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Added some cmap functions.

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Selected species-specific  colors.
Quite some progress on fig_invariance_thresh_lp_species.pdf.
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j-hartling
2026-03-26 17:26:30 +01:00
parent 1a29b95782
commit 92ee4eda6f
11 changed files with 737 additions and 132 deletions
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python/fig_invariance_log-hp_backup.py Normal file
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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 color_functions import load_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, hide_ticks,\
plot_line, strip_zeros, time_bar, zoom_inset,\
letter_subplot, title_subplot
from IPython import embed
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[:, 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):
handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
return handles
# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
species_paths = search_files('*', incl='noise', dir='../data/inv/log_hp/')
stages = ['env', 'log', 'inv']
load_kwargs = dict(
files=stages,
keywords=['scales', 'snip', 'measure']
)
save_path = '../figures/fig_invariance_log_hp.pdf'
compute_ratios = True
show_diag = True
show_noise = True
# GRAPH SETTINGS:
fig_kwargs = dict(
figsize=(32/2.54, 32/2.54),
)
snip_rows = 1
big_rows = 1
super_grid_kwargs = dict(
nrows=2 * snip_rows + big_rows,
ncols=1,
wspace=0,
hspace=0,
left=0,
right=1,
bottom=0,
top=1
)
subfig_specs = dict(
pure=(slice(0, snip_rows), slice(None)),
noise=(slice(snip_rows, 2 * snip_rows), slice(None)),
big=(slice(-big_rows, None), slice(None)),
)
block_height = 0.8
edge_padding = 0.08
pure_grid_kwargs = dict(
nrows=len(stages),
ncols=None,
wspace=0.1,
hspace=0.15,
left=0.11,
right=0.95,
bottom=1 - block_height - edge_padding,
top=1 - edge_padding,
height_ratios=[1, 2, 1]
)
noise_grid_kwargs = dict(
nrows=len(stages),
ncols=None,
wspace=pure_grid_kwargs['wspace'],
hspace=pure_grid_kwargs['hspace'],
left=pure_grid_kwargs['left'],
right=pure_grid_kwargs['right'],
bottom=edge_padding,
top=edge_padding + block_height,
height_ratios=[1, 2, 1]
)
big_grid_kwargs = dict(
nrows=1,
ncols=3,
wspace=0.3,
hspace=0,
left=pure_grid_kwargs['left'],
right=pure_grid_kwargs['right'],
bottom=0.05,
top=1
)
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,
bar=16,
)
colors = load_colors('../data/stage_colors.npz')
lw_snippets = 1
lw_big = 3
xlabels = dict(
big='scale $\\alpha$',
)
ylabels = dict(
env='$x_{\\text{env}}$',
log='$x_{\\text{dB}}$',
inv='$x_{\\text{adapt}}$',
big='$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$',
)
xlab_big_kwargs = dict(
y=0,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
ylab_snip_kwargs = dict(
x=0,
fontsize=fs['lab_tex'],
rotation=0,
ha='left',
va='center',
)
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,
y=1,
ha='center',
va='bottom',
fontsize=fs['tit_norm'],
)
letter_snip_kwargs = dict(
x=0,
yref=0.5,
ha='left',
va='center',
fontsize=fs['letter'],
)
letter_big_kwargs = dict(
x=0,
y=1,
ha='left',
va='bottom',
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,
)
inset_tick_kwargs = dict(
axis='y',
length=3,
pad=1,
left=False,
labelleft=False,
right=True,
labelright=True,
)
bar_time = 5
bar_kwargs = dict(
dur=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'${bar_time}\\,\\text{{s}}$',
text_kwargs=dict(
fontsize=fs['bar'],
ha='right',
va='center',
)
)
diag_kwargs = dict(
c=(0.75, 0.75, 0.75),
lw=2,
ls='--',
zorder=1.9,
)
noise_rel_thresh = 0.95
noise_kwargs = dict(
fc=(0.9, 0.9, 0.9),
ec='none',
lw=0,
zorder=1.5,
)
# PREPARATION:
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')}
species_measures = []
for species_path in species_paths:
species_measure = load_data(species_path, **load_kwargs)[0]['measure_inv']
if compute_ratios:
species_measure /= ref_measures['inv']
species_measures.append(species_measure)
species_measures = np.array(species_measures).T
# EXECUTION:
for data_path in data_paths:
print(f'Processing {data_path}')
# Load invariance data:
pure_data, config = load_data(data_path, **load_kwargs)
noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), **load_kwargs)
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)
fig.canvas.draw()
# Prepare pure-song snippet axes:
pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
pure_axes = add_snip_axes(pure_subfig, pure_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[0, :], pure_data['example_scales']):
pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds)
pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
pure_inset.tick_params(**inset_tick_kwargs)
hide_ticks(pure_inset, 'bottom', ticks=False)
# Prepare noise-song snippet axes:
noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
noise_axes = add_snip_axes(noise_subfig, noise_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[0, :], noise_data['example_scales']):
noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
noise_inset.tick_params(**inset_tick_kwargs)
hide_ticks(noise_inset, 'bottom', ticks=False)
# 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, scales in enumerate([pure_scales, noise_scales, noise_scales]):
ax = big_subfig.add_subplot(big_grid[0, i])
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(ax, 'c', **letter_big_kwargs)
else:
xlabel(ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
letter_subplot(ax, 'd', **letter_big_kwargs)
big_axes[i] = ax
# Plot pure-song envelope 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[1, :], t_full, pure_data['snip_log'],
c=colors['log'], lw=lw_snippets)
# Plot pure-song invariant snippets:
plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
c=colors['inv'], lw=lw_snippets)
# Plot noise-song envelope snippets:
ymin, ymax = pure_axes[0, 0].get_ylim()
handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
ymin, ymax, 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:
ymin, ymax = pure_axes[1, 0].get_ylim()
plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
ymin, ymax, c=colors['log'], lw=lw_snippets)
# Plot noise-song invariant snippets:
ymin, ymax = pure_axes[2, 0].get_ylim()
plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
ymin, ymax, c=colors['inv'], lw=lw_snippets)
# Indicate time scale:
time_bar(noise_axes[-1, -1], **bar_kwargs)
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 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)
# Plot species measures:
big_axes[2].plot(noise_scales, species_measures, 'k', lw=lw_big)
if show_diag:
# Indicate diagonal:
big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
if show_noise:
# Indicate noise floor:
if compute_ratios:
span_measure = noise_data['measure_inv'][-1] - ref_measures['inv']
thresh_measure = ref_measures['inv'] + noise_rel_thresh * span_measure
else:
span_measure = noise_data['measure_inv'][-1] - noise_data['measure_inv'][0]
thresh_measure = noise_data['measure_inv'][0] + noise_rel_thresh * span_measure
thresh_ind = np.nonzero(noise_data['measure_inv'] < thresh_measure)[0][-1]
thresh_scale = noise_scales[thresh_ind]
big_axes[1].axvspan(noise_scales[0], thresh_scale, **noise_kwargs)
if save_path is not None:
fig.savefig(save_path, bbox_inches='tight')
plt.show()
print('Done.')
embed()