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Finished (:D) fig_invariance_log_hp.pdf.

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Added movable label string to time_bar().
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j-hartling
2026-03-23 15:38:49 +01:00
parent a276883454
commit dc4443aa17
18 changed files with 389 additions and 247 deletions
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figures/fig_invariance_full.pdf
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figures/fig_invariance_log_hp.pdf
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figures/fig_invariance_thresh_lp_single_noise.pdf
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main.aux
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\newlabel{eq:toy_log}{{12}{11}{}{}{}}
\newlabel{eq:toy_highpass}{{13}{11}{}{}{}}
\newlabel{eq:toy_snr}{{14}{12}{}{}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf {Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} Envelope $x_{\text {env}}(t)$ is transformed into logarihmically compressed envelope $x_{\text {dB}}(t)$ and further into intensity-adapted envelope $x_{\text {adapt}}(t)$. Indicated time scale is $5\,$s for both \textbf {a} and \textbf {b} (black bars). \textbf {a}:~Ideally, if $x_{\text {env}}(t)$ consists only of song component $s(t)$ rescaled by $\alpha $, then $x_{\text {adapt}}(t)$ is fully intensity-invariant across all $\alpha $. \textbf {b}:~In practice, $x_{\text {env}}(t)$ also contains fixed-scale noise component $\eta (t)$, which limits the effective intensity invariance of $x_{\text {adapt}}(t)$ to sufficiently large $\alpha $. \textbf {c}:~Ratios of the SD of each representation in \textbf {b} at a given $\alpha $ relative to the SD of the representation for $\alpha =0$ (solid lines). The same ratios for the ideal $x_{\text {adapt}}(t)$ in \textbf {a} are shown for comparison (dashed line). }}{12}{}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf {Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} Synthetic input $x_{\text {filt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with (\textbf {c}{} and \textbf {d}) or without (\textbf {a}{} and \textbf {b}) additive noise component $\eta (t)$. Input $x_{\text {filt}}(t)$ is transformed into envelope $x_{\text {env}}(t)$, logarithmically compressed envelope $x_{\text {dB}}(t)$, and intensity-adapted envelope $x_{\text {adapt}}(t)$. \textbf {Left}:~$x_{\text {env}}(t)$, $x_{\text {dB}}(t)$, and $x_{\text {adapt}}(t)$ for different scales $\alpha $. \textbf {Right}:~Ratios of the standard deviation of $x_{\text {env}}(t)$, $x_{\text {dB}}(t)$, and $x_{\text {adapt}}(t)$ relative to the respective reference standard deviation for input $x_{\text {filt}}(t)=\eta (t)$. \textbf {a}{} and \textbf {b}:~Ideally, if $x_{\text {filt}}(t)=\alpha \cdot s(t)$, then $x_{\text {adapt}}(t)$ is intensity-invariant across all $\alpha $. \textbf {c}{} and \textbf {d}:~In practice, if $x_{\text {filt}}(t)=\alpha \cdot s(t)+\eta (t)$, the intensity invariance of $x_{\text {adapt}}(t)$ is limited to sufficiently large $\alpha $. Shaded area indicates saturation of $x_{\text {adapt}}(t)$ at $95\,\%$ curve span. }}{12}{}\protected@file@percent }
\newlabel{fig:inv_log-hp}{{4}{12}{}{}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Thresholding nonlinearity \& temporal averaging}{12}{}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf {Intensity invariance by thresholding and temporal averaging depends on the threshold value with regard to variable range but not saturation level.} Kernel response $c_i(t)$ is rescaled by $\alpha $ and transformed into binary response $b_i(t)$ and further into feature $f_i(t)$. Threshold value $\Theta _i$ is set to different percentiles of the the distribution of $c_i(t)$ at $\alpha =1$. Darker colors indicate higher values of $\Theta _i$. Indicated time scale of $100\,$ms is the same for \textbf {a}-\textbf {c} (black bar). \textbf {a}:~50th percentile. \textbf {b}:~75th percentile. \textbf {c}:~100th percentile. \textbf {d}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$ in \textbf {a}-\textbf {c}. }}{13}{}\protected@file@percent }

