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Nearly finished 1st draft of species-specific Thresh-LP invariance figure (WIP).

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j-hartling
2026-03-13 17:15:03 +01:00
parent 4f5054c8fd
commit 1516fe6090
19 changed files with 735 additions and 239 deletions
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59
python/save_inv_data_thresh-lp.py
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@@ -1,6 +1,5 @@
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
@@ -14,10 +13,9 @@ save_path = '../data/inv/thresh_lp/'
# ANALYSIS SETTINGS:
add_noise = False
thresh_percent = 90
example_scales = np.array([0, 0.5, 1, 10, 50])
scales = np.geomspace(0.01, 50, 100)
example_scales = np.array([0, 1, 10, 50])
scales = np.geomspace(0.01, 100, 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)):
@@ -48,20 +46,14 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
# Reuse threshold from previous noise run:
threshold = np.load(save_name + '_noise.npz')['thresh']
# Prepare snippet storage:
shape = song.shape + (example_scales.size,)
conv = np.zeros(shape, dtype=float)
bi = np.zeros(shape, dtype=float)
feat = np.zeros(shape, dtype=float)
# Prepare measure storage:
shape = (scales.size, song.shape[1])
measure_conv = np.zeros(shape, dtype=float)
# measure_conv = np.zeros(shape, dtype=float)
measure_feat = np.zeros(shape, dtype=float)
# Execute piecewise:
for i, scale in enumerate(scales):
print('Simulating scale ', scale)
print('Simulating scale', scale)
# Rescale song component:
scaled_conv = song * scale
@@ -74,53 +66,16 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
padtype='fixed', padlen=config['padlen'])
# Log snippet data:
if scale in example_scales:
scale_ind = np.nonzero(example_scales == scale)[0][0]
conv[:, :, scale_ind] = scaled_conv
bi[:, :, scale_ind] = scaled_bi
feat[:, :, scale_ind] = scaled_feat
# Get "intensity measure" per stage:
measure_conv[i] = scaled_conv[segment, :].std(axis=0)
# Get intensity measure per stage:
# 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, :]
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.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.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:
data = dict(
scales=scales,
example_scales=example_scales,
conv=conv,
bi=bi,
feat=feat,
measure_conv=measure_conv,
spread_conv=spread_conv,
# measure_conv=measure_conv,
measure_feat=measure_feat,
spread_feat=spread_feat,
thresh=threshold,
thresh_perc=thresh_percent,
)