neuralib.locomotion.position

class neuralib.locomotion.position.CircularPosition[source]

Position information in circular environment.

Dimension parameters:

P = Number of position points (after interpolation)

T = Number of trial(lap)

t: ndarray

1D time array in s. Array[float, P]

p: ndarray

1D position array in cm. Array[float, P]

d: ndarray

1D displacement array in cm. Array[float, P]

v: ndarray

1D velocity array in cm/s. Array[float, P]

trial_time_index: ndarray

1D time index for every trial. Array[int, T]

with_time_range(t0, t1)[source]

With specific time range

Parameters:

  • t0 (float) – time start

  • t1 (float) – time end

Returns:

A new instance of the class with updated subset of attributes (t, p, d, v) based on the time range.

Return type:

Self

with_run_mask1d(**kwargs)[source]

With only the running epoch. Note that shape will be changed (stationary epoch excluded)

Parameters:

kwargs – Additional keyword arguments to pass to running_mask1d function.

Returns:

A new instance of the class with updated subset of attributes (t, p, d, v) based on the mask generated.

Return type:

Self

property trial_array: ndarray

Trial number array as same shape as P

Returns:

Array[int, P]

static __new__(_cls, t, p, d, v, trial_time_index)

Create new instance of CircularPosition(t, p, d, v, trial_time_index)

Parameters:

  • t (ndarray)

  • p (ndarray)

  • d (ndarray)

  • v (ndarray)

  • trial_time_index (ndarray)

neuralib.locomotion.position.interp_pos1d(time, pos, *, sampling_rate=1000, norm_max_value=150, remove_nan=True, renew_trial_value=-100)[source]

Interpolate the raw position data

Dimension parameters:

P = Number of position points (Raw)

Parameters:

  • time (ndarray) – An array of time stamps corresponding to positional data. Array[float, P].

  • pos (ndarray) – An array of positional data (a.u, encoder or other hardware readout). Array[float, P].

  • sampling_rate (float) – The rate at which data points should be sampled, defaults to 1000.

  • norm_max_value (float) – The value by which to normalize the positional data, defaults to 150 (cm).

  • remove_nan (bool) – Flag indicating whether to remove NaN values from interpolated data, defaults to True.

  • renew_trial_value (float) – The value used to detect the overflow index which signifies trial renewals, defaults to -100.

Returns:

CircularPosition

Return type:

CircularPosition

neuralib.locomotion.position.speed_2d(xy, dt=1.0)[source]

Compute speed 2D movement

Parameters:

  • xy (ndarray) – 2D array coordinates in xy. Array[float, [N, 2]]

  • dt (float) – Time period between samples (for smoothing)

Returns:

Speed. Array[float, N]

Return type:

ndarray

neuralib.locomotion.position.direction_2d(xy, dt=1.0)[source]

Compute direction 2D movement

Parameters:

  • xy (ndarray) – 2D array coordinates in xy. Array[float, [N, 2]]

  • dt (float) – Time period between samples (for smoothing)

Returns:

Movement direction. Array[float, N]

Return type:

ndarray

neuralib.locomotion.position.interp_gap2d(t, xy, duration)[source]

UNSTABLE.

Interpolate over gaps in position record.

Parameters:

  • t (ndarray) – Time Vector. Array[float, N]

  • xy (ndarray) – 2D array coordinates in xy. Array[float, [N, 2]]

  • duration (float) – Maximum duration of a gap that should be interpolated over. same unit as t

Returns:

Corrected x,y coordinates. Array[float, [N, 2]]

Return type:

ndarray