gstools.random¶

GStools subpackage for random number generation.

Random Number Generator¶

RNG([seed])

A random number generator for different distributions and multiple streams.

Seed Generator¶

MasterRNG(seed)

Master random number generator for generating seeds.

Distribution factory¶

dist_gen([pdf_in, cdf_in, ppf_in])

Distribution Factory.

class gstools.random.MasterRNG(seed)[source]¶

Master random number generator for generating seeds.

Parameters

seed (int or None, optional) – The seed of the master RNG, if None, a random seed is used. Default: None

Attributes

seed: int: Seed of the master RNG.

Methods

__call__()

Return a random seed.

__call__()[source]¶: Return a random seed.

property seed¶

Seed of the master RNG.

The setter property not only saves the new seed, but also creates a new master RNG function with the new seed.

Type: int

class gstools.random.RNG(seed=None)[source]¶

A random number generator for different distributions and multiple streams.

Parameters

seed (int or None, optional) – The seed of the master RNG, if None, a random seed is used. Default: None

Attributes

random: numpy.random.RandomState: Randomstate.
seed: int: Seed of the master RNG.

Methods

`sample_dist`([pdf, cdf, ppf, size])	Sample from a distribution given by pdf, cdf and/or ppf.
`sample_ln_pdf`(ln_pdf[, size, sample_around, …])	Sample from a distribution given by ln(pdf).
`sample_sphere`(dim[, size])	Uniform sampling on a d-dimensional sphere.

sample_dist(pdf=None, cdf=None, ppf=None, size=None, **kwargs)[source]¶

Sample from a distribution given by pdf, cdf and/or ppf.

Parameters

pdf (callable or None, optional) – Probability density function of the given distribution, that takes a single argument Default: None
cdf (callable or None, optional) – Cumulative distribution function of the given distribution, that takes a single argument Default: None
ppf (callable or None, optional) – Percent point function of the given distribution, that takes a single argument Default: None
size (int or None, optional) – sample size. Default: None
**kwargs – Keyword-arguments that are forwarded to scipy.stats.rv_continuous.

Returns

samples – the samples from the given distribution

Return type

float or numpy.ndarray

Notes

At least pdf or cdf needs to be given.

sample_ln_pdf(ln_pdf, size=None, sample_around=1.0, nwalkers=50, burn_in=20, oversampling_factor=10)[source]¶

Sample from a distribution given by ln(pdf).

This algorithm uses the emcee.EnsembleSampler

Parameters

ln_pdf (callable) – The logarithm of the Probability density function of the given distribution, that takes a single argument
size (int or None, optional) – sample size. Default: None
sample_around (float, optional) – Starting point for initial guess Default: 1.
nwalkers (int, optional) – The number of walkers in the mcmc sampler. Used for the emcee.EnsembleSampler class. Default: 50
burn_in (int, optional) – Number of burn-in runs in the mcmc algorithm. Default: 20
oversampling_factor (int, optional) –
To guess the sample number needed for proper results, we use a factor for oversampling. The intern used sample-size is calculated by

sample_size = max(burn_in, (size/nwalkers)*oversampling_factor)

So at least, as much as the burn-in runs. Default: 10

sample_sphere(dim, size=None)[source]¶

Uniform sampling on a d-dimensional sphere.

Parameters

dim (int) – Dimension of the sphere. Just 1, 2, and 3 supported.
size (int, optional) – sample size

Returns

coord – x[, y[, z]] coordinates on the sphere with shape (dim, size)

Return type

numpy.ndarray

property random¶

Randomstate.

Get a stream to the numpy Random number generator. You can use this, to call any provided distribution from numpy.random.RandomState.

Type: numpy.random.RandomState

property seed¶

Seed of the master RNG.

The setter property not only saves the new seed, but also creates a new master RNG function with the new seed.

Type: int

gstools.random.dist_gen(pdf_in=None, cdf_in=None, ppf_in=None, **kwargs)[source]¶

Distribution Factory.

Parameters

pdf_in (callable or None, optional) – Proprobability distribution function of the given distribution, that takes a single argument Default: None
cdf_in (callable or None, optional) – Cumulative distribution function of the given distribution, that takes a single argument Default: None
ppf_in (callable or None, optional) – Percent point function of the given distribution, that takes a single argument Default: None
**kwargs – Keyword-arguments forwarded to scipy.stats.rv_continuous.

Returns

dist – The constructed distribution.

Return type

scipy.stats.rv_continuous

Notes

At least pdf or cdf needs to be given.