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Feature/surface heights and thermistor depths (#278)
* processes surface heights variables: `z_surf_combined`, `z_ice_surf`, `snow_height`, and thermistors' depths: `d_t_i_1-11` * `variable.csv` was updated accordingly * some clean-up of turbulent fluxes calculation, including renaming functions * handling empty station configuration files and making errors understandable * updated join_l3 so that surface height and thermistor depths in historical data are no longer ignored and to adjust the surface height between the merged datasets * calculated either from `gps_lat, gps_lon, gps_alt` or `lat, lon, alt`, static values called `latitude`, `longitude` and `altitude` are saved as attributes along with `latitude_origin`, `longitude_origin` and `altitude_origin` which states whether they come from gappy observations `gps_lat, gps_lon, gps_alt` or from gap-filled postprocess `lat, lon, alt` * changed "H" to "h" in pandas and added ".iloc" when necessary to remove deprecation warnings * made `make_metadata_csv.py` to update latest location in `aws-l3/AWS_station_metadata.csv` and `aws-l3/AWS_sites_metadata.csv` --------- Co-authored-by: Penny How <[email protected]> * L2toL3 test added (#282) * 3.8 and 3.9 tests removed, tests only for 3.10 and 3.11 * echo syntax changed * updated input file paths ---------
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,214 @@ | ||
| #!/usr/bin/env python | ||
| import os, sys, argparse | ||
| import pandas as pd | ||
| import xarray as xr | ||
| import logging | ||
|
|
||
| logging.basicConfig( | ||
| format="%(asctime)s; %(levelname)s; %(name)s; %(message)s", | ||
| level=logging.INFO, | ||
| stream=sys.stdout, | ||
| ) | ||
| logger = logging.getLogger(__name__) | ||
|
|
||
| def extract_metadata_from_nc(file_path: str, data_type: str, label_s_id: str) -> pd.Series: | ||
| """ | ||
| Extract metadata from a NetCDF file and return it as a pandas Series. | ||
| Parameters: | ||
| - file_path (str): The path to the NetCDF file. | ||
| - data_type (str): The type of data ('station' or 'site'). | ||
| - label_s_id (str): The label for the station or site ID. | ||
| Returns: | ||
| - pd.Series: A pandas Series containing the extracted metadata. | ||
| """ | ||
| try: | ||
| with xr.open_dataset(file_path) as nc_file: | ||
| # Extract attributes | ||
| s_id = nc_file.attrs.get(label_s_id, 'N/A') | ||
| location_type = nc_file.attrs.get('location_type', 'N/A') | ||
| project = nc_file.attrs.get('project', 'N/A') | ||
| if data_type == 'site': | ||
| stations = nc_file.attrs.get('stations', s_id) | ||
| if data_type == 'station': | ||
| number_of_booms = nc_file.attrs.get('number_of_booms', 'N/A') | ||
|
|
||
| # Extract the time variable as datetime64 | ||
| time_var = nc_file['time'].values.astype('datetime64[s]') | ||
|
|
||
| # Extract the first and last timestamps | ||
| date_installation_str = pd.Timestamp(time_var[0]).strftime('%Y-%m-%d') | ||
| last_valid_date_str = pd.Timestamp(time_var[-1]).strftime('%Y-%m-%d') | ||
|
|
||
| # Extract the first and last values of lat, lon, and alt | ||
| lat_installation = nc_file['lat'].isel(time=0).values.item() | ||
| lon_installation = nc_file['lon'].isel(time=0).values.item() | ||
| alt_installation = nc_file['alt'].isel(time=0).values.item() | ||
|
|
||
| lat_last_known = nc_file['lat'].isel(time=-1).values.item() | ||
| lon_last_known = nc_file['lon'].isel(time=-1).values.item() | ||
| alt_last_known = nc_file['alt'].isel(time=-1).values.item() | ||
|
|
||
| # Create a pandas Series for the metadata | ||
