diff --git a/user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook.ipynb b/user_data_notebooks/lpjeosim-wetlandch4-daygrid-v2_User_Notebook.ipynb similarity index 83% rename from user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook.ipynb rename to user_data_notebooks/lpjeosim-wetlandch4-daygrid-v2_User_Notebook.ipynb index 8587b6af..86f825e1 100644 --- a/user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/lpjeosim-wetlandch4-daygrid-v2_User_Notebook.ipynb @@ -93,10 +93,10 @@ "# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n", "\n", "# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\n", - "STAC_API_URL = \"http://ghg.center/api/stac\"\n", + "STAC_API_URL = \"http://dev.ghg.center/ghgcenter/api/stac\"\n", "\n", "# The RASTER API is used to fetch collections for visualization\n", - "RASTER_API_URL = \"https://ghg.center/api/raster\"\n", + "RASTER_API_URL = \"https://dev.ghg.center/ghgcenter/api/raster\"\n", "\n", "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", "# Name of the collection for the wetland methane emissions LPJ-EOSIM Model\n", @@ -176,6 +176,11 @@ " # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n", " # \"href\" is the identifier for each of the tiles stored in the STAC API\n", " items_url = next[0][\"href\"]\n", + " temp = items_url.split('/')\n", + " temp.insert(3, 'ghgcenter')\n", + " temp.insert(4, 'api')\n", + " temp.insert(5, 'stac')\n", + " items_url = '/'.join(temp)\n", "\n", " # Return the information about the total number of granules found associated with the collection\n", " return count" @@ -190,7 +195,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 504 items\n" + "Found 52 items\n" ] } ], @@ -232,7 +237,7 @@ "outputs": [], "source": [ "# Fetch the minimum and maximum values for rescaling\n", - "rescale_values = {'max': 0.2, 'min': 0.0}" + "rescale_values = {'max': 0.0003, 'min': 0.0}" ] }, { @@ -251,7 +256,7 @@ "outputs": [], "source": [ "# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\n", - "items = {item[\"properties\"][\"datetime\"][:7]: item for item in items} " + "items = {item[\"properties\"][\"datetime\"][:10]: item for item in items} " ] }, { @@ -273,7 +278,7 @@ "{'tilejson': '2.2.0',\n", " 'version': '1.0.0',\n", " 'scheme': 'xyz',\n", - " 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1&item=lpjwsl-wetlandch4-monthgrid-v1-200112&assets=ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.2'],\n", + " 'tiles': ['https://dev.ghg.center/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjeosim-wetlandch4-daygrid-v2&item=lpjeosim-wetlandch4-daygrid-v2-19900101day&assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", " 'minzoom': 0,\n", " 'maxzoom': 24,\n", " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", @@ -291,12 +296,13 @@ "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", "color_map = \"magma\" \n", "\n", - "# Make a GET request to retrieve information for the December 2001 tile\n", - "december_2001_tile = requests.get(\n", + "# Make a GET request to retrieve information for the date mentioned below\n", + "date1 = '1990-01-01'\n", + "date1_tile = requests.get(\n", "\n", " # Pass the collection name, collection date, and its ID\n", " # To change the year and month of the observed parameter, you can modify the \"items['YYYY-MM-DD']\" statement\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2001-12-01']['collection']}&item={items['2001-12-01']['id']}\"\n", + " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[date1]['collection']}&item={items[date1]['id']}\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -311,7 +317,7 @@ ").json()\n", "\n", "# Print the properties of the retrieved granule to the console\n", - "december_2001_tile" + "date1_tile" ] }, { @@ -325,7 +331,7 @@ "{'tilejson': '2.2.0',\n", " 'version': '1.0.0',\n", " 'scheme': 'xyz',\n", - " 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1&item=lpjwsl-wetlandch4-monthgrid-v1-202112&assets=ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.2'],\n", + " 'tiles': ['https://dev.ghg.center/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjeosim-wetlandch4-daygrid-v2&item=lpjeosim-wetlandch4-daygrid-v2-19900130day&assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", " 'minzoom': 0,\n", " 'maxzoom': 24,\n", " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", @@ -338,12 +344,13 @@ } ], "source": [ - "# Make a GET request to retrieve information for the December 2021 tile\n", - "december_2021_tile = requests.get(\n", + "# Make a GET request to retrieve information for date mentioned below\n", + "date2 = '1990-01-30'\n", + "date2_tile = requests.get(\n", "\n", " # Pass the collection name, collection date, and its ID\n", " # To change the year and month of the observed parameter, you can modify the \"items['YYYY-MM-DD']\" statement\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-12-01']['collection']}&item={items['2021-12-01']['id']}\"\n", + " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[date2]['collection']}&item={items[date2]['id']}\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -358,7 +365,7 @@ ").json()\n", "\n", "# Print the properties of the retrieved granule to the console\n", - "december_2021_tile" + "date2_tile" ] }, { @@ -391,7 +398,7 @@ " <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n", " <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n", " <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>\n", - " <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>\n", + " <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n", " <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>\n", " <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n", " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>\n", @@ -404,7 +411,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_803dfd24705b959dbbf5990904d942da {\n", + " #map_0cd3bd8a64887a7c792194f7868676c8 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -418,7 +425,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_6d3888ae45782aa85870c8aeb7aec982 {\n", + " #map_8b565a2b7c718653ca79efa5fc88e920 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -433,17 +440,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_803dfd24705b959dbbf5990904d942da" ></div>\n", + " <div class="folium-map" id="map_0cd3bd8a64887a7c792194f7868676c8" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_6d3888ae45782aa85870c8aeb7aec982" ></div>\n", + " <div class="folium-map" id="map_8b565a2b7c718653ca79efa5fc88e920" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_803dfd24705b959dbbf5990904d942da = L.map(\n", - " "map_803dfd24705b959dbbf5990904d942da",\n", + " var map_0cd3bd8a64887a7c792194f7868676c8 = L.map(\n", + " "map_0cd3bd8a64887a7c792194f7868676c8",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -457,26 +464,20 @@ "\n", " \n", " \n", - " var tile_layer_ad5544502d2eb95b321938f68353ac45 = L.tileLayer(\n", - " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", - " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " );\n", - " \n", - " \n", - " tile_layer_ad5544502d2eb95b321938f68353ac45.addTo(map_803dfd24705b959dbbf5990904d942da);\n", + " var tile_layer_351c32047150ae73ab81eead5b16289f = L.tileLayer(\n", + " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", + " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", + " ).addTo(map_0cd3bd8a64887a7c792194f7868676c8);\n", " \n", " \n", - " var tile_layer_35269c24a28ce23f0ec334ffa83a8b86 = L.tileLayer(\n", - " "https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1\\u0026item=lpjwsl-wetlandch4-monthgrid-v1-200112\\u0026assets=ch4-wetlands-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=0.0%2C0.2",\n", + " var tile_layer_58bd8a20b9607db35bbd61510c9b1f3e = L.tileLayer(\n", + " "https://dev.ghg.center/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjeosim-wetlandch4-daygrid-v2\\u0026item=lpjeosim-wetlandch4-daygrid-v2-19900101day\\u0026assets=ensemble-mean-ch4-wetlands-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=0.0%2C0.0003",\n", " {"attribution": "GHG", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", - " );\n", - " \n", - " \n", - " tile_layer_35269c24a28ce23f0ec334ffa83a8b86.addTo(map_803dfd24705b959dbbf5990904d942da);\n", + " ).addTo(map_0cd3bd8a64887a7c792194f7868676c8);\n", " \n", " \n", - " var map_6d3888ae45782aa85870c8aeb7aec982 = L.map(\n", - " "map_6d3888ae45782aa85870c8aeb7aec982",\n", + " var map_8b565a2b7c718653ca79efa5fc88e920 = L.map(\n", + " "map_8b565a2b7c718653ca79efa5fc88e920",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -490,32 +491,26 @@ "\n", " \n", " \n", - " var tile_layer_454ad6651c829dbe0962f5020595bc4b = L.tileLayer(\n", - " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", - " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " );\n", + " var tile_layer_ee29a61ad78967ae48d1576a9b9d404c = L.tileLayer(\n", + " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", + " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", + " ).addTo(map_8b565a2b7c718653ca79efa5fc88e920);\n", " \n", " \n", - " tile_layer_454ad6651c829dbe0962f5020595bc4b.addTo(map_6d3888ae45782aa85870c8aeb7aec982);\n", - " \n", - " \n", - " var tile_layer_900c32add432be26a0004a8ec11d5361 = L.tileLayer(\n", - " "https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1\\u0026item=lpjwsl-wetlandch4-monthgrid-v1-202112\\u0026assets=ch4-wetlands-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=0.0%2C0.2",\n", + " var tile_layer_96d966dbd028b24dadcbb8baf2edec97 = L.tileLayer(\n", + " "https://dev.ghg.center/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjeosim-wetlandch4-daygrid-v2\\u0026item=lpjeosim-wetlandch4-daygrid-v2-19900130day\\u0026assets=ensemble-mean-ch4-wetlands-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=0.0%2C0.0003",\n", " {"attribution": "GHG", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", - " );\n", + " ).addTo(map_8b565a2b7c718653ca79efa5fc88e920);\n", " \n", " \n", - " tile_layer_900c32add432be26a0004a8ec11d5361.addTo(map_6d3888ae45782aa85870c8aeb7aec982);\n", - " \n", - " \n", - " map_803dfd24705b959dbbf5990904d942da.sync(map_6d3888ae45782aa85870c8aeb7aec982);\n", - " map_6d3888ae45782aa85870c8aeb7aec982.sync(map_803dfd24705b959dbbf5990904d942da);\n", + " map_0cd3bd8a64887a7c792194f7868676c8.sync(map_8b565a2b7c718653ca79efa5fc88e920);\n", + " map_8b565a2b7c718653ca79efa5fc88e920.sync(map_0cd3bd8a64887a7c792194f7868676c8);\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -531,27 +526,27 @@ "# 'folium.plugins' allows mapping side-by-side\n", "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", "\n", - "# Define the first map layer for December 2001 tile\n", + "# Define the first map layer for tile fetched for date 1\n", "# The TileLayer library helps in manipulating and displaying raster layers on a map\n", - "map_layer_2001 = TileLayer(\n", - " tiles=december_2001_tile[\"tiles\"][0], # Path to retrieve the tile\n", + "map_layer_date1 = TileLayer(\n", + " tiles=date1_tile[\"tiles\"][0], # Path to retrieve the tile\n", " attr=\"GHG\", # Set the attribution\n", " opacity=0.5, # Adjust the transparency of the layer\n", ")\n", "\n", "# Add the first layer to the Dual Map\n", - "map_layer_2001.add_to(map_.m1)\n", + "map_layer_date1.add_to(map_.m1)\n", "\n", "\n", - "# Define the second map layer for December 2021 tile\n", - "map_layer_2021 = TileLayer(\n", - " tiles=december_2021_tile[\"tiles\"][0], # Path to retrieve the tile\n", + "# Define the second map layer for the tile fetched for date 2\n", + "map_layer_date2 = TileLayer(\n", + " tiles=date2_tile[\"tiles\"][0], # Path to retrieve the tile\n", " attr=\"GHG\", # Set the attribution\n", " opacity=0.5, # Adjust the transparency of the layer\n", ")\n", "\n", "# Add the second layer to the Dual Map\n", - "map_layer_2021.add_to(map_.m2)\n", + "map_layer_date2.add_to(map_.m2)\n", "\n", "# Visualize the Dual Map\n", "map_\n" @@ -614,7 +609,7 @@ " <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n", " <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n", " <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>\n", - " <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>\n", + " <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n", " <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>\n", " <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n", " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>\n", @@ -627,7 +622,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_26d4f0f29a892e40e82f1207f6449295 {\n", + " #map_795d6f917b7901ef903939d254166e74 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -641,14 +636,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_26d4f0f29a892e40e82f1207f6449295" ></div>\n", + " <div class="folium-map" id="map_795d6f917b7901ef903939d254166e74" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_26d4f0f29a892e40e82f1207f6449295 = L.map(\n", - " "map_26d4f0f29a892e40e82f1207f6449295",\n", + " var map_795d6f917b7901ef903939d254166e74 = L.map(\n", + " "map_795d6f917b7901ef903939d254166e74",\n", " {\n", " center: [30.0, -101.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -662,40 +657,35 @@ "\n", " \n", " \n", - " var tile_layer_556c4998af218e469b51c82621d403b9 = L.tileLayer(\n", - " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", - " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " );\n", - " \n", - " \n", - " tile_layer_556c4998af218e469b51c82621d403b9.addTo(map_26d4f0f29a892e40e82f1207f6449295);\n", + " var tile_layer_60be2476d4eba852be06896e3cf1cc78 = L.tileLayer(\n", + " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", + " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", + " ).addTo(map_795d6f917b7901ef903939d254166e74);\n", " \n", " \n", "\n", - " function geo_json_dca6a5d5de87380a92d68193352dedb2_onEachFeature(feature, layer) {\n", + " function geo_json_d0a655636efbd45bb50d41b3227a5b09_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_dca6a5d5de87380a92d68193352dedb2 = L.geoJson(null, {\n", - " onEachFeature: geo_json_dca6a5d5de87380a92d68193352dedb2_onEachFeature,\n", + " var geo_json_d0a655636efbd45bb50d41b3227a5b09 = L.geoJson(null, {\n", + " onEachFeature: geo_json_d0a655636efbd45bb50d41b3227a5b09_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_dca6a5d5de87380a92d68193352dedb2_add (data) {\n", - " geo_json_dca6a5d5de87380a92d68193352dedb2\n", - " .addData(data);\n", + " function geo_json_d0a655636efbd45bb50d41b3227a5b09_add (data) {\n", + " geo_json_d0a655636efbd45bb50d41b3227a5b09\n", + " .addData(data)\n", + " .addTo(map_795d6f917b7901ef903939d254166e74);\n", " }\n", - " geo_json_dca6a5d5de87380a92d68193352dedb2_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_d0a655636efbd45bb50d41b3227a5b09_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", "\n", " \n", - " \n", - " geo_json_dca6a5d5de87380a92d68193352dedb2.addTo(map_26d4f0f29a892e40e82f1207f6449295);\n", - " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -735,17 +725,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 504 items\n" - ] - } - ], + "outputs": [], "source": [ "# Check the total number of items available within the collection\n", "items = requests.get(\n", @@ -775,7 +757,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -817,18 +799,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.4 s, sys: 944 ms, total: 16.4 s\n", - "Wall time: 6min 29s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", @@ -1032,7 +1005,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.16" }, "vscode": { "interpreter": { diff --git a/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb b/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb index 0b109e4c..44390434 100644 --- a/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -86,10 +86,10 @@ "# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n", "\n", "# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\n", - "STAC_API_URL = \"http://ghg.center/api/stac\"\n", + "STAC_API_URL = \"http://dev.ghg.center/ghgcenter/api/stac\"\n", "\n", "# The RASTER API is used to fetch collections for visualization\n", - "RASTER_API_URL = \"https://ghg.center/api/raster\"\n", + "RASTER_API_URL = \"https://dev.ghg.center/ghgcenter/api/raster\"\n", "\n", "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", "# Name of the collection for MiCASA Land Carbon Flux\n", @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -170,6 +170,11 @@ " # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n", " # \"href\" is the identifier for each of the tiles stored in the STAC API\n", " items_url = next[0][\"href\"]\n", + " temp = items_url.split('/')\n", + " temp.insert(3, 'ghgcenter')\n", + " temp.insert(4, 'api')\n", + " temp.insert(5, 'stac')\n", + " items_url = '/'.join(temp)\n", "\n", " # Return the information about the total number of granules found associated with the collection (MiCASA Land Carbon Flux)\n", " return count" @@ -177,14 +182,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Found 180 items\n" + "Found 67 items\n" ] } ], @@ -201,311 +206,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'casagfed-carbonflux-monthgrid-v3-201712',\n", - " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'type': 'Feature',\n", - " 'links': [{'rel': 'collection',\n", - " 'type': 'application/json',\n", - " 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://ghg.center/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://ghg.center/api/stac/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712'}],\n", 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'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n", - " 'fire': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_FIRE_Flux_Monthly_x720_y360_201712.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'fire',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [360.0, 720.0],\n", - " 'description': 'fire emissions',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 0.7556899785995483,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [258952.0, 161.0, 53.0, 22.0, 11.0, 0.0, 0.0, 0.