0_Data_preparation.ipynb

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    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import rasterio\n",
    "import geopandas\n",
    "import ee\n",
    "import geemap\n",
    "from utils import clipAndResample\n",
    "#ee.Initialize()\n",
    "import xarray as xr\n",
    "import rioxarray"
   ]
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   "cell_type": "code",
   "execution_count": 13,
   "id": "969f0ede",
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    "# find new Landcover (NOAA) values at sample points\n",
    "file = pd.read_csv('poc.csv')\n",
    "coords = np.array(file[['Long', 'Lat']])\n",
    "\n",
    "lc_noaa_src = rasterio.open('Low_res_data_05/landcover_noaa_afr_05.tif')\n",
    "lc_noaa_samples = [item for sublist in list(lc_noaa_src.sample(coords)) for item in sublist]"
   ]
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   "execution_count": null,
   "id": "3d6dd5b5",
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    "# code for creating a mean of tiffs\n",
    "\n",
    "from osgeo import gdal\n",
    "import numpy as np\n",
    "\n",
    "def write_raster(raster_array, gt, data_obj, outputpath, dtype=gdal.GDT_UInt16, options=0, color_table=0, nbands=1, nodata=False):\n",
    "\n",
    "    height, width = raster_array.shape\n",
    "\n",
    "    # Prepare destination file\n",
    "    driver = gdal.GetDriverByName(\"GTiff\")\n",
    "    if options != 0:\n",
    "        dest = driver.Create(outputpath, width, height, nbands, dtype, options)\n",
    "    else:\n",
    "        dest = driver.Create(outputpath, width, height, nbands, dtype)\n",
    "\n",
    "    # Write output raster\n",
    "    if color_table != 0:\n",
    "        dest.GetRasterBand(1).SetColorTable(color_table)\n",
    "\n",
    "    dest.GetRasterBand(1).WriteArray(raster_array)\n",
    "\n",
    "    if nodata is not False:\n",
    "        dest.GetRasterBand(1).SetNoDataValue(nodata)\n",
    "\n",
    "    # Set transform and projection\n",
    "    dest.SetGeoTransform(gt)\n",
    "    wkt = data_obj.GetProjection()\n",
    "    srs = osr.SpatialReference()\n",
    "    srs.ImportFromWkt(wkt)\n",
    "    dest.SetProjection(srs.ExportToWkt())\n",
    "\n",
    "    # Close output raster dataset \n",
    "    dest = None\n",
    "\n",
    "tifflist = ['something1.tif', 'something2.tif'] # the paths to each of the tiffs you want to use in averaging\n",
    "for i, tiff in enumerate(tifflist):\n",
    "    gd_obj = gdal.Open(tiff)\n",
    "    array = gd_obj.ReadAsArray()\n",
    "    array = np.expand_dims(array,2)\n",
    "    if i == 0:\n",
    "        allarrays = array\n",
    "    else:\n",
    "        allarrays = np.concatenate((allarrays, array), axis=2)\n",
    "mean_of_tiffs = np.nansum(allarrays, axis=2)\n",
    "\n",
    "outputpath = 'wherever you are saving this guy.tif'\n",
    "write_raster(mean_of_tiffs, gd_obj.GetGeoTransform(), gd_obj, outputpath)"
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   "execution_count": 17,
   "id": "05156ada",
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   "source": [
    "# find annual average of GPCC precip dataset\n",
    "from netCDF4 import Dataset\n",
    "gpcc_dataset = xr.open_dataset(\"GPCC/out.nc\") # lon(720), lat(360), time(360)\n",
    "yearly_prec_nc = gpcc_dataset.resample(time='1y').sum()\n",
    "mean_prec_nc = yearly_prec_nc.mean(dim='time')\n",
    "mean_prec_nc['precip'].rio.to_raster('LTA_GPCC_precip.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8fe3b771",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_prec_nc['precip'].rio.to_raster('LTA_GPCC_precip.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b50973b9",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;precip&#x27; ()&gt;\n",
       "array(2294.9387, dtype=float32)\n",
       "Coordinates:\n",
       "    lon      float64 9.75\n",
