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Polished most of 2 new methods figures.

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j-hartling 2026-02-19 16:34:37 +01:00
parent 652621f782
commit 5afd073de9
13 changed files with 195 additions and 223 deletions
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figures/pathway_stages_feat.pdf
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@ -94,8 +96,6 @@
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@ -134,8 +136,6 @@
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@ -172,11 +172,15 @@
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\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces \textbf {Schematic organisation of the song recognition pathway in grasshoppers compared to the structure of the functional model pathway.} \textbf {a}:~Simplified course of the pathway in the grasshopper, from the tympanal membrane over receptor neurons, local interneurons, and ascending neurons further towards the supraesophageal ganglion. \textbf {b}:~Schematic of synaptic connections between the three neuronal populations within the metathoracic ganglion. \textbf {c}:~Network representation of neuronal connectivity. \textbf {d}:~Flow diagram of the different signal representations and transformations along the model pathway. All representations are time-varying. 1st half: Preprocessing stage (one-dimensional). 2nd half: Feature extraction stage (high-dimensional). }}{6}{}\protected@file@percent } \@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces \textbf {Schematic organisation of the song recognition pathway in grasshoppers compared to the structure of the functional model pathway.} \textbf {a}:~Simplified course of the pathway in the grasshopper, from the tympanal membrane over receptor neurons, local interneurons, and ascending neurons further towards the supraesophageal ganglion. \textbf {b}:~Schematic of synaptic connections between the three neuronal populations within the metathoracic ganglion. \textbf {c}:~Network representation of neuronal connectivity. \textbf {d}:~Flow diagram of the different signal representations and transformations along the model pathway. All representations are time-varying. 1st half: Preprocessing stage (one-dimensional). 2nd half: Feature extraction stage (high-dimensional). }}{6}{}\protected@file@percent }
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main.tex
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@ -1,6 +1,6 @@
\documentclass[a4paper, 12pt]{article} \documentclass[a4paper, 12pt]{article}
\usepackage[left=2.5cm,right=2.5cm,top=2cm,bottom=2cm,includeheadfoot]{geometry} \usepackage[left=2cm,right=2cm,top=2cm,bottom=2cm,includeheadfoot]{geometry}
\usepackage[onehalfspacing]{setspace} \usepackage[onehalfspacing]{setspace}
\usepackage{graphicx} \usepackage{graphicx}
\usepackage{svg} \usepackage{svg}
@ -409,10 +409,17 @@ intensity-adapted envelope $\adapt(t)$ is then passed on from the local
interneurons to the ascending neurons, where it serves as the basis for the interneurons to the ascending neurons, where it serves as the basis for the
following feature extraction stage. following feature extraction stage.
% Cite somewhere:
\begin{figure}[!ht] \begin{figure}[!ht]
\centering \centering
\includegraphics[width=\textwidth]{figures/pathway_stages_pre.pdf} \includegraphics[width=\textwidth]{figures/pathway_stages_pre.pdf}
\caption{} \caption{\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.
}
\label{fig:pre} \label{fig:pre}
\end{figure} \end{figure}
\FloatBarrier \FloatBarrier
@ -516,10 +523,16 @@ the resulting high-dimensional feature space, different species-specific song
patterns are characterized by a distinct combination of feature values, which patterns are characterized by a distinct combination of feature values, which
can be read out by a simple linear classifier. can be read out by a simple linear classifier.
% Cite somewhere:
\begin{figure}[!ht] \begin{figure}[!ht]
\centering \centering
\includegraphics[width=\textwidth]{figures/pathway_stages_feat.pdf} \includegraphics[width=\textwidth]{figures/pathway_stages_feat.pdf}
\caption{} \caption{\textbf{Representations of a song of \textit{O. rufipes} during
the feature extraction stage.}
\textbf{a}:~Kernel-specific filter responses.
\textbf{b}:~Binary responses.
\textbf{c}:~Finalized features.
