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Finished (:D) fig_invariance_thresh_lp_single.pdf.

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Added/modified few plot functions.
Cleaned up simulation/plotting scripts regarding Thresh-LP.
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j-hartling
2026-03-25 16:00:58 +01:00
parent dc4443aa17
commit 1a29b95782
17 changed files with 437 additions and 891 deletions
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figures/fig_invariance_thresh_lp.pdf
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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.} 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 }
\@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 $\sigma _{\eta }$ 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}{}{}{}}
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\@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 }
\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf {Intensity invariance by thresholding and temporal averaging depends on both the threshold value and the noise floor.} Synthetic input $x_{\text {adapt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with additive noise component $\eta (t)$. Input $x_{\text {adapt}}(t)$ is transformed into kernel response $c_i(t)$, binary response $b_i(t)$, and feature $f_i(t)$. Threshold value $\Theta _i$ is set to multiples of the reference standard deviation $\sigma _{\eta }$ of $c_i(t)$ for input $x_{\text {adapt}}(t)=\eta (t)$. Darker colors correspond to higher $\Theta _i$. \textbf {Left}:~$x_{\text {adapt}}(t)$, $c_i(t)$, $b_i(t)$, and $f_i(t)$ for different scales $\alpha $ and threshold values $\Theta _i$. Left-most column is is the pure-noise reference. \textbf {Right}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$. \textbf {a}:~Input $x_{\text {adapt}}(t)$. \textbf {b}-\textbf {d}:~$c_i(t)$, $b_i(t)$, and $f_i(t)$ for the different $\Theta _i$ based on the same $x_{\text {adapt}}(t)$ from \textbf {a}{}. \textbf {e}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$ in \textbf {b}{}-\textbf {d}. }}{13}{}\protected@file@percent }
\newlabel{fig:inv_thresh-lp_single}{{5}{13}{}{}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces \textbf {Intensity invariance by thresholding and temporal averaging depends on noise with regard to variable range but not saturation level.} Kernel response $c_i(t)$ is rescaled by $\alpha $, mixed with fixed-scale noise component $\eta (t)$, 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}. }}{14}{}\protected@file@percent }
\newlabel{fig:inv_thresh-lp_single_noise}{{6}{14}{}{}{}}
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\newlabel{fig:inv_thresh-lp_species}{{7}{15}{}{}{}}
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\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces \textbf {Feature representation of different species-specific songs saturates at different points in feature space.} }}{13}{}\protected@file@percent }
\newlabel{fig:inv_thresh-lp_species}{{6}{13}{}{}{}}
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Output written on main.pdf (17 pages, 164189765 bytes).
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main.tex
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@@ -52,6 +52,7 @@
\newcommand{\figb}{\textbf{b}}
\newcommand{\figc}{\textbf{c}}
\newcommand{\figd}{\textbf{d}}
\newcommand{\fige}{\textbf{e}}
% Math shorthands - Standard symbols:
\newcommand{\dec}{\log_{10}} % Logarithm base 10
@@ -641,8 +642,8 @@ the signal for reliable song recognition.
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
$\filt(t)=\noc(t)$.
respective reference standard deviation $\sigma_{\eta}$
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
@@ -661,49 +662,32 @@ the signal for reliable song recognition.
\centering
\includegraphics[width=\textwidth]{figures/fig_invariance_thresh_lp_single.pdf}
\caption{\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 $\sca$ and
transformed into binary response $b_i(t)$ and further into
feature $f_i(t)$. Threshold value $\thr$ is set to
different percentiles of the the distribution of $c_i(t)$
at $\sca=1$. Darker colors indicate higher values of
$\thr$. 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 $\thr$ in \textbf{a}-\textbf{c}.
averaging depends on both the threshold value and the
noise floor.}
Synthetic input $\adapt(t)$ consists of song component
$\soc(t)$ scaled by $\sca$ with additive noise component
$\noc(t)$. Input $\adapt(t)$ is transformed into kernel
response $c_i(t)$, binary response $b_i(t)$, and feature
$f_i(t)$. Threshold value $\thr$ is set to multiples of
the reference standard deviation $\sigma_{\eta}$ of $c_i(t)$ for input
$\adapt(t)=\noc(t)$. Darker colors correspond to higher
$\thr$.
\textbf{Left}:~$\adapt(t)$, $c_i(t)$, $b_i(t)$, and
$f_i(t)$ for different scales $\sca$ and threshold values
$\thr$. Left-most column is is the pure-noise reference.
\textbf{Right}:~Average value of $f_i(t)$ during the song
for the different $\thr$.
\figa:~Input $\adapt(t)$.
\figb-\figd:~$c_i(t)$, $b_i(t)$, and $f_i(t)$ for the
different $\thr$ based on the same $\adapt(t)$ from
\figa{}.
\fige:~Average value of $f_i(t)$ during the song for
the different $\thr$ in \figb{}-\figd.
}
\label{fig:inv_thresh-lp_single}
\end{figure}
\FloatBarrier
\begin{figure}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/fig_invariance_thresh_lp_single_noise.pdf}
\caption{\textbf{Intensity invariance by thresholding and temporal
averaging depends on noise with regard to variable range
but not saturation level.}
Kernel response $c_i(t)$ is rescaled by $\sca$, mixed with
fixed-scale noise component $\noc(t)$, and transformed
into binary response $b_i(t)$ and further into feature
$f_i(t)$. Threshold value $\thr$ is set to different
percentiles of the the distribution of $c_i(t)$ at
$\sca=1$. Darker colors indicate higher values of $\thr$.