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.00025634843041189015,\n", - " 'stddev': 0.005492232274264097,\n", - " 'maximum': 0.7556899785995483,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.0003858024691358025}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n", - " 'fuel': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_FUEL_Flux_Monthly_x720_y360_201712.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'fuel',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [360.0, 720.0],\n", - " 'description': 'fuel emissions',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 0.020759999752044678,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [257568.0,\n", - " 1150.0,\n", - " 284.0,\n", - " 115.0,\n", - " 47.0,\n", - " 21.0,\n", - " 5.0,\n", - " 6.0,\n", - " 3.0,\n", - " 1.0]},\n", - " 'statistics': {'mean': 5.057307134848088e-05,\n", - " 'stddev': 0.0003876804548781365,\n", - " 'maximum': 0.020759999752044678,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.0003858024691358025}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]}},\n", - " 'geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180, -90],\n", - " [180, -90],\n", - " [180, 90],\n", - " [-180, 90],\n", - " [-180, -90]]]},\n", - " 'collection': 'casagfed-carbonflux-monthgrid-v3',\n", - " 'properties': {'end_datetime': '2017-12-31T00:00:00+00:00',\n", - " 'start_datetime': '2017-12-01T00:00:00+00:00'},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': []}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Examine the first item in the collection\n", "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", @@ -523,12 +226,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\n", - "items = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items}" + "items = {item[\"properties\"][\"datetime\"][:10]: item for item in items}" ] }, { @@ -541,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -559,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -568,14 +271,14 @@ "{'tilejson': '2.2.0',\n", " 'version': '1.0.0',\n", " 'scheme': 'xyz',\n", - " 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=casagfed-carbonflux-monthgrid-v3&item=casagfed-carbonflux-monthgrid-v3-200312&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n", + " 'tiles': ['https://dev.ghg.center/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=micasa-carbonflux-daygrid-v1&item=micasa-carbonflux-daygrid-v1-20010101&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.28565365076065063%2C5.658170223236084'],\n", " 'minzoom': 0,\n", " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" + " 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n", + " 'center': [-2.842170943040401e-14, 0.0, 0]}" ] }, - "execution_count": 10, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -586,12 +289,13 @@ "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", "color_map = \"purd\"\n", "\n", - "# Make a GET request to retrieve information for the December 2003 tile\n", - "december_2003_tile = requests.get(\n", + "# Make a GET request to retrieve information for the date mentioned below\n", + "date1 = '2001-01-01'\n", + "date1_tile = requests.get(\n", "\n", " # Pass the collection name, collection date, and its ID\n", " # To change the year and month of the observed parameter, you can modify the \"items['YYYY-MM-DD']\" statement\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2003-12-01']['collection']}&item={items['2003-12-01']['id']}\"\n", + " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[date1]['collection']}&item={items[date1]['id']}\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -606,21 +310,40 @@ ").json()\n", "\n", "# Print the properties of the retrieved granule to the console\n", - "december_2003_tile" + "date1_tile" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://dev.ghg.center/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=micasa-carbonflux-daygrid-v1&item=micasa-carbonflux-daygrid-v1-20010131&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.28565365076065063%2C5.658170223236084'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n", + " 'center': [-2.842170943040401e-14, 0.0, 0]}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Make a GET request to retrieve information for the December 2017 tile\n", - "december_2017_tile = requests.get(\n", + "# Make a GET request to retrieve information for the date mentioned below\n", + "date2 = '2001-01-31'\n", + "date2_tile = requests.get(\n", "\n", " # Pass the collection name, collection date, and its ID\n", " # To change the year and month of the observed parameter, you can modify the \"items['YYYY-MM-DD']\" statement\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2017-12-01']['collection']}&item={items['2017-12-01']['id']}\"\n", + " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[date2]['collection']}&item={items[date2]['id']}\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -635,7 +358,7 @@ ").json()\n", "\n", "# Print the properties of the retrieved granule to the console\n", - "december_2017_tile" + "date2_tile" ] }, { @@ -648,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -668,7 +391,7 @@ " <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n", " <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n", " <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>\n", - " <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>\n", + " <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n", " <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>\n", " <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n", " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>\n", @@ -681,7 +404,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_859c852e9c147efbb88af97ea5310dc5 {\n", + " #map_6b9d2f3883604b8e3aa0557caa9d2b66 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -696,7 +419,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_0b7389cf63eaeb96680f56aa4b05357a {\n", + " #map_3fa8929adab1fa8d2f34f48657c721a9 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -711,17 +434,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_859c852e9c147efbb88af97ea5310dc5" ></div>\n", + " <div class="folium-map" id="map_6b9d2f3883604b8e3aa0557caa9d2b66" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_0b7389cf63eaeb96680f56aa4b05357a" ></div>\n", + " <div class="folium-map" id="map_3fa8929adab1fa8d2f34f48657c721a9" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_859c852e9c147efbb88af97ea5310dc5 = L.map(\n", - " "map_859c852e9c147efbb88af97ea5310dc5",\n", + " var map_6b9d2f3883604b8e3aa0557caa9d2b66 = L.map(\n", + " "map_6b9d2f3883604b8e3aa0557caa9d2b66",\n", " {\n", " center: [31.9, -99.9],\n", " crs: L.CRS.EPSG3857,\n", @@ -735,31 +458,25 @@ "\n", " \n", " \n", - " var tile_layer_203b223aa16e0df8b58b5f85a40d1bd7 = L.tileLayer(\n", - " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", - " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " );\n", - " \n", - " \n", - " tile_layer_203b223aa16e0df8b58b5f85a40d1bd7.addTo(map_859c852e9c147efbb88af97ea5310dc5);\n", + " var tile_layer_a742a0c22b4e3fa58f8f13d3ae311895 = L.tileLayer(\n", + " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", + " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", + " ).addTo(map_6b9d2f3883604b8e3aa0557caa9d2b66);\n", " \n", " \n", - " var tile_layer_07ba95b287457dec68c962f5f08646cd = L.tileLayer(\n", - " "https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=casagfed-carbonflux-monthgrid-v3\\u0026item=casagfed-carbonflux-monthgrid-v3-200312\\u0026assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0.0%2C0.6039900183677673",\n", + " var tile_layer_4bf45087d20fb9eda91e9a1dfee9594b = L.tileLayer(\n", + " "https://dev.ghg.center/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=micasa-carbonflux-daygrid-v1\\u0026item=micasa-carbonflux-daygrid-v1-20010101\\u0026assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=-0.28565365076065063%2C5.658170223236084",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", - " );\n", - " \n", - " \n", - " tile_layer_07ba95b287457dec68c962f5f08646cd.addTo(map_859c852e9c147efbb88af97ea5310dc5);\n", + " ).addTo(map_6b9d2f3883604b8e3aa0557caa9d2b66);\n", " \n", " \n", - " var marker_ea18a90a13c196de1b87d40e62f78e89 = L.marker(\n", + " var marker_648ee3effb93697d0d1cee6aaa540615 = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_859c852e9c147efbb88af97ea5310dc5);\n", + " ).addTo(map_6b9d2f3883604b8e3aa0557caa9d2b66);\n", " \n", " \n", - " marker_ea18a90a13c196de1b87d40e62f78e89.bindTooltip(\n", + " marker_648ee3effb93697d0d1cee6aaa540615.