06_Aggregation.ipynb

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    "## Aggregations and Composites\n",
    "\n",
    "Aggregation involves summarizing data by reducing its dimensionality or condensing its values. Common aggregation operations include mean, sum, median, max, min, and more.\n",
    "\n",
    "Composites are created by combining multiple datasets or multiple observations into a single dataset, typically to represent the best or a representative view over a period or area.\n",
    "\n",
    "Reasons for the abovementioned operations:\n",
    "1. Cloud-Free Imagery\n",
    "2. Temporal Analysis\n",
    "3. Detect Patterns\n",
    "4. Noise Reductions\n",
    "5. Simplification\n",
    "\n",
    "***"
   ]
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   "cell_type": "markdown",
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   "source": [
    "### Loading Required Libraries"
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    "import pyproj\n",
    "import pystac_client\n",
    "import xarray as xr\n",
    "from shapely.geometry import box\n",
    "from shapely.ops import transform\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from dask.distributed import Client\n",
    "import odc.stac\n",
    "from odc.geo.geobox import GeoBox\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from geogif import gif, dgif\n",
    "from dea_tools.plotting import display_map, rgb"
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   "id": "4a009482",
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     "text": [
      "/home/agriusr/miniconda3/envs/odc/lib/python3.12/site-packages/pystac_client/client.py:190: NoConformsTo: Server does not advertise any conformance classes.\n",
      "  warnings.warn(NoConformsTo())\n",
      "/home/agriusr/miniconda3/envs/odc/lib/python3.12/site-packages/pystac_client/item_search.py:372: DoesNotConformTo: Server does not conform to QUERY\n",
      "  warnings.warn(DoesNotConformTo(\"QUERY\"))\n"
     ]
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   "source": [
    "def digitize_date(month,day):    \n",
    "    if month<10:\n",
    "        month=f'0{month}'\n",
    "    \n",
    "    if day<10:\n",
    "        day=f'0{day}'\n",
    "    \n",
    "    return month,day\n",
    "\n",
    "# STAC connection information for Sentinel 2 COGs\n",
    "STAC_URL = \"https://earth-search.aws.element84.com/v0\"\n",
    "STAC_COLLECTION = \"sentinel-s2-l2a-cogs\"\n",
    "\n",
    "# spatial projection information\n",
    "CRS_STRING = \"epsg:4326\"\n",
    "EPSG = pyproj.CRS.from_string(CRS_STRING).to_epsg()\n",
    "\n",
    "# Area of Interest (Marseille) in WGS84\n",
    "AOI = box(1.279358,43.501373,1.287961,43.514330)\n",
    "bounds = (1.279358,43.501373,1.287961,43.514330)\n",
    "BANDS = [\"B04\", \"B03\", \"B02\"]\n",
    "START_DATE = \"2021-01-01\"\n",
    "END_DATE = \"2023-01-30\"\n",
    "\n",
    "# STAC items store bounding box info in epsg:4326\n",
    "transformer_4326 = pyproj.Transformer.from_crs(\n",
    "    crs_from=CRS_STRING,\n",
    "    crs_to=\"epsg:4326\",\n",
    "    always_xy=True,\n",
    ")\n",
    "bbox_4326 = transform(transformer_4326.transform, AOI).bounds\n",
    "\n",
    "catalog = pystac_client.Client.open(STAC_URL)\n",
    "catalog.add_conforms_to(\"ITEM_SEARCH\")\n",
    "\n",
    "stac_items = catalog.search(\n",
    "    collections=[STAC_COLLECTION],\n",
    "    bbox=bbox_4326,\n",
    "    datetime=[START_DATE, END_DATE],\n",
    "    query={\"eo:cloud_cover\": {\"lt\": 10}}\n",
    ")#.item_collection()\n",
    "\n",
    "# using Open Data Cube stac component\n",
    "dx = 3/3600  # ~90m resolution\n",
    "dx = 20 / 111320 # ~20m res\n",
    "epsg = 4326\n",
    "geobox = GeoBox.from_bbox(bounds, crs=f\"epsg:{epsg}\", resolution=dx)\n",
    "\n",
    "data = odc.stac.load(\n",
    "    stac_items.items(),\n",
    "    chunks={},\n",
    "    geobox=geobox,\n",
    "    bands=BANDS,\n",
    "    resampling=\"bilinear\",\n",
    "    groupby=\"solar_day\" # delete duplicates due to satellite overlap\n",
    ")"
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       "    B04          (time, latitude, longitude) float32 486kB dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;\n",
       "    B03          (time, latitude, longitude) float32 486kB dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;\n",
       "    B02          (time, latitude, longitude) float32 486kB dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</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-7b094d89-928c-40ad-9a42-c77be55bda30' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7b094d89-928c-40ad-9a42-c77be55bda30' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 73</li><li><span class='xr-has-index'>longitude</span>: 49</li><li><span class='xr-has-index'>time</span>: 34</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8e8e0fb6-432d-4aea-96b1-7048e904b2c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e8e0fb6-432d-4aea-96b1-7048e904b2c4' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>43.51 43.51 43.51 ... 43.5 43.5</div><input id='attrs-a3e2e5cd-974e-4852-a81d-b5565f780cc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3e2e5cd-974e-4852-a81d-b5565f780cc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc03d94a-350a-48f9-9497-da58f55d3a5e' class='xr-var-data-in' type='checkbox'><label for='data-bc03d94a-350a-48f9-9497-da58f55d3a5e' 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>units :</span></dt><dd>degrees_north</dd><dt><span>resolution :</span></dt><dd>-0.00017966223499820338</dd><dt><span>crs :</span></dt><dd>EPSG:4326</dd></dl></div><div class='xr-var-data'><pre>array([43.514283, 43.514103, 43.513924, 43.513744, 43.513564, 43.513385,\n",
       "       43.513205, 43.513026, 43.512846, 43.512666, 43.512487, 43.512307,\n",
       "       43.512127, 43.511948, 43.511768, 43.511588, 43.511409, 43.511229,\n",
       "       43.511049, 43.51087 , 43.51069 , 43.51051 , 43.510331, 43.510151,\n",
       "       43.509971, 43.509792, 43.509612, 43.509432, 43.509253, 43.509073,\n",
       "       43.508893, 43.508714, 43.508534, 43.508354, 43.508175, 43.507995,\n",
       "       43.507815, 43.507636, 43.507456, 43.507276, 43.507097, 43.506917,\n",
       "       43.506737, 43.506558, 43.506378, 43.506198, 43.506019, 43.505839,\n",
       "       43.505659, 43.50548 , 43.5053  , 43.50512 , 43.504941, 43.504761,\n",
       "       43.504581, 43.504402, 43.504222, 43.504042, 43.503863, 43.503683,\n",
       "       43.503503, 43.503324, 43.503144, 43.502964, 43.502785, 43.502605,\n",
       "       43.502425, 43.502246, 43.502066, 43.501886, 43.501707, 43.501527,\n",
       "       43.501347])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.279 1.279 1.28 ... 1.288 1.288</div><input id='attrs-4337bf6c-e835-4c8b-be4f-2bbebb3368b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4337bf6c-e835-4c8b-be4f-2bbebb3368b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c38db88-6c08-4a5b-9137-2f76293c7680' class='xr-var-data-in' type='checkbox'><label for='data-2c38db88-6c08-4a5b-9137-2f76293c7680' 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>units :</span></dt><dd>degrees_east</dd><dt><span>resolution :</span></dt><dd>0.00017966223499820338</dd><dt><span>crs :</span></dt><dd>EPSG:4326</dd></dl></div><div class='xr-var-data'><pre>array([1.279285, 1.279465, 1.279644, 1.279824, 1.280004, 1.280183, 1.280363,\n",
