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coefficients by integration. We show\nhow to use the :class:`~openturns.CleaningStrategy` class.\n\n## Polynomial chaos expansion\n\nLet $g : \\mathcal{X} \\rightarrow \\mathbb{R}$ be a function\nwhere $\\mathcal{X} \\subseteq \\mathbb{R}^p$ is the domain of $g$.\nLet $f$ be a probability density function on $\\mathcal{X}$.\nLet $T$ be the iso-probabilistic transformation from the physical\nspace $\\mathcal{X}$ to the standard space $\\mathcal{\\bar{X}}$:\n\n\\begin{align}\\xi = T(\\boldsymbol{x}) \\in \\mathcal{\\bar{X}}\\end{align}\n\nfor any $\\boldsymbol{x} \\in \\mathcal{X}$.\nLet $h$ be the function defined by the equation :\n\n\\begin{align}h(\\boldsymbol{\\xi}) = \\left(g \\circ T^{-1}\\right)(\\boldsymbol{\\xi})\\end{align}\n\nfor any $\\boldsymbol{\\xi} \\in \\mathcal{\\bar{X}}$.\nThe polynomial chaos decomposition of $h$ is ([blatman2009]_ page 73) :\n\n\\begin{align}h(\\boldsymbol{\\xi}) = \\sum_{\\boldsymbol{\\alpha} \\in \\mathbb{N}^p}\n a_{\\boldsymbol{\\alpha}} \\psi_{\\boldsymbol{\\alpha}}(\\boldsymbol{\\xi}) + \\epsilon\\end{align}\n\nwhere $\\boldsymbol{\\alpha} = (\\alpha_1, ..., \\alpha_p) \\in \\mathbb{N}^p$\nis a multiindex, $a_{\\boldsymbol{\\alpha}} \\in \\mathbb{R}$ is the\ncoefficient, $\\psi_{\\boldsymbol{\\alpha}} : \\mathcal{\\bar{X}} \\rightarrow \\mathbb{R}$\nis a multivariate polynomial and $\\epsilon$ is a random variable.\n\n" + "The goal of this example is to show how to create a sparse polynomial chaos\nexpansion (PCE) when we estimate its coefficients by integration. We show\nhow to use the :class:`~openturns.CleaningStrategy` class.\n\n## Polynomial chaos expansion\n\nLet $g : \\mathcal{D} \\rightarrow \\mathbb{R}$ be a function\nwhere $\\mathcal{D} \\subseteq \\mathbb{R}^p$ is the domain of $g$.\nWe consider $\\vect{X}$ a random vector which\nprobability density function is denoted by $f$.\nWe assume that $g(\\vect{X})$ has a finite second order moment.\nLet $T$ be an iso-probabilistic transformation such that $\\vect{Z} = T(\\vect{X})$\nfollows a distribution uniquely defined by all its moments.\nLet $h$ be the function defined by:\n\n\\begin{align}h = g \\circ T^{-1}.\\end{align}\n\nThe polynomial chaos decomposition of $h$ with respect to the measure of\n$\\vect{Z}$ is (see [blatman2009]_ page 73) :\n\n\\begin{align}h(\\vect{z}) = \\sum_{\\vect{\\alpha} \\in \\mathbb{N}^p}\n a_{\\vect{\\alpha}} \\psi_{\\vect{\\alpha}}(\\vect{z})\\end{align}\n\nwhere $\\vect{\\alpha} = (\\alpha_1, ..., \\alpha_p) \\in \\mathbb{N}^p$\nis a multiindex, $a_{\\vect{\\alpha}} \\in \\mathbb{R}$ is the\ncoefficient, $\\psi_{\\vect{\\alpha}} : \\mathcal{\\bar{X}} \\rightarrow \\mathbb{R}$\nis a multivariate polynomial.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Truncated expansion\nIn practice, we cannot consider an infinite series and must truncate\nthe decomposition at a given order. Only a selection of coefficients must\nbe kept. This leads to a subset of all possible multiindices. In the\nremainder of this text, we call this selection the *multiindex set*.\n\nSeveral multiindex sets can be considered. A simple method is to truncate\nthe polynomial up to a given maximum total degree $d \\in \\mathbb{N}$.\nLet $\\mathcal{A}^{d}$ be the multi-index set defined by\n\n\\begin{align}\\mathcal{A}^{d} = \\left\\{ \\boldsymbol{\\alpha} \\in \\mathbb{N}^p\n \\; | \\; \\|\\boldsymbol{\\alpha}\\|_1 \\leq d\\right\\}\\end{align}\n\nwhere\n\n\\begin{align}\\|\\boldsymbol{\\alpha}\\|_d = \\alpha_1 + ... + \\alpha_p\\end{align}\n\nis the 1-norm of the multi-index $\\boldsymbol{\\alpha}$.\nTherefore, the truncated polynomial chaos expansion is:\n\n\\begin{align}h(\\boldsymbol{\\xi}) = \\sum_{\\boldsymbol{\\alpha} \\in \\mathcal{A}^{d}}\n a_{\\boldsymbol{\\alpha}} \\psi_{\\boldsymbol{\\alpha}}(\\boldsymbol{\\xi}) + \\epsilon.\\end{align}\n\nIn order to ensure a low error, we may choose a large value of the\nparameter $P$. This, however, leads to a large number of\ncoefficients $\\boldsymbol{\\alpha} \\in \\mathcal{A}^{d}$ to\nestimate. More precisely, the number of coefficients to estimate\nis ([blatman2009]_ page 73) :\n\n\\begin{align}\\textrm{card}\\left(\\mathcal{A}^{d}\\right) = {p + d \\choose d}\n = \\frac{(p + d)!}{p! d!}\\end{align}\n\nwhere $p!$ is the factorial number of $p$.\n\n" + "## Truncated expansion\nIn practice, we cannot consider an infinite series and must truncate\nthe decomposition at a given order. Only a selection of coefficients must\nbe kept. This leads to a subset of all possible multiindices. In the\nremainder of this text, we call this selection the *multiindex set*.\n\nSeveral multiindex sets can be considered. A simple method is to truncate\nthe polynomial up to a given maximum total degree $d \\in \\mathbb{N}$.\nLet $\\mathcal{A}^{d}$ be the multi-index set defined by\n\n\\begin{align}\\mathcal{A}^{d} = \\left\\{ \\vect{\\alpha} \\in \\mathbb{N}^p\n \\; | \\; \\|\\vect{\\alpha}\\|_1 \\leq d\\right\\}\\end{align}\n\nwhere\n\n\\begin{align}\\|\\vect{\\alpha}\\|_d = \\alpha_1 + ... + \\alpha_p\\end{align}\n\nis the 1-norm of the multi-index $\\vect{\\alpha}$.\nTherefore, the truncated polynomial chaos expansion is:\n\n\\begin{align}\\widetilde{h}(\\vect{z}) = \\sum_{\\vect{\\alpha} \\in \\mathcal{A}^{d}}\n a_{\\vect{\\alpha}} \\psi_{\\vect{\\alpha}}(\\vect{z}).\\end{align}\n\nIn order to ensure a low error, we may choose a large value of the\nparameter $P$. This, however, leads to a large number of\ncoefficients $\\vect{\\alpha} \\in \\mathcal{A}^{d}$ to\nestimate. More precisely, the number of coefficients to estimate\nis (see [blatman2009]_ page 73) :\n\n\\begin{align}\\textrm{card}\\left(\\mathcal{A}^{d}\\right) = {p + d \\choose d}\n = \\frac{(p + d)!}{p! d!}\\end{align}\n\nwhere $p!$ is the factorial number of $p$.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Low-rank polynomial chaos expansion\nFor any $\\boldsymbol{\\alpha} \\in \\mathbb{N}^p$, let\n$\\|\\boldsymbol{\\alpha}\\|_0$ be the rank of the multiindex, that is,\nthe number of nonzero components:\n\n\\begin{align}\\|\\boldsymbol{\\alpha}\\|_0 = \\sum_{i = 1}^p \\boldsymbol{1}_{\\alpha_i > 0}\\end{align}\n\nwhere $\\boldsymbol{1}$ is the indicator function.\nThe multiindex set of maximum total degree $d \\in \\mathbb{N}$\nand maximum rank $j \\in \\mathbb{N}$ is ([blatman2009]_ page 74):\n\n\\begin{align}\\mathcal{A}^{d,j} = \\left\\{ \\boldsymbol{\\alpha} \\in \\mathbb{N}^p\n \\; | \\; \\|\\boldsymbol{\\alpha}\\|_1 \\leq d, \\;\n \\; \\|\\boldsymbol{\\alpha}\\|_0 \\leq j\\right\\}.\\end{align}\n\nTherefore, the rank-`j` polynomial chaos expansion is:\n\n\\begin{align}h(\\boldsymbol{\\xi}) = \\sum_{\\boldsymbol{\\alpha} \\in\n \\mathcal{A}^{d,j}} a_{\\boldsymbol{\\alpha}}\n \\psi_{\\boldsymbol{\\alpha}}(\\boldsymbol{\\xi}) + \\epsilon.\\end{align}\n\nThe rank is now a hyperparameter of the model: [blatman2009]_ suggests\nto use $j = 2, 3, 4$. An example of low-rank PCE for the G-Sobol'\nfunction is given in [blatman2009]_ page 75.\n\n*Note.* It is currently not possible to create a low-rank PCE.\n\n" + "## Low-rank polynomial chaos expansion\nFor any $\\vect{\\alpha} \\in \\mathbb{N}^p$, let\n$\\|\\vect{\\alpha}\\|_0$ be the rank of the multiindex, that is,\nthe number of nonzero components:\n\n\\begin{align}\\|\\vect{\\alpha}\\|_0 = \\sum_{i = 1}^p \\vect{1}_{\\alpha_i > 0}\\end{align}\n\nwhere $\\vect{1}$ is the indicator function.\nThe multiindex set of maximum total degree $d \\in \\mathbb{N}$\nand maximum rank $j \\in \\mathbb{N}$ is ([blatman2009]_ page 74):\n\n\\begin{align}\\mathcal{A}^{d,j} = \\left\\{ \\vect{\\alpha} \\in \\mathbb{N}^p\n \\; | \\; \\|\\vect{\\alpha}\\|_1 \\leq d, \\;\n \\; \\|\\vect{\\alpha}\\|_0 \\leq j\\right\\}.\\end{align}\n\nTherefore, the rank-`j` polynomial chaos expansion is:\n\n\\begin{align}\\widetilde{h}(\\vect{z}) = \\sum_{\\vect{\\alpha} \\in\n \\mathcal{A}^{d,j}} a_{\\vect{\\alpha}}\n \\psi_{\\vect{\\alpha}}(\\vect{z}).\\end{align}\n\nThe rank is now a hyperparameter of the model: [blatman2009]_ suggests\nto use $j = 2, 3, 4$. An example of low-rank PCE for the G-Sobol'\nfunction is given in [blatman2009]_ page 75.\n\n*Note.* It is currently not possible to create a low-rank PCE.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Model selection\nIf $\\textrm{card}\\left(\\mathcal{A}^{d}\\right)$ is large, many coefficients\nmay be poorly estimated, which may reduce the quality of the metamodel. We may\nwant to select a subset of the coefficients which best predict the output.\nIn other words, we may compute a subset\n\n\\begin{align}\\mathcal{A} \\subseteq \\mathcal{A}^{d}\\end{align}\n\nsuch that ([blatman2009]_ page 86) :\n\n\\begin{align}h(\\boldsymbol{\\xi}) = \\sum_{\\boldsymbol{\\alpha} \\in \\mathcal{A}}\n a_{\\boldsymbol{\\alpha}} \\psi_{\\boldsymbol{\\alpha}}(\\boldsymbol{\\xi})\n + \\epsilon.\\end{align}\n\nAn enumeration rule is a function from the set of integers $k$ to\nthe corresponding set of multiindices $\\boldsymbol{\\alpha}$. More\nprecisely, let $r : \\mathbb{N} \\rightarrow \\mathbb{N}^p$ be the\nfunction such that :\n\n\\begin{align}r(k) = \\boldsymbol{\\alpha}\\end{align}\n\nfor any $k \\geq 0$.\nLet $K \\in \\mathbb{N}$ be a parameter representing the number of\ncoefficients considered in the selection. Given an enumeration rule for\nthe multiindices $\\boldsymbol{\\alpha}$, at most $K$ multiindices\nwill be considered. Let $\\mathcal{A}_K$ be the corresponding multiindex set :\n\n\\begin{align}\\mathcal{A}_K = \\left\\{ \\boldsymbol{\\alpha}\n \\; | \\; r^{-1}(\\boldsymbol{\\alpha}) = k \\leq K \\right\\}.\\end{align}\n\n\nLet $\\epsilon > 0$ be a parameter representing the minimum relative\nvalue of a significant coefficient $a_{\\boldsymbol{\\alpha}}$.\nThe :class:`~openturns.CleaningStrategy` uses the following criteria to select the coefficients :\n\n\\begin{align}\\mathcal{A}_\\epsilon =\n \\left\\{\n |a_{\\boldsymbol{\\alpha}}| \\geq \\epsilon \\max_{ a_{\\boldsymbol{\\alpha}}\n \\in \\mathcal{A}_K } |a_{\\boldsymbol{\\alpha}}| \\right\\}\\end{align}\n\nwhere $\\epsilon$ is the significance factor, which by default is\n$\\epsilon = 10^{-4}$. This rule selects only the coefficients which\nare significantly different from zero.\n\n" + "## Model selection\nIf $\\textrm{card}\\left(\\mathcal{A}^{d}\\right)$ is large, many coefficients\nmay be poorly estimated, which may reduce the quality of the metamodel. We may\nwant to select a subset of the coefficients which best predict the output.\nIn other words, we may compute a subset:\n\n\\begin{align}\\mathcal{A} \\subseteq \\mathcal{A}^{d}\\end{align}\n\nsuch that ([blatman2009]_ page 86) :\n\n\\begin{align}\\widetilde{h}(\\vect{z}) = \\sum_{\\vect{\\alpha} \\in \\mathcal{A}}\n a_{\\vect{\\alpha}} \\psi_{\\vect{\\alpha}}(\\vect{z})\\end{align}\n\nAn enumeration rule is a function from the set of integers $k$ to\nthe corresponding set of multiindices $\\vect{\\alpha}$. More\nprecisely, let $r : \\mathbb{N} \\rightarrow \\mathbb{N}^p$ be the\nfunction such that :\n\n\\begin{align}r(k) = \\vect{\\alpha}\\end{align}\n\nfor any $k \\geq 0$.\nLet $K \\in \\mathbb{N}$ be a parameter representing the number of\ncoefficients considered in the selection. Given an enumeration rule for\nthe multiindices $\\vect{\\alpha}$, at most $K$ multiindices\nwill be considered. Let $\\mathcal{A}_K$ be the corresponding multiindex set :\n\n\\begin{align}\\mathcal{A}_K = \\left\\{ \\vect{\\alpha}\n \\; | \\; r^{-1}(\\vect{\\alpha}) = k \\leq K \\right\\}.\\end{align}\n\n\nLet $\\epsilon > 0$ be a parameter representing the minimum relative\nvalue of a significant coefficient $a_{\\vect{\\alpha}}$.\nThe :class:`~openturns.CleaningStrategy` uses the following criteria to select the coefficients :\n\n\\begin{align}\\mathcal{A}_\\epsilon =\n \\left\\{\n |a_{\\vect{\\alpha}}| \\geq \\epsilon \\max_{ a_{\\vect{\\alpha}}\n \\in \\mathcal{A}_K } |a_{\\vect{\\alpha}}| \\right\\}\\end{align}\n\nwhere $\\epsilon$ is the significance factor, which by default is\n$\\epsilon = 10^{-4}$. This rule selects only the coefficients which\nare significantly different from zero.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Sparsity index\nThe sparsity index of a multiindex set is the ratio of the cardinality of\nthe multiindex set to the cardinality of the multiindex set of the\nequivalent multiindex with maximum total degree. For a given multiindex\nset $\\mathcal{A}$, let $d$ be the maximum 1-norm of multiindices\nin the set :\n\n\\begin{align}d := \\textrm{max}_{\\boldsymbol{\\alpha} \\in \\mathcal{A}}\n \\|\\boldsymbol{\\alpha}\\|_1.\\end{align}\n\nThe index of sparsity of $\\mathcal{A}$ is ([blatman2009]_ eq. 4.42 page 86) :\n\n\\begin{align}\\textrm{IS}(\\mathcal{A})\n = \\frac{\\textrm{card}(\\mathcal{A})}{\\textrm{card}\\left(\\mathcal{A}^d\\right)}.\\end{align}\n\n\n*Note.* The index of sparsity as defined by [blatman2009]_ is close to zero when\nthe model is very sparse. The following complementary indicator is close\nto 1 when the model is very sparse:\n\n\\begin{align}\\textrm{IS}_{\\textrm{c}}(\\mathcal{A})\n = 1 - \\frac{\\textrm{card}(\\mathcal{A})}{\\textrm{card}\\left(\\mathcal{A}^d\\right)}.\\end{align}\n\n\n" + "## Sparsity index\nThe sparsity index of a multiindex set is the ratio of the cardinality of\nthe multiindex set to the cardinality of the multiindex set of the\nequivalent multiindex with maximum total degree. For a given multiindex\nset $\\mathcal{A}$, let $d$ be the maximum 1-norm of multiindices\nin the set :\n\n\\begin{align}d := \\textrm{max}_{\\vect{\\alpha} \\in \\mathcal{A}}\n \\|\\vect{\\alpha}\\|_1.\\end{align}\n\nThe index of sparsity of $\\mathcal{A}$ is ([blatman2009]_ eq. 4.42 page 86) :\n\n\\begin{align}\\textrm{IS}(\\mathcal{A})\n = \\frac{\\textrm{card}(\\mathcal{A})}{\\textrm{card}\\left(\\mathcal{A}^d\\right)}.\\end{align}\n\n\n*Note.* The index of sparsity as defined by [blatman2009]_ is close to zero when\nthe model is very sparse. The following complementary indicator is close\nto 1 when the model is very sparse:\n\n\\begin{align}\\textrm{IS}_{\\textrm{c}}(\\mathcal{A})\n = 1 - \\frac{\\textrm{card}(\\mathcal{A})}{\\textrm{card}\\left(\\mathcal{A}^d\\right)}.\\end{align}\n\n\n" ] }, { @@ -251,7 +251,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `CleaningStrategy` has the following algorithm. On input, it considers\nonly the first `maximumConsideredTerms` coefficients\n$a_{\\boldsymbol{\\alpha}}$. On output it selects the `mostSignificant`\nmost significant coefficients. To do this, it uses the\n`significanceFactor` parameter.\n\nThe following function will help to create a sparse PCE using the\n`CleaningStrategy`. It takes into account the number of considered coefficients\nin the expansion, the number of significant coefficients to keep and the\nrelative factor and returns the Q2 score.\n\n" + "The `CleaningStrategy` has the following algorithm. On input, it considers\nonly the first `maximumConsideredTerms` coefficients\n$a_{\\vect{\\alpha}}$. On output it selects the `mostSignificant`\nmost significant coefficients. To do this, it uses the\n`significanceFactor` parameter.\n\nThe following function will help to create a sparse PCE using the\n`CleaningStrategy`. It takes into account the number of considered coefficients\nin the expansion, the number of significant coefficients to keep and the\nrelative factor and returns the Q2 score.\n\n" ] }, { @@ -348,7 +348,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Intermediate steps of the algorithm\n\nIf we set the `verbose` optional input argument of the\n`compute_cleaning_PCE` function to `True`, then intermediate messages are\nprinted in the Terminal. For each step of\nthe adaptivity algorithm, the code prints some of the internal parameters\nof the algorithm. The datastructure uses several variables that we now describe.\n\n- `Psi_k_p_` : the collection of functions in the current active polynomial multiindex set,\n- `I_p_` : the list of indices of the selected coefficients based according to the enumeration rule,\n- `addedPsi_k_ranks_` : the list of indices to add the multiindex set,\n- `removedPsi_k_ranks_` : the list of indices to remove to the multiindex set,\n- `conservedPsi_k_ranks_` : the index of the first polynomial in the selected multiindex set,\n- `currentVectorIndex_` : the current value of the index in the full multiindex set, according to the enumeration rule.\n\nEach time the selection method is called, it is passed a\ncoefficient $a_{\\boldsymbol{\\alpha}}$ which is a new candidate to be\nconsidered by the algorithm. The first time the method is evaluated, the\nactive multiindex set is empty, so that it must be filled with the first\ncoefficients in the multiindex set, according to the enumeration rule. The\nsecond time (and up to the end of the algorithm), the candidate coefficient\nis considered to be added to the multiindex set.\n\nExecuting the function prints messages that we can process to produce the\nfollowing listing. On each step, we print the list of integers corresponding\nto the indices of the coefficients in the active multiindex set.\n\n" + "## Intermediate steps of the algorithm\n\nIf we set the `verbose` optional input argument of the\n`compute_cleaning_PCE` function to `True`, then intermediate messages are\nprinted in the Terminal. For each step of\nthe adaptivity algorithm, the code prints some of the internal parameters\nof the algorithm. The datastructure uses several variables that we now describe.\n\n- `Psi_k_p_` : the collection of functions in the current active polynomial multiindex set,\n- `I_p_` : the list of indices of the selected coefficients based according to the enumeration rule,\n- `addedPsi_k_ranks_` : the list of indices to add the multiindex set,\n- `removedPsi_k_ranks_` : the list of indices to remove to the multiindex set,\n- `conservedPsi_k_ranks_` : the index of the first polynomial in the selected multiindex set,\n- `currentVectorIndex_` : the current value of the index in the full multiindex set, according to the enumeration rule.\n\nEach time the selection method is called, it is passed a\ncoefficient $a_{\\vect{\\alpha}}$ which is a new candidate to be\nconsidered by the algorithm. The first time the method is evaluated, the\nactive multiindex set is empty, so that it must be filled with the first\ncoefficients in the multiindex set, according to the enumeration rule. The\nsecond time (and up to the end of the algorithm), the candidate coefficient\nis considered to be added to the multiindex set.\n\nExecuting the function prints messages that we can process to produce the\nfollowing listing. On each step, we print the list of integers corresponding\nto the indices of the coefficients in the active multiindex set.\n\n" ] }, { diff --git a/openturns/master/_downloads/e3fd5d734a0eba35d5c47a04fa2a07ac/plot_chaos_cleaning_strategy.py b/openturns/master/_downloads/e3fd5d734a0eba35d5c47a04fa2a07ac/plot_chaos_cleaning_strategy.py index 44b2e4823ba..ea1b64d22dc 100644 --- a/openturns/master/_downloads/e3fd5d734a0eba35d5c47a04fa2a07ac/plot_chaos_cleaning_strategy.py +++ b/openturns/master/_downloads/e3fd5d734a0eba35d5c47a04fa2a07ac/plot_chaos_cleaning_strategy.py @@ -11,35 +11,31 @@ # Polynomial chaos expansion # -------------------------- # -# Let :math:`g : \mathcal{X} \rightarrow \mathbb{R}` be a function -# where :math:`\mathcal{X} \subseteq \mathbb{R}^p` is the domain of :math:`g`. -# Let :math:`f` be a probability density function on :math:`\mathcal{X}`. -# Let :math:`T` be the iso-probabilistic transformation from the physical -# space :math:`\mathcal{X}` to the standard space :math:`\mathcal{\bar{X}}`: +# Let :math:`g : \mathcal{D} \rightarrow \mathbb{R}` be a function +# where :math:`\mathcal{D} \subseteq \mathbb{R}^p` is the domain of :math:`g`. +# We consider :math:`\vect{X}` a random vector which +# probability density function is denoted by :math:`f`. +# We assume that :math:`g(\vect{X})` has a finite second order moment. +# Let :math:`T` be an iso-probabilistic transformation such that :math:`\vect{Z} = T(\vect{X})` +# follows a distribution uniquely defined by all its moments. +# Let :math:`h` be the function defined by: # # .. math:: # -# \xi = T(\boldsymbol{x}) \in \mathcal{\bar{X}} +# h = g \circ T^{-1}. # -# for any :math:`\boldsymbol{x} \in \mathcal{X}`. -# Let :math:`h` be the function defined by the equation : +# The polynomial chaos decomposition of :math:`h` with respect to the measure of +# :math:`\vect{Z}` is (see [blatman2009]_ page 73) : # # .. math:: # -# h(\boldsymbol{\xi}) = \left(g \circ T^{-1}\right)(\boldsymbol{\xi}) +# h(\vect{z}) = \sum_{\vect{\alpha} \in \mathbb{N}^p} +# a_{\vect{\alpha}} \psi_{\vect{\alpha}}(\vect{z}) # -# for any :math:`\boldsymbol{\xi} \in \mathcal{\bar{X}}`. -# The polynomial chaos decomposition of :math:`h` is ([blatman2009]_ page 73) : -# -# .. math:: -# -# h(\boldsymbol{\xi}) = \sum_{\boldsymbol{\alpha} \in \mathbb{N}^p} -# a_{\boldsymbol{\alpha}} \psi_{\boldsymbol{\alpha}}(\boldsymbol{\xi}) + \epsilon -# -# where :math:`\boldsymbol{\alpha} = (\alpha_1, ..., \alpha_p) \in \mathbb{N}^p` -# is a multiindex, :math:`a_{\boldsymbol{\alpha}} \in \mathbb{R}` is the -# coefficient, :math:`\psi_{\boldsymbol{\alpha}} : \mathcal{\bar{X}} \rightarrow \mathbb{R}` -# is a multivariate polynomial and :math:`\epsilon` is a random variable. +# where :math:`\vect{\alpha} = (\alpha_1, ..., \alpha_p) \in \mathbb{N}^p` +# is a multiindex, :math:`a_{\vect{\alpha}} \in \mathbb{R}` is the +# coefficient, :math:`\psi_{\vect{\alpha}} : \mathcal{\bar{X}} \rightarrow \mathbb{R}` +# is a multivariate polynomial. # %% # @@ -56,28 +52,28 @@ # # .. math:: # -# \mathcal{A}^{d} = \left\{ \boldsymbol{\alpha} \in \mathbb{N}^p -# \; | \; \|\boldsymbol{\alpha}\|_1 \leq d\right\} +# \mathcal{A}^{d} = \left\{ \vect{\alpha} \in \mathbb{N}^p +# \; | \; \|\vect{\alpha}\|_1 \leq d\right\} # # where # # .. math:: # -# \|\boldsymbol{\alpha}\|_d = \alpha_1 + ... + \alpha_p +# \|\vect{\alpha}\|_d = \alpha_1 + ... + \alpha_p # -# is the 1-norm of the multi-index :math:`\boldsymbol{\alpha}`. +# is the 1-norm of the multi-index :math:`\vect{\alpha}`. # Therefore, the truncated polynomial chaos expansion is: # # .. math:: # -# h(\boldsymbol{\xi}) = \sum_{\boldsymbol{\alpha} \in \mathcal{A}^{d}} -# a_{\boldsymbol{\alpha}} \psi_{\boldsymbol{\alpha}}(\boldsymbol{\xi}) + \epsilon. +# \widetilde{h}(\vect{z}) = \sum_{\vect{\alpha} \in \mathcal{A}^{d}} +# a_{\vect{\alpha}} \psi_{\vect{\alpha}}(\vect{z}). # # In order to ensure a low error, we may choose a large value of the # parameter :math:`P`. This, however, leads to a large number of -# coefficients :math:`\boldsymbol{\alpha} \in \mathcal{A}^{d}` to +# coefficients :math:`\vect{\alpha} \in \mathcal{A}^{d}` to # estimate. More precisely, the number of coefficients to estimate -# is ([blatman2009]_ page 73) : +# is (see [blatman2009]_ page 73) : # # .. math:: # @@ -90,31 +86,31 @@ # # Low-rank polynomial chaos expansion # ----------------------------------- -# For any :math:`\boldsymbol{\alpha} \in \mathbb{N}^p`, let -# :math:`\|\boldsymbol{\alpha}\|_0` be the rank of the multiindex, that is, +# For any :math:`\vect{\alpha} \in \mathbb{N}^p`, let +# :math:`\|\vect{\alpha}\|_0` be the rank of the multiindex, that is, # the number of nonzero components: # # .. math:: # -# \|\boldsymbol{\alpha}\|_0 = \sum_{i = 1}^p \boldsymbol{1}_{\alpha_i > 0} +# \|\vect{\alpha}\|_0 = \sum_{i = 1}^p \vect{1}_{\alpha_i > 0} # -# where :math:`\boldsymbol{1}` is the indicator function. +# where :math:`\vect{1}` is the indicator function. # The multiindex set of maximum total degree :math:`d \in \mathbb{N}` # and maximum rank :math:`j \in \mathbb{N}` is ([blatman2009]_ page 74): # # .. math:: # -# \mathcal{A}^{d,j} = \left\{ \boldsymbol{\alpha} \in \mathbb{N}^p -# \; | \; \|\boldsymbol{\alpha}\|_1 \leq d, \; -# \; \|\boldsymbol{\alpha}\|_0 \leq j\right\}. +# \mathcal{A}^{d,j} = \left\{ \vect{\alpha} \in \mathbb{N}^p +# \; | \; \|\vect{\alpha}\|_1 \leq d, \; +# \; \|\vect{\alpha}\|_0 \leq j\right\}. # # Therefore, the rank-`j` polynomial chaos expansion is: # # .. math:: # -# h(\boldsymbol{\xi}) = \sum_{\boldsymbol{\alpha} \in -# \mathcal{A}^{d,j}} a_{\boldsymbol{\alpha}} -# \psi_{\boldsymbol{\alpha}}(\boldsymbol{\xi}) + \epsilon. +# \widetilde{h}(\vect{z}) = \sum_{\vect{\alpha} \in +# \mathcal{A}^{d,j}} a_{\vect{\alpha}} +# \psi_{\vect{\alpha}}(\vect{z}). # # The rank is now a hyperparameter of the model: [blatman2009]_ suggests # to use :math:`j = 2, 3, 4`. An example of low-rank PCE for the G-Sobol' @@ -129,7 +125,7 @@ # If :math:`\textrm{card}\left(\mathcal{A}^{d}\right)` is large, many coefficients # may be poorly estimated, which may reduce the quality of the metamodel. We may # want to select a subset of the coefficients which best predict the output. -# In other words, we may compute a subset +# In other words, we may compute a subset: # # .. math:: # @@ -139,41 +135,40 @@ # # .. math:: # -# h(\boldsymbol{\xi}) = \sum_{\boldsymbol{\alpha} \in \mathcal{A}} -# a_{\boldsymbol{\alpha}} \psi_{\boldsymbol{\alpha}}(\boldsymbol{\xi}) -# + \epsilon. +# \widetilde{h}(\vect{z}) = \sum_{\vect{\alpha} \in \mathcal{A}} +# a_{\vect{\alpha}} \psi_{\vect{\alpha}}(\vect{z}) # # An enumeration rule is a function from the set of integers :math:`k` to -# the corresponding set of multiindices :math:`\boldsymbol{\alpha}`. More +# the corresponding set of multiindices :math:`\vect{\alpha}`. More # precisely, let :math:`r : \mathbb{N} \rightarrow \mathbb{N}^p` be the # function such that : # # .. math:: # -# r(k) = \boldsymbol{\alpha} +# r(k) = \vect{\alpha} # # for any :math:`k \geq 0`. # Let :math:`K \in \mathbb{N}` be a parameter representing the number of # coefficients considered in the selection. Given an enumeration rule for -# the multiindices :math:`\boldsymbol{\alpha}`, at most :math:`K` multiindices +# the multiindices :math:`\vect{\alpha}`, at most :math:`K` multiindices # will be considered. Let :math:`\mathcal{A}_K` be the corresponding multiindex set : # # .. math:: # -# \mathcal{A}_K = \left\{ \boldsymbol{\alpha} -# \; | \; r^{-1}(\boldsymbol{\alpha}) = k \leq K \right\}. +# \mathcal{A}_K = \left\{ \vect{\alpha} +# \; | \; r^{-1}(\vect{\alpha}) = k \leq K \right\}. # # # Let :math:`\epsilon > 0` be a parameter representing the minimum relative -# value of a significant coefficient :math:`a_{\boldsymbol{\alpha}}`. +# value of a significant coefficient :math:`a_{\vect{\alpha}}`. # The :class:`~openturns.CleaningStrategy` uses the following criteria to select the coefficients : # # .. math:: # # \mathcal{A}_\epsilon = # \left\{ -# |a_{\boldsymbol{\alpha}}| \geq \epsilon \max_{ a_{\boldsymbol{\alpha}} -# \in \mathcal{A}_K } |a_{\boldsymbol{\alpha}}| \right\} +# |a_{\vect{\alpha}}| \geq \epsilon \max_{ a_{\vect{\alpha}} +# \in \mathcal{A}_K } |a_{\vect{\alpha}}| \right\} # # where :math:`\epsilon` is the significance factor, which by default is # :math:`\epsilon = 10^{-4}`. This rule selects only the coefficients which @@ -191,8 +186,8 @@ # # .. math:: # -# d := \textrm{max}_{\boldsymbol{\alpha} \in \mathcal{A}} -# \|\boldsymbol{\alpha}\|_1. +# d := \textrm{max}_{\vect{\alpha} \in \mathcal{A}} +# \|\vect{\alpha}\|_1. # # The index of sparsity of :math:`\mathcal{A}` is ([blatman2009]_ eq. 4.42 page 86) : # @@ -477,7 +472,7 @@ def draw_polynomial_chaos_validation( # # The `CleaningStrategy` has the following algorithm. On input, it considers # only the first `maximumConsideredTerms` coefficients -# :math:`a_{\boldsymbol{\alpha}}`. On output it selects the `mostSignificant` +# :math:`a_{\vect{\alpha}}`. On output it selects the `mostSignificant` # most significant coefficients. To do this, it uses the # `significanceFactor` parameter. # @@ -643,7 +638,7 @@ def compute_cleaning_PCE( # - `currentVectorIndex_` : the current value of the index in the full multiindex set, according to the enumeration rule. # # Each time the selection method is called, it is passed a -# coefficient :math:`a_{\boldsymbol{\alpha}}` which is a new candidate to be +# coefficient :math:`a_{\vect{\alpha}}` which is a new candidate to be # considered by the algorithm. The first time the method is evaluated, the # active multiindex set is empty, so that it must be filled with the first # coefficients in the multiindex set, according to the enumeration rule. 