diff --git a/pr-preview/pr-137/search.json b/pr-preview/pr-137/search.json
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--- a/pr-preview/pr-137/search.json
+++ b/pr-preview/pr-137/search.json
@@ -1,591 +1,790 @@
 [
   {
-    "objectID": "index.html",
-    "href": "index.html",
-    "title": "U.S. Greenhouse Gas Center: Documentation",
+    "objectID": "processingreport.html",
+    "href": "processingreport.html",
+    "title": "U.S. Greenhouse Gas Center: Processing and Verification Reports",
     "section": "",
-    "text": "The U.S. Greenhouse Gas (GHG) Center provides a cloud-based system for exploring and analyzing U.S. government and other curated greenhouse gas datasets.\nOn this site, you can find the technical documentation for the services the center provides, how to load the datasets, and how the datasets were transformed from their source formats (eg. netCDF, HDF, etc.) into cloud-optimized formats that enable efficient cloud data access and visualization.",
+    "text": "Welcome to the U.S. Greenhouse Gas (GHG) Center processing and verification reports. These reports verify that the accuracy and integrity of each dataset in the US GHG Center is maintained once it is processed into the Center.\nThe reports are grouped topically and labeled by dataset name. Click on a dataset name to view the processing and verification report for that dataset.\nExamples of processing that may occur include transforming data from its source format into a could-optimized format, converting the units of the source data into a more common or standard unit, and flagging “nodata” values to ensure accurate data visualization. We strive to handle all data with extreme care, and share these reports to provide transparency and insight into any processing that is applied, while ensuring accuracy and reliability every step of the way.\nJoin us in our mission to make data-driven environmental solutions accessible. Explore, analyze, and make a difference with the US GHG Center.\nView the US GHG Center Data Catalog",
     "crumbs": [
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-    "objectID": "index.html#welcome",
-    "href": "index.html#welcome",
-    "title": "U.S. Greenhouse Gas Center: Documentation",
-    "section": "",
-    "text": "The U.S. Greenhouse Gas (GHG) Center provides a cloud-based system for exploring and analyzing U.S. government and other curated greenhouse gas datasets.\nOn this site, you can find the technical documentation for the services the center provides, how to load the datasets, and how the datasets were transformed from their source formats (eg. netCDF, HDF, etc.) into cloud-optimized formats that enable efficient cloud data access and visualization.",
+    "objectID": "processingreport.html#gridded-anthropogenic-greenhouse-gas-emissions",
+    "href": "processingreport.html#gridded-anthropogenic-greenhouse-gas-emissions",
+    "title": "U.S. Greenhouse Gas Center: Processing and Verification Reports",
+    "section": "Gridded Anthropogenic Greenhouse Gas Emissions",
+    "text": "Gridded Anthropogenic Greenhouse Gas Emissions\n\nOCO-2 MIP Top-Down CO₂ Budgets Processing and Verification Report\nODIAC Fossil Fuel CO₂ Emissions Processing and Verification Report\nTM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report\nU.S. Gridded Anthropogenic Methane Emissions Inventory Processing and Verification Report\nVulcan Fossil Fuel CO₂ Emissions Processing and Verification Report\nGRA²PES Greenhouse Gas and Air Quality Species Processing and Verification Report\nGRA²PES Greenhouse Gas and Air Quality Species Processing and Verification Report",
     "crumbs": [
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-    "objectID": "index.html#contents",
-    "href": "index.html#contents",
-    "title": "U.S. Greenhouse Gas Center: Documentation",
-    "section": "Contents",
-    "text": "Contents\n\nServices provided for accessing and analyzing the US GHG Center datasets, such as the JupyterHub environment for interactive computing.\nDataset usage examples, e.g. for the Wetland Methane Emissions from the LPJ-EOSIM model dataset, that shows how to load the dataset in Python in JupyterHub.\nDataset transformation scripts, which document the code used to transform datasets for display in the US GHG Center. An example is the ODIAC Fossil Fuel CO₂ Emissions dataset transformation code.\nData processing and verification reports that openly present the process we used to check and verify that any transformation did not alter the original source data. An example is the GOSAT-based Top-down Total and Natural Methane Emissions dataset.\nData Flow Diagrams, which provide a high level summary of how each dataset was integrated into the US GHG Center. See the MiCASA Land Carbon Flux Flow Diagram as an example.",
+    "objectID": "processingreport.html#natural-greenhouse-gas-emissions-and-sinks",
+    "href": "processingreport.html#natural-greenhouse-gas-emissions-and-sinks",
+    "title": "U.S. Greenhouse Gas Center: Processing and Verification Reports",
+    "section": "Natural Greenhouse Gas Emissions and Sinks",
+    "text": "Natural Greenhouse Gas Emissions and Sinks\n\nAir-Sea CO₂ Flux, ECCO-Darwin Model v5 Processing and Verification Report\nMiCASA Land Carbon Flux Processing and Verification Report\nGOSAT-based Top-down Total and Natural Methane Emissions Processing and Verification Report\nOCO-2 MIP Top-Down CO₂ Budgets Processing and Verification Report\nTM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report\nWetland Methane Emissions, LPJ-EOSIM model Processing and Verification Report\nWetland Methane Emissions, LPJ-EOSIM model Processing and Verification Report",
     "crumbs": [
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-    "objectID": "index.html#contact",
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-    "title": "U.S. Greenhouse Gas Center: Documentation",
-    "section": "Contact",
-    "text": "Contact\nFor technical help or general questions, please contact the support team using the feedback form.",
+    "objectID": "processingreport.html#large-emissions-events",
+    "href": "processingreport.html#large-emissions-events",
+    "title": "U.S. Greenhouse Gas Center: Processing and Verification Reports",
+    "section": "Large Emissions Events",
+    "text": "Large Emissions Events\n\nEMIT Methane Point Source Plume Complexes Processing and Verification Report",
     "crumbs": [
-      "Welcome"
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     ]
   },
   {
-    "objectID": "services/jupyterhub.html",
-    "href": "services/jupyterhub.html",
-    "title": "JupyterHub",
-    "section": "",
-    "text": "The US GHG Center promotes the use of JupyterHub environments for interactive data science. JupyterHub enables you to analyze massive archives of Earth science data in the cloud in an interactive environment that alleviates the complexities of managing compute resources (virtual machines, roles and permissions, etc).\nUsers affiliated with the US GHG Center can get access to a dedicated JupyterHub service, provided in collaboration with 2i2c: hub.ghg.center. Please find instructions for requesting access below.\nIf you are a scientist affiliated with other NASA projects such as VEDA, EIS, and MAAP, you can also keep using the resources provided by these projects. Through the use of open-source technology, we make sure our services are interoperable and exchangeable.",
+    "objectID": "processingreport.html#greenhouse-gas-concentrations",
+    "href": "processingreport.html#greenhouse-gas-concentrations",
+    "title": "U.S. Greenhouse Gas Center: Processing and Verification Reports",
+    "section": "Greenhouse Gas Concentrations",
+    "text": "Greenhouse Gas Concentrations\n\nAtmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory Processing and Verification Report\nAtmospheric Methane Concentrations from NOAA Global Monitoring Laboratory Processing and Verification Report\nOCO-2 GEOS Column CO₂ Concentrations Processing and Verification Report\nCarbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) Processing and Verification Report\nCarbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project Processing and Verification Report\nCarbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed Processing and Verification Report\nCarbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) Processing and Verification Report\nCarbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project Processing and Verification Report\nCarbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed Processing and Verification Report",
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-    "href": "services/jupyterhub.html#to-get-us-ghg-center-jupyterhub-access",
-    "title": "JupyterHub",
-    "section": "To Get US GHG Center JupyterHub access:",
-    "text": "To Get US GHG Center JupyterHub access:\nThe US GHG Center notebook environment is available to authorized users on an as-need basis. If you are a user affiliated with the US GHG Center, you can gain access by using our Hub Access Request form.\n\nMake sure you have a GitHub Account. Take note of your GitHub username.\nFill out the request form and provide needed information.\nWatch your email for notification of authorization and the invite to join the US GHG Center Hub Access GitHub Team.\nOnce you accept the invitation, you can go to hub.ghg.center and login using your GitHub credentials.",
+    "objectID": "processingreport.html#socioeconomic",
+    "href": "processingreport.html#socioeconomic",
+    "title": "U.S. Greenhouse Gas Center: Processing and Verification Reports",
+    "section": "Socioeconomic",
+    "text": "Socioeconomic\n\nSEDAC Gridded World Population Density Processing and Verification Report",
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-    "href": "services/jupyterhub.html#to-access-user-notebooks",
-    "title": "JupyterHub",
-    "section": "To access User Notebooks",
-    "text": "To access User Notebooks\nThis site provides Jupyter notebooks showing how to load and analyze Earth data in the interactive cloud computing environment.\nFurther instructions are included in each notebook.\nIf you have any questions, please use the feedback form to contact the US GHG Center user support team.",
+    "objectID": "processingreport.html#contact",
+    "href": "processingreport.html#contact",
+    "title": "U.S. Greenhouse Gas Center: Processing and Verification Reports",
+    "section": "Contact",
+    "text": "Contact\nFor technical help or general questions, please contact the support team using the feedback form.",
     "crumbs": [
-      "User Services",
-      "JupyterHub"
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-    "title": "U.S. Greenhouse Gas Center: Data Flow Diagrams",
+    "objectID": "cog_transformation/oco2geos-co2-daygrid-v10r.html",
+    "href": "cog_transformation/oco2geos-co2-daygrid-v10r.html",
+    "title": "OCO-2 GEOS Column CO₂ Concentrations",
     "section": "",
-    "text": "Welcome to the homepage for U.S. Greenhouse Gas (GHG) Center data workflow diagrams. Use these diagrams to discover the journey of each dataset from acquisition to integration in the US GHG Center.\nData flow diagrams are grouped topically and labeled by dataset name. Click on a dataset name to view the data flow diagram for that dataset, which summarizes the process followed to bring the dataset into the US GHG Center.\nJoin us in our mission to make data-driven environmental solutions accessible. Explore, analyze, and make a difference with the US GHG Center.\nView the US GHG Center Data Catalog",
+    "text": "This script was used to transform the OCO-2 GEOS Column CO₂ Concentrations dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nimport os\n\n\nsession = boto3.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"earth_data/geos_oco2\"\ns3_folder_name = \"geos-oco2\"\n\nerror_files = []\ncount = 0\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(FOLDER_NAME):\n    try:\n        xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n        xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n        variable = [var for var in xds.data_vars]\n        filename = name.split(\"/ \")[-1]\n        filename_elements = re.split(\"[_ .]\", filename)\n\n        for time_increment in range(0, len(xds.time)):\n            for var in variable:\n                filename = name.split(\"/ \")[-1]\n                filename_elements = re.split(\"[_ .]\", filename)\n                data = getattr(xds.isel(time=time_increment), var)\n                data = data.isel(lat=slice(None, None, -1))\n                data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n                data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n                # # insert date of generated COG into filename\n                filename_elements[-1] = filename_elements[-3]\n                filename_elements.insert(2, var)\n                filename_elements.pop(-3)\n                cog_filename = \"_\".join(filename_elements)\n                # # add extension\n                cog_filename = f\"{cog_filename}.tif\"\n\n                with tempfile.NamedTemporaryFile() as temp_file:\n                    data.rio.to_raster(\n                        temp_file.name,\n                        driver=\"COG\",\n                    )\n                    s3_client.upload_file(\n                        Filename=temp_file.name,\n                        Bucket=bucket_name,\n                        Key=f\"{s3_folder_name}/{cog_filename}\",\n                    )\n\n                files_processed = files_processed._append(\n                    {\"file_name\": name, \"COGs_created\": cog_filename},\n                    ignore_index=True,\n                )\n        count += 1\n        print(f\"Generated and saved COG: {cog_filename}\")\n    except OSError:\n        error_files.append(name)\n        pass\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{s3_folder_name}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{s3_folder_name}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Flow Diagrams"
+      "Data Transformation Notebooks",
+      "Greenhouse Gas Concentrations",
+      "OCO-2 GEOS Column CO₂ Concentrations"
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-    "title": "U.S. Greenhouse Gas Center: Data Flow Diagrams",
-    "section": "Gridded Anthropogenic Greenhouse Gas Emissions",
-    "text": "Gridded Anthropogenic Greenhouse Gas Emissions\n\nOCO-2 MIP Top-Down CO₂ Budgets Data Flow Diagram\nODIAC Fossil Fuel CO₂ Emissions Data Flow Diagram\nTM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram\nU.S. Gridded Anthropogenic Methane Emissions Inventory Data Flow Diagram\nVulcan Fossil Fuel CO₂ Emissions Data Flow Diagram\nGRA²PES Greenhouse Gas and Air Quality Species Data Flow Diagram\nGRA²PES Greenhouse Gas and Air Quality Species Data Flow Diagram",
-    "crumbs": [
-      "Data Flow Diagrams"
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+    "objectID": "cog_transformation/epa-ch4emission-monthgrid-v2.html",
+    "href": "cog_transformation/epa-ch4emission-monthgrid-v2.html",
+    "title": "Gridded Anthropogenic Methane Emissions Inventory",
+    "section": "",
+    "text": "This script was used to transform the Gridded Anthropogenic Methane Emissions Inventory dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"epa_emissions/monthly_scale\"\ns3_folder_name = \"epa-emissions-monthly-scale-factors\"\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(FOLDER_NAME):\n    xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n    xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    variable = [var for var in xds.data_vars]\n    filename = name.split(\"/ \")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n    start_time = datetime(int(filename_elements[-2]), 1, 1)\n\n    for time_increment in range(0, len(xds.time)):\n        for var in variable:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = getattr(xds.isel(time=time_increment), var)\n            data = data.isel(lat=slice(None, None, -1))\n            data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n            date = start_time + relativedelta(months=+time_increment)\n\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = date.strftime(\"%Y%m\")\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{s3_folder_name}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{s3_folder_name}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{s3_folder_name}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top"
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-    "href": "workflow.html#natural-greenhouse-gas-sources-emissions-and-sinks",
-    "title": "U.S. Greenhouse Gas Center: Data Flow Diagrams",
-    "section": "Natural Greenhouse Gas Sources Emissions and Sinks",
-    "text": "Natural Greenhouse Gas Sources Emissions and Sinks\n\nAir-Sea CO₂ Flux, ECCO-Darwin Model v5 Data Flow Diagram\nMiCASA Land Carbon Flux Data Flow Diagram\nGOSAT-based Top-down Total and Natural Methane Emissions Data Flow Diagram\nOCO-2 MIP Top-Down CO₂ Budgets Data Flow Diagram\nTM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram\nWetland Methane Emissions, LPJ-EOSIM Model Data Flow Diagram",
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+    "href": "cog_transformation/odiac-ffco2-monthgrid-v2022.html",
+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
+    "section": "",
+    "text": "This script was used to transform the ODIAC Fossil Fuel CO₂ Emissions dataset from GeoTIFF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\n\nimport tempfile\nimport boto3\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = \"ghgc-data-store-dev\" # S3 bucket where the COGs are stored after transformation\n\nfold_names = os.listdir(\"ODIAC\")\n\nfiles_processed = pd.DataFrame(columns=[\"file_name\", \"COGs_created\"])   # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor fol_ in fold_names:\n    for name in os.listdir(f\"ODIAC/{fol_}\"):\n        xds = xarray.open_dataarray(f\"ODIAC/{fol_}/{name}\")\n\n        filename = name.split(\"/ \")[-1]\n        filename_elements = re.split(\"[_ .]\", filename)\n        # # insert date of generated COG into filename\n        filename_elements.pop()\n        filename_elements[-1] = fol_ + filename_elements[-1][-2:]\n\n        xds.rio.set_spatial_dims(\"x\", \"y\", inplace=True)\n        xds.rio.write_nodata(-9999, inplace=True)\n        xds.rio.write_crs(\"epsg:4326\", inplace=True)\n\n        cog_filename = \"_\".join(filename_elements)\n        # # add extension\n        cog_filename = f\"{cog_filename}.tif\"\n\n        with tempfile.NamedTemporaryFile() as temp_file:\n            xds.rio.to_raster(\n                temp_file.name,\n                driver=\"COG\",\n            )\n            s3_client.upload_file(\n                Filename=temp_file.name,\n                Bucket=bucket_name,\n                Key=f\"ODIAC_geotiffs_COGs/{cog_filename}\",\n            )\n\n        files_processed = files_processed._append(\n            {\"file_name\": name, \"COGs_created\": cog_filename},\n            ignore_index=True,\n        )\n\n        print(f\"Generated and saved COG: {cog_filename}\")\n\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/ODIAC_COGs/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top"
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-    "title": "U.S. Greenhouse Gas Center: Data Flow Diagrams",
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+    "title": "CASA-GFED3 Land Carbon Flux",
+    "section": "",
+    "text": "Code used to transform CASA-GFED3 Land Carbon Flux data from netcdf to Cloud Optimized Geotiff.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = \"ghgc-data-store-dev\"\ndate_fmt = \"%Y%m\"\n\nfiles_processed = pd.DataFrame(columns=[\"file_name\", \"COGs_created\"])\nfor name in os.listdir(\"geoscarb\"):\n    xds = xarray.open_dataset(\n        f\"geoscarb/{name}\",\n        engine=\"netcdf4\",\n    )\n    xds = xds.assign_coords(\n        longitude=(((xds.longitude + 180) % 360) - 180)\n    ).sortby(\"longitude\")\n    variable = [var for var in xds.data_vars]\n\n    for time_increment in range(0, len(xds.time)):\n        for var in variable[:-1]:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = getattr(xds.isel(time=time_increment), var)\n            data = data.isel(latitude=slice(None, None, -1))\n            data.rio.set_spatial_dims(\"longitude\", \"latitude\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            date = data.time.dt.strftime(date_fmt).item(0)\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = date\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"GEOS-Carbs/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=\"GEOS-Carbs/metadata.json\",\n    )\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/GEOS-Carbs/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top"
   },
   {
-    "objectID": "workflow.html#greenhouse-gas-concentrations",
-    "href": "workflow.html#greenhouse-gas-concentrations",
-    "title": "U.S. Greenhouse Gas Center: Data Flow Diagrams",
-    "section": "Greenhouse Gas Concentrations",
-    "text": "Greenhouse Gas Concentrations\n\nAtmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory Data Flow Diagram\nAtmospheric Methane Concentrations from NOAA Global Monitoring Laboratory Data Flow Diagram\nOCO-2 GEOS Column CO₂ Concentrations Data Flow Diagram\nCarbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) Data Flow Diagram\nCarbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project Data Flow Diagram\nCarbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed Data Flow Diagram\nCarbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) Data Flow Diagram\nCarbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project Data Flow Diagram\nCarbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed Data Flow Diagram",
+    "objectID": "cog_transformation/epa-ch4emission-grid-v2express_layers_update.html",
+    "href": "cog_transformation/epa-ch4emission-grid-v2express_layers_update.html",
+    "title": "Gridded Anthropogenic Methane Emissions Inventory",
+    "section": "",
+    "text": "This script was used to add concatenated layers and transform Gridded Anthropogenic Methane Emissions Inventory dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nfrom datetime import datetime\nimport numpy as np\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nTrue\n\n\n\n# session = boto3.session.Session()\nsession = boto3.Session(\n    aws_access_key_id=os.environ.get(\"AWS_ACCESS_KEY_ID\"),\n    aws_secret_access_key=os.environ.get(\"AWS_SECRET_ACCESS_KEY\"),\n    aws_session_token=os.environ.get(\"AWS_SESSION_TOKEN\"),\n)\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"../data/epa_emissions_express_extension\"\ns3_folder_name = \"epa_express_extension_Mg_km2_yr\"\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(FOLDER_NAME):\n    xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n    xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    variable = [var for var in xds.data_vars]\n    new_variables = {\n        \"all-variables\": variable[:-1],\n        \"agriculture\": variable[17:21],\n        \"natural-gas-systems\": variable[10:15] + [variable[26]],\n        \"petroleum-systems\": variable[5:9],\n        \"waste\": variable[21:26],\n        \"coal-mines\": variable[2:5],\n        \"other\": variable[:2] + [variable[9]] + variable[15:17],\n    }\n    filename = name.split(\"/ \")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n    start_time = datetime(int(filename_elements[-2]), 1, 1)\n\n    for time_increment in range(0, len(xds.time)):\n        for key, value in new_variables.items():\n            data = np.zeros(dtype=np.float32, shape=(len(xds.lat), len(xds.lon)))\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            for var in value:\n                data = data + getattr(xds.isel(time=time_increment), var)\n            # data = np.round(data / pow(10, 9), 2)\n            data.values[data.values==0] = np.nan\n            data = data*((1/(6.022*pow(10,23)))*(16.04*pow(10,-6))*366*pow(10,10)*86400)\n            data = data.fillna(-9999)\n            data = data.isel(lat=slice(None, None, -1))\n            data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = start_time.strftime(\"%Y\")\n            filename_elements.insert(2, key)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{s3_folder_name}/{cog_filename}\",\n                )\n\n                files_processed = files_processed._append(\n                    {\"file_name\": name, \"COGs_created\": cog_filename},\n                    ignore_index=True,\n                )\n\n                print(f\"Generated and saved COG: {cog_filename}\")\nprint(\"Done generating COGs\")\n\nTraceback (most recent call last):\n  File \"_pydevd_bundle/pydevd_cython.pyx\", line 1078, in _pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\n  File \"_pydevd_bundle/pydevd_cython.pyx\", line 297, in _pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\n  File \"/Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages/debugpy/_vendored/pydevd/pydevd.py\", line 1976, in do_wait_suspend\n    keep_suspended = self._do_wait_suspend(thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\n  File \"/Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages/debugpy/_vendored/pydevd/pydevd.py\", line 2011, in _do_wait_suspend\n    time.sleep(0.01)\nKeyboardInterrupt\n\n\n\n---------------------------------------------------------------------------\nKeyboardInterrupt                         Traceback (most recent call last)\n/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb Cell 4 line 4\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=45'>46</a> # data = data*(9.74*pow(10,-11))\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=46'>47</a> # data.values[data.values<=np.nanpercentile(data.values, 50)] = np.nan\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=47'>48</a> data = data.fillna(-9999)\n---> <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=48'>49</a> data = data.isel(lat=slice(None, None, -1))\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=49'>50</a> data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=50'>51</a> data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb Cell 4 line 4\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=45'>46</a> # data = data*(9.74*pow(10,-11))\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=46'>47</a> # data.values[data.values<=np.nanpercentile(data.values, 50)] = np.nan\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=47'>48</a> data = data.fillna(-9999)\n---> <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=48'>49</a> data = data.isel(lat=slice(None, None, -1))\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=49'>50</a> data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=50'>51</a> data.rio.write_crs(\"epsg:4326\", inplace=True)\n\nFile _pydevd_bundle/pydevd_cython.pyx:1363, in _pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__()\n\nFile _pydevd_bundle/pydevd_cython.pyx:662, in _pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch()\n\nFile _pydevd_bundle/pydevd_cython.pyx:1087, in _pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch()\n\nFile _pydevd_bundle/pydevd_cython.pyx:1078, in _pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch()\n\nFile _pydevd_bundle/pydevd_cython.pyx:297, in _pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend()\n\nFile ~/miniconda3/envs/cmip6/lib/python3.9/site-packages/debugpy/_vendored/pydevd/pydevd.py:1976, in PyDB.do_wait_suspend(self, thread, frame, event, arg, exception_type)\n   1973             from_this_thread.append(frame_custom_thread_id)\n   1975     with self._threads_suspended_single_notification.notify_thread_suspended(thread_id, stop_reason):\n-> 1976         keep_suspended = self._do_wait_suspend(thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\n   1978 frames_list = None\n   1980 if keep_suspended:\n   1981     # This means that we should pause again after a set next statement.\n\nFile ~/miniconda3/envs/cmip6/lib/python3.9/site-packages/debugpy/_vendored/pydevd/pydevd.py:2011, in PyDB._do_wait_suspend(self, thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\n   2008         self._call_mpl_hook()\n   2010     self.process_internal_commands()\n-> 2011     time.sleep(0.01)\n   2013 self.cancel_async_evaluation(get_current_thread_id(thread), str(id(frame)))\n   2015 # process any stepping instructions\n\nKeyboardInterrupt: \n\n\n\n\n\n\n Back to top"
+  },
+  {
+    "objectID": "cog_transformation/vulcan-ffco2-yeargrid-v4.html",
+    "href": "cog_transformation/vulcan-ffco2-yeargrid-v4.html",
+    "title": "Vulcan Fossil Fuel CO₂ Emissions",
+    "section": "",
+    "text": "This script was used to transform the VULCAN dataset provided in GeoTIFF format for display in the Greenhouse Gas (GHG) Center with the calaulation of validation statistics.\n\nimport xarray\nimport pandas as pd\nimport boto3\nimport glob\nimport s3fs\nimport tempfile\nfrom datetime import datetime\nimport os\nimport boto3\nfrom pyproj import CRS\nimport numpy as np\n\nimport rasterio\nfrom rasterio.warp import calculate_default_transform, reproject, Resampling\nfrom rasterio.enums import Resampling\nfrom rio_cogeo.cogeo import cog_translate\nfrom rio_cogeo.profiles import cog_profiles\n\n\nconfig = {\n    \"data_acquisition_method\": \"s3\",\n    \"raw_data_bucket\" : \"gsfc-ghg-store\",\n    \"raw_data_prefix\": \"Vulcan/v4.0/grid.1km.mn\",\n    \"cog_data_bucket\": \"ghgc-data-store-develop\",\n    \"cog_data_prefix\": \"transformed_cogs/VULCAN_v4\"\n}\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\n\nraw_data_bucket = config[\"raw_data_bucket\"]\nraw_data_prefix= config[\"raw_data_prefix\"]\n\ncog_data_bucket = config['cog_data_bucket']\ncog_data_prefix= config[\"cog_data_prefix\"]\n\ndate_fmt=config['date_fmt']\n\nfs = s3fs.S3FileSystem()\n\n\ndef get_all_s3_keys(bucket, model_name, ext):\n    \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n    keys = []\n\n    kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n    while True:\n        resp = s3_client.list_objects_v2(**kwargs)\n        for obj in resp[\"Contents\"]:\n            if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n                keys.append(obj[\"Key\"])\n\n        try:\n            kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n        except KeyError:\n            break\n\n    return keys\n\nkeys = get_all_s3_keys(raw_data_bucket, raw_data_prefix, \".tif\")\n\n\nlen(keys)\n\n\n# To calculate the validation stats\noverall= pd.DataFrame(columns=[\"data\",\"min\",\"max\",\"mean\",\"std\"])\n\n\n# Step 1: Reproject the data \n# Define the source and target CRS\n# Also calculate raw - monthly validation stats\nos.makedirs(\"reproj\", exist_ok=True)\nsrc_crs = CRS.from_wkt('PROJCS[\"unknown\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Lambert_Conformal_Conic_2SP\"],PARAMETER[\"latitude_of_origin\",40],PARAMETER[\"central_meridian\",-97],PARAMETER[\"standard_parallel_1\",33],PARAMETER[\"standard_parallel_2\",45],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]')\ndst_crs = CRS.from_epsg(4326)  # WGS 84\ndf = pd.DataFrame(columns=['filename', 'min(raw)', 'max(raw)', 'mean(raw)', 'std(raw)'])\noverall_raw= []\nfor key in keys:\n    url = f\"s3://{raw_data_bucket}/{key}\"\n    with rasterio.open(url) as src:\n        filename_elements = key.split(\"/\")[-1].split(\".\")[:-1]\n        output_tif = \"_\".join(filename_elements) + \".tif\"\n        data = src.read(1)  # Read the first band\n        overall_raw.append(data)\n        \n        # Calculate statistics while ignoring NaN values\n        min_val = np.nanmin(data)\n        max_val = np.nanmax(data)\n        mean_val = np.nanmean(data)\n        std_val = np.nanstd(data)  \n        stats = [output_tif, min_val, max_val, mean_val, std_val]\n        df.loc[len(df)] = stats\n        \n        transform, width, height = calculate_default_transform(\n        src.crs, dst_crs, src.width, src.height, *src.bounds)\n        kwargs = src.meta.copy()\n        kwargs.update({\n        'crs': dst_crs,\n        'transform': transform,\n        'width': width,\n        'height': height,\n        'nodata': -9999\n        })\n\n        with rasterio.open(f\"reproj/{output_tif}\", 'w', **kwargs) as dst:\n            for i in range(1, src.count + 1):\n                reproject(\n                source=rasterio.band(src, i),\n                destination=rasterio.band(dst, i),\n                src_transform=src.transform,\n                src_crs=src.crs,\n                dst_transform=transform,\n                dst_crs=dst_crs,\n                resampling=Resampling.nearest)\n        print(f\"Done for {output_tif}\")\n\n\n\n\n# overall validation of raw data\noverall_raw= np.array(overall_raw)\nnan_min = np.nanmin(overall_raw)\nnan_max = np.nanmax(overall_raw)\nnan_mean = np.nanmean(overall_raw)\nnan_std = np.nanstd(overall_raw)\noverall.loc[len(overall)] = [\"raw\",nan_min,nan_max,nan_mean,nan_std]\n\n\n# validation for reprojected data - yearly calculation\noverall_reproj = []\nfiles = glob.glob(\"reproj/*.tif\")\ndf1 = pd.DataFrame(columns=['filename', 'min(reprojected)', 'max(reprojected)', 'mean(reprojected)', 'std(reprojected)'])\nfor file in files:\n    with rasterio.open(file) as src:\n        filename_elements = file.split(\"/\")[-1].split(\".\")[:-1]\n        output_tif = \"_\".join(filename_elements) + \".tif\"\n        data = src.read(1)  \n        data = np.ma.masked_equal(data, -9999)\n        overall_reproj.append(data)\n        \n        # Calculate statistics while ignoring NaN values\n        min_val = np.nanmin(data)\n        max_val = np.nanmax(data)\n        mean_val = np.nanmean(data)\n        std_val = np.nanstd(data)  \n        stats = [output_tif, min_val, max_val, mean_val, std_val]\n        df1.loc[len(df1)] = stats\n\n\n# overall validation of reprojected  data\noverall_reproj= np.array(overall_reproj)\noverall_reproj = np.ma.masked_equal(overall_reproj, -9999)\nnan_min = np.nanmin(overall_reproj)\nnan_max = np.nanmax(overall_reproj)\nnan_mean = np.nanmean(overall_reproj)\nnan_std = np.nanstd(overall_reproj)\noverall.loc[len(overall)] = [\"reprojected\",nan_min,nan_max,nan_mean,nan_std]\n\n\n# Step 2: Replace nan and 0 values with -9999\nos.makedirs(\"reproj2\", exist_ok=True)\nfiles = glob.glob(\"reproj/*.tif\")\nfor file in files:\n    filename = file.split('/')[-1]\n    xda = xarray.open_dataarray(file).sel(band=1)\n\n    # Multiply data\n    data = data *( 44/12)\n    \n    data = xda.where(xda != 0, -9999)  # Replace 0 with -9999\n    #data = data.where(data != -3.4e+38, -9999)  # Replace -3.4e+38 with -9999\n    data = data.fillna(-9999)  # Ensure all NaNs are replaced with -9999\n    data_array = data.values\n    \n\n    # Open the source raster to get metadata\n    with rasterio.open(file) as src:\n        meta = src.meta\n        meta.update({\n            'nodata': -9999,\n            'dtype': 'float32',\n            'driver': 'COG'\n        })\n        with rasterio.open(f\"reproj2/{filename}\", 'w', **meta) as dst:\n            dst.write(data_array, 1)\n\n\n# validation for reprojected data (non zero) - monthly calculation\noverall_reproj2=[]\nfiles = glob.glob(\"reproj/*.tif\")\ndf11 = pd.DataFrame(columns=['filename', 'min(reproj_nonzero)', 'max(reproj_nonzero)', 'mean(reproj_nonzero)', 'std(reproj_nonzero)'])\nfor file in files:\n    with rasterio.open(file) as src:\n        filename_elements = file.split(\"/\")[-1].split(\".\")[:-1]\n        output_tif = \"_\".join(filename_elements) + \".tif\"\n        data = src.read(1)  \n        data = np.ma.masked_where((data == -9999) | (data == 0), data)\n        overall_reproj2.append(data)\n\n        \n        # Calculate statistics while ignoring NaN values\n        min_val = np.nanmin(data)\n        max_val = np.nanmax(data)\n        mean_val = np.nanmean(data)\n        std_val = np.nanstd(data)  \n        stats = [output_tif, min_val, max_val, mean_val, std_val]\n        df11.loc[len(df11)] = stats\n\n\n# validation for reprojected data (non zero) - overall calculation\noverall_reproj2= np.array(overall_reproj2)\noverall_reproj2 = np.ma.masked_where((overall_reproj2 == -9999) | (overall_reproj2 == 0), overall_reproj2)\nnan_min = np.nanmin(overall_reproj2)\nnan_max = np.nanmax(overall_reproj2)\nnan_mean = np.nanmean(overall_reproj2)\nnan_std = np.nanstd(overall_reproj2)\noverall.loc[len(overall)] = [\"reprojected_non_zero\",nan_min,nan_max,nan_mean,nan_std]\n\n\n# Step 3: To put overviews\nCOG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\nOVERVIEW_LEVELS = 9\nOVERVIEW_RESAMPLING = 'average'\n\nfor file in glob.glob(\"reproj2/*.tif\"):\n    output_path = f\"output/{file.split(\"/\")[-1]}\"\n    \n    # Create a temporary file to hold the COG\n    with tempfile.NamedTemporaryFile(suffix='.tif', delete=False) as temp_file:       \n        # Create COG with overviews and nodata value\n        cog_translate(\n            file,\n            temp_file.name,\n            cog_profiles.get(\"deflate\"),\n            overview_level=OVERVIEW_LEVELS,\n            overview_resampling=OVERVIEW_RESAMPLING,\n            nodata=-9999\n        )\n        # Move the temporary file to the desired local path\n        os.rename(temp_file.name, output_path)\n\n\n# validation for final data with overviews - overall calculation\noverall_final=[]\nfiles = glob.glob(\"output/*.tif\")\ndf2 = pd.DataFrame(columns=['filename', 'min(transformed)', 'max(transformed)', 'mean(transformed)', 'std(transformed)'])\nfor file in files:\n    with rasterio.open(file) as src:\n        filename_elements = file.split(\"/\")[-1].split(\".\")[:-1]\n        output_tif = \"_\".join(filename_elements) + \".tif\"\n        data = src.read(1)  # Read the first band\n        \n        # Mask -9999 values and NaNs for statistics calculation\n        data = np.ma.masked_where((data == -9999) | np.isnan(data), data)\n        # Multiply data - undo the multiplication done during transformation\n        data = data *( 12/44)\n        overall_final.append(data)\n        \n        # Calculate statistics while ignoring NaN values\n        min_val = np.nanmin(data)\n        max_val = np.nanmax(data)\n        mean_val = np.nanmean(data)\n        total = np.nansum(data) \n        stats = [output_tif, min_val, max_val, mean_val, std_val]\n        df2.loc[len(df2)] = stats\n\n\n# validation for final data (with overviews) - overall calculation\noverall_final= np.array(overall_final)\noverall_final = np.ma.masked_where((overall_final == -9999) | np.isnan(overall_final), overall_final)\nnan_min = np.nanmin(overall_final)\nnan_max = np.nanmax(overall_final)\nnan_mean = np.nanmean(overall_final)\nnan_std = np.nanstd(overall_final)\noverall.loc[len(overall)] = [\"Transformed\",nan_min,nan_max,nan_mean,nan_std]\n\n\npd.merge(pd.merge(df,df1, on='filename', how='inner'), pd.merge(df11,df2, on='filename', how='inner'), how='inner',on='filename' )\n\n\noverall\n\n\n# Save to json\noverall.to_json(\"overall_stats.json\")\npd.merge(pd.merge(df,df1, on='filename', how='inner'), pd.merge(df11,df2, on='filename', how='inner'), how='inner',on='filename' ).to_json(\"yearly_stats.json\")\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Flow Diagrams"
+      "Data Transformation Notebooks",
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "Vulcan Fossil Fuel CO₂ Emissions"
     ]
   },
   {
-    "objectID": "workflow.html#socioeconomic",
-    "href": "workflow.html#socioeconomic",
-    "title": "U.S. Greenhouse Gas Center: Data Flow Diagrams",
-    "section": "Socioeconomic",
-    "text": "Socioeconomic\n\nSEDAC Gridded World Population Density Data Flow Diagram",
+    "objectID": "cog_transformation/gosat-based-ch4budget-yeargrid-v1.html",
+    "href": "cog_transformation/gosat-based-ch4budget-yeargrid-v1.html",
+    "title": "GOSAT-based Top-down Methane Budgets",
+    "section": "",
+    "text": "This script was used to transform the GOSAT-based Top-down Methane Budgets dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nimport rasterio\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nyear_ = datetime(2019, 1, 1)\nfolder_name = \"new_data/CH4-inverse-flux\"\n\nCOG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(folder_name):\n    ds = xarray.open_dataset(\n        f\"{folder_name}/{name}\",\n        engine=\"netcdf4\",\n    )\n\n    ds = ds.rename({\"dimy\": \"lat\", \"dimx\": \"lon\"})\n    # assign coords from dimensions\n    ds = ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    ds = ds.assign_coords(lat=((ds.lat / 180) * 180) - 90).sortby(\"lat\")\n\n    variable = [var for var in ds.data_vars]\n\n    for var in variable[2:]:\n        filename = name.split(\"/ \")[-1]\n        filename_elements = re.split(\"[_ .]\", filename)\n        data = ds[var]\n        filename_elements.pop()\n        filename_elements.insert(2, var)\n        cog_filename = \"_\".join(filename_elements)\n        # # add extension\n        cog_filename = f\"{cog_filename}.tif\"\n\n        data = data.reindex(lat=list(reversed(data.lat)))\n\n        data.rio.set_spatial_dims(\"lon\", \"lat\")\n        data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n        # generate COG\n        COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n\n        with tempfile.NamedTemporaryFile() as temp_file:\n            data.rio.to_raster(temp_file.name, **COG_PROFILE)\n            s3_client.upload_file(\n                Filename=temp_file.name,\n                Bucket=bucket_name,\n                Key=f\"ch4_inverse_flux/{cog_filename}\",\n            )\n\n        files_processed = files_processed._append(\n            {\"file_name\": name, \"COGs_created\": cog_filename},\n            ignore_index=True,\n        )\n\n        print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(ds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(ds.dims)}, fp)\n    json.dump({\"data_variables\": list(ds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=\"ch4_inverse_flux/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/ch4_inverse_flux/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Flow Diagrams"
+      "Data Transformation Notebooks",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
+      "GOSAT-based Top-down Methane Budgets"
     ]
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-    "href": "workflow.html#contact",
-    "title": "U.S. Greenhouse Gas Center: Data Flow Diagrams",
-    "section": "Contact",
-    "text": "Contact\nFor technical help or general questions, please contact the support team using the feedback form.",
+    "objectID": "cog_transformation/influx-testbed-ghg-concentrations.html",
+    "href": "cog_transformation/influx-testbed-ghg-concentrations.html",
+    "title": "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)",
+    "section": "",
+    "text": "This script was used to transform the NIST INFLUX dataset into meaningful csv files for ingestion to vector dataset.\n\nimport pandas as pd\nimport glob\nimport os\nimport zipfile\nimport wget\nfrom collections import defaultdict\nfrom io import StringIO\nimport re\nimport warnings\nimport warnings\nfrom datetime import datetime, timedelta\n# Ignore the FutureWarning\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\n\n\n\nselected_level=\"level1\"\nbase_dir = \"data/\"\noutput_dir = \"output/\"\ndat_file_pattern = f\"{base_dir}/*/*.dat\"\noutput_base_dataset_name = \"PSU_INFLUX_INSITU\" \nconstant_variables = [\"datetime\",\"latitude\",\"longitude\",\"level\",\"elevation_m\",\"intake_height_m\",\"Instr\"]\nvariables =[['CO2(ppm)'],['CH4(ppb)']] # exclude CO\nmetadata_link= \"UrbanTestBed-Metadata - INFLUX.csv\"\n\n\n# Functions\ndef filter_dict(site_dict, selected_level):\n    return {key: [x for x in value if selected_level in x] for key, value in site_dict.items()}\n\ndef flag_desired_level(df, desired_level):\n    df['is_max_height_data'] = df['level']== desired_level\n    return df\n\ndef add_location(link, site_number):\n    meta= pd.read_csv(link)\n    location =meta[meta['Station Code']==f\"Site {site_number[-2:]}\"][['City','State']]#(get the actual site number)\n    return location['City'].item()+\",\"+location['State'].item()\n\ndef convert_to_datetime(row):\n    year = int(row['Year'])\n    doy = int(row['DOY'])\n    hour = int(row['Hour'])\n    \n    # Create a datetime object for the start of the year\n    date = datetime(year, 1, 1) + timedelta(days=doy - 1)\n    # Add the hours\n    datetime_obj = date + timedelta(hours=hour)\n    # Format as yyyy-mm-ddThh:mm:ssZ\n    return datetime_obj.strftime('%Y-%m-%dT%H:%M:%SZ')\n\ndef download_and_extract_zip_files(base_dir, levels):\n    \"\"\"\n    Download, extract, and delete zip files for the specified levels.\n\n    Parameters:\n    base_dir (str): The base directory for storing the downloaded files.\n    levels (list): A list of levels to download and extract.\n    \"\"\"\n    # Ensure the base directory exists\n    os.makedirs(base_dir, exist_ok=True)\n\n    # Loop through the levels and handle the download and extraction\n    for level in levels:\n        download_link = f\"https://www.datacommons.psu.edu/download/meteorology/influx/influx-tower-data/wmo-x2019-scale/level{level}.zip\"\n        fname = download_link.split(\"/\")[-1]\n        target_path = os.path.join(base_dir, fname)\n        \n        # Download the zip file\n        wget.download(download_link, target_path)\n        print(f\"Downloaded {download_link} to {target_path}\")\n\n        # Extract the zip file\n        with zipfile.ZipFile(target_path, 'r') as zip_ref:\n            zip_ref.extractall(base_dir)\n            print(f\"Extracted {fname}\")\n\n        # Delete the zip file after extraction\n        os.remove(target_path)\n\ndef create_site_dict(pattern):\n    \"\"\"\n    Creates a dictionary where keys are site numbers extracted from file paths,\n    and values are lists of file paths corresponding to each site number.\n    \n    Args:\n    - pattern (str): Glob pattern to match files.\n    \n    Returns:\n    - dict: Dictionary mapping site numbers to lists of file paths.\n    \"\"\"\n    all_files = glob.glob(pattern)\n    site_dict = defaultdict(list)\n    \n    for file_path in all_files:\n        site_number = file_path.split('_')[-4]\n        site_dict[site_number].append(file_path)\n    \n    return dict(site_dict)\n\ndef process_site_files(site_number, file_list):\n    \"\"\"\n    Process files for a given site number and save the combined DataFrame to CSV.\n    \n    Args:\n    - site_number (str): Site number to process.\n    - file_list (list): List of file paths corresponding to the site number.\n    \"\"\"\n    df = pd.DataFrame()\n    \n    for file_path in file_list:\n        with open(file_path, 'r') as file:\n            data = file.read()\n            \n        contents = data.split(\"\\nSite\")\n        lat = float((re.search(r'LATITUDE:\\s*([0-9.]+)\\s*[NS]', contents[0])).group(1))\n        lat_hemisphere = (re.search(r'LATITUDE:\\s*([0-9.]+)\\s*[NS]', contents[0])).group(0)[-1]\n        \n        lon = float((re.search(r'LONGITUDE:\\s*([0-9.]+)\\s*[EW]', contents[0])).group(1))\n        lon_hemisphere = (re.search(r'LONGITUDE:\\s*([0-9.]+)\\s*[EW]', contents[0])).group(0)[-1]\n        \n        level= file_path.split(\"/\")[-2]\n        \n        elevation= re.search(r'ALTITUDE:\\s*([0-9.]+)\\s*m\\s*ASL', contents[0]).group(1)\n        intake_height= re.search(r'SAMPLING HEIGHT:\\s*([0-9.]+)\\s*m\\s*AGL', contents[0]).group(1)\n\n        \n        data_io = StringIO(contents[1])\n        tmp_data = pd.read_csv(data_io, delim_whitespace=True)\n        tmp_data = tmp_data.reset_index().rename(columns={'index': 'Site'})\n        tmp= tmp_data.query(\"Flag==1\").copy()# 1 means no known problem, 0 is not recommemded, 9 is instrument issue (unrealistic)\n        #tmp['SiteCode'] = int(re.search(r'\\d+', site_number).group()) \n        tmp['latitude'] = lat\n        tmp['longitude'] = lon\n        tmp['level'] = int(re.search(r'\\d+', level).group())\n        tmp['elevation_m'] = elevation\n        tmp['intake_height_m']= intake_height\n\n        if lat_hemisphere == 'S':\n            tmp['latitude'] = -1* tmp[\"latitude\"]\n        if lon_hemisphere == 'W':\n            tmp['longitude'] = -1* tmp[\"longitude\"]\n\n        df = pd.concat([df, tmp], ignore_index=True)\n\n    # Ensure the output directory exists\n    os.makedirs(output_dir, exist_ok=True)\n    os.makedirs(output_dir+\"PSU_INFLUX_INSITU/\", exist_ok=True)\n    \n\n    df['datetime'] = df[[\"Year\",\"DOY\",\"Hour\"]].apply(convert_to_datetime, axis=1)\n    df.reset_index(drop=True, inplace=True)\n    for v in variables:\n        tmp_file=df[constant_variables + v].copy()\n        tmp_file['unit'] = v[0][-4:-1] #CO2(ppm) get  the unit only\n        \n        tmp_file.rename(columns={v[0]: 'value'}, inplace=True)\n        tmp_file['location']= add_location(metadata_link, site_number)\n        tmp_file = flag_desired_level(tmp_file, 1) # Flagging only level 1 data\n\n        # Remove nan\n        tmp_file.dropna(subset=[\"value\"], inplace=True)\n\n        #filter 0 values\n        tmp_file[tmp_file[\"value\"]!=0].to_csv(f\"{output_dir}/PSU_INFLUX_INSITU/NIST-FLUX-IN-{site_number}-{v[0][:-5]}-hourly-concentrations.csv\", index=False)\n        print(f\"CSV Created for Site {site_number}-{v[0][:-5]}!!!\")\n    return \n\n\n\n\n\n\n# Download and extract zip files\nlevels_to_download = range(1, 5)\n#download_and_extract_zip_files(base_dir=base_dir, levels=levels_to_download)\n\n# Create site dictionary\nsite_dict = create_site_dict(dat_file_pattern)\n\n# Comment if you want data from all levels\n#site_dict = filter_dict(site_dict, selected_level)\n\n# Process each site's files\nfor site_number, file_list in site_dict.items():\n    print(f\"Processing Site Number: {site_number}, Total Files: {len(file_list)}\")\n    process_site_files(site_number, file_list)\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Flow Diagrams"
+      "Data Transformation Notebooks",
+      "Greenhouse Gas Concentrations",
+      "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)"
     ]
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-    "title": "U.S. Greenhouse Gas Center Documentation",
+    "objectID": "cog_transformation/lpjwsl-wetlandch4-monthgrid-v1.html",
+    "href": "cog_transformation/lpjwsl-wetlandch4-monthgrid-v1.html",
+    "title": "Wetland Methane Emissions, LPJ-wsl Model",
     "section": "",
-    "text": "import xarray as xr\nimport os\nimport glob\nfrom  datetime import datetime\nimport boto3\nimport s3fs\nimport tempfile\nimport numpy as np\nimport pandas as pd\nimport re\nimport json\n\n\nraw_files = glob.glob(\"data/*.nc4\")\noutput_files= glob.glob(\"output_final2/*.tif\")\n\n\ndef extract_date_from_key(key):\n    # Split the key to isolate the part that contains the date\n    parts = key.split('_')\n    for part in parts:\n        # Check if the part is numeric and has the length of 6 (YYYYMM format)\n        if part.isdigit() and len(part) == 6:\n            return part\n    return None\n\n\noverall_raw= []\nraw= pd.DataFrame(columns=['filename','min_raw','max_raw','mean_raw','std_raw'])\nfor file in raw_files:\n    xds= xr.open_dataset(file)\n    year_month = extract_date_from_key(file)\n    for var in [\"PM25-PRI\",\"CO2\",\"CO\",\"NOX\",\"SOX\"]:\n        data = getattr(xds,var)\n        overall_raw.append(data)\n        data = np.ma.masked_where((data == -9999), data)\n        min_val = np.nanmin(data)\n        max_val = np.nanmax(data)\n        mean_val = np.nanmean(data)\n        std_val = np.nanstd(data)\n        stats = [f\"{var}_{year_month}\", min_val, max_val, mean_val, std_val]\n        raw.loc[len(raw)] = stats\n\n\noverall_cog=[]\ncog= pd.DataFrame(columns=['filename','min_cog','max_cog','mean_cog','std_cog'])\nfor file in output_files:\n    data= xr.open_dataarray(file)\n    \n    year_month = file[:-4][-6:]\n    var = file.split(\"_\")[-2]\n    overall_cog.append(data)\n    data = np.ma.masked_where((data == -9999), data)\n    \n    \n    min_val = np.nanmin(data)\n    max_val = np.nanmax(data)\n    mean_val = np.nanmean(data)\n    std_val = np.nanstd(data)\n    stats = [f\"{var}_{year_month}\", min_val, max_val, mean_val, std_val]\n    cog.loc[len(cog)] = stats\n\n\n# validation for reprojected data (non zero) - overall calculation\noverall_raw= np.array(overall_raw)\noverall_raw= np.ma.masked_where((overall_raw == -9999) , overall_raw)\nnan_min = np.nanmin(overall_raw)\nnan_max = np.nanmax(overall_raw)\nnan_mean = np.nanmean(overall_raw)\nnan_std = np.nanstd(overall_raw)\n[\"overall_raw\",nan_min,nan_max,nan_mean,nan_std]\n\n['overall_raw', 0.0, 110011.766, 5.1753755, 172.26357]\n\n\n\noverall_cog= np.array(overall_cog)\nnan_min = np.nanmin(overall_cog)\nnan_max = np.nanmax(overall_cog)\nnan_mean = np.nanmean(overall_cog)\nnan_std = np.nanstd(overall_cog)\n[\"overall_cog\",nan_min,nan_max,nan_mean,nan_std]\n\n['overall_cog', 0.0, 110011.766, 5.1753297, 172.27177]\n\n\n\npd.merge(cog, raw, on='filename', how='inner').to_json(\"monthly_stats.json\")\n\n\n\nkeys = [\"data\", \"nan_min\", \"nan_max\", \"nan_mean\", \"nan_std\"]\nvalues_set1 = [\"overall_raw\", 0.0, 110011.766, 5.1753297, 172.27177]\nvalues_set2 = [\"overall_cog\", 0.0, 110011.766, 5.1753297, 172.27177]\n\ndata_dict = {key: [val1, val2] for key, val1, val2 in zip(keys, values_set1, values_set2)}\n\n# Save the dictionary as a JSON file\nwith open(\"overall_stats.json\", \"w\") as json_file:\n    json.dump(data_dict, json_file, indent=4)\n\n\n\n\n Back to top"
+    "text": "This script was used to transform the Wetland Methane Emissions, LPJ-wsl Model dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"NASA_GSFC_ch4_wetlands_monthly\"\ndirectory = \"ch4_wetlands_monthly\"\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(directory):\n    xds = xarray.open_dataset(\n        f\"{directory}/{name}\", engine=\"netcdf4\", decode_times=False\n    )\n    xds = xds.assign_coords(longitude=(((xds.longitude + 180) % 360) - 180)).sortby(\n        \"longitude\"\n    )\n    variable = [var for var in xds.data_vars]\n    filename = name.split(\"/ \")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n\n    for time_increment in range(0, len(xds.time)):\n        for var in variable:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = getattr(xds.isel(time=time_increment), var)\n            data = data.isel(latitude=slice(None, None, -1))\n            data = data * 1000\n            data.rio.set_spatial_dims(\"longitude\", \"latitude\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            date = (\n                f\"0{int((data.time.item(0)/732)+1)}\"\n                if len(str(int((data.time.item(0) / 732) + 1))) == 1\n                else f\"{int((data.time.item(0)/732)+1)}\"\n            )\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = filename_elements[-1] + date\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{FOLDER_NAME}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{FOLDER_NAME}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{FOLDER_NAME}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top"
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     "section": "",
-    "text": "Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\nVisualize the time series data",
+    "text": "This script was used to transform the the Los Angeles Megacity Carbon Project dataset into meaningful csv files for ingestion to vector dataset.\n\nimport pandas as pd\nimport glob\nimport os\nimport warnings\nimport warnings \nwarnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n\n\n# download data from https://data.nist.gov/od/id/mds2-2388 into your desired_folder\nsource_dir = \"CA\"\n\n\n# Grouping the files for preparation\nconfig_ca = pd.read_csv(\"LAM_sites-2.csv\") #metadata from providers\nall_files= glob.glob(f\"{source_dir}/*.csv\")\nall_files = [i.split(\"/\")[-1].split('.')[0] for i in glob.glob(f\"{source_dir}/*.csv\") ]\nmy_dict={}\nfor site in list(config_ca.SiteCode):\n    # for each site and variable, append into the dict\n    if (config_ca[config_ca[\"SiteCode\"]==site][\"Tower\"].item()) ==1 :\n\n        co2_files = [f for f in all_files if site in f and \"upwind\" not in f and \"all\" not in f and \"co2\" in f]\n        my_dict[f\"{site}-co2\"] = co2_files\n        # Find the files that do not have \"upwind\" or \"all\" and have \"ch4\"\n        ch4_files = [f for f in all_files if site in f and \"upwind\" not in f and \"all\" not in f and \"ch4\" in f]\n        my_dict[f\"{site}-ch4\"] = ch4_files\n    else:\n        co2_upwind_files = [f for f in all_files if site in f and \"upwind\" in f and \"co2\" in f]\n        my_dict[f\"{site}-co2\"] = co2_upwind_files\n        \n        # Find the files that have \"upwind\" and \"ch4\"\n        ch4_upwind_files = [f for f in all_files if site in f and \"upwind\" in f and \"ch4\" in f]\n        my_dict[f\"{site}-ch4\"] = ch4_upwind_files\n\n        if site in [\"IRV\",\"RAN\"]:\n            co2_files = [f for f in all_files if site in f and \"all\" in f and \"co2\" in f]\n            my_dict[f\"{site}-co2\"] = co2_files\n            ch4_files = [f for f in all_files if site in f and \"all\" in f and \"ch4\" in f]\n            my_dict[f\"{site}-ch4\"] = ch4_files\n        \ndel my_dict['USC2-co2']\ndel my_dict['USC2-ch4']\n\nfor key in my_dict:\n    my_dict[key] = sorted(my_dict[key])\n\n\n# code to generate transformed data for CA\noutput_dir = \"output_LAM\"\nos.makedirs(output_dir,exist_ok=True)\nfor key, value in my_dict.items():\n    df=pd.DataFrame()\n    variable = key.split(\"-\")[-1]\n    val = f\"{variable}_ppm\" if variable == 'co2' else f\"{variable}_ppb\"\n    columns = [\"latitude\",\"longitude\",\"intake_height_m\",\"elevation_m\",\"datetime\",val ]\n    for file in value:\n        tmp = pd.read_csv(f\"CA/{file}.csv\")\n        tmp.dropna(subset=[val], inplace=True)\n        tmp.rename(columns={'datetime_UTC': 'datetime'}, inplace=True)\n        tmp= tmp[columns]\n        tmp.rename(columns={val: 'value'}, inplace=True)\n        tmp['datetime'] = pd.to_datetime(tmp['datetime'])\n        tmp['datetime'] = tmp['datetime'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n        tmp['location'] = config_ca[config_ca['SiteCode']==site][\"Location\"].item()\n        df = pd.concat([df, tmp], ignore_index=True)\n        \n    df['year']= df['datetime'].apply(lambda x: x[:4])\n    result = df.groupby(\"year\").agg(max_height= (\"intake_height_m\",\"max\"))\n    if result['max_height'].std() !=0:\n        print(f\"More than one max height for {file}\",result['max_height'].unique())\n    merged_df=pd.merge(df, result, on='year')\n    merged_df[\"is_max_height_data\"]= merged_df[\"max_height\"] == merged_df[\"intake_height_m\"]\n    merged_df=merged_df.drop(columns=['year','max_height'])\n    merged_df.reset_index(drop=True, inplace=True)\n    merged_df.to_csv(f\"{output_dir}/NIST-testbed-LAM-{key}-hourly-concentrations.csv\", index=False)\n    \n\n\n\n\n Back to top",
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-    "text": "Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\nVisualize the time series data",
+    "text": "Welcome to the U.S. Greenhouse Gas (GHG) Center data usage notebooks, your gateway to exploring and analyzing curated datasets on greenhouse gas emissions. Our cloud-based system offers seamless access to GHG curated datasets. Dive into the data with our data usage Jupyter notebooks, which demonstrate how to explore, access, visualize, and conduct basic data analysis for each GHG Center dataset in a code notebook environment. The data usage notebooks are grouped topically. Click on a notebook to learn more about the dataset and to view the data usage code.\nJoin us in our mission to make data-driven environmental solutions. Explore, analyze, and make a difference with the US GHG Center.\nView the US GHG Center Data Catalog",
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-    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
-    "section": "About the Data",
-    "text": "About the Data\nNIST is engaged in research to improve measurement of greenhouse gas emissions in areas containing multiple emission sources and sinks, such as cities. NIST’s objective is to develop measurement tools supporting independent means to increase the accuracy of greenhouse gas emissions data at urban and regional geospatial scales. NIST has established three test beds in U.S. cities to develop and evaluate the performance of advanced measurement capabilities for emissions independent of their origin. Located in Indianapolis, Indiana, the Los Angeles air basin of California, and the U.S. Northeast corridor (beginning with the Baltimore/Washington D.C. region), the test beds have been selected for their varying meteorology, terrain and emissions characteristics. These test beds will serve as a means to independently diagnose the accuracy of emissions data obtained directly from emission or uptake sources.\nFor more information regarding this dataset, please visit the Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed data overview page.",
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+    "title": "U.S. Greenhouse Gas Center: Data Usage Notebooks",
+    "section": "Gridded Anthropogenic Greenhouse Gas Emissions",
+    "text": "Gridded Anthropogenic Greenhouse Gas Emissions\n\nOCO-2 MIP Top-Down CO₂ Budgets\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the OCO-2 MIP Top-Down CO₂ Budgets dataset.\nIntermediate level notebook to read and visualize National CO₂ Budgets using OCO-2 MIP Top-Down CO₂ Budget country total data. This notebook utilizes the country totals available at https://ceos.org/gst/carbon-dioxide.html, which compliment the global 1° x 1° gridded CO₂ Budget data featured in the US GHG Center.\n\nODIAC Fossil Fuel CO₂ Emissions\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the ODIAC Fossil Fuel CO₂ Emissions dataset.\n\nTM5-4DVar Isotopic CH₄ Inverse Fluxes\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n\nU.S. Gridded Anthropogenic Methane Emissions Inventory\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset.\n\nVulcan Fossil Fuel CO₂ Emissions\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the Vulcan Fossil Fuel CO₂ Emissions, Version 4 dataset.\n\nGRA²PES Greenhouse Gas and Air Quality Species\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the GRA2PES, Version 1 dataset.",
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-    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
-    "section": "Querying the Feature Vector API",
-    "text": "Querying the Feature Vector API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Feature Vector Application Programming Interface (API) where the items for this collection are stored.\n\nFEATURE_API_URL=\"https://earth.gov/ghgcenter/api/features\"\n\n\n# Function to fetch CSV data for a station with a limit parameter\ndef get_station_data_csv(station_code, gas_type, frequency, elevation_m, limit=10000):\n    # Use the ?f=csv and limit query to get more rows\n    url = f\"https://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_nec_{station_code}_{gas_type}_{frequency}_concentrations/items?f=csv&elevation_m={elevation_m}&limit={limit}\"\n    print(url)\n    try:\n        response = requests.get(url)\n        print(response)\n        # Check if the response is successful\n        if response.status_code != 200:\n            print(f\"Failed to fetch data for {station_code}. Status code: {response.status_code}\")\n            return pd.DataFrame()\n\n        # Check if the content type is CSV\n        content_type = response.headers.get('Content-Type')\n        if 'text/csv' not in content_type:\n            print(f\"Unexpected content type for {station_code}: {content_type}\")\n            print(\"Response content:\", response.text)\n            return pd.DataFrame()\n\n        # Read the CSV content into a pandas DataFrame\n        csv_data = StringIO(response.text)\n        return pd.read_csv(csv_data)\n    \n    except requests.exceptions.RequestException as e:\n        print(f\"Request failed: {e}\")\n        return pd.DataFrame()",
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+    "section": "Natural Greenhouse Gas Emissions and Sinks",
+    "text": "Natural Greenhouse Gas Emissions and Sinks\n\nAir-Sea CO₂ Flux, ECCO-Darwin Model v5\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the Air-Sea CO₂ Flux, ECCO-Darwin Model v5 dataset.\n\nMiCASA Land Carbon Flux\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the MiCASA Land Carbon Flux dataset.\n\nGOSAT-based Top-down Total and Natural Methane Emissions\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the GOSAT-based Top-down Total and Natural Methane Emissions dataset.\n\nOCO-2 MIP Top-Down CO₂ Budgets\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the OCO-2 MIP Top-Down CO₂ Budgets dataset.\nIntermediate level notebook to read and visualizeNational CO₂ Budgets using OCO-2 MIP Top-Down CO₂ Budget country total data. This notebook utilizes the country totals available at ceos.org/gst/carbon-dioxide, which compliment the global 1° x 1° gridded CO₂ Budget data featured in the US GHG Center.\n\nTM5-4DVar Isotopic CH₄ Inverse Fluxes\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n\nWetland Methane Emissions, LPJ-EOSIM model\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the Wetland Methane Emissions, LPJ-EOSIM model dataset.",
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-    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
-    "section": "Visualizing the CH₄ data for two NEC stations",
-    "text": "Visualizing the CH₄ data for two NEC stations\n\n# Get station name and elevation from metdata dataframe\n# Fetch data for UNY (elevation 230) and TMD (elevation 489), using limit=10000\n# ch4/co2 select the ghg \nuny_data = get_station_data_csv('uny', 'ch4', 'hourly', 483, limit=10000)\ntmd_data = get_station_data_csv('tmd', 'ch4', 'hourly', 561, limit=10000)\n\n# Check if data was successfully retrieved before proceeding\nif uny_data.empty or tmd_data.empty:\n    print(\"No data available for one or both stations. Exiting.\")\nelse:\n    # Convert the 'datetime' column to datetime for plotting\n    uny_data['datetime'] = pd.to_datetime(uny_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n    tmd_data['datetime'] = pd.to_datetime(tmd_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n\n    # Plot the data\n    plt.figure(figsize=(10, 6))\n    plt.plot(uny_data['datetime'], uny_data['value'], label='UNY (230m)', color='blue', marker='o')\n    plt.plot(tmd_data['datetime'], tmd_data['value'], label='TMD (489m)', color='green', marker='o')\n\n    plt.title('Methane (CH₄) Hourly Concentrations Over Time for UNY and TMD Stations')\n    plt.xlabel('Time')\n    plt.ylabel('CH4 Concentration (ppb)')\n    plt.legend()\n    plt.grid(True)\n\n    # Show plot\n    plt.show()\n\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_nec_uny_ch4_hourly_concentrations/items?f=csv&elevation_m=483&limit=10000\n<Response [200]>\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_nec_tmd_ch4_hourly_concentrations/items?f=csv&elevation_m=561&limit=10000\n<Response [200]>",
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+    "section": "Large Emissions Events",
+    "text": "Large Emissions Events\n\nEMIT Methane Point Source Plume Complexes\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the EMIT Methane Point Source Plume Complexes dataset.",
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     "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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-    "text": "Approach\n\nIdentify 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.\nPass the STAC item into the raster API /stac/tilejson.jsonendpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon.",
+    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. Collection processed in this notebook is SEDAC gridded population density.\nPass the STAC item into raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint\nWe’ll visualize two tiles (side-by-side) allowing for comparison of each of the time points using folium.plugins.DualMap\nAfter the visualization, we’ll perform zonal statistics for a given polygon.",
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-    "text": "About the Data\nThe Greenhouse gas And Air Pollutants Emissions System (GRA2PES) dataset at the GHG Center is an aggregated, regridded, monthly high-resolution (0.036 x 0.036°) data product with emissions of both greenhouse gases and air pollutants developed in a consistent framework. The dataset contains emissions over the contiguous United States covering major anthropogenic sectors, including energy, industrial fuel combustion and processes, commercial and residential combustion, oil and gas production, on-road and off-road transportation, etc. (see Table 1 in the Scientific Details section below for a full sector list). Fossil fuel CO2 (ffCO2) emissions are developed along with those of air pollutants including CO, NOx, SOx, and PM2.5 with consistency in spatial and temporal distributions. Emissions by sectors are grouped into point and area sources, reported as column totals in units of metric tons per km2 per month. Spatial-temporal surrogates are developed to distribute CO2 emissions to grid cells to keep consistency between greenhouse gases and air quality species. The current version of GRA2PES is for 2021. Long-term emissions and more greenhouse gas species (e.g., methane) are under development and will be added in the future.\nFor more information regarding this dataset, please visit the GRA2PES Greenhouse Gas and Air Quality Species, Version 1 data overview page.",
+    "text": "About the Data\nThe SEDAC Gridded Population of the World: Population Density, v4.11 dataset provides annual estimates of population density for the years 2000, 2005, 2010, 2015, and 2020 on a 30 arc-second (~1 km) grid. These data can be used for assessing disaster impacts, risk mapping, and any other applications that include a human dimension. This population density dataset is provided by NASA’s Socioeconomic Data and Applications Center (SEDAC) hosted by the Center for International Earth Science Information Network (CIESIN) at Columbia University. The population estimates are provided as a continuous raster for the entire globe.\nFor more information regarding this dataset, please visit the SEDAC Gridded World Population Density data overview page.",
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     "section": "Querying the STAC API",
-    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# Please use the collection name similar to the one used in the STAC collection.\n# Name of the collection for Vulcan Fossil Fuel CO₂ Emissions, Version 4. \ncollection_name = \"gra2pes-ghg-monthgrid-v1\"\n\n\n# Fetch the collection from STAC collections using the appropriate endpoint\n# the 'requests' library allows a HTTP request possible\ncollection_graapes = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2010 to December 2021. By looking at the dashboard:time density, we observe that the data is periodic with year time density.\n\n# Create a function that would search for the above data collection in the STAC API\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Apply the above function and check the total number of items available within the collection\nnumber_of_items = get_item_count(collection_name)\nitems_graapes = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items_vulcan)} items\")\n\nFound 12 items\n\n\n\n# To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items_graapes} \n# rh = Heterotrophic Respiration\nasset_name = \"co2\"\n\n\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for 2021-01 and again for 2021-05, so that we can visualize each event independently.\n\ncolor_map = \"spectral_r\" # please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n\n# 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\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale=0,150\", \n).json()\n_202101_tile\n\n{'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 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-137.3143, 18.173376, -58.58229999999702, 52.229376000001295],\n 'center': [-97.94829999999851, 35.20137600000065, 0]}\n\n\n\n_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\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale=0,150\", \n).json()\n_202105_tile\n\n{'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 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-137.3143, 18.173376, -58.58229999999702, 52.229376000001295],\n 'center': [-97.94829999999851, 35.20137600000065, 0]}",
+    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for SEDAC population density dataset \ncollection_name = \"sedac-popdensity-yeargrid5yr-v4.11\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\nExamining the contents of our collection under summaries we see that the data is available from January 2000 to December 2020. By looking at the dashboard:time density we observe that the data is available for the years 2000, 2005, 2010, 2015, 2020.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]",
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-    "text": "Visualizing Total Fossil Fuel CO₂ Emissions\n\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n\n# Define the first map layer with the CO2 Flux data for December 2022\nmap_layer_202101 = TileLayer(\n    tiles=_202101_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution \n    name='2021-01 Total CO2 Fossil Fuel Emissions', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.8, # Adjust the transparency of the layer\n)\n# Add the first layer to the Dual Map \nmap_layer_202101.add_to(map_.m1)\n\nmap_layer_202105 = TileLayer(\n    tiles=_202105_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution \n    name='2021-05 Total CO2  Emissions', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.8, # Adjust the transparency of the layer\n)\n# Add the first layer to the Dual Map \nmap_layer_2021.add_to(map_.m2)\n\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "section": "Exploring Changes in the World Population Density using the Raster API",
+    "text": "Exploring Changes in the World Population Density using the Raster API\nWe will explore changes in population density in urban regions. In this notebook, we’ll explore the changes in population density over time. We’ll then visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items} \n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the SEDAC Gridded World Population Density collection, the parameter of interest is “population-density”\nasset_name = \"population-density\"\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetching the min and max values\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for January 2000 and again for January 2020, so that we can visualize each event independently.\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"rainbow\" \n\n# Make a GET request to retrieve information for the 2020 tile\njanuary_2020_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2020-01']['collection']}/items/{items['2020-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format \n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2020_tile\n\n\n# Make a GET request to retrieve information for the 2000 tile\njanuary_2000_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2000-01']['collection']}/items/{items['2000-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format  \n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2000_tile",
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+    "section": "Visualizing Population Density.",
+    "text": "Visualizing Population Density.\n\n# Set initial zoom and center of map for population density Layer\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer (January 2020)\nmap_layer_2020 = TileLayer(\n    tiles=january_2020_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=1, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_2020.add_to(map_.m1)\n\n# Define the second map layer (January 2000)\nmap_layer_2000 = TileLayer(\n    tiles=january_2000_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=1, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_2000.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_",
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+    "title": "SEDAC Gridded World Population Density",
+    "section": "Visualizing the Data as a Time Series",
+    "text": "Visualizing the Data as a Time Series\nWe can now explore the SEDAC population density dataset time series available for the Texas, Dallas area of USA. We can plot the dataset using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\nplt.plot(\n    df[\"date\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum pop density\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Population density over the years\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"Population density\")\n\n# Insert title for the plot\nplt.title(\"Population density over Texas, Dallas (2000-2020)\")\n\n###\n# Add data citation\nplt.text(\n    df[\"date\"].iloc[0],           # X-coordinate of the text\n    df[\"max\"].min(),              # Y-coordinate of the text\n\n\n\n\n    # Text to be displayed\n    \"Source: NASA SEDAC Gridded World Population Density\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"right\",             # Horizontal alignment\n    verticalalignment=\"bottom\",              # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n\n# Plot the time series\nplt.show()\n\n\n# Print the properties for the 3rd item in the collection\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n\n# A GET request is made for the 2010 tile\njanuary2010_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary2010_tile\n\n\n# Create a new map to display the 2010 tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=january2010_tile[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.5\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox",
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     "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully explored, analyzed, and visualized the STAC collection for GRA2PES greenhouse gases Emissions, Version 1 dataset.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the total CO₂ emissions for two distinctive months/years\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
+    "text": "Summary\nIn this notebook we have successfully explored, analyzed and visualized the STAC collection for the SEDAC Gridded World Population Density dataset.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare population density for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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     "text": "Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\nVisualize the time series data",
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     "text": "Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\nVisualize the time series data",
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-    "text": "About the Data\nNIST is engaged in research to improve measurement of greenhouse gas emissions in areas containing multiple emission sources and sinks, such as cities. NIST’s objective is to develop measurement tools supporting independent means to increase the accuracy of greenhouse gas emissions data at urban and regional geospatial scales. NIST has established three test beds in U.S. cities to develop and evaluate the performance of advanced measurement capabilities for emissions independent of their origin. Located in Indianapolis, Indiana, the Los Angeles air basin of California, and the U.S. Northeast corridor (beginning with the Baltimore/Washington D.C. region), the test beds have been selected for their varying meteorology, terrain and emissions characteristics. These test beds will serve as a means to independently diagnose the accuracy of emissions data obtained directly from emission or uptake sources.\nFor more information regarding this dataset, please visit the Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) data overview page.",
+    "text": "About the Data\nNIST is engaged in research to improve measurement of greenhouse gas emissions in areas containing multiple emission sources and sinks, such as ciies. NIST’s objective is to develop measurement tools supporting independent means to increase the accuracy of greenhouse gas emissions data at urban and regional geospatial scales. NIST has established three test beds in U.S. ciies to develop and evaluate the performance of advanced measurement capabilities for emissions independent of their origin. Located in Indianapolis, Indiana, the Los Angeles air basin of California, and the U.S. Northeast corridor (beginning with the Baltimore/Washington D.C. region), the test beds have been selected for their varying meteorology, terrain and emissions characteristics. These test beds will serve as a means to independently diagnose the accuracy of emissions data obtained directly from emission or uptake sources.\nFor more information regarding this dataset, please visit the Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project data overview page.",
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     "section": "Querying the Feature Vector API",
-    "text": "Querying the Feature Vector API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Feature Vector Application Programming Interface (API) where the items for this collection are stored.\n\nFEATURE_API_URL=\"https://earth.gov/ghgcenter/api/features\"\n\n\n# Function to fetch CSV data for a station with a limit parameter\ndef get_station_data_csv(station_code, gas_type, frequency, elevation_m, limit=100000):\n    # Use the ?f=csv and limit query to get more rows\n    url = f\"https://earth.gov/ghgcenter/api/features/collections/public.nist_flux_in_{station_code}_{gas_type}_{frequency}_concentrations/items?f=csv&elevation_m={elevation_m}&limit={limit}\"\n    print(url)\n    try:\n        response = requests.get(url)\n        \n        # Check if the response is successful\n        if response.status_code != 200:\n            print(f\"Failed to fetch data for {station_code}. Status code: {response.status_code}\")\n            return pd.DataFrame()\n\n        # Check if the content type is CSV\n        content_type = response.headers.get('Content-Type')\n        if 'text/csv' not in content_type:\n            print(f\"Unexpected content type for {station_code}: {content_type}\")\n            print(\"Response content:\", response.text)\n            return pd.DataFrame()\n\n        # Read the CSV content into a pandas DataFrame\n        csv_data = StringIO(response.text)\n        return pd.read_csv(csv_data)\n    \n    except requests.exceptions.RequestException as e:\n        print(f\"Request failed: {e}\")\n        return pd.DataFrame()",
+    "text": "Querying the Feature Vector API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Feature Vector Application Programming Interface (API) where the items for this collection are stored.\n\nFEATURE_API_URL=\"https://earth.gov/ghgcenter/api/features\"\n\n\n# Function to fetch CSV data for a station with a limit parameter\ndef get_station_data_csv(station_code, gas_type, frequency, elevation_m, limit=100000):\n    # Use the ?f=csv and limit query to get more rows\n    url = f\"https://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_lam_{station_code}_{gas_type}_{frequency}_concentrations/items?f=csv&elevation_m={elevation_m}&limit={limit}\"\n    print(url)\n    try:\n        response = requests.get(url)\n        \n        # Check if the response is successful\n        if response.status_code != 200:\n            print(f\"Failed to fetch data for {station_code}. Status code: {response.status_code}\")\n            return pd.DataFrame()\n\n        # Check if the content type is CSV\n        content_type = response.headers.get('Content-Type')\n        if 'text/csv' not in content_type:\n            print(f\"Unexpected content type for {station_code}: {content_type}\")\n            print(\"Response content:\", response.text)\n            return pd.DataFrame()\n\n        # Read the CSV content into a pandas DataFrame\n        csv_data = StringIO(response.text)\n        return pd.read_csv(csv_data)\n    \n    except requests.exceptions.RequestException as e:\n        print(f\"Request failed: {e}\")\n        return pd.DataFrame()",
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-    "section": "Visualizing the CH₄ data for two NEC stations",
-    "text": "Visualizing the CH₄ data for two NEC stations\n\n# Get station name and elevation from metdata dataframe\n# Fetch data for site01 (elevation 256) and site09 (elevation 277), using limit=10000\n# ch4/co2 select the ghg \n\nsite01_data = get_station_data_csv('site01', 'ch4', 'hourly', 256,limit=10000)\nsite09_data = get_station_data_csv('site09', 'ch4', 'hourly', 277,limit=10000)\n\n# Check if data was successfully retrieved before proceeding\nif site01_data.empty or site09_data.empty:\n    print(\"No data available for one or both stations. Exiting.\")\nelse:\n    # Convert the 'datetime' column to datetime for plotting\n    site01_data['datetime'] = pd.to_datetime(site01_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n    site09_data['datetime'] = pd.to_datetime(site09_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n\n    # Plot the data\n    plt.figure(figsize=(10, 6))\n    plt.plot(site01_data['datetime'], site01_data['value'], label='site01 (256m)', color='blue', marker='o')\n    plt.plot(site09_data['datetime'], site09_data['value'], label='site09 (277m)', color='green', marker='o')\n\n    plt.title('Methane (CH₄) Hourly Concentrations Over Time for site01 and site09 Stations')\n    plt.xlabel('Time')\n    plt.ylabel('CH₄ Concentration (ppb)')\n    plt.legend()\n    plt.grid(True)\n\n    # Show plot\n    plt.show()\n\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_flux_in_site01_ch4_hourly_concentrations/items?f=csv&elevation_m=256&limit=10000\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_flux_in_site09_ch4_hourly_concentrations/items?f=csv&elevation_m=277&limit=10000",
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+    "section": "Visualizing the CO₂ data for two NEC stations",
+    "text": "Visualizing the CO₂ data for two NEC stations\n\n# Get station name and elevation from metdata dataframe\n# Fetch data for SCI (elevation 489) and COM (elevation 9), using limit=10000\n# ch4/co2 select the ghg \nsci_data = get_station_data_csv('sci', 'co2', 'hourly', 489, limit=10000)\ncom_data = get_station_data_csv('com', 'co2', 'hourly', 9, limit=10000)\n\n# Check if data was successfully retrieved before proceeding\nif sci_data.empty or com_data.empty:\n    print(\"No data available for one or both stations. Exiting.\")\nelse:\n    # Convert the 'datetime' column to datetime for plotting\n    sci_data['datetime'] = pd.to_datetime(sci_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n    com_data['datetime'] = pd.to_datetime(com_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n\n    # Plot the data\n    plt.figure(figsize=(10, 6))\n    plt.plot(sci_data['datetime'], sci_data['value'], label='SCI (489m)', color='blue', marker='o')\n    plt.plot(com_data['datetime'], com_data['value'], label='COM (9m)', color='green', marker='o')\n\n    plt.title('Carbon Dioxide (CO₂) Hourly Concentrations Over Time for SCI and COM Stations')\n    plt.xlabel('Time')\n    plt.ylabel('CO₂ Concentration (ppm)')\n    plt.legend()\n    plt.grid(True)\n\n    # Show plot\n    plt.show()\n\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_lam_sci_co2_hourly_concentrations/items?f=csv&elevation_m=489&limit=10000\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_lam_com_co2_hourly_concentrations/items?f=csv&elevation_m=9&limit=10000",
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     "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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-    "text": "Approach\n\nIdentify 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 Wetland Methane Emissions, LPJ-EOSIM Model data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison.\nAfter the visualization, perform zonal statistics for a given polygon.",
+    "text": "Approach\n\nIdentify 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 TM5-4DVar Isotopic CH₄ Inverse Fluxes Data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.jsonendpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon.",
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-    "text": "About the Data\nMethane (CH₄) emissions from vegetated wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions have thus far been underrepresented. Using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) global CH₄ emissions from wetlands are estimated at 0.5° x 0.5 degree spatial resolution. By simulating wetland extent and using characteristics of inundated areas, such as wetland soil moisture, temperature, and carbon content, the model provides estimates of CH₄ quantities emitted into the atmosphere. This dataset shows concentrated methane sources from tropical and high latitude ecosystems. The LPJ-EOSIM Wetland Methane Emissions dataset consists of global daily model estimates of terrestrial wetland methane emissions from 1990 to the present, with data added bimonthly. The estimates are regularly used in conjunction with NASA’s Goddard Earth Observing System (GEOS) model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, to compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions.\nFor more information regarding this dataset, please visit the U.S. Greenhouse Gas Center.",
+    "text": "About the Data\nSurface methane (CH₄) emissions are derived from atmospheric measurements of methane and its ¹³C carbon isotope content. Different sources of methane contain different ratios of the two stable isotopologues, ¹²CH₄ and ¹³CH₄. This makes normally indistinguishable collocated sources of methane, say from agriculture and oil and gas exploration, distinguishable. The National Oceanic and Atmospheric Administration (NOAA) collects whole air samples from its global cooperative network of flasks (https://gml.noaa.gov/ccgg/about.html), which are then analyzed for methane and other trace gasses. A subset of those flasks are also analyzed for ¹³C of methane in collaboration with the Institute of Arctic and Alpine Research at the University of Colorado Boulder. Scientists at the National Aeronautics and Space Administration (NASA) and NOAA used those measurements of methane and ¹³C of methane in conjunction with a model of atmospheric circulation to estimate emissions of methane separated by three source types, microbial, fossil and pyrogenic.\nFor more information regarding this dataset, please visit the TM5-4DVar Isotopic CH₄ Inverse Fluxes data overview page.",
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       "Data Usage Notebooks",
-      "Natural Greenhouse Gas Sources Emissions and Sinks",
-      "Wetland Methane Emissions, LPJ-EOSIM Model"
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "TM5-4DVar Isotopic CH₄ Inverse Fluxes"
     ]
   },
   {
-    "objectID": "user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.html#query-the-stac-api",
-    "href": "user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.html#query-the-stac-api",
-    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
-    "section": "Query the STAC API",
-    "text": "Query the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for the wetland methane emissions LPJ-EOSIM Model\ncollection_name = \"lpjeosim-wetlandch4-daygrid-v1\"\n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\nasset_name = \"ensemble-mean-ch4-wetlands-emissions\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'lpjeosim-wetlandch4-daygrid-v2',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2/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/lpjeosim-wetlandch4-daygrid-v2'}],\n 'title': '(Daily) Wetland Methane Emissions, LPJ-EOSIM Model v2',\n 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n  'temporal': {'interval': [['1990-01-01 00:00:00+00',\n     '2024-05-31 00:00:00+00']]}},\n 'license': 'CC0-1.0',\n 'renders': {'dashboard': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'era5-ch4-wetlands-emissions': {'assets': ['era5-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'merra2-ch4-wetlands-emissions': {'assets': ['merra2-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'ensemble-mean-ch4-wetlands-emissions': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'}},\n 'providers': [{'name': 'NASA'}],\n 'summaries': {'datetime': ['1990-01-01T00:00:00Z', '2024-05-31T00:00:00Z']},\n 'description': 'Global, daily estimates of methane (CH₄) emissions from terrestrial wetlands at 0.5 x 0.5 degree spatial resolution using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Methane emissions from vegetated wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions have thus far been underrepresented. The LPJ-EOSIM model estimates wetland methane emissions by simulating wetland extent and using characteristics of these inundated areas such as soil moisture, temperature, and carbon content to estimate CH₄ quantities emitted into the atmosphere. Input climate forcing data comes from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data and ECMWF Re-Analysis data (ERA5). An ensemble layer provides the result of the mean of the MERRA-2 and ERA5 layers. The source data can be found at https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E.001 and https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E_LL.001.',\n 'item_assets': {'era5-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.'},\n  'merra2-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.'},\n  'ensemble-mean-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.'}},\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': 'day'}\n\n\nExamining the contents of our collection under summaries, we see that the data is available from January 1990 to December 2024. By looking at dashboard: time density, we can see that these observations are collected monthly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\n\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n        # temp = items_url.split('/')\n        # temp.insert(3, 'ghgcenter')\n        # temp.insert(4, 'api')\n        # temp.insert(5, 'stac')\n        # items_url = '/'.join(temp)\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit=800\"\n).json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 800 items\n\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'lpjeosim-wetlandch4-daygrid-v2-20240531',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531/map?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'era5-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_DCH4E_LL.001/LPJ_EOSIM_L2_DCH4E_LL_001_20240531/LPJ_EOSIM_L2_DCH4E_LL_ERA5_001_20240531.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.1533866629018803e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [60696, 1228, 228, 119, 78, 51, 35, 24, 2, 2]},\n     'statistics': {'mean': 3.818678323370309e-11,\n      'stddev': 1.385319732851768e-10,\n      'maximum': 3.1533866629018803e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09837962962963}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'merra2-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_DCH4E_LL.001/LPJ_EOSIM_L2_DCH4E_LL_001_20240531/LPJ_EOSIM_L2_DCH4E_LL_MERRA2_001_20240531.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 5.284403581384822e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [61618, 503, 152, 101, 53, 21, 5, 6, 0, 1]},\n     'statistics': {'mean': 4.2160033887186084e-11,\n      'stddev': 1.6741675825683113e-10,\n      'maximum': 5.284403581384822e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09722222222222}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'ensemble-mean-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_DCH4E_LL.001/LPJ_EOSIM_L2_DCH4E_LL_001_20240531/LPJ_EOSIM_L2_DCH4E_LL_ensemble_mean_001_20240531.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.8867296048294975e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [61178, 819, 185, 124, 78, 46, 22, 7, 0, 1]},\n     'statistics': {'mean': 4.0174325630166816e-11,\n      'stddev': 1.493077090568075e-10,\n      'maximum': 3.8867296048294975e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09722222222222}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531/preview.png?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'lpjeosim-wetlandch4-daygrid-v2',\n 'properties': {'datetime': '2024-05-31T00:00:00+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']}\n\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetch the minimum and maximum values for rescaling\nrescale_values = {'max': 0.0003, 'min': 0.0}",
+    "objectID": "user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.html#querying-the-stac-api",
+    "href": "user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.html#querying-the-stac-api",
+    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
+    "section": "Querying the STAC API",
+    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for TM5 CH₄ inverse flux dataset \ncollection_name = \"tm54dvar-ch4flux-monthgrid-v1\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'tm54dvar-ch4flux-monthgrid-v1',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/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/tm54dvar-ch4flux-monthgrid-v1'}],\n 'title': 'TM5-4DVar Isotopic CH4 Inverse Fluxes',\n 'assets': None,\n 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n  'temporal': {'interval': [['1999-01-01T00:00:00+00:00',\n     '2016-12-31T00:00:00+00:00']]}},\n 'license': 'CC-BY-4.0',\n 'keywords': None,\n 'providers': None,\n 'summaries': {'datetime': ['1999-01-01T00:00:00Z', '2016-12-31T00:00:00Z']},\n 'description': 'Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 1.',\n 'item_assets': {'total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Total CH4 Emission',\n   'description': 'Total methane emission from microbial, fossil and pyrogenic sources'},\n  'fossil': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil CH4 Emission',\n   'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.'},\n  'microbial': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Microbial CH4 Emission',\n   'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.'},\n  'pyrogenic': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Pyrogenic CH4 Emission',\n   'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.'}},\n 'stac_version': '1.0.0',\n 'stac_extensions': None,\n 'dashboard:is_periodic': True,\n 'dashboard:time_density': 'month'}\n\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 1999 to December 2016. By looking at the dashboard:time density, we observe that the data is periodic with monthly time density.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 216 items\n\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'tm54dvar-ch4flux-monthgrid-v1-201612',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612'}],\n 'assets': {'total': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_total_201612.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Total CH4 Emission',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'description': 'Total methane emission from microbial, fossil and pyrogenic sources',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 207.09559432166358,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64446.0, 253.0, 61.0, 16.0, 14.0, 4.0, 3.0, 0.0, 2.0, 1.0]},\n     'statistics': {'mean': 0.7699816366032659,\n      'stddev': 3.8996905358416045,\n      'maximum': 207.09559432166358,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n  'fossil': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_fossil_201612.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil CH4 Emission',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 202.8189294183266,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64633.0, 107.0, 35.0, 11.0, 8.0, 3.0, 1.0, 1.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.27127687553584495,\n      'stddev': 2.731411670166909,\n      'maximum': 202.8189294183266,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n  'microbial': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_microbial_201612.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Microbial CH4 Emission',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 161.4604621003495,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64610.0, 155.0, 22.0, 5.0, 2.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.46611433673211145,\n      'stddev': 2.2910210071489456,\n      'maximum': 161.4604621003495,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n  'pyrogenic': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_pyrogenic_201612.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Pyrogenic CH4 Emission',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 13.432528617097262,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64440.0, 221.0, 78.0, 24.0, 18.0, 8.0, 3.0, 1.0, 1.0, 6.0]},\n     'statistics': {'mean': 0.032590424335309266,\n      'stddev': 0.28279054181617735,\n      'maximum': 13.432528617097262,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'tm54dvar-ch4flux-monthgrid-v1',\n 'properties': {'end_datetime': '2016-12-31T00:00:00+00:00',\n  'start_datetime': '2016-12-01T00:00:00+00:00'},\n 'stac_version': '1.0.0',\n 'stac_extensions': []}",
     "crumbs": [
       "Data Usage Notebooks",
-      "Natural Greenhouse Gas Sources Emissions and Sinks",
-      "Wetland Methane Emissions, LPJ-EOSIM Model"
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "TM5-4DVar Isotopic CH₄ Inverse Fluxes"
     ]
   },
   {
-    "objectID": "user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.html#explore-changes-in-methane-ch4-emission-levels-using-the-raster-api",
-    "href": "user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.html#explore-changes-in-methane-ch4-emission-levels-using-the-raster-api",
-    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
-    "section": "Explore Changes in Methane (CH4) Emission Levels Using the Raster API",
-    "text": "Explore Changes in Methane (CH4) Emission Levels Using the Raster API\nIn this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"datetime\"][:10]: item for item in items} \n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for date 1 mentioned in the next cell and again for date 2, so we can visualize each event independently.\n\n# Choose a color for displaying the tiles\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"magma\" \n\n# Make a GET request to retrieve information for the date mentioned below\ndate1 = '2024-01-01'\ndate1_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year, month and date of the observed parameter, you can modify the date2 variable above\n    f\"{RASTER_API_URL}/collections/{items[date1]['collection']}/items/{items[date1]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\ndate1_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for date mentioned below\ndate2 = '2024-01-30'\ndate2_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year, month and date of the observed parameter, you can modify the date2 variable above\n    f\"{RASTER_API_URL}/collections/{items[date2]['collection']}/items/{items[date2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format \n).json()\n\n# Print the properties of the retrieved granule to the console\ndate2_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}",
+    "objectID": "user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.html#exploring-changes-in-ch₄-flux-levels-using-the-raster-api",
+    "href": "user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.html#exploring-changes-in-ch₄-flux-levels-using-the-raster-api",
+    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
+    "section": "Exploring Changes in CH₄ flux Levels Using the Raster API",
+    "text": "Exploring Changes in CH₄ flux Levels Using the Raster API\nIn this notebook, we will explore the global changes of CH₄ flux over time in urban regions. We will visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:10]: item for item in items} \n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the TM5-4DVar Isotopic CH₄ Inverse Fluxes collection, the parameter of interest is “fossil”\nasset_name = \"fossil\" #fossil fuel\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for 2016 and again for 1999, so that we can visualize each event independently.\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"purd\"\n\n# Make a GET request to retrieve information for the 2016 tile\nch4_flux_1 = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2016-12-01']['collection']}/items/{items['2016-12-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nch4_flux_1\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for the 1999 tile\nch4_flux_2 = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['1999-12-01']['collection']}/items/{items['1999-12-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nch4_flux_2\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-199912/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}",
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-    "section": "Visualize CH₄ Emissions",
-    "text": "Visualize CH₄ Emissions\n\n# For this study we are going to compare the CH₄ Emissions in date1 and date2 along the coast of California\n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n\n# Set initial zoom and center of map\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer for tile fetched for date 1\n# The TileLayer library helps in manipulating and displaying raster layers on a map\nmap_layer_date1 = TileLayer(\n    tiles=date1_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.5, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_date1.add_to(map_.m1)\n\n\n# Define the second map layer for the tile fetched for date 2\nmap_layer_date2 = TileLayer(\n    tiles=date2_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.5, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_date2.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
+    "section": "Visualizing CH₄ flux Emissions from Fossil Fuel",
+    "text": "Visualizing CH₄ flux Emissions from Fossil Fuel\n\n# For this study we are going to compare CH4 fluxes from fossil fuels in 2016 and 1999 along the coast of California\n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n\n# Set the initial zoom level and center of map for both tiles\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer (2016)\nmap_layer_2016 = TileLayer(\n    tiles=ch4_flux_1[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n# Add the first layer to the Dual Map\nmap_layer_2016.add_to(map_.m1)\n\n\n# Define the second map layer (1999)\nmap_layer_1999 = TileLayer(\n    tiles=ch4_flux_2[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_1999.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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-    "section": "Visualize the Data as a Time Series",
-    "text": "Visualize the Data as a Time Series\nWe can now explore the wetland methane emissions time series (January 1990 – December 2024) available for the Texas area of the U.S. We can plot the data set using the code below:\n\n# Determine the width and height of the plot using the 'matplotlib' library\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\n# Plot the time series\nplt.plot(\n    df[\"date\"], # X-axis: date\n    df[\"max\"], # Y-axis: CH₄ value\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Max daily CH₄ emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"Daily CH4 emissions g/m2\")\n\n# Insert title for the plot\nplt.title(\"Daily CH4 emission Values for Texas, January 2022- March 2024\")\n\nText(0.5, 1.0, 'Daily CH4 emission Values for Texas, January 2022- March 2024')\n\n\n\n\n\n\n\n\n\nTo take a closer look at the CH4 variability across this region, we are going to retrieve and display data collected during the February, 2024 observation.\n\n# The 2024-02-25 observation is the 3rd item in the list\n# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n# Print the start Date Time of the third granule in the collection\nprint(items[2][\"properties\"][\"datetime\"])\n\n2024-05-29T00:00:00+00:00\n\n\n\n# A GET request is made for the 3rd item in the collection\nobserved_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nobserved_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the CH4 variability for the Texas region for Observed tile timeframe\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n    tiles=observed_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", opacity = 0.5 # Set the attribution and transparency\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
+    "section": "Visualizing the Data as a Time Series",
+    "text": "Visualizing the Data as a Time Series\nWe can now explore the fossil fuel emission time series (January 1999 -December 2016) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\nplt.plot(\n    df[\"datetime\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum CH4 flux\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"CH4 emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"g CH₄/m²/year\")\nplt.xticks(rotation = 90)\n\n# Insert title for the plot\nplt.title(\"CH4 emission Values for Texas, Dallas (1999-2016)\")\n\n# Add data citation\nplt.text(\n    df[\"datetime\"].iloc[0],           # X-coordinate of the text\n    df[\"max\"].min(),                  # Y-coordinate of the text\n\n\n\n\n    # Text to be displayed\n    \"Source: NASA/NOAA TM5-4DVar Isotopic CH₄ Inverse Fluxes\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"left\",              # Horizontal alignment\n    verticalalignment=\"top\",                 # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n\n# Plot the time series\nplt.show()\n\n\n\n\n\n\n\n\n\n# Print the properties for the 3rd item in the collection\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n2016-10-01T00:00:00+00:00\n\n\n\n# A GET request is made for the 3rd granule\nch4_flux_3 = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nch4_flux_3\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201610/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=6.8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=ch4_flux_3[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.7\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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       "Data Usage Notebooks",
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+    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
     "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for the Daily Wetland Methane Emissions, LPJ-EOSIM Model data: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map and compare the CH4 levels over the Texas region for two distinctive years 5. Create a table that displays the minimum, maximum, and sum of the CH4 levels for a specified region 6. Generate a time-series graph of the CH4 levels for a specified region\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
+    "text": "Summary\nIn this notebook we have successfully explored, analyzed, and visualized the STAC collection for TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the CH₄ inverse fluxes for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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     "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
+    "text": "Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\nVisualize the time series data",
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     "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
+    "text": "Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\nVisualize the time series data",
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+    "title": "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)",
+    "section": "About the Data",
+    "text": "About the Data\nNIST is engaged in research to improve measurement of greenhouse gas emissions in areas containing multiple emission sources and sinks, such as cities. NIST’s objective is to develop measurement tools supporting independent means to increase the accuracy of greenhouse gas emissions data at urban and regional geospatial scales. NIST has established three test beds in U.S. cities to develop and evaluate the performance of advanced measurement capabilities for emissions independent of their origin. Located in Indianapolis, Indiana, the Los Angeles air basin of California, and the U.S. Northeast corridor (beginning with the Baltimore/Washington D.C. region), the test beds have been selected for their varying meteorology, terrain and emissions characteristics. These test beds will serve as a means to independently diagnose the accuracy of emissions data obtained directly from emission or uptake sources.\nFor more information regarding this dataset, please visit the Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) data overview page.",
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+    "title": "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)",
+    "section": "Querying the Feature Vector API",
+    "text": "Querying the Feature Vector API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Feature Vector Application Programming Interface (API) where the items for this collection are stored.\n\nFEATURE_API_URL=\"https://earth.gov/ghgcenter/api/features\"\n\n\n# Function to fetch CSV data for a station with a limit parameter\ndef get_station_data_csv(station_code, gas_type, frequency, elevation_m, limit=100000):\n    # Use the ?f=csv and limit query to get more rows\n    url = f\"https://earth.gov/ghgcenter/api/features/collections/public.nist_flux_in_{station_code}_{gas_type}_{frequency}_concentrations/items?f=csv&elevation_m={elevation_m}&limit={limit}\"\n    print(url)\n    try:\n        response = requests.get(url)\n        \n        # Check if the response is successful\n        if response.status_code != 200:\n            print(f\"Failed to fetch data for {station_code}. Status code: {response.status_code}\")\n            return pd.DataFrame()\n\n        # Check if the content type is CSV\n        content_type = response.headers.get('Content-Type')\n        if 'text/csv' not in content_type:\n            print(f\"Unexpected content type for {station_code}: {content_type}\")\n            print(\"Response content:\", response.text)\n            return pd.DataFrame()\n\n        # Read the CSV content into a pandas DataFrame\n        csv_data = StringIO(response.text)\n        return pd.read_csv(csv_data)\n    \n    except requests.exceptions.RequestException as e:\n        print(f\"Request failed: {e}\")\n        return pd.DataFrame()",
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+    "href": "user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook.html#visualizing-the-ch₄-data-for-two-nec-stations",
+    "title": "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)",
+    "section": "Visualizing the CH₄ data for two NEC stations",
+    "text": "Visualizing the CH₄ data for two NEC stations\n\n# Get station name and elevation from metdata dataframe\n# Fetch data for site01 (elevation 256) and site09 (elevation 277), using limit=10000\n# ch4/co2 select the ghg \n\nsite01_data = get_station_data_csv('site01', 'ch4', 'hourly', 256,limit=10000)\nsite09_data = get_station_data_csv('site09', 'ch4', 'hourly', 277,limit=10000)\n\n# Check if data was successfully retrieved before proceeding\nif site01_data.empty or site09_data.empty:\n    print(\"No data available for one or both stations. Exiting.\")\nelse:\n    # Convert the 'datetime' column to datetime for plotting\n    site01_data['datetime'] = pd.to_datetime(site01_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n    site09_data['datetime'] = pd.to_datetime(site09_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n\n    # Plot the data\n    plt.figure(figsize=(10, 6))\n    plt.plot(site01_data['datetime'], site01_data['value'], label='site01 (256m)', color='blue', marker='o')\n    plt.plot(site09_data['datetime'], site09_data['value'], label='site09 (277m)', color='green', marker='o')\n\n    plt.title('Methane (CH₄) Hourly Concentrations Over Time for site01 and site09 Stations')\n    plt.xlabel('Time')\n    plt.ylabel('CH₄ Concentration (ppb)')\n    plt.legend()\n    plt.grid(True)\n\n    # Show plot\n    plt.show()\n\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_flux_in_site01_ch4_hourly_concentrations/items?f=csv&elevation_m=256&limit=10000\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_flux_in_site09_ch4_hourly_concentrations/items?f=csv&elevation_m=277&limit=10000",
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+      "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)"
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+    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
+    "section": "",
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
+    "crumbs": [
+      "Data Usage Notebooks",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
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+    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
+    "section": "",
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
+    "crumbs": [
+      "Data Usage Notebooks",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
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+    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
     "section": "Approach",
-    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for a given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the Land-Atmosphere Carbon Flux data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison.\nAfter the visualization, perform zonal statistics for a given polygon."
