{"cells":[{"cell_type":"markdown","metadata":{},"source":["Working with ASCAT data and Xarray\n","==================================\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["## Working with swath files\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["### Creating a SwathFileCollection\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["If we have a collection of time-series swath files, we can create a SwathFileCollection object that will handle them as a group.\n","\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["from datetime import datetime\n","from importlib import reload\n","\n","import ascat.read_native.ragged_array_ts as rat\n","from ascat.read_native import xarray_io\n","\n","from time import time"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["swath_source = \"/home/charriso/p14/data-write/RADAR/hsaf/h121_v1.0/swaths\""]},{"cell_type":"markdown","metadata":{},"source":["It&rsquo;s important to understand the structure of the data, so that the SwathFileCollection can find and parse all of the data that is requested from it. Handily, this package comes with existing parsers for several ASCAT products. These can be used with `SwathFileCollection.from_product_id()`:\n","\n"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["swath_collection = rat.SwathFileCollection.from_product_id(swath_source, \"H121_V1.0\")"]},{"cell_type":"markdown","metadata":{},"source":["The currently included project ids are the keys of `ascat.read_native.xarray_io.swath_io_catalog`\n","\n"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":["dict_keys(['H129', 'H129_V1.0', 'H121_V1.0', 'H122', 'SIG0_6.25', 'SIG0_12.5'])"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["from ascat.read_native.xarray_io import swath_io_catalog\n","swath_io_catalog.keys()"]},{"cell_type":"markdown","metadata":{},"source":["If your data structure does not match anything included in the package, you can write a class inheriting from `ascat.read_native.xarray_io.SwathIOBase`:\n","\n"]},{"cell_type":"code","execution_count":1,"metadata":{"executable":"False"},"outputs":[],"source":["class AscatH121v1Swath(SwathIOBase):\n","    # outlines the basic structure of the swath filenames, with {bracketed} variable names\n","    # in the place of anything that changes according to the data inside\n","    fn_pattern = \"W_IT-HSAF-ROME,SAT,SSM-ASCAT-METOP{sat}-12.5km-H121_C_LIIB_{placeholder}_{placeholder1}_{date}____.nc\"\n","\n","    # defines the names of the subfolder/directory names that contain the data. In this case,\n","    # files are sorted into folders by satellite and year, so that a typical filepath looks like\n","    # \".../metop_b/2021/W_IT-HSAF-ROME...\"\n","    sf_pattern = {\n","        \"satellite_folder\": \"metop_[abc]\",\n","        \"year_folder\": \"{year}\"\n","    }\n","\n","    # specifies the string format of the date that occupies the \"date\" field in fn_pattern\n","    date_format = \"%Y%m%d%H%M%S\"\n","\n","    # provides the grid that the data is based on. In this case, using the grid_cache allows\n","    # us to reuse the same grid for multiple classes in the source code without\n","    # actually generating several different FibGrids.\n","    # however, it would also work to just write, for example:\n","    #     grid = FibGrid(12.5)\n","    grid = grid_cache.fetch_or_store(\"Fib12.5\", FibGrid, 12.5)[\"grid\"]\n","\n","    # specifies the size (in degrees) of the grid cell to be written out by SwathFileCollection.stack()\n","    grid_cell_size = 5\n","\n","    # specifies the filename format for the cells written out by SwathFileCollection.stack()\n","    cell_fn_format = \"{:04d}.nc\"\n","\n","    # names any dataset variables with a \"beams\" dimension - not relevant with this particular product\n","    beams_vars = []\n","\n","    # defines all the dataset's data variable names and the dtype that they should be written as when packed.\n","    ts_dtype = np.dtype([\n","        (\"sat_id\", np.int8),\n","        (\"as_des_pass\", np.int8),\n","        (\"swath_indicator\", np.int8),\n","        (\"surface_soil_moisture\", np.float32),\n","        (\"surface_soil_moisture_noise\", np.float32),\n","        (\"backscatter40\", np.float32),\n","        (\"slope40\", np.float32),\n","        (\"curvature40\", np.float32),\n","        (\"surface_soil_moisture_sensitivity\", np.float32),\n","        (\"backscatter_flag\", np.uint8),\n","        (\"correction_flag\", np.uint8),\n","        (\"processing_flag\", np.uint8),\n","        (\"surface_flag\", np.uint8),\n","        (\"snow_cover_probability\", np.int8),\n","        (\"frozen_soil_probability\", np.int8),\n","        (\"wetland_fraction\", np.int8),\n","        (\"topographic_complexity\", np.int8),\n","        (\"subsurface_scattering_probability\", np.int8),\n","    ])\n","\n","    # a function for generating a filename regex for a particular timestamp\n","    # (used for filtering swath files by date)\n","    @staticmethod\n","    def fn_read_fmt(timestamp):\n","        return {\"date\": timestamp.strftime(\"%Y%m%d*\"),\n","                \"sat\": \"[ABC]\",\n","                \"placeholder\": \"*\",\n","                \"placeholder1\": \"*\"}\n","\n","    # a function that returns subfolder regexes based on a timestamp\n","    # Here, in the case of satellite_folder, we don't actually need to be able to search\n","    # by satellite, so we just have a regex that is inclusive of all three possible satellites.\n","    # However, in the case of year_folder, we want the search year to actually match with the\n","    # timestamp we pass to the function.\n","    @staticmethod\n","    def sf_read_fmt(timestamp):\n","        return {\n","            \"satellite_folder\": {\"satellite\": \"metop_[abc]\"},\n","            \"year_folder\": {\"year\": f\"{timestamp.year}\"},\n","        }\n","\n","    def __init__(self, filename, **kwargs):\n","        super().__init__(filename, \"netcdf4\", **kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["After creating your IO class, you can use it to make a collection by passing it to the SwathFileCollection class:\n","\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["swath_collection = rat.SwathFileCollection(swath_source, ioclass=ASCATH121v1Swath)"]},{"cell_type":"markdown","metadata":{},"source":["Regardless of how you define you define your collection, once created it can be used to read data from your swath collection for any given date range and geographic extent. It can also be used to stack data in the collection into cellwise timeseries in indexed ragged array format, according to the CellGrid defined in the ioclass.\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["Let&rsquo;s start with a rectangular, roughly country-sized area in Central Europe, and a time period of four days.\n","\n"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["bounds = (45, 50, 10, 20) #latmin, latmax, lonmin, lonmax\n","dates = (datetime(2020, 12, 1), datetime(2020, 12, 5))"]},{"cell_type":"markdown","metadata":{},"source":["By calling the `read` method of our SwathFileCollection, we open every dataset within the `date_range` we&rsquo;ve passed, concatenate them together into an xarray dataset, and filter out any observations that don&rsquo;t intersect with the bounding box we&rsquo;ve passed to `bbox`. Other spatial selections we could pass to `read` are `cell` (cell number or list of cell numbers in the swath data&rsquo;s grid system), `location_id` (grid point ID or list of IDs in the swath data&rsquo;s grid system), `coords` (lat/lon coordinate or list of coorinates that will be converted to the nearest grid point ID or list of grid point IDs), or `geom` (a Shapely geometry).\n","\n"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/html":["<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n","<defs>\n","<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n","<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n","<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n","<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n","</symbol>\n","<symbol 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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",".xr-no-icon {\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.Dataset&gt;\n","Dimensions:                            (obs: 45007)\n","Coordinates:\n","    latitude                           (obs) float64 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    longitude                          (obs) float64 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    time                               (obs) datetime64[ns] 2020-12-01T08:09:...\n","Dimensions without coordinates: obs\n","Data variables: (12/19)\n","    location_id                        (obs) float64 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    as_des_pass                        (obs) float32 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    swath_indicator                    (obs) float32 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    surface_soil_moisture              (obs) float32 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    surface_soil_moisture_noise        (obs) float32 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    backscatter40                      (obs) float64 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    ...                                 ...\n","    snow_cover_probability             (obs) float32 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    frozen_soil_probability            (obs) float32 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    wetland_fraction                   (obs) float32 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    topographic_complexity             (obs) float32 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    subsurface_scattering_probability  (obs) float32 dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;\n","    sat_id                             (obs) int64 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d084e493-48bb-44fe-b70d-a13d4dc5c627' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d084e493-48bb-44fe-b70d-a13d4dc5c627' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>obs</span>: 45007</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5df88083-c518-48e0-81db-3fd1e9e4fcff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5df88083-c518-48e0-81db-3fd1e9e4fcff' 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>latitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-ce06a056-5a38-4578-8506-45aa889f8f82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce06a056-5a38-4578-8506-45aa889f8f82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23d2297e-898b-48b6-9199-19dbcefba949' class='xr-var-data-in' type='checkbox'><label for='data-23d2297e-898b-48b6-9199-19dbcefba949' 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><dt><span>valid_min :</span></dt><dd>-90000000</dd><dt><span>valid_max :</span></dt><dd>89999999</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 351.62 kiB </td>\n","                        <td> 18.83 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 74 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n","  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n","  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n","  <line x1=\"32\" y1=\"0\" x2=\"32\" y2=\"25\" />\n","  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n","  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n","  <line x1=\"46\" y1=\"0\" x2=\"46\" y2=\"25\" />\n","  <line x1=\"51\" y1=\"0\" x2=\"51\" y2=\"25\" />\n","  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n","  <line x1=\"70\" y1=\"0\" x2=\"70\" y2=\"25\" />\n","  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n","  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n","  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n","  <line x1=\"91\" y1=\"0\" x2=\"91\" y2=\"25\" />\n","  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n","  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n","  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n","  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Colored Rectangle -->\n","  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n","\n","  <!-- Text -->\n","  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >45007</text>\n","  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-a9b990e4-0a45-4aff-8327-206f1e76de75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9b990e4-0a45-4aff-8327-206f1e76de75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dac4172b-1594-4b60-ab17-6e1bdcd375a3' class='xr-var-data-in' type='checkbox'><label for='data-dac4172b-1594-4b60-ab17-6e1bdcd375a3' 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><dt><span>valid_min :</span></dt><dd>-180000000</dd><dt><span>valid_max :</span></dt><dd>179999999</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 351.62 kiB </td>\n","                        <td> 18.83 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 74 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n","  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n","  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n","  <line x1=\"32\" y1=\"0\" x2=\"32\" y2=\"25\" />\n","  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n","  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n","  <line x1=\"46\" y1=\"0\" x2=\"46\" y2=\"25\" />\n","  <line x1=\"51\" y1=\"0\" x2=\"51\" y2=\"25\" />\n","  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n","  <line x1=\"70\" y1=\"0\" x2=\"70\" y2=\"25\" />\n","  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n","  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n","  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n","  <line x1=\"91\" y1=\"0\" x2=\"91\" y2=\"25\" />\n","  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n","  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n","  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n","  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Colored Rectangle -->\n","  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n","\n","  <!-- Text -->\n","  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >45007</text>\n","  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-12-01T08:09:59.415000064 .....</div><input id='attrs-c52386e7-0012-4fad-b43d-f09df51cc449' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c52386e7-0012-4fad-b43d-f09df51cc449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-695d64b6-f7b0-44bd-a9a8-eba0206ac834' class='xr-var-data-in' type='checkbox'><label for='data-695d64b6-f7b0-44bd-a9a8-eba0206ac834' 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>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-12-01T08:09:59.415000064&#x27;, &#x27;2020-12-01T08:09:59.415000064&#x27;,\n","       &#x27;2020-12-01T08:10:00.264000000&#x27;, ..., &#x27;2020-12-04T20:43:03.397999872&#x27;,\n","       &#x27;2020-12-04T20:43:03.397999872&#x27;, &#x27;2020-12-04T20:43:03.397999872&#x27;],\n","      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f56f2230-3e4e-40b5-9e43-b38ae26aaa18' class='xr-section-summary-in' type='checkbox'  ><label for='section-f56f2230-3e4e-40b5-9e43-b38ae26aaa18' class='xr-section-summary' >Data variables: <span>(19)</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>location_id</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-242c05d6-d6f1-4d18-ba3f-204d9a213c9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-242c05d6-d6f1-4d18-ba3f-204d9a213c9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82edd22f-895c-4eeb-80f0-1b05be15bce1' class='xr-var-data-in' type='checkbox'><label for='data-82edd22f-895c-4eeb-80f0-1b05be15bce1' 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>Location identifier (Grid Point ID)</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3300000</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 351.62 kiB </td>\n","                        <td> 18.83 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n","  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n","  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n","  <line x1=\"32\" y1=\"0\" x2=\"32\" y2=\"25\" />\n","  <line x1=\"38\" 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y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >45007</text>\n","  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>as_des_pass</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-f2a189dd-0fc2-4d66-84f4-292da08d288f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2a189dd-0fc2-4d66-84f4-292da08d288f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74459f53-895f-4321-af31-1575c4091e0e' class='xr-var-data-in' type='checkbox'><label for='data-74459f53-895f-4321-af31-1575c4091e0e' 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>orbit overpass direction (ascending, descending)</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>ascending descending</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" 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x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n","  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n","  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n","  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Colored Rectangle -->\n","  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n","\n","  <!