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b/openturns/master/_images/sphx_glr_plot_pce_design_thumb.png differ diff --git a/openturns/master/_sources/auto_calibration/bayesian_calibration/plot_gibbs.rst.txt b/openturns/master/_sources/auto_calibration/bayesian_calibration/plot_gibbs.rst.txt index bbff7b1c43e..ccc557bb290 100644 --- a/openturns/master/_sources/auto_calibration/bayesian_calibration/plot_gibbs.rst.txt +++ b/openturns/master/_sources/auto_calibration/bayesian_calibration/plot_gibbs.rst.txt @@ -283,7 +283,7 @@ Let us plot the posterior density. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 12.155 seconds) + **Total running time of the script:** (0 minutes 12.547 seconds) .. _sphx_glr_download_auto_calibration_bayesian_calibration_plot_gibbs.py: diff --git a/openturns/master/_sources/auto_calibration/bayesian_calibration/plot_rwmh_python_distribution.rst.txt b/openturns/master/_sources/auto_calibration/bayesian_calibration/plot_rwmh_python_distribution.rst.txt index f4b1b47eb8a..2320b4439df 100644 --- a/openturns/master/_sources/auto_calibration/bayesian_calibration/plot_rwmh_python_distribution.rst.txt +++ b/openturns/master/_sources/auto_calibration/bayesian_calibration/plot_rwmh_python_distribution.rst.txt @@ -425,7 +425,7 @@ Plot posterior marginal plots only as prior cannot be drawn meaningfully. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 5.424 seconds) + **Total running time of the script:** (0 minutes 5.440 seconds) .. _sphx_glr_download_auto_calibration_bayesian_calibration_plot_rwmh_python_distribution.py: diff --git a/openturns/master/_sources/auto_calibration/bayesian_calibration/sg_execution_times.rst.txt b/openturns/master/_sources/auto_calibration/bayesian_calibration/sg_execution_times.rst.txt index 2a687578322..ca5d78092c0 100644 --- a/openturns/master/_sources/auto_calibration/bayesian_calibration/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_calibration/bayesian_calibration/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:22.316** total execution time for 7 files **from auto_calibration/bayesian_calibration**: +**00:22.751** total execution time for 7 files **from auto_calibration/bayesian_calibration**: .. container:: @@ -33,23 +33,23 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_gibbs.py` (``plot_gibbs.py``) - - 00:12.155 + - 00:12.547 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_rwmh_python_distribution.py` (``plot_rwmh_python_distribution.py``) - - 00:05.424 + - 00:05.440 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_bayesian_calibration_flooding.py` (``plot_bayesian_calibration_flooding.py``) - - 00:01.287 + - 00:01.286 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_bayesian_calibration.py` (``plot_bayesian_calibration.py``) - - 00:01.160 + - 00:01.163 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_gibbs_simus.py` (``plot_gibbs_simus.py``) - - 00:01.111 + - 00:01.112 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_ackley_distribution.py` (``plot_ackley_distribution.py``) - - 00:00.902 + - 00:00.921 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_imh_python_distribution.py` (``plot_imh_python_distribution.py``) - - 00:00.277 + - 00:00.282 - 0.0 diff --git a/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_chaboche.rst.txt b/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_chaboche.rst.txt index dcb4905034a..71a92eaa610 100644 --- a/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_chaboche.rst.txt +++ b/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_chaboche.rst.txt @@ -1927,7 +1927,7 @@ Reset default settings .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 6.716 seconds) + **Total running time of the script:** (0 minutes 7.101 seconds) .. _sphx_glr_download_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_chaboche.py: diff --git a/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_deflection_tube.rst.txt b/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_deflection_tube.rst.txt index d41614a0ae4..888a56b1696 100644 --- a/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_deflection_tube.rst.txt +++ b/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_deflection_tube.rst.txt @@ -896,7 +896,7 @@ Reset default settings .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.787 seconds) + **Total running time of the script:** (0 minutes 4.120 seconds) .. _sphx_glr_download_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_deflection_tube.py: diff --git a/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_flooding.rst.txt b/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_flooding.rst.txt index 2c18331af82..ba6c334d1b0 100644 --- a/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_flooding.rst.txt +++ b/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_flooding.rst.txt @@ -1695,7 +1695,7 @@ Reset default settings .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 8.623 seconds) + **Total running time of the script:** (0 minutes 8.970 seconds) .. _sphx_glr_download_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_flooding.py: diff --git a/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/sg_execution_times.rst.txt b/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/sg_execution_times.rst.txt index 555a87a6dd1..50800b7723c 100644 --- a/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_calibration/least_squares_and_gaussian_calibration/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:20.353** total execution time for 8 files **from auto_calibration/least_squares_and_gaussian_calibration**: +**00:21.512** total execution time for 8 files **from auto_calibration/least_squares_and_gaussian_calibration**: .. container:: @@ -33,26 +33,26 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_flooding.py` (``plot_calibration_flooding.py``) - - 00:08.623 + - 00:08.970 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_chaboche.py` (``plot_calibration_chaboche.py``) - - 00:06.716 + - 00:07.101 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_deflection_tube.py` (``plot_calibration_deflection_tube.py``) - - 00:03.787 + - 00:04.120 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_logistic.py` (``plot_calibration_logistic.py``) - - 00:00.702 + - 00:00.774 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_quickstart.py` (``plot_calibration_quickstart.py``) - - 00:00.295 + - 00:00.308 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_withoutobservedinputs.py` (``plot_calibration_withoutobservedinputs.py``) - - 00:00.086 + - 00:00.090 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_generate_chaboche.py` (``plot_generate_chaboche.py``) - - 00:00.073 + - 00:00.077 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_generate_flooding.py` (``plot_generate_flooding.py``) - - 00:00.069 + - 00:00.072 - 0.0 diff --git a/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_conditional_quantile.rst.txt b/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_conditional_quantile.rst.txt index c604f38cb73..08ee921ef2f 100644 --- a/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_conditional_quantile.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_conditional_quantile.rst.txt @@ -525,6 +525,11 @@ Our estimated conditional quantile is a good approximate and should be better th +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 2.022 seconds) + + .. _sphx_glr_download_auto_data_analysis_distribution_fitting_plot_estimate_conditional_quantile.py: .. only:: html diff --git a/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_fremantle.rst.txt b/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_fremantle.rst.txt index 4b5fd04b930..a51a203b799 100644 --- a/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_fremantle.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_fremantle.rst.txt @@ -1061,7 +1061,7 @@ improvements with respect to model tested before. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 5.397 seconds) + **Total running time of the script:** (0 minutes 5.720 seconds) .. _sphx_glr_download_auto_data_analysis_distribution_fitting_plot_estimate_gev_fremantle.py: diff --git a/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_pirie.rst.txt b/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_pirie.rst.txt index e0cfdd9aa0a..859d708fb7c 100644 --- a/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_pirie.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_pirie.rst.txt @@ -947,7 +947,7 @@ the threshold :math:`c_{\alpha}` or if the p-value is less than the Type I error .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.042 seconds) + **Total running time of the script:** (0 minutes 2.145 seconds) .. _sphx_glr_download_auto_data_analysis_distribution_fitting_plot_estimate_gev_pirie.py: diff --git a/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_racetime.rst.txt b/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_racetime.rst.txt index ec5499aea2d..96c8bd7f549 100644 --- a/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_racetime.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_gev_racetime.rst.txt @@ -1108,7 +1108,7 @@ quadratic model explains even better a large variation in the data. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 10.552 seconds) + **Total running time of the script:** (0 minutes 11.135 seconds) .. _sphx_glr_download_auto_data_analysis_distribution_fitting_plot_estimate_gev_racetime.py: diff --git a/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_multivariate_distribution.rst.txt b/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_multivariate_distribution.rst.txt index a043dff4591..9c01a177a9c 100644 --- a/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_multivariate_distribution.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/distribution_fitting/plot_estimate_multivariate_distribution.rst.txt @@ -419,7 +419,7 @@ We build joint distribution from marginal distributions and dependency structure .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 6.356 seconds) + **Total running time of the script:** (0 minutes 6.237 seconds) .. _sphx_glr_download_auto_data_analysis_distribution_fitting_plot_estimate_multivariate_distribution.py: diff --git a/openturns/master/_sources/auto_data_analysis/distribution_fitting/sg_execution_times.rst.txt b/openturns/master/_sources/auto_data_analysis/distribution_fitting/sg_execution_times.rst.txt index 3193fd73b79..c646c0e66f6 100644 --- a/openturns/master/_sources/auto_data_analysis/distribution_fitting/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/distribution_fitting/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:31.008** total execution time for 15 files **from auto_data_analysis/distribution_fitting**: +**00:32.391** total execution time for 15 files **from auto_data_analysis/distribution_fitting**: .. container:: @@ -33,47 +33,47 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_racetime.py` (``plot_estimate_gev_racetime.py``) - - 00:10.552 + - 00:11.135 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_multivariate_distribution.py` (``plot_estimate_multivariate_distribution.py``) - - 00:06.356 + - 00:06.237 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_fremantle.py` (``plot_estimate_gev_fremantle.py``) - - 00:05.397 + - 00:05.720 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_pirie.py` (``plot_estimate_gev_pirie.py``) - - 00:02.042 + - 00:02.145 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_conditional_quantile.py` (``plot_estimate_conditional_quantile.py``) - - 00:01.860 + - 00:02.022 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_non_parametric_distribution.py` (``plot_estimate_non_parametric_distribution.py``) - - 00:00.994 + - 00:01.053 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_venice.py` (``plot_estimate_gev_venice.py``) - - 00:00.792 + - 00:00.817 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_asymptotic_estimators_distribution.py` (``plot_asymptotic_estimators_distribution.py``) - - 00:00.726 - - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_model_singular_multivariate_distribution.py` (``plot_model_singular_multivariate_distribution.py``) - - 00:00.626 + - 00:00.792 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_smoothing_mixture.py` (``plot_smoothing_mixture.py``) - - 00:00.580 + - 00:00.673 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_model_singular_multivariate_distribution.py` (``plot_model_singular_multivariate_distribution.py``) + - 00:00.624 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_fit_extreme_value_distribution.py` (``plot_fit_extreme_value_distribution.py``) - - 00:00.371 + - 00:00.425 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_advanced_mle_estimator.py` (``plot_advanced_mle_estimator.py``) - - 00:00.307 + - 00:00.292 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_normal.py` (``plot_estimate_normal.py``) - - 00:00.263 + - 00:00.269 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_quantilematching_estimator.py` (``plot_quantilematching_estimator.py``) - - 00:00.138 + - 00:00.181 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_maximumlikelihood_estimator.py` (``plot_maximumlikelihood_estimator.py``) - - 00:00.005 + - 00:00.006 - 0.0 diff --git a/openturns/master/_sources/auto_data_analysis/estimate_dependency_and_copulas/sg_execution_times.rst.txt b/openturns/master/_sources/auto_data_analysis/estimate_dependency_and_copulas/sg_execution_times.rst.txt index cb6aaf292fa..a44bc7bf1d8 100644 --- a/openturns/master/_sources/auto_data_analysis/estimate_dependency_and_copulas/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/estimate_dependency_and_copulas/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:01.088** total execution time for 4 files **from auto_data_analysis/estimate_dependency_and_copulas**: +**00:01.106** total execution time for 4 files **from auto_data_analysis/estimate_dependency_and_copulas**: .. container:: @@ -33,14 +33,14 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_non_parametric_copula.py` (``plot_estimate_non_parametric_copula.py``) - - 00:00.448 - - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_dependence_wind.py` (``plot_estimate_dependence_wind.py``) - - 00:00.243 + - 00:00.455 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_dependence_wavesurge.py` (``plot_estimate_dependence_wavesurge.py``) - - 00:00.235 + - 00:00.246 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_dependence_wind.py` (``plot_estimate_dependence_wind.py``) + - 00:00.238 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_copula.py` (``plot_estimate_copula.py``) - - 00:00.162 + - 00:00.168 - 0.0 diff --git a/openturns/master/_sources/auto_data_analysis/estimate_stochastic_processes/plot_estimate_multivariate_arma.rst.txt b/openturns/master/_sources/auto_data_analysis/estimate_stochastic_processes/plot_estimate_multivariate_arma.rst.txt index a547e6ab4ea..0f2d41d6f9a 100644 --- a/openturns/master/_sources/auto_data_analysis/estimate_stochastic_processes/plot_estimate_multivariate_arma.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/estimate_stochastic_processes/plot_estimate_multivariate_arma.rst.txt @@ -159,7 +159,7 @@ Estimate the process from the previous realization .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 4.064 seconds) + **Total running time of the script:** (0 minutes 4.248 seconds) .. _sphx_glr_download_auto_data_analysis_estimate_stochastic_processes_plot_estimate_multivariate_arma.py: diff --git a/openturns/master/_sources/auto_data_analysis/estimate_stochastic_processes/sg_execution_times.rst.txt b/openturns/master/_sources/auto_data_analysis/estimate_stochastic_processes/sg_execution_times.rst.txt index df4d9341630..e045763c3ed 100644 --- a/openturns/master/_sources/auto_data_analysis/estimate_stochastic_processes/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/estimate_stochastic_processes/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:06.388** total execution time for 5 files **from auto_data_analysis/estimate_stochastic_processes**: +**00:06.798** total execution time for 5 files **from auto_data_analysis/estimate_stochastic_processes**: .. container:: @@ -33,17 +33,17 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_multivariate_arma.py` (``plot_estimate_multivariate_arma.py``) - - 00:04.064 + - 00:04.248 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_spectral_density_function.py` (``plot_estimate_spectral_density_function.py``) - - 00:00.873 + - 00:00.962 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_arma.py` (``plot_estimate_arma.py``) - - 00:00.775 + - 00:00.856 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_stationary_covariance_model.py` (``plot_estimate_stationary_covariance_model.py``) - - 00:00.421 + - 00:00.444 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_non_stationary_covariance_model.py` (``plot_estimate_non_stationary_covariance_model.py``) - - 00:00.255 + - 00:00.289 - 0.0 diff --git a/openturns/master/_sources/auto_data_analysis/graphics/plot_sensitivity_par_coo_ishigami.rst.txt b/openturns/master/_sources/auto_data_analysis/graphics/plot_sensitivity_par_coo_ishigami.rst.txt index d107873b917..4a0d7838b84 100644 --- a/openturns/master/_sources/auto_data_analysis/graphics/plot_sensitivity_par_coo_ishigami.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/graphics/plot_sensitivity_par_coo_ishigami.rst.txt @@ -381,7 +381,7 @@ Display figures .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.062 seconds) + **Total running time of the script:** (0 minutes 3.475 seconds) .. _sphx_glr_download_auto_data_analysis_graphics_plot_sensitivity_par_coo_ishigami.py: diff --git a/openturns/master/_sources/auto_data_analysis/graphics/sg_execution_times.rst.txt b/openturns/master/_sources/auto_data_analysis/graphics/sg_execution_times.rst.txt index 9fe6c294745..51cca366f52 100644 --- a/openturns/master/_sources/auto_data_analysis/graphics/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/graphics/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:03.479** total execution time for 3 files **from auto_data_analysis/graphics**: +**00:03.912** total execution time for 3 files **from auto_data_analysis/graphics**: .. container:: @@ -33,11 +33,11 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_sensitivity_par_coo_ishigami.py` (``plot_sensitivity_par_coo_ishigami.py``) - - 00:03.062 + - 00:03.475 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_visualize_clouds.py` (``plot_visualize_clouds.py``) - - 00:00.249 + - 00:00.263 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_visualize_pairs.py` (``plot_visualize_pairs.py``) - - 00:00.168 + - 00:00.174 - 0.0 diff --git a/openturns/master/_sources/auto_data_analysis/manage_data_and_samples/sg_execution_times.rst.txt b/openturns/master/_sources/auto_data_analysis/manage_data_and_samples/sg_execution_times.rst.txt index e0228873f97..cc958ed7704 100644 --- a/openturns/master/_sources/auto_data_analysis/manage_data_and_samples/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/manage_data_and_samples/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:00.873** total execution time for 10 files **from auto_data_analysis/manage_data_and_samples**: +**00:01.019** total execution time for 10 files **from auto_data_analysis/manage_data_and_samples**: .. container:: @@ -33,31 +33,31 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_sample_correlation.py` (``plot_sample_correlation.py``) - - 00:00.422 + - 00:00.470 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_sample_pandas.py` (``plot_sample_pandas.py``) - - 00:00.180 + - 00:00.214 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_linear_regression.py` (``plot_linear_regression.py``) - - 00:00.144 + - 00:00.177 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_quantile_estimation_wilks.py` (``plot_quantile_estimation_wilks.py``) - - 00:00.099 + - 00:00.126 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_quick_start_point_and_sample.py` (``plot_quick_start_point_and_sample.py``) - - 00:00.010 + - 00:00.012 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_sample_manipulation.py` (``plot_sample_manipulation.py``) + - 00:00.007 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_sort_sample.py` (``plot_sort_sample.py``) - 00:00.005 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_estimate_moments.py` (``plot_estimate_moments.py``) - 00:00.005 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_sort_sample.py` (``plot_sort_sample.py``) - - 00:00.004 - - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_import_export_sample_csv.py` (``plot_import_export_sample_csv.py``) - - 00:00.003 + - 00:00.002 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_randomize_sample_lines.py` (``plot_randomize_sample_lines.py``) - 00:00.002 diff --git a/openturns/master/_sources/auto_data_analysis/sample_analysis/sg_execution_times.rst.txt b/openturns/master/_sources/auto_data_analysis/sample_analysis/sg_execution_times.rst.txt index 0988888cd35..44317db2b7d 100644 --- a/openturns/master/_sources/auto_data_analysis/sample_analysis/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/sample_analysis/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:01.421** total execution time for 5 files **from auto_data_analysis/sample_analysis**: +**00:01.760** total execution time for 5 files **from auto_data_analysis/sample_analysis**: .. container:: @@ -33,17 +33,17 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_draw_survival.py` (``plot_draw_survival.py``) - - 00:00.981 + - 00:01.179 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_src_confidence.py` (``plot_src_confidence.py``) - - 00:00.159 + - 00:00.192 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_visualize_empirical_cdf.py` (``plot_visualize_empirical_cdf.py``) - - 00:00.117 + - 00:00.177 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_compare_unconditional_conditional_histograms.py` (``plot_compare_unconditional_conditional_histograms.py``) - - 00:00.102 + - 00:00.110 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_visualize_histogram.py` (``plot_visualize_histogram.py``) - - 00:00.063 + - 00:00.102 - 0.0 diff --git a/openturns/master/_sources/auto_data_analysis/statistical_tests/sg_execution_times.rst.txt b/openturns/master/_sources/auto_data_analysis/statistical_tests/sg_execution_times.rst.txt index 6ad4e3700ac..e5489f65451 100644 --- a/openturns/master/_sources/auto_data_analysis/statistical_tests/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_data_analysis/statistical_tests/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:02.616** total execution time for 11 files **from auto_data_analysis/statistical_tests**: +**00:02.822** total execution time for 11 files **from auto_data_analysis/statistical_tests**: .. container:: @@ -33,31 +33,31 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_distribution.py` (``plot_kolmogorov_distribution.py``) - - 00:01.308 + - 00:01.408 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_fitted_distribution_ranking.py` (``plot_fitted_distribution_ranking.py``) - - 00:00.412 + - 00:00.396 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_test_copula.py` (``plot_test_copula.py``) - - 00:00.276 + - 00:00.293 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_smirnov_test.py` (``plot_smirnov_test.py``) - - 00:00.142 + - 00:00.228 