| if data_type == 'site': | ||
| row = pd.Series({ | ||
| 'project': project.replace('\r',''), | ||
| 'location_type': location_type, | ||
| 'stations': stations, | ||
| 'date_installation': date_installation_str, | ||
| 'latitude_installation': lat_installation, | ||
| 'longitude_installation': lon_installation, | ||
| 'altitude_installation': alt_installation, | ||
| 'date_last_valid': last_valid_date_str, | ||
| 'latitude_last_valid': lat_last_known, | ||
| 'longitude_last_valid': lon_last_known, | ||
| 'altitude_last_valid': alt_last_known | ||
| }, name=s_id) | ||
| else: | ||
| row = pd.Series({ | ||
| 'project': project.replace('\r',''), | ||
| 'number_of_booms': number_of_booms, | ||
| 'location_type': location_type, | ||
| 'date_installation': date_installation_str, | ||
| 'latitude_installation': lat_installation, | ||
| 'longitude_installation': lon_installation, | ||
| 'altitude_installation': alt_installation, | ||
| 'date_last_valid': last_valid_date_str, | ||
| 'latitude_last_valid': lat_last_known, | ||
| 'longitude_last_valid': lon_last_known, | ||
| 'altitude_last_valid': alt_last_known | ||
| }, name=s_id) | ||
| return row | ||
| except Exception as e: | ||
| logger.info(f"Warning: Error processing {file_path}: {str(e)}") | ||
| return pd.Series() # Return an empty Series in case of an error | ||
|
|
||
| def process_files(base_dir: str, csv_file_path: str, data_type: str) -> pd.DataFrame: | ||
| """ | ||
| Process all files in the base directory to generate new metadata. | ||
| Parameters: | ||
| - base_dir (str): The base directory containing the NetCDF files. | ||
| - csv_file_path (str): The path to the existing metadata CSV file. | ||
| - data_type (str): The type of data ('station' or 'site'). | ||
| Returns: | ||
| - pd.DataFrame: The combined metadata DataFrame. | ||
| """ | ||
| label_s_id = 'station_id' if data_type == 'station' else 'site_id' | ||
|
|
||
| # Initialize a list to hold the rows (Series) of DataFrame | ||
| rows = [] | ||
|
|
||
| # Read existing metadata if the CSV file exists | ||
| if os.path.exists(csv_file_path) and os.path.getsize(csv_file_path) > 0: | ||
| logger.info("Updating " + str(csv_file_path)) | ||
| existing_metadata_df = pd.read_csv(csv_file_path, index_col=label_s_id) | ||
| else: | ||
| logger.info("Creating " + str(csv_file_path)) | ||
| existing_metadata_df = pd.DataFrame() | ||
|
|
||
| # Track updated sites or stations to avoid duplicate updates | ||
| updated_s = [] | ||
| new_s = [] | ||
|
|
||
| # Traverse through all the subfolders and files in the base directory | ||
| for subdir, _, files in os.walk(base_dir): | ||
| for file in files: | ||
| if file.endswith('_hour.nc'): | ||
| file_path = os.path.join(subdir, file) | ||
| row = extract_metadata_from_nc(file_path, data_type, label_s_id) | ||
| if not row.empty: | ||
| s_id = row.name | ||
| if s_id in existing_metadata_df.index: | ||
| # Compare with existing metadata | ||
| existing_row = existing_metadata_df.loc[s_id] | ||
| old_date_installation = existing_row['date_installation'] | ||
| old_last_valid_date = existing_row['date_last_valid'] | ||
|
|
||
| # Update the existing metadata | ||
| existing_metadata_df.loc[s_id] = row | ||
|
|
||
| # Print message if dates are updated | ||
| if old_last_valid_date != row['date_last_valid']: | ||
| logger.info(f"Updated {label_s_id}: {s_id} date_last_valid: {old_last_valid_date} --> {row['date_last_valid']}") | ||
|
|
||
| updated_s.append(s_id) | ||
| else: | ||
| new_s.append(s_id) | ||
| # Append new metadata row to the list | ||
| rows.append(row) | ||
|
|
||