+    "text": "Approach\n\nIdentify 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 gridded methane emissions data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.jsonendpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon.",
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-    "title": "CASA-GFED3 Land Carbon Flux",
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+    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
     "section": "About the Data",
-    "text": "About the Data\nThis dataset presents a variety of carbon flux parameters derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA-GFED3) model. The model’s input data includes air temperature, precipitation, incident solar radiation, a soil classification map, and a number of satellite derived products. All model calculations are driven by analyzed meteorological data from NASA’s Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting product provides monthly, global data at 0.5 degree resolution from January 2003 through December 2017. It includes the following carbon flux variables expressed in units of kilograms of carbon per square meter per month (kg Carbon m²/mon) from the following sources: net primary production (NPP), net ecosystem exchange (NEE), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL). This product and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in a number of atmospheric CO₂ transport studies, and through the support of NASA’s Carbon Monitoring System (CMS), it helps characterize, quantify, understand and predict the evolution of global carbon sources and sinks."
+    "text": "About the Data\nThe NASA Carbon Monitoring System Flux (CMS-Flux) team analyzed remote sensing observations from Japan’s Greenhouse gases Observing SATellite (GOSAT) to produce the global Committee on Earth Observation Satellites (CEOS) CH₄ Emissions data product. They used an analytic Bayesian inversion approach and the GEOS-Chem global chemistry transport model to quantify annual methane (CH₄) emissions and their uncertainties at a spatial resolution of 1° by 1° and then projected these to each country for 2019.\nFor more information regarding this dataset, please visit the GOSAT-based Top-down Total and Natural Methane Emissions data overview page.",
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+    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
     "section": "Querying the STAC API",
-    "text": "Querying the STAC API\nPlease run the next cell to import the required libraries.\n\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer \nfrom pystac_client import Client \nimport branca \nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# Please use the collection name similar to the one used in the STAC collection.\n# Name of the collection for CASA GFED Land-Atmosphere Carbon Flux monthly emissions. \ncollection_name = \"casagfed-carbonflux-monthgrid-v3\"\n\n\n# Fetch the collection from STAC collections using the appropriate endpoint\n# the 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\ncollection\n\n{'id': 'casagfed-carbonflux-monthgrid-v3',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3/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/casagfed-carbonflux-monthgrid-v3'}],\n 'title': 'CASA-GFED3 Land Carbon Flux v3',\n 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n  'temporal': {'interval': [['2003-01-01T00:00:00+00:00',\n     '2017-12-31T00:00:00+00:00']]}},\n 'license': 'CC0-1.0',\n 'renders': {'rh': {'assets': ['rh'],\n   'rescale': [[0, 0.3]],\n   'colormap_name': 'purd'},\n  'nee': {'assets': ['nee'],\n   'rescale': [[-0.1, 0.1]],\n   'colormap_name': 'coolwarm'},\n  'npp': {'assets': ['npp'], 'rescale': [[0, 0.3]], 'colormap_name': 'purd'},\n  'fire': {'assets': ['fire'], 'rescale': [[0, 0.3]], 'colormap_name': 'purd'},\n  'fuel': {'assets': ['fuel'],\n   'rescale': [[0, 0.03]],\n   'colormap_name': 'bupu'}},\n 'summaries': {'datetime': ['2003-01-01T00:00:00Z', '2017-12-31T00:00:00Z']},\n 'description': \"This dataset presents a variety of carbon flux parameters derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA-GFED3) model. All model calculations are driven by analyzed meteorological data from NASA's Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting model output provides monthly, global data at 0.5 degree resolution from January 2003 through December 2017. It includes the following carbon flux variables expressed in units of kilograms of carbon per square meter per month (kg Carbon/m2/mon): net primary production (NPP), net ecosystem exchange (NEE), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL). This product and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in a number of atmospheric carbon dioxide (CO₂) transport studies, and through the support of NASA's Carbon Monitoring System (CMS), it helps characterize, quantify, understand and predict the evolution of global carbon sources and sinks. The source dataset can be found at https://doi.org/10.5067/03147VMJE8J9. As of April 2024, this dataset has been replaced by an updated version in the US GHG Center titled MiCASA Land Carbon Flux v1 (STAC ids: micasa-carbonflux-daygrid-v1 and micasa-carbonflux-monthgrid-v1).\",\n 'item_assets': {'rh': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Heterotrophic Respiration (Rh)',\n   'description': 'Model-estimated heterotrophic respiration (Rh), which is the flux of carbon from the soil to the atmosphere.'},\n  'nee': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Net Ecosystem Exchange (NEE)',\n   'description': 'Model-estimated net ecosystem exchange (NEE), which is the net carbon flux to the atmosphere.'},\n  'npp': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Net Primary Production (NPP)',\n   'description': 'Model-estimated net primary production (NPP), which is the amount of carbon available from plants.'},\n  'fire': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fire Emissions (FIRE)',\n   'description': 'Model-estimated flux of carbon to the atmosphere from wildfires.'},\n  'fuel': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Wood Fuel Emissions (FUEL)',\n   'description': 'Model-estimated flux of carbon to the atmosphere from wood burned for fuel.'}},\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': 'month'}\n\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2003 to December 2017. By looking at the dashboard:time density, we observe that the periodic frequency of these observations is monthly.\n\n# Create a function that would search for the above data collection in the STAC API\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Apply the above function and check the total number of items available within the collection\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\nFound 180 items\n\n\n\n# Examine the first item in the collection\nitems[0]\n\n{'id': 'casagfed-carbonflux-monthgrid-v3-201712',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712'}],\n 'assets': {'rh': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_Rh_Flux_Monthly_x720_y360_201712.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Heterotrophic Respiration (Rh)',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [360.0, 720.0],\n   'description': 'Model-estimated heterotrophic respiration (Rh), which is the flux of carbon from the soil to the atmosphere.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.6039900183677673,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [249101.0,\n       7375.0,\n       2429.0,\n       252.0,\n       32.0,\n       5.0,\n       2.0,\n       2.0,\n       0.0,\n       2.0]},\n     'statistics': {'mean': 0.006758838426321745,\n      'stddev': 0.022668374702334404,\n      'maximum': 0.6039900183677673,\n      'minimum': 0.0,\n      'valid_percent': 0.0003858024691358025}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'nee': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NEE_Flux_Monthly_x720_y360_201712.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Net Ecosystem Exchange (NEE)',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [360.0, 720.0],\n   'description': 'Model-estimated net ecosystem exchange (NEE), which is the net carbon flux to the atmosphere.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.48997998237609863,\n      'min': -0.11027999967336655,\n      'count': 11.0,\n      'buckets': [663.0,\n       234393.0,\n       23809.0,\n       282.0,\n       37.0,\n       10.0,\n       4.0,\n       0.0,\n       0.0,\n       2.0]},\n     'statistics': {'mean': 0.0015448036137968302,\n      'stddev': 0.00977976992726326,\n      'maximum': 0.48997998237609863,\n      'minimum': -0.11027999967336655,\n      'valid_percent': 0.0003858024691358025}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'npp': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NPP_Flux_Monthly_x720_y360_201712.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'npp',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [360.0, 720.0],\n   'description': 'Model-estimated net primary production (NPP), which is the amount of carbon available from plants.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.23635999858379364,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [244636.0,\n       3051.0,\n       1928.0,\n       2634.0,\n       4088.0,\n       2211.0,\n       428.0,\n       156.0,\n       59.0,\n       9.0]},\n     'statistics': {'mean': 0.005214035045355558,\n      'stddev': 0.021809572353959084,\n      'maximum': 0.23635999858379364,\n      'minimum': 0.0,\n      'valid_percent': 0.0003858024691358025}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'fire': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_FIRE_Flux_Monthly_x720_y360_201712.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Fire Emissions (FIRE)',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [360.0, 720.0],\n   'description': 'Fire emissions',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.7556899785995483,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [258952.0, 161.0, 53.0, 22.0, 11.0, 0.0, 0.0, 0.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.00025634843041189015,\n      'stddev': 0.005492232274264097,\n      'maximum': 0.7556899785995483,\n      'minimum': 0.0,\n      'valid_percent': 0.0003858024691358025}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'fuel': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_FUEL_Flux_Monthly_x720_y360_201712.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Wood Fuel Emissions (FUEL)',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [360.0, 720.0],\n   'description': 'Fuel emissions',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.020759999752044678,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [257568.0,\n       1150.0,\n       284.0,\n       115.0,\n       47.0,\n       21.0,\n       5.0,\n       6.0,\n       3.0,\n       1.0]},\n     'statistics': {'mean': 5.057307134848088e-05,\n      'stddev': 0.0003876804548781365,\n      'maximum': 0.020759999752044678,\n      'minimum': 0.0,\n      'valid_percent': 0.0003858024691358025}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'casagfed-carbonflux-monthgrid-v3',\n 'properties': {'end_datetime': '2017-12-31T00:00:00+00:00',\n  'start_datetime': '2017-12-01T00:00:00+00:00'},\n 'stac_version': '1.0.0',\n 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n  'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}"
-  },
-  {
-    "objectID": "user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.html#exploring-changes-in-carbon-flux-levels-using-the-raster-api",
-    "href": "user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.html#exploring-changes-in-carbon-flux-levels-using-the-raster-api",
-    "title": "CASA-GFED3 Land Carbon Flux",
-    "section": "Exploring Changes in Carbon Flux Levels Using the Raster API",
-    "text": "Exploring Changes in Carbon Flux Levels Using the Raster API\nWe will explore changes in the land atmosphere Carbon flux Heterotrophic Respiration and examine their impacts over time. We’ll then visualize the outputs on a map using folium.\n\n# To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items} \n# rh = Heterotrophic Respiration\nasset_name = \"rh\"\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.\n\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for December 2003 and again for December 2017, so that we can visualize each event independently.\n\ncolor_map = \"purd\" # please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n\n# 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']\" \ndecember_2003_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items['2003-12']['collection']}/items/{items['2003-12']['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\ndecember_2003_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Now we apply the same process used in the previous step for the December 2017 tile\ndecember_2017_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items['2017-12']['collection']}/items/{items['2017-12']['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\ndecember_2017_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}"
+    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# Please use the collection name similar to the one used in STAC collection.\n\n# Name of the collection for gosat budget methane. \ncollection_name = \"gosat-based-ch4budget-yeargrid-v1\"\n\n\n# Fetching the collection from STAC collections using appropriate endpoint.\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\ncollection\n\n{'id': 'gosat-based-ch4budget-yeargrid-v1',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/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/gosat-based-ch4budget-yeargrid-v1'}],\n 'title': 'GOSAT-based Top-down Total and Natural Methane Emissions v1',\n 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n  'temporal': {'interval': [['2019-01-01T00:00:00+00:00',\n     '2019-12-31T00:00:00+00:00']]}},\n 'license': 'CC-BY-4.0',\n 'renders': {'dashboard': {'assets': ['post-total'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.3]],\n   'colormap_name': 'spectral_r'},\n  'post-total': {'assets': ['post-total'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.3]],\n   'colormap_name': 'spectral_r'},\n  'prior-total': {'assets': ['prior-total'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.3]],\n   'colormap_name': 'spectral_r'},\n  'post-wetland': {'assets': ['post-wetland'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.1]],\n   'colormap_name': 'spectral_r'},\n  'prior-wetland': {'assets': ['prior-wetland'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.1]],\n   'colormap_name': 'spectral_r'},\n  'post-wetland-uncertainty': {'assets': ['post-wetland-uncertainty'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.05]],\n   'colormap_name': 'purd'},\n  'prior-wetland-uncertainty': {'assets': ['prior-wetland-uncertainty'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.05]],\n   'colormap_name': 'purd'}},\n 'summaries': {'datetime': ['2019-01-01T00:00:00Z']},\n 'description': \"As part of the global stock take (GST), countries are asked to provide a record of their greenhouse gas (GHG) emissions to inform decisions on how to reduce GHG emissions. The NASA Carbon Monitoring System Flux (CMS-Flux) team has used remote sensing observations from Japan's Greenhouse gases Observing SATellite (GOSAT) to produce modeled total methane (CH₄) emissions and uncertainties on a 1 degree by 1 degree resolution grid for the year 2019. The GOSAT data is used in the model to inform total emission estimates, as well as wetland (the primary natural source of methane), and various human-related sources such as fossil fuel extraction, transport, agriculture, waste, and fires. A prior GHG emission estimate (and assocated uncertainty) is provided for each layer, which is the emissions estimate without GOSAT data. The posterior GHG emission layers are informed by GOSAT total column methane data. An advanced mathematical approach is used with a global chemistry transport model to quantify annual CH₄ emissions and uncertainties. These estimates are expressed in teragrams of CH₄ per year (Tg/yr). The source data can be found at https://doi.org/10.5281/zenodo.8306874 and more information can also be found on the CEOS website https://ceos.org/gst/methane.html\",\n 'item_assets': {'post-total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Posterior Total Methane Emissions',\n   'description': 'Estimated total methane emissions per grid cell informed by GOSAT satellite total column methane data.'},\n  'prior-total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Prior Total Methane Emissions',\n   'description': 'Total methane emissions per grid cell estimated by various inventories or models, excluding satellite based observations from GOSAT.'},\n  'post-wetland': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Wetland Posterior Methane Emissions',\n   'description': 'Estimated methane emissions per grid cell from wetlands informed by GOSAT satellite total column methane data.'},\n  'prior-wetland': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Wetland Prior Methane Emissions',\n   'description': 'Methane emissions per grid cell from wetlands estimated by various inventories or models, excluding satellite based observations from GOSAT.'},\n  'post-wetland-uncertainty': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Uncertainty - Wetland Posterior Methane Emissions',\n   'description': 'Uncertainty in estimated methane emissions per grid cell from wetlands informed by GOSAT satellite total column methane data.'},\n  'prior-wetland-uncertainty': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Uncertainty - Wetland Prior Methane Emissions',\n   'description': 'Uncertainty in methane emissions per grid cell from wetlands estimated by various inventories or models, excluding satellite based observations from GOSAT.'}},\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': False,\n 'dashboard:time_density': 'year'}\n\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2012 to December 2018. By looking at the dashboard:time density, we observe that the data is available for only one year, i.e. 2019.\n\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Check total number of items available\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\nFound 1 items\n\n\n\n# Examining the first item in the 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'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'prior-livestock-uncertainty': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_prior_unc_livestock_GEOS_CHEM_2019.tif',\n   'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.021834801882505417,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64219.0,\n       326.0,\n       127.0,\n       34.0,\n       19.0,\n       25.0,\n       25.0,\n       17.0,\n       5.0,\n       3.0]},\n     'statistics': {'mean': 7.657577225472778e-05,\n      'stddev': 0.0006582040223293006,\n      'maximum': 0.021834801882505417,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/preview.png?assets=post-total&nodata=9.96921e%2B36&rescale=0%2C0.3&colormap_name=spectral_r',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180.5, -90.5],\n    [179.5, -90.5],\n    [179.5, 89.5],\n    [-180.5, 89.5],\n    [-180.5, -90.5]]]},\n 'collection': 'gosat-based-ch4budget-yeargrid-v1',\n 'properties': {'end_datetime': '2019-12-31T00:00:00+00:00',\n  'start_datetime': '2019-01-01T00:00:00+00:00'},\n 'stac_version': '1.0.0',\n 'stac_extensions': []}\n\n\nBelow, we enter minimum and maximum values to provide our upper and lower bounds in rescale_values.",
+    "crumbs": [
+      "Data Usage Notebooks",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
+      "GOSAT-based Top-down Total and Natural Methane Emissions"
+    ]
   },
   {
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-    "title": "CASA-GFED3 Land Carbon Flux",
-    "section": "Visualizing Land-Atmosphere Carbon Flux (Heterotrophic Respiration)",
-    "text": "Visualizing Land-Atmosphere Carbon Flux (Heterotrophic Respiration)\n\n# For this study we are going to compare the RH level in 2003 and 2017 over the State of Texas \n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n# For example, you can change the current statement \"location=(31.9, -99.9)\" to \"location=(34, -118)\" to monitor the RH level in California instead of Texas\n\n# Set initial zoom and center of map for CO₂ Layer\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(31.9, -99.9), zoom_start=6)\n\n# The TileLayer library helps in manipulating and displaying raster layers on a map\n# December 2003\nmap_layer_2003 = TileLayer(\n    tiles=december_2003_tile[\"tiles\"][0],\n    attr=\"GHG\",\n    opacity=0.8,\n    name=\"December 2003 RH Level\",\n    overlay= True,\n    legendEnabled = True\n)\nmap_layer_2003.add_to(map_.m1)\n\n\n# December 2017\nmap_layer_2017 = TileLayer(\n    tiles=december_2017_tile[\"tiles\"][0],\n    attr=\"GHG\",\n    opacity=0.8,\n    name=\"December 2017 RH Level\",\n    overlay= True,\n    legendEnabled = True\n)\nmap_layer_2017.add_to(map_.m2)\n\n\n# Display data markers (titles) on both maps\nfolium.Marker((40, 5.0), tooltip=\"both\").add_to(map_)\nfolium.LayerControl(collapsed=False).add_to(map_)\n\n\n# Add a legend to the dual map using the 'branca' library. \n# Note: the inserted legend is representing the minimum and maximum values for both tiles.\ncolormap = branca.colormap.linear.PuRd_09.scale(0, 0.3) # minimum value = 0, maximum value = 0.3 (kg Carbon/m2/month)\ncolormap = colormap.to_step(index=[0, 0.07, 0.15, 0.22, 0.3])\ncolormap.caption = 'Rh Values (kg Carbon/m2/month)'\n\ncolormap.add_to(map_.m1)\n\n\n# Visualizing the map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
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+    "href": "user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.html#exploring-changes-in-gosat-methane-budgets-ch4-levels-using-the-raster-api",
+    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
+    "section": "Exploring Changes in GOSAT Methane budgets (CH4) Levels Using the Raster API",
+    "text": "Exploring Changes in GOSAT Methane budgets (CH4) Levels Using the Raster API\nIn this notebook, we will explore the impacts of methane emissions and by examining changes over time in urban regions. We will visualize the outputs on a map using folium.\n\n# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:10]: item for item in items} \nasset_name = \"prior-total\"\n\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\n\nitems.keys()\n\ndict_keys(['2019-01-01'])\n\n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this for first January 2019.\n\ncolor_map = \"rainbow\" # please select the color ramp from matplotlib library.\njanuary_2019_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items['2019-01-01']['collection']}/items/{items['2019-01-01']['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\njanuary_2019_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=prior-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=0.0%2C2.121816635131836'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.5, -90.5, 179.5, 89.5],\n 'center': [-0.5, -0.5, 0]}",
+    "crumbs": [
+      "Data Usage Notebooks",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
+      "GOSAT-based Top-down Total and Natural Methane Emissions"
+    ]
   },
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-    "title": "CASA-GFED3 Land Carbon Flux",
-    "section": "Visualizing the Data as a Time Series",
-    "text": "Visualizing the Data as a Time Series\nWe can now explore the Heterotrophic Respiration time series (January 2003 -December 2017) available for the Dallas, Texas area. We can plot the data set using the code below:\n\nfig = plt.figure(figsize=(20, 10)) #determine the width and height of the plot using the 'matplotlib' library\n\nplt.plot(\n    df[\"date\"],\n    df[\"max\"],\n    color=\"purple\",\n    linestyle=\"-\",\n    linewidth=0.5,\n    label=\"Max monthly Carbon emissions\",\n)\n\nplt.legend()\nplt.xlabel(\"Years\")\nplt.ylabel(\"kg Carbon/m2/month\")\nplt.title(\"Heterotrophic Respiration Values for Dallas, Texas (2003-2017)\")\n\nText(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (2003-2017)')\n\n\n\n\n\n\n\n\n\n\n# Now let's examine the Rh level for the 3rd item in the collection for Dallas, Texas area\n# Keep in mind that a list starts from 0, 1, 2,... therefore items[2] is referring to the third item in the list/collection\nprint(items[2][\"properties\"][\"start_datetime\"]) #print the start Date Time of the third granule in the collection!\n\n2017-10-01T00:00:00+00:00\n\n\n\n# Fetch the third granule in the collection and set the color scheme and rescale values. \noctober_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\",\n).json()\noctober_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Map the Rh level for the Dallas, Texas area for the October, 2017 timeframe\naoi_map_bbox = Map(\n    tiles=\"OpenStreetMap\",\n    location=[\n        32.8, # latitude\n        -96.79, # longitude\n    ],\n    zoom_start=9,\n)\n\nmap_layer = TileLayer(\n    tiles=october_tile[\"tiles\"][0],\n    attr=\"GHG\", opacity = 0.7, name=\"October 2017 RH Level\", overlay= True, legendEnabled = True\n)\n\nmap_layer.add_to(aoi_map_bbox)\n\n# Display data marker (title) on the map\nfolium.Marker((40, 5.9), tooltip=\"both\").add_to(aoi_map_bbox)\nfolium.LayerControl(collapsed=False).add_to(aoi_map_bbox)\n\n# Add a legend\ncolormap = branca.colormap.linear.PuRd_09.scale(0, 0.3) # minimum value = 0, maximum value = 0.3 (kg Carbon/m2/month)\ncolormap = colormap.to_step(index=[0, 0.07, 0.15, 0.22, 0.3])\ncolormap.caption = 'Rh Values (kg Carbon/m2/month)'\n\ncolormap.add_to(aoi_map_bbox)\n\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
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+    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
+    "section": "Visualizing CH₄ Emissions",
+    "text": "Visualizing CH₄ Emissions\n\n# Set initial zoom and center of map for CH₄ Layer\n# Centre of map [latitude,longitude]\nmap_ = folium.Map(location=(34, -118), zoom_start=6)\n\n# January 2019\nmap_layer_2019 = TileLayer(\n    tiles=january_2019_tile[\"tiles\"][0],\n    attr=\"GHG\",\n    opacity=0.7,\n)\nmap_layer_2019.add_to(map_)\n\n# # January 2012\n# map_layer_2012 = TileLayer(\n#     tiles=january_2012_tile[\"tiles\"][0],\n#     attr=\"GHG\",\n#     opacity=0.7,\n# )\n# map_layer_2012.add_to(map_.m2)\n\n# visualising the map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
     "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for CASA GFED Land-Atmosphere Carbon Flux data: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map and compare the Heterotrophic Respiration (Rh) levels over the Dallas, Texas area for two distinctive years 5. Create a table that displays the minimum, maximum, and sum of the Rh values for a specified region 6. Generate a time-series graph of the Rh values for a specified region\nIf you have any questions regarding this user notebook, please contact us using the feedback form."
+    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for the GOSAT-based Top-down Total and Natural Methane Emissions dataset.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap the methane emission levels\nGenerate zonal statistics for the area of interest (AOI)\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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+    "title": "MiCASA Land Carbon Flux",
     "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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+    "title": "MiCASA Land Carbon Flux",
     "section": "Approach",
-    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. Collection processed in this notebook is ODIAC CO₂ emissions version 2022.\nPass the STAC item into raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint\nWe’ll visualize two tiles (side-by-side) allowing for comparison of each of the time points using folium.plugins.DualMap\nAfter the visualization, we’ll perform zonal statistics for a given polygon."
+    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for a given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the Land-Atmosphere Carbon Flux data product\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint\nUsing folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison\nAfter the visualization, perform zonal statistics for a given polygon",
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+    "title": "MiCASA Land Carbon Flux",
     "section": "About the Data",
-    "text": "About the Data\nThe Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) is a high-spatial resolution global emission data product of CO₂ emissions from fossil fuel combustion (Oda and Maksyutov, 2011). ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO₂ emissions. With the innovative emission modeling approach, ODIAC achieved the fine picture of global fossil fuel CO₂ emissions at a 1x1km.\nFor more information regarding this dataset, please visit the ODIAC Fossil Fuel CO₂ Emissions data overview page."
+    "text": "About the Data\nThis dataset presents a variety of carbon flux parameters derived from the Más Informada Carnegie-Ames-Stanford-Approach (MiCASA) model. The model’s input data includes air temperature, precipitation, incident solar radiation, a soil classification map, and several satellite derived products. All model calculations are driven by analyzed meteorological data from NASA’s Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting product provides global, daily data at 0.1 degree resolution from January 2001 through December 2023. It includes carbon flux variables expressed in units of kilograms of carbon per square meter per day (kg Carbon/m²/day) from net primary production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), fuel wood burning emissions (FUEL), net ecosystem exchange (NEE), and net biosphere exchange (NBE). The latter two are derived from the first four (see Scientific Details below). MiCASA is an extensive revision of the CASA – Global Fire Emissions Database, version 3 (CASA-GFED3) product. CASA-GFED3 and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in several atmospheric carbon dioxide (CO₂) transport studies, serve as a community standard for priors of flux inversion systems, and through the support of NASA’s Carbon Monitoring System (CMS), help characterize, quantify, understand and predict the evolution of global carbon sources and sinks.\nFor more information regarding this dataset, please visit the U.S. Greenhouse Gas Center.",
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-    "title": "ODIAC Fossil Fuel CO₂ Emissions",
-    "section": "Querying the STAC API",
-    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for ODIAC dataset \ncollection_name = \"odiac-ffco2-monthgrid-v2022\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'odiac-ffco2-monthgrid-v2022',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022/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/odiac-ffco2-monthgrid-v2022'}],\n 'title': 'ODIAC Fossil Fuel CO₂ Emissions v2022',\n 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n  'temporal': {'interval': [['2000-01-01T00:00:00+00:00',\n     '2021-12-31T00:00:00+00:00']]}},\n 'license': 'CC-BY-4.0',\n 'renders': {'dashboard': {'assets': ['co2-emissions'],\n   'nodata': 0,\n   'rescale': [[-10, 60]],\n   'colormap_name': 'jet'},\n  'co2-emissions': {'assets': ['co2-emissions'],\n   'nodata': 0,\n   'rescale': [[-10, 60]],\n   'colormap_name': 'jet'}},\n 'providers': [{'url': 'https://www.nies.go.jp',\n   'name': 'National Institute for Environmental Studies',\n   'roles': ['producer', 'licensor']}],\n 'summaries': {'datetime': ['2000-01-01T00:00:00Z', '2021-12-31T00:00:00Z']},\n 'description': 'The Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) data product is a monthly high-resolution global data product of modeled fossil fuel carbon dioxide (CO₂) emissions. A complex model incorporates and combines space-based nighttime light data and individual power plant emission/location profiles from the latest country fossil fuel CO₂ estimates (2000-2019) made by the Carbon Dioxide Information Analysis Center (CDIAC) team at the Appalachian State University (CDIAC at AppState, Gilfillan et al. 2021, Hefner et al. 2022). The ODIAC estimated global spatial extent of fossil fuel CO₂ emissions is produced on a 1 km by 1 km grid that details variations in urban regions where emissions are most intense. The ODIAC CO₂ emission data is widely used by the international research community for applications such as CO₂ flux inversion, urban emission estimation, and observing system design experiments. The ODIAC product was first created in 2009 by Dr. Tomohiro Oda with support from the National Institute for Environmental Studies (NIES) GOSAT project. The ODIAC team is now supported by NASA Goddard Space Flight Center, NASA Carbon Monitoring System program, the NASA Orbiting Carbon Observatory mission and NIES. The US GHG Center displays the ODIAC 2022 version containing monthly data from January 2000 to December 2021 that replaces all previous versions. The source dataset can be found at https://doi.org/10.17595/20170411.001',\n 'item_assets': {'co2-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil Fuel CO₂ Emissions',\n   'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.'}},\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': 'month'}\n\n\nExamining the contents of our collection under summaries we see that the data is available from January 2000 to December 2021. By looking at the dashboard:time density we observe that the periodic frequency of these observations is monthly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 264 items\n\n\nThis makes sense as there are 22 years between 2000 - 2021, with 12 months per year, meaning 264 records in total.\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'odiac-ffco2-monthgrid-v2022-202112',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112/map?assets=co2-emissions&nodata=0&rescale=-10%2C60&colormap_name=jet',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'co2-emissions': {'href': 's3://ghgc-data-store/odiac-ffco2-monthgrid-v2022/odiac2022_1km_excl_intl_202112.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil Fuel CO₂ Emissions',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [21600.0, 43200.0],\n   'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 2497.01904296875,\n      'min': -138.71914672851562,\n      'count': 11.0,\n      'buckets': [523457.0, 691.0, 95.0, 28.0, 11.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.9804128408432007,\n      'stddev': 14.766693454324674,\n      'maximum': 2497.01904296875,\n      'minimum': -138.71914672851562,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.008333333333333333,\n    0.0,\n    -180.0,\n    0.0,\n    -0.008333333333333333,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112/preview.png?assets=co2-emissions&nodata=0&rescale=-10%2C60&colormap_name=jet',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'odiac-ffco2-monthgrid-v2022',\n 'properties': {'end_datetime': '2021-12-31T00:00:00+00:00',\n  'start_datetime': '2021-12-01T00:00:00+00:00'},\n 'stac_version': '1.0.0',\n 'stac_extensions': []}"
+    "objectID": "user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.html#query-the-stac-api",
+    "href": "user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.html#query-the-stac-api",
+    "title": "MiCASA Land Carbon Flux",
+    "section": "Query the STAC API",
+    "text": "Query the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for MiCASA Land Carbon Flux\ncollection_name = \"micasa-carbonflux-daygrid-v1\"\n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the MiCASA Land Carbon Flux collection, the parameter of interest is “rh”\n# rh = Heterotrophic Respiration\nasset_name = \"rh\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'micasa-carbonflux-daygrid-v1',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1/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/micasa-carbonflux-daygrid-v1'}],\n 'title': '(Daily) MiCASA Land Carbon Flux v1',\n 'extent': {'spatial': {'bbox': [[-180, -90, 179.99999999999994, 90]]},\n  'temporal': {'interval': [['2001-01-01 00:00:00+00',\n     '2023-12-31 00:00:00+00']]}},\n 'license': 'CC0-1.0',\n 'renders': {'rh': {'assets': ['rh'],\n   'rescale': [[0, 8]],\n   'colormap_name': 'purd'},\n  'nbe': {'assets': ['nbe'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n  'nee': {'assets': ['nee'],\n   'rescale': [[-4, 4]],\n   'colormap_name': 'coolwarm'},\n  'npp': {'assets': ['npp'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n  'atmc': {'assets': ['atmc'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n  'fire': {'assets': ['fire'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n  'fuel': {'assets': ['fuel'], 'rescale': [[0, 0.5]], 'colormap_name': 'purd'},\n  'dashboard': {'assets': ['npp'],\n   'rescale': [[0, 8]],\n   'colormap_name': 'purd'}},\n 'providers': [{'name': 'NASA'}],\n 'summaries': {'datetime': ['2001-01-01T00:00:00Z', '2023-12-31T00:00:00Z']},\n 'description': \"This product provides estimated daily carbon flux to the atmosphere from net primary production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), fuel wood burning emissions (FUEL), net ecosystem exchange (NEE), and net biosphere exchange (NBE) derived from the Más Informada Carnegie-Ames-Stanford-Approach (MiCASA) model. All model calculations are driven by analyzed meteorological data from NASA's Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting product provides global, daily data at 0.1 degree resolution starting from January 2001. The carbon flux variables are expressed in units of kilograms of carbon per square meter per day. MiCASA is an extensive revision of the CASA – Global Fire Emissions Database, version 3 (CASA-GFED3) product. CASA-GFED3 and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in several atmospheric carbon dioxide (CO₂) transport studies, serve as a community standard for priors of flux inversion systems, and through the support of NASA's Carbon Monitoring System (CMS), help characterize, quantify, understand and predict the evolution of global carbon sources and sinks.\",\n 'item_assets': {'rh': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Heterotrophic respiration (Rh), MiCASA Model v1',\n   'description': 'Heterotrophic respiration (carbon flux from the soil to the atmosphere) in units of grams of carbon per square meter per day.'},\n  'nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Net Biosphere Exchange (net carbon flux from the ecosystem), MiCASA Model v1',\n   'description': 'Net Biosphere Exchange (net carbon flux from the ecosystem) in units of grams of carbon per square meter per day.'},\n  'nee': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Net Ecosystem Exchange (NEE), MiCASA Model v1',\n   'description': 'Net Ecosystem Exchange (net carbon flux to the atmosphere) in units of grams of carbon per square meter per day.'},\n  'npp': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Net Primary Production (NPP), MiCASA Model v1',\n   'description': 'Net Primary Production (carbon available from plants) in units of grams of carbon per square meter per day.'},\n  'atmc': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Atmospheric Correction (ATMC), MiCASA Model v1',\n   'description': 'A correction to account for long-term historical changes in the uptake of CO₂ from the atmosphere to the biosphere in units of grams of carbon per square meter per day.'},\n  'fire': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fire emissions (FIRE), MiCASA Model v1',\n   'description': 'Fire emissions (flux of carbon to the atmosphere from wildfires) in units of grams of carbon per square meter per day.'},\n  'fuel': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Wood fuel emissions (FUEL), MiCASA Model v1',\n   'description': 'Wood fuel emissions (flux of carbon to the atmosphere from wood burned for fuel) in units of grams of carbon per square meter per day.'}},\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': 'day'}\n\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2003 to December 2017. By looking at the dashboard:time density, we observe that the periodic frequency of these observations is monthly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n   \n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest (MiCASA Land Carbon Flux) in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n       \n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection (MiCASA Land Carbon Flux) in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n       \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n        # temp = items_url.split('/')\n        # temp.insert(3, 'ghgcenter')\n        # temp.insert(4, 'api')\n        # temp.insert(5, 'stac')\n        # items_url = '/'.join(temp)\n\n    # Return the information about the total number of granules found associated with the collection (MiCASA Land Carbon Flux)\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit=800\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 800 items\n\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'micasa-carbonflux-daygrid-v1-20231231',\n 'bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20231231'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20231231/map?assets=npp&rescale=0%2C8&colormap_name=purd',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'rh': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_Rh_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Heterotrophic respiration (Rh), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Heterotrophic respiration (carbon flux from the soil to the atmosphere) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 7.2141876220703125,\n      'min': -0.35656991600990295,\n      'count': 11,\n      'buckets': [457947,\n       35642,\n       10548,\n       6299,\n       4848,\n       3079,\n       3268,\n       2051,\n       543,\n       63]},\n     'statistics': {'mean': 0.22414785623550415,\n      'stddev': 0.7404906300847516,\n      'maximum': 7.2141876220703125,\n      'minimum': -0.35656991600990295,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'nbe': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_NBE_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Net Biosphere Exchange (NBE), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Net Biosphere Exchange (net carbon flux from the ecosystem) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 4.327333927154541,\n      'min': -3.1603169441223145,\n      'count': 11,\n      'buckets': [374, 1873, 3385, 8057, 481500, 26054, 2405, 543, 93, 4]},\n     'statistics': {'mean': 0.057421840727329254,\n      'stddev': 0.31542102161091723,\n      'maximum': 4.327333927154541,\n      'minimum': -3.1603169441223145,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'nee': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_NEE_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Net Ecosystem Exchange (NEE), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Net Ecosystem Exchange (net carbon flux to the atmosphere) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.59893536567688,\n      'min': -3.1675710678100586,\n      'count': 11,\n      'buckets': [313, 1496, 2644, 4869, 453998, 47409, 12055, 1106, 340, 58]},\n     'statistics': {'mean': 0.05558537319302559,\n      'stddev': 0.3139139632688899,\n      'maximum': 3.59893536567688,\n      'minimum': -3.1675710678100586,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'npp': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_NPP_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Net Primary Production (NPP), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Net Primary Production (carbon available from plants) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 6.099153518676758,\n      'min': -0.40653496980667114,\n      'count': 11,\n      'buckets': [487523,\n       10036,\n       4672,\n       4058,\n       3733,\n       4110,\n       4707,\n       4222,\n       1165,\n       62]},\n     'statistics': {'mean': 0.16241030395030975,\n      'stddev': 0.7038688126658349,\n      'maximum': 6.099153518676758,\n      'minimum': -0.40653496980667114,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'atmc': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_ATMC_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Atmospheric Correction (ATMC), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'A correction to account for long-term historical changes in the uptake of CO₂ from the atmosphere to the biosphere in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.5609952807426453,\n      'min': -0.02930086851119995,\n      'count': 11,\n      'buckets': [496261, 19256, 4265, 1897, 915, 859, 549, 216, 64, 6]},\n     'statistics': {'mean': 0.006152182351797819,\n      'stddev': 0.02767997686822744,\n      'maximum': 0.5609952807426453,\n      'minimum': -0.02930086851119995,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'fire': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_FIRE_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Fire emissions (FIRE), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Fire emissions (flux of carbon to the atmosphere from wildfires) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 4.872155666351318,\n      'min': -0.25238683819770813,\n      'count': 11,\n      'buckets': [524150, 104, 24, 5, 3, 1, 0, 0, 0, 1]},\n     'statistics': {'mean': 0.00028691982151940465,\n      'stddev': 0.014243524521583754,\n      'maximum': 4.872155666351318,\n      'minimum': -0.25238683819770813,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'fuel': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_FUEL_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Wood fuel emissions (FUEL), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Wood fuel emissions (flux of carbon to the atmosphere from wood burned for fuel) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.6249907612800598,\n      'min': -0.021494677290320396,\n      'count': 11,\n      'buckets': [518619, 4684, 688, 188, 65, 24, 6, 3, 7, 4]},\n     'statistics': {'mean': 0.0015495388070121408,\n      'stddev': 0.010684158697696962,\n      'maximum': 0.6249907612800598,\n      'minimum': -0.021494677290320396,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20231231/preview.png?assets=npp&rescale=0%2C8&colormap_name=purd',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [179.99999999999994, -90],\n    [179.99999999999994, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'micasa-carbonflux-daygrid-v1',\n 'properties': {'datetime': '2023-12-31T00:00:00+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']}",
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+      "Natural Greenhouse Gas Sources Emissions and Sinks",
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-    "href": "user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.html#exploring-changes-in-carbon-dioxide-co₂-levels-using-the-raster-api",
-    "title": "ODIAC Fossil Fuel CO₂ Emissions",
-    "section": "Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API",
-    "text": "Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API\nWe will explore changes in fossil fuel emissions in urban egions. In this notebook, we’ll explore the impacts of these emissions and explore these changes over time. We’ll then visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items} \n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the ODIAC Fossil Fuel CO₂ Emissions collection, the parameter of interest is “co2-emissions”\nasset_name = \"co2-emissions\"\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for January 2020 and again for January 2000, so that we can visualize each event independently.\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"rainbow\" \n\n# Make a GET request to retrieve information for the 2020 tile\n# 2020\njanuary_2020_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2020-01']['collection']}/items/{items['2020-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2020_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-138.71914672851562%2C2497.01904296875'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for the 2000 tile\n# 2000\njanuary_2000_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2000-01']['collection']}/items/{items['2000-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2000_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-138.71914672851562%2C2497.01904296875'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}"
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+    "href": "user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.html#explore-changes-in-carbon-flux-levels-using-the-raster-api",
+    "title": "MiCASA Land Carbon Flux",
+    "section": "Explore Changes in Carbon Flux Levels Using the Raster API",
+    "text": "Explore Changes in Carbon Flux Levels Using the Raster API\nWe will explore changes in the land atmosphere Carbon flux Heterotrophic Respiration and examine their impacts over time. We’ll then visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"datetime\"][:10]: item for item in items}\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetch the minimum and maximum values for rescaling\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. This step is done twice, once for December 2003 and again for December 2017, so that we can visualize each event independently.\n\n# Choose a color for displaying the tiles\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"purd\"\n\n# Make a GET request to retrieve information for the date mentioned below\ndate1 = '2023-01-01'\ndate1_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year, month and date of the observed parameter, you can modify the date mentioned above.\n    f\"{RASTER_API_URL}/collections/{items[date1]['collection']}/items/{items[date1]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\ndate1_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.35656991600990295%2C7.2141876220703125'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n 'center': [-2.842170943040401e-14, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for the date mentioned below\ndate2 = '2023-01-31'\ndate2_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year, month and date of the observed parameter, you can modify the date mentioned above.\n    f\"{RASTER_API_URL}/collections/{items[date2]['collection']}/items/{items[date2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\ndate2_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.35656991600990295%2C7.2141876220703125'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n 'center': [-2.842170943040401e-14, 0.0, 0]}",
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+      "Natural Greenhouse Gas Sources Emissions and Sinks",
+      "MiCASA Land Carbon Flux"
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-    "href": "user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.html#visualizing-co₂-emissions",
-    "title": "ODIAC Fossil Fuel CO₂ Emissions",
-    "section": "Visualizing CO₂ emissions",
-    "text": "Visualizing CO₂ emissions\n\n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n\n# Set the initial zoom level and center of map for both tiles\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer (January 2020)\nmap_layer_2020 = TileLayer(\n    tiles=january_2020_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_2020.add_to(map_.m1)\n\n# Define the second map layer (January 2000)\nmap_layer_2000 = TileLayer(\n    tiles=january_2000_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_2000.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
+    "objectID": "user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.html#visualize-land-atmosphere-carbon-flux-heterotrophic-respiration",
+    "href": "user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.html#visualize-land-atmosphere-carbon-flux-heterotrophic-respiration",
+    "title": "MiCASA Land Carbon Flux",
+    "section": "Visualize Land-Atmosphere Carbon Flux (Heterotrophic Respiration)",
+    "text": "Visualize Land-Atmosphere Carbon Flux (Heterotrophic Respiration)\n\n# For this study we are going to compare the Rh level for date1 and date2 over the State of Texas \n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n# For example, you can change the current statement \"location=(31.9, -99.9)\" to \"location=(34, -118)\" to monitor the Rh level in California instead of Texas\n\n# Set initial zoom and center of map for CO₂ Layer\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(31.9, -99.9), zoom_start=6)\n\n\n# Define the first map layer with Rh level for the tile fetched for date 1\n# The TileLayer library helps in manipulating and displaying raster layers on a map\nmap_layer_date1 = TileLayer(\n    tiles=date1_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n    name=f\"{date1} Rh Level\", # Title for the layer\n    overlay= True, # The layer can be overlaid on the map\n    legendEnabled = True # Enable displaying the legend on the map\n)\n\n# Add the first layer to the Dual Map\nmap_layer_date1.add_to(map_.m1)\n\n\n# Define the first map layer with Rh level for the tile fetched for date 2\nmap_layer_date2 = TileLayer(\n    tiles=date2_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n    name=f\"{date2} RH Level\", # Title for the layer\n    overlay= True, # The layer can be overlaid on the map\n    legendEnabled = True # Enable displaying the legend on the map\n)\n\n# Add the second layer to the Dual Map\nmap_layer_date2.add_to(map_.m2)\n\n# Display data markers (titles) on both maps\nfolium.Marker((40, 5.0), tooltip=\"both\").add_to(map_)\n\n# Add a layer control to switch between map layers\nfolium.LayerControl(collapsed=False).add_to(map_)\n\n# Add a legend to the dual map using the 'branca' library. \n# Note: the inserted legend is representing the minimum and maximum values for both tiles.\ncolormap = branca.colormap.linear.PuRd_09.scale(0, 0.3) # minimum value = 0, maximum value = 0.3 (kg Carbon/m2/daily)\n\n# Classify the colormap according to specified Rh values \ncolormap = colormap.to_step(index=[0, 0.07, 0.15, 0.22, 0.3])\n\n# Add the data unit as caption\ncolormap.caption = 'Rh Values (gm Carbon/m2/daily)'\n\n# Display the legend and caption on the map\ncolormap.add_to(map_.m1)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
+    "crumbs": [
+      "Data Usage Notebooks",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
+      "MiCASA Land Carbon Flux"
+    ]
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-    "title": "ODIAC Fossil Fuel CO₂ Emissions",
-    "section": "Visualizing the Data as a Time Series",
-    "text": "Visualizing the Data as a Time Series\nWe can now explore the ODIAC fossil fuel emission time series available (January 2000 -December 2021) for the Texas, Dallas area of USA. We can plot the data set using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\n\nplt.plot(\n    df[\"date\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum CO₂ level\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Max monthly CO₂ emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"CO2 emissions gC/m2/d\")\n\n# Insert title for the plot\nplt.title(\"CO2 emission Values for Texas, Dallas (2000-2021)\")\n\n###\n# Add data citation\nplt.text(\n    df[\"date\"].iloc[0],           # X-coordinate of the text\n    df[\"max\"].min(),              # Y-coordinate of the text\n\n\n\n\n    # Text to be displayed\n    \"Source: NASA ODIAC Fossil Fuel CO₂ Emissions\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"right\",             # Horizontal alignment\n    verticalalignment=\"top\",                 # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n# Plot the time series\nplt.show()\n\n\n\n\n\n\n\n\n\n# Print the properties of the 3rd item in the collection\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n2021-10-01T00:00:00+00:00\n\n\n\n# A GET request is made for the October tile\noctober_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\noctober_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-138.71914672851562%2C2497.01904296875'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the October tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=october_tile[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.5\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
+    "objectID": "user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.html#generate-the-statistics-for-the-aoi",
+    "href": "user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.html#generate-the-statistics-for-the-aoi",
+    "title": "MiCASA Land Carbon Flux",
+    "section": "Generate the statistics for the AOI",
+    "text": "Generate the statistics for the AOI\n\n%%time\n# %%time = Wall time (execution time) for running the code below\n\n# Generate statistics using the created function \"generate_stats\" within the bounding box defined by the \"texas_dallas_aoi\" polygon\nstats = [generate_stats(item, texas_dallas_aoi) for item in items]\n\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': 0.1918513923883438, 'max': 1.1595696210861206, 'mean': 0.7456724643707275, 'count': 147.99998474121094, 'sum': 110.35951232910156, 'std': 0.18667982445867687, 'median': 0.7525566220283508, 'majority': 0.1918513923883438, 'minority': 0.1918513923883438, 'unique': 165.0, 'histogram': [[3.0, 2.0, 5.0, 16.0, 26.0, 31.0, 30.0, 31.0, 15.0, 6.0], [0.1918513923883438, 0.28862321376800537, 0.3853950500488281, 0.4821668267250061, 0.5789386630058289, 0.6757104992866516, 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1.1595696210861206,\n   'mean': 0.7456724643707275,\n   'count': 147.99998474121094,\n   'sum': 110.35951232910156,\n   'std': 0.18667982445867687,\n   'median': 0.7525566220283508,\n   'majority': 0.1918513923883438,\n   'minority': 0.1918513923883438,\n   'unique': 165.0,\n   'histogram': [[3.0, 2.0, 5.0, 16.0, 26.0, 31.0, 30.0, 31.0, 15.0, 6.0],\n    [0.1918513923883438,\n     0.28862321376800537,\n     0.3853950500488281,\n     0.4821668267250061,\n     0.5789386630058289,\n     0.6757104992866516,\n     0.7724822759628296,\n     0.8692541122436523,\n     0.9660259485244751,\n     1.0627977848052979,\n     1.1595696210861206]],\n   'valid_percent': 100.0,\n   'masked_pixels': 0.0,\n   'valid_pixels': 165.0,\n   'percentile_2': 0.27398860454559326,\n   'percentile_98': 1.098567008972168}},\n 'datetime': '2023-12-31'}\n\n\nCreate a function that goes through every single item in the collection and populates their properties - including the minimum, maximum, and sum of their values - in a table.\n\n# Create a function that converts statistics in JSON format into a pandas DataFrame\ndef clean_stats(stats_json) -> pd.DataFrame:\n\n    # Normalize the JSON data\n    df = pd.json_normalize(stats_json)\n\n    # Replace the naming \"statistics.b1\" in the columns\n    df.columns = [col.replace(\"statistics.b1.\", \"\") for col in df.columns]\n\n    # Set the datetime format\n    df[\"date\"] = pd.to_datetime(df[\"datetime\"])\n\n    # Return the cleaned format\n    return df\n\n# Apply the generated function on the stats data\ndf = clean_stats(stats)\n\n# Display the stats for the first 5 granules in the collection in the table\n# Change the value in the parenthesis to show more or a smaller number of rows in the table\ndf.head(5)\n\n\n\n\n\n\n\n\ndatetime\nmin\nmax\nmean\ncount\nsum\nstd\nmedian\nmajority\nminority\nunique\nhistogram\nvalid_percent\nmasked_pixels\nvalid_pixels\npercentile_2\npercentile_98\ndate\n\n\n\n\n0\n2023-12-31\n0.191851\n1.159570\n0.745672\n147.999985\n110.359512\n0.186680\n0.752557\n0.191851\n0.191851\n165.0\n[[3.0, 2.0, 5.0, 16.0, 26.0, 31.0, 30.0, 31.0,...\n100.0\n0.0\n165.0\n0.273989\n1.098567\n2023-12-31\n\n\n1\n2023-12-30\n0.177944\n1.075771\n0.690748\n147.999985\n102.230652\n0.173171\n0.700913\n0.177944\n0.177944\n165.0\n[[3.0, 2.0, 6.0, 15.0, 27.0, 33.0, 27.0, 31.0,...\n100.0\n0.0\n165.0\n0.258274\n1.022774\n2023-12-30\n\n\n2\n2023-12-29\n0.172747\n1.048948\n0.666380\n147.999985\n98.624245\n0.168330\n0.675644\n0.172747\n0.172747\n165.0\n[[3.0, 2.0, 6.0, 18.0, 27.0, 34.0, 26.0, 29.0,...\n100.0\n0.0\n165.0\n0.247735\n0.990478\n2023-12-29\n\n\n3\n2023-12-28\n0.175674\n1.070212\n0.672139\n147.999985\n99.476486\n0.170283\n0.675561\n0.175674\n0.175674\n165.0\n[[3.0, 2.0, 8.0, 18.0, 25.0, 35.0, 29.0, 29.0,...\n100.0\n0.0\n165.0\n0.249545\n1.006913\n2023-12-28\n\n\n4\n2023-12-27\n0.193630\n1.170822\n0.728575\n147.999985\n107.829071\n0.186487\n0.729016\n0.193630\n0.193630\n165.0\n[[3.0, 2.0, 9.0, 18.0, 26.0, 38.0, 28.0, 23.0,...\n100.0\n0.0\n165.0\n0.272505\n1.097846\n2023-12-27",
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+    "title": "MiCASA Land Carbon Flux",
+    "section": "Visualize the Data as a Time Series",
+    "text": "Visualize the Data as a Time Series\nWe can now explore the Heterotrophic Respiration time series (October 2021 - January 2024) available for the Dallas, Texas area. We can plot the data set using the code below:\n\n# Determine the width and height of the plot using the 'matplotlib' library\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10)) \n\n# Plot the time series analysis of the daily Heterotrophic Respiration changes in Dallas, Texas\nplt.plot(\n    df[\"date\"], # X-axis: date\n    df[\"max\"], # Y-axis: Rh value\n    color=\"purple\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"RH Level\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"gm Carbon/m2/day\")\n\n# Insert title for the plot\nplt.title(\"Heterotrophic Respiration Values for Dallas, Texas (October 2021 to January 2024)\")\n\nText(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (October 2021 to January 2024)')\n\n\n\n\n\n\n\n\n\nTo take a closer look at the daily Heterotrophic Respiration variability across this region, we are going to retrieve and display data collected during the December, 2023 observation.\n\n# Fetch the third item in the list as the observation item.\n# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n# Print the start Date Time of the third granule in the collection\nprint(items[2][\"properties\"][\"datetime\"]) \n\n2023-12-29T00:00:00+00:00\n\n\n\n# A GET request is made for the observed tile\nobserved_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console \nobserved_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.35656991600990295%2C7.2141876220703125'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n 'center': [-2.842170943040401e-14, 0.0, 0]}\n\n\n\n# Create a new map to display the Rh level for the Dallas, Texas area for the observed tile timeframe.\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        32.8, # latitude\n        -96.79, # longitude\n    ],\n\n    # Set the zoom value\n    zoom_start=9,\n)\n\n# Define the map layer with the Rh level for observed tile\nmap_layer = TileLayer(\n    tiles=observed_tile[\"tiles\"][0], # Path to retrieve the tile\n\n    # Set the attribution, transparency, and the title along with enabling the visualization of the legend on the map \n    attr=\"GHG\", opacity = 0.7, name=\" Observed tile RH Level\", overlay= True, legendEnabled = True\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Display data marker (title) on the map\nfolium.Marker((40, 5.9), tooltip=\"both\").add_to(aoi_map_bbox)\n\n# Add a layer control\nfolium.LayerControl(collapsed=False).add_to(aoi_map_bbox)\n\n# Add a legend using the 'branca' library\ncolormap = branca.colormap.linear.PuRd_09.scale(0, 0.3) # minimum value = 0, maximum value = 0.3 (gm Carbon/m2/daily)\n\n# Classify the colormap according to the specified Rh values\ncolormap = colormap.to_step(index=[0, 0.07, 0.15, 0.22, 0.3])\n\n# Add the data unit as caption\ncolormap.caption = 'Rh Values (gm Carbon/m2/daily)'\n\n# Display the legend and caption on the map\ncolormap.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "title": "MiCASA Land Carbon Flux",
     "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully explored, analysed and visualized STAC collecetion for ODIAC C02 fossisl fuel emission (2022).\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the CO₂ levels for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form."
