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.filtertools import find_kern_specs
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, 1, 10, 50])
scales = np.geomspace(0.01, 100, 100)
scales = np.unique(np.concatenate((scales, example_scales)))
kernels = np.array([
    [1, 0.002],
    [-1, 0.002],
    [2, 0.004],
    [-2, 0.004],
    [3, 0.032],
    [-3, 0.032]
])
kernels = None
types = None#np.array([-1])
sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])

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

    # Get song recording:
    data, config = load_data(data_path, files='raw')
    song, rate = data['raw'], config['rate']

    # Reduce to kernel subset:
    kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
    config['kernels'] = config['kernels'][:, kern_inds]
    config['k_specs'] = config['k_specs'][kern_inds, :]
    config['k_props'] = [config['k_props'][i] for i in kern_inds]
    config['feat_thresh'] = config['feat_thresh'][kern_inds]

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

    # Normalize song component:
    song /= song[segment].std(axis=0)

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

    # 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(
        snip_raw=np.zeros(shape_low, dtype=float),
        snip_filt=np.zeros(shape_low, dtype=float),
        snip_env=np.zeros(shape_low, dtype=float),
        snip_log=np.zeros(shape_low, dtype=float),
        snip_inv=np.zeros(shape_low, dtype=float),
        snip_conv=np.zeros(shape_high, dtype=float),
        snip_bi=np.zeros(shape_high, dtype=float),
        snip_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[f'snip_{stage}'][:, ..., scale_ind] = signals[stage]

            # Log intensity measure per stage (excluding binary):
            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)
    
    # thresh_y = np.percentile(measures['measure_feat'], 99, axis=0)
    # kern_types = np.unique()
    # thresh_x = np.zeros(thresh_y.shape, dtype=float)
    # for i, thresh in enumerate(thresh_y):
    #     if thresh < 0.1:
    #         thresh_x[i] = scales[-1]
    #         continue
    #     mask = (measures['measure_feat'][:, i] < thresh)
    #     thresh_x[i] = scales[np.nonzero(mask)[0][-1]]
    # inds = np.argsort(thresh_x)
    # print(config['k_specs'][inds, :])

    # fig, axes = plt.subplots(1, 2)
    # axes[0].plot(snippets['snip_feat'][:, inds, -1])
    # axes[1].plot(scales, measures['measure_feat'][:, inds])
    # plt.show()
    # embed()

    # 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()