04_spatial_weights.ipynb

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      "import pysal as ps\n",
      "import numpy as np"
     ],
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    {
     "cell_type": "heading",
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      "Regular Lattice Weights "
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      "w = ps.lat2W()"
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      "$s_0 = \\sum_i \\sum_j w_{i,j}$"
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      "w.s0"
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     "cell_type": "markdown",
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      "$pctnonzero = s_0 / n^2$"
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      "w.s0 / w.n**2"
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      "wq = ps.lat2W(rook=False)"
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    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Spatial Lag"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$l_i = \\sum_{i,j} w_{i,j} y_j$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = np.arange(w.n)\n",
      "y"
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      "wy = ps.lag_spatial(w,y)\n",
      "wy"
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       "metadata": {},
       "output_type": "pyout",
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     "cell_type": "code",
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     "input": [
      "y *= 10"
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      "wy = ps.lag_spatial(w,y)"
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    },
    {
     "cell_type": "markdown",
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     "cell_type": "code",
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      "wry = ps.lag_spatial(w,y)"
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        "\u001b[31mLine.dbf\u001b[m\u001b[m*                          \u001b[31msacramentot2.shp\u001b[m\u001b[m*\r\n",
        "\u001b[31mLine.prj\u001b[m\u001b[m*                          \u001b[31msacramentot2.shx\u001b[m\u001b[m*\r\n",
        "\u001b[31mLine.shp\u001b[m\u001b[m*                          \u001b[31msids2.dbf\u001b[m\u001b[m*\r\n",
        "\u001b[31mLine.shx\u001b[m\u001b[m*                          \u001b[31msids2.gal\u001b[m\u001b[m*\r",
        "\r\n",
        "\u001b[31mNAT.dbf\u001b[m\u001b[m*                           \u001b[31msids2.html\u001b[m\u001b[m*\r\n",
        "\u001b[31mNAT.shp\u001b[m\u001b[m*                           \u001b[31msids2.shp\u001b[m\u001b[m*\r\n",
        "\u001b[31mNAT.shx\u001b[m\u001b[m*                           \u001b[31msids2.shx\u001b[m\u001b[m*\r\n",
        "NAT.zip                            \u001b[31msids2.swm\u001b[m\u001b[m*\r\n",
        "\u001b[31mPoint.dbf\u001b[m\u001b[m*                         south.dbf\r\n",
        "\u001b[31mPoint.prj\u001b[m\u001b[m*                         south.gal\r\n",
        "\u001b[31mPoint.shp\u001b[m\u001b[m*                         south.html\r\n",
        "\u001b[31mPoint.shx\u001b[m\u001b[m*                         south.json\r\n",
        "\u001b[31mPolygon.dbf\u001b[m\u001b[m*                       south.json.webarchive\r\n",
        "\u001b[31mPolygon.prj\u001b[m\u001b[m*                       south.shp\r\n",
        "\u001b[31mPolygon.shp\u001b[m\u001b[m*                       south.shx\r\n",
        "\u001b[31mPolygon.shx\u001b[m\u001b[m*                       \u001b[31msouth.zip\u001b[m\u001b[m*\r\n",
