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Lots of stuff. Syncing to home.

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j-hartling
2026-03-20 16:45:54 +01:00
parent 1516fe6090
commit a276883454
28 changed files with 1106 additions and 562 deletions
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\abx@aux@page{68}{7}
\abx@aux@page{69}{7}
\newlabel{eq:highpass}{{4}{7}{}{}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces \textbf {Representations of a song of \textit {O. rufipes} during the preprocessing stage.} \textbf {a}:~Bandpass-filtered tympanal signal. \textbf {b}:~Signal envelope. \textbf {c}:~Logarithmically scaled envelope. \textbf {d}:~Intensity-adapted envelope. }}{7}{}\protected@file@percent }
\newlabel{fig:pre}{{2}{7}{}{}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces \textbf {Representations of a song of \textit {O. rufipes} during the preprocessing stage.} \textbf {a}:~Bandpass-filtered tympanal signal. \textbf {b}:~Signal envelope. \textbf {c}:~Logarithmically scaled envelope. \textbf {d}:~Intensity-adapted envelope. }}{8}{}\protected@file@percent }
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\newlabel{eq:conv}{{5}{8}{}{}{}}
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@@ -236,33 +236,39 @@
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Values of phase $\phi $ that are specific for the four major groups of Gabor kernels.}}{9}{}\protected@file@percent }
\newlabel{tab:gabor_phases}{{1}{9}{}{}{}}
\newlabel{eq:binary}{{9}{9}{}{}{}}
\abx@aux@page{70}{9}
\abx@aux@page{71}{9}
\abx@aux@page{72}{9}
\newlabel{eq:lowpass}{{10}{9}{}{}{}}
\abx@aux@page{70}{10}
\abx@aux@page{71}{10}
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\newlabel{eq:lowpass}{{10}{10}{}{}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces \textbf {Representations of a song of \textit {O. rufipes} during the feature extraction stage.} Different colors indicate Gabor kernels with different lobe number $n$ and sign, with lighter colors for higher $n$~($1\,\leq \,n\,\leq \,4$; both $+$ and $-$ per $n$; two kernel widths $\sigma $ of $4\,$ms and $32\,$ms per sign). \textbf {a}:~Kernel-specific filter responses. \textbf {b}:~Binary responses. \textbf {c}:~Finalized features. }}{10}{}\protected@file@percent }
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\newlabel{eq:toy_log}{{12}{11}{}{}{}}
\newlabel{eq:toy_highpass}{{13}{11}{}{}{}}
\newlabel{eq:toy_snr}{{14}{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 }
\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 $500\,$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 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 }
\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 $500\,$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 }
\@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{eq:feat_avg}{{16}{15}{}{}{}}
\newlabel{eq:feat_prop}{{17}{15}{}{}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces \textbf {Feature representation of different species-specific songs saturates at different points in feature space.} }}{15}{}\protected@file@percent }
\newlabel{fig:inv_thresh-lp_species}{{7}{15}{}{}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces \textbf {Step-wise emergence of intensity invariant song representation along the model pathway.} }}{15}{}\protected@file@percent }
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\abx@aux@cite{0}{stumpner1991auditory}
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\@writefile{toc}{\contentsline {section}{\numberline {4}Discriminating species-specific song\\patterns in feature space}{16}{}\protected@file@percent }
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[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 587, warning: 6 characters of junk seen at toplevel
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29
main.fdb_latexmk
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@@ -1,14 +1,14 @@
# Fdb version 4
["biber main"] 1773238445.97958 "main.bcf" "main.bbl" "main" 1773392777.69214 0
["biber main"] 1773934381.64397 "main.bcf" "main.bbl" "main" 1774003703.72889 0
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"main.bcf" 1773392777.63678 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex"
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(generated)
"main.bbl"
"main.blg"
(rewritten before read)
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"/home/hartling/phd/paper/paper_2025/main.tex" 1773934444.28996 48517 9087d4f689a47c904bde7fa096405f41 ""
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@@ -152,15 +152,18 @@
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15
main.fls
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@@ -303,6 +303,21 @@ INPUT ./figures/fig_invariance_thresh_lp_single_noise.pdf
INPUT ./figures/fig_invariance_thresh_lp_single_noise.pdf
INPUT ./figures/fig_invariance_thresh_lp_single_noise.pdf
INPUT ./figures/fig_invariance_thresh_lp_single_noise.pdf
INPUT ./figures/fig_invariance_thresh_lp_species.pdf
INPUT ./figures/fig_invariance_thresh_lp_species.pdf
INPUT ./figures/fig_invariance_thresh_lp_species.pdf
INPUT ./figures/fig_invariance_thresh_lp_species.pdf
INPUT ./figures/fig_invariance_thresh_lp_species.pdf
INPUT ./figures/fig_invariance_full.pdf
INPUT ./figures/fig_invariance_full.pdf
INPUT ./figures/fig_invariance_full.pdf
INPUT ./figures/fig_invariance_full.pdf
INPUT ./figures/fig_invariance_full.pdf
INPUT ./figures/fig_noise_env_sd_conversion.pdf
INPUT ./figures/fig_noise_env_sd_conversion.pdf
INPUT ./figures/fig_noise_env_sd_conversion.pdf
INPUT ./figures/fig_noise_env_sd_conversion.pdf
INPUT ./figures/fig_noise_env_sd_conversion.pdf
INPUT main.aux
INPUT main.run.xml
OUTPUT main.run.xml

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@@ -1,4 +1,4 @@
This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28) 13 MAR 2026 10:06
This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28) 20 MAR 2026 11:48
entering extended mode
restricted \write18 enabled.
file:line:error style messages enabled.
@@ -747,42 +747,59 @@ LaTeX Warning: Text page 6 contains only floats.
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File: figures/fig_pre_stages.pdf Graphic file (type pdf)
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Package pdftex.def Info: figures/fig_pre_stages.pdf used on input line 421.
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***********
LaTeX2e <2023-11-01> patch level 1
L3 programming layer <2024-01-22>
@@ -792,18 +809,18 @@ Package logreq Info: Writing requests to 'main.run.xml'.
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Here is how much of TeX's memory you used:
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34
main.tex
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@@ -660,7 +660,7 @@ the signal for reliable song recognition.
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 $500\,$ms is the same for
$\thr$. Indicated time scale of $100\,$ms is the same for
\textbf{a}-\textbf{c} (black bar).
\textbf{a}:~50th percentile.
\textbf{b}:~75th percentile.
@@ -684,7 +684,7 @@ the signal for reliable song recognition.
$f_i(t)$. 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 $500\,$ms is the same for
Indicated time scale of $100\,$ms is the same for
\textbf{a}-\textbf{c} (black bar).
\textbf{a}:~50th percentile.
\textbf{b}:~75th percentile.
@@ -696,6 +696,26 @@ the signal for reliable song recognition.
\end{figure}
\FloatBarrier
\begin{figure}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/fig_invariance_thresh_lp_species.pdf}
\caption{\textbf{Feature representation of different species-specific songs
saturates at different points in feature space.}
}
\label{fig:inv_thresh-lp_species}
\end{figure}
\FloatBarrier
\begin{figure}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/fig_invariance_full.pdf}
\caption{\textbf{Step-wise emergence of intensity invariant song
representation along the model pathway.}
}
\label{fig:inv_thresh-lp_full}
\end{figure}
\FloatBarrier
The second key mechanism for the emergence of intensity invariance along the
model pathway takes place during the transformation of the kernel responses
$c_i(t)$ over the binary responses $b_i(t)$ into the finalized features
@@ -844,4 +864,14 @@ $\rightarrow$ Graded again but highly decorrelated from the acoustic stimulus\\
$\rightarrow$ Parameters of a behavioral response may be graded (e.g. approach speed),
initiation of one behavior over another is categorical (e.g. approach/stay)
\begin{figure}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/fig_noise_env_sd_conversion.pdf}
\caption{\textbf{}
}
\label{}
\end{figure}
\FloatBarrier
\end{document}

111
python/fig_env_sd_conversion.py Normal file
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@@ -0,0 +1,111 @@
import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
from plot_functions import xlabel, ylabel, strip_zeros, letter_subplots
# GENERAL SETTINGS:
data_path = '../data/inv/noise_env/sd_conversion.npz'
save_path = '../figures/fig_noise_env_sd_conversion.pdf'
# PLOT SETTINGS:
fig_kwargs = dict(
figsize=(32/2.54, 16/2.54),
nrows=2,
ncols=1,
sharex=True,
sharey=True,
gridspec_kw=dict(
wspace=0,
hspace=0.1,
left=0.065,
right=0.98,
bottom=0.08,
top=0.95,
)
)
grid_line_kwargs = dict(
visible=True,
which='major',
axis='both',
color='k',
lw=0.5,
)
trial_kwargs = dict(
color='k',
alpha=0.5,
lw=0.5,
)
line_kwargs = dict(
color='black',
lw=1,
)
fill_kwargs = dict(
color='k',
alpha=0.5,
)
xlabels = dict(
bottom='$\\text{scale }\\alpha$',
)
ylabels = dict(
top='$\\sigma_{\\eta}\\,(PLACEHOLDER \\,\\text{realizations})$',
bottom='$\\sigma_{\\eta}\\,(\\text{mean}\\,\\pm\\,\\text{SD})$',
)
xlab_kwargs = dict(
y=0,
fontsize=20,
ha='center',
va='bottom',
)
ylab_kwargs = dict(
x=0,
fontsize=20,
ha='center',
va='top',
)
title_kwargs = dict(
t='$\\sigma_{\\text{filt}}\\,=$',
x=0.5,
y=1,
ha='center',
va='top',
fontsize=20,
)
letter_kwargs = dict(
x=0.005,
y=0.99,
fontsize=22,
ha='left',
va='top',
)
# Fetch data:
data = dict(np.load('../data/inv/noise_env/sd_conversion.npz'))
n = data['n_trials']
# Adjust parameters:
ylabels['top'] = f'$\\sigma_{{\\eta}}\\,({data["n_trials"]}\\text{{ realizations}})$'
title_kwargs['t'] += f'$\\,{strip_zeros(data["sd_factor"])}$'
# Prepare graph:
fig, (ax1, ax2) = plt.subplots(**fig_kwargs)
fig.suptitle(**title_kwargs)
ax1.grid(**grid_line_kwargs)
ax1.set_xlim(data['scales'][0], data['scales'][-1])
ax1.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
ax1.set_ylim(0.4, 1.2)
ylabel(ax1, ylabels['top'], transform=fig.transFigure, **ylab_kwargs)
ax2.grid(**grid_line_kwargs)
xlabel(ax2, xlabels['bottom'], transform=fig.transFigure, **xlab_kwargs)
ylabel(ax2, ylabels['bottom'], transform=fig.transFigure, **ylab_kwargs)
letter_subplots((ax1, ax2), **letter_kwargs)
# Plot individual trials:
ax1.plot(data['scales'], data['trials'], **trial_kwargs)
# Plot mean and spread across trials:
ax2.plot(data['scales'], data['mean'], **line_kwargs)
ax2.fill_between(data['scales'], data['mean'] - data['spread'], data['mean'] + data['spread'], **fill_kwargs)
if save_path is not None:
fig.savefig(save_path)
plt.show()

314
python/fig_invariance_full.py
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@@ -5,46 +5,37 @@ import matplotlib.pyplot as plt
from itertools import product
from thunderhopper.modeltools import load_data
from color_functions import load_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, plot_line, plot_barcode, strip_zeros
from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
plot_line, plot_barcode, strip_zeros, time_bar, super_xlabel
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)
for i, ax in enumerate(axes):
plot_line(ax, time, snippets[:, ..., i], 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)
def plot_bi_snippets(axes, time, snippets, **kwargs):
for i, ax in enumerate(axes):
plot_barcode(ax, time, snippets[:, ..., i], **kwargs)
return None
# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = glob.glob(f'../data/processed/{target}*.npz')
stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
data_paths = glob.glob(f'../data/inv/full/{target}*.npz')
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
load_kwargs = dict(
files=stages,
keywords=['scales', 'measure', 'spread']
keywords=['scales', 'snip', 'measure']
)
save_path = None#'../figures/fig_invariance_full.pdf'
save_path = '../figures/fig_invariance_full.pdf'
# GRAPH SETTINGS:
fig_kwargs = dict(
figsize=(32/2.54, 16/2.54),
)
super_grid_kwargs = dict(
nrows=len(stages),
nrows=1,
ncols=3,
wspace=0,
hspace=0,
@@ -54,104 +45,124 @@ super_grid_kwargs = dict(
top=1
)
subfig_specs = dict(
**{stage: (slice(0, -1), i) for i, stage in enumerate(stages)},
big=(slice(None), -1)
snip=(slice(None), slice(0, -1)),
big=(slice(None), -1),
)
stage_grid_kwargs = dict(
nrows=1,
snip_grid_kwargs = dict(
nrows=len(stages),
ncols=None,
wspace=0.05,
hspace=0,
left=0.07,
wspace=0.1,
hspace=0.4,
left=0.15,
right=0.95,
bottom=0.15,
top=0.9
bottom=0.08,
top=0.95
)
big_grid_kwargs = dict(
nrows=1,
ncols=1,
wspace=0,
hspace=0,
left=0.15,
left=0.2,
right=0.96,
bottom=0.1,
bottom=0.08,
top=0.95
)
# PLOT SETTINGS:
colors = load_colors('../data/stage_colors.npz')
lw_snippets = dict(
colors['raw'] = "#000000"
lw = dict(
raw=0.25,
filt=0.25,
env=0.5,
log=0.5,
inv=0.5,
conv=0.5,
bi=0.01,
feat=2
env=0.25,
log=0.25,
inv=0.25,
conv=0.25,
bi=0,
feat=1,
big=3
)
lw_big = 3
xlabels = dict(
analysis='scale $\\alpha$',
snip='time [s]',
big='scale $\\alpha$',
)
xlab_analysis_kwargs = dict(
y=0.01,
ylabels = dict(
raw='$x$',
filt='$x_{\\text{filt}}$',
env='$x_{\\text{env}}$',
log='$x_{\\text{log}}$',
inv='$x_{\\text{inv}}$',
conv='$c_i$',
bi='$b_i$',
feat='$f_i$',
big='norm. intensity measure'
)
xlab_snip_kwargs = dict(
y=0,
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 $\\sigma_{\\alpha}\\,/\\,\\sigma_{\\min[\\alpha]}$',
xlab_big_kwargs = dict(
y=0,
fontsize=16,
ha='center',
va='bottom',
)
ylab_snip_kwargs = dict(
x=0.01,
x=0,
fontsize=20,
rotation=0,
ha='left',
va='center',
va='center'
)
ylab_analysis_kwargs = dict(
x=0.02,
ylab_big_kwargs = dict(
x=0,
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
yloc = dict(
raw=500,
filt=500,
env=250,
log=25,
inv=10,
conv=1,
feat=1,
)
title_kwargs = dict(
x=0.5,
yref=1,
ha='center',
va='top',
fontsize=16,
)
letter_snip_kwargs = dict(
x=0.02,
y=1,
ha='left',
va='top',
fontsize=22,
fontweight='bold'
)
letter_big_kwargs = dict(
x=0,
y=1,
ha='left',
va='top',
fontsize=22,
fontweight='bold'
)
bar_time = 5
bar_kwargs = dict(
y0=0.8,
y1=0.9,
color='k',
lw=0,
)
# EXECUTION:
for data_path in data_paths:
@@ -159,82 +170,95 @@ for data_path in data_paths:
# Load invariance data:
data, config = load_data(data_path, **load_kwargs)
t_full = np.arange(data['conv'].shape[0]) / config['env_rate']
t_full = np.arange(data['snip_raw'].shape[0]) / config['rate']
# Reduce snippet data to kernel subset:
data['conv'] = data['conv'][:, kernel_ind]
data['bi'] = data['bi'][:, kernel_ind]
data['feat'] = data['feat'][:, kernel_ind]
# Adjust grid parameters:
snip_grid_kwargs['ncols'] = data['example_scales'].size
# 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'] = 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']], data['example_scales']):
ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
pure_subfig.text(s='a', **letter_snip_kwargs)
# Prepare stage-specific snippet axes:
snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
ax = snip_subfig.add_subplot(snip_grid[i, j])
ax.set_xlim(t_full[0], t_full[-1])
hide_axis(ax, 'bottom')
if i == 0:
title = f'$\\alpha={strip_zeros(data["example_scales"][j])}$'
title_subplot(ax, title, ref=snip_subfig, **title_kwargs)
if j == 0:
ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
else:
hide_axis(ax, 'left')
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)
# 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(data['scales'].min(), 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)
# Prepare single 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])
big_ax.set_xlim(data['scales'].min(), data['scales'].max())
big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
big_ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
xlabel(big_ax, xlabels['big'], **xlab_big_kwargs, transform=big_subfig.transSubfigure)
ylabel(big_ax, ylabels['big'], **ylab_big_kwargs, transform=big_subfig.transSubfigure)
# Plot pure-song kernel response snippets:
plot_snippets(pure_axes[snip_specs['conv']], t_full, data['conv'],
c=colors['conv'], lw=lw_snippets['conv'])
# Plot raw snippets:
plot_snippets(snip_axes[0, :], t_full, data['snip_raw'],
c=colors['raw'], lw=lw['raw'])
# Plot pure-song binary snippets:
plot_bi_snippets(pure_axes[snip_specs['bi']], t_full, data['bi'],
color=colors['bi'], lw=0)
# Plot filtered snippets:
plot_snippets(snip_axes[1, :], t_full, data['snip_filt'],
c=colors['filt'], lw=lw['filt'])
# Plot pure-song feature snippets:
plot_snippets(pure_axes[snip_specs['feat']], t_full, data['feat'],
ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat'])
# Plot envelope snippets:
plot_snippets(snip_axes[2, :], t_full, data['snip_env'],
ymin=0, c=colors['env'], lw=lw['env'])
# Indicate time scale:
time_bar(pure_axes[snip_specs['conv']][0], bar_time, **bar_kwargs)
# Plot logarithmic snippets:
plot_snippets(snip_axes[3, :], t_full, data['snip_log'],
ymax=0, c=colors['log'], lw=lw['log'])
# # 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 invariant snippets:
plot_snippets(snip_axes[4, :], t_full, data['snip_inv'],
c=colors['inv'], lw=lw['inv'])
# # Plot noise-song binary snippets:
# plot_bi_snippets(noise_axes[snip_specs['bi']], t_full, noise_data['bi'],
# color=colors['bi'], lw=0)
# Plot kernel response snippets:
plot_snippets(snip_axes[5, :], t_full, data['snip_conv'],
c=colors['conv'], lw=lw['conv'])
# # 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'])
# Plot binary snippets:
plot_bi_snippets(snip_axes[6, :], t_full, data['snip_bi'],
color=colors['bi'], lw=lw['bi'])
# # Indicate time scale:
# time_bar(noise_axes[snip_specs['conv']][0], bar_time, **bar_kwargs)
# Plot feature snippets:
plot_snippets(snip_axes[7, :], t_full, data['snip_feat'],
ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
# Plot noise-song SD ratios (limited):
analysis_ax.plot(data['scales'], data['measure_conv'],
c=colors['conv'], lw=lw_analysis)
lower, upper = data['spread_conv']
analysis_ax.fill_between(data['scales'], lower, upper,
color=colors['conv'], **spread_kwargs)
analysis_ax.plot(data['scales'], data['measure_feat'],
c=colors['feat'], lw=lw_analysis)
lower, upper = data['spread_feat']
analysis_ax.fill_between(data['scales'], lower, upper,
color=colors['feat'], **spread_kwargs)
# Analysis results:
for stage in stages:
key = f'measure_{stage}'
if stage == 'bi':
continue
# Min-max normalization:
base_ind = np.argmin(data['scales'])
data[key] -= data[key][base_ind, ...]
data[key] /= data[key].max(axis=0)
# Condense measure:
if stage in ['conv', 'feat']:
data[key] = np.nanmedian(data[key], axis=1)
# Plot measure over scales:
big_ax.plot(data['scales'], data[key],
c=colors[stage], lw=lw['big'])
if save_path is not None:
fig.savefig(save_path)

280
python/fig_invariance_log-hp.py
View File

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

78
python/fig_invariance_thresh-lp_single.py
View File

@@ -6,17 +6,18 @@ 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,\
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
letter_subplot, letter_subplots, title_subplot
from IPython import embed
def add_snip_axes(fig, grid_kwargs):
def add_snip_axes(fig, grid_kwargs, snip_specs):
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()]
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()]
[hide_axis(ax, 'bottom') for ax in axes.flatten()]
return axes
@@ -88,14 +89,14 @@ subfig_specs = dict(
big=(slice(None), -1),
)
snip_grid_kwargs = dict(
nrows=len(stages),
nrows=len(stages) + 1,
ncols=None,
wspace=0.3,
hspace=0.1,
left=0.1,
hspace=0,
left=0.17,
right=0.93,
bottom=0.05,
top=0.85
bottom=0.15,
top=0.9
)
big_grid_kwargs = dict(
nrows=1,
@@ -107,6 +108,11 @@ 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]
# PLOT SETTINGS:
@@ -119,20 +125,27 @@ lw = dict(
big=4,
)
xlabels = dict(
snip='time [s]',
big='scale $\\alpha$',
)
xlab_big_kwargs = dict(
y=0.01,
fontsize=16,
ha='center',
va='bottom',
)
ylabels = dict(
conv='$c_i$',
bi='$b_i$',
feat='$f_i$',
big='$\\mu_f$',
)
xlab_snip_kwargs = dict(
y=0,
fontsize=16,
ha='center',
va='bottom',
)
xlab_big_kwargs = dict(
y=0,
fontsize=16,
ha='center',
va='bottom',
)
ylab_snip_kwargs = dict(
x=0.08,
fontsize=20,
@@ -141,7 +154,7 @@ ylab_snip_kwargs = dict(
va='center',
)
ylab_super_kwargs = dict(
x=0.005,
x=0,
fontsize=16,
ha='left',
va='center',
@@ -153,11 +166,21 @@ ylab_big_kwargs = dict(
va='top',
)
yloc = dict(
conv=100,
bi=1,
feat=1,
big=0.2,
)
title_kwargs = dict(
x=0.5,
yref=1,
ha='center',
va='top',
fontsize=16,
)
letter_snip_kwargs = dict(
x=0.01,
y=0.9,
x=0,
y=1,
ha='left',
va='top',
fontsize=22,
@@ -178,7 +201,7 @@ dist_fill_kwargs = dict(
color=colors['bi'],
lw=0.1,
)
bar_time = 0.5
bar_time = 0.1
bar_kwargs = dict(
y0=0.3,
y1=0.4,
@@ -190,7 +213,7 @@ kernel = np.array([
[2, 0.004],
[3, 0.002],
])[np.array([1])]
zoom_rel = np.array([0.5, 0.525])
zoom_rel = np.array([0.5, 0.515])
# EXECUTION:
@@ -232,17 +255,22 @@ for data_path in data_paths:
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)
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_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:
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']):
ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
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)
letter_subplots(snip_axes.keys(), **letter_snip_kwargs)
# Prepare analysis axis:

74
python/fig_invariance_thresh-lp_species.py
View File

@@ -213,38 +213,45 @@ lw = dict(
space_kwargs = dict(
s=30,
)
xlabs = dict(
xlabels = dict(
spec='scale $\\alpha$',
big='$\\mu_{f_1}$'
)
ylabs = dict(
ylabels = dict(
spec='$\\mu_f$',
big='$\\mu_{f_2}$',
bar='scale $\\alpha$',
)
xlab_spec_kwargs = dict(
y=0.005,
y=0,
fontsize=16,
ha='center',
va='bottom',
)
xlab_big_kwargs = dict(
y=0,
fontsize=20,
ha='center',
va='bottom',
)
ylab_spec_kwargs = dict(
x=0,
fontsize=20,
ha='left',
va='center',
)
xlab_big_kwargs = dict(
y=0.005,
fontsize=20,
ha='center',
va='bottom',
)
ylab_big_kwargs = dict(
x=0.03,
fontsize=20,
ha='center',
va='center',
)
ylab_cbar_kwargs = dict(
x=1,
fontsize=16,
ha='center',
va='bottom',
)
xloc = dict(
big=0.5,
)
@@ -252,18 +259,18 @@ yloc = dict(
spec=0.5,
big=0.5
)
spec_letter_kwargs = dict(
letter_spec_kwargs = dict(
x=0,
y=1.03,
yref=1,
ha='center',
va='bottom',
va='top',
fontsize=22,
)
big_letter_kwargs = dict(
letter_big_kwargs = dict(
x=0,
yref=spec_letter_kwargs['y'],
yref=1,
ha='center',
va='bottom',
va='top',
fontsize=22,
)
time_bar_kwargs = dict(
@@ -274,7 +281,7 @@ time_bar_kwargs = dict(
lw=0
)
cbar_bounds = [
0.8,
0.05,
big_grid_kwargs['bottom'],
0.15,
big_grid_kwargs['top'] - big_grid_kwargs['bottom']
@@ -297,11 +304,11 @@ for i, j in product(range(spec_grid_kwargs['nrows']), range(n_species)):
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['spec']))
ax.set_ylim(0, 1)
spec_axes[i, j] = ax
super_xlabel(xlabs['spec'], spec_subfig, spec_axes[-1, 0], spec_axes[-1, -1], **xlab_spec_kwargs)
super_ylabel(ylabs['spec'], spec_subfig, spec_axes[-1, 0], spec_axes[0, 0], **ylab_spec_kwargs)
super_xlabel(xlabels['spec'], spec_subfig, spec_axes[-1, 0], spec_axes[-1, -1], **xlab_spec_kwargs)
super_ylabel(ylabels['spec'], spec_subfig, spec_axes[-1, 0], spec_axes[0, 0], **ylab_spec_kwargs)
[hide_ticks(ax, side='bottom') for ax in spec_axes[0, :]]
[hide_ticks(ax, side='left') for ax in spec_axes[:, 1:].ravel()]
letter_subplots(spec_axes[0, :], labels='abc', **spec_letter_kwargs)
letter_subplots(spec_axes[0, :], labels='abc', ref=spec_subfig, **letter_spec_kwargs)
# Prepare kernel insets:
x0 = np.linspace(0, 1, kernels.shape[0] + 1)[:-1] + 1 / kernels.shape[0] / 2
@@ -325,23 +332,17 @@ for i in range(big_axes.size):
ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['big']))
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
ax.set_aspect(**anchor_kwargs)
# ax.set_ylabel(ylabs['big'], **ylab_big_kwargs)
ylabel(ax, ylabs['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
# ax.set_ylabel(ylabels['big'], **ylab_big_kwargs)
ylabel(ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
big_axes[i] = ax
super_xlabel(xlabs['big'], big_subfig, big_axes[1], big_axes[1], **xlab_big_kwargs)
super_xlabel(xlabels['big'], big_subfig, big_axes[1], big_axes[1], **xlab_big_kwargs)
hide_ticks(big_axes[0], side='bottom')
letter_subplot(big_axes[0], 'd', ref=spec_axes[0, 0], **big_letter_kwargs)
letter_subplot(big_axes[0], 'd', ref=big_subfig, **letter_big_kwargs)
# Prepare colorbars:
bar_ax = big_subfig.add_axes(cbar_bounds)
bar_axes = split_subplot(bar_ax, side=['right', 'right'], size=100, pad=0)
bar_axes = [bar_ax] + bar_axes
for ax in bar_axes:
ax.spines[['right', 'top']].set_visible(True)
hide_ticks(ax, 'bottom', ticks=False)
hide_ticks(ax, 'left', ticks=False)
bar_axes[-1].tick_params(axis='y', which='both', right=True, labelright=True)
# plt.show()
cbar_bounds[0] += big_axes[-1].get_position().x1
bar_axes = [big_subfig.add_axes(cbar_bounds)]
bar_axes.extend(split_subplot(bar_axes[0], side=['right', 'right'], size=100, pad=0))
# Plot results per species:
for i, pure_path in enumerate(pure_paths):
@@ -377,10 +378,6 @@ for i, pure_path in enumerate(pure_paths):
inset.set_ylim(ylims)
time_bar(insets[0], parent=spec_axes[0, 0], **time_bar_kwargs)
# Prepare shaded colors:
# factors = np.linspace(*shade_factors, scales.size)
# shaded_colors = shade_colors(spec_colors[i], factors)
# Plot pure feature space:
handle = big_axes[0].scatter(pure_measure[:, 0], pure_measure[:, 1],
c=scales, cmap=spec_cmaps[i], **space_kwargs)
@@ -391,6 +388,11 @@ for i, pure_path in enumerate(pure_paths):
# Indicate scale color code:
big_subfig.colorbar(handle, cax=bar_axes[i])
bar_axes[i].set_yscale('symlog', linthresh=scales[1], linscale=0.2)
if i < len(pure_paths) - 1:
hide_ticks(bar_axes[i], 'right', ticks=False)
else:
ylabel(bar_axes[i], ylabels['bar'], transform=big_subfig.transSubfigure, **ylab_cbar_kwargs)
if save_path is not None:
fig.savefig(save_path)

