CircleCI build openturns 20988

openturns · Nov 22, 2024 · 3e238b7 · 3e238b7
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diff --git a/.../master/_downloads/0cbc0c7f31f83b522c07a946f2a2edc5/plot_graphs_loglikelihood_contour.zip b/.../master/_downloads/0cbc0c7f31f83b522c07a946f2a2edc5/plot_graphs_loglikelihood_contour.zip
diff --git a/openturns/master/_downloads/0e331c7db07837c249c7578327e83050/plot_python_function.zip b/openturns/master/_downloads/0e331c7db07837c249c7578327e83050/plot_python_function.zip
diff --git a/openturns/master/_downloads/0eb193ea6e2b1393ff289eed787f56d0/plot_random_generator.zip b/openturns/master/_downloads/0eb193ea6e2b1393ff289eed787f56d0/plot_random_generator.zip
diff --git a/openturns/master/_downloads/0ef2e737c16c571c8945b202ec11aae3/auto_calibration_jupyter.zip b/openturns/master/_downloads/0ef2e737c16c571c8945b202ec11aae3/auto_calibration_jupyter.zip
diff --git a/...downloads/0fd5651602b730800a9bea40091d8c63/plot_post_analytical_importance_sampling.ipynb b/...downloads/0fd5651602b730800a9bea40091d8c63/plot_post_analytical_importance_sampling.ipynb
@@ -11,7 +11,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "In this example we want to estimate the probability to exceed a threshold through the combination of approximation and simulation methods.\n\n  - perform an FORM or SORM study in order to find the design point,\n\n  - perform an importance sampling study centered around the design point:\n    the importance distribution operates in the standard space and is the\n    standard distribution of the standard space (the standard elliptical\n    distribution in the case of an elliptic copula of the input random vector,\n    the standard normal one in all the other cases).\n\nThe importance sampling technique in the standard space may be of two kinds:\n\n  - the sample is generated according to the new importance distribution:\n    this technique is called post analytical importance sampling,\n\n  - the sample is generated according to the new importance distribution and\n    is controlled by the value of the linearized limit state function:\n    this technique is called post analytical controlled importance sampling.\n\nThis post analytical importance sampling algorithm is created from the result structure of a FORM or SORM algorithm.\n\nIt is parameterized like other simulation algorithm, through the parameters OuterSampling, BlockSize, ... and provide the same type of results.\n\nLet us note that the post FORM/SORM importance sampling method may be\nimplemented thanks to the ImportanceSampling object, where the importance\ndistribution is defined in the standard space: then, it requires that the\nevent initially defined in the pysical space be transformed in the standard space.\n\nThe controlled importance sampling technique is only accessible within the post analytical context.\n\n"
+        "In this example we want to estimate the probability to exceed a threshold through the combination of approximation and simulation methods.\n\n  - perform an FORM or SORM study in order to find the design point,\n  - perform an importance sampling study centered around the design point:\n    the importance distribution operates in the standard space and is the\n    standard distribution of the standard space (the standard elliptical\n    distribution in the case of an elliptic copula of the input random vector,\n    the standard normal one in all the other cases).\n\nThe importance sampling technique in the standard space may be of two kinds:\n\n  - the sample is generated according to the new importance distribution:\n    this technique is called post analytical importance sampling,\n  - the sample is generated according to the new importance distribution and\n    is controlled by the value of the linearized limit state function:\n    this technique is called post analytical controlled importance sampling.