670 lines
26 KiB
Python
670 lines
26 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 tqdm import tqdm
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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.plotstyle import PlotStyle
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from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
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logger = makeLogger(__name__)
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ps = PlotStyle()
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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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print(f"{folder_path}")
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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, "chirp_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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wrong_bh = np.arange(len(category))[category != 2][:-1][
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np.diff(category[category != 2]) == 0
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]
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if onset_ids[0] > offset_ids[0]:
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offset_ids = np.delete(offset_ids, 0)
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help_index = offset_ids[0]
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wrong_bh = np.append(wrong_bh[help_index])
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category = np.delete(category, wrong_bh)
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timestamps = np.delete(timestamps, wrong_bh)
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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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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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logger.info(f"Offsets are greater than onsets 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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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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# timestamps of chirps around event centered on the event timepoint
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centered_chirps = []
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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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time = np.arange(-time_before_event, time_after_event, dt)
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# Kernel density estimation with some if's
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if len(centered_chirps) == 0:
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centered_chirps = np.array([])
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centered_chirps_convolved = np.zeros(len(time))
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else:
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# convert list of arrays to one array for plotting
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centered_chirps = np.concatenate(centered_chirps, axis=0)
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centered_chirps_convolved = (
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acausal_kde1d(centered_chirps, time, width)
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) / 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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foldernames = [
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datapath + x + "/"
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for x in os.listdir(datapath)
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if os.path.isdir(datapath + x)
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]
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nrecording_chirps = []
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nrecording_chirps_fish_ids = []
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nrecording_chasing_onsets = []
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nrecording_chasing_offsets = []
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nrecording_physicals = []
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# Iterate over all recordings and save chirp- and event-timestamps
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for folder in foldernames:
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# exclude folder with empty LED_on_time.npy
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if folder == "../data/mount_data/2020-05-12-10_00/":
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continue
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bh = Behavior(folder)
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# Chirps are already sorted
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category = bh.behavior
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timestamps = bh.start_s
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chirps = bh.chirps
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nrecording_chirps.append(chirps)
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chirps_fish_ids = bh.chirps_ids
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nrecording_chirps_fish_ids.append(chirps_fish_ids)
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fish_ids = np.unique(chirps_fish_ids)
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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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nrecording_chasing_onsets.append(chasing_onsets)
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chasing_offsets = timestamps[category == 1]
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nrecording_chasing_offsets.append(chasing_offsets)
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physical_contacts = timestamps[category == 2]
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nrecording_physicals.append(physical_contacts)
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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 = 1.5 # width of kernel for all recordings, currently gaussian kernel
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recording_width = 2 # width of kernel for each recording
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time = np.arange(-time_before_event, time_after_event, dt)
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##### Chirps around events, all fish, all recordings #####
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# Centered chirps per event type
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nrecording_centered_onset_chirps = []
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nrecording_centered_offset_chirps = []
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nrecording_centered_physical_chirps = []
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# Bootstrapped chirps per recording and per event: 27[1000[n]] 27 recs, 1000 shuffles, n chirps
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nrecording_shuffled_convolved_onset_chirps = []
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nrecording_shuffled_convolved_offset_chirps = []
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nrecording_shuffled_convolved_physical_chirps = []
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nbootstrapping = 100
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for i in range(len(nrecording_chirps)):
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chirps = nrecording_chirps[i]
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chasing_onsets = nrecording_chasing_onsets[i]
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chasing_offsets = nrecording_chasing_offsets[i]
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physical_contacts = nrecording_physicals[i]
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# Chirps around chasing onsets
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(
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_,
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centered_chasing_onset_chirps,
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cc_chasing_onset_chirps,
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) = event_triggered_chirps(
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chasing_onsets,
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chirps,
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time_before_event,
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time_after_event,
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dt,
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recording_width,
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)
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# Chirps around chasing offsets
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(
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_,
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centered_chasing_offset_chirps,
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cc_chasing_offset_chirps,
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) = event_triggered_chirps(
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chasing_offsets,
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chirps,
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time_before_event,
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time_after_event,
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dt,
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recording_width,
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)
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# Chirps around physical contacts
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(
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_,
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centered_physical_chirps,
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cc_physical_chirps,
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) = event_triggered_chirps(
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physical_contacts,
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chirps,
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time_before_event,
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time_after_event,
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dt,
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recording_width,
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)
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nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps)
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nrecording_centered_offset_chirps.append(centered_chasing_offset_chirps)
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nrecording_centered_physical_chirps.append(centered_physical_chirps)
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## Shuffled chirps ##
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nshuffled_onset_chirps = []
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nshuffled_offset_chirps = []
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nshuffled_physical_chirps = []
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# for j in tqdm(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, recording_width)
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# nshuffled_onset_chirps.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, recording_width)
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# nshuffled_offset_chirps.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, recording_width)
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# nshuffled_physical_chirps.append(cc_shuffled_physical_chirps)
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# rec_shuffled_q5_onset, rec_shuffled_median_onset, rec_shuffled_q95_onset = np.percentile(
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# nshuffled_onset_chirps, (5, 50, 95), axis=0)
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# rec_shuffled_q5_offset, rec_shuffled_median_offset, rec_shuffled_q95_offset = np.percentile(
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# nshuffled_offset_chirps, (5, 50, 95), axis=0)
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# rec_shuffled_q5_physical, rec_shuffled_median_physical, rec_shuffled_q95_physical = np.percentile(
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# nshuffled_physical_chirps, (5, 50, 95), axis=0)
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# #### Recording plots ####
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# fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
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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=ps.yellow, zorder=2)
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# ax0 = ax[0].twinx()
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# ax0.eventplot(centered_chasing_onset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
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# ax0.vlines(0, 0, 1.5, ps.white, '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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# ######## median - q5, median + q95
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# ax[0].fill_between(time, rec_shuffled_q5_onset, rec_shuffled_q95_onset, color=ps.gray, alpha=0.5)
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# ax[0].plot(time, rec_shuffled_median_onset, color=ps.black)
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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=ps.orange, zorder=2)
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# ax1 = ax[1].twinx()
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# ax1.eventplot(centered_chasing_offset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
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# ax1.vlines(0, 0, 1.5, ps.white, '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, rec_shuffled_q5_offset, rec_shuffled_q95_offset, color=ps.gray, alpha=0.5)
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# ax[1].plot(time, rec_shuffled_median_offset, color=ps.black)
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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=ps.maroon, zorder=2)
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# ax2 = ax[2].twinx()
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# ax2.eventplot(centered_physical_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
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# ax2.vlines(0, 0, 1.5, ps.white, '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, rec_shuffled_q5_physical, rec_shuffled_q95_physical, color=ps.gray, alpha=0.5)
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# ax[2].plot(time, rec_shuffled_median_physical, ps.black)
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# fig.suptitle(f'Recording: {i}')
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# # plt.show()
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# plt.close()
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# nrecording_shuffled_convolved_onset_chirps.append(nshuffled_onset_chirps)
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# nrecording_shuffled_convolved_offset_chirps.append(nshuffled_offset_chirps)
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# nrecording_shuffled_convolved_physical_chirps.append(nshuffled_physical_chirps)
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#### New shuffle approach ####
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bootstrap_onset = []
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bootstrap_offset = []
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bootstrap_physical = []
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# New bootstrapping approach
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for n in range(nbootstrapping):
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diff_onset = np.diff(np.sort(flatten(nrecording_centered_onset_chirps)))
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diff_offset = np.diff(
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np.sort(flatten(nrecording_centered_offset_chirps))
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)
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diff_physical = np.diff(
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np.sort(flatten(nrecording_centered_physical_chirps))
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)
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np.random.shuffle(diff_onset)
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shuffled_onset = np.cumsum(diff_onset)
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np.random.shuffle(diff_offset)
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shuffled_offset = np.cumsum(diff_offset)
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np.random.shuffle(diff_physical)
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shuffled_physical = np.cumsum(diff_physical)
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kde_onset(acausal_kde1d(shuffled_onset, time, width)) / (27 * 100)
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kde_offset = (acausal_kde1d(shuffled_offset, time, width)) / (27 * 100)
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kde_physical = (acausal_kde1d(shuffled_physical, time, width)) / (
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27 * 100
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)
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bootstrap_onset.append(kde_onset)
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bootstrap_offset.append(kde_offset)
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bootstrap_physical.append(kde_physical)
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# New shuffle approach q5, q50, q95
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onset_q5, onset_median, onset_q95 = np.percentile(
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bootstrap_onset, [5, 50, 95], axis=0
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)
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offset_q5, offset_median, offset_q95 = np.percentile(
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bootstrap_offset, [5, 50, 95], axis=0
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)
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physical_q5, physical_median, physical_q95 = np.percentile(
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bootstrap_physical, [5, 50, 95], axis=0
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)
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# vstack um 1. Dim zu cutten
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# nrecording_shuffled_convolved_onset_chirps = np.vstack(nrecording_shuffled_convolved_onset_chirps)
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# nrecording_shuffled_convolved_offset_chirps = np.vstack(nrecording_shuffled_convolved_offset_chirps)
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# nrecording_shuffled_convolved_physical_chirps = np.vstack(nrecording_shuffled_convolved_physical_chirps)
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# shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(
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# nrecording_shuffled_convolved_onset_chirps, (5, 50, 95), axis=0)
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# shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(
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# nrecording_shuffled_convolved_offset_chirps, (5, 50, 95), axis=0)
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# shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(
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# nrecording_shuffled_convolved_physical_chirps, (5, 50, 95), axis=0)
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# Flatten all chirps
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all_chirps = np.concatenate(nrecording_chirps).ravel() # not centered
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# Flatten event timestamps
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all_onsets = np.concatenate(
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nrecording_chasing_onsets
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).ravel() # not centered
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all_offsets = np.concatenate(
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nrecording_chasing_offsets
|
|
).ravel() # not centered
|
|
all_physicals = np.concatenate(nrecording_physicals).ravel() # not centered
|
|
|
|
# Flatten all chirps around events
|
|
all_onset_chirps = np.concatenate(
|
|
nrecording_centered_onset_chirps
|
|
).ravel() # centered
|
|
all_offset_chirps = np.concatenate(
|
|
nrecording_centered_offset_chirps
|
|
).ravel() # centered
|
|
all_physical_chirps = np.concatenate(
|
|
nrecording_centered_physical_chirps
|
|
).ravel() # centered
|
|
|
|
# Convolute all chirps
|
|
# Divide by total number of each event over all recordings
|
|
all_onset_chirps_convolved = (
|
|
acausal_kde1d(all_onset_chirps, time, width)
|
|
) / len(all_onsets)
|
|
all_offset_chirps_convolved = (
|
|
acausal_kde1d(all_offset_chirps, time, width)
|
|
) / len(all_offsets)
|
|
all_physical_chirps_convolved = (
|
|
acausal_kde1d(all_physical_chirps, time, width)
|
|
) / len(all_physicals)
|
|
|
|
# Plot all events with all shuffled
|
|
fig, ax = plt.subplots(
|
|
1,
|
|
3,
|
|
figsize=(
|
|
28 * ps.cm,
|
|
16 * ps.cm,
|
|
),
|
|
constrained_layout=True,
|
|
sharey="all",
|
|
)
|
|
# offsets = np.arange(1,28,1)
|
|
ax[0].set_xlabel("Time[s]")
|
|
|
|
# Plot chasing onsets
|
|
ax[0].set_ylabel("Chirp rate [Hz]")
|
|
ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2)
|
|
ax0 = ax[0].twinx()
|
|
nrecording_centered_onset_chirps = np.asarray(
|
|
nrecording_centered_onset_chirps, dtype=object
|
|
)
|
|
ax0.eventplot(
|
|
np.array(nrecording_centered_onset_chirps),
|
|
linelengths=0.5,
|
|
colors=ps.gray,
|
|
alpha=0.25,
|
|
zorder=1,
|
|
)
|
|
ax0.vlines(0, 0, 1.5, ps.white, "dashed")
|
|
ax[0].set_zorder(ax0.get_zorder() + 1)
|
|
ax[0].patch.set_visible(False)
|
|
ax0.set_yticklabels([])
|
|
ax0.set_yticks([])
|
|
# ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color=ps.gray, alpha=0.5)
|
|
# ax[0].plot(time, shuffled_median_onset, color=ps.black)
|
|
ax[0].fill_between(time, onset_q5, onset_q95, color=ps.gray, alpha=0.5)
|
|
ax[0].plot(time, onset_median, color=ps.black)
|
|
|
|
# Plot chasing offets
|
|
ax[1].set_xlabel("Time[s]")
|
|
ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2)
|
|
ax1 = ax[1].twinx()
|
|
nrecording_centered_offset_chirps = np.asarray(
|
|
nrecording_centered_offset_chirps, dtype=object
|
|
)
|
|
ax1.eventplot(
|
|
np.array(nrecording_centered_offset_chirps),
|
|
linelengths=0.5,
|
|
colors=ps.gray,
|
|
alpha=0.25,
|
|
zorder=1,
|
|
)
|
|
ax1.vlines(0, 0, 1.5, ps.white, "dashed")
|
|
ax[1].set_zorder(ax1.get_zorder() + 1)
|
|
ax[1].patch.set_visible(False)
|
|
ax1.set_yticklabels([])
|
|
ax1.set_yticks([])
|
|
# ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color=ps.gray, alpha=0.5)
|
|
# ax[1].plot(time, shuffled_median_offset, color=ps.black)
|
|
ax[1].fill_between(time, offset_q5, offset_q95, color=ps.gray, alpha=0.5)
|
|
ax[1].plot(time, offset_median, color=ps.black)
|
|
|
|
# Plot physical contacts
|
|
ax[2].set_xlabel("Time[s]")
|
|
ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2)
|
|
ax2 = ax[2].twinx()
|
|
nrecording_centered_physical_chirps = np.asarray(
|
|
nrecording_centered_physical_chirps, dtype=object
|
|
)
|
|
ax2.eventplot(
|
|
np.array(nrecording_centered_physical_chirps),
|
|
linelengths=0.5,
|
|
colors=ps.gray,
|
|
alpha=0.25,
|
|
zorder=1,
|
|
)
|
|
ax2.vlines(0, 0, 1.5, ps.white, "dashed")
|
|
ax[2].set_zorder(ax2.get_zorder() + 1)
|
|
ax[2].patch.set_visible(False)
|
|
ax2.set_yticklabels([])
|
|
ax2.set_yticks([])
|
|
# ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5)
|
|
# ax[2].plot(time, shuffled_median_physical, ps.black)
|
|
ax[2].fill_between(
|
|
time, physical_q5, physical_q95, color=ps.gray, alpha=0.5
|
|
)
|
|
ax[2].plot(time, physical_median, ps.black)
|
|
fig.suptitle("All recordings")
|
|
plt.show()
|
|
plt.close()
|
|
|
|
embed()
|
|
|
|
# chasing_durations = []
|
|
# # Calculate chasing duration to evaluate a nice time window for kernel density estimation
|
|
# for onset, offset in zip(chasing_onsets, chasing_offsets):
|
|
# duration = offset - onset
|
|
# chasing_durations.append(duration)
|
|
|
|
# fig, ax = plt.subplots()
|
|
# ax.boxplot(chasing_durations)
|
|
# plt.show()
|
|
# plt.close()
|
|
|
|
# # Associate chirps to individual fish
|
|
# fish1 = chirps[chirps_fish_ids == fish_ids[0]]
|
|
# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
|
|
# fish = [len(fish1), len(fish2)]
|
|
|
|
# Convolution over all recordings
|
|
# Rasterplot for each recording
|
|
|
|
# #### Chirps around events, winner VS loser, one recording ####
|
|
# # Load file with fish ids and winner/loser info
|
|
# meta = pd.read_csv('../data/mount_data/order_meta.csv')
|
|
# current_recording = meta[meta.index == 43]
|
|
# fish1 = current_recording['rec_id1'].values
|
|
# fish2 = current_recording['rec_id2'].values
|
|
# # Implement check if fish_ids from meta and chirp detection are the same???
|
|
# winner = current_recording['winner'].values
|
|
|
|
# if winner == fish1:
|
|
# loser = fish2
|
|
# elif winner == fish2:
|
|
# loser = fish1
|
|
|
|
# winner_chirps = chirps[chirps_fish_ids == winner]
|
|
# loser_chirps = chirps[chirps_fish_ids == loser]
|
|
|
|
# # Event triggered winner chirps
|
|
# _, winner_centered_onset, winner_cc_onset = event_triggered_chirps(chasing_onsets, winner_chirps, time_before_event, time_after_event, dt, width)
|
|
# _, winner_centered_offset, winner_cc_offset = event_triggered_chirps(chasing_offsets, winner_chirps, time_before_event, time_after_event, dt, width)
|
|
# _, winner_centered_physical, winner_cc_physical = event_triggered_chirps(physical_contacts, winner_chirps, time_before_event, time_after_event, dt, width)
|
|
|
|
# # Event triggered loser chirps
|
|
# _, loser_centered_onset, loser_cc_onset = event_triggered_chirps(chasing_onsets, loser_chirps, time_before_event, time_after_event, dt, width)
|
|
# _, loser_centered_offset, loser_cc_offset = event_triggered_chirps(chasing_offsets, loser_chirps, time_before_event, time_after_event, dt, width)
|
|
# _, loser_centered_physical, loser_cc_physical = event_triggered_chirps(physical_contacts, loser_chirps, time_before_event, time_after_event, dt, width)
|
|
|
|
# ########## Winner VS Loser plot ##########
|
|
# fig, ax = plt.subplots(2, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='row')
|
|
# offset = [1.35]
|
|
# ax[1][0].set_xlabel('Time[s]')
|
|
# ax[1][1].set_xlabel('Time[s]')
|
|
# ax[1][2].set_xlabel('Time[s]')
|
|
# # Plot winner chasing onsets
|
|
# ax[0][0].set_ylabel('Chirp rate [Hz]')
|
|
# ax[0][0].plot(time, winner_cc_onset, color='tab:blue', zorder=100)
|
|
# ax0 = ax[0][0].twinx()
|
|
# ax0.eventplot(np.array([winner_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
|
|
# ax0.set_ylabel('Event')
|
|
# ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
|
|
# ax[0][0].set_zorder(ax0.get_zorder()+1)
|
|
# ax[0][0].patch.set_visible(False)
|
|
# ax0.set_yticklabels([])
|
|
# ax0.set_yticks([])
|
|
# # Plot winner chasing offets
|
|
# ax[0][1].plot(time, winner_cc_offset, color='tab:blue', zorder=100)
|
|
# ax1 = ax[0][1].twinx()
|
|
# ax1.eventplot(np.array([winner_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
|
|
# ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
|
|
# ax[0][1].set_zorder(ax1.get_zorder()+1)
|
|
# ax[0][1].patch.set_visible(False)
|
|
# ax1.set_yticklabels([])
|
|
# ax1.set_yticks([])
|
|
# # Plot winner physical contacts
|
|
# ax[0][2].plot(time, winner_cc_physical, color='tab:blue', zorder=100)
|
|
# ax2 = ax[0][2].twinx()
|
|
# ax2.eventplot(np.array([winner_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
|
|
# ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
|
|
# ax[0][2].set_zorder(ax2.get_zorder()+1)
|
|
# ax[0][2].patch.set_visible(False)
|
|
# ax2.set_yticklabels([])
|
|
# ax2.set_yticks([])
|
|
# # Plot loser chasing onsets
|
|
# ax[1][0].set_ylabel('Chirp rate [Hz]')
|
|
# ax[1][0].plot(time, loser_cc_onset, color='tab:blue', zorder=100)
|
|
# ax3 = ax[1][0].twinx()
|
|
# ax3.eventplot(np.array([loser_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
|
|
# ax3.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
|
|
# ax[1][0].set_zorder(ax3.get_zorder()+1)
|
|
# ax[1][0].patch.set_visible(False)
|
|
# ax3.set_yticklabels([])
|
|
# ax3.set_yticks([])
|
|
# # Plot loser chasing offsets
|
|
# ax[1][1].plot(time, loser_cc_offset, color='tab:blue', zorder=100)
|
|
# ax4 = ax[1][1].twinx()
|
|
# ax4.eventplot(np.array([loser_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
|
|
# ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
|
|
# ax[1][1].set_zorder(ax4.get_zorder()+1)
|
|
# ax[1][1].patch.set_visible(False)
|
|
# ax4.set_yticklabels([])
|
|
# ax4.set_yticks([])
|
|
# # Plot loser physical contacts
|
|
# ax[1][2].plot(time, loser_cc_physical, color='tab:blue', zorder=100)
|
|
# ax5 = ax[1][2].twinx()
|
|
# ax5.eventplot(np.array([loser_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
|
|
# ax5.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
|
|
# ax[1][2].set_zorder(ax5.get_zorder()+1)
|
|
# ax[1][2].patch.set_visible(False)
|
|
# ax5.set_yticklabels([])
|
|
# ax5.set_yticks([])
|
|
# plt.show()
|
|
# plt.close()
|
|
|
|
# for i in range(len(fish_ids)):
|
|
# fish = fish_ids[i]
|
|
# chirps_temp = chirps[chirps_fish_ids == fish]
|
|
# print(fish)
|
|
|
|
#### Chirps around events, only losers, one recording ####
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Path to the data
|
|
datapath = "../data/mount_data/"
|
|
main(datapath)
|