        "\u001b[31mREADME.txt\u001b[m\u001b[m*                        \u001b[31mspat-sym-us.mat\u001b[m\u001b[m*\r\n",
        "\u001b[31marcgis_ohio.dbf\u001b[m\u001b[m*                   \u001b[31mspat-sym-us.wk1\u001b[m\u001b[m*\r\n",
        "\u001b[31marcgis_txt.txt\u001b[m\u001b[m*                    \u001b[31mspdep_listw2WB_columbus\u001b[m\u001b[m*\r\n",
        "atl_hom.dbf                        spi_download.csv\r\n",
        "atl_hom.shp                        \u001b[31mstata_full.txt\u001b[m\u001b[m*\r\n",
        "atl_hom.shx                        \u001b[31mstata_sparse.txt\u001b[m\u001b[m*\r\n",
        "\u001b[31matl_hom.zip\u001b[m\u001b[m*                       \u001b[31mstates48.gal\u001b[m\u001b[m*\r\n",
        "\u001b[31mbook.gal\u001b[m\u001b[m*                          \u001b[31mstl.gal\u001b[m\u001b[m*\r\n",
        "\u001b[31mbook.txt\u001b[m\u001b[m*                          \u001b[31mstl_hom.csv\u001b[m\u001b[m*\r\n",
        "\u001b[31mburkitt.dbf\u001b[m\u001b[m*                       \u001b[31mstl_hom.dbf\u001b[m\u001b[m*\r\n",
        "\u001b[31mburkitt.shp\u001b[m\u001b[m*                       \u001b[31mstl_hom.html\u001b[m\u001b[m*\r\n",
        "\u001b[31mburkitt.shx\u001b[m\u001b[m*                       \u001b[31mstl_hom.shp\u001b[m\u001b[m*\r\n",
        "\u001b[31mcalempdensity.csv\u001b[m\u001b[m*                 \u001b[31mstl_hom.shx\u001b[m\u001b[m*\r\n",
        "choropleth.py                      \u001b[31mstl_hom.txt\u001b[m\u001b[m*\r\n",
        "col_copy.gal                       \u001b[31mstl_hom.wkt\u001b[m\u001b[m*\r\n",
        "\u001b[31mcolumbus.dbf\u001b[m\u001b[m*                      \u001b[31mstl_hom_rook.gal\u001b[m\u001b[m*\r\n",
        "\u001b[31mcolumbus.gal\u001b[m\u001b[m*                      style.css\r\n",
        "\u001b[31mcolumbus.html\u001b[m\u001b[m*                     \u001b[31mus48.dbf\u001b[m\u001b[m*\r\n",
        "\u001b[31mcolumbus.shp\u001b[m\u001b[m*                      \u001b[31mus48.shp\u001b[m\u001b[m*\r\n",
        "\u001b[31mcolumbus.shx\u001b[m\u001b[m*                      \u001b[31mus48.shx\u001b[m\u001b[m*\r\n",
        "\u001b[31mdesmith.gal\u001b[m\u001b[m*                       usjoin.csv\r\n",
        "\u001b[31mdesmith.txt\u001b[m\u001b[m*                       \u001b[31mvirginia.dbf\u001b[m\u001b[m*\r\n",
        "\u001b[31mexamples.txt\u001b[m\u001b[m*                      \u001b[31mvirginia.gal\u001b[m\u001b[m*\r\n",
        "\u001b[31mgeobugs_scot\u001b[m\u001b[m*                      \u001b[31mvirginia.prj\u001b[m\u001b[m*\r\n",
        "hou_hom.dbf                        \u001b[31mvirginia.shp\u001b[m\u001b[m*\r\n",
        "hou_hom.shp                        \u001b[31mvirginia.shx\u001b[m\u001b[m*\r\n",
        "hou_hom.shx                        \u001b[31mvirginia_queen\u001b[m\u001b[m*\r\n",
        "houston_hom.zip                    \u001b[31mvirginia_queen.dat\u001b[m\u001b[m*\r\n",
        "houston_metadata.html              \u001b[31mvirginia_queen.dbf\u001b[m\u001b[m*\r\n",
        "\u001b[31mjuvenile.dbf\u001b[m\u001b[m*                      \u001b[31mvirginia_queen.gal\u001b[m\u001b[m*\r\n",
        "\u001b[31mjuvenile.gwt\u001b[m\u001b[m*                      \u001b[31mvirginia_queen.mat\u001b[m\u001b[m*\r\n",
        "\u001b[31mjuvenile.html\u001b[m\u001b[m*                     \u001b[31mvirginia_queen.mtx\u001b[m\u001b[m*\r\n",
        "\u001b[31mjuvenile.shp\u001b[m\u001b[m*                      \u001b[31mvirginia_queen.swm\u001b[m\u001b[m*\r\n",