       "    lat      float64 5.25</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'precip'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c4e2da16-f654-47b7-9d97-96dda9722941' class='xr-array-in' type='checkbox' checked><label for='section-c4e2da16-f654-47b7-9d97-96dda9722941' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.295e+03</span></div><div class='xr-array-data'><pre>array(2294.9387, dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-74870b84-f053-4635-aadb-ec021bc021b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74870b84-f053-4635-aadb-ec021bc021b0' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.75</div><input id='attrs-6a168f89-05af-4ee8-8f72-0076d6874f30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a168f89-05af-4ee8-8f72-0076d6874f30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3585bc17-cf84-4fd2-9f1e-16f35011d5a4' class='xr-var-data-in' type='checkbox'><label for='data-3585bc17-cf84-4fd2-9f1e-16f35011d5a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(9.75)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.25</div><input id='attrs-4aa42700-8e09-43b8-908d-389887c313a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4aa42700-8e09-43b8-908d-389887c313a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d816624-a17d-4171-98f2-3e85afb653e9' class='xr-var-data-in' type='checkbox'><label for='data-3d816624-a17d-4171-98f2-3e85afb653e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(5.25)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-055acc3b-8426-41a3-a3fe-efe41ffdd843' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-055acc3b-8426-41a3-a3fe-efe41ffdd843' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'precip' ()>\n",
       "array(2294.9387, dtype=float32)\n",
       "Coordinates:\n",
       "    lon      float64 9.75\n",
       "    lat      float64 5.25"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_prec_nc.get('precip').isel(lon=379, lat=169)"
   ]
  },
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   "execution_count": 172,
   "id": "d5736129",
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   "source": [
    "# find annual average of GPCP dataset\n",
    "gpcp_dataset = xr.open_dataset(\"GPCP/out.nc\") # lon(720), lat(360), time(360)\n",
    "days = [31,28,31,30,31,30,31,31,30,31,30,31, 31,28,31,30,31,30,31,31,30,31,30,31, 31,28,31,30,31,30,31,31,30,31,30,31, 31,29,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,29,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,29,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,29,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,29,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,29,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,29,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31,31,28,31,30,31,30,31,31,30,31,30,31]\n",
    "for (i,d) in enumerate(days):\n",
    "    gpcp_dataset['precip'][i] = gpcp_dataset['precip'][i] * d\n",
    "yearly_prec_nc = gpcp_dataset.resample(time='1y').sum()\n",
    "mean_prec_nc = yearly_prec_nc.resample(time='29y').mean()\n",
    "\n",
    "mean_prec_nc['precip'].rio.to_raster('LTA_GPCP_precip_25.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "dceb1db1",
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       "         176.88115 , 176.93964 ],\n",
       "        [177.71764 , 179.33127 , 174.82571 , ..., 171.5885  ,\n",
       "         176.74818 , 175.42719 ]],\n",
       "\n",
       "       [[109.505585, 106.912415, 105.28148 , ..., 109.71927 ,\n",
       "         116.9897  , 116.55097 ],\n",
       "        [ 77.33448 ,  78.16896 ,  74.30289 , ...,  76.49201 ,\n",
       "          80.23284 ,  78.70794 ],\n",
       "        [ 65.09852 ,  64.2796  ,  55.134758, ...,  72.02436 ,\n",
       "          72.59897 ,  70.23787 ],\n",
       "        ...,\n",
       "        [181.72163 , 181.6312  , 183.65642 , ..., 180.61089 ,\n",
       "         179.01813 , 178.71059 ],\n",
       "        [166.9692  , 169.3949  , 169.47812 , ..., 166.72153 ,\n",
       "         168.54349 , 168.74545 ],\n",
       "        [177.38702 , 179.81345 , 177.86757 , ..., 173.65312 ,\n",
       "         175.26546 , 176.39352 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1981-12-31 2010-12-31\n",
       "  * latitude   (latitude) float32 -88.75 -86.25 -83.75 ... 83.75 86.25 88.75\n",
       "  * longitude  (longitude) float32 1.25 3.75 6.25 8.75 ... 353.8 356.2 358.8</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'precip'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>latitude</span>: 72</li><li><span class='xr-has-index'>longitude</span>: 144</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ea65b8ca-61a1-4311-8ace-98e31520171e' class='xr-array-in' type='checkbox' checked><label for='section-ea65b8ca-61a1-4311-8ace-98e31520171e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>129.2 128.2 126.4 118.2 119.2 112.0 ... 173.9 173.4 173.7 175.3 176.4</span></div><div class='xr-array-data'><pre>array([[[129.19537 , 128.15681 , 126.38945 , ..., 133.26077 ,\n",
