diff --git a/docs/_toc.yml b/docs/_toc.yml
index 3efde76..c262b56 100644
--- a/docs/_toc.yml
+++ b/docs/_toc.yml
@@ -12,7 +12,9 @@ parts:
       - file: notebooks/conflict/README
         sections:
           - file: notebooks/conflict/conflictAnalysis.ipynb
-      - file: notebooks/agriculture/report.ipynb 
+      - file: notebooks/agriculture/report.ipynb
+        sections:
+          - file: notebooks/agriculture/drought.ipynb
       - url: https://raw.githack.com/datapartnership/niger-economic-monitoring/main/notebooks/nighttime-lights/ntl_analysis.html
         title: Nighttime Lights
       - file: notebooks/movement/README
diff --git a/notebooks/agriculture/charts/Niger_2024-04-01_ML1.png b/notebooks/agriculture/charts/Niger_2024-04-01_ML1.png
new file mode 100644
index 0000000..c51e7b4
Binary files /dev/null and b/notebooks/agriculture/charts/Niger_2024-04-01_ML1.png differ
diff --git a/notebooks/agriculture/crop-area-zs.ipynb b/notebooks/agriculture/crop-area-zs.ipynb
index 34bce47..f1e769a 100644
--- a/notebooks/agriculture/crop-area-zs.ipynb
+++ b/notebooks/agriculture/crop-area-zs.ipynb
@@ -539,11714 +539,6 @@
     "histogram_df = pd.concat(histograms_dfs, axis=0).fillna(0)\n",
     "histogram_df.rename(columns=bool_dict, inplace=True)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scale_factor=0.0001\n",
-    "# apply cropland mask to imageCollection\n",
-    "# def cropmask(img):\n",
-    "#   return img.clip(aoi) #.multiply(scale_factor) # unmask(-1)\n",
-    "def cropmask(img):\n",
-    "  return img.updateMask(crop_data).clip(aoi) #.multiply(scale_factor) # unmask(-1)\n",
-    "mxd13q1 = mxd13q1_cleaned_sorted.map(cropmask)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "### Map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "crop_data_raw = lgrip30.select('b1').rename('crop').clip(aoi)\n",
-    "crop_data_viz = crop_data_raw.mask(crop_data_raw.gt(1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_2198204/3884362397.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# one = adm1_ner_sel.iloc[[0]]\n",
-    "one = adm0_ner.iloc[[0]]\n",
-    "centx, centy = one.centroid.x.iloc[0], one.centroid.y.iloc[0]\n",
-    "\n",
-    "viVis2 = {\n",
-    "  'min': 0.2,\n",
-    "  'max': 0.6,\n",
-    "  'palette': [\n",
-    "    'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',\n",
-    "    '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',\n",
-    "    '012E01', '011D01', '011301'\n",
-    "  ],\n",
-    "}\n",
-    "pal = colormaps.get_palette('Greens', 10) # Greens BrBG\n",
-    "crop_vis = {\n",
-    "    'min': 2,\n",
-    "    'max': 3,\n",
-    "    'palette': ['#cc0000', '#529400'],\n",
-    "    'opacity': 0.75\n",
-    "}\n",
-    "ndvi_vis = {\n",
-    "    'min': 0.2,\n",
-    "    'max': 0.6,\n",
-    "    'palette': pal\n",
-    "}\n",
-    "adm_vis = {\n",
-    "    'color': '000000',\n",
-    "    'width': 1,\n",
-    "    'opacity': 0.5\n",
-    "}\n",
-    "\n",
-    "img = mxd13q1.filterDate('2023-08-01', '2023-09-01').select('EVI').first().multiply(scale_factor)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from geemap.foliumap import Map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
-       "&lt;html&gt;\n",
-       "&lt;head&gt;\n",
-       "    \n",
-       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
-       "    \n",
-       "        &lt;script&gt;\n",
-       "            L_NO_TOUCH = false;\n",
-       "            L_DISABLE_3D = false;\n",
-       "        &lt;/script&gt;\n",
-       "    \n",
-       "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
-       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
-       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
-       "    &lt;script src=&quot;https://code.jquery.com/jquery-3.7.1.min.js&quot;&gt;&lt;/script&gt;\n",
-       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
-       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
-       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
-       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
-       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
-       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
-       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
-       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
-       "    \n",
-       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
-       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
-       "            &lt;style&gt;\n",
-       "                #map_87ed547fec9cad61cf8100e912ee37b9 {\n",
-       "                    position: relative;\n",
-       "                    width: 100.0%;\n",
-       "                    height: 100.0%;\n",
-       "                    left: 0.0%;\n",
-       "                    top: 0.0%;\n",
-       "                }\n",
-       "                .leaflet-container { font-size: 1rem; }\n",
-       "            &lt;/style&gt;\n",
-       "        \n",
-       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet.fullscreen@3.0.0/Control.FullScreen.min.js&quot;&gt;&lt;/script&gt;\n",
-       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet.fullscreen@3.0.0/Control.FullScreen.css&quot;/&gt;\n",
-       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.draw/1.0.2/leaflet.draw.js&quot;&gt;&lt;/script&gt;\n",
-       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.draw/1.0.2/leaflet.draw.css&quot;/&gt;\n",
-       "    &lt;script src=&quot;https://unpkg.com/leaflet-control-geocoder/dist/Control.Geocoder.js&quot;&gt;&lt;/script&gt;\n",
-       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://unpkg.com/leaflet-control-geocoder/dist/Control.Geocoder.css&quot;/&gt;\n",
-       "&lt;/head&gt;\n",
-       "&lt;body&gt;\n",
-       "    \n",
-       "    \n",
-       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_87ed547fec9cad61cf8100e912ee37b9&quot; &gt;&lt;/div&gt;\n",
-       "        \n",
-       "&lt;/body&gt;\n",
-       "&lt;script&gt;\n",
-       "    \n",
-       "    \n",
-       "            var map_87ed547fec9cad61cf8100e912ee37b9 = L.map(\n",
-       "                &quot;map_87ed547fec9cad61cf8100e912ee37b9&quot;,\n",
-       "                {\n",
-       "                    center: [17.420444446407295, 9.400408867794077],\n",
-       "                    crs: L.CRS.EPSG3857,\n",
-       "                    zoom: 8,\n",
-       "                    zoomControl: true,\n",
-       "                    preferCanvas: false,\n",
-       "                    eeInitialize: true,\n",
-       "                    addGoogleMap: false,\n",
-       "                    pluginLatlngpopup: false,\n",
-       "                    pluginFullscreen: true,\n",
-       "                    pluginDraw: true,\n",
-       "                    DrawExport: false,\n",
-       "                    pluginMinimap: false,\n",
-       "                    locateControl: false,\n",
-       "                    searchControl: true,\n",
-       "                    layersControl: true,\n",
-       "                }\n",
-       "            );\n",
-       "\n",
-       "            \n",
-       "\n",
-       "        \n",
-       "    \n",
-       "            var tile_layer_9f859830d7b451a75be66d0fdd4a488c = L.tileLayer(\n",
-       "                &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
-       "                {&quot;attribution&quot;: &quot;\\u0026copy; \\u003ca href=\\&quot;https://www.openstreetmap.org/copyright\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
-       "            );\n",
-       "        \n",
-       "    \n",
-       "            tile_layer_9f859830d7b451a75be66d0fdd4a488c.addTo(map_87ed547fec9cad61cf8100e912ee37b9);\n",
-       "        \n",
-       "    \n",
-       "            L.control.fullscreen(\n",
-       "                {&quot;forceSeparateButton&quot;: false, &quot;position&quot;: &quot;topleft&quot;, &quot;title&quot;: &quot;Full Screen&quot;, &quot;titleCancel&quot;: &quot;Exit Full Screen&quot;}\n",
-       "            ).addTo(map_87ed547fec9cad61cf8100e912ee37b9);\n",
-       "        \n",
-       "    \n",
-       "            var options = {\n",
-       "              position: &quot;topleft&quot;,\n",
-       "              draw: {},\n",
-       "              edit: {},\n",
-       "            }\n",
-       "            // FeatureGroup is to store editable layers.\n",
-       "            var drawnItems_draw_control_4de8e96a620336bc46a600456d42bd0c = new L.featureGroup().addTo(\n",
-       "                map_87ed547fec9cad61cf8100e912ee37b9\n",
-       "            );\n",
-       "            options.edit.featureGroup = drawnItems_draw_control_4de8e96a620336bc46a600456d42bd0c;\n",
-       "            var draw_control_4de8e96a620336bc46a600456d42bd0c = new L.Control.Draw(\n",
-       "                options\n",
-       "            ).addTo( map_87ed547fec9cad61cf8100e912ee37b9 );\n",
-       "            map_87ed547fec9cad61cf8100e912ee37b9.on(L.Draw.Event.CREATED, function(e) {\n",
-       "                var layer = e.layer,\n",
-       "                    type = e.layerType;\n",
-       "                var coords = JSON.stringify(layer.toGeoJSON());\n",
-       "                layer.on(&#x27;click&#x27;, function() {\n",
-       "                    alert(coords);\n",
-       "                    console.log(coords);\n",
-       "                });\n",
-       "                drawnItems_draw_control_4de8e96a620336bc46a600456d42bd0c.addLayer(layer);\n",
-       "             });\n",
-       "            map_87ed547fec9cad61cf8100e912ee37b9.on(&#x27;draw:created&#x27;, function(e) {\n",
-       "                drawnItems_draw_control_4de8e96a620336bc46a600456d42bd0c.addLayer(e.layer);\n",
-       "            });\n",
-       "            \n",
-       "        \n",
-       "    \n",
-       "\n",
-       "            var geocoderOpts_geocoder_a72b9ba3927fbbf09929144c06c776ac = {&quot;collapsed&quot;: true, &quot;defaultMarkGeocode&quot;: true, &quot;position&quot;: &quot;topleft&quot;, &quot;provider&quot;: &quot;nominatim&quot;, &quot;providerOptions&quot;: {}, &quot;zoom&quot;: 11};\n",
-       "\n",
-       "            // note: geocoder name should start with lowercase\n",
-       "            var geocoderName_geocoder_a72b9ba3927fbbf09929144c06c776ac = geocoderOpts_geocoder_a72b9ba3927fbbf09929144c06c776ac[&quot;provider&quot;];\n",
-       "\n",
-       "            var customGeocoder_geocoder_a72b9ba3927fbbf09929144c06c776ac = L.Control.Geocoder[ geocoderName_geocoder_a72b9ba3927fbbf09929144c06c776ac ](\n",
-       "                geocoderOpts_geocoder_a72b9ba3927fbbf09929144c06c776ac[&#x27;providerOptions&#x27;]\n",
-       "            );\n",
-       "            geocoderOpts_geocoder_a72b9ba3927fbbf09929144c06c776ac[&quot;geocoder&quot;] = customGeocoder_geocoder_a72b9ba3927fbbf09929144c06c776ac;\n",
-       "\n",
-       "            L.Control.geocoder(\n",
-       "                geocoderOpts_geocoder_a72b9ba3927fbbf09929144c06c776ac\n",
-       "            ).on(&#x27;markgeocode&#x27;, function(e) {\n",
-       "                var zoom = geocoderOpts_geocoder_a72b9ba3927fbbf09929144c06c776ac[&#x27;zoom&#x27;] || map_87ed547fec9cad61cf8100e912ee37b9.getZoom();\n",
-       "                map_87ed547fec9cad61cf8100e912ee37b9.setView(e.geocode.center, zoom);\n",
-       "            }).addTo(map_87ed547fec9cad61cf8100e912ee37b9);\n",
-       "\n",
-       "        \n",
-       "    \n",
-       "            map_87ed547fec9cad61cf8100e912ee37b9.fitBounds(\n",
-       "                [[17.420444446407295, 9.400408867794077], [17.420444446407295, 9.400408867794077]],\n",
-       "                {&quot;maxZoom&quot;: 8}\n",
-       "            );\n",
-       "        \n",
-       "    \n",
-       "            var tile_layer_5a1f710754eb4ba9b3efb63e8f95cdd0 = L.tileLayer(\n",
-       "                &quot;https://earthengine.googleapis.com/v1/projects/earthengine-legacy/maps/bbd025306ade578614635c917a92cc9c-22dca49cca6226364df558b9f351224a/tiles/{z}/{x}/{y}&quot;,\n",
-       "                {&quot;attribution&quot;: &quot;Google Earth Engine&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 24, &quot;maxZoom&quot;: 24, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1.0, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
-       "            );\n",
-       "        \n",
-       "    \n",
-       "            tile_layer_5a1f710754eb4ba9b3efb63e8f95cdd0.addTo(map_87ed547fec9cad61cf8100e912ee37b9);\n",
-       "        \n",
-       "    \n",
-       "            var tile_layer_659ce057240203a81060302c7bc199e5 = L.tileLayer(\n",
-       "                &quot;https://earthengine.googleapis.com/v1/projects/earthengine-legacy/maps/e9befa4b84ebf5f343179240191438c4-4bab19ad7d36b5a2a902f007b8992502/tiles/{z}/{x}/{y}&quot;,\n",
-       "                {&quot;attribution&quot;: &quot;Google Earth Engine&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 24, &quot;maxZoom&quot;: 24, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1.0, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
-       "            );\n",
-       "        \n",
-       "    \n",
-       "            tile_layer_659ce057240203a81060302c7bc199e5.addTo(map_87ed547fec9cad61cf8100e912ee37b9);\n",
-       "        \n",
-       "    \n",
-       "            var tile_layer_c41ef3df729f8a7022292da4553a890d = L.tileLayer(\n",
-       "                &quot;https://earthengine.googleapis.com/v1/projects/earthengine-legacy/maps/5ce0d832fc5f90159adaec89838e0027-d2a6e2943454f08702c2c438ac2ba66c/tiles/{z}/{x}/{y}&quot;,\n",
-       "                {&quot;attribution&quot;: &quot;Google Earth Engine&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 24, &quot;maxZoom&quot;: 24, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1.0, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
-       "            );\n",
-       "        \n",
-       "    \n",
-       "            tile_layer_c41ef3df729f8a7022292da4553a890d.addTo(map_87ed547fec9cad61cf8100e912ee37b9);\n",
-       "        \n",
-       "    \n",
-       "            var layer_control_ec7755c53e41bc78baa6a0a038ac7ed9_layers = {\n",
-       "                base_layers : {\n",
-       "                    &quot;openstreetmap&quot; : tile_layer_9f859830d7b451a75be66d0fdd4a488c,\n",
-       "                },\n",
-       "                overlays :  {\n",
-       "                    &quot;Crop Mask (LGRIP30)&quot; : tile_layer_5a1f710754eb4ba9b3efb63e8f95cdd0,\n",
-       "                    &quot;geoBoundaries&quot; : tile_layer_659ce057240203a81060302c7bc199e5,\n",
-       "                    &quot;VI&quot; : tile_layer_c41ef3df729f8a7022292da4553a890d,\n",
-       "                },\n",
-       "            };\n",
-       "            let layer_control_ec7755c53e41bc78baa6a0a038ac7ed9 = L.control.layers(\n",
-       "                layer_control_ec7755c53e41bc78baa6a0a038ac7ed9_layers.base_layers,\n",
-       "                layer_control_ec7755c53e41bc78baa6a0a038ac7ed9_layers.overlays,\n",
-       "                {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: true, &quot;position&quot;: &quot;topright&quot;}\n",
-       "            ).addTo(map_87ed547fec9cad61cf8100e912ee37b9);\n",
-       "\n",
-       "        \n",
-       "&lt;/script&gt;\n",
-       "&lt;/html&gt;\" width=\"100%\" height=\"600\"style=\"border:none !important;\" \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\"></iframe>"
-      ],
-      "text/plain": [
-       "<geemap.foliumap.Map at 0x7b3cc98d7aa0>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# initialize our map\n",
-    "m = Map(center=[centy, centx], zoom=8) # basemap='Esri.WorldImagery'\n",
-    "m.add_basemap('Esri.WorldImagery')\n",
-    "m.addLayer(crop_data_viz, crop_vis, 'Crop Mask (LGRIP30)') # Green is Rainfed, Red is Irrigated\n",
-    "m.addLayer(aoi, adm_vis, 'geoBoundaries')\n",
-    "m.addLayer(img, ndvi_vis, 'VI')\n",
-    "# m.add_data(labels, column='land_use_t', layer_name='Crop Reference USGS')\n",
-    "m.addLayerControl()\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pull Xarray Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ee.Initialize(opt_url='https://earthengine-highvolume.googleapis.com')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "utm = 'EPSG:32632' # 'EPSG:4326' 'SR-ORG:6974' 'EPSG:23847'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-    },
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-    },
-    {
-     "data": {
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-       "model_id": "54366eaeed7c481f9f02a3a88d412d6f",
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-    },
-    {
-     "data": {
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-       "model_id": "23c469d71b1846c58b4af4b673d86aca",
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-      },
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-      ]
-     },
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-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d960255e484f4544ba8a947a5c95a4ee",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
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-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bef3f7986c354fd0bcc0a471ac327ec4",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-       "  0%|          | 0/12 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c1265e2cd6ab49be9bb0d43e0d85ccd3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/12 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3fcea19ab6474ff7a9298866c6275427",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/12 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_all = []\n",
-    "scale_factor=0.0001\n",
-    "years = range(2001, 2024)\n",
-    "months = range(1, 13)\n",
-    "for year in tqdm(years):\n",
-    "    for month in tqdm(months):\n",
-    "        modis_sel = mxd13q1.filter(ee.Filter.calendarRange(year, year, 'year')).filter(ee.Filter.calendarRange(month, month, 'month'))\n",
-    "        # modis_sel = mxd13q1.filterDate(f'{year}-{month}-01', f'{year}-{month+1}-01')\n",
-    "        ds = xr.open_dataset(\n",
-    "            modis_sel,\n",
-    "            engine = 'ee',\n",
-    "            geometry = aoi.geometry(),\n",
-    "            crs = utm,\n",
-    "            scale = 250,\n",
-    "            )\n",
-    "        ds = ds*scale_factor\n",
-    "        ds = ds.compute()\n",
-    "        ds_all.append(ds)\n",
-    "    # print(f\"Finished {year}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds = xr.concat(ds_all, dim='time')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds = ds.isel(Y=slice(None, None, -1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Save Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_dir = join(expanduser('~'), 'data', 'niger')\n",
-    "# data_dir = join(expanduser('~'), 'tmp', 'niger')\n",
-    "os.path.exists(data_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds.to_netcdf(join(data_dir, \"modis_crop_dea.nc\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds = xr.open_dataset(join(data_dir, \"modis_crop_dea.nc\")) # maradi\n",
-    "# ds = ds.isel(time=range(0,23))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dask Prep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "256"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import multiprocessing\n",
-    "multiprocessing.cpu_count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-48f2e3b2-1b6d-11ef-9a22-3cecef78d5da</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "\n",
-       "        <tr>\n",
-       "        \n",
-       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
-       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
-       "        \n",
-       "        </tr>\n",
-       "\n",
-       "        \n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\"></td>\n",
-       "            </tr>\n",
-       "        \n",
-       "\n",
-       "        </table>\n",
-       "\n",
-       "        \n",
-       "\n",
-       "        \n",
-       "            <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
-       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">e5575bcb</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Workers:</strong> 24\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total threads:</strong> 264\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total memory:</strong> 0.98 TiB\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            \n",
-       "            <tr>\n",
-       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
-       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
-       "</tr>\n",
-       "\n",
-       "            \n",
-       "        </table>\n",
-       "\n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\">\n",
-       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
-       "            </summary>\n",
-       "\n",
-       "            <div style=\"\">\n",
-       "    <div>\n",
-       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
-       "        <div style=\"margin-left: 48px;\">\n",
-       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
