import numpy as np
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data, save_data
from IPython import embed

# GENERAL SETTINGS:
target_species = [
    'Chorthippus_biguttulus',
    'Chorthippus_mollis',
    'Chrysochraon_dispar',
    'Euchorthippus_declivus',
    'Gomphocerippus_rufus',
    'Omocestus_rufipes',
    'Pseudochorthippus_parallelus',
]
search_path = '../data/inv/thresh_lp/'
save_path = '../data/inv/thresh_lp/collected/'

# ANALYSIS SETTINGS:
with_noise = False

# EXECUTION:
for i, species in enumerate(target_species):
    print(f'Processing {species}')

    # Fetch all species-specific song files:
    incl = 'noise' if with_noise else 'pure'
    all_paths = search_files(species, incl=incl, ext='npz', dir=search_path)
    
    # Run through files:
    for j, path in enumerate(all_paths):

        # Load invariance data:
        data, config = load_data(path, ['scales', 'measure_feat', 'thresh_rel'])
        measure = data['measure_feat']

        if j == 0:
            # Prepare species-specific storage:
            spec_data = np.zeros((measure.shape + (len(all_paths),)), dtype=float)

        # Log file data:
        spec_data[..., j] = measure

    # Save collected file data:
    save_name = save_path + species + f'_{incl}'
    archive = dict(
        scales=data['scales'],
        measure_feat=spec_data,
        thresh_rel=data['thresh_rel'])
    save_data(save_name, archive, config)

print('Done.')