-- Text -->\n","  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >45007</text>\n","  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swath_indicator</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-e07d9bc4-8c74-43e9-a8c1-2d92060e29e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e07d9bc4-8c74-43e9-a8c1-2d92060e29e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95e747d1-390d-4891-8774-55b3c9dfba55' class='xr-var-data-in' type='checkbox'><label for='data-95e747d1-390d-4891-8774-55b3c9dfba55' 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>swath indicator (left, right)</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>left right</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","   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>45007</text>\n","  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_soil_moisture</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-ca9a4540-6c1d-4db2-a0e4-f3a732d416cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca9a4540-6c1d-4db2-a0e4-f3a732d416cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e53fb5f-d0a2-4c04-bf28-c4e1d007f9a5' class='xr-var-data-in' type='checkbox'><label for='data-0e53fb5f-d0a2-4c04-bf28-c4e1d007f9a5' 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>surface soil moisture</dd><dt><span>units :</span></dt><dd>percent saturation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 74 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" 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/>\n","  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Colored Rectangle -->\n","  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n","\n","  <!-- Text -->\n","  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >45007</text>\n","  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_soil_moisture_noise</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-ab4f554f-5a2b-4ac7-b423-ca0ff2497662' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab4f554f-5a2b-4ac7-b423-ca0ff2497662' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a35bf20-c556-4189-8016-672575f39403' class='xr-var-data-in' type='checkbox'><label for='data-6a35bf20-c556-4189-8016-672575f39403' 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>surface soil moisture noise</dd><dt><span>units :</span></dt><dd>percent saturation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 74 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","       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</tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backscatter40</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-8ea900ec-5c22-4799-859c-eda6190f933d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ea900ec-5c22-4799-859c-eda6190f933d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dcf6264-ccc9-4c74-b8ce-daeb3daf12a3' class='xr-var-data-in' type='checkbox'><label for='data-8dcf6264-ccc9-4c74-b8ce-daeb3daf12a3' 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>backscatter interpolated at 40 degree incidence angle</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>valid_min :</span></dt><dd>-300000000</dd><dt><span>valid_max :</span></dt><dd>300000000</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 351.62 kiB </td>\n","                        <td> 18.83 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 74 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n","  <line x1=\"18\" y1=\"0\" x2=\"18\" 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id='data-7a5f685b-2b3a-46ff-88f3-d5df58335e72' class='xr-var-data-in' type='checkbox'><label for='data-7a5f685b-2b3a-46ff-88f3-d5df58335e72' 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>slope at 40 degree incidence angle</dd><dt><span>units :</span></dt><dd>dB/deg</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 351.62 kiB </td>\n","                        <td> 18.83 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 74 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" 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meta=np.ndarray&gt;</div><input id='attrs-2c9841e7-fb82-4b72-9104-7e095e6188cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c9841e7-fb82-4b72-9104-7e095e6188cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c9ba825-e622-4dc0-a508-658956ba534d' class='xr-var-data-in' type='checkbox'><label for='data-3c9ba825-e622-4dc0-a508-658956ba534d' 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>curvature at 40 degree incidence angle</dd><dt><span>units :</span></dt><dd>dB/deg^2</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 351.62 kiB </td>\n","                        <td> 18.83 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 74 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n","  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n","  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n","  <line x1=\"32\" y1=\"0\" x2=\"32\" y2=\"25\" />\n","  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n","  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n","  <line x1=\"46\" y1=\"0\" x2=\"46\" y2=\"25\" />\n","  <line x1=\"51\" y1=\"0\" x2=\"51\" y2=\"25\" />\n","  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" 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transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_soil_moisture_sensitivity</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-afc95483-5ba2-423a-b147-2567b6dea817' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afc95483-5ba2-423a-b147-2567b6dea817' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d7034c5-7c73-4aae-9f89-cac9accf726c' class='xr-var-data-in' type='checkbox'><label for='data-8d7034c5-7c73-4aae-9f89-cac9accf726c' 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>surface soil moisture sensitivity</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>valid_min :</span></dt><dd>-300000000</dd><dt><span>valid_max :</span></dt><dd>300000000</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 351.62 kiB </td>\n","                        <td> 18.83 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 74 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" 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xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccd11674-c2ae-4f57-837c-0ea3c54588a9' class='xr-var-data-in' type='checkbox'><label for='data-ccd11674-c2ae-4f57-837c-0ea3c54588a9' 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>backscatter flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>fore_beam_sigma0_valid mid_beam_sigma0_valid aft_beam_sigma0_valid reserved_for_future_use reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n","  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n","  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n","  <line x1=\"32\" y1=\"0\" x2=\"32\" y2=\"25\" />\n","  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n","  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n","  <line x1=\"46\" y1=\"0\" x2=\"46\" y2=\"25\" />\n","  <line x1=\"51\" y1=\"0\" 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text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>correction_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-3c775e23-93c6-4460-b8c6-817f547513aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c775e23-93c6-4460-b8c6-817f547513aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cbc505d-dd37-422d-a47a-e8b649c7fceb' class='xr-var-data-in' type='checkbox'><label for='data-3cbc505d-dd37-422d-a47a-e8b649c7fceb' 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>correction flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>wet_correction_applied ssm_set_to_0%_was_between_-25%_and_0% ssm_set_to_100%_was_between_100%_and_125% reserved_for_future_use reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" 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/>\n","  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n","  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n","  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n","  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Colored Rectangle -->\n","  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n","\n","  <!-- Text -->\n","  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >45007</text>\n","  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>processing_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-a6272ca8-3ef4-44c4-a843-b42cffdd61bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6272ca8-3ef4-44c4-a843-b42cffdd61bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8de5ae4-7f01-439c-8a08-1f6d40652fab' class='xr-var-data-in' type='checkbox'><label for='data-d8de5ae4-7f01-439c-8a08-1f6d40652fab' 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>processing flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>model_parameter_not_usable backscatter_not_usable ssm_below_-25%_saturation ssm_above_125%_saturation reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n","  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n","  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n","  <line x1=\"32\" y1=\"0\" x2=\"32\" y2=\"25\" />\n","  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n","  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n","  <line x1=\"46\" y1=\"0\" x2=\"46\" y2=\"25\" />\n","  <line x1=\"51\" y1=\"0\" x2=\"51\" y2=\"25\" />\n","  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n","  <line x1=\"70\" y1=\"0\" x2=\"70\" y2=\"25\" />\n","  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n","  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n","  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n","  <line x1=\"91\" y1=\"0\" x2=\"91\" y2=\"25\" />\n","  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n","  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n","  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n","  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Colored Rectangle -->\n","  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 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id='data-50908c70-5145-41fb-8d82-84a816798caa' class='xr-var-data-in' type='checkbox'><label for='data-50908c70-5145-41fb-8d82-84a816798caa' 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>surface flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>open_water frozen_soil snow_cover reserved_for_future_use reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","       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</tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_cover_probability</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-3c047f85-4879-40a7-b302-d90121b8ee0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c047f85-4879-40a7-b302-d90121b8ee0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0c604dc-8d8f-4918-94d7-cd5b0dddc2a5' class='xr-var-data-in' type='checkbox'><label for='data-c0c604dc-8d8f-4918-94d7-cd5b0dddc2a5' 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>snow cover probability</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>based on ERA5 Snow Cover data (1980-2020)</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" 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xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02956032-bab9-41cc-9e56-d1bdcdc4131e' class='xr-var-data-in' type='checkbox'><label for='data-02956032-bab9-41cc-9e56-d1bdcdc4131e' 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>frozen soil probability</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>based on ERA5 Soil Temperature Level 1 data (1980-2020)</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","       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</tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wetland_fraction</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-99a20bab-880c-47db-b985-3dde26dadf7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99a20bab-880c-47db-b985-3dde26dadf7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c35dabd-9d1a-4b75-96c4-248190d028f6' class='xr-var-data-in' type='checkbox'><label for='data-3c35dabd-9d1a-4b75-96c4-248190d028f6' 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>wetland fraction</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>based on Global Lakes and Wetlands Database (GLWD) data</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n","  <line 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points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n","\n","  <!-- Text -->\n","  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >45007</text>\n","  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>topographic_complexity</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-c3d31ee6-fe60-435a-be86-329ba46076a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3d31ee6-fe60-435a-be86-329ba46076a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af08e4d5-224d-42e8-b7bb-8c85970a4d36' class='xr-var-data-in' type='checkbox'><label for='data-af08e4d5-224d-42e8-b7bb-8c85970a4d36' 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>topographic complexity</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>normalized standard deviation of CopDEM90m data</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" 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y2=\"25\" />\n","  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n","  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n","  <line x1=\"91\" y1=\"0\" x2=\"91\" y2=\"25\" />\n","  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n","  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n","  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n","  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Colored Rectangle -->\n","  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n","\n","  <!-- Text -->\n","  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >45007</text>\n","  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>subsurface_scattering_probability</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1056,), meta=np.ndarray&gt;</div><input id='attrs-dde85544-7c5b-46d3-97b2-b8134898aad1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dde85544-7c5b-46d3-97b2-b8134898aad1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-508b1b8f-22a1-49ff-bb2c-8d12fe189253' class='xr-var-data-in' type='checkbox'><label for='data-508b1b8f-22a1-49ff-bb2c-8d12fe189253' 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>subsurface scattering probability</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd></dl></div><div class='xr-var-data'><table>\n","    <tr>\n","        <td>\n","            <table style=\"border-collapse: collapse;\">\n","                <thead>\n","                    <tr>\n","                        <td> </td>\n","                        <th> Array </th>\n","                        <th> Chunk </th>\n","                    </tr>\n","                </thead>\n","                <tbody>\n","                    \n","                    <tr>\n","                        <th> Bytes </th>\n","                        <td> 175.81 kiB </td>\n","                        <td> 9.41 kiB </td>\n","                    </tr>\n","                    \n","                    <tr>\n","                        <th> Shape </th>\n","                        <td> (45007,) </td>\n","                        <td> (2410,) </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Dask graph </th>\n","                        <td colspan=\"2\"> 35 chunks in 73 graph layers </td>\n","                    </tr>\n","                    <tr>\n","                        <th> Data type </th>\n","                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n","                    </tr>\n","                </tbody>\n","            </table>\n","        </td>\n","        <td>\n","        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n","\n","  <!