From ca85179fa90f5ebcbe9ea09d9d3bc8226b9454c7 Mon Sep 17 00:00:00 2001 From: vishal Date: Mon, 28 Oct 2024 09:52:25 -0500 Subject: [PATCH 01/16] initial transformation function --- data_transformation_plugins/__init__.py | 0 .../tm54dvar_ch4flux_mask_monthgrid_v5.py | 43 +++++++++++++++++++ 2 files changed, 43 insertions(+) create mode 100644 data_transformation_plugins/__init__.py create mode 100644 data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5.py 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/tm54dvar_ch4flux_mask_monthgrid_v5.py b/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5.py new file mode 100644 index 00000000..99fcc503 --- /dev/null +++ b/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5.py @@ -0,0 +1,43 @@ +import xarray +from datetime import datetime + +def tm54dvar_ch4flux_mask_monthgrid_v5_transformation(file_obj, name, nodata): + """Tranformation 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: + 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 \ No newline at end of file From 79e5c5b38a4b6b50b0c9dd905c9fd813bb37836e Mon Sep 17 00:00:00 2001 From: vishal Date: Mon, 28 Oct 2024 09:52:50 -0500 Subject: [PATCH 02/16] initial transformation function --- .../tm54dvar_ch4flux_mask_monthgrid_v5.py | 1 + 1 file changed, 1 insertion(+) diff --git a/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5.py b/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5.py index 99fcc503..73168450 100644 --- a/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5.py +++ b/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5.py @@ -1,5 +1,6 @@ import xarray from datetime import datetime +import re def tm54dvar_ch4flux_mask_monthgrid_v5_transformation(file_obj, name, nodata): """Tranformation function for the tm5 ch4 influx dataset From c567180b6e0801aa2a0ab0d5cded59383b71ffae Mon Sep 17 00:00:00 2001 From: vishal Date: Mon, 28 Oct 2024 11:46:10 -0500 Subject: [PATCH 03/16] added transformation plugins for different datasets --- data_transformation_plugins/ecco_darwin.py | 47 ++++++++++++++++++++++ data_transformation_plugins/geos_oco2.py | 38 +++++++++++++++++ data_transformation_plugins/gpw.py | 33 +++++++++++++++ data_transformation_plugins/push_to_s3.py | 35 ++++++++++++++++ 4 files changed, 153 insertions(+) create mode 100644 data_transformation_plugins/ecco_darwin.py create mode 100644 data_transformation_plugins/geos_oco2.py create mode 100644 data_transformation_plugins/gpw.py create mode 100644 data_transformation_plugins/push_to_s3.py diff --git a/data_transformation_plugins/ecco_darwin.py b/data_transformation_plugins/ecco_darwin.py new file mode 100644 index 00000000..90ac2da9 --- /dev/null +++ b/data_transformation_plugins/ecco_darwin.py @@ -0,0 +1,47 @@ +import xarray +import re + +def ecco_darwin_transformation(file_obj, name, nodata): + """Tranformation 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" + ) + + variable = [var for var in xds.data_vars] + + for _ in xds.time.values: + for var in variable[2:]: + 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 \ No newline at end of file diff --git a/data_transformation_plugins/geos_oco2.py b/data_transformation_plugins/geos_oco2.py new file mode 100644 index 00000000..f647e33d --- /dev/null +++ b/data_transformation_plugins/geos_oco2.py @@ -0,0 +1,38 @@ +import xarray +import re + +def geos_oco2_transformation(file_obj, name, nodata): + """Tranformation 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 \ No newline at end of file diff --git a/data_transformation_plugins/gpw.py b/data_transformation_plugins/gpw.py new file mode 100644 index 00000000..6f030623 --- /dev/null +++ b/data_transformation_plugins/gpw.py @@ -0,0 +1,33 @@ +import xarray +import re + +def gpw_transformation(file_obj, name, nodata): + """Tranformation 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 \ No newline at end of file diff --git a/data_transformation_plugins/push_to_s3.py b/data_transformation_plugins/push_to_s3.py new file mode 100644 index 00000000..18617f9a --- /dev/null +++ b/data_transformation_plugins/push_to_s3.py @@ -0,0 +1,35 @@ +import boto3 +import os + +def upload_folder_to_s3(folder_path, bucket_name, s3_folder, exclude_files): + """ + Uploads all files in a folder to a specified S3 folder, 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: + # Upload the file + s3.upload_file(file_path, bucket_name, s3_key) + print(f"Uploaded {file_name} to {s3_key}") + except Exception as e: + print(f"Error uploading {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_folder_to_s3("data_transformation_plugins", "ghgc-data-store-develop", "data_transformation_plugins", ["__init__.py", "push_to_s3.py"]) \ No newline at end of file From 08132c8f4b6089c954eaadcaa467d482ca171377 Mon Sep 17 00:00:00 2001 From: vishal Date: Mon, 28 Oct 2024 14:47:23 -0500 Subject: [PATCH 04/16] add readme for the folder --- data_transformation_plugins/README.md | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 data_transformation_plugins/README.md diff --git a/data_transformation_plugins/README.md b/data_transformation_plugins/README.md new file mode 100644 index 00000000..008ca8b1 --- /dev/null +++ b/data_transformation_plugins/README.md @@ -0,0 +1,4 @@ +The naming convention for the python files is as follows: +### collectionname_transformation +where collection name refers to the STAC collection name that we want to provide +for the given dataset followed by the tword `transformation`. Make sure that the collection name in the function is same to the one passed as argument when running the DAG. \ No newline at end of file From 11dac6eac2fd0d7ad560a45e0abb05b07c51e403 Mon Sep 17 00:00:00 2001 From: vishal Date: Mon, 28 Oct 2024 14:58:21 -0500 Subject: [PATCH 05/16] updated the README file --- data_transformation_plugins/README.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/data_transformation_plugins/README.md b/data_transformation_plugins/README.md index 008ca8b1..26524176 100644 --- a/data_transformation_plugins/README.md +++ b/data_transformation_plugins/README.md @@ -1,4 +1,9 @@ -The naming convention for the python files is as follows: -### collectionname_transformation -where collection name refers to the STAC collection name that we want to provide -for the given dataset followed by the tword `transformation`. Make sure that the collection name in the function is same to the one passed as argument when running the DAG. \ No newline at end of file +## 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. \ No newline at end of file From 40f7c369a4677643544345f9d9b0d212632fa71b Mon Sep 17 00:00:00 2001 From: vishal Date: Tue, 29 Oct 2024 12:24:45 -0500 Subject: [PATCH 06/16] updated file names as per the convention and changed the logic for finding files in S3 --- ...darwin.py => ecco_darwin_tranformation.py} | 0 ...os_oco2.py => geos_oco2_transformation.py} | 0 .../{gpw.py => gpw_transformation.py} | 0 data_transformation_plugins/push_to_s3.py | 28 +++++++++++++------ ...4flux_mask_monthgrid_v5_transformation.py} | 0 5 files changed, 20 insertions(+), 8 deletions(-) rename data_transformation_plugins/{ecco_darwin.py => ecco_darwin_tranformation.py} (100%) rename data_transformation_plugins/{geos_oco2.py => geos_oco2_transformation.py} (100%) rename data_transformation_plugins/{gpw.py => gpw_transformation.py} (100%) rename data_transformation_plugins/{tm54dvar_ch4flux_mask_monthgrid_v5.py => tm54dvar_ch4flux_mask_monthgrid_v5_transformation.py} (100%) diff --git a/data_transformation_plugins/ecco_darwin.py b/data_transformation_plugins/ecco_darwin_tranformation.py similarity index 100% rename from data_transformation_plugins/ecco_darwin.py rename to data_transformation_plugins/ecco_darwin_tranformation.py diff --git a/data_transformation_plugins/geos_oco2.py b/data_transformation_plugins/geos_oco2_transformation.py similarity index 100% rename from data_transformation_plugins/geos_oco2.py rename to data_transformation_plugins/geos_oco2_transformation.py diff --git a/data_transformation_plugins/gpw.py b/data_transformation_plugins/gpw_transformation.py similarity index 100% rename from data_transformation_plugins/gpw.py rename to data_transformation_plugins/gpw_transformation.py diff --git a/data_transformation_plugins/push_to_s3.py b/data_transformation_plugins/push_to_s3.py index 18617f9a..85d52c22 100644 --- a/data_transformation_plugins/push_to_s3.py +++ b/data_transformation_plugins/push_to_s3.py @@ -1,9 +1,13 @@ import boto3 import os -def upload_folder_to_s3(folder_path, bucket_name, s3_folder, exclude_files): +import boto3 +import os + +def upload_files_to_s3(folder_path, bucket_name, s3_folder, exclude_files): """ - Uploads all files in a folder to a specified S3 folder, excluding specified 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. @@ -23,13 +27,21 @@ def upload_folder_to_s3(folder_path, bucket_name, s3_folder, exclude_files): s3_key = os.path.join(s3_folder, file_name) try: - # Upload the file - s3.upload_file(file_path, bucket_name, s3_key) - print(f"Uploaded {file_name} to {s3_key}") - except Exception as e: - print(f"Error uploading {file_name}: {e}") + # 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_folder_to_s3("data_transformation_plugins", "ghgc-data-store-develop", "data_transformation_plugins", ["__init__.py", "push_to_s3.py"]) \ No newline at end of file + upload_files_to_s3("data_transformation_plugins", "ghgc-data-store-develop", "data_transformation_plugins", ["__init__.py", "push_to_s3.py", "README.md"]) \ No newline at end of file diff --git a/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5.py b/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5_transformation.py similarity index 100% rename from data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5.py rename to data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5_transformation.py From d80620e241656b3b42b59628de900990fd109ca7 Mon Sep 17 00:00:00 2001 From: vishal Date: Tue, 29 Oct 2024 16:45:36 -0500 Subject: [PATCH 07/16] updated the logic for no data value and a script to transform gosat ch4 --- ...ation.py => ecco_darwin_transformation.py} | 2 +- .../geos_oco2_transformation.py | 2 +- .../gosat_ch4_transformation.py | 36 +++++++++++++++++++ .../gpw_transformation.py | 2 +- ...h4flux_mask_monthgrid_v5_transformation.py | 2 +- 5 files changed, 40 insertions(+), 4 deletions(-) rename data_transformation_plugins/{ecco_darwin_tranformation.py => ecco_darwin_transformation.py} (97%) create mode 100644 data_transformation_plugins/gosat_ch4_transformation.py diff --git a/data_transformation_plugins/ecco_darwin_tranformation.py b/data_transformation_plugins/ecco_darwin_transformation.py similarity index 97% rename from data_transformation_plugins/ecco_darwin_tranformation.py rename to data_transformation_plugins/ecco_darwin_transformation.py index 90ac2da9..9696a372 100644 --- a/data_transformation_plugins/ecco_darwin_tranformation.py +++ b/data_transformation_plugins/ecco_darwin_transformation.py @@ -31,7 +31,7 @@ def ecco_darwin_transformation(file_obj, name, nodata): data = xds[var] data = data.reindex(latitude=list(reversed(data.latitude))) - data = data.where(data == nodata, -9999) + 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) diff --git a/data_transformation_plugins/geos_oco2_transformation.py b/data_transformation_plugins/geos_oco2_transformation.py index f647e33d..4165c91e 100644 --- a/data_transformation_plugins/geos_oco2_transformation.py +++ b/data_transformation_plugins/geos_oco2_transformation.py @@ -22,7 +22,7 @@ def geos_oco2_transformation(file_obj, name, nodata): 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 = 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) diff --git