From a6b7ed2c6cb892e1a357fec13b6594b410e90f49 Mon Sep 17 00:00:00 2001
From: weygoldt <88969563+weygoldt@users.noreply.github.com>
Date: Wed, 25 Jan 2023 10:19:19 +0100
Subject: [PATCH] merging diverged

---
 code/CTCPTC.py                     |  86 ++++++++++++-
 code/eventchirpsplots.py           | 188 +++++++++++++++++------------
 code/extract_chirps.py             |  29 +++--
 code/modules/behaviour_handling.py |  98 ++++++++-------
 poster/main.pdf                    | Bin 379323 -> 403471 bytes
 poster/main.tex                    |   8 +-
 6 files changed, 271 insertions(+), 138 deletions(-)

diff --git a/code/CTCPTC.py b/code/CTCPTC.py
index 1935ba2..82fb2c2 100644
--- a/code/CTCPTC.py
+++ b/code/CTCPTC.py
@@ -1,8 +1,8 @@
-import os 
+import os
 
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt 
+import matplotlib.pyplot as plt
 
 from tqdm import tqdm
 from IPython import embed
@@ -11,8 +11,88 @@ 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()
 
-#### Goal: CTC & PTC for each winner and loser and for all winners and loser ####
+
+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 9117743..cd5c364 100644
--- a/code/eventchirpsplots.py
+++ b/code/eventchirpsplots.py
@@ -1,8 +1,8 @@
-import os 
+import os
 
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt 
+import matplotlib.pyplot as plt
 
 from tqdm import tqdm
 from IPython import embed
@@ -14,42 +14,49 @@ from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
 logger = makeLogger(__name__)
 ps = PlotStyle()
 
+
 class Behavior:
     """Load behavior data from csv file as class attributes
         Attributes
     ----------
     behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
-    behavior_type:         
-    behavioral_category:   
-    comment_start:         
-    comment_stop:          
-    dataframe: pandas dataframe with all the data            
-    duration_s:             
-    media_file:            
-    observation_date:      
-    observation_id:        
-    start_s: start time of the event in seconds               
-    stop_s:  stop time of the event in seconds               
-    total_length:          
+    behavior_type:
+    behavioral_category:
+    comment_start:
+    comment_stop:
+    dataframe: pandas dataframe with all the data
+    duration_s:
+    media_file:
+    observation_date:
+    observation_id:
+    start_s: start time of the event in seconds
+    stop_s:  stop time of the event in seconds
+    total_length:
     """
 
     def __init__(self, folder_path: str) -> None:
         print(f'{folder_path}')
-        LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
-        self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
-        csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0] # check if there are more than one csv file
+        LED_on_time_BORIS = np.load(os.path.join(
+            folder_path, 'LED_on_time.npy'), allow_pickle=True)
+        self.time = np.load(os.path.join(
+            folder_path, "times.npy"), allow_pickle=True)
+        csv_filename = [f for f in os.listdir(folder_path) if f.endswith(
+            '.csv')][0]  # check if there are more than one csv file
         self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
-        self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
-        self.chirps_ids = np.load(os.path.join(folder_path, 'chirp_ids.npy'), allow_pickle=True)
+        self.chirps = np.load(os.path.join(
+            folder_path, 'chirps.npy'), allow_pickle=True)
+        self.chirps_ids = np.load(os.path.join(
+            folder_path, 'chirp_ids.npy'), allow_pickle=True)
 
         for k, key in enumerate(self.dataframe.keys()):
-            key = key.lower() 
+            key = key.lower()
             if ' ' in key:
                 key = key.replace(' ', '_')
                 if '(' in key:
                     key = key.replace('(', '')
                     key = key.replace(')', '')
-            setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
+            setattr(self, key, np.array(
+                self.dataframe[self.dataframe.keys()[k]]))
 
         last_LED_t_BORIS = LED_on_time_BORIS[-1]
         real_time_range = self.time[-1] - self.time[0]
@@ -58,6 +65,7 @@ class Behavior:
         self.start_s = (self.start_s - shift) / factor
         self.stop_s = (self.stop_s - shift) / factor
 
