Kinda finished analysis and figure for Log-HP invariance (WIP).

python/save_inv_data_log-hp.py

@@ -0,0 +1,124 @@
+import plotstyle_plt
+import glob
+import numpy as np
+import matplotlib.pyplot as plt
+from thunderhopper.modeltools import load_data, save_data
+from thunderhopper.filetools import crop_paths
+from thunderhopper.filters import decibel, sosfilter
+from thunderhopper.model import extract_env
+from IPython import embed
+
+# GENERAL SETTINGS:
+target = 'Omocestus_rufipes'
+data_paths = glob.glob(f'../data/processed/{target}*.npz')
+save_path = '../data/inv/log_hp/'
+
+# ANALYSIS SETTINGS:
+scales = np.arange(0, 10.1, 0.1)
+save_scales = np.array([0, 0.5, 1, 2, 5, 10])
+floor_percents = dict(song=100, noise=99)
+single_db_ref = True
+plot_redundant = False
+
+# EXECUTION:
+for data_path, name in zip(data_paths, crop_paths(data_paths)):
+    print(f'Processing {name}')
+
+    # Load song envelope:
+    data, config = load_data(data_path, files='env')
+    song, rate = data['env'], config['env_rate']
+    t_full = np.arange(song.shape[0]) / rate
+
+    # Generate noise envelope:
+    rng = np.random.default_rng()
+    noise = rng.normal(size=song.shape)
+    noise = extract_env(noise, rate, config=config)
+
+    # Normalize each:
+    song /= song.std()
+    noise /= noise.std()
+
+    # Mix song component and noise component:
+    scaled = song[:, None] * scales[None, :]
+    mix = scaled + noise[:, None]
+
+    # Find noise floor (experimental):
+    song_percent = np.percentile(scaled, floor_percents['song'], axis=0)
+    noise_percent = np.percentile(noise, floor_percents['noise'])
+    floor_scale = scales[np.nonzero(song_percent <= noise_percent)[0][-1]]
+
+    # Process mixture:
+    mix_log = decibel(mix, axis=None if single_db_ref else 0)
+    mix_inv = sosfilter(mix_log, rate, config['inv_fcut'], 'hp',
+                        padtype='constant', padlen=config['padlen'])
+
+    # Get variances per stage:
+    var_env = mix.var(axis=0)
+    var_log = mix_log.var(axis=0)
+    var_inv = mix_inv.var(axis=0)
+
+    # Get SNRs against pure noise per stage:
+    base_ind = np.nonzero(scales == 0)[0][0]
+    snr_env = var_env / var_env[base_ind]
+    snr_log = var_log / var_log[base_ind]
+    snr_inv = var_inv / var_inv[base_ind]
+
+    if plot_redundant:
+        # Normalize SNRs:
+        norm_snr_env = snr_env / snr_env.max()
+        norm_snr_log = snr_log / snr_log.max()
+        norm_snr_inv = snr_inv / snr_inv.max()
+
+        # Get SNR gain against env:
+        gain_log = snr_log / snr_env
+        gain_inv = snr_inv / snr_env
+
+        # Plot results:
+        fig, axes = plt.subplots(6, 1, sharex=True, layout='constrained')
+        
+        axes[0].set_title('variance')
+        axes[0].plot(scales, var_env)
+        axes[0].plot(scales, var_log)
+        axes[0].plot(scales, var_inv)
+
+        axes[1].set_title('normalized variance')
+        axes[1].plot(scales, var_env / var_env.max())
+        axes[1].plot(scales, var_log / var_log.max())
+        axes[1].plot(scales, var_inv / var_inv.max())
+
+        axes[2].set_title('SNR')
+        axes[2].plot(scales, snr_env)
+        axes[2].plot(scales, snr_log)
+        axes[2].plot(scales, snr_inv)
+
+        axes[3].set_title('normalized SNR')
+        axes[3].plot(scales, norm_snr_env)
+        axes[3].plot(scales, norm_snr_log)
+        axes[3].plot(scales, norm_snr_inv)
+
+        axes[4].set_title('gain (absolute SNR)')
+        axes[4].plot(scales, gain_log)
+        axes[4].plot(scales, gain_inv)
+
+        axes[5].set_title('gain (normalized SNR)')
+        axes[5].plot(scales, norm_snr_log / norm_snr_env)
+        axes[5].plot(scales, norm_snr_inv / norm_snr_env)
+        plt.show()
+
+    # Save analysis results:
+    save_inds = np.isin(scales, save_scales)
+    if save_path is not None:
+        data = dict(
+            scales=scales,
+            plot_scales=scales[save_inds],
+            floor_scale=floor_scale,
+            env=mix[:, save_inds],
+            log=mix_log[:, save_inds],
+            inv=mix_inv[:, save_inds],
+            snr_env=snr_env,
+            snr_log=snr_log,
+            snr_inv=snr_inv,
+            )
+        save_data(save_path + name, data, config, overwrite=True)
+print('Done.')
+embed()