a/data_transformation_plugins/gosat_ch4_transformation.py b/data_transformation_plugins/gosat_ch4_transformation.py new file mode 100644 index 00000000..a9adbb3e --- /dev/null +++ b/data_transformation_plugins/gosat_ch4_transformation.py @@ -0,0 +1,36 @@ +import xarray +import re + +def gosat_ch4_transformation(file_obj, name, nodata): + """Tranformation 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 != -9999, -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 \ No newline at end of file diff --git a/data_transformation_plugins/gpw_transformation.py b/data_transformation_plugins/gpw_transformation.py index 6f030623..5849f350 100644 --- a/data_transformation_plugins/gpw_transformation.py +++ b/data_transformation_plugins/gpw_transformation.py @@ -21,7 +21,7 @@ def gpw_transformation(file_obj, name, nodata): # # insert date of generated COG into filename filename_elements.pop() filename_elements.append(filename_elements[-3]) - xds = xds.where(xds == nodata, -9999) + 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) diff --git a/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5_transformation.py b/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5_transformation.py index 73168450..7565ec8a 100644 --- a/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5_transformation.py +++ b/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5_transformation.py @@ -27,7 +27,7 @@ def tm54dvar_ch4flux_mask_monthgrid_v5_transformation(file_obj, name, nodata): for var in variable: data = getattr(xds.isel(months=time_increment), var) data = data.isel(lat=slice(None, None, -1)) - data = data.where(data == nodata, -9999) + 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) From 5dcb6022793d942595f600a815f923e72698c448 Mon Sep 17 00:00:00 2001 From: vishal Date: Thu, 31 Oct 2024 09:12:06 -0500 Subject: [PATCH 08/16] resolved the comments --- .../ecco_darwin_transformation.py | 38 +++++++++---------- .../gosat_ch4_transformation.py | 1 + .../gpw_transformation.py | 1 + 3 files changed, 21 insertions(+), 19 deletions(-) diff --git a/data_transformation_plugins/ecco_darwin_transformation.py b/data_transformation_plugins/ecco_darwin_transformation.py index 9696a372..b31c2c13 100644 --- a/data_transformation_plugins/ecco_darwin_transformation.py +++ b/data_transformation_plugins/ecco_darwin_transformation.py @@ -22,26 +22,26 @@ def ecco_darwin_transformation(file_obj, name, nodata): "latitude" ) - variable = [var for var in xds.data_vars] + variables = list(xds.data_vars)[2:] - for _ in xds.time.values: - for var in variable[2:]: - filename = name.split("/")[-1] - filename_elements = re.split("[_ .]", filename) - data = xds[var] + 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) + 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 + 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 \ No newline at end of file diff --git a/data_transformation_plugins/gosat_ch4_transformation.py b/data_transformation_plugins/gosat_ch4_transformation.py index a9adbb3e..7a88b85f 100644 --- a/data_transformation_plugins/gosat_ch4_transformation.py +++ b/data_transformation_plugins/gosat_ch4_transformation.py @@ -33,4 +33,5 @@ def gosat_ch4_transformation(file_obj, name, nodata): 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 \ No newline at end of file diff --git a/data_transformation_plugins/gpw_transformation.py b/data_transformation_plugins/gpw_transformation.py index 5849f350..fde5d1f7 100644 --- a/data_transformation_plugins/gpw_transformation.py +++ b/data_transformation_plugins/gpw_transformation.py @@ -30,4 +30,5 @@ def gpw_transformation(file_obj, name, nodata): # # add extension cog_filename = f"{cog_filename}.tif" var_data_netcdf[cog_filename] = xds + return var_data_netcdf \ No newline at end of file From 0b0870d7352d5364d73bc3fff70619dc6192e95e Mon Sep 17 00:00:00 2001 From: vishal Date: Fri, 1 Nov 2024 10:12:13 -0500 Subject: [PATCH 09/16] added a sample transformation notebook along with updating the README file --- data_transformation_plugins/README.md | 8 +- data_transformation_plugins/push_to_s3.py | 2 +- .../sample_transformation.ipynb | 102 ++++++++++++++++++ 3 files changed, 110 insertions(+), 2 deletions(-) create mode 100644 data_transformation_plugins/sample_transformation.ipynb diff --git a/data_transformation_plugins/README.md b/data_transformation_plugins/README.md index 26524176..9160fa89 100644 --- a/data_transformation_plugins/README.md +++ b/data_transformation_plugins/README.md @@ -6,4 +6,10 @@ These functions/files are created by the `developer` to transform a single data ## 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. \ No newline at end of file +`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/push_to_s3.py b/data_transformation_plugins/push_to_s3.py index 85d52c22..7f941bc9 100644 --- a/data_transformation_plugins/push_to_s3.py +++ b/data_transformation_plugins/push_to_s3.py @@ -44,4 +44,4 @@ def upload_files_to_s3(folder_path, bucket_name, s3_folder, exclude_files): # 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"]) \ No newline at end of file + 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"]) \ No newline at end of file diff --git a/data_transformation_plugins/sample_transformation.ipynb b/data_transformation_plugins/sample_transformation.ipynb new file mode 100644 index 00000000..1f98a392 --- /dev/null +++ b/data_transformation_plugins/sample_transformation.ipynb @@ -0,0 +1,102 @@ +{ + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray\n", + "import re\n", + "from datetime import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read a file of the new dataset\n", + "name = 'test.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": null, + "metadata": {}, + "outputs": [], + "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": null, + "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": "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": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From dcb3db1364c6e1e2ac79d337546986419bfbca50 Mon Sep 17 00:00:00 2001 From: vishal Date: Mon, 11 Nov 2024 14:37:44 -0600 Subject: [PATCH 10/16] tm5 new data transformation plugin --- ...hgrid_v5_transformation.py => tm54dvar_noaa_transformation.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename data_transformation_plugins/{tm54dvar_ch4flux_mask_monthgrid_v5_transformation.py => tm54dvar_noaa_transformation.py} (100%) diff --git a/data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5_transformation.py b/data_transformation_plugins/tm54dvar_noaa_transformation.py similarity index 100% rename from data_transformation_plugins/tm54dvar_ch4flux_mask_monthgrid_v5_transformation.py rename to data_transformation_plugins/tm54dvar_noaa_transformation.py From d94aed272bc59788b8cf59e9e79aaa57223e72fd Mon Sep 17 00:00:00 2001 From: vishal Date: Mon, 11 Nov 2024 15:18:29 -0600 Subject: [PATCH 11/16] updated the tm5 transformation script --- .../sample_transformation.ipynb | 595 +++++++++++++++++- ...> tm5_4dvar_update_noaa_transformation.py} | 4 +- 2 files changed, 590 insertions(+), 9 deletions(-) rename data_transformation_plugins/{tm54dvar_noaa_transformation.py => tm5_4dvar_update_noaa_transformation.py} (91%) diff --git a/data_transformation_plugins/sample_transformation.ipynb b/data_transformation_plugins/sample_transformation.ipynb index 1f98a392..44dd8c02 100644 --- a/data_transformation_plugins/sample_transformation.ipynb +++ b/data_transformation_plugins/sample_transformation.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -21,12 +21,461 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "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 = 'test.nc'\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" @@ -34,9 +483,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "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", @@ -46,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -83,6 +550,104 @@ " 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": {}, @@ -93,8 +658,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "cmip6", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "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, diff --git a/data_transformation_plugins/tm54dvar_noaa_transformation.py b/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py similarity index 91% rename from data_transformation_plugins/tm54dvar_noaa_transformation.py rename to data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py index 7565ec8a..1fd491b3 100644 --- a/data_transformation_plugins/tm54dvar_noaa_transformation.py +++ b/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py @@ -2,7 +2,7 @@ from datetime import datetime import re -def tm54dvar_ch4flux_mask_monthgrid_v5_transformation(file_obj, name, nodata): +def tm5_4dvar_update_noaa_transformation(file_obj, name, nodata): """Tranformation function for the tm5 ch4 influx dataset Args: @@ -25,6 +25,8 @@ def tm54dvar_ch4flux_mask_monthgrid_v5_transformation(file_obj, name, nodata): 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) From 2509f139a7092885f0723b52aee8cbfc917fabff Mon Sep 17 00:00:00 2001 From: Siddharth Chaudhary <118487103+siddharth0248@users.noreply.github.com> Date: Thu, 14 Nov 2024 10:11:18 -0600 Subject: [PATCH 12/16] Merge conflicts From 1151310372d28c4a458c0e836eadf8fff0738ce1 Mon Sep 17 00:00:00 2001 From: Vishal Gaur <34275219+SwordSaintLancelot@users.noreply.github.com> Date: Tue, 19 Nov 2024 09:38:26 -0600 Subject: [PATCH 13/16] Update the content for upcoming release (#141) * ceated python script to fetch json files and generate statistics * rename all TM54Dvar to CT-CH4 * tm5 reports updated * gosat verification report updated * update ct-ch4 notebook * added draft content for goes --------- --- .gitignore | 2 + _quarto.yml | 19 +- ...-v1.ipynb => ct-ch4-monthgrid-v2023.ipynb} | 10 +- .../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 | 4 +- 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 -> 89372 bytes ...-v1_Processing and Verification Report.pdf | Bin 0 -> 82491 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 | 25 + ...