From 060c454bfcf59d9506fea79b0fa162677be79102 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli <46867571+paridhi-parajuli@users.noreply.github.com> Date: Fri, 18 Oct 2024 16:41:52 -0500 Subject: [PATCH 1/2] LPJ collection name change Fixed LPJ-EOSIM version --- _quarto.yml | 6 +++--- data_usage.qmd | 2 +- ...w.qmd => lpjeosim-wetlandch4-grid-v1_Data_Flow.qmd} | 0 datausage.qmd | 10 ++++++++-- index.qmd | 2 +- ...ch4-grid-v1_Processing and Verification Report.qmd} | 0 processingreport.qmd | 5 +++++ ...=> lpjeosim-wetlandch4-grid-v1_User_Notebook.ipynb} | 2 +- ...jeosim-wetlandch4-monthgrid-v1_User_Notebook.ipynb} | 4 ++-- workflow.qmd | 6 +++++- 10 files changed, 26 insertions(+), 11 deletions(-) rename data_workflow/{lpjeosim-wetlandch4-grid-v2_Data_Flow.qmd => lpjeosim-wetlandch4-grid-v1_Data_Flow.qmd} (100%) rename processing_and_verification_reports/{lpjeosim-wetlandch4-grid-v2_Processing and Verification Report.qmd => lpjeosim-wetlandch4-grid-v1_Processing and Verification Report.qmd} (100%) rename user_data_notebooks/{lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb => lpjeosim-wetlandch4-grid-v1_User_Notebook.ipynb} (99%) rename user_data_notebooks/{lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb => lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.ipynb} (99%) diff --git a/_quarto.yml b/_quarto.yml index 0c9fd106..db11916c 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -66,7 +66,7 @@ website: - 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/lpjeosim-wetlandch4-grid-v2_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 @@ -129,7 +129,7 @@ website: - 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/lpjeosim-wetlandch4-grid-v2_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 @@ -162,7 +162,7 @@ website: - 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/lpjeosim-wetlandch4-grid-v2_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 diff --git a/data_usage.qmd b/data_usage.qmd index cbc98c75..68347efb 100644 --- a/data_usage.qmd +++ b/data_usage.qmd @@ -21,7 +21,7 @@ On this site, you can find the technical documentation for the services the cent 3. [EMIT Methane Point Source Plume Complexes](user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory](user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb) 5. [GOSAT-based Top-down Total and Natural Methane Emissions](user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb) -6. [Wetland Methane Emissions, LPJ-wsl Model](user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook.ipynb) +6. [Wetland Methane Emissions, LPJ-wsl Model](user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.ipynb) 7. [OCO-2 MIP Top-Down CO₂ Budgets](user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb) 8. [OCO-2 GEOS Column CO₂ Concentrations](user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb) 9. [ODIAC Fossil Fuel CO₂ Emissions](user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb) diff --git a/data_workflow/lpjeosim-wetlandch4-grid-v2_Data_Flow.qmd b/data_workflow/lpjeosim-wetlandch4-grid-v1_Data_Flow.qmd similarity index 100% rename from data_workflow/lpjeosim-wetlandch4-grid-v2_Data_Flow.qmd rename to data_workflow/lpjeosim-wetlandch4-grid-v1_Data_Flow.qmd diff --git a/datausage.qmd b/datausage.qmd index c1a64a42..a3e5bbe3 100644 --- a/datausage.qmd +++ b/datausage.qmd @@ -23,7 +23,7 @@ Explore, analyze, and make a difference with the US GHG Center. 5. Vulcan Fossil Fuel CO₂ Emissions - [Beginner level notebook](user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Vulcan Fossil Fuel CO₂ Emissions, Version 4 dataset. 6. GRA²PES Greenhouse Gas and Air Quality Species - - [Beginner level notebook](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb.ipynb) to access, visualize, explore statistics, and create a time series of the GRA2PES, Version 1 dataset. + - [Beginner level notebook](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the GRA2PES, Version 1 dataset. ## Natural Greenhouse Gas Emissions and Sinks 1. Air-Sea CO₂ Flux, ECCO-Darwin Model v5 @@ -38,7 +38,7 @@ Explore, analyze, and make a difference with 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. 6. Wetland Methane Emissions, LPJ-EOSIM model - - [Beginner level notebook](user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Wetland Methane Emissions, LPJ-EOSIM model dataset. + - [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 @@ -55,6 +55,12 @@ Explore, analyze, and make a difference with the US GHG Center. - [Beginner level notebook](user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook) 5. Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed - [Beginner level notebook](user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook) +3. Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) + - [Beginner level notebook](user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook) +4. Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project + - [Beginner level notebook](user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook) +5. Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed + - [Beginner level notebook](user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook) ## Socioeconomic 1. SEDAC Gridded World Population Density diff --git a/index.qmd b/index.qmd index e77b2dd9..34935baa 100644 --- a/index.qmd +++ b/index.qmd @@ -13,7 +13,7 @@ On this site, you can find the technical documentation for the services the cent ## Contents 1. **Services** provided for accessing and analyzing the US GHG Center datasets, such as the [JupyterHub](services/jupyterhub.qmd) environment for interactive computing. -2. Dataset **usage** examples, e.g. for the [Wetland Methane Emissions from the LPJ-EOSIM model](user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb) dataset, that shows how to load the dataset in Python in [JupyterHub](services/jupyterhub.qmd). +2. Dataset **usage** examples, e.g. for the [Wetland Methane Emissions from the LPJ-EOSIM model](user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.ipynb) dataset, that shows how to load the dataset in Python in [JupyterHub](services/jupyterhub.qmd). 3. Dataset **transformation** scripts, which document the code used to transform datasets for display in the US GHG Center. An example is the [ODIAC Fossil Fuel CO₂ Emissions](cog_transformation/odiac-ffco2-monthgrid-v2023.ipynb) dataset transformation code. 4. Data **processing and verification reports** that openly present the process we used to check and verify that any transformation did not alter the original source data. An example is the [GOSAT-based Top-down Total and Natural Methane Emissions](processing_and_verification_reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.qmd) dataset. 5. Data **Flow Diagrams**, which provide a high level summary of how each dataset was integrated into the US GHG Center. See the [MiCASA Land Carbon Flux Flow Diagram](data_workflow/micasa-carbonflux-daygrid-v1_Data_Flow.qmd) as an example. diff --git a/processing_and_verification_reports/lpjeosim-wetlandch4-grid-v2_Processing and Verification Report.qmd b/processing_and_verification_reports/lpjeosim-wetlandch4-grid-v1_Processing and Verification Report.qmd similarity index 100% rename from processing_and_verification_reports/lpjeosim-wetlandch4-grid-v2_Processing and Verification Report.qmd rename to processing_and_verification_reports/lpjeosim-wetlandch4-grid-v1_Processing and Verification Report.qmd diff --git a/processingreport.qmd b/processingreport.qmd index 14fa5c3e..b10190be 100644 --- a/processingreport.qmd +++ b/processingreport.qmd @@ -20,6 +20,7 @@ Explore, analyze, and make a difference with the US GHG Center. 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) +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) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Processing and Verification Report](processing_and_verification_reports/eccodarwin-co2flux-monthgrid-v5_Processing and Verification Report.qmd) @@ -27,6 +28,7 @@ Explore, analyze, and make a difference with the US GHG Center. 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) +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 @@ -39,6 +41,9 @@ Explore, analyze, and make a difference with the US GHG Center. 3. [Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) Processing and Verification Report](processing_and_verification_reports/influx-testbed-ghg-concentrations_Processing and Verification Report.qmd) 4. [Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project Processing and Verification Report](processing_and_verification_reports/lam-testbed-ghg-concentrations_Processing and Verification Report.qmd) 5. [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed Processing and Verification Report](processing_and_verification_reports/nec-testbed-ghg-concentrations_Processing and Verification Report.qmd) +3. [Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) Processing and Verification Report](processing_and_verification_reports/influx-testbed-ghg-concentrations_Processing and Verification Report.qmd) +4. [Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project Processing and Verification Report](processing_and_verification_reports/lam-testbed-ghg-concentrations_Processing and Verification Report.qmd) +5. [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed Processing and Verification Report](processing_and_verification_reports/nec-testbed-ghg-concentrations_Processing and Verification Report.qmd) ## Socioeconomic 1. [SEDAC Gridded World Population Density Processing and Verification Report](processing_and_verification_reports/sedac-popdensity-yeargrid5yr-v4.11_Processing and Verification Report.qmd) diff --git a/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb b/user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.ipynb similarity index 99% rename from user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb rename to user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.ipynb index e29792d6..d1adceca 100644 --- a/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb +++ b/user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.ipynb @@ -122,7 +122,7 @@ "\n", "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", "# Name of the collection for the wetland methane emissions LPJ-EOSIM Model\n", - "collection_name = \"lpjeosim-wetlandch4-daygrid-v2\"\n", + "collection_name = \"lpjeosim-wetlandch4-daygrid-v1\"\n", "\n", "# Next, we need to specify the asset name for this collection\n", "# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n", diff --git a/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb b/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.ipynb similarity index 99% rename from user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb rename to user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.ipynb index c860659e..b02d9423 100644 --- a/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb +++ b/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v1_User_Notebook.ipynb @@ -122,7 +122,7 @@ "\n", "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", "# Name of the collection for the wetland methane emissions LPJ-EOSIM Model\n", - "collection_name = \"lpjeosim-wetlandch4-monthgrid-v2\"\n", + "collection_name = \"lpjeosim-wetlandch4-monthgrid-v1\"\n", "\n", "# Next, we need to specify the asset name for this collection\n", "# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n", @@ -1874,7 +1874,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.9.18" }, "vscode": { "interpreter": { diff --git a/workflow.qmd b/workflow.qmd index 78747805..6a0ac856 100644 --- a/workflow.qmd +++ b/workflow.qmd @@ -18,6 +18,7 @@ Explore, analyze, and make a difference with the US GHG Center. 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) +6. [GRA²PES Greenhouse Gas and Air Quality Species Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd) ## Natural Greenhouse Gas Sources Emissions and Sinks @@ -26,7 +27,7 @@ Explore, analyze, and make a difference with the US GHG Center. 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) -6. [Wetland Methane Emissions, LPJ-EOSIM Model Data Flow Diagram](data_workflow/lpjeosim-wetlandch4-grid-v2_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) @@ -38,6 +39,9 @@ Explore, analyze, and make a difference with the US GHG Center. 4. [Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) Data Flow Diagram](data_workflow/influx-testbed-ghg-concentrations_Data_Flow.qmd) 5. [Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project Data Flow Diagram](data_workflow/lam-testbed-ghg-concentrations_Data_Flow.qmd) 6. [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed Data Flow Diagram](data_workflow/nec-testbed-ghg-concentrations_Data_Flow.qmd) +4. [Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) Data Flow Diagram](data_workflow/influx-testbed-ghg-concentrations_Data_Flow.qmd) +5. [Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project Data Flow Diagram](data_workflow/lam-testbed-ghg-concentrations_Data_Flow.qmd) +6. [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed Data Flow Diagram](data_workflow/nec-testbed-ghg-concentrations_Data_Flow.qmd) ## Socioeconomic 1. [SEDAC Gridded World Population Density Data Flow Diagram](data_workflow/sedac-popdensity-yeargrid5yr-v4.11_Data_Flow.qmd) From 5ace3aa7716bfcabf59730c40b20c4581348096e Mon Sep 17 00:00:00 2001 From: Siddharth Chaudhary <118487103+siddharth0248@users.noreply.github.com> Date: Mon, 25 Nov 2024 13:24:54 -0600 Subject: [PATCH 2/2] Methane Release (#146) Update in notebooks and reports for Methane release --- .gitignore | 2 + LICENSE | 2 +- _quarto.yml | 21 +- ...-v1.ipynb => ct-ch4-monthgrid-v2023.ipynb} | 10 +- cog_transformation/goes-ch4plume-v1.ipynb | 3059 +++++++++++ data_transformation_plugins/README.md | 15 + data_transformation_plugins/__init__.py | 0 .../ecco_darwin_transformation.py | 54 + .../geos_oco2_transformation.py | 45 + .../gosat_ch4_transformation.py | 44 + .../gpw_transformation.py | 41 + data_transformation_plugins/push_to_s3.py | 57 + .../sample_transformation.ipynb | 681 +++ .../tm5_4dvar_update_noaa_transformation.py | 53 + .../ct-ch4-monthgrid-v2023_Data_Flow.qmd | 3 + data_workflow/goes-ch4plume-v1_Data_Flow.qmd | 4 + .../ct-ch4-monthgrid-v2023_Data_Flow.png | Bin 0 -> 91702 bytes .../media/goes-ch4plume-v1_Data_Flow.png | Bin 0 -> 156132 bytes ...m54dvar-ch4flux-monthgrid-v1_Data_Flow.png | Bin 104252 -> 0 bytes ...m54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd | 3 - datatransformationcode.qmd | 5 +- datausage.qmd | 10 +- .../generate_statistics.py | 58 + ...023_Processing and Verification Report.qmd | 9 + ...-v1_Processing and Verification Report.qmd | 9 + ...-v1_Processing and Verification Report.qmd | 2 +- ...-v1_Processing and Verification Report.qmd | 2 +- ...023_Processing and Verification Report.pdf | Bin 0 -> 89740 bytes ...-v1_Processing and Verification Report.pdf | Bin 0 -> 87510 bytes ...-v1_Processing and Verification Report.pdf | Bin 171736 -> 85945 bytes ...-v1_Processing and Verification Report.pdf | Bin 198603 -> 0 bytes ...-v1_Processing and Verification Report.qmd | 9 - processingreport.qmd | 5 +- ...ct-ch4-monthgrid-v2023_User_Notebook.ipynb | 1779 +++++++ .../goes-ch4plume-v1_User_Notebook.ipynb | 580 +++ ...-ch4budget-yeargrid-v1_User_Notebook.ipynb | 4610 +++-------------- ...r-ch4flux-monthgrid-v1_User_Notebook.ipynb | 2170 -------- workflow.qmd | 5 +- 38 files changed, 7328 insertions(+), 6019 deletions(-) rename cog_transformation/{tm54dvar-ch4flux-monthgrid-v1.ipynb => ct-ch4-monthgrid-v2023.ipynb} (92%) create mode 100644 cog_transformation/goes-ch4plume-v1.ipynb create mode 100644 data_transformation_plugins/README.md create mode 100644 data_transformation_plugins/__init__.py create mode 100644 data_transformation_plugins/ecco_darwin_transformation.py create mode 100644 data_transformation_plugins/geos_oco2_transformation.py create mode 100644 data_transformation_plugins/gosat_ch4_transformation.py create mode 100644 data_transformation_plugins/gpw_transformation.py create mode 100644 data_transformation_plugins/push_to_s3.py create mode 100644 data_transformation_plugins/sample_transformation.ipynb create mode 100644 data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py create mode 100644 data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd create mode 100644 data_workflow/goes-ch4plume-v1_Data_Flow.qmd create mode 100644 data_workflow/media/ct-ch4-monthgrid-v2023_Data_Flow.png create mode 100644 data_workflow/media/goes-ch4plume-v1_Data_Flow.png delete mode 100644 data_workflow/media/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.png delete mode 100644 data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd create mode 100644 generating_statistics_for_validation/generate_statistics.py create mode 100644 processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd create mode 100644 processing_and_verification_reports/goes-ch4plume-v1_Processing and Verification Report.qmd create mode 100644 processing_and_verification_reports/reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.pdf create mode 100644 processing_and_verification_reports/reports/goes-ch4plume-v1_Processing and Verification Report.pdf delete mode 100644 processing_and_verification_reports/reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.pdf delete mode 100644 processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd create mode 100644 user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb create mode 100644 user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb delete mode 100644 user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb 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/LICENSE b/LICENSE index 3e6e1ff6..185432e2 100644 --- a/LICENSE +++ b/LICENSE @@ -186,7 +186,7 @@ same "printed page" as the copyright notice for easier identification within third-party archives. - Copyright 2024 U.S.GHG Center + Copyright 2024 U.S. Greenhouse Gas Center Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/_quarto.yml b/_quarto.yml index db11916c..1ed1edb9 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 @@ -96,10 +97,12 @@ 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 + - cog_transformation/goes-ch4plume-v1.ipynb + - section: Greenhouse Gas Concentrations contents: - cog_transformation/noaa-gggrn-concentrations.ipynb @@ -118,7 +121,7 @@ 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 +131,12 @@ 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 +155,7 @@ 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 +165,12 @@ 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/cog_transformation/goes-ch4plume-v1.ipynb b/cog_transformation/goes-ch4plume-v1.ipynb new file mode 100644 index 00000000..7248aef8 --- /dev/null +++ b/cog_transformation/goes-ch4plume-v1.ipynb @@ -0,0 +1,3059 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d8c644b3", + "metadata": {}, + "source": [ + "---\n", + "title: Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure\n", + "description: Documentation of data transformation\n", + "author: Paridhi Parajuli \n", + "date: November 15, 2024\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "1ace4dc7", + "metadata": {}, + "source": [ + "This script was used to transform the GOES Methane Point Source Plume dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "91b2b0ea-e970-42b7-9b87-ef3931a1c60f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import glob\n", + "import os\n", + "import rasterio\n", + "from rasterio.warp import calculate_default_transform, reproject, Resampling\n", + "from pyproj import CRS\n", + "import xarray as xr\n", + "import rioxarray \n", + "from datetime import datetime, timedelta\n", + "from rasterio.enums import Resampling\n", + "from rio_cogeo.cogeo import cog_translate\n", + "from rio_cogeo.profiles import cog_profiles\n", + "import tempfile" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "a00a038c-c2ad-4048-bb3f-bcaf96ec00fd", + "metadata": {}, + "outputs": [], + "source": [ + "all_files= glob.glob(\"labelled_plumes2/*/*/*.nc\")" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "5ac87306-4eec-4182-8ca4-18d1973ea207", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "plume_metadata = {\n", + " \"BV1\": {\n", + " \"2019-04-07\": {\n", + " \"Plume_id\": \"BV1-1\",\n", + " \"Plume_Lat\": \"26.298\" ,\n", + " \"Plume_Lon\": \"-104.530\" ,\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " },\n", + " \"2019-05-21\": {\n", + " \"Plume_id\": \"BV1-2\",\n", + " \"Plume_Lat\": \"26.298\", \n", + " \"Plume_Lon\": \"-104.530\",\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " }\n", + " },\n", + " \"BV2\": {\n", + " \"2019-04-09\": {\n", + " \"Plume_id\": \"BV2-1\",\n", + " \"Plume_Lat\": \"26.086\", \n", + " \"Plume_Lon\": \"-104.317\",\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " },\n", + " \"2019-04-10\": {\n", + " \"Plume_id\": \"BV2-2\",\n", + " \"Plume_Lat\": \"26.086\", \n", + " \"Plume_Lon\": \"-104.317\" ,\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " },\n", + " \"2019-04-18\": {\n", + " \"Plume_id\": \"BV2-3\",\n", + " \"Plume_Lat\": \"26.086\", \n", + " \"Plume_Lon\": \"-104.317\" ,\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " },\n", + " \"2019-05-11\": {\n", + " \"Plume_id\": \"BV2-4\", \n", + " \"Plume_Lat\": \"26.086\", \n", + " \"Plume_Lon\": \"-104.317\" ,\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " },\n", + " \"2019-05-12\": {\n", + " \"Plume_id\": \"BV2-5\",\n", + " \"Plume_Lat\": \"26.086\", \n", + " \"Plume_Lon\": \"-104.317\" ,\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " }\n", + " },\n", + " \"BV4\": {\n", + " \"2019-05-16\": {\n", + " \"Plume_id\": \"BV4-1\",\n", + " \"Plume_Lat\": \"26.086\", \n", + " \"Plume_Lon\": \"-104.356\" ,\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " },\n", + " \"2019-05-22\": {\n", + " \"Plume_id\": \"BV4-2\",\n", + " \"Plume_Lat\": \"26.086\", \n", + " \"Plume_Lon\": \"-104.356\" ,\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " },\n", + " \"2019-05-23\": {\n", + " \"Plume_id\": \"BV4-3\",\n", + " \"Plume_Lat\": \"26.086\", \n", + " \"Plume_Lon\": \"-104.356\" ,\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " },\n", + " \"2019-05-24\": {\n", + " \"Plume_id\": \"BV4-4\",\n", + " \"Plume_Lat\": \"26.086\", \n", + " \"Plume_Lon\": \"-104.356\" ,\n", + " \"state\": \"Durango\",\n", + " \"country\": \"Mexico\"\n", + " }\n", + " },\n", + " \"IN-1\": {\n", + " \"2020-06-15\": {\n", + " \"Plume_id\": \"IN-1\",\n", + " \"Plume_Lat\": \"40.650\", \n", + " \"Plume_Lon\": \"-87.000\" ,\n", + " \"state\": \"Indiana\",\n", + " \"country\": \"USA\"\n", + " }\n", + " },\n", + " \"IN-2\": {\n", + " \"2020-06-15\": {\n", + " \"Plume_id\": \"IN-2\",\n", + " \"Plume_Lat\": \"40.650\", \n", + " \"Plume_Lon\": \"-87.000\" ,\n", + " \"state\": \"Indiana\",\n", + " \"country\": \"USA\"\n", + " }\n", + "},\n", + " \"Permian\": {\n", + " \"2023-07-26\": {\n", + " \"Plume_id\": \"PB-1\",\n", + " \"Plume_Lat\": \"33.645\", \n", + " \"Plume_Lon\": \"-104.779\" ,\n", + " \"state\": \"Texas\",\n", + " \"country\": \"USA\"\n", + " }\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "f4e593b3-215d-498f-af6a-cbb4e956464e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:11:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:01:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:21:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:31:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:41:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:51:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T16:41:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T16:51:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T16:46:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T16:56:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T16:36:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T18:01:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:46:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:56:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:26:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:36:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:16:00Z\n", + "GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:06:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:46:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:56:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:26:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T16:06:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:36:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:16:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:46:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:56:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:16:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:06:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:26:00Z\n", + "GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:36:00Z\n", + 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"GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:06:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:16:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:36:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:26:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T16:51:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T16:41:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:56:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:46:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:51:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:41:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T16:56:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T16:46:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:31:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:21:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:01:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:11:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:56:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:46:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:41:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:01:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:36:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:26:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:11:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:31:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:06:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:16:00Z\n", + "GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:21:00Z\n" + ] + } + ], + "source": [ + "src_crs = CRS.from_wkt('PROJCS[\\\"unknown\\\",GEOGCS[\\\"unknown\\\",DATUM[\\\"unknown\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.2572221]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]]],PROJECTION[\\\"Geostationary_Satellite\\\"],PARAMETER[\\\"central_meridian\\\",-75],PARAMETER[\\\"satellite_height\\\",35786023],PARAMETER[\\\"false_easting\\\",0],PARAMETER[\\\"false_northing\\\",0],UNIT[\\\"metre\\\",1,AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],AXIS[\\\"Easting\\\",EAST],AXIS[\\\"Northing\\\",NORTH],EXTENSION[\\\"PROJ4\\\",\\\"+proj=geos +a=6378137.0 +rf=298.2572221 +lon_0=-75.0 +lat_0=0.0 +h=35786023.0 +x_0=0 +y_0=0 +units=m +sweep=x +no_defs\\\"]]')\n", + "dst_crs = CRS.from_epsg(4326) \n", + "for file in all_files:\n", + "\n", + " site_name= file.split('/')[-3].split(\"_\")[0]\n", + " original_time = file.split('/')[-1].split('.')[0]\n", + " parsed_time = datetime.strptime(original_time, '%Y-%m-%dT%Hh%M')\n", + " formatted_time = parsed_time.strftime('%Y-%m-%dT%H:%M:%S') + 'Z'\n", + "\n", + " try:\n", + " plume_id = plume_metadata[site_name][formatted_time.split('T')[0]][\"Plume_id\"]\n", + " state = plume_metadata[site_name][formatted_time.split('T')[0]][\"state\"]\n", + " country = plume_metadata[site_name][formatted_time.split('T')[0]][\"country\"]\n", + " except KeyError:\n", + " print(f\"KeyError for file\", file)\n", + " \n", + " ds = xr.open_dataset(file)\n", + " all_zero = np.all( ds['CH4_plume_mask'] == 0)\n", + " if ~(all_zero):\n", + " \n", + " data_array = ds['Rad'] * ds['CH4_plume_mask']\n", + "\n", + " # Identify the bounding box where CH4_plume_mask is 1\n", + " y, x = np.where(ds['CH4_plume_mask'].values == 1)\n", + " latitudes = ds['y'].values[y]\n", + " longitudes = ds['x'].values[x]\n", + "\n", + " # Calculate the bounding box coordinates\n", + " min_lon, max_lon = longitudes.min(), longitudes.max()\n", + " min_lat, max_lat = latitudes.min(), latitudes.max()\n", + "\n", + " # Assign the spatial coordinates (for rioxarray to work properly)\n", + " data_array = data_array.rio.write_crs(src_crs) # Update CRS as needed\n", + " \n", + " # Crop the data array to the bounding box\n", + " data_array = data_array.rio.clip_box(\n", + " minx=min_lon,\n", + " miny=min_lat,\n", + " maxx=max_lon,\n", + " maxy=max_lat\n", + " )\n", + "\n", + " data_array.rio.write_nodata(0, inplace=True)\n", + " \n", + " data_array.rio.write_crs(src_crs.to_string(), inplace=True)\n", + " reprojected_data = data_array.rio.reproject(dst_crs.to_string())\n", + " reprojected_data.rio.set_spatial_dims(\"x\", \"y\", inplace=True)\n", + " \n", + " reprojected_data.where(reprojected_data != 0,-9999 )\n", + " reprojected_data.rio.write_nodata(0, inplace=True)\n", + " if '_FillValue' in reprojected_data.attrs:\n", + " del reprojected_data.attrs['_FillValue']\n", + "\n", + " \n", + " if site_name in [\"IN-1\", \"IN-2\"]:\n", + " site_name=\"Indiana\"\n", + " \n", + " filename = f\"GOES-CH4_{country}_{state}_{site_name}_{plume_id}_{formatted_time}\"\n", + " print(filename)\n", + " output_file = f\"output3/{filename}.tif\"\n", + " reprojected_data.rio.to_raster(output_file, compress='deflate', nodata=0, driver=\"COG\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "7bf3500a-073e-42cb-9cfd-6120aa633298", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpd1a7ggu6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpagktrif4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp1ymy0puz.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpxevevopk.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpg3kb8b5h.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfnk7pa2v.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T17:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbnhwz15w.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T16:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpjtor1vso.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T16:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp3srhwzr5.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpxpgpkddp.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpyct7nyda.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpj8dhbbq8.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpi0ptkuqs.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpdjiiqn2j.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp28wg0vjc.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T19:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpf74d_330.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpymyzeh0e.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpkqwiv5r7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T19:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpifas1w3_.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T19:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7a1r4raq.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T16:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp8tlazkr1.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T20:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmprmr0nppd.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T16:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp11b5nwuy.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp8pffmfd9.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2u_k97hj.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T17:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp3rwdyz2u.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpc46pr66a.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpqzeow0s3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmph5c861y1.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9z40ins0.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T17:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpgu0ubpw6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_vcysm5x.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpaknzk830.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T19:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp12wn0yqs.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T21:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp8zqx_v28.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9khlc6p7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpsc18iq4k.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T22:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpt5wh14k6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T22:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpta3jk9ji.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp3qdrmbl1.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvyv7j1x3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T17:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp4w0zndhz.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp72k3i0ha.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_ngq02gy.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T16:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpu91c6b7p.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T16:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvss_pjs8.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmph868r7yi.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T14:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpxg3cjv_e.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T14:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpu8abd374.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvq0fjrts.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T16:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp76j9nk8u.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T16:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwko8zw83.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T16:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7fzc0vfq.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpgotc8ulv.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmr7da9g7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T17:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfqvk_ze4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T20:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpspy_bnje.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T20:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpulc2twu_.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T18:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpg2ihudn2.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmphbuzzk77.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpd3253n_3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_7lhkuym.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp1o70f5eo.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2yau47kt.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpdpmi6guj.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpowupxn84.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpk31yc_5w.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpuduu9n50.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmph65k_rv0.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpp1bkcq41.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0oc53vqy.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpkfzz5ldv.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T20:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp50isjnpr.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp4mkb6n21.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmamzp6xw.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp3v32czc1.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpg5k7bm7p.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpndj_qmv3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpnuf9nfwo.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T17:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpf3qnmgq6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T17:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7cxa79fk.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T14:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmprabv8v16.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T16:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmph_yp0wrp.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T14:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpxdup00sz.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvv0bef0e.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfp3auk89.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpclmogkje.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmphvwybmjl.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T15:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpce_d0kgz.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T15:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpqg71c46e.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T22:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwnhzapif.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T16:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpu7fprjd4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T22:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpkgx6rpiy.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T21:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpimkacign.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbsartupd.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpzuzh6rtf.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9_fwu7y6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T17:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp1bhjj357.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpqypxs3in.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T17:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpzbxeklg7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpo3v72lna.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpuyo7uhao.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T20:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpa93aj00e.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T20:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2htwlj2c.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0fby5cge.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpozfzq1_7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0erv5avi.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfr7ztool.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmptin06xnu.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0u892fop.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7shse6w9.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp1w_vq9xj.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T19:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmprp_a4k1k.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwj7bpmcm.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpdi15hsgm.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T19:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpt2zp0mpt.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpe1w38e1h.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T19:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpsarzqjnw.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpazwx43qn.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_au5vxea.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmptymkejt2.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpobtb0ady.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T21:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp1a1mxztb.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpf8fku52n.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T21:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2xejfzqj.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7hc8cqib.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T16:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmphqmjtil2.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpld0d_4ll.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpo3_c242c.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2zzsjuzx.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvfr8ns1x.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpxpd35k4o.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp5q1etn9h.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T16:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmxp4117r.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T14:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpccqgbsd3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T16:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmn6xe9jj.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T14:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp4xo84rdn.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpeop5szlg.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpgdqa50v_.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T17:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfaitm5p9.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T17:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpga3dniuv.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T17:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpk9bgye9c.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T17:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpa47g28gk.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T15:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp3r7ar8wc.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpa7bmuj3a.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2wo1azdv.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T15:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2jjnx2np.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T16:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpnayeagpw.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T16:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmphk7_0cwe.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpqnv8zxlf.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpg4xd4bd_.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T21:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpgc_7q509.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T21:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwle1g31e.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmppfj668ob.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9daudogw.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T18:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpoymlutuv.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpx_x_qke5.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpy3jeu9z1.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpdnmswt2j.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpl3pbai0m.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp8251ngwc.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp5tmo938o.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0bc_t5yp.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpnxyocbgb.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0v3fyz0a.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmprnbk1p6r.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpo69n61ls.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfgun5h6d.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T20:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpr7713tq9.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmppg43aklq.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T17:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp49d0zsvs.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmprnn2qzjm.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T17:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpzbe7otzi.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmplvkcmosp.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmph2k4oz9_.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T16:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbc1kjvt4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T16:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbvcl9_sh.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpx9rp8hoh.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0v5igr2v.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T19:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9ceqe7lb.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpajsurghb.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T16:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmprqnpx5ue.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T16:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpiwigceg7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwgicxhof.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpdk4a4nlb.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpjlzqbxpx.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T19:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpygw8k5p4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T19:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpsqxythbx.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpl3hq5n2l.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpn6jlu33f.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2uuvp9xw.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp5lyw286k.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T20:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpohi7nxvz.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T16:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp1cj3lhe2.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T20:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpo68jb_uh.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpqycppww4.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpprqyqk0x.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpqgi_cxit.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmps4j7eogr.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T16:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpqpnlhk07.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmprdo0vre2.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T16:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbeg331dv.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpto7znk6t.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T18:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpodgt2065.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T18:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9sxap_vj.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T17:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp96iu1w3a.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T17:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpftkumy25.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpe1cmh1oq.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpeuzv3s8q.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0_9phvs7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpxchneni0.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpyn9tawf3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmps46_ehdu.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp6kgxmm4i.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpzeyfaqrn.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpepjhfgfj.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpsx7twdls.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp3hwg3o90.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmphi2sis1q.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T14:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmplezfxo88.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T17:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpre_zjvm9.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T17:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvjq3r8f5.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T20:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7nvdh9gt.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpxklaozh_.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpy9dvrufu.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T17:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpxqocw8q2.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T14:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9pd_ut__.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T14:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwl1u4nm4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpe064ni6j.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T14:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpv55rev9b.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T14:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpt59ghjku.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9zlam255.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T16:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwj28zp_u.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0ngwz92s.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmqf3l3jj.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T16:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbx0xe4h3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T16:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpoodase5r.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpk_vj_nby.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpuunbqre5.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T19:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmptuaqlpo7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T19:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmplji70f0a.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmph1ss4us3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmprifep3pc.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T21:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_r4u5spd.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmhm746fc.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpgpr458y4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T22:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbj7rmhnx.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T22:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpjvb3jnnl.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpr8pat38m.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmplrd1jfc6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7g4rm6xx.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvd09cc9m.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpu3w1h9al.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvsot_phu.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T17:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpjqk_istn.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T16:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpkz6t4m2f.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T16:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpah8zi_xl.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmperki2n7q.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmphqo5uwn0.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T19:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0lcjkx2n.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T16:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpyb2r6_ii.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T16:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpy8bvoa7d.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpzp3oi25c.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_j818cbu.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9y1whuxl.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpglas301y.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmc451281.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpgcin2hy4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T19:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmxi7fksz.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpm6zyr2nu.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0fbkpow2.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp5pum7kxt.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp80zkxm5d.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbr241xx_.