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -767,58 +484,57 @@ " );\n", " \n", " \n", - " var layer_control_77538dab20a91a6e31c141d9f66cd44c_layers = {\n", + " var layer_control_068fe15f2cf9fdbc2b75ca04d1c31eda = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_203b223aa16e0df8b58b5f85a40d1bd7,\n", + " "openstreetmap" : tile_layer_a742a0c22b4e3fa58f8f13d3ae311895,\n", " },\n", " overlays : {\n", - " "December 2003 RH Level" : tile_layer_07ba95b287457dec68c962f5f08646cd,\n", + " "2001-01-01 Rh Level" : tile_layer_4bf45087d20fb9eda91e9a1dfee9594b,\n", " },\n", " };\n", - " let layer_control_77538dab20a91a6e31c141d9f66cd44c = L.control.layers(\n", - " layer_control_77538dab20a91a6e31c141d9f66cd44c_layers.base_layers,\n", - " layer_control_77538dab20a91a6e31c141d9f66cd44c_layers.overlays,\n", + " L.control.layers(\n", + " layer_control_068fe15f2cf9fdbc2b75ca04d1c31eda.base_layers,\n", + " layer_control_068fe15f2cf9fdbc2b75ca04d1c31eda.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_859c852e9c147efbb88af97ea5310dc5);\n", - "\n", + " ).addTo(map_6b9d2f3883604b8e3aa0557caa9d2b66);\n", " \n", " \n", - " var color_map_1c70e55849794370a459c6398906bd16 = {};\n", + " var color_map_7b377ad3c95678f1e6360df6b3f8a9cb = {};\n", "\n", " \n", - " color_map_1c70e55849794370a459c6398906bd16.color = d3.scale.threshold()\n", + " color_map_7b377ad3c95678f1e6360df6b3f8a9cb.color = d3.scale.threshold()\n", " .domain([0.0, 0.0006012024048096192, 0.0012024048096192384, 0.0018036072144288575, 0.002404809619238477, 0.003006012024048096, 0.003607214428857715, 0.004208416833667335, 0.004809619238476954, 0.005410821643286573, 0.006012024048096192, 0.006613226452905812, 0.00721442885771543, 0.00781563126252505, 0.00841683366733467, 0.009018036072144289, 0.009619238476953907, 0.010220440881763526, 0.010821643286573146, 0.011422845691382766, 0.012024048096192385, 0.012625250501002003, 0.013226452905811623, 0.013827655310621242, 0.01442885771543086, 0.01503006012024048, 0.0156312625250501, 0.01623246492985972, 0.01683366733466934, 0.017434869739478956, 0.018036072144288578, 0.018637274549098193, 0.019238476953907815, 0.019839679358717437, 0.02044088176352705, 0.021042084168336674, 0.021643286573146292, 0.02224448897795591, 0.022845691382765532, 0.023446893787575147, 0.02404809619238477, 0.024649298597194388, 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'#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff']);\n", " \n", "\n", - " color_map_1c70e55849794370a459c6398906bd16.x = d3.scale.linear()\n", + " color_map_7b377ad3c95678f1e6360df6b3f8a9cb.x = d3.scale.linear()\n", " .domain([0.0, 0.3])\n", " .range([0, 450 - 50]);\n", "\n", - " color_map_1c70e55849794370a459c6398906bd16.legend = L.control({position: 'topright'});\n", - " color_map_1c70e55849794370a459c6398906bd16.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", - " color_map_1c70e55849794370a459c6398906bd16.legend.addTo(map_859c852e9c147efbb88af97ea5310dc5);\n", + " color_map_7b377ad3c95678f1e6360df6b3f8a9cb.legend = L.control({position: 'topright'});\n", + " color_map_7b377ad3c95678f1e6360df6b3f8a9cb.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_7b377ad3c95678f1e6360df6b3f8a9cb.legend.addTo(map_6b9d2f3883604b8e3aa0557caa9d2b66);\n", "\n", - " color_map_1c70e55849794370a459c6398906bd16.xAxis = d3.svg.axis()\n", - " .scale(color_map_1c70e55849794370a459c6398906bd16.x)\n", + " color_map_7b377ad3c95678f1e6360df6b3f8a9cb.xAxis = d3.svg.axis()\n", + " .scale(color_map_7b377ad3c95678f1e6360df6b3f8a9cb.x)\n", " .orient("top")\n", " .tickSize(1)\n", " .tickValues([0, 0.07, 0.15, 0.22, 0.3]);\n", "\n", - " color_map_1c70e55849794370a459c6398906bd16.svg = d3.select(".legend.leaflet-control").append("svg")\n", + " color_map_7b377ad3c95678f1e6360df6b3f8a9cb.svg = d3.select(".legend.leaflet-control").append("svg")\n", " .attr("id", 'legend')\n", " .attr("width", 450)\n", " .attr("height", 40);\n", "\n", - " color_map_1c70e55849794370a459c6398906bd16.g = color_map_1c70e55849794370a459c6398906bd16.svg.append("g")\n", + " color_map_7b377ad3c95678f1e6360df6b3f8a9cb.g = color_map_7b377ad3c95678f1e6360df6b3f8a9cb.svg.append("g")\n", " .attr("class", "key")\n", " .attr("transform", "translate(25,16)");\n", "\n", - " color_map_1c70e55849794370a459c6398906bd16.g.selectAll("rect")\n", - " .data(color_map_1c70e55849794370a459c6398906bd16.color.range().map(function(d, i) {\n", + " color_map_7b377ad3c95678f1e6360df6b3f8a9cb.g.selectAll("rect")\n", + " .data(color_map_7b377ad3c95678f1e6360df6b3f8a9cb.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_1c70e55849794370a459c6398906bd16.x(color_map_1c70e55849794370a459c6398906bd16.color.domain()[i - 1]) : color_map_1c70e55849794370a459c6398906bd16.x.range()[0],\n", - " x1: i < color_map_1c70e55849794370a459c6398906bd16.color.domain().length ? color_map_1c70e55849794370a459c6398906bd16.x(color_map_1c70e55849794370a459c6398906bd16.color.domain()[i]) : color_map_1c70e55849794370a459c6398906bd16.x.range()[1],\n", + " x0: i ? color_map_7b377ad3c95678f1e6360df6b3f8a9cb.x(color_map_7b377ad3c95678f1e6360df6b3f8a9cb.color.domain()[i - 1]) : color_map_7b377ad3c95678f1e6360df6b3f8a9cb.x.range()[0],\n", + " x1: i < color_map_7b377ad3c95678f1e6360df6b3f8a9cb.color.domain().length ? color_map_7b377ad3c95678f1e6360df6b3f8a9cb.x(color_map_7b377ad3c95678f1e6360df6b3f8a9cb.color.domain()[i]) : color_map_7b377ad3c95678f1e6360df6b3f8a9cb.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -828,13 +544,13 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_1c70e55849794370a459c6398906bd16.g.call(color_map_1c70e55849794370a459c6398906bd16.xAxis).append("text")\n", + " color_map_7b377ad3c95678f1e6360df6b3f8a9cb.g.call(color_map_7b377ad3c95678f1e6360df6b3f8a9cb.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", - " .text("Rh Values (kg Carbon/m2/month)");\n", + " .text("Rh Values (kg Carbon/m2/daily)");\n", " \n", - " var map_0b7389cf63eaeb96680f56aa4b05357a = L.map(\n", - " "map_0b7389cf63eaeb96680f56aa4b05357a",\n", + " var map_3fa8929adab1fa8d2f34f48657c721a9 = L.map(\n", + " "map_3fa8929adab1fa8d2f34f48657c721a9",\n", " {\n", " center: [31.9, -99.9],\n", " crs: L.CRS.EPSG3857,\n", @@ -848,35 +564,29 @@ "\n", " \n", " \n", - " var tile_layer_e8a2240388fe6a29281d5a30ac09c46a = L.tileLayer(\n", - " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", - " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " );\n", - " \n", - " \n", - " tile_layer_e8a2240388fe6a29281d5a30ac09c46a.addTo(map_0b7389cf63eaeb96680f56aa4b05357a);\n", + " var tile_layer_049e746e57971ea84df26f469fab047e = L.tileLayer(\n", + " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", + " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", + " ).addTo(map_3fa8929adab1fa8d2f34f48657c721a9);\n", " \n", " \n", - " var tile_layer_ae2d1409234c2cb287e42c0102715828 = L.tileLayer(\n", - " "https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=casagfed-carbonflux-monthgrid-v3\\u0026item=casagfed-carbonflux-monthgrid-v3-201712\\u0026assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0.0%2C0.6039900183677673",\n", + " var tile_layer_82bf5a53605becd5c1aa68a75be3bae0 = L.tileLayer(\n", + " "https://dev.ghg.center/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=micasa-carbonflux-daygrid-v1\\u0026item=micasa-carbonflux-daygrid-v1-20010131\\u0026assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=-0.28565365076065063%2C5.658170223236084",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", - " );\n", + " ).addTo(map_3fa8929adab1fa8d2f34f48657c721a9);\n", " \n", " \n", - " tile_layer_ae2d1409234c2cb287e42c0102715828.addTo(map_0b7389cf63eaeb96680f56aa4b05357a);\n", + " map_6b9d2f3883604b8e3aa0557caa9d2b66.sync(map_3fa8929adab1fa8d2f34f48657c721a9);\n", + " map_3fa8929adab1fa8d2f34f48657c721a9.sync(map_6b9d2f3883604b8e3aa0557caa9d2b66);\n", " \n", " \n", - " map_859c852e9c147efbb88af97ea5310dc5.sync(map_0b7389cf63eaeb96680f56aa4b05357a);\n", - " map_0b7389cf63eaeb96680f56aa4b05357a.sync(map_859c852e9c147efbb88af97ea5310dc5);\n", - " \n", - " \n", - " var marker_125cbf6433064670af9d879bc1ed6374 = L.marker(\n", + " var marker_2c3e4ad21ccc4ef9874b58448db72f3f = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_0b7389cf63eaeb96680f56aa4b05357a);\n", + " ).addTo(map_3fa8929adab1fa8d2f34f48657c721a9);\n", " \n", " \n", - " marker_125cbf6433064670af9d879bc1ed6374.bindTooltip(\n", + " marker_2c3e4ad21ccc4ef9874b58448db72f3f.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -884,29 +594,28 @@ " );\n", " \n", " \n", - " var layer_control_2db0af31558b4098813e65af1b64de57_layers = {\n", + " var layer_control_82866e90839c47ab8968746193036aa4 = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_e8a2240388fe6a29281d5a30ac09c46a,\n", + " "openstreetmap" : tile_layer_049e746e57971ea84df26f469fab047e,\n", " },\n", " overlays : {\n", - " "December 2017 RH Level" : tile_layer_ae2d1409234c2cb287e42c0102715828,\n", + " "2001-01-31 RH Level" : tile_layer_82bf5a53605becd5c1aa68a75be3bae0,\n", " },\n", " };\n", - " let layer_control_2db0af31558b4098813e65af1b64de57 = L.control.layers(\n", - " layer_control_2db0af31558b4098813e65af1b64de57_layers.base_layers,\n", - " layer_control_2db0af31558b4098813e65af1b64de57_layers.overlays,\n", + " L.control.layers(\n", + " layer_control_82866e90839c47ab8968746193036aa4.base_layers,\n", + " layer_control_82866e90839c47ab8968746193036aa4.