164
python/fig_pathway_stages.py
View File

@@ -4,7 +4,7 @@ import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data
from color_functions import load_colors
from plot_functions import prepare_fig, hide_axis, letter_subplots,\
from plot_functions import hide_axis, letter_subplots,\
ylabel, super_xlabel, plot_line, plot_barcode,\
indicate_zoom, assign_colors, reorder_traces
from IPython import embed
@@ -15,33 +15,28 @@ data_paths = glob.glob(f'../data/processed/{target}*.npz')
stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
save_path = '../figures/'
# PLOT SETTINGS:
# GRAPH SETTINGS:
fig_kwargs = dict(
width=32,
height=12,
figsize=(32/2.54, 16/2.54),
sharex='col',
subplot_kw=dict(
facecolor='none'
),
gridspec_kw=dict(
wspace=0.15,
hspace=0.3,
left=0.1,
right=0.99,
bottom=0.08,
top=0.95
),
)
grid_kwargs = dict(
wspace=0.15,
hspace=0.3,
left=0.1,
right=0.99,
bottom=0.1,
top=0.95
)
ylabels = dict(
filt=r'$x_{\text{filt}}$',
env=r'$x_{\text{env}}$',
log=r'$x_{\text{dB}}$',
inv=r'$x_{\text{adapt}}$',
conv=r'$c_i$',
bi=r'$b_i$',
feat=r'$f_i$'
)
ylab_kwargs = dict(
x=-0.23,
rotation=0,
ha='left',
va='center',
# PLOT SETTINGS:
fs = dict(
lab_tex=20,
lab_norm=16,
letter=22
)
colors = load_colors('../data/stage_colors.npz')
lw_full = dict(
@@ -62,22 +57,58 @@ lw_zoom = dict(
bi=0.2,
feat=1.5
)
loc_full = dict(
xlabels = dict(
super='time [s]',
)
ylabels = dict(
filt=r'$x_{\text{filt}}$',
env=r'$x_{\text{env}}$',
log=r'$x_{\text{dB}}$',
inv=r'$x_{\text{adapt}}$',
conv=r'$c_i$',
bi=r'$b_i$',
feat=r'$f_i$'
)
xlab_kwargs = dict(
y=0,
ha='center',
va='bottom',
fontsize=fs['lab_norm'],
)
ylab_kwargs = dict(
x=0,
rotation=0,
ha='left',
va='center',
fontsize=fs['lab_tex'],
)
xloc = dict(
full=2,
zoom=0.2
)
yloc_full = dict(
filt=0.2,
env=0.1,
log=20,
inv=10,
conv=1,
log=40,
inv=20,
conv=2,
feat=1
)
loc_zoom = dict(
yloc_zoom = dict(
filt=0.1,
env=0.02,
log=20,
inv=10,
conv=0.2,
log=40,
inv=20,
conv=0.5,
feat=1
)
letter_kwargs = dict(
x=0,
y=1,
ha='left',
va='bottom',
fontsize=fs['letter'],
)
zoom_rel = np.array([0.3, 0.4])
zoom_kwargs = dict(
color=3 * (0.85,),
@@ -130,61 +161,62 @@ for data_path in data_paths:
# PART I: PREPROCESSING STAGE
fig, axes = prepare_fig(4, 2, **fig_kwargs, **grid_kwargs)
super_xlabel('time [s]', fig, axes[0, 0], axes[0, -1])
fig, axes = plt.subplots(4, 2, **fig_kwargs)
super_xlabel(xlabels['super'], fig, axes[0, 0], axes[0, -1], **xlab_kwargs)
[hide_axis(ax, 'bottom') for ax in axes[:-1, :].ravel()]
# Bandpass-filtered signal:
ax_full, ax_zoom = axes[0, :]
ylabel(ax_full, ylabels['filt'], **ylab_kwargs)
plot_line(ax_full, t_full, data['filt'], c=colors['filt'], lw=lw_full['filt'], yloc=loc_full['filt'])
plot_line(ax_zoom, t_zoom, data['filt'][zoom_mask], c=colors['filt'], lw=lw_zoom['filt'], yloc=loc_zoom['filt'])
hide_axis(ax_full, 'bottom')
hide_axis(ax_zoom, 'bottom')
ylabel(ax_full, ylabels['filt'], transform=fig.transFigure, **ylab_kwargs)
plot_line(ax_full, t_full, data['filt'], c=colors['filt'], lw=lw_full['filt'], yloc=yloc_full['filt'])
plot_line(ax_zoom, t_zoom, data['filt'][zoom_mask], c=colors['filt'], lw=lw_zoom['filt'], yloc=yloc_zoom['filt'])
# Signal envelope:
ax_full, ax_zoom = axes[1, :]
ylabel(ax_full, ylabels['env'], **ylab_kwargs)
plot_line(ax_full, t_full, data['env'], ymin=0, c=colors['env'], lw=lw_full['env'], yloc=loc_full['env'])
plot_line(ax_zoom, t_zoom, data['env'][zoom_mask], ymin=0, c=colors['env'], lw=lw_zoom['env'], yloc=loc_zoom['env'])
hide_axis(ax_full, 'bottom')
hide_axis(ax_zoom, 'bottom')
ylabel(ax_full, ylabels['env'], transform=fig.transFigure, **ylab_kwargs)
plot_line(ax_full, t_full, data['env'], ymin=0, c=colors['env'], lw=lw_full['env'], yloc=yloc_full['env'])
plot_line(ax_zoom, t_zoom, data['env'][zoom_mask], ymin=0, c=colors['env'], lw=lw_zoom['env'], yloc=yloc_zoom['env'])
# Logarithmic envelope:
ax_full, ax_zoom = axes[2, :]
ylabel(ax_full, ylabels['log'], **ylab_kwargs)
plot_line(ax_full, t_full, data['log'], ymax=0, c=colors['log'], lw=lw_full['log'], yloc=loc_full['log'])
plot_line(ax_zoom, t_zoom, data['log'][zoom_mask], ymax=0, c=colors['log'], lw=lw_zoom['log'], yloc=loc_zoom['log'])
hide_axis(ax_full, 'bottom')
hide_axis(ax_zoom, 'bottom')
ylabel(ax_full, ylabels['log'], transform=fig.transFigure, **ylab_kwargs)
