Polished most of 2 new methods figures.
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j-hartling
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36
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36
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@ -44,6 +44,8 @@
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\abx@aux@page{4}{1}
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\abx@aux@page{5}{1}
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\abx@aux@page{6}{1}
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\abx@aux@page{7}{1}
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\abx@aux@page{8}{1}
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\abx@aux@cite{0}{helversen1977stridulatory}
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\abx@aux@segm{0}{0}{helversen1977stridulatory}
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\abx@aux@cite{0}{stumpner1994song}
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@ -94,8 +96,6 @@
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\abx@aux@segm{0}{0}{clemens2010intensity}
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\abx@aux@cite{0}{hildebrandt2009origin}
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\abx@aux@segm{0}{0}{hildebrandt2009origin}
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\abx@aux@page{7}{2}
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\abx@aux@page{8}{2}
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\abx@aux@page{9}{2}
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\abx@aux@page{10}{2}
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\abx@aux@page{11}{2}
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@ -116,6 +116,8 @@
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\abx@aux@page{26}{2}
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\abx@aux@page{27}{2}
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\abx@aux@page{28}{2}
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\abx@aux@page{29}{2}
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\abx@aux@page{30}{2}
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\abx@aux@cite{0}{clemens2013computational}
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\abx@aux@segm{0}{0}{clemens2013computational}
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\abx@aux@cite{0}{hennig2014time}
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@ -134,8 +136,6 @@
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\abx@aux@segm{0}{0}{clemens2011efficient}
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\abx@aux@cite{0}{clemens2012nonlinear}
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\abx@aux@segm{0}{0}{clemens2012nonlinear}
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\abx@aux@page{29}{3}
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\abx@aux@page{30}{3}
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\abx@aux@page{31}{3}
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\abx@aux@page{32}{3}
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\abx@aux@page{33}{3}
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@ -172,11 +172,15 @@
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\abx@aux@page{48}{4}
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\abx@aux@page{49}{4}
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\abx@aux@page{50}{4}
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\@writefile{toc}{\contentsline {section}{\numberline {2}Developing a functional model of the\\grasshopper song recognition pathway}{4}{}\protected@file@percent }
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\abx@aux@cite{0}{windmill2008time}
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\abx@aux@segm{0}{0}{windmill2008time}
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\abx@aux@cite{0}{malkin2014energy}
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\abx@aux@segm{0}{0}{malkin2014energy}
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\@writefile{toc}{\contentsline {section}{\numberline {2}Developing a functional model of the\\grasshopper song recognition pathway}{5}{}\protected@file@percent }
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\abx@aux@cite{0}{machens2001discrimination}
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\abx@aux@segm{0}{0}{machens2001discrimination}
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\abx@aux@cite{0}{machens2001representation}
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\abx@aux@segm{0}{0}{machens2001representation}
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\abx@aux@page{51}{5}
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\abx@aux@page{52}{5}
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\abx@aux@page{53}{5}
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@ -188,13 +192,12 @@
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Population-driven signal preprocessing}{5}{}\protected@file@percent }
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\abx@aux@page{59}{5}
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\abx@aux@page{60}{5}
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\newlabel{eq:bandpass}{{1}{5}{}{}{}}
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\abx@aux@page{61}{5}
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\abx@aux@page{62}{5}
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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 }
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\providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
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\newlabel{fig:pathway}{{1}{6}{}{}{}}
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\abx@aux@cite{0}{machens2001discrimination}
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\abx@aux@segm{0}{0}{machens2001discrimination}
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\abx@aux@cite{0}{machens2001representation}
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\abx@aux@segm{0}{0}{machens2001representation}
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\abx@aux@cite{0}{suga1960peripheral}
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\abx@aux@segm{0}{0}{suga1960peripheral}
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\abx@aux@cite{0}{gollisch2002energy}
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@ -209,9 +212,6 @@
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\abx@aux@segm{0}{0}{hildebrandt2009origin}
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\abx@aux@cite{0}{clemens2010intensity}
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\abx@aux@segm{0}{0}{clemens2010intensity}
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\newlabel{eq:bandpass}{{1}{7}{}{}{}}
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\abx@aux@page{61}{7}
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\abx@aux@page{62}{7}
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\newlabel{eq:env}{{2}{7}{}{}{}}
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\abx@aux@page{63}{7}
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\abx@aux@page{64}{7}
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@ -222,8 +222,8 @@
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\abx@aux@page{68}{7}
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\abx@aux@page{69}{7}
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\newlabel{eq:highpass}{{4}{7}{}{}{}}
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\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces \relax }}{8}{}\protected@file@percent }
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\newlabel{fig:pre}{{2}{8}{}{}{}}
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\@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 }
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\newlabel{fig:pre}{{2}{7}{}{}{}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}Feature extraction by individual neurons}{8}{}\protected@file@percent }
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\newlabel{eq:conv}{{5}{8}{}{}{}}
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\newlabel{eq:gabor}{{6}{8}{}{}{}}
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@ -239,12 +239,12 @@
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\abx@aux@page{70}{9}
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\abx@aux@page{71}{9}
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\abx@aux@page{72}{9}
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\newlabel{eq:lowpass}{{10}{10}{}{}{}}
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\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces \relax }}{10}{}\protected@file@percent }
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\newlabel{eq:lowpass}{{10}{9}{}{}{}}
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\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces \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. }}{10}{}\protected@file@percent }
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\newlabel{fig:stages_feat}{{3}{10}{}{}{}}
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\@writefile{toc}{\contentsline {section}{\numberline {3}Two mechanisms driving the emergence of intensity-invariant song representation}{10}{}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Logarithmic scaling \& spike-frequency adaptation}{10}{}\protected@file@percent }
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\newlabel{eq:toy_env}{{11}{11}{}{}{}}
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\newlabel{eq:toy_env}{{11}{10}{}{}{}}
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\newlabel{eq:toy_snr}{{12}{11}{}{}{}}
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\newlabel{eq:toy_log}{{13}{11}{}{}{}}
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\newlabel{eq:toy_highpass}{{14}{11}{}{}{}}
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@ -252,7 +252,7 @@
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\newlabel{eq:pdf_split}{{15}{12}{}{}{}}
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\newlabel{eq:pdf}{{16}{12}{}{}{}}
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\newlabel{eq:feat_avg}{{17}{12}{}{}{}}
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\newlabel{eq:feat_prop}{{18}{13}{}{}{}}
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\newlabel{eq:feat_prop}{{18}{12}{}{}{}}
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\abx@aux@cite{0}{stumpner1991auditory}
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\abx@aux@segm{0}{0}{stumpner1991auditory}
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\@writefile{toc}{\contentsline {section}{\numberline {4}Discriminating species-specific song\\patterns in feature space}{14}{}\protected@file@percent }
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[0] Config.pm:307> INFO - This is Biber 2.19
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[36] biber:340> INFO - === Di Feb 17, 2026, 16:10:35
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# Fdb version 4
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</usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmbx10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmbx12.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmbxti10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmex10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmmi10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmmi12.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmmi6.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmmi8.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmr10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmr12.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmr17.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmr6.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmr8.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmsy10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmsy8.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmti12.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/symbols/msbm10.pfb>
|
||||
Output written on main.pdf (14 pages, 14132974 bytes).
|
||||
PDF statistics:
|
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1172 PDF objects out of 1200 (max. 8388607)
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714 compressed objects within 8 object streams
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1210 PDF objects out of 1440 (max. 8388607)
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737 compressed objects within 8 object streams
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0 named destinations out of 1000 (max. 500000)
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28 words of extra memory for PDF output out of 10000 (max. 10000000)
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|
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|
||||
BIN
main.synctex.gz
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main.synctex.gz
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19
main.tex
19
main.tex
@ -1,6 +1,6 @@
|
||||
\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{graphicx}
|
||||
\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
|
||||
following feature extraction stage.
|
||||
|
||||
% Cite somewhere:
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\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}
|
||||
\end{figure}
|
||||
\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
|
||||
can be read out by a simple linear classifier.
