import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data, save_data
from thunderhopper.filetools import search_files, crop_paths
from thunderhopper.filtertools import find_kern_specs
from thunderhopper.filters import sosfilter
from thunderhopper.model import process_signal
from IPython import embed

# GENERAL SETTINGS:
mode = ['song', 'noise'][0]
example_file = dict(
    song='Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms',
    noise='merged_noise'
)[mode]
search_path = f'../data/field/processed/{mode}/'
data_paths = search_files('*', ext='npz', dir=search_path)
thresh_path = '../data/inv/field/thresholds.npz'
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
pre_stages = stages[:-1]
save_path = f'../data/inv/field/{mode}/'

# ANALYSIS SETTINGS:
distances = np.load('../data/field/recording_distances.npy')[::-1]
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])
init_scale = 10000

# SUBSET SETTINGS:
kernels = None
types = None
sigmas = None

# PREPARATION:
thresh_data = dict(np.load(thresh_path))
thresh_abs = thresh_rel[:, None] * thresh_data['sds'][None, :]

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

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

    # Sort max to min distance:
    song = song[:, ::-1] * init_scale

    # Reduce to kernel subset:
    if any(var is not None for var in [kernels, types, sigmas]):
        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]
        thresh_abs = thresh_abs[:, 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)

    # Prepare storage:
    shape = (distances.size, config['k_specs'].shape[0], thresh_rel.size)
    measures = dict(measure_feat=np.zeros(shape, dtype=float))
    if save_detailed:
        shape = (song.shape[0], config['k_specs'].shape[0], distances.size, thresh_rel.size)
        snippets = dict(snip_feat=np.zeros(shape, dtype=float))

    # Process snippet (excluding features):
    signals, rates = process_signal(config, returns=pre_stages, signal=song, rate=rate)

    # Store non-feature results:
    for stage in pre_stages:
        mkey = f'measure_{stage}'

        # Log intensity measures:
        measures[mkey] = signals[stage][segment, ...].std(axis=0)
        if stage == 'conv':
            # Make shape (distances, kernels) for consistency:
            measures[mkey] = np.moveaxis(measures[mkey], 1, 0)

        if save_detailed:
            # Log optional snippet data:
            snippets[f'snip_{stage}'] = signals[stage]
    conv = signals['conv']

    # Execute piecewise per threshold:
    for i, thresholds in enumerate(thresh_abs):
        # Execute piecewise per distance:
        for j in range(conv.shape[-1]):
            feat = sosfilter((conv[:, :, j] > thresholds).astype(float),
                            rate, config['feat_fcut'], 'lp',
                            padtype='fixed', padlen=config['padlen'])
            
            # Log intensity measure:
            measure = feat[segment, ...].mean(axis=0)
            measures['measure_feat'][j, :, i] = measure
            if save_detailed:
                # Log optional snippet data:
                snippets['snip_feat'][:, :, j, i] = feat
    


            # # Log intensity measure, ensuring shape (distances, kernels, thresholds):
            # measures['measure_feat'][:, :, i] = np.moveaxis(feat[segment, ...].mean(axis=0), 1, 0)

            # if save_detailed:
            #     # Log optional snippet data:
            #     snippets['snip_feat'][:, :, :, i] = feat

        # thresholds = thresholds[None, :, None]
        # embed()

        # # Finalize processing:
        # feat = sosfilter((signals['conv'] > thresholds).astype(float),
        #                  rate, config['feat_fcut'], 'lp',
        #                  padtype='fixed', padlen=config['padlen'])
        # if i == thresholds.shape[0] - 1:
        #     fig, axes = plt.subplots(1, 8, sharex=True, sharey=True, figsize=(16, 9))
        #     for j, ax in enumerate(axes):
        #         ax.plot(time, feat[..., j])
        #     plt.show()
        #     embed()

        # # Log intensity measure, ensuring shape (distances, kernels, thresholds):
        # measures['measure_feat'][:, :, i] = np.moveaxis(feat[segment, ...].mean(axis=0), 1, 0)

        # if save_detailed:
        #     # Log optional snippet data:
        #     snippets['snip_feat'][:, :, :, i] = feat

    # Save analysis results:
    if save_path is not None:
        data = dict(
            distances=distances,
            thresh_rel=thresh_rel,
            thresh_abs=thresh_abs,
            )
        data.update(measures)
        if save_detailed:
            data.update(snippets)
        save_data(save_path + name, data, config, overwrite=True)

print('Done.')
embed()