-ch4budget-yeargrid-v1_User_Notebook.ipynb | 12 +- ...r-ch4flux-monthgrid-v1_User_Notebook.ipynb | 2170 ----------------- workflow.qmd | 5 +- 27 files changed, 1931 insertions(+), 2209 deletions(-) rename cog_transformation/{tm54dvar-ch4flux-monthgrid-v1.ipynb => ct-ch4-monthgrid-v2023.ipynb} (92%) 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/_quarto.yml b/_quarto.yml index db11916c..a73212ed 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -55,7 +55,7 @@ website: contents: - user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb - user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb - - user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb + - user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb - user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb - user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb - user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb @@ -65,11 +65,12 @@ website: - user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb - user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb - user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb - - user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb + - user_data_notebooks/ct-ch4-monthgrid-v2023_User_Notebook.ipynb - user_data_notebooks/lpjeosim-wetlandch4-grid-v1_User_Notebook.ipynb - section: Large Emissions Events contents: - user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb + - user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb - section: Greenhouse Gas Concentrations contents: - user_data_notebooks/noaa-insitu_User_Notebook.ipynb @@ -87,7 +88,7 @@ website: contents: - cog_transformation/oco2-mip-co2budget-yeargrid-v1.ipynb - cog_transformation/odiac-ffco2-monthgrid-v2023.ipynb - - cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb + - cog_transformation/ct-ch4-monthgrid-v2023.ipynb - cog_transformation/epa-ch4emission-grid-v2express.ipynb - cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb - cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb @@ -96,7 +97,7 @@ website: - cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb - cog_transformation/gosat-based-ch4budget-yeargrid-v1.ipynb - cog_transformation/oco2-mip-co2budget-yeargrid-v1.ipynb - - cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb + - cog_transformation/ct-ch4-monthgrid-v2023.ipynb - section: Large Emissions Events contents: - cog_transformation/emit-ch4plume-v1.ipynb @@ -118,7 +119,7 @@ website: contents: - processing_and_verification_reports/oco2-mip-co2budget-yeargrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/odiac-ffco2-monthgrid-v2023_Processing and Verification Report.qmd - - processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd + - processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd - processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd - processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd - processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd @@ -128,11 +129,12 @@ website: - processing_and_verification_reports/micasa-carbonflux-daygrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/gosat-based-ch4budget-yeargrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/oco2-mip-co2budget-yeargrid-v1_Processing and Verification Report.qmd - - processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd + - processing_and_verification_reports/ct-ch4-monthgrid-v2023_Processing and Verification Report.qmd - processing_and_verification_reports/lpjeosim-wetlandch4-grid-v1_Processing and Verification Report.qmd - section: Large Emissions Events contents: - processing_and_verification_reports/emit-ch4plume-v1_Processing and Verification Report.qmd + - processing_and_verification_reports/goes-ch4plume-v1_Processing and Verification Report.qmd - section: Greenhouse Gas Concentrations contents: - processing_and_verification_reports/noaa-gggrn-co2-concentrations_Processing and Verification Report.qmd @@ -151,7 +153,7 @@ website: contents: - data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd - data_workflow/odiac-ffco2-monthgrid-v2023_Data_Flow.qmd - - data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd + - data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd - data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd - data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd - data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd @@ -161,11 +163,12 @@ website: - data_workflow/micasa-carbonflux-daygrid-v1_Data_Flow.qmd - data_workflow/gosat-based-ch4budget-yeargrid-v1_Data_Flow.qmd - data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd - - data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd + - data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd - data_workflow/lpjeosim-wetlandch4-grid-v1_Data_Flow.qmd - section: Large Emissions Events contents: - data_workflow/emit-ch4plume-v1_Data_Flow.qmd + - data_workflow/goes-ch4plume-v1_Data_Flow.qmd - section: Greenhouse Gas Concentrations contents: - data_workflow/noaa-gggrn-co2-concentrations_Data_Flow.qmd diff --git a/cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb b/cog_transformation/ct-ch4-monthgrid-v2023.ipynb similarity index 92% rename from cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb rename to cog_transformation/ct-ch4-monthgrid-v2023.ipynb index 9ea6d98f..1872e7c6 100644 --- a/cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb +++ b/cog_transformation/ct-ch4-monthgrid-v2023.ipynb @@ -2,10 +2,14 @@ "cells": [ { "cell_type": "raw", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", - "title: TM5-4DVar Isotopic CH₄ Inverse Fluxes\n", + "title: CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes\n", "description: Documentation of data transformation\n", "author: Vishal Gaur\n", "date: August 31, 2023\n", @@ -18,7 +22,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This script was used to transform the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center." + "This script was used to transform the CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes dataset from netCDF to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center." ] }, { diff --git a/data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd b/data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd new file mode 100644 index 00000000..a7b7ddda --- /dev/null +++ b/data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd @@ -0,0 +1,3 @@ +# CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes + +![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/ct-ch4-monthgrid-v2023_Data_Flow.png) \ No newline at end of file diff --git a/data_workflow/goes-ch4plume-v1_Data_Flow.qmd b/data_workflow/goes-ch4plume-v1_Data_Flow.qmd new file mode 100644 index 00000000..2c6e58ff --- /dev/null +++ b/data_workflow/goes-ch4plume-v1_Data_Flow.qmd @@ -0,0 +1,4 @@ +# Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure + + +![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/goes-ch4plume-v1_Data_Flow.png) \ No newline at end of file diff --git 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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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wKrUSERERERERERERUdXxHSgK+/DDD+Hv76/3GE6hUusteXl5SExM1HsMp7h8+TKWL1+u9xhOodK+stWcVGrltcmcVNlXVToBtVqzs7MxZcoUvcdwCpX2la3mpFIrr03mpNK+qtSqyn2/Ss9V+s/4DhQik9q3bx+aN2+O4uJivUdxOLaaE1vNia3mxFbzUaUTYKtZsdWc2GpObDUntpqTKq2qdNIf46L3APTXcPz4cbzwwgvYtGmT3qM4nEqtZnH16tU7rufm5jppEiIiO16bzEmVfVWlE1Crdf/+/XdcP3LkiJMmcTyV9pWtdmw1Jl6b7LivxqRSqypUeq5S1fEdKARArZNVs7Teddddd1wvLi7GtWvXDN8JAFarFRaLpdJ1EYHFYjFFq0r7ylY7thoTr0123FfjUaUTULO1on/Fu/V7s7VWhq3GxFY7M7by2sR9NSqVWlW571fpuUpVx3egKGLevHl3XD979qyTJnE8VVrz8/MxYsQIREREVLh+8uRJvPnmm06eyjG8vb0xceJEtGrVqsL1tLQ0/O1vf3PyVI6h0r6y1Y6txsRrkx331XhU6QTUar3rrrswc+ZMdOrUqcL1Q4cOoWfPnk6eyjFU2le22rHVmHhtsuO+GpNKrarc96v0XKWq4wGKIkaPHo3AwEC4urpWuF5QUODkiRxHldamTZsiODgYgwYNqnB93759pviHGgA0b94cANChQ4cK1319fSv8myBGpNK+stWOrcbEa5Md99V4VOkE1GqNjIxEZmYmQkJCKly/cuWKaVpV2le22rHVmHhtsuO+GpNKrarc96v0XKWq4wGKIkJCQjBjxgz07du3wvW9e/ciMjLSyVM5hiqt3bt3x5UrVypdv+uuuxAXF+e8gRyof//+uHHjRqXrAQEBmDx5shMnchyV9pWtdmw1Jl6b7LivxqNKJ6BW6/Dhw5GXl1fper169bB06VInTuQ4Ku0rW+3Yaky8NtlxX41JpVZV7vtVeq5S1fE7UBTRp08f3HvvvZgxY0aF6/v27UOzZs1QUlLi5Mm0p1IrERERERERERERETkGD1AUkZKSguvXr6NFixYVrhcWFt7x7YhGolIrERERERERERERETkGD1CIDKygoADfffcdtm3bhqysLAClbzOMjo5Gr169Kv0eGCO6ePEiPv744wpbn3vuOdxzzz06T6gdlfaVrWw1Ol6buK9GpkonoFZrSkoK5s+ff1trVFQUXnzxRYSHh+s8oXZU2le2stXoeG3ivhqdSq2q3Per9FylquEBioJycnLKXBhq1qyp80SOY+bWY8eOITY2FpmZmWjVqhX8/f0BANnZ2dixYwfq1q2LdevW4b777tN50qrbtWsXYmNj4eHhgc6dO5dp3bhxI65fv474+PhK33VkJCrtK1vZanS8NnFfjUyVTkCt1nXr1qF3795o3rw5YmNjy7T++OOPSEpKwr///W/ExsbqPGnVqbSvbGWr0fHaxH01OpVaVbnvV+m5ShoQUsaiRYskLCxMrFZrmZ+wsDBZvHix3uNpSoXWzp07S69evSQnJ+e2tZycHOnVq5fExMToMJn2WrVqJcOGDZOSkpLb1kpKSmTYsGHSunVrHSbTnkr7ytZSbDUuXptKcV+NSZVOEbVaH3zwQZk0aVKl65MnT5aIiAgnTuQ4Ku0rW0ux1bh4bSrFfTUulVpVue9X6blKVccDFEXMnDlTPDw8ZPz48ZKQkCApKSmSkpIiCQkJMmHCBPH09JRZs2bpPaYmVGmtUaOGHDhwoNL1/fv3S40aNZw4keO4u7vL4cOHK10/fPiwuLu7O3Eix1FpX9lqx1Zj4rXJjvtqPKp0iqjXmpqaWul6amqqqVpV2le2lmKrMfHaZMd9NSaVWlW571fpuUpVZ9X7HTDkHPPnz8fSpUvxzjvvoGPHjggLC0NYWBg6duyIt99+G0uWLMG8efP0HlMTqrT6+voiIyOj0vWMjAz4+vo6bR5HCggIwM6dOytd37lzp+3tlkan0r6y1Y6txsRrkx331XhU6QTUaq1fvz6+//77Ste///57hISEOHEix1FpX9lqx1Zj4rXJjvtqTCq1qnLfr9JzlarORe8ByDnOnz+PiIiIStcjIiJw8eJFJ07kOKq0Dh06FHFxcZg0aRI6dep02+c1Tps2DS+99JLOU2pjzJgxGDZsGJKSkipsXbRoEd59912dp9SGSvvKVrYaHa9N3FcjU6UTUKt1ypQp6N+/PzZv3lzh53mvX78eK1as0HlKbai0r2xlq9Hx2sR9NTqVWlW571fpuUoa0PstMOQc7dq1k7i4OCksLLxtraioSOLi4qR9+/Y6TKY9lVqnT58ugYGBYrFYbN/zYrFYJDAwUGbMmKH3eJr68ssvpVWrVuLi4iIWi0UsFou4uLhIq1atZOXKlXqPpymV9pWtbDU6Xpu4r0amSqeIWq1bt26Vp59+WurVqyeurq7i6uoq9erVk6efflp+/fVXvcfTlEr7yla2Gh2vTdxXo1OpVZX7fpWeq1Q1FhERvQ9xyPH279+P2NhYFBYWon379mVOVhMTE+Hq6ooNGzagSZMmOk9adSq13nLixAlkZWUBKH0bYmhoqM4TOU5hYaHtHUR33303qlevrvNEjqPSvrLVnFRq5bXJnFTZV1U6AbVaVaLSvrLVnFRqVQn3lYxOlft+PlfpP+F3oCjiwQcfxNGjRzF16lR4e3sjPT0d6enp8Pb2xrRp05CammqaAwWVWm8JDQ1FVFQUSkpKEBQUpPc4DlW9enUEBgZi8+bNKCgo0Hsch1JpX9lqTiq18tpkTqrsqyqdgFqtt0yfPh1XrlzRewyHUmlf2WpOKrXewmuTOamwr7eo0KrKfb+Kz1X6c/gOFCIT8fHxwd69e9GgQQO9R3E4tpoTW82JrebEVvNRpRNgq1mx1ZzYak5sNSe2mpMqrap00p/Hd6AorHv37jh37pzeYziFKq0qnYey1ZzYak5sNSe2mo8qnQBbzYqt5sRWc2KrObHVnFRpVaWT/jweoCgsMTERN27c0HsMp1CplYiIiIiIiIiIiIiqzkXvAYhIOx9++CH8/f31HsMp1q1bhzp16ug9hlOotK9sNSeVWnltMidV9nXdunWm/nzr31NlTwEgJSWF+2pCfL6ak0r7mpKSotS+qtKq0r6q9M9XVe77VXqu0p/D70BRWJMmTbBu3ToEBwfrPYrDqdCan58PAHBzc9N5EiL6ozZv3oxWrVqhRo0aeo9CGsnPz8eZM2dQt25dXo9NJjs7GyKCgIAAvUfRXHFxMS5evAir1Yp77rlH73EcKicnB1lZWQCAgIAA1KxZU+eJiP7/RAQlJSWoVq2a3qM43CeffILHH3+cz1kTSUtLw6lTpxASEoL77rtP73GoCoqLi8tch3bu3ImSkhI0a9bMlPfDp06dwrlz52C1WtGgQQPUrl1b75Eciq81EfEjvJR28OBBUx8o/J5ZW3/88Ud069YNtWrVgoeHBzw8PFCrVi1069YNP/30k97jOc3hw4dN9SVf+/btw7Rp07BgwQJcvHixzNrVq1fx/PPP6zSZ9hYvXoxBgwZh6dKlAICVK1ciLCwMDRo0wOTJk3WezvFiYmKQkZGh9xiaOn/+fJnHe/fuxaBBg9CmTRv06dMHmzdv1mcwB/jkk0+wbds2AMDNmzcxZMgQeHp6omHDhvDy8sLw4cNt/8JhdBEREZg6dSpOnz6t9ygOd+nSJfTp0wf16tXDiBEjUFxcjKFDhyIwMBB16tRBdHS0ab5X7fvvv0f79u3h6emJoKAgBAQEwNfXFwMHDsSpU6f0Hk9TixcvRnh4OO666y6Eh4eX+Z+XLFmi93hOs2/fPlO92P7DDz9g6NChGDduHFJTU8usXb58GY888ohOk2mrqKgIr732Gjp06GC7P5o1axa8vLzg4eGBQYMGoaCgQOcpHWvYsGHIzMzUewxN7dy5E8XFxbbHa9euRYcOHVCnTh20aNECy5cv13E6bb3zzjvYuHEjgNLnZufOndGoUSM8+uijaNSoEbp27YorV67oO6RGvL29MWTIEPz66696j+JwJ0+eRIsWLeDm5oauXbvi6tWrePTRR9G6dWtER0cjPDwcR48e1XtMzSxYsAAhISEIDQ1FdHQ0WrduDT8/P7Rt2xZJSUl6j6cpvtZkvteZqGp4gKKQ8+fPY9OmTcjJyQFQ+jcpZ86cienTp+PAgQM6T6e99PR0LF++HDNmzMCsWbOwatUqXL16Ve+xNLNs2TJ069YNNWvWxJw5c7B27VqsXbsWc+bMga+vL7p164ZPP/1U7zGdoqCgACdPntR7DE1s2LABLVu2xJdffokZM2agcePGSEhIsK3fuHEDy5Yt03FC7bz33nsYPXo0rl27hokTJ+Ktt97CyJEjMWDAADz33HN477338NFHH+k9piaaN29e4U9RURGefPJJ22MzCAwMtB2i/Prrr2jZsiVOnjyJNm3a2P6lKjExUecptTFlyhRYraW3UpMmTcKmTZvw9ddf49ChQ/jmm2+QkJCASZMm6TylNg4dOoS5c+ciNDQUXbp0wapVq1BUVKT3WA4xduxYHDlyBOPGjcPhw4fx5JNPYteuXfjll1+wZcsWFBUVYfz48XqPWWWffvopnnnmGbRs2RJjxoyBn58fxo0bh+nTp+P06dOIjIxEWlqa3mNqYtasWRg1ahR69eqFjRs34uDBgzh48CA2btyI3r17Y9SoUXj33Xf1HtNpzPIBBCtWrMBjjz2GrKwsbNu2Dc2aNcPnn39uWy8oKMDPP/+s44TaefPNN7F48WK0aNEC33zzDUaMGIF58+bho48+wqJFi7Bx40a89957eo+pibvuuqvCn6KiIkRFRdkem0FUVBR+++03AMCaNWvQq1cv1K9fHxMnTkSzZs0wZMgQfPvttzpPqY0FCxbY9m3cuHG4dOkSkpKScP36dSQnJ+PKlSsYM2aMzlNqIy8vDzt27EDbtm0RFhaG2bNn48KFC3qP5RD/+Mc/4OXlhe+++w4+Pj7o1q0bioqKcPr0aZw9exb3338/Xn31Vb3H1MS7776Lt956C2PHjsWHH36IRo0a4Y033sD333+PBg0aoH379ti9e7feY2qCrzWVMtPrTFR1/AgvRWzevBk9evTA9evX4e/vj/Xr16NHjx6oUaMGrFYrMjIy8L//+7+IiYnRe9Qqy8vLw3PPPYdVq1YBACwWC/z8/HDhwgXUqFED06dPx8iRI3WesuoaNmyIUaNGVdqyYMECzJkzxxQvfrzyyit3XL9w4QJWrFhR5m9wGVV0dDQefvhhvPXWWxARzJo1C1OnTsXXX3+NLl26IDs7G0FBQaZoDQsLw6RJk9C/f3/s2bMHLVu2xAcffIAhQ4YAAJYsWYKFCxea4ka0evXq6Ny5M1q3bm37nYhg6tSpGD58OPz8/ADAFO+6sVqtyMrKgp+fH2JiYhAcHFzmb3ePHj0aBw4csP0tRCNzd3fH0aNHUa9ePTRq1Ahz585Fly5dbOuJiYkYOHCgKW68rVYrzpw5g507d+Ljjz/GunXrUKtWLcTFxWHIkCEICwvTe0TNBAUF4ZtvvkF0dDSys7MRGBiI+Ph4PProowCArVu34umnn8aZM2d0nrRqwsLC8MYbb+Dpp58GAOzevRuPP/44Tp06BYvFgn79+qGgoACrV6/WedKqCwkJwaxZs9C3b98K11euXImxY8ea4l03TzzxxB3Xc3JysHnzZlPcRzRr1gyDBw/Gyy+/DAD46quv8Pzzz2Pu3LkYMmSIqe6Z7r33XsydOxc9evTAsWPH0KhRI6xYscL2/P3qq68wdepUU/ylOG9vb3To0AFPPfWU7XcigqFDh2LKlCm2z6YfNGiQXiNq5vf3TO3atUPbtm3xzjvv2NbffvttrFmzxvZuVyNzd3fHkSNHbH97f9myZWjfvr1tPSkpCT179jTFu4xu7eu5c+ewePFirFixAteuXUOPHj0wdOhQdOnSBRaLRe8xNeHn54cNGzagadOmyMnJQa1atZCYmIi2bdsCAJKTk9GtWzfbR2caWWhoKBYsWICuXbsCAI4ePYro6GhkZWXBxcUFo0aNwuHDh7FhwwadJ606VV5rUul1JtKAkBLatm0rI0eOlNzcXJk1a5bUqVNHRo4caVsfM2aMREdH6zihdoYNGyZt2rSRAwcOSFpamvTp00fGjRsneXl5smTJEvHw8JDPP/9c7zGrzM3NTVJTUytdT01NFXd3dydO5DhWq1WaN28uHTt2rPCnRYsWYrVa9R5TEz4+PnLs2LEyv/v888/F09NT1qxZI1lZWaZprVGjhpw8edL22M3NTQ4ePGh7nJaWJr6+vnqMprktW7bIvffeK6+//roUFxfbfu/i4iKHDh3ScTLtWSwWyc7OFhGRwMBA2bZtW5n1gwcPyt13363HaJoLCQmRTZs2iYhInTp1ZNeuXWXWU1JSxNPTU4/RNPf7fRURyczMlLffflvuv/9+sVqtEhUVJUuWLNFxQu14eHhIRkaG7XH16tXlwIEDtsfp6emm2NcaNWrIiRMnyvzOxcVFzp49KyIiO3bsMM012N3dXVJSUipdP3TokNSoUcOJEzmOi4uLdO3aVZ577rkKfx577DHT3Ed4enpKenp6md9t2rRJvLy8ZOHChaa6Z3J3d5dTp06VeXz48GHb4/T0dPH29tZjNM2lpaXJQw89JHFxcZKbm2v7vdnvmfz8/GT37t1l1lNTU01zHW7YsKGsXbtWRERCQ0Nl69atZdb37NkjPj4+eoymufL3TDdv3pQVK1ZIp06dxGq1St26dWXSpEk6Tqgdb29v23W4uLhYXFxcZO/evbb1tLQ001ybPDw8ytw3lZSUiIuLi2RmZoqIyN69e8XLy0un6bSlymtNKr3ORFXHAxRF/P5F2cLCQnFxcZE9e/bY1o8ePSo1a9bUZziN3X333WVuPi9duiTu7u6Sl5cnIiLz58+Xpk2b6jWeZpo3by5jx46tdH3cuHHSvHlzJ07kOA0bNpRPP/200vU9e/aY5h9s99xzz23/8iQi8sUXX4iHh4csXLjQNK21a9cu84JW3bp1y7xomZaWZpqbUBGRK1euSL9+/aRVq1a267FZXww4duyY5OTkSGhoqCQnJ5dZP3bsmHh4eOg0nbb+53/+R6KiouTy5csyfvx46dmzp+3Fnry8POnbt6/ExMToPKU2rFZrmRcDfi8hIUEGDBhgikMFEZH/+q//kvnz54uIyA8//CDe3t4ye/Zs2/rChQulSZMmeo2nmbCwMPn6669tj5OSksTV1VWKiopEpPQabJY9bdeuncTFxUlhYeFta0VFRRIXFyft27fXYTLtRUREyOLFiytdN9M9U0WH9CIimzdvFi8vL5k4caJpWv39/WX//v22x9HR0XLmzBnb48OHD5vmxWeR0n9fHTdunNx7772yZcsWETHvPVNCQoLs27dPQkJCZOfOnWXWU1NTTXMvPGvWLAkLC5O0tDSZPXu2REVF2e6H09PTpWPHjtKnTx+dp9TGne6ZTpw4Ia+99poEBwc7eSrHaN26tbz22msiIvLxxx+Lv7+/jB8/3rY+ZcoUiYyM1Gs8TTVt2lQ++ugj2+ONGzeKh4eHlJSUiEjp89Ush0WqvNak0utMVHUuer8DhpzD1dUVN2/eBFD6OX4lJSW2x0Dp9ypUr15dr/E0VVRUBB8fH9tjLy8vFBUVIS8vDx4eHoiJiTHF56vOnj0bPXr0wPr169G5c2f4+/sDKP1um40bNyI9PR3ff/+9zlNqo0WLFkhKSsKAAQMqXLdYLKb5PO+mTZsiISEBkZGRZX7fr18/iIgpPq7glsaNG2P//v22j/4p/+XUqampqF+/vg6TOUbNmjXxxRdfYOnSpWjbti3efPNN07x9v7yGDRsCKP3Ijd27d6NZs2a2tUOHDiEoKEiv0TQ1efJkHDx4EA0aNECLFi3wyy+/wN/fH3Xq1EFmZiZq166NH3/8Ue8xNXGna2zHjh3RsWNH03zP2NixYzFo0CC89957OH36ND777DOMGjUKO3bsgNVqxerVq/HPf/5T7zGrbOTIkRg6dCh27doFd3d3LF68GAMHDrR9wfiOHTtsz2Wjmz9/PmJjYxEQEID27duXuWdKTEyEq6urKT5yAwAiIyORnJxs+zjM8tzc3FCvXj0nT+UYLVu2xLp168p8PCYAdOjQAWvWrEGPHj10mkx74eHhSE5ORkREBIDSjxL8vQMHDuD+++/XYzSHcHFxwYwZMxAbG4v+/fvj2WefNe09U6dOnWz/jN26dSseeugh29qePXtM83wdM2YMTp06hfDwcNx7773IyMhAw4YN4eLigqKiIjRv3hxffPGF3mNq4k73TPXr18fUqVMxZcoUJ07kOG+88QZ69+6NmTNnwmq1Ij4+Hi+88AI2bdoEq9WKXbt2YcWKFXqPqYkJEyZgwIAB+Omnn+Du7o7Vq1fj5Zdftl2bNm/ejCZNmug8pTZUea1JpdeZqOr4HSiK6N27N4qLizF+/HgsX74cycnJ8Pf3x8qVK2GxWDBo0CBcu3YN69at03vUKouJiUHDhg0xf/58AKVf9vXPf/7T9nmqe/bsQUxMjCm+yC0jIwMLFy7E9u3bbZ8rGhAQgKioKAwfPtw0Lz5nZWUhPz8fISEheo/icN9++y0SExMxZ86cCtdXrFiBRYsWlflieaPaunUrPD090bRp0wrXFyxYgJKSErz44ovOHcwJ0tLS8Oyzz2L37t04ePAgwsPD9R5JM+W/sDcwMLDMi7Bz585FQUEBxo4d6+zRHGb9+vVYs2YN0tPTUVJSgsDAQLRp0wb9+/eHp6en3uNpYvDgwZg3bx68vb31HsUptm7diu3btyMqKgrR0dFISUnB9OnTcf36dfTs2dM0h9kLFy7EZ599hvz8fMTGxmLSpElwd3cHUHqdKi4uRuPGjXWeUhu5ubn47LPPKrxn6t+/f5m/fGNk+fn5KC4uhoeHh96jONzPP/+MX3/9FRMmTKhwPSEhAcuXL8fSpUudPJn2jh49iurVqyM0NLTC9RUrVsDFxaXS7/kxst9++w0vvPACEhISsH37djRq1EjvkTRT/jvSvLy8ULt2bdvj5cuXAwDi4uKcOpcjHT58GGvXrr3tnqlz586mOSR78803MXbsWCWuw0DpaxJJSUmIjIxE/fr1kZ2djffffx/Xr19H9+7d8fDDD+s9ombWrVtX5r7phRdesK399ttvAFDmOWxkKrzWpNLrTFR1PEBRRFpaGrp3745jx46hcePG+PHHH/H3v/8dP/zwAwCgVq1aWL9+PZo3b67zpFWXnJyMRx99FK6urnB1dUVWVhaWLVuGfv36AQDef/997Ny5E8uWLdN5UiJSWUlJCXJzc+Hj42Oaf2EkIiIiIiIiIjITHqAo5rfffitzIr5x40bcuHEDUVFRpjkpB4Bz585h7dq1yM/PxyOPPGKqv91dXlFREQ4dOmT7WwGBgYEICwszzUey/V751oCAAISHh7PV4NjKVqNjK1uNTJXOOyksLMS5c+dM81E5d1JUVITMzEy2mgxbzUmlVl6HzYmtRGQaunzzChFVWXFxsUycOFF8fX3FYrGU+fH19ZXXXntNiouL9R5TE2xlq9Gxla1Gx1bztarS+Ufs3btXmS8JZas5sdWc2GpObDUns7W+//770qlTJ3nqqafkp59+KrN24cIFCQ0N1WkybanSSVXHL5FXzKZNm7BlyxacO3cOVqsVDRo0wGOPPWaqLx28xeyt48ePxyeffILp06cjNja2zBd7bdiwAZMmTUJBQQFmzJih86RVx1a2Gh1b2Wp0bDVfqyqdRERERPTHzZs3DxMmTMDgwYORk5ODbt264Y033rB951hxcfFt3+FkRKp0kjb4EV6KOH/+PHr27Indu3fDarWipKQEzZo1w9mzZ3HhwgW88sormDlzpt5jakKV1oCAACxbtgyxsbEVrsfHxyMuLg7Z2dlOnkx7bLVjqzGx1Y6txsRWO7O0qtIJ4D9+x9+NGzdw9OhRFBcXO2kix2GrHVuNia12bDUmttqx1ZgeeOABTJw4Ef379wcA/Prrr+jduzeGDx+OKVOmIDs7G0FBQYZvVaWTtMF3oCji5ZdfRlBQEC5fvgw3NzeMGTMGV69exe7du7Fp0yb07dsXderUwahRo/QetcpUac3NzUVQUFCl64GBgcjLy3PiRI7DVju2GhNb7dhqTGy1M0urKp0AkJKSgn79+iE0NLTC9XPnzuHo0aNOnsox2GrHVmNiqx1bjYmtdmw1phMnTiA6Otr2ODo6Gps2bULnzp1RWFiI0aNH6zechlTpJI3o/Rli5Bw+Pj5y8OBB2+Nr165J9erVJScnR0REPv30U2nUqJFe42lKldZu3bpJTEyMXLhw4ba1CxcuSJcuXaR79+46TKY9tpZiq3GxtRRbjYutpczUqkqniEhkZKQsWLCg0vU9e/aY5nPL2WrHVmNiqx1bjYmtdmw1puDgYElMTLzt94cOHRJ/f3+Ji4szRasqnaQNvgNFEW5ubrBYLLbHVqsVxcXFKCoqAlB60pqRkaHTdNpSpfWDDz5At27dEBgYiIiIiDKfXX7gwAGEh4dj7dq1Ok+pDbay1ejYylajY6v5WlXpBIA2bdrgyJEjla57e3ujffv2TpzIcdhqx1ZjYqsdW42JrXZsNaa2bdti9erVaNeuXZnfh4eHY+PGjXj44Yd1mkxbqnSSNvgdKIp44oknYLVasWzZMri6umLcuHFYu3Yt0tLSAAA7duxA7969ce7cOZ0nrTqVWktKShAfH4/t27cjKysLQOlnmkdFRSEmJgZWq1XnCbXDVrYaHVvZanRsNV+rKp1ERERE9Mfs378fSUlJGDx4cIXrBw8exKpVqzB58mQnT6YtVTpJGzxAUUR6ejpiYmJw8uRJWCwWeHp64uuvv0bnzp0BAJ988gmOHDmCd955R+dJq06lViIiIiIiIiIiIiJyDB6gKOT69evYsmULCgoK0Lp1a9x99916j+QwKrXu3LkT27ZtK/M3R6Ojo/HQQw/pPJn22MpWo2MrW42OreZrVaUTqLg1KioKLVu21Hky7bGVrUbHVrYaHVvZanSqtKrSSVWk5xewENH/X3Z2trRt21YsFouEhIRIy5YtpWXLlhISEiIWi0Xatm0r2dnZeo+pCbay1ejYylajY6v5WlXpFGErW42PrWw1Oray1ehUa23Tpo3pW1XpJG3wAEUh169flyVLlsjgwYOlS5cu0q1bN3nxxRflp59+0ns0zanQ+uSTT0pUVJSkpqbetpaamirR0dHSp08fHSbTHltLsdW42FqKrcbF1lJmalWlU4Stt7DVuNhaiq3GxdZSbDUutpYyU6sqnaQNHqAoIi0tTUJCQsTPz0+Cg4PFYrFI9+7dpVWrVlKtWjV56qmnpLCwUO8xNaFKq5eXlyQnJ1e6vnv3bvHy8nLiRI7DVju2GhNb7dhqTGy1M0urKp0ibP09thoTW+3YakxstWOrMbHVziytqnSSNqx6f4QYOcfLL7+MLl26ICsrC6dOncI777yDkpISbN++HYcPH8auXbswbdo0vcfUhCqtbm5uuHr1aqXrubm5cHNzc+JEjsNWO7YaE1vt2GpMbLUzS6sqnQBbf4+txsRWO7YaE1vt2GpMbLUzS6sqnaQRvU9wyDk8PDzk6NGjtsf5+flSvXp1uXjxooiIfPfdd1K/fn29xtOUKq1///vfJSQkRFavXi05OTm23+fk5Mjq1aulfv368uKLL+o4oXbYylajYytbjY6t5mtVpVOErSJsNTq2stXo2MpWo2Or+VpV6SRt8ABFEUFBQZKUlGR7fPnyZbFYLHL16lUREUlPTxc3Nze9xtOUKq03b96U4cOHi6urq1itVnF3dxd3d3exWq3i6uoqI0aMkJs3b+o9pibYylajYytbjY6t5mtVpVOErWw1Pray1ejYylajY6v5WlXpJG1YRET0fhcMOd5zzz2HjIwMfPDBB3Bzc8OECRNw9OhRJCcnAwB+/vlnDBw4EKdOndJ50qpTqRUArl69iqSkJGRlZQEAAgICEBkZCR8fH50n0x5b2Wp0bGWr0bHVfK2qdAJsZavxsZWtRsdWthodW83XqkonVQ0PUBRx/vx59OrVCzt27IDFYkFwcDC+/fZbNGvWDADwzTff4Ny5c3jppZd0nrTqVGolIiIiIiIiIiIiIsfgl8grws/PD9u2bcORI0ewb98+HDt2zHagAAB9+vQxzYGCSq03btzAli1bkJKSctvazZs3sXz5ch2mcgy2lmKrcbG1FFuNi62lzNSqSifA1lvYalxsLcVW42JrKbYaF1tLmalVlU7SgL6fIEZ/FadOnZLBgwfrPYZTmKX1yJEjEhISIhaLRaxWq7Rv317Onj1rW8/KyhKr1arjhNphK1uNjq1sNTq2mq9VlU4RtrLV+NjKVqNjK1uNjq3ma1Wlk7TBd6AQAODSpUtYtmyZ3mM4hVlaX331VTRp0gTnz5/HkSNH4O3tjbZt25rmu11+j61sNTq2stXo2Gq+VlU6Abay1fjYylajYytbjY6t5mtVpZM0ovcJDjnHv//97zv+zJkzxzQnq6q0+vn5yf79+22PS0pKZPjw4VKvXj05fvy4qU7L2cpWo2MrW42OreZrVaVThK1sNT62stXo2MpWo2Or+VpV6SRt8ABFEbfekmaxWCr9McuFQZVWb29vSUlJue33I0eOlLp160piYqIpOkXYKsJWo2MrW42OreZrVaVThK0ibDU6trLV6NjKVqNjq/laVekkbbjo/Q4Yco7AwEAsWLAAvXr1qnB97969iIyMdPJUjqFKa+PGjbF7926EhYWV+f38+fMBAI899pgeYzkEW9lqdGxlq9Gx1XytqnQCbAXYanRsZavRsZWtRsdW87Wq0kna4HegKCIyMhJJSUmVrlssFoiIEydyHFVaH3/8cXzxxRcVrs2fPx/PPPOMKToBtt7CVuNiaym2GhdbS5mpVZVOgK23sNW42FqKrcbF1lJsNS62ljJTqyqdpA2L8L8NSvjll1+Ql5eHLl26VLiel5eH3bt3o0OHDk6eTHsqtRIRERERERERERGRY/AAhYiIiIiIiIiIiIiIqBx+hBcREREREREREREREVE5PEAhIiIiIiIiIiIiIiIqhwcoRERERERERERERERE5fAAhYiIiIiIiIiIiIiIqBweoBARERER0V+eiKBz586IjY29bW3BggXw9fXFmTNndJiMiIiIiIjMigcoRERERET0l2exWLB06VLs2LEDH374oe33J06cwLhx4/Cvf/0LdevW1fT/ZmFhoaZ/HhERERERGQsPUIiIiIiIyBCCg4Mxd+5cjBkzBidOnICIYMiQIYiJiUGzZs3QtWtXeHl5wd/fHwMHDsTFixdt/7vr169H27Zt4evri9q1a6NHjx44fvy4bT0jIwMWiwUrV65Ehw4d4O7ujs8//xwnT55Ez549UatWLXh6euKBBx7ADz/8oEc+ERERERE5mUVERO8hiIiIiIiI/qjevXsjJycHTzzxBKZOnYpDhw7hgQcewNChQxEXF4cbN27g1VdfRVFRETZt2gQAWLVqFSwWCx588EFcu3YNr7/+OjIyMrB3715YrVZkZGQgNDQU9evXx+zZs9GsWTO4u7vjhRdeQEFBAWbPng1PT0+kpKTAx8cH7du31/n/C0RERERE5Gg8QCEiIiIiIkM5f/48HnjgAVy6dAmrVq3CwYMH8csvvyA+Pt72nzlz5gyCg4Nx5MgRNGzY8LY/4+LFi7jnnntw4MABNGnSxHaA8t5772HUqFG2/9yDDz6IJ598EpMnT3ZKGxERERER/XXwI7yIiIiIiMhQ/Pz88Le//Q1hYWHo3bs39u3bh4SEBHh5edl+GjduDAC2j+lKS0vDM888gwYNGsDHxwf169cHAJw6darMn92iRYsyj19++WVMmzYNbdq0weTJk7F//37HBxIRERER0V8CD1CIiIiIiMhwXFxc4OLiAgC4du0aevbsib1795b5SUtLs33UVs+ePXHp0iUsWrQIO3bswI4dOwAABQUFZf5cT0/PMo+HDh2K9PR0DBw4EAcOHECLFi3wr3/9ywmFRERERESkNxe9ByAiIiIiIqqK5s2bY9WqVahfv77tUOX3fvvtNxw5cgSLFi1Cu3btAABbtmz5w39+cHAwhg8fjuHDh2PChAlYtGgRXnrpJc3mJyIiIiKivya+A4WIiIiIiAxt5MiRuHTpEp555hns2rULx48fR3x8PAYPHozi4mLUqlULtWvXxkcffYRjx45h06ZNeOWVV/7Qnz169GjEx8fjxIkTSE5ORkJCAsLCwhxcREREREREfwU8QCEiIiIiIkMLCgrC1q1bUVxcjJiYGERERGD06NHw9fWF1WqF1WrFl19+iaSkJDRp0gT//d//jVmzZv2hP7u4uBgjR45EWFgYunTpgoYNG2LBggUOLiIiIiIior8Ci4iI3kMQERERERERERERERH9lfAdKEREREREREREREREROXwAIWIiIiIiIiIiIiIiKgcHqAQERERERERERERERGVwwMUIiIiIiIiIiIiIiKicniAQkREREREREREREREVA4PUIiIiIiIiIiIiIiIiMrhAQoREREREREREREREVE5PEAhIiIiIiIiIiIiIiIqhwcoRERERERERERERERE5fAAhYiIiIiIiIiIiIiIqBweoBAREREREREREREREZXDAxQiIiIiIiIiIiIiIqJy/g9UQVSKwvGpjAAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure size: 20 representing the width, 10 representing the height\n", + "df = df.sort_values(by=\"datetime\")\n", + "fig = plt.figure(figsize=(20, 10))\n", + "\n", + "plt.plot(\n", + " df[\"datetime\"], # X-axis: sorted datetime\n", + " df[\"max\"], # Y-axis: maximum CH4 flux\n", + " color=\"red\", # Line color\n", + " linestyle=\"-\", # Line style\n", + " linewidth=0.5, # Line width\n", + " label=\"CH4 emissions\", # Legend label\n", + ")\n", + "\n", + "# Display legend\n", + "plt.legend()\n", + "\n", + "# Insert label for the X-axis\n", + "plt.xlabel(\"Years\")\n", + "\n", + "# Insert label for the Y-axis\n", + "plt.ylabel(\"g CH₄/m²/year\")\n", + "plt.xticks(rotation = 90)\n", + "\n", + "interval = 12\n", + "ticks = range(0, len(df), interval) # Tick positions\n", + "plt.xticks(ticks, df[\"datetime\"].iloc[ticks]) # Use the corresponding datetime values\n", + "\n", + "\n", + "# Insert title for the plot\n", + "plt.title(\"CH4 emission Values for Texas, Dallas (1999-2016)\")\n", + "\n", + "# Add data citation\n", + "plt.text(\n", + " df[\"datetime\"].iloc[0], # X-coordinate of the text\n", + " df[\"max\"].min(), # Y-coordinate of the text\n", + "\n", + "\n", + "\n", + "\n", + " # Text to be displayed\n", + " \"Source: NASA/NOAA CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes\", \n", + " fontsize=12, # Font size\n", + " horizontalalignment=\"left\", # Horizontal alignment\n", + " verticalalignment=\"top\", # Vertical alignment\n", + " color=\"blue\", # Text color\n", + ")\n", + "\n", + "\n", + "# Plot the time series\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-10-01T00:00:00+00:00\n" + ] + } + ], + "source": [ + "# Print the properties for the 3rd item in the collection\n", + "print(items[2][\"properties\"][\"start_datetime\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://dev.ghg.center/api/raster/collections/ct-ch4-monthgrid-v2023/items/ct-ch4-monthgrid-v2023-202110/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C198.25427154645362'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# A GET request is made for the 3rd granule\n", + "ch4_flux_3 = requests.get(\n", + "\n", + " # Pass the collection name, the item number in the list, and its ID\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n", + "\n", + " # Pass the asset name\n", + " f\"&assets={asset_name}\"\n", + "\n", + " # Pass the color formula and colormap for custom visualization\n", + " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", + "\n", + " # Pass the minimum and maximum values for rescaling\n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\",\n", + "\n", + "# Return the response in JSON format\n", + ").json()\n", + "\n", + "# Print the properties of the retrieved granule to the console\n", + "ch4_flux_3" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a new map to display the tile\n", + "aoi_map_bbox = Map(\n", + "\n", + " # Base map is set to OpenStreetMap\n", + " tiles=\"OpenStreetMap\",\n", + "\n", + " # Set the center of the map\n", + " location=[\n", + " 30,-100\n", + " ],\n", + "\n", + " # Set the zoom value\n", + " zoom_start=6.8,\n", + ")\n", + "\n", + "# Define the map layer\n", + "map_layer = TileLayer(\n", + "\n", + " # Path to retrieve the tile\n", + " tiles=ch4_flux_3[\"tiles\"][0],\n", + "\n", + " # Set the attribution and adjust the transparency of the layer\n", + " attr=\"GHG\", opacity = 0.7\n", + ")\n", + "\n", + "# Add the layer to the map\n", + "map_layer.add_to(aoi_map_bbox)\n", + "\n", + "# Visualize the map\n", + "aoi_map_bbox" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook we have successfully explored, analyzed, and visualized the STAC collection for TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.