Captioned appendix figures.

Polished some figures.
Shortened existing figure captions.

python/save_env_sd_conversion.py

@@ -1,26 +1,24 @@
-import glob
 import numpy as np
-import matplotlib.pyplot as plt
+from thunderhopper.filetools import search_files
 from thunderhopper.modeltools import load_data
 from thunderhopper.filters import sosfilter
 from IPython import embed
 
 # GENERAL SETTINGS:
 target = 'Omocestus_rufipes'
-data_path = glob.glob(f'../data/processed/{target}*.npz')[0]
+data_path = search_files(target, dir='../data/processed/')[0]
 save_path = '../data/inv/noise_env/'
 
 # ANALYSIS SETTINGS:
-scales = np.geomspace(0.1, 10000, 200)
+scales = np.geomspace(0.01, 1000, 100)
 sd_inputs = np.array([1.0])
-n_trials = 10
-tol_to_one = 0.1
+n_trials = 100
 
 # EXECUTION:
 
 # Load signal data:
-data, config = load_data(data_path, files='filt')
-signal, rate = data['filt'], config['rate']
+data, config = load_data(data_path, files='raw')
+signal, rate = data['raw'], config['rate']
 
 # Reduce to song segment and normalize:
 time = np.arange(signal.shape[0]) / rate
@@ -28,67 +26,60 @@ start, end = data['songs_0'].ravel()
 segment = (time >= start) & (time <= end)
 signal /= signal[segment].std()
 
-# Get rescaled signals (time, scale):
+# Rescale signal (time, scale):
 signal = signal[:, None] * scales[None, :]
 
 # Prepare storage:
-if sd_inputs.size > 1:
-    current_match = 0
-    storage = dict(
-        scales=scales,
-        n_trials=n_trials,
-        sd_factor=np.array([0.]),
-        trials=np.zeros((scales.size, n_trials), dtype=float),
-        mean=np.zeros(scales.size, dtype=float),
-        spread=np.zeros(scales.size, dtype=float),
-    )
+sd_noise = np.zeros((scales.size, n_trials, sd_inputs.size), dtype=float)
 
 # Analyze piece-wise:
 rng = np.random.default_rng()
 for i, sigma in enumerate(sd_inputs):
     print(f'Testing SD: {sigma:.3f} ...')
 
-    # Add Gaussian noise of given SD to rescaled signals (time, scale, trial):
-    mix = signal[..., None] + rng.normal(0, sigma, (*signal.shape, n_trials))
+    # Prepare trial storage:
+    sd_trials = np.zeros((segment.sum(), scales.size, n_trials), dtype=float)
 
-    # Get mixture envelopes (time, scale, trial):
-    mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
-                    padtype='even', padlen=config['padlen'])[segment, ...]
+    # Run trials:
+    for j in range(n_trials):
+        # Mix signals with white noise of target SD:
+        mix = signal + rng.normal(0, sigma, signal.shape)
+
+        # Process mixture:
+        mix = sosfilter(mix, rate, config['bp_fcut'], 'bp',
+                        padtype='fixed', padlen=config['padlen'])
+        mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
+                        padtype='even', padlen=config['padlen'])
+
+        # Log current trial:
+        sd_trials[..., j] = mix[segment, :]
 
     # Get noise remainders of mean over trials:
-    mix -= mix.mean(axis=-1, keepdims=True)
+    sd_trials -= sd_trials.mean(axis=-1, keepdims=True)
 
     # Estimate noise SD:
-    sd = mix.std(axis=0)
-    # Average SD over trials:
-    mean_sd = sd.mean(axis=-1)
+    sd_noise[:, :, i] = sd_trials.std(axis=0)
 
-    # Log single-run results:
-    if sd_inputs.size == 1:
-        storage = dict(
-            scales=scales,
-            n_trials=n_trials,
-            sd_factor=sigma,
-            trials=sd,
-            mean=mean_sd,
-            spread=sd.std(axis=-1),
-        )
-        break
+    # # Add Gaussian noise of given SD to rescaled signals (time, scale, trial):
+    # mix = signal[..., None] + rng.normal(0, sigma, (*signal.shape, n_trials))
 
-    # Update multi-run results if better than previous:
-    n_match = (np.abs(1 - mean_sd) <= tol_to_one).sum()
-    if n_match > current_match:
-        print(f'Found better SD: {sigma:.3f} with {n_match} matches (previous: {current_match})')
-        storage['sd_factor'][0] = sigma
-        storage['trials'][:, :] = sd
-        storage['mean'][:] = mean_sd
-        storage['spread'][:] = sd.std(axis=-1)
-        current_match = n_match
-    del mix
-del signal
+    # # Get mixture envelopes (time, scale, trial):
+    # mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
+    #                 padtype='even', padlen=config['padlen'])[segment, ...]
+
+    # # Get noise remainders of mean over trials:
+    # mix -= mix.mean(axis=-1, keepdims=True)
+
+    # # Estimate noise SD:
+    # sd_noise[:, :, i] = mix.std(axis=0)
 
 if save_path is not None:
-    np.savez(save_path + 'sd_conversion.npz', **storage)
+    archive = dict(
+        scales=scales,
+        sd_input=sd_inputs,
+        sd_noise=sd_noise,
+    )
+    np.savez(save_path + 'sd_conversion.npz', **archive)
 
 print('Done.')
 embed()