-- Horizontal lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n","  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Vertical lines -->\n","  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n","  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n","  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n","  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n","  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n","  <line x1=\"32\" y1=\"0\" x2=\"32\" y2=\"25\" />\n","  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n","  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n","  <line x1=\"46\" y1=\"0\" x2=\"46\" y2=\"25\" />\n","  <line x1=\"51\" y1=\"0\" x2=\"51\" y2=\"25\" />\n","  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n","  <line x1=\"70\" y1=\"0\" x2=\"70\" y2=\"25\" />\n","  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n","  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n","  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n","  <line x1=\"91\" y1=\"0\" x2=\"91\" y2=\"25\" />\n","  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n","  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n","  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n","  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n","\n","  <!-- Colored Rectangle -->\n","  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n","\n","  <!-- Text -->\n","  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >45007</text>\n","  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n","</svg>\n","        </td>\n","    </tr>\n","</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sat_id</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8f88fba4-f540-4921-972d-d161d4d3aa2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f88fba4-f540-4921-972d-d161d4d3aa2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9e70a07-23af-4dc9-a04e-82449b4c731a' class='xr-var-data-in' type='checkbox'><label for='data-e9e70a07-23af-4dc9-a04e-82449b4c731a' 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>[45007 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46716de2-46ad-4f8a-9b22-41ac7508c64b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46716de2-46ad-4f8a-9b22-41ac7508c64b' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ee15fc43-c053-4c36-a65c-eec591cc4ff3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ee15fc43-c053-4c36-a65c-eec591cc4ff3' 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.Dataset>\n","Dimensions:                            (obs: 45007)\n","Coordinates:\n","    latitude                           (obs) float64 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    longitude                          (obs) float64 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    time                               (obs) datetime64[ns] 2020-12-01T08:09:...\n","Dimensions without coordinates: obs\n","Data variables: (12/19)\n","    location_id                        (obs) float64 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    as_des_pass                        (obs) float32 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    swath_indicator                    (obs) float32 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    surface_soil_moisture              (obs) float32 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    surface_soil_moisture_noise        (obs) float32 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    backscatter40                      (obs) float64 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    ...                                 ...\n","    snow_cover_probability             (obs) float32 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    frozen_soil_probability            (obs) float32 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    wetland_fraction                   (obs) float32 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    topographic_complexity             (obs) float32 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    subsurface_scattering_probability  (obs) float32 dask.array<chunksize=(1056,), meta=np.ndarray>\n","    sat_id                             (obs) int64 ..."]},"metadata":{},"output_type":"display_data"}],"source":["output = swath_collection.read(bbox=bounds, date_range=dates)\n","output"]},{"cell_type":"markdown","metadata":{},"source":["Now we have a nice Xarray dataset that we can work with however we wish. In this case, it&rsquo;s one-dimensional, so we are basically working with a tabular data structure. In order to work with it as 3-dimensional (latitude, longitude, time) raster data, we can aggregate it into timesteps.\n","\n","First, to make sure we got the desired data, let&rsquo;s make some plots.\n","\n","This is not a very useful plot, but it shows that the data covers the time range we requested, and that it includes data from all three Metop satellites.\n","\n"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["%matplotlib inline\n","from matplotlib import pyplot as plt\n","import matplotlib.dates as mdates\n","plt.close()\n","fig, ax = plt.subplots()\n","scatter = ax.scatter(output.time, output.longitude, s=0.01, c=output.sat_id, cmap=\"rainbow\", alpha=0.8)\n","legend1 = ax.legend(*scatter.legend_elements(), title=\"Satellite\")\n","for i in range(3):\n","    legend1.get_texts()[i].set_text(f\"Metop {chr(65+i)}\")\n","ax.add_artist(legend1)\n","plt.xlabel(\"Time\")\n","plt.ylabel(\"Latitude (degrees)\")\n","ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y-%m-%d\\n%H:%M\"))\n","plt.xticks(rotation=30)\n","plt.tight_layout()"]},{"cell_type":"markdown","metadata":{},"source":["We can check the spatial coverage of the data by plotting it on a map.\n","\n"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.collections.PathCollection at 0x7fa7d97e7f70>"]},"execution_count":9,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import cartopy.crs as ccrs\n","from shapely import Point\n","\n","plt.close()\n","ax = plt.axes(projection=ccrs.PlateCarree())\n","ax.coastlines()\n","ax.gridlines(draw_labels=True)\n","ax.set_extent([-10, 30, 35, 65])\n","plt.scatter(\n","    output.longitude,\n","    output.latitude,\n","    s=0.1,\n","    alpha=0.1\n",")"]},{"cell_type":"markdown","metadata":{},"source":["Having the data as an Xarray makes it handy to do transformations. For example, we can group by location<sub>id</sub> and get the average surface soil moisture at each. First, we need to load the `location_id` into memory, since it currently exists as a chunked dask array, and the groupby method only works with numpy arrays.\n","\n"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/html":["<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n","<defs>\n","<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n","<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n","<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n","<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n","</symbol>\n","<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n","<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 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break-all;\n","}\n","\n",".xr-icon-database,\n",".xr-icon-file-text2,\n",".xr-no-icon {\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;location_id&#x27; (obs: 45007)&gt;\n","array([1258693., 1259680., 1260057., ..., 1260222., 1260311., 1260455.])\n","Coordinates:\n","    latitude   (obs) float64 49.91 49.96 49.98 49.82 ... 49.98 49.99 49.99 50.0\n","    longitude  (obs) float64 19.94 19.78 19.35 19.84 ... 11.69 10.57 12.38 11.26\n","    time       (obs) datetime64[ns] 2020-12-01T08:09:59.415000064 ... 2020-12...\n","Dimensions without coordinates: obs\n","Attributes:\n","    long_name:  Location identifier (Grid Point ID)\n","    valid_min:  0\n","    valid_max:  3300000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'location_id'</div><ul class='xr-dim-list'><li><span>obs</span>: 45007</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d9b9218a-f8ea-4a9f-a63f-90f54335ac00' class='xr-array-in' type='checkbox' checked><label for='section-d9b9218a-f8ea-4a9f-a63f-90f54335ac00' 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>1.259e+06 1.26e+06 1.26e+06 1.257e+06 ... 1.26e+06 1.26e+06 1.26e+06</span></div><div class='xr-array-data'><pre>array([1258693., 1259680., 1260057., ..., 1260222., 1260311., 1260455.])</pre></div></div></li><li class='xr-section-item'><input id='section-bb5daeb2-aa66-4e02-8ce6-9478015ca474' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bb5daeb2-aa66-4e02-8ce6-9478015ca474' 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>latitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.91 49.96 49.98 ... 49.99 50.0</div><input id='attrs-560ab75a-7845-41f7-a891-c108cc22b3b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-560ab75a-7845-41f7-a891-c108cc22b3b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-552b5f8f-1b20-46d1-8c9f-adfaf317bcfd' class='xr-var-data-in' type='checkbox'><label for='data-552b5f8f-1b20-46d1-8c9f-adfaf317bcfd' 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><dt><span>valid_min :</span></dt><dd>-90000000</dd><dt><span>valid_max :</span></dt><dd>89999999</dd></dl></div><div class='xr-var-data'><pre>array([49.905124, 49.9581  , 49.978352, ..., 49.987216, 49.991996,\n","       49.999736])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.94 19.78 19.35 ... 12.38 11.26</div><input id='attrs-ff4d76a9-f33c-470a-a16a-c364805c3c22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff4d76a9-f33c-470a-a16a-c364805c3c22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1775c68-4101-4a96-84fd-e09adf3c7c7f' class='xr-var-data-in' type='checkbox'><label for='data-c1775c68-4101-4a96-84fd-e09adf3c7c7f' 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><dt><span>valid_min :</span></dt><dd>-180000000</dd><dt><span>valid_max :</span></dt><dd>179999999</dd></dl></div><div class='xr-var-data'><pre>array([19.944564, 19.781448, 19.3544  , ..., 10.573333, 12.382333,\n","       11.26431 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-12-01T08:09:59.415000064 .....</div><input id='attrs-b1fa5f99-f380-4a81-8a3d-782f405ab8b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1fa5f99-f380-4a81-8a3d-782f405ab8b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c17f76da-48de-494f-bf09-5a5d7b773576' class='xr-var-data-in' type='checkbox'><label for='data-c17f76da-48de-494f-bf09-5a5d7b773576' 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>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-12-01T08:09:59.415000064&#x27;, &#x27;2020-12-01T08:09:59.415000064&#x27;,\n","       &#x27;2020-12-01T08:10:00.264000000&#x27;, ...,\n","       &#x27;2020-12-04T20:43:03.397999872&#x27;, &#x27;2020-12-04T20:43:03.397999872&#x27;,\n","       &#x27;2020-12-04T20:43:03.397999872&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6d440bb-01e6-4ec7-ab2d-f85fcc27a281' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f6d440bb-01e6-4ec7-ab2d-f85fcc27a281' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-94d1a239-af7b-459e-a37d-774c0f249ac2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-94d1a239-af7b-459e-a37d-774c0f249ac2' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Location identifier (Grid Point ID)</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3300000</dd></dl></div></li></ul></div></div>"],"text/plain":["<xarray.DataArray 'location_id' (obs: 45007)>\n","array([1258693., 1259680., 1260057., ..., 1260222., 1260311., 1260455.])\n","Coordinates:\n","    latitude   (obs) float64 49.91 49.96 49.98 49.82 ... 49.98 49.99 49.99 50.0\n","    longitude  (obs) float64 19.94 19.78 19.35 19.84 ... 11.69 10.57 12.38 11.26\n","    time       (obs) datetime64[ns] 2020-12-01T08:09:59.415000064 ... 2020-12...\n","Dimensions without coordinates: obs\n","Attributes:\n","    long_name:  Location identifier (Grid Point ID)\n","    valid_min:  0\n","    valid_max:  3300000"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["output[\"location_id\"].load()"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/charriso/micromamba/envs/egu2024/lib/python3.8/site-packages/flox/core.py:2004: PerformanceWarning: Slicing with an out-of-order index is generating 78 times more chunks\n","  result = result[..., 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break-all;\n","}\n","\n",".xr-icon-database,\n",".xr-icon-file-text2,\n",".xr-no-icon {\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;surface_soil_moisture&#x27; (location_id: 2652)&gt;\n","array([55.3      , 73.21357  , 54.573635 , ...,  3.9599998, 41.566666 ,\n","       25.1      ], dtype=float32)\n","Coordinates:\n","  * location_id  (location_id) float64 1.163e+06 1.163e+06 ... 1.26e+06 1.26e+06\n","Attributes:\n","    long_name:  surface soil moisture\n","    units:      percent saturation\n","    valid_min:  0\n","    valid_max:  10000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'surface_soil_moisture'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>location_id</span>: 2652</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-02aa7f60-87c0-43bd-b0ee-abf1f022240e' class='xr-array-in' type='checkbox' checked><label for='section-02aa7f60-87c0-43bd-b0ee-abf1f022240e' 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>55.3 73.21 54.57 82.42 40.4 61.55 ... nan 7.57 12.5 3.96 41.57 25.1</span></div><div class='xr-array-data'><pre>array([55.3      , 73.21357  , 54.573635 , ...,  3.9599998, 41.566666 ,\n","       25.1      ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-2352065b-2bfc-44ea-ae04-1092723d7d91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2352065b-2bfc-44ea-ae04-1092723d7d91' class='xr-section-summary' >Coordinates: <span>(1)</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'>location_id</span></div><div class='xr-var-dims'>(location_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.163e+06 1.163e+06 ... 1.26e+06</div><input id='attrs-f1da0f91-12a2-4b62-9ddb-4cff8ed5f019' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1da0f91-12a2-4b62-9ddb-4cff8ed5f019' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6add485a-accd-4129-8bc2-c0c73afcb6ca' class='xr-var-data-in' type='checkbox'><label for='data-6add485a-accd-4129-8bc2-c0c73afcb6ca' 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>Location identifier (Grid Point ID)</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3300000</dd></dl></div><div class='xr-var-data'><pre>array([1162818., 1162873., 1162907., ..., 1260400., 1260434., 1260455.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de171072-3c56-4c4f-8b90-0d2ea2847a5c' class='xr-section-summary-in' type='checkbox'  ><label for='section-de171072-3c56-4c4f-8b90-0d2ea2847a5c' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>location_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f4be69b-81eb-4555-ad5d-15fbf1f89649' class='xr-index-data-in' type='checkbox'/><label for='index-1f4be69b-81eb-4555-ad5d-15fbf1f89649' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1162818.0, 1162873.0, 1162907.0, 1162928.0, 1162962.0, 1163051.0,\n","       1163140.0, 1163161.0, 1163195.0, 1163284.0,\n","       ...\n","       1260112.0, 1260167.0, 1260201.0, 1260222.0, 1260256.0, 1260311.0,\n","       1260345.0, 1260400.0, 1260434.0, 1260455.0],\n","      dtype=&#x27;float64&#x27;, name=&#x27;location_id&#x27;, length=2652))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da1bc924-cb95-432e-8e5d-b4c3baf1c188' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da1bc924-cb95-432e-8e5d-b4c3baf1c188' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>surface soil moisture</dd><dt><span>units :</span></dt><dd>percent saturation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd></dl></div></li></ul></div></div>"],"text/plain":["<xarray.DataArray 'surface_soil_moisture' (location_id: 2652)>\n","array([55.3      , 73.21357  , 54.573635 , ...,  3.9599998, 41.566666 ,\n","       25.1      ], dtype=float32)\n","Coordinates:\n","  * location_id  (location_id) float64 1.163e+06 1.163e+06 ... 