\n", + "\n", + "1. Install and import the necessary libraries\n", + "2. Fetch the collection from STAC collections using the appropriate endpoints\n", + "3. Count the number of existing granules within the collection\n", + "4. Map and compare the CH₄ inverse fluxes for two distinctive months/years\n", + "5. Generate zonal statistics for the area of interest (AOI)\n", + "6. Visualizing the Data as a Time Series\n", + "\n", + "\n", + "If you have any questions regarding this user notebook, please contact us using the [feedback form](https://docs.google.com/forms/d/e/1FAIpQLSeVWCrnca08Gt_qoWYjTo6gnj1BEGL4NCUC9VEiQnXA02gzVQ/viewform)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb b/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb new file mode 100644 index 00000000..bd8d1854 --- /dev/null +++ b/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb @@ -0,0 +1,25 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure\n", + "description: A sample of methane plumes from point sources observed since 2019 by the U.S. Geostationary Operational Environmental Satellites (GOES) over North and South America.\n", + "author: Siddharth Chaudhary\n", + "date: 15 November 2024\n", + "execute:\n", + " freeze: true\n", + "---" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb index ab47a874..fcdf71c8 100644 --- a/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb @@ -2,13 +2,17 @@ "cells": [ { "cell_type": "raw", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", "title: GOSAT-based Top-down Total and Natural Methane Emissions\n", - "description: Total and natural methane emissions for 2019 summed to a 1° resolution grid. Methane values for both prior to and after inclusion of GOSAT data to the GEOS-Chem global chemistry transport model, version 1.0\n", + "description: Total and wetland yearly methane emissions derived using the GEOS-Chem global chemistry transport model with inclusion of GOSAT data for 2010 to 2022 on a 4 x 5 degree (lat/lon) grid\n", "author: Siddharth Chaudhary, Vishal Gaur\n", - "date: 21 September 2023\n", + "date: 15 November 2024\n", "execute:\n", " freeze: true\n", "---" @@ -4438,7 +4442,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb b/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb deleted file mode 100644 index 221daba3..00000000 --- a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb +++ /dev/null @@ -1,2170 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "title: TM5-4DVar Isotopic CH₄ Inverse Fluxes\n", - "description: Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 1.\n", - "author: Siddharth Chaudhary, Vishal Gaur\n", - "date: 22 August 2023\n", - "execute:\n", - " freeze: true\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run this notebook\n", - "\n", - "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", - "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Ftm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb&branch=main)\n", - " " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Approach\n", - "\n", - "1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API `/stac` endpoint. The collection processed in this notebook is the TM5-4DVar Isotopic CH₄ Inverse Fluxes Data product.\n", - "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json `endpoint.\n", - "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", - "4. After the visualization, we will perform zonal statistics for a given polygon.\n", - "\n", - " " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## About the Data\n", - "\n", - "Surface methane (CH₄) emissions are derived from atmospheric measurements of methane and its ¹³C carbon isotope content. Different sources of methane contain different ratios of the two stable isotopologues, ¹²CH₄ and ¹³CH₄. This makes normally indistinguishable collocated sources of methane, say from agriculture and oil and gas exploration, distinguishable. The National Oceanic and Atmospheric Administration (NOAA) collects whole air samples from its global cooperative network of flasks (https://gml.noaa.gov/ccgg/about.html), which are then analyzed for methane and other trace gasses. A subset of those flasks are also analyzed for ¹³C of methane in collaboration with the Institute of Arctic and Alpine Research at the University of Colorado Boulder. Scientists at the National Aeronautics and Space Administration (NASA) and NOAA used those measurements of methane and ¹³C of methane in conjunction with a model of atmospheric circulation to estimate emissions of methane separated by three source types, microbial, fossil and pyrogenic.\n", - "\n", - "For more information regarding this dataset, please visit the [TM5-4DVar Isotopic CH₄ Inverse Fluxes](https://earth.gov/ghgcenter/data-catalog/tm54dvar-ch4flux-monthgrid-v1) data overview page." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Install the Required Libraries\n", - "Required libraries are pre-installed on the GHG Center Hub. If you need to run this notebook elsewhere, please install them with this line in a code cell:\n", - "\n", - "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# Import the following libraries\n", - "import requests\n", - "import folium\n", - "import folium.plugins\n", - "from folium import Map, TileLayer\n", - "from pystac_client import Client\n", - "import branca\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Querying the STAC API\n", - "First, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Provide the STAC and RASTER API endpoints\n", - "# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n", - "\n", - "# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\n", - "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", - "\n", - "# The RASTER API is used to fetch collections for visualization\n", - "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n", - "\n", - "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", - "# Name of the collection for TM5 CH₄ inverse flux dataset \n", - "collection_name = \"tm54dvar-ch4flux-monthgrid-v1\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'tm54dvar-ch4flux-monthgrid-v1',\n", - " 'type': 'Collection',\n", - " 'links': [{'rel': 'items',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/items'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'}],\n", - " 'title': 'TM5-4DVar Isotopic CH4 Inverse Fluxes',\n", - " 'assets': None,\n", - " 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n", - " 'temporal': {'interval': [['1999-01-01T00:00:00+00:00',\n", - " '2016-12-31T00:00:00+00:00']]}},\n", - " 'license': 'CC-BY-4.0',\n", - " 'keywords': None,\n", - " 'providers': None,\n", - " 'summaries': {'datetime': ['1999-01-01T00:00:00Z', '2016-12-31T00:00:00Z']},\n", - " 'description': 'Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 1.',\n", - " 'item_assets': {'total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total CH4 Emission',\n", - " 'description': 'Total methane emission from microbial, fossil and pyrogenic sources'},\n", - " 'fossil': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Fossil CH4 Emission',\n", - " 'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.'},\n", - " 'microbial': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Microbial CH4 Emission',\n", - " 'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.'},\n", - " 'pyrogenic': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Pyrogenic CH4 Emission',\n", - " 'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.'}},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': None,\n", - " 'dashboard:is_periodic': True,\n", - " 'dashboard:time_density': 'month'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fetch the collection from the STAC API using the appropriate endpoint\n", - "# The 'requests' library allows a HTTP request possible\n", - "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n", - "\n", - "# Print the properties of the collection to the console\n", - "collection" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Examining the contents of our `collection` under the `temporal` variable, we see that the data is available from January 1999 to December 2016. By looking at the `dashboard:time density`, we observe that the data is periodic with monthly time density." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a function that would search for a data collection in the US GHG Center STAC API\n", - "\n", - "# First, we need to define the function\n", - "# The name of the function = \"get_item_count\"\n", - "# The argument that will be passed through the defined function = \"collection_id\"\n", - "def get_item_count(collection_id):\n", - "\n", - " # Set a counter for the number of items existing in the collection\n", - " count = 0\n", - "\n", - " # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n", - " items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n", - "\n", - " # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n", - " while True:\n", - "\n", - " # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path\n", - " response = requests.get(items_url)\n", - "\n", - " # If the items do not exist, print an error message and quit the loop\n", - " if not response.ok:\n", - " print(\"error getting items\")\n", - " exit()\n", - "\n", - " # Return the results of the HTTP response as JSON\n", - " stac = response.json()\n", - "\n", - " # Increase the \"count\" by the number of items (granules) returned in the response\n", - " count += int(stac[\"context\"].get(\"returned\", 0))\n", - "\n", - " # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n", - " next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n", - "\n", - " # Exit the loop if there are no other URLs\n", - " if not next:\n", - " break\n", - " \n", - " # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n", - " # \"href\" is the identifier for each of the tiles stored in the STAC API\n", - " items_url = next[0][\"href\"]\n", - "\n", - " # Return the information about the total number of granules found associated with the collection\n", - " return count" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 216 items\n" - ] - } - ], - "source": [ - "# Apply the function created above \"get_item_count\" to the data collection\n", - "number_of_items = get_item_count(collection_name)\n", - "\n", - "# Get the information about the number of granules found in the collection\n", - "items = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n", - "\n", - "# Print the total number of items (granules) found\n", - "print(f\"Found {len(items)} items\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'tm54dvar-ch4flux-monthgrid-v1-201612',\n", - " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'type': 'Feature',\n", - " 'links': [{'rel': 'collection',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612'}],\n", - " 'assets': {'total': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_total_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Total methane emission from microbial, fossil and pyrogenic sources',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 207.09559432166358,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64446.0, 253.0, 61.0, 16.0, 14.0, 4.0, 3.0, 0.0, 2.0, 1.0]},\n", - " 'statistics': {'mean': 0.7699816366032659,\n", - " 'stddev': 3.8996905358416045,\n", - " 'maximum': 207.09559432166358,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'fossil': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_fossil_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Fossil CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 202.8189294183266,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64633.0, 107.0, 35.0, 11.0, 8.0, 3.0, 1.0, 1.