From b092fd730f1b95bfab02ec76955add91e4e7695b Mon Sep 17 00:00:00 2001 From: Justin Johnson Date: Wed, 26 Nov 2025 09:41:21 -0600 Subject: [PATCH 1/9] Checkpoint from VS Code for coding agent session --- seals/seals_utils.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/seals/seals_utils.py b/seals/seals_utils.py index 44ca65b..c0984a0 100644 --- a/seals/seals_utils.py +++ b/seals/seals_utils.py @@ -1529,6 +1529,8 @@ def convert_regional_change_to_coarse(regional_change_vector_path, regional_chan ### Define the allocation of the total to individual cells + # TODO If the user provides a regional_change_classes_path that has each year as a column, instead of the current format which has + # Creates a dict for each zone_id: to_allocate, which will be reclassified onto the zone ids. for year_c, year in enumerate(years): allocate_per_zone_dict = {} @@ -1539,6 +1541,7 @@ def convert_regional_change_to_coarse(regional_change_vector_path, regional_chan hb.log('Processing ' + column + ' for ' + scenario_label + ', writing to ' + output_path) regions_column_id = regions_column_id.replace('label', 'id') + # TODO Right here, we see that the merged dataframes have years in the columns (instead of a single column with year as a variable). But we actually want landuse class in the columns. Pivot this table so it works. for i, change in merged[column].items(): zone_id = int(merged[regions_column_id][i]) From ca8b299299f5a4f2dcb03ddc63fc7061711d41b5 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 26 Nov 2025 15:46:20 +0000 Subject: [PATCH 2/9] Implement support for year-based CSV format in convert_regional_change_to_coarse Co-authored-by: jandrewjohnson <23742350+jandrewjohnson@users.noreply.github.com> --- seals/seals_utils.py | 145 ++++++++++++++++++++++++++++++++++--------- 1 file changed, 115 insertions(+), 30 deletions(-) diff --git a/seals/seals_utils.py b/seals/seals_utils.py index c0984a0..d662097 100644 --- a/seals/seals_utils.py +++ b/seals/seals_utils.py @@ -1476,13 +1476,16 @@ def load_blocks_list(p, input_dir): def convert_regional_change_to_coarse(regional_change_vector_path, regional_change_classes_path, coarse_ha_per_cell_path, scenario_label, output_dir, output_filename_end, columns_to_process, regions_column_label, years, region_ids_raster_path=None, distribution_algorithm='proportional', coarse_change_raster_path=None): # Converts a regional vector change map to a coarse gridded representation. - # - Regiona_change_vector_path is a gpkg with the boundaries of the regions that are changing. + # - regional_change_vector_path is a gpkg with the boundaries of the regions that are changing. # - regional_change_classes_path is a csv with columns for each changing landuse class (in columns to process) that reports net change in hectares per polygon. - # - Coarse_ha_per_cell_path is a raster that reports the number of hectares per cell in the coarse grid. Necessary to work with unprojected data, which is our assumed form. - # - Output path is the path to save the output raster. - # - Columns to process is a list of the columns headers in the gpkg that represent net changes in LU classes. + # This CSV can be in two formats: + # 1. Class-based format (original): columns are land-use classes (e.g., 'cropland', 'forest'), rows are regions + # 2. Year-based format (new): columns are years (e.g., '2030', '2050'), rows are regions + class combinations with a 'class' column + # - coarse_ha_per_cell_path is a raster that reports the number of hectares per cell in the coarse grid. Necessary to work with unprojected data, which is our assumed form. + # - output_dir is the directory where output rasters will be saved. + # - columns_to_process is a list of the column headers that represent net changes in LU classes. # - distribution_algorithm is a string that indicates how to distribute the change across the cells. default is "proportional". - # - Coarse_change_raster_path is an optional path that, if provided, will be combined with the regional change to produce a combined change raster. + # - coarse_change_raster_path is an optional path that, if provided, will be combined with the regional change to produce a combined change raster. # Read both inputs regional_change_vector = gpd.read_file(regional_change_vector_path) @@ -1529,40 +1532,122 @@ def convert_regional_change_to_coarse(regional_change_vector_path, regional_chan ### Define the allocation of the total to individual cells - # TODO If the user provides a regional_change_classes_path that has each year as a column, instead of the current format which has + # Detect if the CSV has years as columns (year-based format) or classes as columns (class-based format) + # Year-based format: columns are years (e.g., '2030', '2050'), rows have region + class + # Class-based format: columns are classes (e.g., 'cropland', 'forest'), rows have region only + + # Check if any of the columns_to_process appear in the merged dataframe columns + # If not, we assume it's a year-based format that needs pivoting + has_class_columns = any(col in merged.columns for col in columns_to_process) + + # Check if any year appears as a column (as string or int) + year_columns = [col for col in merged.columns if str(col).replace('.', '').replace('-', '').isdigit()] + has_year_columns = len(year_columns) > 0 + + if not has_class_columns and has_year_columns: + # Year-based format detected - need to pivot the data + # Expected format: region_id/label, class_label, year1, year2, ..., yearN + # We need to transform it to: region_id/label, class1, class2, ..., classN (for each year) + + # Find the class column name (usually 'class', 'lulc_class', 'class_label', etc.) + class_column_candidates = ['class', 'lulc_class', 'class_label', 'land_class', 'landuse_class'] + class_column = None + for candidate in class_column_candidates: + if candidate in merged.columns: + class_column = candidate + break + + if class_column is None: + # Try to find any column that might contain class information + for col in merged.columns: + if 'class' in col.lower() and col != regions_column_label: + class_column = col + break + + if class_column is None: + raise NameError('Could not detect class column in year-based format CSV. Expected columns like "class", "lulc_class", or "class_label".') + + hb.log('Detected year-based format in CSV. Pivoting data from class column: ' + class_column) # Creates a dict for each zone_id: to_allocate, which will be reclassified onto the zone ids. for year_c, year in enumerate(years): allocate_per_zone_dict = {} - for column in columns_to_process: - output_path = os.path.join(output_dir, column + output_filename_end) + + # If year-based format, we need to work with data for this specific year + if not has_class_columns and has_year_columns: + # Find the column for this year (could be string or int) + year_col = None + for col in year_columns: + if str(col) == str(year) or int(col) == int(year): + year_col = col + break + + if year_col is None: + hb.log('Warning: Year ' + str(year) + ' not found in CSV columns. Available years: ' + str(year_columns)) + continue - if not hb.path_exists(output_path): - hb.log('Processing ' + column + ' for ' + scenario_label + ', writing to ' + output_path) - regions_column_id = regions_column_id.replace('label', 'id') + # For each class we need to process, extract the data from the pivoted structure + for column in columns_to_process: + output_path = os.path.join(output_dir, column + output_filename_end) - # TODO Right here, we see that the merged dataframes have years in the columns (instead of a single column with year as a variable). But we actually want landuse class in the columns. Pivot this table so it works. - - for i, change in merged[column].items(): - zone_id = int(merged[regions_column_id][i]) + if not hb.path_exists(output_path): + hb.log('Processing ' + column + ' for year ' + str(year) + ' in ' + scenario_label + ', writing to ' + output_path) + regions_column_id = regions_column_id.replace('label', 'id') + + # Filter merged dataframe for this specific class + class_data = merged[merged[class_column] == column] - if int(zone_id) in n_cells_per_zone: - n_cells = n_cells_per_zone[int(zone_id)] # BAD HACK, should be generalized to know ahead of time if it's an int or string - else: - n_cells = 0 + # Now iterate over the filtered data to get regional values for this year and class + for idx, row in class_data.iterrows(): + zone_id = int(row[regions_column_id]) + change = row[year_col] + + if int(zone_id) in n_cells_per_zone: + n_cells = n_cells_per_zone[int(zone_id)] + else: + n_cells = 0 + + if n_cells > 0 and change != 0 and not pd.isna(change): + result = change / n_cells + else: + result = 0.0 + + if 'nan' in str(result).lower() or pd.isna(result): + allocate_per_zone_dict[zone_id] = 0.0 + else: + allocate_per_zone_dict[zone_id] = result + + print('Allocate per zone dict for ' + column + ' (year ' + str(year) + '): ' + str(allocate_per_zone_dict)) + hb.reclassify_raster_hb(region_ids_raster_path, allocate_per_zone_dict, output_path, output_data_type=7, match_path=None, invoke_full_callback=False, existing_values='zero', verbose=False) + else: + # Original class-based format logic + for column in columns_to_process: + output_path = os.path.join(output_dir, column + output_filename_end) + + if not hb.path_exists(output_path): + hb.log('Processing ' + column + ' for ' + scenario_label + ', writing to ' + output_path) + regions_column_id = regions_column_id.replace('label', 'id') + + for i, change in merged[column].items(): + zone_id = int(merged[regions_column_id][i]) - if n_cells > 0 and change != 0: - result = change / n_cells - else: - result = 0.0 - - if 'nan' in str(result).lower(): - allocate_per_zone_dict[zone_id] = 0.0 - else: - allocate_per_zone_dict[zone_id] = result + if int(zone_id) in n_cells_per_zone: + n_cells = n_cells_per_zone[int(zone_id)] # BAD HACK, should be generalized to know ahead of time if it's an int or string + else: + n_cells = 0 - print('Allocate per zone dict for ' + column + ': ' + str(allocate_per_zone_dict)) - hb.reclassify_raster_hb(region_ids_raster_path, allocate_per_zone_dict, output_path, output_data_type=7, match_path=None, invoke_full_callback=False, existing_values='zero', verbose=False) + if n_cells > 0 and change != 0: + result = change / n_cells + else: + result = 0.0 + + if 'nan' in str(result).lower(): + allocate_per_zone_dict[zone_id] = 0.0 + else: + allocate_per_zone_dict[zone_id] = result + + print('Allocate per zone dict for ' + column + ': ' + str(allocate_per_zone_dict)) + hb.reclassify_raster_hb(region_ids_raster_path, allocate_per_zone_dict, output_path, output_data_type=7, match_path=None, invoke_full_callback=False, existing_values='zero', verbose=False) def combine_coarsified_regional_with_coarse_estimate(coarsified_path, coarse_estimate_path, combination_algorithm, output_path): ### ABANDONED? From d03ec65d506b7e75c447bcf71ad6ea72f9691bf0 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 26 Nov 2025 15:50:38 +0000 Subject: [PATCH 3/9] Address code review feedback: improve error handling and consistency Co-authored-by: jandrewjohnson <23742350+jandrewjohnson@users.noreply.github.com> --- seals/seals_utils.py | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/seals/seals_utils.py b/seals/seals_utils.py index d662097..089a57e 100644 --- a/seals/seals_utils.py +++ b/seals/seals_utils.py @@ -1578,9 +1578,15 @@ def convert_regional_change_to_coarse(regional_change_vector_path, regional_chan # Find the column for this year (could be string or int) year_col = None for col in year_columns: - if str(col) == str(year) or int(col) == int(year): - year_col = col - break + try: + if str(col) == str(year) or int(col) == int(year): + year_col = col + break + except (ValueError, TypeError): + # Skip columns that can't be converted to int + if str(col) == str(year): + year_col = col + break if year_col is None: hb.log('Warning: Year ' + str(year) + ' not found in CSV columns. Available years: ' + str(year_columns)) @@ -1607,17 +1613,17 @@ def convert_regional_change_to_coarse(regional_change_vector_path, regional_chan else: n_cells = 0 - if n_cells > 0 and change != 0 and not pd.isna(change): + if n_cells > 0 and not pd.isna(change) and change != 0: result = change / n_cells else: result = 0.0 - if 'nan' in str(result).lower() or pd.isna(result): + if pd.isna(result): allocate_per_zone_dict[zone_id] = 0.0 else: allocate_per_zone_dict[zone_id] = result - print('Allocate per zone dict for ' + column + ' (year ' + str(year) + '): ' + str(allocate_per_zone_dict)) + hb.log('Allocate per zone dict for ' + column + ' (year ' + str(year) + '): ' + str(allocate_per_zone_dict)) hb.reclassify_raster_hb(region_ids_raster_path, allocate_per_zone_dict, output_path, output_data_type=7, match_path=None, invoke_full_callback=False, existing_values='zero', verbose=False) else: # Original class-based format logic @@ -1641,12 +1647,12 @@ def convert_regional_change_to_coarse(regional_change_vector_path, regional_chan else: result = 0.0 - if 'nan' in str(result).lower(): + if pd.isna(result): allocate_per_zone_dict[zone_id] = 0.0 else: allocate_per_zone_dict[zone_id] = result - print('Allocate per zone dict for ' + column + ': ' + str(allocate_per_zone_dict)) + hb.log('Allocate per zone dict for ' + column + ': ' + str(allocate_per_zone_dict)) hb.reclassify_raster_hb(region_ids_raster_path, allocate_per_zone_dict, output_path, output_data_type=7, match_path=None, invoke_full_callback=False, existing_values='zero', verbose=False) def combine_coarsified_regional_with_coarse_estimate(coarsified_path, coarse_estimate_path, combination_algorithm, output_path): From f3cf2912fe4956bad58765d4842cedb8bb631a35 Mon Sep 17 00:00:00 2001 From: Justin Johnson Date: Wed, 26 Nov 2025 10:27:48 -0600 Subject: [PATCH 4/9] Rgit log --oneline -- seals/seals_utils revert "Merge pull request #10 from jandrewjohnson/copilot/swift-bee" This reverts commit f9f30301af777bbf2296079a7a7134642600ed6a, reversing changes made to fce2da7a8a248a22b3604ac6359816557aad8efd. '#' will be ignored, and an empty message aborts the commit. --- seals/seals_utils.py | 150 ++++++++----------------------------------- 1 file changed, 28 insertions(+), 122 deletions(-) diff --git a/seals/seals_utils.py b/seals/seals_utils.py index 089a57e..44ca65b 100644 --- a/seals/seals_utils.py +++ b/seals/seals_utils.py @@ -1476,16 +1476,13 @@ def load_blocks_list(p, input_dir): def convert_regional_change_to_coarse(regional_change_vector_path, regional_change_classes_path, coarse_ha_per_cell_path, scenario_label, output_dir, output_filename_end, columns_to_process, regions_column_label, years, region_ids_raster_path=None, distribution_algorithm='proportional', coarse_change_raster_path=None): # Converts a regional vector change map to a coarse gridded representation. - # - regional_change_vector_path is a gpkg with the boundaries of the regions that are changing. + # - Regiona_change_vector_path is a gpkg with the boundaries of the regions that are changing. # - regional_change_classes_path is a csv with columns for each changing landuse class (in columns to process) that reports net change in hectares per polygon. - # This CSV can be in two formats: - # 1. Class-based format (original): columns are land-use classes (e.g., 'cropland', 'forest'), rows are regions - # 2. Year-based format (new): columns are years (e.g., '2030', '2050'), rows are regions + class combinations with a 'class' column - # - coarse_ha_per_cell_path is a raster that reports the number of hectares per cell in the coarse grid. Necessary to work with unprojected data, which is our assumed form. - # - output_dir is the directory where output rasters will be saved. - # - columns_to_process is a list of the column headers that represent net changes in LU classes. + # - Coarse_ha_per_cell_path is a raster that reports the number of hectares per cell in the coarse grid. Necessary to work with unprojected data, which is our assumed form. + # - Output path is the path to save the output raster. + # - Columns to process is a list of the columns headers in the gpkg that represent net changes in LU classes. # - distribution_algorithm is a string that indicates how to distribute the change across the cells. default is "proportional". - # - coarse_change_raster_path is an optional path that, if provided, will be combined with the regional change to produce a combined change raster. + # - Coarse_change_raster_path is an optional path that, if provided, will be combined with the regional change to produce a combined change raster. # Read both inputs regional_change_vector = gpd.read_file(regional_change_vector_path) @@ -1532,128 +1529,37 @@ def convert_regional_change_to_coarse(regional_change_vector_path, regional_chan ### Define the allocation of the total to individual cells - # Detect if the CSV has years as columns (year-based format) or classes as columns (class-based format) - # Year-based format: columns are years (e.g., '2030', '2050'), rows have region + class - # Class-based format: columns are classes (e.g., 'cropland', 'forest'), rows have region only - - # Check if any of the columns_to_process appear in the merged dataframe columns - # If not, we assume it's a year-based format that needs pivoting - has_class_columns = any(col in merged.columns for col in columns_to_process) - - # Check if any year appears as a column (as string or int) - year_columns = [col for col in merged.columns if str(col).replace('.', '').replace('-', '').isdigit()] - has_year_columns = len(year_columns) > 0 - - if not has_class_columns and has_year_columns: - # Year-based format detected - need to pivot the data - # Expected format: region_id/label, class_label, year1, year2, ..., yearN - # We need to transform it to: region_id/label, class1, class2, ..., classN (for each year) - - # Find the class column name (usually 'class', 'lulc_class', 'class_label', etc.) - class_column_candidates = ['class', 'lulc_class', 'class_label', 'land_class', 'landuse_class'] - class_column = None - for candidate in class_column_candidates: - if candidate in merged.columns: - class_column = candidate - break - - if class_column is None: - # Try to find any column that might contain class information - for col in merged.columns: - if 'class' in col.lower() and col != regions_column_label: - class_column = col - break - - if class_column is None: - raise NameError('Could not detect class column in year-based format CSV. Expected columns like "class", "lulc_class", or "class_label".') - - hb.log('Detected year-based format in CSV. Pivoting data from class column: ' + class_column) - # Creates a dict for each zone_id: to_allocate, which will be reclassified onto the zone ids. for year_c, year in enumerate(years): allocate_per_zone_dict = {} - - # If year-based format, we need to work with data for this specific year - if not has_class_columns and has_year_columns: - # Find the column for this year (could be string or int) - year_col = None - for col in year_columns: - try: - if str(col) == str(year) or int(col) == int(year): - year_col = col - break - except (ValueError, TypeError): - # Skip columns that can't be converted to int - if str(col) == str(year): - year_col = col - break - - if year_col is None: - hb.log('Warning: Year ' + str(year) + ' not found in CSV columns. Available years: ' + str(year_columns)) - continue + for column in columns_to_process: + output_path = os.path.join(output_dir, column + output_filename_end) - # For each class we need to process, extract the data from the pivoted structure - for column in columns_to_process: - output_path = os.path.join(output_dir, column + output_filename_end) + if not hb.path_exists(output_path): + hb.log('Processing ' + column + ' for ' + scenario_label + ', writing to ' + output_path) + regions_column_id = regions_column_id.replace('label', 'id') - if not hb.path_exists(output_path): - hb.log('Processing ' + column + ' for year ' + str(year) + ' in ' + scenario_label + ', writing to ' + output_path) - regions_column_id = regions_column_id.replace('label', 'id') - - # Filter merged dataframe for this specific class - class_data = merged[merged[class_column] == column] - - # Now iterate over the filtered data to get regional values for this year and class - for idx, row in class_data.iterrows(): - zone_id = int(row[regions_column_id]) - change = row[year_col] - - if int(zone_id) in n_cells_per_zone: - n_cells = n_cells_per_zone[int(zone_id)] - else: - n_cells = 0 - - if n_cells > 0 and not pd.isna(change) and change != 0: - result = change / n_cells - else: - result = 0.0 - - if pd.isna(result): - allocate_per_zone_dict[zone_id] = 0.0 - else: - allocate_per_zone_dict[zone_id] = result - - hb.log('Allocate per zone dict for ' + column + ' (year ' + str(year) + '): ' + str(allocate_per_zone_dict)) - hb.reclassify_raster_hb(region_ids_raster_path, allocate_per_zone_dict, output_path, output_data_type=7, match_path=None, invoke_full_callback=False, existing_values='zero', verbose=False) - else: - # Original class-based format logic - for column in columns_to_process: - output_path = os.path.join(output_dir, column + output_filename_end) - - if not hb.path_exists(output_path): - hb.log('Processing ' + column + ' for ' + scenario_label + ', writing to ' + output_path) - regions_column_id = regions_column_id.replace('label', 'id') - for i, change in merged[column].items(): - zone_id = int(merged[regions_column_id][i]) + for i, change in merged[column].items(): + zone_id = int(merged[regions_column_id][i]) + + if int(zone_id) in n_cells_per_zone: + n_cells = n_cells_per_zone[int(zone_id)] # BAD HACK, should be generalized to know ahead of time if it's an int or string + else: + n_cells = 0 - if int(zone_id) in n_cells_per_zone: - n_cells = n_cells_per_zone[int(zone_id)] # BAD HACK, should be generalized to know ahead of time if it's an int or string - else: - n_cells = 0 + if n_cells > 0 and change != 0: + result = change / n_cells + else: + result = 0.0 + + if 'nan' in str(result).lower(): + allocate_per_zone_dict[zone_id] = 0.0 + else: + allocate_per_zone_dict[zone_id] = result - if n_cells > 0 and change != 0: - result = change / n_cells - else: - result = 0.0 - - if pd.isna(result): - allocate_per_zone_dict[zone_id] = 0.0 - else: - allocate_per_zone_dict[zone_id] = result - - hb.log('Allocate per zone dict for ' + column + ': ' + str(allocate_per_zone_dict)) - hb.reclassify_raster_hb(region_ids_raster_path, allocate_per_zone_dict, output_path, output_data_type=7, match_path=None, invoke_full_callback=False, existing_values='zero', verbose=False) + print('Allocate per zone dict for ' + column + ': ' + str(allocate_per_zone_dict)) + hb.reclassify_raster_hb(region_ids_raster_path, allocate_per_zone_dict, output_path, output_data_type=7, match_path=None, invoke_full_callback=False, existing_values='zero', verbose=False) def combine_coarsified_regional_with_coarse_estimate(coarsified_path, coarse_estimate_path, combination_algorithm, output_path): ### ABANDONED? From 42626aac3bfa286ba28394a27ffc7b38f58de4d4 Mon Sep 17 00:00:00 2001 From: Justin Johnson Date: Wed, 26 Nov 2025 15:01:47 -0600 Subject: [PATCH 5/9] Enhance regional change functionality: add support for input override path and improve AOI handling in project_aoi --- seals/run_seals_bonn.py | 149 +++++++++++++++++++++++ seals/seals_process_coarse_timeseries.py | 4 + seals/seals_tasks.py | 21 +++- 3 files changed, 169 insertions(+), 5 deletions(-) create mode 100644 seals/run_seals_bonn.py diff --git a/seals/run_seals_bonn.py b/seals/run_seals_bonn.py new file mode 100644 index 0000000..a5d6e66 --- /dev/null +++ b/seals/run_seals_bonn.py @@ -0,0 +1,149 @@ +import os +import sys + +import hazelbean as hb +import pandas as pd + +from seals import seals_generate_base_data, seals_initialize_project, seals_main, seals_process_coarse_timeseries, seals_tasks, seals_visualization_tasks + + + +def convert_cgebox_output_to_seals_regional_projections_input(p): + + input_path = p.regional_projections_input_path + output_path = os.path.join(p.cur_dir, 'regional_projections_input_pivoted.csv') + p.regional_projections_input_override_path = output_path + + if p.run_this: + + if not hb.path_exists(output_path): + df = hb.df_read(input_path) + + # Step 1: Melt the DataFrame to convert year columns into rows. + + # Get the list of columns to unpivot (years) + years_to_unpivot = [col for col in df.columns if col.isdigit()] + # years_to_unpivot = [] + melted = df.melt( + id_vars=[p.regions_column_label, 'LandCover'], # Assumes the land cover column is named 'LandCover' + value_vars=years_to_unpivot, + var_name='year', + value_name='value' + ) + + # Step 2: Pivot the melted DataFrame. + # We set the region and year as the new index, and create new columns from 'LandCover' categories. + merged_pivoted = melted.pivot_table( + index=[p.regions_column_label, 'year'], + columns='LandCover', + values='value' + ).reset_index() + + + # Now add nuts_id + merged_pivoted['nuts_id'], unique_countries = pd.factorize(merged_pivoted[p.regions_column_label]) + + # Write a new file in the task dir and reassign the project attribute to the new csv + hb.df_write(merged_pivoted, output_path) + + +def build_bonn_task_tree(p): + + # Define the project AOI + p.project_aoi_task = p.add_task(seals_tasks.project_aoi) + p.convert_cgebox_output_to_seals_regional_projections_input_task = p.add_task(convert_cgebox_output_to_seals_regional_projections_input) + + + ##### FINE PROCESSED INPUTS ##### + p.fine_processed_inputs_task = p.add_task(seals_generate_base_data.fine_processed_inputs) + p.generated_kernels_task = p.add_task(seals_generate_base_data.generated_kernels, parent=p.fine_processed_inputs_task, creates_dir=False) + p.lulc_clip_task = p.add_task(seals_generate_base_data.lulc_clip, parent=p.fine_processed_inputs_task, creates_dir=False) + p.lulc_simplifications_task = p.add_task(seals_generate_base_data.lulc_simplifications, parent=p.fine_processed_inputs_task, creates_dir=False) + p.lulc_binaries_task = p.add_task(seals_generate_base_data.lulc_binaries, parent=p.fine_processed_inputs_task, creates_dir=False) + p.lulc_convolutions_task = p.add_task(seals_generate_base_data.lulc_convolutions, parent=p.fine_processed_inputs_task, creates_dir=False) + + ##### COARSE CHANGE ##### + p.coarse_change_task = p.add_task(seals_process_coarse_timeseries.coarse_change, skip_existing=0) + p.extraction_task = p.add_task(seals_process_coarse_timeseries.coarse_extraction, parent=p.coarse_change_task, run=1, skip_existing=0) + p.coarse_simplified_task = p.add_task(seals_process_coarse_timeseries.coarse_simplified_proportion, parent=p.coarse_change_task, skip_existing=0) + p.coarse_simplified_ha_task = p.add_task(seals_process_coarse_timeseries.coarse_simplified_ha, parent=p.coarse_change_task, skip_existing=0) + p.coarse_simplified_ha_difference_from_previous_year_task = p.add_task(seals_process_coarse_timeseries.coarse_simplified_ha_difference_from_previous_year, parent=p.coarse_change_task, skip_existing=0) + + ##### REGIONAL + p.regional_change_task = p.add_task(seals_process_coarse_timeseries.regional_change) + + ##### ALLOCATION ##### + p.allocations_task = p.add_iterator(seals_main.allocations, skip_existing=0) + p.allocation_zones_task = p.add_iterator(seals_main.allocation_zones, run_in_parallel=p.run_in_parallel, parent=p.allocations_task, skip_existing=0) + p.allocation_task = p.add_task(seals_main.allocation, parent=p.allocation_zones_task, skip_existing=0) + + ##### STITCH ZONES ##### + p.stitched_lulc_simplified_scenarios_task = p.add_task(seals_main.stitched_lulc_simplified_scenarios) + + ##### VIZUALIZE EXISTING DATA ##### + p.visualization_task = p.add_task(seals_visualization_tasks.visualization) + p.lulc_pngs_task = p.add_task(seals_visualization_tasks.lulc_pngs, parent=p.visualization_task) + + + + +main = '' +if __name__ == '__main__': + + ### ------- ENVIRONMENT SETTINGS ------------------------------- + # Users should only need to edit lines in this section + + # Create a ProjectFlow Object to organize directories and enable parallel processing. + p = hb.ProjectFlow() + + # Assign project-level attributes to the p object (such as in p.base_data_dir = ... below) + # including where the project_dir and base_data are located. + # The project_name is used to name the project directory below. If the directory exists, each task will not recreate + # files that already exist. + p.user_dir = os.path.expanduser('~') + p.extra_dirs = ['Files', 'seals', 'projects'] + p.project_name = 'bonn_esm_cgebox_seals' + # p.project_name = p.project_name + '_' + hb.pretty_time() # If don't you want to recreate everything each time, comment out this line. + + # Based on the paths above, set the project_dir. All files will be created in this directory. + p.project_dir = os.path.join(p.user_dir, os.sep.join(p.extra_dirs), p.project_name) + p.set_project_dir(p.project_dir) + + p.run_in_parallel = 1 # Must be set before building the task tree if the task tree has parralel iterator tasks. + + # Build the task tree via a building function and assign it to p. IF YOU WANT TO LOOK AT THE MODEL LOGIC, INSPECT THIS FUNCTION + build_bonn_task_tree(p) + + # Set the base data dir. The model will check here to see if it has everything it needs to run. + # If anything is missing, it will download it. You can use the same base_data dir across multiple projects. + # Additionally, if you're clever, you can move files generated in your tasks to the right base_data_dir + # directory so that they are available for future projects and avoids redundant processing. + # The final directory has to be named base_data to match the naming convention on the google cloud bucket. + p.base_data_dir = os.path.join(p.user_dir, 'Files/base_data') + + # ProjectFlow downloads all files automatically via the p.get_path() function. If you want it to download from a different + # bucket than default, provide the name and credentials here. Otherwise uses default public data 'gtap_invest_seals_2023_04_21'. + p.data_credentials_path = None + p.input_bucket_name = None + + ## Set defaults and generate the scenario_definitions.csv if it doesn't exist. + # SEALS will run based on the scenarios defined in a scenario_definitions.csv + # If you have not run SEALS before, SEALS will generate it in your project's input_dir. + # A useful way to get started is to to run SEALS on the test data without modification + # and then edit the scenario_definitions.csv to your project needs. + p.scenario_definitions_filename = 'standard_scenarios.csv' + p.scenario_definitions_path = os.path.join(p.input_dir, p.scenario_definitions_filename) + seals_initialize_project.initialize_scenario_definitions(p) + + # Set processing resolution: determines how large of a chunk should be processed at a time. 4 deg is about max for 64gb memory systems + p.processing_resolution = 1.0 # In degrees. Must be in pyramid_compatible_resolutions + + seals_initialize_project.set_advanced_options(p) + + p.L = hb.get_logger('test_run_seals') + hb.log('Created ProjectFlow object at ' + p.project_dir + '\n from script ' + p.calling_script + '\n with base_data set at ' + p.base_data_dir) + + p.execute() + + result = 'Done!' + diff --git a/seals/seals_process_coarse_timeseries.py b/seals/seals_process_coarse_timeseries.py index 14a0d3d..13ab182 100644 --- a/seals/seals_process_coarse_timeseries.py +++ b/seals/seals_process_coarse_timeseries.py @@ -60,6 +60,10 @@ def regional_change(p): scenario_label = p.scenario_label output_filename_end = '_' + str(year) + '_' + str(previous_year) + '_ha_diff_' + p.exogenous_label + '_' + p.climate_label + '_' + p.model_label + '_' + p.counterfactual_label + '_regional_coarsified.tif' regions_column_label = p.regions_column_label + + if getattr(p, 'regional_projections_input_override_path', None): + regional_change_classes_path = p.regional_projections_input_override_path + # TODOO I should have iterated on class label here, then called the util file only on that one seals_utils.convert_regional_change_to_coarse(regional_change_vector_path, regional_change_classes_path, diff --git a/seals/seals_tasks.py b/seals/seals_tasks.py index e1fa90e..a47f0b0 100644 --- a/seals/seals_tasks.py +++ b/seals/seals_tasks.py @@ -35,11 +35,22 @@ def project_aoi(p): if not p.regions_column_label in gdf.columns: raise ValueError(f"The column '{p.regions_column_label}' is not found in the regions vector file: {p.regions_vector_path}. Please check the column name or the vector file.") - p.aoi_path = os.path.join(p.cur_dir, 'aoi_' + str(p.aoi) + '.gpkg') - p.aoi_label = p.aoi - - filter_column = p.regions_column_label # if it's exactly 3 characters, assume it's an ISO3 code. - filter_value = p.aoi + + if p.aoi == 'from_regional_projections_input_path': + # Read the csv to get the unique AOI values + df = hb.df_read(p.regional_projections_input_path) + unique_aois = list(df[p.regions_column_label].unique()) + filter_column = p.regions_column_label + filter_value = unique_aois + p.aoi_path = os.path.join(p.cur_dir, 'aoi_' + str(p.aoi) + '.gpkg') + p.aoi_label = p.aoi + else: + + p.aoi_path = os.path.join(p.cur_dir, 'aoi_' + str(p.aoi) + '.gpkg') + p.aoi_label = p.aoi + + filter_column = p.regions_column_label # if it's exactly 3 characters, assume it's an ISO3 code. + filter_value = p.aoi for current_aoi_path in hb.list_filtered_paths_nonrecursively(p.cur_dir, include_strings='aoi'): From 6efdadd93de06ad4a352ff73ecf0b8f02077c1ef Mon Sep 17 00:00:00 2001 From: Matt Braaksma Date: Wed, 3 Dec 2025 10:01:46 -0600 Subject: [PATCH 6/9] Add html report to visualize seals processing --- seals/seals_initialize_project.py | 1 + seals/seals_visualization_tasks.py | 868 +++++++++++++++++++++++++++++ 2 files changed, 869 insertions(+) diff --git a/seals/seals_initialize_project.py b/seals/seals_initialize_project.py index 9ac2bb5..8020494 100644 --- a/seals/seals_initialize_project.py +++ b/seals/seals_initialize_project.py @@ -213,6 +213,7 @@ def build_standard_task_tree(p): ##### VIZUALIZE EXISTING DATA ##### p.visualization_task = p.add_task(seals_visualization_tasks.visualization) p.lulc_pngs_task = p.add_task(seals_visualization_tasks.lulc_pngs, parent=p.visualization_task) + p.html_report_task = p.add_task(seals_visualization_tasks.html_report, parent=p.visualization_task) def build_ken_task_tree(p): diff --git a/seals/seals_visualization_tasks.py b/seals/seals_visualization_tasks.py index 56d24fd..764b3f6 100644 --- a/seals/seals_visualization_tasks.py +++ b/seals/seals_visualization_tasks.py @@ -4,6 +4,14 @@ import hazelbean as hb import os import pandas as pd +import geopandas as gpd +from matplotlib.patches import Patch +import contextily as ctx +import rasterio +from rasterio.plot import show +from html import escape as _esc +from datetime import datetime +import getpass, platform # import seals from seals import seals_utils @@ -918,3 +926,863 @@ def simpler_plot_generation(p): show_overall_lulc_fit(lulc_baseline_af.data, lulc_observed_af.data, projected_lulc_af.data, overall_similarity_plot_af.data, output_path, title='Overall LULC and fit') overall_similarity_plot_af = None + + +def html_report(p): + # Generate a HTML report with the generated PNGs. + if p.run_this: + if p.scenario_definitions_path is not None: + p.scenarios_df = pd.read_csv(p.scenario_definitions_path) + + report_assets_dir = os.path.join(p.cur_dir, 'assets') + os.makedirs(report_assets_dir, exist_ok=True) + report_output_path = os.path.join(p.cur_dir, 'seals_visualization_report.html') + + # Add summary table + scenarios_table_rows = [] + for index, row in p.scenarios_df.iterrows(): + seals_utils.assign_df_row_to_object_attributes(p, row) + seals_utils.set_derived_attributes(p) + aoi_val = getattr(p, 'aoi', '') or getattr(p, 'aoi_path', '') + years_val = ','.join([str(y) for y in getattr(p, 'years', [])]) if getattr(p, 'years', None) is not None else '' + scenarios_table_rows.append({ + 'index': index, + 'scenario_label': getattr(p, 'scenario_label', '') or f"{getattr(p,'exogenous_label','')}_{getattr(p,'climate_label','')}_{getattr(p,'model_label','')}_{getattr(p,'counterfactual_label','')}", + 'scenario_type': getattr(p, 'scenario_type', ''), + 'aoi': aoi_val, + 'exogenous_label': getattr(p, 'exogenous_label', ''), + 'climate_label': getattr(p, 'climate_label', ''), + 'model_label': getattr(p, 'model_label', ''), + 'counterfactual_label': getattr(p, 'counterfactual_label', ''), + 'years': years_val + }) + + # render table HTML (simple, small) + scenarios_table_html = ['

