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

Again, numerous changes.

Browse Source 
Mostly figure polishing and fixing.
Crucial fix to "short" invariance analysis.
This commit is contained in:
j-hartling
2026-04-21 17:49:30 +02:00
parent 3b4b7f2161
commit 1a586848e8
41 changed files with 1392 additions and 242 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
python/save_inv_data_short.py
View File

@@ -17,7 +17,7 @@ target_species = [
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
][5]
][6]
example_file = {
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
@@ -31,7 +31,7 @@ data_paths = search_files(target_species, dir='../data/processed/')
noise_path = '../data/processed/white_noise_sd-1.npz'
ref_path = '../data/inv/short/ref_measures.npz'
pre_stages = ['filt', 'env']
stages = pre_stages + ['conv', 'feat']
stages = pre_stages + ['inv', 'conv', 'feat']
save_path = '../data/inv/short/'
# ANALYSIS SETTINGS:
@@ -98,6 +98,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
measures = dict(
measure_filt=np.zeros(shape_low, dtype=float),
measure_env=np.zeros(shape_low, dtype=float),
measure_inv=np.zeros(shape_low, dtype=float),
measure_conv=np.zeros(shape_high, dtype=float),
measure_feat=np.zeros(shape_high, dtype=float)
)
@@ -108,6 +109,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
snippets = dict(
snip_filt=np.zeros(shape_low, dtype=float),
snip_env=np.zeros(shape_low, dtype=float),
snip_inv=np.zeros(shape_low, dtype=float),
snip_conv=np.zeros(shape_high, dtype=float),
snip_feat=np.zeros(shape_high, dtype=float)
)
@@ -124,7 +126,9 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
signal=scaled, rate=rate)
# Process mixture further:
signals['conv'] = convolve_kernels(signals['env'], config['kernels'], config['k_specs'])
signals['inv'] = sosfilter(signals['env'], rate, config['inv_fcut'], 'hp',
padtype='constant', padlen=config['padlen'])
signals['conv'] = convolve_kernels(signals['inv'], config['kernels'], config['k_specs'])
signals['feat'] = sosfilter((signals['conv'] > config['feat_thresh']).astype(float),
rate, config['feat_fcut'], 'lp',
padtype='fixed', padlen=config['padlen'])