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Lots of stuff. Syncing to home.

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j-hartling
2026-03-20 16:45:54 +01:00
parent 1516fe6090
commit a276883454
28 changed files with 1106 additions and 562 deletions
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280
python/fig_invariance_log-hp.py
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@@ -5,9 +5,9 @@ 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, strip_zeros, time_bar,\
letter_subplot, letter_subplots
from plot_functions import hide_axis, ylimits, xlabel, ylabel, hide_ticks,\
plot_line, strip_zeros, time_bar, zoom_inset,\
letter_subplot, letter_subplots, title_subplot
from IPython import embed
def add_snip_axes(fig, grid_kwargs):
@@ -15,26 +15,31 @@ def add_snip_axes(fig, 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, 'left') for ax in axes[:, 1:].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)
handles = []
for ax, snippet in zip(axes, snippets.T):
plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs)
return None
handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
return handles
# GENERAL SETTINGS:
compute_ratios = True
target = 'Omocestus_rufipes'
data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
stages = ['env', 'log', 'inv']
load_kwargs = dict(
files=stages,
keywords=['scales', 'measure']
keywords=['scales', 'snip', 'measure']
)
save_path = '../figures/fig_invariance_log_hp.pdf'
if compute_ratios:
ref_data = load_data('../data/processed/white_noise_sd-1.npz', files=stages)[0]
ref_measures = {k: v.std() for k, v in ref_data.items() if not k.endswith('rate')}
# GRAPH SETTINGS:
fig_kwargs = dict(
@@ -42,7 +47,7 @@ fig_kwargs = dict(
)
super_grid_kwargs = dict(
nrows=2,
ncols=2,
ncols=3,
wspace=0,
hspace=0,
left=0,
@@ -51,99 +56,127 @@ super_grid_kwargs = dict(
top=1
)
subfig_specs = dict(
pure=(0, 0),
noise=(1, 0),
analysis=(slice(None), 1)
pure=(0, slice(0, -1)),
noise=(1, slice(0, -1)),
big=(slice(None), -1),
)
pure_grid_kwargs = dict(
snip_grid_kwargs = dict(
nrows=len(stages),
ncols=None,
wspace=0.05,
hspace=0.1,
left=0.13,
wspace=0.1,
hspace=0.15,
left=0.16,
right=0.95,
bottom=0.15,
top=0.9
bottom=0.1,
top=0.94,
height_ratios=[1, 2, 1]
)
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,
big_grid_kwargs = dict(
nrows=2,
ncols=1,
wspace=0,
hspace=0,
left=0.15,
hspace=0.1,
left=0.19,
right=0.96,
bottom=0.1,
top=0.95
bottom=0.09,
top=0.98
)
snip_specs = dict(
env=(0, slice(None)),
log=(1, slice(None)),
inv=(2, slice(None))
anchor_kwargs = dict(
aspect='equal',
adjustable='box',
anchor=(0.5, 0.5)
)
# PLOT SETTINGS:
fs = dict(
lab_norm=16,
lab_tex=20,
letter=22,
tit_norm=16,
tit_tex=20,
)
colors = load_colors('../data/stage_colors.npz')
lw_snippets = 0.5
lw_analysis = 3
lw_big = 3
xlabels = dict(
analysis='scale $\\alpha$',
)
xlab_analysis_kwargs = dict(
y=0.01,
fontsize=16,
ha='center',
va='bottom',
big='scale $\\alpha$',
)
ylabels = dict(
env='$x_{\\text{env}}$',
log='$x_{\\text{dB}}$',
inv='$x_{\\text{adapt}}$',
analysis='ratio $\\text{SD}_{\\alpha}\\,/\\,\\text{SD}_{\\min[\\alpha]}$',
# analysis='ratio $\\sigma_{\\alpha}\\,/\\,\\sigma_{\\min[\\alpha]}$',
big='$\\sigma_{\\alpha}\\,/\\,\\sigma_{0}$',
)
xlab_big_kwargs = dict(
y=0,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
ylab_snip_kwargs = dict(
x=0.01,
fontsize=20,
x=0,
fontsize=fs['lab_tex'],
rotation=0,
ha='left',
va='center',
)
ylab_analysis_kwargs = dict(
x=0.02,
fontsize=16,
ylab_big_kwargs = dict(
x=0,
fontsize=fs['lab_tex'],
ha='center',
va='top',
)
yloc = dict(
env=1000,
log=40,
inv=20
)
