view-resample_flightline_process_OLI_neon_data_09132020.ipynb

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    "import geopandas as gpd\n",
    "import os\n",
    "import xarray as xr\n",
    "import rioxarray as rxr\n",
    "import rasterio as rio\n",
    "# import regionmask\n",
    "from fiona.crs import from_epsg \n",
    "from shapely.geometry import Point, Polygon\n",
    "import h5py as h5\n",
    "from glob import glob\n",
    "import json\n",
    "import dask as da\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from rasterio.warp import reproject, Resampling\n",
    "\n",
    "import matplotlib \n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'medium',\n",
    "        'size'   : 14}\n",
    "\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "def gen_extent_from_neon_h5(fi, nid='CPER'):\n",
    "    \n",
    "    ds = h5.File(fi, 'r')\n",
    "    map_info = str(ds['CPER']['Reflectance']['Metadata']['Coordinate_System']['Map_Info'][()])\n",
    "    epsg = int(ds['CPER']['Reflectance']['Metadata']['Coordinate_System']['EPSG Code'][()])\n",
    "    data_shape = ds['CPER']['Reflectance']['Reflectance_Data'].shape\n",
    "    \n",
    "    # produce the affine transform components\n",
    "    parts = [p.strip() for p in map_info.split(',')]\n",
    "    aff = [float(p) for p in parts[1:7]]\n",
    "    \n",
    "    px, py, ul_x, ul_y = aff[:4]\n",
    "    py = -py\n",
    "    \n",
    "    num_rows, num_cols, num_bands = data_shape\n",
    "    \n",
    "    # produce the points for the extent polygon\n",
    "    ul = Point(ul_x, ul_y)\n",
    "    ur = Point((ul_x +  num_cols*px), ul_y)\n",
    "    ll = Point(ul_x, (ul_y + num_rows*py))\n",
    "    lr = Point((ul_x + num_cols*px), (ul_y + num_rows*py))\n",
    "\n",
    "    extent_poly = Polygon((ul, ur, lr, ll, ul))\n",
    "    \n",
    "    return extent_poly, epsg\n",
    "\n",
    "# make function for reading h5 tiles\n",
    "def read_h5_file(fi, nid='CPER'):\n",
    "    # Read H5 file\n",
    "    f = h5.File(fi, \"r\")\n",
    "    \n",
    "    # spectral\n",
    "    wavelength = f[nid]['Reflectance']['Metadata']['Spectral_Data']['Wavelength'][:]\n",
    "    fwhm = f[nid]['Reflectance']['Metadata']['Spectral_Data']['FWHM'][:]\n",
    "\n",
    "    # CRS\n",
    "    crs_str = f[nid]['Reflectance']['Metadata']['Coordinate_System']['Coordinate_System_String'][()]\n",
    "    crs_epsg = f[nid]['Reflectance']['Metadata']['Coordinate_System']['EPSG Code'][()]\n",
    "    crs_mapinfo = f[nid]['Reflectance']['Metadata']['Coordinate_System']['Map_Info'][()]\n",
    "    crs_proj4 = f[nid]['Reflectance']['Metadata']['Coordinate_System']['Proj4'][()]\n",
    "\n",
    "    #arr = f[nid]['Radiance']['Radiance_Data'][:]\n",
    "    arr = da.array.from_array(f[nid]['Reflectance']['Reflectance_Data'], chunks=(256,256,256))\n",
    "    arr /= float(f[nid]['Reflectance']['Reflectance_Data'].attrs['Scale_Factor'])\n",
    "    \n",
    "    mapinfo_list = [a.strip() for a in str(crs_mapinfo).split(',')]\n",
    "    mapinfo = [float(a) for a in mapinfo_list[1:7]]\n",
    "    mapinfo\n",
    "    pix_size = mapinfo[0]\n",
    "    x = np.arange(mapinfo[2], mapinfo[2] + pix_size*arr.shape[1], pix_size)\n",
    "    y = np.arange(mapinfo[3], mapinfo[3] - pix_size* arr.shape[0], -pix_size)\n",
    "\n",
    "    xr_cube = xr.DataArray(arr, {'y': y, 'x': x, 'bands': wavelength}, dims=['y', 'x', 'bands'])\n",
    "    xr_cube_ma = xr_cube.where(xr_cube != -0.9999) # scaled nodata\n",
    "    \n",
    "    # add in CRS assignemnt!!!!!!!!!!!!\n",
    "    xr_cube_ma = xr_cube_ma.rio.write_crs(int(crs_epsg))\n",
    "    \n",
    "    return xr_cube_ma\n",
    "\n",
    "def resample_h5_spectral(neon_file, weights_file):\n",
    "    \n",
    "    nis_ds = read_h5_file(neon_file)\n",
    "    \n",
    "    \n",
    "    with open(weights_file, 'r') as fb:\n",
    "        weights = json.load(fb)\n",
    "    \n",
    "    sample_bands = []\n",
