create_shapefile_clips.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import geopandas as gpd\n",
    "from rasterio import features\n",
    "from affine import Affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_from_latlon(lat, lon):\n",
    "    lat = np.asarray(lat)\n",
    "    lon = np.asarray(lon)\n",
    "    trans = Affine.translation(lon[0], lat[0])\n",
    "    scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n",
    "    return trans * scale\n",
    "\n",
    "\n",
    "def rasterize(\n",
    "    shapes, coords, latitude=\"latitude\", longitude=\"longitude\", fill=np.nan, **kwargs\n",
    "):\n",
    "    transform = transform_from_latlon(coords[latitude], coords[longitude])\n",
    "    out_shape = (len(coords[latitude]), len(coords[longitude]))\n",
    "    raster = features.rasterize(\n",
    "        shapes,\n",
    "        out_shape=out_shape,\n",
    "        fill=fill,\n",
    "        transform=transform,\n",
    "        dtype=float,\n",
    "        **kwargs,\n",
    "    )\n",
    "    spatial_coords = {latitude: coords[latitude], longitude: coords[longitude]}\n",
    "    return xr.DataArray(raster, coords=spatial_coords, dims=(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use as base data shape for this project\n",
    "ds = xr.open_dataset('/nobackup/earlacoa/machinelearning/data_annual/predictions/PM2_5_DRY/ds_RES1.0_IND1.0_TRA1.0_AGR1.0_ENE1.0_PM2_5_DRY_popgrid_0.25deg.nc')['PM2_5_DRY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "clips = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "shapefiles = gpd.read_file(\"/nobackup/earlacoa/health/data/gadm28_adm0.shp\")\n",
    "countries = [49, 102, 132, 225]\n",
    "\n",
    "for country in countries:\n",
    "    shapefile = shapefiles.loc[shapefiles.ID_0 == country]\n",
    "    shapes = [(shape, n) for n, shape in enumerate(shapefile.geometry)]\n",
    "    clip = rasterize(shapes, ds.coords, longitude='lon', latitude='lat') \n",
    "    clips.update({shapefile.NAME_ENGLI.values[0]: clip})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "shapefiles = gpd.read_file(\"/nobackup/earlacoa/health/data/gadm36_CHN_1.shp\")\n",
    "provinces = [province for province in shapefiles.NAME_1]\n",
    "\n",
    "for province in provinces:\n",
    "    shapefile = shapefiles.loc[shapefiles.NAME_1 == province]\n",
    "    shapes = [(shape, n) for n, shape in enumerate(shapefile.geometry)]\n",
    "    clip = rasterize(shapes, ds.coords, longitude='lon', latitude='lat') \n",
    "    clips.update({province: clip})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "shapefiles = gpd.read_file(\"/nobackup/earlacoa/health/data/gadm36_CHN_2.shp\")\n",
    "prefecture_ids = [\n",
    "    \"CHN.6.2_1\",\n",
    "    \"CHN.6.3_1\",\n",
    "    \"CHN.6.4_1\",\n",
    "    \"CHN.6.6_1\",\n",
    "    \"CHN.6.7_1\",\n",
    "    \"CHN.6.15_1\",\n",
    "    \"CHN.6.19_1\",\n",
    "    \"CHN.6.20_1\",\n",
    "    \"CHN.6.21_1\",\n",
    "]\n",
    "\n",
    "for prefecture_id in prefecture_ids:\n",
    "    shapefile = shapefiles.loc[shapefiles.GID_2 == prefecture_id]\n",
    "    shapes = [(shape, n) for n, shape in enumerate(shapefile.geometry)]\n",
    "    clip = rasterize(shapes, ds.coords, longitude='lon', latitude='lat') \n",
    "    clips.update({shapefile.NAME_2.values[0]: clip})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['China', 'Hong Kong', 'Macao', 'Taiwan', 'Anhui', 'Beijing', 'Chongqing', 'Fujian', 'Gansu', 'Guangdong', 'Guangxi', 'Guizhou', 'Hainan', 'Hebei', 'Heilongjiang', 'Henan', 'Hubei', 'Hunan', 'Jiangsu', 'Jiangxi', 'Jilin', 'Liaoning', 'Nei Mongol', 'Ningxia Hui', 'Qinghai', 'Shaanxi', 'Shandong', 'Shanghai', 'Shanxi', 'Sichuan', 'Tianjin', 'Xinjiang Uygur', 'Xizang', 'Yunnan', 'Zhejiang', 'Dongguan', 'Foshan', 'Guangzhou', 'Huizhou', 'Jiangmen', 'Shenzhen', 'Zhaoqing', 'Zhongshan', 'Zhuhai'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clips.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b209b3d1490>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clips['China'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/nobackup/earlacoa/machinelearning/data_annual/clips.joblib']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(clips, f'/nobackup/earlacoa/machinelearning/data_annual/clips.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}