diff --git a/.gitignore b/.gitignore index 49c054c3..9111f73b 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,5 @@ .env /.quarto/ _site/ +.yaml +.json \ No newline at end of file diff --git a/_quarto.yml b/_quarto.yml index db11916c..a73212ed 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -55,7 +55,7 @@ website: contents: - user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb - user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb - - user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb + - user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb - user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb - user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb - user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb @@ -65,11 +65,12 @@ website: - user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb - user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb - user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb - - user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb + - user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb - user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.ipynb - section: Large Emissions Events contents: - user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb + - user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb - section: Greenhouse Gas Concentrations contents: - user_data_notebooks/noaa-insitu_User_Notebook.ipynb @@ -87,7 +88,7 @@ website: contents: - cog_transformation/oco2-mip-co2budget-yeargrid-v1.ipynb - cog_transformation/odiac-ffco2-monthgrid-v2023.ipynb - - cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb + - cog_transformation/ct-ch4-monthgrid-v2023.ipynb - cog_transformation/epa-ch4emission-grid-v2express.ipynb - cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb - cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb @@ -96,7 +97,7 @@ website: - cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb - cog_transformation/gosat-based-ch4budget-yeargrid-v1.ipynb - cog_transformation/oco2-mip-co2budget-yeargrid-v1.ipynb - - cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb + - cog_transformation/ct-ch4-monthgrid-v2023.ipynb - section: Large Emissions Events contents: - cog_transformation/emit-ch4plume-v1.ipynb @@ -118,7 +119,7 @@ website: contents: - processing_and_verification_reports/oco2-mip-co2budget-yeargrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/odiac-ffco2-monthgrid-v2023_Processing and Verification Report.qmd - - processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd + - processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd - processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd - processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd - processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd @@ -128,11 +129,12 @@ website: - processing_and_verification_reports/micasa-carbonflux-daygrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/oco2-mip-co2budget-yeargrid-v1_Processing and Verification Report.qmd - - processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd + - processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd - processing_and_verification_reports/lpjeosim-wetlandch4-grid-v1_Processing and Verification Report.qmd - section: Large Emissions Events contents: - processing_and_verification_reports/emit-ch4plume-v1_Processing and Verification Report.qmd + - processing_and_verification_reports/goes-ch4plume-v1_Processing and Verification Report.qmd - section: Greenhouse Gas Concentrations contents: - processing_and_verification_reports/noaa-gggrn-co2-concentrations_Processing and Verification Report.qmd @@ -151,7 +153,7 @@ website: contents: - data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd - data_workflow/odiac-ffco2-monthgrid-v2023_Data_Flow.qmd - - data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd + - data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd - data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd - data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd - data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd @@ -161,11 +163,12 @@ website: - data_workflow/micasa-carbonflux-daygrid-v1_Data_Flow.qmd - data_workflow/gosat-based-ch4budget-yeargrid-v1_Data_Flow.qmd - data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd - - data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd + - data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd - data_workflow/lpjeosim-wetlandch4-grid-v1_Data_Flow.qmd - section: Large Emissions Events contents: - data_workflow/emit-ch4plume-v1_Data_Flow.qmd + - data_workflow/goes-ch4plume-v1_Data_Flow.qmd - section: Greenhouse Gas Concentrations contents: - data_workflow/noaa-gggrn-co2-concentrations_Data_Flow.qmd diff --git a/cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb b/cog_transformation/ct-ch4-monthgrid-v2023.ipynb similarity index 92% rename from cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb rename to cog_transformation/ct-ch4-monthgrid-v2023.ipynb index 9ea6d98f..1872e7c6 100644 --- a/cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb +++ b/cog_transformation/ct-ch4-monthgrid-v2023.ipynb @@ -2,10 +2,14 @@ "cells": [ { "cell_type": "raw", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", - "title: TM5-4DVar Isotopic CH₄ Inverse Fluxes\n", + "title: CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes\n", "description: Documentation of data transformation\n", "author: Vishal Gaur\n", "date: August 31, 2023\n", @@ -18,7 +22,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "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." + "This script was used to transform the CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center." ] }, { diff --git a/data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd b/data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd new file mode 100644 index 00000000..a7b7ddda --- /dev/null +++ b/data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd @@ -0,0 +1,3 @@ +# CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes + +![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/ct-ch4-monthgrid-v2023_Data_Flow.png) \ No newline at end of file diff --git a/data_workflow/goes-ch4plume-v1_Data_Flow.qmd b/data_workflow/goes-ch4plume-v1_Data_Flow.qmd new file mode 100644 index 00000000..2c6e58ff --- /dev/null +++ b/data_workflow/goes-ch4plume-v1_Data_Flow.qmd @@ -0,0 +1,4 @@ +# Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure + + +![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/goes-ch4plume-v1_Data_Flow.png) \ No newline at end of file diff --git a/data_workflow/media/ct-ch4-monthgrid-v2023_Data_Flow.png b/data_workflow/media/ct-ch4-monthgrid-v2023_Data_Flow.png new file mode 100644 index 00000000..940dbf18 Binary files /dev/null and b/data_workflow/media/ct-ch4-monthgrid-v2023_Data_Flow.png differ diff --git a/data_workflow/media/goes-ch4plume-v1_Data_Flow.png b/data_workflow/media/goes-ch4plume-v1_Data_Flow.png new file mode 100644 index 00000000..75ef20f9 Binary files /dev/null and b/data_workflow/media/goes-ch4plume-v1_Data_Flow.png differ diff --git a/data_workflow/media/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.png b/data_workflow/media/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.png deleted file mode 100644 index cbfdbc97..00000000 Binary files a/data_workflow/media/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.png and /dev/null differ diff --git a/data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd b/data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd deleted file mode 100644 index 3aaaee1e..00000000 --- a/data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd +++ /dev/null @@ -1,3 +0,0 @@ -# TM5-4DVar Isotopic CH₄ Inverse Fluxes - -![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.png) \ No newline at end of file diff --git a/datatransformationcode.qmd b/datatransformationcode.qmd index cf6bccf3..f245615c 100644 --- a/datatransformationcode.qmd +++ b/datatransformationcode.qmd @@ -17,7 +17,7 @@ Explore, analyze, and make a difference with the US GHG Center. ## Gridded Anthropogenic Greenhouse Gas Emissions 1. [OCO-2 MIP Top-Down CO₂ Budgets](cog_transformation/oco2-mip-co2budget-yeargrid-v1.ipynb) 2. [ODIAC Fossil Fuel CO₂ Emissions](cog_transformation/odiac-ffco2-monthgrid-v2023.ipynb) -3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb) +3. [CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes](cog_transformation/ct-ch4-monthgrid-v2023.ipynb) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory](cog_transformation/epa-ch4emission-grid-v2express.ipynb) 5. [Vulcan Fossil Fuel CO₂ Emissions](cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb) 6. [GRA²PES Greenhouse Gas and Air Quality Species](cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb) @@ -26,7 +26,7 @@ Explore, analyze, and make a difference with the US GHG Center. 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5](cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb) 2. [GOSAT-based Top-down Total and Natural Methane Emissions](cog_transformation/gosat-based-ch4budget-yeargrid-v1.ipynb) 3. [OCO-2 MIP Top-Down CO₂ Budgets](cog_transformation/oco2-mip-co2budget-yeargrid-v1.ipynb) -4. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb) +4. [CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes](cog_transformation/ct-ch4-monthgrid-v2023.ipynb) ## Large Emissions Events diff --git a/datausage.qmd b/datausage.qmd index a3e5bbe3..ba47f6e9 100644 --- a/datausage.qmd +++ b/datausage.qmd @@ -16,8 +16,8 @@ Explore, analyze, and make a difference with the US GHG Center. - [Intermediate level notebook to read and visualize](user_data_notebooks/oco2-mip-National-co2budget.ipynb) 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](https://ceos.org/gst/carbon-dioxide.html), which compliment the global 1° x 1° gridded CO₂ Budget data featured in the [US GHG Center](https://earth.gov/ghgcenter/data-catalog/oco2-mip-co2budget-yeargrid-v1). 2. ODIAC Fossil Fuel CO₂ Emissions - [Beginner level notebook](user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the ODIAC Fossil Fuel CO₂ Emissions dataset. -3. TM5-4DVar Isotopic CH₄ Inverse Fluxes - - [Beginner level notebook](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. +3. CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes + - [Beginner level notebook](user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. 4. U.S. Gridded Anthropogenic Methane Emissions Inventory - [Beginner level notebook](user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset. 5. Vulcan Fossil Fuel CO₂ Emissions @@ -35,14 +35,16 @@ Explore, analyze, and make a difference with the US GHG Center. 4. OCO-2 MIP Top-Down CO₂ Budgets - [Beginner level notebook](user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the OCO-2 MIP Top-Down CO₂ Budgets dataset. - [Intermediate level notebook to read and visualize](user_data_notebooks/oco2-mip-National-co2budget.ipynb)National 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. -5. TM5-4DVar Isotopic CH₄ Inverse Fluxes - - [Beginner level notebook](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. +5. CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes + - [Beginner level notebook](user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. 6. Wetland Methane Emissions, LPJ-EOSIM model - [Beginner level notebook](user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Wetland Methane Emissions, LPJ-EOSIM model dataset. ## Large Emissions Events 1. EMIT Methane Point Source Plume Complexes - [Beginner level notebook](user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the EMIT Methane Point Source Plume Complexes dataset. +2. Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure + - [Beginner level notebook](user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the EMIT Methane Point Source Plume Complexes dataset. ## Greenhouse Gas Concentrations 1. Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory diff --git a/generating_statistics_for_validation/generate_statistics.py b/generating_statistics_for_validation/generate_statistics.py new file mode 100644 index 00000000..0122d128 --- /dev/null +++ b/generating_statistics_for_validation/generate_statistics.py @@ -0,0 +1,58 @@ +import numpy as np +import boto3 +import pandas as pd +import json +import yaml + +# Step 1: Connect to the S3 bucket +s3 = boto3.client('s3') +bucket_name = 'ghgc-data-store-develop' +prefix = 'transformed_cogs/tm5-4dvar-update-noaa/' # optional: specify if files are in a specific folder within the bucket + +# Step 2: List all JSON files in the bucket +response = s3.list_objects_v2(Bucket=bucket_name, Prefix=prefix) +files = [file['Key'] for file in response.get('Contents', []) if file['Key'].endswith('.json') and 'fossil_emis_2015' in file['Key']] +print(len(files)) +# Step 3: Load each JSON file into a DataFrame and combine +dataframes = [] +for file_key in files: + obj = s3.get_object(Bucket=bucket_name, Key=file_key) + data = json.load(obj['Body']) # Load JSON content + df = pd.json_normalize(data) # Normalize JSON to flatten nested structures if needed + dataframes.append(df) +# Combine all DataFrames into one +all_data = pd.concat(dataframes, ignore_index=True) +for name in all_data.columns[3:]: + all_data[name] = all_data[name].astype(np.float32) + +# Step 4: Calculate mean, std, min, and max for overall data +mean_values_netcdf = all_data["mean_value_netcdf"].mean() +std_values_netcdf = all_data["std_value_netcdf"].std() +min_values_netcdf = all_data["minimum_value_netcdf"].min() +max_values_netcdf = all_data["maximum_value_netcdf"].max() +mean_values_cog = all_data["mean_value_cog"].mean() +std_values_cog = all_data["std_value_cog"].std() +min_values_cog = all_data["minimum_value_cog"].min() +max_values_cog = all_data["maximum_value_cog"].max() + +data_to_save = { + "netcdf":{ + "mean":mean_values_netcdf, + "std":std_values_netcdf, + "min":min_values_netcdf, + "max":max_values_netcdf, + }, + "COG": { + "mean":mean_values_cog, + "std":std_values_cog, + "min":min_values_cog, + "max":max_values_cog, + } + +} +print(data_to_save) +# # Write the data to a YAML file +# output_yaml_path = "config.yaml" +# with open(output_yaml_path, "w") as yaml_file: +# yaml.dump(data_to_save, yaml_file, default_flow_style=False) + diff --git a/processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd b/processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd new file mode 100644 index 00000000..5d74ea26 --- /dev/null +++ b/processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd @@ -0,0 +1,9 @@ +--- +title: CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes +description: "Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 2023" +--- + + +

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+ + \ No newline at end of file diff --git a/processing_and_verification_reports/goes-ch4plume-v1_Processing and Verification Report.qmd b/processing_and_verification_reports/goes-ch4plume-v1_Processing and Verification Report.qmd new file mode 100644 index 00000000..2c1ad2ce --- /dev/null +++ b/processing_and_verification_reports/goes-ch4plume-v1_Processing and Verification Report.qmd @@ -0,0 +1,9 @@ +--- +title: Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure +description: "A sample of methane plumes from point sources observed since 2019 by the U.S. Geostationary Operational Environmental Satellites (GOES) over North and South America" +--- + + +

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+ + \ No newline at end of file diff --git a/processing_and_verification_reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.qmd b/processing_and_verification_reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.qmd index 79be9203..e7c2f649 100644 --- a/processing_and_verification_reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.qmd +++ b/processing_and_verification_reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.qmd @@ -1,6 +1,6 @@ --- title: GOSAT-based Top-down Total and Natural Methane Emissions -description: "Annual methane emissions gridded globally at 1° resolution for 2019, version" +description: "Total and wetland yearly methane emissions derived using the GEOS-Chem global chemistry transport model with inclusion of GOSAT data for 2010 to 2022 on a 4 x 5 degree (lat/lon) grid " --- diff --git a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd index 2a2bfc6e..5e7ba796 100644 --- a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd +++ b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd @@ -3,7 +3,7 @@ title: GRA²PES Greenhouse Gas and Air Quality Species description: "Monthly, 0.036 degree resolution emissions of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxide (NOₓ), sulfur dioxide (SO₂), and particulate matter (PM2.5) emissions for the year 2021 over the Contiguous United States from the Greenhouse gas And Air Pollutants Emissions System (GRA²PES)." --- - +

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 \ No newline at end of file diff --git a/processing_and_verification_reports/reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.pdf b/processing_and_verification_reports/reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.pdf new file mode 100644 index 00000000..9f053f2a Binary files /dev/null and b/processing_and_verification_reports/reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.pdf differ diff --git a/processing_and_verification_reports/reports/goes-ch4plume-v1_Processing and Verification Report.pdf b/processing_and_verification_reports/reports/goes-ch4plume-v1_Processing and Verification Report.pdf new file mode 100644 index 00000000..98ec6800 Binary files /dev/null and b/processing_and_verification_reports/reports/goes-ch4plume-v1_Processing and Verification Report.pdf differ diff --git a/processing_and_verification_reports/reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.pdf b/processing_and_verification_reports/reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.pdf index a7964ff1..c8ace068 100644 Binary files a/processing_and_verification_reports/reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.pdf and b/processing_and_verification_reports/reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.pdf differ diff --git a/processing_and_verification_reports/reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.pdf b/processing_and_verification_reports/reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.pdf deleted file mode 100644 index f0048ea9..00000000 Binary files a/processing_and_verification_reports/reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.pdf and /dev/null differ diff --git a/processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd b/processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd deleted file mode 100644 index 3ffffd04..00000000 --- a/processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd +++ /dev/null @@ -1,9 +0,0 @@ ---- -title: TM5-4DVar Isotopic CH₄ Inverse Fluxes -description: "Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 1" ---- - - -

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- - \ No newline at end of file diff --git a/processingreport.qmd b/processingreport.qmd index b10190be..8efd5786 100644 --- a/processingreport.qmd +++ b/processingreport.qmd @@ -16,7 +16,7 @@ Explore, analyze, and make a difference with the US GHG Center. ## Gridded Anthropogenic Greenhouse Gas Emissions 1. [OCO-2 MIP Top-Down CO₂ Budgets Processing and Verification Report](processing_and_verification_reports/oco2-mip-co2budget-yeargrid-v1_Processing and Verification Report.qmd) 2. [ODIAC Fossil Fuel CO₂ Emissions Processing and Verification Report](processing_and_verification_reports/odiac-ffco2-monthgrid-v2023_Processing and Verification Report.qmd) -3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd) +3. [CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Processing and Verification Report](processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd) 5. [Vulcan Fossil Fuel CO₂ Emissions Processing and Verification Report](processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd) 6. [GRA²PES Greenhouse Gas and Air Quality Species Processing and Verification Report](processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd) @@ -27,12 +27,13 @@ Explore, analyze, and make a difference with the US GHG Center. 2. [MiCASA Land Carbon Flux Processing and Verification Report](processing_and_verification_reports/micasa-carbonflux-daygrid-v1_Processing and Verification Report.qmd) 3. [GOSAT-based Top-down Total and Natural Methane Emissions Processing and Verification Report](processing_and_verification_reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.qmd) 4. [OCO-2 MIP Top-Down CO₂ Budgets Processing and Verification Report](processing_and_verification_reports/oco2-mip-co2budget-yeargrid-v1_Processing and Verification Report.qmd) -5. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd) +5. [CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd) 6. [Wetland Methane Emissions, LPJ-EOSIM model Processing and Verification Report](processing_and_verification_reports/lpjeosim-wetlandch4-grid-v1_Processing and Verification Report.qmd) 6. [Wetland Methane Emissions, LPJ-EOSIM model Processing and Verification Report](processing_and_verification_reports/lpjeosim-wetlandch4-grid-v2_Processing and Verification Report.qmd) ## Large Emissions Events 1. [EMIT Methane Point Source Plume Complexes Processing and Verification Report](processing_and_verification_reports/emit-ch4plume-v1_Processing and Verification Report.qmd) +2. [Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure Processing and Verification Report](processing_and_verification_reports/goes-ch4plume-v1_Processing and Verification Report.qmd) ## Greenhouse Gas Concentrations 1. [Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory Processing and Verification Report](processing_and_verification_reports/noaa-gggrn-co2-concentrations_Processing and Verification Report.qmd) diff --git a/user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb b/user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb new file mode 100644 index 00000000..223e4cca --- /dev/null +++ b/user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb @@ -0,0 +1,1779 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "---\n", + "title: CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes\n", + "description: Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 2023.\n", + "author: Siddharth Chaudhary, Vishal Gaur\n", + "date: 15 November 2024\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run this notebook\n", + "\n", + "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", + "\n", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fct-ch4-monthgrid-v2023_User_Notebook.ipynb&branch=main)\n", + " " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Approach\n", + "\n", + "1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API `/stac` endpoint. The collection processed in this notebook is the CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes Data product.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json `endpoint.\n", + "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", + "4. After the visualization, we will perform zonal statistics for a given polygon.\n", + "\n", + " " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## About the Data\n", + "\n", + "Surface 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.\n", + "\n", + "For more information regarding this dataset, please visit the [CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes](https://earth.gov/ghgcenter/data-catalog/ct-ch4-monthgrid-v2023) data overview page." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Install the Required Libraries\n", + "Required 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", + "\n", + "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the following libraries\n", + "import requests\n", + "import folium\n", + "import folium.plugins\n", + "from folium import Map, TileLayer\n", + "from pystac_client import Client\n", + "import branca\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Querying the STAC API\n", + "First, 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." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# 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.