    "    for k in weights.keys():\n",
    "        b_weights, b_ids = weights[k]\n",
    "        sample_band = (nis_ds.isel(bands=b_ids)*np.array(b_weights)).sum(axis=-1)/ np.array(b_weights).sum()\n",
    "        sample_bands.append(sample_band)\n",
    "            \n",
    "    \n",
    "    sat_ds = xr.concat(sample_bands, dim=list(weights.keys())).rename({'concat_dim':'sat_band'}) # 'sat_band' will be new at dim=0\n",
    "    \n",
    "    return sat_ds\n",
    "\n",
    "def check_valid_pixel_overlap(neon_file, geometry, weights_file):\n",
    "    \n",
    "    nis_ds = resample_h5_spectral(neon_file, weights_file)\n",
    "    #print(nis_ds.spatial_ref)\n",
    "    \n",
    "    try:\n",
    "        sample_clip = nis_ds.rio.clip([geometry])\n",
    "        sample_im_clip = sample_clip.sel(sat_band='L8_Coastal')\n",
    "        \n",
    "        # count pixels vs total\n",
    "        total_pixels = sample_im_clip.size\n",
    "        gt0 = (sample_im_clip > 0.0).sum()\n",
    "        valid_frac = gt0.values/total_pixels\n",
    "        \n",
    "        print(f'valid fraction: {valid_frac}')\n",
    "        if valid_frac > 0.99:\n",
    "            return valid_frac\n",
    "        else:\n",
    "            print('valid pixels are not above 99% of AOI, returning False')\n",
    "            return valid_frac\n",
    "    except Exception as e:\n",
    "        \n",
    "        print(e)\n",
    "        print('not in bounds...')\n",
    "        return 0.0"
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       "      <th>geometry</th>\n",
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       "      <td>POLYGON ((523493.000 4525151.000, 524339.000 4...</td>\n",
       "      <td>NEON_D10_CPER_DP1_20200913_170936_reflectance</td>\n",
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       "      <th>1</th>\n",
       "      <td>POLYGON ((523089.000 4525427.000, 524026.000 4...</td>\n",
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       "      <td>POLYGON ((522775.000 4524952.000, 523653.000 4...</td>\n",
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       "      <th>3</th>\n",
       "      <td>POLYGON ((522399.000 4525726.000, 523277.000 4...</td>\n",
       "      <td>NEON_D10_CPER_DP1_20200913_172932_reflectance</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((521994.000 4524935.000, 522919.000 4...</td>\n",
       "      <td>NEON_D10_CPER_DP1_20200913_173615_reflectance</td>\n",
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       "                                            geometry  \\\n",
       "0  POLYGON ((523493.000 4525151.000, 524339.000 4...   \n",
       "1  POLYGON ((523089.000 4525427.000, 524026.000 4...   \n",
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       "3  POLYGON ((522399.000 4525726.000, 523277.000 4...   \n",
       "4  POLYGON ((521994.000 4524935.000, 522919.000 4...   \n",
       "\n",
       "                                        filename  \n",
       "0  NEON_D10_CPER_DP1_20200913_170936_reflectance  \n",
       "1  NEON_D10_CPER_DP1_20200913_171608_reflectance  \n",
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       "3  NEON_D10_CPER_DP1_20200913_172932_reflectance  \n",
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   "source": [
    "#geodataframe for file extents\n",
    "h5_files = glob('../../NEON_spectrometer-orthorectified-surface-directional-reflectance---flightline-09132020/*.h5')\n",
    "polys = [gen_extent_from_neon_h5(fi)[0] for fi in h5_files]\n",
    "desc = [os.path.basename(fi).split('.')[0] for fi in h5_files]\n",
    "epsg = gen_extent_from_neon_h5(h5_files[0])[1]\n",
    "\n",
    "fl_gdf = gpd.GeoDataFrame({'geometry': polys, 'filename':desc}, crs=from_epsg(epsg))\n",
    "fl_gdf.head()"
   ]
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;truediv-1c7bc32923f4073546fc0f458512ce83&#x27; (y: 11831, x: 846, bands: 426)&gt;\n",
