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[1pt] PR: Function to extract HAND and hydroid values at point locati…
…ons for SRC adjustment tool (#444) This merges in the script, adjust_rc_with_feedback.py, that will be expanded in future issues. The primary function that performs the HAND value and hydroid extraction is ingest_points_layer() but this may change as the overall process evolves. - Added adjust_rc_with_feedback.py with ingest_points_layer(), a function to extract HAND and hydroid values for use in an automatic synthetic rating curve updating mechanism. This resolves #437.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,112 @@ | ||
| import argparse | ||
| import geopandas as gpd | ||
| from geopandas.tools import sjoin | ||
| import os | ||
| import rasterio | ||
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| temp_workspace = r'' | ||
| HAND_CRS = 'EPSG:3857' | ||
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| def update_rating_curve(grouped_median, huc6): | ||
| pass | ||
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| def ingest_points_layer(points_layer, fim_directory, wbd_path): | ||
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| # Read wbd_path and points_layer to determine which HUC6 each point is in. | ||
| wbd_huc8_read = gpd.read_file(wbd_path, layer='WBDHU6') | ||
| points_layer_read = gpd.read_file(points_layer) | ||
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| # Update CRS of points_layer_read. | ||
| points_layer_read = points_layer_read.to_crs(HAND_CRS) | ||
| wbd_huc8_read = wbd_huc8_read.to_crs(HAND_CRS) | ||
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| # Spatial join the two layers. | ||
| water_edge_df = sjoin(points_layer_read, wbd_huc8_read) | ||
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| # Convert to GeoDataFrame. | ||
| gdf = gpd.GeoDataFrame(water_edge_df) | ||
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| # Add two columns for X and Y. | ||
| gdf['X'] = gdf['geometry'].x | ||
| gdf['Y'] = gdf['geometry'].y | ||
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| # Extract information into dictionary. | ||
| huc6_list = [] | ||
| for index, row in gdf.iterrows(): | ||
| huc6 = row['HUC6'] | ||
| if huc6 not in huc6_list: | ||
| huc6_list.append(huc6) | ||
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| # Define coords variable to be used in point raster value attribution. | ||
| coords = [(x,y) for x, y in zip(water_edge_df.X, water_edge_df.Y)] | ||
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| # Define paths to relevant HUC6 HAND data. | ||
| for huc6 in huc6_list: | ||
| print(huc6) | ||
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| # Define paths to relevant HUC6 HAND data and get necessary metadata for point rasterization. | ||
| hand_path = os.path.join(fim_directory, huc6, 'hand_grid_' + huc6 + '.tif') | ||
| if not os.path.exists(hand_path): | ||
| print("HAND grid for " + huc6 + " does not exist.") | ||
| continue | ||
| catchments_path = os.path.join(fim_directory, huc6, 'catchments_' + huc6 + '.tif') | ||
| if not os.path.exists(catchments_path): | ||
| print("Catchments grid for " + huc6 + " does not exist.") | ||
| continue | ||
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| # water_edge_df = water_edge_df[water_edge_df['HUC6'] == huc6] | ||
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| # Use point geometry to determine pixel values at catchment and HAND grids. | ||
| hand_src = rasterio.open(hand_path) | ||
| water_edge_df['hand'] = [h[0] for h in hand_src.sample(coords)] | ||
| hand_src.close() | ||
| catchments_src = rasterio.open(catchments_path) | ||
| water_edge_df['hydroid'] = [c[0] for c in catchments_src.sample(coords)] | ||
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| # Get median HAND value for appropriate groups. | ||
| water_edge_median_ds = water_edge_df.groupby(["hydroid", "flow", "submitter", "coll_time", "flow_unit"])['hand'].median() | ||
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| output_csv = os.path.join(fim_directory, huc6, 'user_supplied_n_vals_' + huc6 + '.csv') | ||
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| water_edge_median_ds.to_csv(output_csv) | ||
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| # 1. Loop and find the corresponding hydroids in the Hydrotable | ||
| # 2. Grab slope, wetted area, hydraulic radius, and feature_id that correspond with the matching hydroids and HAND value for the nearest stage | ||
| # 3. Calculate new column for new roughness using the above info | ||
| # 3b. If multiple flows exist per hydroid, aggregate the resulting Manning Ns | ||
| # 3c. If range of resulting Manning Ns is high, notify human | ||
| # 4. Update Hydrotable | ||
| # 4a. Copy default flow and N columns to new columns with "_default" in the field name | ||
| # 4b. Overwrite the official flow and N columns with the new calculated values | ||
| # 4c. Add last_updated column with timestamp where values were changed, also add "submitter" column | ||
| # 5. What do we do in catchments that match the feature_id? | ||
| # 5a. If these catchments already have known data, then let it use those. If not, use new calculated Ns. | ||
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| update_rating_curve(water_edge_median_ds, huc6) | ||
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| if __name__ == '__main__': | ||
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| # Parse arguments. | ||
| parser = argparse.ArgumentParser(description='Adjusts rating curve given a shapefile containing points of known water boundary.') | ||
| parser.add_argument('-p','--points-layer',help='Path to points layer containing known water boundary locations',required=True) | ||
| parser.add_argument('-d','--fim-directory',help='Parent directory of FIM-required datasets.',required=True) | ||
| parser.add_argument('-w','--wbd-path', help='Path to national HUC6 layer.',required=True) | ||
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| # Assign variables from arguments. | ||
| args = vars(parser.parse_args()) | ||
| points_layer = args['points_layer'] | ||
| fim_directory = args['fim_directory'] | ||
| wbd_path = args['wbd_path'] | ||
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| ingest_points_layer(points_layer, fim_directory, wbd_path) | ||
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| # Open catchment, HAND, and point grids and determine pixel values for Hydroid, HAND value, and discharge value, respectively. | ||
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| # Open rating curve file(s). | ||
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| # Use three values to determine the hydroid rating curve(s) to update, then update them using a variation of Manning's Equation. | ||
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| # Ensure the JSON rating curve is updated and saved (overwitten). Consider adding attributes to document what was performed. |