|
||||
|
||||
% Cite somewhere:
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\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}
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
@ -1,10 +1,12 @@
|
||||
from math import log
|
||||
|
||||
import plotstyle_plt
|
||||
import glob
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from itertools import product
|
||||
from thunderhopper.modeltools import load_data
|
||||
from color_functions import load_colors, shade_colors
|
||||
from color_functions import load_colors
|
||||
from IPython import embed
|
||||
|
||||
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
|
||||
return ax.set_ylim(limits)
|
||||
|
||||
def super_xlabel(label, fig, high_ax, low_ax, **kwargs):
|
||||
x = (low_ax.get_position().x0 + high_ax.get_position().x1) / 2
|
||||
fig.supxlabel(label, x=x, **kwargs)
|
||||
def ylabel(ax, label, x=-0.23, fontsize=20):
|
||||
ax.set_ylabel(label, fontsize=fontsize, rotation=0, ha='left', va='center')
|
||||
ax.yaxis.set_label_coords(x, 0.5)
|
||||
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
|
||||
fig.supylabel(label, y=y, **kwargs)
|
||||
fig.supylabel(label, x=x, y=y, **kwargs)
|
||||
return None
|
||||
|
||||
def hide_axis(ax, side='bottom'):
|
||||
@ -63,10 +70,11 @@ def hide_axis(ax, side='bottom'):
|
||||
return 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)
|
||||
xlimits(ax, time, minval=xmin, maxval=xmax, pad=xpad)
|
||||
ylimits(ax, signal, minval=ymin, maxval=ymax, pad=ypad)
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc))
|
||||
return handles
|
||||
|
||||
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
|
||||
xlimits(ax, time, minval=xmin, maxval=xmax)
|
||||
ax.set_ylim(0, upper)
|
||||
ax.axis('off')
|
||||
hide_axis(ax, 'bottom')
|
||||
hide_axis(ax, 'left')
|
||||
return handles
|
||||
|
||||
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):
|
||||
for handle, type_id in zip(handles, types):
|
||||
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:
|
||||
@ -118,32 +142,57 @@ grid_kwargs = dict(
|
||||
wspace=0.15,
|
||||
hspace=0.3,
|
||||
left=0.1,
|
||||
right=0.95,
|
||||
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'$f_i$'
|
||||
)
|
||||
colors = load_colors('../data/stage_colors.npz')
|
||||
lw_full = dict(
|
||||
filt=0.25,
|
||||
env=0.5,
|
||||
log=0.5,
|
||||
inv=0.5,
|
||||
conv=0.5,
|
||||
bi=1,
|
||||
feat=1
|
||||
conv=0.25,
|
||||
bi=0,
|
||||
feat=2
|
||||
)
|
||||
lw_zoom = dict(
|
||||
filt=0.25,
|
||||
env=0.75,
|
||||
log=0.75,
|
||||
inv=0.75,
|
||||
conv=0.5,
|
||||
bi=1,
|
||||
feat=0.75
|
||||
)
|
||||
filt=0.5,
|
||||
env=1,
|
||||
log=1,
|
||||
inv=1,
|
||||
conv=1.5,
|
||||
bi=0,
|
||||
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_kwargs = dict(
|
||||
color=(0.85, 0.85, 0.85),
|
||||
color=3 * (0.85,),
|
||||
zorder=0,
|
||||
linewidth=0
|
||||
)
|
||||
@ -152,7 +201,7 @@ zoom_kwargs = dict(
|
||||
# [3, 0.032]
|
||||
# ])
|
||||
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])
|
||||
conv_colors = load_colors('../data/conv_colors.npz')
|
||||
bi_colors = load_colors('../data/bi_colors.npz')
|
||||
@ -180,43 +229,44 @@ 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])
|
||||
super_ylabel('amplitude', fig, axes[0, 0], axes[-1, 0])
|
||||
|
||||
# Bandpass-filtered signal:
|
||||
ax_full, ax_zoom = axes[0, :]
|
||||
plot_line(ax_full, t_full, data['filt'], c=colors['filt'], lw=lw_full['filt'])
|
||||
plot_line(ax_zoom, t_zoom, data['filt'][zoom_mask], c=colors['filt'], lw=lw_zoom['filt'])
|
||||
ylabel(ax_full, ylabels['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_zoom, 'bottom')
|
||||
hide_axis(ax_zoom, 'left')
|
||||
|
||||
# Signal envelope:
|
||||
ax_full, ax_zoom = axes[1, :]
|
||||
plot_line(ax_full, t_full, data['env'], ymin=0, c=colors['env'], lw=lw_full['env'])
|
||||