        "\u001b[31mjuvenile.shx\u001b[m\u001b[m*                      \u001b[31mvirginia_queen.txt\u001b[m\u001b[m*\r\n",
        "\u001b[31mlattice10x10.shp\u001b[m\u001b[m*                  \u001b[31mvirginia_queen.wk1\u001b[m\u001b[m*\r\n",
        "\u001b[31mlattice10x10.shx\u001b[m\u001b[m*                  \u001b[31mvirginia_rook.gal\u001b[m\u001b[m*\r\n",
        "mapping.py                         w5_5.shp\r\n",
        "\u001b[31mmexico.csv\u001b[m\u001b[m*                        w5_5.shx\r\n",
        "\u001b[31mmexico.gal\u001b[m\u001b[m*                        \u001b[31mwmat.dat\u001b[m\u001b[m*\r\n",
        "nat.gal                            \u001b[31mwmat.mtx\u001b[m\u001b[m*\r\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wsouth = ps.rook_from_shapefile(\"data/south.shp\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wsouth.n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "1412"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wsouth.pct_nonzero"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "0.0038620805880795125"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wsq = ps.queen_from_shapefile(\"data/south.shp\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wsq.pct_nonzero"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "0.004060701875466459"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wsouth.histogram"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "[(1, 16),\n",
        " (2, 32),\n",
        " (3, 65),\n",
        " (4, 187),\n",
        " (5, 378),\n",
        " (6, 435),\n",
        " (7, 230),\n",
        " (8, 56),\n",
        " (9, 11),\n",
        " (10, 2)]"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wsq.histogram"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "[(1, 16),\n",
        " (2, 29),\n",
        " (3, 54),\n",
        " (4, 141),\n",
        " (5, 306),\n",
        " (6, 437),\n",
        " (7, 319),\n",
        " (8, 86),\n",
        " (9, 18),\n",
        " (10, 5),\n",
        " (11, 1)]"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wh = np.array(wsouth.histogram)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bar(wh[:,0],wh[:,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "<Container object of 10 artists>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Qyy+/LLvdrtraWq1evXpaReL2RsAnrj3c2hIe8MlAwCMeBHzi2sOtjQU/AACSCHgAMBYB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\nDwCGIuABwFAEPAAYioAHAEP9P60eX6l/kJGBAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1096c32d0>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "whq = np.array(wsq.histogram)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bar(whq[:,0],whq[:,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "<Container object of 11 artists>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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v1yeffCKPx6NDhw7p5ptvliTt2rVLL7zwgux2u+rq6rR27dppFYkvNwLeuvYwu1ke8MlA\nwCMRBLx17WF2s3yaJABgdiLgAcBQBDwAGIqABwBDEfAAYCgCHgAMRcADgKEIeAAwFAEPAIYi4AHA\nUAQ8ABiKgAcAQxHwAGAoAh4ADEXAA4ChCHgAMFTcgN+yZYucTqdWrFgRe6+qqkput1s+n08+n0+H\nDx+ObaupqVFubq7y8/PV1taWnKoBAHHFXdHprbfeUnp6ujZv3qz3339fklRdXa25c+dq+/bto/a9\nuh7rsWPHYuuxdnZ2Ki1t9OcIKzohEazoZF17mN0sX9HpnnvuUWZm5nXvj9VIc3OzKioq5HA45PF4\nlJOTo1AoNOliAADWmfIY/L59+1RQUKCtW7dqcHBQktTT0yO32x3bx+12KxKJTL9KAEDC7FM5aNu2\nbXrmmWckSU8//bR27NihAwcOjLnvlT9Br1dVVRV77ff75ff7p1IKABgrGAwqGAxO+fgpBXxWVlbs\ndWVlpdatWydJcrlcCofDsW3d3d1yuVxjnuOLAQ8AuN61nd/q6uqEjp/SEE1vb2/s9SuvvBKbYVNa\nWqrGxkYNDQ2pq6tLJ0+eVFFR0VSaAABMU9wefEVFhd58802dPn1aixYtUnV1tYLBoNrb22Wz2bR4\n8WLt379fkuT1elVeXi6v