brookisme/VectorToRaster-Dev.ipynb

## VectorToRaster-Dev.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shapely\n",
    "import fiona\n",
    "import math\n",
    "import numpy as np\n",
    "from random import choice\n",
    "from rasterio import features\n",
    "import rasterio as rio\n",
    "import json\n",
    "from pprint import pprint\n",
    "from rasterio.crs import CRS\n",
    "from affine import Affine\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mplib_colors\n",
    "import matplotlib.ticker as mplib_ticker\n",
    "import geopandas as gpd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RESOLUTION=1\n",
    "SIZE=math.ceil(37*10/RESOLUTION)\n",
    "SHAPE=(SIZE,SIZE)\n",
    "BUFFER=10\n",
    "NO_DATA=255\n",
    "TARGET_CRS='epsg:32644'\n",
    "VALUE_COLUMN='Land_use'\n",
    "INDEX_COLUMN='Locale_No'\n",
    "DTYPE='uint8'\n",
    "ALL_TOUCHED=False\n",
    "EXPAND_AXIS=0\n",
    "VALUE_MAP={\n",
    "    2: [2,3,4,5],\n",
    "    3: [6]\n",
    "}\n",
    "PAD=16\n",
    "AOI_AREA=10 #ha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "from multiprocessing import Process, cpu_count\n",
    "from multiprocessing import Pool\n",
    "from multiprocessing.pool import ThreadPool\n",
    "\n",
    "\n",
    "#\n",
    "# CONFIG\n",
    "#\n",
    "MAX_POOL_PROCESSES=cpu_count()-1\n",
    "MAX_THREADPOOL_PROCESSES=16\n",
    "\n",
    "\n",
    "\n",
    "def map_with_pool(map_function,args_list,max_processes=MAX_POOL_PROCESSES):\n",
    "    pool=Pool(processes=min(len(args_list),max_processes))\n",
    "    return _run_pool(pool,map_function,args_list)\n",
    "\n",
    "\n",
    "\n",
    "def map_with_threadpool(map_function,args_list,max_processes=MAX_THREADPOOL_PROCESSES):\n",
    "    pool=ThreadPool(processes=min(len(args_list),max_processes))\n",
    "    return _run_pool(pool,map_function,args_list)\n",
    "\n",
    "\n",
    "def _stop_pool(pool,success=True):\n",
    "    pool.close()\n",
    "    pool.join()\n",
    "    return success\n",
    "\n",
    "\n",
    "def _map_async(pool,map_func,objects):\n",
    "    try:\n",
    "        return pool.map_async(map_func,objects)\n",
    "    except KeyboardInterrupt:\n",
    "        print(\"Caught KeyboardInterrupt, terminating workers\")\n",
    "        pool.terminate()\n",
    "        return False\n",
    "    else:\n",
    "        print(\"Failure\")\n",
    "        return _stop_pool(pool,False)\n",
    "\n",
    "\n",
    "def _run_pool(pool,map_function,args_list):\n",
    "    out=_map_async(pool,map_function,args_list)\n",
    "    _stop_pool(pool)\n",
    "    return out.get()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_data(path,src=False):\n",
    "    if src:\n",
    "        src=rio.open(path,'r')\n",
    "        return src, src.read(), src.profile\n",
    "    else:\n",
    "        with rio.open(path,'r') as src:\n",
    "            profile=src.profile\n",
    "            image=src.read()\n",
    "        return image, profile\n",
    "\n",
    "\n",
    "def image_write(im,path,profile):\n",
    "    profile['transform']=profile['affine']\n",
    "    with rio.open(path,'w',**profile) as dst:\n",
    "        dst.write(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "M2_PER_HA=1e4\n",
    "\n",
    "\n",
    "class Rasterizer(object):\n",
    "    @staticmethod\n",
    "    def area_to_pixel_radius(area,resolution=10,conversion=M2_PER_HA):\n",
    "        return math.ceil(math.sqrt(area*conversion/(math.pi))/resolution)\n",
    "\n",
    "    \n",
    "    def __init__(self,\n",
    "            gdf,\n",
    "            crs=TARGET_CRS,\n",
    "            value_map=VALUE_MAP,\n",
    "            buffer=BUFFER,\n",
    "            resolution=RESOLUTION,\n",
    "            no_data=NO_DATA,\n",
    "            index_column=INDEX_COLUMN,\n",
    "            value_column=VALUE_COLUMN,\n",
    "            aoi_area=AOI_AREA,\n",
    "            padding=PAD,\n",
    "            expand_axis=EXPAND_AXIS):\n",
    "        self._set_properties(\n",
    "            value_map,\n",
    "            buffer,\n",
    "            resolution,\n",
    "            no_data,\n",
    "            index_column,\n",
    "            value_column,\n",
    "            aoi_area,\n",
    "            padding,\n",
