braintools.input.Constant

class braintools.input.Constant(I_and_duration)

Generate constant input with specified durations.

Creates a piecewise constant input where each piece has a specific value and duration. This is similar to Section but uses (value, duration) pairs for convenience.

Parameters:

I_and_duration (Sequence[tuple]) – List of (value, duration) pairs. Each tuple specifies the current value and how long it should be maintained. Values can include units.

I_and_duration

The stored (value, duration) pairs.

Type:

Sequence[tuple]

duration

Total duration calculated as sum of all durations.

Type:

Quantity or float

See also

Section

Similar but with separate values and durations lists.

Step

Creates steps at specific time points.

Notes

Constant internally uses the functional constant API. The composable interface allows for easy combination with other inputs and transformations.

Examples

Simple two-phase protocol:

>>> const = Constant([
...     (0 * u.pA, 100 * u.ms),
...     (10 * u.pA, 200 * u.ms)
... ])
>>> array = const()

Multi-step current injection:

>>> # Incrementally increasing steps
>>> steps = Constant([
...     (0 * u.nA, 50 * u.ms),
...     (0.5 * u.nA, 50 * u.ms),
...     (1.0 * u.nA, 50 * u.ms),
...     (1.5 * u.nA, 50 * u.ms),
...     (0 * u.nA, 50 * u.ms),
... ])

Smooth transitions between levels:

>>> # Create sharp steps and smooth them
>>> const = Constant([
...     (0, 100),
...     (1, 100),
...     (0.5, 100),
...     (0, 100)
... ])
>>> smoothed = const.smooth(tau=20 * u.ms)

Combine with oscillations:

>>> from braintools.input import Sinusoidal
>>> baseline = Constant([(0.5, 500)])
>>> oscillation = Sinusoidal(0.2, 5 * u.Hz, 500)
>>> combined = baseline + oscillation

Create complex protocols:

>>> # Paired-pulse protocol
>>> protocol = Constant([
...     (0 * u.pA, 100 * u.ms),    # baseline
...     (5 * u.pA, 20 * u.ms),     # first pulse
...     (0 * u.pA, 50 * u.ms),     # inter-pulse interval
...     (5 * u.pA, 20 * u.ms),     # second pulse
...     (0 * u.pA, 100 * u.ms),    # recovery
... ])
>>> # Add noise for more realistic stimulation
>>> from braintools.input import WienerProcess
>>> noisy_protocol = protocol + WienerProcess(290 * u.ms, sigma=0.1)

Use transformations:

>>> # Scale amplitude
>>> scaled = const.scale(0.5)
>>>
>>> # Clip to physiological range
>>> clipped = const.clip(-80, 40)
>>>
>>> # Repeat pattern
>>> repeated = const.repeat(3)

__init__(I_and_duration)

Initialize constant input.

Parameters:

I_and_duration (Sequence[tuple]) – List of (value, duration) pairs.

Methods

__init__(I_and_duration)	Initialize constant 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 constant input array.
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.