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 $\thr$ in \textbf{a}-\textbf{c}.
}
\label{fig:inv_thresh-lp_single_noise}
\end{figure}
\FloatBarrier
\begin{figure}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/fig_invariance_thresh_lp_species.pdf}

271
python/fig_invariance_thresh-lp.py
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@@ -1,271 +0,0 @@
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,\
plot_line, plot_barcode, strip_zeros, time_bar
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.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']
load_kwargs = dict(
files=stages,
keywords=['scales', 'measure', 'spread']
)
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=0.07,
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=0.07,
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 = dict(
conv=0.5,
feat=2
)
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=0.7,
y1=0.8,
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, **load_kwargs)
noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), **load_kwargs)
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'],
c=colors['conv'], lw=lw_snippets['conv'])
# 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'],
ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat'])
# 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'],
c=colors['conv'], lw=lw_snippets['conv'])
# 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'],
ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat'])
# 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()

287
python/fig_invariance_thresh-lp_single.py
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@@ -1,73 +1,71 @@
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 color_functions import load_colors, shade_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel, super_xlabel,\
plot_line, plot_barcode, strip_zeros, time_bar,\
letter_subplot, letter_subplots, title_subplot
from plot_functions import shift_subplot, hide_axis, ylimits, xlabel, ylabel,\
super_ylabel, plot_line, plot_barcode, strip_zeros,\
time_bar, letter_subplot, letter_subplots, title_subplot,\
set_clip_box
from IPython import embed
def add_snip_axes(fig, grid_kwargs, snip_specs):
def add_snip_axes(fig, grid_kwargs):
grid = fig.add_gridspec(**grid_kwargs)
axes = np.zeros((len(snip_specs), grid.ncols), dtype=object)
for i, specs in enumerate(snip_specs.values()):
for j in range(grid.ncols):
axes[i, j] = fig.add_subplot(grid[specs, j])
[hide_axis(ax, 'left') for ax in axes[:, 1:].flatten()]
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])
if j == 0:
shift_subplot(axes[i, j], dx=snip_col_shift)
[hide_axis(ax, 'left') for ax in axes[:, 2:].flatten()]
[hide_axis(ax, 'bottom') for ax in axes.flatten()]
return axes
def plot_snippets(axes, time, snippets, ymin=None, ymax=None,
def plot_snippets(axes, time, snippets, ymin=None, ymax=None, ypad=0.05,
thresh=None, fill_kwargs={}, **kwargs):
ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=ypad)
handles = []
for ax, snippet in zip(axes, snippets.T):
plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs)
handles.append(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
if thresh is not None:
ax.fill_between(time, thresh, snippet, where=(snippet > thresh), **fill_kwargs)
return None
return handles
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
def side_distributions(axes, snippets, inset_bounds, thresh, nbins=50,
ymin=None, ymax=None, fill_kwargs={}, **kwargs):
limits = np.array([snippets.min(), snippets.max()]) * 1.05
def side_distributions(axes, snippets, inset_bounds, thresh, nbins=1000,
fill_kwargs={}, **kwargs):
limits = np.array([snippets.min(), snippets.max()]) * 1.1
edges = np.linspace(*limits, nbins + 1)
centers = edges[:-1] + (edges[1] - edges[0]) / 2
insets = []
for ax, snippet in zip(axes, snippets.T):
pdf, _ = np.histogram(snippet, edges, density=True)
inset = ax.inset_axes(inset_bounds)
inset.plot(pdf, centers, **kwargs)
inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh),
**fill_kwargs)
ylimits(centers, inset, minval=ymin, maxval=ymax, pad=0)
inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh), **fill_kwargs)
inset.set_xlim(0, pdf.max())
inset.set_ylim(ax.get_ylim())
inset.axis('off')