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpoge53h7m.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T17:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpju4dcpri.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T20:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpj57_hr0z.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpoc67uroi.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpe94sf2lj.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_p5bm2hv.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfwm2melu.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfiu1n0dy.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpen_ltqlu.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T17:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_b8k2g4k.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T20:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpq_0uzzny.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T20:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpprhzxfb1.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7y48vhw0.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmphotvixgj.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbnkcvea6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpams_hqkr.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpjfae7xc7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp950kd_7m.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T16:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp600_d8wd.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T22:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbnxdnj_5.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T22:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp1xl67ck5.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T15:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpevjot2pl.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T15:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfb8jw2xr.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T21:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpenyg8n9r.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T17:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpoiwrd5o4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T17:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvjnkhzoh.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpz9e4njqf.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpr8p_zep5.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T14:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmzi210q_.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T16:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp69ageozy.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T14:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpyfp9p70m.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp4krfm0vq.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T16:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7ou46n13.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp09nr3kqx.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpsy2x5dn6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T18:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpw5l9p88q.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpofnqclx4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0l4lqzvt.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpf9j63sln.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T20:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfmwiqr4f.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9c97rl9e.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvwlg0xxs.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpqy6hgtbc.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7nm_mr_7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpoquwq1xn.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T21:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpoa4t5j6y.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpadmr8zyg.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpkg4isd2n.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp63hmoh08.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpb8bu39gm.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmptiojiuyz.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0y3qy9ad.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T18:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpzz7wsqmz.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpky0nd90o.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpoz5b56iy.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpxotz81sx.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpw52ei2j6.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T18:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpf9oxzqxz.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T16:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9vq46g6o.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpr46k7x55.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpy0lwg_hn.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T20:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp30rbszv4.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T20:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpya38tma6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T17:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpnpv29phv.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T17:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpimmk2b9x.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpy5d_494r.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpp2qx7odm.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp8bzjmyai.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T15:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpptz90o1f.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwcre8b71.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp23vzgd20.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpj8v64pg8.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp8xchimzi.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T16:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpujjf4phe.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T16:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_6pjjxe1.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T15:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpdbd3pmsy.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpx4rj7zvo.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpdu3vohes.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T15:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp8hrnbw6w.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T21:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp9wsx5qil.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T21:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpse79k7kg.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp4v87iwst.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvfboja09.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp3axo_gv9.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T19:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp20dvx0gz.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpqnd1i73_.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp97tp4v8a.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T16:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpilbq8wrd.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T14:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwsj8ig26.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T16:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpub3wvooz.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T14:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpp8aveywa.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T20:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2pgfov1n.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvzhjnopa.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T17:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp1oh4vnml.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T17:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp5j7_tro6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T17:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmq6ib83e.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T17:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpdm3fzpa3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmprbbhomj9.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T19:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpo7s1ecrk.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0g_oqqf5.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpjv_r7k3n.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpgz8flp8i.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpmogy_d8v.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T19:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_vn86y0o.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpl3q2nt88.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T19:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_0la453b.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T17:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpilue2c1b.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpg73td4h_.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T21:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0nlqcnti.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp25i0i5b_.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T21:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_0zf4jfv.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpdpp_4cpu.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T16:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpiabnzsvg.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmph3t6i9k3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpu2ov3osm.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpnr5ropqs.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpeblgz12x.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmplidmnbpj.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmph5_27lf_.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp5zfubhmz.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpneawfqgo.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T14:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpohmpp1nu.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpn5vfkgf9.tif\n", + "Reading input: output3/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T17:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpuj9yy5do.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T17:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp6gqp0alg.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-1_2019-05-16T17:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpfk5we9i2.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpk7qxw931.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T17:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvoqwux0k.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T14:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpdnuv13nn.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T20:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpgctoypwp.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbcnkjd6e.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T18:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpnc9b1to3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpl2n9oq5q.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T19:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpo4_r1q5d.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T16:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpjrwho2e_.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp20gra5yk.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T16:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp248vmvc3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T18:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmptm2rfmrv.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T17:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmps5txqrrs.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T17:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpw3bc4sm3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T20:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2hudjqqz.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T18:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpjz07fb2q.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T20:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp59_es0lh.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T18:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpnne02ctt.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp50hmvjp9.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-4_2019-05-24T17:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp1zpx2te0.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmppr8lrcr3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T20:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpx_8bziv7.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp426rsm9l.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T17:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpcontz4je.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp3ni1rts9.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T17:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpi2zccga0.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-2_2020-06-15T15:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp_65q7ojg.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T20:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp7qkji7sl.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T20:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpgsi3dgkn.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbqvaiz3w.tif\n", + "Reading input: output3/GOES-CH4_USA_Indiana_Indiana_IN-1_2020-06-15T15:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpvqmv5ous.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T19:31:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp607u8q_n.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T19:21:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpual3if3k.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:11:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpgn32czxx.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpws4vcjbt.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-3_2019-04-18T18:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp5fj8jgmd.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-3_2019-05-23T15:01:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp75o7hkrc.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2h1qa719.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T16:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpotblwjjm.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T16:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp5qsykxj3.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T22:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpiswbcw_0.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:36:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpxlt9j6pq.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T21:26:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpd_jqkvum.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp3gktfhrw.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV4_BV4-2_2019-05-22T18:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp3q0y1hxm.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:56:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwhm6uek6.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-2_2019-04-10T17:46:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp5h4ja8pe.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:16:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpwou143zv.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T17:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp8b6_11q2.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-5_2019-05-12T19:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpk0ekejzd.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T17:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbq2sf6ge.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-2_2019-05-21T20:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpbkr29yqn.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-4_2019-05-11T16:06:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpnkf12cca.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp2247hazr.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T18:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmpm_4y1ih9.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:51:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmppbp5pdzm.tif\n", + "Reading input: output3/GOES-CH4_Mexico_Durango_BV2_BV2-1_2019-04-09T18:41:00Z.tif\n", + "\n", + "Adding overviews...\n", + "Updating dataset tags...