plot_line(ax_full, t_full, data['log'], ymax=0, c=colors['log'], lw=lw_full['log'], yloc=yloc_full['log'])
plot_line(ax_zoom, t_zoom, data['log'][zoom_mask], ymax=0, c=colors['log'], lw=lw_zoom['log'], yloc=yloc_zoom['log'])
# Adapted envelope:
ax_full, ax_zoom = axes[3, :]
ylabel(ax_full, ylabels['inv'], **ylab_kwargs)
plot_line(ax_full, t_full, data['inv'], c=colors['inv'], lw=lw_full['inv'], yloc=loc_full['inv'])
plot_line(ax_zoom, t_zoom, data['inv'][zoom_mask], c=colors['inv'], lw=lw_zoom['inv'], yloc=loc_zoom['inv'])
ylabel(ax_full, ylabels['inv'], transform=fig.transFigure, **ylab_kwargs)
plot_line(ax_full, t_full, data['inv'], c=colors['inv'], lw=lw_full['inv'], yloc=yloc_full['inv'])
plot_line(ax_zoom, t_zoom, data['inv'][zoom_mask], c=colors['inv'], lw=lw_zoom['inv'], yloc=yloc_zoom['inv'])
# Posthoc adjustments:
ax_full.set_xlim(t_full[0], t_full[-1])
ax_full.xaxis.set_major_locator(plt.MultipleLocator(xloc['full']))
ax_zoom.set_xlim(t_zoom[0], t_zoom[-1])
ax_zoom.xaxis.set_major_locator(plt.MultipleLocator(xloc['zoom']))
indicate_zoom(fig, axes[0, 0], axes[-1, 0], zoom_abs, **zoom_kwargs)
indicate_zoom(fig, axes[0, 1], axes[-1, 1], zoom_abs, **zoom_kwargs)
letter_subplots(axes[:, 0])
letter_subplots(axes[:, 0], **letter_kwargs)
if save_path is not None:
fig.savefig(f'{save_path}fig_pre_stages.pdf')
# Update parameters:
fig_kwargs['gridspec_kw'].update(
left=0.06,
)
# PART II: FEATURE EXTRACTION STAGE:
fig, axes = prepare_fig(3, 2, **fig_kwargs, **grid_kwargs)
super_xlabel('time [s]', fig, axes[0, 0], axes[0, -1])
fig, axes = plt.subplots(3, 2, **fig_kwargs)
super_xlabel(xlabels['super'], fig, axes[0, 0], axes[0, -1], **xlab_kwargs)
# Convolutional filter responses:
ax_full, ax_zoom = axes[0, :]
ylabel(ax_full, ylabels['conv'], **ylab_kwargs)
ylabel(ax_full, ylabels['conv'], transform=fig.transFigure, **ylab_kwargs)
signal = data['conv'][:, kern_inds]
handles = plot_line(ax_full, t_full, signal, lw=lw_full['conv'], yloc=loc_full['conv'])
handles = plot_line(ax_full, t_full, signal, lw=lw_full['conv'], yloc=yloc_full['conv'])
assign_colors(handles, kern_specs[:, 0], conv_colors)
reorder_traces(handles, signal)
handles = plot_line(ax_zoom, t_zoom, signal[zoom_mask, :], lw=lw_zoom['conv'], yloc=loc_zoom['conv'])
handles = plot_line(ax_zoom, t_zoom, signal[zoom_mask, :], lw=lw_zoom['conv'], yloc=yloc_zoom['conv'])
assign_colors(handles, kern_specs[:, 0], conv_colors)
reorder_traces(handles, signal[zoom_mask, :])
hide_axis(ax_full, 'bottom')
@@ -192,7 +224,7 @@ for data_path in data_paths:
# Binary responses:
ax_full, ax_zoom = axes[1, :]
ylabel(ax_full, ylabels['bi'], **ylab_kwargs)
ylabel(ax_full, ylabels['bi'], transform=fig.transFigure, **ylab_kwargs)
signal = data['bi'][:, kern_inds]
handles = plot_barcode(ax_full, t_full, signal, lw=lw_full['bi'])
assign_colors(handles, kern_specs[:, 0], bi_colors)
@@ -201,19 +233,21 @@ for data_path in data_paths:
# Finalized features:
ax_full, ax_zoom = axes[2, :]
ylabel(ax_full, ylabels['feat'], **ylab_kwargs)
ylabel(ax_full, ylabels['feat'], transform=fig.transFigure, **ylab_kwargs)
signal = data['feat'][:, kern_inds]
handles = plot_line(ax_full, t_full, signal, ymin=0, ymax=1, c=colors['feat'], lw=lw_full['feat'], yloc=loc_full['feat'])
handles = plot_line(ax_full, t_full, signal, ymin=0, ymax=1, c=colors['feat'], lw=lw_full['feat'], yloc=yloc_full['feat'])
assign_colors(handles, kern_specs[:, 0], feat_colors)
handles = plot_line(ax_zoom, t_zoom, signal[zoom_mask, :], ymin=0, ymax=1, c=colors['feat'], lw=lw_zoom['feat'], yloc=loc_zoom['feat'])
handles = plot_line(ax_zoom, t_zoom, signal[zoom_mask, :], ymin=0, ymax=1, c=colors['feat'], lw=lw_zoom['feat'], yloc=yloc_zoom['feat'])
assign_colors(handles, kern_specs[:, 0], feat_colors)
# Posthoc adjustments:
ax_full.set_xlim(t_full[0], t_full[-1])
ax_full.xaxis.set_major_locator(plt.MultipleLocator(xloc['full']))
ax_zoom.set_xlim(t_zoom[0], t_zoom[-1])
ax_zoom.xaxis.set_major_locator(plt.MultipleLocator(xloc['zoom']))
indicate_zoom(fig, axes[0, 0], axes[-1, 0], zoom_abs, **zoom_kwargs)
indicate_zoom(fig, axes[0, 1], axes[-1, 1], zoom_abs, **zoom_kwargs)
letter_subplots(axes[:, 0])
letter_subplots(axes[:, 0], **letter_kwargs)
if save_path is not None:
fig.savefig(f'{save_path}fig_feat_stages.pdf')
plt.show()

