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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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60
python/save_inv_data_thresh-lp_subset.py
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@@ -1,27 +1,29 @@
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.filetools import search_files, crop_paths
from thunderhopper.filters import sosfilter
from thunderhopper.filtertools import find_kern_specs
from thunderhopper.filtertools import find_kern_specs, pdf_proportion
from IPython import embed
# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = glob.glob(f'../data/processed/{target}*.npz')
target = ['Omocestus_rufipes', '*'][0]
data_paths = search_files(target, dir='../data/processed/')
save_path = '../data/inv/thresh_lp/'
# ANALYSIS SETTINGS:
add_noise = True
thresh_percent = np.array([50, 75, 100])
add_noise = False
save_snippets = True
example_scales = np.array([0, 1, 10, 50])
scales = np.geomspace(0.01, 100, 100)
scales = np.geomspace(0.01, 1000, 100)
scales = np.unique(np.concatenate((scales, example_scales)))
thresh_percent = np.array([0.6, 0.75, 0.999])
thresholds = pdf_proportion(thresh_percent, sd=1, mu=0)
plot_results = False
kernels = np.array([
[2, 0.008],
[4, 0.008],
[1, 0.008],
[2, 0.004],
[3, 0.002],
])
# EXECUTION:
@@ -54,22 +56,20 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
noise = rng.normal(size=(conv.shape[0], 1))
noise /= noise[segment].std()
# Prepare snippet storage:
shape = conv.shape + (example_scales.size, thresh_percent.size)
snip_conv = np.zeros(shape, dtype=float)
snip_bi = np.zeros(shape, dtype=float)
snip_feat = np.zeros(shape, dtype=float)
if save_snippets:
# Prepare snippet storage:
shape = conv.shape + (example_scales.size, thresh_percent.size)
snip_conv = np.zeros(shape, dtype=float)
snip_bi = np.zeros(shape, dtype=float)
snip_feat = np.zeros(shape, dtype=float)
# Prepare measure storage:
shape = (scales.size, conv.shape[1], thresh_percent.size)
measure_conv = np.zeros(shape, dtype=float)
# measure_conv = np.zeros(shape, dtype=float)
measure_feat = np.zeros(shape, dtype=float)
# Compute kernel-specific thresholds (thresholds, kernels):
thresholds = np.percentile(conv, thresh_percent, axis=0)
# Execute piecewise analysis:
for i, threshs in enumerate(thresholds):
for i, thresh in enumerate(thresholds):
print('\nSimulating threshold ', thresh_percent[i])
for j, scale in enumerate(scales):
@@ -82,20 +82,20 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
scaled_conv += noise
# Process mixture:
scaled_bi = (scaled_conv > threshs).astype(float)
scaled_bi = (scaled_conv > thresh).astype(float)
scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
padtype='fixed', padlen=config['padlen'])
# Log snippet data:
if scale in example_scales:
if save_snippets and scale in example_scales:
scale_ind = np.nonzero(example_scales == scale)[0][0]
snip_conv[:, :, scale_ind, i] = scaled_conv
snip_bi[:, :, scale_ind, i] = scaled_bi
snip_feat[:, :, scale_ind, i] = scaled_feat
# Get intensity measure per stage:
measure_conv[j, :, i] = scaled_conv[segment, :].std(axis=0)
measure_feat[j, :, i] = scaled_feat[segment, :].mean(axis=0)
# measure_conv[j, :, i] = scaled_conv[segment, :].std(axis=0)
if plot_results:
fig, axes = plt.subplots(thresh_percent.size, kernels.shape[0],
@@ -103,6 +103,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
sharex=True, sharey=True, squeeze=True)
axes[0, 0].set_xscale('symlog', linthresh=scales[scales>0].min(),
linscale=0.25)
axes[0, 0].set_ylim(0, 1)
for i, thresh in enumerate(thresh_percent):
for j, kernel in enumerate(kernels):
@@ -111,7 +112,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
if i == 0:
ax.set_title(f'Kernel {kernel}')
if j == 0:
ax.set_ylabel(f'{thresh}%')
ax.set_ylabel(f'{100 * thresh}%')
plt.show()
# Save analysis results:
@@ -119,14 +120,17 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
data = dict(
scales=scales,
example_scales=example_scales,
snip_conv=snip_conv,
snip_bi=snip_bi,
snip_feat=snip_feat,
measure_conv=measure_conv,
# measure_conv=measure_conv,
measure_feat=measure_feat,
thresh_perc=thresh_percent,
threshs=thresholds,
)
if save_snippets:
data.update(dict(
snip_conv=snip_conv,
snip_bi=snip_bi,
snip_feat=snip_feat,
))
if add_noise:
save_name += '_noise'
save_data(save_name, data, config, overwrite=True)