export functions in modules, plot chirp
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code/modules/behaviour_handling.py
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99
code/modules/behaviour_handling.py
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import numpy as np
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import os
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import numpy as np
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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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logger = makeLogger(__name__)
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class Behavior:
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"""Load behavior data from csv file as class attributes
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Attributes
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----------
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behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
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behavior_type:
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behavioral_category:
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comment_start:
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comment_stop:
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dataframe: pandas dataframe with all the data
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duration_s:
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media_file:
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observation_date:
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observation_id:
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start_s: start time of the event in seconds
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stop_s: stop time of the event in seconds
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total_length:
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"""
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def __init__(self, folder_path: str) -> None:
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LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
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csv_filename = [f for f in os.listdir(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(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.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True)
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self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True)
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self.freq = np.load(os.path.join(folder_path, 'fund_v.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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self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True)
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for k, key in enumerate(self.dataframe.keys()):
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key = key.lower()
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if ' ' in key:
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key = key.replace(' ', '_')
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if '(' in key:
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key = key.replace('(', '')
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key = key.replace(')', '')
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setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
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last_LED_t_BORIS = LED_on_time_BORIS[-1]
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real_time_range = self.time[-1] - self.time[0]
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factor = 1.034141
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shift = last_LED_t_BORIS - real_time_range * factor
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self.start_s = (self.start_s - shift) / factor
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self.stop_s = (self.stop_s - shift) / factor
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def correct_chasing_events(
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category: np.ndarray,
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timestamps: np.ndarray
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) -> tuple[np.ndarray, np.ndarray]:
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onset_ids = np.arange(
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len(category))[category == 0]
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offset_ids = np.arange(
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len(category))[category == 1]
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woring_bh = np.arange(len(category))[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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woring_bh = np.append(woring_bh, help_index)
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category = np.delete(category, woring_bh)
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timestamps = np.delete(timestamps, woring_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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@ -10,100 +10,13 @@ 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.behaviour_handling import Behavior, correct_chasing_events
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ps = PlotStyle()
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logger = makeLogger(__name__)
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class Behavior:
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"""Load behavior data from csv file as class attributes
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Attributes
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----------
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behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
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behavior_type:
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behavioral_category:
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comment_start:
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comment_stop:
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dataframe: pandas dataframe with all the data
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duration_s:
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media_file:
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observation_date:
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observation_id:
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start_s: start time of the event in seconds
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stop_s: stop time of the event in seconds
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total_length:
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"""
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def __init__(self, folder_path: str) -> None:
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LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
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csv_filename = [f for f in os.listdir(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(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.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True)
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self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True)
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self.freq = np.load(os.path.join(folder_path, 'fund_v.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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self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True)
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for k, key in enumerate(self.dataframe.keys()):
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key = key.lower()
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if ' ' in key:
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key = key.replace(' ', '_')
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if '(' in key:
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key = key.replace('(', '')
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key = key.replace(')', '')
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setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
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last_LED_t_BORIS = LED_on_time_BORIS[-1]
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real_time_range = self.time[-1] - self.time[0]
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factor = 1.034141
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shift = last_LED_t_BORIS - real_time_range * factor
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self.start_s = (self.start_s - shift) / factor
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self.stop_s = (self.stop_s - shift) / factor
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def correct_chasing_events(
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category: np.ndarray,
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timestamps: np.ndarray
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) -> tuple[np.ndarray, np.ndarray]:
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onset_ids = np.arange(
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len(category))[category == 0]
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offset_ids = np.arange(
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len(category))[category == 1]
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woring_bh = np.arange(len(category))[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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woring_bh = np.append(woring_bh, help_index)
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category = np.delete(category, woring_bh)
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timestamps = np.delete(timestamps, woring_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 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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@ -10,115 +10,22 @@ 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.behaviour_handling import Behavior, correct_chasing_events
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ps = PlotStyle()