Scenarios

'] + # header + headers = ['#', 'scenario_label', 'scenario_type', 'aoi', 'exogenous', 'climate', 'model', 'counterfactual', 'years'] + ths = ''.join([f'' for h in headers]) + scenarios_table_html.append(f'{ths}') + # rows + for r in scenarios_table_rows: + tds = ''.join([ + f'' + for k in ['index', 'scenario_label', 'scenario_type', 'aoi', 'exogenous_label', 'climate_label', 'model_label', 'counterfactual_label', 'years'] + ]) + scenarios_table_html.append(f'{tds}') + scenarios_table_html.append('
{_esc(h)}
{_esc(str(r.get(k, "")))}
') + scenarios_table_html = '\n'.join(scenarios_table_html) + + # === AOI SECTION === + fig, axes = plt.subplots(1, 3, figsize=(15, 5)) + for ax in axes.flat: + ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, + labelbottom=False, labelleft=False) + # Plot 1: Vector AOI + aoi = gpd.read_file(p.aoi_path) + aoi.plot(ax=axes[0], facecolor='lightblue', edgecolor='black', linewidth=0.5, alpha=0.5) + axes[0].set_title('AOI Boundary') + # Zoom to AOI bounds before adding basemap + minx, miny, maxx, maxy = p.bb + axes[0].set_xlim(minx, maxx) + axes[0].set_ylim(miny, maxy) + ctx.add_basemap(axes[0], source=ctx.providers.OpenStreetMap.Mapnik, crs='EPSG:4326', alpha=0.7, attribution=False) + # Plot 2: Fine resolution raster + with rasterio.open(p.aoi_ha_per_cell_fine_path) as src: + show(src, ax=axes[1], cmap='viridis') + axes[1].set_title('Fine Resolution (ha per cell)') + # Plot 3: Coarse resolution raster + with rasterio.open(p.aoi_ha_per_cell_coarse_path) as src: + show(src, ax=axes[2], cmap='viridis') + axes[2].set_title('Coarse Resolution (ha per cell)') + plt.tight_layout() + plt.savefig(os.path.join(report_assets_dir, 'aoi_plots.png')) + plt.close() + + # === FINE PROCESSED INPUTS SECTION === + # LULC colors (including 0 = nodata at top) + lulc_colors = [ + (1, 1, 1), # 0 = nodata + (212/255,106/255,110/255), # 1 urban + (227/255,167/255,92/255), # 2 cropland + (232/255,232/255,106/255), # 3 grassland + (79/255,169/255,90/255), # 4 forest + (159/255,218/255,143/255), # 5 othernat + (144/255,174/255,224/255), # 6 water + (209/255,209/255,209/255), # 7 other + ] + lulc_cmap = colors.ListedColormap(lulc_colors) + + # Forces clean discrete boundaries + bounds = [i - 0.5 for i in range(9)] # -0.5 → 7.5 + lulc_norm = colors.BoundaryNorm(bounds, lulc_cmap.N) + + fig, axes = plt.subplots(2, 2, figsize=(12, 10)) + for ax in axes.flat: + ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, + labelbottom=False, labelleft=False) + + # Plot 1: Simplified LULC + lulc_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, f'lulc_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}.tif') + with rasterio.open(lulc_path) as src: + show( + src, + ax=axes[0,0], + cmap=lulc_cmap, + norm=lulc_norm + ) + axes[0,0].set_title('SEALS7 Classification (Base Year)') + legend_labels = [ + 'urban', + 'cropland', + 'grassland', + 'forest', + 'othernat', + 'water', + 'other' + ] + # 1–7 only + patches = [ + plt.Rectangle((0,0), 1, 1, fc=lulc_colors[i]) + for i in range(1, 8) + ] + axes[0,0].legend( + patches, + legend_labels, + loc='lower left', + fontsize=8, + title="LULC Classes" + ) + + # Plot 2: Binary cropland + binary_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, 'binaries', str(p.base_years[0]), f'binary_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}_cropland.tif') + with rasterio.open(binary_path) as src: + show(src, ax=axes[0,1], cmap='YlGn') + axes[0,1].set_title('Binary Cropland Mask') + + # Plot 3: Gaussian 1 convolution + conv1_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, 'convolutions', str(p.base_years[0]), f'convolution_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}_cropland_gaussian_1.tif') + with rasterio.open(conv1_path) as src: + show(src, ax=axes[1,0], cmap='YlGn') + axes[1,0].set_title('Cropland Convolution (Gaussian σ=1)') + + # Plot 4: Gaussian 5 convolution + conv5_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, 'convolutions', str(p.base_years[0]), f'convolution_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}_cropland_gaussian_5.tif') + with rasterio.open(conv5_path) as src: + show(src, ax=axes[1,1], cmap='YlGn') + axes[1,1].set_title('Cropland Convolution (Gaussian σ=5)') + + plt.tight_layout() + plt.savefig(os.path.join(report_assets_dir, 'fine_processed_inputs_plots.png')) + plt.close() + + # Plot region IDs (once, before scenarios) + region_ids_path = os.path.join(p.regional_change_dir, 'region_ids.tif') + if os.path.exists(region_ids_path): + fig, ax = plt.subplots(1, 1, figsize=(10, 8)) + + with rasterio.open(region_ids_path) as src: + data = src.read(1) + # Mask out nodata values + if src.nodata is not None: + data_masked = np.ma.masked_equal(data, src.nodata) + else: + data_masked = np.ma.masked_equal(data, 0) + + # Get unique region IDs (excluding masked values) + unique_ids = np.unique(data_masked.compressed()) + n_regions = len(unique_ids) + + # Create discrete colormap for actual regions + cmap = plt.cm.get_cmap('tab20', n_regions) + + # Get extent for basemap + bounds = src.bounds + extent = [bounds.left, bounds.right, bounds.bottom, bounds.top] + + # Add basemap first + ax.set_xlim(bounds.left, bounds.right) + ax.set_ylim(bounds.bottom, bounds.top) + ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik, + crs='EPSG:4326', alpha=0.7, attribution=False) + + # Plot region IDs on top with transparency + im = ax.imshow(data_masked, cmap=cmap, interpolation='nearest', + extent=extent, alpha=0.5, vmin=unique_ids.min(), + vmax=unique_ids.max()) + + ax.set_title('Region IDs', fontsize=12) + ax.axis('off') + + # Create categorical legend + colors_list = [cmap(i) for i in range(n_regions)] + patches = [Patch(facecolor=colors_list[i % len(colors_list)], edgecolor='k', label=str(int(uid))) + for i, uid in enumerate(unique_ids)] + ax.legend(handles=patches, title='Region ID', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=8) + + plt.tight_layout() + plt.savefig(os.path.join(report_assets_dir, 'region_ids.png'), dpi=150, bbox_inches='tight') + plt.close() + region_ids_html = """ +

Region IDs

+
+ Region IDs for regional analysis +
+""" + else: + region_ids_html = "" + + # Prepare collections for coarse and regional sections + coarse_scenario_tabs = [] + regional_scenario_tabs = [] + stitched_scenario_tabs = [] + scenario_list = [] + + # Define LULC classes to plot + lulc_classes = ['cropland', 'urban', 'grassland', 'forest', 'othernat'] + + # Loop through scenarios to generate plots + for index, row in p.scenarios_df.iterrows(): + seals_utils.assign_df_row_to_object_attributes(p, row) + seals_utils.set_derived_attributes(p) + + if p.scenario_type == 'baseline': + continue # Skip baseline for this report + + scenario_name = f"{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}" + scenario_id = f"scenario_{index}" + scenario_list.append((scenario_id, scenario_name)) + + # === COARSE CHANGE SECTION === + coarse_content = "" + + for yi, year in enumerate(p.years): + # for the first year in p.years use the configured base year (p.base_years[0]) + # otherwise use the previous entry in p.years + if yi == 0: + prev_year = p.base_years[0] + else: + prev_year = p.years[yi - 1] + + # Create figure for this year's coarse changes + n_classes = len(lulc_classes) + fig, axes = plt.subplots(1, n_classes, figsize=(4 * n_classes, 4)) + fig.suptitle(f'Coarse Change: {year} (from {prev_year})', fontsize=14) + + for idx, lulc_class in enumerate(lulc_classes): + # Build path to coarse change file + change_fname = f'{lulc_class}_{year}_{prev_year}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}.tif' + change_path = os.path.join( + p.coarse_change_dir, + 'coarse_simplified_ha_difference_from_previous_year', + p.exogenous_label, + p.climate_label, + p.model_label, + p.counterfactual_label, + str(year), + change_fname + ) + + # Plot if file exists + if os.path.exists(change_path): + with rasterio.open(change_path) as src: + data = src.read(1) + # Use diverging colormap for change (red=loss, green=gain) + vmax = np.nanmax(np.abs(data)) + im = axes[idx].imshow(data, cmap='RdYlGn', vmin=-vmax, vmax=vmax) + axes[idx].set_title(f'{lulc_class.capitalize()}', fontsize=10) + axes[idx].axis('off') + plt.colorbar(im, ax=axes[idx], fraction=0.046, pad=0.04, label='ha change') + else: + axes[idx].text(0.5, 0.5, f'{lulc_class}\nNot Found', + ha='center', va='center', transform=axes[idx].transAxes) + axes[idx].axis('off') + + plt.tight_layout() + + # Save figure + output_filename = f'coarse_change_{scenario_name}_{year}.png' + plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') + plt.close() + + # Add to coarse content + coarse_content += f""" +

Year {year}

+
+ Coarse change for {year} +
+""" + + coarse_scenario_tabs.append((scenario_id, coarse_content)) + + # === REGIONAL CHANGE SECTION === + # Region ID plotted earlier + regional_content = "" + + # Check if regional change exists for this scenario + has_regional_change = False + test_year = p.years[0] + test_class = lulc_classes[0] + test_path = os.path.join( + p.regional_change_dir, + p.exogenous_label, + p.climate_label, + p.model_label, + p.counterfactual_label, + str(test_year), + f'{test_class}_{test_year}_{p.base_years[0]}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_regional_coarsified.tif' + ) + has_regional_change = os.path.exists(test_path) + + if not has_regional_change: + regional_content = """ +

No regional change data available for this scenario.

+""" + else: + for yi, year in enumerate(p.years): + if yi == 0: + prev_year = p.base_years[0] + else: + prev_year = p.years[yi - 1] + + # Create 5-panel figure for regional coarsified + n_classes = len(lulc_classes) + fig, axes = plt.subplots(1, n_classes, figsize=(4 * n_classes, 4)) + fig.suptitle(f'Regional Coarsified: {year} (from {prev_year})', fontsize=14) + + for idx, lulc_class in enumerate(lulc_classes): + regional_coarsified_path = os.path.join( + p.regional_change_dir, + p.exogenous_label, + p.climate_label, + p.model_label, + p.counterfactual_label, + str(year), + f'{lulc_class}_{year}_{prev_year}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_regional_coarsified.tif' + ) + + if os.path.exists(regional_coarsified_path): + with rasterio.open(regional_coarsified_path) as src: + data = src.read(1) + vmax = np.nanmax(np.abs(data)) + im = axes[idx].imshow(data, cmap='RdYlGn', vmin=-vmax, vmax=vmax) + axes[idx].set_title(f'{lulc_class.capitalize()}', fontsize=10) + axes[idx].axis('off') + plt.colorbar(im, ax=axes[idx], fraction=0.046, pad=0.04, label='ha change') + else: + axes[idx].text(0.5, 0.5, f'{lulc_class}\nNot Found', + ha='center', va='center', transform=axes[idx].transAxes) + axes[idx].axis('off') + + plt.tight_layout() + output_filename = f'regional_coarsified_{scenario_name}_{year}.png' + plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') + plt.close() + + regional_content += f""" +

Year {year} - Regional Coarsified

+
+ Regional coarsified for {year} +
+""" + + # Create 5-panel figure for covariate sum shift + fig, axes = plt.subplots(1, n_classes, figsize=(4 * n_classes, 4)) + fig.suptitle(f'Covariate Sum Shift: {year} (from {prev_year})', fontsize=14) + + for idx, lulc_class in enumerate(lulc_classes): + covariate_shift_path = os.path.join( + p.regional_change_dir, + p.exogenous_label, + p.climate_label, + p.model_label, + p.counterfactual_label, + str(year), + f'{lulc_class}_{year}_{prev_year}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_covariate_sum_shift.tif' + ) + + if os.path.exists(covariate_shift_path): + with rasterio.open(covariate_shift_path) as src: + data = src.read(1) + vmax = np.nanmax(np.abs(data)) + im = axes[idx].imshow(data, cmap='RdYlGn', vmin=-vmax, vmax=vmax) + axes[idx].set_title(f'{lulc_class.capitalize()}', fontsize=10) + axes[idx].axis('off') + plt.colorbar(im, ax=axes[idx], fraction=0.046, pad=0.04, label='ha change') + else: + axes[idx].text(0.5, 0.5, f'{lulc_class}\nNot Found', + ha='center', va='center', transform=axes[idx].transAxes) + axes[idx].axis('off') + + plt.tight_layout() + output_filename = f'covariate_shift_{scenario_name}_{year}.png' + plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') + plt.close() + + regional_content += f""" +

Year {year} - Covariate Sum Shift

+
+ Covariate sum shift for {year} +
+""" + + regional_scenario_tabs.append((scenario_id, regional_content)) + + # === STITCHED LULC SECTION === + stitched_content = "" + + # Use the same LULC colormap as before + lulc_colors_stitched = [ + (1, 1, 1), # 0 = nodata + (212/255,106/255,110/255), # 1 urban + (227/255,167/255,92/255), # 2 cropland + (232/255,232/255,106/255), # 3 grassland + (79/255,169/255,90/255), # 4 forest + (159/255,218/255,143/255), # 5 othernat + (144/255,174/255,224/255), # 6 water + (209/255,209/255,209/255), # 7 other + ] + lulc_cmap_stitched = colors.ListedColormap(lulc_colors_stitched) + bounds_stitched = [i - 0.5 for i in range(9)] + lulc_norm_stitched = colors.BoundaryNorm(bounds_stitched, lulc_cmap_stitched.N) + + for year in p.years: + # Create 2-panel figure (full and clipped) + fig, axes = plt.subplots(1, 2, figsize=(16, 7)) + + # Full stitched LULC + stitched_path = os.path.join( + p.stitched_lulc_simplified_scenarios_dir, + f'lulc_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_{year}.tif' + ) + + if os.path.exists(stitched_path): + with rasterio.open(stitched_path) as src: + show(src, ax=axes[0], cmap=lulc_cmap_stitched, norm=lulc_norm_stitched) + axes[0].set_title(f'Stitched LULC {year}', fontsize=12) + axes[0].axis('off') + else: + axes[0].text(0.5, 0.5, f'Stitched LULC\n{year}\nNot Found', + ha='center', va='center', transform=axes[0].transAxes) + axes[0].axis('off') + + # Clipped stitched LULC with basemap + clipped_path = os.path.join( + p.stitched_lulc_simplified_scenarios_dir, + f'lulc_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_{year}_clipped.tif' + ) + + if os.path.exists(clipped_path): + with rasterio.open(clipped_path) as src: + # Get extent for basemap + bounds = src.bounds + extent = [bounds.left, bounds.right, bounds.bottom, bounds.top] + + # Add basemap first + axes[1].set_xlim(bounds.left, bounds.right) + axes[1].set_ylim(bounds.bottom, bounds.top) + ctx.add_basemap(axes[1], source=ctx.providers.OpenStreetMap.Mapnik, + crs='EPSG:4326', alpha=0.7, attribution=False) + + # Read and plot LULC data with transparency + data = src.read(1) + # Mask nodata + if src.nodata is not None: + data_masked = np.ma.masked_equal(data, src.nodata) + else: + data_masked = np.ma.masked_equal(data, 0) + + axes[1].imshow(data_masked, cmap=lulc_cmap_stitched, norm=lulc_norm_stitched, + extent=extent, alpha=0.7, interpolation='nearest') + axes[1].set_title(f'Clipped LULC {year}', fontsize=12) + axes[1].axis('off') + else: + axes[1].text(0.5, 0.5, f'Clipped LULC\n{year}\nNot Found', + ha='center', va='center', transform=axes[1].transAxes) + axes[1].axis('off') + + # Add legend to the clipped plot + legend_labels = ['urban', 'cropland', 'grassland', 'forest', 'othernat', 'water', 'other'] + patches = [plt.Rectangle((0,0), 1, 1, fc=lulc_colors_stitched[i]) for i in range(1, 8)] + axes[1].legend(patches, legend_labels, loc='center left', bbox_to_anchor=(1, 0.5), fontsize=10, title="LULC Classes") + + plt.tight_layout() + + # Save figure + output_filename = f'stitched_lulc_{scenario_name}_{year}.png' + plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') + plt.close() + + # Add to stitched content + stitched_content += f""" +

Year {year}

+
+ Stitched LULC for {year} +
+""" + + stitched_scenario_tabs.append((scenario_id, stitched_content)) + + # Build tab buttons and content for coarse section + coarse_tabs_buttons = "" + coarse_tabs_content = "" + for i, (scenario_id, scenario_name) in enumerate(scenario_list): + active_class = "active" if i == 0 else "" + coarse_tabs_buttons += f'\n' + + display_style = "block" if i == 0 else "none" + coarse_tabs_content += f""" +
+{coarse_scenario_tabs[i][1]} +
+""" + + # Build tab buttons and content for regional section + regional_tabs_buttons = "" + regional_tabs_content = "" + for i, (scenario_id, scenario_name) in enumerate(scenario_list): + active_class = "active" if i == 0 else "" + regional_tabs_buttons += f'\n' + + display_style = "block" if i == 0 else "none" + regional_tabs_content += f""" +
+{regional_scenario_tabs[i][1]} +
+""" + + # Build tab buttons and content for stitched LULC section + stitched_tabs_buttons = "" + stitched_tabs_content = "" + for i, (scenario_id, scenario_name) in enumerate(scenario_list): + active_class = "active" if i == 0 else "" + stitched_tabs_buttons += f'\n' + + display_style = "block" if i == 0 else "none" + stitched_tabs_content += f""" +
+{stitched_scenario_tabs[i][1]} +
+""" + + # Function to convert image to base64 + import base64 + def image_to_base64(image_path): + """Convert image file to base64 data URI""" + if os.path.exists(image_path): + with open(image_path, 'rb') as img_file: + img_data = base64.b64encode(img_file.read()).decode('utf-8') + return f'data:image/png;base64,{img_data}' + return '' + + # Collect all image paths and convert to base64 + image_embeds = {} + + # Static images + for img_name in ['aoi_plots.png', 'fine_processed_inputs_plots.png', 'region_ids.png']: + img_path = os.path.join(report_assets_dir, img_name) + image_embeds[img_name] = image_to_base64(img_path) + + # Dynamic scenario images + for filename in os.listdir(report_assets_dir): + if filename.endswith('.png') and filename not in image_embeds: + img_path = os.path.join(report_assets_dir, filename) + image_embeds[filename] = image_to_base64(img_path) + + # Replace all asset references with base64 data + def embed_images_in_html(html_content, image_dict): + """Replace src="assets/..." with base64 data URIs""" + for filename, base64_data in image_dict.items(): + if base64_data: # Only replace if image exists + html_content = html_content.replace(f'src="assets/{filename}"', f'src="{base64_data}"') + return html_content + + # Generate HTML report + html_template = """ + + + + + SEALS Visualization Report + + + + +

SEALS Visualization Report

+

Generated: {date}, User: {user}, OS: {system}

+ {scenarios_table} + +

Area of Interest (AOI)

+
+

Purpose:

+

Defines the geographic boundary for the analysis and creates resolution pyramids for efficient processing at multiple scales.

+
    +
  • Loads the area of interest boundary from the input shapefile
    • +
  • Creates a vector geopackage (.gpkg) file for the AOI
    • +
  • Generates raster "pyramids" at different resolutions: +
      +
    • Fine resolution: Matches the base LULC data (typically 300m)
      • +
    • Coarse resolution: Matches the coarse projection data (typically 0.25°)
      • +
    +
    • +
+

Key outputs:

+ 
intermediate/project_aoi/
+├── aoi_RWA.gpkg                           # Vector boundary
+└── pyramids/
+    ├── aoi_ha_per_cell_coarse.tif    # Coarse resolution
+    └── aoi_ha_per_cell_fine.tif      # Fine resolution
+
+
+ AOI plots showing boundary, fine resolution, and coarse resolution +
+ +

Fine Processed Inputs

+
+

Purpose:

+

Prepares the baseline land-use/land-cover (LULC) data for allocation by simplifying classifications, creating binary masks, and generating spatial convolutions.Binary masks and convolutions inform the allocation model by capturing spatial patterns and neighborhood characteristics.

+
    +
  • Generated Kernels: Creates Gaussian kernels for spatial smoothing
    • +
  • LULC Simplifications: Reclassifies detailed ESA categories into 7 SEALS classes: +
      +
    • Cropland, Forest, Grassland, Urban, Water, Other natural, Other
      • +
    +
    • +
  • LULC Binaries: Creates separate binary masks for each class (1 = present, 0 = absent)
    • +
  • LULC Convolutions: Applies Gaussian smoothing to capture neighborhood characteristics
    • +
+

Key outputs:

+ 
intermediate/fine_processed_inputs/lulc/esa/seals7/
+├── lulc_esa_seals7_2017.tif              # Simplified 7-class LULC
+├── binaries/2017/                         # Binary masks by class
+│   ├── binary_esa_seals7_2017_cropland.tif
+│   └── ...
+└── convolutions/2017/                     # Smoothed neighborhood data
+    ├── convolution_esa_seals7_2017_cropland_gaussian_1.tif
+    └── ...
+
+
+ Fine processed inputs showing LULC classification and convolutions +
+ +

Coarse Change

+
+

Purpose:

+

Extracts and processes land-use change projections from global coarse-resolution models (e.g., LUH2) for the study region. Links exogenous global scenarios (SSP, RCP) to local allocation by quantifying how much of each land class needs to change.

