323 lines
10 KiB
Python
323 lines
10 KiB
Python
import os
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import os
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import numpy as np
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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 scipy.ndimage import gaussian_filter1d
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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(
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os.path.join(folder_path, "LED_on_time.npy"), allow_pickle=True
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)
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self.time = np.load(
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os.path.join(folder_path, "times.npy"), allow_pickle=True
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)
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csv_filename = [
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f for f in os.listdir(folder_path) if f.endswith(".csv")
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][
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0
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] # 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(
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os.path.join(folder_path, "chirps.npy"), allow_pickle=True
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)
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self.chirps_ids = np.load(
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os.path.join(folder_path, "chirps_ids.npy"), allow_pickle=True
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)
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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(
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self, key, np.array(self.dataframe[self.dataframe.keys()[k]])
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)
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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, timestamps: np.ndarray
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) -> tuple[np.ndarray, np.ndarray]:
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onset_ids = np.arange(len(category))[category == 0]
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offset_ids = np.arange(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]) & (
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shorter_array[i] < longer_array[i + 1]
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):
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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(category, wrong_ids)
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timestamps = np.delete(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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) -> tuple[np.ndarray, np.ndarray]:
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event_chirps = [] # chirps that are in specified window around event
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centered_chirps = (
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[]
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) # 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 # timepoint of window start
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stop = event_timestamp + time_after_event # timepoint of window ending
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chirps_around_event = [
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c for c in chirps if (c >= start) & (c <= stop)
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] # get chirps that are in a -5 to +5 sec window around event
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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(
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centered_chirps, axis=0
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) # convert list of arrays to one array for plotting
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return event_chirps, centered_chirps
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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_onset = timestamps[category == 0]
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chasing_offset = timestamps[category == 1]
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physical_contact = timestamps[category == 2]
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# First overview plot
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fig1, ax1 = plt.subplots()
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ax1.scatter(
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chirps,
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np.ones_like(chirps),
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marker="*",
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color="royalblue",
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label="Chirps",
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)
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ax1.scatter(
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chasing_onset,
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np.ones_like(chasing_onset) * 2,
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marker=".",
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color="forestgreen",
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label="Chasing onset",
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)
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ax1.scatter(
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chasing_offset,
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np.ones_like(chasing_offset) * 2.5,
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marker=".",
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color="firebrick",
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label="Chasing offset",
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)
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ax1.scatter(
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physical_contact,
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np.ones_like(physical_contact) * 3,
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marker="x",
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color="black",
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label="Physical contact",
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)
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plt.legend()
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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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##### Chasing triggered chirps CTC #####
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# Evaluate how many chirps were emitted in specific time window around the chasing onset events
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# Iterate over chasing onsets (later over fish)
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time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event
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#### Loop crashes at concatenate in function ####
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# for i in range(len(fish_ids)):
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# fish = fish_ids[i]
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# chirps = chirps[chirps_fish_ids == fish]
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# print(fish)
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chasing_chirps, centered_chasing_chirps = event_triggered_chirps(
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chasing_onset, chirps, time_around_event, time_around_event
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)
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physical_chirps, centered_physical_chirps = event_triggered_chirps(
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physical_contact, chirps, time_around_event, time_around_event
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)
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# Kernel density estimation ???
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# centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5)
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# centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0
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offsets = [0.5, 1]
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fig4, ax4 = plt.subplots(
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figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True
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)
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ax4.eventplot(
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np.array([centered_chasing_chirps, centered_physical_chirps]),
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lineoffsets=offsets,
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linelengths=0.25,
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colors=["g", "r"],
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)
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ax4.vlines(0, 0, 1.5, "tab:grey", "dashed", "Timepoint of event")
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# ax4.plot(centered_chasing_chirps_convolved)
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ax4.set_yticks(offsets)
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ax4.set_yticklabels(["Chasings", "Physical \n contacts"])
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ax4.set_xlabel("Time[s]")
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ax4.set_ylabel("Type of event")
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plt.show()
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# Associate chirps to inidividual 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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### Plots:
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# 1. All recordings, all fish, all chirps
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# One CTC, one PTC
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# 2. All recordings, only winners
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# One CTC, one PTC
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# 3. All recordings, all losers
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# One CTC, one PTC
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#### Chirp counts per fish general #####
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fig2, ax2 = plt.subplots()
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x = ["Fish1", "Fish2"]
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width = 0.35
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ax2.bar(x, fish, width=width)
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ax2.set_ylabel("Chirp count")
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# plt.show()
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plt.close()
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##### Count chirps emitted during chasing events and chirps emitted out of chasing events #####
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chirps_in_chasings = []
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for onset, offset in zip(chasing_onset, chasing_offset):
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chirps_in_chasing = [c for c in chirps if (c > onset) & (c < offset)]
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chirps_in_chasings.append(chirps_in_chasing)
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# chirps out of chasing events
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counts_chirps_chasings = 0
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chasings_without_chirps = 0
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for i in chirps_in_chasings:
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if i:
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chasings_without_chirps += 1
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else:
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counts_chirps_chasings += 1
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# chirps in chasing events
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fig3, ax3 = plt.subplots()
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ax3.bar(
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["Chirps in chasing events", "Chasing events without Chirps"],
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[counts_chirps_chasings, chasings_without_chirps],
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width=width,
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)
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plt.ylabel("Count")
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# plt.show()
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plt.close()
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# comparison between chasing events with and without chirps
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embed()
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exit()
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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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datapath = "../data/mount_data/2020-05-13-10_00/"
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main(datapath)
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