From 1e0e4dd1ed66f3a6077a05d025a8ae6b94c61c50 Mon Sep 17 00:00:00 2001 From: Ram Sharan Rimal Date: Wed, 18 Sep 2024 12:58:20 -0500 Subject: [PATCH 1/2] fix: user notebooks fix --- styles.css | 2 +- ...a2pes-ghg-monthgrid-v1_User_Notebook.ipynb | 93 ++++++++++--------- 2 files changed, 51 insertions(+), 44 deletions(-) diff --git a/styles.css b/styles.css index 01f214f23..c61b29284 100644 --- a/styles.css +++ b/styles.css @@ -5,7 +5,7 @@ /* Adjust height of the cell output in user notebooks*/ .cell-output { - max-height: 500px; + max-height: 600px; overflow-y: auto; overflow-x: auto; background-color:#fefdfe; diff --git a/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb b/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb index 484f55070..37cdbf227 100644 --- a/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb @@ -40,7 +40,7 @@ "## Approach\n", "\n", "1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API `/stac` endpoint. The collection processed in this notebook is the Vulcan Fossil Fuel CO₂ Emissions Data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json `endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json `endpoint.\n", "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", "4. After the visualization, we will perform zonal statistics for a given polygon.\n", "\n", @@ -75,7 +75,16 @@ "execution_count": 1, "id": "503e5661-b99b-421b-b4ba-361bb531e5fa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import requests\n", "import folium\n", @@ -114,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "941eae92-6ec0-46a4-ab8f-739973b59e6f", "metadata": {}, "outputs": [], @@ -134,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "b56d6c87-11b9-48ca-9723-8304984a644b", "metadata": {}, "outputs": [], @@ -164,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "id": "eaaf1487-2e71-490b-a968-e57ee332a919", "metadata": {}, "outputs": [ @@ -180,12 +189,12 @@ "# Apply the above function and check the total number of items available within the collection\n", "number_of_items = get_item_count(collection_name)\n", "items_graapes = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n", - "print(f\"Found {len(items_vulcan)} items\")" + "print(f\"Found {len(items_graapes)} items\")" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "id": "01619ee2-728c-4abc-9edf-a2c85d87128d", "metadata": {}, "outputs": [], @@ -198,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "f9deb11d-4c33-4f59-8532-7829602e3113", "metadata": {}, "outputs": [], @@ -216,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 8, "id": "27b31984-ac8e-473f-ba2d-eb309c9708c1", "metadata": {}, "outputs": [ @@ -226,14 +235,14 @@ "{'tilejson': '2.2.0',\n", " 'version': '1.0.0',\n", " 'scheme': 'xyz',\n", - " 'tiles': ['https://dev.ghg.center/api/raster/collections/gra2pes-co2-monthgrid-v1/items/gra2pes-co2-monthgrid-v1-202101/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=gra2pes-co2-monthgrid-v1&item=gra2pes-co2-monthgrid-v1-202101&assets=co2&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/gra2pes-ghg-monthgrid-v1/items/gra2pes-ghg-monthgrid-v1-202101/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n", " 'minzoom': 0,\n", " 'maxzoom': 24,\n", " 'bounds': [-137.3143, 18.173376, -58.58229999999702, 52.229376000001295],\n", " 'center': [-97.94829999999851, 35.20137600000065, 0]}" ] }, - "execution_count": 30, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -245,8 +254,7 @@ "# To change the year and month of the observed parameter, you can modify the \"items['YYYY-MM']\" statement\n", "# For example, you can change the current statement \"items['2003-12']\" to \"items['2016-10']\" \n", "_202101_tile = requests.get(\n", - " f\"{RASTER_API_URL}/collections/{items['2021-01']['collection']}/items/{items['2021-01']['id']}/tilejson.json?collection={items['2021-01']['collection']}&item={items['2021-01']['id']}\"\n", - "\n", + " f\"{RASTER_API_URL}/collections/{items['2021-01']['collection']}/items/{items['2021-01']['id']}/tilejson.json?\"\n", " f\"&assets={asset_name}\"\n", " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", " f\"&rescale=0,150\", \n", @@ -256,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 9, "id": "80f5933d", "metadata": {}, "outputs": [ @@ -266,22 +274,21 @@ "{'tilejson': '2.2.0',\n", " 'version': '1.0.0',\n", " 'scheme': 'xyz',\n", - " 'tiles': ['https://dev.ghg.center/api/raster/collections/gra2pes-co2-monthgrid-v1/items/gra2pes-co2-monthgrid-v1-202105/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=gra2pes-co2-monthgrid-v1&item=gra2pes-co2-monthgrid-v1-202105&assets=co2&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/gra2pes-ghg-monthgrid-v1/items/gra2pes-ghg-monthgrid-v1-202105/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n", " 'minzoom': 0,\n", " 'maxzoom': 24,\n", " 'bounds': [-137.3143, 18.173376, -58.58229999999702, 52.229376000001295],\n", " 'center': [-97.94829999999851, 35.20137600000065, 0]}" ] }, - "execution_count": 31, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_202105_tile = requests.get(\n", - " f\"{RASTER_API_URL}/collections/{items['2021-05']['collection']}/items/{items['2021-05']['id']}/tilejson.json?collection={items['2021-05']['collection']}&item={items['2021-05']['id']}\"\n", - "\n", + " f\"{RASTER_API_URL}/collections/{items['2021-05']['collection']}/items/{items['2021-05']['id']}/tilejson.json?\"\n", " f\"&assets={asset_name}\"\n", " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", " f\"&rescale=0,150\", \n", @@ -299,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "id": "6cf3c0f1-8d39-4cdd-96c7-96e6229c1584", "metadata": {}, "outputs": [ @@ -333,7 +340,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_edd0ff00f280162cb37a88438dfad200 {\n", + " #map_c94eb2f13aea2887cac7c51b8a32f842 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -347,7 +354,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_ddd6319ec882adf311b2309c43b9d2d5 {\n", + " #map_376345819bea8f69b487f97f7713ee12 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -362,17 +369,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_edd0ff00f280162cb37a88438dfad200" ></div>\n", + " <div class="folium-map" id="map_c94eb2f13aea2887cac7c51b8a32f842" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_ddd6319ec882adf311b2309c43b9d2d5" ></div>\n", + " <div class="folium-map" id="map_376345819bea8f69b487f97f7713ee12" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_edd0ff00f280162cb37a88438dfad200 = L.map(\n", - " "map_edd0ff00f280162cb37a88438dfad200",\n", + " var map_c94eb2f13aea2887cac7c51b8a32f842 = L.map(\n", + " "map_c94eb2f13aea2887cac7c51b8a32f842",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -386,26 +393,26 @@ "\n", " \n", " \n", - " var tile_layer_83789f115bdff9a1c771b85325da703c = L.tileLayer(\n", + " var tile_layer_2bdef7a16107516d1d61f49db46d3801 = 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_83789f115bdff9a1c771b85325da703c.addTo(map_edd0ff00f280162cb37a88438dfad200);\n", + " tile_layer_2bdef7a16107516d1d61f49db46d3801.addTo(map_c94eb2f13aea2887cac7c51b8a32f842);\n", " \n", " \n", - " var tile_layer_d8c169ddc474a63922e903ed89d2e517 = L.tileLayer(\n", - " "https://dev.ghg.center/api/raster/collections/gra2pes-co2-monthgrid-v1/items/gra2pes-co2-monthgrid-v1-202101/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=gra2pes-co2-monthgrid-v1\\u0026item=gra2pes-co2-monthgrid-v1-202101\\u0026assets=co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=spectral_r\\u0026rescale=0%2C150",\n", + " var tile_layer_148570fa458d9cc7e824e1e5f1d985af = L.tileLayer(\n", + " "https://earth.gov/ghgcenter/api/raster/collections/gra2pes-ghg-monthgrid-v1/items/gra2pes-ghg-monthgrid-v1-202101/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=spectral_r\\u0026rescale=0%2C150",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_d8c169ddc474a63922e903ed89d2e517.addTo(map_edd0ff00f280162cb37a88438dfad200);\n", + " tile_layer_148570fa458d9cc7e824e1e5f1d985af.addTo(map_c94eb2f13aea2887cac7c51b8a32f842);\n", " \n", " \n", - " var map_ddd6319ec882adf311b2309c43b9d2d5 = L.map(\n", - " "map_ddd6319ec882adf311b2309c43b9d2d5",\n", + " var map_376345819bea8f69b487f97f7713ee12 = L.map(\n", + " "map_376345819bea8f69b487f97f7713ee12",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -419,35 +426,35 @@ "\n", " \n", " \n", - " var tile_layer_1e14ca0dd8c2166f0d902100d5d29e6a = L.tileLayer(\n", + " var tile_layer_c0d1b6c8928c57c2e7df1ed3718f3bab = 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_1e14ca0dd8c2166f0d902100d5d29e6a.addTo(map_ddd6319ec882adf311b2309c43b9d2d5);\n", + " tile_layer_c0d1b6c8928c57c2e7df1ed3718f3bab.addTo(map_376345819bea8f69b487f97f7713ee12);\n", " \n", " \n", - " var tile_layer_2793f20a58a552418ec77f4c8722f5ef = L.tileLayer(\n", - " "https://dev.ghg.center/api/raster/collections/gra2pes-co2-monthgrid-v1/items/gra2pes-co2-monthgrid-v1-202105/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=gra2pes-co2-monthgrid-v1\\u0026item=gra2pes-co2-monthgrid-v1-202105\\u0026assets=co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=spectral_r\\u0026rescale=0%2C150",\n", + " var tile_layer_1830c9e673ed70c6a6e2d7c0b8891aad = L.tileLayer(\n", + " "https://earth.gov/ghgcenter/api/raster/collections/gra2pes-ghg-monthgrid-v1/items/gra2pes-ghg-monthgrid-v1-202105/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=spectral_r\\u0026rescale=0%2C150",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_2793f20a58a552418ec77f4c8722f5ef.addTo(map_ddd6319ec882adf311b2309c43b9d2d5);\n", + " tile_layer_1830c9e673ed70c6a6e2d7c0b8891aad.addTo(map_376345819bea8f69b487f97f7713ee12);\n", " \n", " \n", - " map_edd0ff00f280162cb37a88438dfad200.sync(map_ddd6319ec882adf311b2309c43b9d2d5);\n", - " map_ddd6319ec882adf311b2309c43b9d2d5.sync(map_edd0ff00f280162cb37a88438dfad200);\n", + " map_c94eb2f13aea2887cac7c51b8a32f842.sync(map_376345819bea8f69b487f97f7713ee12);\n", + " map_376345819bea8f69b487f97f7713ee12.sync(map_c94eb2f13aea2887cac7c51b8a32f842);\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": 32, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -475,7 +482,7 @@ " opacity=0.8, # Adjust the transparency of the layer\n", ")\n", "# Add the first layer to the Dual Map \n", - "map_layer_2021.add_to(map_.m2)\n", + "map_layer_202105.add_to(map_.m2)\n", "\n", "map_" ] @@ -523,7 +530,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.9.6" } }, "nbformat": 4, From e46fde416482bc6a47454b182be9e91e9623a163 Mon Sep 17 00:00:00 2001 From: Ram Sharan Rimal Date: Wed, 18 Sep 2024 17:54:06 -0500 Subject: [PATCH 2/2] run all the user data notebooks --- ...arbonflux-monthgrid-v3_User_Notebook.ipynb | 282 ++-- ...n-co2flux-monthgrid-v5_User_Notebook.ipynb | 552 ++++--- .../emit-ch4plume-v1_User_Notebook.ipynb | 16 +- ...mission-grid-v2express_User_Notebook.ipynb | 1298 +++++++---------- ...-ch4budget-yeargrid-v1_User_Notebook.ipynb | 70 +- ...a2pes-ghg-monthgrid-v1_User_Notebook.ipynb | 46 +- ...bed-ghg-concentrations_User_Notebook.ipynb | 23 +- ...bed-ghg-concentrations_User_Notebook.ipynb | 21 +- ...sim-wetlandch4-grid-v2_User_Notebook.ipynb | 90 +- ...etlandch4-monthgrid-v2_User_Notebook.ipynb | 90 +- ...-carbonflux-daygrid-v1_User_Notebook.ipynb | 210 +-- ...bed-ghg-concentrations_User_Notebook.ipynb | 23 +- .../noaa-insitu_User_Notebook.ipynb | 368 ++--- .../oco2-mip-National-co2budget.ipynb | 28 +- ...-co2budget-yeargrid-v1_User_Notebook.ipynb | 90 +- ...2geos-co2-daygrid-v10r_User_Notebook.ipynb | 90 +- ...-ffco2-monthgrid-v2022_User_Notebook.ipynb | 90 +- ...-ffco2-monthgrid-v2023_User_Notebook.ipynb | 90 +- ...sity-yeargrid5yr-v4.11_User_Notebook.ipynb | 10 +- ...r-ch4flux-monthgrid-v1_User_Notebook.ipynb | 90 +- ...lcan-ffco2-yeargrid-v4_User_Notebook.ipynb | 72 +- 21 files changed, 1775 insertions(+), 1874 deletions(-) diff --git a/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb b/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb index 25f83cd45..8dc7a18a1 100644 --- a/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb +++ b/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -178,7 +178,7 @@ " 'dashboard:time_density': 'month'}" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -550,7 +550,7 @@ " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -572,7 +572,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -592,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -609,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -625,7 +625,7 @@ " 'center': [0.0, 0.0, 0]}" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -647,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -663,7 +663,7 @@ " 'center': [0.0, 0.0, 0]}" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -689,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -722,7 +722,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_2568d271cee858934c7bd78097d8bf6c {\n", + " #map_c4a165a86e35816dc6fe306bc91abda5 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -737,7 +737,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_aaa2c8843b2a25305b4c65014f14d94e {\n", + " #map_4c592a14e1e98db6943cc5d1a25598ed {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -752,17 +752,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_2568d271cee858934c7bd78097d8bf6c" ></div>\n", + " <div class="folium-map" id="map_c4a165a86e35816dc6fe306bc91abda5" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_aaa2c8843b2a25305b4c65014f14d94e" ></div>\n", + " <div class="folium-map" id="map_4c592a14e1e98db6943cc5d1a25598ed" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_2568d271cee858934c7bd78097d8bf6c = L.map(\n", - " "map_2568d271cee858934c7bd78097d8bf6c",\n", + " var map_c4a165a86e35816dc6fe306bc91abda5 = L.map(\n", + " "map_c4a165a86e35816dc6fe306bc91abda5",\n", " {\n", " center: [31.9, -99.9],\n", " crs: L.CRS.EPSG3857,\n", @@ -776,31 +776,31 @@ "\n", " \n", " \n", - " var tile_layer_cfa09e18609b5b286c3a0b927173dda9 = L.tileLayer(\n", + " var tile_layer_49c6f5a19751eb8e73aa29bc7b2f9b90 = 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_cfa09e18609b5b286c3a0b927173dda9.addTo(map_2568d271cee858934c7bd78097d8bf6c);\n", + " tile_layer_49c6f5a19751eb8e73aa29bc7b2f9b90.addTo(map_c4a165a86e35816dc6fe306bc91abda5);\n", " \n", " \n", - " var tile_layer_728763e97e8fed65cc785de578d58220 = L.tileLayer(\n", + " var tile_layer_a8d6e895235856c4e61dc937f620eda3 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-200312/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0.0%2C0.6039900183677673",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_728763e97e8fed65cc785de578d58220.addTo(map_2568d271cee858934c7bd78097d8bf6c);\n", + " tile_layer_a8d6e895235856c4e61dc937f620eda3.addTo(map_c4a165a86e35816dc6fe306bc91abda5);\n", " \n", " \n", - " var marker_2ce7fdb69cd4669c47b849002853be1a = L.marker(\n", + " var marker_7b55ff6982875d34b2d68250d0704649 = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_2568d271cee858934c7bd78097d8bf6c);\n", + " ).addTo(map_c4a165a86e35816dc6fe306bc91abda5);\n", " \n", " \n", - " marker_2ce7fdb69cd4669c47b849002853be1a.bindTooltip(\n", + " marker_7b55ff6982875d34b2d68250d0704649.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -808,58 +808,58 @@ " );\n", " \n", " \n", - " var layer_control_f604ff7163aba74d2b0235581ccd2e37_layers = {\n", + " var layer_control_0a87b8349022f2d6e874fa16bf1e17a4_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_cfa09e18609b5b286c3a0b927173dda9,\n", + " "openstreetmap" : tile_layer_49c6f5a19751eb8e73aa29bc7b2f9b90,\n", " },\n", " overlays : {\n", - " "December 2003 RH Level" : tile_layer_728763e97e8fed65cc785de578d58220,\n", + " "December 2003 RH Level" : tile_layer_a8d6e895235856c4e61dc937f620eda3,\n", " },\n", " };\n", - " let layer_control_f604ff7163aba74d2b0235581ccd2e37 = L.control.layers(\n", - " layer_control_f604ff7163aba74d2b0235581ccd2e37_layers.base_layers,\n", - " layer_control_f604ff7163aba74d2b0235581ccd2e37_layers.overlays,\n", + " let layer_control_0a87b8349022f2d6e874fa16bf1e17a4 = L.control.layers(\n", + " layer_control_0a87b8349022f2d6e874fa16bf1e17a4_layers.base_layers,\n", + " layer_control_0a87b8349022f2d6e874fa16bf1e17a4_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_2568d271cee858934c7bd78097d8bf6c);\n", + " ).addTo(map_c4a165a86e35816dc6fe306bc91abda5);\n", "\n", " \n", " \n", - " var color_map_ce664534ccf357dce37f00fa6c6037ad = {};\n", + " var color_map_8654129a7601173eb057f7559db608ed = {};\n", "\n", " \n", - " color_map_ce664534ccf357dce37f00fa6c6037ad.color = 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'#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff']);\n", " \n", "\n", - " color_map_ce664534ccf357dce37f00fa6c6037ad.x = d3.scale.linear()\n", + " color_map_8654129a7601173eb057f7559db608ed.x = d3.scale.linear()\n", " .domain([0.0, 0.3])\n", " .range([0, 450 - 50]);\n", "\n", - " color_map_ce664534ccf357dce37f00fa6c6037ad.legend = L.control({position: 'topright'});\n", - " color_map_ce664534ccf357dce37f00fa6c6037ad.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", - " color_map_ce664534ccf357dce37f00fa6c6037ad.legend.addTo(map_2568d271cee858934c7bd78097d8bf6c);\n", + " color_map_8654129a7601173eb057f7559db608ed.legend = L.control({position: 'topright'});\n", + " color_map_8654129a7601173eb057f7559db608ed.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_8654129a7601173eb057f7559db608ed.legend.addTo(map_c4a165a86e35816dc6fe306bc91abda5);\n", "\n", - " color_map_ce664534ccf357dce37f00fa6c6037ad.xAxis = d3.svg.axis()\n", - " .scale(color_map_ce664534ccf357dce37f00fa6c6037ad.x)\n", + " color_map_8654129a7601173eb057f7559db608ed.xAxis = d3.svg.axis()\n", + " .scale(color_map_8654129a7601173eb057f7559db608ed.x)\n", " .orient("top")\n", " .tickSize(1)\n", " .tickValues([0.0, 0.07, 0.15, 0.22, 0.3]);\n", "\n", - " color_map_ce664534ccf357dce37f00fa6c6037ad.svg = d3.select(".legend.leaflet-control").append("svg")\n", + " color_map_8654129a7601173eb057f7559db608ed.svg = d3.select(".legend.leaflet-control").append("svg")\n", " .attr("id", 'legend')\n", " .attr("width", 450)\n", " .attr("height", 40);\n", "\n", - " color_map_ce664534ccf357dce37f00fa6c6037ad.g = color_map_ce664534ccf357dce37f00fa6c6037ad.svg.append("g")\n", + " color_map_8654129a7601173eb057f7559db608ed.g = color_map_8654129a7601173eb057f7559db608ed.svg.append("g")\n", " .attr("class", "key")\n", " .attr("transform", "translate(25,16)");\n", "\n", - " color_map_ce664534ccf357dce37f00fa6c6037ad.g.selectAll("rect")\n", - " .data(color_map_ce664534ccf357dce37f00fa6c6037ad.color.range().map(function(d, i) {\n", + " color_map_8654129a7601173eb057f7559db608ed.g.selectAll("rect")\n", + " .data(color_map_8654129a7601173eb057f7559db608ed.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_ce664534ccf357dce37f00fa6c6037ad.x(color_map_ce664534ccf357dce37f00fa6c6037ad.color.domain()[i - 1]) : color_map_ce664534ccf357dce37f00fa6c6037ad.x.range()[0],\n", - " x1: i < color_map_ce664534ccf357dce37f00fa6c6037ad.color.domain().length ? color_map_ce664534ccf357dce37f00fa6c6037ad.x(color_map_ce664534ccf357dce37f00fa6c6037ad.color.domain()[i]) : color_map_ce664534ccf357dce37f00fa6c6037ad.x.range()[1],\n", + " x0: i ? color_map_8654129a7601173eb057f7559db608ed.x(color_map_8654129a7601173eb057f7559db608ed.color.domain()[i - 1]) : color_map_8654129a7601173eb057f7559db608ed.x.range()[0],\n", + " x1: i < color_map_8654129a7601173eb057f7559db608ed.color.domain().length ? color_map_8654129a7601173eb057f7559db608ed.x(color_map_8654129a7601173eb057f7559db608ed.color.domain()[i]) : color_map_8654129a7601173eb057f7559db608ed.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -869,13 +869,13 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_ce664534ccf357dce37f00fa6c6037ad.g.call(color_map_ce664534ccf357dce37f00fa6c6037ad.xAxis).append("text")\n", + " color_map_8654129a7601173eb057f7559db608ed.g.call(color_map_8654129a7601173eb057f7559db608ed.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", " .text("Rh Values (kg Carbon/m2/month)");\n", " \n", - " var map_aaa2c8843b2a25305b4c65014f14d94e = L.map(\n", - " "map_aaa2c8843b2a25305b4c65014f14d94e",\n", + " var map_4c592a14e1e98db6943cc5d1a25598ed = L.map(\n", + " "map_4c592a14e1e98db6943cc5d1a25598ed",\n", " {\n", " center: [31.9, -99.9],\n", " crs: L.CRS.EPSG3857,\n", @@ -889,35 +889,35 @@ "\n", " \n", " \n", - " var tile_layer_4a5503d199ee924e81f9c3e33f3b2c51 = L.tileLayer(\n", + " var tile_layer_c4fdc3a62eaf6fffa060e476735b2f7a = 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_4a5503d199ee924e81f9c3e33f3b2c51.addTo(map_aaa2c8843b2a25305b4c65014f14d94e);\n", + " tile_layer_c4fdc3a62eaf6fffa060e476735b2f7a.addTo(map_4c592a14e1e98db6943cc5d1a25598ed);\n", " \n", " \n", - " var tile_layer_a87971af5b016f5cd8018cae05d47410 = L.tileLayer(\n", + " var tile_layer_c8b3f8b4ab7c344f4e26a0e31d57781b = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0.0%2C0.6039900183677673",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_a87971af5b016f5cd8018cae05d47410.addTo(map_aaa2c8843b2a25305b4c65014f14d94e);\n", + " tile_layer_c8b3f8b4ab7c344f4e26a0e31d57781b.addTo(map_4c592a14e1e98db6943cc5d1a25598ed);\n", " \n", " \n", - " map_2568d271cee858934c7bd78097d8bf6c.sync(map_aaa2c8843b2a25305b4c65014f14d94e);\n", - " map_aaa2c8843b2a25305b4c65014f14d94e.sync(map_2568d271cee858934c7bd78097d8bf6c);\n", + " map_c4a165a86e35816dc6fe306bc91abda5.sync(map_4c592a14e1e98db6943cc5d1a25598ed);\n", + " map_4c592a14e1e98db6943cc5d1a25598ed.sync(map_c4a165a86e35816dc6fe306bc91abda5);\n", " \n", " \n", - " var marker_10036912ba52403c93e7ba7e1c5dc55d = L.marker(\n", + " var marker_3207965280494949a2255cdb4503f02a = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_aaa2c8843b2a25305b4c65014f14d94e);\n", + " ).addTo(map_4c592a14e1e98db6943cc5d1a25598ed);\n", " \n", " \n", - " marker_10036912ba52403c93e7ba7e1c5dc55d.bindTooltip(\n", + " marker_3207965280494949a2255cdb4503f02a.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -925,29 +925,29 @@ " );\n", " \n", " \n", - " var layer_control_44249bb3b87d48759ee835012bcd46af_layers = {\n", + " var layer_control_e8383eb7f71f4ca29297868b97d48a41_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_4a5503d199ee924e81f9c3e33f3b2c51,\n", + " "openstreetmap" : tile_layer_c4fdc3a62eaf6fffa060e476735b2f7a,\n", " },\n", " overlays : {\n", - " "December 2017 RH Level" : tile_layer_a87971af5b016f5cd8018cae05d47410,\n", + " "December 2017 RH Level" : tile_layer_c8b3f8b4ab7c344f4e26a0e31d57781b,\n", " },\n", " };\n", - " let layer_control_44249bb3b87d48759ee835012bcd46af = L.control.layers(\n", - " layer_control_44249bb3b87d48759ee835012bcd46af_layers.base_layers,\n", - " layer_control_44249bb3b87d48759ee835012bcd46af_layers.overlays,\n", + " let layer_control_e8383eb7f71f4ca29297868b97d48a41 = L.control.layers(\n", + " layer_control_e8383eb7f71f4ca29297868b97d48a41_layers.base_layers,\n", + " layer_control_e8383eb7f71f4ca29297868b97d48a41_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_aaa2c8843b2a25305b4c65014f14d94e);\n", + " ).addTo(map_4c592a14e1e98db6943cc5d1a25598ed);\n", "\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": 11, "metadata": {}, "output_type": "execute_result" } @@ -1022,7 +1022,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1048,7 +1048,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1081,7 +1081,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_25e8c40d7ccce990757e5b7c5672128d {\n", + " #map_c146f317ce8913a2100fa2083d272a69 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1095,14 +1095,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_25e8c40d7ccce990757e5b7c5672128d" ></div>\n", + " <div class="folium-map" id="map_c146f317ce8913a2100fa2083d272a69" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_25e8c40d7ccce990757e5b7c5672128d = L.map(\n", - " "map_25e8c40d7ccce990757e5b7c5672128d",\n", + " var map_c146f317ce8913a2100fa2083d272a69 = L.map(\n", + " "map_c146f317ce8913a2100fa2083d272a69",\n", " {\n", " center: [32.81, -96.93],\n", " crs: L.CRS.EPSG3857,\n", @@ -1116,44 +1116,44 @@ "\n", " \n", " \n", - " var tile_layer_79f8737033b060dd64c24293e4a81038 = L.tileLayer(\n", + " var tile_layer_7a482b73c6f9ba19ef830b2515044ba0 = 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_79f8737033b060dd64c24293e4a81038.addTo(map_25e8c40d7ccce990757e5b7c5672128d);\n", + " tile_layer_7a482b73c6f9ba19ef830b2515044ba0.addTo(map_c146f317ce8913a2100fa2083d272a69);\n", " \n", " \n", "\n", - " function geo_json_796b0e919f9eb123f8d4ce20105ef0cf_onEachFeature(feature, layer) {\n", + " function geo_json_4f6f360c36a883cb9140eee219f8ca1f_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_796b0e919f9eb123f8d4ce20105ef0cf = L.geoJson(null, {\n", - " onEachFeature: geo_json_796b0e919f9eb123f8d4ce20105ef0cf_onEachFeature,\n", + " var geo_json_4f6f360c36a883cb9140eee219f8ca1f = L.geoJson(null, {\n", + " onEachFeature: geo_json_4f6f360c36a883cb9140eee219f8ca1f_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_796b0e919f9eb123f8d4ce20105ef0cf_add (data) {\n", - " geo_json_796b0e919f9eb123f8d4ce20105ef0cf\n", + " function geo_json_4f6f360c36a883cb9140eee219f8ca1f_add (data) {\n", + " geo_json_4f6f360c36a883cb9140eee219f8ca1f\n", " .addData(data);\n", " }\n", - " geo_json_796b0e919f9eb123f8d4ce20105ef0cf_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_796b0e919f9eb123f8d4ce20105ef0cf.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_4f6f360c36a883cb9140eee219f8ca1f_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_4f6f360c36a883cb9140eee219f8ca1f.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_796b0e919f9eb123f8d4ce20105ef0cf.addTo(map_25e8c40d7ccce990757e5b7c5672128d);\n", + " geo_json_4f6f360c36a883cb9140eee219f8ca1f.addTo(map_c146f317ce8913a2100fa2083d272a69);\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": 13, "metadata": {}, "output_type": "execute_result" } @@ -1174,7 +1174,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1195,7 +1195,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1496,7 +1496,7 @@ " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1508,7 +1508,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1528,7 +1528,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1556,7 +1556,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1743,8 +1743,8 @@ "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.03646999970078468, 'max': 0.05527999997138977, 'mean': 0.04348228871822357, 'count': 6.300000190734863, 'sum': 0.2739384174346924, 'std': 0.005476967955864938, 'median': 0.04210999980568886, 'majority': 0.03646999970078468, 'minority': 0.03646999970078468, 'unique': 12.0, 'histogram': [[2.0, 0.0, 3.0, 1.0, 1.0, 1.0, 1.0, 2.0, 0.0, 1.0], [0.03646999970078468, 0.03835099935531616, 0.04023199900984764, 0.04211299866437912, 0.0439939983189106, 0.04587499797344208, 0.047756001353263855, 0.049637001007795334, 0.05151800066232681, 0.05339900031685829, 0.05527999997138977]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.03646999970078468, 'percentile_98': 0.05527999997138977}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.017059998586773872, 'max': 0.023930000141263008, 'mean': 0.020088793709874153, 'count': 6.300000190734863, 'sum': 0.12655940651893616, 'std': 0.002199509794210104, 'median': 0.019680000841617584, 'majority': 0.017059998586773872, 'minority': 0.017059998586773872, 'unique': 12.0, 'histogram': [[2.0, 0.0, 1.0, 2.0, 1.0, 0.0, 2.0, 1.0, 1.0, 2.0], [0.017059998586773872, 0.017746997997164726, 0.01843399927020073, 0.019120998680591583, 0.019807999953627586, 0.02049499936401844, 0.021182000637054443, 0.021869000047445297, 0.02255599945783615, 0.023243000730872154, 0.023930000141263008]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.017059998586773872, 'percentile_98': 0.023930000141263008}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.01664000004529953, 'max': 0.02370999939739704, 'mean': 0.019474824890494347, 'count': 6.300000190734863, 'sum': 0.12269140034914017, 'std': 0.0023577335642611815, 'median': 0.019190000370144844, 'majority': 0.01664000004529953, 'minority': 0.01664000004529953, 'unique': 12.0, 'histogram': [[2.0, 1.0, 0.0, 2.0, 1.0, 1.0, 3.0, 0.0, 0.0, 2.0], [0.01664000004529953, 0.017347000539302826, 0.018053999170660973, 0.01876099966466427, 0.019468000158667564, 0.02017499879002571, 0.020881999284029007, 0.021588999778032303, 0.0222960002720356, 0.023002998903393745, 0.02370999939739704]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.01664000004529953, 'percentile_98': 0.02370999939739704}}}}\n", - "CPU times: user 1.24 s, sys: 309 ms, total: 1.55 s\n", - "Wall time: 1min 22s\n" + "CPU times: user 1.12 s, sys: 303 ms, total: 1.42 s\n", + "Wall time: 1min 34s\n" ] } ], @@ -1755,7 +1755,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1791,7 +1791,7 @@ " 'start_datetime': '2017-12-01T00:00:00+00:00'}" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1803,7 +1803,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1994,7 +1994,7 @@ "4 2017-08-01 00:00:00+00:00 " ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2023,7 +2023,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -2032,7 +2032,7 @@ "Text(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (2003-2017)')" ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, @@ -2067,7 +2067,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -2086,7 +2086,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -2102,7 +2102,7 @@ " 'center': [0.0, 0.0, 0]}" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2120,7 +2120,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -2153,7 +2153,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_b18e4e421e4320d93377eb2ec813b7cd {\n", + " #map_d63dc3fcc91fc3b814bc9b368d2f9033 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -2168,14 +2168,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_b18e4e421e4320d93377eb2ec813b7cd" ></div>\n", + " <div class="folium-map" id="map_d63dc3fcc91fc3b814bc9b368d2f9033" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_b18e4e421e4320d93377eb2ec813b7cd = L.map(\n", - " "map_b18e4e421e4320d93377eb2ec813b7cd",\n", + " var map_d63dc3fcc91fc3b814bc9b368d2f9033 = L.map(\n", + " "map_d63dc3fcc91fc3b814bc9b368d2f9033",\n", " {\n", " center: [32.8, -96.79],\n", " crs: L.CRS.EPSG3857,\n", @@ -2189,31 +2189,31 @@ "\n", " \n", " \n", - " var tile_layer_80695508056a49c2fd3ce5e53159d901 = L.tileLayer(\n", + " var tile_layer_2d32a0746069cd0c02e38a4ec068c6f5 = 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_80695508056a49c2fd3ce5e53159d901.addTo(map_b18e4e421e4320d93377eb2ec813b7cd);\n", + " tile_layer_2d32a0746069cd0c02e38a4ec068c6f5.addTo(map_d63dc3fcc91fc3b814bc9b368d2f9033);\n", " \n", " \n", - " var tile_layer_f1948109cb609b88d65d39d9a1f05879 = L.tileLayer(\n", + " var tile_layer_c10a8ef8bf4545e62916981e8994f5d0 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201710/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0.0%2C0.6039900183677673",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.7, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_f1948109cb609b88d65d39d9a1f05879.addTo(map_b18e4e421e4320d93377eb2ec813b7cd);\n", + " tile_layer_c10a8ef8bf4545e62916981e8994f5d0.addTo(map_d63dc3fcc91fc3b814bc9b368d2f9033);\n", " \n", " \n", - " var marker_1c40c6f328d8f4f3102c588cb0f6b17a = L.marker(\n", + " var marker_d20204226564bb900a9294fe360bd5a0 = L.marker(\n", " [40.0, 5.9],\n", " {}\n", - " ).addTo(map_b18e4e421e4320d93377eb2ec813b7cd);\n", + " ).addTo(map_d63dc3fcc91fc3b814bc9b368d2f9033);\n", " \n", " \n", - " marker_1c40c6f328d8f4f3102c588cb0f6b17a.bindTooltip(\n", + " marker_d20204226564bb900a9294fe360bd5a0.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -2221,58 +2221,58 @@ " );\n", " \n", " \n", - " var layer_control_da633cf7b0f52e140121490d7927a778_layers = {\n", + " var layer_control_5e01092467e5c62a74e8b9765eb72bb4_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_80695508056a49c2fd3ce5e53159d901,\n", + " "openstreetmap" : tile_layer_2d32a0746069cd0c02e38a4ec068c6f5,\n", " },\n", " overlays : {\n", - " "October 2017 RH Level" : tile_layer_f1948109cb609b88d65d39d9a1f05879,\n", + " "October 2017 RH Level" : tile_layer_c10a8ef8bf4545e62916981e8994f5d0,\n", " },\n", " };\n", - " let layer_control_da633cf7b0f52e140121490d7927a778 = L.control.layers(\n", - " layer_control_da633cf7b0f52e140121490d7927a778_layers.base_layers,\n", - " layer_control_da633cf7b0f52e140121490d7927a778_layers.overlays,\n", + " let layer_control_5e01092467e5c62a74e8b9765eb72bb4 = L.control.layers(\n", + " layer_control_5e01092467e5c62a74e8b9765eb72bb4_layers.base_layers,\n", + " layer_control_5e01092467e5c62a74e8b9765eb72bb4_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_b18e4e421e4320d93377eb2ec813b7cd);\n", + " ).addTo(map_d63dc3fcc91fc3b814bc9b368d2f9033);\n", "\n", " \n", " \n", - " var color_map_bde09018ab691e4f64c5afe21a73beec = {};\n", + " var color_map_6353bff4833d2c84182c4e6e7029d6ae = {};\n", "\n", " \n", - " color_map_bde09018ab691e4f64c5afe21a73beec.color = d3.scale.threshold()\n", + " color_map_6353bff4833d2c84182c4e6e7029d6ae.color = d3.scale.threshold()\n", " .domain([0.0, 0.0006012024048096192, 0.0012024048096192384, 0.0018036072144288575, 0.002404809619238477, 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" color_map_6353bff4833d2c84182c4e6e7029d6ae.g.selectAll("rect")\n", + " .data(color_map_6353bff4833d2c84182c4e6e7029d6ae.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_bde09018ab691e4f64c5afe21a73beec.x(color_map_bde09018ab691e4f64c5afe21a73beec.color.domain()[i - 1]) : color_map_bde09018ab691e4f64c5afe21a73beec.x.range()[0],\n", - " x1: i < color_map_bde09018ab691e4f64c5afe21a73beec.color.domain().length ? color_map_bde09018ab691e4f64c5afe21a73beec.x(color_map_bde09018ab691e4f64c5afe21a73beec.color.domain()[i]) : color_map_bde09018ab691e4f64c5afe21a73beec.x.range()[1],\n", + " x0: i ? color_map_6353bff4833d2c84182c4e6e7029d6ae.x(color_map_6353bff4833d2c84182c4e6e7029d6ae.color.domain()[i - 1]) : color_map_6353bff4833d2c84182c4e6e7029d6ae.x.range()[0],\n", + " x1: i < color_map_6353bff4833d2c84182c4e6e7029d6ae.color.domain().length ? color_map_6353bff4833d2c84182c4e6e7029d6ae.x(color_map_6353bff4833d2c84182c4e6e7029d6ae.color.domain()[i]) : color_map_6353bff4833d2c84182c4e6e7029d6ae.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -2282,7 +2282,7 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_bde09018ab691e4f64c5afe21a73beec.g.call(color_map_bde09018ab691e4f64c5afe21a73beec.xAxis).append("text")\n", + " color_map_6353bff4833d2c84182c4e6e7029d6ae.g.call(color_map_6353bff4833d2c84182c4e6e7029d6ae.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", " .text("Rh Values (kg Carbon/m2/month)");\n", @@ -2290,10 +2290,10 @@ "</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": 24, "metadata": {}, "output_type": "execute_result" } diff --git a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb index 44ff5ec31..a4ec6e010 100644 --- a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb +++ b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -114,12 +114,13 @@ "from pystac_client import Client \n", "import branca \n", "import pandas as pd\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "from tabulate import tabulate " ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -139,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -185,7 +186,7 @@ " 'dashboard:time_density': 'month'}" ] }, - "execution_count": 27, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -210,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -236,9 +237,69 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Collection Summary
0TitleAir-Sea CO₂ Flux, ECCO-Darwin Model v5
1DescriptionGlobal, monthly average air-sea CO₂ flux (negative into ocean) at ~1/3° resolution from 2020 to 2022. Data are in units of millimoles of CO₂ per meter squared per second (mmol m²/s). Derived using the ECCO-Darwin model v5, which is an ocean biogeochemical model that assimilates Estimating the Circulation and Climate of the Ocean (ECCO) consortium ocean circulation estimates and biogeochemical processes from the Massachusetts Institute of Technology (MIT) Darwin Project.
