101 lines
3.4 KiB
Python
101 lines
3.4 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 = [
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datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
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path_to_csv = (
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'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
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meta_id = read_csv(path_to_csv)
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meta_id['recording'] = meta_id['recording'].str[1:-1]
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chirps_winner = []
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chirps_loser = []
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for foldername in foldernames:
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# behabvior is pandas dataframe with all the data
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if foldername == '../data/mount_data/2020-05-12-10_00/':
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continue
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bh = Behavior(foldername)
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# chirps are not sorted in time (presumably due to prior groupings)
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# get and sort chirps and corresponding fish_ids of the chirps
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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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folder_name = foldername.split('/')[-2]
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winner_row = meta_id[meta_id['recording'] == folder_name]
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winner = winner_row['winner'].values[0].astype(int)
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winner_fish1 = winner_row['fish1'].values[0].astype(int)
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winner_fish2 = winner_row['fish2'].values[0].astype(int)
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if winner == winner_fish1:
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winner_fish_id = winner_row['rec_id1'].values[0]
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loser_fish_id = winner_row['rec_id2'].values[0]
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elif winner == winner_fish2:
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winner_fish_id = winner_row['rec_id2'].values[0]
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loser_fish_id = winner_row['rec_id1'].values[0]
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else:
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continue
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print(foldername)
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all_fish_ids = np.unique(bh.chirps_ids)
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chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
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chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
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chirps_winner.append(chirp_winner)
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chirps_loser.append(chirp_loser)
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fish1_id = all_fish_ids[0]
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fish2_id = all_fish_ids[1]
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print(winner_fish_id)
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print(all_fish_ids)
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fig, ax = plt.subplots()
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scatterwinner = 1.15
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scatterloser = 1.85
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bplot1 = ax.boxplot(chirps_winner, positions=[
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1], showfliers=False, patch_artist=True)
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bplot2 = ax.boxplot(chirps_loser, positions=[
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2], showfliers=False, patch_artist=True)
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ax.scatter(np.ones(len(chirps_winner))*scatterwinner, chirps_winner, color='r')
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ax.scatter(np.ones(len(chirps_loser))*scatterloser, chirps_loser, color='r')
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ax.set_xticklabels(['winner', 'loser'])
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for w, l in zip(chirps_winner, chirps_loser):
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ax.plot([scatterwinner, scatterloser], [w, l], color='r', alpha=0.5, linewidth=0.5)
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colors1 = ps.red
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ps.set_boxplot_color(bplot1, colors1)
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colors1 = ps.orange
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ps.set_boxplot_color(bplot2, colors1)
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ax.set_ylabel('Chirpscounts [n]')
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plt.savefig('../poster/figs/chirps_winner_loser.pdf')
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plt.show()
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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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