172 lines
5.5 KiB
Python
172 lines
5.5 KiB
Python
import numpy as np
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import os
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from IPython import embed
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from pandas import read_csv
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from modules.logger import makeLogger
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from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
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logger = makeLogger(__name__)
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class Behavior:
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"""Load behavior data from csv file as class attributes
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Attributes
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----------
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behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
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behavior_type:
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behavioral_category:
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comment_start:
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comment_stop:
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dataframe: pandas dataframe with all the data
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duration_s:
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media_file:
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observation_date:
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observation_id:
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start_s: start time of the event in seconds
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stop_s: stop time of the event in seconds
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total_length:
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"""
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def __init__(self, folder_path: str) -> None:
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LED_on_time_BORIS = np.load(
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os.path.join(folder_path, "LED_on_time.npy"), allow_pickle=True
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)
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csv_filename = os.path.split(folder_path[:-1])[-1]
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csv_filename = "-".join(csv_filename.split("-")[:-1]) + ".csv"
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# embed()
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# csv_filename = [f for f in os.listdir(
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# folder_path) if f.endswith('.csv')][0]
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# logger.info(f'CSV file: {csv_filename}')
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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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self.ident = np.load(
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os.path.join(folder_path, "ident_v.npy"), allow_pickle=True
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)
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self.idx = np.load(
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os.path.join(folder_path, "idx_v.npy"), allow_pickle=True
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)
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self.freq = np.load(
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os.path.join(folder_path, "fund_v.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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self.spec = np.load(
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os.path.join(folder_path, "spec.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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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 category[category != 2][-1] == 0:
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wrong_bh = np.append(
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wrong_bh, np.arange(len(category))[category != 2][-1]
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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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new_onset_ids = np.arange(len(category))[category == 0]
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new_offset_ids = np.arange(len(category))[category == 1]
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# Check whether on- or offset is longer and calculate length difference
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if len(new_onset_ids) > len(new_offset_ids):
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embed()
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logger.warning("Onsets are greater than offsets")
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elif len(new_onset_ids) < len(new_offset_ids):
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logger.warning("Offsets are greater than onsets")
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elif len(new_onset_ids) == len(new_offset_ids):
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# logger.info('Chasing events are equal')
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pass
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return category, timestamps
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def center_chirps(
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events: 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 events:
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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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if len(chirps_around_event) == 0:
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continue
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centered_chirps.append(chirps_around_event - event_timestamp)
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event_chirps.append(chirps_around_event)
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centered_chirps = np.sort(flatten(centered_chirps))
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event_chirps = np.sort(flatten(event_chirps))
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if len(centered_chirps) != len(event_chirps):
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raise ValueError(
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"Non centered chirps and centered chirps are not equal"
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)
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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 = (acausal_kde1d(
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# centered_chirps, time, width)) / len(event)
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return centered_chirps
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