{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "827e9aae-17df-4df6-9441-d9a6486d4b84",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import rastereasy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b817850-2c77-44c1-8f2b-54cb0fc303d7",
   "metadata": {},
   "source": [
    "# Dealing with shapefiles, ROI, ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c50bfd-4e71-45fe-a7e2-4d716ea063e4",
   "metadata": {},
   "source": [
    "## 1) Read shapefile\n",
    "\n",
    "Two main functions : `rastereasy.shpfiles.shp2raster` and `rastereasy.shpfiles.shp2geoim`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33743715-fc85-412e-93c7-63a98ad997af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function shp2raster in module rastereasy.rastereasy:\n",
      "\n",
      "shp2raster(shapefile_path, dest_name, attribute='code', resolution=10, nodata=0)\n",
      "    Convert a shapefile to a GeoTIFF raster file.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    shapefile_path : str\n",
      "        Path to the input shapefile.\n",
      "    dest_name : str\n",
      "        Path to save the output raster file.\n",
      "    attribute : str, optional\n",
      "        Attribute field in the shapefile to assign values to each pixel.\n",
      "        Default is 'code'.\n",
      "    resolution : float, optional\n",
      "        Spatial resolution of the output raster in meters/degrees.\n",
      "        Default is 10.\n",
      "    nodata : int or float, optional\n",
      "        Value to assign to areas outside the shapes.\n",
      "        Default is 0.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    - The `shapefile_path` should be the full path to a shapefile (.shp) on the disk.\n",
      "    - To get the attributes of a shapefile, see :meth:`shpfiles.get_shapefile_attributes`\n",
      "    - The output raster will be written in GeoTIFF format to the path specified by `dest_name`.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> shpfiles.shp2raster(\"landcover.shp\", \"landcover.tif\", attribute='landtype', resolution=5)\n",
      "\n",
      "Help on function shp2geoim in module rastereasy.rastereasy:\n",
      "\n",
      "shp2geoim(shapefile_path, attribute='code', resolution=10, nodata=0)\n",
      "    Convert a shapefile to a Geoimage object.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    shapefile_path : str\n",
      "        Path to the input shapefile.\n",
      "    attribute : str, optional\n",
      "        Attribute field in the shapefile to assign values to each pixel.\n",
      "        Default is 'code'.\n",
      "    resolution : float, optional\n",
      "        Spatial resolution of the output raster in meters/degrees.\n",
      "        Default is 10.\n",
      "    nodata : int or float, optional\n",
      "        Value to assign to areas outside the shapes.\n",
      "        Default is 0.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    Geoimage\n",
      "        A Geoimage object containing the rasterized data.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    - The `shapefile_path` should be the full path to a shapefile (.shp) on the disk.\n",
      "    - The `attribute` field will be assigned to each pixel in the rasterized Geoimage.\n",
      "    - To get the attributes of a shapefile, see :meth:`shpfiles.get_shapefile_attributes`\n",
      "    - The `resolution` sets the size of each pixel in the output image.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> geo_img = shpfiles.shp2geoim(\"landcover.shp\", attribute='landtype', resolution=5)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(rastereasy.shpfiles.shp2raster)\n",
