kdes work but scale is wrong
This commit is contained in:
weygoldt
parent
9e5ec1d70b
commit
eaa91e1655
@ -1,98 +0,0 @@
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import os
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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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.datahandling import flatten
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from modules.behaviour_handling import Behavior, correct_chasing_events, event_triggered_chirps
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from extract_chirps import get_valid_datasets
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logger = makeLogger(__name__)
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ps = PlotStyle()
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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 = Behavior.chirps[Behavior.chirps_ids == winner_fish_id]
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chirp_loser = 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 None, None
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def main(dataroot):
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foldernames, _ = get_valid_datasets(dataroot)
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meta_path = (
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'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
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meta = pd.read_csv(meta_path)
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meta['recording'] = meta['recording'].str[1:-1]
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winner_chirps = []
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loser_chirps = []
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onsets = []
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offsets = []
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physicals = []
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# Iterate over all recordings and save chirp- and event-timestamps
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for folder in foldernames:
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logger.info('Loading data from folder: {}'.format(folder))
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time_before = 30
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time_after = 60
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dt = 0.1
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kernel_width = 2
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kde_time = np.arange(-time_before, time_after, dt)
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broken_folders = ['../data/mount_data/2020-05-12-10_00/']
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if folder in broken_folders:
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continue
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bh = Behavior(folder)
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winner, loser = get_chirp_winner_loser(folder, bh, meta)
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if winner is None:
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continue
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# Chirps are already sorted
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winner_chirps.append(bh.chirps)
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loser_chirps.append(bh.chirps)
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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(bh.behavior, bh.start_s)
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# Split categories
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onsets.append(timestamps[category == 0])
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offsets.append(timestamps[category == 1])
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physicals.append(timestamps[category == 2])
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# center chirps around events
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if __name__ == '__main__':
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main('../data/mount_data/')
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@ -345,7 +345,7 @@ def main(datapath: str):
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np.random.shuffle(diff_physical)
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shuffled_physical = np.cumsum(diff_physical)
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kde_onset = (acausal_kde1d(shuffled_onset, time, width))/(27*100)
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kde_onset (acausal_kde1d(shuffled_onset, time, width))/(27*100)
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kde_offset = (acausal_kde1d(shuffled_offset, time, width))/(27*100)
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kde_physical = (acausal_kde1d(shuffled_physical, time, width))/(27*100)
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@ -1,13 +1,10 @@
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import numpy as np
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import os
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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.datahandling import causal_kde1d, acausal_kde1d
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from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
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logger = makeLogger(__name__)
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@ -43,7 +40,7 @@ class Behavior:
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# csv_filename = [f for f in os.listdir(
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# folder_path) if f.endswith('.csv')][0]
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logger.info(f'CSV file: {csv_filename}')
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# logger.info(f'CSV file: {csv_filename}')
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self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
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self.chirps = np.load(os.path.join(
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@ -92,6 +89,12 @@ def correct_chasing_events(
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wrong_bh = np.arange(len(category))[
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category != 2][:-1][np.diff(category[category != 2]) == 0]
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if category[category != 2][-1] == 0:
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wrong_bh = np.append(
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wrong_bh,
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np.arange(len(category))[category != 2][-1])
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if onset_ids[0] > offset_ids[0]:
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offset_ids = np.delete(offset_ids, 0)
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help_index = offset_ids[0]
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@ -106,51 +109,61 @@ def correct_chasing_events(
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len(category))[category == 1]
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# Check whether on- or offset is longer and calculate length difference
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if len(new_onset_ids) > len(new_offset_ids):
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len_diff = len(onset_ids) - len(offset_ids)
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logger.info(f'Onsets are greater than offsets by {len_diff}')
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embed()
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logger.warning('Onsets are greater than offsets')
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elif len(new_onset_ids) < len(new_offset_ids):
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len_diff = len(offset_ids) - len(onset_ids)
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logger.info(f'Offsets are greater than onsets by {len_diff}')
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logger.warning('Offsets are greater than onsets')
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elif len(new_onset_ids) == len(new_offset_ids):
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logger.info('Chasing events are equal')
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# logger.info('Chasing events are equal')
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pass
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return category, timestamps
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def event_triggered_chirps(
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event: np.ndarray,
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def center_chirps(
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events: np.ndarray,
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chirps: np.ndarray,
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time_before_event: int,
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time_after_event: int,
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dt: float,
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width: float,
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# dt: float,
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# width: float,
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) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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event_chirps = [] # chirps that are in specified window around event
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# timestamps of chirps around event centered on the event timepoint
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centered_chirps = []
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for event_timestamp in event:
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for event_timestamp in events:
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start = event_timestamp - time_before_event
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stop = event_timestamp + time_after_event
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chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
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event_chirps.append(chirps_around_event)
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if len(chirps_around_event) == 0:
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continue
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else:
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centered_chirps.append(chirps_around_event - event_timestamp)
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event_chirps.append(chirps_around_event)
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centered_chirps = np.sort(flatten(centered_chirps))
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event_chirps = np.sort(flatten(event_chirps))
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if len(centered_chirps) != len(event_chirps):
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raise ValueError(
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'Non centered chirps and centered chirps are not equal')
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time = np.arange(-time_before_event, time_after_event, dt)
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# time = np.arange(-time_before_event, time_after_event, dt)
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# Kernel density estimation with some if's
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if len(centered_chirps) == 0:
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centered_chirps = np.array([])
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centered_chirps_convolved = np.zeros(len(time))
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else:
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# convert list of arrays to one array for plotting
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centered_chirps = np.concatenate(centered_chirps, axis=0)
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centered_chirps_convolved = (acausal_kde1d(
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centered_chirps, time, width)) / len(event)
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# # Kernel density estimation with some if's
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# if len(centered_chirps) == 0:
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# centered_chirps = np.array([])
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# centered_chirps_convolved = np.zeros(len(time))
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# else:
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# # convert list of arrays to one array for plotting
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# centered_chirps = np.concatenate(centered_chirps, axis=0)
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# centered_chirps_convolved = (acausal_kde1d(
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# centered_chirps, time, width)) / len(event)
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return event_chirps, centered_chirps, centered_chirps_convolved
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return centered_chirps
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432
code/plot_kdes.py
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432
code/plot_kdes.py
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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 pandas as pd
