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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import bokeh as bk\n",
    "\n",
    "float(bk.__version__[:2]) >= 0.12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "float(mpl.__version__[:3]) >= 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hvplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "float(pd.__version__[:3]) >= 0.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "float(sklearn.__version__[:4]) >= 0.17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import xlsxwriter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import fiona\n",
    "float(fiona.__version__[:3]) >= 1.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "float(gpd.__version__[:3]) >= 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio as rio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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 ((8.62413 14.23698, 8.55970 14.74245, ...</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 ((8.25279 14.23694, 8.28276 14.22994, ...</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 ((8.65331 14.00809, 8.81814 14.00205, ...</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 ((8.45950 13.82035, 8.47341 13.83227, ...</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 ((8.68527 13.63952, 8.67758 13.72221, ...</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 ((8.62413 14.23698, 8.55970 14.74245, ...  \n",
       "1  0.0  0.0  1000.0  1001.0  POLYGON ((8.25279 14.23694, 8.28276 14.22994, ...  \n",
       "2  1.0  0.0  1000.0  1006.0  POLYGON ((8.65331 14.00809, 8.81814 14.00205, ...  \n",
       "3  0.0  0.0  1000.0  1002.0  POLYGON ((8.45950 13.82035, 8.47341 13.83227, ...  \n",
       "4  1.0  0.0  1000.0  1007.0  POLYGON ((8.68527 13.63952, 8.67758 13.72221, ...  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import libpysal\n",
    "shp = libpysal.examples.get_path('columbus.shp')\n",
    "db = gpd.read_file(shp)\n",
    "db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-5d0dee84d946>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgeopandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'init'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'epsg:26918'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "\n",
    "db.crs['init'] = 'epsg:26918'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot transform naive geometries.  Please set a crs on the object first.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-a95152531016>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdb_wgs84\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_crs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepsg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4326\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdb_wgs84\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pysal-spatialapi20/lib/python3.7/site-packages/geopandas/geodataframe.py\u001b[0m in \u001b[0;36mto_crs\u001b[0;34m(self, crs, epsg, inplace)\u001b[0m\n\u001b[1;32m    814\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    815\u001b[0m             \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 816\u001b[0;31m         \u001b[0mgeom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_crs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepsg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    817\u001b[0m         \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m         \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pysal-spatialapi20/lib/python3.7/site-packages/geopandas/geoseries.py\u001b[0m in \u001b[0;36mto_crs\u001b[0;34m(self, crs, epsg)\u001b[0m\n\u001b[1;32m    525\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    526\u001b[0m             raise ValueError(\n\u001b[0;32m--> 527\u001b[0;31m                 \u001b[0;34m\"Cannot transform naive geometries.  \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    528\u001b[0m                 \u001b[0;34m\"Please set a crs on the object first.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    529\u001b[0m             )\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot transform naive geometries.  Please set a crs on the object first."
     ]
    }
   ],
   "source": [
    "db_wgs84 = db.to_crs(epsg=4326)\n",
    "db_wgs84.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": "3.7.9"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}