+    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for MiCASA Land Carbon Flux data: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map and compare the Heterotrophic Respiration (Rh) levels over the Dallas, Texas area for two distinctive years 5. Create a table that displays the minimum, maximum, and sum of the Rh values for a specified region 6. Generate a time-series graph of the Rh values for a specified region\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
+    "crumbs": [
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     "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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       "Gridded Anthropogenic Greenhouse Gas Emissions",
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+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
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     "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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       "Data Usage Notebooks",
       "Gridded Anthropogenic Greenhouse Gas Emissions",
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+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
     "section": "Approach",
-    "text": "Approach\n\nIdentify 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.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon.",
+    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. Collection processed in this notebook is ODIAC CO₂ emissions version 2023.\nPass the STAC item into raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint\nWe’ll visualize two tiles (side-by-side) allowing for comparison of each of the time points using folium.plugins.DualMap\nAfter the visualization, we’ll perform zonal statistics for a given polygon.",
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+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
     "section": "About the Data",
-    "text": "About the Data\nThe Vulcan version 4.0 data product represents total carbon dioxide (CO2) emissions resulting from the combustion of fossil fuel (ff) for the contiguous United States and District of Columbia. Referred to as ffCO2, the emissions from Vulcan are also categorized into 10 source sectors including; airports, cement production, commercial marine vessels, commercial, power plants, industrial, non-road, on-road, residential and railroads. Data are gridded annually on a 1-km grid for the years 2010 to 2021. These data are annual sums of hourly estimates. Shown is the estimated total annual ffCO2 for the United States, as well as the estimated total annual ffCO2 per sector.\nFor more information regarding this dataset, please visit the Vulcan Fossil Fuel CO₂ Emissions, Version 4 data overview page.",
+    "text": "About the Data\nThe Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) is a high-spatial resolution global emission data product of CO₂ emissions from fossil fuel combustion (Oda and Maksyutov, 2011). ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO₂ emissions. With the innovative emission modeling approach, ODIAC achieved the fine picture of global fossil fuel CO₂ emissions at a 1x1km.\nFor more information regarding this dataset, please visit the ODIAC Fossil Fuel CO₂ Emissions data overview page.",
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+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
     "section": "Querying the STAC API",
-    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# Please use the collection name similar to the one used in the STAC collection.\n# Name of the collection for Vulcan Fossil Fuel CO₂ Emissions, Version 4. \ncollection_name = \"vulcan-ffco2-yeargrid-v4\"\n\n\n# Fetch the collection from STAC collections using the appropriate endpoint\n# the 'requests' library allows a HTTP request possible\ncollection_vulcan = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2010 to December 2021. By looking at the dashboard:time density, we observe that the data is periodic with year time density.\n\ncollection_vulcan\n\n{'id': 'vulcan-ffco2-yeargrid-v4',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4/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/vulcan-ffco2-yeargrid-v4'}],\n 'title': 'Vulcan Fossil Fuel CO₂ Emissions v4.0',\n 'extent': {'spatial': {'bbox': [[-128.22654896758996,\n     22.857766529124284,\n     -65.30917199495289,\n     51.44087947724907]]},\n  'temporal': {'interval': [['2010-01-01 00:00:00+00',\n     '2021-12-31 00:00:00+00']]}},\n 'license': 'CC-BY-NC-4.0',\n 'renders': {'air-co2': {'assets': ['air-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'cmt-co2': {'assets': ['cmt-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'cmv-co2': {'assets': ['cmv-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'com-co2': {'assets': ['com-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'elc-co2': {'assets': ['elc-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'ind-co2': {'assets': ['ind-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'nrd-co2': {'assets': ['nrd-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'onr-co2': {'assets': ['onr-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'res-co2': {'assets': ['res-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'rrd-co2': {'assets': ['rrd-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'dashboard': {'assets': ['total-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'total-co2': {'assets': ['total-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'}},\n 'providers': [{'url': 'https://vulcan.rc.nau.edu/',\n   'name': 'North American Carbon Program',\n   'roles': ['producer', 'licensor']}],\n 'summaries': {'datetime': ['2010-01-01T00:00:00Z', '2021-12-31T00:00:00Z']},\n 'description': 'Annual (2010 - 2021), 1 km resolution estimates of carbon dioxide emissions from fossil fuels and cement production over the contiguous United States, version 4.0',\n 'item_assets': {'air-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Airport Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from taxi, take-off, and landing up to 3000 ft.'},\n  'cmt-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Cement Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from cement production.'},\n  'cmv-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Commercial Marine Vessel Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from commercial marine vessels while maneuvering, hoteling, cruising and traveling within reduced speed zones at ports and shipping lanes. Includes only activity within 12 nautical miles (~22km) from the U.S. shoreline.'},\n  'com-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Commercial Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from Commercial buildings.'},\n  'elc-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Power Plant Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from power plants.'},\n  'ind-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Industrial Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from Industrial buildings.'},\n  'nrd-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Non-Road Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from off-road engines, equipment and vehicles including waterborne pleasure craft.'},\n  'onr-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'On-Road Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from mobile vehicles on roads.'},\n  'res-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Residential Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from Residential buildings.'},\n  'rrd-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Railroad Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual FFCO₂ emissions coming from railroads.'},\n  'total-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Total Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual CO₂ emissions from fossil fuel combustion (ffCO₂) across all sectors..'}},\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'}\n\n\n\n# Create a function that would search for the above data collection in the STAC API\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Apply the above function and check the total number of items available within the collection\nnumber_of_items = get_item_count(collection_name)\nitems_vulcan = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items_vulcan)} items\")\n\nFound 12 items\n\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems_vulcan[0]\n\n{'id': 'vulcan-ffco2-yeargrid-v4-2021',\n 'bbox': [-128.22654896758996,\n  22.857766529124284,\n  -65.30917199495289,\n  51.44087947724907],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021/map?assets=total-co2&rescale=0%2C500&colormap_name=spectral_r',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'air-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/AIR_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Airport CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from taxi, take-off, and landing up to 3000 ft.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 318726.1875,\n      'min': 0.11889950931072235,\n      'count': 11,\n      'buckets': [14659, 40, 6, 2, 4, 0, 2, 0, 0, 1]},\n     'statistics': {'mean': 1190.7966562457523,\n      'stddev': 5906.230747537605,\n      'maximum': 318726.1875,\n      'minimum': 0.11889950931072235,\n      'valid_percent': 3.083506571888412}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'cmt-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/CMT_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Cement CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from cement production.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 538037.5,\n      'min': 14599.9677734375,\n      'count': 11,\n      'buckets': [10, 15, 19, 7, 9, 4, 4, 6, 0, 1]},\n     'statistics': {'mean': 181749.84,\n      'stddev': 114981.70564725697,\n      'maximum': 538037.5,\n      'minimum': 14599.9677734375,\n      'valid_percent': 0.015717207618025753}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'cmv-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/CMV_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Commercial Marine Vessels CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from commercial marine vessels while maneuvering, hoteling, cruising and traveling within reduced speed zones at ports and shipping lanes. Includes only activity within 12 nautical miles (~22km) from the U.S. shoreline.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 15446.8408203125,\n      'min': 8.111214810924139e-07,\n      'count': 11,\n      'buckets': [17370, 16, 5, 1, 2, 0, 1, 0, 0, 1]},\n     'statistics': {'mean': 32.60311997010807,\n      'stddev': 210.77205857399764,\n      'maximum': 15446.8408203125,\n      'minimum': 8.111214810924139e-07,\n      'valid_percent': 3.6455539163090127}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'com-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/COM_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Commercial CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from Commercial buildings.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 41811.0625,\n      'min': 6.441725486361349e-10,\n      'count': 11,\n      'buckets': [178117, 7, 1, 1, 0, 0, 1, 0, 0, 2]},\n     'statistics': {'mean': 10.866918777964285,\n      'stddev': 175.08472372009805,\n      'maximum': 41811.0625,\n      'minimum': 6.441725486361349e-10,\n      'valid_percent': 37.32920634388412}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'elc-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/ELC_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; 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application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Nonroad CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from off-road engines, equipment and vehicles including waterborne pleasure craft.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 2277.109619140625,\n      'min': 1.7788032380394725e-07,\n      'count': 11,\n      'buckets': [227435, 26, 5, 1, 0, 1, 1, 0, 0, 2]},\n     'statistics': {'mean': 6.1029197128425166,\n      'stddev': 14.197089191407585,\n      'maximum': 2277.109619140625,\n      'minimum': 1.7788032380394725e-07,\n      'valid_percent': 47.66945245439914}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'onr-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/ONR_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; 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application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Residential CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from Residential buildings.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 10387.0556640625,\n      'min': 9.804268508162295e-09,\n      'count': 11,\n      'buckets': [204532, 112, 28, 7, 4, 3, 3, 2, 1, 1]},\n     'statistics': {'mean': 12.793920651903093,\n      'stddev': 91.07716732362586,\n      'maximum': 10387.0556640625,\n      'minimum': 9.804268508162295e-09,\n      'valid_percent': 42.896031719420606}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'rrd-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/RRD_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Railroad CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions coming from railroads.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 4935.501953125,\n      'min': 7.793863915139809e-05,\n      'count': 11,\n      'buckets': [43982, 167, 38, 9, 7, 5, 3, 2, 1, 2]},\n     'statistics': {'mean': 24.959290754478015,\n      'stddev': 94.2219837346061,\n      'maximum': 4935.501953125,\n      'minimum': 7.793863915139809e-05,\n      'valid_percent': 9.266027360515022}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'total-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/TOT_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total of all sectors CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual CO₂ emissions from fossil fuel combustion (ffCO₂) across all sectors.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 272530.15625,\n      'min': 1.7858106104995386e-07,\n      'count': 11,\n      'buckets': [227843, 81, 36, 7, 3, 6, 1, 4, 1, 1]},\n     'statistics': {'mean': 162.91311194255712,\n      'stddev': 2080.549384731812,\n      'maximum': 272530.15625,\n      'minimum': 1.7858106104995386e-07,\n      'valid_percent': 47.7767485917382}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021/preview.png?assets=total-co2&rescale=0%2C500&colormap_name=spectral_r',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-128.22654896758996, 22.857766529124284],\n    [-65.30917199495289, 22.857766529124284],\n    [-65.30917199495289, 51.44087947724907],\n    [-128.22654896758996, 51.44087947724907],\n    [-128.22654896758996, 22.857766529124284]]]},\n 'collection': 'vulcan-ffco2-yeargrid-v4',\n 'properties': {'end_datetime': '2021-12-31T00:00:00+00:00',\n  'start_datetime': '2021-01-01T00:00:00+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']}\n\n\n\n# To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:4]: item for item in items_vulcan} \n# rh = Heterotrophic Respiration\nasset_name = \"total-co2\"\n\n\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for 2021 and again for 2010, so that we can visualize each event independently.\n\ncolor_map = \"spectral_r\" # please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n\n# 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_2021_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items['2021']['collection']}/items/{items['2021']['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).json()\n_2021_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-128.22654896758996,\n  22.857766529124284,\n  -65.30917199495289,\n  51.44087947724907],\n 'center': [-96.76786048127143, 37.14932300318668, 0]}\n\n\n\n_2010_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items['2010']['collection']}/items/{items['2010']['id']}/tilejson.json?\"\n\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale=0,150\", \n).json()\n_2010_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-128.22654896758996,\n  22.857766529124284,\n  -65.30917199495289,\n  51.44087947724907],\n 'center': [-96.76786048127143, 37.14932300318668, 0]}",
+    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for ODIAC dataset \ncollection_name = \"odiac-ffco2-monthgrid-v2023\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'odiac-ffco2-monthgrid-v2023',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023/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/odiac-ffco2-monthgrid-v2023'}],\n 'title': 'ODIAC Fossil Fuel CO₂ Emissions v2023',\n 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n  'temporal': {'interval': [['2000-01-01 00:00:00+00',\n     '2022-12-31 00:00:00+00']]}},\n 'license': 'CC-BY-4.0',\n 'renders': {'dashboard': {'assets': ['co2-emissions'],\n   'rescale': [[-10, 60]],\n   'colormap_name': 'jet'},\n  'co2-emissions': {'assets': ['co2-emissions'],\n   'rescale': [[-10, 60]],\n   'colormap_name': 'jet'}},\n 'providers': [{'url': 'https://www.nies.go.jp',\n   'name': 'National Institute for Environmental Studies',\n   'roles': ['producer', 'licensor']}],\n 'summaries': {'datetime': ['2000-01-01T00:00:00Z', '2022-12-31T00:00:00Z']},\n 'description': 'The Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) data product is a monthly high-resolution global data product of modeled fossil fuel carbon dioxide (CO₂) emissions. A complex model incorporates and combines space-based nighttime light data and individual power plant emission/location profiles from the latest country fossil fuel CO₂ estimates (2000-2019) made by the Carbon Dioxide Information Analysis Center (CDIAC) team at the Appalachian State University (CDIAC at AppState, Gilfillan et al. 2021, Hefner et al. 2022). The ODIAC estimated global spatial extent of fossil fuel CO₂ emissions is produced on a 1 km by 1 km grid that details variations in urban regions where emissions are most intense. The ODIAC CO₂ emission data is widely used by the international research community for applications such as CO₂ flux inversion, urban emission estimation, and observing system design experiments. The ODIAC product was first created in 2009 by Dr. Tomohiro Oda with support from the National Institute for Environmental Studies (NIES) GOSAT project. The ODIAC team is now supported by NASA Goddard Space Flight Center, NASA Carbon Monitoring System program, the NASA Orbiting Carbon Observatory mission and NIES. The US GHG Center displays the ODIAC 2023 version containing monthly data from January 2000 to December 2022 that replaces all previous versions. The source dataset can be found at https://doi.org/10.17595/20170411.001',\n 'item_assets': {'co2-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil Fuel CO₂ Emissions',\n   'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.'}},\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': 'month'}\n\n\nExamining the contents of our collection under summaries we see that the data is available from January 2000 to December 2022. By looking at the dashboard:time density we observe that the periodic frequency of these observations is monthly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 276 items\n\n\nThis makes sense as there are 23 years between 2000 - 2023, with 12 months per year, meaning 276 records in total.\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212/map?assets=co2-emissions&rescale=-10%2C60&colormap_name=jet',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'co2-emissions': {'href': 's3://ghgc-data-store/odiac-ffco2-monthgrid-v2023/odiac2023_1km_excl_intl_202212.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil Fuel CO₂ Emissions',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [21600, 43200],\n   'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 31415.447265625,\n      'min': -675.1028442382812,\n      'count': 11,\n      'buckets': [14870, 14, 1, 0, 0, 1, 0, 0, 0, 1]},\n     'statistics': {'mean': 38.57990192785652,\n      'stddev': 332.3921410156093,\n      'maximum': 31415.447265625,\n      'minimum': -675.1028442382812,\n      'valid_percent': 2.8394699096679688}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.008333333333333333,\n    0.0,\n    -180.0,\n    0.0,\n    -0.008333333333333333,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212/preview.png?assets=co2-emissions&rescale=-10%2C60&colormap_name=jet',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'odiac-ffco2-monthgrid-v2023',\n 'properties': {'end_datetime': '2022-12-31T00:00:00+00:00',\n  'start_datetime': '2022-12-01T00:00:00+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']}",
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-    "section": "Visualizing Total Fossil Fuel CO₂ Emissions",
-    "text": "Visualizing Total Fossil Fuel CO₂ Emissions\n\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n\n# Define the first map layer with the CO2 Flux data for December 2022\nmap_layer_2021 = TileLayer(\n    tiles=_2021_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution \n    name='2021 Total CO2 Fossil Fuel Emissions', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.8, # Adjust the transparency of the layer\n)\n# Add the first layer to the Dual Map \nmap_layer_2021.add_to(map_.m1)\n\nmap_layer_2010 = TileLayer(\n    tiles=_2010_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution \n    name='2010 Total CO2 Fossil Fuel Emissions', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.8, # Adjust the transparency of the layer\n)\n# Add the first layer to the Dual Map \nmap_layer_2010.add_to(map_.m2)\n\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
+    "section": "Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API",
+    "text": "Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API\nWe will explore changes in fossil fuel emissions in urban egions. In this notebook, we’ll explore the impacts of these emissions and explore these changes over time. We’ll then visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items} \n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the ODIAC Fossil Fuel CO₂ Emissions collection, the parameter of interest is “co2-emissions”\nasset_name = \"co2-emissions\"\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for January 2020 and again for January 2000, so that we can visualize each event independently.\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"rainbow\" \n\n# Make a GET request to retrieve information for the 2020 tile\n# 2020\njanuary_2020_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2020-01']['collection']}/items/{items['2020-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2020_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-675.1028442382812%2C31415.447265625'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for the 2000 tile\n# 2000\njanuary_2000_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2000-01']['collection']}/items/{items['2000-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2000_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-675.1028442382812%2C31415.447265625'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}",
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+    "section": "Visualizing CO₂ emissions",
+    "text": "Visualizing CO₂ emissions\n\n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n\n# Set the initial zoom level and center of map for both tiles\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer (January 2020)\nmap_layer_2020 = TileLayer(\n    tiles=january_2020_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_2020.add_to(map_.m1)\n\n# Define the second map layer (January 2000)\nmap_layer_2000 = TileLayer(\n    tiles=january_2000_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_2000.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
     "section": "Visualizing the Data as a Time Series",
-    "text": "Visualizing the Data as a Time Series\nWe can now explore the total fossil fuel emission time series (2010 -2021) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\nplt.plot(\n    df[\"datetime\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum CO₂\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"CO₂ emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"tC/km²/year\")\nplt.xticks(rotation = 90)\n\n# Insert title for the plot\nplt.title(\"Total Fossil Fuel CO₂ Emissions for Texas, Dallas (2010-2021)\")\n\n# Add data citation\nplt.text(\n    df[\"datetime\"].iloc[0],           # X-coordinate of the text\n    df[\"max\"].min(),                  # Y-coordinate of the text\n\n\n\n\n    # Text to be displayed\n    \"Source: https://doi.org/10.3334/ORNLDAAC/1741\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"left\",              # Horizontal alignment\n    verticalalignment=\"top\",                 # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n\n# Plot the time series\nplt.show()",
+    "text": "Visualizing the Data as a Time Series\nWe can now explore the ODIAC fossil fuel emission time series available (January 2000 -December 2022) for the Texas, Dallas area of USA. We can plot the data set using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\n\nplt.plot(\n    df[\"date\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum CO₂ level\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Max monthly CO₂ emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"CO2 emissions gC/m2/d\")\n\n# Insert title for the plot\nplt.title(\"CO2 emission Values for Texas, Dallas (2000-2022)\")\n\n###\n# Add data citation\nplt.text(\n    df[\"date\"].iloc[0],           # X-coordinate of the text\n    df[\"max\"].min(),              # Y-coordinate of the text\n\n\n\n\n    # Text to be displayed\n    \"Source: NASA ODIAC Fossil Fuel CO₂ Emissions\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"right\",             # Horizontal alignment\n    verticalalignment=\"top\",                 # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n# Plot the time series\nplt.show()\n\n\n\n\n\n\n\n\n\n# Print the properties of the 3rd item in the collection\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n2022-10-01T00:00:00+00:00\n\n\n\n# A GET request is made for the October tile\noctober_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}//collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\noctober_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-675.1028442382812%2C31415.447265625'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the October tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=october_tile[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.5\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
     "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully explored, analyzed, and visualized the STAC collection for Vulcan Fossil Fuel CO₂ Emissions, Version 4 dataset.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the total fossil fuel CO₂ emissions for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
+    "text": "Summary\nIn this notebook we have successfully explored, analysed and visualized STAC collecetion for ODIAC C02 fossisl fuel emission (2023).\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the CO₂ levels for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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     "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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     "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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     "section": "Approach",
-    "text": "Approach\n\nIdentify 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 TM5-4DVar Isotopic CH₄ Inverse Fluxes Data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.jsonendpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon.",
+    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory.\nVisualize the time series data",
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     "section": "About the Data",
-    "text": "About the Data\nSurface methane (CH₄) emissions are derived from atmospheric measurements of methane and its ¹³C carbon isotope content. Different sources of methane contain different ratios of the two stable isotopologues, ¹²CH₄ and ¹³CH₄. This makes normally indistinguishable collocated sources of methane, say from agriculture and oil and gas exploration, distinguishable. The National Oceanic and Atmospheric Administration (NOAA) collects whole air samples from its global cooperative network of flasks (https://gml.noaa.gov/ccgg/about.html), which are then analyzed for methane and other trace gasses. A subset of those flasks are also analyzed for ¹³C of methane in collaboration with the Institute of Arctic and Alpine Research at the University of Colorado Boulder. Scientists at the National Aeronautics and Space Administration (NASA) and NOAA used those measurements of methane and ¹³C of methane in conjunction with a model of atmospheric circulation to estimate emissions of methane separated by three source types, microbial, fossil and pyrogenic.\nFor more information regarding this dataset, please visit the TM5-4DVar Isotopic CH₄ Inverse Fluxes data overview page.",
+    "text": "About the Data\nThe Global Greenhouse Gas Reference Network (GGGRN) for the Carbon Cycle and Greenhouse Gases (CCGG) Group is part of NOAA’S Global Monitoring Laboratory (GML) in Boulder, CO. The Reference Network measures the atmospheric distribution and trends of the three main long-term drivers of climate change, carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N2O), as well as carbon monoxide (CO) and many other trace gases which help interpretation of the main GHGs. The Reference Network measurement program includes continuous in-situ measurements at 4 baseline observatories (global background sites) and 8 tall towers, as well as flask-air samples collected by volunteers at over 50 additional regional background sites and from small aircraft conducting regular vertical profiles. The air samples are returned to GML for analysis where measurements of about 55 trace gases are done. NOAA’s GGGRN maintains the World Meteorological Organization international calibration scales for CO₂, CH₄, CO, N2O, and SF6 in air. The measurements from the GGGRN serve as a comparison with measurements made by many other international laboratories, and with regional studies. They are widely used in modeling studies that infer space-time patterns of emissions and removals of greenhouse gases that are optimally consistent with the atmospheric observations, given wind patterns. These data serve as an early warning for climate “surprises”. The measurements are also helpful for the ongoing evaluation of remote sensing technologies.\nFor more information regarding this dataset, please visit the Atmospheric Carbon Dioxide Concentrations from NOAA GML data overview page.",
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-    "section": "Querying the STAC API",
-    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for TM5 CH₄ inverse flux dataset \ncollection_name = \"tm54dvar-ch4flux-monthgrid-v1\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'tm54dvar-ch4flux-monthgrid-v1',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/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/tm54dvar-ch4flux-monthgrid-v1'}],\n 'title': 'TM5-4DVar Isotopic CH4 Inverse Fluxes',\n 'assets': None,\n 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n  'temporal': {'interval': [['1999-01-01T00:00:00+00:00',\n     '2016-12-31T00:00:00+00:00']]}},\n 'license': 'CC-BY-4.0',\n 'keywords': None,\n 'providers': None,\n 'summaries': {'datetime': ['1999-01-01T00:00:00Z', '2016-12-31T00:00:00Z']},\n 'description': 'Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 1.',\n 'item_assets': {'total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Total CH4 Emission',\n   'description': 'Total methane emission from microbial, fossil and pyrogenic sources'},\n  'fossil': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil CH4 Emission',\n   'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.'},\n  'microbial': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Microbial CH4 Emission',\n   'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.'},\n  'pyrogenic': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Pyrogenic CH4 Emission',\n   'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.'}},\n 'stac_version': '1.0.0',\n 'stac_extensions': None,\n 'dashboard:is_periodic': True,\n 'dashboard:time_density': 'month'}\n\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 1999 to December 2016. By looking at the dashboard:time density, we observe that the data is periodic with monthly time density.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 216 items\n\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'tm54dvar-ch4flux-monthgrid-v1-201612',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612'}],\n 'assets': {'total': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_total_201612.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Total CH4 Emission',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'description': 'Total methane emission from microbial, fossil and pyrogenic sources',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 207.09559432166358,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64446.0, 253.0, 61.0, 16.0, 14.0, 4.0, 3.0, 0.0, 2.0, 1.0]},\n     'statistics': {'mean': 0.7699816366032659,\n      'stddev': 3.8996905358416045,\n      'maximum': 207.09559432166358,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n  'fossil': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_fossil_201612.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil CH4 Emission',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 202.8189294183266,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64633.0, 107.0, 35.0, 11.0, 8.0, 3.0, 1.0, 1.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.27127687553584495,\n      'stddev': 2.731411670166909,\n      'maximum': 202.8189294183266,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n  'microbial': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_microbial_201612.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Microbial CH4 Emission',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 161.4604621003495,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64610.0, 155.0, 22.0, 5.0, 2.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.46611433673211145,\n      'stddev': 2.2910210071489456,\n      'maximum': 161.4604621003495,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n  'pyrogenic': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_pyrogenic_201612.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Pyrogenic CH4 Emission',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 13.432528617097262,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64440.0, 221.0, 78.0, 24.0, 18.0, 8.0, 3.0, 1.0, 1.0, 6.0]},\n     'statistics': {'mean': 0.032590424335309266,\n      'stddev': 0.28279054181617735,\n      'maximum': 13.432528617097262,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'tm54dvar-ch4flux-monthgrid-v1',\n 'properties': {'end_datetime': '2016-12-31T00:00:00+00:00',\n  'start_datetime': '2016-12-01T00:00:00+00:00'},\n 'stac_version': '1.0.0',\n 'stac_extensions': []}",
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+    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
+    "section": "Reading the NOAA data from GitHub repo",
+    "text": "Reading the NOAA data from GitHub repo\n\ngithub_repo_owner = \"NASA-IMPACT\"\ngithub_repo_name = \"noaa-viz\"\nfolder_path_ch4, folder_path_co2 = \"flask/ch4\", \"flask/c02\"\ncombined_df_co2, combined_df_ch4 = pd.DataFrame(), pd.DataFrame()\n\n\n# Function to fetch and append a file from GitHub\ndef append_github_file(file_url):\n    response = requests.get(file_url)\n    response.raise_for_status()\n    return response.text\n\n# Get the list of CH4 files in the specified directory using GitHub API\ngithub_api_url = f\"https://api.github.com/repos/{github_repo_owner}/{github_repo_name}/contents/{folder_path_ch4}\"\nresponse = requests.get(github_api_url)\nresponse.raise_for_status()\nfile_list_ch4 = response.json()\n\n# Get the list of CO2 files in the specified directory using GitHub API\ngithub_api_url = f\"https://api.github.com/repos/{github_repo_owner}/{github_repo_name}/contents/{folder_path_ch4}\"\nresponse = requests.get(github_api_url)\nresponse.raise_for_status()\nfile_list_co2 = response.json()",
     "crumbs": [
       "Data Usage Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "TM5-4DVar Isotopic CH₄ Inverse Fluxes"
+      "Greenhouse Gas Concentrations",
+      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
     ]
   },
   {
-    "objectID": "user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.html#exploring-changes-in-ch₄-flux-levels-using-the-raster-api",
-    "href": "user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.html#exploring-changes-in-ch₄-flux-levels-using-the-raster-api",
-    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
-    "section": "Exploring Changes in CH₄ flux Levels Using the Raster API",
-    "text": "Exploring Changes in CH₄ flux Levels Using the Raster API\nIn this notebook, we will explore the global changes of CH₄ flux over time in urban regions. We will visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:10]: item for item in items} \n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the TM5-4DVar Isotopic CH₄ Inverse Fluxes collection, the parameter of interest is “fossil”\nasset_name = \"fossil\" #fossil fuel\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for 2016 and again for 1999, so that we can visualize each event independently.\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"purd\"\n\n# Make a GET request to retrieve information for the 2016 tile\nch4_flux_1 = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2016-12-01']['collection']}/items/{items['2016-12-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nch4_flux_1\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for the 1999 tile\nch4_flux_2 = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['1999-12-01']['collection']}/items/{items['1999-12-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nch4_flux_2\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-199912/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}",
+    "objectID": "user_data_notebooks/noaa-insitu_User_Notebook.html#concatenating-the-ch4-data-into-a-single-dataframe",
+    "href": "user_data_notebooks/noaa-insitu_User_Notebook.html#concatenating-the-ch4-data-into-a-single-dataframe",
+    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
+    "section": "Concatenating the CH4 data into a single DataFrame",
+    "text": "Concatenating the CH4 data into a single DataFrame\n\nfor file_info in file_list_ch4:\n    if file_info[\"name\"].endswith(\"txt\"):\n        file_content = append_github_file(file_info[\"download_url\"])\n        Lines = file_content.splitlines()\n        index = Lines.index(\"# VARIABLE ORDER\")+2\n        df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n        combined_df_ch4 = pd.concat([combined_df_ch4, df], ignore_index=True)\n        \n\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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)",
     "crumbs": [
       "Data Usage Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "TM5-4DVar Isotopic CH₄ Inverse Fluxes"
+      "Greenhouse Gas Concentrations",
+      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
     ]
   },
   {
-    "objectID": "user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.html#visualizing-ch₄-flux-emissions-from-fossil-fuel",
-    "href": "user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.html#visualizing-ch₄-flux-emissions-from-fossil-fuel",
-    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
-    "section": "Visualizing CH₄ flux Emissions from Fossil Fuel",
-    "text": "Visualizing CH₄ flux Emissions from Fossil Fuel\n\n# For this study we are going to compare CH4 fluxes from fossil fuels in 2016 and 1999 along the coast of California\n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n\n# Set the initial zoom level and center of map for both tiles\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer (2016)\nmap_layer_2016 = TileLayer(\n    tiles=ch4_flux_1[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n# Add the first layer to the Dual Map\nmap_layer_2016.add_to(map_.m1)\n\n\n# Define the second map layer (1999)\nmap_layer_1999 = TileLayer(\n    tiles=ch4_flux_2[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_1999.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
+    "objectID": "user_data_notebooks/noaa-insitu_User_Notebook.html#concatenating-the-co2-data-into-a-single-dataframe",
+    "href": "user_data_notebooks/noaa-insitu_User_Notebook.html#concatenating-the-co2-data-into-a-single-dataframe",
+    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
+    "section": "Concatenating the CO2 data into a single DataFrame",
+    "text": "Concatenating the CO2 data into a single DataFrame\n\nfor file_info in file_list_co2:\n    if file_info[\"name\"].endswith(\"txt\"):\n        file_content = append_github_file(file_info[\"download_url\"])\n        Lines = file_content.splitlines()\n        index = Lines.index(\"# VARIABLE ORDER\")+2\n        df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n        combined_df_co2 = pd.concat([combined_df_co2, df], ignore_index=True)\n        \n\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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)",
     "crumbs": [
       "Data Usage Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "TM5-4DVar Isotopic CH₄ Inverse Fluxes"
+      "Greenhouse Gas Concentrations",
+      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
     ]
   },
   {
-    "objectID": "user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.html#visualizing-the-data-as-a-time-series",
-    "href": "user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.html#visualizing-the-data-as-a-time-series",
-    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
-    "section": "Visualizing the Data as a Time Series",
-    "text": "Visualizing the Data as a Time Series\nWe can now explore the fossil fuel emission time series (January 1999 -December 2016) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\nplt.plot(\n    df[\"datetime\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum CH4 flux\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"CH4 emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"g CH₄/m²/year\")\nplt.xticks(rotation = 90)\n\n# Insert title for the plot\nplt.title(\"CH4 emission Values for Texas, Dallas (1999-2016)\")\n\n# Add data citation\nplt.text(\n    df[\"datetime\"].iloc[0],           # X-coordinate of the text\n    df[\"max\"].min(),                  # Y-coordinate of the text\n\n\n\n\n    # Text to be displayed\n    \"Source: NASA/NOAA TM5-4DVar Isotopic CH₄ Inverse Fluxes\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"left\",              # Horizontal alignment\n    verticalalignment=\"top\",                 # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n\n# Plot the time series\nplt.show()\n\n\n\n\n\n\n\n\n\n# Print the properties for the 3rd item in the collection\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n2016-10-01T00:00:00+00:00\n\n\n\n# A GET request is made for the 3rd granule\nch4_flux_3 = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nch4_flux_3\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201610/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=6.8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=ch4_flux_3[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.7\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
+    "objectID": "user_data_notebooks/noaa-insitu_User_Notebook.html#visualizing-the-noaa-data-for-ch4-and-co2",
+    "href": "user_data_notebooks/noaa-insitu_User_Notebook.html#visualizing-the-noaa-data-for-ch4-and-co2",
+    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
+    "section": "Visualizing the NOAA data for CH4 and CO2",
+    "text": "Visualizing the NOAA data for CH4 and CO2\n\nsite_to_filter = 'ABP'\nfiltered_df = combined_df_co2[combined_df_co2['site_code'] == site_to_filter]\n\nfiltered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])\n\n# Set the \"Date\" column as the index\nfiltered_df.set_index('datetime', inplace=True)\n\n# Create a time series plot for 'Data' and 'Value'\nplt.figure(figsize=(12, 6))\nplt.plot(filtered_df.index, filtered_df['value'], label='Carbon Dioxide(CO2) Concentration (ppm)')\nplt.xlabel(\"Observed Date/Time\")\nplt.ylabel(\"Carbon Dioxide(CO2) Concentration (ppm)\")\nplt.title(f\"Observed Co2 Concentration {site_to_filter}\")\nplt.legend()\nplt.grid(True)\n# plt.show()\n\n/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/2606016741.py:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  filtered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])\n\n\n\n\n\n\n\n\n\n\nsite_to_filter = 'ABP'\nfiltered_df = combined_df_ch4[combined_df_ch4['site_code'] == site_to_filter]\nfiltered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])\n\n# Set the \"Date\" column as the index\nfiltered_df.set_index('datetime', inplace=True)\n\n# Create a time series plot for 'Data' and 'Value'\nplt.figure(figsize=(12, 6))\nplt.plot(filtered_df.index, filtered_df['value'], label='Methane Ch4 Concentration (ppb)')\nplt.xlabel(\"Observation Date/Time\")\nplt.ylabel(\"Methane Ch4 Concentration (ppb)\")\nplt.title(f\"Observed CH4 Concentration {site_to_filter}\")\nplt.legend()\nplt.grid(True)\nplt.show()\n\n/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1635934907.py:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  filtered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])",
     "crumbs": [
       "Data Usage Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "TM5-4DVar Isotopic CH₄ Inverse Fluxes"
+      "Greenhouse Gas Concentrations",
+      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
     ]
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-    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
+    "objectID": "user_data_notebooks/noaa-insitu_User_Notebook.html#summary",
+    "href": "user_data_notebooks/noaa-insitu_User_Notebook.html#summary",
+    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
     "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully explored, analyzed, and visualized the STAC collection for TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the CH₄ inverse fluxes for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
+    "text": "Summary\nIn this notebook we have successfully visualized the data for Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory.\n\nInstall and import the necessary libraries\nFetch the collection from GitHub API using the appropriate endpoints\nConcatenating the CO2 and CH4 data into a single DataFrame\nVisualizing the NOAA data for CO2 and CH4\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
     "crumbs": [
       "Data Usage Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "TM5-4DVar Isotopic CH₄ Inverse Fluxes"
+      "Greenhouse Gas Concentrations",
+      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
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-    "title": "SEDAC Gridded World Population Density",
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+    "href": "user_data_notebooks/emit-ch4plume-v1_User_Notebook.html",
+    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
     "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this request form and providing the required information.\nAccess the EMIT Methane Point Source Plume Complexes notebook in the US GHG Center JupyterHub.",
     "crumbs": [
       "Data Usage Notebooks",
-      "Socioeconomic",
-      "SEDAC Gridded World Population Density"
+      "Large Emissions Events",
+      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
     ]
   },
   {
-    "objectID": "user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.html#run-this-notebook",
-    "href": "user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.html#run-this-notebook",
-    "title": "SEDAC Gridded World Population Density",
+    "objectID": "user_data_notebooks/emit-ch4plume-v1_User_Notebook.html#access-this-notebook",
+    "href": "user_data_notebooks/emit-ch4plume-v1_User_Notebook.html#access-this-notebook",
+    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
     "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this request form and providing the required information.\nAccess the EMIT Methane Point Source Plume Complexes notebook in the US GHG Center JupyterHub.",
     "crumbs": [
       "Data Usage Notebooks",
-      "Socioeconomic",
-      "SEDAC Gridded World Population Density"
+      "Large Emissions Events",
+      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
+    ]
+  },
+  {
+    "objectID": "user_data_notebooks/emit-ch4plume-v1_User_Notebook.html#table-of-contents",
+    "href": "user_data_notebooks/emit-ch4plume-v1_User_Notebook.html#table-of-contents",
+    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
+    "section": "Table of Contents",
+    "text": "Table of Contents\n\nData Summary and Application\nApproach\nAbout the Data\nInstall the Required Libraries\nQuery the STAC API\nMap Out Selected Tiles\nCalculate Zonal Statistics\nSummary",
+    "crumbs": [
+      "Data Usage Notebooks",
+      "Large Emissions Events",
+      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
     ]
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-    "title": "SEDAC Gridded World Population Density",
-    "section": "Approach",
-    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. Collection processed in this notebook is SEDAC gridded population density.\nPass the STAC item into raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint\nWe’ll visualize two tiles (side-by-side) allowing for comparison of each of the time points using folium.plugins.DualMap\nAfter the visualization, we’ll perform zonal statistics for a given polygon.",
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+    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
+    "section": "Data Summary and Application",
+    "text": "Data Summary and Application\n\nSpatial coverage: 52°N to 52°S latitude within target mask\nSpatial resolution: 60 m\nTemporal extent: August 1, 2022 - Ongoing\nTemporal resolution: Variable\nUnit: Parts per million meter (ppm-m)\nUtility: Methane Emissions, Plume Detection, Climate Monitoring\n\nFor more, visit the EMIT Methane Point Source Plume Complexes data overview page.",
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-    "text": "About the Data\nThe SEDAC Gridded Population of the World: Population Density, v4.11 dataset provides annual estimates of population density for the years 2000, 2005, 2010, 2015, and 2020 on a 30 arc-second (~1 km) grid. These data can be used for assessing disaster impacts, risk mapping, and any other applications that include a human dimension. This population density dataset is provided by NASA’s Socioeconomic Data and Applications Center (SEDAC) hosted by the Center for International Earth Science Information Network (CIESIN) at Columbia University. The population estimates are provided as a continuous raster for the entire globe.\nFor more information regarding this dataset, please visit the SEDAC Gridded World Population Density data overview page.",
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+    "text": "Approach\n\nIdentify 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 Earth Surface Mineral Dust Source Investigation (EMIT) methane emission plumes data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.Map, visualize the plumes.\nAfter the visualization, perform zonal statistics for a given polygon.",
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-    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for SEDAC population density dataset \ncollection_name = \"sedac-popdensity-yeargrid5yr-v4.11\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\nExamining the contents of our collection under summaries we see that the data is available from January 2000 to December 2020. By looking at the dashboard:time density we observe that the data is available for the years 2000, 2005, 2010, 2015, 2020.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]",
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+    "section": "About the Data",
+    "text": "About the Data\nThe Earth Surface Mineral Dust Source Investigation (EMIT) instrument builds upon NASA’s long history of developing advanced imaging spectrometers for new science and applications. EMIT launched to the International Space Station (ISS) on July 14, 2022. The data shows high-confidence research grade methane plumes from point source emitters - updated as they are identified - in keeping with Jet Propulsion Laboratory (JPL) Open Science and Open Data policy.\nLarge methane emissions, typically referred to as point source emissions, represent a significant proportion of total methane emissions from the production, transport, and processing of oil and natural gas, landfills, and other sources. By measuring the spectral fingerprint of methane, EMIT can map areas of high methane concentration over background levels in the atmosphere, identifying plume complexes, and estimating the methane enhancements.\nFor more information regarding this dataset, please visit the EMIT Methane Point Source Plume Complexes data overview page.",
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-    "section": "Exploring Changes in the World Population Density using the Raster API",
-    "text": "Exploring Changes in the World Population Density using the Raster API\nWe will explore changes in population density in urban regions. In this notebook, we’ll explore the changes in population density over time. We’ll then visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items} \n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the SEDAC Gridded World Population Density collection, the parameter of interest is “population-density”\nasset_name = \"population-density\"\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetching the min and max values\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for January 2000 and again for January 2020, so that we can visualize each event independently.\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"rainbow\" \n\n# Make a GET request to retrieve information for the 2020 tile\njanuary_2020_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2020-01']['collection']}/items/{items['2020-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format \n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2020_tile\n\n\n# Make a GET request to retrieve information for the 2000 tile\njanuary_2000_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2000-01']['collection']}/items/{items['2000-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format  \n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2000_tile",
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+    "section": "Querying the STAC API",
+    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport branca.colormap as cm\nimport seaborn as sns\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for methane emission plumes \ncollection_name = \"emit-ch4plume-v1\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\nExamining the contents of our collection under the temporal variable, we note that data is available from August 2022 to May 2023. By looking at the dashboard: time density, we can see that observations are conducted daily and non-periodically (i.e., there are plumes emissions for multiple places on the same dates).\n\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Check total number of items available\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer \nfrom pystac_client import Client \nimport branca \nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom tabulate import tabulate\nimport branca.colormap as cm\nimport seaborn as sns",
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-    "title": "SEDAC Gridded World Population Density",
-    "section": "Visualizing Population Density.",
-    "text": "Visualizing Population Density.\n\n# Set initial zoom and center of map for population density Layer\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer (January 2020)\nmap_layer_2020 = TileLayer(\n    tiles=january_2020_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=1, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_2020.add_to(map_.m1)\n\n# Define the second map layer (January 2000)\nmap_layer_2000 = TileLayer(\n    tiles=january_2000_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=1, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_2000.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_",
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+    "section": "Query the STAC API",
+    "text": "Query the STAC API\nFirst, you need to import the required libraries. Once imported, they allow better execution of a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored. You will learn the functionality of each library throughout the notebook.\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac/\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster/\"\n\nSTAC API Collection Names\nNow, you must fetch the dataset from the STAC API by defining its associated STAC API collection ID as a variable. The collection ID, also known as the collection name, for the EMIT Methane Point Source Plume Complexes dataset is emit-ch4plume-v1\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for methane emission plumes \ncollection_name = \"emit-ch4plume-v1\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection in a table\n# Adjust display settings\npd.set_option('display.max_colwidth', None)  # Set maximum column width to \"None\" to prevent cutting off text\n\n# Extract the relevant information about the collection\ncollection_info = {\n    \"Title\": collection.get(\"title\", \"N/A\"), # Extract the title of the collection \n    \"Description\": collection.get(\"description\", \"N/A\"), # Extract the dataset description\n    \"Temporal Extent\": collection.get(\"extent\", {}).get(\"temporal\", {}).get(\"interval\", \"N/A\"), # Extract the temporal coverage of the collection\n    \"Spatial Extent\": collection.get(\"extent\", {}).get(\"spatial\", {}).get(\"bbox\", \"N/A\"), # Extract the spatial coverage of the collection\n}\n\n# Convert the derived information into a DataFrame format\nproperties_table = pd.DataFrame(list(collection_info.items()), columns=[\"Collection Summary\", \"\"])\n\n# Display the properties in a table\ncollection_summary = properties_table.style.set_properties(**{'text-align': 'left'}) \\\n                                           .set_table_styles([\n    {\n        'selector': 'th.col0, td.col0',    # Select the first column\n        'props': [('min-width', '200px'),  # Set a minimum width\n                  ('text-align', 'left')]  # Align text to the left\n    },\n    {\n        'selector': 'td.col1',             # Select the second column\n        'props': [('text-align', 'left')]  # Align text to the left\n    }\n])\n\n# Print the collection summary table\ncollection_summary\n\nNext, you will examine the contents of the collection under the temporal variable. You’ll see that the data is available since August 2022. Looking at the dashboard: time density, you can see that observations are conducted daily and non-periodically (i.e., there are plumes emissions for multiple places on the same dates).\n\n# Create a function that would search for data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function is \"get_item_count\" \n# The argument that will be passed to the defined function is \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection \n    count = 0 \n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\" \n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path \n        response = requests.get(items_url) \n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} observations\")\n\n# Sort the items based on their date-time attribute\nitems_sorted = sorted(items, key=lambda x: x[\"properties\"][\"datetime\"])\n\n# Create an empty list\ntable_data = []\n# Extract the ID and date-time information for each granule and add them to the list\n# By default, only the first 5 items in the collection are extracted to be displayed in the table. \n# To see the date-time of all existing granules in this collection, remove \"5\" from \"item_sorted[:5]\" in the line below. \nfor item in items_sorted[:5]:\n    table_data.append([item['id'], item['properties']['datetime']])\n\n# Define the table headers\nheaders = [\"Item ID\", \"Date-Time\"]\n\nprint(\"Below you see the first 5 items in the collection, along with their item IDs and corresponding Start Date-Time.\")\n\n# Print the table using tabulate\nprint(tabulate(table_data, headers=headers, tablefmt=\"fancy_grid\"))\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] refers to the first item (granule) in the list/collection\nitems_sorted[0]",
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-    "title": "SEDAC Gridded World Population Density",
-    "section": "Visualizing the Data as a Time Series",
-    "text": "Visualizing the Data as a Time Series\nWe can now explore the SEDAC population density dataset time series available for the Texas, Dallas area of USA. We can plot the dataset using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\nplt.plot(\n    df[\"date\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum pop density\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Population density over the years\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"Population density\")\n\n# Insert title for the plot\nplt.title(\"Population density over Texas, Dallas (2000-2020)\")\n\n###\n# Add data citation\nplt.text(\n    df[\"date\"].iloc[0],           # X-coordinate of the text\n    df[\"max\"].min(),              # Y-coordinate of the text\n\n\n\n\n    # Text to be displayed\n    \"Source: NASA SEDAC Gridded World Population Density\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"right\",             # Horizontal alignment\n    verticalalignment=\"bottom\",              # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n\n# Plot the time series\nplt.show()\n\n\n# Print the properties for the 3rd item in the collection\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n\n# A GET request is made for the 2010 tile\njanuary2010_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary2010_tile\n\n\n# Create a new map to display the 2010 tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=january2010_tile[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.5\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox",
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+    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
+    "section": "Map Out Selected Tiles",
+    "text": "Map Out Selected Tiles\nYou will now explore global methane emission plumes from point sources and visualize the results on a map using folium.\n\n# Once again, apply the function created above \"get_item_count\" to the Air-Sea CO2 Flux ECCO-Darwin collection\n# This step allows retrieving the number of granules “observations” in the collection.\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n\n# Next, you need to create a dictionary where the \"id\" field of each item in the collection are queried more explicitly\nplume_complexes = {items[\"id\"]: items for items in items} \n\n\n# Next, you need to specify the asset name for this collection.\n# The asset name refers to the raster band containing the pixel values for the parameter of interest.\n# For the case of the EMIT Methane Point Source collection, the parameter of interest is “ch4-plume-emissions”.\nasset_name = \"ch4-plume-emissions\"\n\nBelow, you will enter the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":plume_complexes[list(plume_complexes.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":plume_complexes[list(plume_complexes.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, you will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint.\n\n# Select the item ID which you want to visualize. Item ID is in the format yyyymmdd followed by the timestamp. This ID can be extracted from the COG name as well.\n# To browse and select other tiles in the collection, please visit https://search.earthdata.nasa.gov/search/granules?p=C2748088093-LPCLOUD&pg[0][v]=f&pg[0][gsk]=-start_date&q=emit%20plume&tl=1694622854.77!3!!\n\n# You need to copy the entire granule nomenclature \nitem_id = \"EMIT_L2B_CH4PLM_001_20230418T200118_000829\"\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer to choose a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"magma\"\n\n# Make a GET request to retrieve information for the selected tile defined in \"item_id\"\nmethane_plume_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{plume_complexes[item_id]['collection']}/items/{plume_complexes[item_id]['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    \n    # Pass the color formula and colormap for custom visualization\n    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    \n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nmethane_plume_tile\n\n\n# Set a colormap for the granule\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp (https://matplotlib.org/stable/users/explain/colors/colormaps.html)\ncolormap = \"magma\" \n\n\n# Defining the breaks in the colormap \ncolor_map = cm.LinearColormap(colors = ['#310597', '#4C02A1', '#6600A7', '#7E03A8', '#9511A1', '#AA2395', '#BC3587', '#CC4778', '#DA5A6A', '#E66C5C', '#F0804E', '#F89540','#FDAC33', '#FDC527', '#F8DF25'], vmin = 0, vmax = 1500 )\n\n\n# Add an appropriate caption, in this case it would be Parts per million meter\ncolor_map.caption = 'ppm-m'\n\n# Set initial zoom and center of map for plume Layer\nmap_ = folium.Map(location=(methane_plume_tile[\"center\"][1], methane_plume_tile[\"center\"][0]), zoom_start=14, tiles=None, tooltip = 'test tool tip')\nfolium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png', name='ESRI World Imagery', attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(map_)\n\n\n# Use the 'TileLayer' library to display the raster layer, add an appropriate caption, and adjust the transparency of the layer on the map\nmap_layer = TileLayer(\n    tiles=methane_plume_tile[\"tiles\"][0], # Path to retrieve the tile\n    name='Plume Complex Landfill',\n    overlay='True', # The layer can be overlaid on the map\n    attr=\"GHG\", # Set the attribution \n    opacity=1, # Adjust the transparency of the layer\n)\nmap_layer.add_to(map_)\n\n\n# Adjust map elements \nfolium.LayerControl(collapsed=False, position='bottomleft').add_to(map_)\nmap_.add_child(color_map)\nsvg_style = '<style>svg#legend {font-size: 14px; background-color: white;}</style>'\nmap_.get_root().header.add_child(folium.Element(svg_style))\n\n\n# Visualizing the map\nmap_",
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       "Data Usage Notebooks",
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+    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
     "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully explored, analyzed and visualized the STAC collection for the SEDAC Gridded World Population Density dataset.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare population density for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
+    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for the EMIT Methane Point Source Plume Complexes dataset: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map the methane emission plumes 5. Generate statistics for the area of interest (AOI)\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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+    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
     "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
+    "text": "TM5-4DVar Isotopic CH₄ Inverse Fluxes\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "crumbs": [
+      "Data Flow Diagrams",
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "TM5-4DVar Isotopic CH₄ Inverse Fluxes"
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-    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
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+    "title": "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)",
     "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
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-    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
-    "section": "Approach",
-    "text": "Approach\n\nRead in National CO2 Budgets using Pandas\nSub-select the data structure using Pandas\nVisualize the CO2 budgets for a country\nInvestigate uncertainties and metrics for understanding the dataset"
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-    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
-    "section": "About the Data",
-    "text": "About the Data\nThis tutorial guides a user to further explore data from the Carbon Observatory (OCO-2) modeling intercomparison project (MIP). It is designed for those with more understanding of the science and is therefore labeled as intermediate level.\nThe code is used to estimate the annual net terrestrial carbon stock loss (ΔCloss) and net carbon exchange (NCE) for a given country using the “top-down” NCE outputs from the Carbon Observatory (OCO-2) modeling intercomparison project (MIP). Several standardized experiments are studied in this notebook based on the OCO-2 MIP dataset including flux estimates from in situ CO₂ measurements (IS), flux estimates from OCO-2 land CO₂ data (LNLG), combined in situ and OCO-2 land CO₂ data (LNLGIS), and combined in situ and OCO-2 land and ocean CO₂ data (LNLGOGIS). Estimates and uncertainties associated with fossil fuels, riverine fluxes, and wood and crop fluxes are also graphed along with the ΔCloss and NCE variables.\nFor more information about this data collection, please visit the OCO-2 MIP Top-Down CO2 Budgets data overview page.\nFor more information regarding this dataset, please visit the U.S. Greenhouse Gas Center."
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-    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
-    "section": "Import required modules",
-    "text": "Import required modules\nFirst we will need to import the relevant python modules:\n\nimport pandas as pd # for manipulating csv dataset\nimport numpy as np\nimport matplotlib.pyplot as plt # make plots\nfrom scipy.stats import norm # We will use this for understanding significance"
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-    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
-    "section": "Read the CO2 National budget dataset",
-    "text": "Read the CO2 National budget dataset\nNow we will read in the csv dataset from https://ceos.org/gst/carbon-dioxide.html\n\nurl ='https://ceos.org/gst/files/pilot_topdown_CO2_Budget_countries_v1.csv'\ndf_all = pd.read_csv(url, skiprows=52)"
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-    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
-    "section": "Sub-select a single top-down dataset (experiment)",
-    "text": "Sub-select a single top-down dataset (experiment)\nTo simplify the analysis, let’s subselect the results for a single experiment. The experiments are: - IS: estimates fluxes from in situ CO2 measurements - LNLG: estimates fluxes from OCO-2 land CO2 data - LNLGIS: combines in situ and OCO-2 land CO2 data - LNLGOGIS: combines in situ and OCO-2 land and ocean CO2 data\nWe would like to use the experiment that uses the most high-quality CO2 data. There are some concerns about small residual biases in OCO-2 ocean data (Byrne et al., 2023), so let’s use the LNLGIS experiment.\n\n# Choose one experiment from the list ['IS', 'LNLG', 'LNLGIS', 'LNLGOGIS']\nexperiment = 'LNLGIS'\n\n# Subset of columns for a given experiment\nif experiment == 'IS':\n    df = df_all.drop(df_all.columns[[4,5,6,7,8,9,12,13,14,15,16,17,20,21,22,23,24,25,34,35,36]], axis=1)\nif experiment == 'LNLG':\n    df = df_all.drop(df_all.columns[[2,3,6,7,8,9,10,11,14,15,16,17,18,19,22,23,24,25,33,35,36]], axis=1)\nif experiment == 'LNLGIS':\n    df = df_all.drop(df_all.columns[[2,3,4,5,8,9,10,11,12,13,16,17,18,19,20,21,24,25,33,34,36]], axis=1)\nif experiment == 'LNLGOGIS':\n    df = df_all.drop(df_all.columns[[2,3,4,5,6,7,10,11,12,13,14,15,18,19,20,21,22,23,33,34,35]], axis=1)\n\n# We can now look at the colums of data\ndf.head()\n\n\n\n\n\n\n\n\nAlpha 3 Code\nYear\nLNLGIS dC_loss (TgCO2)\nLNLGIS dC_loss unc (TgCO2)\nLNLGIS NBE (TgCO2)\nLNLGIS NBE unc (TgCO2)\nLNLGIS NCE (TgCO2)\nLNLGIS NCE unc (TgCO2)\nRivers (TgCO2)\nRiver unc (TgCO2)\nWood+Crop (TgCO2)\nWood+Crop unc (TgCO2)\nFF (TgCO2)\nFF unc (TgCO2)\nZ-statistic\nFUR LNLGIS\n\n\n\n\n0\nAFG\n2015\n39.3407\n153.746\n40.9643\n153.746\n60.3537\n153.744\n-2.43286\n1.69832\n4.05648\n1.21694\n19.3894\n0.797698\n0.37\n0.19\n\n\n1\nAFG\n2016\n50.6167\n175.454\n52.5114\n175.454\n73.0333\n175.452\n-2.16185\n2.24033\n4.05648\n1.21694\n20.5220\n0.678080\n0.31\n0.19\n\n\n2\nAFG\n2017\n54.5096\n179.794\n56.4726\n179.794\n77.5355\n179.793\n-2.09349\n2.37705\n4.05648\n1.21694\n21.0629\n0.695856\n0.47\n0.19\n\n\n3\nAFG\n2018\n116.4260\n243.057\n118.4610\n243.057\n143.9580\n243.056\n-2.02199\n2.52005\n4.05648\n1.21694\n25.4974\n0.695856\n0.39\n0.19\n\n\n4\nAFG\n2019\n64.0162\n181.516\n66.0388\n181.516\n93.8974\n181.514\n-2.03383\n2.49637\n4.05648\n1.21694\n27.8585\n0.797698\n0.49\n0.19"
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-    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
-    "section": "Sub-select a single country",
-    "text": "Sub-select a single country\nLet’s further filter the dataset to look at a specific country. Choose a country by entering the alpha code in the country_name variable below\n\n# Choose a country\ncountry_name = 'USA' \n\n# We can sub-select the data for the country\ncountry_data = df[df['Alpha 3 Code'] == country_name]\n\n# Now we can look at the data for a specific experiment and country\ncountry_data.head()\n\n\n\n\n\n\n\n\nAlpha 3 Code\nYear\nLNLGIS dC_loss (TgCO2)\nLNLGIS dC_loss unc (TgCO2)\nLNLGIS NBE (TgCO2)\nLNLGIS NBE unc (TgCO2)\nLNLGIS NCE (TgCO2)\nLNLGIS NCE unc (TgCO2)\nRivers (TgCO2)\nRiver unc (TgCO2)\nWood+Crop (TgCO2)\nWood+Crop unc (TgCO2)\nFF (TgCO2)\nFF unc (TgCO2)\nZ-statistic\nFUR LNLGIS\n\n\n\n\n1232\nUSA\n2015\n-1031.83\n721.213\n-1346.46\n721.213\n4017.31\n713.897\n-165.430\n71.7453\n-149.196\n-44.7589\n5363.77\n102.4670\n-0.81\n0.91\n\n\n1233\nUSA\n2016\n-1419.92\n399.738\n-1743.80\n399.738\n3529.45\n387.079\n-174.684\n53.2375\n-149.196\n-44.7589\n5273.24\n99.8012\n0.04\n0.91\n\n\n1234\nUSA\n2017\n-1375.12\n1034.010\n-1696.63\n1034.010\n3515.14\n1029.250\n-172.308\n57.9894\n-149.196\n-44.7589\n5211.76\n99.0981\n0.67\n0.91\n\n\n1235\nUSA\n2018\n-1018.89\n784.463\n-1333.83\n784.463\n4036.65\n778.179\n-165.747\n71.1117\n-149.196\n-44.7589\n5370.48\n99.0981\n-0.20\n0.91\n\n\n1236\nUSA\n2019\n-1161.41\n718.054\n-1504.61\n718.054\n3728.95\n710.705\n-194.005\n14.5948\n-149.196\n-44.7589\n5233.56\n102.4670\n-0.38\n0.91\n\n\n\n\n\n\n\n#This dataset contains fluxes over a five year period, 2015-2020.\nLet’s look at a plot of the annual net terrestrial carbon stock loss (ΔCloss) for each year.\n\n# Make plot\nfig, ax1 = plt.subplots(1, 1, figsize=(6, 4))\nax1.errorbar(country_data['Year'],country_data[experiment+' dC_loss (TgCO2)'],\n                    yerr=country_data[experiment+' dC_loss unc (TgCO2)'],label=experiment,capsize=10)\nax1.legend(loc='upper right')\nax1.set_ylabel('$\\Delta$C$_\\mathrm{loss}$ (TgCO$_2$ year$^{-1}$)')\nax1.set_xlabel('Year')\nax1.set_title('$\\Delta$C$_\\mathrm{loss}$ for '+country_name)\nymin, ymax = ax1.get_ylim()\nmax_abs_y = max(abs(ymin), abs(ymax))\nax1.set_ylim([-max_abs_y, max_abs_y])\nxmin, xmax = ax1.get_xlim()\nax1.plot([xmin,xmax],[0,0],'k',linewidth=0.5)\nax1.set_xlim([xmin, xmax])\n\n\n\n\n\n\n\n\nNext, we can look at the full carbon budget for a given year.\nThe code below creates a plot similar to Fig 13 of Byrne et al. (2023). Each of the bars on the left side of the dashed vertical line (Fossil fuel emissions, lateral C transport by rivers, lateral C transport in crop and wood products, and the net terrestrial carbon stock loss combined to give the net carbon exchange (net surface-atmosphere CO2 flux) shown on the right.\n\n# Pick a specifc year (or mean year)\nyear='mean'\n\n# Make plot\ncountry_data_mean = country_data[country_data['Year'] == year]\na=country_data_mean['Wood+Crop (TgCO2)']\nb=country_data_mean['Wood+Crop unc (TgCO2)']\nprint(b)\n#\nplt.bar(1, country_data_mean['FF (TgCO2)'], yerr=country_data_mean['FF unc (TgCO2)'], label='FF', alpha=0.5)\nplt.bar(2, country_data_mean['Rivers (TgCO2)'], yerr=country_data_mean['River unc (TgCO2)'], label='Rivers', alpha=0.5)\nplt.bar(3, country_data_mean['Wood+Crop (TgCO2)'], yerr=abs(country_data_mean['Wood+Crop unc (TgCO2)']), label='WoodCrop', alpha=0.5)\nplt.bar(4, country_data_mean[experiment+' dC_loss (TgCO2)'], yerr=country_data_mean['LNLGIS dC_loss unc (TgCO2)'], label='dC', alpha=0.5)\nplt.bar(6, country_data_mean[experiment+' NCE (TgCO2)'], yerr=country_data_mean['LNLGIS NCE unc (TgCO2)'], label='NCE', alpha=0.5)\nax = plt.gca()\nymin, ymax = ax.get_ylim()\nplt.plot([5,5],[ymin,ymax],'k:')\nxmin, xmax = ax.get_xlim()\nplt.plot([xmin,xmax],[0,0],'k',linewidth=0.5)\nplt.xlim([xmin,xmax])\nplt.ylim([ymin,ymax])\n#\nplt.xticks([1,2,3,4,6], ['Fossil\\nFuels','Rivers','Wood+\\nCrops','$\\mathrm{\\Delta C _{loss}}$','NCE'])\nplt.title(country_name+' '+year)\nplt.ylabel('CO$_2$ Flux (TgCO$_2$ year$^{-1}$)')\n\n1238   -44.7589\nName: Wood+Crop unc (TgCO2), dtype: float64\n\n\nText(0, 0.5, 'CO$_2$ Flux (TgCO$_2$ year$^{-1}$)')\n\n\n\n\n\n\n\n\n\nUncertainty is an important consideration when analyzing the flux estimates provided by Byrne et al. (2023).\nEach flux estimate is provided with an error estimate representing the standard deviation, and assuming the errors are well prepresented by a normal distribution. This probability dirtribution provided by this uncertainty can be visualized below. We can further quantify the\n\n\n# Select NCE, NBE or dC_loss\nquantity = 'dC_loss'\n\n# Value for comparison\ncomparison_value = 1000 # TgCO2/year\n\n\nMIP_mean = country_data_mean[experiment+' '+quantity+' (TgCO2)'].item()\nMIP_std = country_data_mean[experiment+' '+quantity+' unc (TgCO2)'].item()\n\n# Perform t-test\nt_value = abs(MIP_mean - comparison_value)/(MIP_std / np.sqrt(11))\ncrtical_value = 2.23 # use p=0.05 significance\nif t_value > crtical_value:\n    ttest = 'statistically different'\nif t_value < crtical_value:\n    ttest = 'not statistically\\ndifferent'\n\n# Make plot\nxbounds = abs(MIP_mean)+MIP_std*4\nif abs(crtical_value) > xbounds:\n    xbounds = abs(crtical_value)\nx_axis = np.arange(-1.*xbounds, xbounds, 1) \nplt.plot(x_axis, norm.pdf(x_axis, MIP_mean, MIP_std)) \nax = plt.gca()\nymin, ymax = ax.get_ylim()\nxmin, xmax = ax.get_xlim()\nplt.plot([0,0],[ymin,ymax*1.2],'k:',linewidth=0.5)\nplt.plot([xmin,xmax],[0,0],'k:',linewidth=0.5)\nplt.plot([comparison_value,comparison_value],[ymin,ymax*1.2],'k')\nplt.text(comparison_value+(xmax-xmin)*0.01,ymax*0.96,'value = '+str(comparison_value),ha='left',va='top')\nplt.text(comparison_value+(xmax-xmin)*0.01,ymax*0.9,ttest,ha='left',va='top')\nplt.ylim([ymin,ymax*1.2])\nplt.xlim([xmin,xmax])\nplt.plot(MIP_mean,ymax*1.03,'ko')\nplt.plot([MIP_mean-MIP_std,\n         MIP_mean+MIP_std],\n         [ymax*1.03,ymax*1.03],'k')\nplt.plot([MIP_mean-MIP_std,\n         MIP_mean-MIP_std],\n         [ymax*1.005,ymax*1.055],'k')\nplt.plot([MIP_mean+MIP_std,\n         MIP_mean+MIP_std],\n         [ymax*1.005,ymax*1.055],'k')\nplt.text(MIP_mean,ymax*1.115,\n         str(round(MIP_mean))+' $\\pm$ '+\n         str(round(MIP_std))+' TgCO$_2$',ha='center')\nplt.title(country_name+' '+year+' '+quantity+'')\nplt.yticks([])\nplt.ylabel('Probability')\nplt.xlabel(quantity+' (TgCO$_2$ year$^{-1}$)')\n\nText(0.5, 0, 'dC_loss (TgCO$_2$ year$^{-1}$)')\n\n\n\n\n\n\n\n\n\nFinally, we will examine two metrics that are useful for understanding the confidence in the top-down results:\n\nZ-statistic: metric of agreement in NCE estimates across the experiments that assimilate different CO2 datasets. Experiments are considered significantly different if the magnitude exceeds 1.96\nFractional Uncertainty Reduction (FUR): metric of how strongly the assimilated CO2 data on reduce NCE uncertainties. Values range from 0 to 1, with 0 meaning zero error reduction and 1 meaning complete error reduction\n\nHere we will add a plot of the Z-statistic for each year, and add the FUR value for the country.\n\n# Make plot\nfig, ax1 = plt.subplots(1, 1, figsize=(6, 4))\nax1.plot(country_data['Year'],country_data['Z-statistic'],label=experiment)\nax1.legend(loc='upper right')\nax1.set_ylabel('Z-statistic')\nax1.set_xlabel('Year')\nax1.set_title(country_name)\nymin, ymax = ax1.get_ylim()\nmax_abs_y = max(abs(ymin), abs(ymax))\nax1.set_ylim([-3, 3])\nxmin, xmax = ax1.get_xlim()\nax1.plot([xmin,xmax],[0,0],'k',linewidth=0.5)\nax1.plot([xmin,xmax],[-1.96,-1.96],'k--',linewidth=0.5)\nax1.plot([xmin,xmax],[1.96,1.96],'k--',linewidth=0.5)\nax1.set_xlim([xmin, xmax])\nax1.text(xmin+0.12,2.6,'Fractional error reduction: '+str(country_data['FUR '+experiment].iloc[1]))\n\nText(-0.18000000000000005, 2.6, 'Fractional error reduction: 0.91')"
+    "text": "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "crumbs": [
+      "Data Flow Diagrams",
+      "Greenhouse Gas Concentrations",
+      "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)"
+    ]
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-    "text": "This script was used to add concatenated layers and transform Gridded Anthropogenic Methane Emissions Inventory dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nfrom datetime import datetime\nimport numpy as np\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nTrue\n\n\n\n# session = boto3.session.Session()\nsession = boto3.Session(\n    aws_access_key_id=os.environ.get(\"AWS_ACCESS_KEY_ID\"),\n    aws_secret_access_key=os.environ.get(\"AWS_SECRET_ACCESS_KEY\"),\n    aws_session_token=os.environ.get(\"AWS_SESSION_TOKEN\"),\n)\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"../data/epa_emissions_express_extension\"\ns3_folder_name = \"epa_express_extension_Mg_km2_yr\"\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(FOLDER_NAME):\n    xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n    xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    variable = [var for var in xds.data_vars]\n    new_variables = {\n        \"all-variables\": variable[:-1],\n        \"agriculture\": variable[17:21],\n        \"natural-gas-systems\": variable[10:15] + [variable[26]],\n        \"petroleum-systems\": variable[5:9],\n        \"waste\": variable[21:26],\n        \"coal-mines\": variable[2:5],\n        \"other\": variable[:2] + [variable[9]] + variable[15:17],\n    }\n    filename = name.split(\"/ \")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n    start_time = datetime(int(filename_elements[-2]), 1, 1)\n\n    for time_increment in range(0, len(xds.time)):\n        for key, value in new_variables.items():\n            data = np.zeros(dtype=np.float32, shape=(len(xds.lat), len(xds.lon)))\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            for var in value:\n                data = data + getattr(xds.isel(time=time_increment), var)\n            # data = np.round(data / pow(10, 9), 2)\n            data.values[data.values==0] = np.nan\n            data = data*((1/(6.022*pow(10,23)))*(16.04*pow(10,-6))*366*pow(10,10)*86400)\n            data = data.fillna(-9999)\n            data = data.isel(lat=slice(None, None, -1))\n            data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = start_time.strftime(\"%Y\")\n            filename_elements.insert(2, key)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{s3_folder_name}/{cog_filename}\",\n                )\n\n                files_processed = files_processed._append(\n                    {\"file_name\": name, \"COGs_created\": cog_filename},\n                    ignore_index=True,\n                )\n\n                print(f\"Generated and saved COG: {cog_filename}\")\nprint(\"Done generating COGs\")\n\nTraceback (most recent call last):\n  File \"_pydevd_bundle/pydevd_cython.pyx\", line 1078, in _pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\n  File \"_pydevd_bundle/pydevd_cython.pyx\", line 297, in _pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\n  File \"/Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages/debugpy/_vendored/pydevd/pydevd.py\", line 1976, in do_wait_suspend\n    keep_suspended = self._do_wait_suspend(thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\n  File \"/Users/vgaur/miniconda3/envs/cmip6/lib/python3.9/site-packages/debugpy/_vendored/pydevd/pydevd.py\", line 2011, in _do_wait_suspend\n    time.sleep(0.01)\nKeyboardInterrupt\n\n\n\n---------------------------------------------------------------------------\nKeyboardInterrupt                         Traceback (most recent call last)\n/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb Cell 4 line 4\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=45'>46</a> # data = data*(9.74*pow(10,-11))\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=46'>47</a> # data.values[data.values<=np.nanpercentile(data.values, 50)] = np.nan\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=47'>48</a> data = data.fillna(-9999)\n---> <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=48'>49</a> data = data.isel(lat=slice(None, None, -1))\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=49'>50</a> data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=50'>51</a> data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb Cell 4 line 4\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=45'>46</a> # data = data*(9.74*pow(10,-11))\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=46'>47</a> # data.values[data.values<=np.nanpercentile(data.values, 50)] = np.nan\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=47'>48</a> data = data.fillna(-9999)\n---> <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=48'>49</a> data = data.isel(lat=slice(None, None, -1))\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=49'>50</a> data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n     <a href='vscode-notebook-cell:/Users/vgaur/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express_layers_update.ipynb#W3sZmlsZQ%3D%3D?line=50'>51</a> data.rio.write_crs(\"epsg:4326\", inplace=True)\n\nFile _pydevd_bundle/pydevd_cython.pyx:1363, in _pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__()\n\nFile _pydevd_bundle/pydevd_cython.pyx:662, in _pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch()\n\nFile _pydevd_bundle/pydevd_cython.pyx:1087, in _pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch()\n\nFile _pydevd_bundle/pydevd_cython.pyx:1078, in _pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch()\n\nFile _pydevd_bundle/pydevd_cython.pyx:297, in _pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend()\n\nFile ~/miniconda3/envs/cmip6/lib/python3.9/site-packages/debugpy/_vendored/pydevd/pydevd.py:1976, in PyDB.do_wait_suspend(self, thread, frame, event, arg, exception_type)\n   1973             from_this_thread.append(frame_custom_thread_id)\n   1975     with self._threads_suspended_single_notification.notify_thread_suspended(thread_id, stop_reason):\n-> 1976         keep_suspended = self._do_wait_suspend(thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\n   1978 frames_list = None\n   1980 if keep_suspended:\n   1981     # This means that we should pause again after a set next statement.\n\nFile ~/miniconda3/envs/cmip6/lib/python3.9/site-packages/debugpy/_vendored/pydevd/pydevd.py:2011, in PyDB._do_wait_suspend(self, thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\n   2008         self._call_mpl_hook()\n   2010     self.process_internal_commands()\n-> 2011     time.sleep(0.01)\n   2013 self.cancel_async_evaluation(get_current_thread_id(thread), str(id(frame)))\n   2015 # process any stepping instructions\n\nKeyboardInterrupt: \n\n\n\n\n\n\n Back to top"
