diff --git a/code/behavior.py b/code/behavior.py
index da02838..73ddcc5 100644
--- a/code/behavior.py
+++ b/code/behavior.py
@@ -1,16 +1,19 @@
-from pathlib import Path
+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:
@@ -20,23 +23,37 @@ class Behavior:
media_file:
observation_date:
observation_id:
- start_s:
- stop_s:
+ start_s: start time of the event in seconds
+ stop_s: stop time of the event in seconds
total_length:
"""
- def __init__(self, datapath: str) -> None:
- csv_file = str(sorted(Path(datapath).glob('**/*.csv'))[0])
- self.dataframe = read_csv(csv_file, delimiter=',')
- for key in self.dataframe:
- if ' ' in key:
- new_key = key.replace(' ', '_')
- if '(' in new_key:
- new_key = new_key.replace('(', '')
- new_key = new_key.replace(')', '')
- new_key = new_key.lower()
- setattr(self, new_key, np.array(self.dataframe[key]))
+ 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
+
"""
1 - chasing onset
2 - chasing offset
@@ -64,12 +81,155 @@ temporal encpding needs to be corrected ... not exactly 25FPS.
behavior = data['Behavior']
"""
+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
- behavior = Behavior(datapath)
+
+ # behavior 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]
+ chasing_offset = timestamps[category == 1]
+ physical_contact = timestamps[category == 2]
+
+ ##### TODO Physical contact-triggered chirps (PTC) mit Rasterplot #####
+ # Wahrscheinlichkeit von Phys auf Ch und vice versa
+ # Chasing-triggered chirps (CTC) mit Rasterplot
+ # Wahrscheinlichkeit von Chase auf Ch und vice versa
+
+ # First overview plot
+ fig1, ax1 = plt.subplots()
+ ax1.scatter(chirps, np.ones_like(chirps), marker='*', color='royalblue', label='Chirps')
+ ax1.scatter(chasing_onset, np.ones_like(chasing_onset)*2, marker='.', color='forestgreen', label='Chasing onset')
+ ax1.scatter(chasing_offset, np.ones_like(chasing_offset)*2.5, marker='.', color='firebrick', label='Chasing offset')
+ ax1.scatter(physical_contact, np.ones_like(physical_contact)*3, marker='x', color='black', label='Physical contact')
+ plt.legend()
+ # plt.show()
+ plt.close()
+
+ # Get fish ids
+ all_fish_ids = np.unique(chirps_fish_ids)
+
+ # Associate chirps to inidividual fish
+ fish1 = chirps[chirps_fish_ids == all_fish_ids[0]]
+ fish2 = chirps[chirps_fish_ids == all_fish_ids[1]]
+ fish = [len(fish1), len(fish2)]
+
+ #### Chirp counts per fish general #####
+ fig2, ax2 = plt.subplots()
+ x = ['Fish1', 'Fish2']
+ width = 0.35
+ ax2.bar(x, fish, width=width)
+ ax2.set_ylabel('Chirp count')
+ # plt.show()
+ plt.close()
+
+
+ ##### Count chirps emitted during chasing events and chirps emitted out of chasing events #####
+ chirps_in_chasings = []
+ for onset, offset in zip(chasing_onset, chasing_offset):
+ chirps_in_chasing = [c for c in chirps if (c > onset) & (c < offset)]
+ chirps_in_chasings.append(chirps_in_chasing)
+
+ # chirps out of chasing events
+ counts_chirps_chasings = 0
+ chasings_without_chirps = 0
+ for i in chirps_in_chasings:
+ if i:
+ chasings_without_chirps += 1
+ else:
+ counts_chirps_chasings += 1
+
+ # chirps in chasing events
+ fig3 , ax3 = plt.subplots()
+ ax3.bar(['Chirps in chasing events', 'Chasing events without Chirps'], [counts_chirps_chasings, chasings_without_chirps], width=width)
+ plt.ylabel('Count')
+ plt.show()
+ plt.close()
+
+ # comparison between chasing events with and without chirps
+
+ ##### Chasing triggered chirps CTC #####
+ # Evaluate how many chirps were emitted in specific time window around the chasing onset events
+
+ # Goal:
+ # Plot with Chasing onsets centered at t = 0 on x-axis as a function of event type (0, 1, 2) (or later as a function of recordings) with chirps as rasterplot in background
+
+ # Chasing onset is defined at the point event 'chasing'
+ # Iterate over chasing onsets (later over fish)
+ # Get chirps which in a time window of -5 to +5 seconds aroung the chasing onset and save them
+ # Set Chasing onset at timepoint 0: Chasing onset timestamp - chasing onset timestamp
+ # Calculate chirp timestamps relative to chasing onset: Chirp timestamp - Chasing onset timestamp
+ # For rasterplot look at plt.eventplot() function
+ # Do the plot
+ # Then same with physical onset events (PTC)
+
+
+
+
+
+
+
+
+ embed()
+
+
+
if __name__ == '__main__':
# Path to the data
- datapath = '../data/mount_data/2020-03-13-10_00/'
+ datapath = '../data/mount_data/2020-05-13-10_00/'
main(datapath)