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_ref_measures_field.py

@@ -0,0 +1,42 @@
+import numpy as np
+from thunderhopper.filetools import search_files
+from thunderhopper.model import process_signal
+from thunderhopper.modeltools import load_data
+from IPython import embed
+
+## SETTINGS:
+
+# General:
+stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
+noise_path = search_files('merged_noise', dir='../data/field/processed/noise/')[0]
+save_path = '../data/inv/field/ref_measures.npz'
+channels = np.array([0, 1, 2, 3, 4, 5, 6, 7])
+
+# PROCESSING:
+
+# Load pure-noise starter representation:
+noise_data, config = load_data(noise_path, stages[0])
+# Accumulate channels in time-major order:
+starter = noise_data[stages[0]][:, channels].ravel(order='F')
+
+# Get song segment to be analyzed:
+time = np.arange(starter.shape[0]) / config['rate']
+start, end = noise_data['songs_0'].ravel()
+segment = (time >= start) & (time <= end)
+
+# Run pipeline:
+data = process_signal(config, stages, signal=starter, rate=config['rate'])[0]
+
+# Get measures:
+measures = {}
+for stage in stages:
+    if stage == 'feat':
+        measures[stage] = data[stage][segment, :].mean(axis=0)
+    else:
+        measures[stage] = data[stage][segment, ...].std(axis=0)
+
+# Save results:
+np.savez(save_path, **measures)
+
+print('Done.')
+embed()