       "         141.00227 , 143.09848 ],\n",
       "        [ 84.29381 ,  89.40729 ,  88.67485 , ...,  84.632454,\n",
       "          92.81697 ,  87.94917 ],\n",
       "        [ 56.81002 ,  61.727043,  57.33716 , ...,  64.34771 ,\n",
       "          71.476944,  66.36371 ],\n",
       "        ...,\n",
       "        [199.64218 , 200.10161 , 199.81633 , ..., 190.7377  ,\n",
       "         186.74507 , 189.91205 ],\n",
       "        [176.13716 , 180.60878 , 179.31021 , ..., 172.73724 ,\n",
       "         176.88115 , 176.93964 ],\n",
       "        [177.71764 , 179.33127 , 174.82571 , ..., 171.5885  ,\n",
       "         176.74818 , 175.42719 ]],\n",
       "\n",
       "       [[109.505585, 106.912415, 105.28148 , ..., 109.71927 ,\n",
       "         116.9897  , 116.55097 ],\n",
       "        [ 77.33448 ,  78.16896 ,  74.30289 , ...,  76.49201 ,\n",
       "          80.23284 ,  78.70794 ],\n",
       "        [ 65.09852 ,  64.2796  ,  55.134758, ...,  72.02436 ,\n",
       "          72.59897 ,  70.23787 ],\n",
       "        ...,\n",
       "        [181.72163 , 181.6312  , 183.65642 , ..., 180.61089 ,\n",
       "         179.01813 , 178.71059 ],\n",
       "        [166.9692  , 169.3949  , 169.47812 , ..., 166.72153 ,\n",
       "         168.54349 , 168.74545 ],\n",
       "        [177.38702 , 179.81345 , 177.86757 , ..., 173.65312 ,\n",
       "         175.26546 , 176.39352 ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-be4dd72f-92e8-4bb0-b541-cda688d5ffa1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-be4dd72f-92e8-4bb0-b541-cda688d5ffa1' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1981-12-31 2010-12-31</div><input id='attrs-ccdb7796-58ab-45c5-a1e7-d380ee517863' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccdb7796-58ab-45c5-a1e7-d380ee517863' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18002bd0-a421-445e-9e41-a789c5ef3ae0' class='xr-var-data-in' type='checkbox'><label for='data-18002bd0-a421-445e-9e41-a789c5ef3ae0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-12-31T00:00:00.000000000&#x27;, &#x27;2010-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-88.75 -86.25 ... 86.25 88.75</div><input id='attrs-088ec791-9aee-4a09-a324-8b5d8462638b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-088ec791-9aee-4a09-a324-8b5d8462638b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c4c85e1-b91a-4aeb-aae7-af3a06d2b94f' class='xr-var-data-in' type='checkbox'><label for='data-8c4c85e1-b91a-4aeb-aae7-af3a06d2b94f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[-90.  90.]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bounds</dd></dl></div><div class='xr-var-data'><pre>array([-88.75, -86.25, -83.75, -81.25, -78.75, -76.25, -73.75, -71.25, -68.75,\n",
       "       -66.25, -63.75, -61.25, -58.75, -56.25, -53.75, -51.25, -48.75, -46.25,\n",
       "       -43.75, -41.25, -38.75, -36.25, -33.75, -31.25, -28.75, -26.25, -23.75,\n",
       "       -21.25, -18.75, -16.25, -13.75, -11.25,  -8.75,  -6.25,  -3.75,  -1.25,\n",
       "         1.25,   3.75,   6.25,   8.75,  11.25,  13.75,  16.25,  18.75,  21.25,\n",
       "        23.75,  26.25,  28.75,  31.25,  33.75,  36.25,  38.75,  41.25,  43.75,\n",
       "        46.25,  48.75,  51.25,  53.75,  56.25,  58.75,  61.25,  63.75,  66.25,\n",
       "        68.75,  71.25,  73.75,  76.25,  78.75,  81.25,  83.75,  86.25,  88.75],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.25 3.75 6.25 ... 356.2 358.8</div><input id='attrs-c9714b88-02e9-4d4a-8dee-22c80afe887b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9714b88-02e9-4d4a-8dee-22c80afe887b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ebcf414-0ed8-4cf8-8e43-7d220b291d6d' class='xr-var-data-in' type='checkbox'><label for='data-8ebcf414-0ed8-4cf8-8e43-7d220b291d6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[  0. 360.]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bounds</dd></dl></div><div class='xr-var-data'><pre>array([  1.25,   3.75,   6.25,   8.75,  11.25,  13.75,  16.25,  18.75,  21.25,\n",
