From b2a4d80d17d81f669705b2f00ffc5063ba8b8353 Mon Sep 17 00:00:00 2001 From: sprause Date: Sat, 21 Jan 2023 17:36:05 +0100 Subject: [PATCH] setup --- code/behavior.py | 198 ++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 179 insertions(+), 19 deletions(-) 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)