgets.FloatSlider(min = 100, max = 2000, value = 2000, step = 100, description = 'bandwidth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "\n",
    "ui1 = widgets.HBox([lag,lag_tol,nlag,azi,azi_tol,bandwidth],) # basic widget formatting    \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def f_make(lag,lag_tol,nlag,azi,azi_tol,bandwidth):     # function to take parameters, calculate variogram and plot\n",
    "#    text_trap = io.StringIO()\n",
    "#    sys.stdout = text_trap\n",
    "    global lags,gammas,npps,lags2,gammas2,npps2\n",
    "    tmin = -9999.9; tmax = 9999.9\n",
    "    lags, gammas, npps = geostats.gamv(df,\"X\",\"Y\",\"N\"+feature,tmin,tmax,lag,lag_tol,nlag,azi,azi_tol,bandwidth,isill=1.0)\n",
    "    lags2, gammas2, npps2 = geostats.gamv(df,\"X\",\"Y\",\"N\"+feature,tmin,tmax,lag,lag_tol,nlag,azi+90.0,azi_tol,bandwidth,isill=1.0)\n",
    "    \n",
    "    plt.subplot(111)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azi), alpha = 0.8)\n",
    "    plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azi+90.0), alpha = 0.8)\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol < 90.0:\n",
    "        plt.title('Directional NSCORE ' + feature + ' Variogram - Azi. ' + str(azi) + ', Azi. Tol.' + str(azi_tol))\n",
    "    else: \n",
    "        plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.0, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "    return\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'lag':lag,'lag_tol':lag_tol,'nlag':nlag,'azi':azi,'azi_tol':azi_tol,'bandwidth':bandwidth})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Variogram Calculation Demonstration\n",
    "\n",
    "* calculate omnidirectional and direction experimental variograms \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Calculate interpretable experimental variograms for sparse, irregularly-space spatial data. Note, size of the experimental point is scaled by the number of pairs.\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance\n",
    "\n",
    "* **number of lags** - number of lags in the experimental variogram\n",
    "\n",
    "* **bandwidth** - maximum departure from the lag vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8add34c74d3440b4b7d9a8006afdba23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Calculation Interactive Demonstration, Mich…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7896fb37a7844cbeb941b283bec3f61d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n",
    "l = widgets.Text(value='                              Variogram Modeling, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "nug = widgets.FloatSlider(min = 0, max = 1.0, value = 0.0, step = 0.1, description = r'$c_{nugget}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "nug.style.handle_color = 'gray'\n",
    "it1 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description=r'$Type_1$:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "c1 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.1, description = r'$c_1$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "c1.style.handle_color = 'gray'\n",
    "hmaj1 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 25.0, description = r'$a_{1,maj}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmaj1.style.handle_color = 'black'\n",
    "hmin1 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 25.0, description = r'$a_{1,min}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmin1.style.handle_color = 'red'\n",
    "\n",
    "it2 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description=r'$Type_2$:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "c2 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.0, description = r'$c_2$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "c2.style.handle_color = 'gray'\n",
    "hmaj2 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 100.0, description = r'$a_{2,maj}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmaj2.style.handle_color = 'black'\n",
    "hmin2 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 100.0, description = r'$a_{2,min}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmin2.style.handle_color = 'red'\n",
    "\n",
    "ui1 = widgets.HBox([nug,it1,c1,hmaj1,hmin1,it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "#ui2 = widgets.HBox([it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def convert_type(it):\n",
    "    if it == 'Spherical': \n",
    "        return 1\n",
    "    elif it == 'Exponential':\n",
    "        return 2\n",
    "    else: \n",
    "        return 3\n",
    "\n",
    "def f_make(nug,it1,c1, hmaj1, hmin1, it2, c2, hmaj2, hmin2):                       # function to take parameters, make sample and plot\n",
    "    text_trap = io.StringIO()\n",
    "    sys.stdout = text_trap\n",
    "    \n",
    "    it1 = convert_type(it1); it2 = convert_type(it2)\n",
    "    if c2 > 0.0:\n",
    "        nst = 2\n",
    "    else:\n",
    "        nst = 1\n",
    "    \n",
    "    vario = GSLIB.make_variogram(nug,nst,it1,c1,0.0,hmaj1,hmin1,it2,c2,0.0,hmaj2,hmin2) # make model object\n",
    "    nlag = 100; xlag = 10;                                     \n",
    "    index_maj,h_maj,gam_maj,cov_maj,ro_maj = geostats.vmodel(nlag,xlag,0.0,vario)   # project the model in the major azimuth                                                  # project the model in the 135 azimuth\n",
    "    index_min,h_min,gam_min,cov_min,ro_min = geostats.vmodel(nlag,xlag,90.0,vario)   \n",
    "\n",
    "    plt.subplot(111)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azi.value), alpha = 0.8)\n",
    "    plt.plot(h_maj,gam_maj,color = 'black')\n",
    "    plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azi.value+90.0), alpha = 0.8)\n",
    "    plt.plot(h_min,gam_min,color = 'red')\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol.value < 90.0:\n",
    "        plt.title('Directional NSCORE ' + feature + ' Variogram - Azi. ' + str(azi.value) + ', Azi. Tol.' + str(azi_tol.value))\n",
    "    else: \n",
    "        plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.5, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'nug':nug, 'it1':it1,'c1':c1, 'hmaj1':hmaj1, 'hmin1':hmin1, 'it2':it2, 'c2':c2, 'hmaj2':hmaj2, 'hmin2':hmin2})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Nested Variogram Modeling Demostration\n",
    "\n",
    "* select the nested structures and their types, contributions and major and minor ranges \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Fit a positive definite variogram model based on the addition of multiple structures each describing spatial components of the feature variance \n",
    "\n",
    "* **nug**: nugget effect\n",
    "\n",
    "* **c1 / c2**: contributions of the sill\n",
    "\n",
    "* **hmaj1 / hmaj2**: range in the major direction\n",
    "\n",
    "* **hmin1 / hmin2**: range in the minor direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac03f5cdffc142e68c8d91cea5f329d4",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Modeling, Michael Pyrcz, Associate Professo…"
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     "metadata": {},
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       "model_id": "770e4bcfe7a64bd1933bc501330bee33",
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      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 432x288 with 1 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration / exercise of variogram calculation and modeling for spatial continuity analysis. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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