linear_ramp_pwc

signals.linear_ramp_pwc(duration, segment_count, end_value, start_value=None, start_time=0, end_time=None, segmentation=SegmentationType.UNIFORM, *, name=None)

Create a Pwc representing a linear ramp.

Parameters

  • duration (float) – The duration of the signal, TT.
  • segment_count (int) – The number of segments in the PWC.
  • end_value (float or complex or Tensor) – The value of the ramp at t=tendt = t_\mathrm{end}, aenda_\mathrm{end}. It must either be a scalar or contain a single element.
  • start_value (float or complex or Tensor or None , optional) – The value of the ramp at t=tstartt = t_\mathrm{start}, astarta_\mathrm{start}. If passed, it must either be a scalar or contain a single element. Defaults to −aend-a_\mathrm{end}.
  • start_time (float , optional) – The time at which the linear ramp starts, tstartt_\mathrm{start}. Defaults to 0.
  • end_time (float or None , optional) – The time at which the linear ramp ends, tendt_\mathrm{end}. Defaults to the given duration TT.
  • segmentation (SegmentationType) – The type of segmentation for the signal. With a “MINIMAL” segmentation, most of the segments are placed in the non-constant parts of the signal. Defaults to “UNIFORM”, in which case the segments are uniformly distributed along the signal’s duration.
  • name (str or None , optional) – The name of the node.

Returns

The sampled linear ramp.

Return type

Pwc

SEE ALSO

boulderopal.signals.linear_ramp : Create a Signal object representing a linear ramp.

Graph.signals.linear_ramp_stf : Corresponding operation with Stf output.

Graph.signals.tanh_ramp_pwc : Create a Pwc representing a hyperbolic tangent ramp.

Notes

The linear ramp is defined as

Linear(t)={astartift<tstartastart+(aend−astart)t−tstarttend−tstartiftstart≤t≤tendaendift>tend. \mathop{\mathrm{Linear}}(t) = \begin{cases} a_\mathrm{start} &\mathrm{if} \quad t < t_\mathrm{start}\\ a_\mathrm{start} + (a_\mathrm{end} - a_\mathrm{start}) \frac{t - t_\mathrm{start}}{t_\mathrm{end} - t_\mathrm{start}} &\mathrm{if} \quad t_\mathrm{start} \le t \le t_\mathrm{end} \\ a_\mathrm{end} &\mathrm{if} \quad t > t_\mathrm{end} \end{cases} .

Examples

Define a linear PWC ramp.

>>> graph.signals.linear_ramp_pwc(
...     duration=2.0, segment_count=5, end_value=1.5, start_value=0.5, name="linear_ramp"
... )
<Pwc: name="linear_ramp", operation_name="pwc_signal", value_shape=(), batch_shape=()>
>>> result = bo.execute_graph(graph=graph, output_node_names="linear_ramp")
>>> result["output"]["linear_ramp"]
{
    'durations': array([0.4, 0.4, 0.4, 0.4, 0.4]),
    'values': array([0.6, 0.8, 1. , 1.2, 1.4]),
    'time_dimension': 0
}

Define a linear ramp with start and end times.

>>> graph.signals.linear_ramp_pwc(
...     duration=4,
...     segment_count=8,
...     end_value=2,
...     start_time=1,
...     end_time=3,
...     name="linear",
... )
<Pwc: name="linear", operation_name="pwc_signal", value_shape=(), batch_shape=()>
>>> result = bo.execute_graph(graph=graph, output_node_names="linear")
>>> result["output"]["linear"]
{
    'durations': array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]),
    'values': array([-2. , -2. , -1.5, -0.5,  0.5,  1.5,  2. ,  2. ]),
    'time_dimension': 0
}

Define a linear ramp with minimal segmentation.

>>> graph.signals.linear_ramp_pwc(
...     duration=4,
...     segment_count=6,
...     end_value=2,
...     start_time=1,
...     end_time=3,
...     segmentation="MINIMAL",
...     name="linear",
... )
<Pwc: name="linear", operation_name="time_concatenate_pwc", value_shape=(), batch_shape=()>
>>> result = bo.execute_graph(graph=graph, output_node_names="linear")
>>> result["output"]["linear"]
{
    'durations': array([1. , 0.5, 0.5, 0.5, 0.5, 1. ]),
    'values': array([-2. , -1.5, -0.5,  0.5,  1.5,  2. ]),
    'time_dimension': 0
}

Define a linear ramp with an optimizable slope around 0.

>>> duration = 4.0
>>> slope = graph.optimizable_scalar(
...     lower_bound=-30, upper_bound=30, name="slope"
... )
>>> end_value = slope * duration / 2
>>> graph.signals.linear_ramp_pwc(
...     duration=duration, segment_count=64, end_value=end_value, name="linear_ramp"
... )
<Pwc: name="linear_ramp", operation_name="pwc_signal", value_shape=(), batch_shape=()>

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