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@@ -47,6 +47,12 @@
% \newcommand{\eref}[1]{\mbox{\cref{#1}}}
% \newcommand{\eref}[1]{\mbox{Eq.\,\ref{#1}}}
% Subplot lettering:
\newcommand{\figa}{\textbf{a}}
\newcommand{\figb}{\textbf{b}}
\newcommand{\figc}{\textbf{c}}
\newcommand{\figd}{\textbf{d}}
% Math shorthands - Standard symbols:
\newcommand{\dec}{\log_{10}} % Logarithm base 10
\newcommand{\infint}{\int_{-\infty}^{+\infty}} % Indefinite integral
@@ -625,23 +631,25 @@ the signal for reliable song recognition.
\includegraphics[width=\textwidth]{figures/fig_invariance_log_hp.pdf}
\caption{\textbf{Intensity invariance by logarithmic compression and
adaptation is restricted by the noise floor.}
Envelope $\env(t)$ is transformed into logarihmically
compressed envelope $\db(t)$ and further into
intensity-adapted envelope $\adapt(t)$. Indicated time
scale is $5\,$s for both \textbf{a} and \textbf{b} (black
bars).
\textbf{a}:~Ideally, if $\env(t)$ consists only of song
component $\soc(t)$ rescaled by $\sca$, then $\adapt(t)$
is fully intensity-invariant across all $\sca$.
\textbf{b}:~In practice, $\env(t)$ also contains
fixed-scale noise component $\noc(t)$, which limits the
effective intensity invariance of $\adapt(t)$ to
sufficiently large $\sca$.
\textbf{c}:~Ratios of the SD of each representation in
\textbf{b} at a given $\sca$ relative to the SD of the
representation for $\sca=0$ (solid lines). The same ratios
for the ideal $\adapt(t)$ in \textbf{a} are shown for
comparison (dashed line).
Synthetic input $\filt(t)$ consists of song component
$\soc(t)$ scaled by $\sca$ with (\figc{} and \figd) or
without (\figa{} and \figb) additive noise component
$\noc(t)$. Input $\filt(t)$ is transformed into envelope
$\env(t)$, logarithmically compressed envelope $\db(t)$,
and intensity-adapted envelope $\adapt(t)$.
\textbf{Left}:~$\env(t)$, $\db(t)$, and $\adapt(t)$ for
different scales $\sca$.
\textbf{Right}:~Ratios of the standard deviation of
$\env(t)$, $\db(t)$, and $\adapt(t)$ relative to the
respective reference standard deviation for input
$\filt(t)=\noc(t)$.
\figa{} and \figb:~Ideally, if $\filt(t)=\sca\cdot\soc(t)$, then
$\adapt(t)$ is intensity-invariant across all $\sca$.
\figc{} and \figd:~In practice, if
$\filt(t)=\sca\cdot\soc(t)+\noc(t)$, the intensity
invariance of $\adapt(t)$ is limited to sufficiently large
$\sca$. Shaded area indicates saturation of $\adapt(t)$ at
$95\,\%$ curve span.
}
\label{fig:inv_log-hp}
\end{figure}

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wspace=0,
hspace=0.1,
left=0.065,
left=0.09,
right=0.98,
bottom=0.08,
top=0.95,
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ax1.grid(**grid_line_kwargs)
ax1.set_xlim(data['scales'][0], data['scales'][-1])
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ax1.set_ylim(0.4, 1.2)
ax1.set_ylim(0, 0.1)
ylabel(ax1, ylabels['top'], transform=fig.transFigure, **ylab_kwargs)
ax2.grid(**grid_line_kwargs)
xlabel(ax2, xlabels['bottom'], transform=fig.transFigure, **xlab_kwargs)

