demo of how to generate a shapefile from a csv of GRTS cells

GenerateSahpefileOfGRTS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "#### How do we install geopandas in our container?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "#### Can we have a local copy of the shapefile in this container?\n",
    "#### Or would it make more sense to to hit a wfs?\n",
    "#### Or would it make more sense to generate the geometries by code (would need the CONUS_10km as well as GRTS_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_fname = r\"Z:\\TSH\\DD274_NABat\\SamplingGrid\\DATA\\DERIVED\\GridsWithGRTS\\Conus\\conus_mastersample_10km_GRTS.shp\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Once we have those answers the actual generation of the shapefile is relatively easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = gpd.GeoDataFrame.from_file(grid_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>CONUS_10KM</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>GRTS_ID</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>132883</td>\n",
       "      <td>47.797518</td>\n",
       "      <td>-127.691696</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-2353000 3156000, -2343000 3156000, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74208</td>\n",
       "      <td>39.293360</td>\n",
       "      <td>-108.770110</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((-1093000 1886000, -1083000 1886000, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>520</td>\n",
       "      <td>24.145321</td>\n",
       "      <td>-113.527387</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-1803000 296000, -1793000 296000, -1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CONUS_10KM        lat        long  GRTS_ID  \\\n",
       "0      132883  47.797518 -127.691696        1   \n",
       "1       74208  39.293360 -108.770110        2   \n",
       "2         520  24.145321 -113.527387        3   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-2353000 3156000, -2343000 3156000, ...  \n",
       "1  POLYGON ((-1093000 1886000, -1083000 1886000, ...  \n",
       "2  POLYGON ((-1803000 296000, -1793000 296000, -1...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### This is the csv that would get supplied to the microservice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>GRTS_ID</th>\n",
       "      <th>project</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1768</td>\n",
       "      <td>foo</td>\n",
       "      <td>MYLU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9960</td>\n",
       "      <td>foo</td>\n",
       "      <td>EPFU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33128</td>\n",
       "      <td>foo</td>\n",
       "      <td>EPFU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37224</td>\n",
       "      <td>bar</td>\n",
       "      <td>MYLU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GRTS_ID project species\n",
       "0     1768     foo    MYLU\n",
       "1     9960     foo    EPFU\n",
       "2    33128     foo    EPFU\n",
       "3    37224     bar    MYLU"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_csv = \"\"\"GRTS_ID,project,species\n",
    "1768,foo,MYLU\n",
    "9960,foo,EPFU\n",
    "33128,foo,EPFU\n",
    "37224,bar,MYLU\"\"\"\n",
    "\n",
    "import io\n",
    "\n",
    "df = pd.read_csv(io.StringIO(sample_csv))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### A couple pandas/geopandas lines gives us a shapefile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_cells = grid[grid.GRTS_ID.isin(df.GRTS_ID)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = selected_cells.merge(df)\n",
    "merged.to_file(r\"C:\\temp\\out.shp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quick check of the output..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x18dd812ffd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "m = folium.Map([merged.lat.mean(), merged.long.mean()])\n",
    "folium.GeoJson(merged.to_crs(epsg='4326')).add_to(m)\n",
    "\n",
    "m"
   ]
  }
 ],
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