+
 """
 1 - chasing onset
 2 - chasing offset
@@ -87,16 +95,17 @@ temporal encpding needs to be corrected ... not exactly 25FPS.
 
 
 def correct_chasing_events(
-    category: np.ndarray, 
+    category: np.ndarray,
     timestamps: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
 
     onset_ids = np.arange(
         len(category))[category == 0]
     offset_ids = np.arange(
         len(category))[category == 1]
 
-    wrong_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0]
+    wrong_bh = np.arange(len(category))[
+        category != 2][:-1][np.diff(category[category != 2]) == 0]
     if onset_ids[0] > offset_ids[0]:
         offset_ids = np.delete(offset_ids, 0)
         help_index = offset_ids[0]
@@ -105,7 +114,6 @@ def correct_chasing_events(
     category = np.delete(category, wrong_bh)
     timestamps = np.delete(timestamps, wrong_bh)
 
-
     # Check whether on- or offset is longer and calculate length difference
     if len(onset_ids) > len(offset_ids):
         len_diff = len(onset_ids) - len(offset_ids)
@@ -115,21 +123,22 @@ def correct_chasing_events(
         logger.info(f'Offsets are greater than onsets by {len_diff}')
     elif len(onset_ids) == len(offset_ids):
         logger.info('Chasing events are equal')
-    
+
     return category, timestamps
 
 
 def event_triggered_chirps(
-    event: np.ndarray, 
-    chirps:np.ndarray,
+    event: np.ndarray,
+    chirps: np.ndarray,
     time_before_event: int,
     time_after_event: int,
     dt: float,
     width: float,
-    )-> tuple[np.ndarray, np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
 
     event_chirps = []   # chirps that are in specified window around event
-    centered_chirps = []    # timestamps of chirps around event centered on the event timepoint
+    # timestamps of chirps around event centered on the event timepoint
+    centered_chirps = []
 
     for event_timestamp in event:
         start = event_timestamp - time_before_event
@@ -138,25 +147,28 @@ def event_triggered_chirps(
         event_chirps.append(chirps_around_event)
         if len(chirps_around_event) == 0:
             continue
-        else: 
+        else:
             centered_chirps.append(chirps_around_event - event_timestamp)
-    
+
     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:
-        centered_chirps = np.concatenate(centered_chirps, axis=0)   # convert list of arrays to one array for plotting
-        centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
+        # 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 event_chirps, centered_chirps, centered_chirps_convolved
 
 
 def main(datapath: str):
 
-    foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
+    foldernames = [
+        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
 
     nrecording_chirps = []
     nrecording_chirps_fish_ids = []
@@ -171,7 +183,7 @@ def main(datapath: str):
             continue
 
         bh = Behavior(folder)
-        
+
         # Chirps are already sorted
         category = bh.behavior
         timestamps = bh.start_s
@@ -193,14 +205,12 @@ def main(datapath: str):
         physical_contacts = timestamps[category == 2]
         nrecording_physicals.append(physical_contacts)
 
-    
-
     # Define time window for chirps around event analysis
     time_before_event = 30
     time_after_event = 60
     dt = 0.01
     width = 1.5   # width of kernel for all recordings, currently gaussian kernel
-    recording_width = 2 # width of kernel for each recording
+    recording_width = 2  # width of kernel for each recording
     time = np.arange(-time_before_event, time_after_event, dt)
 
     ##### Chirps around events, all fish, all recordings #####
@@ -222,14 +232,18 @@ def main(datapath: str):
         physical_contacts = nrecording_physicals[i]
 
         # Chirps around chasing onsets
-        _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, recording_width)
+        _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(
+            chasing_onsets, chirps, time_before_event, time_after_event, dt, recording_width)
         # Chirps around chasing offsets
-        _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, recording_width)
+        _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(
+            chasing_offsets, chirps, time_before_event, time_after_event, dt, recording_width)
         # Chirps around physical contacts
-        _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width)
+        _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(
+            physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width)
 
         nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps)
-        nrecording_centered_offset_chirps.append(centered_chasing_offset_chirps)
+        nrecording_centered_offset_chirps.append(
+            centered_chasing_offset_chirps)
         nrecording_centered_physical_chirps.append(centered_physical_chirps)
 
         ## Shuffled chirps ##
@@ -252,7 +266,6 @@ def main(datapath: str):
         #     _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
         #     nshuffled_physical_chirps.append(cc_shuffled_physical_chirps)
 
-
         # rec_shuffled_q5_onset, rec_shuffled_median_onset, rec_shuffled_q95_onset = np.percentile(
         # nshuffled_onset_chirps, (5, 50, 95), axis=0)
         # rec_shuffled_q5_offset, rec_shuffled_median_offset, rec_shuffled_q95_offset = np.percentile(
@@ -260,7 +273,6 @@ def main(datapath: str):
         # rec_shuffled_q5_physical, rec_shuffled_median_physical, rec_shuffled_q95_physical = np.percentile(
         # nshuffled_physical_chirps, (5, 50, 95), axis=0)
 
-
         # #### Recording plots ####
         # fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
         # ax[0].set_xlabel('Time[s]')
@@ -307,7 +319,7 @@ def main(datapath: str):
         # fig.suptitle(f'Recording: {i}')
         # # plt.show()
         # plt.close()
-        
+
         # nrecording_shuffled_convolved_onset_chirps.append(nshuffled_onset_chirps)
         # nrecording_shuffled_convolved_offset_chirps.append(nshuffled_offset_chirps)
         # nrecording_shuffled_convolved_physical_chirps.append(nshuffled_physical_chirps)
@@ -319,9 +331,12 @@ def main(datapath: str):
 
     # New bootstrapping approach
     for n in range(nbootstrapping):
-        diff_onset = np.diff(np.sort(flatten(nrecording_centered_onset_chirps)))
-        diff_offset = np.diff(np.sort(flatten(nrecording_centered_offset_chirps)))
-        diff_physical = np.diff(np.sort(flatten(nrecording_centered_physical_chirps)))
+        diff_onset = np.diff(
+            np.sort(flatten(nrecording_centered_onset_chirps)))
+        diff_offset = np.diff(
+            np.sort(flatten(nrecording_centered_offset_chirps)))
+        diff_physical = np.diff(
+            np.sort(flatten(nrecording_centered_physical_chirps)))
 
         np.random.shuffle(diff_onset)
         shuffled_onset = np.cumsum(diff_onset)
@@ -339,16 +354,18 @@ def main(datapath: str):
         bootstrap_physical.append(kde_physical)
 
     # New shuffle approach q5, q50, q95
-    onset_q5, onset_median, onset_q95 = np.percentile(bootstrap_onset, [5, 50, 95], axis=0)
-    offset_q5, offset_median, offset_q95 = np.percentile(bootstrap_offset, [5, 50, 95], axis=0)
-    physical_q5, physical_median, physical_q95 = np.percentile(bootstrap_physical, [5, 50, 95], axis=0)
-        
+    onset_q5, onset_median, onset_q95 = np.percentile(
+        bootstrap_onset, [5, 50, 95], axis=0)
+    offset_q5, offset_median, offset_q95 = np.percentile(
+        bootstrap_offset, [5, 50, 95], axis=0)
+    physical_q5, physical_median, physical_q95 = np.percentile(
+        bootstrap_physical, [5, 50, 95], axis=0)
 