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logger = makeLogger(__name__)
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class Behavior:
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"""Load behavior data from csv file as class attributes
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Attributes
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----------
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behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
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behavior_type:
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behavioral_category:
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comment_start:
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comment_stop:
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dataframe: pandas dataframe with all the data
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duration_s:
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media_file:
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observation_date:
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observation_id:
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start_s: start time of the event in seconds
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stop_s: stop time of the event in seconds
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total_length:
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"""
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def __init__(self, folder_path: str) -> None:
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LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
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csv_filename = [f for f in os.listdir(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(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
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self.chirps_ids = np.load(os.path.join(folder_path, 'chirps_ids.npy'), allow_pickle=True)
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self.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True)
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self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True)
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self.freq = np.load(os.path.join(folder_path, 'fund_v.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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self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True)
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for k, key in enumerate(self.dataframe.keys()):
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key = key.lower()
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if ' ' in key:
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key = key.replace(' ', '_')
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if '(' in key:
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key = key.replace('(', '')
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key = key.replace(')', '')
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setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
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last_LED_t_BORIS = LED_on_time_BORIS[-1]
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real_time_range = self.time[-1] - self.time[0]
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factor = 1.034141
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shift = last_LED_t_BORIS - real_time_range * factor
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self.start_s = (self.start_s - shift) / factor
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self.stop_s = (self.stop_s - shift) / factor
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def correct_chasing_events(
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category: np.ndarray,
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timestamps: np.ndarray
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) -> tuple[np.ndarray, np.ndarray]:
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onset_ids = np.arange(
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len(category))[category == 0]
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offset_ids = np.arange(
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len(category))[category == 1]
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# Check whether on- or offset is longer and calculate length difference
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if len(onset_ids) > len(offset_ids):
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len_diff = len(onset_ids) - len(offset_ids)
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longer_array = onset_ids
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shorter_array = offset_ids
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logger.info(f'Onsets are greater than offsets by {len_diff}')
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elif len(onset_ids) < len(offset_ids):
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len_diff = len(offset_ids) - len(onset_ids)
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longer_array = offset_ids
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shorter_array = onset_ids
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logger.info(f'Offsets are greater than offsets by {len_diff}')
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elif len(onset_ids) == len(offset_ids):
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logger.info('Chasing events are equal')
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return category, timestamps
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# Correct the wrong chasing events; delete double events
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wrong_ids = []
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for i in range(len(longer_array)-(len_diff+1)):
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if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]):
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pass
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else:
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wrong_ids.append(longer_array[i])
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longer_array = np.delete(longer_array, i)
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category = np.delete(
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category, wrong_ids)
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timestamps = np.delete(
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timestamps, wrong_ids)
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return category, timestamps
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def 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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for foldername in foldernames:
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if foldername == '../data/mount_data/2020-05-12-10_00/':
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continue
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# behabvior is pandas dataframe with all the data
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bh = Behavior(datapath)
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# chirps are not sorted in time (presumably due to prior groupings)
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# get and sort chirps and corresponding fish_ids of the chirps
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chirps = bh.chirps[np.argsort(bh.chirps)]
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chirps_fish_ids = bh.chirps_ids[np.argsort(bh.chirps)]
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bh = Behavior(foldername)
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category = bh.behavior
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timestamps = bh.start_s
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# Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
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chasing_offset = (timestamps[category == 1]/ 60) /60
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physical_contact = (timestamps[category == 2] / 60) /60
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all_fish_ids = np.unique(chirps_fish_ids)
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all_fish_ids = np.unique(bh.chirps_ids)
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fish1_id = all_fish_ids[0]
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fish2_id = all_fish_ids[1]
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# Associate chirps to inidividual fish
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fish1 = (chirps[chirps_fish_ids == fish1_id] / 60) /60
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fish2 = (chirps[chirps_fish_ids == fish2_id] / 60) /60
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fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) /60
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fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) /60
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fish1_color = ps.red
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fish2_color = ps.orange
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@ -190,6 +97,7 @@ def main(datapath: str):
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ax[3].set_ylabel('EODf')
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ax[3].set_xlabel('Time [h]')
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ax[0].set_title(foldername.split('/')[-2])
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plt.show()
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embed()
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@ -199,5 +107,5 @@ def main(datapath: str):
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if __name__ == '__main__':
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# Path to the data
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datapath = '../data/mount_data/2020-05-13-10_00/'
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datapath = '../data/mount_data/'
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main(datapath)
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