gstools.random¶

GStools subpackage for random number generation.

Random Number Generator¶

RNG([seed]) A random number generator for different distributions and multiple streams.

MasterRNG(seed) Master random number generator for generating seeds.

dist_gen([pdf_in, cdf_in, ppf_in]) Distribution Factory.

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.mtrand.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: 100
burn_in (int, optional) – Number of burn-in runs in the mcmc algorithm. Default: 100
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`

random¶

Randomstate.

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

Type:	`numpy.random.mtrand.RandomState`

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.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.

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.