}
\label{fig:stages_feat} \label{fig:stages_feat}
\end{figure} \end{figure}
\FloatBarrier \FloatBarrier

142
python/fig_pathway_stages.py
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@ -1,10 +1,12 @@
from math import log
import plotstyle_plt import plotstyle_plt
import glob import glob
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from itertools import product from itertools import product
from thunderhopper.modeltools import load_data from thunderhopper.modeltools import load_data
from color_functions import load_colors, shade_colors from color_functions import load_colors
from IPython import embed from IPython import embed
def prepare_fig(nrows, ncols, width=8, height=None, rheight=2, def prepare_fig(nrows, ncols, width=8, height=None, rheight=2,
@ -45,14 +47,19 @@ def ylimits(ax, signal, minval=None, maxval=None, pad=0.05):
limits[1] += (limits[1] - limits[0]) * pad limits[1] += (limits[1] - limits[0]) * pad
return ax.set_ylim(limits) return ax.set_ylim(limits)
def super_xlabel(label, fig, high_ax, low_ax, **kwargs): def ylabel(ax, label, x=-0.23, fontsize=20):
x = (low_ax.get_position().x0 + high_ax.get_position().x1) / 2 ax.set_ylabel(label, fontsize=fontsize, rotation=0, ha='left', va='center')
fig.supxlabel(label, x=x, **kwargs) ax.yaxis.set_label_coords(x, 0.5)
return None return None
def super_ylabel(label, fig, high_ax, low_ax, **kwargs): def super_xlabel(label, fig, high_ax, low_ax, y=0.005, **kwargs):
x = (low_ax.get_position().x0 + high_ax.get_position().x1) / 2
fig.supxlabel(label, x=x, y=y, **kwargs)
return None
def super_ylabel(label, fig, high_ax, low_ax, x=0.005, **kwargs):
y = (low_ax.get_position().y0 + high_ax.get_position().y1) / 2 y = (low_ax.get_position().y0 + high_ax.get_position().y1) / 2
fig.supylabel(label, y=y, **kwargs) fig.supylabel(label, x=x, y=y, **kwargs)
return None return None
def hide_axis(ax, side='bottom'): def hide_axis(ax, side='bottom'):
@ -63,10 +70,11 @@ def hide_axis(ax, side='bottom'):
return None return None
def plot_line(ax, time, signal, ymin=None, ymax=None, xmin=None, xmax=None, def plot_line(ax, time, signal, ymin=None, ymax=None, xmin=None, xmax=None,
xpad=None, ypad=0.05, **kwargs): xpad=None, ypad=0.05, yloc=None, **kwargs):
handles = ax.plot(time, signal, **kwargs) handles = ax.plot(time, signal, **kwargs)
xlimits(ax, time, minval=xmin, maxval=xmax, pad=xpad) xlimits(ax, time, minval=xmin, maxval=xmax, pad=xpad)
ylimits(ax, signal, minval=ymin, maxval=ymax, pad=ypad) ylimits(ax, signal, minval=ymin, maxval=ymax, pad=ypad)
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc))
return handles return handles
def plot_barcode(ax, time, binary, offset=0.5, xmin=None, xmax=None, **kwargs): def plot_barcode(ax, time, binary, offset=0.5, xmin=None, xmax=None, **kwargs):
@ -83,7 +91,8 @@ def plot_barcode(ax, time, binary, offset=0.5, xmin=None, xmax=None, **kwargs):
upper += offset + 1 upper += offset + 1
xlimits(ax, time, minval=xmin, maxval=xmax) xlimits(ax, time, minval=xmin, maxval=xmax)
ax.set_ylim(0, upper) ax.set_ylim(0, upper)
ax.axis('off') hide_axis(ax, 'bottom')
hide_axis(ax, 'left')
return handles return handles
def indicate_zoom(fig, high_ax, low_ax, zoom_abs, **kwargs): def indicate_zoom(fig, high_ax, low_ax, zoom_abs, **kwargs):
@ -100,6 +109,21 @@ def indicate_zoom(fig, high_ax, low_ax, zoom_abs, **kwargs):
def assign_colors(handles, types, colors): def assign_colors(handles, types, colors):