     #  vstack um 1. Dim zu cutten
     # nrecording_shuffled_convolved_onset_chirps = np.vstack(nrecording_shuffled_convolved_onset_chirps)
     # nrecording_shuffled_convolved_offset_chirps = np.vstack(nrecording_shuffled_convolved_offset_chirps)
     # nrecording_shuffled_convolved_physical_chirps = np.vstack(nrecording_shuffled_convolved_physical_chirps)
-    
+
     # shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(
     #     nrecording_shuffled_convolved_onset_chirps, (5, 50, 95), axis=0)
     # shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(
@@ -356,27 +373,37 @@ def main(datapath: str):
     # shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(
     #     nrecording_shuffled_convolved_physical_chirps, (5, 50, 95), axis=0)
 
-    # Flatten all chirps 
+    # Flatten all chirps
     all_chirps = np.concatenate(nrecording_chirps).ravel()  # not centered
 
     # Flatten event timestamps
-    all_onsets = np.concatenate(nrecording_chasing_onsets).ravel() # not centered
-    all_offsets = np.concatenate(nrecording_chasing_offsets).ravel() # not centered
-    all_physicals = np.concatenate(nrecording_physicals).ravel() # not centered
+    all_onsets = np.concatenate(
+        nrecording_chasing_onsets).ravel()  # not centered
+    all_offsets = np.concatenate(
+        nrecording_chasing_offsets).ravel()  # not centered
+    all_physicals = np.concatenate(
+        nrecording_physicals).ravel()  # not centered
 
     # Flatten all chirps around events
-    all_onset_chirps = np.concatenate(nrecording_centered_onset_chirps).ravel()   # centered
-    all_offset_chirps = np.concatenate(nrecording_centered_offset_chirps).ravel() # centered
-    all_physical_chirps = np.concatenate(nrecording_centered_physical_chirps).ravel() # centered
+    all_onset_chirps = np.concatenate(
+        nrecording_centered_onset_chirps).ravel()   # centered
+    all_offset_chirps = np.concatenate(
+        nrecording_centered_offset_chirps).ravel()  # centered
+    all_physical_chirps = np.concatenate(
+        nrecording_centered_physical_chirps).ravel()  # centered
 
     # Convolute all chirps
     # Divide by total number of each event over all recordings
-    all_onset_chirps_convolved = (acausal_kde1d(all_onset_chirps, time, width)) / len(all_onsets)
-    all_offset_chirps_convolved = (acausal_kde1d(all_offset_chirps, time, width)) / len(all_offsets)
-    all_physical_chirps_convolved = (acausal_kde1d(all_physical_chirps, time, width)) / len(all_physicals)
+    all_onset_chirps_convolved = (acausal_kde1d(
+        all_onset_chirps, time, width)) / len(all_onsets)
+    all_offset_chirps_convolved = (acausal_kde1d(
+        all_offset_chirps, time, width)) / len(all_offsets)
+    all_physical_chirps_convolved = (acausal_kde1d(
+        all_physical_chirps, time, width)) / len(all_physicals)
 
     # Plot all events with all shuffled
-    fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
+    fig, ax = plt.subplots(1, 3, figsize=(
+        28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
     # offsets = np.arange(1,28,1)
     ax[0].set_xlabel('Time[s]')
 
