{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_9492\\3555289031.py:55: UserWarning: The `geometries` module and `geometries_from_X` functions have been renamed the `features` module and `features_from_X` functions. Use these instead. The `geometries` module and function names are deprecated and will be removed in a future release.\n",
      "  data = ox.geometries_from_polygon(aoi.geometry[0], tags=tags)\n",
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_9492\\3555289031.py:58: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  water_infrastructure = water_infrastructure.append(data)\n",
      "c:\\Users\\jtrum\\miniconda3\\envs\\wash_scan\\lib\\site-packages\\geopandas\\array.py:1406: UserWarning: CRS not set for some of the concatenation inputs. Setting output's CRS as WGS 84 (the single non-null crs provided).\n",
      "  warnings.warn(\n",
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_9492\\3555289031.py:55: UserWarning: The `geometries` module and `geometries_from_X` functions have been renamed the `features` module and `features_from_X` functions. Use these instead. The `geometries` module and function names are deprecated and will be removed in a future release.\n",
      "  data = ox.geometries_from_polygon(aoi.geometry[0], tags=tags)\n",
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_9492\\3555289031.py:58: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  water_infrastructure = water_infrastructure.append(data)\n",
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_9492\\3555289031.py:55: UserWarning: The `geometries` module and `geometries_from_X` functions have been renamed the `features` module and `features_from_X` functions. Use these instead. The `geometries` module and function names are deprecated and will be removed in a future release.\n",
      "  data = ox.geometries_from_polygon(aoi.geometry[0], tags=tags)\n",
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_9492\\3555289031.py:58: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  water_infrastructure = water_infrastructure.append(data)\n",
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_9492\\3555289031.py:55: UserWarning: The `geometries` module and `geometries_from_X` functions have been renamed the `features` module and `features_from_X` functions. Use these instead. The `geometries` module and function names are deprecated and will be removed in a future release.\n",
      "  data = ox.geometries_from_polygon(aoi.geometry[0], tags=tags)\n",
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_9492\\3555289031.py:58: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  water_infrastructure = water_infrastructure.append(data)\n",
      "c:\\Users\\jtrum\\miniconda3\\envs\\wash_scan\\lib\\site-packages\\pyproj\\crs\\crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of grid cells:  4087\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "import osmnx as ox\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from shapely.geometry import Polygon\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "main_dir = 'C:/Users/jtrum/world_bank/'\n",
    "aoi = gpd.read_file(main_dir + 'data/luanda2clean.geojson')\n",
    "catchment = gpd.read_file(main_dir + 'data/catchment.geojson')\n",
    "aoi = aoi.to_crs(epsg=4326)\n",
    "catchment = catchment.to_crs(epsg=4326)\n",
    "\n",
    "\n",
    "#make fishnet grid\n",
    "def createGrid(polygon, gridSize):\n",
    "    xmin, ymin, xmax, ymax = polygon.bounds #creates bounding box of 'co' polygon\n",
    "    rows = int(np.ceil((ymax-ymin)/gridSize)) #number of rows\n",
    "    cols = int(np.ceil((xmax-xmin)/gridSize)) #number of columns\n",
    "    polygons = [] #empty list to hold polygons\n",
    "    for i in range(cols):\n",
    "        for j in range(rows):\n",
    "            polygons.append(Polygon([(xmin+i*gridSize, ymin+j*gridSize), \n",
    "                                     (xmin+(i+1)*gridSize, ymin+j*gridSize), \n",
    "                                     (xmin+(i+1)*gridSize, ymin+(j+1)*gridSize), \n",
    "                                     (xmin+i*gridSize, ymin+(j+1)*gridSize)]))\n",
    "    grid = gpd.GeoDataFrame({'geometry':polygons})\n",
    "    grid.crs = {'init':'epsg:4326'}\n",
    "    grid = grid[grid.geometry.within(polygon)] #keep only grid cells within 'co' polygon\n",
    "    return grid\n",
    "\n",
    "#determine distance to nearest water infrastructure point for each grid cell\n",
    "def distance_to_nearest(row, destination, val):\n",
    "    #row is a row of the grid dataframe\n",
    "    #destination is the water infrastructure point dataframe\n",
    "    #val is the value to return if there are no water infrastructure points\n",
    "    if len(destination) == 0:\n",
    "        return val\n",
    "    else:\n",
    "        dist = destination.distance(row['geometry'])\n",
    "        return dist.min()\n",
    "\n",
    "\n",
    "\n",
    "tags_list = [\n",
    "    {'waterway': True},\n",
    "    {'landuse': ['reservoir', 'basin']},\n",
    "    {'amenity': ['drinking_water', 'watering_place', 'water_point']},\n",
    "    {'man_made': ['water_well', 'water_tower', 'water_works', 'reservoir_covered', 'storage_tank', 'monitoring_station', 'wastewater_plant', 'watermill', 'pipeline']}\n",
    "]        \n",
    "\n",
    "water_infrastructure = pd.DataFrame(columns=['feature', 'geometry'])\n",
    "\n",
    "for tags in tags_list:\n",