    "help(rastereasy.shpfiles.shp2geoim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860bd7d0-73a3-45cf-bad0-2f4067a587ee",
   "metadata": {},
   "source": [
    "## 2) Convert shapefile to tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "282960f2-4b07-4a3c-894d-29828cb615d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "shapefile_path = './data/demo/data_shp/Roi_G5.shp'\n",
    "rastereasy.shpfiles.shp2raster(shapefile_path,'./data/results/shp2raster/raster5m.tif',resolution=5)\n",
    "rastereasy.shpfiles.shp2raster(shapefile_path,'./data/results/shp2raster/raster10m.tif',resolution=10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b71932b9-085c-4fcb-9061-3f988e1bbc65",
   "metadata": {},
   "source": [
    "## 3) Import as Geoimage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f46638e-332b-4cf8-8a5d-b6a734dfb524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Size of the image:\n",
      "   - Rows (height): 3649\n",
      "   - Cols (width): 5097\n",
      "   - Bands: 1\n",
      "- Spatial resolution: 5.000057261870794  meters / degree (depending on projection system)\n",
      "- Central point latitude - longitude coordinates: (47.60953547, -2.57061505)\n",
      "- Driver: GTiff\n",
      "- Data type: int32\n",
      "- Projection system: EPSG:32630\n",
      "- Nodata: 0.0\n",
      "\n",
      "- Given names for spectral bands: \n",
      "   {'1': 1}\n",
      "\n",
      "\n",
      "- Size of the image:\n",
      "   - Rows (height): 1824\n",
      "   - Cols (width): 2548\n",
      "   - Bands: 1\n",
      "- Spatial resolution: 10.002076869605746  meters / degree (depending on projection system)\n",
      "- Central point latitude - longitude coordinates: (47.60951283, -2.57058196)\n",
      "- Driver: GTiff\n",
      "- Data type: int32\n",
      "- Projection system: EPSG:32630\n",
      "- Nodata: 0.0\n",
      "\n",
      "- Given names for spectral bands: \n",
      "   {'1': 1}\n",
      "\n",
      "\n",
      "[0 1 2 3 4 5 6 7]\n",
      "[0 1 2 3 4 5 6 7]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raster10m=rastereasy.Geoimage('./data/results/shp2raster/raster10m.tif')\n",
    "raster5m=rastereasy.Geoimage('./data/results/shp2raster/raster5m.tif')\n",
    "raster5m.info()\n",
    "raster10m.info()\n",
    "print(raster10m.unique())\n",
    "print(raster5m.unique())\n",
    "raster10m.visu()\n",
    "raster5m.visu()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15de8e78-4887-40bf-a1d9-70f98d5473bd",
   "metadata": {},
   "source": [
    "## 4) Convert shapefile to geoimage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f510efc8-a271-4e98-a26d-3f078a500188",
   "metadata": {},
   "outputs": [],
   "source": [
    "georaster5=rastereasy.shpfiles.shp2geoim(shapefile_path,resolution=5)\n",
    "georaster10=rastereasy.shpfiles.shp2geoim(shapefile_path,resolution=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "75ca3b7c-e22f-4dac-8b65-b4c106bcbe72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "georaster5.visu(colorbar=True)    \n",
    "georaster10.visu(colorbar=True)\n",
    "georaster5.save('./data/results/shp2raster/raster5m_fromgeoraster.tif')\n",
    "georaster10.save('./data/results/shp2raster/raster10m_fromgeoraster.tif')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f87b2e65-9419-456d-a2c6-2fa1956115d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "G5_B2=rastereasy.Geoimage('./data/demo/tostack/G5_B2.tif')\n",
    "G5_B3=rastereasy.Geoimage('./data/demo/tostack/G5_B3.tif')\n",
    "G5_B4=rastereasy.Geoimage('./data/demo/tostack/G5_B4.tif')\n",
    "G5_B8=rastereasy.Geoimage('./data/demo/tostack/G5_B8.tif')\n",
