kdes work but scale is wrong

code/CTCPTC.py

@@ -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/')

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)
 

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)
+        embed()
-        logger.info(f'Onsets are greater than offsets by {len_diff}')
+        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.warning('Offsets are greater than onsets')
-        logger.info(f'Offsets are greater than onsets by {len_diff}')
     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(
+def center_chirps(
-    event: np.ndarray,
+    events: np.ndarray,
     chirps: np.ndarray,
     time_before_event: int,
     time_after_event: int,
-    dt: float,
+    # dt: float,
-    width: 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)
+        centered_chirps.append(chirps_around_event - event_timestamp)
-
+        event_chirps.append(chirps_around_event)
-    time = np.arange(-time_before_event, time_after_event, dt)
+
-
+    centered_chirps = np.sort(flatten(centered_chirps))
-    # Kernel density estimation with some if's
+    event_chirps = np.sort(flatten(event_chirps))
-    if len(centered_chirps) == 0:
+
-        centered_chirps = np.array([])
+    if len(centered_chirps) != len(event_chirps):
-        centered_chirps_convolved = np.zeros(len(time))
+        raise ValueError(
-    else:
+            'Non centered chirps and centered chirps are not equal')
-        # convert list of arrays to one array for plotting
+
-        centered_chirps = np.concatenate(centered_chirps, axis=0)
+    # time = np.arange(-time_before_event, time_after_event, dt)
-        centered_chirps_convolved = (acausal_kde1d(
+
-            centered_chirps, time, width)) / len(event)
+    # # Kernel density estimation with some if's
-
+    # if len(centered_chirps) == 0:
-    return event_chirps, centered_chirps, centered_chirps_convolved
+    #     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

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/')