diff --git a/code/CTCPTC.py b/code/CTCPTC.py
deleted file mode 100644
index 82fb2c2..0000000
--- a/code/CTCPTC.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import os
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-
-from tqdm import tqdm
-from IPython import embed
-from pandas import read_csv
-from modules.logger import makeLogger
-from modules.plotstyle import PlotStyle
-from modules.datahandling import flatten
-from modules.behaviour_handling import Behavior, correct_chasing_events, event_triggered_chirps
-from extract_chirps import get_valid_datasets
-
-logger = makeLogger(__name__)
-ps = PlotStyle()
-
-
-def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
-
- foldername = folder_name.split('/')[-2]
- winner_row = order_meta_df[order_meta_df['recording'] == foldername]
- winner = winner_row['winner'].values[0].astype(int)
- winner_fish1 = winner_row['fish1'].values[0].astype(int)
- winner_fish2 = winner_row['fish2'].values[0].astype(int)
-
- if winner > 0:
- if winner == winner_fish1:
- winner_fish_id = winner_row['rec_id1'].values[0]
- loser_fish_id = winner_row['rec_id2'].values[0]
-
- elif winner == winner_fish2:
- winner_fish_id = winner_row['rec_id2'].values[0]
- loser_fish_id = winner_row['rec_id1'].values[0]
-
- chirp_winner = Behavior.chirps[Behavior.chirps_ids == winner_fish_id]
- chirp_loser = Behavior.chirps[Behavior.chirps_ids == loser_fish_id]
-
- return chirp_winner, chirp_loser
- else:
- return None, None
-
-
-def main(dataroot):
-
- foldernames, _ = get_valid_datasets(dataroot)
-
- meta_path = (
- '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
- meta = pd.read_csv(meta_path)
- meta['recording'] = meta['recording'].str[1:-1]
-
- winner_chirps = []
- loser_chirps = []
- onsets = []
- offsets = []
- physicals = []
-
- # Iterate over all recordings and save chirp- and event-timestamps
- for folder in foldernames:
-
- logger.info('Loading data from folder: {}'.format(folder))
-
- time_before = 30
- time_after = 60
- dt = 0.1
- kernel_width = 2
- kde_time = np.arange(-time_before, time_after, dt)
-
- broken_folders = ['../data/mount_data/2020-05-12-10_00/']
- if folder in broken_folders:
- continue
-
- bh = Behavior(folder)
- winner, loser = get_chirp_winner_loser(folder, bh, meta)
-
- if winner is None:
- continue
-
- # Chirps are already sorted
- winner_chirps.append(bh.chirps)
- loser_chirps.append(bh.chirps)
-
- # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
- # Get rid of tracking faults (two onsets or two offsets after another)
- category, timestamps = correct_chasing_events(bh.behavior, bh.start_s)
-
- # Split categories
- onsets.append(timestamps[category == 0])
- offsets.append(timestamps[category == 1])
- physicals.append(timestamps[category == 2])
-
- # center chirps around events
-
-
-if __name__ == '__main__':
- main('../data/mount_data/')
diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py
index cd5c364..4ebaa66 100644
--- a/code/eventchirpsplots.py
+++ b/code/eventchirpsplots.py
@@ -345,7 +345,7 @@ def main(datapath: str):
np.random.shuffle(diff_physical)
shuffled_physical = np.cumsum(diff_physical)
- kde_onset = (acausal_kde1d(shuffled_onset, time, width))/(27*100)
+ kde_onset (acausal_kde1d(shuffled_onset, time, width))/(27*100)
kde_offset = (acausal_kde1d(shuffled_offset, time, width))/(27*100)
kde_physical = (acausal_kde1d(shuffled_physical, time, width))/(27*100)
diff --git a/code/modules/behaviour_handling.py b/code/modules/behaviour_handling.py
index a3618ed..94a0ca1 100644
--- a/code/modules/behaviour_handling.py
+++ b/code/modules/behaviour_handling.py
@@ -1,13 +1,10 @@
import numpy as np
-
import os
-
from IPython import embed
-
from pandas import read_csv
from modules.logger import makeLogger
-from modules.datahandling import causal_kde1d, acausal_kde1d
+from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
logger = makeLogger(__name__)
@@ -43,7 +40,7 @@ class Behavior:
# csv_filename = [f for f in os.listdir(
# folder_path) if f.endswith('.csv')][0]
- logger.info(f'CSV file: {csv_filename}')
+ # logger.info(f'CSV file: {csv_filename}')
self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
self.chirps = np.load(os.path.join(
@@ -92,6 +89,12 @@ def correct_chasing_events(
wrong_bh = np.arange(len(category))[
category != 2][:-1][np.diff(category[category != 2]) == 0]
+
+ if category[category != 2][-1] == 0:
+ wrong_bh = np.append(
+ wrong_bh,
+ np.arange(len(category))[category != 2][-1])
+
if onset_ids[0] > offset_ids[0]:
offset_ids = np.delete(offset_ids, 0)