1yu73a76+vpxh2gAAMkVd5pkUhplmiQSwDRJ69rD7Gb5NEngWhkZ82Wz\n2Sz/yciYn+pLA4xCDx4JM7lHPdPt0YNHIujBAwAkEfAAYCwCHgAMRcADgKEIeAAwFAEPAIYi4AHA\nUAQ8ABiKgAcAQxHwAGAoAh4ADEXAA4ChCHgAMBQBDwCGihvwW7ZskdPpjC3LJ0kDAwMqKSlRXl6e\n1qxZo8HBwdi2mpoa5ebmKj8/X21tbcmpGgAQV9yAf+SRR3TkyJFR79XW1qqkpESdnZ0qLi5WbW2t\nJKmjo0MHDx5UR0eHjhw5oscee0wjIyPJqRwAMKG4AX/PPfcoMzNz1HstLS0KBAKSpEAgoKamJklS\nc3OzKioq5HA45PF4lJOTo1AolISyAQDxTGkMvr+/X06nU5LkdDrV398vSerp6ZHb7Y7t53a7FYlE\nLCgTAJAo+3RPcHU9zYm2j6Wqqir22u/3y+/3T7cUADBKMBhUMBic8vFTCnin06m+vj5lZ2ert7dX\nWVlZkiSXy6VwOBzbr7u7Wy6Xa8xzfDHgAQDXu7bzW11dndDxUxqiKS0tVUNDgySpoaFBZWVlsfcb\nGxs1NDSkrq4unTx5UkVFRVNpAgAwTXF78BUVFXrzzTd1+vRpLVq0SD//+c/1xBNPqLy8XAcOHJDH\n49GhQ4ckSV6vV+Xl5fJ6vbLb7aqvr59w+AYAkDy2aDQanfFGbTaloFlY5MqHdjL++439e2Fye8lr\na+z2MLslmp18kxUADEXAA4ChCHjgSyQjY35sarPVPxkZ81N9ebgGY/BImMlj4jPd3kyPwTPmP7sx\nBg8AkETAA4CxCHgAMBQBDwCGIuABwFAEPAAYioAHAEMR8ABgKAIeAAxFwAOAoQh4ADAUAQ8AhprW\notsej0cZGRm66aab5HA4FAqFNDAwoO9973v65z//GVvt6eabb7aqXgDAJE2rB2+z2RQMBnXixAmF\nQiFJUm1trUpKStTZ2ani4mLV1tZaUigAIDHTHqK59tGVLS0tCgQCkqRAIKCmpqbpNoE4kvWMb57v\nDcxu03oe/O2336558+bppptu0g9+8AM9+uijyszM1JkzZyRdCf/58+fH/h1rlOfBW8rk56Wb3h7P\ng0ciEs3OaY3Bv/3221q4cKH+9a9/qaSkRPn5+dcVc+UX6npVVVWx136/X36/fzqlAIBxgsGggsHg\nlI+3bEWn6upqpaen6/nnn1cwGFR2drZ6e3u1atUq/f3vfx/dKD14S5ncwzW9PXrwSMSMreh08eJF\nnT9/XpL0n//8R21tbVqxYoVKS0vV0NAgSWpoaFBZWdlUmwAATMOUe/BdXV164IEHJEmXL1/W97//\nfT355JMaGBhQeXm5Pvnkk3GnSdKDt5bJPVzT26MHj0Qkmp0sum0AkwPQ9PYIeCSCRbcBAJIIeAAw\nFgEPAIYi4AHAUAQ8ABiKgE8Cng0D4EYwrUcVYGznz59RMqainT8/9mMfAGAs9OABwFAEPAAYioAH\nAEMR8ABgKAIeQNIka0YZs8om50sxiyYjY/5/Z7ZYa+7cTJ07N2D5eQFTJGtG2ZVzM6ssni9FwDNt\nEcCXEUM0AGCopAT8kSNHlJ+fr9zcXO3evTsZTQAA4rA84IeHh/XDH/5QR44cUUdHh15++WV9+OGH\nVjcDANeZzgLVJrI84EOhkHJycuTxeORwOPTQQw+pubnZ6mYA4Dpr1/4/Zux8geUBH4lEtGjRoti/\n3W63IpGI1c0AwHWGhi7pyoQKa3+SMQtvJlge8FfWfAQA893oT461fJqky+VSOByO/TscDsvtdo/a\nZ8mSJSn4IEhOe+NfB+3R3mTbS97/C7SX7LaS4/z5M2O2t2TJkoTOY4tavAz65cuX9fWvf11vvPGG\nbr31VhUVFenll1/W0qVLrWwGABCH5T14u92uX//611q7dq2Gh4e1detWwh0AUsDyHjwA4MYw499k\nNflLUOFwWKtWrdKyZcu0fPlyPffcc6kuyXLDw8Py+Xxat25dqkux3ODgoDZs2KClS5fK6/XqnXfe\nSXVJlqqpqdGyZcu0YsUKbdy4UZ999lmqS5qWLVu2yOl0asWKFbH3BgYGVFJSory8PK1Zs0aDg4Mp\nrHB6xrq+n/zkJ1q6dKkKCgr04IMP6uzZsxOeY0YD3vQvQTkcDu3du1cffPCB3nnnHf3mN78x6vok\nqa6uTl6v18jZUj/60Y9033336cMPP9Tf/vY3o4YWT506peeff17Hjx/X+++/r+HhYTU2Nqa6rGl5\n5JFHdOTIkVHv1dbWqqSkRJ2dnSouLlZtbW2Kqpu+sa5vzZo1+uCDD/Tee+8pLy9PNTU1E55jRgPe\n9C9BZWdnq7CwUJKUnp6upUuXqqenJ8VVWae7u1utra2qrKyUaSN7Z8+e1VtvvaUtW7ZIunIvad68\neSmuyjoZGRlyOBy6ePGiLl++rIsXL8rlcqW6rGm55557lJmZOeq9lpYWBQIBSVIgEFBTU1MqSrPE\nWNdXUlKitLQrsb1y5Up1d3dPeI4ZDfgv05egTp06pRMnTmjlypWpLsUyjz/+uPbs2RP7BTNJV1eX\nvva1r+mRRx7RN77xDT366KO6ePFiqsuyzPz587Vjxw7ddtttuvXWW3XzzTdr9erVqS7Lcv39/XI6\nnZIkp9Op/v7+FFeUPC+88ILuu+++CfeZ0f9TTfyzfiwXLlzQhg0bVFdXp/T09FSXY4lXX31VWVlZ\n8vl8xvXepSvTe48fP67HHntMx48f11e/+tVZ/ef9tT7++GP96le/0qlTp9TT06MLFy7oD3/4Q6rL\nSqqrXxoy0S9+8Qv93//9nzZu3DjhfjMa8JP5EtRs9/nnn2v9+vV6+OGHVVZWlupyLHP06FG1tLRo\n8eLFqqio0J/+9Cdt3rw51WVZxu12y+1265vf/KYkacOGDTp+/HiKq7LOu+++q29/+9tasGCB7Ha7\nHnzwQR09ejTVZVnO6XSqr69PktTb26usrKwUV2S93/3ud2ptbZ3UB/SMBvxdd92lkydP6tSpUxoa\nGtLBgwdVWlo6kyUkVTQa1datW+X1evXjH/841eVYateuXQqHw+rq6lJjY6Puvfdevfjii6kuyzLZ\n2dlatGiROjs7JUmvv/66li1bluKqrJOfn6933nlHn376qaLRqF5//XV5vd5Ul2W50tJSNTQ0SJIa\nGhqM6mRJV2Yh7tmzR83NzfrKV74S/4DoDGttbY3m5eVFlyxZEt21a9dMN59Ub731VtRms0ULCgqi\nhYWF0cLCwujhw4dTXZblgsFgdN26dakuw3Lt7e3Ru+66K3rHHXdEH3jggejg4GCqS7LU7t27o16v\nN7p8+fLo5s2bo0NDQ6kuaVoeeuih6MKFC6MOhyPqdrujL7zwQvTf//53tLi4OJqbmxstKSmJnjlz\nJtVlTtm113fgwIFoTk5O9Lbbbovly7Zt2yY8B190AgBDmTcdAgAgiYAHAGMR8ABgKAIeAAxFwAOA\noQh4ADAUAQ8AhiLgAcBQ/x+WgXGiTTCn9QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109788bd0>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Distance Based Weights"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x,y = np.indices((5,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.shape = (25,1)\n",