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_qqplot_graph.py` (``plot_qqplot_graph.py``) - - 00:00.142 + - 00:00.155 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_test_normality.py` (``plot_test_normality.py``) - - 00:00.129 + - 00:00.136 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_pvalue.py` (``plot_kolmogorov_pvalue.py``) - - 00:00.093 + - 00:00.096 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_statistics.py` (``plot_kolmogorov_statistics.py``) - - 00:00.067 + - 00:00.071 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_test.py` (``plot_kolmogorov_test.py``) - - 00:00.039 + - 00:00.032 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_test_independence.py` (``plot_test_independence.py``) - 00:00.005 diff --git a/openturns/master/_sources/auto_functional_modeling/field_functions/sg_execution_times.rst.txt b/openturns/master/_sources/auto_functional_modeling/field_functions/sg_execution_times.rst.txt index e64722313fe..705a815d482 100644 --- a/openturns/master/_sources/auto_functional_modeling/field_functions/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_functional_modeling/field_functions/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:00.764** total execution time for 6 files **from auto_functional_modeling/field_functions**: +**00:00.804** total execution time for 6 files **from auto_functional_modeling/field_functions**: .. container:: @@ -33,16 +33,16 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_function_manipulation.py` (``plot_function_manipulation.py``) - - 00:00.409 + - 00:00.433 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_logistic_growth_model.py` (``plot_logistic_growth_model.py``) - - 00:00.197 + - 00:00.205 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_viscous_fall_field_function.py` (``plot_viscous_fall_field_function.py``) - - 00:00.083 + - 00:00.087 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_viscous_fall_field_function_connection.py` (``plot_viscous_fall_field_function_connection.py``) - - 00:00.072 + - 00:00.075 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_vertexvalue_function.py` (``plot_vertexvalue_function.py``) - 00:00.002 diff --git a/openturns/master/_sources/auto_functional_modeling/link_to_an_external_code/sg_execution_times.rst.txt b/openturns/master/_sources/auto_functional_modeling/link_to_an_external_code/sg_execution_times.rst.txt index 5237b3cbda8..66ead82b0f0 100644 --- a/openturns/master/_sources/auto_functional_modeling/link_to_an_external_code/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_functional_modeling/link_to_an_external_code/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:00.023** total execution time for 1 file **from auto_functional_modeling/link_to_an_external_code**: +**00:00.027** total execution time for 1 file **from auto_functional_modeling/link_to_an_external_code**: .. container:: @@ -33,5 +33,5 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_functional_modeling_link_to_an_external_code_plot_link_computer_code_coupling_tools.py` (``plot_link_computer_code_coupling_tools.py``) - - 00:00.023 + - 00:00.027 - 0.0 diff --git a/openturns/master/_sources/auto_functional_modeling/univariate_functions/sg_execution_times.rst.txt b/openturns/master/_sources/auto_functional_modeling/univariate_functions/sg_execution_times.rst.txt index 121cf3b71fe..af57683c499 100644 --- a/openturns/master/_sources/auto_functional_modeling/univariate_functions/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_functional_modeling/univariate_functions/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:00.223** total execution time for 1 file **from auto_functional_modeling/univariate_functions**: +**00:00.250** total execution time for 1 file **from auto_functional_modeling/univariate_functions**: .. container:: @@ -33,5 +33,5 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_functional_modeling_univariate_functions_plot_createUnivariateFunction.py` (``plot_createUnivariateFunction.py``) - - 00:00.223 + - 00:00.250 - 0.0 diff --git a/openturns/master/_sources/auto_functional_modeling/vectorial_functions/sg_execution_times.rst.txt b/openturns/master/_sources/auto_functional_modeling/vectorial_functions/sg_execution_times.rst.txt index 96ace6d3fcc..fd4ef7070c8 100644 --- a/openturns/master/_sources/auto_functional_modeling/vectorial_functions/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_functional_modeling/vectorial_functions/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:00.280** total execution time for 11 files **from auto_functional_modeling/vectorial_functions**: +**00:00.318** total execution time for 11 files **from auto_functional_modeling/vectorial_functions**: .. container:: @@ -33,16 +33,16 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_symbolic_function.py` (``plot_symbolic_function.py``) - - 00:00.150 + - 00:00.175 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_quadratic_function.py` (``plot_quadratic_function.py``) - - 00:00.063 + - 00:00.069 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_quick_start_functions.py` (``plot_quick_start_functions.py``) - - 00:00.048 + - 00:00.054 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_functions_inputDim.py` (``plot_functions_inputDim.py``) - - 00:00.003 + - 00:00.004 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_createMultivariateFunction.py` (``plot_createMultivariateFunction.py``) - 00:00.003 @@ -59,9 +59,9 @@ Computation times * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_linear_combination_function.py` (``plot_linear_combination_function.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_composed_function.py` (``plot_composed_function.py``) + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_functions_outputDim.py` (``plot_functions_outputDim.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_functions_outputDim.py` (``plot_functions_outputDim.py``) + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_composed_function.py` (``plot_composed_function.py``) - 00:00.002 - 0.0 diff --git a/openturns/master/_sources/auto_graphs/sg_execution_times.rst.txt b/openturns/master/_sources/auto_graphs/sg_execution_times.rst.txt index e12bd770af6..aa142280851 100644 --- a/openturns/master/_sources/auto_graphs/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_graphs/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:01.999** total execution time for 3 files **from auto_graphs**: +**00:01.905** total execution time for 3 files **from auto_graphs**: .. container:: @@ -33,11 +33,11 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_graphs_plot_graphs_basics.py` (``plot_graphs_basics.py``) - - 00:01.112 + - 00:01.054 - 0.0 * - :ref:`sphx_glr_auto_graphs_plot_graphs_loglikelihood_contour.py` (``plot_graphs_loglikelihood_contour.py``) - - 00:00.723 + - 00:00.711 - 0.0 * - :ref:`sphx_glr_auto_graphs_plot_graphs_fill_area.py` (``plot_graphs_fill_area.py``) - - 00:00.163 + - 00:00.140 - 0.0 diff --git a/openturns/master/_sources/auto_meta_modeling/fields_metamodels/plot_fieldfunction_metamodel.rst.txt b/openturns/master/_sources/auto_meta_modeling/fields_metamodels/plot_fieldfunction_metamodel.rst.txt index f1cee309eb7..1b223e5f08d 100644 --- a/openturns/master/_sources/auto_meta_modeling/fields_metamodels/plot_fieldfunction_metamodel.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/fields_metamodels/plot_fieldfunction_metamodel.rst.txt @@ -498,7 +498,7 @@ Reset default settings .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 17.698 seconds) + **Total running time of the script:** (0 minutes 19.058 seconds) .. _sphx_glr_download_auto_meta_modeling_fields_metamodels_plot_fieldfunction_metamodel.py: diff --git a/openturns/master/_sources/auto_meta_modeling/fields_metamodels/plot_viscous_fall_metamodel.rst.txt b/openturns/master/_sources/auto_meta_modeling/fields_metamodels/plot_viscous_fall_metamodel.rst.txt index 35914d27619..65ac92a5fd0 100644 --- a/openturns/master/_sources/auto_meta_modeling/fields_metamodels/plot_viscous_fall_metamodel.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/fields_metamodels/plot_viscous_fall_metamodel.rst.txt @@ -407,6 +407,11 @@ Reset ResourceMap +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 2.073 seconds) + + .. _sphx_glr_download_auto_meta_modeling_fields_metamodels_plot_viscous_fall_metamodel.py: .. only:: html diff --git a/openturns/master/_sources/auto_meta_modeling/fields_metamodels/sg_execution_times.rst.txt b/openturns/master/_sources/auto_meta_modeling/fields_metamodels/sg_execution_times.rst.txt index 8b72e8721a9..9109cccc8dc 100644 --- a/openturns/master/_sources/auto_meta_modeling/fields_metamodels/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/fields_metamodels/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:19.952** total execution time for 3 files **from auto_meta_modeling/fields_metamodels**: +**00:21.633** total execution time for 3 files **from auto_meta_modeling/fields_metamodels**: .. container:: @@ -33,11 +33,11 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_meta_modeling_fields_metamodels_plot_fieldfunction_metamodel.py` (``plot_fieldfunction_metamodel.py``) - - 00:17.698 + - 00:19.058 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_fields_metamodels_plot_viscous_fall_metamodel.py` (``plot_viscous_fall_metamodel.py``) - - 00:01.807 + - 00:02.073 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_fields_metamodels_plot_karhunenloeve_validation.py` (``plot_karhunenloeve_validation.py``) - - 00:00.446 + - 00:00.502 - 0.0 diff --git a/openturns/master/_sources/auto_meta_modeling/general_purpose_metamodels/sg_execution_times.rst.txt b/openturns/master/_sources/auto_meta_modeling/general_purpose_metamodels/sg_execution_times.rst.txt index 47ee0d8a8c5..0451a489ea3 100644 --- a/openturns/master/_sources/auto_meta_modeling/general_purpose_metamodels/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/general_purpose_metamodels/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:02.472** total execution time for 8 files **from auto_meta_modeling/general_purpose_metamodels**: +**00:03.257** total execution time for 8 files **from auto_meta_modeling/general_purpose_metamodels**: .. container:: @@ -32,27 +32,27 @@ Computation times * - Example - Time - Mem (MB) - * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_overfitting_model_selection.py` (``plot_overfitting_model_selection.py``) - - 00:00.700 - - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_linear_model.py` (``plot_linear_model.py``) - - 00:00.683 + - 00:00.879 + - 0.0 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_overfitting_model_selection.py` (``plot_overfitting_model_selection.py``) + - 00:00.858 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_stepwise.py` (``plot_stepwise.py``) - - 00:00.358 + - 00:00.452 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_export_metamodel.py` (``plot_export_metamodel.py``) - - 00:00.271 + - 00:00.331 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_expert_mixture.py` (``plot_expert_mixture.py``) - - 00:00.171 + - 00:00.307 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_taylor_approximation.py` (``plot_taylor_approximation.py``) - - 00:00.138 + - 00:00.238 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_general_linear_model.py` (``plot_general_linear_model.py``) - - 00:00.088 + - 00:00.111 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_create_linear_least_squares_model.py` (``plot_create_linear_least_squares_model.py``) - - 00:00.064 + - 00:00.082 - 0.0 diff --git a/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/plot_kriging_categorical.rst.txt b/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/plot_kriging_categorical.rst.txt index 5297453a73a..0f7588f6f10 100644 --- a/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/plot_kriging_categorical.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/plot_kriging_categorical.rst.txt @@ -556,7 +556,7 @@ than relying on multiple purely continuous Gaussian processes. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 9.530 seconds) + **Total running time of the script:** (0 minutes 10.578 seconds) .. _sphx_glr_download_auto_meta_modeling_kriging_metamodel_plot_kriging_categorical.py: diff --git a/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/plot_kriging_multioutput_firesatellite.rst.txt b/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/plot_kriging_multioutput_firesatellite.rst.txt index 9c44e1c577e..736f5b08011 100644 --- a/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/plot_kriging_multioutput_firesatellite.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/plot_kriging_multioutput_firesatellite.rst.txt @@ -308,7 +308,7 @@ Then, we use the `MetaModelValidation` class to validate the metamodel. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 7.010 seconds) + **Total running time of the script:** (0 minutes 7.445 seconds) .. _sphx_glr_download_auto_meta_modeling_kriging_metamodel_plot_kriging_multioutput_firesatellite.py: diff --git a/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/sg_execution_times.rst.txt b/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/sg_execution_times.rst.txt index 0c783857662..75e20863368 100644 --- a/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/kriging_metamodel/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:22.043** total execution time for 18 files **from auto_meta_modeling/kriging_metamodel**: +**00:23.860** total execution time for 18 files **from auto_meta_modeling/kriging_metamodel**: .. container:: @@ -33,56 +33,56 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_categorical.py` (``plot_kriging_categorical.py``) - - 00:09.530 + - 00:10.578 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_multioutput_firesatellite.py` (``plot_kriging_multioutput_firesatellite.py``) - - 00:07.010 + - 00:07.445 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_draw_covariance_models.py` (``plot_draw_covariance_models.py``) - - 00:01.180 + - 00:01.273 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_advanced.py` (``plot_kriging_advanced.py``) - - 00:00.882 + - 00:00.926 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_sequential.py` (``plot_kriging_sequential.py``) - - 00:00.605 + - 00:00.616 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_chose_trend.py` (``plot_kriging_chose_trend.py``) - - 00:00.506 + - 00:00.539 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_branin_function.py` (``plot_kriging_branin_function.py``) - - 00:00.388 + - 00:00.398 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_propagate_kriging_ishigami.py` (``plot_propagate_kriging_ishigami.py``) - - 00:00.345 + - 00:00.353 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_1d.py` (``plot_kriging_1d.py``) - - 00:00.303 + - 00:00.343 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_isotropic.py` (``plot_kriging_isotropic.py``) - - 00:00.243 + - 00:00.252 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_simulate.py` (``plot_kriging_simulate.py``) - - 00:00.227 + - 00:00.240 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_cantilever_beam_hmat.py` (``plot_kriging_cantilever_beam_hmat.py``) - - 00:00.178 + - 00:00.196 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_cantilever_beam.py` (``plot_kriging_cantilever_beam.py``) - - 00:00.174 + - 00:00.187 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_hyperparameters_optimization.py` (``plot_kriging_hyperparameters_optimization.py``) - - 00:00.166 + - 00:00.179 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_beam_trend.py` (``plot_kriging_beam_trend.py``) - - 00:00.155 + - 00:00.163 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_likelihood.py` (``plot_kriging_likelihood.py``) - - 00:00.076 + - 00:00.092 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging.py` (``plot_kriging.py``) - - 00:00.067 + - 00:00.071 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_beam_arbitrary_trend.py` (``plot_kriging_beam_arbitrary_trend.py``) - - 00:00.008 + - 00:00.009 - 0.0 diff --git a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_beam_sensitivity_degree.rst.txt b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_beam_sensitivity_degree.rst.txt index 4f47c9b75c6..591288b0783 100644 --- a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_beam_sensitivity_degree.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_beam_sensitivity_degree.rst.txt @@ -577,7 +577,7 @@ References .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 6.189 seconds) + **Total running time of the script:** (0 minutes 7.290 seconds) .. _sphx_glr_download_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_beam_sensitivity_degree.py: diff --git a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_build_distribution.rst.txt b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_build_distribution.rst.txt index 3fe86022cb9..694fcd8a991 100644 --- a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_build_distribution.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_build_distribution.rst.txt @@ -290,6 +290,11 @@ The previous constructor is the main topic of the example +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 2.839 seconds) + + .. _sphx_glr_download_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_build_distribution.py: .. only:: html diff --git a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_cleaning_strategy.rst.txt b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_cleaning_strategy.rst.txt index ecca378797c..acc58fafcd0 100644 --- a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_cleaning_strategy.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_cleaning_strategy.rst.txt @@ -21,7 +21,7 @@ Create a sparse chaos by integration ==================================== -.. GENERATED FROM PYTHON SOURCE LINES 6-42 +.. GENERATED FROM PYTHON SOURCE LINES 6-38 The goal of this example is to show how to create a sparse polynomial chaos expansion (PCE) when we estimate its coefficients by integration. We show @@ -30,37 +30,33 @@ how to use the :class:`~openturns.CleaningStrategy` class. Polynomial chaos expansion -------------------------- -Let :math:`g : \mathcal{X} \rightarrow \mathbb{R}` be a function -where :math:`\mathcal{X} \subseteq \mathbb{R}^p` is the domain of :math:`g`. -Let :math:`f` be a probability density function on :math:`\mathcal{X}`. -Let :math:`T` be the iso-probabilistic transformation from the physical -space :math:`\mathcal{X}` to the standard space :math:`\mathcal{\bar{X}}`: +Let :math:`g : \mathcal{D} \rightarrow \mathbb{R}` be a function +where :math:`\mathcal{D} \subseteq \mathbb{R}^p` is the domain of :math:`g`. +We consider :math:`\vect{X}` a random vector which +probability density function is denoted by :math:`f`. +We assume that :math:`g(\vect{X})` has a finite second order moment. +Let :math:`T` be an iso-probabilistic transformation such that :math:`\vect{Z} = T(\vect{X})` +follows a distribution uniquely defined by all its moments. +Let :math:`h` be the function defined by: .. math:: - \xi = T(\boldsymbol{x}) \in \mathcal{\bar{X}} + h = g \circ T^{-1}. -for any :math:`\boldsymbol{x} \in \mathcal{X}`. -Let :math:`h` be the function defined by the equation : +The polynomial chaos decomposition of :math:`h` with respect to the measure of +:math:`\vect{Z}` is (see [blatman2009]_ page 73) : .. math:: - h(\boldsymbol{\xi}) = \left(g \circ T^{-1}\right)(\boldsymbol{\xi}) + h(\vect{z}) = \sum_{\vect{\alpha} \in \mathbb{N}^p} + a_{\vect{\alpha}} \psi_{\vect{\alpha}}(\vect{z}) -for any :math:`\boldsymbol{\xi} \in \mathcal{\bar{X}}`. -The polynomial chaos decomposition of :math:`h` is ([blatman2009]_ page 73) : +where :math:`\vect{\alpha} = (\alpha_1, ..., \alpha_p) \in \mathbb{N}^p` +is a multiindex, :math:`a_{\vect{\alpha}} \in \mathbb{R}` is the +coefficient, :math:`\psi_{\vect{\alpha}} : \mathcal{\bar{X}} \rightarrow \mathbb{R}` +is a multivariate polynomial. -.. math:: - - h(\boldsymbol{\xi}) = \sum_{\boldsymbol{\alpha} \in \mathbb{N}^p} - a_{\boldsymbol{\alpha}} \psi_{\boldsymbol{\alpha}}(\boldsymbol{\xi}) + \epsilon - -where :math:`\boldsymbol{\alpha} = (\alpha_1, ..., \alpha_p) \in \mathbb{N}^p` -is a multiindex, :math:`a_{\boldsymbol{\alpha}} \in \mathbb{R}` is the -coefficient, :math:`\psi_{\boldsymbol{\alpha}} : \mathcal{\bar{X}} \rightarrow \mathbb{R}` -is a multivariate polynomial and :math:`\epsilon` is a random variable. - -.. GENERATED FROM PYTHON SOURCE LINES 45-87 +.. GENERATED FROM PYTHON SOURCE LINES 41-83 Truncated expansion ------------------- @@ -75,28 +71,28 @@ Let :math:`\mathcal{A}^{d}` be the multi-index set defined by .. math:: - \mathcal{A}^{d} = \left\{ \boldsymbol{\alpha} \in \mathbb{N}^p - \; | \; \|\boldsymbol{\alpha}\|_1 \leq d\right\} + \mathcal{A}^{d} = \left\{ \vect{\alpha} \in \mathbb{N}^p + \; | \; \|\vect{\alpha}\|_1 \leq d\right\} where .. math:: - \|\boldsymbol{\alpha}\|_d = \alpha_1 + ... + \alpha_p + \|\vect{\alpha}\|_d = \alpha_1 + ... + \alpha_p -is the 1-norm of the multi-index :math:`\boldsymbol{\alpha}`. +is the 1-norm of the multi-index :math:`\vect{\alpha}`. Therefore, the truncated polynomial chaos expansion is: .. math:: - h(\boldsymbol{\xi}) = \sum_{\boldsymbol{\alpha} \in \mathcal{A}^{d}} - a_{\boldsymbol{\alpha}} \psi_{\boldsymbol{\alpha}}(\boldsymbol{\xi}) + \epsilon. + \widetilde{h}(\vect{z}) = \sum_{\vect{\alpha} \in \mathcal{A}^{d}} + a_{\vect{\alpha}} \psi_{\vect{\alpha}}(\vect{z}). In order to ensure a low error, we may choose a large value of the parameter :math:`P`. This, however, leads to a large number of -coefficients :math:`\boldsymbol{\alpha} \in \mathcal{A}^{d}` to +coefficients :math:`\vect{\alpha} \in \mathcal{A}^{d}` to estimate. More precisely, the number of coefficients to estimate -is ([blatman2009]_ page 73) : +is (see [blatman2009]_ page 73) : .. math:: @@ -105,35 +101,35 @@ is ([blatman2009]_ page 73) : where :math:`p!` is the factorial number of :math:`p`. -.. GENERATED FROM PYTHON SOURCE LINES 90-123 +.. GENERATED FROM PYTHON SOURCE LINES 86-119 Low-rank polynomial chaos expansion ----------------------------------- -For any :math:`\boldsymbol{\alpha} \in \mathbb{N}^p`, let -:math:`\|\boldsymbol{\alpha}\|_0` be the rank of the multiindex, that is, +For any :math:`\vect{\alpha} \in \mathbb{N}^p`, let +:math:`\|\vect{\alpha}\|_0` be the rank of the multiindex, that is, the number of nonzero components: .. math:: - \|\boldsymbol{\alpha}\|_0 = \sum_{i = 1}^p \boldsymbol{1}_{\alpha_i > 0} + \|\vect{\alpha}\|_0 = \sum_{i = 1}^p \vect{1}_{\alpha_i > 0} -where :math:`\boldsymbol{1}` is the indicator function. +where :math:`\vect{1}` is the indicator function. The multiindex set of maximum total degree :math:`d \in \mathbb{N}` and maximum rank :math:`j \in \mathbb{N}` is ([blatman2009]_ page 74): .. math:: - \mathcal{A}^{d,j} = \left\{ \boldsymbol{\alpha} \in \mathbb{N}^p - \; | \; \|\boldsymbol{\alpha}\|_1 \leq d, \; - \; \|\boldsymbol{\alpha}\|_0 \leq j\right\}. + \mathcal{A}^{d,j} = \left\{ \vect{\alpha} \in \mathbb{N}^p + \; | \; \|\vect{\alpha}\|_1 \leq d, \; + \; \|\vect{\alpha}\|_0 \leq j\right\}. Therefore, the rank-`j` polynomial chaos expansion is: .. math:: - h(\boldsymbol{\xi}) = \sum_{\boldsymbol{\alpha} \in - \mathcal{A}^{d,j}} a_{\boldsymbol{\alpha}} - \psi_{\boldsymbol{\alpha}}(\boldsymbol{\xi}) + \epsilon. + \widetilde{h}(\vect{z}) = \sum_{\vect{\alpha} \in + \mathcal{A}^{d,j}} a_{\vect{\alpha}} + \psi_{\vect{\alpha}}(\vect{z}). The rank is now a hyperparameter of the model: [blatman2009]_ suggests to use :math:`j = 2, 3, 4`. An example of low-rank PCE for the G-Sobol' @@ -141,14 +137,14 @@ function is given in [blatman2009]_ page 75. *Note.