       "dask.array&lt;where, shape=(11831, 846, 426), dtype=float64, chunksize=(256, 256, 256), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * y            (y) float64 4.525e+06 4.525e+06 ... 4.513e+06 4.513e+06\n",
       "  * x            (x) float64 5.235e+05 5.235e+05 ... 5.243e+05 5.243e+05\n",
       "  * bands        (bands) float64 381.6 386.6 391.6 ... 2.506e+03 2.511e+03\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'truediv-1c7bc32923f4073546fc0f458512ce83'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 11831</li><li><span class='xr-has-index'>x</span>: 846</li><li><span class='xr-has-index'>bands</span>: 426</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-39a7e892-ab4a-438b-81f6-cf8f94c466f9' class='xr-array-in' type='checkbox' checked><label for='section-39a7e892-ab4a-438b-81f6-cf8f94c466f9' 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>dask.array&lt;chunksize=(256, 256, 256), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 34.11 GB </td> <td> 134.22 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (11831, 846, 426) </td> <td> (256, 256, 256) </td></tr>\n",
       "    <tr><th> Count </th><td> 1505 Tasks </td><td> 376 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-cafd53fc-68fe-4c4e-b8fc-ded1110d02e1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cafd53fc-68fe-4c4e-b8fc-ded1110d02e1' class='xr-section-summary' >Coordinates: <span>(4)</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'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.525e+06 4.525e+06 ... 4.513e+06</div><input id='attrs-b91f2587-85da-4182-92f8-fd8784092512' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b91f2587-85da-4182-92f8-fd8784092512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-943e6b7f-07bf-492f-b15c-1dabfd9d4b46' class='xr-var-data-in' type='checkbox'><label for='data-943e6b7f-07bf-492f-b15c-1dabfd9d4b46' 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([4525151., 4525150., 4525149., ..., 4513323., 4513322., 4513321.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.235e+05 5.235e+05 ... 5.243e+05</div><input id='attrs-4e7c8c3f-5a0f-4bcf-a7be-fc7f61f9f849' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e7c8c3f-5a0f-4bcf-a7be-fc7f61f9f849' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49d8d85f-1367-4076-b79c-5cdbb4241257' class='xr-var-data-in' type='checkbox'><label for='data-49d8d85f-1367-4076-b79c-5cdbb4241257' 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([523493., 523494., 523495., ..., 524336., 524337., 524338.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bands</span></div><div class='xr-var-dims'>(bands)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>381.6 386.6 ... 2.506e+03 2.511e+03</div><input id='attrs-99ef3f46-091a-4fff-b9df-19ff16a620ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99ef3f46-091a-4fff-b9df-19ff16a620ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-073187a9-2d81-48b9-8cff-c8c98987dee9' class='xr-var-data-in' type='checkbox'><label for='data-073187a9-2d81-48b9-8cff-c8c98987dee9' 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([ 381.603485,  386.61319 ,  391.622894, ..., 2500.722412, 2505.732178,\n",
       "       2510.741699])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-706e4165-2d20-4dfe-a9e6-63efc5d909d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-706e4165-2d20-4dfe-a9e6-63efc5d909d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c00e6d6-2007-44a4-ac7a-07482872608b' class='xr-var-data-in' type='checkbox'><label for='data-3c00e6d6-2007-44a4-ac7a-07482872608b' 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>crs_wkt :</span></dt><dd>PROJCRS[&quot;WGS 84 / UTM zone 13N&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,DATUM[&quot;World Geodetic System 1984&quot;,ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;UTM zone 13N&quot;,METHOD[&quot;Transverse Mercator&quot;,ID[&quot;EPSG&quot;,9807]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,-105,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,0.9996,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,500000,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;easting&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;northing&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],ID[&quot;EPSG&quot;,32613]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 13N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-105.