romtools.workflows.parameters#

Classes

GaussianParameter(parameter_name[, mean, std])

Normally distributed parameter

Parameter()

Abstract implementation

ScipyDistributionParameter(parameter_name, ...)

Random parameter with distribution described by a scipy.stats.rv_continuous object

StringParameter(parameter_name, value)

Constant string-valued parameter

TriangularParameter(parameter_name[, ...])

Random parameter with a triangular distribution

UniformParameter(parameter_name[, ...])

Uniformly distributed floating point
class romtools.workflows.parameters.GaussianParameter(parameter_name, mean=0, std=1)[source]#

Bases: Parameter

Normally distributed parameter

Parameters:

  • parameter_name (str)

  • mean (float)

  • std (float)

get_dimensionality()[source]#

Returns dimensionality of parameter for vector quantities. Returns 1 for scalar parameters

Return type:

int

get_name()[source]#

Returns parameter name

Return type:

str

scale_samples(uniform_dist_samples)[source]#

Generates samples from the desired distribution given a set of samples from a uniform distribution on (0,1)

uniform_dist_samples should be of shape (number_of_samples, self.get_dimensionality())

Returns np.array of the same shape

Parameters:

uniform_dist_samples (array)

Return type:

array

class romtools.workflows.parameters.Parameter[source]#

Bases: ABC

Abstract implementation

abstractmethod get_dimensionality()[source]#

Returns dimensionality of parameter for vector quantities. Returns 1 for scalar parameters

Return type:

int

abstractmethod get_name()[source]#

Returns parameter name

Return type:

str

abstractmethod scale_samples(uniform_dist_samples)[source]#

Generates samples from the desired distribution given a set of samples from a uniform distribution on (0,1)

uniform_dist_samples should be of shape (number_of_samples, self.get_dimensionality())

Returns np.array of the same shape

Return type:

array

class romtools.workflows.parameters.ScipyDistributionParameter(parameter_name, distribution, **kwargs)[source]#

Bases: Parameter

Random parameter with distribution described by a scipy.stats.rv_continuous object

Parameters:

  • parameter_name (str)

  • distribution (rv_continuous)

get_dimensionality()[source]#

Returns dimensionality of parameter for vector quantities. Returns 1 for scalar parameters

Return type:

int

get_name()[source]#

Returns parameter name

Return type:

str

scale_samples(uniform_dist_samples)[source]#

Generates samples from the desired distribution given a set of samples from a uniform distribution on (0,1)

uniform_dist_samples should be of shape (number_of_samples, self.get_dimensionality())

Returns np.array of the same shape

Parameters:

uniform_dist_samples (array)

Return type:

array

class romtools.workflows.parameters.StringParameter(parameter_name, value)[source]#

Bases: Parameter

Constant string-valued parameter

Parameters:

parameter_name (str)

get_dimensionality()[source]#

Returns dimensionality of parameter for vector quantities. Returns 1 for scalar parameters

Return type:

int

get_name()[source]#

Returns parameter name

Return type:

str

scale_samples(uniform_dist_samples)[source]#

Generates samples from the desired distribution given a set of samples from a uniform distribution on (0,1)

uniform_dist_samples should be of shape (number_of_samples, self.get_dimensionality())

Returns np.array of the same shape

Parameters:

uniform_dist_samples (array)

Return type:

array

class romtools.workflows.parameters.TriangularParameter(parameter_name, lower_bound=-1, peak=0, upper_bound=1)[source]#

Bases: Parameter

Random parameter with a triangular distribution

Parameters:

  • parameter_name (str)

  • lower_bound (float)

  • peak (float)

  • upper_bound (float)

get_dimensionality()[source]#

Returns dimensionality of parameter for vector quantities. Returns 1 for scalar parameters

Return type:

int

get_name()[source]#

Returns parameter name

Return type:

str

scale_samples(uniform_dist_samples)[source]#

Generates samples from the desired distribution given a set of samples from a uniform distribution on (0,1)

uniform_dist_samples should be of shape (number_of_samples, self.get_dimensionality())

Returns np.array of the same shape

Parameters:

uniform_dist_samples (array)

Return type:

array

class romtools.workflows.parameters.UniformParameter(parameter_name, lower_bound=0, upper_bound=1)[source]#

Bases: Parameter

Uniformly distributed floating point

Parameters:

  • parameter_name (str)

  • lower_bound (float)

  • upper_bound (float)

get_dimensionality()[source]#

Returns dimensionality of parameter for vector quantities. Returns 1 for scalar parameters

Return type:

int

get_name()[source]#

Returns parameter name

Return type:

str

scale_samples(uniform_dist_samples)[source]#

Generates samples from the desired distribution given a set of samples from a uniform distribution on (0,1)

uniform_dist_samples should be of shape (number_of_samples, self.get_dimensionality())

Returns np.array of the same shape

Parameters:

uniform_dist_samples (array)

Return type:

array