diff --git a/code/chirpdetection.py b/code/chirpdetection.py index 415da4a..541015d 100755 --- a/code/chirpdetection.py +++ b/code/chirpdetection.py @@ -102,7 +102,7 @@ class ChirpPlotBuffer: self.t0 = 0 fig = plt.figure( - figsize=(14 / 2.54, 20 / 2.54) + figsize=(14 * ps.cm, 18 * ps.cm) ) gs0 = gr.GridSpec( @@ -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 @@ -628,7 +652,7 @@ 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 # t0 = 0 @@ -651,7 +675,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None: multiwindow_chirps = [] multiwindow_ids = [] - for st, window_start_index in enumerate(window_start_indices): + for st, window_start_index in enumerate(window_start_indices[3175:]): logger.info(f"Processing window {st+1} of {len(window_start_indices)}") @@ -886,25 +910,25 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None: # normalize all three feature arrays to the same range to make # peak detection simpler - baseline_envelope = minmaxnorm([baseline_envelope])[0] - search_envelope = minmaxnorm([search_envelope])[0] - baseline_frequency_filtered = minmaxnorm( - [baseline_frequency_filtered] - )[0] + # baseline_envelope = minmaxnorm([baseline_envelope])[0] + # search_envelope = minmaxnorm([search_envelope])[0] + # baseline_frequency_filtered = minmaxnorm( + # [baseline_frequency_filtered] + # )[0] # PEAK DETECTION ---------------------------------------------- # detect peaks baseline_enelope baseline_peak_indices, _ = find_peaks( - baseline_envelope, prominence=config.prominence + baseline_envelope, prominence=config.baseline_prominence ) # detect peaks search_envelope search_peak_indices, _ = find_peaks( - search_envelope, prominence=config.prominence + search_envelope, prominence=config.search_prominence ) # detect peaks inst_freq_filtered frequency_peak_indices, _ = find_peaks( - baseline_frequency_filtered, prominence=config.prominence + baseline_frequency_filtered, prominence=config.frequency_prominence ) # DETECT CHIRPS IN SEARCH WINDOW ------------------------------ @@ -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="fish") + chirpdetection(datapath, plot="save", debug="false") diff --git a/code/chirpdetector_conf.yml b/code/chirpdetector_conf.yml index 4bc7d63..058448d 100755 --- a/code/chirpdetector_conf.yml +++ b/code/chirpdetector_conf.yml @@ -1,47 +1,41 @@ -# directory setup -dataroot: "../data/" -outputdir: "../output/" +# Path setup ------------------------------------------------------------------ -# Duration and overlap of the analysis window in seconds -window: 5 -overlap: 1 -edge: 0.25 +dataroot: "../data/" # path to data +outputdir: "../output/" # path to save plots to -# Number of electrodes to go over -number_electrodes: 3 -minimum_electrodes: 2 +# Rolling window parameters --------------------------------------------------- -# Search window bandwidth and minimal baseline bandwidth -minimal_bandwidth: 20 +window: 5 # rolling window length in seconds +overlap: 1 # window overlap in seconds +edge: 0.25 # window edge cufoffs to mitigate filter edge effects -# Instantaneous frequency smoothing usint a gaussian kernel of this width -baseline_frequency_smoothing: 5 +# Electrode iteration parameters ---------------------------------------------- -# Baseline processing parameters -baseline_envelope_cutoff: 25 -baseline_envelope_bandpass_lowf: 2 -baseline_envelope_bandpass_highf: 100 -# baseline_envelope_envelope_cutoff: 4 +number_electrodes: 2 # number of electrodes to go over +minimum_electrodes: 1 # mimumun number of electrodes a chirp must be on -# search envelope processing parameters -search_envelope_cutoff: 10 +# Feature extraction parameters ----------------------------------------------- -# Instantaneous frequency bandpass filter cutoff frequencies -# baseline_frequency_highpass_cutoff: 0.000005 -# baseline_frequency_envelope_cutoff: 0.000005 +search_df_lower: 20 # start searching this far above the baseline +search_df_upper: 100 # stop searching this far above the baseline +search_res: 1 # search window resolution +default_search_freq: 60 # search here if no need for a search frequency +minimal_bandwidth: 10 # minimal bandpass filter width for baseline +search_bandwidth: 10 # minimal bandpass filter width for search frequency +baseline_frequency_smoothing: 10 # instantaneous frequency smoothing -# peak detecion parameters -prominence: 0.7 +# Feature processing parameters ----------------------------------------------- -# search freq parameter -search_df_lower: 20 -search_df_upper: 100 -search_res: 1 -search_bandwidth: 20 -default_search_freq: 60 - -# Classify events as chirps if they are less than this time apart -chirp_window_threshold: 0.015 +baseline_envelope_cutoff: 25 # envelope estimation cutoff +baseline_envelope_bandpass_lowf: 2 # envelope badpass lower cutoff +baseline_envelope_bandpass_highf: 100 # envelope bandbass higher cutoff +search_envelope_cutoff: 10 # search envelope estimation cufoff +# Peak detecion parameters ---------------------------------------------------- +baseline_prominence: 0.00005 # peak prominence threshold for baseline envelope +search_prominence: 0.000004 # peak prominence threshold for search envelope +frequency_prominence: 2 # peak prominence threshold for baseline freq +# Classify events as chirps if they are less than this time apart +chirp_window_threshold: 0.02 diff --git a/code/extract_chirps.py b/code/extract_chirps.py index 4e229fa..5d5580e 100644 --- a/code/extract_chirps.py +++ b/code/extract_chirps.py @@ -4,11 +4,13 @@ import numpy as np from chirpdetection import chirpdetection from IPython import embed +# check rec ../data/mount_data/2020-03-25-10_00/ starting at 3175 + def main(datapaths): for path in datapaths: - chirpdetection(path, plot='show', debug='electrode') + chirpdetection(path, plot='show') if __name__ == '__main__': @@ -43,6 +45,7 @@ if __name__ == '__main__': recs = pd.DataFrame(columns=['recording'], data=valid_datasets) recs.to_csv('../recs.csv', index=False) - # main(datapaths) + datapaths = ['../data/mount_data/2020-03-25-10_00/'] + main(datapaths) # window 1524 + 244 in dataset index 4 is nice example diff --git a/code/modules/behaviour_handling.py b/code/modules/behaviour_handling.py new file mode 100644 index 0000000..90a18ab --- /dev/null +++ b/code/modules/behaviour_handling.py @@ -0,0 +1,99 @@ +import numpy as np + +import os + +import numpy as np +from IPython import embed + + +from pandas import read_csv +from modules.logger import makeLogger + + +logger = makeLogger(__name__) + + +class Behavior: + """Load behavior data from csv file as class attributes + Attributes + ---------- + behavior: 0: chasing onset, 1: chasing offset, 2: physical contact + behavior_type: + behavioral_category: + comment_start: + comment_stop: + dataframe: pandas dataframe with all the data + duration_s: + media_file: + observation_date: + observation_id: + start_s: start time of the event in seconds + stop_s: stop time of the event in seconds + total_length: + """ + + def __init__(self, folder_path: str) -> None: + + LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True) + + csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0] + logger.info(f'CSV file: {csv_filename}') + self.dataframe = read_csv(os.path.join(folder_path, csv_filename)) + + self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True) + self.chirps_ids = np.load(os.path.join(folder_path, 'chirp_ids.npy'), allow_pickle=True) + + self.