title_kwargs = dict(
x=0.5,
yref=1,
ha='center',
va='top',
fontsize=fs['tit_norm'],
)
letter_snip_kwargs = dict(
x=0.02,
y=0.97,
x=0,
y=1,
ha='left',
va='top',
fontsize=22,
fontsize=fs['letter'],
)
letter_analysis_kwargs = dict(
letter_big_kwargs = dict(
x=0,
yref=letter_snip_kwargs['y'],
ha='left',
va='top',
fontsize=22,
fontsize=fs['letter'],
)
zoom_inset_bounds = [0.1, 0.2, 0.8, 0.6]
zoom_kwargs = dict(
x0=0.45,
x1=0.55,
y0=0,
y1=0.0006,
low_left=True,
low_right=True,
ec='k',
lw=1,
alpha=1,
)
bar_time = 5
bar_kwargs = dict(
y0=0.5,
y1=0.6,
y0=-0.2,
y1=-0.05,
color='k',
lw = 0,
lw=0,
clip_on=False,
)
diag_kwargs = dict(
c=(0.75, 0.75, 0.75),
lw=2,
ls='--',
zorder=1.9,
)
# EXECUTION:
@@ -153,91 +186,116 @@ for data_path in data_paths:
# Load invariance data:
pure_data, config = load_data(data_path, **load_kwargs)
noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), **load_kwargs)
t_full = np.arange(pure_data['env'].shape[0]) / config['env_rate']
pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']
# Prepare overall graph:
fig = plt.figure(**fig_kwargs)
super_grid = fig.add_gridspec(**super_grid_kwargs)
# Prepare pure-song snippet axes:
snip_grid_kwargs['ncols'] = pure_data['example_scales'].size
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)
pure_axes = add_snip_axes(pure_subfig, snip_grid_kwargs)
for ax, stage in zip(pure_axes[:, 0], stages):
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
transform=pure_subfig.transSubfigure)
for ax, scale in zip(pure_axes[snip_specs['env']], pure_data['example_scales']):
ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=pure_subfig, **title_kwargs)
pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds)
pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
hide_ticks(pure_inset, 'bottom', ticks=False)
hide_ticks(pure_inset, 'left', ticks=False)
# Prepare noise-song snippet axes:
snip_grid_kwargs['ncols'] = noise_data['example_scales'].size
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)
noise_axes = add_snip_axes(noise_subfig, snip_grid_kwargs)
for ax, stage in zip(noise_axes[:, 0], stages):
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
transform=noise_subfig.transSubfigure)
for ax, scale in zip(noise_axes[snip_specs['env']], noise_data['example_scales']):
ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
letter_subplots([pure_subfig, noise_subfig], **letter_snip_kwargs)
for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=noise_subfig, **title_kwargs)
letter_subplots([pure_subfig, noise_subfig], 'ac', **letter_snip_kwargs)
noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
hide_ticks(noise_inset, 'bottom', ticks=False)
hide_ticks(noise_inset, 'left', ticks=False)
# 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.set_xscale('symlog', linthresh=pure_data['scales'][1], linscale=0.5)
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)
letter_subplot(analysis_subfig, 'c', **letter_analysis_kwargs, ref=pure_subfig)
# 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.nrows,), dtype=object)
for i, scales in enumerate([pure_scales, noise_scales]):
ax = big_subfig.add_subplot(big_grid[i, 0])
ax.set_xlim(scales[0], scales[-1])
ax.set_ylim(scales[0], scales[-1])
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
ax.set_aspect(**anchor_kwargs)
ylabel(ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
if i == 0:
hide_ticks(ax, 'bottom')
letter_subplot(big_subfig, 'b', ref=pure_subfig, **letter_big_kwargs)
else:
xlabel(ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
letter_subplot(big_subfig, 'd', ref=noise_subfig, **letter_big_kwargs)
