Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection
This commit is contained in:
commit
c13b7357b5
@ -17,7 +17,7 @@ def main():
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data = LoadData(datapath)
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# good chirp times for data: 2022-06-02-10_00
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window_start_seconds = 3 * 60 * 60 + 6 * 60 + 43.5 + 9 + 6.25
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window_start_seconds = 3 * 60 * 60 + 6 * 60 + 43.5 + 9 + 6.24
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window_start_index = window_start_seconds * data.raw_rate
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window_duration_seconds = 0.2
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window_duration_index = window_duration_seconds * data.raw_rate
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@ -27,8 +27,8 @@ def main():
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raw = data.raw[window_start_index:window_start_index +
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window_duration_index, 10]
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fig, (ax1, ax2, ax3) = plt.subplots(
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3, 1, figsize=(12 * ps.cm, 10*ps.cm), sharex=True, sharey=True)
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fig, ax = plt.subplots(
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1, 1, figsize=(14 * ps.cm, 6*ps.cm), sharex=True, sharey=True)
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# plot instantaneous frequency
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filtered1 = bandpass_filter(
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@ -41,13 +41,14 @@ def main():
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freqtime2, freq2 = instantaneous_frequency(
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filtered2, data.raw_rate, smoothing_window=3)
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ax1.plot(freqtime1*timescaler, freq1, color=ps.red,
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lw=2, label=f"fish 1, {np.median(freq1):.0f} Hz")
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ax1.plot(freqtime2*timescaler, freq2, color=ps.orange,
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lw=2, label=f"fish 2, {np.median(freq2):.0f} Hz")
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ax1.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower center",
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mode="normal", borderaxespad=0, ncol=2)
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ps.hide_xax(ax1)
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ax.plot(freqtime1*timescaler, freq1, color=ps.red,
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lw=2, label="fish 1")
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ax.plot(freqtime2*timescaler, freq2, color=ps.orange,
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lw=2, label="fish 2")
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ax.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower center",
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mode="normal", borderaxespad=0, ncol=2)
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# ax.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
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# # ps.hide_xax(ax1)
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# plot fine spectrogram
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spec_power, spec_freqs, spec_times = spectrogram(
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@ -57,11 +58,11 @@ def main():
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overlap_frac=0.2,
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)
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ylims = [300, 1200]
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ylims = [300, 1300]
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fmask = np.zeros(spec_freqs.shape, dtype=bool)
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fmask[(spec_freqs > ylims[0]) & (spec_freqs < ylims[1])] = True
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ax2.imshow(
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ax.imshow(
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decibel(spec_power[fmask, :]),
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extent=[
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spec_times[0]*timescaler,
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@ -73,45 +74,47 @@ def main():
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origin="lower",
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interpolation="gaussian",
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alpha=1,
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vmin=-100,
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vmax=-80,
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)
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ps.hide_xax(ax2)
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# plot coarse spectrogram
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spec_power, spec_freqs, spec_times = spectrogram(
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raw,
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ratetime=data.raw_rate,
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freq_resolution=10,
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overlap_frac=0.3,
