tidy3d.plugins.design.DesignSpace

class DesignSpace

Bases: Tidy3dBaseModel

Specification of a design problem / combination of several parameters + algorithm.

Parameters:

• parameters (Attribute: parameters) –

  Type	Tuple[Union[ParameterInt, ParameterFloat, ParameterAny], …]
  Default	= ()
  Description	Set of parameters defining the dimensions and allowed values for the design space.

• method (Attribute: method) –

  Type	Union[MethodMonteCarlo, MethodGrid, MethodRandom, MethodRandomCustom]
  Default
  Description	Specifications for the procedure used to explore the parameter space.

• name (Attribute: name) –

  Type	Optional[str]
  Default	= None
  Description	Optional name for the design space.

Example

>>> import tidy3d.plugins.design as tdd
>>> param = tdd.ParameterFloat(name="x", span=(0,1))
>>> method = tdd.MethodMonteCarlo(num_points=10)
>>> design_space = tdd.DesignSpace(parameters=[param], method=method)
>>> fn = lambda x: x**2
>>> result = design_space.run(fn)
>>> df = result.to_dataframe()
>>> im = df.plot()

Attributes

design_parameter_dict	Mapping of design parameter name to design parameter.
dims	dimensions defined by the design parameter names.
method	Defines the methods used for parameter sweep.

Methods

get_fn_source(function)	Get the function source as a string, return None if not available.
run(function, **kwargs)	Run the design problem on a user defined function of the design parameters.
run_batch(fn_pre, fn_post[, path_dir])	Run a design problem where the function is split into pre and post processing steps.

parameters

method

name

property dims

dimensions defined by the design parameter names.

property design_parameter_dict

Mapping of design parameter name to design parameter.

static get_fn_source(function)

Get the function source as a string, return None if not available.

run(function, **kwargs)

Run the design problem on a user defined function of the design parameters.

Parameters:

function (Callable) – Function accepting arguments that correspond to the .name fields of the DesignSpace.parameters.

Returns:

Object containing the results of the design space exploration. Can be converted to pandas.DataFrame with .to_dataframe().

Return type:

Result

run_batch(fn_pre, fn_post, path_dir=None, **batch_kwargs)

Run a design problem where the function is split into pre and post processing steps.

Parameters:

• fn_pre (Callable[Union[SimulationData, List[SimulationData], Dict[str, SimulationData]], Any]) – Function accepting arguments that correspond to the .name fields of the DesignSpace.parameters. Returns either a Simulation, list of Simulation`s or a `dict of :class:`.Simulation`s to be run in a batch.

• fn_pre – Function accepting the SimulationData object(s) corresponding to the fn_pre and returning the result of the parameter sweep function. If fn_pre returns a single simulation, it will be passed as a single argument to fn_post. If fn_pre returns a list of simulations, their data will be passed as *args. If fn_pre returns a dict of simulations, their data will be passed as **kwargs with the keys corresponding to the argument names.

• path_dir (str = None) – Optional directory in which to store the batch results.

Returns:

Object containing the results of the design space exploration. Can be converted to pandas.DataFrame with .to_dataframe().

Return type:

Result

__hash__()

Hash method.