ibllib.io.extractors.training_wheel

Functions

extract_all	Extract the wheel data.
extract_first_movement_times	Extracts the time of the first sufficiently large wheel movement for each trial.
extract_wheel_moves	Extract wheel positions and times from sync fronts dictionary
get_trial_start_times
get_wheel_position	Gets wheel timestamps and position from Bpod data.
infer_wheel_units	Given an array of wheel positions, infer the rotary encoder resolution, encoding type and units
sync_rotary_encoder

Classes

Wheel

Get wheel data from raw files and converts positions into radians mathematical convention

get_trial_start_times(session_path, data=None, task_collection='raw_behavior_data')

sync_rotary_encoder(session_path, bpod_data=None, re_events=None, task_collection='raw_behavior_data')

get_wheel_position(session_path, bp_data=None, display=False, task_collection='raw_behavior_data')

Gets wheel timestamps and position from Bpod data. Position is in radian (constant above for radius is 1) mathematical convention.

Parameters:

• session_path –

• (optional) (display) – bpod trials read from jsonable file

• (optional) – (bool)

Returns:

timestamps (np.array)

Returns:

positions (np.array)

infer_wheel_units(pos)

Given an array of wheel positions, infer the rotary encoder resolution, encoding type and units

The encoding type varies across hardware (Bpod uses X1 while FPGA usually extracted as X4), and older data were extracted in linear cm rather than radians.

Parameters:

pos – a 1D array of extracted wheel positions

Return units:

the position units, assumed to be either ‘rad’ or ‘cm’

Return resolution:

the number of decoded fronts per 360 degree rotation

Return encoding:

one of {‘X1’, ‘X2’, ‘X4’}

extract_wheel_moves(re_ts, re_pos, display=False)

Extract wheel positions and times from sync fronts dictionary

Parameters:

• re_ts – numpy array of rotary encoder timestamps

• re_pos – numpy array of rotary encoder positions

• display – bool: show the wheel position and velocity for full session with detected

movements highlighted :return: wheel_moves dictionary

extract_first_movement_times(wheel_moves, trials, min_qt=None)

Extracts the time of the first sufficiently large wheel movement for each trial. To be counted, the movement must occur between go cue / stim on and before feedback / response time. The movement onset is sometimes just before the cue (occurring in the gap between quiescence end and cue start, or during the quiescence period but sub- threshold). The movement is sufficiently large if it is greater than or equal to THRESH

Parameters:

wheel_moves – dictionary of detected wheel movement onsets and peak amplitudes for

use in extracting each trial’s time of first movement. :param trials: dictionary of trial data :param min_qt: the minimum quiescence period, if None a default is used :return: numpy array of first movement times, bool array indicating whether movement crossed response threshold, and array of indices for wheel_moves arrays

class Wheel(session_path=None)

Bases: BaseBpodTrialsExtractor

Get wheel data from raw files and converts positions into radians mathematical convention

(anti-clockwise = +) and timestamps into seconds relative to Bpod clock.

Optional: saves _ibl_wheel.times.npy and _ibl_wheel.position.npy

Times: Gets Rotary Encoder timestamps (us) for each position and converts to times. Synchronize with Bpod and outputs

Positions: Radians mathematical convention

save_names = ('_ibl_wheel.timestamps.npy', '_ibl_wheel.position.npy', '_ibl_wheelMoves.intervals.npy', '_ibl_wheelMoves.peakAmplitude.npy', None, '_ibl_trials.firstMovement_times.npy', None)

var_names = ('wheel_timestamps', 'wheel_position', 'wheelMoves_intervals', 'wheelMoves_peakAmplitude', 'peakVelocity_times', 'firstMovement_times', 'is_final_movement')

extract_all(session_path, bpod_trials=None, settings=None, save=False, task_collection='raw_behavior_data', save_path=None)

Extract the wheel data.

NB: Wheel extraction is now called through ibllib.io.training_trials.extract_all

Parameters:

• session_path (str, pathlib.Path) – The path to the session

• save (bool) – If true save the data files to ALF

• bpod_trials (list of dicts) – The Bpod trial dicts loaded from the _iblrig_taskData.raw dataset

• settings (dict) – The Bpod settings loaded from the _iblrig_taskSettings.raw dataset

Return type:

A list of extracted data and a list of file paths if save is True (otherwise None)