    "            expand_axis)\n",
    "        if isinstance(gdf,str):\n",
    "            self.regions=gpd.read_file(gdf)\n",
    "        self._set_projection(crs)\n",
    "        self.indices=self.regions[self.index_column].unique().tolist()\n",
    "\n",
    "        \n",
    "    def get(self,index=None):\n",
    "        if index is None:\n",
    "            index=choice(self.indices)\n",
    "        return self.regions[self.regions[self.index_column]==index]\n",
    "    \n",
    "    \n",
    "    def select(self,index=None):\n",
    "        self.region=self.get(index)\n",
    "    \n",
    "    \n",
    "    def plot(self,region=None,index=None,**kwargs):\n",
    "        region=self._get_region(region,index)\n",
    "        region.plot(self.value_column,**kwargs)\n",
    "\n",
    "    \n",
    "    def affine(self,region=None,index=None):\n",
    "        region=self._get_region(region,index)\n",
    "        centroid=region.dissolve(self.index_column).iloc[0].geometry.centroid\n",
    "        boundry=centroid.buffer((self.aoi_pixel_radius+self.padding)*self.resolution,resolution=self.resolution)\n",
    "        xmin,_,_,ymax=boundry.bounds\n",
    "        return Affine(self.resolution, 0.0, xmin,0.0, -self.resolution, ymax)\n",
    "    \n",
    "    \n",
    "    def rasterize(self,region=None,index=None):\n",
    "        region=self._get_region(region,index)\n",
    "        affine=self.affine(region)\n",
    "        geoms_values=self._geometry_values_list(region)\n",
    "        im=features.rasterize(\n",
    "                    geoms_values,\n",
    "                    fill=self.no_data,\n",
    "                    transform=affine,\n",
    "                    out_shape=self.shape,\n",
    "                    all_touched=ALL_TOUCHED,\n",
    "                    dtype=DTYPE)\n",
    "        if self.value_map:\n",
    "            im=self._map_values(im)\n",
    "        h,w=im.shape\n",
    "        if self.expand_axis is not None:\n",
    "            im=np.expand_dims(im,axis=self.expand_axis)\n",
    "        return im, self._profile(affine,h,w)        \n",
    "        \n",
    "        \n",
    "    def _set_projection(self,crs):\n",
    "        if crs:\n",
    "            if isinstance(crs,str):\n",
    "                self.crs=CRS({'init':crs})\n",
    "            self.regions=self.regions.to_crs(self.crs)\n",
    "\n",
    "\n",
    "    def _set_properties(self,\n",
    "            value_map,\n",
    "            buffer,\n",
    "            resolution,\n",
    "            no_data,\n",
    "            index_column,\n",
    "            value_column,\n",
    "            aoi_area,\n",
    "            padding,\n",
    "            expand_axis):\n",
    "        self.value_map=value_map\n",
    "        self.index_column=index_column\n",
    "        self.value_column=value_column\n",
    "        self.resolution=resolution\n",
    "        self.buffer=BUFFER\n",
    "        self.no_data=no_data\n",
    "        self.aoi_pixel_radius=Rasterizer.area_to_pixel_radius(\n",
    "            area=aoi_area,\n",
    "            resolution=resolution)\n",
    "        self.shape=[2*(self.aoi_pixel_radius+padding)]*2\n",
    "        self.padding=padding\n",
    "        self.expand_axis=expand_axis\n",
    "        self.region=None\n",
    "\n",
    "    \n",
    "    def _get_region(self,region=None,index=None):\n",
    "        if region is None:\n",
    "            if index is None:\n",
    "                region=self.region\n",
    "            else:\n",
    "                region=self.get(index)\n",
    "        return region\n",
    "    \n",
    "\n",
    "    def _geometry_values_list(self,region):\n",
    "        return [(row.geometry,int(row[self.value_column])) for i,row in region.iterrows()]\n",
    "    \n",
    "    \n",
    "    def _map_values(self,im):\n",
    "        for k,v in self.value_map.items():\n",
    "            im=np.where(np.isin(im,v),k,im)\n",
    "        return im\n",
    "    \n",
    "    \n",
    "    def _profile(self,affine,height,width):\n",
    "        return {\n",
    "            'affine': affine,\n",
    "             'count': 1,\n",
    "             'crs': self.crs,\n",
    "             'driver': 'GTiff',\n",
    "             'dtype':  DTYPE,\n",
