# tanh_ramp_stf

signals.tanh_ramp_stf(center_time, ramp_duration, end_value, start_value=None)

Create an Stf representing a hyperbolic tangent ramp.

Parameters:
• center_time (float or Tensor) – The time at which the ramp has its greatest slope, $$t_0$$. It must either be a scalar or contain a single element.

• ramp_duration (float or Tensor) – The characteristic time for the hyperbolic tangent ramp, $$t_\mathrm{ramp}$$. It must either be a scalar or contain a single element.

• end_value (float or complex or Tensor) – The asymptotic value of the ramp towards $$t \to +\infty$$, $$a_+$$. It must either be a scalar or contain a single element.

• start_value (float or complex or Tensor, optional) – The asymptotic value of the ramp towards $$t \to -\infty$$, $$a_-$$. If passed, it must either be a scalar or contain a single element. Defaults to minus end_value.

Returns:

The sampleable hyperbolic tangent ramp.

Return type:

Stf

Graph.signals.linear_ramp_stf

Create an Stf representing a linear ramp.

boulderopal.signals.tanh_ramp

Create a Signal object representing a hyperbolic tangent ramp.

Graph.signals.tanh_ramp_pwc

Corresponding operation with Pwc output.

Graph.tanh

Calculate the element-wise hyperbolic tangent of an object.

Notes

The hyperbolic tangent ramp is defined as

$\mathop{\mathrm{Tanh}}(t) = \frac{a_+ + a_-}{2} + \frac{a_+ - a_-}{2} \tanh\left( \frac{t - t_0}{t_\mathrm{ramp}} \right) ,$

where the function’s asymptotic values $$a_\pm$$ are defined by:

$a_\pm := \lim_{t\to\pm\infty} \mathop{\mathrm{Tanh}}(t) ,$

and $$t_0$$ is related to $$t_\mathrm{ramp}$$ by:

$\left.\frac{{\rm d}\mathop{\mathrm{Tanh}}(t)}{{\rm d}t}\right|_{t=t_0} = \frac{ (a_+ - a_-)}{2 t_\mathrm{ramp}} .$

With the default value of start_value ($$a_-$$), the ramp expression simplifies to

$\mathop{\mathrm{Tanh}}(t) = A \tanh\left( \frac{t - t_0}{t_\mathrm{ramp}} \right) ,$

where $$A = a_+$$ is the end value (the start value is then $$-A$$).

Examples

Define a simple sampleable hyperbolic tangent ramp.

>>> tanh = graph.signals.tanh_ramp_stf(
...     center_time=0.4, ramp_duration=0.2, end_value=2, start_value=-1
... )
>>> graph.discretize_stf(tanh, duration=1, segment_count=5, name="tanh")
<Pwc: name="tanh", operation_name="discretize_stf", value_shape=(), batch_shape=()>
>>> result = bo.execute_graph(graph=graph, output_node_names="tanh")
>>> result["output"]["tanh"]
{'durations': array([0.2, 0.2, 0.2, 0.2, 0.2]),
'values': array([-0.85772238, -0.19317574,  1.19317574,  1.85772238,  1.97992145]),
'time_dimension': 0}


Define a hyperbolic tangent ramp with optimizable parameters.

>>> center_time = graph.optimizable_scalar(
...     lower_bound=0.25e-6, upper_bound=0.75e-6, name="center_time"
... )
>>> ramp_duration = graph.optimizable_scalar(
...     lower_bound=0.1e-6, upper_bound=0.3e-6, name="ramp_duration"
... )
>>> end_value = graph.optimizable_scalar(
...     lower_bound=0, upper_bound=3e6, name="end_value"
... )
>>> graph.signals.tanh_ramp_stf(
...     center_time=center_time, ramp_duration=ramp_duration, end_value=end_value
... )