       "        23.75,  26.25,  28.75,  31.25,  33.75,  36.25,  38.75,  41.25,  43.75,\n",
       "        46.25,  48.75,  51.25,  53.75,  56.25,  58.75,  61.25,  63.75,  66.25,\n",
       "        68.75,  71.25,  73.75,  76.25,  78.75,  81.25,  83.75,  86.25,  88.75,\n",
       "        91.25,  93.75,  96.25,  98.75, 101.25, 103.75, 106.25, 108.75, 111.25,\n",
       "       113.75, 116.25, 118.75, 121.25, 123.75, 126.25, 128.75, 131.25, 133.75,\n",
       "       136.25, 138.75, 141.25, 143.75, 146.25, 148.75, 151.25, 153.75, 156.25,\n",
       "       158.75, 161.25, 163.75, 166.25, 168.75, 171.25, 173.75, 176.25, 178.75,\n",
       "       181.25, 183.75, 186.25, 188.75, 191.25, 193.75, 196.25, 198.75, 201.25,\n",
       "       203.75, 206.25, 208.75, 211.25, 213.75, 216.25, 218.75, 221.25, 223.75,\n",
       "       226.25, 228.75, 231.25, 233.75, 236.25, 238.75, 241.25, 243.75, 246.25,\n",
       "       248.75, 251.25, 253.75, 256.25, 258.75, 261.25, 263.75, 266.25, 268.75,\n",
       "       271.25, 273.75, 276.25, 278.75, 281.25, 283.75, 286.25, 288.75, 291.25,\n",
       "       293.75, 296.25, 298.75, 301.25, 303.75, 306.25, 308.75, 311.25, 313.75,\n",
       "       316.25, 318.75, 321.25, 323.75, 326.25, 328.75, 331.25, 333.75, 336.25,\n",
       "       338.75, 341.25, 343.75, 346.25, 348.75, 351.25, 353.75, 356.25, 358.75],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-532bd10a-e7ec-4805-8e22-23e25beb179d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-532bd10a-e7ec-4805-8e22-23e25beb179d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'precip' (time: 2, latitude: 72, longitude: 144)>\n",
       "array([[[129.19537 , 128.15681 , 126.38945 , ..., 133.26077 ,\n",
       "         141.00227 , 143.09848 ],\n",
       "        [ 84.29381 ,  89.40729 ,  88.67485 , ...,  84.632454,\n",
       "          92.81697 ,  87.94917 ],\n",
       "        [ 56.81002 ,  61.727043,  57.33716 , ...,  64.34771 ,\n",
       "          71.476944,  66.36371 ],\n",
       "        ...,\n",
       "        [199.64218 , 200.10161 , 199.81633 , ..., 190.7377  ,\n",
       "         186.74507 , 189.91205 ],\n",
       "        [176.13716 , 180.60878 , 179.31021 , ..., 172.73724 ,\n",
       "         176.88115 , 176.93964 ],\n",
       "        [177.71764 , 179.33127 , 174.82571 , ..., 171.5885  ,\n",
       "         176.74818 , 175.42719 ]],\n",
       "\n",
       "       [[109.505585, 106.912415, 105.28148 , ..., 109.71927 ,\n",
       "         116.9897  , 116.55097 ],\n",
       "        [ 77.33448 ,  78.16896 ,  74.30289 , ...,  76.49201 ,\n",
       "          80.23284 ,  78.70794 ],\n",
       "        [ 65.09852 ,  64.2796  ,  55.134758, ...,  72.02436 ,\n",
       "          72.59897 ,  70.23787 ],\n",
       "        ...,\n",
       "        [181.72163 , 181.6312  , 183.65642 , ..., 180.61089 ,\n",
       "         179.01813 , 178.71059 ],\n",
       "        [166.9692  , 169.3949  , 169.47812 , ..., 166.72153 ,\n",
       "         168.54349 , 168.74545 ],\n",
       "        [177.38702 , 179.81345 , 177.86757 , ..., 173.65312 ,\n",
       "         175.26546 , 176.39352 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1981-12-31 2010-12-31\n",
       "  * latitude   (latitude) float32 -88.75 -86.25 -83.75 ... 83.75 86.25 88.75\n",
       "  * longitude  (longitude) float32 1.25 3.75 6.25 8.75 ... 353.8 356.2 358.8"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_prec_nc['precip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "c605b943",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method resample in module xarray.core.common:\n",
      "\n",
      "resample(indexer: Mapping[Hashable, str] = None, skipna=None, closed: str = None, label: str = None, base: int = 0, keep_attrs: bool = None, loffset=None, restore_coord_dims: bool = None, **indexer_kwargs: str) method of xarray.core.dataset.Dataset instance\n",
      "    Returns a Resample object for performing resampling operations.\n",
      "    \n",
      "    Handles both downsampling and upsampling. The resampled\n",
      "    dimension must be a datetime-like coordinate. If any intervals\n",
      "    contain no values from the original object, they will be given\n",
      "    the value ``NaN``.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    indexer : {dim: freq}, optional\n",
