Create generate_metadata.py

generate_metadata.py

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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()