+
    +
  • Coarse Extraction: Clips global projection data to the region's bounding box
    • +
  • Coarse Simplified Proportion: Calculates proportions of each SEALS7 class
    • +
  • Coarse Simplified Ha: Converts proportions to hectares
    • +
  • Ha Difference: Calculates change in hectares between time periods (e.g., 2017 → 2030 → 2050)
    • +
+

Key outputs:

+ 
intermediate/coarse_change/coarse_simplified_ha_difference_from_previous_year/
+└── ssp2/rcp45/luh2-message/bau/2030/
+    ├── cropland_2030_2017_ha_diff_ssp2_rcp45_luh2-message_bau.tif
+    ├── forest_2030_2017_ha_diff_ssp2_rcp45_luh2-message_bau.tif
+    └── ...
+
+
+
+{coarse_tabs_buttons} +
+{coarse_tabs_content} +
+ +

Regional Change

+
+

Purpose:

+

Adjusts coarse-resolution changes using regional covariates to account for local constraints and opportunities.

+
    +
  • Creates a region ID map dividing AOI into separately processed zones
    • +
  • Applies algorithms (e.g., proportional, covariate sum shift) to downscale from coarse to regional resolution
    • +
  • Regional Coarsified: Aggregated regional change
    • +
  • Covariate Sum Shift: Change adjusted by spatial covariates (infrastructure, topography, current land use)
    • +
+

Key outputs:

+ 
intermediate/regional_change/
+├── region_ids.tif                          # Regional zone identifiers
+└── ssp2/rcp45/luh2-message/bau/2030/
+    ├── cropland_2030_2017_ha_diff_..._regional_coarsified.tif
+    └── cropland_2030_2017_ha_diff_..._covariate_sum_shift.tif
+
+ {region_ids_section} +
+
+{regional_tabs_buttons} +
+{regional_tabs_content} +
+ +

Stitched LULC Scenarios

+
+

Purpose:

+

Combines all allocation zones back into complete region-wide LULC maps for each scenario.

+
    +
  • Stitches together individual allocation blocks
    • +
  • Clips to exact boundary
    • +
  • Generates outputs for all scenario combinations (bau, policy scenarios, etc.)
    • +
  • Creates both full extent and clipped versions
    • +
+

Key outputs:

+ 
intermediate/stitched_lulc_simplified_scenarios/
+├── lulc_esa_seals7_ssp2_rcp45_luh2-message_bau_2030.tif
+├── lulc_esa_seals7_ssp2_rcp45_luh2-message_bau_2050.tif
+└── lulc_esa_seals7_ssp2_rcp45_luh2-message_bau_2030_clipped.tif
+
+
+
+{stitched_tabs_buttons} +
+{stitched_tabs_content} +
+ + +""" + + # Format the HTML with current date and dynamic sections + html_content = html_template.format( + date=datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + user=getpass.getuser(), + system=platform.system(), + scenarios_table=scenarios_table_html, + region_ids_section=region_ids_html, + coarse_tabs_buttons=coarse_tabs_buttons, + coarse_tabs_content=coarse_tabs_content, + regional_tabs_buttons=regional_tabs_buttons, + regional_tabs_content=regional_tabs_content, + stitched_tabs_buttons=stitched_tabs_buttons, + stitched_tabs_content=stitched_tabs_content + ) + + # Embed all images as base64 + html_content = embed_images_in_html(html_content, image_embeds) + + # Write HTML file + with open(report_output_path, 'w') as f: + f.write(html_content) + \ No newline at end of file From e5bed421ddd87e1e6f19a7c1e0afd84787a0aa7b Mon Sep 17 00:00:00 2001 From: Justin Johnson Date: Fri, 5 Dec 2025 09:23:42 -0600 Subject: [PATCH 7/9] bonn done in devstack but not in release script. waiting for final feedback from bonn team. --- seals/run_seals_bonn.py | 105 +++++++++++++++-------- seals/seals_process_coarse_timeseries.py | 21 +++-- seals/seals_utils.py | 6 +- 3 files changed, 87 insertions(+), 45 deletions(-) diff --git a/seals/run_seals_bonn.py b/seals/run_seals_bonn.py index a5d6e66..7dbcf46 100644 --- a/seals/run_seals_bonn.py +++ b/seals/run_seals_bonn.py @@ -4,49 +4,84 @@ import hazelbean as hb import pandas as pd -from seals import seals_generate_base_data, seals_initialize_project, seals_main, seals_process_coarse_timeseries, seals_tasks, seals_visualization_tasks +from seals import seals_generate_base_data, seals_initialize_project, seals_main, seals_process_coarse_timeseries, seals_tasks, seals_utils, seals_visualization_tasks +# TODO +# 1. make regional projections be the change not the total. +# 2. decide how to handle project_dir in the project repo so it uses the git-cloned inputs. +# 3. test the setup.bat and decide if it's dumb to put it in the src (tho with git ignore listed) def convert_cgebox_output_to_seals_regional_projections_input(p): - input_path = p.regional_projections_input_path - output_path = os.path.join(p.cur_dir, 'regional_projections_input_pivoted.csv') - p.regional_projections_input_override_path = output_path + + p.regional_projections_input_override_paths = {} if p.run_this: - - if not hb.path_exists(output_path): - df = hb.df_read(input_path) - - # Step 1: Melt the DataFrame to convert year columns into rows. - - # Get the list of columns to unpivot (years) - years_to_unpivot = [col for col in df.columns if col.isdigit()] - # years_to_unpivot = [] - melted = df.melt( - id_vars=[p.regions_column_label, 'LandCover'], # Assumes the land cover column is named 'LandCover' - value_vars=years_to_unpivot, - var_name='year', - value_name='value' - ) - - # Step 2: Pivot the melted DataFrame. - # We set the region and year as the new index, and create new columns from 'LandCover' categories. - merged_pivoted = melted.pivot_table( - index=[p.regions_column_label, 'year'], - columns='LandCover', - values='value' - ).reset_index() - + for index, row in p.scenarios_df.iterrows(): + seals_utils.assign_df_row_to_object_attributes(p, row) + + if p.scenario_type != 'baseline': + input_path = p.regional_projections_input_path + output_path = os.path.join(p.cur_dir, f'regional_projections_input_pivoted_{p.exogenous_label}.csv') + + # START HERE, the override doesn't work cause it doesn't iterate over years.... use a catear? + p.regional_projections_input_override_paths[p.scenario_label] = output_path + + if not hb.path_exists(output_path): + df = hb.df_read(input_path) + + # Step 1: Melt the DataFrame to convert year columns into rows. + + # Get the list of columns to unpivot (years) + years_to_unpivot = [col for col in df.columns if col.isdigit()] + # years_to_unpivot = [] + melted = df.melt( + id_vars=[p.regions_column_label, 'LandCover'], # Assumes the land cover column is named 'LandCover' + value_vars=years_to_unpivot, + var_name='year', + value_name='value' + ) + + # Step 2: Pivot the melted DataFrame. + # We set the region and year as the new index, and create new columns from 'LandCover' categories. + merged_pivoted = melted.pivot_table( + index=[p.regions_column_label, 'year'], + columns='LandCover', + values='value' + ).reset_index() + + + # Now add nuts_id + merged_pivoted['nuts_id'], unique_countries = pd.factorize(merged_pivoted[p.regions_column_label]) + merged_pivoted['nuts_id'] = merged_pivoted['nuts_id'] + 1 + + # Define the columns for which the year-over-year change should be calculated + land_use_columns = ['cropland', 'forest', 'grassland', 'other', 'othernat', 'urban', 'water'] + + # Sort the DataFrame by 'nuts_label' and 'year' to ensure correct chronological order + df_sorted = merged_pivoted.sort_values(by=['nuts_label', 'year']) + + # Group by 'nuts_label' and calculate the difference for the specified columns + # .diff() calculates the difference from the previous row within each group + # .fillna(0) replaces the initial NaN values with 0 + df_sorted[land_use_columns] = df_sorted.groupby('nuts_label')[land_use_columns].diff().fillna(0) + + # The 'df_sorted' DataFrame now contains the year-over-year change. + # You can display the first few rows of the result for a specific region to verify. + print("Year-over-year changes for CZ03:") + print(df_sorted[df_sorted['nuts_label'] == 'CZ03'].head()) + + # multiply by 1000 because i think cgebox outputs in thousands of ha + for col in land_use_columns: + df_sorted[col] = df_sorted[col] * 1000 + # 2019 2020 2021 2023 2025 2027 2029 2030 2031 2033 2035 2037 2039 2040 2041 2043 2045 2047 2049 2050 + # repeat these numbers + + # Write a new file in the task dir and reassign the project attribute to the new csv + hb.df_write(df_sorted, output_path) + - # Now add nuts_id - merged_pivoted['nuts_id'], unique_countries = pd.factorize(merged_pivoted[p.regions_column_label]) - - # Write a new file in the task dir and reassign the project attribute to the new csv - hb.df_write(merged_pivoted, output_path) - - def build_bonn_task_tree(p): # Define the project AOI diff --git a/seals/seals_process_coarse_timeseries.py b/seals/seals_process_coarse_timeseries.py index 13ab182..03b0233 100644 --- a/seals/seals_process_coarse_timeseries.py +++ b/seals/seals_process_coarse_timeseries.py @@ -61,8 +61,8 @@ def regional_change(p): output_filename_end = '_' + str(year) + '_' + str(previous_year) + '_ha_diff_' + p.exogenous_label + '_' + p.climate_label + '_' + p.model_label + '_' + p.counterfactual_label + '_regional_coarsified.tif' regions_column_label = p.regions_column_label - if getattr(p, 'regional_projections_input_override_path', None): - regional_change_classes_path = p.regional_projections_input_override_path + if getattr(p, 'regional_projections_input_override_paths', None): + regional_change_classes_path = p.regional_projections_input_override_paths[p.scenario_label] # TODOO I should have iterated on class label here, then called the util file only on that one seals_utils.convert_regional_change_to_coarse(regional_change_vector_path, @@ -102,7 +102,7 @@ def regional_change(p): regional_coarsified_raster = hb.as_array(regional_coarsified_path) # covariate_additive - covariate_additive = (current_luc_coarse_projections + regional_coarsified_raster) * 1000.0 + covariate_additive = (current_luc_coarse_projections + regional_coarsified_raster) hb.save_array_as_geotiff(covariate_additive, hb.suri(output_path_template, 'covariate_additive'), current_luc_coarse_projections_path) @@ -152,8 +152,11 @@ def regional_change(p): covariate_sum_shift_path = hb.suri(output_path_template, 'covariate_sum_shift') input = ((regional_coarsified_path, 1), (current_luc_coarse_projections_path, 1), (target_raster_path, 1)) + + print('WARNING! sometimes need to use *1000 here to convert from ha to m2. Check if this is desired behavior. GTAP NEEDS THIS OTHERS DONT') def op(a, b, c): - return (a - (c-b)) * 1000.0 + return (a - (c-b)) + # return (a - (c-b)) * 1000 hb.raster_calculator(input, op, covariate_sum_shift_path, 7, -9999.) #### COVARIATE MULTIPLY SHIFT @@ -230,7 +233,7 @@ def op(a, b, c): covariate_multiply_shift_path = hb.suri(output_path_template, 'covariate_multiply_shift') input = ((current_luc_coarse_projections_path, 1), (covariate_multiply_regional_change_sum_path, 1), (covariate_multiply_regional_change_sum_path, 1)) def op(a, b, c): - return (a * (b/c)) * 1000.0 + return (a * (b/c)) hb.raster_calculator(input, op, covariate_multiply_shift_path, 7, -9999.) # This is the one i want to use so also save it as the template. Can choose from different algorithms above. @@ -238,7 +241,7 @@ def op(a, b, c): hb.path_copy(alg_to_use_path, output_path_template) 5 # Given all of these, copy the one that we want to use to the name without a label - + # 2019 2020 2021 2023 2025 2027 2029 2030 2031 2033 2035 2037 2039 2040 2041 2043 2045 2047 2049 2050 else: @@ -342,7 +345,7 @@ def regional_change_new_test(p): regional_coarsified_raster = hb.as_array(regional_coarsified_path) # covariate_additive - covariate_additive = (current_luc_coarse_projections + regional_coarsified_raster) * 1000.0 + covariate_additive = (current_luc_coarse_projections + regional_coarsified_raster) hb.save_array_as_geotiff(covariate_additive, hb.suri(output_path_template, 'covariate_additive'), current_luc_coarse_projections_path) @@ -393,7 +396,7 @@ def regional_change_new_test(p): covariate_sum_shift_path = hb.suri(output_path_template, 'covariate_sum_shift') input = ((regional_coarsified_path, 1), (current_luc_coarse_projections_path, 1), (target_raster_path, 1)) def op(a, b, c): - return (a - (c-b)) * 1000 + return (a - (c-b)) hb.raster_calculator(input, op, covariate_sum_shift_path, 7, -9999.) #### COVARIATE MULTIPLY SHIFT @@ -469,7 +472,7 @@ def op(a, b, c): covariate_multiply_shift_path = hb.suri(output_path_template, 'covariate_multiply_shift') input = ((current_luc_coarse_projections_path, 1), (covariate_multiply_regional_change_sum_path, 1), (covariate_multiply_regional_change_sum_path, 1)) def op(a, b, c): - return (a * (b/c)) * 1000 + return (a * (b/c)) hb.raster_calculator(input, op, covariate_multiply_shift_path, 7, -9999.) if p.region_to_coarse_algorithm == 'covariate_additive': diff --git a/seals/seals_utils.py b/seals/seals_utils.py index 44ca65b..1e8c1d4 100644 --- a/seals/seals_utils.py +++ b/seals/seals_utils.py @@ -1508,7 +1508,11 @@ def convert_regional_change_to_coarse(regional_change_vector_path, regional_chan if region_ids_raster_path is None: region_ids_raster_path = os.path.join(output_dir, 'region_ids.tif') + + merged_vector_output_path = os.path.join(os.path.split(region_ids_raster_path)[0], 'regional_change_with_ids_' + scenario_label + '.gpkg') + + merged.to_file(merged_vector_output_path, driver='GPKG') ### Rasterize the regions vector to a raster. @@ -1519,7 +1523,7 @@ def convert_regional_change_to_coarse(regional_change_vector_path, regional_chan # TODOO NOTE that here we are not using all_touched. This is a fundamental problem with coarse reclassification. Lots of the polygon will be missed. Ideally, you use all_touched=False for # country-country borders but all_touched=True for country-coastline boarders. Or join with EEZs? - hb.rasterize_to_match(regional_change_vector_path, coarse_ha_per_cell_path, region_ids_raster_path, burn_column_name=regions_column_id, burn_values=None, datatype=13, ndv=0, all_touched=False) + hb.rasterize_to_match(merged_vector_output_path, coarse_ha_per_cell_path, region_ids_raster_path, burn_column_name=regions_column_id, burn_values=None, datatype=13, ndv=0, all_touched=False) ### Get the number of cells per zone. From e9dc2cd0f8740222255189eb322d7111047e7b90 Mon Sep 17 00:00:00 2001 From: Justin Johnson Date: Sun, 7 Dec 2025 17:27:39 -0600 Subject: [PATCH 8/9] Add initial scenarios CSV for Bonn input with baseline and BAU scenarios - Introduced a new CSV file `scenarios.csv` in the Bonn input directory. - Defined two scenarios: `baseline_luh2-message` and `ssp2_rcp45_luh2-message_bau`. - Included relevant parameters such as scenario type, area of interest, climate labels, and input paths. --- seals/input/bonn_input/SSP1.csv | 243 ++++ seals/input/bonn_input/SSP2.csv | 243 ++++ seals/input/bonn_input/SSP3.csv | 243 ++++ seals/input/bonn_input/SSP4.csv | 243 ++++ seals/input/bonn_input/SSP5.csv | 243 ++++ seals/input/bonn_input/scenarios.csv | 3 + seals/run_seals_bonn.py | 105 +- seals/seals_initialize_project.py | 10 +- seals/seals_main.py | 5 +- seals/seals_process_coarse_timeseries.py | 18 +- seals/seals_utils.py | 5 +- seals/seals_visualization_tasks.py | 1564 +++++++++++----------- 12 files changed, 2097 insertions(+), 828 deletions(-) create mode 100644 seals/input/bonn_input/SSP1.csv create mode 100644 seals/input/bonn_input/SSP2.csv create mode 100644 seals/input/bonn_input/SSP3.csv create mode 100644 seals/input/bonn_input/SSP4.csv create mode 100644 seals/input/bonn_input/SSP5.csv create mode 100644 seals/input/bonn_input/scenarios.csv diff --git a/seals/input/bonn_input/SSP1.csv b/seals/input/bonn_input/SSP1.csv new file mode 100644 index 0000000..8b37e46 --- /dev/null +++ b/seals/input/bonn_input/SSP1.csv @@ -0,0 +1,243 @@ +"","nuts_label","LandCover","2017","2019","2020","2021","2023","2025","2027","2029","2030","2031","2033","2035","2037","2039","2040","2041","2043","2045","2047","2049","2050" +"1","CZ03","cropland",390.541935056382,392.406598613809,392.561332936339,392.242231004472,390.159841733171,384.710356624636,376.232933109571,366.874944024223,363.774974662034,361.887563856674,361.312314977787,362.024844511158,362.690176744187,363.693180703337,364.308820338354,364.9667900636,366.712833668761,368.977108861135,371.358607807415,374.10471501366,375.601034936779 +"2","CZ03","forest",647.104114417311,644.280436209375,644.492176322881,645.367921735793,648.137545579223,653.966426773988,663.033885482455,671.295150796053,673.599231155563,674.877702422069,675.640056912748,675.386637877157,674.900611086011,674.07404288199,673.453229578207,672.764181380658,671.29441750046,669.525570703103,667.66771149595,665.506932031959,663.836410898808 +"3","CZ03","grassland",173.468010493361,173.570818576615,173.069329087736,172.361751795824,171.50856117285,171.343683088832,171.960907981454,175.107145409986,176.531634021084,177.807712477319,178.311425875907,178.704063466002,179.520142031957,180.673331950004,181.37152713317,182.06622184113,183.313679710169,184.395622496445,185.17243753764,185.862180593139,186.636508524957 +"4","CZ03","other",14.2307936031706,14.2307085578372,14.2306175201826,14.2305180451336,14.2302823735467,14.2299749610835,14.2294948783793,14.2288967572625,14.2286778430619,14.2284850549964,14.2282384650934,14.2279792635636,14.2276981154209,14.2273716547782,14.2272072559597,14.2270500406382,14.2267142081946,14.2263756250615,14.2260831851638,14.2257986114247,14.2256641092709 +"5","CZ03","othernat",205.061919916509,204.434757075204,203.763404143443,203.029830036968,201.291881105302,199.024882063274,195.484534293015,191.073718320844,189.459345768063,188.037639179791,186.219173452021,184.307703871406,182.234390018092,179.826921249573,178.614569837062,177.45519278836,174.978612110956,172.481746602612,170.32516222993,168.22658647587,167.234706622072 +"6","CZ03","urban",79.3486808928063,80.8322203920326,81.6387704519461,82.5234773193859,84.4278536450699,86.4809495098127,88.8149973594582,91.1774959170796,92.1637066898432,92.9186599368641,94.0467998340599,95.1070397298593,96.1855318716209,97.2640278882494,97.7836865839971,98.279761827685,99.2332765759745,100.153448069294,101.010162541448,101.834236645233,102.226258781552 +"7","CZ03","water",14.2307936031706,14.2307085578372,14.2306175201826,14.2305180451336,14.2302823735467,14.2299749610835,14.2294948783793,14.2288967572625,14.2286778430619,14.2284850549964,14.2282384650934,14.2279792635636,14.2276981154209,14.2273716547782,14.2272072559597,14.2270500406382,14.2267142081946,14.2263756250615,14.2260831851638,14.2257986114247,14.2256641092709 +"8","CZ04","cropland",241.247529181886,242.318590784185,242.203120301475,241.753573179502,239.867417213834,235.178648232965,228.058060576479,220.320169621765,217.888822920875,215.896135687718,214.987185134138,215.026379520803,215.007708080385,215.101173139266,215.217482871658,215.311745308983,215.848898217498,216.850186468287,217.980954665722,219.663333442503,220.548051053506 +"9","CZ04","forest",440.445609114921,438.582058983524,438.597380144275,439.468906983702,442.1523960813,447.330759643875,456.293652930126,465.378941527115,468.323105184149,469.925680035587,471.717692564041,472.856261014895,473.945828295239,474.921378511227,475.229589332514,475.41438440035,475.881073105777,476.202032903906,476.212334116385,476.119386016209,475.618819568341 +"10","CZ04","grassland",67.6227744771883,67.7295827560009,67.3769069514281,67.0146776813113,66.3933399894072,65.7474204218374,65.4901075606586,66.3486914169325,66.8457789792384,67.1745979844622,67.3575668630225,67.518255000572,67.840049264185,68.2486216969705,68.4822818963884,68.7077747548578,69.1176424353431,69.478398940482,69.7269713077531,69.9732971326531,70.2688992793571 +"11","CZ04","other",3.96157178787712,3.9615573989242,3.96155185244963,3.96152994390124,3.96148143724401,3.96143898400306,3.96134059300544,3.96122389225926,3.96117264436451,3.96115714204827,3.96110105593442,3.96103877371556,3.96097453209637,3.96090787605164,3.96087728800114,3.96085160764253,3.96078962313248,3.96072074200347,3.96066165313877,3.96059062316769,3.96056233723292 +"12","CZ04","othernat",141.504429135409,140.998485621744,140.803460810539,140.03311372352,138.327525733203,136.834787148436,133.375168108967,129.271743938824,127.469768485808,126.9246769567,124.952580329598,122.762616623523,120.503756598108,118.160000684945,117.084465457063,116.181494202213,114.001998515747,111.58000431984,109.502325144533,107.004773490671,106.010185119919 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+"239","NL130000","other",4.17402689385742,4.17236758733089,4.1718075373871,4.17136187942557,4.17102471613267,4.17101007439522,4.17139258684086,4.17187865290775,4.17197008144388,4.17195490433719,4.17170956647858,4.17139594940025,4.17106960483697,4.17074581511251,4.17058392894302,4.17042505779164,4.1701291409404,4.16974579776456,4.16926426947757,4.16871893792733,4.16845310798361 +"240","NL130000","othernat",37.9797373291509,31.2810218632091,29.0200681695848,27.2209214586915,25.8597739420761,25.8006648795684,27.3448890522731,29.307165598719,29.6762678346176,29.6149969856547,28.624554001608,27.3584638973011,26.0409922076123,24.7338345485854,24.0802908121425,23.4389188764711,22.2442856026785,20.6967072500617,18.7527499810506,16.5512152854237,15.4780445801127 +"241","NL130000","urban",49.7097547459059,50.5370270392994,50.9955989632542,51.5056632446751,52.6084971475912,53.7059516366739,54.7531814380684,55.7807088142657,56.2883843753474,56.7879346661295,57.7735364313613,58.7676018142183,59.7868443487328,60.813510356719,61.3205581167964,61.8199177883606,62.8061492841908,63.7733101952976,64.6994944134598,65.6045239150494,66.0431243254189 +"242","NL130000","water",4.17402689385742,4.17236758733089,4.1718075373871,4.17136187942557,4.17102471613267,4.17101007439522,4.17139258684086,4.17187865290775,4.17197008144388,4.17195490433719,4.17170956647858,4.17139594940025,4.17106960483697,4.17074581511251,4.17058392894302,4.17042505779164,4.1701291409404,4.16974579776456,4.16926426947757,4.16871893792733,4.16845310798361 diff --git a/seals/input/bonn_input/scenarios.csv b/seals/input/bonn_input/scenarios.csv new file mode 100644 index 0000000..d78c650 --- /dev/null +++ b/seals/input/bonn_input/scenarios.csv @@ -0,0 +1,3 @@ +scenario_label,scenario_type,aoi,exogenous_label,climate_label,model_label,counterfactual_label,years,baseline_reference_label,base_years,key_base_year,comparison_counterfactual_labels,time_dim_adjustment,coarse_projections_input_path,lulc_src_label,lulc_simplification_label,lulc_correspondence_path,coarse_src_label,coarse_simplification_label,coarse_correspondence_path,lc_class_varname,dimensions,calibration_parameters_source,base_year_lulc_path,regional_projections_input_path,regions_vector_path,regions_column_label +baseline_luh2-message,baseline,from_regional_projections_input_path,baseline,,luh2-message,,2017,,2017,2017,,add2015,luh2/raw_data/rcp45_ssp2/multiple-states_input4MIPs_landState_ScenarioMIP_UofMD-MESSAGE-ssp245-2-1-f_gn_2015-2100.nc,esa,seals7,seals/default_inputs/esa_seals7_correspondence.csv,luh2-14,seals7,seals/default_inputs/luh2-14_seals7_correspondence.csv,all_variables,time,seals/default_inputs/default_global_coefficients.csv,lulc\esa\lulc_esa_2017.tif,ssp1.csv,europe_nuts2_2016.gpkg,nuts_label +ssp2_rcp45_luh2-message_bau,bau,from_regional_projections_input_path,ssp2,rcp45,luh2-message,bau,2050,baseline_luh2-message,2017,2017,,add2015,luh2/raw_data/rcp45_ssp2/multiple-states_input4MIPs_landState_ScenarioMIP_UofMD-MESSAGE-ssp245-2-1-f_gn_2015-2100.nc,esa,seals7,seals/default_inputs/esa_seals7_correspondence.csv,luh2-14,seals7,seals/default_inputs/luh2-14_seals7_correspondence.csv,all_variables,time,seals/default_inputs/default_global_coefficients.csv,lulc\esa\lulc_esa_2017.tif,ssp1.csv,europe_nuts2_2016.gpkg,nuts_label diff --git a/seals/run_seals_bonn.py b/seals/run_seals_bonn.py index 7dbcf46..e0c1f7f 100644 --- a/seals/run_seals_bonn.py +++ b/seals/run_seals_bonn.py @@ -7,14 +7,20 @@ from seals import seals_generate_base_data, seals_initialize_project, seals_main, seals_process_coarse_timeseries, seals_tasks, seals_utils, seals_visualization_tasks # TODO -# 1. make regional projections be the change not the total. # 2. decide how to handle project_dir in the project repo so it uses the git-cloned inputs. # 3. test the setup.bat and decide if it's dumb to put it in the src (tho with git ignore listed) +### ENVIRONMENT VARIABLES (nothing else should need to be edited besides here) +base_data_dir = '../../../base_data/' # Automatically downloaded data will go here. +project_name = 'bonn_esm_cgebox_seals' # Name of the project. Also is used to set the project dir +project_dir = os.path.join('../../Projects', project_name) # New files will be written here + +input_dir = os.path.join(project_dir, 'input') # By default this is just in the project dir but you can specify it elsewhere here. +input_data_dir = 'input/bonn_input' # In the event the user just cloned the repo and that repo has an input dir, this will be copied from the repo input to the project input. +scenario_definitions_path = os.path.join(input_dir, 'scenarios.csv') # Path to the scenario definitions file. -def convert_cgebox_output_to_seals_regional_projections_input(p): - +def convert_cgebox_output_to_seals_regional_projections_input(p): p.regional_projections_input_override_paths = {} if p.run_this: @@ -125,53 +131,80 @@ def build_bonn_task_tree(p): main = '' if __name__ == '__main__': - ### ------- ENVIRONMENT SETTINGS ------------------------------- - # Users should only need to edit lines in this section - # Create a ProjectFlow Object to organize directories and enable parallel processing. p = hb.ProjectFlow() - - # Assign project-level attributes to the p object (such as in p.base_data_dir = ... below) - # including where the project_dir and base_data are located. - # The project_name is used to name the project directory below. If the directory exists, each task will not recreate - # files that already exist. + + # Default locations (to be used if local vars not defined above) p.user_dir = os.path.expanduser('~') - p.extra_dirs = ['Files', 'seals', 'projects'] - p.project_name = 'bonn_esm_cgebox_seals' - # p.project_name = p.project_name + '_' + hb.pretty_time() # If don't you want to recreate everything each time, comment out this line. - - # Based on the paths above, set the project_dir. All files will be created in this directory. - p.project_dir = os.path.join(p.user_dir, os.sep.join(p.extra_dirs), p.project_name) - p.set_project_dir(p.project_dir) - - p.run_in_parallel = 1 # Must be set before building the task tree if the task tree has parralel iterator tasks. - - # Build the task tree via a building function and assign it to p. IF YOU WANT TO LOOK AT THE MODEL LOGIC, INSPECT THIS FUNCTION - build_bonn_task_tree(p) - + p.extra_dirs = ['Files', 'seals', 'projects'] + + # Set processing resolution: determines how large of a chunk should be processed at a time. 4 deg is about max for 64gb memory systems + p.processing_resolution = 1.0 # In degrees. Must be in pyramid_compatible_resolutions + + # Check for locally-set versions of base_data_dir, project_dir, and input_dir + # Set the base data dir. The model will check here to see if it has everything it needs to run. # If anything is missing, it will download it. You can use the same base_data dir across multiple projects. # Additionally, if you're clever, you can move files generated in your tasks to the right base_data_dir # directory so that they are available for future projects and avoids redundant processing. # The final directory has to be named base_data to match the naming convention on the google cloud bucket. - p.base_data_dir = os.path.join(p.user_dir, 'Files/base_data') + if 'base_data_dir' in globals(): + hb.log(f'Using locally set base_data_dir: {base_data_dir}') + p.base_data_dir = base_data_dir + else: + p.base_data_dir = os.path.join(p.user_dir, 'Files/base_data') + + if 'project_name' in globals(): + hb.log(f'Using locally set project_name: {project_name}') + p.project_name = project_name + generate_new_project_dir_with_timestamp_for_every_run = False # If true, every run goes into a new and unique folder. This can help with debuging, but also means each run will be very slow as it will not use precalculated results. + if generate_new_project_dir_with_timestamp_for_every_run: + p.project_name = p.project_name + '_' + hb.pretty_time() + + if 'project_dir' in globals(): + hb.log(f'Using locally set project_dir: {project_dir}') + p.project_dir = project_dir + else: + p.project_dir = os.path.join(p.user_dir, os.sep.join(p.extra_dirs), p.project_name) + p.set_project_dir(p.project_dir) + + if 'input_dir' in globals(): + hb.log(f'Using locally set input_dir: {input_dir}') + p.input_dir = input_dir + + else: + p.input_dir = os.path.join(p.project_dir, 'input') + + if 'input_data_dir' in globals(): + hb.log(f'Detected locally set input_data_dir: {input_data_dir}. This happens when the data used for project setup (not the raw spatial data) is obtianed by git cloning. The assumed behavior here is that it will copy it from the repo input dir to the project input dir.') + p.input_data_dir = input_data_dir + # Copy the input data dir to the project input dir if it exists and the project input dir doesn't exist. + if hb.path_exists(p.input_data_dir, verbose=True): + hb.copy_file_tree_to_new_root(p.input_data_dir, p.input_dir, skip_existing=True) + hb.log(f'Copied input data from {p.input_data_dir} to {p.input_dir}.') + + ## Set defaults and generate the scenario_definitions.csv if it doesn't exist. + # SEALS will run based on the scenarios defined in a scenario_definitions.csv + # If you have not run SEALS before, SEALS will generate it in your project's input_dir. + # A useful way to get started is to to run SEALS on the test data without modification + # and then edit the scenario_definitions.csv to your project needs. + if 'scenario_definitions_path' in globals(): + hb.log(f'Using locally set scenarios_file_path: {scenario_definitions_path}') + p.scenario_definitions_path = scenario_definitions_path + else: + p.scenario_definitions_path = os.path.join(p.input_dir, 'scenarios.csv') + + p.run_in_parallel = 1 # Must be set before building the task tree if the task tree has parralel iterator tasks. + + # Build the task tree via a building function and assign it to p. IF YOU WANT TO LOOK AT THE MODEL LOGIC, INSPECT THIS FUNCTION + build_bonn_task_tree(p) # ProjectFlow downloads all files automatically via the p.get_path() function. If you want it to download from a different # bucket than default, provide the name and credentials here. Otherwise uses default public data 'gtap_invest_seals_2023_04_21'. p.data_credentials_path = None p.input_bucket_name = None - ## Set defaults and generate the scenario_definitions.csv if it doesn't exist. - # SEALS will run based on the scenarios defined in a scenario_definitions.csv - # If you have not run SEALS before, SEALS will generate it in your project's input_dir. - # A useful way to get started is to to run SEALS on the test data without modification - # and then edit the scenario_definitions.csv to your project needs. - p.scenario_definitions_filename = 'standard_scenarios.csv' - p.scenario_definitions_path = os.path.join(p.input_dir, p.scenario_definitions_filename) - seals_initialize_project.initialize_scenario_definitions(p) - - # Set processing resolution: determines how large of a chunk should be processed at a time. 4 deg is about max for 64gb memory systems - p.processing_resolution = 1.0 # In degrees. Must be in pyramid_compatible_resolutions + seals_initialize_project.initialize_scenario_definitions(p) seals_initialize_project.set_advanced_options(p) diff --git a/seals/seals_initialize_project.py b/seals/seals_initialize_project.py index 8020494..5879604 100644 --- a/seals/seals_initialize_project.py +++ b/seals/seals_initialize_project.py @@ -82,12 +82,16 @@ def set_advanced_options(p): def initialize_scenario_definitions(p): - + # TODOO NOTE: This has some dumb legacy code that needs to get fixed where it doesn't just TRUST that the get_path function works. # If the scenarios csv doesn't exist, generate it and put it in the input_dir if not hb.path_exists(p.scenario_definitions_path): + + # Check if p has attribute + if not hasattr(p, 'scenario_definitions_filename'): + p.scenario_definitions_filename = 'scenarios.csv' # Before generating a new scenarios file, check if there's not one in the base data with the matching name. - possible_path = p.get_path('seals', 'default_inputs', p.scenario_definitions_filename) + possible_path = p.get_path('seals', 'default_inputs', p.scenario_definitions_filename, raise_error_if_fail=False) if hb.path_exists(possible_path): hb.path_copy(possible_path, p.scenario_definitions_path) @@ -213,7 +217,7 @@ def build_standard_task_tree(p): ##### VIZUALIZE EXISTING DATA ##### p.visualization_task = p.add_task(seals_visualization_tasks.visualization) p.lulc_pngs_task = p.add_task(seals_visualization_tasks.lulc_pngs, parent=p.visualization_task) - p.html_report_task = p.add_task(seals_visualization_tasks.html_report, parent=p.visualization_task) + # p.html_report_task = p.add_task(seals_visualization_tasks.html_report, parent=p.visualization_task) def build_ken_task_tree(p): diff --git a/seals/seals_main.py b/seals/seals_main.py index cec57e5..4187f81 100644 --- a/seals/seals_main.py +++ b/seals/seals_main.py @@ -1769,7 +1769,10 @@ def allocation_zones(p): f.write(str(k) + ',' + str(line[0]) + '_' + str(line[1]) + '\n') # Load the calibration variable used for all the zones if relying ona precalcualted one. - if hb.path_exists(os.path.join(p.input_dir, p.calibration_parameters_source)): + # TODO I never fixed the case where the data is just in the default base data and was resolved by get_path() the desired way. Could probably delete the code below cause the p.get_path found by the scenarios iteration should be sufficient. + if hb.path_exists(p.calibration_parameters_source): + calibration_parameters_path = p.calibration_parameters_source + elif hb.path_exists(os.path.join(p.input_dir, p.calibration_parameters_source)): calibration_parameters_path = os.path.join(p.input_dir, p.calibration_parameters_source) elif hb.path_exists(os.path.join(p.base_data_dir, p.calibration_parameters_source)): calibration_parameters_path = os.path.join(p.base_data_dir, p.calibration_parameters_source) diff --git a/seals/seals_process_coarse_timeseries.py b/seals/seals_process_coarse_timeseries.py index 03b0233..10abfbb 100644 --- a/seals/seals_process_coarse_timeseries.py +++ b/seals/seals_process_coarse_timeseries.py @@ -832,10 +832,14 @@ def coarse_extraction(p): if p.scenario_type == 'baseline': - if hb.path_exists(os.path.join(p.input_dir, p.coarse_projections_input_path)): + + if hb.path_exists(p.coarse_projections_input_path): + src_nc_path = p.coarse_projections_input_path + elif hb.path_exists(os.path.join(p.input_dir, p.coarse_projections_input_path)): src_nc_path = os.path.join(p.input_dir, p.coarse_projections_input_path) elif hb.path_exists(os.path.join(p.base_data_dir, p.coarse_projections_input_path)): src_nc_path = os.path.join(p.base_data_dir, p.coarse_projections_input_path) + else: hb.log('Could not find ' + str(p.coarse_projections_input_path) + ' in either ' + str(p.input_dir) + ' or ' + str(p.base_data_dir)) @@ -853,13 +857,15 @@ def coarse_extraction(p): extract_global_netcdf(src_nc_path, dst_dir, adjustment_dict, filter_dict, skip_if_exists=True, verbose=0) else: if p.coarse_projections_input_path: - if hb.path_exists(os.path.join(p.input_dir, p.coarse_projections_input_path)): + if hb.path_exists(p.coarse_projections_input_path): + src_nc_path = p.coarse_projections_input_path + elif hb.path_exists(os.path.join(p.input_dir, p.coarse_projections_input_path)): src_nc_path = os.path.join(p.input_dir, p.coarse_projections_input_path) elif hb.path_exists(os.path.join(p.base_data_dir, p.coarse_projections_input_path)): - src_nc_path = os.path.join(p.base_data_dir, p.coarse_projections_input_path) + src_nc_path = os.path.join(p.base_data_dir, p.coarse_projections_input_path) else: hb.log('No understandible input_source.') - raise NameError('No understandible input_source for coarse_projections_input_path. The ref_path is specified in the scenario csv file, which resolves to a local or downloadable path. If you got here, it means that the file was not on the online storage bucket you are pointing to. The manual workaround is just to download the file manually and put it in your base_data directly. Path in question is ' + str(p.coarse_projections_input_path)) + raise NameError('No understandible input_source for coarse_projections_input_path. The ref_path is specified in the scenario csv file, which resolves to a local or downloadable path. If you got here, it means that the file was not on the online storage bucket you are pointing to. The manual workaround is just to download the file manually and put it in your base_data directly. Path in question is ' + str(p.coarse_projections_input_path) + ' at abspath ' + str(os.path.abspath(p.coarse_projections_input_path))) else: hb.log('No coarse change listed') @@ -1140,7 +1146,9 @@ def coarse_simplified_ha_difference_from_previous_year(p): seals_utils.assign_df_row_to_object_attributes(p, row) hb.log('Converting coarse_extraction to simplified proportion for scenario ' + str(index) + ' of ' + str(len(p.scenarios_df)) + ' with row ' + str([i for i in row])) - if hb.path_exists(os.path.join(p.input_dir, p.coarse_correspondence_path)): + if hb.path_exists(p.coarse_correspondence_path): + p.lulc_correspondence_dict = hb.utils.get_reclassification_dict_from_df(p.coarse_correspondence_path, 'src_id', 'dst_id', 'src_label', 'dst_label') + elif hb.path_exists(os.path.join(p.input_dir, p.coarse_correspondence_path)): p.lulc_correspondence_dict = hb.utils.get_reclassification_dict_from_df(os.path.join(p.input_dir, p.coarse_correspondence_path), 'src_id', 'dst_id', 'src_label', 'dst_label') elif hb.path_exists(os.path.join(p.base_data_dir, p.coarse_correspondence_path)): p.lulc_correspondence_dict = hb.utils.get_reclassification_dict_from_df(os.path.join(p.base_data_dir, p.coarse_correspondence_path), 'src_id', 'dst_id', 'src_label', 'dst_label') diff --git a/seals/seals_utils.py b/seals/seals_utils.py index 1e8c1d4..46b9beb 100644 --- a/seals/seals_utils.py +++ b/seals/seals_utils.py @@ -590,7 +590,10 @@ def assign_df_row_to_object_attributes(input_object, input_row): # First check if is numeric # Then check if has extension, is path for attribute_name, attribute_value in list(zip(input_row.index, input_row.values)): - + if attribute_name == 'coarse_correspondence_path': + pass + + try: float(attribute_value) is_floatable = True diff --git a/seals/seals_visualization_tasks.py b/seals/seals_visualization_tasks.py index 764b3f6..3fad165 100644 --- a/seals/seals_visualization_tasks.py +++ b/seals/seals_visualization_tasks.py @@ -6,7 +6,7 @@ import pandas as pd import geopandas as gpd from matplotlib.patches import Patch -import contextily as ctx +# import contextily as ctx import rasterio from rasterio.plot import show from html import escape as _esc @@ -927,862 +927,862 @@ def simpler_plot_generation(p): overall_similarity_plot_af = None - -def html_report(p): - # Generate a HTML report with the generated PNGs. - if p.run_this: - if p.scenario_definitions_path is not None: - p.scenarios_df = pd.read_csv(p.scenario_definitions_path) - - report_assets_dir = os.path.join(p.cur_dir, 'assets') - os.makedirs(report_assets_dir, exist_ok=True) - report_output_path = os.path.join(p.cur_dir, 'seals_visualization_report.html') - - # Add summary table - scenarios_table_rows = [] - for index, row in p.scenarios_df.iterrows(): - seals_utils.assign_df_row_to_object_attributes(p, row) - seals_utils.set_derived_attributes(p) - aoi_val = getattr(p, 'aoi', '') or getattr(p, 'aoi_path', '') - years_val = ','.join([str(y) for y in getattr(p, 'years', [])]) if getattr(p, 'years', None) is not None else '' - scenarios_table_rows.append({ - 'index': index, - 'scenario_label': getattr(p, 'scenario_label', '') or f"{getattr(p,'exogenous_label','')}_{getattr(p,'climate_label','')}_{getattr(p,'model_label','')}_{getattr(p,'counterfactual_label','')}", - 'scenario_type': getattr(p, 'scenario_type', ''), - 'aoi': aoi_val, - 'exogenous_label': getattr(p, 'exogenous_label', ''), - 'climate_label': getattr(p, 'climate_label', ''), - 'model_label': getattr(p, 'model_label', ''), - 'counterfactual_label': getattr(p, 'counterfactual_label', ''), - 'years': years_val - }) - - # render table HTML (simple, small) - scenarios_table_html = ['