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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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.datahandling import flatten, causal_kde1d, acausal_kde1d
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from modules.behaviour_handling import (
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Behavior, correct_chasing_events, center_chirps)
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from modules.plotstyle import PlotStyle
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logger = makeLogger(__name__)
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ps = PlotStyle()
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def jackknife(data, nresamples, subsetsize, kde_time, kernel_width):
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if len(data) == 0:
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return []
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jackknifed_kdes = []
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data = np.sort(data)
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subsetsize = int(np.round(len(data)*subsetsize))
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for n in range(nresamples):
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subset = np.random.choice(data, subsetsize, replace=False)
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subset_kde = acausal_kde1d(subset, time=kde_time, width=kernel_width)
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jackknifed_kdes.append(subset_kde)
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return jackknifed_kdes
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def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before, time_after):
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bootstrapped_kdes = []
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data = data[data <= 3*60*60] # only night time
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if len(data) == 0:
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logger.info('No data for bootstrap, added zeros')
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return [np.zeros_like(kde_time) for i in range(nresamples)]
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diff_data = np.diff(np.sort(data), prepend=np.sort(data)[0])
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for i in tqdm(range(nresamples)):
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np.random.shuffle(diff_data)
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bootstrapped_data = np.cumsum(diff_data)
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bootstrap_data_centered = center_chirps(
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bootstrapped_data, event_times, time_before, time_after)
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bootstrapped_kde = acausal_kde1d(
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bootstrap_data_centered, time=kde_time, width=kernel_width)
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bootstrapped_kdes.append(bootstrapped_kde)
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return bootstrapped_kdes
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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 = Behavior.chirps[Behavior.chirps_ids == winner_fish_id]
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chirp_loser = Behavior.chirps[Behavior.chirps_ids == loser_fish_id]
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return chirp_winner, chirp_loser
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return None, None
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def main(dataroot):
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foldernames, _ = get_valid_datasets(dataroot)
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plot_all = False
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time_before = 60
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time_after = 60
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dt = 0.001
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kernel_width = 1
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kde_time = np.arange(-time_before, time_after, dt)
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nbootstraps = 2
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meta_path = (
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'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
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meta = pd.read_csv(meta_path)
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meta['recording'] = meta['recording'].str[1:-1]
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winner_onsets = []
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winner_offsets = []
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winner_physicals = []
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loser_onsets = []
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loser_offsets = []
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loser_physicals = []
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winner_onsets_boot = []
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winner_offsets_boot = []
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winner_physicals_boot = []
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loser_onsets_boot = []
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loser_offsets_boot = []
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loser_physicals_boot = []
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onset_count = 0
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offset_count = 0
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physical_count = 0
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# Iterate over all recordings and save chirp- and event-timestamps
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for folder in tqdm(foldernames):
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foldername = folder.split('/')[-2]
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# logger.info('Loading data from folder: {}'.format(foldername))
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broken_folders = ['../data/mount_data/2020-05-12-10_00/']
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if folder in broken_folders:
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continue
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bh = Behavior(folder)
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category, timestamps = correct_chasing_events(bh.behavior, bh.start_s)
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winner, loser = get_chirp_winner_loser(folder, bh, meta)
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if winner is None:
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continue
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onsets = (timestamps[category == 0])
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offsets = (timestamps[category == 1])
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physicals = (timestamps[category == 2])
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onset_count += len(onsets)
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offset_count += len(offsets)
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physical_count += len(physicals)
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winner_onsets.append(center_chirps(
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winner, onsets, time_before, time_after))
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winner_offsets.append(center_chirps(
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winner, offsets, time_before, time_after))
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winner_physicals.append(center_chirps(
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winner, physicals, time_before, time_after))
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loser_onsets.append(center_chirps(
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loser, onsets, time_before, time_after))
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loser_offsets.append(center_chirps(
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loser, offsets, time_before, time_after))
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loser_physicals.append(center_chirps(
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loser, physicals, time_before, time_after))
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# bootstrap
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winner_onsets_boot.append(bootstrap(
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winner,
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nresamples=nbootstraps,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=onsets,
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time_before=time_before,
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time_after=time_after))
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winner_offsets_boot.append(bootstrap(
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winner,
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nresamples=nbootstraps,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=offsets,
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time_before=time_before,
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time_after=time_after))
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winner_physicals_boot.append(bootstrap(
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winner,
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nresamples=nbootstraps,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=physicals,
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time_before=time_before,
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time_after=time_after))
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loser_onsets_boot.append(bootstrap(
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loser,
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nresamples=nbootstraps,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=onsets,
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time_before=time_before,
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time_after=time_after))
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loser_offsets_boot.append(bootstrap(
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loser,
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nresamples=nbootstraps,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=offsets,
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time_before=time_before,
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time_after=time_after))
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loser_physicals_boot.append(bootstrap(
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loser,
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nresamples=nbootstraps,
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kde_time=kde_time,
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kernel_width=kernel_width,
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event_times=physicals,
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time_before=time_before,
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time_after=time_after))
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if plot_all:
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winner_onsets_conv = acausal_kde1d(
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winner_onsets[-1], kde_time, kernel_width)
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winner_offsets_conv = acausal_kde1d(
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winner_offsets[-1], kde_time, kernel_width)
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winner_physicals_conv = acausal_kde1d(
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winner_physicals[-1], kde_time, kernel_width)
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loser_onsets_conv = acausal_kde1d(
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loser_onsets[-1], kde_time, kernel_width)
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loser_offsets_conv = acausal_kde1d(
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loser_offsets[-1], kde_time, kernel_width)
|
||||
loser_physicals_conv = acausal_kde1d(
|
||||
loser_physicals[-1], kde_time, kernel_width)
|
||||
|
||||
fig, ax = plt.subplots(2, 3, figsize=(
|
||||
21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
|
||||
ax[0, 0].set_title(
|
||||
f"{foldername}, onsets {len(onsets)}, offsets {len(offsets)}, physicals {len(physicals)},winner {len(winner)}, looser {len(loser)} , onsets")
|
||||
ax[0, 0].plot(kde_time, winner_onsets_conv/len(onsets))
|
||||
ax[0, 1].plot(kde_time, winner_offsets_conv/len(offsets))
|
||||
ax[0, 2].plot(kde_time, winner_physicals_conv/len(physicals))
|
||||
ax[1, 0].plot(kde_time, loser_onsets_conv/len(onsets))
|
||||
ax[1, 1].plot(kde_time, loser_offsets_conv/len(offsets))
|
||||
ax[1, 2].plot(kde_time, loser_physicals_conv/len(physicals))
|
||||
|
||||
# # plot bootstrap lines
|
||||
# for kde in winner_onsets_boot[-1]:
|
||||
# ax[0, 0].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
# for kde in winner_offsets_boot[-1]:
|
||||
# ax[0, 1].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
# for kde in winner_physicals_boot[-1]:
|
||||
# ax[0, 2].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
# for kde in loser_onsets_boot[-1]:
|
||||