2Temporal Extent[['2020-01-01T00:00:00+00:00', '2022-12-31T00:00:00+00:00']]
3Spatial Extent[[-180.0, -90.0, 180.0, 90.0]]
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# The collection name is used to fetch the dataset from the STAC API. First, you must define the collection name as a variable. \n", "# The collection name in the STAC API\n", @@ -291,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -342,14 +403,28 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Found 36 items\n" + "Found 36 observations\n", + "Below you see the first 5 items in the collection, along with their item IDs and corresponding Start Date-Time.\n", + "╒════════════════════════════════════════╤═══════════════════════════╕\n", + "│ Item ID │ Start Date-Time │\n", + "╞════════════════════════════════════════╪═══════════════════════════╡\n", + "│ eccodarwin-co2flux-monthgrid-v5-202001 │ 2020-01-01T00:00:00+00:00 │\n", + "├────────────────────────────────────────┼───────────────────────────┤\n", + "│ eccodarwin-co2flux-monthgrid-v5-202002 │ 2020-02-01T00:00:00+00:00 │\n", + "├────────────────────────────────────────┼───────────────────────────┤\n", + "│ eccodarwin-co2flux-monthgrid-v5-202003 │ 2020-03-01T00:00:00+00:00 │\n", + "├────────────────────────────────────────┼───────────────────────────┤\n", + "│ eccodarwin-co2flux-monthgrid-v5-202004 │ 2020-04-01T00:00:00+00:00 │\n", + "├────────────────────────────────────────┼───────────────────────────┤\n", + "│ eccodarwin-co2flux-monthgrid-v5-202005 │ 2020-05-01T00:00:00+00:00 │\n", + "╘════════════════════════════════════════╧═══════════════════════════╛\n" ] } ], @@ -385,13 +460,13 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'id': 'eccodarwin-co2flux-monthgrid-v5-202212',\n", + "{'id': 'eccodarwin-co2flux-monthgrid-v5-202001',\n", " 'bbox': [-180.125, -90.124826629681, 179.875, 89.875173370319],\n", " 'type': 'Feature',\n", " 'links': [{'rel': 'collection',\n", @@ -405,12 +480,12 @@ " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", " {'rel': 'self',\n", " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212'},\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202001'},\n", " {'title': 'Map of Item',\n", - " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212/map?assets=co2&nodata=nan&rescale=-0.0007%2C0.0002&colormap_name=bwr',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202001/map?assets=co2&nodata=nan&rescale=-0.0007%2C0.0002&colormap_name=bwr',\n", " 'rel': 'preview',\n", " 'type': 'text/html'}],\n", - " 'assets': {'co2': {'href': 's3://ghgc-data-store/eccodarwin-co2flux-monthgrid-v5/ECCO-Darwin_CO2_flux_202212.tif',\n", + " 'assets': {'co2': {'href': 's3://ghgc-data-store/eccodarwin-co2flux-monthgrid-v5/ECCO-Darwin_CO2_flux_202001.tif',\n", " 'type': 'image/tiff; application=geotiff',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Air-Sea CO₂ Flux',\n", @@ -423,13 +498,13 @@ " 'sampling': 'area',\n", " 'data_type': 'float64',\n", " 'histogram': {'max': 1e+20,\n", - " 'min': -0.0560546528687938,\n", + " 'min': -0.0035127835016991096,\n", " 'count': 11.0,\n", " 'buckets': [338606.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 186706.0]},\n", " 'statistics': {'mean': 3.554192556042885e+19,\n", " 'stddev': 4.786401658343999e+19,\n", " 'maximum': 1e+20,\n", - " 'minimum': -0.0560546528687938,\n", + " 'minimum': -0.0035127835016991096,\n", " 'valid_percent': 0.0001903630604288499}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", " 'coordinates': [[[-180.125, -90.124826629681],\n", @@ -465,7 +540,7 @@ " 0.0,\n", " 1.0]},\n", " 'rendered_preview': {'title': 'Rendered preview',\n", - " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212/preview.png?assets=co2&nodata=nan&rescale=-0.0007%2C0.0002&colormap_name=bwr',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202001/preview.png?assets=co2&nodata=nan&rescale=-0.0007%2C0.0002&colormap_name=bwr',\n", " 'rel': 'preview',\n", " 'roles': ['overview'],\n", " 'type': 'image/png'}},\n", @@ -476,14 +551,14 @@ " [-180.125, 89.875173370319],\n", " [-180.125, -90.124826629681]]]},\n", " 'collection': 'eccodarwin-co2flux-monthgrid-v5',\n", - " 'properties': {'end_datetime': '2022-12-31T00:00:00+00:00',\n", - " 'start_datetime': '2022-12-01T00:00:00+00:00'},\n", + " 'properties': {'end_datetime': '2020-01-31T00:00:00+00:00',\n", + " 'start_datetime': '2020-01-01T00:00:00+00:00'},\n", " 'stac_version': '1.0.0',\n", " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" ] }, - "execution_count": 30, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -506,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -533,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -551,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -567,7 +642,7 @@ " 'center': [-0.125, -0.1248266296809959, 0]}" ] }, - "execution_count": 33, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -609,7 +684,7 @@ " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", " \n", " # Pass the minimum and maximum values for rescaling \n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"),\n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"\n", " \n", "# Return the response in JSON format\n", ").json()\n", @@ -621,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -637,7 +712,7 @@ " 'center': [-0.125, -0.1248266296809959, 0]}" ] }, - "execution_count": 34, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -665,7 +740,7 @@ " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", " \n", " # Pass the minimum and maximum values for rescaling \n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"),\n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"\n", " \n", "# Return the response in JSON format\n", ").json()\n", @@ -686,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -719,7 +794,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_1d84f425760589c5c57dfd3456c41c8c {\n", + " #map_8b0c6cf84ebc5af16a1e3b06894f91a6 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -733,7 +808,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_2b8f4ebabc9915b6d1c296c332a5802a {\n", + " #map_e427f884bfff929d3cc2947e0ca4449b {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -766,21 +841,21 @@ " #FF66CC 80%, #FF3399); height: 10px;"></div>\n", "</div>\n", " \n", - " <div class="folium-map" id="map_1d84f425760589c5c57dfd3456c41c8c" ></div>\n", + " <div class="folium-map" id="map_8b0c6cf84ebc5af16a1e3b06894f91a6" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_2b8f4ebabc9915b6d1c296c332a5802a" ></div>\n", + " <div class="folium-map" id="map_e427f884bfff929d3cc2947e0ca4449b" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_1d84f425760589c5c57dfd3456c41c8c = L.map(\n", - " "map_1d84f425760589c5c57dfd3456c41c8c",\n", + " var map_8b0c6cf84ebc5af16a1e3b06894f91a6 = L.map(\n", + " "map_8b0c6cf84ebc5af16a1e3b06894f91a6",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", - " zoom: 6,\n", + " zoom: 7,\n", " zoomControl: true,\n", " preferCanvas: false,\n", " }\n", @@ -790,31 +865,31 @@ "\n", " \n", " \n", - " var tile_layer_f773cac2c5118d02914331befe7893dd = L.tileLayer(\n", + " var tile_layer_686aa78a244ce52a36f9f5585379440d = 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_f773cac2c5118d02914331befe7893dd.addTo(map_1d84f425760589c5c57dfd3456c41c8c);\n", + " tile_layer_686aa78a244ce52a36f9f5585379440d.addTo(map_8b0c6cf84ebc5af16a1e3b06894f91a6);\n", " \n", " \n", - " var tile_layer_db1f3a71765c955789913dd105059d89 = L.tileLayer(\n", + " var tile_layer_0519ac4f42679588a4a515ec9fc05d01 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=-0.0007%2C0.0007",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_db1f3a71765c955789913dd105059d89.addTo(map_1d84f425760589c5c57dfd3456c41c8c);\n", + " tile_layer_0519ac4f42679588a4a515ec9fc05d01.addTo(map_8b0c6cf84ebc5af16a1e3b06894f91a6);\n", " \n", " \n", - " var marker_a4b7208dfb15251816520d172570fa5c = L.marker(\n", + " var marker_ceddcdc655d17e004f5814dc04709531 = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_1d84f425760589c5c57dfd3456c41c8c);\n", + " ).addTo(map_8b0c6cf84ebc5af16a1e3b06894f91a6);\n", " \n", " \n", - " marker_a4b7208dfb15251816520d172570fa5c.bindTooltip(\n", + " marker_ceddcdc655d17e004f5814dc04709531.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -822,28 +897,28 @@ " );\n", " \n", " \n", - " var layer_control_4292915797c7d99b89b7677cac7fd245_layers = {\n", + " var layer_control_65eca1e629f9e6d04121a2491198fba2_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_f773cac2c5118d02914331befe7893dd,\n", + " "openstreetmap" : tile_layer_686aa78a244ce52a36f9f5585379440d,\n", " },\n", " overlays : {\n", - " "December 2022 CO2 Flux" : tile_layer_db1f3a71765c955789913dd105059d89,\n", + " "December 2022 CO2 Flux" : tile_layer_0519ac4f42679588a4a515ec9fc05d01,\n", " },\n", " };\n", - " let layer_control_4292915797c7d99b89b7677cac7fd245 = L.control.layers(\n", - " layer_control_4292915797c7d99b89b7677cac7fd245_layers.base_layers,\n", - " layer_control_4292915797c7d99b89b7677cac7fd245_layers.overlays,\n", + " let layer_control_65eca1e629f9e6d04121a2491198fba2 = L.control.layers(\n", + " layer_control_65eca1e629f9e6d04121a2491198fba2_layers.base_layers,\n", + " layer_control_65eca1e629f9e6d04121a2491198fba2_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_1d84f425760589c5c57dfd3456c41c8c);\n", + " ).addTo(map_8b0c6cf84ebc5af16a1e3b06894f91a6);\n", "\n", " \n", " \n", - " var map_2b8f4ebabc9915b6d1c296c332a5802a = L.map(\n", - " "map_2b8f4ebabc9915b6d1c296c332a5802a",\n", + " var map_e427f884bfff929d3cc2947e0ca4449b = L.map(\n", + " "map_e427f884bfff929d3cc2947e0ca4449b",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", - " zoom: 6,\n", + " zoom: 7,\n", " zoomControl: true,\n", " preferCanvas: false,\n", " }\n", @@ -853,35 +928,35 @@ "\n", " \n", " \n", - " var tile_layer_de846f26ee32feff54adeb0e6ae775c8 = L.tileLayer(\n", + " var tile_layer_66fb13673280cbbbbade7caa3c954ca8 = 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_de846f26ee32feff54adeb0e6ae775c8.addTo(map_2b8f4ebabc9915b6d1c296c332a5802a);\n", + " tile_layer_66fb13673280cbbbbade7caa3c954ca8.addTo(map_e427f884bfff929d3cc2947e0ca4449b);\n", " \n", " \n", - " var tile_layer_c27e59e6968a77db4ae47767c742f632 = L.tileLayer(\n", + " var tile_layer_5bd2d38756057194d670db3d31c2abce = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202104/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=-0.0007%2C0.0007",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_c27e59e6968a77db4ae47767c742f632.addTo(map_2b8f4ebabc9915b6d1c296c332a5802a);\n", + " tile_layer_5bd2d38756057194d670db3d31c2abce.addTo(map_e427f884bfff929d3cc2947e0ca4449b);\n", " \n", " \n", - " map_1d84f425760589c5c57dfd3456c41c8c.sync(map_2b8f4ebabc9915b6d1c296c332a5802a);\n", - " map_2b8f4ebabc9915b6d1c296c332a5802a.sync(map_1d84f425760589c5c57dfd3456c41c8c);\n", + " map_8b0c6cf84ebc5af16a1e3b06894f91a6.sync(map_e427f884bfff929d3cc2947e0ca4449b);\n", + " map_e427f884bfff929d3cc2947e0ca4449b.sync(map_8b0c6cf84ebc5af16a1e3b06894f91a6);\n", " \n", " \n", - " var marker_ede3a5b5ad9c4ba9b53e1fb23748df93 = L.marker(\n", + " var marker_223470160b064b56b095ee21f83cdf09 = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_2b8f4ebabc9915b6d1c296c332a5802a);\n", + " ).addTo(map_e427f884bfff929d3cc2947e0ca4449b);\n", " \n", " \n", - " marker_ede3a5b5ad9c4ba9b53e1fb23748df93.bindTooltip(\n", + " marker_223470160b064b56b095ee21f83cdf09.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -889,29 +964,29 @@ " );\n", " \n", " \n", - " var layer_control_c54cf0816afb496fb472f1dac46d88f4_layers = {\n", + " var layer_control_0b0742c9274d4ef29422b460bbe59cfc_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_de846f26ee32feff54adeb0e6ae775c8,\n", + " "openstreetmap" : tile_layer_66fb13673280cbbbbade7caa3c954ca8,\n", " },\n", " overlays : {\n", - " "April 2021 CO2 Flux" : tile_layer_c27e59e6968a77db4ae47767c742f632,\n", + " "April 2021 CO2 Flux" : tile_layer_5bd2d38756057194d670db3d31c2abce,\n", " },\n", " };\n", - " let layer_control_c54cf0816afb496fb472f1dac46d88f4 = L.control.layers(\n", - " layer_control_c54cf0816afb496fb472f1dac46d88f4_layers.base_layers,\n", - " layer_control_c54cf0816afb496fb472f1dac46d88f4_layers.overlays,\n", + " let layer_control_0b0742c9274d4ef29422b460bbe59cfc = L.control.layers(\n", + " layer_control_0b0742c9274d4ef29422b460bbe59cfc_layers.base_layers,\n", + " layer_control_0b0742c9274d4ef29422b460bbe59cfc_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_2b8f4ebabc9915b6d1c296c332a5802a);\n", + " ).addTo(map_e427f884bfff929d3cc2947e0ca4449b);\n", "\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": 35, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1007,7 +1082,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1032,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1065,7 +1140,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_b026227a03ef1c8c8fdc2f21a0a0d90f {\n", + " #map_d60179c7f39aa4adf54fc99ae36be817 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1079,14 +1154,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_b026227a03ef1c8c8fdc2f21a0a0d90f" ></div>\n", + " <div class="folium-map" id="map_d60179c7f39aa4adf54fc99ae36be817" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_b026227a03ef1c8c8fdc2f21a0a0d90f = L.map(\n", - " "map_b026227a03ef1c8c8fdc2f21a0a0d90f",\n", + " var map_d60179c7f39aa4adf54fc99ae36be817 = L.map(\n", + " "map_d60179c7f39aa4adf54fc99ae36be817",\n", " {\n", " center: [35.0, -120.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1100,44 +1175,44 @@ "\n", " \n", " \n", - " var tile_layer_504c4687cdc33c3c47acf1f05ceefe5e = L.tileLayer(\n", + " var tile_layer_697d7374797b4bfd48a93d4c95b1197b = 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_504c4687cdc33c3c47acf1f05ceefe5e.addTo(map_b026227a03ef1c8c8fdc2f21a0a0d90f);\n", + " tile_layer_697d7374797b4bfd48a93d4c95b1197b.addTo(map_d60179c7f39aa4adf54fc99ae36be817);\n", " \n", " \n", "\n", - " function geo_json_69aa59710193d53dfbcdc75743e28c75_onEachFeature(feature, layer) {\n", + " function geo_json_e225c973c87bd705c29f5e6d66005e40_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_69aa59710193d53dfbcdc75743e28c75 = L.geoJson(null, {\n", - " onEachFeature: geo_json_69aa59710193d53dfbcdc75743e28c75_onEachFeature,\n", + " var geo_json_e225c973c87bd705c29f5e6d66005e40 = L.geoJson(null, {\n", + " onEachFeature: geo_json_e225c973c87bd705c29f5e6d66005e40_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_69aa59710193d53dfbcdc75743e28c75_add (data) {\n", - " geo_json_69aa59710193d53dfbcdc75743e28c75\n", + " function geo_json_e225c973c87bd705c29f5e6d66005e40_add (data) {\n", + " geo_json_e225c973c87bd705c29f5e6d66005e40\n", " .addData(data);\n", " }\n", - " geo_json_69aa59710193d53dfbcdc75743e28c75_add({"geometry": {"coordinates": [[[-124.19, 37.86], [-123.11, 37.86], [-119.96, 33.16], [-121.13, 33.16], [-124.19, 37.86]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", - " geo_json_69aa59710193d53dfbcdc75743e28c75.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_e225c973c87bd705c29f5e6d66005e40_add({"geometry": {"coordinates": [[[-124.19, 37.86], [-123.11, 37.86], [-119.96, 33.16], [-121.13, 33.16], [-124.19, 37.86]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_e225c973c87bd705c29f5e6d66005e40.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_69aa59710193d53dfbcdc75743e28c75.addTo(map_b026227a03ef1c8c8fdc2f21a0a0d90f);\n", + " geo_json_e225c973c87bd705c29f5e6d66005e40.addTo(map_d60179c7f39aa4adf54fc99ae36be817);\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": 37, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1176,7 +1251,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1220,7 +1295,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1243,7 +1318,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1341,7 +1416,7 @@ " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" ] }, - "execution_count": 40, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1353,7 +1428,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1385,7 +1460,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1428,8 +1503,8 @@ "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-124.19, 37.86], [-123.11, 37.86], [-119.96, 33.16], [-121.13, 33.16], [-124.19, 37.86]]]}, 'properties': {'statistics': {'b1': {'min': -0.00011453414031127196, 'max': -2.5153009306526816e-05, 'mean': -5.606484279560035e-05, 'count': 86.62000274658203, 'sum': -0.004856336836941592, 'std': 1.3576859561140137e-05, 'median': -5.2092425995657114e-05, 'majority': -0.00011453414031127196, 'minority': -0.00011453414031127196, 'unique': 115.0, 'histogram': [[2.0, 2.0, 3.0, 1.0, 5.0, 18.0, 27.0, 39.0, 14.0, 4.0], [-0.00011453414031127196, -0.00010559602721079744, -9.665791411032294e-05, -8.771980100984842e-05, -7.87816879093739e-05, -6.984357480889939e-05, -6.090546170842488e-05, -5.196734860795037e-05, -4.302923550747585e-05, -3.409112240700133e-05, -2.5153009306526816e-05]], 'valid_percent': 31.94, 'masked_pixels': 245.0, 'valid_pixels': 115.0, 'percentile_2': -9.761120728455703e-05, 'percentile_98': -3.4258626892058585e-05}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-124.19, 37.86], [-123.11, 37.86], [-119.96, 33.16], [-121.13, 33.16], [-124.19, 37.86]]]}, 'properties': {'statistics': {'b1': {'min': -0.00021918676108497462, 'max': -3.927832631416977e-05, 'mean': -0.00012144348425158578, 'count': 86.62000274658203, 'sum': -0.010519434939426852, 'std': 2.9769797386936854e-05, 'median': -0.00011626481094279005, 'majority': -0.00021918676108497462, 'minority': -0.00021918676108497462, 'unique': 115.0, 'histogram': [[5.0, 2.0, 5.0, 6.0, 12.0, 35.0, 28.0, 12.0, 8.0, 2.0], [-0.00021918676108497462, -0.00020119591760789414, -0.00018320507413081366, -0.00016521423065373315, -0.00014722338717665267, -0.0001292325436995722, -0.0001112417002224917, -9.32508567454112e-05, -7.526001326833072e-05, -5.726916979125024e-05, -3.927832631416977e-05]], 'valid_percent': 31.94, 'masked_pixels': 245.0, 'valid_pixels': 115.0, 'percentile_2': -0.0002064151684468108, 'percentile_98': -6.805256999965305e-05}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-124.19, 37.86], [-123.11, 37.86], [-119.96, 33.16], [-121.13, 33.16], [-124.19, 37.86]]]}, 'properties': {'statistics': {'b1': {'min': -0.00015412138841721786, 'max': -3.145271371838777e-05, 'mean': -9.593350082157177e-05, 'count': 86.62000274658203, 'sum': -0.008309760104653776, 'std': 2.9374048721393294e-05, 'median': -8.783572604921066e-05, 'majority': -0.00015412138841721786, 'minority': -0.00015412138841721786, 'unique': 115.0, 'histogram': [[10.0, 10.0, 11.0, 3.0, 15.0, 28.0, 20.0, 9.0, 5.0, 4.0], [-0.00015412138841721786, -0.00014185452094733484, -0.00012958765347745183, -0.00011732078600756883, -0.00010505391853768583, -9.278705106780281e-05, -8.05201835979198e-05, -6.825331612803679e-05, -5.5986448658153776e-05, -4.371958118827076e-05, -3.145271371838777e-05]], 'valid_percent': 31.94, 'masked_pixels': 245.0, 'valid_pixels': 115.0, 'percentile_2': -0.00015409053848538357, 'percentile_98': -4.834538500993245e-05}}}}\n", - "CPU times: user 143 ms, sys: 35.5 ms, total: 178 ms\n", - "Wall time: 11.5 s\n" + "CPU times: user 164 ms, sys: 40.8 ms, total: 205 ms\n", + "Wall time: 17.1 s\n" ] } ], @@ -1447,7 +1522,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1483,7 +1558,7 @@ " 'datetime': '2022-12-01T00:00:00+00:00'}" ] }, - "execution_count": 43, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1503,7 +1578,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1551,106 +1626,106 @@ " \n", " 0\n", " 2022-12-01T00:00:00+00:00\n", - " -0.000105\n", - " -0.000026\n", - " -0.000067\n", - " 86.620003\n", - " -0.005815\n", - " 0.000020\n", - " -0.000066\n", - " -0.000105\n", - " -0.000105\n", - " 115.0\n", - " [[9.0, 8.0, 9.0, 8.0, 22.0, 10.0, 18.0, 16.0, ...\n", - " 31.94\n", - " 245.0\n", - " 115.0\n", - " -0.000104\n", - " -0.000032\n", + " -0.00010515466364758998\n", + " -0.00002633915608704389\n", + " -0.00006713517054587462\n", + " 86.62000274658203125000\n", + " -0.00581524865707591307\n", + " 0.00001997011065738988\n", + " -0.00006633380711657132\n", + " -0.00010515466364758998\n", + " -0.00010515466364758998\n", + " 115.00000000000000000000\n", + " [[9.0, 8.0, 9.0, 8.0, 22.0, 10.0, 18.0, 16.0, 8.0, 7.0], [-0.00010515466364758998, -9.727311289153537e-05, -8.939156213548077e-05, -8.151001137942615e-05, -7.362846062337154e-05, -6.574690986731694e-05, -5.786535911126232e-05, -4.9983808355207714e-05, -4.2102257599153106e-05, -3.422070684309849e-05, -2.6339156087043894e-05]]\n", + " 31.94000000000000127898\n", + " 245.00000000000000000000\n", + " 115.00000000000000000000\n", + " -0.00010359588859230771\n", + " -0.00003194067887033325\n", " 2022-12-01 00:00:00+00:00\n", " \n", " \n", " 1\n", " 2022-11-01T00:00:00+00:00\n", - 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" datetime min max mean count \\\n", - "0 2022-12-01T00:00:00+00:00 -0.000105 -0.000026 -0.000067 86.620003 \n", - "1 2022-11-01T00:00:00+00:00 -0.000053 0.000010 -0.000030 86.620003 \n", - "2 2022-10-01T00:00:00+00:00 -0.000034 0.000047 -0.000002 86.620003 \n", - "3 2022-09-01T00:00:00+00:00 -0.000047 0.000050 -0.000007 86.620003 \n", - "4 2022-08-01T00:00:00+00:00 -0.000091 -0.000002 -0.000038 86.620003 \n", + " datetime min max \\\n", + "0 2022-12-01T00:00:00+00:00 -0.00010515466364758998 -0.00002633915608704389 \n", + "1 2022-11-01T00:00:00+00:00 -0.00005327701115205930 0.00001028134560749642 \n", + "2 2022-10-01T00:00:00+00:00 -0.00003414260442530581 0.00004739613790624944 \n", + "3 2022-09-01T00:00:00+00:00 -0.00004692151229820521 0.00004959876485207447 \n", + "4 2022-08-01T00:00:00+00:00 -0.00009080547748822169 -0.00000155375716192473 \n", + "\n", + " mean count sum \\\n", + "0 -0.00006713517054587462 86.62000274658203125000 -0.00581524865707591307 \n", + "1 -0.00002951114864331012 86.62000274658203125000 -0.00255625577653831359 \n", + "2 -0.00000174851221190252 86.62000274658203125000 -0.00015145613259742822 \n", + "3 -0.00000688822897101456 86.62000274658203125000 -0.00059665841238836686 \n", + "4 -0.00003822837370543178 86.62000274658203125000 -0.00331134183536186495 \n", + "\n", + " std median majority \\\n", + "0 0.00001997011065738988 -0.00006633380711657132 -0.00010515466364758998 \n", + "1 0.00001407494890675066 -0.00003181141450025719 -0.00005327701115205930 \n", + "2 0.00001837792521916367 -0.00000250581129329143 -0.00003414260442530581 \n", + "3 0.00002092700549779860 -0.00000422793679848356 -0.00004692151229820521 \n", + "4 0.00002081685202185498 -0.00003259453885688862 -0.00009080547748822169 \n", "\n", - " sum std median majority minority unique \\\n", - "0 -0.005815 0.000020 -0.000066 -0.000105 -0.000105 115.0 \n", - "1 -0.002556 0.000014 -0.000032 -0.000053 -0.000053 115.0 \n", - "2 -0.000151 0.000018 -0.000003 -0.000034 -0.000034 115.0 \n", - "3 -0.000597 0.000021 -0.000004 -0.000047 -0.000047 115.0 \n", - "4 -0.003311 0.000021 -0.000033 -0.000091 -0.000091 115.0 \n", + " minority unique \\\n", + "0 -0.00010515466364758998 115.00000000000000000000 \n", + "1 -0.00005327701115205930 115.00000000000000000000 \n", + "2 -0.00003414260442530581 115.00000000000000000000 \n", + "3 -0.00004692151229820521 115.00000000000000000000 \n", + "4 -0.00009080547748822169 115.00000000000000000000 \n", "\n", - " histogram valid_percent \\\n", - "0 [[9.0, 8.0, 9.0, 8.0, 22.0, 10.0, 18.0, 16.0, ... 31.94 \n", - "1 [[6.0, 21.0, 17.0, 16.0, 13.0, 15.0, 11.0, 9.0... 31.94 \n", - "2 [[10.0, 15.0, 16.0, 13.0, 14.0, 15.0, 14.0, 12... 31.94 \n", - "3 [[6.0, 13.0, 20.0, 14.0, 17.0, 12.0, 24.0, 5.0... 31.94 \n", - "4 [[2.0, 10.0, 4.0, 8.0, 11.0, 15.0, 21.0, 20.0,... 31.94 \n", + " histogram \\\n", + "0 [[9.0, 8.0, 9.0, 8.0, 22.0, 10.0, 18.0, 16.0, 8.0, 7.0], [-0.00010515466364758998, -9.727311289153537e-05, -8.939156213548077e-05, -8.151001137942615e-05, -7.362846062337154e-05, -6.574690986731694e-05, -5.786535911126232e-05, -4.9983808355207714e-05, -4.2102257599153106e-05, -3.422070684309849e-05, -2.6339156087043894e-05]] \n", + "1 [[6.0, 21.0, 17.0, 16.0, 13.0, 15.0, 11.0, 9.0, 5.0, 2.0], [-5.3277011152059295e-05, -4.692117547610372e-05, -4.056533980014815e-05, -3.4209504124192576e-05, -2.7853668448237005e-05, -2.1497832772281433e-05, -1.5141997096325857e-05, -8.786161420370289e-06, -2.4303257444147137e-06, 3.9255099315408615e-06, 1.0281345607496425e-05]] \n", + "2 [[10.0, 15.0, 16.0, 13.0, 14.0, 15.0, 14.0, 12.0, 3.0, 3.0], [-3.414260442530581e-05, -2.5988730192150287e-05, -1.7834855958994763e-05, -9.680981725839238e-06, -1.5271074926837138e-06, 6.626766740471814e-06, 1.4780640973627335e-05, 2.2934515206782857e-05, 3.1088389439938384e-05, 3.924226367309391e-05, 4.739613790624944e-05]] \n", + "3 [[6.0, 13.0, 20.0, 14.0, 17.0, 12.0, 24.0, 5.0, 1.0, 3.0], [-4.692151229820521e-05, -3.726948458317724e-05, -2.7617456868149273e-05, -1.7965429153121305e-05, -8.313401438093338e-06, 1.338626276934629e-06, 1.0990653991962596e-05, 2.0642681706990564e-05, 3.029470942201853e-05, 3.99467371370465e-05, 4.9598764852074465e-05]] \n", + "4 [[2.0, 10.0, 4.0, 8.0, 11.0, 15.0, 21.0, 20.0, 12.0, 12.0], [-9.080547748822169e-05, -8.1880305455592e-05, -7.29551334229623e-05, -6.40299613903326e-05, -5.5104789357702906e-05, -4.6179617325073214e-05, -3.7254445292443516e-05, -2.8329273259813817e-05, -1.9404101227184125e-05, -1.0478929194554433e-05, -1.5537571619247306e-06]] \n", "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", - "0 245.0 115.0 -0.000104 -0.000032 \n", - "1 245.0 115.0 -0.000051 -0.000001 \n", - "2 245.0 115.0 -0.000032 0.000034 \n", - "3 245.0 115.0 -0.000045 0.000027 \n", - "4 245.0 115.0 -0.000081 -0.000007 \n", + " valid_percent masked_pixels valid_pixels \\\n", + "0 31.94000000000000127898 245.00000000000000000000 115.00000000000000000000 \n", + "1 31.94000000000000127898 245.00000000000000000000 115.00000000000000000000 \n", + "2 31.94000000000000127898 245.00000000000000000000 115.00000000000000000000 \n", + "3 31.94000000000000127898 245.00000000000000000000 115.00000000000000000000 \n", + "4 31.94000000000000127898 245.00000000000000000000 115.00000000000000000000 \n", "\n", - " date \n", - "0 2022-12-01 00:00:00+00:00 \n", - "1 2022-11-01 00:00:00+00:00 \n", - "2 2022-10-01 00:00:00+00:00 \n", - "3 2022-09-01 00:00:00+00:00 \n", - "4 2022-08-01 00:00:00+00:00 " + " percentile_2 percentile_98 date \n", + "0 -0.00010359588859230771 -0.00003194067887033325 2022-12-01 00:00:00+00:00 \n", + "1 -0.00005065607151994821 -0.00000110899758273440 2022-11-01 00:00:00+00:00 \n", + "2 -0.00003200012649207952 0.00003440232174632397 2022-10-01 00:00:00+00:00 \n", + "3 -0.00004511987338653629 0.00002716666299628972 2022-09-01 00:00:00+00:00 \n", + "4 -0.00008052887732505406 -0.00000747190884265286 2022-08-01 00:00:00+00:00 " ] }, - "execution_count": 44, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1735,7 +1824,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1806,7 +1895,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1825,7 +1914,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -1841,7 +1930,7 @@ " 'center': [-0.125, -0.1248266296809959, 0]}" ] }, - "execution_count": 47, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1851,8 +1940,9 @@ "September2022_co2_flux = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/collections/{items[3]['collection']}/items/{items[3]['id']}/tilejson.json?\"\n", + " f\"{RASTER_API_URL}/collections/{items[3]['collection']}/items/{items[3]['id']}/tilejson.json?\")\n", "\n", + "date_of_interest = '202209'\n", "# Don't change anything here\n", "observation_of_interest = f'eccodarwin-co2flux-monthgrid-v5-{date_of_interest}'\n", "\n", @@ -1870,7 +1960,7 @@ " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", " \n", " # Pass the minimum and maximum values for rescaling \n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"),\n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\")\n", " \n", "# Return the response in JSON format\n", ").json()\n", @@ -1882,7 +1972,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1915,7 +2005,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_b1804ab1fa95f7e34309234e1d555da9 {\n", + " #map_c42fe458956fc51a129672c3f3b42a45 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1952,14 +2042,14 @@ " September 2022\n", "</div>\n", " \n", - " <div class="folium-map" id="map_b1804ab1fa95f7e34309234e1d555da9" ></div>\n", + " <div class="folium-map" id="map_c42fe458956fc51a129672c3f3b42a45" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_b1804ab1fa95f7e34309234e1d555da9 = L.map(\n", - " "map_b1804ab1fa95f7e34309234e1d555da9",\n", + " var map_c42fe458956fc51a129672c3f3b42a45 = L.map(\n", + " "map_c42fe458956fc51a129672c3f3b42a45",\n", " {\n", " center: [34.0, -120.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1973,31 +2063,31 @@ "\n", " \n", " \n", - " var tile_layer_8d0bec0ea33a0290d6db1b68fc287faf = L.tileLayer(\n", + " var tile_layer_8645c74db95f4e59424d63595825faab = 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_8d0bec0ea33a0290d6db1b68fc287faf.addTo(map_b1804ab1fa95f7e34309234e1d555da9);\n", + " tile_layer_8645c74db95f4e59424d63595825faab.addTo(map_c42fe458956fc51a129672c3f3b42a45);\n", " \n", " \n", - " var tile_layer_1e40c05ea4db66e2a66bd10b54db2229 = L.tileLayer(\n", + " var tile_layer_e27e07b88a6c43cf2b18cda6f1c0f171 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202209/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=-0.0007%2C0.0007",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.7, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_1e40c05ea4db66e2a66bd10b54db2229.addTo(map_b1804ab1fa95f7e34309234e1d555da9);\n", + " tile_layer_e27e07b88a6c43cf2b18cda6f1c0f171.addTo(map_c42fe458956fc51a129672c3f3b42a45);\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": 48, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -2114,7 +2204,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb b/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb index 39539b933..7a0ed042b 100644 --- a/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb +++ b/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -430,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -459,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -566,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -648,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -825,7 +825,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.9.6" }, "vscode": { "interpreter": { diff --git a/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb b/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb index 2f5a9e179..5085f2106 100644 --- a/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb +++ b/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -172,343 +172,57 @@ "outputs": [ { "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Collection Summary
0TitleU.S. Gridded Anthropogenic Methane Emissions Inventory v2 Express Extension
1DescriptionThe gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions for the contiguous United States (CONUS) from 2012 - 2020, consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI). This dataset contains methane emissions provided as fluxes, in units of megagrams of methane per square kilometer per year (Mg CH₄/km²/yr). It contains 34 data layers including a 'Total' layer with emissions fluxes from all anthropogenic sources of methane in the U.S. GHGI; 6 aggregate layers with emission fluxes from Agriculture, Natural Gas, Petroleum, Waste, Industry, and ‘Other’ source categories; and 27 layers representing methane emission fluxes from individual sector categories (i.e. the individual layers that make up each of the aggregate layers and the 'Total' layer). The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) and scaled to Mg/km²/yr for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends. The source data and addition information can be found at https://doi.org/10.5281/zenodo.8367082
2Temporal Extent[['2012-01-01T00:00:00+00:00', '2020-12-31T00:00:00+00:00']]
3Spatial Extent[[-180.0, -90.0, 180.0, 90.0]]