\n", + "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", + "\n", + "# The RASTER API is used to fetch collections for visualization\n", + "RASTER_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 \n", + "collection_name = \"ct-ch4-monthgrid-v2023\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'ct-ch4-monthgrid-v2023',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://dev.ghg.center/api/stac/collections/ct-ch4-monthgrid-v2023/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://dev.ghg.center/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://dev.ghg.center/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://dev.ghg.center/api/stac/collections/ct-ch4-monthgrid-v2023'}],\n", + " 'title': 'CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes v2023',\n", + " 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n", + " 'temporal': {'interval': [['1998-01-01 00:00:00+00',\n", + " '2021-12-31 00:00:00+00']]}},\n", + " 'license': 'CC0-1.0',\n", + " 'renders': {'total': {'assets': ['total'],\n", + " 'rescale': [[0, 50]],\n", + " 'colormap_name': 'purd'},\n", + " 'fossil': {'assets': ['fossil'],\n", + " 'rescale': [[0, 50]],\n", + " 'colormap_name': 'purd'},\n", + " 'dashboard': {'assets': ['total'],\n", + " 'rescale': [[0, 50]],\n", + " 'colormap_name': 'purd'},\n", + " 'microbial': {'assets': ['microbial'],\n", + " 'rescale': [[0, 30]],\n", + " 'colormap_name': 'purd'},\n", + " 'pyrogenic': {'assets': ['pyrogenic'],\n", + " 'rescale': [[0, 8]],\n", + " 'colormap_name': 'purd'}},\n", + " 'summaries': {'datetime': ['1998-01-01T00:00:00Z', '2021-12-31T00:00:00Z']},\n", + " 'description': 'Surface 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 gases. 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 NOAA and the National Aeronautics and Space Administration (NASA) 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. This dataset presents monthly methane emissions in units of grams of methane per square meter per year (g CH₄/m²/year) from microbial, fossil and pyrogenic sources, along with a layer of total methane emissions from all three sources combined, at 1° resolution from 1998 to 2021. The source data can be found at https://doi.org/10.25925/40jt-qd67',\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, ruminants, 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 residue burning.'}},\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'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fetch the collection from the STAC API using the appropriate endpoint\n", + "# The 'requests' library allows a HTTP request possible\n", + "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n", + "\n", + "# Print the properties of the collection to the console\n", + "collection" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examining 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." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# 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", + "def 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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 288 items\n" + ] + } + ], + "source": [ + "# Apply the function created above \"get_item_count\" to the data collection\n", + "number_of_items = get_item_count(collection_name)\n", + "\n", + "# Get the information about the number of granules found in the collection\n", + "items = 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\n", + "print(f\"Found {len(items)} items\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Examine the first item in the collection\n", + "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", + "items[0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring Changes in CH₄ flux Levels Using the Raster API\n", + "\n", + "In 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`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\n", + "items = {item[\"properties\"][\"start_datetime\"][: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”\n", + "asset_name = \"fossil\" #fossil fuel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, we are entering the minimum and maximum values to provide our upper and lower bounds in the `rescale_values`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Fetching the min and max values for a specific item\n", + "rescale_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\"]}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, 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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 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\n", + "color_map = \"purd\"\n", + "\n", + "# Make a GET request to retrieve information for the 2016 tile\n", + "ch4_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\n", + "ch4_flux_1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a GET request to retrieve information for the 1999 tile\n", + "ch4_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\n", + "ch4_flux_2" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing CH₄ flux Emissions from Fossil Fuel" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 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\n", + "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", + "\n", + "# Define the first map layer (2016)\n", + "map_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\n", + "map_layer_2016.add_to(map_.m1)\n", + "\n", + "\n", + "# Define the second map layer (1999)\n", + "map_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\n", + "map_layer_1999.add_to(map_.m2)\n", + "\n", + "# Visualize the Dual Map\n", + "map_" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculating Zonal Statistics" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a polygon for the area of interest (aoi)\n", + "texas_aoi = {\n", + " \"type\": \"Feature\", # Create a feature object\n", + " \"properties\": {},\n", + " \"geometry\": { # Set the bounding coordinates for the polygon\n", + " \"coordinates\": [\n", + " [\n", + " [-95, 29], # South-east bounding coordinate\n", + " [-95, 33], # North-east bounding coordinate\n", + " [-104,33], # North-west bounding coordinate\n", + " [-104,29], # South-west bounding coordinate\n", + " [-95, 29] # South-east bounding coordinate (closing the polygon)\n", + " ]\n", + " ],\n", + " \"type\": \"Polygon\",\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a new map to display the generated polygon\n", + "# We'll plug in the coordinates for a location\n", + "# Central to the study area and a reasonable zoom level\n", + "aoi_map = Map(\n", + "\n", + " # Base map is set to OpenStreetMap\n", + " tiles=\"OpenStreetMap\",\n", + "\n", + " # Define the spatial properties for the map\n", + " location=[\n", + " 30,-100\n", + " ],\n", + "\n", + " # Set the zoom value\n", + " zoom_start=6,\n", + ")\n", + "\n", + "# Insert the polygon to the map\n", + "folium.GeoJson(texas_aoi, name=\"Texas, USA\").add_to(aoi_map)\n", + "\n", + "# Visualize the map\n", + "aoi_map" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 288 items\n" + ] + } + ], + "source": [ + "# Check total number of items available within the collection\n", + "items = requests.get(\n", + " f\"{STAC_API_URL}/collections/{collection_name}/items?limit=600\"\n", + ").json()[\"features\"]\n", + "\n", + "# Print the total number of items (granules) found\n", + "print(f\"Found {len(items)} items\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Examine the first item in the collection\n", + "items[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we created the polygon for the area of interest, we need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection\n", + "# Create a function that retrieves information regarding a specific granule using its asset name and raster identifier and generates the statistics for it\n", + "\n", + "# The function takes an item (granule) and a JSON (polygon) as input parameters\n", + "def generate_stats(item, geojson):\n", + "\n", + " # A POST request is made to submit the data associated with the item of interest (specific observation) within the boundaries of the polygon to compute its statistics\n", + " result = requests.post(\n", + "\n", + " # Raster API Endpoint for computing statistics\n", + " f\"{RASTER_API_URL}/cog/statistics\",\n", + "\n", + " # Pass the URL to the item, asset name, and raster identifier as parameters\n", + " params={\"url\": item[\"assets\"][asset_name][\"href\"]},\n", + "\n", + " # Send the GeoJSON object (polygon) along with the request\n", + " json=geojson,\n", + "\n", + " # Return the response in JSON format\n", + " ).json()\n", + "\n", + " # Print the result\n", + " print(result)\n", + "\n", + " # Return a dictionary containing the computed statistics along with the item's datetime information.\n", + " return {\n", + " **result[\"properties\"],\n", + " \"datetime\": item[\"properties\"][\"start_datetime\"][:10],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-12-01\n" + ] + } + ], + "source": [ + "# Generate a for loop that iterates over all the existing items in the collection\n", + "for item in items:\n", + "\n", + " # The loop will then retrieve the information for the start datetime of each item in the list\n", + " print(item[\"properties\"][\"start_datetime\"][:10])\n", + "\n", + " # Exit the loop after printing the start datetime for the first item in the collection\n", + " break" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the function above we can generate the statistics for the AOI." + ] + }, + { 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[0.052018797181943, 1.7396911286134804, 3.427363460045018, 5.115035791476555, 6.802708122908093, 8.490380454339629, 10.178052785771166, 11.865725117202704, 13.553397448634241, 15.241069780065779, 16.928742111497318]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.052018797181943, 'percentile_98': 16.928742111497318}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.04866568769103654, 'max': 15.40446170465091, 'mean': 3.0925303410411136, 'count': 36.0, 'sum': 111.3310922774801, 'std': 3.080247911241435, 'median': 1.7905992166111768, 'majority': 0.04866568769103654, 'minority': 0.04866568769103654, 'unique': 36.0, 'histogram': [[15.0, 8.0, 3.0, 6.0, 1.0, 2.0, 0.0, 0.0, 0.0, 1.0], [0.04866568769103654, 1.5842452893870238, 3.1198248910830113, 4.655404492778998, 6.190984094474985, 7.726563696170973, 9.26214329786696, 10.797722899562947, 12.333302501258935, 13.868882102954922, 15.40446170465091]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.04866568769103654, 'percentile_98': 15.40446170465091}}}}\n", + "CPU times: user 2.77 s, sys: 366 ms, total: 3.14 s\n", + "Wall time: 3min 35s\n" + ] + } + ], + "source": [ + "%%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 polygon\n", + "stats = [generate_stats(item, texas_aoi) for item in items]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create 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." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': 0.042764596042364544,\n", + " 'max': 45.46200998420616,\n", + " 'mean': 9.320847132019729,\n", + " 'count': 36.0,\n", + " 'sum': 335.5504967527103,\n", + " 'std': 11.191973503272377,\n", + " 'median': 5.158028075849703,\n", + " 'majority': 0.042764596042364544,\n", + " 'minority': 0.042764596042364544,\n", + " 'unique': 36.0,\n", + " 'histogram': [[17.0, 7.0, 4.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0, 1.0],\n", + " [0.042764596042364544,\n", + " 4.584689134858745,\n", + " 9.126613673675124,\n", + " 13.668538212491503,\n", + " 18.210462751307883,\n", + " 22.752387290124265,\n", + " 27.294311828940643,\n", + " 31.83623636775702,\n", + " 36.378160906573406,\n", + " 40.92008544538979,\n", + " 45.46200998420616]],\n", + " 'valid_percent': 100.0,\n", + " 'masked_pixels': 0.0,\n", + " 'valid_pixels': 36.0,\n", + " 'percentile_2': 0.042764596042364544,\n", + " 'percentile_98': 45.46200998420616}},\n", + " 'datetime': '2021-12-01'}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print the stats for the first item in the collection\n", + "stats[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02021-12-010.04276545.4620109.32084736.0335.55049711.1919745.1580280.0427650.04276536.0[[17.0, 7.0, 4.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0...100.00.036.00.04276545.4620102021-12-01
12021-11-010.04496447.7926569.88173736.0355.74254711.8112065.5399230.0449640.04496436.0[[17.0, 7.0, 4.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0...100.00.036.00.04496447.7926562021-11-01
22021-10-010.04735450.74800710.48213436.0377.35682812.5448515.8757160.0473540.04735436.0[[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0...100.00.036.00.04735450.7480072021-10-01
32021-09-010.04978553.75799711.03849936.0397.38596813.2610916.1603180.0497850.04978536.0[[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0...100.00.036.00.04978553.7579972021-09-01