* It is currently not possible to create a low-rank PCE. -.. GENERATED FROM PYTHON SOURCE LINES 126-180 +.. GENERATED FROM PYTHON SOURCE LINES 122-175 Model selection --------------- If :math:`\textrm{card}\left(\mathcal{A}^{d}\right)` is large, many coefficients may be poorly estimated, which may reduce the quality of the metamodel. We may want to select a subset of the coefficients which best predict the output. -In other words, we may compute a subset +In other words, we may compute a subset: .. math:: @@ -158,47 +154,46 @@ such that ([blatman2009]_ page 86) : .. math:: - h(\boldsymbol{\xi}) = \sum_{\boldsymbol{\alpha} \in \mathcal{A}} - a_{\boldsymbol{\alpha}} \psi_{\boldsymbol{\alpha}}(\boldsymbol{\xi}) - + \epsilon. + \widetilde{h}(\vect{z}) = \sum_{\vect{\alpha} \in \mathcal{A}} + a_{\vect{\alpha}} \psi_{\vect{\alpha}}(\vect{z}) An enumeration rule is a function from the set of integers :math:`k` to -the corresponding set of multiindices :math:`\boldsymbol{\alpha}`. More +the corresponding set of multiindices :math:`\vect{\alpha}`. More precisely, let :math:`r : \mathbb{N} \rightarrow \mathbb{N}^p` be the function such that : .. math:: - r(k) = \boldsymbol{\alpha} + r(k) = \vect{\alpha} for any :math:`k \geq 0`. Let :math:`K \in \mathbb{N}` be a parameter representing the number of coefficients considered in the selection. Given an enumeration rule for -the multiindices :math:`\boldsymbol{\alpha}`, at most :math:`K` multiindices +the multiindices :math:`\vect{\alpha}`, at most :math:`K` multiindices will be considered. Let :math:`\mathcal{A}_K` be the corresponding multiindex set : .. math:: - \mathcal{A}_K = \left\{ \boldsymbol{\alpha} - \; | \; r^{-1}(\boldsymbol{\alpha}) = k \leq K \right\}. + \mathcal{A}_K = \left\{ \vect{\alpha} + \; | \; r^{-1}(\vect{\alpha}) = k \leq K \right\}. Let :math:`\epsilon > 0` be a parameter representing the minimum relative -value of a significant coefficient :math:`a_{\boldsymbol{\alpha}}`. +value of a significant coefficient :math:`a_{\vect{\alpha}}`. The :class:`~openturns.CleaningStrategy` uses the following criteria to select the coefficients : .. math:: \mathcal{A}_\epsilon = \left\{ - |a_{\boldsymbol{\alpha}}| \geq \epsilon \max_{ a_{\boldsymbol{\alpha}} - \in \mathcal{A}_K } |a_{\boldsymbol{\alpha}}| \right\} + |a_{\vect{\alpha}}| \geq \epsilon \max_{ a_{\vect{\alpha}} + \in \mathcal{A}_K } |a_{\vect{\alpha}}| \right\} where :math:`\epsilon` is the significance factor, which by default is :math:`\epsilon = 10^{-4}`. This rule selects only the coefficients which are significantly different from zero. -.. GENERATED FROM PYTHON SOURCE LINES 183-213 +.. GENERATED FROM PYTHON SOURCE LINES 178-208 Sparsity index -------------- @@ -210,8 +205,8 @@ in the set : .. math:: - d := \textrm{max}_{\boldsymbol{\alpha} \in \mathcal{A}} - \|\boldsymbol{\alpha}\|_1. + d := \textrm{max}_{\vect{\alpha} \in \mathcal{A}} + \|\vect{\alpha}\|_1. The index of sparsity of :math:`\mathcal{A}` is ([blatman2009]_ eq. 4.42 page 86) : @@ -231,7 +226,7 @@ to 1 when the model is very sparse: = 1 - \frac{\textrm{card}(\mathcal{A})}{\textrm{card}\left(\mathcal{A}^d\right)}. -.. GENERATED FROM PYTHON SOURCE LINES 216-222 +.. GENERATED FROM PYTHON SOURCE LINES 211-217 .. code-block:: Python @@ -248,13 +243,13 @@ to 1 when the model is very sparse: -.. GENERATED FROM PYTHON SOURCE LINES 223-226 +.. GENERATED FROM PYTHON SOURCE LINES 218-221 The following function takes a polynomial chaos result as input and prints a given maximum number of coefficients of this polynomial. It can take into account a threshold, so that we can avoid to print coefficients which are very close to zero. -.. GENERATED FROM PYTHON SOURCE LINES 230-266 +.. GENERATED FROM PYTHON SOURCE LINES 225-261 .. code-block:: Python @@ -301,13 +296,13 @@ a threshold, so that we can avoid to print coefficients which are very close to -.. GENERATED FROM PYTHON SOURCE LINES 267-270 +.. GENERATED FROM PYTHON SOURCE LINES 262-265 The next function computes the polynomial chaos Q2 score using simple validation on a test sample generated by Monte-Carlo sampling. The actual computation is performed by the :class:`~openturns.MetaModelValidation` class. -.. GENERATED FROM PYTHON SOURCE LINES 274-306 +.. GENERATED FROM PYTHON SOURCE LINES 269-301 .. code-block:: Python @@ -350,13 +345,13 @@ is performed by the :class:`~openturns.MetaModelValidation` class. -.. GENERATED FROM PYTHON SOURCE LINES 307-310 +.. GENERATED FROM PYTHON SOURCE LINES 302-305 The following function creates a validation plot using the :meth:`~openturns.MetaModelValidation.drawValidation` method of the `MetaModelValidation` class. -.. GENERATED FROM PYTHON SOURCE LINES 314-350 +.. GENERATED FROM PYTHON SOURCE LINES 309-345 .. code-block:: Python @@ -403,7 +398,7 @@ of the `MetaModelValidation` class. -.. GENERATED FROM PYTHON SOURCE LINES 351-357 +.. GENERATED FROM PYTHON SOURCE LINES 346-352 We consider the Ishigami model. Its three inputs are i.i.d. random variables that follow the uniform distribution on the @@ -412,7 +407,7 @@ purpose because it is highly non linear, so that a high polynomial degree will be required in order to produce a polynomial chaos expansion with Q2 score sufficiently close to 1. -.. GENERATED FROM PYTHON SOURCE LINES 360-366 +.. GENERATED FROM PYTHON SOURCE LINES 355-361 .. code-block:: Python @@ -435,7 +430,7 @@ score sufficiently close to 1. -.. GENERATED FROM PYTHON SOURCE LINES 367-378 +.. GENERATED FROM PYTHON SOURCE LINES 362-373 Then we create the multivariate basis onto which the function is expanded. By default, it is associated with the linear enumeration rule. Since our @@ -449,7 +444,7 @@ of the enumeration function computes the number of layers necessary to achieve that total degree. Then the number of functions up to that layer is computed with the :meth:`~openturns.EnumerateFunction.getStrataCumulatedCardinal` method. -.. GENERATED FROM PYTHON SOURCE LINES 381-395 +.. GENERATED FROM PYTHON SOURCE LINES 376-390 .. code-block:: Python @@ -482,7 +477,7 @@ with the :meth:`~openturns.EnumerateFunction.getStrataCumulatedCardinal` method. -.. GENERATED FROM PYTHON SOURCE LINES 396-403 +.. GENERATED FROM PYTHON SOURCE LINES 391-398 We compute the coefficients using a multivariate tensor product Gaussian quadrature rule. Since the coefficients are computed in the standardized @@ -492,7 +487,7 @@ order to get that standardized distribution. Then we use the using 6 nodes on each of the dimensions. -.. GENERATED FROM PYTHON SOURCE LINES 406-417 +.. GENERATED FROM PYTHON SOURCE LINES 401-412 .. code-block:: Python @@ -521,7 +516,7 @@ of the dimensions. -.. GENERATED FROM PYTHON SOURCE LINES 418-423 +.. GENERATED FROM PYTHON SOURCE LINES 413-418 We see that 216 nodes are involved in this quadrature rule, which is the result of :math:`6^3 = 216`. @@ -529,7 +524,7 @@ of :math:`6^3 = 216`. In the next cell, we compute the coefficients of the polynomial chaos expansion using integration. -.. GENERATED FROM PYTHON SOURCE LINES 426-433 +.. GENERATED FROM PYTHON SOURCE LINES 421-428 .. code-block:: Python @@ -547,14 +542,14 @@ using integration. -.. GENERATED FROM PYTHON SOURCE LINES 434-438 +.. GENERATED FROM PYTHON SOURCE LINES 429-433 We now validate the metamodel by drawing the validation graph. We see that many points are close to the red test line, which indicates that the predictions of the polynomial chaos expansion are close to the output observations from the model. -.. GENERATED FROM PYTHON SOURCE LINES 441-443 +.. GENERATED FROM PYTHON SOURCE LINES 436-438 .. code-block:: Python @@ -572,12 +567,12 @@ observations from the model. -.. GENERATED FROM PYTHON SOURCE LINES 444-446 +.. GENERATED FROM PYTHON SOURCE LINES 439-441 In order to have a closer look on the result, we use the `printCoefficientsTable` function in order to print the first 10 coefficients. -.. GENERATED FROM PYTHON SOURCE LINES 449-452 +.. GENERATED FROM PYTHON SOURCE LINES 444-447 .. code-block:: Python @@ -609,7 +604,7 @@ In order to have a closer look on the result, we use the -.. GENERATED FROM PYTHON SOURCE LINES 453-459 +.. GENERATED FROM PYTHON SOURCE LINES 448-454 We see that there are 56 coefficients in the metamodel and that many of these coefficients are close to zero. @@ -618,7 +613,7 @@ If we print only coefficients greater than :math:`10^{-14}`, we see that only a fraction of them are significant and that these significant coefficients have a relatively large polynomial degree. -.. GENERATED FROM PYTHON SOURCE LINES 462-465 +.. GENERATED FROM PYTHON SOURCE LINES 457-460 .. code-block:: Python @@ -648,21 +643,21 @@ have a relatively large polynomial degree. -.. GENERATED FROM PYTHON SOURCE LINES 466-468 +.. GENERATED FROM PYTHON SOURCE LINES 461-463 The previous experiments suggest to keep only the coefficients which are significant in the model: this is the topic of the next section. -.. GENERATED FROM PYTHON SOURCE LINES 471-473 +.. GENERATED FROM PYTHON SOURCE LINES 466-468 Use a model selection method ---------------------------- -.. GENERATED FROM PYTHON SOURCE LINES 477-487 +.. GENERATED FROM PYTHON SOURCE LINES 472-482 The `CleaningStrategy` has the following algorithm. On input, it considers only the first `maximumConsideredTerms` coefficients -:math:`a_{\boldsymbol{\alpha}}`. On output it selects the `mostSignificant` +:math:`a_{\vect{\alpha}}`. On output it selects the `mostSignificant` most significant coefficients. To do this, it uses the `significanceFactor` parameter. @@ -671,7 +666,7 @@ The following function will help to create a sparse PCE using the in the expansion, the number of significant coefficients to keep and the relative factor and returns the Q2 score. -.. GENERATED FROM PYTHON SOURCE LINES 491-537 +.. GENERATED FROM PYTHON SOURCE LINES 486-532 .. code-block:: Python @@ -728,12 +723,12 @@ relative factor and returns the Q2 score. -.. GENERATED FROM PYTHON SOURCE LINES 538-540 +.. GENERATED FROM PYTHON SOURCE LINES 533-535 In the next cell, we consider at most 500 coefficients and keep only the 5 most significant coefficients. The factor is set to a relatively low value. -.. GENERATED FROM PYTHON SOURCE LINES 543-551 +.. GENERATED FROM PYTHON SOURCE LINES 538-546 .. code-block:: Python @@ -766,7 +761,7 @@ the 5 most significant coefficients. The factor is set to a relatively low value -.. GENERATED FROM PYTHON SOURCE LINES 552-559 +.. GENERATED FROM PYTHON SOURCE LINES 547-554 We see that when we keep only 5 coefficients among the first 500 ones, these coefficients have a very high polynomial degree. Indeed, it occurs that @@ -776,7 +771,7 @@ criteria selects them as significant coefficients, which leads to a poor metamod Let us reduce the number of considered coefficients and increase the number of selected coefficients. -.. GENERATED FROM PYTHON SOURCE LINES 562-570 +.. GENERATED FROM PYTHON SOURCE LINES 557-565 .. code-block:: Python @@ -812,7 +807,7 @@ of selected coefficients. -.. GENERATED FROM PYTHON SOURCE LINES 571-583 +.. GENERATED FROM PYTHON SOURCE LINES 566-578 When we keep only 10 coefficients among the first 56 ones, the polynomial chaos metamodel is much better: the coefficients are associated with a low @@ -827,7 +822,7 @@ combination with highest :math:`Q^2` coefficient. As shown in [muller2016]_ page 268, the computed :math:`Q^2` may be optimistic, but this is not the point of the current example. -.. GENERATED FROM PYTHON SOURCE LINES 587-608 +.. GENERATED FROM PYTHON SOURCE LINES 582-603 .. code-block:: Python @@ -867,13 +862,13 @@ point of the current example. -.. GENERATED FROM PYTHON SOURCE LINES 609-612 +.. GENERATED FROM PYTHON SOURCE LINES 604-607 We see that the best solution could be to select at most 16 significant coefficients among the first 101 ones. Let us see the Q2 score and the coefficients in this situation. -.. GENERATED FROM PYTHON SOURCE LINES 615-620 +.. GENERATED FROM PYTHON SOURCE LINES 610-615 .. code-block:: Python @@ -908,14 +903,14 @@ coefficients in this situation. -.. GENERATED FROM PYTHON SOURCE LINES 621-625 +.. GENERATED FROM PYTHON SOURCE LINES 616-620 These parameters lead to a total number of coefficients equal to 12. Among the 16 most significant coefficients, only 12 satisfy the criteria. Most of the coefficients have a small polynomial degree although some have a total degree as large as 7. -.. GENERATED FROM PYTHON SOURCE LINES 628-655 +.. GENERATED FROM PYTHON SOURCE LINES 623-650 Intermediate steps of the algorithm ----------------------------------- @@ -934,7 +929,7 @@ of the algorithm. The datastructure uses several variables that we now describe. - `currentVectorIndex_` : the current value of the index in the full multiindex set, according to the enumeration rule. Each time the selection method is called, it is passed a -coefficient :math:`a_{\boldsymbol{\alpha}}` which is a new candidate to be +coefficient :math:`a_{\vect{\alpha}}` which is a new candidate to be considered by the algorithm. The first time the method is evaluated, the active multiindex set is empty, so that it must be filled with the first coefficients in the multiindex set, according to the enumeration rule. The @@ -945,7 +940,7 @@ Executing the function prints messages that we can process to produce the following listing. On each step, we print the list of integers corresponding to the indices of the coefficients in the active multiindex set. -.. GENERATED FROM PYTHON SOURCE LINES 658-694 +.. GENERATED FROM PYTHON SOURCE LINES 653-689 .. code-block:: python @@ -984,7 +979,7 @@ to the indices of the coefficients in the active multiindex set. Step 86: [0, 1, 7, 10, 15, 30, 35, 40, 49, 84, 89, 98] Step 87: [0, 1, 7, 10, 15, 30, 35, 40, 49, 84, 89, 98] -.. GENERATED FROM PYTHON SOURCE LINES 697-740 +.. GENERATED FROM PYTHON SOURCE LINES 692-735 The previous text (and a detailed analysis of the output file) allows one to understand what exactly happens in the algorithm. To understand each step, @@ -1030,7 +1025,7 @@ more coefficients since we provided 16 slots to fill thanks to the `mostSignificant` parameter. The considered coefficients were, however, too close to zero and were below the threshold. -.. GENERATED FROM PYTHON SOURCE LINES 744-750 +.. GENERATED FROM PYTHON SOURCE LINES 739-745 Conclusion ---------- @@ -1042,7 +1037,7 @@ produce the best Q2 score. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.174 seconds) + **Total running time of the script:** (0 minutes 2.736 seconds) .. _sphx_glr_download_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cleaning_strategy.py: diff --git a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_cv.rst.txt b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_cv.rst.txt index 902c732d7a7..48ca4adc6f2 100644 --- a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_cv.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_cv.rst.txt @@ -672,7 +672,7 @@ When estimating the :math:`Q^2` score with the best parameters, the test set is .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 10.612 seconds) + **Total running time of the script:** (0 minutes 12.021 seconds) .. _sphx_glr_download_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cv.py: diff --git a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_sobol_confidence.rst.txt b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_sobol_confidence.rst.txt index f9d40f73e5a..b2ea575aaa1 100644 --- a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_sobol_confidence.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_chaos_sobol_confidence.rst.txt @@ -619,7 +619,7 @@ Hence, there is no evidence that the Sobol' indices of E are zero. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.809 seconds) + **Total running time of the script:** (0 minutes 3.196 seconds) .. _sphx_glr_download_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_sobol_confidence.py: diff --git a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_enumeratefunction.rst.txt b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_enumeratefunction.rst.txt index aa86318f4c8..5691b131578 100644 --- a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_enumeratefunction.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_enumeratefunction.rst.txt @@ -402,6 +402,11 @@ Reset default settings +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 2.266 seconds) + + .. _sphx_glr_download_auto_meta_modeling_polynomial_chaos_metamodel_plot_enumeratefunction.py: .. only:: html diff --git a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos.rst.txt b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos.rst.txt index a5c8000fad0..28b5be57846 100644 --- a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos.rst.txt @@ -907,7 +907,7 @@ Reset default settings .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 10.942 seconds) + **Total running time of the script:** (0 minutes 15.343 seconds) .. _sphx_glr_download_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos.py: diff --git a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/sg_execution_times.rst.txt b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/sg_execution_times.rst.txt index b2320b56edc..28651627409 100644 --- a/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_meta_modeling/polynomial_chaos_metamodel/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:37.609** total execution time for 16 files **from auto_meta_modeling/polynomial_chaos_metamodel**: +**00:47.135** total execution time for 16 files **from auto_meta_modeling/polynomial_chaos_metamodel**: .. container:: @@ -33,50 +33,50 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos.py` (``plot_functional_chaos.py``) - - 00:10.942 + - 00:15.343 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cv.py` (``plot_chaos_cv.py``) - - 00:10.612 + - 00:12.021 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_beam_sensitivity_degree.py` (``plot_chaos_beam_sensitivity_degree.py``) - - 00:06.189 + - 00:07.290 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_sobol_confidence.py` (``plot_chaos_sobol_confidence.py``) - - 00:02.809 - - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cleaning_strategy.py` (``plot_chaos_cleaning_strategy.py``) - - 00:02.174 + - 00:03.196 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_build_distribution.py` (``plot_chaos_build_distribution.py``) - - 00:01.970 + - 00:02.839 + - 0.0 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cleaning_strategy.py` (``plot_chaos_cleaning_strategy.py``) + - 00:02.736 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_enumeratefunction.py` (``plot_enumeratefunction.py``) - - 00:01.809 + - 00:02.266 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_ishigami.py` (``plot_chaos_ishigami.py``) - - 00:00.366 + - 00:00.462 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cantilever_beam_integration.py` (``plot_chaos_cantilever_beam_integration.py``) - - 00:00.278 + - 00:00.391 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_graphs.py` (``plot_functional_chaos_graphs.py``) - - 00:00.235 + - 00:00.280 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_draw_validation.py` (``plot_chaos_draw_validation.py``) - - 00:00.122 + - 00:00.164 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_database.py` (``plot_functional_chaos_database.py``) - - 00:00.051 + - 00:00.081 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_exploitation.py` (``plot_functional_chaos_exploitation.py``) - - 00:00.023 + - 00:00.029 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_ishigami_grouped_indices.py` (``plot_chaos_ishigami_grouped_indices.py``) - - 00:00.019 + - 00:00.022 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_advanced_ctors.py` (``plot_functional_chaos_advanced_ctors.py``) - - 00:00.006 + - 00:00.010 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_distribution_transformation.py` (``plot_chaos_distribution_transformation.py``) - - 00:00.003 + - 00:00.004 - 0.0 diff --git a/openturns/master/_sources/auto_numerical_methods/general_methods/plot_ifs.rst.txt b/openturns/master/_sources/auto_numerical_methods/general_methods/plot_ifs.rst.txt index d427268380a..e589893ee95 100644 --- a/openturns/master/_sources/auto_numerical_methods/general_methods/plot_ifs.rst.txt +++ b/openturns/master/_sources/auto_numerical_methods/general_methods/plot_ifs.rst.txt @@ -275,7 +275,7 @@ Sierpinski triangle .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 7.370 seconds) + **Total running time of the script:** (0 minutes 7.338 seconds) .. _sphx_glr_download_auto_numerical_methods_general_methods_plot_ifs.py: diff --git a/openturns/master/_sources/auto_numerical_methods/general_methods/plot_pce_design.rst.txt b/openturns/master/_sources/auto_numerical_methods/general_methods/plot_pce_design.rst.txt index 355540a6e0c..f9506560f07 100644 --- a/openturns/master/_sources/auto_numerical_methods/general_methods/plot_pce_design.rst.txt +++ b/openturns/master/_sources/auto_numerical_methods/general_methods/plot_pce_design.rst.txt @@ -547,7 +547,7 @@ Compute leave-one-out error .. code-block:: none - mseLOO = 12.803440112606266 + mseLOO = 13.842823814127744 @@ -685,8 +685,8 @@ perform elementwise division and exponentiation .. code-block:: none - MSE LOO = 12.803440112606296 - Q2 LOO = -0.0005040512152780785 + MSE LOO = 13.84282381412771 + Q2 LOO = 0.2177983482283371 diff --git a/openturns/master/_sources/auto_numerical_methods/general_methods/plot_random_generator.rst.txt b/openturns/master/_sources/auto_numerical_methods/general_methods/plot_random_generator.rst.txt index 9e7e0529e25..1034296499a 100644 --- a/openturns/master/_sources/auto_numerical_methods/general_methods/plot_random_generator.rst.txt +++ b/openturns/master/_sources/auto_numerical_methods/general_methods/plot_random_generator.rst.txt @@ -127,7 +127,7 @@ Example 3: using a previously saved generator state .. raw:: html
- class=Point name=Unnamed dimension=1 values=[0.512882] + class=Point name=Unnamed dimension=1 values=[1.87841]


@@ -150,7 +150,7 @@ load the generator state .. raw:: html
- class=Point name=Unnamed dimension=1 values=[0.512882] + class=Point name=Unnamed dimension=1 values=[1.87841]


diff --git a/openturns/master/_sources/auto_numerical_methods/general_methods/sg_execution_times.rst.txt b/openturns/master/_sources/auto_numerical_methods/general_methods/sg_execution_times.rst.txt index 7c968d90dc4..b734caa9849 100644 --- a/openturns/master/_sources/auto_numerical_methods/general_methods/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_numerical_methods/general_methods/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:08.390** total execution time for 8 files **from auto_numerical_methods/general_methods**: +**00:08.344** total execution time for 8 files **from auto_numerical_methods/general_methods**: .. container:: @@ -33,25 +33,25 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_ifs.py` (``plot_ifs.py``) - - 00:07.370 + - 00:07.338 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_regression_sinus.py` (``plot_regression_sinus.py``) - - 00:00.410 + - 00:00.399 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_pce_design.py` (``plot_pce_design.py``) - - 00:00.382 + - 00:00.374 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_estimate_integral_iterated_quadrature.py` (``plot_estimate_integral_iterated_quadrature.py``) - - 00:00.127 + - 00:00.130 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_regression_interval.py` (``plot_regression_interval.py``) - - 00:00.089 + - 00:00.090 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_study_save_load.py` (``plot_study_save_load.py``) - 00:00.010 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_random_generator.py` (``plot_random_generator.py``) - - 00:00.001 + - 00:00.002 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_combinatorial_generator.py` (``plot_combinatorial_generator.py``) - 00:00.001 diff --git a/openturns/master/_sources/auto_numerical_methods/iterative_statistics/sg_execution_times.rst.txt b/openturns/master/_sources/auto_numerical_methods/iterative_statistics/sg_execution_times.rst.txt index af46e93ef15..ca62fbb2796 100644 --- a/openturns/master/_sources/auto_numerical_methods/iterative_statistics/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_numerical_methods/iterative_statistics/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:00.580** total execution time for 3 files **from auto_numerical_methods/iterative_statistics**: +**00:00.587** total execution time for 3 files **from auto_numerical_methods/iterative_statistics**: .. container:: @@ -36,8 +36,8 @@ Computation times - 00:00.283 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_extrema.py` (``plot_iterative_extrema.py``) - - 00:00.157 + - 00:00.161 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_moments.py` (``plot_iterative_moments.py``) - - 00:00.140 + - 00:00.144 - 0.0 diff --git a/openturns/master/_sources/auto_numerical_methods/optimization/plot_control_termination.rst.txt b/openturns/master/_sources/auto_numerical_methods/optimization/plot_control_termination.rst.txt index 7d20524ad38..7b89344d689 100644 --- a/openturns/master/_sources/auto_numerical_methods/optimization/plot_control_termination.rst.txt +++ b/openturns/master/_sources/auto_numerical_methods/optimization/plot_control_termination.rst.txt @@ -209,8 +209,8 @@ Run algorithm .. code-block:: none - event probability: 0.14060949681077267 - calls number: 7055 + event probability: 0.1409395973154362 + calls number: 6556 diff --git a/openturns/master/_sources/auto_numerical_methods/optimization/plot_ego.rst.txt b/openturns/master/_sources/auto_numerical_methods/optimization/plot_ego.rst.txt index 5b6b67ed2d8..a0b01ad0497 100644 --- a/openturns/master/_sources/auto_numerical_methods/optimization/plot_ego.rst.txt +++ b/openturns/master/_sources/auto_numerical_methods/optimization/plot_ego.rst.txt @@ -341,7 +341,7 @@ In the Ackley example, we choose to perform 10 iterations of the algorithm. .. raw:: html
- class=Point name=Unnamed dimension=2 values=[0.0115245,0.174226] + class=Point name=Unnamed dimension=2 values=[0.0113859,0.174231]


@@ -360,7 +360,7 @@ In the Ackley example, we choose to perform 10 iterations of the algorithm. .. raw:: html
- class=Point name=Unnamed dimension=1 values=[1.13547] + class=Point name=Unnamed dimension=1 values=[1.13543]


@@ -491,7 +491,7 @@ This is why we finalize the process by adding a local optimization step. .. raw:: html
- class=Point name=Unnamed dimension=2 values=[2.08532e-07,-1.02771e-07] + class=Point name=Unnamed dimension=2 values=[-2.31186e-07,8.12325e-07]


@@ -840,7 +840,7 @@ We run the algorithm and get the result: .. raw:: html
- class=Point name=Unnamed dimension=2 values=[0.547934,0.166686] + class=Point name=Unnamed dimension=2 values=[0.547937,0.166695]