    "ROI_g5=rastereasy.Geoimage('./data/demo/data_shp/ROI_g5.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e94c618e-b10c-4a1f-b79a-a4e14cb24fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROI_g5.visu()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17cacb50-579c-4e09-b784-bfe5dd86f0e5",
   "metadata": {},
   "source": [
    "## 5) Select interesting areas with `extract_from_shapefile`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d455a004-345b-4bb3-bdbb-6e6bc7d6d2a6",
   "metadata": {},
   "source": [
    "### 1) Without modifying the image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537cf186-7187-4d63-93f7-39fcd705d491",
   "metadata": {},
   "source": [
    "Read a stack (for the example. `im` can be an image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0c4e4581-e2fe-430b-8eb9-3155716407fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Size of the image:\n",
      "   - Rows (height): 2370\n",
      "   - Cols (width): 3144\n",
      "   - Bands: 4\n",
      "- Spatial resolution: 10.0  meters / degree (depending on projection system)\n",
      "- Central point latitude - longitude coordinates: (47.60615500, -2.57780166)\n",
      "- Driver: GTiff\n",
      "- Data type: uint16\n",
      "- Projection system: EPSG:32630\n",
      "\n",
      "- Given names for spectral bands: \n",
      "   {'1': 1, '2': 2, '3': 3, '4': 4}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "im=rastereasy.Geoimage('./data/demo/tostack/G5_B2.tif')\n",
    "im_stack = im.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B3.tif'),reformat_names=True)\n",
    "im_stack = im_stack.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B4.tif'),reformat_names=True)\n",
    "im_stack = im_stack.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B8.tif'),reformat_names=True)\n",
    "im_stack.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3f7c8df-dabc-4602-8c43-9d1d833d9af1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Size of the image:\n",
      "   - Rows (height): 2370\n",
      "   - Cols (width): 3144\n",
      "   - Bands: 4\n",
      "- Spatial resolution: 10.0  meters / degree (depending on projection system)\n",
      "- Central point latitude - longitude coordinates: (47.60615500, -2.57780166)\n",
      "- Driver: GTiff\n",
      "- Data type: uint16\n",
      "- Projection system: EPSG:32630\n",
      "- Nodata: 0\n",
      "\n",
      "- Given names for spectral bands: \n",
      "   {'1': 1, '2': 2, '3': 3, '4': 4}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "keep_size=True\n",
    "value=3\n",
    "ime=im_stack.extract_from_shapefile(shapefile_path,value,keep_size=keep_size)\n",
    "ime.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf86f8b3-1c7b-484b-bec7-43fe9a51a78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(rastereasy.shpfiles.shp2geoim(shapefile_path,resolution=im.resolution)==value).visu(title='shapefile = %d'%value)\n",
    "if keep_size is True:\n",
    "    ((ime-im)==0).visu(1,title='extracted areas')\n",
    "else:\n",
    "    ime,imc = rastereasy.extract_common_areas(ime,im,resolution=None)\n",
    "    ((ime-imc)==0).visu(1,title='extracted areas')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b6c94d6-f3bf-4f38-9fe0-908f0be01a38",
   "metadata": {},
   "source": [
    "### 2) With modifying the image (option `inplace=True`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49d362c6-ef99-43ce-a2b6-198e2280a3a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Size of the image:\n",
      "   - Rows (height): 2370\n",
      "   - Cols (width): 3144\n",
      "   - Bands: 4\n",