42021-08-010.05131755.69460311.38766536.0409.95593813.7173796.3371740.0513170.05131736.0[[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0...100.00.036.00.05131755.6946032021-08-01
\n", + "
" + ], + "text/plain": [ + " datetime min max mean count sum std \\\n", + "0 2021-12-01 0.042765 45.462010 9.320847 36.0 335.550497 11.191974 \n", + "1 2021-11-01 0.044964 47.792656 9.881737 36.0 355.742547 11.811206 \n", + "2 2021-10-01 0.047354 50.748007 10.482134 36.0 377.356828 12.544851 \n", + "3 2021-09-01 0.049785 53.757997 11.038499 36.0 397.385968 13.261091 \n", + "4 2021-08-01 0.051317 55.694603 11.387665 36.0 409.955938 13.717379 \n", + "\n", + " median majority minority unique \\\n", + "0 5.158028 0.042765 0.042765 36.0 \n", + "1 5.539923 0.044964 0.044964 36.0 \n", + "2 5.875716 0.047354 0.047354 36.0 \n", + "3 6.160318 0.049785 0.049785 36.0 \n", + "4 6.337174 0.051317 0.051317 36.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[17.0, 7.0, 4.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0... 100.0 \n", + "1 [[17.0, 7.0, 4.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0... 100.0 \n", + "2 [[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0... 100.0 \n", + "3 [[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0... 100.0 \n", + "4 [[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0... 100.0 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 date \n", + "0 0.0 36.0 0.042765 45.462010 2021-12-01 \n", + "1 0.0 36.0 0.044964 47.792656 2021-11-01 \n", + "2 0.0 36.0 0.047354 50.748007 2021-10-01 \n", + "3 0.0 36.0 0.049785 53.757997 2021-09-01 \n", + "4 0.0 36.0 0.051317 55.694603 2021-08-01 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", + "def 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\n", + "df = 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\n", + "df.head(5)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing the Data as a Time Series\n", + "We 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:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure size: 20 representing the width, 10 representing the height\n", + "df = df.sort_values(by=\"datetime\")\n", + "fig = plt.figure(figsize=(20, 10))\n", + "\n", + "plt.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\n", + "plt.legend()\n", + "\n", + "# Insert label for the X-axis\n", + "plt.xlabel(\"Years\")\n", + "\n", + "# Insert label for the Y-axis\n", + "plt.ylabel(\"g CH₄/m²/year\")\n", + "plt.xticks(rotation = 90)\n", + "\n", + "interval = 12\n", + "ticks = range(0, len(df), interval) # Tick positions\n", + "plt.xticks(ticks, df[\"datetime\"].iloc[ticks]) # Use the corresponding datetime values\n", + "\n", + "\n", + "# Insert title for the plot\n", + "plt.title(\"CH4 emission Values for Texas, Dallas (1999-2016)\")\n", + "\n", + "# Add data citation\n", + "plt.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 CarbonTracker-CH₄ Isotopic Methane 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\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-10-01T00:00:00+00:00\n" + ] + } + ], + "source": [ + "# Print the properties for the 3rd item in the collection\n", + "print(items[2][\"properties\"][\"start_datetime\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://dev.ghg.center/api/raster/collections/ct-ch4-monthgrid-v2023/items/ct-ch4-monthgrid-v2023-202110/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C198.25427154645362'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# A GET request is made for the 3rd granule\n", + "ch4_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\n", + "ch4_flux_3" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a new map to display the tile\n", + "aoi_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\n", + "map_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\n", + "map_layer.add_to(aoi_map_bbox)\n", + "\n", + "# Visualize the map\n", + "aoi_map_bbox" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook we have successfully explored, analyzed, and visualized the STAC collection for TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n", + "\n", + "1. Install and import the necessary libraries\n", + "2. Fetch the collection from STAC collections using the appropriate endpoints\n", + "3. Count the number of existing granules within the collection\n", + "4. Map and compare the CH₄ inverse fluxes for two distinctive months/years\n", + "5. Generate zonal statistics for the area of interest (AOI)\n", + "6. Visualizing the Data as a Time Series\n", + "\n", + "\n", + "If you have any questions regarding this user notebook, please contact us using the [feedback form](https://docs.google.com/forms/d/e/1FAIpQLSeVWCrnca08Gt_qoWYjTo6gnj1BEGL4NCUC9VEiQnXA02gzVQ/viewform)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb b/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb new file mode 100644 index 00000000..bd8d1854 --- /dev/null +++ b/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb @@ -0,0 +1,25 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure\n", + "description: A sample of methane plumes from point sources observed since 2019 by the U.S. Geostationary Operational Environmental Satellites (GOES) over North and South America.\n", + "author: Siddharth Chaudhary\n", + "date: 15 November 2024\n", + "execute:\n", + " freeze: true\n", + "---" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb index ab47a874..fcdf71c8 100644 --- a/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb @@ -2,13 +2,17 @@ "cells": [ { "cell_type": "raw", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", "title: GOSAT-based Top-down Total and Natural Methane Emissions\n", - "description: Total and natural methane emissions for 2019 summed to a 1° resolution grid. Methane values for both prior to and after inclusion of GOSAT data to the GEOS-Chem global chemistry transport model, version 1.0\n", + "description: Total and wetland yearly methane emissions derived using the GEOS-Chem global chemistry transport model with inclusion of GOSAT data for 2010 to 2022 on a 4 x 5 degree (lat/lon) grid\n", "author: Siddharth Chaudhary, Vishal Gaur\n", - "date: 21 September 2023\n", + "date: 15 November 2024\n", "execute:\n", " freeze: true\n", "---" @@ -4438,7 +4442,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb b/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb deleted file mode 100644 index 221daba3..00000000 --- a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb +++ /dev/null @@ -1,2170 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "title: TM5-4DVar Isotopic CH₄ Inverse Fluxes\n", - "description: Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 1.\n", - "author: Siddharth Chaudhary, Vishal Gaur\n", - "date: 22 August 2023\n", - "execute:\n", - " freeze: true\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run this notebook\n", - "\n", - "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", - "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Ftm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb&branch=main)\n", - " " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Approach\n", - "\n", - "1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API `/stac` endpoint. The collection processed in this notebook is the TM5-4DVar Isotopic CH₄ Inverse Fluxes Data product.\n", - "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json `endpoint.\n", - "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", - "4. After the visualization, we will perform zonal statistics for a given polygon.\n", - "\n", - " " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## About the Data\n", - "\n", - "Surface 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.\n", - "\n", - "For more information regarding this dataset, please visit the [TM5-4DVar Isotopic CH₄ Inverse Fluxes](https://earth.gov/ghgcenter/data-catalog/tm54dvar-ch4flux-monthgrid-v1) data overview page." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Install the Required Libraries\n", - "Required 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", - "\n", - "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# Import the following libraries\n", - "import requests\n", - "import folium\n", - "import folium.plugins\n", - "from folium import Map, TileLayer\n", - "from pystac_client import Client\n", - "import branca\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Querying the STAC API\n", - "First, 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." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# 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.\n", - "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", - "\n", - "# The RASTER API is used to fetch collections for visualization\n", - "RASTER_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 \n", - "collection_name = \"tm54dvar-ch4flux-monthgrid-v1\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'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'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fetch the collection from the STAC API using the appropriate endpoint\n", - "# The 'requests' library allows a HTTP request possible\n", - "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n", - "\n", - "# Print the properties of the collection to the console\n", - "collection" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Examining 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." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# 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", - "def 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" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 216 items\n" - ] - } - ], - "source": [ - "# Apply the function created above \"get_item_count\" to the data collection\n", - "number_of_items = get_item_count(collection_name)\n", - "\n", - "# Get the information about the number of granules found in the collection\n", - "items = 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\n", - "print(f\"Found {len(items)} items\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'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': []}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Examine the first item in the collection\n", - "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", - "items[0]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploring Changes in CH₄ flux Levels Using the Raster API\n", - "\n", - "In 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`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\n", - "items = {item[\"properties\"][\"start_datetime\"][: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”\n", - "asset_name = \"fossil\" #fossil fuel" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we are entering the minimum and maximum values to provide our upper and lower bounds in the `rescale_values`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Fetching the min and max values for a specific item\n", - "rescale_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\"]}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, 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." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'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]}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 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\n", - "color_map = \"purd\"\n", - "\n", - "# Make a GET request to retrieve information for the 2016 tile\n", - "ch4_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\n", - "ch4_flux_1" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'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]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Make a GET request to retrieve information for the 1999 tile\n", - "ch4_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\n", - "ch4_flux_2" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing CH₄ flux Emissions from Fossil Fuel" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 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\n", - "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", - "\n", - "# Define the first map layer (2016)\n", - "map_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\n", - "map_layer_2016.add_to(map_.m1)\n", - "\n", - "\n", - "# Define the second map layer (1999)\n", - "map_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\n", - "map_layer_1999.add_to(map_.m2)\n", - "\n", - "# Visualize the Dual Map\n", - "map_" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Calculating Zonal Statistics" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a polygon for the area of interest (aoi)\n", - "texas_aoi = {\n", - " \"type\": \"Feature\", # Create a feature object\n", - " \"properties\": {},\n", - " \"geometry\": { # Set the bounding coordinates for the polygon\n", - " \"coordinates\": [\n", - " [\n", - " [-95, 29], # South-east bounding coordinate\n", - " [-95, 33], # North-east bounding coordinate\n", - " [-104,33], # North-west bounding coordinate\n", - " [-104,29], # South-west bounding coordinate\n", - " [-95, 29] # South-east bounding coordinate (closing the polygon)\n", - " ]\n", - " ],\n", - " \"type\": \"Polygon\",\n", - " },\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a new map to display the generated polygon\n", - "# We'll plug in the coordinates for a location\n", - "# Central to the study area and a reasonable zoom level\n", - "aoi_map = Map(\n", - "\n", - " # Base map is set to OpenStreetMap\n", - " tiles=\"OpenStreetMap\",\n", - "\n", - " # Define the spatial properties for the map\n", - " location=[\n", - " 30,-100\n", - " ],\n", - "\n", - " # Set the zoom value\n", - " zoom_start=6,\n", - ")\n", - "\n", - "# Insert the polygon to the map\n", - "folium.GeoJson(texas_aoi, name=\"Texas, USA\").add_to(aoi_map)\n", - "\n", - "# Visualize the map\n", - "aoi_map" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 216 items\n" - ] - } - ], - "source": [ - "# Check total number of items available within the collection\n", - "items = requests.get(\n", - " f\"{STAC_API_URL}/collections/{collection_name}/items?limit=600\"\n", - ").json()[\"features\"]\n", - "\n", - "# Print the total number of items (granules) found\n", - "print(f\"Found {len(items)} items\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'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': []}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Examine the first item in the collection\n", - "items[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we created the polygon for the area of interest, we need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection\n", - "# Create a function that retrieves information regarding a specific granule using its asset name and raster identifier and generates the statistics for it\n", - "\n", - "# The function takes an item (granule) and a JSON (polygon) as input parameters\n", - "def generate_stats(item, geojson):\n", - "\n", - " # A POST request is made to submit the data associated with the item of interest (specific observation) within the boundaries of the polygon to compute its statistics\n", - " result = requests.post(\n", - "\n", - " # Raster API Endpoint for computing statistics\n", - " f\"{RASTER_API_URL}/cog/statistics\",\n", - "\n", - " # Pass the URL to the item, asset name, and raster identifier as parameters\n", - " params={\"url\": item[\"assets\"][asset_name][\"href\"]},\n", - "\n", - " # Send the GeoJSON object (polygon) along with the request\n", - " json=geojson,\n", - "\n", - " # Return the response in JSON format\n", - " ).json()\n", - "\n", - " # Print the result\n", - " print(result)\n", - "\n", - " # Return a dictionary containing the computed statistics along with the item's datetime information.\n", - " return {\n", - " **result[\"properties\"],\n", - " \"datetime\": item[\"properties\"][\"start_datetime\"][:10],\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2016-12-01\n" - ] - } - ], - "source": [ - "# Generate a for loop that iterates over all the existing items in the collection\n", - "for item in items:\n", - "\n", - " # The loop will then retrieve the information for the start datetime of each item in the list\n", - " print(item[\"properties\"][\"start_datetime\"][:10])\n", - "\n", - " # Exit the loop after printing the start datetime for the first item in the collection\n", - " break" - ] - }, - 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"{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0809581013139931, 'max': 24.822949625050086, 'mean': 4.3481885376528915, 'count': 36.0, 'sum': 156.5347873555041, 'std': 4.633345420525916, 'median': 2.5974040079993728, 'majority': 0.0809581013139931, 'minority': 0.0809581013139931, 'unique': 36.0, 'histogram': [[17.0, 8.0, 6.0, 2.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0], [0.0809581013139931, 2.5551572536876024, 5.029356406061212, 7.5035555584348215, 9.97775471080843, 12.45195386318204, 14.926153015555649, 17.40035216792926, 19.874551320302867, 22.348750472676475, 24.822949625050086]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0809581013139931, 'percentile_98': 24.822949625050086}}}}\n", - "CPU times: user 2.27 s, sys: 465 ms, total: 2.73 s\n", - "Wall time: 2min\n" - ] - } - ], - "source": [ - "%%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 polygon\n", - "stats = [generate_stats(item, texas_aoi) for item in items]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create 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." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'statistics': {'b1': {'min': 0.0464402866499578,\n", - " 'max': 49.61378870603235,\n", - " 'mean': 9.039553150168388,\n", - " 'count': 36.0,\n", - " 'sum': 325.42391340606196,\n", - " 'std': 11.97160706711745,\n", - " 'median': 3.662876577293575,\n", - " 'majority': 0.0464402866499578,\n", - " 'minority': 0.0464402866499578,\n", - " 'unique': 36.0,\n", - " 'histogram': [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0, 1.0],\n", - " [0.0464402866499578,\n", - " 5.003175128588197,\n", - " 9.959909970526436,\n", - " 14.916644812464675,\n", - " 19.873379654402914,\n", - " 24.830114496341153,\n", - " 29.786849338279392,\n", - " 34.74358418021763,\n", - " 39.700319022155874,\n", - " 44.65705386409412,\n", - " 49.61378870603235]],\n", - " 'valid_percent': 100.0,\n", - " 'masked_pixels': 0.0,\n", - " 'valid_pixels': 36.0,\n", - " 'percentile_2': 0.0464402866499578,\n", - " 'percentile_98': 49.61378870603235}},\n", - " 'datetime': '2016-12-01'}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Print the stats for the first item in the collection\n", - "stats[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02016-12-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-12-01
12016-11-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-11-01
22016-10-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-10-01
32016-09-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-09-01
42016-08-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-08-01
\n", - "
" - ], - "text/plain": [ - " datetime min max mean count sum std \\\n", - "0 2016-12-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "1 2016-11-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "2 2016-10-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "3 2016-09-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "4 2016-08-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "\n", - " median majority minority unique \\\n", - "0 3.662877 0.04644 0.04644 36.0 \n", - "1 3.662877 0.04644 0.04644 36.0 \n", - "2 3.662877 0.04644 0.04644 36.0 \n", - "3 3.662877 0.04644 0.04644 36.0 \n", - "4 3.662877 0.04644 0.04644 36.0 \n", - "\n", - " histogram valid_percent \\\n", - "0 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "1 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "2 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "3 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "4 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 date \n", - "0 0.0 36.0 0.04644 49.613789 2016-12-01 \n", - "1 0.0 36.0 0.04644 49.613789 2016-11-01 \n", - "2 0.0 36.0 0.04644 49.613789 2016-10-01 \n", - "3 0.0 36.0 0.04644 49.613789 2016-09-01 \n", - "4 0.0 36.0 0.04644 49.613789 2016-08-01 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", - "def 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\n", - "df = 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\n", - "df.head(5)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing the Data as a Time Series\n", - "We 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:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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+ffpYvL7ZkiVLrPYVHMMCihvZv38/rV+/nrKzs4mIKCoqipo1a0Y1atSgy5cv09atWy3q0tLS6PTp03Zj7G2PiGyWV6tWjVasWEEbNmywquvYsSMdPHjQKq5JkyZ0/PhxmzH33XcfrVy50qquSZMmxMy0adMmp2OaNWtGCQkJtHHjxnLlrSRGSd7NmjWjuXPn2oyJjIyknTt3Oh1Tv359Kioqoh07dljF3H///bR48eJyxTz88MO0fv36cu2rkrZT8r7WrVuX/v33X5sx9vJ2FGOvfdRuO0f76qqxomafU9o+9mIc5aB1n6vI49Ve+ziaIwIDA8s9F9ibPxz1YT3MEUpywHjFeNXLHFFR51clbao0B1fl3aJFC/rmm2+sYho0aEBhYWF06NAhp2Nq1apFzEz79u2ziunWrRstWbLEZvvYq3OUg5L5Q+s+p4fxqofvotWqVaOzZ8/Stm3brGIef/xx+vPPP8sVY6//KOmPjvqc2mPcncarkjlZzbGipM+p3XaOYuzloPYYd6fxquYcoYfvw678P5jWebuqz6k9Xs+dO0d//fUXjRkzhtq3by8vrkydOpUmT55MRqORevToQSNHjiQisejy1ltv0aZNm6hXr140ZswYOWbMmDH09ddfkyRJlJGRQW+88YYc88ILL9Bvv/1GHTp0oMmTJ1ss4qxYsYJef/11KiwsJGaWf2x48+ZNIiJq3rw5TZ06leLj48uMycvLI0mSyKz0DxdTUlLo8OHDVj98txdDROTp6UlvvvkmpaenWy1YT5gwgZ5++ml5X6FsWEABK6dOnaKYmBgyGHCLHAAAsIQ5AgAAAAAAALQ0adIkmjp1KmVnZ8sLCcxMQUFBFBAQQP/++y/dunWLiIiMRiOlpaVRlSpVKDMz0yrG39+fiIiuXr1qUR4VFUX16tWj7du3W8VERUXRyJEjaejQoVY/Nvzjjz/o888/L1eM+ewbez9ctPejRnsxn332mc32MecwatSoO/K+3LVu7x70UFEFBATwkSNHylWndkydOnX4xIkT5dqeoxh7dUpilOStdF+V5N2lSxc+ffq0KjH2ypXGuKq9lbyOkryVtI+jOiU56GGsqNnnHNUpidFzn1Oat9bjVcnnvaM6JTF66Pdq54DxivF6OzFqzhFKt6f1/Kp0TtZz3k899RSfPXtWlRh75Urr1J4/tO5zjuJcNV718F3UUV9QEqNmf3TlGHen8ar13KakzzmKU7s/umqMu9N4xTET9ecpPY9XtT/PypvD0aNH+a+//uK//vqLjx49Kpfn5+fz6dOn+fTp05yfn+9UjL3ysurMTp48ybdu3bqtmLLKyxvjTA5QNiyguCl/f3+7E5e9OlfF6CEH7Kv6MXrIAfuqjxg95IB9VT9GDzlgX9WP0UMO2Ff1Y/SQA/ZVsHcwSUmM2ovfemgfd+oLrtpXR31BSYya/VHv7yvGq+v6nKM4tfuj1u3jqO5uG696H+Nax+ghB1f1OUd1Sj8z1q5dyzdv3rQqnzt3Ll+9erVcMfbKy6qzl7uSGLXbzlEOUDZcfwMAAAAAAEADrOBqyvZiHG1LaR3cfdTsc47q7sY+h/GqjNKc1Wy7ithuRBiv4Hqu6nOO6pT2uc6dO9M///xjVT5kyBDKyckpV4y98rLq7OWuJEbttnOUA5QNCyhu6vHHH6fAwECbda+88gqFhoaWK8ZenaOYli1bko+PT7m25yjGXp2SGCV5K91XJXknJiaSp6enKjH2ypXGuKq9lbyOkryVtI+jOiU56GGsqNnnHNUpidFzn1Oat9bjVckc4ShOyfyhh36vdg4YrxivtxOj5hyhdHtaz69K52Q9522Pkhi1qT1/aN3nHMW5arzq4buoI1r3O1eOcXcar1rPbY7oue0wXpW/Po6ZqD9PYbyWnYOrFiLUXmRyFT3kUJHhJvIAAAAAAAAAAAAAUCEFBATQjh07qHLlyk6Vqx1DRPT222/TU089RcHBwbcdY69caYyjHKBsWEBxEzdu3KCtW7dSaGgo1apVy6Lu5s2b9N1331FGRoZF+blz52jKlCk0Z84ciomJISKiqKgoat68OfXt25d+++03Wrt2Lf37779kMBiocuXKFBUVRXl5eZSenk4tWrSgVatW0XvvvUdHjx6lfv360ejRo61yO3XqFHl7e1N4eDgREf3555/03//+l06cOEGJiYk0bNgwatasGTEzrV69mg4fPkzR0dGUnp5utfJcuXJlmjdvHl27do3q1q1LoaGhdO7cOfryyy8pLy+PHnroIQoICKDg4GDy9/e3iC0oKKD169dTq1atLMpzcnLo3XffpXHjxjkdc/LkSXr++ecpNjaWsrOzLdque/futHbtWqu2S0lJoXPnzlGbNm2ocuXKtGfPHpo2bRodPnyYhg0bRt27d7d4jby8PDIYDHIbHDlyhL766iu53QYOHEjJycny848dOya3XZ06dSy21a5dO5o5cyYlJCRQVlYWxcfHk4eHB+Xn59PChQspLy+POnXqRBEREVbvX1FREZ06dYoSEhKs6i5evEizZ8+mQYMGqdJ2gYGBtHHjRot2e+CBB+j69eu0detWi7Z77rnnKC4ujvr06UPp6emK285Ru93Jttu9ezf99ddf9OSTTzrVbvn5+TRr1iyaOXMmJSUlWbSbHvucue2++uorYmab7dalSxcKDQ0lg8H6ZEl7badln6vI4/XGjRu0evVq2r59u9XntL05gohox44dNGjQIIqLi7Nou0aNGpGfnx/t2LHDou0efPBBunHjBm3atMlinhg2bBhFRETQY489ZtXn9T5HEBFt27aNFixYQG+99ZZTMRivAsar6+YINdvOlfOr2n2OiOj8+fP0yy+/WH2mOYr57bffaNasWRQfHy+3W7Nmzahx48Z09OhRq/Far149On78OKWlpVFQUBDl5OTQ7Nmz6eTJk/Tkk09Samqq1WuUdOnSJfr+++/ldnvooYcoKChIri8oKKCsrCyKiIiwKCcieuKJJ+itt96imJgYunXrFhmNRrlu48aNlJeXR82aNSv3L0ivXbtGy5Yto06dOlXY73RE2oxXV/S5Dh060M2bN2n37t0W/c7cF7p27Xpb/c5RnyMq7neRkZGq9rnNmzdTmzZtrOowXh1T0nYXL16kDz/8kEJDQy3Ga7NmzSg8PNzlfU6rtrty5Qpt27bN6vMM49UxtecIPX4fvpPHTBYtWkT9+/d3Ok5v49XZPncnPuu2bt0q9625c+dS9+7dyc/Pz+J5a9eupUaNGpHJZLLahr0Ye+Vl1dmjJIZI9ElzX7tdSnOA/0/le6qADh04cIATExNZkiQ2GAzcqlUr/ueff+T67OxsNhgMFjGbNm3ikJAQjoiIYCLiUaNG8ahRo7hfv34cFRXFBoOBQ0NDOT4+niVJ4q5du3JSUhITEYeEhHBgYCB//fXXHBAQwIMGDWIiYiLiatWq8bx58zgvL09+rcaNG/OSJUuYmfmnn35ig8HADzzwAFeuXJm7devGnp6ePHfuXG7SpAlLksSVKlViSZI4IiKC33rrLZ46dar8MBgMbDKZWJIk9vX15S1btnBycjKnpKRYtIHRaOT/+7//4ytXrjhsh9OnT3Pt2rWZiJyOOXToEMfFxTERcevWrblPnz7cp08fbt26NZtMJjaZTGwwGNjDw4MNBgOnpaVxcHAwExH7+Piwv78/r1y5koODg/m+++6T265t27a8YcMG+XVat27N33//PTOLm0GZTCa+5557uG/fvhweHs4+Pj78119/8fXr17lXr14sSRITEUuSxKmpqTxv3jxetGgRL1q0iI1GI7/++uscERHBBoOBq1atykePHuW0tDT29fVlo9HIRMRhYWH8+uuvc2FhocM2ULvtmjVrJudfst3MfVGSJA4LC7NoO/NzJUnicePGWbyOvbarUqUK33PPPezr68uZmZlyu5kfpdvtTrTd6dOnuVGjRmwwGJiInG63ypUrs8lkYiLSVZ9z1HbmzxGDwcDR0dFyu/n5+bGPjw97eXmxyWTiiIgIp9tOyz5XUcdryTmCiJyaI5jFPBEYGMhExP369ZPniR49erDRaGRJkjgyMlKeI8yf4ZIkcYMGDSzmCfO+GgwGzsjIqFBzBMYrxqsrx6uSPqd227lqflW7z+Xm5vJDDz0kjz1nYnJycvjee++V26hx48bcuHFjTkxMlN838z4YDAa53cz9ISoqirdv385xcXGckpIi97n+/ftbfM726