# constant_pwc

Graph.constant_pwc(constant, duration, batch_dimension_count=0, *, name=None)

Create a piecewise-constant function of time that is constant over a specified duration.

### Parameters

• constant (number or np.ndarray or Tensor) – The value $c$ of the function on the constant segment. To create a batch of $B_1 \times \ldots \times B_n$ piecewise-constant functions of shape $D_1 \times \ldots \times D_m$, provide this constant parameter as an object of shape $B_1\times\ldots\times B_n\times D_1\times\ldots\times D_m$
• duration (float) – The duration $\tau$
• batch_dimension_count (int , optional) – The number of batch dimensions, $n$ in constant. If provided, the first $n$
• name (str or None , optional) – The name of the node.

### Returns

The constant function $f(t) = c$ (for $0\leq t\leq\tau$

### Return type

Pwc

Graph.constant_pwc_operator : Create constant Pwc operators.

Graph.constant_stf : Corresponding operation for Stfs.

Graph.pwc : Create piecewise-constant functions.

## Notes

For more information on Pwc nodes see the Working with time-dependent functions in Boulder Opal topic.

## Examples

Create a batched piecewise-constant function.

>>> constant = np.arange(12).reshape((2, 2, 3))
>>> graph.constant_pwc(
...     constant=constant, duration=0.1, batch_dimension_count=1, name="constant"
... )
<Pwc: name="constant", operation_name="constant_pwc", value_shape=(2, 3), batch_shape=(2,)>
>>> result = bo.execute_graph(graph=graph, output_node_names="constant")
>>> result["output"]["constant"]
{
'durations': array([0.1]),
'values': array([
[[[ 0.,  1.,  2.], [ 3.,  4.,  5.]]],
[[[ 6.,  7.,  8.], [ 9., 10., 11.]]]
]),
'time_dimension': 1
}

See more examples in the Simulate the dynamics of a single qubit using computational graphs tutorial.