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True) + self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True) + self.freq = np.load(os.path.join(folder_path, 'fund_v.npy'), allow_pickle=True) + self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True) + self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True) + + for k, key in enumerate(self.dataframe.keys()): + key = key.lower() + if ' ' in key: + key = key.replace(' ', '_') + if '(' in key: + key = key.replace('(', '') + key = key.replace(')', '') + setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]])) + + last_LED_t_BORIS = LED_on_time_BORIS[-1] + real_time_range = self.time[-1] - self.time[0] + factor = 1.034141 + shift = last_LED_t_BORIS - real_time_range * factor + self.start_s = (self.start_s - shift) / factor + self.stop_s = (self.stop_s - shift) / factor + + +def correct_chasing_events( + category: np.ndarray, + timestamps: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + + onset_ids = np.arange( + len(category))[category == 0] + offset_ids = np.arange( + len(category))[category == 1] + + woring_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0] + if onset_ids[0] > offset_ids[0]: + offset_ids = np.delete(offset_ids, 0) + help_index = offset_ids[0] + woring_bh = np.append(woring_bh, help_index) + + category = np.delete(category, woring_bh) + timestamps = np.delete(timestamps, woring_bh) + + # Check whether on- or offset is longer and calculate length difference + if len(onset_ids) > len(offset_ids): + len_diff = len(onset_ids) - len(offset_ids) + logger.info(f'Onsets are greater than offsets by {len_diff}') + elif len(onset_ids) < len(offset_ids): + len_diff = len(offset_ids) - len(onset_ids) + logger.info(f'Offsets are greater than onsets by {len_diff}') + elif len(onset_ids) == len(offset_ids): + logger.info('Chasing events are equal') + + + return category, timestamps \ No newline at end of file diff --git a/code/modules/filehandling.py b/code/modules/filehandling.py index 334aefa..c3c71f2 100644 --- a/code/modules/filehandling.py +++ b/code/modules/filehandling.py @@ -3,6 +3,7 @@ import os import yaml import numpy as np from thunderfish.dataloader import DataLoader +import matplotlib.pyplot as plt class ConfLoader: diff --git a/code/modules/plotstyle.py b/code/modules/plotstyle.py index 2325f62..b4a8a41 100644 --- a/code/modules/plotstyle.py +++ b/code/modules/plotstyle.py @@ -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): @@ -229,7 +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" @@ -261,31 +261,33 @@ def PlotStyle() -> None: # 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 @@ -295,12 +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) @@ -308,14 +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() @@ -328,22 +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() @@ -355,24 +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) diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py new file mode 100644 index 0000000..6d5c782 --- /dev/null +++ b/code/plot_chirp_bodylegth.py @@ -0,0 +1,160 @@ +import numpy as np + +import os + +import numpy as np +import matplotlib.pyplot as plt +from thunderfish.powerspectrum import decibel + +from IPython import embed +from pandas import read_csv +from modules.logger import makeLogger +from modules.plotstyle import PlotStyle +from modules.behaviour_handling import Behavior, correct_chasing_events + +ps = PlotStyle() + +logger = makeLogger(__name__) + + +def main(datapath: str): + + foldernames = [ + datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] + path_order_meta = ( + '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv' + order_meta_df = read_csv(path_order_meta) + order_meta_df['recording'] = order_meta_df['recording'].str[1:-1] + path_id_meta = ( + '/').join(foldernames[0].split('/')[:-2]) + '/id_meta.csv' + id_meta_df = read_csv(path_id_meta) + + chirps_winner = [] + size_diff = [] + chirps_diff = [] + chirps_loser = [] + freq_diff = [] + + + for foldername in foldernames: + # behabvior is pandas dataframe with all the data + if foldername == '../data/mount_data/2020-05-12-10_00/': + continue + bh = Behavior(foldername) + # chirps are not sorted in time (presumably due to prior groupings) + # get and sort chirps and corresponding fish_ids of the chirps + category = bh.behavior + timestamps = bh.start_s + # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs + # Get rid of tracking faults (two onsets or two offsets after another) + category, timestamps = correct_chasing_events(category, timestamps) + + folder_name = foldername.split('/')[-2] + winner_row = order_meta_df[order_meta_df['recording'] == folder_name] + winner = winner_row['winner'].values[0].astype(int) + winner_fish1 = winner_row['fish1'].values[0].astype(int) + winner_fish2 = winner_row['fish2'].values[0].astype(int) + + groub = winner_row['group'].values[0].astype(int) + size_rows = id_meta_df[id_meta_df['group'] == groub] + + + if winner == winner_fish1: + winner_fish_id = winner_row['rec_id1'].values[0] + loser_fish_id = winner_row['rec_id2'].values[0] + + size_winners = [] + for l in ['l1', 'l2', 'l3']: + size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0] + size_winners.append(size_winner) + mean_size_winner = np.nanmean(size_winners) + + + size_losers = [] + for l in ['l1', 'l2', 'l3']: + size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0] + size_losers.append(size_loser) + mean_size_loser = np.nanmean(size_losers) + + size_diff.append(mean_size_winner - mean_size_loser) + + elif winner == winner_fish2: + winner_fish_id = winner_row['rec_id2'].values[0] + loser_fish_id = winner_row['rec_id1'].values[0] + + size_winners = [] + for l in ['l1', 'l2', 'l3']: + size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0] + size_winners.append(size_winner) + mean_size_winner = np.nanmean(size_winners) + + size_losers = [] + for l in ['l1', 'l2', 'l3']: + size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0] + size_losers.append(size_loser) + mean_size_loser = np.nanmean(size_losers) + + size_diff.append(mean_size_winner - mean_size_loser) + else: + continue + + print(foldername) + all_fish_ids = np.unique(bh.chirps_ids) + chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id]) + chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id]) + + freq_winner = np.nanmedian(bh.freq[bh.ident==winner_fish_id]) + freq_loser = np.nanmedian(bh.freq[bh.ident==loser_fish_id]) + + + chirps_winner.append(chirp_winner) + chirps_loser.append(chirp_loser) + + chirps_diff.append(chirp_winner - chirp_loser) + freq_diff.append(freq_winner - freq_loser) + + fish1_id = all_fish_ids[0] + fish2_id = all_fish_ids[1] + print(winner_fish_id) + print(all_fish_ids) + + fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(10,5)) + scatterwinner = 1.15 + scatterloser = 1.85 + bplot1 = ax1.boxplot(chirps_winner, positions=[ + 1], showfliers=False, patch_artist=True) + bplot2 = ax1.boxplot(chirps_loser, positions=[ + 2], showfliers=False, patch_artist=True) + ax1.scatter(np.ones(len(chirps_winner))*scatterwinner, chirps_winner, color='r') + ax1.scatter(np.ones(len(chirps_loser))*scatterloser, chirps_loser, color='r') + ax1.set_xticklabels(['winner', 'loser']) + ax1.text(0.9, 0.9, f'n = {len(chirps_winner)}', transform=ax1.transAxes, color= ps.white) + + for w, l in zip(chirps_winner, chirps_loser): + ax1.plot([scatterwinner, scatterloser], [w, l], color='r', alpha=0.5, linewidth=0.5) + + colors1 = ps.red + ps.set_boxplot_color(bplot1, colors1) + colors1 = ps.orange + ps.set_boxplot_color(bplot2, colors1) + ax1.set_ylabel('Chirpscounts [n]') + + ax2.scatter(size_diff, chirps_diff, color='r') + ax2.set_xlabel('Size difference [mm]') + ax2.set_ylabel('Chirps difference [n]') + + ax3.scatter(freq_diff, chirps_diff, color='r') + ax3.set_xlabel('Frequency difference [Hz]') + ax3.set_yticklabels([]) + ax3.set + + plt.savefig('../poster/figs/chirps_winner_loser.pdf') + plt.show() + + +if __name__ == '__main__': + + # Path to the data + datapath = '../data/mount_data/' + + main(datapath) diff --git a/code/plot_event_timeline.py b/code/plot_event_timeline.py index 6c984be..96f4e31 100644 --- a/code/plot_event_timeline.py +++ b/code/plot_event_timeline.py @@ -10,194 +10,102 @@ from IPython import embed from pandas import read_csv from modules.logger import makeLogger from modules.plotstyle import PlotStyle +from modules.behaviour_handling import Behavior, correct_chasing_events ps = PlotStyle() logger = makeLogger(__name__) -class Behavior: - """Load behavior data from csv file as class attributes - Attributes - ---------- - behavior: 0: chasing onset, 1: chasing offset, 2: physical contact - behavior_type: - behavioral_category: - comment_start: - comment_stop: - dataframe: pandas dataframe with all the data - duration_s: - media_file: - observation_date: - observation_id: - start_s: start time of the event in seconds - stop_s: stop time of the event in seconds - total_length: - """ - - def __init__(self, folder_path: str) -> None: - - - LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True) - - csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0] - logger.info(f'CSV file: {csv_filename}') - self.dataframe = read_csv(os.path.join(folder_path, csv_filename)) - - self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True) - self.chirps_ids = np.load(os.path.join(folder_path, 'chirps_ids.npy'), allow_pickle=True) - - self.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True) - self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True) - self.freq = np.load(os.path.join(folder_path, 'fund_v.npy'), allow_pickle=True) - self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True) - self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True) - - for k, key in enumerate(self.dataframe.keys()): - key = key.lower() - if ' ' in key: - key = key.replace(' ', '_') - if '(' in key: - key = key.replace('(', '') - key = key.replace(')', '') - setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]])) - - last_LED_t_BORIS = LED_on_time_BORIS[-1] - real_time_range = self.time[-1] - self.time[0] - factor = 1.034141 - shift = last_LED_t_BORIS - real_time_range * factor - self.start_s = (self.start_s - shift) / factor - self.stop_s = (self.stop_s - shift) / factor - -def correct_chasing_events( - category: np.ndarray, - timestamps: np.ndarray - ) -> tuple[np.ndarray, np.ndarray]: - - onset_ids = np.arange( - len(category))[category == 0] - offset_ids = np.arange( - len(category))[category == 1] - - # Check whether on- or offset is longer and calculate length difference - if len(onset_ids) > len(offset_ids): - len_diff = len(onset_ids) - len(offset_ids) - longer_array = onset_ids - shorter_array = offset_ids - logger.info(f'Onsets are greater than offsets by {len_diff}') - elif len(onset_ids) < len(offset_ids): - len_diff = len(offset_ids) - len(onset_ids) - longer_array = offset_ids - shorter_array = onset_ids - logger.info(f'Offsets are greater than offsets by {len_diff}') - elif len(onset_ids) == len(offset_ids): - logger.info('Chasing events are equal') - return category, timestamps - - - # Correct the wrong chasing events; delete double events - wrong_ids = [] - for i in range(len(longer_array)-(len_diff+1)): - if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]): - pass - else: - wrong_ids.append(longer_array[i]) - longer_array = np.delete(longer_array, i) - - category = np.delete( - category, wrong_ids) - timestamps = np.delete( - timestamps, wrong_ids) - return category, timestamps - - - def main(datapath: str): - # behabvior is pandas dataframe with all the data - bh = Behavior(datapath) - # chirps are not sorted in time (presumably due to prior groupings) - # get and sort chirps and corresponding fish_ids of the chirps - chirps = bh.chirps[np.argsort(bh.chirps)] - chirps_fish_ids = bh.chirps_ids[np.argsort(bh.chirps)] - category = bh.behavior - timestamps = bh.start_s - # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs - # Get rid of tracking faults (two onsets or two offsets after another) - category, timestamps = correct_chasing_events(category, timestamps) - - # split categories - chasing_onset = (timestamps[category == 0]/ 60) /60 - chasing_offset = (timestamps[category == 1]/ 60) /60 - physical_contact = (timestamps[category == 2] / 60) /60 - - all_fish_ids = np.unique(chirps_fish_ids) - fish1_id = all_fish_ids[0] - fish2_id = all_fish_ids[1] - # Associate chirps to inidividual fish - fish1 = (chirps[chirps_fish_ids == fish1_id] / 60) /60 - fish2 = (chirps[chirps_fish_ids == fish2_id] / 60) /60 - fish1_color = ps.red - fish2_color = ps.orange - - fig, ax = plt.subplots(4, 1, figsize=(10, 5), height_ratios=[0.5, 0.5, 0.5, 6], sharex=True) - # marker size - s = 200 - ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='firebrick', marker='|', s=s) - ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', marker='|', s=s ) - ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color=fish1_color, marker='|', s=s) - ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color=fish2_color, marker='|', s=s) - - - freq_temp = bh.freq[bh.ident==fish1_id] - time_temp = bh.time[bh.idx[bh.ident==fish1_id]] - ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish1_color) - - freq_temp = bh.freq[bh.ident==fish2_id] - time_temp = bh.time[bh.idx[bh.ident==fish2_id]] - ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish2_color) - - #ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower') - - # Hide grid lines - ax[0].grid(False) - ax[0].set_frame_on(False) - ax[0].set_xticks([]) - ax[0].set_yticks([]) - ps.hide_ax(ax[0]) - - - ax[1].grid(False) - ax[1].set_frame_on(False) - ax[1].set_xticks([]) - ax[1].set_yticks([]) - ps.hide_ax(ax[1]) - - ax[2].grid(False) - ax[2].set_frame_on(False) - ax[2].set_yticks([]) - ax[2].set_xticks([]) - ps.hide_ax(ax[2]) - - - - ax[3].axvspan(0, 3, 0, 5, facecolor='grey', alpha=0.5) - ax[3].set_xticks(np.arange(0, 6.1, 0.5)) - - labelpad = 40 - ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad) - ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad) - ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad) - ax[3].set_ylabel('EODf') - - ax[3].set_xlabel('Time [h]') - - plt.show() - embed() + + foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] + for foldername in foldernames: + if foldername == '../data/mount_data/2020-05-12-10_00/': + continue + # behabvior is pandas dataframe with all the data + bh = Behavior(foldername) + + category = bh.behavior + timestamps = bh.start_s + # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs + # Get rid of tracking faults (two onsets or two offsets after another) + category, timestamps = correct_chasing_events(category, timestamps) + + # split categories + chasing_onset = (timestamps[category == 0]/ 60) /60 + chasing_offset = (timestamps[category == 1]/ 60) /60 + physical_contact = (timestamps[category == 2] / 60) /60 + + all_fish_ids = np.unique(bh.chirps_ids) + fish1_id = all_fish_ids[0] + fish2_id = all_fish_ids[1] + # Associate chirps to inidividual fish + fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) /60 + fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) /60 + fish1_color = ps.red + fish2_color = ps.orange + + fig, ax = plt.subplots(4, 1, figsize=(10, 5), height_ratios=[0.5, 0.5, 0.5, 6], sharex=True) + # marker size + s = 200 + ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='firebrick', marker='|', s=s) + ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', marker='|', s=s ) + ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color=fish1_color, marker='|', s=s) + ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color=fish2_color, marker='|', s=s) + + + freq_temp = bh.freq[bh.ident==fish1_id] + time_temp = bh.time[bh.idx[bh.ident==fish1_id]] + ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish1_color) + + freq_temp = bh.freq[bh.ident==fish2_id] + time_temp = bh.time[bh.idx[bh.ident==fish2_id]] + ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish2_color) + + #ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower') + + # Hide grid lines + ax[0].grid(False) + ax[0].set_frame_on(False) + ax[0].set_xticks([]) + ax[0].set_yticks([]) + ps.hide_ax(ax[0]) + + + ax[1].grid(False) + ax[1].set_frame_on(False) + ax[1].set_xticks([]) + ax[1].set_yticks([]) + ps.hide_ax(ax[1]) + + ax[2].grid(False) + ax[2].set_frame_on(False) + ax[2].set_yticks([]) + ax[2].set_xticks([]) + ps.hide_ax(ax[2]) + + + + ax[3].axvspan(3, 6, 0, 5, facecolor='grey', alpha=0.5) + ax[3].set_xticks(np.arange(0, 6.1, 0.5)) + + labelpad = 40 + ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad) + ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad) + ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad) + ax[3].set_ylabel('EODf') + + ax[3].set_xlabel('Time [h]') + ax[0].set_title(foldername.split('/')[-2]) + # 2020-03-31-9_59 + plt.show() + embed() # plot chirps if __name__ == '__main__': # Path to the data - datapath = '../data/mount_data/2020-05-13-10_00/' + datapath = '../data/mount_data/' main(datapath) diff --git a/code/plot_introduction_specs.py b/code/plot_introduction_specs.py index 3f8395e..20fb562 100644 --- a/code/plot_introduction_specs.py +++ b/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) diff --git a/poster/figs/algorithm.pdf b/poster/figs/algorithm.pdf index 2e2c453..359d7e6 100644 Binary files a/poster/figs/algorithm.pdf and b/poster/figs/algorithm.pdf differ 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diff --git a/poster/main.pdf b/poster/main.pdf index 4c1a7c1..92b0871 100644 Binary files a/poster/main.pdf and b/poster/main.pdf differ diff --git a/poster/main.tex b/poster/main.tex index ca20bb3..1738024 100644 --- a/poster/main.tex +++ b/poster/main.tex @@ -1,4 +1,4 @@ -\documentclass[25pt, a0paper, landscape, margin=0mm, innermargin=20mm, +\documentclass[25pt, a0paper, portrait, margin=0mm, innermargin=20mm, blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default values for poster format options. \input{packages} @@ -7,113 +7,84 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val \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} +\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} \maketitle \renewcommand{\baselinestretch}{1.4} \begin{columns} -\column{0.5} +\column{0.4} \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} + 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. + % \begin{tikzfigure}[] + % \label{griddrawing} + % \includegraphics[width=1\linewidth]{figs/introplot} + % \end{tikzfigure} } - -\column{0.5} -\myblock[TranspBlock]{Chirps and diadic competitions}{ - \begin{minipage}[t]{0.7\linewidth} +\myblock[TranspBlock]{Chirp detection}{ \begin{tikzfigure}[] - \label{modulations} - \includegraphics[width=\linewidth]{figs/placeholder1} + \label{fig:example_a} + \includegraphics[width=1\linewidth]{figs/algorithm} \end{tikzfigure} - \end{minipage} \hfill - \begin{minipage}[t]{0.25\linewidth} - \lipsum[3][1-3] - \end{minipage} + \vspace{0cm} +} - \begin{minipage}[t]{0.7\linewidth} +\column{0.6} +\myblock[TranspBlock]{Chirps during competition}{ \begin{tikzfigure}[] - \label{modulations} - \includegraphics[width=\linewidth]{figs/placeholder1} + \label{fig:example_b} + \includegraphics[width=0.5\linewidth]{example-image-b} \end{tikzfigure} - \end{minipage} \hfill - \begin{minipage}[t]{0.25\linewidth} - \lipsum[3][1-3] - \end{minipage} + \noindent +} - \begin{minipage}[t]{0.7\linewidth} +\myblock[TranspBlock]{Interactions at modulations}{ + \vspace{-1.2cm} \begin{tikzfigure}[] - \label{modulations} - \includegraphics[width=\linewidth]{figs/placeholder1} + \label{fig:example_c} + \includegraphics[width=0.5\linewidth]{example-image-c} \end{tikzfigure} - \end{minipage} \hfill - \begin{minipage}[t]{0.25\linewidth} - \lipsum[3][1-3] - \end{minipage} - -} - -\myblock[TranspBlock]{Conclusion}{ - \lipsum[3][1-9] + \begin{multicols}{2} + \begin{itemize} + \setlength\itemsep{0.5em} + \item $\Delta$EOD$f$ does not appear to decrease during synchronous modulations (). + \item Individuals that rise their EOD$f$ first appear to rise their frequency higher compared to reactors (\textbf{B}). + \vfill + \null + \columnbreak + \item Synchronized fish keep distances below 1 m (\textbf{C}) but distances over 3 m also occur (see \textbf{movie}). + \item Spatial interactions increase \textbf{after} the start of a synchronous modulation (\textbf{D}). + \end{itemize} + \end{multicols} + \vspace{-1cm} } -% \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, +\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. + \item The recorded fish do not exhibit jamming avoidance behavior while close during synchronous modulations. + \item Synchronous signals \textbf{initiate} spatio-temporal interactions. + \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}}; + color=boxes] at (-43.6,-61) { + \textcolor{white}{ + \normalsize Contact: \{name\}.