103
python/plot_functions.py
View File

@@ -2,21 +2,6 @@ import string
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.transforms import BboxTransformTo
from itertools import product
def prepare_fig(nrows, ncols, width=8, height=None, rheight=2, unit=1/2.54,
left=0.01, right=0.95, bottom=0.01, top=0.95,
wspace=0.4, hspace=0.4):
if height is None:
height = rheight * nrows
fig = plt.figure(figsize=(width * unit, height * unit))
grid = fig.add_gridspec(nrows=nrows, ncols=ncols, wspace=wspace, hspace=hspace,
left=left, right=right, top=top, bottom=bottom)
axes = np.zeros((nrows, ncols), dtype=object)
for i, j in product(range(nrows), range(ncols)):
axes[i, j] = fig.add_subplot(grid[i, j])
axes[i, j].set_facecolor('none')
return fig, axes
def hide_ticks(ax, side='bottom', ticks=True):
axis = 'x' if side in ['top', 'bottom'] else 'y'
@@ -31,6 +16,20 @@ def hide_axis(ax, side='bottom'):
which='both', **params)
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 = BboxTransformTo(artist.bbox)
if xref is not None or yref is not None:
transform = BboxTransformTo(ref.bbox) + 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
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)
@@ -141,6 +140,8 @@ def plot_line(ax, time, signal, ymin=None, ymax=None, xmin=None, xmax=None,
return handles
def plot_barcode(ax, time, binary, offset=0.5, xmin=None, xmax=None, **kwargs):
if binary.ndim == 1:
binary = binary[:, None]
lower, upper, handles = 0, 1, []
for i in range(binary.shape[1]):
h = ax.fill_between(time, lower, upper, where=binary[:, i], **kwargs)
@@ -186,21 +187,63 @@ 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, transform=None, **kwargs):
t_lims = ax.get_xlim()
span = t_lims[1] - t_lims[0]
if parent is not None or transform is not None:
if transform is None:
transform = BboxTransformTo(parent.bbox)
kwargs['transform'] = transform
transform = ax.transData + transform.inverted()
x0 = transform.transform((t_lims[0], 0))[0]
x1 = transform.transform((t_lims[0] + dur, 0))[0]
dur = x1 - x0
span = 1
elif parent is None:
def time_bar(ax, dur, y0=0.9, y1=0.95, xshift=0.5, parent=None, **kwargs):
if parent is None:
parent = ax
x0 = (span - dur) * xshift
x1 = x0 + dur
trans_parent = BboxTransformTo(parent.bbox)
kwargs['transform'] = trans_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
def zoom_inset(ax, inset, handle, x0=None, x1=None, y0=None, y1=None, ref='x',
transform = None,
low_left=False, up_left=False, low_right=False, up_right=False,
props=['c', 'lw', 'ls', 'zorder', 'alpha'], **kwargs):
if not kwargs:
kwargs = dict(edgecolor='k', alpha=1, lw=2)
if transform is not None:
transform = transform + ax.transData.inverted()
xlims = ax.get_xlim()
ylims = ax.get_ylim()
if x0 is None:
x0 = xlims[0]
elif transform is not None:
x0 = transform.transform((x0, 0))[0]
if x1 is None:
x1 = xlims[1]
elif transform is not None:
x1 = transform.transform((x1, 0))[0]
if y0 is None:
y0 = ylims[0]
elif transform is not None:
y0 = transform.transform((0, y0))[1]
if y1 is None:
y1 = ylims[1]
elif transform is not None:
y1 = transform.transform((0, y1))[1]
inset.set_xlim(x0, x1)
inset.set_ylim(y0, y1)
x = handle.get_xdata()
y = handle.get_ydata()
if ref == 'x':
zoom_inds = (x >= x0) & (x <= x1)
elif ref == 'y':
zoom_inds = (y >= y0) & (y <= y1)
x = x[zoom_inds]
y = y[zoom_inds]
inset_handle = inset.plot(x, y)[0]
inset_handle.set(**{prop: plt.getp(handle, prop) for prop in props})
elements = ax.indicate_inset_zoom(inset, **kwargs)
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

82
python/save_env_sd_conversion.py Normal file
View File

@@ -0,0 +1,82 @@
import glob
import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data
from thunderhopper.filters import sosfilter
from IPython import embed
# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_path = glob.glob(f'../data/processed/{target}*.npz')[0]
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)
n_trials = 10
tol_to_one = 0.1
# EXECUTION:
# Load signal data:
data, config = load_data(data_path, files='filt')
signal, rate = data['filt'], config['rate']
# Reduce to song segment and normalize:
time = np.arange(signal.shape[0]) / rate
start, end = data['songs_0'].ravel()
segment = (time >= start) & (time <= end)
signal /= signal[segment].std()
# Get rescaled signals (time, scale):
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),
)
# Analyze piece-wise:
rng = np.random.default_rng()
for i, sigma in enumerate(sd_inputs):
print(f'Testing SD: {sigma:.3f} ...')
# Add Gaussian noise of given SD to rescaled signals (time, scale, trial):
mix = signal[..., None] + rng.normal(0, sigma, (*signal.shape, n_trials))
# Get mixture envelopes (time, scale, trial):
mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
padtype='even', padlen=config['padlen'])[segment, ...]
# Get noise remainders of mean over trials:
mix -= mix.mean(axis=-1, keepdims=True)
# Estimate noise SD:
sd = mix.std(axis=0)
mean_sd = sd.mean(axis=-1)
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})')
storage['sd_factor'][0] = sigma
storage['trials'][:, :] = sd
storage['mean'][:] = mean_sd
storage['spread'][:] = sd.std(axis=-1)
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()

