new poster template

code/chirpdetection.py

@@ -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)
+                         zorder=10, color=ps.black)
-
+
-        ax0.fill_between(
+        # ax0.fill_between(
-            np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate),
+        #     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,
-            q50 + self.config.minimal_bandwidth / 2,
+        #     q50 + self.config.minimal_bandwidth / 2,
-            color=ps.gblue1,
+        #     color=ps.gblue1,
-            lw=1,
+        #     lw=1,
-            ls="dashed",
+        #     ls="dashed",
-            alpha=0.5,
+        #     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",
+            marker=".",
-            facecolors="none",
+            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",
+            marker=".",
-            facecolors="none",
+            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",
+            marker=".",
-            facecolors="none",
+            s=70,
+            # facecolors="none",
         )
 
         ax0.set_ylabel("frequency [Hz]")
-        ax1.set_ylabel("a.u.")
+        ax1.set_ylabel(r"$\mu$V")
-        ax2.set_ylabel("a.u.")
+        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_start_index = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate
-    # window_duration_index = 60 * 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_start_index = 0
-    window_duration_index = data.raw.shape[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")

code/modules/plotstyle.py

@@ -23,16 +23,16 @@ def PlotStyle() -> None:
         sky = "#89dceb"
         teal = "#94e2d5"
         green = "#a6e3a1"
-        yellow = "#f9e2af"
+        yellow = "#f9d67f"
-        orange = "#fab387"
+        orange = "#faa472"
-        maroon = "#eba0ac"
+        maroon = "#eb8486"
-        red = "#f38ba8"
+        red = "#f37588"
-        purple = "#cba6f7"
+        purple = "#d89bf7"
-        pink = "#f5c2e7"
+        pink = "#f59edb"
         lavender = "#b4befe"
-        gblue1 = "#8cb8ff"
+        gblue1 = "#89b4fa"
-        gblue2 = "#7cdcdc"
+        gblue2 = "#89dceb"
-        gblue3 = "#82e896"
+        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"
@@ -247,11 +250,11 @@ 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.whiskerprops.color"] = gray
-    plt.rcParams["boxplot.capprops.color"] = white
+    plt.rcParams["boxplot.capprops.color"] = gray
-    plt.rcParams["boxplot.medianprops.color"] = white
+    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
@@ -264,20 +267,22 @@ def PlotStyle() -> None:
     plt.rcParams["axes.spines.top"] = False
     plt.rcParams["axes.spines.right"] = False
     plt.rcParams["axes.prop_cycle"] = cycler(
-        'color', [
+        "color",
-            '#b4befe',
+        [
-            '#89b4fa',
+            "#b4befe",
-            '#74c7ec',
+            "#89b4fa",
-            '#89dceb',
+            "#74c7ec",
-            '#94e2d5',
+            "#89dceb",
-            '#a6e3a1',
+            "#94e2d5",
-            '#f9e2af',
+            "#a6e3a1",
-            '#fab387',
+            "#f9e2af",
-            '#eba0ac',
+            "#fab387",
-            '#f38ba8',
+            "#eba0ac",
-            '#cba6f7',
+            "#f38ba8",
-            '#f5c2e7',
+            "#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
@@ -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)
+    Z1 = np.exp(-(X**2) - Y**2)
-    Z2 = np.exp(-(X - 1)**2 - (Y - 1)**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,
+    im = ax.imshow(
-                   origin='lower', extent=[-3, 3, -3, 3],
+        Z,
-                   vmax=abs(Z).max(), vmin=-abs(Z).max())
+        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,
+    axs[0].violinplot(all_data, showmeans=False, showmedians=True)
-                      showmeans=False,
+    axs[0].set_title("Violin plot")
-                      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))],
+        ax.set_xticks(
-                      labels=['x1', 'x2', 'x3', 'x4'])
+            [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_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',
+    methods = [
-               'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
+        None,
-               'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']
+        "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),
+    fig, axs = plt.subplots(
-                            subplot_kw={'xticks': [], 'yticks': []})
+        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)

code/plot_introduction_specs.py

@@ -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)

poster/main.tex

@@ -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}[]
-    \begin{tikzfigure}[]
+    %     \label{griddrawing}
-        \label{griddrawing}
+    %     \includegraphics[width=1\linewidth]{figs/introplot}
-        \includegraphics[width=0.5\linewidth]{example-image-a}
+    % \end{tikzfigure}
-    \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.

poster/style.tex

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