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paper_2025/python/fig_invariance_field.py
j-hartling f14de13823 Syncing to home.
2026-05-08 18:21:47 +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 reduce_kernel_set, divide_by_zero, 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, xlabel, 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, distances, measures, **kwargs):
if measures.ndim == 1:
handles = ax.plot(distances, measures, **kwargs)
return handles, measures
median_measure = np.nanmedian(measures, axis=1)
line_handle = ax.plot(distances, median_measure, **kwargs)[0]
return line_handle, median_measure
def crop_noise_snippets(snippets, nin, nout, stages=['filt', 'env', 'log', 'inv', 'conv', 'feat']):
half_offset = int((nin - nout) / 2)
segment = np.arange(half_offset, half_offset + nout)
for stage in stages:
key = f'snip_{stage}'
snippets[key] = snippets[key][segment, ...]
return snippets
# GENERAL SETTINGS:
search_target = 'Pseudochorthippus_parallelus'
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
song_example = 'Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms'
noise_example = 'merged_noise'
song_path = search_files(song_example, dir='../data/inv/field/song/')[0]
noise_path = search_files(noise_example, dir='../data/inv/field/noise/')[0]
save_path = '../figures/fig_invariance_field.pdf'
# ANALYSIS SETTINGS:
offset_distance = 10 # centimeter
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4]
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=0.25,
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_dist_bounds = [1.01, 0, 0.95, 1]
inset_ax_bounds = [raw_grid_kwargs['left'], 0.1, raw_grid_kwargs['right'] - raw_grid_kwargs['left'], 0.01]
# 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,
log=0.25,
inv=0.25,
conv=0.25,
feat=1,
single=3,
swarm=1,
legend=5,
dist=1
)
xlabels = dict(
high='$1\\,/\\,d\\,\\sim\\,\\alpha$ [cm$^{-1}$]',
low='distance $d$ [cm]',
)
ylabels = dict(
filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$',
env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
log='$x_{\\text{log}}$\n$[\\text{dB}]$',
inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
conv='$c_i$\n$[\\text{dB}]$',
feat='$f_i$',
raw=['$m$', '$\\mu_{f_i}$'],
base=['$m\\,/\\,m_{\\eta}$', '$\\sigma_{c_i}\\,/\\,\\sigma_{\\eta_i}$', '$\\mu_{f_i}\\,/\\,\\mu_{\\eta_i}$']
)
xlab_high_kwargs = dict(
y=0.15,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
xlab_low_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',
ma='center'
)
ylab_big_kwargs = dict(
x=0,
fontsize=fs['lab_norm'],
ha='center',
va='top',
)
yloc = dict(
filt=300,
env=100,
log=50,
inv=20,
conv=0.5,
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='bottom',
fontsize=fs['letter'],
)
song_bar_time = 1
song_bar_kwargs = dict(
dur=song_bar_time,
y0=-0.25,
y1=-0.1,
xshift=1,
color='k',
lw=0,
clip_on=False,
text_pos=(-0.1, 0.5),
text_str=f'${song_bar_time}\\,\\text{{s}}$',
text_kwargs=dict(
fontsize=fs['bar'],
ha='right',
va='center',
)
)
noise_bar_time = 0.5
noise_bar_kwargs = song_bar_kwargs.copy()
noise_bar_kwargs['dur'] = noise_bar_time
noise_bar_kwargs['text_str'] = f'${int(1000 * noise_bar_time)}\\,\\text{{ms}}$'
leg_labels = dict(
filt='$x_{\\text{filt}}$',
env='$x_{\\text{env}}$',
log='$x_{\\text{log}}$',
inv='$x_{\\text{adapt}}$',
conv='$c_i$',
feat='$f_i$'
)
leg_kwargs = dict(
ncols=3,
loc='upper left',
bbox_to_anchor=(0.025, 0.9, 0.95, 0.1),
frameon=False,
