import numpy as np
from thunderhopper.filetools import search_files
from thunderhopper.model import process_signal
from thunderhopper.modeltools import load_data
from IPython import embed

## SETTINGS:

# General:
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
noise_path = search_files('merged_noise', dir='../data/field/processed/noise/')[0]
save_path = '../data/inv/field/ref_measures.npz'
channels = np.array([0, 1, 2, 3, 4, 5, 6, 7])

# PROCESSING:

# Load pure-noise starter representation:
noise_data, config = load_data(noise_path, stages[0])
# Accumulate channels in time-major order:
starter = noise_data[stages[0]][:, channels].ravel(order='F')

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

# Run pipeline:
data = process_signal(config, stages, signal=starter, rate=config['rate'])[0]

# Get measures:
measures = {}
for stage in stages:
    if stage == 'feat':
        measures[stage] = data[stage][segment, :].mean(axis=0)
    else:
        measures[stage] = data[stage][segment, ...].std(axis=0)

# Save results:
np.savez(save_path, **measures)

print('Done.')
embed()