-       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-08525b0c-c2b0-442d-9a5c-fa3739dbebaa</p>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:33099\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Workers:</strong> 24\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total threads:</strong> 264\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Started:</strong> Just now\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total memory:</strong> 0.98 TiB\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "            </table>\n",
-       "        </div>\n",
-       "    </div>\n",
-       "\n",
-       "    <details style=\"margin-left: 48px;\">\n",
-       "        <summary style=\"margin-bottom: 20px;\">\n",
-       "            <h3 style=\"display: inline;\">Workers</h3>\n",
-       "        </summary>\n",
-       "\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:33161\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:33463/status\" target=\"_blank\">http://127.0.0.1:33463/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33105\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-g6nvz6zd\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:35841\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:42379/status\" target=\"_blank\">http://127.0.0.1:42379/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46397\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-ofh78don\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:44783\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:42625/status\" target=\"_blank\">http://127.0.0.1:42625/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40009\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-u5379y6p\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:46785\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34085/status\" target=\"_blank\">http://127.0.0.1:34085/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34153\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-cya9f5so\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:33655\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:32947/status\" target=\"_blank\">http://127.0.0.1:32947/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33951\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-dl8p2bd9\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:37883\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:46385/status\" target=\"_blank\">http://127.0.0.1:46385/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38203\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-4wy28e3r\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 6</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:41485\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:33401/status\" target=\"_blank\">http://127.0.0.1:33401/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42071\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-1cqoghdc\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 7</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:34053\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:35119/status\" target=\"_blank\">http://127.0.0.1:35119/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46861\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-vxy_20xo\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 8</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:33727\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:41349/status\" target=\"_blank\">http://127.0.0.1:41349/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43067\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-2i1f1sp5\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 9</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:33425\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:44395/status\" target=\"_blank\">http://127.0.0.1:44395/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43815\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-vkp281co\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 10</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:46017\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:37649/status\" target=\"_blank\">http://127.0.0.1:37649/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38325\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-ysoz8h5w\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 11</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:40353\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:40433/status\" target=\"_blank\">http://127.0.0.1:40433/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35917\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-tihtx8d1\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 12</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:35651\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:45705/status\" target=\"_blank\">http://127.0.0.1:45705/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37945\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-8t6g7cfc\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 13</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:40937\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:44463/status\" target=\"_blank\">http://127.0.0.1:44463/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43051\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-wdi2b8h7\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 14</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:36487\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:36023/status\" target=\"_blank\">http://127.0.0.1:36023/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41065\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-s324wolb\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 15</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:46213\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:42799/status\" target=\"_blank\">http://127.0.0.1:42799/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33513\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-g425rk21\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 16</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:38333\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:39931/status\" target=\"_blank\">http://127.0.0.1:39931/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35345\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-2u96e633\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 17</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:34733\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:35343/status\" target=\"_blank\">http://127.0.0.1:35343/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43075\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-sas643xa\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 18</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:36399\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:40539/status\" target=\"_blank\">http://127.0.0.1:40539/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43125\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-evw9vmlz\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 19</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:43167\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:33563/status\" target=\"_blank\">http://127.0.0.1:33563/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43587\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-x2dxhr0d\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 20</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:43647\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:41495/status\" target=\"_blank\">http://127.0.0.1:41495/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41133\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-2zjf9u3_\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 21</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:43539\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:40759/status\" target=\"_blank\">http://127.0.0.1:40759/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37373\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-m9lr4ff4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 22</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:44679\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:40541/status\" target=\"_blank\">http://127.0.0.1:40541/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41063\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-saaxav73\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 23</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:46101\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 11\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34161/status\" target=\"_blank\">http://127.0.0.1:34161/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 41.98 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37097\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-uqlak544\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "\n",
-       "    </details>\n",
-       "</div>\n",
-       "\n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>\n",
-       "            </details>\n",
-       "        \n",
-       "\n",
-       "    </div>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "<Client: 'tcp://127.0.0.1:33099' processes=24 threads=264, memory=0.98 TiB>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Let's start simple with a LocalCluster that makes use of all the cores and RAM we have on a single machine\n",
-    "# cluster = LocalCluster()\n",
-    "# explicitly connect to the cluster we just created\n",
-    "# client = Client(cluster)\n",
-    "# client\n",
-    "\n",
-    "# Dask Prep\n",
-    "workers = 24 # 50\n",
-    "cluster = LocalCluster(n_workers=workers)\n",
-    "client = Client(cluster)\n",
-    "client"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "client.close()\n",
-    "client.shutdown()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Process by year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
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-     "text": [
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/utils.py:73: RuntimeWarning: All-NaN slice encountered\n",
-      "  return func(*args, **kwargs)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished 2004\n",
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished 2005\n",
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished 2006\n",
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished 2007\n",
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished 2008\n",
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished 2009\n",
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished 2010\n",
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished 2011\n",
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished 2012\n",
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/tornado/ioloop.py:937: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  val = self.callback()\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/xarray/backends/lru_cache.py:71: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
-      "  del self._cache[key]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished 2013\n",
-      "Outlier removal method: median with a user factor of: 2\n",
-      "> Generated roll window size is an even number, added 1 to make it odd (7).\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Outlier removal successful.\n",
-      "\n",
-      "Interpolating dataset using method: interpolate_na.\n",
-      "> Warning: dataset still contains nan values. The first and/or last time slices may be empty.\n",
-      "> Interpolation successful.\n",
-      "\n",
-      "Smoothing method: savitsky with window length: 3 and polyorder: 1.\n",
-      "> Warning: dataset contains nan values. You may want to interpolate next.\n",
-      "> Smoothing successful.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds_all = []\n",
-    "years = range(2004, 2024) # 2001 2024\n",
-    "for year in tqdm(years):\n",
-    "    ds_sel = ds.sel(time=ds.time.dt.year==year)\n",
-    "    dask_chunks={\"time\": -1, \"X\": 500, \"Y\": 500}\n",
-    "    ds_sel = ds_sel.chunk(dask_chunks)\n",
-    "    ds_sel = ds_sel.rename_vars({'EVI':'veg_index'})\n",
-    "    ds_sel = phenolopy.remove_outliers(ds=ds_sel, method='median', user_factor=2, z_pval=0.05)\n",
-    "    ds_sel = ds_sel.chunk(dask_chunks)\n",
-    "    ds_sel = phenolopy.interpolate(ds=ds_sel, method='interpolate_na')\n",
-    "    ds_sel = phenolopy.smooth(ds=ds_sel, method='savitsky', window_length=3, polyorder=1)\n",
-    "    ds_sel = ds_sel.compute()\n",
-    "    ds_all.append(ds_sel)\n",
-    "    print(f\"Finished {year}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
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-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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-       "            </style>\n",
-       "            "
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-      "text/plain": [
-       "<IPython.core.display.HTML object>"
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-       "20"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(ds_all)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds2 = xr.concat(ds_all, dim='time')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.backends.zarr.ZarrStore at 0x7a798d846740>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Flip the data along the y-axis\n",
-    "# ds2 = ds2.isel(Y=slice(None, None, -1))\n",
-    "ds_geo = ds2.copy()\n",
-    "ds_geo.rio.write_crs(utm, inplace=True)\n",
-    "ds_geo = ds_geo.transpose('time', 'Y', 'X')\n",
-    "ds_geo = ds_geo.rename({'Y': \"y\", 'X': \"x\"})\n",
-    "ds_geo.rio.set_spatial_dims(x_dim='x', y_dim='y', inplace=True)\n",
-    "ds_geo.to_zarr(join(data_dir, \"modis_crop_dea_processed.zarr\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load Zarr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_dir = join(expanduser('~'), 'data', 'niger')\n",
-    "# data_dir = join(expanduser('~'), 'tmp', 'niger')\n",
-    "os.path.exists(data_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: 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; Size: 130GB\n",
-       "Dimensions:      (time: 920, y: 5242, x: 6727)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 8B ...\n",
-       "  * time         (time) datetime64[ns] 7kB 2004-01-01 2004-01-09 ... 2023-12-27\n",
-       "  * x            (x) float64 54kB -4.661e+05 -4.658e+05 ... 1.215e+06 1.215e+06\n",
-       "  * y            (y) float64 42kB 2.618e+06 2.618e+06 ... 1.308e+06 1.308e+06\n",
-       "Data variables:\n",
-       "    veg_index    (time, y, x) float32 130GB dask.array&lt;chunksize=(29, 164, 421), 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-57eece04-f18c-4244-b0f8-ccdac88e172d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-57eece04-f18c-4244-b0f8-ccdac88e172d' 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'>time</span>: 920</li><li><span class='xr-has-index'>y</span>: 5242</li><li><span class='xr-has-index'>x</span>: 6727</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8e88b1b1-8592-4d1a-8290-584774cbfdcc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e88b1b1-8592-4d1a-8290-584774cbfdcc' 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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c33bc409-8ef3-46c0-b8e3-da9f7c25b7a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c33bc409-8ef3-46c0-b8e3-da9f7c25b7a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6717d53-0e6f-49bb-9c69-b779f58f4da4' class='xr-var-data-in' type='checkbox'><label for='data-f6717d53-0e6f-49bb-9c69-b779f58f4da4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</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>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 32N</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</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>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</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'>2004-01-01 ... 2023-12-27</div><input id='attrs-a45a28fe-f93e-41d6-8ddf-683ca318f773' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a45a28fe-f93e-41d6-8ddf-683ca318f773' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1937c30-4c0d-4114-89ac-2ef33d3c13ac' class='xr-var-data-in' type='checkbox'><label for='data-c1937c30-4c0d-4114-89ac-2ef33d3c13ac' 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;2004-01-01T00:00:00.000000000&#x27;, &#x27;2004-01-09T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2004-01-17T00:00:00.000000000&#x27;, ..., &#x27;2023-12-11T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-12-19T00:00:00.000000000&#x27;, &#x27;2023-12-27T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.661e+05 -4.658e+05 ... 1.215e+06</div><input id='attrs-a89b2663-af84-4cb9-aac8-4b4655ea6df1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a89b2663-af84-4cb9-aac8-4b4655ea6df1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28dc8377-954b-4d3a-9b7c-d9aed8aa02a0' class='xr-var-data-in' type='checkbox'><label for='data-28dc8377-954b-4d3a-9b7c-d9aed8aa02a0' 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([-466083.452738, -465833.452738, -465583.452738, ..., 1214916.547262,\n",
-       "       1215166.547262, 1215416.547262])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.618e+06 2.618e+06 ... 1.308e+06</div><input id='attrs-28930fff-a4a6-4c43-8d07-827970be6e8c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28930fff-a4a6-4c43-8d07-827970be6e8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eda3cc03-14c8-4aac-96de-db6409334ff0' class='xr-var-data-in' type='checkbox'><label for='data-eda3cc03-14c8-4aac-96de-db6409334ff0' 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([2618053.851129, 2617803.851129, 2617553.851129, ..., 1308303.851129,\n",
-       "       1308053.851129, 1307803.851129])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-faf1caa3-327f-4d83-916b-bfe4622e2db4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-faf1caa3-327f-4d83-916b-bfe4622e2db4' class='xr-section-summary' >Data variables: <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>veg_index</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29, 164, 421), meta=np.ndarray&gt;</div><input id='attrs-d386bc46-4b11-4c85-b473-1efe227ce1aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d386bc46-4b11-4c85-b473-1efe227ce1aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-722a2755-d82a-4e8a-881c-45f8816232b0' class='xr-var-data-in' type='checkbox'><label for='data-722a2755-d82a-4e8a-881c-45f8816232b0' 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> 120.86 GiB </td>\n",
-       "                        <td> 7.64 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (920, 5242, 6727) </td>\n",
-       "                        <td> (29, 164, 421) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 16384 chunks in 2 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",
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-       "  <line x1=\"33\" y1=\"90\" x2=\"153\" y2=\"90\" />\n",
-       "  <line x1=\"33\" y1=\"96\" x2=\"153\" y2=\"96\" />\n",
-       "  <line x1=\"33\" y1=\"99\" x2=\"153\" y2=\"99\" />\n",
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-       "  <line x1=\"33\" y1=\"117\" x2=\"153\" y2=\"117\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
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-       "  <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"117\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"33.677591009400075,23.67759100940008 153.6775910094001,23.67759100940008 153.6775910094001,117.18732789062499 33.677591009400075,117.18732789062499\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"93.677591\" y=\"137.187328\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6727</text>\n",
-       "  <text x=\"173.677591\" y=\"70.432459\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.677591,70.432459)\">5242</text>\n",
-       "  <text x=\"11.838796\" y=\"125.348532\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.838796,125.348532)\">920</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee817ad7-6656-403c-901c-299adc3829dc' class='xr-section-summary-in' type='checkbox'  ><label for='section-ee817ad7-6656-403c-901c-299adc3829dc' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e62cdd2-6334-410d-9536-56c47ac5ee73' class='xr-index-data-in' type='checkbox'/><label for='index-8e62cdd2-6334-410d-9536-56c47ac5ee73' 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;2004-01-01&#x27;, &#x27;2004-01-09&#x27;, &#x27;2004-01-17&#x27;, &#x27;2004-01-25&#x27;,\n",
-       "               &#x27;2004-02-02&#x27;, &#x27;2004-02-10&#x27;, &#x27;2004-02-18&#x27;, &#x27;2004-02-26&#x27;,\n",
-       "               &#x27;2004-03-05&#x27;, &#x27;2004-03-13&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2023-10-16&#x27;, &#x27;2023-10-24&#x27;, &#x27;2023-11-01&#x27;, &#x27;2023-11-09&#x27;,\n",
-       "               &#x27;2023-11-17&#x27;, &#x27;2023-11-25&#x27;, &#x27;2023-12-03&#x27;, &#x27;2023-12-11&#x27;,\n",
-       "               &#x27;2023-12-19&#x27;, &#x27;2023-12-27&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=920, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a05e57be-2d53-45fb-a119-6318c52b7b89' class='xr-index-data-in' type='checkbox'/><label for='index-a05e57be-2d53-45fb-a119-6318c52b7b89' 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([-466083.4527382861, -465833.4527382861, -465583.4527382861,\n",