+    "text": "SEDAC Gridded World Population Data\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
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+      "Data Flow Diagrams",
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+      "SEDAC Gridded World Population Data"
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+    "href": "data_workflow/emit-ch4plume-v1_Data_Flow.html",
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     "section": "",
-    "text": "This script was used to read the EMIT Methane Point Source Plume Complexes dataset provided in Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\n\n\nsession_ghgc = boto3.session.Session(profile_name=\"ghg_user\")\ns3_client_ghgc = session_ghgc.client(\"s3\")\nsession_veda_smce = boto3.session.Session()\ns3_client_veda_smce = session_veda_smce.client(\"s3\")\n\n# Since the plume emissions were already COGs, we just had to transform their naming convention to be stored in the STAC collection.\nSOURCE_BUCKET_NAME = \"ghgc-data-staging-uah\"\nTARGET_BUCKET_NAME = \"ghgc-data-store-dev\"\n\n\nkeys = []\nresp = s3_client_ghgc.list_objects_v2(Bucket=SOURCE_BUCKET_NAME)\nfor obj in resp[\"Contents\"]:\n    if \"l3\" in obj[\"Key\"]:\n        keys.append(obj[\"Key\"])\n\nfor key in keys:\n    s3_obj = s3_client_ghgc.get_object(Bucket=SOURCE_BUCKET_NAME, Key=key)[\n        \"Body\"\n    ]\n    filename = key.split(\"/\")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n\n    date = re.search(\"t\\d\\d\\d\\d\\d\\d\\d\\dt\", key).group(0)\n    filename_elements.insert(-1, date[1:-1])\n    filename_elements.pop()\n\n    cog_filename = \"_\".join(filename_elements)\n    # # add extension\n    cog_filename = f\"{cog_filename}.tif\"\n    s3_client_veda_smce.upload_fileobj(\n        Fileobj=s3_obj,\n        Bucket=TARGET_BUCKET_NAME,\n        Key=f\"plum_data/{cog_filename}\",\n    )\n\n\n\n\n Back to top",
+    "text": "EMIT Methane Point Source Plume Complexes\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks",
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+    "title": "MiCASA Land Carbon Flux - Data Workflow",
     "section": "",
-    "text": "This script was used to transform the VULCAN dataset provided in GeoTIFF format for display in the Greenhouse Gas (GHG) Center with the calaulation of validation statistics.\n\nimport xarray\nimport pandas as pd\nimport boto3\nimport glob\nimport s3fs\nimport tempfile\nfrom datetime import datetime\nimport os\nimport boto3\nfrom pyproj import CRS\nimport numpy as np\n\nimport rasterio\nfrom rasterio.warp import calculate_default_transform, reproject, Resampling\nfrom rasterio.enums import Resampling\nfrom rio_cogeo.cogeo import cog_translate\nfrom rio_cogeo.profiles import cog_profiles\n\n\nconfig = {\n    \"data_acquisition_method\": \"s3\",\n    \"raw_data_bucket\" : \"gsfc-ghg-store\",\n    \"raw_data_prefix\": \"Vulcan/v4.0/grid.1km.mn\",\n    \"cog_data_bucket\": \"ghgc-data-store-develop\",\n    \"cog_data_prefix\": \"transformed_cogs/VULCAN_v4\"\n}\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\n\nraw_data_bucket = config[\"raw_data_bucket\"]\nraw_data_prefix= config[\"raw_data_prefix\"]\n\ncog_data_bucket = config['cog_data_bucket']\ncog_data_prefix= config[\"cog_data_prefix\"]\n\ndate_fmt=config['date_fmt']\n\nfs = s3fs.S3FileSystem()\n\n\ndef get_all_s3_keys(bucket, model_name, ext):\n    \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n    keys = []\n\n    kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n    while True:\n        resp = s3_client.list_objects_v2(**kwargs)\n        for obj in resp[\"Contents\"]:\n            if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n                keys.append(obj[\"Key\"])\n\n        try:\n            kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n        except KeyError:\n            break\n\n    return keys\n\nkeys = get_all_s3_keys(raw_data_bucket, raw_data_prefix, \".tif\")\n\n\nlen(keys)\n\n\n# To calculate the validation stats\noverall= pd.DataFrame(columns=[\"data\",\"min\",\"max\",\"mean\",\"std\"])\n\n\n# Step 1: Reproject the data \n# Define the source and target CRS\n# Also calculate raw - monthly validation stats\nos.makedirs(\"reproj\", exist_ok=True)\nsrc_crs = CRS.from_wkt('PROJCS[\"unknown\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Lambert_Conformal_Conic_2SP\"],PARAMETER[\"latitude_of_origin\",40],PARAMETER[\"central_meridian\",-97],PARAMETER[\"standard_parallel_1\",33],PARAMETER[\"standard_parallel_2\",45],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]')\ndst_crs = CRS.from_epsg(4326)  # WGS 84\ndf = pd.DataFrame(columns=['filename', 'min(raw)', 'max(raw)', 'mean(raw)', 'std(raw)'])\noverall_raw= []\nfor key in keys:\n    url = f\"s3://{raw_data_bucket}/{key}\"\n    with rasterio.open(url) as src:\n        filename_elements = key.split(\"/\")[-1].split(\".\")[:-1]\n        output_tif = \"_\".join(filename_elements) + \".tif\"\n        data = src.read(1)  # Read the first band\n        overall_raw.append(data)\n        \n        # Calculate statistics while ignoring NaN values\n        min_val = np.nanmin(data)\n        max_val = np.nanmax(data)\n        mean_val = np.nanmean(data)\n        std_val = np.nanstd(data)  \n        stats = [output_tif, min_val, max_val, mean_val, std_val]\n        df.loc[len(df)] = stats\n        \n        transform, width, height = calculate_default_transform(\n        src.crs, dst_crs, src.width, src.height, *src.bounds)\n        kwargs = src.meta.copy()\n        kwargs.update({\n        'crs': dst_crs,\n        'transform': transform,\n        'width': width,\n        'height': height,\n        'nodata': -9999\n        })\n\n        with rasterio.open(f\"reproj/{output_tif}\", 'w', **kwargs) as dst:\n            for i in range(1, src.count + 1):\n                reproject(\n                source=rasterio.band(src, i),\n                destination=rasterio.band(dst, i),\n                src_transform=src.transform,\n                src_crs=src.crs,\n                dst_transform=transform,\n                dst_crs=dst_crs,\n                resampling=Resampling.nearest)\n        print(f\"Done for {output_tif}\")\n\n\n\n\n# overall validation of raw data\noverall_raw= np.array(overall_raw)\nnan_min = np.nanmin(overall_raw)\nnan_max = np.nanmax(overall_raw)\nnan_mean = np.nanmean(overall_raw)\nnan_std = np.nanstd(overall_raw)\noverall.loc[len(overall)] = [\"raw\",nan_min,nan_max,nan_mean,nan_std]\n\n\n# validation for reprojected data - yearly calculation\noverall_reproj = []\nfiles = glob.glob(\"reproj/*.tif\")\ndf1 = pd.DataFrame(columns=['filename', 'min(reprojected)', 'max(reprojected)', 'mean(reprojected)', 'std(reprojected)'])\nfor file in files:\n    with rasterio.open(file) as src:\n        filename_elements = file.split(\"/\")[-1].split(\".\")[:-1]\n        output_tif = \"_\".join(filename_elements) + \".tif\"\n        data = src.read(1)  \n        data = np.ma.masked_equal(data, -9999)\n        overall_reproj.append(data)\n        \n        # Calculate statistics while ignoring NaN values\n        min_val = np.nanmin(data)\n        max_val = np.nanmax(data)\n        mean_val = np.nanmean(data)\n        std_val = np.nanstd(data)  \n        stats = [output_tif, min_val, max_val, mean_val, std_val]\n        df1.loc[len(df1)] = stats\n\n\n# overall validation of reprojected  data\noverall_reproj= np.array(overall_reproj)\noverall_reproj = np.ma.masked_equal(overall_reproj, -9999)\nnan_min = np.nanmin(overall_reproj)\nnan_max = np.nanmax(overall_reproj)\nnan_mean = np.nanmean(overall_reproj)\nnan_std = np.nanstd(overall_reproj)\noverall.loc[len(overall)] = [\"reprojected\",nan_min,nan_max,nan_mean,nan_std]\n\n\n# Step 2: Replace nan and 0 values with -9999\nos.makedirs(\"reproj2\", exist_ok=True)\nfiles = glob.glob(\"reproj/*.tif\")\nfor file in files:\n    filename = file.split('/')[-1]\n    xda = xarray.open_dataarray(file).sel(band=1)\n\n    # Multiply data\n    data = data *( 44/12)\n    \n    data = xda.where(xda != 0, -9999)  # Replace 0 with -9999\n    #data = data.where(data != -3.4e+38, -9999)  # Replace -3.4e+38 with -9999\n    data = data.fillna(-9999)  # Ensure all NaNs are replaced with -9999\n    data_array = data.values\n    \n\n    # Open the source raster to get metadata\n    with rasterio.open(file) as src:\n        meta = src.meta\n        meta.update({\n            'nodata': -9999,\n            'dtype': 'float32',\n            'driver': 'COG'\n        })\n        with rasterio.open(f\"reproj2/{filename}\", 'w', **meta) as dst:\n            dst.write(data_array, 1)\n\n\n# validation for reprojected data (non zero) - monthly calculation\noverall_reproj2=[]\nfiles = glob.glob(\"reproj/*.tif\")\ndf11 = pd.DataFrame(columns=['filename', 'min(reproj_nonzero)', 'max(reproj_nonzero)', 'mean(reproj_nonzero)', 'std(reproj_nonzero)'])\nfor file in files:\n    with rasterio.open(file) as src:\n        filename_elements = file.split(\"/\")[-1].split(\".\")[:-1]\n        output_tif = \"_\".join(filename_elements) + \".tif\"\n        data = src.read(1)  \n        data = np.ma.masked_where((data == -9999) | (data == 0), data)\n        overall_reproj2.append(data)\n\n        \n        # Calculate statistics while ignoring NaN values\n        min_val = np.nanmin(data)\n        max_val = np.nanmax(data)\n        mean_val = np.nanmean(data)\n        std_val = np.nanstd(data)  \n        stats = [output_tif, min_val, max_val, mean_val, std_val]\n        df11.loc[len(df11)] = stats\n\n\n# validation for reprojected data (non zero) - overall calculation\noverall_reproj2= np.array(overall_reproj2)\noverall_reproj2 = np.ma.masked_where((overall_reproj2 == -9999) | (overall_reproj2 == 0), overall_reproj2)\nnan_min = np.nanmin(overall_reproj2)\nnan_max = np.nanmax(overall_reproj2)\nnan_mean = np.nanmean(overall_reproj2)\nnan_std = np.nanstd(overall_reproj2)\noverall.loc[len(overall)] = [\"reprojected_non_zero\",nan_min,nan_max,nan_mean,nan_std]\n\n\n# Step 3: To put overviews\nCOG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\nOVERVIEW_LEVELS = 9\nOVERVIEW_RESAMPLING = 'average'\n\nfor file in glob.glob(\"reproj2/*.tif\"):\n    output_path = f\"output/{file.split(\"/\")[-1]}\"\n    \n    # Create a temporary file to hold the COG\n    with tempfile.NamedTemporaryFile(suffix='.tif', delete=False) as temp_file:       \n        # Create COG with overviews and nodata value\n        cog_translate(\n            file,\n            temp_file.name,\n            cog_profiles.get(\"deflate\"),\n            overview_level=OVERVIEW_LEVELS,\n            overview_resampling=OVERVIEW_RESAMPLING,\n            nodata=-9999\n        )\n        # Move the temporary file to the desired local path\n        os.rename(temp_file.name, output_path)\n\n\n# validation for final data with overviews - overall calculation\noverall_final=[]\nfiles = glob.glob(\"output/*.tif\")\ndf2 = pd.DataFrame(columns=['filename', 'min(transformed)', 'max(transformed)', 'mean(transformed)', 'std(transformed)'])\nfor file in files:\n    with rasterio.open(file) as src:\n        filename_elements = file.split(\"/\")[-1].split(\".\")[:-1]\n        output_tif = \"_\".join(filename_elements) + \".tif\"\n        data = src.read(1)  # Read the first band\n        \n        # Mask -9999 values and NaNs for statistics calculation\n        data = np.ma.masked_where((data == -9999) | np.isnan(data), data)\n        # Multiply data - undo the multiplication done during transformation\n        data = data *( 12/44)\n        overall_final.append(data)\n        \n        # Calculate statistics while ignoring NaN values\n        min_val = np.nanmin(data)\n        max_val = np.nanmax(data)\n        mean_val = np.nanmean(data)\n        total = np.nansum(data) \n        stats = [output_tif, min_val, max_val, mean_val, std_val]\n        df2.loc[len(df2)] = stats\n\n\n# validation for final data (with overviews) - overall calculation\noverall_final= np.array(overall_final)\noverall_final = np.ma.masked_where((overall_final == -9999) | np.isnan(overall_final), overall_final)\nnan_min = np.nanmin(overall_final)\nnan_max = np.nanmax(overall_final)\nnan_mean = np.nanmean(overall_final)\nnan_std = np.nanstd(overall_final)\noverall.loc[len(overall)] = [\"Transformed\",nan_min,nan_max,nan_mean,nan_std]\n\n\npd.merge(pd.merge(df,df1, on='filename', how='inner'), pd.merge(df11,df2, on='filename', how='inner'), how='inner',on='filename' )\n\n\noverall\n\n\n# Save to json\noverall.to_json(\"overall_stats.json\")\npd.merge(pd.merge(df,df1, on='filename', how='inner'), pd.merge(df11,df2, on='filename', how='inner'), how='inner',on='filename' ).to_json(\"yearly_stats.json\")\n\n\n\n\n Back to top",
+    "text": "MiCASA Land Carbon Flux - Data Workflow\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "Vulcan Fossil Fuel CO₂ Emissions"
+      "Data Flow Diagrams",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
+      "MiCASA Land Carbon Flux - Data Workflow"
     ]
   },
   {
-    "objectID": "cog_transformation/odiac-ffco2-monthgrid-v2022.html",
-    "href": "cog_transformation/odiac-ffco2-monthgrid-v2022.html",
-    "title": "ODIAC Fossil Fuel CO₂ Emissions",
+    "objectID": "data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.html",
+    "href": "data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.html",
+    "title": "GRA²PES Greenhouse Gas and Air Quality Species",
     "section": "",
-    "text": "This script was used to transform the ODIAC Fossil Fuel CO₂ Emissions dataset from GeoTIFF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\n\nimport tempfile\nimport boto3\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = \"ghgc-data-store-dev\" # S3 bucket where the COGs are stored after transformation\n\nfold_names = os.listdir(\"ODIAC\")\n\nfiles_processed = pd.DataFrame(columns=[\"file_name\", \"COGs_created\"])   # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor fol_ in fold_names:\n    for name in os.listdir(f\"ODIAC/{fol_}\"):\n        xds = xarray.open_dataarray(f\"ODIAC/{fol_}/{name}\")\n\n        filename = name.split(\"/ \")[-1]\n        filename_elements = re.split(\"[_ .]\", filename)\n        # # insert date of generated COG into filename\n        filename_elements.pop()\n        filename_elements[-1] = fol_ + filename_elements[-1][-2:]\n\n        xds.rio.set_spatial_dims(\"x\", \"y\", inplace=True)\n        xds.rio.write_nodata(-9999, inplace=True)\n        xds.rio.write_crs(\"epsg:4326\", inplace=True)\n\n        cog_filename = \"_\".join(filename_elements)\n        # # add extension\n        cog_filename = f\"{cog_filename}.tif\"\n\n        with tempfile.NamedTemporaryFile() as temp_file:\n            xds.rio.to_raster(\n                temp_file.name,\n                driver=\"COG\",\n            )\n            s3_client.upload_file(\n                Filename=temp_file.name,\n                Bucket=bucket_name,\n                Key=f\"ODIAC_geotiffs_COGs/{cog_filename}\",\n            )\n\n        files_processed = files_processed._append(\n            {\"file_name\": name, \"COGs_created\": cog_filename},\n            ignore_index=True,\n        )\n\n        print(f\"Generated and saved COG: {cog_filename}\")\n\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/ODIAC_COGs/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top"
+    "text": "GRA²PES Greenhouse Gas and Air Quality Species\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "crumbs": [
+      "Data Flow Diagrams",
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "GRA²PES Greenhouse Gas and Air Quality Species"
+    ]
   },
   {
-    "objectID": "cog_transformation/casagfed-carbonflux-monthgrid-v3.html",
-    "href": "cog_transformation/casagfed-carbonflux-monthgrid-v3.html",
-    "title": "CASA-GFED3 Land Carbon Flux",
+    "objectID": "data_workflow/gosat-based-ch4budget-yeargrid-v1_Data_Flow.html",
+    "href": "data_workflow/gosat-based-ch4budget-yeargrid-v1_Data_Flow.html",
+    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
     "section": "",
-    "text": "Code used to transform CASA-GFED3 Land Carbon Flux data from netcdf to Cloud Optimized Geotiff.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = \"ghgc-data-store-dev\"\ndate_fmt = \"%Y%m\"\n\nfiles_processed = pd.DataFrame(columns=[\"file_name\", \"COGs_created\"])\nfor name in os.listdir(\"geoscarb\"):\n    xds = xarray.open_dataset(\n        f\"geoscarb/{name}\",\n        engine=\"netcdf4\",\n    )\n    xds = xds.assign_coords(\n        longitude=(((xds.longitude + 180) % 360) - 180)\n    ).sortby(\"longitude\")\n    variable = [var for var in xds.data_vars]\n\n    for time_increment in range(0, len(xds.time)):\n        for var in variable[:-1]:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = getattr(xds.isel(time=time_increment), var)\n            data = data.isel(latitude=slice(None, None, -1))\n            data.rio.set_spatial_dims(\"longitude\", \"latitude\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            date = data.time.dt.strftime(date_fmt).item(0)\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = date\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"GEOS-Carbs/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=\"GEOS-Carbs/metadata.json\",\n    )\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/GEOS-Carbs/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top"
+    "text": "GOSAT-based Top-down Total and Natural Methane Emissions\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "crumbs": [
+      "Data Flow Diagrams",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
+      "GOSAT-based Top-down Total and Natural Methane Emissions"
+    ]
   },
   {
-    "objectID": "cog_transformation/tm54dvar-ch4flux-monthgrid-v1.html",
-    "href": "cog_transformation/tm54dvar-ch4flux-monthgrid-v1.html",
-    "title": "TM5-4DVar Isotopic CH₄ Inverse Fluxes",
+    "objectID": "data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.html",
+    "href": "data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.html",
+    "title": "OCO-2 MIP Top-Down CO₂ Budgets",
     "section": "",
-    "text": "This script was used to transform the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nfrom datetime import datetime\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"tm5-ch4-inverse-flux\"\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(FOLDER_NAME):\n    xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n    xds = xds.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})\n    xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    variable = [var for var in xds.data_vars if \"global\" not in var]\n\n    for time_increment in range(0, len(xds.months)):\n        filename = name.split(\"/ \")[-1]\n        filename_elements = re.split(\"[_ .]\", filename)\n        start_time = datetime(int(filename_elements[-2]), time_increment + 1, 1)\n        for var in variable:\n            data = getattr(xds.isel(months=time_increment), var)\n            data = data.isel(lat=slice(None, None, -1))\n            data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = start_time.strftime(\"%Y%m\")\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{FOLDER_NAME}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{FOLDER_NAME}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{FOLDER_NAME}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
+    "text": "OCO-2 MIP Top-Down CO₂ Budgets\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks",
+      "Data Flow Diagrams",
       "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "TM5-4DVar Isotopic CH₄ Inverse Fluxes"
+      "OCO-2 MIP Top-Down CO₂ Budgets"
     ]
   },
   {
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-    "href": "cog_transformation/lpjwsl-wetlandch4-monthgrid-v1.html",
-    "title": "Wetland Methane Emissions, LPJ-wsl Model",
+    "objectID": "data_workflow/lam-testbed-ghg-concentrations_Data_Flow.html",
+    "href": "data_workflow/lam-testbed-ghg-concentrations_Data_Flow.html",
+    "title": "Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project",
+    "section": "",
+    "text": "Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "crumbs": [
+      "Data Flow Diagrams",
+      "Greenhouse Gas Concentrations",
+      "Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project"
+    ]
+  },
+  {
+    "objectID": "index.html",
+    "href": "index.html",
+    "title": "U.S. Greenhouse Gas Center: Documentation",
     "section": "",
-    "text": "This script was used to transform the Wetland Methane Emissions, LPJ-wsl Model dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"NASA_GSFC_ch4_wetlands_monthly\"\ndirectory = \"ch4_wetlands_monthly\"\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(directory):\n    xds = xarray.open_dataset(\n        f\"{directory}/{name}\", engine=\"netcdf4\", decode_times=False\n    )\n    xds = xds.assign_coords(longitude=(((xds.longitude + 180) % 360) - 180)).sortby(\n        \"longitude\"\n    )\n    variable = [var for var in xds.data_vars]\n    filename = name.split(\"/ \")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n\n    for time_increment in range(0, len(xds.time)):\n        for var in variable:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = getattr(xds.isel(time=time_increment), var)\n            data = data.isel(latitude=slice(None, None, -1))\n            data = data * 1000\n            data.rio.set_spatial_dims(\"longitude\", \"latitude\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            date = (\n                f\"0{int((data.time.item(0)/732)+1)}\"\n                if len(str(int((data.time.item(0) / 732) + 1))) == 1\n                else f\"{int((data.time.item(0)/732)+1)}\"\n            )\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = filename_elements[-1] + date\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{FOLDER_NAME}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{FOLDER_NAME}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{FOLDER_NAME}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top"
+    "text": "The U.S. Greenhouse Gas (GHG) Center provides a cloud-based system for exploring and analyzing U.S. government and other curated greenhouse gas datasets.\nOn this site, you can find the technical documentation for the services the center provides, how to load the datasets, and how the datasets were transformed from their source formats (eg. netCDF, HDF, etc.) into cloud-optimized formats that enable efficient cloud data access and visualization.",
+    "crumbs": [
+      "Welcome"
+    ]
   },
   {
-    "objectID": "cog_transformation/odiac-ffco2-monthgrid-v2023.html",
-    "href": "cog_transformation/odiac-ffco2-monthgrid-v2023.html",
-    "title": "ODIAC Fossil Fuel CO₂ Emissions",
+    "objectID": "index.html#welcome",
+    "href": "index.html#welcome",
+    "title": "U.S. Greenhouse Gas Center: Documentation",
     "section": "",
-    "text": "This script was used to transform the ODIAC Fossil Fuel CO₂ Emissions dataset from GeoTIFF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport xarray\nimport re\nimport tempfile\nimport numpy as np\nimport boto3\nimport os\nimport gzip,shutil, wget\nimport s3fs\nimport hashlib\nimport json\n\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nfs = s3fs.S3FileSystem()\n\ndata_dir = \"data/\"\ndataset_name = \"odiac-ffco2-monthgrid-v2023\"\ncog_data_bucket = \"ghgc-data-store-develop\"\ncog_data_prefix= f\"transformed_cogs/{dataset_name}\"\ncog_checksum_prefix= \"checksum\"\n\n\n# Retrieve the checksum of raw files\nchecksum_dict ={}\nfor year in range(2000,2023):\n    checksum_url = f\"https://db.cger.nies.go.jp/nies_data/10.17595/20170411.001/odiac2023/1km_tiff/{year}/odiac2023_1km_checksum_{year}.md5.txt\"\n    response = requests.get(checksum_url)\n    content = response.text\n    tmp={}\n    \n    # Split the content into lines\n    lines = content.splitlines()\n    \n    for line in lines:\n        checksum, filename = line.split()\n        tmp[filename[:-3]] = checksum\n    checksum_dict.update(tmp)\nchecksum_dict = {k: v for k, v in checksum_dict.items() if k.endswith('.tif')}\n\n\n\ndef calculate_md5(file_path):\n    \"\"\"\n    Calculate the MD5 hash of a file.\n\n    Parameters:\n    file_path (str): The path to the file.\n\n    Returns:\n    str: The MD5 hash of the file.\n    \"\"\"\n    hash_md5 = hashlib.md5()\n    with open(file_path, \"rb\") as f:\n        for chunk in iter(lambda: f.read(4096), b\"\"):\n            hash_md5.update(chunk)\n    return hash_md5.hexdigest()\n\n\n#Code to download raw ODIAC data in your local machine\n\n# Creating  a base directory for ODIAC data\nif not os.path.exists(data_dir):\n        os.makedirs(data_dir)\n\nchecksum_dict_local={}\n# Download and unzip data for the years you want\nfor year in range(2000,2023):\n    year_dir = os.path.join(data_dir, str(year))\n    checksum_download_link = f\"https://db.cger.nies.go.jp/nies_data/10.17595/20170411.001/odiac2023/1km_tiff/{year}/odiac2023_1km_checksum_{year}.md5.txt\"\n    wget.download(checksum_download_link, year_dir)\n    # Make a subfolder for each year\n    if not os.path.exists(year_dir):\n        os.makedirs(year_dir)\n\n    for month in range(1,13):\n        month = f\"{month:02d}\"\n        download_link = f\"https://db.cger.nies.go.jp/nies_data/10.17595/20170411.001/odiac2023/1km_tiff/{year}/odiac2023_1km_excl_intl_{str(year)[-2:]}{month}.tif.gz\"\n        target_folder = f\"{data_dir}/{year}/\"\n        fname = os.path.basename(download_link)\n        target_path = os.path.join(target_folder, fname)\n\n        # Download the file\n        wget.download(download_link, target_path)\n\n        # Unzip the file\n        with gzip.open(target_path, 'rb') as f_in:\n            with open(target_path[:-3], 'wb') as f_out:\n                shutil.copyfileobj(f_in, f_out)\n                \n        # Calculate checksum of the .gz file \n        checksum_dict_local[target_path.split(\"/\")[-1][:-3]]=calculate_md5(target_path)\n        \n        # Remove the zip file\n        os.remove(target_path)\n    \n\n\n# check if the checksums match\nchecksum_dict_local == checksum_dict\n\n\n# List of years you want to run the transformation on\nfold_names=[str(i) for i in range(2020,2023)]\n\nfor fol_ in fold_names:\n    names= os.listdir(f\"{data_dir}{fol_}\")\n    names= [name for name in names if name.endswith('.tif')]\n    print(\"For year: \" ,fol_)\n    for name in names:\n        xds = xarray.open_dataarray(f\"{data_dir}{fol_}/{name}\")\n        filename = name.split(\"/ \")[-1]\n        filename_elements = re.split(\"[_ .]\", filename)\n        \n        # Remove the extension\n        filename_elements.pop()\n        # Extract and insert date of generated COG into filename\n        filename_elements[-1] = fol_ + filename_elements[-1][-2:]\n\n        # Replace 0 values  with -9999\n        xds = xds.where(xds!=0, -9999)\n        xds.rio.set_spatial_dims(\"x\", \"y\", inplace=True)\n        xds.rio.write_nodata(-9999, inplace=True)\n        xds.rio.write_crs(\"epsg:4326\", inplace=True)\n\n        cog_filename = \"_\".join(filename_elements)\n        cog_filename = f\"{cog_filename}.tif\"\n\n        # Write the cog file to s3 \n        with tempfile.NamedTemporaryFile() as temp_file:\n            xds.rio.to_raster(\n                temp_file.name,\n                driver=\"COG\",\n                compress=\"DEFLATE\"\n            )\n            s3_client.upload_file(\n                Filename=temp_file.name,\n                Bucket=cog_data_bucket,\n                Key=f\"{cog_data_prefix}/{cog_filename}\",\n            )\n\n        print(f\"Generated and saved COG: {cog_filename}\")\n\nprint(\"ODIAC COGs generation completed!!!\")\n\n\n# This block is used to calculate the SHA for each COG file and store in a JSON.\n\ndef get_all_s3_keys(bucket, model_name, ext):\n    \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n    keys = []\n\n    kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n    while True:\n        resp = s3_client.list_objects_v2(**kwargs)\n        for obj in resp[\"Contents\"]:\n            if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n                keys.append(obj[\"Key\"])\n\n        try:\n            kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n        except KeyError:\n            break\n\n    return keys\n\nkeys = get_all_s3_keys(cog_data_bucket, cog_data_prefix,\".tif\")\n\n\ndef compute_sha256(url):\n    \"\"\"Compute SHA-256 checksum for a given file.\"\"\"\n    sha256_hash = hashlib.sha256()\n    with fs.open(url) as f:\n        for byte_block in iter(lambda: f.read(4096), b\"\"):\n            sha256_hash.update(byte_block)\n    return sha256_hash.hexdigest()\n\nsha_mapping = {}\nfor key in keys:\n    sha_mapping[key.split(\"/\")[-1]]=compute_sha256(f\"s3://{cog_data_bucket}/{key}\")\n\n\njson_data = json.dumps(sha_mapping, indent=4)\ns3_client.put_object(Bucket=cog_data_bucket, Key=f\"{cog_checksum_prefix}/{dataset_name}.json\", Body=json_data)\n\nprint(\"Checksums created for ODIAC!!!\")\n\n\n\n\n Back to top",
+    "text": "The U.S. Greenhouse Gas (GHG) Center provides a cloud-based system for exploring and analyzing U.S. government and other curated greenhouse gas datasets.\nOn this site, you can find the technical documentation for the services the center provides, how to load the datasets, and how the datasets were transformed from their source formats (eg. netCDF, HDF, etc.) into cloud-optimized formats that enable efficient cloud data access and visualization.",
     "crumbs": [
-      "Data Transformation Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "ODIAC Fossil Fuel CO₂ Emissions"
+      "Welcome"
     ]
   },
   {
-    "objectID": "cog_transformation/eccodarwin-co2flux-monthgrid-v5.html",
-    "href": "cog_transformation/eccodarwin-co2flux-monthgrid-v5.html",
-    "title": "Air-Sea CO₂ Flux, ECCO-Darwin Model v5",
-    "section": "",
-    "text": "This script was used to transform the Air-Sea CO₂ Flux, ECCO-Darwin Mode dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nimport rasterio\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\n\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"ecco-darwin\"\ns3_fol_name = \"ecco_darwin\"\n\n# Reading the raw netCDF files from local machine\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\nfor name in os.listdir(FOLDER_NAME):\n    xds = xarray.open_dataset(\n        f\"{FOLDER_NAME}/{name}\",\n        engine=\"netcdf4\",\n    )\n    xds = xds.rename({\"y\": \"latitude\", \"x\": \"longitude\"})\n    xds = xds.assign_coords(longitude=((xds.longitude / 1440) * 360) - 180).sortby(\n        \"longitude\"\n    )\n    xds = xds.assign_coords(latitude=((xds.latitude / 721) * 180) - 90).sortby(\n        \"latitude\"\n    )\n\n    variable = [var for var in xds.data_vars]\n\n    for time_increment in xds.time.values:\n        for var in variable[2:]:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = xds[var]\n\n            data = data.reindex(latitude=list(reversed(data.latitude)))\n            data.rio.set_spatial_dims(\"longitude\", \"latitude\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            # generate COG\n            COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n\n            filename_elements.pop()\n            filename_elements[-1] = filename_elements[-2] + filename_elements[-1]\n            filename_elements.pop(-2)\n            # # insert date of generated COG into filename\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(temp_file.name, **COG_PROFILE)\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{s3_fol_name}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n            del data\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=\"s3_fol_name/metadata.json\",\n    )\n\n# A csv file to store the names of all the files converted.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{s3_fol_name}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
+    "objectID": "index.html#contents",
+    "href": "index.html#contents",
+    "title": "U.S. Greenhouse Gas Center: Documentation",
+    "section": "Contents",
+    "text": "Contents\n\nServices provided for accessing and analyzing the US GHG Center datasets, such as the JupyterHub environment for interactive computing.\nDataset usage examples, e.g. for the Wetland Methane Emissions from the LPJ-EOSIM model dataset, that shows how to load the dataset in Python in JupyterHub.\nDataset transformation scripts, which document the code used to transform datasets for display in the US GHG Center. An example is the ODIAC Fossil Fuel CO₂ Emissions dataset transformation code.\nData processing and verification reports that openly present the process we used to check and verify that any transformation did not alter the original source data. An example is the GOSAT-based Top-down Total and Natural Methane Emissions dataset.\nData Flow Diagrams, which provide a high level summary of how each dataset was integrated into the US GHG Center. See the MiCASA Land Carbon Flux Flow Diagram as an example.",
     "crumbs": [
-      "Data Transformation Notebooks",
-      "Natural Greenhouse Gas Sources Emissions and Sinks",
-      "Air-Sea CO₂ Flux, ECCO-Darwin Model v5"
+      "Welcome"
     ]
   },
   {
-    "objectID": "cog_transformation/influx-testbed-ghg-concentrations.html",
-    "href": "cog_transformation/influx-testbed-ghg-concentrations.html",
-    "title": "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)",
-    "section": "",
-    "text": "This script was used to transform the NIST INFLUX dataset into meaningful csv files for ingestion to vector dataset.\n\nimport pandas as pd\nimport glob\nimport os\nimport zipfile\nimport wget\nfrom collections import defaultdict\nfrom io import StringIO\nimport re\nimport warnings\nimport warnings\nfrom datetime import datetime, timedelta\n# Ignore the FutureWarning\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\n\n\n\nselected_level=\"level1\"\nbase_dir = \"data/\"\noutput_dir = \"output/\"\ndat_file_pattern = f\"{base_dir}/*/*.dat\"\noutput_base_dataset_name = \"PSU_INFLUX_INSITU\" \nconstant_variables = [\"datetime\",\"latitude\",\"longitude\",\"level\",\"elevation_m\",\"intake_height_m\",\"Instr\"]\nvariables =[['CO2(ppm)'],['CH4(ppb)']] # exclude CO\nmetadata_link= \"UrbanTestBed-Metadata - INFLUX.csv\"\n\n\n# Functions\ndef filter_dict(site_dict, selected_level):\n    return {key: [x for x in value if selected_level in x] for key, value in site_dict.items()}\n\ndef flag_desired_level(df, desired_level):\n    df['is_max_height_data'] = df['level']== desired_level\n    return df\n\ndef add_location(link, site_number):\n    meta= pd.read_csv(link)\n    location =meta[meta['Station Code']==f\"Site {site_number[-2:]}\"][['City','State']]#(get the actual site number)\n    return location['City'].item()+\",\"+location['State'].item()\n\ndef convert_to_datetime(row):\n    year = int(row['Year'])\n    doy = int(row['DOY'])\n    hour = int(row['Hour'])\n    \n    # Create a datetime object for the start of the year\n    date = datetime(year, 1, 1) + timedelta(days=doy - 1)\n    # Add the hours\n    datetime_obj = date + timedelta(hours=hour)\n    # Format as yyyy-mm-ddThh:mm:ssZ\n    return datetime_obj.strftime('%Y-%m-%dT%H:%M:%SZ')\n\ndef download_and_extract_zip_files(base_dir, levels):\n    \"\"\"\n    Download, extract, and delete zip files for the specified levels.\n\n    Parameters:\n    base_dir (str): The base directory for storing the downloaded files.\n    levels (list): A list of levels to download and extract.\n    \"\"\"\n    # Ensure the base directory exists\n    os.makedirs(base_dir, exist_ok=True)\n\n    # Loop through the levels and handle the download and extraction\n    for level in levels:\n        download_link = f\"https://www.datacommons.psu.edu/download/meteorology/influx/influx-tower-data/wmo-x2019-scale/level{level}.zip\"\n        fname = download_link.split(\"/\")[-1]\n        target_path = os.path.join(base_dir, fname)\n        \n        # Download the zip file\n        wget.download(download_link, target_path)\n        print(f\"Downloaded {download_link} to {target_path}\")\n\n        # Extract the zip file\n        with zipfile.ZipFile(target_path, 'r') as zip_ref:\n            zip_ref.extractall(base_dir)\n            print(f\"Extracted {fname}\")\n\n        # Delete the zip file after extraction\n        os.remove(target_path)\n\ndef create_site_dict(pattern):\n    \"\"\"\n    Creates a dictionary where keys are site numbers extracted from file paths,\n    and values are lists of file paths corresponding to each site number.\n    \n    Args:\n    - pattern (str): Glob pattern to match files.\n    \n    Returns:\n    - dict: Dictionary mapping site numbers to lists of file paths.\n    \"\"\"\n    all_files = glob.glob(pattern)\n    site_dict = defaultdict(list)\n    \n    for file_path in all_files:\n        site_number = file_path.split('_')[-4]\n        site_dict[site_number].append(file_path)\n    \n    return dict(site_dict)\n\ndef process_site_files(site_number, file_list):\n    \"\"\"\n    Process files for a given site number and save the combined DataFrame to CSV.\n    \n    Args:\n    - site_number (str): Site number to process.\n    - file_list (list): List of file paths corresponding to the site number.\n    \"\"\"\n    df = pd.DataFrame()\n    \n    for file_path in file_list:\n        with open(file_path, 'r') as file:\n            data = file.read()\n            \n        contents = data.split(\"\\nSite\")\n        lat = float((re.search(r'LATITUDE:\\s*([0-9.]+)\\s*[NS]', contents[0])).group(1))\n        lat_hemisphere = (re.search(r'LATITUDE:\\s*([0-9.]+)\\s*[NS]', contents[0])).group(0)[-1]\n        \n        lon = float((re.search(r'LONGITUDE:\\s*([0-9.]+)\\s*[EW]', contents[0])).group(1))\n        lon_hemisphere = (re.search(r'LONGITUDE:\\s*([0-9.]+)\\s*[EW]', contents[0])).group(0)[-1]\n        \n        level= file_path.split(\"/\")[-2]\n        \n        elevation= re.search(r'ALTITUDE:\\s*([0-9.]+)\\s*m\\s*ASL', contents[0]).group(1)\n        intake_height= re.search(r'SAMPLING HEIGHT:\\s*([0-9.]+)\\s*m\\s*AGL', contents[0]).group(1)\n\n        \n        data_io = StringIO(contents[1])\n        tmp_data = pd.read_csv(data_io, delim_whitespace=True)\n        tmp_data = tmp_data.reset_index().rename(columns={'index': 'Site'})\n        tmp= tmp_data.query(\"Flag==1\").copy()# 1 means no known problem, 0 is not recommemded, 9 is instrument issue (unrealistic)\n        #tmp['SiteCode'] = int(re.search(r'\\d+', site_number).group()) \n        tmp['latitude'] = lat\n        tmp['longitude'] = lon\n        tmp['level'] = int(re.search(r'\\d+', level).group())\n        tmp['elevation_m'] = elevation\n        tmp['intake_height_m']= intake_height\n\n        if lat_hemisphere == 'S':\n            tmp['latitude'] = -1* tmp[\"latitude\"]\n        if lon_hemisphere == 'W':\n            tmp['longitude'] = -1* tmp[\"longitude\"]\n\n        df = pd.concat([df, tmp], ignore_index=True)\n\n    # Ensure the output directory exists\n    os.makedirs(output_dir, exist_ok=True)\n    os.makedirs(output_dir+\"PSU_INFLUX_INSITU/\", exist_ok=True)\n    \n\n    df['datetime'] = df[[\"Year\",\"DOY\",\"Hour\"]].apply(convert_to_datetime, axis=1)\n    df.reset_index(drop=True, inplace=True)\n    for v in variables:\n        tmp_file=df[constant_variables + v].copy()\n        tmp_file['unit'] = v[0][-4:-1] #CO2(ppm) get  the unit only\n        \n        tmp_file.rename(columns={v[0]: 'value'}, inplace=True)\n        tmp_file['location']= add_location(metadata_link, site_number)\n        tmp_file = flag_desired_level(tmp_file, 1) # Flagging only level 1 data\n\n        # Remove nan\n        tmp_file.dropna(subset=[\"value\"], inplace=True)\n\n        #filter 0 values\n        tmp_file[tmp_file[\"value\"]!=0].to_csv(f\"{output_dir}/PSU_INFLUX_INSITU/NIST-FLUX-IN-{site_number}-{v[0][:-5]}-hourly-concentrations.csv\", index=False)\n        print(f\"CSV Created for Site {site_number}-{v[0][:-5]}!!!\")\n    return \n\n\n\n\n\n\n# Download and extract zip files\nlevels_to_download = range(1, 5)\n#download_and_extract_zip_files(base_dir=base_dir, levels=levels_to_download)\n\n# Create site dictionary\nsite_dict = create_site_dict(dat_file_pattern)\n\n# Comment if you want data from all levels\n#site_dict = filter_dict(site_dict, selected_level)\n\n# Process each site's files\nfor site_number, file_list in site_dict.items():\n    print(f\"Processing Site Number: {site_number}, Total Files: {len(file_list)}\")\n    process_site_files(site_number, file_list)\n\n\n\n\n Back to top",
+    "objectID": "index.html#contact",
+    "href": "index.html#contact",
+    "title": "U.S. Greenhouse Gas Center: Documentation",
+    "section": "Contact",
+    "text": "Contact\nFor technical help or general questions, please contact the support team using the feedback form.",
     "crumbs": [
-      "Data Transformation Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)"
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@@ -1216,183 +1431,139 @@
     "text": "Explore Data Usage Notebook\n\nCASA-GFED3 Land Carbon Flux\nAir-Sea CO₂ Flux, ECCO-Darwin Model v5\nEMIT Methane Point Source Plume Complexes\nU.S. Gridded Anthropogenic Methane Emissions Inventory\nGOSAT-based Top-down Total and Natural Methane Emissions\nWetland Methane Emissions, LPJ-wsl Model\n\nOCO-2 MIP Top-Down CO₂ Budgets\nOCO-2 GEOS Column CO₂ Concentrations\nODIAC Fossil Fuel CO₂ Emissions\nSEDAC Gridded World Population Density\nTM5-4DVar Isotopic CH₄ Inverse Fluxes\nAtmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory\nVulcan Fossil Fuel CO₂ Emissions\nGreenhouse Gas And Air Pollutants Emissions System"
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-    "text": "OCO-2 GEOS Column CO₂ Concentrations\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "text": "The US GHG Center promotes the use of JupyterHub environments for interactive data science. JupyterHub enables you to analyze massive archives of Earth science data in the cloud in an interactive environment that alleviates the complexities of managing compute resources (virtual machines, roles and permissions, etc).\nUsers affiliated with the US GHG Center can get access to a dedicated JupyterHub service, provided in collaboration with 2i2c: hub.ghg.center. Please find instructions for requesting access below.\nIf you are a scientist affiliated with other NASA projects such as VEDA, EIS, and MAAP, you can also keep using the resources provided by these projects. Through the use of open-source technology, we make sure our services are interoperable and exchangeable.",
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-    "text": "EMIT Methane Point Source Plume Complexes\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "objectID": "services/jupyterhub.html#to-get-us-ghg-center-jupyterhub-access",
+    "href": "services/jupyterhub.html#to-get-us-ghg-center-jupyterhub-access",
+    "title": "JupyterHub",
+    "section": "To Get US GHG Center JupyterHub access:",
+    "text": "To Get US GHG Center JupyterHub access:\nThe US GHG Center notebook environment is available to authorized users on an as-need basis. If you are a user affiliated with the US GHG Center, you can gain access by using our Hub Access Request form.\n\nMake sure you have a GitHub Account. Take note of your GitHub username.\nFill out the request form and provide needed information.\nWatch your email for notification of authorization and the invite to join the US GHG Center Hub Access GitHub Team.\nOnce you accept the invitation, you can go to hub.ghg.center and login using your GitHub credentials.",
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+    "objectID": "services/jupyterhub.html#to-access-user-notebooks",
+    "href": "services/jupyterhub.html#to-access-user-notebooks",
+    "title": "JupyterHub",
+    "section": "To access User Notebooks",
+    "text": "To access User Notebooks\nThis site provides Jupyter notebooks showing how to load and analyze Earth data in the interactive cloud computing environment.\nFurther instructions are included in each notebook.\nIf you have any questions, please use the feedback form to contact the US GHG Center user support team.",
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-    "text": "Air-Sea CO₂ Flux, ECCO-Darwin Model v5\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
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-      "Data Flow Diagrams",
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+    "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport rasterio\nfrom glob import glob\nimport pathlib\nimport boto3\nimport pandas as pd\nimport calendar\nimport seaborn as sns\nimport json\nimport re\n\n\n# Enter the year you want to run validation on\nvyear=2022 # summary json files will be later generated for the year you provide here\ndata_dir=\"data/\" # make sure you have the data for vyear in your data directory\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\n\ndataset_name= \"odiac-ffco2-monthgrid-v2023\"\ncog_data_bucket=\"ghgc-data-store-develop\"\ncog_data_prefix = f\"transformed_cogs/{dataset_name}\"\n\n\ndef get_all_s3_keys(bucket, model_name, ext):\n    \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n    keys = []\n\n    kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n    while True:\n        resp = s3_client.list_objects_v2(**kwargs)\n        for obj in resp[\"Contents\"]:\n            if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n                keys.append(obj[\"Key\"])\n\n        try:\n            kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n        except KeyError:\n            break\n\n    return keys\n\nkeys = get_all_s3_keys(cog_data_bucket, cog_data_prefix, \".tif\")\n\n# Extract only the COGs for selected year\npattern = re.compile(rf'{vyear}(0[1-9]|1[0-2])')\nkeys = [path for path in keys if pattern.search(path)]\n\n\n# Initialize the summary variables\nsummary_dict_netcdf, summary_dict_cog = {}, {}\noverall_stats_netcdf, overall_stats_cog = {}, {}\nfull_data_df_netcdf, full_data_df_cog = pd.DataFrame(), pd.DataFrame()\n\n\n# Process the COGs to get the statistics\nfor key in keys:\n    url=f\"s3://{cog_data_bucket}/{key}\"\n    with rasterio.open(url) as src:\n        filename_elements = re.split(\"[_ ? . ]\", url)\n        for band in src.indexes:\n            print(\"_\".join(filename_elements[1:6]))\n            idx = pd.MultiIndex.from_product(\n                    [\n                        [\"_\".join(filename_elements[1:6])],\n                        [filename_elements[5]],\n                        [x for x in np.arange(1, src.height + 1)],\n                    ]\n                )\n            raster_data = src.read(band)\n            raster_data[raster_data == -9999] = 0 # because we did that in the transformation script\n            temp = pd.DataFrame(index=idx, data=raster_data)\n            full_data_df_cog = full_data_df_cog._append(temp, ignore_index=False)\n\n            # Calculate summary statistics\n            min_value = np.float64(temp.values.min())\n            max_value = np.float64(temp.values.max())\n            mean_value = np.float64(temp.values.mean())\n            std_value = np.float64(temp.values.std())\n\n            summary_dict_cog[\n                    f'{\"_\".join(filename_elements[1:5])}_{filename_elements[5][:4]}_{calendar.month_name[int(filename_elements[5][4:])]}'\n                ] = {\n                    \"min_value\": min_value,\n                    \"max_value\": max_value,\n                    \"mean_value\": mean_value,\n                    \"std_value\": std_value,\n                }\n\n\n# Process the raw files for selected year to get the statistics \ntif_files = glob(f\"{data_dir}{vyear}/*.tif\", recursive=True)\nfor tif_file in tif_files:\n    file_name = pathlib.Path(tif_file).name[:-4]\n    print(file_name)\n    with rasterio.open(tif_file) as src:\n        for band in src.indexes:\n            idx = pd.MultiIndex.from_product(\n                [\n                    [pathlib.Path(tif_file).name[:-9]],\n                    [pathlib.Path(tif_file).name[-8:-4]],\n                    [x for x in np.arange(1, src.height + 1)],\n                ]\n            )\n            # Read the raster data\n            raster_data = src.read(band)\n            #raster_data[raster_data == -9999] = np.nan\n            temp = pd.DataFrame(index=idx, data=raster_data)\n            full_data_df_netcdf = full_data_df_netcdf._append(temp, ignore_index=False)\n\n            # Calculate summary statistics\n            min_value = np.float64(temp.values.min())\n            max_value = np.float64(temp.values.max())\n            mean_value = np.float64(temp.values.mean())\n            std_value = np.float64(temp.values.std())\n\n            summary_dict_netcdf[\n                f'{tif_file.split(\"/\")[-1][:-9]}_{calendar.month_name[int(tif_file.split(\"/\")[-1][-6:-4])]}'\n            ] = {\n                \"min_value\": min_value,\n                \"max_value\": max_value,\n                \"mean_value\": mean_value,\n                \"std_value\": std_value,\n            }\n            \n\n\n# Merge monthly stats for COGs and raw files in a csv file \ncog_df = pd.DataFrame(summary_dict_cog).T.reset_index()\nraw_df = pd.DataFrame(summary_dict_netcdf).T.reset_index()\ncog_df['date']= cog_df[\"index\"].apply(lambda x: (x.split(\"_\")[-1]+x.split(\"_\")[-2]) )\nraw_df['date']= raw_df[\"index\"].apply(lambda x: (x.split(\"_\")[-1]+str(vyear)) )\ncheck_df=pd.merge(cog_df, raw_df[[\"min_value\",\"max_value\",\"mean_value\",\"std_value\",\"date\"]], how='inner', on='date',suffixes=('', '_raw'))\ncheck_df.to_csv(f\"monthly_stats_{vyear}.csv\")\n\n\n# Calculate the overall data stat for that year\noverall_stats_netcdf[\"min_value\"] = np.float64(full_data_df_netcdf.values.min())\noverall_stats_netcdf[\"max_value\"] = np.float64(full_data_df_netcdf.values.max())\noverall_stats_netcdf[\"mean_value\"] = np.float64(full_data_df_netcdf.values.mean())\noverall_stats_netcdf[\"std_value\"] = np.float64(full_data_df_netcdf.values.std())\n\noverall_stats_cog[\"min_value\"] = np.float64(full_data_df_cog.values.min())\noverall_stats_cog[\"max_value\"] = np.float64(full_data_df_cog.values.max())\noverall_stats_cog[\"mean_value\"] = np.float64(full_data_df_cog.values.mean())\noverall_stats_cog[\"std_value\"] = np.float64(full_data_df_cog.values.std())\n\n\n\ndata = {\n    \"Stats for raw netCDF files.\": summary_dict_netcdf,\n    \"Stats for transformed COG files.\": summary_dict_cog\n}\n\n# Writing to JSON file\nwith open(f\"monthly_stats_{vyear}.json\", \"w\") as fp:\n    json.dump(data, fp, indent=4) \n\ndata = {\n    \"Stats for raw netCDF files.\": overall_stats_netcdf,\n    \"Stats for transformed COG files.\": overall_stats_cog\n}\n\n# Writing to JSON file\nwith open(f\"overall_stats_{vyear}.json\", \"w\") as fp:\n    json.dump(data, fp, indent=4) \n\n\n\n\n Back to top"
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-    "text": "Welcome to the U.S. Greenhouse Gas (GHG) Center processing and verification reports. These reports verify that the accuracy and integrity of each dataset in the US GHG Center is maintained once it is processed into the Center.\nThe reports are grouped topically and labeled by dataset name. Click on a dataset name to view the processing and verification report for that dataset.\nExamples of processing that may occur include transforming data from its source format into a could-optimized format, converting the units of the source data into a more common or standard unit, and flagging “nodata” values to ensure accurate data visualization. We strive to handle all data with extreme care, and share these reports to provide transparency and insight into any processing that is applied, while ensuring accuracy and reliability every step of the way.\nJoin us in our mission to make data-driven environmental solutions accessible. Explore, analyze, and make a difference with the US GHG Center.\nView the US GHG Center Data Catalog",
+    "text": "Welcome to the homepage for U.S. Greenhouse Gas (GHG) Center data workflow diagrams. Use these diagrams to discover the journey of each dataset from acquisition to integration in the US GHG Center.\nData flow diagrams are grouped topically and labeled by dataset name. Click on a dataset name to view the data flow diagram for that dataset, which summarizes the process followed to bring the dataset into the US GHG Center.\nJoin us in our mission to make data-driven environmental solutions accessible. Explore, analyze, and make a difference with the US GHG Center.\nView the US GHG Center Data Catalog",
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-    "text": "Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "text": "import xarray as xr\nimport os\nimport glob\nfrom  datetime import datetime\nimport boto3\nimport s3fs\nimport tempfile\nimport numpy as np\nimport pandas as pd\nimport re\nimport json\n\n\nraw_files = glob.glob(\"data/*.nc4\")\noutput_files= glob.glob(\"output_final2/*.tif\")\n\n\ndef extract_date_from_key(key):\n    # Split the key to isolate the part that contains the date\n    parts = key.split('_')\n    for part in parts:\n        # Check if the part is numeric and has the length of 6 (YYYYMM format)\n        if part.isdigit() and len(part) == 6:\n            return part\n    return None\n\n\noverall_raw= []\nraw= pd.DataFrame(columns=['filename','min_raw','max_raw','mean_raw','std_raw'])\nfor file in raw_files:\n    xds= xr.open_dataset(file)\n    year_month = extract_date_from_key(file)\n    for var in [\"PM25-PRI\",\"CO2\",\"CO\",\"NOX\",\"SOX\"]:\n        data = getattr(xds,var)\n        overall_raw.append(data)\n        data = np.ma.masked_where((data == -9999), data)\n        min_val = np.nanmin(data)\n        max_val = np.nanmax(data)\n        mean_val = np.nanmean(data)\n        std_val = np.nanstd(data)\n        stats = [f\"{var}_{year_month}\", min_val, max_val, mean_val, std_val]\n        raw.loc[len(raw)] = stats\n\n\noverall_cog=[]\ncog= pd.DataFrame(columns=['filename','min_cog','max_cog','mean_cog','std_cog'])\nfor file in output_files:\n    data= xr.open_dataarray(file)\n    \n    year_month = file[:-4][-6:]\n    var = file.split(\"_\")[-2]\n    overall_cog.append(data)\n    data = np.ma.masked_where((data == -9999), data)\n    \n    \n    min_val = np.nanmin(data)\n    max_val = np.nanmax(data)\n    mean_val = np.nanmean(data)\n    std_val = np.nanstd(data)\n    stats = [f\"{var}_{year_month}\", min_val, max_val, mean_val, std_val]\n    cog.loc[len(cog)] = stats\n\n\n# validation for reprojected data (non zero) - overall calculation\noverall_raw= np.array(overall_raw)\noverall_raw= np.ma.masked_where((overall_raw == -9999) , overall_raw)\nnan_min = np.nanmin(overall_raw)\nnan_max = np.nanmax(overall_raw)\nnan_mean = np.nanmean(overall_raw)\nnan_std = np.nanstd(overall_raw)\n[\"overall_raw\",nan_min,nan_max,nan_mean,nan_std]\n\n['overall_raw', 0.0, 110011.766, 5.1753755, 172.26357]\n\n\n\noverall_cog= np.array(overall_cog)\nnan_min = np.nanmin(overall_cog)\nnan_max = np.nanmax(overall_cog)\nnan_mean = np.nanmean(overall_cog)\nnan_std = np.nanstd(overall_cog)\n[\"overall_cog\",nan_min,nan_max,nan_mean,nan_std]\n\n['overall_cog', 0.0, 110011.766, 5.1753297, 172.27177]\n\n\n\npd.merge(cog, raw, on='filename', how='inner').to_json(\"monthly_stats.json\")\n\n\n\nkeys = [\"data\", \"nan_min\", \"nan_max\", \"nan_mean\", \"nan_std\"]\nvalues_set1 = [\"overall_raw\", 0.0, 110011.766, 5.1753297, 172.27177]\nvalues_set2 = [\"overall_cog\", 0.0, 110011.766, 5.1753297, 172.27177]\n\ndata_dict = {key: [val1, val2] for key, val1, val2 in zip(keys, values_set1, values_set2)}\n\n# Save the dictionary as a JSON file\nwith open(\"overall_stats.json\", \"w\") as json_file:\n    json.dump(data_dict, json_file, indent=4)\n\n\n\n\n Back to top"
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+    "title": "APIs",
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+    "text": "Open Source\nMost of the US GHG Center APIs are hosted out of a single project (veda-backend) that combines multiple standalone services.",
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+      "User Services",
+      "APIs"
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+    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
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+    "text": "Welcome to the U.S. Greenhouse Gas (GHG) Center data transformation notebooks, where we harness the power of Cloud Optimized Geotiffs (COGs) to offer a dynamic, cloud-based platform for exploring and analyzing greenhouse gas datasets. Dive into our curated collection of GHG datasets, all optimized for seamless accessibility and analysis.\nDiscover the journey of each dataset from its original format to COGs through the below Jupyter notebooks. The transformation examples are grouped topically. The dataset product type (model output, satellite observation, etc.) is noted next to the notebook name. Click on a notebook to learn more about the dataset and to view the transformation code.\nJoin us in our mission to make data-driven environmental solutions. Explore, analyze, and make a difference with the US GHG Center.\nNote: Not all datasets have a transformation code\nView the US GHG Center Data Catalog",
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+    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
+    "section": "Gridded Anthropogenic Greenhouse Gas Emissions",
+    "text": "Gridded Anthropogenic Greenhouse Gas Emissions\n\nOCO-2 MIP Top-Down CO₂ Budgets\nODIAC Fossil Fuel CO₂ Emissions\nTM5-4DVar Isotopic CH₄ Inverse Fluxes\nU.S. Gridded Anthropogenic Methane Emissions Inventory\nVulcan Fossil Fuel CO₂ Emissions\nGRA²PES Greenhouse Gas and Air Quality Species",
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+    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
+    "section": "Natural Greenhouse Gas Emissions and Sinks",
+    "text": "Natural Greenhouse Gas Emissions and Sinks\n\nAir-Sea CO₂ Flux, ECCO-Darwin Model v5\nGOSAT-based Top-down Total and Natural Methane Emissions\nOCO-2 MIP Top-Down CO₂ Budgets\nTM5-4DVar Isotopic CH₄ Inverse Fluxes",
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+    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
+    "section": "Large Emissions Events",
+    "text": "Large Emissions Events\n\nEMIT Methane Point Source Plume Complexes",
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+      "Data Transformation Notebooks"
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+    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
+    "section": "Greenhouse Gas Concentrations",
+    "text": "Greenhouse Gas Concentrations\n\nAtmospheric Carbon Dioxide and Methane Concentrations from NOAA Global Monitoring Laboratory\nOCO-2 GEOS Column CO₂ Concentrations\nCarbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)\nCarbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project\nCarbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
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+    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
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+    "text": "Socioeconomic\n\nSEDAC Gridded World Population Density",
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+      "Data Transformation Notebooks"
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+  },
+  {
+    "objectID": "datatransformationcode.html#contact",
+    "href": "datatransformationcode.html#contact",
+    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
+    "section": "Contact",
+    "text": "Contact\nFor technical help or general questions, please contact the support team using the feedback form.",
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+      "Data Transformation Notebooks"
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+    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
+    "section": "",
+    "text": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
     "crumbs": [
       "Data Flow Diagrams",
       "Greenhouse Gas Concentrations",
-      "Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project"
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+    "href": "data_workflow/noaa-gggrn-ch4-concentrations_Data_Flow.html",
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     "section": "",
-    "text": "ODIAC Fossil Fuel CO₂ Emissions\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "text": "Atmospheric Methane Concentrations from the NOAA Global Monitoring Laboratory\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
     "crumbs": [
       "Data Flow Diagrams",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "ODIAC Fossil Fuel CO₂ Emissions"
+      "Greenhouse Gas Concentrations",
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@@ -1707,1005 +1953,846 @@
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+    "href": "data_workflow/epa-ch4emission-grid-v2express_Data_Flow.html",
+    "title": "Gridded Anthropogenic Methane Emissions Inventory",
     "section": "",
-    "text": "GRA²PES Greenhouse Gas and Air Quality Species\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "text": "Gridded Anthropogenic Methane Emissions Inventory\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
     "crumbs": [
       "Data Flow Diagrams",
       "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "GRA²PES Greenhouse Gas and Air Quality Species"
+      "Gridded Anthropogenic Methane Emissions Inventory"
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   {
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+    "href": "data_workflow/oco2geos-co2-daygrid-v10r_Data_Flow.html",
+    "title": "OCO-2 GEOS Column CO₂ Concentrations",
     "section": "",
-    "text": "Atmospheric Carbon Dioxide Concentrations from the NOAA Global Monitoring Laboratory\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "text": "OCO-2 GEOS Column CO₂ Concentrations\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
     "crumbs": [
       "Data Flow Diagrams",
       "Greenhouse Gas Concentrations",
-      "Atmospheric Carbon Dioxide Concentrations from the NOAA Global Monitoring Laboratory"
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-    "section": "",
-    "text": "MiCASA Land Carbon Flux - Data Workflow\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
-    "crumbs": [
-      "Data Flow Diagrams",
-      "Natural Greenhouse Gas Sources Emissions and Sinks",
-      "MiCASA Land Carbon Flux - Data Workflow"
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-    "title": "SEDAC Gridded World Population Data",
-    "section": "",
-    "text": "SEDAC Gridded World Population Data\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
-    "crumbs": [
-      "Data Flow Diagrams",
-      "Socioeconomic",
-      "SEDAC Gridded World Population Data"
+      "OCO-2 GEOS Column CO₂ Concentrations"
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-    "href": "data_workflow/influx-testbed-ghg-concentrations_Data_Flow.html",
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+    "href": "data_workflow/noaa-gggrn-co2-concentrations_Data_Flow.html",
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     "section": "",
-    "text": "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
+    "text": "Atmospheric Carbon Dioxide Concentrations from the NOAA Global Monitoring Laboratory\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
     "crumbs": [
       "Data Flow Diagrams",
       "Greenhouse Gas Concentrations",
-      "Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)"
+      "Atmospheric Carbon Dioxide Concentrations from the NOAA Global Monitoring Laboratory"
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   {
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-    "href": "cog_transformation/lpjwsl-wetlandch4-daygrid-v1.html",
-    "title": "Wetland Methane Emissions, LPJ-wsl Model",
-    "section": "",
-    "text": "This script was used to transform the Wetland Methane Emissions, LPJ-wsl Model dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nfrom datetime import datetime, timedelta\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"NASA_GSFC_ch4_wetlands_daily\"\ndirectory = \"ch4_wetlands_daily\"\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(directory):\n    xds = xarray.open_dataset(\n        f\"{directory}/{name}\", engine=\"netcdf4\", decode_times=False\n    )\n    xds = xds.assign_coords(longitude=(((xds.longitude + 180) % 360) - 180)).sortby(\n        \"longitude\"\n    )\n    variable = [var for var in xds.data_vars]\n    filename = name.split(\"/ \")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n    start_time = datetime(int(filename_elements[-2]), 1, 1)\n\n    for time_increment in range(0, len(xds.time)):\n        for var in variable:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = getattr(xds.isel(time=time_increment), var)\n            data = data.isel(latitude=slice(None, None, -1))\n            data = data * 1000\n            data.rio.set_spatial_dims(\"longitude\", \"latitude\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n            date = start_time + timedelta(hours=data.time.item(0))\n\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = date.strftime(\"%Y%m%d\")\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{FOLDER_NAME}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{FOLDER_NAME}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{FOLDER_NAME}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top"
-  },
-  {
-    "objectID": "cog_transformation/gosat-based-ch4budget-yeargrid-v1.html",
-    "href": "cog_transformation/gosat-based-ch4budget-yeargrid-v1.html",
-    "title": "GOSAT-based Top-down Methane Budgets",
+    "objectID": "data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.html",
+    "href": "data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.html",
+    "title": "Air-Sea CO₂ Flux, ECCO-Darwin Model v5",
     "section": "",
-    "text": "This script was used to transform the GOSAT-based Top-down Methane Budgets dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nimport rasterio\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nyear_ = datetime(2019, 1, 1)\nfolder_name = \"new_data/CH4-inverse-flux\"\n\nCOG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(folder_name):\n    ds = xarray.open_dataset(\n        f\"{folder_name}/{name}\",\n        engine=\"netcdf4\",\n    )\n\n    ds = ds.rename({\"dimy\": \"lat\", \"dimx\": \"lon\"})\n    # assign coords from dimensions\n    ds = ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    ds = ds.assign_coords(lat=((ds.lat / 180) * 180) - 90).sortby(\"lat\")\n\n    variable = [var for var in ds.data_vars]\n\n    for var in variable[2:]:\n        filename = name.split(\"/ \")[-1]\n        filename_elements = re.split(\"[_ .]\", filename)\n        data = ds[var]\n        filename_elements.pop()\n        filename_elements.insert(2, var)\n        cog_filename = \"_\".join(filename_elements)\n        # # add extension\n        cog_filename = f\"{cog_filename}.tif\"\n\n        data = data.reindex(lat=list(reversed(data.lat)))\n\n        data.rio.set_spatial_dims(\"lon\", \"lat\")\n        data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n        # generate COG\n        COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n\n        with tempfile.NamedTemporaryFile() as temp_file:\n            data.rio.to_raster(temp_file.name, **COG_PROFILE)\n            s3_client.upload_file(\n                Filename=temp_file.name,\n                Bucket=bucket_name,\n                Key=f\"ch4_inverse_flux/{cog_filename}\",\n            )\n\n        files_processed = files_processed._append(\n            {\"file_name\": name, \"COGs_created\": cog_filename},\n            ignore_index=True,\n        )\n\n        print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(ds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(ds.dims)}, fp)\n    json.dump({\"data_variables\": list(ds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=\"ch4_inverse_flux/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/ch4_inverse_flux/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
+    "text": "Air-Sea CO₂ Flux, ECCO-Darwin Model v5\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks",
+      "Data Flow Diagrams",
       "Natural Greenhouse Gas Sources Emissions and Sinks",
-      "GOSAT-based Top-down Methane Budgets"
+      "Air-Sea CO₂ Flux, ECCO-Darwin Model v5"
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-    "href": "cog_transformation/oco2geos-co2-daygrid-v10r.html",
-    "title": "OCO-2 GEOS Column CO₂ Concentrations",
+    "objectID": "data_workflow/odiac-ffco2-monthgrid-v2023_Data_Flow.html",
+    "href": "data_workflow/odiac-ffco2-monthgrid-v2023_Data_Flow.html",
+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
     "section": "",
-    "text": "This script was used to transform the OCO-2 GEOS Column CO₂ Concentrations dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nimport os\n\n\nsession = boto3.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"earth_data/geos_oco2\"\ns3_folder_name = \"geos-oco2\"\n\nerror_files = []\ncount = 0\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(FOLDER_NAME):\n    try:\n        xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n        xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n        variable = [var for var in xds.data_vars]\n        filename = name.split(\"/ \")[-1]\n        filename_elements = re.split(\"[_ .]\", filename)\n\n        for time_increment in range(0, len(xds.time)):\n            for var in variable:\n                filename = name.split(\"/ \")[-1]\n                filename_elements = re.split(\"[_ .]\", filename)\n                data = getattr(xds.isel(time=time_increment), var)\n                data = data.isel(lat=slice(None, None, -1))\n                data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n                data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n                # # insert date of generated COG into filename\n                filename_elements[-1] = filename_elements[-3]\n                filename_elements.insert(2, var)\n                filename_elements.pop(-3)\n                cog_filename = \"_\".join(filename_elements)\n                # # add extension\n                cog_filename = f\"{cog_filename}.tif\"\n\n                with tempfile.NamedTemporaryFile() as temp_file:\n                    data.rio.to_raster(\n                        temp_file.name,\n                        driver=\"COG\",\n                    )\n                    s3_client.upload_file(\n                        Filename=temp_file.name,\n                        Bucket=bucket_name,\n                        Key=f\"{s3_folder_name}/{cog_filename}\",\n                    )\n\n                files_processed = files_processed._append(\n                    {\"file_name\": name, \"COGs_created\": cog_filename},\n                    ignore_index=True,\n                )\n        count += 1\n        print(f\"Generated and saved COG: {cog_filename}\")\n    except OSError:\n        error_files.append(name)\n        pass\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{s3_folder_name}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{s3_folder_name}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
+    "text": "ODIAC Fossil Fuel CO₂ Emissions\n\n\n\nData Flow Diagram Extending From Acquisition/Creation to User Delivery\n\n\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks",
-      "Greenhouse Gas Concentrations",
-      "OCO-2 GEOS Column CO₂ Concentrations"
+      "Data Flow Diagrams",
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "ODIAC Fossil Fuel CO₂ Emissions"
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   {
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-    "href": "cog_transformation/epa-ch4emission-monthgrid-v2.html",
-    "title": "Gridded Anthropogenic Methane Emissions Inventory",
+    "objectID": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html",
+    "href": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html",
+    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
     "section": "",
-    "text": "This script was used to transform the Gridded Anthropogenic Methane Emissions Inventory dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"epa_emissions/monthly_scale\"\ns3_folder_name = \"epa-emissions-monthly-scale-factors\"\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(FOLDER_NAME):\n    xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n    xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    variable = [var for var in xds.data_vars]\n    filename = name.split(\"/ \")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n    start_time = datetime(int(filename_elements[-2]), 1, 1)\n\n    for time_increment in range(0, len(xds.time)):\n        for var in variable:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = getattr(xds.isel(time=time_increment), var)\n            data = data.isel(lat=slice(None, None, -1))\n            data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n            date = start_time + relativedelta(months=+time_increment)\n\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = date.strftime(\"%Y%m\")\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{s3_folder_name}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{s3_folder_name}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{s3_folder_name}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top"
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
   },
   {
-    "objectID": "cog_transformation/lam-testbed-ghg-concentrations.html",
-    "href": "cog_transformation/lam-testbed-ghg-concentrations.html",
-    "title": "Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project",
+    "objectID": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#run-this-notebook",
+    "href": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#run-this-notebook",
+    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
     "section": "",
-    "text": "This script was used to transform the the Los Angeles Megacity Carbon Project dataset into meaningful csv files for ingestion to vector dataset.\n\nimport pandas as pd\nimport glob\nimport os\nimport warnings\nimport warnings \nwarnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n\n\n# download data from https://data.nist.gov/od/id/mds2-2388 into your desired_folder\nsource_dir = \"CA\"\n\n\n# Grouping the files for preparation\nconfig_ca = pd.read_csv(\"LAM_sites-2.csv\") #metadata from providers\nall_files= glob.glob(f\"{source_dir}/*.csv\")\nall_files = [i.split(\"/\")[-1].split('.')[0] for i in glob.glob(f\"{source_dir}/*.csv\") ]\nmy_dict={}\nfor site in list(config_ca.SiteCode):\n    # for each site and variable, append into the dict\n    if (config_ca[config_ca[\"SiteCode\"]==site][\"Tower\"].item()) ==1 :\n\n        co2_files = [f for f in all_files if site in f and \"upwind\" not in f and \"all\" not in f and \"co2\" in f]\n        my_dict[f\"{site}-co2\"] = co2_files\n        # Find the files that do not have \"upwind\" or \"all\" and have \"ch4\"\n        ch4_files = [f for f in all_files if site in f and \"upwind\" not in f and \"all\" not in f and \"ch4\" in f]\n        my_dict[f\"{site}-ch4\"] = ch4_files\n    else:\n        co2_upwind_files = [f for f in all_files if site in f and \"upwind\" in f and \"co2\" in f]\n        my_dict[f\"{site}-co2\"] = co2_upwind_files\n        \n        # Find the files that have \"upwind\" and \"ch4\"\n        ch4_upwind_files = [f for f in all_files if site in f and \"upwind\" in f and \"ch4\" in f]\n        my_dict[f\"{site}-ch4\"] = ch4_upwind_files\n\n        if site in [\"IRV\",\"RAN\"]:\n            co2_files = [f for f in all_files if site in f and \"all\" in f and \"co2\" in f]\n            my_dict[f\"{site}-co2\"] = co2_files\n            ch4_files = [f for f in all_files if site in f and \"all\" in f and \"ch4\" in f]\n            my_dict[f\"{site}-ch4\"] = ch4_files\n        \ndel my_dict['USC2-co2']\ndel my_dict['USC2-ch4']\n\nfor key in my_dict:\n    my_dict[key] = sorted(my_dict[key])\n\n\n# code to generate transformed data for CA\noutput_dir = \"output_LAM\"\nos.makedirs(output_dir,exist_ok=True)\nfor key, value in my_dict.items():\n    df=pd.DataFrame()\n    variable = key.split(\"-\")[-1]\n    val = f\"{variable}_ppm\" if variable == 'co2' else f\"{variable}_ppb\"\n    columns = [\"latitude\",\"longitude\",\"intake_height_m\",\"elevation_m\",\"datetime\",val ]\n    for file in value:\n        tmp = pd.read_csv(f\"CA/{file}.csv\")\n        tmp.dropna(subset=[val], inplace=True)\n        tmp.rename(columns={'datetime_UTC': 'datetime'}, inplace=True)\n        tmp= tmp[columns]\n        tmp.rename(columns={val: 'value'}, inplace=True)\n        tmp['datetime'] = pd.to_datetime(tmp['datetime'])\n        tmp['datetime'] = tmp['datetime'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n        tmp['location'] = config_ca[config_ca['SiteCode']==site][\"Location\"].item()\n        df = pd.concat([df, tmp], ignore_index=True)\n        \n    df['year']= df['datetime'].apply(lambda x: x[:4])\n    result = df.groupby(\"year\").agg(max_height= (\"intake_height_m\",\"max\"))\n    if result['max_height'].std() !=0:\n        print(f\"More than one max height for {file}\",result['max_height'].unique())\n    merged_df=pd.merge(df, result, on='year')\n    merged_df[\"is_max_height_data\"]= merged_df[\"max_height\"] == merged_df[\"intake_height_m\"]\n    merged_df=merged_df.drop(columns=['year','max_height'])\n    merged_df.reset_index(drop=True, inplace=True)\n    merged_df.to_csv(f\"{output_dir}/NIST-testbed-LAM-{key}-hourly-concentrations.csv\", index=False)\n    \n\n\n\n\n Back to top",
-    "crumbs": [
-      "Data Transformation Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project"
-    ]
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
   },
   {
-    "objectID": "cog_transformation/gra2pes-ghg-monthgrid-v1.html",
-    "href": "cog_transformation/gra2pes-ghg-monthgrid-v1.html",
-    "title": "GRA²PES Greenhouse Gas and Air Quality Species",
-    "section": "",
-    "text": "This script was used to transform the GRA2PES dataset to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport xarray as xr\nimport os\nimport glob\nfrom  datetime import datetime\nimport boto3\nimport s3fs\nimport tempfile\nimport numpy as np\n\nimport rasterio\nfrom rasterio.enums import Resampling\nfrom rio_cogeo.cogeo import cog_translate\nfrom rio_cogeo.profiles import cog_profiles\n\n\nconfig = {\n    \"data_acquisition_method\": \"s3\",\n    \"raw_data_bucket\" : \"gsfc-ghg-store\",\n    \"raw_data_prefix\": \"GRA2PES/monthly_subset_regrid/2021\", \n    \"cog_data_bucket\": \"ghgc-data-store-develop\",\n    \"cog_data_prefix\": \"transformed_cogs/GRAAPES\",\n    \"date_fmt\" :\"%Y%m\",\n    \"transformation\": {}\n}\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\n\nraw_data_bucket = config[\"raw_data_bucket\"]\nraw_data_prefix= config[\"raw_data_prefix\"]\n\ncog_data_bucket = config['cog_data_bucket']\ncog_data_prefix= config[\"cog_data_prefix\"]\n\ndate_fmt=config['date_fmt']\n\nfs = s3fs.S3FileSystem()\n\n\ndef get_all_s3_keys(bucket, model_name, ext):\n    \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n    keys = []\n\n    kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n    while True:\n        resp = s3_client.list_objects_v2(**kwargs)\n        for obj in resp[\"Contents\"]:\n            if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n                keys.append(obj[\"Key\"])\n\n        try:\n            kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n        except KeyError:\n            break\n\n    return keys\n\nkeys = get_all_s3_keys(raw_data_bucket, raw_data_prefix, \".nc4\")\n\ndef download_s3_objects(bucket, keys, download_dir):\n    \"\"\"Download all S3 objects listed in keys to the specified local directory.\"\"\"\n    if not os.path.exists(download_dir):\n        os.makedirs(download_dir)\n\n    for key in keys:\n        local_filename = os.path.join(download_dir, os.path.basename(key))\n        try:\n            s3_client.download_file(bucket, key, local_filename)\n            print(f\"Downloaded {key} to {local_filename}\")\n        except (NoCredentialsError, PartialCredentialsError) as e:\n            print(f\"Credentials error: {e}\")\n        except Exception as e:\n            print(f\"Failed to download {key}: {e}\")\n\ndownload_s3_objects(raw_data_bucket, keys, \"data\")\n\n\n\ndef extract_date_from_key(key):\n    # Split the key to isolate the part that contains the date\n    parts = key.split('_')\n    for part in parts:\n        # Check if the part is numeric and has the length of 6 (YYYYMM format)\n        if part.isdigit() and len(part) == 6:\n            return part\n    return None\n\n\nCOG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\nOVERVIEW_LEVELS = 4 \nOVERVIEW_RESAMPLING = 'average'\n\nfor key in glob.glob(\"data/*.nc4\"):\n    xds= xr.open_dataset(key)\n    xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    \n    for var in [\"PM25-PRI\",\"CO2\",\"CO\",\"NOX\",\"SOX\"]:\n        yearmonth = extract_date_from_key(key)\n        filename = f\"output/GRA2PESv1.0_total_{(\"-\").join(var.split('_'))}_{yearmonth}.tif\"\n        data = getattr(xds,var)\n        data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n        data.rio.write_crs(\"epsg:4326\", inplace=True)\n        \n        # Create a temporary file to hold the COG\n        with tempfile.NamedTemporaryFile(suffix='.tif', delete=False) as temp_file:\n            data.rio.to_raster(f\"temp_{yearmonth}_{var}.tif\", **COG_PROFILE, nodata=-9999)\n            # Create COG with overviews and nodata value\n            cog_translate(\n                f\"temp_{yearmonth}_{var}.tif\",\n                temp_file.name,\n                cog_profiles.get(\"deflate\"),\n                overview_level=OVERVIEW_LEVELS,\n                overview_resampling=OVERVIEW_RESAMPLING,\n                nodata=-9999\n            )\n            \n            # Move the temporary file to the desired local path\n            os.rename(temp_file.name, filename)\n    \n        if os.path.exists(f\"temp_{yearmonth}_{var}.tif\"):\n            os.remove(f\"temp_{yearmonth}_{var}.tif\")\n        del data\n        print(f\"Done for: {filename}\")\n    \n\n\n\n\n Back to top",
-    "crumbs": [
-      "Data Transformation Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "GRA²PES Greenhouse Gas and Air Quality Species"
-    ]
+    "objectID": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#approach",
+    "href": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#approach",
+    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
+    "section": "Approach",
+    "text": "Approach\n\nIdentify 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 Wetland Methane Emissions, LPJ-EOSIM Model data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison.\nAfter the visualization, perform zonal statistics for a given polygon."
   },
   {
-    "objectID": "cog_transformation/epa-ch4emission-grid-v2express.html",
-    "href": "cog_transformation/epa-ch4emission-grid-v2express.html",
-    "title": "U.S. Gridded Anthropogenic Methane Emissions Inventory",
-    "section": "",
-    "text": "This script was used to transform the Gridded Anthropogenic Methane Emissions Inventory monthly dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nfrom datetime import datetime\nimport numpy as np\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nTrue\n\n\n\n# session = boto3.session.Session()\nsession = boto3.Session(\n    aws_access_key_id=os.environ.get(\"AWS_ACCESS_KEY_ID\"),\n    aws_secret_access_key=os.environ.get(\"AWS_SECRET_ACCESS_KEY\"),\n    aws_session_token=os.environ.get(\"AWS_SESSION_TOKEN\"),\n)\ns3_client = session.client(\"s3\")\nbucket_name = \"ghgc-data-store-dev\" # S3 bucket where the COGs are stored after transformation\nFOLDER_NAME = \"../data/epa_emissions_express_extension\"\ns3_folder_name = \"epa_express_extension_Mg_km2_yr\"\n# raw gridded data [molec/cm2/s] * 1/6.022x10^23 [molec/mol] * 16.04x10^-6 [ Mg/mol] * 366 [days/yr] * 1x10^10 [cm2/km2]\n\nfiles_processed = pd.DataFrame(columns=[\"file_name\", \"COGs_created\"])   # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(FOLDER_NAME):\n    xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n    xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    variable = [var for var in xds.data_vars]\n    filename = name.split(\"/ \")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n    start_time = datetime(int(filename_elements[-2]), 1, 1)\n\n    for time_increment in range(0, len(xds.time)):\n        for var in variable:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = getattr(xds.isel(time=time_increment), var)\n            data.values[data.values==0] = np.nan\n            data = data*((1/(6.022*pow(10,23)))*(16.04*pow(10,-6))*366*pow(10,10)*86400)\n            data = data.fillna(-9999)\n            data = data.isel(lat=slice(None, None, -1))\n            data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = start_time.strftime(\"%Y\")\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{s3_folder_name}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{s3_folder_name}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{s3_folder_name}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2018.tif\nDone generating COGs\n\n\n\n\n\n Back to top",
-    "crumbs": [
-      "Data Transformation Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "U.S. Gridded Anthropogenic Methane Emissions Inventory"
-    ]
+    "objectID": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#about-the-data",
+    "href": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#about-the-data",
+    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
+    "section": "About the Data",
+    "text": "About the Data\nMethane (CH₄) emissions from vegetated wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions have thus far been underrepresented. Using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) global CH₄ emissions from wetlands are estimated at 0.5° x 0.5 degree spatial resolution. By simulating wetland extent and using characteristics of inundated areas, such as wetland soil moisture, temperature, and carbon content, the model provides estimates of CH₄ quantities emitted into the atmosphere. This dataset shows concentrated methane sources from tropical and high latitude ecosystems. The LPJ-EOSIM Wetland Methane Emissions dataset consists of global daily model estimates of terrestrial wetland methane emissions from 1990 to the present, with data added bimonthly. The monthly data has been curated by aggregating the daily files. The estimates are regularly used in conjunction with NASA’s Goddard Earth Observing System (GEOS) model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, to compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions.\nFor more information regarding this dataset, please visit the U.S. Greenhouse Gas Center."