      "        Mapping from the dimension name to resample frequency [1]_. The\n",
      "        dimension must be datetime-like.\n",
      "    skipna : bool, optional\n",
      "        Whether to skip missing values when aggregating in downsampling.\n",
      "    closed : {\"left\", \"right\"}, optional\n",
      "        Side of each interval to treat as closed.\n",
      "    label : {\"left\", \"right\"}, optional\n",
      "        Side of each interval to use for labeling.\n",
      "    base : int, optional\n",
      "        For frequencies that evenly subdivide 1 day, the \"origin\" of the\n",
      "        aggregated intervals. For example, for \"24H\" frequency, base could\n",
      "        range from 0 through 23.\n",
      "    loffset : timedelta or str, optional\n",
      "        Offset used to adjust the resampled time labels. Some pandas date\n",
      "        offset strings are supported.\n",
      "    restore_coord_dims : bool, optional\n",
      "        If True, also restore the dimension order of multi-dimensional\n",
      "        coordinates.\n",
      "    **indexer_kwargs : {dim: freq}\n",
      "        The keyword arguments form of ``indexer``.\n",
      "        One of indexer or indexer_kwargs must be provided.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    resampled : same type as caller\n",
      "        This object resampled.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    Downsample monthly time-series data to seasonal data:\n",
      "    \n",
      "    >>> da = xr.DataArray(\n",
      "    ...     np.linspace(0, 11, num=12),\n",
      "    ...     coords=[\n",
      "    ...         pd.date_range(\n",
      "    ...             \"15/12/1999\",\n",
      "    ...             periods=12,\n",
      "    ...             freq=pd.DateOffset(months=1),\n",
      "    ...         )\n",
      "    ...     ],\n",
      "    ...     dims=\"time\",\n",
      "    ... )\n",
      "    >>> da\n",
      "    <xarray.DataArray (time: 12)>\n",
      "    array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])\n",
      "    Coordinates:\n",
      "      * time     (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15\n",
      "    >>> da.resample(time=\"QS-DEC\").mean()\n",
      "    <xarray.DataArray (time: 4)>\n",
      "    array([ 1.,  4.,  7., 10.])\n",
      "    Coordinates:\n",
      "      * time     (time) datetime64[ns] 1999-12-01 2000-03-01 2000-06-01 2000-09-01\n",
      "    \n",
      "    Upsample monthly time-series data to daily data:\n",
      "    \n",
      "    >>> da.resample(time=\"1D\").interpolate(\"linear\")  # +doctest: ELLIPSIS\n",
      "    <xarray.DataArray (time: 337)>\n",
      "    array([ 0.        ,  0.03225806,  0.06451613,  0.09677419,  0.12903226,\n",
      "            0.16129032,  0.19354839,  0.22580645,  0.25806452,  0.29032258,\n",
      "            0.32258065,  0.35483871,  0.38709677,  0.41935484,  0.4516129 ,\n",
      "    ...\n",
      "           10.80645161, 10.83870968, 10.87096774, 10.90322581, 10.93548387,\n",
      "           10.96774194, 11.        ])\n",
      "    Coordinates:\n",
      "      * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15\n",
      "    \n",
      "    Limit scope of upsampling method\n",
      "    \n",
      "    >>> da.resample(time=\"1D\").nearest(tolerance=\"1D\")\n",
      "    <xarray.DataArray (time: 337)>\n",
      "    array([ 0.,  0., nan, ..., nan, 11., 11.])\n",
      "    Coordinates:\n",
      "      * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    pandas.Series.resample\n",
      "    pandas.DataFrame.resample\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    .. [1] http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(gpcp_dataset.resample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63036229",
   "metadata": {},
   "outputs": [],
   "source": [
    "clipAndResample('Recharge_files/LTA_soil_moisture_Africa.tif', 'CRU_precip_CGIAR_AI_data/Africa_continent_shape.shp', \n",
    "                'SM.tif', 1/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c3b441a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "clipAndResample('Recharge_files/LTA_NDVI.asc', 'CRU_precip_CGIAR_AI_data/Africa_continent_shape.shp', \n",
    "                'test.tif', 1/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "728a43fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24336\n"
