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paper_2025/python/fig_invariance_short.py
j-hartling f6d353c5ea Finished fig_invariance_full.pdf and fig_invariance_short.pdf. 
Some renaming shenanigans.
2026-04-29 19:04:21 +02:00

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import plotstyle_plt
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 thunderhopper.filtertools import find_kern_specs
from misc_functions import get_saturation, reduce_kernel_set, exclude_zero_scale,\
divide_by_zero, x_dist, y_dist
from color_functions import load_colors
from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
plot_line, strip_zeros, time_bar, assign_colors,\
letter_subplot, letter_subplots, hide_ticks
from IPython import embed
def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
handles = []
for i, ax in enumerate(axes):
handles.append(plot_line(ax, time, snippets[:, ..., i],
ymin=ymin, ymax=ymax, **kwargs))
return handles
def plot_curves(ax, scales, measures, **kwargs):
if measures.ndim == 1:
handles = ax.plot(scales, measures, **kwargs)
return handles, measures
median_measure = np.nanmedian(measures, axis=1)
line_handle = ax.plot(scales, median_measure, **kwargs)[0]
return line_handle, median_measure
# GENERAL SETTINGS:
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]
stages = ['filt', 'env', 'inv', 'conv', 'feat']
data_path = search_files(example_file, dir='../data/inv/short/')[0]
save_path = '../figures/fig_invariance_short.pdf'
# ANALYSIS SETTINGS:
exclude_zero = True
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4]
percentiles = np.array([0, 100])
scale_subset_kwargs = dict(
combis=[['measure'], stages],
)
kern_subset_kwargs = dict(
combis=[['measure', 'snip'], ['conv', 'feat']],
keys=['thresh_abs'],
)
# SUBSET SETTINGS:
types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016])
# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
kernels = None
reduce_kernels = any(var is not None for var in [kernels, types, sigmas])
# GRAPH SETTINGS:
fig_kwargs = dict(
figsize=(32/2.54, 32/2.54),
)
super_grid_kwargs = dict(
nrows=2,
ncols=2,
wspace=0,
hspace=0,
left=0,
right=1,
bottom=0,
top=1,
height_ratios=[1, 1]
)
subfig_specs = dict(
snip=(0, slice(None)),
raw=(1, 0),
base=(1, 1),
)
snip_grid_kwargs = dict(
nrows=len(stages),
ncols=None,
wspace=0.1,
hspace=0.4,
left=0.13,
right=0.98,
bottom=0.05,
top=0.95
)
raw_grid_kwargs = dict(
nrows=2,
ncols=1,
wspace=0,
hspace=0.15,
left=0.14,
right=0.9,
bottom=0.1,
top=0.95,
height_ratios=[0.8, 0.2]
)
base_grid_kwargs = dict(
nrows=3,
ncols=1,
wspace=0,
hspace=0.25,
left=raw_grid_kwargs['left'],
right=raw_grid_kwargs['right'],
bottom=raw_grid_kwargs['bottom'],
top=raw_grid_kwargs['top'],
)
inset_bounds = [1.01, 0, 0.95, 1]
# PLOT SETTINGS:
fs = dict(
lab_norm=16,
lab_tex=20,
letter=22,
tit_norm=16,
tit_tex=20,
bar=16,
)
stage_colors = load_colors('../data/stage_colors.npz')
kern_colors = dict(
conv=load_colors('../data/conv_colors_subset.npz'),
feat=load_colors('../data/feat_colors_subset.npz')
)
lw = dict(
filt=0.25,
env=0.25,
inv=0.25,
conv=0.25,
feat=1,
single=3,
swarm=1,
plateau=1.5,
legend=5,
dist=1
)
xlabels = dict(
big='scale $\\alpha$',
)
ylabels = dict(
filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$',
env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
conv='$c_i$\n$[\\text{dB}]$',
feat='$f_i$',
raw=['$m$', '$\\mu_{f_i}$'],
base=['$m\\,/\\,m_{\\eta}$', '$\\mu_{f_i}$', '$\\text{PDF}_{\\alpha}$']
)
xlab_big_kwargs = dict(
y=0,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
ylab_snip_kwargs = dict(
x=0.03,
fontsize=fs['lab_tex'],
rotation=0,
ha='center',
va='center',
)
ylab_big_kwargs = dict(
x=0,
fontsize=fs['lab_norm'],
ha='center',
va='top',
)
yloc = dict(
filt=3000,
env=1000,
inv=500,
conv=10,
feat=1,
)
title_kwargs = dict(
x=0.5,
yref=1,
ha='center',
va='top',
fontsize=fs['tit_norm'],
)
letter_snip_kwargs = dict(
x=0,
yref=0.5,
ha='left',
va='center',
fontsize=fs['letter'],
)
letter_big_kwargs = dict(
xref=0,
y=1,
ha='left',
va='center',
fontsize=fs['letter'],
)
bar_time = 5
bar_kwargs = dict(
dur=bar_time,
y0=-0.3,
y1=-0.15,
xshift=1,
color='k',
lw=0,
clip_on=False,
text_pos=(-0.1, 0.5),
text_str=f'${bar_time}\\,\\text{{s}}$',
text_kwargs=dict(
fontsize=fs['bar'],
ha='right',
va='center',
)
)
leg_kwargs = dict(
ncols=1,
loc='upper left',
bbox_to_anchor=(0.025, 0.5, 0.5, 0.5),
frameon=False,
prop=dict(
size=20,
),
borderpad=0,
borderaxespad=0,
handlelength=1,
columnspacing=1,
handletextpad=0.5,
labelspacing=0.1
)
leg_labels = dict(
filt='$x_{\\text{filt}}$',
env='$x_{\\text{env}}$',
inv='$x_{\\text{adapt}}$',
conv='$c_i$',
feat='$f_i$'
)
dist_line_kwargs = dict(
lw=lw['dist'],
)
dist_fill_kwargs = dict(
lw=lw['dist'],
)
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:
# Load invariance data:
data, config = load_data(data_path, keywords=['snip', 'scales', 'measure', 'thresh'])
t_full = np.arange(data['snip_filt'].shape[0]) / config['rate']
# Reduce kernels:
if reduce_kernels:
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
data = reduce_kernel_set(data, kern_inds, **kern_subset_kwargs)
config['k_specs'] = config['k_specs'][kern_inds, :]
config['kernels'] = config['kernels'][:, kern_inds]
# Reduce thresholds:
thresh_ind = np.nonzero(data['thresh_rel'] == thresh_rel)[0][0]
data['measure_feat'] = data['measure_feat'][:, :, thresh_ind]
data['snip_feat'] = data['snip_feat'][:, :, :, thresh_ind]
# Remember pure-noise reference measures:
ref_data = {stage: data[f'measure_{stage}'][0, ...] for stage in stages}
# Reduce scales:
if exclude_zero:
data = exclude_zero_scale(data, **scale_subset_kwargs)
scales = data['scales']
snip_scales = data['example_scales']
# Adjust grid parameters:
snip_grid_kwargs['ncols'] = snip_scales.size
# Prepare overall graph:
fig = plt.figure(**fig_kwargs)
super_grid = fig.add_gridspec(**super_grid_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)
# Prepare raw analysis axes:
raw_subfig = fig.add_subfigure(super_grid[subfig_specs['raw']])
raw_grid = raw_subfig.add_gridspec(**raw_grid_kwargs)
raw_axes = np.zeros((raw_grid.nrows,), dtype=object)
for i in range(raw_grid.nrows):
ax = raw_subfig.add_subplot(raw_grid[i, 0])
ax.set_xlim(scales[0], scales[-1])
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
ylabel(ax, ylabels['raw'][i], transform=raw_subfig.transSubfigure, **ylab_big_kwargs)
if i == 0:
ax.set_yscale('symlog', linthresh=0.0001, linscale=0.1)
hide_ticks(ax, 'bottom')
else:
transform = raw_subfig.transSubfigure + ax.transAxes.inverted()
inset_x1 = transform.transform((inset_bounds[2], 0))[0]
inset_bounds[2] = inset_x1 - inset_bounds[0]
raw_inset = ax.inset_axes(inset_bounds)
raw_inset.axis('off')
raw_axes[i] = ax
letter_subplots(raw_axes, 'bc', ref=raw_subfig, **letter_big_kwargs)
# Prepare base analysis axes:
base_subfig = fig.add_subfigure(super_grid[subfig_specs['base']])
base_grid = base_subfig.add_gridspec(**base_grid_kwargs)
base_axes = np.zeros((base_grid.nrows,), dtype=object)
for i in range(base_grid.nrows):
ax = base_subfig.add_subplot(base_grid[i, 0])
ax.set_xlim(scales[0], scales[-1])
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
ylabel(ax, ylabels['base'][i], transform=base_subfig.transSubfigure, **ylab_big_kwargs)
if i < base_grid_kwargs['nrows'] - 1:
ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
hide_ticks(ax, 'bottom')
if i == 1:
base_inset = ax.inset_axes(inset_bounds)
base_inset.set_yscale('symlog', linthresh=0.01, linscale=0.1)
base_inset.axis('off')
base_axes[i] = ax
letter_subplots(base_axes, 'def', ref=base_subfig, **letter_big_kwargs)
super_xlabel(xlabels['big'], fig, raw_axes[-1], base_axes[-1],
left_fig=raw_subfig, right_fig=base_subfig, **xlab_big_kwargs)
if True:
# Plot filtered snippets:
plot_snippets(snip_axes[0, :], t_full, data['snip_filt'],
c=stage_colors['filt'], lw=lw['filt'])
# Plot envelope snippets:
plot_snippets(snip_axes[1, :], t_full, data['snip_env'],
ymin=0, c=stage_colors['env'], lw=lw['env'])
# Plot invariant snippets:
plot_snippets(snip_axes[2, :], t_full, data['snip_inv'],
c=stage_colors['inv'], lw=lw['inv'])
# Plot kernel response snippets:
all_handles = plot_snippets(snip_axes[3, :], t_full, data['snip_conv'],
c=stage_colors['conv'], lw=lw['conv'])
for i, handles in enumerate(all_handles):
assign_colors(handles, config['k_specs'][:, 0], kern_colors['conv'])
reorder_by_sd(handles, data['snip_conv'][..., i])
# Plot feature snippets:
all_handles = plot_snippets(snip_axes[4, :], t_full, data['snip_feat'],