\n" + ], "text/plain": [ - "{'id': 'epa-ch4emission-yeargrid-v2express',\n", - " 'type': 'Collection',\n", - " 'links': [{'rel': 'items',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express/items'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'}],\n", - " 'title': 'U.S. Gridded Anthropogenic Methane Emissions Inventory v2 Express Extension',\n", - " 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n", - " 'temporal': {'interval': [['2012-01-01T00:00:00+00:00',\n", - " '2020-12-31T00:00:00+00:00']]}},\n", - " 'license': 'CC-BY-4.0',\n", - " 'renders': {'dashboard': {'assets': ['total-methane'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'dwtd-waste': {'assets': ['dwtd-waste'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'iwtd-waste': {'assets': ['iwtd-waste'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'post-meter': {'assets': ['post-meter'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'refining-ps': {'assets': ['refining-ps'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'total-other': {'assets': ['total-other'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'total-waste': {'assets': ['total-waste'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'surface-coal': {'assets': ['surface-coal'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'transport-ps': {'assets': ['transport-ps'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'abn-ong-other': {'assets': ['abn-ong-other'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'field-burning': {'assets': ['field-burning'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'production-ps': {'assets': ['production-ps'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'total-methane': {'assets': ['total-methane'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'exploration-ps': {'assets': ['exploration-ps'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'processing-ngs': {'assets': ['processing-ngs'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'production-ngs': {'assets': ['production-ngs'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'exploration-ngs': {'assets': ['exploration-ngs'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'composting-waste': {'assets': ['composting-waste'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'distribution-ngs': {'assets': ['distribution-ngs'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'rice-cultivation': {'assets': ['rice-cultivation'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'total-coal-mines': {'assets': ['total-coal-mines'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'underground-coal': {'assets': ['underground-coal'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'manure-management': {'assets': ['manure-management'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'total-agriculture': {'assets': ['total-agriculture'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'msw-landfill-waste': {'assets': ['msw-landfill-waste'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'abn-underground-coal': {'assets': ['abn-underground-coal'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'enteric-fermentation': {'assets': ['enteric-fermentation'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'petro-production-other': {'assets': ['petro-production-other'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'mobile-combustion-other': {'assets': ['mobile-combustion-other'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'total-petroleum-systems': {'assets': ['total-petroleum-systems'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'transmission-storage-ngs': {'assets': ['transmission-storage-ngs'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'industrial-landfill-waste': {'assets': ['industrial-landfill-waste'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'total-natural-gas-systems': {'assets': ['total-natural-gas-systems'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'ferroalloy-production-other': {'assets': ['ferroalloy-production-other'],\n", - " 'nodata': -9999,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'},\n", - " 'stationary-combustion-other': {'assets': ['stationary-combustion-other'],\n", - " 'maxzoom': 5,\n", - " 'minzoom': 0,\n", - " 'rescale': [[0, 20]],\n", - " 'colormap_name': 'epa-ghgi-ch4'}},\n", - " 'summaries': {'datetime': ['2012-01-01T00:00:00Z', '2020-01-01T00:00:00Z']},\n", - " 'description': \"The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions for the contiguous United States (CONUS) from 2012 - 2020, consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI). This dataset contains methane emissions provided as fluxes, in units of megagrams of methane per square kilometer per year (Mg CH₄/km²/yr). It contains 34 data layers including a 'Total' layer with emissions fluxes from all anthropogenic sources of methane in the U.S. GHGI; 6 aggregate layers with emission fluxes from Agriculture, Natural Gas, Petroleum, Waste, Industry, and ‘Other’ source categories; and 27 layers representing methane emission fluxes from individual sector categories (i.e. the individual layers that make up each of the aggregate layers and the 'Total' layer). The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) and scaled to Mg/km²/yr for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends. The source data and addition information can be found at https://doi.org/10.5281/zenodo.8367082\",\n", - " 'item_assets': {'dwtd-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Waste - Domestic Wastewater Treatment & Discharge (annual)',\n", - " 'description': 'Annual methane emissions from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category).'},\n", - " 'iwtd-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Waste - Industrial Wastewater Treatment & Discharge (annual)',\n", - " 'description': 'Annual methane emissions from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category).'},\n", - " 'post-meter': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Natural Gas - Post Meter (annual)',\n", - " 'description': 'Annual methane emissions downstream of residential, commercial, industrial natural gas distribution meters (i.e., “Post Meter”) (inventory Energy 1B2b sub-category).'},\n", - " 'refining-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Petroleum - Refining (annual)',\n", - " 'description': 'Annual methane emissions from Petroleum Refining (inventory Energy 1B2a sub-category).'},\n", - " 'total-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total Other (annual)',\n", - " 'description': 'Total annual methane emission fluxes from ‘Other’ remaining sources (sum of inventory categories 1A (Energy Combustion), 2B8 & 2C2 (Petrochemical & Ferroalloy Production) and 1B2a & 1B2b (Abandoned Oil & Gas Well Emissions)).'},\n", - " 'total-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total Waste (annual)',\n", - " 'description': 'Total annual methane emission fluxes from Waste (sum of inventory Waste categories: Municipal Solid Waste (MSW) and Industrial Landfills (5A1), Composting (5B1), Domestic and Industrial Wastewater Treatment and Discharge (5D)).'},\n", - " 'surface-coal': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Coal Mining - Surface Mining (annual)',\n", - " 'description': 'Annual methane emissions from active Surface Coal Mining (inventory Energy 1B1a sub-category).'},\n", - " 'transport-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Petroleum - Transportation (annual)',\n", - " 'description': 'Annual methane emissions from Petroleum Transportation (inventory Energy 1B2a sub-category).'},\n", - " 'abn-ong-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Other - Abandoned Oil and Gas Wells (annual)',\n", - " 'description': 'Annual methane emissions from Abandoned Oil and Gas Wells (inventory Energy 1B2a and 1B2b sub-categories).'},\n", - " 'field-burning': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Agriculture - Field Burning (annual)',\n", - " 'description': 'Annual methane emissions from field burning of agricultural residues (inventory Agriculture category 3F).'},\n", - " 'production-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Petroleum - Production (annual)',\n", - " 'description': 'Annual methane emissions from Petroleum Production (inventory Energy 1B2a sub-category).'},\n", - " 'total-methane': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total Methane (annual)',\n", - " 'description': 'Total annual methane emission fluxes from all Agriculture, Energy, Waste, and ‘Other’ sources included in this dataset.'},\n", - " 'exploration-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Petroleum - Exploration (annual)',\n", - " 'description': 'Annual methane emissions from Petroleum Exploration (inventory Energy 1B2a sub-category).'},\n", - " 'processing-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Natural Gas - Processing (annual)',\n", - " 'description': 'Annual methane emissions from Natural Gas Processing (inventory Energy 1B2b sub-category).'},\n", - " 'production-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Natural Gas - Production (annual)',\n", - " 'description': 'Annual methane emissions from Natural Gas Production (inventory Energy 1B2b sub-category).'},\n", - " 'exploration-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Natural Gas - Exploration (annual)',\n", - " 'description': 'Annual methane emissions from Natural Gas Exploration (inventory Energy 1B2b sub-category).'},\n", - " 'composting-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Waste - Composting (annual)',\n", - " 'description': 'Annual methane emissions from Composting (inventory Waste category 5B1).'},\n", - " 'distribution-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Natural Gas - Distribution (annual)',\n", - " 'description': 'Annual methane emissions from Natural Gas Distribution (inventory Energy 1B2b sub-category).'},\n", - " 'rice-cultivation': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Agriculture - Rice Cultivation (annual)',\n", - " 'description': 'Annual methane emissions from rice cultivation (inventory Agriculture category 3C).'},\n", - " 'total-coal-mines': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total Coal Mines (annual)',\n", - " 'description': 'Total annual methane emission fluxes from Coal Mines (sum of inventory 1B1a sub-categories which includes Underground Coal Mining, Surface Coal Mining and Abandoned Underground Coal Mines).'},\n", - " 'underground-coal': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Coal Mining - Underground Mining (annual)',\n", - " 'description': 'Annual methane emissions from active Underground Coal Mining (inventory Energy 1B1a sub-category).'},\n", - " 'manure-management': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Agriculture - Manure Management (annual)',\n", - " 'description': 'Annual methane emissions from livestock manure management (inventory Agriculture category 3B).'},\n", - " 'total-agriculture': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total Agriculture (annual)',\n", - " 'description': 'Total annual methane emission fluxes from Agriculture sources (sum of inventory categories: Enteric Fermentation (3A), Manure Management (3B), Rice Cultivation (3C), Field Burning of Agricultural Residues (3F)).'},\n", - " 'msw-landfill-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Waste - Municipal Solid Waste (MSW) Landfills (annual)',\n", - " 'description': 'Annual methane emissions from Municipal Solid Waste Landfills (inventory Waste 5A1 sub-category).'},\n", - " 'abn-underground-coal': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Coal Mining - Abandoned Underground Mines (annual)',\n", - " 'description': 'Annual methane emissions from Abandoned Underground Coal Mines (inventory Energy 1B1a sub-category).'},\n", - " 'enteric-fermentation': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Agriculture - Enteric Fermentation (annual)',\n", - " 'description': 'Annual methane emissions from enteric fermentation which is methane emitted as a by-product of the normal livestock digestive process (inventory Agriculture category 3A).'},\n", - " 'petro-production-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Other - Petrochemical Production (annual)',\n", - " 'description': 'Annual methane emissions from Petrochemical Production (inventory Industrial Processes and Product Use category 2B8).'},\n", - " 'mobile-combustion-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Other - Mobile Combustion (annual)',\n", - " 'description': 'Annual methane emissions from Mobile Combustion (inventory Energy 1A sub-category).'},\n", - " 'total-petroleum-systems': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total Petroleum Systems (annual)',\n", - " 'description': 'Total annual methane emission fluxes from Petroleum Systems (sum of inventory Energy 1B2a sub-categories which includes Petroleum Production, Refining, Exploration and Transport).'},\n", - " 'transmission-storage-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Natural Gas - Transmission and Storage (annual)',\n", - " 'description': 'Annual methane emissions from Natural Gas Transmission and Storage (inventory Energy 1B2b sub-category).'},\n", - " 'industrial-landfill-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Waste - Industrial Landfills (annual)',\n", - " 'description': 'Annual methane emissions from Industrial Landfills (inventory Waste 5A1 sub-category).'},\n", - " 'total-natural-gas-systems': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total Natural Gas Systems (annual)',\n", - " 'description': 'Total annual methane emission fluxes from Natural Gas Systems (sum of inventory Energy 1B2b sub-categories which includes Natural Gas Production, Transmission & Storage, Processing, Distribution and Exploration).'},\n", - " 'ferroalloy-production-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Other - Ferroalloy Production (annual)',\n", - " 'description': 'Annual methane emissions from Ferroalloy Production (inventory Industrial Processes and Product Use category 2C2).'},\n", - " 'stationary-combustion-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Other - Stationary Combustion (annual)',\n", - " 'description': 'Annual methane emissions from Stationary Combustion (inventory Energy 1A sub-category).'}},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", - " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", - " 'dashboard:is_periodic': True,\n", - " 'dashboard:time_density': 'year'}" + "" ] }, "execution_count": 3, @@ -568,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -626,7 +340,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 9 items\n" + "Found 9 items\n", + "Below you see the first 5 items in the collection, along with their item IDs and corresponding Start Date-Time.\n", + "╒═════════════════════════════════════════╤═══════════════════════════╕\n", + "│ Item ID │ Start Date-Time │\n", + "╞═════════════════════════════════════════╪═══════════════════════════╡\n", + "│ epa-ch4emission-yeargrid-v2express-2012 │ 2012-01-01T00:00:00+00:00 │\n", + "├─────────────────────────────────────────┼───────────────────────────┤\n", + "│ epa-ch4emission-yeargrid-v2express-2013 │ 2013-01-01T00:00:00+00:00 │\n", + "├─────────────────────────────────────────┼───────────────────────────┤\n", + "│ epa-ch4emission-yeargrid-v2express-2014 │ 2014-01-01T00:00:00+00:00 │\n", + "├─────────────────────────────────────────┼───────────────────────────┤\n", + "│ epa-ch4emission-yeargrid-v2express-2015 │ 2015-01-01T00:00:00+00:00 │\n", + "├─────────────────────────────────────────┼───────────────────────────┤\n", + "│ epa-ch4emission-yeargrid-v2express-2016 │ 2016-01-01T00:00:00+00:00 │\n", + "╘═════════════════════════════════════════╧═══════════════════════════╛\n" ] } ], @@ -675,7 +403,7 @@ { "data": { "text/plain": [ - "{'id': 'epa-ch4emission-yeargrid-v2express-2020',\n", + "{'id': 'epa-ch4emission-yeargrid-v2express-2012',\n", " 'bbox': [-129.99999694387628,\n", " 19.99999923487448,\n", " -60.00000305612369,\n", @@ -692,12 +420,12 @@ " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", " {'rel': 'self',\n", " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020'},\n", + " 'href': 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+ " 'histogram': {'max': 465.1289367675781,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [169000.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 76000.0]},\n", - " 'statistics': {'mean': -6897.05615234375,\n", - " 'stddev': 4625.62646484375,\n", - " 'maximum': 552.9092407226562,\n", + " 'buckets': [169001.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75999.0]},\n", + " 'statistics': {'mean': -6897.08447265625,\n", + " 'stddev': 4625.62841796875,\n", + " 'maximum': 465.1289367675781,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -1039,7 +767,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'surface-coal': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'surface-coal': {'href': 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{'mean': -9532.515625,\n", - " 'stddev': 2108.737060546875,\n", - " 'maximum': 2.4141974449157715,\n", + " 'buckets': [233173.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 11827.0]},\n", + " 'statistics': {'mean': -9516.3134765625,\n", + " 'stddev': 2143.221923828125,\n", + " 'maximum': 2.1466116905212402,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -1153,7 +881,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'abn-ong-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'abn-ong-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Other - Abandoned Oil and Gas Wells 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's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'field-burning': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Agriculture - Field Burning (annual)',\n", @@ -1225,13 +953,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 0.24409635365009308,\n", + " 'histogram': {'max': 0.26811718940734863,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [225969.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 19031.0]},\n", - " 'statistics': {'mean': -9222.2998046875,\n", - " 'stddev': 2676.369873046875,\n", - " 'maximum': 0.24409635365009308,\n", + " 'buckets': [225487.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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'histogram': {'max': 161.6470489501953,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [233515.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 11485.0]},\n", - " 'statistics': {'mean': -9530.2080078125,\n", - " 'stddev': 2113.833251953125,\n", - " 'maximum': 140.06321716308594,\n", + " 'buckets': [233076.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 11924.0]},\n", + " 'statistics': {'mean': -9512.30078125,\n", + " 'stddev': 2151.78271484375,\n", + " 'maximum': 161.6470489501953,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -1324,7 +1052,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'total-methane': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_all-variables_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'total-methane': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_all-variables_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Total Methane (annual)',\n", @@ -1339,13 +1067,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 1317.5279541015625,\n", + " 'histogram': {'max': 1910.80810546875,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [153603.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 91349.0, 48.0]},\n", - " 'statistics': {'mean': -6267.79736328125,\n", - " 'stddev': 4837.07958984375,\n", - " 'maximum': 1317.5279541015625,\n", + " 'buckets': [153469.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 91522.0, 9.0]},\n", + " 'statistics': {'mean': -6262.291015625,\n", + " 'stddev': 4838.56201171875,\n", + " 'maximum': 1910.80810546875,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -1381,7 +1109,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'exploration-ps': {'href': 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's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Natural Gas - Production (annual)',\n", @@ -1510,13 +1238,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 190.11717224121094,\n", + " 'histogram': {'max': 124.75360107421875,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [232277.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 12723.0]},\n", - " 'statistics': {'mean': -9479.6015625,\n", - " 'stddev': 2219.260498046875,\n", - " 'maximum': 190.11717224121094,\n", + " 'buckets': [231589.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 13411.0]},\n", + " 'statistics': {'mean': -9451.5068359375,\n", + " 'stddev': 2275.126953125,\n", + " 'maximum': 124.75360107421875,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -1552,7 +1280,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'exploration-ngs': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'exploration-ngs': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Natural Gas - Exploration (annual)',\n", @@ -1567,13 +1295,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 3.1922497749328613,\n", + " 'histogram': {'max': 25.08983612060547,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [235806.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9194.0]},\n", - " 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-1624,13 +1352,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 5.681565761566162,\n", + " 'histogram': {'max': 10.551192283630371,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [241655.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3345.0]},\n", - " 'statistics': {'mean': -9862.478515625,\n", - " 'stddev': 1160.3760986328125,\n", - " 'maximum': 5.681565761566162,\n", + " 'buckets': [241611.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3389.0]},\n", + " 'statistics': {'mean': -9860.6826171875,\n", + " 'stddev': 1167.8707275390625,\n", + " 'maximum': 10.551192283630371,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -1666,7 +1394,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'distribution-ngs': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'distribution-ngs': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Natural Gas - Distribution (annual)',\n", @@ -1681,13 +1409,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 32.086830139160156,\n", + " 'histogram': {'max': 36.85501480102539,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", " 'buckets': [169148.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75852.0]},\n", - " 'statistics': {'mean': -6903.2861328125,\n", - " 'stddev': 4622.859375,\n", - " 'maximum': 32.086830139160156,\n", + " 'statistics': {'mean': -6903.2841796875,\n", + " 'stddev': 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'title': 'Total Coal Mines (annual)',\n", @@ -1795,13 +1523,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 1312.8009033203125,\n", + " 'histogram': {'max': 1884.5418701171875,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [243327.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1644.0, 29.0]},\n", - " 'statistics': {'mean': -9930.6416015625,\n", - " 'stddev': 824.4235229492188,\n", - " 'maximum': 1312.8009033203125,\n", + " 'buckets': [243246.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1746.0, 8.0]},\n", + " 'statistics': {'mean': -9927.2919921875,\n", + " 'stddev': 844.4795532226562,\n", + " 'maximum': 1884.5418701171875,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -1837,7 +1565,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'underground-coal': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'underground-coal': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Coal Mining - Underground Mining (annual)',\n", @@ -1852,13 +1580,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 1312.8009033203125,\n", + " 'histogram': {'max': 1884.4639892578125,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [244819.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 156.0, 25.0]},\n", - " 'statistics': {'mean': -9991.5537109375,\n", - " 'stddev': 273.90582275390625,\n", - " 'maximum': 1312.8009033203125,\n", + " 'buckets': [244714.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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'layer'],\n", " 'title': 'Total Agriculture (annual)',\n", @@ -1966,13 +1694,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 64.48854064941406,\n", + " 'histogram': {'max': 65.74567413330078,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", " 'buckets': [161625.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 83375.0]},\n", - " 'statistics': {'mean': -6595.85009765625,\n", - " 'stddev': 4738.24462890625,\n", - " 'maximum': 64.48854064941406,\n", + " 'statistics': {'mean': -6595.87744140625,\n", + " 'stddev': 4738.2060546875,\n", + " 'maximum': 65.74567413330078,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -2008,7 +1736,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'msw-landfill-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'msw-landfill-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Waste - Municipal Solid Waste (MSW) Landfills (annual)',\n", @@ -2023,13 +1751,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 381.43365478515625,\n", + " 'histogram': {'max': 464.0804443359375,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [242963.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2037.0]},\n", - " 'statistics': {'mean': -9915.712890625,\n", - " 'stddev': 909.60986328125,\n", - " 'maximum': 381.43365478515625,\n", + " 'buckets': [242848.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2152.0]},\n", + " 'statistics': {'mean': -9911.0078125,\n", + " 'stddev': 934.75,\n", + " 'maximum': 464.0804443359375,\n", " 'minimum': -9999.0,\n", " 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'Agriculture - Enteric Fermentation (annual)',\n", @@ -2137,13 +1865,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 34.75341796875,\n", + " 'histogram': {'max': 38.10722351074219,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", " 'buckets': [161630.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 83370.0]},\n", - " 'statistics': {'mean': -6596.18310546875,\n", - " 'stddev': 4737.99560546875,\n", - " 'maximum': 34.75341796875,\n", + " 'statistics': {'mean': -6596.2001953125,\n", + " 'stddev': 4737.97314453125,\n", + " 'maximum': 38.10722351074219,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -2179,7 +1907,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'petro-production-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'petro-production-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Other - Petrochemical Production (annual)',\n", @@ -2194,13 +1922,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 39.25223159790039,\n", + " 'histogram': {'max': 2.9906651973724365,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [244974.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 26.0]},\n", - " 'statistics': {'mean': -9997.9384765625,\n", - " 'stddev': 103.04502868652344,\n", - " 'maximum': 39.25223159790039,\n", + " 'buckets': [244976.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 24.0]},\n", + " 'statistics': {'mean': -9998.0205078125,\n", + " 'stddev': 98.96927642822266,\n", + " 'maximum': 2.9906651973724365,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -2236,7 +1964,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'mobile-combustion-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'mobile-combustion-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Other - Mobile Combustion (annual)',\n", @@ -2251,13 +1979,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 1.7823798656463623,\n", + " 'histogram': {'max': 1.8657997846603394,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [158134.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 86866.0]},\n", - " 'statistics': {'mean': -6453.80078125,\n", - " 'stddev': 4783.30908203125,\n", - " 'maximum': 1.7823798656463623,\n", + " 'buckets': [157770.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 87230.0]},\n", + " 'statistics': {'mean': -6438.94287109375,\n", + " 'stddev': 4787.802734375,\n", + " 'maximum': 1.8657997846603394,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -2293,7 +2021,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'total-petroleum-systems': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_petroleum-systems_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'total-petroleum-systems': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_petroleum-systems_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Total Petroleum Systems (annual)',\n", @@ -2308,13 +2036,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 140.2111053466797,\n", + " 'histogram': {'max': 163.31410217285156,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [233456.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 11544.0]},\n", - " 'statistics': {'mean': -9527.798828125,\n", - " 'stddev': 2118.995849609375,\n", - " 'maximum': 140.2111053466797,\n", + " 'buckets': [233014.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 11986.0]},\n", + " 'statistics': {'mean': -9509.7529296875,\n", + " 'stddev': 2157.157958984375,\n", + " 'maximum': 163.31410217285156,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -2350,7 +2078,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'transmission-storage-ngs': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'transmission-storage-ngs': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Natural Gas - Transmission and Storage (annual)',\n", @@ -2365,13 +2093,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 174.0918426513672,\n", + " 'histogram': {'max': 971.8997192382812,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [215921.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 29079.0]},\n", - " 'statistics': {'mean': -8812.154296875,\n", - " 'stddev': 3234.086669921875,\n", - " 'maximum': 174.0918426513672,\n", + " 'buckets': [215923.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 29077.0]},\n", + " 'statistics': {'mean': -8812.2470703125,\n", + " 'stddev': 3233.96142578125,\n", + " 'maximum': 971.8997192382812,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -2407,7 +2135,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'industrial-landfill-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'industrial-landfill-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Waste - Industrial Landfills (annual)',\n", @@ -2422,13 +2150,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 362.9629211425781,\n", + " 'histogram': {'max': 351.3695983886719,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [240762.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4238.0]},\n", - " 'statistics': {'mean': -9826.0126953125,\n", - " 'stddev': 1303.853515625,\n", - " 'maximum': 362.9629211425781,\n", + " 'buckets': [240759.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4241.0]},\n", + " 'statistics': {'mean': -9825.890625,\n", + " 'stddev': 1304.30224609375,\n", + " 'maximum': 351.3695983886719,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -2464,7 +2192,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'total-natural-gas-systems': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_natural-gas-systems_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'total-natural-gas-systems': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_natural-gas-systems_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Total Natural Gas Systems (annual)',\n", @@ -2479,13 +2207,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 539.842529296875,\n", + " 'histogram': {'max': 971.9710083007812,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [167033.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 77967.0]},\n", - " 'statistics': {'mean': -6816.72021484375,\n", - " 'stddev': 4657.83544921875,\n", - " 'maximum': 539.842529296875,\n", + " 'buckets': [166931.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 78069.0]},\n", + " 'statistics': {'mean': -6812.556640625,\n", + " 'stddev': 4659.45947265625,\n", + " 'maximum': 971.9710083007812,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -2521,7 +2249,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'ferroalloy-production-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'ferroalloy-production-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Other - Ferroalloy Production (annual)',\n", @@ -2536,13 +2264,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 17.590591430664062,\n", + " 'histogram': {'max': 4.439168930053711,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [244990.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.0]},\n", - " 'statistics': {'mean': -9998.5908203125,\n", - " 'stddev': 63.95193862915039,\n", - " 'maximum': 17.590591430664062,\n", + " 'buckets': [244992.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.0]},\n", + " 'statistics': {'mean': -9998.673828125,\n", + " 'stddev': 57.15283203125,\n", + " 'maximum': 4.439168930053711,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -2578,7 +2306,7 @@ " 0.0,\n", " 0.0,\n", " 1.0]},\n", - " 'stationary-combustion-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2020.tif',\n", + " 'stationary-combustion-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2012.tif',\n", " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", " 'roles': ['data', 'layer'],\n", " 'title': 'Other - Stationary Combustion (annual)',\n", @@ -2593,13 +2321,13 @@ " 'offset': 0.0,\n", " 'sampling': 'area',\n", " 'data_type': 'float32',\n", - " 'histogram': {'max': 8.104816436767578,\n", + " 'histogram': {'max': 8.125173568725586,\n", " 'min': -9999.0,\n", " 'count': 11.0,\n", - " 'buckets': [169107.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75893.0]},\n", - " 'statistics': {'mean': -6901.62255859375,\n", - " 'stddev': 4623.533203125,\n", - " 'maximum': 8.104816436767578,\n", + " 'buckets': [169091.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75909.0]},\n", + " 'statistics': {'mean': -6900.970703125,\n", + " 'stddev': 4623.80078125,\n", + " 'maximum': 8.125173568725586,\n", " 'minimum': -9999.0,\n", " 'valid_percent': 0.0004081632653061224}}],\n", " 'proj:geometry': {'type': 'Polygon',\n", @@ -2636,7 +2364,7 @@ " 0.0,\n", " 1.0]},\n", " 'rendered_preview': {'title': 'Rendered preview',\n", - " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020/preview.png?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2012/preview.png?