NFDbrfdu3dzeHg4V6pUiZs0acL+/v4cGRnJe/fu5cLCQn7++efZy8uLJUlio9HI3bt35y1btvCOHTt4x44d7OnpyV9++SXXq1ePjUYjt2rVii9cuMBdu3aV9yMkJISTkpK4UaNG/OWXX1rssx7Hq5I+p4fx6qo+ZzQa2dvbW+4XJfsdEbGnpycbjUaeO3euxWvZ63fx8fEcERHBUVFRvGvXLrnPmefH0n1ux44d7OHhwTVq1GCj0chpaWm33edyc3O5Q4cOTEROj3GM19tru/r16zMRcWJiotx28fHxcjvcyT6ndtuZc/b09OR69eo53W4Yr9rPEXr4PuzKYyYVcbwq6XNqj1ez7du3261zxs2bN2/rpurdunXj//3vf3z9+nWnnl9QUGBV9s8///CYMWP40Ucf5eeff5737dtnUe/p6cl79+7lX3/9lceMGcO///47MzOvWbOGO3XqxM2bN+fBgwfzL7/8YvF/aGbmq1evWn1vAuWwgOIGHnzwQe7atSufPXuWDx06xF27duXk5GQ+fvw4L1q0iGfPns2SJFn8p7VatWqcnp7OU6ZMsfpA6ty5M9esWZObNGnCzMzvvPMOd+7cmWvVqsVvvvkmJyUlcUZGBnt7e/O0adOYmVmSJB44cCD7+/uzp6cnh4WF8YgRI3jXrl3s5+fHR48eZWbmJk2a8DvvvCPH5OTk8Mcff8zh4eFcq1Yt+XmSJLGnpycHBARwUlKS/CAi9vX15YSEBA4NDeW4uDgeNGgQMzNnZGRwpUqVuHXr1rxy5UpOS0vjhg0b8p9//sk7duzgVatWMRFZfNDff//9XLVqVZYkyemYpk2bcsOGDVmSJKv3omfPnhwVFcXt2rXjmzdv8vDhwzkjI4MbNGjAAwYM4LCwMM7IyODg4GAeP368vK/p6ens4+PDkiRx7dq1+YMPPuCAgAA+ePAgM4svBs8++6z8OgaDgZ999llu0aIFjx49muPi4njVqlXyRFpyYjU/SpaNHDmSa9asyd27d+fhw4dzSkoKp6WlcZMmTTgxMZG7du3KeXl5nJuby4cOHWIi4tzcXIvHI488wrVq1VKl7fr27cvdunXjLl268H333Se3GzNzSkoK+/j48Icffsjffvut3HaSJPH48eM5NjaWiUhut3PnznFgYKDNtjMYDJyTk8OvvfYax8XFye127do1qza6U233yCOPcMOGDXn69OksSZLcbhcuXHDYbm3btuWZM2dajVet+5yjtjOXmR/mdsvPz+dhw4axn58ft2zZkqdPny633dmzZx22nZZ9rqKO127dunF6ejovWbJEXuwoa44wzxPmA2Gl54gnn3ySBw8ezE2bNpXnCGbmqlWrclhYGI8dO5ZXrVolzxOSJPHMmTPl/wzoaY5wNPbuv/9+Tk1N5YkTJ2K8Yry6ZLwqmSMq6vyqpM85aruhQ4dy1apV5X1wJuaBBx7gxo0b85w5c6zGa58+fTggIIA7dOjAhw4d4t69e/OoUaO4efPm3K5dO/bx8eFHH32UY2NjediwYXKfu+eee9jT05M9PDy4a9euvHDhQg4JCZH/w9y5c2d+9NFH5f8EGwwGfvTRR7ljx448efJkDgkJ4a+++sqinRx9L+nbty83b96cW7ZsyadOneKRI0eyp6cnN2/enF999VUOCgriJ598Ut6v7Oxsq36SkZHBqampFeo7nR7Gq6v63LVr1zglJYU9PDz4yy+/5MmTJ8v9TpIkHjx4MPv6+spz/MKFC7mwsNBuvzMYDHzq1CkeOHAgV6tWTe5ze/bsKbPPmR+32+eeeeYZ+YcNzo5xjFfHbedoTn7ggQe4Tp06Vtt68sknuUGDBly3bl3u1KnTHetzarfds88+y5UqVeKkpCROS0tzut2qVavGkydPxnjVcI5g1v77sCuPmQwdOlT+UXJFGa9K+pza49Vs/vz5LEkST5s2jc+ePWtRl5uby0888YRVzIwZM9jHx4eDg4PZYDCwwWDg4OBgbtGiBd93332cnJzM3t7e7Ovry3Xq1OGXX36Zhw8fzi1btpT/HzphwgT28/OT9ycoKIiHDh3KW7ZsYWbmZcuW8c6dO5mZ+datWzx+/HiOiYmRfyjy6quvclFREe/Zs4eDgoK4atWqHBsby/7+/mw0Grldu3bco0cP7tGjBxsMBq5duzZLksTBwcHs7+/PM2fO5ODgYO7evTt7eXkxEbGXlxdXrVqVd+/ebdF29haYDh8+zG3btrVZB7ZhAcUNREREyIOXmbmoqIiHDh3KCQkJFpODvQ+30gPO19eXly1bxt7e3szMnJeXx56enuzj48PHjx/nn376iZOSktjT05N37drFzGJC27x5M/v6+nJOTg5PmjSJa9SoIa9Sjxkzhi9fvswRERG8Y8cOOSYnJ4cPHz4sH7wzGzJkCFepUoVjY2MtciMiXrx4MTMz5+fns8Fg4I0bNzIzc0xMDM+ePVuOuXnzJt9///1MRPIHmb12MLeBkpiSAgMD+ccff2QfHx9mFivCnp6e7Ofnx8eOHeOvv/6aq1Wrxp6envJ7JkkSb9y4kf39/XnLli381FNPyb++SE9P519//ZUjIyN5+/bt8uuUjKlTp478a4JOnTpx165defbs2VytWjX5+R4eHhwaGsrbtm2T85Ikif/8809OSEjgzMxMXrduHSckJPDZs2e5cePG3LFjR4v+Y558zA812y4wMJB3797NO3fuZB8fH7ndcnNz2c/Pj6dMmcLVq1fnoqIiue3M/efIkSPs4+Mjt5vJZGKj0cgzZsxgZrZou5J9zmAwWPwKo1OnTtywYUOuXLmyRW5qt13JNjEYDHK71atXz+F4LRmjpz7nqO2IiBcsWGDVbszMCQkJ/PHHH3NCQgIzs9x2JdvLVttp2efUbjtXjdfS/ceZOcJR2/n6+vLBgwd537597O3tLc8R586dYx8fH54+fTonJSUxM8vzhHnsHTt2jH18fHQ1R5Rn7GG8Yry6erw60+cq6vyqpM+Vp+2UxJQUHh7OX3/9Nfv7+zMz84ULF9jb25sDAwP58OHD/Mknn3DdunXZw8NDfv8lSeL169dzYGAgL1iwgLt06SL/YnXw4MF84MABjo6O5r///tuiz/31118cFBTE9evX588//5yZmevWrctdu3bld999l1NSUjgrK4uPHTsm97kff/yRs7Ky+Pz58yxJEv/222/MLBayJ0yYILf3oUOHuGrVquzl5cUhISHyGAgJCZEfkiSxn59fhfpOp8fxeqf63LVr1zgwMJDHjh3L9erV44KCArnfmdtu79697OvrK/e5yMhI9vDw4JUrVzIzW/Q7c8yBAwfYYDDIfc7c7+rVq8dVq1blrKwsud8REU+dOpWzsrJ4+/btt93nDAYD+/r6ym2A8Xr7bVeeOblku23ZsoW3bNnC/v7+d6zPqd12VatW5SVLlvDvv//OlStXltutf//+DtvN39+fAwMDMV41nCOYtf8+rOUxk4owXpX0ObXHa0hICPv7+8ttkZCQwGFhYbxq1So5F1uLB7NmzWIPDw8mIp45cyYvXbqUly5dys8995x8BYXmzZuzr68vDx8+nF966SUOCgpio9HIQ4cO5Zo1a/J//vMfTkhI4G+++YYlSeKEhARu2bIlp6amssFg4Lp163JERAT/8ssvzMw8ceJEDgsL4ylTpvCyZctYksQVE9555x3u3r0733///VxQUMCSJHHr1q05OTmZ4+LiuH///ty/f382GAwcFBTEjRs35v79+/Nvv/3GPj4+PGXKFL7vvvv4iSee4HfffZebNGnCTz31FIeFhcnv1Z08e8cdYQHFDQQEBPDevXutyocNG8YGg4Hfeustq4GTlJTEs2fP5m3btlnVxcTE8Lhx4zgxMZGZmS9evMiSJHFsbCz/8ccffPToUfn0QPOHhiRJvHDhQo6Li7PY1h9//MHx8fHyhJiens5Tp06VY86cOcPTp09no9FosZLKzPzf//6XiYg//vhjuYyI+Ndff5X/7e/vz0eOHGFmZj8/P169erW88MMsTqHz8vLiuLg4Xr58OUuSJH/QZ2Vlsa+vL3/xxRcWbVBWTEREBI8dO9bmh1GlSpX4k08+4ejoaGZmvn79OhsMBo6MjOQtW7bwkSNH5LbLzMyU22H58uUcFRUlb+fGjRtcq1Ytrly5MhsM4pI0s2fPluslSeIvv/ySExISODw83KLtpkyZwjExMezl5SWXeXh4sLe3Nx8/ftyi7Q4fPsw+Pj589OhRPnHiBJtMJmZmvnz5Mjdr1oyNRiO/9NJLLEkSr1692uLh7e3NEyZMUKXtKlWqxHv27OHFixdzdHS03G7nz5/nqKgo/umnn9hkMvGFCxfktjNP7Js2bZLb7saNG/y///2Pg4ODWZIkTkpK4ubNm8ttZ+5zCxYsYIPBYNXnXnvtNZYkSb6c0J1oO29vb/7mm294+vTpcjsUFBTwgw8+yEajkceNG2ez3b788kv+5ZdfbLad1n3OXtsRkfylrGS7MTP7+PjIp1mbXb58mY1GI1epUoW//fZbm22nZZ+7E23nivHq6+vLs2fPtuhzzI7nCGYxT4wfP97mHLF161aePXs2JyYmynPE5cuXOS4ujufNm8cmk4n/+ecfeZ4wj9fVq1dbzBN6mCMcjT3z5YhKjj2MVwHjVdDDHFFR51clfc5R25lMJv72228t2q6smMDAQP7www+tPh+ZmYODg/nrr7/msLAwZhaLsh4eHhwWFsa7d+/mgwcPypcgWb9+vdwOq1at4vDwcHk7p06d4ri4OA4PD2eDwcB+fn68cOFCiz733XffcVRUFIeFhck/TsrLy+MRI0ZwSkqKxeeWh4cHm0wmPnHihFzm5+fHhw4dsmg784Epcw6SJHHNmjX5gw8+YEmSeNasWfLDZDLxM888U6G+05nbTsvx6qo+d+bMGQ4PD+dffvlFPlhm7nfmttuxY4fc706dOsXjx4+XL2nZsmVLrl+/vtzvzOP1119/ZUmS5D5n7nf9+/dnSZIsDn4TkXwAVo0+5+XlxSNHjrRoA4zX22s7R3NyYGAgv/DCCzbb7eDBg5yZmclhYWF3rM+p3XY+Pj587NgxPn78uNx2p06d4mrVqrHRaOQhQ4bYbLfJkyfzuHHjMF41nCOY9fF92FXHTEwmE0+cOLFCjVclfU7t8Tpr1iyuWrUqt2zZkg0G8QPASZMmsb+/P/fo0YOfffZZHjJkCBMRP/vss/IjODhYvhRuSfXq1ePPPvuMp02bxlWrVuVff/2Va9SowczM8fHxXL9+fe7fvz8fOXKEDQYD//TTT3JbzJ8/Xz42unHjRn7yySeZiNhkMvEjjzzCycnJ/N1331n0u2+++YarVq3K8fHx/McffzAz87fffstxcXE8duxYue+b287cv8w8PT15x44dHBISwgcOHOB9+/bJnzVvv/02h4SE8PPPP88TJkxgSZIsLmltfowaNQoLKOWEBRQ30KhRI/7f//5nsy4pKUn+sljSJ598Ig94IuINGzbwhg0beNGiRVyzZk02GAz8+uuv89GjR7lv375cv359HjZsGKekpPDAgQPZy8uL+/XrxzVq1OBly5axwWDgmjVr8oABA6xy2Lt3L4eGhnKzZs34zTffZH9/f3788cflRRnzl9OS/6lmZt6wYQOHh4dzu3btuFOnTvzvv/8yEVlcM/Hnn3+Wr0dovsxK6UWcDh06cM2aNTkhIcGqHVJTU/m9995jSZKcjnn99dfl+wLs2LGDs7OzOTs7m3fs2MGpqans6enJo0eP5vz8fB45ciRXrVqVH3/8cW7SpAm/8cYbbDKZOD09nZs2bcr79u1jg8HAzZo14969e1u8jvkXOk8//TR37NiRw8PD+bXXXuM5c+bIp/Dde++9HBERYXHAkJl57ty5bDQa+cknn+Rr166xh4cHx8fHy7/sZGb+9NNP+fLly1y9enX+5ZdfeOvWrRZfSK5cucKBgYEcHR1t84NXzbZr164d33PPPRwSEsKvvfaa3G7MzI8//jjXrl2bg4KC+P7775fbzmAw8Lp167h169Y22y4gIIDvvfde+fI/5oM3aWlpbDKZ2N/f36rdtm7dysHBwVyrVq071napqam8YMEC+ddAZgUFBRwREcFBQUE22808SRKRLvucrbYjIovPJnO7MTNXr16dp06datFuzMwtW7bkhIQErlu37h0dr0r6XEUdr+Y5onSfY7Y/RzCLecJ8yvCiRYvkeaJdu3YcGxvLJpOJx40bJ88RzGJRJi4uTv4VjXmeMBgMPG/ePE5NTbWaJ7SeIxyNPYxXjFdXj1clfU7ttnPV/KqkzzlqO3N7l247RzH/+c9/ODExkadMmcJExf9tys3N5bp163JAQAAPHz6cmZknT57M0dHR3L17d+7WrRt/9dVX7O3tzQ0bNuSuXbvy1atX2WAwcLdu3bhTp04Wr/Pzzz9zaGgov/jii9y0aVNOSkriGTNm8Lp161iSxKUh2rVrx9HR0bxmzRqLWPNlECdOnMi3bt1iDw8Pjo6Ols+uY2Z+6aWX+Pz588zMnJyczJ9//rnFQWFm5oYNG3KlSpW4Q4cOdsdr6XI9f6fTw3h1VZ9jZu7evTu3bNlSvvSJud8ZDAY+duwY9+7d22a/CwgI4KZNm/LMmTPlfidJEvfs2ZODg4PZz8/Pqs/t2LGD/f39OS4uTu53RMTffvut/Jzb7XPVq1fnjz/+2Koc41V52zmak//zn/9wdHQ0E4lLBZm1bduWO3fuzElJSTx8+PA71ufUbrvk5GT+7bffePv27RZt988//7C3tzdXr17d4XgtWYfxKrhqjtDT92FXHDMxj9fS34f1PF6V9Dm1xyuzOFNpyZIlFnXm97VKlSrcvHlzJiJu06aN/JAkievUqWO1PW9vbz527Bjv37+fvb295bNgT58+zT4+Pvz9999zpUqVmFksXpgX2ySp+Eo7JUVFRfFrr73G9957LxORxWKSwWDgDRs2sI+PDycmJspXV2BmPnbsGKelpbHBYOALFy4ws1hACQwM5P3798vPM/8IMCQkhHfs2MFHjx61yGHy5MlMRBwaGspEZHFJa/PDfEkxcB4WUNzAxIkT5evPl/bHH3/INwwrbd68edyoUSP5VDZJktjDw4MbNGjAKSkpLEniVMTExET++++/+erVqzx48GCOj4/n5s2bc15eHk+ePFk+wNa8eXPOycmxmcfhw4f54Ycf5oCAAPm1JEmc2tauXTvu378/z58/3yLmxRdf5PT0dC4qKuKJEydyVFQUS5LEkydPtvkao0aN4uTkZO7Zs6dF+Y8//sizZs3iBx54wOoDZNSoUdy2bVueNWuW0zHMzGPHjpV/EWcwiFMtzfsTFhbGHh4e7OnpycHBwbxy5UrOzs7mDh06sLe3NycnJ/OlS5d4+PDhLEni9Nbk5GT5V5cl/fXXX9y0aVOLNpMkSb7+oXmimD59ukXcm2++yffeey8PGTKEU1JS2Gg0cp8+fayex8z89NNPc+/evfntt9/mLl26WNR99NFHnJiYaLMNRo0axW3atOE33njjttuOSNyQzmAwWLQbszgtsXbt2uzl5cXp6ely25lPE05JSXG67cxfosxtZ6vdWrduzdevX79jbTdq1Cju2LEjZ2dnW7Xd999/z/Xr17fZbu+88448BvTa50q3nSRZ3wy2ZNvVqVPHqt2++OILnjRpEjdp0sRm22nZ5yrqeDXPEbb6nKM5glmcBl2zZk355r6SJG706e/vbzVHMItT3du3b8/R0dH85JNPyvOE+fTrNm3a2JwntJwjmO2PPfN4vXDhgtU8gfGK8aqnOULNtnPV/KqkzzlqO3N722o7ezE3b97koUOHyr909fb2Zm9vbzYYDPKvKaOiojghIYG9vLz422+/5YMHD8r5BwUF8alTp/iBBx6QLxkRFhbGW7dutcp7wYIFHBcXJ38elGy7wMBA+T+8H3zwgUXchx9+yA0aNODOnTtzy5Yt2cPDg9u2bcsffvihzbYbOHAgN2vWjNu1a2dR/tZbb/Fzzz3HVatWtdnvWrVqxf3797co1/N3Oj2MV1f1OWbmgwcPcqVKlZhI3C/I3O/M7V2pUiWn+525vc39zlafa9q0KWdnZ8v9TpIkfvnll622z6yszz399NPcpUsXqz6ndtvdjePVXts5mpNv3rzJGRkZ8tg0t515n3x9fTk+Pv6O9Tm1227gwIE8YMAA/uSTT6za7qWXXuLQ0FC74/XEiRNWbYfx6ro5Qm/fh+/0MZOnn36au3XrZjVH6Hm8Kulzao9XZnGm0s8//2zV76Kjo9nLy4s/++wzq7gGDRpwv379rMqrVKnCy5cv51GjRnGDBg340KFDbDQaOS8vj6tXr84fffQR+/j48KZNm9jLy4u/+uorZhYLKJ9//jmnpKRYbO8///kPd+vWjQsLC7lv3748aNAgLioqkmPMlyD19PTkr7/+2iJ2+fLlHBQUxPHx8bx8+XL29PTk2rVry2e9MItF2qKiIm7ZsiV/9tlnvHLlSovLzTEzh4SEsIeHh93/F9i62hA4JjEzE4ADBQUFdO7cOSIiCg8PJ09PTyIiOnToEOXl5VGNGjXIw8PDbvzNmzepoKCAAgICynwtZqYzZ85QUVGRxWvZcu3aNTIajeTt7U1ERFu3bqW1a9dSRkYGhYSEWD2/sLCQzp07RyEhIWQymWzW//PPP5SYmGhRdv36dQoMDLSZg62Yko4dO0bZ2dlERBQVFUXJycl0/fp1Wrt2LeXn51PTpk0pPDzc7j4ePXqUDh8+TG3btnXYFmfPnqWjR49SUVERRUdHU1JSkt3nmrfr5eVFcXFxtHjxYsrMzKTRo0dTRESE1XMvXrxIp0+fJl9fX/L29qbo6GiL+itXrtDff/9NrVu3tihXu+0iIyOdbjcioj///JOCg4OpZs2aDvtnedquZLsR0R1pO3foc0TOtd2mTZvonnvusWo3Ite0XXn7XEUer7fL1jzh7BxB5Pw8gTnCEsar+43X2203ortnfi1vnzO3d+3atW3m4Ojz8fLly7R161aLdktLS6Nr167Rzz//THl5edSuXTuqVauWHHP+/HkKCwuT//3777/Tpk2baMCAARQZGWkzh1u3btHWrVvp2LFjcrulpaU5/GzcsGEDmUwmql+/Pn300UeUmZlJH3/8sdyWpR0/fpwWLVpE7dq1ozp16ljVnz59mlauXEn9+vWTy7Qer3eizxHd+fHq6j5HZN3vpkyZQlWqVKF7773Xoryk8va7kn2OiMrsd0r6HMaroIe2i42NpTVr1mja54jK13bHjx+n/fv3U0hICPn6+lq1nR76HNHdM17d4fvwnfo/2N04XpX0ufKOVyKijh07UseOHemFF16wKH/sscfo4sWL9Ntvv9GtW7fo1q1bct3q1aupS5cudOPGDXr22Wflz/dFixbRli1byGAw0MiRI+mXX36hlJQU+vHHH+nDDz+kl156iTw9PcnLy4vGjRtHEydOpIyMDJo3bx5dunSJXnjhBXr99dfl18nNzaX77ruPLl26RM2aNaPvv/+eIiMjqVq1arRt2za6du0avfDCC1SlShWqXr06NW3aVI5988036eLFi9SzZ0/KyMig48eP0wcffED16tWjVq1aWezrjBkzaM2aNVS7dm26du0avfnmm3Jd79696dy5c3T8+HE6duyYVbvv2LGD6tevT0VFRTbfR7CGBRQAAAAAAAAAAAAA0L2FCxfSH3/8QR988IFFeXZ2NuXl5dG6deto+vTplJmZaVGflZVFn332GW3YsEFefIqMjKT8/HzKysqiwsJCSk9Pp6lTp8oLd2+++SZt27aNHnroIXrkkUdo9erVNGbMGLp+/Trdf//99Prrr5PBYLB4nYKCAvryyy9pyZIlFgt3LVq0oKeeesruwnlJV69epSNHjlCNGjVs/sDPkb1799L169epYcOGNusLCgro9OnTDn90BZawgAJ05MgRGjx4MK1atcrpuuXLl9PAgQMpIiLCYsW7evXqRER04MABi/JmzZpRp06daPny5bR+/Xqn65RsT+2Y4cOHW63cm508eZLGjh1LX331lVPlRGKld9CgQRQXF2fxWnXr1qWioiLatWuXRXnz5s3p/vvvp8WLF1vlbS+mWbNm9MADD5QrRsnrOIpp3rw59e/fnypVqqRK2507d46mTJlCc+bMoZiYmDuW9z333EPMXO59dVXbOepb9uq07nNqt4+jGK373N04XpXMEXv37qW33nqLFi9eTP7+/hb7qufPeyXzlKN5AuMV47UizBF6nl+Vto+StsvJyaHPP/+cxowZ43TdyZMn6fnnn6fY2FiL12ncuDExM23evNnq9Tt37kxLly61ys1VMUq31717d/Ly8rJqG0d9S+s+p/fxqmaf00PfUjvGUZ/DeFW/7eyV5+fn06xZs2jmzJnyL/b1tK9q52Cv7TBeXTdHEGn/fVgPcwSRfserHsZ4Wf3O1XJzcy3yCwoKUhzjaFtKYkAdWEAB2rFjBzVo0MDi1DZHdcuWLaPu3btTQUEBjR07Vj7tbe3atfTtt98SEdGjjz5KLVq0ICLx4f7dd9/Rvn37qEaNGtS3b185xlGdku2pHbNy5UraunUrLVq0iNLT051uO3vlmzdvpvvuu48uX75M/fr1k3PYs2cPLV26lIiIunTpIp/CmZOTQ8uWLaOzZ89SpUqVqHPnzhUiJicnh37//Xe6fv06rVixwuaqd3nabvPmzZSenk6enp505swZGjVqlK721ZVtV97xqnWfc+V7pHWf00N/1EOfW7ZsGT344INUo0YN2rlzJ3366afy6+j5817JPFXWPIHxivGq9/Gq5/lV7T6sdtuZLxdy6tQpat26tfw6WVlZtHnzZiIiatSokXwAIycnh9avX09FRUVkNBqpadOmLo9Rur2NGzdSXFwcLVu2jKpWrepU+9grd1Wf00t/dEWf00PfUjtGaZ8rb9vpYV/1PF4PHz5M6enp9M8//1BeXh716dNHV/uqdg6O2g7j1TV9jkj778N6mSPK23auGq96GOPO9Dulyrt4MWPGDJoyZQodOHDAYjvVq1en559/nnr37u10TEREBEmSRDk5OUQkLl8tSRJVr16dGjVqRJs3by4zpvTrDxw4EIsrKsECihv46KOP7NatWbOGcnNzadWqVfThhx9alBORzbpJkyZRYmIibdy40eIDu27dutS9e3cyGAz0448/0s6dOy3qgoOD6eLFixbljuqUbE/tmMWLF9O3335L69evt2jHjRs3EpH44P7qq6/op59+sqizVU5E9OKLL1JYWBht2LDB4lqDTZs2pXvuuYeIiHbt2kXr16+3qMvNzaWgoCDasGFDhYghEteR/PTTTykrK4smT558W2334osvUnJyMqWnp9MLL7xg0e/0sK9qbm/x4sXEzHbbzl672WtTrfuc2ttzFKN1n3PlvqoZ89FHHxEz03fffUf//PMPPffcc3KdvTnCUd2kSZMoNTWV6tatS++9957VPKHXz3sl89TixYuJiOzOExivGK9qxyiZI9RuOz23j6O6nTt3EjPTm2++SQcPHqRvvvlGrjt48CAdO3aMXnrpJdq+fbtFORHZrBsyZAgVFhbS1q1bLcZrhw4dyGQyETNTYWEhrVixQq5r27Yt7d+/n2rVqkW///67y2OUbm/evHn0wQcfUH5+Po0bN04u1/N3OrW3pyTGVX2OSPu+pXbMX3/9Ra+++irl5eXRf//7X4t9xXhV1naO2sdemw4ZMoR8fHwoIyODBg4caDFe9bCvasbs3LmTrl69arftMF5dM0cQaf992JXfWXbs2GF3jiDS53jVwxgnEveAycjIoBs3bljElGzb8izczZgxg95++206evSoxeW4HC1ehIeH06VLl+i5556j9PR0iwWed999l5YvX05ERJIkyYsh9mKmTp0qn401cOBAeuaZZ+RtjR8/njIzM6lt27Y0ZswYp2J+/fVXev/99yk4OFi+V2nJfTIvrkA53PZt6EH3JEnimJgYTkpKsnoQERsMBiYiq3Kj0chGo9GqTpIkjoiIYIPBYPE63t7evH//ft6/fz97e3tb1S1dutSq3FGdku2pHSNJktw+kiTJDyKyeNirK1less5W2+3bt4/37dtnM79ffvnFZrleY9Ruu5JlttpO631Vc3vmfTTvd3nax1GdVn1O7e05itG6z7lyX9XuczExMRwbG8uSJDk1RziqkySJY2NjOSYmxmbb6fXzXsk8hfGqPAbj1XVzREWdX9Xuw64arz4+Prxr1y7euXMn+/j4WNX98MMPNstdEaN0e1qPV71/nmnd55i171tqx9jrcxivyttO6Xg1/91W22m9r2rGYLwqj1Gzz+nh+7Arv7NUxPGqhzFuZivGbPv27SxJklPl7777Lvv6+vKAAQOYiHjv3r28d+9ezszM5LZt2zIRcdu2bTkzM9OiLjAwkE0mE0+ePNnm9rp3786VKlVyKiYhIYHnz5/P