278 lines
10 KiB
Python
278 lines
10 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 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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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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mean_size_winner = 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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mean_size_loser = np.nanmean(size_losers)
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if mean_size_winner > mean_size_loser:
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size_diff = mean_size_winner - mean_size_loser
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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 mean_size_winner < mean_size_loser:
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size_diff = mean_size_loser - mean_size_winner
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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 = 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_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
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Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
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return size_diff, chirp_diff
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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['rec_id1'].values[0].astype(int)
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fish2 = folder_row['rec_id2'].values[0].astype(int)
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chirp_freq_fish1 = np.nanmedian(
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Behavior.freq[Behavior.ident == fish1])
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chirp_freq_fish2 = np.nanmedian(
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Behavior.freq[Behavior.ident == fish2])
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if chirp_freq_fish1 > chirp_freq_fish2:
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freq_diff = chirp_freq_fish1 - chirp_freq_fish2
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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 chirp_freq_fish1 < chirp_freq_fish2:
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freq_diff = chirp_freq_fish2 - chirp_freq_fish1
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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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chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
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Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
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return freq_diff, chirp_diff
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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 = []
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size_chirps_diffs = []
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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, chirp_diff = get_chirp_size(
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# foldername, bh, order_meta_df, id_meta_df)
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# size_diffs.append(size_diff)
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# size_chirps_diffs.append(chirp_diff)
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# freq_diff, freq_chirps_diff = get_chirp_freq(
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# foldername, bh, order_meta_df)
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# freq_diffs.append(freq_diff)
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# freq_chirps_diffs.append(freq_chirps_diff)
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folder_name = foldername.split('/')[-2]
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winner_row = order_meta_df[order_meta_df['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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groub = winner_row['group'].values[0].astype(int)
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size_rows = id_meta_df[id_meta_df['group'] == groub]
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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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size_winners = []
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for l in ['l1', 'l2', 'l3']:
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size_winner = size_rows[size_rows['fish']
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== winner_fish1][l].values[0]
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size_winners.append(size_winner)
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mean_size_winner = np.nanmean(size_winners)
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size_losers = []
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for l in ['l1', 'l2', 'l3']:
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size_loser = size_rows[size_rows['fish']
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== winner_fish2][l].values[0]
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size_losers.append(size_loser)
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mean_size_loser = np.nanmean(size_losers)
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size_diffs.append(mean_size_winner - mean_size_loser)
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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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size_winners = []
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for l in ['l1', 'l2', 'l3']:
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size_winner = size_rows[size_rows['fish']
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== winner_fish2][l].values[0]
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size_winners.append(size_winner)
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mean_size_winner = np.nanmean(size_winners)
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size_losers = []
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for l in ['l1', 'l2', 'l3']:
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size_loser = size_rows[size_rows['fish']
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== winner_fish1][l].values[0]
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size_losers.append(size_loser)
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mean_size_loser = np.nanmean(size_losers)
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size_diffs.append(mean_size_winner - mean_size_loser)
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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_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
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chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
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freq_winner = np.nanmedian(bh.freq[bh.ident == winner_fish_id])
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freq_loser = np.nanmedian(bh.freq[bh.ident == loser_fish_id])
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chirps_winner.append(chirp_winner)
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chirps_loser.append(chirp_loser)
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size_chirps_diffs.append(chirp_winner - chirp_loser)
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freq_diffs.append(freq_winner - freq_loser)
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fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(
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22*ps.cm, 12*ps.cm), width_ratios=[1.5, 1, 1])
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plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
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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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bplot1 = ax1.boxplot(chirps_winner, positions=[
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1], showfliers=False, patch_artist=True)
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bplot2 = ax1.boxplot(chirps_loser, positions=[
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2], showfliers=False, patch_artist=True)
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ax1.scatter(np.ones(len(chirps_winner)) *
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scatterwinner, chirps_winner, color='r')
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ax1.scatter(np.ones(len(chirps_loser)) *
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scatterloser, chirps_loser, color='r')
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ax1.set_xticklabels(['winner', 'loser'])
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ax1.text(0.1, 0.9, 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='r', alpha=0.5, linewidth=0.5)
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ax1.set_ylabel('Chirps [n]', color=ps.white)
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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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ax2.scatter(size_diffs, size_chirps_diffs, color='r')
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ax2.set_xlabel('Size difference [mm]')
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ax2.set_ylabel('Chirps [n]')
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ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
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# ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
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ax3.set_xlabel('Frequency difference [Hz]')
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ax3.set_yticklabels([])
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ax3.set
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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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