pystra.distributions.maximum.Maximum#

class Maximum(name, parent, N, input_type=None, startpoint=None)[source]#

Bases: Distribution

Distribution of maximima from the passed in parent distribution

Attributes:

  • name (str): Name of the random variable

  • mean (float): Mean

  • stdv (float): Standard deviation

  • parent (Distribution): Parent distribution object

  • N (float): Power to which distribution is raised

  • input_type (any): Change meaning of mean and stdv

  • startpoint (float): Start point for seach

Methods

cdf

Cumulative distribution function

getMean

getName

getStartPoint

getStdv

jacobian

Compute the Jacobian (e.g.

pdf

Probability density function

plot

Plots the PDF of the distribution

ppf

inverse cumulative distribution function

sample

Return a sample of the distribution of length n

setStartPoint

set_location

Updating the parent distribution location parameter.

set_scale

Updating the parent distribution scale parameter.

u_to_x

Transformation from u to x

update_stats

Updates the mean and stdv estimates - used for sensitivity analysis where the parent distribution params may change after instantiation

x_to_u

Transformation from x to u

zero_distn

Attributes

std_normal

pdf(x)[source]#

Probability density function

cdf(x)[source]#

Cumulative distribution function

ppf(p)[source]#

inverse cumulative distribution function

u_to_x(u)[source]#

Transformation from u to x

x_to_u(x)[source]#

Transformation from x to u

jacobian(u, x)[source]#

Compute the Jacobian (e.g. Lemaire, eq. 4.9)

set_location(loc=0)[source]#

Updating the parent distribution location parameter.

set_scale(scale=1)[source]#

Updating the parent distribution scale parameter.

update_stats()[source]#

Updates the mean and stdv estimates - used for sensitivity analysis where the parent distribution params may change after instantiation

plot(ax=None, **kwargs)#

Plots the PDF of the distribution

sample(n=1000)#

Return a sample of the distribution of length n