39
python/fig_invariance_full.py
View File

@@ -70,6 +70,14 @@ big_grid_kwargs = dict(
)
# 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')
colors['raw'] = "#000000"
lw = dict(
@@ -100,26 +108,26 @@ ylabels = dict(
)
xlab_snip_kwargs = dict(
y=0,
fontsize=16,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
xlab_big_kwargs = dict(
y=0,
fontsize=16,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
ylab_snip_kwargs = dict(
x=0,
fontsize=20,
fontsize=fs['lab_tex'],
rotation=0,
ha='left',
va='center'
)
ylab_big_kwargs = dict(
x=0,
fontsize=16,
fontsize=fs['lab_norm'],
ha='center',
va='top',
)
@@ -137,14 +145,14 @@ title_kwargs = dict(
yref=1,
ha='center',
va='top',
fontsize=16,
fontsize=fs['tit_norm'],
)
letter_snip_kwargs = dict(
x=0.02,
y=1,
ha='left',
va='top',
fontsize=22,
fontsize=fs['letter'],
fontweight='bold'
)
letter_big_kwargs = dict(
@@ -152,15 +160,25 @@ letter_big_kwargs = dict(
y=1,
ha='left',
va='top',
fontsize=22,
fontsize=fs['letter'],
fontweight='bold'
)
bar_time = 5
bar_kwargs = dict(
y0=0.8,
y1=0.9,
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',
)
)
@@ -197,8 +215,7 @@ for data_path in data_paths:
if stages[i] != 'bi':
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
snip_axes[i, j] = ax
super_xlabel(xlabels['snip'], snip_subfig, snip_axes[-1, 0], snip_axes[-1, -1], **xlab_snip_kwargs)
time_bar(snip_axes[0, 0], bar_time, **bar_kwargs)
time_bar(snip_axes[-1, -1], **bar_kwargs)
# Prepare single analysis axis:
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])

136
python/fig_invariance_log-hp.py
View File

@@ -7,7 +7,7 @@ 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, letter_subplots, title_subplot
letter_subplot, title_subplot
from IPython import embed
def add_snip_axes(fig, grid_kwargs):
@@ -28,7 +28,6 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
# GENERAL SETTINGS:
compute_ratios = True
target = 'Omocestus_rufipes'
data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
stages = ['env', 'log', 'inv']
@@ -37,10 +36,14 @@ load_kwargs = dict(
keywords=['scales', 'snip', 'measure']
)
save_path = '../figures/fig_invariance_log_hp.pdf'
compute_ratios = True
show_diag = True
show_noise = True
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(
figsize=(32/2.54, 16/2.54),
@@ -60,22 +63,35 @@ subfig_specs = dict(
noise=(1, slice(0, -1)),
big=(slice(None), -1),
)
snip_grid_kwargs = dict(
block_height = 0.8
edge_padding = 0.08
pure_grid_kwargs = dict(
nrows=len(stages),
ncols=None,
wspace=0.1,
hspace=0.15,
left=0.16,
right=0.95,
bottom=0.1,
top=0.94,
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=0.1,
hspace=0.15,
left=0.16,
right=0.95,
bottom=edge_padding,
top=edge_padding + block_height,
height_ratios=[1, 2, 1]
)
big_grid_kwargs = dict(
nrows=2,
ncols=1,
wspace=0,
hspace=0.1,
hspace=0.3,
left=0.19,
right=0.96,
bottom=0.09,
@@ -94,9 +110,10 @@ fs = dict(
letter=22,
tit_norm=16,
tit_tex=20,
bar=16,
)
colors = load_colors('../data/stage_colors.npz')
lw_snippets = 0.5
lw_snippets = 1
lw_big = 3
xlabels = dict(
big='scale $\\alpha$',
@@ -105,7 +122,7 @@ ylabels = dict(
env='$x_{\\text{env}}$',
log='$x_{\\text{dB}}$',
inv='$x_{\\text{adapt}}$',
big='$\\sigma_{\\alpha}\\,/\\,\\sigma_{0}$',
big='$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$',
)
xlab_big_kwargs = dict(
y=0,
@@ -121,7 +138,7 @@ ylab_snip_kwargs = dict(
va='center',
)
ylab_big_kwargs = dict(
x=0,
x=0.05,
fontsize=fs['lab_tex'],
ha='center',
va='top',
@@ -133,23 +150,23 @@ yloc = dict(
)
title_kwargs = dict(
x=0.5,
yref=1,
y=1,
ha='center',
va='top',
va='bottom',
fontsize=fs['tit_norm'],
)
letter_snip_kwargs = dict(
x=0,
y=1,
yref=0.5,
ha='left',
va='top',
va='center',
fontsize=fs['letter'],
)
letter_big_kwargs = dict(
x=0,
yref=letter_snip_kwargs['y'],
x=0.05,
yref=letter_snip_kwargs['yref'],
ha='left',
va='top',
va='center',
fontsize=fs['letter'],
)
zoom_inset_bounds = [0.1, 0.2, 0.8, 0.6]
@@ -164,13 +181,31 @@ zoom_kwargs = dict(
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(
y0=-0.2,
y1=-0.05,
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),
@@ -178,6 +213,13 @@ diag_kwargs = dict(
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,
)
# EXECUTION:
for data_path in data_paths:
@@ -192,37 +234,39 @@ for data_path in data_paths:
# Prepare overall graph:
fig = plt.figure(**fig_kwargs)
super_grid = fig.add_gridspec(**super_grid_kwargs)
fig.canvas.draw()
# Prepare pure-song snippet axes:
snip_grid_kwargs['ncols'] = pure_data['example_scales'].size
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, snip_grid_kwargs)
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']):
title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=pure_subfig, **title_kwargs)
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)
hide_ticks(pure_inset, 'left', ticks=False)
# Prepare noise-song snippet axes:
snip_grid_kwargs['ncols'] = noise_data['example_scales'].size
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, snip_grid_kwargs)
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']):
title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=noise_subfig, **title_kwargs)
letter_subplots([pure_subfig, noise_subfig], 'ac', **letter_snip_kwargs)
noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
letter_subplot(noise_subfig, 'c', 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)
hide_ticks(noise_inset, 'left', ticks=False)
# Prepare analysis axes:
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
@@ -238,17 +282,17 @@ for data_path in data_paths:
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)
letter_subplot(big_subfig, 'b', ref=pure_title, **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)
letter_subplot(big_subfig, 'd', ref=noise_title, **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)
@@ -258,20 +302,23 @@ for data_path in data_paths:
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=0, c=colors['env'], lw=lw_snippets)[0]
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'],
c=colors['log'], lw=lw_snippets)
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'],
c=colors['inv'], lw=lw_snippets)
ymin, ymax, c=colors['inv'], lw=lw_snippets)
# Indicate time scale:
time_bar(noise_axes[2, -1], bar_time, **bar_kwargs)
time_bar(noise_axes[-1, -1], **bar_kwargs)
if compute_ratios:
# Relate pure-song measures to zero scale:
@@ -293,12 +340,25 @@ for data_path in data_paths:
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 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)
fig.savefig(save_path, bbox_inches='tight')
plt.show()
print('Done.')

