pystra.dependence.copula.Copula#

Use it: User guide · Worked example · Theory

class Copula[source]#

Bases: object

Interface for continuous dependence on the unit cube.

Coordinates and returned samples follow the original marginal order. Subclasses provide cdf, logpdf, rosenblatt and inverse_rosenblatt. The Rosenblatt maps take/return uniform coordinates, not normal scores.

Methods

pdf

Evaluate the copula density at interior uniform coordinates.

rvs

Sample rows using a local NumPy random generator.
pdf(probabilities)[source]#

Evaluate the copula density at interior uniform coordinates.

Parameters:

probabilities (ArrayLike) – Marginal probabilities strictly between zero and one, in original order.

Returns:

Density for one point or one value per row of points.

Return type:

float | ndarray

Raises:

ValueError – If the coordinates have an invalid shape or lie outside the interior.

rvs(size=1, seed=None)[source]#

Sample rows using a local NumPy random generator.

Parameters:

  • size (int) – Nonnegative number of observations.

  • seed (int | integer | Sequence[int] | ndarray | SeedSequence | BitGenerator | Generator | None) – Input to numpy.random.default_rng. A supplied generator is reused and advances; other seeds construct a local generator.

Returns:

Uniform marginal coordinates with this copula’s dependence.

Return type:

ndarray