Scenarios

'] - # header - headers = ['#', 'scenario_label', 'scenario_type', 'aoi', 'exogenous', 'climate', 'model', 'counterfactual', 'years'] - ths = ''.join([f'' for h in headers]) - scenarios_table_html.append(f'{ths}') - # rows - for r in scenarios_table_rows: - tds = ''.join([ - f'' - for k in ['index', 'scenario_label', 'scenario_type', 'aoi', 'exogenous_label', 'climate_label', 'model_label', 'counterfactual_label', 'years'] - ]) - scenarios_table_html.append(f'{tds}') - scenarios_table_html.append('
{_esc(h)}
{_esc(str(r.get(k, "")))}
') - scenarios_table_html = '\n'.join(scenarios_table_html) - - # === AOI SECTION === - fig, axes = plt.subplots(1, 3, figsize=(15, 5)) - for ax in axes.flat: - ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, - labelbottom=False, labelleft=False) - # Plot 1: Vector AOI - aoi = gpd.read_file(p.aoi_path) - aoi.plot(ax=axes[0], facecolor='lightblue', edgecolor='black', linewidth=0.5, alpha=0.5) - axes[0].set_title('AOI Boundary') - # Zoom to AOI bounds before adding basemap - minx, miny, maxx, maxy = p.bb - axes[0].set_xlim(minx, maxx) - axes[0].set_ylim(miny, maxy) - ctx.add_basemap(axes[0], source=ctx.providers.OpenStreetMap.Mapnik, crs='EPSG:4326', alpha=0.7, attribution=False) - # Plot 2: Fine resolution raster - with rasterio.open(p.aoi_ha_per_cell_fine_path) as src: - show(src, ax=axes[1], cmap='viridis') - axes[1].set_title('Fine Resolution (ha per cell)') - # Plot 3: Coarse resolution raster - with rasterio.open(p.aoi_ha_per_cell_coarse_path) as src: - show(src, ax=axes[2], cmap='viridis') - axes[2].set_title('Coarse Resolution (ha per cell)') - plt.tight_layout() - plt.savefig(os.path.join(report_assets_dir, 'aoi_plots.png')) - plt.close() +# Temoporarily disable HTML report generation until we rebuild the release system to include contextily +# def html_report(p): +# # Generate a HTML report with the generated PNGs. +# if p.run_this: +# if p.scenario_definitions_path is not None: +# p.scenarios_df = pd.read_csv(p.scenario_definitions_path) + +# report_assets_dir = os.path.join(p.cur_dir, 'assets') +# os.makedirs(report_assets_dir, exist_ok=True) +# report_output_path = os.path.join(p.cur_dir, 'seals_visualization_report.html') + +# # Add summary table +# scenarios_table_rows = [] +# for index, row in p.scenarios_df.iterrows(): +# seals_utils.assign_df_row_to_object_attributes(p, row) +# seals_utils.set_derived_attributes(p) +# aoi_val = getattr(p, 'aoi', '') or getattr(p, 'aoi_path', '') +# years_val = ','.join([str(y) for y in getattr(p, 'years', [])]) if getattr(p, 'years', None) is not None else '' +# scenarios_table_rows.append({ +# 'index': index, +# 'scenario_label': getattr(p, 'scenario_label', '') or f"{getattr(p,'exogenous_label','')}_{getattr(p,'climate_label','')}_{getattr(p,'model_label','')}_{getattr(p,'counterfactual_label','')}", +# 'scenario_type': getattr(p, 'scenario_type', ''), +# 'aoi': aoi_val, +# 'exogenous_label': getattr(p, 'exogenous_label', ''), +# 'climate_label': getattr(p, 'climate_label', ''), +# 'model_label': getattr(p, 'model_label', ''), +# 'counterfactual_label': getattr(p, 'counterfactual_label', ''), +# 'years': years_val +# }) + +# # render table HTML (simple, small) +# scenarios_table_html = ['

Scenarios

'] +# # header +# headers = ['#', 'scenario_label', 'scenario_type', 'aoi', 'exogenous', 'climate', 'model', 'counterfactual', 'years'] +# ths = ''.join([f'' for h in headers]) +# scenarios_table_html.append(f'{ths}') +# # rows +# for r in scenarios_table_rows: +# tds = ''.join([ +# f'' +# for k in ['index', 'scenario_label', 'scenario_type', 'aoi', 'exogenous_label', 'climate_label', 'model_label', 'counterfactual_label', 'years'] +# ]) +# scenarios_table_html.append(f'{tds}') +# scenarios_table_html.append('
{_esc(h)}
{_esc(str(r.get(k, "")))}
') +# scenarios_table_html = '\n'.join(scenarios_table_html) + +# # === AOI SECTION === +# fig, axes = plt.subplots(1, 3, figsize=(15, 5)) +# for ax in axes.flat: +# ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, +# labelbottom=False, labelleft=False) +# # Plot 1: Vector AOI +# aoi = gpd.read_file(p.aoi_path) +# aoi.plot(ax=axes[0], facecolor='lightblue', edgecolor='black', linewidth=0.5, alpha=0.5) +# axes[0].set_title('AOI Boundary') +# # Zoom to AOI bounds before adding basemap +# minx, miny, maxx, maxy = p.bb +# axes[0].set_xlim(minx, maxx) +# axes[0].set_ylim(miny, maxy) +# ctx.add_basemap(axes[0], source=ctx.providers.OpenStreetMap.Mapnik, crs='EPSG:4326', alpha=0.7, attribution=False) +# # Plot 2: Fine resolution raster +# with rasterio.open(p.aoi_ha_per_cell_fine_path) as src: +# show(src, ax=axes[1], cmap='viridis') +# axes[1].set_title('Fine Resolution (ha per cell)') +# # Plot 3: Coarse resolution raster +# with rasterio.open(p.aoi_ha_per_cell_coarse_path) as src: +# show(src, ax=axes[2], cmap='viridis') +# axes[2].set_title('Coarse Resolution (ha per cell)') +# plt.tight_layout() +# plt.savefig(os.path.join(report_assets_dir, 'aoi_plots.png')) +# plt.close() - # === FINE PROCESSED INPUTS SECTION === - # LULC colors (including 0 = nodata at top) - lulc_colors = [ - (1, 1, 1), # 0 = nodata - (212/255,106/255,110/255), # 1 urban - (227/255,167/255,92/255), # 2 cropland - (232/255,232/255,106/255), # 3 grassland - (79/255,169/255,90/255), # 4 forest - (159/255,218/255,143/255), # 5 othernat - (144/255,174/255,224/255), # 6 water - (209/255,209/255,209/255), # 7 other - ] - lulc_cmap = colors.ListedColormap(lulc_colors) +# # === FINE PROCESSED INPUTS SECTION === +# # LULC colors (including 0 = nodata at top) +# lulc_colors = [ +# (1, 1, 1), # 0 = nodata +# (212/255,106/255,110/255), # 1 urban +# (227/255,167/255,92/255), # 2 cropland +# (232/255,232/255,106/255), # 3 grassland +# (79/255,169/255,90/255), # 4 forest +# (159/255,218/255,143/255), # 5 othernat +# (144/255,174/255,224/255), # 6 water +# (209/255,209/255,209/255), # 7 other +# ] +# lulc_cmap = colors.ListedColormap(lulc_colors) - # Forces clean discrete boundaries - bounds = [i - 0.5 for i in range(9)] # -0.5 → 7.5 - lulc_norm = colors.BoundaryNorm(bounds, lulc_cmap.N) +# # Forces clean discrete boundaries +# bounds = [i - 0.5 for i in range(9)] # -0.5 → 7.5 +# lulc_norm = colors.BoundaryNorm(bounds, lulc_cmap.N) - fig, axes = plt.subplots(2, 2, figsize=(12, 10)) - for ax in axes.flat: - ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, - labelbottom=False, labelleft=False) +# fig, axes = plt.subplots(2, 2, figsize=(12, 10)) +# for ax in axes.flat: +# ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, +# labelbottom=False, labelleft=False) - # Plot 1: Simplified LULC - lulc_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, f'lulc_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}.tif') - with rasterio.open(lulc_path) as src: - show( - src, - ax=axes[0,0], - cmap=lulc_cmap, - norm=lulc_norm - ) - axes[0,0].set_title('SEALS7 Classification (Base Year)') - legend_labels = [ - 'urban', - 'cropland', - 'grassland', - 'forest', - 'othernat', - 'water', - 'other' - ] - # 1–7 only - patches = [ - plt.Rectangle((0,0), 1, 1, fc=lulc_colors[i]) - for i in range(1, 8) - ] - axes[0,0].legend( - patches, - legend_labels, - loc='lower left', - fontsize=8, - title="LULC Classes" - ) +# # Plot 1: Simplified LULC +# lulc_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, f'lulc_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}.tif') +# with rasterio.open(lulc_path) as src: +# show( +# src, +# ax=axes[0,0], +# cmap=lulc_cmap, +# norm=lulc_norm +# ) +# axes[0,0].set_title('SEALS7 Classification (Base Year)') +# legend_labels = [ +# 'urban', +# 'cropland', +# 'grassland', +# 'forest', +# 'othernat', +# 'water', +# 'other' +# ] +# # 1–7 only +# patches = [ +# plt.Rectangle((0,0), 1, 1, fc=lulc_colors[i]) +# for i in range(1, 8) +# ] +# axes[0,0].legend( +# patches, +# legend_labels, +# loc='lower left', +# fontsize=8, +# title="LULC Classes" +# ) - # Plot 2: Binary cropland - binary_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, 'binaries', str(p.base_years[0]), f'binary_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}_cropland.tif') - with rasterio.open(binary_path) as src: - show(src, ax=axes[0,1], cmap='YlGn') - axes[0,1].set_title('Binary Cropland Mask') +# # Plot 2: Binary cropland +# binary_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, 'binaries', str(p.base_years[0]), f'binary_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}_cropland.tif') +# with rasterio.open(binary_path) as src: +# show(src, ax=axes[0,1], cmap='YlGn') +# axes[0,1].set_title('Binary Cropland Mask') - # Plot 3: Gaussian 1 convolution - conv1_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, 'convolutions', str(p.base_years[0]), f'convolution_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}_cropland_gaussian_1.tif') - with rasterio.open(conv1_path) as src: - show(src, ax=axes[1,0], cmap='YlGn') - axes[1,0].set_title('Cropland Convolution (Gaussian σ=1)') +# # Plot 3: Gaussian 1 convolution +# conv1_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, 'convolutions', str(p.base_years[0]), f'convolution_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}_cropland_gaussian_1.tif') +# with rasterio.open(conv1_path) as src: +# show(src, ax=axes[1,0], cmap='YlGn') +# axes[1,0].set_title('Cropland Convolution (Gaussian σ=1)') - # Plot 4: Gaussian 5 convolution - conv5_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, 'convolutions', str(p.base_years[0]), f'convolution_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}_cropland_gaussian_5.tif') - with rasterio.open(conv5_path) as src: - show(src, ax=axes[1,1], cmap='YlGn') - axes[1,1].set_title('Cropland Convolution (Gaussian σ=5)') +# # Plot 4: Gaussian 5 convolution +# conv5_path = os.path.join(p.fine_processed_inputs_dir, 'lulc', p.lulc_src_label, p.lulc_simplification_label, 'convolutions', str(p.base_years[0]), f'convolution_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.base_years[0]}_cropland_gaussian_5.tif') +# with rasterio.open(conv5_path) as src: +# show(src, ax=axes[1,1], cmap='YlGn') +# axes[1,1].set_title('Cropland Convolution (Gaussian σ=5)') - plt.tight_layout() - plt.savefig(os.path.join(report_assets_dir, 'fine_processed_inputs_plots.png')) - plt.close() - - # Plot region IDs (once, before scenarios) - region_ids_path = os.path.join(p.regional_change_dir, 'region_ids.tif') - if os.path.exists(region_ids_path): - fig, ax = plt.subplots(1, 1, figsize=(10, 8)) +# plt.tight_layout() +# plt.savefig(os.path.join(report_assets_dir, 'fine_processed_inputs_plots.png')) +# plt.close() + +# # Plot region IDs (once, before scenarios) +# region_ids_path = os.path.join(p.regional_change_dir, 'region_ids.tif') +# if os.path.exists(region_ids_path): +# fig, ax = plt.subplots(1, 1, figsize=(10, 8)) - with rasterio.open(region_ids_path) as src: - data = src.read(1) - # Mask out nodata values - if src.nodata is not None: - data_masked = np.ma.masked_equal(data, src.nodata) - else: - data_masked = np.ma.masked_equal(data, 0) +# with rasterio.open(region_ids_path) as src: +# data = src.read(1) +# # Mask out nodata values +# if src.nodata is not None: +# data_masked = np.ma.masked_equal(data, src.nodata) +# else: +# data_masked = np.ma.masked_equal(data, 0) - # Get unique region IDs (excluding masked values) - unique_ids = np.unique(data_masked.compressed()) - n_regions = len(unique_ids) +# # Get unique region IDs (excluding masked values) +# unique_ids = np.unique(data_masked.compressed()) +# n_regions = len(unique_ids) - # Create discrete colormap for actual regions - cmap = plt.cm.get_cmap('tab20', n_regions) +# # Create discrete colormap for actual regions +# cmap = plt.cm.get_cmap('tab20', n_regions) - # Get extent for basemap - bounds = src.bounds - extent = [bounds.left, bounds.right, bounds.bottom, bounds.top] +# # Get extent for basemap +# bounds = src.bounds +# extent = [bounds.left, bounds.right, bounds.bottom, bounds.top] - # Add basemap first - ax.set_xlim(bounds.left, bounds.right) - ax.set_ylim(bounds.bottom, bounds.top) - ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik, - crs='EPSG:4326', alpha=0.7, attribution=False) +# # Add basemap first +# ax.set_xlim(bounds.left, bounds.right) +# ax.set_ylim(bounds.bottom, bounds.top) +# ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik, +# crs='EPSG:4326', alpha=0.7, attribution=False) - # Plot region IDs on top with transparency - im = ax.imshow(data_masked, cmap=cmap, interpolation='nearest', - extent=extent, alpha=0.5, vmin=unique_ids.min(), - vmax=unique_ids.max()) +# # Plot region IDs on top with transparency +# im = ax.imshow(data_masked, cmap=cmap, interpolation='nearest', +# extent=extent, alpha=0.5, vmin=unique_ids.min(), +# vmax=unique_ids.max()) - ax.set_title('Region IDs', fontsize=12) - ax.axis('off') +# ax.set_title('Region IDs', fontsize=12) +# ax.axis('off') - # Create categorical legend - colors_list = [cmap(i) for i in range(n_regions)] - patches = [Patch(facecolor=colors_list[i % len(colors_list)], edgecolor='k', label=str(int(uid))) - for i, uid in enumerate(unique_ids)] - ax.legend(handles=patches, title='Region ID', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=8) +# # Create categorical legend +# colors_list = [cmap(i) for i in range(n_regions)] +# patches = [Patch(facecolor=colors_list[i % len(colors_list)], edgecolor='k', label=str(int(uid))) +# for i, uid in enumerate(unique_ids)] +# ax.legend(handles=patches, title='Region ID', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=8) - plt.tight_layout() - plt.savefig(os.path.join(report_assets_dir, 'region_ids.png'), dpi=150, bbox_inches='tight') - plt.close() - region_ids_html = """ -

Region IDs

-
- Region IDs for regional analysis -
-""" - else: - region_ids_html = "" - - # Prepare collections for coarse and regional sections - coarse_scenario_tabs = [] - regional_scenario_tabs = [] - stitched_scenario_tabs = [] - scenario_list = [] +# plt.tight_layout() +# plt.savefig(os.path.join(report_assets_dir, 'region_ids.png'), dpi=150, bbox_inches='tight') +# plt.close() +# region_ids_html = """ +#

