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Finished a good part of analysis and figure for Thresh-LP invariance (WIP).

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j-hartling
2026-03-06 14:47:22 +01:00
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from pyparsing import alphanums
import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.transforms import BboxTransformTo
from itertools import product
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data
from color_functions import load_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, plot_line, plot_barcode, strip_zeros
from IPython import embed
def time_bar(ax, dur, y0=0.9, y1=0.95, xshift=0.5, parent=None, transform=None, **kwargs):
t0, t1 = ax.get_xlim()
offset = (t1 - t0 - dur) * xshift
x0 = t0 + offset
x1 = x0 + dur
if parent is None:
parent = ax
if transform is None:
transform = BboxTransformTo(parent.bbox)
if transform is not ax.transData:
trans = ax.transData + transform.inverted()
x0 = trans.transform((x0, 0))[0]
x1 = trans.transform((x1, 0))[0]
parent.add_artist(plt.Rectangle((x0, y0), x1 - x0, y1 - y0,
transform=transform, **kwargs))
return None
def add_snip_axes(fig, grid_kwargs):
grid = fig.add_gridspec(**grid_kwargs)
axes = np.zeros((grid.nrows, grid.ncols), dtype=object)
for i, j in product(range(grid.nrows), range(grid.ncols)):
axes[i, j] = fig.add_subplot(grid[i, j])
[hide_axis(ax, 'left') for ax in axes.flatten()]
[hide_axis(ax, 'bottom') for ax in axes.flatten()]
return axes
def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
for ax, snippet in zip(axes, snippets.T):
plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs)
return None
def plot_bi_snippets(axes, time, binary, **kwargs):
for ax, binary in zip(axes, binary.T):
plot_barcode(ax, time, binary[:, None], **kwargs)
return None
# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = search_files(target, excl='noise', dir='../data/inv/thresh_lp/')
stages = ['conv', 'bi', 'feat']
files = stages + ['scales', 'example_scales', 'measure_conv', 'spread_conv',
'measure_feat', 'spread_feat']
save_path = '../figures/fig_invariance_thresh_lp.pdf'
# GRAPH SETTINGS:
fig_kwargs = dict(
figsize=(32/2.54, 16/2.54),
)
super_grid_kwargs = dict(
nrows=2,
ncols=2,
wspace=0,
hspace=0,
left=0,
right=1,
bottom=0,
top=1
)
subfig_specs = dict(
pure=(0, 0),
noise=(1, 0),
analysis=(slice(None), 1)
)
pure_grid_kwargs = dict(
nrows=len(stages),
ncols=None,
wspace=0.05,
hspace=0.1,
left=0.13,
right=0.95,
bottom=0.15,
top=0.9
)
noise_grid_kwargs = dict(
nrows=len(stages),
ncols=None,
wspace=0.05,
hspace=0.1,
left=0.13,
right=0.95,
bottom=0.15,
top=0.9
)
analysis_grid_kwargs = dict(
nrows=1,
ncols=1,
wspace=0,
hspace=0,
left=0.15,
right=0.96,
bottom=0.1,
top=0.95
)
snip_specs = dict(
conv=(0, slice(None)),
bi=(1, slice(None)),
feat=(2, slice(None))
)
# PLOT SETTINGS:
colors = load_colors('../data/stage_colors.npz')
lw_snippets = 0.5
lw_analysis = 3
xlabels = dict(
analysis='scale $\\alpha$',
)
xlab_analysis_kwargs = dict(
y=0.01,
fontsize=16,
ha='center',
va='bottom',
)
ylabels = dict(
conv='$c_i$',
bi='$b_i$',
feat='$f_i$',
analysis='ratio $\\text{SD}_{\\alpha}\\,/\\,\\text{SD}_{\\min[\\alpha]}$',
# analysis='ratio $\\sigma_{\\alpha}\\,/\\,\\sigma_{\\min[\\alpha]}$',
)
ylab_snip_kwargs = dict(
x=0.01,
fontsize=20,
rotation=0,
ha='left',
va='center',
)
ylab_analysis_kwargs = dict(
x=0.02,
fontsize=16,
ha='center',
va='top',
)
xloc = dict(
analysis=10,
)
letter_snip_kwargs = dict(
x=0.02,
y=1,
ha='left',
va='top',
fontsize=22,
fontweight='bold'
)
letter_analysis_kwargs = dict(
x=0,
y=1,
ha='left',
va='top',
fontsize=22,
fontweight='bold'
)
bar_time = 5
bar_kwargs = dict(
y0=0.5,
y1=0.6,
color='k',
lw = 0,
)
spread_kwargs = dict(
alpha=0.3,
lw=0,
zorder=0
)
kernel_ind = 0
# EXECUTION:
for data_path in data_paths:
print(f'Processing {data_path}')
# Load invariance data:
pure_data, config = load_data(data_path, files)
noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), files)
t_full = np.arange(pure_data['conv'].shape[0]) / config['env_rate']