       "       1.280543, 1.280722, 1.280902, 1.281082, 1.281261, 1.281441, 1.281621,\n",
       "       1.2818  , 1.28198 , 1.28216 , 1.282339, 1.282519, 1.282699, 1.282878,\n",
       "       1.283058, 1.283238, 1.283417, 1.283597, 1.283777, 1.283956, 1.284136,\n",
       "       1.284315, 1.284495, 1.284675, 1.284854, 1.285034, 1.285214, 1.285393,\n",
       "       1.285573, 1.285753, 1.285932, 1.286112, 1.286292, 1.286471, 1.286651,\n",
       "       1.286831, 1.28701 , 1.28719 , 1.28737 , 1.287549, 1.287729, 1.287909])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-18f7d715-6b11-4d19-8e42-7c6387abf832' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18f7d715-6b11-4d19-8e42-7c6387abf832' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76a80614-101e-46ae-a1b0-828fd4752131' class='xr-var-data-in' type='checkbox'><label for='data-76a80614-101e-46ae-a1b0-828fd4752131' 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>spatial_ref :</span></dt><dd>GEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2296)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],CS[ellipsoidal,2],AXIS[&quot;geodetic latitude (Lat)&quot;,north,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],AXIS[&quot;geodetic longitude (Lon)&quot;,east,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],USAGE[SCOPE[&quot;Horizontal component of 3D system.&quot;],AREA[&quot;World.&quot;],BBOX[-90,-180,90,180]],ID[&quot;EPSG&quot;,4326]]</dd><dt><span>crs_wkt :</span></dt><dd>GEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2296)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],CS[ellipsoidal,2],AXIS[&quot;geodetic latitude (Lat)&quot;,north,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],AXIS[&quot;geodetic longitude (Lon)&quot;,east,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],USAGE[SCOPE[&quot;Horizontal component of 3D system.&quot;],AREA[&quot;World.&quot;],BBOX[-90,-180,90,180]],ID[&quot;EPSG&quot;,4326]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984 ensemble</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>GeoTransform :</span></dt><dd>1.279195113187208132643491 0.00017966223499820338028 0 43.514372978799855218312587 0 -0.00017966223499820338028</dd></dl></div><div class='xr-var-data'><pre>array(4326, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-02-14T10:58:57 ... 2023-01-...</div><input id='attrs-4dcd50f2-75b1-40b8-84c5-d1423a38543c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4dcd50f2-75b1-40b8-84c5-d1423a38543c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-933b8fea-48d7-445e-b816-f12df04cbf39' class='xr-var-data-in' type='checkbox'><label for='data-933b8fea-48d7-445e-b816-f12df04cbf39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-02-14T10:58:57.000000000&#x27;, &#x27;2021-02-19T10:58:56.000000000&#x27;,\n",
       "       &#x27;2021-02-24T10:58:58.000000000&#x27;, &#x27;2021-03-01T10:58:57.000000000&#x27;,\n",
       "       &#x27;2021-03-31T10:58:57.000000000&#x27;, &#x27;2021-04-05T10:58:54.000000000&#x27;,\n",
       "       &#x27;2021-04-15T10:58:52.000000000&#x27;, &#x27;2021-05-05T10:58:57.000000000&#x27;,\n",
       "       &#x27;2021-05-15T10:58:58.000000000&#x27;, &#x27;2021-07-19T10:59:00.000000000&#x27;,\n",
       "       &#x27;2021-08-23T10:59:00.000000000&#x27;, &#x27;2021-08-28T10:58:56.000000000&#x27;,\n",
       "       &#x27;2021-09-17T10:58:54.000000000&#x27;, &#x27;2021-09-22T10:59:01.000000000&#x27;,\n",
       "       &#x27;2021-12-16T10:58:52.000000000&#x27;, &#x27;2021-12-31T10:59:00.000000000&#x27;,\n",
       "       &#x27;2022-01-15T10:58:54.000000000&#x27;, &#x27;2022-01-25T10:58:52.000000000&#x27;,\n",
       "       &#x27;2022-02-09T10:58:57.000000000&#x27;, &#x27;2022-04-05T10:58:55.000000000&#x27;,\n",
       "       &#x27;2022-04-25T10:58:52.000000000&#x27;, &#x27;2022-05-25T10:59:04.000000000&#x27;,\n",
       "       &#x27;2022-06-29T10:59:13.000000000&#x27;, &#x27;2022-07-09T10:59:11.000000000&#x27;,\n",
       "       &#x27;2022-07-14T10:59:03.000000000&#x27;, &#x27;2022-07-24T10:59:02.000000000&#x27;,\n",
       "       &#x27;2022-08-03T10:59:03.000000000&#x27;, &#x27;2022-08-08T10:59:14.000000000&#x27;,\n",
       "       &#x27;2022-08-28T10:59:15.000000000&#x27;, &#x27;2022-09-22T10:58:59.000000000&#x27;,\n",
       "       &#x27;2022-11-11T10:58:58.000000000&#x27;, &#x27;2022-11-16T10:59:00.000000000&#x27;,\n",
       "       &#x27;2022-12-26T10:58:57.000000000&#x27;, &#x27;2023-01-20T10:58:56.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48fca32a-4ffc-4252-971e-8cd0bd88e190' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48fca32a-4ffc-4252-971e-8cd0bd88e190' class='xr-section-summary' >Data variables: <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>B04</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</div><input id='attrs-366517bd-0791-447b-9139-80a605424c24' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-366517bd-0791-447b-9139-80a605424c24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b37fbb2-3e88-42dd-bc5d-b9c553930405' class='xr-var-data-in' type='checkbox'><label for='data-6b37fbb2-3e88-42dd-bc5d-b9c553930405' 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'><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> 475.07 kiB </td>\n",
       "                        <td> 13.97 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (34, 73, 49) </td>\n",
       "                        <td> (1, 73, 49) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 34 chunks in 1 graph layer </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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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B03</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</div><input id='attrs-eaee5838-5ab4-432f-81aa-71fac73b88f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eaee5838-5ab4-432f-81aa-71fac73b88f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-121b65a1-0790-438c-a1eb-4631ce13b5bc' class='xr-var-data-in' type='checkbox'><label for='data-121b65a1-0790-438c-a1eb-4631ce13b5bc' 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'><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> 475.07 kiB </td>\n",
       "                        <td> 13.97 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (34, 73, 49) </td>\n",
       "                        <td> (1, 73, 49) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 34 chunks in 1 graph layer </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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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B02</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</div><input id='attrs-132493b7-594e-4ba7-a7aa-a2147988b100' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-132493b7-594e-4ba7-a7aa-a2147988b100' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8728ab07-e0af-45ac-8c60-4369be6d53a0' class='xr-var-data-in' type='checkbox'><label for='data-8728ab07-e0af-45ac-8c60-4369be6d53a0' 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'><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> 475.07 kiB </td>\n",
