161 lines
4.7 KiB
Python
161 lines
4.7 KiB
Python
import numpy as np
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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from thunderfish.powerspectrum import decibel
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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.plotstyle import PlotStyle
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from modules.behaviour_handling import Behavior, correct_chasing_events
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from extract_chirps import get_valid_datasets
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ps = PlotStyle()
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logger = makeLogger(__name__)
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def main(datapath: str):
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foldernames = [
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datapath + x + "/"
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for x in os.listdir(datapath)
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if os.path.isdir(datapath + x)
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]
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foldernames, _ = get_valid_datasets(datapath)
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for foldername in foldernames[3:4]:
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print(foldername)
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# foldername = foldernames[0]
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if foldername == "../data/mount_data/2020-05-12-10_00/":
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continue
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# behabvior is pandas dataframe with all the data
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bh = Behavior(foldername)
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# 2020-06-11-10
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category = bh.behavior
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timestamps = bh.start_s
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# Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
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# Get rid of tracking faults (two onsets or two offsets after another)
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category, timestamps = correct_chasing_events(category, timestamps)
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# split categories
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chasing_onset = (timestamps[category == 0] / 60) / 60
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chasing_offset = (timestamps[category == 1] / 60) / 60
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physical_contact = (timestamps[category == 2] / 60) / 60
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all_fish_ids = np.unique(bh.chirps_ids)
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fish1_id = all_fish_ids[0]
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fish2_id = all_fish_ids[1]
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# Associate chirps to inidividual fish
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fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) / 60
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fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) / 60
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embed()
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exit()
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fish1_color = ps.gblue2
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fish2_color = ps.gblue1
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fig, ax = plt.subplots(
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5,
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1,
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figsize=(21 * ps.cm, 10 * ps.cm),
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height_ratios=[0.5, 0.5, 0.5, 0.2, 6],
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sharex=True,
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)
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# marker size
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s = 80
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ax[0].scatter(
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physical_contact,
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np.ones(len(physical_contact)),
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color=ps.gray,
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marker="|",
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s=s,
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)
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ax[1].scatter(
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chasing_onset,
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np.ones(len(chasing_onset)),
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color=ps.gray,
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marker="|",
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s=s,
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)
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ax[2].scatter(
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fish1,
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np.ones(len(fish1)) - 0.25,
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color=fish1_color,
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marker="|",
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s=s,
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)
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ax[2].scatter(
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fish2,
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np.zeros(len(fish2)) + 0.25,
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color=fish2_color,
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marker="|",
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s=s,
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)
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freq_temp = bh.freq[bh.ident == fish1_id]
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time_temp = bh.time[bh.idx[bh.ident == fish1_id]]
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ax[4].plot((time_temp / 60) / 60, freq_temp, color=fish1_color)
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freq_temp = bh.freq[bh.ident == fish2_id]
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time_temp = bh.time[bh.idx[bh.ident == fish2_id]]
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ax[4].plot((time_temp / 60) / 60, freq_temp, color=fish2_color)
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# ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower')
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# Hide grid lines
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ax[0].grid(False)
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ax[0].set_frame_on(False)
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ax[0].set_xticks([])
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ax[0].set_yticks([])
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ps.hide_ax(ax[0])
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ax[1].grid(False)
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ax[1].set_frame_on(False)
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ax[1].set_xticks([])
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ax[1].set_yticks([])
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ps.hide_ax(ax[1])
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ax[2].grid(False)
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ax[2].set_frame_on(False)
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ax[2].set_yticks([])
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ax[2].set_xticks([])
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ps.hide_ax(ax[2])
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ax[4].axvspan(0, 3, 0, 5, facecolor="grey", alpha=0.5)
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ax[4].set_xticks(np.arange(0, 6.1, 0.5))
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ps.hide_ax(ax[3])
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labelpad = 30
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fsize = 12
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ax[0].set_ylabel(
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"Contact", rotation=0, labelpad=labelpad, fontsize=fsize
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)
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ax[0].yaxis.set_label_coords(-0.062, -0.08)
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ax[1].set_ylabel(
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"Chasing", rotation=0, labelpad=labelpad, fontsize=fsize
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)
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ax[1].yaxis.set_label_coords(-0.06, -0.08)
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ax[2].set_ylabel(
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"Chirps", rotation=0, labelpad=labelpad, fontsize=fsize
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)
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ax[2].yaxis.set_label_coords(-0.07, -0.08)
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ax[4].set_ylabel("EODf")
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ax[4].set_xlabel("Time [h]")
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# ax[0].set_title(foldername.split('/')[-2])
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# 2020-03-31-9_59
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plt.subplots_adjust(left=0.158, right=0.987, top=0.918, bottom=0.136)
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plt.savefig("../poster/figs/timeline.svg")
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plt.show()
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# plot chirps
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if __name__ == "__main__":
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# Path to the data
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datapath = "../data/mount_data/"
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main(datapath)
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