j_hartling/paper_2025
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Added loads of units in nearly all graphs.

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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
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22
python/condense_inv_data_log-hp.py
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@@ -2,7 +2,7 @@ import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data, save_data
from misc_functions import shorten_species, sort_files_by_rec
from misc_functions import shorten_species, sort_files_by_rec, divide_by_zero
from IPython import embed
# GENERAL SETTINGS:
@@ -41,7 +41,7 @@ suffix = dict(
base='_norm-base',
range='_norm-range'
)[normalization]
plot_overview = True
plot_overview = False
# PREPARATION:
if plot_overview:
@@ -59,7 +59,7 @@ for i, species in enumerate(target_species):
# Fetch all species-specific song files:
all_paths = search_files(species, incl=mode, ext='npz', dir=search_path)
# Sort song files by recording (one or more per source):
sorted_paths = sort_files_by_rec(all_paths, sources)
@@ -82,18 +82,22 @@ for i, species in enumerate(target_species):
# Log song file data:
if normalization == 'min':
# Minimum normalization:
measure /= measure.min(axis=0, keepdims=True)
measure = divide_by_zero(measure, measure.min(axis=0, keepdims=True))
# measure /= measure.min(axis=0, keepdims=True)
elif normalization == 'max':
# Maximum normalization:
measure /= measure.max(axis=0, keepdims=True)
measure = divide_by_zero(measure, measure.max(axis=0, keepdims=True))
# measure /= measure.max(axis=0, keepdims=True)
elif normalization == 'base':
# Noise baseline normalization:
measure /= measure[0]
measure = divide_by_zero(measure, measure[0])
# measure /= measure[0]
elif normalization == 'range':
# Min-max normalization:
min_measure = measure.min(axis=0, keepdims=True)
max_measure = measure.max(axis=0, keepdims=True)
measure = (measure - min_measure) / (max_measure - min_measure)
measure = divide_by_zero(measure - min_measure, max_measure - min_measure)
# measure = (measure - min_measure) / (max_measure - min_measure)
file_data[:, k] = measure
@@ -101,8 +105,8 @@ for i, species in enumerate(target_species):
axes[0, i].plot(scales, measure, c='k', alpha=0.5)
# Get recording statistics:
rec_mean[:, j] = file_data.mean(axis=1)
rec_sd[:, j] = file_data.std(axis=1)
rec_mean[:, j] = np.nanmean(file_data, axis=1)
rec_sd[:, j] = np.nanstd(file_data, axis=1)
if plot_overview:
axes[1, i].plot(scales, rec_mean[:, j], c='k')