       "                        <td> 13.97 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (34, 73, 49) </td>\n",
       "                        <td> (1, 73, 49) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 34 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "  <text x=\"16.438356\" y=\"156.438356\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,16.438356,156.438356)\">34</text>\n",
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       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0cef4d3-d22a-4d64-aea5-ba29e55a6317' class='xr-section-summary-in' type='checkbox'  ><label for='section-e0cef4d3-d22a-4d64-aea5-ba29e55a6317' class='xr-section-summary' >Indexes: <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-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-671d7f84-a624-45a8-acdd-0358c28970be' class='xr-index-data-in' type='checkbox'/><label for='index-671d7f84-a624-45a8-acdd-0358c28970be' 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([43.514283147682356,  43.51410348544736,  43.51392382321236,\n",
       "        43.51374416097736,  43.51356449874236,  43.51338483650736,\n",
       "       43.513205174272365, 43.513025512037366,  43.51284584980237,\n",
       "        43.51266618756737,  43.51248652533237,  43.51230686309738,\n",
       "        43.51212720086238,  43.51194753862738,  43.51176787639238,\n",
       "       43.511588214157385, 43.511408551922386,  43.51122888968739,\n",
       "        43.51104922745239,  43.51086956521739,  43.51068990298239,\n",
       "       43.510510240747394, 43.510330578512395,   43.5101509162774,\n",
       "         43.5099712540424,   43.5097915918074,   43.5096119295724,\n",
       "         43.5094322673374, 43.509252605102404, 43.509072942867405,\n",
       "        43.50889328063241,  43.50871361839741,  43.50853395616242,\n",
       "        43.50835429392742,  43.50817463169242,  43.50799496945742,\n",
       "        43.50781530722242, 43.507635644987424, 43.507455982752425,\n",
       "        43.50727632051743,  43.50709665828243,  43.50691699604743,\n",
       "        43.50673733381243,  43.50655767157743, 43.506378009342434,\n",
       "       43.506198347107436,  43.50601868487244,  43.50583902263744,\n",
       "        43.50565936040244,  43.50547969816744,  43.50530003593244,\n",
       "       43.505120373697444,  43.50494071146245, 43.504761049227454,\n",
       "       43.504581386992456,  43.50440172475746,  43.50422206252246,\n",
       "        43.50404240028746,  43.50386273805246,  43.50368307581746,\n",
       "       43.503503413582465, 43.503323751347466,  43.50314408911247,\n",
       "        43.50296442687747,  43.50278476464247,  43.50260510240747,\n",
       "        43.50242544017247, 43.502245777937475, 43.502066115702476,\n",
       "        43.50188645346748,  43.50170679123248,  43.50152712899748,\n",
       "        43.50134746676248],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4fa007ad-c24b-419b-a6ed-03dcae93bbc0' class='xr-index-data-in' type='checkbox'/><label for='index-4fa007ad-c24b-419b-a6ed-03dcae93bbc0' 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([1.2792849443047072, 1.2794646065397053, 1.2796442687747036,\n",
       "       1.2798239310097017,    1.2800035932447, 1.2801832554796981,\n",
       "       1.2803629177146965, 1.2805425799496946, 1.2807222421846929,\n",
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       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7e76aef-cf2e-428d-8374-835958fc555a' class='xr-index-data-in' type='checkbox'/><label for='index-f7e76aef-cf2e-428d-8374-835958fc555a' 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(DatetimeIndex([&#x27;2021-02-14 10:58:57&#x27;, &#x27;2021-02-19 10:58:56&#x27;,\n",
       "               &#x27;2021-02-24 10:58:58&#x27;, &#x27;2021-03-01 10:58:57&#x27;,\n",
       "               &#x27;2021-03-31 10:58:57&#x27;, &#x27;2021-04-05 10:58:54&#x27;,\n",
       "               &#x27;2021-04-15 10:58:52&#x27;, &#x27;2021-05-05 10:58:57&#x27;,\n",
       "               &#x27;2021-05-15 10:58:58&#x27;, &#x27;2021-07-19 10:59:00&#x27;,\n",
       "               &#x27;2021-08-23 10:59:00&#x27;, &#x27;2021-08-28 10:58:56&#x27;,\n",
       "               &#x27;2021-09-17 10:58:54&#x27;, &#x27;2021-09-22 10:59:01&#x27;,\n",
       "               &#x27;2021-12-16 10:58:52&#x27;, &#x27;2021-12-31 10:59:00&#x27;,\n",
       "               &#x27;2022-01-15 10:58:54&#x27;, &#x27;2022-01-25 10:58:52&#x27;,\n",
       "               &#x27;2022-02-09 10:58:57&#x27;, &#x27;2022-04-05 10:58:55&#x27;,\n",
       "               &#x27;2022-04-25 10:58:52&#x27;, &#x27;2022-05-25 10:59:04&#x27;,\n",
       "               &#x27;2022-06-29 10:59:13&#x27;, &#x27;2022-07-09 10:59:11&#x27;,\n",
       "               &#x27;2022-07-14 10:59:03&#x27;, &#x27;2022-07-24 10:59:02&#x27;,\n",
       "               &#x27;2022-08-03 10:59:03&#x27;, &#x27;2022-08-08 10:59:14&#x27;,\n",
       "               &#x27;2022-08-28 10:59:15&#x27;, &#x27;2022-09-22 10:58:59&#x27;,\n",
       "               &#x27;2022-11-11 10:58:58&#x27;, &#x27;2022-11-16 10:59:00&#x27;,\n",
       "               &#x27;2022-12-26 10:58:57&#x27;, &#x27;2023-01-20 10:58:56&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa144b23-0c67-4a61-99d8-ecffc0d5617a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fa144b23-0c67-4a61-99d8-ecffc0d5617a' 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>"
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    "### Resample Data\n",
    "\n",
    "The xarray.resample command is a powerful tool for aggregating data along the time dimension by grouping it into regular intervals and applying a reduction function to each group. This is particularly useful for temporal data analysis, such as converting daily data into monthly or yearly summaries.\n",
    "\n"
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      "/home/agriusr/miniconda3/envs/odc/lib/python3.12/site-packages/xarray/core/groupby.py:668: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
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       "    B04          (time, latitude, longitude) float32 343kB dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;\n",
       "    B03          (time, latitude, longitude) float32 343kB dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;\n",
       "    B02          (time, latitude, longitude) float32 343kB dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</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-10a06a9b-ca05-40ea-8926-829355271128' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-10a06a9b-ca05-40ea-8926-829355271128' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 73</li><li><span class='xr-has-index'>longitude</span>: 49</li><li><span class='xr-has-index'>time</span>: 24</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b206e868-ff2a-4b25-a00f-4571e8b67c4d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b206e868-ff2a-4b25-a00f-4571e8b67c4d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>43.51 43.51 43.51 ... 43.5 43.5</div><input id='attrs-891d2ef9-0458-4c4b-a035-820bfe08ac89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-891d2ef9-0458-4c4b-a035-820bfe08ac89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d80fccd3-c77f-4c6b-b66c-0c34ee22e442' class='xr-var-data-in' type='checkbox'><label for='data-d80fccd3-c77f-4c6b-b66c-0c34ee22e442' 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>units :</span></dt><dd>degrees_north</dd><dt><span>resolution :</span></dt><dd>-0.00017966223499820338</dd><dt><span>crs :</span></dt><dd>EPSG:4326</dd></dl></div><div class='xr-var-data'><pre>array([43.514283, 43.514103, 43.513924, 43.513744, 43.513564, 43.513385,\n",