   },
   {
-    "objectID": "cog_transformation/sedac-popdensity-yeargrid5yr-v4.11.html",
-    "href": "cog_transformation/sedac-popdensity-yeargrid5yr-v4.11.html",
-    "title": "SEDAC Gridded World Population Data",
-    "section": "",
-    "text": "This script was used to transform SEDAC Gridded World Population Data from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\n\nimport tempfile\nimport boto3\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\n\nfold_names = os.listdir(\"gpw\")\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor fol_ in fold_names:\n    for name in os.listdir(f\"gpw/{fol_}\"):\n        if name.endswith(\".tif\"):\n            xds = xarray.open_dataarray(f\"gpw/{fol_}/{name}\")\n\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements.append(filename_elements[-3])\n\n            xds.rio.set_spatial_dims(\"x\", \"y\", inplace=True)\n            xds.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                xds.rio.to_raster(temp_file.name, driver=\"COG\")\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"gridded_population_cog/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/gridded_population_cog/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
-    "crumbs": [
-      "Data Transformation Notebooks",
-      "Socioeconomic",
-      "SEDAC Gridded World Population Data"
-    ]
+    "objectID": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#query-the-stac-api",
+    "href": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#query-the-stac-api",
+    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
+    "section": "Query the STAC API",
+    "text": "Query the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for the wetland methane emissions LPJ-EOSIM Model\ncollection_name = \"lpjeosim-wetlandch4-monthgrid-v1\"\n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\nasset_name = \"ensemble-mean-ch4-wetlands-emissions\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'lpjeosim-wetlandch4-monthgrid-v2',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2/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/lpjeosim-wetlandch4-monthgrid-v2'}],\n 'title': '(Monthly) Wetland Methane Emissions, LPJ-EOSIM Model v2',\n 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n  'temporal': {'interval': [['1990-01-01 00:00:00+00',\n     '2024-05-31 00:00:00+00']]}},\n 'license': 'CC0-1.0',\n 'renders': {'dashboard': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'era5-ch4-wetlands-emissions': {'assets': ['era5-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'merra2-ch4-wetlands-emissions': {'assets': ['merra2-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'ensemble-mean-ch4-wetlands-emissions': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'}},\n 'providers': [{'name': 'NASA'}],\n 'summaries': {'datetime': ['1990-01-01T00:00:00Z', '2024-05-31T00:00:00Z']},\n 'description': 'Global, monthly estimates of methane (CH₄) emissions from terrestrial wetlands at 0.5 x 0.5 degree spatial resolution using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Methane emissions from vegetated wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions have thus far been underrepresented. The LPJ-EOSIM model estimates wetland methane emissions by simulating wetland extent and using characteristics of these inundated areas such as soil moisture, temperature, and carbon content to estimate CH₄ quantities emitted into the atmosphere. Input climate forcing data comes from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data and ECMWF Re-Analysis data (ERA5). An ensemble layer provides the result of the mean of the MERRA-2 and ERA5 layers. The source data can be found at https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_MCH4E.001 and https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_MCH4E_LL.001.',\n 'item_assets': {'era5-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.'},\n  'merra2-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.'},\n  'ensemble-mean-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.'}},\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': 'month'}\n\n\nExamining the contents of our collection under summaries, we see that the data is available from January 1990 to December 2024. By looking at dashboard: time density, we can see that these observations are collected monthly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\n\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n        # temp = items_url.split('/')\n        # temp.insert(3, 'ghgcenter')\n        # temp.insert(4, 'api')\n        # temp.insert(5, 'stac')\n        # items_url = '/'.join(temp)\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\"\n).json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 413 items\n\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'lpjeosim-wetlandch4-monthgrid-v2-202405',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405/map?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'era5-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_MCH4E_LL.001/LPJ_EOSIM_L2_MCH4E_LL_001_202405/LPJ_EOSIM_L2_MCH4E_LL_ERA5_001_202405.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.2603939548181415e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [61251, 731, 214, 98, 77, 34, 28, 25, 3, 2]},\n     'statistics': {'mean': 3.2184362818990856e-11,\n      'stddev': 1.3179453937864483e-10,\n      'maximum': 3.2603939548181415e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09837962962963}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'merra2-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_MCH4E_LL.001/LPJ_EOSIM_L2_MCH4E_LL_001_202405/LPJ_EOSIM_L2_MCH4E_LL_MERRA2_001_202405.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.9324570266785486e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [61424, 584, 191, 119, 55, 42, 23, 13, 6, 3]},\n     'statistics': {'mean': 3.452568789087992e-11,\n      'stddev': 1.504910798913e-10,\n      'maximum': 3.9324570266785486e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09722222222222}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'ensemble-mean-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_MCH4E_LL.001/LPJ_EOSIM_L2_MCH4E_LL_001_202405/LPJ_EOSIM_L2_MCH4E_LL_ensemble_mean_001_202405.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 2.8522617601112188e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [60942, 894, 247, 132, 89, 60, 38, 31, 17, 10]},\n     'statistics': {'mean': 3.335580191462005e-11,\n      'stddev': 1.37447713656579e-10,\n      'maximum': 2.8522617601112188e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09722222222222}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405/preview.png?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'lpjeosim-wetlandch4-monthgrid-v2',\n 'properties': {'end_datetime': '2024-05-31T00:00:00+00:00',\n  'start_datetime': '2024-05-01T00:00:00+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']}\n\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetch the minimum and maximum values for rescaling\nrescale_values = {'max': 0.0003, 'min': 0.0}"
   },
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-    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
-    "section": "",
-    "text": "This script was used to transform the Northeast Corridor (NEC) Urban Test Bed dataset into meaningful csv files for ingestion to vector dataset.\n\nimport pandas as pd\nimport glob\nimport os\nimport warnings\nimport subprocess\nimport tarfile\nimport warnings \nimport requests\nwarnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n\n\nconfig = pd.read_csv(\"NEC_sites.csv\")  #https://data.nist.gov/od/id/mds2-3012\n\n\n# Code to download the files into csv folder \nsites = list(config.SiteCode)\nfor SiteCode in config.SiteCode[:2]:\n    print(SiteCode)\n    download_link = f\"https://data.nist.gov/od/ds/ark:/88434/mds2-3012/{SiteCode}.tgz\"\n    \n    # Check if the file exists on the server\n    response = requests.head(download_link)\n    if response.status_code != 404:\n        # File exists, proceed with download\n        result = subprocess.run([\"wget\", download_link, \"-O\", f\"{SiteCode}.tgz\"], \n                                stdout=subprocess.DEVNULL,\n                                stderr=subprocess.DEVNULL)\n\n        # Check if wget succeeded\n        if result.returncode == 0:\n            # Ensure the file is not empty\n            if os.path.getsize(f\"{SiteCode}.tgz\") > 0:\n                # Extract the files\n                with tarfile.open(f\"{SiteCode}.tgz\", \"r:gz\") as tar:\n                    tar.extractall()\n\n                # Delete the .tgz file\n                os.remove(f\"{SiteCode}.tgz\")\n            else:\n                print(f\"File {SiteCode}.tgz is empty.\")\n                sites.remove(SiteCode)\n                os.remove(f\"{SiteCode}.tgz\")  # Remove the empty file\n        else:\n            print(f\"Failed to download {SiteCode}.tgz.\")\n            sites.remove(SiteCode)\n    else:\n        print(f\"File {SiteCode}.tgz does not exist on the server.\")\n        sites.remove(SiteCode)\n\n\nsites = list(config.SiteCode)\n# These are not available\nsites.remove('AWS')\nsites.remove('BVA')\nsites.remove('DNC')\n\n\nvariables = ['ch4','co2']\noutput_dir =\"output_NEC\"\nos.makedirs(output_dir,exist_ok=True)\n\n\nfor site in sites:\n    for variable in variables:\n        df = pd.DataFrame()\n        files = glob.glob(f\"csv/{site}-*-{variable}-*.csv\")\n        val = f\"{variable}_ppm\" if variable == 'co2' else f\"{variable}_ppb\"\n        for file in files:\n            tmp = pd.read_csv(file)\n            tmp.dropna(subset=[val], inplace=True)\n            tmp.rename(columns={'datetime_UTC': 'datetime'}, inplace=True)\n            columns = [\"latitude\",\"longitude\",\"intake_height_m\",\"elevation_m\",\"datetime\",val ]\n            tmp= tmp[columns]\n            tmp.rename(columns={val: 'value'}, inplace=True)\n            tmp['datetime'] = pd.to_datetime(tmp['datetime'])\n            tmp['datetime'] = tmp['datetime'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n            tmp['location'] = config[config['SiteCode']==site][\"Location\"].item()\n            df = pd.concat([df, tmp], ignore_index=True)\n            \n        df['year']= df['datetime'].apply(lambda x: x[:4])\n        result = df.groupby(\"year\").agg(max_height= (\"intake_height_m\",\"max\"))\n        if result['max_height'].std() !=0:\n            print(f\"More than one max height for {file}\",result['max_height'].unique())\n        merged_df=pd.merge(df, result, on='year')\n        merged_df[\"is_max_height_data\"]= merged_df[\"max_height\"] == merged_df[\"intake_height_m\"]\n        merged_df=merged_df.drop(columns=['year','max_height'])\n        merged_df.reset_index(drop=True, inplace=True)\n        merged_df.to_csv(f\"{output_dir}/NIST-testbed-NEC-{site}-{variable}-hourly-concentrations.csv\", index=False)\n\n\n\n\n Back to top",
-    "crumbs": [
-      "Data Transformation Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed"
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+    "href": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#explore-changes-in-methane-ch4-emission-levels-using-the-raster-api",
+    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
+    "section": "Explore Changes in Methane (CH4) Emission Levels Using the Raster API",
+    "text": "Explore Changes in Methane (CH4) Emission Levels Using the Raster API\nIn this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items} \n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for month 1 mentioned in the next cell and again for month 2, so we can visualize each event independently.\n\n# Choose a color for displaying the tiles\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"magma\" \n\n# Make a GET request to retrieve information for the date mentioned below\nmonth1 = '1990-01'\nmonth1_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year and month of the observed parameter, you can modify month mentioned above.\n    f\"{RASTER_API_URL}/collections/{items[month1]['collection']}/items/{items[month1]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nmonth1_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for date mentioned below\nmonth2 = '1990-08'\nmonth2_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year and month of the observed parameter, you can modify the month mentioned above.\n    f\"{RASTER_API_URL}/collections/{items[month2]['collection']}/items/{items[month2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format \n).json()\n\n# Print the properties of the retrieved granule to the console\nmonth2_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}"
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-    "title": "Atmospheric Carbon Dioxide and Methane Concentrations from NOAA Global Monitoring Laboratory",
-    "section": "",
-    "text": "This script was used to transform the CO₂ and CH₄ datasets in txt format with hourly granularity to JSON in daily and monthly granularity for visualization in the Greenhouse Gas (GHG) Center.\n\nimport sys\nimport json\nimport pandas as pd\n\n\ndef daily_aggregate(filepath):\n    \"\"\"\n    Reads hourly data from a .txt file, aggregates it to daily, and returns a list of JSON objects that can be readily visualized in chart.\n\n    Parameters:\n        filepath (str): The path to the file containing the data to be aggregated.\n\n    Returns:\n        list: A list of dictionaries representing aggregated data, with each dictionary containing\n              'date' and 'value' keys.\n\n    Description:\n        This function reads data from the specified file, aggregates it, and returns a list of JSON objects.\n        The function performs the following steps:\n        - Reads the content of the file.\n        - Extracts the header lines from the file to determine the structure of the data.\n        - Processes the data into a DataFrame.\n        - Filters and aggregates the data.\n        - Converts the aggregated data into a list of JSON objects, where each object contains 'date' and 'value' keys.\n\n    Exceptions:\n        - FileNotFoundError: If the specified file is not found.\n        - Exception: If any other exception occurs during the processing, the exception message is returned.\n\n    Note:\n        - The input file is expected to have a .txt format with header lines indicating the structure of the data.\n        - The function aggregates data from hourly to daily intervals.\n        - The returned JSON list is suitable for use in frontend applications to visualize the aggregated data.\n\n    Example:\n        aggregated_data = daily_aggregate(\"/path/to/data_file.txt\")\n    \"\"\"\n    try:\n        with open(filepath, \"r\", encoding=\"utf-8\") as file:\n            file_content_str = file.read()\n            # split the string text based on new line\n            file_content_list = file_content_str.split(\"\\n\")\n            # get the header lines. its mentioned in the file's first line.\n            header_lines = file_content_list[0].split(\":\")[-1]\n            header_lines = int(header_lines)\n            # Slice the non header part of the data. and the last empty element\n            str_datas = file_content_list[header_lines - 1: -1]\n            data = [data.replace(\"\\n\", \"\").split(\" \") for data in str_datas]\n            # seperate table body and head to form dataframe\n            table_head = data[0]\n            table_body = data[1:]\n            dataframe = pd.DataFrame(table_body, columns=table_head)\n            dataframe['value'] = dataframe['value'].astype(float)\n            # Filter data\n            mask = (dataframe[\"qcflag\"] == \"...\") & (dataframe[\"value\"] != 0) & (dataframe[\"value\"] != -999)\n            filtered_df = dataframe[mask].reset_index(drop=True)\n            # Aggregate data (hourly into daily)\n            aggregated_df = filtered_df.groupby(['year', 'month', 'day'])['value'].mean().reset_index()\n            aggregated_df['value'] = aggregated_df['value'].round(2)\n            # necessary columns, processed df\n            aggregated_df['datetime'] = pd.to_datetime(aggregated_df[['year', 'month', 'day']])\n            aggregated_df['datetime'] = aggregated_df['datetime'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n            processed_df = aggregated_df[['datetime', 'value']]\n            processed_df = processed_df.sort_values(by='datetime')\n            # dict formation, needed for frontend [{date: , value: }]\n            json_list = []\n            for _, row in processed_df.iterrows():\n                json_obj = {'date': row['datetime'], 'value': row['value']}\n                json_list.append(json_obj)\n            return json_list\n    except FileNotFoundError:\n        return \"File not found\"\n    except Exception as e:\n        return f\"Exception occured {e}\"\n\n\ndef monthly_aggregate(filepath):\n    \"\"\"\n    Reads hourly data from a .txt file, aggregates it to monthly, and returns a list of JSON objects that can be readily visualized in chart.\n\n    Parameters:\n        filepath (str): The path to the file containing the data to be aggregated.\n\n    Returns:\n        list: A list of dictionaries representing aggregated data, with each dictionary containing\n              'date' and 'value' keys.\n\n    Description:\n        This function reads data from the specified file, aggregates it, and returns a list of JSON objects.\n        The function performs the following steps:\n        - Reads the content of the file.\n        - Extracts the header lines from the file to determine the structure of the data.\n        - Processes the data into a DataFrame.\n        - Filters and aggregates the data.\n        - Converts the aggregated data into a list of JSON objects, where each object contains 'date' and 'value' keys.\n\n    Exceptions:\n        - FileNotFoundError: If the specified file is not found.\n        - Exception: If any other exception occurs during the processing, the exception message is returned.\n\n    Note:\n        - The input file is expected to have a .txt format with header lines indicating the structure of the data.\n        - The function aggregates data from hourly to daily intervals.\n        - The returned JSON list is suitable for use in frontend applications to visualize the aggregated data.\n\n    Example:\n        aggregated_data = monthly_aggregate(\"/path/to/data_file.txt\")\n    \"\"\"\n    try:\n        with open(filepath, \"r\", encoding=\"utf-8\") as file:\n            file_content_str = file.read()\n            # split the string text based on new line\n            file_content_list = file_content_str.split(\"\\n\")\n            # get the header lines. its mentioned in the file's first line.\n            header_lines = file_content_list[0].split(\":\")[-1]\n            header_lines = int(header_lines)\n            # Slice the non header part of the data. and the last empty element\n            str_datas = file_content_list[header_lines - 1: -1]\n            data = [data.replace(\"\\n\", \"\").split(\" \") for data in str_datas]\n            # seperate table body and head to form dataframe\n            table_head = data[0]\n            table_body = data[1:]\n            dataframe = pd.DataFrame(table_body, columns=table_head)\n            dataframe['value'] = dataframe['value'].astype(float)\n            # Filter data\n            mask = (dataframe[\"qcflag\"] == \"...\") & (dataframe[\"value\"] != 0) & (dataframe[\"value\"] != -999)\n            filtered_df = dataframe[mask].reset_index(drop=True)\n            # Aggregate data (hourly into monthly)\n            aggregated_df = filtered_df.groupby(['year', 'month'])['value'].mean().reset_index()\n            aggregated_df['value'] = aggregated_df['value'].round(2)\n            # necessary columns, processed df\n            aggregated_df['datetime'] = pd.to_datetime(aggregated_df[['year', 'month']].assign(day=1))\n            aggregated_df['datetime'] = aggregated_df['datetime'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n            processed_df = aggregated_df[['datetime', 'value']]\n            processed_df = processed_df.sort_values(by='datetime')\n            # dict formation, needed for frontend [{date: , value: }]\n            json_list = []\n            for _, row in processed_df.iterrows():\n                json_obj = {'date': row['datetime'], 'value': row['value']}\n                json_list.append(json_obj)\n            return json_list\n    except FileNotFoundError:\n        return \"File not found\"\n    except Exception as e:\n        return f\"Exception occured {e}\"\n\n\nif __name__ == \"__main__\":\n    # Check if filepath argument is provided\n    if len(sys.argv) != 2:\n        print(\"Usage: python aggregrate.py <daily|monthly> <filepath>\")\n        sys.exit(1)\n\n    # Get the filepath from command line argument\n    frequency = sys.argv[1]\n    hourly_data_filepath = sys.argv[2]\n\n    # Call the aggregate function with the provided filepath\n    if (frequency == \"daily\"):\n        result = daily_aggregate(hourly_data_filepath)\n    elif (frequency == \"monthly\"):\n        result = monthly_aggregate(hourly_data_filepath)\n    else:\n        print(\"Usage: python aggregrate.py <daily|monthly> <filepath>\")\n        sys.exit(1)\n\n    if result is not None:\n        print(result)\n        # save the json file for reference\n        out_path = f\"{hourly_data_filepath.split(\"/\")[-1]}.json\"\n        with open(out_path, \"w\", encoding=\"utf-8\") as file:\n            json.dump(result, file)\n\n\n\n\n Back to top",
-    "crumbs": [
-      "Data Transformation Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Atmospheric Carbon Dioxide and Methane Concentrations from NOAA Global Monitoring Laboratory"
-    ]
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+    "href": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#visualize-ch₄-emissions",
+    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
+    "section": "Visualize CH₄ Emissions",
+    "text": "Visualize CH₄ Emissions\n\n# For this study we are going to compare the CH₄ Emissions for month1 and month2 along the coast of California\n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n\n# Set initial zoom and center of map\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer for tile fetched for month 1\n# The TileLayer library helps in manipulating and displaying raster layers on a map\nmap_layer_month1 = TileLayer(\n    tiles=month1_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.5, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_month1.add_to(map_.m1)\n\n\n# Define the second map layer for the tile fetched for month 2\nmap_layer_month2 = TileLayer(\n    tiles=month2_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.5, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_month2.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
   },
   {
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-    "title": "OCO-2 MIP Top-Down CO₂ Budgets",
-    "section": "",
-    "text": "This script was used to transform the OCO-2 MIP Top-Down CO₂ Budgets dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nimport rasterio\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = \"ghgc-data-store-dev\" # S3 bucket where the COGs are to be stored\nyear_ = datetime(2015, 1, 1)    # Initialize the starting date time of the dataset.\n\nCOG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n\n# Reading the raw netCDF files from local machine\nfiles_processed = pd.DataFrame(columns=[\"file_name\", \"COGs_created\"])   # A dataframe to keep track of the files that are converted into COGs\nfor name in os.listdir(\"new_data\"):\n    ds = xarray.open_dataset(\n        f\"new_data/{name}\",\n        engine=\"netcdf4\",\n    )\n    ds = ds.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})\n    # assign coords from dimensions\n    ds = ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    ds = ds.assign_coords(lat=list(ds.lat))\n\n    variable = [var for var in ds.data_vars]\n\n    for time_increment in range(0, len(ds.year)):\n        for var in variable[2:]:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            try:\n                data = ds[var].sel(year=time_increment)\n                date = year_ + relativedelta(years=+time_increment)\n                filename_elements[-1] = date.strftime(\"%Y\")\n                # # insert date of generated COG into filename\n                filename_elements.insert(2, var)\n                cog_filename = \"_\".join(filename_elements)\n                # # add extension\n                cog_filename = f\"{cog_filename}.tif\"\n            except KeyError:\n                data = ds[var]\n                date = year_ + relativedelta(years=+(len(ds.year) - 1))\n                filename_elements.pop()\n                filename_elements.append(year_.strftime(\"%Y\"))\n                filename_elements.append(date.strftime(\"%Y\"))\n                filename_elements.insert(2, var)\n                cog_filename = \"_\".join(filename_elements)\n                # # add extension\n                cog_filename = f\"{cog_filename}.tif\"\n\n            data = data.reindex(lat=list(reversed(data.lat)))\n\n            data.rio.set_spatial_dims(\"lon\", \"lat\")\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            # generate COG\n            COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(temp_file.name, **COG_PROFILE)\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"ceos_co2_flux/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/ceos_co2_flux/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
-    "crumbs": [
-      "Data Transformation Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "OCO-2 MIP Top-Down CO₂ Budgets"
-    ]
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+    "href": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#visualize-the-data-as-a-time-series",
+    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
+    "section": "Visualize the Data as a Time Series",
+    "text": "Visualize the Data as a Time Series\nWe can now explore the wetland methane emissions time series (January 1990 – December 2024) available for the Texas area of the U.S. We can plot the data set using the code below:\n\n# Determine the width and height of the plot using the 'matplotlib' library\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\n# Plot the time series\nplt.plot(\n    df[\"date\"], # X-axis: date\n    df[\"max\"], # Y-axis: CH₄ value\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Max monthly CH₄ emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"Monthly CH4 emissions g/m2\")\n\n# Insert title for the plot\nplt.title(\"Monthly CH4 emission Values for Texas, 1990-2024\")\n\nText(0.5, 1.0, 'Monthly CH4 emission Values for Texas, 1990-2024')\n\n\n\n\n\n\n\n\n\nTo take a closer look at the CH4 variability across this region, we are going to retrieve and display data collected for the observation mentioned below.\n\n# The 2023-11-01 observation is the 3rd item in the list\n# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n# Print the start Date Time of the third granule in the collection\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n2024-03-01T00:00:00+00:00\n\n\n\n# A GET request is made for the 3rd item in the collection\nobserved_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nobserved_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the CH4 variability for the Texas region for the time in previous cell.\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n    tiles=observed_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", opacity = 0.5 # Set the attribution and transparency\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
   },
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-    "objectID": "user_data_notebooks/emit-ch4plume-v1_User_Notebook.html",
-    "href": "user_data_notebooks/emit-ch4plume-v1_User_Notebook.html",
-    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
-    "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this request form and providing the required information.\nAccess the EMIT Methane Point Source Plume Complexes notebook in the US GHG Center JupyterHub.",
-    "crumbs": [
-      "Data Usage Notebooks",
-      "Large Emissions Events",
-      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
-    ]
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+    "href": "user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.html#summary",
+    "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
+    "section": "Summary",
+    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for the Monthly Wetland Methane Emissions, LPJ-EOSIM Model data: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map and compare the CH4 levels over the Texas region for two distinctive years 5. Create a table that displays the minimum, maximum, and sum of the CH4 levels for a specified region 6. Generate a time-series graph of the CH4 levels for a specified region\nIf you have any questions regarding this user notebook, please contact us using the feedback form."
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-    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
+    "objectID": "user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.html",
+    "href": "user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.html",
+    "title": "Vulcan Fossil Fuel CO₂ Emissions",
     "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this request form and providing the required information.\nAccess the EMIT Methane Point Source Plume Complexes notebook in the US GHG Center JupyterHub.",
-    "crumbs": [
-      "Data Usage Notebooks",
-      "Large Emissions Events",
-      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
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-    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
-    "section": "Table of Contents",
-    "text": "Table of Contents\n\nData Summary and Application\nApproach\nAbout the Data\nInstall the Required Libraries\nQuery the STAC API\nMap Out Selected Tiles\nCalculate Zonal Statistics\nSummary",
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
     "crumbs": [
       "Data Usage Notebooks",
-      "Large Emissions Events",
-      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
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-    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
-    "section": "Data Summary and Application",
-    "text": "Data Summary and Application\n\nSpatial coverage: 52°N to 52°S latitude within target mask\nSpatial resolution: 60 m\nTemporal extent: August 1, 2022 - Ongoing\nTemporal resolution: Variable\nUnit: Parts per million meter (ppm-m)\nUtility: Methane Emissions, Plume Detection, Climate Monitoring\n\nFor more, visit the EMIT Methane Point Source Plume Complexes data overview page.",
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "Vulcan Fossil Fuel CO₂ Emissions"
+    ]
+  },
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+    "objectID": "user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.html#run-this-notebook",
+    "href": "user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.html#run-this-notebook",
+    "title": "Vulcan Fossil Fuel CO₂ Emissions",
+    "section": "",
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
     "crumbs": [
       "Data Usage Notebooks",
-      "Large Emissions Events",
-      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "Vulcan Fossil Fuel CO₂ Emissions"
     ]
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-    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
+    "objectID": "user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.html#approach",
+    "href": "user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.html#approach",
+    "title": "Vulcan Fossil Fuel CO₂ Emissions",
     "section": "Approach",
-    "text": "Approach\n\nIdentify 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 Earth Surface Mineral Dust Source Investigation (EMIT) methane emission plumes data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.Map, visualize the plumes.\nAfter the visualization, perform zonal statistics for a given polygon.",
+    "text": "Approach\n\nIdentify 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.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon.",
     "crumbs": [
       "Data Usage Notebooks",
-      "Large Emissions Events",
-      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "Vulcan Fossil Fuel CO₂ Emissions"
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-    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
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+    "href": "user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.html#about-the-data",
+    "title": "Vulcan Fossil Fuel CO₂ Emissions",
     "section": "About the Data",
-    "text": "About the Data\nThe Earth Surface Mineral Dust Source Investigation (EMIT) instrument builds upon NASA’s long history of developing advanced imaging spectrometers for new science and applications. EMIT launched to the International Space Station (ISS) on July 14, 2022. The data shows high-confidence research grade methane plumes from point source emitters - updated as they are identified - in keeping with Jet Propulsion Laboratory (JPL) Open Science and Open Data policy.\nLarge methane emissions, typically referred to as point source emissions, represent a significant proportion of total methane emissions from the production, transport, and processing of oil and natural gas, landfills, and other sources. By measuring the spectral fingerprint of methane, EMIT can map areas of high methane concentration over background levels in the atmosphere, identifying plume complexes, and estimating the methane enhancements.\nFor more information regarding this dataset, please visit the EMIT Methane Point Source Plume Complexes data overview page.",
+    "text": "About the Data\nThe Vulcan version 4.0 data product represents total carbon dioxide (CO2) emissions resulting from the combustion of fossil fuel (ff) for the contiguous United States and District of Columbia. Referred to as ffCO2, the emissions from Vulcan are also categorized into 10 source sectors including; airports, cement production, commercial marine vessels, commercial, power plants, industrial, non-road, on-road, residential and railroads. Data are gridded annually on a 1-km grid for the years 2010 to 2021. These data are annual sums of hourly estimates. Shown is the estimated total annual ffCO2 for the United States, as well as the estimated total annual ffCO2 per sector.\nFor more information regarding this dataset, please visit the Vulcan Fossil Fuel CO₂ Emissions, Version 4 data overview page.",
     "crumbs": [
       "Data Usage Notebooks",
-      "Large Emissions Events",
-      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "Vulcan Fossil Fuel CO₂ Emissions"
     ]
   },
   {
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-    "href": "user_data_notebooks/emit-ch4plume-v1_User_Notebook.html#querying-the-stac-api",
-    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
+    "objectID": "user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.html#querying-the-stac-api",
+    "href": "user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.html#querying-the-stac-api",
+    "title": "Vulcan Fossil Fuel CO₂ Emissions",
     "section": "Querying the STAC API",
-    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport branca.colormap as cm\nimport seaborn as sns\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for methane emission plumes \ncollection_name = \"emit-ch4plume-v1\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\nExamining the contents of our collection under the temporal variable, we note that data is available from August 2022 to May 2023. By looking at the dashboard: time density, we can see that observations are conducted daily and non-periodically (i.e., there are plumes emissions for multiple places on the same dates).\n\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Check total number of items available\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer \nfrom pystac_client import Client \nimport branca \nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom tabulate import tabulate\nimport branca.colormap as cm\nimport seaborn as sns",
+    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# Please use the collection name similar to the one used in the STAC collection.\n# Name of the collection for Vulcan Fossil Fuel CO₂ Emissions, Version 4. \ncollection_name = \"vulcan-ffco2-yeargrid-v4\"\n\n\n# Fetch the collection from STAC collections using the appropriate endpoint\n# the 'requests' library allows a HTTP request possible\ncollection_vulcan = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2010 to December 2021. By looking at the dashboard:time density, we observe that the data is periodic with year time density.\n\ncollection_vulcan\n\n{'id': 'vulcan-ffco2-yeargrid-v4',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4/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/vulcan-ffco2-yeargrid-v4'}],\n 'title': 'Vulcan Fossil Fuel CO₂ Emissions v4.0',\n 'extent': {'spatial': {'bbox': [[-128.22654896758996,\n     22.857766529124284,\n     -65.30917199495289,\n     51.44087947724907]]},\n  'temporal': {'interval': [['2010-01-01 00:00:00+00',\n     '2021-12-31 00:00:00+00']]}},\n 'license': 'CC-BY-NC-4.0',\n 'renders': {'air-co2': {'assets': ['air-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'cmt-co2': {'assets': ['cmt-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'cmv-co2': {'assets': ['cmv-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'com-co2': {'assets': ['com-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'elc-co2': {'assets': ['elc-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'ind-co2': {'assets': ['ind-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'nrd-co2': {'assets': ['nrd-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'onr-co2': {'assets': ['onr-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'res-co2': {'assets': ['res-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'rrd-co2': {'assets': ['rrd-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'dashboard': {'assets': ['total-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'},\n  'total-co2': {'assets': ['total-co2'],\n   'rescale': [[0, 500]],\n   'colormap_name': 'spectral_r'}},\n 'providers': [{'url': 'https://vulcan.rc.nau.edu/',\n   'name': 'North American Carbon Program',\n   'roles': ['producer', 'licensor']}],\n 'summaries': {'datetime': ['2010-01-01T00:00:00Z', '2021-12-31T00:00:00Z']},\n 'description': 'Annual (2010 - 2021), 1 km resolution estimates of carbon dioxide emissions from fossil fuels and cement production over the contiguous United States, version 4.0',\n 'item_assets': {'air-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Airport Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from taxi, take-off, and landing up to 3000 ft.'},\n  'cmt-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Cement Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from cement production.'},\n  'cmv-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Commercial Marine Vessel Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from commercial marine vessels while maneuvering, hoteling, cruising and traveling within reduced speed zones at ports and shipping lanes. Includes only activity within 12 nautical miles (~22km) from the U.S. shoreline.'},\n  'com-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Commercial Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from Commercial buildings.'},\n  'elc-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Power Plant Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from power plants.'},\n  'ind-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Industrial Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from Industrial buildings.'},\n  'nrd-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Non-Road Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from off-road engines, equipment and vehicles including waterborne pleasure craft.'},\n  'onr-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'On-Road Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from mobile vehicles on roads.'},\n  'res-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Residential Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual ffCO₂ emissions from Residential buildings.'},\n  'rrd-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Railroad Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual FFCO₂ emissions coming from railroads.'},\n  'total-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Total Fossil Fuel CO₂ Emissions',\n   'description': 'Estimated total annual CO₂ emissions from fossil fuel combustion (ffCO₂) across all sectors..'}},\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'}\n\n\n\n# Create a function that would search for the above data collection in the STAC API\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Apply the above function and check the total number of items available within the collection\nnumber_of_items = get_item_count(collection_name)\nitems_vulcan = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items_vulcan)} items\")\n\nFound 12 items\n\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems_vulcan[0]\n\n{'id': 'vulcan-ffco2-yeargrid-v4-2021',\n 'bbox': [-128.22654896758996,\n  22.857766529124284,\n  -65.30917199495289,\n  51.44087947724907],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021/map?assets=total-co2&rescale=0%2C500&colormap_name=spectral_r',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'air-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/AIR_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Airport CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from taxi, take-off, and landing up to 3000 ft.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 318726.1875,\n      'min': 0.11889950931072235,\n      'count': 11,\n      'buckets': [14659, 40, 6, 2, 4, 0, 2, 0, 0, 1]},\n     'statistics': {'mean': 1190.7966562457523,\n      'stddev': 5906.230747537605,\n      'maximum': 318726.1875,\n      'minimum': 0.11889950931072235,\n      'valid_percent': 3.083506571888412}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'cmt-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/CMT_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Cement CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from cement production.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 538037.5,\n      'min': 14599.9677734375,\n      'count': 11,\n      'buckets': [10, 15, 19, 7, 9, 4, 4, 6, 0, 1]},\n     'statistics': {'mean': 181749.84,\n      'stddev': 114981.70564725697,\n      'maximum': 538037.5,\n      'minimum': 14599.9677734375,\n      'valid_percent': 0.015717207618025753}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'cmv-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/CMV_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Commercial Marine Vessels CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from commercial marine vessels while maneuvering, hoteling, cruising and traveling within reduced speed zones at ports and shipping lanes. Includes only activity within 12 nautical miles (~22km) from the U.S. shoreline.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 15446.8408203125,\n      'min': 8.111214810924139e-07,\n      'count': 11,\n      'buckets': [17370, 16, 5, 1, 2, 0, 1, 0, 0, 1]},\n     'statistics': {'mean': 32.60311997010807,\n      'stddev': 210.77205857399764,\n      'maximum': 15446.8408203125,\n      'minimum': 8.111214810924139e-07,\n      'valid_percent': 3.6455539163090127}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'com-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/COM_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Commercial CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from Commercial buildings.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 41811.0625,\n      'min': 6.441725486361349e-10,\n      'count': 11,\n      'buckets': [178117, 7, 1, 1, 0, 0, 1, 0, 0, 2]},\n     'statistics': {'mean': 10.866918777964285,\n      'stddev': 175.08472372009805,\n      'maximum': 41811.0625,\n      'minimum': 6.441725486361349e-10,\n      'valid_percent': 37.32920634388412}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'elc-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/ELC_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Powerplants CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from power plants.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 5685384.0,\n      'min': 1.3666567610925995e-05,\n      'count': 11,\n      'buckets': [3813, 90, 29, 26, 8, 8, 6, 2, 0, 1]},\n     'statistics': {'mean': 94311.64850615115,\n      'stddev': 355198.87374596845,\n      'maximum': 5685384.0,\n      'minimum': 1.3666567610925995e-05,\n      'valid_percent': 0.8346885059012876}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'ind-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/IND_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Industrial CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from Industrial buildings.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3248811.0,\n      'min': 7.467048507292517e-11,\n      'count': 11,\n      'buckets': [110441, 0, 0, 0, 0, 0, 0, 0, 0, 1]},\n     'statistics': {'mean': 120.20799152496333,\n      'stddev': 9843.60430747931,\n      'maximum': 3248811.0,\n      'minimum': 7.467048507292517e-11,\n      'valid_percent': 23.14453125}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'nrd-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/NRD_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Nonroad CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from off-road engines, equipment and vehicles including waterborne pleasure craft.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 2277.109619140625,\n      'min': 1.7788032380394725e-07,\n      'count': 11,\n      'buckets': [227435, 26, 5, 1, 0, 1, 1, 0, 0, 2]},\n     'statistics': {'mean': 6.1029197128425166,\n      'stddev': 14.197089191407585,\n      'maximum': 2277.109619140625,\n      'minimum': 1.7788032380394725e-07,\n      'valid_percent': 47.66945245439914}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'onr-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/ONR_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Onroad CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from mobile vehicles on roads.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 16120.27734375,\n      'min': 0.0011055185459554195,\n      'count': 11,\n      'buckets': [195583, 803, 88, 22, 3, 2, 0, 0, 0, 1]},\n     'statistics': {'mean': 64.57338856601969,\n      'stddev': 230.03362908149538,\n      'maximum': 16120.27734375,\n      'minimum': 0.0011055185459554195,\n      'valid_percent': 41.179503084763944}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'res-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/RES_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Residential CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions from Residential buildings.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 10387.0556640625,\n      'min': 9.804268508162295e-09,\n      'count': 11,\n      'buckets': [204532, 112, 28, 7, 4, 3, 3, 2, 1, 1]},\n     'statistics': {'mean': 12.793920651903093,\n      'stddev': 91.07716732362586,\n      'maximum': 10387.0556640625,\n      'minimum': 9.804268508162295e-09,\n      'valid_percent': 42.896031719420606}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'rrd-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/RRD_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total Railroad CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual ffCO₂ emissions coming from railroads.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 4935.501953125,\n      'min': 7.793863915139809e-05,\n      'count': 11,\n      'buckets': [43982, 167, 38, 9, 7, 5, 3, 2, 1, 2]},\n     'statistics': {'mean': 24.959290754478015,\n      'stddev': 94.2219837346061,\n      'maximum': 4935.501953125,\n      'minimum': 7.793863915139809e-05,\n      'valid_percent': 9.266027360515022}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'total-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/TOT_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Total of all sectors CO₂ Emissions',\n   'proj:bbox': [-128.22654896758996,\n    22.857766529124284,\n    -65.30917199495289,\n    51.44087947724907],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [2649, 5831],\n   'description': 'Estimated total annual CO₂ emissions from fossil fuel combustion (ffCO₂) across all sectors.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 272530.15625,\n      'min': 1.7858106104995386e-07,\n      'count': 11,\n      'buckets': [227843, 81, 36, 7, 3, 6, 1, 4, 1, 1]},\n     'statistics': {'mean': 162.91311194255712,\n      'stddev': 2080.549384731812,\n      'maximum': 272530.15625,\n      'minimum': 1.7858106104995386e-07,\n      'valid_percent': 47.7767485917382}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-128.22654896758996, 22.857766529124284],\n      [-65.30917199495289, 22.857766529124284],\n      [-65.30917199495289, 51.44087947724907],\n      [-128.22654896758996, 51.44087947724907],\n      [-128.22654896758996, 22.857766529124284]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.01079015211329739,\n    0.0,\n    -128.22654896758996,\n    0.0,\n    -0.01079015211329739,\n    51.44087947724907,\n    0.0,\n    0.0,\n    1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021/preview.png?assets=total-co2&rescale=0%2C500&colormap_name=spectral_r',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-128.22654896758996, 22.857766529124284],\n    [-65.30917199495289, 22.857766529124284],\n    [-65.30917199495289, 51.44087947724907],\n    [-128.22654896758996, 51.44087947724907],\n    [-128.22654896758996, 22.857766529124284]]]},\n 'collection': 'vulcan-ffco2-yeargrid-v4',\n 'properties': {'end_datetime': '2021-12-31T00:00:00+00:00',\n  'start_datetime': '2021-01-01T00:00:00+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']}\n\n\n\n# To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:4]: item for item in items_vulcan} \n# rh = Heterotrophic Respiration\nasset_name = \"total-co2\"\n\n\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for 2021 and again for 2010, so that we can visualize each event independently.\n\ncolor_map = \"spectral_r\" # please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n\n# 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_2021_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items['2021']['collection']}/items/{items['2021']['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).json()\n_2021_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-128.22654896758996,\n  22.857766529124284,\n  -65.30917199495289,\n  51.44087947724907],\n 'center': [-96.76786048127143, 37.14932300318668, 0]}\n\n\n\n_2010_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items['2010']['collection']}/items/{items['2010']['id']}/tilejson.json?\"\n\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale=0,150\", \n).json()\n_2010_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-128.22654896758996,\n  22.857766529124284,\n  -65.30917199495289,\n  51.44087947724907],\n 'center': [-96.76786048127143, 37.14932300318668, 0]}",
     "crumbs": [
       "Data Usage Notebooks",
-      "Large Emissions Events",
-      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "Vulcan Fossil Fuel CO₂ Emissions"
     ]
   },
   {
-    "objectID": "user_data_notebooks/emit-ch4plume-v1_User_Notebook.html#query-the-stac-api",
-    "href": "user_data_notebooks/emit-ch4plume-v1_User_Notebook.html#query-the-stac-api",
-    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
-    "section": "Query the STAC API",
-    "text": "Query the STAC API\nFirst, you need to import the required libraries. Once imported, they allow better execution of a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored. You will learn the functionality of each library throughout the notebook.\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac/\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster/\"\n\nSTAC API Collection Names\nNow, you must fetch the dataset from the STAC API by defining its associated STAC API collection ID as a variable. The collection ID, also known as the collection name, for the EMIT Methane Point Source Plume Complexes dataset is emit-ch4plume-v1\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for methane emission plumes \ncollection_name = \"emit-ch4plume-v1\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection in a table\n# Adjust display settings\npd.set_option('display.max_colwidth', None)  # Set maximum column width to \"None\" to prevent cutting off text\n\n# Extract the relevant information about the collection\ncollection_info = {\n    \"Title\": collection.get(\"title\", \"N/A\"), # Extract the title of the collection \n    \"Description\": collection.get(\"description\", \"N/A\"), # Extract the dataset description\n    \"Temporal Extent\": collection.get(\"extent\", {}).get(\"temporal\", {}).get(\"interval\", \"N/A\"), # Extract the temporal coverage of the collection\n    \"Spatial Extent\": collection.get(\"extent\", {}).get(\"spatial\", {}).get(\"bbox\", \"N/A\"), # Extract the spatial coverage of the collection\n}\n\n# Convert the derived information into a DataFrame format\nproperties_table = pd.DataFrame(list(collection_info.items()), columns=[\"Collection Summary\", \"\"])\n\n# Display the properties in a table\ncollection_summary = properties_table.style.set_properties(**{'text-align': 'left'}) \\\n                                           .set_table_styles([\n    {\n        'selector': 'th.col0, td.col0',    # Select the first column\n        'props': [('min-width', '200px'),  # Set a minimum width\n                  ('text-align', 'left')]  # Align text to the left\n    },\n    {\n        'selector': 'td.col1',             # Select the second column\n        'props': [('text-align', 'left')]  # Align text to the left\n    }\n])\n\n# Print the collection summary table\ncollection_summary\n\nNext, you will examine the contents of the collection under the temporal variable. You’ll see that the data is available since August 2022. Looking at the dashboard: time density, you can see that observations are conducted daily and non-periodically (i.e., there are plumes emissions for multiple places on the same dates).\n\n# Create a function that would search for data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function is \"get_item_count\" \n# The argument that will be passed to the defined function is \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection \n    count = 0 \n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\" \n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path \n        response = requests.get(items_url) \n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} observations\")\n\n# Sort the items based on their date-time attribute\nitems_sorted = sorted(items, key=lambda x: x[\"properties\"][\"datetime\"])\n\n# Create an empty list\ntable_data = []\n# Extract the ID and date-time information for each granule and add them to the list\n# By default, only the first 5 items in the collection are extracted to be displayed in the table. \n# To see the date-time of all existing granules in this collection, remove \"5\" from \"item_sorted[:5]\" in the line below. \nfor item in items_sorted[:5]:\n    table_data.append([item['id'], item['properties']['datetime']])\n\n# Define the table headers\nheaders = [\"Item ID\", \"Date-Time\"]\n\nprint(\"Below you see the first 5 items in the collection, along with their item IDs and corresponding Start Date-Time.\")\n\n# Print the table using tabulate\nprint(tabulate(table_data, headers=headers, tablefmt=\"fancy_grid\"))\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] refers to the first item (granule) in the list/collection\nitems_sorted[0]",
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+    "href": "user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.html#visualizing-total-fossil-fuel-co₂-emissions",
+    "title": "Vulcan Fossil Fuel CO₂ Emissions",
+    "section": "Visualizing Total Fossil Fuel CO₂ Emissions",
+    "text": "Visualizing Total Fossil Fuel CO₂ Emissions\n\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n\n# Define the first map layer with the CO2 Flux data for December 2022\nmap_layer_2021 = TileLayer(\n    tiles=_2021_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution \n    name='2021 Total CO2 Fossil Fuel Emissions', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.8, # Adjust the transparency of the layer\n)\n# Add the first layer to the Dual Map \nmap_layer_2021.add_to(map_.m1)\n\nmap_layer_2010 = TileLayer(\n    tiles=_2010_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution \n    name='2010 Total CO2 Fossil Fuel Emissions', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.8, # Adjust the transparency of the layer\n)\n# Add the first layer to the Dual Map \nmap_layer_2010.add_to(map_.m2)\n\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
     "crumbs": [
       "Data Usage Notebooks",
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-      "Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters"
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-    "title": "Utilizing NASA’s EMIT Instrument to Monitor Methane Plumes from Point Source Emitters",
-    "section": "Map Out Selected Tiles",
-    "text": "Map Out Selected Tiles\nYou will now explore global methane emission plumes from point sources and visualize the results on a map using folium.\n\n# Once again, apply the function created above \"get_item_count\" to the Air-Sea CO2 Flux ECCO-Darwin collection\n# This step allows retrieving the number of granules “observations” in the collection.\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n\n# Next, you need to create a dictionary where the \"id\" field of each item in the collection are queried more explicitly\nplume_complexes = {items[\"id\"]: items for items in items} \n\n\n# Next, you need to specify the asset name for this collection.\n# The asset name refers to the raster band containing the pixel values for the parameter of interest.\n# For the case of the EMIT Methane Point Source collection, the parameter of interest is “ch4-plume-emissions”.\nasset_name = \"ch4-plume-emissions\"\n\nBelow, you will enter the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":plume_complexes[list(plume_complexes.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":plume_complexes[list(plume_complexes.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, you will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint.\n\n# Select the item ID which you want to visualize. Item ID is in the format yyyymmdd followed by the timestamp. This ID can be extracted from the COG name as well.\n# To browse and select other tiles in the collection, please visit https://search.earthdata.nasa.gov/search/granules?p=C2748088093-LPCLOUD&pg[0][v]=f&pg[0][gsk]=-start_date&q=emit%20plume&tl=1694622854.77!3!!\n\n# You need to copy the entire granule nomenclature \nitem_id = \"EMIT_L2B_CH4PLM_001_20230418T200118_000829\"\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer to choose a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"magma\"\n\n# Make a GET request to retrieve information for the selected tile defined in \"item_id\"\nmethane_plume_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{plume_complexes[item_id]['collection']}/items/{plume_complexes[item_id]['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    \n    # Pass the color formula and colormap for custom visualization\n    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    \n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nmethane_plume_tile\n\n\n# Set a colormap for the granule\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp (https://matplotlib.org/stable/users/explain/colors/colormaps.html)\ncolormap = \"magma\" \n\n\n# Defining the breaks in the colormap \ncolor_map = cm.LinearColormap(colors = ['#310597', '#4C02A1', '#6600A7', '#7E03A8', '#9511A1', '#AA2395', '#BC3587', '#CC4778', '#DA5A6A', '#E66C5C', '#F0804E', '#F89540','#FDAC33', '#FDC527', '#F8DF25'], vmin = 0, vmax = 1500 )\n\n\n# Add an appropriate caption, in this case it would be Parts per million meter\ncolor_map.caption = 'ppm-m'\n\n# Set initial zoom and center of map for plume Layer\nmap_ = folium.Map(location=(methane_plume_tile[\"center\"][1], methane_plume_tile[\"center\"][0]), zoom_start=14, tiles=None, tooltip = 'test tool tip')\nfolium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png', name='ESRI World Imagery', attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(map_)\n\n\n# Use the 'TileLayer' library to display the raster layer, add an appropriate caption, and adjust the transparency of the layer on the map\nmap_layer = TileLayer(\n    tiles=methane_plume_tile[\"tiles\"][0], # Path to retrieve the tile\n    name='Plume Complex Landfill',\n    overlay='True', # The layer can be overlaid on the map\n    attr=\"GHG\", # Set the attribution \n    opacity=1, # Adjust the transparency of the layer\n)\nmap_layer.add_to(map_)\n\n\n# Adjust map elements \nfolium.LayerControl(collapsed=False, position='bottomleft').add_to(map_)\nmap_.add_child(color_map)\nsvg_style = '<style>svg#legend {font-size: 14px; background-color: white;}</style>'\nmap_.get_root().header.add_child(folium.Element(svg_style))\n\n\n# Visualizing the map\nmap_",
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+    "title": "Vulcan Fossil Fuel CO₂ Emissions",
+    "section": "Visualizing the Data as a Time Series",
+    "text": "Visualizing the Data as a Time Series\nWe can now explore the total fossil fuel emission time series (2010 -2021) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\nplt.plot(\n    df[\"datetime\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum CO₂\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"CO₂ emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"tC/km²/year\")\nplt.xticks(rotation = 90)\n\n# Insert title for the plot\nplt.title(\"Total Fossil Fuel CO₂ Emissions for Texas, Dallas (2010-2021)\")\n\n# Add data citation\nplt.text(\n    df[\"datetime\"].iloc[0],           # X-coordinate of the text\n    df[\"max\"].min(),                  # Y-coordinate of the text\n\n\n\n\n    # Text to be displayed\n    \"Source: https://doi.org/10.3334/ORNLDAAC/1741\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"left\",              # Horizontal alignment\n    verticalalignment=\"top\",                 # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n\n# Plot the time series\nplt.show()",
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     "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for the EMIT Methane Point Source Plume Complexes dataset: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map the methane emission plumes 5. Generate statistics for the area of interest (AOI)\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
+    "text": "Summary\nIn this notebook we have successfully explored, analyzed, and visualized the STAC collection for Vulcan Fossil Fuel CO₂ Emissions, Version 4 dataset.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the total fossil fuel CO₂ emissions for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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     "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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     "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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-    "text": "Approach\n\nIdentify 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 gridded methane emissions data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.jsonendpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon.",
+    "text": "Approach\n\nIdentify 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 OCO-2 GEOS Column CO₂ Concentrations data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison.\nAfter the visualization, perform zonal statistics for a given polygon.",
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     "section": "About the Data",
-    "text": "About the Data\nThe NASA Carbon Monitoring System Flux (CMS-Flux) team analyzed remote sensing observations from Japan’s Greenhouse gases Observing SATellite (GOSAT) to produce the global Committee on Earth Observation Satellites (CEOS) CH₄ Emissions data product. They used an analytic Bayesian inversion approach and the GEOS-Chem global chemistry transport model to quantify annual methane (CH₄) emissions and their uncertainties at a spatial resolution of 1° by 1° and then projected these to each country for 2019.\nFor more information regarding this dataset, please visit the GOSAT-based Top-down Total and Natural Methane Emissions data overview page.",
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-    "section": "Querying the STAC API",
-    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# Please use the collection name similar to the one used in STAC collection.\n\n# Name of the collection for gosat budget methane. \ncollection_name = \"gosat-based-ch4budget-yeargrid-v1\"\n\n\n# Fetching the collection from STAC collections using appropriate endpoint.\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\ncollection\n\n{'id': 'gosat-based-ch4budget-yeargrid-v1',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/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/gosat-based-ch4budget-yeargrid-v1'}],\n 'title': 'GOSAT-based Top-down Total and Natural Methane Emissions v1',\n 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n  'temporal': {'interval': [['2019-01-01T00:00:00+00:00',\n     '2019-12-31T00:00:00+00:00']]}},\n 'license': 'CC-BY-4.0',\n 'renders': {'dashboard': {'assets': ['post-total'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.3]],\n   'colormap_name': 'spectral_r'},\n  'post-total': {'assets': ['post-total'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.3]],\n   'colormap_name': 'spectral_r'},\n  'prior-total': {'assets': ['prior-total'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.3]],\n   'colormap_name': 'spectral_r'},\n  'post-wetland': {'assets': ['post-wetland'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.1]],\n   'colormap_name': 'spectral_r'},\n  'prior-wetland': {'assets': ['prior-wetland'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.1]],\n   'colormap_name': 'spectral_r'},\n  'post-wetland-uncertainty': {'assets': ['post-wetland-uncertainty'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.05]],\n   'colormap_name': 'purd'},\n  'prior-wetland-uncertainty': {'assets': ['prior-wetland-uncertainty'],\n   'nodata': 9.96921e+36,\n   'rescale': [[0, 0.05]],\n   'colormap_name': 'purd'}},\n 'summaries': {'datetime': ['2019-01-01T00:00:00Z']},\n 'description': \"As part of the global stock take (GST), countries are asked to provide a record of their greenhouse gas (GHG) emissions to inform decisions on how to reduce GHG emissions. The NASA Carbon Monitoring System Flux (CMS-Flux) team has used remote sensing observations from Japan's Greenhouse gases Observing SATellite (GOSAT) to produce modeled total methane (CH₄) emissions and uncertainties on a 1 degree by 1 degree resolution grid for the year 2019. The GOSAT data is used in the model to inform total emission estimates, as well as wetland (the primary natural source of methane), and various human-related sources such as fossil fuel extraction, transport, agriculture, waste, and fires. A prior GHG emission estimate (and assocated uncertainty) is provided for each layer, which is the emissions estimate without GOSAT data. The posterior GHG emission layers are informed by GOSAT total column methane data. An advanced mathematical approach is used with a global chemistry transport model to quantify annual CH₄ emissions and uncertainties. These estimates are expressed in teragrams of CH₄ per year (Tg/yr). The source data can be found at https://doi.org/10.5281/zenodo.8306874 and more information can also be found on the CEOS website https://ceos.org/gst/methane.html\",\n 'item_assets': {'post-total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Posterior Total Methane Emissions',\n   'description': 'Estimated total methane emissions per grid cell informed by GOSAT satellite total column methane data.'},\n  'prior-total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Prior Total Methane Emissions',\n   'description': 'Total methane emissions per grid cell estimated by various inventories or models, excluding satellite based observations from GOSAT.'},\n  'post-wetland': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Wetland Posterior Methane Emissions',\n   'description': 'Estimated methane emissions per grid cell from wetlands informed by GOSAT satellite total column methane data.'},\n  'prior-wetland': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Wetland Prior Methane Emissions',\n   'description': 'Methane emissions per grid cell from wetlands estimated by various inventories or models, excluding satellite based observations from GOSAT.'},\n  'post-wetland-uncertainty': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Uncertainty - Wetland Posterior Methane Emissions',\n   'description': 'Uncertainty in estimated methane emissions per grid cell from wetlands informed by GOSAT satellite total column methane data.'},\n  'prior-wetland-uncertainty': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Uncertainty - Wetland Prior Methane Emissions',\n   'description': 'Uncertainty in methane emissions per grid cell from wetlands estimated by various inventories or models, excluding satellite based observations from GOSAT.'}},\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': False,\n 'dashboard:time_density': 'year'}\n\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2012 to December 2018. By looking at the dashboard:time density, we observe that the data is available for only one year, i.e. 2019.\n\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Check total number of items available\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\nFound 1 items\n\n\n\n# Examining the first item in the collection\nitems[0]\n\n{'id': 'gosat-based-ch4budget-yeargrid-v1-2019',\n 'bbox': [-180.5, -90.5, 179.5, 89.5],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/map?assets=post-total&nodata=9.96921e%2B36&rescale=0%2C0.3&colormap_name=spectral_r',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'post-gas': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_gas_GEOS_CHEM_2019.tif',\n   'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.6140491962432861,\n      'min': -0.4103066623210907,\n      'count': 11.0,\n      'buckets': [1.0, 0.0, 2.0, 23.0, 64734.0, 30.0, 7.0, 2.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.00043242290848866105,\n      'stddev': 0.006180576980113983,\n      'maximum': 0.6140491962432861,\n      'minimum': -0.4103066623210907,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'post-geo': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_geo_GEOS_CHEM_2019.tif',\n   'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 1.0034276247024536,\n      'min': -1.0016025304794312,\n      'count': 11.0,\n      'buckets': [1.0, 0.0, 1.0, 5.0, 63425.0, 1354.0, 10.0, 2.0, 1.0, 1.0]},\n     'statistics': {'mean': 0.0003479516308289021,\n      'stddev': 0.0093332938849926,\n      'maximum': 1.0034276247024536,\n      'minimum': -1.0016025304794312,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'post-oil': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_oil_GEOS_CHEM_2019.tif',\n   'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.457329273223877,\n      'min': -0.7987076640129089,\n      'count': 11.0,\n      'buckets': [2.0, 64681.0, 108.0, 4.0, 3.0, 1.0, 0.0, 0.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.0004447368555702269,\n      'stddev': 0.01879083551466465,\n      'maximum': 3.457329273223877,\n      'minimum': -0.7987076640129089,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'post-coal': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_coal_GEOS_CHEM_2019.tif',\n   'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 1.1035711765289307,\n      'min': -0.9143016934394836,\n      'count': 11.0,\n      'buckets': [1.0, 1.0, 1.0, 1.0, 64710.0, 62.0, 19.0, 3.0, 1.0, 1.0]},\n     'statistics': {'mean': 0.0003904950572177768,\n      'stddev': 0.01172551792114973,\n      'maximum': 1.1035711765289307,\n      'minimum': -0.9143016934394836,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'post-fire': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_fire_GEOS_CHEM_2019.tif',\n   'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.7065173387527466,\n      'min': -0.08211488276720047,\n      'count': 11.0,\n      'buckets': [103.0, 64685.0, 11.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.00020585705351550132,\n      'stddev': 0.00356540665961802,\n      'maximum': 0.7065173387527466,\n      'minimum': -0.08211488276720047,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'post-rice': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_rice_GEOS_CHEM_2019.tif',\n   'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 1.3836066722869873,\n      'min': -1.1384793519973755,\n      'count': 11.0,\n      'buckets': [1.0, 4.0, 12.0, 20.0, 64581.0, 132.0, 30.0, 11.0, 4.0, 5.0]},\n     'statistics': {'mean': 0.0010437712771818042,\n      'stddev': 0.024994080886244774,\n      'maximum': 1.3836066722869873,\n      'minimum': -1.1384793519973755,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'prior-gas': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_prior_gas_GEOS_CHEM_2019.tif',\n   'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.2977725863456726,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64659.0, 93.0, 27.0, 8.0, 2.0, 4.0, 2.0, 2.0, 2.0, 1.0]},\n     'statistics': {'mean': 0.00037746498128399253,\n      'stddev': 0.00403926195576787,\n      'maximum': 0.2977725863456726,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'prior-geo': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_prior_geo_GEOS_CHEM_2019.tif',\n   'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 1.8356599807739258,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64780.0, 15.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.0004932624287903309,\n      'stddev': 0.009640775620937347,\n      'maximum': 1.8356599807739258,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'prior-oil': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_prior_oil_GEOS_CHEM_2019.tif',\n   'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n   'proj:epsg': 4326.0,\n   'proj:shape': [180.0, 360.0],\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 1.287477731704712,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [64734.0, 40.0, 15.0, 3.0, 1.0, 4.0, 0.0, 1.0, 1.0, 1.0]},\n     'statistics': {'mean': 0.0006414719391614199,\n      'stddev': 0.01284099742770195,\n      'maximum': 1.287477731704712,\n      'minimum': 0.0,\n      'valid_percent': 0.00154320987654321}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'post-total': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_total_GEOS_CHEM_2019.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   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'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.5, -90.5],\n      [179.5, -90.5],\n      [179.5, 89.5],\n      [-180.5, 89.5],\n      [-180.5, -90.5]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/preview.png?assets=post-total&nodata=9.96921e%2B36&rescale=0%2C0.3&colormap_name=spectral_r',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180.5, -90.5],\n    [179.5, -90.5],\n    [179.5, 89.5],\n    [-180.5, 89.5],\n    [-180.5, -90.5]]]},\n 'collection': 'gosat-based-ch4budget-yeargrid-v1',\n 'properties': {'end_datetime': '2019-12-31T00:00:00+00:00',\n  'start_datetime': '2019-01-01T00:00:00+00:00'},\n 'stac_version': '1.0.0',\n 'stac_extensions': []}\n\n\nBelow, we enter minimum and maximum values to provide our upper and lower bounds in rescale_values.",
-    "crumbs": [
-      "Data Usage Notebooks",
-      "Natural Greenhouse Gas Sources Emissions and Sinks",
-      "GOSAT-based Top-down Total and Natural Methane Emissions"
-    ]
-  },
-  {
-    "objectID": "user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.html#exploring-changes-in-gosat-methane-budgets-ch4-levels-using-the-raster-api",
-    "href": "user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.html#exploring-changes-in-gosat-methane-budgets-ch4-levels-using-the-raster-api",
-    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
-    "section": "Exploring Changes in GOSAT Methane budgets (CH4) Levels Using the Raster API",
-    "text": "Exploring Changes in GOSAT Methane budgets (CH4) Levels Using the Raster API\nIn this notebook, we will explore the impacts of methane emissions and by examining changes over time in urban regions. We will visualize the outputs on a map using folium.\n\n# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:10]: item for item in items} \nasset_name = \"prior-total\"\n\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\n\nitems.keys()\n\ndict_keys(['2019-01-01'])\n\n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this for first January 2019.\n\ncolor_map = \"rainbow\" # please select the color ramp from matplotlib library.\njanuary_2019_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items['2019-01-01']['collection']}/items/{items['2019-01-01']['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\njanuary_2019_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=prior-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=0.0%2C2.121816635131836'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.5, -90.5, 179.5, 89.5],\n 'center': [-0.5, -0.5, 0]}",
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-    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
-    "section": "Visualizing CH₄ Emissions",
-    "text": "Visualizing CH₄ Emissions\n\n# Set initial zoom and center of map for CH₄ Layer\n# Centre of map [latitude,longitude]\nmap_ = folium.Map(location=(34, -118), zoom_start=6)\n\n# January 2019\nmap_layer_2019 = TileLayer(\n    tiles=january_2019_tile[\"tiles\"][0],\n    attr=\"GHG\",\n    opacity=0.7,\n)\nmap_layer_2019.add_to(map_)\n\n# # January 2012\n# map_layer_2012 = TileLayer(\n#     tiles=january_2012_tile[\"tiles\"][0],\n#     attr=\"GHG\",\n#     opacity=0.7,\n# )\n# map_layer_2012.add_to(map_.m2)\n\n# visualising the map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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-    "title": "GOSAT-based Top-down Total and Natural Methane Emissions",
-    "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for the GOSAT-based Top-down Total and Natural Methane Emissions dataset.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap the methane emission levels\nGenerate zonal statistics for the area of interest (AOI)\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
+    "text": "About the Data\nIn July 2014, NASA successfully launched the first dedicated Earth remote sensing satellite to study atmospheric carbon dioxide (CO₂) from space. The Orbiting Carbon Observatory-2 (OCO-2) is an exploratory science mission designed to collect space-based global measurements of atmospheric CO₂ with the precision, resolution, and coverage needed to characterize sources and sinks (fluxes) on regional scales (≥1000 km). This dataset provides global gridded, daily column-averaged carbon dioxide (XCO₂) concentrations from January 1, 2015 - February 28, 2022. The data are derived from OCO-2 observations that were input to the Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS), a modeling and data assimilation system maintained by NASA’s Global Modeling and Assimilation Office (GMAO). Concentrations are measured in moles of carbon dioxide per mole of dry air (mol CO₂/mol dry) at a spatial resolution of 0.5° x 0.625°. Data assimilation synthesizes simulations and observations, adjusting modeled atmospheric constituents like CO₂ to reflect observed values. With the support of NASA’s Carbon Monitoring System (CMS) Program and the OCO Science Team, this dataset was produced as part of the OCO-2 mission which provides the highest quality space-based XCO₂ retrievals to date.\nFor more information regarding this dataset, please visit the OCO-2 GEOS Column CO₂ Concentrations data overview page.",
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-    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
-    "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this request form and providing the required information.\nAccess the U.S. Gridded Anthropogenic Methane Emissions Inventory notebook in the US GHG Center JupyterHub.",
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+    "title": "OCO-2 GEOS Column CO₂ Concentrations",
+    "section": "Install the Required Libraries",
+    "text": "Install the Required Libraries\nRequired libraries are pre-installed on the GHG Center Hub. If you need to run this notebook elsewhere, please install them with this line in a code cell:\n%pip install requests folium rasterstats pystac_client pandas matplotlib –quiet\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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(",
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-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this request form and providing the required information.\nAccess the U.S. Gridded Anthropogenic Methane Emissions Inventory notebook in the US GHG Center JupyterHub.",
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+    "title": "OCO-2 GEOS Column CO₂ Concentrations",
+    "section": "Querying the STAC API",
+    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# Please use the collection name similar to the one used in STAC collection.\n# Name of the collection for OCO-2 GEOS Column CO₂ Concentrations. \ncollection_name = \"oco2geos-co2-daygrid-v10r\"\n\n\n# Fetching the collection from STAC collections using appropriate endpoint.\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\ncollection\n\n{'id': 'oco2geos-co2-daygrid-v10r',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r/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/oco2geos-co2-daygrid-v10r'}],\n 'title': 'OCO-2 GEOS Column CO₂ Concentrations v10r',\n 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n  'temporal': {'interval': [['2015-01-01T00:00:00+00:00',\n     '2022-02-28T00:00:00+00:00']]}},\n 'license': 'CC0-1.0',\n 'renders': {'xco2': {'assets': ['xco2'],\n   'nodata': 0,\n   'rescale': [[412, 422]],\n   'colormap_name': 'magma'},\n  'dashboard': {'assets': ['xco2'],\n   'nodata': 0,\n   'rescale': [[412, 422]],\n   'colormap_name': 'magma'}},\n 'summaries': {'datetime': ['2015-01-01T00:00:00Z', '2022-02-28T00:00:00Z']},\n 'description': 'This dataset provides global gridded, daily column-averaged carbon dioxide (XCO₂) concentrations from January 1, 2015 - February 28, 2022. The data are derived from Orbiting Carbon Observatory-2 (OCO-2) satellite observations that were input to the Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS), a modeling and data assimilation system maintained by NASA’s Global Modeling and Assimilation Office (GMAO). Concentrations are measured in moles of carbon dioxide per mole of dry air (mol CO₂/mol dry) at a spatial resolution of 0.5° x 0.625°. Data assimilation synthesizes simulations and observations, adjusting modeled atmospheric constituents like CO₂ to reflect observed values. With the support of NASA’s Carbon Monitoring System (CMS) Program and the OCO Science Team, this dataset was produced as part of the OCO-2 mission which provides the highest quality space-based XCO₂ retrievals to date. The source data can be found at https://doi.org/10.5067/Y9M4NM9MPCGH',\n 'item_assets': {'xco2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Average Dry-Air Column CO₂ (XCO₂)',\n   'description': 'Daily dry air column-averaged mole fractions of carbon dioxide created from data assimilations of OCO-2 satellite retrievals.'},\n  'xco2prec': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Average Dry-Air Column CO₂ Precision (XCO₂PREC)',\n   'description': 'Random errors for daily dry air column-averaged mole fractions of carbon dioxide calculated using a posteriori diagnostics.'}},\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': 'day'}\n\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2015 to February 2022. By looking at the dashboard:time density, we can see that these observations are collected daily.\n\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Check total number of items available\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\nFound 2615 items\n\n\n\n# Examining the first item in the collection\nitems[0]\n\n{'id': 'oco2geos-co2-daygrid-v10r-20220228',\n 'bbox': [-180.3125, -90.25, 179.6875, 90.25],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228/map?assets=xco2&nodata=0&rescale=412%2C422&colormap_name=magma',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'xco2': {'href': 's3://ghgc-data-store/oco2geos-co2-daygrid-v10r/oco2_GEOS_XCO2_L3CO2_day_B10206Ar_20220228.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Average Dry-Air Column CO₂ (XCO₂)',\n   'proj:bbox': [-180.3125, -90.25, 179.6875, 90.25],\n   'proj:epsg': 4326.0,\n   'proj:shape': [361.0, 576.0],\n   'description': 'Daily dry air column-averaged mole fractions of carbon dioxide created from data assimilations of OCO-2 satellite retrievals.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 423.60419320175424,\n      'min': 411.7429234611336,\n      'count': 11.0,\n      'buckets': [37851.0,\n       30550.0,\n       19173.0,\n       11220.0,\n       15304.0,\n       31151.0,\n       45205.0,\n       15819.0,\n       1524.0,\n       139.0]},\n     'statistics': {'mean': 416.40504944204235,\n      'stddev': 2.967704894550985,\n      'maximum': 423.60419320175424,\n      'minimum': 411.7429234611336,\n      'valid_percent': 0.00048091720529393656}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.3125, -90.25],\n      [179.6875, -90.25],\n      [179.6875, 90.25],\n      [-180.3125, 90.25],\n      [-180.3125, -90.25]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.625, 0.0, -180.3125, 0.0, -0.5, 90.25, 0.0, 0.0, 1.0]},\n  'xco2prec': {'href': 's3://ghgc-data-store/oco2geos-co2-daygrid-v10r/oco2_GEOS_XCO2PREC_L3CO2_day_B10206Ar_20220228.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Average Dry-Air Column CO₂ Precision (XCO₂PREC)',\n   'proj:bbox': [-180.3125, -90.25, 179.6875, 90.25],\n   'proj:epsg': 4326.0,\n   'proj:shape': [361.0, 576.0],\n   'description': 'Random errors for daily dry air column-averaged mole fractions of carbon dioxide calculated using a posteriori diagnostics.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 1.0,\n      'min': 0.09999999999999999,\n      'count': 11.0,\n      'buckets': [73789.0,\n       19836.0,\n       7943.0,\n       4684.0,\n       3634.0,\n       3060.0,\n       3094.0,\n       3093.0,\n       3814.0,\n       84989.0]},\n     'statistics': {'mean': 0.5499856972588942,\n      'stddev': 0.4024318718400779,\n      'maximum': 1.0,\n      'minimum': 0.09999999999999999,\n      'valid_percent': 0.00048091720529393656}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.3125, -90.25],\n      [179.6875, -90.25],\n      [179.6875, 90.25],\n      [-180.3125, 90.25],\n      [-180.3125, -90.25]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.625, 0.0, -180.3125, 0.0, -0.5, 90.25, 0.0, 0.0, 1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228/preview.png?assets=xco2&nodata=0&rescale=412%2C422&colormap_name=magma',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180.3125, -90.25],\n    [179.6875, -90.25],\n    [179.6875, 90.25],\n    [-180.3125, 90.25],\n    [-180.3125, -90.25]]]},\n 'collection': 'oco2geos-co2-daygrid-v10r',\n 'properties': {'datetime': '2022-02-28T00:00:00+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']}\n\n\nBelow, we enter minimum and maximum values to provide our upper and lower bounds in rescale_values.",
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-    "section": "Table of Contents",
-    "text": "Table of Contents\n\nData Summary and Application\nApproach\nAbout the Data\nInstall the Required Libraries\nQuery the STAC API\nVisual Comparison Across Time Periods\nMap Out Selected Tiles\nCalculate Zonal Statistics\nTime-Series Analysis\nSummary",
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+    "title": "OCO-2 GEOS Column CO₂ Concentrations",
+    "section": "Exploring Changes in Column-Averaged XCO₂ Concentrations Levels Using the Raster API",
+    "text": "Exploring Changes in Column-Averaged XCO₂ Concentrations Levels Using the Raster API\nIn this notebook, we will explore the temporal impacts of CO₂ emissions. We will visualize the outputs on a map using folium.\n\n# To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"datetime\"]: item for item in items} \nasset_name = \"xco2\" #fossil fuel\n\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for 2022-02-08 and again for 2022-01-27, so that we can visualize each event independently.\n\ncolor_map = \"magma\"\noco2_1 = requests.get(\n    f\"{RASTER_API_URL}/collections/{items[list(items.keys())[0]]['collection']}/items/{items[list(items.keys())[0]]['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\noco2_1\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=411.7429234611336%2C423.60419320175424'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.3125, -90.25, 179.6875, 90.25],\n 'center': [-0.3125, 0.0, 0]}\n\n\n\noco2_2 = requests.get(\n    f\"{RASTER_API_URL}/collections/{items[list(items.keys())[1]]['collection']}/items/{items[list(items.keys())[1]]['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\noco2_2\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=411.7429234611336%2C423.60419320175424'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.3125, -90.25, 179.6875, 90.25],\n 'center': [-0.3125, 0.0, 0]}",
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-    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
-    "section": "Data Summary and Application",
-    "text": "Data Summary and Application\n\nSpatial coverage: Contiguous United States\nSpatial resolution: 0.1° x 0.1°\nTemporal extent: 2012 - 2020\nTemporal resolution: Annual\nUnit: Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr)\nUtility: Methane Monitoring, Anthropogenic Emissions Analysis, Climate Research\n\nFor more, visit the U.S. Gridded Anthropogenic Methane Emissions Inventory data overview page.",
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+    "title": "OCO-2 GEOS Column CO₂ Concentrations",
+    "section": "Visualizing Daily Column-Averaged XCO₂ Concentrations",
+    "text": "Visualizing Daily Column-Averaged XCO₂ Concentrations\n\n# Set initial zoom and center of map for XCO₂ Layer\n# Centre of map [latitude,longitude]\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n\nmap_layer_2020 = TileLayer(\n    tiles=oco2_1[\"tiles\"][0],\n    attr=\"GHG\",\n    opacity=0.5,\n)\nmap_layer_2020.add_to(map_.m1)\n\nmap_layer_2019 = TileLayer(\n    tiles=oco2_2[\"tiles\"][0],\n    attr=\"GHG\",\n    opacity=0.5,\n)\nmap_layer_2019.add_to(map_.m2)\n\n# visualising the map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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-    "section": "Approach",
-    "text": "Approach\n\nIdentify 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 gridded methane emissions data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon.",
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+    "title": "OCO-2 GEOS Column CO₂ Concentrations",
+    "section": "Visualizing the Data as a Time Series",
+    "text": "Visualizing the Data as a Time Series\nWe can now explore the XCO₂ concentrations time series (January 1, 2015 - February 28, 2022) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:\n\nfig = plt.figure(figsize=(20, 10))\n\n\nplt.plot(\n    df[\"datetime\"],\n    df[\"max\"],\n    color=\"red\",\n    linestyle=\"-\",\n    linewidth=0.5,\n    label=\"CO₂ concentrations\",\n)\n\nplt.legend()\nplt.xlabel(\"Years\")\nplt.ylabel(\"CO2 concentrations ppm\")\nplt.title(\"CO₂ concentrations Values for Texas, Dallas (Jan 2015- Feb 2022)\")\n\nText(0.5, 1.0, 'CO₂ concentrations Values for Texas, Dallas (Jan 2015- Feb 2022)')\n\n\n\n\n\n\n\n\n\n\nprint(items[2][\"properties\"][\"datetime\"])\n\n2022-02-26T00:00:00+00:00\n\n\n\noco2_3 = requests.get(\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\",\n).json()\noco2_3\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220226/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=xco2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=411.7429234611336%2C423.60419320175424'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.3125, -90.25, 179.6875, 90.25],\n 'center': [-0.3125, 0.0, 0]}\n\n\n\n# Use bbox initial zoom and map\n# Set up a map located w/in event bounds\naoi_map_bbox = Map(\n    tiles=\"OpenStreetMap\",\n    location=[\n        30,-100\n    ],\n    zoom_start=6.8,\n)\n\nmap_layer = TileLayer(\n    tiles=oco2_3[\"tiles\"][0],\n    attr=\"GHG\", opacity = 0.7\n)\n\nmap_layer.add_to(aoi_map_bbox)\n\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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-    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
-    "section": "About the Data",
-    "text": "About the Data\nThe 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)), 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).\nThis V2 Express Extension dataset contains methane emissions provided as fluxes, in units of molecules of methane per square cm per second, for over 25 individual emission source categories, including those from agriculture, petroleum and natural gas systems, coal mining, and waste. The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends.\nThe gridded dataset currently includes 34 data layers. The first data layer includes annual 2012-2020 gridded methane emissions fluxes from all anthropogenic sources of methane in the U.S. GHGI (excluding Land Use, Land-Use Change and Forestry (LULUCF) sources, which are not included in the gridded GHGI). The next six data layers include annual 2012-2020 gridded methane fluxes from sources within the aggregate Agriculture, Natural Gas, Petroleum, Waste, Industry, and ‘Other’ source categories. The remaining 27 data layers include annual 2012-2020 gridded methane emissions fluxes from individual emission sectors within each of the aggregate categories.\nFor more information regarding this dataset, please visit the U.S. Gridded Anthropogenic Methane Emissions Inventory data overview page.",
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+    "title": "OCO-2 GEOS Column CO₂ Concentrations",
+    "section": "Summary",
+    "text": "Summary\nIn this notebook, we have successfully explored, analyzed, and visualized the STAC collection for OCO-2 GEOS Column CO₂ Concentrations.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the Column-Averaged XCO₂ Concentrations Levels for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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-    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
-    "section": "Query the STAC API",
-    "text": "Query the STAC API\nFirst, you need to import the required libraries. Once imported, they allow better execution of a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored. You will learn the functionality of each library throughout the notebook.\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\nSTAC API Collection Names\nNow, you must fetch the dataset from the STAC API by defining its associated STAC API collection ID as a variable. The collection ID, also known as the collection name, for the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset is epa-ch4emission-yeargrid-v2express\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for gridded methane dataset \ncollection_name = \"epa-ch4emission-yeargrid-v2express\"\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection in a table\n# Adjust display settings\npd.set_option('display.max_colwidth', None)  # Set maximum column width to \"None\" to prevent cutting off text\n\n# Extract the relevant information about the collection\ncollection_info = {\n    \"Title\": collection.get(\"title\", \"N/A\"), # Extract the title of the collection \n    \"Description\": collection.get(\"description\", \"N/A\"), # Extract the dataset description\n    \"Temporal Extent\": collection.get(\"extent\", {}).get(\"temporal\", {}).get(\"interval\", \"N/A\"), # Extract the temporal coverage of the collection\n    \"Spatial Extent\": collection.get(\"extent\", {}).get(\"spatial\", {}).get(\"bbox\", \"N/A\"), # Extract the spatial coverage of the collection\n}\n\n# Convert the derived information into a DataFrame format\nproperties_table = pd.DataFrame(list(collection_info.items()), columns=[\"Collection Summary\", \"\"])\n\n# Display the properties in a table\ncollection_summary = properties_table.style.set_properties(**{'text-align': 'left'}) \\\n                                           .set_table_styles([\n    {\n        'selector': 'th.col0, td.col0',    # Select the first column\n        'props': [('min-width', '200px'),  # Set a minimum width\n                  ('text-align', 'left')]  # Align text to the left\n    },\n    {\n        'selector': 'td.col1',             # Select the second column\n        'props': [('text-align', 'left')]  # Align text to the left\n    }\n])\n\n# Print the collection summary table\ncollection_summary\n\n{'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'}\n\n\nNext, you will examine the contents of the collection under the temporal variable. You’ll see that the data is available from January 2012 to December 2020. By looking at the dashboard:time density, you can observe that the periodic frequency of these observations is yearly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\n# Sort the items based on their date-time attribute\nitems_sorted = sorted(items, key=lambda x: x[\"properties\"][\"datetime\"])\n\n# Create an empty list\ntable_data = []\n# Extract the ID and date-time information for each granule and add them to the list\n# By default, only the first 5 items in the collection are extracted to be displayed in the table. \n# To see the date-time of all existing granules in this collection, remove \"5\" from \"item_sorted[:5]\" in the line below. \nfor item in items_sorted[:5]:\n    table_data.append([item['id'], item['properties']['datetime']])\n\n# Define the table headers\nheaders = [\"Item ID\", \"Start Date-Time\"]\n\nprint(\"Below you see the first 5 items in the collection, along with their item IDs and corresponding Start Date-Time.\")\n\n# Print the table using tabulate\nprint(tabulate(table_data, headers=headers, tablefmt=\"fancy_grid\"))\n\nFound 9 items\n\n\nThis makes sense as there are 9 years between 2012 - 2020, meaning 9 records in total.\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems_sorted[0]\n\n{'id': 'epa-ch4emission-yeargrid-v2express-2020',\n 'bbox': [-129.99999694387628,\n  19.99999923487448,\n  -60.00000305612369,\n  55.00000076512553],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},\n  {'rel': 'root',\n   'type': 'application/json',\n   '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  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020/map?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'dwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2020.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Waste - Domestic Wastewater Treatment & Discharge (annual)',\n   'proj:bbox': [-129.99999694387628,\n    19.99999923487448,\n    -60.00000305612369,\n    55.00000076512553],\n   'proj:epsg': 4326.0,\n   'proj:shape': [350.0, 700.0],\n   'description': 'Annual methane emissions from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 250.26608276367188,\n      'min': -9999.0,\n      'count': 11.0,\n      'buckets': [169028.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75972.0]},\n     'statistics': {'mean': -6898.392578125,\n      'stddev': 4624.86865234375,\n      'maximum': 250.26608276367188,\n      'minimum': -9999.0,\n      'valid_percent': 0.0004081632653061224}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-129.99999694387628, 19.99999923487448],\n      [-60.00000305612369, 19.99999923487448],\n      [-60.00000305612369, 55.00000076512553],\n      [-129.99999694387628, 55.00000076512553],\n      [-129.99999694387628, 19.99999923487448]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999126821799,\n    0.0,\n    -129.99999694387628,\n    0.0,\n    -0.10000000437214586,\n    55.00000076512553,\n    0.0,\n    0.0,\n    1.0]},\n  'iwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2020.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Waste - Industrial Wastewater Treatment & Discharge (annual)',\n   'proj:bbox': [-129.99999694387628,\n    19.99999923487448,\n    -60.00000305612369,\n    55.00000076512553],\n   'proj:epsg': 4326.0,\n   'proj:shape': [350.0, 700.0],\n   'description': 'Annual methane emissions from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 552.8455200195312,\n      'min': -9999.0,\n      'count': 11.0,\n      'buckets': [244120.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 880.0]},\n     'statistics': {'mean': -9963.07421875,\n      'stddev': 598.360107421875,\n      'maximum': 552.8455200195312,\n      'minimum': -9999.0,\n      'valid_percent': 0.0004081632653061224}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-129.99999694387628, 19.99999923487448],\n      [-60.00000305612369, 19.99999923487448],\n      [-60.00000305612369, 55.00000076512553],\n      [-129.99999694387628, 55.00000076512553],\n      [-129.99999694387628, 19.99999923487448]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999126821799,\n    0.0,\n    -129.99999694387628,\n    0.0,\n    -0.10000000437214586,\n    55.00000076512553,\n    0.0,\n    0.0,\n    1.0]},\n  'post-meter': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2020.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Natural Gas - Post Meter (annual)',\n   'proj:bbox': [-129.99999694387628,\n    19.99999923487448,\n    -60.00000305612369,\n    55.00000076512553],\n   'proj:epsg': 4326.0,\n   'proj:shape': [350.0, 700.0],\n   'description': 'Annual methane emissions downstream of residential, commercial, industrial natural gas distribution meters (i.e., “Post Meter”) (inventory Energy 1B2b sub-category).',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 32.81692123413086,\n      'min': -9999.0,\n      'count': 11.0,\n      'buckets': [169110.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75890.0]},\n     'statistics': {'mean': -6901.73974609375,\n      'stddev': 4623.49169921875,\n      'maximum': 32.81692123413086,\n      'minimum': -9999.0,\n      'valid_percent': 0.0004081632653061224}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-129.99999694387628, 19.99999923487448],\n      [-60.00000305612369, 19.99999923487448],\n      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+    "objectID": "user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.html",
+    "href": "user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.html",
+    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
+    "section": "",
+    "text": "Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\nVisualize the time series data",
     "crumbs": [
       "Data Usage Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions"
+      "Greenhouse Gas Concentrations",
+      "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed"
     ]
   },
   {
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-    "href": "user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html#visual-comparison-across-time-periods",
-    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
-    "section": "Visual Comparison Across Time Periods",
-    "text": "Visual Comparison Across Time Periods\nIn this notebook, you will explore the impacts of methane emissions and by examining changes over time in urban regions. You will visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"datetime\"][:7]: item for item in items} \n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the U.S. Gridded Anthropogenic Methane Emissions Inventory collection, the parameter of interest is “surface-coal”\nasset_name = \"surface-coal\"\n\nBelow, you will enter the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\n\nitems\n\n{'2020-01': {'id': 'epa-ch4emission-yeargrid-v2express-2020',\n  'bbox': [-129.99999694387628,\n   19.99999923487448,\n   -60.00000305612369,\n   55.00000076512553],\n  'type': 'Feature',\n  'links': [{'rel': 'collection',\n    'type': 'application/json',\n    'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},\n   {'rel': 'parent',\n    'type': 'application/json',\n    'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},\n   {'rel': 'root',\n    'type': 'application/json',\n    '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   {'title': 'Map of Item',\n    'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020/map?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',\n    'rel': 'preview',\n    'type': 'text/html'}],\n  'assets': {'dwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Waste - Domestic Wastewater Treatment & Discharge (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\n    'description': 'Annual methane emissions from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',\n    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 250.26608276367188,\n       'min': -9999.0,\n       'count': 11.0,\n       'buckets': [169028.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75972.0]},\n      'statistics': {'mean': -6898.392578125,\n       'stddev': 4624.86865234375,\n       'maximum': 250.26608276367188,\n       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     0.0,\n     1.0]},\n   'iwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Waste - Industrial Wastewater Treatment & Discharge (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\n    'description': 'Annual methane emissions from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',\n    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 552.8455200195312,\n       'min': -9999.0,\n       'count': 11.0,\n       'buckets': [244120.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 880.0]},\n      'statistics': {'mean': -9963.07421875,\n       'stddev': 598.360107421875,\n       'maximum': 552.8455200195312,\n       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     0.0,\n     1.0]},\n   'post-meter': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Natural Gas - Post Meter (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\n    'description': 'Annual methane emissions downstream of residential, commercial, industrial natural gas distribution meters (i.e., “Post Meter”) (inventory Energy 1B2b sub-category).',\n    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 32.81692123413086,\n       'min': -9999.0,\n       'count': 11.0,\n       'buckets': [169110.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75890.0]},\n      'statistics': {'mean': -6901.73974609375,\n       'stddev': 4623.49169921875,\n       'maximum': 32.81692123413086,\n       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     0.0,\n     1.0]},\n   'refining-ps': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Petroleum - Refining (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\n    'description': 'Annual methane emissions from Petroleum Refining (inventory Energy 1B2a sub-category).',\n    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 22.515766143798828,\n       'min': -9999.0,\n       'count': 11.0,\n       'buckets': [244892.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 108.0]},\n      'statistics': {'mean': -9994.5908203125,\n       'stddev': 209.9478759765625,\n       'maximum': 22.515766143798828,\n       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     0.0,\n     1.0]},\n   'total-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_other_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Total Other (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\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    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 43.67214584350586,\n       'min': -9999.0,\n       'count': 11.0,\n       'buckets': [157503.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 87497.0]},\n      'statistics': {'mean': -6428.02197265625,\n       'stddev': 4791.09814453125,\n       'maximum': 43.67214584350586,\n       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     0.0,\n     1.0]},\n   'total-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_waste_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Total Waste (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\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    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 552.9092407226562,\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       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     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    'type': 'image/tiff; 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application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Other - Stationary Combustion (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\n    'description': 'Annual methane emissions from Stationary Combustion (inventory Energy 1A sub-category).',\n    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 8.125173568725586,\n       'min': -9999.0,\n       'count': 11.0,\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     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     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-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  'geometry': {'type': 'Polygon',\n   'coordinates': [[[-129.99999694387628, 19.99999923487448],\n     [-60.00000305612369, 19.99999923487448],\n     [-60.00000305612369, 55.00000076512553],\n     [-129.99999694387628, 55.00000076512553],\n     [-129.99999694387628, 19.99999923487448]]]},\n  'collection': 'epa-ch4emission-yeargrid-v2express',\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']}}\n\n\nNow, you will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. This step is done twice, once for January 2018 and again for January 2012, so that you can visualize each event independently.\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"rainbow\" \n\nobservation_date_1 = '2018'\n\n# Don't change anything here\nobservation_1 = f'epa-ch4emission-yeargrid-v2express-{observation_date_1}'\n\n# Make a GET request to retrieve information for the 2018 tile \njanuary_2018_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2018-01']['collection']}/items/{items['2018-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2018_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-129.99999694387628,\n  19.99999923487448,\n  -60.00000305612369,\n  55.00000076512553],\n 'center': [-94.99999999999999, 37.5, 0]}\n\n\n\n# You will repeat the same approach used in the previous step to retrieve the second observation of interest\nobservation_date_2 = '2012'\n\n# Don't change anything here\nobservation_2 = f'epa-ch4emission-yeargrid-v2express-{observation_date_2}'\n\n# Make a GET request to retrieve information for the 2018 tile \njanuary_2012_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2012-01']['collection']}/items/{items['2012-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2012_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-129.99999694387628,\n  19.99999923487448,\n  -60.00000305612369,\n  55.00000076512553],\n 'center': [-94.99999999999999, 37.5, 0]}",
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+    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
+    "section": "",
+    "text": "Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\nVisualize the time series data",
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       "Data Usage Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions"
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+      "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed"
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-    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
-    "section": "Map Out Selected Tiles",
-    "text": "Map Out Selected Tiles\n\n# Set initial zoom and center of map for CH₄ Layer\n# Centre of map [latitude,longitude]\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(38.9, -80.0), zoom_start=8)\n\n# Define the first map layer (January 2018)\nmap_layer_2018 = TileLayer(\n    tiles=january_2018_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    name='January 2018', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.7, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_2018.add_to(map_.m1)\n\n# Define the second map layer (January 2012)\nmap_layer_2012 = TileLayer(\n    tiles=january_2012_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    name='January 2012', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.7, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_2012.add_to(map_.m2)\n\n# Display data markers (titles) on both maps\nfolium.Marker((40, 5.0), tooltip=\"both\").add_to(map_)\n\n# Add a layer control to switch between map layers\nfolium.LayerControl(collapsed=False).add_to(map_)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "href": "user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.html#about-the-data",
+    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
+    "section": "About the Data",
+    "text": "About the Data\nNIST is engaged in research to improve measurement of greenhouse gas emissions in areas containing multiple emission sources and sinks, such as cities. NIST’s objective is to develop measurement tools supporting independent means to increase the accuracy of greenhouse gas emissions data at urban and regional geospatial scales. NIST has established three test beds in U.S. cities to develop and evaluate the performance of advanced measurement capabilities for emissions independent of their origin. Located in Indianapolis, Indiana, the Los Angeles air basin of California, and the U.S. Northeast corridor (beginning with the Baltimore/Washington D.C. region), the test beds have been selected for their varying meteorology, terrain and emissions characteristics. These test beds will serve as a means to independently diagnose the accuracy of emissions data obtained directly from emission or uptake sources.\nFor more information regarding this dataset, please visit the Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed data overview page.",
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       "Data Usage Notebooks",
-      "Gridded Anthropogenic Greenhouse Gas Emissions",
-      "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions"
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-    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
-    "section": "Time-Series Analysis",
-    "text": "Time-Series Analysis\nYou can now explore the gridded methane emission (Domestic Wastewater Treatment & Discharge (5D)) time series (January 2000 -December 2021) available for the Pittsburgh Pennsylvania area of the U.S. You can plot the data set using the code below:\n\n# Ensure 'datetime' column is in datetime format\ndf['datetime'] = pd.to_datetime(df['datetime'])\n\n# Sort the DataFrame by the datetime column so the plot displays the values from left to right (2020 -> 2022)\ndf_sorted = df.sort_values(by=\"datetime\")\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\nplt.plot(\n    df[\"date\"], # X-axis: sorted date\n    df[\"max\"],  # Y-axis: maximum CH4 emission\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Max CH4 emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"CH4 emissions Molecules CH₄/cm²/s\")\n\n# Insert title for the plot\nplt.title(\"CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Pittsburgh, PA (2012-2021)\")\n\n\n# Add data citation\nplt.text(\n    df_sorted[\"datetime\"].iloc[0],           # X-coordinate of the text \n    df_sorted[\"max\"].min(),                  # Y-coordinate of the text \n\n    # Text to be displayed\n    \"Source: EPA Gridded Anthropogenic Methane Emissions Inventory\",                   \n    fontsize=12,                             # Font size\n    horizontalalignment=\"left\",              # Horizontal alignment\n    verticalalignment=\"bottom\",              # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n# Plot the time series\nplt.show()\n\nText(0.5, 1.0, 'CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Texas, Dallas (2012-202)')\n\n\n\n\n\n\n\n\n\n\n# Print the properties for the 3rd item in the collection\nprint(items[2][\"properties\"][\"datetime\"])\n\n2018-01-01T00:00:00+00:00\n\n\n\n# You will repeat the same approach used in the previous step to retrieve the second observation of interest\nobservation_date_3 = '2016'\n\n# Don't change anything here\nobservation_3 = f'epa-ch4emission-yeargrid-v2express-{observation_date_3}'\n\n# Make a GET request to retrieve information for the 2018 tile \ntile_2016 = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\ntile_2016\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-129.99999694387628,\n  19.99999923487448,\n  -60.00000305612369,\n  55.00000076512553],\n 'center': [-94.99999999999999, 37.5, 0]}\n\n\n\n# Create a new map to display the 2016 tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        39.9,-79.4\n    ],\n\n    # Set the zoom value\n    zoom_start=9,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=tile_2016[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.5\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
+    "section": "Querying the Feature Vector API",
+    "text": "Querying the Feature Vector API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Feature Vector Application Programming Interface (API) where the items for this collection are stored.\n\nFEATURE_API_URL=\"https://earth.gov/ghgcenter/api/features\"\n\n\n# Function to fetch CSV data for a station with a limit parameter\ndef get_station_data_csv(station_code, gas_type, frequency, elevation_m, limit=10000):\n    # Use the ?f=csv and limit query to get more rows\n    url = f\"https://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_nec_{station_code}_{gas_type}_{frequency}_concentrations/items?f=csv&elevation_m={elevation_m}&limit={limit}\"\n    print(url)\n    try:\n        response = requests.get(url)\n        print(response)\n        # Check if the response is successful\n        if response.status_code != 200:\n            print(f\"Failed to fetch data for {station_code}. Status code: {response.status_code}\")\n            return pd.DataFrame()\n\n        # Check if the content type is CSV\n        content_type = response.headers.get('Content-Type')\n        if 'text/csv' not in content_type:\n            print(f\"Unexpected content type for {station_code}: {content_type}\")\n            print(\"Response content:\", response.text)\n            return pd.DataFrame()\n\n        # Read the CSV content into a pandas DataFrame\n        csv_data = StringIO(response.text)\n        return pd.read_csv(csv_data)\n    \n    except requests.exceptions.RequestException as e:\n        print(f\"Request failed: {e}\")\n        return pd.DataFrame()",
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-    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
-    "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset:\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the anthropogenic methane emissions for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nGenerate a time-series graph of the anthropogenic methane emissions for a specified region\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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+    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
+    "section": "Visualizing the CH₄ data for two NEC stations",
+    "text": "Visualizing the CH₄ data for two NEC stations\n\n# Get station name and elevation from metdata dataframe\n# Fetch data for UNY (elevation 230) and TMD (elevation 489), using limit=10000\n# ch4/co2 select the ghg \nuny_data = get_station_data_csv('uny', 'ch4', 'hourly', 483, limit=10000)\ntmd_data = get_station_data_csv('tmd', 'ch4', 'hourly', 561, limit=10000)\n\n# Check if data was successfully retrieved before proceeding\nif uny_data.empty or tmd_data.empty:\n    print(\"No data available for one or both stations. Exiting.\")\nelse:\n    # Convert the 'datetime' column to datetime for plotting\n    uny_data['datetime'] = pd.to_datetime(uny_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n    tmd_data['datetime'] = pd.to_datetime(tmd_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n\n    # Plot the data\n    plt.figure(figsize=(10, 6))\n    plt.plot(uny_data['datetime'], uny_data['value'], label='UNY (230m)', color='blue', marker='o')\n    plt.plot(tmd_data['datetime'], tmd_data['value'], label='TMD (489m)', color='green', marker='o')\n\n    plt.title('Methane (CH₄) Hourly Concentrations Over Time for UNY and TMD Stations')\n    plt.xlabel('Time')\n    plt.ylabel('CH4 Concentration (ppb)')\n    plt.legend()\n    plt.grid(True)\n\n    # Show plot\n    plt.show()\n\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_nec_uny_ch4_hourly_concentrations/items?f=csv&elevation_m=483&limit=10000\n<Response [200]>\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_nec_tmd_ch4_hourly_concentrations/items?f=csv&elevation_m=561&limit=10000\n<Response [200]>",
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-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
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-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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     "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
     "section": "Approach",
-    "text": "Approach\n\nIdentify 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 Wetland Methane Emissions, LPJ-EOSIM Model data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison.\nAfter the visualization, perform zonal statistics for a given polygon."
