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

Added loads of units in nearly all graphs.

Browse Source 
Overhauled fig_invariance_full.pdf.
Added some legends, somewhere.
This commit is contained in:
j-hartling
2026-04-28 19:43:05 +02:00
parent 7e1aa8721a
commit e70d100655
40 changed files with 965 additions and 471 deletions
Download Patch File Download Diff File Expand all files Collapse all files

36
python/condense_inv_data_short.py
View File

@@ -1,18 +1,18 @@
import numpy as np
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data, save_data
from misc_functions import sort_files_by_rec
from misc_functions import sort_files_by_rec, divide_by_zero
from IPython import embed
# GENERAL SETTINGS:
target_species = [
'Chorthippus_biguttulus',
'Chorthippus_mollis',
'Chrysochraon_dispar',
'Euchorthippus_declivus',
'Gomphocerippus_rufus',
# 'Chorthippus_biguttulus',
# 'Chorthippus_mollis',
# 'Chrysochraon_dispar',
# 'Euchorthippus_declivus',
# 'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
# 'Pseudochorthippus_parallelus',
]
sources = [
'BM04',
@@ -32,7 +32,7 @@ normalization = [
'max',
'base',
'range',
][2]
][4]
suffix = dict(
none='_unnormed',
min='_norm-min',
@@ -58,7 +58,7 @@ for i, species in enumerate(target_species):
for k, path in enumerate(rec_paths):
# Load invariance data:
data, config = load_data(path, 'scales', 'measure')
data, config = load_data(path, 'scales', ['measure', 'thresh'])
if k == 0:
# Prepare song file-specific storage:
@@ -80,18 +80,22 @@ for i, species in enumerate(target_species):
if normalization == 'min':
# Minimum normalization:
data[mkey] /= data[mkey].min(axis=0, keepdims=True)
data[mkey] = divide_by_zero(data[mkey], data[mkey].min(axis=0))
# data[mkey] /= data[mkey].min(axis=0, keepdims=True)
elif normalization == 'max':
# Maximum normalization:
data[mkey] /= data[mkey].max(axis=0, keepdims=True)
data[mkey] = divide_by_zero(data[mkey], data[mkey].max(axis=0))
# data[mkey] /= data[mkey].max(axis=0, keepdims=True)
elif normalization == 'base':
# Noise baseline normalization:
data[mkey] /= data[mkey][0]
data[mkey] = divide_by_zero(data[mkey], data[mkey][0])
# data[mkey] /= data[mkey][0]
elif normalization == 'range':
# Min-max normalization:
min_measure = data[mkey].min(axis=0, keepdims=True)
max_measure = data[mkey].max(axis=0, keepdims=True)
data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
data[mkey] = divide_by_zero(data[mkey] - min_measure, max_measure - min_measure)
# data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
file_data[stage][..., k] = data[mkey]
@@ -101,7 +105,11 @@ for i, species in enumerate(target_species):
rec_sd[f'sd_{stage}'][..., j] = np.nanstd(file_data[stage], axis=-1)
# Save condensed recording data:
archive = dict(scales=data['scales'])
archive = dict(
scales=data['scales'],
thresh_rel=data['thresh_rel'],
thresh_abs=data['thresh_abs']
)
archive.update(rec_mean)
archive.update(rec_sd)
save_data(save_path + species + suffix, archive, config)