raab/GP2023_chirp_detection
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sprause 2023-01-24 17:59:22 +01:00
commit b48270f475
27 changed files with 1378 additions and 414 deletions
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120
code/chirpdetection.py
View File

@ -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")

64
code/chirpdetector_conf.yml
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@ -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

7
code/extract_chirps.py
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@ -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

99
code/modules/behaviour_handling.py Normal file
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@ -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

1
code/modules/filehandling.py
View File

@ -3,6 +3,7 @@ import os
import yaml
import numpy as np
from thunderfish.dataloader import DataLoader
import matplotlib.pyplot as plt
class ConfLoader:

126
code/modules/plotstyle.py
View File

@ -23,16 +23,16 @@ def PlotStyle() -> None:
sky = "#89dceb"
teal = "#94e2d5"
green = "#a6e3a1"
yellow = "#f9e2af"
orange = "#fab387"
maroon = "#eba0ac"
red = "#f38ba8"
purple = "#cba6f7"
pink = "#f5c2e7"
yellow = "#f9d67f"
orange = "#faa472"
maroon = "#eb8486"
red = "#f37588"
purple = "#d89bf7"
pink = "#f59edb"
lavender = "#b4befe"
gblue1 = "#8cb8ff"
gblue2 = "#7cdcdc"
gblue3 = "#82e896"
gblue1 = "#89b4fa"
gblue2 = "#89dceb"
gblue3 = "#a6e3a1"
@classmethod
def lims(cls, track1, track2):
@ -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)

160
code/plot_chirp_bodylegth.py Normal file
View File

@ -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)

262
code/plot_event_timeline.py
View File

@ -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)

4
code/plot_introduction_specs.py
View File

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

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poster/figs/algorithm.pdf
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poster/figs/logo_all.pdf Normal file
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141
poster/main.tex
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@ -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}

5
poster/packages.tex
View File

@ -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}

25
poster/style.tex
View File

@ -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

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\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}

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\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}

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\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}}