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)
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fmask = np.zeros(spec_freqs.shape, dtype=bool)
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fmask[(spec_freqs > ylims[0]) & (spec_freqs < ylims[1])] = True
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ax3.imshow(
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decibel(spec_power[fmask, :]),
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extent=[
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spec_times[0]*timescaler,
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spec_times[-1]*timescaler,
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spec_freqs[fmask][0],
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spec_freqs[fmask][-1],
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],
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aspect="auto",
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origin="lower",
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interpolation="gaussian",
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alpha=1,
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)
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# ps.hide_xax(ax3)
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ax3.set_xlabel("time [ms]")
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ax2.set_ylabel("frequency [Hz]")
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ax1.set_yticks(np.arange(400, 1201, 400))
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ax1.spines.left.set_bounds((400, 1200))
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ax2.set_yticks(np.arange(400, 1201, 400))
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ax2.spines.left.set_bounds((400, 1200))
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ax3.set_yticks(np.arange(400, 1201, 400))
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ax3.spines.left.set_bounds((400, 1200))
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plt.subplots_adjust(left=0.17, right=0.98, top=0.9,
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bottom=0.14, hspace=0.35)
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# ps.hide_xax(ax2)
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# # plot coarse spectrogram
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# spec_power, spec_freqs, spec_times = spectrogram(
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# raw,
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# ratetime=data.raw_rate,
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# freq_resolution=10,
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# overlap_frac=0.3,
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# )
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# fmask = np.zeros(spec_freqs.shape, dtype=bool)
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# fmask[(spec_freqs > ylims[0]) & (spec_freqs < ylims[1])] = True
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# ax3.imshow(
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# decibel(spec_power[fmask, :]),
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# extent=[
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# spec_times[0]*timescaler,
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# spec_times[-1]*timescaler,
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# spec_freqs[fmask][0],
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# spec_freqs[fmask][-1],
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# ],
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# aspect="auto",
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# origin="lower",
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# interpolation="gaussian",
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# alpha=1,
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# )
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# # ps.hide_xax(ax3)
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ax.set_xlabel("time [ms]")
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ax.set_ylabel("frequency [Hz]")
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# ax.set_yticks(np.arange(400, 1201, 400))
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# ax.spines.left.set_bounds((400, 1200))
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# ax2.set_yticks(np.arange(400, 1201, 400))
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# ax2.spines.left.set_bounds((400, 1200))
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# ax3.set_yticks(np.arange(400, 1201, 400))
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# ax3.spines.left.set_bounds((400, 1200))
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plt.subplots_adjust(left=0.17, right=0.98, top=0.87,
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bottom=0.24, hspace=0.35)
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plt.savefig('../poster/figs/introplot.pdf')
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plt.show()
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@ -1,18 +1,18 @@
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from extract_chirps import get_valid_datasets
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import os
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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from IPython import embed
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from pandas import read_csv
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from modules.logger import makeLogger
