324 lines
12 KiB
Python
324 lines
12 KiB
Python
import numpy as np
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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 matplotlib.pyplot as plt
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from scipy.stats import pearsonr, spearmanr, wilcoxon
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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 get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
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foldername = folder_name.split('/')[-2]
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winner_row = order_meta_df[order_meta_df['recording'] == foldername]
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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 > 0:
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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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chirp_winner = len(
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Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
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chirp_loser = len(
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Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
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return chirp_winner, chirp_loser
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else:
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return np.nan, np.nan
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def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
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foldername = folder_name.split('/')[-2]
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folder_row = order_meta_df[order_meta_df['recording'] == foldername]
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fish1 = folder_row['fish1'].values[0].astype(int)
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fish2 = folder_row['fish2'].values[0].astype(int)
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winner = folder_row['winner'].values[0].astype(int)
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groub = folder_row['group'].values[0].astype(int)
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size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & (
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id_meta_df['fish'] == fish1)]
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size_fish2_row = id_meta_df[(id_meta_df['group'] == groub) & (
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id_meta_df['fish'] == fish2)]
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size_winners = [size_fish1_row[col].values[0]
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for col in ['l1', 'l2', 'l3']]
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size_fish1 = np.nanmean(size_winners)
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size_losers = [size_fish2_row[col].values[0] for col in ['l1', 'l2', 'l3']]
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size_fish2 = np.nanmean(size_losers)
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if winner == fish1:
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if size_fish1 > size_fish2:
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size_diff_bigger = size_fish1 - size_fish2
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size_diff_smaller = size_fish2 - size_fish1
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elif size_fish1 < size_fish2:
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size_diff_bigger = size_fish1 - size_fish2
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size_diff_smaller = size_fish2 - size_fish1
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else:
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size_diff_bigger = np.nan
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size_diff_smaller = np.nan
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winner_fish_id = np.nan
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loser_fish_id = np.nan
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return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
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winner_fish_id = folder_row['rec_id1'].values[0]
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loser_fish_id = folder_row['rec_id2'].values[0]
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elif winner == fish2:
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if size_fish2 > size_fish1:
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size_diff_bigger = size_fish2 - size_fish1
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size_diff_smaller = size_fish1 - size_fish2
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elif size_fish2 < size_fish1:
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size_diff_bigger = size_fish2 - size_fish1
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size_diff_smaller = size_fish1 - size_fish2
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else:
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size_diff_bigger = np.nan
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size_diff_smaller = np.nan
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winner_fish_id = np.nan
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loser_fish_id = np.nan
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return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
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winner_fish_id = folder_row['rec_id2'].values[0]
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loser_fish_id = folder_row['rec_id1'].values[0]
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else:
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size_diff_bigger = np.nan
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size_diff_smaller = np.nan
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winner_fish_id = np.nan
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loser_fish_id = np.nan
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return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
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chirp_winner = len(
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Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
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chirp_loser = len(
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Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
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return size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser
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def get_chirp_freq(folder_name, Behavior, order_meta_df):
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foldername = folder_name.split('/')[-2]
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folder_row = order_meta_df[order_meta_df['recording'] == foldername]
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fish1 = folder_row['fish1'].values[0].astype(int)
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fish2 = folder_row['fish2'].values[0].astype(int)
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fish1_freq = folder_row['rec_id1'].values[0].astype(int)
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fish2_freq = folder_row['rec_id2'].values[0].astype(int)
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winner = folder_row['winner'].values[0].astype(int)
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chirp_freq_fish1 = np.nanmedian(
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Behavior.freq[Behavior.ident == fish1_freq])
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chirp_freq_fish2 = np.nanmedian(
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Behavior.freq[Behavior.ident == fish2_freq])
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if winner == fish1:
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# if chirp_freq_fish1 > chirp_freq_fish2:
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# freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
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# freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
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# elif chirp_freq_fish1 < chirp_freq_fish2:
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# freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
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# freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
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# else:
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# freq_diff_higher = np.nan
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# freq_diff_lower = np.nan
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# winner_fish_id = np.nan
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# loser_fish_id = np.nan
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winner_fish_id = folder_row['rec_id1'].values[0]
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winner_fish_freq = chirp_freq_fish1
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loser_fish_id = folder_row['rec_id2'].values[0]
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loser_fish_freq = chirp_freq_fish2
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elif winner == fish2:
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# if chirp_freq_fish2 > chirp_freq_fish1:
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# freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
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# freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
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# elif chirp_freq_fish2 < chirp_freq_fish1:
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# freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
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# freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