\{surname\}@student.uni-tuebingen.de}}; \end{document} diff --git a/poster/packages.tex b/poster/packages.tex index 82f951e..50d9f0e 100644 --- a/poster/packages.tex +++ b/poster/packages.tex @@ -1,11 +1,10 @@ \usepackage[utf8]{inputenc} \usepackage[scaled]{helvet} -\renewcommand\familydefault{\sfdefault} +\renewcommand\familydefault{\sfdefault} \usepackage[T1]{fontenc} \usepackage{wrapfig} \usepackage{setspace} \usepackage{multicol} \setlength{\columnsep}{1.5cm} \usepackage{xspace} -\usepackage{tikz} -\usepackage{lipsum} +\usepackage{tikz} \ No newline at end of file diff --git a/poster/style.tex b/poster/style.tex index ac800ce..0397831 100644 --- a/poster/style.tex +++ b/poster/style.tex @@ -16,10 +16,11 @@ \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} +\begin{minipage}[c]{0.8\paperwidth} % \centering \vspace{2.5cm}\hspace{1.5cm} \color{text}{\Huge{\textbf{\@title}} \par} @@ -30,26 +31,28 @@ \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} + % \vspace{1cm} + \hspace{-10cm} + \includegraphics[width=\linewidth]{example-image-a} \end{minipage}} +% \begin{minipage}[c]{0.2\paperwidth} +% \vspace{1cm}\hspace{1cm} +% \centering +% \includegraphics[width=\linewidth]{example-image-a} +% \end{minipage}} -% definie title style with background box +% define title style with background box (currently white) \definetitlestyle{sampletitle}{ - width=1189mm, + width=841mm, roundedcorners=0, linewidth=0pt, innersep=15pt, titletotopverticalspace=0mm, titletoblockverticalspace=5pt }{ - \begin{scope}[line width=\titlelinewidth, rounded corners=\titleroundedcorners] + \begin{scope}[line width=\titlelinewidth, + rounded corners=\titleroundedcorners] \draw[fill=text, color=boxes] (\titleposleft,\titleposbottom) rectangle diff --git a/poster/figs/Untitled.png b/poster_old/figs/Untitled.png similarity index 100% rename from poster/figs/Untitled.png rename to poster_old/figs/Untitled.png diff --git a/poster_old/figs/algorithm.pdf b/poster_old/figs/algorithm.pdf new file mode 100644 index 0000000..2e2c453 Binary files /dev/null and b/poster_old/figs/algorithm.pdf differ diff --git a/poster_old/figs/introplot.pdf b/poster_old/figs/introplot.pdf new file mode 100644 index 0000000..cbead3e Binary files /dev/null and b/poster_old/figs/introplot.pdf differ diff --git a/poster/figs/logo.png b/poster_old/figs/logo.png similarity index 100% rename from poster/figs/logo.png rename to poster_old/figs/logo.png diff --git a/poster/figs/logo.svg b/poster_old/figs/logo.svg similarity index 100% rename from poster/figs/logo.svg rename to poster_old/figs/logo.svg diff --git a/poster/figs/placeholder1.png b/poster_old/figs/placeholder1.png similarity index 100% rename from poster/figs/placeholder1.png rename to poster_old/figs/placeholder1.png diff --git a/poster_old/main.pdf b/poster_old/main.pdf new file mode 100644 index 0000000..4c1a7c1 Binary files /dev/null and b/poster_old/main.pdf differ diff --git a/poster_old/main.tex b/poster_old/main.tex new file mode 100644 index 0000000..ca20bb3 --- /dev/null +++ b/poster_old/main.tex @@ -0,0 +1,119 @@ +\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} diff --git a/poster_old/packages.tex b/poster_old/packages.tex new file mode 100644 index 0000000..82f951e --- /dev/null +++ b/poster_old/packages.tex @@ -0,0 +1,11 @@ +\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} diff --git a/poster_old/style.tex b/poster_old/style.tex new file mode 100644 index 0000000..ac800ce --- /dev/null +++ b/poster_old/style.tex @@ -0,0 +1,119 @@ +\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}}