@@ -974,7 +974,7 @@ Reset default settings .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.654 seconds) + **Total running time of the script:** (0 minutes 2.755 seconds) .. _sphx_glr_download_auto_numerical_methods_optimization_plot_ego.py: diff --git a/openturns/master/_sources/auto_numerical_methods/optimization/plot_optimization_dlib.rst.txt b/openturns/master/_sources/auto_numerical_methods/optimization/plot_optimization_dlib.rst.txt index d3dde75f073..bd2dd8d7c50 100644 --- a/openturns/master/_sources/auto_numerical_methods/optimization/plot_optimization_dlib.rst.txt +++ b/openturns/master/_sources/auto_numerical_methods/optimization/plot_optimization_dlib.rst.txt @@ -450,13 +450,13 @@ Retrieve results .. code-block:: none - x^ = [2.75542,1.1809,0.5] - f(x^) = [1.18329e-30] - Iteration number: 9 - Evaluation number: 12 - Absolute error: 1.5447704398252394e-09 - Relative error: 5.082779161445124e-10 - Residual error: 5.151497309257793e-18 + x^ = [2.65188,1.13652,0.5] + f(x^) = [1.09024e-21] + Iteration number: 7 + Evaluation number: 8 + Absolute error: 4.382527526005035e-06 + Relative error: 1.4966833956274179e-06 + Residual error: 1.0420355815718745e-11 Constraint error: 0.0 diff --git a/openturns/master/_sources/auto_numerical_methods/optimization/sg_execution_times.rst.txt b/openturns/master/_sources/auto_numerical_methods/optimization/sg_execution_times.rst.txt index 0663d05d3a8..1cf7d160045 100644 --- a/openturns/master/_sources/auto_numerical_methods/optimization/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_numerical_methods/optimization/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:05.228** total execution time for 11 files **from auto_numerical_methods/optimization**: +**00:05.347** total execution time for 11 files **from auto_numerical_methods/optimization**: .. container:: @@ -33,25 +33,25 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_ego.py` (``plot_ego.py``) - - 00:02.654 + - 00:02.755 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rastrigin.py` (``plot_optimization_rastrigin.py``) - - 00:00.951 + - 00:00.972 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rosenbrock.py` (``plot_optimization_rosenbrock.py``) - - 00:00.840 + - 00:00.848 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_dlib.py` (``plot_optimization_dlib.py``) - - 00:00.369 + - 00:00.355 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_pagmo.py` (``plot_optimization_pagmo.py``) - - 00:00.164 + - 00:00.167 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_bonmin.py` (``plot_optimization_bonmin.py``) - 00:00.092 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_constraints.py` (``plot_optimization_constraints.py``) - - 00:00.069 + - 00:00.070 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_nlopt.py` (``plot_optimization_nlopt.py``) - 00:00.068 @@ -60,7 +60,7 @@ Computation times - 00:00.015 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_minmax_optimization.py` (``plot_minmax_optimization.py``) - - 00:00.003 + - 00:00.004 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_minmax_by_random_design.py` (``plot_minmax_by_random_design.py``) - 00:00.003 diff --git a/openturns/master/_sources/auto_probabilistic_modeling/copulas/sg_execution_times.rst.txt b/openturns/master/_sources/auto_probabilistic_modeling/copulas/sg_execution_times.rst.txt index 123d9c1123d..15f31a6498a 100644 --- a/openturns/master/_sources/auto_probabilistic_modeling/copulas/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_probabilistic_modeling/copulas/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:00.411** total execution time for 4 files **from auto_probabilistic_modeling/copulas**: +**00:00.452** total execution time for 4 files **from auto_probabilistic_modeling/copulas**: .. container:: @@ -33,14 +33,14 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_ordinal_sum_copula.py` (``plot_ordinal_sum_copula.py``) - - 00:00.407 + - 00:00.448 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_composed_copula.py` (``plot_composed_copula.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_extract_copula.py` (``plot_extract_copula.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_create_copula.py` (``plot_create_copula.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_composed_copula.py` (``plot_composed_copula.py``) - 00:00.001 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_extract_copula.py` (``plot_extract_copula.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_create_copula.py` (``plot_create_copula.py``) - 00:00.001 - 0.0 diff --git a/openturns/master/_sources/auto_probabilistic_modeling/distributions/sg_execution_times.rst.txt b/openturns/master/_sources/auto_probabilistic_modeling/distributions/sg_execution_times.rst.txt index 35d8c7e9ab3..17200ec12b9 100644 --- a/openturns/master/_sources/auto_probabilistic_modeling/distributions/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_probabilistic_modeling/distributions/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:06.847** total execution time for 19 files **from auto_probabilistic_modeling/distributions**: +**00:08.101** total execution time for 19 files **from auto_probabilistic_modeling/distributions**: .. container:: @@ -33,58 +33,58 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_draw_multivariate_distributions.py` (``plot_create_draw_multivariate_distributions.py``) - - 00:01.084 + - 00:01.516 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_quick_start_guide_distributions.py` (``plot_quick_start_guide_distributions.py``) - - 00:00.957 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_minimum_volume_level_sets.py` (``plot_minimum_volume_level_sets.py``) - - 00:00.714 + - 00:01.124 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_transformation.py` (``plot_distribution_transformation.py``) - - 00:00.634 + - 00:00.787 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_minimum_volume_level_sets.py` (``plot_minimum_volume_level_sets.py``) + - 00:00.764 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_order_statistics_distribution.py` (``plot_order_statistics_distribution.py``) - - 00:00.580 + - 00:00.605 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_truncated_distribution.py` (``plot_truncated_distribution.py``) - - 00:00.428 + - 00:00.474 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_overview_univariate_distributions.py` (``plot_overview_univariate_distributions.py``) - - 00:00.385 + - 00:00.446 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_manipulation.py` (``plot_distribution_manipulation.py``) - - 00:00.299 + - 00:00.363 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_random_mixture.py` (``plot_create_random_mixture.py``) + - 00:00.311 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_extreme_value_distribution.py` (``plot_create_extreme_value_distribution.py``) - - 00:00.273 + - 00:00.297 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_and_draw_scalar_distributions.py` (``plot_create_and_draw_scalar_distributions.py``) - - 00:00.251 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_mixture_distribution.py` (``plot_mixture_distribution.py``) + - 00:00.289 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_random_mixture.py` (``plot_create_random_mixture.py``) - - 00:00.244 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_and_draw_scalar_distributions.py` (``plot_create_and_draw_scalar_distributions.py``) + - 00:00.271 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_bayes_distribution.py` (``plot_bayes_distribution.py``) - - 00:00.243 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_mixture_distribution.py` (``plot_mixture_distribution.py``) - - 00:00.229 + - 00:00.249 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_generate_by_inversion.py` (``plot_generate_by_inversion.py``) - - 00:00.161 + - 00:00.218 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_python_distribution.py` (``plot_python_distribution.py``) - - 00:00.145 + - 00:00.155 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_conditional_distribution.py` (``plot_conditional_distribution.py``) - - 00:00.094 + - 00:00.099 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_your_own_dist.py` (``plot_create_your_own_dist.py``) - - 00:00.064 + - 00:00.068 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_maximum_distribution.py` (``plot_maximum_distribution.py``) - - 00:00.059 + - 00:00.063 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_conditional_random_vector.py` (``plot_conditional_random_vector.py``) - 00:00.001 diff --git a/openturns/master/_sources/auto_probabilistic_modeling/random_vectors/sg_execution_times.rst.txt b/openturns/master/_sources/auto_probabilistic_modeling/random_vectors/sg_execution_times.rst.txt index bd8970596d7..2121ab13afb 100644 --- a/openturns/master/_sources/auto_probabilistic_modeling/random_vectors/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_probabilistic_modeling/random_vectors/sg_execution_times.rst.txt @@ -32,12 +32,12 @@ Computation times * - Example - Time - Mem (MB) - * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_random_vector_manipulation.py` (``plot_random_vector_manipulation.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_composite_random_vector.py` (``plot_composite_random_vector.py``) - 00:00.003 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_python_randomvector.py` (``plot_python_randomvector.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_random_vector_manipulation.py` (``plot_random_vector_manipulation.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_composite_random_vector.py` (``plot_composite_random_vector.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_python_randomvector.py` (``plot_python_randomvector.py``) - 00:00.002 - 0.0 diff --git a/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_create_mesh.rst.txt b/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_create_mesh.rst.txt index 490f8dafd67..3a8a8e02922 100644 --- a/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_create_mesh.rst.txt +++ b/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_create_mesh.rst.txt @@ -386,6 +386,11 @@ Display figures +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 2.289 seconds) + + .. _sphx_glr_download_auto_probabilistic_modeling_stochastic_processes_plot_create_mesh.py: .. only:: html diff --git a/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_gaussian_process_covariance_hmat.rst.txt b/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_gaussian_process_covariance_hmat.rst.txt index b1a1e49d324..c925a7f9219 100644 --- a/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_gaussian_process_covariance_hmat.rst.txt +++ b/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_gaussian_process_covariance_hmat.rst.txt @@ -228,7 +228,7 @@ Reset default settings .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.134 seconds) + **Total running time of the script:** (0 minutes 2.498 seconds) .. _sphx_glr_download_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_process_covariance_hmat.py: diff --git a/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_userdefined_covariance_model.rst.txt b/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_userdefined_covariance_model.rst.txt index 58de6a33a21..f6ead697ca3 100644 --- a/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_userdefined_covariance_model.rst.txt +++ b/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_userdefined_covariance_model.rst.txt @@ -157,7 +157,7 @@ Draw the covariance model .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.370 seconds) + **Total running time of the script:** (0 minutes 2.588 seconds) .. _sphx_glr_download_auto_probabilistic_modeling_stochastic_processes_plot_userdefined_covariance_model.py: diff --git a/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/sg_execution_times.rst.txt b/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/sg_execution_times.rst.txt index fab2557373b..3f9cd99e2c4 100644 --- a/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:12.843** total execution time for 24 files **from auto_probabilistic_modeling/stochastic_processes**: +**00:14.793** total execution time for 24 files **from auto_probabilistic_modeling/stochastic_processes**: .. container:: @@ -33,74 +33,74 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_userdefined_covariance_model.py` (``plot_userdefined_covariance_model.py``) - - 00:02.370 + - 00:02.588 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_process_covariance_hmat.py` (``plot_gaussian_process_covariance_hmat.py``) - - 00:02.134 + - 00:02.498 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_mesh.py` (``plot_create_mesh.py``) - - 00:01.876 + - 00:02.289 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_field_manipulation.py` (``plot_field_manipulation.py``) - - 00:01.720 + - 00:01.979 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_box_cox_transform.py` (``plot_box_cox_transform.py``) - - 00:01.238 + - 00:01.353 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_processes_comparison.py` (``plot_gaussian_processes_comparison.py``) - - 00:00.685 + - 00:00.793 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_process_manipulation.py` (``plot_process_manipulation.py``) - - 00:00.456 + - 00:00.511 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_random_walk_process.py` (``plot_random_walk_process.py``) - - 00:00.296 + - 00:00.345 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_and_manipulate_arma_process.py` (``plot_create_and_manipulate_arma_process.py``) - - 00:00.276 + - 00:00.344 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_discrete_markov_chain_process.py` (``plot_discrete_markov_chain_process.py``) - - 00:00.263 + - 00:00.310 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_kronecker_covmodel.py` (``plot_kronecker_covmodel.py``) - - 00:00.252 + - 00:00.306 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_white_noise_process.py` (``plot_white_noise_process.py``) + - 00:00.191 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_mix_rv_process.py` (``plot_mix_rv_process.py``) - - 00:00.159 + - 00:00.173 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_add_trend.py` (``plot_add_trend.py``) + - 00:00.164 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_export_field_vtk.py` (``plot_export_field_vtk.py``) - 00:00.155 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_white_noise_process.py` (``plot_white_noise_process.py``) - - 00:00.154 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_add_trend.py` (``plot_add_trend.py``) - - 00:00.134 - - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_normal_process.py` (``plot_create_normal_process.py``) - - 00:00.127 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_trend_transform.py` (``plot_trend_transform.py``) - - 00:00.119 + - 00:00.152 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_aggregated_process.py` (``plot_aggregated_process.py``) - - 00:00.116 + - 00:00.144 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_timeseries_manipulation.py` (``plot_timeseries_manipulation.py``) - - 00:00.113 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_trend_transform.py` (``plot_trend_transform.py``) + - 00:00.140 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_functional_basis_process.py` (``plot_functional_basis_process.py``) - - 00:00.072 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_timeseries_manipulation.py` (``plot_timeseries_manipulation.py``) + - 00:00.126 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_user_stationary_covmodel.py` (``plot_user_stationary_covmodel.py``) - - 00:00.065 + - 00:00.087 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_userdefined_spectral_model.py` (``plot_userdefined_spectral_model.py``) - - 00:00.060 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_functional_basis_process.py` (``plot_functional_basis_process.py``) + - 00:00.080 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_stationary_covmodel.py` (``plot_create_stationary_covmodel.py``) - - 00:00.001 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_userdefined_spectral_model.py` (``plot_userdefined_spectral_model.py``) + - 00:00.063 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_parametric_spectral_density.py` (``plot_parametric_spectral_density.py``) - - 00:00.001 + - 00:00.002 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_stationary_covmodel.py` (``plot_create_stationary_covmodel.py``) + - 00:00.002 - 0.0 diff --git a/openturns/master/_sources/auto_reliability_sensitivity/central_dispersion/sg_execution_times.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/central_dispersion/sg_execution_times.rst.txt index 1a3c6137d89..712eb5fd693 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/central_dispersion/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/central_dispersion/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:00.597** total execution time for 3 files **from auto_reliability_sensitivity/central_dispersion**: +**00:00.615** total execution time for 3 files **from auto_reliability_sensitivity/central_dispersion**: .. container:: @@ -33,11 +33,11 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_expectation_simulation_algorithm.py` (``plot_expectation_simulation_algorithm.py``) - - 00:00.429 + - 00:00.438 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_central_tendency.py` (``plot_central_tendency.py``) - - 00:00.122 + - 00:00.129 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_estimate_moments_taylor.py` (``plot_estimate_moments_taylor.py``) - - 00:00.046 + - 00:00.048 - 0.0 diff --git a/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/plot_smolyak_experiment.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/plot_smolyak_experiment.rst.txt index f8e97734ce3..969afc9f032 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/plot_smolyak_experiment.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/plot_smolyak_experiment.rst.txt @@ -294,7 +294,7 @@ This growth depends on the dimension of the problem. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.158 seconds) + **Total running time of the script:** (0 minutes 2.378 seconds) .. _sphx_glr_download_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_experiment.py: diff --git a/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/plot_smolyak_quadrature.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/plot_smolyak_quadrature.rst.txt index 79f784b4223..fce8feaec65 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/plot_smolyak_quadrature.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/plot_smolyak_quadrature.rst.txt @@ -584,7 +584,7 @@ We can finally create the graph. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 7.234 seconds) + **Total running time of the script:** (0 minutes 7.966 seconds) .. _sphx_glr_download_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_quadrature.py: diff --git a/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/sg_execution_times.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/sg_execution_times.rst.txt index 330835b0f19..65e7db3c808 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/design_of_experiments/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:14.538** total execution time for 17 files **from auto_reliability_sensitivity/design_of_experiments**: +**00:15.824** total execution time for 17 files **from auto_reliability_sensitivity/design_of_experiments**: .. container:: @@ -33,53 +33,53 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_quadrature.py` (``plot_smolyak_quadrature.py``) - - 00:07.234 + - 00:07.966 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_experiment.py` (``plot_smolyak_experiment.py``) - - 00:02.158 + - 00:02.378 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_plot_design.py` (``plot_plot_design.py``) - - 00:01.210 + - 00:01.262 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_design_of_experiments.py` (``plot_design_of_experiments.py``) - - 00:01.010 + - 00:01.110 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_low_discrepancy_sequence.py` (``plot_low_discrepancy_sequence.py``) - - 00:00.528 + - 00:00.552 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_optimal_lhs.py` (``plot_optimal_lhs.py``) - - 00:00.464 + - 00:00.534 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_merge.py` (``plot_smolyak_merge.py``) - - 00:00.463 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_deterministic_design.py` (``plot_deterministic_design.py``) - - 00:00.313 + - 00:00.487 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_create_deterministic_doe.py` (``plot_create_deterministic_doe.py``) - - 00:00.283 + - 00:00.310 + - 0.0 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_deterministic_design.py` (``plot_deterministic_design.py``) + - 00:00.278 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_indices.py` (``plot_smolyak_indices.py``) - - 00:00.254 + - 00:00.266 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_mixed_design.py` (``plot_mixed_design.py``) - - 00:00.176 + - 00:00.184 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_create_random_doe.py` (``plot_create_random_doe.py``) - - 00:00.118 + - 00:00.132 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_design_of_experiment_continuous_discrete.py` (``plot_design_of_experiment_continuous_discrete.py``) - - 00:00.115 + - 00:00.121 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_gauss_product_experiment.py` (``plot_gauss_product_experiment.py``) - - 00:00.057 + - 00:00.075 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_probabilistic_design.py` (``plot_probabilistic_design.py``) - - 00:00.055 + - 00:00.058 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_composite_experiment.py` (``plot_composite_experiment.py``) - - 00:00.053 + - 00:00.057 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_monte_carlo_experiment.py` (``plot_monte_carlo_experiment.py``) - - 00:00.048 + - 00:00.052 - 0.0 diff --git a/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_estimate_probability_adaptive_directional_sampling.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_estimate_probability_adaptive_directional_sampling.rst.txt index 1781f703446..fd2089d0e15 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_estimate_probability_adaptive_directional_sampling.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_estimate_probability_adaptive_directional_sampling.rst.txt @@ -216,6 +216,11 @@ Retrieve results. +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 2.100 seconds) + + .. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_estimate_probability_adaptive_directional_sampling.py: .. only:: html diff --git a/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_estimate_probability_form_oscillator.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_estimate_probability_form_oscillator.rst.txt index 7fa19caf80e..f3fbba1788e 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_estimate_probability_form_oscillator.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_estimate_probability_form_oscillator.rst.txt @@ -724,7 +724,7 @@ We can see that this post-treatment of FORM result allows one to greatly improve .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 4.877 seconds) + **Total running time of the script:** (0 minutes 5.128 seconds) .. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_estimate_probability_form_oscillator.py: diff --git a/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_nais.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_nais.rst.txt index 9661a6a8268..f5f2f0f3802 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_nais.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_nais.rst.txt @@ -586,7 +586,7 @@ Draw them! They are all in the event space. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.708 seconds) + **Total running time of the script:** (0 minutes 2.988 seconds) .. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_nais.py: diff --git a/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_proba_system_event.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_proba_system_event.rst.txt index c0015f9332b..031fe3d38d7 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_proba_system_event.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_proba_system_event.rst.txt @@ -387,7 +387,7 @@ Draw the graphs! .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.059 seconds) + **Total running time of the script:** (0 minutes 2.234 seconds) .. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_proba_system_event.py: diff --git a/openturns/master/_sources/auto_reliability_sensitivity/reliability/sg_execution_times.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/reliability/sg_execution_times.rst.txt index b42f1c98e0f..79af19ef7d8 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/reliability/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/reliability/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:19.628** total execution time for 24 files **from auto_reliability_sensitivity/reliability**: +**00:20.923** total execution time for 24 files **from auto_reliability_sensitivity/reliability**: .. container:: @@ -33,67 +33,67 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_form_oscillator.py` (``plot_estimate_probability_form_oscillator.py``) - - 00:04.877 + - 00:05.128 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_nais.py` (``plot_nais.py``) - - 00:02.708 + - 00:02.988 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_proba_system_event.py` (``plot_proba_system_event.py``) - - 00:02.059 + - 00:02.234 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_adaptive_directional_sampling.py` (``plot_estimate_probability_adaptive_directional_sampling.py``) - - 00:01.996 + - 00:02.100 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_crossentropy.py` (``plot_crossentropy.py``) - - 00:01.381 + - 00:01.411 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_form_explained.py` (``plot_form_explained.py``) - - 00:00.884 + - 00:00.954 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_randomized_qmc.py` (``plot_estimate_probability_randomized_qmc.py``) - - 00:00.786 + - 00:00.836 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_create_domain_event.py` (``plot_create_domain_event.py``) - - 00:00.690 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_directional_sampling.py` (``plot_estimate_probability_directional_sampling.py``) - - 00:00.651 + - 00:00.729 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_axial_stressed_beam.py` (``plot_axial_stressed_beam.py``) - - 00:00.638 + - 00:00.688 + - 0.0 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_directional_sampling.py` (``plot_estimate_probability_directional_sampling.py``) + - 00:00.659 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_system.py` (``plot_event_system.py``) - - 00:00.607 + - 00:00.634 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_multi_form.py` (``plot_multi_form.py``) - - 00:00.578 + - 00:00.618 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_subset_sampling.py` (``plot_subset_sampling.py``) - - 00:00.566 + - 00:00.598 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_axial_stressed_beam_quickstart.py` (``plot_axial_stressed_beam_quickstart.py``) - - 00:00.349 + - 00:00.418 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_form.py` (``plot_estimate_probability_form.py``) - - 00:00.327 + - 00:00.349 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_importance_sampling.py` (``plot_estimate_probability_importance_sampling.py``) - - 00:00.140 + - 00:00.153 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_probability_simulation_results.py` (``plot_probability_simulation_results.py``) - - 00:00.132 + - 00:00.141 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_flood_model.py` (``plot_flood_model.py``) - - 00:00.113 + - 00:00.124 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_create_threshold_event.py` (``plot_create_threshold_event.py``) - - 00:00.071 + - 00:00.079 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_probability_simulation_parametrization.py` (``plot_probability_simulation_parametrization.py``) - - 00:00.032 + - 00:00.037 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_monte_carlo.py` (``plot_estimate_probability_monte_carlo.py``) - - 00:00.032 + - 00:00.034 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_strong_maximum_test.py` (``plot_strong_maximum_test.py``) - 00:00.005 diff --git a/openturns/master/_sources/auto_reliability_sensitivity/reliability_processes/plot_field_fca_sobol.