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from modules.datahandling import flatten, causal_kde1d, acausal_kde1d
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from modules.plotstyle import PlotStyle
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from modules.behaviour_handling import (
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Behavior, correct_chasing_events, center_chirps)
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from modules.plotstyle import PlotStyle
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from modules.datahandling import flatten, causal_kde1d, acausal_kde1d
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from modules.logger import makeLogger
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from pandas import read_csv
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from IPython import embed
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from tqdm import tqdm
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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import os
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from extract_chirps import get_valid_datasets
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logger = makeLogger(__name__)
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ps = PlotStyle()
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@ -23,16 +23,16 @@ def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before
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bootstrapped_kdes = []
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data = data[data <= 3*60*60] # only night time
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# diff_data = np.diff(np.sort(data), prepend=0)
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diff_data = np.diff(np.sort(data), prepend=0)
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# if len(data) != 0:
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# mean_chirprate = (len(data) - 1) / (data[-1] - data[0])
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for i in tqdm(range(nresamples)):
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# np.random.shuffle(diff_data)
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np.random.shuffle(diff_data)
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# bootstrapped_data = np.cumsum(diff_data)
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bootstrapped_data = data + np.random.randn(len(data)) * 10
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bootstrapped_data = np.cumsum(diff_data)
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# bootstrapped_data = data + np.random.randn(len(data)) * 10
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bootstrap_data_centered = center_chirps(
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bootstrapped_data, event_times, time_before, time_after)
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@ -40,8 +40,8 @@ def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before
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bootstrapped_kde = acausal_kde1d(
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bootstrap_data_centered, time=kde_time, width=kernel_width)
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# bootstrapped_kdes = list(np.asarray(
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# bootstrapped_kdes) / len(event_times))
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bootstrapped_kde = list(np.asarray(
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bootstrapped_kde) / len(event_times))
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bootstrapped_kdes.append(bootstrapped_kde)
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@ -58,21 +58,21 @@ def jackknife(data, nresamples, subsetsize, kde_time, kernel_width, event_times,
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for i in tqdm(range(nresamples)):
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bootstrapped_data = np.random.sample(data, subsetsize, replace=False)
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bootstrapped_data = np.cumsum(diff_data)
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jackknifed_data = np.random.choice(
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diff_data, subsetsize, replace=False)
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bootstrap_data_centered = center_chirps(
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bootstrapped_data, event_times, time_before, time_after)
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jackknifed_data = np.cumsum(jackknifed_data)
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bootstrapped_kde = acausal_kde1d(
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bootstrap_data_centered, time=kde_time, width=kernel_width)
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jackknifed_data_centered = center_chirps(
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jackknifed_data, event_times, time_before, time_after)
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# bootstrapped_kdes = list(np.asarray(
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# bootstrapped_kdes) / len(event_times))
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jackknifed_kde = acausal_kde1d(
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jackknifed_data_centered, time=kde_time, width=kernel_width)
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jackknife_kdes.append(bootstrapped_kde)