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',\n", " 'rel': 'preview',\n", " 'roles': ['overview'],\n", " 'type': 'image/png'}},\n", @@ -2647,7 +2375,7 @@ " [-129.99999694387628, 55.00000076512553],\n", " [-129.99999694387628, 19.99999923487448]]]},\n", " 'collection': 'epa-ch4emission-yeargrid-v2express',\n", - " 'properties': {'datetime': '2020-01-01T00:00:00+00:00'},\n", + " 'properties': {'datetime': '2012-01-01T00:00:00+00:00'},\n", " 'stac_version': '1.0.0',\n", " 'stac_extensions': ['https://stac-extensions.github.io/projection/v1.0.0/schema.json',\n", " 'https://stac-extensions.github.io/raster/v1.1.0/schema.json']}" @@ -2676,7 +2404,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -2698,7 +2426,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -20657,7 +20385,7 @@ " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", "\n", " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"), \n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"\n", "\n", "# Return the response in JSON format\n", ").json()\n", @@ -20712,7 +20440,7 @@ " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", "\n", " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"), \n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"\n", "\n", "# Return the response in JSON format\n", ").json()\n", @@ -20764,7 +20492,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_8c848d91580490eeb0d73a991cf95883 {\n", + " #map_a2f25b044660f123f033aea1506c5864 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -20778,7 +20506,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_fb488d61fdfb85a048b85493e3c7e284 {\n", + " #map_3bed02b90fe8bf7a5c59590ce826837e {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -20793,21 +20521,21 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_8c848d91580490eeb0d73a991cf95883" ></div>\n", + " <div class="folium-map" id="map_a2f25b044660f123f033aea1506c5864" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_fb488d61fdfb85a048b85493e3c7e284" ></div>\n", + " <div class="folium-map" id="map_3bed02b90fe8bf7a5c59590ce826837e" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_8c848d91580490eeb0d73a991cf95883 = L.map(\n", - " "map_8c848d91580490eeb0d73a991cf95883",\n", + " var map_a2f25b044660f123f033aea1506c5864 = L.map(\n", + " "map_a2f25b044660f123f033aea1506c5864",\n", " {\n", - " center: [34.0, -118.0],\n", + " center: [38.9, -80.0],\n", " crs: L.CRS.EPSG3857,\n", - " zoom: 6,\n", + " zoom: 8,\n", " zoomControl: true,\n", " preferCanvas: false,\n", " }\n", @@ -20817,30 +20545,60 @@ "\n", " \n", " \n", - " var tile_layer_dd21e2bda57a674d105327202ae469b2 = L.tileLayer(\n", + " var tile_layer_979e334a752af82d888f0a406162c826 = 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_dd21e2bda57a674d105327202ae469b2.addTo(map_8c848d91580490eeb0d73a991cf95883);\n", + " tile_layer_979e334a752af82d888f0a406162c826.addTo(map_a2f25b044660f123f033aea1506c5864);\n", " \n", " \n", - " var tile_layer_eb89668d2a78f388a4b9e8bd9ab2e4fb = L.tileLayer(\n", + " var tile_layer_c525d8b6930e24f13315675a6581affc = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2018/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=surface-coal\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=-9999.0%2C569.109130859375",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.7, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_eb89668d2a78f388a4b9e8bd9ab2e4fb.addTo(map_8c848d91580490eeb0d73a991cf95883);\n", + " tile_layer_c525d8b6930e24f13315675a6581affc.addTo(map_a2f25b044660f123f033aea1506c5864);\n", + " \n", + " \n", + " var marker_fde073f33067e3b1f2c4e3eb8d552719 = L.marker(\n", + " [40.0, 5.0],\n", + " {}\n", + " ).addTo(map_a2f25b044660f123f033aea1506c5864);\n", " \n", " \n", - " var map_fb488d61fdfb85a048b85493e3c7e284 = L.map(\n", - " "map_fb488d61fdfb85a048b85493e3c7e284",\n", + " marker_fde073f33067e3b1f2c4e3eb8d552719.bindTooltip(\n", + " `<div>\n", + " both\n", + " </div>`,\n", + " {"sticky": true}\n", + " );\n", + " \n", + " \n", + " var layer_control_8916f679cb9328002da6b4de6cad3d63_layers = {\n", + " base_layers : {\n", + " "openstreetmap" : tile_layer_979e334a752af82d888f0a406162c826,\n", + " },\n", + " overlays : {\n", + " "January 2018" : tile_layer_c525d8b6930e24f13315675a6581affc,\n", + " },\n", + " };\n", + " let layer_control_8916f679cb9328002da6b4de6cad3d63 = L.control.layers(\n", + " layer_control_8916f679cb9328002da6b4de6cad3d63_layers.base_layers,\n", + " layer_control_8916f679cb9328002da6b4de6cad3d63_layers.overlays,\n", + " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", + " ).addTo(map_a2f25b044660f123f033aea1506c5864);\n", + "\n", + " \n", + " \n", + " var map_3bed02b90fe8bf7a5c59590ce826837e = L.map(\n", + " "map_3bed02b90fe8bf7a5c59590ce826837e",\n", " {\n", - " center: [34.0, -118.0],\n", + " center: [38.9, -80.0],\n", " crs: L.CRS.EPSG3857,\n", - " zoom: 6,\n", + " zoom: 8,\n", " zoomControl: true,\n", " preferCanvas: false,\n", " }\n", @@ -20850,32 +20608,62 @@ "\n", " \n", " \n", - " var tile_layer_eedc2b3d8ebede3c7d210df432344f79 = L.tileLayer(\n", + " var tile_layer_28d9d40564668012b1cf0ce2ea3308e1 = 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_eedc2b3d8ebede3c7d210df432344f79.addTo(map_fb488d61fdfb85a048b85493e3c7e284);\n", + " tile_layer_28d9d40564668012b1cf0ce2ea3308e1.addTo(map_3bed02b90fe8bf7a5c59590ce826837e);\n", " \n", " \n", - " var tile_layer_db9178209b74ae8e1c764675581b0890 = L.tileLayer(\n", + " var tile_layer_713a43bd2fbe9161297cce1f309c8e6d = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2012/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=surface-coal\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=-9999.0%2C569.109130859375",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.7, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_db9178209b74ae8e1c764675581b0890.addTo(map_fb488d61fdfb85a048b85493e3c7e284);\n", + " tile_layer_713a43bd2fbe9161297cce1f309c8e6d.addTo(map_3bed02b90fe8bf7a5c59590ce826837e);\n", + " \n", + " \n", + " map_a2f25b044660f123f033aea1506c5864.sync(map_3bed02b90fe8bf7a5c59590ce826837e);\n", + " map_3bed02b90fe8bf7a5c59590ce826837e.sync(map_a2f25b044660f123f033aea1506c5864);\n", + " \n", + " \n", + " var marker_896ac496b21c4783a8faf6f7dd534b62 = L.marker(\n", + " [40.0, 5.0],\n", + " {}\n", + " ).addTo(map_3bed02b90fe8bf7a5c59590ce826837e);\n", " \n", " \n", - " map_8c848d91580490eeb0d73a991cf95883.sync(map_fb488d61fdfb85a048b85493e3c7e284);\n", - " map_fb488d61fdfb85a048b85493e3c7e284.sync(map_8c848d91580490eeb0d73a991cf95883);\n", + " marker_896ac496b21c4783a8faf6f7dd534b62.bindTooltip(\n", + " `<div>\n", + " both\n", + " </div>`,\n", + " {"sticky": true}\n", + " );\n", + " \n", + " \n", + " var layer_control_6ac3348b50e044988e7557b61ad5913d_layers = {\n", + " base_layers : {\n", + " "openstreetmap" : tile_layer_28d9d40564668012b1cf0ce2ea3308e1,\n", + " },\n", + " overlays : {\n", + " "January 2012" : tile_layer_713a43bd2fbe9161297cce1f309c8e6d,\n", + " },\n", + " };\n", + " let layer_control_6ac3348b50e044988e7557b61ad5913d = L.control.layers(\n", + " layer_control_6ac3348b50e044988e7557b61ad5913d_layers.base_layers,\n", + " layer_control_6ac3348b50e044988e7557b61ad5913d_layers.overlays,\n", + " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", + " ).addTo(map_3bed02b90fe8bf7a5c59590ce826837e);\n", + "\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, @@ -20934,7 +20722,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -20992,7 +20780,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_7ec4dafa62c4223da85605712dc8f9b9 {\n", + " #map_20c950fff03fbf1007218ff6f40f4b40 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -21006,18 +20794,18 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_7ec4dafa62c4223da85605712dc8f9b9" ></div>\n", + " <div class="folium-map" id="map_20c950fff03fbf1007218ff6f40f4b40" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_7ec4dafa62c4223da85605712dc8f9b9 = L.map(\n", - " "map_7ec4dafa62c4223da85605712dc8f9b9",\n", + " var map_20c950fff03fbf1007218ff6f40f4b40 = L.map(\n", + " "map_20c950fff03fbf1007218ff6f40f4b40",\n", " {\n", - " center: [30.0, -100.0],\n", + " center: [39.9, -79.4],\n", " crs: L.CRS.EPSG3857,\n", - " zoom: 6,\n", + " zoom: 9,\n", " zoomControl: true,\n", " preferCanvas: false,\n", " }\n", @@ -21027,41 +20815,41 @@ "\n", " \n", " \n", - " var tile_layer_e82cad60ceac778fe78c8e3fcb5486c6 = L.tileLayer(\n", + " var tile_layer_fd7116a2130c262922d64141a0fd3182 = 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_e82cad60ceac778fe78c8e3fcb5486c6.addTo(map_7ec4dafa62c4223da85605712dc8f9b9);\n", + " tile_layer_fd7116a2130c262922d64141a0fd3182.addTo(map_20c950fff03fbf1007218ff6f40f4b40);\n", " \n", " \n", "\n", - " function geo_json_c2e05c2439fa6a02d91a89a3455dbc6b_onEachFeature(feature, layer) {\n", + " function geo_json_e86b4698571efcc16f8a86619b60c9de_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_c2e05c2439fa6a02d91a89a3455dbc6b = L.geoJson(null, {\n", - " onEachFeature: geo_json_c2e05c2439fa6a02d91a89a3455dbc6b_onEachFeature,\n", + " var geo_json_e86b4698571efcc16f8a86619b60c9de = L.geoJson(null, {\n", + " onEachFeature: geo_json_e86b4698571efcc16f8a86619b60c9de_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_c2e05c2439fa6a02d91a89a3455dbc6b_add (data) {\n", - " geo_json_c2e05c2439fa6a02d91a89a3455dbc6b\n", + " function geo_json_e86b4698571efcc16f8a86619b60c9de_add (data) {\n", + " geo_json_e86b4698571efcc16f8a86619b60c9de\n", " .addData(data);\n", " }\n", - " geo_json_c2e05c2439fa6a02d91a89a3455dbc6b_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", - " geo_json_c2e05c2439fa6a02d91a89a3455dbc6b.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_e86b4698571efcc16f8a86619b60c9de_add({"geometry": {"coordinates": [[[-79.4, 39.9], [-79.4, 40.9], [-80.5, 40.9], [-80.5, 39.9], [-79.4, 39.9]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_e86b4698571efcc16f8a86619b60c9de.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_c2e05c2439fa6a02d91a89a3455dbc6b.addTo(map_7ec4dafa62c4223da85605712dc8f9b9);\n", + " geo_json_e86b4698571efcc16f8a86619b60c9de.addTo(map_20c950fff03fbf1007218ff6f40f4b40);\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, @@ -23122,7 +22910,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -23170,8 +22958,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 25 ms, sys: 7.31 ms, total: 32.3 ms\n", - "Wall time: 5.72 s\n" + "CPU times: user 33.8 ms, sys: 9.17 ms, total: 43 ms\n", + "Wall time: 3.07 s\n" ] } ], @@ -23195,34 +22983,34 @@ { "data": { "text/plain": [ - "{'statistics': {'b1': {'min': 0.23683340847492218,\n", - " 'max': 22.17084503173828,\n", - " 'mean': 9.193570137023926,\n", - " 'count': 3.0,\n", - " 'sum': 27.580711364746094,\n", - " 'std': 9.39498967501574,\n", - " 'median': 5.173033714294434,\n", - " 'majority': 0.23683340847492218,\n", - " 'minority': 0.23683340847492218,\n", - " 'unique': 3.0,\n", - " 'histogram': [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],\n", - " [0.23683340847492218,\n", - " 2.430234432220459,\n", - " 4.623635768890381,\n", - " 6.8170366287231445,\n", - " 9.010437965393066,\n", - " 11.203839302062988,\n", - " 13.397239685058594,\n", - " 15.590641021728516,\n", - " 17.784042358398438,\n", - " 19.97744369506836,\n", - " 22.17084503173828]],\n", - " 'valid_percent': 0.08,\n", - " 'masked_pixels': 3728.0,\n", - " 'valid_pixels': 3.0,\n", - " 'percentile_2': 0.23683340847492218,\n", - " 'percentile_98': 22.17084503173828}},\n", - " 'datetime': '2020-01-01T00:00:00+00:00'}" + "{'statistics': {'b1': {'min': 0.02134246937930584,\n", + " 'max': 1.3142744302749634,\n", + " 'mean': 0.2896881103515625,\n", + " 'count': 11.0,\n", + " 'sum': 3.1865692138671875,\n", + " 'std': 0.3474250195933299,\n", + " 'median': 0.17199701070785522,\n", + " 'majority': 0.02134246937930584,\n", + " 'minority': 0.02134246937930584,\n", + " 'unique': 11.0,\n", + " 'histogram': [[4.0, 3.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],\n", + " [0.02134246937930584,\n", + " 0.15063565969467163,\n", + " 0.2799288332462311,\n", + " 0.4092220067977905,\n", + " 0.5385152101516724,\n", + " 0.6678084135055542,\n", + " 0.7971015572547913,\n", + " 0.9263947606086731,\n", + " 1.0556880235671997,\n", + " 1.1849812269210815,\n", + " 1.3142744302749634]],\n", + " 'valid_percent': 8.33,\n", + " 'masked_pixels': 121.0,\n", + " 'valid_pixels': 11.0,\n", + " 'percentile_2': 0.02134246937930584,\n", + " 'percentile_98': 1.3142744302749634}},\n", + " 'datetime': '2016-01-01T00:00:00+00:00'}" ] }, "execution_count": 19, @@ -23293,106 +23081,106 @@ " \n", " 0\n", " 2020-01-01T00:00:00+00:00\n", - " 0.236833\n", - " 22.170845\n", - " 9.193570\n", - " 3.0\n", - " 27.580711\n", - " 9.394990\n", - " 5.173034\n", - " 0.236833\n", - " 0.236833\n", - " 3.0\n", - " [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 0.08\n", - " 3728.0\n", - " 3.0\n", - " 0.236833\n", - " 22.170845\n", + " 0.00274410564452409744\n", + " 1.83296215534210205078\n", + " 0.36944574117660522461\n", + " 7.00000000000000000000\n", + " 2.58612012863159179688\n", + " 0.60635886443843034499\n", + " 0.07520373910665512085\n", + " 0.00274410564452409744\n", + " 0.00274410564452409744\n", + " 7.00000000000000000000\n", + " [[4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0027441056445240974, 0.18576592206954956, 0.3687877357006073, 0.551809549331665, 0.7348313331604004, 0.9178531169891357, 1.1008750200271606, 1.283896803855896, 1.4669185876846313, 1.6499403715133667, 1.832962155342102]]\n", + " 5.29999999999999982236\n", + " 125.00000000000000000000\n", + " 7.00000000000000000000\n", + " 0.00274410564452409744\n", + " 1.83296215534210205078\n", " 2020-01-01 00:00:00+00:00\n", " \n", " \n", " 1\n", " 2019-01-01T00:00:00+00:00\n", - " 0.311410\n", - " 29.152185\n", - " 12.088519\n", - " 3.0\n", - " 36.265556\n", - " 12.353363\n", - " 6.801961\n", - " 0.311410\n", - " 0.311410\n", - " 3.0\n", - " [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 0.08\n", - " 3728.0\n", - " 3.0\n", - " 0.311410\n", - " 29.152185\n", + " 0.00360819255001842976\n", + " 2.41014075279235839844\n", + " 0.48577991127967834473\n", + " 7.00000000000000000000\n", + " 3.40045928955078125000\n", + " 0.79729428200101326585\n", + " 0.09888452291488647461\n", + " 0.00360819255001842976\n", + " 0.00360819255001842976\n", + " 7.00000000000000000000\n", + " [[4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0036081925500184298, 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0.00396033935248851776\n", + " 7.00000000000000000000\n", + " [[4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.003960339352488518, 0.26810047030448914, 0.5322405695915222, 0.7963806986808777, 1.060520887374878, 1.3246610164642334, 1.5888011455535889, 1.8529412746429443, 2.1170814037323, 2.3812215328216553, 2.6453616619110107]]\n", + " 5.29999999999999982236\n", + " 125.00000000000000000000\n", + " 7.00000000000000000000\n", + " 0.00396033935248851776\n", + " 2.64536166191101074219\n", " 2018-01-01 00:00:00+00:00\n", " \n", " \n", " 3\n", " 2017-01-01T00:00:00+00:00\n", - " 4.555885\n", - " 40.591602\n", - " 19.690639\n", - " 4.0\n", - " 78.762558\n", - " 14.679013\n", - " 7.337633\n", - " 4.555885\n", - " 4.555885\n", - " 4.0\n", - " [[2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0,...\n", - " 0.11\n", - " 3727.0\n", - " 4.0\n", - " 4.555885\n", - " 40.591602\n", + " 0.00975672807544469833\n", + " 3.13506197929382324219\n", + " 0.68930435180664062500\n", + " 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5.0\n", - " 4.969536\n", - " 34.220993\n", + " 0.02134246937930583954\n", + " 1.31427443027496337891\n", + " 0.28968811035156250000\n", + " 11.00000000000000000000\n", + " 3.18656921386718750000\n", + " 0.34742501959332988681\n", + " 0.17199701070785522461\n", + " 0.02134246937930583954\n", + " 0.02134246937930583954\n", + " 11.00000000000000000000\n", + " [[4.0, 3.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.02134246937930584, 0.15063565969467163, 0.2799288332462311, 0.4092220067977905, 0.5385152101516724, 0.6678084135055542, 0.7971015572547913, 0.9263947606086731, 1.0556880235671997, 1.1849812269210815, 1.3142744302749634]]\n", + " 8.33000000000000007105\n", + " 121.00000000000000000000\n", + " 11.00000000000000000000\n", + " 0.02134246937930583954\n", + " 1.31427443027496337891\n", " 2016-01-01 00:00:00+00:00\n", " \n", " \n", @@ -23400,40 +23188,54 @@ "" ], "text/plain": [ - " datetime min max mean count \\\n", - "0 2020-01-01T00:00:00+00:00 0.236833 22.170845 9.193570 3.0 \n", - "1 2019-01-01T00:00:00+00:00 0.311410 29.152185 12.088519 3.0 \n", - "2 2018-01-01T00:00:00+00:00 0.341802 31.997334 13.268314 3.0 \n", - "3 2017-01-01T00:00:00+00:00 4.555885 40.591602 19.690639 4.0 \n", - "4 2016-01-01T00:00:00+00:00 4.969536 34.220993 17.191929 5.0 \n", + " datetime min max \\\n", + "0 2020-01-01T00:00:00+00:00 0.00274410564452409744 1.83296215534210205078 \n", + "1 2019-01-01T00:00:00+00:00 0.00360819255001842976 2.41014075279235839844 \n", + "2 2018-01-01T00:00:00+00:00 0.00396033935248851776 2.64536166191101074219 \n", + "3 2017-01-01T00:00:00+00:00 0.00975672807544469833 3.13506197929382324219 \n", + "4 2016-01-01T00:00:00+00:00 0.02134246937930583954 1.31427443027496337891 \n", "\n", - " sum std median majority minority unique \\\n", - "0 27.580711 9.394990 5.173034 0.236833 0.236833 3.0 \n", - "1 36.265556 12.353363 6.801961 0.311410 0.311410 3.0 \n", - "2 39.804943 13.559006 7.465808 0.341802 0.341802 3.0 \n", - "3 78.762558 14.679013 7.337633 4.555885 4.555885 4.0 \n", - "4 85.959641 11.040686 13.514935 4.969536 4.969536 5.0 \n", + " mean count sum \\\n", + "0 0.36944574117660522461 7.00000000000000000000 2.58612012863159179688 \n", + "1 0.48577991127967834473 7.00000000000000000000 3.40045928955078125000 \n", + "2 0.53319025039672851562 7.00000000000000000000 3.73233175277709960938 \n", + "3 0.68930435180664062500 8.00000000000000000000 5.51443481445312500000 \n", + "4 0.28968811035156250000 11.00000000000000000000 3.18656921386718750000 \n", "\n", - " histogram valid_percent \\\n", - "0 [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 0.08 \n", - "1 [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 0.08 \n", - "2 [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 0.08 \n", - "3 [[2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0,... 0.11 \n", - "4 [[2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0,... 0.13 \n", + " std median majority \\\n", + "0 0.60635886443843034499 0.07520373910665512085 0.00274410564452409744 \n", + "1 0.79729428200101326585 0.09888452291488647461 0.00360819255001842976 \n", + "2 0.87510717961693595957 0.10853529721498489380 0.00396033935248851776 \n", + "3 0.98443012991432565784 0.31279709935188293457 0.00975672807544469833 \n", + "4 0.34742501959332988681 0.17199701070785522461 0.02134246937930583954 \n", "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", - "0 3728.0 3.0 0.236833 22.170845 \n", - "1 3728.0 3.0 0.311410 29.152185 \n", - "2 3728.0 3.0 0.341802 31.997334 \n", - "3 3727.0 4.0 4.555885 40.591602 \n", - "4 3726.0 5.0 4.969536 34.220993 \n", + " minority unique \\\n", + "0 0.00274410564452409744 7.00000000000000000000 \n", + "1 0.00360819255001842976 7.00000000000000000000 \n", + "2 0.00396033935248851776 7.00000000000000000000 \n", + "3 0.00975672807544469833 8.00000000000000000000 \n", + "4 0.02134246937930583954 11.00000000000000000000 \n", "\n", - " date \n", - "0 2020-01-01 00:00:00+00:00 \n", - "1 2019-01-01 00:00:00+00:00 \n", - "2 2018-01-01 00:00:00+00:00 \n", - "3 2017-01-01 00:00:00+00:00 \n", - "4 2016-01-01 00:00:00+00:00 " + " histogram \\\n", + "0 [[4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0027441056445240974, 0.18576592206954956, 0.3687877357006073, 0.551809549331665, 0.7348313331604004, 0.9178531169891357, 1.1008750200271606, 1.283896803855896, 1.4669185876846313, 1.6499403715133667, 1.832962155342102]] \n", + "1 [[4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0036081925500184298, 0.24426144361495972, 0.4849146902561188, 0.7255678772926331, 0.9662211537361145, 1.2068744897842407, 1.4475276470184326, 1.688180923461914, 1.9288341999053955, 2.169487476348877, 2.4101407527923584]] \n", + "2 [[4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.003960339352488518, 0.26810047030448914, 0.5322405695915222, 0.7963806986808777, 1.060520887374878, 1.3246610164642334, 1.5888011455535889, 1.8529412746429443, 2.1170814037323, 2.3812215328216553, 2.6453616619110107]] \n", + "3 [[4.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.009756728075444698, 0.3222872316837311, 0.6348177790641785, 0.9473482966423035, 1.2598787546157837, 1.5724092721939087, 1.8849397897720337, 2.1974704265594482, 2.5100009441375732, 2.8225314617156982, 3.1350619792938232]] \n", + "4 [[4.0, 3.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.02134246937930584, 0.15063565969467163, 0.2799288332462311, 0.4092220067977905, 0.5385152101516724, 0.6678084135055542, 0.7971015572547913, 0.9263947606086731, 1.0556880235671997, 1.1849812269210815, 1.3142744302749634]] \n", + "\n", + " valid_percent masked_pixels valid_pixels \\\n", + "0 5.29999999999999982236 125.00000000000000000000 7.00000000000000000000 \n", + "1 5.29999999999999982236 125.00000000000000000000 7.00000000000000000000 \n", + "2 5.29999999999999982236 125.00000000000000000000 7.00000000000000000000 \n", + "3 6.05999999999999960920 124.00000000000000000000 8.00000000000000000000 \n", + "4 8.33000000000000007105 121.00000000000000000000 11.00000000000000000000 \n", + "\n", + " percentile_2 percentile_98 date \n", + "0 0.00274410564452409744 1.83296215534210205078 2020-01-01 00:00:00+00:00 \n", + "1 0.00360819255001842976 2.41014075279235839844 2019-01-01 00:00:00+00:00 \n", + "2 0.00396033935248851776 2.64536166191101074219 2018-01-01 00:00:00+00:00 \n", + "3 0.00975672807544469833 3.13506197929382324219 2017-01-01 00:00:00+00:00 \n", + "4 0.02134246937930583954 1.31427443027496337891 2016-01-01 00:00:00+00:00 " ] }, "execution_count": 20, @@ -23482,17 +23284,7 @@ "outputs": [ { "data": { - "text/plain": [ - "Text(0.5, 1.0, 'CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Texas, Dallas (2012-202)')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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"text/plain": [ "
" ] @@ -23614,7 +23406,7 @@ " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", "\n", " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"), \n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"\n", "\n", "# Return the response in JSON format\n", ").json()\n", @@ -23658,7 +23450,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_93933e3e02402a6aee5eeb71ed5acc1e {\n", + " #map_7ddce5f3e011c89773e7c98f7b480441 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -23672,18 +23464,18 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_93933e3e02402a6aee5eeb71ed5acc1e" ></div>\n", + " <div class="folium-map" id="map_7ddce5f3e011c89773e7c98f7b480441" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_93933e3e02402a6aee5eeb71ed5acc1e = L.map(\n", - " "map_93933e3e02402a6aee5eeb71ed5acc1e",\n", + " var map_7ddce5f3e011c89773e7c98f7b480441 = L.map(\n", + " "map_7ddce5f3e011c89773e7c98f7b480441",\n", " {\n", - " center: [30.0, -100.0],\n", + " center: [39.9, -79.4],\n", " crs: L.CRS.EPSG3857,\n", - " zoom: 8,\n", + " zoom: 9,\n", " zoomControl: true,\n", " preferCanvas: false,\n", " }\n", @@ -23693,28 +23485,28 @@ "\n", " \n", " \n", - " var tile_layer_a066337c287f42cfd088c9928bdb5a87 = L.tileLayer(\n", + " var tile_layer_09074a1b80e522ba3dcc6b522e6a0061 = 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_a066337c287f42cfd088c9928bdb5a87.addTo(map_93933e3e02402a6aee5eeb71ed5acc1e);\n", + " tile_layer_09074a1b80e522ba3dcc6b522e6a0061.addTo(map_7ddce5f3e011c89773e7c98f7b480441);\n", " \n", " \n", - " var tile_layer_764918b1cfb125b2a6f18cfc35455951 = L.tileLayer(\n", + " var tile_layer_d121a4f80b1990eca6bb4006504a5c1e = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2018/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=surface-coal\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=-9999.0%2C569.109130859375",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_764918b1cfb125b2a6f18cfc35455951.addTo(map_93933e3e02402a6aee5eeb71ed5acc1e);\n", + " tile_layer_d121a4f80b1990eca6bb4006504a5c1e.addTo(map_7ddce5f3e011c89773e7c98f7b480441);\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": 24, @@ -23791,7 +23583,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.9.6" }, "vscode": { "interpreter": { diff --git a/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb index ab47a8740..948754a8c 100644 --- a/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb @@ -2084,7 +2084,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_7a62581dcff03d4f316c66fc6cd81085 {\n", + " #map_3d68dfb07e247335ea3561e7a3b5aa19 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -2098,14 +2098,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_7a62581dcff03d4f316c66fc6cd81085" ></div>\n", + " <div class="folium-map" id="map_3d68dfb07e247335ea3561e7a3b5aa19" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_7a62581dcff03d4f316c66fc6cd81085 = L.map(\n", - " "map_7a62581dcff03d4f316c66fc6cd81085",\n", + " var map_3d68dfb07e247335ea3561e7a3b5aa19 = L.map(\n", + " "map_3d68dfb07e247335ea3561e7a3b5aa19",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -2119,28 +2119,28 @@ "\n", " \n", " \n", - " var tile_layer_413e082a1469ab544809dcdc052ce67a = L.tileLayer(\n", + " var tile_layer_9f6b2f5be16b8364fe2040a2e196da32 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; 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See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import requests\n", "import pandas as pd\n", @@ -77,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -141,7 +150,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -198,7 +207,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb b/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb index 6730fa700..3e0293c3e 100644 --- a/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb +++ b/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb @@ -58,7 +58,16 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import requests\n", "import pandas as pd\n", @@ -77,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -141,7 +150,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -197,7 +206,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb b/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb index e29792d67..9bf74a1b8 100644 --- a/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb +++ b/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb @@ -683,7 +683,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_3188b1be673d802837393ef950a0dd45 {\n", + " #map_20ca33df23920a6d05bfa39af7987452 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -697,7 +697,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_84545a0f8d30d5018bbc5b8f55177980 {\n", + " #map_29c57efa53aadc432da900549279f795 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -712,17 +712,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_3188b1be673d802837393ef950a0dd45" ></div>\n", + " <div class="folium-map" id="map_20ca33df23920a6d05bfa39af7987452" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_84545a0f8d30d5018bbc5b8f55177980" ></div>\n", + " <div class="folium-map" id="map_29c57efa53aadc432da900549279f795" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_3188b1be673d802837393ef950a0dd45 = L.map(\n", - " "map_3188b1be673d802837393ef950a0dd45",\n", + " var map_20ca33df23920a6d05bfa39af7987452 = L.map(\n", + " "map_20ca33df23920a6d05bfa39af7987452",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -736,26 +736,26 @@ "\n", " \n", " \n", - " var tile_layer_8339b91845bed783c10754c953d55182 = L.tileLayer(\n", + " var tile_layer_418137896306f7fdc8b4d9caec18b84a = 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_8339b91845bed783c10754c953d55182.addTo(map_3188b1be673d802837393ef950a0dd45);\n", + " tile_layer_418137896306f7fdc8b4d9caec18b84a.addTo(map_20ca33df23920a6d05bfa39af7987452);\n", " \n", " \n", - " var tile_layer_c10e3c76bf326e16b6299c6e7fe190da = L.tileLayer(\n", + " var tile_layer_f6d692e7b17873d3ce3c7b202db584ef = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240101/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=0.0%2C0.0003",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_c10e3c76bf326e16b6299c6e7fe190da.addTo(map_3188b1be673d802837393ef950a0dd45);\n", + " tile_layer_f6d692e7b17873d3ce3c7b202db584ef.addTo(map_20ca33df23920a6d05bfa39af7987452);\n", " \n", " \n", - " var map_84545a0f8d30d5018bbc5b8f55177980 = L.map(\n", - " "map_84545a0f8d30d5018bbc5b8f55177980",\n", + " var map_29c57efa53aadc432da900549279f795 = L.map(\n", + " "map_29c57efa53aadc432da900549279f795",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -769,32 +769,32 @@ "\n", " \n", " \n", - " var tile_layer_7817880779d9793230a86c6e93244c70 = L.tileLayer(\n", + " var tile_layer_d73a0f74bb2afec6c4a4e057f40464ca = 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_7817880779d9793230a86c6e93244c70.addTo(map_84545a0f8d30d5018bbc5b8f55177980);\n", + " tile_layer_d73a0f74bb2afec6c4a4e057f40464ca.addTo(map_29c57efa53aadc432da900549279f795);\n", " \n", " \n", - " var tile_layer_d3069329aaf010cd894b10cb0e54c37d = L.tileLayer(\n", + " var tile_layer_bfb24453a71682fb5160bd536b3ab457 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240130/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=0.0%2C0.0003",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_d3069329aaf010cd894b10cb0e54c37d.addTo(map_84545a0f8d30d5018bbc5b8f55177980);\n", + " tile_layer_bfb24453a71682fb5160bd536b3ab457.addTo(map_29c57efa53aadc432da900549279f795);\n", " \n", " \n", - " map_3188b1be673d802837393ef950a0dd45.sync(map_84545a0f8d30d5018bbc5b8f55177980);\n", - " map_84545a0f8d30d5018bbc5b8f55177980.sync(map_3188b1be673d802837393ef950a0dd45);\n", + " map_20ca33df23920a6d05bfa39af7987452.sync(map_29c57efa53aadc432da900549279f795);\n", + " map_29c57efa53aadc432da900549279f795.sync(map_20ca33df23920a6d05bfa39af7987452);\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, @@ -906,7 +906,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_1a965f34e31b9b2476dc5f21209a317e {\n", + " #map_504e775d20dca20cbdf7acb87513d492 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -920,14 +920,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_1a965f34e31b9b2476dc5f21209a317e" ></div>\n", + " <div class="folium-map" id="map_504e775d20dca20cbdf7acb87513d492" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_1a965f34e31b9b2476dc5f21209a317e = L.map(\n", - " "map_1a965f34e31b9b2476dc5f21209a317e",\n", + " var map_504e775d20dca20cbdf7acb87513d492 = L.map(\n", + " "map_504e775d20dca20cbdf7acb87513d492",\n", " {\n", " center: [30.0, -101.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -941,41 +941,41 @@ "\n", " \n", " \n", - " var tile_layer_a3c259b90472e557d4d5bbcd49789919 = L.tileLayer(\n", + " var tile_layer_cb5e52c924a4cd55a5ae9275df910b24 = 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_a3c259b90472e557d4d5bbcd49789919.addTo(map_1a965f34e31b9b2476dc5f21209a317e);\n", + " tile_layer_cb5e52c924a4cd55a5ae9275df910b24.addTo(map_504e775d20dca20cbdf7acb87513d492);\n", " \n", " \n", "\n", - " function geo_json_4523cd45c842df369cb58289d42d7356_onEachFeature(feature, layer) {\n", + " function geo_json_71881f84e78a6e5da3640402f8b121a4_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_4523cd45c842df369cb58289d42d7356 = L.geoJson(null, {\n", - " onEachFeature: geo_json_4523cd45c842df369cb58289d42d7356_onEachFeature,\n", + " var geo_json_71881f84e78a6e5da3640402f8b121a4 = L.geoJson(null, {\n", + " onEachFeature: geo_json_71881f84e78a6e5da3640402f8b121a4_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_4523cd45c842df369cb58289d42d7356_add (data) {\n", - " geo_json_4523cd45c842df369cb58289d42d7356\n", + " function geo_json_71881f84e78a6e5da3640402f8b121a4_add (data) {\n", + " geo_json_71881f84e78a6e5da3640402f8b121a4\n", " .addData(data);\n", " }\n", - " geo_json_4523cd45c842df369cb58289d42d7356_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", - " geo_json_4523cd45c842df369cb58289d42d7356.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_71881f84e78a6e5da3640402f8b121a4_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_71881f84e78a6e5da3640402f8b121a4.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_4523cd45c842df369cb58289d42d7356.addTo(map_1a965f34e31b9b2476dc5f21209a317e);\n", + " geo_json_71881f84e78a6e5da3640402f8b121a4.addTo(map_504e775d20dca20cbdf7acb87513d492);\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, @@ -1293,8 +1293,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 8.37 s, sys: 2.02 s, total: 10.4 s\n", - "Wall time: 7min 25s\n" + "CPU times: user 5.74 s, sys: 1.34 s, total: 7.08 s\n", + "Wall time: 6min 24s\n" ] } ], @@ -1744,7 +1744,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_dee8d51f798384c13b7ec9659120de68 {\n", + " #map_1e6baf37f5af966091ee0a2fafbdf8dd {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1758,14 +1758,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_dee8d51f798384c13b7ec9659120de68" ></div>\n", + " <div class="folium-map" id="map_1e6baf37f5af966091ee0a2fafbdf8dd" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_dee8d51f798384c13b7ec9659120de68 = L.map(\n", - " "map_dee8d51f798384c13b7ec9659120de68",\n", + " var map_1e6baf37f5af966091ee0a2fafbdf8dd = L.map(\n", + " "map_1e6baf37f5af966091ee0a2fafbdf8dd",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1779,28 +1779,28 @@ "\n", " \n", " \n", - " var tile_layer_0c585c9282ff7027bf2030bc65e17e4a = L.tileLayer(\n", + " var tile_layer_c6810a093b1f5dd39f380e2da06f4057 = 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_0c585c9282ff7027bf2030bc65e17e4a.addTo(map_dee8d51f798384c13b7ec9659120de68);\n", + " tile_layer_c6810a093b1f5dd39f380e2da06f4057.addTo(map_1e6baf37f5af966091ee0a2fafbdf8dd);\n", " \n", " \n", - " var tile_layer_235a0ffc9c237d66fdb3fee5aa1db5c1 = L.tileLayer(\n", + " var tile_layer_3ea36086c19939f5af8ca00139d40ee2 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240529/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=0.0%2C0.0003",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_235a0ffc9c237d66fdb3fee5aa1db5c1.addTo(map_dee8d51f798384c13b7ec9659120de68);\n", + " tile_layer_3ea36086c19939f5af8ca00139d40ee2.addTo(map_1e6baf37f5af966091ee0a2fafbdf8dd);\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": 23, diff --git a/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb b/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb index c860659ef..686bffa7a 100644 --- a/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb +++ b/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb @@ -684,7 +684,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_d78471e3b40a36c9067f171943944df4 {\n", + " #map_778bed359851b8823fc4be1297951261 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -698,7 +698,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_5ff53030476e881f5a0bfd5cff79f778 {\n", + " #map_fc19c3bc2f85fcfb6cf5d874cf36db2a {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -713,17 +713,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_d78471e3b40a36c9067f171943944df4" ></div>\n", + " <div class="folium-map" id="map_778bed359851b8823fc4be1297951261" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_5ff53030476e881f5a0bfd5cff79f778" ></div>\n", + " <div class="folium-map" id="map_fc19c3bc2f85fcfb6cf5d874cf36db2a" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_d78471e3b40a36c9067f171943944df4 = L.map(\n", - " "map_d78471e3b40a36c9067f171943944df4",\n", + " var map_778bed359851b8823fc4be1297951261 = L.map(\n", + " "map_778bed359851b8823fc4be1297951261",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -737,26 +737,26 @@ "\n", " \n", " \n", - " var tile_layer_5a6fe7d6c35dda49f3d6c6ec2611a0ee = L.tileLayer(\n", + " var tile_layer_213638103266468c77d5aa438ad4e05d = 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_5a6fe7d6c35dda49f3d6c6ec2611a0ee.addTo(map_d78471e3b40a36c9067f171943944df4);\n", + " tile_layer_213638103266468c77d5aa438ad4e05d.addTo(map_778bed359851b8823fc4be1297951261);\n", " \n", " \n", - " var tile_layer_9d744dfcb492edf094b87bf3d2f4f4f1 = L.tileLayer(\n", + " var tile_layer_a7a5458c7f7f96a218485ceab2b55a0f = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-199001/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=0.0%2C0.0003",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_9d744dfcb492edf094b87bf3d2f4f4f1.addTo(map_d78471e3b40a36c9067f171943944df4);\n", + " tile_layer_a7a5458c7f7f96a218485ceab2b55a0f.addTo(map_778bed359851b8823fc4be1297951261);\n", " \n", " \n", - " var map_5ff53030476e881f5a0bfd5cff79f778 = L.map(\n", - " "map_5ff53030476e881f5a0bfd5cff79f778",\n", + " var map_fc19c3bc2f85fcfb6cf5d874cf36db2a = L.map(\n", + " "map_fc19c3bc2f85fcfb6cf5d874cf36db2a",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -770,32 +770,32 @@ "\n", " \n", " \n", - " var tile_layer_7e0121c2bc78509ecf7775f45c282908 = L.tileLayer(\n", + " var tile_layer_38571577a29aecbd7f3dd4288fe27076 = 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_7e0121c2bc78509ecf7775f45c282908.addTo(map_5ff53030476e881f5a0bfd5cff79f778);\n", + " tile_layer_38571577a29aecbd7f3dd4288fe27076.addTo(map_fc19c3bc2f85fcfb6cf5d874cf36db2a);\n", " \n", " \n", - " var tile_layer_5d30793eb7a4164b5915fa2996611e03 = L.tileLayer(\n", + " var tile_layer_5bd326ee90c04b747605eb6328ff1d74 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-199008/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=0.0%2C0.0003",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_5d30793eb7a4164b5915fa2996611e03.addTo(map_5ff53030476e881f5a0bfd5cff79f778);\n", + " tile_layer_5bd326ee90c04b747605eb6328ff1d74.addTo(map_fc19c3bc2f85fcfb6cf5d874cf36db2a);\n", " \n", " \n", - " map_d78471e3b40a36c9067f171943944df4.sync(map_5ff53030476e881f5a0bfd5cff79f778);\n", - " map_5ff53030476e881f5a0bfd5cff79f778.sync(map_d78471e3b40a36c9067f171943944df4);\n", + " map_778bed359851b8823fc4be1297951261.sync(map_fc19c3bc2f85fcfb6cf5d874cf36db2a);\n", + " map_fc19c3bc2f85fcfb6cf5d874cf36db2a.sync(map_778bed359851b8823fc4be1297951261);\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, @@ -907,7 +907,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_8fb9b42e28b6d208588ec3c8eae2ef74 {\n", + " #map_7fad4354c65f31985cd10834c5345b0a {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -921,14 +921,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_8fb9b42e28b6d208588ec3c8eae2ef74" ></div>\n", + " <div class="folium-map" id="map_7fad4354c65f31985cd10834c5345b0a" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_8fb9b42e28b6d208588ec3c8eae2ef74 = L.map(\n", - " "map_8fb9b42e28b6d208588ec3c8eae2ef74",\n", + " var map_7fad4354c65f31985cd10834c5345b0a = L.map(\n", + " "map_7fad4354c65f31985cd10834c5345b0a",\n", " {\n", " center: [30.0, -101.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -942,41 +942,41 @@ "\n", " \n", " \n", - " var tile_layer_675e5a3268fe44ac70beb7c71eae3944 = L.tileLayer(\n", + " var tile_layer_5270c53e86278bd8518c8f77eae913a8 = 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_675e5a3268fe44ac70beb7c71eae3944.addTo(map_8fb9b42e28b6d208588ec3c8eae2ef74);\n", + " tile_layer_5270c53e86278bd8518c8f77eae913a8.addTo(map_7fad4354c65f31985cd10834c5345b0a);\n", " \n", " \n", "\n", - " function geo_json_2c5593b114ef9c7fe4088f9d985ba6c7_onEachFeature(feature, layer) {\n", + " function geo_json_a1edf8e89f7926fb378e913b14986320_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_2c5593b114ef9c7fe4088f9d985ba6c7 = L.geoJson(null, {\n", - " onEachFeature: geo_json_2c5593b114ef9c7fe4088f9d985ba6c7_onEachFeature,\n", + " var geo_json_a1edf8e89f7926fb378e913b14986320 = L.geoJson(null, {\n", + " onEachFeature: geo_json_a1edf8e89f7926fb378e913b14986320_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_2c5593b114ef9c7fe4088f9d985ba6c7_add (data) {\n", - " geo_json_2c5593b114ef9c7fe4088f9d985ba6c7\n", + " function geo_json_a1edf8e89f7926fb378e913b14986320_add (data) {\n", + " geo_json_a1edf8e89f7926fb378e913b14986320\n", " .addData(data);\n", " }\n", - " geo_json_2c5593b114ef9c7fe4088f9d985ba6c7_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", - " geo_json_2c5593b114ef9c7fe4088f9d985ba6c7.