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/reliability_processes/plot_field_fca_sobol.rst.txt index 60c7517bf53..45346ae44f9 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/reliability_processes/plot_field_fca_sobol.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/reliability_processes/plot_field_fca_sobol.rst.txt @@ -519,7 +519,7 @@ Reset default settings .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 6.299 seconds) + **Total running time of the script:** (0 minutes 6.595 seconds) .. _sphx_glr_download_auto_reliability_sensitivity_reliability_processes_plot_field_fca_sobol.py: diff --git a/openturns/master/_sources/auto_reliability_sensitivity/reliability_processes/sg_execution_times.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/reliability_processes/sg_execution_times.rst.txt index b8f9b7d3219..0d9e9cd0b84 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/reliability_processes/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/reliability_processes/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:06.473** total execution time for 3 files **from auto_reliability_sensitivity/reliability_processes**: +**00:06.779** total execution time for 3 files **from auto_reliability_sensitivity/reliability_processes**: .. container:: @@ -33,10 +33,10 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_field_fca_sobol.py` (``plot_field_fca_sobol.py``) - - 00:06.299 + - 00:06.595 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_estimate_probability_monte_carlo_process.py` (``plot_estimate_probability_monte_carlo_process.py``) - - 00:00.173 + - 00:00.182 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_event_process.py` (``plot_event_process.py``) - 00:00.002 diff --git a/openturns/master/_sources/auto_reliability_sensitivity/sensitivity_analysis/plot_sensitivity_wingweight.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/sensitivity_analysis/plot_sensitivity_wingweight.rst.txt index 193370d7f10..46ae54f2ef2 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/sensitivity_analysis/plot_sensitivity_wingweight.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/sensitivity_analysis/plot_sensitivity_wingweight.rst.txt @@ -1280,7 +1280,7 @@ We can also see that the asymptotic p-values and p-values estimated by permutati .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 5.449 seconds) + **Total running time of the script:** (0 minutes 5.713 seconds) .. _sphx_glr_download_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_wingweight.py: diff --git a/openturns/master/_sources/auto_reliability_sensitivity/sensitivity_analysis/sg_execution_times.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/sensitivity_analysis/sg_execution_times.rst.txt index 362718d1897..15138357204 100644 --- a/openturns/master/_sources/auto_reliability_sensitivity/sensitivity_analysis/sg_execution_times.rst.txt +++ b/openturns/master/_sources/auto_reliability_sensitivity/sensitivity_analysis/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:08.432** total execution time for 8 files **from auto_reliability_sensitivity/sensitivity_analysis**: +**00:09.022** total execution time for 8 files **from auto_reliability_sensitivity/sensitivity_analysis**: .. container:: @@ -33,26 +33,26 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_wingweight.py` (``plot_sensitivity_wingweight.py``) - - 00:05.449 + - 00:05.713 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_functional_chaos_sensitivity.py` (``plot_functional_chaos_sensitivity.py``) - - 00:00.898 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_hsic_estimators_ishigami.py` (``plot_hsic_estimators_ishigami.py``) - - 00:00.712 + - 00:01.011 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_par_coo.py` (``plot_sensitivity_par_coo.py``) - - 00:00.688 + - 00:00.788 + - 0.0 + * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_hsic_estimators_ishigami.py` (``plot_hsic_estimators_ishigami.py``) + - 00:00.768 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_sobol.py` (``plot_sensitivity_sobol.py``) - - 00:00.400 + - 00:00.424 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_ancova.py` (``plot_sensitivity_ancova.py``) - - 00:00.111 + - 00:00.128 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_sobol_multivariate.py` (``plot_sensitivity_sobol_multivariate.py``) - - 00:00.099 + - 00:00.103 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_fast.py` (``plot_sensitivity_fast.py``) - - 00:00.076 + - 00:00.087 - 0.0 diff --git a/openturns/master/_sources/sg_execution_times.rst.txt b/openturns/master/_sources/sg_execution_times.rst.txt index e1403528535..c53660e9612 100644 --- a/openturns/master/_sources/sg_execution_times.rst.txt +++ b/openturns/master/_sources/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**04:18.882** total execution time for 263 files **from all galleries**: +**04:44.057** total execution time for 263 files **from all galleries**: .. container:: @@ -33,709 +33,712 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_meta_modeling_fields_metamodels_plot_fieldfunction_metamodel.py` (``examples/meta_modeling/fields_metamodels/plot_fieldfunction_metamodel.py``) - - 00:17.698 - - 0.0 - * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_gibbs.py` (``examples/calibration/bayesian_calibration/plot_gibbs.py``) - - 00:12.155 + - 00:19.058 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos.py``) - - 00:10.942 + - 00:15.343 + - 0.0 + * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_gibbs.py` (``examples/calibration/bayesian_calibration/plot_gibbs.py``) + - 00:12.547 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cv.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_cv.py``) - - 00:10.612 + - 00:12.021 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_racetime.py` (``examples/data_analysis/distribution_fitting/plot_estimate_gev_racetime.py``) - - 00:10.552 + - 00:11.135 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_categorical.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_categorical.py``) - - 00:09.530 + - 00:10.578 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_flooding.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_calibration_flooding.py``) - - 00:08.623 - - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_ifs.py` (``examples/numerical_methods/general_methods/plot_ifs.py``) - - 00:07.370 + - 00:08.970 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_quadrature.py` (``examples/reliability_sensitivity/design_of_experiments/plot_smolyak_quadrature.py``) - - 00:07.234 + - 00:07.966 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_multioutput_firesatellite.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_multioutput_firesatellite.py``) - - 00:07.010 + - 00:07.445 - 0.0 - * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_chaboche.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_calibration_chaboche.py``) - - 00:06.716 + * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_ifs.py` (``examples/numerical_methods/general_methods/plot_ifs.py``) + - 00:07.338 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_multivariate_distribution.py` (``examples/data_analysis/distribution_fitting/plot_estimate_multivariate_distribution.py``) - - 00:06.356 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_beam_sensitivity_degree.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_beam_sensitivity_degree.py``) + - 00:07.290 + - 0.0 + * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_chaboche.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_calibration_chaboche.py``) + - 00:07.101 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_field_fca_sobol.py` (``examples/reliability_sensitivity/reliability_processes/plot_field_fca_sobol.py``) - - 00:06.299 + - 00:06.595 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_beam_sensitivity_degree.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_beam_sensitivity_degree.py``) - - 00:06.189 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_multivariate_distribution.py` (``examples/data_analysis/distribution_fitting/plot_estimate_multivariate_distribution.py``) + - 00:06.237 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_fremantle.py` (``examples/data_analysis/distribution_fitting/plot_estimate_gev_fremantle.py``) + - 00:05.720 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_wingweight.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_wingweight.py``) - - 00:05.449 + - 00:05.713 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_rwmh_python_distribution.py` (``examples/calibration/bayesian_calibration/plot_rwmh_python_distribution.py``) - - 00:05.424 - - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_fremantle.py` (``examples/data_analysis/distribution_fitting/plot_estimate_gev_fremantle.py``) - - 00:05.397 + - 00:05.440 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_form_oscillator.py` (``examples/reliability_sensitivity/reliability/plot_estimate_probability_form_oscillator.py``) - - 00:04.877 + - 00:05.128 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_multivariate_arma.py` (``examples/data_analysis/estimate_stochastic_processes/plot_estimate_multivariate_arma.py``) - - 00:04.064 + - 00:04.248 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_deflection_tube.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_calibration_deflection_tube.py``) - - 00:03.787 + - 00:04.120 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_sensitivity_par_coo_ishigami.py` (``examples/data_analysis/graphics/plot_sensitivity_par_coo_ishigami.py``) - - 00:03.062 + - 00:03.475 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_sobol_confidence.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_sobol_confidence.py``) - - 00:02.809 + - 00:03.196 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_nais.py` (``examples/reliability_sensitivity/reliability/plot_nais.py``) - - 00:02.708 + - 00:02.988 + - 0.0 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_build_distribution.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_build_distribution.py``) + - 00:02.839 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_ego.py` (``examples/numerical_methods/optimization/plot_ego.py``) - - 00:02.654 + - 00:02.755 + - 0.0 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cleaning_strategy.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_cleaning_strategy.py``) + - 00:02.736 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_userdefined_covariance_model.py` (``examples/probabilistic_modeling/stochastic_processes/plot_userdefined_covariance_model.py``) - - 00:02.370 + - 00:02.588 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cleaning_strategy.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_cleaning_strategy.py``) - - 00:02.174 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_process_covariance_hmat.py` (``examples/probabilistic_modeling/stochastic_processes/plot_gaussian_process_covariance_hmat.py``) + - 00:02.498 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_experiment.py` (``examples/reliability_sensitivity/design_of_experiments/plot_smolyak_experiment.py``) - - 00:02.158 + - 00:02.378 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_process_covariance_hmat.py` (``examples/probabilistic_modeling/stochastic_processes/plot_gaussian_process_covariance_hmat.py``) - - 00:02.134 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_mesh.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_mesh.py``) + - 00:02.289 + - 0.0 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_enumeratefunction.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_enumeratefunction.py``) + - 00:02.266 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_proba_system_event.py` (``examples/reliability_sensitivity/reliability/plot_proba_system_event.py``) - - 00:02.059 + - 00:02.234 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_pirie.py` (``examples/data_analysis/distribution_fitting/plot_estimate_gev_pirie.py``) - - 00:02.042 + - 00:02.145 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_adaptive_directional_sampling.py` (``examples/reliability_sensitivity/reliability/plot_estimate_probability_adaptive_directional_sampling.py``) - - 00:01.996 - - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_build_distribution.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_build_distribution.py``) - - 00:01.970 + - 00:02.100 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_mesh.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_mesh.py``) - - 00:01.876 + * - :ref:`sphx_glr_auto_meta_modeling_fields_metamodels_plot_viscous_fall_metamodel.py` (``examples/meta_modeling/fields_metamodels/plot_viscous_fall_metamodel.py``) + - 00:02.073 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_conditional_quantile.py` (``examples/data_analysis/distribution_fitting/plot_estimate_conditional_quantile.py``) - - 00:01.860 - - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_enumeratefunction.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_enumeratefunction.py``) - - 00:01.809 - - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_fields_metamodels_plot_viscous_fall_metamodel.py` (``examples/meta_modeling/fields_metamodels/plot_viscous_fall_metamodel.py``) - - 00:01.807 + - 00:02.022 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_field_manipulation.py` (``examples/probabilistic_modeling/stochastic_processes/plot_field_manipulation.py``) - - 00:01.720 + - 00:01.979 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_draw_multivariate_distributions.py` (``examples/probabilistic_modeling/distributions/plot_create_draw_multivariate_distributions.py``) + - 00:01.516 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_crossentropy.py` (``examples/reliability_sensitivity/reliability/plot_crossentropy.py``) - - 00:01.381 + - 00:01.411 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_distribution.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_distribution.py``) - - 00:01.308 + - 00:01.408 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_box_cox_transform.py` (``examples/probabilistic_modeling/stochastic_processes/plot_box_cox_transform.py``) + - 00:01.353 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_bayesian_calibration_flooding.py` (``examples/calibration/bayesian_calibration/plot_bayesian_calibration_flooding.py``) - - 00:01.287 + - 00:01.286 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_box_cox_transform.py` (``examples/probabilistic_modeling/stochastic_processes/plot_box_cox_transform.py``) - - 00:01.238 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_draw_covariance_models.py` (``examples/meta_modeling/kriging_metamodel/plot_draw_covariance_models.py``) + - 00:01.273 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_plot_design.py` (``examples/reliability_sensitivity/design_of_experiments/plot_plot_design.py``) - - 00:01.210 + - 00:01.262 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_draw_covariance_models.py` (``examples/meta_modeling/kriging_metamodel/plot_draw_covariance_models.py``) - - 00:01.180 + * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_draw_survival.py` (``examples/data_analysis/sample_analysis/plot_draw_survival.py``) + - 00:01.179 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_bayesian_calibration.py` (``examples/calibration/bayesian_calibration/plot_bayesian_calibration.py``) - - 00:01.160 + - 00:01.163 - 0.0 - * - :ref:`sphx_glr_auto_graphs_plot_graphs_basics.py` (``examples/graphs/plot_graphs_basics.py``) - - 00:01.112 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_quick_start_guide_distributions.py` (``examples/probabilistic_modeling/distributions/plot_quick_start_guide_distributions.py``) + - 00:01.124 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_gibbs_simus.py` (``examples/calibration/bayesian_calibration/plot_gibbs_simus.py``) - - 00:01.111 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_draw_multivariate_distributions.py` (``examples/probabilistic_modeling/distributions/plot_create_draw_multivariate_distributions.py``) - - 00:01.084 + - 00:01.112 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_design_of_experiments.py` (``examples/reliability_sensitivity/design_of_experiments/plot_design_of_experiments.py``) - - 00:01.010 + - 00:01.110 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_non_parametric_distribution.py` (``examples/data_analysis/distribution_fitting/plot_estimate_non_parametric_distribution.py``) - - 00:00.994 + * - :ref:`sphx_glr_auto_graphs_plot_graphs_basics.py` (``examples/graphs/plot_graphs_basics.py``) + - 00:01.054 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_draw_survival.py` (``examples/data_analysis/sample_analysis/plot_draw_survival.py``) - - 00:00.981 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_non_parametric_distribution.py` (``examples/data_analysis/distribution_fitting/plot_estimate_non_parametric_distribution.py``) + - 00:01.053 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_quick_start_guide_distributions.py` (``examples/probabilistic_modeling/distributions/plot_quick_start_guide_distributions.py``) - - 00:00.957 + * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_functional_chaos_sensitivity.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_functional_chaos_sensitivity.py``) + - 00:01.011 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rastrigin.py` (``examples/numerical_methods/optimization/plot_optimization_rastrigin.py``) - - 00:00.951 - - 0.0 - * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_ackley_distribution.py` (``examples/calibration/bayesian_calibration/plot_ackley_distribution.py``) - - 00:00.902 + - 00:00.972 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_functional_chaos_sensitivity.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_functional_chaos_sensitivity.py``) - - 00:00.898 + * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_spectral_density_function.py` (``examples/data_analysis/estimate_stochastic_processes/plot_estimate_spectral_density_function.py``) + - 00:00.962 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_form_explained.py` (``examples/reliability_sensitivity/reliability/plot_form_explained.py``) - - 00:00.884 + - 00:00.954 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_advanced.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_advanced.py``) - - 00:00.882 + - 00:00.926 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_spectral_density_function.py` (``examples/data_analysis/estimate_stochastic_processes/plot_estimate_spectral_density_function.py``) - - 00:00.873 + * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_ackley_distribution.py` (``examples/calibration/bayesian_calibration/plot_ackley_distribution.py``) + - 00:00.921 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rosenbrock.py` (``examples/numerical_methods/optimization/plot_optimization_rosenbrock.py``) - - 00:00.840 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_linear_model.py` (``examples/meta_modeling/general_purpose_metamodels/plot_linear_model.py``) + - 00:00.879 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_venice.py` (``examples/data_analysis/distribution_fitting/plot_estimate_gev_venice.py``) - - 00:00.792 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_overfitting_model_selection.py` (``examples/meta_modeling/general_purpose_metamodels/plot_overfitting_model_selection.py``) + - 00:00.858 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_arma.py` (``examples/data_analysis/estimate_stochastic_processes/plot_estimate_arma.py``) + - 00:00.856 + - 0.0 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rosenbrock.py` (``examples/numerical_methods/optimization/plot_optimization_rosenbrock.py``) + - 00:00.848 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_randomized_qmc.py` (``examples/reliability_sensitivity/reliability/plot_estimate_probability_randomized_qmc.py``) - - 00:00.786 + - 00:00.836 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_arma.py` (``examples/data_analysis/estimate_stochastic_processes/plot_estimate_arma.py``) - - 00:00.775 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_venice.py` (``examples/data_analysis/distribution_fitting/plot_estimate_gev_venice.py``) + - 00:00.817 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_asymptotic_estimators_distribution.py` (``examples/data_analysis/distribution_fitting/plot_asymptotic_estimators_distribution.py``) - - 00:00.726 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_processes_comparison.py` (``examples/probabilistic_modeling/stochastic_processes/plot_gaussian_processes_comparison.py``) + - 00:00.793 - 0.0 - * - :ref:`sphx_glr_auto_graphs_plot_graphs_loglikelihood_contour.py` (``examples/graphs/plot_graphs_loglikelihood_contour.py``) - - 00:00.723 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_asymptotic_estimators_distribution.py` (``examples/data_analysis/distribution_fitting/plot_asymptotic_estimators_distribution.py``) + - 00:00.792 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_minimum_volume_level_sets.py` (``examples/probabilistic_modeling/distributions/plot_minimum_volume_level_sets.py``) - - 00:00.714 + * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_par_coo.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_par_coo.py``) + - 00:00.788 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_hsic_estimators_ishigami.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_hsic_estimators_ishigami.py``) - - 00:00.712 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_transformation.py` (``examples/probabilistic_modeling/distributions/plot_distribution_transformation.py``) + - 00:00.787 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_logistic.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_calibration_logistic.py``) - - 00:00.702 + - 00:00.774 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_overfitting_model_selection.py` (``examples/meta_modeling/general_purpose_metamodels/plot_overfitting_model_selection.py``) - - 00:00.700 + * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_hsic_estimators_ishigami.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_hsic_estimators_ishigami.py``) + - 00:00.768 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_minimum_volume_level_sets.py` (``examples/probabilistic_modeling/distributions/plot_minimum_volume_level_sets.py``) + - 00:00.764 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_create_domain_event.py` (``examples/reliability_sensitivity/reliability/plot_create_domain_event.py``) - - 00:00.690 + - 00:00.729 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_par_coo.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_par_coo.py``) - - 00:00.688 + * - :ref:`sphx_glr_auto_graphs_plot_graphs_loglikelihood_contour.py` (``examples/graphs/plot_graphs_loglikelihood_contour.py``) + - 00:00.711 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_processes_comparison.py` (``examples/probabilistic_modeling/stochastic_processes/plot_gaussian_processes_comparison.py``) - - 00:00.685 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_axial_stressed_beam.py` (``examples/reliability_sensitivity/reliability/plot_axial_stressed_beam.py``) + - 00:00.688 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_linear_model.py` (``examples/meta_modeling/general_purpose_metamodels/plot_linear_model.py``) - - 00:00.683 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_smoothing_mixture.py` (``examples/data_analysis/distribution_fitting/plot_smoothing_mixture.py``) + - 00:00.673 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_directional_sampling.py` (``examples/reliability_sensitivity/reliability/plot_estimate_probability_directional_sampling.py``) - - 00:00.651 + - 00:00.659 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_axial_stressed_beam.py` (``examples/reliability_sensitivity/reliability/plot_axial_stressed_beam.py``) - - 00:00.638 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_transformation.py` (``examples/probabilistic_modeling/distributions/plot_distribution_transformation.py``) + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_system.py` (``examples/reliability_sensitivity/reliability/plot_event_system.py``) - 00:00.634 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_model_singular_multivariate_distribution.py` (``examples/data_analysis/distribution_fitting/plot_model_singular_multivariate_distribution.py``) - - 00:00.626 + - 00:00.624 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_system.py` (``examples/reliability_sensitivity/reliability/plot_event_system.py``) - - 00:00.607 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_multi_form.py` (``examples/reliability_sensitivity/reliability/plot_multi_form.py``) + - 00:00.618 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_sequential.