1.26e+06 1.26e+06\n","Attributes:\n","    long_name:  surface soil moisture\n","    units:      percent saturation\n","    valid_min:  0\n","    valid_max:  10000"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["%%time\n","avg_ssm = output[\"surface_soil_moisture\"].groupby(output[\"location_id\"]).mean(\"obs\")\n","avg_ssm.load()"]},{"cell_type":"markdown","metadata":{},"source":["This takes ~1.2 seconds on my own machine, which isn&rsquo;t too bad, but we&rsquo;re only working with 5 days of data here for a relatively small area. This operation could easily balloon in complexity and become intractable, especially if it needs to be repeated often. We also get warnings about slicing with an out-of-order index.\n","\n","However, if we use flox directly (a package from the developers of Xarray that is created to do faster groupbys with Xarray datasets), we can accomplish the same operation in a fraction of the time (~90ms on my machine). When scaling up to much longer time periods and larger surface areas, these savings can make a huge difference.\n","\n"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["from flox.xarray import xarray_reduce"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["CPU times: user 93.7 ms, sys: 14.4 ms, total: 108 ms\n","Wall time: 93.4 ms\n"]},{"data":{"text/html":["<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n","<defs>\n","<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n","<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n","<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n","<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n","</symbol>\n","<symbol 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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",".xr-no-icon {\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;surface_soil_moisture&#x27; (location_id: 2652)&gt;\n","array([55.3      , 73.21357  , 54.57363  , ...,  3.9599998, 41.566666 ,\n","       25.100002 ], dtype=float32)\n","Coordinates:\n","  * location_id  (location_id) float64 1.163e+06 1.163e+06 ... 1.26e+06 1.26e+06\n","Attributes:\n","    long_name:  surface soil moisture\n","    units:      percent saturation\n","    valid_min:  0\n","    valid_max:  10000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'surface_soil_moisture'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>location_id</span>: 2652</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fca2f8cd-838b-4298-8207-a3a48d820dd2' class='xr-array-in' type='checkbox' checked><label for='section-fca2f8cd-838b-4298-8207-a3a48d820dd2' 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>55.3 73.21 54.57 82.42 40.4 61.55 ... nan 7.57 12.5 3.96 41.57 25.1</span></div><div class='xr-array-data'><pre>array([55.3      , 73.21357  , 54.57363  , ...,  3.9599998, 41.566666 ,\n","       25.100002 ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-4b05fcdc-6976-48ba-8df3-c361ef1986de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4b05fcdc-6976-48ba-8df3-c361ef1986de' class='xr-section-summary' >Coordinates: <span>(1)</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'>location_id</span></div><div class='xr-var-dims'>(location_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.163e+06 1.163e+06 ... 1.26e+06</div><input id='attrs-b07bb99d-6860-468d-9a4f-ee5caf953e2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b07bb99d-6860-468d-9a4f-ee5caf953e2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b0e9bed-f3a1-41fb-af7e-68ecacd1e26b' class='xr-var-data-in' type='checkbox'><label for='data-4b0e9bed-f3a1-41fb-af7e-68ecacd1e26b' 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>Location identifier (Grid Point ID)</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3300000</dd></dl></div><div class='xr-var-data'><pre>array([1162818., 1162873., 1162907., ..., 1260400., 1260434., 1260455.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ffa9a08-9cfc-4d06-a0ee-044448fbf29a' class='xr-section-summary-in' type='checkbox'  ><label for='section-7ffa9a08-9cfc-4d06-a0ee-044448fbf29a' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>location_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8f1c9861-b6cb-4507-8ba4-b4ba4f601f6a' class='xr-index-data-in' type='checkbox'/><label for='index-8f1c9861-b6cb-4507-8ba4-b4ba4f601f6a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1162818.0, 1162873.0, 1162907.0, 1162928.0, 1162962.0, 1163051.0,\n","       1163140.0, 1163161.0, 1163195.0, 1163284.0,\n","       ...\n","       1260112.0, 1260167.0, 1260201.0, 1260222.0, 1260256.0, 1260311.0,\n","       1260345.0, 1260400.0, 1260434.0, 1260455.0],\n","      dtype=&#x27;float64&#x27;, name=&#x27;location_id&#x27;, length=2652))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-111f5389-c78f-4a72-a35d-4e3f46e1aab5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-111f5389-c78f-4a72-a35d-4e3f46e1aab5' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>surface soil moisture</dd><dt><span>units :</span></dt><dd>percent saturation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd></dl></div></li></ul></div></div>"],"text/plain":["<xarray.DataArray 'surface_soil_moisture' (location_id: 2652)>\n","array([55.3      , 73.21357  , 54.57363  , ...,  3.9599998, 41.566666 ,\n","       25.100002 ], dtype=float32)\n","Coordinates:\n","  * location_id  (location_id) float64 1.163e+06 1.163e+06 ... 1.26e+06 1.26e+06\n","Attributes:\n","    long_name:  surface soil moisture\n","    units:      percent saturation\n","    valid_min:  0\n","    valid_max:  10000"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["%%time\n","avg_ssm_flox = xarray_reduce(output[\"surface_soil_moisture\"], output[\"location_id\"], func=\"mean\")\n","avg_ssm_flox.load()"]},{"cell_type":"markdown","metadata":{},"source":["Note: if, when using flox, you get an error about needing to provide `expected_groups`, make sure you&rsquo;ve `load` -ed the variables you&rsquo;ll be grouping your data by into memory first. If your dataset is too big for that, you can calculate the unique values of those variables and pass them in a tuple to the `expected_groups` parameter. For example, if we want to calculate seasonal soil moisture averages per location, we can add a grouping of the `time` variable to our `xarray_reduce` arguments. However, if we haven&rsquo;t loaded `location_id` into memory yet, we&rsquo;ll get an error:\n","\n"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"ename":"ValueError","evalue":"Please provide expected_groups if not grouping by a numpy array.","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[14], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m short_dates \u001b[38;5;241m=\u001b[39m (datetime(\u001b[38;5;241m2020\u001b[39m, \u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m1\u001b[39m), datetime(\u001b[38;5;241m2020\u001b[39m, \u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m2\u001b[39m))\n\u001b[1;32m      2\u001b[0m ds \u001b[38;5;241m=\u001b[39m swath_collection\u001b[38;5;241m.\u001b[39mread(bbox\u001b[38;5;241m=\u001b[39mbounds, date_range\u001b[38;5;241m=\u001b[39mdates)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mxarray_reduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msurface_soil_moisture\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlocation_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhour\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/micromamba/envs/egu2024/lib/python3.8/site-packages/flox/xarray.py:337\u001b[0m, in \u001b[0;36mxarray_reduce\u001b[0;34m(obj, func, expected_groups, isbin, sort, dim, fill_value, dtype, method, engine, keep_attrs, skipna, min_count, reindex, *by, **finalize_kwargs)\u001b[0m\n\u001b[1;32m    332\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isbin_:\n\u001b[1;32m    333\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    334\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease provided bin edges for group variable \u001b[39m\u001b[38;5;132;01m{\u001b[39;00midx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    335\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnamed \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgroup_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m in expected_groups.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    336\u001b[0m         )\n\u001b[0;32m--> 337\u001b[0m     expect_ \u001b[38;5;241m=\u001b[39m \u001b[43m_get_expected_groups\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb_\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    338\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    339\u001b[0m     expect_ \u001b[38;5;241m=\u001b[39m expect\n","File \u001b[0;32m~/micromamba/envs/egu2024/lib/python3.8/site-packages/flox/core.py:103\u001b[0m, in \u001b[0;36m_get_expected_groups\u001b[0;34m(by, sort)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_expected_groups\u001b[39m(by: T_By, sort: \u001b[38;5;28mbool\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m pd\u001b[38;5;241m.\u001b[39mIndex:\n\u001b[1;32m    102\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_duck_dask_array(by):\n\u001b[0;32m--> 103\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease provide expected_groups if not grouping by a numpy array.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    104\u001b[0m     flatby \u001b[38;5;241m=\u001b[39m by\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    105\u001b[0m     expected \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39munique(flatby[\u001b[38;5;241m~\u001b[39misnull(flatby)])\n","\u001b[0;31mValueError\u001b[0m: Please provide expected_groups if not grouping by a numpy array."]}],"source":["short_dates = (datetime(2020, 12, 1), datetime(2020, 12, 2))\n","ds = swath_collection.read(bbox=bounds, date_range=dates)\n","xarray_reduce(ds[\"surface_soil_moisture\"], ds[\"location_id\"], ds[\"time\"].dt.hour, func=\"mean\")"]},{"cell_type":"markdown","metadata":{},"source":["We didn&rsquo;t get this error before because we had already loaded `location_id` into memory. Loading a single variable into memory shouldn&rsquo;t be much of a problem, but if it is, here&rsquo;s how you would use `expected_groups` to solve it instead:\n","\n"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"data":{"text/html":["<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n","<defs>\n","<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n","<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n","<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n","<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n","</symbol>\n","<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n","<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 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break-all;\n","}\n","\n",".xr-icon-database,\n",".xr-icon-file-text2,\n",".xr-no-icon {\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;surface_soil_moisture&#x27; (location_id: 2652, hour: 9)&gt;\n","array([[91.71     , 37.46     , 71.503334 , ..., 55.53     , 40.95     ,\n","               nan],\n","       [75.7      , 56.953327 , 63.656666 , ..., 80.432495 , 94.45     ,\n","               nan],\n","       [52.       , 42.173332 , 57.54333  , ..., 58.72333  ,        nan,\n","               nan],\n","       ...,\n","       [ 3.9599998,        nan,        nan, ...,        nan,        nan,\n","               nan],\n","       [       nan, 26.9      , 70.9      , ...,        nan,        nan,\n","               nan],\n","       [14.49     , 21.055    , 12.83     , ...,        nan, 31.404999 ,\n","               nan]], dtype=float32)\n","Coordinates:\n","  * location_id  (location_id) float64 1.163e+06 1.163e+06 ... 1.26e+06 1.26e+06\n","  * hour         (hour) int32 7 8 9 10 17 18 19 20 21\n","Attributes:\n","    long_name:  surface soil moisture\n","    units:      percent saturation\n","    valid_min:  0\n","    valid_max:  10000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'surface_soil_moisture'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>location_id</span>: 2652</li><li><span class='xr-has-index'>hour</span>: 9</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2f9658df-f6eb-48f6-bd31-bd4e673a794a' class='xr-array-in' type='checkbox' checked><label for='section-2f9658df-f6eb-48f6-bd31-bd4e673a794a' 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>91.71 37.46 71.5 nan nan nan 55.53 ... nan nan 43.46 nan 31.4 nan</span></div><div class='xr-array-data'><pre>array([[91.71     , 37.46     , 71.503334 , ..., 55.53     , 40.95     ,\n","               nan],\n","       [75.7      , 56.953327 , 63.656666 , ..., 80.432495 , 94.45     ,\n","               nan],\n","       [52.       , 42.173332 , 57.54333  , ..., 58.72333  ,        nan,\n","               nan],\n","       ...,\n","       [ 3.9599998,        nan,        nan, ...,        nan,        nan,\n","               nan],\n","       [       nan, 26.9      , 70.9      , ...,        nan,        nan,\n","               nan],\n","       [14.49     , 21.055    , 12.83     , ...,        nan, 31.404999 ,\n","               nan]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-0de22435-7f0e-4444-a02e-4e06a44cbfd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0de22435-7f0e-4444-a02e-4e06a44cbfd8' 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 class='xr-has-index'>location_id</span></div><div class='xr-var-dims'>(location_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.163e+06 1.163e+06 ... 1.26e+06</div><input id='attrs-518b9e0a-0f3a-4a17-90a5-5f4a6c2a3ca0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-518b9e0a-0f3a-4a17-90a5-5f4a6c2a3ca0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b530c67-a547-4584-8bae-639cc1d6bfbf' class='xr-var-data-in' type='checkbox'><label for='data-2b530c67-a547-4584-8bae-639cc1d6bfbf' 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>Location identifier (Grid Point ID)</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3300000</dd></dl></div><div class='xr-var-data'><pre>array([1162818., 1162873., 1162907., ..., 1260400., 1260434., 1260455.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>hour</span></div><div class='xr-var-dims'>(hour)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>7 8 9 10 17 18 19 20 21</div><input id='attrs-30396750-2f6f-4f7f-b480-501d72924c35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30396750-2f6f-4f7f-b480-501d72924c35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7128c249-6c02-47bd-88ef-ace719911299' class='xr-var-data-in' type='checkbox'><label for='data-7128c249-6c02-47bd-88ef-ace719911299' 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([ 7,  8,  9, 10, 17, 18, 19, 20, 21], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3de9fc1-f901-40e3-8796-e18953e5fc8c' class='xr-section-summary-in' type='checkbox'  ><label for='section-a3de9fc1-f901-40e3-8796-e18953e5fc8c' class='xr-section-summary' >Indexes: <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-index-name'><div>location_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7e8bc10c-dc09-4995-bc7a-8aaae727ce2c' class='xr-index-data-in' type='checkbox'/><label for='index-7e8bc10c-dc09-4995-bc7a-8aaae727ce2c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1162818.0, 1162873.0, 1162907.0, 1162928.0, 1162962.0, 1163051.0,\n","       1163140.0, 1163161.0, 1163195.0, 1163284.0,\n","       ...