       "       43.513205, 43.513026, 43.512846, 43.512666, 43.512487, 43.512307,\n",
       "       43.512127, 43.511948, 43.511768, 43.511588, 43.511409, 43.511229,\n",
       "       43.511049, 43.51087 , 43.51069 , 43.51051 , 43.510331, 43.510151,\n",
       "       43.509971, 43.509792, 43.509612, 43.509432, 43.509253, 43.509073,\n",
       "       43.508893, 43.508714, 43.508534, 43.508354, 43.508175, 43.507995,\n",
       "       43.507815, 43.507636, 43.507456, 43.507276, 43.507097, 43.506917,\n",
       "       43.506737, 43.506558, 43.506378, 43.506198, 43.506019, 43.505839,\n",
       "       43.505659, 43.50548 , 43.5053  , 43.50512 , 43.504941, 43.504761,\n",
       "       43.504581, 43.504402, 43.504222, 43.504042, 43.503863, 43.503683,\n",
       "       43.503503, 43.503324, 43.503144, 43.502964, 43.502785, 43.502605,\n",
       "       43.502425, 43.502246, 43.502066, 43.501886, 43.501707, 43.501527,\n",
       "       43.501347])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.279 1.279 1.28 ... 1.288 1.288</div><input id='attrs-0fb3910f-dd6b-4a54-b193-853fd9533212' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fb3910f-dd6b-4a54-b193-853fd9533212' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55b92688-2c38-478e-9440-b4b8d832db55' class='xr-var-data-in' type='checkbox'><label for='data-55b92688-2c38-478e-9440-b4b8d832db55' 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>units :</span></dt><dd>degrees_east</dd><dt><span>resolution :</span></dt><dd>0.00017966223499820338</dd><dt><span>crs :</span></dt><dd>EPSG:4326</dd></dl></div><div class='xr-var-data'><pre>array([1.279285, 1.279465, 1.279644, 1.279824, 1.280004, 1.280183, 1.280363,\n",
       "       1.280543, 1.280722, 1.280902, 1.281082, 1.281261, 1.281441, 1.281621,\n",
       "       1.2818  , 1.28198 , 1.28216 , 1.282339, 1.282519, 1.282699, 1.282878,\n",
       "       1.283058, 1.283238, 1.283417, 1.283597, 1.283777, 1.283956, 1.284136,\n",
       "       1.284315, 1.284495, 1.284675, 1.284854, 1.285034, 1.285214, 1.285393,\n",
       "       1.285573, 1.285753, 1.285932, 1.286112, 1.286292, 1.286471, 1.286651,\n",
       "       1.286831, 1.28701 , 1.28719 , 1.28737 , 1.287549, 1.287729, 1.287909])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-02-28 ... 2023-01-31</div><input id='attrs-d9a39bc7-f49f-4bc6-ba00-ca5e1caaadb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9a39bc7-f49f-4bc6-ba00-ca5e1caaadb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b27c9514-b441-4714-9c83-7152dad607c3' class='xr-var-data-in' type='checkbox'><label for='data-b27c9514-b441-4714-9c83-7152dad607c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-02-28T00:00:00.000000000&#x27;, &#x27;2021-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-04-30T00:00:00.000000000&#x27;, &#x27;2021-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-06-30T00:00:00.000000000&#x27;, &#x27;2021-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-31T00:00:00.000000000&#x27;, &#x27;2021-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-10-31T00:00:00.000000000&#x27;, &#x27;2021-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-31T00:00:00.000000000&#x27;, &#x27;2022-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-02-28T00:00:00.000000000&#x27;, &#x27;2022-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-30T00:00:00.000000000&#x27;, &#x27;2022-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-30T00:00:00.000000000&#x27;, &#x27;2022-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-31T00:00:00.000000000&#x27;, &#x27;2022-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-10-31T00:00:00.000000000&#x27;, &#x27;2022-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-12-31T00:00:00.000000000&#x27;, &#x27;2023-01-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-b917a5f0-17f8-41e2-a10b-df64782c776c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b917a5f0-17f8-41e2-a10b-df64782c776c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acb5ebb1-92af-4d48-a1ee-6290b78fd1d3' class='xr-var-data-in' type='checkbox'><label for='data-acb5ebb1-92af-4d48-a1ee-6290b78fd1d3' 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>spatial_ref :</span></dt><dd>GEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2296)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],CS[ellipsoidal,2],AXIS[&quot;geodetic latitude (Lat)&quot;,north,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],AXIS[&quot;geodetic longitude (Lon)&quot;,east,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],USAGE[SCOPE[&quot;Horizontal component of 3D system.&quot;],AREA[&quot;World.&quot;],BBOX[-90,-180,90,180]],ID[&quot;EPSG&quot;,4326]]</dd><dt><span>crs_wkt :</span></dt><dd>GEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2296)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],CS[ellipsoidal,2],AXIS[&quot;geodetic latitude (Lat)&quot;,north,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],AXIS[&quot;geodetic longitude (Lon)&quot;,east,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],USAGE[SCOPE[&quot;Horizontal component of 3D system.&quot;],AREA[&quot;World.&quot;],BBOX[-90,-180,90,180]],ID[&quot;EPSG&quot;,4326]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984 ensemble</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>GeoTransform :</span></dt><dd>1.279195113187208132643491 0.00017966223499820338028 0 43.514372978799855218312587 0 -0.00017966223499820338028</dd></dl></div><div class='xr-var-data'><pre>array(4326, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4857118d-80af-485b-bc63-a03fad884686' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4857118d-80af-485b-bc63-a03fad884686' class='xr-section-summary' >Data variables: <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>B04</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</div><input id='attrs-ab608f7a-f1f0-4149-a6cc-67065db22eb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab608f7a-f1f0-4149-a6cc-67065db22eb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44184a82-761e-4e4b-b35c-6c630fbcb706' class='xr-var-data-in' type='checkbox'><label for='data-44184a82-761e-4e4b-b35c-6c630fbcb706' 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'><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> 335.34 kiB </td>\n",
       "                        <td> 41.92 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (24, 73, 49) </td>\n",
