Again, numerous changes.

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

python/save_saturation_log-hp.py

@@ -0,0 +1,79 @@
+import numpy as np
+from thunderhopper.filetools import search_files
+from thunderhopper.modeltools import load_data, save_data
+from misc_functions import get_saturation
+from IPython import embed
+
+# GENERAL SETTINGS:
+target_species = [
+    'Chorthippus_biguttulus',
+    'Chorthippus_mollis',
+    'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+]
+search_path = '../data/inv/log_hp/collected/'
+save_path = '../data/inv/log_hp/saturation/'
+
+# ANALYSIS SETTINGS:
+plateau_settings = dict(
+    low=0.05,
+    high=0.95,
+    first=True,
+    last=True,
+    condense=None,
+)
+compute_hist = True
+bins = 50
+pad = 0.05
+
+# PREPARATION:
+if compute_hist:
+    species_scales = []
+    min_scale, max_scale = [], []
+    archives = [{} for _ in target_species]
+
+# EXECUTION:
+for i, species in enumerate(target_species):
+    print(f'Processing {species}')
+
+    # Load accumulated invariance data:
+    path = search_files(species, dir=search_path)[0]
+    data, config = load_data(path, ['scales', 'measure_inv'])
+
+    # Find upper saturation point per song file:
+    crit_inds = np.array(get_saturation(data['measure_inv'], **plateau_settings)[1])
+    crit_scales = data['scales'][crit_inds]
+
+    # Output options:
+    if not compute_hist:
+        # Save species data immediately:
+        archive = dict(crit_inds=crit_inds, crit_scales=crit_scales, scales=data['scales'])
+        save_data(save_path + species, archive, config, overwrite=True)
+        continue
+
+    # Log but don't save data yet:
+    archives[i]['crit_inds'] = crit_inds
+    archives[i]['crit_scales'] = crit_scales
+    archives[i]['scales'] = data['scales']
+    min_scale.append(crit_scales.min())
+    max_scale.append(crit_scales.max())
+
+# Optional histogram:
+if compute_hist:
+    # Generated shared histogram edges:
+    min_scale, max_scale = min(min_scale), max(max_scale)
+    pad *= (max_scale - min_scale)
+    edges = np.linspace(max(0, min_scale - pad), max_scale + pad, bins + 1)
+    centers = edges[:-1] + np.diff(edges) / 2
+
+    # Compute histogram and save species data:
+    for i, (species, archive) in enumerate(zip(target_species, archives)):
+        hist = np.histogram(archive['crit_scales'], bins=edges, density=True)[0]
+        archive['hist'] = hist
+        archive['bins'] = centers
+        save_data(save_path + species, archive, config, overwrite=True)
+
+print('Done.')