\n","       1260112.0, 1260167.0, 1260201.0, 1260222.0, 1260256.0, 1260311.0,\n","       1260345.0, 1260400.0, 1260434.0, 1260455.0],\n","      dtype=&#x27;float64&#x27;, name=&#x27;location_id&#x27;, length=2652))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>hour</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-708cb14f-c06e-4340-903d-4d4af7e67833' class='xr-index-data-in' type='checkbox'/><label for='index-708cb14f-c06e-4340-903d-4d4af7e67833' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([7, 8, 9, 10, 17, 18, 19, 20, 21], dtype=&#x27;int32&#x27;, name=&#x27;hour&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81ce9a65-c606-422a-b831-6f43d01b9e4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81ce9a65-c606-422a-b831-6f43d01b9e4c' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>surface soil moisture</dd><dt><span>units :</span></dt><dd>percent saturation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd></dl></div></li></ul></div></div>"],"text/plain":["<xarray.DataArray 'surface_soil_moisture' (location_id: 2652, hour: 9)>\n","array([[91.71     , 37.46     , 71.503334 , ..., 55.53     , 40.95     ,\n","               nan],\n","       [75.7      , 56.953327 , 63.656666 , ..., 80.432495 , 94.45     ,\n","               nan],\n","       [52.       , 42.173332 , 57.54333  , ..., 58.72333  ,        nan,\n","               nan],\n","       ...,\n","       [ 3.9599998,        nan,        nan, ...,        nan,        nan,\n","               nan],\n","       [       nan, 26.9      , 70.9      , ...,        nan,        nan,\n","               nan],\n","       [14.49     , 21.055    , 12.83     , ...,        nan, 31.404999 ,\n","               nan]], dtype=float32)\n","Coordinates:\n","  * location_id  (location_id) float64 1.163e+06 1.163e+06 ... 1.26e+06 1.26e+06\n","  * hour         (hour) int32 7 8 9 10 17 18 19 20 21\n","Attributes:\n","    long_name:  surface soil moisture\n","    units:      percent saturation\n","    valid_min:  0\n","    valid_max:  10000"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["import numpy as np\n","xarray_reduce(\n","    ds[\"surface_soil_moisture\"],\n","    ds[\"location_id\"],\n","    ds[\"time\"].dt.hour,\n","    expected_groups=(np.unique(output[\"location_id\"].values),\n","                     np.unique(output[\"time\"].dt.hour.values)),\n","    func=\"mean\"\n",").load()"]},{"cell_type":"markdown","metadata":{},"source":["I&rsquo;ll step away from the data for a second and write a quick function for plotting it on a map:\n","\n"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["from matplotlib import pyplot as plt\n","import cartopy.crs as ccrs\n","\n","def simple_map(lons, lats, color_var, cmap, dates=None, cbar_label=None):\n","    plt.close()\n","    ax = plt.axes(projection=ccrs.PlateCarree())\n","    ax.coastlines()\n","    gl = ax.gridlines(draw_labels=True)\n","    gl.bottom_labels = False\n","    gl.right_labels = False\n","    ax.set_extent([lons.min()-5, lons.max()+5, lats.min()-5, lats.max()+5])\n","    # ax.set_extent([-10, 30, 35, 65])\n","    plt.scatter(\n","        lons,\n","        lats,\n","        c=color_var,\n","        cmap=cmap,\n","        s=1,\n","        # alpha=0.8,\n","        # clim=(0, 100)\n","    )\n","    if cbar_label is None:\n","        cbar_label = (\n","            f\"Average {color_var.long_name}\\n\"\n","            f\"({color_var.units})\\n\"\n","        )\n","    if dates is not None:\n","        cbar_label += f\"\\n{np.datetime_as_string(dates[0], unit='s')} - {np.datetime_as_string(dates[1], unit='s')}\"\n","\n","    plt.colorbar(label=(cbar_label),\n","                 shrink=0.5,\n","                 pad=0.05,\n","                 orientation=\"horizontal\"\n","    )\n","    plt.tight_layout()"]},{"cell_type":"markdown","metadata":{},"source":["And here is our mean soil moisture!\n","\n"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import cmcrameri.cm as cmc\n","lons, lats = swath_collection.grid.gpi2lonlat(avg_ssm_flox.location_id.values)\n","simple_map(lons, lats, avg_ssm_flox, cmc.roma, (output.time.values.min(), output.time.values.max()))"]},{"cell_type":"markdown","metadata":{},"source":["Now it&rsquo;s easy to make a map of any of the other variables in the dataset. Here&rsquo;s the average backscatter at 40 degrees incidence angle:\n","\n"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["avg_backscatter40 = xarray_reduce(output[\"backscatter40\"], output[\"location_id\"], func=\"mean\")\n","simple_map(lons, lats, avg_backscatter40, \"viridis\", (output.time.values.min(), output.time.values.max()))"]},{"cell_type":"markdown","metadata":{},"source":["Or we could make a timeseries plot of a variable at a single location or a collection of locations:\n","\n"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[],"source":["week_dates = (datetime(2020, 12, 1), datetime(2020, 12, 8))\n","week_data = swath_collection.read(date_range=week_dates, bbox=bounds)\n","date_groups = week_data.groupby(\"time.date\")"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["for dt, ds in date_groups:\n","    plt.scatter(ds[\"time.date\"], ds.backscatter40, color=\"black\", s=1, alpha=0.01)\n","\n","plt.plot(date_groups.groups.keys(), date_groups.mean().backscatter40.values, color=\"red\")\n","\n","plt.title(\"Daily backscatter values, Metop A, B and C\\n\"\n","          \"Latitudes 45-50, Longitudes 10-20\")\n","plt.ylabel(f\"{ds.backscatter40.units}\")\n","plt.xlabel(f\"date\")\n","plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n","plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=1))\n","plt.xticks(rotation=30)\n","plt.tight_layout()"]},{"cell_type":"markdown","metadata":{},"source":["We can make a 5-year climatology for our data in three lines of code, but it will take a while to run, since we&rsquo;ll have to read metadata from thousands of files to compile the xarray dataset. I do not recommend running this cell.\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["# five year climatology\n","five_years = [datetime(2015, 1, 1), datetime(2020, 1, 1)]\n","five_years_data = swath_collection.read(location_id=gpis, date_range=five_years)#.load()\n","climatology = output.groupby(\"time.dayofyear\").mean(\"obs\")"]},{"cell_type":"markdown","metadata":{},"source":["If you need to do several operations on larger chunks of time, it could be useful to convert the data to a cell file collection and work off of that. (see CellFileCollection section below)\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["### Converting swath collections to cell collections\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["To convert a collection of swath files into a collection of cell files, we only need to call a single method from our SwathFileCollection. We pass it at least an output directory path (`out_dir`), where the outputs will be written, and we can also pass it several other options.\n","\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["# without setting this variable as False, the package will warn the user and wait for confirmation before running, since a careless use of `stack` pointing to the wrong directory could delete or ruin a lot of data.\n","rat.process_warnings = False\n","# where to save the files\n","cell_file_directory = \"\"\n","# a list of swath file names to write, if you have a specific list\n","fnames = None\n","# the date range to stack data from\n","date_range = None\n","# mode : \"w\" for creating new files if any already exist, \"a\" to append data to existing cell files\n","# note that old data and new data will not be sorted after the append\n","mode = \"w\"\n","# the number of processes to use when writing the data.\n","# does NOT have anything to do with xarray's dask processing\n","# I've found that using too many processes, even on machines with many cores, may not be optimal.\n","# A good number is 8.\n","processes = 8\n","\n","# the maximum size of the data buffer before dumping to file (actual maximum memory used will be higher)\n","# default is 6144MB\n","buffer_memory_mb = None\n","\n","# # This command commented to avoid accidental execution\n","# swath_collection.stack(\n","#     output_dir=cell_file_directory,\n","#     fnames=fnames,\n","#     date_range=date_range,\n","#     mode=mode,\n","#     processes=processes,\n","#     buffer_memory_mb=buffer_memory_mb\n","# )"]},{"cell_type":"markdown","metadata":{},"source":["The output cells are in *indexed ragged array* format. In order to convert them to *contiguous* ragged array format, we can create a `CellFileCollection` from the output directory, and call the method `to_contiguous()`:\n","\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["cell_collection = rat.CellFileCollection.from_product_id(cell_file_directory, product_id=\"H121_v1.0\")\n","contiguous_cell_file_directory = \"\"\n","cell_collection.to_contiguous(contiguous_cell_file_directory)"]},{"cell_type":"markdown","metadata":{},"source":["This will sort the entire dataset first by time and then by `locationIndex`, and then replace the dataset&rsquo;s `locationIndex` variable with a `row_size` variable. At this point it is no longer practically possible to append new data to the dataset without first re-converting it to indexed ragged array format and then converting back.\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["## Working with collections of cell files\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["Right now, although `CellFileCollection` exists, it currently is optimized for use under the hood of `CellFileCollectionStack`. Ideally both could be used by users, but there are still some bugs to be worked out and some refactoring to do. To work with a single collection of cell files, simply create a `CellFileCollectionStack` with a single collection inside.\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["## Working with stacks of cell file collections\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["### Creating a cell file collection\n","\n"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[],"source":["from datetime import datetime\n","from importlib import reload\n","from time import time\n","\n","import ascat.read_native.ragged_array_ts as rat"]},{"cell_type":"markdown","metadata":{},"source":["Our cell files, in this case, all live in a single directory, so that&rsquo;s the path we&rsquo;ll pass to `rat.CellFileCollectionStack.from_product_id()`. If we had multiple sets of cell files contained in different directories, we could pass a list of these directories&rsquo; paths, assuming they were all of the same product type (and therefore had the same dimensions, data variables, etc).\n","\n","The product id, `\"H121_V1.0\"`, refers to a specific handler class defined in `ascat.read_native.xarray_io`. There are several of these already defined for various products we use, and it is also possible to define your own handler class if you need to process a product we haven&rsquo;t included in this package already.\n","\n"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/charriso/Projects/ascat/src/ascat/read_native/ragged_array_ts.py:945: UserWarning: Could not determine date range for collection 'metop_abc' from directory name. Using min/max datetime from files instead.\n","  warnings.warn(\n"]}],"source":["cell_source = \"/home/charriso/p14/data-write/USERS/charriso/h121_merged/metop_abc/\"\n","cell_collection = rat.CellFileCollectionStack.from_product_id(cell_source, \"H121_V1.0\")"]},{"cell_type":"markdown","metadata":{},"source":["### Reading from a cell file collection\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["We can read data from a specific geographic and temporal extent, but if you have a single collection, it may actually take longer to create an xarray dataset if you try to trim down the time range. In this case it is best to only subset by geographic extent on read, and then do any temporal subsetting after the xarray dataset is created, but before the data is actually loaded into memory with `.load()`.\n","\n","On the other hand, if you have a stack with multiple collections that cover different time ranges, you can possibly save a lot of time when reading using temporal subsetting. (Imagine you have dozens of weekly cell collections and only need two weeks - no need to even look at the other files).\n","\n","Our options for geographic extent are `cell`, `bbox`, `geom`, and `location_id`. `cell` is a list of cell indices, `bbox` is a tuple of (latmin, latmax, lonmin, lonmax), `geom` is a shapely geometry object, and `location_id` is a list of location indices.\n","\n"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[],"source":["bounds = (43, 51, 11, 21) #latmin, latmax, lonmin, lonmax\n","dates = (np.datetime64(datetime(2020, 12, 1)), np.datetime64(datetime(2020, 12, 15)))"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"data":{"text/html":["<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n","<defs>\n","<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n","<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n","<path d=\"M16 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auto;\n","}\n","\n",".xr-attrs dt,\n",".xr-attrs 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",".xr-no-icon {\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.Dataset&gt;\n","Dimensions:                            (obs: 59143813, locations: 4378)\n","Coordinates:\n","    time                               (obs) datetime64[ns] 2007-01-01T08:00:...\n","    lon                                (locations) float32 ...\n","    lat                                (locations) float32 ...\n","    alt                                (locations) float32 ...\n","Dimensions without coordinates: obs, locations\n","Data variables: (12/22)\n","    locationIndex                      (obs) int64 ...\n","    as_des_pass                        (obs) float32 ...\n","    swath_indicator                    (obs) float32 ...\n","    surface_soil_moisture              (obs) float32 ...\n","    surface_soil_moisture_noise        (obs) float32 ...\n","    backscatter40                      (obs) float32 ...\n","    ...                                 ...\n","    topographic_complexity             (obs) float32 ...\n","    subsurface_scattering_probability  (obs) float32 ...\n","    global_attributes_flag             (locations) int64 ...\n","    sat_id                             (obs) float32 ...\n","    location_id                        (locations) int64 ...\n","    location_description               (locations) object ...\n","Attributes: (12/15)\n","    title:             ASCAT surface soil moisture near real-time product\n","    summary:           ASCAT surface soil moisture expressed in degree of sat...\n","    doi:               unset\n","    keywords:          Metop-A ASCAT surface soil moisture\n","    history:           original generated product\n","    institution:       H SAF\n","    ...                ...\n","    disposition_mode:  Operational\n","    environment:       Operational\n","    references:        h-saf.eumetsat.int\n","    software_version:  warp_h_nrt 0.0.0\n","    conventions:       CF-1.10\n","    featureType:       timeSeries</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1306b871-31a2-4cfc-9650-8695fabc4423' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1306b871-31a2-4cfc-9650-8695fabc4423' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>obs</span>: 59143813</li><li><span>locations</span>: 4378</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8e198061-8908-46e1-822a-dc731fa8f89c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e198061-8908-46e1-822a-dc731fa8f89c' 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>time</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2007-01-01T08:00:00.707000320 .....