       "                        <td> (3, 73, 49) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 19 chunks in 89 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B03</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</div><input id='attrs-a6489a0c-b2c6-41dd-952b-69cd4faa000b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6489a0c-b2c6-41dd-952b-69cd4faa000b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2853b8bc-5c4c-4d39-8b9f-acf4b32f468d' class='xr-var-data-in' type='checkbox'><label for='data-2853b8bc-5c4c-4d39-8b9f-acf4b32f468d' 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'><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> 335.34 kiB </td>\n",
       "                        <td> 41.92 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (24, 73, 49) </td>\n",
       "                        <td> (3, 73, 49) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 19 chunks in 89 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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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B02</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</div><input id='attrs-16d48ec5-c780-4d7e-b866-831b5d6215c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16d48ec5-c780-4d7e-b866-831b5d6215c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50823030-55cc-429d-8d33-2a4ebab11f7b' class='xr-var-data-in' type='checkbox'><label for='data-50823030-55cc-429d-8d33-2a4ebab11f7b' 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'><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> 335.34 kiB </td>\n",
       "                        <td> 41.92 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (24, 73, 49) </td>\n",
       "                        <td> (3, 73, 49) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 19 chunks in 89 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b1c4efdb-4c38-4021-a3cd-6edbb1d9177e' class='xr-section-summary-in' type='checkbox'  ><label for='section-b1c4efdb-4c38-4021-a3cd-6edbb1d9177e' class='xr-section-summary' >Indexes: <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-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9bd4e14e-207d-40da-8037-77a014f76880' class='xr-index-data-in' type='checkbox'/><label for='index-9bd4e14e-207d-40da-8037-77a014f76880' 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([43.514283147682356,  43.51410348544736,  43.51392382321236,\n",
       "        43.51374416097736,  43.51356449874236,  43.51338483650736,\n",
       "       43.513205174272365, 43.513025512037366,  43.51284584980237,\n",
       "        43.51266618756737,  43.51248652533237,  43.51230686309738,\n",
       "        43.51212720086238,  43.51194753862738,  43.51176787639238,\n",
       "       43.511588214157385, 43.511408551922386,  43.51122888968739,\n",
       "        43.51104922745239,  43.51086956521739,  43.51068990298239,\n",
       "       43.510510240747394, 43.510330578512395,   43.5101509162774,\n",
       "         43.5099712540424,   43.5097915918074,   43.5096119295724,\n",
       "         43.5094322673374, 43.509252605102404, 43.509072942867405,\n",
       "        43.50889328063241,  43.50871361839741,  43.50853395616242,\n",
       "        43.50835429392742,  43.50817463169242,  43.50799496945742,\n",
       "        43.50781530722242, 43.507635644987424, 43.507455982752425,\n",
       "        43.50727632051743,  43.50709665828243,  43.50691699604743,\n",
       "        43.50673733381243,  43.50655767157743, 43.506378009342434,\n",
       "       43.506198347107436,  43.50601868487244,  43.50583902263744,\n",
       "        43.50565936040244,  43.50547969816744,  43.50530003593244,\n",
       "       43.505120373697444,  43.50494071146245, 43.504761049227454,\n",
       "       43.504581386992456,  43.50440172475746,  43.50422206252246,\n",
       "        43.50404240028746,  43.50386273805246,  43.50368307581746,\n",
       "       43.503503413582465, 43.503323751347466,  43.50314408911247,\n",
       "        43.50296442687747,  43.50278476464247,  43.50260510240747,\n",
       "        43.50242544017247, 43.502245777937475, 43.502066115702476,\n",
       "        43.50188645346748,  43.50170679123248,  43.50152712899748,\n",
       "        43.50134746676248],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2d638df0-3cab-4034-8d4b-7b30ef29c291' class='xr-index-data-in' type='checkbox'/><label for='index-2d638df0-3cab-4034-8d4b-7b30ef29c291' 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([1.2792849443047072, 1.2794646065397053, 1.2796442687747036,\n",
       "       1.2798239310097017,    1.2800035932447, 1.2801832554796981,\n",
       "       1.2803629177146965, 1.2805425799496946, 1.2807222421846929,\n",
       "        1.280901904419691, 1.2810815666546893, 1.2812612288896874,\n",
       "       1.2814408911246857, 1.2816205533596838,  1.281800215594682,\n",
       "       1.2819798778296803, 1.2821595400646784, 1.2823392022996767,\n",
       "       1.2825188645346748,  1.282698526769673, 1.2828781890046712,\n",
       "       1.2830578512396695, 1.2832375134746676,  1.283417175709666,\n",
       "        1.283596837944664, 1.2837765001796622, 1.2839561624146605,\n",
       "       1.2841358246496586,  1.284315486884657,  1.284495149119655,\n",
       "       1.2846748113546533, 1.2848544735896514, 1.2850341358246498,\n",
       "       1.2852137980596479, 1.2853934602946462, 1.2855731225296443,\n",
       "       1.2857527847646426, 1.2859324469996407, 1.2861121092346388,\n",
       "       1.2862917714696371, 1.2864714337046352, 1.2866510959396336,\n",
       "       1.2868307581746317,   1.28701042040963,  1.287190082644628,\n",
       "       1.2873697448796264, 1.2875494071146245, 1.2877290693496228,\n",
       "        1.287908731584621],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-79cfb5f1-5c9a-47da-aed3-6b06644b2a51' class='xr-index-data-in' type='checkbox'/><label for='index-79cfb5f1-5c9a-47da-aed3-6b06644b2a51' 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(DatetimeIndex([&#x27;2021-02-28&#x27;, &#x27;2021-03-31&#x27;, &#x27;2021-04-30&#x27;, &#x27;2021-05-31&#x27;,\n",
       "               &#x27;2021-06-30&#x27;, &#x27;2021-07-31&#x27;, &#x27;2021-08-31&#x27;, &#x27;2021-09-30&#x27;,\n",
       "               &#x27;2021-10-31&#x27;, &#x27;2021-11-30&#x27;, &#x27;2021-12-31&#x27;, &#x27;2022-01-31&#x27;,\n",
       "               &#x27;2022-02-28&#x27;, &#x27;2022-03-31&#x27;, &#x27;2022-04-30&#x27;, &#x27;2022-05-31&#x27;,\n",
       "               &#x27;2022-06-30&#x27;, &#x27;2022-07-31&#x27;, &#x27;2022-08-31&#x27;, &#x27;2022-09-30&#x27;,\n",
       "               &#x27;2022-10-31&#x27;, &#x27;2022-11-30&#x27;, &#x27;2022-12-31&#x27;, &#x27;2023-01-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;ME&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-43053b71-a608-4218-8541-cf5e8dc7edaf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-43053b71-a608-4218-8541-cf5e8dc7edaf' 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>"
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       "<xarray.Dataset> Size: 1MB\n",
       "Dimensions:      (latitude: 73, longitude: 49, time: 24)\n",
       "Coordinates:\n",
       "  * latitude     (latitude) float64 584B 43.51 43.51 43.51 ... 43.5 43.5 43.5\n",
       "  * longitude    (longitude) float64 392B 1.279 1.279 1.28 ... 1.288 1.288 1.288\n",
       "  * time         (time) datetime64[ns] 192B 2021-02-28 2021-03-31 ... 2023-01-31\n",