help_index = offset_ids[0]
@@ -106,51 +109,61 @@ def correct_chasing_events(
len(category))[category == 1]
# Check whether on- or offset is longer and calculate length difference
+
if len(new_onset_ids) > len(new_offset_ids):
- len_diff = len(onset_ids) - len(offset_ids)
- logger.info(f'Onsets are greater than offsets by {len_diff}')
+ embed()
+ logger.warning('Onsets are greater than offsets')
elif len(new_onset_ids) < len(new_offset_ids):
- len_diff = len(offset_ids) - len(onset_ids)
- logger.info(f'Offsets are greater than onsets by {len_diff}')
+ logger.warning('Offsets are greater than onsets')
elif len(new_onset_ids) == len(new_offset_ids):
- logger.info('Chasing events are equal')
+ # logger.info('Chasing events are equal')
+ pass
return category, timestamps
-def event_triggered_chirps(
- event: np.ndarray,
+def center_chirps(
+ events: np.ndarray,
chirps: np.ndarray,
time_before_event: int,
time_after_event: int,
- dt: float,
- width: float,
+ # dt: float,
+ # width: float,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
event_chirps = [] # chirps that are in specified window around event
# timestamps of chirps around event centered on the event timepoint
centered_chirps = []
- for event_timestamp in event:
+ for event_timestamp in events:
+
start = event_timestamp - time_before_event
stop = event_timestamp + time_after_event
chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
- event_chirps.append(chirps_around_event)
+
if len(chirps_around_event) == 0:
continue
- else:
- centered_chirps.append(chirps_around_event - event_timestamp)
- time = np.arange(-time_before_event, time_after_event, dt)
+ centered_chirps.append(chirps_around_event - event_timestamp)
+ event_chirps.append(chirps_around_event)
- # Kernel density estimation with some if's
- if len(centered_chirps) == 0:
- centered_chirps = np.array([])
- centered_chirps_convolved = np.zeros(len(time))
- else:
- # convert list of arrays to one array for plotting
- centered_chirps = np.concatenate(centered_chirps, axis=0)
- centered_chirps_convolved = (acausal_kde1d(
- centered_chirps, time, width)) / len(event)
+ centered_chirps = np.sort(flatten(centered_chirps))
+ event_chirps = np.sort(flatten(event_chirps))
- return event_chirps, centered_chirps, centered_chirps_convolved
+ if len(centered_chirps) != len(event_chirps):
+ raise ValueError(
+ 'Non centered chirps and centered chirps are not equal')
+
+ # time = np.arange(-time_before_event, time_after_event, dt)
+
+ # # Kernel density estimation with some if's
+ # if len(centered_chirps) == 0:
+ # centered_chirps = np.array([])
+ # centered_chirps_convolved = np.zeros(len(time))
+ # else:
+ # # convert list of arrays to one array for plotting
+ # centered_chirps = np.concatenate(centered_chirps, axis=0)
+ # centered_chirps_convolved = (acausal_kde1d(
+ # centered_chirps, time, width)) / len(event)
+
+ return centered_chirps
diff --git a/code/plot_kdes.py b/code/plot_kdes.py
new file mode 100644
index 0000000..03c6621
--- /dev/null
+++ b/code/plot_kdes.py
@@ -0,0 +1,432 @@
+from extract_chirps import get_valid_datasets
+import os
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from tqdm import tqdm
+from IPython import embed
+from pandas import read_csv
+from modules.logger import makeLogger
+from modules.datahandling import flatten, causal_kde1d, acausal_kde1d
+from modules.behaviour_handling import (
+ Behavior, correct_chasing_events, center_chirps)
+from modules.plotstyle import PlotStyle
+
+logger = makeLogger(__name__)
+ps = PlotStyle()
+
+
+def jackknife(data, nresamples, subsetsize, kde_time, kernel_width):
+
+ if len(data) == 0:
+ return []
+
+ jackknifed_kdes = []
+ data = np.sort(data)
+ subsetsize = int(np.round(len(data)*subsetsize))
+
+ for n in range(nresamples):
+
+ subset = np.random.choice(data, subsetsize, replace=False)
+ subset_kde = acausal_kde1d(subset, time=kde_time, width=kernel_width)
+ jackknifed_kdes.append(subset_kde)
+
+ return jackknifed_kdes
+
+
+def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before, time_after):
+
+ bootstrapped_kdes = []
+ data = data[data <= 3*60*60] # only night time
+
+ if len(data) == 0:
+ logger.info('No data for bootstrap, added zeros')
+ return [np.zeros_like(kde_time) for i in range(nresamples)]
+
+ diff_data = np.diff(np.sort(data), prepend=np.sort(data)[0])
+
+ for i in tqdm(range(nresamples)):
+
+ np.random.shuffle(diff_data)
+ bootstrapped_data = np.cumsum(diff_data)
+ bootstrap_data_centered = center_chirps(
