"""Replay the saved seed-7 synthetic gyro samples; no physical sensor measurements.
100 Hz; affine changes to constant bias and white-noise amplitude; prefix duration.
Analyze every selected sample; display only the first 10 s of the raw trace.
"""
import math
from pathlib import Path
import time
import numpy as np
from imu_kernel import analyze, BLOCK_SIZES

_saved = None


def samples():
    global _saved
    if _saved is None:
        local = Path(__file__).with_name('imu-synthetic.csv')
        path = local if local.exists() else Path(__file__).parent.parent/'expansion/imu-synthetic.csv'
        _saved = np.loadtxt(path, delimiter=',', skiprows=1)
    return _saved


def run(name, p):
    if name != 'imu' or set(p) != {'bias', 'noise_std', 'duration'}:
        raise ValueError('Unknown experiment or parameters')
    for key, lo, hi in [('bias', -.05, .05), ('noise_std', .0005, .01), ('duration', 10, 600)]:
        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 p['duration'] % 10 != 0:
        raise ValueError('Duration must use 10-second steps')
    start = time.perf_counter()
    source = samples()[:int(100*p['duration'])]
    t = source[:, 0]
    y = p['bias']+(source[:, 1]-.01)*(p['noise_std']/.002)
    stats, rows = analyze(t, y, BLOCK_SIZES[len(t)])
    def series(ident, x, y, unit='rad/s'):
        return dict(id=ident, x=x.tolist(), y=y.tolist(), x_unit='s', y_unit=unit)
    return dict(
        metrics=[dict(id='gyro_mean', value=stats['mean_rad_s'], unit='rad/s'),
                 dict(id='gyro_std', value=stats['std_rad_s'], unit='rad/s'),
                 dict(id='gyro_samples', value=stats['samples'], unit='count'),
                 dict(id='allan_longest_tau', value=float(rows[-1, 0]), unit='s'),
                 dict(id='allan_min_pairs', value=int(rows[:, 2].min()), unit='count')],
        series=[series('gyro_trace', t[:1000], y[:1000]),
                series('allan_deviation', rows[:, 0], rows[:, 1]),
                series('white_expectation', rows[:, 0], p['noise_std']/np.sqrt(rows[:, 0]*100)),
                series('adjacent_pairs', rows[:, 0], rows[:, 2], 'count')],
        stdout='', provenance=dict(data_type='synthetic', compute_ms=(time.perf_counter()-start)*1000))


if __name__ == '__main__':
    import argparse
    import json
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument('--parameters', default='{"bias":0.01,"noise_std":0.002,"duration":600}')
    args = parser.parse_args()
    print(json.dumps(run('imu', json.loads(args.parameters)), allow_nan=False))