# pwc¶

Graph.pwc(durations, values, time_dimension=0, *, name=None)

Creates a piecewise-constant function of time.

Parameters
• durations (np.ndarray (1D, real)) – The durations $$\{\delta t_n\}$$ of the $$N$$ constant segments.

• values (np.ndarray or Tensor) – The values $$\{v_n\}$$ of the function on the constant segments. The dimension corresponding to time_dimension must be the same length as durations. To create a batch of $$B_1 \times \ldots \times B_n$$ piecewise-constant tensors of shape $$D_1 \times \ldots \times D_m$$, provide this values parameter as an object of shape $$B_1\times\ldots\times B_n\times N\times D_1\times\ldots\times D_m$$.

• time_dimension (int, optional) – The axis along values corresponding to time. All dimensions that come before the time_dimension are batch dimensions: if there are $$n$$ batch dimensions, then time_dimension is also $$n$$. Defaults to 0, which corresponds to no batch. Note that you can pass a negative value to refer to the time dimension.

• name (str, optional) – The name of the node.

Returns

The piecewise-constant function of time $$v(t)$$, satisfying $$v(t)=v_n$$ for $$t_{n-1}\leq t\leq t_n$$, where $$t_0=0$$ and $$t_n=t_{n-1}+\delta t_n$$. If you provide a batch of values, the returned Pwc represents a corresponding batch of $$B_1 \times \ldots \times B_n$$ functions $$v(t)$$, each of shape $$D_1 \times \ldots \times D_m$$.

Return type

Pwc

pwc_operator()
pwc_signal()
pwc_sum()