116 lines
3.9 KiB
Python
116 lines
3.9 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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ps = PlotStyle()
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logger = makeLogger(__name__)
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def main(datapath: str):
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foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
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for foldername in foldernames:
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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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fish1_color = ps.red
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fish2_color = ps.orange
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fig, ax = plt.subplots(4, 1, figsize=(21*ps.cm, 13*ps.cm), height_ratios=[0.5, 0.5, 0.5, 6], sharex=True)
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# marker size
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s = 200
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ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='firebrick', marker='|', s=s)
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ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', marker='|', s=s )
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ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color=fish1_color, marker='|', s=s)
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ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color=fish2_color, marker='|', s=s)
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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[3].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[3].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[3].axvspan(3, 6, 0, 5, facecolor='grey', alpha=0.5)
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ax[3].set_xticks(np.arange(0, 6.1, 0.5))
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labelpad = 40
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fsize = 12
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ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad, fontsize=fsize)
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ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad, fontsize=fsize)
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ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad, fontsize=fsize)
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ax[3].set_ylabel('EODf')
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ax[3].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)
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#plt.savefig('../poster/figs/timeline.pdf')
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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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