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCRS[&quot;WGS 84 / UTM zone 13N&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,DATUM[&quot;World Geodetic System 1984&quot;,ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;UTM zone 13N&quot;,METHOD[&quot;Transverse Mercator&quot;,ID[&quot;EPSG&quot;,9807]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,-105,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,0.9996,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,500000,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;easting&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;northing&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],ID[&quot;EPSG&quot;,32613]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-18b12f20-c125-46d0-9666-5196bf86d163' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18b12f20-c125-46d0-9666-5196bf86d163' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'truediv-1c7bc32923f4073546fc0f458512ce83' (y: 11831, x: 846, bands: 426)>\n",
       "dask.array<where, shape=(11831, 846, 426), dtype=float64, chunksize=(256, 256, 256), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * y            (y) float64 4.525e+06 4.525e+06 ... 4.513e+06 4.513e+06\n",
       "  * x            (x) float64 5.235e+05 5.235e+05 ... 5.243e+05 5.243e+05\n",
       "  * bands        (bands) float64 381.6 386.6 391.6 ... 2.506e+03 2.511e+03\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = read_h5_file(r'../../NEON_spectrometer-orthorectified-surface-directional-reflectance---flightline-09132020\\NEON_D10_CPER_DP1_20200913_170936_reflectance.h5')\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(nan)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[0,0,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try the resample_match technique for landsat\n",
    "landsat_file = '../../OutputsL8/LS8_20200913_lat40lon10423_r32p33_vmsk_mclds_topshad_rad_srefdem_stdsref.tif'\n",
    "landsat_ds = rxr.open_rasterio(landsat_file)\n",
    "l8_weights_file = './spectral_response/NIS_weights_L8.json'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## loop over the geometries and do the groupby method to produce STD and MEAN per satellite pixel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../NEON_spectrometer-orthorectified-surface-directional-reflectance---flightline-09132020\\NEON_D10_CPER_DP1_20200913_170936_reflectance.h5\n"
     ]
    }
   ],
   "source": [
    "# lists to store results\n",
    "res_L8_means, res_S2_means, res_L8_stds, res_S2_stds =  [],[],[],[]\n",
    "L8_arrs, S2_arrs = [],[]\n",
    "\n",
    "num_fl = 1\n",
    "for  geom, fi in fl_gdf[['geometry', 'filename']].values[:num_fl]:\n",
    "    \n",
    "    fi = os.path.join('../../NEON_spectrometer-orthorectified-surface-directional-reflectance---flightline-09132020', fi + '.h5')\n",
    "    desc=fi\n",
    "    print(desc)\n",
    "    \n",
    "    # spectral resample to each satellite\n",
    "    neon_ds_L8 = resample_h5_spectral(fi, l8_weights_file)\n",
    "#     neon_ds_S2 = resample_h5_spectral(fi, s2_weights_file)\n",
    "    \n",
    "    # clip to current AOI\n",
    "    neon_ds_L8 = neon_ds_L8.rio.clip([geom], all_touched=True, from_disk=True)\n",
    "#     neon_ds_S2 = neon_ds_S2.rio.clip([geom], all_touched=True, from_disk=True)\n",
    "    \n",
    "    # clip landsat and sentinel datasets, add to list\n",
    "    L8_arrs.append(landsat_ds.rio.clip([geom], all_touched=True, from_disk=True))\n",
    "#     S2_arrs.append(s2_ds.rio.clip([geom], all_touched=True, from_disk=True))\n",
