import glob
import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data, save_data
from thunderhopper.filetools import crop_paths
from thunderhopper.model import process_signal
from IPython import embed

# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = glob.glob(f'../data/processed/{target}*.npz')
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
save_path = '../data/inv/full/'

# ANALYSIS SETTINGS:
example_scales = np.array([0, 0.5, 1, 5, 10])
scales = np.linspace(0, 10, 100)
scales = np.unique(np.concatenate((scales, example_scales)))

# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
    print(f'Processing {name}')

    # Get normalized song recording:
    data, config = load_data(data_path, files='raw')
    song, rate = data['raw'], config['rate']
    song /= song.std(axis=0)

    # Get normalized noise:
    rng = np.random.default_rng()
    noise = rng.normal(size=song.shape[0])
    noise /= noise.std()

    # Get song segment to be analyzed:
    time = np.arange(song.shape[0]) / rate
    start, end = data['songs_0'].ravel()
    segment = (time >= start) & (time <= end)

    # Prepare snippet storage:
    shape_low = (song.shape[0], example_scales.size)
    shape_high = (song.shape[0], config['k_specs'].shape[0], example_scales.size)
    snippets = dict(
        raw=np.zeros(shape_low, dtype=float),
        filt=np.zeros(shape_low, dtype=float),
        env=np.zeros(shape_low, dtype=float),
        log=np.zeros(shape_low, dtype=float),
        inv=np.zeros(shape_low, dtype=float),
        conv=np.zeros(shape_high, dtype=float),
        bi=np.zeros(shape_high, dtype=float),
        feat=np.zeros(shape_high, dtype=float)
    )

    # Prepare measure storage:
    shape_low = (scales.size,)
    shape_high = (scales.size, config['k_specs'].shape[0])
    measures = dict(
        measure_raw=np.zeros(shape_low, dtype=float),
        measure_filt=np.zeros(shape_low, dtype=float),
        measure_env=np.zeros(shape_low, dtype=float),
        measure_log=np.zeros(shape_low, dtype=float),
        measure_inv=np.zeros(shape_low, dtype=float),
        measure_conv=np.zeros(shape_high, dtype=float),
        measure_feat=np.zeros(shape_high, dtype=float)
    )

    # Execute piecewise:
    for i, scale in enumerate(scales):
        print('Simulating scale ', scale)

        # Rescale song and add noise:
        scaled = song * scale + noise

        # Process mixture:
        signals, rates = process_signal(config, returns=stages,
                                        signal=scaled, rate=rate)
        # Store results:
        for stage in stages:
            key = f'measure_{stage}'

            # Log snippet data:
            if scale in example_scales:
                scale_ind = np.nonzero(example_scales == scale)[0][0]
                snippets[stage][:, ..., scale_ind] = signals[stage]

            # Log "intensity measure" per stage:
            if stage in ['raw', 'filt', 'env', 'log', 'inv', 'conv']:
                measures[key][i] = signals[stage][segment, ...].std(axis=0)
            elif stage == 'feat':
                measures[key][i] = signals[stage][segment, :].mean(axis=0) / signals[stage][segment, :].std(axis=0)

    # Relate to smallest scale:
    base_ind = np.argmin(scales)
    for stage in stages:
        if stage == 'bi':
            continue
        key = f'measure_{stage}'
        measures[key] /= measures[key][base_ind, ...]
        if stage in ['conv', 'feat']:
            spread = np.zeros((2, scales.size))
            spread[0] = np.percentile(measures[key], 25, axis=1)
            spread[1] = np.percentile(measures[key], 75, axis=1)
            measures[f'spread_{stage}'] = spread
            measures[key] = np.median(measures[key], axis=1)

    # Save analysis results:
    if save_path is not None:
        data = dict(
            scales=scales,
            example_scales=example_scales,
            )
        data.update(snippets)
        data.update(measures)
        save_data(save_path + name, data, config, overwrite=True)
print('Done.')
embed()