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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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265
python/fig_invariance_full.py
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@@ -1,13 +1,13 @@
import plotstyle_plt
import glob
import numpy as np
import matplotlib.pyplot as plt
from itertools import product
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data
from misc_functions import get_saturation
from color_functions import load_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
plot_line, plot_barcode, strip_zeros, time_bar,\
plot_line, strip_zeros, time_bar,\
letter_subplot, letter_subplots
from IPython import embed
@@ -17,11 +17,6 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
return None
def plot_bi_snippets(axes, time, snippets, **kwargs):
for i, ax in enumerate(axes):
plot_barcode(ax, time, snippets[:, ..., i], **kwargs)
return None
def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
if measures.ndim == 1:
ax.plot(scales, measures, **kwargs)[0]
@@ -39,8 +34,28 @@ def show_saturation(ax, scales, measures, high=0.95, **kwargs):
marker='o', ms=10, zorder=6, clip_on=False, **kwargs)
# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = glob.glob(f'../data/inv/full/{target}*.npz')
target_species = [
'Chorthippus_biguttulus',
'Chorthippus_mollis',
'Chrysochraon_dispar',
'Euchorthippus_declivus',
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
][5]
example_file = {
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
'Chrysochraon_dispar': 'Chrysochraon_dispar_DJN_26_T28C_DT-32s134ms-34s432ms',
'Euchorthippus_declivus': 'Euchorthippus_declivus_FTN_79-2s167ms-2s563ms',
'Gomphocerippus_rufus': 'Gomphocerippus_rufus_FTN_91-3-884ms-10s427ms',
'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
}[target_species]
raw_path = search_files(target_species, incl='raw', dir='../data/inv/full/condensed/')[0]
norm_path = search_files(target_species, incl='norm', dir='../data/inv/full/condensed/')[0]
snip_path = search_files(example_file, dir='../data/inv/full/')[0]
trace_path = search_files(target_species, dir='../data/inv/full/collected/')[0]
ref_path = '../data/inv/full/ref_measures.npz'
save_path = '../figures/fig_invariance_full.pdf'
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
@@ -105,9 +120,9 @@ lw = dict(
log=0.25,
inv=0.25,
conv=0.25,
bi=0,
feat=1,
big=3
big=3,
plateau=1.5,
)
xlabels = dict(
big='scale $\\alpha$',
@@ -118,7 +133,6 @@ ylabels = dict(
log='$x_{\\text{db}}$',
inv='$x_{\\text{inv}}$',
conv='$c_i$',
bi='$b_i$',
feat='$f_i$',
big=['intensity', 'rel. intensity', 'norm. intensity']
)
@@ -187,121 +201,160 @@ bar_kwargs = dict(
va='center',
)
)
# PREPARATION:
ref_data = dict(np.load(ref_path))
plateau_settings = dict(
low=0.05,
high=0.95,
first=True,
last=True,
condense=None,
)
plateau_line_kwargs = dict(
lw=lw['plateau'],
ls='--',
zorder=1,
)
plateau_dot_kwargs = dict(
marker='o',
markersize=8,
markeredgewidth=1,
clip_on=False,
)
# EXECUTION:
for data_path in data_paths:
print(f'Processing {data_path}')
# Load invariance data:
data, config = load_data(data_path, **load_kwargs)
t_full = np.arange(data['snip_filt'].shape[0]) / config['rate']
# Load invariance data:
raw_data, config = load_data(raw_path, files='scales', keywords='mean')
norm_data, _ = load_data(norm_path, files='scales', keywords='mean')
scales = raw_data['scales']
# Adjust grid parameters:
snip_grid_kwargs['ncols'] = data['example_scales'].size
# Load snippet data:
snip, _ = load_data(snip_path, files='example_scales', keywords='snip')
t_full = np.arange(snip['snip_filt'].shape[0]) / config['rate']
snip_scales = snip['example_scales']
# Prepare overall graph:
fig = plt.figure(**fig_kwargs)
