| """Convert NASA Tecplot-zone .dat files to flat CSV. |
| |
| Each .dat file in a given directory is read via the same parser used in |
| the original NASA challenge submission (readNasaZoneFile), then flattened |
| to a single CSV with one row per (zone, sample) and a `zone` column to |
| preserve the multi-zone structure. |
| |
| Usage: |
| python dat_to_csv.py <dir1> [<dir2> ...] |
| """ |
|
|
| import sys |
| import os |
| import glob |
| import numpy as np |
| import pandas as pd |
|
|
|
|
| def _text_in_line(line): |
| if line == '': |
| return False |
| return max( |
| [(ord(c) > 96) & (ord(c) < 101) | (ord(c) > 101) & (ord(c) < 123) |
| for c in line.lower()] |
| ) |
|
|
|
|
| def read_nasa_zone_file(path): |
| """Parse a NASA Tecplot-zone .dat file into a dict {zone: {var: array}}.""" |
| with open(path) as f: |
| data = f.read() |
|
|
| data_lower = data.lower() |
|
|
| var_start = data_lower.find('variables') |
| var_end = var_start + data_lower[var_start:].find('\n') |
| var_line = data[var_start:var_end] |
| variables = var_line.split('"')[1::2] |
|
|
| zones = [] |
| zone_inds = [] |
| data_start_inds = [] |
| find_data_start = False |
|
|
| if 'zone' not in data_lower: |
| zones.append('zone') |
| zone_inds.append(0) |
| find_data_start = True |
|
|
| lines = data.split('\n') |
| lower_lines = data_lower.split('\n') |
| for i, (line, lower) in enumerate(zip(lines, lower_lines)): |
| if 'zone t=' in lower or 'zone, t=' in lower: |
| zones.append(line.split('"')[1]) |
| zone_inds.append(i) |
| find_data_start = True |
| if find_data_start and not _text_in_line(line): |
| data_start_inds.append(i) |
| find_data_start = False |
|
|
| zone_inds.append(i + 1) |
|
|
| out = {} |
| for i, zone in enumerate(zones): |
| buff = np.genfromtxt( |
| path, |
| skip_header=data_start_inds[i], |
| max_rows=zone_inds[i + 1] - data_start_inds[i], |
| ) |
| if buff.ndim == 1: |
| buff = buff.reshape(1, -1) |
| out[zone] = {variables[j]: buff[:, j] for j in range(len(variables))} |
| return out |
|
|
|
|
| def dat_to_dataframe(path): |
| zones = read_nasa_zone_file(path) |
| frames = [] |
| for zone_name, var_dict in zones.items(): |
| df = pd.DataFrame(var_dict) |
| df.insert(0, 'zone', zone_name) |
| frames.append(df) |
| return pd.concat(frames, ignore_index=True) |
|
|
|
|
| def convert_directory(directory): |
| dat_files = sorted(glob.glob(os.path.join(directory, '*.dat'))) |
| if not dat_files: |
| print(f' (no .dat files in {directory})') |
| return |
| for dat_path in dat_files: |
| try: |
| df = dat_to_dataframe(dat_path) |
| except Exception as exc: |
| print(f' FAILED {dat_path}: {exc}') |
| continue |
| csv_path = dat_path[:-4] + '.csv' |
| df.to_csv(csv_path, index=False) |
| print(f' {os.path.basename(dat_path)} -> {os.path.basename(csv_path)}' |
| f' ({len(df)} rows, {len(df.columns)} cols)') |
|
|
|
|
| if __name__ == '__main__': |
| if len(sys.argv) < 2: |
| print(__doc__) |
| sys.exit(1) |
| for d in sys.argv[1:]: |
| print(f'\n[{d}]') |
| convert_directory(d) |
|
|