# Reduce snippet data to kernel subset:
pure_data['conv'] = pure_data['conv'][:, kernel_ind]
pure_data['bi'] = pure_data['bi'][:, kernel_ind]
pure_data['feat'] = pure_data['feat'][:, kernel_ind]
noise_data['conv'] = noise_data['conv'][:, kernel_ind]
noise_data['bi'] = noise_data['bi'][:, kernel_ind]
noise_data['feat'] = noise_data['feat'][:, kernel_ind]
# Prepare overall graph:
fig = plt.figure(**fig_kwargs)
super_grid = fig.add_gridspec(**super_grid_kwargs)
# Prepare pure-song snippet axes:
pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
pure_grid_kwargs['nrows' if pure_grid_kwargs['nrows'] is None else 'ncols'] = pure_data['example_scales'].size
pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
for ax, stage in zip(pure_axes[:, 0], stages):
ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
transform=pure_subfig.transSubfigure)
for ax, scale in zip(pure_axes[snip_specs['conv']], pure_data['example_scales']):
ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
pure_subfig.text(s='a', **letter_snip_kwargs)
# Prepare noise-song snippet axes:
noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
noise_grid_kwargs['nrows' if noise_grid_kwargs['nrows'] is None else 'ncols'] = noise_data['example_scales'].size
noise_grid = noise_subfig.add_gridspec(**noise_grid_kwargs)
noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
for ax, stage in zip(noise_axes[:, 0], stages):
ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
transform=noise_subfig.transSubfigure)
for ax, scale in zip(noise_axes[snip_specs['conv']], noise_data['example_scales']):
ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
noise_subfig.text(s='b', **letter_snip_kwargs)
# Prepare analysis axis:
analysis_subfig = fig.add_subfigure(super_grid[subfig_specs['analysis']])
analysis_grid = analysis_subfig.add_gridspec(**analysis_grid_kwargs)
analysis_ax = analysis_subfig.add_subplot(analysis_grid[0, 0])
analysis_ax.set_xlim(noise_data['scales'].min(), noise_data['scales'].max())
analysis_ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['analysis']))
xlabel(analysis_ax, xlabels['analysis'], **xlab_analysis_kwargs,
transform=analysis_subfig.transSubfigure)
# analysis_ax.set_yscale('log')
ylabel(analysis_ax, ylabels['analysis'], **ylab_analysis_kwargs,
transform=analysis_subfig.transSubfigure)
analysis_subfig.text(s='c', **letter_analysis_kwargs)
# Plot pure-song kernel response snippets:
plot_snippets(pure_axes[snip_specs['conv']], t_full, pure_data['conv'],
ymin=0, c=colors['conv'], lw=lw_snippets)
# Plot pure-song binary snippets:
plot_bi_snippets(pure_axes[snip_specs['bi']], t_full, pure_data['bi'],
color=colors['bi'], lw=0)
# Plot pure-song feature snippets:
plot_snippets(pure_axes[snip_specs['feat']], t_full, pure_data['feat'],
c=colors['feat'], lw=lw_snippets)
# Indicate time scale:
time_bar(pure_axes[snip_specs['conv']][0], bar_time, **bar_kwargs)
# Plot noise-song kernel response snippets:
plot_snippets(noise_axes[snip_specs['conv']], t_full, noise_data['conv'],
ymin=0, c=colors['conv'], lw=lw_snippets)
# Plot noise-song binary snippets:
plot_bi_snippets(noise_axes[snip_specs['bi']], t_full, noise_data['bi'],
color=colors['bi'], lw=0)
# Plot noise-song feature snippets:
plot_snippets(noise_axes[snip_specs['feat']], t_full, noise_data['feat'],
c=colors['feat'], lw=lw_snippets)
# Indicate time scale:
time_bar(noise_axes[snip_specs['conv']][0], bar_time, **bar_kwargs)
# Plot noise-song SD ratios (limited):
analysis_ax.plot(noise_data['scales'], noise_data['measure_conv'],
c=colors['conv'], lw=lw_analysis)
lower, upper = noise_data['spread_conv']
analysis_ax.fill_between(noise_data['scales'], lower, upper,
color=colors['conv'], **spread_kwargs)
analysis_ax.plot(noise_data['scales'], noise_data['measure_feat'],
c=colors['feat'], lw=lw_analysis)
lower, upper = noise_data['spread_feat']
analysis_ax.fill_between(noise_data['scales'], lower, upper,
color=colors['feat'], **spread_kwargs)
if save_path is not None:
fig.savefig(save_path)
plt.show()
print('Done.')
embed()