</div><input id='attrs-4e54692d-1ed4-4461-8a21-37fede9ca628' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e54692d-1ed4-4461-8a21-37fede9ca628' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4488ad40-cdea-4c1d-ba5d-b169507c30fa' class='xr-var-data-in' type='checkbox'><label for='data-4488ad40-cdea-4c1d-ba5d-b169507c30fa' 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>_FillValue :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>time of measurement</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2007-01-01T08:00:00.707000320&#x27;, &#x27;2007-01-01T19:23:10.243000320&#x27;,\n","       &#x27;2007-01-02T09:19:26.544999424&#x27;, ...,\n","       &#x27;2022-12-30T08:33:06.198000128&#x27;, &#x27;2022-12-30T10:13:12.200999936&#x27;,\n","       &#x27;2022-12-31T19:39:52.947999744&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-209c81f4-96bf-4f41-8385-7550fa5fd250' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-209c81f4-96bf-4f41-8385-7550fa5fd250' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8c39535-b312-4db8-b391-74cd1dacbd19' class='xr-var-data-in' type='checkbox'><label for='data-d8c39535-b312-4db8-b391-74cd1dacbd19' 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>[4378 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b7eb7206-460a-473c-8e6b-bc43e1312f66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7eb7206-460a-473c-8e6b-bc43e1312f66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d47bf794-e011-475d-97c4-901fd7e5d9d7' class='xr-var-data-in' type='checkbox'><label for='data-d47bf794-e011-475d-97c4-901fd7e5d9d7' 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>[4378 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2954a655-d7fb-4514-bedb-c75afe8fa61d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2954a655-d7fb-4514-bedb-c75afe8fa61d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d0ec406-8b90-4d6a-a020-cb0c217d5708' class='xr-var-data-in' type='checkbox'><label for='data-1d0ec406-8b90-4d6a-a020-cb0c217d5708' 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>[4378 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8feffc7a-651b-400f-8ca4-edf8191e6825' class='xr-section-summary-in' type='checkbox'  ><label for='section-8feffc7a-651b-400f-8ca4-edf8191e6825' class='xr-section-summary' >Data variables: <span>(22)</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>locationIndex</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7984c0f8-a38d-4261-b934-d5c62ca9e7aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7984c0f8-a38d-4261-b934-d5c62ca9e7aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b56d18f8-f571-48e1-9818-3b06a645153e' class='xr-var-data-in' type='checkbox'><label for='data-b56d18f8-f571-48e1-9818-3b06a645153e' 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>[59143813 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>as_des_pass</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-819981cd-15d7-4f14-9775-d94de2fdbbdf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-819981cd-15d7-4f14-9775-d94de2fdbbdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd117c8a-5220-4149-a303-4e3ba78fe7c7' class='xr-var-data-in' type='checkbox'><label for='data-fd117c8a-5220-4149-a303-4e3ba78fe7c7' 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>orbit overpass direction (ascending, descending)</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>ascending descending</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swath_indicator</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f9ec7c41-4b27-467f-b09e-a48be2919a56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9ec7c41-4b27-467f-b09e-a48be2919a56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4d377f0-b79d-4115-8b40-076b91538c24' class='xr-var-data-in' type='checkbox'><label for='data-a4d377f0-b79d-4115-8b40-076b91538c24' 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>swath indicator (left, right)</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>left right</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_soil_moisture</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd720502-2a9d-4290-a3d8-b4b36cca3380' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd720502-2a9d-4290-a3d8-b4b36cca3380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8aceddd2-5b36-4c0f-a16c-358e0663a60b' class='xr-var-data-in' type='checkbox'><label for='data-8aceddd2-5b36-4c0f-a16c-358e0663a60b' 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>surface soil moisture</dd><dt><span>units :</span></dt><dd>percent saturation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_soil_moisture_noise</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-719ab287-eab4-43ee-80e7-19848e748d18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-719ab287-eab4-43ee-80e7-19848e748d18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e233ef78-6add-458e-b6d1-a5476eaae540' class='xr-var-data-in' type='checkbox'><label for='data-e233ef78-6add-458e-b6d1-a5476eaae540' 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>surface soil moisture noise</dd><dt><span>units :</span></dt><dd>percent saturation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backscatter40</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f443acee-8ae2-48e9-9473-78aad0f43bdc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f443acee-8ae2-48e9-9473-78aad0f43bdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bec73f03-2c68-4147-b9ff-ef2ad384b83b' class='xr-var-data-in' type='checkbox'><label for='data-bec73f03-2c68-4147-b9ff-ef2ad384b83b' 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>backscatter interpolated at 40 degree incidence angle</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>valid_min :</span></dt><dd>-300000000</dd><dt><span>valid_max :</span></dt><dd>300000000</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope40</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bdbad549-6e55-48da-af9b-51d978730720' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdbad549-6e55-48da-af9b-51d978730720' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77e35a3e-741b-4f8a-ae37-f47421d55c4e' class='xr-var-data-in' type='checkbox'><label for='data-77e35a3e-741b-4f8a-ae37-f47421d55c4e' 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>slope at 40 degree incidence angle</dd><dt><span>units :</span></dt><dd>dB/deg</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>curvature40</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4465c72-d6c9-4225-bf33-e68f9f3cb377' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4465c72-d6c9-4225-bf33-e68f9f3cb377' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa383ffe-3cdd-447d-b231-be8a248fa9da' class='xr-var-data-in' type='checkbox'><label for='data-aa383ffe-3cdd-447d-b231-be8a248fa9da' 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>curvature at 40 degree incidence angle</dd><dt><span>units :</span></dt><dd>dB/deg^2</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_soil_moisture_sensitivity</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45b5ad44-cfe2-4d12-9327-3f78cb41713f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45b5ad44-cfe2-4d12-9327-3f78cb41713f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-812d067c-30d6-48e8-b1b9-ede2aa1b3afd' class='xr-var-data-in' type='checkbox'><label for='data-812d067c-30d6-48e8-b1b9-ede2aa1b3afd' 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>surface soil moisture sensitivity</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>valid_min :</span></dt><dd>-300000000</dd><dt><span>valid_max :</span></dt><dd>300000000</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backscatter_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5fa22f5-20cd-46d1-887d-ffa7523838ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5fa22f5-20cd-46d1-887d-ffa7523838ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-406de92e-1cce-4f7f-98fe-f19830e65181' class='xr-var-data-in' type='checkbox'><label for='data-406de92e-1cce-4f7f-98fe-f19830e65181' 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>backscatter flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>fore_beam_sigma0_valid mid_beam_sigma0_valid aft_beam_sigma0_valid reserved_for_future_use reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>correction_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-acfe173e-08d0-44e2-a2de-dac60eea4067' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acfe173e-08d0-44e2-a2de-dac60eea4067' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b9665a6-6d20-4ce9-b8f7-6d80542708ab' class='xr-var-data-in' type='checkbox'><label for='data-4b9665a6-6d20-4ce9-b8f7-6d80542708ab' 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>correction flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>wet_correction_applied ssm_set_to_0%_was_between_-25%_and_0% ssm_set_to_100%_was_between_100%_and_125% reserved_for_future_use reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>processing_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45f0def4-915b-47d9-997b-57812aee481c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45f0def4-915b-47d9-997b-57812aee481c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-078006e2-8bb6-434b-8c4c-a47cead70da8' class='xr-var-data-in' type='checkbox'><label for='data-078006e2-8bb6-434b-8c4c-a47cead70da8' 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>processing flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>model_parameter_not_usable backscatter_not_usable ssm_below_-25%_saturation ssm_above_125%_saturation reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-026e79d2-b887-4064-9c1e-8417d7c0ee95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-026e79d2-b887-4064-9c1e-8417d7c0ee95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e43674e8-1aea-4c16-babb-3b0acf4fd3e0' class='xr-var-data-in' type='checkbox'><label for='data-e43674e8-1aea-4c16-babb-3b0acf4fd3e0' 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>surface flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>open_water frozen_soil snow_cover reserved_for_future_use reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_cover_probability</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-286c7385-ed6a-49e7-aa0c-0c2f257331c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-286c7385-ed6a-49e7-aa0c-0c2f257331c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84ddbc5a-5399-455c-a467-c3fd7bedc568' class='xr-var-data-in' type='checkbox'><label for='data-84ddbc5a-5399-455c-a467-c3fd7bedc568' 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>snow cover probability</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>based on ERA5 Snow Cover data (1980-2020)</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frozen_soil_probability</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8a744e0a-5b7a-4ecd-8208-3b21e0ff3c55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a744e0a-5b7a-4ecd-8208-3b21e0ff3c55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bada02c-c246-4794-b54a-12e8858d67c6' class='xr-var-data-in' type='checkbox'><label for='data-2bada02c-c246-4794-b54a-12e8858d67c6' 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>frozen soil probability</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>based on ERA5 Soil Temperature Level 1 data (1980-2020)</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wetland_fraction</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-30290329-519e-414b-9b7b-3b191c35953a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30290329-519e-414b-9b7b-3b191c35953a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24b06593-0591-436a-ac7d-7cf50f194e70' class='xr-var-data-in' type='checkbox'><label for='data-24b06593-0591-436a-ac7d-7cf50f194e70' 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>wetland fraction</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>based on Global Lakes and Wetlands Database (GLWD) data</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>topographic_complexity</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a682031-720c-43ec-bf4f-efb42a4efc61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a682031-720c-43ec-bf4f-efb42a4efc61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc700b89-cef8-4a5e-a7a9-059e000e03e3' class='xr-var-data-in' type='checkbox'><label for='data-fc700b89-cef8-4a5e-a7a9-059e000e03e3' 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>topographic complexity</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>normalized standard deviation of CopDEM90m data</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>subsurface_scattering_probability</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-96cfdfe0-702e-4b5a-a23d-c653f7b302ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96cfdfe0-702e-4b5a-a23d-c653f7b302ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc12121a-09f2-4739-9441-5eb73e50757d' class='xr-var-data-in' type='checkbox'><label for='data-dc12121a-09f2-4739-9441-5eb73e50757d' 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>subsurface scattering probability</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>global_attributes_flag</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e925a5d7-5af3-4d9c-91e7-c46a558d90b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e925a5d7-5af3-4d9c-91e7-c46a558d90b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5e9b178-af6a-45d3-929e-ad0c438bf797' class='xr-var-data-in' type='checkbox'><label for='data-a5e9b178-af6a-45d3-929e-ad0c438bf797' 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>[4378 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sat_id</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b4c3c73b-ec14-4ada-a9df-9c5cc35e1ec4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4c3c73b-ec14-4ada-a9df-9c5cc35e1ec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea89823-da84-4805-987a-97d86c1d18fd' class='xr-var-data-in' type='checkbox'><label for='data-5ea89823-da84-4805-987a-97d86c1d18fd' 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>[59143813 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>location_id</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d4263d29-a58a-4e21-950f-7a435bad7c45' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4263d29-a58a-4e21-950f-7a435bad7c45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f74bd85-2f74-477d-a7f9-caa6fffca1af' class='xr-var-data-in' type='checkbox'><label for='data-1f74bd85-2f74-477d-a7f9-caa6fffca1af' 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>[4378 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>location_description</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c02ff9fa-ba9c-4305-9619-d31a81859d74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c02ff9fa-ba9c-4305-9619-d31a81859d74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cb8f1e7-e86f-4d1c-b833-4df686d6034b' class='xr-var-data-in' type='checkbox'><label for='data-8cb8f1e7-e86f-4d1c-b833-4df686d6034b' 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>[4378 values with dtype=object]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79e6a003-2d95-43fb-88b1-5a6f0ac35268' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79e6a003-2d95-43fb-88b1-5a6f0ac35268' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-19bccc54-ad9a-4a14-ba98-8ce7263295d9' class='xr-section-summary-in' type='checkbox'  ><label for='section-19bccc54-ad9a-4a14-ba98-8ce7263295d9' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ASCAT surface soil moisture near real-time product</dd><dt><span>summary :</span></dt><dd>ASCAT surface soil moisture expressed in degree of saturation</dd><dt><span>doi :</span></dt><dd>unset</dd><dt><span>keywords :</span></dt><dd>Metop-A ASCAT surface soil moisture</dd><dt><span>history :</span></dt><dd>original generated product</dd><dt><span>institution :</span></dt><dd>H SAF</dd><dt><span>instrument :</span></dt><dd>ASCAT</dd><dt><span>product_level :</span></dt><dd>2</dd><dt><span>mission_type :</span></dt><dd>Global</dd><dt><span>disposition_mode :</span></dt><dd>Operational</dd><dt><span>environment :</span></dt><dd>Operational</dd><dt><span>references :</span></dt><dd>h-saf.eumetsat.int</dd><dt><span>software_version :</span></dt><dd>warp_h_nrt 0.0.0</dd><dt><span>conventions :</span></dt><dd>CF-1.10</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd></dl></div></li></ul></div></div>"],"text/plain":["<xarray.Dataset>\n","Dimensions:                            (obs: 59143813, locations: 4378)\n","Coordinates:\n","    time                               (obs) datetime64[ns] 2007-01-01T08:00:...\n","    lon                                (locations) float32 ...\n","    lat                                (locations) float32 ...\n","    alt                                (locations) float32 ...\n","Dimensions without coordinates: obs, locations\n","Data variables: (12/22)\n","    locationIndex                      (obs) int64 ...\n","    as_des_pass                        (obs) float32 ...\n","    swath_indicator                    (obs) float32 ...\n","    surface_soil_moisture              (obs) float32 ...\n","    surface_soil_moisture_noise        (obs) float32 ...\n","    backscatter40                      (obs) float32 ...\n","    ...                                 ...