Region IDs

+#
+# Region IDs for regional analysis +#
+# """ +# else: +# region_ids_html = "" + +# # Prepare collections for coarse and regional sections +# coarse_scenario_tabs = [] +# regional_scenario_tabs = [] +# stitched_scenario_tabs = [] +# scenario_list = [] - # Define LULC classes to plot - lulc_classes = ['cropland', 'urban', 'grassland', 'forest', 'othernat'] +# # Define LULC classes to plot +# lulc_classes = ['cropland', 'urban', 'grassland', 'forest', 'othernat'] - # Loop through scenarios to generate plots - for index, row in p.scenarios_df.iterrows(): - seals_utils.assign_df_row_to_object_attributes(p, row) - seals_utils.set_derived_attributes(p) +# # Loop through scenarios to generate plots +# for index, row in p.scenarios_df.iterrows(): +# seals_utils.assign_df_row_to_object_attributes(p, row) +# seals_utils.set_derived_attributes(p) - if p.scenario_type == 'baseline': - continue # Skip baseline for this report +# if p.scenario_type == 'baseline': +# continue # Skip baseline for this report - scenario_name = f"{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}" - scenario_id = f"scenario_{index}" - scenario_list.append((scenario_id, scenario_name)) +# scenario_name = f"{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}" +# scenario_id = f"scenario_{index}" +# scenario_list.append((scenario_id, scenario_name)) - # === COARSE CHANGE SECTION === - coarse_content = "" +# # === COARSE CHANGE SECTION === +# coarse_content = "" - for yi, year in enumerate(p.years): - # for the first year in p.years use the configured base year (p.base_years[0]) - # otherwise use the previous entry in p.years - if yi == 0: - prev_year = p.base_years[0] - else: - prev_year = p.years[yi - 1] +# for yi, year in enumerate(p.years): +# # for the first year in p.years use the configured base year (p.base_years[0]) +# # otherwise use the previous entry in p.years +# if yi == 0: +# prev_year = p.base_years[0] +# else: +# prev_year = p.years[yi - 1] - # Create figure for this year's coarse changes - n_classes = len(lulc_classes) - fig, axes = plt.subplots(1, n_classes, figsize=(4 * n_classes, 4)) - fig.suptitle(f'Coarse Change: {year} (from {prev_year})', fontsize=14) - - for idx, lulc_class in enumerate(lulc_classes): - # Build path to coarse change file - change_fname = f'{lulc_class}_{year}_{prev_year}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}.tif' - change_path = os.path.join( - p.coarse_change_dir, - 'coarse_simplified_ha_difference_from_previous_year', - p.exogenous_label, - p.climate_label, - p.model_label, - p.counterfactual_label, - str(year), - change_fname - ) +# # Create figure for this year's coarse changes +# n_classes = len(lulc_classes) +# fig, axes = plt.subplots(1, n_classes, figsize=(4 * n_classes, 4)) +# fig.suptitle(f'Coarse Change: {year} (from {prev_year})', fontsize=14) + +# for idx, lulc_class in enumerate(lulc_classes): +# # Build path to coarse change file +# change_fname = f'{lulc_class}_{year}_{prev_year}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}.tif' +# change_path = os.path.join( +# p.coarse_change_dir, +# 'coarse_simplified_ha_difference_from_previous_year', +# p.exogenous_label, +# p.climate_label, +# p.model_label, +# p.counterfactual_label, +# str(year), +# change_fname +# ) - # Plot if file exists - if os.path.exists(change_path): - with rasterio.open(change_path) as src: - data = src.read(1) - # Use diverging colormap for change (red=loss, green=gain) - vmax = np.nanmax(np.abs(data)) - im = axes[idx].imshow(data, cmap='RdYlGn', vmin=-vmax, vmax=vmax) - axes[idx].set_title(f'{lulc_class.capitalize()}', fontsize=10) - axes[idx].axis('off') - plt.colorbar(im, ax=axes[idx], fraction=0.046, pad=0.04, label='ha change') - else: - axes[idx].text(0.5, 0.5, f'{lulc_class}\nNot Found', - ha='center', va='center', transform=axes[idx].transAxes) - axes[idx].axis('off') +# # Plot if file exists +# if os.path.exists(change_path): +# with rasterio.open(change_path) as src: +# data = src.read(1) +# # Use diverging colormap for change (red=loss, green=gain) +# vmax = np.nanmax(np.abs(data)) +# im = axes[idx].imshow(data, cmap='RdYlGn', vmin=-vmax, vmax=vmax) +# axes[idx].set_title(f'{lulc_class.capitalize()}', fontsize=10) +# axes[idx].axis('off') +# plt.colorbar(im, ax=axes[idx], fraction=0.046, pad=0.04, label='ha change') +# else: +# axes[idx].text(0.5, 0.5, f'{lulc_class}\nNot Found', +# ha='center', va='center', transform=axes[idx].transAxes) +# axes[idx].axis('off') - plt.tight_layout() +# plt.tight_layout() - # Save figure - output_filename = f'coarse_change_{scenario_name}_{year}.png' - plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') - plt.close() +# # Save figure +# output_filename = f'coarse_change_{scenario_name}_{year}.png' +# plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') +# plt.close() - # Add to coarse content - coarse_content += f""" -

Year {year}

-
- Coarse change for {year} -
-""" +# # Add to coarse content +# coarse_content += f""" +#

Year {year}

+#
+# Coarse change for {year} +#
+# """ - coarse_scenario_tabs.append((scenario_id, coarse_content)) +# coarse_scenario_tabs.append((scenario_id, coarse_content)) - # === REGIONAL CHANGE SECTION === - # Region ID plotted earlier - regional_content = "" +# # === REGIONAL CHANGE SECTION === +# # Region ID plotted earlier +# regional_content = "" - # Check if regional change exists for this scenario - has_regional_change = False - test_year = p.years[0] - test_class = lulc_classes[0] - test_path = os.path.join( - p.regional_change_dir, - p.exogenous_label, - p.climate_label, - p.model_label, - p.counterfactual_label, - str(test_year), - f'{test_class}_{test_year}_{p.base_years[0]}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_regional_coarsified.tif' - ) - has_regional_change = os.path.exists(test_path) +# # Check if regional change exists for this scenario +# has_regional_change = False +# test_year = p.years[0] +# test_class = lulc_classes[0] +# test_path = os.path.join( +# p.regional_change_dir, +# p.exogenous_label, +# p.climate_label, +# p.model_label, +# p.counterfactual_label, +# str(test_year), +# f'{test_class}_{test_year}_{p.base_years[0]}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_regional_coarsified.tif' +# ) +# has_regional_change = os.path.exists(test_path) - if not has_regional_change: - regional_content = """ -

No regional change data available for this scenario.

-""" - else: - for yi, year in enumerate(p.years): - if yi == 0: - prev_year = p.base_years[0] - else: - prev_year = p.years[yi - 1] +# if not has_regional_change: +# regional_content = """ +#

No regional change data available for this scenario.

+# """ +# else: +# for yi, year in enumerate(p.years): +# if yi == 0: +# prev_year = p.base_years[0] +# else: +# prev_year = p.years[yi - 1] - # Create 5-panel figure for regional coarsified - n_classes = len(lulc_classes) - fig, axes = plt.subplots(1, n_classes, figsize=(4 * n_classes, 4)) - fig.suptitle(f'Regional Coarsified: {year} (from {prev_year})', fontsize=14) +# # Create 5-panel figure for regional coarsified +# n_classes = len(lulc_classes) +# fig, axes = plt.subplots(1, n_classes, figsize=(4 * n_classes, 4)) +# fig.suptitle(f'Regional Coarsified: {year} (from {prev_year})', fontsize=14) - for idx, lulc_class in enumerate(lulc_classes): - regional_coarsified_path = os.path.join( - p.regional_change_dir, - p.exogenous_label, - p.climate_label, - p.model_label, - p.counterfactual_label, - str(year), - f'{lulc_class}_{year}_{prev_year}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_regional_coarsified.tif' - ) +# for idx, lulc_class in enumerate(lulc_classes): +# regional_coarsified_path = os.path.join( +# p.regional_change_dir, +# p.exogenous_label, +# p.climate_label, +# p.model_label, +# p.counterfactual_label, +# str(year), +# f'{lulc_class}_{year}_{prev_year}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_regional_coarsified.tif' +# ) - if os.path.exists(regional_coarsified_path): - with rasterio.open(regional_coarsified_path) as src: - data = src.read(1) - vmax = np.nanmax(np.abs(data)) - im = axes[idx].imshow(data, cmap='RdYlGn', vmin=-vmax, vmax=vmax) - axes[idx].set_title(f'{lulc_class.capitalize()}', fontsize=10) - axes[idx].axis('off') - plt.colorbar(im, ax=axes[idx], fraction=0.046, pad=0.04, label='ha change') - else: - axes[idx].text(0.5, 0.5, f'{lulc_class}\nNot Found', - ha='center', va='center', transform=axes[idx].transAxes) - axes[idx].axis('off') +# if os.path.exists(regional_coarsified_path): +# with rasterio.open(regional_coarsified_path) as src: +# data = src.read(1) +# vmax = np.nanmax(np.abs(data)) +# im = axes[idx].imshow(data, cmap='RdYlGn', vmin=-vmax, vmax=vmax) +# axes[idx].set_title(f'{lulc_class.capitalize()}', fontsize=10) +# axes[idx].axis('off') +# plt.colorbar(im, ax=axes[idx], fraction=0.046, pad=0.04, label='ha change') +# else: +# axes[idx].text(0.5, 0.5, f'{lulc_class}\nNot Found', +# ha='center', va='center', transform=axes[idx].transAxes) +# axes[idx].axis('off') - plt.tight_layout() - output_filename = f'regional_coarsified_{scenario_name}_{year}.png' - plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') - plt.close() +# plt.tight_layout() +# output_filename = f'regional_coarsified_{scenario_name}_{year}.png' +# plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') +# plt.close() - regional_content += f""" -

Year {year} - Regional Coarsified

-
- Regional coarsified for {year} -
-""" +# regional_content += f""" +#

Year {year} - Regional Coarsified

+#
+# Regional coarsified for {year} +#
+# """ - # Create 5-panel figure for covariate sum shift - fig, axes = plt.subplots(1, n_classes, figsize=(4 * n_classes, 4)) - fig.suptitle(f'Covariate Sum Shift: {year} (from {prev_year})', fontsize=14) +# # Create 5-panel figure for covariate sum shift +# fig, axes = plt.subplots(1, n_classes, figsize=(4 * n_classes, 4)) +# fig.suptitle(f'Covariate Sum Shift: {year} (from {prev_year})', fontsize=14) - for idx, lulc_class in enumerate(lulc_classes): - covariate_shift_path = os.path.join( - p.regional_change_dir, - p.exogenous_label, - p.climate_label, - p.model_label, - p.counterfactual_label, - str(year), - f'{lulc_class}_{year}_{prev_year}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_covariate_sum_shift.tif' - ) +# for idx, lulc_class in enumerate(lulc_classes): +# covariate_shift_path = os.path.join( +# p.regional_change_dir, +# p.exogenous_label, +# p.climate_label, +# p.model_label, +# p.counterfactual_label, +# str(year), +# f'{lulc_class}_{year}_{prev_year}_ha_diff_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_covariate_sum_shift.tif' +# ) - if os.path.exists(covariate_shift_path): - with rasterio.open(covariate_shift_path) as src: - data = src.read(1) - vmax = np.nanmax(np.abs(data)) - im = axes[idx].imshow(data, cmap='RdYlGn', vmin=-vmax, vmax=vmax) - axes[idx].set_title(f'{lulc_class.capitalize()}', fontsize=10) - axes[idx].axis('off') - plt.colorbar(im, ax=axes[idx], fraction=0.046, pad=0.04, label='ha change') - else: - axes[idx].text(0.5, 0.5, f'{lulc_class}\nNot Found', - ha='center', va='center', transform=axes[idx].transAxes) - axes[idx].axis('off') +# if os.path.exists(covariate_shift_path): +# with rasterio.open(covariate_shift_path) as src: +# data = src.read(1) +# vmax = np.nanmax(np.abs(data)) +# im = axes[idx].imshow(data, cmap='RdYlGn', vmin=-vmax, vmax=vmax) +# axes[idx].set_title(f'{lulc_class.capitalize()}', fontsize=10) +# axes[idx].axis('off') +# plt.colorbar(im, ax=axes[idx], fraction=0.046, pad=0.04, label='ha change') +# else: +# axes[idx].text(0.5, 0.5, f'{lulc_class}\nNot Found', +# ha='center', va='center', transform=axes[idx].transAxes) +# axes[idx].axis('off') - plt.tight_layout() - output_filename = f'covariate_shift_{scenario_name}_{year}.png' - plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') - plt.close() +# plt.tight_layout() +# output_filename = f'covariate_shift_{scenario_name}_{year}.png' +# plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') +# plt.close() - regional_content += f""" -

Year {year} - Covariate Sum Shift

-
- Covariate sum shift for {year} -
-""" +# regional_content += f""" +#

Year {year} - Covariate Sum Shift

+#
+# Covariate sum shift for {year} +#
+# """ - regional_scenario_tabs.append((scenario_id, regional_content)) +# regional_scenario_tabs.append((scenario_id, regional_content)) - # === STITCHED LULC SECTION === - stitched_content = "" +# # === STITCHED LULC SECTION === +# stitched_content = "" - # Use the same LULC colormap as before - lulc_colors_stitched = [ - (1, 1, 1), # 0 = nodata - (212/255,106/255,110/255), # 1 urban - (227/255,167/255,92/255), # 2 cropland - (232/255,232/255,106/255), # 3 grassland - (79/255,169/255,90/255), # 4 forest - (159/255,218/255,143/255), # 5 othernat - (144/255,174/255,224/255), # 6 water - (209/255,209/255,209/255), # 7 other - ] - lulc_cmap_stitched = colors.ListedColormap(lulc_colors_stitched) - bounds_stitched = [i - 0.5 for i in range(9)] - lulc_norm_stitched = colors.BoundaryNorm(bounds_stitched, lulc_cmap_stitched.N) +# # Use the same LULC colormap as before +# lulc_colors_stitched = [ +# (1, 1, 1), # 0 = nodata +# (212/255,106/255,110/255), # 1 urban +# (227/255,167/255,92/255), # 2 cropland +# (232/255,232/255,106/255), # 3 grassland +# (79/255,169/255,90/255), # 4 forest +# (159/255,218/255,143/255), # 5 othernat +# (144/255,174/255,224/255), # 6 water +# (209/255,209/255,209/255), # 7 other +# ] +# lulc_cmap_stitched = colors.ListedColormap(lulc_colors_stitched) +# bounds_stitched = [i - 0.5 for i in range(9)] +# lulc_norm_stitched = colors.BoundaryNorm(bounds_stitched, lulc_cmap_stitched.N) - for year in p.years: - # Create 2-panel figure (full and clipped) - fig, axes = plt.subplots(1, 2, figsize=(16, 7)) +# for year in p.years: +# # Create 2-panel figure (full and clipped) +# fig, axes = plt.subplots(1, 2, figsize=(16, 7)) - # Full stitched LULC - stitched_path = os.path.join( - p.stitched_lulc_simplified_scenarios_dir, - f'lulc_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_{year}.tif' - ) +# # Full stitched LULC +# stitched_path = os.path.join( +# p.stitched_lulc_simplified_scenarios_dir, +# f'lulc_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_{year}.tif' +# ) - if os.path.exists(stitched_path): - with rasterio.open(stitched_path) as src: - show(src, ax=axes[0], cmap=lulc_cmap_stitched, norm=lulc_norm_stitched) - axes[0].set_title(f'Stitched LULC {year}', fontsize=12) - axes[0].axis('off') - else: - axes[0].text(0.5, 0.5, f'Stitched LULC\n{year}\nNot Found', - ha='center', va='center', transform=axes[0].transAxes) - axes[0].axis('off') +# if os.path.exists(stitched_path): +# with rasterio.open(stitched_path) as src: +# show(src, ax=axes[0], cmap=lulc_cmap_stitched, norm=lulc_norm_stitched) +# axes[0].set_title(f'Stitched LULC {year}', fontsize=12) +# axes[0].axis('off') +# else: +# axes[0].text(0.5, 0.5, f'Stitched LULC\n{year}\nNot Found', +# ha='center', va='center', transform=axes[0].transAxes) +# axes[0].axis('off') - # Clipped stitched LULC with basemap - clipped_path = os.path.join( - p.stitched_lulc_simplified_scenarios_dir, - f'lulc_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_{year}_clipped.tif' - ) +# # Clipped stitched LULC with basemap +# clipped_path = os.path.join( +# p.stitched_lulc_simplified_scenarios_dir, +# f'lulc_{p.lulc_src_label}_{p.lulc_simplification_label}_{p.exogenous_label}_{p.climate_label}_{p.model_label}_{p.counterfactual_label}_{year}_clipped.tif' +# ) - if os.path.exists(clipped_path): - with rasterio.open(clipped_path) as src: - # Get extent for basemap - bounds = src.bounds - extent = [bounds.left, bounds.right, bounds.bottom, bounds.top] +# if os.path.exists(clipped_path): +# with rasterio.open(clipped_path) as src: +# # Get extent for basemap +# bounds = src.bounds +# extent = [bounds.left, bounds.right, bounds.bottom, bounds.top] - # Add basemap first - axes[1].set_xlim(bounds.left, bounds.right) - axes[1].set_ylim(bounds.bottom, bounds.top) - ctx.add_basemap(axes[1], source=ctx.providers.OpenStreetMap.Mapnik, - crs='EPSG:4326', alpha=0.7, attribution=False) +# # Add basemap first +# axes[1].set_xlim(bounds.left, bounds.right) +# axes[1].set_ylim(bounds.bottom, bounds.top) +# ctx.add_basemap(axes[1], source=ctx.providers.OpenStreetMap.Mapnik, +# crs='EPSG:4326', alpha=0.7, attribution=False) - # Read and plot LULC data with transparency - data = src.read(1) - # Mask nodata - if src.nodata is not None: - data_masked = np.ma.masked_equal(data, src.nodata) - else: - data_masked = np.ma.masked_equal(data, 0) +# # Read and plot LULC data with transparency +# data = src.read(1) +# # Mask nodata +# if src.nodata is not None: +# data_masked = np.ma.masked_equal(data, src.nodata) +# else: +# data_masked = np.ma.masked_equal(data, 0) - axes[1].imshow(data_masked, cmap=lulc_cmap_stitched, norm=lulc_norm_stitched, - extent=extent, alpha=0.7, interpolation='nearest') - axes[1].set_title(f'Clipped LULC {year}', fontsize=12) - axes[1].axis('off') - else: - axes[1].text(0.5, 0.5, f'Clipped LULC\n{year}\nNot Found', - ha='center', va='center', transform=axes[1].transAxes) - axes[1].axis('off') +# axes[1].imshow(data_masked, cmap=lulc_cmap_stitched, norm=lulc_norm_stitched, +# extent=extent, alpha=0.7, interpolation='nearest') +# axes[1].set_title(f'Clipped LULC {year}', fontsize=12) +# axes[1].axis('off') +# else: +# axes[1].text(0.5, 0.5, f'Clipped LULC\n{year}\nNot Found', +# ha='center', va='center', transform=axes[1].transAxes) +# axes[1].axis('off') - # Add legend to the clipped plot - legend_labels = ['urban', 'cropland', 'grassland', 'forest', 'othernat', 'water', 'other'] - patches = [plt.Rectangle((0,0), 1, 1, fc=lulc_colors_stitched[i]) for i in range(1, 8)] - axes[1].legend(patches, legend_labels, loc='center left', bbox_to_anchor=(1, 0.5), fontsize=10, title="LULC Classes") +# # Add legend to the clipped plot +# legend_labels = ['urban', 'cropland', 'grassland', 'forest', 'othernat', 'water', 'other'] +# patches = [plt.Rectangle((0,0), 1, 1, fc=lulc_colors_stitched[i]) for i in range(1, 8)] +# axes[1].legend(patches, legend_labels, loc='center left', bbox_to_anchor=(1, 0.5), fontsize=10, title="LULC Classes") - plt.tight_layout() +# plt.tight_layout() - # Save figure - output_filename = f'stitched_lulc_{scenario_name}_{year}.png' - plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') - plt.close() +# # Save figure +# output_filename = f'stitched_lulc_{scenario_name}_{year}.png' +# plt.savefig(os.path.join(report_assets_dir, output_filename), dpi=150, bbox_inches='tight') +# plt.close() - # Add to stitched content - stitched_content += f""" -

