diff --git a/code/plot_kdes.py b/code/plot_kdes.py index 1d2400d..efbd5c0 100644 --- a/code/plot_kdes.py +++ b/code/plot_kdes.py @@ -275,14 +275,15 @@ def main(dataroot): # loser_physicals_conv = acausal_kde1d( # loser_physicals[-1], kde_time, kernel_width) - ax[i].plot(kde_time, loser_offsets_conv/len(offsets)) + ax[i].plot(kde_time, loser_offsets_conv / + len(offsets), lw=2, zorder=100) ax[i].fill_between( kde_time, np.percentile(loser_offsets_boot[-1], 1, axis=0), np.percentile(loser_offsets_boot[-1], 99, axis=0), color=ps.white, - alpha=0.5) + alpha=0.4) ax[i].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0), color=ps.black, linewidth=2) @@ -519,7 +520,7 @@ def main(dataroot): # loser_physicals_boot_quarts[2], # color=ps.gray, # alpha=0.5) - plt.subplots_adjust(bottom=0.21) + plt.subplots_adjust(bottom=0.21, top=0.93) plt.savefig('../poster/figs/kde.pdf') plt.show() diff --git a/poster/figs/kde.pdf b/poster/figs/kde.pdf index 365d8e1..ba87f34 100644 Binary files a/poster/figs/kde.pdf and b/poster/figs/kde.pdf differ diff --git a/poster/main.tex b/poster/main.tex index 918cdd6..3305b5b 100644 --- a/poster/main.tex +++ b/poster/main.tex @@ -7,7 +7,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val \begin{document} \renewcommand{\baselinestretch}{1} -\title{\parbox{1500pt}{Bypassing time-frequency uncertainty in the detection of transient communication signals in weakly electric fish}} +\title{\parbox{1600pt}{Bypassing time-frequency uncertainty in the detection of transient communication signals in weakly electric fish}} \author{Sina Prause, Alexander Wendt, and Patrick Weygoldt} \institute{Supervised by Till Raab \& Jan Benda, Neuroethology Lab, University of Tuebingen} \usetitlestyle[]{sampletitle} @@ -17,7 +17,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val \begin{columns} \column{0.4} \myblock[TranspBlock]{Introduction}{ - \textbf{Chirps} are the most common communication signals in weakly electric fish. They are characterized by \textbf{short frequency excursions} and are emitted during various social contexts. It is nearly impossible to reliably \textbf{detect and assign} chirps in freely interacting fish using only a Fourier transform. To overcome these limits, we developed a new method of \textbf{dynamic feature extraction} and classification. + \textbf{Chirps} are the most common communication signals in weakly electric fish. They are characterized by \textbf{short frequency excursions} and are emitted during various social contexts. It is nearly impossible to reliably \textbf{detect and assign} chirps in freely interacting fish using only a Fourier transform. To overcome these limits, we developed a new method of \textbf{dynamic feature extraction} and classification. \vspace{1cm} \begin{tikzfigure}[] \label{griddrawing} @@ -52,7 +52,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val \item Experiment had a 3 hour long darkphase and a 3 hour long light phase. \end{itemize} \end{multicols} - + \noindent \begin{tikzfigure}[]