    "    \n",
    "    # group by for mean\n",
    "    target_res = 30\n",
    "    reduced_L8_mean = (\n",
    "        neon_ds_L8\n",
    "        .groupby(((neon_ds_L8.x//target_res) + 0.5) * target_res)\n",
    "        .mean(dim='x')\n",
    "        .groupby(((neon_ds_L8.y//target_res) + 0.5) * target_res)\n",
    "        .mean(dim='y'))\n",
    "\n",
    "#     target_res = 10\n",
    "#     reduced_S2_mean = (\n",
    "#         neon_ds_S2\n",
    "#         .groupby(((neon_ds_S2.x//target_res) + 0.5) * target_res)\n",
    "#         .mean(dim='x')\n",
    "#         .groupby(((neon_ds_S2.y//target_res) + 0.5) * target_res)\n",
    "#         .mean(dim='y'))\n",
    "    \n",
    "    # group by for STD\n",
    "    target_res = 30\n",
    "    reduced_L8_std = (\n",
    "        neon_ds_L8\n",
    "        .groupby(((neon_ds_L8.x//target_res) + 0.5) * target_res)\n",
    "        .std(dim='x')\n",
    "        .groupby(((neon_ds_L8.y//target_res) + 0.5) * target_res)\n",
    "        .std(dim='y'))\n",
    "\n",
    "#     target_res = 10\n",
    "#     reduced_S2_std = (\n",
    "#         neon_ds_S2\n",
    "#         .groupby(((neon_ds_S2.x//target_res) + 0.5) * target_res)\n",
    "#         .std(dim='x')\n",
    "#         .groupby(((neon_ds_S2.y//target_res) + 0.5) * target_res)\n",
    "#         .std(dim='y'))\n",
    "    \n",
    "    # append to lists\n",
    "    res_L8_means.append(reduced_L8_mean)\n",
    "#     res_S2_means.append(reduced_S2_mean)\n",
    "    res_L8_stds.append(reduced_L8_std)\n",
    "#     res_S2_stds.append(reduced_S2_std)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get samples and corresponding 1-m spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "nis_ds = read_h5_file(fi)\n",
    "sat_ds = L8_arrs[0]\n",
    "res_nis_ds = res_L8_means[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(523920.), array(4519230.))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = sat_ds.x.median().values\n",
    "y = sat_ds.y.median().values\n",
    "x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7, 1, 1), (7, 1, 1), (30, 30, 426))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psize=30\n",
    "cond = (res_nis_ds.x >= x) & (res_nis_ds.x < x+psize) & (res_nis_ds.y >= y) & (res_nis_ds.y < y+psize)\n",
    "res_nis_sample = res_nis_ds.where(cond, drop=True)\n",
    "\n",
    "cond = (sat_ds.x >= x) & (sat_ds.x < x+psize) & (sat_ds.y >= y) & (sat_ds.y < y+psize)\n",
    "sat_sample = sat_ds.where(cond, drop=True)\n",
    "\n",
    "cond = (nis_ds.x >= x) & (nis_ds.x < x+psize) & (nis_ds.y >= y) & (nis_ds.y < y+psize)\n",
    "nis_sample = nis_ds.where(cond, drop=True)\n",
    "\n",
    "res_nis_sample.shape, sat_sample.shape, nis_sample.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "l8_wavs = [440, 475, 550, 650, 870, 1600, 2200]\n",
    "\n",
    "wav_cond1 = (nis_sample.bands >=1300) & (nis_sample.bands < 1450)\n",
    "wav_cond2 = (nis_sample.bands >=1800) & (nis_sample.bands < 1935)\n",
    "wav_cond = wav_cond1 | wav_cond2\n",
    "\n",
    "nis_plot_values = nis_sample.where(~wav_cond)\n",
    "nis_plot_values = nis_plot_values.values.reshape(nis_sample.shape[0]*nis_sample.shape[1], nis_sample.shape[2]).T\n",
    "nis_plot_values = np.where(nis_plot_values>1.0, np.nan, nis_plot_values)\n",
    "\n",
    "ax.plot(nis_sample.bands.values, nis_plot_values, '-', color='lightgrey', alpha=0.14)\n",
    "ax.plot(l8_wavs, res_nis_sample.values.ravel(), 'o-', color='darkgreen', label='NIS spectra resampled to L8')\n",
    "ax.plot(l8_wavs, sat_sample.values.ravel()/1000, 'o-', color='red', alpha=0.5, label='Original L8 spectra')\n",
    "ax.grid('on')\n",
    "\n",
    "plt.xlabel('Wavelength [nm]')\n",
    "plt.ylabel('Surface Reflectance Units')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (x-python)",
   "language": "python",
   "name": "x-python"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}