return None
insets.append(inset)
return insets
# GENERAL SETTINGS:
with_noise = True
target = 'Omocestus_rufipes'
search_kwargs = dict(
incl=['subset', 'noise'] if with_noise else 'subset',
excl=None if with_noise else 'noise',
dir='../data/inv/thresh_lp/'
)
data_paths = search_files(target, **search_kwargs)
data_paths = search_files(target, incl='noise', dir='../data/inv/thresh_lp/')
stages = ['conv', 'bi', 'feat']
load_kwargs = dict(
files=stages,
keywords=['scales', 'snip', 'measure', 'thresh']
)
save_path = '../figures/fig_invariance_thresh_lp_single.pdf'
if with_noise and save_path is not None:
save_path = save_path.replace('.pdf', '_noise.pdf')
exclude_zero = True
# GRAPH SETTINGS:
fig_kwargs = dict(
@@ -83,19 +81,34 @@ super_grid_kwargs = dict(
bottom=0,
top=1
)
input_rows = 1
snip_rows = 2
subfig_specs = dict(
snip=(slice(None), slice(super_grid_kwargs['ncols'] - 1)),
input=(slice(input_rows), slice(-1)),
snip=[np.array([input_rows, input_rows + snip_rows]), slice(-1)],
big=(slice(None), -1),
)
snip_col_shift = -0.07
snip_grid_kwargs = dict(
nrows=len(stages) + 1,
nrows=len(stages),
ncols=None,
wspace=0.3,
hspace=0,
left=0.17,
left=0.25,
right=0.93,
bottom=0.15,
top=0.9
top=0.95,
height_ratios=[2, 1, 1]
)
input_grid_kwargs = dict(
nrows=1,
ncols=None,
wspace=snip_grid_kwargs['wspace'],
hspace=0,
left=snip_grid_kwargs['left'],
right=snip_grid_kwargs['right'],
bottom=0.15,
top=0.75,
)
big_grid_kwargs = dict(
nrows=1,
@@ -107,12 +120,7 @@ big_grid_kwargs = dict(
bottom=0.1,
top=0.99
)
snip_specs = dict(
conv=slice(0, -2),
bi=-2,
feat=-1
)
inset_bounds = [1.02, 0, 0.2, 1]
dist_inset_bounds = [1.02, 0, 0.2, 1]
# PLOT SETTINGS:
fs = dict(
@@ -124,29 +132,24 @@ fs = dict(
bar=16,
)
colors = load_colors('../data/stage_colors.npz')
color_factors = [0.2, -0.2]
shade_factors = [0.2, -0.2]
lw = dict(
conv=1,
inv=1.5,
conv=1.5,
bi=0.1,
feat=3,
big=4,
)
xlabels = dict(
snip='time [s]',
big='scale $\\alpha$',
)
ylabels = dict(
inv='$x_{\\text{adapt}}$',
conv='$c_i$',
bi='$b_i$',
feat='$f_i$',
big='$\\mu_f$',
)
xlab_snip_kwargs = dict(
y=0,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
xlab_big_kwargs = dict(
y=0,
fontsize=fs['lab_norm'],
@@ -157,7 +160,7 @@ ylab_snip_kwargs = dict(
x=0.08,
fontsize=fs['lab_tex'],
rotation=0,
ha='right',
ha='center',
va='center',
)
ylab_super_kwargs = dict(
@@ -168,14 +171,20 @@ ylab_super_kwargs = dict(
)
ylab_big_kwargs = dict(
x=0,
fontsize=fs['lab_norm'],
fontsize=fs['lab_tex'],
ha='center',
va='top',
)
ypad = dict(
inv=0.05,
conv=0.05,
big=0.075
)
yloc = dict(
conv=100,
bi=1,
feat=1,
inv=(2, 200),
conv=(0.02, 2),
bi=(1, 1),
feat=(1, 1),
big=0.2,
)
title_kwargs = dict(
@@ -200,7 +209,6 @@ letter_big_kwargs = dict(
fontsize=fs['letter'],
)
dist_kwargs = dict(
nbins=50,
c='k',
lw=1,
)
@@ -211,8 +219,8 @@ dist_fill_kwargs = dict(
bar_time = 0.1
bar_kwargs = dict(
dur=bar_time,
y0=-0.25,
y1=-0.1,
y0=-0.5,
y1=-0.35,
xshift=1,
color='k',
lw=0,
@@ -225,7 +233,16 @@ bar_kwargs = dict(
va='center',
)
)
kernel = np.array([
leg_kwargs = dict(
ncols=2,
loc='center',
bbox_to_anchor=(0, 0.95, 1, 0.05),
frameon=False,
fontsize=fs['tit_norm'],
handlelength=1.5,
columnspacing=1,
)
kern_specs = np.array([
[1, 0.008],
[2, 0.004],
[3, 0.002],
@@ -238,96 +255,138 @@ for data_path in data_paths:
print(f'Processing {data_path}')
# Load invariance data:
data, config = load_data(data_path, **load_kwargs)
t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate']
noise_data, config = load_data(data_path, **load_kwargs)
pure_data, _ = load_data(data_path.replace('noise', 'pure'), **load_kwargs)
# Unpack shared variables:
scales = noise_data['scales']
plot_scales = noise_data['example_scales']
thresh_rel = noise_data['thresh_rel']
thresh_abs = noise_data['thresh_abs']
# Reduce to kernel subset and crop to zoom frame:
t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
zoom_abs = zoom_rel * t_full[-1]
zoom_inds = (t_full >= zoom_abs[0]) & (t_full <= zoom_abs[1])
kern_ind = find_kern_specs(config['k_specs'], kerns=kernel)[0]
# Reduce to kernel subset and crop time to zoom frame:
data['snip_conv'] = data['snip_conv'][zoom_inds, kern_ind, ...]
data['snip_bi'] = data['snip_bi'][zoom_inds, kern_ind, ...]
data['snip_feat'] = data['snip_feat'][zoom_inds, kern_ind, ...]
