raab/GP2023_chirp_detection
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new poster template

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weygoldt 2023-01-24 17:27:37 +01:00
parent fe41f8f59c
commit 70c4b012be
8 changed files with 159 additions and 110 deletions
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106
code/chirpdetection.py
View File

@ -133,8 +133,10 @@ class ChirpPlotBuffer:
data_oi,
self.data.raw_rate,
self.t0 - 5,
[np.min(self.frequency) - 100, np.max(self.frequency) + 200]
[np.min(self.frequency) - 300, np.max(self.frequency) + 300]
)
ax0.set_ylim(np.min(self.frequency) - 100,
np.max(self.frequency) + 200)
for track_id in self.data.ids:
@ -157,27 +159,35 @@ class ChirpPlotBuffer:
zorder=10, color=ps.gblue1)
else:
ax0.plot(t-self.t0_old, f, lw=lw,
zorder=10, color=ps.gray, alpha=0.5)
ax0.fill_between(
np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate),
q50 - self.config.minimal_bandwidth / 2,
q50 + self.config.minimal_bandwidth / 2,
color=ps.gblue1,
lw=1,
ls="dashed",
alpha=0.5,
)
zorder=10, color=ps.black)
# ax0.fill_between(
# np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate),
# q50 - self.config.minimal_bandwidth / 2,
# q50 + self.config.minimal_bandwidth / 2,
# color=ps.gblue1,
# lw=1,
# ls="dashed",
# alpha=0.5,
# )
# ax0.fill_between(
# np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate),
# search_lower,
# search_upper,
# color=ps.gblue2,
# lw=1,
# ls="dashed",
# alpha=0.5,
# )
ax0.axhline(q50 - self.config.minimal_bandwidth / 2,
color=ps.gblue1, lw=1, ls="dashed")
ax0.axhline(q50 + self.config.minimal_bandwidth / 2,
color=ps.gblue1, lw=1, ls="dashed")
ax0.axhline(search_lower, color=ps.gblue2, lw=1, ls="dashed")
ax0.axhline(search_upper, color=ps.gblue2, lw=1, ls="dashed")
ax0.fill_between(
np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate),
search_lower,
search_upper,
color=ps.gblue2,
lw=1,
ls="dashed",
alpha=0.5,
)
# ax0.axhline(q50, spec_times[0], spec_times[-1],
# color=ps.gblue1, lw=2, ls="dashed")
# ax0.axhline(q50 + self.search_frequency,
@ -187,7 +197,11 @@ class ChirpPlotBuffer:
if len(chirps) > 0:
for chirp in chirps:
ax0.scatter(
chirp, np.median(self.frequency) + 150, c=ps.black, marker="v"
chirp, np.median(self.frequency), c=ps.red, marker=".",
edgecolors=ps.red,
facecolors=ps.red,
zorder=10,
s=70,
)
# plot waveform of filtered signal
@ -207,25 +221,31 @@ class ChirpPlotBuffer:
c=ps.gblue3, lw=lw, label="baseline inst. freq.")
# plot filtered and rectified envelope
ax4.plot(self.time, self.baseline_envelope, c=ps.gblue1, lw=lw)
ax4.plot(self.time, self.baseline_envelope *
waveform_scaler, c=ps.gblue1, lw=lw)
ax4.scatter(
(self.time)[self.baseline_peaks],
self.baseline_envelope[self.baseline_peaks],
(self.baseline_envelope*waveform_scaler)[self.baseline_peaks],
edgecolors=ps.red,
facecolors=ps.red,
zorder=10,
marker="o",
facecolors="none",
marker=".",
s=70,
# facecolors="none",
)
# plot envelope of search signal
ax5.plot(self.time, self.search_envelope, c=ps.gblue2, lw=lw)
ax5.plot(self.time, self.search_envelope *
waveform_scaler, c=ps.gblue2, lw=lw)
ax5.scatter(
(self.time)[self.search_peaks],
self.search_envelope[self.search_peaks],
(self.search_envelope*waveform_scaler)[self.search_peaks],
edgecolors=ps.red,
facecolors=ps.red,
zorder=10,
marker="o",
facecolors="none",
marker=".",
s=70,
# facecolors="none",
)
# plot filtered instantaneous frequency
@ -235,16 +255,20 @@ class ChirpPlotBuffer:
self.frequency_time[self.frequency_peaks],
self.frequency_filtered[self.frequency_peaks],
edgecolors=ps.red,
facecolors=ps.red,
zorder=10,
marker="o",
facecolors="none",
marker=".",
s=70,
# facecolors="none",
)
ax0.set_ylabel("frequency [Hz]")
ax1.set_ylabel("a.u.")
ax2.set_ylabel("a.u.")
ax1.set_ylabel(r"$\mu$V")
ax2.set_ylabel(r"$\mu$V")
ax3.set_ylabel("Hz")
ax5.set_ylabel("a.u.")
ax4.set_ylabel(r"$\mu$V")
ax5.set_ylabel(r"$\mu$V")
ax6.set_ylabel("Hz")
ax6.set_xlabel("time [s]")
plt.setp(ax0.get_xticklabels(), visible=False)
@ -323,7 +347,7 @@ def plot_spectrogram(
aspect="auto",
origin="lower",
interpolation="gaussian",
alpha=0.6,
# alpha=0.6,
)
# axis.use_sticky_edges = False
return spec_times
@ -628,16 +652,16 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
raw_time = np.arange(data.raw.shape[0]) / data.raw_rate
# good chirp times for data: 2022-06-02-10_00
# window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5 + 5) * data.raw_rate
# window_duration_index = 60 * data.raw_rate
window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate
window_duration_index = 60 * data.raw_rate
# t0 = 0
# dt = data.raw.shape[0]
# window_start_seconds = (23495 + ((28336-23495)/3)) * data.raw_rate
# window_duration_seconds = (28336 - 23495) * data.raw_rate
window_start_index = 0
window_duration_index = data.raw.shape[0]
# window_start_index = 0
# window_duration_index = data.raw.shape[0]
# generate starting points of rolling window
window_start_indices = np.arange(
@ -1097,4 +1121,4 @@ if __name__ == "__main__":
datapath = "../data/2022-06-02-10_00/"
# datapath = "/home/weygoldt/Data/uni/efishdata/2016-colombia/fishgrid/2016-04-09-22_25/"
# datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/"
chirpdetection(datapath, plot="show", debug="false")
chirpdetection(datapath, plot="save", debug="false")

137
code/modules/plotstyle.py
View File

@ -23,16 +23,16 @@ def PlotStyle() -> None:
sky = "#89dceb"
teal = "#94e2d5"
green = "#a6e3a1"
yellow = "#f9e2af"
orange = "#fab387"
maroon = "#eba0ac"
red = "#f38ba8"
purple = "#cba6f7"
pink = "#f5c2e7"
yellow = "#f9d67f"
orange = "#faa472"
maroon = "#eb8486"
red = "#f37588"
purple = "#d89bf7"
pink = "#f59edb"
lavender = "#b4befe"
gblue1 = "#8cb8ff"
gblue2 = "#7cdcdc"
gblue3 = "#82e896"
gblue1 = "#89b4fa"
gblue2 = "#89dceb"
gblue3 = "#a6e3a1"
@classmethod
def lims(cls, track1, track2):
@ -108,6 +108,9 @@ def PlotStyle() -> None:
@classmethod
def set_boxplot_color(cls, bp, color):
plt.setp(bp["boxes"], color=color)
plt.setp(bp["whiskers"], color=color)
plt.setp(bp["caps"], color=color)
plt.setp(bp["medians"], color=color)
@classmethod
def label_subplots(cls, labels, axes, fig):
@ -226,7 +229,7 @@ def PlotStyle() -> None:
plt.rc("legend", fontsize=SMALL_SIZE) # legend fontsize
plt.rc("figure", titlesize=BIGGER_SIZE) # fontsize of the figure title
plt.rcParams["image.cmap"] = 'cmo.haline'
plt.rcParams["image.cmap"] = "cmo.haline"
plt.rcParams["axes.xmargin"] = 0.05
plt.rcParams["axes.ymargin"] = 0.1
plt.rcParams["axes.titlelocation"] = "left"
@ -247,42 +250,44 @@ def PlotStyle() -> None:
# dark mode modifications
plt.rcParams["boxplot.flierprops.color"] = white
plt.rcParams["boxplot.flierprops.markeredgecolor"] = white
plt.rcParams["boxplot.flierprops.markeredgecolor"] = gray
plt.rcParams["boxplot.boxprops.color"] = gray
plt.rcParams["boxplot.whiskerprops.color"] = white
plt.rcParams["boxplot.capprops.color"] = white
plt.rcParams["boxplot.medianprops.color"] = white
plt.rcParams["boxplot.whiskerprops.color"] = gray
plt.rcParams["boxplot.capprops.color"] = gray
plt.rcParams["boxplot.medianprops.color"] = gray
plt.rcParams["text.color"] = white
plt.rcParams["axes.facecolor"] = black # axes background color
plt.rcParams["axes.edgecolor"] = gray # axes edge color
# plt.rcParams["axes.grid"] = True # display grid or not
# plt.rcParams["axes.grid.axis"] = "y" # which axis the grid is applied to
plt.rcParams["axes.labelcolor"] = white
plt.rcParams["axes.axisbelow"] = True # draw axis gridlines and ticks:
plt.rcParams["axes.axisbelow"] = True # draw axis gridlines and ticks:
plt.rcParams["axes.spines.left"] = True # display axis spines
plt.rcParams["axes.spines.bottom"] = True
plt.rcParams["axes.spines.top"] = False
plt.rcParams["axes.spines.right"] = False
plt.rcParams["axes.prop_cycle"] = cycler(
'color', [
'#b4befe',
'#89b4fa',
'#74c7ec',
'#89dceb',
'#94e2d5',
'#a6e3a1',
'#f9e2af',
'#fab387',
'#eba0ac',
'#f38ba8',
'#cba6f7',
'#f5c2e7',
])
"color",
[
"#b4befe",
"#89b4fa",
"#74c7ec",
"#89dceb",
"#94e2d5",
"#a6e3a1",
"#f9e2af",
"#fab387",
"#eba0ac",
"#f38ba8",
"#cba6f7",
"#f5c2e7",
],
)
plt.rcParams["xtick.color"] = gray # color of the ticks
plt.rcParams["ytick.color"] = gray # color of the ticks
plt.rcParams["grid.color"] = dark_gray # grid color
plt.rcParams["figure.facecolor"] = black # figure face color
plt.rcParams["figure.edgecolor"] = black # figure edge color
plt.rcParams["figure.facecolor"] = black # figure face color
plt.rcParams["figure.edgecolor"] = black # figure edge color
plt.rcParams["savefig.facecolor"] = black # figure face color when saving
return style
@ -292,12 +297,11 @@ if __name__ == "__main__":
s = PlotStyle()
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
from matplotlib.path import Path
from matplotlib.patches import PathPatch
from matplotlib.path import Path
# Fixing random state for reproducibility
np.random.seed(19680801)
@ -305,14 +309,20 @@ if __name__ == "__main__":
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z1 = np.exp(-(X**2) - Y**2)
Z2 = np.exp(-((X - 1) ** 2) - (Y - 1) ** 2)
Z = (Z1 - Z2) * 2
fig1, ax = plt.subplots()
im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
origin='lower', extent=[-3, 3, -3, 3],
vmax=abs(Z).max(), vmin=-abs(Z).max())
im = ax.imshow(
Z,
interpolation="bilinear",
cmap=cm.RdYlGn,
origin="lower",
extent=[-3, 3, -3, 3],
vmax=abs(Z).max(),
vmin=-abs(Z).max(),
)
plt.show()
@ -325,22 +335,21 @@ if __name__ == "__main__":
all_data = [np.random.normal(0, std, 100) for std in range(6, 10)]
# plot violin plot
axs[0].violinplot(all_data,
showmeans=False,
showmedians=True)
axs[0].set_title('Violin plot')
axs[0].violinplot(all_data, showmeans=False, showmedians=True)
axs[0].set_title("Violin plot")
# plot box plot
axs[1].boxplot(all_data)
axs[1].set_title('Box plot')
axs[1].set_title("Box plot")
# adding horizontal grid lines
for ax in axs:
ax.yaxis.grid(True)
ax.set_xticks([y + 1 for y in range(len(all_data))],
labels=['x1', 'x2', 'x3', 'x4'])
ax.set_xlabel('Four separate samples')
ax.set_ylabel('Observed values')
ax.set_xticks(
[y + 1 for y in range(len(all_data))], labels=["x1", "x2", "x3", "x4"]
)
ax.set_xlabel("Four separate samples")
ax.set_ylabel("Observed values")
plt.show()
@ -352,24 +361,42 @@ if __name__ == "__main__":
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
radii = 10 * np.random.rand(N)
width = np.pi / 4 * np.random.rand(N)
colors = cmo.cm.haline(radii / 10.)
colors = cmo.cm.haline(radii / 10.0)
ax = plt.subplot(projection='polar')
ax = plt.subplot(projection="polar")
ax.bar(theta, radii, width=width, bottom=0.0, color=colors, alpha=0.5)
plt.show()
methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',
'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']
methods = [
None,
"none",
"nearest",
"bilinear",
"bicubic",
"spline16",
"spline36",
"hanning",
"hamming",
"hermite",
"kaiser",
"quadric",
"catrom",
"gaussian",
"bessel",
"mitchell",
"sinc",
"lanczos",
]
# Fixing random state for reproducibility
np.random.seed(19680801)
grid = np.random.rand(4, 4)
fig, axs = plt.subplots(nrows=3, ncols=6, figsize=(9, 6),
subplot_kw={'xticks': [], 'yticks': []})
fig, axs = plt.subplots(
nrows=3, ncols=6, figsize=(9, 6), subplot_kw={"xticks": [], "yticks": []}
)
for ax, interp_method in zip(axs.flat, methods):
ax.imshow(grid, interpolation=interp_method)

4
code/plot_introduction_specs.py
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@ -41,9 +41,9 @@ def main():
freqtime2, freq2 = instantaneous_frequency(
filtered2, data.raw_rate, smoothing_window=3)
ax1.plot(freqtime1*timescaler, freq1, color=ps.gblue1,
ax1.plot(freqtime1*timescaler, freq1, color=ps.red,
lw=2, label=f"fish 1, {np.median(freq1):.0f} Hz")
ax1.plot(freqtime2*timescaler, freq2, color=ps.gblue3,
ax1.plot(freqtime2*timescaler, freq2, color=ps.orange,
lw=2, label=f"fish 2, {np.median(freq2):.0f} Hz")
ax1.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower center",
mode="normal", borderaxespad=0, ncol=2)

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poster/figs/algorithm.pdf Normal file
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poster/figs/introplot.pdf Normal file
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poster/main.pdf
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16
poster/main.tex
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@ -7,7 +7,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
\begin{document}
\renewcommand{\baselinestretch}{1}
\title{\parbox{1500pt}{Poster}}
\title{\parbox{1500pt}{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}
@ -21,17 +21,15 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
sender identification of freely interacting individuals impossible.
This profoundly limits our current understanding of chirps to experiments
with single - or physically separated - individuals.
\vspace{0.6cm}
\begin{tikzfigure}[]
\label{griddrawing}
\includegraphics[width=0.5\linewidth]{example-image-a}
\end{tikzfigure}
% \begin{tikzfigure}[]
% \label{griddrawing}
% \includegraphics[width=1\linewidth]{figs/introplot}
% \end{tikzfigure}
}
\myblock[TranspBlock]{Chirp detection}{
\begin{tikzfigure}[]
\label{fig:example_a}
\includegraphics[width=0.5\linewidth]{example-image-a}
\includegraphics[width=1\linewidth]{figs/algorithm}
\end{tikzfigure}
\vspace{0cm}
}
@ -67,7 +65,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
\vspace{-1cm}
}
\myblock[TranspBlock]{Conclusion}{
\myblock[GrayBlock]{Conclusion}{
\begin{itemize}
\setlength\itemsep{0.5em}
\item Our analysis is the first to indicate that \textit{A. leptorhynchus} uses long, diffuse and synchronized EOD$f$ signals to communicate in addition to chirps and rises.

6
poster/style.tex
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@ -1,9 +1,9 @@
\tikzposterlatexaffectionproofoff
\usetheme{Default}
\definecolor{text}{HTML}{ffffff}
\definecolor{background}{HTML}{080808}
\definecolor{boxes}{HTML}{1E1E1E}
\definecolor{text}{HTML}{e0e4f7}
\definecolor{background}{HTML}{111116}
\definecolor{boxes}{HTML}{2a2a32}
\definecolor{unired}{HTML}{a51e37}
\colorlet{blocktitlefgcolor}{text}