view-resample_flightline_process_S2A_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\")"
   ]
  },
  {
   "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_band = sample_clip.sat_band.values[0]\n",
    "        sample_im_clip = sample_clip.sel(sat_band=sample_band)\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",
       "      <th>filename</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",
       "      <td>NEON_D10_CPER_DP1_20200913_171608_reflectance</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((522775.000 4524952.000, 523653.000 4...</td>\n",
       "      <td>NEON_D10_CPER_DP1_20200913_172248_reflectance</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",
       "2  POLYGON ((522775.000 4524952.000, 523653.000 4...   \n",
       "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",
       "2  NEON_D10_CPER_DP1_20200913_172248_reflectance  \n",
       "3  NEON_D10_CPER_DP1_20200913_172932_reflectance  \n",
       "4  NEON_D10_CPER_DP1_20200913_173615_reflectance  "
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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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   "cell_type": "code",
   "execution_count": 4,
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       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  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-690ac11cc6581c72f653462b14882b84&#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-690ac11cc6581c72f653462b14882b84'</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-ad9a1004-4800-4285-93f1-b1bced1940de' class='xr-array-in' type='checkbox' checked><label for='section-ad9a1004-4800-4285-93f1-b1bced1940de' 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",
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       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-890a861f-7aa9-4300-9e81-4e8c5d7227f8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-890a861f-7aa9-4300-9e81-4e8c5d7227f8' 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-1828d808-b9e8-4dd1-bb46-cb91219588ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1828d808-b9e8-4dd1-bb46-cb91219588ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa4d77ae-c4c4-4fe0-9483-081b009945d3' class='xr-var-data-in' type='checkbox'><label for='data-aa4d77ae-c4c4-4fe0-9483-081b009945d3' 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-ea2fb7ee-406b-46c7-9e20-639cdb1150c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea2fb7ee-406b-46c7-9e20-639cdb1150c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f786144-95c6-4740-a7b5-21fdb0be0a69' class='xr-var-data-in' type='checkbox'><label for='data-8f786144-95c6-4740-a7b5-21fdb0be0a69' 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-238e1474-3631-4d46-b312-a4a92e0f0ab9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-238e1474-3631-4d46-b312-a4a92e0f0ab9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-038912c6-3865-481f-86e2-9cae8b6d824d' class='xr-var-data-in' type='checkbox'><label for='data-038912c6-3865-481f-86e2-9cae8b6d824d' 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-19d9d456-1bad-494e-9283-44aa5333d7be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19d9d456-1bad-494e-9283-44aa5333d7be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bbc0d56-0c4c-49c5-8cb0-81cf6ad82a9c' class='xr-var-data-in' type='checkbox'><label for='data-6bbc0d56-0c4c-49c5-8cb0-81cf6ad82a9c' 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-73c432ce-a034-4496-904f-0047f3180c15' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73c432ce-a034-4496-904f-0047f3180c15' 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-690ac11cc6581c72f653462b14882b84' (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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try the resample_match technique for landsat\n",
    "s2 = '../../OutputsSen2_10m_09132020/SEN2_20200913_lat41lon10435_T13TEF_ORB141_utm13n_vmsk_rad_srefdem.tif'\n",
    "s2 = '../../OutputsSen2_10m_09132020_AOTL8/SEN2_20200913_lat41lon10435_T13TEF_ORB141_utm13n_vmsk_rad_srefdem.tif' # same AOT as L8\n",
    "s2 = '../../OutputsSen2_10m_09132020_AOTL8_STDSREF/SEN2_20200913_lat41lon10435_T13TEF_ORB141_utm13n_vmsk_topshad_rad_srefdem_stdsref.tif' # same AOT as L8 and standardized surface reflectance\n",
    "\n",
    "\n",
    "# s2 = '../../OutputsSen2_10m_09132020_STDSREF/SEN2_20200913_lat41lon10435_T13TEF_ORB141_utm13n_vmsk_topshad_rad_srefdem_stdsref.tif' # standardized SR, DOS method for AOT\n",
    "# s2 = r'C:\\projects\\arcsi\\S2A_MSIL2A_20200913T174941_N9999_R141_T13TEF_20210301T150241.SAFE\\GRANULE\\L2A_T13TEF_A027308_20200913T175722\\IMG_DATA\\s2_stack.vrt' # sen2cor\n",
    "\n",
    "s2_ds = rxr.open_rasterio(s2)\n",
    "s2_ds = s2_ds/1000 # arcsi scale factor\n",
    "s2_weights_file = './spectral_response_cutoff/NIS_weights_S2A.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": 26,
   "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "nis_ds = read_h5_file(fi)\n",
    "sat_ds = S2_arrs[0]\n",
    "res_nis_ds = res_S2_means[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(523915.), array(4519235.))"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((13, 1, 1), (10, 1, 1), (10, 10, 426))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psize=10\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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "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",
    "s2_wavs = [442.7, 492.4, 559.8, 664.6, 704.1, 740.5,782.8, 832.8, 864.7, 945.1, 1373.5, ]\n",
    "s2_wavs =        [492.4, 559.8, 664.6, 704.1, 740.5,782.8, 832.8, 864.7,1613.7, 2202.4]\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",
    "\n",
    "# since arcsi doesn't keep the 60m data\n",
    "s2_nis_vals = res_nis_sample.values.ravel()\n",
    "idx = [1,2,3,4,5,6,7,8, -2, -1]\n",
    "s2_nis_vals = s2_nis_vals[idx]\n",
    "ax.plot(s2_wavs, s2_nis_vals, 'o-', color='darkgreen', label='NIS spectra resampled to S2A')\n",
    "\n",
    "ax.plot(s2_wavs, sat_sample.values.ravel(), 'o-', color='red', alpha=0.5, label='Original S2A spectra')\n",
    "ax.grid('on')\n",
    "\n",
    "plt.xlabel('Wavelength [nm]')\n",
    "plt.ylabel('Surface Reflectance Units')\n",
    "plt.legend(loc='lower right')\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
}