397 lines
16 KiB
Python
397 lines
16 KiB
Python
import os
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from IPython import embed
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from pandas import read_csv
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from modules.logger import makeLogger
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from modules.datahandling import causal_kde1d, acausal_kde1d
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logger = makeLogger(__name__)
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class Behavior:
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"""Load behavior data from csv file as class attributes
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Attributes
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----------
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behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
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behavior_type:
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behavioral_category:
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comment_start:
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comment_stop:
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dataframe: pandas dataframe with all the data
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duration_s:
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media_file:
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observation_date:
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observation_id:
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start_s: start time of the event in seconds
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stop_s: stop time of the event in seconds
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total_length:
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"""
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def __init__(self, folder_path: str) -> None:
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LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
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self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
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csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0] # check if there are more than one csv file
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self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
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self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
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self.chirps_ids = np.load(os.path.join(folder_path, 'chirps_ids.npy'), allow_pickle=True)
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for k, key in enumerate(self.dataframe.keys()):
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key = key.lower()
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if ' ' in key:
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key = key.replace(' ', '_')
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if '(' in key:
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key = key.replace('(', '')
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key = key.replace(')', '')
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setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
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last_LED_t_BORIS = LED_on_time_BORIS[-1]
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real_time_range = self.time[-1] - self.time[0]
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factor = 1.034141
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shift = last_LED_t_BORIS - real_time_range * factor
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self.start_s = (self.start_s - shift) / factor
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self.stop_s = (self.stop_s - shift) / factor
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"""
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1 - chasing onset
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2 - chasing offset
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3 - physical contact event
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temporal encpding needs to be corrected ... not exactly 25FPS.
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### correspinding python code ###
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factor = 1.034141
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LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
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last_LED_t_BORIS = LED_on_time_BORIS[-1]
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real_time_range = times[-1] - times[0]
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shift = last_LED_t_BORIS - real_time_range * factor
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data = pd.read_csv(os.path.join(folder_path, file[1:-7] + '.csv'))
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boris_times = data['Start (s)']
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data_times = []
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for Cevent_t in boris_times:
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Cevent_boris_times = (Cevent_t - shift) / factor
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data_times.append(Cevent_boris_times)
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data_times = np.array(data_times)
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behavior = data['Behavior']
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"""
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def correct_chasing_events(
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category: np.ndarray,
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timestamps: np.ndarray
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) -> tuple[np.ndarray, np.ndarray]:
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onset_ids = np.arange(
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len(category))[category == 0]
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offset_ids = np.arange(
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len(category))[category == 1]
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# Check whether on- or offset is longer and calculate length difference
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if len(onset_ids) > len(offset_ids):
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len_diff = len(onset_ids) - len(offset_ids)
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longer_array = onset_ids
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shorter_array = offset_ids
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logger.info(f'Onsets are greater than offsets by {len_diff}')
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elif len(onset_ids) < len(offset_ids):
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len_diff = len(offset_ids) - len(onset_ids)
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longer_array = offset_ids
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shorter_array = onset_ids
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logger.info(f'Offsets are greater than offsets by {len_diff}')
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elif len(onset_ids) == len(offset_ids):
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logger.info('Chasing events are equal')
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return category, timestamps
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# Correct the wrong chasing events; delete double events
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wrong_ids = []
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for i in range(len(longer_array)-(len_diff+1)):
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if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]):
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pass
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else:
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wrong_ids.append(longer_array[i])
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longer_array = np.delete(longer_array, i)
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category = np.delete(
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category, wrong_ids)
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timestamps = np.delete(
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timestamps, wrong_ids)
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return category, timestamps
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def event_triggered_chirps(
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event: np.ndarray,
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chirps:np.ndarray,
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time_before_event: int,
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time_after_event: int,
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dt: float,
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width: float,
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)-> tuple[np.ndarray, np.ndarray, np.ndarray]:
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event_chirps = [] # chirps that are in specified window around event
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centered_chirps = [] # timestamps of chirps around event centered on the event timepoint
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for event_timestamp in event:
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start = event_timestamp - time_before_event
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stop = event_timestamp + time_after_event
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chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
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event_chirps.append(chirps_around_event)
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if len(chirps_around_event) == 0:
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continue
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else:
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centered_chirps.append(chirps_around_event - event_timestamp)
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centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
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# Kernel density estimation
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time = np.arange(-time_before_event, time_after_event, dt)
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centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
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return event_chirps, centered_chirps, centered_chirps_convolved
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def main(datapath: str):
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# behavior is pandas dataframe with all the data
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bh = Behavior(datapath)
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# chirps are not sorted in time (presumably due to prior groupings)
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# get and sort chirps and corresponding fish_ids of the chirps
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chirps = bh.chirps[np.argsort(bh.chirps)]
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chirps_fish_ids = bh.chirps_ids[np.argsort(bh.chirps)]
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category = bh.behavior
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timestamps = bh.start_s
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# Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
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# Get rid of tracking faults (two onsets or two offsets after another)
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category, timestamps = correct_chasing_events(category, timestamps)
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# split categories
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chasing_onsets = timestamps[category == 0]
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chasing_offsets = timestamps[category == 1]
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physical_contacts = timestamps[category == 2]
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chasing_durations = []
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# Calculate chasing duration to evaluate a nice time window for kernel density estimation
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for onset, offset in zip(chasing_onsets, chasing_offsets):
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duration = offset - onset
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chasing_durations.append(duration)
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# fig, ax = plt.subplots()
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# ax.boxplot(chasing_durations)
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# plt.show()
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# plt.close()
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# Get fish ids
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fish_ids = np.unique(chirps_fish_ids)
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# # Associate chirps to individual fish
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# fish1 = chirps[chirps_fish_ids == fish_ids[0]]
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# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
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# fish = [len(fish1), len(fish2)]
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# Define time window for chirps around event analysis
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time_before_event = 30
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time_after_event = 60
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dt = 0.01
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width = 1
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time = np.arange(-time_before_event, time_after_event, dt)
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##### Chirps around events, all fish, one recording #####
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# Chirps around chasing onsets
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_, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width)
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# Chirps around chasing offsets
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_, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width)
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# Chirps around physical contacts
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_, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width)
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## Shuffled chirps ##
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nbootstrapping = 1000
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nshuffled_chirps_onset = []
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nshuffled_chirps_offset = []
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nshuffled_chirps_physical = []
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for i in range(nbootstrapping):
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# Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths
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interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps))
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np.random.shuffle(interchirp_intervals)
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shuffled_chirps = np.cumsum(interchirp_intervals)
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# Shuffled chasing onset chirps
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_, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width)
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nshuffled_chirps_onset.append(cc_shuffled_onset_chirps)
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# Shuffled chasing offset chirps
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_, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width)
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nshuffled_chirps_offset.append(cc_shuffled_offset_chirps)
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# Shuffled physical contact chirps
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_, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width)
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nshuffled_chirps_physical.append(cc_shuffled_physical_chirps)
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shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(nshuffled_chirps_onset, (5, 50, 95), axis=0)
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shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(nshuffled_chirps_offset, (5, 50, 95), axis=0)
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shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(nshuffled_chirps_physical, (5, 50, 95), axis=0)
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# Plot all events with all shuffled
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fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all')
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offset = [1.35]
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ax[0].set_xlabel('Time[s]')
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# Plot chasing onsets
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ax[0].set_ylabel('Chirp rate [Hz]')
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ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=2)
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ax0 = ax[0].twinx()
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ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=1)
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ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax[0].set_zorder(ax0.get_zorder()+1)
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ax[0].patch.set_visible(False)
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ax0.set_yticklabels([])
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ax0.set_yticks([])
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ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5)
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ax[0].plot(time, shuffled_median_onset, color='k')
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# Plot chasing offets
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ax[1].set_xlabel('Time[s]')
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ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=2)
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ax1 = ax[1].twinx()
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ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=1)
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ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax[1].set_zorder(ax1.get_zorder()+1)
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ax[1].patch.set_visible(False)
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ax1.set_yticklabels([])
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ax1.set_yticks([])
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ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5)
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ax[1].plot(time, shuffled_median_offset, color='k')
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# Plot physical contacts
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ax[2].set_xlabel('Time[s]')
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ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=2)
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ax2 = ax[2].twinx()
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ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=1)
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ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax[2].set_zorder(ax2.get_zorder()+1)
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ax[2].patch.set_visible(False)
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ax2.set_yticklabels([])
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ax2.set_yticks([])
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ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5)
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ax[2].plot(time, shuffled_median_physical, color='k')
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plt.show()
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# plt.close()
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#### Chirps around events, winner VS loser, one recording ####
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# Load file with fish ids and winner/loser info
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meta = pd.read_csv('../data/mount_data/order_meta.csv')
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current_recording = meta[meta.index == 43]
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fish1 = current_recording['rec_id1'].values
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fish2 = current_recording['rec_id2'].values
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# Implement check if fish_ids from meta and chirp detection are the same???
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winner = current_recording['winner'].values
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if winner == fish1:
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loser = fish2
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elif winner == fish2:
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loser = fish1
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winner_chirps = chirps[chirps_fish_ids == winner]
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loser_chirps = chirps[chirps_fish_ids == loser]
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# Event triggered winner chirps
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_, winner_centered_onset, winner_cc_onset = event_triggered_chirps(chasing_onsets, winner_chirps, time_before_event, time_after_event, dt, width)
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_, winner_centered_offset, winner_cc_offset = event_triggered_chirps(chasing_offsets, winner_chirps, time_before_event, time_after_event, dt, width)
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_, winner_centered_physical, winner_cc_physical = event_triggered_chirps(physical_contacts, winner_chirps, time_before_event, time_after_event, dt, width)
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# Event triggered loser chirps
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_, loser_centered_onset, loser_cc_onset = event_triggered_chirps(chasing_onsets, loser_chirps, time_before_event, time_after_event, dt, width)
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_, loser_centered_offset, loser_cc_offset = event_triggered_chirps(chasing_offsets, loser_chirps, time_before_event, time_after_event, dt, width)
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_, loser_centered_physical, loser_cc_physical = event_triggered_chirps(physical_contacts, loser_chirps, time_before_event, time_after_event, dt, width)
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########## Winner VS Loser plot ##########
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fig, ax = plt.subplots(2, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='row')
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offset = [1.35]
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ax[1][0].set_xlabel('Time[s]')
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ax[1][1].set_xlabel('Time[s]')
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ax[1][2].set_xlabel('Time[s]')
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# Plot winner chasing onsets
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ax[0][0].set_ylabel('Chirp rate [Hz]')
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ax[0][0].plot(time, winner_cc_onset, color='tab:blue', zorder=100)
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ax0 = ax[0][0].twinx()
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ax0.eventplot(np.array([winner_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
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ax0.set_ylabel('Event')
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ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax[0][0].set_zorder(ax0.get_zorder()+1)
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ax[0][0].patch.set_visible(False)
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ax0.set_yticklabels([])
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ax0.set_yticks([])
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# Plot winner chasing offets
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ax[0][1].plot(time, winner_cc_offset, color='tab:blue', zorder=100)
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ax1 = ax[0][1].twinx()
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ax1.eventplot(np.array([winner_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
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ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax[0][1].set_zorder(ax1.get_zorder()+1)
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ax[0][1].patch.set_visible(False)
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ax1.set_yticklabels([])
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ax1.set_yticks([])
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# Plot winner physical contacts
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ax[0][2].plot(time, winner_cc_physical, color='tab:blue', zorder=100)
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ax2 = ax[0][2].twinx()
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ax2.eventplot(np.array([winner_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
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ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax[0][2].set_zorder(ax2.get_zorder()+1)
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ax[0][2].patch.set_visible(False)
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ax2.set_yticklabels([])
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ax2.set_yticks([])
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# Plot loser chasing onsets
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ax[1][0].set_ylabel('Chirp rate [Hz]')
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ax[1][0].plot(time, loser_cc_onset, color='tab:blue', zorder=100)
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ax3 = ax[1][0].twinx()
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ax3.eventplot(np.array([loser_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
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ax3.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax[1][0].set_zorder(ax3.get_zorder()+1)
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ax[1][0].patch.set_visible(False)
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ax3.set_yticklabels([])
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ax3.set_yticks([])
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# Plot loser chasing offsets
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ax[1][1].plot(time, loser_cc_offset, color='tab:blue', zorder=100)
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ax4 = ax[1][1].twinx()
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ax4.eventplot(np.array([loser_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
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ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax[1][1].set_zorder(ax4.get_zorder()+1)
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ax[1][1].patch.set_visible(False)
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ax4.set_yticklabels([])
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ax4.set_yticks([])
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# Plot loser physical contacts
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ax[1][2].plot(time, loser_cc_physical, color='tab:blue', zorder=100)
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ax5 = ax[1][2].twinx()
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ax5.eventplot(np.array([loser_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
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ax5.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
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ax[1][2].set_zorder(ax5.get_zorder()+1)
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ax[1][2].patch.set_visible(False)
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ax5.set_yticklabels([])
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ax5.set_yticks([])
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plt.show()
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# plt.close()
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embed()
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exit()
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for i in range(len(fish_ids)):
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fish = fish_ids[i]
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chirps_temp = chirps[chirps_fish_ids == fish]
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print(fish)
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#### Chirps around events, only losers, one recording ####
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if __name__ == '__main__':
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# Path to the data
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datapath = '../data/mount_data/2020-05-13-10_00/'
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main(datapath)
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