data['measure_feat'] = data['measure_feat'][:, kern_ind, :]
t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate']
kern_ind = find_kern_specs(config['k_specs'], kerns=kern_specs)[0]
noise_data['snip_inv'] = noise_data['snip_inv'][zoom_inds, :]
noise_data['snip_conv'] = noise_data['snip_conv'][zoom_inds, kern_ind, :]
noise_data['snip_bi'] = noise_data['snip_bi'][zoom_inds, kern_ind, :, :]
noise_data['snip_feat'] = noise_data['snip_feat'][zoom_inds, kern_ind, :, :]
noise_data['measure_feat'] = noise_data['measure_feat'][:, kern_ind, :]
pure_data['measure_feat'] = pure_data['measure_feat'][:, kern_ind, :]
thresh_abs = thresh_abs[:, kern_ind]
t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
if exclude_zero:
# Reduce to nonzero scales:
nonzero_inds = scales > 0
scales = scales[nonzero_inds]
noise_data['measure_feat'] = noise_data['measure_feat'][nonzero_inds, :]
pure_data['measure_feat'] = pure_data['measure_feat'][nonzero_inds, :]
# Get threshold-specific colors:
factors = np.linspace(*color_factors, data['threshs'].size)
colors = dict(
factors = np.linspace(*shade_factors, thresh_rel.size)
shaded = dict(
conv=shade_colors(colors['conv'], factors),
bi=shade_colors(colors['bi'], factors),
feat=shade_colors(colors['feat'], factors),
)
# Adjust grid parameters:
super_grid_kwargs['nrows'] = data['threshs'].size
snip_grid_kwargs['ncols'] = data['example_scales'].size
# Adjust grid parameters to loaded data:
super_grid_kwargs['nrows'] = snip_rows * thresh_rel.size + 1
input_grid_kwargs['ncols'] = plot_scales.size
snip_grid_kwargs['ncols'] = plot_scales.size
# Prepare overall graph:
fig = plt.figure(**fig_kwargs)
super_grid = fig.add_gridspec(**super_grid_kwargs)
# Prepare input snippet axes:
input_subfig = fig.add_subfigure(super_grid[subfig_specs['input']])
input_axes = add_snip_axes(input_subfig, input_grid_kwargs).ravel()
input_axes[0].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][0]))
input_axes[1].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][1]))
ylabel(input_axes[0], ylabels['inv'], transform=input_subfig.transSubfigure, **ylab_snip_kwargs)
for ax, scale in zip(input_axes, plot_scales):
title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=input_subfig, **title_kwargs)
letter_subplot(input_subfig, 'a', **letter_snip_kwargs)
# Prepare snippet axes:
snip_axes = {}
for i in range(data['threshs'].size):
subfig_specs['snip'] = (i, subfig_specs['snip'][1])
snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
axes = add_snip_axes(snip_subfig, snip_grid_kwargs, snip_specs)
snip_axes[snip_subfig] = axes
super_ylabel(f'{strip_zeros(100 * data["thresh_perc"][i])}%',
snip_subfigs, snip_axes = [], []
for i in range(thresh_rel.size):
subfig_spec = subfig_specs['snip'].copy()
subfig_spec[0] = slice(*(subfig_spec[0] + i * snip_rows))
snip_subfig = fig.add_subfigure(super_grid[*subfig_spec])
axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
[hide_axis(ax, 'left') for ax in axes[1:, 1]]
super_ylabel(f'$\\Theta={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
for ax, stage in zip(axes[:, 0], stages):
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
transform=snip_subfig.transSubfigure)
if i == 0:
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:
for (ax1, ax2), stage in zip(axes[:, :2], stages):
ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
ylabel(ax1, ylabels[stage], transform=snip_subfig.transSubfigure, **ylab_snip_kwargs)
# for ax, scale in zip(axes[0, :], plot_scales):
# title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=snip_subfig, **title_kwargs)
if i == thresh_rel.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)
snip_subfigs.append(snip_subfig)
snip_axes.append(axes)
letter_subplots(snip_subfigs, 'bcd', **letter_snip_kwargs)
# Prepare analysis axis:
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
big_ax = big_subfig.add_subplot(big_grid[0, 0])
xlabel(big_ax, xlabels['big'], **xlab_big_kwargs,
transform=big_subfig.transSubfigure)
ylabel(big_ax, ylabels['big'], **ylab_big_kwargs,
transform=big_subfig.transSubfigure)
big_ax.set_xlim(data['scales'].min(), data['scales'].max())
big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
ylimits(data['measure_feat'], big_ax, minval=0, pad=0.01)
big_ax.set_xlim(scales[0], scales[-1])
big_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
ylimits(noise_data['measure_feat'], big_ax, minval=0, pad=ypad['big'])
big_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
letter_subplot(big_subfig, 'd', **letter_big_kwargs, ref=list(snip_axes.keys())[0])
xlabel(big_ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
ylabel(big_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
letter_subplot(big_subfig, 'e', **letter_big_kwargs, ref=input_subfig)
# Plot intensity-adapted snippets:
plot_snippets(input_axes, t_full, noise_data['snip_inv'],
ypad=ypad['inv'], c=colors['inv'], lw=lw['inv'])
