pwc_signal

Graph.pwc_signal(values, duration, *, name=None)

Create a piecewise-constant signal (scalar-valued function of time).

Use this function to create a piecewise-constant signal in which the constant segments all have the same duration.

Parameters

• values (np.ndarray or Tensor) – The values $\{\alpha_n\}$ of the $N$ constant segments. These can represent either a single sequence of segment values or a batch of them. To create a batch of $B_1 \times \ldots \times B_n$ signals, represent these values as a tensor of shape $B_1 \times \ldots \times B_n \times N$
• duration (float) – The total duration $\tau$
• name (str or None , optional) – The name of the node.

Returns

The piecewise-constant function of time $\alpha(t)$, satisfying $\alpha(t)=\alpha_n$ for $t_{n-1}\leq t\leq t_n$, where $t_n=n\tau/N$ (where $N$ is the number of values in $\{\alpha_n\}$). If you provide a batch of values, the returned Pwc represents a corresponding batch of $B_1 \times \ldots \times B_n$ functions $\alpha(t)$

Return type

Pwc

Graph.complex_pwc_signal : Create complex Pwc signals from their moduli and phases.

Graph.pwc : Corresponding operation with support for segments of different durations.

Graph.pwc_operator : Create Pwc operators.

Graph.pwc_sum : Sum multiple Pwcs.

Graph.symmetrize_pwc : Symmetrize Pwcs.

Notes

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

Examples

Create a piecewise-constant signal with uniform segment duration.

>>> graph.pwc_signal(duration=0.1, values=np.array([2, 3]), name="signal")
<Pwc: name="signal", operation_name="pwc_signal", value_shape=(), batch_shape=()>
>>> result = bo.execute_graph(graph=graph, output_node_names="signal")
>>> result["output"]["signal"]
{
'durations': array([0.05, 0.05]),
'values': array([2., 3.]),
'time_dimension': 0
}

See more examples in the Get familiar with graphs tutorial.