Fetched bunch of species-specific song snippets.

Worked those into LogHP analysis.
Worked results into fig_invariance_log-hp.pdf.
Put details into new fig_invariance_log-hp_species.pdf (appendix).

python/save_inv_data_log-hp.py

@@ -5,16 +5,17 @@ from thunderhopper.filters import decibel, sosfilter
 from IPython import embed
 
 # GENERAL SETTINGS:
-target = ['Omocestus_rufipes', '*'][0]
-data_paths = search_files(target, excl='noise', dir='../data/processed/')
+example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
+search_target = ['*', example_file][1]
+data_paths = search_files(search_target, excl='noise', dir='../data/processed/')
 noise_path = '../data/processed/white_noise_sd-1.npz'
 save_path = '../data/inv/log_hp/'
 
 # ANALYSIS SETTINGS:
-add_noise = target == '*' or False
-save_snippets = target == 'Omocestus_rufipes'
+add_noise = search_target == '*' or False
+save_detailed = search_target == example_file
 example_scales = np.array([0.1, 1, 10, 30, 100, 300])
-scales = np.geomspace(0.1, 10000, 500)
+scales = np.geomspace(0.01, 10000, 1000)
 scales = np.unique(np.concatenate((scales, example_scales)))
 
 # PREPARATION:
@@ -35,47 +36,67 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
 
     # Normalize song component:
     song /= song[segment].std()
-
-    # Rescale song component:
-    mix = song[:, None] * scales[None, :]
-
     if add_noise:
-        # Add normalized noise component:
+        # Get normalized noise component:
         noise = pure_noise[:song.shape[0]]
         noise /= noise[segment].std()
-        mix += noise[:, None]
 
-    # Process mixture:
-    mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
-                    padtype='even', padlen=config['padlen'])
-    mix_log = decibel(mix, ref=1)
-    mix_inv = sosfilter(mix_log, rate, config['inv_fcut'], 'hp',
-                        padtype='constant', padlen=config['padlen'])
+    # Prepare storage:
+    measure_inv = np.zeros_like(scales)
+    if save_detailed:
+        # Prepare optional storage:
+        measure_env = np.zeros_like(scales)
+        measure_log = np.zeros_like(scales)
+        snip_env = np.zeros((song.shape[0], example_scales.size))
+        snip_log = np.zeros((song.shape[0], example_scales.size))
+        snip_inv = np.zeros((song.shape[0], example_scales.size))
 
-    # Get intensity measure per stage:
-    measure_env = mix[segment, :].std(axis=0)
-    measure_log = mix_log[segment, :].std(axis=0)
-    measure_inv = mix_inv[segment, :].std(axis=0)
+    # Execute piecewise:
+    for i, scale in enumerate(scales):
+
+        # Get scaled mixture:
+        mix = song * scale
+        if add_noise:
+            mix += noise
+
+        # Process mixture:
+        mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
+                        padtype='even', padlen=config['padlen'])
+        mix_log = decibel(mix, ref=1)
+        mix_inv = sosfilter(mix_log, rate, config['inv_fcut'], 'hp',
+                            padtype='constant', padlen=config['padlen'])
+
+        # Log intensity measures:
+        measure_inv[i] = mix_inv[segment].std()
+        if save_detailed:
+            measure_env[i] = mix[segment].std()
+            measure_log[i] = mix_log[segment].std()
+            if scale in example_scales:
+                # Log snippet data:
+                save_ind = np.nonzero(example_scales == scale)[0][0]
+                snip_env[:, save_ind] = mix
+                snip_log[:, save_ind] = mix_log
+                snip_inv[:, save_ind] = mix_inv
 
     # Save analysis results:
-    save_inds = np.nonzero(np.isin(scales, example_scales))[0]
     if save_path is not None:
-        data = dict(
+        archive = dict(
             scales=scales,
             example_scales=example_scales,
-            measure_env=measure_env,
-            measure_log=measure_log,
             measure_inv=measure_inv,
-            )
-        if save_snippets:
-            data.update(
-                snip_env=mix[:, save_inds],
-                snip_log=mix_log[:, save_inds],
-                snip_inv=mix_inv[:, save_inds],
+        )
+        if save_detailed:
+            archive.update(
+                measure_env=measure_env,
+                measure_log=measure_log,
+                snip_env=snip_env,
+                snip_log=snip_log,
+                snip_inv=snip_inv,
             )
         file_name = save_path + name
         if add_noise:
             file_name += '_noise'
-        save_data(file_name, data, config, overwrite=True)
+        save_data(file_name, archive, config, overwrite=True)
+
 print('Done.')
 embed()