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_0b7389cf63eaeb96680f56aa4b05357a);\n", - "\n", + " ).addTo(map_3fa8929adab1fa8d2f34f48657c721a9);\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -921,33 +630,33 @@ "map_ = folium.plugins.DualMap(location=(31.9, -99.9), zoom_start=6)\n", "\n", "\n", - "# Define the first map layer with Rh level for December 2003\n", + "# Define the first map layer with Rh level for the tile fetched for date 1\n", "# The TileLayer library helps in manipulating and displaying raster layers on a map\n", - "map_layer_2003 = TileLayer(\n", - " tiles=december_2003_tile[\"tiles\"][0], # Path to retrieve the tile\n", + "map_layer_date1 = TileLayer(\n", + " tiles=date1_tile[\"tiles\"][0], # Path to retrieve the tile\n", " attr=\"GHG\", # Set the attribution\n", " opacity=0.8, # Adjust the transparency of the layer\n", - " name=\"December 2003 Rh Level\", # Title for the layer\n", + " name=f\"{date1} Rh Level\", # Title for the layer\n", " overlay= True, # The layer can be overlaid on the map\n", " legendEnabled = True # Enable displaying the legend on the map\n", ")\n", "\n", "# Add the first layer to the Dual Map\n", - "map_layer_2003.add_to(map_.m1)\n", + "map_layer_date1.add_to(map_.m1)\n", "\n", "\n", - "# Define the first map layer with Rh level for December 2017\n", - "map_layer_2017 = TileLayer(\n", - " tiles=december_2017_tile[\"tiles\"][0], # Path to retrieve the tile\n", + "# Define the first map layer with Rh level for the tile fetched for date 2\n", + "map_layer_date2 = TileLayer(\n", + " tiles=date2_tile[\"tiles\"][0], # Path to retrieve the tile\n", " attr=\"GHG\", # Set the attribution\n", " opacity=0.8, # Adjust the transparency of the layer\n", - " name=\"December 2017 RH Level\", # Title for the layer\n", + " name=f\"{date2} RH Level\", # Title for the layer\n", " overlay= True, # The layer can be overlaid on the map\n", " legendEnabled = True # Enable displaying the legend on the map\n", ")\n", "\n", "# Add the second layer to the Dual Map\n", - "map_layer_2017.add_to(map_.m2)\n", + "map_layer_date2.add_to(map_.m2)\n", "\n", "# Display data markers (titles) on both maps\n", "folium.Marker((40, 5.0), tooltip=\"both\").add_to(map_)\n", @@ -983,7 +692,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1009,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1029,7 +738,7 @@ " <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n", " <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n", " <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>\n", - " <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>\n", + " <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n", " <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>\n", " <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n", " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>\n", @@ -1042,7 +751,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_90a69a0d47d01e6ca7ac1f3525e447e9 {\n", + " #map_3f168b48e210038477a91c0afaad4fb8 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1056,14 +765,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_90a69a0d47d01e6ca7ac1f3525e447e9" ></div>\n", + " <div class="folium-map" id="map_3f168b48e210038477a91c0afaad4fb8" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_90a69a0d47d01e6ca7ac1f3525e447e9 = L.map(\n", - " "map_90a69a0d47d01e6ca7ac1f3525e447e9",\n", + " var map_3f168b48e210038477a91c0afaad4fb8 = L.map(\n", + " "map_3f168b48e210038477a91c0afaad4fb8",\n", " {\n", " center: [32.81, -96.93],\n", " crs: L.CRS.EPSG3857,\n", @@ -1077,43 +786,38 @@ "\n", " \n", " \n", - " var tile_layer_1bfb46b3448d47bebd1bc30a2dd6935a = L.tileLayer(\n", - " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", - " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " );\n", - " \n", - " \n", - " tile_layer_1bfb46b3448d47bebd1bc30a2dd6935a.addTo(map_90a69a0d47d01e6ca7ac1f3525e447e9);\n", + " var tile_layer_8a733779b25cf866841544a394e7c068 = L.tileLayer(\n", + " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", + " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", + " ).addTo(map_3f168b48e210038477a91c0afaad4fb8);\n", " \n", " \n", "\n", - " function geo_json_9c291b416d477515d3f8427f3f529d9c_onEachFeature(feature, layer) {\n", + " function geo_json_402cc8948bd910b0cac759727e73a103_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_9c291b416d477515d3f8427f3f529d9c = L.geoJson(null, {\n", - " onEachFeature: geo_json_9c291b416d477515d3f8427f3f529d9c_onEachFeature,\n", + " var geo_json_402cc8948bd910b0cac759727e73a103 = L.geoJson(null, {\n", + " onEachFeature: geo_json_402cc8948bd910b0cac759727e73a103_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_9c291b416d477515d3f8427f3f529d9c_add (data) {\n", - " geo_json_9c291b416d477515d3f8427f3f529d9c\n", - " .addData(data);\n", + " function geo_json_402cc8948bd910b0cac759727e73a103_add (data) {\n", + " geo_json_402cc8948bd910b0cac759727e73a103\n", + " .addData(data)\n", + " .addTo(map_3f168b48e210038477a91c0afaad4fb8);\n", " }\n", - " geo_json_9c291b416d477515d3f8427f3f529d9c_add({"geometry": {"coordinates": [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_402cc8948bd910b0cac759727e73a103_add({"geometry": {"coordinates": [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", "\n", " \n", - " \n", - " geo_json_9c291b416d477515d3f8427f3f529d9c.addTo(map_90a69a0d47d01e6ca7ac1f3525e447e9);\n", - " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1150,14 +854,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Found 180 items\n" + "Found 67 items\n" ] } ], @@ -1190,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1221,20 +925,20 @@ " # Return a dictionary containing the computed statistics along with the item's datetime information\n", " return {\n", " **result[\"properties\"],\n", - " \"start_datetime\": item[\"properties\"][\"start_datetime\"],\n", + " \"datetime\": item[\"properties\"][\"datetime\"][:10],\n", " }" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2017-12-01T00:00:00+00:00\n" + "2001-03-08T00:00:00+00:00\n" ] } ], @@ -1243,7 +947,7 @@ "for item in items:\n", "\n", " # The loop will then retrieve the information for the start datetime of each item in the list\n", - " print(item[\"properties\"][\"start_datetime\"])\n", + " print(item[\"properties\"][\"datetime\"])\n", "\n", " # Exit the loop after printing the start datetime for the first item in the collection\n", " break" @@ -1272,43 +976,43 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'statistics': {'b1': {'min': 0.024049999192357063,\n", - " 'max': 0.03717999905347824,\n", - " 'mean': 0.02940833071867625,\n", - " 'count': 6.0,\n", - " 'sum': 0.1764499843120575,\n", - " 'std': 0.004277999495895477,\n", - " 'median': 0.028450001031160355,\n", - " 'majority': 0.024049999192357063,\n", - " 'minority': 0.024049999192357063,\n", - " 'unique': 6.0,\n", - " 'histogram': [[1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0],\n", - " [0.024049999192357063,\n", - " 0.025362998247146606,\n", - " 0.0266759991645813,\n", - " 0.02798900008201599,\n", - " 0.029301999136805534,\n", - " 0.030614998191595078,\n", - " 0.03192799910902977,\n", - " 0.03324100002646446,\n", - " 0.034553997218608856,\n", - " 0.03586699813604355,\n", - " 0.03717999905347824]],\n", + "{'statistics': {'b1': {'min': 0.08267466723918915,\n", + " 'max': 0.9830807447433472,\n", + " 'mean': 0.6214667558670044,\n", + " 'count': 150.0,\n", + " 'sum': 93.22001647949219,\n", + " 'std': 0.16884362462505265,\n", + " 'median': 0.6536906361579895,\n", + " 'majority': 0.08267466723918915,\n", + " 'minority': 0.08267466723918915,\n", + " 'unique': 150.0,\n", + " 'histogram': [[2.0, 3.0, 3.0, 13.0, 19.0, 30.0, 40.0, 18.0, 16.0, 6.0],\n", + " [0.08267466723918915,\n", + " 0.17271527647972107,\n", + " 0.2627558708190918,\n", + " 0.3527964949607849,\n", + " 0.44283708930015564,\n", + " 0.5328776836395264,\n", + " 0.6229183077812195,\n", + " 0.7129589319229126,\n", + " 0.8029995560646057,\n", + " 0.893040120601654,\n", + " 0.9830807447433472]],\n", " 'valid_percent': 100.0,\n", " 'masked_pixels': 0.0,\n", - " 'valid_pixels': 6.0,\n", - " 'percentile_2': 0.02427899930626154,\n", - " 'percentile_98': 0.036659999191761015}},\n", - " 'start_datetime': '2017-12-01T00:00:00+00:00'}" + " 'valid_pixels': 150.0,\n", + " 'percentile_2': 0.18744821846485138,\n", + " 'percentile_98': 0.9215888977050781}},\n", + " 'datetime': '2001-03-08'}" ] }, - "execution_count": 20, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1327,7 +1031,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1351,7 +1055,7 @@ " \n", " \n", " \n", - " start_datetime\n", + " datetime\n", " min\n", " max\n", " mean\n", @@ -1374,151 +1078,144 @@ " \n", " \n", " 0\n", - " 2017-12-01T00:00:00+00:00\n", - " 0.02405\n", - " 0.03718\n", - " 0.029408\n", - " 6.0\n", - " 0.17645\n", - " 0.004278\n", - " 0.028450\n", - " 0.02405\n", - " 0.02405\n", - " 6.0\n", - " [[1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0,...