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jackknifed_kde = list(np.asarray(
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jackknifed_kde) / len(event_times))
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jackknife_kdes.append(jackknifed_kde)
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return jackknife_kdes
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@ -102,14 +102,14 @@ def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
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def main(dataroot):
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foldernames, _ = get_valid_datasets(dataroot)
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foldernames, _ = np.asarray(get_valid_datasets(dataroot))
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plot_all = True
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time_before = 60
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time_after = 60
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time_before = 90
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time_after = 90
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dt = 0.001
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kernel_width = 1
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kernel_width = 2
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kde_time = np.arange(-time_before, time_after, dt)
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nbootstraps = 2
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nbootstraps = 50
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meta_path = (
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'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
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@ -135,9 +135,19 @@ def main(dataroot):
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onset_count = 0
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offset_count = 0
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physical_count = 0
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# winner_count = 0
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# loser_count = 0
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# winner_onset_chirpcount = 0
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# winner_offset_chirpcount = 0
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# winner_physical_chirpcount = 0
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# loser_onset_chirpcount = 0
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# loser_offset_chirpcount = 0
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# loser_physical_chirpcount = 0
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fig, ax = plt.subplots(1, 2, figsize=(
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14 * ps.cm, 7*ps.cm), sharey=True, sharex=True)
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# Iterate over all recordings and save chirp- and event-timestamps
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for folder in tqdm(foldernames):
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good_recs = np.asarray([0, 15])
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for i, folder in tqdm(enumerate(foldernames[good_recs])):
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foldername = folder.split('/')[-2]
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# logger.info('Loading data from folder: {}'.format(foldername))
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@ -153,9 +163,10 @@ def main(dataroot):
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timestamps = timestamps[timestamps < 3*60*60] # only night time
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winner, loser = get_chirp_winner_loser(folder, bh, meta)
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if winner is None:
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continue
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# winner_count += len(winner)
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# loser_count += len(loser)
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onsets = (timestamps[category == 0])
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offsets = (timestamps[category == 1])
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@ -179,42 +190,48 @@ def main(dataroot):
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loser_physicals.append(center_chirps(
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loser, physicals, time_before, time_after))
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# winner_onset_chirpcount += len(winner_onsets[-1])
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# winner_offset_chirpcount += len(winner_offsets[-1])
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# winner_physical_chirpcount += len(winner_physicals[-1])
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# loser_onset_chirpcount += len(loser_onsets[-1])
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# loser_offset_chirpcount += len(loser_offsets[-1])
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# loser_physical_chirpcount += len(loser_physicals[-1])
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# bootstrap
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# chirps = [winner, winner, winner, loser, loser, loser]
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winner_onsets_boot.append(bootstrap(
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winner,
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nresamples=nbootstraps,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=onsets,