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_a1edf8e89f7926fb378e913b14986320_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_a1edf8e89f7926fb378e913b14986320.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_2c5593b114ef9c7fe4088f9d985ba6c7.addTo(map_8fb9b42e28b6d208588ec3c8eae2ef74);\n", + " geo_json_a1edf8e89f7926fb378e913b14986320.addTo(map_7fad4354c65f31985cd10834c5345b0a);\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, @@ -1295,8 +1295,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 3.82 s, sys: 784 ms, total: 4.6 s\n", - "Wall time: 3min 55s\n" + "CPU times: user 1.98 s, sys: 498 ms, total: 2.48 s\n", + "Wall time: 3min 17s\n" ] } ], @@ -1746,7 +1746,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_2a562f924c77c4c45bd4daf735c57f6f {\n", + " #map_69138f92dad8548e3b7d7a0986a2ccd4 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1760,14 +1760,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_2a562f924c77c4c45bd4daf735c57f6f" ></div>\n", + " <div class="folium-map" id="map_69138f92dad8548e3b7d7a0986a2ccd4" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_2a562f924c77c4c45bd4daf735c57f6f = L.map(\n", - " "map_2a562f924c77c4c45bd4daf735c57f6f",\n", + " var map_69138f92dad8548e3b7d7a0986a2ccd4 = L.map(\n", + " "map_69138f92dad8548e3b7d7a0986a2ccd4",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1781,28 +1781,28 @@ "\n", " \n", " \n", - " var tile_layer_b50d9d913657c9570ee9b83dad7c2111 = L.tileLayer(\n", + " var tile_layer_cee767bc8213a196fe05e699248fd0db = 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_b50d9d913657c9570ee9b83dad7c2111.addTo(map_2a562f924c77c4c45bd4daf735c57f6f);\n", + " tile_layer_cee767bc8213a196fe05e699248fd0db.addTo(map_69138f92dad8548e3b7d7a0986a2ccd4);\n", " \n", " \n", - " var tile_layer_2f2e1043d49301d5d898e7486e0efce8 = L.tileLayer(\n", + " var tile_layer_70957806af5ce95cd9f32ce55a97e057 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202403/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=0.0%2C0.0003",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_2f2e1043d49301d5d898e7486e0efce8.addTo(map_2a562f924c77c4c45bd4daf735c57f6f);\n", + " tile_layer_70957806af5ce95cd9f32ce55a97e057.addTo(map_69138f92dad8548e3b7d7a0986a2ccd4);\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": 23, 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 bc6aec8fe..efdb17ce5 100644 --- a/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb @@ -967,7 +967,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_c416cba82fc90d99ed0c7e106a1534f8 {\n", + " #map_3c08b5cd143ad9c0225845d4d084f94a {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -982,7 +982,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_b5da17e67439e3ecef16807985f9e3da {\n", + " #map_a813a36ca707bd1aac44863fead052c1 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -997,17 +997,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_c416cba82fc90d99ed0c7e106a1534f8" ></div>\n", + " <div class="folium-map" id="map_3c08b5cd143ad9c0225845d4d084f94a" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_b5da17e67439e3ecef16807985f9e3da" ></div>\n", + " <div class="folium-map" id="map_a813a36ca707bd1aac44863fead052c1" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_c416cba82fc90d99ed0c7e106a1534f8 = L.map(\n", - " "map_c416cba82fc90d99ed0c7e106a1534f8",\n", + " var map_3c08b5cd143ad9c0225845d4d084f94a = L.map(\n", + " "map_3c08b5cd143ad9c0225845d4d084f94a",\n", " {\n", " center: [31.9, -99.9],\n", " crs: L.CRS.EPSG3857,\n", @@ -1021,31 +1021,31 @@ "\n", " \n", " \n", - " var tile_layer_f03b186486bc55426f39bc61789f41b3 = L.tileLayer(\n", + " var tile_layer_72052f8d7d531dca08a3599f74244253 = 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_f03b186486bc55426f39bc61789f41b3.addTo(map_c416cba82fc90d99ed0c7e106a1534f8);\n", + " tile_layer_72052f8d7d531dca08a3599f74244253.addTo(map_3c08b5cd143ad9c0225845d4d084f94a);\n", " \n", " \n", - " var tile_layer_a7c06cac99b39841d0c13c10e1f53097 = L.tileLayer(\n", + " var tile_layer_fc04029e2e1db98c5f38917bc35f5a9d = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20230101/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=-0.35656991600990295%2C7.2141876220703125",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_a7c06cac99b39841d0c13c10e1f53097.addTo(map_c416cba82fc90d99ed0c7e106a1534f8);\n", + " tile_layer_fc04029e2e1db98c5f38917bc35f5a9d.addTo(map_3c08b5cd143ad9c0225845d4d084f94a);\n", " \n", " \n", - " var marker_e6ac61d5f4ae722ebdb8174e0741d6bd = L.marker(\n", + " var marker_5c0625cd1fea132eccda97774b5d242e = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_c416cba82fc90d99ed0c7e106a1534f8);\n", + " ).addTo(map_3c08b5cd143ad9c0225845d4d084f94a);\n", " \n", " \n", - " marker_e6ac61d5f4ae722ebdb8174e0741d6bd.bindTooltip(\n", + " marker_5c0625cd1fea132eccda97774b5d242e.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -1053,58 +1053,58 @@ " );\n", " \n", " \n", - " var layer_control_c7ded6b1acf06ea324fdad9f4872fc33_layers = {\n", + " var layer_control_5fdd459855c5631ec7e9667c9030d096_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_f03b186486bc55426f39bc61789f41b3,\n", + " "openstreetmap" : tile_layer_72052f8d7d531dca08a3599f74244253,\n", " },\n", " overlays : {\n", - " "2023-01-01 Rh Level" : tile_layer_a7c06cac99b39841d0c13c10e1f53097,\n", + " "2023-01-01 Rh Level" : tile_layer_fc04029e2e1db98c5f38917bc35f5a9d,\n", " },\n", " };\n", - " let layer_control_c7ded6b1acf06ea324fdad9f4872fc33 = L.control.layers(\n", - " layer_control_c7ded6b1acf06ea324fdad9f4872fc33_layers.base_layers,\n", - " layer_control_c7ded6b1acf06ea324fdad9f4872fc33_layers.overlays,\n", + " let layer_control_5fdd459855c5631ec7e9667c9030d096 = L.control.layers(\n", + " layer_control_5fdd459855c5631ec7e9667c9030d096_layers.base_layers,\n", + " layer_control_5fdd459855c5631ec7e9667c9030d096_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_c416cba82fc90d99ed0c7e106a1534f8);\n", + " ).addTo(map_3c08b5cd143ad9c0225845d4d084f94a);\n", "\n", " \n", " \n", - " var color_map_c8ce69387f16660f893cdc647cad43fc = {};\n", + " var color_map_7644aa715058bcf5d43b72a5f1644235 = {};\n", "\n", " \n", - " color_map_c8ce69387f16660f893cdc647cad43fc.color = d3.scale.threshold()\n", + " color_map_7644aa715058bcf5d43b72a5f1644235.color = d3.scale.threshold()\n", " .domain([0.0, 0.0006012024048096192, 0.0012024048096192384, 0.0018036072144288575, 0.002404809619238477, 0.003006012024048096, 0.003607214428857715, 0.004208416833667335, 0.004809619238476954, 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d3.scale.linear()\n", " .domain([0.0, 0.3])\n", " .range([0, 450 - 50]);\n", "\n", - " color_map_c8ce69387f16660f893cdc647cad43fc.legend = L.control({position: 'topright'});\n", - " color_map_c8ce69387f16660f893cdc647cad43fc.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", - " color_map_c8ce69387f16660f893cdc647cad43fc.legend.addTo(map_c416cba82fc90d99ed0c7e106a1534f8);\n", + " color_map_7644aa715058bcf5d43b72a5f1644235.legend = L.control({position: 'topright'});\n", + " color_map_7644aa715058bcf5d43b72a5f1644235.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_7644aa715058bcf5d43b72a5f1644235.legend.addTo(map_3c08b5cd143ad9c0225845d4d084f94a);\n", "\n", - " color_map_c8ce69387f16660f893cdc647cad43fc.xAxis = d3.svg.axis()\n", - " .scale(color_map_c8ce69387f16660f893cdc647cad43fc.x)\n", + " color_map_7644aa715058bcf5d43b72a5f1644235.xAxis = d3.svg.axis()\n", + " .scale(color_map_7644aa715058bcf5d43b72a5f1644235.x)\n", " .orient("top")\n", " .tickSize(1)\n", " .tickValues([0.0, 0.07, 0.15, 0.22, 0.3]);\n", "\n", - " color_map_c8ce69387f16660f893cdc647cad43fc.svg = d3.select(".legend.leaflet-control").append("svg")\n", + " color_map_7644aa715058bcf5d43b72a5f1644235.svg = d3.select(".legend.leaflet-control").append("svg")\n", " .attr("id", 'legend')\n", " .attr("width", 450)\n", " .attr("height", 40);\n", "\n", - " color_map_c8ce69387f16660f893cdc647cad43fc.g = color_map_c8ce69387f16660f893cdc647cad43fc.svg.append("g")\n", + " color_map_7644aa715058bcf5d43b72a5f1644235.g = color_map_7644aa715058bcf5d43b72a5f1644235.svg.append("g")\n", " .attr("class", "key")\n", " .attr("transform", "translate(25,16)");\n", "\n", - " color_map_c8ce69387f16660f893cdc647cad43fc.g.selectAll("rect")\n", - " .data(color_map_c8ce69387f16660f893cdc647cad43fc.color.range().map(function(d, i) {\n", + " color_map_7644aa715058bcf5d43b72a5f1644235.g.selectAll("rect")\n", + " .data(color_map_7644aa715058bcf5d43b72a5f1644235.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_c8ce69387f16660f893cdc647cad43fc.x(color_map_c8ce69387f16660f893cdc647cad43fc.color.domain()[i - 1]) : color_map_c8ce69387f16660f893cdc647cad43fc.x.range()[0],\n", - " x1: i < color_map_c8ce69387f16660f893cdc647cad43fc.color.domain().length ? color_map_c8ce69387f16660f893cdc647cad43fc.x(color_map_c8ce69387f16660f893cdc647cad43fc.color.domain()[i]) : color_map_c8ce69387f16660f893cdc647cad43fc.x.range()[1],\n", + " x0: i ? color_map_7644aa715058bcf5d43b72a5f1644235.x(color_map_7644aa715058bcf5d43b72a5f1644235.color.domain()[i - 1]) : color_map_7644aa715058bcf5d43b72a5f1644235.x.range()[0],\n", + " x1: i < color_map_7644aa715058bcf5d43b72a5f1644235.color.domain().length ? color_map_7644aa715058bcf5d43b72a5f1644235.x(color_map_7644aa715058bcf5d43b72a5f1644235.color.domain()[i]) : color_map_7644aa715058bcf5d43b72a5f1644235.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -1114,13 +1114,13 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_c8ce69387f16660f893cdc647cad43fc.g.call(color_map_c8ce69387f16660f893cdc647cad43fc.xAxis).append("text")\n", + " color_map_7644aa715058bcf5d43b72a5f1644235.g.call(color_map_7644aa715058bcf5d43b72a5f1644235.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", " .text("Rh Values (gm Carbon/m2/daily)");\n", " \n", - " var map_b5da17e67439e3ecef16807985f9e3da = L.map(\n", - " "map_b5da17e67439e3ecef16807985f9e3da",\n", + " var map_a813a36ca707bd1aac44863fead052c1 = L.map(\n", + " "map_a813a36ca707bd1aac44863fead052c1",\n", " {\n", " center: [31.9, -99.9],\n", " crs: L.CRS.EPSG3857,\n", @@ -1134,35 +1134,35 @@ "\n", " \n", " \n", - " var tile_layer_0335d695e467ba797817150608781903 = L.tileLayer(\n", + " var tile_layer_385b2ac11497266730cb740d6dbc9c07 = 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_0335d695e467ba797817150608781903.addTo(map_b5da17e67439e3ecef16807985f9e3da);\n", + " tile_layer_385b2ac11497266730cb740d6dbc9c07.addTo(map_a813a36ca707bd1aac44863fead052c1);\n", " \n", " \n", - " var tile_layer_513dd255d7c5376f138b1dcfd0592e71 = L.tileLayer(\n", + " var tile_layer_e207efdfdb23ae86013cc69dff4ff960 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20230131/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=-0.35656991600990295%2C7.2141876220703125",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_513dd255d7c5376f138b1dcfd0592e71.addTo(map_b5da17e67439e3ecef16807985f9e3da);\n", + " tile_layer_e207efdfdb23ae86013cc69dff4ff960.addTo(map_a813a36ca707bd1aac44863fead052c1);\n", " \n", " \n", - " map_c416cba82fc90d99ed0c7e106a1534f8.sync(map_b5da17e67439e3ecef16807985f9e3da);\n", - " map_b5da17e67439e3ecef16807985f9e3da.sync(map_c416cba82fc90d99ed0c7e106a1534f8);\n", + " map_3c08b5cd143ad9c0225845d4d084f94a.sync(map_a813a36ca707bd1aac44863fead052c1);\n", + " map_a813a36ca707bd1aac44863fead052c1.sync(map_3c08b5cd143ad9c0225845d4d084f94a);\n", " \n", " \n", - " var marker_6673d988ec29429c846107e7d5ead59b = L.marker(\n", + " var marker_b6de12954d134d53b5244ee6115a5766 = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_b5da17e67439e3ecef16807985f9e3da);\n", + " ).addTo(map_a813a36ca707bd1aac44863fead052c1);\n", " \n", " \n", - " marker_6673d988ec29429c846107e7d5ead59b.bindTooltip(\n", + " marker_b6de12954d134d53b5244ee6115a5766.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -1170,26 +1170,26 @@ " );\n", " \n", " \n", - " var layer_control_3860a9f1204b44e3bf4201b00d26d8eb_layers = {\n", + " var layer_control_2d8d84e921f14c10b34b809a930023d9_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_0335d695e467ba797817150608781903,\n", + " "openstreetmap" : tile_layer_385b2ac11497266730cb740d6dbc9c07,\n", " },\n", " overlays : {\n", - " "2023-01-31 RH Level" : tile_layer_513dd255d7c5376f138b1dcfd0592e71,\n", + " "2023-01-31 RH Level" : tile_layer_e207efdfdb23ae86013cc69dff4ff960,\n", " },\n", " };\n", - " let layer_control_3860a9f1204b44e3bf4201b00d26d8eb = L.control.layers(\n", - " layer_control_3860a9f1204b44e3bf4201b00d26d8eb_layers.base_layers,\n", - " layer_control_3860a9f1204b44e3bf4201b00d26d8eb_layers.overlays,\n", + " let layer_control_2d8d84e921f14c10b34b809a930023d9 = L.control.layers(\n", + " layer_control_2d8d84e921f14c10b34b809a930023d9_layers.base_layers,\n", + " layer_control_2d8d84e921f14c10b34b809a930023d9_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_b5da17e67439e3ecef16807985f9e3da);\n", + " ).addTo(map_a813a36ca707bd1aac44863fead052c1);\n", "\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, @@ -1328,7 +1328,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_464b6077fb346bf174d0608e88ade387 {\n", + " #map_f4fa259dfe2dc2d398c7c451f455f6a6 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1342,14 +1342,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_464b6077fb346bf174d0608e88ade387" ></div>\n", + " <div class="folium-map" id="map_f4fa259dfe2dc2d398c7c451f455f6a6" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_464b6077fb346bf174d0608e88ade387 = L.map(\n", - " "map_464b6077fb346bf174d0608e88ade387",\n", + " var map_f4fa259dfe2dc2d398c7c451f455f6a6 = L.map(\n", + " "map_f4fa259dfe2dc2d398c7c451f455f6a6",\n", " {\n", " center: [32.81, -96.93],\n", " crs: L.CRS.EPSG3857,\n", @@ -1363,41 +1363,41 @@ "\n", " \n", " \n", - " var tile_layer_989ff79c7c54b870368eb37b2dd7e173 = L.tileLayer(\n", + " var tile_layer_985651133c0d44c627cff6e4edb34938 = 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_989ff79c7c54b870368eb37b2dd7e173.addTo(map_464b6077fb346bf174d0608e88ade387);\n", + " tile_layer_985651133c0d44c627cff6e4edb34938.addTo(map_f4fa259dfe2dc2d398c7c451f455f6a6);\n", " \n", " \n", "\n", - " function geo_json_5ae82f03e6067914a0403d999526be4f_onEachFeature(feature, layer) {\n", + " function geo_json_71dd924684afce1e8c606f4317977134_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_5ae82f03e6067914a0403d999526be4f = L.geoJson(null, {\n", - " onEachFeature: geo_json_5ae82f03e6067914a0403d999526be4f_onEachFeature,\n", + " var geo_json_71dd924684afce1e8c606f4317977134 = L.geoJson(null, {\n", + " onEachFeature: geo_json_71dd924684afce1e8c606f4317977134_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_5ae82f03e6067914a0403d999526be4f_add (data) {\n", - " geo_json_5ae82f03e6067914a0403d999526be4f\n", + " function geo_json_71dd924684afce1e8c606f4317977134_add (data) {\n", + " geo_json_71dd924684afce1e8c606f4317977134\n", " .addData(data);\n", " }\n", - " geo_json_5ae82f03e6067914a0403d999526be4f_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_5ae82f03e6067914a0403d999526be4f.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_71dd924684afce1e8c606f4317977134_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_71dd924684afce1e8c606f4317977134.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_5ae82f03e6067914a0403d999526be4f.addTo(map_464b6077fb346bf174d0608e88ade387);\n", + " geo_json_71dd924684afce1e8c606f4317977134.addTo(map_f4fa259dfe2dc2d398c7c451f455f6a6);\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, @@ -2815,8 +2815,8 @@ "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.3459377884864807, 'max': 2.7621078491210938, 'mean': 1.8180646896362305, 'count': 147.99998474121094, 'sum': 269.07354736328125, 'std': 0.43199905772238856, 'median': 1.8053152561187744, 'majority': 0.3459377884864807, 'minority': 0.3459377884864807, 'unique': 165.0, 'histogram': [[2.0, 0.0, 3.0, 10.0, 31.0, 34.0, 26.0, 34.0, 19.0, 6.0], [0.3459377884864807, 0.5875548124313354, 0.8291717767715454, 1.070788860321045, 1.3124058246612549, 1.5540227890014648, 1.7956397533416748, 2.037256956100464, 2.278873920440674, 2.520490884780884, 2.7621078491210938]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 165.0, 'percentile_2': 0.8396886587142944, 'percentile_98': 2.6329331398010254}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.3362884819507599, 'max': 2.780261993408203, 'mean': 1.8850572109222412, 'count': 147.99998474121094, 'sum': 278.9884338378906, 'std': 0.42190765325133345, 'median': 1.8817213773727417, 'majority': 0.3362884819507599, 'minority': 0.3362884819507599, 'unique': 165.0, 'histogram': [[1.0, 1.0, 2.0, 4.0, 29.0, 30.0, 38.0, 33.0, 15.0, 12.0], [0.3362884819507599, 0.5806858539581299, 0.8250831961631775, 1.069480538368225, 1.3138779401779175, 1.5582752227783203, 1.8026726245880127, 2.047070026397705, 2.2914674282073975, 2.5358645915985107, 2.780261993408203]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 165.0, 'percentile_2': 0.9102519750595093, 'percentile_98': 2.6507318019866943}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.3196641504764557, 'max': 2.580934524536133, 'mean': 1.782191514968872, 'count': 147.99998474121094, 'sum': 263.7643127441406, 'std': 0.39324181475308584, 'median': 1.788713812828064, 'majority': 0.3196641504764557, 'minority': 0.3196641504764557, 'unique': 165.0, 'histogram': [[1.0, 1.0, 2.0, 4.0, 24.0, 31.0, 39.0, 32.0, 15.0, 16.0], [0.3196641504764557, 0.5457911491394043, 0.7719181776046753, 0.9980452060699463, 1.2241722345352173, 1.4502992630004883, 1.6764262914657593, 1.9025533199310303, 2.128680467605591, 2.3548076152801514, 2.580934524536133]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 165.0, 'percentile_2': 0.8592413067817688, 'percentile_98': 2.4906678199768066}}}}\n", - "CPU times: user 9.55 s, sys: 2.22 s, total: 11.8 s\n", - "Wall time: 9min 27s\n" + "CPU times: user 6.5 s, sys: 1.49 s, total: 7.99 s\n", + "Wall time: 8min 3s\n" ] } ], @@ -3269,7 +3269,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_23405972e4a814028a80ca8a93cea19a {\n", + " #map_d63f72bc9147efc435e8625525112f3b {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -3284,14 +3284,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_23405972e4a814028a80ca8a93cea19a" ></div>\n", + " <div class="folium-map" id="map_d63f72bc9147efc435e8625525112f3b" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_23405972e4a814028a80ca8a93cea19a = L.map(\n", - " "map_23405972e4a814028a80ca8a93cea19a",\n", + " var map_d63f72bc9147efc435e8625525112f3b = L.map(\n", + " "map_d63f72bc9147efc435e8625525112f3b",\n", " {\n", " center: [32.8, -96.79],\n", " crs: L.CRS.EPSG3857,\n", @@ -3305,31 +3305,31 @@ "\n", " \n", " \n", - " var tile_layer_7c8240c7356eef93643a1648269142a1 = L.tileLayer(\n", + " var tile_layer_af24f8814a134a48861332d867dda4da = 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_7c8240c7356eef93643a1648269142a1.addTo(map_23405972e4a814028a80ca8a93cea19a);\n", + " tile_layer_af24f8814a134a48861332d867dda4da.addTo(map_d63f72bc9147efc435e8625525112f3b);\n", " \n", " \n", - " var tile_layer_459218b87fd95b686fa2195bb2ac910c = L.tileLayer(\n", + " var tile_layer_1513b973da340d8396860d7babdba1f6 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20231229/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=-0.35656991600990295%2C7.2141876220703125",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.7, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_459218b87fd95b686fa2195bb2ac910c.addTo(map_23405972e4a814028a80ca8a93cea19a);\n", + " tile_layer_1513b973da340d8396860d7babdba1f6.addTo(map_d63f72bc9147efc435e8625525112f3b);\n", " \n", " \n", - " var marker_336c66b5601fa16837a2b381a903750e = L.marker(\n", + " var marker_ddabe8fcd187d6ed7824abdfe0e4f039 = L.marker(\n", " [40.0, 5.9],\n", " {}\n", - " ).addTo(map_23405972e4a814028a80ca8a93cea19a);\n", + " ).addTo(map_d63f72bc9147efc435e8625525112f3b);\n", " \n", " \n", - " marker_336c66b5601fa16837a2b381a903750e.bindTooltip(\n", + " marker_ddabe8fcd187d6ed7824abdfe0e4f039.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -3337,58 +3337,58 @@ " );\n", " \n", " \n", - " var layer_control_c74501e447cbbc44cba267e14acd407d_layers = {\n", + " var layer_control_8ffa00fa0a9969b70b2f850190c6eecc_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_7c8240c7356eef93643a1648269142a1,\n", + " "openstreetmap" : tile_layer_af24f8814a134a48861332d867dda4da,\n", " },\n", " overlays : {\n", - " " Observed tile RH Level" : tile_layer_459218b87fd95b686fa2195bb2ac910c,\n", + " " Observed tile RH Level" : tile_layer_1513b973da340d8396860d7babdba1f6,\n", " },\n", " };\n", - " let layer_control_c74501e447cbbc44cba267e14acd407d = L.control.layers(\n", - " layer_control_c74501e447cbbc44cba267e14acd407d_layers.base_layers,\n", - " layer_control_c74501e447cbbc44cba267e14acd407d_layers.overlays,\n", + " let layer_control_8ffa00fa0a9969b70b2f850190c6eecc = L.control.layers(\n", + " layer_control_8ffa00fa0a9969b70b2f850190c6eecc_layers.base_layers,\n", + " layer_control_8ffa00fa0a9969b70b2f850190c6eecc_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_23405972e4a814028a80ca8a93cea19a);\n", + " ).addTo(map_d63f72bc9147efc435e8625525112f3b);\n", "\n", " \n", " \n", - " var color_map_2e6e44f2ad112be850a57b9d778ac010 = {};\n", + " var color_map_812876e34287f884ac10c343ab2a3186 = {};\n", "\n", " \n", - " color_map_2e6e44f2ad112be850a57b9d778ac010.color = 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color_map_812876e34287f884ac10c343ab2a3186.x.range()[0],\n", + " x1: i < color_map_812876e34287f884ac10c343ab2a3186.color.domain().length ? color_map_812876e34287f884ac10c343ab2a3186.x(color_map_812876e34287f884ac10c343ab2a3186.color.domain()[i]) : color_map_812876e34287f884ac10c343ab2a3186.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -3398,7 +3398,7 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_2e6e44f2ad112be850a57b9d778ac010.g.call(color_map_2e6e44f2ad112be850a57b9d778ac010.xAxis).append("text")\n", + " color_map_812876e34287f884ac10c343ab2a3186.g.call(color_map_812876e34287f884ac10c343ab2a3186.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", " .text("Rh Values (gm Carbon/m2/daily)");\n", @@ -3406,7 +3406,7 @@ "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, "execution_count": 24, diff --git a/user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.ipynb b/user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.ipynb index c8b78070b..d080eed05 100644 --- a/user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.ipynb +++ b/user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.ipynb @@ -56,9 +56,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import requests\n", "import pandas as pd\n", @@ -77,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -143,7 +152,7 @@ }, { "data": { - "image/png": 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w5ptvolmzZjh69KjZ504VIBA5yNdffy0AEH7//Xfhk08+EXx9fYWioiJBEAThySefFCIjIwVBEITg4GBh8ODBuv1SU1MFAMLq1av1jrdt2zaD8rZt2wq9e/c2ee6+ffsKarVaVz5t2jRBKpUK165d05Vp21TWiy++KHh7ewt37tzRlfXu3VsAIHzzzTe6srt37wpNmjQRhg8friv79ttvBRcXFyE1NVXvmMuXLxcACPv37zf+gv2nZ8+eQufOnQ3K/fz8hA4dOpjdtyxte839fPjhh6KPJwiCcP/99wsDBw60WC84ONjiuTds2KCrP2zYMMHd3V04d+6cruzChQuCr6+v0KtXL13Z7NmzBWMfW9rfd3p6ukEbtm3bZrR9o0eP1j3+7LPPBABCWlqarqy4uFho2LChXj1jOnXqJNStW9dsnbLH9Pf3F9q1ayfcvn1bV75lyxYBgDBr1ixd2ejRowUAwrx58wzOV/b62LVrlwBAmDp1qsH5tNd+RkaGIJVKhfnz5+ttP3HihODq6qpXLvY6//333wUAwtdff21wXu0xli9fbrBN7Ptt8ODBQnBwsEHd9PR0g/N27NhR8Pf3F/Lz83Vlx48fF1xcXITnnntOV6a9fsaNG6d3zCeeeEJo0KCB7vGSJUsEAMLly5cNzm9OXFycAED4448/TNY5evSoAECYPn26IAiCcObMGQGA8PHHH+vVe+mllwQfHx/d62XN56K5a98SU5+p2uOWfT9o33f9+/fX+5zt1q2bIJFIhIkTJ+rKSktLBblcrndsa56TMWX/xmjZ8h57/fXXzZ5HS3v9mLouyr922mv1ww8/FEpLS4WWLVsKHTp00L1Wpo5XUlIidOzYUahfv74AQPjss89Ete/OnTuCSqXSK0tPTxc8PDz0Pkd2794tABDCwsKEu3fv6soTEhIEAMKJEycEQbj3Wnbs2FGv3ooVKwQAJq8TYy5fviwAEGbPnm10++jRo4U6deoIgiAI0dHRQp8+fQRBEASVSiU0adJEmDt3rt7rWf65lP17Ul6HDh0EPz8/s+3Ly8sTGjVqJAAQWrduLUycOFFYs2aN3r2ClqnPptLSUr3XSRAE4erVq0Ljxo31PnPMvRbl/8YdO3ZMACA8//zzevVmzJghABB27dqlK9O+7/fu3asru3TpkuDh4SG88sorurIOHTro3XOR47FniypFTEwMbt++jS1btuDGjRvYsmWLySEBGzZsQN26ddGvXz+9HpHOnTvDx8dHr0vekgkTJuh1yYeHh0OlUuH8+fO6srLzGG7cuIErV64gPDwcRUVFOH36tN7xfHx89Mabu7u746GHHsK///6r1/6wsDC0bt1ar/3aCbqW2p+fnw8/Pz+D8sLCQvj6+op85hohISFITk42+Pnuu+9M7qMd2mCsR8DPz0/0HLuHH37Y6LnLZ0BUqVTYsWMHhg0bhvvuu09XHhAQgFGjRmHfvn0oLCwU94TLCQ0NRf/+/S3Wi4mJgaenp17v1vbt23HlyhWT8wu0rPm9HD58GJcuXcJLL72kN+9o8ODBaN26NX766SeDfSZOnKj3ODw8XO9627hxIyQSCWbPnm2wr/baVyqVUKvViImJ0bsmmzRpgpYtWxpck2Kuc0s8PDwwduxYg3Jr3m9i5Obm4tixYxgzZgzq16+vK3/ggQfQr18/bN261WAfY69pfn6+7jrTzgHavHkz1Gq16LbcuHEDAMxeD9pt2nPdf//96NixI9avX6+ro1KpkJSUhCFDhuheL2s/F8Ve+/Ywfvx4vc/Zhx9+GIIgYPz48boyqVSKLl26GHxW2uuzXsuW99ikSZOsPo+1yvZuff/992brurq6YsWKFSgoKMAjjzwiegkQDw8PXQITlUqF/Px83RBYY70WY8eO1ZuLFB4eDgC635H2tZw4caJevTFjxqBu3bqi2mSLUaNGISUlBXl5edi1axfy8vKsHkJYlo+Pj+69aUrjxo1x/PhxTJw4EVevXsXy5csxatQo+Pv745133rE4/BPQ/I61r5NarUZBQQFKS0vRpUsXm3uNtJ9f2mGlWq+88goAGFzPbdq00f0eAU1PWqtWrfTed/Xq1cOpU6dw9uxZm9pE1uMsPKoUjRo1Qt++fbFmzRoUFRVBpVKZHA539uxZXL9+Hf7+/ka3aydFi9GsWTO9x9ogpuwwulOnTuGtt97Crl27DG7qyw95k8vlBuOp/fz88Oeff+q1Py0tTTcu25b2G/tgl8lkFv9glFenTh2jWbqMBVIA8Ndff5kdjy8Igug00A0bNjR67vKTfy9fvoyioiK0atXKoG5YWBjUajWysrLQtm1bUectKzQ0VFS9evXqYciQIVizZg3eeecdAJohhIGBgRazWMlkMtFBiDbIN/ZcW7dubbBEgnb+VVl+fn561++5c+fQtGlTvUCjvLNnz0IQBLRs2dLo9vKZ/cRc55YEBgYanVRuzftNDHOvaVhYGLZv326Q9MDc54JMJsNTTz2FL774As8//zxef/119OnTBwqFAtHR0Waz8WkDKXPvU2MB2VNPPYX/+7//Q05ODgIDA5GSkoJLly7pDTu29nNR7LVvD+VfT+2NeFBQkEF52WvXnp/1Wta+x1xdXSGXy60+jynmPh+ffvppvPPOO5g3bx6GDRtm9jjaOZGdO3cW/Zmrnbu5dOlSpKen682DbNCggUF9S38fta9l+c8NNzc3vS/G7E07f279+vU4duwYunbtihYtWpj8u2XJzZs3RX0hFhAQgGXLlmHp0qU4e/Ystm/fjvfffx+zZs1CQEAAnn/+eYvHWLVqFRYtWoTTp0/rDYO19f14/vx5uLi4GGQmbtKkCerVq6f3xTFg+DsFDP9mzJs3D0OHDsX999+Pdu3aYcCAAXj22Wf1EpqQfTHYokozatQovPDCC8jLy8PAgQNNZpBSq9Xw9/c3OocGgMkgxhipVGq0XBvMXLt2Db1794ZMJsO8efPQvHlzeHp64ujRo3jttdcMvtW2dDxt+9u3b4/FixcbrVv+BqS8Bg0aGJ1T1bp1axw7dgzFxcUOyYykVqsxefJkLFu2DGFhYUbrXL161eQNe2UwddNRPmGEljWZB5977jls2LABBw4cQPv27fHDDz/gpZdespjqunXr1vjjjz+QlZVl8XdrLVPXm7XUajUkEgl+/vlno8csn11SzHVuibHX3tr3m6NYen5eXl7Yu3cvdu/ejZ9++gnbtm3D+vXr8eijj2LHjh0m99e+b/7880+Tc6S0AWubNm10ZU899RTeeOMNbNiwAXFxcUhMTETdunUxYMAAXR1rPxftnXXTHFOvh7Hy8p+V9vqst1XZ3iBLtD1lxtaBAjQZN81lytT2bo0ZMwabN2+2vrEWvPfee3j77bcxbtw4vPPOO6hfvz5cXFwQFxdn9L1lj/e5I3h4eEChUGDVqlX4999/dckjbFFSUoK///7b5JqTxkgkEtx///24//77MXjwYLRs2RKrV6+2GGx99913GDNmDIYNG4ZXX30V/v7+unmD586ds/k5aNskhpjfaa9evXDu3Dls3rwZO3bswBdffIElS5Zg+fLlogJKsh6DLao0TzzxBF588UUcOnRIb8hMec2bN8fOnTvRo0cPizcMFV1sMyUlBfn5+VAqlXqT+9PT020+ZvPmzXH8+HH06dPHpva1bt3aaHrbIUOG4ODBg9i4cSNGjhxpc/tM+eSTT/Doo48azXQIaCaSZ2Vl4fHHH7freRs1agRvb2+cOXPGYNvp06fh4uKiC2K037xeu3ZNL1gv/+2eLQYMGIBGjRph9erVePjhh1FUVCQq6cWQIUOwdu1afPfdd3jjjTfM1tUmPTlz5oxBj9mZM2cMkqKI0bx5c2zfvh0FBQUme7eaN28OQRAQGhqK+++/3+pzGGPLtW3N+03s8cu+puWdPn0aDRs2tCmVt4uLC/r06YM+ffpg8eLFeO+99/Dmm29i9+7dJtd0GjhwIKRSKb799luTSTK++eYbuLq66gVSoaGheOihh7B+/XpMmTIFSqUSw4YN00u1bs3norNwxHNyxHvM2LHLf7FSVFSErKwsREVFmT3GM888g3fffVeXTMSekpKSEBkZiS+//FKv/Nq1a2jYsKHVx9M+37Nnz+q9liUlJUhPT0eHDh0q1mAzRo0aha+++gouLi4mE2mJkZSUhNu3b9s8pPa+++6Dn58fcnNzdWWmPpuSkpJw3333QalU6tUpP8Tbms/O4OBgqNVqnD17Vu9L0IsXL+LatWs2X8/169fH2LFjMXbsWNy8eRO9evXCnDlzGGw5COdsUaXx8fHBsmXLMGfOHAwZMsRkvZiYGKhUKt1wrrJKS0v1sknVqVPHILuUNbTfApX91qe4uBhLly61+ZgxMTHIyckxuubV7du3cevWLbP7d+vWDVevXjUYmjZx4kQEBATglVdeMbpA7qVLl/Duu+/a1ObMzEysXbvW7GKKf/31F+7cuWOwBklFSaVSREVFYfPmzXrDRC5evKhbDFsmkwHQ3JwBwN69e3X1bt26hVWrVlW4Ha6urhg5ciQSExOxcuVKtG/fXtSwiujoaLRv3x7z58/HwYMHDbbfuHFDlyWzS5cu8Pf3x/Lly/XS9v78889IS0vD4MGDrW738OHDIQiC0cVOtde1QqGAVCrF3LlzDb61FgQB+fn5Vp9XG8BY8/6z5v1Wp04dUcMKAwIC0LFjR6xatUqvLSdPnsSOHTswaNAg0e3TKigoMCjT9lSZW74hKCgIY8eOxc6dO42uo7V8+XLs2rUL48ePNxi69tRTT+HQoUP46quvcOXKFYPMpdZ8LjoLRzwnR7zHtPr06QN3d3csW7bMoKdoxYoVKC0ttZieXdu7dezYMV0mU3uRSqUG7+8NGzYYpLwXq0uXLmjUqBGWL1+O4uJiXfnKlSsdfr1FRkbinXfewSeffIImTZrYdIzjx48jLi4Ofn5+mDx5stm6v/76q9G/zb/99hvy8/P1hqWa+mwy9vn266+/Gvxd0K6VJuY11H5+lc9GqR05Y8v1XP7z3sfHBy1atBC1NA3Zhj1bVKm066KY07t3b7z44otYsGABjh07hqioKLi5ueHs2bPYsGEDEhISdPO9OnfujGXLluHdd99FixYt4O/vb3GOTVndu3eHn58fRo8ejalTp0IikeDbb7+t0DCKZ599FomJiZg4cSJ2796NHj16QKVS4fTp00hMTNStfWPK4MGD4erqip07d+pSGwOaXp1NmzZh0KBB6NixI5555hl07twZAHD06FGsXbsW3bp1s6nNBw4cQE5ODtq3b68r69OnD5RKpe7by+TkZHh7e6Nfv342ncOcd999V7eu0UsvvQRXV1d89tlnuHv3rt76TlFRUWjWrBnGjx+PV199FVKpFF999RUaNWqEzMzMCrfjueeew0cffYTdu3eLXojYzc0NSqUSffv2Ra9evRATE4MePXrAzc0Np06dwpo1a+Dn54f58+fDzc0N77//PsaOHYvevXtj5MiRuHjxIhISEhASEoJp06ZZ3ebIyEg8++yz+Oijj3D27FkMGDAAarUaqampiIyMxJQpU9C8eXO8++67eOONN5CRkYFhw4bB19cX6enp2LRpEyZMmIAZM2ZYdd7mzZujXr16WL58OXx9fVGnTh08/PDDZucmWPN+69y5M9avX4/p06eja9eu8PHxMfklzYcffoiBAweiW7duGD9+PG7fvo2PP/4YdevWtWkI0rx587B3714MHjwYwcHBuHTpEpYuXQq5XG50LbWylixZgtOnT+Oll17Ctm3bdD1Y27dvx+bNm3Xp+8uLiYnBjBkzMGPGDNSvX9+g98yaz0Vn4Yjn5Ij3mJa/vz9mzZqFt956C7169cLjjz8Ob29vHDhwAGvXrkVUVJTZLxK1tHO3tOnA7eWxxx7DvHnzMHbsWHTv3h0nTpzA6tWrbZ5f5ebmhnfffRcvvvgiHn30UTz11FNIT0/H119/7dA5W4CmZ9madSNTU1Nx584dXWKQ/fv344cffkDdunWxadMmiwHbt99+i9WrV+OJJ55A586d4e7ujrS0NHz11Vfw9PTUW2PP1GfTY489BqVSiSeeeAKDBw9Geno6li9fjjZt2uDmzZu6/b28vNCmTRusX78e999/P+rXr4927doZHerYoUMHjB49GitWrNANw/7tt9+watUqDBs2zOgafZa0adMGERER6Ny5M+rXr4/Dhw8jKSnJ6NqAZCeVmPmQahljaXmNKZ/6XWvFihVC586dBS8vL8HX11do3769MHPmTOHChQu6Onl5ecLgwYMFX19fvVS0ps6tTRO7e/duXdn+/fuFRx55RPDy8hKaNm0qzJw5U9i+fbtBvd69ewtt27Y1aOfo0aMN0sAWFxcL77//vtC2bVvBw8ND8PPzEzp37izMnTtXuH79utnXQxAE4fHHH9elvi3vwoULwrRp04T7779f8PT0FLy9vYXOnTsL8+fP1zu2qfYKgmA0hW5ZKJdGXRAE4eGHHxaeeeYZi20XBNO/U0Ewnar36NGjQv/+/QUfHx/B29tbiIyMFA4cOGCw/5EjR4SHH35YcHd3F5o1ayYsXrzYZOp3U20on8K6rLZt2wouLi5Cdna2qOeqdfXqVWHWrFlC+/btBW9vb8HT01No166d8MYbbwi5ubl6ddevXy906tRJ8PDwEOrXry88/fTTBucrmwq5LGPp70tLS4UPP/xQaN26teDu7i40atRIGDhwoHDkyBG9ehs3bhR69uwp1KlTR6hTp47QunVrYfLkycKZM2d0day5zjdv3iy0adNGcHV11UvHbu7aE/t+u3nzpjBq1CihXr16AgDduY2lfhcEQdi5c6fQo0cPwcvLS5DJZMKQIUOEv/76y+hrVz7Vdvnr55dffhGGDh0qNG3aVHB3dxeaNm0qjBw5Uvj777+NPqfy7t69KyxZskTo3LmzUKdOHcHb21t48MEHhfj4eKG4uNjkfj169DCa5rksMZ+L5q59S2xJ/V7+c9bU62zqmhbznIwx9zemIu8xS7777jvhkUceEerUqSN4eHgIrVu3FubOnau3dIEgmP+c1bbd2OukBUCYPHmy6HbduXNHeOWVV4SAgADBy8tL6NGjh3Dw4EGhd+/eer9TU5/Bpt5bS5cuFUJDQwUPDw+hS5cuwt69ew2OaYk1qd9NMZf6Xfvj5uYmNGrUSOjVq5cwf/584dKlS6La9+effwqvvvqq8OCDDwr169cXXF1dhYCAAOHJJ58Ujh49qlfX1GeTWq0W3nvvPSE4OFjw8PAQOnXqJGzZssXoZ+eBAweEzp07C+7u7nqvi7HP95KSEmHu3LlCaGio4ObmJgQFBQlvvPGGwfVm6n1f/nf17rvvCg899JBQr149wcvLS2jdurUwf/58s59NVDESQajimZBEZCA1NRURERE4ffp0lSak0Dp27BgefPBBHD161KGLo1YHnTp1Qv369fHLL79UdVOIiIjIyTHYIqqmBg4cCLlcbnTuV2UbMWIE1Go1EhMTq7opDnX48GF07doVK1euFDXklYiIiMgcBltEVOudPHkSR44cwaJFi3DlyhX8+++/ZlM4ExEREYnBbIREVOslJSVh7NixKCkpwdq1axloERERkV2wZ4uIiIiIiMgB2LNFRERERETkAAy2iIiIiIiIHICLGoukVqtx4cIF+Pr6QiKRVHVziIiIiIioigiCgBs3bqBp06ZwcTHdf8VgS6QLFy4gKCioqptBRERERETVRFZWFuRyucntDLZE8vX1BaB5QWUyWZW2paSkBDt27EBUVBTc3NyqtC3kPHjdkC143ZAteN2QLXjdkC2q6ropLCxEUFCQLkYwhcGWSNqhgzKZrFoEW97e3pDJZPwwItF43ZAteN2QLXjdkC143ZAtqvq6sTS9iAkyiIiIiIiIHIDBFhERERERkQMw2CIiIiIiInIAztkiIiIiolpLEASUlpZCpVJVdVPIBiUlJXB1dcWdO3fs+juUSqVwdXWt8JJPDLaIiIiIqFYqLi5Gbm4uioqKqropZCNBENCkSRNkZWXZfS1cb29vBAQEwN3d3eZjMNgiIiIiolpHrVYjPT0dUqkUTZs2hbu7u91v1snx1Go1bt68CR8fH7OLC1tDEAQUFxfj8uXLSE9PR8uWLW0+NoMtIiIiIqp1iouLoVarERQUBG9v76puDtlIrVajuLgYnp6edgu2AMDLywtubm44f/687vi2YIIMIiIiIqq17HmDTjWLPa4NXl1EREREREQOwGCLiIiIiIjIARhsERERERFVgEoFpKQAa9dq/l+bssj36tULa9asqepmiPLXX39BLpfj1q1blXZOBltERERERDZSKoGQECAyEhg1SvP/kBBNuaNEREQgLi7OoHzlypWoV6+e7vGcOXMgkUgwceJEvXrHjh2DRCJBRkYG9uzZAzc3N+zbt0+vzq1bt3DfffdhxowZJtvxww8/4OLFixgxYgQAoKCgAC+//DJatWoFLy8vNGvWDFOnTsX169d1++Tn52PAgAFo2rQpPDw8EBQUhClTpqCwsFDv2CkpKXjwwQfh4eGBFi1aYOXKlSJfHdPatGmDRx55BIsXL67wscRisEVEREREZAOlEoiOBrKz9ctzcjTljgy4xPL09MSXX36Js2fPGt3eu3dvvPzyyxgzZoxej8/MmTPh5eWFd9991+SxP/roI4wdO1aXSOLChQu4cOECFi5ciJMnT2LlypXYtm0bxo8fr9vHxcUFQ4cOxQ8//IC///4bK1euxM6dO/UCwvT0dAwePBiRkZE4duwY4uLi8Pzzz2P79u0VfTkwduxYLFu2DKWlpRU+lhgMtoiIiMgklVqFlIwUrD2xFikZKVCpa9H4KKp1BAG4dUvcT2EhMHWqZh9jxwGA2FhNPUvHMnYMe2nVqhUiIyPx5ptvmqzz3nvvwd3dHa+99hoAYPfu3fjiiy/wzTffmEx5fvnyZezatQtDhgzRlbVr1w4bN27EkCFD0Lx5czz66KOYP38+fvzxR11w4+fnh0mTJqFLly4IDg5Gnz598NJLLyE1NVV3nOXLlyM0NBSLFi1CWFgYpkyZgujoaCxZskRXJyIiAi+//DKmTZuGkJAQBAQE4PPPP8etW7cwduxY+Pr6okWLFvj555/12t2vXz8UFBRgz5491r+YNmCwRUREREYp05QISQhB5KpIjFKOQuSqSIQkhECZVg2+ridygKIiwMdH3E/dupoeLFMEQdPjVbeu5WMVFTn2ef3vf//Dxo0bcfjwYaPbPT098c0332DFihXYvHkzxo0bh//7v/9D586dTR5z37598Pb2RlhYmNlzX79+HTKZDK6uxpf3vXDhApRKJXr37q0rO3jwIPr27atXr3///jh48KBe2apVq9CwYUP88ssvmDJlCiZNmoQnn3wS3bt3x9GjRxEVFYVnn30WRWVeYHd3d3Ts2FEvuHMkBltERERkQJmmRHRiNLIL9cdH5RTmIDoxmgEXkRN58MEHERMTo+u5MqZLly544403oFAo0KBBA7M9YQBw/vx5NG7c2OxaVFeuXME777yDCRMmGGwbOXIkvL29ERgYCJlMhi+++EK3LS8vD40bN9ar37hxYxQWFuL27du6sg4dOuDNN99E8+bN8frrr8PT0xMNGzbECy+8gJYtW2LWrFnIz8/Hn3/+qXespk2b4vz582afn70w2CIiIiI9KrUKsdtiIcBwbJO2LG5bHIcUUo3j7Q3cvCnuZ+tWccfcutXysby9Hfu8AODdd99FamoqduzYYbLO22+/DbVajddff91kT5TW7du3TQ4xBIDCwkIMHjwYbdq0wZw5cwy2L1myBEePHsXmzZtx7tw5TJ8+XfRz0XrggQd0/5ZKpWjQoAHat2+vK9MGbJcuXdLbz8vLS6+3y5EYbBEREZGe1MxUgx6tsgQIyCrMQmpm5QzDIaosEglQp464n6goQC7X7GPqWEFBmnqWjmXqGKbIZDK9DH9a165dQ926dY3u07x5c7zwwgt4/fXXIZiYJKYNsCwFWgDQsGFDXL161ei2GzduYMCAAfD19cWmTZvg5uZmUKdJkyZo3bo1Hn/8cXz22WdYtmwZcnNzddsuXryoV//ixYuQyWTw8vLSlZU/rkQi0SuT/PfCqtVqvXoFBQVo1KiRxedoDwy2iIiISE/ujVy71iOqiaRSICFB8+/ywZL2cXy8pp69tWrVCkePHjUoP3r0KO6//36T+82aNQt///031q1bV+E2dOrUCXl5eQYBV2FhIaKiouDu7o4ffvjBbO+XljYYunv3LgCgW7du+OWXX/TqJCcno1u3bhVuNwCcPHkSnTp1ssuxLGGwRURERHoCfAPsWo+oplIogKQkIDBQv1wu15QrFI4576RJk/D3339j6tSp+PPPP3HmzBksXrwYa9euxSuvvGJyv8aNG2P69On46KOPKtyGTp06oWHDhti/f7+uTBto3bp1C19++SUKCwuRl5eHvLw8qP5b6Xnr1q34+uuvcfLkSWRkZOCnn37CxIkT0aNHD4SEhAAAJk6ciH///RczZ87E6dOnsXTpUiQmJmLatGkVbndGRgZycnIMEnA4CoMtIiIi0hPeLBxymRwSGB/bJIEEQbIghDcLr+SWEVU/CgWQkQHs3g2sWaP5f3q64wItALjvvvuwd+9enD59Gn379sXDDz+MxMREbNiwAQMGDDC774wZM+Dj41PhNkilUowdOxarV6/WlR09ehS//vorTpw4gRYtWiAgIED3k5WVBUAzX+rzzz9Hz549ERYWhmnTpuHxxx/Hli1bdMcJDQ3FTz/9hOTkZHTo0AGLFi3CF198gf79+1e43WvXrkVUVBSCg4MrfCwxLA/IJCIiolpF6iJFwoAERCdGQwKJXqIMbQAWPyAeUhcHjI8ickJSKRARUbnn7Nq1q9lkFwAwZ84cg+QUMpkMly9fNrmPqflcxkybNg1t27bF+fPnERwcjIiICIv7R0ZG4sCBAxaPHRERgT/++MPk9pSUFAD687EyMjIM6pVtT3FxMZYvX441a9ZYPL+9sGeLiIiIDCjCFEiKSUKgTH98lFwmR1JMEhRhDvzanoicQpMmTfDll18iMzOzqpsiSmZmJv7v//4PPXr0qLRzsmeLiIiIjFKEKTC01VC4vqO5XXitx2uY/+h89mgRkc6wYcOqugmitWjRAi1atKjUc7Jni4iIiEwqG1iFNQxjoEVEZAUGW0RERERERA7AYIuIiIiIiMgBGGwRERERERE5AIMtIiIiIiIiB2CwRURERKKUXW+LiIgsY7BFRERERETkAAy2iIiISBQJJFXdBKJqSaVWISUjBWtPrEVKRgpUalVVN6nSffnll4iKiqrqZoj2+uuv4+WXX3b4eRhsERERERHZSJmmREhCCCJXRWKUchQiV0UiJCEEyjSlQ84nkUjM/syZMwcZGRmQSCSQSqXIycnR2z83Nxeurq6QSCTIyMgAAF197Y+vry/atm2LyZMn4+zZsxbbdOfOHbz99tuYPXu20e3r1q2DRCIxWAD55s2bmDJlCuRyOby8vNCmTRssX75cr865c+fwxBNPoFGjRpDJZIiJicHFixfFv2AmzJgxA6tWrcK///5b4WOZw2CLiIiIiMgGyjQlohOjkV2YrVeeU5iD6MRohwRcubm5up/4+HjIZDK9shkzZujqBgYG4ptvvtHbf9WqVQgMDDR67J07dyI3NxfHjx/He++9h7S0NHTo0AG//PKL2TYlJSVBJpOhR48eBtsyMjIwY8YMhIeHG2ybPn06tm3bhu+++w5paWmIi4vDlClT8MMPPwAAbt26haioKEgkEuzatQv79+9HcXExhgwZArVabfG1Mqdhw4bo378/li1bVqHjWMJgi4iIiIgIgCAIuFV8S9RP4Z1CTP15qtHEMdqy2J9jUXin0OKxBEF88pkmTZrofurWrQuJRKJX5uPjo6s7evRofP3113r7f/311xg9erTRYzdo0ABNmjTBfffdh6FDh2Lnzp14+OGHMX78eKhUpodGrlu3DkOGDDEoV6lUePrppzF37lzcd999BtsPHDiA0aNHIyIiAiEhIZgwYQI6dOiA3377DQCwf/9+ZGRkYOXKlWjfvj3at2+PVatW4fDhw9i1axcATTDn5+eHxMREhIeHw8vLC127dsXff/+N33//HV26dIGPjw8GDhyIy5cv651/yJAhWLduncnnZQ+uDj06EREREZGTKCopgs8CH8sVRRAgIPtGNuq+X9di3Ztv3EQd9zp2OW9Zjz/+OJYvX459+/ahZ8+e2LdvH65evYohQ4bgnXfesbi/i4sLYmNj8cQTT+DIkSN46KGHjNbbt28fnn32WYPyefPmwd/fH+PHj0dqaqrB9u7du+OHH37AuHHj0LRpU6SkpODvv//GkiVLAAB3796FRCKBh4eHbh9PT0+4uLhg37596