       "    spatial_ref  int32 4B 4326\n",
       "Data variables:\n",
       "    B04          (time, latitude, longitude) float32 343kB dask.array<chunksize=(1, 73, 49), meta=np.ndarray>\n",
       "    B03          (time, latitude, longitude) float32 343kB dask.array<chunksize=(1, 73, 49), meta=np.ndarray>\n",
       "    B02          (time, latitude, longitude) float32 343kB dask.array<chunksize=(1, 73, 49), meta=np.ndarray>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_mean = data.resample(time='M').mean()\n",
    "monthly_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4561a5e",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "array(['2021-02-28T00:00:00.000000000', '2021-03-31T00:00:00.000000000',\n",
       "       '2021-04-30T00:00:00.000000000', '2021-05-31T00:00:00.000000000',\n",
       "       '2021-06-30T00:00:00.000000000', '2021-07-31T00:00:00.000000000',\n",
       "       '2021-08-31T00:00:00.000000000', '2021-09-30T00:00:00.000000000',\n",
       "       '2021-10-31T00:00:00.000000000', '2021-11-30T00:00:00.000000000',\n",
       "       '2021-12-31T00:00:00.000000000', '2022-01-31T00:00:00.000000000',\n",
       "       '2022-02-28T00:00:00.000000000', '2022-03-31T00:00:00.000000000',\n",
       "       '2022-04-30T00:00:00.000000000', '2022-05-31T00:00:00.000000000',\n",
       "       '2022-06-30T00:00:00.000000000', '2022-07-31T00:00:00.000000000',\n",
       "       '2022-08-31T00:00:00.000000000', '2022-09-30T00:00:00.000000000',\n",
       "       '2022-10-31T00:00:00.000000000', '2022-11-30T00:00:00.000000000',\n",
       "       '2022-12-31T00:00:00.000000000', '2023-01-31T00:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_mean.time.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980cba13",
   "metadata": {},
   "source": [
    "### Group By"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "919f2e0e",
   "metadata": {},
   "source": [
    "Similar to resample, grouping works by looking at part of the date, but ignoring other parts. For instance, 'time.month' would group together all January data together, no matter what year it is from.\n",
    "\n",
    "Some examples are:\n",
    "\n",
    "- 'time.day' - groups by the day of the month (1-31)\n",
    "- 'time.dayofyear' - groups by the day of the year (1-365)\n",
    "- 'time.week' - groups by week (1-52)\n",
    "- 'time.month' - groups by the month (1-12)\n",
    "- 'time.season' - groups into 3-month seasons:\n",
    "    - 'DJF' December, Jaunary, February\n",
    "    - 'MAM' March, April, May\n",
    "    - 'JJA' June, July, August\n",
    "    - 'SON' September, October, November\n",
    "- 'time.year' - groups by the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea811182",
   "metadata": {},
   "outputs": [
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       "    B04          (month, latitude, longitude) float32 157kB dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;\n",
       "    B03          (month, latitude, longitude) float32 157kB dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;\n",
       "    B02          (month, latitude, longitude) float32 157kB dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</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-aa9a2296-3e63-43a9-a9da-99782aee3318' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aa9a2296-3e63-43a9-a9da-99782aee3318' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 11</li><li><span class='xr-has-index'>latitude</span>: 73</li><li><span class='xr-has-index'>longitude</span>: 49</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a276eb1b-89fe-4218-9ad4-8a3a87675fee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a276eb1b-89fe-4218-9ad4-8a3a87675fee' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>43.51 43.51 43.51 ... 43.5 43.5</div><input id='attrs-efa35acf-3dd9-44a0-9ba8-b554274c7b76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efa35acf-3dd9-44a0-9ba8-b554274c7b76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d9cce08-b145-47f3-8b82-43d8acf7eb60' class='xr-var-data-in' type='checkbox'><label for='data-1d9cce08-b145-47f3-8b82-43d8acf7eb60' 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>units :</span></dt><dd>degrees_north</dd><dt><span>resolution :</span></dt><dd>-0.00017966223499820338</dd><dt><span>crs :</span></dt><dd>EPSG:4326</dd></dl></div><div class='xr-var-data'><pre>array([43.514283, 43.514103, 43.513924, 43.513744, 43.513564, 43.513385,\n",
       "       43.513205, 43.513026, 43.512846, 43.512666, 43.512487, 43.512307,\n",
       "       43.512127, 43.511948, 43.511768, 43.511588, 43.511409, 43.511229,\n",
       "       43.511049, 43.51087 , 43.51069 , 43.51051 , 43.510331, 43.510151,\n",
       "       43.509971, 43.509792, 43.509612, 43.509432, 43.509253, 43.509073,\n",
       "       43.508893, 43.508714, 43.508534, 43.508354, 43.508175, 43.507995,\n",
       "       43.507815, 43.507636, 43.507456, 43.507276, 43.507097, 43.506917,\n",
       "       43.506737, 43.506558, 43.506378, 43.506198, 43.506019, 43.505839,\n",
       "       43.505659, 43.50548 , 43.5053  , 43.50512 , 43.504941, 43.504761,\n",
       "       43.504581, 43.504402, 43.504222, 43.504042, 43.503863, 43.503683,\n",
       "       43.503503, 43.503324, 43.503144, 43.502964, 43.502785, 43.502605,\n",
       "       43.502425, 43.502246, 43.502066, 43.501886, 43.501707, 43.501527,\n",
       "       43.501347])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.279 1.279 1.28 ... 1.288 1.288</div><input id='attrs-392d3a36-2b2c-481b-86fd-6c58fa2ba03c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-392d3a36-2b2c-481b-86fd-6c58fa2ba03c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-079847ab-a5ed-4a36-9ae2-d37d14732606' class='xr-var-data-in' type='checkbox'><label for='data-079847ab-a5ed-4a36-9ae2-d37d14732606' 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>units :</span></dt><dd>degrees_east</dd><dt><span>resolution :</span></dt><dd>0.00017966223499820338</dd><dt><span>crs :</span></dt><dd>EPSG:4326</dd></dl></div><div class='xr-var-data'><pre>array([1.279285, 1.279465, 1.279644, 1.279824, 1.280004, 1.280183, 1.280363,\n",
       "       1.280543, 1.280722, 1.280902, 1.281082, 1.281261, 1.281441, 1.281621,\n",
       "       1.2818  , 1.28198 , 1.28216 , 1.282339, 1.282519, 1.282699, 1.282878,\n",
       "       1.283058, 1.283238, 1.283417, 1.283597, 1.283777, 1.283956, 1.284136,\n",
       "       1.284315, 1.284495, 1.284675, 1.284854, 1.285034, 1.285214, 1.285393,\n",
       "       1.285573, 1.285753, 1.285932, 1.286112, 1.286292, 1.286471, 1.286651,\n",