46
python/fig_invariance_thresh-lp_single.py
View File

@@ -1,7 +1,6 @@
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
@@ -116,6 +115,14 @@ snip_specs = dict(
inset_bounds = [1.02, 0, 0.2, 1]
# 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')
color_factors = [0.2, -0.2]
lw = dict(
@@ -136,32 +143,32 @@ ylabels = dict(
)
xlab_snip_kwargs = dict(
y=0,
fontsize=16,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
xlab_big_kwargs = dict(
y=0,
fontsize=16,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
ylab_snip_kwargs = dict(
x=0.08,
fontsize=20,
fontsize=fs['lab_tex'],
rotation=0,
ha='right',
va='center',
)
ylab_super_kwargs = dict(
x=0,
fontsize=16,
fontsize=fs['lab_norm'],
ha='left',
va='center',
)
ylab_big_kwargs = dict(
x=0,
fontsize=20,
fontsize=fs['lab_norm'],
ha='center',
va='top',
)
@@ -176,21 +183,21 @@ title_kwargs = dict(
yref=1,
ha='center',
va='top',
fontsize=16,
fontsize=fs['tit_norm'],
)
letter_snip_kwargs = dict(
x=0,
y=1,
ha='left',
va='top',
fontsize=22,
fontsize=fs['letter'],
)
letter_big_kwargs = dict(
x=0,
yref=letter_snip_kwargs['y'],
ha='left',
va='top',
fontsize=22,
fontsize=fs['letter'],
)
dist_kwargs = dict(
nbins=50,
@@ -203,10 +210,20 @@ dist_fill_kwargs = dict(
)
bar_time = 0.1
bar_kwargs = dict(
y0=0.3,
y1=0.4,
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'${int(1000 * bar_time)}\\,\\text{{ms}}$',
text_kwargs=dict(
fontsize=fs['bar'],
ha='right',
va='center',
)
)
kernel = np.array([
[1, 0.008],
@@ -264,13 +281,12 @@ for data_path in data_paths:
ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
transform=snip_subfig.transSubfigure)
if i == 0:
axes[0, 0].set_xlim(t_full[0], t_full[-1])
time_bar(axes[0, 0], bar_time, **bar_kwargs)
for ax, scale in zip(axes[0, :], data['example_scales']):
title = f'$\\alpha={strip_zeros(scale)}$'
title_subplot(ax, title, **title_kwargs, ref=fig)
elif i == data['threshs'].size - 1:
super_xlabel(xlabels['snip'], snip_subfig, axes[-1, 0], axes[-1, -1], **xlab_snip_kwargs)
elif i == data['threshs'].size - 1:
axes[-1, -1].set_xlim(t_full[0], t_full[-1])
time_bar(axes[-1, -1], **bar_kwargs)
letter_subplots(snip_axes.keys(), **letter_snip_kwargs)
# Prepare analysis axis:

22
python/fig_invariance_thresh-lp_species.py
View File

@@ -200,6 +200,14 @@ inset_kwargs = dict(
)
# PLOT SETTINGS:
fs = dict(
lab_norm=16,
lab_tex=20,
letter=22,
tit_norm=16,
tit_tex=20,
bar=16,
)
base_color = load_colors('../data/stage_colors.npz')['feat']
spec_cmaps = [
'Reds',
@@ -224,31 +232,31 @@ ylabels = dict(
)
xlab_spec_kwargs = dict(
y=0,
fontsize=16,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
xlab_big_kwargs = dict(
y=0,
fontsize=20,
fontsize=fs['lab_tex'],
ha='center',
va='bottom',
)
ylab_spec_kwargs = dict(
x=0,
fontsize=20,
fontsize=fs['lab_tex'],
ha='left',
va='center',
)
ylab_big_kwargs = dict(
x=0.03,
fontsize=20,
fontsize=fs['lab_tex'],
ha='center',
va='center',
)
ylab_cbar_kwargs = dict(
x=1,
fontsize=16,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
@@ -264,14 +272,14 @@ letter_spec_kwargs = dict(
yref=1,
ha='center',
va='top',
fontsize=22,
fontsize=fs['letter'],
)
letter_big_kwargs = dict(
x=0,
yref=1,
ha='center',
va='top',
fontsize=22,
fontsize=fs['letter'],
)
time_bar_kwargs = dict(
dur=0.05,