for handle, type_id in zip(handles, types): for handle, type_id in zip(handles, types):
handle.set_color(colors[str(int(type_id))]) handle.set_color(colors[str(int(type_id))])
return None
def reorder_traces(handles, signal, zlow=2, zhigh=2.5):
inds = np.argsort(signal.std(axis=0))
zorders = np.linspace(zlow, zhigh, len(inds))[::-1]
for ind, z in zip(inds, zorders):
handles[ind].set_zorder(z)
return None
def letter_subplots(axes, labels='abcd', x=0.02, y=1, ha='left', va='bottom',
fontsize=16, fontweight='bold', **kwargs):
for ax, label in zip(axes, labels):
ax.text(x, y, label, transform=ax.transAxes, ha=ha, va=va,
fontsize=fontsize, fontweight=fontweight, **kwargs)
return None
# GENERAL SETTINGS: # GENERAL SETTINGS:
@ -118,32 +142,57 @@ grid_kwargs = dict(
wspace=0.15, wspace=0.15,
hspace=0.3, hspace=0.3,
left=0.1, left=0.1,
right=0.95, right=0.99,
bottom=0.1, bottom=0.1,
top=0.95 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'$f_i$'
)
colors = load_colors('../data/stage_colors.npz') colors = load_colors('../data/stage_colors.npz')
lw_full = dict( lw_full = dict(
filt=0.25, filt=0.25,
env=0.5, env=0.5,
log=0.5, log=0.5,
inv=0.5, inv=0.5,
conv=0.5, conv=0.25,
bi=1, bi=0,
feat=1 feat=2
) )
lw_zoom = dict( lw_zoom = dict(
filt=0.25, filt=0.5,
env=0.75, env=1,
log=0.75, log=1,
inv=0.75, inv=1,
conv=0.5, conv=1.5,
bi=1, bi=0,
feat=0.75 feat=2
) )
loc_full = dict(
filt=0.2,
env=0.1,
log=20,
inv=10,
conv=1,
feat=1
)
loc_zoom = dict(
filt=0.1,
env=0.02,
log=20,
inv=10,
conv=0.2,
feat=1
)
zoom_rel = np.array([0.3, 0.4]) zoom_rel = np.array([0.3, 0.4])
zoom_kwargs = dict( zoom_kwargs = dict(
color=(0.85, 0.85, 0.85), color=3 * (0.85,),
zorder=0, zorder=0,
linewidth=0 linewidth=0
) )
@ -152,7 +201,7 @@ zoom_kwargs = dict(
# [3, 0.032] # [3, 0.032]
# ]) # ])
kern_types = np.array([1, -1, 2, -2, 3, -3, 4, -4]) kern_types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
kern_sigmas = np.array([0.008, 0.016, 0.032]) kern_sigmas = np.array([0.008, 0.032])
kernels = np.array([[k, s] for k in kern_types for s in kern_sigmas]) kernels = np.array([[k, s] for k in kern_types for s in kern_sigmas])
conv_colors = load_colors('../data/conv_colors.npz') conv_colors = load_colors('../data/conv_colors.npz')
bi_colors = load_colors('../data/bi_colors.npz') bi_colors = load_colors('../data/bi_colors.npz')
@ -180,43 +229,44 @@ for data_path in data_paths:
# PART I: PREPROCESSING STAGE # PART I: PREPROCESSING STAGE
fig, axes = prepare_fig(4, 2, **fig_kwargs, **grid_kwargs) fig, axes = prepare_fig(4, 2, **fig_kwargs, **grid_kwargs)
super_xlabel('time [s]', fig, axes[0, 0], axes[0, -1]) super_xlabel('time [s]', fig, axes[0, 0], axes[0, -1])
super_ylabel('amplitude', fig, axes[0, 0], axes[-1, 0])
# Bandpass-filtered signal: # Bandpass-filtered signal:
ax_full, ax_zoom = axes[0, :] ax_full, ax_zoom = axes[0, :]
plot_line(ax_full, t_full, data['filt'], c=colors['filt'], lw=lw_full['filt']) ylabel(ax_full, ylabels['filt'])
plot_line(ax_zoom, t_zoom, data['filt'][zoom_mask], c=colors['filt'], lw=lw_zoom['filt']) 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_full, 'bottom')
hide_axis(ax_zoom, 'bottom') hide_axis(ax_zoom, 'bottom')
hide_axis(ax_zoom, 'left')
# Signal envelope: # Signal envelope:
ax_full, ax_zoom = axes[1, :] ax_full, ax_zoom = axes[1, :]
plot_line(ax_full, t_full, data['env'], ymin=0, c=colors['env'], lw=lw_full['env']) ylabel(ax_full, ylabels['env'])