8+bN4/j4eKu6ESNG2Cy3F/Puu++yyWTigIAAq7xHjx7Nfn5+VjmAY5Z3uYG7UmJiIn3wwQd07Ngxq0dSUhK9/fbbZDAYrMrnzp1LW7ZssaqrXr06Pf7441avk5SURL/88gv98ssvVjciSkpKov/+9782b1Bkr07J9tSOiY6Opn79+lH16tWpqKhIfsTExNBPP/1E27ZtI4PBYFU3ZcoUq/KioiJKSkqi8ePHW7VBVFQUbdq0iTZt2iSvJpes++mnn2yW6zXG3HZPP/00JSYm3nbbJSUl0ezZs+nvv/+22XZa76ua24uOjqYff/yRZs2aZbPtbLWPozbVus+pvT1HMVr3OVfuq5ox5jli4sSJlJCQ4NQc4aiuevXq9Nxzz9Evv/xi1T56/rxXMk+Zx+t7771nc57AeMV4VTtGyRxRUedXtftwaGgoTZ8+nd577z2KjY21+NwKCQmhESNGkCRJVuXvvvsuzZw506ouIiKCxo4da/X6wcHBlJWVRVlZWRQcHGxVt337dpvlrohRur3o6Gh6+eWXKTo6usJ8p1N7e0piXNXniLTvW2rHhIaG0rBhw6hSpUpW3z8wXpW1naP2sdemERERNGPGDPr5559VyU3tfVUzxjxep0+fbrPtMF5dM0fo4fuwK7+zhIaG0hNPPGFzjtDreNXDGCci6tmzJz355JNUVFREPXv2lB8xMTEUExNDHTp0IGa2qrNVPnbsWKpduzZlZWWRwWCgmjVrUs2aNalNmzZ05MgRGjFiBB0+fJjatGljUZefn08TJ060uurPJ598QjNnzqSJEyfS1atXnYo5c+YMpaamUmpqqtUZI2fOnKH27dvbLLcX88knn9DEiROpsLDQKu+JEyfSl19+6fBqRWDNQ+sE4M5LS0ujrVu3ytdALF23b98+4lJXcjPHVK9e3apu/Pjx9Oijj1JRURF99NFH8kTQtGlTeuGFF4iIqH///jR//nwiEqeP+fv70+LFi6lhw4YWMY7qlGxP7Zhbt27R7Nmz6bvvvrPZPklJSTbbbv/+/VblREQvvPACPffcc1RUVESLFy+Wc+jYsSMNGDCAiIgGDRpkcbprYmIiTZ8+ndq0aVNhYnJycshkMtG0adNo6tSpt912L7zwAj355JPUs2dPKioq0t2+qrm92NhYmjhxIu3atYvee+89p9rHUZtq3edc+R5p3ef00B+VxERHR9O7775Le/futdnnbM0RjurMc0SLFi2oqKhIk89uNWMc1YWGhtKoUaMoKyuL5s6da9U+GK8Yr3qYI9RuOz23j6O6+Ph4mj59Ou3YsYPee+89iwXRRo0aUWFhIRGRVXleXh7Vr1/fqm7IkCH0wQcfUFFREe3cuVN+nfvvv5/69OlDkiRRRkaGfE3qnJwcqlOnDk2YMIE6dOigSYzS7YWEhNCUKVPopZdesupXev1Op4f+6Ko+p4e+pXZMeHg4zZgxg0aNGmX14wWMV2Vt56h97LXpkCFD6IUXXqABAwZQUVGR7vZVzZhq1arRggULaOPGjfIPPO5En9PDvqodo+YcQaT992FXfmfx8/Oj2bNn09SpUyvMeNXDGM/JyaFFixaR0Wik2rVrW9zTIzs7m2JiYsjb25uIyKquUqVKVuX5+fkUGxtrdbk9ouLFiy+++MKqrlGjRrRmzRo6e/asVUytWrVo0qRJ1KhRI6diGjVqRG+//TYxs1VMw4YNaeTIkVb3yXEUc+bMGVqzZo1VuZmtRRcog6tOdQHt7Nmzhzdv3my3bv369ZyVlWUzJj8/36qOmXnNmjXcrVs3TkhIYC8vL/by8uKEhARu3749t2/f3qq8b9++/Pnnn3Pfvn3LVadke2rGtGvXjqdMmWK1/3/88QcvW7aMr169yqtXr7aqW7hwoVW52axZs7hmzZrs4eEhn2Lp4eHBVatW5apVq1qVN2nShEeMGMFNmjSpUDE1a9bk0aNHq9Z28+bN40aNGrHRaNTdvqq5PaPRyNWrV+f58+fbbDtb7eOoTfXQ51z1Humhz+mhPyrpc7Vr17bZ5+zNEWXVrVu3jh966CGOjY2tMJ/3SuapiIgIbtWqFf/1118YrxivLhuv5Z0j7kTb6bV9HNUZDAauUqWKzbb78ccf+b///S/PmjXLqvzrr7/mCxcuWNUxM48dO5aDg4Pl7Zsv/xIQEMABAQFW5dHR0dy5c2eOjo7WLEbJ9sLCwnjAgAGajlc9f57poc/poW+pGRMSEsJ9+/a12n+MV+Vt56h97LUpM/M777zDUVFRutxXNWOIiIOCgnjSpEkYrxrPEczafx92VUxKSgoPHz68wo1XPYxxDw8P7tWrl1UbpKam8owZM3jbtm1WlzJLTU3lMWPGWJW3bNmSMzIyePPmzVZ19957L1euXJlbtmxp9Vp///03+/j4sIeHB/fo0YOHDh3KQ4cO5bCwMDaZTBwVFcW7du1yKqZdu3Zy32jfvr1c3qNHDw4KCmKDwcDBwcFOx3h4eLCPjw9v27bNKu/CwkLOyMjgVq1aWdWBfbiJPIBGCgoK5BXf8PBw8vT0dFheUWPUpvd9rYhtV1HbR+t208u+6rnPgXJ66AsYr/qOUZue91Xt91Vtx44do+zsbCISl+pITk52WK6HGKXbU5Me+kJFHa967lt67nN631c9t53e91XtHNSk533Vc58j0v6zG3OE+jFqbW/8+PHk6+tL06ZNs9j3J554gnx9fWn48OHUpUsXOnbsmEVdXl4erV+/3qJ8586dlJ6eTjdv3qT8/HzKyMggInGmy6pVq+jKlSsUGBhIbdu2tTgL5o8//iAPDw8aPHgwnThxQs7PZDLRmjVryMPDg1q3bu1UTFRUFNWvX5+Kiopox44dFuXNmjWj+++/nxYvXkwbNmxwKiY+Pp5mzJhBhYWF1KpVK6scvLy86Ndff6U6deoQOEnrFRzQxttvv80XL14sV52rYvSQg6OYuXPn8tWrV50uV1p3t8XoIQfsqz5i9JAD9tVx3d342e2qeUrP76s79WHsq/5z0MO+rl27lm/evGkzxl7d3RajdHtav68Vtd/r/X2tiDF6yAH7qn6MHnLAviqPuds+u105T+n5fdVTv7958yZfu3bNqt5eeVl1ly9f5k8//ZQzMjK4Y8eO3LFjR87IyODPPvuMT506ZbcuNze33NuzF6M2PeRwN8ECipsKCAjgI0eOlKvOVTF6yAH7qn6MHnLAvuojRg85YF/Vj9FDDthX9WP0kAP2Vf0YPeSAfdVHjB5ywL6qH6OHHLCv6sfoIQfsq/oxeshBzzF6yAH7qn7MndieHnTp0oVPnz6tSoyjbSmJgdtj0PoMGNAGO7hym706V8XoIQfsq/oxesgB+6qPGD3kgH1VP0YPOWBf1Y/RQw7YV/Vj9JAD9lUfMXrIAfuqfowecsC+qh+jhxywr+rH6CEHPcfoIQfsq/oxd2J7Xbt2pX///dfpcrVjiIj++OMPunHjhiox9sqVxjjKAcqGBRQAAAAAAAAAAAAAqJBctRDhqK68ubmSHnKo0Bjc0okTJ/jWrVvlqnNVjB5ycBTz559/8o0bN5wuV1p3t8WY62xdy9JeeUWNuRM56PV9rQj9/m7rC66IOXHiBBcWFtqMUVJ3t8U4qtPz+1oR+rA7fTapGaP391WvfXjOnDl2r0Fur+5ui1G6Pa3f14ra7/X+vlbEGD3kgH1VP0YPOWBflcfcbZ/drpyn9Py+6rnfMzP7+/vbvLyXvXK1Y5iZa9euzSdOnFAlxl650hhHOUDZJGYH5z/BXevQoUN04sQJSkxMpKpVqzpV56oYPeTgKAZuz6xZs6hHjx4UFBTkVHlFjVF7e8xMRUVFZDQarWLs1ek5xpU5ALhCbm4uZWdnExFRVFSUxRi2V6fnGFfmcOvWLTp37hwZDAaqVKlSmeVqx+ghB6V5m+Xk5BAzU1RUlCp1d1tMWXVQfnl5eXTq1CmKi4sjk8lUZrkeYvSSAyi3evVqatKkCfn4+Dhdd7fFKN0eAOhfXl4eEZHNOcVWeUWNuRPbIyKqU6cOLVu2jOLj450qVzvGESUxatNDDhWaq1ZqQDsTJ07k3377jZmZL1y4wO3bt2dJkuRHo0aN+OLFixZ1RMSSJHGnTp342LFjdyxGDzmUFZOens4XL15kZuYlS5Zwq1atODw8nBs0aMCzZ8+2KI+JieEaNWrwzJkz5fZ3pk7J9vQcExMTw2lpaXJMSZ6enrx3716nyytqjNLteXh48JAhQ7hVq1Y8ZswYZmZ+99132dfXlz09PTk1NZVbtmxpUefj48MGg4EjIyP51Vdf1VWMK3Pw8vLidu3a8RNPPMEvvvgi79u3T27XX375hR9//HGOj4+3KGdmnj9/PkdHR6sSo/b2XJn3hQsXuG3btlza9u3b2WAwWJUrrbvbYqZPn86VK1dmImKDwSA/atasyRkZGVyzZk2LcoPBwFFRURwdHW1VrocYV+bwzDPPcMuWLdlkMsnlQUFB3KZNG27cuLFV+eOPP85fffWVajFqb88VMQEBAZyQkMAxMTE8dOhQLiws5IEDB8rfWUJDQ8tVR0RMROzl5cUZGRkVPqas7UmSxA0aNLC6uWadOnV4/PjxVr/Ys1deUWOUbm/mzJn8119/MTPzjRs3eMCAAWw0GlmSJDYajTxkyBC+dOmSXG7ur927d+ebN29qEqOHHCRJkr/X3bx5k7dt28YZGRncvHlz7tq1K2dmZsptbK5r2LAh9+rVS65zJkbt7ekhpmR5SXr+7q+n/2NMnz6dMzIy+KuvvmJm5nnz5nGNGjU4PDyc77nnHqvy5ORk7tatm6YxesghJiaG27Rpw9OmTeOzZ89atO3atWu5fv36VnXbt2/n1157jZs2bep0jNrb00NMbm4uP/HEE2zL3r17OTk52elypXUVNWbGjBns4+PDwcHB8pwSHBzMjRo14kaNGlmVd+7cmd955x3u3LlzhYq5E9tbuXKlzTZ1tSNHjvDs2bP5nXfe4XfffZcXLFjAubm5imIcbUtJDKgHCyhuIC4ujv/++29mZh40aBDXr1+f//77b75x4wZHRkZyamoqDxw40KIuNjaW582bx02bNuVq1ardsRg95OAoxmAwcFpaGg8cOJAXL17MBoOBMzIyWJIkfuyxx9jDw4NHjx4tl0+bNk3+D9aPP/5oEeOoTsn29Bwzbdo0NplMTETs7+/PISEh8sN8AMP8MJeXLCtZrveYO5WD+YDZ0KFDOS4ujr/55ht+4IEH2GAwcMuWLblWrVpyXffu3TkoKIj9/f05IiJCVzGuzGHo0KFMRFyjRg2+99572dvbm7/55hueM2cOG41Gvu+++5iI5HJmluuI6LZj1N6eK/NmZs7Ozra5QLB9+3aWJMnm/KKk7m6KMS/eDRgwgImI9+7dy3v37uXMzExu27YtExG3bduWMzMz5bohQ4awp6cne3p68tChQ3UV48ocunXrxkTErVq14ldffZWjoqL45Zdf5v79+7PRaGQfHx9+6qmn5PLPPvuMa9SowZIk8YABA247Ru3tuSqmefPm7Ofnx35+fty4cWPu3r0733PPPbx27Vru1q0b+/j4cOPGjbl169ZO1XXt2pUrV67MCQkJHBkZWeFjHNVJksRBQUFMRBwTE8MLFizggoICZmaWJInDwsLYaDRyenq6XGevvKLGKN1ecnIyb9iwgZmZX3jhBU5KSuIff/yR4+Li+N133+Vq1apxo0aN5PJ9+/ZxZGQkx8fH84svvqhJjB5yMBgMPGvWLK5WrRo/+uij7Onpya1bt+YXX3yRiYiNRiOvWbOG161bJ9dJksStW7dmDw8PnjZtmlMxam9PDzEBAQFMRJySksL14LojXQAAKlJJREFU69eXH+bvyD4+Puzj42NRR0Q26ypqjNLtRUZGyovx0dHRPGHCBA4LC+MuXbqwl5cXe3h4cFBQkFw+YcIE7tSpExMR169fX5MYPeTQr18/eVE0NDSUw8LCeNWqVczMvGLFCvby8mIi4oSEBLnOXF69enUmIqdi1N6eHmKY7f8/wvwd2t7/Mcobo/b2tI6ZNWsWe3h4MBHxzJkzeenSpbx06VKLH4UMHjxYLp85cyY3adKEiYibNm1aYWLuxPYeeeQR9vT05Pfee88lCxG26k6fPs29e/eW8zf/eMxoNLK/vz+/8cYbTscYDAb5xxelt+Xn5yd/xjsb4+/vz5988gkWV1SEBRQ3YDKZOCsri5mZk5KSeM2aNRZ1S5Ys4ejoaIs6c8yWLVvkL7x3IkYPOTiKkSSJf/31V46OjuZ7772XX375Zbk8JyeH33rrLQ4ICJDLzXWjR4/mpk2bWsQ4qlOyPT3HMIvrK1avXp2rVKnCs2bN4lmzZvHMmTOZiLh69eo8aNAgHjRokFxuNBrZy8uL69atK5dXhJg7kcP//d//8axZs3jSpElsMBh43rx5zMxcuXJlHjVqFNepU4cPHTok11WuXJmXLFnC8+fP55SUFF3FuDKHevXqcf/+/blOnTrMLM628PPz4/j4eJ46dar8xd5cPmPGDK5Xrx5PmDBB/kJ7OzFqb89VMfXr1+cePXpwly5dmIi4R48e8iM6OporVapkVe6oLjo62mFdRYtxVOfj48ONGjXidu3aWf2nKCEhgUeMGMHx8fFW5fPnz+d58+bZrNMyxpU51KhRg5955hm5fPPmzRwXF8c1atTgefPmcd++fblHjx5yeVFREdeoUYObNWvGPXr0uO0Ytbfnqpjo6Ghet24d9+3blzt37ix/T2Fmjo6O5s8++4xjY2M5OzvbqTrz9tauXctRUVEVPsZRnSRJ/M8///DEiRPZ29ubPTw8uFKlSvz888/LdQsXLuT7779friMiXr16tVV5RY1Ruj0vLy8+fvw4MzNXq1aNly1bxsziO/Tx48d5zZo17OHhIZeb67777jtOSEjQJEYPOZi/Q69Zs4a9vb15wIABcoz5QFG7du24Q4cOcp05ZsSIERwSEuJUjNrb00OMh4cHJyYmcnJyMr/xxhv8xhtv8NixY+VfH1etWpXbtGljUWc+4NumTRu5riLHKN1ecHAw9+zZk9944w3++++/2cPDg2fMmME1atTgOXPm8IwZM7hmzZpyObOYkwcPHsxpaWmaxOghh2bNmvErr7zCM2bM4AYNGvCkSZPY39+fly1bxs2aNeMRI0awwWDgoqIiua5mzZr8yiuvyHONMzFqb08PMc8++ywPGTKEiYifffZZ+dGgQQNu0KAB16hRw2adrXJHdUq2p+eYZ599Vj77ovT/I1JSUviTTz7hadOmcdWqVa3q+vTpY7NcrzF3YntXr16VF5Tv5EKEvRij0cgeHh5cuXJl3rVrFx86dIh79+7No0aN4jNnznBaWppVbo5iBgwYwFWrVmVvb29+//335fJr165x69at2WAw8DvvvON0zKefflrm4gqUDxZQ3EC1atX4559/ZmbxC7J169ZZ1H300UccGBhoUWeO2bZtG0uSdMdi9JCDoxhJkvj333/nwMBAjoiI4C1btsjlOTk5vH//fpYkSS43161bt46Dg4MtYhzVKdmenmOYmQ8dOsSpqans6enJV65ckcuNRiPXqVOHMzIyLMo9PDx46dKl3KhRI6s6PceovT0iki+5x8zs7e0tX2rJ29ub165dywEBARZ13t7efOLECT569CgHBAToKsaVOfj5+fGaNWvkGGbmVatWMRHxm2++afHLqFWrVrG/vz+bTCbeuHGjxZdWpTFqb8+VMbVr1+a+ffsyEXH//v3lhyRJHBERYVXuqE6SJI6NjeX4+Hint6fnGEd1RqORH3zwQfmMqZK8vb158eLF7OPjY1W+d+9e3rNnj806LWNcmYOPjw//+uuvFuUeHh7s7e3Nx44d440bN3JwcLBc/s8//7CPjw8vXLhQLr+dGLW356oYHx8fzsrKkmM8PT15165dzMzs6+vLf/75J/v5+TEzO1Xn6+vLWVlZfPToUfb7f+3deViU5f4/8PczMAwMiHBYBBQXMgTFBTdE3APU0lxzze1ofk3NtCv3k2VZ4nHplB1NzQyzy8rczfSUouUu4hKSgCIuueBSuCPi5/eH1zw/YWZwxAEe9P26Lq7reN++bz/vpzxiNzO4upb5TGF7ps9nTOedO3dOPvroI3n++ecFgDRs2FAWL14sIqLumf7iGxkZKYsXLy7zmSc5r2bNmrJ48WKpWLGi7N+/X0REqlSpIlu3bpWUlBRRFEVdN+0tWbJEXF1dSyWjhRlM/86ZMrt371YziqLI9u3bxdvbW/z9/dU9UyY5OVl0Op1NGXufp4XMjh07JDAwUFxcXCQvL09dd3R0lK+//lqee+45mTJlSr49BwcHCQwMNFsvq5minufs7KxeeIo8uOBLTk4WFxcXOXXqlKSnp4uHh4e6LvLgz+Tt27erf+aUdEYLM7i7u8vx48fVjMiDV2+7urqK0WiUPXv25Ptc75tvvhEAsnDhwnyfdz8qY+/ztJDR6XRSp04dAaBe6rVq1UqAB+9GYXpFWcE9o9Fotl7YXlHO03KmVatWoiiKhIWFmf09wmAwyLFjx+TYsWPi7Oxstrdx40aL61rNFMd5Q4cOlfr164uTk1OxXkRYy9y8eVPc3NzE2dlZvvnmGxF58JbYpi+YiIqKkkmTJkloaKhNGW9vb0lMTJTPPvtM6tWrp67fvHlTvL29Zdy4cVKvXj2bM0OHDpWgoCCpUaOG2dyLFy8Wo9GozkC24QXKM2DmzJkSGhoq6enpMnv2bImMjJTjx4+LiMjEiRPFaDRKu3bt8u3NnDlTqlevLo0bN5Y6deoUW0YLMxSWURRF6tatK9HR0VKlShXZt2+fup6QkCBr164VRVHUddNefHy8uLi45MsUtleU87ScMUlOTha9Xi/PPfec7NixQ0QefGJ/+PBhGTdunNn60aNHJTc31+KeljP2PA+ArF69Wn2GTZs2lbNnz4qISIUKFWTt2rXi7u6eb69ChQpy5MgR+eOPP8Td3V1TmZKcwd/fX5YvX65mTLy8vMTFxUUmT56c75PTbdu2iaIoMnDgQLNPWouSsfd5JZUJCgoSg8FglhERqV27tkyZMsXiS86t7dWuXVu++OILOXjwoM3naTlT2F7z5s2lf//+sn//frO9Zs2aSVBQkDRv3tws069fP3n11VelRYsWmsqU5AyhoaHSsmVLdf3AgQPi5OQkoaGhsmLFCklPTxdXV1d1/d69exIaGiqffvqp+h/GnyRj7/NKKlO3bl357LPPJD09XQwGg5QrV05mz54tIiJ169aVnj17SlhYmGzcuNGmPdN58+fPlypVqpT5TGF7Op1OLl68KPPnz1dfqWii0+mke/fu6vN+eH3VqlXy6quvWtwra5minte3b1/x9vYWo9EoEyZMkI4dO8r169dl0qRJEhERIZ07d5Zq1aqp6yIiY8eOFS8vL2ndunWpZLQwg6Io8vvvv6uvWDS9pbLI//8iLRcXF6lWrZq6pyiKHD9+XP3CLlsy9j5PCxmRB98TxcHBQSIiItS/m5k+h/7777+lV69eZnt79uyxuF5WM0U5z8PDI9/3R6lUqZJkZmaKl5eXpKSkSHp6uri5uanrIg8+f/zpp5/Ezc2tVDJamMHHx0cSExPVjMny5csFgEyYMMHscz13d3dxdnaW+fPn59srLGPv87SQsfZ3jODgYPn6668tft4dHByc75XxtuwV5TwtZ0RE6tevLwMGDDDbq1+/vowdO1bGjRsn9evXN9tr3LixxXWtZorjPG9vb+nfv7+6V1wXEdYyN2/eFA8PD5kyZYq6d/fuXXF0dBQvLy9JTEyUtLQ0cXZ2tilTvnx5SUtLUzOm9aysLPHw8JD//e9/6kWSLRlvb29ZtWqVmnl4BhFRu5LteIHyjHjjjTdEr9dLSEiIODs7i06nEycnJ9HpdOLr62txz/SVaKab2OLKaGEGaxkA6p6iKPLxxx+LiKgvgVOUB9+E3rRu2jOtP5wpbK8o52k5Y7J8+XKpWbOmbNmyRSpXriwTJ04UvV4vR48eFRGxul7YnpYz9jhPURT58MMPxZLWrVvL8OHDpUGDBmbrX331lXz//fcW90ozU5IzdOrUSbp3725xvX///upXSD0sKipK9Hq92XpRMvY+r6QyAwcOlE6dOlnMDBw4UHr37i1Vq1Y162ptb+DAgTJ8+HBJSUmxuFfWMoXtHT58WPz8/MTDw0OMRqMMGzZMhg0bJl26dJHy5cuLTvfgrTS6dOmi7rVp00b9/84XXnhBU5mSnMHV1VUURZFBgwbJlClTJCAgQAYPHiyfffaZlC9fXjp06CB+fn7qusiDT/SNRqP4+vo+ccbe55VUZtmyZeLg4CAVKlQQRVFkxYoVEhAQID169JAmTZoIAPHx8RGDwWDTXkREhPrnuKOjY5nPFLYHQDp37ixOTk5mb1tg+kr4gu8JbVoXEat7ZSlT1PNycnLk5ZdfFg8PD4mJiRFnZ2cxGo1SvXp19XsGREVFqevPP/+8GI1GcXFxkfLly5dKRgszmP4eYfo8eeHChfmet06nM9szrZv2bMnY+zwtZERE1q5dK9WrV5cvv/xS/Pz8ZMGCBWafQ1vbe9oyj3NeeHi4+va2D4uKipJvv/1W1q9fb3aJHBUVpb51bmlktDBDTEyMzJw502Kmdu3a6r+rBTOm729k6QtzLGXsfZ4WMk2bNrWY6dOnj4wePdri9xHs06eP9O3b12y9sL2inKfljIhIQkKCuLi4qG/vFRcXJ3FxcdK9e3f1mb7yyivq+pgxYyQoKEgASFBQUJnJFMd5Dg4OYjQa1bfgL66LCGuZrKwsiYmJkb59+6p7M2fOFH9/f/Hw8JC0tDRJSkoSb29vmzIxMTEyYsQINWNaF3nw+69Hjx7i7e1tc8bDw0PWrFmjZh6eQUTUrmQ7RUQE9Ez4448/sGHDBmRkZOD+/fvw9/dHVFQUoqOjcezYMYt7FStWxI8//ljsGS3MYClTvXp1NGvWDIqiAADc3Nzg5eWFU6dOAQBWrlwJABgwYAC8vLwAAKdOnVLXu3XrpmYK2yvKeVrOmCxduhQA0L9/f1y5cgWvvfYaEhISsGfPHtSoUQMArK4XtqflzJOe98svv2D16tV44YUXUFBaWho2btyIgIAA9OjRI9+6Xq/H7t274ejoaLZXmpmSnGH79u2YN28eunXrZra+a9cuNGnSBEuXLsWSJUvy7S1btgz37t0zW3/cjL3PK6lMTk4O8vLysHfvXrOMac9oNKIga3tPW+ZRe9evX8eyZcuwZ88eXLhwAQDg5+eHyMhIdOzYEevWrTPbCw8Px/3793H48GHNZUpyhlu3bmHlypXIyclB27Zt8c4778DZ2Rnz58/HF198gZycHHTq1EldB4CpU6di7dq10Ol0T5yx93klldm5cyfWr1+PunXronfv3khJSUFcXBxu3bqFkJAQeHp6IjIyEk2bNrVp7/Tp0/Dz88OoUaOeioy1vZdffhm1atVC165dMWDAgHy/jwcNGoRPP/0U5cqVs2m9rGaKep7Jpk2bsH79erPPoX18fPDzzz+brffp0we//fZbqWZKcwZFURAWFobo6Gi4uLjA398fwcHBAB78mfzDDz8gNzcXvXv3Vve2b98OAOreW2+99ciMvc/TQgYAPvnkE9y9exdjx45Feno6+vbti8TERCQnJ6NmzZrqz7O297RlbD1v6dKlCAsLQ7169fL9/t25cydcXV2xa9cu3L9/HyNHjsy3t2HDBlSsWNFsvSQyWphh9erV+PXXX/H888+bZVavXo2FCxfizp07SEhIMMs0atQIixYtMtuzlLH3eVrIXLhwAd999x3WrFljtp6Tk4MqVaqgoKLsPW0Zk8zMTMyfP9/sc+iaNWtCRPDHH3+YfQ7doUMHrF+/vkxl7H3e6dOn0bx5c/W/Oc2aNQtz5sxBWFgYgoODMXjwYMTGxmL8+PGYM2cOzp07h9jYWHh6emLr1q24dOnSE2XOnTuHpKQktGnTBteuXUNgYCAuXLiA+Ph4fPnllwgODkZoaCj27duH2rVrPzITHByMmJgY3Lt3D3l5ecjNzUV8fDx69eqFpKQkNG/eHCICHx8fmzKxsbHIyclB1apVER8fn29uADh48CBiY2Nx6dIli/9ekjleoBARERERERGRZt2/fx/Xr1+Hu7u7+sVtj9p72jJFPY+I6GmTlJSEmJgYODk5wcnJqdguIqxlevXqBQD46KOPsH79evTt2xdt2rRBzZo1rc5WWAYAzp8/jw0bNiAnJyffemF71tYfNcN///tf7Nu3D/Hx8SX2z6ys4wUKITc3F+fPn0flypVt3iupjBZmKCxz7949nDt3zmzP2npR9562jBZmYFdtZLQwA7uya3F11cKfU1r/M/nMmTO4fv262VehKYqCo0ePmq3r9Xrcu3fP4l5RMvY+T8sZLczArtrIaGEGdmVXdmVXLc7ArtrIaGGG0uzq7++P0NBQs/NM62U1Y+/zLl++/FiXCoXtFSVTmKJk7E0LMzxVSuN9w0hbDh06ZPG9OQvbK6mMFmZgV/tntDADu2ojo4UZ2NX+GS3MwK72z9jzvLy8PBkyZEi+7zNm+n4XBoNBnJ2d860riiLly5eXqKgo8fDweOKMvc/TckYLM7CrNjJamIFd2ZVd2ZVdy97cfD7sqqWuiqKIh4eH/Otf/5K8vDyLf/8oSVu2bJGpU6fKsGHDZPjw4TJr1ixJS0srUqaws4qSIfvR2fMyhoiIiIjoUSZMmICVK1dCURRkZGTg5s2buHnzJoYOHQonJyfo9XoMHTpUXc/IyEBERAR27dqFxo0bP3HG3udpOaOFGdhVGxktzMCu7Mqu7MquZW9uPh921VLXjIwMzJgxAwsXLkSfPn3w/vvv4/XXX8eIESMwe/ZspKenY+vWrRbXAVjde9zMnj17EBERgZiYGHzwwQdYuHAh9u7di1mzZiE0NBQ9e/a0OTNjxgwEBwcjOjra7KyQkBAEBAQ8ViY0NBTjxo0rtBM9ptK+waHiFx4ebvXDxcVFDAaDADBbL2zPXhktzMCu7MqufD7syq7sWrIzODo6SqVKlcxemVKhQgXZtGmTbNq0SXx9fc32pk2bZnH9cTP2Pk/LGS3MwK7ayGhhBnZlV3ZlV3Yte3Pz+bCrlrqKiFy8eFFq1KghAMTR0VF0Op00aNBAfHx8BHjwCveH1/38/ESn04m/v7/odLonzvj5+YmiKFK9enXJzs6WO3fuyMiRI6V///5y8eJFCQkJEQCi0+lsyvTs2VOioqLE09NTZs2apa6LiLRq1Ur0er3ExcXZnPnhhx/E0dHRYicHBwcZO3as2TOlwjk+xl0LlVEpKSno1asXqlWrZrZ35MgRPPfcc0hPT0enTp3yrYeFhcHR0RFJSUlme/bKaGEGdmVXduXzYVd2ZdeSnSE5ORlNmjTBqlWr8v36169fR0BAAEQEN2/eNNsLCQmxuP64GXufp+WMFmZgV21ktDADu7Iru7Iru5a9ufl82FVLXQFg1KhRCAgIwJkzZ3D16lW8/fbbuHbtGqpXr46zZ88iJSUFkydPRmZmJq5du4bExES0bt0aO3fuxEcffYTRo0c/USYxMRFubm64dOkSlixZgjfffBNxcXHw9PTEjRs3EBISgrfeegszZ85E27ZtH5lxcXHBrl27cPDgQUybNg0HDhyAp6cn5s6di6SkJLz//vtYsmQJxo8fb1NmxYoVqFu3LrKzs5GcnJxv7q1bt6JHjx6oWLEi3nzzTbNnS1bY9TqGNKlBgwYyb948q3sTJ040+wpQU+bgwYMW9+yV0cIM7Mqu7Mrnw67syq4lO8OLL74okZGRoiiK2XqrVq2kTZs28tJLL+Xbi46OFi8vL4mJiXnijL3P03JGCzOwqzYyWpiBXdmVXdmVXcve3Hw+7KqlriIi5cqVk6ioKDVz48YN0ev14u7uLsnJyfL1119LjRo11PXs7Gxxd3eX6dOnS40aNZ44k52dLT4+PhIXF6fu3bp1S3Q6nZQrV06Sk5PlxIkTYjAYbMp4e3vL0aNH1Yxp/cqVK+Lj4yObNm0Sg8Fgc8bd3V1++uknNfPwDCKidiXb8RUoz4CoqCikpqZa3Tt//jxatGhhMRMbG2txz14ZLczAruzKrnw+7Mqu7FqyM3z++eeIjo4GANSvXx8VKlQAAJw5cwZHjx4FAISFhaF9+/YAgIsXL+LIkSPQ6/VISEh44oy9z9NyRgszsKs2MlqYgV3ZlV3ZlV3L3tx8Puyqpa4XL17E9evXcenSJSxfvhwAoNPpkJeXBycnJyiKgqZNmyIzM1Ndv3fvHgwGA8LDw/Hee+89cebevXto1qwZtm3bhpMnTyI3NxeTJk1CUFAQsrOzoSgKLl26BE9PT5sytWvXxpQpUzBy5Eh4eHio6//4xz/QrFkzzJgxA+XLl7c5YzAY8Pfff8PT09NsbgBqV7KdIiJS2kMQERER0bPl/v372Lx5M/bs2YMLFy4AAPz8/BAREQERwb59+/KtR0ZGIjo6Gj///LNdMvY+T8sZLczArtrIaGEGdmVXdmVXdi17c/P5sKuWuv7yyy/w8/PD0qVL4eTkhHHjxmHDhg2oXbs2dDodRo4ciV69eqF3797YsGED0tPT0bVrV/z99984evQozp49+0SZ9PR0ZGRkoGXLljh79iz0ej1cXV2xYsUKzJs3DzqdDjExMThx4gRyc3MfmQkKCkJsbCwyMzMhInB3d8eKFSsQHR2NjIwMREZG4vLly3BwcLAp07VrV5w5cwYtWrRAXFxcvrkBYO/evejcuTPOnz8Psg0vUIiIiIiIiIiIiIhI8zIyMhAbG4tTp05BUZRiu4iwljG9kn7BggVISEjAq6++iiZNmsDb29vqbIVlAODWrVvYsWMH7t69m2+9sD1r64+a4auvvkJqaiqmT59eYv/MyjpeoDxD9u3bh927d5vd6DZu3NjqHoASyWhhBnZlV3bl82FXdmXX0p+hadOmEBGL640aNbJrxt7naTmjhRnYVRsZLczAruzKruyqxRnYVRsZLczArtrvWqtWrce6VChsryiZwhQlY29amOFpwguUZ0BWVha6deuGnTt3onLlyvneN/DUqVNwd3fH9evX8+2dO3cOZ8+eBQAEBgbC39+/WDJamIFd2ZVd+XzYlV3ZVRszsCu7siufD7uyK7tqYwZ2ZVc+H3bVYteLFy/i9OnTiIqKwsqVK+Hr64vScvv2bSxfvhw7duzA+fPnodPpEBQUhM6dO+OFF154rEz79u1x4cIFi2c1bdr0sTPWfn0qIqGnXrdu3SQyMlKOHTtmthcbGyvlypWTtm3bmmXq1asn4eHh0r1792LLaGEGdmVXduXzYVd2ZVdtzMCu7MqufD7syq7sqo0Z2JVd+XzYVYtdRUSOHTsmERER0qBBAxk0aJC0a9dOXnzxRRk5cqT8+OOPsnjxYrP1X375RW7dumVxryiZ+Ph4qVKlivj6+kpgYKAoiiIvvfSSREREiE6nk4YNG8qAAQNsytStW1cAiMFgsHiW0Wh8rIyDg4N07dpVFi5caLETPT5eoDwD3NzcJCkpyeresmXLxM3NzWImMTHR4p69MlqYgV3ZlV35fNiVXdlVGzOwK7uyK58Pu7Iru2pjBnZlVz4fdtViVxGR9PR08ff3F0VRivUiwlomIiJCAEhQUJDcvXtXRETi4uKkffv2kp6eLhUrVhSdTifu7u42Zdq3by+9evWSKlWqyLvvvquui4i0aNFC3NzcZMqUKTZnfv75Z3FwcBBXV1eLlyuvvPKK5Obmmj1Xss6xNF/9QiXDYDDg2rVrVvcuXboEg8FgMSMiFvfsldHCDOzKruzK58Ou7Mqu2piBXdmVXfl82JVd2VUbM7Aru/L5sKsWuwLAqFGj0LBhQ+zcuROnT5/GjBkzsH37dgQEBCA0NBS7d+/GwIED4eLigu3bt2PPnj1o2bIlkpKSMGzYMEydOvWJMnv27IHRaEROTg4+/PBDvPfeexgzZgzeeecdvP766+jQoQPatWuHMWPGYNKkSY/M6PV6HDp0CCkpKRg9ejRSU1Pxzjvv4MqVK0hMTMSsWbPw73//G1OnTrUpM2fOHERHR+PYsWPIzMzMN3d6ejpiY2Mxbdo0vPfee2bPlqyw9aaFyq7hw4dLlSpVZNWqVZKdna2uZ2dnS7t27cTBwUHat2+fb2/IkCHi4+MjPj4+MnTo0GLLaGEGdmVXduXzYVd2ZVdtzMCu7MqufD7syq7sqo0Z2JVd+XzYVYtds7OzxWAwSMWKFWXkyJEiIpKTkyN6vV6MRqOkpaXJmjVrpGrVqur65cuXxWg0yrx586Rq1apPnLl8+bIEBATI7Nmz1b2//vpLFEVRz8vIyBCDwWBTxt/fXw4cOKBmTOvXrl2TgIAAWbdunRgMBpszRqNRtmzZomYenkFE1K5kO16gPAPu3Lkjw4YNEycnJ9HpdOLs7CzOzs6i0+lEr9dLzZo1zfYURRGdTqd+FFdGCzOwK7uyK58Pu7Iru2pjBnZlV3bl82FXdmVXbczAruzK58OuWuyq0+kEgHTv3l3u3LkjIsV3EWEtc+3aNRkwYIBERESIXq+XjIwM6dmzp4SHh0tAQIAcOHBAtm3bJoGBgTZlBgwYIC1btpT4+Hjx9/dX10VEBgwYIHXr1hU/Pz+bMwEBAbJw4UIJDAw0m1tE1K5kO0VEpLRfBUMl49q1azhw4AAuXLgAAPDz80ODBg3g7u5udQ9AiWS0MAO7siu78vmwK7uyqzZmYFd2ZVc+H3ZlV3bVxgzsyq58Puyqta5ffPEF/vzzT3z++ecwGAyYOHEi0tLSUKdOHWRmZuKf//wnJkyYgBYtWiAtLQ1JSUkYOHAgDh06hIsXL2LXrl1PlElKSkJWVhZat26NlJQU6HQ6BAYGYvXq1fjkk0+QmZmJLl264K+//sKxY8cemalYsSI6deqEvXv3AgAqV66M1atXIzw8HFlZWWjWrBmOHz8ORVFsygwcOBD79+9Hly5dMHjw4HxzA8D27dvRr18/nD59GmQbXqAQERERERERERERkeZlZWWplwe2XioU5SLCWiY8PBwA8MMPP+Dw4cPo2bMnQkJC4OjoaHW2wjIm6enpyMnJMVsvbM/Sui0znD9/Hm+88UYx/RN6+uhKewAqGbdv38aOHTuQkpJitvfXX3/hX//6l9ne7du3sWXLFkyfPr1YM1qYgV3ZlV2LL6OFGdiVXdmVXdm17M3NrvbPaGEGdmVXdmVXdi17c/P5sKuWuvr6+iIhIQFxcXE4fPgwjh8/jvDwcPj6+mL37t1ITU3FkSNH1HVTJi0tDampqU+cMenevTs++OADhIWFqZcXpvO2bduGl19+2aaMibOzM+bMmWO2fubMGUyfPt3mjK+vL77//nt06dLF6ty8PHlMpfsOYlQSUlNTpUqVKup7C7Zo0UL+/PNPda9SpUoCIN/ewxkAxZbRwgzsyq7syufDruzKrtqYgV3ZlV35fNiVXdlVGzOwK7vy+bCrFruaXLhwQXQ6nVhy+vRpGTRokM3r9s6IiBw6dMjifEXJWFsvaqawGcgyvgLlGTB+/HiEhYUhKysLqampKFeuHJo1a4bTp09j/PjxCAkJgaIo+fZGjRqFsLAwJCcnQ1GUYstoYQZ2ZVd25fNhV3ZlV23MwK7syq58PuzKruyqjRnYlV35fNhVi11t+b4dV69eRXx8vM3rRcmsW7cOq1atwldffYV169aZfSQkJNicmTx5MiZPnoy5c+dCRMz2rK1b27P269vyHMiKUr7AoRLg6+srR44cUX98//59GTZsmFSuXFm8vLwkISFBvZU07el0Otm4caN6o1tcGS3MwK7syq58PuzKruyqjRnYlV3Zlc+HXdmVXbUxA7uyK58Pu2qxa+XKlWXBggUSHx8viqLI2rVr1Y9JkybJpEmTZPDgwRb3rK0/bmbt2rWiKIr6yhnT/zb92PRRMGdL5lHnPU7G0txr166Vjz/+WH2uZBteoDwDypUrJykpKWbrI0aMEEVRZOnSpWa/cfR6vVSoUEF+/fXXfHv2zmhhBnZlV3bl82FXdmVXbczAruzKrnw+7Mqu7KqNGdiVXfl82FWLXUeMGKG+rVdJXUQUXH94r+B8ivLg2yeYfo4tmYCAAFmzZo0cPHjQ4t6cOXMeK2OawdLcpo+CGSocL1CeAY0aNZKlS5da3PP19RWj0Wj2G6dRo0YSHR0tHh4eZnv2zGhhBnZlV3bl82FXdmVXbczAruzKrnw+7Mqu7KqNGdiVXfl82FWLXUVEXF1dxdXV1WzP3hcR1jIiIh07dpTXXnvN6nmHDh0SRVFsynTs2FHeeecdq5mhQ4daXLeWCQgIkI8//ths3cRSVyocvwfKM6BLly5Yvny5xb3Ro0fD29sbImKW0ev16N27t9mePTNamIFd2ZVd+XzYlV3ZVRszsCu7siufD7uyK7tqYwZ2ZVc+H3bVYlcAaNOmDUJCQsz2GjRogAMHDkBRFIt7x44ds0sGAMaOHYt69epZPa969epm34fEWmbs2LFo2rSp1Uz79u0trlvLNGjQABcuXLD6fVAsdaXCKcInRkREREREREREREQa99tvv+HmzZto166dxfXmzZsjMTERLVu2zLd35coVeHp6mq0/bsbk5s2bFjOWZissY29amOFpwwsUIiIiIiIiIiIiIiKiAvgWXkRERERERERERERERAXwAoWIiIiIiIiIiIiIiKgAXqAQEREREREREREREREVwAsUIiIiIiIiIiIiIiKiAniBQkREREREmiYiiI6ORtu2bc325s2bBw8PD5w9e7YUJiMiIiIioqcZL1CIiIiIiEjTFEXBkiVLsHfvXixYsEBdP3nyJMaNG4e5c+eiUqVKdv01c3Nz7XoeERERERGVPbxAISIiIiIizQsMDMQnn3yCt99+GydPnoSIYPDgwYiNjUV4eDjat28PNzc3VKhQAf369cPly5fV7KZNm9CsWTN4eHjAy8sLHTp0wIkTJ9T9zMxMKIqC7777Di1btoSzszO++eYbnDp1Ch07doSnpydcXV1Rq1YtbNy4sTTqExERERFRKVBEREp7CCIiIiIiIlt07twZ2dnZ6Nq1Kz744AMcPXoUtWrVwpAhQ9C/f3/cvn0b48ePx71797B161YAwMqVK6EoCurUqYMbN25gypQpyMzMxKFDh6DT6ZCZmYlq1aqhatWqmD17NsLDw+Hs7IzXXnsNd+/exezZs+Hq6oqUlBS4u7ujRYsWpfwUiIiIiIioJPAChYiIiIiIyoysrCzUqlULV69excqVK5GcnIzffvsNmzdvVn/O2bNnERgYiNTUVAQHB5udcfnyZfj4+OD3339HWFiYeoHyn//8B2+++ab68+rUqYNu3brh3XffLZFuRERERESkLXwLLyIiIiIiKjN8fX3xf//3fwgNDUXnzp1x+PBhJCQkwM3NTf0ICQkBAPVtutLT09G7d28EBQXB3d0dVatWBQCcPn0639kNGzbM9+NRo0Zh2rRpiIqKwrvvvosjR44Uf0EiIiIiItIMXqAQEREREVGZ4ujoCEdHRwDAjRs30LFjRxw6dCjfR3p6uvpWWx07dsTVq1exaNEi7N27F3v37gUA3L17N9+5rq6u+X48ZMgQZGRkoF+/fvj999/RsGFDzJ07twQaEhERERGRFjiW9gBERERERERFVb9+faxcuRJVq1ZVL1UeduXKFaSmpmLRokVo3rw5AGDHjh02nx8YGIhhw4Zh2LBhmDhxIhYtWoQ33njDbvMTEREREZF28RUoRERERERUZo0YMQJXr15F7969sX//fpw4cQKbN2/GoEGDkJeXB09PT3h5eWHhwoU4fvw4tm7dirfeesums0ePHo3Nmzfj5MmTSEpKQkJCAkJDQ4u5ERERERERaQUvUIiIiIiIqMwKCAjAzp07kZeXh9jYWNSuXRujR4+Gh4cHdDoddDodvv32Wxw4cABhYWEYM2YMZs6cadPZeXl5GDFiBEJDQ9GuXTsEBwdj3rx5xdyIiIiIiIi0QhERKe0hiIiIiIiIiIiIiIiItISvQCEiIiIiIiIiIiIiIiqAFyhEREREREREREREREQF8AKFiIiIiIiIiIiIiIioAF6gEBERERERERERERERFcALFCIiIiIiIiIiIiIiogJ4gUJERERERERERERERFQAL1CIiIiIiIiIiIiIiIgK4AUKERERERERERERERFRAbxAISIiIiIiIiIiIiIiKoAXKERERERERERERERERAXwAoWIiIiIiIiIiIiIiKgAXqAQEREREREREREREREV8P8A1vaANo4TiQYAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Figure size: 20 representing the width, 10 representing the height\n", - "fig = plt.figure(figsize=(20, 10))\n", - "\n", - "plt.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\n", - "plt.legend()\n", - "\n", - "# Insert label for the X-axis\n", - "plt.xlabel(\"Years\")\n", - "\n", - "# Insert label for the Y-axis\n", - "plt.ylabel(\"g CH₄/m²/year\")\n", - "plt.xticks(rotation = 90)\n", - "\n", - "# Insert title for the plot\n", - "plt.title(\"CH4 emission Values for Texas, Dallas (1999-2016)\")\n", - "\n", - "# Add data citation\n", - "plt.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\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2016-10-01T00:00:00+00:00\n" - ] - } - ], - "source": [ - "# Print the properties for the 3rd item in the collection\n", - "print(items[2][\"properties\"][\"start_datetime\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'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]}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# A GET request is made for the 3rd granule\n", - "ch4_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\n", - "ch4_flux_3" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a new map to display the tile\n", - "aoi_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\n", - "map_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\n", - "map_layer.add_to(aoi_map_bbox)\n", - "\n", - "# Visualize the map\n", - "aoi_map_bbox" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "In this notebook we have successfully explored, analyzed, and visualized the STAC collection for TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n", - "\n", - "1. Install and import the necessary libraries\n", - "2. Fetch the collection from STAC collections using the appropriate endpoints\n", - "3. Count the number of existing granules within the collection\n", - "4. Map and compare the CH₄ inverse fluxes for two distinctive months/years\n", - "5. Generate zonal statistics for the area of interest (AOI)\n", - "6. Visualizing the Data as a Time Series\n", - "\n", - "\n", - "If you have any questions regarding this user notebook, please contact us using the [feedback form](https://docs.google.com/forms/d/e/1FAIpQLSeVWCrnca08Gt_qoWYjTo6gnj1BEGL4NCUC9VEiQnXA02gzVQ/viewform)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/workflow.qmd b/workflow.qmd index 6a0ac856..711cfc6f 100644 --- a/workflow.qmd +++ b/workflow.qmd @@ -14,7 +14,7 @@ Explore, analyze, and make a difference with the US GHG Center. ## Gridded Anthropogenic Greenhouse Gas Emissions 1. [OCO-2 MIP Top-Down CO₂ Budgets Data Flow Diagram](data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd) 2. [ODIAC Fossil Fuel CO₂ Emissions Data Flow Diagram](data_workflow/odiac-ffco2-monthgrid-v2023_Data_Flow.qmd) -3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram](data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd) +3. [CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes Data Flow Diagram](data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Data Flow Diagram](data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd) 5. [Vulcan Fossil Fuel CO₂ Emissions Data Flow Diagram](data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd) 6. [GRA²PES Greenhouse Gas and Air Quality Species Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd) @@ -26,11 +26,12 @@ Explore, analyze, and make a difference with the US GHG Center. 2. [MiCASA Land Carbon Flux Data Flow Diagram](data_workflow/micasa-carbonflux-daygrid-v1_Data_Flow.qmd) 3. [GOSAT-based Top-down Total and Natural Methane Emissions Data Flow Diagram](data_workflow/gosat-based-ch4budget-yeargrid-v1_Data_Flow.qmd) 4. [OCO-2 MIP Top-Down CO₂ Budgets Data Flow Diagram](data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd) -5. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram](data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd) +5. [CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes Data Flow Diagram](data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd) 6. [Wetland Methane Emissions, LPJ-EOSIM Model Data Flow Diagram](data_workflow/lpjeosim-wetlandch4-grid-v1_Data_Flow.qmd) ## Large Emissions Events 1. [EMIT Methane Point Source Plume Complexes Data Flow Diagram](data_workflow/emit-ch4plume-v1_Data_Flow.qmd) +2. [Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure Complexes Data Flow Diagram](data_workflow/goes-ch4plume-v1_Data_Flow.qmd) ## Greenhouse Gas Concentrations 1. [Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory Data Flow Diagram](data_workflow/noaa-gggrn-co2-concentrations_Data_Flow.qmd)