\n\nThis post analytical importance sampling algorithm is created from the result structure of a FORM or SORM algorithm.\n\nIt is parameterized like other simulation algorithm, through the parameters OuterSampling, BlockSize, ... and provide the same type of results.\n\nLet us note that the post FORM/SORM importance sampling method may be\nimplemented thanks to the ImportanceSampling object, where the importance\ndistribution is defined in the standard space: then, it requires that the\nevent initially defined in the pysical space be transformed in the standard space.\n\nThe controlled importance sampling technique is only accessible within the post analytical context.\n\n"
       ]
     },
     {

diff --git a/...ster/_downloads/113bcf8fb299a8a1ffa667d986351fb5/plot_quick_start_guide_distributions.zip b/...ster/_downloads/113bcf8fb299a8a1ffa667d986351fb5/plot_quick_start_guide_distributions.zip
diff --git a/...turns/master/_downloads/13532f1cc71ed6a9093a0a7b9e049a88/plot_kriging_branin_function.zip b/...turns/master/_downloads/13532f1cc71ed6a9093a0a7b9e049a88/plot_kriging_branin_function.zip
diff --git a/openturns/master/_downloads/13868e5a95906ad1cc6c5b66f0d709d9/plot_kronecker_covmodel.zip b/openturns/master/_downloads/13868e5a95906ad1cc6c5b66f0d709d9/plot_kronecker_covmodel.zip
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diff --git a/openturns/master/_downloads/2a1e7a17fdd66aa7667a38a867dec696/plot_functional_chaos.zip b/openturns/master/_downloads/2a1e7a17fdd66aa7667a38a867dec696/plot_functional_chaos.zip
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diff --git a/openturns/master/_downloads/2a7cf8c224eca448791e340e83b10c16/plot_aggregated_function.zip b/openturns/master/_downloads/2a7cf8c224eca448791e340e83b10c16/plot_aggregated_function.zip
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diff --git a/openturns/master/_downloads/2c7edf7675deb263148b74debd92a02c/plot_event_process.zip b/openturns/master/_downloads/2c7edf7675deb263148b74debd92a02c/plot_event_process.zip
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diff --git a/openturns/master/_downloads/2e93b8c6a2a654d5f374cd0b16c9e4af/plot_quick_start_functions.zip b/openturns/master/_downloads/2e93b8c6a2a654d5f374cd0b16c9e4af/plot_quick_start_functions.zip
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diff --git a/...urns/master/_downloads/309f49c10d5e51bde01d1de415c7ec7f/plot_optimization_constraints.zip b/...urns/master/_downloads/309f49c10d5e51bde01d1de415c7ec7f/plot_optimization_constraints.zip
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diff --git a/openturns/master/_downloads/398cba8baeb494f3230b195cb1856dc3/plot_gibbs.zip b/openturns/master/_downloads/398cba8baeb494f3230b195cb1856dc3/plot_gibbs.zip
diff --git a/...s/master/_downloads/3b736a15b18a14ecb45c9a923b720669/plot_distribution_transformation.zip b/...s/master/_downloads/3b736a15b18a14ecb45c9a923b720669/plot_distribution_transformation.zip
diff --git a/...turns/master/_downloads/3b980ef69e4e49ae98082750379327c2/plot_imh_python_distribution.zip b/...turns/master/_downloads/3b980ef69e4e49ae98082750379327c2/plot_imh_python_distribution.zip
diff --git a/...r/_downloads/3ccaf2cef908bd4413d896996f9f0d78/plot_post_analytical_importance_sampling.py b/...r/_downloads/3ccaf2cef908bd4413d896996f9f0d78/plot_post_analytical_importance_sampling.py
@@ -7,7 +7,6 @@
 # In this example we want to estimate the probability to exceed a threshold through the combination of approximation and simulation methods.
