Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection
This commit is contained in:
commit
d4ee13d56b
@ -1,18 +0,0 @@
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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 flatten
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from modules.behaviour_handling import Behavior, correct_chasing_events, event_triggered_chirps
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logger = makeLogger(__name__)
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ps = PlotStyle()
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#### Goal: CTC & PTC for each winner and loser and for all winners and loser ####
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@ -1,8 +1,8 @@
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import os
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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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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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@ -14,42 +14,49 @@ 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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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(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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LED_on_time_BORIS = np.load(os.path.join(
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folder_path, 'LED_on_time.npy'), allow_pickle=True)
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self.time = np.load(os.path.join(
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folder_path, "times.npy"), allow_pickle=True)
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csv_filename = [f for f in os.listdir(folder_path) if f.endswith(
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'.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, 'chirp_ids.npy'), allow_pickle=True)
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self.chirps = np.load(os.path.join(
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folder_path, 'chirps.npy'), allow_pickle=True)
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self.chirps_ids = np.load(os.path.join(
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folder_path, 'chirp_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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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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setattr(self, key, np.array(
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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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@ -58,6 +65,7 @@ class Behavior:
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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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@ -87,16 +95,17 @@ temporal encpding needs to be corrected ... not exactly 25FPS.
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def correct_chasing_events(
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category: np.ndarray,
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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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) -> 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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wrong_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0]
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wrong_bh = np.arange(len(category))[
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category != 2][:-1][np.diff(category[category != 2]) == 0]
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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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@ -105,7 +114,6 @@ def correct_chasing_events(
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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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@ -115,21 +123,22 @@ def correct_chasing_events(
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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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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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) -> 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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# 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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@ -138,25 +147,28 @@ def event_triggered_chirps(
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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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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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centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
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centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
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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 = (acausal_kde1d(
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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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foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