59
python/save_inv_data_full.py
View File

@@ -3,6 +3,7 @@ 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.filtertools import find_kern_specs
from thunderhopper.model import process_signal
from IPython import embed
@@ -13,9 +14,20 @@ stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
save_path = '../data/inv/full/'
# ANALYSIS SETTINGS:
example_scales = np.array([0, 0.5, 1, 5, 10])
scales = np.geomspace(0.01, 10, 100)
example_scales = np.array([0, 1, 10, 50])
scales = np.geomspace(0.01, 100, 100)
scales = np.unique(np.concatenate((scales, example_scales)))
kernels = np.array([
[1, 0.002],
[-1, 0.002],
[2, 0.004],
[-2, 0.004],
[3, 0.032],
[-3, 0.032]
])
kernels = None
types = None#np.array([-1])
sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
@@ -25,6 +37,13 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
data, config = load_data(data_path, files='raw')
song, rate = data['raw'], config['rate']
# Reduce to kernel subset:
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
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]
config['feat_thresh'] = config['feat_thresh'][kern_inds]
# Get song segment to be analyzed:
time = np.arange(song.shape[0]) / rate
start, end = data['songs_0'].ravel()
@@ -42,14 +61,14 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
shape_low = (song.shape[0], example_scales.size)
shape_high = (song.shape[0], config['k_specs'].shape[0], example_scales.size)
snippets = dict(
raw=np.zeros(shape_low, dtype=float),
filt=np.zeros(shape_low, dtype=float),
env=np.zeros(shape_low, dtype=float),
log=np.zeros(shape_low, dtype=float),
inv=np.zeros(shape_low, dtype=float),
conv=np.zeros(shape_high, dtype=float),
bi=np.zeros(shape_high, dtype=float),
feat=np.zeros(shape_high, dtype=float)
snip_raw=np.zeros(shape_low, dtype=float),
snip_filt=np.zeros(shape_low, dtype=float),
snip_env=np.zeros(shape_low, dtype=float),
snip_log=np.zeros(shape_low, dtype=float),
snip_inv=np.zeros(shape_low, dtype=float),
snip_conv=np.zeros(shape_high, dtype=float),
snip_bi=np.zeros(shape_high, dtype=float),
snip_feat=np.zeros(shape_high, dtype=float)
)
# Prepare measure storage:
@@ -82,13 +101,31 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
# Log snippet data:
if scale in example_scales:
scale_ind = np.nonzero(example_scales == scale)[0][0]
snippets[stage][:, ..., scale_ind] = signals[stage]
snippets[f'snip_{stage}'][:, ..., scale_ind] = signals[stage]
# Log intensity measure per stage (excluding binary):
if stage in ['raw', 'filt', 'env', 'log', 'inv', 'conv']:
measures[key][i] = signals[stage][segment, ...].std(axis=0)
elif stage == 'feat':
measures[key][i] = signals[stage][segment, :].mean(axis=0)
# thresh_y = np.percentile(measures['measure_feat'], 99, axis=0)
# kern_types = np.unique()
# thresh_x = np.zeros(thresh_y.shape, dtype=float)
# for i, thresh in enumerate(thresh_y):
# if thresh < 0.1:
# thresh_x[i] = scales[-1]
# continue
# mask = (measures['measure_feat'][:, i] < thresh)
# thresh_x[i] = scales[np.nonzero(mask)[0][-1]]
# inds = np.argsort(thresh_x)
# print(config['k_specs'][inds, :])
# fig, axes = plt.subplots(1, 2)
# axes[0].plot(snippets['snip_feat'][:, inds, -1])
# axes[1].plot(scales, measures['measure_feat'][:, inds])
# plt.show()
# embed()
# Save analysis results:
if save_path is not None:

33
python/save_inv_data_log-hp.py
View File

@@ -3,7 +3,6 @@ import numpy as np
from thunderhopper.modeltools import load_data, save_data
from thunderhopper.filetools import crop_paths
from thunderhopper.filters import decibel, sosfilter
from thunderhopper.model import extract_env
from IPython import embed
# GENERAL SETTINGS:
@@ -12,21 +11,18 @@ data_paths = glob.glob(f'../data/processed/{target}*.npz')
save_path = '../data/inv/log_hp/'
# ANALYSIS SETTINGS:
add_noise = True
single_db_ref = True
example_scales = np.array([0, 0.1, 1, 10, 100, 200])
scales = np.geomspace(0.1, 1000, 100)
if not add_noise:
example_scales = example_scales[example_scales > 0]
add_noise = False
example_scales = np.array([0.1, 1, 10, 30, 100, 300])
scales = np.geomspace(0.1, 10000, 1000)
scales = np.unique(np.concatenate((scales, example_scales)))
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
print(f'Processing {name}')
# Get song envelope:
data, config = load_data(data_path, files='env')
song, rate = data['env'], config['env_rate']
# Get filtered song (prior to envelope extraction):
data, config = load_data(data_path, files='filt')
song, rate = data['filt'], config['rate']
# Get song segment to be analyzed:
time = np.arange(song.shape[0]) / rate
@@ -40,19 +36,20 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
mix = song[:, None] * scales[None, :]
if add_noise:
# Add normalized noise envelope:
# Add normalized envelopenoise:
rng = np.random.default_rng()
noise = rng.normal(size=song.shape)
noise = extract_env(noise, rate, config=config)
noise = rng.normal(scale=1, size=song.shape)
noise /= noise[segment].std()
mix += noise[:, None]
# Process mixture:
mix_log = decibel(mix, axis=None if single_db_ref else 0)
mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
padtype='even', padlen=config['padlen'])
mix_log = decibel(mix, ref=1)
mix_inv = sosfilter(mix_log, rate, config['inv_fcut'], 'hp',
padtype='constant', padlen=config['padlen'])
# Get "intensity measure" per stage:
# Get intensity measure per stage:
measure_env = mix[segment, :].std(axis=0)
measure_log = mix_log[segment, :].std(axis=0)
measure_inv = mix_inv[segment, :].std(axis=0)
@@ -63,9 +60,9 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
data = dict(
scales=scales,
example_scales=example_scales,
env=mix[:, save_inds],
log=mix_log[:, save_inds],
inv=mix_inv[:, save_inds],
snip_env=mix[:, save_inds],
snip_log=mix_log[:, save_inds],
snip_inv=mix_inv[:, save_inds],
measure_env=measure_env,
measure_log=measure_log,
measure_inv=measure_inv,

57
python/save_noise_data.py Normal file
View File

@@ -0,0 +1,57 @@
import numpy as np
from thunderhopper.model import configuration, process_signal
from thunderhopper.modeltools import load_data
from IPython import embed
## SETTINGS:
# General:
save_path = '../data/processed/white_noise'
stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
sds = [1]
dur = 10
# Interactivity:
reload_saved = False
# Processing:
rate = 44100.0
env_rate = 44100.0
feat_rate = 44100.0
sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5,
6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
config = configuration(env_rate, feat_rate, types=types, sigmas=sigmas)
config.update({
'bp_fcut': None,
'rate_ratio': None,
'env_fcut': 250,
'inv_fcut': 5,
'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2,
'feat_fcut': 0.5,
'label_channels': 0,
'label_thresh': 0.5,
})
## PREPARATION:
n_samples = int(dur * env_rate)
rng = np.random.default_rng()
# PROCESSING:
for sd in sds:
print('Processing: SD =', sd)
# Generate white noise signal:
noise = rng.normal(loc=0, scale=sd, size=n_samples)
# Fetch and store representations:
save = None if save_path is None else save_path + f'_sd-{sd}.npz'
process_signal(config, stages, signal=noise, rate=rate, save=save)
# Cross-control:
if reload_saved:
data, params = load_data(save, stages, ['songs', 'noise'])
embed()
print('Done.')

2
python/save_snippet_data.py
View File

@@ -30,7 +30,7 @@ config.update({
'rate_ratio': None,
'env_fcut': 250,
'inv_fcut': 5,
'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.1,
'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2,
'feat_fcut': 0.5,
'label_channels': 0,
'label_thresh': 0.5,