import numpy as np
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.model import process_signal
from thunderhopper.filters import sosfilter
from misc_functions import draw_noise_segment
from IPython import embed

# GENERAL SETTINGS:
target_species = [
    'Chorthippus_biguttulus',
    'Chorthippus_mollis',
    # 'Chrysochraon_dispar',
    # 'Euchorthippus_declivus',
    # 'Gomphocerippus_rufus',
    # 'Omocestus_rufipes',
    # 'Pseudochorthippus_parallelus',
][1]
example_file = {
    'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
    'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
    'Chrysochraon_dispar': 'Chrysochraon_dispar_DJN_26_T28C_DT-32s134ms-34s432ms',
    'Euchorthippus_declivus': 'Euchorthippus_declivus_FTN_79-2s167ms-2s563ms',
    'Gomphocerippus_rufus': 'Gomphocerippus_rufus_FTN_91-3-884ms-10s427ms',
    'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
    'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
}[target_species]
data_paths = search_files(target_species, dir='../data/processed/')
noise_path = '../data/processed/white_noise_sd-1.npz'
thresh_path = '../data/inv/full/thresholds.npz'
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
pre_stages = stages[:-1]
save_path = '../data/inv/full/'

# ANALYSIS SETTINGS:
example_scales = np.array([0.1, 1, 10, 30, 100, 300])
scales = np.geomspace(0.01, 10000, 500)
scales = np.unique(np.concatenate(([0], scales, example_scales)))
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])

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

# PREPARATION:
pure_noise = np.load(noise_path)['raw']
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']

    # 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)

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

    # Get normalized noise component:
    noise = draw_noise_segment(pure_noise, song.shape[0])
    noise /= noise[segment].std()

    # Prepare storage:
    shape_low = (scales.size,)
    shape_high = (scales.size, config['k_specs'].shape[0])
    measures = dict(
        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 + (thresh_rel.size,), dtype=float)
    )
    if save_detailed:
        # Prepare optional 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_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_feat=np.zeros(shape_high + (thresh_rel.size,), 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 (excluding features):
        signals, rates = process_signal(config, returns=pre_stages,
                                        signal=scaled, rate=rate)
        # Store non-feature results:
        for stage in pre_stages:
            # Log intensity measures:
            measures[f'measure_{stage}'][i] = signals[stage][segment, ...].std(axis=0)

            # Log optional snippet data:
            if save_detailed and scale in example_scales:
                scale_ind = np.nonzero(example_scales == scale)[0][0]
                snippets[f'snip_{stage}'][:, ..., scale_ind] = signals[stage]

        # Execute piecewise again:
        for j, thresholds in enumerate(thresh_abs):
            # Finalize processing:
            feat = sosfilter((signals['conv'] > thresholds).astype(float),
                             rate, config['feat_fcut'], 'lp',
                             padtype='fixed', padlen=config['padlen'])

            # Log intensity measure:
            measures['measure_feat'][i, :, j] = feat[segment, :].mean(axis=0)

            # Log optional snippet data:
            if save_detailed and scale in example_scales:
                snippets['snip_feat'][:, :, scale_ind, j] = feat

    # Save analysis results:
    if save_path is not None:
        data = dict(
            scales=scales,
            example_scales=example_scales,
            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()