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_thresholds.py

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+import numpy as np
+from thunderhopper.filters import sosfilter
+from thunderhopper.model import convolve_kernels, process_signal
+from thunderhopper.modeltools import load_data
+from IPython import embed
+
+## SETTINGS:
+
+# General:
+mode = ['thresh_lp', 'full', 'short', 'field'][3]
+if mode == 'field':
+    noise_path = '../data/field/processed/noise/merged_noise.npz'
+    channels = np.array([0, 1, 2, 3, 4, 5, 6, 7])
+else:
+    noise_path = '../data/processed/white_noise_sd-1.npz'
+save_path = '../data/inv/'
+start_stage = dict(
+    thresh_lp='inv',
+    full='raw',
+    short='raw',
+    field='raw'
+)[mode]
+
+# Analysis:
+factors = np.concatenate([np.arange(-4, 0, 0.01), np.arange(0, 4.01, 0.01)])
+pad = np.array([0.1, 0.9])
+
+# PROCESSING:
+
+print(f'Fetching threshold data in {mode} mode...')
+
+# Load pure-noise starter representation:
+noise_data, config = load_data(noise_path, start_stage)
+starter = noise_data[start_stage]
+
+# Prepare buffered measurement segment:
+pad = (pad * starter.shape[0]).astype(int)
+segment = np.arange(starter.shape[0])[pad[0]:pad[1]]
+
+if mode != 'field':
+    # Normalize starter:
+    starter /= starter[segment].std()
+
+# Run (partial) pipeline:
+print('Running pipeline...')
+if mode == 'thresh_lp':
+    conv = convolve_kernels(starter, config['kernels'], config['k_specs'])
+elif mode == 'full':
+    conv = process_signal(config, 'conv', signal=starter, rate=config['rate'])[0]['conv']
+elif mode == 'short':
+    env = process_signal(config, 'env', signal=starter, rate=config['rate'])[0]['env']
+    inv = sosfilter(env, config['env_rate'], config['inv_fcut'], 'hp',
+                    padtype='constant', padlen=config['padlen'])
+    conv = convolve_kernels(inv, config['kernels'], config['k_specs'])
+elif mode == 'field':
+    starter = starter[:, channels].ravel(order='F')
+    conv = process_signal(config, 'conv', signal=starter, rate=config['rate'])[0]['conv']
+
+# Get baseline kernel response SDs:
+sds = conv[segment, :].std(axis=0)
+
+# Get corresponding supra-threshold proportions:
+percs = np.zeros((len(factors), conv.shape[1]))
+for i, factor in enumerate(factors):
+    print(f'Processing factor {i + 1} / {factors.size}...')
+    percs[i] = (conv > (factor * sds)).sum(axis=0) / conv.shape[0]
+
+# Save results:
+np.savez(save_path + f'{mode}/thresholds.npz', factors=factors, sds=sds, percs=percs)
+
+print('Done.')
+embed()