+    "text": "Approach\n\nIdentify 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 Wetland Methane Emissions, LPJ-EOSIM Model data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison.\nAfter the visualization, perform zonal statistics for a given polygon.",
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     "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
     "section": "About the Data",
-    "text": "About the Data\nMethane (CH₄) emissions from vegetated wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions have thus far been underrepresented. Using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) global CH₄ emissions from wetlands are estimated at 0.5° x 0.5 degree spatial resolution. By simulating wetland extent and using characteristics of inundated areas, such as wetland soil moisture, temperature, and carbon content, the model provides estimates of CH₄ quantities emitted into the atmosphere. This dataset shows concentrated methane sources from tropical and high latitude ecosystems. The LPJ-EOSIM Wetland Methane Emissions dataset consists of global daily model estimates of terrestrial wetland methane emissions from 1990 to the present, with data added bimonthly. The monthly data has been curated by aggregating the daily files. The estimates are regularly used in conjunction with NASA’s Goddard Earth Observing System (GEOS) model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, to compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions.\nFor more information regarding this dataset, please visit the U.S. Greenhouse Gas Center."
+    "text": "About the Data\nMethane (CH₄) emissions from vegetated wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions have thus far been underrepresented. Using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) global CH₄ emissions from wetlands are estimated at 0.5° x 0.5 degree spatial resolution. By simulating wetland extent and using characteristics of inundated areas, such as wetland soil moisture, temperature, and carbon content, the model provides estimates of CH₄ quantities emitted into the atmosphere. This dataset shows concentrated methane sources from tropical and high latitude ecosystems. The LPJ-EOSIM Wetland Methane Emissions dataset consists of global daily model estimates of terrestrial wetland methane emissions from 1990 to the present, with data added bimonthly. The estimates are regularly used in conjunction with NASA’s Goddard Earth Observing System (GEOS) model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, to compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions.\nFor more information regarding this dataset, please visit the U.S. Greenhouse Gas Center.",
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     "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
     "section": "Query the STAC API",
-    "text": "Query the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for the wetland methane emissions LPJ-EOSIM Model\ncollection_name = \"lpjeosim-wetlandch4-monthgrid-v1\"\n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\nasset_name = \"ensemble-mean-ch4-wetlands-emissions\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'lpjeosim-wetlandch4-monthgrid-v2',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2/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/lpjeosim-wetlandch4-monthgrid-v2'}],\n 'title': '(Monthly) Wetland Methane Emissions, LPJ-EOSIM Model v2',\n 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n  'temporal': {'interval': [['1990-01-01 00:00:00+00',\n     '2024-05-31 00:00:00+00']]}},\n 'license': 'CC0-1.0',\n 'renders': {'dashboard': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'era5-ch4-wetlands-emissions': {'assets': ['era5-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'merra2-ch4-wetlands-emissions': {'assets': ['merra2-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'ensemble-mean-ch4-wetlands-emissions': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'}},\n 'providers': [{'name': 'NASA'}],\n 'summaries': {'datetime': ['1990-01-01T00:00:00Z', '2024-05-31T00:00:00Z']},\n 'description': 'Global, monthly estimates of methane (CH₄) emissions from terrestrial wetlands at 0.5 x 0.5 degree spatial resolution using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Methane emissions from vegetated wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions have thus far been underrepresented. The LPJ-EOSIM model estimates wetland methane emissions by simulating wetland extent and using characteristics of these inundated areas such as soil moisture, temperature, and carbon content to estimate CH₄ quantities emitted into the atmosphere. Input climate forcing data comes from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data and ECMWF Re-Analysis data (ERA5). An ensemble layer provides the result of the mean of the MERRA-2 and ERA5 layers. The source data can be found at https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_MCH4E.001 and https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_MCH4E_LL.001.',\n 'item_assets': {'era5-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.'},\n  'merra2-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.'},\n  'ensemble-mean-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.'}},\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': 'month'}\n\n\nExamining the contents of our collection under summaries, we see that the data is available from January 1990 to December 2024. By looking at dashboard: time density, we can see that these observations are collected monthly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\n\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n        # temp = items_url.split('/')\n        # temp.insert(3, 'ghgcenter')\n        # temp.insert(4, 'api')\n        # temp.insert(5, 'stac')\n        # items_url = '/'.join(temp)\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\"\n).json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 413 items\n\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'lpjeosim-wetlandch4-monthgrid-v2-202405',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405/map?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'era5-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_MCH4E_LL.001/LPJ_EOSIM_L2_MCH4E_LL_001_202405/LPJ_EOSIM_L2_MCH4E_LL_ERA5_001_202405.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.2603939548181415e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [61251, 731, 214, 98, 77, 34, 28, 25, 3, 2]},\n     'statistics': {'mean': 3.2184362818990856e-11,\n      'stddev': 1.3179453937864483e-10,\n      'maximum': 3.2603939548181415e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09837962962963}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'merra2-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_MCH4E_LL.001/LPJ_EOSIM_L2_MCH4E_LL_001_202405/LPJ_EOSIM_L2_MCH4E_LL_MERRA2_001_202405.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.9324570266785486e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [61424, 584, 191, 119, 55, 42, 23, 13, 6, 3]},\n     'statistics': {'mean': 3.452568789087992e-11,\n      'stddev': 1.504910798913e-10,\n      'maximum': 3.9324570266785486e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09722222222222}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'ensemble-mean-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_MCH4E_LL.001/LPJ_EOSIM_L2_MCH4E_LL_001_202405/LPJ_EOSIM_L2_MCH4E_LL_ensemble_mean_001_202405.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Monthly) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 2.8522617601112188e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [60942, 894, 247, 132, 89, 60, 38, 31, 17, 10]},\n     'statistics': {'mean': 3.335580191462005e-11,\n      'stddev': 1.37447713656579e-10,\n      'maximum': 2.8522617601112188e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09722222222222}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405/preview.png?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'lpjeosim-wetlandch4-monthgrid-v2',\n 'properties': {'end_datetime': '2024-05-31T00:00:00+00:00',\n  'start_datetime': '2024-05-01T00:00:00+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']}\n\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetch the minimum and maximum values for rescaling\nrescale_values = {'max': 0.0003, 'min': 0.0}"
+    "text": "Query the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for the wetland methane emissions LPJ-EOSIM Model\ncollection_name = \"lpjeosim-wetlandch4-daygrid-v1\"\n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\nasset_name = \"ensemble-mean-ch4-wetlands-emissions\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'lpjeosim-wetlandch4-daygrid-v2',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2/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/lpjeosim-wetlandch4-daygrid-v2'}],\n 'title': '(Daily) Wetland Methane Emissions, LPJ-EOSIM Model v2',\n 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n  'temporal': {'interval': [['1990-01-01 00:00:00+00',\n     '2024-05-31 00:00:00+00']]}},\n 'license': 'CC0-1.0',\n 'renders': {'dashboard': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'era5-ch4-wetlands-emissions': {'assets': ['era5-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'merra2-ch4-wetlands-emissions': {'assets': ['merra2-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'},\n  'ensemble-mean-ch4-wetlands-emissions': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n   'rescale': [[0, 3e-09]],\n   'colormap_name': 'magma'}},\n 'providers': [{'name': 'NASA'}],\n 'summaries': {'datetime': ['1990-01-01T00:00:00Z', '2024-05-31T00:00:00Z']},\n 'description': 'Global, daily estimates of methane (CH₄) emissions from terrestrial wetlands at 0.5 x 0.5 degree spatial resolution using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Methane emissions from vegetated wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions have thus far been underrepresented. The LPJ-EOSIM model estimates wetland methane emissions by simulating wetland extent and using characteristics of these inundated areas such as soil moisture, temperature, and carbon content to estimate CH₄ quantities emitted into the atmosphere. Input climate forcing data comes from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data and ECMWF Re-Analysis data (ERA5). An ensemble layer provides the result of the mean of the MERRA-2 and ERA5 layers. The source data can be found at https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E.001 and https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E_LL.001.',\n 'item_assets': {'era5-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.'},\n  'merra2-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.'},\n  'ensemble-mean-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n   'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.'}},\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': 'day'}\n\n\nExamining the contents of our collection under summaries, we see that the data is available from January 1990 to December 2024. By looking at dashboard: time density, we can see that these observations are collected monthly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\n\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n        # temp = items_url.split('/')\n        # temp.insert(3, 'ghgcenter')\n        # temp.insert(4, 'api')\n        # temp.insert(5, 'stac')\n        # items_url = '/'.join(temp)\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit=800\"\n).json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 800 items\n\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'lpjeosim-wetlandch4-daygrid-v2-20240531',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531/map?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'era5-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_DCH4E_LL.001/LPJ_EOSIM_L2_DCH4E_LL_001_20240531/LPJ_EOSIM_L2_DCH4E_LL_ERA5_001_20240531.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.1533866629018803e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [60696, 1228, 228, 119, 78, 51, 35, 24, 2, 2]},\n     'statistics': {'mean': 3.818678323370309e-11,\n      'stddev': 1.385319732851768e-10,\n      'maximum': 3.1533866629018803e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09837962962963}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'merra2-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_DCH4E_LL.001/LPJ_EOSIM_L2_DCH4E_LL_001_20240531/LPJ_EOSIM_L2_DCH4E_LL_MERRA2_001_20240531.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 5.284403581384822e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [61618, 503, 152, 101, 53, 21, 5, 6, 0, 1]},\n     'statistics': {'mean': 4.2160033887186084e-11,\n      'stddev': 1.6741675825683113e-10,\n      'maximum': 5.284403581384822e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09722222222222}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'ensemble-mean-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_DCH4E_LL.001/LPJ_EOSIM_L2_DCH4E_LL_001_20240531/LPJ_EOSIM_L2_DCH4E_LL_ensemble_mean_001_20240531.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': '(Daily) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [360, 720],\n   'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -99999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.8867296048294975e-09,\n      'min': 0.0,\n      'count': 11,\n      'buckets': [61178, 819, 185, 124, 78, 46, 22, 7, 0, 1]},\n     'statistics': {'mean': 4.0174325630166816e-11,\n      'stddev': 1.493077090568075e-10,\n      'maximum': 3.8867296048294975e-09,\n      'minimum': 0.0,\n      'valid_percent': 24.09722222222222}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531/preview.png?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'lpjeosim-wetlandch4-daygrid-v2',\n 'properties': {'datetime': '2024-05-31T00:00:00+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']}\n\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetch the minimum and maximum values for rescaling\nrescale_values = {'max': 0.0003, 'min': 0.0}",
+    "crumbs": [
+      "Data Usage Notebooks",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
+      "Wetland Methane Emissions, LPJ-EOSIM Model"
+    ]
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     "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
     "section": "Explore Changes in Methane (CH4) Emission Levels Using the Raster API",
-    "text": "Explore Changes in Methane (CH4) Emission Levels Using the Raster API\nIn this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items} \n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for month 1 mentioned in the next cell and again for month 2, so we can visualize each event independently.\n\n# Choose a color for displaying the tiles\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"magma\" \n\n# Make a GET request to retrieve information for the date mentioned below\nmonth1 = '1990-01'\nmonth1_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year and month of the observed parameter, you can modify month mentioned above.\n    f\"{RASTER_API_URL}/collections/{items[month1]['collection']}/items/{items[month1]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nmonth1_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for date mentioned below\nmonth2 = '1990-08'\nmonth2_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year and month of the observed parameter, you can modify the month mentioned above.\n    f\"{RASTER_API_URL}/collections/{items[month2]['collection']}/items/{items[month2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format \n).json()\n\n# Print the properties of the retrieved granule to the console\nmonth2_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}"
+    "text": "Explore Changes in Methane (CH4) Emission Levels Using the Raster API\nIn this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"datetime\"][:10]: item for item in items} \n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for date 1 mentioned in the next cell and again for date 2, so we can visualize each event independently.\n\n# Choose a color for displaying the tiles\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"magma\" \n\n# Make a GET request to retrieve information for the date mentioned below\ndate1 = '2024-01-01'\ndate1_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year, month and date of the observed parameter, you can modify the date2 variable above\n    f\"{RASTER_API_URL}/collections/{items[date1]['collection']}/items/{items[date1]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\ndate1_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for date mentioned below\ndate2 = '2024-01-30'\ndate2_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year, month and date of the observed parameter, you can modify the date2 variable above\n    f\"{RASTER_API_URL}/collections/{items[date2]['collection']}/items/{items[date2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format \n).json()\n\n# Print the properties of the retrieved granule to the console\ndate2_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}",
+    "crumbs": [
+      "Data Usage Notebooks",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
+      "Wetland Methane Emissions, LPJ-EOSIM Model"
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     "section": "Visualize CH₄ Emissions",
-    "text": "Visualize CH₄ Emissions\n\n# For this study we are going to compare the CH₄ Emissions for month1 and month2 along the coast of California\n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n\n# Set initial zoom and center of map\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer for tile fetched for month 1\n# The TileLayer library helps in manipulating and displaying raster layers on a map\nmap_layer_month1 = TileLayer(\n    tiles=month1_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.5, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_month1.add_to(map_.m1)\n\n\n# Define the second map layer for the tile fetched for month 2\nmap_layer_month2 = TileLayer(\n    tiles=month2_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.5, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_month2.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
+    "text": "Visualize CH₄ Emissions\n\n# For this study we are going to compare the CH₄ Emissions in date1 and date2 along the coast of California\n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n\n# Set initial zoom and center of map\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer for tile fetched for date 1\n# The TileLayer library helps in manipulating and displaying raster layers on a map\nmap_layer_date1 = TileLayer(\n    tiles=date1_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.5, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_date1.add_to(map_.m1)\n\n\n# Define the second map layer for the tile fetched for date 2\nmap_layer_date2 = TileLayer(\n    tiles=date2_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.5, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_date2.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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     "title": "Wetland Methane Emissions, LPJ-EOSIM Model",
     "section": "Visualize the Data as a Time Series",
-    "text": "Visualize the Data as a Time Series\nWe can now explore the wetland methane emissions time series (January 1990 – December 2024) available for the Texas area of the U.S. We can plot the data set using the code below:\n\n# Determine the width and height of the plot using the 'matplotlib' library\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\n# Plot the time series\nplt.plot(\n    df[\"date\"], # X-axis: date\n    df[\"max\"], # Y-axis: CH₄ value\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Max monthly CH₄ emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"Monthly CH4 emissions g/m2\")\n\n# Insert title for the plot\nplt.title(\"Monthly CH4 emission Values for Texas, 1990-2024\")\n\nText(0.5, 1.0, 'Monthly CH4 emission Values for Texas, 1990-2024')\n\n\n\n\n\n\n\n\n\nTo take a closer look at the CH4 variability across this region, we are going to retrieve and display data collected for the observation mentioned below.\n\n# The 2023-11-01 observation is the 3rd item in the list\n# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n# Print the start Date Time of the third granule in the collection\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n2024-03-01T00:00:00+00:00\n\n\n\n# A GET request is made for the 3rd item in the collection\nobserved_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nobserved_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the CH4 variability for the Texas region for the time in previous cell.\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n    tiles=observed_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", opacity = 0.5 # Set the attribution and transparency\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
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-    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for the Monthly Wetland Methane Emissions, LPJ-EOSIM Model data: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map and compare the CH4 levels over the Texas region for two distinctive years 5. Create a table that displays the minimum, maximum, and sum of the CH4 levels for a specified region 6. Generate a time-series graph of the CH4 levels for a specified region\nIf you have any questions regarding this user notebook, please contact us using the feedback form."
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-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
+    "text": "Visualize the Data as a Time Series\nWe can now explore the wetland methane emissions time series (January 1990 – December 2024) available for the Texas area of the U.S. We can plot the data set using the code below:\n\n# Determine the width and height of the plot using the 'matplotlib' library\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\n# Plot the time series\nplt.plot(\n    df[\"date\"], # X-axis: date\n    df[\"max\"], # Y-axis: CH₄ value\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Max daily CH₄ emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"Daily CH4 emissions g/m2\")\n\n# Insert title for the plot\nplt.title(\"Daily CH4 emission Values for Texas, January 2022- March 2024\")\n\nText(0.5, 1.0, 'Daily CH4 emission Values for Texas, January 2022- March 2024')\n\n\n\n\n\n\n\n\n\nTo take a closer look at the CH4 variability across this region, we are going to retrieve and display data collected during the February, 2024 observation.\n\n# The 2024-02-25 observation is the 3rd item in the list\n# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n# Print the start Date Time of the third granule in the collection\nprint(items[2][\"properties\"][\"datetime\"])\n\n2024-05-29T00:00:00+00:00\n\n\n\n# A GET request is made for the 3rd item in the collection\nobserved_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nobserved_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the CH4 variability for the Texas region for Observed tile timeframe\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n    tiles=observed_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", opacity = 0.5 # Set the attribution and transparency\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for the Daily Wetland Methane Emissions, LPJ-EOSIM Model data: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map and compare the CH4 levels over the Texas region for two distinctive years 5. Create a table that displays the minimum, maximum, and sum of the CH4 levels for a specified region 6. Generate a time-series graph of the CH4 levels for a specified region\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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-    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for a given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the Land-Atmosphere Carbon Flux data product\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint\nUsing folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison\nAfter the visualization, perform zonal statistics for a given polygon",
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-    "text": "About the Data\nThis dataset presents a variety of carbon flux parameters derived from the Más Informada Carnegie-Ames-Stanford-Approach (MiCASA) model. The model’s input data includes air temperature, precipitation, incident solar radiation, a soil classification map, and several satellite derived products. All model calculations are driven by analyzed meteorological data from NASA’s Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting product provides global, daily data at 0.1 degree resolution from January 2001 through December 2023. It includes carbon flux variables expressed in units of kilograms of carbon per square meter per day (kg Carbon/m²/day) from net primary production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), fuel wood burning emissions (FUEL), net ecosystem exchange (NEE), and net biosphere exchange (NBE). The latter two are derived from the first four (see Scientific Details below). MiCASA is an extensive revision of the CASA – Global Fire Emissions Database, version 3 (CASA-GFED3) product. CASA-GFED3 and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in several atmospheric carbon dioxide (CO₂) transport studies, serve as a community standard for priors of flux inversion systems, and through the support of NASA’s Carbon Monitoring System (CMS), help characterize, quantify, understand and predict the evolution of global carbon sources and sinks.\nFor more information regarding this dataset, please visit the U.S. Greenhouse Gas Center.",
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-    "text": "Query the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for MiCASA Land Carbon Flux\ncollection_name = \"micasa-carbonflux-daygrid-v1\"\n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the MiCASA Land Carbon Flux collection, the parameter of interest is “rh”\n# rh = Heterotrophic Respiration\nasset_name = \"rh\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'micasa-carbonflux-daygrid-v1',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1/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/micasa-carbonflux-daygrid-v1'}],\n 'title': '(Daily) MiCASA Land Carbon Flux v1',\n 'extent': {'spatial': {'bbox': [[-180, -90, 179.99999999999994, 90]]},\n  'temporal': {'interval': [['2001-01-01 00:00:00+00',\n     '2023-12-31 00:00:00+00']]}},\n 'license': 'CC0-1.0',\n 'renders': {'rh': {'assets': ['rh'],\n   'rescale': [[0, 8]],\n   'colormap_name': 'purd'},\n  'nbe': {'assets': ['nbe'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n  'nee': {'assets': ['nee'],\n   'rescale': [[-4, 4]],\n   'colormap_name': 'coolwarm'},\n  'npp': {'assets': ['npp'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n  'atmc': {'assets': ['atmc'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n  'fire': {'assets': ['fire'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n  'fuel': {'assets': ['fuel'], 'rescale': [[0, 0.5]], 'colormap_name': 'purd'},\n  'dashboard': {'assets': ['npp'],\n   'rescale': [[0, 8]],\n   'colormap_name': 'purd'}},\n 'providers': [{'name': 'NASA'}],\n 'summaries': {'datetime': ['2001-01-01T00:00:00Z', '2023-12-31T00:00:00Z']},\n 'description': \"This product provides estimated daily carbon flux to the atmosphere from net primary production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), fuel wood burning emissions (FUEL), net ecosystem exchange (NEE), and net biosphere exchange (NBE) derived from the Más Informada Carnegie-Ames-Stanford-Approach (MiCASA) model. All model calculations are driven by analyzed meteorological data from NASA's Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting product provides global, daily data at 0.1 degree resolution starting from January 2001. The carbon flux variables are expressed in units of kilograms of carbon per square meter per day. MiCASA is an extensive revision of the CASA – Global Fire Emissions Database, version 3 (CASA-GFED3) product. CASA-GFED3 and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in several atmospheric carbon dioxide (CO₂) transport studies, serve as a community standard for priors of flux inversion systems, and through the support of NASA's Carbon Monitoring System (CMS), help characterize, quantify, understand and predict the evolution of global carbon sources and sinks.\",\n 'item_assets': {'rh': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Heterotrophic respiration (Rh), MiCASA Model v1',\n   'description': 'Heterotrophic respiration (carbon flux from the soil to the atmosphere) in units of grams of carbon per square meter per day.'},\n  'nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Net Biosphere Exchange (net carbon flux from the ecosystem), MiCASA Model v1',\n   'description': 'Net Biosphere Exchange (net carbon flux from the ecosystem) in units of grams of carbon per square meter per day.'},\n  'nee': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Net Ecosystem Exchange (NEE), MiCASA Model v1',\n   'description': 'Net Ecosystem Exchange (net carbon flux to the atmosphere) in units of grams of carbon per square meter per day.'},\n  'npp': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Net Primary Production (NPP), MiCASA Model v1',\n   'description': 'Net Primary Production (carbon available from plants) in units of grams of carbon per square meter per day.'},\n  'atmc': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Atmospheric Correction (ATMC), MiCASA Model v1',\n   'description': 'A correction to account for long-term historical changes in the uptake of CO₂ from the atmosphere to the biosphere in units of grams of carbon per square meter per day.'},\n  'fire': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fire emissions (FIRE), MiCASA Model v1',\n   'description': 'Fire emissions (flux of carbon to the atmosphere from wildfires) in units of grams of carbon per square meter per day.'},\n  'fuel': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Wood fuel emissions (FUEL), MiCASA Model v1',\n   'description': 'Wood fuel emissions (flux of carbon to the atmosphere from wood burned for fuel) in units of grams of carbon per square meter per day.'}},\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': 'day'}\n\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2003 to December 2017. By looking at the dashboard:time density, we observe that the periodic frequency of these observations is monthly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n   \n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest (MiCASA Land Carbon Flux) in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n       \n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection (MiCASA Land Carbon Flux) in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n       \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n        # temp = items_url.split('/')\n        # temp.insert(3, 'ghgcenter')\n        # temp.insert(4, 'api')\n        # temp.insert(5, 'stac')\n        # items_url = '/'.join(temp)\n\n    # Return the information about the total number of granules found associated with the collection (MiCASA Land Carbon Flux)\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit=800\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 800 items\n\n\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'micasa-carbonflux-daygrid-v1-20231231',\n 'bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20231231'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20231231/map?assets=npp&rescale=0%2C8&colormap_name=purd',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'rh': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_Rh_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Heterotrophic respiration (Rh), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Heterotrophic respiration (carbon flux from the soil to the atmosphere) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 7.2141876220703125,\n      'min': -0.35656991600990295,\n      'count': 11,\n      'buckets': [457947,\n       35642,\n       10548,\n       6299,\n       4848,\n       3079,\n       3268,\n       2051,\n       543,\n       63]},\n     'statistics': {'mean': 0.22414785623550415,\n      'stddev': 0.7404906300847516,\n      'maximum': 7.2141876220703125,\n      'minimum': -0.35656991600990295,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'nbe': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_NBE_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Net Biosphere Exchange (NBE), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Net Biosphere Exchange (net carbon flux from the ecosystem) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 4.327333927154541,\n      'min': -3.1603169441223145,\n      'count': 11,\n      'buckets': [374, 1873, 3385, 8057, 481500, 26054, 2405, 543, 93, 4]},\n     'statistics': {'mean': 0.057421840727329254,\n      'stddev': 0.31542102161091723,\n      'maximum': 4.327333927154541,\n      'minimum': -3.1603169441223145,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'nee': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_NEE_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Net Ecosystem Exchange (NEE), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Net Ecosystem Exchange (net carbon flux to the atmosphere) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 3.59893536567688,\n      'min': -3.1675710678100586,\n      'count': 11,\n      'buckets': [313, 1496, 2644, 4869, 453998, 47409, 12055, 1106, 340, 58]},\n     'statistics': {'mean': 0.05558537319302559,\n      'stddev': 0.3139139632688899,\n      'maximum': 3.59893536567688,\n      'minimum': -3.1675710678100586,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'npp': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_NPP_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Net Primary Production (NPP), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Net Primary Production (carbon available from plants) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 6.099153518676758,\n      'min': -0.40653496980667114,\n      'count': 11,\n      'buckets': [487523,\n       10036,\n       4672,\n       4058,\n       3733,\n       4110,\n       4707,\n       4222,\n       1165,\n       62]},\n     'statistics': {'mean': 0.16241030395030975,\n      'stddev': 0.7038688126658349,\n      'maximum': 6.099153518676758,\n      'minimum': -0.40653496980667114,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'atmc': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_ATMC_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Atmospheric Correction (ATMC), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'A correction to account for long-term historical changes in the uptake of CO₂ from the atmosphere to the biosphere in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.5609952807426453,\n      'min': -0.02930086851119995,\n      'count': 11,\n      'buckets': [496261, 19256, 4265, 1897, 915, 859, 549, 216, 64, 6]},\n     'statistics': {'mean': 0.006152182351797819,\n      'stddev': 0.02767997686822744,\n      'maximum': 0.5609952807426453,\n      'minimum': -0.02930086851119995,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'fire': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_FIRE_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Fire emissions (FIRE), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Fire emissions (flux of carbon to the atmosphere from wildfires) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 4.872155666351318,\n      'min': -0.25238683819770813,\n      'count': 11,\n      'buckets': [524150, 104, 24, 5, 3, 1, 0, 0, 0, 1]},\n     'statistics': {'mean': 0.00028691982151940465,\n      'stddev': 0.014243524521583754,\n      'maximum': 4.872155666351318,\n      'minimum': -0.25238683819770813,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'fuel': {'href': 's3://ghgc-data-store/micasa-carbonflux-daygrid-v1/MiCASA_v1_FUEL_x3600_y1800_daily_20231231.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Wood fuel emissions (FUEL), MiCASA Model v1',\n   'proj:bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [1800, 3600],\n   'description': 'Wood fuel emissions (flux of carbon to the atmosphere from wood burned for fuel) in units of grams of carbon per square meter per day.',\n   'raster:bands': [{'unit': 'g C m-2 day-1',\n     'scale': 1.0,\n     'nodata': 'nan',\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.6249907612800598,\n      'min': -0.021494677290320396,\n      'count': 11,\n      'buckets': [518619, 4684, 688, 188, 65, 24, 6, 3, 7, 4]},\n     'statistics': {'mean': 0.0015495388070121408,\n      'stddev': 0.010684158697696962,\n      'maximum': 0.6249907612800598,\n      'minimum': -0.021494677290320396,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [179.99999999999994, -90.0],\n      [179.99999999999994, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999999999999,\n    0.0,\n    -180.0,\n    0.0,\n    -0.1,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20231231/preview.png?assets=npp&rescale=0%2C8&colormap_name=purd',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [179.99999999999994, -90],\n    [179.99999999999994, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'micasa-carbonflux-daygrid-v1',\n 'properties': {'datetime': '2023-12-31T00:00:00+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']}",
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+    "title": "GRA²PES Greenhouse Gas and Air Quality Species",
+    "section": "Approach",
+    "text": "Approach\n\nIdentify 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.\nPass the STAC item into the raster API /stac/tilejson.jsonendpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon.",
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-    "title": "MiCASA Land Carbon Flux",
-    "section": "Explore Changes in Carbon Flux Levels Using the Raster API",
-    "text": "Explore Changes in Carbon Flux Levels Using the Raster API\nWe will explore changes in the land atmosphere Carbon flux Heterotrophic Respiration and examine their impacts over time. We’ll then visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"datetime\"][:10]: item for item in items}\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetch the minimum and maximum values for rescaling\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. This step is done twice, once for December 2003 and again for December 2017, so that we can visualize each event independently.\n\n# Choose a color for displaying the tiles\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"purd\"\n\n# Make a GET request to retrieve information for the date mentioned below\ndate1 = '2023-01-01'\ndate1_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year, month and date of the observed parameter, you can modify the date mentioned above.\n    f\"{RASTER_API_URL}/collections/{items[date1]['collection']}/items/{items[date1]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\ndate1_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.35656991600990295%2C7.2141876220703125'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n 'center': [-2.842170943040401e-14, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for the date mentioned below\ndate2 = '2023-01-31'\ndate2_tile = requests.get(\n\n    # Pass the collection name, collection date, and its ID\n    # To change the year, month and date of the observed parameter, you can modify the date mentioned above.\n    f\"{RASTER_API_URL}/collections/{items[date2]['collection']}/items/{items[date2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\ndate2_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.35656991600990295%2C7.2141876220703125'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n 'center': [-2.842170943040401e-14, 0.0, 0]}",
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+    "title": "GRA²PES Greenhouse Gas and Air Quality Species",
+    "section": "About the Data",
+    "text": "About the Data\nThe Greenhouse gas And Air Pollutants Emissions System (GRA2PES) dataset at the GHG Center is an aggregated, regridded, monthly high-resolution (0.036 x 0.036°) data product with emissions of both greenhouse gases and air pollutants developed in a consistent framework. The dataset contains emissions over the contiguous United States covering major anthropogenic sectors, including energy, industrial fuel combustion and processes, commercial and residential combustion, oil and gas production, on-road and off-road transportation, etc. (see Table 1 in the Scientific Details section below for a full sector list). Fossil fuel CO2 (ffCO2) emissions are developed along with those of air pollutants including CO, NOx, SOx, and PM2.5 with consistency in spatial and temporal distributions. Emissions by sectors are grouped into point and area sources, reported as column totals in units of metric tons per km2 per month. Spatial-temporal surrogates are developed to distribute CO2 emissions to grid cells to keep consistency between greenhouse gases and air quality species. The current version of GRA2PES is for 2021. Long-term emissions and more greenhouse gas species (e.g., methane) are under development and will be added in the future.\nFor more information regarding this dataset, please visit the GRA2PES Greenhouse Gas and Air Quality Species, Version 1 data overview page.",
     "crumbs": [
       "Data Usage Notebooks",
-      "Natural Greenhouse Gas Sources Emissions and Sinks",
-      "MiCASA Land Carbon Flux"
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-    "title": "MiCASA Land Carbon Flux",
-    "section": "Visualize Land-Atmosphere Carbon Flux (Heterotrophic Respiration)",
-    "text": "Visualize Land-Atmosphere Carbon Flux (Heterotrophic Respiration)\n\n# For this study we are going to compare the Rh level for date1 and date2 over the State of Texas \n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n# For example, you can change the current statement \"location=(31.9, -99.9)\" to \"location=(34, -118)\" to monitor the Rh level in California instead of Texas\n\n# Set initial zoom and center of map for CO₂ Layer\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(31.9, -99.9), zoom_start=6)\n\n\n# Define the first map layer with Rh level for the tile fetched for date 1\n# The TileLayer library helps in manipulating and displaying raster layers on a map\nmap_layer_date1 = TileLayer(\n    tiles=date1_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n    name=f\"{date1} Rh Level\", # Title for the layer\n    overlay= True, # The layer can be overlaid on the map\n    legendEnabled = True # Enable displaying the legend on the map\n)\n\n# Add the first layer to the Dual Map\nmap_layer_date1.add_to(map_.m1)\n\n\n# Define the first map layer with Rh level for the tile fetched for date 2\nmap_layer_date2 = TileLayer(\n    tiles=date2_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n    name=f\"{date2} RH Level\", # Title for the layer\n    overlay= True, # The layer can be overlaid on the map\n    legendEnabled = True # Enable displaying the legend on the map\n)\n\n# Add the second layer to the Dual Map\nmap_layer_date2.add_to(map_.m2)\n\n# Display data markers (titles) on both maps\nfolium.Marker((40, 5.0), tooltip=\"both\").add_to(map_)\n\n# Add a layer control to switch between map layers\nfolium.LayerControl(collapsed=False).add_to(map_)\n\n# Add a legend to the dual map using the 'branca' library. \n# Note: the inserted legend is representing the minimum and maximum values for both tiles.\ncolormap = branca.colormap.linear.PuRd_09.scale(0, 0.3) # minimum value = 0, maximum value = 0.3 (kg Carbon/m2/daily)\n\n# Classify the colormap according to specified Rh values \ncolormap = colormap.to_step(index=[0, 0.07, 0.15, 0.22, 0.3])\n\n# Add the data unit as caption\ncolormap.caption = 'Rh Values (gm Carbon/m2/daily)'\n\n# Display the legend and caption on the map\ncolormap.add_to(map_.m1)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "href": "user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.html#querying-the-stac-api",
+    "title": "GRA²PES Greenhouse Gas and Air Quality Species",
+    "section": "Querying the STAC API",
+    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# Please use the collection name similar to the one used in the STAC collection.\n# Name of the collection for Vulcan Fossil Fuel CO₂ Emissions, Version 4. \ncollection_name = \"gra2pes-ghg-monthgrid-v1\"\n\n\n# Fetch the collection from STAC collections using the appropriate endpoint\n# the 'requests' library allows a HTTP request possible\ncollection_graapes = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2010 to December 2021. By looking at the dashboard:time density, we observe that the data is periodic with year time density.\n\n# Create a function that would search for the above data collection in the STAC API\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Apply the above function and check the total number of items available within the collection\nnumber_of_items = get_item_count(collection_name)\nitems_graapes = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items_vulcan)} items\")\n\nFound 12 items\n\n\n\n# To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items_graapes} \n# rh = Heterotrophic Respiration\nasset_name = \"co2\"\n\n\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for 2021-01 and again for 2021-05, so that we can visualize each event independently.\n\ncolor_map = \"spectral_r\" # please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n\n# 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\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale=0,150\", \n).json()\n_202101_tile\n\n{'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 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-137.3143, 18.173376, -58.58229999999702, 52.229376000001295],\n 'center': [-97.94829999999851, 35.20137600000065, 0]}\n\n\n\n_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\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale=0,150\", \n).json()\n_202105_tile\n\n{'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 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-137.3143, 18.173376, -58.58229999999702, 52.229376000001295],\n 'center': [-97.94829999999851, 35.20137600000065, 0]}",
     "crumbs": [
       "Data Usage Notebooks",
-      "Natural Greenhouse Gas Sources Emissions and Sinks",
-      "MiCASA Land Carbon Flux"
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "GRA²PES Greenhouse Gas and Air Quality Species"
     ]
   },
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-    "href": "user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.html#generate-the-statistics-for-the-aoi",
-    "title": "MiCASA Land Carbon Flux",
-    "section": "Generate the statistics for the AOI",
-    "text": "Generate the statistics for the AOI\n\n%%time\n# %%time = Wall time (execution time) for running the code below\n\n# Generate statistics using the created function \"generate_stats\" within the bounding box defined by the \"texas_dallas_aoi\" polygon\nstats = [generate_stats(item, texas_dallas_aoi) for item in items]\n\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': 0.1918513923883438, 'max': 1.1595696210861206, 'mean': 0.7456724643707275, 'count': 147.99998474121094, 'sum': 110.35951232910156, 'std': 0.18667982445867687, 'median': 0.7525566220283508, 'majority': 0.1918513923883438, 'minority': 0.1918513923883438, 'unique': 165.0, 'histogram': [[3.0, 2.0, 5.0, 16.0, 26.0, 31.0, 30.0, 31.0, 15.0, 6.0], [0.1918513923883438, 0.28862321376800537, 0.3853950500488281, 0.4821668267250061, 0.5789386630058289, 0.6757104992866516, 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165.0, 'percentile_2': 0.24773457646369934, 'percentile_98': 0.9904780983924866}}}}\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': 0.17567388713359833, 'max': 1.070212483406067, 'mean': 0.672138512134552, 'count': 147.99998474121094, 'sum': 99.47648620605469, 'std': 0.17028310351448978, 'median': 0.675561249256134, 'majority': 0.17567388713359833, 'minority': 0.17567388713359833, 'unique': 165.0, 'histogram': [[3.0, 2.0, 8.0, 18.0, 25.0, 35.0, 29.0, 29.0, 10.0, 6.0], [0.17567388713359833, 0.2651277482509613, 0.3545815944671631, 0.44403547048568726, 0.5334893465042114, 0.6229432225227356, 0.712397038936615, 0.8018509149551392, 0.8913047909736633, 0.9807586669921875, 1.070212483406067]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 165.0, 'percentile_2': 0.24954460561275482, 'percentile_98': 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1.1595696210861206,\n   'mean': 0.7456724643707275,\n   'count': 147.99998474121094,\n   'sum': 110.35951232910156,\n   'std': 0.18667982445867687,\n   'median': 0.7525566220283508,\n   'majority': 0.1918513923883438,\n   'minority': 0.1918513923883438,\n   'unique': 165.0,\n   'histogram': [[3.0, 2.0, 5.0, 16.0, 26.0, 31.0, 30.0, 31.0, 15.0, 6.0],\n    [0.1918513923883438,\n     0.28862321376800537,\n     0.3853950500488281,\n     0.4821668267250061,\n     0.5789386630058289,\n     0.6757104992866516,\n     0.7724822759628296,\n     0.8692541122436523,\n     0.9660259485244751,\n     1.0627977848052979,\n     1.1595696210861206]],\n   'valid_percent': 100.0,\n   'masked_pixels': 0.0,\n   'valid_pixels': 165.0,\n   'percentile_2': 0.27398860454559326,\n   'percentile_98': 1.098567008972168}},\n 'datetime': '2023-12-31'}\n\n\nCreate a function that goes through every single item in the collection and populates their properties - including the minimum, maximum, and sum of their values - in a table.\n\n# Create a function that converts statistics in JSON format into a pandas DataFrame\ndef clean_stats(stats_json) -> pd.DataFrame:\n\n    # Normalize the JSON data\n    df = pd.json_normalize(stats_json)\n\n    # Replace the naming \"statistics.b1\" in the columns\n    df.columns = [col.replace(\"statistics.b1.\", \"\") for col in df.columns]\n\n    # Set the datetime format\n    df[\"date\"] = pd.to_datetime(df[\"datetime\"])\n\n    # Return the cleaned format\n    return df\n\n# Apply the generated function on the stats data\ndf = clean_stats(stats)\n\n# Display the stats for the first 5 granules in the collection in the table\n# Change the value in the parenthesis to show more or a smaller number of rows in the table\ndf.head(5)\n\n\n\n\n\n\n\n\ndatetime\nmin\nmax\nmean\ncount\nsum\nstd\nmedian\nmajority\nminority\nunique\nhistogram\nvalid_percent\nmasked_pixels\nvalid_pixels\npercentile_2\npercentile_98\ndate\n\n\n\n\n0\n2023-12-31\n0.191851\n1.159570\n0.745672\n147.999985\n110.359512\n0.186680\n0.752557\n0.191851\n0.191851\n165.0\n[[3.0, 2.0, 5.0, 16.0, 26.0, 31.0, 30.0, 31.0,...\n100.0\n0.0\n165.0\n0.273989\n1.098567\n2023-12-31\n\n\n1\n2023-12-30\n0.177944\n1.075771\n0.690748\n147.999985\n102.230652\n0.173171\n0.700913\n0.177944\n0.177944\n165.0\n[[3.0, 2.0, 6.0, 15.0, 27.0, 33.0, 27.0, 31.0,...\n100.0\n0.0\n165.0\n0.258274\n1.022774\n2023-12-30\n\n\n2\n2023-12-29\n0.172747\n1.048948\n0.666380\n147.999985\n98.624245\n0.168330\n0.675644\n0.172747\n0.172747\n165.0\n[[3.0, 2.0, 6.0, 18.0, 27.0, 34.0, 26.0, 29.0,...\n100.0\n0.0\n165.0\n0.247735\n0.990478\n2023-12-29\n\n\n3\n2023-12-28\n0.175674\n1.070212\n0.672139\n147.999985\n99.476486\n0.170283\n0.675561\n0.175674\n0.175674\n165.0\n[[3.0, 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+    "text": "Visualizing Total Fossil Fuel CO₂ Emissions\n\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n\n# Define the first map layer with the CO2 Flux data for December 2022\nmap_layer_202101 = TileLayer(\n    tiles=_202101_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution \n    name='2021-01 Total CO2 Fossil Fuel Emissions', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.8, # Adjust the transparency of the layer\n)\n# Add the first layer to the Dual Map \nmap_layer_202101.add_to(map_.m1)\n\nmap_layer_202105 = TileLayer(\n    tiles=_202105_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution \n    name='2021-05 Total CO2  Emissions', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.8, # Adjust the transparency of the layer\n)\n# Add the first layer to the Dual Map \nmap_layer_2021.add_to(map_.m2)\n\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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-    "text": "Visualize the Data as a Time Series\nWe can now explore the Heterotrophic Respiration time series (October 2021 - January 2024) available for the Dallas, Texas area. We can plot the data set using the code below:\n\n# Determine the width and height of the plot using the 'matplotlib' library\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10)) \n\n# Plot the time series analysis of the daily Heterotrophic Respiration changes in Dallas, Texas\nplt.plot(\n    df[\"date\"], # X-axis: date\n    df[\"max\"], # Y-axis: Rh value\n    color=\"purple\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"RH Level\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"gm Carbon/m2/day\")\n\n# Insert title for the plot\nplt.title(\"Heterotrophic Respiration Values for Dallas, Texas (October 2021 to January 2024)\")\n\nText(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (October 2021 to January 2024)')\n\n\n\n\n\n\n\n\n\nTo take a closer look at the daily Heterotrophic Respiration variability across this region, we are going to retrieve and display data collected during the December, 2023 observation.\n\n# Fetch the third item in the list as the observation item.\n# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n# Print the start Date Time of the third granule in the collection\nprint(items[2][\"properties\"][\"datetime\"]) \n\n2023-12-29T00:00:00+00:00\n\n\n\n# A GET request is made for the observed tile\nobserved_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console \nobserved_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.35656991600990295%2C7.2141876220703125'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n 'center': [-2.842170943040401e-14, 0.0, 0]}\n\n\n\n# Create a new map to display the Rh level for the Dallas, Texas area for the observed tile timeframe.\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        32.8, # latitude\n        -96.79, # longitude\n    ],\n\n    # Set the zoom value\n    zoom_start=9,\n)\n\n# Define the map layer with the Rh level for observed tile\nmap_layer = TileLayer(\n    tiles=observed_tile[\"tiles\"][0], # Path to retrieve the tile\n\n    # Set the attribution, transparency, and the title along with enabling the visualization of the legend on the map \n    attr=\"GHG\", opacity = 0.7, name=\" Observed tile RH Level\", overlay= True, legendEnabled = True\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Display data marker (title) on the map\nfolium.Marker((40, 5.9), tooltip=\"both\").add_to(aoi_map_bbox)\n\n# Add a layer control\nfolium.LayerControl(collapsed=False).add_to(aoi_map_bbox)\n\n# Add a legend using the 'branca' library\ncolormap = branca.colormap.linear.PuRd_09.scale(0, 0.3) # minimum value = 0, maximum value = 0.3 (gm Carbon/m2/daily)\n\n# Classify the colormap according to the specified Rh values\ncolormap = colormap.to_step(index=[0, 0.07, 0.15, 0.22, 0.3])\n\n# Add the data unit as caption\ncolormap.caption = 'Rh Values (gm Carbon/m2/daily)'\n\n# Display the legend and caption on the map\ncolormap.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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+    "text": "Summary\nIn this notebook we have successfully explored, analyzed, and visualized the STAC collection for GRA2PES greenhouse gases Emissions, Version 1 dataset.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the total CO₂ emissions for two distinctive months/years\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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-    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for MiCASA Land Carbon Flux data: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map and compare the Heterotrophic Respiration (Rh) levels over the Dallas, Texas area for two distinctive years 5. Create a table that displays the minimum, maximum, and sum of the Rh values for a specified region 6. Generate a time-series graph of the Rh values for a specified region\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this request form and providing the required information.\nAccess the U.S. Gridded Anthropogenic Methane Emissions Inventory notebook in the US GHG Center JupyterHub.",
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-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this request form and providing the required information.\nAccess the U.S. Gridded Anthropogenic Methane Emissions Inventory notebook in the US GHG Center JupyterHub.",
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-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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+    "section": "Table of Contents",
+    "text": "Table of Contents\n\nData Summary and Application\nApproach\nAbout the Data\nInstall the Required Libraries\nQuery the STAC API\nVisual Comparison Across Time Periods\nMap Out Selected Tiles\nCalculate Zonal Statistics\nTime-Series Analysis\nSummary",
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-    "text": "Approach\n\nIdentify 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 OCO-2 GEOS Column CO₂ Concentrations data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison.\nAfter the visualization, perform zonal statistics for a given polygon.",
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+    "section": "Data Summary and Application",
+    "text": "Data Summary and Application\n\nSpatial coverage: Contiguous United States\nSpatial resolution: 0.1° x 0.1°\nTemporal extent: 2012 - 2020\nTemporal resolution: Annual\nUnit: Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr)\nUtility: Methane Monitoring, Anthropogenic Emissions Analysis, Climate Research\n\nFor more, visit the U.S. Gridded Anthropogenic Methane Emissions Inventory data overview page.",
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-    "title": "OCO-2 GEOS Column CO₂ Concentrations",
-    "section": "About the Data",
-    "text": "About the Data\nIn July 2014, NASA successfully launched the first dedicated Earth remote sensing satellite to study atmospheric carbon dioxide (CO₂) from space. The Orbiting Carbon Observatory-2 (OCO-2) is an exploratory science mission designed to collect space-based global measurements of atmospheric CO₂ with the precision, resolution, and coverage needed to characterize sources and sinks (fluxes) on regional scales (≥1000 km). This dataset provides global gridded, daily column-averaged carbon dioxide (XCO₂) concentrations from January 1, 2015 - February 28, 2022. The data are derived from OCO-2 observations that were input to the Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS), a modeling and data assimilation system maintained by NASA’s Global Modeling and Assimilation Office (GMAO). Concentrations are measured in moles of carbon dioxide per mole of dry air (mol CO₂/mol dry) at a spatial resolution of 0.5° x 0.625°. Data assimilation synthesizes simulations and observations, adjusting modeled atmospheric constituents like CO₂ to reflect observed values. With the support of NASA’s Carbon Monitoring System (CMS) Program and the OCO Science Team, this dataset was produced as part of the OCO-2 mission which provides the highest quality space-based XCO₂ retrievals to date.\nFor more information regarding this dataset, please visit the OCO-2 GEOS Column CO₂ Concentrations data overview page.",
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+    "text": "Approach\n\nIdentify 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 gridded methane emissions data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon.",
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-    "text": "Install the Required Libraries\nRequired libraries are pre-installed on the GHG Center Hub. If you need to run this notebook elsewhere, please install them with this line in a code cell:\n%pip install requests folium rasterstats pystac_client pandas matplotlib –quiet\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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(",
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+    "text": "About the Data\nThe 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)), 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).\nThis V2 Express Extension dataset contains methane emissions provided as fluxes, in units of molecules of methane per square cm per second, for over 25 individual emission source categories, including those from agriculture, petroleum and natural gas systems, coal mining, and waste. The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends.\nThe gridded dataset currently includes 34 data layers. The first data layer includes annual 2012-2020 gridded methane emissions fluxes from all anthropogenic sources of methane in the U.S. GHGI (excluding Land Use, Land-Use Change and Forestry (LULUCF) sources, which are not included in the gridded GHGI). The next six data layers include annual 2012-2020 gridded methane fluxes from sources within the aggregate Agriculture, Natural Gas, Petroleum, Waste, Industry, and ‘Other’ source categories. The remaining 27 data layers include annual 2012-2020 gridded methane emissions fluxes from individual emission sectors within each of the aggregate categories.\nFor more information regarding this dataset, please visit the U.S. Gridded Anthropogenic Methane Emissions Inventory data overview page.",
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-    "title": "OCO-2 GEOS Column CO₂ Concentrations",
-    "section": "Querying the STAC API",
-    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# Please use the collection name similar to the one used in STAC collection.\n# Name of the collection for OCO-2 GEOS Column CO₂ Concentrations. \ncollection_name = \"oco2geos-co2-daygrid-v10r\"\n\n\n# Fetching the collection from STAC collections using appropriate endpoint.\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\ncollection\n\n{'id': 'oco2geos-co2-daygrid-v10r',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r/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/oco2geos-co2-daygrid-v10r'}],\n 'title': 'OCO-2 GEOS Column CO₂ Concentrations v10r',\n 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n  'temporal': {'interval': [['2015-01-01T00:00:00+00:00',\n     '2022-02-28T00:00:00+00:00']]}},\n 'license': 'CC0-1.0',\n 'renders': {'xco2': {'assets': ['xco2'],\n   'nodata': 0,\n   'rescale': [[412, 422]],\n   'colormap_name': 'magma'},\n  'dashboard': {'assets': ['xco2'],\n   'nodata': 0,\n   'rescale': [[412, 422]],\n   'colormap_name': 'magma'}},\n 'summaries': {'datetime': ['2015-01-01T00:00:00Z', '2022-02-28T00:00:00Z']},\n 'description': 'This dataset provides global gridded, daily column-averaged carbon dioxide (XCO₂) concentrations from January 1, 2015 - February 28, 2022. The data are derived from Orbiting Carbon Observatory-2 (OCO-2) satellite observations that were input to the Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS), a modeling and data assimilation system maintained by NASA’s Global Modeling and Assimilation Office (GMAO). Concentrations are measured in moles of carbon dioxide per mole of dry air (mol CO₂/mol dry) at a spatial resolution of 0.5° x 0.625°. Data assimilation synthesizes simulations and observations, adjusting modeled atmospheric constituents like CO₂ to reflect observed values. With the support of NASA’s Carbon Monitoring System (CMS) Program and the OCO Science Team, this dataset was produced as part of the OCO-2 mission which provides the highest quality space-based XCO₂ retrievals to date. The source data can be found at https://doi.org/10.5067/Y9M4NM9MPCGH',\n 'item_assets': {'xco2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Average Dry-Air Column CO₂ (XCO₂)',\n   'description': 'Daily dry air column-averaged mole fractions of carbon dioxide created from data assimilations of OCO-2 satellite retrievals.'},\n  'xco2prec': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Average Dry-Air Column CO₂ Precision (XCO₂PREC)',\n   'description': 'Random errors for daily dry air column-averaged mole fractions of carbon dioxide calculated using a posteriori diagnostics.'}},\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': 'day'}\n\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2015 to February 2022. By looking at the dashboard:time density, we can see that these observations are collected daily.\n\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Check total number of items available\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\nFound 2615 items\n\n\n\n# Examining the first item in the collection\nitems[0]\n\n{'id': 'oco2geos-co2-daygrid-v10r-20220228',\n 'bbox': [-180.3125, -90.25, 179.6875, 90.25],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228/map?assets=xco2&nodata=0&rescale=412%2C422&colormap_name=magma',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'xco2': {'href': 's3://ghgc-data-store/oco2geos-co2-daygrid-v10r/oco2_GEOS_XCO2_L3CO2_day_B10206Ar_20220228.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Average Dry-Air Column CO₂ (XCO₂)',\n   'proj:bbox': [-180.3125, -90.25, 179.6875, 90.25],\n   'proj:epsg': 4326.0,\n   'proj:shape': [361.0, 576.0],\n   'description': 'Daily dry air column-averaged mole fractions of carbon dioxide created from data assimilations of OCO-2 satellite retrievals.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 423.60419320175424,\n      'min': 411.7429234611336,\n      'count': 11.0,\n      'buckets': [37851.0,\n       30550.0,\n       19173.0,\n       11220.0,\n       15304.0,\n       31151.0,\n       45205.0,\n       15819.0,\n       1524.0,\n       139.0]},\n     'statistics': {'mean': 416.40504944204235,\n      'stddev': 2.967704894550985,\n      'maximum': 423.60419320175424,\n      'minimum': 411.7429234611336,\n      'valid_percent': 0.00048091720529393656}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.3125, -90.25],\n      [179.6875, -90.25],\n      [179.6875, 90.25],\n      [-180.3125, 90.25],\n      [-180.3125, -90.25]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.625, 0.0, -180.3125, 0.0, -0.5, 90.25, 0.0, 0.0, 1.0]},\n  'xco2prec': {'href': 's3://ghgc-data-store/oco2geos-co2-daygrid-v10r/oco2_GEOS_XCO2PREC_L3CO2_day_B10206Ar_20220228.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Average Dry-Air Column CO₂ Precision (XCO₂PREC)',\n   'proj:bbox': [-180.3125, -90.25, 179.6875, 90.25],\n   'proj:epsg': 4326.0,\n   'proj:shape': [361.0, 576.0],\n   'description': 'Random errors for daily dry air column-averaged mole fractions of carbon dioxide calculated using a posteriori diagnostics.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float64',\n     'histogram': {'max': 1.0,\n      'min': 0.09999999999999999,\n      'count': 11.0,\n      'buckets': [73789.0,\n       19836.0,\n       7943.0,\n       4684.0,\n       3634.0,\n       3060.0,\n       3094.0,\n       3093.0,\n       3814.0,\n       84989.0]},\n     'statistics': {'mean': 0.5499856972588942,\n      'stddev': 0.4024318718400779,\n      'maximum': 1.0,\n      'minimum': 0.09999999999999999,\n      'valid_percent': 0.00048091720529393656}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.3125, -90.25],\n      [179.6875, -90.25],\n      [179.6875, 90.25],\n      [-180.3125, 90.25],\n      [-180.3125, -90.25]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.625, 0.0, -180.3125, 0.0, -0.5, 90.25, 0.0, 0.0, 1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228/preview.png?assets=xco2&nodata=0&rescale=412%2C422&colormap_name=magma',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180.3125, -90.25],\n    [179.6875, -90.25],\n    [179.6875, 90.25],\n    [-180.3125, 90.25],\n    [-180.3125, -90.25]]]},\n 'collection': 'oco2geos-co2-daygrid-v10r',\n 'properties': {'datetime': '2022-02-28T00:00:00+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']}\n\n\nBelow, we enter minimum and maximum values to provide our upper and lower bounds in rescale_values.",
+    "objectID": "user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html#query-the-stac-api",
+    "href": "user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html#query-the-stac-api",
+    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
+    "section": "Query the STAC API",
+    "text": "Query the STAC API\nFirst, you need to import the required libraries. Once imported, they allow better execution of a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored. You will learn the functionality of each library throughout the notebook.\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\nSTAC API Collection Names\nNow, you must fetch the dataset from the STAC API by defining its associated STAC API collection ID as a variable. The collection ID, also known as the collection name, for the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset is epa-ch4emission-yeargrid-v2express\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for gridded methane dataset \ncollection_name = \"epa-ch4emission-yeargrid-v2express\"\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection in a table\n# Adjust display settings\npd.set_option('display.max_colwidth', None)  # Set maximum column width to \"None\" to prevent cutting off text\n\n# Extract the relevant information about the collection\ncollection_info = {\n    \"Title\": collection.get(\"title\", \"N/A\"), # Extract the title of the collection \n    \"Description\": collection.get(\"description\", \"N/A\"), # Extract the dataset description\n    \"Temporal Extent\": collection.get(\"extent\", {}).get(\"temporal\", {}).get(\"interval\", \"N/A\"), # Extract the temporal coverage of the collection\n    \"Spatial Extent\": collection.get(\"extent\", {}).get(\"spatial\", {}).get(\"bbox\", \"N/A\"), # Extract the spatial coverage of the collection\n}\n\n# Convert the derived information into a DataFrame format\nproperties_table = pd.DataFrame(list(collection_info.items()), columns=[\"Collection Summary\", \"\"])\n\n# Display the properties in a table\ncollection_summary = properties_table.style.set_properties(**{'text-align': 'left'}) \\\n                                           .set_table_styles([\n    {\n        'selector': 'th.col0, td.col0',    # Select the first column\n        'props': [('min-width', '200px'),  # Set a minimum width\n                  ('text-align', 'left')]  # Align text to the left\n    },\n    {\n        'selector': 'td.col1',             # Select the second column\n        'props': [('text-align', 'left')]  # Align text to the left\n    }\n])\n\n# Print the collection summary table\ncollection_summary\n\n{'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'}\n\n\nNext, you will examine the contents of the collection under the temporal variable. You’ll see that the data is available from January 2012 to December 2020. By looking at the dashboard:time density, you can observe that the periodic frequency of these observations is yearly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\n# Sort the items based on their date-time attribute\nitems_sorted = sorted(items, key=lambda x: x[\"properties\"][\"datetime\"])\n\n# Create an empty list\ntable_data = []\n# Extract the ID and date-time information for each granule and add them to the list\n# By default, only the first 5 items in the collection are extracted to be displayed in the table. \n# To see the date-time of all existing granules in this collection, remove \"5\" from \"item_sorted[:5]\" in the line below. \nfor item in items_sorted[:5]:\n    table_data.append([item['id'], item['properties']['datetime']])\n\n# Define the table headers\nheaders = [\"Item ID\", \"Start Date-Time\"]\n\nprint(\"Below you see the first 5 items in the collection, along with their item IDs and corresponding Start Date-Time.\")\n\n# Print the table using tabulate\nprint(tabulate(table_data, headers=headers, tablefmt=\"fancy_grid\"))\n\nFound 9 items\n\n\nThis makes sense as there are 9 years between 2012 - 2020, meaning 9 records in total.\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems_sorted[0]\n\n{'id': 'epa-ch4emission-yeargrid-v2express-2020',\n 'bbox': [-129.99999694387628,\n  19.99999923487448,\n  -60.00000305612369,\n  55.00000076512553],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},\n  {'rel': 'root',\n   'type': 'application/json',\n   '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  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020/map?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'dwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2020.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Waste - Domestic Wastewater Treatment & Discharge (annual)',\n   'proj:bbox': [-129.99999694387628,\n    19.99999923487448,\n    -60.00000305612369,\n    55.00000076512553],\n   'proj:epsg': 4326.0,\n   'proj:shape': [350.0, 700.0],\n   'description': 'Annual methane emissions from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 250.26608276367188,\n      'min': -9999.0,\n      'count': 11.0,\n      'buckets': [169028.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75972.0]},\n     'statistics': {'mean': -6898.392578125,\n      'stddev': 4624.86865234375,\n      'maximum': 250.26608276367188,\n      'minimum': -9999.0,\n      'valid_percent': 0.0004081632653061224}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-129.99999694387628, 19.99999923487448],\n      [-60.00000305612369, 19.99999923487448],\n      [-60.00000305612369, 55.00000076512553],\n      [-129.99999694387628, 55.00000076512553],\n      [-129.99999694387628, 19.99999923487448]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999126821799,\n    0.0,\n    -129.99999694387628,\n    0.0,\n    -0.10000000437214586,\n    55.00000076512553,\n    0.0,\n    0.0,\n    1.0]},\n  'iwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2020.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Waste - Industrial Wastewater Treatment & Discharge (annual)',\n   'proj:bbox': [-129.99999694387628,\n    19.99999923487448,\n    -60.00000305612369,\n    55.00000076512553],\n   'proj:epsg': 4326.0,\n   'proj:shape': [350.0, 700.0],\n   'description': 'Annual methane emissions from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 552.8455200195312,\n      'min': -9999.0,\n      'count': 11.0,\n      'buckets': [244120.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 880.0]},\n     'statistics': {'mean': -9963.07421875,\n      'stddev': 598.360107421875,\n      'maximum': 552.8455200195312,\n      'minimum': -9999.0,\n      'valid_percent': 0.0004081632653061224}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-129.99999694387628, 19.99999923487448],\n      [-60.00000305612369, 19.99999923487448],\n      [-60.00000305612369, 55.00000076512553],\n      [-129.99999694387628, 55.00000076512553],\n      [-129.99999694387628, 19.99999923487448]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999126821799,\n    0.0,\n    -129.99999694387628,\n    0.0,\n    -0.10000000437214586,\n    55.00000076512553,\n    0.0,\n    0.0,\n    1.0]},\n  'post-meter': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2020.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Natural Gas - Post Meter (annual)',\n   'proj:bbox': [-129.99999694387628,\n    19.99999923487448,\n    -60.00000305612369,\n    55.00000076512553],\n   'proj:epsg': 4326.0,\n   'proj:shape': [350.0, 700.0],\n   'description': 'Annual methane emissions downstream of residential, commercial, industrial natural gas distribution meters (i.e., “Post Meter”) (inventory Energy 1B2b sub-category).',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 32.81692123413086,\n      'min': -9999.0,\n      'count': 11.0,\n      'buckets': [169110.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75890.0]},\n     'statistics': {'mean': -6901.73974609375,\n      'stddev': 4623.49169921875,\n      'maximum': 32.81692123413086,\n      'minimum': -9999.0,\n      'valid_percent': 0.0004081632653061224}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-129.99999694387628, 19.99999923487448],\n      [-60.00000305612369, 19.99999923487448],\n      [-60.00000305612369, 55.00000076512553],\n      [-129.99999694387628, 55.00000076512553],\n      [-129.99999694387628, 19.99999923487448]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.09999999126821799,\n    0.0,\n    -129.99999694387628,\n    0.0,\n    -0.10000000437214586,\n    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-    "title": "OCO-2 GEOS Column CO₂ Concentrations",
-    "section": "Exploring Changes in Column-Averaged XCO₂ Concentrations Levels Using the Raster API",
-    "text": "Exploring Changes in Column-Averaged XCO₂ Concentrations Levels Using the Raster API\nIn this notebook, we will explore the temporal impacts of CO₂ emissions. We will visualize the outputs on a map using folium.\n\n# To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"datetime\"]: item for item in items} \nasset_name = \"xco2\" #fossil fuel\n\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for 2022-02-08 and again for 2022-01-27, so that we can visualize each event independently.\n\ncolor_map = \"magma\"\noco2_1 = requests.get(\n    f\"{RASTER_API_URL}/collections/{items[list(items.keys())[0]]['collection']}/items/{items[list(items.keys())[0]]['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\noco2_1\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=411.7429234611336%2C423.60419320175424'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.3125, -90.25, 179.6875, 90.25],\n 'center': [-0.3125, 0.0, 0]}\n\n\n\noco2_2 = requests.get(\n    f\"{RASTER_API_URL}/collections/{items[list(items.keys())[1]]['collection']}/items/{items[list(items.keys())[1]]['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\noco2_2\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=magma&rescale=411.7429234611336%2C423.60419320175424'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.3125, -90.25, 179.6875, 90.25],\n 'center': [-0.3125, 0.0, 0]}",
+    "objectID": "user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html#visual-comparison-across-time-periods",
+    "href": "user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html#visual-comparison-across-time-periods",
+    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
+    "section": "Visual Comparison Across Time Periods",
+    "text": "Visual Comparison Across Time Periods\nIn this notebook, you will explore the impacts of methane emissions and by examining changes over time in urban regions. You will visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"datetime\"][:7]: item for item in items} \n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the U.S. Gridded Anthropogenic Methane Emissions Inventory collection, the parameter of interest is “surface-coal”\nasset_name = \"surface-coal\"\n\nBelow, you will enter the minimum and maximum values to provide our upper and lower bounds in the rescale_values.\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\n\nitems\n\n{'2020-01': {'id': 'epa-ch4emission-yeargrid-v2express-2020',\n  'bbox': [-129.99999694387628,\n   19.99999923487448,\n   -60.00000305612369,\n   55.00000076512553],\n  'type': 'Feature',\n  'links': [{'rel': 'collection',\n    'type': 'application/json',\n    'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},\n   {'rel': 'parent',\n    'type': 'application/json',\n    'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'},\n   {'rel': 'root',\n    'type': 'application/json',\n    '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   {'title': 'Map of Item',\n    'href': 'https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2020/map?assets=total-methane&maxzoom=5&minzoom=0&rescale=0%2C20&colormap_name=epa-ghgi-ch4',\n    'rel': 'preview',\n    'type': 'text/html'}],\n  'assets': {'dwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Waste - Domestic Wastewater Treatment & Discharge (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\n    'description': 'Annual methane emissions from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',\n    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 250.26608276367188,\n       'min': -9999.0,\n       'count': 11.0,\n       'buckets': [169028.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75972.0]},\n      'statistics': {'mean': -6898.392578125,\n       'stddev': 4624.86865234375,\n       'maximum': 250.26608276367188,\n       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     0.0,\n     1.0]},\n   'iwtd-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Waste - Industrial Wastewater Treatment & Discharge (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\n    'description': 'Annual methane emissions from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category).',\n    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 552.8455200195312,\n       'min': -9999.0,\n       'count': 11.0,\n       'buckets': [244120.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 880.0]},\n      'statistics': {'mean': -9963.07421875,\n       'stddev': 598.360107421875,\n       'maximum': 552.8455200195312,\n       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     0.0,\n     1.0]},\n   'post-meter': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Natural Gas - Post Meter (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\n    'description': 'Annual methane emissions downstream of residential, commercial, industrial natural gas distribution meters (i.e., “Post Meter”) (inventory Energy 1B2b sub-category).',\n    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 32.81692123413086,\n       'min': -9999.0,\n       'count': 11.0,\n       'buckets': [169110.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75890.0]},\n      'statistics': {'mean': -6901.73974609375,\n       'stddev': 4623.49169921875,\n       'maximum': 32.81692123413086,\n       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     0.0,\n     1.0]},\n   'refining-ps': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Petroleum - Refining (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\n    'description': 'Annual methane emissions from Petroleum Refining (inventory Energy 1B2a sub-category).',\n    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 22.515766143798828,\n       'min': -9999.0,\n       'count': 11.0,\n       'buckets': [244892.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 108.0]},\n      'statistics': {'mean': -9994.5908203125,\n       'stddev': 209.9478759765625,\n       'maximum': 22.515766143798828,\n       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     0.0,\n     1.0]},\n   'total-other': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_other_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Total Other (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\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    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 43.67214584350586,\n       'min': -9999.0,\n       'count': 11.0,\n       'buckets': [157503.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 87497.0]},\n      'statistics': {'mean': -6428.02197265625,\n       'stddev': 4791.09814453125,\n       'maximum': 43.67214584350586,\n       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     0.0,\n     1.0]},\n   'total-waste': {'href': 's3://ghgc-data-store/epa-ch4emission-yeargrid-v2express/Express_Extension_waste_Gridded_GHGI_Methane_v2_2020.tif',\n    'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Total Waste (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\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    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 552.9092407226562,\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       'minimum': -9999.0,\n       'valid_percent': 0.0004081632653061224}}],\n    'proj:geometry': {'type': 'Polygon',\n     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     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    'type': 'image/tiff; 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application=geotiff; profile=cloud-optimized',\n    'roles': ['data', 'layer'],\n    'title': 'Other - Stationary Combustion (annual)',\n    'proj:bbox': [-129.99999694387628,\n     19.99999923487448,\n     -60.00000305612369,\n     55.00000076512553],\n    'proj:epsg': 4326.0,\n    'proj:shape': [350.0, 700.0],\n    'description': 'Annual methane emissions from Stationary Combustion (inventory Energy 1A sub-category).',\n    'raster:bands': [{'scale': 1.0,\n      'offset': 0.0,\n      'sampling': 'area',\n      'data_type': 'float32',\n      'histogram': {'max': 8.125173568725586,\n       'min': -9999.0,\n       'count': 11.0,\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     'coordinates': [[[-129.99999694387628, 19.99999923487448],\n       [-60.00000305612369, 19.99999923487448],\n       [-60.00000305612369, 55.00000076512553],\n       [-129.99999694387628, 55.00000076512553],\n       [-129.99999694387628, 19.99999923487448]]]},\n    'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n     'name': 'WGS 84',\n     'type': 'GeographicCRS',\n     'datum': {'name': 'World Geodetic System 1984',\n      'type': 'GeodeticReferenceFrame',\n      'ellipsoid': {'name': 'WGS 84',\n       'semi_major_axis': 6378137.0,\n       'inverse_flattening': 298.257223563}},\n     '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n     'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n        'unit': 'degree',\n        'direction': 'north',\n        'abbreviation': 'Lat'},\n       {'name': 'Geodetic longitude',\n        'unit': 'degree',\n        'direction': 'east',\n        'abbreviation': 'Lon'}],\n      'subtype': 'ellipsoidal'}},\n    'proj:transform': [0.09999999126821799,\n     0.0,\n     -129.99999694387628,\n     0.0,\n     -0.10000000437214586,\n     55.00000076512553,\n     0.0,\n     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-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  'geometry': {'type': 'Polygon',\n   'coordinates': [[[-129.99999694387628, 19.99999923487448],\n     [-60.00000305612369, 19.99999923487448],\n     [-60.00000305612369, 55.00000076512553],\n     [-129.99999694387628, 55.00000076512553],\n     [-129.99999694387628, 19.99999923487448]]]},\n  'collection': 'epa-ch4emission-yeargrid-v2express',\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']}}\n\n\nNow, you will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. This step is done twice, once for January 2018 and again for January 2012, so that you can visualize each event independently.\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"rainbow\" \n\nobservation_date_1 = '2018'\n\n# Don't change anything here\nobservation_1 = f'epa-ch4emission-yeargrid-v2express-{observation_date_1}'\n\n# Make a GET request to retrieve information for the 2018 tile \njanuary_2018_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2018-01']['collection']}/items/{items['2018-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2018_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-129.99999694387628,\n  19.99999923487448,\n  -60.00000305612369,\n  55.00000076512553],\n 'center': [-94.99999999999999, 37.5, 0]}\n\n\n\n# You will repeat the same approach used in the previous step to retrieve the second observation of interest\nobservation_date_2 = '2012'\n\n# Don't change anything here\nobservation_2 = f'epa-ch4emission-yeargrid-v2express-{observation_date_2}'\n\n# Make a GET request to retrieve information for the 2018 tile \njanuary_2012_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2012-01']['collection']}/items/{items['2012-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2012_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-129.99999694387628,\n  19.99999923487448,\n  -60.00000305612369,\n  55.00000076512553],\n 'center': [-94.99999999999999, 37.5, 0]}",
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       "Data Usage Notebooks",
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-    "title": "OCO-2 GEOS Column CO₂ Concentrations",
-    "section": "Visualizing Daily Column-Averaged XCO₂ Concentrations",
-    "text": "Visualizing Daily Column-Averaged XCO₂ Concentrations\n\n# Set initial zoom and center of map for XCO₂ Layer\n# Centre of map [latitude,longitude]\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n\nmap_layer_2020 = TileLayer(\n    tiles=oco2_1[\"tiles\"][0],\n    attr=\"GHG\",\n    opacity=0.5,\n)\nmap_layer_2020.add_to(map_.m1)\n\nmap_layer_2019 = TileLayer(\n    tiles=oco2_2[\"tiles\"][0],\n    attr=\"GHG\",\n    opacity=0.5,\n)\nmap_layer_2019.add_to(map_.m2)\n\n# visualising the map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
+    "objectID": "user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html#map-out-selected-tiles",
+    "href": "user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html#map-out-selected-tiles",
+    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
+    "section": "Map Out Selected Tiles",
+    "text": "Map Out Selected Tiles\n\n# Set initial zoom and center of map for CH₄ Layer\n# Centre of map [latitude,longitude]\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(38.9, -80.0), zoom_start=8)\n\n# Define the first map layer (January 2018)\nmap_layer_2018 = TileLayer(\n    tiles=january_2018_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    name='January 2018', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.7, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_2018.add_to(map_.m1)\n\n# Define the second map layer (January 2012)\nmap_layer_2012 = TileLayer(\n    tiles=january_2012_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    name='January 2012', # Title for the layer\n    overlay=True, # The layer can be overlaid on the map\n    opacity=0.7, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_2012.add_to(map_.m2)\n\n# Display data markers (titles) on both maps\nfolium.Marker((40, 5.0), tooltip=\"both\").add_to(map_)\n\n# Add a layer control to switch between map layers\nfolium.LayerControl(collapsed=False).add_to(map_)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
     "crumbs": [
       "Data Usage Notebooks",
-      "Greenhouse Gas Concentrations",
-      "OCO-2 GEOS Column CO₂ Concentrations"
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-    "title": "OCO-2 GEOS Column CO₂ Concentrations",
-    "section": "Visualizing the Data as a Time Series",
-    "text": "Visualizing the Data as a Time Series\nWe can now explore the XCO₂ concentrations time series (January 1, 2015 - February 28, 2022) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:\n\nfig = plt.figure(figsize=(20, 10))\n\n\nplt.plot(\n    df[\"datetime\"],\n    df[\"max\"],\n    color=\"red\",\n    linestyle=\"-\",\n    linewidth=0.5,\n    label=\"CO₂ concentrations\",\n)\n\nplt.legend()\nplt.xlabel(\"Years\")\nplt.ylabel(\"CO2 concentrations ppm\")\nplt.title(\"CO₂ concentrations Values for Texas, Dallas (Jan 2015- Feb 2022)\")\n\nText(0.5, 1.0, 'CO₂ concentrations Values for Texas, Dallas (Jan 2015- Feb 2022)')\n\n\n\n\n\n\n\n\n\n\nprint(items[2][\"properties\"][\"datetime\"])\n\n2022-02-26T00:00:00+00:00\n\n\n\noco2_3 = requests.get(\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\",\n).json()\noco2_3\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220226/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=xco2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=411.7429234611336%2C423.60419320175424'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.3125, -90.25, 179.6875, 90.25],\n 'center': [-0.3125, 0.0, 0]}\n\n\n\n# Use bbox initial zoom and map\n# Set up a map located w/in event bounds\naoi_map_bbox = Map(\n    tiles=\"OpenStreetMap\",\n    location=[\n        30,-100\n    ],\n    zoom_start=6.8,\n)\n\nmap_layer = TileLayer(\n    tiles=oco2_3[\"tiles\"][0],\n    attr=\"GHG\", opacity = 0.7\n)\n\nmap_layer.add_to(aoi_map_bbox)\n\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
+    "objectID": "user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html#time-series-analysis",
+    "href": "user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html#time-series-analysis",
+    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
+    "section": "Time-Series Analysis",
+    "text": "Time-Series Analysis\nYou can now explore the gridded methane emission (Domestic Wastewater Treatment & Discharge (5D)) time series (January 2000 -December 2021) available for the Pittsburgh Pennsylvania area of the U.S. You can plot the data set using the code below:\n\n# Ensure 'datetime' column is in datetime format\ndf['datetime'] = pd.to_datetime(df['datetime'])\n\n# Sort the DataFrame by the datetime column so the plot displays the values from left to right (2020 -> 2022)\ndf_sorted = df.sort_values(by=\"datetime\")\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\nplt.plot(\n    df[\"date\"], # X-axis: sorted date\n    df[\"max\"],  # Y-axis: maximum CH4 emission\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Max CH4 emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"CH4 emissions Molecules CH₄/cm²/s\")\n\n# Insert title for the plot\nplt.title(\"CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Pittsburgh, PA (2012-2021)\")\n\n\n# Add data citation\nplt.text(\n    df_sorted[\"datetime\"].iloc[0],           # X-coordinate of the text \n    df_sorted[\"max\"].min(),                  # Y-coordinate of the text \n\n    # Text to be displayed\n    \"Source: EPA Gridded Anthropogenic Methane Emissions Inventory\",                   \n    fontsize=12,                             # Font size\n    horizontalalignment=\"left\",              # Horizontal alignment\n    verticalalignment=\"bottom\",              # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n# Plot the time series\nplt.show()\n\nText(0.5, 1.0, 'CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Texas, Dallas (2012-202)')\n\n\n\n\n\n\n\n\n\n\n# Print the properties for the 3rd item in the collection\nprint(items[2][\"properties\"][\"datetime\"])\n\n2018-01-01T00:00:00+00:00\n\n\n\n# You will repeat the same approach used in the previous step to retrieve the second observation of interest\nobservation_date_3 = '2016'\n\n# Don't change anything here\nobservation_3 = f'epa-ch4emission-yeargrid-v2express-{observation_date_3}'\n\n# Make a GET request to retrieve information for the 2018 tile \ntile_2016 = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\ntile_2016\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-129.99999694387628,\n  19.99999923487448,\n  -60.00000305612369,\n  55.00000076512553],\n 'center': [-94.99999999999999, 37.5, 0]}\n\n\n\n# Create a new map to display the 2016 tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        39.9,-79.4\n    ],\n\n    # Set the zoom value\n    zoom_start=9,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=tile_2016[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.5\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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-    "title": "OCO-2 GEOS Column CO₂ Concentrations",
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+    "href": "user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html#summary",
+    "title": "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions",
     "section": "Summary",
-    "text": "Summary\nIn this notebook, we have successfully explored, analyzed, and visualized the STAC collection for OCO-2 GEOS Column CO₂ Concentrations.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the Column-Averaged XCO₂ Concentrations Levels for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
+    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset:\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the anthropogenic methane emissions for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nGenerate a time-series graph of the anthropogenic methane emissions for a specified region\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
     "crumbs": [
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-      "Greenhouse Gas Concentrations",
-      "OCO-2 GEOS Column CO₂ Concentrations"
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions"
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-    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
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+    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
     "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
-    "crumbs": [
-      "Data Usage Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
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+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
   },
   {
-    "objectID": "user_data_notebooks/noaa-insitu_User_Notebook.html#run-this-notebook",
-    "href": "user_data_notebooks/noaa-insitu_User_Notebook.html#run-this-notebook",
-    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
+    "objectID": "user_data_notebooks/oco2-mip-National-co2budget.html#run-this-notebook",
+    "href": "user_data_notebooks/oco2-mip-National-co2budget.html#run-this-notebook",
+    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
     "section": "",
-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
-    "crumbs": [
-      "Data Usage Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
-    ]
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
   },
   {
-    "objectID": "user_data_notebooks/noaa-insitu_User_Notebook.html#approach",
-    "href": "user_data_notebooks/noaa-insitu_User_Notebook.html#approach",
-    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
+    "objectID": "user_data_notebooks/oco2-mip-National-co2budget.html#approach",
+    "href": "user_data_notebooks/oco2-mip-National-co2budget.html#approach",
+    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
     "section": "Approach",
-    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory.\nVisualize the time series data",
-    "crumbs": [
-      "Data Usage Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
-    ]
+    "text": "Approach\n\nRead in National CO2 Budgets using Pandas\nSub-select the data structure using Pandas\nVisualize the CO2 budgets for a country\nInvestigate uncertainties and metrics for understanding the dataset"
   },
   {
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-    "href": "user_data_notebooks/noaa-insitu_User_Notebook.html#about-the-data",
-    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
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+    "href": "user_data_notebooks/oco2-mip-National-co2budget.html#about-the-data",
+    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
     "section": "About the Data",
-    "text": "About the Data\nThe Global Greenhouse Gas Reference Network (GGGRN) for the Carbon Cycle and Greenhouse Gases (CCGG) Group is part of NOAA’S Global Monitoring Laboratory (GML) in Boulder, CO. The Reference Network measures the atmospheric distribution and trends of the three main long-term drivers of climate change, carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N2O), as well as carbon monoxide (CO) and many other trace gases which help interpretation of the main GHGs. The Reference Network measurement program includes continuous in-situ measurements at 4 baseline observatories (global background sites) and 8 tall towers, as well as flask-air samples collected by volunteers at over 50 additional regional background sites and from small aircraft conducting regular vertical profiles. The air samples are returned to GML for analysis where measurements of about 55 trace gases are done. NOAA’s GGGRN maintains the World Meteorological Organization international calibration scales for CO₂, CH₄, CO, N2O, and SF6 in air. The measurements from the GGGRN serve as a comparison with measurements made by many other international laboratories, and with regional studies. They are widely used in modeling studies that infer space-time patterns of emissions and removals of greenhouse gases that are optimally consistent with the atmospheric observations, given wind patterns. These data serve as an early warning for climate “surprises”. The measurements are also helpful for the ongoing evaluation of remote sensing technologies.\nFor more information regarding this dataset, please visit the Atmospheric Carbon Dioxide Concentrations from NOAA GML data overview page.",
-    "crumbs": [
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-    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
-    "section": "Reading the NOAA data from GitHub repo",
-    "text": "Reading the NOAA data from GitHub repo\n\ngithub_repo_owner = \"NASA-IMPACT\"\ngithub_repo_name = \"noaa-viz\"\nfolder_path_ch4, folder_path_co2 = \"flask/ch4\", \"flask/c02\"\ncombined_df_co2, combined_df_ch4 = pd.DataFrame(), pd.DataFrame()\n\n\n# Function to fetch and append a file from GitHub\ndef append_github_file(file_url):\n    response = requests.get(file_url)\n    response.raise_for_status()\n    return response.text\n\n# Get the list of CH4 files in the specified directory using GitHub API\ngithub_api_url = f\"https://api.github.com/repos/{github_repo_owner}/{github_repo_name}/contents/{folder_path_ch4}\"\nresponse = requests.get(github_api_url)\nresponse.raise_for_status()\nfile_list_ch4 = response.json()\n\n# Get the list of CO2 files in the specified directory using GitHub API\ngithub_api_url = f\"https://api.github.com/repos/{github_repo_owner}/{github_repo_name}/contents/{folder_path_ch4}\"\nresponse = requests.get(github_api_url)\nresponse.raise_for_status()\nfile_list_co2 = response.json()",
-    "crumbs": [
-      "Data Usage Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
-    ]
+    "text": "About the Data\nThis tutorial guides a user to further explore data from the Carbon Observatory (OCO-2) modeling intercomparison project (MIP). It is designed for those with more understanding of the science and is therefore labeled as intermediate level.\nThe code is used to estimate the annual net terrestrial carbon stock loss (ΔCloss) and net carbon exchange (NCE) for a given country using the “top-down” NCE outputs from the Carbon Observatory (OCO-2) modeling intercomparison project (MIP). Several standardized experiments are studied in this notebook based on the OCO-2 MIP dataset including flux estimates from in situ CO₂ measurements (IS), flux estimates from OCO-2 land CO₂ data (LNLG), combined in situ and OCO-2 land CO₂ data (LNLGIS), and combined in situ and OCO-2 land and ocean CO₂ data (LNLGOGIS). Estimates and uncertainties associated with fossil fuels, riverine fluxes, and wood and crop fluxes are also graphed along with the ΔCloss and NCE variables.\nFor more information about this data collection, please visit the OCO-2 MIP Top-Down CO2 Budgets data overview page.\nFor more information regarding this dataset, please visit the U.S. Greenhouse Gas Center."