       "       1.286831, 1.28701 , 1.28719 , 1.28737 , 1.287549, 1.287729, 1.287909])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-65557835-4b7e-4a47-ba3e-50dda1691887' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65557835-4b7e-4a47-ba3e-50dda1691887' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3355e849-84a3-47dc-9f49-5c1e63e19cac' class='xr-var-data-in' type='checkbox'><label for='data-3355e849-84a3-47dc-9f49-5c1e63e19cac' 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>spatial_ref :</span></dt><dd>GEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2296)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],CS[ellipsoidal,2],AXIS[&quot;geodetic latitude (Lat)&quot;,north,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],AXIS[&quot;geodetic longitude (Lon)&quot;,east,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],USAGE[SCOPE[&quot;Horizontal component of 3D system.&quot;],AREA[&quot;World.&quot;],BBOX[-90,-180,90,180]],ID[&quot;EPSG&quot;,4326]]</dd><dt><span>crs_wkt :</span></dt><dd>GEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2296)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],CS[ellipsoidal,2],AXIS[&quot;geodetic latitude (Lat)&quot;,north,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],AXIS[&quot;geodetic longitude (Lon)&quot;,east,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],USAGE[SCOPE[&quot;Horizontal component of 3D system.&quot;],AREA[&quot;World.&quot;],BBOX[-90,-180,90,180]],ID[&quot;EPSG&quot;,4326]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984 ensemble</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>GeoTransform :</span></dt><dd>1.279195113187208132643491 0.00017966223499820338028 0 43.514372978799855218312587 0 -0.00017966223499820338028</dd></dl></div><div class='xr-var-data'><pre>array(4326, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 11 12</div><input id='attrs-279b4968-c990-4523-8348-495cf36c7ccc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-279b4968-c990-4523-8348-495cf36c7ccc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-185821ef-5db9-473c-acd8-7a8363c519c3' class='xr-var-data-in' type='checkbox'><label for='data-185821ef-5db9-473c-acd8-7a8363c519c3' 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,  3,  4,  5,  6,  7,  8,  9, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6aee8ff-e1ef-42cc-aebb-6de85c8b69c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6aee8ff-e1ef-42cc-aebb-6de85c8b69c2' class='xr-section-summary' >Data variables: <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>B04</span></div><div class='xr-var-dims'>(month, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</div><input id='attrs-89d7cc42-c4dd-4750-b0fa-9a4b05e92798' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89d7cc42-c4dd-4750-b0fa-9a4b05e92798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cf54e59-1bc1-4970-8aa3-d292b2b111fb' class='xr-var-data-in' type='checkbox'><label for='data-3cf54e59-1bc1-4970-8aa3-d292b2b111fb' 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'><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> 153.70 kiB </td>\n",
       "                        <td> 13.97 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (11, 73, 49) </td>\n",
       "                        <td> (1, 73, 49) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 11 chunks in 47 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=\"164\" height=\"193\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <text x=\"134.505795\" y=\"83.957850\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,134.505795,83.957850)\">73</text>\n",
       "  <text x=\"11.978925\" y=\"151.978925\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.978925,151.978925)\">11</text>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B03</span></div><div class='xr-var-dims'>(month, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</div><input id='attrs-c18c3a8d-a75c-4bbe-a1c9-25a587b4416e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c18c3a8d-a75c-4bbe-a1c9-25a587b4416e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e496343-5d5f-4d4b-8c94-e293a28cbd54' class='xr-var-data-in' type='checkbox'><label for='data-2e496343-5d5f-4d4b-8c94-e293a28cbd54' 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'><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> 153.70 kiB </td>\n",
       "                        <td> 13.97 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (11, 73, 49) </td>\n",
       "                        <td> (1, 73, 49) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 11 chunks in 47 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B02</span></div><div class='xr-var-dims'>(month, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 73, 49), meta=np.ndarray&gt;</div><input id='attrs-60327204-fc6b-45c6-ae9b-b3061c0328d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60327204-fc6b-45c6-ae9b-b3061c0328d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bae02927-c457-4555-a207-f3e35f57ed6c' class='xr-var-data-in' type='checkbox'><label for='data-bae02927-c457-4555-a207-f3e35f57ed6c' 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'><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> 153.70 kiB </td>\n",
       "                        <td> 13.97 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (11, 73, 49) </td>\n",
       "                        <td> (1, 73, 49) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 11 chunks in 47 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc60aa15-2128-4a67-8e32-8b2eb923dc3a' class='xr-section-summary-in' type='checkbox'  ><label for='section-bc60aa15-2128-4a67-8e32-8b2eb923dc3a' class='xr-section-summary' >Indexes: <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-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89213cf9-241e-416f-a7f1-b8e9e22eef2f' class='xr-index-data-in' type='checkbox'/><label for='index-89213cf9-241e-416f-a7f1-b8e9e22eef2f' 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([43.514283147682356,  43.51410348544736,  43.51392382321236,\n",
       "        43.51374416097736,  43.51356449874236,  43.51338483650736,\n",
       "       43.513205174272365, 43.513025512037366,  43.51284584980237,\n",
       "        43.51266618756737,  43.51248652533237,  43.51230686309738,\n",
       "        43.51212720086238,  43.51194753862738,  43.51176787639238,\n",
       "       43.511588214157385, 43.511408551922386,  43.51122888968739,\n",
       "        43.51104922745239,  43.51086956521739,  43.51068990298239,\n",
       "       43.510510240747394, 43.510330578512395,   43.5101509162774,\n",
       "         43.5099712540424,   43.5097915918074,   43.5096119295724,\n",
       "         43.5094322673374, 43.509252605102404, 43.509072942867405,\n",
       "        43.50889328063241,  43.50871361839741,  43.50853395616242,\n",
       "        43.50835429392742,  43.50817463169242,  43.50799496945742,\n",
       "        43.50781530722242, 43.507635644987424, 43.507455982752425,\n",
       "        43.50727632051743,  43.50709665828243,  43.50691699604743,\n",
       "        43.50673733381243,  43.50655767157743, 43.506378009342434,\n",
       "       43.506198347107436,  43.50601868487244,  43.50583902263744,\n",
       "        43.50565936040244,  43.50547969816744,  43.50530003593244,\n",
       "       43.505120373697444,  43.50494071146245, 43.504761049227454,\n",
       "       43.504581386992456,  43.50440172475746,  43.50422206252246,\n",
       "        43.50404240028746,  43.50386273805246,  43.50368307581746,\n",
       "       43.503503413582465, 43.503323751347466,  43.50314408911247,\n",
       "        43.50296442687747,  43.50278476464247,  43.50260510240747,\n",
       "        43.50242544017247, 43.502245777937475, 43.502066115702476,\n",
       "        43.50188645346748,  43.50170679123248,  43.50152712899748,\n",