      "- Spatial resolution: 10.0  meters / degree (depending on projection system)\n",
      "- Central point latitude - longitude coordinates: (47.60615500, -2.57780166)\n",
      "- Driver: GTiff\n",
      "- Data type: uint16\n",
      "- Projection system: EPSG:32630\n",
      "\n",
      "- Given names for spectral bands: \n",
      "   {'1': 1, '2': 2, '3': 3, '4': 4}\n",
      "\n",
      "\n",
      "- Size of the image:\n",
      "   - Rows (height): 1824\n",
      "   - Cols (width): 2548\n",
      "   - Bands: 4\n",
      "- Spatial resolution: 10.0  meters / degree (depending on projection system)\n",
      "- Central point latitude - longitude coordinates: (47.60943270, -2.57056349)\n",
      "- Driver: GTiff\n",
      "- Data type: uint16\n",
      "- Projection system: EPSG:32630\n",
      "- Nodata: 0\n",
      "\n",
      "- Given names for spectral bands: \n",
      "   {'1': 1, '2': 2, '3': 3, '4': 4}\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "keep_size=False\n",
    "value=4\n",
    "\n",
    "#Read a stack\n",
    "im=rastereasy.Geoimage('./data/demo/tostack/G5_B2.tif')\n",
    "im.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B3.tif'),reformat_names=True, inplace=True)\n",
    "im.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B4.tif'),reformat_names=True, inplace=True)\n",
    "im.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B8.tif'),reformat_names=True, inplace=True)\n",
    "imoriginal=im.copy()\n",
    "imoriginal.info()\n",
    "\n",
    "im.extract_from_shapefile(shapefile_path,value,keep_size=keep_size,inplace=True)\n",
    "im.save('./data/results/shp2raster/extract4.tif')\n",
    "im.info()\n",
    "(rastereasy.shpfiles.shp2geoim(shapefile_path,resolution=im.resolution)==value).visu(title='shapefile = %d'%value)\n",
    "if keep_size is True:\n",
    "    ((imoriginal-im)==0).visu(1,title='extracted areas')\n",
    "else:\n",
    "    ime,imc = rastereasy.extract_common_areas(imoriginal,im,resolution=None)\n",
    "    ((ime-imc)==0).visu(1,title='extracted areas')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed876807-36c1-454f-a58e-9d2e04e7cccf",
   "metadata": {},
   "source": [
    "### 3) By having charged the shapefile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060d6e38-30f1-4370-b620-6fc572e08652",
   "metadata": {},
   "source": [
    "#### 1) Without modifying the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f868e0a2-bd87-4462-98d8-c2088cb2adbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Size of the image:\n",
      "   - Rows (height): 2370\n",
      "   - Cols (width): 3144\n",
      "   - Bands: 4\n",
      "- Spatial resolution: 10.0  meters / degree (depending on projection system)\n",
      "- Central point latitude - longitude coordinates: (47.60615500, -2.57780166)\n",
      "- Driver: GTiff\n",
      "- Data type: uint16\n",
      "- Projection system: EPSG:32630\n",
      "\n",
      "- Given names for spectral bands: \n",
      "   {'1': 1, '2': 2, '3': 3, '4': 4}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Read a stack\n",
    "shapefile_path = './data/demo/data_shp/Roi_G5.shp'\n",
    "\n",
    "shp=rastereasy.shpfiles.shp2geoim(shapefile_path,resolution=10)\n",
    "im=rastereasy.Geoimage('./data/demo/tostack/G5_B2.tif')\n",
    "im_stack = im.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B3.tif'),reformat_names=True)\n",
    "im_stack = im_stack.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B4.tif'),reformat_names=True)\n",
    "im_stack = im_stack.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B8.tif'),reformat_names=True)\n",
    "im_stack.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ba02d549-a271-4859-bd3a-a85fb8a010f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Size of the image:\n",