plot_line(ax_zoom, t_zoom, data['env'][zoom_mask], ymin=0, c=colors['env'], lw=lw_zoom['env'])
|
||||
ylabel(ax_full, ylabels['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_zoom, 'bottom')
|
||||
hide_axis(ax_zoom, 'left')
|
||||
|
||||
# Logarithmic envelope:
|
||||
ax_full, ax_zoom = axes[2, :]
|
||||
plot_line(ax_full, t_full, data['log'], ymax=0, c=colors['log'], lw=lw_full['log'])
|
||||
plot_line(ax_zoom, t_zoom, data['log'][zoom_mask], ymax=0, c=colors['log'], lw=lw_zoom['log'])
|
||||
ylabel(ax_full, ylabels['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_zoom, 'bottom')
|
||||
hide_axis(ax_zoom, 'left')
|
||||
|
||||
# Adapted envelope:
|
||||
ax_full, ax_zoom = axes[3, :]
|
||||
plot_line(ax_full, t_full, data['inv'], c=colors['inv'], lw=lw_full['inv'])
|
||||
plot_line(ax_zoom, t_zoom, data['inv'][zoom_mask], c=colors['inv'], lw=lw_zoom['inv'])
|
||||
hide_axis(ax_zoom, 'left')
|
||||
ylabel(ax_full, ylabels['inv'])
|
||||
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'])
|
||||
|
||||
# Posthoc adjustments:
|
||||
ax_full.set_xlim(t_full[0], t_full[-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, 1], axes[-1, 1], zoom_abs, **zoom_kwargs)
|
||||
letter_subplots(axes[:, 0])
|
||||
# fig.align_ylabels(axes[:, 0])
|
||||
if save_name is not None:
|
||||
fig.savefig(f'{save_name}_pre.pdf')
|
||||
|
||||
@ -224,41 +274,45 @@ for data_path in data_paths:
|
||||
# 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])
|
||||
super_ylabel('amplitude', fig, axes[0, 0], axes[-1, 0])
|
||||
|
||||
# Convolutional filter responses:
|
||||
ax_full, ax_zoom = axes[0, :]
|
||||
ylabel(ax_full, ylabels['conv'])
|
||||
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)
|
||||
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)
|
||||
reorder_traces(handles, signal[zoom_mask, :])
|
||||
hide_axis(ax_full, 'bottom')
|
||||
hide_axis(ax_zoom, 'bottom')
|
||||
hide_axis(ax_zoom, 'left')
|
||||
|
||||
# Binary responses:
|
||||
ax_full, ax_zoom = axes[1, :]
|
||||
ylabel(ax_full, ylabels['bi'])
|
||||
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)
|
||||
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)
|
||||
|
||||
# Finalized features:
|
||||
ax_full, ax_zoom = axes[2, :]
|
||||
ylabel(ax_full, ylabels['feat'])
|
||||
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)
|
||||
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)
|
||||
hide_axis(ax_zoom, 'left')
|
||||
|
||||
# Posthoc adjustments:
|
||||
ax_full.set_xlim(t_full[0], t_full[-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, 1], axes[-1, 1], zoom_abs, **zoom_kwargs)
|
||||
letter_subplots(axes[:, 0])
|
||||
# fig.align_ylabels(axes[:, 0])
|
||||
if save_name is not None:
|
||||
fig.savefig(f'{save_name}_feat.pdf')
|
||||
plt.show()
|
||||
|
||||
@ -5,9 +5,8 @@ mpl.rcParams['text.usetex'] = False
|
||||
|
||||
# Font style:
|
||||
mpl.rcParams['font.style'] = 'normal'
|
||||
mpl.rcParams['font.family'] = 'serif'
|
||||
mpl.rcParams['font.family'] = 'sans-serif'
|
||||
mpl.rcParams['mathtext.fontset'] = 'cm'
|
||||
mpl.rcParams['mathtext.default'] = 'regular'
|
||||
|
||||
# Font sizes:
|
||||
mpl.rcParams['font.size'] = 14
|
||||
@ -41,7 +40,7 @@ mpl.rcParams['axes.spines.right'] = False
|
||||
mpl.rcParams['axes.xmargin'] = 0
|
||||
mpl.rcParams['axes.ymargin'] = 0
|
||||
mpl.rcParams['axes.autolimit_mode'] = 'round_numbers'
|
||||
mpl.rcParams['axes.labelpad'] = 3
|
||||
mpl.rcParams['axes.labelpad'] = 5
|
||||
|
||||
# Major tick parameters:
|
||||
mpl.rcParams['xtick.major.size'] = 5
|
||||
|
||||
@ -4,7 +4,7 @@ from color_functions import load_colors, shade_colors
|
||||
# Settings:
|
||||
stages = ['conv', 'bi', 'feat']
|
||||
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:
|
||||
stage_colors = load_colors('../data/stage_colors.npz')
|
||||
|
||||
Loading…
Reference in New Issue
Block a user