-       "       -465333.4527382861, -465083.4527382861, -464833.4527382861,\n",
-       "       -464583.4527382861, -464333.4527382861, -464083.4527382861,\n",
-       "       -463833.4527382861,\n",
-       "       ...\n",
-       "        1213166.547261714,  1213416.547261714,  1213666.547261714,\n",
-       "        1213916.547261714,  1214166.547261714,  1214416.547261714,\n",
-       "        1214666.547261714,  1214916.547261714,  1215166.547261714,\n",
-       "        1215416.547261714],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=6727))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-56982b0c-7095-48e5-b03c-1517f1e6344b' class='xr-index-data-in' type='checkbox'/><label for='index-56982b0c-7095-48e5-b03c-1517f1e6344b' 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([2618053.8511287076, 2617803.8511287076, 2617553.8511287076,\n",
-       "       2617303.8511287076, 2617053.8511287076, 2616803.8511287076,\n",
-       "       2616553.8511287076, 2616303.8511287076, 2616053.8511287076,\n",
-       "       2615803.8511287076,\n",
-       "       ...\n",
-       "       1310053.8511287074, 1309803.8511287074, 1309553.8511287074,\n",
-       "       1309303.8511287074, 1309053.8511287074, 1308803.8511287074,\n",
-       "       1308553.8511287074, 1308303.8511287074, 1308053.8511287074,\n",
-       "       1307803.8511287074],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=5242))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b2bcef6-134c-420d-afb9-8ffe02b14e85' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4b2bcef6-134c-420d-afb9-8ffe02b14e85' 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: 130GB\n",
-       "Dimensions:      (time: 920, y: 5242, x: 6727)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 8B ...\n",
-       "  * time         (time) datetime64[ns] 7kB 2004-01-01 2004-01-09 ... 2023-12-27\n",
-       "  * x            (x) float64 54kB -4.661e+05 -4.658e+05 ... 1.215e+06 1.215e+06\n",
-       "  * y            (y) float64 42kB 2.618e+06 2.618e+06 ... 1.308e+06 1.308e+06\n",
-       "Data variables:\n",
-       "    veg_index    (time, y, x) float32 130GB dask.array<chunksize=(29, 164, 421), meta=np.ndarray>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_geo = xr.open_zarr(join(data_dir, \"modis_crop_dea_processed.zarr\"))\n",
-    "ds_geo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Anomalies per Pixel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# add a new date format to the dataset\n",
-    "# ds_ref['md'] = ds_ref.time.dt.strftime('%m-%d')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ds_geo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'M' is deprecated and will be removed in a future version. Please use 'ME' instead of 'M'.\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds_monthly = ds_geo.resample(time='1M').mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ds_monthly['m'] = ds_monthly.time.dt.strftime('%m')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: 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; Size: 34GB\n",
-       "Dimensions:      (time: 240, y: 5242, x: 6727)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 8B ...\n",
-       "  * x            (x) float64 54kB -4.661e+05 -4.658e+05 ... 1.215e+06 1.215e+06\n",
-       "  * y            (y) float64 42kB 2.618e+06 2.618e+06 ... 1.308e+06 1.308e+06\n",
-       "  * time         (time) datetime64[ns] 2kB 2004-01-31 2004-02-29 ... 2023-12-31\n",
-       "Data variables:\n",
-       "    veg_index    (time, y, x) float32 34GB dask.array&lt;chunksize=(1, 164, 421), 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-321db9ba-2ef2-4b79-ba1c-19d3f44d0003' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-321db9ba-2ef2-4b79-ba1c-19d3f44d0003' 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'>time</span>: 240</li><li><span class='xr-has-index'>y</span>: 5242</li><li><span class='xr-has-index'>x</span>: 6727</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-425368b2-a000-4103-b889-845f28c75e40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-425368b2-a000-4103-b889-845f28c75e40' 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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e2d92cf8-952d-4bdf-a0d6-fddcf9314407' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2d92cf8-952d-4bdf-a0d6-fddcf9314407' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3658648-53df-4ecb-a879-fa97306b37e0' class='xr-var-data-in' type='checkbox'><label for='data-b3658648-53df-4ecb-a879-fa97306b37e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</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>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 32N</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</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>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.661e+05 -4.658e+05 ... 1.215e+06</div><input id='attrs-fbec79cf-35c0-4d95-9c56-f37dd9292b6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbec79cf-35c0-4d95-9c56-f37dd9292b6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acfc1f07-75bb-48c2-a1c5-da161e36cea8' class='xr-var-data-in' type='checkbox'><label for='data-acfc1f07-75bb-48c2-a1c5-da161e36cea8' 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([-466083.452738, -465833.452738, -465583.452738, ..., 1214916.547262,\n",
-       "       1215166.547262, 1215416.547262])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.618e+06 2.618e+06 ... 1.308e+06</div><input id='attrs-8b71240e-c0c9-4d97-9709-8cfcf98890b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b71240e-c0c9-4d97-9709-8cfcf98890b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f699d53b-c8be-49b1-a04f-b9f5c55b9902' class='xr-var-data-in' type='checkbox'><label for='data-f699d53b-c8be-49b1-a04f-b9f5c55b9902' 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([2618053.851129, 2617803.851129, 2617553.851129, ..., 1308303.851129,\n",
-       "       1308053.851129, 1307803.851129])</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'>2004-01-31 ... 2023-12-31</div><input id='attrs-2f3c9919-9f06-4b06-9712-77909b29c07e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f3c9919-9f06-4b06-9712-77909b29c07e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2eb54672-bc79-4379-bf60-73617dfd6a69' class='xr-var-data-in' type='checkbox'><label for='data-2eb54672-bc79-4379-bf60-73617dfd6a69' 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;2004-01-31T00:00:00.000000000&#x27;, &#x27;2004-02-29T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2004-03-31T00:00:00.000000000&#x27;, ..., &#x27;2023-10-31T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-11-30T00:00:00.000000000&#x27;, &#x27;2023-12-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c8b4339-9fe5-4f7f-b416-ace14f31ef85' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c8b4339-9fe5-4f7f-b416-ace14f31ef85' class='xr-section-summary' >Data variables: <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>veg_index</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 164, 421), meta=np.ndarray&gt;</div><input id='attrs-fd862413-aebb-4f0f-96f7-d2c870cf78d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd862413-aebb-4f0f-96f7-d2c870cf78d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aca8a050-9c94-42d0-a956-e538b4880efc' class='xr-var-data-in' type='checkbox'><label for='data-aca8a050-9c94-42d0-a956-e538b4880efc' 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> 31.53 GiB </td>\n",
-       "                        <td> 269.70 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (240, 5242, 6727) </td>\n",
-       "                        <td> (1, 164, 421) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 122880 chunks in 963 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4dda50d-3fb0-4a99-9914-ddcf6c6a53db' class='xr-section-summary-in' type='checkbox'  ><label for='section-b4dda50d-3fb0-4a99-9914-ddcf6c6a53db' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fc465a87-dbdb-4232-ae1d-a8949ac5f4af' class='xr-index-data-in' type='checkbox'/><label for='index-fc465a87-dbdb-4232-ae1d-a8949ac5f4af' 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([-466083.4527382861, -465833.4527382861, -465583.4527382861,\n",
-       "       -465333.4527382861, -465083.4527382861, -464833.4527382861,\n",
-       "       -464583.4527382861, -464333.4527382861, -464083.4527382861,\n",
-       "       -463833.4527382861,\n",
-       "       ...\n",
-       "        1213166.547261714,  1213416.547261714,  1213666.547261714,\n",
-       "        1213916.547261714,  1214166.547261714,  1214416.547261714,\n",
-       "        1214666.547261714,  1214916.547261714,  1215166.547261714,\n",
-       "        1215416.547261714],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=6727))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-852b41e0-3286-4220-a51c-ace5a4ed59a9' class='xr-index-data-in' type='checkbox'/><label for='index-852b41e0-3286-4220-a51c-ace5a4ed59a9' 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([2618053.8511287076, 2617803.8511287076, 2617553.8511287076,\n",
-       "       2617303.8511287076, 2617053.8511287076, 2616803.8511287076,\n",
-       "       2616553.8511287076, 2616303.8511287076, 2616053.8511287076,\n",
-       "       2615803.8511287076,\n",
-       "       ...\n",
-       "       1310053.8511287074, 1309803.8511287074, 1309553.8511287074,\n",
-       "       1309303.8511287074, 1309053.8511287074, 1308803.8511287074,\n",
-       "       1308553.8511287074, 1308303.8511287074, 1308053.8511287074,\n",
-       "       1307803.8511287074],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=5242))</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-cae6a08a-a1ea-4425-a2f0-38179f322011' class='xr-index-data-in' type='checkbox'/><label for='index-cae6a08a-a1ea-4425-a2f0-38179f322011' 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;2004-01-31&#x27;, &#x27;2004-02-29&#x27;, &#x27;2004-03-31&#x27;, &#x27;2004-04-30&#x27;,\n",
-       "               &#x27;2004-05-31&#x27;, &#x27;2004-06-30&#x27;, &#x27;2004-07-31&#x27;, &#x27;2004-08-31&#x27;,\n",
-       "               &#x27;2004-09-30&#x27;, &#x27;2004-10-31&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2023-03-31&#x27;, &#x27;2023-04-30&#x27;, &#x27;2023-05-31&#x27;, &#x27;2023-06-30&#x27;,\n",
-       "               &#x27;2023-07-31&#x27;, &#x27;2023-08-31&#x27;, &#x27;2023-09-30&#x27;, &#x27;2023-10-31&#x27;,\n",
-       "               &#x27;2023-11-30&#x27;, &#x27;2023-12-31&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=240, freq=&#x27;M&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2e1fc55-03b8-49ff-88c8-35f428e6865a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e2e1fc55-03b8-49ff-88c8-35f428e6865a' 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: 34GB\n",
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-       "Coordinates:\n",
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-       "  * y            (y) float64 42kB 2.618e+06 2.618e+06 ... 1.308e+06 1.308e+06\n",
-       "  * time         (time) datetime64[ns] 2kB 2004-01-31 2004-02-29 ... 2023-12-31\n",
-       "Data variables:\n",
-       "    veg_index    (time, y, x) float32 34GB dask.array<chunksize=(1, 164, 421), meta=np.ndarray>"
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-     "execution_count": 16,
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-    "### Percentage Change"
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-   "execution_count": 109,
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-   "outputs": [
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-       "                    --jp-layout-color3: #383838;\n",
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-   ],
-   "source": [
-    "# ds_ref = ds_monthly.sel(time=slice(None, end_reference)) # ds_geo\n",
-    "# ds_filt = ds_monthly.sel(time=ds_monthly.time.dt.year==2023)\n",
-    "# ds_2023_09 = ds_filt.sel(time=ds_filt.time.dt.month==9).isel(time=0)\n",
-    "# ds_ref_09 = ds_ref.sel(time=ds_ref.time.dt.month==9).mean(dim='time')\n",
-    "# ds_diff = ds_2023_09 - ds_ref_09\n",
-    "# ds_pct_ch = (ds_diff / ds_ref_09)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ds_pct_ch = ds_pct_ch.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ds_pct_ch['veg_index'].rio.write_nodata(np.nan, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ds_pct_ch['veg_index'].rio.to_raster(join(data_dir, 'pct_ch_2023_09.tif'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Z Score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_ref = ds_monthly.sel(time=slice(None, end_reference)) # ds_geo\n",
-    "ds_filt = ds_monthly.sel(time=ds_monthly.time.dt.year==2023)\n",
-    "\n",
-    "ds_2023_09 = ds_filt.sel(time=ds_filt.time.dt.month==9).isel(time=0)\n",
-    "ds_ref_09_mean = ds_ref.sel(time=ds_ref.time.dt.month==9).mean(dim='time')\n",
-    "ds_ref_09_sdt = ds_ref.sel(time=ds_ref.time.dt.month==9).std(dim='time')\n",
-    "\n",
-    "ds_diff = ds_2023_09 - ds_ref_09_mean\n",
-    "ds_zscore = (ds_diff / ds_ref_09_sdt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n",
-      "  x = np.divide(x1, x2, out)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds_zscore = ds_zscore.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
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-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: 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.DataArray &#x27;veg_index&#x27; (y: 5242, x: 6727)&gt; Size: 141MB\n",
-       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 8B 0\n",
-       "  * x            (x) float64 54kB -4.661e+05 -4.658e+05 ... 1.215e+06 1.215e+06\n",
-       "  * y            (y) float64 42kB 2.618e+06 2.618e+06 ... 1.308e+06 1.308e+06\n",
-       "    time         datetime64[ns] 8B 2023-09-30\n",
-       "Attributes:\n",
-       "    _FillValue:  nan</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'>'veg_index'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 5242</li><li><span class='xr-has-index'>x</span>: 6727</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ff8dd592-b027-491f-870a-157e001f507d' class='xr-array-in' type='checkbox' checked><label for='section-ff8dd592-b027-491f-870a-157e001f507d' 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>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-f74997c0-a69b-4030-8868-8186d1af2346' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f74997c0-a69b-4030-8868-8186d1af2346' 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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ad872980-524a-48c6-b221-36506ae3942e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad872980-524a-48c6-b221-36506ae3942e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5e9899b-4f2a-4fcb-8f04-b28cf13928f3' class='xr-var-data-in' type='checkbox'><label for='data-f5e9899b-4f2a-4fcb-8f04-b28cf13928f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</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>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 32N</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</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>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.661e+05 -4.658e+05 ... 1.215e+06</div><input id='attrs-0942cad2-af34-4e2c-986b-059e1c0ed6b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0942cad2-af34-4e2c-986b-059e1c0ed6b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23f93b9e-0ec3-46b2-b4f4-340ef2d5705b' class='xr-var-data-in' type='checkbox'><label for='data-23f93b9e-0ec3-46b2-b4f4-340ef2d5705b' 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([-466083.452738, -465833.452738, -465583.452738, ..., 1214916.547262,\n",
-       "       1215166.547262, 1215416.547262])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.618e+06 2.618e+06 ... 1.308e+06</div><input id='attrs-dc94f448-d24a-4e02-ad3e-977c5c5853ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc94f448-d24a-4e02-ad3e-977c5c5853ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c8d868c-d82d-4dbf-a4fc-6d12085bf9ab' class='xr-var-data-in' type='checkbox'><label for='data-7c8d868c-d82d-4dbf-a4fc-6d12085bf9ab' 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([2618053.851129, 2617803.851129, 2617553.851129, ..., 1308303.851129,\n",
-       "       1308053.851129, 1307803.851129])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-09-30</div><input id='attrs-f4e9710d-217a-4730-9629-cc208a1e0774' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4e9710d-217a-4730-9629-cc208a1e0774' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6129f13e-4410-427d-90d4-cc1315096653' class='xr-var-data-in' type='checkbox'><label for='data-6129f13e-4410-427d-90d4-cc1315096653' 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;2023-09-30T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44d66b5d-d1d1-4a90-8ca4-787eaf32c5be' class='xr-section-summary-in' type='checkbox'  ><label for='section-44d66b5d-d1d1-4a90-8ca4-787eaf32c5be' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7fd3b5a4-20a2-4a59-8288-273be10428ce' class='xr-index-data-in' type='checkbox'/><label for='index-7fd3b5a4-20a2-4a59-8288-273be10428ce' 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([-466083.4527382861, -465833.4527382861, -465583.4527382861,\n",
-       "       -465333.4527382861, -465083.4527382861, -464833.4527382861,\n",
-       "       -464583.4527382861, -464333.4527382861, -464083.4527382861,\n",
-       "       -463833.4527382861,\n",
-       "       ...\n",
-       "        1213166.547261714,  1213416.547261714,  1213666.547261714,\n",
-       "        1213916.547261714,  1214166.547261714,  1214416.547261714,\n",
-       "        1214666.547261714,  1214916.547261714,  1215166.547261714,\n",
-       "        1215416.547261714],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=6727))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b10ec2a-4cc1-4ec7-b673-bcb8b1e678d6' class='xr-index-data-in' type='checkbox'/><label for='index-9b10ec2a-4cc1-4ec7-b673-bcb8b1e678d6' 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([2618053.8511287076, 2617803.8511287076, 2617553.8511287076,\n",
-       "       2617303.8511287076, 2617053.8511287076, 2616803.8511287076,\n",
-       "       2616553.8511287076, 2616303.8511287076, 2616053.8511287076,\n",
-       "       2615803.8511287076,\n",
-       "       ...\n",
-       "       1310053.8511287074, 1309803.8511287074, 1309553.8511287074,\n",
-       "       1309303.8511287074, 1309053.8511287074, 1308803.8511287074,\n",
-       "       1308553.8511287074, 1308303.8511287074, 1308053.8511287074,\n",
-       "       1307803.8511287074],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=5242))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-03d9d9a0-0aab-47c0-9c1a-d8ef2f2e5f4e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03d9d9a0-0aab-47c0-9c1a-d8ef2f2e5f4e' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'veg_index' (y: 5242, x: 6727)> Size: 141MB\n",
-       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 8B 0\n",
-       "  * x            (x) float64 54kB -4.661e+05 -4.658e+05 ... 1.215e+06 1.215e+06\n",
-       "  * y            (y) float64 42kB 2.618e+06 2.618e+06 ... 1.308e+06 1.308e+06\n",
-       "    time         datetime64[ns] 8B 2023-09-30\n",
-       "Attributes:\n",
-       "    _FillValue:  nan"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_zscore['veg_index'].rio.write_nodata(np.nan, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_zscore['veg_index'].rio.to_raster(join(data_dir, 'zcore_dea_2023_09.tif'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "### Try Leafmap"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# import leafmap\n",
-    "import leafmap.foliumap as leafmap\n",
-    "\n",
-    "import rasterio\n",
-    "import rioxarray\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading...\n",
-      "From: https://open.gishub.org/data/raster/landsat.tif\n",
-      "To: /home/jupyter-wb514197/landsat.tif\n",
-      "100%|██████████| 10.1M/10.1M [00:00<00:00, 76.2MB/s]\n",
-      "Downloading...\n",
-      "From: https://open.gishub.org/data/raster/srtm90.tif\n",
-      "To: /home/jupyter-wb514197/srtm90.tif\n",
-      "100%|██████████| 16.6M/16.6M [00:00<00:00, 88.6MB/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "url1 = \"https://open.gishub.org/data/raster/landsat.tif\"\n",
-    "url2 = \"https://open.gishub.org/data/raster/srtm90.tif\"\n",
-    "satellite = leafmap.download_file(url1, \"landsat.tif\", overwrite=True)\n",
-    "dem = leafmap.download_file(url2, \"srtm90.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dataset = rasterio.open(satellite)\n",
-    "nir = dataset.read(4).astype(float)\n",
-    "red = dataset.read(1).astype(float)\n",
-    "ndvi = (nir - red) / (nir + red)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ndvi_image = leafmap.array_to_image(ndvi, source=satellite)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'driver': 'GTiff', 'dtype': 'float64', 'nodata': None, 'width': 2127, 'height': 1564, 'count': 1, 'crs': CRS.from_epsg(3857), 'transform': Affine(180.0, 0.0, -13442580.0,\n",