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foldernames = [
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datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
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nrecording_chirps = []
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nrecording_chirps_fish_ids = []
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@ -171,7 +183,7 @@ def main(datapath: str):
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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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@ -193,14 +205,12 @@ def main(datapath: str):
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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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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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@ -222,14 +232,18 @@ def main(datapath: str):
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physical_contacts = nrecording_physicals[i]
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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, recording_width)
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_, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(
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chasing_onsets, chirps, time_before_event, time_after_event, dt, recording_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, recording_width)
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_, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(
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chasing_offsets, chirps, time_before_event, time_after_event, dt, recording_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, recording_width)
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_, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(
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physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width)
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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_offset_chirps.append(
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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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@ -252,7 +266,6 @@ def main(datapath: str):
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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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@ -260,7 +273,6 @@ def main(datapath: str):
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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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@ -307,7 +319,7 @@ def main(datapath: str):
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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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@ -319,9 +331,12 @@ def main(datapath: str):
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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(np.sort(flatten(nrecording_centered_offset_chirps)))
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diff_physical = np.diff(np.sort(flatten(nrecording_centered_physical_chirps)))
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diff_onset = np.diff(
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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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diff_physical = np.diff(
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np.sort(flatten(nrecording_centered_physical_chirps)))
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np.random.shuffle(diff_onset)
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shuffled_onset = np.cumsum(diff_onset)
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@ -330,7 +345,7 @@ def main(datapath: str):
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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_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))/(27*100)
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@ -339,16 +354,18 @@ def main(datapath: str):
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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(bootstrap_onset, [5, 50, 95], axis=0)
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offset_q5, offset_median, offset_q95 = np.percentile(bootstrap_offset, [5, 50, 95], axis=0)
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physical_q5, physical_median, physical_q95 = np.percentile(bootstrap_physical, [5, 50, 95], axis=0)
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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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offset_q5, offset_median, offset_q95 = np.percentile(
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bootstrap_offset, [5, 50, 95], axis=0)
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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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# 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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@ -356,27 +373,37 @@ def main(datapath: str):
