Lots of stuff. Syncing to home.

python/save_noise_data.py

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+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 = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
+sds = [1]
+dur = 10
+
+# 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({
+    'bp_fcut': None,
+    'rate_ratio': None,
+    'env_fcut': 250,
+    '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.')