plot_line(ax_zoom, t_zoom, data['env'][zoom_mask], ymin=0, c=colors['env'], lw=lw_zoom['env']) 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_full, 'bottom')
hide_axis(ax_zoom, 'bottom') hide_axis(ax_zoom, 'bottom')
hide_axis(ax_zoom, 'left')
# Logarithmic envelope: # Logarithmic envelope:
ax_full, ax_zoom = axes[2, :] ax_full, ax_zoom = axes[2, :]
plot_line(ax_full, t_full, data['log'], ymax=0, c=colors['log'], lw=lw_full['log']) ylabel(ax_full, ylabels['log'])
plot_line(ax_zoom, t_zoom, data['log'][zoom_mask], ymax=0, c=colors['log'], lw=lw_zoom['log']) 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_full, 'bottom')
hide_axis(ax_zoom, 'bottom') hide_axis(ax_zoom, 'bottom')
hide_axis(ax_zoom, 'left')
# Adapted envelope: # Adapted envelope:
ax_full, ax_zoom = axes[3, :] ax_full, ax_zoom = axes[3, :]
plot_line(ax_full, t_full, data['inv'], c=colors['inv'], lw=lw_full['inv']) ylabel(ax_full, ylabels['inv'])
plot_line(ax_zoom, t_zoom, data['inv'][zoom_mask], c=colors['inv'], lw=lw_zoom['inv']) plot_line(ax_full, t_full, data['inv'], c=colors['inv'], lw=lw_full['inv'], yloc=loc_full['inv'])
hide_axis(ax_zoom, 'left') plot_line(ax_zoom, t_zoom, data['inv'][zoom_mask], c=colors['inv'], lw=lw_zoom['inv'], yloc=loc_zoom['inv'])
# Posthoc adjustments: # Posthoc adjustments:
ax_full.set_xlim(t_full[0], t_full[-1]) ax_full.set_xlim(t_full[0], t_full[-1])
ax_zoom.set_xlim(t_zoom[0], t_zoom[-1]) ax_zoom.set_xlim(t_zoom[0], t_zoom[-1])
indicate_zoom(fig, axes[0, 0], axes[-1, 0], zoom_abs, **zoom_kwargs) 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) indicate_zoom(fig, axes[0, 1], axes[-1, 1], zoom_abs, **zoom_kwargs)
letter_subplots(axes[:, 0])
# fig.align_ylabels(axes[:, 0])
if save_name is not None: if save_name is not None:
fig.savefig(f'{save_name}_pre.pdf') fig.savefig(f'{save_name}_pre.pdf')
@ -224,41 +274,45 @@ for data_path in data_paths:
# PART II: FEATURE EXTRACTION STAGE: # PART II: FEATURE EXTRACTION STAGE:
fig, axes = prepare_fig(3, 2, **fig_kwargs, **grid_kwargs) fig, axes = prepare_fig(3, 2, **fig_kwargs, **grid_kwargs)
super_xlabel('time [s]', fig, axes[0, 0], axes[0, -1]) super_xlabel('time [s]', fig, axes[0, 0], axes[0, -1])
super_ylabel('amplitude', fig, axes[0, 0], axes[-1, 0])
# Convolutional filter responses: # Convolutional filter responses:
ax_full, ax_zoom = axes[0, :] ax_full, ax_zoom = axes[0, :]
ylabel(ax_full, ylabels['conv'])
signal = data['conv'][:, kern_inds] signal = data['conv'][:, kern_inds]
handles = plot_line(ax_full, t_full, signal, lw=lw_full['conv']) handles = plot_line(ax_full, t_full, signal, lw=lw_full['conv'], yloc=loc_full['conv'])
assign_colors(handles, kernel_specs[:, 0], conv_colors) assign_colors(handles, kernel_specs[:, 0], conv_colors)
handles = plot_line(ax_zoom, t_zoom, signal[zoom_mask, :], lw=lw_zoom['conv']) reorder_traces(handles, signal)
handles = plot_line(ax_zoom, t_zoom, signal[zoom_mask, :], lw=lw_zoom['conv'], yloc=loc_zoom['conv'])
assign_colors(handles, kernel_specs[:, 0], conv_colors) assign_colors(handles, kernel_specs[:, 0], conv_colors)
reorder_traces(handles, signal[zoom_mask, :])
hide_axis(ax_full, 'bottom') hide_axis(ax_full, 'bottom')
hide_axis(ax_zoom, 'bottom') hide_axis(ax_zoom, 'bottom')
hide_axis(ax_zoom, 'left')
# Binary responses: # Binary responses:
ax_full, ax_zoom = axes[1, :] ax_full, ax_zoom = axes[1, :]
ylabel(ax_full, ylabels['bi'])
signal = data['bi'][:, kern_inds] signal = data['bi'][:, kern_inds]
