import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data
from thunderhopper.filetools import search_files
from thunderhopper.model import process_signal

paths = search_files('Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms', dir='../data/field/processed/song/')
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[-1]
thresh_abs = np.load('../data/inv/field/thresholds.npz')['sds'] * thresh_rel

for path in paths:
    print(f'Processing {path}')

    data, config = load_data(path, files='raw')
    config['feat_thresh'] = thresh_abs

    song, rate = data['raw'], config['rate']
    time = np.arange(song.shape[0]) / rate
    start, end = data['songs_0'].ravel()
    segment = (time >= start) & (time <= end)

    signals, rates = process_signal(config, 'feat', signal=song, rate=rate)
    feat = signals['feat']

    fig, axes = plt.subplots(1, 8, sharex=True, sharey=True, figsize=(16, 9))
    for i, ax in enumerate(axes):
        ax.plot(feat[..., i])
    plt.show()