\n", + " 2001-03-08\n", + " 0.082675\n", + " 0.983081\n", + " 0.621467\n", + " 150.0\n", + " 93.220016\n", + " 0.168844\n", + " 0.653691\n", + " 0.082675\n", + " 0.082675\n", + " 150.0\n", + " [[2.0, 3.0, 3.0, 13.0, 19.0, 30.0, 40.0, 18.0,...\n", " 100.0\n", " 0.0\n", - " 6.0\n", - " 0.024279\n", - " 0.036660\n", - " 2017-12-01 00:00:00+00:00\n", + " 150.0\n", + " 0.187448\n", + " 0.921589\n", + " 2001-03-08\n", " \n", " \n", " 1\n", - " 2017-11-01T00:00:00+00:00\n", - " 0.02307\n", - " 0.05224\n", - " 0.033735\n", - " 6.0\n", - " 0.20241\n", - " 0.009451\n", - " 0.033080\n", - " 0.02307\n", - " 0.02307\n", - " 6.0\n", - " [[2.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0,...\n", + " 2001-03-07\n", + " 0.082977\n", + " 0.985993\n", + " 0.623370\n", + " 150.0\n", + " 93.505447\n", + " 0.169345\n", + " 0.655875\n", + " 0.082977\n", + " 0.082977\n", + " 150.0\n", + " [[2.0, 3.0, 3.0, 13.0, 19.0, 30.0, 41.0, 17.0,...\n", " 100.0\n", " 0.0\n", - " 6.0\n", - " 0.023289\n", - " 0.050584\n", - " 2017-11-01 00:00:00+00:00\n", + " 150.0\n", + " 0.188115\n", + " 0.924291\n", + " 2001-03-07\n", " \n", " \n", " 2\n", - " 2017-10-01T00:00:00+00:00\n", - " 0.02672\n", - " 0.05809\n", - " 0.040735\n", - " 6.0\n", - " 0.24441\n", - " 0.010850\n", - " 0.036945\n", - " 0.02672\n", - " 0.02672\n", - " 6.0\n", - " [[1.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0,...\n", + " 2001-03-06\n", + " 0.083282\n", + " 0.988925\n", + " 0.625286\n", + " 150.0\n", + " 93.792953\n", + " 0.169849\n", + " 0.658075\n", + " 0.083282\n", + " 0.083282\n", + " 150.0\n", + " [[2.0, 3.0, 3.0, 13.0, 19.0, 30.0, 41.0, 17.0,...\n", " 100.0\n", " 0.0\n", - " 6.0\n", - " 0.027453\n", - " 0.057447\n", - " 2017-10-01 00:00:00+00:00\n", + " 150.0\n", + " 0.188787\n", + " 0.927012\n", + " 2001-03-06\n", " \n", " \n", " 3\n", - " 2017-09-01T00:00:00+00:00\n", - " 0.01629\n", - " 0.03278\n", - " 0.021902\n", - " 6.0\n", - " 0.13141\n", - " 0.005631\n", - " 0.020090\n", - " 0.01629\n", - " 0.01629\n", - " 6.0\n", - " [[2.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,...\n", + " 2001-03-05\n", + " 0.083589\n", + " 0.991878\n", + " 0.627217\n", + " 150.0\n", + " 94.082520\n", + " 0.170358\n", + " 0.660291\n", + " 0.083589\n", + " 0.083589\n", + " 150.0\n", + " [[2.0, 3.0, 3.0, 13.0, 20.0, 29.0, 41.0, 17.0,...\n", " 100.0\n", " 0.0\n", - " 6.0\n", - " 0.016382\n", - " 0.031997\n", - " 2017-09-01 00:00:00+00:00\n", + " 150.0\n", + " 0.189463\n", + " 0.929751\n", + " 2001-03-05\n", " \n", " \n", " 4\n", - " 2017-08-01T00:00:00+00:00\n", - " 0.03309\n", - " 0.06435\n", - " 0.047835\n", - " 6.0\n", - " 0.28701\n", - " 0.010822\n", - " 0.046425\n", - " 0.03309\n", - " 0.03309\n", - " 6.0\n", - " [[1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0,...\n", + " 2001-03-04\n", + " 0.083898\n", + " 0.994851\n", + " 0.629161\n", + " 150.0\n", + " 94.374184\n", + " 0.170871\n", + " 0.662523\n", + " 0.083898\n", + " 0.083898\n", + " 150.0\n", + " [[2.0, 3.0, 3.0, 13.0, 20.0, 29.0, 41.0, 17.0,...\n", " 100.0\n", " 0.0\n", - " 6.0\n", - " 0.033677\n", - " 0.063691\n", - " 2017-08-01 00:00:00+00:00\n", + " 150.0\n", + " 0.190144\n", + " 0.932509\n", + " 2001-03-04\n", " \n", " \n", "\n", "" ], "text/plain": [ - " start_datetime min max mean count sum \\\n", - "0 2017-12-01T00:00:00+00:00 0.02405 0.03718 0.029408 6.0 0.17645 \n", - "1 2017-11-01T00:00:00+00:00 0.02307 0.05224 0.033735 6.0 0.20241 \n", - "2 2017-10-01T00:00:00+00:00 0.02672 0.05809 0.040735 6.0 0.24441 \n", - "3 2017-09-01T00:00:00+00:00 0.01629 0.03278 0.021902 6.0 0.13141 \n", - "4 2017-08-01T00:00:00+00:00 0.03309 0.06435 0.047835 6.0 0.28701 \n", + " datetime min max mean count sum std \\\n", + "0 2001-03-08 0.082675 0.983081 0.621467 150.0 93.220016 0.168844 \n", + "1 2001-03-07 0.082977 0.985993 0.623370 150.0 93.505447 0.169345 \n", + "2 2001-03-06 0.083282 0.988925 0.625286 150.0 93.792953 0.169849 \n", + "3 2001-03-05 0.083589 0.991878 0.627217 150.0 94.082520 0.170358 \n", + "4 2001-03-04 0.083898 0.994851 0.629161 150.0 94.374184 0.170871 \n", "\n", - " std median majority minority unique \\\n", - "0 0.004278 0.028450 0.02405 0.02405 6.0 \n", - "1 0.009451 0.033080 0.02307 0.02307 6.0 \n", - "2 0.010850 0.036945 0.02672 0.02672 6.0 \n", - "3 0.005631 0.020090 0.01629 0.01629 6.0 \n", - "4 0.010822 0.046425 0.03309 0.03309 6.0 \n", + " median majority minority unique \\\n", + "0 0.653691 0.082675 0.082675 150.0 \n", + "1 0.655875 0.082977 0.082977 150.0 \n", + "2 0.658075 0.083282 0.083282 150.0 \n", + "3 0.660291 0.083589 0.083589 150.0 \n", + "4 0.662523 0.083898 0.083898 150.0 \n", "\n", " histogram valid_percent \\\n", - "0 [[1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0,... 100.0 \n", - "1 [[2.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0,... 100.0 \n", - "2 [[1.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0,... 100.0 \n", - "3 [[2.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,... 100.0 \n", - "4 [[1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0,... 100.0 \n", - "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", - "0 0.0 6.0 0.024279 0.036660 \n", - "1 0.0 6.0 0.023289 0.050584 \n", - "2 0.0 6.0 0.027453 0.057447 \n", - "3 0.0 6.0 0.016382 0.031997 \n", - "4 0.0 6.0 0.033677 0.063691 \n", + "0 [[2.0, 3.0, 3.0, 13.0, 19.0, 30.0, 40.0, 18.0,... 100.0 \n", + "1 [[2.0, 3.0, 3.0, 13.0, 19.0, 30.0, 41.0, 17.0,... 100.0 \n", + "2 [[2.0, 3.0, 3.0, 13.0, 19.0, 30.0, 41.0, 17.0,... 100.0 \n", + "3 [[2.0, 3.0, 3.0, 13.0, 20.0, 29.0, 41.0, 17.0,... 100.0 \n", + "4 [[2.0, 3.0, 3.0, 13.0, 20.0, 29.0, 41.0, 17.0,... 100.0 \n", "\n", - " date \n", - "0 2017-12-01 00:00:00+00:00 \n", - "1 2017-11-01 00:00:00+00:00 \n", - "2 2017-10-01 00:00:00+00:00 \n", - "3 2017-09-01 00:00:00+00:00 \n", - "4 2017-08-01 00:00:00+00:00 " + " masked_pixels valid_pixels percentile_2 percentile_98 date \n", + "0 0.0 150.0 0.187448 0.921589 2001-03-08 \n", + "1 0.0 150.0 0.188115 0.924291 2001-03-07 \n", + "2 0.0 150.0 0.188787 0.927012 2001-03-06 \n", + "3 0.0 150.0 0.189463 0.929751 2001-03-05 \n", + "4 0.0 150.0 0.190144 0.932509 2001-03-04 " ] }, - "execution_count": 21, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1534,7 +1231,7 @@ " df.columns = [col.replace(\"statistics.b1.\", \"\") for col in df.columns]\n", "\n", " # Set the datetime format\n", - " df[\"date\"] = pd.to_datetime(df[\"start_datetime\"])\n", + " df[\"date\"] = pd.to_datetime(df[\"datetime\"])\n", "\n", " # Return the cleaned format\n", " return df\n", @@ -1558,22 +1255,22 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (2003-2017)')" + "Text(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (January 2001 to March 2001)')" ] }, - "execution_count": 22, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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AgIoymUy1RwAAAKgpogoAAAAAAEAKogoAAAAAAEAKogoAAAAAAEAKogoAAAAAAEAKogoAAAAAAEAKogoAAAAAAEAKogoAAAAAAEAKogoAAAAAAEAKogoAAFCmrdAWDVmXCgAAAG/nSgkAAChTyBeisXdjtccAAACoOaIKAABQprBFVAEAAKhEVAEAAMoUthQi2ztb7TEAAABqjqgCAACUKeaL0ZizUgUAAODtRBUAAKCM238BAABUJqoAAABlRBUAAIDKRBUAAKCMqAIAAFCZqAIAAJQRVQAAACoTVQAAgDKFvKgCAABQiagCAACUKWwpRDaXrfYYAAAANUdUAQAAyrj9FwAAQGWiCgAAUEZUAQAAqExUAQAAyogqAAAAlYkqAABAGVEFAACgMlEFAAAoU8wXozEnqgAAALydqAIAAJSxUgUAAKAyUQUAACgjqgAAAFQmqgAAAGVEFQAAgMpEFQAAoIyoAgAAUJmoAgAAlCnmi5HNZas9BgAAQM0RVQAAgDJWqgAAAFQmqgAAAGUKWwrRmBNVAAAA3k5UAQAAyrQV26Kh0aUCAADA27lSAgAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAASpIkqfYIAAAANUtUAQAAStoKbZHdI1vtMQAAAGqSqAIAAJQUthSisXdjtccAAACoSaIKAABQUthSiGxvK1UAAAAqEVUAAICSYr4YjTkrVQAAACoRVQAAgBK3/wIAAOiYqAIAAJSIKgAAAB2relSZM2dODB8+PHr37h1jx46NBx98sMN9zzrrrMhkMu0eo0eP3oUTAwBAzyWqAAAAdKyqUWXevHkxY8aMuPzyy6OlpSWOPfbYmDRpUqxatari/jfddFOsWbOm9HjxxRdj3333jY997GO7eHIAAOiZRBUAAICOVTWqzJ49O6ZOnRrTpk2LUaNGxY033hhDhw6NW265peL+TU1NMWjQoNLj8ccfj1deeSU++clP7uLJAQCgZyrkC5HNZas9BgAAQE2qWlTZunVrLFu2LCZOnFi2feLEifHII4+kOsZtt90WH/zgB2PYsGEd7pPP56O1tbXsAQAAVGalCgAAQMeqFlXWr18fxWIxmpuby7Y3NzfH2rVrt/v6NWvWxE9/+tOYNm1ap/vNmjUrmpqaSo+hQ4f+TXMDAEBPJqoAAAB0rOpfVJ/JZMp+TpKk3bZK/uM//iP23nvvOPnkkzvd77LLLouNGzeWHi+++OLfMi4AAPRoogoAAEDHqna1tP/++0c2m223KmXdunXtVq+8XZIkcfvtt8cZZ5wRvXr16nTfXC4XuVzub54XAADqQWFLIfbcf89qjwEAAFCTqrZSpVevXjF27NhYuHBh2faFCxfG0Ucf3elrlyxZEr///e9j6tSpO3NEAACoO8V8MRpzVqoAAABUUtWrpZkzZ8YZZ5wR48aNi6OOOipuvfXWWLVqVUyfPj0itt2666WXXorvfve7Za+77bbb4h//8R9jzJgx1RgbAAB6LLf/AgAA6FhVr5ZOPfXU2LBhQ1x99dWxZs2aGDNmTCxYsCCGDRsWEdu+jH7VqlVlr9m4cWP86Ec/iptuuqkaIwMAQI8mqgAAAHQskyRJUu0hdqXW1tZoamqKjRs3Rv/+/as9DgAA1JTFX1gco/7fqBj0/w2q9igAAAC7RFe6QdW+UwUAAKg9VqoAAAB0TFQBAABKCvlCZHPZao8BAABQk0QVAACgxEoVAACAjokqAABASXFLUVQBAADogKgCAACUWKkCAADQMVEFAAAoKeQL0ZgTVQAAACoRVQAAgJKkLYlMQ6baYwAAANQkUQUAAAAAACAFUQUAAAAAACAFUQUAAAAAACAFUQUAAAAAACAFUQUAAAAAACAFUQUAAAAAACAFUQUAAAAAACAFUQUAAAAAACAFUQUAAIiIiCRJqj0CAABATRNVAACAiIgobi1GY66x2mMAAADULFEFAACIiIhivhjZXLbaYwAAANQsUQUAAIiIiMKWQjT2tlIFAACgI6IKAAAQEaIKAADA9ogqAABARIgqAAAA2yOqAAAAESGqAAAAbI+oAgAAREREIV/wRfUAAACdEFUAAICIsFIFAABge0QVAAAgIkQVAACA7RFVAACAiBBVAAAAtkdUAQAAIkJUAQAA2B5RBQAAiIiIYr4YjTlRBQAAoCOiCgAAEBFWqgAAAGyPqAIAAESEqAIAALA9ogoAABARogoAAMD2iCoAAEBEiCoAAADbI6oAAAAREVHIFyKby1Z7DAAAgJolqgAAABFhpQoAAMD2iCoAAEBEiCoAAADbI6oAAAARIaoAAABsj6gCAABERERxS1FUAQAA6ISoAgAARISVKgAAANsjqgAAABERUdxajGyvbLXHAAAAqFmiCgAAEBERSZJEJpOp9hgAAAA1S1QBAAAAAABIQVQBAAAAAABIQVQBAAAAAABIQVQBAAAAAABIQVQBAAAAAABIQVQBAAAAAABIQVQBAAAAAABIQVQBAAAAAABIQVQBAAAiSZLIZDLVHgMAAKCmiSoAAEAU88XI5rLVHgMAAKCmiSoAAEAUthSisXdjtccAAACoaaIKAAAgqgAAAKQgqgAAAKIKAABACqIKAAAQhXzBd6oAAABsh6gCAABYqQIAAJCCqAIAAIgqAAAAKYgqAACAqAIAAJCCqAIAAIgqAAAAKYgqAABAFPPFaMyJKgAAAJ0RVQAAACtVAAAAUhBVAAAAUQUAACAFUQUAABBVAAAAUhBVAAAAUQUAACAFUQUAABBVAAAAUhBVAACAKOQLkc1lqz0GAABATRNVAAAAK1UAAABSEFUAAABRBQAAIAVRBQAAEFUAAABSEFUAAABRBQAAIAVRBQAAiGK+GI05UQUAAKAzogoAAGClCgAAQAqiCgAAEMWtxWjYw+UBAABAZ1w1AQAAERGRyWSqPQIAAEBNE1UAAAAAAABSEFUAAAAAAABSEFUAAAAAAABSEFUAAAAAAABSEFUAAAAAAABSEFUAAAAAAABSEFUAAAAAAABSEFUAAAAAAABSEFUAAKDOtRXbItOQqfYYAAAANU9UAQCAOlfMF6Oxd2O1xwAAAKh5ogoAANS5wpaCqAIAAJCCqAIAAHWusKUQ2Vy22mMAAADUPFEFAADqXCFvpQoAAEAaogoAANQ5t/8CAABIR1QBAIA6J6oAAACkI6oAAECdE1UAAADSEVUAAKDOiSoAAADpiCoAAFDnivliNOZEFQAAgO0RVQAAoM5ZqQIAAJCOqAIAAHVOVAEAAEhHVAEAgDonqgAAAKQjqgAAQJ0TVQAAANIRVQAAoM4V8oXI5rLVHgMAAKDmiSoAAFDnrFQBAABIR1QBAIA6J6oAAACkI6oAAECdE1UAAADSEVUAAKDOiSoAAADpiCoAAFDnivliNOZEFQAAgO0RVQAAoM5ZqQIAAJCOqAIAAHVOVAEAAEhHVAEAgDonqgAAAKQjqgAAQJ1rK7RFQ6NLAwAAgO1x5QQAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAHWsrdAWDY0uCwAAANJw9QQAAHWssKUQjb0bqz0GAADAbkFUAQCAOlbIF6IxJ6oAAACkIaoAAEAdK2wpRLZ3ttpjAAAA7BZEFQAAqGNu/wUAAJCeqAIAAHVMVAEAAEhPVAEAgDomqgAAAKQnqgAAQB0r5ou+qB4AACAlUQUAAOqYlSoAAADpiSoAAFDHRBUAAID0RBUAAKhjogoAAEB6ogoAANQxUQUAACA9UQUAAOpYIV+IbC5b7TEAAAB2C6IKAADUMStVAAAA0hNVAACgjokqAAAA6YkqAABQx0QVAACA9EQVAACoY6IKAABAeqIKAADUsWK+GI05UQUAACANUQUAAOqYlSoAAADpiSoAAFDHRBUAAID0RBUAAKhjogoAAEB6ogoAANQxUQUAACA9UQUAAOpYYUshsrlstccAAADYLYgqAABQx5K2JBqyLgsAAADSqPrV05w5c2L48OHRu3fvGDt2bDz44IOd7p/P5+Pyyy+PYcOGRS6Xi3e84x1x++2376JpAQAAAACAelXVmyfPmzcvZsyYEXPmzInx48fHt771rZg0aVI8/fTTcdBBB1V8zZQpU+Lll1+O2267LUaMGBHr1q2LQqGwiycHAAAAAADqTVWjyuzZs2Pq1Kkxbdq0iIi48cYb44EHHohbbrklZs2a1W7/+++/P5YsWRLPPfdc7LvvvhERcfDBB+/KkXu0p+Y9FU99/6mybUmSREREJpMp/bnLkojIpN8905CJTCaz7Z//94hMlP38xvPRUHn/zl7T1X0q7rej+3fymu2+bjuv7Y5HZLb9uwYAAAAAoL2qRZWtW7fGsmXL4rOf/WzZ9okTJ8YjjzxS8TX33XdfjBs3Lq677rr43ve+F3379o0TTzwxrrnmmujTp0/F1+Tz+cjn86WfW1tbu+9N9DBjTh0TY04dU+0xIkmSSNq2PSKJ0p+TtiT1czuyT1ux7c39K+zX6XHf/kiSaCu0bftzsf3x0ry+s99dNmt3Pf5vzr9FpSCTJEm77R39ns726ygENWQb3oxCDdHhc2WPbMdhqd1rsjX8fDbddrEMAAAAALpH1aLK+vXro1gsRnNzc9n25ubmWLt2bcXXPPfcc/HQQw9F79694957743169fHpz/96fjzn//c4feqzJo1K6666qpun5+dJ5PZ9h+FI1vtSagVSVIeot6ISm3Fts4jUYXtXX1Nu/072aet0BbFrcWy/TqctdjB7yh28vuLKWbq4Ljp/qKjS6vKunrMUvjJdhKCOnmuw9d35efu2reLz3W2n+AFAAAAsPuo6u2/Iir/n+kd/Qemtra2yGQycdddd0VTU1NEbLuF2D//8z/HN77xjYqrVS677LKYOXNm6efW1tYYOnRoN74DYGfLZN68NVpERFZx2+2UVl9VCkNv31YpDL11n+0938HPb3+u+Hr7ANbRvn/Tc8UK7/Ot0a3Lf5nxt8WvJMqCVYfxp9JzHcWjTo7T4fPdsa3SDCm2iVkAAADAjqpaVNl///0jm822W5Wybt26dqtX3jB48OA44IADSkElImLUqFGRJEmsXr06/v7v/77da3K5XORyue4dHoAusQKttiRJ+wj09tjz9j93FI9Sva7CtrbX26KYL253v462VZqhLGp19vr/u5ViRLwZqHbiSq23B56yuFNh+277z8YGsQoAAIAer2pRpVevXjF27NhYuHBhnHLKKaXtCxcujJNOOqnia8aPHx8//OEP4y9/+Uv069cvIiJWrFgRDQ0NceCBB+6SuQFgd5fJZCLTuO0/grNzlQWsNKGoG/9ZyBd2ynE7e+7t7727I8sbx+ww6nQQfXboucaGdNs6OEa7bSmO19DYUFqVCQAAQG2q6u2/Zs6cGWeccUaMGzcujjrqqLj11ltj1apVMX369IjYduuul156Kb773e9GRMTpp58e11xzTXzyk5+Mq666KtavXx+f+cxn4uyzz+7wi+oBAKpFwOp+lUJVW6F95Km0LSlu+/6rVNvecoy3Bqq0vyvNtoq/u7PvwXr7Cqsu/+Vtu/1fpQgEAABAOlWNKqeeemps2LAhrr766lizZk2MGTMmFixYEMOGDYuIiDVr1sSqVatK+/fr1y8WLlwYF1xwQYwbNy7222+/mDJlSlx77bXVegsAAOxCQtXf5o1VUm8NPI25qn/NIgAAwG4jkyRJJ/87XM/T2toaTU1NsXHjxujfv3+1xwEAAAAAAKqoK93A/+IHAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQgqgCAAAAAACQQmO1B9jVkiSJiIjW1tYqTwIAAAAAAFTbG73gjX7QmbqLKps2bYqIiKFDh1Z5EgAAAAAAoFZs2rQpmpqaOt0nk6RJLz1IW1tb/PGPf4y99torMplMtcepKa2trTF06NB48cUXo3///tUeB6Ai5yqA2udcDewOnKuAWuc8BbtOkiSxadOmGDJkSDQ0dP6tKXW3UqWhoSEOPPDAao9R0/r37+9EDdQ85yqA2udcDewOnKuAWuc8BbvG9laovMEX1QMAAAAAAKQgqgAAAAAAAKQgqlCSy+XiiiuuiFwuV+1RADrkXAVQ+5yrgd2BcxVQ65ynoDbV3RfVAwAAAAAA7AgrVQAAAAAAAFIQVQAAAAAAAFIQVQAAAAAAAFIQVQAAAAAAAFIQVXaxWbNmxbve9a7Ya6+9YuDAgXHyySfH7373u7J9kiSJK6+8MoYMGRJ9+vSJ9773vfGb3/ymbJ98Ph8XXHBB7L///tG3b9848cQTY/Xq1WX7fPGLX4yjjz469txzz9h7771Tz7h8+fKYMGFC9OnTJw444IC4+uqrI0mS0vNr1qyJ008/PUaOHBkNDQ0xY8aM1MeeM2dODB8+PHr37h1jx46NBx98sOz5+fPnx/HHHx/7779/ZDKZePLJJ1MfG+g+9XyuWrp0aUyePDmGDBkSmUwm/uu//qvdPmeddVZkMpmyxz/90z+lnh2gO/SEc/X8+fPjuOOOiwEDBkT//v3jqKOOigceeCDVsX2uhN1DPZ+rfK6E3UdPOFc99NBDMX78+Nhvv/2iT58+ceihh8ZXv/rVVMf2uQq6RlTZxZYsWRLnnXde/PKXv4yFCxdGoVCIiRMnxubNm0v7XHfddTF79uy4+eab47HHHotBgwbFcccdF5s2bSrtM2PGjLj33ntj7ty58dBDD8Vf/vKX+MhHPhLFYrG0z9atW+NjH/tYnHvuuanna21tjeOOOy6GDBkSjz32WHz961+P66+/PmbPnl3aJ5/Px4ABA+Lyyy+PI444IvWx582bFzNmzIjLL788Wlpa4thjj41JkybFqlWrSvts3rw5xo8fH1/+8pdTHxfofvV8rtq8eXMcccQRcfPNN3e63wknnBBr1qwpPRYsWJD6dwB0h55wrl66dGkcd9xxsWDBgli2bFm8733vi8mTJ0dLS0unx/a5EnYf9Xyu8rkSdh894VzVt2/fOP/882Pp0qXxzDPPxOc///n4/Oc/H7feemunx/a5CnZAQlWtW7cuiYhkyZIlSZIkSVtbWzJo0KDky1/+cmmfLVu2JE1NTck3v/nNJEmS5NVXX0322GOPZO7cuaV9XnrppaShoSG5//772/2OO+64I2lqako1z5w5c5KmpqZky5YtpW2zZs1KhgwZkrS1tbXbf8KECclFF12U6tjvfve7k+nTp5dtO/TQQ5PPfvaz7fZduXJlEhFJS0tLqmMDO1c9naveKiKSe++9t932M888MznppJO6fDyAnWl3P1e/4R/+4R+Sq666qtNj+1wJu696Ole9lc+VsHvpKeeqU045Jfn4xz/e6bF9roKus1KlyjZu3BgREfvuu29ERKxcuTLWrl0bEydOLO2Ty+ViwoQJ8cgjj0RExLJly+L1118v22fIkCExZsyY0j476n/+539iwoQJkcvlStuOP/74+OMf/xjPP//8Dh9369atsWzZsrKZIyImTpz4N88M7Hz1cq7qil/84hcxcODAOOSQQ+JTn/pUrFu3bpf8XoCO9IRzdVtbW2zatKn0HirxuRJ2b/VyruoKnyuh9vSEc1VLS0s88sgjMWHChA6P63MV7BhRpYqSJImZM2fGMcccE2PGjImIiLVr10ZERHNzc9m+zc3NpefWrl0bvXr1in322afDfXbU2rVrK/7ut862I9avXx/FYrHT9wXUpno6V6U1adKkuOuuu2LRokVxww03xGOPPRbvf//7I5/P7/TfDVBJTzlX33DDDbF58+aYMmVKh8f1uRJ2X/V0rkrL50qoPbv7uerAAw+MXC4X48aNi/POOy+mTZvW4XF9roIdI6pU0fnnnx+//vWv4+677273XCaTKfs5SZJ2294uzT5vNXr06OjXr1/069cvJk2a1OnvrrS9Iw8++GDpuP369Yu77rqr02N3ZWZg16vHc9X2nHrqqfHhD384xowZE5MnT46f/vSnsWLFivjJT36S+hgA3aknnKvvvvvuuPLKK2PevHkxcODAiPC5EnqaejxXbY/PlVB7dvdz1YMPPhiPP/54fPOb34wbb7yx9D58roLu01jtAerVBRdcEPfdd18sXbo0DjzwwNL2QYMGRcS2yjx48ODS9nXr1pWq8aBBg2Lr1q3xyiuvlNXvdevWxdFHH516hgULFsTrr78eERF9+vQpHfvtJfqNpcdvr9YdGTduXDz55JOln5ubmyOXy0U2m6147LTHBXa9ejtX7ajBgwfHsGHD4tlnn93hYwDsqJ5wrp43b15MnTo1fvjDH8YHP/jB0nafK6HnqLdz1Y7yuRKqqyecq4YPHx4REYcddli8/PLLceWVV8Zpp53mcxV0IytVdrEkSeL888+P+fPnx6JFi0onujcMHz48Bg0aFAsXLixt27p1ayxZsqR0Ah47dmzsscceZfusWbMmnnrqqS6dpIcNGxYjRoyIESNGxAEHHBAREUcddVQsXbo0tm7dWtrvZz/7WQwZMiQOPvjgVMft06dP6bgjRoyIvfbaK3r16hVjx44tmzkiYuHChV2aGdg16vVctaM2bNgQL774YtmHa4Cdraecq+++++4466yz4vvf/358+MMfLjuuz5Ww+6vXc9WO8rkSqqOnnKsqva83bifocxV0o27+4nu249xzz02ampqSX/ziF8maNWtKj9dee620z5e//OWkqakpmT9/frJ8+fLktNNOSwYPHpy0traW9pk+fXpy4IEHJj//+c+TJ554Inn/+9+fHHHEEUmhUCjt88ILLyQtLS3JVVddlfTr1y9paWlJWlpakk2bNnU436uvvpo0Nzcnp512WrJ8+fJk/vz5Sf/+/ZPrr7++bL83jjV27Njk9NNPT1paWpLf/OY3nb73uXPnJnvssUdy2223JU8//XQyY8aMpG/fvsnzzz9f2mfDhg1JS0tL8pOf/CSJiGTu3LlJS0tLsmbNmtR/x8Dfrp7PVZs2bSq9LiKS2bNnJy0tLckLL7xQev6SSy5JHnnkkWTlypXJ4sWLk6OOOio54IADyt47wM7WE87V3//+95PGxsbkG9/4Rtl7ePXVVzt97z5Xwu6jns9VPlfC7qMnnKtuvvnm5L777ktWrFiRrFixIrn99tuT/v37J5dffnmn793nKug6UWUXi4iKjzvuuKO0T1tbW3LFFVckgwYNSnK5XPKe97wnWb58edlx/vrXvybnn39+su+++yZ9+vRJPvKRjySrVq0q2+fMM8+s+LsWL17c6Yy//vWvk2OPPTbJ5XLJoEGDkiuvvDJpa2vb7vsYNmzYdt//N77xjWTYsGFJr169kne+853JkiVLyp6/4447Kh77iiuu2O6xge5Tz+eqxYsXV3zdmWeemSRJkrz22mvJxIkTkwEDBiR77LFHctBBByVnnnlmu/cFsLP1hHP1hAkTOj3ndsbnStg91PO5yudK2H30hHPV1772tWT06NHJnnvumfTv3z858sgjkzlz5iTFYnG779/nKuiaTJL837caAQAAAAAA0CHfqQIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAAAAAJCCqAIAAOx2kiSJD37wg3H88ce3e27OnDnR1NQUq1atqsJkAABATyaqAAAAu51MJhN33HFHPProo/Gtb32rtH3lypVx6aWXxk033RQHHXRQt/7O119/vVuPBwAA7H5EFQAAYLc0dOjQuOmmm+Lf/u3fYuXKlZEkSUydOjU+8IEPxLvf/e740Ic+FP369Yvm5uY444wzYv369aXX3n///XHMMcfE3nvvHfvtt1985CMfiT/84Q+l559//vnIZDLxgx/8IN773vdG7969484774wXXnghJk+eHPvss0/07ds3Ro8eHQsWLKjG2wcAAKogkyRJUu0hAAAAdtTJJ58cr776anz0ox+Na665Jh577LEYN25cfOpTn4pPfOIT8de//jUuvfTSKBQKsWjRooiI+NGPfhSZTCYOO+yw2Lx5c3zhC1+I559/Pp588sloaGiI559/PoYPHx4HH3xw3HDDDXHkkUdGLpeLc845J7Zu3Ro33HBD9O3bN55++uno379/vOc976ny3wIAALAriCoAAMBubd26dTFmzJjYsGFD3HPPPdHS0hKPPvpoPPDAA6V9Vq9eHUOHDo3f/e53ccghh7Q7xp/+9KcYOHBgLF++PMaMGVOKKjfeeGNcdNFFpf0OP/zw+OhHPxpXXHHFLnlvAABAbXH7LwAAYLc2cODAOOecc2LUqFFxyimnxLJly2Lx4sXRr1+/0uPQQw+NiCjd4usPf/hDnH766fF3f/d30b9//xg+fHhERLsvtx83blzZzxdeeGFce+21MX78+Ljiiivi17/+9S54hwAAQK0QVQAAgN1eY2NjNDY2RkREW1tbTJ48OZ588smyx7PPPlu6TdfkyZNjw4YN8e1vfzseffTRePTRRyMiYuvWrWXH7du3b9nP06ZNi+eeey7OOOOMWL58eYwbNy6+/vWv74J3CAAA1AJRBQAA6FHe+c53xm9+85s4+OCDY8SIEWWPvn37xoYNG+KZZ56Jz3/+8/GBD3wgRo0aFa+88krq4w8dOjSmT58e8+fPj0suuSS+/e1v78R3AwAA1BJRBQAA6FHOO++8+POf/xynnXZa/OpXv4rnnnsufvazn8XZZ58dxWIx9tlnn9hvv/3i1ltvjd///vexaNGimDlzZqpjz5gxIx544IFYuXJlPPHEE7Fo0aIYNWrUTn5HAABArRBVAACAHmXIkCHx8MMPR7FYjOOPPz7GjBkTF110UTQ1NUVDQ0M0NDTE3LlzY9myZTFmzJi4+OKL49///d9THbtYLMZ5550Xo0aNihNOOCFGjhwZc+bM2cnvCAAAqBWZJEmSag8BAAAAAABQ66xUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASEFUAQAAAAAASOH/Bzrmlmk/Crh4AAAAAElFTkSuQmCC", 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" ] @@ -1607,7 +1304,7 @@ "plt.ylabel(\"kg Carbon/m2/day\")\n", "\n", "# Insert title for the plot\n", - "plt.title(\"Heterotrophic Respiration Values for Dallas, Texas (2003-2017)\")" + "plt.title(\"Heterotrophic Respiration Values for Dallas, Texas (January 2001 to March 2001)\")" ] }, { @@ -1619,14 +1316,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2017-10-01T00:00:00+00:00\n" + "2001-03-06T00:00:00+00:00\n" ] } ], @@ -1634,12 +1331,12 @@ "# The 2017-10 observation is the 3rd item in the list\n", "# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n", "# Print the start Date Time of the third granule in the collection\n", - "print(items[2][\"properties\"][\"start_datetime\"]) " + "print(items[2][\"properties\"][\"datetime\"]) " ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1648,14 +1345,14 @@ "{'tilejson': '2.2.0',\n", " 'version': '1.0.0',\n", " 'scheme': 'xyz',\n", - " 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=casagfed-carbonflux-monthgrid-v3&item=casagfed-carbonflux-monthgrid-v3-201710&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n", + " 'tiles': ['https://dev.ghg.center/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=micasa-carbonflux-daygrid-v1&item=micasa-carbonflux-daygrid-v1-20010306&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.28565365076065063%2C5.658170223236084'],\n", " 'minzoom': 0,\n", " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" + " 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n", + " 'center': [-2.842170943040401e-14, 0.0, 0]}" ] }, - "execution_count": 24, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1685,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1705,7 +1402,7 @@ " <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n", " <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n", " <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>\n", - " <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>\n", + " <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n", " <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>\n", " <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n", " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>\n", @@ -1718,7 +1415,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_f66b4e2e6f6cb7486bb6c0a768b97aca {\n", + " #map_b76f194958b0ab1aa75b2d7bc5f1365e {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1733,14 +1430,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_f66b4e2e6f6cb7486bb6c0a768b97aca" ></div>\n", + " <div class="folium-map" id="map_b76f194958b0ab1aa75b2d7bc5f1365e" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_f66b4e2e6f6cb7486bb6c0a768b97aca = L.map(\n", - " "map_f66b4e2e6f6cb7486bb6c0a768b97aca",\n", + " var map_b76f194958b0ab1aa75b2d7bc5f1365e = L.map(\n", + " "map_b76f194958b0ab1aa75b2d7bc5f1365e",\n", " {\n", " center: [32.8, -96.79],\n", " crs: L.CRS.EPSG3857,\n", @@ -1754,31 +1451,25 @@ "\n", " \n", " \n", - " var tile_layer_91ad101f4ac7b2a79b67fc8b98e29767 = L.tileLayer(\n", - " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", - " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " );\n", - " \n", - " \n", - " tile_layer_91ad101f4ac7b2a79b67fc8b98e29767.addTo(map_f66b4e2e6f6cb7486bb6c0a768b97aca);\n", + " var tile_layer_1bcfaf73a3c0f34ee5ad078784b420e9 = L.tileLayer(\n", + " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", + " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", + " ).addTo(map_b76f194958b0ab1aa75b2d7bc5f1365e);\n", " \n", " \n", - " var tile_layer_6cfcb9550cafc548dd0b03eb8ea46f12 = L.tileLayer(\n", - " "https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=casagfed-carbonflux-monthgrid-v3\\u0026item=casagfed-carbonflux-monthgrid-v3-201710\\u0026assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0.0%2C0.6039900183677673",\n", + " var tile_layer_49de6e7c0377b54c355b9d86a28abb08 = L.tileLayer(\n", + " "https://dev.ghg.center/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=micasa-carbonflux-daygrid-v1\\u0026item=micasa-carbonflux-daygrid-v1-20010306\\u0026assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=-0.28565365076065063%2C5.658170223236084",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.7, "subdomains": "abc", "tms": false}\n", - " );\n", + " ).addTo(map_b76f194958b0ab1aa75b2d7bc5f1365e);\n", " \n", " \n", - " tile_layer_6cfcb9550cafc548dd0b03eb8ea46f12.addTo(map_f66b4e2e6f6cb7486bb6c0a768b97aca);\n", - " \n", - " \n", - " var marker_2ce29a8c2f7a97aebeb42b4dbb5918c7 = L.marker(\n", + " var marker_bdfdbcec9b83e45a17cf335fd05a454a = L.marker(\n", " [40.0, 5.9],\n", " {}\n", - " ).addTo(map_f66b4e2e6f6cb7486bb6c0a768b97aca);\n", + " ).addTo(map_b76f194958b0ab1aa75b2d7bc5f1365e);\n", " \n", " \n", - " marker_2ce29a8c2f7a97aebeb42b4dbb5918c7.bindTooltip(\n", + " marker_bdfdbcec9b83e45a17cf335fd05a454a.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -1786,58 +1477,57 @@ " );\n", " \n", " \n", - " var layer_control_63659aa7f929c3d17046c47f574e9069_layers = {\n", + " var layer_control_7daa073e4e20648999776be7357eb75e = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_91ad101f4ac7b2a79b67fc8b98e29767,\n", + " "openstreetmap" : tile_layer_1bcfaf73a3c0f34ee5ad078784b420e9,\n", " },\n", " overlays : {\n", - " "October 2017 RH Level" : tile_layer_6cfcb9550cafc548dd0b03eb8ea46f12,\n", + " "October 2017 RH Level" : tile_layer_49de6e7c0377b54c355b9d86a28abb08,\n", " },\n", " };\n", - " let layer_control_63659aa7f929c3d17046c47f574e9069 = L.control.layers(\n", - " layer_control_63659aa7f929c3d17046c47f574e9069_layers.base_layers,\n", - " layer_control_63659aa7f929c3d17046c47f574e9069_layers.overlays,\n", + " L.control.layers(\n", + " layer_control_7daa073e4e20648999776be7357eb75e.base_layers,\n", + " layer_control_7daa073e4e20648999776be7357eb75e.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_f66b4e2e6f6cb7486bb6c0a768b97aca);\n", - "\n", + " ).addTo(map_b76f194958b0ab1aa75b2d7bc5f1365e);\n", " \n", " \n", - " var color_map_9b243c39dd25bcfa4f3384fa48a61768 = {};\n", + " var color_map_64e28d0e9f56ec2214425beff1f8082f = {};\n", "\n", " \n", - " color_map_9b243c39dd25bcfa4f3384fa48a61768.color = d3.scale.threshold()\n", + " color_map_64e28d0e9f56ec2214425beff1f8082f.color = d3.scale.threshold()\n", " .domain([0.0, 0.0006012024048096192, 0.0012024048096192384, 0.0018036072144288575, 0.002404809619238477, 0.003006012024048096, 0.003607214428857715, 0.004208416833667335, 0.004809619238476954, 0.005410821643286573, 0.006012024048096192, 0.006613226452905812, 0.00721442885771543, 0.00781563126252505, 0.00841683366733467, 0.009018036072144289, 0.009619238476953907, 0.010220440881763526, 0.010821643286573146, 0.011422845691382766, 0.012024048096192385, 0.012625250501002003, 0.013226452905811623, 0.013827655310621242, 0.01442885771543086, 0.01503006012024048, 0.0156312625250501, 0.01623246492985972, 0.01683366733466934, 0.017434869739478956, 0.018036072144288578, 0.018637274549098193, 0.019238476953907815, 0.019839679358717437, 0.02044088176352705, 0.021042084168336674, 0.021643286573146292, 0.02224448897795591, 0.022845691382765532, 0.023446893787575147, 0.02404809619238477, 0.024649298597194388, 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color_map_64e28d0e9f56ec2214425beff1f8082f.legend.addTo(map_b76f194958b0ab1aa75b2d7bc5f1365e);\n", "\n", - " color_map_9b243c39dd25bcfa4f3384fa48a61768.xAxis = d3.svg.axis()\n", - " .scale(color_map_9b243c39dd25bcfa4f3384fa48a61768.x)\n", + " color_map_64e28d0e9f56ec2214425beff1f8082f.xAxis = d3.svg.axis()\n", + " .scale(color_map_64e28d0e9f56ec2214425beff1f8082f.x)\n", " .orient("top")\n", " .tickSize(1)\n", " .tickValues([0, 0.07, 0.15, 0.22, 0.3]);\n", "\n", - " color_map_9b243c39dd25bcfa4f3384fa48a61768.svg = d3.select(".legend.leaflet-control").append("svg")\n", + " color_map_64e28d0e9f56ec2214425beff1f8082f.svg = d3.select(".legend.leaflet-control").append("svg")\n", " .attr("id", 'legend')\n", " .attr("width", 450)\n", " .attr("height", 40);\n", "\n", - " color_map_9b243c39dd25bcfa4f3384fa48a61768.g = color_map_9b243c39dd25bcfa4f3384fa48a61768.svg.append("g")\n", + " color_map_64e28d0e9f56ec2214425beff1f8082f.g = color_map_64e28d0e9f56ec2214425beff1f8082f.svg.append("g")\n", " 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color_map_64e28d0e9f56ec2214425beff1f8082f.x.range()[0],\n", + " x1: i < color_map_64e28d0e9f56ec2214425beff1f8082f.color.domain().length ? color_map_64e28d0e9f56ec2214425beff1f8082f.x(color_map_64e28d0e9f56ec2214425beff1f8082f.color.domain()[i]) : color_map_64e28d0e9f56ec2214425beff1f8082f.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -1847,18 +1537,18 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_9b243c39dd25bcfa4f3384fa48a61768.g.call(color_map_9b243c39dd25bcfa4f3384fa48a61768.xAxis).append("text")\n", + " color_map_64e28d0e9f56ec2214425beff1f8082f.g.call(color_map_64e28d0e9f56ec2214425beff1f8082f.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", - " .text("Rh Values (kg Carbon/m2/month)");\n", + " .text("Rh Values (kg Carbon/m2/daily)");\n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1948,7 +1638,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.16" } }, "nbformat": 4,