@@ -384,8 +411,10 @@ def main(datapath: str):
     ax[0].set_ylabel('Chirp rate [Hz]')
     ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2)
     ax0 = ax[0].twinx()
-    nrecording_centered_onset_chirps = np.asarray(nrecording_centered_onset_chirps, dtype=object)
-    ax0.eventplot(np.array(nrecording_centered_onset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
+    nrecording_centered_onset_chirps = np.asarray(
+        nrecording_centered_onset_chirps, dtype=object)
+    ax0.eventplot(np.array(nrecording_centered_onset_chirps),
+                  linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
     ax0.vlines(0, 0, 1.5, ps.white, 'dashed')
     ax[0].set_zorder(ax0.get_zorder()+1)
     ax[0].patch.set_visible(False)
@@ -400,8 +429,10 @@ def main(datapath: str):
     ax[1].set_xlabel('Time[s]')
     ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2)
     ax1 = ax[1].twinx()
-    nrecording_centered_offset_chirps = np.asarray(nrecording_centered_offset_chirps, dtype=object)
-    ax1.eventplot(np.array(nrecording_centered_offset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
+    nrecording_centered_offset_chirps = np.asarray(
+        nrecording_centered_offset_chirps, dtype=object)
+    ax1.eventplot(np.array(nrecording_centered_offset_chirps),
+                  linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
     ax1.vlines(0, 0, 1.5, ps.white, 'dashed')
     ax[1].set_zorder(ax1.get_zorder()+1)
     ax[1].patch.set_visible(False)
@@ -416,8 +447,10 @@ def main(datapath: str):
     ax[2].set_xlabel('Time[s]')
     ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2)
     ax2 = ax[2].twinx()
-    nrecording_centered_physical_chirps = np.asarray(nrecording_centered_physical_chirps, dtype=object)
-    ax2.eventplot(np.array(nrecording_centered_physical_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
+    nrecording_centered_physical_chirps = np.asarray(
+        nrecording_centered_physical_chirps, dtype=object)
+    ax2.eventplot(np.array(nrecording_centered_physical_chirps),
+                  linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
     ax2.vlines(0, 0, 1.5, ps.white, 'dashed')
     ax[2].set_zorder(ax2.get_zorder()+1)
     ax[2].patch.set_visible(False)
@@ -425,14 +458,15 @@ def main(datapath: str):
     ax2.set_yticks([])
     # ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5)
     # ax[2].plot(time, shuffled_median_physical, ps.black)
-    ax[2].fill_between(time, physical_q5, physical_q95, color=ps.gray, alpha=0.5)
+    ax[2].fill_between(time, physical_q5, physical_q95,
+                       color=ps.gray, alpha=0.5)
     ax[2].plot(time, physical_median, ps.black)
     fig.suptitle('All recordings')
     plt.show()
     plt.close()
 
     embed()
-    
+
     # chasing_durations = []
     # # Calculate chasing duration to evaluate a nice time window for kernel density estimation
     # for onset, offset in zip(chasing_onsets, chasing_offsets):
@@ -444,7 +478,6 @@ def main(datapath: str):
     # plt.show()
     # plt.close()
 
-
     # # Associate chirps to individual fish
     # fish1 = chirps[chirps_fish_ids == fish_ids[0]]
     # fish2 = chirps[chirps_fish_ids == fish_ids[1]]
@@ -453,7 +486,6 @@ def main(datapath: str):
     # Convolution over all recordings
     # Rasterplot for each recording
 
-
     # #### Chirps around events, winner VS loser, one recording ####
     # # Load file with fish ids and winner/loser info
     # meta = pd.read_csv('../data/mount_data/order_meta.csv')
@@ -462,7 +494,7 @@ def main(datapath: str):
     # fish2 = current_recording['rec_id2'].values
     # # Implement check if fish_ids from meta and chirp detection are the same???
     # winner = current_recording['winner'].values
-    
+
     # if winner == fish1:
     #     loser = fish2
     # elif winner == fish2:
@@ -546,7 +578,6 @@ def main(datapath: str):
     # ax5.set_yticks([])
     # plt.show()
     # plt.close()
-    
 
     # for i in range(len(fish_ids)):
     #     fish = fish_ids[i]
@@ -556,7 +587,6 @@ def main(datapath: str):
     #### Chirps around events, only losers, one recording ####
 
 
-
 if __name__ == '__main__':
     # Path to the data
     datapath = '../data/mount_data/'
diff --git a/code/extract_chirps.py b/code/extract_chirps.py
index 5d5580e..77e3e8d 100644
--- a/code/extract_chirps.py
+++ b/code/extract_chirps.py
@@ -7,21 +7,12 @@ from IPython import embed
 # check rec ../data/mount_data/2020-03-25-10_00/ starting at 3175
 
