lmoments3 Library documentation

Version 1.0

This library was designed to use L-moments to calculate optimal parameters for a number of distributions. This library extends a number of scipy distributions and provides some additional distributions frequently used in Extreme Value Analyses.

Name lmoments3 name scipy name Parameters
Exponential exp expon loc, scale
Gamma gam gamma a, loc, scale (The location parameter is not calculated using L-moments and assumed to be zero.)
Generalised Extreme Value gev genextreme c, loc, scale
Generalised Logistic glo n/a k, loc, scale
Generalised Normal gno n/a k, loc, scale
Generalised Pareto gpa genpareto c, loc, scale
Gumbel gum gumbel_r loc, scale
Kappa kap n/a k, h, loc, scale
Normal nor norm loc, scale
Pearson III pe3 pearson3 skew, loc, scale
Wakeby wak n/a beta, gamma, delta, loc, scale
Weibull wei weibull_min c, loc, scale

All distributions in the table above are included in the lmoments3.distr module.

L-moment estimation from sample data

The primary purpose of this library is to estimate L-moments from a sample dataset.

The function lmoments3.lmom_ratios(data, nmom)() takes an input list or numpy array data and the number of L-moments to estimate from the dataset.


>>> import lmoments3 as lm
>>> data = [2.0, 3.0, 4.0, 2.4, 5.5, 1.2, 5.4, 2.2, 7.1, 1.3, 1.5]
>>> lm.lmom_ratios(data, nmom=5)
[3.2363636363636363, 1.1418181818181818, 0.27388535031847133, 0.023354564755838598, -0.042462845010615709]

This returns the first five sample L-moments, in the structured as l1, l2, t3, t4, t5. Where t3..5 = l3..5 / l2.

Fitting distribution functions to sample data

Sample data can be fitted directly to statistical distribution functions using the lmoments3 library.

For example, using the gamma distribution:

>>> import lmoments3 as lm
>>> from lmoments3 import distr
>>> data = [2.0, 3.0, 4.0, 2.4, 5.5, 1.2, 5.4, 2.2, 7.1, 1.3, 1.5]
>>> paras = distr.gam.lmom_fit(data)
>>> paras
OrderedDict([('a', 2.295206110128833), ('loc', 0), ('scale', 1.4100535991436407)])

This returns the distribution’s parameters as an OrderedDict in the same order as a standard scipy list of distribution function parameters. The distribution parameters can be used, for example, like this:

>>> fitted_gam = distr.gam(**paras)
>>> median = fitted_gam.ppf(0.5)
>>> median

For full details of distribution function methods, see the scipy.stats documentation. Some useful methods include:

  • pdf: Probability density function
  • cdf: Cumulative distribution function
  • ppf: Inverse cumulative distribution function (also known as quantile function or percentage point function)
  • rvs: Random numbers generator

Computing L-moments from distribution parameters

The lmoments3 package provides two additional methods to compute the L-moments (λ1..n) or L-moment ratios (λ1, λ2, τ3..n) for a distribution with given parameters.


>>> distr.gam.lmom(nmom=3, **paras)
[3.2363636363636363, 1.1418181181569327, 0.24963415541016151]
>>> distr.gam.lmom_ratios(nmom=4, **paras)
[3.2363636363636363, 1.1418181181569327, 0.21862865148182167, 0.13877337951549581]

Or using the frozen distribution:

>>> moments = fitted_gam.lmom(nmom=3)
>>> ratios = fitted_gam.lmom_ratios(nmom=4)

Modified implementation of negative log likelihood function

nnlf(data, *args, **kwds)()

Calculates the Negative Log Likelihood. Provide data to calculate the negeative log likelihood. If no distribution parameters are provided, the scipy defaults of loc=0 and scale=1 are used.

Example: Calculate the Negative Log Likelihood of a Gamma distribution fitted to data:

>>> from lmoments3 import distr
>>> paras = distr.gam.lmom_fit(data)
>>> distr.gam.nnlf(data, **paras)

Example: Calculate the Negative Log Likelihood of a Gamma distribution with parameters 2.5 and 1.0 when fitted to data:

>>> from lmoments3 import distr
>>> from collections import OrderedDict
>>> distr.gam.nnlf(data, a=2.5, scale=1)

Other statistical methods

The lmoments3.stats module provides some additional statistical parametes to evaluate fitting of data to distribution function.

AIC(data, distr_name, distr_paras)()

Calculate the Akaike Information Criterion (AIC) using the chosen dataset and distribution.

Example: Calculate the Akaike Information Criterion for the weibull distribution using the input dataset data:

>>> from lmoments3 import stats, distr
>>> paras = {'loc': 0.67, 'scale': 2.71, 'c': 1.18}
>>> stats.AIC(data, 'wei', paras)

Functions AICc() and BIC() have a similar structure and calculate the corrected Akaike Information Criterion and the Bayesian Information Criterion respectively.