\n", + "Writing output to: /var/folders/6f/r0g105hx7l5g3qw69th62h100000gp/T/tmp0chyrnfg.tif\n" + ] + } + ], + "source": [ + "# Add overviews\n", + "COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n", + "OVERVIEW_LEVELS = 3\n", + "OVERVIEW_RESAMPLING = 'average'\n", + "\n", + "for file in glob.glob(\"output3/*.tif\"):\n", + " output_path = f\"output3/{file.split(\"/\")[-1]}\"\n", + " \n", + " # Create a temporary file to hold the COG\n", + " with tempfile.NamedTemporaryFile(suffix='.tif', delete=False) as temp_file: \n", + " # Create COG with overviews and nodata value\n", + " cog_translate(\n", + " file,\n", + " temp_file.name,\n", + " cog_profiles.get(\"deflate\"),\n", + " overview_level=OVERVIEW_LEVELS,\n", + " overview_resampling=OVERVIEW_RESAMPLING,\n", + " nodata=0\n", + " )\n", + " # Move the temporary file to the desired local path\n", + " os.rename(temp_file.name, output_path)" + ] + } + ], + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/data_transformation_plugins/README.md b/data_transformation_plugins/README.md new file mode 100644 index 00000000..9160fa89 --- /dev/null +++ b/data_transformation_plugins/README.md @@ -0,0 +1,15 @@ +## Information about the folder +This folder is a part of the `automation pipeline using DAG`. It contains the functions which are essential for transforming the given dataset into COGs. There are python files for transforming every dataset which will be used as plugins into the pipeline. + +## How this folder fits in the automation pipeline +These functions/files are created by the `developer` to transform a single data file to COG. Once the COGs are validated, these python scripts are pushed to the `GHGC-SMCE S3` These files are then fetched by the `SM2A DAG` to complete the transformation of entire dataset automatically. + +## Naming convention for the transformation files in the folder +- `name of python file` - `collectionname_transformation.py` +`collectionname` refers to the STAC collection name of the dataset followed by the word `transformation`. Make sure the `collectionname` within the filename matches with the `collectionname` passed as a `parameter` to the DAG. + +## Steps for running the pipeline +- Test convert a single netCDF file for a new dataset using the `sample_transformation.ipynb` notebook. +- Create a new `data transformation plugin` python file for the new dataset using the convention mentioned above. +- `push_to_s3.py` is not yet plugged into the `CI/CD pipeline` so after creating the plugin, run the python file in the terminal. Running the python file will only push the files that are not present on the S3 folder. +- At this point, the tasks from `ghgc-docs` are completed. \ No newline at end of file diff --git a/data_transformation_plugins/__init__.py b/data_transformation_plugins/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/data_transformation_plugins/ecco_darwin_transformation.py b/data_transformation_plugins/ecco_darwin_transformation.py new file mode 100644 index 00000000..9eb518b0 --- /dev/null +++ b/data_transformation_plugins/ecco_darwin_transformation.py @@ -0,0 +1,54 @@ +import re +from typing import Dict + +import xarray +from s3fs import S3File +from xarray import DataArray + + +def ecco_darwin_transformation( + file_obj: S3File, name: str, nodata: int +) -> Dict[str, DataArray]: + """Transformation function for the ecco darwin dataset + + Args: + file_obj (s3fs object): s3fs sile object for one file of the dataset + name (str): name of the file to be transformed + nodata (int): Nodata value as specified by the data provider + + Returns: + dict: Dictionary with the COG name and its corresponding data array. + """ + var_data_netcdf = {} + xds = xarray.open_dataset(file_obj) + xds = xds.rename({"y": "latitude", "x": "longitude"}) + xds = xds.assign_coords( + longitude=((xds.longitude / 1440) * 360) - 180 + ).sortby("longitude") + xds = xds.assign_coords(latitude=((xds.latitude / 721) * 180) - 90).sortby( + "latitude" + ) + + variables = list(xds.data_vars)[2:] + + for var in variables: + filename = name.split("/")[-1] + filename_elements = re.split("[_ .]", filename) + data = xds[var] + + data = data.reindex(latitude=list(reversed(data.latitude))) + data = data.where(data != nodata, -9999) + data.rio.set_spatial_dims("longitude", "latitude", inplace=True) + data.rio.write_crs("epsg:4326", inplace=True) + data.rio.write_nodata(-9999, inplace=True) + + filename_elements.pop() + filename_elements[-1] = filename_elements[-2] + filename_elements[-1] + filename_elements.pop(-2) + # # insert date of generated COG into filename + cog_filename = "_".join(filename_elements) + # # add extension + cog_filename = f"{cog_filename}.tif" + var_data_netcdf[cog_filename] = data + + return var_data_netcdf diff --git a/data_transformation_plugins/geos_oco2_transformation.py b/data_transformation_plugins/geos_oco2_transformation.py new file mode 100644 index 00000000..dc18dfbc --- /dev/null +++ b/data_transformation_plugins/geos_oco2_transformation.py @@ -0,0 +1,45 @@ +import re +from typing import Dict + +import xarray +from s3fs import S3File +from xarray import DataArray + + +def geos_oco2_transformation( + file_obj: S3File, name: str, nodata: int +) -> Dict[str, DataArray]: + """Transformation function for the oco2 geos dataset + + Args: + file_obj (s3fs object): s3fs sile object for one file of the dataset + name (str): name of the file to be transformed + nodata (int): Nodata value as specified by the data provider + + Returns: + dict: Dictionary with the COG name and its corresponding data array. + """ + var_data_netcdf = {} + xds = xarray.open_dataset(file_obj) + xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby("lon") + variable = [var for var in xds.data_vars] + for time_increment in range(0, len(xds.time)): + for var in variable: + filename = name.split("/ ")[-1] + filename_elements = re.split("[_ .]", filename) + data = getattr(xds.isel(time=time_increment), var) + data = data.isel(lat=slice(None, None, -1)) + data = data.where(data != nodata, -9999) + data.rio.set_spatial_dims("lon", "lat", inplace=True) + data.rio.write_crs("epsg:4326", inplace=True) + data.rio.write_nodata(-9999, inplace=True) + # # insert date of generated COG into filename + filename_elements[-1] = filename_elements[-3] + filename_elements.insert(2, var) + filename_elements.pop(-3) + cog_filename = "_".join(filename_elements) + # # add extension + cog_filename = f"{cog_filename}.tif" + var_data_netcdf[cog_filename] = data + + return var_data_netcdf diff --git a/data_transformation_plugins/gosat_ch4_transformation.py b/data_transformation_plugins/gosat_ch4_transformation.py new file mode 100644 index 00000000..ac299264 --- /dev/null +++ b/data_transformation_plugins/gosat_ch4_transformation.py @@ -0,0 +1,44 @@ +import re +from typing import Dict + +import xarray +from s3fs import S3File +from xarray import DataArray + + +def gosat_ch4_transformation( + file_obj: S3File, name: str, nodata: int +) -> Dict[str, DataArray]: + """Transformation function for the ecco darwin dataset + + Args: + file_obj (s3fs object): s3fs sile object for one file of the dataset + name (str): name of the file to be transformed + nodata (int): Nodata value as specified by the data provider + + Returns: + dict: Dictionary with the COG name and its corresponding data array. + """ + var_data_netcdf = {} + ds = xarray.open_dataset(file_obj) + variable = [var for var in ds.data_vars] + + for var in variable: + filename = name.split("/")[-1] + filename_elements = re.split("[_ .]", filename) + data = ds[var] + filename_elements.pop() + filename_elements.insert(2, var) + cog_filename = "_".join(filename_elements) + # # add extension + cog_filename = f"{cog_filename}.tif" + + data = data.reindex(lat=list(reversed(data.lat))) + data = data.where(data != nodata, -9999) + data.rio.write_nodata(-9999, inplace=True) + + data.rio.set_spatial_dims("lon", "lat") + data.rio.write_crs("epsg:4326", inplace=True) + var_data_netcdf[cog_filename] = data + + return var_data_netcdf diff --git a/data_transformation_plugins/gpw_transformation.py b/data_transformation_plugins/gpw_transformation.py new file mode 100644 index 00000000..9db17e90 --- /dev/null +++ b/data_transformation_plugins/gpw_transformation.py @@ -0,0 +1,41 @@ +import re +from typing import Dict + +import xarray +from s3fs import S3File +from xarray import DataArray + + +def gpw_transformation( + file_obj: S3File, name: str, nodata: int +) -> Dict[str, DataArray]: + """Transformation function for the gridded population dataset + + Args: + file_obj (s3fs object): s3fs sile object for one file of the dataset + name (str): name of the file to be transformed + nodata (int): Nodata value as specified by the data provider + + Returns: + dict: Dictionary with the COG name and its corresponding data array. + """ + + var_data_netcdf = {} + xds = xarray.open_dataarray(file_obj, engine="rasterio") + + filename = name.split("/")[-1] + filename_elements = re.split("[_ .]", filename) + # # insert date of generated COG into filename + filename_elements.pop() + filename_elements.append(filename_elements[-3]) + xds = xds.where(xds != nodata, -9999) + xds.rio.set_spatial_dims("x", "y", inplace=True) + xds.rio.write_crs("epsg:4326", inplace=True) + xds.rio.write_nodata(-9999, inplace=True) + + cog_filename = "_".join(filename_elements) + # # add extension + cog_filename = f"{cog_filename}.tif" + var_data_netcdf[cog_filename] = xds + + return var_data_netcdf diff --git a/data_transformation_plugins/push_to_s3.py b/data_transformation_plugins/push_to_s3.py new file mode 100644 index 00000000..03cdfaa6 --- /dev/null +++ b/data_transformation_plugins/push_to_s3.py @@ -0,0 +1,57 @@ +import os + +import boto3 + + +def upload_files_to_s3(folder_path, bucket_name, s3_folder, exclude_files): + """ + Uploads all files in a folder to a specified S3 folder only if the file does not already exist in S3, + and excluding specified files. + + Parameters: + - folder_path (str): Path to the local folder containing files to upload. + - bucket_name (str): Name of the S3 bucket. + - s3_folder (str): Destination folder path in the S3 bucket. + - exclude_files (list): List of files to exclude from uploading. + """ + # Initialize S3 client + s3 = boto3.client("s3") + + # Loop through files in the local folder + for file_name in os.listdir(folder_path): + file_path = os.path.join(folder_path, file_name) + + # Check if it's a file and not in the exclude list + if os.path.isfile(file_path) and file_name not in exclude_files: + s3_key = os.path.join(s3_folder, file_name) + + try: + # Check if the file already exists in S3 + s3.head_object(Bucket=bucket_name, Key=s3_key) + print(f"Skipped {file_name} (already exists in S3)") + except s3.exceptions.ClientError as e: + # If the file does not exist, upload it + if e.response["Error"]["Code"] == "404": + try: + s3.upload_file(file_path, bucket_name, s3_key) + print(f"Uploaded {file_name} to {s3_key}") + except Exception as upload_error: + print(f"Error uploading {file_name}: {upload_error}") + else: + print(f"Error checking existence of {file_name}: {e}") + + +# Example usage: +# upload_folder_to_s3("path/to/local/folder", "my-s3-bucket", "my/s3/folder", ["exclude1.ext", "exclude2.ext"]) +if __name__ == "__main__": + upload_files_to_s3( + "data_transformation_plugins", + "ghgc-data-store-develop", + "data_transformation_plugins", + [ + "__init__.py", + "push_to_s3.py", + "README.md", + "sample_transformation.ipynb", + ], + ) diff --git a/data_transformation_plugins/sample_transformation.ipynb b/data_transformation_plugins/sample_transformation.ipynb new file mode 100644 index 00000000..44dd8c02 --- /dev/null +++ b/data_transformation_plugins/sample_transformation.ipynb @@ -0,0 +1,681 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instructions on how to transform a single file\n", + "Install the required libraries in an isolated environment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray\n", + "import re\n", + "from datetime import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The netcdf file contains information as shown below\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:               (latitude: 180, longitude: 360, months: 12)\n",
+       "Coordinates:\n",
+       "  * latitude              (latitude) float64 -89.5 -88.5 -87.5 ... 88.5 89.5\n",
+       "  * longitude             (longitude) float64 -179.5 -178.5 ... 178.5 179.5\n",
+       "  * months                (months) int32 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "Data variables:\n",
+       "    fossil                (months, latitude, longitude) float64 ...\n",
+       "    fossil_global_sum     (months) float64 ...\n",
+       "    microbial             (months, latitude, longitude) float64 ...\n",
+       "    microbial_global_sum  (months) float64 ...\n",
+       "    pyrogenic             (months, latitude, longitude) float64 ...\n",
+       "    pyrogenic_global_sum  (months) float64 ...\n",
+       "    total                 (months, latitude, longitude) float64 ...\n",
+       "    total_global_sum      (months) float64 ...\n",
+       "    surface_area          (latitude, longitude) float64 ...\n",
+       "Attributes:\n",
+       "    email:        carbontracker.team@noaa.gov\n",
+       "    url:          http://gml.noaa.gov/ccgg/carbontracker-ch4/\n",
+       "    institution:  NOAA Global Monitoring Laboratory\n",
+       "    version:      CTCH4 v2023 1x1 monthly fluxes as of 08-Nov-2024 01:58:47 UTC
" + ], + "text/plain": [ + "\n", + "Dimensions: (latitude: 180, longitude: 360, months: 12)\n", + "Coordinates:\n", + " * latitude (latitude) float64 -89.5 -88.5 -87.5 ... 88.5 89.5\n", + " * longitude (longitude) float64 -179.5 -178.5 ... 178.5 179.5\n", + " * months (months) int32 1 2 3 4 5 6 7 8 9 10 11 12\n", + "Data variables:\n", + " fossil (months, latitude, longitude) float64 ...\n", + " fossil_global_sum (months) float64 ...\n", + " microbial (months, latitude, longitude) float64 ...\n", + " microbial_global_sum (months) float64 ...\n", + " pyrogenic (months, latitude, longitude) float64 ...\n", + " pyrogenic_global_sum (months) float64 ...\n", + " total (months, latitude, longitude) float64 ...\n", + " total_global_sum (months) float64 ...\n", + " surface_area (latitude, longitude) float64 ...\n", + "Attributes:\n", + " email: carbontracker.team@noaa.gov\n", + " url: http://gml.noaa.gov/ccgg/carbontracker-ch4/\n", + " institution: NOAA Global Monitoring Laboratory\n", + " version: CTCH4 v2023 1x1 monthly fluxes as of 08-Nov-2024 01:58:47 UTC" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read a file of the new dataset\n", + "name = '/Users/vgaur/Downloads/CTCH4_methane_emis_1998.nc'\n", + "ds = xarray.open_dataset(name) # open the file\n", + "print('The netcdf file contains information as shown below')\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data valriables in the netCDF file are Data variables:\n", + " fossil (months, latitude, longitude) float64 ...\n", + " fossil_global_sum (months) float64 ...\n", + " microbial (months, latitude, longitude) float64 ...\n", + " microbial_global_sum (months) float64 ...\n", + " pyrogenic (months, latitude, longitude) float64 ...\n", + " pyrogenic_global_sum (months) float64 ...\n", + " total (months, latitude, longitude) float64 ...\n", + " total_global_sum (months) float64 ...\n", + " surface_area (latitude, longitude) float64 ...\n", + "Dimensions mentioned in the netCDF file are Frozen({'latitude': 180, 'longitude': 360, 'months': 12})\n" + ] + } + ], + "source": [ + "# Explore the netCDF file\n", + "print('Data valriables in the netCDF file are', ds.data_vars)\n", + "print('Dimensions mentioned in the netCDF file are', ds.dims)\n", + "#..... explore the netCDf file for more information" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# once explored, look at the latitude, longitude and other values and comvert them if needed.\n", + "ds = ds.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})\n", + "ds = ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180)).sortby(\"lon\")\n", + "variable = [var for var in ds.data_vars]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Parse through the time variable to extract single timestamp and create a dataarray for the given timestamp.\n", + "for time_increment in range(0, len(ds.months)):\n", + " # split the original file name to create a unique COG name\n", + " filename = name.split(\"/ \")[-1]\n", + " filename_elements = re.split(\"[_ .]