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time_before=time_before,
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time_after=time_after))
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winner_offsets_boot.append(bootstrap(
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winner,
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nresamples=nbootstraps,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=offsets,
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time_before=time_before,
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time_after=time_after))
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winner_physicals_boot.append(bootstrap(
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winner,
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nresamples=nbootstraps,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=physicals,
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time_before=time_before,
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time_after=time_after))
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loser_onsets_boot.append(bootstrap(
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loser,
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nresamples=nbootstraps,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=onsets,
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time_before=time_before,
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time_after=time_after))
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# winner_onsets_boot.append(bootstrap(
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# winner,
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# nresamples=nbootstraps,
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# kde_time=kde_time,
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# kernel_width=kernel_width,
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# event_times=onsets,
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# time_before=time_before,
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# time_after=time_after))
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# winner_offsets_boot.append(bootstrap(
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# winner,
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# nresamples=nbootstraps,
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# kde_time=kde_time,
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# kernel_width=kernel_width,
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# event_times=offsets,
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# time_before=time_before,
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# time_after=time_after))
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# winner_physicals_boot.append(bootstrap(
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# winner,
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# nresamples=nbootstraps,
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# kde_time=kde_time,
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# kernel_width=kernel_width,
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# event_times=physicals,
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# time_before=time_before,
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# time_after=time_after))
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# loser_onsets_boot.append(bootstrap(
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# loser,
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# nresamples=nbootstraps,
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# kde_time=kde_time,
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# kernel_width=kernel_width,
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# event_times=onsets,
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# time_before=time_before,
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# time_after=time_after))
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loser_offsets_boot.append(bootstrap(
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loser,
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nresamples=nbootstraps,
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@ -223,61 +240,99 @@ def main(dataroot):
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event_times=offsets,
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time_before=time_before,
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time_after=time_after))
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loser_physicals_boot.append(bootstrap(
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# loser_physicals_boot.append(bootstrap(
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# loser,
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# nresamples=nbootstraps,
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# kde_time=kde_time,
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# kernel_width=kernel_width,
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# event_times=physicals,
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# time_before=time_before,
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# time_after=time_after))
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loser_offsets_jackknife = jackknife(
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loser,
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nresamples=nbootstraps,