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# else:
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# freq_diff_higher = np.nan
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# freq_diff_lower = np.nan
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# winner_fish_id = np.nan
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# loser_fish_id = np.nan
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winner_fish_id = folder_row['rec_id2'].values[0]
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winner_fish_freq = chirp_freq_fish2
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loser_fish_id = folder_row['rec_id1'].values[0]
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loser_fish_freq = chirp_freq_fish1
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else:
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winner_fish_freq = np.nan
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loser_fish_freq = np.nan
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winner_fish_id = np.nan
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loser_fish_id = np.nan
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chirp_winner = len(
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Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
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chirp_loser = len(
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Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
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return winner_fish_freq, chirp_winner, loser_fish_freq, chirp_loser
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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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foldernames, _ = get_valid_datasets(datapath)
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path_order_meta = (
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'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
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order_meta_df = read_csv(path_order_meta)
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order_meta_df['recording'] = order_meta_df['recording'].str[1:-1]
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path_id_meta = (
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'/').join(foldernames[0].split('/')[:-2]) + '/id_meta.csv'
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id_meta_df = read_csv(path_id_meta)
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chirps_winner = []
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size_diffs_winner = []
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size_diffs_loser = []
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size_chirps_winner = []
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size_chirps_loser = []
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freq_diffs_higher = []
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freq_diffs_lower = []
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freq_chirps_winner = []
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freq_chirps_loser = []
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chirps_loser = []
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freq_diffs = []
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freq_chirps_diffs = []
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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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winner_chirp, loser_chirp = get_chirp_winner_loser(
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foldername, bh, order_meta_df)
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chirps_winner.append(winner_chirp)
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chirps_loser.append(loser_chirp)
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size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser = get_chirp_size(
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foldername, bh, order_meta_df, id_meta_df)
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freq_winner, chirp_freq_winner, freq_loser, chirp_freq_loser = get_chirp_freq(
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foldername, bh, order_meta_df)
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freq_diffs_higher.append(freq_winner)
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freq_diffs_lower.append(freq_loser)
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freq_chirps_winner.append(chirp_freq_winner)
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freq_chirps_loser.append(chirp_freq_loser)
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if np.isnan(size_diff_bigger):
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continue
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size_diffs_winner.append(size_diff_bigger)
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size_diffs_loser.append(size_diff_smaller)
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size_chirps_winner.append(chirp_winner)
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size_chirps_loser.append(chirp_loser)
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size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner)
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size_loser_pearsonr = pearsonr(size_diffs_loser, size_chirps_loser)
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fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(
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21*ps.cm, 7*ps.cm), width_ratios=[1, 0.8, 0.8], sharey=True)
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plt.subplots_adjust(left=0.11, right=0.948, top=0.86,
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wspace=0.343, bottom=0.198)
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scatterwinner = 1.15
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scatterloser = 1.85
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chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
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chirps_loser = np.asarray(chirps_loser)[~np.isnan(chirps_loser)]
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stat = wilcoxon(chirps_winner, chirps_loser)
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print(stat)
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winner_color = ps.gblue2
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loser_color = ps.gblue1
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bplot1 = ax1.boxplot(chirps_winner, positions=[
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0.9], showfliers=False, patch_artist=True)
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bplot2 = ax1.boxplot(chirps_loser, positions=[
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2.1], showfliers=False, patch_artist=True)
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ax1.scatter(np.ones(len(chirps_winner)) *
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scatterwinner, chirps_winner, color=winner_color)
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ax1.scatter(np.ones(len(chirps_loser)) *
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scatterloser, chirps_loser, color=loser_color)
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ax1.set_xticklabels(['Winner', 'Loser'])
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ax1.text(0.1, 0.95, f'n={len(chirps_winner)}',
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transform=ax1.transAxes, color=ps.white)
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for w, l in zip(chirps_winner, chirps_loser):
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ax1.plot([scatterwinner, scatterloser], [w, l],
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color=ps.white, alpha=0.6, linewidth=1, zorder=-1)
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ax1.set_ylabel('Chirp counts', color=ps.white)
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ax1.set_xlabel('Competition outcome', color=ps.white)
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ps.set_boxplot_color(bplot1, winner_color)
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ps.set_boxplot_color(bplot2, loser_color)
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ax2.scatter(size_diffs_winner, size_chirps_winner,
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color=winner_color, label=f'Winner')
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ax2.scatter(size_diffs_loser, size_chirps_loser,
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color=loser_color, label='Loser')
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ax2.text(0.05, 0.95, f'n={len(size_chirps_winner)}',
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transform=ax2.transAxes, color=ps.white)
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ax2.set_xlabel('Size difference [cm]')
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# ax2.set_xticks(np.arange(-10, 10.1, 2))
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ax3.scatter(freq_diffs_higher, freq_chirps_winner, color=winner_color)
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ax3.scatter(freq_diffs_lower, freq_chirps_loser, color=loser_color)
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ax3.text(0.1, 0.95, f'n={len(freq_chirps_loser)}',
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transform=ax3.transAxes, color=ps.white)
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ax3.set_xlabel('EODf [Hz]')
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handles, labels = ax2.get_legend_handles_labels()
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fig.legend(handles, labels, loc='upper center',
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ncol=2, bbox_to_anchor=(0.5, 1.04))
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# pearson r
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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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