braintools.input.Clipped

class braintools.input.Clipped(input_obj, min_val=None, max_val=None)

Clipped version of an input.

Clips input values to stay within specified bounds, useful for modeling saturation or rectification.

Parameters:

• input_obj (Input) – The input to clip.

• min_val (float | None) – Minimum value. If None, no lower bound.

• max_val (float | None) – Maximum value. If None, no upper bound.

Examples

>>> ramp = Ramp(-2, 2, 200 * u.ms)
>>>
>>> # Clip to [0, 1] range
>>> saturated = Clipped(ramp, 0, 1)
>>>
>>> # Only lower bound (rectification)
>>> rectified = Clipped(ramp, min_val=0)
>>>
>>> # Only upper bound (saturation)
>>> capped = Clipped(ramp, max_val=1.5)
>>>
>>> # Usually created via clip() method
>>> saturated = ramp.clip(0, 1)

__init__(input_obj, min_val=None, max_val=None)

Initialize clipped input.

Parameters:

• input_obj (Input) – The input to clip.

• min_val (float | None) – Minimum value.

• max_val (float | None) – Maximum value.

Methods

__init__(input_obj[, min_val, max_val])	Initialize clipped input.
apply(func)	Apply a custom function to the input.
clip([min_val, max_val])	Clip the input values to a range.
generate()	Generate the clipped input.
repeat(n_times)	Repeat the input pattern n times.
scale(factor)	Scale the input by a factor.
shift(time_shift)	Shift the input in time.
smooth(tau)	Apply exponential smoothing to the input.

Attributes

dt	Get the time step from global environment.
n_steps	Get the number of time steps.
shape	Get the shape of the input array.