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.27127687553584495,\n", - " 'stddev': 2.731411670166909,\n", - " 'maximum': 202.8189294183266,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'microbial': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_microbial_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Microbial CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 161.4604621003495,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64610.0, 155.0, 22.0, 5.0, 2.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.46611433673211145,\n", - " 'stddev': 2.2910210071489456,\n", - " 'maximum': 161.4604621003495,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'pyrogenic': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_pyrogenic_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Pyrogenic CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 13.432528617097262,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64440.0, 221.0, 78.0, 24.0, 18.0, 8.0, 3.0, 1.0, 1.0, 6.0]},\n", - " 'statistics': {'mean': 0.032590424335309266,\n", - " 'stddev': 0.28279054181617735,\n", - " 'maximum': 13.432528617097262,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]}},\n", - " 'geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180, -90],\n", - " [180, -90],\n", - " [180, 90],\n", - " [-180, 90],\n", - " [-180, -90]]]},\n", - " 'collection': 'tm54dvar-ch4flux-monthgrid-v1',\n", - " 'properties': {'end_datetime': '2016-12-31T00:00:00+00:00',\n", - " 'start_datetime': '2016-12-01T00:00:00+00:00'},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': []}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Examine the first item in the collection\n", - "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", - "items[0]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploring Changes in CH₄ flux Levels Using the Raster API\n", - "\n", - "In this notebook, we will explore the global changes of CH₄ flux over time in urban regions. We will visualize the outputs on a map using `folium`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\n", - "items = {item[\"properties\"][\"start_datetime\"][:10]: item for item in items} \n", - "\n", - "# Next, we need to specify the asset name for this collection\n", - "# The asset name is referring to the raster band containing the pixel values for the parameter of interest\n", - "# For the case of the TM5-4DVar Isotopic CH₄ Inverse Fluxes collection, the parameter of interest is “fossil”\n", - "asset_name = \"fossil\" #fossil fuel" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we are entering the minimum and maximum values to provide our upper and lower bounds in the `rescale_values`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Fetching the min and max values for a specific item\n", - "rescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we will pass the item id, collection name, asset name, and the `rescaling factor` to the `Raster API` endpoint. We will do this twice, once for 2016 and again for 1999, so that we can visualize each event independently." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Choose a color map for displaying the first observation (event)\n", - "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", - "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", - "color_map = \"purd\"\n", - "\n", - "# Make a GET request to retrieve information for the 2016 tile\n", - "ch4_flux_1 = requests.get(\n", - "\n", - " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/collections/{items['2016-12-01']['collection']}/items/{items['2016-12-01']['id']}/tilejson.json?\"\n", - "\n", - " # Pass the asset name\n", - " f\"&assets={asset_name}\"\n", - "\n", - " # Pass the color formula and colormap for custom visualization\n", - " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", - "\n", - " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", - "\n", - "# Return the response in JSON format\n", - ").json()\n", - "\n", - "# Print the properties of the retrieved granule to the console\n", - "ch4_flux_1" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-199912/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Make a GET request to retrieve information for the 1999 tile\n", - "ch4_flux_2 = requests.get(\n", - "\n", - " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/collections/{items['1999-12-01']['collection']}/items/{items['1999-12-01']['id']}/tilejson.json?\"\n", - "\n", - " # Pass the asset name\n", - " f\"&assets={asset_name}\"\n", - "\n", - " # Pass the color formula and colormap for custom visualization\n", - " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", - "\n", - " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", - "\n", - "# Return the response in JSON format\n", - ").json()\n", - "\n", - "# Print the properties of the retrieved granule to the console\n", - "ch4_flux_2" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing CH₄ flux Emissions from Fossil Fuel" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For this study we are going to compare CH4 fluxes from fossil fuels in 2016 and 1999 along the coast of California\n", - "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", - "\n", - "# Set the initial zoom level and center of map for both tiles\n", - "# 'folium.plugins' allows mapping side-by-side\n", - "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", - "\n", - "# Define the first map layer (2016)\n", - "map_layer_2016 = TileLayer(\n", - " tiles=ch4_flux_1[\"tiles\"][0], # Path to retrieve the tile\n", - " attr=\"GHG\", # Set the attribution\n", - " opacity=0.8, # Adjust the transparency of the layer\n", - ")\n", - "# Add the first layer to the Dual Map\n", - "map_layer_2016.add_to(map_.m1)\n", - "\n", - "\n", - "# Define the second map layer (1999)\n", - "map_layer_1999 = TileLayer(\n", - " tiles=ch4_flux_2[\"tiles\"][0], # Path to retrieve the tile\n", - " attr=\"GHG\", # Set the attribution\n", - " opacity=0.8, # Adjust the transparency of the layer\n", - ")\n", - "\n", - "# Add the second layer to the Dual Map\n", - "map_layer_1999.add_to(map_.m2)\n", - "\n", - "# Visualize the Dual Map\n", - "map_" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Calculating Zonal Statistics" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a polygon for the area of interest (aoi)\n", - "texas_aoi = {\n", - " \"type\": \"Feature\", # Create a feature object\n", - " \"properties\": {},\n", - " \"geometry\": { # Set the bounding coordinates for the polygon\n", - " \"coordinates\": [\n", - " [\n", - " [-95, 29], # South-east bounding coordinate\n", - " [-95, 33], # North-east bounding coordinate\n", - " [-104,33], # North-west bounding coordinate\n", - " [-104,29], # South-west bounding coordinate\n", - " [-95, 29] # South-east bounding coordinate (closing the polygon)\n", - " ]\n", - " ],\n", - " \"type\": \"Polygon\",\n", - " },\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a new map to display the generated polygon\n", - "# We'll plug in the coordinates for a location\n", - "# Central to the study area and a reasonable zoom level\n", - "aoi_map = Map(\n", - "\n", - " # Base map is set to OpenStreetMap\n", - " tiles=\"OpenStreetMap\",\n", - "\n", - " # Define the spatial properties for the map\n", - " location=[\n", - " 30,-100\n", - " ],\n", - "\n", - " # Set the zoom value\n", - " zoom_start=6,\n", - ")\n", - "\n", - "# Insert the polygon to the map\n", - "folium.GeoJson(texas_aoi, name=\"Texas, USA\").add_to(aoi_map)\n", - "\n", - "# Visualize the map\n", - "aoi_map" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 216 items\n" - ] - } - ], - "source": [ - "# Check total number of items available within the collection\n", - "items = requests.get(\n", - " f\"{STAC_API_URL}/collections/{collection_name}/items?limit=600\"\n", - ").json()[\"features\"]\n", - "\n", - "# Print the total number of items (granules) found\n", - "print(f\"Found {len(items)} items\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'tm54dvar-ch4flux-monthgrid-v1-201612',\n", - " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'type': 'Feature',\n", - " 'links': [{'rel': 'collection',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612'}],\n", - " 'assets': {'total': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_total_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Total methane emission from microbial, fossil and pyrogenic sources',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 207.09559432166358,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64446.0, 253.0, 61.0, 16.0, 14.0, 4.0, 3.0, 0.0, 2.0, 1.0]},\n", - " 'statistics': {'mean': 0.7699816366032659,\n", - " 'stddev': 3.8996905358416045,\n", - " 'maximum': 207.09559432166358,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'fossil': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_fossil_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Fossil CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 202.8189294183266,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64633.0, 107.0, 35.0, 11.0, 8.0, 3.0, 1.0, 1.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.27127687553584495,\n", - " 'stddev': 2.731411670166909,\n", - " 'maximum': 202.8189294183266,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'microbial': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_microbial_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Microbial CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 161.4604621003495,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64610.0, 155.0, 22.0, 5.0, 2.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.46611433673211145,\n", - " 'stddev': 2.2910210071489456,\n", - " 'maximum': 161.4604621003495,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", - " 'pyrogenic': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_pyrogenic_201612.tif',\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Pyrogenic CH4 Emission',\n", - " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.',\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float64',\n", - " 'histogram': {'max': 13.432528617097262,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64440.0, 221.0, 78.0, 24.0, 18.0, 8.0, 3.0, 1.0, 1.0, 6.0]},\n", - " 'statistics': {'mean': 0.032590424335309266,\n", - " 'stddev': 0.28279054181617735,\n", - " 'maximum': 13.432528617097262,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.0, -90.0],\n", - " [180.0, -90.0],\n", - " [180.0, 90.0],\n", - " [-180.0, 90.0],\n", - " [-180.0, -90.0]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]}},\n", - " 'geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180, -90],\n", - " [180, -90],\n", - " [180, 90],\n", - " [-180, 90],\n", - " [-180, -90]]]},\n", - " 'collection': 'tm54dvar-ch4flux-monthgrid-v1',\n", - " 'properties': {'end_datetime': '2016-12-31T00:00:00+00:00',\n", - " 'start_datetime': '2016-12-01T00:00:00+00:00'},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': []}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Examine the first item in the collection\n", - "items[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we created the polygon for the area of interest, we need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection\n", - "# Create a function that retrieves information regarding a specific granule using its asset name and raster identifier and generates the statistics for it\n", - "\n", - "# The function takes an item (granule) and a JSON (polygon) as input parameters\n", - "def generate_stats(item, geojson):\n", - "\n", - " # A POST request is made to submit the data associated with the item of interest (specific observation) within the boundaries of the polygon to compute its statistics\n", - " result = requests.post(\n", - "\n", - " # Raster API Endpoint for computing statistics\n", - " f\"{RASTER_API_URL}/cog/statistics\",\n", - "\n", - " # Pass the URL to the item, asset name, and raster identifier as parameters\n", - " params={\"url\": item[\"assets\"][asset_name][\"href\"]},\n", - "\n", - " # Send the GeoJSON object (polygon) along with the request\n", - " json=geojson,\n", - "\n", - " # Return the response in JSON format\n", - " ).json()\n", - "\n", - " # Print the result\n", - " print(result)\n", - "\n", - " # Return a dictionary containing the computed statistics along with the item's datetime information.