+ bootstrapped_data, event_times, time_before, time_after)
+ bootstrapped_kde = acausal_kde1d(
+ bootstrap_data_centered, time=kde_time, width=kernel_width)
+
+ bootstrapped_kdes.append(bootstrapped_kde)
+
+ return bootstrapped_kdes
+
+
+def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
+
+ foldername = folder_name.split('/')[-2]
+ winner_row = order_meta_df[order_meta_df['recording'] == foldername]
+ winner = winner_row['winner'].values[0].astype(int)
+ winner_fish1 = winner_row['fish1'].values[0].astype(int)
+ winner_fish2 = winner_row['fish2'].values[0].astype(int)
+
+ if winner > 0:
+ if winner == winner_fish1:
+ winner_fish_id = winner_row['rec_id1'].values[0]
+ loser_fish_id = winner_row['rec_id2'].values[0]
+
+ elif winner == winner_fish2:
+ winner_fish_id = winner_row['rec_id2'].values[0]
+ loser_fish_id = winner_row['rec_id1'].values[0]
+
+ chirp_winner = Behavior.chirps[Behavior.chirps_ids == winner_fish_id]
+ chirp_loser = Behavior.chirps[Behavior.chirps_ids == loser_fish_id]
+
+ return chirp_winner, chirp_loser
+ return None, None
+
+
+def main(dataroot):
+
+ foldernames, _ = get_valid_datasets(dataroot)
+ plot_all = False
+ time_before = 60
+ time_after = 60
+ dt = 0.001
+ kernel_width = 1
+ kde_time = np.arange(-time_before, time_after, dt)
+ nbootstraps = 2
+
+ meta_path = (
+ '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
+ meta = pd.read_csv(meta_path)
+ meta['recording'] = meta['recording'].str[1:-1]
+
+ winner_onsets = []
+ winner_offsets = []
+ winner_physicals = []
+
+ loser_onsets = []
+ loser_offsets = []
+ loser_physicals = []
+
+ winner_onsets_boot = []
+ winner_offsets_boot = []
+ winner_physicals_boot = []
+
+ loser_onsets_boot = []
+ loser_offsets_boot = []
+ loser_physicals_boot = []
+
+ onset_count = 0
+ offset_count = 0
+ physical_count = 0
+
+ # Iterate over all recordings and save chirp- and event-timestamps
+ for folder in tqdm(foldernames):
+
+ foldername = folder.split('/')[-2]
+ # logger.info('Loading data from folder: {}'.format(foldername))
+
+ broken_folders = ['../data/mount_data/2020-05-12-10_00/']
+ if folder in broken_folders:
+ continue
+
+ bh = Behavior(folder)
+ category, timestamps = correct_chasing_events(bh.behavior, bh.start_s)
+
+ winner, loser = get_chirp_winner_loser(folder, bh, meta)
+
+ if winner is None:
+ continue
+
+ onsets = (timestamps[category == 0])
+ offsets = (timestamps[category == 1])
+ physicals = (timestamps[category == 2])
+
+ onset_count += len(onsets)
+ offset_count += len(offsets)
+ physical_count += len(physicals)
+
+ winner_onsets.append(center_chirps(
+ winner, onsets, time_before, time_after))
+ winner_offsets.append(center_chirps(
+ winner, offsets, time_before, time_after))
+ winner_physicals.append(center_chirps(
+ winner, physicals, time_before, time_after))
+
+ loser_onsets.append(center_chirps(
+ loser, onsets, time_before, time_after))
+ loser_offsets.append(center_chirps(
+ loser, offsets, time_before, time_after))
+ loser_physicals.append(center_chirps(
+ loser, physicals, time_before, time_after))
+
+ # bootstrap
+ winner_onsets_boot.append(bootstrap(
+ winner,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=onsets,
+ time_before=time_before,
+ time_after=time_after))
+ winner_offsets_boot.append(bootstrap(
+ winner,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=offsets,
+ time_before=time_before,
+ time_after=time_after))
+ winner_physicals_boot.append(bootstrap(
+ winner,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=physicals,
+ time_before=time_before,
+ time_after=time_after))
+
+ loser_onsets_boot.append(bootstrap(
+ loser,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=onsets,
+ time_before=time_before,
+ time_after=time_after))
+ loser_offsets_boot.append(bootstrap(
+ loser,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=offsets,
+ time_before=time_before,
+ time_after=time_after))
+ loser_physicals_boot.append(bootstrap(
+ loser,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=physicals,
+ time_before=time_before,
+ time_after=time_after))
+
+ if plot_all:
+
+ winner_onsets_conv = acausal_kde1d(
+ winner_onsets[-1], kde_time, kernel_width)
+ winner_offsets_conv = acausal_kde1d(
+ winner_offsets[-1], kde_time, kernel_width)
+ winner_physicals_conv = acausal_kde1d(
+ winner_physicals[-1], kde_time, kernel_width)
+
+ loser_onsets_conv = acausal_kde1d(
+ loser_onsets[-1], kde_time, kernel_width)
+ loser_offsets_conv = acausal_kde1d(
+ 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/')