ylimits(noise_data['snip_inv'][:, 0], input_axes[0], pad=ypad['inv'])
# Plot representation snippets per threshold:
conv_min, conv_max = ylimits(data['snip_conv'], pad=0.02)
for i, (subfig, axes) in enumerate(snip_axes.items()):
dist_fill_kwargs['color'] = colors['bi'][i]
for i, (subfig, axes) in enumerate(zip(snip_subfigs, snip_axes)):
dist_fill_kwargs['color'] = shaded['bi'][i]
# Plot kernel response snippets:
plot_snippets(axes[0, :], t_full, data['snip_conv'][:, :, i],
thresh=data['threshs'][i], ymin=conv_min, ymax=conv_max,
fill_kwargs=dist_fill_kwargs, c=colors['conv'][i], lw=lw['conv'])
plot_snippets(axes[0, :], t_full, noise_data['snip_conv'], thresh=thresh_abs[i],
ypad=ypad['conv'], fill_kwargs=dist_fill_kwargs, c=shaded['conv'][i], lw=lw['conv'])
ylimits(noise_data['snip_conv'][:, 0], axes[0, 0], pad=ypad['conv'])
# Plot kernel response distributions:
side_distributions(axes[0, :1], noise_data['snip_conv'][:, :1], dist_inset_bounds,
thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
side_distributions(axes[0, 1:], noise_data['snip_conv'][:, 1:], dist_inset_bounds,
thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
# Plot binary snippets:
plot_bi_snippets(axes[1, :], t_full, data['snip_bi'][:, :, i],
color=colors['bi'][i], lw=lw['bi'])
plot_bi_snippets(axes[1, :], t_full, noise_data['snip_bi'][:, :, i],
color=shaded['bi'][i], lw=lw['bi'])
# Plot feature snippets:
plot_snippets(axes[2, :], t_full, data['snip_feat'][:, :, i],
ymin=0, ymax=1, c=colors['feat'][i], lw=lw['feat'])
handles = plot_snippets(axes[2, :], t_full, noise_data['snip_feat'][:, :, i],
ymin=0, ymax=1, c=shaded['feat'][i], lw=lw['feat'])
[set_clip_box(h[0], ax, bounds=[[0, -0.05], [1, 1.05]]) for h, ax in zip(handles, axes[2, :])]
# Plot kernel response distribution:
side_distributions(axes[0, :], data['snip_conv'][:, :, i], inset_bounds,
data['threshs'][i], ymin=conv_min, ymax=conv_max,
fill_kwargs=dist_fill_kwargs, **dist_kwargs)
# Plot pure-song analysis results:
handles = big_ax.plot(scales, pure_data['measure_feat'], lw=lw['big'], ls='dotted')
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
# Plot analysis results:
handles = big_ax.plot(data['scales'], data['measure_feat'], lw=lw['big'])
[h.set_color(c) for h, c in zip(handles, colors['feat'])]
# Plot noise-song analysis results:
handles = big_ax.plot(scales, noise_data['measure_feat'], lw=lw['big'])
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
# Add proxy legend:
h1 = big_ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
h2 = big_ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
big_ax.legend(handles=[h1, h2], **leg_kwargs)
if save_path is not None:
fig.savefig(save_path)

160
python/fig_invariance_thresh-lp_subset.py
View File

@@ -1,160 +0,0 @@
import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data
from thunderhopper.filtertools import find_kern_specs
from color_functions import load_colors, shade_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel,\
plot_line, plot_barcode, strip_zeros, time_bar,\
letter_subplot, letter_subplots, hide_ticks,\
super_xlabel, super_ylabel, assign_colors
from IPython import embed
# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
search_kwargs = dict(
incl='subset',
excl='noise',
dir='../data/inv/thresh_lp/'
)
pure_paths = search_files(target, **search_kwargs)
load_kwargs = dict(
keywords=['scales', 'measure', 'thresh']
)
save_path = None#'../figures/fig_invariance_thresh_lp_subset.pdf'
# GRAPH SETTINGS:
fig_kwargs = dict(
figsize=(32/2.54, 16/2.54),
)
super_grid_kwargs = dict(
nrows=1,
ncols=1,
wspace=0,
hspace=0,
left=0,
right=1,
bottom=0,
top=1
)
grid_kwargs = dict(
nrows=2,
ncols=1,
wspace=0,
hspace=0.1,
left=0.15,
right=0.95,
bottom=0.1,
top=0.85
)
inset_bounds = [0.2, 1.01, 0.6, 0.4]
# PLOT SETTINGS:
colors = load_colors('../data/stage_colors.npz')
color_factors = [-0.5, 0.5]
lw = dict(
one=3,
kern=3,
all=1,
)
ax_labels = dict(
x='scale $\\alpha$',
y='$\\mu_f$',
)
xlab_kwargs = dict(
y=0.005,
fontsize=16,
ha='center',
va='bottom',
)
ylab_kwargs = dict(
x=0,
fontsize=20,
ha='left',
va='center',
)
yloc = 0.2
# EXECUTION:
for pure_path in pure_paths:
print(f'Processing {pure_path}')
noise_path = pure_path.replace('.npz', '_noise.npz')
# Load kernel invariance data:
pure_data, config = load_data(pure_path, **load_kwargs)
noise_data, _ = load_data(noise_path, **load_kwargs)
scales = pure_data['scales']
# Adjust grid parameters:
n_columns = config['k_specs'].shape[0] + 1
super_grid_kwargs['ncols'] = n_columns
# Prepare overall graph:
fig = plt.figure(**fig_kwargs)
super_grid = fig.add_gridspec(**super_grid_kwargs)
# Prepare axes:
all_axes = np.zeros((grid_kwargs['nrows'], n_columns), dtype=object)
subfigs = []
for i in range(n_columns):
subfig = fig.add_subfigure(super_grid[0, i])
grid = subfig.add_gridspec(**grid_kwargs)
subfigs.append(subfig)
for j in range(grid_kwargs['nrows']):
ax = subfig.add_subplot(grid[j, 0])
ax.set_xlim(scales[0], scales[-1])
ax.set_ylim(0, 1)
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc))
if i > 0:
hide_ticks(ax, side='left')
all_axes[j, i] = ax
hide_ticks(all_axes[0, i], side='bottom')
super_xlabel(ax_labels['x'], fig, all_axes[-1, 0], all_axes[-1, -1], **xlab_kwargs)
super_ylabel(ax_labels['y'], fig, all_axes[0, 0], all_axes[1, 0], **ylab_kwargs)
# Plot kernel-specific results:
in_min, in_high = ylimits(config['kernels'], pad=0.05)
for i in range(config['k_specs'].shape[0]):
pure_ax, noise_ax = all_axes[:, i]
# Plot results of pure-song analysis:
pure_ax.plot(scales, pure_data['measure_feat'][:, i, :],
c=colors['feat'], lw=lw['one'])
# Plot results of noise-song analysis:
noise_ax.plot(scales, noise_data['measure_feat'][:, i, :],
c=colors['feat'], lw=lw['one'])
# Indicate kernel waveform:
inset = pure_ax.inset_axes(inset_bounds)
inset.plot(config['k_times'], config['kernels'][:, i], c='k', lw=lw['kern'])
inset.set_xlim(config['k_times'][0], config['k_times'][-1])
inset.set_ylim(in_min, in_high)
inset.axis('off')
# Load population invariance data:
pure_data, config = load_data(pure_path.replace('_subset', ''), **load_kwargs)
noise_data, _ = load_data(noise_path.replace('_subset', ''), **load_kwargs)
scales = pure_data['scales']
# Get kernel type-specific colors:
types, ind = np.unique(config['k_specs'][:, 0], return_index=True)
types = types[np.argsort(ind)].astype(int)
factors = np.linspace(*color_factors, types.size)
kern_colors = shade_colors(colors['feat'], factors)
kern_colors = dict(zip(types.astype(str), kern_colors))
# Plot population-wide results:
pure_ax, noise_ax = all_axes[:, -1]
handles = pure_ax.plot(scales, pure_data['measure_feat'], c='k', lw=lw['all'])
assign_colors(handles, config['k_specs'][:, 0], kern_colors)
handles = noise_ax.plot(scales, noise_data['measure_feat'], c='k', lw=lw['all'])
assign_colors(handles, config['k_specs'][:, 0], kern_colors)
if save_path is not None:
fig.savefig(save_path)
plt.show()
print('Done.')
embed()

22
python/plot_functions.py
View File

@@ -1,7 +1,7 @@
import string
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.transforms import BboxTransformTo
from matplotlib.transforms import Bbox, BboxTransformTo, TransformedBbox
def hide_ticks(ax, side='bottom', ticks=True):
axis = 'x' if side in ['top', 'bottom'] else 'y'
@@ -33,6 +33,14 @@ def get_trans_artist(artist):
return BboxTransformTo(artist.get_tightbbox(renderer))
raise ValueError('Artist does not have a bounding box to use as transform.')
def shift_subplot(ax, dx=0, dy=0, ref=None):
if ref is not None:
transform = get_trans_artist(ref) + get_trans_artist(ax.figure).inverted()
dx, dy = transform.transform((dx, dy)) - transform.transform((0, 0))
pos = ax.get_position()
ax.set_position([pos.x0 + dx, pos.y0 + dy, pos.width, pos.height])
return None
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 = get_trans_artist(artist)
@@ -68,6 +76,10 @@ def letter_subplots(artists, labels=None, x=None, y=None, xref=None, yref=None,
return handles
def xlimits(time, ax=None, minval=None, maxval=None, pad=0.05):
if minval is not None and maxval is not None:
if ax is not None:
return ax.set_xlim([minval, maxval])
return [minval, maxval]
limits = [minval, maxval]
if minval is None:
limits[0] = time[0]
@@ -83,6 +95,10 @@ def xlimits(time, ax=None, minval=None, maxval=None, pad=0.05):
return limits
def ylimits(signal, ax=None, minval=None, maxval=None, pad=0.05):
if minval is not None and maxval is not None:
if ax is not None:
return ax.set_ylim([minval, maxval])
return [minval, maxval]
limits = [minval, maxval]
if minval is None:
limits[0] = signal.min()
@@ -267,3 +283,7 @@ def zoom_inset(ax, inset, handle, x0=None, x1=None, y0=None, y1=None, ref='x',
visibility = low_left, up_left, low_right, up_right
[l.set_visible(v) for l, v in zip(elements.connectors, visibility)]
return inset_handle, elements.rectangle, elements.connectors
def set_clip_box(artist, ax, bounds=[[0, -0.05], [1, 1.05]]):
artist.set_clip_box(TransformedBbox(Bbox(bounds), ax.transAxes))
return None

137
python/save_inv_data_thresh-lp.py
View File

@@ -1,86 +1,151 @@
import glob
import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data, save_data
from thunderhopper.filetools import crop_paths
from thunderhopper.filetools import search_files, crop_paths
from thunderhopper.filters import sosfilter
from thunderhopper.filtertools import find_kern_specs
from thunderhopper.model import convolve_kernels
from IPython import embed
# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = glob.glob(f'../data/processed/{target}*.npz')
target = ['Omocestus_rufipes', '*'][0]
data_paths = search_files(target, dir='../data/processed/')
noise_path = '../data/processed/white_noise_sd-1.npz'
save_path = '../data/inv/thresh_lp/'
# ANALYSIS SETTINGS:
add_noise = False
thresh_percent = 90
example_scales = np.array([0, 1, 10, 50])
scales = np.geomspace(0.01, 100, 100)
add_noise = True
save_snippets = add_noise and True
plot_results = False
example_scales = np.array([0, 1, 10, 30, 100])
scales = np.geomspace(0.01, 10000, 100)
scales = np.unique(np.concatenate((scales, example_scales)))
thresh_rel = np.array([0.5, 1, 3])
kern_specs = np.array([
[1, 0.008],
[2, 0.004],
[3, 0.002],
])
# PREPARATION:
pure_noise = np.load(noise_path)['inv']
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
print(f'Processing {name}')
save_name = save_path + name
# Get pure-song kernel responses:
data, config = load_data(data_path, files='conv')
song, rate = data['conv'], data['conv_rate']
# Get adapted envelope (prior to convolution):
data, config = load_data(data_path, files='inv')
song, rate = data['inv'], data['inv_rate']
# Get song segment to be analyzed:
time = np.arange(song.shape[0]) / rate
start, end = data['songs_0'].ravel()
segment = (time >= start) & (time <= end)
# Reduce to kernel subset:
kern_inds = find_kern_specs(config['k_specs'], kerns=kern_specs)
config['kernels'] = config['kernels'][:, kern_inds]
config['k_specs'] = config['k_specs'][kern_inds, :]
config['k_props'] = [config['k_props'][i] for i in kern_inds]
# Normalize song component:
song /= song[segment, :].std(axis=0)
song /= song[segment].std()
if add_noise:
# Get normalized noise:
rng = np.random.default_rng()
noise = rng.normal(size=(song.shape[0], 1))
noise /= noise[segment].std()
# Get normalized noise component:
noise = pure_noise[:song.shape[0]]
noise /= noise[segment].std()
# Prepare noise-bound threshold:
threshold = np.percentile(noise, thresh_percent, axis=0)
else:
# Reuse threshold from previous noise run:
threshold = np.load(save_name + '_noise.npz')['thresh']
# Define kernel-specific threshold values based on pure-noise response SD:
ref_conv = convolve_kernels(noise, config['kernels'], config['k_specs'])
thresh_abs = ref_conv[segment, :].std(axis=0, keepdims=True) * thresh_rel[:, None]
# Prepare measure storage:
shape = (scales.size, song.shape[1])
# measure_conv = np.zeros(shape, dtype=float)
shape = (scales.size, kern_specs.shape[0], thresh_rel.size)
measure_feat = np.zeros(shape, dtype=float)
if save_snippets:
# Prepare snippet storage:
snip_inv = np.zeros((song.size, example_scales.size), dtype=float)
shape = (song.size, kern_specs.shape[0], example_scales.size, thresh_rel.size)
snip_conv = np.zeros(shape[:-1], dtype=float)
snip_bi = np.zeros(shape, dtype=float)
snip_feat = np.zeros(shape, dtype=float)
# Execute piecewise:
for i, scale in enumerate(scales):
print('Simulating scale', scale)
# Rescale song component:
scaled_conv = song * scale
scaled_song = song * scale
if add_noise:
# Add noise:
scaled_conv += noise
scaled_song += noise
# Process mixture:
scaled_bi = (scaled_conv > threshold).astype(float)
scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
padtype='fixed', padlen=config['padlen'])
scaled_conv = convolve_kernels(scaled_song, config['kernels'], config['k_specs'])
# Log threshold-independent snippet data:
if save_snippets and scale in example_scales:
save_ind = np.nonzero(example_scales == scale)[0][0]
snip_inv[:, save_ind] = scaled_song
snip_conv[:, :, save_ind] = scaled_conv
# Get intensity measure per stage:
# measure_conv[i] = scaled_conv[segment, :].std(axis=0)
measure_feat[i] = scaled_feat[segment, :].mean(axis=0)
# Execute piecewise again:
for j, thresholds in enumerate(thresh_abs):
# Process mixture further:
scaled_bi = (scaled_conv > thresholds).astype(float)
scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
padtype='fixed', padlen=config['padlen'])
# Log threshold-dependent snippet data:
if save_snippets and scale in example_scales:
snip_bi[:, :, save_ind, j] = scaled_bi
snip_feat[:, :, save_ind, j] = scaled_feat
# Log intensity measure:
measure_feat[i, :, j] = scaled_feat[segment, :].mean(axis=0)
# Overview plot:
if plot_results:
fig, axes = plt.subplots(thresh_rel.size, kern_specs.shape[0],
figsize=(16, 9), layout='constrained',
sharex=True, sharey=True, squeeze=True)
axes[0, 0].set_xscale('symlog', linthresh=scales[scales>0].min(),
linscale=0.25)
axes[0, 0].set_ylim(0, 1)
for i, thresh in enumerate(thresh_rel):
for j, kernel in enumerate(kern_specs):
ax = axes[i, j]
ax.plot(scales, measure_feat[:, j, i], 'k')
if i == 0:
ax.set_title(f'Kernel {kernel}')
if j == 0:
ax.set_ylabel(f'{thresh} * SD')
plt.show()
# Save analysis results:
if save_path is not None:
data = dict(
scales=scales,
# measure_conv=measure_conv,
example_scales=example_scales,
measure_feat=measure_feat,
thresh=threshold,
thresh_perc=thresh_percent,
thresh_rel=thresh_rel,
thresh_abs=thresh_abs,
)
if save_snippets:
data.update(dict(
snip_inv=snip_inv,
snip_conv=snip_conv,
snip_bi=snip_bi,
snip_feat=snip_feat,
))
if add_noise:
save_name += '_noise'
else:
save_name += '_pure'
save_data(save_name, data, config, overwrite=True)
print('Done.')
embed()

138
python/save_inv_data_thresh-lp_subset.py
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@@ -1,138 +0,0 @@
import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data, save_data
from thunderhopper.filetools import search_files, crop_paths
from thunderhopper.filters import sosfilter
from thunderhopper.filtertools import find_kern_specs, pdf_proportion
from IPython import embed
# GENERAL SETTINGS:
target = ['Omocestus_rufipes', '*'][0]
data_paths = search_files(target, dir='../data/processed/')
save_path = '../data/inv/thresh_lp/'
# ANALYSIS SETTINGS:
add_noise = False
save_snippets = True
example_scales = np.array([0, 1, 10, 50])
scales = np.geomspace(0.01, 1000, 100)
scales = np.unique(np.concatenate((scales, example_scales)))
thresh_percent = np.array([0.6, 0.75, 0.999])
thresholds = pdf_proportion(thresh_percent, sd=1, mu=0)
plot_results = False
kernels = np.array([
[1, 0.008],
[2, 0.004],
[3, 0.002],
])
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
print(f'Processing {name}')
save_name = save_path + name + '_subset'
# Get pure-song kernel responses:
data, config = load_data(data_path, files='conv')
conv, rate = data['conv'], data['conv_rate']
# Get song segment to be analyzed:
time = np.arange(conv.shape[0]) / rate
start, end = data['songs_0'].ravel()
segment = (time >= start) & (time <= end)
# Reduce to kernel subset:
kern_inds = find_kern_specs(config['k_specs'], kerns=kernels)
config['kernels'] = config['kernels'][:, kern_inds]
config['k_specs'] = config['k_specs'][kern_inds]
config['k_props'] = [config['k_props'][i] for i in kern_inds]
conv = conv[:, kern_inds]
# Normalize kernel responses:
conv /= conv[segment, :].std(axis=0)
if add_noise:
# Get normalized noise:
rng = np.random.default_rng()
noise = rng.normal(size=(conv.shape[0], 1))
noise /= noise[segment].std()
if save_snippets:
# Prepare snippet storage:
shape = conv.shape + (example_scales.size, thresh_percent.size)
snip_conv = np.zeros(shape, dtype=float)
snip_bi = np.zeros(shape, dtype=float)
snip_feat = np.zeros(shape, dtype=float)
# Prepare measure storage:
shape = (scales.size, conv.shape[1], thresh_percent.size)
# measure_conv = np.zeros(shape, dtype=float)
measure_feat = np.zeros(shape, dtype=float)
# Execute piecewise analysis:
for i, thresh in enumerate(thresholds):
print('\nSimulating threshold ', thresh_percent[i])
for j, scale in enumerate(scales):
print('Simulating scale ', scale)
# Rescale conv component:
scaled_conv = conv * scale
if add_noise:
# Add noise:
scaled_conv += noise
# Process mixture:
scaled_bi = (scaled_conv > thresh).astype(float)
scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
padtype='fixed', padlen=config['padlen'])
# Log snippet data:
if save_snippets and scale in example_scales:
scale_ind = np.nonzero(example_scales == scale)[0][0]
snip_conv[:, :, scale_ind, i] = scaled_conv
snip_bi[:, :, scale_ind, i] = scaled_bi
snip_feat[:, :, scale_ind, i] = scaled_feat
# Get intensity measure per stage:
measure_feat[j, :, i] = scaled_feat[segment, :].mean(axis=0)
# measure_conv[j, :, i] = scaled_conv[segment, :].std(axis=0)
if plot_results:
fig, axes = plt.subplots(thresh_percent.size, kernels.shape[0],
figsize=(16, 9), layout='constrained',
sharex=True, sharey=True, squeeze=True)
axes[0, 0].set_xscale('symlog', linthresh=scales[scales>0].min(),
linscale=0.25)
axes[0, 0].set_ylim(0, 1)
for i, thresh in enumerate(thresh_percent):
for j, kernel in enumerate(kernels):
ax = axes[i, j]
ax.plot(scales, measure_feat[:, j, i], 'k')
if i == 0:
ax.set_title(f'Kernel {kernel}')
if j == 0:
ax.set_ylabel(f'{100 * thresh}%')
plt.show()
# Save analysis results:
if save_path is not None:
data = dict(
scales=scales,
example_scales=example_scales,
# measure_conv=measure_conv,
measure_feat=measure_feat,
thresh_perc=thresh_percent,
threshs=thresholds,
)
if save_snippets:
data.update(dict(
snip_conv=snip_conv,
snip_bi=snip_bi,
snip_feat=snip_feat,
))
if add_noise:
save_name += '_noise'
save_data(save_name, data, config, overwrite=True)
print('Done.')
embed()