gnome.utilities.distributions

Classes that generate various types of probability distributions

Attributes

ALL_DISTS

Classes

DistributionBase	Base class for distributions, just to make it easier to know
UniformDistribution	Base class for distributions, just to make it easier to know
NormalDistribution	Base class for distributions, just to make it easier to know
LogNormalDistribution	Base class for distributions, just to make it easier to know
WeibullDistribution	Base class for distributions, just to make it easier to know
RayleighDistribution

Functions

get_distribution_by_name(dist_name)

Return a distribution object corresponding to its name

Module Contents

gnome.utilities.distributions.get_distribution_by_name(dist_name)

Return a distribution object corresponding to its name

note: this isn’t really helpful until / unless we standardize the initialization interface.

class gnome.utilities.distributions.DistributionBase(name=None, _appearance=None, *args, **kwargs)

Bases: gnome.gnomeobject.GnomeId

Base class for distributions, just to make it easier to know what is a distribution class

At them moment, all they need is a set_values method

NOTE: if possible, it would be good to have the same interface to all distributions. For example, see: the distributions in numpy.random. for example:

random.normal(loc=0.0, scale=1.0, size=None)

all distribution have a location and a scale, which have different meanings depending on the distribution.

abstract set_values(np_array)

class gnome.utilities.distributions.UniformDistribution(low=0.0, high=0.1, **kwargs)

Bases: DistributionBase

Base class for distributions, just to make it easier to know what is a distribution class

At them moment, all they need is a set_values method

NOTE: if possible, it would be good to have the same interface to all distributions. For example, see: the distributions in numpy.random. for example:

random.normal(loc=0.0, scale=1.0, size=None)

all distribution have a location and a scale, which have different meanings depending on the distribution.

Parameters:

• low – For the Uniform distribution, it is lower bound.

• high – For the Uniform distribution, it is upper bound.

low = 0.0

high = 0.1

set_values(np_array)

class gnome.utilities.distributions.NormalDistribution(mean=0.0, sigma=0.1, **kwargs)

Bases: DistributionBase

Base class for distributions, just to make it easier to know what is a distribution class

At them moment, all they need is a set_values method

NOTE: if possible, it would be good to have the same interface to all distributions. For example, see: the distributions in numpy.random. for example:

random.normal(loc=0.0, scale=1.0, size=None)

all distribution have a location and a scale, which have different meanings depending on the distribution.

Parameters:

• mean – The mean of the normal distribution

• sigma – The standard deviation of normal distribution

mean = 0.0

sigma = 0.1

set_values(np_array)

class gnome.utilities.distributions.LogNormalDistribution(mean=0.0, sigma=0.1, **kwargs)

Bases: DistributionBase

Base class for distributions, just to make it easier to know what is a distribution class

At them moment, all they need is a set_values method

NOTE: if possible, it would be good to have the same interface to all distributions. For example, see: the distributions in numpy.random. for example:

random.normal(loc=0.0, scale=1.0, size=None)

all distribution have a location and a scale, which have different meanings depending on the distribution.

Parameters:

• mean – The mean of the normal distribution

• sigma – The standard deviation of normal distribution

mean = 0.0

sigma = 0.1

set_values(np_array)

class gnome.utilities.distributions.WeibullDistribution(alpha=None, lambda_=1.0, min_=None, max_=None, **kwargs)

Bases: DistributionBase

Base class for distributions, just to make it easier to know what is a distribution class

At them moment, all they need is a set_values method

NOTE: if possible, it would be good to have the same interface to all distributions. For example, see: the distributions in numpy.random. for example:

random.normal(loc=0.0, scale=1.0, size=None)

all distribution have a location and a scale, which have different meanings depending on the distribution.

Parameters:

• alpha – The shape parameter ‘alpha’ - labeled as ‘a’ in numpy.random.weibull distribution

• lambda – The scale parameter for the distribution - required for 2-parameter weibull distribution (Rosin-Rammler).

NOTE: underscore names because min, max, and lambda are Python

builtins

alpha = None

lambda_ = 1.0

min_ = None

max_ = None

set_values(np_array)

class gnome.utilities.distributions.RayleighDistribution

classmethod sigma_from_wind(avg_speed)

classmethod pdf(x, sigma)

classmethod cdf(x, sigma)

classmethod quantile(f, sigma)

gnome.utilities.distributions.ALL_DISTS