    "             'height': height,\n",
    "             'interleave': 'band',\n",
    "             'nodata': self.no_data,\n",
    "             'tiled': False,\n",
    "             'width': width}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE_COLORS={\n",
    "    'OpenSpace':\"#009246\",\n",
    "    'NonResidential':\"#7bc043\",\n",
    "    'Residential':\"#dcedc1\",\n",
    "    'Roads':\"#414a4c\",\n",
    "    'NoData': \"#ffffff\"\n",
    "}\n",
    "\n",
    "def catshow(im,ax=None,figsize=(6,4),nb_cats=4,alpha=1,color_bar=True):\n",
    "    if im.ndim==3:\n",
    "        im=im[0]\n",
    "    cmap=mplib_colors.ListedColormap(BASE_COLORS.values())\n",
    "    if not ax:\n",
    "        fig,ax=plt.subplots(1,figsize=figsize)\n",
    "    showax=ax.imshow(\n",
    "            im,\n",
    "            vmin=0,\n",
    "            vmax=nb_cats,\n",
    "            cmap=cmap,\n",
    "            alpha=alpha)\n",
    "    if color_bar:\n",
    "        categories=BASE_COLORS.keys()\n",
    "        cbar=plt.colorbar(showax,ax=ax)\n",
    "        cbar.ax.yaxis.set_major_locator(mplib_ticker.LinearLocator(\n",
    "            numticks=len(categories)))\n",
    "        cbar.ax.yaxis.set_ticklabels(categories)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rzr=Rasterizer('Hindupur_Complete.geojson',padding=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'affine': Affine(1.0, 0.0, 120616.94702658665,\n",
      "       0.0, -1.0, 1533087.4351218508),\n",
      " 'count': 1,\n",
      " 'crs': CRS.from_dict(init='epsg:32644'),\n",
      " 'driver': 'GTiff',\n",
      " 'dtype': 'uint8',\n",
      " 'height': 378,\n",
      " 'interleave': 'band',\n",
      " 'nodata': 255,\n",
      " 'tiled': False,\n",
      " 'width': 378}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 378, 378)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "rzr.select(index=19)\n",
    "rzr.plot()\n",
    "im,p=rzr.rasterize()\n",
    "pprint(p)\n",
    "np.unique(im,return_counts=True)\n",
    "catshow(im)\n",
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_write(im,'test-affine.tif',p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'count': 1,\n",
      " 'crs': CRS.from_dict(init='epsg:32644'),\n",
      " 'driver': 'GTiff',\n",
      " 'dtype': 'uint8',\n",
      " 'height': 378,\n",
      " 'interleave': 'band',\n",
      " 'nodata': 255.0,\n",
      " 'tiled': False,\n",
      " 'transform': Affine(1.0, 0.0, 120616.94702658665,\n",
      "       0.0, -1.0, 1533087.4351218508),\n",
      " 'width': 378}\n"
     ]
    }
   ],
   "source": [
    "im_t,p_t=image_data('test-affine.tif')\n",
    "pprint(p_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120402390>"
      ]
     },
     "execution_count": 11,
     "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": [
    "catshow(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'affine': Affine(1.0, 0.0, 120539.6574046311,\n",
      "       0.0, -1.0, 1532246.470642275),\n",
      " 'count': 1,\n",
      " 'crs': CRS.from_dict(init='epsg:32644'),\n",
      " 'driver': 'GTiff',\n",
      " 'dtype': 'uint8',\n",
      " 'height': 378,\n",
      " 'interleave': 'band',\n",
      " 'nodata': 255,\n",
      " 'tiled': False,\n",
      " 'width': 378}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 378, 378)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "rzr.select(index=2)\n",
    "rzr.plot()\n",
    "im,p=rzr.rasterize()\n",
    "pprint(p)\n",
    "np.unique(im,return_counts=True)\n",
    "catshow(im)\n",
    "im.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "RZR=Rasterizer('Hindupur_Complete.geojson')\n",
    "INDICES=RZR.indices\n",
    "REGION='hindupur'\n",
    "ERRORS=[]\n",
    "\n",
    "def save_raster(index):\n",
    "    try:\n",
    "        im,p=RZR.rasterize(index=index)\n",
    "        path=f'{REGION}/{RESOLUTION}/aue_{index}.tif'\n",
    "        image_write(im,path,p)\n",
    "        return { index: path }\n",
    "    except Exception as e:\n",
    "        return {index: str(e)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.14 s, sys: 201 ms, total: 1.34 s\n",
      "Wall time: 765 ms\n"
     ]
    }
   ],
   "source": [
    "RESOLUTION=3\n",
    "RZR=Rasterizer('Hindupur_Complete.geojson',resolution=RESOLUTION)\n",
