From 5d8c41c8995e7bdacb4d89bd57784f9c436591c4 Mon Sep 17 00:00:00 2001 From: sprause Date: Mon, 23 Jan 2023 09:49:19 +0100 Subject: [PATCH] new plotfile for CTC and PTC --- code/behavior.py | 50 +++++---- code/eventchirpsplots.py | 225 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 253 insertions(+), 22 deletions(-) create mode 100644 code/eventchirpsplots.py diff --git a/code/behavior.py b/code/behavior.py index 2e0eb3d..bbfda4b 100644 --- a/code/behavior.py +++ b/code/behavior.py @@ -131,7 +131,6 @@ def event_triggered_chirps( )-> tuple[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 @@ -188,36 +187,43 @@ def main(datapath: str): # Iterate over chasing onsets (later over fish) time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event - #### Loop crashes at concatenate in function #### - for i in range(len(fish_ids)): - fish = fish_ids[i] - chirps = chirps[chirps_fish_ids == fish] - print(fish) + # for i in range(len(fish_ids)): + # fish = fish_ids[i] + # chirps = chirps[chirps_fish_ids == fish] + # print(fish) - chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) - physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) + chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) + physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) - # Kernel density estimation ??? - # centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) - - # centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 - offsets = [0.5, 1] - fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True) - ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) - ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') - # ax4.plot(centered_chasing_chirps_convolved) - ax4.set_yticks(offsets) - ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) - ax4.set_xlabel('Time[s]') - ax4.set_ylabel('Type of event') - plt.show() + # Kernel density estimation ??? + # centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) + + # centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 + offsets = [0.5, 1] + fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True) + ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) + ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') + # ax4.plot(centered_chasing_chirps_convolved) + ax4.set_yticks(offsets) + ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) + ax4.set_xlabel('Time[s]') + ax4.set_ylabel('Type of event') + plt.show() # Associate chirps to inidividual fish fish1 = chirps[chirps_fish_ids == fish_ids[0]] fish2 = chirps[chirps_fish_ids == fish_ids[1]] fish = [len(fish1), len(fish2)] + ### Plots: + # 1. All recordings, all fish, all chirps + # One CTC, one PTC + # 2. All recordings, only winners + # One CTC, one PTC + # 3. All recordings, all losers + # One CTC, one PTC + #### Chirp counts per fish general ##### fig2, ax2 = plt.subplots() x = ['Fish1', 'Fish2'] diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py new file mode 100644 index 0000000..e0adb14 --- /dev/null +++ b/code/eventchirpsplots.py @@ -0,0 +1,225 @@ +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 + +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 + + +""" +1 - chasing onset +2 - chasing offset +3 - physical contact event + +temporal encpding needs to be corrected ... not exactly 25FPS. + +### correspinding python code ### + + factor = 1.034141 + LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True) + last_LED_t_BORIS = LED_on_time_BORIS[-1] + real_time_range = times[-1] - times[0] + shift = last_LED_t_BORIS - real_time_range * factor + + data = pd.read_csv(os.path.join(folder_path, file[1:-7] + '.csv')) + boris_times = data['Start (s)'] + data_times = [] + + for Cevent_t in boris_times: + Cevent_boris_times = (Cevent_t - shift) / factor + data_times.append(Cevent_boris_times) + + data_times = np.array(data_times) + 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 event_triggered_chirps( + event: np.ndarray, + chirps:np.ndarray, + time_before_event: int, + time_after_event: int + )-> tuple[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 + + for event_timestamp in event: + start = event_timestamp - time_before_event # timepoint of window start + stop = event_timestamp + time_after_event # timepoint of window ending + chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)] # get chirps that are in a -5 to +5 sec window around event + event_chirps.append(chirps_around_event) + if len(chirps_around_event) == 0: + continue + else: + centered_chirps.append(chirps_around_event - event_timestamp) + centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting + + return event_chirps, centered_chirps + + +def main(datapath: str): + + # 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] + + # Get fish ids + fish_ids = np.unique(chirps_fish_ids) + + ##### Chasing triggered chirps CTC ##### + # Evaluate how many chirps were emitted in specific time window around the chasing onset events + + # Iterate over chasing onsets (later over fish) + time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event + #### Loop crashes at concatenate in function #### + # for i in range(len(fish_ids)): + # fish = fish_ids[i] + # chirps = chirps[chirps_fish_ids == fish] + # print(fish) + + chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) + physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) + + # Kernel density estimation ??? + # centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) + + # centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 + offsets = [0.5, 1] + fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True) + ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) + ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') + # ax4.plot(centered_chasing_chirps_convolved) + ax4.set_yticks(offsets) + ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) + ax4.set_xlabel('Time[s]') + ax4.set_ylabel('Type of event') + plt.show() + + # Associate chirps to inidividual fish + fish1 = chirps[chirps_fish_ids == fish_ids[0]] + fish2 = chirps[chirps_fish_ids == fish_ids[1]] + fish = [len(fish1), len(fish2)] + + ### Plots: + # 1. All recordings, all fish, all chirps + # One CTC, one PTC + # 2. All recordings, only winners + # One CTC, one PTC + # 3. All recordings, all losers + # One CTC, one PTC + + + embed() + exit() + + + +if __name__ == '__main__': + # Path to the data + datapath = '../data/mount_data/2020-05-13-10_00/' + main(datapath)