    "    data = ox.geometries_from_polygon(aoi.geometry[0], tags=tags)\n",
    "    data = data[['geometry']]\n",
    "    data['feature'] = list(tags.keys())[0]  # Extract the feature type from the tags\n",
    "    water_infrastructure = water_infrastructure.append(data)\n",
    "\n",
    "water_infrastructure = water_infrastructure.reset_index(drop=True)\n",
    "water_infrastructure = gpd.GeoDataFrame(water_infrastructure, geometry='geometry', crs=aoi.crs)\n",
    "\n",
    "# in 'water_infrastructure', give a value of 1 if the feature is a waterway, and 0 if it is not, in a column called 'layer'\n",
    "water_infrastructure['layer'] = water_infrastructure['feature'].apply(lambda x: 1 if x == 'waterway' else 0)\n",
    "#turn into geodataframe\n",
    "water_infrastructure = gpd.GeoDataFrame(water_infrastructure, geometry='geometry') \n",
    "\n",
    "#convert water_infrastructure crs into one that measures distance in meters\n",
    "water_infrastructure = water_infrastructure.to_crs(epsg=32632)\n",
    "aoi = aoi.to_crs(epsg=32632)\n",
    "catchment = catchment.to_crs(epsg=32632)\n",
    "water_infrastructure\n",
    "\n",
    "#make a subset of water_infrastructure to include only 0 values in the 'layer' column\n",
    "water_infrastructure_0 = water_infrastructure[water_infrastructure['layer'] == 0].reset_index().drop(columns=['index'])\n",
    "water_infrastructure_0['geometry'] = water_infrastructure_0['geometry'].centroid #take centroids of all of the polygons in water_infrastructure_0 to get point data\n",
    "#make a subset of water_infrastructure to include only 1 values in the 'layer' column\n",
    "water_infrastructure_1 = water_infrastructure[water_infrastructure['layer'] == 1].reset_index().drop(columns=['index'])\n",
    "\n",
    "#make a fishnet grid of Philadelphia\n",
    "gridSize = 750 #grid size in meters\n",
    "grid = createGrid(aoi.geometry[0], gridSize)\n",
    "\n",
    "print('Number of grid cells: ', len(grid))\n",
    "grid = grid.reset_index().rename(columns={'index':'grid_id'})    \n",
    "grid['dist_to_water_infrastructure'] = grid.apply(lambda row: distance_to_nearest(row, water_infrastructure_0, 0), axis=1)\n",
    "grid['dist_to_water_infrastructure'] = grid['dist_to_water_infrastructure'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'grid' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39m#ax = catchment.plot(color='black', edgecolor='black', alpha=0.1)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m ax \u001b[39m=\u001b[39m grid\u001b[39m.\u001b[39mplot(column\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mdist_to_water_infrastructure\u001b[39m\u001b[39m'\u001b[39m, cmap\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39msummer\u001b[39m\u001b[39m'\u001b[39m, legend\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, figsize\u001b[39m=\u001b[39m(\u001b[39m16\u001b[39m, \u001b[39m16\u001b[39m), aspect\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mequal\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      3\u001b[0m ax \u001b[39m=\u001b[39m water_infrastructure_1\u001b[39m.\u001b[39mplot(ax\u001b[39m=\u001b[39max, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblue\u001b[39m\u001b[39m'\u001b[39m, alpha\u001b[39m=\u001b[39m\u001b[39m0.25\u001b[39m, edgecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblue\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[39m# add points of water infrastructure\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'grid' is not defined"
     ]
    }
   ],
   "source": [
    "#ax = catchment.plot(color='black', edgecolor='black', alpha=0.1)\n",
    "ax = grid.plot(column='dist_to_water_infrastructure', cmap='summer', legend=True, figsize=(16, 16), aspect='equal')\n",
    "ax = water_infrastructure_1.plot(ax=ax, color='blue', alpha=0.25, edgecolor='blue')\n",
    "# add points of water infrastructure\n",
    "ax = water_infrastructure_0.plot(ax=ax, alpha=1, color='black', markersize=1, edgecolor='black')\n",
    "# No ticks\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# No frame\n",
    "ax.set_frame_on(False)\n",
    "# Change font to Helvetica\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "# Add title\n",
    "plt.title('Distance to Nearest Water\\n Infrastructure Point in Luanda, Angola', fontsize=16);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1600x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "catchment = catchment.to_crs(aoi.crs)\n",
    "ax = catchment.plot(color='black', edgecolor='black', alpha=0.1, figsize=(16, 16))\n",
    "ax = grid.plot(ax=ax, column='dist_to_water_infrastructure', cmap='summer', legend=True, figsize=(16, 16), aspect='equal')\n",
    "ax = water_infrastructure_1.plot(ax=ax, color='blue', alpha=0.25, edgecolor='blue')\n",
    "# add points of water infrastructure\n",
    "ax = water_infrastructure_0.plot(ax=ax, alpha=1, color='black', markersize=1, edgecolor='black')\n",
    "# No ticks\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "# No frame\n",
    "ax.set_frame_on(False)\n",
    "# Change font to Helvetica\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "# Add title\n",
    "plt.title('Distance to Nearest Water\\n Infrastructure Point in Luanda, Angola', fontsize=16);\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "wash_scan",
   "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": "3.8.17"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}