      "y.shape = (25,1)\n",
      "data = np.hstack([x,y])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "array([[0, 0],\n",
        "       [0, 1],\n",
        "       [0, 2],\n",
        "       [0, 3],\n",
        "       [0, 4],\n",
        "       [1, 0],\n",
        "       [1, 1],\n",
        "       [1, 2],\n",
        "       [1, 3],\n",
        "       [1, 4],\n",
        "       [2, 0],\n",
        "       [2, 1],\n",
        "       [2, 2],\n",
        "       [2, 3],\n",
        "       [2, 4],\n",
        "       [3, 0],\n",
        "       [3, 1],\n",
        "       [3, 2],\n",
        "       [3, 3],\n",
        "       [3, 4],\n",
        "       [4, 0],\n",
        "       [4, 1],\n",
        "       [4, 2],\n",
        "       [4, 3],\n",
        "       [4, 4]])"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "knn weights"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wknn3 = ps.knnW(data, k = 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wknn3[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "{1: 1.0, 5: 1.0, 6: 1.0}"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wknn3.histogram"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "[(3, 25)]"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Distance Bands"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdb = ps.threshold_binaryW_from_array(data,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdb[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "{1: 1, 2: 1, 5: 1, 6: 1, 10: 1}"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdb.histogram"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "[(5, 4), (6, 0), (7, 8), (8, 4), (9, 0), (10, 4), (11, 4), (12, 1)]"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdb.cardinalities"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "{0: 5,\n",
        " 1: 7,\n",
        " 2: 8,\n",
        " 3: 7,\n",
        " 4: 5,\n",
        " 5: 7,\n",
        " 6: 10,\n",
        " 7: 11,\n",
        " 8: 10,\n",
        " 9: 7,\n",
        " 10: 8,\n",
        " 11: 11,\n",
        " 12: 12,\n",
        " 13: 11,\n",
        " 14: 8,\n",
        " 15: 7,\n",
        " 16: 10,\n",
        " 17: 11,\n",
        " 18: 10,\n",
        " 19: 7,\n",
        " 20: 5,\n",
        " 21: 7,\n",
        " 22: 8,\n",
        " 23: 7,\n",
        " 24: 5}"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Writing to external files"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w = ps.rook_from_shapefile('data/nat.shp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w.n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "3085"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gal = ps.open('data/nat_copy.gal', 'w')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gal.write(w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gal.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gal = ps.open('data/nat_copy.gal', 'r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w1 = gal.read()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w1.n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "3085"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w.pct_nonzero == w1.pct_nonzero"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}