| # Convert the list of rows to a DataFrame | ||
| new_metadata_df = pd.DataFrame(rows) | ||
|
|
||
| # Concatenate the existing metadata with the new metadata | ||
| combined_metadata_df = pd.concat([existing_metadata_df, new_metadata_df], ignore_index=False) | ||
|
|
||
| # Exclude some sites | ||
| sites_to_exclude = [s for s in ['XXX', 'Roof_GEUS', 'Roof_PROMICE'] if s in combined_metadata_df.index] | ||
| excluded_metadata_df = combined_metadata_df.loc[sites_to_exclude].copy() | ||
| combined_metadata_df.drop(sites_to_exclude, inplace=True) | ||
|
|
||
| # Sort the DataFrame by index (s_id) | ||
| combined_metadata_df.sort_index(inplace=True) | ||
|
|
||
| # Print excluded lines | ||
| if not excluded_metadata_df.empty: | ||
| pd.set_option('display.max_columns', None) # Show all columns | ||
| pd.set_option('display.max_colwidth', None) # Show full width of columns | ||
| pd.set_option('display.width', None) # Disable line wrapping | ||
| logger.info("\nExcluded lines from combined metadata.csv:") | ||
| print(excluded_metadata_df) | ||
|
|
||
| # Drop excluded lines from combined_metadata_df | ||
| combined_metadata_df.drop(sites_to_exclude, errors='ignore', inplace=True) | ||
|
|
||
| # Save to csv | ||
| combined_metadata_df.to_csv(csv_file_path, index_label=label_s_id) | ||
|
|
||
| return combined_metadata_df, existing_metadata_df, new_s, updated_s | ||
|
|
||
| def compare_and_log_updates(combined_metadata_df: pd.DataFrame, existing_metadata_df: pd.DataFrame, new_s: list, updated_s: list): | ||
| """ | ||
| Compare the combined metadata with the existing metadata and log the updates. | ||
| Parameters: | ||
| - combined_metadata_df (pd.DataFrame): The combined metadata DataFrame. | ||
| - existing_metadata_df (pd.DataFrame): The existing metadata DataFrame. | ||
| - new_s (list): List of new station/site IDs. | ||
| - updated_s (list): List of updated station/site IDs. | ||
| """ | ||
| # Determine which lines were not updated (reused) and which were added | ||
| if not existing_metadata_df.empty: | ||
| reused_s = [s_id for s_id in existing_metadata_df.index if ((s_id not in new_s) & (s_id not in updated_s))] | ||
| reused_lines = existing_metadata_df.loc[reused_s] | ||
| added_lines = combined_metadata_df.loc[combined_metadata_df.index.difference(existing_metadata_df.index)] | ||
|
|
||
| logger.info("\nLines from the old metadata.csv that are reused (not updated):") | ||
| print(reused_lines) | ||
|
|
||
| if not added_lines.empty: | ||
| logger.info("\nLines that were not present in the old metadata.csv and are added:") | ||
| print(added_lines) | ||
| else: | ||
| logger.info("\nAll lines are added (no old metadata.csv found)") | ||
|
|
||
| def main(): | ||
| parser = argparse.ArgumentParser(description='Process station or site data.') | ||
| parser.add_argument('-t', '--type', choices=['station', 'site'], | ||
| required=True, | ||
| help='Type of data to process: "station" or "site"') | ||
| parser.add_argument('-r', '--root_dir', required=True, help='Root directory ' + | ||
| 'containing the aws-l3 station or site folder') | ||
| parser.add_argument('-m','--metadata_file', required=True, | ||
| help='File path to metadata csv file (existing or '+ | ||
| 'intended output path') | ||
|
|
||
| args = parser.parse_args() | ||
| combined_metadata_df, existing_metadata_df, new_s, updated_s = process_files(args.root_dir, args.metadata_file, args.type) | ||
| compare_and_log_updates(combined_metadata_df, existing_metadata_df, new_s, updated_s) | ||
|
|
||
| if __name__ == '__main__': | ||
| main() |
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