"""Synthetic fixed-offset 24-hour schedule; interval means, no crop model."""
import math
import time
from datetime import datetime, timedelta
from vpd_kernel import analyze

BOUNDS = {'temperature': (0, 50), 'rh_light': (0, 100), 'rh_dark': (0, 100),
          'ppfd': (0, 3000), 'light_start': (0, 23.5), 'light_hours': (0, 24)}
DEFAULT = dict(temperature=25, rh_light=50, rh_dark=50, ppfd=200, light_start=8, light_hours=16)


def schedule(p):
    if set(p) != set(BOUNDS):
        raise ValueError('Unknown or missing parameters')
    for key, (lo, hi) in BOUNDS.items():
        value = p[key]
        if type(value) not in (int, float) or not math.isfinite(value) or not lo <= value <= hi:
            raise ValueError('Parameter out of range: ' + key)
        if key in ('light_start', 'light_hours') and abs(value * 2 - round(value * 2)) > 1e-8:
            raise ValueError('Lighting times must use half-hour steps')
    # Retain published unequal interval boundaries; add exact lighting transitions.
    cuts = sorted({0, 5, 8, 9, 15, 24, p['light_start'], (p['light_start'] + p['light_hours']) % 24})
    midnight = datetime.fromisoformat('2026-09-11T00:00:00+09:00')
    rows = []
    for a, b in zip(cuts, cuts[1:]):
        lit = ((a + b) / 2 - p['light_start']) % 24 < p['light_hours']
        rows.append(dict(start=(midnight + timedelta(hours=a)).isoformat(),
                         end=(midnight + timedelta(hours=b)).isoformat(),
                         temp_c=p['temperature'], rh_pct=p['rh_light'] if lit else p['rh_dark'],
                         ppfd_umol_m2_s=p['ppfd'] if lit else 0))
    return rows


def run(name, p):
    if name != 'vpd':
        raise ValueError('Unknown experiment')
    start = time.perf_counter()
    rows = schedule(p)
    result = analyze(rows)
    hours = [0.0]
    dose = [0.0]
    step_x, vpd, ppfd = [], [], []
    mean = 0.0
    for row, interval in zip(rows, result['intervals']):
        end = hours[-1] + interval['seconds'] / 3600
        step_x.extend([hours[-1], end])
        vpd.extend([interval['air_vpd_kpa']] * 2)
        ppfd.extend([row['ppfd_umol_m2_s']] * 2)
        mean += interval['air_vpd_kpa'] * interval['seconds'] / 86400
        hours.append(end)
        dose.append(interval['cumulative_mol_m2'])
    def series(ident, x, y, unit):
        return dict(id=ident, x=x, y=y, x_unit='h', y_unit=unit)
    return dict(metrics=[dict(id='mean_air_vpd', value=mean, unit='kPa'),
                         dict(id='daily_light', value=result['dli_mol_m2_day'], unit='mol m⁻² day⁻¹'),
                         dict(id='day_coverage', value=hours[-1], unit='h')],
                series=[series('air_vpd', step_x, vpd, 'kPa'),
                        series('light_dose', hours, dose, 'mol m⁻²'),
                        series('light_schedule', step_x, ppfd, 'μmol m⁻² s⁻¹')],
                stdout='', provenance=dict(data_type='synthetic', compute_ms=(time.perf_counter()-start)*1000))


if __name__ == '__main__':
    import argparse, json
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument('--parameters', default=json.dumps(DEFAULT))
    print(json.dumps(run('vpd', json.loads(parser.parse_args().parameters)), allow_nan=False))