     ]
    }
   ],
   "source": [
    "with rasterio.open('Low_res_data_05/LTA_FLDAS_Soil_moisture_Afr_05.tif') as src:\n",
    "    print(np.count_nonzero(~np.isnan(src.read())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f7a5f864",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 26424, 32352)\n"
     ]
    }
   ],
   "source": [
    "with rasterio.open('Recharge_files/GLOBCOVER_L4_200901_200912_V2.3_AfricaClip.tif') as src:\n",
    "    print(src.read().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08275ed9",
   "metadata": {},
   "outputs": [
    {
     "ename": "RasterioIOError",
     "evalue": "High_res_data_01/RF_recharge.tif: No such file or directory",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCPLE_OpenFailedError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32mrasterio/_base.pyx\u001b[0m in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mrasterio/_shim.pyx\u001b[0m in \u001b[0;36mrasterio._shim.open_dataset\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mrasterio/_err.pyx\u001b[0m in \u001b[0;36mrasterio._err.exc_wrap_pointer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mCPLE_OpenFailedError\u001b[0m: High_res_data_01/RF_recharge.tif: No such file or directory",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mRasterioIOError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-1b4b307b550b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mrasterio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'High_res_data_01/RF_recharge.tif'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gee/lib/python3.9/site-packages/rasterio/env.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0menv_ctor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gee/lib/python3.9/site-packages/rasterio/__init__.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode, driver, width, height, count, crs, transform, dtype, nodata, sharing, **kwargs)\u001b[0m\n\u001b[1;32m    218\u001b[0m         \u001b[0;31m# None.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDatasetReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdriver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msharing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"r+\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m             s = get_writer_for_path(path, driver=driver)(\n",
      "\u001b[0;32mrasterio/_base.pyx\u001b[0m in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mRasterioIOError\u001b[0m: High_res_data_01/RF_recharge.tif: No such file or directory"
     ]
    }
   ],
   "source": [
    "with rasterio.open('High_res_data_01/RF_recharge.tif') as src:\n",
    "    print(src.read().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c6e3115e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reproject\n",
    "import numpy as np\n",
    "import rasterio\n",
    "from rasterio.warp import calculate_default_transform, reproject, Resampling\n",
    "\n",
    "dst_crs = 'EPSG:4326'\n",
    "\n",
    "with rasterio.open('CPC_ARC/africa_arc.19840104.tif') as src:\n",
    "    transform, width, height = calculate_default_transform(\n",
    "        src.crs, dst_crs, src.width, src.height, *src.bounds)\n",
    "    kwargs = src.meta.copy()\n",
    "    kwargs.update({\n",
    "        'crs': dst_crs,\n",
    "        'transform': transform,\n",
    "        'width': width,\n",
    "        'height': height\n",
    "    })\n",
    "\n",
    "    with rasterio.open('CPC_ARC/africa_arc.19840104_n.tif', 'w', **kwargs) as dst:\n",
    "        for i in range(1, src.count + 1):\n",
    "            reproject(\n",
    "                source=rasterio.band(src, i),\n",
    "                destination=rasterio.band(dst, i),\n",
    "                src_transform=src.transform,\n",
    "                src_crs=src.crs,\n",
    "                dst_transform=transform,\n",
    "                dst_crs=dst_crs,\n",
    "                resampling=Resampling.nearest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b90bbf22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:4326\n",
      "(2, 145, 178)\n"
     ]
    }
   ],
   "source": [
    "with rasterio.open('Precip_sensitivity/LTA_GPCC_precip_05.tif') as src:\n",
    "    print(src.crs)\n",