Nu3r6587ty5iI+PR7NmzTBu3DiMGjUKvr6+SEhIgLe3N2JiYjBr1iy9nqyHHnoI2dnZyMjIQEhIiMXXwxbs2SIiIiIiqoHc3NzwzDPP4KuvvgIAfPXVV3jmmWfg5uYm+hitW7cGAN38rvKuXbuG69evo2nTpnrl+/btw5dffonPP//c5LE//vhjtGnTBnK5HO7u7hgwYAA+/fRT9OrVCwDwyCOPoE6dOnjttddQVFSEW7duYcaMGVCpVMjNzdU71vTp09G/f3+EhYUhNjYWR44cwdtvv40ePXqgU6dOGD9+PHbv3q23j7bN58+fF/16WIs9W0REREREALzdvHHzjZui6u49vxeD1gyyWG/rqK3oFdzL4nkdZdy4cejevTvee+89bNiwAQcPHkRpaano/bVDHCUS49lIb9++DUDT46R148YNPPvss/j888/RsGFDk8f++OOPcejQIfzwww8IDg7G3r17MXnyZDRt2hR9+/ZFo0aNsGHDBkyaNAkfffQRXFxcMHLkSDz44INwcdHvM3rggQd0/27cuDEAoH379nplly5d0tvHy8sLAFBUVGTxdbAVgy0iIiIiImgCCrHD+aKaR0EukyOnMMfovC0JJJDL5IhqHgWpi9TeTRWtffv2aN26NUaOHImwsDC0a9cOx44dE71/WloaACA0NNTo9gYNGkAikeDq1au6snPnziEjI0NvHpc2oYWrqyvOnDmDpk2b4v/+7/+wadMmDB48GIAmYDp27BgWLlyoGyIYFRWFc+fO4cqVK3B1dUW9evV088rKKttbpw0My5eVT6pRUFAAAGjUqJHo18NaHEZIRERERGQlqYsUCQMSABiuQad9HD8gvkoDLa1x48YhJSUF48aNs2o/tVqNjz76CKGhoejUqZPROu7u7mjTpg3++usvXVnr1q1x4sQJHDt2TPfz+OOPIzIyEseOHUNQUBBKSkpQUlJi0EMllUqNZhps2LAh6tWrh127duHSpUt4/PHHrXouxpw8eRJubm5o27ZthY9lCnu2iIiISBRj394T1WaKMAWSYpIQuy1WL/27XCZH/IB4KMIUVdi6e1544QU8+eSTqFevntl6+fn5yMvLQ1FREU6ePIn4+Hj89ttv+OmnnyCVmg4a+/fvj3379iEuLg6AZkhhu3bt9Opoz60td3d3R+/evfHqq6/Cy8sLwcHB2LNnD7755hssXrxYt9/XX3+NsLAwNGrUCAcPHkRsbCymTZuGVq1aWf9ClJOamqrLYOgoDLaIiIiIiGykCFNgaKuhSM1MRe6NXAT4BiC8WXi16NHScnV1NTt3Sks7dM/b2xvBwcGIjIzEihUr0KJFC7P7jR8/Hl26dMH169dRt67l7Ita69atwxtvvIGnn34aBQUFCA4Oxvz58zFx4kRdnTNnzuCNN95AQUEBQkJC8Oabb2LatGmiz2Hp/HPmzLHLsUxhsEVEREREVAFSFykiQiIq/bxjxozBmDFjDMpDQkLMrt3VsWNHve2W6lvSpk0bDB48GEuXLsUbb7xhtM7KlSsNypo0aWKwDlh5//vf//C///3P5PaQkBBcvXoVMplMVxYREWHwfMq/Vj///DNcXFwQHR1t9vwVxTlbRERERERUIR9++KHegsrV3a1bt/D111/D1dWxfU/s2SIiIiJRyicBICLSCgkJwcsvv1zVzRDN0T1aWuzZIiIiIiIicgAGW0RERERERA7AYIuIiIiIaq2KJIagms0e1waDLSIiIiKqddzc3AAARUVFVdwSqq6014b2WrEFE2QQERERUa0jlUpRr149XLp0CYBmbSmJhElgnI1arUZxcTHu3LkDFxf79CMJgoCioiJcunQJ9erVM7ugsyUMtoiIiIioVmrSpAkA6AIucj6CIOD27dvw8vKye7Bcr1493TViKwZbRERERFQrSSQSBAQEwN/fHyUlJVXdHLJBSUkJ9u7di169elVouF95bm5uFerR0mKwRURERKIIYCIBqpmkUqldbqyp8kmlUpSWlsLT09OuwZa9MEEGERERERGRAzDYIiIiIiIicgAGW0RERERERA7AYIuIiIiIiMgBGGwRERGRKBJwDSIiImtUabC1YMECdO3aFb6+vvD398ewYcNw5swZo3UFQcDAgQMhkUjw/fff623LzMzE4MGD4e3tDX9/f7z66qsoLS3Vq5OSkoIHH3wQHh4eaNGiBVauXOmgZ0VERERERFTFwdaePXswefJkHDp0CMnJySgpKUFUVBRu3bplUDc+Pt7oQmUqlQqDBw9GcXExDhw4gFWrVmHlypWYNWuWrk56ejoGDx6MyMhIHDt2DHFxcXj++eexfft2hz4/IiIiIiKqvap0na1t27bpPV65ciX8/f1x5MgR9OrVS1d+7NgxLFq0CIcPH0ZAQIDePjt27MBff/2FnTt3onHjxujYsSPeeecdvPbaa5gzZw7c3d2xfPlyhIaGYtGiRQCAsLAw7Nu3D0uWLEH//v0d/0SJiIiIiKjWqVaLGl+/fh0AUL9+fV1ZUVERRo0ahU8//RRNmjQx2OfgwYNo3749GjdurCvr378/Jk2ahFOnTqFTp044ePAg+vbtq7df//79ERcXZ7Itd+/exd27d3WPCwsLAWhWqa7qFca156/qdpBz4XVDtuB1Q2WVqkpFXQu8bsgWvG7IFlV13Yg9X7UJttRqNeLi4tCjRw+0a9dOVz5t2jR0794dQ4cONbpfXl6eXqAFQPc4Ly/PbJ3CwkLcvn0bXl5eBsddsGAB5s6da1C+Y8cOeHt7W/fkHCQ5Obmqm0BOiNcN2YLXTe2lElS6f2/9bSv8svwglUhF7cvrhmzB64ZsUdnXTVFRkah61SbYmjx5Mk6ePIl9+/bpyn744Qfs2rULf/zxR6W354033sD06dN1jwsLCxEUFISoqCjIZLJKb09ZJSUlSE5ORr9+/eDm5lalbSHnweuGbMHrpnbbdHoTpiff+1uovKTEr7d/xeJ+i/FE6ydM7sfrhmzB64ZsUVXXjXbUmyXVItiaMmUKtmzZgr1790Iul+vKd+3ahXPnzqFevXp69YcPH47w8HCkpKSgSZMm+O233/S2X7x4EQB0ww6bNGmiKytbRyaTGe3VAgAPDw94eHgYlLu5uVWbD4Dq1BZyHrxuyBa8bmofZZoSI5QjIEDQK79w4wJGKEcgKSYJijCF2WPwuiFb8LohW1T2dSP2XFWajVAQBEyZMgWbNm3Crl27EBoaqrf99ddfx59//oljx47pfgBgyZIl+PrrrwEA3bp1w4kTJ3Dp0iXdfsnJyZDJZGjTpo2uzi+//KJ37OTkZHTr1s2Bz46IiMg5qdQqxG6LNQi0AOjK4rbFQaVWGWwnIqJ7qrRna/LkyVizZg02b94MX19f3RyrunXrwsvLC02aNDGaFKNZs2a6wCwqKgpt2rTBs88+iw8++AB5eXl46623MHnyZF3P1MSJE/HJJ59g5syZGDduHHbt2oXExET89NNPlfdkiYiInERqZiqyC7NNbhcgIKswC6mZqYgIiai8hhEROZkq7dlatmwZrl+/joiICAQEBOh+1q9fL/oYUqkUW7ZsgVQqRbdu3fDMM8/gueeew7x583R1QkND8dNPPyE5ORkdOnTAokWL8MUXXzDtOxERkRG5N3LtWo+IqLaq0p4tQTAcnmDLPsHBwdi6davZ/SIiIqok0QYREZGzCfANsFzJinpERLVVlfZsERERUfUT3iwccpkcEkiMbpdAgiBZEMKbhVdyy4iInAuDLSIiItIjdZEiYUACABgEXNrH8QPiIXURt94WEVFtxWCLiIiIDCjCFEiKSUKgLFCvXC6Ti0r7TkREDLaIiIjIBEWYAhmxGbrHr/V4Demx6Qy0iIhEYrBFREREJpUdKhjWMIxDB4mIrMBgi4iIiIiIyAEYbBEREZEoAqxfsoWIqDZjsEVEREREROQADLaIiIiIiIgcgMEWERERERGRA7hWdQOIiIiocqjUKqRmpiL3Ri4CfAMQ3iyc2QWJiByIwRYREVEtoExTInZbLLILs3VlcpkcCQMSuG4WEZGDcBghERFRDadMUyI6MVov0AKAnMIcRCdGQ5mmrKKWERHVbAy2iIiIajCVWoXYbbFG07Zry+K2xUGlVlV204iIajwGW0RERDVYamaqQY9WWQIEZBVmITUz1eKxJJDYs2lERDUegy0iIqIaLPdGrl3rERGReAy2iIiIarAA3wC71iMiIvEYbBEREdVg4c3CIZfJTQ4BlECCIFkQwpuFV3LLiIhqPgZbRERENZjURYqEAQkADOdcaR/HD4gXtd6WsSQbRERkGoMtIiKiGk4RpkBSTBICZYF65XKZHEkxSVxni4jIQbioMRERUS2gCFNgaKuhcH1H86ffU+qJ9Nh0UT1aRERkG/ZsERER1RJlAyupi5SBFhGRgzHYIiIiqoU4/4qIyPEYbBERERERETkAgy0iIiIiIiIHYLBFRERERETkAAy2iIiIaiFB4JwtIiJHY7BFREREopRfFJmIiMxjsEVERFQLSSQMnIiIHI3BFhEREYnCdPFERNZhsEVERFQLcc4WEZHjMdgiIiIiIiJyAAZbREREREREDsBgi4iIiIiIyAFcq7oBREREVPmY7IKIAEClAlJTgdxcICAACA8HpNKqblXNwWCLiIiIiKgWUiqB2FggO/temVwOJCQACkXVtasm4TBCIiIiIqJaRqkEoqP1Ay0AyMnRlCuVVdOumobBFhERUS2hUqv0/l32MRHVHiqVpkfL2AoQ2rK4OE09qhgGW0RERLWAMk2JkIQQ3eMSdQlCEkKgTOPX10S1TWqqYY9WWYIAZGVp6lHFMNgiIiKq4ZRpSkQnRiO7UP/uKqcwB9GJ0Qy4iGqZ3Fz71iPTGGwRERHVYCq1CrHbYo1mHxT++y9uW5yoIYUSSBzRRCKqZAEB9q1HpjHYIiIiqsFSM1MNerTKyyrMQmqm5fFCTBdPVDOEh2uyDkpMfH8ikQBBQZp6VDEMtoiIiGqwnMIcu9YjIucnlWrSuwOGAZf2cXw819uyBwZbRERENdjlost2rUdENYNCASQlAYGB+uVyuaac62zZBxc1JiIickIqtQqpmanIvZGLAN8AhDcLh9TF8GvoRt6NRB1PbD0iqjkUCmDoUMD1v4igcWMgPZ09WvbEYIuIiMjJKNOUiN0WqzcXSy6TI2FAAhRh+l9HB8oCy+9ulNh6RFSzlA2svLwYaNkbhxESERE5EWvTuIc3C4dcJjd7zCBZEMKbcSY8EZG9MdgiIiJyEpbSuAMwSOMudZEiYUCCybTtEkgQPyDe6BBEIiKqGAZbRERETsJSGncBgtE07oowBZJikuAh9dArD5IFISkmyWDoIRER2QfnbBERETmJ3Bu5NtdThCnQPag7dmfsBgC4urgiPTadPVpERA7Eni0iIiInEeAbYLd6LhIXBlpERA7GYIuIiMhJ2DPZhSAYzvsiIiL7YrBFRETkJKQuUoxsN9JsnRHtRrDHiohswu9g7I/BFhERkZNQqVVYe3Kt2TrrTq7Ty0ZYlkQiMfpvIiJyDAZbRERETsJSNkIARrMRapUdOqgW1CaDMiIisg8GW0RERE6iItkIlWlKHMg6oHtcqi5FSEKIwSLIRERkPwy2iIiInISt2QiVaUpEJ0bjruquXnlOYQ6iE6MZcBEROQiDLSIiIiehzUYogfH5VhJIDLIRqtQqxG6LhQDDme/asrhtcRxSSETkAAy2iIiInITURYqEAQlGt2kDsPgB8XrZCC3N8xIgmJ3nVdVUahVSMlKw9sRapGSkMCgkIqfiWtUNICIiIvEUYQokxSRh9PejcbP4pq5cLpMjfkA8FGEKvfoVmedV1ZRpSsRui9ULFuUyORIGJBg8TyKi6qhKe7YWLFiArl27wtfXF/7+/hg2bBjOnDmjV+fFF19E8+bN4eXlhUaNGmHo0KE4ffq0Xp3MzEwMHjwY3t7e8Pf3x6uvvorS0lK9OikpKXjwwQfh4eGBFi1aYOXKlY5+ekRERA6hCFPg+U7P6x7vHr0b6bHpRgMQW+d5VTXtPLPyvXKcZ0ZEzqRKg609e/Zg8uTJOHToEJKTk1FSUoKoqCjcunVLV6dz5874+uuvkZaWhu3bt0MQBERFRUGl0gwjUKlUGDx4MIqLi3HgwAGsWrUKK1euxKxZs3THSE9Px+DBgxEZGYljx44hLi4Ozz//PLZv317pz5mIiMgeXCT3/oRHhESYXMhYO8/LnPLzvKoa55kRUU1RpcMIt23bpvd45cqV8Pf3x5EjR9CrVy8AwIQJE3TbQ0JC8O6776JDhw7IyMhA8+bNsWPHDvz111/YuXMnGjdujI4dO+Kdd97Ba6+9hjlz5sDd3R3Lly9HaGgoFi1aBAAICwvDvn37sGTJEvTv37/ynjAREZGdiF2UWOoixch2I/HhgQ9N1hnRboTJYK0qWDPPLCIkovIaRkRkpWo1Z+v69esAgPr16xvdfuvWLXz99dcIDQ1FUFAQAODgwYNo3749GjdurKvXv39/TJo0CadOnUKnTp1w8OBB9O3bV+9Y/fv3R1xcnMm23L17F3fv3kuRW1hYCAAoKSlBSUmJTc/PXrTnr+p2kHPhdUO2sHTdqNQq7Mvah9ybuQjwCUDPoJ7V6qa9JitV3Rsuv/OfnSZfe5VahTUn1pg91tqTazGv1zyLvzuVSiXqM6SinzdZ17JE1+NnWs3Bv1NVye2//wsoKSk1W7O6qarrRuz5qk2wpVarERcXhx49eqBdu3Z625YuXYqZM2fi1q1baNWqFZKTk+Hu7g4AyMvL0wu0AOge5+Xlma1TWFiI27dvw8vLy6A9CxYswNy5cw3Kd+zYAW9vb9ufqB0lJydXdRPICfG6IVsYu24OXjuIL3K+QH5Jvq6sgVsDPB/4PLrV61aZzat1Dl47iM8yP9M97re6n8nX/sSNE8i5kWP2eNmF2Vi4YSHa+7Y3W+/48ePYmr1VVBtVggqLlYtxtfQq/Fz90ManDaQScYH4+RvnxdU7eR5bz4trDzkP/p2qCkMBAEVFt7F1q3O+/pV93RQVFYmqV22CrcmTJ+PkyZPYt2+fwbann34a/fr1Q25uLhYuXIiYmBjs378fnp6eDmvPG2+8genTp+seFxYWIigoCFFRUZDJZA47rxglJSVITk5Gv3794ObmZnkHIvC6IduYum42nd6ED5QfGMypKSgpwAcZH2CdYh2eaP1EZTe3VrD2tS88VQics3zc4HbBGNR2kPGNxzT/e+CBBzCog4k6ZSSdSsLLW1/WC8QDfQOxuN9iUddFf3V/LP90OS7cuGB03pYEEgTKAjHjyRnsSa1B+Heq6nl5eWHQIMvv8eqkqq4b7ag3S6pFsDVlyhRs2bIFe/fuhVxuOIm3bt26qFu3Llq2bIlHHnkEfn5+2LRpE0aOHIkmTZrgt99+06t/8eJFAECTJk10/9eWla0jk8mM9moBgIeHBzw8PAzK3dzcqs0HQHVqCzkPXjdki7LXjUqtwis7XzGbvGDKtikYFjYM7q7uldrOms7Say+BBDN2zsDwtsN1QUhQvSBRxw6qF2Txs8HV1dViHWWaEk9vftqgjRduXMAI5QgkxSRZTNvuBjd8NPAjRCdGG2zTrieWMCABnh6O+9KVqg7/TlUlidO+9pV93Yg9V5VmIxQEAVOmTMGmTZuwa9cuhIaGitpHEATdfKpu3brhxIkTuHTpkq5OcnIyZDIZ2rRpo6vzyy+/6B0nOTkZ3bpxmAsRkbUsJS8AgMtFlxG4OJDpue3MlgWKtdkItUFKeRJI7JaN0J5ZBLXriZUfeiiXyUUFbERE1UGVBluTJ0/Gd999hzVr1sDX1xd5eXnIy8vD7du3AQD//vsvFixYgCNHjiAzMxMHDhzAk08+qdfFGRUVhTZt2uDZZ5/F8ePHsX37drz11luYPHmyrmdq4sSJ+PfffzFz5kycPn0aS5cuRWJiIqZNm1Zlz52IyFmJXfz2yu0rGJ44nAGXHdmyQLHURYqEAQlG62kDsPgB8XYZjmdLMGiOIkyB4LrBusfm1hMjIqqOqjTYWrZsGa5fv46IiAgEBAToftavXw8A8PT0RGpqKgYNGoQWLVrgqaeegq+vLw4cOAB/f38AgFQqxZYtWyCVStGtWzc888wzeO655zBv3jzdeUJDQ/HTTz8hOTkZHTp0wKJFi/DFF18w7TsRkQ2sXfx2wo8TuB6Sndi6QLG2l8hDqj883t69RLYEg5aUTXFvbj0xIqo4kStKkBWqdM6WIBgOMyiradOm2LrVcpah4OBgi/UiIiLwxx9/WNU+IiIy1F3eHVKJFCpBXACVfzsfKRkp6HNfHwe3rObTDgnMKcwxmThCLpMbHRKoCFOgm7wbUs6nAABc4IL02HSbgheVWoXUzFTk3shFgG8AwpuFQ+oitTkYJCKqqaq0Z4uIiJzPgewDogMtrZSMFMc0ppbRDgk0FmgBmmF6YocEqqFGSkaK1b2OyjQlQhJCELkqEqOUoxC5KhIhCSFQpikdMj+s7BeztrSXiKgqMdgiIiKrWDMEjKoPZZoS+7L0l1fp+21fNF7YWPS8usMXDiM6MdpgXlZOYQ6iE6Ox+cxmu84PU6Ypcf76vTW3ygZ2RETOgMEWERFZxZYhYBEhEfZvSC2kzfZnigQSo9n+lGlKDE8cjlJ1qcE++bfzRScyWXNijcVMg0NbDcU6xTo0cGugV8fa+WHKNCWiE6MNelG1gR0DLiJyBjYFW5mZmUhNTcX27dtx9OhRXRp2IiKq+SwNFSuvgVcDBlt2Yku2P5Vahak/T7V47NhtsRaH6F29c1XUuZ9o/QRWtFmh27agzwKrsgjaM4U8EVFVEh1sZWRk4LXXXkNwcDBCQ0PRu3dvDBw4EF26dEHdunXRr18/bNiwAWq12pHtJSKiKmYulbgxK4asYAY5O7El219qZipybuRY3Ce7MFt0SnYx5y67PtYDjR+w6hqwdwp5IqKqIirYmjp1Kjp06ID09HS8++67+Ouvv3D9+nUUFxcjLy8PW7duRc+ePTFr1iw88MAD+P333x3dbiIiqkLaVOJ13OqYrCOXybExZiPXRLIjW7L9WTPHzh7z8Yy10VL2YVvbwfmDRFTdiUr9XqdOHfz7779o0KCBwTZ/f388+uijePTRRzF79mxs27YNWVlZ6Nq1q90bS0RE1YciTIEd53bgsyOfAQB2PrsTfb/tCwB4pv0zWDlsJXu07ExM2n2pRIru8u66x9bMsbNU18/TD9fuXLOYdl6tqtgoF6aQJ6KaQlTP1oIFC4wGWsYMGDAACgW/xSQiqg1cJPf+jJRdRyukXggDLQcQk3ZfJahwIPuA7nF4s3AE+gZaPLap9bnKGtVuFAAYzNezJdOgOY5IIU9EVBVszkZ46dIlpKamIjU1FZcuXbJnm4iIiMgIW4bXSV2k+GjgRxb3SRiQYDFQ6hLYBUkxSQiU6Qdv1mYatMTcvEB7B3ZEdI+VI35JBKuDrRs3buDZZ59FYGAgevfujd69eyMwMBDPPPMMrl+/7og2EhEREWwfXqcIU2BjzEa9nkgtF7jg1e6vig6UFGEKZMRm6B67ubhZzDRoahFmS+dJiknSS7QB2D+wIyJyJKuDreeffx6//vortmzZgmvXruHatWvYsmULDh8+jBdffNERbSQiIidjy801WVbR4XVqwXAulRpqLDyw0Kp1q8r2KLlIXBzWw6QIUyBIFqR7vHv0bqtSyBMRVTWrg60tW7bgq6++Qv/+/SGTySCTydC/f398/vnn+PHHHx3RRiIiIoLtw+ssLYYMVN91q1xc7t2qRIREcOggETkVq4OtBg0aoG7dugbldevWhZ+fn10aRURERMZph9f5uPvolZsbXlfV61ZZm/qdiKimsDrYeuuttzB9+nTk5eXpyvLy8vDqq6/i7bfftmvjiIjIOZka5kb2oQhT4IUHX9A9tjS8zpHrVkkk/F0TEZkiap2tTp066X2Ynj17Fs2aNUOzZs0AAJmZmfDw8MDly5c5b4uIiKgSlE12ERESYbauM69bxV4xInJmooKtYcOGObgZRETkjNiD5Ry0iTVMDSUsuyAxERHZj6hga/bs2Y5uBxERETmINrHG8MThBtusWbfK1l4mZqckotpKVLBlzOHDh5GWlgYAaNOmDTp37my3RhEREZF9KcIUaNeoHU5ePqlXLpfJET8gnunUiYgcwOpgKzs7GyNHjsT+/ftRr149AMC1a9fQvXt3rFu3DnK53N5tJCIiIjtoVKcRcPne492jdyO8WXiF0qlzKCkRVZRKBaSmArm5QEAAEB4OSGvIKg82LWpcUlKCtLQ0FBQUoKCgAGlpaVCr1Xj++ecd0UYiIiKyg/KLGlc00KoMHIJIVLMplUBICBAZCYwapfl/SIimvCawOtjas2cPli1bhlatWunKWrVqhY8//hh79+61a+OIiMg58Qa5+lGmKXEo+5BeWUhCCJRpjr+jYUZBIjJGqQSio4Hscrl7cnI05TUh4LI62AoKCkJJSYlBuUqlQtOmTe3SKCIiIrIfZZoS0YnRuKu6q1eeU5iD6MRo0QEX19Qiqtkq83sRlQqIjTV+Tm1ZXJymnjOzOtj68MMP8fLLL+Pw4cO6ssOHDyM2NhYLFy60a+OIiIioYlRqFWK3xRrtbdSWxW2Lg0rt5Hc0RORUUlMNe7TKEgQgK0tTz5lZnSBjzJgxKCoqwsMPPwxXV83upaWlcHV1xbhx4zBu3Dhd3YKCAvu1lIiIqh32dFR/qZmpJtfXAjQBV1ZhFlIzUy0ujmwMrwEiskVurn3rVVdWB1vx8fEOaAYRERE5Qu4NcXcqYuuVJ2Y+FufwEVF5AQH2rVddWR1sjR492hHtICIiIgcI8BV3pyK2XmVjcg2imik8HJDLNckwjL3NJRLN9vDwym+bPdm0qLFKpcKmTZv0FjUeOnSoblghERHVblx7qfoIbxaOBl4NkH8732SdBl4NEN7Mye9oiMipSKVAQoIm62B52tHJ8fHOv96W1QkyTp06hfvvvx+jR4/Gpk2bsGnTJowePRotW7bEyZMnLR+AiIhqDPY61AzlsxRaQ8ycLV4nRGSMQgEkJQEymX65XK4pVyiqpl32ZNOixm3btkV2djaOHj2Ko0ePIisrCw888AAmTJjgiDYSERGRjVIzU832agHAzeKbmJ863+KxGDQR1WxVke9GoQBeeeXe4927gfT0mhFoATYEW8eOHcOCBQvg5+enK/Pz88P8+fPxxx9/2LVxREREZJzYoZpiE1989OtH1TL9O7MdEtV8LmUikogI5x86WJbVwdb999+PixcvGpRfunQJLVq0sEujiIjIuTH7nOOJfY3FJr7Iv52P1EzHLGjDgImIaiurg60FCxZg6tSpSEpKQnZ2NrKzs5GUlIS4uDi8//77KCws1P0QERGRY6gFte7fKRkpJnulusu7270XzFoVGX7IoYtE5MysTh/42GOPAQBiYmJ031RpPwiHDBmieyyRSKBSVb/hCEREZD9leyzK3uyfv3YeKrUKUpcaNBakGlGmKfH50c91jyNXRUIukyNhQAIUYfoTHQ5kH7B7L1hZzDxJRGSa1cHW7t27HdEOIiJyciEJIbp/f3fiO6ScTzF6808Vo0xTIjox2iCAyinMQXRiNJJikvRec7G9VY5O/65Sq5CamYrcG7kI8A1AeLNwBuNEBABQ3+uoR0qKZm2tmjJvy+pgq3fv3o5oBxERObnswmy9x6Zu/sl2KrUKsdtijfZUCRAggQRx2+IwtNVQXSDjX8df1LGnPDTFYvBj69yrQ9mHMHXbVL1rxFRPHBHVLkolsGjRvceRkZrU7wkJNSMjoag5W5mZmVYdNCcnx6bGEBGRcyk7b6g8bUAQty2uWma5c0apmakGQW1ZAgRkFWbZlOjCkb1aHxz4wGQwrkxTOuy8RGSdyp4iqVRqFjUun+ohJ0dTrqwBHw+igq2uXbvixRdfxO+//26yzvXr1/H555+jXbt22Lhxo90aSERE1ZelIWoVufknQ2KHBJatd+nWJVH7iK1nL2KDcbGJQIjIOiqVZsje2rVVd/7YWOMBnrYsLk5Tz5mJGkb4119/Yf78+ejXrx88PT3RuXNnNG3aFJ6enrh69Sr++usvnDp1Cg8++CA++OADDBo0yNHtJiKiauBWyS1R9RyV5a62EZvAomw9W/axRkXSupcNxiNCIgy2K9OUuHDjgu6xuUQgRCSeUqkJdLLLdZQXFVVeG1JTDc9fliAAWVmaehERldYsuxPVs9WgQQMsXrwYubm5+OSTT9CyZUtcuXIFZ8+eBQA8/fTTOHLkCA4ePMhAi4ioFqnjVkdUPVtv5ElfeLNwyGVykxkAJZAgSBakNyTQln0qm7FgXJsIRCXof63N4YdEFaMdumcs0MnPr7yhe7kiv4MTW6+6sipBhpeXF6KjoxEdHe2o9hARkRNp7NPYYh2pRIru8u6V0JqaT+oiRcKABEQnGv4d1gZT8QPi9RJd2LJPZSsfjKvUKsT+bF0iECKyzNzQPa24OGDoUMdnAwwQ+R2c2HrVldWLGhMREWldvHnRYh2VoMKB7AOV0JraQRGmQFJMEnzcffTKA2WBJjM/avfxkHrolctlcquyRdp7gWFTvWrzU+cj+4ZjEoEQ1WaWhu4B94buOVp4uCbroKmRyBIJEBSkqefMGGwREZHNikrEDfDnnC37Kx/4WAqEFGEKPCJ/RK8sPTa9wnOfbF3U2FSvmjJNidkps0Udg9cVkXWq09A9qVST3t0YbQAWH+/8620x2CIiIpt5u3mLqsc5W/ajnctUPjnJhRsXLM5lUqv1U/UXlxY7pI3lzeg2Aw29G+qVGetV064jJhavKyLrVLehewoFkJQEyGT65XK5przWrLNFRERkTFPfpma3V4fkCzWJpUWNAdOp1IetG4bULP2xQd4LvDFs3TCL59Q6feW0TanXH5Y/jG+GfqN7vKT/Evzz8j8GvWqW1hEri9cVkfUsDd0DKn/onkIBTJ9+7/Hu3UB6es0ItAAGW0REVAEuknt/RsoPJ6suyRdqElsXNR62bhg2n9lsdJ/NZzabDLiUaUqEJIToHn9w4AOEJIRYnQnw1+xf8dzm53SPp22fhuYfNTc4jjXDAnldEVnP3NA9raoYuudSJiKJiHD+oYNlWZWNUOvs2bPYvXs3Ll26ZDAkYdasWXZpGBEROZdAWaBeICCXyRE/IJ7rIdmRLYsa3y6+bTLQ0tp8ZjNuF9+Gl7uXrkw7XLF8L5o29bo1Fh5caFCWfSMbwxOHY2PMRt01wmGBRI6nHbr35JNAudt4NGhQc3qUqgurg63PP/8ckyZNQsOGDdGkSRO9xQwlEgmDLSKiWurcy+fgMf9etrt/Xv4H7q7uVdiimseWBYpf3fmqqH1e3fkqPhn0CQDLwxVtTYphzIQfJ+hSuIc3C0cDrwbIv51vdh+mfieqGIUCaNQIuFguoay3uGm4ZAWrhxG+++67mD9/PvLy8nDs2DH88ccfup+jR486oo1EROQEmn/c3OAxF561L1sWKD6bf1bUscvWEzNc0V7yb+cjJSPFqn2Y+p2o4szN26psdl5VolqxOti6evUqnnzySUe0hYiInFj5m3PtcDMGXPajXaDYGFNz5JrXb260fnll61V2SnVtsJWamWqxV6sspn4nsp2xYMtS0KNSASkpwNq1mv+rrM+XU+tYHWw9+eST2LFjhyPaQkRETsZcD4el7HhkG+0CxXXc6uiVB/oaX9R46P1DRR23bL2qmjuVU5hjVX3O8SKqPEolEBICREYCo0Zp/h8Soikn06yes9WiRQu8/fbbOHToENq3bw83Nze97VOnTrVb44iIqHq7cOOC2e1lh3tFhERUTqNqCYnIMUAFdwqsrqcdrphTmGNxyKDYdpijvTYuF10WvQ9TvxNVHqUSiI427PnKydGU15Q1sRzB6mBrxYoV8PHxwZ49e7Bnzx69bRKJhMEWEVEtUlRSJKoeh3vZj8ksgTc0wzbL92751/EXddyy9bTDFcVkHSxRlYhsuXENvBrogq1G3o1E78fU70SVQ6UCYmONDzEUBM1wxLg4YOjQmpWy3V6sDrbS09Md0Q4iInJC3m7iUldxuJd9iMkSaGuWPpVahZSMFOTeyEWAbwCGthqK9dHrMXLjSKgE08NA75Te0Q0TTc1MRe6NXNT3qI/jN46LOu+KISt0bQ2UBYraZ27EXC4pQFRJUlOBbDNrjQsCkJWlqRcRUWnNcho2rbOlJfwX4tpjCAERETmfpr5NzW6XQAK5TM7hXnZizaLG2t6ivJt5oo49cuNIvaGEcpkcLzz4gtlAS3vO+anz8fnRz022zdPVE64SV9wsuXnv+L5yJAxM0AuatMMXzT1Hua8cb4a/Keo5EVHF5YocmCC2Xm1jdYIMAPjmm2/Qvn17eHl5wcvLCw888AC+/fZbe7eNiIiqORfJvT8j5dORm8qOR7azZVFjsfOgys/tyinMweyU2aL2nZ0y22yAdKf0jl6gtXv0bmTEZRj0TmmHL5pLbZ8wMIHXE1ElChA5MEFsvdrG6mBr8eLFmDRpEgYNGoTExEQkJiZiwIABmDhxIpYsWeKINhIRkRMoPwRMLpMbzY5HtrNlUeMGXg1sOpc919IqLyIkwmTApAhTYEb3GZBKDLfP6D6D1xORnYgdmBYeDsjlputLJEBQkKYeGbJ6GOHHH3+MZcuW4bnnntOVPf7442jbti3mzJmDadOm2bWBRETkHDJiM+D6zr0/K+mx6eyBsDNLWQKNDdu0Zt2qyqJSq0xeG8o0JRYeWGj0+S08sBCPyB9hwEVUiaRSICFBk3VQItFPlKENwOLjmRzDFKt7tnJzc9G9e3eD8u7duyOXgzWJiGqt8jfPDLTsz5ZFjW3t2XKk1MxUo+XmEoBocd02Iscx1XulUGjSuweWy2Ejl9sn7bulxZSdmdXBVosWLZCYmGhQvn79erRs2dIujSIiIiLjtIsa+7r76pWbGrYpNkFGZTI198yaBCBEVLkUCiAj497jqCggPZ3ra1li9TDCuXPn4qmnnsLevXvRo0cPAMD+/fvxyy+/GA3CiIiIyL4UYQoczDqIhQcXAtAknAhvFm60N/FY3jGHtkXbo2bNHC9Tc89sSQBCRJWn7FDBxo05dFAMq3u2hg8fjl9//RUNGzbE999/j++//x4NGzbEb7/9hieeeMIRbSQiIidQfmgXh3o5VtlMkOYSTohdeNoaZbMFerl5Wb2/qaUAbEkAQkRUndmU+r1z58747rvvcOTIERw5cgTfffcdOnXqZPVxFixYgK5du8LX1xf+/v4YNmwYzpw5o9teUFCAl19+Ga1atYKXlxeaNWuGqVOn4vr163rHyczMxODBg+Ht7Q1/f3+8+uqrKC0t1auTkpKCBx98EB4eHmjRogVWrlxpy1MnIiITQhJCDB4r05RV05haoGxPUkpGisngtmezng49t7vUHUkxSSbTtRtjKjDUJgAxdyypRIort66IbywRiVZVc6dq8pK9ooKtwsJCvX+b+7HGnj17MHnyZBw6dAjJyckoKSlBVFQUbt26BQC4cOECLly4gIULF+LkyZNYuXIltm3bhvHjx+uOoVKpMHjwYBQXF+PAgQNYtWoVVq5ciVmzZunqpKenY/DgwYiMjMSxY8cQFxeH559/Htu3b7eqvUREZFr5uTY5hTmIToxmwOUAyjQllh9ernscuSrSZHD78kMvWxUIAcBb4W9ZVV8RpoCn1NOqfYwxlwBESyWoEJMUw+uqBlKpgBMnGmDdOglSUjSPyXGqU4BT9ndd0373ooItPz8/XLp0CQBQr149+Pn5Gfxoy62xbds2jBkzBm3btkWHDh2wcuVKZGZm4siRIwCAdu3aYePGjRgyZAiaN2+ORx99FPPnz8ePP/6o67nasWMH/vrrL3z33Xfo2LEjBg4ciHfeeQeffvopiouLAQDLly9HaGgoFi1ahLCwMEyZMgXR0dFcF4yIqIJK1aUmtwn//Tdxy0Ss/nO12d4XEk+ZpkR0YjRuFN/QKzcV3Lq7umN6t+lWnaPPfX2sbpe91uVShCmwPnq9xXrMSlizKJVAixauePvtnnjuOVdERgIhIZpyqtmUSk3qeK2a9rsXlSBj165dqF+/PgBg9+7dDmuMdnig9lym6shkMri6app+8OBBtG/fHo0bN9bV6d+/PyZNmoRTp06hU6dOOHjwIPr27at3nP79+yMuLs7kee7evYu7d+/qHmt77UpKSlBSUmL1c7Mn7fmruh3kXHjdkC3MXTebTm/C1398bfEYl4su45lNzwAAAn0DsbjfYjzRmnN8baFSqzD156lGAxsBAiSQIHZbLAbdN0hvqN6CyAX4+8rf+PHsj0aPK4FE75iPBDyCQN9AXLhxweR6XtpyCSQoKSmxKtgy9zmkUquQeS3T7P7arIS7/92N3sG9RZ+XqqdNmyQYMUJqMIQtJ0dAdDSwbp0KTzxRg3ODVxlXoFyvd1GRgJ07VejZUzCT/MINAKBWq1FSUrEvPPR/9/faYs3vvqrub8SeT1Sw1bv3vQ+y0NBQBAUFQVKu71EQBGRlZVnRRH1qtRpxcXHo0aMH2rVrZ7TOlStX8M4772DChAm6sry8PL1AC4DucV5entk6hYWFuH37Nry8DCf3LliwAHPnzjUo37FjB7y9va17cg6SnJxc1U0gJ8TrhmxR/ro5eO0g3s943+rj5NzIwVPKp/BayGvoVq+bvZpXa5y4cQI5N3JMbhcgILswGws3LER73/Z628bXGY9DrodwufSyXnlDt4YYHzhe7/e5fdt2PNPgGbx/w/jvuGxgVVJSgq1bt0KtVot+Hu+vfx9tfNpAKtG/mzt47SC+yPkC+SXiFmL+ed/PuHXqlujzUvWjUgEvvRQFQZCi/I2/IEgACJg8uRiursnMfGdnd+5EAdC/By4okKBfP1c0aHAbzz9/At26Gcv8ORQAkJOTg61bj9p8fnv/7iv7/qaoSFzyIatTv4eGhiI3Nxf+/v565QUFBQgNDYXKxkGWkydPxsmTJ7Fv3z6j2wsLCzF48GC0adMGc+bMsekc1njjjTcwffq9YReFhYUICgpCVFQUZDKZw89vTklJCZKTk9GvXz+4ublVaVvIefC6IVsYu25UahUmfzrZ5mNKIMHqgtWYM2IOFz62UuGpQuCc5XrB7YIxqO0gvbJNpzfh2p/X9MoaejXExwM+xvCw4Xj/vXuB1aBBgzAIg/Dg6QcxPXm6QYC3XrEeTymfAgC4ublh0KBBcD/lLvqb3rfPvW3Qy7np9CZ8oPzAqh6ygT0HsmfLye3ZI0F+vrnbUQmuXPGGTDYYvXuzd8uePD1Nv+4FBZ744IOuZnuWmjYNxKBBTWw+v71+91V1fyM2V4XVwZYgCAa9WgBw8+ZNeHraNjl2ypQp2LJlC/bu3Qu5XG6w/caNGxgwYAB8fX2xadMmvReySZMm+O233/TqX7x4UbdN+39tWdk6MpnMaK8WAHh4eMDDw8Og3M3NrdrcqFantpDz4HVDtih73ezP2G+2d8USbe/LodxDiAiJsFMLa4emsqai65V9nyvTlBihHGEQyOTfzseoTaOQ5JakV67dN6Z9DIa3HQ7Xd/RvF2Lax+iCLQEC3Nzc9O4Nyg9LNObCjQsYoRyBpJgkDG01FK/sfEV0oCWBBHKZHJH3RTJgd3KXL1uuo6nnCv7psi9zmQcFQQKJBJgxwxXDhxtfT8vFxQVubjYlNgdg/999Zd/fiD2X6GBL28sjkUjw9ttv6w2lU6lU+PXXX9GxY0erGikIAl5++WVs2rQJKSkpCA0NNahTWFiI/v37w8PDAz/88INBQNetWzfMnz8fly5d0vW2JScnQyaToU2bNro6W7du1dsvOTkZ3bpxCAsRkbXstaAsF6a1XnFpsdX1VGoVYrfFmp3nFbctzuSxbAlmmvo2tRiQlz13XY+6BtksLYkfEM9AqwYIELlkmth6JF6xhY8TQQCysoDUVCAiwv7nry2/e9HB1h9//AFAEyCdOHEC7u7uum3u7u7o0KEDZsyYYdXJJ0+ejDVr1mDz5s3w9fXVzbGqW7cuvLy8UFhYiKioKBQVFeG7777TSy/fqFEjSKVSREVFoU2bNnj22WfxwQcfIC8vD2+99RYmT56s65maOHEiPvnkE8ycORPjxo3Drl27kJiYiJ9++smq9hIRkf0WlOXCtNZbc3KN6HoD7x8IAEjNTDUbyGiTTdhKm1a+bHr5fyb/A6//WV7sWHvulIwUq845J2IOFGEKq/ah6ik8HJDLgZwc4z0tEolme7jxdbCpAsTO/Ml10PditeV3LzrY0mYhHDt2LBISEuwyb2nZsmUAgIhy4fLXX3+NMWPG4OjRo/j1118BAC1atNCrk56ejpCQEEilUmzZsgWTJk1Ct27dUKdOHYwePRrz5s3T1Q0NDcVPP/2EadOmISEhAXK5HF988QX69+9f4edARFTbaBeezSnMsSndt3YIWHgzJ/8LWgVuFt+0ul5FexCrY3r15n7Nq7oJZCdSKZCQAAwfDgACDBMlaNKCMzmG/Yl9TR3Vs6T93UdHG27TjkquCb97q+dsff215TS/YgkWlqmOiIiwWAcAgoODDYYJGjuWtneOiIhsp114NjrRyF9IkTgEzDY9m/XE92e+F1VPy7+Ov5ma5inTlIjdFmu03JwWn7Ywu728iJAIrDy+UvRQwl/Sf8HTDzxt1TmISF+ZQWpGGetZKtsblpeneVyRYEihAJKSgLFjgbL5JuRyTaClqAEd2DbNajt8+DBmzpyJESNGQKFQ6P0QEVHNpwhTICkmCZ6u1iVGksvkSIpJsjgETKVWISUjBWtPrOViyGW8/NDLcLHwp9sFLnj5oZcrfC7t4snGAiBLgbY1CVR83H0QERKBke1Git7n62NfWwz4yDmoVECsLp43TMAmkQBxceKHvJF4RvLd6Snfq6hUahYb1tq5U9ziwyoVkJICrF2r+X/536VCUfYaAHbvBtLTa0agBdgQbK1btw7du3dHWloaNm3ahJKSEpw6dQq7du1C3bp1HdFGIiKqhhRhCvRq1kt0/bkRc5ERm2Ex0FKmKRGSEILIVZEYpRyFyFWRCEkI4c01AHdXdwxpNcRsnSGthsDd9d5X1nk386w+j7mkGuUZy1BsjVvFt1BcWoy1J9datV/ctjgG4TVAaiqQbaZDs2ySBqo6SqVmuF/531VOjqbcVMClDdAiI4FRozT/NxaguZSJSCIinH/oYFlWB1vvvfcelixZgh9//BHu7u5ISEjA6dOnERMTg2bNmjmijUREVE35uPuIrjur9yyLQwdN9abkFOYgOjG61gdcKrUKR3KPmK1zNPeoXhBy8dZFM7WNE5NUw6Btgm2BjwAB03dMtzobYVZhFlIzeQfu7MQmX3BUkobazNJMHW2vYnGxpufJWH1B0PxMnGiY3dDWAK2msTrYOnfuHAYPHgxAk4Xw1q1bkEgkmDZtGlasWGH3BpJjWeraJSIyx54ZBS2lKAfYm2EpCAIMg5DLt0QuZlOGLUk1xMyxNuXwhcM27cflA5xfbUn/XR2JTf2+dKn53kdAs2aWXH4vgNIODzUVoAG1Z3io1cGWn58fbty4AQAIDAzEyZMnAQDXrl1DUVGRfVtHDiW2a5eIyBRbhqiZIjZFeW3uzRAbXJStZ22PEWBbEO0isX1x07oetk1D4PIBzk+b/tucoCDnT/9d3SiVQH6+uLrnzomrd/nyvR4rDg+9x+pPxl69eiE5ORkA8OSTTyI2NhYvvPACRo4ciT59+ti9geQY7NolInu4VXzLbseyJZCobcQGF2XrNatr/RB/lVoFua9cb+2ssoyVSyW2T7Lod18/q+pLIEGQLIjLB9QAUikwUpcbxXjv6IgRNWsOT1XT3gOK7YxubuVKC3FxmvtJMbTDQyvQMV7tWR1sffLJJxgxYgQA4M0338T06dNx8eJFDB8+HF9++aXdG0j2x65dIrKXOu517HYsWwKJ2ka7xpk55YOQR0Mftfo8fb/ti9ultyFAMBpYlR3qqVvU2MZEGT7uPpDXtdC1YQSXD6gZVCrNVAYN49fQunW8J7EXc/eA5Ukkml7Fl17S9D6KeYtre6wuixy9XBuGh1oVbJWWlmLLli2Q/vf1gouLC15//XX88MMPWLRoEfz8/BzSSLIvdu0Skb009W1qt2NpAwlzvSm1vTdD6iK1mCJ9RLsRekFIeLNwi+nijSm4XQAAqO9VX1R9W+dsrRq2Ck18mli1z5yIORazWpJzsHRPAvCexJ7EvN5lxcdr1uNKSLDuPI0amQ/QtIGcdnhoBZOaVmtWffq6urpi4sSJuHPnjqPaQ5WAmX+IqDrSLpZsjDYAq+29GSq1ymKK9HUn1+klETmQfQBqqK0+l65Xy0IMdVd1V9M2K7MRBvoGYmPMRpuCppb1W1q9D1VPvCepXGJfxwYNNIsNa9e60i4+3LChuP0DAy0HaGXX8KrJrP6q66GHHsKxY8cc0BSqLMz8Q0T2cuHGBbseT7tYcnliF0Ou6WzJRliROW4CBOTfMT+Lvqi4CCq1StSaXFq7R+/G+bjzut/npVuXrGpXbR5KWtPwnqRyiX0d1683XFRYobA8F6tsj5VCAcyYob+GFqAJsGbM0D++sY7xmpIx29XaHV566SVMnz4dWVlZ6Ny5M+rU0R+v/8ADD9itceQY2sw/OTnGL26JRLOdmX+IyJKbxTftfszyAdXu0bsR3iy8VvdoadmSRMS/jr+jmgMAUEON1MxUk8M/jYkIidB73MCrgeh9a/tQ0pqG9ySVy9LrrRURYbzc3d14uZYg3OuxUiqBhQsNz6NSacofecQwoNNSKjVzy8oOeZTLNb1lpvaprqwOtrTJMaZOnaork0gkEAQBEokEKmcNO2sRqVRzsUZHaz7Eyr4JtGNma0vXLhFVTFGx45f8KH9jXptV1yQiOYU5cHWx+pZC58SlE6Lrlp+TRs6t7D2JZsyqYdDOexL70X+9zderCEuJOARBk4xt6FDDc5nKlqjNmF12eKMzsHoYYXp6usHPv//+q/s/OQft2NvAQP1yudz5LmIiqjp3SjmHtzLZkkTEnmuhmTJt+zSUqkv12mGNjGsZouuWn5NGzk873Kw8Y8PNqOK094C2BFSW+lQkEk0QlZIiPvGJSgVkZNwrr2kZs60Ots6fP4/AwEAEBwfr/QQGBuL8+fOOaCM5iEKhf3EHBADp6fxQIyLxrEn3rUyzbQG/lIyUWndzrVKrkJKRgrUn1uo9f1uSiFwuEpmD2QQxgdOVoiu4VXJvzTVr5m8BQEi9ENF1a/vC1jWRdrhZeWq1ppxrf9qfQmHbPDhLWSG1Ga137RJ3vO+/B4KDgW++uVdW0zJmWx1sRUZGoqCgwKD8+vXriIyMtEujqPKU/VbDy4vd9ERkHUtrPpUVty1OVNBUPiiLXBWJkIQQm4M1Z6NMUyIkIQSRqyIxSjnK6PM3lo7dz9PPaBKRRt6NKtQeMYGTtcFVee3921tVvzYvbF3T6A830w/snbUnw1nYkm5dbDbDzExx9RISxC+AbEs7qgOrgy3t3Kzy8vPzDZJlEBFRzWZNsCWmR0KZpkR0ouFkgpzCHEQnRtf4gEv7/MtnHMwuzEZ0YjRmJs9EdGI08m8bZggsuFOAQ9mHDMoDZYEGZVUt+VyyXuBtbe8bsxHWHFz707mI7Q1rar8lGCvUjupA9GxWxX9jyyQSCcaMGQMPDw/dNpVKhT///BPdu3e3fwuJiKjasmYYIWC+R0KlViF2W6zRXhLtmk9x2+IwtNVQp0yQoFKrkJqZitwbuQjwDTDIsGju+QOa12DxwcVme5E+PPAhHmr6EKLb3gtYu8u7QyqRml0Hy9J2e4v6LgpymRwJAxKgCFNYNa9MKpGiu5z3GzUF19mqOrb0bFnKCqnNHllYaFubLHHG7JSie7bq1q2LunXrQhAE+Pr66h7XrVsXTZo0wYQJE/Ddd985sq1EROTkzPVIWFpDSoDgtPN1xAwNFLOGlpiA6KWtLxksamxpv8oMtLTK9lZeKboiej+VoMKB7AMObBlVJq6zVT0VFxsvNzfdpGxG6/Jra9mDs2bMFt2z9fXXXwMAQkJCMGPGDA4ZJCIiq1jqkbBlDSlnoB0aWL5HShtsaOdZ5RTaMHHBiMtFl5GamapLmV9dX6+yvZWhdUOt2tderxVVPa6zVT15ewPTpwMffGC6jqcncKdMQlq5XBMIKRTi52xZo+zxnYnVcefs2bMZaBERkdUs9UhU1zWkKsLS0EjgXuKQimYNLKtsgFUZr5e1qd61tL2Vv134zar9Lt66aNP5qPrRrvukCbSMvE8E5+vJqAlUKuDDD4GZM03XGTDg3r+/+04/o/VLL9m3d2vJEuCff5wv0AJsCLYuXryIZ599Fk2bNoWrqyukUqneDzkvcyuJExEZI1j5wWGuR8KWNaSqO7FDI+MPxeNs/lm7nbdsgBXeLBwNvBqYrW9puyVymRw+7j42739HZd16bVduiR92SETGqdWW6yxebHpIYdk5X488oh8QS6Wa3jF7mTYNaN7cOZcBsHq59zFjxiAzMxNvv/02AgICrJ4cTURENUfODeuGc5nrvdGuIWUsG6GpNaSqO7FD+GYkG1nR1UYyd5nDA9LE6ETEJMUAADykHvh66NeI2RDj0HOWlX3Dwmqp5DRUKmDCBO0j4/eUEyYAQ4eyd8vebt60XEelApYu1aTfL6/sd23lw4HUVHHHt0ZODhAdrVmQ2Zl6uKwOtvbt24fU1FR07NjRAc0hIiJncqv4luVKZVha80kRpkBSTBKGJw7XK5fL5IgfEG+whlR1VyVDHsvf9GSmGk0VX5al7eWVDXjvqu6i77d94WL9YBmbyX3FLzlA1VtKCpBv4fLLz9fU69OnMlpUe5SWiqu3Zw/QuLEmSYnYuXPnz9veLlMEQRPUxcU5V/Bt9SdjUFCQ1cNGiIioZqrjbt0c3nNXz1msUz6g2j16N9Jj050u0AIsD410hMK7hXoZGx2RIKN8MAwAaogYk2SCh9TDcqUyGtZpaPO5qHrZtcu+9Ug8V5FdLt9/D4waBURGAiEhxuuUDw0cNdzPGdddszrYio+Px+uvv46MjAwHNIeIiJxJoK91C+bOSZlj9cLEESERTjV0sCzt0MjKVtkJMirK2mGPlnpIyXlkZdm3HonnY8M0S3MLUJeV4+CEoc607prVwdZTTz2FlJQUNG/eHL6+vqhfv77eDxER1R4uEuuHjmmz79UW2qGR9b0q72+kfx1/3b/FJh6pSg28rUvQYe2wR6q+gkReemLrkXgVzRZobqCbo0MCZ1p3zeo5W/Hx8Q5oBtmDSqXpVs3NvTeu1lnGsxJR9aRSq3DixgkUnipEUD1NJsCyvUxq4d7QsUbejSymLy+7MLF2HajaQBGmQF2Puuj7bd9KOV/Z34u2d83Y0D9A8zuJHxBvcntlyL5uXcIL9mzVHI8+Crz3nrh6VL1cKZMUtHyCjOnTgeRkx5y3QQPnWnfN6mBr9OjRjmgHVZBSCcTG6nfvyuWatSvEZmxhYkkiKkuZpsTUn6dqMg7+N9VKLpMjYUACFGEKKNOU+OrYV7r61qwTVV0X2nWkygwuUzJS0K95v0o7X0VZO2crUGbd8FWqvsLDNfcf5npJJBLnurmuLW7fNr2NwfE9NnUgnjt3Dm+99RZGjhyJS5cuAQB+/vlnnDp1yq6NI3GUSk0qzPLjaLUpMp1xTQIiqlrKNCWiE6MNUrvnFOYgOjEaM5NnIjoxGjeLbcvt6wzziOytMuedZV7P1P1bu7CyOXHb4vQe13GzLvFJRTTwamAq47dRzrbWGpmXmmp5nU9BcK6ECLWFqfW3tOniHSU/37muB6uDrT179qB9+/b49ddfoVQqcfO/JPrHjx/H7Nmz7d5AMk+l0vRoGfug0pbFxWnqERGJob05F2D4wSL899/ig4uNbhdDKpGiu7x7RZvp1DxdPR16/GZ1m+n+bWlhZQDIKtTPPuDm4uaQdplizYLIzrbWGpnHbITOy93dsEyp1GQsnDbNseeu0QkyXn/9dbz77rtITk6Ge5lX+dFHH8WhQ4fs2jiyLDXVfGYYZ0yRSURVS8zNuUqw/RsclaDCgewDNu/vrMpmYbxTekf374ebPqz79+7Ru7EhegOkkooFE4+G3hvDY8uQTUkljivPv52Ppr5NRdV99oFnDZYAUKlVSMlIwdoTa5GSkVKrkq/UBMxGWHUq+ja/e1f/samRVo5QoxNknDhxAmvWrDEo9/f3x5WyM+WoUoiN7MXU4/JpRARUznyq2jZnSzss05hfL/yq+7d2XlfgjkBkFmYarW+Jm4ub3vwwZxiy6evuK6reyLYj9R4r05SI3Rar9+VA2XmFVP0xG6HzKtuzpVabHmllTxKJJieBM83hs7pnq169esg1cuf+xx9/IDCQE1Yrm7+/5TrW1CMiqoyb87MFZx1+jurC3LBMkyrwjXOpuhTFpcW63h6VWoWGXtV7EeA/8v4QVW/1idW6nittAFu+F1Y7r9Da9dyoaohNpMCEC9WPR5m8Nr//7vgeLW1PXHy8c2XbtjrYGjFiBF577TXk5eVBIpFArVZj//79mDFjBp577jlHtJGIiCqRpXWZAM28K3PbLfn86Oe1ZriXmGGZ9iRAQMDiAESuisQo5Sj0/bZvhdalknnI7Ng6426XmElrVsbqk6sREh+CDac2mJ1XCNS+9dycVUSEJpW3OQ0aaOpR9fVfvjyHksuBpCTxWbarC6uDrffeew+tW7dGUFAQbt68iTZt2qBXr17o3r073nrrLUe0kcwQe3FXxpuAiGoG7bpMAAwCKsl//03vNr1C58guzEZqZu2YTFoVQyav372u99jaZCYlqhLdvwe1GGSXNpkTUi9EdN3sG9mISYoxG8CWXc+NqjepFBg3znydFSucqyejOlOpgJQUYO1a4M4di9XNKpuN0NEjqJYsAf75x/kCLcCGYMvd3R2ff/45/v33X2zZsgXfffcdTp8+jW+//RZSvhMqHYcREpEjKMIUSIpJMkhcIJfJkRSThA/6fYCkmCSrssiVV1vmbVkzZPKXf3+pFr0xN0vupfRv06iNQ8/VwKsBQvxCHHLs2nKNOTOlEli40PT2V191zhvs6kibKTAyEhg1Crh4sWLHKzuM8KGHND1PjsqtM20a0Ly5cy5nZNM6WwAQFBSEQYMGISYmBi1btrRnm8gKarV96xERaSnCFPhn8j+6x1HNo5Aem65LPKAIU2B8p/E2H98ZEjdUlEqtwoojK0TX7/ttX4QkhOBW8S0Htso6f13+y6HHFwQBy39f7pBj14ZrzJmZW75Ga906Ll9jD47IFOjnd+/fUqmm98mRCTKcdf1Yq4Ot4cOH4/333zco/+CDD/Dkk0/apVEkHtenICJHKrueUROfJgbrG9kyb0sCSa1ZmDY1M9VgYWhLcgpzKjTHyt5++PsHhx6/4E4B8u/Y9/nWpmvMmVlavgbg8jX2ICaotUXZJOQffeT4tbWcdf1Yq4OtvXv3YtAgw/HbAwcOxN69e+3SKBLv8GHr6pUdq5uS4lwXKxFVP+UXw7VEG5zVloVpcwqtC7QAw/lVFUlEYg9FJUVVen5LjM0rBGrPNebM7Ll8DZkmJqi1Rdl70CVLKmd9LWdcP9bqYOvmzZt6ixlrubm5obCw0C6NIvG8vcXVu3bNcKxuZCQQHHyvzu3bDL6IyDpZ160LtrRzvmrLGkiXiy5X+BhJMUmQy+R2aE3NM7v3bATK9JedqW3XmDMTuzCtMy1gWx05KlityikqzhSAWx1stW/fHuvXrzcoX7duHdq0cewkWjLUtKnlOgBw4oTxsbo5Zb50zcvTBGPONhaWiKrOjbs3RNdt6N0Qi6IW1aqb4EbejSp8DEWYAhmxGRVvTDXlAhf4efpZrlhOA68GeLvX23qvTfl5hVS9hYebT6ogkWgWM3amBWyro5oYrDrTc3K1doe3334bCoUC586dw6P/rTD3yy+/YO3atdiwYYPdG0jmde8OLBcxr/juXXHH004+dMZ1DIio8vm6+4que6XoCp5KegpSF2mtuRku3+tiq5o8HE4NNYa2GoqVx1datd+KISsMXhdj8wqp+pJKgYQEzX2HRKI/p8hZF7CtSiqVZnhdbq4mGAkP17x22qA2J8exCSwqg0SieS7OFIBb3bM1ZMgQfP/99/jnn3/w0ksv4ZVXXkF2djZ27tyJYcOGOaCJZE5QkH2P56yTD4nIMcqmIc+7mWeQlrxZ3WZWH7M2LTarXSCazHs09FGr6vdq1qvWBOw1nUKh+YI3sNz3Es66gG1VMTZVRDtaSRvUOjtnDcBtSv0+ePBg7N+/H7du3cKVK1ewa9cu9O7d295tIxG6dwdcbE7gb5wzTj4kIvtTpinR4tMWusc7zu1ASHwIlGn3xhrL61oXSNS2xWa1C0RrF4MWo3y9lIyUGh+c5t3Ms6p+96DuDmoJVQWFAsjI0C9LT2egJZZSCQwfbjhVJDtbU65Umg5qnYmzBuA236YXFxcjOzsbmZmZej9UuQ4ccNwERWeafEhE9qVMU2J44nCDtOXZN7IxPHG4LuBykdj2Z8TSYrNVnYHPnrQLRIsdUljfq77e849cFYng+GAzezi3IFkQjucdt2qfht4Ndf+21PtKzqF8T4Uz9VxUJZUKmDDBfJ0JEzT1FArgyy8rp1321rmz8wbgVv+VPHv2LMLDw+Hl5YXg4GCEhoYiNDQUISEhCA0NdUQbyQxHBkTONPmQiOxHpVZhwo/m/3pP+HECVGoVJKZmtlvgX8ff7Pay6c9rQs+O2CQXcyPmouB2gUH6d2vX6nImI9qNwK0S6xZxbuLTBIDmS4GQhBBd+Y5zOxCSoN/7Ss6h/NQFTmUQJyUFyLewTF1+vqYeAKxe7egWOUaLFpbrVFdWB1tjxoyBi4sLtmzZgiNHjuDo0aM4evQo/vjjDxw9etQRbSQzrA2IxNwXMfsPUe2WkpFicVHd/Nv5SMlIgeCA2dblb5QjV0WKuoFWqVVIyUjB2hNrq2WAJiZxw+dHPzcItGq6dSfX4RH5I1btEygLhDJNiejEaGQX6o+dyinMQXRiNAMuJ6Kdb1QWsyOLow2ixNa7IT6BrNUc+SX9+vVA48bOeU1YnY3w2LFjOHLkCFq3bu2I9pCVwsMBT0/gzh1xddPTzS8656yTD4nIflIyUkTX+zv/b5vOcenWJaPl2hvo8rQ30KbWT1KmKRG7LVbvxlsukyNhQIJTJVIoHzjUBlmFWbAmvgySBaG7vDuaf9zcaGAqQIAEEsRti8PQVkOZnbCaUyo12QjLf2/D7MjiiJ1Koq0XHg58/719zl02g+TatZo8AsEOHPGcn6+Zg7Zxo3NdE1b3bLVp0wZXrlxxRFvIBlIp0LWruLrNmulPQB02TDPZsCyxkw9VKs23JGvXav5fk7v7a9NzJbLG6SunseXsFpv2DfA1/ApUpVYhdlusyRtoAIj9ORa//PuLXu8Vezic38Gcg6Lrjmg3AgeyD5gNTGtbIhZnpVIBsbHG05ELguaH2ZHNq1fPunqW5ndZo+zv7fp14BHrOqhtFhvrXNeE1cHW+++/j5kzZyIlJQX5+fkoLCzU+6HK10jkmpnBwfq9VUOH6gdfTZqIm3xoLr1oTVObniuRVkRIhKh6KedTrD62BBIEyYIQ3sxwnHJqZqrFG+jsG9no+21fjFKO0iWOmPDjBLMBmjWp5h05FJFBn2m3isXP2frqj6+QUyhuDpulRCxUtVJTzY+2AZgd2ZKCAvH1lErAUekVJk2qvMRq2dnOdU1YHWz17dsXhw4dQp8+feDv7w8/Pz/4+fmhXr168POzfgV4qhiVCkhOFlf3USPLmJQNvry8LA8d1Hb3l/9w1Hb316QgpDY9V6KyIkIi0MCrgdk6MncZrhRZN8pBm2EvfkC80aFdttwY59zIMTu/zJoeDm2yhchVkbpgzl7JFrS9dlVh57M7Ifet3mt9+XmKv3/Iv52Pn8/+LKqusR5Uqj5yROZ9WbjQse1wZpaCVa0DBzT3Lo4anFbZiyU7U8Zsq+ds7d692xHtIBulpIib7OjuDkREVOxclrr7JRJNd//Qoc4/36s2PVei8qQuUqwYsgLDE4ebrDPuwXGIPxRv1XH9vPwQ+3AshrYaanT72YKzVh3PGpYCOe1QRIMsgBbmiollqdfOkSJCIpAwMMHs77OqXb191ar6q0+aT6kmgQRymdxoDypVH5cvi6u3dStQXKy5lyF9TZuKq/fHH5UfEDmSM2XMtrpnq3fv3mZ/qHKJzUJTXFzx8a2Wuvtr0mLItem5EhmjCFNgY8xGNPXR/0su95VjY8xGkwGTOQW3CzA7ZTaC44MNeotUahVWHFlRoTabY66HQ8xcMWuGIhpTlcPZUjNToQhTINC3eq5mGiQLgre7t92OZ6kHlaqPBuY70HUEAVi61LFtcVZiZ/A4MgthZWvUyLkyZtu0GuW1a9ewaNEiPP/883j++eexZMkSXL9+3d5tIzv75BP9x9Z+wyG2y9aZunZNqU3PlcgURZgC56ac0z2Oah6FjLgMKMIUCG8WDrnMtqFpOTdyMDxxODac2qArS81MdchaUubmiJU9t6OTLVTlcDZtoGfrmmiOFj8gXrdulj3IZfIK90RS5bC0PlRZ585ZrlMbudi2rrxTa9bMuUYVWf0rOnz4MJo3b44lS5agoKAABQUFWLx4MZo3b851tqqANUMD9+zR9HBp7dih/9hS8CW2y9aZunZNEfttm9h6RM6qbM9AE58musdSFykSBiRU6NgjkkYg6VQSAMf0/Ijt4RB77oq0sSLBaUUF+AZAmaaslmnlPV09MbTVUPh52WfOd9/QvkiPTWeg5SSs+Rtafh0u0mjZsqpbUPmOHtW/f63urA62pk2bhscffxwZGRlQKpVQKpVIT0/HY489hri4OAc0kcyJiBD/rcahQ4B3mZEa69ZpkmKI1b275W8SpFJNPWdgLqX7iRPijiG2HlFNpAhT4PH7H7d5fzXUeDLpSSjTlBXq+ZFAggZeDQySQIjt4RB77oq00R7Bqa26y7tXWXIOS+6U3sEv//6CCzcu2OV4jX0ac+igE7GmZ6tNG8e1w5m99JLlezMXF6B+/cppT2UQBODjj6u6FeLZ1LP12muvwdX1Xm4NV1dXzJw5E4cPH7Zr48gyqRTw9xdX99Ilw3lbYhfDAzSZbCzN+1KpNPWqO0sp3cumxDdHbD0iZ1V2nlLujVyDeUuhfhXPIxy3LQ7d5d1t6vnR9l6tGLICGXEZunKpRCq6h6O7vDukEvN3K1KJFN3lFfsmqap6W6oyOYcYq46vqvbZEskxrOnZ2rfPce1wZu7uwPTp5utMm6ZJ7FWTONP1YHWwJZPJkJmZaVCelZUFX19fuzSKxFOpgIsX7XOsmzfNb68p85jEpHQXuw6Fo9arIKoOlGlKtPi0he5x8r/JeqnQlWlKfHb4swqfJ6swCweyD2Bx1GKr9y3be1W2R8NF4iK6h+NA9gGoBPPfJKkEFQ5kO8E3SUakZKRUdRPM2n5uOxrWaVjVzaAqYE3PVlaW49rh7D74AHj1VcMeLqlUU/7YY9a91s7AmpFZVc3qYOupp57C+PHjsX79emRlZSErKwvr1q3D888/j5EjRzqijWRGaqr9Unnm55vvuRLbgya2XlWwlNId0KR0DwsTd7y2be3WNKJqRZsKvXzSCm0q9JnJMxGdGI07qjt2OV/ujVykXUmzap+5EXPtMj+nMuZskWn5t/Ph52GfOVvGMkpS9dWokfi6cnZ+mvXBB0BR0b3HzZtrHn/wQfX/EtwWMllVt0A8q9fZWrhwISQSCZ577jmUlpYCANzc3DBp0iT873//s3sDyTx7voEEQRO8VXQ9rupMbEr3tWvFHW/fPqB/f/u0jai6EJMKfdHBRXa9sfWv44/JWydbtc+clDlo59/OaLBlTdsqY84WALssjmwLiUQCqURqsfeuKm06s6mqm0BVINCK1Qhq0pwjRym7DpmvL7BxoyZpWXX+EtxWzpSN0Opgy93dHQkJCViwYAHO/ZeHs3nz5vD2tt8aGSSevTP/mQveLl0Sdwyx9aqC2OC0Jq1HQWQtMXN81IIVEz4taOSt+Xr76h3rFrYFNPO9hrYaWqGkCNpMgTmFOUaDNHsskKsNYKvCssPLqnWgBWh6TO3h4s2LUKlVTJLhJMLDAR8fy9MYAOCq9R8PtdqxY5o56YAmqG3QACgosO/Cxi4u1s39t6fmzavmvLYQPYxQpVLhzz//xO3btwEA3t7eaN++Pdq3bw+JRII///wTaitf8QULFqBr167w9fWFv78/hg0bhjNnzujVWbFiBSIiIiCTySCRSHDt2jWD4xQUFODpp5+GTCZDvXr1MH78eNws9879888/ER4eDk9PTwQFBeGDDz6wqq3V1cMP2/d45oK3mpD6XWzbxC6WV5N7Aan2quzhchO7TMSlW9Z/S2OP9a8A/UyB2oQbWvZaILcqk1RcKbpSJee1ip1uAH9J/8XootlUPUmlQFRUVbei5lCauOwvXNBMFbFnoAVUXaAFOFd2StHB1rfffotx48bBvWwf5X/c3Nwwbtw4rFmzxqqT79mzB5MnT8ahQ4eQnJyMkpISREVF4datW7o6RUVFGDBgAP7v//7P5HGefvppnDp1CsnJydiyZQv27t2LCRMm6LYXFhYiKioKwcHBOHLkCD788EPMmTMHK1assKq91dFnFZ+brsdc2vaakPo9PFwz7ttUVh6JBAgKAiZNspxS38XFuVYwJxLLv07ljjn59PdPcbbgrM372yM4VIQpkBSThEBf/XFN9loglzf/5tkjq6WWdtFsvubOQWxuNQ4jNE87J90YbZDVoAHQsIbkotm/v6pbIJ7oYOvLL7/EjBkzIDVyt61N/W5t8LJt2zaMGTMGbdu2RYcOHbBy5UpkZmbiyJEjujpxcXF4/fXX8cgjjxg9RlpaGrZt24YvvvgCDz/8MHr27ImPP/4Y69atw4ULmnU7Vq9ejeLiYnz11Vdo27YtRowYgalTp2LxYuszX1U39l5RPdXMF8Q1IfW7VAok/LfUTfmAS/s4Ph749VfL39io1dX7uRI5i4LbBZidMhv1PW27mzI2l0qw4StcRZgC/0z9R/d41bBVdknAoVKr8PmRzyt0jIrSDtWsro7lHbP7MSf8OMFgqQKqXlQq4IcfxNVt0sSxbXF2luakA5rereefr5z20D2i52ydOXPGZMADAF27dkVamnWZpMq7fv06AKC+FV9fHDx4EPXq1UOXLl10ZX379oWLiwt+/fVXPPHEEzh48CB69eql1yvXv39/vP/++7h69Sr8/AyzIN29exd3797VPS4sLAQAlJSUoKSkxOrnZk/a85eUlCAkxAWA/camJyer0KuX8SgjK0sCMZdMVlYpSkqqb0aoIUOAdeskmD5dipycexFXYKCARYtUGDJEwJo14p7r+fPV+7mWVfa6ITLnQqF9Fpi1hgQSSGxYCEbuK8cjAY8Yva5tudbLDod/sPGDUKvUUKsqNlZmz/k9dsvaaItG3o3wUf+PMHJT9cwYLIEE567Z+ZtDaLIc7vxnJx4NfdTuxyb72LNHgqtXxd2KNmrkPH9vq8L770shpg9l0SIBgPMvutW9+73roarub8SeT3SwdevWLV3AYcyNGzdQVDbnpJXUajXi4uLQo0cPtGvXTvR+eXl58C+XZsXV1RX169dHXl6erk5ouQWRGjdurNtmLNhasGAB5s6da1C+Y8eOapMMJDk5Gc2aAcAQ2JDF36iDB3OwdesfRredP98AQE+Lxzh//hC2bq3eCzp4eAAffQQMHz4UABAWdgX9+2fg77/v4Mcf87F3730A2ls8zt69aahX718Ht9a+kpOTq7oJVM2dv3G+0s8pQED+bes/N3p698T2bdsNjycI2Lp1q9XHK5tIYu+evTjnWfEg4MvsLyt8jIpo4dYCXulekEllKFSZ/jteVRyZrv3LXV/iTkDVBbpk3t69gQC6WKwHAIcOHcJtGz4jaoOVK9tg27YWlisCqCnft/7++yEUF+tfD5V9fyM27hEdbLVs2RIHDhzAAw88YHT7vn370LJlS7GHMzB58mScPHkS+6rJktBvvPEGppdZkruwsBBBQUGIioqCrIqT+5eUlCA5ORn9+vWDm5sbgoMFnLfTvZFEIsegQcazSPTvDyxfLuDCBUAQDL8VkUgEBAYCM2Y87FQpOQEgLa0h0tI0A5kDAwU88YS4b7J79QrDoEGtHdk0uyl/3RCZ0l/dH8s/XY4LNy5U+3WLBjw8AIPaDbpXcEzzP4lEgkGDBhndx5xSdSlwXPPv8F7haN2wYu9vlVqF8fHjK3SMigoNCsXt0NsoPFb9Ai1Ha9GiBQb1tv46oMpRp44EYmd0hIY+gkGDqvfnUVUoLgYUCmuSizt/rxYABAffux6q6v7GXCdUWaJ/O6NGjcJbb72F7t27GwRcx48fx6xZszBz5kzrWvmfKVOm6BJbyK1cta5Jkya4VC7XeGlpKQoKCtDkvwG+TZo0wcWLF/XqaB83MTEI2MPDAx4eHgblbm5u1eZGVdsWMSlTxfrrLxe4uRnvJXNz0/YGGd9XECRISAA8PavH62OJ6aw9EnzyibhoMTjYFdXkchCtOl3DVD25wQ0fDfwI0YnRkEBSrQOu/Dv5Jq9nW65zifrejYg93iv7M/Yj/07Vfhu/5uQarD0pcvHAGqbPfX34eVeN9eqlmS8tZoplUJDz/b2tDJ9+WrVZAavK1auG10Nl39+IPZfosWfTpk1D+/bt0blzZwwcOBDTpk3DtGnTMHDgQHTp0gXt2rXDtGnTrGqkIAiYMmUKNm3ahF27dhkM9ROjW7duuHbtml5SjV27dkGtVuPh//Kid+vWDXv37tUbW5mcnIxWrVoZHULobEQG1qL8l9nfpEOHKra9uhCTtcdS71xQELMRUs2lzc7X1LdpVTfFrPwi44FMdQkQKzuNvinV5fWoTA28GiAiJKKqm0FmpKaKC7Qkkuqd6bgq2TtRmrNoVL1z/ugRHWy5ublhx44dmD9/PnJzc7FixQp89tlnyM3Nxfz587Fjxw6ro8nJkyfju+++w5o1a+Dr64u8vDzk5eXp1vICNHOqjh07hn/+0WSIOnHiBI4dO4aCggIAQFhYGAYMGIAXXngBv/32G/bv348pU6ZgxIgRaNpUc5MwatQouLu7Y/z48Th16hTWr1+PhIQEvWGCzkqlsu/42zp1TG8rLobF7v7FizX1qjsxWXssZV6Mj3euFcyJrKUIU+Cfyf9YrliFXCT2ma9qjC0ZDcszlimRKseKISu4uHE1t2uXuHqCYD5bsqOoVEBKCrB2reb/lu4LquK8zrS4rz05U3ZKq/5Kubm5YebMmTh27Bhu3bqFoqIiHDt2DDNnzjS6/pYly5Ytw/Xr1xEREYGAgADdz/r163V1li9fjk6dOuGFF14AAPTq1QudOnXCD2Vyha5evRqtW7dGnz59MGjQIPTs2VMvDX3dunWxY8cOpKeno3PnznjllVcwa9YsvbW4nJW9P3zMLRK3dKm41O9Ll9q3TY6QWz2+bCaq9qr7zWp4M+Pdy+UXJ64q4c3CDdbuIseSy+TYGLOxwin7yfGsmW+ekuKwZhilVAIhIUBkJDBqlOb/ISGmpyBU1XlfesnyuqBUtayZUWd3Yr41nDNnDubMmWO2Tv369S0uqPzAAw8gtSq+FnEwewcNdeua3ia2q9oZurQD7PBl84QJwNCh7N0iqkr2Dgbt0ZtVltRFio8GfoThiSYmu5JdRYZEIvnZ5Gr/JQFpVNe5RkolEB1tOMQxJ0dTnpQEKBwQy9tyXnd3YOBA4Kef7N+e6qxcuoZqjbGwE1OpgHJ5PyrMXHe12K7q6t6lrVJpfiq6Gn1+fuV/00ZE+i7dugSVWoWUjBSsPXEvCUR1mqOkCFMgMiSyqptRK/jX8WegVUNFRFTOebRzuo1976Iti4uz/5DCipz30Vq4lJw9vjSvLAy2nNTBgwFo0cIVVuYksejaNdNDE8WuOl4dVic3Nd5Z2z3fty/w37S/CmGwRbWJI+dH2epM/hk0W9IMkasiMUo5Sldu7x6qilCpVThz5UxVN4PIabm7V16wZWlOtyAAWVn2n8ZRkfNWk1WTKk3Dhs6VoKxKhxGSbTZtkuD997s67PimhiYuWyZu/2XLgFdftV97rKVUar4dKvuhJZcDI0cCCxdaznwkNg0tUW1TnQIYrbl7DBefBzQ9W8o0pUPm7ajUKqRmpiL3Ri4CfAMQ3ixcrzeluLQYSw8vxbmCc2hevznaNmqLCzcv2L0dZKg69WiS/Tz0UOUN2Rc7PcPe0zhsPa9KBWzbZt+2VHdqNbB5s2OGcjpChYOt9PR0BAUFwdWVcVtlUKmA8eO1nziOmQBuqmt282Zx+2/eXHXBlqnxztnZwIcfijuGNfeTlfVNG1FVUanvjVlxthvZ0d+PxtBWQ20eVmbs+SrTlJj681Tk3MjRlQX6BuKjgR9BEabAzOSZWHxwMVTCvdetuiTrIKpuxCZ2CA52bDvKEjs8zd7D2Gw9b2qq5WV7apqCAs2arxs3OkfAVeExIa1atcLZs2ft0Rb6//buPL6JMv8D+CdJ7yOFll70oMiNIKCyUrRYQIpyCLQsyiqy7oIiZ0Fxfyor4rmicijgLqsLrooCUkURQeRquUQQFJQFRW7actNCS1vS+f0xpm3aJJ0kM5mZ9PPeV19tkyfzfMOO6XzneZ7vI8GGDcCVKwYouQO4XveycDbfWQkhIUy2yLflHMhBy/kt1Q7DbVfKr2D9b+tlO17OgRxkLcuySbQA4FTxKWQty8LgjwfjtW2v2SRagP6SVD1jYqsvUpMobyZbaWnibBiDg1PJYFBmn013+5V6I9wXPfKI98rxe0LycFSmg9TRYrFg4sSJCA8PBwDkKF0Ts4F7/33l+8jLA3r3rvv4oEHA1q31v37QIPljkkLK3llyWrSIlQjJd+UcyMHQZUO9migEmgJRZimT9Zjv//g+MlpmSG5fcyRv56mdaBPVBiajCZZKCx75wvl2ISsPNuCrHo0ovFoIS6WFRTJ04s47gZdfltbOW0wmYO5cceTEHkFQZp9Na79Dh9ZdzmBNwGr3m5MjPtZQWQuV9eihdiTOSR7Z+uyzz3DhwgVERETYfAFAWFiYze+knOJi5ftwtMngpEmO77hYGQxiOzV4c++sQYOAYcO81x+RN1kqLZi0ZpLXR2TkTrQAcXRLqpwDOWjxZnU51YdXPoxmc5oh50AONh3dhPOl52WPj+S16egmpMxNQc4B3vjVA6kJy7ZtysahFZmZYnn3hFpb8yUm1i37brGIIzsNnR4KlUlOtpYsWYLDhw+jR48eWLRoUdWX0WjESy+9VPU7KcsbU/yOHrX/eEAAcO+9zl97771iOzUoUQbUXnIZEgI89JD8fRFpRd7xPJws8uIwsYK6J0n70LROEaxdyMI6RfDtXRIrBJHqThWdwtBlQ5lw6YDUvZLmzfPedDHrkgRnlCj9bpWZCfz2W/XvrVsDR47UXZu0aZM4stPQaXWvtpokJ1v3338/8vLy8O677yIrKwsXL15UMi5yoFMn5ftwtHeXxVJ/edEtW9SbP1vffGdXhYXZX/9VUiJOL+CMWfJV+cVeHCZWWOe4zvW2kTJFcPWh1TJFREqzjshmr8m2mRZK2iP1Jun58/KXWndEypIEJUq/11