Overhauled Thresh-LP analysis and figures (WIP).

python/save_inv_data_full.py

@@ -0,0 +1,115 @@
+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.model import process_signal
+from IPython import embed
+
+# GENERAL SETTINGS:
+target = 'Omocestus_rufipes'
+data_paths = glob.glob(f'../data/processed/{target}*.npz')
+stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
+save_path = '../data/inv/full/'
+
+# ANALYSIS SETTINGS:
+example_scales = np.array([0, 0.5, 1, 5, 10])
+scales = np.linspace(0, 10, 100)
+scales = np.unique(np.concatenate((scales, example_scales)))
+
+# EXECUTION:
+for data_path, name in zip(data_paths, crop_paths(data_paths)):
+    print(f'Processing {name}')
+
+    # Get normalized song recording:
+    data, config = load_data(data_path, files='raw')
+    song, rate = data['raw'], config['rate']
+    song /= song.std(axis=0)
+
+    # Get normalized noise:
+    rng = np.random.default_rng()
+    noise = rng.normal(size=song.shape[0])
+    noise /= noise.std()
+
+    # Get song segment to be analyzed:
+    time = np.arange(song.shape[0]) / rate
+    start, end = data['songs_0'].ravel()
+    segment = (time >= start) & (time <= end)
+
+    # Prepare snippet storage:
+    shape_low = (song.shape[0], example_scales.size)
+    shape_high = (song.shape[0], config['k_specs'].shape[0], example_scales.size)
+    snippets = dict(
+        raw=np.zeros(shape_low, dtype=float),
+        filt=np.zeros(shape_low, dtype=float),
+        env=np.zeros(shape_low, dtype=float),
+        log=np.zeros(shape_low, dtype=float),
+        inv=np.zeros(shape_low, dtype=float),
+        conv=np.zeros(shape_high, dtype=float),
+        bi=np.zeros(shape_high, dtype=float),
+        feat=np.zeros(shape_high, dtype=float)
+    )
+
+    # Prepare measure storage:
+    shape_low = (scales.size,)
+    shape_high = (scales.size, config['k_specs'].shape[0])
+    measures = dict(
+        measure_raw=np.zeros(shape_low, dtype=float),
+        measure_filt=np.zeros(shape_low, dtype=float),
+        measure_env=np.zeros(shape_low, dtype=float),
+        measure_log=np.zeros(shape_low, dtype=float),
+        measure_inv=np.zeros(shape_low, dtype=float),
+        measure_conv=np.zeros(shape_high, dtype=float),
+        measure_feat=np.zeros(shape_high, dtype=float)
+    )
+
+    # Execute piecewise:
+    for i, scale in enumerate(scales):
+        print('Simulating scale ', scale)
+
+        # Rescale song and add noise:
+        scaled = song * scale + noise
+
+        # Process mixture:
+        signals, rates = process_signal(config, returns=stages,
+                                        signal=scaled, rate=rate)
+        # Store results:
+        for stage in stages:
+            key = f'measure_{stage}'
+
+            # Log snippet data:
+            if scale in example_scales:
+                scale_ind = np.nonzero(example_scales == scale)[0][0]
+                snippets[stage][:, ..., scale_ind] = signals[stage]
+
+            # Log "intensity measure" per stage:
+            if stage in ['raw', 'filt', 'env', 'log', 'inv', 'conv']:
+                measures[key][i] = signals[stage][segment, ...].std(axis=0)
+            elif stage == 'feat':
+                measures[key][i] = signals[stage][segment, :].mean(axis=0) / signals[stage][segment, :].std(axis=0)
+
+    # Relate to smallest scale:
+    base_ind = np.argmin(scales)
+    for stage in stages:
+        if stage == 'bi':
+            continue
+        key = f'measure_{stage}'
+        measures[key] /= measures[key][base_ind, ...]
+        if stage in ['conv', 'feat']:
+            spread = np.zeros((2, scales.size))
+            spread[0] = np.percentile(measures[key], 25, axis=1)
+            spread[1] = np.percentile(measures[key], 75, axis=1)
+            measures[f'spread_{stage}'] = spread
+            measures[key] = np.median(measures[key], axis=1)
+
+    # Save analysis results:
+    if save_path is not None:
+        data = dict(
+            scales=scales,
+            example_scales=example_scales,
+            )
+        data.update(snippets)
+        data.update(measures)
+        save_data(save_path + name, data, config, overwrite=True)
+print('Done.')
+embed()