 #
 #   - perform an FORM or SORM study in order to find the design point,
-#
 #   - perform an importance sampling study centered around the design point:
 #     the importance distribution operates in the standard space and is the
 #     standard distribution of the standard space (the standard elliptical
@@ -18,7 +17,6 @@
 #
 #   - the sample is generated according to the new importance distribution:
 #     this technique is called post analytical importance sampling,
-#
 #   - the sample is generated according to the new importance distribution and
 #     is controlled by the value of the linearized limit state function:
 #     this technique is called post analytical controlled importance sampling.

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diff --git a/...rns/master/_downloads/4ee390b5ccc95498710f43a77cb4b6a7/plot_conditional_random_vector.zip b/...rns/master/_downloads/4ee390b5ccc95498710f43a77cb4b6a7/plot_conditional_random_vector.zip
diff --git a/openturns/master/_downloads/4eea463a8688d668770d903a8c72fc5e/plot_function_manipulation.zip b/openturns/master/_downloads/4eea463a8688d668770d903a8c72fc5e/plot_function_manipulation.zip
diff --git a/...master/_downloads/5054b46f4cdf35d7731b997adbac5056/plot_estimate_conditional_quantile.zip b/...master/_downloads/5054b46f4cdf35d7731b997adbac5056/plot_estimate_conditional_quantile.zip
diff --git a/.../master/_downloads/50eb8d70654816c928f9d642a2ef4a76/plot_quick_start_point_and_sample.zip b/.../master/_downloads/50eb8d70654816c928f9d642a2ef4a76/plot_quick_start_point_and_sample.zip
diff --git a/...urns/master/_downloads/518be0da6f937a76737e11eea3ba1508/plot_createUnivariateFunction.zip b/...urns/master/_downloads/518be0da6f937a76737e11eea3ba1508/plot_createUnivariateFunction.zip
diff --git a/...turns/master/_downloads/52878e7a369d4e78d5357bed0031e830/plot_timeseries_manipulation.zip b/...turns/master/_downloads/52878e7a369d4e78d5357bed0031e830/plot_timeseries_manipulation.zip
diff --git a/...urns/master/_downloads/52af42ee236d1fb67799a76c7ba89173/plot_estimate_dependence_wind.zip b/...urns/master/_downloads/52af42ee236d1fb67799a76c7ba89173/plot_estimate_dependence_wind.zip
diff --git a/...993c0aeab9708075323f3dbe9bf10/plot_estimate_probability_adaptive_directional_sampling.zip b/...993c0aeab9708075323f3dbe9bf10/plot_estimate_probability_adaptive_directional_sampling.zip
diff --git a/openturns/master/_downloads/541c748d6ce6ab3bbe2b01d37f67d57e/plot_generate_flooding.zip b/openturns/master/_downloads/541c748d6ce6ab3bbe2b01d37f67d57e/plot_generate_flooding.zip
diff --git a/openturns/master/_downloads/545c537577c0514acf664d660c3d6673/plot_event_manipulation.zip b/openturns/master/_downloads/545c537577c0514acf664d660c3d6673/plot_event_manipulation.zip
diff --git a/openturns/master/_downloads/58127b3948c7459557773bdd5881ef20/plot_smirnov_test.zip b/openturns/master/_downloads/58127b3948c7459557773bdd5881ef20/plot_smirnov_test.zip
diff --git a/openturns/master/_downloads/589122e5e0a996cebc673636c01ac7a9/plot_mix_rv_process.zip b/openturns/master/_downloads/589122e5e0a996cebc673636c01ac7a9/plot_mix_rv_process.zip
diff --git a/openturns/master/_downloads/58b7cedd7592157d47e84db4a498c83f/auto_data_analysis_jupyter.zip b/openturns/master/_downloads/58b7cedd7592157d47e84db4a498c83f/auto_data_analysis_jupyter.zip
diff --git a/openturns/master/_downloads/591696734b2f30020c482b44bc81f7bd/plot_functions_inputDim.zip b/openturns/master/_downloads/591696734b2f30020c482b44bc81f7bd/plot_functions_inputDim.zip
diff --git a/openturns/master/_downloads/599573e70640a0f39bc3497cb30cc18a/plot_random_walk_process.zip b/openturns/master/_downloads/599573e70640a0f39bc3497cb30cc18a/plot_random_walk_process.zip
diff --git a/...master/_downloads/5a308943de755c6667a6ef3e92623e41/plot_gaussian_processes_comparison.zip b/...master/_downloads/5a308943de755c6667a6ef3e92623e41/plot_gaussian_processes_comparison.zip