-       "       0.0, -180.0, 4670100.0), 'blockxsize': 512, 'blockysize': 512, 'tiled': True, 'compress': 'deflate', 'interleave': 'band'}"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ndvi_image.profile"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# import localtileserver\n",
-    "# print(localtileserver.Report())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# m = leafmap.Map()\n",
-    "# m.add_raster(satellite, indexes=[4, 1, 2], vmin=0, vmax=120, layer_name=\"Landsat 7\")\n",
-    "# m.add_raster(ndvi_image, colormap=\"Greens\", layer_name=\"NDVI\")\n",
-    "# m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "da = ds_pct_ch['veg_index']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# m = leafmap.Map()\n",
-    "# m.add_raster(da, colormap=\"BrBG\", layer_name=\"VEG Index\")\n",
-    "# # m.add_raster(dem, colormap=\"terrain\", layer_name=\"DEM\")\n",
-    "# # m.add_raster(masked_array, colormap=\"coolwarm\", layer_name=\"Classified DEM\")\n",
-    "# m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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",
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-       "\n",
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-       "\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",
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-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
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-       "\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",
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-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;veg_index&#x27; ()&gt; Size: 4B\n",
-       "array(2383.3328, dtype=float32)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 8B 0\n",
-       "    time         datetime64[ns] 8B 2023-09-30</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'>'veg_index'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-dd8ccb75-5492-4a52-a3a1-e8d8e654d759' class='xr-array-in' type='checkbox' checked><label for='section-dd8ccb75-5492-4a52-a3a1-e8d8e654d759' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.383e+03</span></div><div class='xr-array-data'><pre>array(2383.3328, dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-340d648c-092b-40d5-87bc-df5aefd9389a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-340d648c-092b-40d5-87bc-df5aefd9389a' 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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-1d7fa848-2dc1-4371-a7aa-af12d8f2cb36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d7fa848-2dc1-4371-a7aa-af12d8f2cb36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d29519a-459c-4f2a-a74d-5790ebf9437f' class='xr-var-data-in' type='checkbox'><label for='data-8d29519a-459c-4f2a-a74d-5790ebf9437f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</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>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 32N</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</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>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-09-30</div><input id='attrs-aabef282-8769-4159-8282-f1a24d62199b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aabef282-8769-4159-8282-f1a24d62199b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd5d9e8c-5a3d-41f0-9bf8-58f1ff2f5ce0' class='xr-var-data-in' type='checkbox'><label for='data-cd5d9e8c-5a3d-41f0-9bf8-58f1ff2f5ce0' 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;2023-09-30T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6c39803-ca2b-41ee-8f87-d5250a4500df' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d6c39803-ca2b-41ee-8f87-d5250a4500df' 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-5414d66b-ce14-4e02-abaf-15fc72a0795a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5414d66b-ce14-4e02-abaf-15fc72a0795a' 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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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;veg_index&#x27; ()&gt; Size: 4B\n",
-       "array(-119.153114, dtype=float32)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 8B 0\n",
-       "    time         datetime64[ns] 8B 2023-09-30</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'>'veg_index'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d42eeb1a-a973-40a8-b494-10d1274fd048' class='xr-array-in' type='checkbox' checked><label for='section-d42eeb1a-a973-40a8-b494-10d1274fd048' 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>-119.2</span></div><div class='xr-array-data'><pre>array(-119.153114, dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-0b83b00c-a257-4b58-9496-8e9e696c8378' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b83b00c-a257-4b58-9496-8e9e696c8378' 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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-3ad5e2cd-668d-4791-b18e-57d349851b76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ad5e2cd-668d-4791-b18e-57d349851b76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1c53f83-d352-4220-bf84-d6ff95cf2f6f' class='xr-var-data-in' type='checkbox'><label for='data-f1c53f83-d352-4220-bf84-d6ff95cf2f6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</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>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 32N</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</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>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-09-30</div><input id='attrs-7acdbcdb-4457-4625-a764-7bc519faf527' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7acdbcdb-4457-4625-a764-7bc519faf527' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97ddc774-94d9-4a42-857c-b93e5caf6928' class='xr-var-data-in' type='checkbox'><label for='data-97ddc774-94d9-4a42-857c-b93e5caf6928' 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;2023-09-30T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee119b4c-fe71-4e36-9608-6af328c4b83e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ee119b4c-fe71-4e36-9608-6af328c4b83e' 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-51f42a61-74fd-4500-b8c1-93b3886e0d5b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-51f42a61-74fd-4500-b8c1-93b3886e0d5b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'veg_index' ()> Size: 4B\n",
-       "array(-119.153114, dtype=float32)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 8B 0\n",
-       "    time         datetime64[ns] 8B 2023-09-30"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_pct_ch['veg_index'].min()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Summary of Anomalies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# data = ds_pct_ch['veg_index'].values\n",
-    "data = ds_zscore['veg_index'].values\n",
-    "data = data[~np.isnan(data)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data = data[~np.isnan(data)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[-20, -2, -1, -0.5, 0.5, 1, 2, 20]"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# bins = np.linspace(-0.5, 0.5, 21)\n",
-    "# bins = range(-5, 5, 1)\n",
-    "# bins = np.linspace(-3, 3, 13)\n",
-    "bins = [-20, -2, -1, -0.5, 0.5, 1, 2, 20]\n",
-    "bin_labels = ['< -2', '(-2, -1]', '(-1, -0.5]', '(-0.5, -0.5]', '(0.5, 1]', '(1, 2]', '> 2']\n",
-    "bins"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Numpy Way"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "vals, bins = np.histogram(data, bins=bins)\n",
-    "df = pd.DataFrame({'vals': vals, 'bins': bins[:-1]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='bins'>"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "df.plot(kind='bar', x='bins', y='vals', ax=ax)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pandas way"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bins = [-20, -2, -1, -0.5, 0.5, 1, 2, 20]\n",
-    "bin_labels = ['< -2', '(-2, -1]', '(-1, -0.5]', '(-0.5, -0.5]', '(0.5, 1]', '(1, 2]', '> 2']\n",
-    "\n",
-    "df = pd.DataFrame(data, columns=['zscore'])\n",
-    "df.loc[:, 'zscore_bin'] = pd.cut(data, bins=bins, include_lowest=True, labels=bin_labels)\n",
-    "df2 = df['zscore_bin'].value_counts().sort_index()\n",
-    "df2 = pd.DataFrame(df2).reset_index()\n",
-    "df2.loc[:, 'pct'] = df2['count'] / df2['count'].sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "font = \"Liberation Sans\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>zscore_bin</th>\n",
-       "      <th>count</th>\n",
-       "      <th>pct</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>&lt; -2</td>\n",
-       "      <td>58652</td>\n",
-       "      <td>0.033413</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>(-2, -1]</td>\n",
-       "      <td>189878</td>\n",
-       "      <td>0.108170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>(-1, -0.5]</td>\n",
-       "      <td>192149</td>\n",
-       "      <td>0.109463</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>(-0.5, -0.5]</td>\n",
-       "      <td>577571</td>\n",
-       "      <td>0.329031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>(0.5, 1]</td>\n",
-       "      <td>266766</td>\n",
-       "      <td>0.151971</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>(1, 2]</td>\n",
-       "      <td>333452</td>\n",
-       "      <td>0.189961</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>&gt; 2</td>\n",
-       "      <td>136904</td>\n",
-       "      <td>0.077991</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     zscore_bin   count       pct\n",
-       "0          < -2   58652  0.033413\n",
-       "1      (-2, -1]  189878  0.108170\n",
-       "2    (-1, -0.5]  192149  0.109463\n",
-       "3  (-0.5, -0.5]  577571  0.329031\n",
-       "4      (0.5, 1]  266766  0.151971\n",
-       "5        (1, 2]  333452  0.189961\n",
-       "6           > 2  136904  0.077991"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "\n",
-    "(\n",
-    "ggplot(df2, aes(x=\"zscore_bin\", y=\"count\", fill=\"zscore_bin\"))\n",
-    "    + geom_bar(alpha=1, stat='identity', color='black') #  fill='#006d2c'\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_fill_brewer(type='div', palette=\"BrBG\", direction=1)\n",
-    "    # + scale_fill_brewer(type='div', palette=\"RdYlGn\", direction=1)\n",
-    "    # + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "\n",
-    "(\n",
-    "ggplot(df2, aes(x=\"zscore_bin\", y=\"pct\", fill=\"zscore_bin\"))\n",
-    "    + geom_bar(alpha=1, stat='identity') #  fill='#006d2c'\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_fill_brewer(type='div', palette=\"BrBG\", direction=1)\n",
-    "    # + scale_fill_brewer(type='div', palette=\"RdYlGn\", direction=1)\n",
-    "    # + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Less Bins"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bins = [-20, -1, 1, 20]\n",
-    "bin_labels = ['< -1', '(-1, 1]', '> 1']\n",
-    "\n",
-    "df = pd.DataFrame(data, columns=['zscore'])\n",
-    "df.loc[:, 'zscore_bin'] = pd.cut(data, bins=bins, include_lowest=True, labels=bin_labels)\n",
-    "df2 = df['zscore_bin'].value_counts().sort_index()\n",
-    "df2 = pd.DataFrame(df2).reset_index()\n",
-    "df2.loc[:, 'pct'] = df2['count'] / df2['count'].sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "\n",
-    "(\n",
-    "ggplot(df2, aes(x=\"zscore_bin\", y=\"count\", fill=\"zscore_bin\"))\n",
-    "    + geom_bar(alpha=1, stat='identity') #  fill='#006d2c'\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_fill_brewer(type='div', palette=\"BrBG\", direction=1)\n",
-    "    # + scale_fill_brewer(type='div', palette=\"RdYlGn\", direction=1)\n",
-    "    # + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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juf322/Pmm2/mxRdfzIsvvpgVVlghe+65Z3bYYYcF+gfjDjvskB122GExVA4Ai0/tUR+NTZvhOgcATWf8+PEZOHBgBg4cmAkTJiRJqqqqstFGG2WvvfbKRhttZCpKoBQEIAALqWXLltl+++2z/fbb5+WXX07//v3zxBNP5IMPPsgll1ySa6+9Nrvsskt23333dO7cuanLBYDFaqmllkqzZs1SXV2dzz77rKnLAQBqGTFiRPr375+HH344M2bMSDJ73a1evXpljz32yEorrdTEFQIsWgIQgEWoR48e6dGjR8aMGZM77rgj99xzTz777LPcfPPNufXWW/PHP/4x6623XlOXCQCLTYsWLbLMMstk3LhxGTNmTFOXAwD8fwMGDMill15a+b1r167Zfffds9NOO6Vdu3ZNWBnA4iMAAVgMunTpkkMPPTQHHXRQHnjggQwYMCDvvvtuJk2a1NSlAcBit8IKK2TcuHEZO3ZsZsyYkZYtWzZ1SQDwlffpp58mmf3Fvb322iubbrppmjdv3sRVASxeAhCAxah169bp3bt3evfunWHDhmXZZZddLMcpiiJTp06d4/7q6urK7SlTpsyxfamlllos9QDw1bbGGmvkhRdeyMyZMzNy5MisueaaC9XforrOnXfeeXnggQeSJGecccZ8j8p0vQXgy2ydddZJ3759s/rqqy/2Y82aNavRtcCqq6vnuGY2a9YsrVu3XtylAV8xAhCAL8hGG2202PoePXp0jjjiiEb36dOnzxz39e/ff3GVBMBXWI8ePXLLLbckSV566aWFDkCWlOvcklIHACyIDTbY4As71qBBg3L++ec3uP3ll1+e45q57rrr5swzz1zcpQFfMc2augAAAKBcNtxww8o3OIcMGdLE1dSvqqqqqUsAAAAWs6okRTJ7ODcAAMCicO6552bQoEFp1qxZLr/88nTu3LmpS0qS3H///Tn//PO/sClAGnLSSSflxRdfTNeuXXP55Zc3WR0AAFBWVVVVRoAAAACL3p577plk9hzfd999dxNX839GjhyZJOnWrVvTFgIAACx2AhAAAGCRW3PNNbPxxhsnSe6444589tlnTVzR7AVZH3300ayxxhpp165dU5cDAAAsZgIQAABgsTj88MPTvHnzfPbZZ/nvf//b1OVk4MCBGTduXPbaa6+mLgUAAPgCCEAAAIDFYpVVVsn++++fJOnfv39GjBjRpPXsscce6d+/f7bbbrsv/NizZs3KlClTKj/V1dVfeA0AAPBV06KpCwAAAMrrgAMOyLBhw/Laa6/lvPPOy5///Oe0atWqqcv6wr388sv53e9+19RlAADAV4oRIAAAwGLTokWLnHTSSVluueUyYsSI/OMf/2jqkgAAgK+IqiRFkhRF0cSlAAAAAAAALLyqqiojQAAAAAAAgPIRgAAAAAAAAKUjAAEAAAAAAEpHAAIAAAAAAJSOAAQAAAAAACgdAQgAAAAAAFA6AhAAAAAAAKB0BCAAAAAAAEDpCEAAAAAAAIDSEYAAAAAAAAClIwABAAAAAABKRwACAAAAAACUjgAEAAAAAAAoHQEIAAAAAABQOgIQAAAAAACgdAQgAAAAAABAk+revXuqqqqy3XbbLbI+BSAAAAAAfGW98847Ofvss7PrrrtmlVVWSbt27dKiRYu0b98+a6yxRvbYY4/07ds3Y8eObepS+ZyqqqrKT+fOnTNjxoz5an/uuefW6ePKK6+ca5uiKHL33XfnuOOOy7e//e2ssMIKad26ddq2bZtVV101u+22W/r27ZtRo0bNUw01H/jW99OmTZussMIK2XDDDXPkkUfm2muvzeTJk+ep3z/84Q+Vfm699dZG9x0zZkwuuOCC7LnnnllttdXSoUOHtGjRIu3atUv37t2z00475Ywzzsi77747T8eel5oa+mnZsmU6d+6czTffPCeeeGLeeuuthTomCEAAAAAA+MopiiJ/+MMfsuaaa+bEE0/M3XffnXfffTeTJk3KrFmz8tlnn2X48OG544478stf/jLdu3fP1Vdf3dRl04Bx48blzjvvnK8211xzzXztP2jQoGy88cbZddddc/7552fo0KEZNWpUpk+fnsmTJ+edd97JwIED88tf/jJf//rXc/zxx2fSpEnzdYzapk2bllGjRuW5557LpZdemu9///tZZZVVcvrpp2fmzJkL3G9tF198cVZfffX84he/yIABAzJixIhMnDgxs2bNyqRJk/L222/nvvvuy8knn5zVVlstZ5999iI5bkNmzpyZcePG5YknnsjZZ5+dtdZaa7Efk3ITgAAAAADwlfPrX/86p512WqZPn56VV14555xzToYMGZKPPvoo48aNy8svv5xbbrkl++yzT5Jk0qRJOfTQQ3PLLbc0ceV8XosWLZJkvgKqV155JcOGDau0nZuLLrooO+64Y5599tlUVVVln332ybXXXptXX301Y8eOzTvvvJNHHnkkJ598crp27Zpp06blvPPOy2abbZYPPvhgrv137NgxEydOrPMzevTovPLKK7nhhhtyxBFHZOmll864cePy+9//Ptttt10+/PDDeX689bngggty9NFHZ+LEiVluueVy6qmn5rHHHsuoUaPy8ccf57XXXsvAgQPzgx/8IC1atMjMmTNz4okn5vzzz1+o4ybJ9ddfP8fjnThxYsaMGZOhQ4fm9NNPzzLLLJMZM2bkxBNPzDnnnLPQx+SrqSpJkcxOvQEAAACg7F555ZWss846KYoi66+/fh566KF06tSpwf0vvfTSHHnkkUmSFVdcMcOHD0/r1q2/oGppSFVVVZJk6623ziOPPJLWrVtn1KhRWWaZZeba9qSTTspZZ52VLbbYIo8//niS5Iorrsihhx46x7433nhj+vTpkyTp1q1b+vXrly222KLBvidMmJAjjzwyN9xwQ5Jkgw02yOOPP56ll156jn27d++et99+Ox07dswnn3zSaM2jR4/OkUceWZnSauONN84jjzxSb79/+MMfctpppyVJbrnllnznO9+ps338+PFZccUVM2XKlKywwgp58skns9JKKzV47AEDBmTffffN9OnT07Zt2wwfPjzdunVrtN75renzhg8fnk033TTjxo1LmzZtMnz48KywwgrzdUy+XGrOh2233TaDBg1a6P6qqqqMAAEAAADgq+XWW2+tfBn4d7/7XaPhR5L85Cc/yU477ZRk9ofQjzzyyOIukfmw1VZbpX379pk2bVpuvPHGue5fFEWuu+66JMkuu+zS6L7vv/9+fvzjHydJ2rdvn4cffrjR8COZPZrjuuuuy3e/+90kyXPPPZc//OEP8/BIGte1a9f069cvhxxySJLkmWeeyfHHH79Afd11112ZMmVKkuTYY49tNPxIkj322KNy3ClTpsz3dGMLYvXVV8+vfvWrJMnUqVPTr1+/xX5MykcAAgAAAMBXyttvv125veqqq85Tm4svvjjDhg3LZ599lh133LHefaqrq3PNNddk7733zkorrZQ2bdqkQ4cO+eY3v5kf/ehHeeyxxxrsf7vttqssBN2Y2gtJjxw5ss62Qw89NFVVVWnXrl2S5P77788WW2yR9u3bNxjy1EzzteKKK6Z169bp1KlTttxyy5x//vmZNm1ao7W89dZb+eUvf5n11lsvnTp1Sps2bbLSSitln332yY033tjojDM1tVZVVS30N71btWqV3r17J5m3abAeffTRvP3222nWrFl23XXXRvc999xzM3HixCTJn/70p3zjG9+Yp5qaNWuWSy65JMstt1yS5G9/+1vGjBkzT20bU1VVlX/84x9ZY401kiT/+te/MmLEiPnuZ0HOgdNOOy1PPvlkPvvssxx22GHzfcwFscMOO1RuP/fcc3W2Lcjr/fbbb8+BBx6Y7t27Z+mll07btm2z2mqr5eCDD56nUKe6ujpXXnlldtlllyy//PJp1apVlltuueywww658sorU11d3Wj75557LkcddVTWWmuttG/fPksvvXS6d++egw8+OHfffXejbYuiyE033ZR99tknq666apZaaqm0bNky3bp1S69evfK3v/2t0TVnnnzyyfz4xz/O2muvnfbt26dFixZZZpll0rNnz5x00kl57733Gj3+p59+mrPPPjtbbbVVunTpklatWqVbt27Zbrvtcu6552by5MmNtp85c2b+9re/ZZNNNknHjh3Trl27rLXWWvnVr36V999/v9G2C6tIUgAAAADAV8Gxxx5b1HwmdtFFFy2SPj/66KOiZ8+elX4b+jnqqKOKmTNnztF+2223Leblc7pTTz21st+IESPqbDvkkEOKJEXbtm2LZ599tmjZsmVl344dO9bZd/LkycUee+zRaK1rr712MXLkyHrruOiii4pWrVo12n7bbbctPvnkk3rb19SapHjwwQcbfcwNqWl/6qmnFtdee23l9zfffLPRdj/5yU+KJMU222xTjBgxotLuiiuuqLPf9OnTiw4dOhRJis6dOxdTp06d7xp/+9vfVvr/y1/+Msf2VVddtd6/z9xcfPHFlX5/+9vfzrG99uvklltumWN73759K9v/53/+Z76OvaDmVlN9Xn/99Uqb/fffv862+Xm9f/bZZ8Vuu+021/Nz7733LiZPnlxvLWPHji022WSTRttvtdVWxccff1xv+5NOOqlo1qxZo+0POOCAYtq0aXO0nTJlStG7d++51r/GGmsUb731Vr3PfVVVVaNt27VrV9x555311v7YY48VXbt2bbT9yiuvXDz//PP1tp88eXKx9dZbN9i2a9euxTPPPFM5H7bddtt6+5lfSQojQAAAAAD4Svn2t79duf373/8+gwcPXqj+Zs6cmd69e2fIkCFJkv322y8PP/xwxowZk/fffz+33HJLNthggySzR5KccsopC3W8eXHKKadktdVWy/33359x48blhRdeqLP9kEMOyYABA5IkRx55ZJ555pmMHTs2zz33XI4//vg0a9Ysr7zySnbfffdMnTq1Tttrr702P/3pTzN9+vSsttpqueaaa/Lee+9lzJgxefTRR7P//vsnSR566KHKNFCL25577pmllloqSfLvf/+7wf2mT5+em266KUly8MEHN9rn0KFD8+mnnyZJ9t577wVa9+Wggw6q3B44cOB8t29Inz59KqOFHnjggfluX/scuOCCC3LbbbctstoWpXHjxlVuL7vssg3uN7fX+0EHHVQZ4dGrV6/cfffd+fDDDzNq1Kjcfffd2W677ZIkt912W2XKs9qqq6uzxx575KmnnkqLFi1y4okn5qWXXsqYMWPy9NNP5wc/+EGS1Hn913bGGWfkzDPPTHV1dTbccMPceuutGTVqVEaPHp177rknvXr1SjJ7vZmjjjpqjvZnnnlm5fXz3e9+Nw899FDee++9jB49OkOGDMlxxx2XZs2a5c0338z3vve9Om0feOCBnHbaaSmKIhtssEH69euXt956K+PGjcvLL7+cv/71r1l22WXz2WefpU+fPhk7dmyd9q+//np23nnnjB49Ou3atcvZZ5+d119/PR9//HGef/75nHTSSWnRokXefffd7LDDDnO0T5Lf/OY3lakDv/3tb+fee+/NmDFj8s477+Saa65J27Zt873vfS/Tp0+f84+7CBgBAgAAAMBXxuTJkyvfNE5SNGvWrNh3332LG264oRg3btx893fBBRdU+vrxj39c7z6ffvpp8Y1vfKNIUrRo0aJ455136mxflCNAmjdvXrRr126O7TXuvffeSh8///nP692n9siFf/zjH3UeR6dOnYokRffu3YsxY8bU2/7oo4+utL/xxhsbfUwLqqb/U089tSiKojjooIOKJMXqq6/eYJt+/foVSYpWrVoV48aNa3QEyHnnnVfZdumlly5QjTNnzizatm1bJCmWW265ObYv6AiQoiiKddZZp/L3njFjRp1tcxttUV1dXWy66aZ1voW/8847F1dccUXxwQcfzHct82JBRoCcccYZlTaXX355nW3z+nq//fbbK33ssssu9Y7AmjFjRrHNNttU9nv88cfrbL/ssssq2/7617/We5w+ffpU9hk4cGDl/uHDh1dGp/Ts2bOYNGnSHG1nzZpVZ0TWk08+WWf7aqutViQp1l133WLWrFn1Hv8vf/lLpf2wYcMq9x9++OGV97kPP/yw3rZPP/10pW3fvn3rbOvVq1eRpGjZsmXx2GOP1dv+hhtuqLQ/5phj6mwbNWpU5fGvuuqqxYQJE+ZoP2rUqGL55Zev9GEECAAAAAAsoKWWWiq33HJLunbtmmT2t7tvvvnm9OnTJ126dMlGG22UX/ziF7nxxhvrfAO9IZdddlmS2WtRnH322fXu0759+5xwwglJZo8YueaaaxbRo5nTrFmzsv/++6d79+71br/kkkuSzF6n4uSTT653n2OPPbayvkLtETLXXnttPvnkkySzv5XeuXPnetufeeaZadu2bZL/e34Wt5pv4Q8fPjyPP/54vfvUPO+9e/dudERBknz44YeV2w09l3PTvHnzrLzyyklmj2aoWU9kUVhllVWSzP57z8vrtLaqqqrceOONlbVEkuSee+7JYYcdlhVWWCFrr712fvKTn+Tf//53Pvjgg0VW8/x455130rdv3yRJhw4d8p3vfKfe/eb2eq/9+vvzn/+c5s2bz7FPixYt6ixUf+WVV9bZXnPOLLvssvnZz35W73FqFqTv0KFDHn300cr9l156aWbMmJEkOf/887P00kvP0bZZs2Y599xzK79ffvnldbbXrM+x1lprpVmz+j/SP/roo/PAAw9kxIgRWW+99eZou+yyy6Zbt271tv3Wt76VQYMG5ZVXXqkzAuXVV1+tjDA67LDDssUWW9Tb/oADDsiWW26ZZPZ7xJQpUyrb+vfvX3n8P/vZz9KhQ4c52i+//PKVBe8XNQEIAAAAAF85G220UZ5//vn85Cc/SatWrSr3V1dX59lnn80FF1yQPn36pFu3btl+++3Tr1+/ehf1rpkGJkm22mqryqLX9dltt90qt2t/QLo4NLRQezJ7segkWX/99Ssh0Od97Wtfy9SpUzNx4sRcddVVlfvvueeeJLM/sN1jjz0aPEanTp2y/fbbJ0keeeSRzJw5c74fw/zaeeedKx/w1rcY+oQJE3LHHXckyRzTBNVn/PjxldsNLao9L5ZZZpnK7UUZgNSu6eOPP57v9qusskqeeeaZ/Pa3v60sJF7j1VdfzWWXXZYf/vCHWWmllbLJJpvkiiuuqHyQvbhMnz49b731Vi666KJssskmlemUzjnnnEbPrYZe70VR5KGHHkqSrLbaanWCgc/beuutK89D7fPzk08+ydNPP50k2Wabbeq8X9S26aabZvr06ZkwYUJOP/30yv0150yXLl2y+eabN3j8NddcM+uuu26SOac1W3755ZMk9913X95888162y+99NLZfvvt07179zohT03bsWPH5r///W+Dx992222z1lprpU2bNnPUniR77bVXg22TZJ999kky+zwbOnRo5f4nn3yycrv2ovaft+eeezba/4ISgAAAAADwldStW7dccsklGTVqVP71r39l//33nyMQmDVrVgYNGpR99903W265ZZ1RAUnqrDXQ2IerNcer+TD8pZdeWkSPon7f+MY36r3/nXfeqXyw39A+Ner7oPfZZ59NMvtD/aqqqnz22WcN/qy11lpJkqlTp+b1119fiEczb5o3b15Zc+PGG2+cYz2Bm266KdOmTUuHDh3m6cPW2oHXwgQ4tds29O39BTFp0qTK7fpGNcyL9u3b5+yzz86oUaPyn//8Jz/84Q8rI1ZqFEWRIUOG5PDDD896662X1157baHqTmZ/WF5VVTXHT+vWrbP66qvnZz/7WT766KO0bNkyffv2zZFHHtlof4293idMmJBk7udnixYtsuaaayZJXnvttcrfrSbgbOw4NVq2bFnn91mzZlXeI1ZeeeVGz5fPPvssa6+9dpLkrbfeqvP3PfTQQ5PMDmM23HDD/PznP899992XadOmNVpPMnu9nxoHHHBA9ttvv9x4443zFJrVnO9J0rVr10Zrr3nukrrPWe1zv/Y+n7fGGmvM8fwtCi0WeY8AAAAA8CWy7LLL5rDDDsthhx2WZPa33x955JE8+OCDueuuuyqBweDBg7P55ptn2LBhlW/f117wt6HRFLV17tw548ePX6Bv7M+Pjh071nt/7Xprj0yYV2PGjEkyezqn9u3bz3e7xe373/9+zjvvvIwfPz4DBgyoswh7zfRX++67b51vuTek9nNYM+3Xgqj5AP7zfS6sjz76qHJ7Qf6WtbVr1y59+vRJnz59kiQjR46snAMDBw6sBH+vvfZaNt988zzzzDMLPC3Y3LRq1Sprrrlmdtppp/z0pz+tM01XQ+bl9T6v52cyO7j49NNPs+yyyy7UOfPJJ59UgpRnnnlmns+ZoigyduzYyjRyv//97/Paa6/lhhtuyKRJk3LhhRfmwgsvTJs2bbL11ltnjz32yAEHHFAZ7VFbr1690rdv3/z617/OrFmzcvPNN+fmm29Os2bNstFGG2W33XbL/vvvX29AVPu83WSTTeb5cdduV/P+2bp16zlGGtXWvHnzLLPMMhk9evQ8H2deGAECAAAAALWstdZaOeKII3Ldddflgw8+yHnnnVeZt3/kyJGVdQmS1Jnrfl4+VK/ZZ/LkyYu46roa+iZ17eM2NJVPY2o/3vmxKKd+asy3vvWtyrfoa0+D9e677+bhhx9OMm/TXyXJ6quvXrn96quvLlA9M2bMyNtvv50kWWGFFbLUUkstUD+fVxRFRo4cmWT2h/9dunRZJP3W6N69e37wgx/kX//6V959991cffXVlSmoxo8fn1NPPXWh+r/++uszceLEOX4mT56cadOm5cUXX0zfvn3nKfxIGn69L+j5mfzfubIw58yCni9J3XOmRYsW+c9//pNbb701u+yyS1q0mD2uYerUqbn33ntz7LHHZpVVVsnPf/7zeo953HHHZciQIfnhD39YCWGqq6szdOjQ/O///m/WX3/99O7du/KaWtj6a9de08e8PHcL8p40NwIQAAAAAGhAmzZtcuyxx+aKK66o3DdgwIDK7doLGk+dOnWu/dV8GNjYN6EXp/mt9/Nq6l577bVTFMU8/zS2XsiiVrMY+p133llZHPzaa69NURRZYYUVKmuTzE3Pnj0rt2svBD8/nn/++co0RfPzDfq5efrppysjQDbbbLNF1m99WrRokR/84AeV9VOSuufAgmjTpk3atWs3x8+iCohqLOj5mfzfa31hzpna53nv3r3n65ypWQ+ktr333jt33XVXxowZk5tuuik/+clPKlOWzZgxIxdeeGH23HPPetcr2mijjXLVVVdl7Nixuf/++3PCCSdkww03rGy/6667ssUWW9SZ5q92/R999NE81/6Xv/yl0q5169ZJMseUdPVZmMCoIQIQAAAAAJiL/fffv/Jh4HvvvVe5v/Y37z+/Pkh9aqZ3aWxB5/o+vKyxsCMpatc7atSo+W5fM41Q7WmBljTf+973UlVVlRkzZqRfv35JZo84SJIDDzxwntfh2HDDDStTCg0YMGCBpsH6z3/+U7m9++67z3f7htx0002V24tr8ejP23TTTbPOOuskmb3o+uL4sHpRm9/zsyZUatmyZTp06DBHH/N7znTo0KESACzKc6ZTp07Zb7/9cskll+Ttt9/OHXfcUQlC7r///vTv37/Btq1atUqvXr1y1llnZdiwYXnhhRey9dZbJ5n9+P70pz9V9q09bdiC1l8zPdm0adMaHfk2bdq0ynRZi5IABAAAAICvjGeeeSannHJKevfunfvvv3