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-    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
-    "section": "Concatenating the CH4 data into a single DataFrame",
-    "text": "Concatenating the CH4 data into a single DataFrame\n\nfor file_info in file_list_ch4:\n    if file_info[\"name\"].endswith(\"txt\"):\n        file_content = append_github_file(file_info[\"download_url\"])\n        Lines = file_content.splitlines()\n        index = Lines.index(\"# VARIABLE ORDER\")+2\n        df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n        combined_df_ch4 = pd.concat([combined_df_ch4, df], ignore_index=True)\n        \n\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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)",
-    "crumbs": [
-      "Data Usage Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
-    ]
+    "objectID": "user_data_notebooks/oco2-mip-National-co2budget.html#import-required-modules",
+    "href": "user_data_notebooks/oco2-mip-National-co2budget.html#import-required-modules",
+    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
+    "section": "Import required modules",
+    "text": "Import required modules\nFirst we will need to import the relevant python modules:\n\nimport pandas as pd # for manipulating csv dataset\nimport numpy as np\nimport matplotlib.pyplot as plt # make plots\nfrom scipy.stats import norm # We will use this for understanding significance"
   },
   {
-    "objectID": "user_data_notebooks/noaa-insitu_User_Notebook.html#concatenating-the-co2-data-into-a-single-dataframe",
-    "href": "user_data_notebooks/noaa-insitu_User_Notebook.html#concatenating-the-co2-data-into-a-single-dataframe",
-    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
-    "section": "Concatenating the CO2 data into a single DataFrame",
-    "text": "Concatenating the CO2 data into a single DataFrame\n\nfor file_info in file_list_co2:\n    if file_info[\"name\"].endswith(\"txt\"):\n        file_content = append_github_file(file_info[\"download_url\"])\n        Lines = file_content.splitlines()\n        index = Lines.index(\"# VARIABLE ORDER\")+2\n        df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n        combined_df_co2 = pd.concat([combined_df_co2, df], ignore_index=True)\n        \n\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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  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  df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)",
-    "crumbs": [
-      "Data Usage Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
-    ]
+    "objectID": "user_data_notebooks/oco2-mip-National-co2budget.html#read-the-co2-national-budget-dataset",
+    "href": "user_data_notebooks/oco2-mip-National-co2budget.html#read-the-co2-national-budget-dataset",
+    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
+    "section": "Read the CO2 National budget dataset",
+    "text": "Read the CO2 National budget dataset\nNow we will read in the csv dataset from https://ceos.org/gst/carbon-dioxide.html\n\nurl ='https://ceos.org/gst/files/pilot_topdown_CO2_Budget_countries_v1.csv'\ndf_all = pd.read_csv(url, skiprows=52)"
   },
   {
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-    "href": "user_data_notebooks/noaa-insitu_User_Notebook.html#visualizing-the-noaa-data-for-ch4-and-co2",
-    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
-    "section": "Visualizing the NOAA data for CH4 and CO2",
-    "text": "Visualizing the NOAA data for CH4 and CO2\n\nsite_to_filter = 'ABP'\nfiltered_df = combined_df_co2[combined_df_co2['site_code'] == site_to_filter]\n\nfiltered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])\n\n# Set the \"Date\" column as the index\nfiltered_df.set_index('datetime', inplace=True)\n\n# Create a time series plot for 'Data' and 'Value'\nplt.figure(figsize=(12, 6))\nplt.plot(filtered_df.index, filtered_df['value'], label='Carbon Dioxide(CO2) Concentration (ppm)')\nplt.xlabel(\"Observed Date/Time\")\nplt.ylabel(\"Carbon Dioxide(CO2) Concentration (ppm)\")\nplt.title(f\"Observed Co2 Concentration {site_to_filter}\")\nplt.legend()\nplt.grid(True)\n# plt.show()\n\n/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/2606016741.py:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  filtered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])\n\n\n\n\n\n\n\n\n\n\nsite_to_filter = 'ABP'\nfiltered_df = combined_df_ch4[combined_df_ch4['site_code'] == site_to_filter]\nfiltered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])\n\n# Set the \"Date\" column as the index\nfiltered_df.set_index('datetime', inplace=True)\n\n# Create a time series plot for 'Data' and 'Value'\nplt.figure(figsize=(12, 6))\nplt.plot(filtered_df.index, filtered_df['value'], label='Methane Ch4 Concentration (ppb)')\nplt.xlabel(\"Observation Date/Time\")\nplt.ylabel(\"Methane Ch4 Concentration (ppb)\")\nplt.title(f\"Observed CH4 Concentration {site_to_filter}\")\nplt.legend()\nplt.grid(True)\nplt.show()\n\n/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1635934907.py:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  filtered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])",
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-      "Data Usage Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
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+    "objectID": "user_data_notebooks/oco2-mip-National-co2budget.html#sub-select-a-single-top-down-dataset-experiment",
+    "href": "user_data_notebooks/oco2-mip-National-co2budget.html#sub-select-a-single-top-down-dataset-experiment",
+    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
+    "section": "Sub-select a single top-down dataset (experiment)",
+    "text": "Sub-select a single top-down dataset (experiment)\nTo simplify the analysis, let’s subselect the results for a single experiment. The experiments are: - IS: estimates fluxes from in situ CO2 measurements - LNLG: estimates fluxes from OCO-2 land CO2 data - LNLGIS: combines in situ and OCO-2 land CO2 data - LNLGOGIS: combines in situ and OCO-2 land and ocean CO2 data\nWe would like to use the experiment that uses the most high-quality CO2 data. There are some concerns about small residual biases in OCO-2 ocean data (Byrne et al., 2023), so let’s use the LNLGIS experiment.\n\n# Choose one experiment from the list ['IS', 'LNLG', 'LNLGIS', 'LNLGOGIS']\nexperiment = 'LNLGIS'\n\n# Subset of columns for a given experiment\nif experiment == 'IS':\n    df = df_all.drop(df_all.columns[[4,5,6,7,8,9,12,13,14,15,16,17,20,21,22,23,24,25,34,35,36]], axis=1)\nif experiment == 'LNLG':\n    df = df_all.drop(df_all.columns[[2,3,6,7,8,9,10,11,14,15,16,17,18,19,22,23,24,25,33,35,36]], axis=1)\nif experiment == 'LNLGIS':\n    df = df_all.drop(df_all.columns[[2,3,4,5,8,9,10,11,12,13,16,17,18,19,20,21,24,25,33,34,36]], axis=1)\nif experiment == 'LNLGOGIS':\n    df = df_all.drop(df_all.columns[[2,3,4,5,6,7,10,11,12,13,14,15,18,19,20,21,22,23,33,34,35]], axis=1)\n\n# We can now look at the colums of data\ndf.head()\n\n\n\n\n\n\n\n\nAlpha 3 Code\nYear\nLNLGIS dC_loss (TgCO2)\nLNLGIS dC_loss unc (TgCO2)\nLNLGIS NBE (TgCO2)\nLNLGIS NBE unc (TgCO2)\nLNLGIS NCE (TgCO2)\nLNLGIS NCE unc (TgCO2)\nRivers (TgCO2)\nRiver unc (TgCO2)\nWood+Crop (TgCO2)\nWood+Crop unc (TgCO2)\nFF (TgCO2)\nFF unc (TgCO2)\nZ-statistic\nFUR LNLGIS\n\n\n\n\n0\nAFG\n2015\n39.3407\n153.746\n40.9643\n153.746\n60.3537\n153.744\n-2.43286\n1.69832\n4.05648\n1.21694\n19.3894\n0.797698\n0.37\n0.19\n\n\n1\nAFG\n2016\n50.6167\n175.454\n52.5114\n175.454\n73.0333\n175.452\n-2.16185\n2.24033\n4.05648\n1.21694\n20.5220\n0.678080\n0.31\n0.19\n\n\n2\nAFG\n2017\n54.5096\n179.794\n56.4726\n179.794\n77.5355\n179.793\n-2.09349\n2.37705\n4.05648\n1.21694\n21.0629\n0.695856\n0.47\n0.19\n\n\n3\nAFG\n2018\n116.4260\n243.057\n118.4610\n243.057\n143.9580\n243.056\n-2.02199\n2.52005\n4.05648\n1.21694\n25.4974\n0.695856\n0.39\n0.19\n\n\n4\nAFG\n2019\n64.0162\n181.516\n66.0388\n181.516\n93.8974\n181.514\n-2.03383\n2.49637\n4.05648\n1.21694\n27.8585\n0.797698\n0.49\n0.19"
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-    "href": "user_data_notebooks/noaa-insitu_User_Notebook.html#summary",
-    "title": "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory",
-    "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully visualized the data for Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory.\n\nInstall and import the necessary libraries\nFetch the collection from GitHub API using the appropriate endpoints\nConcatenating the CO2 and CH4 data into a single DataFrame\nVisualizing the NOAA data for CO2 and CH4\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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-      "Data Usage Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory"
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+    "objectID": "user_data_notebooks/oco2-mip-National-co2budget.html#sub-select-a-single-country",
+    "href": "user_data_notebooks/oco2-mip-National-co2budget.html#sub-select-a-single-country",
+    "title": "OCO-2 MIP National Top-Down CO2 Budgets",
+    "section": "Sub-select a single country",
+    "text": "Sub-select a single country\nLet’s further filter the dataset to look at a specific country. Choose a country by entering the alpha code in the country_name variable below\n\n# Choose a country\ncountry_name = 'USA' \n\n# We can sub-select the data for the country\ncountry_data = df[df['Alpha 3 Code'] == country_name]\n\n# Now we can look at the data for a specific experiment and country\ncountry_data.head()\n\n\n\n\n\n\n\n\nAlpha 3 Code\nYear\nLNLGIS dC_loss (TgCO2)\nLNLGIS dC_loss unc (TgCO2)\nLNLGIS NBE (TgCO2)\nLNLGIS NBE unc (TgCO2)\nLNLGIS NCE (TgCO2)\nLNLGIS NCE unc (TgCO2)\nRivers (TgCO2)\nRiver unc (TgCO2)\nWood+Crop (TgCO2)\nWood+Crop unc (TgCO2)\nFF (TgCO2)\nFF unc (TgCO2)\nZ-statistic\nFUR LNLGIS\n\n\n\n\n1232\nUSA\n2015\n-1031.83\n721.213\n-1346.46\n721.213\n4017.31\n713.897\n-165.430\n71.7453\n-149.196\n-44.7589\n5363.77\n102.4670\n-0.81\n0.91\n\n\n1233\nUSA\n2016\n-1419.92\n399.738\n-1743.80\n399.738\n3529.45\n387.079\n-174.684\n53.2375\n-149.196\n-44.7589\n5273.24\n99.8012\n0.04\n0.91\n\n\n1234\nUSA\n2017\n-1375.12\n1034.010\n-1696.63\n1034.010\n3515.14\n1029.250\n-172.308\n57.9894\n-149.196\n-44.7589\n5211.76\n99.0981\n0.67\n0.91\n\n\n1235\nUSA\n2018\n-1018.89\n784.463\n-1333.83\n784.463\n4036.65\n778.179\n-165.747\n71.1117\n-149.196\n-44.7589\n5370.48\n99.0981\n-0.20\n0.91\n\n\n1236\nUSA\n2019\n-1161.41\n718.054\n-1504.61\n718.054\n3728.95\n710.705\n-194.005\n14.5948\n-149.196\n-44.7589\n5233.56\n102.4670\n-0.38\n0.91\n\n\n\n\n\n\n\n#This dataset contains fluxes over a five year period, 2015-2020.\nLet’s look at a plot of the annual net terrestrial carbon stock loss (ΔCloss) for each year.\n\n# Make plot\nfig, ax1 = plt.subplots(1, 1, figsize=(6, 4))\nax1.errorbar(country_data['Year'],country_data[experiment+' dC_loss (TgCO2)'],\n                    yerr=country_data[experiment+' dC_loss unc (TgCO2)'],label=experiment,capsize=10)\nax1.legend(loc='upper right')\nax1.set_ylabel('$\\Delta$C$_\\mathrm{loss}$ (TgCO$_2$ year$^{-1}$)')\nax1.set_xlabel('Year')\nax1.set_title('$\\Delta$C$_\\mathrm{loss}$ for '+country_name)\nymin, ymax = ax1.get_ylim()\nmax_abs_y = max(abs(ymin), abs(ymax))\nax1.set_ylim([-max_abs_y, max_abs_y])\nxmin, xmax = ax1.get_xlim()\nax1.plot([xmin,xmax],[0,0],'k',linewidth=0.5)\nax1.set_xlim([xmin, xmax])\n\n\n\n\n\n\n\n\nNext, we can look at the full carbon budget for a given year.\nThe code below creates a plot similar to Fig 13 of Byrne et al. (2023). Each of the bars on the left side of the dashed vertical line (Fossil fuel emissions, lateral C transport by rivers, lateral C transport in crop and wood products, and the net terrestrial carbon stock loss combined to give the net carbon exchange (net surface-atmosphere CO2 flux) shown on the right.\n\n# Pick a specifc year (or mean year)\nyear='mean'\n\n# Make plot\ncountry_data_mean = country_data[country_data['Year'] == year]\na=country_data_mean['Wood+Crop (TgCO2)']\nb=country_data_mean['Wood+Crop unc (TgCO2)']\nprint(b)\n#\nplt.bar(1, country_data_mean['FF (TgCO2)'], yerr=country_data_mean['FF unc (TgCO2)'], label='FF', alpha=0.5)\nplt.bar(2, country_data_mean['Rivers (TgCO2)'], yerr=country_data_mean['River unc (TgCO2)'], label='Rivers', alpha=0.5)\nplt.bar(3, country_data_mean['Wood+Crop (TgCO2)'], yerr=abs(country_data_mean['Wood+Crop unc (TgCO2)']), label='WoodCrop', alpha=0.5)\nplt.bar(4, country_data_mean[experiment+' dC_loss (TgCO2)'], yerr=country_data_mean['LNLGIS dC_loss unc (TgCO2)'], label='dC', alpha=0.5)\nplt.bar(6, country_data_mean[experiment+' NCE (TgCO2)'], yerr=country_data_mean['LNLGIS NCE unc (TgCO2)'], label='NCE', alpha=0.5)\nax = plt.gca()\nymin, ymax = ax.get_ylim()\nplt.plot([5,5],[ymin,ymax],'k:')\nxmin, xmax = ax.get_xlim()\nplt.plot([xmin,xmax],[0,0],'k',linewidth=0.5)\nplt.xlim([xmin,xmax])\nplt.ylim([ymin,ymax])\n#\nplt.xticks([1,2,3,4,6], ['Fossil\\nFuels','Rivers','Wood+\\nCrops','$\\mathrm{\\Delta C _{loss}}$','NCE'])\nplt.title(country_name+' '+year)\nplt.ylabel('CO$_2$ Flux (TgCO$_2$ year$^{-1}$)')\n\n1238   -44.7589\nName: Wood+Crop unc (TgCO2), dtype: float64\n\n\nText(0, 0.5, 'CO$_2$ Flux (TgCO$_2$ year$^{-1}$)')\n\n\n\n\n\n\n\n\n\nUncertainty is an important consideration when analyzing the flux estimates provided by Byrne et al. (2023).\nEach flux estimate is provided with an error estimate representing the standard deviation, and assuming the errors are well prepresented by a normal distribution. This probability dirtribution provided by this uncertainty can be visualized below. We can further quantify the\n\n\n# Select NCE, NBE or dC_loss\nquantity = 'dC_loss'\n\n# Value for comparison\ncomparison_value = 1000 # TgCO2/year\n\n\nMIP_mean = country_data_mean[experiment+' '+quantity+' (TgCO2)'].item()\nMIP_std = country_data_mean[experiment+' '+quantity+' unc (TgCO2)'].item()\n\n# Perform t-test\nt_value = abs(MIP_mean - comparison_value)/(MIP_std / np.sqrt(11))\ncrtical_value = 2.23 # use p=0.05 significance\nif t_value > crtical_value:\n    ttest = 'statistically different'\nif t_value < crtical_value:\n    ttest = 'not statistically\\ndifferent'\n\n# Make plot\nxbounds = abs(MIP_mean)+MIP_std*4\nif abs(crtical_value) > xbounds:\n    xbounds = abs(crtical_value)\nx_axis = np.arange(-1.*xbounds, xbounds, 1) \nplt.plot(x_axis, norm.pdf(x_axis, MIP_mean, MIP_std)) \nax = plt.gca()\nymin, ymax = ax.get_ylim()\nxmin, xmax = ax.get_xlim()\nplt.plot([0,0],[ymin,ymax*1.2],'k:',linewidth=0.5)\nplt.plot([xmin,xmax],[0,0],'k:',linewidth=0.5)\nplt.plot([comparison_value,comparison_value],[ymin,ymax*1.2],'k')\nplt.text(comparison_value+(xmax-xmin)*0.01,ymax*0.96,'value = '+str(comparison_value),ha='left',va='top')\nplt.text(comparison_value+(xmax-xmin)*0.01,ymax*0.9,ttest,ha='left',va='top')\nplt.ylim([ymin,ymax*1.2])\nplt.xlim([xmin,xmax])\nplt.plot(MIP_mean,ymax*1.03,'ko')\nplt.plot([MIP_mean-MIP_std,\n         MIP_mean+MIP_std],\n         [ymax*1.03,ymax*1.03],'k')\nplt.plot([MIP_mean-MIP_std,\n         MIP_mean-MIP_std],\n         [ymax*1.005,ymax*1.055],'k')\nplt.plot([MIP_mean+MIP_std,\n         MIP_mean+MIP_std],\n         [ymax*1.005,ymax*1.055],'k')\nplt.text(MIP_mean,ymax*1.115,\n         str(round(MIP_mean))+' $\\pm$ '+\n         str(round(MIP_std))+' TgCO$_2$',ha='center')\nplt.title(country_name+' '+year+' '+quantity+'')\nplt.yticks([])\nplt.ylabel('Probability')\nplt.xlabel(quantity+' (TgCO$_2$ year$^{-1}$)')\n\nText(0.5, 0, 'dC_loss (TgCO$_2$ year$^{-1}$)')\n\n\n\n\n\n\n\n\n\nFinally, we will examine two metrics that are useful for understanding the confidence in the top-down results:\n\nZ-statistic: metric of agreement in NCE estimates across the experiments that assimilate different CO2 datasets. Experiments are considered significantly different if the magnitude exceeds 1.96\nFractional Uncertainty Reduction (FUR): metric of how strongly the assimilated CO2 data on reduce NCE uncertainties. Values range from 0 to 1, with 0 meaning zero error reduction and 1 meaning complete error reduction\n\nHere we will add a plot of the Z-statistic for each year, and add the FUR value for the country.\n\n# Make plot\nfig, ax1 = plt.subplots(1, 1, figsize=(6, 4))\nax1.plot(country_data['Year'],country_data['Z-statistic'],label=experiment)\nax1.legend(loc='upper right')\nax1.set_ylabel('Z-statistic')\nax1.set_xlabel('Year')\nax1.set_title(country_name)\nymin, ymax = ax1.get_ylim()\nmax_abs_y = max(abs(ymin), abs(ymax))\nax1.set_ylim([-3, 3])\nxmin, xmax = ax1.get_xlim()\nax1.plot([xmin,xmax],[0,0],'k',linewidth=0.5)\nax1.plot([xmin,xmax],[-1.96,-1.96],'k--',linewidth=0.5)\nax1.plot([xmin,xmax],[1.96,1.96],'k--',linewidth=0.5)\nax1.set_xlim([xmin, xmax])\nax1.text(xmin+0.12,2.6,'Fractional error reduction: '+str(country_data['FUR '+experiment].iloc[1]))\n\nText(-0.18000000000000005, 2.6, 'Fractional error reduction: 0.91')"
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-    "text": "Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\nVisualize the time series data",
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+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
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-    "text": "Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\nVisualize the time series data",
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+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
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+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
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+    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. Collection processed in this notebook is ODIAC CO₂ emissions version 2022.\nPass the STAC item into raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint\nWe’ll visualize two tiles (side-by-side) allowing for comparison of each of the time points using folium.plugins.DualMap\nAfter the visualization, we’ll perform zonal statistics for a given polygon."
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-    "text": "About the Data\nNIST is engaged in research to improve measurement of greenhouse gas emissions in areas containing multiple emission sources and sinks, such as ciies. NIST’s objective is to develop measurement tools supporting independent means to increase the accuracy of greenhouse gas emissions data at urban and regional geospatial scales. NIST has established three test beds in U.S. ciies to develop and evaluate the performance of advanced measurement capabilities for emissions independent of their origin. Located in Indianapolis, Indiana, the Los Angeles air basin of California, and the U.S. Northeast corridor (beginning with the Baltimore/Washington D.C. region), the test beds have been selected for their varying meteorology, terrain and emissions characteristics. These test beds will serve as a means to independently diagnose the accuracy of emissions data obtained directly from emission or uptake sources.\nFor more information regarding this dataset, please visit the Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project data overview page.",
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+    "text": "About the Data\nThe Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) is a high-spatial resolution global emission data product of CO₂ emissions from fossil fuel combustion (Oda and Maksyutov, 2011). ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO₂ emissions. With the innovative emission modeling approach, ODIAC achieved the fine picture of global fossil fuel CO₂ emissions at a 1x1km.\nFor more information regarding this dataset, please visit the ODIAC Fossil Fuel CO₂ Emissions data overview page."
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-    "text": "Querying the Feature Vector API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Feature Vector Application Programming Interface (API) where the items for this collection are stored.\n\nFEATURE_API_URL=\"https://earth.gov/ghgcenter/api/features\"\n\n\n# Function to fetch CSV data for a station with a limit parameter\ndef get_station_data_csv(station_code, gas_type, frequency, elevation_m, limit=100000):\n    # Use the ?f=csv and limit query to get more rows\n    url = f\"https://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_lam_{station_code}_{gas_type}_{frequency}_concentrations/items?f=csv&elevation_m={elevation_m}&limit={limit}\"\n    print(url)\n    try:\n        response = requests.get(url)\n        \n        # Check if the response is successful\n        if response.status_code != 200:\n            print(f\"Failed to fetch data for {station_code}. Status code: {response.status_code}\")\n            return pd.DataFrame()\n\n        # Check if the content type is CSV\n        content_type = response.headers.get('Content-Type')\n        if 'text/csv' not in content_type:\n            print(f\"Unexpected content type for {station_code}: {content_type}\")\n            print(\"Response content:\", response.text)\n            return pd.DataFrame()\n\n        # Read the CSV content into a pandas DataFrame\n        csv_data = StringIO(response.text)\n        return pd.read_csv(csv_data)\n    \n    except requests.exceptions.RequestException as e:\n        print(f\"Request failed: {e}\")\n        return pd.DataFrame()",
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+    "section": "Querying the STAC API",
+    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for ODIAC dataset \ncollection_name = \"odiac-ffco2-monthgrid-v2022\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'odiac-ffco2-monthgrid-v2022',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022/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/odiac-ffco2-monthgrid-v2022'}],\n 'title': 'ODIAC Fossil Fuel CO₂ Emissions v2022',\n 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n  'temporal': {'interval': [['2000-01-01T00:00:00+00:00',\n     '2021-12-31T00:00:00+00:00']]}},\n 'license': 'CC-BY-4.0',\n 'renders': {'dashboard': {'assets': ['co2-emissions'],\n   'nodata': 0,\n   'rescale': [[-10, 60]],\n   'colormap_name': 'jet'},\n  'co2-emissions': {'assets': ['co2-emissions'],\n   'nodata': 0,\n   'rescale': [[-10, 60]],\n   'colormap_name': 'jet'}},\n 'providers': [{'url': 'https://www.nies.go.jp',\n   'name': 'National Institute for Environmental Studies',\n   'roles': ['producer', 'licensor']}],\n 'summaries': {'datetime': ['2000-01-01T00:00:00Z', '2021-12-31T00:00:00Z']},\n 'description': 'The Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) data product is a monthly high-resolution global data product of modeled fossil fuel carbon dioxide (CO₂) emissions. A complex model incorporates and combines space-based nighttime light data and individual power plant emission/location profiles from the latest country fossil fuel CO₂ estimates (2000-2019) made by the Carbon Dioxide Information Analysis Center (CDIAC) team at the Appalachian State University (CDIAC at AppState, Gilfillan et al. 2021, Hefner et al. 2022). The ODIAC estimated global spatial extent of fossil fuel CO₂ emissions is produced on a 1 km by 1 km grid that details variations in urban regions where emissions are most intense. The ODIAC CO₂ emission data is widely used by the international research community for applications such as CO₂ flux inversion, urban emission estimation, and observing system design experiments. The ODIAC product was first created in 2009 by Dr. Tomohiro Oda with support from the National Institute for Environmental Studies (NIES) GOSAT project. The ODIAC team is now supported by NASA Goddard Space Flight Center, NASA Carbon Monitoring System program, the NASA Orbiting Carbon Observatory mission and NIES. The US GHG Center displays the ODIAC 2022 version containing monthly data from January 2000 to December 2021 that replaces all previous versions. The source dataset can be found at https://doi.org/10.17595/20170411.001',\n 'item_assets': {'co2-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil Fuel CO₂ Emissions',\n   'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.'}},\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': 'month'}\n\n\nExamining the contents of our collection under summaries we see that the data is available from January 2000 to December 2021. By looking at the dashboard:time density we observe that the periodic frequency of these observations is monthly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 264 items\n\n\nThis makes sense as there are 22 years between 2000 - 2021, with 12 months per year, meaning 264 records in total.\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'odiac-ffco2-monthgrid-v2022-202112',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112/map?assets=co2-emissions&nodata=0&rescale=-10%2C60&colormap_name=jet',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'co2-emissions': {'href': 's3://ghgc-data-store/odiac-ffco2-monthgrid-v2022/odiac2022_1km_excl_intl_202112.tif',\n   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil Fuel CO₂ Emissions',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [21600.0, 43200.0],\n   'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 2497.01904296875,\n      'min': -138.71914672851562,\n      'count': 11.0,\n      'buckets': [523457.0, 691.0, 95.0, 28.0, 11.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n     'statistics': {'mean': 0.9804128408432007,\n      'stddev': 14.766693454324674,\n      'maximum': 2497.01904296875,\n      'minimum': -138.71914672851562,\n      'valid_percent': 100.0}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.008333333333333333,\n    0.0,\n    -180.0,\n    0.0,\n    -0.008333333333333333,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112/preview.png?assets=co2-emissions&nodata=0&rescale=-10%2C60&colormap_name=jet',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'odiac-ffco2-monthgrid-v2022',\n 'properties': {'end_datetime': '2021-12-31T00:00:00+00:00',\n  'start_datetime': '2021-12-01T00:00:00+00:00'},\n 'stac_version': '1.0.0',\n 'stac_extensions': []}"
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-    "title": "Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project",
-    "section": "Visualizing the CO₂ data for two NEC stations",
-    "text": "Visualizing the CO₂ data for two NEC stations\n\n# Get station name and elevation from metdata dataframe\n# Fetch data for SCI (elevation 489) and COM (elevation 9), using limit=10000\n# ch4/co2 select the ghg \nsci_data = get_station_data_csv('sci', 'co2', 'hourly', 489, limit=10000)\ncom_data = get_station_data_csv('com', 'co2', 'hourly', 9, limit=10000)\n\n# Check if data was successfully retrieved before proceeding\nif sci_data.empty or com_data.empty:\n    print(\"No data available for one or both stations. Exiting.\")\nelse:\n    # Convert the 'datetime' column to datetime for plotting\n    sci_data['datetime'] = pd.to_datetime(sci_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n    com_data['datetime'] = pd.to_datetime(com_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n\n    # Plot the data\n    plt.figure(figsize=(10, 6))\n    plt.plot(sci_data['datetime'], sci_data['value'], label='SCI (489m)', color='blue', marker='o')\n    plt.plot(com_data['datetime'], com_data['value'], label='COM (9m)', color='green', marker='o')\n\n    plt.title('Carbon Dioxide (CO₂) Hourly Concentrations Over Time for SCI and COM Stations')\n    plt.xlabel('Time')\n    plt.ylabel('CO₂ Concentration (ppm)')\n    plt.legend()\n    plt.grid(True)\n\n    # Show plot\n    plt.show()\n\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_lam_sci_co2_hourly_concentrations/items?f=csv&elevation_m=489&limit=10000\nhttps://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_lam_com_co2_hourly_concentrations/items?f=csv&elevation_m=9&limit=10000",
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-      "Data Usage Notebooks",
-      "Greenhouse Gas Concentrations",
-      "Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project"
-    ]
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+    "href": "user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.html#exploring-changes-in-carbon-dioxide-co₂-levels-using-the-raster-api",
+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
+    "section": "Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API",
+    "text": "Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API\nWe will explore changes in fossil fuel emissions in urban egions. In this notebook, we’ll explore the impacts of these emissions and explore these changes over time. We’ll then visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items} \n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the ODIAC Fossil Fuel CO₂ Emissions collection, the parameter of interest is “co2-emissions”\nasset_name = \"co2-emissions\"\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for January 2020 and again for January 2000, so that we can visualize each event independently.\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"rainbow\" \n\n# Make a GET request to retrieve information for the 2020 tile\n# 2020\njanuary_2020_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2020-01']['collection']}/items/{items['2020-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2020_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-138.71914672851562%2C2497.01904296875'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for the 2000 tile\n# 2000\njanuary_2000_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2000-01']['collection']}/items/{items['2000-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2000_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-138.71914672851562%2C2497.01904296875'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}"
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+    "href": "user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.html#visualizing-co₂-emissions",
+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
+    "section": "Visualizing CO₂ emissions",
+    "text": "Visualizing CO₂ emissions\n\n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n\n# Set the initial zoom level and center of map for both tiles\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer (January 2020)\nmap_layer_2020 = TileLayer(\n    tiles=january_2020_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_2020.add_to(map_.m1)\n\n# Define the second map layer (January 2000)\nmap_layer_2000 = TileLayer(\n    tiles=january_2000_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_2000.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
+  },
+  {
+    "objectID": "user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.html#visualizing-the-data-as-a-time-series",
+    "href": "user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.html#visualizing-the-data-as-a-time-series",
+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
+    "section": "Visualizing the Data as a Time Series",
+    "text": "Visualizing the Data as a Time Series\nWe can now explore the ODIAC fossil fuel emission time series available (January 2000 -December 2021) for the Texas, Dallas area of USA. We can plot the data set using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\n\nplt.plot(\n    df[\"date\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum CO₂ level\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Max monthly CO₂ emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"CO2 emissions gC/m2/d\")\n\n# Insert title for the plot\nplt.title(\"CO2 emission Values for Texas, Dallas (2000-2021)\")\n\n###\n# Add data citation\nplt.text(\n    df[\"date\"].iloc[0],           # X-coordinate of the text\n    df[\"max\"].min(),              # Y-coordinate of the text\n\n\n\n\n    # Text to be displayed\n    \"Source: NASA ODIAC Fossil Fuel CO₂ Emissions\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"right\",             # Horizontal alignment\n    verticalalignment=\"top\",                 # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n# Plot the time series\nplt.show()\n\n\n\n\n\n\n\n\n\n# Print the properties of the 3rd item in the collection\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n2021-10-01T00:00:00+00:00\n\n\n\n# A GET request is made for the October tile\noctober_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\noctober_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-138.71914672851562%2C2497.01904296875'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the October tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=october_tile[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.5\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
+  },
+  {
+    "objectID": "user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.html#summary",
+    "href": "user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.html#summary",
+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
+    "section": "Summary",
+    "text": "Summary\nIn this notebook we have successfully explored, analysed and visualized STAC collecetion for ODIAC C02 fossisl fuel emission (2022).\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the CO₂ levels for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form."
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@@ -2796,300 +2883,213 @@
     "href": "user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.html#visualizing-the-data-as-a-time-series",
     "title": "OCO-2 MIP Top-Down CO₂ Budgets",
     "section": "Visualizing the Data as a Time Series",
-    "text": "Visualizing the Data as a Time Series\nWe can now explore the fossil fuel emission time series (January 2015 -December 2020) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\nplt.plot(\n    df[\"datetime\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum CO₂ emission\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"CO2 emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"CO2 emissions gC/m2/year1\")\n\n# Insert title for the plot\nplt.title(\"CO2 emission Values for Texas, Dallas (2015-2020)\")\n\n# Add data citation\nplt.text(\n    df[\"datetime\"].iloc[0],           # X-coordinate of the text \n    df[\"max\"].min(),                  # Y-coordinate of the text \n\n\n    # Text to be displayed\n    \"Source: NASA/NOAA OCO-2 MIP Top-Down CO₂ Budgets\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"left\",              # Horizontal alignment\n    verticalalignment=\"top\",                 # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n# Plot the time series\nplt.show()\n\n\n\n\n\n\n\n\n\n# The 2018-01-01 observation is the 3rd item in the list.\n# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n2018-01-01T00:00:00+00:00\n\n\n\n# A GET request is made for the 2018-01-01 tile\nco2_flux_3 = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nco2_flux_3\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0%2C450'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the 2018-01-01 tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=6.8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=co2_flux_3[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.7\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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-    "title": "OCO-2 MIP Top-Down CO₂ Budgets",
-    "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully explored, analyzed, and visualized the STAC collection for OCO-2 MIP Top-Down CO₂ Budgets.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nVisualizing CO₂ Emissions for two distinctive months/years\nGenerate zonal statistics for a specified region\nGenerate a time-series graph\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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-    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)",
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-    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. Collection processed in this notebook is ODIAC CO₂ emissions version 2023.\nPass the STAC item into raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint\nWe’ll visualize two tiles (side-by-side) allowing for comparison of each of the time points using folium.plugins.DualMap\nAfter the visualization, we’ll perform zonal statistics for a given polygon.",
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-    "text": "About the Data\nThe Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) is a high-spatial resolution global emission data product of CO₂ emissions from fossil fuel combustion (Oda and Maksyutov, 2011). ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO₂ emissions. With the innovative emission modeling approach, ODIAC achieved the fine picture of global fossil fuel CO₂ emissions at a 1x1km.\nFor more information regarding this dataset, please visit the ODIAC Fossil Fuel CO₂ Emissions data overview page.",
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-    "text": "Querying the STAC API\nFirst, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.\n\n# Import the following libraries\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\nimport branca\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide the STAC and RASTER API endpoints\n# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n\n# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n\n# The RASTER API is used to fetch collections for visualization\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n# Name of the collection for ODIAC dataset \ncollection_name = \"odiac-ffco2-monthgrid-v2023\"\n\n\n# Fetch the collection from the STAC API using the appropriate endpoint\n# The 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n\n# Print the properties of the collection to the console\ncollection\n\n{'id': 'odiac-ffco2-monthgrid-v2023',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023/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/odiac-ffco2-monthgrid-v2023'}],\n 'title': 'ODIAC Fossil Fuel CO₂ Emissions v2023',\n 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n  'temporal': {'interval': [['2000-01-01 00:00:00+00',\n     '2022-12-31 00:00:00+00']]}},\n 'license': 'CC-BY-4.0',\n 'renders': {'dashboard': {'assets': ['co2-emissions'],\n   'rescale': [[-10, 60]],\n   'colormap_name': 'jet'},\n  'co2-emissions': {'assets': ['co2-emissions'],\n   'rescale': [[-10, 60]],\n   'colormap_name': 'jet'}},\n 'providers': [{'url': 'https://www.nies.go.jp',\n   'name': 'National Institute for Environmental Studies',\n   'roles': ['producer', 'licensor']}],\n 'summaries': {'datetime': ['2000-01-01T00:00:00Z', '2022-12-31T00:00:00Z']},\n 'description': 'The Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) data product is a monthly high-resolution global data product of modeled fossil fuel carbon dioxide (CO₂) emissions. A complex model incorporates and combines space-based nighttime light data and individual power plant emission/location profiles from the latest country fossil fuel CO₂ estimates (2000-2019) made by the Carbon Dioxide Information Analysis Center (CDIAC) team at the Appalachian State University (CDIAC at AppState, Gilfillan et al. 2021, Hefner et al. 2022). The ODIAC estimated global spatial extent of fossil fuel CO₂ emissions is produced on a 1 km by 1 km grid that details variations in urban regions where emissions are most intense. The ODIAC CO₂ emission data is widely used by the international research community for applications such as CO₂ flux inversion, urban emission estimation, and observing system design experiments. The ODIAC product was first created in 2009 by Dr. Tomohiro Oda with support from the National Institute for Environmental Studies (NIES) GOSAT project. The ODIAC team is now supported by NASA Goddard Space Flight Center, NASA Carbon Monitoring System program, the NASA Orbiting Carbon Observatory mission and NIES. The US GHG Center displays the ODIAC 2023 version containing monthly data from January 2000 to December 2022 that replaces all previous versions. The source dataset can be found at https://doi.org/10.17595/20170411.001',\n 'item_assets': {'co2-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil Fuel CO₂ Emissions',\n   'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.'}},\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': 'month'}\n\n\nExamining the contents of our collection under summaries we see that the data is available from January 2000 to December 2022. By looking at the dashboard:time density we observe that the periodic frequency of these observations is monthly.\n\n# Create a function that would search for a data collection in the US GHG Center STAC API\n\n# First, we need to define the function\n# The name of the function = \"get_item_count\"\n# The argument that will be passed through the defined function = \"collection_id\"\ndef get_item_count(collection_id):\n\n    # Set a counter for the number of items existing in the collection\n    count = 0\n\n    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n    while True:\n\n        # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n        response = requests.get(items_url)\n\n        # If the items do not exist, print an error message and quit the loop\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        # Return the results of the HTTP response as JSON\n        stac = response.json()\n\n        # Increase the \"count\" by the number of items (granules) returned in the response\n        count += int(stac[\"context\"].get(\"returned\", 0))\n\n        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        # Exit the loop if there are no other URLs\n        if not next:\n            break\n        \n        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n        # \"href\" is the identifier for each of the tiles stored in the STAC API\n        items_url = next[0][\"href\"]\n\n    # Return the information about the total number of granules found associated with the collection\n    return count\n\n\n# Apply the function created above \"get_item_count\" to the data collection\nnumber_of_items = get_item_count(collection_name)\n\n# Get the information about the number of granules found in the collection\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n# Print the total number of items (granules) found\nprint(f\"Found {len(items)} items\")\n\nFound 276 items\n\n\nThis makes sense as there are 23 years between 2000 - 2023, with 12 months per year, meaning 276 records in total.\n\n# Examine the first item in the collection\n# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\nitems[0]\n\n{'id': 'odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212'},\n  {'title': 'Map of Item',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212/map?assets=co2-emissions&rescale=-10%2C60&colormap_name=jet',\n   'rel': 'preview',\n   'type': 'text/html'}],\n 'assets': {'co2-emissions': {'href': 's3://ghgc-data-store/odiac-ffco2-monthgrid-v2023/odiac2023_1km_excl_intl_202212.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Fossil Fuel CO₂ Emissions',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326,\n   'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n   'proj:shape': [21600, 43200],\n   'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.',\n   'raster:bands': [{'scale': 1.0,\n     'nodata': -9999.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 31415.447265625,\n      'min': -675.1028442382812,\n      'count': 11,\n      'buckets': [14870, 14, 1, 0, 0, 1, 0, 0, 0, 1]},\n     'statistics': {'mean': 38.57990192785652,\n      'stddev': 332.3921410156093,\n      'maximum': 31415.447265625,\n      'minimum': -675.1028442382812,\n      'valid_percent': 2.8394699096679688}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.008333333333333333,\n    0.0,\n    -180.0,\n    0.0,\n    -0.008333333333333333,\n    90.0,\n    0.0,\n    0.0,\n    1.0]},\n  'rendered_preview': {'title': 'Rendered preview',\n   'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212/preview.png?assets=co2-emissions&rescale=-10%2C60&colormap_name=jet',\n   'rel': 'preview',\n   'roles': ['overview'],\n   'type': 'image/png'}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'odiac-ffco2-monthgrid-v2023',\n 'properties': {'end_datetime': '2022-12-31T00:00:00+00:00',\n  'start_datetime': '2022-12-01T00:00:00+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']}",
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-    "section": "Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API",
-    "text": "Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API\nWe will explore changes in fossil fuel emissions in urban egions. In this notebook, we’ll explore the impacts of these emissions and explore these changes over time. We’ll then visualize the outputs on a map using folium.\n\n# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items} \n\n# Next, we need to specify the asset name for this collection\n# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n# For the case of the ODIAC Fossil Fuel CO₂ Emissions collection, the parameter of interest is “co2-emissions”\nasset_name = \"co2-emissions\"\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for January 2020 and again for January 2000, so that we can visualize each event independently.\n\n# Choose a color map for displaying the first observation (event)\n# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\ncolor_map = \"rainbow\" \n\n# Make a GET request to retrieve information for the 2020 tile\n# 2020\njanuary_2020_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2020-01']['collection']}/items/{items['2020-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2020_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-675.1028442382812%2C31415.447265625'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Make a GET request to retrieve information for the 2000 tile\n# 2000\njanuary_2000_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items['2000-01']['collection']}/items/{items['2000-01']['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\njanuary_2000_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-675.1028442382812%2C31415.447265625'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}",
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-    "text": "Visualizing CO₂ emissions\n\n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n\n# Set the initial zoom level and center of map for both tiles\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# Define the first map layer (January 2020)\nmap_layer_2020 = TileLayer(\n    tiles=january_2020_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n\n# Add the first layer to the Dual Map\nmap_layer_2020.add_to(map_.m1)\n\n# Define the second map layer (January 2000)\nmap_layer_2000 = TileLayer(\n    tiles=january_2000_tile[\"tiles\"][0], # Path to retrieve the tile\n    attr=\"GHG\", # Set the attribution\n    opacity=0.8, # Adjust the transparency of the layer\n)\n\n# Add the second layer to the Dual Map\nmap_layer_2000.add_to(map_.m2)\n\n# Visualize the Dual Map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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-    "title": "ODIAC Fossil Fuel CO₂ Emissions",
-    "section": "Visualizing the Data as a Time Series",
-    "text": "Visualizing the Data as a Time Series\nWe can now explore the ODIAC fossil fuel emission time series available (January 2000 -December 2022) for the Texas, Dallas area of USA. We can plot the data set using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\n\nplt.plot(\n    df[\"date\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum CO₂ level\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"Max monthly CO₂ emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"CO2 emissions gC/m2/d\")\n\n# Insert title for the plot\nplt.title(\"CO2 emission Values for Texas, Dallas (2000-2022)\")\n\n###\n# Add data citation\nplt.text(\n    df[\"date\"].iloc[0],           # X-coordinate of the text\n    df[\"max\"].min(),              # Y-coordinate of the text\n\n\n\n\n    # Text to be displayed\n    \"Source: NASA ODIAC Fossil Fuel CO₂ Emissions\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"right\",             # Horizontal alignment\n    verticalalignment=\"top\",                 # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n# Plot the time series\nplt.show()\n\n\n\n\n\n\n\n\n\n# Print the properties of the 3rd item in the collection\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n2022-10-01T00:00:00+00:00\n\n\n\n# A GET request is made for the October tile\noctober_tile = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}//collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\noctober_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-675.1028442382812%2C31415.447265625'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the October tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=october_tile[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.5\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
+    "text": "Visualizing the Data as a Time Series\nWe can now explore the fossil fuel emission time series (January 2015 -December 2020) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:\n\n# Figure size: 20 representing the width, 10 representing the height\nfig = plt.figure(figsize=(20, 10))\n\nplt.plot(\n    df[\"datetime\"], # X-axis: sorted datetime\n    df[\"max\"], # Y-axis: maximum CO₂ emission\n    color=\"red\", # Line color\n    linestyle=\"-\", # Line style\n    linewidth=0.5, # Line width\n    label=\"CO2 emissions\", # Legend label\n)\n\n# Display legend\nplt.legend()\n\n# Insert label for the X-axis\nplt.xlabel(\"Years\")\n\n# Insert label for the Y-axis\nplt.ylabel(\"CO2 emissions gC/m2/year1\")\n\n# Insert title for the plot\nplt.title(\"CO2 emission Values for Texas, Dallas (2015-2020)\")\n\n# Add data citation\nplt.text(\n    df[\"datetime\"].iloc[0],           # X-coordinate of the text \n    df[\"max\"].min(),                  # Y-coordinate of the text \n\n\n    # Text to be displayed\n    \"Source: NASA/NOAA OCO-2 MIP Top-Down CO₂ Budgets\",                  \n    fontsize=12,                             # Font size\n    horizontalalignment=\"left\",              # Horizontal alignment\n    verticalalignment=\"top\",                 # Vertical alignment\n    color=\"blue\",                            # Text color\n)\n\n# Plot the time series\nplt.show()\n\n\n\n\n\n\n\n\n\n# The 2018-01-01 observation is the 3rd item in the list.\n# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\nprint(items[2][\"properties\"][\"start_datetime\"])\n\n2018-01-01T00:00:00+00:00\n\n\n\n# A GET request is made for the 2018-01-01 tile\nco2_flux_3 = requests.get(\n\n    # Pass the collection name, the item number in the list, and its ID\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n\n    # Pass the asset name\n    f\"&assets={asset_name}\"\n\n    # Pass the color formula and colormap for custom visualization\n    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\n# Return the response in JSON format\n).json()\n\n# Print the properties of the retrieved granule to the console\nco2_flux_3\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0%2C450'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Create a new map to display the 2018-01-01 tile\naoi_map_bbox = Map(\n\n    # Base map is set to OpenStreetMap\n    tiles=\"OpenStreetMap\",\n\n    # Set the center of the map\n    location=[\n        30,-100\n    ],\n\n    # Set the zoom value\n    zoom_start=6.8,\n)\n\n# Define the map layer\nmap_layer = TileLayer(\n\n    # Path to retrieve the tile\n    tiles=co2_flux_3[\"tiles\"][0],\n\n    # Set the attribution and adjust the transparency of the layer\n    attr=\"GHG\", opacity = 0.7\n)\n\n# Add the layer to the map\nmap_layer.add_to(aoi_map_bbox)\n\n# Visualize the map\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook",
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       "Data Usage Notebooks",
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     "section": "Summary",
-    "text": "Summary\nIn this notebook we have successfully explored, analysed and visualized STAC collecetion for ODIAC C02 fossisl fuel emission (2023).\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nMap and compare the CO₂ levels for two distinctive months/years\nGenerate zonal statistics for the area of interest (AOI)\nVisualizing the Data as a Time Series\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
+    "text": "Summary\nIn this notebook we have successfully explored, analyzed, and visualized the STAC collection for OCO-2 MIP Top-Down CO₂ Budgets.\n\nInstall and import the necessary libraries\nFetch the collection from STAC collections using the appropriate endpoints\nCount the number of existing granules within the collection\nVisualizing CO₂ Emissions for two distinctive months/years\nGenerate zonal statistics for a specified region\nGenerate a time-series graph\n\nIf you have any questions regarding this user notebook, please contact us using the feedback form.",
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+    "title": "CASA-GFED3 Land Carbon Flux",
     "section": "",
-    "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport rasterio\nfrom glob import glob\nimport pathlib\nimport boto3\nimport pandas as pd\nimport calendar\nimport seaborn as sns\nimport json\nimport re\n\n\n# Enter the year you want to run validation on\nvyear=2022 # summary json files will be later generated for the year you provide here\ndata_dir=\"data/\" # make sure you have the data for vyear in your data directory\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\n\ndataset_name= \"odiac-ffco2-monthgrid-v2023\"\ncog_data_bucket=\"ghgc-data-store-develop\"\ncog_data_prefix = f\"transformed_cogs/{dataset_name}\"\n\n\ndef get_all_s3_keys(bucket, model_name, ext):\n    \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n    keys = []\n\n    kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n    while True:\n        resp = s3_client.list_objects_v2(**kwargs)\n        for obj in resp[\"Contents\"]:\n            if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n                keys.append(obj[\"Key\"])\n\n        try:\n            kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n        except KeyError:\n            break\n\n    return keys\n\nkeys = get_all_s3_keys(cog_data_bucket, cog_data_prefix, \".tif\")\n\n# Extract only the COGs for selected year\npattern = re.compile(rf'{vyear}(0[1-9]|1[0-2])')\nkeys = [path for path in keys if pattern.search(path)]\n\n\n# Initialize the summary variables\nsummary_dict_netcdf, summary_dict_cog = {}, {}\noverall_stats_netcdf, overall_stats_cog = {}, {}\nfull_data_df_netcdf, full_data_df_cog = pd.DataFrame(), pd.DataFrame()\n\n\n# Process the COGs to get the statistics\nfor key in keys:\n    url=f\"s3://{cog_data_bucket}/{key}\"\n    with rasterio.open(url) as src:\n        filename_elements = re.split(\"[_ ? . ]\", url)\n        for band in src.indexes:\n            print(\"_\".join(filename_elements[1:6]))\n            idx = pd.MultiIndex.from_product(\n                    [\n                        [\"_\".join(filename_elements[1:6])],\n                        [filename_elements[5]],\n                        [x for x in np.arange(1, src.height + 1)],\n                    ]\n                )\n            raster_data = src.read(band)\n            raster_data[raster_data == -9999] = 0 # because we did that in the transformation script\n            temp = pd.DataFrame(index=idx, data=raster_data)\n            full_data_df_cog = full_data_df_cog._append(temp, ignore_index=False)\n\n            # Calculate summary statistics\n            min_value = np.float64(temp.values.min())\n            max_value = np.float64(temp.values.max())\n            mean_value = np.float64(temp.values.mean())\n            std_value = np.float64(temp.values.std())\n\n            summary_dict_cog[\n                    f'{\"_\".join(filename_elements[1:5])}_{filename_elements[5][:4]}_{calendar.month_name[int(filename_elements[5][4:])]}'\n                ] = {\n                    \"min_value\": min_value,\n                    \"max_value\": max_value,\n                    \"mean_value\": mean_value,\n                    \"std_value\": std_value,\n                }\n\n\n# Process the raw files for selected year to get the statistics \ntif_files = glob(f\"{data_dir}{vyear}/*.tif\", recursive=True)\nfor tif_file in tif_files:\n    file_name = pathlib.Path(tif_file).name[:-4]\n    print(file_name)\n    with rasterio.open(tif_file) as src:\n        for band in src.indexes:\n            idx = pd.MultiIndex.from_product(\n                [\n                    [pathlib.Path(tif_file).name[:-9]],\n                    [pathlib.Path(tif_file).name[-8:-4]],\n                    [x for x in np.arange(1, src.height + 1)],\n                ]\n            )\n            # Read the raster data\n            raster_data = src.read(band)\n            #raster_data[raster_data == -9999] = np.nan\n            temp = pd.DataFrame(index=idx, data=raster_data)\n            full_data_df_netcdf = full_data_df_netcdf._append(temp, ignore_index=False)\n\n            # Calculate summary statistics\n            min_value = np.float64(temp.values.min())\n            max_value = np.float64(temp.values.max())\n            mean_value = np.float64(temp.values.mean())\n            std_value = np.float64(temp.values.std())\n\n            summary_dict_netcdf[\n                f'{tif_file.split(\"/\")[-1][:-9]}_{calendar.month_name[int(tif_file.split(\"/\")[-1][-6:-4])]}'\n            ] = {\n                \"min_value\": min_value,\n                \"max_value\": max_value,\n                \"mean_value\": mean_value,\n                \"std_value\": std_value,\n            }\n            \n\n\n# Merge monthly stats for COGs and raw files in a csv file \ncog_df = pd.DataFrame(summary_dict_cog).T.reset_index()\nraw_df = pd.DataFrame(summary_dict_netcdf).T.reset_index()\ncog_df['date']= cog_df[\"index\"].apply(lambda x: (x.split(\"_\")[-1]+x.split(\"_\")[-2]) )\nraw_df['date']= raw_df[\"index\"].apply(lambda x: (x.split(\"_\")[-1]+str(vyear)) )\ncheck_df=pd.merge(cog_df, raw_df[[\"min_value\",\"max_value\",\"mean_value\",\"std_value\",\"date\"]], how='inner', on='date',suffixes=('', '_raw'))\ncheck_df.to_csv(f\"monthly_stats_{vyear}.csv\")\n\n\n# Calculate the overall data stat for that year\noverall_stats_netcdf[\"min_value\"] = np.float64(full_data_df_netcdf.values.min())\noverall_stats_netcdf[\"max_value\"] = np.float64(full_data_df_netcdf.values.max())\noverall_stats_netcdf[\"mean_value\"] = np.float64(full_data_df_netcdf.values.mean())\noverall_stats_netcdf[\"std_value\"] = np.float64(full_data_df_netcdf.values.std())\n\noverall_stats_cog[\"min_value\"] = np.float64(full_data_df_cog.values.min())\noverall_stats_cog[\"max_value\"] = np.float64(full_data_df_cog.values.max())\noverall_stats_cog[\"mean_value\"] = np.float64(full_data_df_cog.values.mean())\noverall_stats_cog[\"std_value\"] = np.float64(full_data_df_cog.values.std())\n\n\n\ndata = {\n    \"Stats for raw netCDF files.\": summary_dict_netcdf,\n    \"Stats for transformed COG files.\": summary_dict_cog\n}\n\n# Writing to JSON file\nwith open(f\"monthly_stats_{vyear}.json\", \"w\") as fp:\n    json.dump(data, fp, indent=4) \n\ndata = {\n    \"Stats for raw netCDF files.\": overall_stats_netcdf,\n    \"Stats for transformed COG files.\": overall_stats_cog\n}\n\n# Writing to JSON file\nwith open(f\"overall_stats_{vyear}.json\", \"w\") as fp:\n    json.dump(data, fp, indent=4) \n\n\n\n\n Back to top"
+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
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-    "text": "Welcome to the U.S. Greenhouse Gas (GHG) Center data usage notebooks, your gateway to exploring and analyzing curated datasets on greenhouse gas emissions. Our cloud-based system offers seamless access to GHG curated datasets. Dive into the data with our data usage Jupyter notebooks, which demonstrate how to explore, access, visualize, and conduct basic data analysis for each GHG Center dataset in a code notebook environment. The data usage notebooks are grouped topically. Click on a notebook to learn more about the dataset and to view the data usage code.\nJoin us in our mission to make data-driven environmental solutions. Explore, analyze, and make a difference with the US GHG Center.\nView the US GHG Center Data Catalog",
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+    "text": "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\nLaunch in the US GHG Center JupyterHub (requires access)"
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-    "section": "Gridded Anthropogenic Greenhouse Gas Emissions",
-    "text": "Gridded Anthropogenic Greenhouse Gas Emissions\n\nOCO-2 MIP Top-Down CO₂ Budgets\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the OCO-2 MIP Top-Down CO₂ Budgets dataset.\nIntermediate level notebook to read and visualize National CO₂ Budgets using OCO-2 MIP Top-Down CO₂ Budget country total data. This notebook utilizes the country totals available at https://ceos.org/gst/carbon-dioxide.html, which compliment the global 1° x 1° gridded CO₂ Budget data featured in the US GHG Center.\n\nODIAC Fossil Fuel CO₂ Emissions\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the ODIAC Fossil Fuel CO₂ Emissions dataset.\n\nTM5-4DVar Isotopic CH₄ Inverse Fluxes\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n\nU.S. Gridded Anthropogenic Methane Emissions Inventory\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset.\n\nVulcan Fossil Fuel CO₂ Emissions\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the Vulcan Fossil Fuel CO₂ Emissions, Version 4 dataset.\n\nGRA²PES Greenhouse Gas and Air Quality Species\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the GRA2PES, Version 1 dataset.",
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+    "title": "CASA-GFED3 Land Carbon Flux",
+    "section": "Approach",
+    "text": "Approach\n\nIdentify available dates and temporal frequency of observations for a given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the Land-Atmosphere Carbon Flux data product.\nPass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.json endpoint.\nUsing folium.plugins.DualMap, visualize two tiles (side-by-side), allowing time point comparison.\nAfter the visualization, perform zonal statistics for a given polygon."
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-    "section": "Natural Greenhouse Gas Emissions and Sinks",
-    "text": "Natural Greenhouse Gas Emissions and Sinks\n\nAir-Sea CO₂ Flux, ECCO-Darwin Model v5\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the Air-Sea CO₂ Flux, ECCO-Darwin Model v5 dataset.\n\nMiCASA Land Carbon Flux\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the MiCASA Land Carbon Flux dataset.\n\nGOSAT-based Top-down Total and Natural Methane Emissions\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the GOSAT-based Top-down Total and Natural Methane Emissions dataset.\n\nOCO-2 MIP Top-Down CO₂ Budgets\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the OCO-2 MIP Top-Down CO₂ Budgets dataset.\nIntermediate level notebook to read and visualizeNational CO₂ Budgets using OCO-2 MIP Top-Down CO₂ Budget country total data. This notebook utilizes the country totals available at ceos.org/gst/carbon-dioxide, which compliment the global 1° x 1° gridded CO₂ Budget data featured in the US GHG Center.\n\nTM5-4DVar Isotopic CH₄ Inverse Fluxes\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n\nWetland Methane Emissions, LPJ-EOSIM model\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the Wetland Methane Emissions, LPJ-EOSIM model dataset.",
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+    "title": "CASA-GFED3 Land Carbon Flux",
+    "section": "About the Data",
+    "text": "About the Data\nThis dataset presents a variety of carbon flux parameters derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA-GFED3) model. The model’s input data includes air temperature, precipitation, incident solar radiation, a soil classification map, and a number of satellite derived products. All model calculations are driven by analyzed meteorological data from NASA’s Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting product provides monthly, global data at 0.5 degree resolution from January 2003 through December 2017. It includes the following carbon flux variables expressed in units of kilograms of carbon per square meter per month (kg Carbon m²/mon) from the following sources: net primary production (NPP), net ecosystem exchange (NEE), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL). This product and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in a number of atmospheric CO₂ transport studies, and through the support of NASA’s Carbon Monitoring System (CMS), it helps characterize, quantify, understand and predict the evolution of global carbon sources and sinks."
