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Added multi-thresh simulation to "full" and "short" (currently running).

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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>
This commit is contained in:
j-hartling
2026-04-24 16:50:14 +02:00
parent 1a586848e8
commit 5411a309f7
48 changed files with 1549 additions and 300 deletions
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30
python/save_inv_data_field.py
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@@ -6,16 +6,20 @@ 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]
mode = ['song', 'noise'][0]
example_file = dict(
song='Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms',
noise='merged_noise'
)[mode]
search_path = f'../data/field/processed/{mode}/'
data_paths = search_files(target, ext='npz', dir=search_path)
data_paths = search_files('*', ext='npz', dir=search_path)
ref_path = '../data/inv/field/ref_measures.npz'
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')
distances = np.load('../data/field/recording_distances.npy')[::-1]
thresh_rel = 0.5
# SUBSET SETTINGS:
kernels = np.array([
@@ -30,6 +34,11 @@ kernels = None
types = None#np.array([-1])
sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
# PREPARATION:
if thresh_rel is not None:
# Get threshold values from pure-noise response SD:
thresh_abs = np.load(ref_path)['conv'] * thresh_rel
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
save_detailed = example_file in name
@@ -39,6 +48,10 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
data, config = load_data(data_path, files='raw')
song, rate = data['raw'], config['rate']
if thresh_rel is not None:
# Set kernel-specific thresholds:
config['feat_thresh'] = thresh_abs
# 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)
@@ -59,6 +72,9 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
# Process snippet:
signals, rates = process_signal(config, returns=stages, signal=song, rate=rate)
for stage in stages:
# Sort largest to smallest distance:
signals[stage] = signals[stage][..., ::-1]
# Store results:
for stage in stages:
@@ -68,6 +84,10 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
measures[mkey] = signals[stage][segment, ...].mean(axis=0)
else:
measures[mkey] = signals[stage][segment, ...].std(axis=0)
if measures[mkey].ndim == 2:
# Make shape (distances, kernels):
measures[mkey] = np.moveaxis(measures[mkey], 1, 0)
# Log optional snippet data:
if save_detailed: