diff --git a/plot_event_timeline.py b/plot_event_timeline.py
new file mode 100644
index 0000000..239dcaa
--- /dev/null
+++ b/plot_event_timeline.py
@@ -0,0 +1,183 @@
+import numpy as np
+
+import os
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from IPython import embed
+from pandas import read_csv
+from modules.logger import makeLogger
+
+logger = makeLogger(__name__)
+
+
+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:
+ """
+
+ 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)
+ 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, 'chirps_ids.npy'), allow_pickle=True)
+
+ for k, key in enumerate(self.dataframe.keys()):
+ 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]]))
+
+ last_LED_t_BORIS = LED_on_time_BORIS[-1]
+ real_time_range = self.time[-1] - self.time[0]
+ factor = 1.034141
+ shift = last_LED_t_BORIS - real_time_range * factor
+ self.start_s = (self.start_s - shift) / factor
+ self.stop_s = (self.stop_s - shift) / factor
+
+def correct_chasing_events(
+ category: np.ndarray,
+ timestamps: np.ndarray
+ ) -> tuple[np.ndarray, np.ndarray]:
+
+ onset_ids = np.arange(
+ len(category))[category == 0]
+ offset_ids = np.arange(
+ len(category))[category == 1]
+
+ # 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)
+ longer_array = onset_ids
+ shorter_array = offset_ids
+ logger.info(f'Onsets are greater than offsets by {len_diff}')
+ elif len(onset_ids) < len(offset_ids):
+ len_diff = len(offset_ids) - len(onset_ids)
+ longer_array = offset_ids
+ shorter_array = onset_ids
+ logger.info(f'Offsets are greater than offsets by {len_diff}')
+ elif len(onset_ids) == len(offset_ids):
+ logger.info('Chasing events are equal')
+ return category, timestamps
+
+
+ # Correct the wrong chasing events; delete double events
+ wrong_ids = []
+ for i in range(len(longer_array)-(len_diff+1)):
+ if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]):
+ pass
+ else:
+ wrong_ids.append(longer_array[i])
+ longer_array = np.delete(longer_array, i)
+
+ category = np.delete(
+ category, wrong_ids)
+ timestamps = np.delete(
+ timestamps, wrong_ids)
+ return category, timestamps
+
+
+
+def main(datapath: str):
+ # behabvior is pandas dataframe with all the data
+ bh = Behavior(datapath)
+ # chirps are not sorted in time (presumably due to prior groupings)
+ # get and sort chirps and corresponding fish_ids of the chirps
+ chirps = bh.chirps[np.argsort(bh.chirps)]
+ chirps_fish_ids = bh.chirps_ids[np.argsort(bh.chirps)]
+ category = bh.behavior
+ timestamps = bh.start_s
+ # 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(category, timestamps)
+
+ # split categories
+ chasing_onset = (timestamps[category == 0]/ 60) /60
+ chasing_offset = (timestamps[category == 1]/ 60) /60
+ physical_contact = (timestamps[category == 2] / 60) /60
+
+ all_fish_ids = np.unique(chirps_fish_ids)
+ # Associate chirps to inidividual fish
+ fish1 = (chirps[chirps_fish_ids == all_fish_ids[0]] / 60) /60
+ fish2 = (chirps[chirps_fish_ids == all_fish_ids[1]] / 60) /60
+
+ fig, ax = plt.subplots(4, 1, figsize=(10, 5), height_ratios=[0.5, 0.5, 0.5, 6])
+ # marker size
+ s = 200
+ ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='red', marker='|', s=s)
+ ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='blue', marker='|', s=s )
+ ax[1].scatter(chasing_offset, np.ones(len(chasing_offset)), color='green', marker='|', s=s)
+ ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color='blue', marker='|', s=s)
+ ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color='green', marker='|', s=s)
+ ax[3].scatter(fish2, np.zeros(len(fish2))+0.25, color='green', marker='|', s=s)
+
+ # Hide grid lines
+ ax[0].grid(False)
+ ax[0].set_frame_on(False)
+ ax[0].set_xticks([])
+ ax[0].set_yticks([])
+
+ ax[1].grid(False)
+ ax[1].set_frame_on(False)
+ ax[1].set_xticks([])
+ ax[1].set_yticks([])
+
+ ax[2].grid(False)
+ ax[2].set_frame_on(False)
+ ax[2].set_yticks([])
+ ax[2].set_xticks([])
+
+ ax[3].axvspan(0, 3, 0, 5, facecolor='grey', alpha=0.5)
+
+ labelpad = 40
+ ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad)
+ ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad)
+ ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad)
+ ax[3].set_ylabel('EODf')
+
+ ax[3].set_xlabel('Time [h]')
+
+ plt.show()
+
+ # plot chirps
+
+
+ """
+ for track_id in np.unique(ident):
+ # window_index for time array in time window
+ window_index = np.arange(len(idx))[(ident == track_id) &
+ (time[idx] >= t0) &
+ (time[idx] <= (t0+dt))]
+ freq_temp = freq[window_index]
+ time_temp = time[idx[window_index]]
+ #mean_freq = np.mean(freq_temp)
+ #fdata = bandpass_filter(data_oi[:, track_id], data.samplerate, mean_freq-5, mean_freq+200)
+ ax.plot(time_temp - t0, freq_temp)
+ """
+
+if __name__ == '__main__':
+ # Path to the data
+ datapath = '../data/mount_data/2020-05-13-10_00/'
+ main(datapath)