+e21VVVVW+nV57Hv/1118/VVVVSZLnnnuu0T7eeeedfPrpp0mSDTbYoM622lP41F78+PNeeeWVea65PiuvvHJlHYPaC7jXZ9iwYXn00UczZMiQyn013xgfM2ZMnSBoSbLKKqtk2223TZIMHDgww4cPryzK/P3vf3+e+2nWrFmOOOKIJLO/mX7uuefOVx1jx46tjBzq2LFjDjzwwPlq35ARI0bkwgsvTDL7G/rz85iS5I033sgZZ5yRvffeO//+97/nq23NaIiWLVvO05RSTW3llVfOsssum6Tuwtz1mTZtWt54440kybrrrlsJymqvjTG3c2bw4MF59NFHK8dq1qxZ1l9//STJSy+9NE+jIOZXVVVVdtttt1x33XWV+x588MF5br/uuutmwIABlfeF2m1rjxB55plnFqi+2lPJDR8+vMH9XnzxxVRXVy/QMRojAAEAAADgK+OVV17J//7v/+auu+7KOeecM1/tahb2rf2hYMeOHStTJQ0ePLjRBXxrfyu7V69edbbVLKqeZI55+GuMGTMmDzzwwDzX3JAddtghSfL22283+KHw5MmTs/nmm2frrbfOL3/5y8r9O++8c+V2zaiKhtxwww3p379/o4HO4lIzDVbtb8Ovvfba2Wijjearn+OPP74yWufss8+e56mwiqLIMcccU5mC6+STT14k055NmTIlP/rRjyqjL0466aT5Xlh99OjROfnkk9O/f/+cffbZlUW652b8+PGVAKB28Lek23HHHZPMfr0PGzaswf3uvffeyhRXtc/PTp065dvf/naS2ed4QyMh3nrrrWyxxRbZeuut8+c//7lyf805M3ny5Nx2222N1nrZZZfl3nvvrROUjBw5Mv/+979z1llnNdq2dqha0378+PG59dZb8/vf/z4ff/xxg207dOiQr3/963XaJslOO+1UuT238/3JJ5/MFVdcMcdIm29961uV24MGDWqw/a233tpo/wtKAAIAAADAV8Z+++1X+aDvnnvuyYknntjolFNJ8sknn+RHP/pR5feaUQE1jjzyyCSz5+A/4YQT6u1jzJgxlall2rVrN8dC3DVTCyV1pzeqUV1dnaOPPjqzZs1qtNZ5Ubv+E044od7Hf+GFF1bWrthvv/0q9x944IGVb4qfddZZDX6j+9lnn83hhx+evffeO8ccc8xC1zy/9ttvv7Rp0yaffvpp/va3vyWZ98XPa1tmmWXyz3/+szKl1i677FJnLYz6TJw4MT/4wQ8qf8cddtghxx9//Pw/iM/56KOPsv3221e+ob/NNtvkf/7nf+a7ny233DJbbLFFkuTll1/O4YcfPteRCdOmTcvhhx9eCQg+fw4syWrOzyT5zW9+U+85NGXKlJxyyilJkubNm8/x+Gp+nz59ek4++eR6j1N73Yva58yPfvSjygivE044ocEA5e67786RRx6ZnXfeOWeccUbl/ltvvTU//OEPc9JJJ+X2229v8HHWDkdr3k/efvvt7LPPPjn99NPr9Pl5H374YV5++eU6bZNkzTXXrARId955Z2VKuc8bP358fvzjH+fwww/PlltuWSdU23vvvSth2YUXXljvNFgjR46sjGpaHIokBQAAAAB8FQwePLhYaqmliprPxTbYYIPiggsuKJ5++uli9OjRxYQJE4r333+/ePjhh4s//OEPRbdu3Sr7/vSnP52jv1mzZhVbbrllZZ8+ffoUgwcPLsaNG1e8/fbbxTXXXFOsscYale0XX3zxHH08++yzle0tWrQoTjnllOKJJ54oXnrppeKWW24ptt1226Jly5bFSSedVNlvxIgRdfo45JBDGtz2efvuu29l3z322KMYPHhwMXbs2OL5558vfv3rXxfNmzcvkhTf/OY3iylTptRpe80111TaduvWrfj73/9ejBgxohg7dmzx7LPPFqeddlrRoUOHIkmx9NJLFyNHjpzj+LVrffDBB+f6N6tPTftTTz213u19+vSp7FNVVVXvczJixIjKPldccUWDx7rssssqz0mSYtdddy2uvvrq4pVXXinGjRtXvPvuu8Wjjz5a/P73v6/zetlmm22KTz75pMF+V1111SJJ0bFjx2LixIlz/HzwwQfF/fffXxx//PFF+/btK/1uscUWxfjx4xvs99RTT63se8stt8yx/Y033ii6dOlS2Wf11Vcv/vSnPxWDBw8uPvzww2LChAnFhx9+WDzxxBPFOeecU6y22mp1Xi/V1dUNHntBa5pf8/N6P+iggyr77rjjjsUDDzxQjBkzpnj//feLW265pdh4440r20844YQ52s+cObPYZJNNKvsceuihxbBhw4qxY8cWTz/9dHHYYYdVtm277bZzPD+nn356nef6mmuuKd59991i9OjRxZAhQ4pf/epXRatWrYokxfLLL19MmDCh0vaTTz4punbtWiQpWrVqVZxwwgnFk08+WXzwwQfF+PHji1dffbW44IILik6dOhVJis6dOxdjx46ttN9ll13qvDfdc889xciRI4tPPvmkGDlyZHHTTTcVPXr0qJwngwYNqlP7a6+9VrRt27bO8V944YVi3Lhxxauvvlpcfvnldd7fbrzxxjmevwMPPLCyfcsttywefPDBYuzYscU777xTXHHFFcWKK65Y9OjRo1hzzTUrz+Gi8P+PKQABAAAA4Kvl2WefrfOh59x+2rdvX/z5z39usL+PP/64TghS30/z5s2LP/3pTw328dOf/rTBti1atCj+9a9/FTfddFPlvjfeeKNO+/n5QHjSpEnFrrvu2mi93/zmN4u33nqr3vZ///vfKx/YNvTTrVu34sknn6y3/RcRgAwYMKDOh671mdcApCiK4rHHHivWW2+9eXq9tGvXrvjjH/9YzJgxo9E+awKQef1ZaqmlipNPPrmYPn16o/3OS9gwYsSIolevXvN87FatWhW/+c1v5nrshalpfszP633KlCnFPvvsM9fHeOyxxxazZs2qt4/Ro0cX3/72txttv/nmmxfjxo2rt/1JJ51UNGvWrNH2a6655hzndVHMDm1rB1YN/XTt2rV4/PHH56j7W9/61jz9ff/+97/XW/tjjz1WCWEae4+6/PLL620/bty4Yt11122wbefOnYuhQ4cWPXv2bPR8nV9JihYBAAAAgK+YDTbYIEOHDs3999+f2267LUOHDs3w4cMzYcKEzJgxI+3atcvyyy+fddddNzvttFP233//ymLK9VlmmWXyyCOP5Prrr89//vOfDB06NGPHjk3r1q2zyiqrpFevXvnpT3+ab37zmw32ceGFF6Znz57517/+leeffz6TJk1Kt27dsuWWW+b444/Ppptumv/+97+V/eubSmZeLb300hk4cGBuvvnmXH311RkyZEjGjh2bpZZaKj169MgBBxyQo446qrLw++cdffTR6d27dy688MLce++9efvttzN16tR069Yt3bt3z/7775/vf//7ddY2+aLtsssu6dKlS8aMGbNA01993hZbbJHnnnsu9913X/r165ennnoqb731ViZOnJhWrVqlW7duWW+99bLLLrvkoIMOavT1Mq9atWqVzp07V/r93ve+l65duy50v0nSvXv33H///XnyySfTr1+/PPHEE3nzzTczfvz4TJs2LW3btk2XLl2yzjrrZLvttkufPn2y4oorLpJjf9HatGmTfv365Y477sjVV19dWa+nRYsWWWGFFbLtttvmyCOPrKz1UZ8uXbrkiSeeyFVXXZUbbrghzz77bD7++OO0b98+G264Yb73ve/l0EMPTfPmzettf8YZZ6RPnz65+OKL8+CDD+a9997LzJkzs8IKK2T11VfPwQcfnAMOOKCy0Hxtm222WV577bVcdtllueuuu/LKK6/k448/TlEUWWaZZdKjR4/svvvuOeKII+ZYE6am7uuvvz79+vXLs88+m48++ijTp09Pu3btssYaa6RXr1458sgj6yxYXtsWW2yRN998M//4xz8yYMCAvPLKK5kwYUKWW265rLjiitlll13yox/9KKuttlq97Zdddtk89dRTOe+883LjjTfmzTffTHV1dVZcccX07t07v/rVr7LKKqtUal+Y97bPq0r+/zCQucx1CAAAAAAA8GVQVVVlEXQAAAAAAKB8BCAAAAAAAEDpCEAAAAAAAIDSEYAAAAAAAAClIwABAAAAAABKRwACAAAAAACUjgAEAAAAAAAoHQEIAAAAAABQOgIQAAAAAACgdAQgAAAAAABA6QhAAAAAAACA0hGAAAAAAAAApSMAAQAAAAAASkcAAgAAAAAAlI4ABAAAAAAAKB0BCAAAAAAAUDoCEAAAAAAAoHQEIAAAAAAAQOkIQAAAAAAAgNIRgAAAAAAAAKUjAAEAAAAAAEqnKknR1EUAAAAAAAAsSv8PdsYJO9TxzJoAAAAASUVORK5CYII="
-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "\n",
-    "(\n",
-    "ggplot(df2, aes(x=\"zscore_bin\", y=\"pct\", fill=\"zscore_bin\"))\n",
-    "    + geom_bar(alpha=1, stat='identity') #  fill='#006d2c'\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_fill_brewer(type='div', palette=\"RdYlGn\", direction=1)\n",
-    "    # + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='zscore_bin'>"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "df['zscore_bin'].value_counts().sort_index().plot(kind='bar', ax=ax)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# df.reclassify(column='zscore', bins=bins)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# df.plot(kind='bar', bins=bins, alpha=0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# # make a histogram plot of the data\n",
-    "fig, ax = plt.subplots(figsize=(10, 6))\n",
-    "plt.hist(data, bins=bins, color='c', alpha=0.7)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loop years"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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-    "start_reference = \"2004-01-01\"\n",
-    "end_reference = \"2019-01-01\""
-   ]
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-   "cell_type": "code",
-   "execution_count": 166,
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-    "ds_ref = ds_monthly.sel(time=slice(None, end_reference)) # ds_geo\n",
-    "ds_ref_09_mean = ds_ref.sel(time=ds_ref.time.dt.month==9).mean(dim='time')\n",
-    "ds_ref_09_sdt = ds_ref.sel(time=ds_ref.time.dt.month==9).std(dim='time')\n",
-    "\n",
-    "\n",
-    "# ds_filt = ds_monthly.sel(time=ds_monthly.time.dt.year==2023)\n",
-    "# ds_2023_09 = ds_filt.sel(time=ds_filt.time.dt.month==9).isel(time=0)\n",
-    "# ds_diff = ds_2023_09 - ds_ref_09_mean\n",
-    "# ds_zscore = (ds_diff / ds_ref_09_sdt)"
-   ]
-  },
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-   "execution_count": 169,
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calc anomalies for 2019\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calc anomalies for 2020\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calc anomalies for 2021\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calc anomalies for 2022\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calc anomalies for 2023\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n"
-     ]
-    }
-   ],
-   "source": [
-    "res = []\n",
-    "years = range(2019, 2024)\n",
-    "for year in tqdm(years):\n",
-    "    print(f\"Calc anomalies for {year}\")\n",
-    "    ds_filt = ds_monthly.sel(time=ds_monthly.time.dt.year==year)\n",
-    "    ds_filt_09 = ds_filt.sel(time=ds_filt.time.dt.month==9).isel(time=0)\n",
-    "    ds_diff = ds_filt_09 - ds_ref_09_mean\n",
-    "    ds_zscore = (ds_diff / ds_ref_09_sdt)\n",
-    "    ds_zscore = ds_zscore.compute()\n",
-    "    data = ds_zscore['veg_index'].values\n",
-    "    data = data[~np.isnan(data)]\n",
-    "    res.append(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
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-    "[len(r) for r in res]"
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-   "execution_count": 176,
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-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
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-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
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-   "source": [
-    "bins = [-20, -2, -1, -0.5, 0.5, 1, 2, 20]\n",
-    "bin_labels = ['< -2', '(-2, -1]', '(-1, -0.5]', '(-0.5, -0.5]', '(0.5, 1]', '(1, 2]', '> 2']"
-   ]
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-   "execution_count": 186,
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-       "\n",
-       "                .geemap-colab {\n",
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-       "\n",
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-   "source": [
-    "res2 = []\n",
-    "for i, year in enumerate(years):\n",
-    "    data = res[i]\n",
-    "    df = pd.DataFrame(data, columns=['zscore'])\n",
-    "    df.loc[:, 'zscore_bin'] = pd.cut(data, bins=bins, include_lowest=True, labels=bin_labels)\n",
-    "    df = df['zscore_bin'].value_counts().sort_index()\n",
-    "    df = pd.DataFrame(df).reset_index()\n",
-    "    df.loc[:, 'pct'] = df['count'] / df['count'].sum()\n",
-    "    df.loc[:, 'year'] = year\n",
-    "    # df.rename(columns={'count':f'count_{year}', 'pct': f'pct_{year}'}, inplace=True)\n",
-    "    res2.append(df)"
-   ]
-  },
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-   "execution_count": 188,
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-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
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-       "                .geemap-colab {\n",
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-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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-    "df = pd.concat(res2, axis=0)"
-   ]
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-   "execution_count": 229,
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-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "\n",
-    "(\n",
-    "ggplot(df2, aes(x=\"zscore_bin\", y=\"pct\", fill=\"zscore_bin\"))\n",
-    "    + geom_bar(alpha=1, stat='identity') #  fill='#006d2c'\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_fill_brewer(type='div', palette=\"BrBG\", direction=1)\n",
-    "    # + scale_fill_brewer(type='div', palette=\"RdYlGn\", direction=1)\n",
-    "    # + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 247,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df.loc[:, \"zscore_bin_cat\"] = pd.Categorical(df['zscore_bin'], categories=bin_labels, ordered=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "zscore_bin_cat\n",
-       "< -2            5\n",
-       "(-2, -1]        5\n",
-       "(-1, -0.5]      5\n",
-       "(-0.5, -0.5]    5\n",
-       "(0.5, 1]        5\n",
-       "(1, 2]          5\n",
-       "> 2             5\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 249,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.zscore_bin_cat.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<plotnine.positions.position_fill.position_fill at 0x73a3d789d1c0>"
-      ]
-     },
-     "execution_count": 253,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "position_fill(reverse=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "(\n",
-    "ggplot(df, aes(x=\"year\", y=\"pct\", fill=\"zscore_bin\"))\n",
-    "    + geom_bar(alpha=1, stat='identity', color='black', position = position_fill(reverse=True)) #  fill='#006d2c'\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_fill_brewer(type='div', palette=\"BrBG\", direction=1)\n",
-    "    # + scale_fill_brewer(type='div', palette=\"RdYlGn\", direction=1)\n",
-    "    # + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 265,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "charts_dir = join(expanduser('~'), 'Repos', 'niger')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 270,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# import plotnine\n",
-    "# plotnine.options.figure_size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Evi Anomalies Chart"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/plotnine/ggplot.py:606: PlotnineWarning: Saving 8 x 6 in image.\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/plotnine/ggplot.py:607: PlotnineWarning: Filename: /home/jupyter-wb514197/Repos/niger/evi-anomalies-pct.jpeg\n"
-     ]
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "p = (\n",
-    "ggplot(df, aes(x=\"year\", y=\"pct\", fill=\"zscore_bin\"))\n",
-    "    + geom_bar(alpha=1, stat='identity', color='black', width=0.5, position = position_fill(reverse=True)) #  fill='#006d2c'\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"% of Crop Area\", title=\"Anomalies in Agricultural Productivity (EVI)\", \n",
-    "        y=\"\", caption=\"Source: MODIS Terra and Aqua\", fill=\"Z-Score\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"bottom\"\n",
-    "        )\n",
-    "    + scale_fill_brewer(type='div', palette=\"BrBG\", direction=1)\n",
-    "    + coord_flip()\n",
-    "    + scale_y_continuous(labels=percent_format())\n",
-    "\n",
-    "    \n",
-    "    # + scale_fill_brewer(type='div', palette=\"RdYlGn\", direction=1)\n",
-    "    # + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")\n",
-    "p.save(\n",
-    "    filename=join(\n",
-    "        charts_dir, \"evi-anomalies-pct.jpeg\"\n",
-    "    ),\n",
-    "    dpi=300,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>zscore</th>\n",
-       "      <th>zscore_bin</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.569313</td>\n",
-       "      <td>(0.5, 1]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.032612</td>\n",
-       "      <td>(-0.5, -0.5]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.379615</td>\n",
-       "      <td>(-0.5, -0.5]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.538359</td>\n",
-       "      <td>(-1, -0.5]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.399906</td>\n",
-       "      <td>(-0.5, -0.5]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1755367</th>\n",
-       "      <td>1.553685</td>\n",
-       "      <td>(1, 2]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1755368</th>\n",
-       "      <td>0.289687</td>\n",
-       "      <td>(-0.5, -0.5]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1755369</th>\n",
-       "      <td>-0.530185</td>\n",
-       "      <td>(-1, -0.5]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1755370</th>\n",
-       "      <td>-1.447524</td>\n",
-       "      <td>(-2, -1]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1755371</th>\n",
-       "      <td>-1.312369</td>\n",
-       "      <td>(-2, -1]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1755372 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           zscore    zscore_bin\n",
-       "0        0.569313      (0.5, 1]\n",
-       "1       -0.032612  (-0.5, -0.5]\n",
-       "2        0.379615  (-0.5, -0.5]\n",
-       "3       -0.538359    (-1, -0.5]\n",
-       "4        0.399906  (-0.5, -0.5]\n",
-       "...           ...           ...\n",
-       "1755367  1.553685        (1, 2]\n",
-       "1755368  0.289687  (-0.5, -0.5]\n",
-       "1755369 -0.530185    (-1, -0.5]\n",
-       "1755370 -1.447524      (-2, -1]\n",
-       "1755371 -1.312369      (-2, -1]\n",
-       "\n",
-       "[1755372 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 174,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# bins = [-20, -2, -1, -0.5, 0.5, 1, 2, 20]\n",
-    "# bin_labels = ['< -2', '(-2, -1]', '(-1, -0.5]', '(-0.5, -0.5]', '(0.5, 1]', '(1, 2]', '> 2']\n",
-    "\n",
-    "# df = pd.DataFrame(data, columns=['zscore'])\n",
-    "# df.loc[:, 'zscore_bin'] = pd.cut(data, bins=bins, include_lowest=True, labels=bin_labels)\n",
-    "# df2 = df['zscore_bin'].value_counts().sort_index()\n",
-    "# df2 = pd.DataFrame(df2).reset_index()\n",
-    "# df2.loc[:, 'pct'] = df2['count'] / df2['count'].sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Zonal Stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 274,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Affine(250.0, 0.0, -466208.4527382861,\n",
-       "       0.0, -250.00000000000006, 2618178.8511287076)"
-      ]
-     },
-     "execution_count": 274,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = ds_geo.rio.transform()\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 275,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "utm = 'EPSG:32632' # 'EPSG:4326' 'SR-ORG:6974' 'EPSG:23847'\n",
-    "# adm_df = adm0_ner.copy()\n",
-    "adm_df = adm1_ner.copy()\n",
-    "adm_df = adm_df.to_crs(utm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 276,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "01b1c136d53c4c8fbe6648b34691a68d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/920 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# res = []\n",
-    "var = 'evi'\n",
-    "for date in tqdm(ds_geo.time.values):\n",
-    "    date = pd.to_datetime(date).strftime('%Y%m%d')\n",
-    "    ds_sel = ds_geo.sel(time=date)\n",
-    "    data = ds_sel['veg_index'].values\n",
-    "    zs = pd.DataFrame(zonal_stats(\n",
-    "        adm_df, data, affine=a, stats=['median'], nodata=np.nan #  'median', 'std', 'count'\n",
-    "        ), index=adm_df.index\n",
-    "    ).rename(columns={'median': f'{var}-median_{date}'})\n",
-    "    adm_df = adm_df.join(zs)\n",
-    "    # .rename(columns={'mean':'mean_'+str(date), 'median':'median_'+str(date), 'std':'std_'+str(date), 'count':'count_'+str(date)})\n",
-    "    # res.append(zs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>shapeISO</th>\n",
-       "      <th>shapeID</th>\n",
-       "      <th>shapeGroup</th>\n",
-       "      <th>shapeType</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>evi-median_20040101</th>\n",
-       "      <th>evi-median_20040109</th>\n",
-       "      <th>evi-median_20040117</th>\n",
-       "      <th>evi-median_20040125</th>\n",
-       "      <th>...</th>\n",
-       "      <th>evi-median_20231016</th>\n",
-       "      <th>evi-median_20231024</th>\n",
-       "      <th>evi-median_20231101</th>\n",
-       "      <th>evi-median_20231109</th>\n",
-       "      <th>evi-median_20231117</th>\n",
-       "      <th>evi-median_20231125</th>\n",
-       "      <th>evi-median_20231203</th>\n",
-       "      <th>evi-median_20231211</th>\n",
-       "      <th>evi-median_20231219</th>\n",
-       "      <th>evi-median_20231227</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Tahoua/Agadez</td>\n",
-       "      <td>NE-1</td>\n",
-       "      <td>87150794B58281898249288</td>\n",
-       "      <td>NER</td>\n",
-       "      <td>ADM1</td>\n",
-       "      <td>POLYGON ((1196661.737 2002495.058, 1196625.057...</td>\n",
-       "      <td>0.13325</td>\n",
-       "      <td>0.130967</td>\n",
-       "      <td>0.1293</td>\n",
-       "      <td>0.125800</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.161867</td>\n",
-       "      <td>0.149767</td>\n",
-       "      <td>0.144167</td>\n",
-       "      <td>0.1386</td>\n",
-       "      <td>0.137067</td>\n",
-       "      <td>0.134133</td>\n",
-       "      <td>0.132867</td>\n",
-       "      <td>0.129567</td>\n",
-       "      <td>0.128533</td>\n",
-       "      <td>0.126883</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Dossa</td>\n",
-       "      <td>NE-3</td>\n",
-       "      <td>87150794B47488127174201</td>\n",
-       "      <td>NER</td>\n",
-       "      <td>ADM1</td>\n",
-       "      <td>POLYGON ((-11577.870 1625850.497, -12410.899 1...</td>\n",
-       "      <td>0.14725</td>\n",
-       "      <td>0.146500</td>\n",
-       "      <td>0.1457</td>\n",
-       "      <td>0.141733</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.183333</td>\n",
-       "      <td>0.174100</td>\n",
-       "      <td>0.165433</td>\n",
-       "      <td>0.1587</td>\n",
-       "      <td>0.156333</td>\n",
-       "      <td>0.152633</td>\n",
-       "      <td>0.150200</td>\n",
-       "      <td>0.146400</td>\n",
-       "      <td>0.146633</td>\n",
-       "      <td>0.146150</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2 rows × 926 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       shapeName shapeISO                  shapeID shapeGroup shapeType  \\\n",
-       "0  Tahoua/Agadez     NE-1  87150794B58281898249288        NER      ADM1   \n",
-       "1          Dossa     NE-3  87150794B47488127174201        NER      ADM1   \n",
-       "\n",
-       "                                            geometry  evi-median_20040101  \\\n",
-       "0  POLYGON ((1196661.737 2002495.058, 1196625.057...              0.13325   \n",
-       "1  POLYGON ((-11577.870 1625850.497, -12410.899 1...              0.14725   \n",
-       "\n",
-       "   evi-median_20040109  evi-median_20040117  evi-median_20040125  ...  \\\n",
-       "0             0.130967               0.1293             0.125800  ...   \n",
-       "1             0.146500               0.1457             0.141733  ...   \n",
-       "\n",
-       "   evi-median_20231016  evi-median_20231024  evi-median_20231101  \\\n",
-       "0             0.161867             0.149767             0.144167   \n",
-       "1             0.183333             0.174100             0.165433   \n",
-       "\n",
-       "   evi-median_20231109  evi-median_20231117  evi-median_20231125  \\\n",
-       "0               0.1386             0.137067             0.134133   \n",
-       "1               0.1587             0.156333             0.152633   \n",
-       "\n",
-       "   evi-median_20231203  evi-median_20231211  evi-median_20231219  \\\n",
-       "0             0.132867             0.129567             0.128533   \n",
-       "1             0.150200             0.146400             0.146633   \n",