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subsetsize=0.5,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=physicals,
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event_times=offsets,
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time_before=time_before,
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time_after=time_after))
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time_after=time_after)
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if plot_all:
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winner_onsets_conv = acausal_kde1d(
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winner_onsets[-1], kde_time, kernel_width)
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winner_offsets_conv = acausal_kde1d(
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winner_offsets[-1], kde_time, kernel_width)
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winner_physicals_conv = acausal_kde1d(
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winner_physicals[-1], kde_time, kernel_width)
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# winner_onsets_conv = acausal_kde1d(
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# winner_onsets[-1], kde_time, kernel_width)
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# winner_offsets_conv = acausal_kde1d(
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# winner_offsets[-1], kde_time, kernel_width)
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# winner_physicals_conv = acausal_kde1d(
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# winner_physicals[-1], kde_time, kernel_width)
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loser_onsets_conv = acausal_kde1d(
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loser_onsets[-1], kde_time, kernel_width)
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# loser_onsets_conv = acausal_kde1d(
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# loser_onsets[-1], kde_time, kernel_width)
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loser_offsets_conv = acausal_kde1d(
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loser_offsets[-1], kde_time, kernel_width)
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loser_physicals_conv = acausal_kde1d(
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loser_physicals[-1], kde_time, kernel_width)
|
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|
||||
fig, ax = plt.subplots(2, 3, figsize=(
|
||||
21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
|
||||
ax[0, 0].set_title(
|
||||
f"{foldername}, onsets {len(onsets)}, offsets {len(offsets)}, physicals {len(physicals)},winner {len(winner)}, looser {len(loser)} , onsets")
|
||||
ax[0, 0].plot(kde_time, winner_onsets_conv/len(onsets))
|
||||
ax[0, 1].plot(kde_time, winner_offsets_conv/len(offsets))
|
||||
ax[0, 2].plot(kde_time, winner_physicals_conv/len(physicals))
|
||||
ax[1, 0].plot(kde_time, loser_onsets_conv/len(onsets))
|
||||
ax[1, 1].plot(kde_time, loser_offsets_conv/len(offsets))
|
||||
ax[1, 2].plot(kde_time, loser_physicals_conv/len(physicals))
|
||||
# loser_physicals_conv = acausal_kde1d(
|
||||
# loser_physicals[-1], kde_time, kernel_width)
|
||||
|
||||
ax[i].plot(kde_time, loser_offsets_conv/len(offsets))
|
||||
|
||||
ax[i].fill_between(
|
||||
kde_time,
|
||||
np.percentile(loser_offsets_boot[-1], 5, axis=0),
|
||||
np.percentile(loser_offsets_boot[-1], 95, axis=0),
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
|
||||
ax[i].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0),
|
||||
color=ps.black, linewidth=2)
|
||||
|
||||
ax[i].fill_between(
|
||||
kde_time,
|
||||
np.percentile(loser_offsets_jackknife, 5, axis=0),
|
||||
np.percentile(loser_offsets_jackknife, 95, axis=0),
|
||||
color=ps.blue,
|
||||
alpha=0.5)
|
||||
ax[i].plot(kde_time, np.median(loser_offsets_jackknife, axis=0),
|
||||
color=ps.white, linewidth=2)
|
||||
|
||||
ax[i].set_xlim(-60, 60)
|
||||
|
||||
embed()
|
||||
|
||||
# fig, ax = plt.subplots(2, 3, figsize=(
|
||||
# 21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
|
||||
# ax[0, 0].set_title(
|
||||
# f"{foldername}, onsets {len(onsets)}, offsets {len(offsets)}, physicals {len(physicals)},winner {len(winner)}, looser {len(loser)} , onsets")
|
||||
# ax[0, 0].plot(kde_time, winner_onsets_conv/len(onsets))
|
||||
# ax[0, 1].plot(kde_time, winner_offsets_conv /
|
||||
# len(offsets))
|
||||
# ax[0, 2].plot(kde_time, winner_physicals_conv /
|
||||
# len(physicals))
|
||||
# ax[1, 0].plot(kde_time, loser_onsets_conv/len(onsets))
|
||||
# ax[1, 1].plot(kde_time, loser_offsets_conv/len(offsets))
|
||||
# ax[1, 2].plot(kde_time, loser_physicals_conv /
|
||||
# len(physicals))
|
||||
|
||||
# # plot bootstrap lines
|
||||
for kde in winner_onsets_boot[-1]:
|
||||
ax[0, 0].plot(kde_time, kde/len(onsets),
|
||||
color='gray')
|
||||
for kde in winner_offsets_boot[-1]:
|
||||
ax[0, 1].plot(kde_time, kde/len(offsets),
|
||||
color='gray')
|
||||
for kde in winner_physicals_boot[-1]:
|
||||
ax[0, 2].plot(kde_time, kde/len(physicals),
|
||||
color='gray')
|
||||
for kde in loser_onsets_boot[-1]:
|
||||
ax[1, 0].plot(kde_time, kde/len(onsets),
|
||||
color='gray')
|
||||
for kde in loser_offsets_boot[-1]:
|
||||
ax[1, 1].plot(kde_time, kde/len(offsets),
|
||||
color='gray')
|
||||
for kde in loser_physicals_boot[-1]:
|
||||
ax[1, 2].plot(kde_time, kde/len(physicals),
|
||||
color='gray')
|
||||
# for kde in winner_onsets_boot[-1]:
|
||||
# ax[0, 0].plot(kde_time, kde,
|
||||
# color='gray')
|
||||
# for kde in winner_offsets_boot[-1]:
|
