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| "Parallel processing for midi files" | |
| import csv | |
| from fastprogress.fastprogress import master_bar, progress_bar | |
| from pathlib import Path | |
| from pebble import ProcessPool | |
| from concurrent.futures import TimeoutError | |
| import numpy as np | |
| # https://stackoverflow.com/questions/20991968/asynchronous-multiprocessing-with-a-worker-pool-in-python-how-to-keep-going-aft | |
| def process_all(func, arr, timeout_func=None, total=None, max_workers=None, timeout=None): | |
| with ProcessPool() as pool: | |
| future = pool.map(func, arr, timeout=timeout) | |
| iterator = future.result() | |
| results = [] | |
| for i in progress_bar(range(len(arr)), total=len(arr)): | |
| try: | |
| result = next(iterator) | |
| if result: results.append(result) | |
| except StopIteration: | |
| break | |
| except TimeoutError as error: | |
| if timeout_func: timeout_func(arr[i], error.args[1]) | |
| return results | |
| def process_file(file_path, tfm_func=None, src_path=None, dest_path=None): | |
| "Utility function that transforms midi file to numpy array." | |
| output_file = Path(str(file_path).replace(str(src_path), str(dest_path))).with_suffix('.npy') | |
| if output_file.exists(): return output_file | |
| output_file.parent.mkdir(parents=True, exist_ok=True) | |
| # Call tfm_func and save file | |
| npenc = tfm_func(file_path) | |
| if npenc is not None: | |
| np.save(output_file, npenc) | |
| return output_file | |
| def arr2csv(arr, out_file): | |
| "Convert metadata array to csv" | |
| all_keys = {k for d in arr for k in d.keys()} | |
| arr = [format_values(x) for x in arr] | |
| with open(out_file, 'w') as f: | |
| dict_writer = csv.DictWriter(f, list(all_keys)) | |
| dict_writer.writeheader() | |
| dict_writer.writerows(arr) | |
| def format_values(d): | |
| "Format array values for csv encoding" | |
| def format_value(v): | |
| if isinstance(v, list): return ','.join(v) | |
| return v | |
| return {k:format_value(v) for k,v in d.items()} |