j_hartling/paper_2025
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Holiday syncing :)

Browse Source
This commit is contained in:
j-hartling
2026-04-02 16:00:56 +02:00
parent 298969a067
commit 0b9264b1e1
14 changed files with 627 additions and 667 deletions
Download Patch File Download Diff File Expand all files Collapse all files

67
python/save_inv_data_full.py
View File

@@ -4,19 +4,23 @@ import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data, save_data
from thunderhopper.filetools import crop_paths
from thunderhopper.filtertools import find_kern_specs
from thunderhopper.model import process_signal
from thunderhopper.model import process_signal, convolve_kernels
from IPython import embed
# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = glob.glob(f'../data/processed/{target}*.npz')
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
noise_path = '../data/processed/white_noise_sd-1.npz'
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
save_path = '../data/inv/full/'
# ANALYSIS SETTINGS:
example_scales = np.array([0, 1, 10, 50])
scales = np.geomspace(0.01, 100, 100)
example_scales = np.array([0.1, 1, 10, 30, 100, 300])
scales = np.geomspace(0.01, 10000, 100)
scales = np.unique(np.concatenate((scales, example_scales)))
thresh_rel = 3
# SUBSET SETTINGS:
kernels = np.array([
[1, 0.002],
[-1, 0.002],
@@ -29,20 +33,29 @@ kernels = None
types = None#np.array([-1])
sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
# PREPARATION:
noise_data = np.load(noise_path)
pure_noise = noise_data['raw']
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
print(f'Processing {name}')
# Get song recording:
# Get song recording (prior to anything):
data, config = load_data(data_path, files='raw')
song, rate = data['raw'], config['rate']
if thresh_rel is not None:
# Get noise-bound kernel-specific thresholds:
config['feat_thresh'] = noise_data['conv'].std(axis=0) * thresh_rel
# Reduce to kernel subset:
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
config['kernels'] = config['kernels'][:, kern_inds]
config['k_specs'] = config['k_specs'][kern_inds, :]
config['k_props'] = [config['k_props'][i] for i in kern_inds]
config['feat_thresh'] = config['feat_thresh'][kern_inds]
if any(var is not None for var in [kernels, types, sigmas]):
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
config['kernels'] = config['kernels'][:, kern_inds]
config['k_specs'] = config['k_specs'][kern_inds, :]
config['k_props'] = [config['k_props'][i] for i in kern_inds]
config['feat_thresh'] = config['feat_thresh'][kern_inds]
# Get song segment to be analyzed:
time = np.arange(song.shape[0]) / rate
@@ -52,22 +65,19 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
# Normalize song component:
song /= song[segment].std(axis=0)
# Get normalized noise:
rng = np.random.default_rng()
noise = rng.normal(size=song.shape[0])
# Get normalized noise component:
noise = pure_noise[:song.shape[0]]
noise /= noise[segment].std()
# Prepare snippet storage:
shape_low = (song.shape[0], example_scales.size)
shape_high = (song.shape[0], config['k_specs'].shape[0], example_scales.size)
snippets = dict(
snip_raw=np.zeros(shape_low, dtype=float),
snip_filt=np.zeros(shape_low, dtype=float),
snip_env=np.zeros(shape_low, dtype=float),
snip_log=np.zeros(shape_low, dtype=float),
snip_inv=np.zeros(shape_low, dtype=float),
snip_conv=np.zeros(shape_high, dtype=float),
snip_bi=np.zeros(shape_high, dtype=float),
snip_feat=np.zeros(shape_high, dtype=float)
)
@@ -75,7 +85,6 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
shape_low = (scales.size,)
shape_high = (scales.size, config['k_specs'].shape[0])
measures = dict(
measure_raw=np.zeros(shape_low, dtype=float),
measure_filt=np.zeros(shape_low, dtype=float),
measure_env=np.zeros(shape_low, dtype=float),
measure_log=np.zeros(shape_low, dtype=float),
@@ -96,36 +105,18 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
signal=scaled, rate=rate)
# Store results:
for stage in stages:
key = f'measure_{stage}'
mkey, skey = f'measure_{stage}', f'snip_{stage}'
# Log snippet data:
if scale in example_scales:
scale_ind = np.nonzero(example_scales == scale)[0][0]
snippets[f'snip_{stage}'][:, ..., scale_ind] = signals[stage]
snippets[skey][:, ..., scale_ind] = signals[stage]
# Log intensity measure per stage (excluding binary):
if stage in ['raw', 'filt', 'env', 'log', 'inv', 'conv']:
measures[key][i] = signals[stage][segment, ...].std(axis=0)
measures[mkey][i] = signals[stage][segment, ...].std(axis=0)
elif stage == 'feat':
measures[key][i] = signals[stage][segment, :].mean(axis=0)
# thresh_y = np.percentile(measures['measure_feat'], 99, axis=0)
# kern_types = np.unique()
# thresh_x = np.zeros(thresh_y.shape, dtype=float)
# for i, thresh in enumerate(thresh_y):
# if thresh < 0.1:
# thresh_x[i] = scales[-1]
# continue
# mask = (measures['measure_feat'][:, i] < thresh)
# thresh_x[i] = scales[np.nonzero(mask)[0][-1]]
# inds = np.argsort(thresh_x)
# print(config['k_specs'][inds, :])
# fig, axes = plt.subplots(1, 2)
# axes[0].plot(snippets['snip_feat'][:, inds, -1])
# axes[1].plot(scales, measures['measure_feat'][:, inds])
# plt.show()
# embed()
measures[mkey][i] = signals[stage][segment, :].mean(axis=0)
# Save analysis results:
if save_path is not None: