added plotting shellscript
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chirpdetection.bib
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1421
chirpdetection.bib
Normal file
File diff suppressed because it is too large
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@ -7,7 +7,7 @@ import matplotlib.pyplot as plt
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import matplotlib.gridspec as gr
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from scipy.signal import find_peaks
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from thunderfish.powerspectrum import spectrogram, decibel
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from sklearn.preprocessing import normalize
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# from sklearn.preprocessing import normalize
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from modules.filters import bandpass_filter, envelope, highpass_filter
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from modules.filehandling import ConfLoader, LoadData, make_outputdir
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@ -18,7 +18,7 @@ from modules.datahandling import (
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purge_duplicates,
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group_timestamps,
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instantaneous_frequency,
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minmaxnorm
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minmaxnorm,
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)
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logger = makeLogger(__name__)
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@ -101,13 +101,9 @@ class ChirpPlotBuffer:
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self.t0_old = self.t0
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self.t0 = 0
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fig = plt.figure(
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figsize=(14 * ps.cm, 18 * ps.cm)
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)
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fig = plt.figure(figsize=(14 * ps.cm, 18 * ps.cm))
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gs0 = gr.GridSpec(
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3, 1, figure=fig, height_ratios=[1, 1, 1]
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)
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gs0 = gr.GridSpec(3, 1, figure=fig, height_ratios=[1, 1, 1])
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gs1 = gs0[0].subgridspec(1, 1)
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gs2 = gs0[1].subgridspec(3, 1, hspace=0.4)
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gs3 = gs0[2].subgridspec(3, 1, hspace=0.4)
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@ -133,10 +129,9 @@ class ChirpPlotBuffer:
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data_oi,
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self.data.raw_rate,
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self.t0 - 5,
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[np.min(self.frequency) - 300, np.max(self.frequency) + 300]
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[np.min(self.frequency) - 300, np.max(self.frequency) + 300],
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)
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ax0.set_ylim(np.min(self.frequency) - 100,
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np.max(self.frequency) + 200)
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ax0.set_ylim(np.min(self.frequency) - 100, np.max(self.frequency) + 200)
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for track_id in self.data.ids:
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@ -155,11 +150,9 @@ class ChirpPlotBuffer:
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# tmask = (t >= t0_track) & (t <= (t0_track + dt_track))
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t = self.data.time[self.data.idx[window_idx]]
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if track_id == self.track_id:
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ax0.plot(t-self.t0_old, f, lw=lw,
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zorder=10, color=ps.gblue1)
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ax0.plot(t - self.t0_old, f, lw=lw, zorder=10, color=ps.gblue1)
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else:
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ax0.plot(t-self.t0_old, f, lw=lw,
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zorder=10, color=ps.black)
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ax0.plot(t - self.t0_old, f, lw=lw, zorder=10, color=ps.black)
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# ax0.fill_between(
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# np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate),
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@ -181,10 +174,12 @@ class ChirpPlotBuffer:
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# alpha=0.5,
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# )
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ax0.axhline(q50 - self.config.minimal_bandwidth / 2,
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color=ps.gblue1, lw=1, ls="dashed")
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ax0.axhline(q50 + self.config.minimal_bandwidth / 2,
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color=ps.gblue1, lw=1, ls="dashed")
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ax0.axhline(
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q50 - self.config.minimal_bandwidth / 2, color=ps.gblue1, lw=1, ls="dashed"
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)
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ax0.axhline(
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q50 + self.config.minimal_bandwidth / 2, color=ps.gblue1, lw=1, ls="dashed"
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)
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ax0.axhline(search_lower, color=ps.gblue2, lw=1, ls="dashed")
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ax0.axhline(search_upper, color=ps.gblue2, lw=1, ls="dashed")
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@ -197,7 +192,10 @@ class ChirpPlotBuffer:
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if len(chirps) > 0:
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for chirp in chirps:
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ax0.scatter(
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chirp, np.median(self.frequency), c=ps.red, marker=".",
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chirp,
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np.median(self.frequency),
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c=ps.red,
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marker=".",
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edgecolors=ps.black,
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facecolors=ps.red,
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zorder=10,
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@ -205,27 +203,43 @@ class ChirpPlotBuffer:
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)
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# plot waveform of filtered signal
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ax1.plot(self.time, self.baseline * waveform_scaler,
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c=ps.gray, lw=lw, alpha=0.5)
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ax1.plot(self.time, self.baseline_envelope_unfiltered *
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waveform_scaler, c=ps.gblue1, lw=lw, label="baseline envelope")
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ax1.plot(
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self.time, self.baseline * waveform_scaler, c=ps.gray, lw=lw, alpha=0.5
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)
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ax1.plot(
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self.time,
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self.baseline_envelope_unfiltered * waveform_scaler,
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c=ps.gblue1,
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lw=lw,
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label="baseline envelope",
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)
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# plot waveform of filtered search signal
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ax2.plot(self.time, self.search * waveform_scaler,
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c=ps.gray, lw=lw, alpha=0.5)
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ax2.plot(self.time, self.search_envelope_unfiltered *
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waveform_scaler, c=ps.gblue2, lw=lw, label="search envelope")
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ax2.plot(self.time, self.search * waveform_scaler, c=ps.gray, lw=lw, alpha=0.5)
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ax2.plot(
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self.time,
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self.search_envelope_unfiltered * waveform_scaler,
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c=ps.gblue2,
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lw=lw,
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label="search envelope",
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)
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# plot baseline instantaneous frequency
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ax3.plot(self.frequency_time, self.frequency,
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c=ps.gblue3, lw=lw, label="baseline inst. freq.")
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ax3.plot(
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self.frequency_time,
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self.frequency,
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c=ps.gblue3,
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lw=lw,
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label="baseline inst. freq.",
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)
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# plot filtered and rectified envelope
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ax4.plot(self.time, self.baseline_envelope *
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waveform_scaler, c=ps.gblue1, lw=lw)
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ax4.plot(
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self.time, self.baseline_envelope * waveform_scaler, c=ps.gblue1, lw=lw
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)
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ax4.scatter(
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(self.time)[self.baseline_peaks],
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(self.baseline_envelope*waveform_scaler)[self.baseline_peaks],
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(self.baseline_envelope * waveform_scaler)[self.baseline_peaks],
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edgecolors=ps.black,
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facecolors=ps.red,
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zorder=10,
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@ -235,11 +249,10 @@ class ChirpPlotBuffer:
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)
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# plot envelope of search signal
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ax5.plot(self.time, self.search_envelope *
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waveform_scaler, c=ps.gblue2, lw=lw)
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ax5.plot(self.time, self.search_envelope * waveform_scaler, c=ps.gblue2, lw=lw)
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ax5.scatter(
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(self.time)[self.search_peaks],
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(self.search_envelope*waveform_scaler)[self.search_peaks],
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(self.search_envelope * waveform_scaler)[self.search_peaks],
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edgecolors=ps.black,
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facecolors=ps.red,
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zorder=10,
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@ -249,8 +262,7 @@ class ChirpPlotBuffer:
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)
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# plot filtered instantaneous frequency
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ax6.plot(self.frequency_time,
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self.frequency_filtered, c=ps.gblue3, lw=lw)
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ax6.plot(self.frequency_time, self.frequency_filtered, c=ps.gblue3, lw=lw)
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ax6.scatter(
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self.frequency_time[self.frequency_peaks],
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self.frequency_filtered[self.frequency_peaks],
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@ -284,8 +296,7 @@ class ChirpPlotBuffer:
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# ax7.spines.bottom.set_bounds((0, 5))
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ax0.set_xlim(0, self.config.window)
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plt.subplots_adjust(left=0.165, right=0.975,
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top=0.98, bottom=0.074, hspace=0.2)
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plt.subplots_adjust(left=0.165, right=0.975, top=0.98, bottom=0.074, hspace=0.2)
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fig.align_labels()
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if plot == "show":
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@ -306,7 +317,7 @@ def plot_spectrogram(
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signal: np.ndarray,
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samplerate: float,
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window_start_seconds: float,
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ylims: list[float]
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ylims: list[float],
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) -> np.ndarray:
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"""
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Plot a spectrogram of a signal.
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@ -390,9 +401,7 @@ def extract_frequency_bands(
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q25, q75 = q50 - minimal_bandwidth / 2, q50 + minimal_bandwidth / 2
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# filter baseline
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filtered_baseline = bandpass_filter(
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raw_data, samplerate, lowf=q25, highf=q75
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)
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filtered_baseline = bandpass_filter(raw_data, samplerate, lowf=q25, highf=q75)
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# filter search area
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filtered_search_freq = bandpass_filter(
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@ -442,15 +451,13 @@ def window_median_all_track_ids(
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window_idx = np.arange(len(data.idx))[
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(data.ident == track_id)
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& (data.time[data.idx] >= window_start_seconds)
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& (
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data.time[data.idx]
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<= (window_start_seconds + window_duration_seconds)
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)
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& (data.time[data.idx] <= (window_start_seconds + window_duration_seconds))
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]
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if len(data.freq[window_idx]) > 0:
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frequency_percentiles.append(
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np.percentile(data.freq[window_idx], [25, 50, 75]))
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np.percentile(data.freq[window_idx], [25, 50, 75])
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)
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track_ids.append(track_id)
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# convert to numpy array
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@ -477,7 +484,7 @@ def array_center(array: np.ndarray) -> float:
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"""
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if len(array) % 2 == 0:
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return np.mean(array[int(len(array) / 2) - 1:int(len(array) / 2) + 1])
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return np.mean(array[int(len(array) / 2) - 1 : int(len(array) / 2) + 1])
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else:
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return array[int(len(array) / 2)]
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@ -533,8 +540,7 @@ def find_searchband(
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# get tracks that fall into search window
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check_track_ids = percentiles_ids[
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(q25 > current_median) & (
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q75 < search_window[-1])
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(q25 > current_median) & (q75 < search_window[-1])
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]
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# iterate through theses tracks
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@ -547,8 +553,7 @@ def find_searchband(
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bool_lower[search_window > q25_temp - config.search_res] = False
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bool_upper[search_window < q75_temp + config.search_res] = False
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search_window_bool[(bool_lower == False) &
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(bool_upper == False)] = False
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search_window_bool[(bool_lower == False) & (bool_upper == False)] = False
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# find gaps in search window
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search_window_indices = np.arange(len(search_window))
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@ -567,9 +572,7 @@ def find_searchband(
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# if the first value is -1, the array starst with true, so a gap
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if nonzeros[0] == -1:
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stops = search_window_indices[search_window_gaps == -1]
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starts = np.append(
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0, search_window_indices[search_window_gaps == 1]
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)
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starts = np.append(0, search_window_indices[search_window_gaps == 1])
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# if the last value is -1, the array ends with true, so a gap
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if nonzeros[-1] == 1:
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@ -605,7 +608,7 @@ def find_searchband(
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return config.default_search_freq
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def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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assert plot in [
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"save",
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@ -623,7 +626,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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assert plot == "show", "debug mode only runs when plot is 'show'"
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# load raw file
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print('datapath', datapath)
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print("datapath", datapath)
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data = LoadData(datapath)
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# load config file
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@ -691,9 +694,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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)
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# iterate through all fish
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for tr, track_id in enumerate(
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np.unique(data.ident[~np.isnan(data.ident)])
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):
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for tr, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
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logger.debug(f"Processing track {tr} of {len(data.ids)}")
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@ -714,11 +715,11 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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# approximate sampling rate to compute expected durations if there
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# is data available for this time window for this fish id
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# track_samplerate = np.mean(1 / np.diff(data.time))
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# expected_duration = (
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# (window_start_seconds + window_duration_seconds)
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# - window_start_seconds
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# ) * track_samplerate
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# track_samplerate = np.mean(1 / np.diff(data.time))
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# expected_duration = (
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# (window_start_seconds + window_duration_seconds)
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# - window_start_seconds
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# ) * track_samplerate
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# check if tracked data available in this window
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if len(current_frequencies) < 3:
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@ -731,17 +732,16 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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nanchecker = np.unique(np.isnan(current_powers))
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if (len(nanchecker) == 1) and nanchecker[0] is True:
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logger.warning(
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f"No powers available for track {track_id} window {st},"
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"skipping."
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f"No powers available for track {track_id} window {st}," "skipping."
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)
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continue
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# find the strongest electrodes for the current fish in the current
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# window
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best_electrode_index = np.argsort(
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np.nanmean(current_powers, axis=0)
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)[-config.number_electrodes:]
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best_electrode_index = np.argsort(np.nanmean(current_powers, axis=0))[
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-config.number_electrodes :
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]
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# find a frequency above the baseline of the current fish in which
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# no other fish is active to search for chirps there
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@ -763,8 +763,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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for el, electrode_index in enumerate(best_electrode_index):
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logger.debug(
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f"Processing electrode {el+1} of "
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f"{len(best_electrode_index)}"
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f"Processing electrode {el+1} of " f"{len(best_electrode_index)}"
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)
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# LOAD DATA FOR CURRENT ELECTRODE AND CURRENT FISH ------------
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@ -773,9 +772,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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current_raw_data = data.raw[
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window_start_index:window_stop_index, electrode_index
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]
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current_raw_time = raw_time[
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window_start_index:window_stop_index
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]
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current_raw_time = raw_time[window_start_index:window_stop_index]
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# EXTRACT FEATURES --------------------------------------------
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@ -897,9 +894,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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baseline_frequency_time <= no_edges_t1
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)
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baseline_frequency_filtered = baseline_frequency_filtered[
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no_edges
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]
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baseline_frequency_filtered = baseline_frequency_filtered[no_edges]
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baseline_frequency = baseline_frequency[no_edges]
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baseline_frequency_time = (
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baseline_frequency_time[no_edges] + window_start_seconds
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@ -934,11 +929,8 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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# DETECT CHIRPS IN SEARCH WINDOW ------------------------------
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# get the peak timestamps from the peak indices
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baseline_peak_timestamps = current_raw_time[
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baseline_peak_indices
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]
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search_peak_timestamps = current_raw_time[
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search_peak_indices]
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baseline_peak_timestamps = current_raw_time[baseline_peak_indices]
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search_peak_timestamps = current_raw_time[search_peak_indices]
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frequency_peak_timestamps = baseline_frequency_time[
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frequency_peak_indices
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@ -953,7 +945,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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or len(frequency_peak_timestamps) == 0
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)
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if one_feature_empty and (debug == 'false'):
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if one_feature_empty and (debug == "false"):
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continue
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# group peak across feature arrays but only if they
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@ -974,18 +966,18 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
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# check it there are chirps detected after grouping, continue
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# with the loop if not
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if (len(singleelectrode_chirps) == 0) and (debug == 'false'):
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if (len(singleelectrode_chirps) == 0) and (debug == "false"):
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continue
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# append chirps from this electrode to the multilectrode list
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multielectrode_chirps.append(singleelectrode_chirps)
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# only initialize the plotting buffer if chirps are detected
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chirp_detected = (el == (config.number_electrodes - 1)
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& (plot in ["show", "save"])
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)
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chirp_detected = el == (config.number_electrodes - 1) & (
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plot in ["show", "save"]
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)
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if chirp_detected or (debug != 'elecrode'):
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if chirp_detected or (debug != "elecrode"):
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logger.debug("Detected chirp, ititialize buffer ...")
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@ -1016,9 +1008,8 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
|
||||
logger.debug("Buffer initialized!")
|
||||
|
||||
if debug == "electrode":
|
||||
logger.info(f'Plotting electrode {el} ...')
|
||||
buffer.plot_buffer(
|
||||
chirps=singleelectrode_chirps, plot=plot)
|
||||
logger.info(f"Plotting electrode {el} ...")
|
||||
buffer.plot_buffer(chirps=singleelectrode_chirps, plot=plot)
|
||||
|
||||
logger.debug(
|
||||
f"Processed all electrodes for fish {track_id} for this"
|
||||
@ -1028,7 +1019,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
|
||||
# check if there are chirps detected in multiple electrodes and
|
||||
# continue the loop if not
|
||||
|
||||
if (len(multielectrode_chirps) == 0) and (debug == 'false'):
|
||||
if (len(multielectrode_chirps) == 0) and (debug == "false"):
|
||||
continue
|
||||
|
||||
# validate multielectrode chirps, i.e. check if they are
|
||||
@ -1054,11 +1045,14 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
|
||||
# chirps, otheswise try to delete the buffer if it exists
|
||||
|
||||
if debug == "fish":
|
||||
logger.info(f'Plotting fish {track_id} ...')
|
||||
logger.info(f"Plotting fish {track_id} ...")
|
||||
buffer.plot_buffer(multielectrode_chirps_validated, plot)
|
||||
|
||||
if ((len(multielectrode_chirps_validated) > 0) &
|
||||
(plot in ["show", "save"]) & (debug == 'false')):
|
||||
if (
|
||||
(len(multielectrode_chirps_validated) > 0)
|
||||
& (plot in ["show", "save"])
|
||||
& (debug == "false")
|
||||
):
|
||||
try:
|
||||
buffer.plot_buffer(multielectrode_chirps_validated, plot)
|
||||
del buffer
|
||||
@ -1085,9 +1079,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
|
||||
|
||||
# add flattened chirps to the list
|
||||
multiwindow_chirps_flat.extend(current_track_chirps)
|
||||
multiwindow_ids_flat.extend(
|
||||
list(np.ones_like(current_track_chirps) * track_id)
|
||||
)
|
||||
multiwindow_ids_flat.extend(list(np.ones_like(current_track_chirps) * track_id))
|
||||
|
||||
# purge duplicates, i.e. chirps that are very close to each other
|
||||
# duplites arise due to overlapping windows
|
||||
|
||||
@ -16,8 +16,8 @@ def PlotStyle() -> None:
|
||||
|
||||
# colors
|
||||
black = "#111116"
|
||||
white = "#e0e4f7"
|
||||
gray = "#6c6e7d"
|
||||
white = "#111116"
|
||||
gray = "#111116"
|
||||
blue = "#89b4fa"
|
||||
sapphire = "#74c7ec"
|
||||
sky = "#89dceb"
|
||||
@ -216,10 +216,10 @@ def PlotStyle() -> None:
|
||||
SMALL_SIZE = 12
|
||||
MEDIUM_SIZE = 14
|
||||
BIGGER_SIZE = 16
|
||||
black = "#111116"
|
||||
white = "#e0e4f7"
|
||||
gray = "#6c6e7d"
|
||||
dark_gray = "#2a2a32"
|
||||
black = "#e0e4f7"
|
||||
white = "#111116"
|
||||
# gray = "#6c6e7d"
|
||||
# dark_gray = "#2a2a32"
|
||||
|
||||
# rcparams
|
||||
plt.rc("font", size=MEDIUM_SIZE) # controls default text sizes
|
||||
@ -250,46 +250,46 @@ def PlotStyle() -> None:
|
||||
# plt.rcParams["font.sans-serif"] = "Helvetica Now Text"
|
||||
|
||||
# dark mode modifications
|
||||
plt.rcParams["boxplot.flierprops.color"] = white
|
||||
plt.rcParams["boxplot.flierprops.markeredgecolor"] = gray
|
||||
plt.rcParams["boxplot.boxprops.color"] = gray
|
||||
plt.rcParams["boxplot.whiskerprops.color"] = gray
|
||||
plt.rcParams["boxplot.capprops.color"] = gray
|
||||
plt.rcParams["boxplot.medianprops.color"] = black
|
||||
plt.rcParams["text.color"] = white
|
||||
plt.rcParams["axes.facecolor"] = black # axes background color
|
||||
plt.rcParams["axes.edgecolor"] = white # 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.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",
|
||||
],
|
||||
)
|
||||
plt.rcParams["xtick.color"] = white # color of the ticks
|
||||
plt.rcParams["ytick.color"] = white # color of the ticks
|
||||
plt.rcParams["grid.color"] = white # grid 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
|
||||
# plt.rcParams["boxplot.flierprops.color"] = white
|
||||
# plt.rcParams["boxplot.flierprops.markeredgecolor"] = gray
|
||||
# plt.rcParams["boxplot.boxprops.color"] = gray
|
||||
# plt.rcParams["boxplot.whiskerprops.color"] = gray
|
||||
# plt.rcParams["boxplot.capprops.color"] = gray
|
||||
# plt.rcParams["boxplot.medianprops.color"] = black
|
||||
# plt.rcParams["text.color"] = white
|
||||
# plt.rcParams["axes.facecolor"] = black # axes background color
|
||||
# plt.rcParams["axes.edgecolor"] = white # 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.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",
|
||||
# ],
|
||||
# )
|
||||
# plt.rcParams["xtick.color"] = white # color of the ticks
|
||||
# plt.rcParams["ytick.color"] = white # color of the ticks
|
||||
# plt.rcParams["grid.color"] = white # grid 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
|
||||
|
||||
|
||||
407
code/modules/plotstyle_dark.py
Normal file
407
code/modules/plotstyle_dark.py
Normal file
@ -0,0 +1,407 @@
|
||||
import cmocean as cmo
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from cycler import cycler
|
||||
from matplotlib.colors import ListedColormap
|
||||
|
||||
|
||||
def PlotStyle() -> None:
|
||||
class style:
|
||||
|
||||
# lightcmap = cmocean.tools.lighten(cmocean.cm.haline, 0.8)
|
||||
|
||||
# units
|
||||
cm = 1 / 2.54
|
||||
mm = 1 / 25.4
|
||||
|
||||
# colors
|
||||
black = "#111116"
|
||||
white = "#e0e4f7"
|
||||
gray = "#6c6e7d"
|
||||
blue = "#89b4fa"
|
||||
sapphire = "#74c7ec"
|
||||
sky = "#89dceb"
|
||||
teal = "#94e2d5"
|
||||
green = "#a6e3a1"
|
||||
yellow = "#f9d67f"
|
||||
orange = "#faa472"
|
||||
maroon = "#eb8486"
|
||||
red = "#e0e4f7"
|
||||
purple = "#d89bf7"
|
||||
pink = "#f59edb"
|
||||
lavender = "#b4befe"
|
||||
gblue1 = "#f37588"
|
||||
gblue2 = "#faa472"
|
||||
gblue3 = "#f9d67f"
|
||||
g = "#f3626c"
|
||||
|
||||
@classmethod
|
||||
def lims(cls, track1, track2):
|
||||
"""Helper function to get frequency y axis limits from two
|
||||
fundamental frequency tracks.
|
||||
|
||||
Args:
|
||||
track1 (array): First track
|
||||
track2 (array): Second track
|
||||
start (int): Index for first value to be plotted
|
||||
stop (int): Index for second value to be plotted
|
||||
padding (int): Padding for the upper and lower limit
|
||||
|
||||
Returns:
|
||||
lower (float): lower limit
|
||||
upper (float): upper limit
|
||||
|
||||
"""
|
||||
allfunds_tmp = (
|
||||
np.concatenate(
|
||||
[
|
||||
track1,
|
||||
track2,
|
||||
]
|
||||
)
|
||||
.ravel()
|
||||
.tolist()
|
||||
)
|
||||
lower = np.min(allfunds_tmp)
|
||||
upper = np.max(allfunds_tmp)
|
||||
return lower, upper
|
||||
|
||||
@classmethod
|
||||
def circled_annotation(cls, text, axis, xpos, ypos, padding=0.25):
|
||||
axis.text(
|
||||
xpos,
|
||||
ypos,
|
||||
text,
|
||||
ha="center",
|
||||
va="center",
|
||||
zorder=1000,
|
||||
bbox=dict(
|
||||
boxstyle=f"circle, pad={padding}", fc="white", ec="black", lw=1
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def fade_cmap(cls, cmap):
|
||||
|
||||
my_cmap = cmap(np.arange(cmap.N))
|
||||
my_cmap[:, -1] = np.linspace(0, 1, cmap.N)
|
||||
my_cmap = ListedColormap(my_cmap)
|
||||
|
||||
return my_cmap
|
||||
|
||||
@classmethod
|
||||
def hide_ax(cls, ax):
|
||||
ax.xaxis.set_visible(False)
|
||||
plt.setp(ax.spines.values(), visible=False)
|
||||
ax.tick_params(left=False, labelleft=False)
|
||||
ax.patch.set_visible(False)
|
||||
|
||||
@classmethod
|
||||
def hide_xax(cls, ax):
|
||||
ax.xaxis.set_visible(False)
|
||||
ax.spines["bottom"].set_visible(False)
|
||||
|
||||
@classmethod
|
||||
def hide_yax(cls, ax):
|
||||
ax.yaxis.set_visible(False)
|
||||
ax.spines["left"].set_visible(False)
|
||||
|
||||
@classmethod
|
||||
def set_boxplot_color(cls, bp, color):
|
||||
plt.setp(bp["boxes"], color=color)
|
||||
plt.setp(bp["whiskers"], color=white)
|
||||
plt.setp(bp["caps"], color=white)
|
||||
plt.setp(bp["medians"], color=black)
|
||||
|
||||
@classmethod
|
||||
def label_subplots(cls, labels, axes, fig):
|
||||
for axis, label in zip(axes, labels):
|
||||
X = axis.get_position().x0
|
||||
Y = axis.get_position().y1
|
||||
fig.text(X, Y, label, weight="bold")
|
||||
|
||||
@classmethod
|
||||
def letter_subplots(
|
||||
cls, axes=None, letters=None, xoffset=-0.1, yoffset=1.0, **kwargs
|
||||
):
|
||||
"""Add letters to the corners of subplots (panels). By default each axis is
|
||||
given an uppercase bold letter label placed in the upper-left corner.
|
||||
Args
|
||||
axes : list of pyplot ax objects. default plt.gcf().axes.
|
||||
letters : list of strings to use as labels, default ["A", "B", "C", ...]
|
||||
xoffset, yoffset : positions of each label relative to plot frame
|
||||
(default -0.1,1.0 = upper left margin). Can also be a list of
|
||||
offsets, in which case it should be the same length as the number of
|
||||
axes.
|
||||
Other keyword arguments will be passed to annotate() when panel letters
|
||||
are added.
|
||||
Returns:
|
||||
list of strings for each label added to the axes
|
||||
Examples:
|
||||
Defaults:
|
||||
>>> fig, axes = plt.subplots(1,3)
|
||||
>>> letter_subplots() # boldfaced A, B, C
|
||||
|
||||
Common labeling schemes inferred from the first letter:
|
||||
>>> fig, axes = plt.subplots(1,4)
|
||||
# panels labeled (a), (b), (c), (d)
|
||||
>>> letter_subplots(letters='(a)')
|
||||
Fully custom lettering:
|
||||
>>> fig, axes = plt.subplots(2,1)
|
||||
>>> letter_subplots(axes, letters=['(a.1)', '(b.2)'], fontweight='normal')
|
||||
Per-axis offsets:
|
||||
>>> fig, axes = plt.subplots(1,2)
|
||||
>>> letter_subplots(axes, xoffset=[-0.1, -0.15])
|
||||
|
||||
Matrix of axes:
|
||||
>>> fig, axes = plt.subplots(2,2, sharex=True, sharey=True)
|
||||
# fig.axes is a list when axes is a 2x2 matrix
|
||||
>>> letter_subplots(fig.axes)
|
||||
"""
|
||||
|
||||
# get axes:
|
||||
if axes is None:
|
||||
axes = plt.gcf().axes
|
||||
# handle single axes:
|
||||
try:
|
||||
iter(axes)
|
||||
except TypeError:
|
||||
axes = [axes]
|
||||
|
||||
# set up letter defaults (and corresponding fontweight):
|
||||
fontweight = "bold"
|
||||
ulets = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ"[: len(axes)])
|
||||
llets = list("abcdefghijklmnopqrstuvwxyz"[: len(axes)])
|
||||
if letters is None or letters == "A":
|
||||
letters = ulets
|
||||
elif letters == "(a)":
|
||||
letters = ["({})".format(lett) for lett in llets]
|
||||
fontweight = "normal"
|
||||
elif letters == "(A)":
|
||||
letters = ["({})".format(lett) for lett in ulets]
|
||||
fontweight = "normal"
|
||||
elif letters in ("lower", "lowercase", "a"):
|
||||
letters = llets
|
||||
|
||||
# make sure there are x and y offsets for each ax in axes:
|
||||
if isinstance(xoffset, (int, float)):
|
||||
xoffset = [xoffset] * len(axes)
|
||||
else:
|
||||
assert len(xoffset) == len(axes)
|
||||
if isinstance(yoffset, (int, float)):
|
||||
yoffset = [yoffset] * len(axes)
|
||||
else:
|
||||
assert len(yoffset) == len(axes)
|
||||
|
||||
# defaults for annotate (kwargs is second so it can overwrite these defaults):
|
||||
my_defaults = dict(
|
||||
fontweight=fontweight,
|
||||
fontsize="large",
|
||||
ha="center",
|
||||
va="center",
|
||||
xycoords="axes fraction",
|
||||
annotation_clip=False,
|
||||
)
|
||||
kwargs = dict(list(my_defaults.items()) + list(kwargs.items()))
|
||||
|
||||
list_txts = []
|
||||
for ax, lbl, xoff, yoff in zip(axes, letters, xoffset, yoffset):
|
||||
t = ax.annotate(lbl, xy=(xoff, yoff), **kwargs)
|
||||
list_txts.append(t)
|
||||
return list_txts
|
||||
|
||||
pass
|
||||
|
||||
# rcparams text setup
|
||||
SMALL_SIZE = 12
|
||||
MEDIUM_SIZE = 14
|
||||
BIGGER_SIZE = 16
|
||||
black = "#111116"
|
||||
white = "#e0e4f7"
|
||||
gray = "#6c6e7d"
|
||||
dark_gray = "#2a2a32"
|
||||
|
||||
# rcparams
|
||||
plt.rc("font", size=MEDIUM_SIZE) # controls default text sizes
|
||||
plt.rc("axes", titlesize=MEDIUM_SIZE) # fontsize of the axes title
|
||||
plt.rc("axes", labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
|
||||
plt.rc("xtick", labelsize=SMALL_SIZE) # fontsize of the tick labels
|
||||
plt.rc("ytick", labelsize=SMALL_SIZE) # fontsize of the tick labels
|
||||
plt.rc("legend", fontsize=SMALL_SIZE) # legend fontsize
|
||||
plt.rc("figure", titlesize=BIGGER_SIZE) # fontsize of the figure title
|
||||
|
||||
plt.rcParams["image.cmap"] = "cmo.thermal"
|
||||
plt.rcParams["axes.xmargin"] = 0.05
|
||||
plt.rcParams["axes.ymargin"] = 0.1
|
||||
plt.rcParams["axes.titlelocation"] = "left"
|
||||
plt.rcParams["axes.titlesize"] = BIGGER_SIZE
|
||||
# plt.rcParams["axes.titlepad"] = -10
|
||||
plt.rcParams["legend.frameon"] = False
|
||||
plt.rcParams["legend.loc"] = "best"
|
||||
plt.rcParams["legend.borderpad"] = 0.4
|
||||
plt.rcParams["legend.facecolor"] = black
|
||||
plt.rcParams["legend.edgecolor"] = black
|
||||
plt.rcParams["legend.framealpha"] = 0.7
|
||||
plt.rcParams["legend.borderaxespad"] = 0.5
|
||||
plt.rcParams["legend.fancybox"] = False
|
||||
|
||||
# # specify the custom font to use
|
||||
# plt.rcParams["font.family"] = "sans-serif"
|
||||
# plt.rcParams["font.sans-serif"] = "Helvetica Now Text"
|
||||
|
||||
# dark mode modifications
|
||||
plt.rcParams["boxplot.flierprops.color"] = white
|
||||
plt.rcParams["boxplot.flierprops.markeredgecolor"] = gray
|
||||
plt.rcParams["boxplot.boxprops.color"] = gray
|
||||
plt.rcParams["boxplot.whiskerprops.color"] = gray
|
||||
plt.rcParams["boxplot.capprops.color"] = gray
|
||||
plt.rcParams["boxplot.medianprops.color"] = black
|
||||
plt.rcParams["text.color"] = white
|
||||
plt.rcParams["axes.facecolor"] = black # axes background color
|
||||
plt.rcParams["axes.edgecolor"] = white # 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.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",
|
||||
],
|
||||
)
|
||||
plt.rcParams["xtick.color"] = white # color of the ticks
|
||||
plt.rcParams["ytick.color"] = white # color of the ticks
|
||||
plt.rcParams["grid.color"] = white # grid 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
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
s = PlotStyle()
|
||||
|
||||
import matplotlib.cbook as cbook
|
||||
import matplotlib.cm as cm
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.patches import PathPatch
|
||||
from matplotlib.path import Path
|
||||
|
||||
# Fixing random state for reproducibility
|
||||
np.random.seed(19680801)
|
||||
|
||||
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)
|
||||
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(),
|
||||
)
|
||||
|
||||
plt.show()
|
||||
|
||||
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))
|
||||
|
||||
# Fixing random state for reproducibility
|
||||
np.random.seed(19680801)
|
||||
|
||||
# generate some random test data
|
||||
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")
|
||||
|
||||
# plot box plot
|
||||
axs[1].boxplot(all_data)
|
||||
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")
|
||||
|
||||
plt.show()
|
||||
|
||||
# Fixing random state for reproducibility
|
||||
np.random.seed(19680801)
|
||||
|
||||
# Compute pie slices
|
||||
N = 20
|
||||
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 / 10.0)
|
||||
|
||||
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",
|
||||
]
|
||||
|
||||
# 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": []}
|
||||
)
|
||||
|
||||
for ax, interp_method in zip(axs.flat, methods):
|
||||
ax.imshow(grid, interpolation=interp_method)
|
||||
ax.set_title(str(interp_method))
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
@ -277,7 +277,7 @@ def main(datapath: str):
|
||||
scatterloser, chirps_loser, color=loser_color)
|
||||
ax1.set_xticklabels(['Winner', 'Loser'])
|
||||
|
||||
ax1.text(0.1, 0.95, f'n={len(chirps_winner)}',
|
||||
ax1.text(0.1, 0.85, f'n={len(chirps_loser)}',
|
||||
transform=ax1.transAxes, color=ps.white)
|
||||
|
||||
for w, l in zip(chirps_winner, chirps_loser):
|
||||
@ -294,7 +294,7 @@ def main(datapath: str):
|
||||
ax2.scatter(size_diffs_loser, size_chirps_loser,
|
||||
color=loser_color, label='Loser')
|
||||
|
||||
ax2.text(0.05, 0.95, f'n={len(size_chirps_winner)}',
|
||||
ax2.text(0.05, 0.85, f'n={len(size_chirps_loser)}',
|
||||
transform=ax2.transAxes, color=ps.white)
|
||||
|
||||
ax2.set_xlabel('Size difference [cm]')
|
||||
@ -303,7 +303,7 @@ def main(datapath: str):
|
||||
ax3.scatter(freq_diffs_higher, freq_chirps_winner, color=winner_color)
|
||||
ax3.scatter(freq_diffs_lower, freq_chirps_loser, color=loser_color)
|
||||
|
||||
ax3.text(0.1, 0.95, f'n={len(freq_chirps_loser)}',
|
||||
ax3.text(0.1, 0.85, f'n={len(np.asarray(freq_chirps_winner)[~np.isnan(freq_chirps_loser)])}',
|
||||
transform=ax3.transAxes, color=ps.white)
|
||||
|
||||
ax3.set_xlabel('EODf [Hz]')
|
||||
|
||||
@ -282,8 +282,8 @@ def main(dataroot):
|
||||
kde_time,
|
||||
np.percentile(loser_offsets_boot[-1], 1, axis=0),
|
||||
np.percentile(loser_offsets_boot[-1], 99, axis=0),
|
||||
color=ps.white,
|
||||
alpha=0.4)
|
||||
color='gray',
|
||||
alpha=0.8)
|
||||
|
||||
ax[i].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0),
|
||||
color=ps.black, linewidth=2)
|
||||
|
||||
14
code/requirements.txt
Normal file
14
code/requirements.txt
Normal file
@ -0,0 +1,14 @@
|
||||
audioio==0.9.5
|
||||
cmocean==2.0
|
||||
cycler==0.11.0
|
||||
ipython==8.10.0
|
||||
matplotlib==3.7.0
|
||||
numpy==1.23.5
|
||||
pandas==1.5.3
|
||||
paramiko==2.11.1
|
||||
PyYAML==6.0
|
||||
scikit_learn==1.2.1
|
||||
scipy==1.10.1
|
||||
scp==0.14.5
|
||||
thunderfish==1.9.9
|
||||
tqdm==4.64.1
|
||||
10
code/run_plots.sh
Executable file
10
code/run_plots.sh
Executable file
@ -0,0 +1,10 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
color='\033[1;91m'
|
||||
nocolor='\033[0m'
|
||||
message='Running scripts in directory: '
|
||||
|
||||
for py_file in $(ls plot_*); do
|
||||
echo -e $message$color$py_file$nocolor
|
||||
python $py_file
|
||||
done
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
BIN
poster_frozen/figs/algorithm.pdf
Normal file
BIN
poster_frozen/figs/algorithm.pdf
Normal file
Binary file not shown.
BIN
poster_frozen/figs/algorithm1.pdf
Normal file
BIN
poster_frozen/figs/algorithm1.pdf
Normal file
Binary file not shown.
BIN
poster_frozen/figs/chirps_in_chasing.pdf
Normal file
BIN
poster_frozen/figs/chirps_in_chasing.pdf
Normal file
Binary file not shown.
BIN
poster_frozen/figs/chirps_winner_loser.pdf
Normal file
BIN
poster_frozen/figs/chirps_winner_loser.pdf
Normal file
Binary file not shown.
BIN
poster_frozen/figs/efishlogo.pdf
Normal file
BIN
poster_frozen/figs/efishlogo.pdf
Normal file
Binary file not shown.
BIN
poster_frozen/figs/introplot.pdf
Normal file
BIN
poster_frozen/figs/introplot.pdf
Normal file
Binary file not shown.
BIN
poster_frozen/figs/kde.pdf
Normal file
BIN
poster_frozen/figs/kde.pdf
Normal file
Binary file not shown.
529
poster_frozen/figs/logo_all.pdf
Normal file
529
poster_frozen/figs/logo_all.pdf
Normal file
File diff suppressed because one or more lines are too long
BIN
poster_frozen/figs/timeline.pdf
Normal file
BIN
poster_frozen/figs/timeline.pdf
Normal file
Binary file not shown.
BIN
poster_frozen/main.pdf
Normal file
BIN
poster_frozen/main.pdf
Normal file
Binary file not shown.
Binary file not shown.
|
Before Width: | Height: | Size: 116 KiB |
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
Before Width: | Height: | Size: 40 KiB |
File diff suppressed because it is too large
Load Diff
|
Before Width: | Height: | Size: 84 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 157 KiB |
Binary file not shown.
@ -1,119 +0,0 @@
|
||||
\documentclass[25pt, a0paper, landscape, margin=0mm, innermargin=20mm,
|
||||
blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default values for poster format options.
|
||||
|
||||
\input{packages}
|
||||
\input{style}
|
||||
|
||||
\begin{document}
|
||||
|
||||
\renewcommand{\baselinestretch}{1}
|
||||
\title{\parbox{1900pt}{Pushing the limits of time-frequency uncertainty in the
|
||||
detection of transient communication signals in weakly electric fish}}
|
||||
\author{Sina Prause, Alexander Wendt, Patrick Weygoldt}
|
||||
\institute{Supervised by Till Raab \& Jan Benda, Neurothology Group,
|
||||
University of Tübingen}
|
||||
\usetitlestyle[]{sampletitle}
|
||||
\maketitle
|
||||
\renewcommand{\baselinestretch}{1.4}
|
||||
|
||||
\begin{columns}
|
||||
\column{0.5}
|
||||
\myblock[TranspBlock]{Introduction}{
|
||||
\begin{minipage}[t]{0.55\linewidth}
|
||||
The time-frequency tradeoff makes reliable signal detecion and simultaneous
|
||||
sender identification of freely interacting individuals impossible.
|
||||
This profoundly limits our current understanding of chirps to experiments
|
||||
with single - or physically separated - individuals.
|
||||
\end{minipage} \hfill
|
||||
\begin{minipage}[t]{0.40\linewidth}
|
||||
\vspace{-1.5cm}
|
||||
\begin{tikzfigure}[]
|
||||
\label{tradeoff}
|
||||
\includegraphics[width=\linewidth]{figs/introplot}
|
||||
\end{tikzfigure}
|
||||
\end{minipage}
|
||||
}
|
||||
|
||||
\myblock[TranspBlock]{A chirp detection algorithm}{
|
||||
\begin{tikzfigure}[]
|
||||
\label{modulations}
|
||||
\includegraphics[width=\linewidth]{figs/algorithm}
|
||||
\end{tikzfigure}
|
||||
}
|
||||
|
||||
\column{0.5}
|
||||
\myblock[TranspBlock]{Chirps and diadic competitions}{
|
||||
\begin{minipage}[t]{0.7\linewidth}
|
||||
\begin{tikzfigure}[]
|
||||
\label{modulations}
|
||||
\includegraphics[width=\linewidth]{figs/placeholder1}
|
||||
\end{tikzfigure}
|
||||
\end{minipage} \hfill
|
||||
\begin{minipage}[t]{0.25\linewidth}
|
||||
\lipsum[3][1-3]
|
||||
\end{minipage}
|
||||
|
||||
\begin{minipage}[t]{0.7\linewidth}
|
||||
\begin{tikzfigure}[]
|
||||
\label{modulations}
|
||||
\includegraphics[width=\linewidth]{figs/placeholder1}
|
||||
\end{tikzfigure}
|
||||
\end{minipage} \hfill
|
||||
\begin{minipage}[t]{0.25\linewidth}
|
||||
\lipsum[3][1-3]
|
||||
\end{minipage}
|
||||
|
||||
\begin{minipage}[t]{0.7\linewidth}
|
||||
\begin{tikzfigure}[]
|
||||
\label{modulations}
|
||||
\includegraphics[width=\linewidth]{figs/placeholder1}
|
||||
\end{tikzfigure}
|
||||
\end{minipage} \hfill
|
||||
\begin{minipage}[t]{0.25\linewidth}
|
||||
\lipsum[3][1-3]
|
||||
\end{minipage}
|
||||
|
||||
|
||||
}
|
||||
|
||||
\myblock[TranspBlock]{Conclusion}{
|
||||
\lipsum[3][1-9]
|
||||
}
|
||||
|
||||
% \column{0.3}
|
||||
% \myblock[TranspBlock]{More Results}{
|
||||
% \begin{tikzfigure}[]
|
||||
% \label{results}
|
||||
% \includegraphics[width=\linewidth]{example-image-a}
|
||||
% \end{tikzfigure}
|
||||
|
||||
% \begin{multicols}{2}
|
||||
% \lipsum[5][1-8]
|
||||
% \end{multicols}
|
||||
% \vspace{-1cm}
|
||||
% }
|
||||
|
||||
% \myblock[TranspBlock]{Conclusion}{
|
||||
% \begin{itemize}
|
||||
% \setlength\itemsep{0.5em}
|
||||
% \item \lipsum[1][1]
|
||||
% \item \lipsum[1][1]
|
||||
% \item \lipsum[1][1]
|
||||
% \end{itemize}
|
||||
% \vspace{0.2cm}
|
||||
% }
|
||||
\end{columns}
|
||||
|
||||
\node[
|
||||
above right,
|
||||
text=white,
|
||||
outer sep=45pt,
|
||||
minimum width=\paperwidth,
|
||||
align=center,
|
||||
draw,
|
||||
fill=boxes,
|
||||
color=boxes,
|
||||
] at (-0.51\paperwidth,-43.5) {
|
||||
\textcolor{text}{\normalsize Contact: \{name\}.\{surname\}@student.uni-tuebingen.de}};
|
||||
|
||||
\end{document}
|
||||
@ -1,11 +0,0 @@
|
||||
\usepackage[utf8]{inputenc}
|
||||
\usepackage[scaled]{helvet}
|
||||
\renewcommand\familydefault{\sfdefault}
|
||||
\usepackage[T1]{fontenc}
|
||||
\usepackage{wrapfig}
|
||||
\usepackage{setspace}
|
||||
\usepackage{multicol}
|
||||
\setlength{\columnsep}{1.5cm}
|
||||
\usepackage{xspace}
|
||||
\usepackage{tikz}
|
||||
\usepackage{lipsum}
|
||||
@ -1,119 +0,0 @@
|
||||
\tikzposterlatexaffectionproofoff
|
||||
\usetheme{Default}
|
||||
|
||||
\definecolor{text}{HTML}{e0e4f7}
|
||||
\definecolor{background}{HTML}{111116}
|
||||
\definecolor{boxes}{HTML}{2a2a32}
|
||||
\definecolor{unired}{HTML}{a51e37}
|
||||
|
||||
\colorlet{blocktitlefgcolor}{text}
|
||||
\colorlet{backgroundcolor}{background}
|
||||
\colorlet{blocktitlebgcolor}{background}
|
||||
\colorlet{blockbodyfgcolor}{text}
|
||||
\colorlet{innerblocktitlebgcolor}{background}
|
||||
\colorlet{innerblocktitlefgcolor}{text}
|
||||
\colorlet{notefrcolor}{text}
|
||||
\colorlet{notefgcolor}{background}
|
||||
\colorlet{notebgcolor}{background}
|
||||
|
||||
% Title setup
|
||||
\settitle{
|
||||
% Rearrange the order of the minipages to e.g. center the title between the logos
|
||||
\begin{minipage}[c]{0.6\paperwidth}
|
||||
% \centering
|
||||
\vspace{2.5cm}\hspace{1.5cm}
|
||||
\color{text}{\Huge{\textbf{\@title}} \par}
|
||||
\vspace*{2em}\hspace{1.5cm}
|
||||
\color{text}{\LARGE \@author \par}
|
||||
\vspace*{2em}\hspace{1.5cm}
|
||||
\color{text}{\Large \@institute}
|
||||
\vspace{2.5cm}
|
||||
\end{minipage}
|
||||
\begin{minipage}[c]{0.2\paperwidth}
|
||||
% \centering
|
||||
\vspace{1cm}\hspace{1cm}
|
||||
\includegraphics[scale=1]{example-image-a}
|
||||
\end{minipage}
|
||||
\begin{minipage}[c]{0.2\paperwidth}
|
||||
% \vspace{1cm}\hspace{1cm}
|
||||
\centering
|
||||
\includegraphics[scale=1]{example-image-a}
|
||||
\end{minipage}}
|
||||
|
||||
% definie title style with background box
|
||||
\definetitlestyle{sampletitle}{
|
||||
width=1189mm,
|
||||
roundedcorners=0,
|
||||
linewidth=0pt,
|
||||
innersep=15pt,
|
||||
titletotopverticalspace=0mm,
|
||||
titletoblockverticalspace=5pt
|
||||
}{
|
||||
\begin{scope}[line width=\titlelinewidth, rounded corners=\titleroundedcorners]
|
||||
\draw[fill=text, color=boxes]
|
||||
(\titleposleft,\titleposbottom)
|
||||
rectangle
|
||||
(\titleposright,\titlepostop);
|
||||
\end{scope}
|
||||
}
|
||||
|
||||
% define coustom block style for visible blocks
|
||||
\defineblockstyle{GrayBlock}{
|
||||
titlewidthscale=1,
|
||||
bodywidthscale=1,
|
||||
% titlecenter,
|
||||
titleleft,
|
||||
titleoffsetx=0pt,
|
||||
titleoffsety=-30pt,
|
||||
bodyoffsetx=0pt,
|
||||
bodyoffsety=-40pt,
|
||||
bodyverticalshift=0mm,
|
||||
roundedcorners=25,
|
||||
linewidth=1pt,
|
||||
titleinnersep=20pt,
|
||||
bodyinnersep=38pt
|
||||
}{
|
||||
\draw[rounded corners=\blockroundedcorners, inner sep=\blockbodyinnersep,
|
||||
line width=\blocklinewidth, color=background,
|
||||
top color=boxes, bottom color=boxes,
|
||||
]
|
||||
(blockbody.south west) rectangle (blockbody.north east); %
|
||||
\ifBlockHasTitle%
|
||||
\draw[rounded corners=\blockroundedcorners, inner sep=\blocktitleinnersep,
|
||||
top color=background, bottom color=background,
|
||||
line width=2, color=background, %fill=blocktitlebgcolor
|
||||
]
|
||||
(blocktitle.south west) rectangle (blocktitle.north east); %
|
||||
\fi%
|
||||
}
|
||||
\newcommand\myblock[3][GrayBlock]{\useblockstyle{#1}\block{#2}{#3}\useblockstyle{Default}}
|
||||
|
||||
% Define blockstyle for tranparent block
|
||||
\defineblockstyle{TranspBlock}{
|
||||
titlewidthscale=0.99,
|
||||
bodywidthscale=0.99,
|
||||
titleleft,
|
||||
titleoffsetx=15pt,
|
||||
titleoffsety=-40pt,
|
||||
bodyoffsetx=0pt,
|
||||
bodyoffsety=-40pt,
|
||||
bodyverticalshift=0mm,
|
||||
roundedcorners=25,
|
||||
linewidth=1pt,
|
||||
titleinnersep=20pt,
|
||||
bodyinnersep=38pt
|
||||
}{
|
||||
\draw[rounded corners=\blockroundedcorners, inner sep=\blockbodyinnersep,
|
||||
line width=\blocklinewidth, color=background,
|
||||
top color=background, bottom color=background,
|
||||
]
|
||||
(blockbody.south west) rectangle (blockbody.north east); %
|
||||
\ifBlockHasTitle%
|
||||
\draw[rounded corners=\blockroundedcorners, inner sep=\blocktitleinnersep,
|
||||
top color=background, bottom color=background,
|
||||
line width=2, color=background, %fill=blocktitlebgcolor
|
||||
]
|
||||
(blocktitle.south west) rectangle (blocktitle.north east); %
|
||||
\fi%
|
||||
}
|
||||
\renewcommand\myblock[3][TranspBlock]{\useblockstyle{#1}\block{#2}{#3}\useblockstyle{Default}}
|
||||
148
requirements.txt
148
requirements.txt
@ -1,140 +1,14 @@
|
||||
asttokens==2.2.1
|
||||
backcall==0.2.0
|
||||
contourpy==1.0.6
|
||||
cycler==0.11.0
|
||||
decorator==5.1.1
|
||||
executing==1.2.0
|
||||
fonttools==4.38.0
|
||||
ipython==8.8.0
|
||||
jedi==0.18.2
|
||||
kiwisolver==1.4.4
|
||||
matplotlib==3.6.2
|
||||
matplotlib-inline==0.1.6
|
||||
numpy==1.24.1
|
||||
packaging==23.0
|
||||
pandas==1.5.2
|
||||
parso==0.8.3
|
||||
pexpect==4.8.0
|
||||
pickleshare==0.7.5
|
||||
Pillow==9.4.0
|
||||
prompt-toolkit==3.0.36
|
||||
ptyprocess==0.7.0
|
||||
pure-eval==0.2.2
|
||||
Pygments==2.14.0
|
||||
pyparsing==3.0.9
|
||||
python-dateutil==2.8.2
|
||||
pytz==2022.7
|
||||
scipy==1.10.0
|
||||
six==1.16.0
|
||||
sklearn==0.0.post1
|
||||
stack-data==0.6.2
|
||||
traitlets==5.8.0
|
||||
wcwidth==0.2.5
|
||||
asttokens==2.2.1
|
||||
audioio==0.10.0
|
||||
backcall==0.2.0
|
||||
contourpy==1.0.6
|
||||
cycler==0.11.0
|
||||
decorator==5.1.1
|
||||
executing==1.2.0
|
||||
fonttools==4.38.0
|
||||
ipython==8.8.0
|
||||
jedi==0.18.2
|
||||
kiwisolver==1.4.4
|
||||
matplotlib==3.6.2
|
||||
matplotlib-inline==0.1.6
|
||||
numpy==1.24.1
|
||||
packaging==23.0
|
||||
pandas==1.5.2
|
||||
parso==0.8.3
|
||||
pexpect==4.8.0
|
||||
pickleshare==0.7.5
|
||||
Pillow==9.4.0
|
||||
prompt-toolkit==3.0.36
|
||||
ptyprocess==0.7.0
|
||||
pure-eval==0.2.2
|
||||
Pygments==2.14.0
|
||||
pyparsing==3.0.9
|
||||
python-dateutil==2.8.2
|
||||
pytz==2022.7
|
||||
scipy==1.10.0
|
||||
six==1.16.0
|
||||
sklearn==0.0.post1
|
||||
stack-data==0.6.2
|
||||
thunderfish==1.9.10
|
||||
traitlets==5.8.0
|
||||
wcwidth==0.2.5
|
||||
appnope==0.1.3
|
||||
asttokens==2.2.1
|
||||
audioio==0.10.0
|
||||
backcall==0.2.0
|
||||
contourpy==1.0.6
|
||||
cycler==0.11.0
|
||||
decorator==5.1.1
|
||||
executing==1.2.0
|
||||
fonttools==4.38.0
|
||||
ipython==8.8.0
|
||||
jedi==0.18.2
|
||||
kiwisolver==1.4.4
|
||||
matplotlib==3.6.2
|
||||
matplotlib-inline==0.1.6
|
||||
numpy==1.24.1
|
||||
packaging==23.0
|
||||
pandas==1.5.2
|
||||
parso==0.8.3
|
||||
pexpect==4.8.0
|
||||
pickleshare==0.7.5
|
||||
Pillow==9.4.0
|
||||
prompt-toolkit==3.0.36
|
||||
ptyprocess==0.7.0
|
||||
pure-eval==0.2.2
|
||||
Pygments==2.14.0
|
||||
pyparsing==3.0.9
|
||||
python-dateutil==2.8.2
|
||||
pytz==2022.7
|
||||
scipy==1.10.0
|
||||
six==1.16.0
|
||||
sklearn==0.0.post1
|
||||
stack-data==0.6.2
|
||||
thunderfish==1.9.10
|
||||
traitlets==5.8.0
|
||||
wcwidth==0.2.5
|
||||
asttokens==2.2.1
|
||||
audioio==0.10.0
|
||||
backcall==0.2.0
|
||||
audioio==0.9.5
|
||||
cmocean==2.0
|
||||
contourpy==1.0.6
|
||||
cycler==0.11.0
|
||||
decorator==5.1.1
|
||||
executing==1.2.0
|
||||
fonttools==4.38.0
|
||||
ipython==8.8.0
|
||||
jedi==0.18.2
|
||||
joblib==1.2.0
|
||||
kiwisolver==1.4.4
|
||||
matplotlib==3.6.2
|
||||
matplotlib-inline==0.1.6
|
||||
numpy==1.24.1
|
||||
packaging==23.0
|
||||
pandas==1.5.2
|
||||
parso==0.8.3
|
||||
pexpect==4.8.0
|
||||
pickleshare==0.7.5
|
||||
Pillow==9.4.0
|
||||
prompt-toolkit==3.0.36
|
||||
ptyprocess==0.7.0
|
||||
pure-eval==0.2.2
|
||||
Pygments==2.14.0
|
||||
pyparsing==3.0.9
|
||||
python-dateutil==2.8.2
|
||||
pytz==2022.7
|
||||
ipython==8.10.0
|
||||
matplotlib==3.7.0
|
||||
numpy==1.24.2
|
||||
pandas==1.5.3
|
||||
paramiko==2.11.1
|
||||
PyYAML==6.0
|
||||
scikit-learn==1.2.0
|
||||
scipy==1.10.0
|
||||
six==1.16.0
|
||||
sklearn==0.0.post1
|
||||
stack-data==0.6.2
|
||||
threadpoolctl==3.1.0
|
||||
thunderfish==1.9.10
|
||||
traitlets==5.8.0
|
||||
wcwidth==0.2.5
|
||||
scikit_learn==1.2.1
|
||||
scipy==1.10.1
|
||||
scp==0.14.5
|
||||
thunderfish==1.9.9
|
||||
tqdm==4.64.1
|
||||
|
||||
Loading…
Reference in New Issue
Block a user