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import pandas as pd
import xarray as xr
from glob import glob
from typing import Optional, List
def extract_region_id(filepath: str) -> str:
"""Extract region ID from netCDF file attributes."""
ds = xr.open_dataset(filepath)
original_id = ds.attrs.get('original_id', '')
ice_service = ds.attrs.get('ice_service', '')
ds.close()
parts = original_id.split('_')
if ice_service == "dmi":
return parts[-2] + "_" + parts[-1].split('.')[0]
return parts[-4]
def load_split_data(splits: List[str]) -> pd.DataFrame:
"""Load and preprocess data from split directories."""
dfs = []
for split in splits:
paths = glob(f"{split}/*.nc")
split_df = pd.DataFrame(paths, columns=["path"])
split_df["split"] = split
dfs.append(split_df)
df = pd.concat(dfs, ignore_index=True)
df['date'] = pd.to_datetime(df['path'].str.extract(r'(\d{8}T\d{6})')[0], format='%Y%m%dT%H%M%S')
df['ice_service'] = df['path'].str.extract(r'_(dmi|cis)_')[0]
df['is_reference'] = df['path'].str.contains('reference')
return df
def process_test_data(test_data: pd.DataFrame) -> pd.DataFrame:
"""Process test split data to pair inputs with references."""
test_pairs = []
for (date, ice_service), group in test_data.groupby(['date', 'ice_service']):
input_file = group[~group['is_reference']]['path'].iloc[0]
ref_file = group[group['is_reference']]['path'].iloc[0]
test_pairs.append({
'input_path': input_file,
'reference_path': ref_file,
'date': date,
'ice_service': ice_service,
'split': 'test'
})
return pd.DataFrame(test_pairs)
def create_summary_df() -> pd.DataFrame:
"""Create summary DataFrame with all samples."""
splits = ["train", "test"]
df = load_split_data(splits)
# Process train data
train_data = df[df['split'] == 'train'].copy()
train_data['input_path'] = train_data['path']
train_data['reference_path'] = None
# Process test data
test_data = process_test_data(df[df['split'] == 'test'])
# Combine and add region IDs
summary_df = pd.concat([
train_data[['input_path', 'reference_path', 'date', 'ice_service', 'split']],
test_data
])
summary_df['region_id'] = summary_df['input_path'].apply(extract_region_id)
return summary_df
def main():
"""Main function to generate metadata summary."""
summary_df = create_summary_df()
print("\nFinal Summary:")
print(summary_df)
if __name__ == '__main__':
main() |