Datasets:
Download scripts/merge_embed.py from wxcai/California_ISO: direct link, hf CLI and curl.
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- Download file 3.69 kB
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https://huggingface.co/datasets/wxcai/California_ISO/resolve/main/scripts/merge_embed.py
- Command line
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hf download hf://datasets/wxcai/California_ISO/scripts/merge_embed.py
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curl -L -o merge_embed.py https://huggingface.co/datasets/wxcai/California_ISO/resolve/main/scripts/merge_embed.py
3.69 kB
| import os | |
| import sys | |
| import glob | |
| import joblib | |
| import numpy as np | |
| from tqdm import tqdm | |
| def merge_and_stack_folders(folder1_path, folder2_path, output_dir): | |
| """ | |
| Merge same-name .pkl files from two folders and stack their contents by timestamp. | |
| """ | |
| # 1. Validate existence of input paths | |
| if not os.path.isdir(folder1_path): | |
| print(f"Error: Folder not found -> {folder1_path}") | |
| return | |
| if not os.path.isdir(folder2_path): | |
| print(f"Error: Folder not found -> {folder2_path}") | |
| return | |
| # 2. Ensure output directory exists | |
| os.makedirs(output_dir, exist_ok=True) | |
| print(f"Input folder 1: {folder1_path}") | |
| print(f"Input folder 2: {folder2_path}") | |
| print(f"Output will be saved to: {output_dir}") | |
| # 3. Find all .pkl files in first folder | |
| folder1_files = glob.glob(os.path.join(folder1_path, "*.pkl")) | |
| if not folder1_files: | |
| print(f"Warning: No .pkl files found in {folder1_path}.") | |
| return | |
| print(f"\nFound {len(folder1_files)} files, starting pairwise merging...") | |
| # 4. Traverse files, find pairs and process | |
| for file1_path in tqdm(folder1_files, desc="Processing file pairs"): | |
| file_name = os.path.basename(file1_path) | |
| file2_path = os.path.join(folder2_path, file_name) | |
| # Check if paired file exists in second folder | |
| if not os.path.exists(file2_path): | |
| print(f"\nSkipped: Paired file not found in {folder2_path} for {file_name}") | |
| continue | |
| try: | |
| # Load data from both files | |
| with open(file1_path, 'rb') as f: | |
| data1 = joblib.load(f) | |
| with open(file2_path, 'rb') as f: | |
| data2 = joblib.load(f) | |
| except Exception as e: | |
| print(f"\nError: Failed to load file pair {file_name}: {e}") | |
| continue | |
| # Create new dictionary for merged data | |
| merged_data = {} | |
| # Find common timestamps (keys) in both dictionaries | |
| common_keys = data1.keys() & data2.keys() | |
| for key in common_keys: | |
| vec1 = data1[key] | |
| vec2 = data2[key] | |
| # Verify vectors meet expectations | |
| if isinstance(vec1, np.ndarray) and isinstance(vec2, np.ndarray) and \ | |
| vec1.ndim == 2 and vec2.ndim == 2 and \ | |
| vec1.shape[1] == vec2.shape[1]: # Ensure same number of columns | |
| # Vertically stack (N, D) and (M, D) -> (N+M, D) | |
| # Example: (7, 256) and (7, 256) -> (14, 256) | |
| stacked_vec = np.vstack((vec1, vec2)) | |
| merged_data[key] = stacked_vec | |
| else: | |
| print(f"\nWarning: Data format mismatch at timestamp {key} in file {file_name}, skipped.") | |
| # Save result if any data was successfully merged | |
| if merged_data: | |
| output_path = os.path.join(output_dir, file_name) | |
| try: | |
| with open(output_path, 'wb') as f: | |
| joblib.dump(merged_data, f) | |
| except Exception as e: | |
| print(f"\nError: Failed to save merged file {output_path}: {e}") | |
| def main(): | |
| """ | |
| Main execution function. | |
| """ | |
| folder1 = "./data/California_ISO/weather/san-francisco" | |
| folder2 = "./data/California_ISO/weather/san-diego" | |
| output_folder = "./data/California_ISO/weather/merged_report_embedding" | |
| merge_and_stack_folders(folder1, folder2, output_folder) | |
| print("\nAll matching files merged and stacked successfully!") | |
| if __name__ == "__main__": | |
| main() | |