\", filename)\n", + " start_time = datetime(int(filename_elements[-2]), time_increment + 1, 1)\n", + " for var in variable: # Parse through the data variables\n", + " data = getattr(ds.isel(months=time_increment), var)\n", + " data = data.isel(lat=slice(None, None, -1))\n", + " data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n", + " data.rio.write_crs(\"epsg:4326\", inplace=True)\n", + "\n", + " # # insert date of generated COG into filename\n", + " filename_elements.pop()\n", + " filename_elements[-1] = start_time.strftime(\"%Y%m\")\n", + " filename_elements.insert(2, var)\n", + " cog_filename = \"_\".join(filename_elements)\n", + " # # add extension\n", + " cog_filename = f\"{cog_filename}.tif\"" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CTCH4_methane_fossil_emis_199801.tif\n", + "CTCH4_methane_microbial_emis_199801.tif\n", + "CTCH4_methane_pyrogenic_emis_199801.tif\n", + "CTCH4_methane_total_emis_199801.tif\n", + "CTCH4_methane_surface_area_emis_199801.tif\n", + "CTCH4_methane_fossil_emis_199802.tif\n", + "CTCH4_methane_microbial_emis_199802.tif\n", + "CTCH4_methane_pyrogenic_emis_199802.tif\n", + "CTCH4_methane_total_emis_199802.tif\n", + "CTCH4_methane_surface_area_emis_199802.tif\n", + "CTCH4_methane_fossil_emis_199803.tif\n", + "CTCH4_methane_microbial_emis_199803.tif\n", + "CTCH4_methane_pyrogenic_emis_199803.tif\n", + "CTCH4_methane_total_emis_199803.tif\n", + "CTCH4_methane_surface_area_emis_199803.tif\n", + "CTCH4_methane_fossil_emis_199804.tif\n", + "CTCH4_methane_microbial_emis_199804.tif\n", + "CTCH4_methane_pyrogenic_emis_199804.tif\n", + "CTCH4_methane_total_emis_199804.tif\n", + "CTCH4_methane_surface_area_emis_199804.tif\n", + "CTCH4_methane_fossil_emis_199805.tif\n", + "CTCH4_methane_microbial_emis_199805.tif\n", + "CTCH4_methane_pyrogenic_emis_199805.tif\n", + "CTCH4_methane_total_emis_199805.tif\n", + "CTCH4_methane_surface_area_emis_199805.tif\n", + "CTCH4_methane_fossil_emis_199806.tif\n", + "CTCH4_methane_microbial_emis_199806.tif\n", + "CTCH4_methane_pyrogenic_emis_199806.tif\n", + "CTCH4_methane_total_emis_199806.tif\n", + "CTCH4_methane_surface_area_emis_199806.tif\n", + "CTCH4_methane_fossil_emis_199807.tif\n", + "CTCH4_methane_microbial_emis_199807.tif\n", + "CTCH4_methane_pyrogenic_emis_199807.tif\n", + "CTCH4_methane_total_emis_199807.tif\n", + "CTCH4_methane_surface_area_emis_199807.tif\n", + "CTCH4_methane_fossil_emis_199808.tif\n", + "CTCH4_methane_microbial_emis_199808.tif\n", + "CTCH4_methane_pyrogenic_emis_199808.tif\n", + "CTCH4_methane_total_emis_199808.tif\n", + "CTCH4_methane_surface_area_emis_199808.tif\n", + "CTCH4_methane_fossil_emis_199809.tif\n", + "CTCH4_methane_microbial_emis_199809.tif\n", + "CTCH4_methane_pyrogenic_emis_199809.tif\n", + "CTCH4_methane_total_emis_199809.tif\n", + "CTCH4_methane_surface_area_emis_199809.tif\n", + "CTCH4_methane_fossil_emis_199810.tif\n", + "CTCH4_methane_microbial_emis_199810.tif\n", + "CTCH4_methane_pyrogenic_emis_199810.tif\n", + "CTCH4_methane_total_emis_199810.tif\n", + "CTCH4_methane_surface_area_emis_199810.tif\n", + "CTCH4_methane_fossil_emis_199811.tif\n", + "CTCH4_methane_microbial_emis_199811.tif\n", + "CTCH4_methane_pyrogenic_emis_199811.tif\n", + "CTCH4_methane_total_emis_199811.tif\n", + "CTCH4_methane_surface_area_emis_199811.tif\n", + "CTCH4_methane_fossil_emis_199812.tif\n", + "CTCH4_methane_microbial_emis_199812.tif\n", + "CTCH4_methane_pyrogenic_emis_199812.tif\n", + "CTCH4_methane_total_emis_199812.tif\n", + "CTCH4_methane_surface_area_emis_199812.tif\n" + ] + } + ], + "source": [ + "variable = [var for var in ds.data_vars if \"global\" not in var]\n", + "\n", + "for time_increment in range(0, len(ds.months)):\n", + " filename = name.split(\"/\")[-1]\n", + " filename_elements = re.split(\"[_ .]\", filename)\n", + " start_time = datetime(int(filename_elements[-2]), time_increment + 1, 1)\n", + " for var in variable:\n", + " filename = name.split(\"/\")[-1]\n", + " filename_elements = re.split(\"[_ .]\", filename)\n", + " data = getattr(ds.isel(months=time_increment), var)\n", + " data = data.isel(lat=slice(None, None, -1))\n", + " data = data.where(data != -9999, -9999)\n", + " data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n", + " data.rio.write_crs(\"epsg:4326\", inplace=True)\n", + " data.rio.write_nodata(-9999, inplace=True)\n", + "\n", + " # # insert date of generated COG into filename\n", + " filename_elements.pop()\n", + " filename_elements[-1] = start_time.strftime(\"%Y%m\")\n", + " filename_elements.insert(2, var)\n", + " cog_filename = \"_\".join(filename_elements)\n", + " # # add extension\n", + " cog_filename = f\"{cog_filename}.tif\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Validate the COG using QGIS or any other means.\n", + "Once validated, follow the steps mentioned in the README in the folder `data_transformation_plugins`" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cmip6", + "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": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py b/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py new file mode 100644 index 00000000..8b13fc79 --- /dev/null +++ b/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py @@ -0,0 +1,53 @@ +import re +from datetime import datetime +from typing import Dict + +import xarray +from s3fs import S3File +from xarray import DataArray + + +def tm5_4dvar_update_noaa_transformation( + file_obj: S3File, name: str, nodata: int +) -> Dict[str, DataArray]: + """Transformation function for the tm5 ch4 influx dataset + + Args: + file_obj (s3fs object): s3fs sile object for one file of the dataset + name (str): name of the file to be transformed + nodata (int): Nodata value as specified by the data provider + + Returns: + dict: Dictionary with the COG name and its corresponding data array. + """ + + var_data_netcdf = {} + xds = xarray.open_dataset(file_obj) + xds = xds.rename({"latitude": "lat", "longitude": "lon"}) + xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby("lon") + variable = [var for var in xds.data_vars if "global" not in var] + + for time_increment in range(0, len(xds.months)): + filename = name.split("/")[-1] + filename_elements = re.split("[_ .]", filename) + start_time = datetime(int(filename_elements[-2]), time_increment + 1, 1) + for var in variable: + filename = name.split("/")[-1] + filename_elements = re.split("[_ .]", filename) + data = getattr(xds.isel(months=time_increment), var) + data = data.isel(lat=slice(None, None, -1)) + data = data.where(data != nodata, -9999) + data.rio.set_spatial_dims("lon", "lat", inplace=True) + data.rio.write_crs("epsg:4326", inplace=True) + data.rio.write_nodata(-9999, inplace=True) + + # # insert date of generated COG into filename + filename_elements.pop() + filename_elements[-1] = start_time.strftime("%Y%m") + filename_elements.insert(2, var) + cog_filename = "_".join(filename_elements) + # # add extension + cog_filename = f"{cog_filename}.tif" + var_data_netcdf[cog_filename] = data + + return var_data_netcdf 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 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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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z7`%Em=HV|{tMku2(pa2*Pq?}0!AIzF|2ntH@~B$oqif{}l@O+Pt{xM-zEQ~@Q)&a#_t?;X#zY|yKw{q+0}6;jcf~}3R1E=in6&-e7Xg8T z0B3q<3=S0oh~e!q;I$}XZwv(B?7PcB05g1d3=RSE1k(1`ae9NHuo%gd-`*DB9Ereo^$m!D5PQl&5wJbSg#kqP?)qThUzCG@_MCHI%G|v#9E?Eh zDF=Zec8?1j;No|eLx4%ooObpNn3ne(7kJ7+dWy7T3=j}8*q-`eVtdXd0xkmGbuL8_ za1fxd|E$mXh6zCa-=F|=eWbOU1?iR%)^>1kCOrrGK6`(+_ep>{=?{RDv&jwT?>84v P4;%!c;Np_kRG|1D!E>Wh 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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"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..e4441013 --- /dev/null +++ b/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb @@ -0,0 +1,580 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "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", + "---" + ] + }, + { + "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%2Fgoes-ch4plume-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 gridded methane emissions 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", + " " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## About the Data\n", + "\n", + "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", + "\n", + "For more information regarding this dataset, please visit the [Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure](https://earth.gov/ghgcenter/data-catalog/goes-ch4plume-v1) data overview page." + ] + }, + { + "attachments": {}, + "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" + ] + }, + { + "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": 7, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Provide STAC and RASTER API endpoints\n", + "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", + "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n", + "\n", + "# Please use the collection name similar to the one used in STAC collection.\n", + "\n", + "# Name of the collection for gosat budget methane. \n", + "collection_name = \"goes-ch4plume-v1\"" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'goes-ch4plume-v1',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/goes-ch4plume-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/goes-ch4plume-v1'}],\n", + " 'title': 'Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure',\n", + " 'extent': {'spatial': {'bbox': [[-104.72692922440127,\n", + " 25.251622016105113,\n", + " -86.82596069124111,\n", + " 41.12229053684302]]},\n", + " 'temporal': {'interval': [['2019-04-07 16:16:00+00',\n", + " '2023-07-26 18:01:00+00']]}},\n", + " 'license': 'CC-BY-4.0',\n", + " 'renders': {'rad': {'assets': ['rad'],\n", + " 'rescale': [[0.0, 0.4]],\n", + " 'colormap_name': 'plasma'},\n", + " 'dashboard': {'assets': ['rad'],\n", + " 'rescale': [[0.0, 0.4]],\n", + " 'colormap_name': 'plasma'}},\n", + " 'summaries': {'datetime': ['2019-04-07T16:16:00Z', '2023-07-26T18:01:00Z']},\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", + " 'item_assets': {'rad': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Methane Column Enhancement',\n", + " 'description': 'Sample of methane plumes from point sources.'}},\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': 'daily'}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fetching the collection from STAC collections using appropriate endpoint.\n", + "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\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 April 7, 2019 . By looking at the `dashboard:time density`, we observe that the data is available for multiple days." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_item_count(collection_id):\n", + " count = 0\n", + " items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n", + "\n", + " while True:\n", + " response = requests.get(items_url)\n", + "\n", + " if not response.ok:\n", + " print(\"error getting items\")\n", + " exit()\n", + "\n", + " stac = response.json()\n", + " count += int(stac[\"context\"].get(\"returned\", 0))\n", + " next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n", + "\n", + " if not next:\n", + " break\n", + " items_url = next[0][\"href\"]\n", + "\n", + " return count" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 457 items\n" + ] + } + ], + "source": [ + "# Check total number of items available\n", + "number_of_items = get_item_count(collection_name)\n", + "items = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n", + "print(f\"Found {len(items)} items\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'goes-ch4plume-v1-GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T18:01:00Z',\n", + " 'bbox': [-104.05592607423101,\n", + " 31.603935693136833,\n", + " -103.94719869425685,\n", + " 31.697130590257547],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/goes-ch4plume-v1'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/goes-ch4plume-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/goes-ch4plume-v1/items/goes-ch4plume-v1-GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T18:01:00Z'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/goes-ch4plume-v1/items/goes-ch4plume-v1-GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T18:01:00Z/map?assets=rad&rescale=0.0%2C0.4&colormap_name=plasma',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'rad': {'href': 's3://ghgc-data-store/goes-ch4plume-v1/GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T18:01:00Z.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Methane Column Enhancement',\n", + " 'proj:bbox': [-104.05592607423101,\n", + " 31.603935693136833,\n", + " -103.94719869425685,\n", + " 31.697130590257547],\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [6, 7],\n", + " 'description': 'Sample of methane plumes from point sources',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': 0.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 0.11519252942615393,\n", + " 'min': 0.021999548698559218,\n", + " 'count': 11,\n", + " 'buckets': [2, 3, 2, 1, 1, 0, 2, 2, 1, 2]},\n", + " 'statistics': {'mean': 0.0651174719845713,\n", + " 'stddev': 0.02977607649044455,\n", + " 'maximum': 0.11519252942615393,\n", + " 'minimum': 0.021999548698559218,\n", + " 'valid_percent': 38.095238095238095}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-104.05592607423101, 31.603935693136833],\n", + " [-103.94719869425685, 31.603935693136833],\n", + " [-103.94719869425685, 31.697130590257547],\n", + " [-104.05592607423101, 31.697130590257547],\n", + " [-104.05592607423101, 31.603935693136833]]]},\n", + " 'proj:transform': [0.015532482853451768,\n", + " 0.0,\n", + " -104.05592607423101,\n", + " 0.0,\n", + " -0.015532482853452479,\n", + " 31.697130590257547,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/goes-ch4plume-v1/items/goes-ch4plume-v1-GOES-CH4_USA_Texas_Permian_PB-1_2023-07-26T18:01:00Z/preview.png?assets=rad&rescale=0.0%2C0.4&colormap_name=plasma',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-104.05592607423101, 31.603935693136833],\n", + " [-103.94719869425685, 31.603935693136833],\n", + " [-103.94719869425685, 31.697130590257547],\n", + " [-104.05592607423101, 31.697130590257547],\n", + " [-104.05592607423101, 31.603935693136833]]]},\n", + " 'collection': 'goes-ch4plume-v1',\n", + " 'properties': {'datetime': '2023-07-26T18:01:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Examining the first item in the collection\n", + "items[0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, we enter minimum and maximum values to provide our upper and lower bounds in `rescale_values.`" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring Changes in GOES Methne (CH4) Levels Using the Raster API\n", + "\n", + "In this notebook, we will explore the impacts of methane emissions and by examining changes over time in urban regions. We will visualize the outputs on a map using `folium.`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)\n", + "items = {item[\"properties\"][\"datetime\"][:10]: item for item in items} \n", + "asset_name = \"rad\"" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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\"]}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "items.keys()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will pass the item id, collection name, and `rescaling_factor` to the `Raster API` endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "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/goes-ch4plume-v1/items/goes-ch4plume-v1-GOES-CH4_Mexico_Durango_BV1_BV1-1_2019-04-07T16:16:00Z/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rad&color_formula=gamma+r+1.05&colormap_name=plasma&rescale=0.026619306576566443%2C0.07300545309991482'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-104.5738874776177,\n", + " 26.1684406557021,\n", + " -104.43664236204742,\n", + " 26.305685771272373],\n", + " 'center': [-104.50526491983257, 26.237063213487236, 0]}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "color_map = \"plasma\" # please select the color ramp from matplotlib library.\n", + "april_2019_tile = requests.get(\n", + " f\"{RASTER_API_URL}/collections/{items['2019-04-07']['collection']}/items/{items['2019-04-07']['id']}/tilejson.json?\"\n", + " f\"&assets={asset_name}\"\n", + " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", + ").json()\n", + "april_2019_tile" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing CH₄ Emissions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set initial zoom and center of map for CH₄ Layer\n", + "# Centre of map [latitude,longitude]\n", + "map_ = folium.Map(location=(26.29, -104.53), zoom_start=10)\n", + "\n", + "# January 2019\n", + "map_layer_2019 = TileLayer(\n", + " tiles=april_2019_tile[\"tiles\"][0],\n", + " attr=\"GHG\",\n", + " opacity=0.7,\n", + ")\n", + "map_layer_2019.add_to(map_)\n", + "map_" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook we have successfully completed the following steps for the STAC collection for the GOSAT-based Top-down Total and Natural Methane Emissions dataset.\n", + "\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 the methane emission levels\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/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb index ab47a874..03e87ca3 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", "---" @@ -78,16 +82,7 @@ "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" - ] - } - ], + "outputs": [], "source": [ "# Import the following libraries\n", "import requests\n", @@ -102,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -118,97 +113,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'gosat-based-ch4budget-yeargrid-v1',\n", - " 'type': 'Collection',\n", - " 'links': [{'rel': 'items',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'}],\n", - " 'title': 'GOSAT-based Top-down Total and Natural Methane Emissions v1',\n", - " 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n", - " 'temporal': {'interval': [['2019-01-01T00:00:00+00:00',\n", - " '2019-12-31T00:00:00+00:00']]}},\n", - " 'license': 'CC-BY-4.0',\n", - " 'renders': {'dashboard': {'assets': ['post-total'],\n", - " 'nodata': 9.96921e+36,\n", - " 'rescale': [[0, 0.3]],\n", - " 'colormap_name': 'spectral_r'},\n", - " 'post-total': {'assets': ['post-total'],\n", - " 'nodata': 9.96921e+36,\n", - " 'rescale': [[0, 0.3]],\n", - " 'colormap_name': 'spectral_r'},\n", - " 'prior-total': {'assets': ['prior-total'],\n", - " 'nodata': 9.96921e+36,\n", - " 'rescale': [[0, 0.3]],\n", - " 'colormap_name': 'spectral_r'},\n", - " 'post-wetland': {'assets': ['post-wetland'],\n", - " 'nodata': 9.96921e+36,\n", - " 'rescale': [[0, 0.1]],\n", - " 'colormap_name': 'spectral_r'},\n", - " 'prior-wetland': {'assets': ['prior-wetland'],\n", - " 'nodata': 9.96921e+36,\n", - " 'rescale': [[0, 0.1]],\n", - " 'colormap_name': 'spectral_r'},\n", - " 'post-wetland-uncertainty': {'assets': ['post-wetland-uncertainty'],\n", - " 'nodata': 9.96921e+36,\n", - " 'rescale': [[0, 0.05]],\n", - " 'colormap_name': 'purd'},\n", - " 'prior-wetland-uncertainty': {'assets': ['prior-wetland-uncertainty'],\n", - " 'nodata': 9.96921e+36,\n", - " 'rescale': [[0, 0.05]],\n", - " 'colormap_name': 'purd'}},\n", - " 'summaries': {'datetime': ['2019-01-01T00:00:00Z']},\n", - " 'description': \"As part of the global stock take (GST), countries are asked to provide a record of their greenhouse gas (GHG) emissions to inform decisions on how to reduce GHG emissions. The NASA Carbon Monitoring System Flux (CMS-Flux) team has used remote sensing observations from Japan's Greenhouse gases Observing SATellite (GOSAT) to produce modeled total methane (CH₄) emissions and uncertainties on a 1 degree by 1 degree resolution grid for the year 2019. The GOSAT data is used in the model to inform total emission estimates, as well as wetland (the primary natural source of methane), and various human-related sources such as fossil fuel extraction, transport, agriculture, waste, and fires. A prior GHG emission estimate (and assocated uncertainty) is provided for each layer, which is the emissions estimate without GOSAT data. The posterior GHG emission layers are informed by GOSAT total column methane data. An advanced mathematical approach is used with a global chemistry transport model to quantify annual CH₄ emissions and uncertainties. These estimates are expressed in teragrams of CH₄ per year (Tg/yr). The source data can be found at https://doi.org/10.5281/zenodo.8306874 and more information can also be found on the CEOS website https://ceos.org/gst/methane.html\",\n", - " 'item_assets': {'post-total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Posterior Total Methane Emissions',\n", - " 'description': 'Estimated total methane emissions per grid cell informed by GOSAT satellite total column methane data.'