       "        43.50134746676248],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-22070f80-ea97-4cab-affa-7a5c67cce4a2' class='xr-index-data-in' type='checkbox'/><label for='index-22070f80-ea97-4cab-affa-7a5c67cce4a2' 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([1.2792849443047072, 1.2794646065397053, 1.2796442687747036,\n",
       "       1.2798239310097017,    1.2800035932447, 1.2801832554796981,\n",
       "       1.2803629177146965, 1.2805425799496946, 1.2807222421846929,\n",
       "        1.280901904419691, 1.2810815666546893, 1.2812612288896874,\n",
       "       1.2814408911246857, 1.2816205533596838,  1.281800215594682,\n",
       "       1.2819798778296803, 1.2821595400646784, 1.2823392022996767,\n",
       "       1.2825188645346748,  1.282698526769673, 1.2828781890046712,\n",
       "       1.2830578512396695, 1.2832375134746676,  1.283417175709666,\n",
       "        1.283596837944664, 1.2837765001796622, 1.2839561624146605,\n",
       "       1.2841358246496586,  1.284315486884657,  1.284495149119655,\n",
       "       1.2846748113546533, 1.2848544735896514, 1.2850341358246498,\n",
       "       1.2852137980596479, 1.2853934602946462, 1.2855731225296443,\n",
       "       1.2857527847646426, 1.2859324469996407, 1.2861121092346388,\n",
       "       1.2862917714696371, 1.2864714337046352, 1.2866510959396336,\n",
       "       1.2868307581746317,   1.28701042040963,  1.287190082644628,\n",
       "       1.2873697448796264, 1.2875494071146245, 1.2877290693496228,\n",
       "        1.287908731584621],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bfa163d1-db48-49f9-b38d-28ab76ac23f4' class='xr-index-data-in' type='checkbox'/><label for='index-bfa163d1-db48-49f9-b38d-28ab76ac23f4' 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, 3, 4, 5, 6, 7, 8, 9, 11, 12], dtype=&#x27;int64&#x27;, name=&#x27;month&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-633cf944-9b0e-412e-b560-eec5ed0bf0fe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-633cf944-9b0e-412e-b560-eec5ed0bf0fe' 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> Size: 473kB\n",
       "Dimensions:      (month: 11, latitude: 73, longitude: 49)\n",
       "Coordinates:\n",
       "  * latitude     (latitude) float64 584B 43.51 43.51 43.51 ... 43.5 43.5 43.5\n",
       "  * longitude    (longitude) float64 392B 1.279 1.279 1.28 ... 1.288 1.288 1.288\n",
       "    spatial_ref  int32 4B 4326\n",
       "  * month        (month) int64 88B 1 2 3 4 5 6 7 8 9 11 12\n",
       "Data variables:\n",
       "    B04          (month, latitude, longitude) float32 157kB dask.array<chunksize=(1, 73, 49), meta=np.ndarray>\n",
       "    B03          (month, latitude, longitude) float32 157kB dask.array<chunksize=(1, 73, 49), meta=np.ndarray>\n",
       "    B02          (month, latitude, longitude) float32 157kB dask.array<chunksize=(1, 73, 49), meta=np.ndarray>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_mean_all_years = data.groupby('time.month').mean()\n",
    "monthly_mean_all_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e1620340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 11, 12])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_mean_all_years.month.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b97890a4",
   "metadata": {},
   "source": [
    "### Generate a median by binning data into six-monthly time-spans\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3bbf6778",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_groupby_season = data.groupby('time.season').median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cbfe72d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/agriusr/miniconda3/envs/odc/lib/python3.12/site-packages/rasterio/warp.py:344: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  _reproject(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[355.69666, 333.51883, 348.56732, ..., 685.38007, 689.4693 ,\n",
       "         681.23346],\n",
       "        [356.664  , 390.07428, 438.8332 , ..., 723.5681 , 725.36   ,\n",
       "         683.36536],\n",
       "        [411.7394 , 413.94547, 431.42297, ..., 755.0623 , 773.1621 ,\n",
       "         742.8051 ],\n",
       "        ...,\n",
       "        [375.36444, 283.29102, 279.3695 , ..., 389.8036 , 418.7879 ,\n",
       "         541.82983],\n",
       "        [287.14252, 227.20892, 263.29614, ..., 466.42426, 489.18585,\n",
       "         645.2578 ],\n",
       "        [299.3303 , 308.95462, 312.62604, ..., 484.15744, 500.19928,\n",
       "         509.7285 ]],\n",
       "\n",
       "       [[742.67535, 681.3662 , 487.95105, ..., 703.5788 , 725.93823,\n",
       "         757.3825 ],\n",
       "        [718.8767 , 703.79504, 642.2698 , ..., 742.4879 , 743.5075 ,\n",
       "         755.28815],\n",
       "        [712.85767, 720.0801 , 680.46826, ..., 667.5729 , 679.78064,\n",
       "         740.01917],\n",
       "        ...,\n",
       "        [514.79675, 430.8197 , 369.89014, ..., 412.39526, 409.00476,\n",
       "         537.07916],\n",
       "        [385.04205, 388.78595, 349.88123, ..., 632.1194 , 589.7724 ,\n",
       "         592.58057],\n",
       "        [434.24692, 454.0072 , 402.49664, ..., 697.5773 , 685.9939 ,\n",
       "         643.75574]],\n",
       "\n",
       "       [[365.9543 , 499.45026, 642.49115, ..., 380.25122, 375.55295,\n",
       "         382.41116],\n",
       "        [388.18042, 656.44977, 933.6429 , ..., 392.92987, 385.47473,\n",
       "         396.76318],\n",
       "        [433.90686, 754.5218 , 861.9297 , ..., 367.0896 , 380.13605,\n",
       "         393.0351 ],\n",
       "        ...,\n",
       "        [455.81824, 406.3106 , 336.68378, ..., 316.4497 , 345.00504,\n",
       "         453.97757],\n",
       "        [359.7812 , 300.60767, 338.28152, ..., 383.72873, 435.01218,\n",
       "         545.68896],\n",
       "        [431.28668, 457.4635 , 436.89853, ..., 412.75925, 440.26755,\n",
       "         447.2657 ]],\n",
       "\n",
       "       [[365.64737, 339.35648, 344.7363 , ..., 410.68958, 398.96548,\n",
       "         423.13437],\n",
       "        [339.3574 , 510.9704 , 565.73914, ..., 421.41632, 438.51797,\n",
       "         464.7918 ],\n",
       "        [356.04285, 504.84155, 539.89294, ..., 387.944  , 410.98654,\n",
       "         459.67435],\n",
       "        ...,\n",
       "        [379.1729 , 257.77667, 236.66492, ..., 261.20242, 270.4375 ,\n",
       "         367.839  ],\n",
       "        [209.74838, 216.33948, 224.06099, ..., 320.0244 , 285.3671 ,\n",
       "         393.4396 ],\n",
       "        [352.37698, 351.50336, 314.2097 , ..., 386.2046 , 371.39462,\n",
       "         364.30655]]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_groupby_season.B02.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8f80da66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1710.96x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "rgb(ds_groupby_season,col=\"season\", bands=[\"B04\", \"B03\", \"B02\"])"
   ]
  },
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   "execution_count": null,
   "id": "3992bdba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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