373 lines
12 KiB
Python
373 lines
12 KiB
Python
import os
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import matplotlib.pyplot as plt
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import numpy as np
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from extract_chirps import get_valid_datasets
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from IPython import embed
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from modules.behaviour_handling import Behavior, correct_chasing_events
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from modules.logger import makeLogger
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from modules.plotstyle import PlotStyle
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from pandas import read_csv
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from scipy.stats import pearsonr, wilcoxon
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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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)
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chirp_loser = len(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[
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(id_meta_df["group"] == groub) & (id_meta_df["fish"] == fish1)
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]
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size_fish2_row = id_meta_df[
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(id_meta_df["group"] == groub) & (id_meta_df["fish"] == fish2)
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]
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size_winners = [size_fish1_row[col].values[0] 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 = 0
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size_diff_smaller = 0
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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 = 0
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size_diff_smaller = 0
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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 (
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size_diff_bigger,
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size_diff_smaller,
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winner_fish_id,
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loser_fish_id,
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)
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chirp_winner = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
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chirp_loser = len(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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chirp_freq_fish1 = np.nanmedian(Behavior.freq[Behavior.ident == fish1_freq])
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chirp_freq_fish2 = np.nanmedian(Behavior.freq[Behavior.ident == fish2_freq])
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winner = folder_row["winner"].values[0].astype(int)
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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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return winner_fish_freq, winner_fish_id, loser_fish_freq, loser_fish_id
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chirp_winner = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
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chirp_loser = len(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 + "/"
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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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path_order_meta = ("/").join(
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foldernames[0].split("/")[:-2]
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) + "/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 = ("/").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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chirps_loser = []
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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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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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)
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chirps_winner.append(winner_chirp)
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chirps_loser.append(loser_chirp)
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(
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size_diff_bigger,
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chirp_winner,
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size_diff_smaller,
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chirp_loser,
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) = get_chirp_size(foldername, bh, order_meta_df, id_meta_df)
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(
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freq_winner,
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chirp_freq_winner,
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freq_loser,
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chirp_freq_loser,
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) = get_chirp_freq(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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pearsonr(size_diffs_winner, size_chirps_winner)
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pearsonr(size_diffs_loser, size_chirps_loser)
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fig, (ax1, ax2, ax3) = plt.subplots(
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1,
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3,
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figsize=(21 * ps.cm, 7 * ps.cm),
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width_ratios=[1, 0.8, 0.8],
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sharey=True,
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)
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plt.subplots_adjust(
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left=0.11, right=0.948, top=0.86, wspace=0.343, bottom=0.198
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)
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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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embed()
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exit()
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freq_diffs_higher = np.asarray(freq_diffs_higher)[
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~np.isnan(freq_diffs_higher)
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]
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freq_diffs_lower = np.asarray(freq_diffs_lower)[~np.isnan(freq_diffs_lower)]
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freq_chirps_winner = np.asarray(freq_chirps_winner)[
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~np.isnan(freq_chirps_winner)
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]
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freq_chirps_loser = np.asarray(freq_chirps_loser)[
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~np.isnan(freq_chirps_loser)
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]
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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(
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chirps_winner, positions=[0.9], showfliers=False, patch_artist=True
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)
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bplot2 = ax1.boxplot(
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chirps_loser, positions=[2.1], showfliers=False, patch_artist=True
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)
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ax1.scatter(
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np.ones(len(chirps_winner)) * scatterwinner,
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chirps_winner,
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color=winner_color,
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)
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ax1.scatter(
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np.ones(len(chirps_loser)) * scatterloser,
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chirps_loser,
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color=loser_color,
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)
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ax1.set_xticklabels(["Winner", "Loser"])
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ax1.text(
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0.1,
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0.85,
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f"n={len(chirps_loser)}",
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transform=ax1.transAxes,
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color=ps.white,
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)
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for w, l in zip(chirps_winner, chirps_loser):
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ax1.plot(
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[scatterwinner, scatterloser],
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[w, l],
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color=ps.white,
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alpha=0.6,
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linewidth=1,
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zorder=-1,
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)
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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(
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size_diffs_winner,
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size_chirps_winner,
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color=winner_color,
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label="Winner",
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)
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ax2.scatter(
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size_diffs_loser, size_chirps_loser, color=loser_color, label="Loser"
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)
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ax2.text(
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0.05,
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0.85,
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f"n={len(size_chirps_loser)}",
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transform=ax2.transAxes,
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color=ps.white,
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)
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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(
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0.1,
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0.85,
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f"n={len(np.asarray(freq_chirps_winner)[~np.isnan(freq_chirps_loser)])}",
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transform=ax3.transAxes,
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color=ps.white,
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)
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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(
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handles, labels, loc="upper center", ncol=2, bbox_to_anchor=(0.5, 1.04)
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)
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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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