-       "\n",
-       "   evi-median_20231227  \n",
-       "0             0.126883  \n",
-       "1             0.146150  \n",
-       "\n",
-       "[2 rows x 926 columns]"
-      ]
-     },
-     "execution_count": 283,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "adm_df.head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 278,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_re = pd.wide_to_long(\n",
-    "    adm_df,\n",
-    "    stubnames = ['evi-median'],\n",
-    "    i = 'shapeName',\n",
-    "    j = 'ymd',\n",
-    "    sep = \"_\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_re.reset_index(inplace=True)\n",
-    "df_re.drop(columns=['geometry'], inplace=True)\n",
-    "df_re.to_csv(join(data_dir, 'modis_crop_dea_niger_adm1.csv'), index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load Table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_re = pd.read_csv(join(data_dir, 'modis_crop_niger_adm1.csv'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>ymd</th>\n",
-       "      <th>shapeType</th>\n",
-       "      <th>shapeISO</th>\n",
-       "      <th>shapeID</th>\n",
-       "      <th>shapeGroup</th>\n",
-       "      <th>evi-median</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Tahoua/Agadez</td>\n",
-       "      <td>20040101</td>\n",
-       "      <td>ADM1</td>\n",
-       "      <td>NE-1</td>\n",
-       "      <td>87150794B58281898249288</td>\n",
-       "      <td>NER</td>\n",
-       "      <td>0.13325</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Dossa</td>\n",
-       "      <td>20040101</td>\n",
-       "      <td>ADM1</td>\n",
-       "      <td>NE-3</td>\n",
-       "      <td>87150794B47488127174201</td>\n",
-       "      <td>NER</td>\n",
-       "      <td>0.14725</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       shapeName       ymd shapeType shapeISO                  shapeID  \\\n",
-       "0  Tahoua/Agadez  20040101      ADM1     NE-1  87150794B58281898249288   \n",
-       "1          Dossa  20040101      ADM1     NE-3  87150794B47488127174201   \n",
-       "\n",
-       "  shapeGroup  evi-median  \n",
-       "0        NER     0.13325  \n",
-       "1        NER     0.14725  "
-      ]
-     },
-     "execution_count": 280,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_re.head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_re.ymd = pd.to_datetime(df_re.ymd, format=\"%Y%m%d\")\n",
-    "df_re.loc[:, \"year\"] = df_re.ymd.dt.year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = df_re.copy()\n",
-    "# df.set_index('ymd', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>ymd</th>\n",
-       "      <th>shapeType</th>\n",
-       "      <th>shapeISO</th>\n",
-       "      <th>shapeID</th>\n",
-       "      <th>shapeGroup</th>\n",
-       "      <th>evi-median</th>\n",
-       "      <th>year</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Tahoua/Agadez</td>\n",
-       "      <td>2004-01-01</td>\n",
-       "      <td>ADM1</td>\n",
-       "      <td>NE-1</td>\n",
-       "      <td>87150794B58281898249288</td>\n",
-       "      <td>NER</td>\n",
-       "      <td>0.133250</td>\n",
-       "      <td>2004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Dossa</td>\n",
-       "      <td>2004-01-01</td>\n",
-       "      <td>ADM1</td>\n",
-       "      <td>NE-3</td>\n",
-       "      <td>87150794B47488127174201</td>\n",
-       "      <td>NER</td>\n",
-       "      <td>0.147250</td>\n",
-       "      <td>2004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Niamey</td>\n",
-       "      <td>2004-01-01</td>\n",
-       "      <td>ADM1</td>\n",
-       "      <td>NE-6</td>\n",
-       "      <td>87150794B29979307394387</td>\n",
-       "      <td>NER</td>\n",
-       "      <td>0.128108</td>\n",
-       "      <td>2004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Tillaberi</td>\n",
-       "      <td>2004-01-01</td>\n",
-       "      <td>ADM1</td>\n",
-       "      <td>NE-5</td>\n",
-       "      <td>87150794B24708462658640</td>\n",
-       "      <td>NER</td>\n",
-       "      <td>0.135967</td>\n",
-       "      <td>2004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Maradi</td>\n",
-       "      <td>2004-01-01</td>\n",
-       "      <td>ADM1</td>\n",
-       "      <td>NE-4</td>\n",
-       "      <td>87150794B5839560144768</td>\n",
-       "      <td>NER</td>\n",
-       "      <td>0.153567</td>\n",
-       "      <td>2004</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       shapeName        ymd shapeType shapeISO                  shapeID  \\\n",
-       "0  Tahoua/Agadez 2004-01-01      ADM1     NE-1  87150794B58281898249288   \n",
-       "1          Dossa 2004-01-01      ADM1     NE-3  87150794B47488127174201   \n",
-       "2         Niamey 2004-01-01      ADM1     NE-6  87150794B29979307394387   \n",
-       "3      Tillaberi 2004-01-01      ADM1     NE-5  87150794B24708462658640   \n",
-       "4         Maradi 2004-01-01      ADM1     NE-4   87150794B5839560144768   \n",
-       "\n",
-       "  shapeGroup  evi-median  year  \n",
-       "0        NER    0.133250  2004  \n",
-       "1        NER    0.147250  2004  \n",
-       "2        NER    0.128108  2004  \n",
-       "3        NER    0.135967  2004  \n",
-       "4        NER    0.153567  2004  "
-      ]
-     },
-     "execution_count": 285,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 288,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "(\n",
-    "ggplot(df, aes(x=\"ymd\", y=\"evi-median\", group=\"shapeName\"))\n",
-    "    + geom_line(alpha=1, color='#006d2c') #\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_x_datetime(breaks=date_breaks(\"1 year\"), labels=date_format(\"%Y\"))\n",
-    "    + facet_wrap('shapeName', ncol=1)\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 289,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dates = df.ymd.unique()\n",
-    "dates_copy = []\n",
-    "leap_years = [2004, 2008, 2012, 2016, 2020, 2024]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 290,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for date in dates:\n",
-    "    date_copy = date\n",
-    "    if (date.year in leap_years) and (date.month > 2):\n",
-    "        date_copy = date + pd.DateOffset(days=1)\n",
-    "    dates_copy.append(date_copy)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dates_df = pd.DataFrame({'ymd': dates, 'ymd2': dates_copy})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = df.merge(dates_df, on='ymd', how='left')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df.loc[:, \"md\"] = df.ymd2.dt.strftime(\"%m-%d\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 296,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# df.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reference ZS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "start_reference = \"2004-01-01\"\n",
-    "end_reference = \"2019-01-01\"\n",
-    "df_ref = df.loc[(df.ymd >= start_reference) & (df.ymd < end_reference)].copy()\n",
-    "\n",
-    "df_ref = df_ref.groupby([\"shapeName\", \"md\"])[\n",
-    "    [\"evi-median\"]\n",
-    "].mean()\n",
-    "df_ref.reset_index(inplace=True)\n",
-    "df_ref.rename(\n",
-    "    columns={\n",
-    "        \"evi-median\": \"evi-median_ref\"\n",
-    "    },\n",
-    "    inplace=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 299,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>md</th>\n",
-       "      <th>evi-median_ref</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>274</th>\n",
-       "      <td>Zinder/Diffa</td>\n",
-       "      <td>12-19</td>\n",
-       "      <td>0.140971</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>275</th>\n",
-       "      <td>Zinder/Diffa</td>\n",
-       "      <td>12-27</td>\n",
-       "      <td>0.138536</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        shapeName     md  evi-median_ref\n",
-       "274  Zinder/Diffa  12-19        0.140971\n",
-       "275  Zinder/Diffa  12-27        0.138536"
-      ]
-     },
-     "execution_count": 299,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_ref.tail(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_filt = df.loc[(df.ymd >= \"2023-01-01\")].copy()\n",
-    "df_filt = df_filt.merge(df_ref, on=[\"shapeName\", \"md\"], how=\"left\", validate=\"m:1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# calculate z score \n",
-    "# df_filt.loc[:, \"zscore\"] = zscore(df_filt[\"evi-median\"], nan_policy='omit')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>ymd</th>\n",
-       "      <th>shapeType</th>\n",
-       "      <th>shapeISO</th>\n",
-       "      <th>shapeID</th>\n",
-       "      <th>shapeGroup</th>\n",
-       "      <th>evi-median</th>\n",
-       "      <th>year</th>\n",
-       "      <th>ymd2</th>\n",
-       "      <th>md</th>\n",
-       "      <th>evi-median_ref</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Tahoua/Agadez</td>\n",
-       "      <td>2023-01-01</td>\n",
-       "      <td>ADM1</td>\n",
-       "      <td>NE-1</td>\n",
-       "      <td>87150794B58281898249288</td>\n",
-       "      <td>NER</td>\n",
-       "      <td>0.13035</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>2023-01-01</td>\n",
-       "      <td>01-01</td>\n",
-       "      <td>0.128127</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       shapeName        ymd shapeType shapeISO                  shapeID  \\\n",
-       "0  Tahoua/Agadez 2023-01-01      ADM1     NE-1  87150794B58281898249288   \n",
-       "\n",
-       "  shapeGroup  evi-median  year       ymd2     md  evi-median_ref  \n",
-       "0        NER     0.13035  2023 2023-01-01  01-01        0.128127  "
-      ]
-     },
-     "execution_count": 302,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_filt.head(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 303,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_filt.loc[:, \"evi_pct_ch\"] = df_filt.apply(\n",
-    "    lambda x: (x['evi-median'] - x['evi-median_ref']) / (x['evi-median_ref']), axis=1\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_filt = df.loc[(df.ymd >= \"2022-01-01\") & (df.ymd < \"2023-01-01\")].copy()\n",
-    "df_filt = df_filt.merge(df_ref, on=[\"shapeName\", \"md\"], how=\"left\", validate=\"m:1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 389,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_filt = df.loc[(df.ymd >= \"2022-01-01\")].copy()\n",
-    "df_filt = df_filt.merge(df_ref, on=[\"shapeName\", \"md\"], how=\"left\", validate=\"m:1\")\n",
-    "df_filt.loc[:, \"ymd3\"] = \"2023-\"+df_filt.md\n",
-    "df_filt.ymd3 = pd.to_datetime(df_filt.ymd3, format=\"%Y-%m-%d\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 390,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([2022, 2023], dtype=int32)"
-      ]
-     },
-     "execution_count": 390,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_filt.year.unique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Admin 1 Median Chart"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 395,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/plotnine/ggplot.py:606: PlotnineWarning: Saving 8 x 10 in image.\n",
-      "/home/jupyter-wb514197/.conda/envs/rtmis/lib/python3.12/site-packages/plotnine/ggplot.py:607: PlotnineWarning: Filename: /home/jupyter-wb514197/Repos/niger/evi-median-adm1.jpeg\n"
-     ]
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
-    "p2 = (\n",
-    "ggplot(df_filt, aes(x=\"ymd3\", y=\"evi-median\", group=\"factor(year)\", color=\"factor(year)\"))\n",
-    "    + geom_line(alpha=1, size=1.4) # color='#006d2c'\n",
-    "    + geom_line(df_filt.loc[df_filt.year==2023], \n",
-    "                aes(x=\"ymd3\", y=\"evi-median_ref\", group=\"shapeName\"), alpha=0.8, size=1.4, color='gray', linetype='dotted'\n",
-    "                ) #\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"EVI\", title=\"Median EVI vs. Historical Average\", \n",
-    "        y=\"\", caption=\"Source: MODIS Processed\", color=\"Year\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 10), # 10, 16\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"right\",\n",
-    "        )\n",
-    "    # + theme(figure_size=(10, 16))\n",
-    "    + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    + facet_wrap('shapeName', ncol=1)\n",
-    "    + scale_color_brewer(type='qual', palette=\"Set1\")\n",
-    "    # + scale_color_brewer(type='div', palette=\"PiYG\", direction=1)\n",
-    "    # + scale_color_brewer(type='seq', palette=\"Greens\", direction=1)\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")\n",
-    "p2.save(\n",
-    "    filename=join(\n",
-    "        charts_dir, \"evi-median-adm1.jpeg\"\n",
-    "    ),\n",
-    "    dpi=300,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
-    "(\n",
-    "ggplot(df_filt, aes(x=\"ymd2\t\", y=\"evi-median\", group=\"shapeName\"))\n",
-    "    + geom_line(alpha=1, color='#006d2c') #\n",
-    "    + geom_line(aes(x=\"ymd2\t\", y=\"evi-median_ref\", group=\"shapeName\"), alpha=1, color='gray') #\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    + facet_wrap('shapeName', ncol=1)\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# import matplotlib.font_manager\n",
-    "# matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# font_names = [\n",
-    "#     \"LiberationSansNarrow\",\n",
-    "#     \"Quicksand\",\n",
-    "#     \"UbuntuMono\",\n",
-    "#     \"NimbusRoman\",\n",
-    "#     \"DejaVuSansCondensed\",\n",
-    "#     \"Trebuchet MS\",\n",
-    "#     \"NimbusSans\",\n",
-    "#     \"Ubuntu\",\n",
-    "#     \"NimbusMonoPS\",\n",
-    "#     \"DejaVuSans\"\n",
-    "# ]\n",
-    "font = \"Liberation Sans\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
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"
-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "(\n",
-    "ggplot(df_filt, aes(x=\"ymd2\t\", y=\"evi-median\", group=\"shapeName\"))\n",
-    "    + geom_line(alpha=1, color='#006d2c') #\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "(\n",
-    "ggplot(df_filt, aes(x=\"ymd2\t\", y=\"evi_pct_ch\", group=\"shapeName\"))\n",
-    "    + geom_line(alpha=1) # color='#006d2c'\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        plot_background=element_rect(fill=\"white\"),\n",
-    "        figure_size=(8, 6),\n",
-    "        text=element_text(family=font, size=11),\n",
-    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
-    "        # axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_x_datetime(breaks=date_breaks(\"1 month\"), labels=date_format(\"%m\"))\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(\n",
-    "ggplot(df_re, aes(x=\"ymd\", y=\"evi-median\", group=\"shapeName\"))\n",
-    "    + geom_line(alpha=1, color='#006d2c')\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        figure_size=(8, 6),\n",
-    "        # text=element_text(family=\"Roboto\", size=11),\n",
-    "        # plot_title=element_text(family=\"Roboto\", size=14, weight=\"bold\"),\n",
-    "        axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_x_datetime(breaks=date_breaks(\"1 year\"), labels=date_format(\"%Y\"))\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 800
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(\n",
-    "ggplot(df_re, aes(x=\"ymd\", y=\"evi-median\", group=\"shapeName\"))\n",
-    "    + geom_line(alpha=1, color='#006d2c')\n",
-    "    + labs(\n",
-    "        x=\"\", subtitle=\"NDVI\", title=\"EVI for a Admin Area\", y=\"\", caption=\"Source: MODIS Processed\"\n",
-    "    )\n",
-    "    + theme_minimal() \n",
-    "    + theme(\n",
-    "        figure_size=(8, 6),\n",
-    "        # text=element_text(family=\"Roboto\", size=11),\n",
-    "        # plot_title=element_text(family=\"Roboto\", size=14, weight=\"bold\"),\n",
-    "        axis_text_x=element_text(rotation=45),\n",
-    "        legend_position=\"none\"\n",
-    "        )\n",
-    "    + scale_x_datetime(breaks=date_breaks(\"1 year\"), labels=date_format(\"%Y\"))\n",
-    "    # + scale_y_continuous(labels=percent_format())\n",
-    "    # + theme(legend_position='bottom')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/agriculture/drought.ipynb b/notebooks/agriculture/drought.ipynb
new file mode 100644
index 0000000..a46bc43
--- /dev/null
+++ b/notebooks/agriculture/drought.ipynb
@@ -0,0 +1,610 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Drought Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Request\n",
+    "Using NASA remote sensing data, prepare historical drought analysis for Niger and bordering countries. Estimate hectares of farmland where drought has occurred over the past 5 years. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Drought Index\n",
+    "There are several drought indices in the literature, the majority of them look a trends in rainfall and temperature to identify droughts at different time intervals. We use the [Standardised Precipitation-Evapotranspiration Index (SPEI)](https://spei.csic.es/home.html)  \n",
+    "\n",
+    "### SPEI\n",
+    "The [SPEI](https://spei.csic.es) is an extension of the widely used Standardized Precipitation Index (SPI). The SPEI is designed to take into account both `precipitation` and `potential evapotranspiration` (PET) in determining drought. Thus, unlike the SPI, the SPEI captures the main impact of increased temperatures on water demand.  \n",
+    "\n",
+    "The idea behind the SPEI is to compare the highest possible evapotranspiration (what we call the evaporative demand by the atmosphere) with the current water availability. Thus, precipitation (accumulated over a period of time) in the SPEI stands for the water availability, while ETo stands for the atmospheric water demand.  \n",
+    "\n",
+    "### Time Scales\n",
+    "The SPEI can measure different types of drought (levels of severity and duration) depending on the time timescale factor. At the shortest time scales (3/6 months) the drought series show a high frequency of drought, and moist periods of short duration. In contrast, at the longest time scales (12/18 months) the drought periods are of longer duration and lower frequency.\n",
+    "\n",
+    "### Interpretation\n",
+    "\n",
+    "Negative SPEI values represent rainfall deficit and high potential epotranspiration (dry conditions), starting when the SPEI value is equal or below -1.0. Whereas positive SPEI values indicate rainfall surplus and low potential epotranspiration (wet conditions), starting when the SPEI value is equal or above 1.0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Maps of Monthly SPEI (6-month window)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each row in this panel of maps is a year, and each column is a month. Red values signify dry conditions (drought), and blue values signify wet conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\WB514197\\AppData\\Local\\Temp\\ipykernel_28692\\3436898147.py:27: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
+      "\n",
+      "  centx, centy = one.centroid.x.iloc[0], one.centroid.y.iloc[0]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2800x800 with 49 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Imports\n",
+    "import ee\n",
+    "ee.Initialize()\n",
+    "from geemap.foliumap import Map\n",
+    "import requests\n",
+    "import geopandas as gpd\n",
+    "from geemap.conversion import *\n",
+    "from geemap import colormaps\n",
+    "import xee\n",
+    "import rioxarray\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Admin Data\n",
+    "adm = 'ADM0'\n",
+    "iso3 = 'NER'\n",
+    "release_type = 'gbOpen'\n",
+    "geo_url = f'https://www.geoboundaries.org/api/current/{release_type}/{iso3}/{adm}/'\n",
+    "try:\n",
+    "  res = requests.get(geo_url).json()\n",
+    "  adm0_ner = gpd.read_file(res['gjDownloadURL'])\n",
+    "  # adm0_ner.to_file('../../data/adm0_ner.geojson', driver='GeoJSON')\n",
+    "except:\n",
+    "  adm0_ner = gpd.read_file('../../data/adm0_ner.geojson')\n",
+    "  \n",
+    "one = adm0_ner.iloc[[0]]\n",
+    "centx, centy = one.centroid.x.iloc[0], one.centroid.y.iloc[0]\n",
+    "aoi = gdf_to_ee(adm0_ner)\n",
+    "\n",
+    "spei = ee.ImageCollection(\"CSIC/SPEI/2_9\").select('SPEI_06_month').filterDate('2019-01-01', '2023-12-31')\n",
+    "def clip_image(image):\n",
+    "    return image.clip(aoi)\n",
+    "spei = spei.map(clip_image)\n",
+    "\n",
+    "ds = xr.open_dataset(\n",
+    "    spei,\n",
+    "    engine = 'ee',\n",
+    "    geometry = aoi.geometry(),\n",
+    "    crs = 'EPSG:4326',\n",
+    "    scale = 0.5,\n",
+    "    )\n",
+    "ax = ds['SPEI_06_month'].plot.imshow(robust=True, x='lon', y='lat', cmap='RdYlBu', vmin=-2.33, vmax=2.33, col='time', col_wrap=12,\n",
+    "                              figsize=(28, 8)) # figsize=(10, 10) aspect=2, size=10\n",
+    "ax.map(lambda: plt.axis('off'))\n",
+    "for a in ax.axs.flat:\n",
+    "    a.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Global SPEI Database only contains data up to 2022. The authors also provide less accurate but real-time data through the [SPEI Global Drought Monitor](https://spei.csic.es/map/maps.html#months=1#month=3#year=2024). In the maps below, we show the progression of SPEI for 2023 and 2024, using the coarser (1 degree resolution) real-time data.  \n",
+    "\n",
