"""CSV storage for the eight one-dimensional terminal-path arrays.

Float columns use Python's binary64 round-trip representation. Integer columns
are restored as int8 (cause) and int32 (retained_marks). The string nan denotes
an unavailable terminal mark on a path that did not terminate at a jump.
"""
from __future__ import annotations

import csv
from pathlib import Path
import numpy as np

DTYPES = {
    'cause': np.dtype('int8'),
    'time_lower': np.dtype('float64'),
    'time_upper': np.dtype('float64'),
    'pre_h': np.dtype('float64'),
    'terminal_mark': np.dtype('float64'),
    'final_v': np.dtype('float64'),
    'retained_marks': np.dtype('int32'),
    'occupation_left_001': np.dtype('float64'),
}


def write_paths(path: Path, paths: dict[str, np.ndarray]) -> None:
    """Write one record per path, preserving the schema and binary64 values."""
    if set(paths) != set(DTYPES):
        raise ValueError('Unexpected path-array fields')
    n = len(paths['cause'])
    for name, dtype in DTYPES.items():
        if paths[name].shape != (n,) or paths[name].dtype != dtype:
            raise ValueError(f'Unexpected shape or dtype for {name}')
    path = Path(path)
    path.parent.mkdir(parents=True, exist_ok=True)
    with path.open('w', newline='', encoding='utf-8') as stream:
        writer = csv.writer(stream, lineterminator='\n')
        writer.writerow(DTYPES)
        for i in range(n):
            writer.writerow([
                str(int(paths[name][i])) if dtype.kind == 'i'
                else repr(float(paths[name][i]))
                for name, dtype in DTYPES.items()
            ])


def read_paths(path: Path) -> dict[str, np.ndarray]:
    """Read CSV fields with explicit dtypes and round-trip float parsing."""
    values = {name: [] for name in DTYPES}
    with Path(path).open(newline='', encoding='utf-8') as stream:
        reader = csv.reader(stream)
        if next(reader, None) != list(DTYPES):
            raise ValueError('Unexpected path CSV header')
        for row in reader:
            if len(row) != len(DTYPES):
                raise ValueError('Unexpected number of fields in path CSV')
            for (name, dtype), value in zip(DTYPES.items(), row):
                values[name].append(int(value) if dtype.kind == 'i' else float(value))
    return {name: np.asarray(values[name], dtype=dtype)
            for name, dtype in DTYPES.items()}