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Overhauled Thresh-LP analysis and figures (WIP).

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j-hartling
2026-03-10 17:48:10 +01:00
parent 0407053c20
commit 4494bc7783
12 changed files with 952 additions and 107 deletions
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71
python/save_inv_data_thresh-lp.py
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@@ -1,5 +1,6 @@
import glob
import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data, save_data
from thunderhopper.filetools import crop_paths
from thunderhopper.filters import sosfilter
@@ -12,30 +13,40 @@ save_path = '../data/inv/thresh_lp/'
# ANALYSIS SETTINGS:
add_noise = False
threshold = 0.5
example_scales = np.array([threshold, 0.6, 1, 10, 50, 100])
scales = np.linspace(threshold + 0.1, 100, 100)
if not add_noise:
example_scales = example_scales[example_scales > threshold]
thresh_percent = 90
example_scales = np.array([0, 0.5, 1, 10, 50])
scales = np.geomspace(0.01, 50, 100)
scales = np.unique(np.concatenate((scales, example_scales)))
plot_results = True
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
print(f'Processing {name}')
save_name = save_path + name
# Get normalized pure-song kernel responses:
# Get pure-song kernel responses:
data, config = load_data(data_path, files='conv')
song, rate = data['conv'], data['conv_rate']
song /= song.std(axis=0)
# Prepare kernel-specific thresholds:
threshold *= song.max(axis=0, keepdims=True)
# Get song segment to be analyzed:
time = np.arange(song.shape[0]) / rate
start, end = data['songs_0'].ravel()
segment = (time >= start) & (time <= end)
# Normalize song component:
song /= song[segment, :].std(axis=0)
if add_noise:
# Get normalized noise:
rng = np.random.default_rng()
noise = rng.normal(size=(song.shape[0], 1))
noise /= noise.std()
noise /= noise[segment].std()
# Prepare noise-bound threshold:
threshold = np.percentile(noise, thresh_percent, axis=0)
else:
# Reuse threshold from previous noise run:
threshold = np.load(save_name + '_noise.npz')['thresh']
# Prepare snippet storage:
shape = song.shape + (example_scales.size,)
@@ -71,23 +82,32 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
feat[:, :, scale_ind] = scaled_feat
# Get "intensity measure" per stage:
measure_conv[i] = scaled_conv.std(axis=0)
measure_feat[i] = scaled_feat.mean(axis=0)
measure_conv[i] = scaled_conv[segment, :].std(axis=0)
measure_feat[i] = scaled_feat[segment, :].mean(axis=0)
# Relate to smallest scale:
base_ind = np.argmin(scales)
measure_conv /= measure_conv[base_ind, :]
measure_feat /= measure_feat[base_ind, :]
# # Relate to smallest scale:
# base_ind = np.argmin(scales)
# measure_conv /= measure_conv[base_ind, :]
if plot_results:
fig, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(scales, measure_conv)
ax1.plot(scales, measure_conv.mean(axis=1), c='k')
ax1.plot(scales, np.median(measure_conv, axis=1), c='k', ls='--')
ax2.plot(scales, measure_feat)
ax2.plot(scales, np.nanmean(measure_feat, axis=1), c='k')
ax2.plot(scales, np.nanmedian(measure_feat, axis=1), c='k', ls='--')
plt.show()
# Condense measures across kernels:
spread_conv = np.zeros((2, scales.size))
spread_conv[0] = np.percentile(measure_conv, 25, axis=1)
spread_conv[1] = np.percentile(measure_conv, 75, axis=1)
measure_conv = np.median(measure_conv, axis=1)
spread_conv[0] = np.nanpercentile(measure_conv, 25, axis=1)
spread_conv[1] = np.nanpercentile(measure_conv, 75, axis=1)
measure_conv = np.nanmedian(measure_conv, axis=1)
spread_feat = np.zeros((2, scales.size))
spread_feat[0] = np.percentile(measure_feat, 25, axis=1)
spread_feat[1] = np.percentile(measure_feat, 75, axis=1)
measure_feat = np.median(measure_feat, axis=1)
spread_feat[0] = np.nanpercentile(measure_feat, 25, axis=1)
spread_feat[1] = np.nanpercentile(measure_feat, 75, axis=1)
measure_feat = np.nanmedian(measure_feat, axis=1)
# Save analysis results:
if save_path is not None:
@@ -101,10 +121,11 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
spread_conv=spread_conv,
measure_feat=measure_feat,
spread_feat=spread_feat,
thresh=threshold,
thresh_perc=thresh_percent,
)
file_name = save_path + name
if add_noise:
file_name += '_noise'
save_data(file_name, data, config, overwrite=True)
save_name += '_noise'
save_data(save_name, data, config, overwrite=True)
print('Done.')
embed()