ymin=0, ymax=1, c=stage_colors['feat'], lw=lw['feat'])
for i, handles in enumerate(all_handles):
assign_colors(handles, config['k_specs'][:, 0], kern_colors['feat'])
reorder_by_sd(handles, data['snip_feat'][..., i])
# Plot analysis results:
leg_handles = []
for stage in stages:
mkey = f'measure_{stage}'
measure = data[mkey]
color = stage_colors[stage]
## UNNORMALIZED MEASURE:
# Plot single raw intensity curve (median where necessary):
handles, curve = plot_curves(raw_axes[0], scales, measure, c=color, lw=lw['single'])
# Add stage-specific proxy legend artist:
leg_handles.append(raw_axes[0].plot([], [], c=color, label=leg_labels[stage])[0])
# Plot curve swarm:
if stage == 'feat':
# Sync y-limits:
ylimits(measure, raw_axes[1], minval=0, pad=0.05)
raw_inset.set_ylim(raw_axes[1].get_ylim())
# Plot swarm:
handles = raw_axes[1].plot(scales, measure, lw=lw['swarm'])
assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
reorder_by_sd(handles, measure)
# Plot distribution of saturation levels:
line_kwargs = dist_line_kwargs | dict(c=color)
fill_kwargs = dist_fill_kwargs | dict(color=color)
y_dist(raw_inset, measure[-1], nbins=75, log=False,
line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)
# Indicate saturation point:
if stage == 'feat':
crit_ind = get_saturation(curve, **plateau_settings)[1]
crit_scale = scales[crit_ind]
raw_axes[0].plot(crit_scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=raw_axes[0].get_xaxis_transform())
raw_axes[0].plot(crit_scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
transform=raw_axes[0].get_xaxis_transform())
raw_axes[0].vlines(crit_scale, raw_axes[0].get_ylim()[0], curve[crit_ind],
color=color, **plateau_line_kwargs)
## NORMALIZED MEASURE:
# Relate to noise baseline:
measure = divide_by_zero(data[mkey], ref_data[stage])
# Plot single baseline-normalized intensity curve (median where necessary):
handles, curve = plot_curves(base_axes[0], scales, measure, c=color, lw=lw['single'])
# Plot curve swarm:
if stage == 'feat':
# Sync y-limits:
ylimits(measure, base_axes[1], minval=0.9, pad=0.05)
base_inset.set_ylim(base_axes[1].get_ylim())
# Plot swarm:
handles = base_axes[1].plot(scales, measure, lw=lw['swarm'])
assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
reorder_by_sd(handles, measure)
# Plot distribution of saturation levels:
line_kwargs = dist_line_kwargs | dict(c=color)
fill_kwargs = dist_fill_kwargs | dict(color=color)
y_dist(base_inset, measure[-1], nbins=100, log=True,
line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)
# Plot distribution of saturation points:
inds = np.array(get_saturation(measure, **plateau_settings)[1])
if np.isnan(inds).sum():
inds = inds[~np.isnan(inds)].astype(int)
crit_scales = scales[inds]
bin_lims = [0.01, 1.1 * crit_scales.max()]
line_kwargs = dist_line_kwargs | dict(c=stage_colors['feat'])
fill_kwargs = dist_fill_kwargs | dict(color=stage_colors['feat'])
x_dist(base_axes[-1], crit_scales, nbins=75, limits=bin_lims, log=True,
line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)
# Add single curve saturation point:
base_axes[-1].plot(crit_scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=base_axes[-1].get_xaxis_transform())
base_axes[-1].plot(crit_scale, 0, mfc=stage_colors['feat'], mec='k', alpha=0.75,
zorder=6, **plateau_dot_kwargs, transform=base_axes[-1].get_xaxis_transform())
# Indicate saturation point:
if stage == 'feat':
base_axes[0].plot(crit_scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=base_axes[0].get_xaxis_transform())
base_axes[0].plot(crit_scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
transform=base_axes[0].get_xaxis_transform())
base_axes[0].vlines(crit_scale, base_axes[0].get_ylim()[0], curve[crit_ind],
color=color, **plateau_line_kwargs)
# Posthoc adjustments:
raw_axes[0].set_ylim(bottom=0.0001)
base_axes[0].set_ylim(1, 100)
# Add legend to first analysis axis:
legend = raw_axes[0].legend(handles=leg_handles, **leg_kwargs)
[handle.set_lw(lw['legend']) for handle in legend.get_lines()]
# Save graph:
if save_path is not None:
file_name = save_path.replace('.pdf', f'_{target_species}.pdf')
fig.savefig(file_name)
plt.show()
print('Done.')
embed()
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