Added multi-thresh simulation to "full" and "short" (currently running).

Added complete "rect-lp" analysis except figure.
Added multiple appendix figs.
Overhauled normalization options across all condense scripts.

Co-authored-by: Copilot <copilot@github.com>

python/save_inv_data_rect-lp.py

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+import numpy as np
+from thunderhopper.modeltools import load_data, save_data
+from thunderhopper.filetools import search_files, crop_paths
+from thunderhopper.filters import sosfilter
+from misc_functions import draw_noise_segment
+from IPython import embed
+
+# GENERAL SETTINGS:
+example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
+data_paths = search_files('*', excl='noise', dir='../data/processed/')
+noise_path = '../data/processed/white_noise_sd-1.npz'
+save_path = '../data/inv/rect_lp/'
+
+# ANALYSIS SETTINGS:
+mode = ['pure', 'noise'][1]
+example_scales = np.array([0.1, 1, 10, 30, 100, 300])
+scales = np.geomspace(0.01, 10000, 1000)
+scales = np.unique(np.concatenate(([0], scales, example_scales)))
+cutoffs = np.array([np.nan, 125, 250, 500])
+
+# PREPARATION:
+if mode == 'noise':
+    pure_noise = np.load(noise_path)['raw']
+
+# EXECUTION:
+for data_path, name in zip(data_paths, crop_paths(data_paths)):
+    save_detailed = example_file in name
+    print(f'Processing {name}')
+
+    # Get filtered song (prior to envelope extraction):
+    data, config = load_data(data_path, files='raw')
+    song, rate = data['raw'], config['rate']
+
+    # Get song segment to be analyzed:
+    time = np.arange(song.shape[0]) / rate
+    start, end = data['songs_0'].ravel()
+    segment = (time >= start) & (time <= end)
+
+    # Normalize song component:
+    song /= song[segment].std()
+    if mode == 'noise':
+        # Get normalized noise component:
+        noise = draw_noise_segment(pure_noise, song.shape[0])
+        noise /= noise[segment].std()
+
+    # Prepare storage:
+    measure_filt = np.zeros_like(scales)
+    measure_env = np.zeros((scales.size, len(cutoffs)), dtype=float)
+    if save_detailed:
+        # Prepare optional storage:
+        shape = (song.shape[0], example_scales.size)
+        snip_raw = np.zeros(shape)
+        snip_filt = np.zeros(shape)
+        snip_env = np.zeros(shape + (len(cutoffs),))
+
+    # Execute piecewise:
+    for i, scale in enumerate(scales):
+
+        # Get scaled mixture:
+        mix = song * scale
+        if mode == 'noise':
+            mix += noise
+
+        # Process mixture:
+        mix = sosfilter(mix, rate, config['bp_fcut'], 'bp',
+                        padtype='fixed', padlen=config['padlen'])
+        mix_rect = np.abs(mix)
+
+        # Store non-envelope results:
+        measure_filt[i] = mix[segment].std()
+        if save_detailed and scale in example_scales:
+            scale_ind = np.nonzero(example_scales == scale)[0][0]
+            snip_raw[:, scale_ind] = mix
+            snip_filt[:, scale_ind] = mix
+
+        # Process piecewise again:
+        for j, cutoff in enumerate(cutoffs):
+            if np.isnan(cutoff):
+                mix_env = mix_rect
+            else:
+                mix_env = sosfilter(mix_rect, rate, cutoff, 'lp',
+                                    padtype='even', padlen=config['padlen'])
+
+            # Store envelope results:
+            measure_env[i, j] = mix_env[segment].std()
+            if save_detailed and scale in example_scales:
+                snip_env[:, scale_ind, j] = mix_env
+
+    # Save analysis results:
+    if save_path is not None:
+        archive = dict(
+            scales=scales,
+            example_scales=example_scales,
+            cutoffs=cutoffs,
+            measure_filt=measure_filt,
+            measure_env=measure_env,
+        )
+        if save_detailed:
+            archive.update(
+                snip_raw=snip_raw,
+                snip_filt=snip_filt,
+                snip_env=snip_env,
+            )
+        save_name = save_path + name + '_' + mode
+        save_data(save_name, archive, config, overwrite=True)
+
+print('Done.')
+embed()