\n", - " return {\n", - " **result[\"properties\"],\n", - " \"datetime\": item[\"properties\"][\"start_datetime\"][:10],\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2016-12-01\n" - ] - } - ], - "source": [ - "# Generate a for loop that iterates over all the existing items in the collection\n", - "for item in items:\n", - "\n", - " # The loop will then retrieve the information for the start datetime of each item in the list\n", - " print(item[\"properties\"][\"start_datetime\"][:10])\n", - "\n", - " # Exit the loop after printing the start datetime for the first item in the collection\n", - " break" - ] - }, - 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"{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0809581013139931, 'max': 24.822949625050086, 'mean': 4.3481885376528915, 'count': 36.0, 'sum': 156.5347873555041, 'std': 4.633345420525916, 'median': 2.5974040079993728, 'majority': 0.0809581013139931, 'minority': 0.0809581013139931, 'unique': 36.0, 'histogram': [[17.0, 8.0, 6.0, 2.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0], [0.0809581013139931, 2.5551572536876024, 5.029356406061212, 7.5035555584348215, 9.97775471080843, 12.45195386318204, 14.926153015555649, 17.40035216792926, 19.874551320302867, 22.348750472676475, 24.822949625050086]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0809581013139931, 'percentile_98': 24.822949625050086}}}}\n", - "CPU times: user 2.27 s, sys: 465 ms, total: 2.73 s\n", - "Wall time: 2min\n" - ] - } - ], - "source": [ - "%%time\n", - "# %%time = Wall time (execution time) for running the code below\n", - "\n", - "# Generate statistics using the created function \"generate_stats\" within the bounding box defined by the polygon\n", - "stats = [generate_stats(item, texas_aoi) for item in items]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a function that goes through every single item in the collection and populates their properties - including the minimum, maximum, and sum of their values - in a table." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'statistics': {'b1': {'min': 0.0464402866499578,\n", - " 'max': 49.61378870603235,\n", - " 'mean': 9.039553150168388,\n", - " 'count': 36.0,\n", - " 'sum': 325.42391340606196,\n", - " 'std': 11.97160706711745,\n", - " 'median': 3.662876577293575,\n", - " 'majority': 0.0464402866499578,\n", - " 'minority': 0.0464402866499578,\n", - " 'unique': 36.0,\n", - " 'histogram': [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0, 1.0],\n", - " [0.0464402866499578,\n", - " 5.003175128588197,\n", - " 9.959909970526436,\n", - " 14.916644812464675,\n", - " 19.873379654402914,\n", - " 24.830114496341153,\n", - " 29.786849338279392,\n", - " 34.74358418021763,\n", - " 39.700319022155874,\n", - " 44.65705386409412,\n", - " 49.61378870603235]],\n", - " 'valid_percent': 100.0,\n", - " 'masked_pixels': 0.0,\n", - " 'valid_pixels': 36.0,\n", - " 'percentile_2': 0.0464402866499578,\n", - " 'percentile_98': 49.61378870603235}},\n", - " 'datetime': '2016-12-01'}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Print the stats for the first item in the collection\n", - "stats[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02016-12-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-12-01
12016-11-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-11-01
22016-10-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-10-01
32016-09-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-09-01
42016-08-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-08-01
\n", - "
" - ], - "text/plain": [ - " datetime min max mean count sum std \\\n", - "0 2016-12-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "1 2016-11-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "2 2016-10-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "3 2016-09-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "4 2016-08-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", - "\n", - " median majority minority unique \\\n", - "0 3.662877 0.04644 0.04644 36.0 \n", - "1 3.662877 0.04644 0.04644 36.0 \n", - "2 3.662877 0.04644 0.04644 36.0 \n", - "3 3.662877 0.04644 0.04644 36.0 \n", - "4 3.662877 0.04644 0.04644 36.0 \n", - "\n", - " histogram valid_percent \\\n", - "0 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "1 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "2 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "3 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "4 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", - "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 date \n", - "0 0.0 36.0 0.04644 49.613789 2016-12-01 \n", - "1 0.0 36.0 0.04644 49.613789 2016-11-01 \n", - "2 0.0 36.0 0.04644 49.613789 2016-10-01 \n", - "3 0.0 36.0 0.04644 49.613789 2016-09-01 \n", - "4 0.0 36.0 0.04644 49.613789 2016-08-01 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", - "def clean_stats(stats_json) -> pd.DataFrame:\n", - "\n", - " # Normalize the JSON data\n", - " df = pd.json_normalize(stats_json)\n", - "\n", - " # Replace the naming \"statistics.b1\" in the columns\n", - " df.columns = [col.replace(\"statistics.b1.\", \"\") for col in df.columns]\n", - "\n", - " # Set the datetime format\n", - " df[\"date\"] = pd.to_datetime(df[\"datetime\"])\n", - "\n", - " # Return the cleaned format\n", - " return df\n", - "\n", - "# Apply the generated function on the stats data\n", - "df = clean_stats(stats)\n", - "\n", - "# Display the stats for the first 5 granules in the collection in the table\n", - "# Change the value in the parenthesis to show more or a smaller number of rows in the table\n", - "df.head(5)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing the Data as a Time Series\n", - "We can now explore the fossil fuel emission time series (January 1999 -December 2016) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "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) From e6a6ee9ae51d820b30f71da91fdd13252568ff87 Mon Sep 17 00:00:00 2001 From: vishal Date: Tue, 19 Nov 2024 11:11:45 -0600 Subject: [PATCH 14/16] reformatting files --- .../ecco_darwin_transformation.py | 14 ++++++----- .../geos_oco2_transformation.py | 6 +++-- .../gosat_ch4_transformation.py | 8 ++++--- .../gpw_transformation.py | 8 ++++--- data_transformation_plugins/push_to_s3.py | 24 +++++++++++++------ .../tm5_4dvar_update_noaa_transformation.py | 8 ++++--- 6 files changed, 44 insertions(+), 24 deletions(-) diff --git a/data_transformation_plugins/ecco_darwin_transformation.py b/data_transformation_plugins/ecco_darwin_transformation.py index b31c2c13..93be21cd 100644 --- a/data_transformation_plugins/ecco_darwin_transformation.py +++ b/data_transformation_plugins/ecco_darwin_transformation.py @@ -1,6 +1,8 @@ -import xarray import re +import xarray + + def ecco_darwin_transformation(file_obj, name, nodata): """Tranformation function for the ecco darwin dataset @@ -15,9 +17,9 @@ def ecco_darwin_transformation(file_obj, name, nodata): 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( + longitude=((xds.longitude / 1440) * 360) - 180 + ).sortby("longitude") xds = xds.assign_coords(latitude=((xds.latitude / 721) * 180) - 90).sortby( "latitude" ) @@ -43,5 +45,5 @@ def ecco_darwin_transformation(file_obj, name, nodata): # # add extension cog_filename = f"{cog_filename}.tif" var_data_netcdf[cog_filename] = data - - return var_data_netcdf \ No newline at end of file + + return var_data_netcdf diff --git a/data_transformation_plugins/geos_oco2_transformation.py b/data_transformation_plugins/geos_oco2_transformation.py index 4165c91e..61b9702a 100644 --- a/data_transformation_plugins/geos_oco2_transformation.py +++ b/data_transformation_plugins/geos_oco2_transformation.py @@ -1,6 +1,8 @@ -import xarray import re +import xarray + + def geos_oco2_transformation(file_obj, name, nodata): """Tranformation function for the oco2 geos dataset @@ -35,4 +37,4 @@ def geos_oco2_transformation(file_obj, name, nodata): cog_filename = f"{cog_filename}.tif" var_data_netcdf[cog_filename] = data - return var_data_netcdf \ No newline at end of file + return var_data_netcdf diff --git a/data_transformation_plugins/gosat_ch4_transformation.py b/data_transformation_plugins/gosat_ch4_transformation.py index 7a88b85f..ea5552ec 100644 --- a/data_transformation_plugins/gosat_ch4_transformation.py +++ b/data_transformation_plugins/gosat_ch4_transformation.py @@ -1,6 +1,8 @@ -import xarray import re +import xarray + + def gosat_ch4_transformation(file_obj, name, nodata): """Tranformation function for the ecco darwin dataset @@ -33,5 +35,5 @@ def gosat_ch4_transformation(file_obj, name, nodata): 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 \ No newline at end of file + + return var_data_netcdf diff --git a/data_transformation_plugins/gpw_transformation.py b/data_transformation_plugins/gpw_transformation.py index fde5d1f7..fb1e99d8 100644 --- a/data_transformation_plugins/gpw_transformation.py +++ b/data_transformation_plugins/gpw_transformation.py @@ -1,6 +1,8 @@ -import xarray import re +import xarray + + def gpw_transformation(file_obj, name, nodata): """Tranformation function for the gridded population dataset @@ -30,5 +32,5 @@ def gpw_transformation(file_obj, name, nodata): # # add extension cog_filename = f"{cog_filename}.tif" var_data_netcdf[cog_filename] = xds - - return var_data_netcdf \ No newline at end of file + + return var_data_netcdf diff --git a/data_transformation_plugins/push_to_s3.py b/data_transformation_plugins/push_to_s3.py index 7f941bc9..03cdfaa6 100644 --- a/data_transformation_plugins/push_to_s3.py +++ b/data_transformation_plugins/push_to_s3.py @@ -1,8 +1,7 @@ -import boto3 import os import boto3 -import os + def upload_files_to_s3(folder_path, bucket_name, s3_folder, exclude_files): """ @@ -16,23 +15,23 @@ def upload_files_to_s3(folder_path, bucket_name, s3_folder, exclude_files): - exclude_files (list): List of files to exclude from uploading. """ # Initialize S3 client - s3 = boto3.client('s3') + 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': + if e.response["Error"]["Code"] == "404": try: s3.upload_file(file_path, bucket_name, s3_key) print(f"Uploaded {file_name} to {s3_key}") @@ -41,7 +40,18 @@ def upload_files_to_s3(folder_path, bucket_name, s3_folder, exclude_files): 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"]) \ No newline at end of file + 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/tm5_4dvar_update_noaa_transformation.py b/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py index 1fd491b3..a7f3a8ac 100644 --- a/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py +++ b/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py @@ -1,6 +1,8 @@ -import xarray -from datetime import datetime import re +from datetime import datetime + +import xarray + def tm5_4dvar_update_noaa_transformation(file_obj, name, nodata): """Tranformation function for the tm5 ch4 influx dataset @@ -43,4 +45,4 @@ def tm5_4dvar_update_noaa_transformation(file_obj, name, nodata): cog_filename = f"{cog_filename}.tif" var_data_netcdf[cog_filename] = data - return var_data_netcdf \ No newline at end of file + return var_data_netcdf From 6c85d0ca6bd56e2d6028476358942da33bb4bc00 Mon Sep 17 00:00:00 2001 From: vishal Date: Wed, 20 Nov 2024 10:01:32 -0600 Subject: [PATCH 15/16] reformatting files along with resolving the comments --- .../ecco_darwin_transformation.py | 9 +++++++-- .../geos_oco2_transformation.py | 9 +++++++-- .../gosat_ch4_transformation.py | 11 ++++++++--- data_transformation_plugins/gpw_transformation.py | 9 +++++++-- .../tm5_4dvar_update_noaa_transformation.py | 9 +++++++-- 5 files changed, 36 insertions(+), 11 deletions(-) diff --git a/data_transformation_plugins/ecco_darwin_transformation.py b/data_transformation_plugins/ecco_darwin_transformation.py index 93be21cd..9eb518b0 100644 --- a/data_transformation_plugins/ecco_darwin_transformation.py +++ b/data_transformation_plugins/ecco_darwin_transformation.py @@ -1,10 +1,15 @@ import re +from typing import Dict import xarray +from s3fs import S3File +from xarray import DataArray -def ecco_darwin_transformation(file_obj, name, nodata): - """Tranformation function for the ecco darwin dataset +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 diff --git a/data_transformation_plugins/geos_oco2_transformation.py b/data_transformation_plugins/geos_oco2_transformation.py index 61b9702a..dc18dfbc 100644 --- a/data_transformation_plugins/geos_oco2_transformation.py +++ b/data_transformation_plugins/geos_oco2_transformation.py @@ -1,10 +1,15 @@ import re +from typing import Dict import xarray +from s3fs import S3File +from xarray import DataArray -def geos_oco2_transformation(file_obj, name, nodata): - """Tranformation function for the oco2 geos dataset +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 diff --git a/data_transformation_plugins/gosat_ch4_transformation.py b/data_transformation_plugins/gosat_ch4_transformation.py index ea5552ec..ac299264 100644 --- a/data_transformation_plugins/gosat_ch4_transformation.py +++ b/data_transformation_plugins/gosat_ch4_transformation.py @@ -1,10 +1,15 @@ import re +from typing import Dict import xarray +from s3fs import S3File +from xarray import DataArray -def gosat_ch4_transformation(file_obj, name, nodata): - """Tranformation function for the ecco darwin dataset +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 @@ -29,7 +34,7 @@ def gosat_ch4_transformation(file_obj, name, nodata): cog_filename = f"{cog_filename}.tif" data = data.reindex(lat=list(reversed(data.lat))) - data = data.where(data != -9999, -9999) + data = data.where(data != nodata, -9999) data.rio.write_nodata(-9999, inplace=True) data.rio.set_spatial_dims("lon", "lat") diff --git a/data_transformation_plugins/gpw_transformation.py b/data_transformation_plugins/gpw_transformation.py index fb1e99d8..9db17e90 100644 --- a/data_transformation_plugins/gpw_transformation.py +++ b/data_transformation_plugins/gpw_transformation.py @@ -1,10 +1,15 @@ import re +from typing import Dict import xarray +from s3fs import S3File +from xarray import DataArray -def gpw_transformation(file_obj, name, nodata): - """Tranformation function for the gridded population dataset +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 diff --git a/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py b/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py index a7f3a8ac..8b13fc79 100644 --- a/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py +++ b/data_transformation_plugins/tm5_4dvar_update_noaa_transformation.py @@ -1,11 +1,16 @@ 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, name, nodata): - """Tranformation function for the tm5 ch4 influx dataset +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 From 062982082a2e69eff461a23dd95570cb8f8010f3 Mon Sep 17 00:00:00 2001 From: Siddharth Chaudhary <118487103+siddharth0248@users.noreply.github.com> Date: Fri, 22 Nov 2024 10:23:04 -0600 Subject: [PATCH 16/16] Methane release (#143) Transformation notebook and data usage notebook for goes-ch4plume-v1 added --- LICENSE | 2 +- _quarto.yml | 2 + cog_transformation/goes-ch4plume-v1.ipynb | 3059 +++++++++++ datatransformationcode.qmd | 1 + ...-v1_Processing and Verification Report.pdf | Bin 82491 -> 88684 bytes .../goes-ch4plume-v1_User_Notebook.ipynb | 563 +- ...-ch4budget-yeargrid-v1_User_Notebook.ipynb | 4598 +++-------------- 7 files changed, 4411 insertions(+), 3814 deletions(-) create mode 100644 cog_transformation/goes-ch4plume-v1.ipynb 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 a73212ed..1ed1edb9 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -101,6 +101,8 @@ website: - 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 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", + 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"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/datatransformationcode.qmd b/datatransformationcode.qmd index f245615c..53dae8b9 100644 --- a/datatransformationcode.qmd +++ b/datatransformationcode.qmd @@ -31,6 +31,7 @@ Explore, analyze, and make a difference with the US GHG Center. ## Large Emissions Events 1. [EMIT Methane Point Source Plume Complexes](cog_transformation/emit-ch4plume-v1.ipynb) +2. [Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure](cog_transformation/goes-ch4plume-v1.ipynb) ## Greenhouse Gas Concentrations 1. 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zOqYvdlr}cu(hthd2VTJjR0O)d%rh@7UjfVpmN3O7iA5z9MX70A7KY}=#$2kZuKsRZ E0Bx?5Z2$lO diff --git a/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb b/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb index bd8d1854..e4441013 100644 --- a/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb +++ b/user_data_notebooks/goes-ch4plume-v1_User_Notebook.ipynb @@ -1,8 +1,12 @@ { "cells": [ { - "cell_type": "markdown", - "metadata": {}, + "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", @@ -13,13 +17,564 @@ " 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": { - "name": "python" + "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": 2 + "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 fcdf71c8..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 @@ -82,16 +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", @@ -106,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -122,97 +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", @@ -224,12 +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": [ @@ -257,14 +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" ] } ], @@ -277,399 +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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" '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", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", - " 'post-oil': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_oil_GEOS_CHEM_2019.tif',\n", - " 'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 3.457329273223877,\n", - " 'min': -0.7987076640129089,\n", - " 'count': 11.0,\n", - " 'buckets': [2.0, 64681.0, 108.0, 4.0, 3.0, 1.0, 0.0, 0.0, 0.0, 1.0]},\n", - " 'statistics': {'mean': 0.0004447368555702269,\n", - " 'stddev': 0.01879083551466465,\n", - " 'maximum': 3.457329273223877,\n", - " 'minimum': -0.7987076640129089,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.5, -90.5],\n", - " [179.5, -90.5],\n", - " [179.5, 89.5],\n", - " [-180.5, 89.5],\n", - " [-180.5, -90.5]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", - " 'post-coal': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_coal_GEOS_CHEM_2019.tif',\n", - " 'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 1.1035711765289307,\n", - " 'min': -0.9143016934394836,\n", - " 'count': 11.0,\n", - " 'buckets': [1.0, 1.0, 1.0, 1.0, 64710.0, 62.0, 19.0, 3.0, 1.0, 1.0]},\n", - " 'statistics': {'mean': 0.0003904950572177768,\n", - " 'stddev': 0.01172551792114973,\n", - " 'maximum': 1.1035711765289307,\n", - 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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", - " '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", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", - " 'post-livestock-uncertainty': {'href': 's3://ghgc-data-store/gosat-based-ch4budget-yeargrid-v1/TopDownEmissions_GOSAT_post_unc_livestock_GEOS_CHEM_2019.tif',\n", - " 'proj:bbox': [-180.5, -90.5, 179.5, 89.5],\n", - " 'proj:epsg': 4326.0,\n", - " 'proj:shape': [180.0, 360.0],\n", - " 'raster:bands': [{'scale': 1.0,\n", - " 'offset': 0.0,\n", - " 'sampling': 'area',\n", - " 'data_type': 'float32',\n", - " 'histogram': {'max': 0.016047537326812744,\n", - " 'min': 0.0,\n", - " 'count': 11.0,\n", - " 'buckets': [64206.0,\n", - " 360.0,\n", - " 119.0,\n", - " 35.0,\n", - " 30.0,\n", - " 20.0,\n", - " 14.0,\n", - " 9.0,\n", - " 6.0,\n", - " 1.0]},\n", - " 'statistics': {'mean': 5.696367225027643e-05,\n", - " 'stddev': 0.00044628031901083887,\n", - " 'maximum': 0.016047537326812744,\n", - " 'minimum': 0.0,\n", - " 'valid_percent': 0.00154320987654321}}],\n", - " 'proj:geometry': {'type': 'Polygon',\n", - " 'coordinates': [[[-180.5, -90.5],\n", - " [179.5, -90.5],\n", - " [179.5, 89.5],\n", - " [-180.5, 89.5],\n", - " [-180.5, -90.5]]]},\n", - " 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n", - " 'name': 'WGS 84',\n", - " 'type': 'GeographicCRS',\n", - " 'datum': {'name': 'World Geodetic System 1984',\n", - " 'type': 'GeodeticReferenceFrame',\n", - " 'ellipsoid': {'name': 'WGS 84',\n", - " 'semi_major_axis': 6378137.0,\n", - " 'inverse_flattening': 298.257223563}},\n", - " '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n", - " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'north',\n", - " 'abbreviation': 'Lat'},\n", - " {'name': 'Geodetic longitude',\n", - " 'unit': 'degree',\n", - " 'direction': 'east',\n", - " 'abbreviation': 'Lon'}],\n", - " 'subtype': 'ellipsoidal'}},\n", - " 'proj:transform': [1.0, 0.0, -180.5, 0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", - " 'prior-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]" @@ -4042,7 +922,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -4055,7 +935,7 @@ " ).json()\n", " return {\n", " **result[\"properties\"],\n", - " \"datetime\": item[\"properties\"][\"start_datetime\"][:10],\n", + " \"datetime\": item[\"properties\"][\"datetime\"][:10],\n", " }" ] }, @@ -4069,15 +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" ] } ], @@ -4088,43 +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" } @@ -4135,7 +1015,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -4182,44 +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" } @@ -4238,24 +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": [ { @@ -4264,14 +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" } @@ -4288,7 +1268,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -4313,7 +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", @@ -4321,7 +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", @@ -4335,18 +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", @@ -4356,31 +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" } @@ -4394,7 +1374,7 @@ " location=[\n", " 30,-100\n", " ],\n", - " zoom_start=8,\n", + " zoom_start=2,\n", ")\n", "\n", "map_layer = TileLayer(\n",

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