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Seriously, no idea. Wild amount of changes. Good luck.

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j-hartling
2026-04-17 17:19:30 +02:00
parent 36ac504efa
commit 3b4b7f2161
40 changed files with 2067 additions and 672 deletions
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54
python/condense_inv_data_log-hp.py
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@@ -1,8 +1,8 @@
import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.filetools import search_files, crop_paths
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data, save_data
from misc_functions import shorten_species
from misc_functions import shorten_species, sort_files_by_rec
from IPython import embed
# GENERAL SETTINGS:
@@ -23,7 +23,6 @@ sources = [
'FTN'
]
search_path = '../data/inv/log_hp/'
ref_path = '../data/inv/log_hp/ref_measures.npz'
save_path = '../data/inv/log_hp/condensed/'
# ANALYSIS SETTINGS:
@@ -31,14 +30,12 @@ compute_ratios = True
plot_overview = True
# PREPARATION:
if compute_ratios:
ref_measure = np.load(ref_path)['inv']
if plot_overview:
fig, axes = plt.subplots(3, len(target_species), figsize=(16, 9),
sharex=True, sharey=True, layout='constrained')
axes[0, 0].set_ylabel('songs')
axes[1, 0].set_ylabel('recordings\n(mean ± SD)')
axes[2, 0].set_ylabel('total\n(mean ± SEM)')
axes[2, 0].set_ylabel('total\n(mean ± SD)')
# EXECUTION:
for i, species in enumerate(target_species):
@@ -48,49 +45,21 @@ for i, species in enumerate(target_species):
# Fetch all species-specific song files:
all_paths = search_files(species, incl='noise', ext='npz', dir=search_path)
# Separate by source:
sorted_paths = {}
for source in sources:
# Check for any source-specific song files:
source_paths = [path for path in all_paths if source in path]
if not source_paths:
continue
# Separate by recording:
sorted_paths[source] = [[]]
for path, name in zip(source_paths, crop_paths(source_paths)):
# Find numerical ID behind source tag:
id_ind = name.find(source) + len(source) + 1
# Check if ID is followed by sub-ID:
sub_id = name[id_ind:].split('-')[1]
if 's' in sub_id:
# Single (time stamp in next spot):
sorted_paths[source][0].append(path)
continue
sub_id = int(sub_id)
# Multiple (sub-ID in next spot):
if sub_id > len(sorted_paths[source]):
# Open new recording-specific slot:
sorted_paths[source].append([])
sorted_paths[source][sub_id - 1].append(path)
# Re-sort song files only by recording (discarding source separation):
sorted_paths = [path for paths in sorted_paths.values() for path in paths]
# Sort song files by recording (one or more per source):
sorted_paths = sort_files_by_rec(all_paths, sources)
# Condense across song files per recording:
for j, rec_paths in enumerate(sorted_paths):
for k, path in enumerate(rec_paths):
# Load invariance data:
data, _ = load_data(path, ['scales', 'measure_inv'])
data, config = load_data(path, ['scales', 'measure_inv'])
scales, measure = data['scales'], data['measure_inv']
# Relate to noise:
if compute_ratios:
measure /= ref_measure
measure /= measure[0]
if k == 0:
# Prepare song file-specific storage:
@@ -116,7 +85,8 @@ for i, species in enumerate(target_species):
rec_mean[:, j] + rec_sd[:, j], color='k', alpha=0.2)
# Save condensed recording data for current species:
np.savez(save_path + species, scales=scales, mean=rec_mean, sd=rec_sd)
archive = dict(scales=scales, mean_inv=rec_mean, sd_inv=rec_sd)
save_data(save_path + species, archive, config, overwrite=True)
if plot_overview:
spec_mean = rec_mean.mean(axis=1)
@@ -128,9 +98,7 @@ for i, species in enumerate(target_species):
print('Done.')
if plot_overview:
axes[0, 0].set_xlim(scales[0], scales[-1])
axes[0, 0].set_xscale('log')
axes[0, 0].set_yscale('log')
plt.show()
axes[0, 0].set_xlim(scales[1], scales[-1])
plt.show()