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 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)
ref_path = '../data/inv/field/ref_measures.npz'
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
save_path = f'../data/inv/field/{mode}/'

# ANALYSIS SETTINGS:
distances = np.load('../data/field/recording_distances.npy')[::-1]
thresh_rel = 0.5

# SUBSET SETTINGS:
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])

# PREPARATION:
if thresh_rel is not None:
    # Get threshold values from pure-noise response SD:
    thresh_abs = np.load(ref_path)['conv'] * thresh_rel

# 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']

    if thresh_rel is not None:
        # Set kernel-specific thresholds:
        config['feat_thresh'] = thresh_abs

    # 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]
        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)

    # Prepare storage:
    measures = {}
    if save_detailed:
        snippets = {}

    # Process snippet:
    signals, rates = process_signal(config, returns=stages, signal=song, rate=rate)
    for stage in stages:
        # Sort largest to smallest distance:
        signals[stage] = signals[stage][..., ::-1]

    # Store results:
    for stage in stages:
        # Log intensity measures:
        mkey = f'measure_{stage}'
        if stage == 'feat':
            measures[mkey] = signals[stage][segment, ...].mean(axis=0)
        else:
            measures[mkey] = signals[stage][segment, ...].std(axis=0)
        
        if measures[mkey].ndim == 2:
            # Make shape (distances, kernels):
            measures[mkey] = np.moveaxis(measures[mkey], 1, 0)

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

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

print('Done.')
embed()