"""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)