check_workshop.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check `PySAL Workshop` stack\n",
    "\n",
    "This notebook checks all software requirements for the PySAL Workshop are correctly installed. \n",
    "\n",
    "A successful run of the notebook implies no errors returned in any cell *and* every cell beyond the first one returning a printout of `True`. This ensures a correct environment installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import bokeh as bk\n",
    "\n",
    "float(bk.__version__[:2]) >= 0.12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "float(mpl.__version__[:3]) >= 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mplleaflet as mpll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "float(sns.__version__[:3]) >= 0.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "float(pd.__version__[:4]) >= 0.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "float(sklearn.__version__[:4]) >= 0.17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "float(sm.version.version[:3]) >= 0.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlsxwriter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import fiona\n",
    "float(fiona.__version__[:3]) >= 1.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "float(gpd.__version__[:3]) >= 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pysal\n",
    "from pysal.explore import esda\n",
    "from pysal.viz import mapclassify\n",
    "from pysal.explore import giddy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio as rio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>AREA</th>\n",
       "      <th>PERIMETER</th>\n",
       "      <th>COLUMBUS_</th>\n",
       "      <th>COLUMBUS_I</th>\n",
       "      <th>POLYID</th>\n",
       "      <th>NEIG</th>\n",
       "      <th>HOVAL</th>\n",
       "      <th>INC</th>\n",
       "      <th>CRIME</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>...</th>\n",
       "      <th>DISCBD</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>NSA</th>\n",
       "      <th>NSB</th>\n",
       "      <th>EW</th>\n",
       "      <th>CP</th>\n",
       "      <th>THOUS</th>\n",
       "      <th>NEIGNO</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.309441</td>\n",
       "      <td>2.440629</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>80.467003</td>\n",
       "      <td>19.531</td>\n",
       "      <td>15.725980</td>\n",
       "      <td>2.850747</td>\n",
       "      <td>...</td>\n",
       "      <td>5.03</td>\n",
       "      <td>38.799999</td>\n",
       "      <td>44.070000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>POLYGON ((8.624129295349121 14.23698043823242,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.259329</td>\n",
       "      <td>2.236939</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>44.567001</td>\n",
       "      <td>21.232</td>\n",
       "      <td>18.801754</td>\n",
       "      <td>5.296720</td>\n",
       "      <td>...</td>\n",
       "      <td>4.27</td>\n",
       "      <td>35.619999</td>\n",
       "      <td>42.380001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>POLYGON ((8.252790451049805 14.23694038391113,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.192468</td>\n",
       "      <td>2.187547</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>26.350000</td>\n",
       "      <td>15.956</td>\n",
       "      <td>30.626781</td>\n",
       "      <td>4.534649</td>\n",
       "      <td>...</td>\n",
       "      <td>3.89</td>\n",
       "      <td>39.820000</td>\n",
       "      <td>41.180000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>POLYGON ((8.653305053710938 14.00809001922607,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.083841</td>\n",
       "      <td>1.427635</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>33.200001</td>\n",
       "      <td>4.477</td>\n",
       "      <td>32.387760</td>\n",
       "      <td>0.394427</td>\n",
       "      <td>...</td>\n",
       "      <td>3.70</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>40.520000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>POLYGON ((8.459499359130859 13.82034969329834,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.488888</td>\n",
       "      <td>2.997133</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>23.225000</td>\n",
       "      <td>11.252</td>\n",
       "      <td>50.731510</td>\n",
       "      <td>0.405664</td>\n",
       "      <td>...</td>\n",
       "      <td>2.83</td>\n",
       "      <td>40.009998</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>POLYGON ((8.685274124145508 13.63951969146729,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AREA  PERIMETER  COLUMBUS_  COLUMBUS_I  POLYID  NEIG      HOVAL  \\\n",
       "0  0.309441   2.440629          2           5       1     5  80.467003   \n",
       "1  0.259329   2.236939          3           1       2     1  44.567001   \n",
       "2  0.192468   2.187547          4           6       3     6  26.350000   \n",
       "3  0.083841   1.427635          5           2       4     2  33.200001   \n",
       "4  0.488888   2.997133          6           7       5     7  23.225000   \n",
       "\n",
       "      INC      CRIME      OPEN  ...  DISCBD          X          Y  NSA  NSB  \\\n",
       "0  19.531  15.725980  2.850747  ...    5.03  38.799999  44.070000  1.0  1.0   \n",
       "1  21.232  18.801754  5.296720  ...    4.27  35.619999  42.380001  1.0  1.0   \n",
       "2  15.956  30.626781  4.534649  ...    3.89  39.820000  41.180000  1.0  1.0   \n",
       "3   4.477  32.387760  0.394427  ...    3.70  36.500000  40.520000  1.0  1.0   \n",
       "4  11.252  50.731510  0.405664  ...    2.83  40.009998  38.000000  1.0  1.0   \n",
       "\n",
       "    EW   CP   THOUS  NEIGNO                                           geometry  \n",
       "0  1.0  0.0  1000.0  1005.0  POLYGON ((8.624129295349121 14.23698043823242,...  \n",
       "1  0.0  0.0  1000.0  1001.0  POLYGON ((8.252790451049805 14.23694038391113,...  \n",
       "2  1.0  0.0  1000.0  1006.0  POLYGON ((8.653305053710938 14.00809001922607,...  \n",
       "3  0.0  0.0  1000.0  1002.0  POLYGON ((8.459499359130859 13.82034969329834,...  \n",
       "4  1.0  0.0  1000.0  1007.0  POLYGON ((8.685274124145508 13.63951969146729,...  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp = pysal.lib.examples.get_path('columbus.shp')\n",
    "db = gpd.read_file(shp)\n",
    "db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.crs['init'] = 'epsg:26918'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "db_wgs84 = db.to_crs(epsg=4326)\n",
    "db_wgs84.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "db.plot(column='INC', scheme='fisher_jenks', cmap=plt.matplotlib.cm.Blues)\n",
    "#plt.show()\n",
    "plt.savefig(fname='inc.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": 2
}