||||
# ax[0, 1].plot(kde_time, kde,
|
||||
# color='gray')
|
||||
# for kde in winner_physicals_boot[-1]:
|
||||
# ax[0, 2].plot(kde_time, kde,
|
||||
# color='gray')
|
||||
# for kde in loser_onsets_boot[-1]:
|
||||
# ax[1, 0].plot(kde_time, kde,
|
||||
# color='gray')
|
||||
# for kde in loser_offsets_boot[-1]:
|
||||
# ax[1, 1].plot(kde_time, kde,
|
||||
# color='gray')
|
||||
# for kde in loser_physicals_boot[-1]:
|
||||
# ax[1, 2].plot(kde_time, kde,
|
||||
# color='gray')
|
||||
|
||||
# plot bootstrap percentiles
|
||||
# ax[0, 0].fill_between(
|
||||
@ -335,79 +390,79 @@ def main(dataroot):
|
||||
# ax[1, 2].plot(kde_time, np.median(loser_physicals_boot[-1], axis=0),
|
||||
# color='black', linewidth=2)
|
||||
|
||||
ax[0, 0].set_xlim(-30, 30)
|
||||
plt.show()
|
||||
|
||||
winner_onsets = np.sort(flatten(winner_onsets))
|
||||
winner_offsets = np.sort(flatten(winner_offsets))
|
||||
winner_physicals = np.sort(flatten(winner_physicals))
|
||||
loser_onsets = np.sort(flatten(loser_onsets))
|
||||
loser_offsets = np.sort(flatten(loser_offsets))
|
||||
loser_physicals = np.sort(flatten(loser_physicals))
|
||||
|
||||
winner_onsets_conv = acausal_kde1d(
|
||||
winner_onsets, kde_time, kernel_width)
|
||||
winner_offsets_conv = acausal_kde1d(
|
||||
winner_offsets, kde_time, kernel_width)
|
||||
winner_physicals_conv = acausal_kde1d(
|
||||
winner_physicals, kde_time, kernel_width)
|
||||
loser_onsets_conv = acausal_kde1d(
|
||||
loser_onsets, kde_time, kernel_width)
|
||||
loser_offsets_conv = acausal_kde1d(
|
||||
loser_offsets, kde_time, kernel_width)
|
||||
loser_physicals_conv = acausal_kde1d(
|
||||
loser_physicals, kde_time, kernel_width)
|
||||
|
||||
winner_onsets_conv = winner_onsets_conv / onset_count
|
||||
winner_offsets_conv = winner_offsets_conv / offset_count
|
||||
winner_physicals_conv = winner_physicals_conv / physical_count
|
||||
loser_onsets_conv = loser_onsets_conv / onset_count
|
||||
loser_offsets_conv = loser_offsets_conv / offset_count
|
||||
loser_physicals_conv = loser_physicals_conv / physical_count
|
||||
|
||||
winner_onsets_boot = np.concatenate(
|
||||
winner_onsets_boot)
|
||||
winner_offsets_boot = np.concatenate(
|
||||
winner_offsets_boot)
|
||||
winner_physicals_boot = np.concatenate(
|
||||
winner_physicals_boot)
|
||||
loser_onsets_boot = np.concatenate(
|
||||
loser_onsets_boot)
|
||||
loser_offsets_boot = np.concatenate(
|
||||
loser_offsets_boot)
|
||||
loser_physicals_boot = np.concatenate(
|
||||
loser_physicals_boot)
|
||||
|
||||
percs = [5, 50, 95]
|
||||
winner_onsets_boot_quarts = np.percentile(
|
||||
winner_onsets_boot, percs, axis=0)
|
||||
winner_offsets_boot_quarts = np.percentile(
|
||||
winner_offsets_boot, percs, axis=0)
|
||||
winner_physicals_boot_quarts = np.percentile(
|
||||
winner_physicals_boot, percs, axis=0)
|
||||
loser_onsets_boot_quarts = np.percentile(
|
||||
loser_onsets_boot, percs, axis=0)
|
||||
loser_offsets_boot_quarts = np.percentile(
|
||||
loser_offsets_boot, percs, axis=0)
|
||||
loser_physicals_boot_quarts = np.percentile(
|
||||
loser_physicals_boot, percs, axis=0)
|
||||
|
||||
fig, ax = plt.subplots(2, 3, figsize=(
|
||||
21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
|
||||
|
||||
ax[0, 0].plot(kde_time, winner_onsets_conv)
|
||||
ax[0, 1].plot(kde_time, winner_offsets_conv)
|
||||
ax[0, 2].plot(kde_time, winner_physicals_conv)
|
||||
ax[1, 0].plot(kde_time, loser_onsets_conv)
|
||||
ax[1, 1].plot(kde_time, loser_offsets_conv)
|
||||
ax[1, 2].plot(kde_time, loser_physicals_conv)
|
||||
|
||||
ax[0, 0].plot(kde_time, winner_onsets_boot_quarts[1], c=ps.black)
|
||||
ax[0, 1].plot(kde_time, winner_offsets_boot_quarts[1], c=ps.black)
|
||||
ax[0, 2].plot(kde_time, winner_physicals_boot_quarts[1], c=ps.black)
|
||||
ax[1, 0].plot(kde_time, loser_onsets_boot_quarts[1], c=ps.black)
|
||||
ax[1, 1].plot(kde_time, loser_offsets_boot_quarts[1], c=ps.black)
|
||||
ax[1, 2].plot(kde_time, loser_physicals_boot_quarts[1], c=ps.black)
|
||||
# ax[0, 0].set_xlim(-30, 30)
|
||||
plt.show()
|
||||
|
||||
# winner_onsets = np.sort(flatten(winner_onsets))
|
||||
# winner_offsets = np.sort(flatten(winner_offsets))
|
||||
# winner_physicals = np.sort(flatten(winner_physicals))
|
||||
# loser_onsets = np.sort(flatten(loser_onsets))
|
||||
# loser_offsets = np.sort(flatten(loser_offsets))
|
||||
# loser_physicals = np.sort(flatten(loser_physicals))
|
||||
|
||||
# winner_onsets_conv = acausal_kde1d(
|
||||
# winner_onsets, kde_time, kernel_width)
|
||||
# winner_offsets_conv = acausal_kde1d(
|
||||
# winner_offsets, kde_time, kernel_width)
|
||||
# winner_physicals_conv = acausal_kde1d(
|
||||
# winner_physicals, kde_time, kernel_width)
|
||||
# loser_onsets_conv = acausal_kde1d(
|
||||
# loser_onsets, kde_time, kernel_width)
|
||||
# loser_offsets_conv = acausal_kde1d(
|
||||
# loser_offsets, kde_time, kernel_width)
|
||||
# loser_physicals_conv = acausal_kde1d(
|
||||
# loser_physicals, kde_time, kernel_width)
|
||||
|
||||
# winner_onsets_conv = winner_onsets_conv / onset_count
|
||||
# winner_offsets_conv = winner_offsets_conv / offset_count
|
||||
# winner_physicals_conv = winner_physicals_conv / physical_count
|
||||
# loser_onsets_conv = loser_onsets_conv / onset_count
|
||||
# loser_offsets_conv = loser_offsets_conv / offset_count
|
||||
# loser_physicals_conv = loser_physicals_conv / physical_count
|
||||
|
||||
# winner_onsets_boot = np.concatenate(
|
||||
# winner_onsets_boot)
|
||||
# winner_offsets_boot = np.concatenate(
|
||||
# winner_offsets_boot)
|
||||