big_axes[i] = ax
# Plot pure-song envelope snippets:
plot_snippets(pure_axes[snip_specs['env']], t_full, pure_data['env'],
ymin=0, c=colors['env'], lw=lw_snippets)
handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
ymin=0, c=colors['env'], lw=lw_snippets)[0]
zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)
# Plot pure-song logarithmic snippets:
plot_snippets(pure_axes[snip_specs['log']], t_full, pure_data['log'],
ymax=None, c=colors['log'], lw=lw_snippets)
plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
c=colors['log'], lw=lw_snippets)
# Plot pure-song invariant snippets:
plot_snippets(pure_axes[snip_specs['inv']], t_full, pure_data['inv'],
plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
c=colors['inv'], lw=lw_snippets)
# Indicate time scale:
time_bar(pure_axes[snip_specs['env']][0], bar_time, **bar_kwargs)
# Plot noise-song envelope snippets:
plot_snippets(noise_axes[snip_specs['env']], t_full, noise_data['env'],
ymin=0, c=colors['env'], lw=lw_snippets)
handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
ymin=0, c=colors['env'], lw=lw_snippets)[0]
zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs)
# Plot noise-song logarithmic snippets:
plot_snippets(noise_axes[snip_specs['log']], t_full, noise_data['log'],
ymax=None, c=colors['log'], lw=lw_snippets)
plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
c=colors['log'], lw=lw_snippets)
# Plot noise-song invariant snippets:
plot_snippets(noise_axes[snip_specs['inv']], t_full, noise_data['inv'],
plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
c=colors['inv'], lw=lw_snippets)
# Indicate time scale:
time_bar(noise_axes[snip_specs['env']][0], bar_time, **bar_kwargs)
time_bar(noise_axes[2, -1], bar_time, **bar_kwargs)
# Plot pure-song SD ratios (ideal):
base_ind = np.argmin(pure_data['scales'])
measure_inv = pure_data['measure_inv'] / pure_data['measure_inv'][base_ind]
analysis_ax.plot(pure_data['scales'], measure_inv, c=colors['inv'], lw=lw_analysis, ls='--')
if compute_ratios:
# Relate pure-song measures to zero scale:
pure_data['measure_env'] /= ref_measures['env']
pure_data['measure_log'] /= ref_measures['log']
pure_data['measure_inv'] /= ref_measures['inv']
# Relate noise-song measures to zero scale:
noise_data['measure_env'] /= ref_measures['env']
noise_data['measure_log'] /= ref_measures['log']
noise_data['measure_inv'] /= ref_measures['inv']
# Plot noise-song SD ratios (limited):
base_ind = np.argmin(noise_data['scales'])
measure_env = noise_data['measure_env'] / noise_data['measure_env'][base_ind]
measure_log = noise_data['measure_log'] / noise_data['measure_log'][base_ind]
measure_inv = noise_data['measure_inv'] / noise_data['measure_inv'][base_ind]
analysis_ax.plot(noise_data['scales'], measure_env, c=colors['env'], lw=lw_analysis)
analysis_ax.plot(noise_data['scales'], measure_log, c=colors['log'], lw=lw_analysis)
analysis_ax.plot(noise_data['scales'], measure_inv, c=colors['inv'], lw=lw_analysis)
analysis_ax.set_ylim(0.9, measure_env.max())
# Plot pure-song measures (ideal):
big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw_big)
big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw_big)
big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw_big)
# Plot noise-song measures (limited):
big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw_big)
big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw_big)
big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw_big)
# Indicate diagonal:
big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
if save_path is not None:
fig.savefig(save_path)