Year {year}

-
- Stitched LULC for {year} -
-""" +# # Add to stitched content +# stitched_content += f""" +#

Year {year}

+#
+# Stitched LULC for {year} +#
+# """ - stitched_scenario_tabs.append((scenario_id, stitched_content)) - - # Build tab buttons and content for coarse section - coarse_tabs_buttons = "" - coarse_tabs_content = "" - for i, (scenario_id, scenario_name) in enumerate(scenario_list): - active_class = "active" if i == 0 else "" - coarse_tabs_buttons += f'\n' +# stitched_scenario_tabs.append((scenario_id, stitched_content)) + +# # Build tab buttons and content for coarse section +# coarse_tabs_buttons = "" +# coarse_tabs_content = "" +# for i, (scenario_id, scenario_name) in enumerate(scenario_list): +# active_class = "active" if i == 0 else "" +# coarse_tabs_buttons += f'\n' - display_style = "block" if i == 0 else "none" - coarse_tabs_content += f""" -
-{coarse_scenario_tabs[i][1]} -
-""" +# display_style = "block" if i == 0 else "none" +# coarse_tabs_content += f""" +#
+# {coarse_scenario_tabs[i][1]} +#
+# """ - # Build tab buttons and content for regional section - regional_tabs_buttons = "" - regional_tabs_content = "" - for i, (scenario_id, scenario_name) in enumerate(scenario_list): - active_class = "active" if i == 0 else "" - regional_tabs_buttons += f'\n' +# # Build tab buttons and content for regional section +# regional_tabs_buttons = "" +# regional_tabs_content = "" +# for i, (scenario_id, scenario_name) in enumerate(scenario_list): +# active_class = "active" if i == 0 else "" +# regional_tabs_buttons += f'\n' - display_style = "block" if i == 0 else "none" - regional_tabs_content += f""" -
-{regional_scenario_tabs[i][1]} -
-""" +# display_style = "block" if i == 0 else "none" +# regional_tabs_content += f""" +#
+# {regional_scenario_tabs[i][1]} +#
+# """ - # Build tab buttons and content for stitched LULC section - stitched_tabs_buttons = "" - stitched_tabs_content = "" - for i, (scenario_id, scenario_name) in enumerate(scenario_list): - active_class = "active" if i == 0 else "" - stitched_tabs_buttons += f'\n' +# # Build tab buttons and content for stitched LULC section +# stitched_tabs_buttons = "" +# stitched_tabs_content = "" +# for i, (scenario_id, scenario_name) in enumerate(scenario_list): +# active_class = "active" if i == 0 else "" +# stitched_tabs_buttons += f'\n' - display_style = "block" if i == 0 else "none" - stitched_tabs_content += f""" -
-{stitched_scenario_tabs[i][1]} -
-""" - - # Function to convert image to base64 - import base64 - def image_to_base64(image_path): - """Convert image file to base64 data URI""" - if os.path.exists(image_path): - with open(image_path, 'rb') as img_file: - img_data = base64.b64encode(img_file.read()).decode('utf-8') - return f'data:image/png;base64,{img_data}' - return '' +# display_style = "block" if i == 0 else "none" +# stitched_tabs_content += f""" +#
+# {stitched_scenario_tabs[i][1]} +#
+# """ + +# # Function to convert image to base64 +# import base64 +# def image_to_base64(image_path): +# """Convert image file to base64 data URI""" +# if os.path.exists(image_path): +# with open(image_path, 'rb') as img_file: +# img_data = base64.b64encode(img_file.read()).decode('utf-8') +# return f'data:image/png;base64,{img_data}' +# return '' - # Collect all image paths and convert to base64 - image_embeds = {} +# # Collect all image paths and convert to base64 +# image_embeds = {} - # Static images - for img_name in ['aoi_plots.png', 'fine_processed_inputs_plots.png', 'region_ids.png']: - img_path = os.path.join(report_assets_dir, img_name) - image_embeds[img_name] = image_to_base64(img_path) +# # Static images +# for img_name in ['aoi_plots.png', 'fine_processed_inputs_plots.png', 'region_ids.png']: +# img_path = os.path.join(report_assets_dir, img_name) +# image_embeds[img_name] = image_to_base64(img_path) - # Dynamic scenario images - for filename in os.listdir(report_assets_dir): - if filename.endswith('.png') and filename not in image_embeds: - img_path = os.path.join(report_assets_dir, filename) - image_embeds[filename] = image_to_base64(img_path) +# # Dynamic scenario images +# for filename in os.listdir(report_assets_dir): +# if filename.endswith('.png') and filename not in image_embeds: +# img_path = os.path.join(report_assets_dir, filename) +# image_embeds[filename] = image_to_base64(img_path) - # Replace all asset references with base64 data - def embed_images_in_html(html_content, image_dict): - """Replace src="assets/..." with base64 data URIs""" - for filename, base64_data in image_dict.items(): - if base64_data: # Only replace if image exists - html_content = html_content.replace(f'src="assets/{filename}"', f'src="{base64_data}"') - return html_content - - # Generate HTML report - html_template = """ - - - - - SEALS Visualization Report - - - - -

SEALS Visualization Report

-

Generated: {date}, User: {user}, OS: {system}

- {scenarios_table} +# // Show the selected tab and mark button as active +# document.getElementById(tabId).style.display = 'block'; +# evt.currentTarget.className += ' active'; +# }} +# +# +# +#

SEALS Visualization Report

+#

Generated: {date}, User: {user}, OS: {system}

+# {scenarios_table} -

Area of Interest (AOI)

-
-

Purpose:

-

Defines the geographic boundary for the analysis and creates resolution pyramids for efficient processing at multiple scales.

-
    -
  • Loads the area of interest boundary from the input shapefile
    • -
  • Creates a vector geopackage (.gpkg) file for the AOI
    • -
  • Generates raster "pyramids" at different resolutions: -
      -
    • Fine resolution: Matches the base LULC data (typically 300m)
      • -
    • Coarse resolution: Matches the coarse projection data (typically 0.25°)
      • -
    -
    • -
-

Key outputs:

- 
intermediate/project_aoi/
-├── aoi_RWA.gpkg                           # Vector boundary
-└── pyramids/
-    ├── aoi_ha_per_cell_coarse.tif    # Coarse resolution
-    └── aoi_ha_per_cell_fine.tif      # Fine resolution
-
-
- AOI plots showing boundary, fine resolution, and coarse resolution -
+#

Area of Interest (AOI)

+#
+#

Purpose:

+#

Defines the geographic boundary for the analysis and creates resolution pyramids for efficient processing at multiple scales.

+#
    +#
  • Loads the area of interest boundary from the input shapefile
    • +#
  • Creates a vector geopackage (.gpkg) file for the AOI
    • +#
  • Generates raster "pyramids" at different resolutions: +#
      +#
    • Fine resolution: Matches the base LULC data (typically 300m)
      • +#
    • Coarse resolution: Matches the coarse projection data (typically 0.25°)
      • +#
    +#
    • +#
+#

Key outputs:

+# 
intermediate/project_aoi/
+# ├── aoi_RWA.gpkg                           # Vector boundary
+# └── pyramids/
+#     ├── aoi_ha_per_cell_coarse.tif    # Coarse resolution
+#     └── aoi_ha_per_cell_fine.tif      # Fine resolution
+#
+#
+# AOI plots showing boundary, fine resolution, and coarse resolution +#
-

Fine Processed Inputs

-
-

Purpose:

-

Prepares the baseline land-use/land-cover (LULC) data for allocation by simplifying classifications, creating binary masks, and generating spatial convolutions.Binary masks and convolutions inform the allocation model by capturing spatial patterns and neighborhood characteristics.

-
    -
  • Generated Kernels: Creates Gaussian kernels for spatial smoothing
    • -
  • LULC Simplifications: Reclassifies detailed ESA categories into 7 SEALS classes: -
      -
    • Cropland, Forest, Grassland, Urban, Water, Other natural, Other
      • -
    -
    • -
  • LULC Binaries: Creates separate binary masks for each class (1 = present, 0 = absent)
    • -
  • LULC Convolutions: Applies Gaussian smoothing to capture neighborhood characteristics
    • -
-

Key outputs:

- 
intermediate/fine_processed_inputs/lulc/esa/seals7/
-├── lulc_esa_seals7_2017.tif              # Simplified 7-class LULC
-├── binaries/2017/                         # Binary masks by class
-│   ├── binary_esa_seals7_2017_cropland.tif
-│   └── ...
-└── convolutions/2017/                     # Smoothed neighborhood data
-    ├── convolution_esa_seals7_2017_cropland_gaussian_1.tif
-    └── ...
-
-
- Fine processed inputs showing LULC classification and convolutions -
+#

Fine Processed Inputs

+#
+#

Purpose:

+#

Prepares the baseline land-use/land-cover (LULC) data for allocation by simplifying classifications, creating binary masks, and generating spatial convolutions.Binary masks and convolutions inform the allocation model by capturing spatial patterns and neighborhood characteristics.

+#
    +#
  • Generated Kernels: Creates Gaussian kernels for spatial smoothing
    • +#
  • LULC Simplifications: Reclassifies detailed ESA categories into 7 SEALS classes: +#
      +#
    • Cropland, Forest, Grassland, Urban, Water, Other natural, Other
      • +#
    +#
    • +#
  • LULC Binaries: Creates separate binary masks for each class (1 = present, 0 = absent)
    • +#
  • LULC Convolutions: Applies Gaussian smoothing to capture neighborhood characteristics
    • +#
+#

Key outputs:

+# 
intermediate/fine_processed_inputs/lulc/esa/seals7/
+# ├── lulc_esa_seals7_2017.tif              # Simplified 7-class LULC
+# ├── binaries/2017/                         # Binary masks by class
+# │   ├── binary_esa_seals7_2017_cropland.tif
+# │   └── ...
+# └── convolutions/2017/                     # Smoothed neighborhood data
+#     ├── convolution_esa_seals7_2017_cropland_gaussian_1.tif
+#     └── ...
+#
+#
+# Fine processed inputs showing LULC classification and convolutions +#
-

Coarse Change

-
-

Purpose:

-

Extracts and processes land-use change projections from global coarse-resolution models (e.g., LUH2) for the study region. Links exogenous global scenarios (SSP, RCP) to local allocation by quantifying how much of each land class needs to change.

-
    -
  • Coarse Extraction: Clips global projection data to the region's bounding box
    • -
  • Coarse Simplified Proportion: Calculates proportions of each SEALS7 class
    • -
  • Coarse Simplified Ha: Converts proportions to hectares
    • -
  • Ha Difference: Calculates change in hectares between time periods (e.g., 2017 → 2030 → 2050)
    • -
-

Key outputs:

- 
intermediate/coarse_change/coarse_simplified_ha_difference_from_previous_year/
-└── ssp2/rcp45/luh2-message/bau/2030/
-    ├── cropland_2030_2017_ha_diff_ssp2_rcp45_luh2-message_bau.tif
-    ├── forest_2030_2017_ha_diff_ssp2_rcp45_luh2-message_bau.tif
-    └── ...
-
-
-
-{coarse_tabs_buttons} -
-{coarse_tabs_content} -
+#

Coarse Change

+#
+#

Purpose:

+#

Extracts and processes land-use change projections from global coarse-resolution models (e.g., LUH2) for the study region. Links exogenous global scenarios (SSP, RCP) to local allocation by quantifying how much of each land class needs to change.

+#
    +#
  • Coarse Extraction: Clips global projection data to the region's bounding box
    • +#
  • Coarse Simplified Proportion: Calculates proportions of each SEALS7 class
    • +#
  • Coarse Simplified Ha: Converts proportions to hectares
    • +#
  • Ha Difference: Calculates change in hectares between time periods (e.g., 2017 → 2030 → 2050)
    • +#
+#

Key outputs:

+# 
intermediate/coarse_change/coarse_simplified_ha_difference_from_previous_year/
+# └── ssp2/rcp45/luh2-message/bau/2030/
+#     ├── cropland_2030_2017_ha_diff_ssp2_rcp45_luh2-message_bau.tif
+#     ├── forest_2030_2017_ha_diff_ssp2_rcp45_luh2-message_bau.tif
+#     └── ...
+#
+#
+#
+# {coarse_tabs_buttons} +#
+# {coarse_tabs_content} +#
-

Regional Change

-
-

Purpose:

-

Adjusts coarse-resolution changes using regional covariates to account for local constraints and opportunities.

-
    -
  • Creates a region ID map dividing AOI into separately processed zones
    • -
  • Applies algorithms (e.g., proportional, covariate sum shift) to downscale from coarse to regional resolution
    • -
  • Regional Coarsified: Aggregated regional change
    • -
  • Covariate Sum Shift: Change adjusted by spatial covariates (infrastructure, topography, current land use)
    • -
-

Key outputs:

- 
intermediate/regional_change/
-├── region_ids.tif                          # Regional zone identifiers
-└── ssp2/rcp45/luh2-message/bau/2030/
-    ├── cropland_2030_2017_ha_diff_..._regional_coarsified.tif
-    └── cropland_2030_2017_ha_diff_..._covariate_sum_shift.tif
-
- {region_ids_section} -
-
-{regional_tabs_buttons} -
-{regional_tabs_content} -
+#

Regional Change

+#
+#

Purpose:

+#

Adjusts coarse-resolution changes using regional covariates to account for local constraints and opportunities.

+#
    +#
  • Creates a region ID map dividing AOI into separately processed zones
    • +#
  • Applies algorithms (e.g., proportional, covariate sum shift) to downscale from coarse to regional resolution
    • +#
  • Regional Coarsified: Aggregated regional change
    • +#
  • Covariate Sum Shift: Change adjusted by spatial covariates (infrastructure, topography, current land use)
    • +#
+#

Key outputs:

+# 
intermediate/regional_change/
+# ├── region_ids.tif                          # Regional zone identifiers
+# └── ssp2/rcp45/luh2-message/bau/2030/
+#     ├── cropland_2030_2017_ha_diff_..._regional_coarsified.tif
+#     └── cropland_2030_2017_ha_diff_..._covariate_sum_shift.tif
+#
+# {region_ids_section} +#
+#
+# {regional_tabs_buttons} +#
+# {regional_tabs_content} +#
-

Stitched LULC Scenarios

-
-

Purpose:

-

Combines all allocation zones back into complete region-wide LULC maps for each scenario.

-
    -
  • Stitches together individual allocation blocks
    • -
  • Clips to exact boundary
    • -
  • Generates outputs for all scenario combinations (bau, policy scenarios, etc.)
    • -
  • Creates both full extent and clipped versions
    • -
-

Key outputs:

- 
intermediate/stitched_lulc_simplified_scenarios/
-├── lulc_esa_seals7_ssp2_rcp45_luh2-message_bau_2030.tif
-├── lulc_esa_seals7_ssp2_rcp45_luh2-message_bau_2050.tif
-└── lulc_esa_seals7_ssp2_rcp45_luh2-message_bau_2030_clipped.tif
-
-
-
-{stitched_tabs_buttons} -
-{stitched_tabs_content} -
+#

Stitched LULC Scenarios

+#
+#

Purpose:

+#

Combines all allocation zones back into complete region-wide LULC maps for each scenario.

+#
    +#
  • Stitches together individual allocation blocks
    • +#
  • Clips to exact boundary
    • +#
  • Generates outputs for all scenario combinations (bau, policy scenarios, etc.)
    • +#
  • Creates both full extent and clipped versions
    • +#
+#

Key outputs:

+# 
intermediate/stitched_lulc_simplified_scenarios/
+# ├── lulc_esa_seals7_ssp2_rcp45_luh2-message_bau_2030.tif
+# ├── lulc_esa_seals7_ssp2_rcp45_luh2-message_bau_2050.tif
+# └── lulc_esa_seals7_ssp2_rcp45_luh2-message_bau_2030_clipped.tif
+#
+#
+#
+# {stitched_tabs_buttons} +#
+# {stitched_tabs_content} +#
- -""" - - # Format the HTML with current date and dynamic sections - html_content = html_template.format( - date=datetime.now().strftime("%Y-%m-%d %H:%M:%S"), - user=getpass.getuser(), - system=platform.system(), - scenarios_table=scenarios_table_html, - region_ids_section=region_ids_html, - coarse_tabs_buttons=coarse_tabs_buttons, - coarse_tabs_content=coarse_tabs_content, - regional_tabs_buttons=regional_tabs_buttons, - regional_tabs_content=regional_tabs_content, - stitched_tabs_buttons=stitched_tabs_buttons, - stitched_tabs_content=stitched_tabs_content - ) - - # Embed all images as base64 - html_content = embed_images_in_html(html_content, image_embeds) +# +# """ + +# # Format the HTML with current date and dynamic sections +# html_content = html_template.format( +# date=datetime.now().strftime("%Y-%m-%d %H:%M:%S"), +# user=getpass.getuser(), +# system=platform.system(), +# scenarios_table=scenarios_table_html, +# region_ids_section=region_ids_html, +# coarse_tabs_buttons=coarse_tabs_buttons, +# coarse_tabs_content=coarse_tabs_content, +# regional_tabs_buttons=regional_tabs_buttons, +# regional_tabs_content=regional_tabs_content, +# stitched_tabs_buttons=stitched_tabs_buttons, +# stitched_tabs_content=stitched_tabs_content +# ) + +# # Embed all images as base64 +# html_content = embed_images_in_html(html_content, image_embeds) - # Write HTML file - with open(report_output_path, 'w') as f: - f.write(html_content) +# # Write HTML file +# with open(report_output_path, 'w') as f: +# f.write(html_content) \ No newline at end of file From 7fd733f7c4452afd3c4c980a63b7de47886dc5df Mon Sep 17 00:00:00 2001 From: Justin Johnson Date: Tue, 9 Dec 2025 15:03:25 -0600 Subject: [PATCH 9/9] Remove warning print statement in regional_change function --- seals/seals_process_coarse_timeseries.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/seals/seals_process_coarse_timeseries.py b/seals/seals_process_coarse_timeseries.py index 10abfbb..2505539 100644 --- a/seals/seals_process_coarse_timeseries.py +++ b/seals/seals_process_coarse_timeseries.py @@ -153,7 +153,7 @@ def regional_change(p): covariate_sum_shift_path = hb.suri(output_path_template, 'covariate_sum_shift') input = ((regional_coarsified_path, 1), (current_luc_coarse_projections_path, 1), (target_raster_path, 1)) - print('WARNING! sometimes need to use *1000 here to convert from ha to m2. Check if this is desired behavior. GTAP NEEDS THIS OTHERS DONT') + # print('WARNING! sometimes need to use *1000 here to convert from ha to m2. Check if this is desired behavior. GTAP NEEDS THIS OTHERS DONT') def op(a, b, c): return (a - (c-b)) # return (a - (c-b)) * 1000