diff --git a/...er/_downloads/5b4293c2d959aa20763107c6ad85215c/plot_link_computer_code_coupling_tools.zip b/...er/_downloads/5b4293c2d959aa20763107c6ad85215c/plot_link_computer_code_coupling_tools.zip
diff --git a/...s/master/_downloads/5d0c7b03067e5614ac647ab1e8bcde4b/plot_calibration_deflection_tube.zip b/...s/master/_downloads/5d0c7b03067e5614ac647ab1e8bcde4b/plot_calibration_deflection_tube.zip
diff --git a/..._downloads/5e7302bdc094106801271d8bba8a6aa7/plot_estimate_stationary_covariance_model.zip b/..._downloads/5e7302bdc094106801271d8bba8a6aa7/plot_estimate_stationary_covariance_model.zip
diff --git a/openturns/master/_downloads/60642898d696254e40ed2c2f8d0d885f/plot_calibration_quickstart.zip b/openturns/master/_downloads/60642898d696254e40ed2c2f8d0d885f/plot_calibration_quickstart.zip
diff --git a/openturns/master/_downloads/60a4d760fd43f421838e34b483ce05ee/plot_smolyak_merge.zip b/openturns/master/_downloads/60a4d760fd43f421838e34b483ce05ee/plot_smolyak_merge.zip
diff --git a/openturns/master/_downloads/62116af902203365c4c062bcc54f3f8a/plot_estimate_gpd_rain.zip b/openturns/master/_downloads/62116af902203365c4c062bcc54f3f8a/plot_estimate_gpd_rain.zip
diff --git a/openturns/master/_downloads/623afba8553c4c1b94d6fef9e7b4ee5d/plot_form_explained.zip b/openturns/master/_downloads/623afba8553c4c1b94d6fef9e7b4ee5d/plot_form_explained.zip
diff --git a/...er/_downloads/63042d3025a409300f5967643cc18f4c/plot_calibration_withoutobservedinputs.zip b/...er/_downloads/63042d3025a409300f5967643cc18f4c/plot_calibration_withoutobservedinputs.zip
diff --git a/openturns/master/_downloads/63d936015b9fe8c1084d030a9bdcaf21/plot_smolyak_quadrature.zip b/openturns/master/_downloads/63d936015b9fe8c1084d030a9bdcaf21/plot_smolyak_quadrature.zip
diff --git a/openturns/master/_downloads/6419bb826c2a13b8b1a53ba3ea2c3dec/plot_test_copula.zip b/openturns/master/_downloads/6419bb826c2a13b8b1a53ba3ea2c3dec/plot_test_copula.zip
diff --git a/openturns/master/_downloads/655eb768eb526d2af1ea0e0433eeac49/plot_ackley_distribution.zip b/openturns/master/_downloads/655eb768eb526d2af1ea0e0433eeac49/plot_ackley_distribution.zip
diff --git a/openturns/master/_downloads/6562cb5f440391f8b800d51ffb554a83/plot_study_save_load.zip b/openturns/master/_downloads/6562cb5f440391f8b800d51ffb554a83/plot_study_save_load.zip
diff --git a/...urns/master/_downloads/661a148897102186e052aa62075814d0/plot_rwmh_python_distribution.zip b/...urns/master/_downloads/661a148897102186e052aa62075814d0/plot_rwmh_python_distribution.zip
diff --git a/.../master/_downloads/669c88e63bc2f77f36b4dd5143b089d9/plot_userdefined_covariance_model.zip b/.../master/_downloads/669c88e63bc2f77f36b4dd5143b089d9/plot_userdefined_covariance_model.zip
diff --git a/openturns/master/_downloads/68559af0df72168ca0cda598cfe34f34/plot_trend_transform.zip b/openturns/master/_downloads/68559af0df72168ca0cda598cfe34f34/plot_trend_transform.zip
diff --git a/openturns/master/_downloads/6869db87f34b684e38dda5f58b080bc2/plot_optimization_dlib.zip b/openturns/master/_downloads/6869db87f34b684e38dda5f58b080bc2/plot_optimization_dlib.zip
diff --git a/openturns/master/_downloads/69a7d9a687b1401899213788f81c5c70/plot_smolyak_experiment.zip b/openturns/master/_downloads/69a7d9a687b1401899213788f81c5c70/plot_smolyak_experiment.zip
diff --git a/...ter/_downloads/6b3dc8c62080028f6fe87ecac8bb1afb/plot_estimate_probability_monte_carlo.zip b/...ter/_downloads/6b3dc8c62080028f6fe87ecac8bb1afb/plot_estimate_probability_monte_carlo.zip
diff --git a/...er/_downloads/6bd45d367567d19dd3fb62fb724a75b9/plot_overview_univariate_distributions.zip b/...er/_downloads/6bd45d367567d19dd3fb62fb724a75b9/plot_overview_univariate_distributions.zip
diff --git a/openturns/master/_downloads/6d40c35de5da53e59e3736b961357f44/plot_ordinal_sum_copula.zip b/openturns/master/_downloads/6d40c35de5da53e59e3736b961357f44/plot_ordinal_sum_copula.zip
