gemseo / algos / doe

lib_openturns module

OpenTUNRS DOE algorithms wrapper

class gemseo.algos.doe.lib_openturns.OpenTURNS

Bases: gemseo.algos.doe.doe_lib.DOELibrary

OpenTURNS library of DOE algorithms wrapping

Constructor Unless mentioned, DOE are normalized between [0,1]

ALGO_LIST = ['OT_SOBOL', 'OT_HASELGROVE', 'OT_REVERSE_HALTON', 'OT_HALTON', 'OT_FAURE', 'OT_AXIAL', 'OT_FACTORIAL', 'OT_MONTE_CARLO', 'OT_LHS', 'OT_LHSC', 'OT_RANDOM', 'OT_FULLFACT', 'OT_COMPOSITE', 'OT_SOBOL_INDICES']

CENTER_KEYWORD = 'centers'

DESC_LIST = ['Sobol sequence implemented in openTURNS library', 'Haselgrove sequence implemented in openTURNS library', 'Reverse Halton sequence implemented in openTURNS library', 'Halton sequence implemented in openTURNS library', 'Faure sequence implemented in openTURNS library', 'Axial design implemented in openTURNS library', 'Factorial design implemented in openTURNS library', 'Monte Carlo sequence implemented in openTURNS library', 'Latin Hypercube Sampling implemented in openTURNS library', 'Centered Latin Hypercube Sampling implemented in openTURNS library', 'Random sampling implemented in openTURNS library', 'Full factorial design implementedin openTURNS library', 'Composite design implemented in openTURNS library', 'Sobol indices']

DISTRIBUTION_DEFAULT = 'Uniform'

DISTRIBUTION_KEYWORD = 'distribution_name'

DISTRIBUTION_LIST = ['Arcsine', 'Beta', 'Dirichlet', 'Normal', 'TruncatedNormal', 'Triangular', 'Trapezoidal', 'Uniform']

DOE_SETTINGS_OPTIONS = ['levels', 'distribution_name', 'mu', 'sigma', 'start', 'end', 'centers']

END_KEYWORD = 'end'

LEVEL_KEYWORD = 'levels'

MEAN_KEYWORD = 'mu'

OT_ARCSINE = 'Arcsine'

OT_AXIAL = 'OT_AXIAL'

OT_AXIAL_DESC = 'Axial design implemented in openTURNS library'

OT_AXIAL_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Axial.html'

OT_BETA = 'Beta'

OT_COMPOSITE = 'OT_COMPOSITE'

OT_COMPOSITE_DESC = 'Composite design implemented in openTURNS library'

OT_COMPOSITE_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Composite.html'

OT_DIRICHLET = 'Dirichlet'

OT_DOC = 'http://openturns.github.io/openturns/master/user_manual/'

OT_FACTORIAL = 'OT_FACTORIAL'

OT_FACTORIAL_DESC = 'Factorial design implemented in openTURNS library'

OT_FACTORIAL_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Factorial.html'

OT_FAURE = 'OT_FAURE'

OT_FAURE_DESC = 'Faure sequence implemented in openTURNS library'

OT_FAURE_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.FaureSequence.html'

OT_FULLFACT = 'OT_FULLFACT'

OT_FULLFACT_DESC = 'Full factorial design implementedin openTURNS library'

OT_FULLFACT_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Box.html'

OT_HALTON = 'OT_HALTON'

OT_HALTON_DESC = 'Halton sequence implemented in openTURNS library'

OT_HALTON_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.HaltonSequence.html'

OT_HASEL = 'OT_HASELGROVE'

OT_HASEL_DESC = 'Haselgrove sequence implemented in openTURNS library'

OT_HASEL_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.HaselgroveSequence.html'

OT_LHS = 'OT_LHS'

OT_LHSC = 'OT_LHSC'

OT_LHSC_DESC = 'Centered Latin Hypercube Sampling implemented in openTURNS library'

OT_LHSC_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.LHS.html'

OT_LHS_DESC = 'Latin Hypercube Sampling implemented in openTURNS library'

OT_LHS_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.LHS.html'

OT_MC = 'OT_MONTE_CARLO'

OT_MC_DESC = 'Monte Carlo sequence implemented in openTURNS library'

OT_MC_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.RandomGenerator.html'

OT_NORMAL = 'Normal'

OT_RANDOM = 'OT_RANDOM'

OT_RANDOM_DESC = 'Random sampling implemented in openTURNS library'

OT_RANDOM_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.RandomGenerator.html'

OT_REVERSE_HALTON = 'OT_REVERSE_HALTON'

OT_REVERSE_HALTON_DESC = 'Reverse Halton sequence implemented in openTURNS library'

OT_REVERSE_HALTON_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.ReverseHaltonSequence.html'

OT_SOBOL = 'OT_SOBOL'

OT_SOBOL_DESC = 'Sobol sequence implemented in openTURNS library'

OT_SOBOL_INDICES = 'OT_SOBOL_INDICES'

OT_SOBOL_INDICES_DESC = 'Sobol indices'

OT_SOBOL_INDICES_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.SobolIndicesAlgorithm.html'

OT_SOBOL_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.SobolSequence.html'

OT_TRAPEZOIDAL = 'Trapezoidal'

OT_TRIANGULAR = 'Triangular'

OT_TRUNCNORMAL = 'TruncatedNormal'

OT_UNIFORM = 'Uniform'

START_KEYWORD = 'start'

STD_KEYWORD = 'sigma'

WEB_LIST = ['http://openturns.github.io/openturns/master/user_manual/_generated/openturns.SobolSequence.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.HaselgroveSequence.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.ReverseHaltonSequence.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.HaltonSequence.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.FaureSequence.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Axial.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Factorial.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.RandomGenerator.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.LHS.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.LHS.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.RandomGenerator.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Box.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Composite.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.SobolIndicesAlgorithm.html']

check_distribution_name(distribution_name)

Check that distribution is available

Parameters

distribution_name (string) – name of the distribution

create_composed_distributions()

Create a composed distribution from a list of distributions

create_distribution(distribution_name='Uniform', **options)

Create a distribution for all design vectors and add it to the list of distributions

Parameters

• distribution_name (str) – name of the distribution (Default value = “Uniform”)

• options (dict) – optional parameters

• options – OT distributions options

display_distributions_list()

Display list of distributions use or that will be used for DOE design based on LHS or Monte-Carlo methods

get_composed_distributions()

Returns the composed distributions

Returns

composed distributions

Return type

openturns.ComposedDistribution

get_distributions_list()

Accessor for distributions list

Returns

distribution list

Return type

list

static plot_distribution(distribution, show=False)

Plot the density PDF & the CDF (cumulative) of a given distribution

Parameters

• distribution (openturns.Distribution) – the distribution to plot

• show (bool) – show plot (Default value = False)