    "    print(src.read().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d762837f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert .asc to .tif\n",
    "import rasterio.crs\n",
    "import rasterio.mask\n",
    "from rasterio.enums import Resampling\n",
    "import fiona\n",
    "from rasterio.io import MemoryFile\n",
    "\n",
    "# read continent outline\n",
    "with fiona.open(\"CRU_precip_CGIAR_AI_data/Africa_continent_shape.shp\", \"r\") as shapefile:\n",
    "    shapes = [feature[\"geometry\"] for feature in shapefile]\n",
    "\n",
    "crs = rasterio.crs.CRS({\"init\": \"epsg:4326\"})\n",
    "\n",
    "# define the scaling factor\n",
    "scale_factor = 1/5\n",
    "\n",
    "# read text file\n",
    "with rasterio.open('High_res_data_01/landcover_Afr_01.tif', mode='r+') as src:\n",
    "    #src.crs = crs\n",
    "    # mask out the ocean\n",
    "    out_image, out_transform = rasterio.mask.mask(src, shapes, crop=True, all_touched=True)\n",
    "    out_meta = src.meta\n",
    "    # config for intermediate in-memory dataset\n",
    "    out_meta.update({\"height\": out_image.shape[1], \"width\": out_image.shape[2],\"transform\": out_transform})\n",
    "    \n",
    "    # create in-memory cropped raster\n",
    "    with MemoryFile() as memfile:\n",
    "        with memfile.open(**out_meta) as dataset:  # Open as DatasetWriter\n",
    "            dataset.write(out_image)\n",
    "        with memfile.open() as dataset:  # Reopen as DatasetReader\n",
    "            # resample data to target shape\n",
    "            data = dataset.read(\n",
    "                out_shape=(\n",
    "                    dataset.count,\n",
    "                    int(dataset.height * scale_factor),\n",
    "                    int(dataset.width * scale_factor)\n",
    "                ),\n",
    "                resampling=Resampling.mode\n",
    "            )\n",
    "\n",
    "            # scale image transform\n",
    "            transform = dataset.transform * dataset.transform.scale(\n",
    "                (dataset.width / data.shape[-1]),\n",
    "                (dataset.height / data.shape[-2])\n",
    "            )\n",
    "            # final raster config\n",
    "            profile = dataset.profile\n",
    "            profile.update(transform=transform, driver='GTiff', \n",
    "                height=int(dataset.height * scale_factor), width=int(dataset.width * scale_factor))\n",
    "\n",
    "# write file\n",
    "with rasterio.open(\"Low_res_data_05/landcover_Afr_05_2.tif\", \"w\", **profile) as dest:\n",
    "    dest.write(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b92a47a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clip\n",
    "import rasterio.crs\n",
    "import rasterio.mask\n",
    "from rasterio.enums import Resampling\n",
    "import fiona\n",
    "from rasterio.io import MemoryFile\n",
    "\n",
    "# read continent outline\n",
    "with fiona.open(\"CRU_precip_CGIAR_AI_data/Africa_continent_shape.shp\", \"r\") as shapefile:\n",
    "    shapes = [feature[\"geometry\"] for feature in shapefile]\n",
    "\n",
    "crs = rasterio.crs.CRS({\"init\": \"epsg:4326\"})\n",
    "\n",
    "# read text file\n",
    "with rasterio.open('LTA_GPCC_precip.tif', mode='r+') as src:\n",
    "    src.crs = crs\n",
    "    # mask out the ocean\n",
    "    out_image, out_transform = rasterio.mask.mask(src, shapes, crop=True, all_touched=True)\n",
    "    out_meta = src.meta\n",
    "    # config for intermediate in-memory dataset\n",
    "    out_meta.update({\"height\": out_image.shape[1], \"width\": out_image.shape[2],\"transform\": out_transform})\n",
    "\n",
    "# write file\n",
    "with rasterio.open(\"Precip_sensitivity/LTA_GPCC_precip_Africa_05.tif\", \"w\", **out_meta) as dest:\n",
    "    dest.write(out_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ccaf1c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert .asc to .tif\n",
    "import rasterio.crs\n",
    "import rasterio.mask\n",
    "from rasterio.enums import Resampling\n",
    "import fiona\n",
    "from rasterio.io import MemoryFile\n",
    "\n",
    "# define the scaling factor\n",
    "scale_factor = 1/150\n",
    "\n",
    "# read text file\n",
    "with rasterio.open('Recharge_files/GLOBCOVER_L4_200901_200912_V2.3_AfricaClip.tif', mode='r') as dataset:\n",
    "    data = dataset.read(\n",
    "        out_shape=(\n",