diff --git a/openturns/master/_downloads/6e04fbf02802d048d6f2f49b2cdb54c1/plot_estimate_moments.zip b/openturns/master/_downloads/6e04fbf02802d048d6f2f49b2cdb54c1/plot_estimate_moments.zip
diff --git a/...master/_downloads/6e1be84ade199a4f6af4c22825566ae9/plot_functional_chaos_exploitation.zip b/...master/_downloads/6e1be84ade199a4f6af4c22825566ae9/plot_functional_chaos_exploitation.zip
diff --git a/...urns/master/_downloads/6e20a49211011af7e84a81b89b2ac70f/plot_chaos_build_distribution.zip b/...urns/master/_downloads/6e20a49211011af7e84a81b89b2ac70f/plot_chaos_build_distribution.zip
diff --git a/...aster/_downloads/6e518ec737e8c5eb60654ad7ec3146cf/auto_probabilistic_modeling_jupyter.zip b/...aster/_downloads/6e518ec737e8c5eb60654ad7ec3146cf/auto_probabilistic_modeling_jupyter.zip
diff --git a/...nloads/6eaa9cec49834fa341fe3c12979d425e/plot_model_singular_multivariate_distribution.zip b/...nloads/6eaa9cec49834fa341fe3c12979d425e/plot_model_singular_multivariate_distribution.zip
diff --git a/...aster/_downloads/6f5e8023d44074bc0dea8e1a8077bc41/plot_distribution_linear_regression.zip b/...aster/_downloads/6f5e8023d44074bc0dea8e1a8077bc41/plot_distribution_linear_regression.zip
diff --git a/...ter/_downloads/708d09b7bdfce339e18287966c732912/plot_expectation_simulation_algorithm.zip b/...ter/_downloads/708d09b7bdfce339e18287966c732912/plot_expectation_simulation_algorithm.zip
diff --git a/openturns/master/_downloads/70aeb039c9b0fabc4a4522c8f23b7b2f/plot_kriging_categorical.zip b/openturns/master/_downloads/70aeb039c9b0fabc4a4522c8f23b7b2f/plot_kriging_categorical.zip
diff --git a/openturns/master/_downloads/70b03af17c7f5138c806a95f8899e0db/plot_field_fca_sobol.zip b/openturns/master/_downloads/70b03af17c7f5138c806a95f8899e0db/plot_field_fca_sobol.zip
diff --git a/openturns/master/_downloads/71aec4e01968ab2c8be52211336adbb2/auto_meta_modeling_python.zip b/openturns/master/_downloads/71aec4e01968ab2c8be52211336adbb2/auto_meta_modeling_python.zip
diff --git a/openturns/master/_downloads/7214c34045afefc34c7ebdea60927f49/plot_calibration_logistic.zip b/openturns/master/_downloads/7214c34045afefc34c7ebdea60927f49/plot_calibration_logistic.zip
diff --git a/openturns/master/_downloads/721ea9b85a5cdd56b948dfb35e84d99b/plot_sample_pandas.zip b/openturns/master/_downloads/721ea9b85a5cdd56b948dfb35e84d99b/plot_sample_pandas.zip
diff --git a/openturns/master/_downloads/72470bd840148df8d813b0ca21b3d8c5/plot_kolmogorov_test.zip b/openturns/master/_downloads/72470bd840148df8d813b0ca21b3d8c5/plot_kolmogorov_test.zip
diff --git a/...rns/master/_downloads/7263f63092b58313bb0867d9aebe4337/auto_numerical_methods_jupyter.zip b/...rns/master/_downloads/7263f63092b58313bb0867d9aebe4337/auto_numerical_methods_jupyter.zip
diff --git a/...ownloads/72a10ca6fbae493981b89b8ef45af136/plot_probability_simulation_parametrization.zip b/...ownloads/72a10ca6fbae493981b89b8ef45af136/plot_probability_simulation_parametrization.zip
diff --git a/openturns/master/_downloads/7300e74c30077a98a88316bee7ad1e6f/plot_gpr_cantilever_beam.zip b/openturns/master/_downloads/7300e74c30077a98a88316bee7ad1e6f/plot_gpr_cantilever_beam.zip
diff --git a/...r/_downloads/7652574c40576c5a9d532fa8a8c6377d/plot_estimate_spectral_density_function.zip b/...r/_downloads/7652574c40576c5a9d532fa8a8c6377d/plot_estimate_spectral_density_function.zip
diff --git a/.../master/_downloads/79a97d9a347050e36aa7c6fa164845aa/plot_functional_chaos_sensitivity.zip b/.../master/_downloads/79a97d9a347050e36aa7c6fa164845aa/plot_functional_chaos_sensitivity.zip
diff --git a/...master/_downloads/7b02572378003f7f5b8c26c7fe775e7f/plot_chaos_beam_sensitivity_degree.zip b/...master/_downloads/7b02572378003f7f5b8c26c7fe775e7f/plot_chaos_beam_sensitivity_degree.zip
diff --git a/openturns/master/_downloads/7bf8d0459e8bcb2bc728124f8a7aee7f/plot_linear_regression.zip b/openturns/master/_downloads/7bf8d0459e8bcb2bc728124f8a7aee7f/plot_linear_regression.zip