    "%time out3=map_with_threadpool(save_raster,INDICES,max_processes=MAX_THREADPOOL_PROCESSES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.2 s, sys: 200 ms, total: 1.4 s\n",
      "Wall time: 793 ms\n"
     ]
    }
   ],
   "source": [
    "RESOLUTION=5\n",
    "RZR=Rasterizer('Hindupur_Complete.geojson',resolution=RESOLUTION)\n",
    "%time out5=map_with_threadpool(save_raster,INDICES,max_processes=MAX_THREADPOOL_PROCESSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.25 s, sys: 192 ms, total: 1.44 s\n",
      "Wall time: 766 ms\n"
     ]
    }
   ],
   "source": [
    "RESOLUTION=10\n",
    "RZR=Rasterizer('Hindupur_Complete.geojson',resolution=RESOLUTION)\n",
    "%time out10=map_with_threadpool(save_raster,INDICES,max_processes=MAX_THREADPOOL_PROCESSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 120469.95404033182 1531374.5860329489 at 120469.95404033182 1531374.5860329489\n",
      "TopologyException: Input geom 1 is invalid: Self-intersection at or near point 121979.93080939387 1532939.2537923492 at 121979.93080939387 1532939.2537923492\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.34 s, sys: 237 ms, total: 1.57 s\n",
      "Wall time: 850 ms\n"
     ]
    }
   ],
   "source": [
    "RESOLUTION=1\n",
    "RZR=Rasterizer('Hindupur_Complete.geojson',resolution=RESOLUTION)\n",
    "%time out1=map_with_threadpool(save_raster,INDICES,max_processes=MAX_THREADPOOL_PROCESSES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "im5,p5=image_data('hindupur/5/aue_2.tif')\n",
    "im3,p3=image_data('hindupur/3/aue_2.tif')\n",
    "im10,p10=image_data('hindupur/10/aue_2.tif')\n",
    "im1,p1=image_data('hindupur/1/aue_2.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'count': 1,\n",
      " 'crs': CRS.from_dict(init='epsg:32644'),\n",
      " 'driver': 'GTiff',\n",
      " 'dtype': 'uint8',\n",
      " 'height': 104,\n",
      " 'interleave': 'band',\n",
      " 'nodata': 255.0,\n",
      " 'tiled': False,\n",
      " 'transform': Affine(5.0, 0.0, 120468.6574046311,\n",
      "       0.0, -5.0, 1532317.470642275),\n",
      " 'width': 104}\n",
      "{'count': 1,\n",
      " 'crs': CRS.from_dict(init='epsg:32644'),\n",
      " 'driver': 'GTiff',\n",
      " 'dtype': 'uint8',\n",
      " 'height': 152,\n",
      " 'interleave': 'band',\n",
      " 'nodata': 255.0,\n",
      " 'tiled': False,\n",
      " 'transform': Affine(3.0, 0.0, 120500.6574046311,\n",
      "       0.0, -3.0, 1532285.470642275),\n",
      " 'width': 152}\n",
      "{'count': 1,\n",
      " 'crs': CRS.from_dict(init='epsg:32644'),\n",
      " 'driver': 'GTiff',\n",
      " 'dtype': 'uint8',\n",
      " 'height': 390,\n",
      " 'interleave': 'band',\n",
      " 'nodata': 255.0,\n",
      " 'tiled': False,\n",
      " 'transform': Affine(1.0, 0.0, 121520.78441811203,\n",
      "       0.0, -1.0, 1530897.3268872676),\n",
      " 'width': 390}\n"
     ]
    }
   ],
   "source": [
    "pprint(p5)\n",
    "pprint(p3)\n",
    "_,p=image_data('hindupur/1/aue_12.tif')\n",
    "pprint(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12156ac18>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(1,4,figsize=(28,5))\n",
    "[catshow(img,ax,color_bar=False) for img,ax in zip([im1,im3,im5],axs)]\n",
    "catshow(im10,axs[3],color_bar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{4: 'No Shapely geometry can be created from null value'}\n",
      "{9: 'No Shapely geometry can be created from null value'}\n",
      "{4: 'No Shapely geometry can be created from null value'}\n",
      "{9: 'No Shapely geometry can be created from null value'}\n",
      "{4: 'No Shapely geometry can be created from null value'}\n",
      "{9: 'No Shapely geometry can be created from null value'}\n",
      "{4: 'No Shapely geometry can be created from null value'}\n",
      "{9: 'No Shapely geometry can be created from null value'}\n"
     ]
    }
   ],
   "source": [
    "for out in [out1,out3,out5,out10]:\n",
    "    for o in out:\n",
    "        if '.tif' not in list(o.values())[0]:\n",
    "            print(o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1, 152, 152), (1, 104, 104), (1, 390, 390))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im3.shape,im5.shape,im1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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