86
python/plot_functions.py
View File

@@ -16,41 +16,56 @@ def hide_axis(ax, side='bottom'):
which='both', **params)
return None
def get_trans_artist(artist):
artist_type = type(artist).__name__
if artist_type == 'Axes':
return artist.transAxes
elif artist_type == 'Figure':
return artist.transFigure
elif artist_type == 'Subfigure':
return artist.transSubfigure
elif hasattr(artist, 'bbox'):
return BboxTransformTo(artist.bbox)
renderer = artist.get_figure(root=True).canvas.get_renderer()
if hasattr(artist, 'get_window_extent'):
return BboxTransformTo(artist.get_window_extent(renderer))
elif hasattr(artist, 'get_tightbbox'):
return BboxTransformTo(artist.get_tightbbox(renderer))
raise ValueError('Artist does not have a bounding box to use as transform.')
def title_subplot(artist, title, x=0.5, y=1.0, xref=None, yref=None, ref=None,
ha='center', va='bottom', fontsize=16, fontweight='normal', **kwargs):
trans_artist = BboxTransformTo(artist.bbox)
trans_artist = get_trans_artist(artist)
if xref is not None or yref is not None:
transform = BboxTransformTo(ref.bbox) + trans_artist.inverted()
transform = get_trans_artist(ref) + trans_artist.inverted()
if xref is not None:
x = transform.transform((xref, 0))[0]
if yref is not None:
y = transform.transform((0, yref))[1]
artist.text(x, y, title, transform=trans_artist, ha=ha, va=va,
fontsize=fontsize, fontweight=fontweight, **kwargs)
return None
return artist.text(x, y, title, transform=trans_artist, ha=ha, va=va,
fontsize=fontsize, fontweight=fontweight, **kwargs)
def letter_subplot(artist, label, x=None, y=None, xref=None, yref=None, ref=None,
ha='left', va='bottom', fontsize=16, fontweight='bold', **kwargs):
trans_artist = BboxTransformTo(artist.bbox)
trans_artist = get_trans_artist(artist)
if x is None or y is None:
transform = BboxTransformTo(ref.bbox) + trans_artist.inverted()
transform = get_trans_artist(ref) + trans_artist.inverted()
if x is None:
x = transform.transform([xref, 0])[0]
if y is None:
y = transform.transform([0, yref])[1]
artist.text(x, y, label, transform=trans_artist, ha=ha, va=va,
fontsize=fontsize, fontweight=fontweight, **kwargs)
return None
return artist.text(x, y, label, transform=trans_artist, ha=ha, va=va,
fontsize=fontsize, fontweight=fontweight, **kwargs)
def letter_subplots(artists, labels=None, x=None, y=None, xref=None, yref=None, ref=None,
ha='left', va='bottom', fontsize=16, fontweight='bold', **kwargs):
if labels is None:
labels = string.ascii_lowercase
handles = []
for artist, label in zip(artists, labels):
letter_subplot(artist, label, x, y, xref, yref, ref=ref, ha=ha, va=va,
fontsize=fontsize, fontweight=fontweight, **kwargs)
return None
handles.append(letter_subplot(artist, label, x, y, xref, yref, ref,
ha=ha, va=va, fontsize=fontsize, fontweight=fontweight, **kwargs))
return handles
def xlimits(time, ax=None, minval=None, maxval=None, pad=0.05):
limits = [minval, maxval]
@@ -83,34 +98,32 @@ def ylimits(signal, ax=None, minval=None, maxval=None, pad=0.05):
return limits
def xlabel(ax, label, x=None, y=-0.1, fontsize=20, transform=None, **kwargs):
ax.set_xlabel(label, fontsize=fontsize, **kwargs)
if x is None:
x = 0.5
if transform is not None:
x = (ax.transAxes + transform.inverted()).transform((x, 0))[0]
ax.xaxis.set_label_coords(x, y, transform=transform)
return None
return ax.set_xlabel(label, fontsize=fontsize, **kwargs)
def ylabel(ax, label, x=-0.2, y=None, fontsize=20, transform=None, **kwargs):
ax.set_ylabel(label, fontsize=fontsize, **kwargs)
if y is None:
y = 0.5
if transform is not None:
y = (ax.transAxes + transform.inverted()).transform((0, y))[1]
ax.yaxis.set_label_coords(x, y, transform=transform)
return None
return ax.set_ylabel(label, fontsize=fontsize, **kwargs)
def super_xlabel(label, fig, left_ax, right_ax, y=0.005,
left_fig=None, right_fig=None, **kwargs):
left_x = left_ax.get_position().x0
right_x = right_ax.get_position().x1
if left_fig is not None or right_fig is not None:
trans_fig = BboxTransformTo(fig.bbox)
trans_fig = get_trans_artist(fig)
if left_fig is not None:
transform = BboxTransformTo(left_fig.bbox) + trans_fig.inverted()
transform = get_trans_artist(left_fig) + trans_fig.inverted()
left_x = transform.transform((left_x, 0))[0]
if right_fig is not None:
transform = BboxTransformTo(right_fig.bbox) + trans_fig.inverted()
transform = get_trans_artist(right_fig) + trans_fig.inverted()
right_x = transform.transform((right_x, 0))[0]
return fig.supxlabel(label, x=(left_x + right_x) / 2, y=y, **kwargs)
@@ -119,12 +132,12 @@ def super_ylabel(label, fig, low_ax, high_ax, x=0.005,
low_y = high_ax.get_position().y0
high_y = low_ax.get_position().y1
if low_fig is not None or high_fig is not None:
trans_fig = BboxTransformTo(fig.bbox)
trans_fig = get_trans_artist(fig)
if low_fig is not None:
transform = BboxTransformTo(low_fig.bbox) + trans_fig.inverted()
transform = get_trans_artist(low_fig) + trans_fig.inverted()
low_y = transform.transform((0, low_y))[1]
if high_fig is not None:
transform = BboxTransformTo(high_fig.bbox) + trans_fig.inverted()
transform = get_trans_artist(high_fig) + trans_fig.inverted()
high_y = transform.transform((0, high_y))[1]
return fig.supylabel(label, x=x, y=(low_y + high_y) / 2, **kwargs)
@@ -161,9 +174,8 @@ def indicate_zoom(fig, high_ax, low_ax, zoom_abs, **kwargs):
transform = low_ax.transData + fig.transFigure.inverted()
x0 = transform.transform((zoom_abs[0], 0))[0]
x1 = transform.transform((zoom_abs[1], 0))[0]
fig.add_artist(plt.Rectangle((x0, y0), x1 - x0, y1 - y0,
transform=fig.transFigure, **kwargs))
return None
return fig.add_artist(plt.Rectangle((x0, y0), x1 - x0, y1 - y0,
transform=fig.transFigure, **kwargs))
def assign_colors(handles, types, colors):
for handle, type_id in zip(handles, types):
@@ -187,22 +199,30 @@ def strip_zeros(num, right_digits=5):
return f'{left}.{right}'
return left
def time_bar(ax, dur, y0=0.9, y1=0.95, xshift=0.5, parent=None, **kwargs):
def time_bar(ax, dur, y0=0.9, y1=0.95, xshift=0.5, parent=None,
text_pos=None, text_str=None, text_kwargs={}, **kwargs):
if parent is None:
parent = ax
trans_parent = BboxTransformTo(parent.bbox)
kwargs['transform'] = trans_parent
trans_parent = get_trans_artist(parent)
transform = ax.transData + trans_parent.inverted()
t0 = ax.get_xlim()[0]
x0 = transform.transform((t0, 0))[0]
x1 = transform.transform((t0 + dur, 0))[0]
dur = x1 - x0
x0 = (1 - dur) * xshift
parent.add_artist(plt.Rectangle((x0, y0), dur, y1 - y0, **kwargs))
return None
rect = parent.add_artist(plt.Rectangle((x0, y0), dur, y1 - y0,
transform=trans_parent, **kwargs))
if text_pos is not None:
trans_bar = get_trans_artist(rect)
text_pos = (trans_bar + trans_parent.inverted()).transform(text_pos)
if text_str is None:
text_str = f'{dur:.2f} s'
t = parent.text(*text_pos, text_str, transform=trans_parent, **text_kwargs)
return rect, t
return rect
def zoom_inset(ax, inset, handle, x0=None, x1=None, y0=None, y1=None, ref='x',
transform = None,
transform=None,
low_left=False, up_left=False, low_right=False, up_right=False,
props=['c', 'lw', 'ls', 'zorder', 'alpha'], **kwargs):
if not kwargs:

41
python/save_env_sd_conversion.py
View File

@@ -12,7 +12,7 @@ save_path = '../data/inv/noise_env/'
# ANALYSIS SETTINGS:
scales = np.geomspace(0.1, 10000, 200)
sd_inputs = np.arange(10.9, 11.1, 0.01)
sd_inputs = np.array([1.0])
n_trials = 10
tol_to_one = 0.1
@@ -32,15 +32,16 @@ signal /= signal[segment].std()
signal = signal[:, None] * scales[None, :]
# Prepare storage:
current_match = 0
storage = dict(
scales=scales,
n_trials=n_trials,
sd_factor=np.array([0.]),
trials=np.zeros((scales.size, n_trials), dtype=float),
mean=np.zeros(scales.size, dtype=float),
spread=np.zeros(scales.size, dtype=float),
)
if sd_inputs.size > 1:
current_match = 0
storage = dict(
scales=scales,
n_trials=n_trials,
sd_factor=np.array([0.]),
trials=np.zeros((scales.size, n_trials), dtype=float),
mean=np.zeros(scales.size, dtype=float),
spread=np.zeros(scales.size, dtype=float),
)
# Analyze piece-wise:
rng = np.random.default_rng()
@@ -59,7 +60,22 @@ for i, sigma in enumerate(sd_inputs):
# Estimate noise SD:
sd = mix.std(axis=0)
# Average SD over trials:
mean_sd = sd.mean(axis=-1)
# Log single-run results:
if sd_inputs.size == 1:
storage = dict(
scales=scales,
n_trials=n_trials,
sd_factor=sigma,
trials=sd,
mean=mean_sd,
spread=sd.std(axis=-1),
)
break
# Update multi-run results if better than previous:
n_match = (np.abs(1 - mean_sd) <= tol_to_one).sum()
if n_match > current_match:
print(f'Found better SD: {sigma:.3f} with {n_match} matches (previous: {current_match})')
@@ -70,13 +86,10 @@ for i, sigma in enumerate(sd_inputs):
current_match = n_match
del mix
del signal
if save_path is not None:
np.savez(save_path + 'sd_conversion.npz', **storage)
plt.plot(scales, storage['mean'], 'k')
plt.show()
embed()
print('Done.')
embed()