},\n", - " 'prior-total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Prior Total Methane Emissions',\n", - " 'description': 'Total methane emissions per grid cell estimated by various inventories or models, excluding satellite based observations from GOSAT.'},\n", - " 'post-wetland': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Wetland Posterior Methane Emissions',\n", - " 'description': 'Estimated methane emissions per grid cell from wetlands informed by GOSAT satellite total column methane data.'},\n", - " 'prior-wetland': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Wetland Prior Methane Emissions',\n", - " 'description': 'Methane emissions per grid cell from wetlands estimated by various inventories or models, excluding satellite based observations from GOSAT.'},\n", - " 'post-wetland-uncertainty': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Uncertainty - Wetland Posterior Methane Emissions',\n", - " 'description': 'Uncertainty in estimated methane emissions per grid cell from wetlands informed by GOSAT satellite total column methane data.'},\n", - " 'prior-wetland-uncertainty': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Uncertainty - Wetland Prior Methane Emissions',\n", - " 'description': 'Uncertainty in methane emissions per grid cell from wetlands estimated by various inventories or models, excluding satellite based observations from GOSAT.'}},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", - " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", - " 'dashboard:is_periodic': False,\n", - " 'dashboard:time_density': 'year'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Fetching the collection from STAC collections using appropriate endpoint.\n", "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n", @@ -220,12 +127,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Examining the contents of our `collection` under the `temporal` variable, we see that the data is available from January 2012 to December 2018. By looking at the `dashboard:time density`, we observe that the data is available for only one year, i.e. 2019." + "Examining the contents of our `collection` under the `temporal` variable, we see that the data is available from January 2010 to December 2022. By looking at the `dashboard:time density`, we observe that the data is available for thirteen years." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -253,14 +160,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Found 1 items\n" + "Found 13 items\n" ] } ], @@ -273,399 +180,69 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'id': 'gosat-based-ch4budget-yeargrid-v1-2019',\n", - " 'bbox': [-180.5, -90.5, 179.5, 89.5],\n", + "{'id': 'gosat-based-ch4budget-yeargrid-v1-test-2022',\n", + " 'bbox': [-182.5, -90.9777777777778, 177.5, 90.97777777777777],\n", " 'type': 'Feature',\n", " 'links': [{'rel': 'collection',\n", " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},\n", + " 'href': 'https://dev.ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1-test'},\n", " {'rel': 'parent',\n", " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},\n", + " 'href': 'https://dev.ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1-test'},\n", " {'rel': 'root',\n", " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " 'href': 'https://dev.ghg.center/api/stac/'},\n", " {'rel': 'self',\n", " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019'},\n", + " 'href': 'https://dev.ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1-test/items/gosat-based-ch4budget-yeargrid-v1-test-2022'},\n", " {'title': 'Map of Item',\n", - " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/map?assets=post-total&nodata=9.96921e%2B36&rescale=0%2C0.3&colormap_name=spectral_r',\n", + " 'href': 'https://dev.ghg.center/api/raster/collections/gosat-based-ch4budget-yeargrid-v1-test/items/gosat-based-ch4budget-yeargrid-v1-test-2022/map?assets=post-total&rescale=0%2C0.3&colormap_name=spectral_r',\n", " 'rel': 'preview',\n", " 'type': 'text/html'}],\n", - 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" 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", - " 'prior-livestock-uncertainty': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_prior_unc_livestock_GEOS_CHEM_2019.tif',\n", - " 'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 0.021834801882505417,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64219.0,\n", - " 326.0,\n", - " 127.0,\n", - " 34.0,\n", - " 19.0,\n", - " 25.0,\n", - " 25.0,\n", - " 17.0,\n", - " 5.0,\n", - " 3.0]},\n", - " 'statistics': {'mean': 7.657577225472778e-05,\n", - " 'stddev': 0.0006582040223293006,\n", - " 'maximum': 0.021834801882505417,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.5, -90.5],\n", - " [179.5, -90.5],\n", - " [179.5, 89.5],\n", - " [-180.5, 89.5],\n", - " [-180.5, -90.5]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", - " 'rendered_preview': {'title': 'Rendered preview',\n", - " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/preview.png?assets=post-total&nodata=9.96921e%2B36&rescale=0%2C0.3&colormap_name=spectral_r',\n", - " 'rel': 'preview',\n", - " 'roles': ['overview'],\n", - " 'type': 'image/png'}},\n", - " 'geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.5, -90.5],\n", - " [179.5, -90.5],\n", - " [179.5, 89.5],\n", - " [-180.5, 89.5],\n", - " [-180.5, -90.5]]]},\n", - " 'collection': 'gosat-based-ch4budget-yeargrid-v1',\n", - " 'properties': {'end_datetime': '2019-12-31T00:00:00+00:00',\n", - " 'start_datetime': '2019-01-01T00:00:00+00:00'},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': []}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Examining the first item in the collection\n", - "items[0]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we enter minimum and maximum values to provide our upper and lower bounds in `rescale_values.`" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploring Changes in GOSAT Methane budgets (CH4) Levels Using the Raster API\n", - "\n", - "In this notebook, we will explore the impacts of methane emissions and by examining changes over time in urban regions. We will visualize the outputs on a map using `folium.`" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)\n", - "items = {item[\"properties\"][\"start_datetime\"][:10]: item for item in items} \n", - "asset_name = \"prior-total\"" - ] - }, - { - "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\"]}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['2019-01-01'])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "items.keys()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we will pass the item id, collection name, and `rescaling_factor` to the `Raster API` endpoint. We will do this for first January 2019." - ] - }, - { - "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/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=prior-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=0.0%2C2.121816635131836'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.5, -90.5, 179.5, 89.5],\n", - " 'center': [-0.5, -0.5, 0]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "color_map = \"rainbow\" # please select the color ramp from matplotlib library.\n", - "january_2019_tile = requests.get(\n", - " f\"{RASTER_API_URL}/collections/{items['2019-01-01']['collection']}/items/{items['2019-01-01']['id']}/tilejson.json?\"\n", - " f\"&assets={asset_name}\"\n", - " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", - ").json()\n", - "january_2019_tile" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing CH₄ Emissions\n" - ] - }, - { - "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": [ - "# Set initial zoom and center of map for CH₄ Layer\n", - "# Centre of map [latitude,longitude]\n", - "map_ = folium.Map(location=(34, -118), zoom_start=6)\n", - "\n", - "# January 2019\n", - "map_layer_2019 = TileLayer(\n", - " tiles=january_2019_tile[\"tiles\"][0],\n", - " attr=\"GHG\",\n", - " opacity=0.7,\n", - ")\n", - "map_layer_2019.add_to(map_)\n", - "\n", - "# # January 2012\n", - "# map_layer_2012 = TileLayer(\n", - "# tiles=january_2012_tile[\"tiles\"][0],\n", - "# attr=\"GHG\",\n", - "# opacity=0.7,\n", - "# )\n", - "# map_layer_2012.add_to(map_.m2)\n", - "\n", - "# visualising the map\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": [ - "# Texas, USA\n", - "texas_aoi = {\n", - " \"type\": \"Feature\",\n", - " \"properties\": {},\n", - " \"geometry\": {\n", - " \"coordinates\": [\n", - " [\n", - " # [13.686159004559698, -21.700046934333145],\n", - " # [13.686159004559698, -23.241974326585833],\n", - " # [14.753560168039911, -23.241974326585833],\n", - " # [14.753560168039911, -21.700046934333145],\n", - " # [13.686159004559698, -21.700046934333145],\n", - " [-95, 29],\n", - " [-95, 33],\n", - " [-104, 33],\n", - " [-104,29],\n", - " [-95, 29]\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": [ - "# We will plug in the coordinates for a location inside the the polygon and a zoom level\n", - "\n", - "aoi_map = Map(\n", - " tiles=\"OpenStreetMap\",\n", - " location=[\n", - " 30,-100\n", - " ],\n", - " zoom_start=6,\n", - ")\n", - "\n", - "folium.GeoJson(texas_aoi, name=\"Texas, USA\").add_to(aoi_map)\n", - "aoi_map" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 1 items\n" - ] - } - ], - "source": [ - "# Check total number of items available\n", - "items = requests.get(\n", - " f\"{STAC_API_URL}/collections/{collection_name}/items?limit=300\"\n", - ").json()[\"features\"]\n", - "print(f\"Found {len(items)} items\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'gosat-based-ch4budget-yeargrid-v1-2019',\n", - " 'bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'type': 'Feature',\n", - " 'links': [{'rel': 'collection',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019'},\n", - " {'title': 'Map of Item',\n", - " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/map?assets=post-total&nodata=9.96921e%2B36&rescale=0%2C0.3&colormap_name=spectral_r',\n", - " 'rel': 'preview',\n", - " 'type': 'text/html'}],\n", - " 'assets': {'post-gas': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_gas_GEOS_CHEM_2019.tif',\n", - " 'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 0.6140491962432861,\n", - " 'min': -0.4103066623210907,\n", - " 'count': 11.0,\n", - " 'buckets': [1.0, 0.0, 2.0, 23.0, 64734.0, 30.0, 7.0, 2.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.00043242290848866105,\n", - " 'stddev': 0.006180576980113983,\n", - " 'maximum': 0.6140491962432861,\n", - " 'minimum': -0.4103066623210907,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.5, -90.5],\n", - " [179.5, -90.5],\n", - " [179.5, 89.5],\n", - " [-180.5, 89.5],\n", - " [-180.5, -90.5]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", - " 'post-geo': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_geo_GEOS_CHEM_2019.tif',\n", - " 'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 1.0034276247024536,\n", - " 'min': -1.0016025304794312,\n", - " 'count': 11.0,\n", - " 'buckets': [1.0, 0.0, 1.0, 5.0, 63425.0, 1354.0, 10.0, 2.0, 1.0, 1.0]},\n", - " 'statistics': {'mean': 0.0003479516308289021,\n", - " 'stddev': 0.0093332938849926,\n", - " 'maximum': 1.0034276247024536,\n", - " 'minimum': -1.0016025304794312,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.5, -90.5],\n", - " [179.5, -90.5],\n", - " [179.5, 89.5],\n", - " [-180.5, 89.5],\n", - " [-180.5, -90.5]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", - " 'post-oil': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_oil_GEOS_CHEM_2019.tif',\n", - " 'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 3.457329273223877,\n", - 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" 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Uncertainty - Wetland Posterior Methane Emissions',\n", - " 'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Uncertainty in estimated methane emissions per grid cell from wetlands informed by GOSAT satellite total column methane data.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 0.36633968353271484,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64677.0, 68.0, 19.0, 14.0, 5.0, 8.0, 3.0, 4.0, 0.0, 2.0]},\n", - " 'statistics': {'mean': 0.00034577888436615467,\n", - " 'stddev': 0.005308355204761028,\n", - " 'maximum': 0.36633968353271484,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - 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" 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Uncertainty - Wetland Prior Methane Emissions',\n", - " 'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Uncertainty in methane emissions per grid cell from wetlands estimated by various inventories or models, excluding satellite based observations from GOSAT.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 1.5251290798187256,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64704.0, 49.0, 21.0, 11.0, 2.0, 3.0, 3.0, 3.0, 1.0, 3.0]},\n", - " 'statistics': {'mean': 0.0009943766053766012,\n", - " 'stddev': 0.020392030477523804,\n", - " 'maximum': 1.5251290798187256,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - 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" 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", - " 'prior-livestock-uncertainty': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_prior_unc_livestock_GEOS_CHEM_2019.tif',\n", - " 'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 0.021834801882505417,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64219.0,\n", - " 326.0,\n", - " 127.0,\n", - " 34.0,\n", - " 19.0,\n", - " 25.0,\n", - " 25.0,\n", - " 17.0,\n", - " 5.0,\n", - " 3.0]},\n", - " 'statistics': {'mean': 7.657577225472778e-05,\n", - " 'stddev': 0.0006582040223293006,\n", - " 'maximum': 0.021834801882505417,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.5, -90.5],\n", - " [179.5, -90.5],\n", - " [179.5, 89.5],\n", - " [-180.5, 89.5],\n", - " [-180.5, -90.5]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", + " 'proj:transform': [5.0,\n", + " 0.0,\n", + " -182.5,\n", + " 0.0,\n", + " -3.9555555555555557,\n", + " 90.97777777777777,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", " 'rendered_preview': {'title': 'Rendered preview',\n", - " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/preview.png?assets=post-total&nodata=9.96921e%2B36&rescale=0%2C0.3&colormap_name=spectral_r',\n", + " 'href': 'https://dev.ghg.center/api/raster/collections/gosat-based-ch4budget-yeargrid-v1-test/items/gosat-based-ch4budget-yeargrid-v1-test-2022/preview.png?assets=post-total&rescale=0%2C0.3&colormap_name=spectral_r',\n", " 'rel': 'preview',\n", " 'roles': ['overview'],\n", " 'type': 'image/png'}},\n", " 'geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.5, -90.5],\n", - " [179.5, -90.5],\n", - " [179.5, 89.5],\n", - " [-180.5, 89.5],\n", - " [-180.5, -90.5]]]},\n", - " 'collection': 'gosat-based-ch4budget-yeargrid-v1',\n", - " 'properties': {'end_datetime': '2019-12-31T00:00:00+00:00',\n", - " 'start_datetime': '2019-01-01T00:00:00+00:00'},\n", + " 'coordinates': [[[-182.5, -90.9777777777778],\n", + " [177.5, -90.9777777777778],\n", + " [177.5, 90.97777777777777],\n", + " [-182.5, 90.97777777777777],\n", + " [-182.5, -90.9777777777778]]]},\n", + " 'collection': 'gosat-based-ch4budget-yeargrid-v1-test',\n", + " 'properties': {'datetime': '2022-01-01T00:00:00+00:00'},\n", " 'stac_version': '1.0.0',\n", - " 'stac_extensions': []}" + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Examining the first item in the collection\n", + "items[0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, we enter minimum and maximum values to provide our upper and lower bounds in `rescale_values.`" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring Changes in GOSAT Methane budgets (CH4) Levels Using the Raster API\n", + "\n", + "In this notebook, we will explore the impacts of methane emissions and by examining changes over time in urban regions. We will visualize the outputs on a map using `folium.