super_grid = fig.add_gridspec(**super_grid_kwargs)
# Adjust grid parameters:
snip_grid_kwargs['ncols'] = snip_scales.size
# Prepare stage-specific snippet axes:
snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
ax = snip_subfig.add_subplot(snip_grid[i, j])
ax.set_xlim(t_full[0], t_full[-1])
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
hide_axis(ax, 'bottom')
if i == 0:
title = title_subplot(ax, f'$\\alpha={strip_zeros(data["example_scales"][j])}$',
ref=snip_subfig, **title_kwargs)
if j == 0:
ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
else:
hide_axis(ax, 'left')
snip_axes[i, j] = ax
time_bar(snip_axes[-1, -1], **bar_kwargs)
letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
# Prepare overall graph:
fig = plt.figure(**fig_kwargs)
super_grid = fig.add_gridspec(**super_grid_kwargs)
# Prepare analysis axes:
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
big_axes = np.zeros((big_grid.ncols,), dtype=object)
for i in range(big_grid.ncols):
ax = big_subfig.add_subplot(big_grid[0, i])
ax.set_xlim(data['scales'][0], data['scales'][-1])
ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
xlabel(ax, xlabels['big'], transform=big_subfig, **xlab_big_kwargs)
ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
big_axes[i] = ax
letter_subplots(big_axes, 'bc', **letter_big_kwargs)
# Prepare stage-specific snippet axes:
snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
ax = snip_subfig.add_subplot(snip_grid[i, j])
ax.set_xlim(t_full[0], t_full[-1])
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
hide_axis(ax, 'bottom')
if i == 0:
title = title_subplot(ax, f'$\\alpha={strip_zeros(snip_scales[j])}$',
ref=snip_subfig, **title_kwargs)
if j == 0:
ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
else:
hide_axis(ax, 'left')
snip_axes[i, j] = ax
time_bar(snip_axes[-1, -1], **bar_kwargs)
letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
# # Plot filtered snippets:
# plot_snippets(snip_axes[0, :], t_full, data['snip_filt'],
# c=colors['filt'], lw=lw['filt'])
# Prepare analysis axes:
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
big_axes = np.zeros((big_grid.ncols,), dtype=object)
for i in range(big_grid.ncols):
ax = big_subfig.add_subplot(big_grid[0, i])
ax.set_xlim(scales[0], scales[-1])
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
xlabel(ax, xlabels['big'], transform=big_subfig, **xlab_big_kwargs)
ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
big_axes[i] = ax
letter_subplots(big_axes, 'bc', **letter_big_kwargs)
# # Plot envelope snippets:
# plot_snippets(snip_axes[1, :], t_full, data['snip_env'],
# ymin=0, c=colors['env'], lw=lw['env'])
if False:
# Plot filtered snippets:
plot_snippets(snip_axes[0, :], t_full, snip['snip_filt'],
c=colors['filt'], lw=lw['filt'])
# # Plot logarithmic snippets:
# plot_snippets(snip_axes[2, :], t_full, data['snip_log'],
# c=colors['log'], lw=lw['log'])
# Plot envelope snippets:
plot_snippets(snip_axes[1, :], t_full, snip['snip_env'],
ymin=0, c=colors['env'], lw=lw['env'])
# # Plot invariant snippets:
# plot_snippets(snip_axes[3, :], t_full, data['snip_inv'],
# c=colors['inv'], lw=lw['inv'])
# Plot logarithmic snippets:
plot_snippets(snip_axes[2, :], t_full, snip['snip_log'],
c=colors['log'], lw=lw['log'])
# # Plot kernel response snippets:
# plot_snippets(snip_axes[4, :], t_full, data['snip_conv'],
# c=colors['conv'], lw=lw['conv'])
# Plot invariant snippets:
plot_snippets(snip_axes[3, :], t_full, snip['snip_inv'],
c=colors['inv'], lw=lw['inv'])
# # Plot feature snippets:
# plot_snippets(snip_axes[5, :], t_full, data['snip_feat'],
# ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
# Plot kernel response snippets:
plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
c=colors['conv'], lw=lw['conv'])
# Analysis results:
scales_rel = data['scales'] - data['scales'][0]
scales_rel /= scales_rel[-1]
for stage in stages:
measure = data[f'measure_{stage}']
# Plot feature snippets:
plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
# Plot unmodified intensity measures:
curve = plot_curves(big_axes[0], data['scales'], measure, c=colors[stage], lw=lw['big'],
fill_kwargs=dict(color=colors[stage], alpha=0.25))
if stage in ['log', 'inv', 'conv', 'feat']:
show_saturation(big_axes[0], data['scales'], curve, c=colors[stage])
# Plot analysis results:
for stage in stages:
# Get average unnormed measure across recordings:
raw_measure = raw_data[f'mean_{stage}'].mean(axis=-1)
# # Relate to pure-noise reference:
# norm_measure = measure / ref_data[stage]
# Plot unmodified intensity measures:
curve = plot_curves(big_axes[0], scales, raw_measure, c=colors[stage], lw=lw['big'],
fill_kwargs=dict(color=colors[stage], alpha=0.25))
# Indicate saturation point:
if stage in ['log', 'inv', 'conv', 'feat']:
ind = get_saturation(curve, **plateau_settings)[1]
scale = scales[ind]
big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=big_axes[0].get_xaxis_transform())
big_axes[0].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
transform=big_axes[0].get_xaxis_transform())
big_axes[0].vlines(scale, big_axes[0].get_ylim()[0], curve[ind],
color=colors[stage], **plateau_line_kwargs)
# # Plot noise-related intensity measures:
# big_axes[1].plot(data['scales'], norm_measure, c=colors[stage], lw=lw['big'])
# Get average noise-related measure across recordings:
norm_measure = norm_data[f'mean_{stage}'].mean(axis=-1)
# Normalize measure to [0, 1]:
min_measure = measure.min(axis=0)
max_measure = measure.max(axis=0)
norm_measure = (measure - min_measure) / (max_measure - min_measure)
# Plot noise-related intensity measure:
curve = plot_curves(big_axes[1], scales, norm_measure, c=colors[stage], lw=lw['big'],
fill_kwargs=dict(color=colors[stage], alpha=0.25))
# Plot normalized intensity measures:
curve = plot_curves(big_axes[1], data['scales'], norm_measure, c=colors[stage], lw=lw['big'],
fill_kwargs=dict(color=colors[stage], alpha=0.25))
if stage in ['log', 'inv', 'conv', 'feat']:
show_saturation(big_axes[1], data['scales'], curve, c=colors[stage])
# Indicate saturation point:
if stage in ['log', 'inv', 'conv', 'feat']:
big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=big_axes[1].get_xaxis_transform())
big_axes[1].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
transform=big_axes[1].get_xaxis_transform())
big_axes[1].vlines(scale, big_axes[1].get_ylim()[0], curve[ind],
color=colors[stage], **plateau_line_kwargs)
# # Plot over relative scales:
# plot_curves(big_axes[2], scales_rel, norm_measure, c=colors[stage], lw=lw['big'],
# fill_kwargs=dict(color=colors[stage], alpha=0.25))
# scales_rel = curve - curve.min()
# scales_rel /= scales_rel.max()
# Normalize measure to [0, 1]:
min_measure = raw_measure.min(axis=0)
max_measure = raw_measure.max(axis=0)
norm_measure = (raw_measure - min_measure) / (max_measure - min_measure)
if save_path is not None:
fig.savefig(save_path)
plt.show()
# Plot range-normalized intensity measure:
curve = plot_curves(big_axes[2], scales, norm_measure, c=colors[stage], lw=lw['big'],
fill_kwargs=dict(color=colors[stage], alpha=0.25))
# Indicate saturation point:
if stage in ['log', 'inv', 'conv', 'feat']:
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=big_axes[2].get_xaxis_transform())
big_axes[2].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
transform=big_axes[2].get_xaxis_transform())
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], curve[ind],
color=colors[stage], **plateau_line_kwargs)
if save_path is not None:
fig.savefig(save_path)
plt.show()
print('Done.')
embed()