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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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# 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(nrecording_chasing_onsets).ravel() # not centered
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all_offsets = np.concatenate(nrecording_chasing_offsets).ravel() # not centered
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all_physicals = np.concatenate(nrecording_physicals).ravel() # not centered
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all_onsets = np.concatenate(
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nrecording_chasing_onsets).ravel() # not centered
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all_offsets = np.concatenate(
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nrecording_chasing_offsets).ravel() # not centered
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all_physicals = np.concatenate(
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nrecording_physicals).ravel() # not centered
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# Flatten all chirps around events
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all_onset_chirps = np.concatenate(nrecording_centered_onset_chirps).ravel() # centered
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all_offset_chirps = np.concatenate(nrecording_centered_offset_chirps).ravel() # centered
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all_physical_chirps = np.concatenate(nrecording_centered_physical_chirps).ravel() # centered
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all_onset_chirps = np.concatenate(
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nrecording_centered_onset_chirps).ravel() # centered
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all_offset_chirps = np.concatenate(
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nrecording_centered_offset_chirps).ravel() # centered
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all_physical_chirps = np.concatenate(
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nrecording_centered_physical_chirps).ravel() # centered
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# Convolute all chirps
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# Divide by total number of each event over all recordings
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all_onset_chirps_convolved = (acausal_kde1d(all_onset_chirps, time, width)) / len(all_onsets)
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all_offset_chirps_convolved = (acausal_kde1d(all_offset_chirps, time, width)) / len(all_offsets)
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all_physical_chirps_convolved = (acausal_kde1d(all_physical_chirps, time, width)) / len(all_physicals)
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all_onset_chirps_convolved = (acausal_kde1d(
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all_onset_chirps, time, width)) / len(all_onsets)
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all_offset_chirps_convolved = (acausal_kde1d(
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all_offset_chirps, time, width)) / len(all_offsets)
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all_physical_chirps_convolved = (acausal_kde1d(
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all_physical_chirps, time, width)) / len(all_physicals)
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# Plot all events with all shuffled
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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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fig, ax = plt.subplots(1, 3, figsize=(
|
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28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
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# offsets = np.arange(1,28,1)
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ax[0].set_xlabel('Time[s]')
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@ -384,8 +411,10 @@ def main(datapath: str):
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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)
|
||||
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)
|
||||
@ -400,8 +429,10 @@ def main(datapath: str):
|
||||
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)
|
||||
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)
|
||||
@ -416,8 +447,10 @@ def main(datapath: str):
|
||||
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)
|
||||
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)
|
||||
@ -425,14 +458,15 @@ def main(datapath: str):
|
||||
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].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):
|
||||
@ -444,7 +478,6 @@ def main(datapath: str):
|
||||
# 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]]
|
||||
@ -453,7 +486,6 @@ def main(datapath: str):
|
||||
# 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')
|
||||
@ -462,7 +494,7 @@ def main(datapath: str):
|
||||
# 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:
|
||||
@ -546,7 +578,6 @@ def main(datapath: str):
|
||||
# ax5.set_yticks([])
|
||||
# plt.show()
|
||||
# plt.close()
|
||||
|
||||
|
||||
# for i in range(len(fish_ids)):
|
||||
# fish = fish_ids[i]
|
||||
@ -556,7 +587,6 @@ def main(datapath: str):