RzxO/CBfsjgI5mITU0nu6Z6g0uFchISUlBbm4uOnTogE6dOmHt2rUwyHVlS5KcPat8H9eu2X9cyl2U8+fF+cO197fyBut8Z6BuwmUwiF9TpwLR0dKOd/Wq8+f1sjCTyFUxoTFqhyCbcyXn6m0jZYpgqaWBlfvSOQECThSdQN5xL12hk1vS0qRfLHtrqcCpU/W3AcSESyk1b/SeOwdcsTMb+vhx5frXk7g4tSOon8vVCI1GI2bMmIElS5bgscceg4VXm17ljWQrJcX+41LnxT7xBPCnPwE9e4rH8uYIkHW+c2ys7ePW+c4zZ4qlQqWor6qhdWEmka+pFHQwL0MiKYnjpqObJB0r2C/Yw2jI23xplNYXmUzS13krsVTAnm++kdZuwgTvXd8cO1b3seRk7/StdbXXt2mR26Xf77jjDvz444/4/vvv0aJFi/pfQLKQOirjiQcesP+4O/NiT50SK+t4O+HKza3+fe1a2/nOcg7GMtkiX5R7LLf+Rg1Q/1b93X5tvxb9YPR8txVyUXy4l67QyW3t2tXfRolS6/bk5ACLF0tre/my965v7K2l98a/h9ZFROjj38GjT/6wsDB06tQJgYGBcsVD9fBGBr93r/3HGzd2/VjW0SElF5PaU3N+c48etr87en9E5HvOXK1/BX56SrqkY425dQxWDFuBuDDX5630uqEXKuE7I4Z6YIAB3RN1unFkA2GxAFOm1N9u1izlt1pxt7qfEtc3tWfW2Eu2fvpJ3j71qF07fWzBI3mfrS5dukhan/X99997FBA5l5YGhIUJv8/fVWa93JIlwFNP1X3c3ZooglC9mFQLmwDLWb1HC++HSG7pKel4Me9FtcOQhZSRjfSUdEQFRzldtxUVHIX0lHSYjCZ0ju2MFm+JMzo6xXTCD2d+qLePrSckbFJIshIgYNvJbZKTafI+qftjNmmifCzuVPfz1vWNvWTLUeXohmTHDqC8XN4ZS0qQnGwNHjy46mdBEPDKK69gzJgxiNRDGRAfYrHYXygpp/JyZY7rzX2waqp9hygqSp7jms1Mtsg3SUk+9MAAAz478BkAIC05zeFGtyajCQsHLkTWMge7mAJYOHAhAHF914+FP1Y9PqDNAEnJ1qpDq1yInOTCNVvaJvW6wBvXD1LXatmjdHz2Eiuu4BH17Qt8/bXaUTgnOdmaPn26ze9vvPEGJk2ahBtuuEH2oMixefMApUa0rNq0sf94o0aeHddbi1sB53c5OnSQp4+//EUfw9dErpKSfOiBAAFzd87F3J1zkRCegDfveROZ7TLtts1sl4kVw1Zg4lcTcaq4uhxZojkRc+8Wy5ymzE2ps/+Y1P3IKior3HwX5Amu2dI2qdcF3rh++O4791+rdHz2kq1HHwUmT1a2Xz3YtEm5QQK5cLWuznhjn4n+DtaAX7rk/jG9tbhViv375TnOoEHyHIdIi6zJR9OwpmqHIgvr5sTONrod1GYQ/j3w31W//3vgv3F00lEAwNBlQ+0mVu/98J7ssZI8IoMjkZaskT88ZJeU/TG9df3gTil3g0GZ+KSs2dq2Td4+9WzBAm2nM9qOjuoID1e+j//+1/7jnuzSff/96o0C5eba7vslR8IaFKSd5JFIKZntMnF4/GG1w5DVAzkP2N3oNudADlLmpqDfkn5Vj03bMA05B3Iwac2kqo1yST86xXZyOHWUtKHm/piOeOP6IScHOHTItddYE8Q5c5SPz95eW9zUuNqWLdpetMVkS2dGjFC+j9On7T/uycjWxx+rtwHw3XdX7/sVGwssW+b5Ma9dA7L0PcOKSBJfu1i9dv0ant/8vM1jOQdy7I5cFV4txLBPhkmeKkjasvHoRqcjmaQNmZni/pyOvP66suXVLRbpe33VZDSKcWfan5ksG2siV3uvrd9+U7ZfPQkJ0fbNMMlrtt58802b369fv47FixejSa0SMRMnTpQnMrKrVy/lqxE6qnli9CA193Y1wjVr7D8uZyXClSuB0lIgmPucEunKq1tfxbN3PgtALHgx+ovRHLnyUZPWTMKgNoN87qaBL7FYxNknzmRni1P3lRhBkloRsTaLRUwEu3WTP+GqOY0wOVncK/ToUeDGG6sfP1P/rhYNRliY2hE4JznZmj17ts3vcXFxeP/9920eMxgMTLYUtnKl9Sflhkz/+Ef7j7dq5dlx3ZkPLZXFIk4T3LRJnO74z38q11dNjz8OLFjgnb6ISB5lljK8kPsC3vn+HZtiGOR7ThadRN7xPJZ/17D6kh2ly6t7WklQyUQQAFJSqpOtmq5dU6Y/PfKZ0u9HjhxRMg6SICfHO1PXTpwQk5a0NNsPj7FjxeTC3bVb336rzDTInBxxI0I5R62k2rnT+30SkedmbJ6hdgjkJSz/rm1ql3/3pJKg3Ilgebl4A7fm+rGUFPF77WQrJMTz/nyF1itDc82WTtjOKVY2hV+wQFzflJJiO0/aZPLsP24l1mxZE1A1Ei0iItI+ln/XNrXLv3fv7vnFevWsI/c9+aS4LGHyZODtt6sftyZetZOtzp0979NX/OEP2p4GLjnZ2rBhA9q3b4+ioqI6z12+fBk33ngjcnNzZQ2Oqrk7p9gTp04BQ4dWJ1x5eZ5tqCx3smWxAGrPWvV0aiURESknOiSa5d81TkqyYzKJ7ZSwbZvn1ydz5nhWxOPJJ4HXXrM/c2jrVvF77WSruNj9/nxNXJzaETgnOdmaM2cORo8eDbPZXOe5iIgIPProo3XWdZF8TqmwrEAQxK/sbPGDyNMhfE82DLQnL0+df5eatD50TSQnk4EnPOnLH5r+gcUxNE5KsmOxKLevlFzryR95xL2krbwceOON+tvVrj6o9vWPluzbp+1FW5KTrR9++AF33323w+czMjKwe/duWYKiuhyVY/cG63xkT4fwa5ctBaoLW9TcB0sqLXzQJCerHQERETmy9eRWu/uqkXaovWZr+3Z5jnP+vHgd46p586Sthb9wwXZ2kav7gvkye5s+a4nkZKuwsBD+/v4On/fz88PZs2dlCYrq2rtX3f5Pnare6d1d1sTKmlDl5Ijrwnr2rN4Hq/Y6MWe0cLrV2vmAyKdZBF60kr5cunYJecdl2MmeFKP2mi05b2a7k2y5sgKn5k3r0FDX+/JVLVqoHYFzkpOthIQE7N+/3+HzP/74I+KV+i+BcPWquv0XFIhT5oYPd/8YRUXVCdWTT4rrwWqvQzt50nadmDPR0e7HIpeoKLUjIFIWRwVI71iNUNuk3MhNShLbKUHtPZpcWQtfcwTHk5vfvsRgAMaMcbNMtpdITrb69euHv//977hmp7B/aWkppk+fjgEDBsgaHFVTe/HfhQvSNh6U4uRJcSGo4KB4TM11Ys6o/W8CyL8OjUhLcg7koOX8lmqHQeSRmNAYtUMgJ0ymmqNW9i8M4uKUWyNdc6NgT7lT/j3GhdOzZrLVtq3rffkif3/tr5+XvM/WtGnTkJOTg9atW2P8+PFo06YNAOB///sf5s+fD4vFgmeeeUaxQBu6yEh1+zcavVsRUckNDOVUUaF2BETKyDmQg6HLhkJwcPFDpBeVgrbvejd0paU1b1zaL3Tw3Xdiu+Bg+fuXa5lGcDBw552uv65ZM+ltayZbchX20LvycmDLFh8pkBEbG4tt27ahQ4cOeOqppzBkyBAMGTIETz/9NDp06IAtW7YgNjZWyVhJRenp3v8P21EBDOvar88+82Y09p05o3YERPKzVFowac0kJlrkEzYc2aB2COTE1KnytnPVunXyHKe0FPi//3M8a8cRV26m10y2WJOumlLFU+QieWQLAJo1a4bVq1fj4sWL+PXXXyEIAlq1aoXGjRsrFR/97tIl9fo2GsW50lOmeLdfewUwcnLEzZ29veeYI64M/xPpRd7xPJws0sh/ZEQe2nV6l9ohkBNSq+opVX1PzjXxb7whJl1vvSVeO0lx4YL049dMtuxse9tgxcaK/+5a5VKyZdW4cWN07dpV7ljICYOKI6SVleL+Fq7erfHUmTPiGrH4eDHZW7kSyMrybgz14cgW+SIWFCBfEuIXonYI5ESIxP97pLZzp//ycs+PM2oU8O67wIIFQEkJ8M470tYSuXLzuGayJTWZawi8fX3qKreSLfK+Vq3U7f/UKe/H8Mor1T83barNuzhaKNJBJDcWFCBf0tTcVO0QyIm2bcWbqVLaKSE5WZ7ZQw88IC65GDkSWLxYHGl5/32xgIMzrlQVPHdOrF4YFqb9ohDedOYMYDarHYVjzIt14uGH1e3/5Eng0UfV6//0adfKo3rLDTeoHQGR/FhQgHxJ96TuaodATtSXjLjazlVyFLoKCxNn4DzwALB0qRjr0qXAsGFAWZnz17q6jc1vv4nf1VxeojVa3/OUyZZO/O1v6vb/zTfAt9+qG4MWSdn1nUhvNh7ZqHYIRLJpGs6RLS2TWnVYqerEJSWeH6PmlL6sLODTT4HAQLGQ1+DBzvtwtbbcl1+KhcK0eANaLfv3+0g1QlLXRpWvfS5e1H61FzXs2KF2BETy25XPggLkO7gxt7alpdW//shaqEsJctw0LSoSt6ux6t9fTIpCQoA1a8TfHSVHri5HOHQIeOkl92P1RUeOqB2Bc0y2dCIgQN3+mzatuekgWam98zyREoJNCmxmQ6SSzcc2qx0CObFtW/0Jj7VQlxLkWg9ee3uc3r2BtWuB8HBxu5qMDHmm/h08CEyf7vlxfIkre5WpgcmWTjz4oLr9t2sHdO/O6je1jRihdgRE8kuISFA7BCLZHL98XO0QyAlHe2q6285VckwjBOwvtbjjDmD9eqBxY2D7djEBO3/ets3p0671w/219IeXzjoxcaK6/fv5iUPkXKNULSxM/OAk8jW3Nb1N7RCIZJMckax2COSEvT01PWmnll9+sf94167iUpDoaOD778W1Z4WF1c+7uh5ejjL1vqZmSXwtYrKlE2oXp4iMFIfB5ZCUJO4EbzCou3+YpxYtYulV8k3nSs+pHQKRbNJT0tUOgZyIipK3nauCguQ5Tm6uWLjCnk6dgM2bxeUY+/cDPXpU76+l9T2iyHNMtnRCqeFzqWJi5BnVevJJcSHjzJnAJ5/oex1Y48ZqR0CkjNPFLs5rIdIwo4GXOlpWUCBvO1fJddP02jXbIhm1tWsnJmTJyWKRix49xOshbiHjuZtv1nbGyk8gnVB7+Pz8eXF0y1OtW1d/sP33v67PVdYSuUb6iLRm6U9L1Q6BSDYFVxS6SidZ7JJY/FRqO1c5Go1yR303xlu2FBOuFi3ERKtHD/eurfQ8K0gJ33+v7X8QJls64eqmd0r0HxPj+XEOHRK/Dx4sbcd4LZPzA5pIS66UcwMX8h1nSzS+2KeBk7pMQqnlFGazfMeScmO8WTMx4WrXTpxKmJ3tej+cemhL6/UEmGzphKv7MMgtIUHc2NhTly4BpaX6T7SAuhWFiHxFk+AmaodAJJuoYIUW+5Asrl6Vt52rMjLkO5bUG+NNm4qzYzp1cq/0fL9+rr/Gl2l9pI/Jlk6oOYoSFCSWfc/J8fxYERHAlCmeH0cLalYTIvIlD3d+WO0QiGRz5uoZtUMgJyoq5G3nKj8/+Y7lyo3xmBhgwwZxeYWrtJ5ceFtoqNoROMdkSyc2blSv72vXxCo6jnY/d8WVK2IVP1/ADY3JVyU3Yqls8h2FV3hnTMuuX5e3nasOH1bmuFJERorVmV1VXCx/LHq2Z4/aETgnYz5PSlJqYahU770nz3Heflue42jBTTepHQGRMs6Xco4s+Y4NRzeoHQI5IXXmjlIzfORMttypmHj5suuvuXpVHN3i2i1Rfr62h/o4sqUTgYHq9q/1DePUcPGi2hEQKYNrXMiXCLwi1TSpU+KUmjpXWirfsdxJti5ccP01hw4x0arJnX9Db2KypRNqn0iJiUBwsLoxaM3x42pHQKQMVm8jX9I0vKnaIZAT/v7ytnNVSYl8x3Jnmx6jG1finEZoS851d0pgsqUTct55cUdJCTfxrY0LVMlXnS/hNELyHe2j26sdAjnRqJG87VzlTrLjyHffuf6a9HTpbY1GXnvY00TjBXSZbOmE2pXvSkuV271dr+Lj1Y6AiIjqE2AKUDsEcqJFC3nbuSInx73S644cPOj6a9LSpCd8f/wjpw/ao/UElMmWTii1v4RUYWHa3zTO29z5UCXSg8ZBHMYm35Gekq52COTEbbdJayf3yJbFAowYIe8x3bkxvm2b9Osr3uS1T+1r5Pow2dIJteej3nuvuv1r0enTakdApAxWIyRf0j2xu9ohkBN33SWt3aefyrPfp9WGDfKu1wLcq5iYny+97X/+4/rxGwKt77Ol8SVlZHXbbcBXX6nTt9GofoEOLeIaNvJVxy4fUzsEItks2LUAj3d/XO0wyIH0dCAoSNzTsz5jxojVma9fB8rKnH+Vlzt//scfFX9rkrgyWlVUJCYWWh/J8bbQUG3PrWSypRN33KFeslVZCZw7p07fWjZ+vNoRECmjsIibwJLv2HJ8C5MtDbNYxORHirNngQEDlI3HE+6sp5I6jdKqUydx6iFV++EHbS/aYrKlE+5UuJHTyZPq9q9Fv/6qdgREyvj29Ldqh0Akm1B/jc8xauDeesu1JKVZM3E0KDBQ/AoIqP7Z0Ze9NhMnauNG8r/+5Vr7G29kslWblFFRNTHZ0omwMHX7T0wU141dv65uHFrCjZ7JF5WWl+Lqdc5RId/RMbaj2iGQE1u2uNZ+8WLXyqU78re/eX4MORw+7Fr7zp0VCUPXTCa1I3COyZZOjBgBfPCBev1HRYnzhC9fVi8GrVGiDC2R2qZ+M1XtEIhkVVQmY21vkp0rN5OTksRS6XK4ckWe4zgiCOL0yKIicRNie9+LioD//c+1444bp0y8emY2qx2Bc0y2dKJ3b3H39IoKdfq/cIGJVm2PPqp2BETyO3TukNohEFED4srN5Dlz5BvFKC2V5zi1NW9enVApcc0WHi4em6r5+6sdgXOqln5/5ZVX0LVrV4SHhyMmJgaDBw/GwVqbF127dg3jxo1DVFQUwsLCkJWVhcJaGxkcP34c/fv3R0hICGJiYjB16lRcrzXfbdOmTbj55psRGBiIli1bYvHixUq/PVlZLOolWgDXbNmTm6t2BETyC/IPUjsEIlmZAzV+27uB691b2ujWE08AmZn2n6uoEG8KHzsG7NsHbN0KrFkDLFsGvPsuMHs28Pzz4jEeeQQYPly5dT5Hj4qx1LxmCwsT15m1aQPceivQqxcweLCYaPbp49rxP/0UGDJEzoj1T+vVoVUd2dq8eTPGjRuHrl274vr163j66aeRkZGBn3/+GaG/F82fPHkyvvzySyxfvhwREREYP348MjMzsXXrVgCAxWJB//79ERcXh23btiE/Px8PPfQQ/P398fLLLwMAjhw5gv79+2PMmDH48MMPsX79eowaNQrx8fHo27evau/fFXPnqtt/06bq9q9Fr74K6OT0IZKsXZN2+OLQF2qHQSSbfYX71A6B7LBYqqfTvfACMHkyAAgA7FeWe/11YONGMXGpPRVPSwUSduwQR5/MZvErNNT5aNymTcC6ddKPv2GDujfftejBB1n63aE1a9bY/L548WLExMRg9+7d6NGjBy5fvox3330XS5YsQa9evQAAixYtQrt27bBjxw5069YNX3/9NX7++Wd88803iI2NRefOnfHCCy/gb3/7G5577jkEBATgn//8J5o3b4433ngDANCuXTts2bIFs2fP1k2ytXKluv1fvKhu/1qklT06iOQUYApQOwQiWV2pUHhxTgNSWSmudXK0/sjZ2qTaj9nfUNh5Ce/du53HFxRkm+hYf6793WwGVqwA8vLc/qewKzTU9VLuaWnifqaVldLab90KbN7semy+bOLESnzzjdpROKapNVuXf18UFBkZCQDYvXs3KioqcFeN7cXbtm2L5ORkbN++Hd26dcP27dvRsWNHxMbGVrXp27cvHnvsMfz000/o0qULtm/fbnMMa5vs7GyHsZSVlaGsxsYPRUXiAtuKigpUqHBL4eJFE9Sc9blvX6Wq/WtRSUklKirc2C5eJdbzVo3zl/QjLVGm1edEGhETHNOgP/cEQdwE15roFBcbbJKgK1cMNZ4Tn6/5e1GRoSrBunJF/v2M/P0FRESIFY8LCuo//jPPWHDbbcLvyZNgk0i5snbn1Ckj8vLkLWPXo4cFFRUSs6YaOnY04ocfpMXy7bfWURxt7y3lLTfdZIHBoM71jdT+NJNsVVZWIjs7G7fffjs6dOgAACgoKEBAQAAaNWpk0zY2NhYFBQVVbWomWtbnrc85a1NUVITS0lIEBwfXieeVV17BjBkz6jz+9ddfIyQkxL036QGTqRuA2HrbKeX8+fMAolXrX4sqKiqxevVqtcNw2TpX5itQg1Ny3e7tZiLdOnvirO4+qwUBKC83oaTED6Wlfr9/96/xc/VXSYm/3cdq/i4I8l6Ym0yVCAm5jqCg6wgJqUBw8HWEhFxHcPD133+uqPq5+jnbdtbv/v5icpKbm4BZs26tt+/S0j2orDyFS5eAS5fcfw9Hj7YB0Nb9A9QhYMSIVXDnVCstvR1AE0ltr11jklXTrbf+jHXrfgPg/eubEvvDs3VoJtkaN24c9u/fjy2ubrigkKeeegpTpkyp+r2oqAhJSUnIyMiAWYUak1u3GrFPxWnnZnOkep1rVGioEf369VM7DMkqKiqwbt069OnTB/5aL91Dqpm4ZqLaIRDJ6nLoZa98Vtcs9W1vFMk6giT+LI4YVY8m2f5eVARUVsp7UW00ClUjQOJokFD1s/X3sLDqaXZhYTXbCzZT8AIDAYPBAMD/9y/PhYYaMGtW/e3uuacz7ryzk8f9BQcbsHy5x4epkphYiaFD3TvP/vEPjW8UpYqa67Ds/bcgwGQC3nyzLQyGFqpc31hnvdVHE8nW+PHjsWrVKuTm5iIxMbHq8bi4OJSXl+PSpUs2o1uFhYWIi4urarNz506b41mrFdZsU7uCYWFhIcxms91RLQAIDAxEYGBgncf9/f1VuVBNTwdee83r3VbZvZsfBLW1bWuEv7/+plaqdQ6TPhy+6OIOm0QaV1BS4PQzr6zMtbVGzp6rVQjZYwZDzWTI8fojZ2uTrD8HBxtgsLlm1dYISc+eQEKCgFOnAHuxGQxAYiLQs6efLOXfe/cW9xA9f97zYwFiwuTv715gUtdrNSwGxMYCtS7fbZ6fMgUIDfWvKhji7esbqX2pmmwJgoAJEybg008/xaZNm9C8eXOb52+55Rb4+/tj/fr1yMrKAgAcPHgQx48fR2pqKgAgNTUVL730Es6cOYOYmBgA4jCi2WxG+/btq9rUnkKwbt26qmPowYoV6vYvaLvQiypuv13tCIjkx9Lv5Gt+KziP++93nDSVl8vfp3WESEoS5Oy5kBCxeEJDYDIBs2ZZcN99JhgMgs3UR2uSKOc+WyYTsHAh8PvlpccSEtx/7YkT8sTgKwwG8b/NsDDgySeBWbPE6pVWJhMwZQowc6Z6MbpC1WRr3LhxWLJkCVauXInw8PCqNVYREREIDg5GREQE/vrXv2LKlCmIjIyE2WzGhAkTkJqaim7dugEAMjIy0L59e4wYMQIzZ85EQUEBpk2bhnHjxlWNTI0ZMwbz5s3Dk08+ib/85S/YsGEDli1bhi+//FK19+6qzz9XOwKqzfHdFiL9atGohdohEMmqrKIES5fW3y4kxLPEyPo9LKzhJEhyGzJEwN/+9h0++KDr7yNcosREMdFytM+WuzIzxZvZkyZ5tp9okyZiVUF3nTvn/mt9UXx89d5rM2cCL74ILFgAHD4MtGgBjB0LBOiocK6qydbbb78NAEhPT7d5fNGiRfjzn/8MAJg9ezaMRiOysrJQVlaGvn37YsGCBVVtTSYTVq1ahcceewypqakIDQ3FyJEj8fzzz1e1ad68Ob788ktMnjwZc+fORWJiIt555x3dlH0HxEpCpC1yTxch0oLc49ytm3yLIbgcs+c4T5rCwsRqeKS+1NR8PPfcdezY4Y/8fPHCOy1NvhGt2jIzgUGDxDLwU6YAe/a4foz+/T2LrwEXy7RrwADb3wMCACcFxDVP9WmE9QkKCsL8+fMxf/58h22aNWtWb6Wh9PR07HHnvyCN4F0y7TlzRu0IiORnMGhrHQeRpwy4jkmT1I6CXGEyiWvVvd1fbq6YgLuK1wPymjNH7QjkxUt4nYhVr+o7OfDbb2pHQCS/zrGd1Q6BSFaVYPUBkiYsDOja1fXXbd9uu6aI3JeaCjioXadbTLZ0ok8ftSOg2ji1k3yRlBkHRHoSaKhbWZjIkZ07XU+4Ll0SpyG6iwWCq9WqlecTmGzpxOuvqx0B1abC3tZEitt8dLPaIRDJqnFQY7VDIJ3ZuVOsVjl4MBAksUBrfr77/THZqvbjj2pHID8mWzrx8MNqR0C1RUWpHQGR/E5dOVV/IyIdCQsMUzsE0qGwMODTT4FVq6S1/333IbeUlkpva2cLWK9SqlCJVUSEssdXA5MtHSgvBz75RO0oqLYa+28T+QyjgX8WyLc0a9RM7RBIx6SuxfJkzZYrs7eTk8Vy9Z7s6+WJF16w339iomcJp9XgwZ4fQ2v4V1UHalS6Jw1R64OOSEnxIfFqh0Akq8dTH1c7BNIxqWuxPFmz5UoR2NdeE8vVHzsG1FOIWxGPP17d/8aNwJIl4vejRz2fDmkwABMnyhKmpnBXCR04eFDtCMges1ntCIhElZViwZaiInGdgb3vUp+7OqAF0O6w2m+JGoCBrQbiRNEJ7C3cq1gfRhiR0TJDseMTySE6Wnr5eOt6cZPJ+xv7PvFEdZ/2yvM3agSbzag9Ob4vYbKlAwUFakdA9uzapXYEpGeCAJSUeJYYWb9fueLaNBSn4n+W6UBEzk3qNgl7C/Zi77q9ivXxTNozMBkVXmRCPi09HXjxRWnt3DVxIjBtmrS2ubnVFao3bXK/T1cNGiSOqjlz++3ATz+5d3w/P2DmTPdeq3VMtnQgnrN6NKmwUO0IyNsEAbh2zbPEqOb3Spm3/zGZxA05zWbxy/pz7e/OHuvxKXCa2xqQwqKCo5Cekg5LpXKbEwX5BWF6+nTFjk8NQ3q6WBDr/HnHbaKiPEu2pk6VnmzV9L//ud+nVBERwL/+Bdx3X/1tb77Z/X7k/nuoJUy2dKBlS7UjIHvCWOBKN8rKPEuMav4s98aVBoPriZGj54KDXZv7b0/nuJtw+vBJed4ceU2SOQknik6oHYZkCwcuhMlowvlSJ1ewHvow80OOapHHTCZg4UIgK8txm4ULPavSFxAA/PGPwPLl9be1JnU5OcoXTxs4UKzIKPW9zZvnfl9SS+zrEZMtHWjfXu0IyB5PL2rJufJyzxMj6/eKCvnjCw/3LDGyfg8N1da51DG2I1YfVmHVNXmkR3IPfLj/Q7XDqFeiORFz756LzHaZAID4cGlTN4a1H4ZlPy+T1DYhLAFv9nuzqg8iT2VmihX4Jk0CTta4F5WYCMydKz7vqY8+Ar74Qpw94Yh1BM1iAR55xPM+pXAlibx0yf1+brjB/ddqHZMtHdi6Ve0IyB6ObNV1/bprSZCzpKmsTP74QkM9S4ysz4WGAkYfreXKkQB90nJ58+iQaMzuOxsJ5gSkJafZnGPdE7vDCCMq4XgOkRFGvD/kfZRZyrDy4Mo6z9/b+l5MTp2M/OJ8xIfH1+mDSA6ZmeK6pbw8cQPj+HggLU2+fadMJuDDD6WNoK1f73xao1xc7SMpyTYZdcX+/cCTT/rmui0mWzpwlesnNKlNG7UjkIfFIhZYkGMdkisbM0oVHCxPghQWpvxmjL6gcXBjtUMgN/Rq3guL9ixC/tV8tUOp42zJWSSYE5Cekl7nubzjeU4TLQCoRCVe3vIyPj/4ud3nvzj0BUZ2HonhHYfLES6RQ/Yq8MnJOoI2caJtVb/aI2jeKozh6qyLqVM9G+WbNUssRuJrFQmZbOlATo7aEZA99VXlUVLNUt9SE6PLl004erQ7XnjBZNNOiWQ+MFC+dUh+/JTyqgslF9QOgVyUZE5Ceko65vWfh6xlTm6LK2BAqwFY9cuqetvlF9tPAjcd3SSpn1nbZ0GA45Kb2WuyMajNII5oke4pPYLmihtvdK29p1WaLRZxb9nsbM+OozW8jNEBbwwVk2v8/Fy/8yIIYmIjxzqk4mJ3ojYCiHb4rL+/54mR9buv3ZVqSI4XHVc7BHKBAQbMuXsOTEYTMttlYsWwFbj/k/tRUSnfQsUBrQZg9S+rbUagjDDi8e6Po1+rfpKSLalrsxwpLnf8oSdAwImiE8g7nmd39IxIb+obQZNajt5Td9zhWvvffvO8z8M+uM0jky0d8OVymHp1/Trw7LPi0L7UpEmpUt9SE6PQ0Ov49de96NGjMxo39qvzusBAeWMjfarkB46uPNH9iTqFIGJDY3GyWL6Kko93fxwrhq3Agl0LcPjCYbSIbIGxt45FgF8ALJUWJJoTcarolN2RJwMMSDQnIi05ze6xezTrAeTJE6ej0TMiXyOlHL0z0dHA2bP1t0tKcu245865F09NLVp4fgytYbKlAyEh4uanpC0vv+ze62qX+vZkul1QkPQ51RUVAlavPoV+/TrB39+92Mn3GQ0+WvnDR328/2O80vsVmIwm5BzIwdBlQ51Ot3NH98TuCPALQHa37DrPmYwmzL17LoYuGwoDDHb7to682SPn+ebp6BmRXkgpR++IwQDMnw9MmeK8mEVSkjh90RWhoa7HU5PJBIwd69kxtIjJlg64N2WMlHbzzUCzZq4nTSEh2ir1TVRTUoSLtzJJVdbpc2nJaZi0ZpLsiRYAbDu5zen0vMx2mfhk2CeYtGYSThZVX7018W+C+QPnOy3BfubqGUkxRAZH4mLpRbdGz4h8UX3FNIC6peqTkoA5c8TXmkzA0KHiEofaDAaxnavrxNLSgJV1C4ZKNmWKby5DYLKlA5zVoz1GI7BzJ6vbke+JCo5SOwRyUX5xPvKO59kkOnI6cbn+zZIz22ViUJtByDueh/zifEQHR6NofxEGth3o9HVSR6Mm3TYJz216rs7omQHinStno2dEvqq+YhrOnsvMFDdFdpaQuerRR4Ennqi/ncFgm+SZTGKi5Ytl3wEmW7oQGKjMpqzkvspKJlrkm/KvcN2L3sSHx+NU0an6G7pp64mtGNFpRL3tTEZT1QhYRUUFVv9U/+bYaclpktZ8PZP2DDrEdKgzepZoTsScu+dwA2NqsJwV06iv0IbclQ/feUdau5kzxUJjhw+La7TGjvXNES0rJls6EB4u7oNERKS0Lw5+oXYI5ILwgHCkJafhrZ1vKdbH/jP7FTu2szVftUetMttlYkCrAQh8Sazm065JO+x9dC8C/Hz4Ko1IYXLuHSa1kuCxY8Bbyn1kaQ5XQuvApUtqR0C1sXIf+arrldfVDoFccLX8KiyVFkWnfypdodK65ivBnGDzeKI5EZ8M+6Rq1CrnQA5avFVdquzAuQNo8VYL5BzgZpREWiC1kqAvVhx0hiNbOmCxqB0B1ebLw93UsHWI7YAjl4+oHQZJVIlKLNi1QNE+BHsr6GVWe81XfHg80pLTqtZhOaq0eKroFIYuG2qTlBGROsaOFddsObtu9dWKg85wZEsHIiLUjoBq43ot8lVLhixR9PiNgxorevyG6PCFw4gOcbxhuafKLeWKHbsm65qv4R2HIz0lvSrRslRaHFZatD6WvSYblkremSRSU0CAWOjCGV+tOOgMky0dCA9XOwKqLS5O7QiIlBEWFIYbGt2g2PGfTnsao7uMVuz4DVGLyBaIC1PuQykyOFKxY0tRX6VFAUJVCXwiUtfMmcDUqXVvSptM4uO+WnHQGSZbOnD1qtoRUG1emFVDpJp3B70rqd19N95XVcRAqoulFxHox0WPcjEajBh7q7Jzcnrd0EvR49cnv1hahUyp7YhIWTNnAiUlwOzZwPjx4veSkoaZaAFcs6ULkZFAYaHaUVBNTIDJl0ktx/1h5of47+D/YsGuBXj/h/fxfcH39R7baDCiRWQDWx2toCndpiDAL0Dy5sDuKC4rVuzYUkjdi0tqOyJSXkAAkJ2tdhTawJEtHYjn3w/NadJE7QiIlGMtxw2gzshV7XLcAX4ByO6WjZl9pN2yTE9Jx9hbx8Jk4MJHTw1qMwivZbwGQNlE4+ilo4odWwpr8u9oFNUAA5LMSUhLTvNyZERE9WOypQPRyq17Jje1bq12BETKspbjbhre1Obx2uW4rdJT0ustPx4VHIX0lHQE+AVgSmo9q6jJqTaN2+Cz+z+r+r2+hMQTZ6+elf2YrnAl+Sci0homWzrAynfa07Kl2hEQKS+zXSZ+HfcrXmjxAv476L/YOHIjjkw6YrfEtslowsKBC50eb+HAhVUXxDP7zESLRpxOaIABYf5hLr/u4MWDNvtLOUtI7An1C5XcV2ig9LZKkboXFxGR1jDZ0oGUFLUjoNruvFPtCIi8w2Q0oWN4R9x/4/025bjtyWyXiRXDViAxPNHm8URzIlYMW1Hngrh54+ayxmrU4Z+0J7o/UeffS6ra5c4dJSRJ5iQsG7oMG0duxJLMJdg4ciMevOlByf3cnnS7W/HJLbNdJo5OOmrzPhwl/0REWsECGTrQqxfw8stqR0E1cbSRyL76Nqet6damt+KbI9/I1nclKmU7lre8tu01RAe5N1fcWu48PSW96jGp//5L9y+V3E+n2E5uxacE615cRER6wWRLB9LTgago4Px5tSMhqzPKFf4i0j2pF8R33XAX/rH1H8oHpHFnr7m/JuqNbW/U+beW8u/vyvqm86X840NE5C79zblogEwmYKHzpRDkZawQSeQ5KUU1yLkvf/kS5dfLXX7dbQm3SW4bExrj8vGJiEjEZEsnMjOBFSuAhIT625Ly0lhhmMhjUopqKMEcYEZcSJzX+1WCAAHzvpvn8uuSIpIUiIaIiGpjsqUjmZnAsWPAzJmW+huTorhmi0gejopqKCnRnIirFb6zM3nu0VyXX5OWnIbGQY0ltS24UuDy8YmISMRkS2dMJuDcObWjaNgCA9WOgMi3ZLbLxNHsoxhx0wiv9PdQp4dQbnF96p1WuZM4mowm3NvmXkltmWwREbmPyZYO7d4t/6aVJJ0gqB0Bke8xGU0Y2WmkV/qa3G0yjAbf+fMXGRzp1utiQ2IltTtzhRWBiIjc5Tt/bRqQkBDtXu03hOl1fqzhSaQIKQUzzAFmj/qY2n0qAvwCEBsqLdHQgwslF9x63fcF38vajoiI6mKypUNaLs7Qty8QHq52FMqK9Z1rNCJNkVIwY+HAhTAZnN/VMaDu6L8BBkztPhUz+8wEAAT7B7sfqMaEBoa69zp/aa+T2o6IiOpisqVDo0ZVAhB+/5ImJgbYuBFYsgR4+mnFQkNBAdCnj3LH14IOHdSOgMh3WQtm1C7ekBieiBXDViA2LBYWwXmRIAEC1j6wFrP7zsb4ruMxu+9sXHvmWlWi5WvSkt27A5fWTNrrpLYjIqK6OCFKh955xwjYuXNbW3Aw0KIFsHkzEFljSv+mTcDLLysTW1AQ0LatMsfWijvvVDsCIt+24+QOXLx20eax08WnsePkDnSJ6yLpGOdLzyO7W7bD54NMQZ6EqCljbx3r1usm/GECnlz3JCpR6bCNEUZM+MMEd0MjImrwOLKlQ1u2SGvXty+wb59togUA3bsrt7aqeXOgsbRqwrpkNAITeN1BpJgn1z2J17a9VufxSlTitW2v4bP/fSbpOPVtxBvop52yovamPbri29PfuvW6AL8APN79cadtHu/+OAL8Atw6PhERMdnSpfBwaX+YHa2d2rYNsCi0VddDDwEX3FurrQuPPw4E8LqDSBHl18sxa/ssp22W/7xclr7KrpfJchw5DGg1wKPX5xfnu/3amX1mYmr3qXXWwZkMJps1bkRE5B5OI9ShBx6oxIcf1p8nj3CwZU2+xL/LDz4INGokTkV89FFx3deVK47bh4UBvXsDH3wg7fh6064dMJPXHUSKWbBrgaT1WFKcueq8XLmWCmRsPLYRCaEJOHX1lFuvr28Urz4z+8zEiz1fxIJdC3D4wmG0iGyBsbeO5YgWEZEMmGzpUM+eAoKCKnDtmh8crd0KDwd69bL/+vh4af389a9Aenr17++9B2RlOW7/3nvi9MSkJGnH15s5c9SOgMi3Hb5wWLZjxYc7/6Br1qgZtp3aJlt/nrhSfgVBRnXXkAX4BThd40ZERO7hNEIdMpmAm24667RNr16O12WlpQGJiYDBwWxEg0FMmGqXmM/MBFasABISbB9PSBAfz8ys7tvXWEftiEg5LSJbSGoXERjhcJ2TAQYkmZPqrdCntXLmtQuCuKK+UTwiIlIPky0dKi8Hdu2Kc9pm1SqxnT0mEzB3rvhz7YTL+vucOfaTtcxM4Nix6jLyGzeKv1sTLUAcDYtyvi+p7lhH7YhIOWNvHVvvHlomgwkLB4h7cdVOuKy/z7l7DkxG58c5cO6AB5GK/I3+Hh/DSur0SHvqG8UjIiL1MNnSoX/+04jKSufl3y0WYMECx8fIzAQ++aTuKFViovh4zeSpNpNJTKiGDxe/105CTCZgofN9SXWj9qgdESknwC8AU1KnOG0zJXUKhnUYhk+GfYIEs+0HWKI5EZ8M+wSZ7er/D9ZZufP6DG4zGGsfWItKwf1j1BYe4Ppu8FJH8YiISD1cs6VDBw/K0y4zExg0CMjLE4tmxMeLUwflGMGxTjmcOBE45d6ab1WFhQGffWY/mSQi5Vir383aPsumWIbJYMKU1ClVz2e2y8SgNoOQdzwP+cX5iA+PR1pyWr0jWlbRwdFux9g+uj1+PvdzvcU8XNEprhNyj+dKbu/KKB4REamHyZYOFRTI1846SqUEe8ncbbeJ5dPffluZPuVy5Yr4b8NEi8j7pFbHMxlNSE9Jd6uPhIiE+hs5EBkciV/O/+L26+2JCnZt7nWiORFz7p4jaRSPiIjUw2RLh6RWE5TaTkn2krm33gKWLQPOn1clJMn0OCJH5CuUro7XOqq126+NC4vDkUtHZIwG+ObIN5LaPX3H0+jToo9Lo3hERKQertnSodYSrxGktvM2vazpOuu84CMR6ZiUYhyOJJgTcFvCbbLGU1xeXG+bAGMAnu/5PNJT0ploERHpBJMtHRozphIGQyXgpHqV0QiMHeu9mFxlXdOVmKh2JI41bqx2BESkFCnFOOyxFqRoGt5UgaicK68sh6VSvnViRESkPCZbOmQyAYGBzv/ghoZqf71RZiZw9Gh1GfnZs9WOyNZ336kdAREpaWafmZjafaqkES7D7/+zFqSQmvSMvnm0p2HaeGvnW7Iej4iIlMVkS4e2bDHg2jV/OCv9XlwsFqbQuppl5MeOrT9BNJmApUu9MyImuL/tDRHpxMw+M1HydAlm952N8V3HY3bf2ViatRSJZtsPmdpl5Tcf2yzp+I2DGjvcgLmmQGOgpOPlHpNesZCIiNTHAhk6lJ8vbzut2LZN3B/MGYsFiIkRR8SsVQ6jooB9+4D164GvvpIvnubN5TsWEWmXvWIcWe2znJaVP1F0QtKxv8//XtKGxZHBkci/Wv+H9qliVu4hItITJls6pKdqhK5wJYmsXeUwI0MsKZ+TA0yYAJw+rUiIRNRA1FdWPiFcWun4kooSSe1C/UIltWsa6v21YkRE5D5OI9ShO+4QEBVVCoPB/t1SgwFIShI3KNaTmBjP22VmAsePV68D++YbIDLSvXiOHXPvdUTk+5qENJHULtgvWFI7c5BZUrvERhquKkRERHUw2dIhkwkYNWofADGxqsn6+5w52i+QUVt9Uwiltqu5Dqx3b2DSJPfiSUlx73VE5PviwuIktYsJk3YXqXUTaXt1pCamSmpHRETawGRLp1JT8/HxxxYk1JrJkpgIfPKJOMKjN5ulrTeX3M7q3DnXYwGANm3cex0R+T6pyZbU6YbNGjWT1C4pIklSOyIi0gZVk63c3FwMHDgQTZs2hcFgwGeffWbzfGFhIf785z+jadOmCAkJwd13341ffvnFps21a9cwbtw4REVFISwsDFlZWSgsLLRpc/z4cfTv3x8hISGIiYnB1KlTcf36daXfnuKGDBFsSqdv3AgcOaLPRAsATkhbby65HSCOgr3lZqXkpUvdex0R+b7y6+WS2m34bYOkdkVlRXUqINZm3eOLiIj0Q9Vk6+rVq+jUqRPmz59f5zlBEDB48GD89ttvWLlyJfbs2YNmzZrhrrvuwtWrV6vaTZ48GV988QWWL1+OzZs34/Tp08iskW1YLBb0798f5eXl2LZtG9577z0sXrwYzz77rFfeo9JqTplLT9ff1MGakiTesJXaDgBWr3YvFkAsn09EZM+S/UsktdtTuEdSOwMMmHv33Kr9vGo/V3OPLyIi0g9Vk6177rkHL774IoYMGVLnuV9++QU7duzA22+/ja5du6JNmzZ4++23UVpaio8++ggAcPnyZbz77ruYNWsWevXqhVtuuQWLFi3Ctm3bsGPHDgDA119/jZ9//hkffPABOnfujHvuuQcvvPAC5s+fj/JyaXcmyTt69ZK3HQBMnuxeLID+qjkSkfcUl0m7GyOl7DsAtIpqhcx2mfhk2CdIMNtOPay9xxcREemHZku/l5WVAQCCgoKqHjMajQgMDMSWLVswatQo7N69GxUVFbjrrruq2rRt2xbJycnYvn07unXrhu3bt6Njx46IjY2tatO3b1889thj+Omnn9ClSxeH/VtjAICioiIAQEVFBSoqKmR9r66y9q92HHK7/XYgMtIPFy4A9jdsFhAVBdx++3VIfesnTpjg7j2FP/zhOioqfGdnY189b0hZPG/saxIsrRohII5MOUu6TAYTRncajYqKCgxsORD9xvbDlhNbkH8lH/Fh8bgj6Q6YjCZd/X/A84bcwfOG3KHWeSO1P80mW9ak6amnnsK//vUvhIaGYvbs2Th58iTyf9+QqaCgAAEBAWjUqJHNa2NjY1FQUFDVpmaiZX3e+pwjr7zyCmbMmFHn8a+//hohISGevDXZrFu3Tu0QZDd6dDxefbUrAAG2CZd4oTJq1HdYu1b6bs0WS3+4m2ydOrUDq1efd+u1WuaL5w0pj+eNrUNHD0lu2yq4FQ6VOm4/sMlAfPP1N3UeN8OMq7iKtT+tdStGLeB5Q+7geUPu8PZ5U1IibR9FzSZb/v7+yMnJwV//+ldERkbCZDLhrrvuwj333ANBUH604amnnsKUKVOqfi8qKkJSUhIyMjJgNkvbD0UpFRUVWLduHfr06QN/f39VY5Fbv37AzTdbMHmyyWZj4oQEYNYsC4YM6QLA/mikPX5+Rskl5asJSEwEnnjiNl2vgavNl88bUg7PG/v+88l/gMvS2o7qPgoFVwowZ+ccVAqVVY+bDCZM+sMk/KP3PxSKUj08b8gdPG/IHWqdN9ZZb/XRbLIFALfccgv27t2Ly5cvo7y8HNHR0bjttttw6623AgDi4uJQXl6OS5cu2YxuFRYWIi4urqrNzp07bY5rrVZobWNPYGAgAgMD6zzu7++vmQ8ALcUip2HDgKwsIC8PyM8X106lpRlgMrl+ugYGAjVmg0piMBgwdy4QFOR7/7aA7543pCyeN7Z6NOuBzw99LqltU3NTTL1jKl656xUs2LUAhy8cRovIFhh761gE+AUoHKm6eN6QO3jekDu8fd5I7UsX+2xFREQgOjoav/zyC3bt2oVBgwYBEJMxf39/rF+/vqrtwYMHcfz4caSmihs/pqamYt++fThz5kxVm3Xr1sFsNqN9+/befSMkmVxVFl0dhExK0u8+ZUTkPRP+MKFO1UBHrAUvAvwCkN0tG2/1ewvZ3bJ9PtEiIiKVR7auXLmCX3/9ter3I0eOYO/evYiMjERycjKWL1+O6OhoJCcnY9++fZg0aRIGDx6MjIwMAGIS9te//hVTpkxBZGQkzGYzJkyYgNTUVHTr1g0AkJGRgfbt22PEiBGYOXMmCgoKMG3aNIwbN87uyBX5Flf+Lx49Gnj7bX2Xzyci7wjwC8CU1Cl4Y/sbTttxbywiooZN1WRr165d6NmzZ9Xv1jVSI0eOxOLFi5Gfn48pU6agsLAQ8fHxeOihh/D3v//d5hizZ8+G0WhEVlYWysrK0LdvXyxYsKDqeZPJhFWrVuGxxx5DamoqQkNDMXLkSDz//PPeeZOkqvBw6W1TUphoEZF0r2e8jl8v/IqVB1fafZ57YxERkarJVnp6utNiFxMnTsTEiROdHiMoKAjz58+3uzGyVbNmzbDak91tSbcCXJil89FHwNNPKxcLEfmez+7/DMv2L8Mjqx7B5bLqihlJ5iTMuXsO98YiImrgNF0gg8hTTqr718E9ronIHcM6DENW+yzkHc9DfnE+4sPjkZacxhEtIiJiskW+7eJF6W1rzGglInKJyWhCekq62mEQEZHG6KIaIZG7IiOlt331VeXiICIiIqKGh8kW+bTevaW3ffdd5eIgIiIiooaHyRb5tMaNpbfNzVUuDiIiIiJqeJhskU/bsEF625IS5eIgIiIiooaHyRb5tDNnpLe9+Wbl4iAiIiKihofJFvk0s1l62+ho5eIgIiIiooaHyRb5tKQk6W3j4pSLg4iIiIgaHiZb5NMOHpTeNiFBuTiIiIiIqOFhskU+rbhYWjuDAUhLUzYWIiIiImpYmGyRT0tJkd7OZFIyEiIiIiJqaJhskU+bMUNauzffVDYOIiIiImp4mGyRT7vnHsDf33kbf3+xHRERERGRnJhskU8zmYCPP3be5uOPOYWQiIiIiOTHZIt8XmYmsGIFEB9v+3h8vPh4ZqY6cRERERGRb/NTOwAib8jMBAYNAvLygPx8MdFKS+OIFhEREREph8kWNRgmE5CernYURERERNRQcBohERERERGRAphsERERERERKYDJFhERERERkQKYbBERERERESmAyRYREREREZECmGwREREREREpgMkWERERERGRAphsERERERERKYDJFhERERERkQKYbBERERERESmAyRYREREREZECmGwREREREREpgMkWERERERGRAvzUDkAvBEEAABQVFakcCVBRUYGSkhIUFRXB399f7XBIJ3jekDt43pA7eN6QO3jekDvUOm+sOYE1R3CEyZZExcXFAICkpCSVIyEiIiIiIi0oLi5GRESEw+cNQn3pGAEAKisrcfr0aYSHh8NgMKgaS1FREZKSknDixAmYzWZVYyH94HlD7uB5Q+7geUPu4HlD7lDrvBEEAcXFxWjatCmMRscrsziyJZHRaERiYqLaYdgwm838MCKX8bwhd/C8IXfwvCF38Lwhd6hx3jgb0bJigQwiIiIiIiIFMNkiIiIiIiJSAJMtHQoMDMT06dMRGBiodiikIzxvyB08b8gdPG/IHTxvyB1aP29YIIOIiIiIiEgBHNkiIiIiIiJSAJMtIiIiIiIiBTDZIiIiIiIiUgCTLSIiIiIiIgUw2VLBK6+8gq5duyI8PBwxMTEYPHgwDh48aNPm2rVrGDduHKKiohAWFoasrCwUFhbatJk4cSJuueUWBAYGonPnznb7Wrt2Lbp164bw8HBER0cjKysLR48eVeidkZK8ed4sW7YMnTt3RkhICJo1a4bXXntNqbdFCpPjvPnhhx8wfPhwJCUlITg4GO3atcPcuXPr9LVp0ybcfPPNCAwMRMuWLbF48WKl3x4pyFvnTn5+Pv70pz+hdevWMBqNyM7O9sbbI4V467zJyclBnz59EB0dDbPZjNTUVKxdu9Yr75Hk563zZsuWLbj99tsRFRWF4OBgtG3bFrNnz1b0vTHZUsHmzZsxbtw47NixA+vWrUNFRQUyMjJw9erVqjaTJ0/GF198geXLl2Pz5s04ffo0MjMz6xzrL3/5C+677z67/Rw5cgSDBg1Cr169sHfvXqxduxbnzp2zexzSPm+dN1999RUeeOABjBkzBvv378eCBQswe/ZszJs3T7H3RsqR47zZvXs3YmJi8MEHH+Cnn37CM888g6eeesrmnDhy5Aj69++Pnj17Yu/evcjOzsaoUaN48aNj3jp3ysrKEB0djWnTpqFTp05efY8kP2+dN7m5uejTpw9Wr16N3bt3o2fPnhg4cCD27Nnj1fdL8vDWeRMaGorx48cjNzcXBw4cwLRp0zBt2jQsXLhQuTcnkOrOnDkjABA2b94sCIIgXLp0SfD39xeWL19e1ebAgQMCAGH79u11Xj99+nShU6dOdR5fvny54OfnJ1gslqrHPv/8c8FgMAjl5eXyvxHyKqXOm+HDhwtDhw61eezNN98UEhMThcrKSnnfBHmdp+eN1dixY4WePXtW/f7kk08KN954o02b++67T+jbt6/M74DUotS5U9Odd94pTJo0Sda4SV3eOG+s2rdvL8yYMUOewElV3jxvhgwZIjz44IPyBG4HR7Y04PLlywCAyMhIAGJmXlFRgbvuuquqTdu2bZGcnIzt27dLPu4tt9wCo9GIRYsWwWKx4PLly3j//fdx1113wd/fX943QV6n1HlTVlaGoKAgm8eCg4Nx8uRJHDt2TIbISU1ynTeXL1+uOgYAbN++3eYYANC3b1+Xzj3SNqXOHfJt3jpvKisrUVxczHPLR3jrvNmzZw+2bduGO++8U6bI62KypbLKykpkZ2fj9ttvR4cOHQAABQUFCAgIQKNGjWzaxsbGoqCgQPKxmzdvjq+//hpPP/00AgMD0ahRI5w8eRLLli2T8y2QCpQ8b/r27YucnBysX78elZWVOHToEN544w0A4toK0i+5zptt27Zh6dKleOSRR6oeKygoQGxsbJ1jFBUVobS0VN43Ql6n5LlDvsub583rr7+OK1euYNiwYbLFT+rwxnmTmJiIwMBA3HrrrRg3bhxGjRol+/uw8lPsyCTJuHHjsH//fmzZskX2YxcUFGD06NEYOXIkhg8fjuLiYjz77LMYOnQo1q1bB4PBIHuf5B1KnjejR4/G4cOHMWDAAFRUVMBsNmPSpEl47rnnYDTy/oyeyXHe7N+/H4MGDcL06dORkZEhY3SkZTx3yB3eOm+WLFmCGTNmYOXKlYiJiXG7L9IGb5w3eXl5uHLlCnbs2IH/+7//Q8uWLTF8+HBPwnaIyZaKxo8fj1WrViE3NxeJiYlVj8fFxaG8vByXLl2yyeALCwsRFxcn+fjz589HREQEZs6cWfXYBx98gKSkJHz77bfo1q2bLO+DvEvp88ZgMODVV1/Fyy+/jIKCAkRHR2P9+vUAgBtuuEG290HeJcd58/PPP6N379545JFHMG3aNJvn4uLi6lS+LCwshNlsRnBwsPxviLxG6XOHfJO3zpuPP/4Yo0aNwvLly+tMZSb98dZ507x5cwBAx44dUVhYiOeee06xZIu3qVUgCALGjx+PTz/9FBs2bKj6P9zqlltugb+/f9UFLgAcPHgQx48fR2pqquR+SkpK6oxEmEwmAOIQLemLt84bK5PJhISEBAQEBOCjjz5CamoqoqOjPX4f5F1ynTc//fQTevbsiZEjR+Kll16q009qaqrNMQBg3bp1bp17pA3eOnfIt3jzvPnoo4/w8MMP46OPPkL//v2VeUPkFWp+3lRWVqKsrEyeN2KPYqU3yKHHHntMiIiIEDZt2iTk5+dXfZWUlFS1GTNmjJCcnCxs2LBB2LVrl5CamiqkpqbaHOeXX34R9uzZIzz66KNC69athT179gh79uwRysrKBEEQhPXr1wsGg0GYMWOGcOjQIWH37t1C3759hWbNmtn0RfrgrfPm7Nmzwttvvy0cOHBA2LNnjzBx4kQhKChI+Pbbb736fkkecpw3+/btE6Kjo4UHH3zQ5hhnzpypavPbb78JISEhwtSpU4UDBw4I8+fPF0wmk7BmzRqvvl+Sj7fOHUEQqj6HbrnlFuFPf/qTsGfPHuGnn37y2nsl+XjrvPnwww8FPz8/Yf78+TZtLl265NX3S/Lw1nkzb9484fPPPxcOHTokHDp0SHjnnXeE8PBw4ZlnnlHsvTHZUgEAu1+LFi2qalNaWiqMHTtWaNy4sRASEiIMGTJEyM/PtznOnXfeafc4R44cqWrz0UcfCV26dBFCQ0OF6Oho4d577xUOHDjgpXdKcvLWeXP27FmhW7duQmhoqBASEiL07t1b2LFjhxffKclJjvNm+vTpdo/RrFkzm742btwodO7cWQgICBBuuOEGmz5If7x57khpQ/rgrfPG0d+ykSNHeu/Nkmy8dd68+eabwo033iiEhIQIZrNZ6NKli7BgwQKbbZLkZvj9DRIREREREZGMuGaLiIiIiIhIAUy2iIiIiIiIFMBki4iIiIiISAFMtoiIiIiIiBTAZIuIiIiIiEgBTLaIiIiIiIgUwGSLiIiIiIhIAUy2iIiIiIiIFMBki4iIqIY///nPGDx4sNphEBGRD/BTOwAiIiJvMRgMTp+fPn065s6dC0EQvBQRERH5MiZbRETUYOTn51f9vHTpUjz77LM4ePBg1WNhYWEICwtTIzQiIvJBnEZIREQNRlxcXNVXREQEDAaDzWNhYWF1phGmp6djwoQJyM7ORuPGjREbG4t///vfuHr1Kh5++GGEh4ejZcuW+Oqrr2z62r9/P+655x6EhYUhNjYWI0aMwLlz57z8jomISE1MtoiIiOrx3nvvoUmTJti5cycmTJiAxx57DH/84x/RvXt3fP/998jIyMCIESNQUlICALh06RJ69eqFLl26YNeuXVizZg0KCwsxbNgwld8JERF5E5MtIiKienTq1AnTpk1Dq1at8NRTTyEoKAhNmjTB6NGj0apVKzz77LM4f/48fvzxRwDAvHnz0KVLF7z88sto27YtunTpgv/85z/YuHEjDh06pPK7ISIib+GaLSIionrcdNNNVT+bTCZERUWhY8eOVY/FxsYCAM6cOQMA+OGHH7Bx40a7678OHz6M1q1bKxwxERFpAZMtIiKievj7+9v8bjAYbB6zVjmsrKwEAFy5cgUDBw7Eq6++WudY8fHxCkZKRERawmSLiIhIZjfffDNWrFiBlJQU+PnxTy0RUUPFNVtEREQyGzduHC5cuIDhw4fju+++w+HDh7F27Vo8/PDDsFgsaodHRERewmSLiIhIZk2bNsXWrVthsViQkZGBjh07Ijs7G40aNYLRyD+9REQNhUEQBEHtIIiIiIiIiHwNb68REREREREpgMkWERERERGRAphsERERERERKYDJFhERERERkQKYbBERERERESmAyRYREREREZECmGwREREREREpgMkWERERERGRAphsERERERERKYDJFhERERERkQKYbBERERERESng/wHY6BqO/MhRfQAAAABJRU5ErkJggg==", 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", 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" ] @@ -199,7 +208,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/noaa-insitu_User_Notebook.ipynb b/user_data_notebooks/noaa-insitu_User_Notebook.ipynb index 1b4469d80..0e7263e89 100644 --- a/user_data_notebooks/noaa-insitu_User_Notebook.ipynb +++ b/user_data_notebooks/noaa-insitu_User_Notebook.ipynb @@ -146,187 +146,187 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n" ] } @@ -358,187 +358,187 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n" ] } @@ -570,7 +570,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/2606016741.py:4: SettingWithCopyWarning: \n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/2606016741.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -618,7 +618,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1635934907.py:3: SettingWithCopyWarning: \n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_67809/1635934907.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", diff --git a/user_data_notebooks/oco2-mip-National-co2budget.ipynb b/user_data_notebooks/oco2-mip-National-co2budget.ipynb index b25a4de8f..90e5decc4 100644 --- a/user_data_notebooks/oco2-mip-National-co2budget.ipynb +++ b/user_data_notebooks/oco2-mip-National-co2budget.ipynb @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "id": "8ff420ae-1455-4a3b-8dfe-1da8abf16049", "metadata": { "tags": [] @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "id": "10194dc3-274b-419c-96ab-b2bc51e6d19d", "metadata": { "tags": [] @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "id": "7f380763-4b59-4f53-9993-803c37995a3b", "metadata": { "tags": [] @@ -303,7 +303,7 @@ "4 0.49 0.19 " ] }, - "execution_count": 3, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -338,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "id": "dc8b03d3-2ff7-43db-bf7d-0054c3a7f2a8", "metadata": { "tags": [] @@ -520,7 +520,7 @@ "1236 -0.38 0.91 " ] }, - "execution_count": 4, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -548,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "id": "f7be864d-4d01-442f-b3cb-a1aefff616ae", "metadata": { "tags": [] @@ -560,7 +560,7 @@ "(-0.30000000000000004, 6.3)" ] }, - "execution_count": 5, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "id": "3f956003-05f7-4394-9764-56545ecc9ec9", "metadata": { "tags": [] @@ -624,7 +624,7 @@ "Text(0, 0.5, 'CO$_2$ Flux (TgCO$_2$ year$^{-1}$)')" ] }, - "execution_count": 6, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -679,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "id": "d6cb1421-a44c-4e54-9bb1-fda73bc3ff27", "metadata": {}, "outputs": [ @@ -689,7 +689,7 @@ "Text(0.5, 0, 'dC_loss (TgCO$_2$ year$^{-1}$)')" ] }, - "execution_count": 7, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -775,7 +775,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "id": "65fb149a-a252-4018-876c-3245f1197a03", "metadata": { "tags": [] @@ -787,7 +787,7 @@ "Text(-0.18000000000000005, 2.6, 'Fractional error reduction: 0.91')" ] }, - 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"" + "" ] }, "execution_count": 13, @@ -4340,8 +4340,8 @@ "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0910222977399826, 'max': 8138.96484375, 'mean': 791.9455965670446, 'count': 36.0, 'sum': 28510.041476413608, 'std': 1550.8237787859084, 'median': 102.0539779663086, 'majority': 0.0910222977399826, 'minority': 0.0910222977399826, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0910222977399826, 813.9784044429659, 1627.865786588192, 2441.7531687334176, 3255.640550878644, 4069.52793302387, 4883.415315169095, 5697.302697314321, 6511.190079459548, 7325.077461604774, 8138.96484375]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0910222977399826, 'percentile_98': 8138.96484375}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.08842560648918152, 'max': 8243.259765625, 'mean': 801.7388236944875, 'count': 36.0, 'sum': 28862.597653001547, 'std': 1570.4892862189242, 'median': 103.01327514648438, 'majority': 0.08842560648918152, 'minority': 0.08842560648918152, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.08842560648918152, 824.4055596083402, 1648.7226936101913, 2473.0398276120422, 3297.3569616138934, 4121.674095615745, 4945.991229617595, 5770.308363619447, 6594.625497621298, 7418.942631623148, 8243.259765625]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.08842560648918152, 'percentile_98': 8243.259765625}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.08379923552274704, 'max': 8397.8779296875, 'mean': 816.0609874193453, 'count': 36.0, 'sum': 29378.19554709643, 'std': 1599.6065142777418, 'median': 104.36005401611328, 'majority': 0.08379923552274704, 'minority': 0.08379923552274704, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.08379923552274704, 839.8632122807205, 1679.6426253259183, 2519.4220383711163, 3359.201451416314, 4198.980864461511, 5038.76027750671, 5878.539690551907, 6718.319103597105, 7558.0985166423025, 8397.8779296875]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.08379923552274704, 'percentile_98': 8397.8779296875}}}}\n", - "CPU times: user 23.3 ms, sys: 6.84 ms, total: 30.2 ms\n", - "Wall time: 3.97 s\n" + "CPU times: user 27.6 ms, sys: 8.21 ms, total: 35.8 ms\n", + "Wall time: 2.07 s\n" ] } ], @@ -4797,7 +4797,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_0ef456d2f1a7fa3f0dca50e77ca5f4d7 {\n", + " #map_4d60c130a9ad6cbd7d1714dfd842822a {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -4811,14 +4811,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_0ef456d2f1a7fa3f0dca50e77ca5f4d7" ></div>\n", + " <div class="folium-map" id="map_4d60c130a9ad6cbd7d1714dfd842822a" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_0ef456d2f1a7fa3f0dca50e77ca5f4d7 = L.map(\n", - " "map_0ef456d2f1a7fa3f0dca50e77ca5f4d7",\n", + " var map_4d60c130a9ad6cbd7d1714dfd842822a = L.map(\n", + " "map_4d60c130a9ad6cbd7d1714dfd842822a",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -4832,28 +4832,28 @@ "\n", " \n", " \n", - " var tile_layer_ef4c727358f069fd4a9e2cddb9d12720 = L.tileLayer(\n", + " var tile_layer_33fec0a08645886ea23c50e9e74266b2 = 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_ef4c727358f069fd4a9e2cddb9d12720.addTo(map_0ef456d2f1a7fa3f0dca50e77ca5f4d7);\n", + " tile_layer_33fec0a08645886ea23c50e9e74266b2.addTo(map_4d60c130a9ad6cbd7d1714dfd842822a);\n", " \n", " \n", - " var tile_layer_2778311726e5899b31ba931516c93dba = L.tileLayer(\n", + " var tile_layer_34190667b972a6426d5fa9fa366e1f62 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2018/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ff\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0%2C450",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.7, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_2778311726e5899b31ba931516c93dba.addTo(map_0ef456d2f1a7fa3f0dca50e77ca5f4d7);\n", + " tile_layer_34190667b972a6426d5fa9fa366e1f62.addTo(map_4d60c130a9ad6cbd7d1714dfd842822a);\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": 24, diff --git a/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb b/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb index 2d4edf58c..9b1c2fc48 100644 --- a/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb +++ b/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb @@ -556,7 +556,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_f9d55b26764e40b892188decca65885d {\n", + " #map_145a984b69add673c7d51df1fd9c1a16 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -570,7 +570,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_2e5e32c93f8dfaa40503e74f5b9d4676 {\n", + " #map_004f74f5dd78d23eea3c65c82386815c {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -585,17 +585,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_f9d55b26764e40b892188decca65885d" ></div>\n", + " <div class="folium-map" id="map_145a984b69add673c7d51df1fd9c1a16" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_2e5e32c93f8dfaa40503e74f5b9d4676" ></div>\n", + " <div class="folium-map" id="map_004f74f5dd78d23eea3c65c82386815c" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_f9d55b26764e40b892188decca65885d = L.map(\n", - " "map_f9d55b26764e40b892188decca65885d",\n", + " var map_145a984b69add673c7d51df1fd9c1a16 = L.map(\n", + " "map_145a984b69add673c7d51df1fd9c1a16",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -609,26 +609,26 @@ "\n", " \n", " \n", - " var tile_layer_a2c307788ca6b385ed288b3c7e9c79a3 = L.tileLayer(\n", + " var tile_layer_b302532ce609dc6c4de960e0937dd66e = 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_a2c307788ca6b385ed288b3c7e9c79a3.addTo(map_f9d55b26764e40b892188decca65885d);\n", + " tile_layer_b302532ce609dc6c4de960e0937dd66e.addTo(map_145a984b69add673c7d51df1fd9c1a16);\n", " \n", " \n", - " var tile_layer_3503e70ab9c9e94470c016e4fce4b389 = L.tileLayer(\n", + " var tile_layer_cc76b6cc87e9813385104478fc27041a = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=xco2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=411.7429234611336%2C423.60419320175424",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_3503e70ab9c9e94470c016e4fce4b389.addTo(map_f9d55b26764e40b892188decca65885d);\n", + " tile_layer_cc76b6cc87e9813385104478fc27041a.addTo(map_145a984b69add673c7d51df1fd9c1a16);\n", " \n", " \n", - " var map_2e5e32c93f8dfaa40503e74f5b9d4676 = L.map(\n", - " "map_2e5e32c93f8dfaa40503e74f5b9d4676",\n", + " var map_004f74f5dd78d23eea3c65c82386815c = L.map(\n", + " "map_004f74f5dd78d23eea3c65c82386815c",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -642,32 +642,32 @@ "\n", " \n", " \n", - " var tile_layer_99e5669333bdfc2a62d62ff31e3c8aee = L.tileLayer(\n", + " var tile_layer_b80990d350f24d64275390c2a3988ef7 = 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_99e5669333bdfc2a62d62ff31e3c8aee.addTo(map_2e5e32c93f8dfaa40503e74f5b9d4676);\n", + " tile_layer_b80990d350f24d64275390c2a3988ef7.addTo(map_004f74f5dd78d23eea3c65c82386815c);\n", " \n", " \n", - " var tile_layer_74e87290520c26dff9a4ef65c375373e = L.tileLayer(\n", + " var tile_layer_0b41d01d414164ca573a07eafe4f77b1 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220227/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=xco2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=411.7429234611336%2C423.60419320175424",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_74e87290520c26dff9a4ef65c375373e.addTo(map_2e5e32c93f8dfaa40503e74f5b9d4676);\n", + " tile_layer_0b41d01d414164ca573a07eafe4f77b1.addTo(map_004f74f5dd78d23eea3c65c82386815c);\n", " \n", " \n", - " map_f9d55b26764e40b892188decca65885d.sync(map_2e5e32c93f8dfaa40503e74f5b9d4676);\n", - " map_2e5e32c93f8dfaa40503e74f5b9d4676.sync(map_f9d55b26764e40b892188decca65885d);\n", + " map_145a984b69add673c7d51df1fd9c1a16.sync(map_004f74f5dd78d23eea3c65c82386815c);\n", + " map_004f74f5dd78d23eea3c65c82386815c.sync(map_145a984b69add673c7d51df1fd9c1a16);\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, @@ -775,7 +775,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_a990e731e4d1999a94ac76294bf9453c {\n", + " #map_94e0536defadc5d27f3bc270b8e45b15 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -789,14 +789,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_a990e731e4d1999a94ac76294bf9453c" ></div>\n", + " <div class="folium-map" id="map_94e0536defadc5d27f3bc270b8e45b15" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_a990e731e4d1999a94ac76294bf9453c = L.map(\n", - " "map_a990e731e4d1999a94ac76294bf9453c",\n", + " var map_94e0536defadc5d27f3bc270b8e45b15 = L.map(\n", + " "map_94e0536defadc5d27f3bc270b8e45b15",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -810,41 +810,41 @@ "\n", " \n", " \n", - " var tile_layer_02feeec69f72fa6c5d0b87639cc48438 = L.tileLayer(\n", + " var tile_layer_22cca04947fbafd80d611aee6c5172b7 = 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_02feeec69f72fa6c5d0b87639cc48438.addTo(map_a990e731e4d1999a94ac76294bf9453c);\n", + " tile_layer_22cca04947fbafd80d611aee6c5172b7.addTo(map_94e0536defadc5d27f3bc270b8e45b15);\n", " \n", " \n", "\n", - " function geo_json_ccdc1d5b284d3635f2d8d2119b582732_onEachFeature(feature, layer) {\n", + " function geo_json_49c30cba9408aacbc4b6ad8f2bca84d7_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_ccdc1d5b284d3635f2d8d2119b582732 = L.geoJson(null, {\n", - " onEachFeature: geo_json_ccdc1d5b284d3635f2d8d2119b582732_onEachFeature,\n", + " var geo_json_49c30cba9408aacbc4b6ad8f2bca84d7 = L.geoJson(null, {\n", + " onEachFeature: geo_json_49c30cba9408aacbc4b6ad8f2bca84d7_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_ccdc1d5b284d3635f2d8d2119b582732_add (data) {\n", - " geo_json_ccdc1d5b284d3635f2d8d2119b582732\n", + " function geo_json_49c30cba9408aacbc4b6ad8f2bca84d7_add (data) {\n", + " geo_json_49c30cba9408aacbc4b6ad8f2bca84d7\n", " .addData(data);\n", " }\n", - " geo_json_ccdc1d5b284d3635f2d8d2119b582732_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", - " geo_json_ccdc1d5b284d3635f2d8d2119b582732.