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-    "section": "Large Emissions Events",
-    "text": "Large Emissions Events\n\nEMIT Methane Point Source Plume Complexes\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the EMIT Methane Point Source Plume Complexes dataset.",
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+    "title": "CASA-GFED3 Land Carbon Flux",
+    "section": "Querying the STAC API",
+    "text": "Querying the STAC API\nPlease run the next cell to import the required libraries.\n\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer \nfrom pystac_client import Client \nimport branca \nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n/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\n\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\nRASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n\n# Please use the collection name similar to the one used in the STAC collection.\n# Name of the collection for CASA GFED Land-Atmosphere Carbon Flux monthly emissions. \ncollection_name = \"casagfed-carbonflux-monthgrid-v3\"\n\n\n# Fetch the collection from STAC collections using the appropriate endpoint\n# the 'requests' library allows a HTTP request possible\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\ncollection\n\n{'id': 'casagfed-carbonflux-monthgrid-v3',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3/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/casagfed-carbonflux-monthgrid-v3'}],\n 'title': 'CASA-GFED3 Land Carbon Flux v3',\n 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n  'temporal': {'interval': [['2003-01-01T00:00:00+00:00',\n     '2017-12-31T00:00:00+00:00']]}},\n 'license': 'CC0-1.0',\n 'renders': {'rh': {'assets': ['rh'],\n   'rescale': [[0, 0.3]],\n   'colormap_name': 'purd'},\n  'nee': {'assets': ['nee'],\n   'rescale': [[-0.1, 0.1]],\n   'colormap_name': 'coolwarm'},\n  'npp': {'assets': ['npp'], 'rescale': [[0, 0.3]], 'colormap_name': 'purd'},\n  'fire': {'assets': ['fire'], 'rescale': [[0, 0.3]], 'colormap_name': 'purd'},\n  'fuel': {'assets': ['fuel'],\n   'rescale': [[0, 0.03]],\n   'colormap_name': 'bupu'}},\n 'summaries': {'datetime': ['2003-01-01T00:00:00Z', '2017-12-31T00:00:00Z']},\n 'description': \"This dataset presents a variety of carbon flux parameters derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA-GFED3) model. All model calculations are driven by analyzed meteorological data from NASA's Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting model output provides monthly, global data at 0.5 degree resolution from January 2003 through December 2017. It includes the following carbon flux variables expressed in units of kilograms of carbon per square meter per month (kg Carbon/m2/mon): net primary production (NPP), net ecosystem exchange (NEE), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL). This product and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in a number of atmospheric carbon dioxide (CO₂) transport studies, and through the support of NASA's Carbon Monitoring System (CMS), it helps characterize, quantify, understand and predict the evolution of global carbon sources and sinks. The source dataset can be found at https://doi.org/10.5067/03147VMJE8J9. As of April 2024, this dataset has been replaced by an updated version in the US GHG Center titled MiCASA Land Carbon Flux v1 (STAC ids: micasa-carbonflux-daygrid-v1 and micasa-carbonflux-monthgrid-v1).\",\n 'item_assets': {'rh': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Heterotrophic Respiration (Rh)',\n   'description': 'Model-estimated heterotrophic respiration (Rh), which is the flux of carbon from the soil to the atmosphere.'},\n  'nee': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Net Ecosystem Exchange (NEE)',\n   'description': 'Model-estimated net ecosystem exchange (NEE), which is the net carbon flux to the atmosphere.'},\n  'npp': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Net Primary Production (NPP)',\n   'description': 'Model-estimated net primary production (NPP), which is the amount of carbon available from plants.'},\n  'fire': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Fire Emissions (FIRE)',\n   'description': 'Model-estimated flux of carbon to the atmosphere from wildfires.'},\n  'fuel': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n   'roles': ['data', 'layer'],\n   'title': 'Wood Fuel Emissions (FUEL)',\n   'description': 'Model-estimated flux of carbon to the atmosphere from wood burned for fuel.'}},\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': 'month'}\n\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2003 to December 2017. By looking at the dashboard:time density, we observe that the periodic frequency of these observations is monthly.\n\n# Create a function that would search for the above data collection in the STAC API\ndef get_item_count(collection_id):\n    count = 0\n    items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n    while True:\n        response = requests.get(items_url)\n\n        if not response.ok:\n            print(\"error getting items\")\n            exit()\n\n        stac = response.json()\n        count += int(stac[\"context\"].get(\"returned\", 0))\n        next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n        if not next:\n            break\n        items_url = next[0][\"href\"]\n\n    return count\n\n\n# Apply the above function and check the total number of items available within the collection\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\nFound 180 items\n\n\n\n# Examine the first item in the collection\nitems[0]\n\n{'id': 'casagfed-carbonflux-monthgrid-v3-201712',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},\n  {'rel': 'parent',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},\n  {'rel': 'root',\n   'type': 'application/json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/'},\n  {'rel': 'self',\n   'type': 'application/geo+json',\n   'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712'}],\n 'assets': {'rh': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_Rh_Flux_Monthly_x720_y360_201712.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Heterotrophic Respiration (Rh)',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [360.0, 720.0],\n   'description': 'Model-estimated heterotrophic respiration (Rh), which is the flux of carbon from the soil to the atmosphere.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.6039900183677673,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [249101.0,\n       7375.0,\n       2429.0,\n       252.0,\n       32.0,\n       5.0,\n       2.0,\n       2.0,\n       0.0,\n       2.0]},\n     'statistics': {'mean': 0.006758838426321745,\n      'stddev': 0.022668374702334404,\n      'maximum': 0.6039900183677673,\n      'minimum': 0.0,\n      'valid_percent': 0.0003858024691358025}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'nee': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NEE_Flux_Monthly_x720_y360_201712.tif',\n   'type': 'image/tiff; application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Net Ecosystem Exchange (NEE)',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [360.0, 720.0],\n   'description': 'Model-estimated net ecosystem exchange (NEE), which is the net carbon flux to the atmosphere.',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.48997998237609863,\n      'min': -0.11027999967336655,\n      'count': 11.0,\n      'buckets': [663.0,\n       234393.0,\n       23809.0,\n       282.0,\n       37.0,\n       10.0,\n       4.0,\n       0.0,\n       0.0,\n       2.0]},\n     'statistics': {'mean': 0.0015448036137968302,\n      'stddev': 0.00977976992726326,\n      'maximum': 0.48997998237609863,\n      'minimum': -0.11027999967336655,\n      'valid_percent': 0.0003858024691358025}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]},\n  'npp': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NPP_Flux_Monthly_x720_y360_201712.tif',\n   'type': 'image/tiff; 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application=geotiff',\n   'roles': ['data', 'layer'],\n   'title': 'Wood Fuel Emissions (FUEL)',\n   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n   'proj:epsg': 4326.0,\n   'proj:shape': [360.0, 720.0],\n   'description': 'Fuel emissions',\n   'raster:bands': [{'scale': 1.0,\n     'offset': 0.0,\n     'sampling': 'area',\n     'data_type': 'float32',\n     'histogram': {'max': 0.020759999752044678,\n      'min': 0.0,\n      'count': 11.0,\n      'buckets': [257568.0,\n       1150.0,\n       284.0,\n       115.0,\n       47.0,\n       21.0,\n       5.0,\n       6.0,\n       3.0,\n       1.0]},\n     'statistics': {'mean': 5.057307134848088e-05,\n      'stddev': 0.0003876804548781365,\n      'maximum': 0.020759999752044678,\n      'minimum': 0.0,\n      'valid_percent': 0.0003858024691358025}}],\n   'proj:geometry': {'type': 'Polygon',\n    'coordinates': [[[-180.0, -90.0],\n      [180.0, -90.0],\n      [180.0, 90.0],\n      [-180.0, 90.0],\n      [-180.0, -90.0]]]},\n   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n    'name': 'WGS 84',\n    'type': 'GeographicCRS',\n    'datum': {'name': 'World Geodetic System 1984',\n     'type': 'GeodeticReferenceFrame',\n     'ellipsoid': {'name': 'WGS 84',\n      'semi_major_axis': 6378137.0,\n      'inverse_flattening': 298.257223563}},\n    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n       'unit': 'degree',\n       'direction': 'north',\n       'abbreviation': 'Lat'},\n      {'name': 'Geodetic longitude',\n       'unit': 'degree',\n       'direction': 'east',\n       'abbreviation': 'Lon'}],\n     'subtype': 'ellipsoidal'}},\n   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]}},\n 'geometry': {'type': 'Polygon',\n  'coordinates': [[[-180, -90],\n    [180, -90],\n    [180, 90],\n    [-180, 90],\n    [-180, -90]]]},\n 'collection': 'casagfed-carbonflux-monthgrid-v3',\n 'properties': {'end_datetime': '2017-12-31T00:00:00+00:00',\n  'start_datetime': '2017-12-01T00:00:00+00:00'},\n 'stac_version': '1.0.0',\n 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n  'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}"
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-    "section": "Greenhouse Gas Concentrations",
-    "text": "Greenhouse Gas Concentrations\n\nAtmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory dataset.\n\nOCO-2 GEOS Column CO₂ Concentrations\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the OCO-2 GEOS Column CO₂ Concentrations dataset.\n\nCarbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)\n\nBeginner level notebook\n\nCarbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project\n\nBeginner level notebook\n\nCarbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed\n\nBeginner level notebook\n\nCarbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)\n\nBeginner level notebook\n\nCarbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project\n\nBeginner level notebook\n\nCarbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed\n\nBeginner level notebook",
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+    "title": "CASA-GFED3 Land Carbon Flux",
+    "section": "Exploring Changes in Carbon Flux Levels Using the Raster API",
+    "text": "Exploring Changes in Carbon Flux Levels Using the Raster API\nWe will explore changes in the land atmosphere Carbon flux Heterotrophic Respiration and examine their impacts over time. We’ll then visualize the outputs on a map using folium.\n\n# To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items} \n# rh = Heterotrophic Respiration\nasset_name = \"rh\"\n\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.\n\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for December 2003 and again for December 2017, so that we can visualize each event independently.\n\ncolor_map = \"purd\" # please refer to matplotlib library if you'd prefer choosing a different color ramp.\n# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n\n# 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']\" \ndecember_2003_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items['2003-12']['collection']}/items/{items['2003-12']['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\ndecember_2003_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Now we apply the same process used in the previous step for the December 2017 tile\ndecember_2017_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items['2017-12']['collection']}/items/{items['2017-12']['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\ndecember_2017_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}"
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-    "section": "Socioeconomic",
-    "text": "Socioeconomic\n\nSEDAC Gridded World Population Density\n\nBeginner level notebook to access, visualize, explore statistics, and create a time series of the SEDAC Gridded World Population Density dataset.",
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-      "Data Usage Notebooks"
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+    "href": "user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.html#visualizing-land-atmosphere-carbon-flux-heterotrophic-respiration",
+    "title": "CASA-GFED3 Land Carbon Flux",
+    "section": "Visualizing Land-Atmosphere Carbon Flux (Heterotrophic Respiration)",
+    "text": "Visualizing Land-Atmosphere Carbon Flux (Heterotrophic Respiration)\n\n# For this study we are going to compare the RH level in 2003 and 2017 over the State of Texas \n# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n# For example, you can change the current statement \"location=(31.9, -99.9)\" to \"location=(34, -118)\" to monitor the RH level in California instead of Texas\n\n# Set initial zoom and center of map for CO₂ Layer\n# 'folium.plugins' allows mapping side-by-side\nmap_ = folium.plugins.DualMap(location=(31.9, -99.9), zoom_start=6)\n\n# The TileLayer library helps in manipulating and displaying raster layers on a map\n# December 2003\nmap_layer_2003 = TileLayer(\n    tiles=december_2003_tile[\"tiles\"][0],\n    attr=\"GHG\",\n    opacity=0.8,\n    name=\"December 2003 RH Level\",\n    overlay= True,\n    legendEnabled = True\n)\nmap_layer_2003.add_to(map_.m1)\n\n\n# December 2017\nmap_layer_2017 = TileLayer(\n    tiles=december_2017_tile[\"tiles\"][0],\n    attr=\"GHG\",\n    opacity=0.8,\n    name=\"December 2017 RH Level\",\n    overlay= True,\n    legendEnabled = True\n)\nmap_layer_2017.add_to(map_.m2)\n\n\n# Display data markers (titles) on both maps\nfolium.Marker((40, 5.0), tooltip=\"both\").add_to(map_)\nfolium.LayerControl(collapsed=False).add_to(map_)\n\n\n# Add a legend to the dual map using the 'branca' library. \n# Note: the inserted legend is representing the minimum and maximum values for both tiles.\ncolormap = branca.colormap.linear.PuRd_09.scale(0, 0.3) # minimum value = 0, maximum value = 0.3 (kg Carbon/m2/month)\ncolormap = colormap.to_step(index=[0, 0.07, 0.15, 0.22, 0.3])\ncolormap.caption = 'Rh Values (kg Carbon/m2/month)'\n\ncolormap.add_to(map_.m1)\n\n\n# Visualizing the map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
   },
   {
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-    "href": "datausage.html#contact",
-    "title": "U.S. Greenhouse Gas Center: Data Usage Notebooks",
-    "section": "Contact",
-    "text": "Contact\nFor technical help or general questions, please contact the support team using the feedback form.",
+    "objectID": "user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.html#visualizing-the-data-as-a-time-series",
+    "href": "user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.html#visualizing-the-data-as-a-time-series",
+    "title": "CASA-GFED3 Land Carbon Flux",
+    "section": "Visualizing the Data as a Time Series",
+    "text": "Visualizing the Data as a Time Series\nWe can now explore the Heterotrophic Respiration time series (January 2003 -December 2017) available for the Dallas, Texas area. We can plot the data set using the code below:\n\nfig = plt.figure(figsize=(20, 10)) #determine the width and height of the plot using the 'matplotlib' library\n\nplt.plot(\n    df[\"date\"],\n    df[\"max\"],\n    color=\"purple\",\n    linestyle=\"-\",\n    linewidth=0.5,\n    label=\"Max monthly Carbon emissions\",\n)\n\nplt.legend()\nplt.xlabel(\"Years\")\nplt.ylabel(\"kg Carbon/m2/month\")\nplt.title(\"Heterotrophic Respiration Values for Dallas, Texas (2003-2017)\")\n\nText(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (2003-2017)')\n\n\n\n\n\n\n\n\n\n\n# Now let's examine the Rh level for the 3rd item in the collection for Dallas, Texas area\n# Keep in mind that a list starts from 0, 1, 2,... therefore items[2] is referring to the third item in the list/collection\nprint(items[2][\"properties\"][\"start_datetime\"]) #print the start Date Time of the third granule in the collection!\n\n2017-10-01T00:00:00+00:00\n\n\n\n# Fetch the third granule in the collection and set the color scheme and rescale values. \noctober_tile = requests.get(\n    f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n    f\"&assets={asset_name}\"\n    f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n    f\"&rescale={rescale_values['min']},{rescale_values['max']}\",\n).json()\noctober_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['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&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\n# Map the Rh level for the Dallas, Texas area for the October, 2017 timeframe\naoi_map_bbox = Map(\n    tiles=\"OpenStreetMap\",\n    location=[\n        32.8, # latitude\n        -96.79, # longitude\n    ],\n    zoom_start=9,\n)\n\nmap_layer = TileLayer(\n    tiles=october_tile[\"tiles\"][0],\n    attr=\"GHG\", opacity = 0.7, name=\"October 2017 RH Level\", overlay= True, legendEnabled = True\n)\n\nmap_layer.add_to(aoi_map_bbox)\n\n# Display data marker (title) on the map\nfolium.Marker((40, 5.9), tooltip=\"both\").add_to(aoi_map_bbox)\nfolium.LayerControl(collapsed=False).add_to(aoi_map_bbox)\n\n# Add a legend\ncolormap = branca.colormap.linear.PuRd_09.scale(0, 0.3) # minimum value = 0, maximum value = 0.3 (kg Carbon/m2/month)\ncolormap = colormap.to_step(index=[0, 0.07, 0.15, 0.22, 0.3])\ncolormap.caption = 'Rh Values (kg Carbon/m2/month)'\n\ncolormap.add_to(aoi_map_bbox)\n\naoi_map_bbox\n\nMake this Notebook Trusted to load map: File -> Trust Notebook"
+  },
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+    "href": "user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.html#summary",
+    "title": "CASA-GFED3 Land Carbon Flux",
+    "section": "Summary",
+    "text": "Summary\nIn this notebook we have successfully completed the following steps for the STAC collection for CASA GFED Land-Atmosphere Carbon Flux data: 1. Install and import the necessary libraries 2. Fetch the collection from STAC collections using the appropriate endpoints 3. Count the number of existing granules within the collection 4. Map and compare the Heterotrophic Respiration (Rh) levels over the Dallas, Texas area for two distinctive years 5. Create a table that displays the minimum, maximum, and sum of the Rh values for a specified region 6. Generate a time-series graph of the Rh values for a specified region\nIf you have any questions regarding this user notebook, please contact us using the feedback form."
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+    "section": "",
+    "text": "This script was used to read the EMIT Methane Point Source Plume Complexes dataset provided in Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\n\n\nsession_ghgc = boto3.session.Session(profile_name=\"ghg_user\")\ns3_client_ghgc = session_ghgc.client(\"s3\")\nsession_veda_smce = boto3.session.Session()\ns3_client_veda_smce = session_veda_smce.client(\"s3\")\n\n# Since the plume emissions were already COGs, we just had to transform their naming convention to be stored in the STAC collection.\nSOURCE_BUCKET_NAME = \"ghgc-data-staging-uah\"\nTARGET_BUCKET_NAME = \"ghgc-data-store-dev\"\n\n\nkeys = []\nresp = s3_client_ghgc.list_objects_v2(Bucket=SOURCE_BUCKET_NAME)\nfor obj in resp[\"Contents\"]:\n    if \"l3\" in obj[\"Key\"]:\n        keys.append(obj[\"Key\"])\n\nfor key in keys:\n    s3_obj = s3_client_ghgc.get_object(Bucket=SOURCE_BUCKET_NAME, Key=key)[\n        \"Body\"\n    ]\n    filename = key.split(\"/\")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n\n    date = re.search(\"t\\d\\d\\d\\d\\d\\d\\d\\dt\", key).group(0)\n    filename_elements.insert(-1, date[1:-1])\n    filename_elements.pop()\n\n    cog_filename = \"_\".join(filename_elements)\n    # # add extension\n    cog_filename = f\"{cog_filename}.tif\"\n    s3_client_veda_smce.upload_fileobj(\n        Fileobj=s3_obj,\n        Bucket=TARGET_BUCKET_NAME,\n        Key=f\"plum_data/{cog_filename}\",\n    )\n\n\n\n\n Back to top",
     "crumbs": [
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+      "Large Emissions Events",
+      "EMIT Methane Point Source Plume Complexes"
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+    "href": "cog_transformation/tm54dvar-ch4flux-monthgrid-v1.html",
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     "section": "",
-    "text": "Please note: while some of our services are already very mature, the US GHG Center platform is currently in the beta phase and will undergo many changes in coming months.",
+    "text": "This script was used to transform the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nfrom datetime import datetime\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"tm5-ch4-inverse-flux\"\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(FOLDER_NAME):\n    xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n    xds = xds.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})\n    xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    variable = [var for var in xds.data_vars if \"global\" not in var]\n\n    for time_increment in range(0, len(xds.months)):\n        filename = name.split(\"/ \")[-1]\n        filename_elements = re.split(\"[_ .]\", filename)\n        start_time = datetime(int(filename_elements[-2]), time_increment + 1, 1)\n        for var in variable:\n            data = getattr(xds.isel(months=time_increment), var)\n            data = data.isel(lat=slice(None, None, -1))\n            data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = start_time.strftime(\"%Y%m\")\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{FOLDER_NAME}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{FOLDER_NAME}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{FOLDER_NAME}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
     "crumbs": [
-      "User Services",
-      "APIs"
+      "Data Transformation Notebooks",
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "TM5-4DVar Isotopic CH₄ Inverse Fluxes"
     ]
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   {
-    "objectID": "services/apis.html#open-source",
-    "href": "services/apis.html#open-source",
-    "title": "APIs",
-    "section": "Open Source",
-    "text": "Open Source\nMost of the US GHG Center APIs are hosted out of a single project (veda-backend) that combines multiple standalone services.",
+    "objectID": "cog_transformation/noaa-gggrn-concentrations.html",
+    "href": "cog_transformation/noaa-gggrn-concentrations.html",
+    "title": "Atmospheric Carbon Dioxide and Methane Concentrations from NOAA Global Monitoring Laboratory",
+    "section": "",
+    "text": "This script was used to transform the CO₂ and CH₄ datasets in txt format with hourly granularity to JSON in daily and monthly granularity for visualization in the Greenhouse Gas (GHG) Center.\n\nimport sys\nimport json\nimport pandas as pd\n\n\ndef daily_aggregate(filepath):\n    \"\"\"\n    Reads hourly data from a .txt file, aggregates it to daily, and returns a list of JSON objects that can be readily visualized in chart.\n\n    Parameters:\n        filepath (str): The path to the file containing the data to be aggregated.\n\n    Returns:\n        list: A list of dictionaries representing aggregated data, with each dictionary containing\n              'date' and 'value' keys.\n\n    Description:\n        This function reads data from the specified file, aggregates it, and returns a list of JSON objects.\n        The function performs the following steps:\n        - Reads the content of the file.\n        - Extracts the header lines from the file to determine the structure of the data.\n        - Processes the data into a DataFrame.\n        - Filters and aggregates the data.\n        - Converts the aggregated data into a list of JSON objects, where each object contains 'date' and 'value' keys.\n\n    Exceptions:\n        - FileNotFoundError: If the specified file is not found.\n        - Exception: If any other exception occurs during the processing, the exception message is returned.\n\n    Note:\n        - The input file is expected to have a .txt format with header lines indicating the structure of the data.\n        - The function aggregates data from hourly to daily intervals.\n        - The returned JSON list is suitable for use in frontend applications to visualize the aggregated data.\n\n    Example:\n        aggregated_data = daily_aggregate(\"/path/to/data_file.txt\")\n    \"\"\"\n    try:\n        with open(filepath, \"r\", encoding=\"utf-8\") as file:\n            file_content_str = file.read()\n            # split the string text based on new line\n            file_content_list = file_content_str.split(\"\\n\")\n            # get the header lines. its mentioned in the file's first line.\n            header_lines = file_content_list[0].split(\":\")[-1]\n            header_lines = int(header_lines)\n            # Slice the non header part of the data. and the last empty element\n            str_datas = file_content_list[header_lines - 1: -1]\n            data = [data.replace(\"\\n\", \"\").split(\" \") for data in str_datas]\n            # seperate table body and head to form dataframe\n            table_head = data[0]\n            table_body = data[1:]\n            dataframe = pd.DataFrame(table_body, columns=table_head)\n            dataframe['value'] = dataframe['value'].astype(float)\n            # Filter data\n            mask = (dataframe[\"qcflag\"] == \"...\") & (dataframe[\"value\"] != 0) & (dataframe[\"value\"] != -999)\n            filtered_df = dataframe[mask].reset_index(drop=True)\n            # Aggregate data (hourly into daily)\n            aggregated_df = filtered_df.groupby(['year', 'month', 'day'])['value'].mean().reset_index()\n            aggregated_df['value'] = aggregated_df['value'].round(2)\n            # necessary columns, processed df\n            aggregated_df['datetime'] = pd.to_datetime(aggregated_df[['year', 'month', 'day']])\n            aggregated_df['datetime'] = aggregated_df['datetime'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n            processed_df = aggregated_df[['datetime', 'value']]\n            processed_df = processed_df.sort_values(by='datetime')\n            # dict formation, needed for frontend [{date: , value: }]\n            json_list = []\n            for _, row in processed_df.iterrows():\n                json_obj = {'date': row['datetime'], 'value': row['value']}\n                json_list.append(json_obj)\n            return json_list\n    except FileNotFoundError:\n        return \"File not found\"\n    except Exception as e:\n        return f\"Exception occured {e}\"\n\n\ndef monthly_aggregate(filepath):\n    \"\"\"\n    Reads hourly data from a .txt file, aggregates it to monthly, and returns a list of JSON objects that can be readily visualized in chart.\n\n    Parameters:\n        filepath (str): The path to the file containing the data to be aggregated.\n\n    Returns:\n        list: A list of dictionaries representing aggregated data, with each dictionary containing\n              'date' and 'value' keys.\n\n    Description:\n        This function reads data from the specified file, aggregates it, and returns a list of JSON objects.\n        The function performs the following steps:\n        - Reads the content of the file.\n        - Extracts the header lines from the file to determine the structure of the data.\n        - Processes the data into a DataFrame.\n        - Filters and aggregates the data.\n        - Converts the aggregated data into a list of JSON objects, where each object contains 'date' and 'value' keys.\n\n    Exceptions:\n        - FileNotFoundError: If the specified file is not found.\n        - Exception: If any other exception occurs during the processing, the exception message is returned.\n\n    Note:\n        - The input file is expected to have a .txt format with header lines indicating the structure of the data.\n        - The function aggregates data from hourly to daily intervals.\n        - The returned JSON list is suitable for use in frontend applications to visualize the aggregated data.\n\n    Example:\n        aggregated_data = monthly_aggregate(\"/path/to/data_file.txt\")\n    \"\"\"\n    try:\n        with open(filepath, \"r\", encoding=\"utf-8\") as file:\n            file_content_str = file.read()\n            # split the string text based on new line\n            file_content_list = file_content_str.split(\"\\n\")\n            # get the header lines. its mentioned in the file's first line.\n            header_lines = file_content_list[0].split(\":\")[-1]\n            header_lines = int(header_lines)\n            # Slice the non header part of the data. and the last empty element\n            str_datas = file_content_list[header_lines - 1: -1]\n            data = [data.replace(\"\\n\", \"\").split(\" \") for data in str_datas]\n            # seperate table body and head to form dataframe\n            table_head = data[0]\n            table_body = data[1:]\n            dataframe = pd.DataFrame(table_body, columns=table_head)\n            dataframe['value'] = dataframe['value'].astype(float)\n            # Filter data\n            mask = (dataframe[\"qcflag\"] == \"...\") & (dataframe[\"value\"] != 0) & (dataframe[\"value\"] != -999)\n            filtered_df = dataframe[mask].reset_index(drop=True)\n            # Aggregate data (hourly into monthly)\n            aggregated_df = filtered_df.groupby(['year', 'month'])['value'].mean().reset_index()\n            aggregated_df['value'] = aggregated_df['value'].round(2)\n            # necessary columns, processed df\n            aggregated_df['datetime'] = pd.to_datetime(aggregated_df[['year', 'month']].assign(day=1))\n            aggregated_df['datetime'] = aggregated_df['datetime'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n            processed_df = aggregated_df[['datetime', 'value']]\n            processed_df = processed_df.sort_values(by='datetime')\n            # dict formation, needed for frontend [{date: , value: }]\n            json_list = []\n            for _, row in processed_df.iterrows():\n                json_obj = {'date': row['datetime'], 'value': row['value']}\n                json_list.append(json_obj)\n            return json_list\n    except FileNotFoundError:\n        return \"File not found\"\n    except Exception as e:\n        return f\"Exception occured {e}\"\n\n\nif __name__ == \"__main__\":\n    # Check if filepath argument is provided\n    if len(sys.argv) != 2:\n        print(\"Usage: python aggregrate.py <daily|monthly> <filepath>\")\n        sys.exit(1)\n\n    # Get the filepath from command line argument\n    frequency = sys.argv[1]\n    hourly_data_filepath = sys.argv[2]\n\n    # Call the aggregate function with the provided filepath\n    if (frequency == \"daily\"):\n        result = daily_aggregate(hourly_data_filepath)\n    elif (frequency == \"monthly\"):\n        result = monthly_aggregate(hourly_data_filepath)\n    else:\n        print(\"Usage: python aggregrate.py <daily|monthly> <filepath>\")\n        sys.exit(1)\n\n    if result is not None:\n        print(result)\n        # save the json file for reference\n        out_path = f\"{hourly_data_filepath.split(\"/\")[-1]}.json\"\n        with open(out_path, \"w\", encoding=\"utf-8\") as file:\n            json.dump(result, file)\n\n\n\n\n Back to top",
     "crumbs": [
-      "User Services",
-      "APIs"
+      "Data Transformation Notebooks",
+      "Greenhouse Gas Concentrations",
+      "Atmospheric Carbon Dioxide and Methane Concentrations from NOAA Global Monitoring Laboratory"
     ]
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-    "objectID": "datatransformationcode.html",
-    "href": "datatransformationcode.html",
-    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
+    "objectID": "cog_transformation/gra2pes-ghg-monthgrid-v1.html",
+    "href": "cog_transformation/gra2pes-ghg-monthgrid-v1.html",
+    "title": "GRA²PES Greenhouse Gas and Air Quality Species",
     "section": "",
-    "text": "Welcome to the U.S. Greenhouse Gas (GHG) Center data transformation notebooks, where we harness the power of Cloud Optimized Geotiffs (COGs) to offer a dynamic, cloud-based platform for exploring and analyzing greenhouse gas datasets. Dive into our curated collection of GHG datasets, all optimized for seamless accessibility and analysis.\nDiscover the journey of each dataset from its original format to COGs through the below Jupyter notebooks. The transformation examples are grouped topically. The dataset product type (model output, satellite observation, etc.) is noted next to the notebook name. Click on a notebook to learn more about the dataset and to view the transformation code.\nJoin us in our mission to make data-driven environmental solutions. Explore, analyze, and make a difference with the US GHG Center.\nNote: Not all datasets have a transformation code\nView the US GHG Center Data Catalog",
+    "text": "This script was used to transform the GRA2PES dataset to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport xarray as xr\nimport os\nimport glob\nfrom  datetime import datetime\nimport boto3\nimport s3fs\nimport tempfile\nimport numpy as np\n\nimport rasterio\nfrom rasterio.enums import Resampling\nfrom rio_cogeo.cogeo import cog_translate\nfrom rio_cogeo.profiles import cog_profiles\n\n\nconfig = {\n    \"data_acquisition_method\": \"s3\",\n    \"raw_data_bucket\" : \"gsfc-ghg-store\",\n    \"raw_data_prefix\": \"GRA2PES/monthly_subset_regrid/2021\", \n    \"cog_data_bucket\": \"ghgc-data-store-develop\",\n    \"cog_data_prefix\": \"transformed_cogs/GRAAPES\",\n    \"date_fmt\" :\"%Y%m\",\n    \"transformation\": {}\n}\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\n\nraw_data_bucket = config[\"raw_data_bucket\"]\nraw_data_prefix= config[\"raw_data_prefix\"]\n\ncog_data_bucket = config['cog_data_bucket']\ncog_data_prefix= config[\"cog_data_prefix\"]\n\ndate_fmt=config['date_fmt']\n\nfs = s3fs.S3FileSystem()\n\n\ndef get_all_s3_keys(bucket, model_name, ext):\n    \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n    keys = []\n\n    kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n    while True:\n        resp = s3_client.list_objects_v2(**kwargs)\n        for obj in resp[\"Contents\"]:\n            if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n                keys.append(obj[\"Key\"])\n\n        try:\n            kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n        except KeyError:\n            break\n\n    return keys\n\nkeys = get_all_s3_keys(raw_data_bucket, raw_data_prefix, \".nc4\")\n\ndef download_s3_objects(bucket, keys, download_dir):\n    \"\"\"Download all S3 objects listed in keys to the specified local directory.\"\"\"\n    if not os.path.exists(download_dir):\n        os.makedirs(download_dir)\n\n    for key in keys:\n        local_filename = os.path.join(download_dir, os.path.basename(key))\n        try:\n            s3_client.download_file(bucket, key, local_filename)\n            print(f\"Downloaded {key} to {local_filename}\")\n        except (NoCredentialsError, PartialCredentialsError) as e:\n            print(f\"Credentials error: {e}\")\n        except Exception as e:\n            print(f\"Failed to download {key}: {e}\")\n\ndownload_s3_objects(raw_data_bucket, keys, \"data\")\n\n\n\ndef extract_date_from_key(key):\n    # Split the key to isolate the part that contains the date\n    parts = key.split('_')\n    for part in parts:\n        # Check if the part is numeric and has the length of 6 (YYYYMM format)\n        if part.isdigit() and len(part) == 6:\n            return part\n    return None\n\n\nCOG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\nOVERVIEW_LEVELS = 4 \nOVERVIEW_RESAMPLING = 'average'\n\nfor key in glob.glob(\"data/*.nc4\"):\n    xds= xr.open_dataset(key)\n    xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    \n    for var in [\"PM25-PRI\",\"CO2\",\"CO\",\"NOX\",\"SOX\"]:\n        yearmonth = extract_date_from_key(key)\n        filename = f\"output/GRA2PESv1.0_total_{(\"-\").join(var.split('_'))}_{yearmonth}.tif\"\n        data = getattr(xds,var)\n        data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n        data.rio.write_crs(\"epsg:4326\", inplace=True)\n        \n        # Create a temporary file to hold the COG\n        with tempfile.NamedTemporaryFile(suffix='.tif', delete=False) as temp_file:\n            data.rio.to_raster(f\"temp_{yearmonth}_{var}.tif\", **COG_PROFILE, nodata=-9999)\n            # Create COG with overviews and nodata value\n            cog_translate(\n                f\"temp_{yearmonth}_{var}.tif\",\n                temp_file.name,\n                cog_profiles.get(\"deflate\"),\n                overview_level=OVERVIEW_LEVELS,\n                overview_resampling=OVERVIEW_RESAMPLING,\n                nodata=-9999\n            )\n            \n            # Move the temporary file to the desired local path\n            os.rename(temp_file.name, filename)\n    \n        if os.path.exists(f\"temp_{yearmonth}_{var}.tif\"):\n            os.remove(f\"temp_{yearmonth}_{var}.tif\")\n        del data\n        print(f\"Done for: {filename}\")\n    \n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks"
+      "Data Transformation Notebooks",
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "GRA²PES Greenhouse Gas and Air Quality Species"
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-    "href": "datatransformationcode.html#gridded-anthropogenic-greenhouse-gas-emissions",
-    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
-    "section": "Gridded Anthropogenic Greenhouse Gas Emissions",
-    "text": "Gridded Anthropogenic Greenhouse Gas Emissions\n\nOCO-2 MIP Top-Down CO₂ Budgets\nODIAC Fossil Fuel CO₂ Emissions\nTM5-4DVar Isotopic CH₄ Inverse Fluxes\nU.S. Gridded Anthropogenic Methane Emissions Inventory\nVulcan Fossil Fuel CO₂ Emissions\nGRA²PES Greenhouse Gas and Air Quality Species",
+    "objectID": "cog_transformation/eccodarwin-co2flux-monthgrid-v5.html",
+    "href": "cog_transformation/eccodarwin-co2flux-monthgrid-v5.html",
+    "title": "Air-Sea CO₂ Flux, ECCO-Darwin Model v5",
+    "section": "",
+    "text": "This script was used to transform the Air-Sea CO₂ Flux, ECCO-Darwin Mode dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nimport rasterio\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\n\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"ecco-darwin\"\ns3_fol_name = \"ecco_darwin\"\n\n# Reading the raw netCDF files from local machine\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\nfor name in os.listdir(FOLDER_NAME):\n    xds = xarray.open_dataset(\n        f\"{FOLDER_NAME}/{name}\",\n        engine=\"netcdf4\",\n    )\n    xds = xds.rename({\"y\": \"latitude\", \"x\": \"longitude\"})\n    xds = xds.assign_coords(longitude=((xds.longitude / 1440) * 360) - 180).sortby(\n        \"longitude\"\n    )\n    xds = xds.assign_coords(latitude=((xds.latitude / 721) * 180) - 90).sortby(\n        \"latitude\"\n    )\n\n    variable = [var for var in xds.data_vars]\n\n    for time_increment in xds.time.values:\n        for var in variable[2:]:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = xds[var]\n\n            data = data.reindex(latitude=list(reversed(data.latitude)))\n            data.rio.set_spatial_dims(\"longitude\", \"latitude\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            # generate COG\n            COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n\n            filename_elements.pop()\n            filename_elements[-1] = filename_elements[-2] + filename_elements[-1]\n            filename_elements.pop(-2)\n            # # insert date of generated COG into filename\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(temp_file.name, **COG_PROFILE)\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{s3_fol_name}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n            del data\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=\"s3_fol_name/metadata.json\",\n    )\n\n# A csv file to store the names of all the files converted.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{s3_fol_name}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks"
+      "Data Transformation Notebooks",
+      "Natural Greenhouse Gas Sources Emissions and Sinks",
+      "Air-Sea CO₂ Flux, ECCO-Darwin Model v5"
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-    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
-    "section": "Natural Greenhouse Gas Emissions and Sinks",
-    "text": "Natural Greenhouse Gas Emissions and Sinks\n\nAir-Sea CO₂ Flux, ECCO-Darwin Model v5\nGOSAT-based Top-down Total and Natural Methane Emissions\nOCO-2 MIP Top-Down CO₂ Budgets\nTM5-4DVar Isotopic CH₄ Inverse Fluxes",
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+    "title": "SEDAC Gridded World Population Data",
+    "section": "",
+    "text": "This script was used to transform SEDAC Gridded World Population Data from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\n\nimport tempfile\nimport boto3\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\n\nfold_names = os.listdir(\"gpw\")\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor fol_ in fold_names:\n    for name in os.listdir(f\"gpw/{fol_}\"):\n        if name.endswith(\".tif\"):\n            xds = xarray.open_dataarray(f\"gpw/{fol_}/{name}\")\n\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements.append(filename_elements[-3])\n\n            xds.rio.set_spatial_dims(\"x\", \"y\", inplace=True)\n            xds.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                xds.rio.to_raster(temp_file.name, driver=\"COG\")\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"gridded_population_cog/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/gridded_population_cog/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks"
+      "Data Transformation Notebooks",
+      "Socioeconomic",
+      "SEDAC Gridded World Population Data"
     ]
   },
   {
-    "objectID": "datatransformationcode.html#large-emissions-events",
-    "href": "datatransformationcode.html#large-emissions-events",
-    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
-    "section": "Large Emissions Events",
-    "text": "Large Emissions Events\n\nEMIT Methane Point Source Plume Complexes",
+    "objectID": "cog_transformation/epa-ch4emission-grid-v2express.html",
+    "href": "cog_transformation/epa-ch4emission-grid-v2express.html",
+    "title": "U.S. Gridded Anthropogenic Methane Emissions Inventory",
+    "section": "",
+    "text": "This script was used to transform the Gridded Anthropogenic Methane Emissions Inventory monthly dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nfrom datetime import datetime\nimport numpy as np\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nTrue\n\n\n\n# session = boto3.session.Session()\nsession = boto3.Session(\n    aws_access_key_id=os.environ.get(\"AWS_ACCESS_KEY_ID\"),\n    aws_secret_access_key=os.environ.get(\"AWS_SECRET_ACCESS_KEY\"),\n    aws_session_token=os.environ.get(\"AWS_SESSION_TOKEN\"),\n)\ns3_client = session.client(\"s3\")\nbucket_name = \"ghgc-data-store-dev\" # S3 bucket where the COGs are stored after transformation\nFOLDER_NAME = \"../data/epa_emissions_express_extension\"\ns3_folder_name = \"epa_express_extension_Mg_km2_yr\"\n# raw gridded data [molec/cm2/s] * 1/6.022x10^23 [molec/mol] * 16.04x10^-6 [ Mg/mol] * 366 [days/yr] * 1x10^10 [cm2/km2]\n\nfiles_processed = pd.DataFrame(columns=[\"file_name\", \"COGs_created\"])   # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(FOLDER_NAME):\n    xds = xarray.open_dataset(f\"{FOLDER_NAME}/{name}\", engine=\"netcdf4\")\n    xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    variable = [var for var in xds.data_vars]\n    filename = name.split(\"/ \")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n    start_time = datetime(int(filename_elements[-2]), 1, 1)\n\n    for time_increment in range(0, len(xds.time)):\n        for var in variable:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = getattr(xds.isel(time=time_increment), var)\n            data.values[data.values==0] = np.nan\n            data = data*((1/(6.022*pow(10,23)))*(16.04*pow(10,-6))*366*pow(10,10)*86400)\n            data = data.fillna(-9999)\n            data = data.isel(lat=slice(None, None, -1))\n            data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = start_time.strftime(\"%Y\")\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{s3_folder_name}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{s3_folder_name}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{s3_folder_name}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2015.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2020.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2014.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2013.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2017.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2016.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2012.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2019.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Mobile_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1A_Combustion_Stationary_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Abandoned_Coal_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Surface_Coal_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B1a_Underground_Coal_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Exploration_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Production_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Refining_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2a_Petroleum_Systems_Transport_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2ab_Abandoned_Oil_Gas_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Distribution_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Exploration_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Processing_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_Production_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_1B2b_Natural_Gas_TransmissionStorage_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2B8_Industry_Petrochemical_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_2C2_Industry_Ferroalloy_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3A_Enteric_Fermentation_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3B_Manure_Management_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3C_Rice_Cultivation_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_3F_Field_Burning_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_Industrial_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5A1_Landfills_MSW_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5B1_Composting_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Domestic_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_5D_Wastewater_Treatment_Industrial_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_emi_ch4_Supp_1B2b_PostMeter_Gridded_GHGI_Methane_v2_2018.tif\nGenerated and saved COG: Express_Extension_grid_cell_area_Gridded_GHGI_Methane_v2_2018.tif\nDone generating COGs\n\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks"
+      "Data Transformation Notebooks",
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "U.S. Gridded Anthropogenic Methane Emissions Inventory"
     ]
   },
   {
-    "objectID": "datatransformationcode.html#greenhouse-gas-concentrations",
-    "href": "datatransformationcode.html#greenhouse-gas-concentrations",
-    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
-    "section": "Greenhouse Gas Concentrations",
-    "text": "Greenhouse Gas Concentrations\n\nAtmospheric Carbon Dioxide and Methane Concentrations from NOAA Global Monitoring Laboratory\nOCO-2 GEOS Column CO₂ Concentrations\nCarbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)\nCarbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project\nCarbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
+    "objectID": "cog_transformation/lpjwsl-wetlandch4-daygrid-v1.html",
+    "href": "cog_transformation/lpjwsl-wetlandch4-daygrid-v1.html",
+    "title": "Wetland Methane Emissions, LPJ-wsl Model",
+    "section": "",
+    "text": "This script was used to transform the Wetland Methane Emissions, LPJ-wsl Model dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nfrom datetime import datetime, timedelta\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = (\n    \"ghgc-data-store-dev\"  # S3 bucket where the COGs are stored after transformation\n)\nFOLDER_NAME = \"NASA_GSFC_ch4_wetlands_daily\"\ndirectory = \"ch4_wetlands_daily\"\n\nfiles_processed = pd.DataFrame(\n    columns=[\"file_name\", \"COGs_created\"]\n)  # A dataframe to keep track of the files that we have transformed into COGs\n\n# Reading the raw netCDF files from local machine\nfor name in os.listdir(directory):\n    xds = xarray.open_dataset(\n        f\"{directory}/{name}\", engine=\"netcdf4\", decode_times=False\n    )\n    xds = xds.assign_coords(longitude=(((xds.longitude + 180) % 360) - 180)).sortby(\n        \"longitude\"\n    )\n    variable = [var for var in xds.data_vars]\n    filename = name.split(\"/ \")[-1]\n    filename_elements = re.split(\"[_ .]\", filename)\n    start_time = datetime(int(filename_elements[-2]), 1, 1)\n\n    for time_increment in range(0, len(xds.time)):\n        for var in variable:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            data = getattr(xds.isel(time=time_increment), var)\n            data = data.isel(latitude=slice(None, None, -1))\n            data = data * 1000\n            data.rio.set_spatial_dims(\"longitude\", \"latitude\", inplace=True)\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n            date = start_time + timedelta(hours=data.time.item(0))\n\n            # # insert date of generated COG into filename\n            filename_elements.pop()\n            filename_elements[-1] = date.strftime(\"%Y%m%d\")\n            filename_elements.insert(2, var)\n            cog_filename = \"_\".join(filename_elements)\n            # # add extension\n            cog_filename = f\"{cog_filename}.tif\"\n\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(\n                    temp_file.name,\n                    driver=\"COG\",\n                )\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"{FOLDER_NAME}/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# Generate the json file with the metadata that is present in the netCDF files.\nwith tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n    json.dump(xds.attrs, fp)\n    json.dump({\"data_dimensions\": dict(xds.dims)}, fp)\n    json.dump({\"data_variables\": list(xds.data_vars)}, fp)\n    fp.flush()\n\n    s3_client.upload_file(\n        Filename=fp.name,\n        Bucket=bucket_name,\n        Key=f\"{FOLDER_NAME}/metadata.json\",\n    )\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/{FOLDER_NAME}/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top"
+  },
+  {
+    "objectID": "cog_transformation/nec-testbed-ghg-concentrations.html",
+    "href": "cog_transformation/nec-testbed-ghg-concentrations.html",
+    "title": "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed",
+    "section": "",
+    "text": "This script was used to transform the Northeast Corridor (NEC) Urban Test Bed dataset into meaningful csv files for ingestion to vector dataset.\n\nimport pandas as pd\nimport glob\nimport os\nimport warnings\nimport subprocess\nimport tarfile\nimport warnings \nimport requests\nwarnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n\n\nconfig = pd.read_csv(\"NEC_sites.csv\")  #https://data.nist.gov/od/id/mds2-3012\n\n\n# Code to download the files into csv folder \nsites = list(config.SiteCode)\nfor SiteCode in config.SiteCode[:2]:\n    print(SiteCode)\n    download_link = f\"https://data.nist.gov/od/ds/ark:/88434/mds2-3012/{SiteCode}.tgz\"\n    \n    # Check if the file exists on the server\n    response = requests.head(download_link)\n    if response.status_code != 404:\n        # File exists, proceed with download\n        result = subprocess.run([\"wget\", download_link, \"-O\", f\"{SiteCode}.tgz\"], \n                                stdout=subprocess.DEVNULL,\n                                stderr=subprocess.DEVNULL)\n\n        # Check if wget succeeded\n        if result.returncode == 0:\n            # Ensure the file is not empty\n            if os.path.getsize(f\"{SiteCode}.tgz\") > 0:\n                # Extract the files\n                with tarfile.open(f\"{SiteCode}.tgz\", \"r:gz\") as tar:\n                    tar.extractall()\n\n                # Delete the .tgz file\n                os.remove(f\"{SiteCode}.tgz\")\n            else:\n                print(f\"File {SiteCode}.tgz is empty.\")\n                sites.remove(SiteCode)\n                os.remove(f\"{SiteCode}.tgz\")  # Remove the empty file\n        else:\n            print(f\"Failed to download {SiteCode}.tgz.\")\n            sites.remove(SiteCode)\n    else:\n        print(f\"File {SiteCode}.tgz does not exist on the server.\")\n        sites.remove(SiteCode)\n\n\nsites = list(config.SiteCode)\n# These are not available\nsites.remove('AWS')\nsites.remove('BVA')\nsites.remove('DNC')\n\n\nvariables = ['ch4','co2']\noutput_dir =\"output_NEC\"\nos.makedirs(output_dir,exist_ok=True)\n\n\nfor site in sites:\n    for variable in variables:\n        df = pd.DataFrame()\n        files = glob.glob(f\"csv/{site}-*-{variable}-*.csv\")\n        val = f\"{variable}_ppm\" if variable == 'co2' else f\"{variable}_ppb\"\n        for file in files:\n            tmp = pd.read_csv(file)\n            tmp.dropna(subset=[val], inplace=True)\n            tmp.rename(columns={'datetime_UTC': 'datetime'}, inplace=True)\n            columns = [\"latitude\",\"longitude\",\"intake_height_m\",\"elevation_m\",\"datetime\",val ]\n            tmp= tmp[columns]\n            tmp.rename(columns={val: 'value'}, inplace=True)\n            tmp['datetime'] = pd.to_datetime(tmp['datetime'])\n            tmp['datetime'] = tmp['datetime'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n            tmp['location'] = config[config['SiteCode']==site][\"Location\"].item()\n            df = pd.concat([df, tmp], ignore_index=True)\n            \n        df['year']= df['datetime'].apply(lambda x: x[:4])\n        result = df.groupby(\"year\").agg(max_height= (\"intake_height_m\",\"max\"))\n        if result['max_height'].std() !=0:\n            print(f\"More than one max height for {file}\",result['max_height'].unique())\n        merged_df=pd.merge(df, result, on='year')\n        merged_df[\"is_max_height_data\"]= merged_df[\"max_height\"] == merged_df[\"intake_height_m\"]\n        merged_df=merged_df.drop(columns=['year','max_height'])\n        merged_df.reset_index(drop=True, inplace=True)\n        merged_df.to_csv(f\"{output_dir}/NIST-testbed-NEC-{site}-{variable}-hourly-concentrations.csv\", index=False)\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks"
+      "Data Transformation Notebooks",
+      "Greenhouse Gas Concentrations",
+      "Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed"
     ]
   },
   {
-    "objectID": "datatransformationcode.html#socioeconomic",
-    "href": "datatransformationcode.html#socioeconomic",
-    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
-    "section": "Socioeconomic",
-    "text": "Socioeconomic\n\nSEDAC Gridded World Population Density",
+    "objectID": "cog_transformation/oco2-mip-co2budget-yeargrid-v1.html",
+    "href": "cog_transformation/oco2-mip-co2budget-yeargrid-v1.html",
+    "title": "OCO-2 MIP Top-Down CO₂ Budgets",
+    "section": "",
+    "text": "This script was used to transform the OCO-2 MIP Top-Down CO₂ Budgets dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport os\nimport xarray\nimport re\nimport pandas as pd\nimport json\nimport tempfile\nimport boto3\nimport rasterio\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nbucket_name = \"ghgc-data-store-dev\" # S3 bucket where the COGs are to be stored\nyear_ = datetime(2015, 1, 1)    # Initialize the starting date time of the dataset.\n\nCOG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n\n# Reading the raw netCDF files from local machine\nfiles_processed = pd.DataFrame(columns=[\"file_name\", \"COGs_created\"])   # A dataframe to keep track of the files that are converted into COGs\nfor name in os.listdir(\"new_data\"):\n    ds = xarray.open_dataset(\n        f\"new_data/{name}\",\n        engine=\"netcdf4\",\n    )\n    ds = ds.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})\n    # assign coords from dimensions\n    ds = ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180)).sortby(\"lon\")\n    ds = ds.assign_coords(lat=list(ds.lat))\n\n    variable = [var for var in ds.data_vars]\n\n    for time_increment in range(0, len(ds.year)):\n        for var in variable[2:]:\n            filename = name.split(\"/ \")[-1]\n            filename_elements = re.split(\"[_ .]\", filename)\n            try:\n                data = ds[var].sel(year=time_increment)\n                date = year_ + relativedelta(years=+time_increment)\n                filename_elements[-1] = date.strftime(\"%Y\")\n                # # insert date of generated COG into filename\n                filename_elements.insert(2, var)\n                cog_filename = \"_\".join(filename_elements)\n                # # add extension\n                cog_filename = f\"{cog_filename}.tif\"\n            except KeyError:\n                data = ds[var]\n                date = year_ + relativedelta(years=+(len(ds.year) - 1))\n                filename_elements.pop()\n                filename_elements.append(year_.strftime(\"%Y\"))\n                filename_elements.append(date.strftime(\"%Y\"))\n                filename_elements.insert(2, var)\n                cog_filename = \"_\".join(filename_elements)\n                # # add extension\n                cog_filename = f\"{cog_filename}.tif\"\n\n            data = data.reindex(lat=list(reversed(data.lat)))\n\n            data.rio.set_spatial_dims(\"lon\", \"lat\")\n            data.rio.write_crs(\"epsg:4326\", inplace=True)\n\n            # generate COG\n            COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n            with tempfile.NamedTemporaryFile() as temp_file:\n                data.rio.to_raster(temp_file.name, **COG_PROFILE)\n                s3_client.upload_file(\n                    Filename=temp_file.name,\n                    Bucket=bucket_name,\n                    Key=f\"ceos_co2_flux/{cog_filename}\",\n                )\n\n            files_processed = files_processed._append(\n                {\"file_name\": name, \"COGs_created\": cog_filename},\n                ignore_index=True,\n            )\n\n            print(f\"Generated and saved COG: {cog_filename}\")\n\n# creating the csv file with the names of files transformed.\nfiles_processed.to_csv(\n    f\"s3://{bucket_name}/ceos_co2_flux/files_converted.csv\",\n)\nprint(\"Done generating COGs\")\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks"
+      "Data Transformation Notebooks",
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "OCO-2 MIP Top-Down CO₂ Budgets"
     ]
   },
   {
-    "objectID": "datatransformationcode.html#contact",
-    "href": "datatransformationcode.html#contact",
-    "title": "U.S. Greenhouse Gas Center: Data Transformation Notebooks",
-    "section": "Contact",
-    "text": "Contact\nFor technical help or general questions, please contact the support team using the feedback form.",
+    "objectID": "cog_transformation/odiac-ffco2-monthgrid-v2023.html",
+    "href": "cog_transformation/odiac-ffco2-monthgrid-v2023.html",
+    "title": "ODIAC Fossil Fuel CO₂ Emissions",
+    "section": "",
+    "text": "This script was used to transform the ODIAC Fossil Fuel CO₂ Emissions dataset from GeoTIFF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center.\n\nimport xarray\nimport re\nimport tempfile\nimport numpy as np\nimport boto3\nimport os\nimport gzip,shutil, wget\nimport s3fs\nimport hashlib\nimport json\n\n\n\nsession = boto3.session.Session()\ns3_client = session.client(\"s3\")\nfs = s3fs.S3FileSystem()\n\ndata_dir = \"data/\"\ndataset_name = \"odiac-ffco2-monthgrid-v2023\"\ncog_data_bucket = \"ghgc-data-store-develop\"\ncog_data_prefix= f\"transformed_cogs/{dataset_name}\"\ncog_checksum_prefix= \"checksum\"\n\n\n# Retrieve the checksum of raw files\nchecksum_dict ={}\nfor year in range(2000,2023):\n    checksum_url = f\"https://db.cger.nies.go.jp/nies_data/10.17595/20170411.001/odiac2023/1km_tiff/{year}/odiac2023_1km_checksum_{year}.md5.txt\"\n    response = requests.get(checksum_url)\n    content = response.text\n    tmp={}\n    \n    # Split the content into lines\n    lines = content.splitlines()\n    \n    for line in lines:\n        checksum, filename = line.split()\n        tmp[filename[:-3]] = checksum\n    checksum_dict.update(tmp)\nchecksum_dict = {k: v for k, v in checksum_dict.items() if k.endswith('.tif')}\n\n\n\ndef calculate_md5(file_path):\n    \"\"\"\n    Calculate the MD5 hash of a file.\n\n    Parameters:\n    file_path (str): The path to the file.\n\n    Returns:\n    str: The MD5 hash of the file.\n    \"\"\"\n    hash_md5 = hashlib.md5()\n    with open(file_path, \"rb\") as f:\n        for chunk in iter(lambda: f.read(4096), b\"\"):\n            hash_md5.update(chunk)\n    return hash_md5.hexdigest()\n\n\n#Code to download raw ODIAC data in your local machine\n\n# Creating  a base directory for ODIAC data\nif not os.path.exists(data_dir):\n        os.makedirs(data_dir)\n\nchecksum_dict_local={}\n# Download and unzip data for the years you want\nfor year in range(2000,2023):\n    year_dir = os.path.join(data_dir, str(year))\n    checksum_download_link = f\"https://db.cger.nies.go.jp/nies_data/10.17595/20170411.001/odiac2023/1km_tiff/{year}/odiac2023_1km_checksum_{year}.md5.txt\"\n    wget.download(checksum_download_link, year_dir)\n    # Make a subfolder for each year\n    if not os.path.exists(year_dir):\n        os.makedirs(year_dir)\n\n    for month in range(1,13):\n        month = f\"{month:02d}\"\n        download_link = f\"https://db.cger.nies.go.jp/nies_data/10.17595/20170411.001/odiac2023/1km_tiff/{year}/odiac2023_1km_excl_intl_{str(year)[-2:]}{month}.tif.gz\"\n        target_folder = f\"{data_dir}/{year}/\"\n        fname = os.path.basename(download_link)\n        target_path = os.path.join(target_folder, fname)\n\n        # Download the file\n        wget.download(download_link, target_path)\n\n        # Unzip the file\n        with gzip.open(target_path, 'rb') as f_in:\n            with open(target_path[:-3], 'wb') as f_out:\n                shutil.copyfileobj(f_in, f_out)\n                \n        # Calculate checksum of the .gz file \n        checksum_dict_local[target_path.split(\"/\")[-1][:-3]]=calculate_md5(target_path)\n        \n        # Remove the zip file\n        os.remove(target_path)\n    \n\n\n# check if the checksums match\nchecksum_dict_local == checksum_dict\n\n\n# List of years you want to run the transformation on\nfold_names=[str(i) for i in range(2020,2023)]\n\nfor fol_ in fold_names:\n    names= os.listdir(f\"{data_dir}{fol_}\")\n    names= [name for name in names if name.endswith('.tif')]\n    print(\"For year: \" ,fol_)\n    for name in names:\n        xds = xarray.open_dataarray(f\"{data_dir}{fol_}/{name}\")\n        filename = name.split(\"/ \")[-1]\n        filename_elements = re.split(\"[_ .]\", filename)\n        \n        # Remove the extension\n        filename_elements.pop()\n        # Extract and insert date of generated COG into filename\n        filename_elements[-1] = fol_ + filename_elements[-1][-2:]\n\n        # Replace 0 values  with -9999\n        xds = xds.where(xds!=0, -9999)\n        xds.rio.set_spatial_dims(\"x\", \"y\", inplace=True)\n        xds.rio.write_nodata(-9999, inplace=True)\n        xds.rio.write_crs(\"epsg:4326\", inplace=True)\n\n        cog_filename = \"_\".join(filename_elements)\n        cog_filename = f\"{cog_filename}.tif\"\n\n        # Write the cog file to s3 \n        with tempfile.NamedTemporaryFile() as temp_file:\n            xds.rio.to_raster(\n                temp_file.name,\n                driver=\"COG\",\n                compress=\"DEFLATE\"\n            )\n            s3_client.upload_file(\n                Filename=temp_file.name,\n                Bucket=cog_data_bucket,\n                Key=f\"{cog_data_prefix}/{cog_filename}\",\n            )\n\n        print(f\"Generated and saved COG: {cog_filename}\")\n\nprint(\"ODIAC COGs generation completed!!!\")\n\n\n# This block is used to calculate the SHA for each COG file and store in a JSON.\n\ndef get_all_s3_keys(bucket, model_name, ext):\n    \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n    keys = []\n\n    kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n    while True:\n        resp = s3_client.list_objects_v2(**kwargs)\n        for obj in resp[\"Contents\"]:\n            if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n                keys.append(obj[\"Key\"])\n\n        try:\n            kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n        except KeyError:\n            break\n\n    return keys\n\nkeys = get_all_s3_keys(cog_data_bucket, cog_data_prefix,\".tif\")\n\n\ndef compute_sha256(url):\n    \"\"\"Compute SHA-256 checksum for a given file.\"\"\"\n    sha256_hash = hashlib.sha256()\n    with fs.open(url) as f:\n        for byte_block in iter(lambda: f.read(4096), b\"\"):\n            sha256_hash.update(byte_block)\n    return sha256_hash.hexdigest()\n\nsha_mapping = {}\nfor key in keys:\n    sha_mapping[key.split(\"/\")[-1]]=compute_sha256(f\"s3://{cog_data_bucket}/{key}\")\n\n\njson_data = json.dumps(sha_mapping, indent=4)\ns3_client.put_object(Bucket=cog_data_bucket, Key=f\"{cog_checksum_prefix}/{dataset_name}.json\", Body=json_data)\n\nprint(\"Checksums created for ODIAC!!!\")\n\n\n\n\n Back to top",
     "crumbs": [
-      "Data Transformation Notebooks"
+      "Data Transformation Notebooks",
+      "Gridded Anthropogenic Greenhouse Gas Emissions",
+      "ODIAC Fossil Fuel CO₂ Emissions"
     ]
   }
 ]
\ No newline at end of file
diff --git a/pr-preview/pr-137/site_libs/bootstrap/bootstrap-dark.min.css b/pr-preview/pr-137/site_libs/bootstrap/bootstrap-dark.min.css
index a696bdee..2c8223a7 100644
--- a/pr-preview/pr-137/site_libs/bootstrap/bootstrap-dark.min.css
+++ b/pr-preview/pr-137/site_libs/bootstrap/bootstrap-dark.min.css
@@ -2,7 +2,7 @@
  * Bootstrap  v5.3.1 (https://getbootstrap.com/)
  * Copyright 2011-2023 The Bootstrap Authors
  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
- */@import"https://fonts.googleapis.com/css2?family=Lato:ital,wght@0,400;0,700;1,400&display=swap";:root,[data-bs-theme=light]{--bs-blue: #375a7f;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #e83e8c;--bs-red: #e74c3c;--bs-orange: #fd7e14;--bs-yellow: #f39c12;--bs-green: #00bc8c;--bs-teal: #20c997;--bs-cyan: #3498db;--bs-black: #000;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #ebebeb;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #444;--bs-gray-800: #343a40;--bs-gray-900: #222;--bs-default: #434343;--bs-primary: #375a7f;--bs-secondary: #434343;--bs-success: #00bc8c;--bs-info: #3498db;--bs-warning: #f39c12;--bs-danger: #e74c3c;--bs-light: #6f6f6f;--bs-dark: #2d2d2d;--bs-default-rgb: 67, 67, 67;--bs-primary-rgb: 55, 90, 127;--bs-secondary-rgb: 67, 67, 67;--bs-success-rgb: 0, 188, 140;--bs-info-rgb: 52, 152, 219;--bs-warning-rgb: 243, 156, 18;--bs-danger-rgb: 231, 76, 60;--bs-light-rgb: 111, 111, 111;--bs-dark-rgb: 45, 45, 45;--bs-primary-text-emphasis: #162433;--bs-secondary-text-emphasis: #1b1b1b;--bs-success-text-emphasis: #004b38;--bs-info-text-emphasis: #153d58;--bs-warning-text-emphasis: #613e07;--bs-danger-text-emphasis: #5c1e18;--bs-light-text-emphasis: #444;--bs-dark-text-emphasis: #444;--bs-primary-bg-subtle: #d7dee5;--bs-secondary-bg-subtle: #d9d9d9;--bs-success-bg-subtle: #ccf2e8;--bs-info-bg-subtle: #d6eaf8;--bs-warning-bg-subtle: #fdebd0;--bs-danger-bg-subtle: #fadbd8;--bs-light-bg-subtle: #fcfcfd;--bs-dark-bg-subtle: #ced4da;--bs-primary-border-subtle: #afbdcc;--bs-secondary-border-subtle: #b4b4b4;--bs-success-border-subtle: #99e4d1;--bs-info-border-subtle: #aed6f1;--bs-warning-border-subtle: #fad7a0;--bs-danger-border-subtle: #f5b7b1;--bs-light-border-subtle: #ebebeb;--bs-dark-border-subtle: #adb5bd;--bs-white-rgb: 255, 255, 255;--bs-black-rgb: 0, 0, 0;--bs-font-sans-serif: Lato, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--bs-gradient: linear-gradient(180deg, rgba(255, 255, 255, 0.15), rgba(255, 255, 255, 0));--bs-root-font-size: 17px;--bs-body-font-family: Lato, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--bs-body-font-size:1rem;--bs-body-font-weight: 400;--bs-body-line-height: 1.5;--bs-body-color: #fff;--bs-body-color-rgb: 255, 255, 255;--bs-body-bg: #222;--bs-body-bg-rgb: 34, 34, 34;--bs-emphasis-color: #000;--bs-emphasis-color-rgb: 0, 0, 0;--bs-secondary-color: rgba(255, 255, 255, 0.75);--bs-secondary-color-rgb: 255, 255, 255;--bs-secondary-bg: #ebebeb;--bs-secondary-bg-rgb: 235, 235, 235;--bs-tertiary-color: rgba(255, 255, 255, 0.5);--bs-tertiary-color-rgb: 255, 255, 255;--bs-tertiary-bg: #f8f9fa;--bs-tertiary-bg-rgb: 248, 249, 250;--bs-heading-color: inherit;--bs-link-color: #00bc8c;--bs-link-color-rgb: 0, 188, 140;--bs-link-decoration: underline;--bs-link-hover-color: #009670;--bs-link-hover-color-rgb: 0, 150, 112;--bs-code-color: #7d12ba;--bs-highlight-bg: #fdebd0;--bs-border-width: 1px;--bs-border-style: solid;--bs-border-color: #dee2e6;--bs-border-color-translucent: rgba(0, 0, 0, 0.175);--bs-border-radius: 0.25rem;--bs-border-radius-sm: 0.2em;--bs-border-radius-lg: 0.5rem;--bs-border-radius-xl: 1rem;--bs-border-radius-xxl: 2rem;--bs-border-radius-2xl: var(--bs-border-radius-xxl);--bs-border-radius-pill: 50rem;--bs-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-box-shadow-sm: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-box-shadow-lg: 0 1rem 3rem rgba(0, 0, 0, 0.175);--bs-box-shadow-inset: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-focus-ring-width: 0.25rem;--bs-focus-ring-opacity: 0.25;--bs-focus-ring-color: rgba(55, 90, 127, 0.25);--bs-form-valid-color: 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #dee2e6;--bs-navbar-hover-color: rgba(255, 255, 255, 0.8);--bs-navbar-disabled-color: rgba(222, 226, 230, 0.75);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: #dee2e6;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(222, 226, 230, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #2d2d2d;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 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var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #fff;--bs-accordion-bg: #222;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s 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0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #9badbf;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #162433;--bs-accordion-active-bg: #d7dee5}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23879cb2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23879cb2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: #434343;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(255, 255, 255, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #00bc8c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #00efb2;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #009670;--bs-pagination-focus-bg: #ebebeb;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #00efb2;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #fff;--bs-pagination-disabled-bg: #007053;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #434343;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #375a7f;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #fff;--bs-list-group-bg: #2d2d2d;--bs-list-group-border-color: #434343;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(255, 255, 255, 0.75);--bs-list-group-action-hover-color: #fff;--bs-list-group-action-hover-bg: #434343;--bs-list-group-action-active-color: #fff;--bs-list-group-action-active-bg: #222;--bs-list-group-disabled-color: rgba(255, 255, 255, 0.75);--bs-list-group-disabled-bg: #2d2d2d;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #375a7f;--bs-list-group-active-border-color: #375a7f;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down 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.modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #222;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:Lato,-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI 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var(--bslib-sidebar-transition-duration);border:var(--bslib-sidebar-border);border-radius:var(--bslib-sidebar-border-radius)}@media(prefers-reduced-motion: reduce){.bslib-sidebar-layout{transition:none}}.bslib-sidebar-layout[data-bslib-sidebar-border=false]{border:none}.bslib-sidebar-layout[data-bslib-sidebar-border-radius=false]{border-radius:initial}.bslib-sidebar-layout>.main,.bslib-sidebar-layout>.sidebar{grid-row:1/2;border-radius:inherit;overflow:auto}.bslib-sidebar-layout>.main{grid-column:2/3;border-top-left-radius:0;border-bottom-left-radius:0;padding:var(--bslib-sidebar-padding);transition:padding var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration);color:var(--bslib-sidebar-main-fg);background-color:var(--bslib-sidebar-main-bg)}.bslib-sidebar-layout>.sidebar{grid-column:1/2;width:100%;height:100%;border-right:var(--bslib-sidebar-vert-border);border-top-right-radius:0;border-bottom-right-radius:0;color:var(--bslib-sidebar-fg);background-color:var(--bslib-sidebar-bg);backdrop-filter:blur(5px)}.bslib-sidebar-layout>.sidebar>.sidebar-content{display:flex;flex-direction:column;gap:var(--bslib-spacer, 1rem);padding:var(--bslib-sidebar-padding);padding-top:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout>.sidebar>.sidebar-content>:last-child:not(.sidebar-title){margin-bottom:0}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion{margin-left:calc(-1*var(--bslib-sidebar-padding));margin-right:calc(-1*var(--bslib-sidebar-padding))}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:last-child{margin-bottom:calc(-1*var(--bslib-sidebar-padding))}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:not(:last-child){margin-bottom:1rem}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion .accordion-body{display:flex;flex-direction:column}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:not(:first-child) .accordion-item:first-child{border-top:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:not(:last-child) .accordion-item:last-child{border-bottom:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.bslib-sidebar-layout>.sidebar>.sidebar-content.has-accordion>.sidebar-title{border-bottom:none;padding-bottom:0}.bslib-sidebar-layout>.sidebar .shiny-input-container{width:100%}.bslib-sidebar-layout[data-bslib-sidebar-open=always]>.sidebar>.sidebar-content{padding-top:var(--bslib-sidebar-padding)}.bslib-sidebar-layout>.collapse-toggle{grid-row:1/2;grid-column:1/2;display:inline-flex;align-items:center;position:absolute;right:calc(var(--bslib-sidebar-icon-size));top:calc(var(--bslib-sidebar-icon-size, 1rem)/2);border:none;border-radius:var(--bslib-collapse-toggle-border-radius);height:var(--bslib-sidebar-icon-button-size, 2rem);width:var(--bslib-sidebar-icon-button-size, 2rem);display:flex;align-items:center;justify-content:center;padding:0;color:var(--bslib-sidebar-fg);background-color:unset;transition:color var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),top var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),right var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),left var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration)}.bslib-sidebar-layout>.collapse-toggle:hover{background-color:var(--bslib-sidebar-toggle-bg)}.bslib-sidebar-layout>.collapse-toggle>.collapse-icon{opacity:.8;width:var(--bslib-sidebar-icon-size);height:var(--bslib-sidebar-icon-size);transform:rotateY(var(--bslib-collapse-toggle-transform));transition:transform var(--bslib-sidebar-toggle-transition-easing) var(--bslib-sidebar-transition-duration)}.bslib-sidebar-layout>.collapse-toggle:hover>.collapse-icon{opacity:1}.bslib-sidebar-layout .sidebar-title{font-size:1.25rem;line-height:1.25;margin-top:0;margin-bottom:1rem;padding-bottom:1rem;border-bottom:var(--bslib-sidebar-border)}.bslib-sidebar-layout.sidebar-right{grid-template-columns:var(--bslib-sidebar-column-main) min(100% - var(--bslib-sidebar-icon-size),var(--bslib-sidebar-width, 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auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:.25rem}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(1px*-1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n+3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(1px*-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 2rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #00bc8c;--bs-nav-link-hover-color: #009670;--bs-nav-link-disabled-color: #6f6f6f;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out}@media(prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem 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#fff;--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25;--bs-navbar-toggler-padding-x: 0;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(222, 226, 230, 0);--bs-navbar-toggler-border-radius: 0.25rem;--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #dee2e6;--bs-navbar-hover-color: rgba(255, 255, 255, 0.8);--bs-navbar-disabled-color: rgba(222, 226, 230, 0.75);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: #dee2e6;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(222, 226, 230, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #2d2d2d;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #9badbf;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #162433;--bs-accordion-active-bg: #d7dee5}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23879cb2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23879cb2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: #434343;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(255, 255, 255, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #00bc8c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #00efb2;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #009670;--bs-pagination-focus-bg: #ebebeb;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #00efb2;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #fff;--bs-pagination-disabled-bg: #007053;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #434343;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #375a7f;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #fff;--bs-list-group-bg: #2d2d2d;--bs-list-group-border-color: #434343;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(255, 255, 255, 0.75);--bs-list-group-action-hover-color: #fff;--bs-list-group-action-hover-bg: #434343;--bs-list-group-action-active-color: #fff;--bs-list-group-action-active-bg: #222;--bs-list-group-disabled-color: rgba(255, 255, 255, 0.75);--bs-list-group-disabled-bg: #2d2d2d;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #375a7f;--bs-list-group-active-border-color: #375a7f;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: #434343;--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(255, 255, 255, 0.75);--bs-toast-header-bg: #2d2d2d;--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #2d2d2d;--bs-modal-border-color: #434343;--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #434343;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #434343;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down 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var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen .card:hover>*>.bslib-full-screen-enter{display:none}@media(max-width: 575.98px){.bslib-full-screen-enter{display:none !important}}.bslib-full-screen-exit{position:relative;top:1.35rem;font-size:.9rem;cursor:pointer;text-decoration:none;display:flex;float:right;margin-right:2.15rem;align-items:center;color:rgba(var(--bs-body-bg-rgb), 0.8)}.bslib-full-screen-exit:hover{color:rgba(var(--bs-body-bg-rgb), 1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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30'%3e%3cpath stroke='%23ccd1d5' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23ccd1d5' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 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var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #212529;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s 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6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #969fa8;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #121920;--bs-accordion-active-bg: #d5d8dc}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #13967d;--bs-pagination-focus-bg: #ecf0f1;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #ecf0f1;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #ecf0f1;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto 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var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) #1dc499;color:#fff}.bg-gradient-teal-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #28b5b2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) #28b5b2;color:#fff}.bg-gradient-cyan-blue{--bslib-color-fg: #fff;--bslib-color-bg: #3174a3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #2c3e50 var(--bg-gradient-end, 180%)) #3174a3;color:#fff}.bg-gradient-cyan-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4862e4;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4862e4;color:#fff}.bg-gradient-cyan-purple{--bslib-color-fg: #fff;--bslib-color-bg: #4c76d1;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #4c76d1;color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: #7c74bb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #7c74bb;color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: #7c7a9b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) #7c7a9b;color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: #848e8b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #848e8b;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #809a8b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) #809a8b;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #29a6c2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) #29a6c2;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #2cacc0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #2cacc0;color:#fff}.bslib-sidebar-layout{--bslib-sidebar-transition-duration: 500ms;--bslib-sidebar-transition-easing-x: cubic-bezier(0.8, 0.78, 0.22, 1.07);--bslib-sidebar-border: var(--bs-card-border-width, 1px) solid var(--bs-card-border-color, rgba(0, 0, 0, 0.175));--bslib-sidebar-border-radius: var(--bs-border-radius);--bslib-sidebar-vert-border: var(--bs-card-border-width, 1px) solid var(--bs-card-border-color, rgba(0, 0, 0, 0.175));--bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--bslib-sidebar-fg: var(--bs-emphasis-color, black);--bslib-sidebar-main-fg: var(--bs-card-color, var(--bs-body-color));--bslib-sidebar-main-bg: var(--bs-card-bg, var(--bs-body-bg));--bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--bslib-sidebar-padding: calc(var(--bslib-spacer) * 1.5);--bslib-sidebar-icon-size: var(--bslib-spacer, 1rem);--bslib-sidebar-icon-button-size: calc(var(--bslib-sidebar-icon-size, 1rem) * 2);--bslib-sidebar-padding-icon: calc(var(--bslib-sidebar-icon-button-size, 2rem) * 1.5);--bslib-collapse-toggle-border-radius: var(--bs-border-radius, 0.25rem);--bslib-collapse-toggle-transform: 0deg;--bslib-sidebar-toggle-transition-easing: cubic-bezier(1, 0, 0, 1);--bslib-collapse-toggle-right-transform: 180deg;--bslib-sidebar-column-main: minmax(0, 1fr);display:grid !important;grid-template-columns:min(100% - var(--bslib-sidebar-icon-size),var(--bslib-sidebar-width, 250px)) var(--bslib-sidebar-column-main);position:relative;transition:grid-template-columns ease-in-out var(--bslib-sidebar-transition-duration);border:var(--bslib-sidebar-border);border-radius:var(--bslib-sidebar-border-radius)}@media(prefers-reduced-motion: reduce){.bslib-sidebar-layout{transition:none}}.bslib-sidebar-layout[data-bslib-sidebar-border=false]{border:none}.bslib-sidebar-layout[data-bslib-sidebar-border-radius=false]{border-radius:initial}.bslib-sidebar-layout>.main,.bslib-sidebar-layout>.sidebar{grid-row:1/2;border-radius:inherit;overflow:auto}.bslib-sidebar-layout>.main{grid-column:2/3;border-top-left-radius:0;border-bottom-left-radius:0;padding:var(--bslib-sidebar-padding);transition:padding var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration);color:var(--bslib-sidebar-main-fg);background-color:var(--bslib-sidebar-main-bg)}.bslib-sidebar-layout>.sidebar{grid-column:1/2;width:100%;height:100%;border-right:var(--bslib-sidebar-vert-border);border-top-right-radius:0;border-bottom-right-radius:0;color:var(--bslib-sidebar-fg);background-color:var(--bslib-sidebar-bg);backdrop-filter:blur(5px)}.bslib-sidebar-layout>.sidebar>.sidebar-content{display:flex;flex-direction:column;gap:var(--bslib-spacer, 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.accordion-item:last-child{border-bottom:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.bslib-sidebar-layout>.sidebar>.sidebar-content.has-accordion>.sidebar-title{border-bottom:none;padding-bottom:0}.bslib-sidebar-layout>.sidebar .shiny-input-container{width:100%}.bslib-sidebar-layout[data-bslib-sidebar-open=always]>.sidebar>.sidebar-content{padding-top:var(--bslib-sidebar-padding)}.bslib-sidebar-layout>.collapse-toggle{grid-row:1/2;grid-column:1/2;display:inline-flex;align-items:center;position:absolute;right:calc(var(--bslib-sidebar-icon-size));top:calc(var(--bslib-sidebar-icon-size, 1rem)/2);border:none;border-radius:var(--bslib-collapse-toggle-border-radius);height:var(--bslib-sidebar-icon-button-size, 2rem);width:var(--bslib-sidebar-icon-button-size, 2rem);display:flex;align-items:center;justify-content:center;padding:0;color:var(--bslib-sidebar-fg);background-color:unset;transition:color var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),top var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),right var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),left var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration)}.bslib-sidebar-layout>.collapse-toggle:hover{background-color:var(--bslib-sidebar-toggle-bg)}.bslib-sidebar-layout>.collapse-toggle>.collapse-icon{opacity:.8;width:var(--bslib-sidebar-icon-size);height:var(--bslib-sidebar-icon-size);transform:rotateY(var(--bslib-collapse-toggle-transform));transition:transform var(--bslib-sidebar-toggle-transition-easing) var(--bslib-sidebar-transition-duration)}.bslib-sidebar-layout>.collapse-toggle:hover>.collapse-icon{opacity:1}.bslib-sidebar-layout .sidebar-title{font-size:1.25rem;line-height:1.25;margin-top:0;margin-bottom:1rem;padding-bottom:1rem;border-bottom:var(--bslib-sidebar-border)}.bslib-sidebar-layout.sidebar-right{grid-template-columns:var(--bslib-sidebar-column-main) min(100% - var(--bslib-sidebar-icon-size),var(--bslib-sidebar-width, 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0}.bslib-sidebar-layout.sidebar-collapsed:not(.transitioning)>.sidebar>*{display:none}.bslib-sidebar-layout.sidebar-collapsed>.main{border-radius:inherit}.bslib-sidebar-layout.sidebar-collapsed:not(.sidebar-right)>.main{padding-left:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout.sidebar-collapsed.sidebar-right>.main{padding-right:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout.sidebar-collapsed>.collapse-toggle{color:var(--bslib-sidebar-main-fg);top:calc(var(--bslib-sidebar-overlap-counter, 0)*(var(--bslib-sidebar-icon-size) + var(--bslib-sidebar-padding)) + var(--bslib-sidebar-icon-size, 1rem)/2);right:calc(-2.5*var(--bslib-sidebar-icon-size) - var(--bs-card-border-width, 1px))}.bslib-sidebar-layout.sidebar-collapsed.sidebar-right>.collapse-toggle{left:calc(-2.5*var(--bslib-sidebar-icon-size) - var(--bs-card-border-width, 1px));right:unset}@media(min-width: 576px){.bslib-sidebar-layout.transitioning>.sidebar>.sidebar-content{display:none}}@media(max-width: 575.98px){.bslib-sidebar-layout[data-bslib-sidebar-open=desktop]{--bslib-sidebar-js-init-collapsed: true}.bslib-sidebar-layout>.sidebar,.bslib-sidebar-layout.sidebar-right>.sidebar{border:none}.bslib-sidebar-layout>.main,.bslib-sidebar-layout.sidebar-right>.main{grid-column:1/3}.bslib-sidebar-layout[data-bslib-sidebar-open=always]{display:block !important}.bslib-sidebar-layout[data-bslib-sidebar-open=always]>.sidebar{max-height:var(--bslib-sidebar-max-height-mobile);overflow-y:auto;border-top:var(--bslib-sidebar-vert-border)}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]){grid-template-columns:100% 0}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]):not(.sidebar-collapsed)>.sidebar{z-index:1}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]):not(.sidebar-collapsed)>.collapse-toggle{z-index:1}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-right{grid-template-columns:0 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inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #2c3e50;--bs-btn-disabled-border-color: #2c3e50}.btn-secondary{--bs-btn-color: #fff;--bs-btn-bg: #6c757d;--bs-btn-border-color: #6c757d;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #5c636a;--bs-btn-hover-border-color: #565e64;--bs-btn-focus-shadow-rgb: 130, 138, 145;--bs-btn-active-color: #fff;--bs-btn-active-bg: #565e64;--bs-btn-active-border-color: #51585e;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #6c757d;--bs-btn-disabled-border-color: #6c757d}.btn-success{--bs-btn-color: #fff;--bs-btn-bg: #18bc9c;--bs-btn-border-color: #18bc9c;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #14a085;--bs-btn-hover-border-color: #13967d;--bs-btn-focus-shadow-rgb: 59, 198, 171;--bs-btn-active-color: #fff;--bs-btn-active-bg: #13967d;--bs-btn-active-border-color: #128d75;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #18bc9c;--bs-btn-disabled-border-color: #18bc9c}.btn-info{--bs-btn-color: #fff;--bs-btn-bg: #3498db;--bs-btn-border-color: #3498db;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2c81ba;--bs-btn-hover-border-color: #2a7aaf;--bs-btn-focus-shadow-rgb: 82, 167, 224;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2a7aaf;--bs-btn-active-border-color: #2772a4;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #3498db;--bs-btn-disabled-border-color: #3498db}.btn-warning{--bs-btn-color: #fff;--bs-btn-bg: #f39c12;--bs-btn-border-color: #f39c12;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #cf850f;--bs-btn-hover-border-color: #c27d0e;--bs-btn-focus-shadow-rgb: 245, 171, 54;--bs-btn-active-color: #fff;--bs-btn-active-bg: #c27d0e;--bs-btn-active-border-color: #b6750e;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #f39c12;--bs-btn-disabled-border-color: #f39c12}.btn-danger{--bs-btn-color: #fff;--bs-btn-bg: #e74c3c;--bs-btn-border-color: #e74c3c;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #c44133;--bs-btn-hover-border-color: #b93d30;--bs-btn-focus-shadow-rgb: 235, 103, 89;--bs-btn-active-color: #fff;--bs-btn-active-bg: #b93d30;--bs-btn-active-border-color: #ad392d;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #e74c3c;--bs-btn-disabled-border-color: #e74c3c}.btn-light{--bs-btn-color: #000;--bs-btn-bg: #ecf0f1;--bs-btn-border-color: #ecf0f1;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #c9cccd;--bs-btn-hover-border-color: #bdc0c1;--bs-btn-focus-shadow-rgb: 201, 204, 205;--bs-btn-active-color: #000;--bs-btn-active-bg: #bdc0c1;--bs-btn-active-border-color: #b1b4b5;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #000;--bs-btn-disabled-bg: #ecf0f1;--bs-btn-disabled-border-color: #ecf0f1}.btn-dark{--bs-btn-color: #fff;--bs-btn-bg: #7b8a8b;--bs-btn-border-color: #7b8a8b;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #8f9c9c;--bs-btn-hover-border-color: #889697;--bs-btn-focus-shadow-rgb: 143, 156, 156;--bs-btn-active-color: #fff;--bs-btn-active-bg: #95a1a2;--bs-btn-active-border-color: #889697;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #7b8a8b;--bs-btn-disabled-border-color: #7b8a8b}.btn-outline-default{--bs-btn-color: #6c757d;--bs-btn-border-color: #6c757d;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #6c757d;--bs-btn-hover-border-color: #6c757d;--bs-btn-focus-shadow-rgb: 108, 117, 125;--bs-btn-active-color: #fff;--bs-btn-active-bg: #6c757d;--bs-btn-active-border-color: #6c757d;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #6c757d;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-primary{--bs-btn-color: #2c3e50;--bs-btn-border-color: #2c3e50;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2c3e50;--bs-btn-hover-border-color: #2c3e50;--bs-btn-focus-shadow-rgb: 44, 62, 80;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2c3e50;--bs-btn-active-border-color: #2c3e50;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #2c3e50;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #2c3e50;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #6c757d;--bs-btn-border-color: #6c757d;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #6c757d;--bs-btn-hover-border-color: #6c757d;--bs-btn-focus-shadow-rgb: 108, 117, 125;--bs-btn-active-color: #fff;--bs-btn-active-bg: #6c757d;--bs-btn-active-border-color: #6c757d;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #6c757d;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-success{--bs-btn-color: #18bc9c;--bs-btn-border-color: #18bc9c;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #18bc9c;--bs-btn-hover-border-color: #18bc9c;--bs-btn-focus-shadow-rgb: 24, 188, 156;--bs-btn-active-color: #fff;--bs-btn-active-bg: #18bc9c;--bs-btn-active-border-color: #18bc9c;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #18bc9c;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #18bc9c;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-info{--bs-btn-color: #3498db;--bs-btn-border-color: #3498db;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #3498db;--bs-btn-hover-border-color: #3498db;--bs-btn-focus-shadow-rgb: 52, 152, 219;--bs-btn-active-color: #fff;--bs-btn-active-bg: #3498db;--bs-btn-active-border-color: #3498db;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #3498db;--bs-btn-disabled-bg: 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#e74c3c;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #e74c3c;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #ecf0f1;--bs-btn-border-color: #ecf0f1;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #ecf0f1;--bs-btn-hover-border-color: #ecf0f1;--bs-btn-focus-shadow-rgb: 236, 240, 241;--bs-btn-active-color: #000;--bs-btn-active-bg: #ecf0f1;--bs-btn-active-border-color: #ecf0f1;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #ecf0f1;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ecf0f1;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #7b8a8b;--bs-btn-border-color: #7b8a8b;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #7b8a8b;--bs-btn-hover-border-color: #7b8a8b;--bs-btn-focus-shadow-rgb: 123, 138, 139;--bs-btn-active-color: #fff;--bs-btn-active-bg: #7b8a8b;--bs-btn-active-border-color: #7b8a8b;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 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6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #13967d;--bs-pagination-focus-bg: #ecf0f1;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #ecf0f1;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #ecf0f1;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 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