Again, numerous changes.

Mostly figure polishing and fixing.
Crucial fix to "short" invariance analysis.

python/save_inv_data_field.py

@@ -0,0 +1,87 @@
+import numpy as np
+from thunderhopper.modeltools import load_data, save_data
+from thunderhopper.filetools import search_files, crop_paths
+from thunderhopper.filtertools import find_kern_specs
+from thunderhopper.model import process_signal
+from IPython import embed
+
+# GENERAL SETTINGS:
+target = '*'
+example_file = 'Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms'
+mode = ['song', 'noise'][1]
+search_path = f'../data/field/processed/{mode}/'
+data_paths = search_files(target, ext='npz', dir=search_path)
+stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
+save_path = f'../data/inv/field/{mode}/'
+
+# ANALYSIS SETTINGS:
+distances = np.load('../data/field/recording_distances.npy')
+
+# SUBSET SETTINGS:
+kernels = np.array([
+    [1, 0.002],
+    [-1, 0.002],
+    [2, 0.004],
+    [-2, 0.004],
+    [3, 0.032],
+    [-3, 0.032]
+])
+kernels = None
+types = None#np.array([-1])
+sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
+
+# EXECUTION:
+for data_path, name in zip(data_paths, crop_paths(data_paths)):
+    save_detailed = example_file in name
+    print(f'Processing {name}')
+
+    # Get song recording (prior to anything):
+    data, config = load_data(data_path, files='raw')
+    song, rate = data['raw'], config['rate']
+
+    # Reduce to kernel subset:
+    if any(var is not None for var in [kernels, types, sigmas]):
+        kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
+        config['kernels'] = config['kernels'][:, kern_inds]
+        config['k_specs'] = config['k_specs'][kern_inds, :]
+        config['k_props'] = [config['k_props'][i] for i in kern_inds]
+        config['feat_thresh'] = config['feat_thresh'][kern_inds]
+
+    # 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 storage:
+    measures = {}
+    if save_detailed:
+        snippets = {}
+
+    # Process snippet:
+    signals, rates = process_signal(config, returns=stages, signal=song, rate=rate)
+
+    # Store results:
+    for stage in stages:
+        # Log intensity measures:
+        mkey = f'measure_{stage}'
+        if stage == 'feat':
+            measures[mkey] = signals[stage][segment, ...].mean(axis=0)
+        else:
+            measures[mkey] = signals[stage][segment, ...].std(axis=0)
+
+        # Log optional snippet data:
+        if save_detailed:
+            snippets[f'snip_{stage}'] = signals[stage]
+
+    # Save analysis results:
+    if save_path is not None:
+        data = dict(
+            distances=distances,
+            )
+        data.update(measures)
+        if save_detailed:
+            data.update(snippets)
+        save_data(save_path + name, data, config, overwrite=True)
+
+print('Done.')
+embed()