# winner_physicals_boot = np.concatenate(
|
||||
# winner_physicals_boot)
|
||||
# loser_onsets_boot = np.concatenate(
|
||||
# loser_onsets_boot)
|
||||
# loser_offsets_boot = np.concatenate(
|
||||
# loser_offsets_boot)
|
||||
# loser_physicals_boot = np.concatenate(
|
||||
# loser_physicals_boot)
|
||||
|
||||
# percs = [5, 50, 95]
|
||||
# winner_onsets_boot_quarts = np.percentile(
|
||||
# winner_onsets_boot, percs, axis=0)
|
||||
# winner_offsets_boot_quarts = np.percentile(
|
||||
# winner_offsets_boot, percs, axis=0)
|
||||
# winner_physicals_boot_quarts = np.percentile(
|
||||
# winner_physicals_boot, percs, axis=0)
|
||||
# loser_onsets_boot_quarts = np.percentile(
|
||||
# loser_onsets_boot, percs, axis=0)
|
||||
# loser_offsets_boot_quarts = np.percentile(
|
||||
# loser_offsets_boot, percs, axis=0)
|
||||
# loser_physicals_boot_quarts = np.percentile(
|
||||
# loser_physicals_boot, percs, axis=0)
|
||||
|
||||
# fig, ax = plt.subplots(2, 3, figsize=(
|
||||
# 21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
|
||||
|
||||
# ax[0, 0].plot(kde_time, winner_onsets_conv)
|
||||
# ax[0, 1].plot(kde_time, winner_offsets_conv)
|
||||
# ax[0, 2].plot(kde_time, winner_physicals_conv)
|
||||
# ax[1, 0].plot(kde_time, loser_onsets_conv)
|
||||
# ax[1, 1].plot(kde_time, loser_offsets_conv)
|
||||
# ax[1, 2].plot(kde_time, loser_physicals_conv)
|
||||
|
||||
# ax[0, 0].plot(kde_time, winner_onsets_boot_quarts[1], c=ps.black)
|
||||
# ax[0, 1].plot(kde_time, winner_offsets_boot_quarts[1], c=ps.black)
|
||||
# ax[0, 2].plot(kde_time, winner_physicals_boot_quarts[1], c=ps.black)
|
||||
# ax[1, 0].plot(kde_time, loser_onsets_boot_quarts[1], c=ps.black)
|
||||
# ax[1, 1].plot(kde_time, loser_offsets_boot_quarts[1], c=ps.black)
|
||||
# ax[1, 2].plot(kde_time, loser_physicals_boot_quarts[1], c=ps.black)
|
||||
|
||||
# for kde in winner_onsets_boot:
|
||||
# ax[0, 0].plot(kde_time, kde,
|
||||
@ -428,43 +483,43 @@ def main(dataroot):
|
||||
# ax[1, 2].plot(kde_time, kde,
|
||||
# color='gray')
|
||||
|
||||
ax[0, 0].fill_between(kde_time,
|
||||
winner_onsets_boot_quarts[0],
|
||||
winner_onsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
|
||||
ax[0, 1].fill_between(kde_time,
|
||||
winner_offsets_boot_quarts[0],
|
||||
winner_offsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
|
||||
ax[0, 2].fill_between(kde_time,
|
||||
loser_physicals_boot_quarts[0],
|
||||
loser_physicals_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
|
||||
ax[1, 0].fill_between(kde_time,
|
||||
loser_onsets_boot_quarts[0],
|
||||
loser_onsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
|
||||
ax[1, 1].fill_between(kde_time,
|
||||
loser_offsets_boot_quarts[0],
|
||||
loser_offsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
|
||||
ax[1, 2].fill_between(kde_time,
|
||||
loser_physicals_boot_quarts[0],
|
||||
loser_physicals_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
|
||||
plt.show()
|
||||
# ax[0, 0].fill_between(kde_time,
|
||||
# winner_onsets_boot_quarts[0],
|
||||
# winner_onsets_boot_quarts[2],
|
||||
# color=ps.gray,
|
||||
# alpha=0.5)
|
||||
|
||||
# ax[0, 1].fill_between(kde_time,
|
||||
# winner_offsets_boot_quarts[0],
|
||||
# winner_offsets_boot_quarts[2],
|
||||
# color=ps.gray,
|
||||
# alpha=0.5)
|
||||
|
||||
# ax[0, 2].fill_between(kde_time,
|
||||
# loser_physicals_boot_quarts[0],
|
||||
# loser_physicals_boot_quarts[2],
|
||||
# color=ps.gray,
|
||||
# alpha=0.5)
|
||||
|
||||
# ax[1, 0].fill_between(kde_time,
|
||||
# loser_onsets_boot_quarts[0],
|
||||
# loser_onsets_boot_quarts[2],
|
||||
# color=ps.gray,
|
||||
# alpha=0.5)
|
||||
|
||||
# ax[1, 1].fill_between(kde_time,
|
||||
# loser_offsets_boot_quarts[0],
|
||||
# loser_offsets_boot_quarts[2],
|
||||
# color=ps.gray,
|
||||
# alpha=0.5)
|
||||
|
||||
# ax[1, 2].fill_between(kde_time,
|
||||
# loser_physicals_boot_quarts[0],
|
||||
# loser_physicals_boot_quarts[2],
|
||||
# color=ps.gray,
|
||||
# alpha=0.5)
|
||||
|
||||
# plt.show()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@ -16,21 +16,22 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
|
||||
|
||||
\begin{columns}
|
||||
\column{0.4}
|
||||
\myblock[GrayBlock]{Introduction}{
|
||||
\myblock[TranspBlock]{Introduction}{
|
||||
The time-frequency tradeoff makes reliable signal detecion and simultaneous
|
||||
sender identification by simple Fourier decomposition in freely interacting
|
||||
weakly electric fish impossible. This profoundly limits our current
|
||||
understanding of chirps to experiments
|
||||
with single - or physically separated - individuals.
|
||||
\vspace{1cm}
|
||||
\begin{tikzfigure}[]
|
||||
\label{griddrawing}
|
||||
\includegraphics[width=0.6\linewidth]{figs/introplot}
|
||||
\includegraphics[width=\linewidth]{figs/introplot}
|
||||
\end{tikzfigure}
|
||||
}
|
||||
\myblock[TranspBlock]{Chirp detection}{
|
||||
\begin{tikzfigure}[]
|
||||
\label{fig:alg1}
|
||||
\includegraphics[width=0.6\linewidth]{figs/algorithm1}
|
||||
\includegraphics[width=0.9\linewidth]{figs/algorithm1}
|
||||
\end{tikzfigure}
|
||||
\vspace{2cm}
|
||||
\begin{tikzfigure}[]
|
||||
|
||||
@ -33,8 +33,8 @@
|
||||
\begin{minipage}[c]{0.2\paperwidth}
|
||||
\centering
|
||||
% \vspace{1cm}
|
||||
\hspace{-10cm}
|
||||
\includegraphics[width=0.8\linewidth]{figs/efishlogo.pdf}
|
||||
\hspace{-7cm}
|
||||
\includegraphics[width=0.7\linewidth]{figs/efishlogo.pdf}
|
||||
\end{minipage}}
|
||||
% \begin{minipage}[c]{0.2\paperwidth}
|
||||
% \vspace{1cm}\hspace{1cm}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user