handles = plot_barcode(ax_full, t_full, signal) handles = plot_barcode(ax_full, t_full, signal, lw=lw_full['bi'])
assign_colors(handles, kernel_specs[:, 0], bi_colors) assign_colors(handles, kernel_specs[:, 0], bi_colors)
handles = plot_barcode(ax_zoom, t_zoom, signal[zoom_mask, :]) handles = plot_barcode(ax_zoom, t_zoom, signal[zoom_mask, :], lw=lw_zoom['bi'])
assign_colors(handles, kernel_specs[:, 0], bi_colors) assign_colors(handles, kernel_specs[:, 0], bi_colors)
# Finalized features: # Finalized features:
ax_full, ax_zoom = axes[2, :] ax_full, ax_zoom = axes[2, :]
ylabel(ax_full, ylabels['feat'])
signal = data['feat'][:, kern_inds] signal = data['feat'][:, kern_inds]
handles = plot_line(ax_full, t_full, signal, ymin=0, ymax=1, c=colors['feat'], lw=lw_full['feat']) handles = plot_line(ax_full, t_full, signal, ymin=0, ymax=1, c=colors['feat'], lw=lw_full['feat'], yloc=loc_full['feat'])
assign_colors(handles, kernel_specs[:, 0], feat_colors) assign_colors(handles, kernel_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']) 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'])
assign_colors(handles, kernel_specs[:, 0], feat_colors) assign_colors(handles, kernel_specs[:, 0], feat_colors)
hide_axis(ax_zoom, 'left')
# Posthoc adjustments: # Posthoc adjustments:
ax_full.set_xlim(t_full[0], t_full[-1]) ax_full.set_xlim(t_full[0], t_full[-1])
ax_zoom.set_xlim(t_zoom[0], t_zoom[-1]) ax_zoom.set_xlim(t_zoom[0], t_zoom[-1])
indicate_zoom(fig, axes[0, 0], axes[-1, 0], zoom_abs, **zoom_kwargs) 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) indicate_zoom(fig, axes[0, 1], axes[-1, 1], zoom_abs, **zoom_kwargs)
letter_subplots(axes[:, 0])
# fig.align_ylabels(axes[:, 0])
if save_name is not None: if save_name is not None:
fig.savefig(f'{save_name}_feat.pdf') fig.savefig(f'{save_name}_feat.pdf')
plt.show() plt.show()

5
python/plotstyle_plt.py
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@ -5,9 +5,8 @@ mpl.rcParams['text.usetex'] = False
# Font style: # Font style:
mpl.rcParams['font.style'] = 'normal' mpl.rcParams['font.style'] = 'normal'
mpl.rcParams['font.family'] = 'serif' mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['mathtext.fontset'] = 'cm' mpl.rcParams['mathtext.fontset'] = 'cm'
mpl.rcParams['mathtext.default'] = 'regular'
# Font sizes: # Font sizes:
mpl.rcParams['font.size'] = 14 mpl.rcParams['font.size'] = 14
@ -41,7 +40,7 @@ mpl.rcParams['axes.spines.right'] = False
mpl.rcParams['axes.xmargin'] = 0 mpl.rcParams['axes.xmargin'] = 0
mpl.rcParams['axes.ymargin'] = 0 mpl.rcParams['axes.ymargin'] = 0
mpl.rcParams['axes.autolimit_mode'] = 'round_numbers' mpl.rcParams['axes.autolimit_mode'] = 'round_numbers'
mpl.rcParams['axes.labelpad'] = 3 mpl.rcParams['axes.labelpad'] = 5
# Major tick parameters: # Major tick parameters:
mpl.rcParams['xtick.major.size'] = 5 mpl.rcParams['xtick.major.size'] = 5

2
python/save_kernel_colors.py
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@ -4,7 +4,7 @@ from color_functions import load_colors, shade_colors
# Settings: # Settings:
stages = ['conv', 'bi', 'feat'] stages = ['conv', 'bi', 'feat']
kern_types = np.array([1, -1, 2, -2, 3, -3, 4, -4]) kern_types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
shade_factors = np.linspace(-0.5, 0.5, kern_types.size) shade_factors = np.linspace(-0.6, 0.2, kern_types.size)
# Main colors: # Main colors:
stage_colors = load_colors('../data/stage_colors.npz') stage_colors = load_colors('../data/stage_colors.npz')