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_sequential.py``) - - 00:00.605 + - 00:00.616 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_order_statistics_distribution.py` (``examples/probabilistic_modeling/distributions/plot_order_statistics_distribution.py``) - - 00:00.580 - - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_smoothing_mixture.py` (``examples/data_analysis/distribution_fitting/plot_smoothing_mixture.py``) - - 00:00.580 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_multi_form.py` (``examples/reliability_sensitivity/reliability/plot_multi_form.py``) - - 00:00.578 + - 00:00.605 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_subset_sampling.py` (``examples/reliability_sensitivity/reliability/plot_subset_sampling.py``) - - 00:00.566 + - 00:00.598 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_low_discrepancy_sequence.py` (``examples/reliability_sensitivity/design_of_experiments/plot_low_discrepancy_sequence.py``) - - 00:00.528 + - 00:00.552 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_chose_trend.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_chose_trend.py``) - - 00:00.506 + - 00:00.539 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_optimal_lhs.py` (``examples/reliability_sensitivity/design_of_experiments/plot_optimal_lhs.py``) - - 00:00.464 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_merge.py` (``examples/reliability_sensitivity/design_of_experiments/plot_smolyak_merge.py``) - - 00:00.463 + - 00:00.534 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_process_manipulation.py` (``examples/probabilistic_modeling/stochastic_processes/plot_process_manipulation.py``) - - 00:00.456 - - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_non_parametric_copula.py` (``examples/data_analysis/estimate_dependency_and_copulas/plot_estimate_non_parametric_copula.py``) - - 00:00.448 + - 00:00.511 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_fields_metamodels_plot_karhunenloeve_validation.py` (``examples/meta_modeling/fields_metamodels/plot_karhunenloeve_validation.py``) - - 00:00.446 + - 00:00.502 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_expectation_simulation_algorithm.py` (``examples/reliability_sensitivity/central_dispersion/plot_expectation_simulation_algorithm.py``) - - 00:00.429 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_merge.py` (``examples/reliability_sensitivity/design_of_experiments/plot_smolyak_merge.py``) + - 00:00.487 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_truncated_distribution.py` (``examples/probabilistic_modeling/distributions/plot_truncated_distribution.py``) - - 00:00.428 + - 00:00.474 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_sample_correlation.py` (``examples/data_analysis/manage_data_and_samples/plot_sample_correlation.py``) - - 00:00.422 + - 00:00.470 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_stationary_covariance_model.py` (``examples/data_analysis/estimate_stochastic_processes/plot_estimate_stationary_covariance_model.py``) - - 00:00.421 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_ishigami.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_ishigami.py``) + - 00:00.462 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_fitted_distribution_ranking.py` (``examples/data_analysis/statistical_tests/plot_fitted_distribution_ranking.py``) - - 00:00.412 + * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_non_parametric_copula.py` (``examples/data_analysis/estimate_dependency_and_copulas/plot_estimate_non_parametric_copula.py``) + - 00:00.455 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_regression_sinus.py` (``examples/numerical_methods/general_methods/plot_regression_sinus.py``) - - 00:00.410 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_stepwise.py` (``examples/meta_modeling/general_purpose_metamodels/plot_stepwise.py``) + - 00:00.452 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_ordinal_sum_copula.py` (``examples/probabilistic_modeling/copulas/plot_ordinal_sum_copula.py``) + - 00:00.448 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_overview_univariate_distributions.py` (``examples/probabilistic_modeling/distributions/plot_overview_univariate_distributions.py``) + - 00:00.446 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_stationary_covariance_model.py` (``examples/data_analysis/estimate_stochastic_processes/plot_estimate_stationary_covariance_model.py``) + - 00:00.444 + - 0.0 + * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_expectation_simulation_algorithm.py` (``examples/reliability_sensitivity/central_dispersion/plot_expectation_simulation_algorithm.py``) + - 00:00.438 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_function_manipulation.py` (``examples/functional_modeling/field_functions/plot_function_manipulation.py``) - - 00:00.409 + - 00:00.433 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_ordinal_sum_copula.py` (``examples/probabilistic_modeling/copulas/plot_ordinal_sum_copula.py``) - - 00:00.407 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_fit_extreme_value_distribution.py` (``examples/data_analysis/distribution_fitting/plot_fit_extreme_value_distribution.py``) + - 00:00.425 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_sobol.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_sobol.py``) - - 00:00.400 + - 00:00.424 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_branin_function.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_branin_function.py``) - - 00:00.388 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_axial_stressed_beam_quickstart.py` (``examples/reliability_sensitivity/reliability/plot_axial_stressed_beam_quickstart.py``) + - 00:00.418 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_overview_univariate_distributions.py` (``examples/probabilistic_modeling/distributions/plot_overview_univariate_distributions.py``) - - 00:00.385 + * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_regression_sinus.py` (``examples/numerical_methods/general_methods/plot_regression_sinus.py``) + - 00:00.399 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_pce_design.py` (``examples/numerical_methods/general_methods/plot_pce_design.py``) - - 00:00.382 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_branin_function.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_branin_function.py``) + - 00:00.398 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_fit_extreme_value_distribution.py` (``examples/data_analysis/distribution_fitting/plot_fit_extreme_value_distribution.py``) - - 00:00.371 + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_fitted_distribution_ranking.py` (``examples/data_analysis/statistical_tests/plot_fitted_distribution_ranking.py``) + - 00:00.396 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_dlib.py` (``examples/numerical_methods/optimization/plot_optimization_dlib.py``) - - 00:00.369 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cantilever_beam_integration.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_cantilever_beam_integration.py``) + - 00:00.391 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_ishigami.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_ishigami.py``) - - 00:00.366 + * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_pce_design.py` (``examples/numerical_methods/general_methods/plot_pce_design.py``) + - 00:00.374 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_stepwise.py` (``examples/meta_modeling/general_purpose_metamodels/plot_stepwise.py``) - - 00:00.358 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_manipulation.py` (``examples/probabilistic_modeling/distributions/plot_distribution_manipulation.py``) + - 00:00.363 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_axial_stressed_beam_quickstart.py` (``examples/reliability_sensitivity/reliability/plot_axial_stressed_beam_quickstart.py``) - - 00:00.349 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_dlib.py` (``examples/numerical_methods/optimization/plot_optimization_dlib.py``) + - 00:00.355 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_propagate_kriging_ishigami.py` (``examples/meta_modeling/kriging_metamodel/plot_propagate_kriging_ishigami.py``) - - 00:00.345 + - 00:00.353 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_form.py` (``examples/reliability_sensitivity/reliability/plot_estimate_probability_form.py``) - - 00:00.327 + - 00:00.349 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_deterministic_design.py` (``examples/reliability_sensitivity/design_of_experiments/plot_deterministic_design.py``) - - 00:00.313 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_random_walk_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_random_walk_process.py``) + - 00:00.345 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_advanced_mle_estimator.py` (``examples/data_analysis/distribution_fitting/plot_advanced_mle_estimator.py``) - - 00:00.307 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_and_manipulate_arma_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_and_manipulate_arma_process.py``) + - 00:00.344 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_1d.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_1d.py``) - - 00:00.303 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_manipulation.py` (``examples/probabilistic_modeling/distributions/plot_distribution_manipulation.py``) - - 00:00.299 + - 00:00.343 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_random_walk_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_random_walk_process.py``) - - 00:00.296 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_export_metamodel.py` (``examples/meta_modeling/general_purpose_metamodels/plot_export_metamodel.py``) + - 00:00.331 - 0.0 - * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_quickstart.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_calibration_quickstart.py``) - - 00:00.295 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_random_mixture.py` (``examples/probabilistic_modeling/distributions/plot_create_random_mixture.py``) + - 00:00.311 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_create_deterministic_doe.py` (``examples/reliability_sensitivity/design_of_experiments/plot_create_deterministic_doe.py``) - - 00:00.283 - - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_threshold.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_threshold.py``) - - 00:00.283 + - 00:00.310 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_cantilever_beam_integration.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_cantilever_beam_integration.py``) - - 00:00.278 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_discrete_markov_chain_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_discrete_markov_chain_process.py``) + - 00:00.310 - 0.0 - * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_imh_python_distribution.py` (``examples/calibration/bayesian_calibration/plot_imh_python_distribution.py``) - - 00:00.277 + * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_quickstart.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_calibration_quickstart.py``) + - 00:00.308 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_test_copula.py` (``examples/data_analysis/statistical_tests/plot_test_copula.py``) - - 00:00.276 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_expert_mixture.py` (``examples/meta_modeling/general_purpose_metamodels/plot_expert_mixture.py``) + - 00:00.307 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_and_manipulate_arma_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_and_manipulate_arma_process.py``) - - 00:00.276 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_kronecker_covmodel.py` (``examples/probabilistic_modeling/stochastic_processes/plot_kronecker_covmodel.py``) + - 00:00.306 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_extreme_value_distribution.py` (``examples/probabilistic_modeling/distributions/plot_create_extreme_value_distribution.py``) - - 00:00.273 + - 00:00.297 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_export_metamodel.py` (``examples/meta_modeling/general_purpose_metamodels/plot_export_metamodel.py``) - - 00:00.271 + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_test_copula.py` (``examples/data_analysis/statistical_tests/plot_test_copula.py``) + - 00:00.293 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_discrete_markov_chain_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_discrete_markov_chain_process.py``) - - 00:00.263 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_advanced_mle_estimator.py` (``examples/data_analysis/distribution_fitting/plot_advanced_mle_estimator.py``) + - 00:00.292 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_normal.py` (``examples/data_analysis/distribution_fitting/plot_estimate_normal.py``) - - 00:00.263 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_mixture_distribution.py` (``examples/probabilistic_modeling/distributions/plot_mixture_distribution.py``) + - 00:00.289 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_non_stationary_covariance_model.py` (``examples/data_analysis/estimate_stochastic_processes/plot_estimate_non_stationary_covariance_model.py``) - - 00:00.255 + - 00:00.289 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_indices.py` (``examples/reliability_sensitivity/design_of_experiments/plot_smolyak_indices.py``) - - 00:00.254 + * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_threshold.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_threshold.py``) + - 00:00.283 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_kronecker_covmodel.py` (``examples/probabilistic_modeling/stochastic_processes/plot_kronecker_covmodel.py``) - - 00:00.252 + * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_imh_python_distribution.py` (``examples/calibration/bayesian_calibration/plot_imh_python_distribution.py``) + - 00:00.282 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_and_draw_scalar_distributions.py` (``examples/probabilistic_modeling/distributions/plot_create_and_draw_scalar_distributions.py``) - - 00:00.251 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_graphs.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos_graphs.py``) + - 00:00.280 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_visualize_clouds.py` (``examples/data_analysis/graphics/plot_visualize_clouds.py``) - - 00:00.249 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_deterministic_design.py` (``examples/reliability_sensitivity/design_of_experiments/plot_deterministic_design.py``) + - 00:00.278 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_random_mixture.py` (``examples/probabilistic_modeling/distributions/plot_create_random_mixture.py``) - - 00:00.244 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_and_draw_scalar_distributions.py` (``examples/probabilistic_modeling/distributions/plot_create_and_draw_scalar_distributions.py``) + - 00:00.271 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_bayes_distribution.py` (``examples/probabilistic_modeling/distributions/plot_bayes_distribution.py``) - - 00:00.243 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_normal.py` (``examples/data_analysis/distribution_fitting/plot_estimate_normal.py``) + - 00:00.269 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_dependence_wind.py` (``examples/data_analysis/estimate_dependency_and_copulas/plot_estimate_dependence_wind.py``) - - 00:00.243 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_indices.py` (``examples/reliability_sensitivity/design_of_experiments/plot_smolyak_indices.py``) + - 00:00.266 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_visualize_clouds.py` (``examples/data_analysis/graphics/plot_visualize_clouds.py``) + - 00:00.263 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_isotropic.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_isotropic.py``) - - 00:00.243 + - 00:00.252 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_dependence_wavesurge.py` (``examples/data_analysis/estimate_dependency_and_copulas/plot_estimate_dependence_wavesurge.py``) - - 00:00.235 + * - :ref:`sphx_glr_auto_functional_modeling_univariate_functions_plot_createUnivariateFunction.py` (``examples/functional_modeling/univariate_functions/plot_createUnivariateFunction.py``) + - 00:00.250 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_graphs.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos_graphs.py``) - - 00:00.235 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_bayes_distribution.py` (``examples/probabilistic_modeling/distributions/plot_bayes_distribution.py``) + - 00:00.249 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_mixture_distribution.py` (``examples/probabilistic_modeling/distributions/plot_mixture_distribution.py``) - - 00:00.229 + * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_dependence_wavesurge.py` (``examples/data_analysis/estimate_dependency_and_copulas/plot_estimate_dependence_wavesurge.py``) + - 00:00.246 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_simulate.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_simulate.py``) - - 00:00.227 + - 00:00.240 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_univariate_functions_plot_createUnivariateFunction.py` (``examples/functional_modeling/univariate_functions/plot_createUnivariateFunction.py``) - - 00:00.223 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_taylor_approximation.py` (``examples/meta_modeling/general_purpose_metamodels/plot_taylor_approximation.py``) + - 00:00.238 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_logistic_growth_model.py` (``examples/functional_modeling/field_functions/plot_logistic_growth_model.py``) - - 00:00.197 + * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_dependence_wind.py` (``examples/data_analysis/estimate_dependency_and_copulas/plot_estimate_dependence_wind.py``) + - 00:00.238 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_smirnov_test.py` (``examples/data_analysis/statistical_tests/plot_smirnov_test.py``) + - 00:00.228 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_generate_by_inversion.py` (``examples/probabilistic_modeling/distributions/plot_generate_by_inversion.py``) + - 00:00.218 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_sample_pandas.py` (``examples/data_analysis/manage_data_and_samples/plot_sample_pandas.py``) - - 00:00.180 + - 00:00.214 + - 0.0 + * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_logistic_growth_model.py` (``examples/functional_modeling/field_functions/plot_logistic_growth_model.py``) + - 00:00.205 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_cantilever_beam_hmat.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_cantilever_beam_hmat.py``) - - 00:00.178 + - 00:00.196 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_mixed_design.py` (``examples/reliability_sensitivity/design_of_experiments/plot_mixed_design.py``) - - 00:00.176 + * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_src_confidence.py` (``examples/data_analysis/sample_analysis/plot_src_confidence.py``) + - 00:00.192 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_white_noise_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_white_noise_process.py``) + - 00:00.191 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_cantilever_beam.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_cantilever_beam.py``) - - 00:00.174 + - 00:00.187 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_estimate_probability_monte_carlo_process.py` (``examples/reliability_sensitivity/reliability_processes/plot_estimate_probability_monte_carlo_process.py``) - - 00:00.173 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_mixed_design.py` (``examples/reliability_sensitivity/design_of_experiments/plot_mixed_design.py``) + - 00:00.184 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_expert_mixture.py` (``examples/meta_modeling/general_purpose_metamodels/plot_expert_mixture.py``) - - 00:00.171 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_estimate_probability_monte_carlo_process.py` (``examples/reliability_sensitivity/reliability_processes/plot_estimate_probability_monte_carlo_process.py``) + - 00:00.182 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_visualize_pairs.py` (``examples/data_analysis/graphics/plot_visualize_pairs.py``) - - 00:00.168 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_quantilematching_estimator.py` (``examples/data_analysis/distribution_fitting/plot_quantilematching_estimator.py``) + - 00:00.181 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_hyperparameters_optimization.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_hyperparameters_optimization.py``) - - 00:00.166 + - 00:00.179 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_pagmo.py` (``examples/numerical_methods/optimization/plot_optimization_pagmo.py``) - - 00:00.164 + * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_linear_regression.py` (``examples/data_analysis/manage_data_and_samples/plot_linear_regression.py``) + - 00:00.177 - 0.0 - * - :ref:`sphx_glr_auto_graphs_plot_graphs_fill_area.py` (``examples/graphs/plot_graphs_fill_area.py``) - - 00:00.163 + * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_visualize_empirical_cdf.py` (``examples/data_analysis/sample_analysis/plot_visualize_empirical_cdf.py``) + - 00:00.177 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_copula.py` (``examples/data_analysis/estimate_dependency_and_copulas/plot_estimate_copula.py``) - - 00:00.162 + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_symbolic_function.py` (``examples/functional_modeling/vectorial_functions/plot_symbolic_function.py``) + - 00:00.175 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_generate_by_inversion.py` (``examples/probabilistic_modeling/distributions/plot_generate_by_inversion.py``) - - 00:00.161 + * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_visualize_pairs.py` (``examples/data_analysis/graphics/plot_visualize_pairs.py``) + - 00:00.174 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_mix_rv_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_mix_rv_process.py``) - - 00:00.159 + - 00:00.173 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_src_confidence.py` (``examples/data_analysis/sample_analysis/plot_src_confidence.py``) - - 00:00.159 + * - :ref:`sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_copula.py` (``examples/data_analysis/estimate_dependency_and_copulas/plot_estimate_copula.py``) + - 00:00.168 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_extrema.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_extrema.py``) - - 00:00.157 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_pagmo.py` (``examples/numerical_methods/optimization/plot_optimization_pagmo.py``) + - 00:00.167 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_export_field_vtk.py` (``examples/probabilistic_modeling/stochastic_processes/plot_export_field_vtk.py``) - - 00:00.155 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_draw_validation.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_draw_validation.py``) + - 00:00.164 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_add_trend.py` (``examples/probabilistic_modeling/stochastic_processes/plot_add_trend.py``) + - 00:00.164 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_beam_trend.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_beam_trend.py``) - - 00:00.155 + - 00:00.163 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_white_noise_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_white_noise_process.py``) - - 00:00.154 + * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_extrema.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_extrema.py``) + - 00:00.161 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_symbolic_function.py` (``examples/functional_modeling/vectorial_functions/plot_symbolic_function.py``) - - 00:00.150 + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_qqplot_graph.py` (``examples/data_analysis/statistical_tests/plot_qqplot_graph.py``) + - 00:00.155 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_python_distribution.py` (``examples/probabilistic_modeling/distributions/plot_python_distribution.py``) - - 00:00.145 - - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_linear_regression.py` (``examples/data_analysis/manage_data_and_samples/plot_linear_regression.py``) - - 00:00.144 - - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_smirnov_test.py` (``examples/data_analysis/statistical_tests/plot_smirnov_test.py``) - - 00:00.142 + - 00:00.155 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_qqplot_graph.py` (``examples/data_analysis/statistical_tests/plot_qqplot_graph.py``) - - 00:00.142 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_export_field_vtk.py` (``examples/probabilistic_modeling/stochastic_processes/plot_export_field_vtk.py``) + - 00:00.155 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_importance_sampling.py` (``examples/reliability_sensitivity/reliability/plot_estimate_probability_importance_sampling.py``) - - 00:00.140 + - 00:00.153 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_normal_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_normal_process.py``) + - 00:00.152 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_moments.