\n","    topographic_complexity             (obs) float32 ...\n","    subsurface_scattering_probability  (obs) float32 ...\n","    global_attributes_flag             (locations) int64 ...\n","    sat_id                             (obs) float32 ...\n","    location_id                        (locations) int64 ...\n","    location_description               (locations) object ...\n","Attributes: (12/15)\n","    title:             ASCAT surface soil moisture near real-time product\n","    summary:           ASCAT surface soil moisture expressed in degree of sat...\n","    doi:               unset\n","    keywords:          Metop-A ASCAT surface soil moisture\n","    history:           original generated product\n","    institution:       H SAF\n","    ...                ...\n","    disposition_mode:  Operational\n","    environment:       Operational\n","    references:        h-saf.eumetsat.int\n","    software_version:  warp_h_nrt 0.0.0\n","    conventions:       CF-1.10\n","    featureType:       timeSeries"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["output_bbox = cell_collection.read(bbox=bounds)#, date_range=dates)\n","output_bbox"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"data":{"text/html":["<div><svg style=\"position: absolute; 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var(--xr-font-color2);\n","}\n","\n",".xr-var-name,\n",".xr-var-dims,\n",".xr-var-dtype,\n",".xr-preview,\n",".xr-attrs dt {\n","  white-space: nowrap;\n","  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",".xr-index-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",".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",".xr-index-data,\n",".xr-attrs {\n","  padding-left: 25px !important;\n","}\n","\n",".xr-attrs,\n",".xr-var-attrs,\n",".xr-var-data,\n",".xr-index-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,\n",".xr-attrs 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",".xr-no-icon {\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.Dataset&gt;\n","Dimensions:                            (obs: 226965, locations: 4378)\n","Coordinates:\n","    time                               (obs) datetime64[ns] 2020-12-01T08:11:...\n","    lon                                (locations) float32 ...\n","    lat                                (locations) float32 ...\n","    alt                                (locations) float32 ...\n","Dimensions without coordinates: obs, locations\n","Data variables: (12/22)\n","    locationIndex                      (obs) int64 ...\n","    as_des_pass                        (obs) float32 ...\n","    swath_indicator                    (obs) float32 ...\n","    surface_soil_moisture              (obs) float32 ...\n","    surface_soil_moisture_noise        (obs) float32 ...\n","    backscatter40                      (obs) float32 ...\n","    ...                                 ...\n","    topographic_complexity             (obs) float32 ...\n","    subsurface_scattering_probability  (obs) float32 ...\n","    global_attributes_flag             (locations) int64 ...\n","    sat_id                             (obs) float32 ...\n","    location_id                        (locations) int64 ...\n","    location_description               (locations) object ...\n","Attributes: (12/15)\n","    title:             ASCAT surface soil moisture near real-time product\n","    summary:           ASCAT surface soil moisture expressed in degree of sat...\n","    doi:               unset\n","    keywords:          Metop-A ASCAT surface soil moisture\n","    history:           original generated product\n","    institution:       H SAF\n","    ...                ...\n","    disposition_mode:  Operational\n","    environment:       Operational\n","    references:        h-saf.eumetsat.int\n","    software_version:  warp_h_nrt 0.0.0\n","    conventions:       CF-1.10\n","    featureType:       timeSeries</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-93f2b0ac-04cb-4d0e-9caf-f78cfdccd84a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-93f2b0ac-04cb-4d0e-9caf-f78cfdccd84a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>obs</span>: 226965</li><li><span>locations</span>: 4378</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-381f9df7-76f9-4700-8fff-ec76bb85eedb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-381f9df7-76f9-4700-8fff-ec76bb85eedb' 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>time</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-12-01T08:11:51.520000 ... 2...</div><input id='attrs-2bc24d7f-7c15-4b31-8cb9-bad6afe8afc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bc24d7f-7c15-4b31-8cb9-bad6afe8afc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ad66531-9b18-47fa-adb7-72537f81f62f' class='xr-var-data-in' type='checkbox'><label for='data-6ad66531-9b18-47fa-adb7-72537f81f62f' 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>_FillValue :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>time of measurement</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-12-01T08:11:51.520000000&#x27;, &#x27;2020-12-01T17:54:59.309000192&#x27;,\n","       &#x27;2020-12-02T19:14:17.520000512&#x27;, ...,\n","       &#x27;2020-12-13T19:16:58.832000000&#x27;, &#x27;2020-12-14T18:56:24.828000256&#x27;,\n","       &#x27;2020-12-14T20:36:23.400000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c77fcbb8-de37-4c9c-a225-7884d885f20d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c77fcbb8-de37-4c9c-a225-7884d885f20d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64d8fac2-e145-4b1d-b720-6f7c4ac02229' class='xr-var-data-in' type='checkbox'><label for='data-64d8fac2-e145-4b1d-b720-6f7c4ac02229' 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>[4378 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aec5fb3c-2ec7-47cd-8aa9-b2dad1dd6eb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aec5fb3c-2ec7-47cd-8aa9-b2dad1dd6eb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ede8c661-3478-448f-9c50-aec586774074' class='xr-var-data-in' type='checkbox'><label for='data-ede8c661-3478-448f-9c50-aec586774074' 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>[4378 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-56c61c94-63fc-412f-a659-ef94e027e0a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56c61c94-63fc-412f-a659-ef94e027e0a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05bcea2f-6c8d-48b0-8ed4-6baece605706' class='xr-var-data-in' type='checkbox'><label for='data-05bcea2f-6c8d-48b0-8ed4-6baece605706' 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>[4378 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d697ac5-11fd-4835-b6a5-0cc63c4b2172' class='xr-section-summary-in' type='checkbox'  ><label for='section-2d697ac5-11fd-4835-b6a5-0cc63c4b2172' class='xr-section-summary' >Data variables: <span>(22)</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>locationIndex</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-81f0efee-c3ae-45ec-bc0c-a7cbb98b2d68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81f0efee-c3ae-45ec-bc0c-a7cbb98b2d68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ba7e6d7-8960-441a-acaf-50b511f7e57b' class='xr-var-data-in' type='checkbox'><label for='data-4ba7e6d7-8960-441a-acaf-50b511f7e57b' 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>[226965 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>as_des_pass</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-533db5db-3111-4d18-baa7-e136ce6976e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-533db5db-3111-4d18-baa7-e136ce6976e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa4535e9-c0c2-4c9e-907d-b577b4afa6ac' class='xr-var-data-in' type='checkbox'><label for='data-aa4535e9-c0c2-4c9e-907d-b577b4afa6ac' 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>orbit overpass direction (ascending, descending)</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>ascending descending</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swath_indicator</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-20aae9bc-fae7-4591-afe8-12cff4ac17c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20aae9bc-fae7-4591-afe8-12cff4ac17c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70ccaf9c-1567-483e-8f11-ee6ac65a9596' class='xr-var-data-in' type='checkbox'><label for='data-70ccaf9c-1567-483e-8f11-ee6ac65a9596' 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>swath indicator (left, right)</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>left right</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_soil_moisture</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-afaa93db-5b86-46b8-83e9-2be87c000505' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afaa93db-5b86-46b8-83e9-2be87c000505' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a0a3754-fa1d-4ba8-a0f3-cbc0e6693fc3' class='xr-var-data-in' type='checkbox'><label for='data-5a0a3754-fa1d-4ba8-a0f3-cbc0e6693fc3' 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>surface soil moisture</dd><dt><span>units :</span></dt><dd>percent saturation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_soil_moisture_noise</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad7b39ad-9407-46a2-9b7c-6d91e32259aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad7b39ad-9407-46a2-9b7c-6d91e32259aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa6f4f23-b811-421f-bd08-bb601a17591c' class='xr-var-data-in' type='checkbox'><label for='data-aa6f4f23-b811-421f-bd08-bb601a17591c' 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>surface soil moisture noise</dd><dt><span>units :</span></dt><dd>percent saturation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backscatter40</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a02f960c-afd3-4d54-9295-f4046efe5f3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a02f960c-afd3-4d54-9295-f4046efe5f3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1dbc70c-bf1f-4039-b4f9-92cc2cb7134f' class='xr-var-data-in' type='checkbox'><label for='data-f1dbc70c-bf1f-4039-b4f9-92cc2cb7134f' 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>backscatter interpolated at 40 degree incidence angle</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>valid_min :</span></dt><dd>-300000000</dd><dt><span>valid_max :</span></dt><dd>300000000</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope40</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ae59a1ca-e2a5-4409-add5-3d8b90d463ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae59a1ca-e2a5-4409-add5-3d8b90d463ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ee8ed04-77da-4a0c-ba41-ed48145568e7' class='xr-var-data-in' type='checkbox'><label for='data-3ee8ed04-77da-4a0c-ba41-ed48145568e7' 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>slope at 40 degree incidence angle</dd><dt><span>units :</span></dt><dd>dB/deg</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>curvature40</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-08b07694-05fa-4088-9d9d-e41071f49dfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08b07694-05fa-4088-9d9d-e41071f49dfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35b3ea07-1890-42b4-8227-337ab2dad67c' class='xr-var-data-in' type='checkbox'><label for='data-35b3ea07-1890-42b4-8227-337ab2dad67c' 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>curvature at 40 degree incidence angle</dd><dt><span>units :</span></dt><dd>dB/deg^2</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_soil_moisture_sensitivity</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f22bae28-f814-4ebf-94c4-f5526e45751b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f22bae28-f814-4ebf-94c4-f5526e45751b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57b32871-7af3-4283-8ba5-c4702eed8d23' class='xr-var-data-in' type='checkbox'><label for='data-57b32871-7af3-4283-8ba5-c4702eed8d23' 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>surface soil moisture sensitivity</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>valid_min :</span></dt><dd>-300000000</dd><dt><span>valid_max :</span></dt><dd>300000000</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backscatter_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31148829-6e50-4102-8cc6-3f5c4e31c650' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31148829-6e50-4102-8cc6-3f5c4e31c650' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53091759-4c3c-4f0f-849a-0a808e84983a' class='xr-var-data-in' type='checkbox'><label for='data-53091759-4c3c-4f0f-849a-0a808e84983a' 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>backscatter flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>fore_beam_sigma0_valid mid_beam_sigma0_valid aft_beam_sigma0_valid reserved_for_future_use reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>correction_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e6a8cd3-d434-4e7c-9379-2294a64166e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e6a8cd3-d434-4e7c-9379-2294a64166e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1031498b-6c50-42ef-836b-c6b267b9fb84' class='xr-var-data-in' type='checkbox'><label for='data-1031498b-6c50-42ef-836b-c6b267b9fb84' 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>correction flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>wet_correction_applied ssm_set_to_0%_was_between_-25%_and_0% ssm_set_to_100%_was_between_100%_and_125% reserved_for_future_use reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>processing_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9baafc9-3a10-44db-b7d3-8a2730dec75b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9baafc9-3a10-44db-b7d3-8a2730dec75b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1123de59-8943-4ce1-81a7-f1ca4dfd1abf' class='xr-var-data-in' type='checkbox'><label for='data-1123de59-8943-4ce1-81a7-f1ca4dfd1abf' 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>processing flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>model_parameter_not_usable backscatter_not_usable ssm_below_-25%_saturation ssm_above_125%_saturation reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b0c3644-ddef-46a9-a240-bcce421ae5da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b0c3644-ddef-46a9-a240-bcce421ae5da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17b6bf4e-a39c-4636-8a1b-6d870d3fd225' class='xr-var-data-in' type='checkbox'><label for='data-17b6bf4e-a39c-4636-8a1b-6d870d3fd225' 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>surface flag</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16 32 64]</dd><dt><span>flag_meanings :</span></dt><dd>open_water frozen_soil snow_cover reserved_for_future_use reserved_for_future_use reserved_for_future_use reserved_for_future_use</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_cover_probability</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e0d114e-5caf-455f-a56f-7b21f57bb577' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e0d114e-5caf-455f-a56f-7b21f57bb577' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb5e57ea-2fed-4487-8c9f-0837b76d2606' class='xr-var-data-in' type='checkbox'><label for='data-bb5e57ea-2fed-4487-8c9f-0837b76d2606' 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>snow cover probability</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>based on ERA5 Snow Cover data (1980-2020)</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frozen_soil_probability</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-351329b7-72f3-4443-adf8-e698ec3a30e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-351329b7-72f3-4443-adf8-e698ec3a30e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afb511bd-1539-4796-b172-3fc813587368' class='xr-var-data-in' type='checkbox'><label for='data-afb511bd-1539-4796-b172-3fc813587368' 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>frozen soil probability</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>based on ERA5 Soil Temperature Level 1 data (1980-2020)</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wetland_fraction</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2fedc7d4-7cd2-4722-8618-9e4e0f943d99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fedc7d4-7cd2-4722-8618-9e4e0f943d99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22aad0f2-043e-4afa-a956-86eb6a3ac58c' class='xr-var-data-in' type='checkbox'><label for='data-22aad0f2-043e-4afa-a956-86eb6a3ac58c' 