      "   - Rows (height): 2370\n",
      "   - Cols (width): 3144\n",
      "   - Bands: 4\n",
      "- Spatial resolution: 10.0  meters / degree (depending on projection system)\n",
      "- Central point latitude - longitude coordinates: (47.60615500, -2.57780166)\n",
      "- Driver: GTiff\n",
      "- Data type: uint16\n",
      "- Projection system: EPSG:32630\n",
      "- Nodata: 0\n",
      "\n",
      "- Given names for spectral bands: \n",
      "   {'1': 1, '2': 2, '3': 3, '4': 4}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "keep_size=True\n",
    "value=3\n",
    "ime=im_stack.extract_from_shapeimage(shp,value,keep_size=keep_size)\n",
    "ime.save('./data/results/shp2raster//extract3.tif')\n",
    "ime.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "644b6d5a-ace6-48d2-9879-4f62a6350bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(rastereasy.shpfiles.shp2geoim(shapefile_path,resolution=im.resolution)==value).visu(title='shapefile = %d'%value)\n",
    "if keep_size is True:\n",
    "    ((ime-im)==0).visu(1,title='extracted areas')\n",
    "else:\n",
    "    ime,imc = rastereasy.extract_common_areas(ime,im,resolution=None)\n",
    "    ((ime-imc)==0).visu(1,title='extracted areas')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8857e813-12b3-41e2-b624-9f974ce00907",
   "metadata": {},
   "source": [
    "#### 2) With modifying the  image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b8a41d4-9704-472c-bd2e-855a3eb89535",
   "metadata": {},
   "outputs": [],
   "source": [
    "shp=rastereasy.shpfiles.shp2geoim(shapefile_path,resolution=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8d341273-e8d3-47a9-8563-87a4b1b104b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Size of the image:\n",
      "   - Rows (height): 2370\n",
      "   - Cols (width): 3144\n",
      "   - Bands: 4\n",
      "- Spatial resolution: 10.0  meters / degree (depending on projection system)\n",
      "- Central point latitude - longitude coordinates: (47.60615500, -2.57780166)\n",
      "- Driver: GTiff\n",
      "- Data type: uint16\n",
      "- Projection system: EPSG:32630\n",
      "\n",
      "- Given names for spectral bands: \n",
      "   {'1': 1, '2': 2, '3': 3, '4': 4}\n",
      "\n",
      "\n",
      "- Size of the image:\n",
      "   - Rows (height): 2370\n",
      "   - Cols (width): 3144\n",
      "   - Bands: 4\n",
      "- Spatial resolution: 10.0  meters / degree (depending on projection system)\n",
      "- Central point latitude - longitude coordinates: (47.60615500, -2.57780166)\n",
      "- Driver: GTiff\n",
      "- Data type: uint16\n",
      "- Projection system: EPSG:32630\n",
      "- Nodata: 0\n",
      "\n",
      "- Given names for spectral bands: \n",
      "   {'1': 1, '2': 2, '3': 3, '4': 4}\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dU8pJ3Nb0awCemK7O2zAOEkBjlBIcnSkfgiU097j25jGEAAmgAUoPilqZyFqt+1nLkrxYS4EACaByaj9pW2eUan8/oJ1aCgRIAFAMW8HRlROetzIKBG79wTAPANDMVevtLdfa1e7BOeafuaufPzJIAFAlBDtwN0hek64S0XGHz0MGCQC6orVMkquPldKJSWswUQD4Ba5C7539Ca5zp01TCZzp/QvyCitKqX3McaqpqaGwsDAba6GKFXzM+guslkCplnKCZyg90Ng6DtEL2LPQBgk+o/STjjMwM4z2qeE4tTezjPVyNbwfX+L9U3vRuXVRxQo+4ehLquTMzBMnGOMve2++T5wI3ae2zMud40hN78/b+8jZuVgRIEGz00Q5w1HQ8tZJxRvvW28nQK2TywQ9ddzo5Vgx2NhfzgZI9GIFvx6o/uxRaHxduddXW09HNZUV3Pu++PvHpB6hDRIUwdtzbzoKIOadcJQcbDAtG3jyGAL7ECBB019IV19bSScP8yo1W1kuaA+q35UDARK8yt2TuJ57izZnaixQP39d+1BrDB7YhwiQ4DVqyhyV0h4LYH0sNPdEr8fjyoDLXYGSeeNL6cxgfaWfDJAZgK9qU5T+XVADZJDgUb74UmJWG9A7e98BBEby2DkC4yChWfBldJ6aM19QNqX3vlbadw/jIMHr8IXEfgRlwHfRO1DFCuBlyBwB/Kc51awIkColN7DdfMwc5vxUfo9E/OoHUDa0QSqcdbBz9aSKLuLKY2/wPwBoZC7WZcuWUUBAAGVnZ4v7VVVV4r7crbCw0O62Dh8+TFOmTKHw8HAKDg6m4cOH04kTJ0yPjx07tsk2H3jgAdIy87lC3Z0bFCdh5VHbPK8AagyMnqhFcztAFhcX0+rVq2ngwIGmZdHR0XTmzBmL26JFiygkJISSk5NtbquyspJGjRpFcXFxtGvXLjp48CAtXLiQWrdubbFeZmamxbafffZZ0jJPfci2Tsb2TtI4iQOAGnmyecmtNshLly7RjBkzaM2aNbRkyRLT8pYtW1JUVJTFuhs3bqRp06aJIGnL448/TpMmTbIIeL17926yXtu2bZtsHzwTJDGAHQDUwuCjtny3MsisrCyaPHkyjR8/3u56JSUlVFZWRhkZGTbXaWxspM2bN1NMTAwlJSVRZGQkJSQk0KZNm5qs+84771CnTp2of//+lJOTQ3V1dTa3W19fT7W1tRY3cAyZIwAomcEqQzTWtHmqxq1ZGeT69euptLRUVLE6kp+fT3379qWRI0faXKe6ulpkpNyeydnoM888Q1u3bqWpU6fSzp07acyYMWK9u+66i7p3705du3YVVbALFiygiooKeu+992S3m5ubK6p3wTloEwMApTP4eBJ3lwLkyZMnae7cubRt27Ym7YPWrly5QuvWrRNtifZwBsluvfVWmjdvnvh78ODBtH//flq1apUpQM6aNcv0nAEDBlCXLl0oMTFRtF/KVcdyhvnwww+b7nMGyW2kAAAAHq9i5SpTzvji4+MpMDBQ3Hbv3k0vv/yy+LuhocG07oYNG0QVaFpamt1tcpUpP7dfv34WyznzNO/Fao2rYdmxY8dkHw8KChLdd81vAACgTgY/XALMpQySM7by8nKLZenp6aL3KVd5cicd8+pVHrYRERFhd5utWrUSQzq4utTc0aNHRZWqLdy2yTiT1DrMswgAemXw47UxXQqQoaGhooOMOR6z2LFjR4vlnNXt2bOHtmzZIrsdDqjcRpiSkiLuz58/n+644w4aPXo0jRs3TrRBfvDBB2LIB+NqVK6u5Z6u/FrcBsnVsby++TATAADQDoOfLxztlanmCgoKqFu3bjRhwgTZxzlb5BkMjDhQcnsjB805c+ZQbGwsvfvuu2JspDHL3L59O61YsYIuX74s2hJTU1PpiSeeIK1D5xkA0BODn4OiOUw1p1AIjACgNwYfBUdc7krFEBwBQG8MCsocjXA1DwVBYAQAvUpS4KxeCJB+hqAIAPA/3r5cnyuadTUPAAC9UspJHLwHAdKPkD0CqJcSqwS1IqkZl/jzJPRi9TN/HwAAAGph8NCPEfRiBQAATUnycUKBTjp+hmnksO8AoHlB0lvV3GiDVAC0YbgH1dMA4E3IIEH1kIUD6I/BB52jECAVAJkQ9h8AKK/GDQESAAAUyeDnITRog1TIQeDvAwEAQEkMCjgnIkAqiBIOCDXCfgPQFoNCzoUIkAqjlANDTdCGC6AdBgWdAxEgFQYnewAAZUAnHQeByZe/ZhAcAUCvDArKHI2QQTqghAlzAQC0zKDA4MiQQTqZuZmvYz0wXakfLgCA0hkUfP5EBunGB2QdUD2VYSr5QAEA0BtkkB5iHSTdDXaYNg0A9CRJwdfVRAb5X57+cNBuCQDg3kWSlXL+RID004w29g4CJf6SAgDwJSUEyQBJkiTSgdraWgoPD6efjvai22PjXQpC3v6g7JVFCQcJAICveTNRqL3YSO1jjlNNTQ2FhYXZXE93AXIs3UqBAde5/EH4M0j64vUBAJTEgADpnwwyLLRFs4ZuyK3Hy8z/docSAjUAgL8ZvNzMhAzSiQDpKPA050PydCAzL4uWgyR68QLol8FH/S8QIF0MkJ6k5QAG4C78+AEmV9Pm646JzgZIjIP0Ar1kewCuwHcB1NZbH8M8vEwNB4FaYd8CqItBZedDZJBuzvZg3inHEev18EvaM7AfAdTDoLLgyBAgm3FSdneKpOb2dgV1QFU7gLohQHqAXKB0JngiUGqTGn8pA0BTaIP08HR01lPIOZMl4oSq3uMAnx2AY2r9niCDdPMDtvWBu5sVulodhy7zyqHWLz+ALxhU/P1oVga5bNkyCggIoOzsbHG/qqpK3Je7FRYW2t3W4cOHacqUKWKsYnBwMA0fPpxOnDhhevznn3+mrKws6tixI4WEhFBqaiqdO3eOlMo8u3B1dnq5YGy+zPi3mg88tXPl80Rbs23mxzayctBMBllcXEyrV6+mgQMHmpZFR0fTmTNnLNbLy8uj5cuXU3Jyss1tVVZW0qhRoygjI4MWLVokBm4eOnSIWrdubVpn3rx5tHnzZhFoOYjOnj2bpk6dSp9++impQXM68jjbGUjucZycvcfTsy/pma0aGhy/4E9uTVZ+6dIlio+Pp9dee42WLFlCgwcPphUrVsiuO2TIELFufn6+ze1Nnz6drrvuOnrrrbdkH+fZDiIiImjdunV02223iWVHjhyhvn37UlFREd10002KmknHVxdjdiZw4gTjWxjS49l9ieNX/QwK/NHo7Ew6bkUKruqcPHkyjR8/3u56JSUlVFZWJjJDWxobG0VmGBMTQ0lJSRQZGUkJCQm0adMmi+1cvXrV4vXi4uLo//7v/0SAlFNfXy+CovlNrayrWM0DoztZJXgHgqNnITiqn0Hl5x+XA+T69euptLSUcnNzHa7LWSNneSNHjrS5TnV1tchIuT1z4sSJ9PHHH1NKSoqoPt29e7dY5+zZs9SqVStq166dxXM7d+4sHpPD5eOM0Xjj6l+1c/ZqH7YyTvAuV3svg/Lhu+MerfTwdqkN8uTJkzR37lzatm2bRfugnCtXrogq0YULF9pdjzNIduutt4p2RsZVtvv376dVq1bRmDFjyB05OTn08MMPm+5zBqmVIGkMgnIHoK1qKVRXefYzAH3ADx19fz9cCpBc1ckZH7cpGjU0NNCePXto5cqVolqzZcuWYvmGDRuorq6O0tLS7G6zU6dOFBgYSP369bNYzpnnvn37xN9RUVH0yy+/0IULFyyySO7Fyo/JCQoKEjctQmcd/7K+9ieqVkHPDBoKiM0KkImJiVReXm6xLD09XbQHLliwwBQcjdWrPGyDO9fYw1WnPKSjoqLCYvnRo0epe/fu4u+hQ4eKTjyffPKJGN7BeH0eBjJixAhX3gKAR6A6FZQA0xkqKECGhoZS//79LZbxmEUem2i+/NixYyKr3LJli+x2OKByGyG3NbL58+fTHXfcQaNHj6Zx48bR1q1b6YMPPqBdu3aJx7kNkTv6cJVphw4dRK+jhx56SARHZ3qwapkrk6YDgDb4+ztv0Mk5xysz6RQUFFC3bt1owoQJso9z9sfda404UHJ7IwfNOXPmUGxsLL377rtibKTRiy++SC1atBAZJFflco9XHmaid+Zti3o5aAH0yp/fcYMOzy9ujYNUIzWPg3SnA4GWJw1AhyPQK3+OeTZoKEB6dRwkKIMrB6yWgqP5/wBKgONRmxAgdXZ1EfPnqZ0W3gNog69+gPrrh65Bp981XM1DI1w9gLWSUQLojXWfA198l5PMXkNPwRJtkDqgtWDoj67t6E4P0JRag6WzbZAIkDqh5SDprffo7Jdfa/sWwFkGjQdItEFqmBZP3PbaXD3xZXXnmoRqPUkAgH0IkBomd5FlNfPme2ju5Mpa2L8AYAkBElTNupeuuxemdue1AEDb0ItV4Tw