+    "*An important difference between the data sources is that the SPEI Global Drought Monitor is based on the Thortnthwaite equation for estimating potential evapotranspiration, PET. This is due to the lack of real-time data sources for computing more robust PET estimations which have larger data requirements.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "                    \n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "                \n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "                    \n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
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+    },
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+      "text/plain": [
+       "<Figure size 2800x400 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from os.path import join, expanduser\n",
+    "# Download from https://spei.csic.es/map/maps.html\n",
+    "ds2 = xr.open_dataset(join(expanduser('~'), 'Downloads', 'spei06.nc'))\n",
+    "ds2 = ds2.isel(lat=slice(None, None, -1))\n",
+    "# slice time dimension\n",
+    "ds2 = ds2.sel(time=slice('2023-01-01', '2024-12-31'))\n",
+    "ds2.rio.set_spatial_dims(x_dim='lon', y_dim='lat', inplace=True)\n",
+    "ds2.rio.write_crs(\"EPSG:4326\", inplace=True)\n",
+    "clipped = ds2.rio.clip(adm0_ner.geometry.values, adm0_ner.crs, drop=True, invert=False)\n",
+    "ax = clipped['spei'].plot(robust=True, x='lon', y='lat', cmap='RdYlBu', vmin=-2.33, vmax=2.33, col='time', col_wrap=12,\n",
+    "                                                        figsize=(28, 4)) # figsize=(10, 10) aspect=2, size=10\n",
+    "ax.map(lambda: plt.axis('off'))\n",
+    "for a in ax.axs.flat:\n",
+    "    a.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trend of SPEI (6-month) in Niger"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The chart below shows the historical trend in SPEI 6-month from 2016 to 2023 using the median value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "                    \n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "                \n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "                    \n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\WBG\\Anaconda3\\envs\\rtmis\\Lib\\site-packages\\plotnine\\layer.py:344: PlotnineWarning: position_stack : Removed 1 rows containing missing values.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ThSAAAMAqSRdYAACwGigqKqpR+BERUVJSUqMnRgAAABoTT4AAAMBq5urr/xyFPQqzzi+aUBS/O+3clVgjAACAuicAAQCA1Uxhj8LYbodtG7oaAAAA9UoXWAAAAAAAQOIIQAAAAAAAgMQRgAAAAAAAAIkjAAEAAAAAABJHAAIAAAAAACSOAAQAAAAAAEgcAQgAAAAAAJA4AhAAAAAAACBxBCAAAAAAAEDiCEAAAAAAAIDEEYAAAAAAAACJIwABAAAAAAASRwACAAAAAAAkjgAEAAAAAABIHAEIAAAAAACQOAIQAAAAAAAgcXIbugIAAACsnoqLi6OoqCj9d2lpaRQXF0dERH5+fjRt2rTC8oWFhZGfn79S6wgAwKpLAAIAAMBKV1xcHAUFBVFSUlLtMm3bto3p06cLQQAAqBYBCCTMsnfRlVm8eHH6dW7uT03fXXQAAKunbL8bs6nr341FRUU1Cj8iIkpKSqKoqCj69etXZ/UAACC5BCCQIO6iAwCgOhrb78Zrb7gsevTcPFKpn27cyc3NjZycpfMnjJ8UZ516YZ1vFwCAZBOAQIK4iw4AgOpobL8be/TcPLbbYetIpSIWLVoUERF5eXnpAAQAAGpDAAIJVXYXXZlUKpV+nZOT4y46AIBVWF12X7Xs78Zl+d0IAKyqGrrLTxqeAAQSquwuujLLBiAAAKya6rr7qmV/NwIAJEFj6/KThtGkoSsAAABA9a1I91UAAKsLv5mI8AQIAADAKmvEDZfGFj27Z50/cfzkOPPU36/EGgEAND5+M62+BCAAAACrqC16do/tdV8FAFAlv5lWX7rAAgAAAAAAEkcAAgAAAAAAJI4ABAAAAAAASBwBCAAAAAAAkDgCEAAAAAAAIHEEIAAAAAAAQOIIQAAAAAAAgMQRgAAAAAAAAIkjAAEAAAAAABJHAAIAAAAAACSOAAQAAAAAAEgcAQgAAAAAAJA4AhAAAAAAACBxBCAAAAAAAEDiCEAAAAAAAIDEEYAAAAAAAACJIwABAAAAAAASRwACAAAAAAAkjgAEAAAAAABIHAEIAAAAAACQOAIQAAAAAAAgcXIbugIAAACrm+Li4igqKqr28oWFhZGfn1+PNQIAgOQRgACNjgsCAECSFRcXR0FBQZSUlFS7TNu2bWP69Ol+8wAAQA0IQIBGxQUBACDpioqKavRbJyKipKQkioqKol+/fvVUKwAASB4BCNCouCAAAKxOrrthWPTouVnW+RPGfxinnzps5VUIAAASRAACNFrX3XBRNS4IXLwSawQAULd69Nwsdui3VUNXAwAAEkkAAjRaLggAAAAAALUlAAHqnEHMAQAAAICGJgAB6pRBzAEAAACAxqBJQ1cASJYVGcQcAAAAAKCueAIEqDfXj/zjcgcxP+2UP63EGgEAAAAAqwsBCFBvlg5i/rOGrgYAAAAAqyDjzLKiBCAAAAAAADQqxpmlLhgDBAAAAACARsU4s9QFT4AAAAAAANBoGWeW2hKAAAAAAADQaBlnltrSBRYAAAAAAJA4ngChUSkuLq52P32FhYWJHNDIPgAAAAAAWHECEBqN4uLiKCgoqPbgRm3bto3p06cnKgCwDwAAAAAA6oYusGg0ioqKqn3hPyKipKSk2k9KrCrsAwAAAACAuuEJEBqlG0b+Pnr22jTjvPHjpsSpp1y6kmu08tkHAAAAAAC1JwChUerZa9PYod/PGroaDco+AAAAAACoPQEIUEFNBmGPMBA7AAAAANA4CUCAtJoOwh5hIHYAAAAAoHEyCDqQVtNB2CMMxA4AAAAANE6eAAEyqmoQ9ggDsQMArO50nQoAQGMnAAEy6tlr0+jXb8uGrgYAAI2QrlMBAFgV6AILAACAGtF1KgAAqwJPgABAFWrSvYeuPQBYHek6FQCAxkoAAgBZ1LR7D117ALA60nUqAACNlS6wACCLmnbvoWsPAAAAgMbDEyAAUA03jLwwevbaJOO88eOmxqmnXLaSawQAAACNV026lI7QrTT1QwACANXQs9cmuvcAAACAaqhpl9IRupWmfugCCwAAAACAOlPTLqUjdCtN/UjsEyBLliyJl19+OV577bX49NNPo6SkJFq1ahVdu3aN7bbbLgYMGBDNmzdv6GoCAAAAACTWyBsvyNqldMTSbqVPOfnylVgjVieJDEDmzp0bw4YNi8mTJ1eY/sMPP8QPP/wQkyZNimeeeSYuueSSWGuttRqolgAAAAAAyaZLaRpSIgOQq6++Oh1+rLPOOrHrrrvGGmusEd99912MGjUqPv3005gxY0ZcfPHFcf3110deXl4D1xgAAAAAAKhLiQtAxo0bF2PGjImIiM033zz+9Kc/Vejqar/99osrr7wy3n777fjyyy/j+eefj3322aehqgvUk+Li4hr1G1lYWGiQLQAAAABIkMQFIKNGjUq/PvHEEyuN89G0adM48cQTY+zYsbF48eJ45ZVXBCCQMMXFxVFQUFCjwbbatm0b06dPF4IAAAAAQEI0aegK1LUPPvggIiK6du0aG2ywQcZlOnXqFIWFhRERMXXq1Jg/f/5Kqx9Q/4qKimoUfkRElJSU1OiJEQAAAACgcUvUEyBz586NWbNmRUTEhhtuWOWyG220UYwbNy5SqVR8/vnnsckmm6yMKgIr2cgbz49evbK373HjpsYpJ1+xEmsEAKs33VQCAAArS6ICkLLwIyKic+fOVS5bfv73339fX1UCGlivXptEv359GroaAEDophIAAFi5EtUF1rx589KvmzVrVuWy5ccGKV8OAACoH7qpBAAAVqZEPQGyaNGi9OucnJwql23atGnGcgAAQP278abzlttN5cknXbkSawQAACRNogKQVCrV0FUAAACqQTeV1AVjygAAUJVEBSAAAACsHowpAwDA8ghAYvndZVWltLS0Dmuy6iq/H4rGj49IpSIVEaWLl05vmts0yvZy0YQJFcqVlS2/jnHjpkS2B3rGj5+SsXxd3P21outY0fIrug/Klx8/blKFp6LKv87JyYkJ4ydXWkfF8lMiqnioqjp1mDD+w+wrWGZ+pjrUpvyydRj3wZQqnw4bP25qxnXUx/G0ZMmSmD17dkREtG/fPpo0aVKj8jWpQ9Law7QpRZGq4oD8dMqkCuWWPZY+mVxU4Xguv66cyIlPPiyqVH7ZOixtE5nrMH585uMoouH3Y2Otg/bQeOqwePHi9Ovc3J9+Gq5oHer6PZRvVxMnTIiqTlITJ0xMv87WposmTKxyHUUTKn8vZPq9k7388n/vlL2PVLnpTZv+9Jsp0/soX/7jSROrPFd/PLli+fImTqj6c5iY4f2XqavP8YMPplZ5jh437qN6qUNV5avbHir8XpkwMevHUJ1jcfz4yVHVBzl+eb+Xxk2q8jiYML7yObJSHZb53Vh5Hcv73fhhle2h+r9dIyJSsfj//x8iN7dpxP+3iEzvY/z48bUaU2b8+PHRr1+/iFj538+r0jmmNu1h8sTxUdUBOXni8r8bp380qcrffZ99VPXx+OmUoiqPx0+nLr9NfPHx5Kxv44tPKm9/2fJffz6lyvcw4/PKvx3Ll/9sSrk2map4joiciM+nLr8Ok5ZznpqU4TzVmH+v1KQOK6om54j62Ad1sY6kfrf5f4j6rUNNylenPVQ4z38wteprTFmuzVS+RlTV75XKvzfq+jdTbc5z5ct/WG5+Jh9mOU9mUn5e0q9Plx/KojYSFYDk5eWlXy/vg1+yZEn6dfkB0WtqxowZtS6bJMXFxenX551xZo3Kle3D8us47ZRLa1T++++/j2233TbmzJlT7W23adMm3n777ejQoUNExAqvoy7qsCL7YNnyZ592YbXrUbaO8uVPreb2q6rDaaf8aYXqcPqpF69wHU45pfp9hzfk8VSXx2NEJK49/PXS86u9nkzH0vUXn1vj8svW4dRTLq9x+cawH1fFOmgPyahDXb+H8u3xnNPPqfY6srXpc07/XY3XUb78BWeeVgd1qPn7KF/+2j/WrHx5Q0+r3fdiXX6Op5xc83N0XdShrs9R1f0csx0HZ9Ti90758med9ocal1+2Div+u/GyBq9DTSTlN19j+X4v/zkMG7ri/y94+5W/r/E6ypf/22UXrHAd7htx0QqVf+aOmn+/lS9/x/Ca74Nl63DeGWfUaB0ffvhhIn6vrKiGbtN1sY7G8L2wKr6H8uuIWD3/H6I+zw+nVPP/p8vK1cc1prr4zVSb81z58hefU7vz5PLMmjWr2utdFa2zzjorVL7J8hdZdbRo0SL9ev78+VUuu2DBgvTrli1b1ludVhebbLJJtGnTpkZl2rRpE5ts8tPAlzVdR/nyU6dOrdFJISJizpw5MXXqTwnziq6jLuqwIvugNuWXXUdDfI6NsQ4NcTzV5fG4uraHiIY/lpYt3xj246pYB+0hGXWo6/fQGM4Pq3od6qL+jeFzbAztwW+2ZNQhCd+NjeE9JOFYqM06kvAeyq8jKb9XVlRDt+m6WEdj+F5YFd9D+XU09Pbronxt1uH80HjrwIpJ1BMgnTp1Sr/+5ptvqly2fDK25ppr1nqbXbp0qXXZJOnSpUtMmzat2t2bRFR+NC7TOqpSvnz59dx4/dnRq+dGWcuNG/9xnHzaNelyZZ9hhXVcd2b06rFh9nVM+CROPn1EhXVUKD/itOjVY4Mqyk+Lk8+8vlIdVmQfLK98dR5hr+n2a1qH6qyjMdSh/LpGDv9N9NqiW9Zy4yZ+Gqec87d0uUzH08hTDoieG64dEamKj69HToz/5H9xysjHqy5/8n7Rs9vaWesw/tP/xSk3/qtSuYiIG47aPXqul/07bvzn38Spd/+76u3/6ajotdl62ffBh5/HKX+8u8I6KpS/+rfRq7Db/7F3n9FxVGcDgF/ZcrcMC8YGDNh0WIzpVaEXE1oKJCF0kkAINaGFwJcAoYReQu8tCSSEmhBCYjrB9IANohfTizEYN1yk+X44GrSWdrWyJEsaPc85PmetuffOu7tzZ2fmnbm3eP2at+OQoy5tFEN7bkvlDun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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 600,
+       "width": 800
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (800 x 600)>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from plotnine import *\n",
+    "from mizani.breaks import date_breaks\n",
+    "from mizani.formatters import date_format, percent_format\n",
+    "\n",
+    "spei = ee.ImageCollection(\"CSIC/SPEI/2_9\").select('SPEI_06_month').filterDate('2016-01-01', '2023-12-31')\n",
+    "def clip_image(image):\n",
+    "    return image.clip(aoi)\n",
+    "spei = spei.map(clip_image)\n",
+    "\n",
+    "ds = xr.open_dataset(\n",
+    "    spei,\n",
+    "    engine = 'ee',\n",
+    "    geometry = aoi.geometry(),\n",
+    "    crs = 'EPSG:4326',\n",
+    "    scale = 0.5,\n",
+    "    )\n",
+    "\n",
+    "country_median = ds.median(dim=['lat', 'lon'])\n",
+    "df = country_median['SPEI_06_month'].to_dataframe().reset_index()\n",
+    "df.loc[:, 'ymd'] = df['time'].dt.strftime('%Y-%m-%d')\n",
+    "df.loc[:, 'group'] = 1\n",
+    "\n",
+    "font = \"Roboto\"\n",
+    "p = (\n",
+    "ggplot(df, aes(x=\"ymd\", y=\"SPEI_06_month\", group=\"group\", fill=\"SPEI_06_month\"))\n",
+    "    + geom_bar(alpha=0.8, stat='identity', color='black', width=20)\n",
+    "    + labs(\n",
+    "        x=\"\", subtitle=\"Standardised Precipitation-Evapotranspiration Index\", title=\"Drought Index\", \n",
+    "        y=\"\", caption=\"Source: SPEI Global Database (Spanish National Research Council)\"\n",
+    "    )\n",
+    "    + theme_minimal() \n",
+    "    + theme(\n",
+    "        plot_background=element_rect(fill=\"white\"),\n",
+    "        figure_size=(8, 6),\n",
+    "        text=element_text(family=font, size=11),\n",
+    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
+    "        legend_position=\"none\"\n",
+    "        )\n",
+    "    + scale_fill_distiller(type='div', palette=\"RdYlBu\", direction=1, limits=(-1.5, 1.5))\n",
+    "    + scale_y_continuous(limits=(-1.5, 1.5))\n",
+    "    + scale_x_datetime(breaks=date_breaks(\"1 year\"), labels=date_format(\"%Y\")) # 6 month \"%b, %y\"\n",
+    ")\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trend of SPEI (12-month) in Niger"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "                    \n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "                \n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "                    \n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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wvTvttFPG6eeee27Wz7nclVdemfEOpXKzZ8+OSy+9tMr03r17R/PmzStNW7RoUYwaNWqF9QIAAAAAqx8BSI48+OCDscMOO0RJSUmVeeeee26svfba9batww8/vMq0jz/+OI455phYvHhxpelffvllHHzwwZFKpSpNb9myZRx88MGVpq277roZtzdmzJgq06ZNmxannnrqCmtt2bJl7LXXXlWmv/vuu3HaaadVOTn+xRdfNIoT2MufWI+ImDVrVsbPd3Xyi1/8IuPQZxdffHFMmDCh0rRUKhVnn312TJkyZYXr3XLLLaNv375Vpr/44otx6KGHZnwGTWlpaRx33HFx5plnxtChQ+Pbb7/NuO7zzz8/5s6dW2X6TTfdFL/97W+rTH/ooYfipZdeWmHNAAAAAMDqpVmuC0i6f/zjH/Hf//43IpY9WPyzzz6LZ599NuvwPIMHD46zzjqrXms48sgj47LLLqvy7IY77rgj/vOf/8R+++0XXbt2jY8++ijuv//+jCeWjzzyyCqhzKBBgzJu79JLL4233norBg8eHKlUKoqKiuKJJ56IBQsW1Kje448/Ph544IEq06+99tp44YUXYu+99462bdvG+++/Hw899FCjeJB1ppBg8eLFseOOO8Y+++wTTZs2jT59+sSee+6Zg+pWXosWLeKwww6LsWPHVppeWloagwcPjgMOOCB69eoVJSUl8cQTT8SkSZNqtN68vLy45JJLMoZd99xzT/zrX/+KfffdNzbeeOOYP39+TJ06NZ588skoLS2NiGV3T+23337x9NNPR4sWLdJtJ02aFDfffHOVde6///4xaNCg2GSTTeKOO+6oso+feuqp8fbbb2cc1g0AAAAAWD0JQBrYDTfcUONl+/TpEw899FClE7r1oU2bNnHnnXfGLrvsUuUOig8//DCuvPLKattvttlm8Yc//KHK9PXWWy+22267jFfP/+tf/4p//etfK1XvdtttF7/+9a/jnnvuqTKvqKgoioqKVmq9DWnAgAEZp7/zzjvxzjvvRETEySefvNoFIBERv/vd7+Kuu+6K2bNnV5q+cOHCuPPOO1d6vXvuuWecfPLJce2111aZN2fOnLjpppuqbf+f//wnJk6cWOm9P+2006oMPdaiRYu44oorImJZUHXuuedWGXru3Xffjdtuuy2OOOKIlX05AAAAAEAj43LnRmKvvfaKl19+OdZcc80GWf+OO+4YN910UzRrVrvMa6ONNorHH3882rVrl3H+mDFjarzOvLy8Gm933Lhx0b9//xot2xiu2t9qq62qPCQ+KTp37hx33HFHjYO52uzDV199dfzmN7+pdU3NmzePe+65p1L48fjjj8ezzz5bZdnjjz8+Ntxww/TvJ510UvTo0aPKcr/73e9i3rx5ta4FAAAAAGiccn/m+Cdus802i/vuuy8effTR6NSpU4Nu64gjjoh//etfUVhYuMJl8/Ly4sADD4zXX389Nt5446zLDRw4MO67775o3bp1tevr0qVLPProozWuNT8/P5577rnYbbfdql2uR48eMW7cuBqvt6Hk5eXF3Xff3WABVq7tuuuu8cgjj0S3bt2yLpOXlxennHJKxruFsmnatGnceOONceedd8b6669fozaDBw+Od955J4YNG5aetnjx4jjjjDOqLJufnx/nn39+pWktW7aMyy67rMqy//vf/+Lyyy+vce0AAAAAQOMmAFmFWrRoEWussUZsvfXWcfLJJ8cLL7wQxcXFceCBB66yGnbYYYeYOHFiPPjggzFy5Mjo2bNn5OfnR/PmzWOttdaKgQMHxjnnnBPvvvtu3HfffdG1a9cVrnPYsGExbdq0GDVqVPTr1y86dOgQzZs3jzXXXDN22GGHuOqqq2LGjBmx5557Rvv27Wtca5cuXeLJJ5+MJ554Ig488MAoKCiIli1bRqdOnWKrrbaKMWPGxOTJk6OgoCBj+5YtW9Z4W/WhT58+8d5778WZZ54Zffr0ibZt26bfh2233TZ+8YtfrNJ66ttuu+0WU6ZMicsvvzwGDhwYnTt3jpYtW0ZBQUGMGDEiXnvttRg7dmyNg4yKDjnkkPjggw/i3nvvjREjRkTPnj2jc+fO0bx581hnnXViwIABceqpp8Zrr70Wr7zySvTu3btS+z/96U/x/vvvV1nv7373u8jPz68y/aCDDoqBAwdWmX7NNdfEzJkza10/AAAAAND45EVEKiIilUrluBRYOVdffXXGq/+nTZsWm2yySQ4qAgAAAAAgl/Ly8twBQuP1+eefr3A4pSVLlsStt95aZXrbtm0rPfcBAAAAAICfFgEIjVIqlYqRI0fG2WefHYceemjMmTOnyjILFiyII444IiZPnlxl3u677x5NmzZdFaUCAAAAANAIGQKLRumaa66J008/Pf1769atY7fddovevXtH27ZtY8aMGfHoo4/G//73vypt8/Ly4vXXX8/4jAcAAAAAAJIvLy9PAELjM2fOnFh33XVj4cKFK9X+wgsvjNGjR9dvUQAAAAAArDY8A4RGqWvXrnHZZZdFXl5erduOHDkyLrzwwgaoCgAAAACA1YkAhEbptNNOi/Hjx0f//v1rtPwaa6wR9913X9x6660rFZwAAAAAAJAshsCiUUulUvHaa6/Fgw8+GC+99FJ89tln8fXXX0fr1q1j3XXXjf79+8duu+0Ww4cPj9atW+e6XAAAAAAAGgHPAAEAAAAAABLHM0AAAAAAAIBEEoAAAAAAAACJIwABAAAAAAASRwACAAAAAAAkjgAEAAAAAABIHAEIAAAAAACQOAIQAAAAAAAgcQQgAAAAAABA4ghAAAAAAACAxBGAAAAAAAAAiSMAAQAAAAAAEkcAAgAAAAAAJI4ABAAAAAAASBwBCAAAAAAAkDgCEAAAAAAAIHEEIDS4F198MfLy8iIvLy9Gjx6d63JqpLze7bffPtelNIiRI0emX+PHH3+c63IAgCx8ZwMAAKw8AUgDeOutt+K3v/1t9O3bNzp16hQtW7aM9ddfP3bYYYe4/vrrY86cObkukToYN25ctG/fPgYMGBDffPNNrstZ5SqeiMn0r1mzZtG1a9cYOHBgjBo1KqZNm5brkuts0aJFseOOO0a7du3ivPPOa5BtPPTQQ9G5c+fYbLPN4oMPPmiQbdSns88+O9q1axc777xzLFq0KNflrLQV7c/Z/sGq8vXXX8fPfvaz6NChQ/z5z3/OdTk1cvDBB0fbtm3jsMMOy3UpK+22225b7S7eAAAAoCoBSD36/vvv49BDD42BAwfGjTfeGEVFRTF37txYtGhRfPrpp/Hiiy/GSSedFJtuumncfvvtWdfTo0ePyMvLix49eqy64qmxG2+8MebNmxcTJkyI8ePH57qcRqesrCy+/vrreOutt+Kqq66KzTffPM4666woKyvLdWkrbeLEifH888/H999/H9ddd12DbONvf/tblJSUxLRp0+LRRx+tMn/06NHpk3Evvvhig9RQriZ3QI0bNy6+//77ePbZZ6OoqKhB62H1kfS753Jh/Pjx8e6770ZpaWn86U9/ymktH3/8cfozHjlyZMZl5syZE/fcc0/Mnz8/7rzzzvj6669XbZEAAABQQbNcF5AUS5YsiaFDh8bLL78cERFt27aNQw45JDbffPNYc801Y86cOfHSSy/Fo48+GiUlJTFy5MgoLS2NE044IceVU1tHHnlknH/++bHxxhvHdtttl+tycmr06NHRq1evStOWLl2a3t8feuihWLJkSYwZMyZKSkrir3/9a44qrZvevXvHtttuG2+//XYce+yxDbKNww47LP7zn/9Ely5dYo899miQbdSnY445Jm688cYYNGhQ9O7dO9fl1ItM+zPk2rbbbht9+vSJGTNmxFFHHZXrclaoa9eusf/++8fjjz8ee++9d3Tp0iXXJQEAAPATJgCpJ9dff306/PjFL34RDz/8cJX/9J944okxYcKE2HXXXWPOnDlxyimnxA477OCE22rm1FNPjVNPPTXXZTQK2223XdYrvY877rh4++23Y+edd46SkpK46aabYvjw4bHzzjuv2iLrQatWreKll15q0G0ccMABccABBzToNurTNddcE9dcc02uy6hX1e3PkCtrrrlmTJw4Mddl1Mrf//73XJcAAAAAEWEIrHpz2223pX++9dZbs17xuOWWW8YNN9wQEcuGCrrqqqtWRXmQEwMGDIhx48alf7/xxhtzWA0AAAAA8FMiAKkn06dPj4iItdZaKzbaaKNql91nn32iVatWERHxyiuvRETE9ttvnx5Xe+bMmRERMXPmzBU+dHf+/Pkxbty42GGHHWKttdaKFi1axBprrBE77bRT3HbbbVmfu/Diiy+m13vjjTdGKpWKO++8M4YMGRJdu3aNli1bxgYbbBDHHXdcfP755yt8/U888UQMHTo01lhjjWjVqlVsuOGGccwxx9TqYc6fffZZXHDBBfGzn/0sOnfunH54/AEHHFDtszYqPhvh7bffjkceeSS233776NixY+Tl5cUjjzxSpc3kyZNj5MiRsd5660XLli1jrbXWiv322y9eeOGFFdZZ8aHJH3/8ccZl5syZE6NHj44tt9wyOnToEC1btoz11lsvhg8fHk8//XSN3o9nn3029tlnn1hzzTWjRYsWsc4668S+++4bjz322Arb/vDDD3HppZdGv379om3bttGxY8cYOHBgXH311bFw4cIabb++DBs2LFq2bBkREa+99lqleeX7fbt27WLx4sVx9dVXR9++faN169aRl5cX3377bZX1lZSUxIUXXhh9+vSJ1q1bR4cOHaJfv35x7rnnxuzZs1dYz5w5c+LCCy+MLbfcMtq3bx+tWrWKHj16xIgRI+Ktt97K2q66ZxuUP7dn+PDhERHx6KOPxh577BHdunVLf/YjRoyISZMmZV1/pmd8VHwI70UXXZRedocddkhPrxi+VvTcc8/FYYcdFhtuuGG0bt062rdvH/369Yuzzjorvvjii6yvoeKx5qWXXkpPW/6ZRBWPWdWZNm1aHH/88bHppptGq1atomPHjtGvX784++yzM9ZRrr6PUQ3h7bffTte4oqHRDjnkkPSyU6dOrTL/m2++iSuuuCK23nrrWGONNaJFixax9tprx5577hkPP/xw1vVW3EcmTZoUs2fPjrPOOit69eoV7dq1i44dO8ZWW20V119/fSxZsmSFr+mHH36Ia6+9NgYPHhwdO3aMVq1aRUFBQfz617+O5557LmOb2u475dP32GOPmDNnTpx00knRo0ePaN68efTv37/K+lOpVDz00EMxfPjwKCgoSO9HAwcOjEsuuSTjcWL5bR100EEREfHvf/87DjzwwOjevXu0aNEiunXrFvvtt1+8+eab1b4vqVQq7r///hg6dGh069YtmjdvHp07d47/+7//i6uvvjrmzZuXsV3Ffj1v3rz4+OOP4/jjj49NNtkkWrduHZ07d47tt98+7rzzzqzbXtFzN1bF+1m+jQ022CA97fbbb896XKzps8zefvvtOPzww6NHjx7RsmXL6Ny5cwwcODAuvvjiaj/Xivv9008/HYsXL45x48bFNttsE/n5+dG6devYbLPN4qyzzoqSkpJqa1hZFf8WWLBgQcyZMyfOOeecKCwsjLZt20b79u1j4MCBccMNN6zwGVj19Z39+eefx2mnnRabbrpptGzZMvLz82PgwIFx2WWXZdxHb7zxxvRrqG5Iz5122im93B/+8Ica1wMAANBYpCIiRd106tQpFRGpNm3apBYsWLDC5f/4xz+mLr744tQVV1yRSqVSqe222y5V/llk+7e8V199NbX++utX22bbbbdNlZaWVmk7fvz49DJXX311aqeddsq6jm7duqU++OCDrK/l+OOPz9q2ZcuWqZNOOin9+4UXXphxHX/5y19Sbdq0qfa1nHrqqRnbXnjhhell9ttvvyrtHn744UrL33XXXakWLVpk3c6oUaPSP2+33XZVtjdixIj0/BkzZlSZ/8orr6TWWGONal/Lr3/969SiRYsyvp4lS5akjjnmmGrb/+pXv8ra/tNPP01tuummWdv26dMntcsuu1T7GqpT8fWPHz++Rm3WWWedVESkWrRoUWl6+X7funXr1G677Val1pKSkkrLv/XWW6lu3bplfW35+fmpl19+OWsdL7/8cqpLly5Z2+fl5aXOO++8jG2r2ycKCgrS/e3QQw/Nuv4WLVqk7rrrrozrr7gfl7+vt9566wqPC7feemul9cybNy+19957V9smPz8/9eKLL2Z8Ddn+FRQUZPzsMh2byt18882p5s2bZ11nu3btUvfee2/GtvV5jKrOyuzPFW200UapiEittdZaqbKysozLLF68OP0d0bdv3yrzH3nkkWr3y4hI7b///qnFixdXaVtxHxkzZkyqc+fOWdex9dZbp+bOnZv1tUyfPr3aY0f5sWvhwoWV2tV23ymfvuWWW1Zp269fv0rLfvnll6nBgwdXu/511103NXny5IyvqXyZAw88MHXOOeek8vLyMq6jWbNmqUceeSTjOkpLS6vd/yIitcEGG6QmTZpUpW3Ffn399denWrVqlXUd++yzT8bj+owZM9LLjBgxIutrbMj3c0XHoeWPi+V1LP/ZV3TRRRdl/TwiIrXGGmukXnjhhYxtK+73t99+e2qLLbbIup5NN9009c0332StI5uK28j0t0vFY8cLL7yQ6tq1a7X9N5v6+s5+8sknU+3atcu6nvXWWy9VXFxcqc3SpUtTW2+9dXqZv//971XW++CDD1aqJdNxCAAAoLGKiJRngNSTbbbZJp588smYP39+XHrppfH73/++2uVPPvnkSr9fdNFF6avXjzvuuJg9e3asscYa8ac//Slj+ylTpsQuu+wSpaWlERGx5557xk477RTt27ePqVOnxl/+8pf49ttv4+WXX47jjjsu7rjjjqy1XHjhhTFv3rzYbLPNYsSIEdG9e/eYMWNG3HjjjfHll1/GrFmz4pRTTsl458Hll1+eHtKrRYsWMWLEiNhmm21i0aJF8e9//zvuv//+uO6666p9L+666674zW9+ExHLnrUwYsSIGDBgQCxZsiTefPPNuOOOO6KsrCzGjh0bvXv3jiOOOCLruh566KFo27ZtHHbYYTFgwIBo2rRp9O3bNz3/lVdeiREjRqSvxtxtt91ir732itatW0dRUVHcdtttceWVV1Zbb3U+//zz2GOPPWLu3LmRl5cXe+21V+y4447Rtm3b+O9//xs33XRT/PDDD3HPPfdE586d4/rrr6+yjoMPPjjuv//+iIjo27dvHHroodG9e/f45JNP4tZbb433338/7r333lhjjTXi2muvrdR20aJFMXTo0Jg2bVpERGywwQZxxBFHxAYbbBBffvll3HffffH2229HUVHRSr/G2lqyZEl88803ERHRsWPHjMv88MMP8dRTT0X37t3jiCOOiE022SQWLFgQbdq0SS8zYcKE2H777eP777+Pli1bxmGHHRaDBw+OpUuXxksvvRR33313lJSUxO677x6TJk2K9ddfv9I2Xnvttdhxxx1j0aJF6bs1dtxxx2jTpk28+eabccstt6T7b/v27eOss86q9Wstfw5Q+R0lG2+8ccyZMyfuueeeeOutt2LRokUxcuTI2GSTTWLgwIErXN8OO+wQDzzwQEQsG1O//OeKD+v++c9/XqnNPvvsk75Sf6ONNoqRI0dGQUFBfPnll/HAAw/EW2+9FSUlJbHvvvvG9OnT08P1/fnPf47vv/8+IiL233//iIgoLCxM33nStm3bWr0X48aNixNPPDEilvXrkSNHxqBBg2Lx4sXx3HPPxd///veYN29eHHzwwdG8efMYNmxY1nXV5RjV0A488MC47LLL4ssvv4xXX301/u///q/KMi+99FL6avYDDzyw0rzx48fH/vvvH4sXL46mTZvGQQcdFIMHD44WLVrEf//737j55ptjwYIF8cADD0TPnj2r/W4588wzo2nTprH//vvHL3/5y2jTpk28/fbb6X37tddei0MOOST++c9/Vmk7c+bMGDRoUHz99dcRsex5KMOGDYv8/PwoLi6Ov/3tbzFr1qy455574vvvv690Z93K7jsTJkyIiGVDQx500EGxzjrrRIsWLdLzFy5cGEOGDInJkydHRET//v3jV7/6Vay99trx6aefxu233x7Tpk2Lzz77LPbee++YPHlypfYVPfHEEzFv3rzIz8+Po48+Onr16hWzZs2K+++/PyZMmBBLliyJo48+OnbZZZf0HZrlRo4cGc8++2xERGy88cZx+OGHx3rrrReff/553H777TF16tSYMWNG7LLLLlFcXBwdOnTIWMOJJ54YrVq1isMPPzy22WabaNq0abz88stx1113xZIlS+KRRx6J008/fYXfmdk05PtZfuyZPXt2HHfccRGx7C6w448/PiIi1lhjjVrVOmrUqPQQoJ06dYojjjgi+vfvH/PmzYvHHnssnnrqqZg9e3YMHTo0Xnjhhdh6662zruv444+PefPmxYABA+Kggw6Krl27xpQpU+LPf/5zfPfddzFt2rS44IILMn7f1pe99torvv/++9hvv/1ixx13jKZNm8Zrr70Wd9xxRyxdujQeeOCBePDBB6sc5+rrO/vxxx+PfffdN5YsWRLt27ePI488Mrbccsv44Ycf4umnn46HH344Pv3009hll11i0qRJ6X00Ly8v/vrXv8bPfvazWLx4cYwaNSr23HPPdB9YsGBBnH766RER0aRJk/jrX/8azZr5rwMAALD6SUW4A6Su/v3vf6eaNGmSvkruoIMOSk2fPn2l1lWTKycrXuF99913V5n/ySefpPLz81MRy65q//LLLyvNr3h1dUSkDjvssNSSJUsqLTNr1qz0VctNmjRJzZ49u8o2yq9mbdu2ber111+vUserr76a6tChQ9arKBcuXJhac801UxHLrkrPdAXtY489lm5fWFhYZX7FK2y7dOmSmjhxYsb3bOnSpalevXqllx03blyVZf73v/+l+vbtm/Wq1lSq+jtAzjrrrPS8K6+8skrbKVOmpDp27Jh+T5f/XO6///50+0MPPbTKZ/LDDz+kr0TOy8tLTZkypdL8MWPGVKp93rx5Vd6DijVmeg0rUtsr5v/5z3+ml99xxx0rzat4F8HAgQNT3377bcZ1LFq0KP25tG3bNvXOO+9UWeaZZ55JNW3aNBWx7A6ZihYuXJgqLCxMRSy7C+OJJ56o0r6oqCh99Wzr1q1Ts2bNqjS/un2i4lXXQ4cOTX3//feV5peVlaVOOOGE9DKDBg2qso5Md4DUZn4qlUo9/PDDlepY/mrysrKy1P77759eZsyYMRnXU91rLVfdHSDTp09PtW7dOhURqa5du2bsk08//XSqZcuW6b7fEMeomqjrHSATJ05Mt892l1rFz375O1X69euX3i8zXe0+YcKE9F00HTt2rHL1dcWr1Fu2bJl6+umnq6yjuLi40tXpmZapeIdD+Z2JFc2ZMyf1s5/9LL3MX//614yvtSb7TsXP9Te/+U1q6dKlGZcbO3ZserljjjmmynILFy5c4dXrFbfVv3//1FdffVVp/pIlS1LbbrttepmHHnqo0vwpU6ak5w0YMCD1ww8/VJq/aNGi1B577JH1uF+x33bu3Dk1YcKEKjW+9NJL6f7SpEmTKlfp1/QOkFXxfq6olnLV/R3zyiuvpO/82HjjjVOffvpplWVuueWW9DIbbLBBlTtrl787LtOde8XFxalmzZqlIiLVqVOnrO9LNrW5A6Rly5apxx57rMoyN910U3qZvfbaq8r8+vjOLikpSa299tqpiEits846Gb/Tb7nllnT7c845p8r8s88+Oz3/4osvTk+/6KKL0tN/+9vfZnmnAAAAGq///38aAUh9+etf/5r+z3b5CeoddtghNW7cuNQnn3xS4/XUJAAZPHhwqlevXqmhQ4dmXeaMM85I17L8f8wrnlzs169f1iENTj755PRyzz77bKV5F1xwQdaTPhXdeOONWU8iTJs2LdWrV69Ur169sg6PlUqlUgMGDEivY/khvSqeYLruuuuyruOFF15IL7f77rtnXW7q1KnVnsSrLgDZdddd0/OWH76p3DnnnJNeZvkhVzbZZJNURKR69OhR5URIuRkzZqRPDJ111lnp6UuXLk316NEjFRGpVq1aZd3nlh/yoiEDkKlTp6bWXXfd9PK33XZbpfkVT6JnC65SqVTq7rvvTi93ww03ZF3u8MMPT59Mrhim/OMf/0i3r/ieLe/6669PL/e3v/2t0rzq9onyPtuxY8esIc7ixYvTIUxEpIqKiirNr48AZMyYMen+lCkkSqVSqUmTJqXXM2zYsIzL1OQkdnUByBFHHJGet/wQdMvXW77c+eefX2lefRyjaqLi/lyTf5lO+pZ/rj169Mi4jfXWWy8VsWyIoormzZuX6t27d6pXr16po446KmuNw4cPz7rfrOgkbbl77rkn6+f+8ssvp+ftvffeWdcxY8aM9In6Hj16VAmkUqnaBSDdu3evEihUdMopp6T35+UDyXKPP/54en2nn3561m21adMm9fHHH2dcR8XgcPkT6ffdd1963tixYzO2f//999PL7LPPPpXmVey3yw9XV9Fll12W9XXUNABZFe9nfQQgv/zlL1MRy8Ket99+O+s6jjvuuPS2brnllkrzKu731X2fV7xYpLYXpdQmAMkWJpeVlaUvRunevXulefX1nX3ppZem52UK98vtsMMOqYhlQwYub/78+akNN9wwFbHsIoPPPvssNXPmzHR/X3vttbN+twEAADRmEZHyEPR6dPTRR8fLL78cAwYMiIiIVCoV48ePjxNOOCHWX3/92GqrreLGG2+M+fPn13lb//73v2PSpEnxxBNPZF1ms802S/9cPgRRJvvss0/WIQ023njj9M9z5sypNK98282aNYsjjzyyRnUsb5NNNolJkybFpEmTYvTo0TVaR3WvpU+fPlnnVXyvqntgcXX1rkjFIZ7ef//9jMucffbZMX369Jg+fXr88pe/TE//73//G9OnT4+IiBEjRmQdNqZHjx6x6aabRsSy/aDc5MmT0w9l33333WO99dbL2D4vLy/dvq5eeuml+Mc//lHp39///vf405/+FIccckj0798/Pvvss4hYNlzKoYcemnVd1X12f//73yNi2VBK1Q2BtvPOO0fEsmFFKj7Q/MEHH0z/XN1nv88++0THjh2jY8eOK/Vg7U6dOmUd5qtZs2bpoWMiIp5//vlar39FRo0ale5PW2yxRcZlatqXVtbixYvTD+3eeOONY5999sm67PHHH58eiqV82LdMVvYYtaqUD2v18ccfx7vvvltp3jvvvBOffvppREQccMABlea1bds2ioqKYtKkSXHTTTdlXX9NP7PqHjh9wAEHxFprrRURES+88EKleRXf+1GjRlW7/oMPPjgilr3WFT04fEU23njjKsNNVTR27Nj0/rzmmmtmXKam783Pf/7zKCgoyFpHueX3oZoc0zfddNP0Mb18SMhMqvt8jj322PRwUyt7bFiV7+