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>wetland fraction</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>based on Global Lakes and Wetlands Database (GLWD) data</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>topographic_complexity</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8616d1c9-8a95-4271-b160-767a8a912f07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8616d1c9-8a95-4271-b160-767a8a912f07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8861ec5d-9003-46e0-b2c8-0c1c8d6599b0' class='xr-var-data-in' type='checkbox'><label for='data-8861ec5d-9003-46e0-b2c8-0c1c8d6599b0' 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>topographic complexity</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>comment :</span></dt><dd>normalized standard deviation of CopDEM90m data</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>subsurface_scattering_probability</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c8ee8d88-d308-4079-adf6-b6084b2bfe77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8ee8d88-d308-4079-adf6-b6084b2bfe77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4519e2d8-ec95-4523-92ef-42a3059e2628' class='xr-var-data-in' type='checkbox'><label for='data-4519e2d8-ec95-4523-92ef-42a3059e2628' 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>subsurface scattering probability</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div><div class='xr-var-data'><pre>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>global_attributes_flag</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-62432da0-3b72-4e6e-9fa9-5e3153e776eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62432da0-3b72-4e6e-9fa9-5e3153e776eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cab981f-e8a3-4db9-9f8c-d1ef9ba3d340' class='xr-var-data-in' type='checkbox'><label for='data-2cab981f-e8a3-4db9-9f8c-d1ef9ba3d340' 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>[4378 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sat_id</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8751ce0c-e545-4ce9-b83f-4daa03326fb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8751ce0c-e545-4ce9-b83f-4daa03326fb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-983b5a70-dbeb-4122-ae01-ef8b2cf1ac6e' class='xr-var-data-in' type='checkbox'><label for='data-983b5a70-dbeb-4122-ae01-ef8b2cf1ac6e' 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>[226965 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>location_id</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df6c48bb-7184-4c08-8ccf-394a8d207478' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df6c48bb-7184-4c08-8ccf-394a8d207478' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecaa985b-ec9d-459a-a0ba-f6701bfc7e1d' class='xr-var-data-in' type='checkbox'><label for='data-ecaa985b-ec9d-459a-a0ba-f6701bfc7e1d' 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>[4378 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>location_description</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21c77841-e5ba-405d-94ca-a6bc66d81291' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21c77841-e5ba-405d-94ca-a6bc66d81291' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a53f53a8-6b0e-4729-9b9f-e158e4c656dd' class='xr-var-data-in' type='checkbox'><label for='data-a53f53a8-6b0e-4729-9b9f-e158e4c656dd' 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>[4378 values with dtype=object]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bcab46b5-4260-4c96-9e84-4a5b6253d92f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bcab46b5-4260-4c96-9e84-4a5b6253d92f' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ff3a2a9d-1810-45eb-a2fb-58d6052e99f6' class='xr-section-summary-in' type='checkbox'  ><label for='section-ff3a2a9d-1810-45eb-a2fb-58d6052e99f6' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ASCAT surface soil moisture near real-time product</dd><dt><span>summary :</span></dt><dd>ASCAT surface soil moisture expressed in degree of saturation</dd><dt><span>doi :</span></dt><dd>unset</dd><dt><span>keywords :</span></dt><dd>Metop-A ASCAT surface soil moisture</dd><dt><span>history :</span></dt><dd>original generated product</dd><dt><span>institution :</span></dt><dd>H SAF</dd><dt><span>instrument :</span></dt><dd>ASCAT</dd><dt><span>product_level :</span></dt><dd>2</dd><dt><span>mission_type :</span></dt><dd>Global</dd><dt><span>disposition_mode :</span></dt><dd>Operational</dd><dt><span>environment :</span></dt><dd>Operational</dd><dt><span>references :</span></dt><dd>h-saf.eumetsat.int</dd><dt><span>software_version :</span></dt><dd>warp_h_nrt 0.0.0</dd><dt><span>conventions :</span></dt><dd>CF-1.10</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd></dl></div></li></ul></div></div>"],"text/plain":["<xarray.Dataset>\n","Dimensions:                            (obs: 226965, locations: 4378)\n","Coordinates:\n","    time                               (obs) datetime64[ns] 2020-12-01T08:11:...\n","    lon                                (locations) float32 ...\n","    lat                                (locations) float32 ...\n","    alt                                (locations) float32 ...\n","Dimensions without coordinates: obs, locations\n","Data variables: (12/22)\n","    locationIndex                      (obs) int64 ...\n","    as_des_pass                        (obs) float32 ...\n","    swath_indicator                    (obs) float32 ...\n","    surface_soil_moisture              (obs) float32 ...\n","    surface_soil_moisture_noise        (obs) float32 ...\n","    backscatter40                      (obs) float32 ...\n","    ...                                 ...\n","    topographic_complexity             (obs) float32 ...\n","    subsurface_scattering_probability  (obs) float32 ...\n","    global_attributes_flag             (locations) int64 ...\n","    sat_id                             (obs) float32 ...\n","    location_id                        (locations) int64 ...\n","    location_description               (locations) object ...\n","Attributes: (12/15)\n","    title:             ASCAT surface soil moisture near real-time product\n","    summary:           ASCAT surface soil moisture expressed in degree of sat...\n","    doi:               unset\n","    keywords:          Metop-A ASCAT surface soil moisture\n","    history:           original generated product\n","    institution:       H SAF\n","    ...                ...\n","    disposition_mode:  Operational\n","    environment:       Operational\n","    references:        h-saf.eumetsat.int\n","    software_version:  warp_h_nrt 0.0.0\n","    conventions:       CF-1.10\n","    featureType:       timeSeries"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["date_range_data = output_bbox.sel(obs=(output_bbox[\"time\"] > dates[0]) & (output_bbox[\"time\"] < dates[1]))\n","date_range_data"]},{"cell_type":"markdown","metadata":{},"source":["Now let&rsquo;s map the average surface soil moisture over the area and time range we selected.\n","\n"]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[{"data":{"text/html":["<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n","<defs>\n","<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n","<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n","<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n","<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n","</symbol>\n","<symbol 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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",".xr-no-icon {\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;surface_soil_moisture&#x27; (locationIndex: 4000)&gt;\n","array([73.23913 , 85.52    , 52.248085, ..., 64.45    , 35.750626,\n","       62.632   ], dtype=float32)\n","Coordinates:\n","  * locationIndex  (locationIndex) int64 1 2 4 5 6 ... 4373 4374 4375 4376 4377\n","Attributes:\n","    long_name:    surface soil moisture\n","    units:        percent saturation\n","    valid_min:    0\n","    valid_max:    10000\n","    coordinates:  time latitude longitude</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'surface_soil_moisture'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>locationIndex</span>: 4000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c650a226-c64c-42cc-94ac-676bb5d25e95' class='xr-array-in' type='checkbox' checked><label for='section-c650a226-c64c-42cc-94ac-676bb5d25e95' 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>73.24 85.52 52.25 76.47 63.89 60.19 ... 53.8 72.28 64.45 35.75 62.63</span></div><div class='xr-array-data'><pre>array([73.23913 , 85.52    , 52.248085, ..., 64.45    , 35.750626,\n","       62.632   ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-02b2f4af-86db-43da-a42a-a3f93b4b245d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-02b2f4af-86db-43da-a42a-a3f93b4b245d' class='xr-section-summary' >Coordinates: <span>(1)</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'>locationIndex</span></div><div class='xr-var-dims'>(locationIndex)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 4 5 6 ... 4374 4375 4376 4377</div><input id='attrs-d637b7e9-8842-4bf5-8dd6-7575ffa67914' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d637b7e9-8842-4bf5-8dd6-7575ffa67914' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c0408cb-a244-495b-b5bf-fdebfb0128e6' class='xr-var-data-in' type='checkbox'><label for='data-7c0408cb-a244-495b-b5bf-fdebfb0128e6' 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([   1,    2,    4, ..., 4375, 4376, 4377])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-351ffd70-2ba9-41ec-915d-1fed9d855cf3' class='xr-section-summary-in' type='checkbox'  ><label for='section-351ffd70-2ba9-41ec-915d-1fed9d855cf3' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>locationIndex</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-522f62ad-409b-45a9-8c04-0d58e2451315' class='xr-index-data-in' type='checkbox'/><label for='index-522f62ad-409b-45a9-8c04-0d58e2451315' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    4,    5,    6,    8,    9,   11,   12,   14,\n","       ...\n","       4368, 4369, 4370, 4371, 4372, 4373, 4374, 4375, 4376, 4377],\n","      dtype=&#x27;int64&#x27;, name=&#x27;locationIndex&#x27;, length=4000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-061435ff-bc40-40c2-a913-96fb345fb354' class='xr-section-summary-in' type='checkbox'  checked><label for='section-061435ff-bc40-40c2-a913-96fb345fb354' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>surface soil moisture</dd><dt><span>units :</span></dt><dd>percent saturation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>coordinates :</span></dt><dd>time latitude longitude</dd></dl></div></li></ul></div></div>"],"text/plain":["<xarray.DataArray 'surface_soil_moisture' (locationIndex: 4000)>\n","array([73.23913 , 85.52    , 52.248085, ..., 64.45    , 35.750626,\n","       62.632   ], dtype=float32)\n","Coordinates:\n","  * locationIndex  (locationIndex) int64 1 2 4 5 6 ... 4373 4374 4375 4376 4377\n","Attributes:\n","    long_name:    surface soil moisture\n","    units:        percent saturation\n","    valid_min:    0\n","    valid_max:    10000\n","    coordinates:  time latitude longitude"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["from flox.xarray import xarray_reduce\n","avg_sm = xarray_reduce(date_range_data[\"surface_soil_moisture\"], date_range_data[\"locationIndex\"], func=\"mean\")\n","avg_sm"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","lons = date_range_data.lon.values[avg_sm.locationIndex.values]\n","lats = date_range_data.lat.values[avg_sm.locationIndex.values]\n","simple_map(lons, lats, avg_sm, cmc.roma, (date_range_data.time.values.min(), date_range_data.time.values.max()))"]},{"cell_type":"markdown","metadata":{},"source":["When we read data using cell ids, the process is just as easy:\n","\n"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[],"source":["output_cells = cell_collection.read(cell=[1431, 1432, 1395, 1396])"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["avg_sm = xarray_reduce(output_cells[\"surface_soil_moisture\"], output_cells[\"locationIndex\"], func=\"mean\")\n","lons = output_cells.lon.values[avg_sm.locationIndex.values]\n","lats = output_cells.lat.values[avg_sm.locationIndex.values]\n","simple_map(lons, lats, avg_sm, cmc.roma, (output_cells.time.values.min(), output_cells.time.values.max()))"]},{"cell_type":"markdown","metadata":{},"source":["I forgot to filter by time range, but it took flox only a few seconds to calculate the average surface soil moisture over the entire time range of the dataset for these cells!\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Using geometries\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["If you have a shapefile you would like to use to filter your data, you will have to turn it into a shapely geometry object. There are a few ways you could do this (using geopandas, fiona, or ogr, for example). This function uses cartopy&rsquo;s shapereader to fetch a world country boundaries shapefile from Natural Earth, and then uses shapely to create a geometry object from the desired country names.\n","\n"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[],"source":["import cartopy.io.shapereader as shpreader\n","from shapely.ops import unary_union\n","\n","def get_country_geometries(country_names, resolution=\"10m\", ne_product=\"admin_0_countries\"):\n","    countries = shpreader.Reader(\n","        shpreader.natural_earth(\n","            resolution=resolution,\n","            category=\"cultural\",\n","            name=ne_product,\n","        )\n","    ).records()\n","    if isinstance(country_names, str):\n","        country_names = [country_names]\n","    for i in range(len(country_names)):\n","        country_names[i] = country_names[i].lower()\n","\n","    geometries = []\n","    desired_shp = None\n","    for loop_country in countries:\n","        if loop_country.attributes[\"SOVEREIGNT\"].lower() in country_names:\n","            desired_shp = loop_country.geometry\n","            if desired_shp is not None:\n","                geometries.append(desired_shp)\n","    return unary_union(geometries)"]},{"cell_type":"markdown","metadata":{},"source":["If we are interested in the Baltic countries, for example, we can simply pass a list of their names to `get_country_geometries`, then pass the resulting geometry to the `geom` argument of `cell_collection.read()`.\n","\n"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[],"source":["baltics = [\"Estonia\", \"Latvia\", \"Lithuania\"]\n","country_data = cell_collection.read(geom=get_country_geometries(baltics))"]},{"cell_type":"markdown","metadata":{},"source":["Groupby operations are easy with flox. Here we calculate the average summer soil moisture for each location in the Baltics across the entire time range of the dataset.\n","\n"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","from flox.xarray import xarray_reduce\n","\n","baltic_summer = country_data.sel(obs=(country_data.time.dt.season == \"JJA\"))\n","avg_sm = xarray_reduce(baltic_summer[\"surface_soil_moisture\"], baltic_summer[\"locationIndex\"], func=\"mean\")\n","lons = country_data.lon.values[avg_sm.locationIndex.values]\n","lats = country_data.lat.values[avg_sm.locationIndex.values]\n","label = (\n","        f\"Average summer soil moisture \"\n","        f\"in the Baltic countries\\n\"\n","        f\"({avg_sm.units})\\n\"\n","        f\"June, July, and August of 2007 - 2022\"\n",")\n","simple_map(lons, lats, avg_sm, cmc.roma, cbar_label=label)"]},{"cell_type":"markdown","metadata":{},"source":["Remember that climatology we were going to make in the swaths section? Let&rsquo;s do that now, it&rsquo;s simple:\n","\n"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# 15-year climatology\n","ssm_climatology = xarray_reduce(country_data[\"surface_soil_moisture\"], country_data[\"time\"].dt.dayofyear, func=\"mean\")\n","plt.close()\n","plt.plot(ssm_climatology)\n","plt.xlabel(\"Day of year\")\n","plt.ylabel(\"Average surface soil moisture\\n(% saturation)\")\n","plt.title(\"Average surface soil moisture per day of year\\n(Estonia, Latvia, and Lithuania; 2010-2019)\")\n","plt.tight_layout()"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.18"},"org":null},"nbformat":4,"nbformat_minor":0}