 
-def main(datapaths):
-
-    for path in datapaths:
-        chirpdetection(path, plot='show')
-
-
-if __name__ == '__main__':
-
-    dataroot = '../data/mount_data/'
+def get_valid_datasets(dataroot):
 
     datasets = sorted([name for name in os.listdir(dataroot) if os.path.isdir(
         os.path.join(dataroot, name))])
 
     valid_datasets = []
-
     for dataset in datasets:
 
         path = os.path.join(dataroot, dataset)
@@ -43,9 +34,25 @@ if __name__ == '__main__':
     datapaths = [os.path.join(dataroot, dataset) +
                  '/' for dataset in valid_datasets]
 
+    return datapaths, valid_datasets
+
+
+def main(datapaths):
+
+    for path in datapaths:
+        chirpdetection(path, plot='show')
+
+
+if __name__ == '__main__':
+
+    dataroot = '../data/mount_data/'
+
+
+    datapaths, valid_datasets= get_valid_datasets(dataroot)
+
     recs = pd.DataFrame(columns=['recording'], data=valid_datasets)
     recs.to_csv('../recs.csv', index=False)
-    datapaths = ['../data/mount_data/2020-03-25-10_00/']
+    # datapaths = ['../data/mount_data/2020-03-25-10_00/']
     main(datapaths)
 
 # window 1524 + 244 in dataset index 4 is nice example
diff --git a/code/modules/behaviour_handling.py b/code/modules/behaviour_handling.py
index 6641309..f846d45 100644
--- a/code/modules/behaviour_handling.py
+++ b/code/modules/behaviour_handling.py
@@ -1,8 +1,7 @@
 import numpy as np
 
-import os 
+import os
 
-import numpy as np
 from IPython import embed
 
 
@@ -19,46 +18,60 @@ class Behavior:
         Attributes
     ----------
     behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
-    behavior_type:         
-    behavioral_category:   
-    comment_start:         
-    comment_stop:          
-    dataframe: pandas dataframe with all the data            
-    duration_s:             
-    media_file:            
-    observation_date:      
-    observation_id:        
-    start_s: start time of the event in seconds               
-    stop_s:  stop time of the event in seconds               
-    total_length:          
+    behavior_type:
+    behavioral_category:
+    comment_start:
+    comment_stop:
+    dataframe: pandas dataframe with all the data
+    duration_s:
+    media_file:
+    observation_date:
+    observation_id:
+    start_s: start time of the event in seconds
+    stop_s:  stop time of the event in seconds
+    total_length:
     """
 
     def __init__(self, folder_path: str) -> None:
 
-        LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
+        LED_on_time_BORIS = np.load(os.path.join(
+            folder_path, 'LED_on_time.npy'), allow_pickle=True)
+
+        csv_filename = os.path.split(folder_path[:-1])[-1]
+        csv_filename = '-'.join(csv_filename.split('-')[:-1]) + '.csv'
+        # embed()
 
-        csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
+        # csv_filename = [f for f in os.listdir(
+        #     folder_path) if f.endswith('.csv')][0]
         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(folder_path, 'chirps.npy'), allow_pickle=True)
-        self.chirps_ids = np.load(os.path.join(folder_path, 'chirp_ids.npy'), allow_pickle=True)
-
-        self.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True)
-        self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True)
-        self.freq = np.load(os.path.join(folder_path, 'fund_v.npy'), allow_pickle=True)
-        self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
-        self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True)    
+        self.chirps = np.load(os.path.join(
+            folder_path, 'chirps.npy'), allow_pickle=True)
+        self.chirps_ids = np.load(os.path.join(
+            folder_path, 'chirp_ids.npy'), allow_pickle=True)
+
+        self.ident = np.load(os.path.join(
+            folder_path, 'ident_v.npy'), allow_pickle=True)
+        self.idx = np.load(os.path.join(
+            folder_path, 'idx_v.npy'), allow_pickle=True)
+        self.freq = np.load(os.path.join(
+            folder_path, 'fund_v.npy'), allow_pickle=True)
+        self.time = np.load(os.path.join(
+            folder_path, "times.npy"), allow_pickle=True)
+        self.spec = np.load(os.path.join(
+            folder_path, "spec.npy"), allow_pickle=True)
 