diff --git a/...er/_downloads/7c241020aa2fa43ac8e7b0761f299c8a/plot_chaos_cantilever_beam_integration.zip b/...er/_downloads/7c241020aa2fa43ac8e7b0761f299c8a/plot_chaos_cantilever_beam_integration.zip
diff --git a/...nloads/7cafadbda97f6816d5623bc9db869a25/plot_estimate_non_stationary_covariance_model.zip b/...nloads/7cafadbda97f6816d5623bc9db869a25/plot_estimate_non_stationary_covariance_model.zip
diff --git a/openturns/master/_downloads/7d24cd52377821f689d84e79b36a61e5/plot_create_copula.zip b/openturns/master/_downloads/7d24cd52377821f689d84e79b36a61e5/plot_create_copula.zip
diff --git a/openturns/master/_downloads/7d586d39c413da0400fa78b92b306895/plot_value_function.zip b/openturns/master/_downloads/7d586d39c413da0400fa78b92b306895/plot_value_function.zip
diff --git a/openturns/master/_downloads/7e76296b4bc7855cdf20bf0133743601/plot_kriging_1d.zip b/openturns/master/_downloads/7e76296b4bc7855cdf20bf0133743601/plot_kriging_1d.zip
diff --git a/...nloads/7ec8ed71df0eb9d82633c91962e59ebf/plot_estimate_probability_importance_sampling.zip b/...nloads/7ec8ed71df0eb9d82633c91962e59ebf/plot_estimate_probability_importance_sampling.zip
diff --git a/...ownloads/7f93192076929a5cc83ea94c7ba560fb/plot_viscous_fall_field_function_connection.zip b/...ownloads/7f93192076929a5cc83ea94c7ba560fb/plot_viscous_fall_field_function_connection.zip
diff --git a/...ns/master/_downloads/7fba9fbf4155fba4caf3a896351a91d4/plot_estimate_multivariate_arma.zip b/...ns/master/_downloads/7fba9fbf4155fba4caf3a896351a91d4/plot_estimate_multivariate_arma.zip
diff --git a/openturns/master/_downloads/801fdae2ddbb17a82ef192802e7301a2/plot_regression_sinus.zip b/openturns/master/_downloads/801fdae2ddbb17a82ef192802e7301a2/plot_regression_sinus.zip
diff --git a/openturns/master/_downloads/80369ad61786c1b3b82dc25d1576d70f/plot_event_system.zip b/openturns/master/_downloads/80369ad61786c1b3b82dc25d1576d70f/plot_event_system.zip
diff --git a/openturns/master/_downloads/803a8387a539184ea684abf35b850382/plot_estimate_gev_pirie.zip b/openturns/master/_downloads/803a8387a539184ea684abf35b850382/plot_estimate_gev_pirie.zip
diff --git a/openturns/master/_downloads/840cbdfc0bbce30c7fda0cd3bd141927/plot_kriging_isotropic.zip b/openturns/master/_downloads/840cbdfc0bbce30c7fda0cd3bd141927/plot_kriging_isotropic.zip
diff --git a/openturns/master/_downloads/84be1b5bc7311b75dc50e7ac58454ad3/plot_composite_experiment.zip b/openturns/master/_downloads/84be1b5bc7311b75dc50e7ac58454ad3/plot_composite_experiment.zip
diff --git a/...loads/86b07936c02c03e63bbd15967c07df94/plot_estimate_probability_directional_sampling.zip b/...loads/86b07936c02c03e63bbd15967c07df94/plot_estimate_probability_directional_sampling.zip
diff --git a/openturns/master/_downloads/86e25f41100f0dff2d1689ebb2592d6f/plot_chaos_sobol_confidence.zip b/openturns/master/_downloads/86e25f41100f0dff2d1689ebb2592d6f/plot_chaos_sobol_confidence.zip
diff --git a/openturns/master/_downloads/88d3828120a999fc8ca5897c5dd2db69/auto_graphs_python.zip b/openturns/master/_downloads/88d3828120a999fc8ca5897c5dd2db69/auto_graphs_python.zip
diff --git a/openturns/master/_downloads/899030c6d5f36486afde5e6a31ee950b/plot_src_confidence.zip b/openturns/master/_downloads/899030c6d5f36486afde5e6a31ee950b/plot_src_confidence.zip
diff --git a/...downloads/89d55db4e6fa5b7885da77440713586c/plot_create_draw_multivariate_distributions.py b/...downloads/89d55db4e6fa5b7885da77440713586c/plot_create_draw_multivariate_distributions.py
@@ -13,40 +13,82 @@
 
 
 # %%
-# Create a multivariate model with `JointDistribution`
-# -------------------------------------------------------
+# Create a multivariate model with a :class:`~openturns.JointDistribution`
+# ------------------------------------------------------------------------
 #
 # In this paragraph we use :class:`~openturns.JointDistribution` class to
-# build multidimensional distribution described by its marginal distributions and optionally its dependence structure (a particular copula).