|
||||
#### Chirps around events, only losers, one recording ####
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# Path to the data
|
||||
datapath = '../data/mount_data/'
|
||||
|
||||
@ -7,21 +7,12 @@ from IPython import embed
|
||||
# check rec ../data/mount_data/2020-03-25-10_00/ starting at 3175
|
||||
|
||||
|
||||
def main(datapaths):
|
||||
|
||||
for path in datapaths:
|
||||
chirpdetection(path, plot='show')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
dataroot = '../data/mount_data/'
|
||||
def get_valid_datasets(dataroot):
|
||||
|
||||
datasets = sorted([name for name in os.listdir(dataroot) if os.path.isdir(
|
||||
os.path.join(dataroot, name))])
|
||||
|
||||
valid_datasets = []
|
||||
|
||||
for dataset in datasets:
|
||||
|
||||
path = os.path.join(dataroot, dataset)
|
||||
@ -43,9 +34,25 @@ if __name__ == '__main__':
|
||||
datapaths = [os.path.join(dataroot, dataset) +
|
||||
'/' for dataset in valid_datasets]
|
||||
|
||||
return datapaths, valid_datasets
|
||||
|
||||
|
||||
def main(datapaths):
|
||||
|
||||
for path in datapaths:
|
||||
chirpdetection(path, plot='show')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
dataroot = '../data/mount_data/'
|
||||
|
||||
|
||||
datapaths, valid_datasets= get_valid_datasets(dataroot)
|
||||
|
||||
recs = pd.DataFrame(columns=['recording'], data=valid_datasets)
|
||||
recs.to_csv('../recs.csv', index=False)
|
||||
datapaths = ['../data/mount_data/2020-03-25-10_00/']
|
||||
# datapaths = ['../data/mount_data/2020-03-25-10_00/']
|
||||
main(datapaths)
|
||||
|
||||
# window 1524 + 244 in dataset index 4 is nice example
|
||||
|
||||
@ -1,14 +1,10 @@
|
||||
import numpy as np
|
||||
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
import os
|
||||
from IPython import embed
|
||||
|
||||
|
||||
from pandas import read_csv
|
||||
from modules.logger import makeLogger
|
||||
from modules.datahandling import causal_kde1d, acausal_kde1d
|
||||
from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
|
||||
|
||||
|
||||
logger = makeLogger(__name__)
|
||||
@ -19,46 +15,60 @@ class Behavior:
|
||||
Attributes
|
||||
----------
|
||||
behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
|
||||
behavior_type:
|
||||
behavioral_category:
|
||||
comment_start:
|
||||
comment_stop:
|
||||
dataframe: pandas dataframe with all the data
|
||||
duration_s:
|
||||
media_file:
|
||||
observation_date:
|
||||
observation_id:
|
||||
start_s: start time of the event in seconds
|
||||
stop_s: stop time of the event in seconds
|
||||
total_length:
|
||||
behavior_type:
|
||||
behavioral_category:
|
||||
comment_start:
|
||||
comment_stop:
|
||||
dataframe: pandas dataframe with all the data
|
||||
duration_s:
|
||||
media_file:
|
||||
observation_date:
|
||||
observation_id:
|
||||
start_s: start time of the event in seconds
|
||||
stop_s: stop time of the event in seconds
|
||||
total_length:
|
||||
"""
|
||||
|
||||
def __init__(self, folder_path: str) -> None:
|
||||
|
||||
LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
|
||||
LED_on_time_BORIS = np.load(os.path.join(
|
||||
folder_path, 'LED_on_time.npy'), allow_pickle=True)
|
||||
|
||||
csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
|
||||
logger.info(f'CSV file: {csv_filename}')
|
||||
self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
|
||||
csv_filename = os.path.split(folder_path[:-1])[-1]
|
||||
csv_filename = '-'.join(csv_filename.split('-')[:-1]) + '.csv'
|
||||
# embed()
|
||||
|
||||
self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
|
||||
self.chirps_ids = np.load(os.path.join(folder_path, 'chirp_ids.npy'), allow_pickle=True)
|
||||
# csv_filename = [f for f in os.listdir(
|
||||
# folder_path) if f.endswith('.csv')][0]
|
||||
# logger.info(f'CSV file: {csv_filename}')
|
||||
self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
|
||||
|
||||
self.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True)
|
||||
self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True)
|
||||
self.freq = np.load(os.path.join(folder_path, 'fund_v.npy'), allow_pickle=True)
|
||||
self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
|
||||
self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True)
|
||||
self.chirps = np.load(os.path.join(
|
||||
folder_path, 'chirps.npy'), allow_pickle=True)
|
||||
self.chirps_ids = np.load(os.path.join(
|
||||
folder_path, 'chirp_ids.npy'), allow_pickle=True)
|
||||
|
||||
self.ident = np.load(os.path.join(
|
||||
folder_path, 'ident_v.npy'), allow_pickle=True)
|
||||
self.idx = np.load(os.path.join(
|
||||
folder_path, 'idx_v.npy'), allow_pickle=True)
|
||||
self.freq = np.load(os.path.join(
|
||||
folder_path, 'fund_v.npy'), allow_pickle=True)
|
||||
self.time = np.load(os.path.join(
|
||||
folder_path, "times.npy"), allow_pickle=True)
|
||||
self.spec = np.load(os.path.join(
|
||||
folder_path, "spec.npy"), allow_pickle=True)
|
||||
|
||||
for k, key in enumerate(self.dataframe.keys()):
|
||||
key = key.lower()
|
||||
key = key.lower()
|
||||
if ' ' in key:
|
||||
key = key.replace(' ', '_')
|
||||
if '(' in key:
|
||||
key = key.replace('(', '')
|
||||
key = key.replace(')', '')
|
||||
setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
|
||||
|
||||
setattr(self, key, np.array(
|
||||
self.dataframe[self.dataframe.keys()[k]]))
|
||||
|
||||
last_LED_t_BORIS = LED_on_time_BORIS[-1]
|
||||
real_time_range = self.time[-1] - self.time[0]
|
||||
factor = 1.034141
|
||||
@ -68,16 +78,23 @@ class Behavior:
|
||||
|
||||
|
||||
def correct_chasing_events(
|
||||
category: np.ndarray,
|
||||
category: np.ndarray,
|
||||
timestamps: np.ndarray
|
||||
) -> tuple[np.ndarray, np.ndarray]:
|
||||
) -> tuple[np.ndarray, np.ndarray]:
|
||||
|
||||
onset_ids = np.arange(
|
||||
len(category))[category == 0]
|
||||
offset_ids = np.arange(
|
||||
len(category))[category == 1]
|
||||
|
||||
wrong_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0]
|
||||
wrong_bh = np.arange(len(category))[
|
||||
category != 2][:-1][np.diff(category[category != 2]) == 0]
|
||||
|
||||
if category[category != 2][-1] == 0:
|
||||
wrong_bh = np.append(
|
||||
wrong_bh,
|
||||
np.arange(len(category))[category != 2][-1])
|
||||
|
||||
if onset_ids[0] > offset_ids[0]:
|
||||
offset_ids = np.delete(offset_ids, 0)
|
||||
help_index = offset_ids[0]
|
||||
@ -92,49 +109,61 @@ def correct_chasing_events(
|
||||
len(category))[category == 1]
|
||||
|
||||
# Check whether on- or offset is longer and calculate length difference
|
||||
|
||||
if len(new_onset_ids) > len(new_offset_ids):
|
||||
len_diff = len(onset_ids) - len(offset_ids)
|
||||
logger.info(f'Onsets are greater than offsets by {len_diff}')
|
||||
embed()
|
||||
logger.warning('Onsets are greater than offsets')
|
||||
elif len(new_onset_ids) < len(new_offset_ids):
|
||||
len_diff = len(offset_ids) - len(onset_ids)
|
||||
logger.info(f'Offsets are greater than onsets by {len_diff}')
|
||||
logger.warning('Offsets are greater than onsets')
|
||||
elif len(new_onset_ids) == len(new_offset_ids):
|
||||
logger.info('Chasing events are equal')
|
||||
|
||||
# logger.info('Chasing events are equal')
|
||||
pass
|
||||
|
||||
return category, timestamps
|
||||
|
||||
|
||||
def event_triggered_chirps(
|
||||
event: np.ndarray,
|
||||
chirps:np.ndarray,
|
||||
def center_chirps(
|
||||
events: np.ndarray,
|
||||
chirps: np.ndarray,
|
||||
time_before_event: int,
|
||||
time_after_event: int,
|
||||
dt: float,
|
||||
width: float,
|
||||
)-> tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
# dt: float,
|
||||
# width: float,
|
||||
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
|
||||
event_chirps = [] # chirps that are in specified window around event
|
||||
centered_chirps = [] # timestamps of chirps around event centered on the event timepoint
|
||||
# timestamps of chirps around event centered on the event timepoint
|
||||
centered_chirps = []
|
||||
|
||||
for event_timestamp in events:
|
||||
|
||||
for event_timestamp in event:
|
||||
start = event_timestamp - time_before_event
|
||||
stop = event_timestamp + time_after_event
|
||||
chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
|
||||
event_chirps.append(chirps_around_event)
|
||||
|
||||
if len(chirps_around_event) == 0:
|
||||
continue
|
||||
else:
|
||||
centered_chirps.append(chirps_around_event - event_timestamp)
|
||||
|
||||
time = np.arange(-time_before_event, time_after_event, dt)
|
||||
|
||||
# Kernel density estimation with some if's
|
||||
if len(centered_chirps) == 0:
|
||||
centered_chirps = np.array([])
|
||||
centered_chirps_convolved = np.zeros(len(time))
|
||||
else:
|
||||
centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
|
||||
centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
|
||||
|
||||
return event_chirps, centered_chirps, centered_chirps_convolved
|
||||
|
||||
centered_chirps.append(chirps_around_event - event_timestamp)
|
||||
event_chirps.append(chirps_around_event)
|
||||
|
||||
centered_chirps = np.sort(flatten(centered_chirps))
|
||||
event_chirps = np.sort(flatten(event_chirps))
|
||||
|
||||
if len(centered_chirps) != len(event_chirps):
|
||||
raise ValueError(
|
||||
'Non centered chirps and centered chirps are not equal')
|
||||
|
||||
# time = np.arange(-time_before_event, time_after_event, dt)
|
||||
|
||||
# # Kernel density estimation with some if's
|
||||
# if len(centered_chirps) == 0:
|
||||
# centered_chirps = np.array([])
|
||||
# centered_chirps_convolved = np.zeros(len(time))
|
||||
# else:
|
||||
# # convert list of arrays to one array for plotting
|
||||
# centered_chirps = np.concatenate(centered_chirps, axis=0)
|
||||
# centered_chirps_convolved = (acausal_kde1d(
|
||||
# centered_chirps, time, width)) / len(event)
|
||||
|
||||
return centered_chirps
|
||||
|
||||
432
code/plot_kdes.py
Normal file
432
code/plot_kdes.py
Normal file
@ -0,0 +1,432 @@
|
||||
from extract_chirps import get_valid_datasets
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from tqdm import tqdm
|
||||
from IPython import embed
|
||||
from pandas import read_csv
|
||||
from modules.logger import makeLogger
|
||||
from modules.datahandling import flatten, causal_kde1d, acausal_kde1d
|
||||
from modules.behaviour_handling import (
|
||||
Behavior, correct_chasing_events, center_chirps)
|
||||
from modules.plotstyle import PlotStyle
|
||||
|
||||
logger = makeLogger(__name__)
|
||||
ps = PlotStyle()
|
||||
|
||||
|
||||
def jackknife(data, nresamples, subsetsize, kde_time, kernel_width):
|
||||
|
||||
if len(data) == 0:
|
||||
return []
|
||||
|
||||
jackknifed_kdes = []
|
||||
data = np.sort(data)
|
||||
subsetsize = int(np.round(len(data)*subsetsize))
|
||||
|
||||
for n in range(nresamples):
|
||||
|
||||
subset = np.random.choice(data, subsetsize, replace=False)
|
||||
subset_kde = acausal_kde1d(subset, time=kde_time, width=kernel_width)
|
||||
jackknifed_kdes.append(subset_kde)
|
||||
|
||||
return jackknifed_kdes
|
||||
|
||||
|
||||
def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before, time_after):
|
||||
|
||||
bootstrapped_kdes = []
|
||||
data = data[data <= 3*60*60] # only night time
|
||||
|
||||
if len(data) == 0:
|
||||
logger.info('No data for bootstrap, added zeros')
|
||||
return [np.zeros_like(kde_time) for i in range(nresamples)]
|
||||
|
||||
diff_data = np.diff(np.sort(data), prepend=np.sort(data)[0])
|
||||
|
||||
for i in tqdm(range(nresamples)):
|
||||
|
||||
np.random.shuffle(diff_data)
|
||||
bootstrapped_data = np.cumsum(diff_data)
|
||||
bootstrap_data_centered = center_chirps(
|
||||
bootstrapped_data, event_times, time_before, time_after)
|
||||
bootstrapped_kde = acausal_kde1d(
|
||||
bootstrap_data_centered, time=kde_time, width=kernel_width)
|
||||
|
||||
bootstrapped_kdes.append(bootstrapped_kde)
|
||||
|
||||
return bootstrapped_kdes
|
||||
|
||||
|
||||
def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
|
||||
|
||||
foldername = folder_name.split('/')[-2]
|
||||
winner_row = order_meta_df[order_meta_df['recording'] == foldername]
|
||||
winner = winner_row['winner'].values[0].astype(int)
|
||||
winner_fish1 = winner_row['fish1'].values[0].astype(int)
|
||||
winner_fish2 = winner_row['fish2'].values[0].astype(int)
|
||||
|
||||
if winner > 0:
|
||||
if winner == winner_fish1:
|
||||
winner_fish_id = winner_row['rec_id1'].values[0]
|
||||
loser_fish_id = winner_row['rec_id2'].values[0]
|
||||
|
||||
elif winner == winner_fish2:
|
||||
winner_fish_id = winner_row['rec_id2'].values[0]
|
||||
loser_fish_id = winner_row['rec_id1'].values[0]
|
||||
|
||||
chirp_winner = Behavior.chirps[Behavior.chirps_ids == winner_fish_id]
|
||||
chirp_loser = Behavior.chirps[Behavior.chirps_ids == loser_fish_id]
|
||||
|
||||
return chirp_winner, chirp_loser
|
||||
return None, None
|
||||
|
||||
|
||||
def main(dataroot):
|
||||
|
||||
foldernames, _ = get_valid_datasets(dataroot)
|
||||
plot_all = False
|
||||
time_before = 60
|
||||
time_after = 60
|
||||
dt = 0.001
|
||||
kernel_width = 1
|
||||
kde_time = np.arange(-time_before, time_after, dt)
|
||||
nbootstraps = 2
|
||||
|
||||
meta_path = (
|
||||
'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
|
||||
meta = pd.read_csv(meta_path)
|
||||
meta['recording'] = meta['recording'].str[1:-1]
|
||||
|
||||
winner_onsets = []
|
||||
winner_offsets = []
|
||||
winner_physicals = []
|
||||
|
||||
loser_onsets = []
|
||||
loser_offsets = []
|
||||
loser_physicals = []
|
||||
|
||||
winner_onsets_boot = []
|
||||
winner_offsets_boot = []
|
||||
winner_physicals_boot = []
|
||||
|
||||
loser_onsets_boot = []
|
||||
loser_offsets_boot = []
|
||||
loser_physicals_boot = []
|
||||
|
||||
onset_count = 0
|
||||
offset_count = 0
|
||||
physical_count = 0
|
||||
|
||||
# Iterate over all recordings and save chirp- and event-timestamps
|
||||
for folder in tqdm(foldernames):
|
||||
|
||||
foldername = folder.split('/')[-2]
|
||||
# logger.info('Loading data from folder: {}'.format(foldername))
|
||||
|
||||
broken_folders = ['../data/mount_data/2020-05-12-10_00/']
|
||||
if folder in broken_folders:
|
||||
continue
|
||||
|
||||
bh = Behavior(folder)
|
||||
category, timestamps = correct_chasing_events(bh.behavior, bh.start_s)
|
||||
|
||||
winner, loser = get_chirp_winner_loser(folder, bh, meta)
|
||||
|
||||
if winner is None:
|
||||
continue
|
||||
|
||||
onsets = (timestamps[category == 0])
|
||||
offsets = (timestamps[category == 1])
|
||||
physicals = (timestamps[category == 2])
|
||||
|
||||
onset_count += len(onsets)
|
||||
offset_count += len(offsets)
|
||||
physical_count += len(physicals)
|
||||
|
||||
winner_onsets.append(center_chirps(
|
||||
winner, onsets, time_before, time_after))
|
||||
winner_offsets.append(center_chirps(
|
||||
winner, offsets, time_before, time_after))
|
||||
winner_physicals.append(center_chirps(
|
||||
winner, physicals, time_before, time_after))
|
||||
|
||||
loser_onsets.append(center_chirps(
|
||||
loser, onsets, time_before, time_after))
|
||||
loser_offsets.append(center_chirps(
|
||||
loser, offsets, time_before, time_after))
|
||||
loser_physicals.append(center_chirps(
|
||||
loser, physicals, time_before, time_after))
|
||||
|
||||
# bootstrap
|
||||
winner_onsets_boot.append(bootstrap(
|
||||
winner,
|
||||
nresamples=nbootstraps,
|
||||
kde_time=kde_time,
|
||||
kernel_width=kernel_width,
|
||||
event_times=onsets,
|
||||
time_before=time_before,
|
||||
time_after=time_after))
|
||||
winner_offsets_boot.append(bootstrap(
|
||||
winner,
|
||||
nresamples=nbootstraps,
|
||||
kde_time=kde_time,
|
||||
kernel_width=kernel_width,
|
||||
event_times=offsets,
|
||||
time_before=time_before,
|
||||
time_after=time_after))
|
||||
winner_physicals_boot.append(bootstrap(
|
||||
winner,
|
||||
nresamples=nbootstraps,
|
||||
kde_time=kde_time,
|
||||
kernel_width=kernel_width,
|
||||
event_times=physicals,
|
||||
time_before=time_before,
|
||||
time_after=time_after))
|
||||
|
||||
loser_onsets_boot.append(bootstrap(
|
||||
loser,
|
||||
nresamples=nbootstraps,
|
||||
kde_time=kde_time,
|
||||
kernel_width=kernel_width,
|
||||
event_times=onsets,
|
||||
time_before=time_before,
|
||||
time_after=time_after))
|
||||
loser_offsets_boot.append(bootstrap(
|
||||
loser,
|
||||
nresamples=nbootstraps,
|
||||
kde_time=kde_time,
|
||||
kernel_width=kernel_width,
|
||||
event_times=offsets,
|
||||
time_before=time_before,
|
||||
time_after=time_after))
|
||||
loser_physicals_boot.append(bootstrap(
|
||||
loser,
|
||||
nresamples=nbootstraps,
|
||||
kde_time=kde_time,
|
||||
kernel_width=kernel_width,
|
||||
event_times=physicals,
|
||||
time_before=time_before,
|
||||
time_after=time_after))
|
||||
|
||||
if plot_all:
|
||||
|
||||
winner_onsets_conv = acausal_kde1d(
|
||||
winner_onsets[-1], kde_time, kernel_width)
|
||||
winner_offsets_conv = acausal_kde1d(
|
||||
winner_offsets[-1], kde_time, kernel_width)
|
||||
winner_physicals_conv = acausal_kde1d(
|
||||
winner_physicals[-1], kde_time, kernel_width)
|
||||
|
||||
loser_onsets_conv = acausal_kde1d(
|
||||
loser_onsets[-1], kde_time, kernel_width)
|
||||
loser_offsets_conv = acausal_kde1d(
|
||||
loser_offsets[-1], kde_time, kernel_width)
|
||||
loser_physicals_conv = acausal_kde1d(
|
||||
loser_physicals[-1], kde_time, kernel_width)
|
||||
|
||||
fig, ax = plt.subplots(2, 3, figsize=(
|
||||
21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
|
||||
ax[0, 0].set_title(
|
||||
f"{foldername}, onsets {len(onsets)}, offsets {len(offsets)}, physicals {len(physicals)},winner {len(winner)}, looser {len(loser)} , onsets")
|
||||
ax[0, 0].plot(kde_time, winner_onsets_conv/len(onsets))
|
||||
ax[0, 1].plot(kde_time, winner_offsets_conv/len(offsets))
|
||||
ax[0, 2].plot(kde_time, winner_physicals_conv/len(physicals))
|
||||
ax[1, 0].plot(kde_time, loser_onsets_conv/len(onsets))
|
||||
ax[1, 1].plot(kde_time, loser_offsets_conv/len(offsets))
|
||||
ax[1, 2].plot(kde_time, loser_physicals_conv/len(physicals))
|
||||
|
||||
# # plot bootstrap lines
|
||||
# for kde in winner_onsets_boot[-1]:
|
||||
# ax[0, 0].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
# for kde in winner_offsets_boot[-1]:
|
||||
# ax[0, 1].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
# for kde in winner_physicals_boot[-1]:
|
||||
# ax[0, 2].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
# for kde in loser_onsets_boot[-1]:
|
||||
# ax[1, 0].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
# for kde in loser_offsets_boot[-1]:
|
||||
# ax[1, 1].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
# for kde in loser_physicals_boot[-1]:
|
||||
# ax[1, 2].plot(kde_time, kde/len(offsets),
|
||||
# color='gray')
|
||||
|
||||
# plot bootstrap percentiles
|
||||
ax[0, 0].fill_between(
|
||||
kde_time,
|
||||
np.percentile(winner_onsets_boot[-1], 5, axis=0)/len(onsets),
|
||||
np.percentile(winner_onsets_boot[-1], 95, axis=0)/len(onsets),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
ax[0, 1].fill_between(
|
||||
kde_time,
|
||||
np.percentile(winner_offsets_boot[-1], 5, axis=0)/len(offsets),
|
||||
np.percentile(
|
||||
winner_offsets_boot[-1], 95, axis=0)/len(offsets),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
ax[0, 2].fill_between(
|
||||
kde_time,
|
||||
np.percentile(
|
||||
winner_physicals_boot[-1], 5, axis=0)/len(physicals),
|
||||
np.percentile(
|
||||
winner_physicals_boot[-1], 95, axis=0)/len(physicals),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
ax[1, 0].fill_between(
|
||||
kde_time,
|
||||
np.percentile(loser_onsets_boot[-1], 5, axis=0)/len(onsets),
|
||||
np.percentile(loser_onsets_boot[-1], 95, axis=0)/len(onsets),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
ax[1, 1].fill_between(
|
||||
kde_time,
|
||||
np.percentile(loser_offsets_boot[-1], 5, axis=0)/len(offsets),
|
||||
np.percentile(loser_offsets_boot[-1], 95, axis=0)/len(offsets),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
ax[1, 2].fill_between(
|
||||
kde_time,
|
||||
np.percentile(
|
||||
loser_physicals_boot[-1], 5, axis=0)/len(physicals),
|
||||
np.percentile(
|
||||
loser_physicals_boot[-1], 95, axis=0)/len(physicals),
|
||||
color='gray',
|
||||
alpha=0.5)
|
||||
|
||||
ax[0, 0].plot(kde_time, np.median(winner_onsets_boot[-1], axis=0)/len(onsets),
|
||||
color='black', linewidth=2)
|
||||
ax[0, 1].plot(kde_time, np.median(winner_offsets_boot[-1], axis=0)/len(offsets),
|
||||
color='black', linewidth=2)
|
||||
ax[0, 2].plot(kde_time, np.median(winner_physicals_boot[-1], axis=0)/len(physicals),
|
||||
color='black', linewidth=2)
|
||||
ax[1, 0].plot(kde_time, np.median(loser_onsets_boot[-1], axis=0)/len(onsets),
|
||||
color='black', linewidth=2)
|
||||
ax[1, 1].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0)/len(offsets),
|
||||
color='black', linewidth=2)
|
||||
ax[1, 2].plot(kde_time, np.median(loser_physicals_boot[-1], axis=0)/len(physicals),
|
||||
color='black', linewidth=2)
|
||||
|
||||
ax[0, 0].set_xlim(-30, 30)
|
||||
plt.show()
|
||||
|
||||
winner_onsets = np.sort(flatten(winner_onsets))
|
||||
winner_offsets = np.sort(flatten(winner_offsets))
|
||||
winner_physicals = np.sort(flatten(winner_physicals))
|
||||
loser_onsets = np.sort(flatten(loser_onsets))
|
||||
loser_offsets = np.sort(flatten(loser_offsets))
|
||||
loser_physicals = np.sort(flatten(loser_physicals))
|
||||
|
||||
winner_onsets_conv = acausal_kde1d(
|
||||
winner_onsets, kde_time, kernel_width)
|
||||
winner_offsets_conv = acausal_kde1d(
|
||||
winner_offsets, kde_time, kernel_width)
|
||||
winner_physicals_conv = acausal_kde1d(
|
||||
winner_physicals, kde_time, kernel_width)
|
||||
loser_onsets_conv = acausal_kde1d(
|
||||
loser_onsets, kde_time, kernel_width)
|
||||
loser_offsets_conv = acausal_kde1d(
|
||||
loser_offsets, kde_time, kernel_width)
|
||||
loser_physicals_conv = acausal_kde1d(
|
||||
loser_physicals, kde_time, kernel_width)
|
||||
|
||||
winner_onsets_conv = winner_onsets_conv / onset_count
|
||||
winner_offsets_conv = winner_offsets_conv / offset_count
|
||||
winner_physicals_conv = winner_physicals_conv / physical_count
|
||||
loser_onsets_conv = loser_onsets_conv / onset_count
|
||||
loser_offsets_conv = loser_offsets_conv / offset_count
|
||||
loser_physicals_conv = loser_physicals_conv / physical_count
|
||||
|
||||
embed()
|
||||
|
||||
winner_onsets_boot = np.concatenate(
|
||||
winner_onsets_boot) / onset_count
|
||||
winner_offsets_boot = np.concatenate(
|
||||
winner_offsets_boot) / offset_count
|
||||
winner_physicals_boot = np.concatenate(
|
||||
winner_physicals_boot) / physical_count
|
||||
loser_onsets_boot = np.concatenate(
|
||||
loser_onsets_boot) / onset_count
|
||||
loser_offsets_boot = np.concatenate(
|
||||
loser_offsets_boot) / offset_count
|
||||
loser_physicals_boot = np.concatenate(
|
||||
loser_physicals_boot) / physical_count
|
||||
|
||||
percs = [5, 50, 95]
|
||||
winner_onsets_boot_quarts = np.percentile(
|
||||
winner_onsets_boot, percs, axis=0)
|
||||
winner_offsets_boot_quarts = np.percentile(
|
||||
winner_offsets_boot, percs, axis=0)
|
||||
winner_physicals_boot_quarts = np.percentile(
|
||||
winner_physicals_boot, percs, axis=0)
|
||||
loser_onsets_boot_quarts = np.percentile(
|
||||
loser_onsets_boot, percs, axis=0)
|
||||
loser_offsets_boot_quarts = np.percentile(
|
||||
loser_offsets_boot, percs, axis=0)
|
||||
loser_physicals_boot_quarts = np.percentile(
|
||||
loser_physicals_boot, percs, axis=0)
|
||||
|
||||
fig, ax = plt.subplots(2, 3, figsize=(
|
||||
21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
|
||||
|
||||
ax[0, 0].plot(kde_time, winner_onsets_conv)
|
||||
ax[0, 0].fill_between(kde_time,
|
||||
winner_onsets_boot_quarts[0],
|
||||
winner_onsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[0, 0].plot(kde_time, winner_onsets_boot_quarts[1], c=ps.black)
|
||||
|
||||
ax[0, 1].plot(kde_time, winner_offsets_conv)
|
||||
ax[0, 1].fill_between(kde_time,
|
||||
winner_offsets_boot_quarts[0],
|
||||
winner_offsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[0, 1].plot(kde_time, winner_offsets_boot_quarts[1], c=ps.black)
|
||||
|
||||
ax[0, 2].plot(kde_time, winner_physicals_conv)
|
||||
ax[0, 2].fill_between(kde_time,
|
||||
loser_physicals_boot_quarts[0],
|
||||
loser_physicals_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[0, 2].plot(kde_time, winner_physicals_boot_quarts[1], c=ps.black)
|
||||
|
||||
ax[1, 0].plot(kde_time, loser_onsets_conv)
|
||||
ax[1, 0].fill_between(kde_time,
|
||||
loser_onsets_boot_quarts[0],
|
||||
loser_onsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[1, 0].plot(kde_time, loser_onsets_boot_quarts[1], c=ps.black)
|
||||
|
||||
ax[1, 1].plot(kde_time, loser_offsets_conv)
|
||||
ax[1, 1].fill_between(kde_time,
|
||||
loser_offsets_boot_quarts[0],
|
||||
loser_offsets_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[1, 1].plot(kde_time, loser_offsets_boot_quarts[1], c=ps.black)
|
||||
|
||||
ax[1, 2].plot(kde_time, loser_physicals_conv)
|
||||
ax[1, 2].fill_between(kde_time,
|
||||
loser_physicals_boot_quarts[0],
|
||||
loser_physicals_boot_quarts[2],
|
||||
color=ps.gray,
|
||||
alpha=0.5)
|
||||
ax[1, 2].plot(kde_time, loser_physicals_boot_quarts[1], c=ps.black)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main('../data/mount_data/')
|
||||
BIN
poster/main.pdf
BIN
poster/main.pdf
Binary file not shown.
@ -23,7 +23,10 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
|
||||
with single - or physically separated - individuals.
|
||||
\begin{tikzfigure}[]
|
||||
\label{griddrawing}
|
||||
<<<<<<< HEAD
|
||||
=======
|
||||
\includegraphics[width=0.8\linewidth]{figs/introplot}
|
||||
>>>>>>> cdcf9564df07914cf57225de5a8bdaa642fbad0e
|
||||
\end{tikzfigure}
|
||||
}
|
||||
\myblock[TranspBlock]{Chirp detection}{
|
||||
@ -43,10 +46,10 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
|
||||
\noindent
|
||||
\begin{itemize}
|
||||
\setlength\itemsep{0.5em}
|
||||
\item Two fish compete for one hidding place in one tank,
|
||||
\item Two fish compete for one hidding place in one tank,
|
||||
\item Experiment had a 3 hour long darkphase and a 3 hour long light phase.
|
||||
\end{itemize}
|
||||
|
||||
|
||||
\noindent
|
||||
\begin{minipage}[c]{0.7\linewidth}
|
||||
\begin{tikzfigure}[]
|
||||
@ -57,8 +60,8 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
|
||||
\begin{minipage}[c]{0.2\linewidth}
|
||||
\begin{itemize}
|
||||
\setlength\itemsep{0.5em}
|
||||
\item Fish who won the competition chirped more often than the fish who lost.
|
||||
\item
|
||||
\item Fish who won the competition chirped more often than the fish who lost.
|
||||
\item
|
||||
\end{itemize}
|
||||
\end{minipage}
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user