# ax[1, 0].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
# for kde in loser_offsets_boot[-1]:
|
||||
# ax[1, 1].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
# for kde in loser_physicals_boot[-1]:
|
||||
# ax[1, 2].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
|
||||
# plot bootstrap percentiles
|
||||
ax[0, 0].fill_between(
|
||||
kde_time,
|
||||
np.percentile(winner_onsets_boot[-1], 5, axis=0)/len(onsets),
|
||||
np.percentile(winner_onsets_boot[-1], 95, axis=0)/len(onsets),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
ax[0, 1].fill_between(
|
||||
kde_time,
|
||||
np.percentile(winner_offsets_boot[-1], 5, axis=0)/len(offsets),
|
||||
np.percentile(
|
||||
winner_offsets_boot[-1], 95, axis=0)/len(offsets),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
ax[0, 2].fill_between(
|
||||
kde_time,
|
||||
np.percentile(
|
||||
winner_physicals_boot[-1], 5, axis=0)/len(physicals),
|
||||
np.percentile(
|
||||
winner_physicals_boot[-1], 95, axis=0)/len(physicals),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
ax[1, 0].fill_between(
|
||||
kde_time,
|
||||
np.percentile(loser_onsets_boot[-1], 5, axis=0)/len(onsets),
|
||||
np.percentile(loser_onsets_boot[-1], 95, axis=0)/len(onsets),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
ax[1, 1].fill_between(
|
||||
kde_time,
|
||||
np.percentile(loser_offsets_boot[-1], 5, axis=0)/len(offsets),
|
||||
np.percentile(loser_offsets_boot[-1], 95, axis=0)/len(offsets),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
ax[1, 2].fill_between(
|
||||
kde_time,
|
||||
np.percentile(
|
||||
loser_physicals_boot[-1], 5, axis=0)/len(physicals),
|
||||
np.percentile(
|
||||
loser_physicals_boot[-1], 95, axis=0)/len(physicals),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
|
||||
ax[0, 0].plot(kde_time, np.median(winner_onsets_boot[-1], axis=0)/len(onsets),
|
||||
color='black', linewidth=2)
|
||||
ax[0, 1].plot(kde_time, np.median(winner_offsets_boot[-1], axis=0)/len(offsets),
|
||||
color='black', linewidth=2)
|
||||
ax[0, 2].plot(kde_time, np.median(winner_physicals_boot[-1], axis=0)/len(physicals),
|
||||
color='black', linewidth=2)
|
||||
ax[1, 0].plot(kde_time, np.median(loser_onsets_boot[-1], axis=0)/len(onsets),
|
||||
color='black', linewidth=2)
|
||||
ax[1, 1].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0)/len(offsets),
|
||||
color='black', linewidth=2)
|
||||
ax[1, 2].plot(kde_time, np.median(loser_physicals_boot[-1], axis=0)/len(physicals),
|
||||
color='black', linewidth=2)
|
||||
|
||||
ax[0, 0].set_xlim(-30, 30)
|
||||
plt.show()
|
||||
|
||||
winner_onsets = np.sort(flatten(winner_onsets))
|
||||
winner_offsets = np.sort(flatten(winner_offsets))
|
||||
winner_physicals = np.sort(flatten(winner_physicals))
|
||||
loser_onsets = np.sort(flatten(loser_onsets))
|
||||
loser_offsets = np.sort(flatten(loser_offsets))
|
||||
loser_physicals = np.sort(flatten(loser_physicals))
|
||||
|
||||
winner_onsets_conv = acausal_kde1d(
|
||||
winner_onsets, kde_time, kernel_width)
|
||||
winner_offsets_conv = acausal_kde1d(
|
||||
winner_offsets, kde_time, kernel_width)
|
||||
winner_physicals_conv = acausal_kde1d(
|
||||
winner_physicals, kde_time, kernel_width)
|
||||
loser_onsets_conv = acausal_kde1d(
|
||||
loser_onsets, kde_time, kernel_width)
|
||||
loser_offsets_conv = acausal_kde1d(
|
||||
loser_offsets, kde_time, kernel_width)
|
||||
loser_physicals_conv = acausal_kde1d(
|
||||
loser_physicals, kde_time, kernel_width)
|
||||
|
||||
winner_onsets_conv = winner_onsets_conv / onset_count
|
||||
winner_offsets_conv = winner_offsets_conv / offset_count
|
||||
winner_physicals_conv = winner_physicals_conv / physical_count
|
||||
loser_onsets_conv = loser_onsets_conv / onset_count
|
||||
loser_offsets_conv = loser_offsets_conv / offset_count
|
||||
loser_physicals_conv = loser_physicals_conv / physical_count
|
||||
|
||||
embed()
|
||||
|
||||
winner_onsets_boot = np.concatenate(
|
||||
winner_onsets_boot) / onset_count
|
||||
winner_offsets_boot = np.concatenate(
|
||||
winner_offsets_boot) / offset_count
|
||||
winner_physicals_boot = np.concatenate(
|
||||
winner_physicals_boot) / physical_count
|
||||
loser_onsets_boot = np.concatenate(
|
||||
loser_onsets_boot) / onset_count
|
||||
loser_offsets_boot = np.concatenate(
|
||||
loser_offsets_boot) / offset_count
|
||||
loser_physicals_boot = np.concatenate(
|
||||
loser_physicals_boot) / physical_count
|
||||
|
||||
percs = [5, 50, 95]
|
||||
winner_onsets_boot_quarts = np.percentile(
|
||||
winner_onsets_boot, percs, axis=0)
|
||||
winner_offsets_boot_quarts = np.percentile(
|
||||
winner_offsets_boot, percs, axis=0)
|
||||
winner_physicals_boot_quarts = np.percentile(
|
||||
winner_physicals_boot, percs, axis=0)
|
||||
loser_onsets_boot_quarts = np.percentile(
|
||||
loser_onsets_boot, percs, axis=0)
|
||||
loser_offsets_boot_quarts = np.percentile(
|
||||
loser_offsets_boot, percs, axis=0)
|
||||
loser_physicals_boot_quarts = np.percentile(
|
||||
loser_physicals_boot, percs, axis=0)
|
||||
|
||||
fig, ax = plt.subplots(2, 3, figsize=(
|
||||
21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
|
||||
|
||||
ax[0, 0].plot(kde_time, winner_onsets_conv)
|
||||
ax[0, 0].fill_between(kde_time,
|
||||
winner_onsets_boot_quarts[0],
|
||||
winner_onsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[0, 0].plot(kde_time, winner_onsets_boot_quarts[1], c=ps.black)
|
||||
|
||||
ax[0, 1].plot(kde_time, winner_offsets_conv)
|
||||
ax[0, 1].fill_between(kde_time,
|
||||
winner_offsets_boot_quarts[0],
|
||||
winner_offsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[0, 1].plot(kde_time, winner_offsets_boot_quarts[1], c=ps.black)
|
||||
|
||||
ax[0, 2].plot(kde_time, winner_physicals_conv)
|
||||
ax[0, 2].fill_between(kde_time,
|
||||
loser_physicals_boot_quarts[0],
|
||||
loser_physicals_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[0, 2].plot(kde_time, winner_physicals_boot_quarts[1], c=ps.black)
|
||||
|
||||
ax[1, 0].plot(kde_time, loser_onsets_conv)
|
||||
ax[1, 0].fill_between(kde_time,
|
||||
loser_onsets_boot_quarts[0],
|
||||
loser_onsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[1, 0].plot(kde_time, loser_onsets_boot_quarts[1], c=ps.black)
|
||||
|
||||
ax[1, 1].plot(kde_time, loser_offsets_conv)
|
||||
ax[1, 1].fill_between(kde_time,
|
||||
loser_offsets_boot_quarts[0],
|
||||
loser_offsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[1, 1].plot(kde_time, loser_offsets_boot_quarts[1], c=ps.black)
|
||||
|
||||
ax[1, 2].plot(kde_time, loser_physicals_conv)
|
||||
ax[1, 2].fill_between(kde_time,
|
||||
loser_physicals_boot_quarts[0],
|
||||
loser_physicals_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[1, 2].plot(kde_time, loser_physicals_boot_quarts[1], c=ps.black)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main('../data/mount_data/')
|
||||
Loading…
Reference in New Issue
Block a user