+# build a multivariate distribution of dimension :math:`\inputDim`, from:
 #
-
-
-# %%
-# We first create the marginals of the distribution :
+# - :math:`\inputDim` scalar distributions whose cumulative distribution functions are
+#   denoted by :math:`(F_1, \dots, F_\inputDim)`, called the  *instrumental marginals*,
+# - and a core :math:`K` which is a multivariate distribution of dimension :math:`\inputDim` whose range is
+#   included in :math:`[0,1]^\inputDim`.
 #
-#   - a :class:`~openturns.Normal` distribution ;
+# First case: the core is a copula
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# In this case, we use a core which is a copula. Thus, the instrumental marginals
+# are the marginals of the multivariate distribution.
+#
+# We first create the marginals of the distribution:
+#
+#   - a :class:`~openturns.Normal` distribution;
 #   - a :class:`~openturns.Gumbel` distribution.
-
+#
+# We use a :class:`~openturns.ClaytonCopula` as dependence structure.
 marginals = [ot.Normal(), ot.Gumbel()]
-
-# %%
-# We draw their PDF. We recall that the `drawPDF` command just generates the graph data. It is the viewer module that enables the actual display.
-graphNormalPDF = marginals[0].drawPDF()
-graphNormalPDF.setTitle("PDF of the first marginal")
-graphGumbelPDF = marginals[1].drawPDF()
-graphGumbelPDF.setTitle("PDF of the second marginal")
-view = otv.View(graphNormalPDF)
-view = otv.View(graphGumbelPDF)
-
-# %%
-# The CDF is also available with the `drawCDF` method.
-
-# %%
-# We then have the minimum required to create a bivariate distribution, assuming no dependency structure :
-distribution = ot.JointDistribution(marginals)
-
-# %%
-# We can draw the PDF (here in dimension 2) :
+theta = 2.0
+cop = ot.ClaytonCopula(theta)
+distribution = ot.JointDistribution(marginals, cop)
+
+# %%
+# We can check here that the instrumental marginals really are  the marginal distributions.
+# In the following graphs, we draw the instrumental marginals and the real marginals, obtained with
+# the method :meth:`~openturns.Distribution.getMarginal`.
+# First, we draw the probability density functions of each component.
+graph_PDF_0 = marginals[0].drawPDF()
+graph_PDF_0.add(distribution.getMarginal(0).drawPDF())
+graph_PDF_0.setLegends(['instrumental marg', 'marg'])
+graph_PDF_0.setTitle("First component")
+view = otv.View(graph_PDF_0)
+
+graph_PDF_1 = marginals[1].drawPDF()
+graph_PDF_1.add(distribution.getMarginal(1).drawPDF())
+graph_PDF_1.setLegends(['instrumental marg', 'marg'])
+graph_PDF_1.setTitle("Second component")
+view = otv.View(graph_PDF_1)
+
+# %%
+# Then, we draw the cumulative distribution functions.
+graph_CDF_0 = marginals[0].drawCDF()
+graph_CDF_0.add(distribution.getMarginal(0).drawCDF())
+graph_CDF_0.setLegends(['instrumental marg', 'marg'])
+graph_CDF_0.setTitle("First component")
+view = otv.View(graph_CDF_0)
+
+graph_CDF_1 = marginals[1].drawCDF()
+graph_CDF_1.add(distribution.getMarginal(1).drawCDF())
+graph_CDF_1.setLegends(['instrumental marg', 'marg'])
+graph_CDF_1.setTitle("Second component")
+view = otv.View(graph_CDF_1)
+
+# %%
+# At last, we check that the copula of the multivariate distribution is the specified core
+# which was a copula.
+cop_dist = distribution.getCopula()
+graph_cop = cop_dist.drawPDF()
+# Get the Contour Drawable's actual implementation from the Graph
+# produced by drawPDF in order to access all its methods
+contour_cop = cop.drawPDF().getDrawable(1).getImplementation()
+contour_cop.setLineStyle('dashed')
+# Remove the colorbar
+contour_cop.setColorBarPosition("")
+graph_cop.add(contour_cop)
+# Add the contour without a colorbargraph_cop.add(cop.drawPDF())
+graph_cop.setTitle('Distribution copula and core')
+view = otv.View(graph_cop)
+
+# %%
+# We can draw the PDF of the bivariate distribution:
 graph = distribution.drawPDF()
 view = otv.View(graph)
 
@@ -57,26 +99,85 @@
 
 
 # %%
-# If a dependence between marginals is needed we have to create the copula specifying the dependency structure, here a :class:`~openturns.NormalCopula` :
-R = ot.CorrelationMatrix(2)
-R[0, 1] = 0.3
-copula = ot.NormalCopula(R)
-print(copula)
-
-# %%
-# We create the bivariate distribution with the desired copula and draw it.
