import numpy as np
from thunderhopper.model import configuration, process_signal
from thunderhopper.modeltools import load_data
from IPython import embed

## SETTINGS:

# General:
save_path = '../data/processed/white_noise'
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
sds = [1]
dur = 60

# Interactivity:
reload_saved = False

# Processing:
rate = 44100.0
env_rate = 44100.0
feat_rate = 44100.0
sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5,
         6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
config = configuration(env_rate, feat_rate, types=types, sigmas=sigmas)
config.update({
    'rate_ratio': None,
    'env_fcut': 250,
    'db_ref': 1,
    'inv_fcut': 5,
    'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2,
    'feat_fcut': 0.5,
    'label_channels': 0,
    'label_thresh': 0.5,
    })

## PREPARATION:

n_samples = int(dur * env_rate)
rng = np.random.default_rng()

# PROCESSING:

for sd in sds:
    print('Processing: SD =', sd)

    # Generate white noise signal:
    noise = rng.normal(loc=0, scale=sd, size=n_samples)

    # Fetch and store representations:
    save = None if save_path is None else save_path + f'_sd-{sd}.npz'
    process_signal(config, stages, signal=noise, rate=rate, save=save)

    # Cross-control:
    if reload_saved:
        data, params = load_data(save, stages, ['songs', 'noise'])
        embed()
print('Done.')