1S47aT+yO2hxdfX/OZNeubFPt+xcAmkIGqRLNPQGruQpQ7moZ7u4PrVynDkAJkjReq4IMUkWaOxbJl+OmtPAjwdn9pdb9CQD2IUCqlCfa3dRyYvfUFUtc3WfG10XGCaBPCJAK58wAdT1czcPTwdzePrNu29TyfgUA2zBRgIaChR4nL/cVT3TkAdDrj3qDwn5kYqIAFWhORxNPbg8cw74FcK3jTnN6mSsFqlj9wJsHizNVgmo9WP0N+w3APUrLIJ2FYR5+4Kkp0dzZNk7yAADOQQapgSCp1Hp+AAA1QwapI8geAQCchwCpwMDkzrRpqFoFAKUxqHwcMapYfcDTE2g7OzYPGSMAgPsQIFUI2SIAqEGSyvtHIEBqBLJFAFAig0qDI0MbpAYgOAIAeB4CpAao+RcaAIBSoYoVAAA8zqCBH+7IIDVCCwcjAGiDQSPnI2SQGjso0R4JAP4492gRAqSGIDgC+J7eh10ZNBocGQKkRmj9Swig1uDgzEXPQZnQBqkB+NIBqIPap16zpqX3IgcZJACAB36cuhIsrK/EA8qEAKlySviCWZ8YlFAmAFDuDwS1QIAEl2nxiwAA7jNo9JyANkjwaqcEAJCnpZqWpK6DNfV+jBAgVUyNB6SxkwKCKGgJjmf1npO8FiCXLVtGAQEBlJ2dLe5XVVWJ+3K3wsJCm9uZOXNmk/UnTpxosU6PHj2arMOvr1f+OBDdOQmYB0ScRECrvH1RdDVJ0lA26XYbZHFxMa1evZoGDhxoWhYdHU1nzpyxWC8vL4+WL19OycnJdrfHAXHt2rWm+0FBQU3WWbx4MWVmZpruh4aGkp6o6aBDMAS9Ufu1D8FDAfLSpUs0Y8YMWrNmDS1ZssS0vGXLlhQVFWWx7saNG2natGkUEhJid5scEK2fa40DoqN1tMzdruFKnIJOaeUB8FWgxLGv8SrWrKwsmjx5Mo0fP97ueiUlJVRWVkYZGRkOt7lr1y6KjIyk2NhYevDBB+n8+fNN1uEq1Y4dO9KQIUNEVnrt2jWb26uvr6fa2lqLm1bIffEcVWMqrZpTKeUA8HZVo/VNDwwa+X67nEGuX7+eSktLRRWrI/n5+dS3b18aOXKkw+rVqVOnUs+ePamyspIee+wxUSVbVFQkslI2Z84cio+Ppw4dOtD+/fspJydHVOe+8MILstvMzc2lRYsWkR4PQEcHJ8YtAoC3GDQSHFmAJEmSsyufPHmShg0bRtu2bTO1PY4dO5YGDx5MK1assFj3ypUr1KVLF1q4cCE98sgjLhXq+PHj1Lt3b9q+fTslJibKrlNQUED333+/qO6Va6/kDJJvRpxBchvpT0d7UVgoOu+ac/ZXrTcOfL38ogbQA4NKgmPtxUZqH3OcampqKCwszOZ6LkUKrjKtrq4WmVxgYKC47d69m15++WXxd0NDg2ndDRs2UF1dHaWlpblc+F69elGnTp3o2LFjNtdJSEgQVazcc1YOB01+4+Y3UB61fKEAwDYlNd/4rYqVs7ny8nKLZenp6RQXF0cLFiwwVYcaq1enTJlCERERLhfq1KlTog2SM1BbuG2zRYsWot0S1E2JnYgAwDlaDIxuBUjuRdq/f3+LZcHBwaLjjPlyzvz27NlDW7Zskd0OB1RuI0xJSRFVpNxWmJqaKnqochvko48+Sn369KGkpCSxPrdFHjhwgMaNGyfKwPfnzZtHd999N7Vv3969dw66PvABABzxSmMctw9269aNJkyYIPt4RUWFqPtlnHUePHhQZJsxMTGix+vQoUNp7969prZF/p87B40ZM4ZuuOEGevrpp0WA5DGW4L1esf7k79cHAHCpk46acSed8PBwdNJR6GBnZ64KgGpYAOUxqPDHrLOddBAgQVUQJAGU01/AoMLg6EqAxOWuQBUQGAH8x2AWCPV0sWdkkKA61l9M8y+vHr60AP5iUGnGaA0ZJGgW2igBwBcwpQwAgI9odUC9VqENEjTD2ROPXqph9dRWpMZ9h89H+RAgATTA/GTriwzF3mtoLSAbe26a/2/OfJmz7ePWnV7Uss+SfHR8KQU66YDuqOVk5ApXT1q+mKBeC/vZl8FATfvLoPIg6ZXJygG0QO1fbm+0acl142/udpV2DVJXebvcagqI5tT6eboDVaygS2pv//FE8LKuMpMLkuAdxuNOrcefXiBAgq6pqWOPN4IWAqHypl0E5UCABHCC3EnSF0ETJ2dQEoPOfiwgQAK4Sa43Y3OeD6B0STrrxYoACeCjdk09nVjAMTVOkWjQ2TGMYR4AXqS3X9y2eDsANDebd+c1vEGJgdKgweMXc7ECKIAWTy5q2L+ezs589Tn6e9IAHK+WUMUKALoY0mLOlSDk66Dhy8n4ERDtQ4AEAL+xFby8feJW4zjY5mSXany/SoA2SADQPbnAobTsqrnBUW5bSnuPvoKp5gAAnKSGQOFuGW0FfzW8Z3/DXKwAAJhqD2QgQAIA/JfSsypXy4dMsXkQIAEANBwkwX0IkAAAGoVeq82DAAkAoDLIIn0DARIAQMOQRboPEwUAAKgEgp1vIUACAGg8EGLSfPegihUAQEGQJSoHAiQAgA464CDwug5VrAAACqPGiylrETJIAACdZJQItq5BBgkAoHCevEYkOuw4DxkkAIBKYcIA70KABABQMXcmMAcfBMhly5ZRQEAAZWdni/tVVVXivtytsLDQ5nZmzpzZZP2JEydarPPjjz/SjBkzKCwsjNq1a0cZGRl06dKl5hQfAEATcNUOhbVBFhcX0+rVq2ngwIGmZdHR0XTmzBmL9fLy8mj58uWUnJxsd3scENeuXWu6HxQUZPE4B0fe9rZt2+jq1auUnp5Os2bNonXr1rn7FgAANMWYHaIzjh8zSM7cOGCtWbOG2rdvb1resmVLioqKsrht3LiRpk2bRiEhIXa3yQHR/Hnm2z18+DBt3bqV3njjDUpISKBRo0bRK6+8QuvXr6fTp0+78xYAADQL1ah+DJBZWVk0efJkGj9+vN31SkpKqKysTFSHOrJr1y6KjIyk2NhYevDBB+n8+fOmx4qKikS16rBhw0zL+LVbtGhBBw4ckN1efX091dbWWtwAAPTCkz1f9crlKlbO2kpLS0UVqyP5+fnUt29fGjlypMPq1alTp1LPnj2psrKSHnvsMVEly4GRs9KzZ8+K4GlR8MBA6tChg3hMTm5uLi1atKjJ8tpLjQ7LDQCgBYUVpeL/lJgB4v+NR8vF/7UXSddq/xsHJEmyv6LkghMnTkiRkZHSl19+aVo2ZswYae7cuU3Wraurk8LDw6XnnntOclVlZSWXWtq+fbu4//TTT0sxMTFN1ouIiJBee+012W38/PPPUk1Njen29ddfi23ihn2AYwDHAI4BHANEJJ08edJuLHIpg+Qq0+rqaoqPjzcta2hooD179tDKlStFtSZnfGzDhg1UV1dHaWlpLkf3Xr16UadOnejYsWOUmJgo2iT5dc1du3ZN9Gzlx2y1aZp39OE20JMnT1JoaKjoJesvXNXLnZm4LNwjVy3UWm41l12t5VZz2dVabjWXvdYP5ebM8eLFi9S1a1e767kUIDlYlZf//xTdiHuTxsXF0YIFC0zB0Vi9OmXKFIqIiHC17HTq1CnRBtmlSxdxf8SIEXThwgURoIcOHSqW7dixgxobG0WnHWdwe2W3bt1IKfhAUNNBrPZyq7nsai23msuu1nKruexhPi53eHi4w3VcCpCcffXv399iWXBwMHXs2NFiOWd+nFVu2bJFdjscULmNMCUlRfSI5bbC1NRUkQ1yG+Sjjz5Kffr0oaSkJLE+t2NyO2VmZiatWrVKDPOYPXs2TZ8+3eEvAAAAAMXMpFNQUCCytQkTJsg+XlFRQTU1NeJvzjoPHjwoss2YmBjR45WzxL1791pUkb7zzjsisHIWO2nSJDHUg8dYAgAAKHKych6eYW3p0qXiZot5z6E2bdqQwWBw+DrcY1ULkwJw0H/qqaeaTISgdGott5rLrtZyq7nsai23mssepOByB3BPHX8XAgAAQGkwWTkAAIAMBEgAAAAZCJAAAAAyECABAABkIEACAADIQID0IL5gNI/j5EnXefhK7969RfflX375xe5znLnItNzjPHG8Gsp+4sQJcfWXtm3biknn58+fL6YK9Fe5jXgy/FtuuUVMdsEzeIwePZquXLlierxHjx5N3hdfJNxTvFl2b15g3N1yjx07tsn+fOCBByzWUeJx7mzZlXqcMx6swBeA4HJv2rRJFfvcmbJ7c597ZBwk/M+RI0fE9Hd8IWmeCeirr74Ss/9cvnyZnnvuOdld5cpFpvmC0jyjkBGf+JRedp6rlw9gniVp//79Yn2en/e6666zO1bWm+U2Bhjelzk5OeLaonx1mC+//FJMSWhu8eLFYnvms0l5ijfL7s0LjLtbbsbr8T414hObNaUd586UXanHudGKFSvszkGt1H1ur+ze3ueCy5faAJc8++yzUs+ePV16zuDBg6V7773XYhl/VBs3blRd2bds2SK1aNFCOnv2rGnZ66+/LoWFhUn19fWSv8qdkJAgPfHEE3bX6d69u/Tiiy9KvuSJshuvXFNcXGxa9tFHH0kBAQHS999/L/mr3Lau/KOG49xR2ZV6nLMvvvhCuv7666UzZ87I7l+l7nNHZffFPkcVq5fxlHo8C5Cz7F1kmi9UzVc5ufHGG8V0ft6e48ETZedsZ8CAAdS5c2fTMp5jl2fwP3ToEPmj3HxlGL7QNlfJ8LVKuWxjxoyhffv2NVmXq1R5ruEhQ4aIzNiT1TfeKrs7Fxj3drnNp4zkY5jnbuYMmK/4o5bj3F7ZlXicMy7jXXfdRa+++qrNKx8pdZ/XOSi7T/a5R8IsyPrmm2/Er5m8vDyn99CDDz4o9e3bt8nyxYsXS/v27ZNKS0ulZcuWSUFBQdJLL72k+LJnZmZKEyZMsFh2+fJl8WuQfwH6o9xFRUXi9Tt06CAVFBSIfZqdnS21atVKOnr0qGm9559/Xtq5c6e4/in/Mm3Xrp00b948j5fZ02V35/qp3i43W716tbR161bp4MGD0ttvvy0yg5SUFFUc547KrsTjnM2aNUvKyMiwmy0qdZ/PclB2X+xzBEgnLFiwwOEFVg8fPmzxnFOnTkm9e/e2+IAdceUi0wsXLpS6deum+LK7exB7s9yffvqpeH5OTo7F8gEDBkh/+tOfbD4vPz9fCgwMFBfjtsffZXc3QPrqWDH65JNPxDaPHTummuPcVtmVeJy///77Up8+faSLFy+6VJ2qhH3+vhNl90WARCcdJzzyyCM0c+ZMhxd5Njp9+jSNGzdOVIG5csURVy4yzdfB/Mtf/iIuUm1vkl9/l52rRj777DOLZefOnTM95o9yG68z2q9fP4vlfFk17hVnb59zFSv3youNjVVs2d25wLi3yy3HeC1Xvjwe92xUw3Fuq+xKPM75mrl8+UDrDjd8acGbb75Z9kITStnnO5wou7v73CUeCbNg8QvpV7/6lTR9+nTp2rVrLu0Z7giQmprq1LpLliyR2rdvr/iyGxvSz507Z1FdxVUsjjIxb5W7sbFR6tq1a5OOLtzByDozM8dVa/xefvzxR8lTvFF2Yyedzz//3PS4wWDwaCed5hwrRlytx+XkKmy1HOe2yq7E45w7tpSXl1vcuMxcfXr8+HFF7/MzTpTdF/scAdLDBwFXCyQmJoq/+UM23szXiY2NlQ4cONCkXp5PYNzb0Nq//vUvac2aNeIg4fW4mqxt27bSk08+qfiy85ehf//+oiqkrKxMtONwVZ+9QOSLcnPvVP4iFRYWivJzwGndurWpymz//v1iHS5zZWWlCI5c7rS0NI+U25tlZxMnTpSGDBkinscncz453XnnnX4rN5eN27o4aH/77beiCq1Xr17S6NGjFX+cO1N2pR7n1qyrKZW6z50pu7f3uXhNj20JpLVr19qsizfiLxjf584f5vhDjY6OlhoaGprsSQ48nCGEhIRIwcHB0qBBg6RVq1bJrqu0srOqqiopOTlZatOmjdSpUyfpkUceka5ever3cufm5oq2Fj4hjBgxQtq7d6/psZKSEjGcgttVOfhw56OlS5d67JepN8vOzp8/LwIiHzMcTNPT0y3ac3xd7hMnToiAwp2LuBMInzDnz58v1dTUKP44d6bsSj7O7QUZpe5zOXLtp97c5wzXgwQAAJCBcZAAAAAyECABAABkIEACAADIQIAEAACQgQAJAAAgAwESAABABgIkAACADARIAAAAGQiQAAAAMhAgAQAAZCBAAgAAUFP/Dz0XroVpo+O3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "keep_size=True\n",
    "value=4\n",
    "\n",
    "#Read a stack\n",
    "im=rastereasy.Geoimage('./data/demo/tostack/G5_B2.tif')\n",
    "im.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B3.tif'),reformat_names=True, inplace=True)\n",
    "im.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B4.tif'),reformat_names=True, inplace=True)\n",
    "im.stack(rastereasy.Geoimage('./data/demo/tostack/G5_B8.tif'),reformat_names=True, inplace=True)\n",
    "imoriginal=im.copy()\n",
    "imoriginal.info()\n",
    "\n",
    "im.extract_from_shapeimage(shp,value,keep_size=keep_size,inplace=True)\n",
    "im.save('./data/results/shp2raster//extract4.tif')\n",
    "im.info()\n",
    "(shp==value).visu(title='shapefile = %d'%value)\n",
    "if keep_size is True:\n",
    "    ((imoriginal-im)==0).visu(1,title='extracted areas')\n",
    "else:\n",
    "    ime,imc = rastereasy.extract_common_areas(imoriginal,im,resolution=None)\n",
    "    ((ime-imc)==0).visu(1,title='extracted areas')\n"
   ]
  }
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