-distribution = ot.JointDistribution(marginals, copula)
+# Second case: the core is not a copula
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# In this case, we use a core which is not a copula. Thus, the instrumental marginals
+# are not the marginals of the multivariate distribution.
+#
+# We first create the instrumental marginals of the distribution:
+#
+# - a :class:`~openturns.Normal` distribution;
+# - a :class:`~openturns.Gumbel` distribution.
+#
+# We use a :class:`~openturns.Dirichlet` as the core.
+inst_marginals = [ot.Normal(), ot.Gumbel()]
+core_dir = ot.Dirichlet([2.0, 1.5, 2.5])
+distribution = ot.JointDistribution(inst_marginals, core_dir)
+
+# %%
+# We can check here that the instrumental marginals are not the marginal distributions.
+# In the following graphs, we draw the instrumental marginals and the real marginals, obtained with
+# the method :meth:`~openturns.Distribution.getMarginal`.
+# First, we draw the probability density functions of each component.
+graph_PDF_0 = inst_marginals[0].drawPDF()
+graph_PDF_0.add(distribution.getMarginal(0).drawPDF())
+graph_PDF_0.setLegends(['instrumental marg', 'marg'])
+graph_PDF_0.setTitle("First component")
+view = otv.View(graph_PDF_0)
+
+graph_PDF_1 = inst_marginals[1].drawPDF()
+graph_PDF_1.add(distribution.getMarginal(1).drawPDF())
+graph_PDF_1.setLegends(['instrumental marg', 'marg'])
+graph_PDF_1.setTitle("Second component")
+view = otv.View(graph_PDF_1)
+
+# %%
+# Then, we draw the cumulative distribution functions.
+graph_CDF_0 = inst_marginals[0].drawCDF()
+graph_CDF_0.add(distribution.getMarginal(0).drawCDF())
+graph_CDF_0.setLegends(['instrumental marg', 'marg'])
+graph_CDF_0.setTitle("First component")
+view = otv.View(graph_CDF_0)
+
+graph_CDF_1 = inst_marginals[1].drawCDF()
+graph_CDF_1.add(distribution.getMarginal(1).drawCDF())
+graph_CDF_1.setLegends(['instrumental marg', 'marg'])
+graph_CDF_1.setTitle("Second component")
+view = otv.View(graph_CDF_1)
+
+# %%
+# At last, we check that the copula of the multivariate distribution is not the specified core.
+cop_dist = distribution.getCopula()
+graph_cop = cop_dist.drawPDF()
+cop_dist_draw = graph_cop.getDrawable(1)
+levels = cop_dist_draw.getLevels()
+graph_core = core_dir.drawPDF()
+core_draw = graph_core.getDrawable(0).getImplementation()
+core_draw.setColorBarPosition("")
+core_draw.setLevels(levels)
+core_draw.setLineStyle('dashed')
+
+graph_cop.add(core_draw)
+graph_cop.setTitle('Distribution copula and core')
+view = otv.View(graph_cop)
+
+# %%
+# We can draw the PDF of the bivariate distribution.
 graph = distribution.drawPDF()
 view = otv.View(graph)
 
+# %%
+# We also draw the CDF.
+graph = distribution.drawCDF()
+view = otv.View(graph)
 
 # %%
-# Multivariate models
-# -------------------
-# Some models in the library are natively multivariate. We present examples of three of them :
+# Use some native multivariate models
+# -----------------------------------
+# Some models in the library are natively multivariate. We present examples of three of them:
 #
-#  - the :class:`~openturns.Normal` distribution ;
-#  - the :class:`~openturns.Student` distribution ;
+#  - the :class:`~openturns.Normal` distribution,
+#  - the :class:`~openturns.Student` distribution,
 #  - the :class:`~openturns.UserDefined` distribution.
 #
 # The Normal distribution
@@ -135,7 +236,7 @@
 graph.setTitle("User defined PDF")
 
 # %%
-# We can draw a sample from this distribution with the `getSample` method :
+# We can generate and display a sample from this distribution.
 omega = distribution.getSample(100)
 cloud = ot.Cloud(omega, "black", "fdiamond", "Sample from UserDefined distribution")
 graph.add(cloud)

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