    "            dataset.count,\n",
    "            int(dataset.height * scale_factor),\n",
    "            int(dataset.width * scale_factor)\n",
    "        ),\n",
    "        resampling=Resampling.mode\n",
    "    )\n",
    "    # scale image transform\n",
    "    transform = dataset.transform * dataset.transform.scale(\n",
    "        (dataset.width / data.shape[-1]),\n",
    "        (dataset.height / data.shape[-2])\n",
    "    )\n",
    "    # final raster config\n",
    "    profile = dataset.profile\n",
    "    profile.update(transform=transform, driver='GTiff', \n",
    "                   height=int(dataset.height * scale_factor), width=int(dataset.width * scale_factor))\n",
    "\n",
    "# write file\n",
    "with rasterio.open(\"Low_res_data_05/landcover_Afr_05.tif\", \"w\", **profile) as dest:\n",
    "    dest.write(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "accfd6d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ee.Image({\n",
      "  \"functionInvocationValue\": {\n",
      "    \"functionName\": \"Image.clip\",\n",
      "    \"arguments\": {\n",
      "      \"geometry\": {\n",
      "        \"functionInvocationValue\": {\n",
      "          \"functionName\": \"GeometryConstructors.Polygon\",\n",
      "          \"arguments\": {\n",
      "            \"coordinates\": {\n",
      "              \"constantValue\": [\n",
      "                [\n",
      "                  [\n",
      "                    -29.262235,\n",
      "                    -37.679961\n",
      "                  ],\n",
      "                  [\n",
      "                    68.400326,\n",
      "                    -37.679961\n",
      "                  ],\n",
      "                  [\n",
      "                    68.400326,\n",
      "                    38.738848\n",
      "                  ],\n",
      "                  [\n",
      "                    -29.262235,\n",
      "                    38.738848\n",
      "                  ],\n",
      "                  [\n",
      "                    -29.262235,\n",
      "                    -37.679961\n",
      "                  ]\n",
      "                ]\n",
      "              ]\n",
      "            },\n",
      "            \"crs\": {\n",
      "              \"functionInvocationValue\": {\n",
      "                \"functionName\": \"Projection\",\n",
      "                \"arguments\": {\n",
      "                  \"crs\": {\n",
      "                    \"constantValue\": false\n",
      "                  }\n",
      "                }\n",
      "              }\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "      },\n",
      "      \"input\": {\n",
      "        \"functionInvocationValue\": {\n",
      "          \"functionName\": \"Image.rename\",\n",
      "          \"arguments\": {\n",
      "            \"input\": {\n",
      "              \"functionInvocationValue\": {\n",
      "                \"functionName\": \"Image.load\",\n",
      "                \"arguments\": {\n",
      "                  \"id\": {\n",
      "                    \"constantValue\": \"CGIAR/SRTM90_V4\"\n",
      "                  }\n",
      "                }\n",
      "              }\n",
      "            },\n",
      "            \"names\": {\n",
      "              \"constantValue\": [\n",
      "                \"dem\"\n",
      "              ]\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "# # get and resample digital elevation data\n",
    "# ee_dem = ee.Image(\"CGIAR/SRTM90_V4\").rename('dem')\n",
    "\n",
    "# # Africa only\n",
    "# geometry = ee.Geometry.Polygon([[[-29.262235, -37.679961],\n",
    "#     [68.400326, -37.679961],\n",
    "#     [68.400326, 38.738848],\n",
    "#     [-29.262235, 38.738848],\n",
    "#     [-29.262235, -37.679961]]], evenOdd=False)\n",
    "# featureCollection = geemap.vector_to_ee('CRU_precip_CGIAR_AI_data/Africa_continent_shape.shp')\n",
    "# ee_dem_africa = ee_dem.clip(geometry)\n",
    "# ee_dem_ll = ee_dem.resample('bilinear').reproject(crs='EPSG:4326', scale=50000.0)\n",
    "\n",
    "# print(ee_dem_africa)\n",
    "# #geemap.ee_export_image(ee_dem_ll, 'SRTM5000.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a7c7d07",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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