`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)\n", + "items = {item[\"properties\"][\"datetime\"][:10]: item for item in items} \n", + "asset_name = \"post-total\"" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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\"]}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "items.keys()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will pass the item id, collection name, and `rescaling_factor` to the `Raster API` endpoint. We will do this for first January 2019." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "color_map = \"rainbow\" # please select the color ramp from matplotlib library.\n", + "january_2019_tile = requests.get(\n", + " f\"{RASTER_API_URL}/collections/{items['2019-01-01']['collection']}/items/{items['2019-01-01']['id']}/tilejson.json?\"\n", + " f\"&assets={asset_name}\"\n", + " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", + ").json()\n", + "january_2019_tile" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing CH₄ Emissions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set initial zoom and center of map for CH₄ Layer\n", + "# Centre of map [latitude,longitude]\n", + "map_ = folium.Map(location=(34, -118), zoom_start=2)\n", + "\n", + "# January 2019\n", + "map_layer_2019 = TileLayer(\n", + " tiles=january_2019_tile[\"tiles\"][0],\n", + " attr=\"GHG\",\n", + " opacity=0.7,\n", + ")\n", + "map_layer_2019.add_to(map_)\n", + "\n", + "# # January 2012\n", + "# map_layer_2012 = TileLayer(\n", + "# tiles=january_2012_tile[\"tiles\"][0],\n", + "# attr=\"GHG\",\n", + "# opacity=0.7,\n", + "# )\n", + "# map_layer_2012.add_to(map_.m2)\n", + "\n", + "# visualising the map\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": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Texas, USA\n", + "texas_aoi = {\n", + " \"type\": \"Feature\",\n", + " \"properties\": {},\n", + " \"geometry\": {\n", + " \"coordinates\": [\n", + " [\n", + " # [13.686159004559698, -21.700046934333145],\n", + " # [13.686159004559698, -23.241974326585833],\n", + " # [14.753560168039911, -23.241974326585833],\n", + " # [14.753560168039911, -21.700046934333145],\n", + " # [13.686159004559698, -21.700046934333145],\n", + " [-95, 29],\n", + " [-95, 33],\n", + " [-104, 33],\n", + " [-104,29],\n", + " [-95, 29]\n", + " ]\n", + " ],\n", + " \"type\": \"Polygon\",\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" ] }, - "execution_count": 15, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], + "source": [ + "# We will plug in the coordinates for a location inside the the polygon and a zoom level\n", + "\n", + "aoi_map = Map(\n", + " tiles=\"OpenStreetMap\",\n", + " location=[\n", + " 30,-100\n", + " ],\n", + " zoom_start=6,\n", + ")\n", + "\n", + "folium.GeoJson(texas_aoi, name=\"Texas, USA\").add_to(aoi_map)\n", + "aoi_map" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 13 items\n" + ] + } + ], + "source": [ + "# Check total number of items available\n", + "items = requests.get(\n", + " f\"{STAC_API_URL}/collections/{collection_name}/items?limit=300\"\n", + ").json()[\"features\"]\n", + "print(f\"Found {len(items)} items\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Explore the first item\n", "items[0]" @@ -4038,7 +922,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -4051,7 +935,7 @@ " ).json()\n", " return {\n", " **result[\"properties\"],\n", - " \"datetime\": item[\"properties\"][\"start_datetime\"][:10],\n", + " \"datetime\": item[\"properties\"][\"datetime\"][:10],\n", " }" ] }, @@ -4065,15 +949,15 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 2.87 ms, sys: 1.29 ms, total: 4.17 ms\n", - "Wall time: 844 ms\n" + "CPU times: user 160 ms, sys: 22.7 ms, total: 182 ms\n", + "Wall time: 7.4 s\n" ] } ], @@ -4084,43 +968,43 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'statistics': {'b1': {'min': 0.0002109968481818214,\n", - " 'max': 0.2379249483346939,\n", - " 'mean': 0.06609854102134705,\n", - " 'count': 36.0,\n", - " 'sum': 2.379547357559204,\n", - " 'std': 0.05244022027065174,\n", - " 'median': 0.06046847254037857,\n", - " 'majority': 0.0002109968481818214,\n", - " 'minority': 0.0002109968481818214,\n", - " 'unique': 50.0,\n", - " 'histogram': [[14.0, 7.0, 7.0, 10.0, 5.0, 3.0, 0.0, 1.0, 2.0, 1.0],\n", - " [0.0002109968481818214,\n", - " 0.023982390761375427,\n", - " 0.04775378480553627,\n", - " 0.07152518630027771,\n", - " 0.09529657661914825,\n", - " 0.1190679669380188,\n", - " 0.14283937215805054,\n", - " 0.16661076247692108,\n", - " 0.19038215279579163,\n", - " 0.21415354311466217,\n", - " 0.2379249483346939]],\n", + "{'statistics': {'b1': {'min': 0.45840875428297767,\n", + " 'max': 2.563434123331244,\n", + " 'mean': 1.3501146985384829,\n", + " 'count': 1.9799998998641968,\n", + " 'sum': 2.6732269679113765,\n", + " 'std': 0.5669339642223294,\n", + " 'median': 1.0878720382884925,\n", + " 'majority': 0.45840875428297767,\n", + " 'minority': 0.45840875428297767,\n", + " 'unique': 6.0,\n", + " 'histogram': [[2.0, 0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 1.0],\n", + " [0.45840875428297767,\n", + " 0.6689112911878043,\n", + " 0.8794138280926309,\n", + " 1.0899163649974577,\n", + " 1.300418901902284,\n", + " 1.5109214388071108,\n", + " 1.7214239757119376,\n", + " 1.931926512616764,\n", + " 2.1424290495215907,\n", + " 2.352931586426417,\n", + " 2.563434123331244]],\n", " 'valid_percent': 100.0,\n", " 'masked_pixels': 0.0,\n", - " 'valid_pixels': 50.0,\n", - " 'percentile_2': 0.002050149254500866,\n", - " 'percentile_98': 0.2056385725736618}},\n", - " 'datetime': '2019-01-01'}" + " 'valid_pixels': 6.0,\n", + " 'percentile_2': 0.45840875428297767,\n", + " 'percentile_98': 2.563434123331244}},\n", + " 'datetime': '2022-01-01'}" ] }, - "execution_count": 18, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -4131,7 +1015,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -4178,44 +1062,144 @@ " \n", " \n", " 0\n", + " 2022-01-01\n", + " 0.458409\n", + " 2.563434\n", + " 1.350115\n", + " 1.98\n", + " 2.673227\n", + " 0.566934\n", + " 1.087872\n", + " 0.458409\n", + " 0.458409\n", + " 6.0\n", + " [[2.0, 0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0,...\n", + " 100.0\n", + " 0.0\n", + " 6.0\n", + " 0.458409\n", + " 2.563434\n", + " 2022-01-01\n", + " \n", + " \n", + " 1\n", + " 2021-01-01\n", + " 0.417406\n", + " 2.977322\n", + " 1.409865\n", + " 1.98\n", + " 2.791533\n", + " 0.719124\n", + " 1.029306\n", + " 0.417406\n", + " 0.417406\n", + " 6.0\n", + " [[2.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0,...\n", + " 100.0\n", + " 0.0\n", + " 6.0\n", + " 0.417406\n", + " 2.977322\n", + " 2021-01-01\n", + " \n", + " \n", + " 2\n", + " 2020-01-01\n", + " 0.392606\n", + " 2.619608\n", + " 1.301333\n", + " 1.98\n", + " 2.576640\n", + " 0.623242\n", + " 0.978943\n", + " 0.392606\n", + " 0.392606\n", + " 6.0\n", + " [[2.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0,...\n", + " 100.0\n", + " 0.0\n", + " 6.0\n", + " 0.392606\n", + " 2.619608\n", + " 2020-01-01\n", + " \n", + " \n", + " 3\n", " 2019-01-01\n", - " 0.000211\n", - " 0.237925\n", - " 0.066099\n", - " 36.0\n", - " 2.379547\n", - " 0.05244\n", - " 0.060468\n", - " 0.000211\n", - " 0.000211\n", - " 50.0\n", - " [[14.0, 7.0, 7.0, 10.0, 5.0, 3.0, 0.0, 1.0, 2....\n", + " 0.521856\n", + " 2.678034\n", + " 1.409689\n", + " 1.98\n", + " 2.791184\n", + " 0.667938\n", + " 0.953206\n", + " 0.521856\n", + " 0.521856\n", + " 6.0\n", + " [[1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0,...\n", " 100.0\n", " 0.0\n", - " 50.0\n", - " 0.00205\n", - " 0.205639\n", + " 6.0\n", + " 0.521856\n", + " 2.678034\n", " 2019-01-01\n", " \n", + " \n", + " 4\n", + " 2018-01-01\n", + " 0.552891\n", + " 2.624957\n", + " 1.427609\n", + " 1.98\n", + " 2.826666\n", + " 0.582425\n", + " 1.086807\n", + " 0.552891\n", + " 0.552891\n", + " 6.0\n", + " [[1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,...\n", + " 100.0\n", + " 0.0\n", + " 6.0\n", + " 0.552891\n", + " 2.624957\n", + " 2018-01-01\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " datetime min max mean count sum std \\\n", - "0 2019-01-01 0.000211 0.237925 0.066099 36.0 2.379547 0.05244 \n", + " datetime min max mean count sum std \\\n", + "0 2022-01-01 0.458409 2.563434 1.350115 1.98 2.673227 0.566934 \n", + "1 2021-01-01 0.417406 2.977322 1.409865 1.98 2.791533 0.719124 \n", + "2 2020-01-01 0.392606 2.619608 1.301333 1.98 2.576640 0.623242 \n", + "3 2019-01-01 0.521856 2.678034 1.409689 1.98 2.791184 0.667938 \n", + "4 2018-01-01 0.552891 2.624957 1.427609 1.98 2.826666 0.582425 \n", "\n", " median majority minority unique \\\n", - "0 0.060468 0.000211 0.000211 50.0 \n", + "0 1.087872 0.458409 0.458409 6.0 \n", + "1 1.029306 0.417406 0.417406 6.0 \n", + "2 0.978943 0.392606 0.392606 6.0 \n", + "3 0.953206 0.521856 0.521856 6.0 \n", + "4 1.086807 0.552891 0.552891 6.0 \n", "\n", " histogram valid_percent \\\n", - "0 [[14.0, 7.0, 7.0, 10.0, 5.0, 3.0, 0.0, 1.0, 2.... 100.0 \n", + "0 [[2.0, 0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0,... 100.0 \n", + "1 [[2.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0,... 100.0 \n", + "2 [[2.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0,... 100.0 \n", + "3 [[1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0,... 100.0 \n", + "4 [[1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,... 100.0 \n", "\n", " masked_pixels valid_pixels percentile_2 percentile_98 date \n", - "0 0.0 50.0 0.00205 0.205639 2019-01-01 " + "0 0.0 6.0 0.458409 2.563434 2022-01-01 \n", + "1 0.0 6.0 0.417406 2.977322 2021-01-01 \n", + "2 0.0 6.0 0.392606 2.619608 2020-01-01 \n", + "3 0.0 6.0 0.521856 2.678034 2019-01-01 \n", + "4 0.0 6.0 0.552891 2.624957 2018-01-01 " ] }, - "execution_count": 19, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -4234,24 +1218,24 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2019-01-01\n" + "2022-01-01\n" ] } ], "source": [ - "print(items[0][\"properties\"][\"start_datetime\"][:10])" + "print(items[0][\"properties\"][\"datetime\"][:10])" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -4260,14 +1244,14 @@ "{'tilejson': '2.2.0',\n", " 'version': '1.0.0',\n", " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=prior-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=0.0%2C2.121816635131836'],\n", + " 'tiles': ['https://dev.ghg.center/api/raster/collections/gosat-based-ch4budget-yeargrid-v1-test/items/gosat-based-ch4budget-yeargrid-v1-test-2022/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=post-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-4.4208666115704%2C8.355603568623742'],\n", " 'minzoom': 0,\n", " 'maxzoom': 24,\n", - " 'bounds': [-180.5, -90.5, 179.5, 89.5],\n", - " 'center': [-0.5, -0.5, 0]}" + " 'bounds': [-182.5, -90.9777777777778, 177.5, 90.97777777777777],\n", + " 'center': [-2.5, -1.4210854715202004e-14, 0]}" ] }, - "execution_count": 21, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -4284,7 +1268,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -4309,7 +1293,7 @@ " <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n", " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>\n", " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css"/>\n", - " <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap-glyphicons.css"/>\n", + " <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css"/>\n", " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css"/>\n", " <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>\n", " <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>\n", @@ -4317,7 +1301,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_505d38387e6eedd3872e4802424c35d3 {\n", + " #map_aa2c0d3b3a0dfdea9e134176befadd23 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -4331,18 +1315,18 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_505d38387e6eedd3872e4802424c35d3" ></div>\n", + " <div class="folium-map" id="map_aa2c0d3b3a0dfdea9e134176befadd23" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_505d38387e6eedd3872e4802424c35d3 = L.map(\n", - " "map_505d38387e6eedd3872e4802424c35d3",\n", + " var map_aa2c0d3b3a0dfdea9e134176befadd23 = L.map(\n", + " "map_aa2c0d3b3a0dfdea9e134176befadd23",\n", " {\n", " center: [30.0, -100.0],\n", " crs: L.CRS.EPSG3857,\n", - " zoom: 8,\n", + " zoom: 2,\n", " zoomControl: true,\n", " preferCanvas: false,\n", " }\n", @@ -4352,31 +1336,31 @@ "\n", " \n", " \n", - " var tile_layer_042ed3985023b76053dd48c577816b85 = L.tileLayer(\n", + " var tile_layer_4a9e5026d3494f12131894da8ee67a93 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_042ed3985023b76053dd48c577816b85.addTo(map_505d38387e6eedd3872e4802424c35d3);\n", + " tile_layer_4a9e5026d3494f12131894da8ee67a93.addTo(map_aa2c0d3b3a0dfdea9e134176befadd23);\n", " \n", " \n", - " var tile_layer_5bf127a76ba506fed5de3bb8e44aca5c = L.tileLayer(\n", - " "https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=prior-total\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=0.0%2C2.121816635131836",\n", - " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", + " var tile_layer_e895499063df5b7264a2176146257181 = L.tileLayer(\n", + " "https://dev.ghg.center/api/raster/collections/gosat-based-ch4budget-yeargrid-v1-test/items/gosat-based-ch4budget-yeargrid-v1-test-2022/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=post-total\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=rainbow\\u0026rescale=-4.4208666115704%2C8.355603568623742",\n", + " {"attribution": "GHG", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.5, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_5bf127a76ba506fed5de3bb8e44aca5c.addTo(map_505d38387e6eedd3872e4802424c35d3);\n", + " tile_layer_e895499063df5b7264a2176146257181.addTo(map_aa2c0d3b3a0dfdea9e134176befadd23);\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -4390,7 +1374,7 @@ " location=[\n", " 30,-100\n", " ],\n", - " zoom_start=8,\n", + " zoom_start=2,\n", ")\n", "\n", "map_layer = TileLayer(\n", @@ -4438,7 +1422,7 @@ "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" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the function above we can generate the statistics for the AOI." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'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': 0.0464402866499578, 'max': 49.61378870603235, 'mean': 9.039553150168388, 'count': 36.0, 'sum': 325.42391340606196, 'std': 11.97160706711745, 'median': 3.662876577293575, 'majority': 0.0464402866499578, 'minority': 0.0464402866499578, 'unique': 36.0, 'histogram': [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0, 1.0], [0.0464402866499578, 5.003175128588197, 9.959909970526436, 14.916644812464675, 19.873379654402914, 24.830114496341153, 29.786849338279392, 34.74358418021763, 39.700319022155874, 44.65705386409412, 49.61378870603235]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0464402866499578, 'percentile_98': 49.61378870603235}}}}\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': 0.0464402866499578, 'max': 49.61378870603235, 'mean': 9.039553150168388, 'count': 36.0, 'sum': 325.42391340606196, 'std': 11.97160706711745, 'median': 3.662876577293576, 'majority': 0.0464402866499578, 'minority': 0.0464402866499578, 'unique': 36.0, 'histogram': [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0, 1.0], [0.0464402866499578, 5.003175128588197, 9.959909970526436, 14.916644812464675, 19.873379654402914, 24.830114496341153, 29.786849338279392, 34.74358418021763, 39.700319022155874, 44.65705386409412, 49.61378870603235]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0464402866499578, 'percentile_98': 49.61378870603235}}}}\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': 0.0464402866499578, 'max': 49.61378870603235, 'mean': 9.039553150168388, 'count': 36.0, 'sum': 325.42391340606196, 'std': 11.97160706711745, 'median': 3.662876577293575, 'majority': 0.0464402866499578, 'minority': 0.0464402866499578, 'unique': 36.0, 'histogram': [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0, 1.0], [0.0464402866499578, 5.003175128588197, 9.959909970526436, 14.916644812464675, 19.873379654402914, 24.830114496341153, 29.786849338279392, 34.74358418021763, 39.700319022155874, 44.65705386409412, 49.61378870603235]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0464402866499578, 'percentile_98': 49.61378870603235}}}}\n", - 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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", - "{'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': 4.348188537652892, 'count': 36.0, 'sum': 156.53478735550414, 'std': 4.633345420525916, 'median': 2.5974040079993723, '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", - "{'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", - "{'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': 4.348188537652892, 'count': 36.0, 'sum': 156.53478735550414, 'std': 4.633345420525916, 'median': 2.5974040079993723, '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", - "{'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", - "{'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': 0.08095810131399311, 'max': 24.822949625050086, 'mean': 4.348188537652892, 'count': 36.0, 'sum': 156.53478735550414, 'std': 4.633345420525916, 'median': 2.5974040079993723, 'majority': 0.08095810131399311, 'minority': 0.08095810131399311, 'unique': 36.0, 'histogram': [[17.0, 8.0, 6.0, 2.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0], [0.08095810131399311, 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': 0.08095810131399311, 'percentile_98': 24.822949625050086}}}}\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': 0.0809581013139931, '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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", - "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)

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