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_49c30cba9408aacbc4b6ad8f2bca84d7_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_49c30cba9408aacbc4b6ad8f2bca84d7.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_ccdc1d5b284d3635f2d8d2119b582732.addTo(map_a990e731e4d1999a94ac76294bf9453c);\n", + " geo_json_49c30cba9408aacbc4b6ad8f2bca84d7.addTo(map_94e0536defadc5d27f3bc270b8e45b15);\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, @@ -1708,8 +1708,8 @@ "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 413.8769618293736, 'max': 414.4926060689613, 'mean': 414.0766919599859, 'count': 115.20000457763672, 'sum': 47701.636809283045, 'std': 0.11449538158699511, 'median': 414.04915100429207, 'majority': 413.8769618293736, 'minority': 413.8769618293736, 'unique': 135.0, 'histogram': [[9.0, 32.0, 35.0, 15.0, 18.0, 10.0, 4.0, 7.0, 4.0, 1.0], [413.8769618293736, 413.9385262533324, 414.00009067729115, 414.0616551012499, 414.1232195252087, 414.18478394916747, 414.24634837312624, 414.307912797085, 414.3694772210438, 414.43104164500255, 414.4926060689613]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 135.0, 'percentile_2': 413.9032644161489, 'percentile_98': 414.3582555116154}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 413.88225508853793, 'max': 414.3805199419148, 'mean': 414.06020590057466, 'count': 115.20000457763672, 'sum': 47699.7376151634, 'std': 0.10172649448593125, 'median': 414.04588773730217, 'majority': 413.88225508853793, 'minority': 413.88225508853793, 'unique': 135.0, 'histogram': [[12.0, 16.0, 22.0, 36.0, 14.0, 16.0, 9.0, 4.0, 4.0, 2.0], [413.88225508853793, 413.9320815738756, 413.9819080592133, 414.031734544551, 414.0815610298887, 414.1313875152264, 414.18121400056407, 414.23104048590176, 414.28086697123945, 414.33069345657714, 414.3805199419148]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 135.0, 'percentile_2': 413.89060424990015, 'percentile_98': 414.302725403104}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 413.8595249969512, 'max': 414.43683949182747, 'mean': 414.1013446241451, 'count': 115.20000457763672, 'sum': 47704.47679630703, 'std': 0.14015944443389658, 'median': 414.1040444665123, 'majority': 413.8595249969512, 'minority': 413.8595249969512, 'unique': 135.0, 'histogram': [[15.0, 13.0, 12.0, 19.0, 20.0, 25.0, 10.0, 10.0, 7.0, 4.0], [413.8595249969512, 413.91725644643884, 413.97498789592646, 414.0327193454141, 414.0904507949017, 414.1481822443893, 414.205913693877, 414.2636451433646, 414.32137659285223, 414.37910804233985, 414.43683949182747]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 135.0, 'percentile_2': 413.8741860515438, 'percentile_98': 414.4011363678146}}}}\n", - "CPU times: user 6.82 s, sys: 1.39 s, total: 8.22 s\n", - "Wall time: 5min 22s\n" + "CPU times: user 4.02 s, sys: 926 ms, total: 4.94 s\n", + "Wall time: 4min 53s\n" ] } ], @@ -2114,7 +2114,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_cd1fda2a3f429d5e1f622a74d8638de6 {\n", + " #map_366d70eb5070904af9bfb1281a140500 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -2128,14 +2128,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_cd1fda2a3f429d5e1f622a74d8638de6" ></div>\n", + " <div class="folium-map" id="map_366d70eb5070904af9bfb1281a140500" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_cd1fda2a3f429d5e1f622a74d8638de6 = L.map(\n", - " "map_cd1fda2a3f429d5e1f622a74d8638de6",\n", + " var map_366d70eb5070904af9bfb1281a140500 = L.map(\n", + " "map_366d70eb5070904af9bfb1281a140500",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -2149,28 +2149,28 @@ "\n", " \n", " \n", - 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" tile_layer_193a13ae1bac9e48b934e12c689bd828.addTo(map_cd1fda2a3f429d5e1f622a74d8638de6);\n", + " tile_layer_ec0f1b206f5cae8c070ead924a262d2c.addTo(map_366d70eb5070904af9bfb1281a140500);\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": 24, diff --git a/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb b/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb index bbc12dbc0..4044b4410 100644 --- a/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb +++ b/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb @@ -581,7 +581,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_89285ac073aeb372cc1824c0cd7844ee {\n", + " #map_d2ac65e8f91b6ddeeb188d518009e853 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -595,7 +595,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_194651bda221ac7b3d69892d93ee6b49 {\n", + " #map_792c6d65fa31cc1ec3f3806ac340b432 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -610,17 +610,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_89285ac073aeb372cc1824c0cd7844ee" ></div>\n", + " <div class="folium-map" id="map_d2ac65e8f91b6ddeeb188d518009e853" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_194651bda221ac7b3d69892d93ee6b49" ></div>\n", + " <div class="folium-map" id="map_792c6d65fa31cc1ec3f3806ac340b432" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_89285ac073aeb372cc1824c0cd7844ee = L.map(\n", - " "map_89285ac073aeb372cc1824c0cd7844ee",\n", + " var map_d2ac65e8f91b6ddeeb188d518009e853 = L.map(\n", + " "map_d2ac65e8f91b6ddeeb188d518009e853",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -634,26 +634,26 @@ "\n", " \n", " \n", - " var tile_layer_49ecf861722f2aea0bfa441fd95a42ca = L.tileLayer(\n", + " var tile_layer_fb201e562b5c3e9928692993f00b1b13 = 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_49ecf861722f2aea0bfa441fd95a42ca.addTo(map_89285ac073aeb372cc1824c0cd7844ee);\n", + " tile_layer_fb201e562b5c3e9928692993f00b1b13.addTo(map_d2ac65e8f91b6ddeeb188d518009e853);\n", " \n", " \n", - " var tile_layer_5e5204678e564a810cc63b35e4cf0ab8 = L.tileLayer(\n", + " var tile_layer_5a01e09a02fb94307fa501fafc4f5ad8 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202001/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=-138.71914672851562%2C2497.01904296875",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_5e5204678e564a810cc63b35e4cf0ab8.addTo(map_89285ac073aeb372cc1824c0cd7844ee);\n", + " tile_layer_5a01e09a02fb94307fa501fafc4f5ad8.addTo(map_d2ac65e8f91b6ddeeb188d518009e853);\n", " \n", " \n", - " var map_194651bda221ac7b3d69892d93ee6b49 = L.map(\n", - " "map_194651bda221ac7b3d69892d93ee6b49",\n", + " var map_792c6d65fa31cc1ec3f3806ac340b432 = L.map(\n", + " "map_792c6d65fa31cc1ec3f3806ac340b432",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -667,32 +667,32 @@ "\n", " \n", " \n", - " var tile_layer_ac5a36152d64a973328f9dce6c239bee = L.tileLayer(\n", + " var tile_layer_2b6ed9fd6f4e7b94abb15aa581b211b5 = 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_ac5a36152d64a973328f9dce6c239bee.addTo(map_194651bda221ac7b3d69892d93ee6b49);\n", + " tile_layer_2b6ed9fd6f4e7b94abb15aa581b211b5.addTo(map_792c6d65fa31cc1ec3f3806ac340b432);\n", " \n", " \n", - " var tile_layer_f73f6636372385bfe0bd01da556a4ea9 = L.tileLayer(\n", + " var tile_layer_4f18289edf3373d9ce5731caa9e94677 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-200001/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=-138.71914672851562%2C2497.01904296875",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_f73f6636372385bfe0bd01da556a4ea9.addTo(map_194651bda221ac7b3d69892d93ee6b49);\n", + " tile_layer_4f18289edf3373d9ce5731caa9e94677.addTo(map_792c6d65fa31cc1ec3f3806ac340b432);\n", " \n", " \n", - " map_89285ac073aeb372cc1824c0cd7844ee.sync(map_194651bda221ac7b3d69892d93ee6b49);\n", - " map_194651bda221ac7b3d69892d93ee6b49.sync(map_89285ac073aeb372cc1824c0cd7844ee);\n", + " map_d2ac65e8f91b6ddeeb188d518009e853.sync(map_792c6d65fa31cc1ec3f3806ac340b432);\n", + " map_792c6d65fa31cc1ec3f3806ac340b432.sync(map_d2ac65e8f91b6ddeeb188d518009e853);\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, @@ -805,7 +805,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_b093dcc88542ddb632a148171819568c {\n", + " #map_fe00e95d4a738370e836fe71ad3b8edf {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -819,14 +819,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_b093dcc88542ddb632a148171819568c" ></div>\n", + " <div class="folium-map" id="map_fe00e95d4a738370e836fe71ad3b8edf" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_b093dcc88542ddb632a148171819568c = L.map(\n", - " "map_b093dcc88542ddb632a148171819568c",\n", + " var map_fe00e95d4a738370e836fe71ad3b8edf = L.map(\n", + " "map_fe00e95d4a738370e836fe71ad3b8edf",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -840,41 +840,41 @@ "\n", " \n", " \n", - " var tile_layer_0fccf620f58bbb149704a0871e74c884 = L.tileLayer(\n", + " var tile_layer_1f92abed8e407758d72a9f42e4bc38de = 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_0fccf620f58bbb149704a0871e74c884.addTo(map_b093dcc88542ddb632a148171819568c);\n", + " tile_layer_1f92abed8e407758d72a9f42e4bc38de.addTo(map_fe00e95d4a738370e836fe71ad3b8edf);\n", " \n", " \n", "\n", - " function geo_json_e5afbe43a1c14add5b7e0f263688b521_onEachFeature(feature, layer) {\n", + " function geo_json_117c0f87b9e4ba7f999433f7fb9ce5cf_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_e5afbe43a1c14add5b7e0f263688b521 = L.geoJson(null, {\n", - " onEachFeature: geo_json_e5afbe43a1c14add5b7e0f263688b521_onEachFeature,\n", + " var geo_json_117c0f87b9e4ba7f999433f7fb9ce5cf = L.geoJson(null, {\n", + " onEachFeature: geo_json_117c0f87b9e4ba7f999433f7fb9ce5cf_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_e5afbe43a1c14add5b7e0f263688b521_add (data) {\n", - " geo_json_e5afbe43a1c14add5b7e0f263688b521\n", + " function geo_json_117c0f87b9e4ba7f999433f7fb9ce5cf_add (data) {\n", + " geo_json_117c0f87b9e4ba7f999433f7fb9ce5cf\n", " .addData(data);\n", " }\n", - 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"" + "" ] }, "execution_count": 13, @@ -1098,8 +1098,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 2.39 s, sys: 525 ms, total: 2.91 s\n", - "Wall time: 5min 22s\n" + "CPU times: user 1 s, sys: 260 ms, total: 1.26 s\n", + "Wall time: 5min\n" ] } ], @@ -1553,7 +1553,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_dbeb287a5a316d0fcfab2a659ff0bc81 {\n", + " #map_26762e875f8dd09caac20a399c2a36ea {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1567,14 +1567,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_dbeb287a5a316d0fcfab2a659ff0bc81" ></div>\n", + " <div class="folium-map" id="map_26762e875f8dd09caac20a399c2a36ea" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_dbeb287a5a316d0fcfab2a659ff0bc81 = L.map(\n", - " "map_dbeb287a5a316d0fcfab2a659ff0bc81",\n", + " var map_26762e875f8dd09caac20a399c2a36ea = L.map(\n", + " "map_26762e875f8dd09caac20a399c2a36ea",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1588,28 +1588,28 @@ "\n", " \n", " \n", - " var tile_layer_507c5c96f2cbe2461954c32cef43f987 = L.tileLayer(\n", + " var tile_layer_5c5d6f5f42570f1977ded3a26b776270 = 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_507c5c96f2cbe2461954c32cef43f987.addTo(map_dbeb287a5a316d0fcfab2a659ff0bc81);\n", + " tile_layer_5c5d6f5f42570f1977ded3a26b776270.addTo(map_26762e875f8dd09caac20a399c2a36ea);\n", " \n", " \n", - " var tile_layer_326e0cdfcf0b770fc3ae9be9056e3173 = L.tileLayer(\n", + " var tile_layer_9905d158349baf01fa3abb4b5fa9793c = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202110/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=-138.71914672851562%2C2497.01904296875",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_326e0cdfcf0b770fc3ae9be9056e3173.addTo(map_dbeb287a5a316d0fcfab2a659ff0bc81);\n", + " tile_layer_9905d158349baf01fa3abb4b5fa9793c.addTo(map_26762e875f8dd09caac20a399c2a36ea);\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": 23, diff --git a/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb b/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb index d25a55e14..f80de484e 100644 --- a/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb +++ b/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb @@ -584,7 +584,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_bcffbb79452c5682f897a520eefde96b {\n", + " #map_eae033b4df041144e7303f6cad9ef2a0 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -598,7 +598,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_6a2dea0a8cfc82cd84894f916fd15381 {\n", + " #map_f36f8c25bbe243fcaa7c525e9d467ad8 {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -613,17 +613,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_bcffbb79452c5682f897a520eefde96b" ></div>\n", + " <div class="folium-map" id="map_eae033b4df041144e7303f6cad9ef2a0" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_6a2dea0a8cfc82cd84894f916fd15381" ></div>\n", + " <div class="folium-map" id="map_f36f8c25bbe243fcaa7c525e9d467ad8" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_bcffbb79452c5682f897a520eefde96b = L.map(\n", - " "map_bcffbb79452c5682f897a520eefde96b",\n", + " var map_eae033b4df041144e7303f6cad9ef2a0 = L.map(\n", + " "map_eae033b4df041144e7303f6cad9ef2a0",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -637,26 +637,26 @@ "\n", " \n", " \n", - " var tile_layer_f29fc29697f90efd57aaf69e3c83683a = L.tileLayer(\n", + " var tile_layer_68ca74e005a71cfec51b55fb0e849212 = 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_f29fc29697f90efd57aaf69e3c83683a.addTo(map_bcffbb79452c5682f897a520eefde96b);\n", + " tile_layer_68ca74e005a71cfec51b55fb0e849212.addTo(map_eae033b4df041144e7303f6cad9ef2a0);\n", " \n", " \n", - " var tile_layer_bbaeab4f56cd92f64a8ea2064dbed7c1 = L.tileLayer(\n", + " var tile_layer_2c2afd2a48b20c28e64cda150483f17d = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202001/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=-675.1028442382812%2C31415.447265625",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_bbaeab4f56cd92f64a8ea2064dbed7c1.addTo(map_bcffbb79452c5682f897a520eefde96b);\n", + " tile_layer_2c2afd2a48b20c28e64cda150483f17d.addTo(map_eae033b4df041144e7303f6cad9ef2a0);\n", " \n", " \n", - " var map_6a2dea0a8cfc82cd84894f916fd15381 = L.map(\n", - " "map_6a2dea0a8cfc82cd84894f916fd15381",\n", + " var map_f36f8c25bbe243fcaa7c525e9d467ad8 = L.map(\n", + " "map_f36f8c25bbe243fcaa7c525e9d467ad8",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -670,32 +670,32 @@ "\n", " \n", " \n", - " var tile_layer_c086ba82112faf51f5885ce356091a3c = L.tileLayer(\n", + " var tile_layer_015acae7a2017176bfaf7d02b29ad9c9 = 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_c086ba82112faf51f5885ce356091a3c.addTo(map_6a2dea0a8cfc82cd84894f916fd15381);\n", + " tile_layer_015acae7a2017176bfaf7d02b29ad9c9.addTo(map_f36f8c25bbe243fcaa7c525e9d467ad8);\n", " \n", " \n", - " var tile_layer_632325c6d01845189c434e0b98ce264e = L.tileLayer(\n", + " var tile_layer_4dac231d385d45477705a93853c083bd = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_200001/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=-675.1028442382812%2C31415.447265625",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_632325c6d01845189c434e0b98ce264e.addTo(map_6a2dea0a8cfc82cd84894f916fd15381);\n", + " tile_layer_4dac231d385d45477705a93853c083bd.addTo(map_f36f8c25bbe243fcaa7c525e9d467ad8);\n", " \n", " \n", - " map_bcffbb79452c5682f897a520eefde96b.sync(map_6a2dea0a8cfc82cd84894f916fd15381);\n", - " map_6a2dea0a8cfc82cd84894f916fd15381.sync(map_bcffbb79452c5682f897a520eefde96b);\n", + " map_eae033b4df041144e7303f6cad9ef2a0.sync(map_f36f8c25bbe243fcaa7c525e9d467ad8);\n", + " map_f36f8c25bbe243fcaa7c525e9d467ad8.sync(map_eae033b4df041144e7303f6cad9ef2a0);\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, @@ -808,7 +808,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_3540651f4b1fe15566eee23721007424 {\n", + " #map_27a7ebd8e454a7dc077de85a3321ad5d {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -822,14 +822,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_3540651f4b1fe15566eee23721007424" ></div>\n", + " <div class="folium-map" id="map_27a7ebd8e454a7dc077de85a3321ad5d" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_3540651f4b1fe15566eee23721007424 = L.map(\n", - " "map_3540651f4b1fe15566eee23721007424",\n", + " var map_27a7ebd8e454a7dc077de85a3321ad5d = L.map(\n", + " "map_27a7ebd8e454a7dc077de85a3321ad5d",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -843,41 +843,41 @@ "\n", " \n", " \n", - " var tile_layer_14bcecd3c77ecc4705c232ccd283a711 = L.tileLayer(\n", + " var tile_layer_b6637d0f53efa54cce6e09cc0e537039 = 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_14bcecd3c77ecc4705c232ccd283a711.addTo(map_3540651f4b1fe15566eee23721007424);\n", + " tile_layer_b6637d0f53efa54cce6e09cc0e537039.addTo(map_27a7ebd8e454a7dc077de85a3321ad5d);\n", " \n", " \n", "\n", - " function geo_json_1f711d298aa7b57a2f34b26eaf4543e6_onEachFeature(feature, layer) {\n", + " function geo_json_3bef6bd789224ee5885d50ec13bf4267_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_1f711d298aa7b57a2f34b26eaf4543e6 = L.geoJson(null, {\n", - " onEachFeature: geo_json_1f711d298aa7b57a2f34b26eaf4543e6_onEachFeature,\n", + " var geo_json_3bef6bd789224ee5885d50ec13bf4267 = L.geoJson(null, {\n", + " onEachFeature: geo_json_3bef6bd789224ee5885d50ec13bf4267_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_1f711d298aa7b57a2f34b26eaf4543e6_add (data) {\n", - " geo_json_1f711d298aa7b57a2f34b26eaf4543e6\n", + " function geo_json_3bef6bd789224ee5885d50ec13bf4267_add (data) {\n", + " geo_json_3bef6bd789224ee5885d50ec13bf4267\n", " .addData(data);\n", " }\n", - " geo_json_1f711d298aa7b57a2f34b26eaf4543e6_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", - " geo_json_1f711d298aa7b57a2f34b26eaf4543e6.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_3bef6bd789224ee5885d50ec13bf4267_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_3bef6bd789224ee5885d50ec13bf4267.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_1f711d298aa7b57a2f34b26eaf4543e6.addTo(map_3540651f4b1fe15566eee23721007424);\n", + " geo_json_3bef6bd789224ee5885d50ec13bf4267.addTo(map_27a7ebd8e454a7dc077de85a3321ad5d);\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, @@ -1103,8 +1103,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 2.5 s, sys: 543 ms, total: 3.04 s\n", - "Wall time: 5min 6s\n" + "CPU times: user 1.09 s, sys: 277 ms, total: 1.37 s\n", + "Wall time: 4min 54s\n" ] } ], @@ -1558,7 +1558,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_37ff139368ac2e420349168f9fb3b594 {\n", + " #map_5eda73303f522adc0f48cac89ccd8dcf {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1572,14 +1572,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_37ff139368ac2e420349168f9fb3b594" ></div>\n", + " <div class="folium-map" id="map_5eda73303f522adc0f48cac89ccd8dcf" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_37ff139368ac2e420349168f9fb3b594 = L.map(\n", - " "map_37ff139368ac2e420349168f9fb3b594",\n", + " var map_5eda73303f522adc0f48cac89ccd8dcf = L.map(\n", + " "map_5eda73303f522adc0f48cac89ccd8dcf",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1593,28 +1593,28 @@ "\n", " \n", " \n", - " var tile_layer_693b1f71974b993079b8db8ee81e63a1 = L.tileLayer(\n", + " var tile_layer_f2795ce101d9e9c6053cd4fa98e54cb9 = 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_693b1f71974b993079b8db8ee81e63a1.addTo(map_37ff139368ac2e420349168f9fb3b594);\n", + " tile_layer_f2795ce101d9e9c6053cd4fa98e54cb9.addTo(map_5eda73303f522adc0f48cac89ccd8dcf);\n", " \n", " \n", - " var tile_layer_23d047a0125d08f6c00b3469cdc161dd = L.tileLayer(\n", + " var tile_layer_530b76f1963fb8566cddeaa514a6bae9 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202210/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=-675.1028442382812%2C31415.447265625",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_23d047a0125d08f6c00b3469cdc161dd.addTo(map_37ff139368ac2e420349168f9fb3b594);\n", + " tile_layer_530b76f1963fb8566cddeaa514a6bae9.addTo(map_5eda73303f522adc0f48cac89ccd8dcf);\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": 23, diff --git a/user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb b/user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb index 0b8df6988..120972249 100644 --- a/user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb +++ b/user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ diff --git a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb b/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb index 221daba3a..6d895d31b 100644 --- a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb @@ -691,7 +691,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_0324a499a0aea743364fe3044b13f33e {\n", + " #map_1564213018361a64d821587590b8d85b {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -705,7 +705,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - 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" tile_layer_d4d7915f6987fa76c96462a37b267af1.addTo(map_0324a499a0aea743364fe3044b13f33e);\n", + " tile_layer_e85b1b184e34e18111f5ca0d87d594b1.addTo(map_1564213018361a64d821587590b8d85b);\n", " \n", " \n", - " var tile_layer_cb94436a5da74a96a5c7f8c88d106ff0 = L.tileLayer(\n", + " var tile_layer_15a1616991256ac04a9ba7fbcfb7e018 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0.0%2C202.8189294183266",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_cb94436a5da74a96a5c7f8c88d106ff0.addTo(map_0324a499a0aea743364fe3044b13f33e);\n", + " tile_layer_15a1616991256ac04a9ba7fbcfb7e018.addTo(map_1564213018361a64d821587590b8d85b);\n", " \n", " \n", - 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"" + "" ] }, "execution_count": 11, @@ -918,7 +918,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_22cb4d35c6f9b00aca2c746378251a82 {\n", + " #map_c1f718329ae2316af062e26b106b960b {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -932,14 +932,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_22cb4d35c6f9b00aca2c746378251a82" ></div>\n", + " <div class="folium-map" id="map_c1f718329ae2316af062e26b106b960b" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_22cb4d35c6f9b00aca2c746378251a82 = L.map(\n", - " "map_22cb4d35c6f9b00aca2c746378251a82",\n", + " var map_c1f718329ae2316af062e26b106b960b = L.map(\n", + " "map_c1f718329ae2316af062e26b106b960b",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -953,41 +953,41 @@ "\n", " \n", " \n", - " var tile_layer_9bc397a2d816fcd6309be8f4b606f7b6 = L.tileLayer(\n", + " var tile_layer_b47d1ff178d2810f05b68c9c35ab51de = 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_9bc397a2d816fcd6309be8f4b606f7b6.addTo(map_22cb4d35c6f9b00aca2c746378251a82);\n", + " tile_layer_b47d1ff178d2810f05b68c9c35ab51de.addTo(map_c1f718329ae2316af062e26b106b960b);\n", " \n", " \n", "\n", - " function geo_json_7658d2479dbd9dd746159373cba3e583_onEachFeature(feature, layer) {\n", + " function geo_json_72e134b02d49ee8135ee70d3588c0870_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_7658d2479dbd9dd746159373cba3e583 = L.geoJson(null, {\n", - " onEachFeature: geo_json_7658d2479dbd9dd746159373cba3e583_onEachFeature,\n", + " var geo_json_72e134b02d49ee8135ee70d3588c0870 = L.geoJson(null, {\n", + " onEachFeature: geo_json_72e134b02d49ee8135ee70d3588c0870_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_7658d2479dbd9dd746159373cba3e583_add (data) {\n", - " geo_json_7658d2479dbd9dd746159373cba3e583\n", + " function geo_json_72e134b02d49ee8135ee70d3588c0870_add (data) {\n", + " geo_json_72e134b02d49ee8135ee70d3588c0870\n", " .addData(data);\n", " }\n", - 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"CPU times: user 2.27 s, sys: 465 ms, total: 2.73 s\n", - "Wall time: 2min\n" + "CPU times: user 815 ms, sys: 204 ms, total: 1.02 s\n", + "Wall time: 1min 37s\n" ] } ], @@ -2028,7 +2028,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_27d3e6ad386f0217cd90b21b1e80c684 {\n", + " #map_f40dc866d94d3428084f80f892d73f49 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -2042,14 +2042,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_27d3e6ad386f0217cd90b21b1e80c684" ></div>\n", + " <div class="folium-map" id="map_f40dc866d94d3428084f80f892d73f49" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_27d3e6ad386f0217cd90b21b1e80c684 = L.map(\n", - " "map_27d3e6ad386f0217cd90b21b1e80c684",\n", + " var map_f40dc866d94d3428084f80f892d73f49 = L.map(\n", + " "map_f40dc866d94d3428084f80f892d73f49",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -2063,28 +2063,28 @@ "\n", " \n", " \n", - 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" tile_layer_4997c1e1fe6530dab46a0635cbcfced9.addTo(map_27d3e6ad386f0217cd90b21b1e80c684);\n", + " tile_layer_3806a1bdd136b9b42b1b9d9741dd5873.addTo(map_f40dc866d94d3428084f80f892d73f49);\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": 24, diff --git a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb index 1590f0f84..a9fb90cd8 100644 --- a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb +++ b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb @@ -1185,7 +1185,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_fb0e6f7604b887e3038f2e34c387daf6 {\n", + " #map_01e30781db5546fd33c936564bb55b9e {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -1199,7 +1199,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_33e4646c6221daf11b8bc827692ad44d {\n", + " #map_96ae4ef77daae07d6517a46cb132078e {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -1214,17 +1214,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_fb0e6f7604b887e3038f2e34c387daf6" ></div>\n", + " <div class="folium-map" id="map_01e30781db5546fd33c936564bb55b9e" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_33e4646c6221daf11b8bc827692ad44d" ></div>\n", + " <div class="folium-map" id="map_96ae4ef77daae07d6517a46cb132078e" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_fb0e6f7604b887e3038f2e34c387daf6 = L.map(\n", - " "map_fb0e6f7604b887e3038f2e34c387daf6",\n", + " var map_01e30781db5546fd33c936564bb55b9e = L.map(\n", + " "map_01e30781db5546fd33c936564bb55b9e",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1238,26 +1238,26 @@ "\n", " \n", " \n", - " var tile_layer_3af008ef80a8815cec0254fac65ad52a = L.tileLayer(\n", + " var tile_layer_358d5f4205c7ef05badea23731934487 = 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_3af008ef80a8815cec0254fac65ad52a.addTo(map_fb0e6f7604b887e3038f2e34c387daf6);\n", + " tile_layer_358d5f4205c7ef05badea23731934487.addTo(map_01e30781db5546fd33c936564bb55b9e);\n", " \n", " \n", - " var tile_layer_6e9e857cb364999634c67fd9fae39765 = L.tileLayer(\n", + " var tile_layer_2de7eea223d5c3c231e7bb0a886c7158 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=total-co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=spectral_r\\u0026rescale=0%2C150",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_6e9e857cb364999634c67fd9fae39765.addTo(map_fb0e6f7604b887e3038f2e34c387daf6);\n", + " tile_layer_2de7eea223d5c3c231e7bb0a886c7158.addTo(map_01e30781db5546fd33c936564bb55b9e);\n", " \n", " \n", - " var map_33e4646c6221daf11b8bc827692ad44d = L.map(\n", - " "map_33e4646c6221daf11b8bc827692ad44d",\n", + " var map_96ae4ef77daae07d6517a46cb132078e = L.map(\n", + " "map_96ae4ef77daae07d6517a46cb132078e",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1271,32 +1271,32 @@ "\n", " \n", " \n", - " var tile_layer_4c8a4d4af0e468b3a8e29ca52ad0e2cf = L.tileLayer(\n", + " var tile_layer_4ececd6c1677b874e98e0fc98a153b84 = 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_4c8a4d4af0e468b3a8e29ca52ad0e2cf.addTo(map_33e4646c6221daf11b8bc827692ad44d);\n", + " tile_layer_4ececd6c1677b874e98e0fc98a153b84.addTo(map_96ae4ef77daae07d6517a46cb132078e);\n", " \n", " \n", - " var tile_layer_3e8cd97bad7974c10408d84bb81a83cf = L.tileLayer(\n", + " var tile_layer_70acc13fa1fec86f69bde7d15f60e08a = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2010/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=total-co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=spectral_r\\u0026rescale=0%2C150",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_3e8cd97bad7974c10408d84bb81a83cf.addTo(map_33e4646c6221daf11b8bc827692ad44d);\n", + " tile_layer_70acc13fa1fec86f69bde7d15f60e08a.addTo(map_96ae4ef77daae07d6517a46cb132078e);\n", " \n", " \n", - " map_fb0e6f7604b887e3038f2e34c387daf6.sync(map_33e4646c6221daf11b8bc827692ad44d);\n", - " map_33e4646c6221daf11b8bc827692ad44d.sync(map_fb0e6f7604b887e3038f2e34c387daf6);\n", + " map_01e30781db5546fd33c936564bb55b9e.sync(map_96ae4ef77daae07d6517a46cb132078e);\n", + " map_96ae4ef77daae07d6517a46cb132078e.sync(map_01e30781db5546fd33c936564bb55b9e);\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, @@ -1403,7 +1403,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_b1b86cb0f6f01931d6cb3d72865f2611 {\n", + " #map_df34be553540c80e323fd461d98e745b {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1417,14 +1417,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_b1b86cb0f6f01931d6cb3d72865f2611" ></div>\n", + " <div class="folium-map" id="map_df34be553540c80e323fd461d98e745b" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_b1b86cb0f6f01931d6cb3d72865f2611 = L.map(\n", - " "map_b1b86cb0f6f01931d6cb3d72865f2611",\n", + " var map_df34be553540c80e323fd461d98e745b = L.map(\n", + " "map_df34be553540c80e323fd461d98e745b",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1438,41 +1438,41 @@ "\n", " \n", " \n", - " var tile_layer_e7fbe53a67e52db58cdd982ed6500387 = L.tileLayer(\n", + " var tile_layer_7b4d89df0e526c587fae17696473fdc2 = 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_e7fbe53a67e52db58cdd982ed6500387.addTo(map_b1b86cb0f6f01931d6cb3d72865f2611);\n", + " tile_layer_7b4d89df0e526c587fae17696473fdc2.addTo(map_df34be553540c80e323fd461d98e745b);\n", " \n", " \n", "\n", - " function geo_json_9736b9c5a395f87c57939285c079f83e_onEachFeature(feature, layer) {\n", + " function geo_json_4ab63d93727fece0045d8e3c552162ce_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_9736b9c5a395f87c57939285c079f83e = L.geoJson(null, {\n", - " onEachFeature: geo_json_9736b9c5a395f87c57939285c079f83e_onEachFeature,\n", + " var geo_json_4ab63d93727fece0045d8e3c552162ce = L.geoJson(null, {\n", + " onEachFeature: geo_json_4ab63d93727fece0045d8e3c552162ce_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_9736b9c5a395f87c57939285c079f83e_add (data) {\n", - " geo_json_9736b9c5a395f87c57939285c079f83e\n", + " function geo_json_4ab63d93727fece0045d8e3c552162ce_add (data) {\n", + " geo_json_4ab63d93727fece0045d8e3c552162ce\n", " .addData(data);\n", " }\n", - " geo_json_9736b9c5a395f87c57939285c079f83e_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", - " geo_json_9736b9c5a395f87c57939285c079f83e.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_4ab63d93727fece0045d8e3c552162ce_add({"geometry": {"coordinates": [[[-95, 29], [-95, 33], [-104, 33], [-104, 29], [-95, 29]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_4ab63d93727fece0045d8e3c552162ce.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_9736b9c5a395f87c57939285c079f83e.addTo(map_b1b86cb0f6f01931d6cb3d72865f2611);\n", + " geo_json_4ab63d93727fece0045d8e3c552162ce.addTo(map_df34be553540c80e323fd461d98e745b);\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, @@ -2339,8 +2339,8 @@ "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.8786006705795444e-07, 'max': 3873315.0, 'mean': 251.08050537109375, 'count': 296120.0625, 'sum': 74349976.0, 'std': 12656.74018063103, 'median': 7.482449054718018, 'majority': 0.5272803902626038, 'minority': 1.8786006705795444e-07, 'unique': 242919.0, 'histogram': [[296500.0, 12.0, 3.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0], [1.8786006705795444e-07, 387331.5, 774663.0, 1161994.5, 1549326.0, 1936657.5, 2323989.0, 2711320.5, 3098652.0, 3485983.5, 3873315.0]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.212051123380661, 'percentile_98': 1615.6910400390625}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.9114715144041838e-07, 'max': 4798060.5, 'mean': 255.62869262695312, 'count': 296120.0625, 'sum': 75696784.0, 'std': 14444.495145210165, 'median': 7.578545093536377, 'majority': 0.5363049507141113, 'minority': 1.9114715144041838e-07, 'unique': 242933.0, 'histogram': [[296505.0, 10.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0], [1.9114715144041838e-07, 479806.0625, 959612.125, 1439418.25, 1919224.25, 2399030.25, 2878836.5, 3358642.5, 3838448.5, 4318254.5, 4798060.5]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.21587365865707397, 'percentile_98': 1612.6146240234375}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.898683592571615e-07, 'max': 4486994.5, 'mean': 243.4835662841797, 'count': 296120.0625, 'sum': 72100368.0, 'std': 13255.782134600735, 'median': 7.461791038513184, 'majority': 0.5328000783920288, 'minority': 1.898683592571615e-07, 'unique': 239140.0, 'histogram': [[296505.0, 10.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0], [1.898683592571615e-07, 448699.4375, 897398.875, 1346098.25, 1794797.75, 2243497.25, 2692196.5, 3140896.0, 3589595.5, 4038295.0, 4486994.5]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.2142595797777176, 'percentile_98': 1595.547119140625}}}}\n", - "CPU times: user 92.6 ms, sys: 21 ms, total: 114 ms\n", - "Wall time: 23.5 s\n" + "CPU times: user 46.7 ms, sys: 13.4 ms, total: 60.1 ms\n", + "Wall time: 10.9 s\n" ] } ],