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_moments.py``) - - 00:00.140 + - 00:00.144 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_quantilematching_estimator.py` (``examples/data_analysis/distribution_fitting/plot_quantilematching_estimator.py``) - - 00:00.138 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_aggregated_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_aggregated_process.py``) + - 00:00.144 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_taylor_approximation.py` (``examples/meta_modeling/general_purpose_metamodels/plot_taylor_approximation.py``) - - 00:00.138 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_probability_simulation_results.py` (``examples/reliability_sensitivity/reliability/plot_probability_simulation_results.py``) + - 00:00.141 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_add_trend.py` (``examples/probabilistic_modeling/stochastic_processes/plot_add_trend.py``) - - 00:00.134 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_trend_transform.py` (``examples/probabilistic_modeling/stochastic_processes/plot_trend_transform.py``) + - 00:00.140 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_probability_simulation_results.py` (``examples/reliability_sensitivity/reliability/plot_probability_simulation_results.py``) - - 00:00.132 + * - :ref:`sphx_glr_auto_graphs_plot_graphs_fill_area.py` (``examples/graphs/plot_graphs_fill_area.py``) + - 00:00.140 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_test_normality.py` (``examples/data_analysis/statistical_tests/plot_test_normality.py``) - - 00:00.129 + - 00:00.136 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_normal_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_normal_process.py``) - - 00:00.127 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_create_random_doe.py` (``examples/reliability_sensitivity/design_of_experiments/plot_create_random_doe.py``) + - 00:00.132 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_estimate_integral_iterated_quadrature.py` (``examples/numerical_methods/general_methods/plot_estimate_integral_iterated_quadrature.py``) - - 00:00.127 + - 00:00.130 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_central_tendency.py` (``examples/reliability_sensitivity/central_dispersion/plot_central_tendency.py``) - - 00:00.122 - - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_draw_validation.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_draw_validation.py``) - - 00:00.122 + - 00:00.129 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_trend_transform.py` (``examples/probabilistic_modeling/stochastic_processes/plot_trend_transform.py``) - - 00:00.119 + * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_ancova.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_ancova.py``) + - 00:00.128 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_create_random_doe.py` (``examples/reliability_sensitivity/design_of_experiments/plot_create_random_doe.py``) - - 00:00.118 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_timeseries_manipulation.py` (``examples/probabilistic_modeling/stochastic_processes/plot_timeseries_manipulation.py``) + - 00:00.126 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_visualize_empirical_cdf.py` (``examples/data_analysis/sample_analysis/plot_visualize_empirical_cdf.py``) - - 00:00.117 + * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_quantile_estimation_wilks.py` (``examples/data_analysis/manage_data_and_samples/plot_quantile_estimation_wilks.py``) + - 00:00.126 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_aggregated_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_aggregated_process.py``) - - 00:00.116 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_flood_model.py` (``examples/reliability_sensitivity/reliability/plot_flood_model.py``) + - 00:00.124 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_design_of_experiment_continuous_discrete.py` (``examples/reliability_sensitivity/design_of_experiments/plot_design_of_experiment_continuous_discrete.py``) - - 00:00.115 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_timeseries_manipulation.py` (``examples/probabilistic_modeling/stochastic_processes/plot_timeseries_manipulation.py``) - - 00:00.113 + - 00:00.121 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_flood_model.py` (``examples/reliability_sensitivity/reliability/plot_flood_model.py``) - - 00:00.113 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_ancova.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_ancova.py``) + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_general_linear_model.py` (``examples/meta_modeling/general_purpose_metamodels/plot_general_linear_model.py``) - 00:00.111 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_compare_unconditional_conditional_histograms.py` (``examples/data_analysis/sample_analysis/plot_compare_unconditional_conditional_histograms.py``) - - 00:00.102 + - 00:00.110 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_sobol_multivariate.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_sobol_multivariate.py``) - - 00:00.099 + - 00:00.103 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_quantile_estimation_wilks.py` (``examples/data_analysis/manage_data_and_samples/plot_quantile_estimation_wilks.py``) - - 00:00.099 + * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_visualize_histogram.py` (``examples/data_analysis/sample_analysis/plot_visualize_histogram.py``) + - 00:00.102 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_conditional_distribution.py` (``examples/probabilistic_modeling/distributions/plot_conditional_distribution.py``) - - 00:00.094 + - 00:00.099 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_pvalue.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_pvalue.py``) - - 00:00.093 + - 00:00.096 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_bonmin.py` (``examples/numerical_methods/optimization/plot_optimization_bonmin.py``) - 00:00.092 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_regression_interval.py` (``examples/numerical_methods/general_methods/plot_regression_interval.py``) - - 00:00.089 - - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_general_linear_model.py` (``examples/meta_modeling/general_purpose_metamodels/plot_general_linear_model.py``) - - 00:00.088 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_likelihood.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_likelihood.py``) + - 00:00.092 - 0.0 * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_calibration_withoutobservedinputs.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_calibration_withoutobservedinputs.py``) - - 00:00.086 + - 00:00.090 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_viscous_fall_field_function.py` (``examples/functional_modeling/field_functions/plot_viscous_fall_field_function.py``) - - 00:00.083 + * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_regression_interval.py` (``examples/numerical_methods/general_methods/plot_regression_interval.py``) + - 00:00.090 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_likelihood.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_likelihood.py``) - - 00:00.076 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_user_stationary_covmodel.py` (``examples/probabilistic_modeling/stochastic_processes/plot_user_stationary_covmodel.py``) + - 00:00.087 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_fast.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_fast.py``) - - 00:00.076 + - 00:00.087 - 0.0 - * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_generate_chaboche.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_generate_chaboche.py``) - - 00:00.073 + * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_viscous_fall_field_function.py` (``examples/functional_modeling/field_functions/plot_viscous_fall_field_function.py``) + - 00:00.087 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_viscous_fall_field_function_connection.py` (``examples/functional_modeling/field_functions/plot_viscous_fall_field_function_connection.py``) - - 00:00.072 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_create_linear_least_squares_model.py` (``examples/meta_modeling/general_purpose_metamodels/plot_create_linear_least_squares_model.py``) + - 00:00.082 + - 0.0 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_database.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos_database.py``) + - 00:00.081 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_functional_basis_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_functional_basis_process.py``) - - 00:00.072 + - 00:00.080 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_create_threshold_event.py` (``examples/reliability_sensitivity/reliability/plot_create_threshold_event.py``) - - 00:00.071 + - 00:00.079 - 0.0 - * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_generate_flooding.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_generate_flooding.py``) - - 00:00.069 + * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_generate_chaboche.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_generate_chaboche.py``) + - 00:00.077 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_constraints.py` (``examples/numerical_methods/optimization/plot_optimization_constraints.py``) - - 00:00.069 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_gauss_product_experiment.py` (``examples/reliability_sensitivity/design_of_experiments/plot_gauss_product_experiment.py``) + - 00:00.075 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_nlopt.py` (``examples/numerical_methods/optimization/plot_optimization_nlopt.py``) - - 00:00.068 + * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_viscous_fall_field_function_connection.py` (``examples/functional_modeling/field_functions/plot_viscous_fall_field_function_connection.py``) + - 00:00.075 + - 0.0 + * - :ref:`sphx_glr_auto_calibration_least_squares_and_gaussian_calibration_plot_generate_flooding.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_generate_flooding.py``) + - 00:00.072 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_statistics.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_statistics.py``) - - 00:00.067 + - 00:00.071 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging.py``) - - 00:00.067 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_user_stationary_covmodel.py` (``examples/probabilistic_modeling/stochastic_processes/plot_user_stationary_covmodel.py``) - - 00:00.065 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_your_own_dist.py` (``examples/probabilistic_modeling/distributions/plot_create_your_own_dist.py``) - - 00:00.064 + - 00:00.071 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_create_linear_least_squares_model.py` (``examples/meta_modeling/general_purpose_metamodels/plot_create_linear_least_squares_model.py``) - - 00:00.064 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_constraints.py` (``examples/numerical_methods/optimization/plot_optimization_constraints.py``) + - 00:00.070 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_quadratic_function.py` (``examples/functional_modeling/vectorial_functions/plot_quadratic_function.py``) - - 00:00.063 + - 00:00.069 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_visualize_histogram.py` (``examples/data_analysis/sample_analysis/plot_visualize_histogram.py``) - - 00:00.063 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_your_own_dist.py` (``examples/probabilistic_modeling/distributions/plot_create_your_own_dist.py``) + - 00:00.068 + - 0.0 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_nlopt.py` (``examples/numerical_methods/optimization/plot_optimization_nlopt.py``) + - 00:00.068 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_userdefined_spectral_model.py` (``examples/probabilistic_modeling/stochastic_processes/plot_userdefined_spectral_model.py``) - - 00:00.060 + - 00:00.063 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_maximum_distribution.py` (``examples/probabilistic_modeling/distributions/plot_maximum_distribution.py``) - - 00:00.059 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_gauss_product_experiment.py` (``examples/reliability_sensitivity/design_of_experiments/plot_gauss_product_experiment.py``) - - 00:00.057 + - 00:00.063 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_probabilistic_design.py` (``examples/reliability_sensitivity/design_of_experiments/plot_probabilistic_design.py``) - - 00:00.055 + - 00:00.058 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_composite_experiment.py` (``examples/reliability_sensitivity/design_of_experiments/plot_composite_experiment.py``) - - 00:00.053 + - 00:00.057 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_database.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos_database.py``) - - 00:00.051 + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_quick_start_functions.py` (``examples/functional_modeling/vectorial_functions/plot_quick_start_functions.py``) + - 00:00.054 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_monte_carlo_experiment.py` (``examples/reliability_sensitivity/design_of_experiments/plot_monte_carlo_experiment.py``) - - 00:00.048 - - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_quick_start_functions.py` (``examples/functional_modeling/vectorial_functions/plot_quick_start_functions.py``) - - 00:00.048 + - 00:00.052 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_estimate_moments_taylor.py` (``examples/reliability_sensitivity/central_dispersion/plot_estimate_moments_taylor.py``) - - 00:00.046 - - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_test.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_test.py``) - - 00:00.039 + - 00:00.048 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_probability_simulation_parametrization.py` (``examples/reliability_sensitivity/reliability/plot_probability_simulation_parametrization.py``) - - 00:00.032 + - 00:00.037 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_monte_carlo.py` (``examples/reliability_sensitivity/reliability/plot_estimate_probability_monte_carlo.py``) + - 00:00.034 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_test.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_test.py``) - 00:00.032 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_exploitation.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos_exploitation.py``) - - 00:00.023 + - 00:00.029 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_link_to_an_external_code_plot_link_computer_code_coupling_tools.py` (``examples/functional_modeling/link_to_an_external_code/plot_link_computer_code_coupling_tools.py``) - - 00:00.023 + - 00:00.027 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_ishigami_grouped_indices.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_ishigami_grouped_indices.py``) - - 00:00.019 + - 00:00.022 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_control_termination.py` (``examples/numerical_methods/optimization/plot_control_termination.py``) - 00:00.015 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_quick_start_point_and_sample.py` (``examples/data_analysis/manage_data_and_samples/plot_quick_start_point_and_sample.py``) + - 00:00.012 + - 0.0 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_advanced_ctors.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos_advanced_ctors.py``) - 00:00.010 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_study_save_load.py` (``examples/numerical_methods/general_methods/plot_study_save_load.py``) - 00:00.010 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_beam_arbitrary_trend.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_beam_arbitrary_trend.py``) - - 00:00.008 - - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos_advanced_ctors.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos_advanced_ctors.py``) - - 00:00.006 + - 00:00.009 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_sample_manipulation.py` (``examples/data_analysis/manage_data_and_samples/plot_sample_manipulation.py``) - - 00:00.005 + - 00:00.007 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_strong_maximum_test.py` (``examples/reliability_sensitivity/reliability/plot_strong_maximum_test.py``) + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_maximumlikelihood_estimator.py` (``examples/data_analysis/distribution_fitting/plot_maximumlikelihood_estimator.py``) + - 00:00.006 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_sort_sample.py` (``examples/data_analysis/manage_data_and_samples/plot_sort_sample.py``) - 00:00.005 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_estimate_moments.py` (``examples/data_analysis/manage_data_and_samples/plot_estimate_moments.py``) - 00:00.005 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_maximumlikelihood_estimator.py` (``examples/data_analysis/distribution_fitting/plot_maximumlikelihood_estimator.py``) + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_test_independence.py` (``examples/data_analysis/statistical_tests/plot_test_independence.py``) - 00:00.005 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_post_analytical_importance_sampling.py` (``examples/reliability_sensitivity/reliability/plot_post_analytical_importance_sampling.py``) + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_strong_maximum_test.py` (``examples/reliability_sensitivity/reliability/plot_strong_maximum_test.py``) - 00:00.005 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_test_independence.py` (``examples/data_analysis/statistical_tests/plot_test_independence.py``) + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_post_analytical_importance_sampling.py` (``examples/reliability_sensitivity/reliability/plot_post_analytical_importance_sampling.py``) - 00:00.005 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_sort_sample.py` (``examples/data_analysis/manage_data_and_samples/plot_sort_sample.py``) + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_distribution_transformation.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_distribution_transformation.py``) - 00:00.004 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_minmax_optimization.py` (``examples/numerical_methods/optimization/plot_minmax_optimization.py``) - - 00:00.003 + - 00:00.004 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_functions_inputDim.py` (``examples/functional_modeling/vectorial_functions/plot_functions_inputDim.py``) - - 00:00.003 + - 00:00.004 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_createMultivariateFunction.py` (``examples/functional_modeling/vectorial_functions/plot_createMultivariateFunction.py``) - 00:00.003 @@ -743,17 +746,17 @@ Computation times * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_python_function.py` (``examples/functional_modeling/vectorial_functions/plot_python_function.py``) - 00:00.003 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_random_vector_manipulation.py` (``examples/probabilistic_modeling/random_vectors/plot_random_vector_manipulation.py``) - - 00:00.003 - - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_minmax_by_random_design.py` (``examples/numerical_methods/optimization/plot_minmax_by_random_design.py``) - 00:00.003 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_distribution_transformation.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_distribution_transformation.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_composite_random_vector.py` (``examples/probabilistic_modeling/random_vectors/plot_composite_random_vector.py``) - 00:00.003 - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_random_vector_manipulation.py` (``examples/probabilistic_modeling/random_vectors/plot_random_vector_manipulation.py``) + - 00:00.002 + - 0.0 * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_import_export_sample_csv.py` (``examples/data_analysis/manage_data_and_samples/plot_import_export_sample_csv.py``) - - 00:00.003 + - 00:00.002 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_manipulation.py` (``examples/reliability_sensitivity/reliability/plot_event_manipulation.py``) - 00:00.002 @@ -764,25 +767,25 @@ Computation times * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_vertexvalue_function.py` (``examples/functional_modeling/field_functions/plot_vertexvalue_function.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_functional_basis_plot_multidimensional_basis.py` (``examples/functional_modeling/functional_basis/plot_multidimensional_basis.py``) + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_aggregated_function.py` (``examples/functional_modeling/vectorial_functions/plot_aggregated_function.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_aggregated_function.py` (``examples/functional_modeling/vectorial_functions/plot_aggregated_function.py``) + * - :ref:`sphx_glr_auto_functional_modeling_functional_basis_plot_multidimensional_basis.py` (``examples/functional_modeling/functional_basis/plot_multidimensional_basis.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_python_randomvector.py` (``examples/probabilistic_modeling/random_vectors/plot_python_randomvector.py``) + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_linear_combination_function.py` (``examples/functional_modeling/vectorial_functions/plot_linear_combination_function.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_randomize_sample_lines.py` (``examples/data_analysis/manage_data_and_samples/plot_randomize_sample_lines.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_python_randomvector.py` (``examples/probabilistic_modeling/random_vectors/plot_python_randomvector.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_linear_combination_function.py` (``examples/functional_modeling/vectorial_functions/plot_linear_combination_function.py``) + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_functions_outputDim.py` (``examples/functional_modeling/vectorial_functions/plot_functions_outputDim.py``) - 00:00.002 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_value_function.py` (``examples/functional_modeling/field_functions/plot_value_function.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_composite_random_vector.py` (``examples/probabilistic_modeling/random_vectors/plot_composite_random_vector.py``) + * - :ref:`sphx_glr_auto_data_analysis_manage_data_and_samples_plot_randomize_sample_lines.py` (``examples/data_analysis/manage_data_and_samples/plot_randomize_sample_lines.py``) - 00:00.002 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_composed_function.py` (``examples/functional_modeling/vectorial_functions/plot_composed_function.py``) @@ -791,33 +794,30 @@ Computation times * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_event_process.py` (``examples/reliability_sensitivity/reliability_processes/plot_event_process.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_functions_outputDim.py` (``examples/functional_modeling/vectorial_functions/plot_functions_outputDim.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_parametric_spectral_density.py` (``examples/probabilistic_modeling/stochastic_processes/plot_parametric_spectral_density.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_composed_copula.py` (``examples/probabilistic_modeling/copulas/plot_composed_copula.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_extract_copula.py` (``examples/probabilistic_modeling/copulas/plot_extract_copula.py``) + - 00:00.002 + - 0.0 + * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_random_generator.py` (``examples/numerical_methods/general_methods/plot_random_generator.py``) - 00:00.002 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_stationary_covmodel.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_stationary_covmodel.py``) - - 00:00.001 + - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_chi2_fitting_test.py` (``examples/data_analysis/statistical_tests/plot_chi2_fitting_test.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_composed_copula.py` (``examples/probabilistic_modeling/copulas/plot_composed_copula.py``) - 00:00.001 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_random_generator.py` (``examples/numerical_methods/general_methods/plot_random_generator.py``) + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_chi2_fitting_test.py` (``examples/data_analysis/statistical_tests/plot_chi2_fitting_test.py``) - 00:00.001 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_conditional_random_vector.py` (``examples/probabilistic_modeling/distributions/plot_conditional_random_vector.py``) - 00:00.001 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_parametric_spectral_density.py` (``examples/probabilistic_modeling/stochastic_processes/plot_parametric_spectral_density.py``) - - 00:00.001 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_create_copula.py` (``examples/probabilistic_modeling/copulas/plot_create_copula.py``) - - 00:00.001 - - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_combinatorial_generator.py` (``examples/numerical_methods/general_methods/plot_combinatorial_generator.py``) - 00:00.001 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_extract_copula.py` (``examples/probabilistic_modeling/copulas/plot_extract_copula.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_create_copula.py` (``examples/probabilistic_modeling/copulas/plot_create_copula.py``) - 00:00.001 - 0.0 diff --git a/openturns/master/auto_calibration/bayesian_calibration/plot_gibbs.html b/openturns/master/auto_calibration/bayesian_calibration/plot_gibbs.html index 786af4bc1c0..1e2dca9fd2a 100644 --- a/openturns/master/auto_calibration/bayesian_calibration/plot_gibbs.html +++ b/openturns/master/auto_calibration/bayesian_calibration/plot_gibbs.html @@ -243,7 +243,7 @@

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