prop=dict(
size=20,
),
borderpad=0,
borderaxespad=0,
handlelength=1,
columnspacing=1,
handletextpad=0.5,
labelspacing=0.1
)
dist_line_kwargs = dict(
lw=lw['dist'],
)
dist_fill_kwargs = dict(
lw=lw['dist'],
)
# EXECUTION:
# Load song invariance data:
song_data, config = load_data(song_path, files='distances', keywords=['measure', 'snip', 'thresh'])
t_song = np.arange(song_data['snip_filt'].shape[0]) / config['rate']
dists = song_data['distances'] + offset_distance
scales = 1 / dists
snip_dists = ['noise'] + [f'{int(d)}$\\,$cm' for d in dists]
# Load noise invariance data:
noise_data, _ = load_data(noise_path, keywords=['measure', 'snip', 'thresh'])
noise_data = crop_noise_snippets(noise_data, noise_data['snip_filt'].shape[0], t_song.size)
t_noise = np.arange(noise_data['snip_filt'].shape[0]) / config['rate']
# Reduce kernels:
if reduce_kernels:
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
config['k_specs'] = config['k_specs'][kern_inds, :]
config['kernels'] = config['kernels'][:, kern_inds]
song_data = reduce_kernel_set(song_data, kern_inds, **kern_subset_kwargs)
noise_data = reduce_kernel_set(noise_data, kern_inds, **kern_subset_kwargs)
# Reduce thresholds:
thresh_ind = np.nonzero(song_data['thresh_rel'] == thresh_rel)[0][0]
song_data['measure_feat'] = song_data['measure_feat'][:, :, thresh_ind]
song_data['snip_feat'] = song_data['snip_feat'][:, :, :, thresh_ind]
noise_data['measure_feat'] = noise_data['measure_feat'][:, :, thresh_ind]
noise_data['snip_feat'] = noise_data['snip_feat'][:, :, :, thresh_ind]
# Adjust grid parameters:
snip_grid_kwargs['ncols'] = len(snip_dists)
# 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.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
hide_axis(ax, 'bottom')
if i == 0:
title = title_subplot(ax, snip_dists[j], ref=snip_subfig, **title_kwargs)
if j == 0:
ax.set_xlim(t_noise[0], t_noise[-1])
ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
else:
ax.set_xlim(t_song[0], t_song[-1])
hide_axis(ax, 'left')
snip_axes[i, j] = ax
time_bar(snip_axes[-1, -1], **song_bar_kwargs)
# time_bar(snip_axes[-1, 0], **noise_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('log')
ylabel(ax, ylabels['raw'][i], transform=raw_subfig.transSubfigure, **ylab_big_kwargs)
if i == 0:
ax.set_yscale('symlog', linthresh=0.00001, linscale=0.1)
hide_ticks(ax, 'bottom')
else:
transform = raw_subfig.transSubfigure + ax.transAxes.inverted()
inset_x1 = transform.transform((inset_dist_bounds[2], 0))[0]
inset_dist_bounds[2] = inset_x1 - inset_dist_bounds[0]
raw_inset = ax.inset_axes(inset_dist_bounds)
raw_inset.axis('off')
raw_axes[i] = ax
letter_subplots(raw_axes, 'bc', ref=raw_subfig, **letter_big_kwargs)
xlabel(raw_axes[-1], xlabels['high'], transform=raw_subfig.transSubfigure, **xlab_high_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)
base_insets = np.zeros((base_grid.nrows - 1,), 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('log')
ax.set_yscale('log')
ylabel(ax, ylabels['base'][i], transform=base_subfig.transSubfigure, **ylab_big_kwargs)
if i < base_grid_kwargs['nrows'] - 1:
hide_ticks(ax, 'bottom')
if i > 0:
inset = ax.inset_axes(inset_dist_bounds)
inset.set_yscale('log')
inset.axis('off')
base_insets[i - 1] = inset
base_axes[i] = ax
letter_subplots(base_axes, 'def', ref=base_subfig, **letter_big_kwargs)
xlabel(base_axes[-1], xlabels['high'], transform=base_subfig.transSubfigure, **xlab_high_kwargs)
if True:
# Plot filtered snippets:
plot_snippets(snip_axes[0, 1:], t_song, song_data['snip_filt'],
c=stage_colors['filt'], lw=lw['filt'])
plot_line(snip_axes[0, 0], t_noise, noise_data['snip_filt'][:, 0],
*snip_axes[0, 1].get_ylim(), c=stage_colors['filt'], lw=lw['filt'])
# Plot envelope snippets:
plot_snippets(snip_axes[1, 1:], t_song, song_data['snip_env'],
ymin=0, c=stage_colors['env'], lw=lw['env'])
plot_line(snip_axes[1, 0], t_noise, noise_data['snip_env'][:, 0],
*snip_axes[1, 1].get_ylim(), c=stage_colors['env'], lw=lw['env'])
# Plot logarithmic snippets:
plot_snippets(snip_axes[2, 1:], t_song, song_data['snip_log'],
c=stage_colors['log'], lw=lw['log'])
plot_line(snip_axes[2, 0], t_noise, noise_data['snip_log'][:, 0],
*snip_axes[2, 1].get_ylim(), c=stage_colors['log'], lw=lw['log'])
# Plot invariant snippets:
plot_snippets(snip_axes[3, 1:], t_song, song_data['snip_inv'],
c=stage_colors['inv'], lw=lw['inv'])
plot_line(snip_axes[3, 0], t_noise, noise_data['snip_inv'][:, 0],
*snip_axes[3, 1].get_ylim(), c=stage_colors['inv'], lw=lw['inv'])
# Plot kernel response snippets:
all_handles = plot_snippets(snip_axes[4, 1:], t_song, song_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, song_data['snip_conv'][..., i])
handles = plot_line(snip_axes[4, 0], t_noise, noise_data['snip_conv'][:, 0],
*snip_axes[4, 1].get_ylim(), c=stage_colors['conv'], lw=lw['conv'])
assign_colors(handles, config['k_specs'][:, 0], kern_colors['conv'])
reorder_by_sd(handles, noise_data['snip_conv'][:, 0])
# Plot feature snippets:
all_handles = plot_snippets(snip_axes[5, 1:], t_song, song_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, song_data['snip_feat'][..., i])
handles = plot_line(snip_axes[5, 0], t_noise, noise_data['snip_feat'][:, 0],
ymin=0, ymax=1, c=stage_colors['feat'], lw=lw['feat'])
assign_colors(handles, config['k_specs'][:, 0], kern_colors['feat'])
reorder_by_sd(handles, noise_data['snip_feat'][:, 0])
# Plot analysis results:
leg_handles = []
for stage in stages:
mkey = f'measure_{stage}'
measure = song_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)
## NORMALIZED MEASURE:
# Relate to noise baseline:
measure = divide_by_zero(song_data[mkey], noise_data[mkey].mean(axis=0))
# 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 in ['conv', 'feat']:
i0, i1 = (1, 0) if stage == 'conv' else (2, 1)
# Sync y-limits:
ylimits(measure, base_axes[i0], minval=0.9, pad=0.05)
base_insets[i1].set_ylim(base_axes[i0].get_ylim())
# Plot swarm:
handles = base_axes[i0].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_insets[i1], measure[-1], nbins=100, log=True,
line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)
# Posthoc adjustments:
raw_axes[0].set_ylim(top=100)
base_axes[0].set_ylim(1, 100)
base_axes[1].set_ylim(bottom=1)
base_insets[0].set_ylim(bottom=1)
base_axes[2].set_ylim(bottom=1)
base_insets[1].set_ylim(bottom=1)
# Add secondary x-axes:
for subfig in [raw_subfig, base_subfig]:
dual_ax = subfig.add_subplot(inset_ax_bounds)
dual_ax.set_xlim(scales[0], scales[-1])
dual_ax.set_xscale('log')
dual_ax.tick_params(axis='x', which='minor', bottom=False)
dual_ax.tick_params(axis='x', which='major', labelrotation=45)
dual_ax.set_xticks(scales, dists)
hide_axis(dual_ax, 'left')
xlabel(dual_ax, xlabels['low'], transform=subfig.transSubfigure, **xlab_low_kwargs)
# 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:
fig.savefig(save_path)
plt.show()
print('Done.')
embed()
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