         for k, key in enumerate(self.dataframe.keys()):
-            key = key.lower() 
+            key = key.lower()
             if ' ' in key:
                 key = key.replace(' ', '_')
                 if '(' in key:
                     key = key.replace('(', '')
                     key = key.replace(')', '')
-            setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
-        
+            setattr(self, key, np.array(
+                self.dataframe[self.dataframe.keys()[k]]))
+
         last_LED_t_BORIS = LED_on_time_BORIS[-1]
         real_time_range = self.time[-1] - self.time[0]
         factor = 1.034141
@@ -68,16 +81,17 @@ class Behavior:
 
 
 def correct_chasing_events(
-    category: np.ndarray, 
+    category: np.ndarray,
     timestamps: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
 
     onset_ids = np.arange(
         len(category))[category == 0]
     offset_ids = np.arange(
         len(category))[category == 1]
 
-    wrong_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0]
+    wrong_bh = np.arange(len(category))[
+        category != 2][:-1][np.diff(category[category != 2]) == 0]
     if onset_ids[0] > offset_ids[0]:
         offset_ids = np.delete(offset_ids, 0)
         help_index = offset_ids[0]
@@ -95,21 +109,22 @@ def correct_chasing_events(
         logger.info(f'Offsets are greater than onsets by {len_diff}')
     elif len(onset_ids) == len(offset_ids):
         logger.info('Chasing events are equal')
-    
+
     return category, timestamps
 
 
 def event_triggered_chirps(
-    event: np.ndarray, 
-    chirps:np.ndarray,
+    event: np.ndarray,
+    chirps: np.ndarray,
     time_before_event: int,
     time_after_event: int,
     dt: float,
     width: float,
-    )-> tuple[np.ndarray, np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
 
     event_chirps = []   # chirps that are in specified window around event
-    centered_chirps = []    # timestamps of chirps around event centered on the event timepoint
+    # timestamps of chirps around event centered on the event timepoint
+    centered_chirps = []
 
     for event_timestamp in event:
         start = event_timestamp - time_before_event
@@ -118,18 +133,19 @@ def event_triggered_chirps(
         event_chirps.append(chirps_around_event)
         if len(chirps_around_event) == 0:
             continue
-        else: 
+        else:
             centered_chirps.append(chirps_around_event - event_timestamp)
-    
+
     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:
-        centered_chirps = np.concatenate(centered_chirps, axis=0)   # convert list of arrays to one array for plotting
-        centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
+        # 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 event_chirps, centered_chirps, centered_chirps_convolved
-
diff --git a/poster/main.pdf b/poster/main.pdf
index 8e5c90ad4548fb1a5cd4ed8d02f9cbf2898c9fd6..a1d28bebf08ab172246e4f2aae5322485e84004d 100644
GIT binary patch
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diff --git a/poster/main.tex b/poster/main.tex
index 03e6fb1..38c038d 100644
--- a/poster/main.tex
+++ b/poster/main.tex
@@ -21,10 +21,10 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
     sender identification of freely interacting individuals impossible.
     This profoundly limits our current understanding of chirps to experiments
     with single - or physically separated - individuals.
-    % \begin{tikzfigure}[]
-    %     \label{griddrawing}
-    %     \includegraphics[width=1\linewidth]{figs/introplot}
-    % \end{tikzfigure}
+    \begin{tikzfigure}[]
+        \label{griddrawing}
+        \includegraphics[width=1\linewidth]{figs/introplot}
+    \end{tikzfigure}
 }
 \myblock[TranspBlock]{Chirp detection}{
     \begin{tikzfigure}[]