fKmjVrVrz00ksREfHLX/4yttxyy6zLnn322dGkybI/U6s7PgwfPjzrvFV1fCgf+m15TZo0iQ033DDj9uvrO7v8+7GgoCCGDh2adbny78dZs2bFBx98UGle69at00Oufv/993HWWWfF6aefHj/88ENERFx77bVZv9sAAAAaOwP51rOtt9463nrrrXj22WfjpptuiieeeCIdeLz55pvx5ptvxkUXXRR/+tOfYt9996237c6aNSvef//9mDt3bixcuDAilp1QL7d06dKVWm+7du3SPy9YsCD9c1lZWXpM6l69ekV+fv5KrX95qVQqPvroo/j444+jtLQ0lixZEhFR6WT0yr6Wiu9HpnH668N+++2XPlGz3377xe9///sYPnx4pRMHHTp0yDhGfPnzIyKWvb//+Mc/sm4nLy8vIiI9bnjEqnl9y6sutKpo2LBh8be//S19Mqu2yt+b9ddfPx5//PGsy02ZMiX987Rp02LHHXeMiGUnoSMi1llnnWpPQq677rrpZzU0hO222y7988SJExtsO+V++OGHmDJlSnz11Vfx/fffx7Lg+0cr25eqM3ny5CgpKYmIiJ122qnaZdu0aRPbbrttPP744zFt2rT4+uuv088kqalsx6jq+s92222X8ZkFFZ+tkk2m/eeggw6KCy+8MCKWPYeoYvhU8VkZywcgmXz66afx4Ycfxty5c2Px4sURUbmfr+xn1rRp0xg8eHA8+OCDUVJSEp9++mn6hGt5kNq2bdtqn7UQEbHrrrvGzTffHBHLnquzouXrU2lpaRQXF8fXX38dP/zwQ6RSqfSzsyLq/3suImLbbbeNLl26xNdffx1/+ctfomXLlnHsscdGz549Ky1X8UT7ysjPz48+ffrEhAkTYvLkyVFWVhZNmzat0zpXpKHez+q89tpr6Wdwrej4sN5660VhYWFMmjQpXnvttZXaXnWf7apSXkP532bl6uM7+7vvvov33nsvIpZ9f1V33Pvkk0/SP0+bNq3KPrvLLrvEr371q7j33nvj7rvvTk/ffffdswY8AAAAqwMBSAPZaaedYqeddor58+fHM888Ew8++GA8/PDDMX/+/Pjyyy9jv/32i0svvTTOPffcld7GwoUL4/rrr4+bbrqp0gmyVeHbb7+NRYsWRcSyh3bW1axZs+LSSy+Ne++9t8Gu0pw1a1ZELLtKv1OnTg2yjQMOOCD+85//xHXXXRdffPFFHHXUUXHsscfGlltuGTvvvHPsvvvusdVWW2Vs+8UXX6R/vuSSS2q0vblz56Z/Ln99EfXzmaysJk2aRIcOHaKgoCC23nrrOPTQQ2ObbbZZ6fUtWLAgfUJ92rRpNT4RU/G9+fLLLyNi2QmiXKp48rwhr0YeP358jBkzJp577rl0iLiqVAwrN9lkkxUuX3GZzz//vNYBSDbV7Sfjx4+P7bffvsr07bbbLuP0Fdl0001jiy22iHfffTceeuihuPjii9PzHn300YiI2GqrrbKGb999911ceeWVcfvtt6fvFmkIy+9/5QFI+We20UYbrTCkrPh5ld/d1dAefPDBuPbaa+Pf//53lRCvobVp0yb+8Y9/xD777BNz586Na6+9Nq699tooKCiIIUOGxC677BJ77LFHtGnTps7b6tGjR0yYMCEWL14c3333Xb1dWLC8XL6fK3N8mDRpUpSWlsZ3332X9QHzq6P6+M6u+HfDf/7zn/jPf/5To3YVvx8rGjt2bDz99NPp79w2bdpUe1cTAADA6sAQWA2sTZs2se+++8Zdd90Vn332WZxzzjnpqzp/97vfrfRQF7Nnz45BgwbFqFGjVnn4ERGVhvGqeIXlynjjjTeisLAwrr/++gY9KVxec13rXZFrr702nnnmmfjlL38ZeXl5sWTJknjjjTfi4osvjkGDBkXfvn0zDl1W8crbmioPoSLq9zOpqfHjx0cqlar0r6ysLEpKSuK///1v/PnPf65T+BGxcu9LROWrbb/77ruIWDbMRy5V3P68efMaZBvnnXde/PKXv4ynn356lYcfEZWHzanJycqKJ47LP6fV0UEHHRQREcXFxelj8owZM9J3+pQPk7W8Dz74IPr27RuXXHJJg4YfEdn3v/LPrLF9XmVlZTFixIgYPnx4vPLKK6v8ZH257bffPiZPnhzHHXdctG/fPiIiZs6cGX/729/iwAMPjO7du8d5552XHi5oZTX08aExvJ8/1eNDJvXxnV0f348VdevWrdKwhbvuumvWoeMAAABWFwKQVSg/Pz8uu+yyuP766yNi2XBP11xzzUqt68gjj0wPn9CrV6+4+eab4/3334/vv/8+li5dGqlUKm699db6Kr2Kis+nyPYf6ZooLS2NYcOGpU+KDB06NB555JGYOXNmLFiwIH1SfcSIEfVWc13qramdd945nn/++fjiiy/izjvvjCOPPDK6d+8eERFFRUWxxx57xBVXXFGpTcuWLdM/T5kypUqwkO3f8q8vYtW8xlWl4vty4IEH1vh9qTg8V/nJpbqeoKyriic16+OK8eU99NBDcdlll6XXf84558Rrr70WX3/9dSxZsqTKPtMQyk8QR9RsyJnS0tL0z/V5xXt1+8bK3OWxIhWHt3rooYci4sfhr/Ly8jLekbJ06dLYf//9Y+bMmRERsc0228Q999wTH374YXpIolQqlR5eq66y7X/ln1kuP69Mrr322rjjjjsiIqJr165x6aWXxoQJE+Lbb7+NsrKySKVSMWPGjAatoVz37t3jhhtuiNmzZ8dzzz0X5557bgwaNCjy8vLi22+/jcsuuyy23XbbOgUXDX18aAzvZ2M5PjQG9fGdXfH78ayzzqrx9+PIkSMzrm/y5Mlx1113pX9/5JFH4o033lip2gAAABoLQ2DVg9tuuy39QMlTTz11hUO4HHXUUXHGGWfE/PnzY8KECbXe3owZM+Kxxx6LiIgtttgiXn311WoffNoQOnXqFK1atYoFCxZUGle6th588MH0kBi//e1v0w/hbAhrrbVWFBUVxddffx3z589vkBNMmbZ5yCGHxCGHHBKpVCr+8Y9/xOGHHx7ff/99nHfeeTFs2LD0MCAVn0kwc+bMKmPM12Rb5erymTQ2nTt3jiZNmsTSpUvTJ4prq1u3blFaWlppuJBc+PDDD9M/d+3atd7Xf91110XEshPuzz//fAwaNKjet7Ei5UFfROXXm03FO9jWWWedBqlpVejRo0cMGjQoXn/99XjooYfi7LPPTg9/NXjw4IzDr7388svpIHv33XePf/7znyv9nJyayLb/de/ePebOnRsfffTRCtexKj+v8v25Xbt28frrr8dGG23UoNuriZYtW8aQIUNiyJAhERExffr0GDlyZLz66qvx9ttvx5gxY+L3v//9Sq27/PNp3rx5gzxwujG8nyt7fOjQoUOlwCAJ6uM7e/m/G+oilUrFMcccE4sXL46WLVtG8+bNY968eXH00UfHO++8E82a+S8DAACwenIHSD3417/+FZdeemlceuml8e67765w+ebNm6evgsw2DnN1Kj44c8SIEas8/IhY9pyHfv36RUTEe++9V+kqzdqo+H795je/qZfasvnZz34WEcuuun711Vfrff2zZ8+O2267LW677baM+0H5VeAXXXRRuo7nnnsuPX/AgAHpn5955plab7/89UX8+FDjJGjWrFn0798/IpY9zHxlhkkrf28/++yzaocZ+uGHH2L06NExevToeOGFF1aq3uq88sor6Z/L+099Kt/v+vXrl5PwIyKif//+6aF8nn322WqX/eGHH9Jj1m+++ear/RXe5cNgvf322/Hf//433Q+zPfy84nHi6KOPbtDwo6ysLP0g6U6dOlUa1qZ8mLpvvvlmhaF8xWNWXYe3q05JSUn6hO6OO+6Ys/Dj4Ycfjttuuy0eeOCBjPM32WSTePjhh9Of3cocuyMivv7665gyZUpERBQWFtb7yebG8n5W3GdWdHz49NNP4/3336/SLinq4zu7R48e6YtuXnjhhVi8ePFK1/PnP/85/bfRqaeeGr/73e8iYtldq1deeeVKrxcAACDXBCD1oPxK0IiIG2+8cYXLf/TRR/HVV19FRGR8KG75M0KWLl2asX3F/+BWN9zGe++9t8Ja6mKPPfaIiGXDWFQ33FZRUVHWeTV5LWVlZTF58uSVrPJH5fVGRLV3mlRXb3Xmz58fhx9+eBx++OFx9dVXZ11uww03TP9c8TVvv/326THRb7nllmofMDxr1qx48MEHK03r2bNnbLzxxhGxbNiK//3vfxnbLl26tF7ez1Vp7733johlzzy5/PLLq1323nvvrTTOfETEXnvtlf65uj769NNPx0UXXRQXXXRR+q6u2pg7d27WcerLysrir3/9a/r3nXfeuVbrLj8uRKz42FDX40L5ydxs26lO8+bNY999942IiEmTJsVTTz2VddmbbropHZ7W9OH2jdkBBxwQTZo0SV9JXVZWFk2aNInhw4dnXL6+j+XVXUX+wAMPpMPD5fe9igHNmDFjsq7jiy++iHvvvTciIgoKCmKrrbaqskxd9p2KGsv33HXXXReHH354HHroofH9999nXGaNNdZIX9RQXa3VfT433XRT+pk9u+yySx0qzqy+3s+aHIeq071793SY8dRTT1X7fTt27Nj0kH3ZQsTVWX18Z+fl5aW/37766qsV/g36t7/9LePQY1988UWcc845EbHszq7zzjsvTj311PQdqhdffHGN7tgBAABojAQg9eDggw9OX0354IMPxmmnnZZ1POeSkpIYMWJE+j/15VcMV1Q+rMisWbMy3iHSu3fv9M+33357xpMZ48aNSw93ERFZT9zUxRFHHJEeRur888/PeOXwv//97zjvvPOyrqPia7nhhhuqzP/hhx/iiCOOiLfffjs9bWVfy//93//FFltsERHLruq96aabqizzxRdfxK9+9auVWn9BQUH06dMnIpadbMx0ReeSJUvi5ptvTv9evnzEsrHRTzzxxIhY9rDXvffeOx2UVTRz5szYeeedY/jw4TF27NhK80444YSIWPYeHXbYYVWeeZFKpWLUqFGV3s/VwXHHHZceEuaPf/xjpSChorFjx8bBBx8cgwYNqvTeDR8+PB02XnXVVfGvf/2rStuPPvoojj/++IhYNkTMsGHDal3nt99+G4ccckilh9tGLHvfTz/99CguLo6IZftiYWFhrdZdcbih8quil1fenz788MOMV6IXFRXFfvvtl/49W18q39aHH34YZWVltaozYtlY9OVXsB9xxBHpK9srevnll9Mn3Lp06ZLed1dna6+9dvziF7+IiIi33norIiK23XbbWHvttTMuX/H495e//KXKe11WVhbnnHNO+lkiEdUf/y6//PJKd2iUmzp1apx88snp34855phK83faaacYOHBgRET8/e9/r3JciVgW7h1wwAHp7V9wwQUZa6jrvlNujTXWiG7dukXEsu+R8ofJV/TSSy/FUUcdlf69Ib7n9txzz4hY9oyGs88+O+NJ//vuuy/9XV3xmL68008/vdIdnOX+/e9/x8UXXxwRywKGI444oh4qr6y+3s+11lorHXJlOw6tSPnfBGVlZXHAAQdkPPF///33p/+G2WSTTVb6e7mxq4/v7FGjRkXz5s0jIuLMM8+Mxx9/vMoyZWVlcfrpp8eRRx4Z22+/fZW/UU844YR0eD9mzJho165dtGjRIv74xz9GxLK/xRr6Ll0AAICGYkDfetCqVat48sknY9ttt41Zs2bF2LFj4/7774/hw4dHnz59on379vHdd9/Fu+++G3//+9/j66+/johlJ79GjRpVZX19+/aNf//737Fo0aLYddddY6+99oq5c+emH5pdWFgYO+64Yzz33HMxffr06NOnTxx99NGx/vrrx2effRb3339/lZMs5dusT+uss05cfPHFcfrpp8d3330XgwcPjsMPPzy23nrrWLhwYbz88stx//33Vzskw69//eu48MILY86cOXHvvffGJ598EgcccEB07NgxpkyZEnfeeWeV5zbU5bX86U9/im233TYWL14cxxxzTDz22GOxxx57RMuWLaOoqChuvfXWKncP1MbVV18du+22WyxatCi22267GDZsWPzf//1fdO7cOT755JO4++670yfB+/fvHzvuuGOl9hdccEE899xz8cYbb8Q777wTPXv2jMMPPzz69+8fCxYsiLfffjvuueeemDdvXrRo0SK23nrrSu2PP/74uP322+Pdd9+N5557Lvr06RNHHnlkFBQUpK/efuedd1b69eVK165d49Zbb43hw4fH0qVL4ze/+U3ce++9se+++0aXLl3is88+iwceeCAdwhUWFlYaG7158+Zx++23x5AhQ2LRokWx2267xf777x9DhgyJZs2axYQJE+Jvf/tb+uTTNddcs8Jn+WTz2GOPRe/evWPEiBGx0UYbxTfffBP33HNP+kGyzZs3T59Uqo2+ffumfz7vvPPi008/jbZt28aQIUPSw12dfPLJ6efN7LXXXnH44YfHVlttFQsWLIiXX345HnzwwUr9MVtf6tu3b3z22WfxxRdfxO677x477LBDLF26NB1Y1KTWyy67LM4888z48ssvY8stt4yRI0fGVlttFWVlZfHiiy/GPffcE2VlZZGXlxfjxo2r9HnlyksvvVTjIdaGDh2a8TlCBx10ULz00kvp3w888MCs69h5552jZ8+eMXXq1HjllVdiiy22iBEjRkS3bt3iww8/jLvvvjumT59eqU11x78FCxbErrvuGsOGDYsddtgh2rZtGxMmTIibb745fTJ7v/32q3TXYrk777wzttpqq/j222/jtNNOi3/+858xbNiwyM/Pj/fffz/+9re/pZ/XNHTo0Kwn6eu675TLy8uLE088MX73u9/FggULYvDgwfGb3/wm+vbtG3Pnzo1nnnkmnnrqqUqBREN8z/3mN7+Jm2++OaZMmRLjxo2LF154IX79619HQUFBzJ07N8aPH58OqJo0aRKnnXZa1nXNmTMntt566/j1r38dW2+9dTRr1ixeeeWVuOOOO9J3f5x88smx2Wab1fvrqK/3s3nz5tGzZ88oLi6ON998Mw466KD42c9+Fu3atYvjjjuuRrUMHTo0jjvuuPjTn/4UU6dOjV69esWRRx4Z/fr1i/nz58eTTz6Zfn5OixYt4pZbbsnJUJ+rQn18Z2+++eZx5ZVXximnnBILFiyIPffcM/baa6/Ybbfdol27djFjxoy4++6704HVwIEDKz08/dFHH42HH344IpYNNXbwwQen5w0dOjR23333eOKJJ+L555+P22+/PUaMGNEA7wQAAEDDSkVEirqbMWNGas8990yVv6fV/dt9991Tc+bMybieDz74INW8efMqbSr63//+lyosLMy6/jXXXDN18cUXp3/fe++9K7UfP358et6FF16Y9TXdeuut6eVuvfXWjMuccsopWeto3rx56qKLLqp2Wy+//HKqU6dOWdcxYMCA1FFHHZX+fezYsZXaX3jhhel548ePz/payt13332pli1bZt3eCSeckFpnnXVSEZHabrvtqrQfMWJEetkZM2ZkXH/Hjh2r/fz79u2b+vTTTzPWN3fu3NSuu+5abfuuXbumnnrqqYztv/jii9Tmm2+etW3Pnj1X+BqqU7FtTd7v6my33XYZ9+9s/vGPf6Tat29f7Xtz4IEHpkpLSzO2f/zxx6v9bFq3bp264YYbMrYtXybTPlFQUJCKiFT37t1TBx98cNb1t2jRInXvvfdmXH9N9uNf/vKXVda5fL887bTTsm4/Ly8vde6556bWW2+9VESkmjRpkvr666+rbOfll1+u0ragoKDSMjX57MaMGZPxWFbx/f7HP/6RsW19HqOqU3F/rs2/bP1m9uzZqWbNmqUiItW0adPUV199Ve32p0yZkurevXvW7Wy88capUaNGpX8/+eSTs77+o48+OpWfn591Xdtss03WvpFKpVL//e9/0/tytn+HHHJIauHChVnXUZN9p7q+VNGSJUtSBxxwQNZamjVrlvrjH/+Yfr87d+6cKisrq/W2ZsyYkV5uxIgRVeZ/8sknqZ///OfVvi9t2rRJ3XPPPVXaVuzXJ510UqpVq1ZZ17HvvvumFi9eXOv6VuX7mUqlUnfccUeVtstvu3w/Wv6zL1dWVpY6+eSTU3l5eVnr6dKlS+rll1/O2L6m/b62fx9k20amY1BNv0dXdKysr+/s66+/PtWiRYtq99OTTjqp0j723XffpdZdd91UxLLvgwkTJlRZ7/Tp09Pr7dKlS2r27NnVvm8AAACNSUSkDIFVj3r06BH//Oc/44033ohRo0bFlltuGd26dYtmzZpFx44do2/fvnHMMcfESy+9FI8//njWK8w32mijePrpp2PQoEHRsmXLaNu2bWy55ZaVlllrrbXirbfeissvvzy22GKLaNOmTbRp0yb69OkTF1xwQUydOjVOOeWU2GmnnWLIkCGVxu2ub2PHjo0nn3wydt111+jSpUu0bNkyevToEUceeWQUFRW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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 600,
+       "width": 800
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (800 x 600)>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spei = ee.ImageCollection(\"CSIC/SPEI/2_9\").select('SPEI_12_month').filterDate('2016-01-01', '2023-12-31')\n",
+    "def clip_image(image):\n",
+    "    return image.clip(aoi)\n",
+    "spei = spei.map(clip_image)\n",
+    "\n",
+    "ds = xr.open_dataset(\n",
+    "    spei,\n",
+    "    engine = 'ee',\n",
+    "    geometry = aoi.geometry(),\n",
+    "    crs = 'EPSG:4326',\n",
+    "    scale = 0.5,\n",
+    "    )\n",
+    "\n",
+    "country_median = ds.median(dim=['lat', 'lon'])\n",
+    "df = country_median['SPEI_12_month'].to_dataframe().reset_index()\n",
+    "df.loc[:, 'ymd'] = df['time'].dt.strftime('%Y-%m-%d')\n",
+    "df.loc[:, 'group'] = 1\n",
+    "\n",
+    "font = \"Roboto\"\n",
+    "p = (\n",
+    "ggplot(df, aes(x=\"ymd\", y=\"SPEI_12_month\", group=\"group\", fill=\"SPEI_12_month\"))\n",
+    "    + geom_bar(alpha=0.8, stat='identity', color='black', width=20)\n",
+    "    + labs(\n",
+    "        x=\"\", subtitle=\"Standardised Precipitation-Evapotranspiration Index\", title=\"Drought Index\", \n",
+    "        y=\"\", caption=\"Source: SPEI Global Database (Spanish National Research Council)\"\n",
+    "    )\n",
+    "    + theme_minimal() \n",
+    "    + theme(\n",
+    "        plot_background=element_rect(fill=\"white\"),\n",
+    "        figure_size=(8, 6),\n",
+    "        text=element_text(family=font, size=11),\n",
+    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
+    "        legend_position=\"none\"\n",
+    "        )\n",
+    "    + scale_fill_distiller(type='div', palette=\"RdYlBu\", direction=1, limits=(-1.5, 1.5))\n",
+    "    + scale_y_continuous(limits=(-1.5, 1.5))\n",
+    "    + scale_x_datetime(breaks=date_breaks(\"1 year\"), labels=date_format(\"%Y\")) # 6 month \"%b, %y\"\n",
+    ")\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "                    \n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "                \n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "                    \n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 600,
+       "width": 800
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (800 x 600)>"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# pal = colormaps.get_palette('RdYlBu', 10) # Greens BrBG\n",
+    "# visParams = {\n",
+    "#   'min': -2.33,\n",
+    "#   'max':  2.33,\n",
+    "#   'palette': pal\n",
+    "# }\n",
+    "\n",
+    "# spei = ee.ImageCollection(\"IDAHO_EPSCOR/TERRACLIMATE\").select('spei').filterDate('2019-01-01', '2023-12-31')\n",
+    "# one = spei.first().clip(aoi)\n",
+    "\n",
+    "\n",
+    "# m = Map(center=[centy, centx], zoom=6) # basemap='Esri.WorldImagery'\n",
+    "# m.addLayer(one, visParams, 'SPEI')\n",
+    "# # m.add_time_slider(spei, visParams, time_interval=1)\n",
+    "# m.addLayerControl()\n",
+    "# m\n",
+    "\n",
+    "# Download from https://spei.csic.es/map/maps.html\n",
+    "ds2 = xr.open_dataset(join(expanduser('~'), 'Downloads', 'spei06.nc'))\n",
+    "ds2 = ds2.isel(lat=slice(None, None, -1))\n",
+    "# slice time dimension\n",
+    "ds2 = ds2.sel(time=slice('2019-01-01', '2023-12-31'))\n",
+    "ds2.rio.set_spatial_dims(x_dim='lon', y_dim='lat', inplace=True)\n",
+    "ds2.rio.write_crs(\"EPSG:4326\", inplace=True)\n",
+    "clipped = ds2.rio.clip(adm0_ner.geometry.values, adm0_ner.crs, drop=True, invert=False)\n",
+    "\n",
+    "country_median = clipped.median(dim=['lat', 'lon'])\n",
+    "df = country_median['spei'].to_dataframe().reset_index()\n",
+    "df.loc[:, 'ymd'] = df['time'].dt.strftime('%Y-%m-%d')\n",
+    "df.loc[:, 'group'] = 1\n",
+    "\n",
+    "font = \"Roboto\"\n",
+    "p = (\n",
+    "ggplot(df, aes(x=\"ymd\", y=\"spei\", group=\"group\", fill=\"spei\"))\n",
+    "    + geom_bar(alpha=0.8, stat='identity', color='black', width=20)\n",
+    "    + labs(\n",
+    "        x=\"\", subtitle=\"Standardised Precipitation-Evapotranspiration Index\", title=\"Drought Index\", \n",
+    "        y=\"\", caption=\"Source: Global Drought Monitor (Spanish National Research Council)\"\n",
+    "    )\n",
+    "    + theme_minimal() \n",
+    "    + theme(\n",
+    "        plot_background=element_rect(fill=\"white\"),\n",
+    "        figure_size=(8, 6),\n",
+    "        text=element_text(family=font, size=11),\n",
+    "        plot_title=element_text(family=font, size=14, weight=\"bold\"),\n",
+    "        legend_position=\"none\"\n",
+    "        )\n",
+    "    + scale_fill_distiller(type='div', palette=\"RdYlBu\", direction=1, limits=(-1.5, 1.5))\n",
+    "    + scale_x_datetime(breaks=date_breaks(\"1 year\"), labels=date_format(\"%Y\"))\n",
+    ")\n",
+    "p"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rtmis",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/agriculture/evi-classification.ipynb b/notebooks/agriculture/evi-classification.ipynb
index e525ecd..b84dbd6 100644
--- a/notebooks/agriculture/evi-classification.ipynb
+++ b/notebooks/agriculture/evi-classification.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Niger Phenolopy"
+    "# Crop Classification"
    ]
   },
   {
@@ -244,102 +244,6 @@
     "adm2_ner = gpd.read_file(res['gjDownloadURL'])"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# labels = gpd.read_file(join(expanduser('~'), 'tmp', 'usgs', 'usgs-niger.shp'))\n",
-    "# labels = labels.loc[labels.land_use_t==1].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# adm2_ner.explore()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -721,7 +625,11 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
    "outputs": [
     {
      "data": {
@@ -2721,7 +2629,11 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
    "outputs": [
     {
      "data": {
@@ -5380,64 +5292,6 @@
     "rf_best_fit = rf_best.fit(X_train, y_train.ravel())"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ACCURACY OF THE MODEL:  0.8016528925619835\n",
-      "R-Squared OF THE MODEL:  0.1942286348501664\n"
-     ]
-    }
-   ],
-   "source": [
-    "# performing predictions on the test dataset\n",
-    "y_pred = rf_best_fit.predict(X_test)\n",
-    "y_train_pred = rf_best_fit.predict(X_train)\n",
-    "\n",
-    "# using metrics module for accuracy calculation\n",
-    "print(\"ACCURACY OF THE MODEL: \", metrics.accuracy_score(y_test, y_pred))\n",
-    "print(\"R-Squared OF THE MODEL: \", metrics.r2_score(y_test, y_pred))"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 178,
@@ -5549,48 +5403,6 @@
     "print(\"ACCURACY OF THE MODEL: \", metrics.accuracy_score(y_train, y_train_pred))\n",
     "print(\"R-Squared OF THE MODEL: \", metrics.r2_score(y_train, y_train_pred))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/agriculture/evi-reference-clean-dea.ipynb b/notebooks/agriculture/evi-reference.ipynb
similarity index 99%
rename from notebooks/agriculture/evi-reference-clean-dea.ipynb
rename to notebooks/agriculture/evi-reference.ipynb
index 8bd62b6..b5dfe14 100644
--- a/notebooks/agriculture/evi-reference-clean-dea.ipynb
+++ b/notebooks/agriculture/evi-reference.ipynb
@@ -21,16 +21,6 @@
     "## Setup"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import ee\n",
-    "# ee.Authenticate()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 146,
@@ -12305,18 +12295,6 @@
    "display_name": "RTMIS",
    "language": "python",
    "name": "rtmis"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/agriculture/evi-timesat.ipynb b/notebooks/agriculture/evi-timesat.ipynb
index 8bf85b7..6154a6a 100644
--- a/notebooks/agriculture/evi-timesat.ipynb
+++ b/notebooks/agriculture/evi-timesat.ipynb
@@ -3016,7 +3016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
@@ -7083,6 +7083,18 @@
    "display_name": "RTMIS",
    "language": "python",
    "name": "rtmis"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/agriculture/report.ipynb b/notebooks/agriculture/report.ipynb
index d0eef39..48b3e15 100644
--- a/notebooks/agriculture/report.ipynb
+++ b/notebooks/agriculture/report.ipynb
@@ -861,15 +861,15 @@
    "metadata": {},
    "source": [
     "### Crop Seasonality\n",
-    "Using this time series dataset of EVI images, we apply several pre-processing steps to extract critical phenological parameters: start of season (SOS), middle of season (MOS), end of season (EOS), length of season (LOS), etc. This workflow is heavily inspired by the [TIMESAT](https://web.nateko.lu.se/timesat/timesat.asp) software, although in this implementation we use the [Phenolopy](https://github.com/frontiersi/phenolopy/tree/main) open-source package.\n",
-    "|\n",
+    "Using this time series dataset of EVI images, we apply several pre-processing steps to extract critical phenological parameters: start of season (SOS), middle of season (MOS), end of season (EOS), length of season (LOS), etc. This workflow is heavily inspired by the [TIMESAT](https://web.nateko.lu.se/timesat/timesat.asp) software, although in this implementation we use the [Phenolopy](https://github.com/frontiersi/phenolopy/tree/main) open-source package.  \n",
+    "\n",
     "**Pre-processing steps**  \n",
     "1. Remove outliers from dataset on per-pixel basis using median method: outlier if median from a moving window < or > standard deviation of time-series times 2.\n",
     "2. Interpolate missing values linearly\n",
-    "3. Smooth data on per-pixel basis (using Savitsky Golay filter, window length of 3, and polyorder of 1)\n",
+    "3. Smooth data on per-pixel basis (using Savitsky Golay filter, window length of 3, and polyorder of 1)  \n",
     "\n",
     "**Phenology Process**  \n",
-    "We then extract crop seasonality metrics using the seasonal amplitude method from the phenolopy package.\n",
+    "We then extract crop seasonality metrics using the seasonal amplitude method from the phenolopy package. \n",
     "\n",
     "The chart below shows the result of this process for a single crop pixel. The green dots represent the raw EVI values, the black line represents the processed EVI values, and the red dotted lines represent season parameters extracted for that pixel: start of season, peak of season, and end of season."
    ]
@@ -923,19 +923,12 @@
     "![map](./charts/map-anomalies.jpg)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Interactive Map of Anomalies (experimental)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
     "tags": [
-     "hide-input"
+     "remove-cell"
     ]
    },
    "outputs": [
@@ -1258,7 +1251,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the plot below, we aggregate the median EVI value for each region and compare that with the historical average (gray dotted line)."
+    "In the plot below, we aggregate the median EVI value for each region and compare that with the historical average (gray dotted line). Most provinces show productivity trends close or slightly above the baseline for 2023, except for Diffa and Dosso where the median trend is below the baseline."
    ]
   },
   {
@@ -1267,6 +1260,49 @@
    "source": [
     "![chart2](./charts/evi-median-region.jpeg)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Crop Classification\n",
+    "With the objective of creating crop extent maps for different years, we ran a classification model using the EVI time series and crop seasonality parameters (250 meter resolution). We trained the model by selecting data for crop polygons from [Digital Earth Africa](https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.html#Training-and-Validation-data), resulting in a training dataset of ~500 areas and 18 features (EVI-derived crop season parameters).   \n",
+    "\n",
+    "We used a Random Forest Classifier and validated the method on a test dataset (20% of training data not used in the model). Although the method was able to identify crops from the test dataset, the R-squared was quite low meaning the model is not fit for general use yet. \n",
+    "\n",
+    "**Accuracy of the Model**:  0.809  \n",
+    "**R-Squared**:  0.227  \n",
+    "\n",
+    "See more details in [notebook](evi-classification.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outlook for 2024\n",
+    "Despite the favorable vegetation conditions in 2023, food security agencies are projecting large food security shocks across Niger in 2024. The key factors are increasing food prices and market disruptions due to insecurity and sociopolitical factors. An in-depth analysis can be found from the [FEWS NET Food Security Outlook Februrary 2024](https://fews.net/west-africa/niger/food-security-outlook/february-2024)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Food Security IPC Classification \n",
+    "Source: FEWS NET"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Niger_2024-02-01_ML1.png](./charts/Niger_2024-04-01_ML1.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {