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z!em9?p>Uq91Yr_REHHVw`S`8?f*}}f6DRRwdaQo&XZPY)0BV=Eb(6FC~ z8+a4MSO=6N0{=)N**tRLyUe)`b74iEeFj*Wwoy*G4RS`Oq - class=Point name=Unnamed dimension=1 values=[-0.553319] + class=Point name=Unnamed dimension=1 values=[-0.440425]

@@ -150,7 +150,7 @@ load the generator state .. raw:: html

- class=Point name=Unnamed dimension=1 values=[-0.553319] + class=Point name=Unnamed dimension=1 values=[-0.440425]


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 a6603a50a9f..627846c3bb9 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 ================= -**00:08.473** total execution time for 8 files **from auto_numerical_methods/general_methods**: +**00:08.782** 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``) - - 00:07.426 + - 00:07.735 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_regression_sinus.py` (``plot_regression_sinus.py``) - - 00:00.403 + - 00:00.436 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_pce_design.py` (``plot_pce_design.py``) - - 00:00.383 + - 00:00.373 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_estimate_integral_iterated_quadrature.py` (``plot_estimate_integral_iterated_quadrature.py``) - - 00:00.135 + - 00:00.132 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_regression_interval.py` (``plot_regression_interval.py``) - - 00:00.112 + - 00:00.093 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_study_save_load.py` (``plot_study_save_load.py``) - - 00:00.012 + - 00:00.010 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_random_generator.py` (``plot_random_generator.py``) - - 00:00.002 + - 00:00.001 - 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 434b9180f2a..cdbd632215e 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 ================= -**00:00.664** total execution time for 3 files **from auto_numerical_methods/iterative_statistics**: +**00:00.602** total execution time for 3 files **from auto_numerical_methods/iterative_statistics**: .. container:: @@ -33,11 +33,11 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_threshold.py` (``plot_iterative_threshold.py``) - - 00:00.317 + - 00:00.288 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_extrema.py` (``plot_iterative_extrema.py``) - - 00:00.183 + - 00:00.166 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_moments.py` (``plot_iterative_moments.py``) - - 00:00.164 + - 00:00.148 - 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 b3f839ea86e..d6648cfcc78 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 @@ -210,8 +210,8 @@ Run algorithm .. code-block:: none - event probability: 0.14109406270847227 - calls number: 6098 + event probability: 0.14108865829237185 + calls number: 5595 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 23caf72d64a..7fa8c51c1ba 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=[0.0115253,0.17423] + class=Point name=Unnamed dimension=2 values=[0.0116529,0.174234]


@@ -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=[1.13551] + class=Point name=Unnamed dimension=1 values=[1.13562]


@@ -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=[1.93445e-07,-6.51134e-07] + class=Point name=Unnamed dimension=2 values=[-1.08864e-06,-1.00913e-06]


@@ -840,7 +840,7 @@ We run the algorithm and get the result: .. raw:: html
- class=Point name=Unnamed dimension=2 values=[0.547927,0.166704] + class=Point name=Unnamed dimension=2 values=[0.547933,0.166691]


@@ -974,7 +974,7 @@ Reset default settings .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.749 seconds) + **Total running time of the script:** (0 minutes 2.575 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 892940a7d3a..cad9827a8f4 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^ = [2.74742,1.17747,0.5] - f(x^) = [2.1235e-23] + x^ = [2.81297,1.20556,0.5] + f(x^) = [1.5135e-24] Iteration number: 7 Evaluation number: 8 - Absolute error: 1.653344515803232e-06 - Relative error: 5.455437574215886e-07 - Residual error: 1.489466995323662e-12 + Absolute error: 8.626885208779728e-07 + Relative error: 2.7819715276910214e-07 + Residual error: 4.019911002292566e-13 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 281e9eb1840..922669ba878 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 ================= -**00:05.424** total execution time for 11 files **from auto_numerical_methods/optimization**: +**00:05.149** total execution time for 11 files **from auto_numerical_methods/optimization**: .. container:: @@ -33,29 +33,29 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_ego.py` (``plot_ego.py``) - - 00:02.749 + - 00:02.575 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rastrigin.py` (``plot_optimization_rastrigin.py``) - - 00:01.076 + - 00:01.003 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rosenbrock.py` (``plot_optimization_rosenbrock.py``) - - 00:00.679 + - 00:00.731 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_dlib.py` (``plot_optimization_dlib.py``) - - 00:00.387 + - 00:00.434 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_pagmo.py` (``plot_optimization_pagmo.py``) - - 00:00.289 - - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_bonmin.py` (``plot_optimization_bonmin.py``) - - 00:00.077 + - 00:00.185 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_constraints.py` (``plot_optimization_constraints.py``) - - 00:00.074 + - 00:00.071 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_nlopt.py` (``plot_optimization_nlopt.py``) - 00:00.071 - 0.0 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_bonmin.py` (``plot_optimization_bonmin.py``) + - 00:00.057 + - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_control_termination.py` (``plot_control_termination.py``) - 00:00.014 - 0.0 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 ef60212b8f7..ba0476789af 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 ================= -**00:00.392** total execution time for 4 files **from auto_probabilistic_modeling/copulas**: +**00:00.471** total execution time for 4 files **from auto_probabilistic_modeling/copulas**: .. container:: @@ -33,7 +33,7 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_ordinal_sum_copula.py` (``plot_ordinal_sum_copula.py``) - - 00:00.389 + - 00:00.468 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_extract_copula.py` (``plot_extract_copula.py``) - 00:00.001 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 aab8e18b209..f46c48ab9ce 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 ================= -**00:06.157** total execution time for 19 files **from auto_probabilistic_modeling/distributions**: +**00:07.593** total execution time for 19 files **from auto_probabilistic_modeling/distributions**: .. container:: @@ -33,59 +33,59 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_quick_start_guide_distributions.py` (``plot_quick_start_guide_distributions.py``) - - 00:00.873 + - 00:00.994 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_draw_multivariate_distributions.py` (``plot_create_draw_multivariate_distributions.py``) - - 00:00.852 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_minimum_volume_level_sets.py` (``plot_minimum_volume_level_sets.py``) - - 00:00.633 + - 00:00.975 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_transformation.py` (``plot_distribution_transformation.py``) - - 00:00.603 + - 00:00.739 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_order_statistics_distribution.py` (``plot_order_statistics_distribution.py``) - - 00:00.520 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_minimum_volume_level_sets.py` (``plot_minimum_volume_level_sets.py``) + - 00:00.694 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_truncated_distribution.py` (``plot_truncated_distribution.py``) - - 00:00.388 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_order_statistics_distribution.py` (``plot_order_statistics_distribution.py``) + - 00:00.629 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_overview_univariate_distributions.py` (``plot_overview_univariate_distributions.py``) - - 00:00.365 + - 00:00.534 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_manipulation.py` (``plot_distribution_manipulation.py``) - - 00:00.286 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_truncated_distribution.py` (``plot_truncated_distribution.py``) + - 00:00.498 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_extreme_value_distribution.py` (``plot_create_extreme_value_distribution.py``) - - 00:00.276 + - 00:00.371 - 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_distribution_manipulation.py` (``plot_distribution_manipulation.py``) + - 00:00.369 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_random_mixture.py` (``plot_create_random_mixture.py``) - - 00:00.237 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_and_draw_scalar_distributions.py` (``plot_create_and_draw_scalar_distributions.py``) + - 00:00.347 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_bayes_distribution.py` (``plot_bayes_distribution.py``) - - 00:00.207 + - 00:00.297 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_random_mixture.py` (``plot_create_random_mixture.py``) + - 00:00.287 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_mixture_distribution.py` (``plot_mixture_distribution.py``) - - 00:00.188 + - 00:00.258 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_generate_by_inversion.py` (``plot_generate_by_inversion.py``) - - 00:00.162 + - 00:00.211 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_python_distribution.py` (``plot_python_distribution.py``) - - 00:00.102 + - 00:00.116 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_conditional_distribution.py` (``plot_conditional_distribution.py``) - - 00:00.092 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_your_own_dist.py` (``plot_create_your_own_dist.py``) - - 00:00.062 + - 00:00.101 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_maximum_distribution.py` (``plot_maximum_distribution.py``) - - 00:00.058 + - 00:00.086 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_your_own_dist.py` (``plot_create_your_own_dist.py``) + - 00:00.085 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_conditional_random_vector.py` (``plot_conditional_random_vector.py``) - - 00:00.001 + - 00:00.002 - 0.0 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 864ad93a4a2..95429cd2b52 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 @@ -6,7 +6,7 @@ Computation times ================= -**00:00.006** total execution time for 3 files **from auto_probabilistic_modeling/random_vectors**: +**00:00.008** total execution time for 3 files **from auto_probabilistic_modeling/random_vectors**: .. container:: @@ -33,10 +33,10 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_random_vector_manipulation.py` (``plot_random_vector_manipulation.py``) - - 00:00.002 + - 00:00.003 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_python_randomvector.py` (``plot_python_randomvector.py``) - - 00:00.002 + - 00:00.003 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_composite_random_vector.py` (``plot_composite_random_vector.py``) - 00:00.002 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..681c99299a2 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 2.771 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_field_manipulation.rst.txt b/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_field_manipulation.rst.txt index 89015fcf099..c4b1fa50bf2 100644 --- a/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_field_manipulation.rst.txt +++ b/openturns/master/_sources/auto_probabilistic_modeling/stochastic_processes/plot_field_manipulation.rst.txt @@ -262,6 +262,11 @@ Export to the VTK format +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 2.198 seconds) + + .. _sphx_glr_download_auto_probabilistic_modeling_stochastic_processes_plot_field_manipulation.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 42d080d2a0a..86744234b37 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 2.135 seconds) + **Total running time of the script:** (0 minutes 2.475 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 92453dad3a6..c016cab326e 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 2.089 seconds) + **Total running time of the script:** (0 minutes 2.779 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 e6900929b5d..1ad98759fb2 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 ================= -**00:12.403** total execution time for 24 files **from auto_probabilistic_modeling/stochastic_processes**: +**00:15.1000** total execution time for 24 files **from auto_probabilistic_modeling/stochastic_processes**: .. container:: @@ -32,75 +32,75 @@ Computation times * - Example - Time - Mem (MB) - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_process_covariance_hmat.py` (``plot_gaussian_process_covariance_hmat.py``) - - 00:02.135 - - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_userdefined_covariance_model.py` (``plot_userdefined_covariance_model.py``) - - 00:02.089 + - 00:02.779 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_mesh.py` (``plot_create_mesh.py``) - - 00:01.975 + - 00:02.771 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_process_covariance_hmat.py` (``plot_gaussian_process_covariance_hmat.py``) + - 00:02.475 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_field_manipulation.py` (``plot_field_manipulation.py``) - - 00:01.738 + - 00:02.198 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_box_cox_transform.py` (``plot_box_cox_transform.py``) - - 00:01.009 + - 00:01.217 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_processes_comparison.py` (``plot_gaussian_processes_comparison.py``) - - 00:00.689 + - 00:00.899 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_process_manipulation.py` (``plot_process_manipulation.py``) - - 00:00.462 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_random_walk_process.py` (``plot_random_walk_process.py``) - - 00:00.291 + - 00:00.653 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_and_manipulate_arma_process.py` (``plot_create_and_manipulate_arma_process.py``) - - 00:00.272 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_discrete_markov_chain_process.py` (``plot_discrete_markov_chain_process.py``) - - 00:00.258 + - 00:00.374 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_kronecker_covmodel.py` (``plot_kronecker_covmodel.py``) - - 00:00.256 + - 00:00.355 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_random_walk_process.py` (``plot_random_walk_process.py``) + - 00:00.338 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_discrete_markov_chain_process.py` (``plot_discrete_markov_chain_process.py``) + - 00:00.308 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_mix_rv_process.py` (``plot_mix_rv_process.py``) - - 00:00.157 + - 00:00.223 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_white_noise_process.py` (``plot_white_noise_process.py``) - - 00:00.153 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_timeseries_manipulation.py` (``plot_timeseries_manipulation.py``) + - 00:00.176 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_export_field_vtk.py` (``plot_export_field_vtk.py``) - - 00:00.130 + - 00:00.175 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_normal_process.py` (``plot_create_normal_process.py``) - - 00:00.128 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_aggregated_process.py` (``plot_aggregated_process.py``) + - 00:00.170 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_add_trend.py` (``plot_add_trend.py``) - - 00:00.126 + - 00:00.166 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_trend_transform.py` (``plot_trend_transform.py``) - - 00:00.120 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_white_noise_process.py` (``plot_white_noise_process.py``) + - 00:00.165 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_timeseries_manipulation.py` (``plot_timeseries_manipulation.py``) - - 00:00.110 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_trend_transform.py` (``plot_trend_transform.py``) + - 00:00.163 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_aggregated_process.py` (``plot_aggregated_process.py``) - - 00:00.109 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_normal_process.py` (``plot_create_normal_process.py``) + - 00:00.162 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_functional_basis_process.py` (``plot_functional_basis_process.py``) - - 00:00.070 + - 00:00.089 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_user_stationary_covmodel.py` (``plot_user_stationary_covmodel.py``) - - 00:00.066 + - 00:00.076 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_userdefined_spectral_model.py` (``plot_userdefined_spectral_model.py``) - - 00:00.059 + - 00:00.065 - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_stationary_covmodel.py` (``plot_create_stationary_covmodel.py``) - - 00:00.001 + - 00:00.003 - 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 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 6d99f745015..a129a87097a 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 ================= -**00:00.618** total execution time for 3 files **from auto_reliability_sensitivity/central_dispersion**: +**00:00.627** 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``) - - 00:00.446 + - 00:00.443 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_central_tendency.py` (``plot_central_tendency.py``) - - 00:00.125 + - 00:00.133 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_estimate_moments_taylor.py` (``plot_estimate_moments_taylor.py``) - - 00:00.047 + - 00:00.051 - 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 32ac905391f..cdb0c5c3227 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 2.247 seconds) + **Total running time of the script:** (0 minutes 2.405 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 f61e8fb6e61..e64eead807a 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 7.476 seconds) + **Total running time of the script:** (0 minutes 8.062 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 1bc494dd496..56b6f594384 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 ================= -**00:14.921** total execution time for 17 files **from auto_reliability_sensitivity/design_of_experiments**: +**00:16.038** total execution time for 17 files **from auto_reliability_sensitivity/design_of_experiments**: .. container:: @@ -33,52 +33,52 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_quadrature.py` (``plot_smolyak_quadrature.py``) - - 00:07.476 + - 00:08.062 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_experiment.py` (``plot_smolyak_experiment.py``) - - 00:02.247 + - 00:02.405 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_plot_design.py` (``plot_plot_design.py``) - - 00:01.253 + - 00:01.303 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_design_of_experiments.py` (``plot_design_of_experiments.py``) - - 00:01.045 + - 00:01.115 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_low_discrepancy_sequence.py` (``plot_low_discrepancy_sequence.py``) - - 00:00.546 + - 00:00.561 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_merge.py` (``plot_smolyak_merge.py``) - - 00:00.482 + - 00:00.541 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_optimal_lhs.py` (``plot_optimal_lhs.py``) - - 00:00.462 + - 00:00.473 + - 0.0 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_indices.py` (``plot_smolyak_indices.py``) + - 00:00.364 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_create_deterministic_doe.py` (``plot_create_deterministic_doe.py``) - - 00:00.290 + - 00:00.323 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_deterministic_design.py` (``plot_deterministic_design.py``) - - 00:00.271 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_indices.py` (``plot_smolyak_indices.py``) - - 00:00.259 + - 00:00.287 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_mixed_design.py` (``plot_mixed_design.py``) - - 00:00.133 + - 00:00.137 - 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``) - - 00:00.121 + - 00:00.123 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_create_random_doe.py` (``plot_create_random_doe.py``) - - 00:00.119 + - 00:00.122 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_gauss_product_experiment.py` (``plot_gauss_product_experiment.py``) - - 00:00.059 + - 00:00.060 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_probabilistic_design.py` (``plot_probabilistic_design.py``) + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_composite_experiment.py` (``plot_composite_experiment.py``) - 00:00.056 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_composite_experiment.py` (``plot_composite_experiment.py``) - - 00:00.054 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_probabilistic_design.py` (``plot_probabilistic_design.py``) + - 00:00.056 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_monte_carlo_experiment.py` (``plot_monte_carlo_experiment.py``) - 00:00.049 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 dfc087c69c4..5a7506b2c6a 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 @@ -218,7 +218,7 @@ Retrieve results. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.066 seconds) + **Total running time of the script:** (0 minutes 2.173 seconds) .. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_estimate_probability_adaptive_directional_sampling.py: 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 9e67171134e..3a43015cd61 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 5.197 seconds) + **Total running time of the script:** (0 minutes 5.391 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_proba_system_event.rst.txt b/openturns/master/_sources/auto_reliability_sensitivity/reliability/plot_proba_system_event.rst.txt index 710c3152528..ca2a952b4fb 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 2.168 seconds) + **Total running time of the script:** (0 minutes 2.219 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 ffff8d19f78..019ab7c7908 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 ================= -**00:18.000** total execution time for 24 files **from auto_reliability_sensitivity/reliability**: +**00:18.549** total execution time for 24 files **from auto_reliability_sensitivity/reliability**: .. container:: @@ -33,74 +33,74 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_form_oscillator.py` (``plot_estimate_probability_form_oscillator.py``) - - 00:05.197 + - 00:05.391 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_proba_system_event.py` (``plot_proba_system_event.py``) - - 00:02.168 + - 00:02.219 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_adaptive_directional_sampling.py` (``plot_estimate_probability_adaptive_directional_sampling.py``) - - 00:02.066 + - 00:02.173 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_crossentropy.py` (``plot_crossentropy.py``) - - 00:01.407 + - 00:01.431 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_form_explained.py` (``plot_form_explained.py``) - - 00:00.824 + - 00:00.871 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_randomized_qmc.py` (``plot_estimate_probability_randomized_qmc.py``) - - 00:00.804 + - 00:00.809 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_directional_sampling.py` (``plot_estimate_probability_directional_sampling.py``) - - 00:00.692 + - 00:00.713 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_axial_stressed_beam.py` (``plot_axial_stressed_beam.py``) - - 00:00.677 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_system.py` (``plot_event_system.py``) - - 00:00.627 + - 00:00.705 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_create_domain_event.py` (``plot_create_domain_event.py``) - - 00:00.613 + - 00:00.648 + - 0.0 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_system.py` (``plot_event_system.py``) + - 00:00.641 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_subset_sampling.py` (``plot_subset_sampling.py``) - - 00:00.607 + - 00:00.623 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_multi_form.py` (``plot_multi_form.py``) - - 00:00.518 + - 00:00.517 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_nais.py` (``plot_nais.py``) - - 00:00.493 + - 00:00.463 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_axial_stressed_beam_quickstart.py` (``plot_axial_stressed_beam_quickstart.py``) - - 00:00.395 + - 00:00.390 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_form.py` (``plot_estimate_probability_form.py``) - - 00:00.349 + - 00:00.368 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_importance_sampling.py` (``plot_estimate_probability_importance_sampling.py``) - - 00:00.151 + - 00:00.155 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_probability_simulation_results.py` (``plot_probability_simulation_results.py``) - - 00:00.139 + - 00:00.145 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_flood_model.py` (``plot_flood_model.py``) - - 00:00.114 + - 00:00.123 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_create_threshold_event.py` (``plot_create_threshold_event.py``) - - 00:00.081 + - 00:00.085 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_monte_carlo.py` (``plot_estimate_probability_monte_carlo.py``) + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_probability_simulation_parametrization.py` (``plot_probability_simulation_parametrization.py``) - 00:00.033 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_probability_simulation_parametrization.py` (``plot_probability_simulation_parametrization.py``) + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_monte_carlo.py` (``plot_estimate_probability_monte_carlo.py``) - 00:00.032 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_post_analytical_importance_sampling.py` (``plot_post_analytical_importance_sampling.py``) - - 00:00.005 - - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_strong_maximum_test.py` (``plot_strong_maximum_test.py``) + - 00:00.006 + - 0.0 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_post_analytical_importance_sampling.py` (``plot_post_analytical_importance_sampling.py``) - 00:00.005 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_manipulation.py` (``plot_event_manipulation.py``) - - 00:00.002 + - 00:00.004 - 0.0 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 cf2231f1164..959e6fedcd0 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 @@ -521,7 +521,7 @@ Reset default settings .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 6.607 seconds) + **Total running time of the script:** (0 minutes 6.549 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 c773bda88d7..3db087b7632 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 ================= -**00:06.786** total execution time for 3 files **from auto_reliability_sensitivity/reliability_processes**: +**00:06.732** total execution time for 3 files **from auto_reliability_sensitivity/reliability_processes**: .. container:: @@ -33,11 +33,11 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_field_fca_sobol.py` (``plot_field_fca_sobol.py``) - - 00:06.607 + - 00:06.549 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_estimate_probability_monte_carlo_process.py` (``plot_estimate_probability_monte_carlo_process.py``) - - 00:00.177 + - 00:00.180 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_event_process.py` (``plot_event_process.py``) - - 00:00.002 + - 00:00.003 - 0.0 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 599f71dd760..38a055c5221 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 @@ -1188,7 +1188,7 @@ 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 5.645 seconds) + **Total running time of the script:** (0 minutes 5.701 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 7e5ed9fa18c..e03aeeb2aff 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 ================= -**00:08.859** total execution time for 8 files **from auto_reliability_sensitivity/sensitivity_analysis**: +**00:08.908** 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``) - - 00:05.645 + - 00:05.701 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_functional_chaos_sensitivity.py` (``plot_functional_chaos_sensitivity.py``) - - 00:00.953 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_par_coo.py` (``plot_sensitivity_par_coo.py``) - - 00:00.766 + - 00:00.954 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_hsic_estimators_ishigami.py` (``plot_hsic_estimators_ishigami.py``) - - 00:00.759 + - 00:00.764 + - 0.0 + * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_par_coo.py` (``plot_sensitivity_par_coo.py``) + - 00:00.754 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_sobol.py` (``plot_sensitivity_sobol.py``) - - 00:00.423 + - 00:00.428 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_ancova.py` (``plot_sensitivity_ancova.py``) - - 00:00.117 + - 00:00.124 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_sobol_multivariate.py` (``plot_sensitivity_sobol_multivariate.py``) - - 00:00.115 + - 00:00.105 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_fast.py` (``plot_sensitivity_fast.py``) - - 00:00.082 + - 00:00.078 - 0.0 diff --git a/openturns/master/_sources/sg_execution_times.rst.txt b/openturns/master/_sources/sg_execution_times.rst.txt index 6a41d08d2cc..5479b2c9d73 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 ================= -**04:11.550** total execution time for 262 files **from all galleries**: +**04:39.606** total execution time for 262 files **from all galleries**: .. container:: @@ -33,785 +33,785 @@ 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``) - - 00:18.730 + - 00:18.665 + - 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``) + - 00:12.746 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_gibbs.py` (``examples/calibration/bayesian_calibration/plot_gibbs.py``) - - 00:12.846 + - 00:12.487 - 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``) - - 00:10.892 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_racetime.py` (``examples/data_analysis/distribution_fitting/plot_estimate_gev_racetime.py``) + - 00:12.264 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_categorical.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_categorical.py``) - - 00:10.297 + - 00:11.735 - 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``) - - 00:09.992 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_functional_chaos.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_functional_chaos.py``) + - 00:10.581 - 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``) - - 00:09.121 + - 00:09.090 - 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``) - - 00:07.708 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_multivariate_distribution.py` (``examples/data_analysis/distribution_fitting/plot_estimate_multivariate_distribution.py``) + - 00:08.315 - 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``) - - 00:07.476 + - 00:08.062 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_ifs.py` (``examples/numerical_methods/general_methods/plot_ifs.py``) - - 00:07.426 + * - :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``) + - 00:07.981 - 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``) - - 00:07.411 + * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_ifs.py` (``examples/numerical_methods/general_methods/plot_ifs.py``) + - 00:07.735 - 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``) - - 00:07.151 + - 00:07.142 - 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``) - - 00:06.607 - - 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``) - - 00:06.396 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_fremantle.py` (``examples/data_analysis/distribution_fitting/plot_estimate_gev_fremantle.py``) + - 00:06.865 - 0.0 - * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_rwmh_python_distribution.py` (``examples/calibration/bayesian_calibration/plot_rwmh_python_distribution.py``) - - 00:05.693 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_field_fca_sobol.py` (``examples/reliability_sensitivity/reliability_processes/plot_field_fca_sobol.py``) + - 00:06.549 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_wingweight.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_wingweight.py``) - - 00:05.645 + - 00:05.701 - 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``) - - 00:05.388 + * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_rwmh_python_distribution.py` (``examples/calibration/bayesian_calibration/plot_rwmh_python_distribution.py``) + - 00:05.585 - 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``) - - 00:05.197 - - 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``) - - 00:03.952 + - 00:05.391 - 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``) - - 00:03.897 + - 00:04.751 + - 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``) + - 00:03.914 - 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``) - - 00:02.947 + - 00:03.698 - 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``) - - 00:02.763 + - 00:03.292 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_ego.py` (``examples/numerical_methods/optimization/plot_ego.py``) - - 00:02.749 + * - :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``) + - 00:02.864 - 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``) - - 00:02.247 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_userdefined_covariance_model.py` (``examples/probabilistic_modeling/stochastic_processes/plot_userdefined_covariance_model.py``) + - 00:02.779 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_mesh.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_mesh.py``) + - 00:02.771 - 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``) - - 00:02.220 + - 00:02.653 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_proba_system_event.py` (``examples/reliability_sensitivity/reliability/plot_proba_system_event.py``) - - 00:02.168 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_ego.py` (``examples/numerical_methods/optimization/plot_ego.py``) + - 00:02.575 - 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``) - - 00:02.135 + - 00:02.475 - 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``) - - 00:02.089 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_experiment.py` (``examples/reliability_sensitivity/design_of_experiments/plot_smolyak_experiment.py``) + - 00:02.405 - 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``) - - 00:02.085 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_gev_pirie.py` (``examples/data_analysis/distribution_fitting/plot_estimate_gev_pirie.py``) + - 00:02.399 - 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``) - - 00:02.066 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_proba_system_event.py` (``examples/reliability_sensitivity/reliability/plot_proba_system_event.py``) + - 00:02.219 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_mesh.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_mesh.py``) - - 00:01.975 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_field_manipulation.py` (``examples/probabilistic_modeling/stochastic_processes/plot_field_manipulation.py``) + - 00:02.198 - 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``) - - 00:01.963 + * - :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``) + - 00:02.173 - 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``) - - 00:01.935 + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_enumeratefunction.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_enumeratefunction.py``) + - 00:02.134 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_field_manipulation.py` (``examples/probabilistic_modeling/stochastic_processes/plot_field_manipulation.py``) - - 00:01.738 + * - :ref:`sphx_glr_auto_meta_modeling_fields_metamodels_plot_viscous_fall_metamodel.py` (``examples/meta_modeling/fields_metamodels/plot_viscous_fall_metamodel.py``) + - 00:02.075 - 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``) - - 00:01.553 - - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_crossentropy.py` (``examples/reliability_sensitivity/reliability/plot_crossentropy.py``) - - 00:01.407 + - 00:01.932 - 0.0 - * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_bayesian_calibration_flooding.py` (``examples/calibration/bayesian_calibration/plot_bayesian_calibration_flooding.py``) - - 00:01.397 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_draw_covariance_models.py` (``examples/meta_modeling/kriging_metamodel/plot_draw_covariance_models.py``) + - 00:01.508 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_enumeratefunction.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_enumeratefunction.py``) - - 00:01.383 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_crossentropy.py` (``examples/reliability_sensitivity/reliability/plot_crossentropy.py``) + - 00:01.431 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_distribution.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_distribution.py``) - - 00:01.313 + - 00:01.403 + - 0.0 + * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_bayesian_calibration_flooding.py` (``examples/calibration/bayesian_calibration/plot_bayesian_calibration_flooding.py``) + - 00:01.328 - 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``) - - 00:01.253 + - 00:01.303 - 0.0 - * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_gibbs_simus.py` (``examples/calibration/bayesian_calibration/plot_gibbs_simus.py``) - - 00:01.196 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_box_cox_transform.py` (``examples/probabilistic_modeling/stochastic_processes/plot_box_cox_transform.py``) + - 00:01.217 - 0.0 * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_bayesian_calibration.py` (``examples/calibration/bayesian_calibration/plot_bayesian_calibration.py``) - - 00:01.192 + - 00:01.180 - 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``) - - 00:01.184 - - 0.0 - * - :ref:`sphx_glr_auto_graphs_plot_graphs_basics.py` (``examples/graphs/plot_graphs_basics.py``) - - 00:01.140 - - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rastrigin.py` (``examples/numerical_methods/optimization/plot_optimization_rastrigin.py``) - - 00:01.076 + * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_draw_survival.py` (``examples/data_analysis/sample_analysis/plot_draw_survival.py``) + - 00:01.149 - 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``) - - 00:01.045 + * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_gibbs_simus.py` (``examples/calibration/bayesian_calibration/plot_gibbs_simus.py``) + - 00:01.148 - 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``) - - 00:01.030 + - 00:01.125 - 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``) - - 00:01.009 + * - :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``) + - 00:01.116 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_draw_survival.py` (``examples/data_analysis/sample_analysis/plot_draw_survival.py``) - - 00:00.983 + * - :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``) + - 00:01.115 - 0.0 - * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_ackley_distribution.py` (``examples/calibration/bayesian_calibration/plot_ackley_distribution.py``) - - 00:00.955 + * - :ref:`sphx_glr_auto_graphs_plot_graphs_basics.py` (``examples/graphs/plot_graphs_basics.py``) + - 00:01.074 - 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``) - - 00:00.953 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rastrigin.py` (``examples/numerical_methods/optimization/plot_optimization_rastrigin.py``) + - 00:01.003 - 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``) - - 00:00.873 + - 00:00.994 - 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``) - - 00:00.872 + - 00:00.991 - 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``) - - 00:00.852 + - 00:00.975 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_form_explained.py` (``examples/reliability_sensitivity/reliability/plot_form_explained.py``) - - 00:00.824 + * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_functional_chaos_sensitivity.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_functional_chaos_sensitivity.py``) + - 00:00.954 - 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``) - - 00:00.804 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_linear_model.py` (``examples/meta_modeling/general_purpose_metamodels/plot_linear_model.py``) + - 00:00.952 - 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``) - - 00:00.788 + * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_ackley_distribution.py` (``examples/calibration/bayesian_calibration/plot_ackley_distribution.py``) + - 00:00.931 - 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``) - - 00:00.787 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_processes_comparison.py` (``examples/probabilistic_modeling/stochastic_processes/plot_gaussian_processes_comparison.py``) + - 00:00.899 - 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``) - - 00:00.766 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_form_explained.py` (``examples/reliability_sensitivity/reliability/plot_form_explained.py``) + - 00:00.871 - 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``) - - 00:00.759 + * - :ref:`sphx_glr_auto_data_analysis_estimate_stochastic_processes_plot_estimate_arma.py` (``examples/data_analysis/estimate_stochastic_processes/plot_estimate_arma.py``) + - 00:00.829 - 0.0 * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_advanced.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_advanced.py``) - - 00:00.753 + - 00:00.815 + - 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``) + - 00:00.809 + - 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``) + - 00:00.796 + - 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``) + - 00:00.764 - 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``) - - 00:00.745 + - 00:00.755 - 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``) - - 00:00.712 + * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_par_coo.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_par_coo.py``) + - 00:00.754 - 0.0 - * - :ref:`sphx_glr_auto_graphs_plot_graphs_loglikelihood_contour.py` (``examples/graphs/plot_graphs_loglikelihood_contour.py``) - - 00:00.708 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_transformation.py` (``examples/probabilistic_modeling/distributions/plot_distribution_transformation.py``) + - 00:00.739 - 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``) - - 00:00.705 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rosenbrock.py` (``examples/numerical_methods/optimization/plot_optimization_rosenbrock.py``) + - 00:00.731 - 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``) - - 00:00.692 + - 00:00.713 - 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``) - - 00:00.689 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_sequential.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_sequential.py``) + - 00:00.712 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_rosenbrock.py` (``examples/numerical_methods/optimization/plot_optimization_rosenbrock.py``) - - 00:00.679 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_asymptotic_estimators_distribution.py` (``examples/data_analysis/distribution_fitting/plot_asymptotic_estimators_distribution.py``) + - 00:00.707 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_axial_stressed_beam.py` (``examples/reliability_sensitivity/reliability/plot_axial_stressed_beam.py``) - - 00:00.677 + - 00:00.705 - 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``) - - 00:00.633 + - 00:00.694 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_system.py` (``examples/reliability_sensitivity/reliability/plot_event_system.py``) - - 00:00.627 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_smoothing_mixture.py` (``examples/data_analysis/distribution_fitting/plot_smoothing_mixture.py``) + - 00:00.653 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_sequential.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_sequential.py``) - - 00:00.621 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_chose_trend.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_chose_trend.py``) + - 00:00.653 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_create_domain_event.py` (``examples/reliability_sensitivity/reliability/plot_create_domain_event.py``) - - 00:00.613 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_process_manipulation.py` (``examples/probabilistic_modeling/stochastic_processes/plot_process_manipulation.py``) + - 00:00.653 - 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``) - - 00:00.612 + * - :ref:`sphx_glr_auto_graphs_plot_graphs_loglikelihood_contour.py` (``examples/graphs/plot_graphs_loglikelihood_contour.py``) + - 00:00.650 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_subset_sampling.py` (``examples/reliability_sensitivity/reliability/plot_subset_sampling.py``) - - 00:00.607 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_create_domain_event.py` (``examples/reliability_sensitivity/reliability/plot_create_domain_event.py``) + - 00:00.648 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_transformation.py` (``examples/probabilistic_modeling/distributions/plot_distribution_transformation.py``) - - 00:00.603 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_system.py` (``examples/reliability_sensitivity/reliability/plot_event_system.py``) + - 00:00.641 - 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``) - - 00:00.567 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_order_statistics_distribution.py` (``examples/probabilistic_modeling/distributions/plot_order_statistics_distribution.py``) + - 00:00.629 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_smoothing_mixture.py` (``examples/data_analysis/distribution_fitting/plot_smoothing_mixture.py``) - - 00:00.566 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_subset_sampling.py` (``examples/reliability_sensitivity/reliability/plot_subset_sampling.py``) + - 00:00.623 - 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``) - - 00:00.547 + - 00:00.609 + - 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``) + - 00:00.608 + - 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``) + - 00:00.572 - 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``) - - 00:00.546 + - 00:00.561 - 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``) + * - :ref:`sphx_glr_auto_meta_modeling_polynomial_chaos_metamodel_plot_chaos_ishigami.py` (``examples/meta_modeling/polynomial_chaos_metamodel/plot_chaos_ishigami.py``) + - 00:00.553 + - 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``) - 00:00.541 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_order_statistics_distribution.py` (``examples/probabilistic_modeling/distributions/plot_order_statistics_distribution.py``) - - 00:00.520 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_overview_univariate_distributions.py` (``examples/probabilistic_modeling/distributions/plot_overview_univariate_distributions.py``) + - 00:00.534 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_multi_form.py` (``examples/reliability_sensitivity/reliability/plot_multi_form.py``) - - 00:00.518 + * - :ref:`sphx_glr_auto_meta_modeling_fields_metamodels_plot_karhunenloeve_validation.py` (``examples/meta_modeling/fields_metamodels/plot_karhunenloeve_validation.py``) + - 00:00.534 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_nais.py` (``examples/reliability_sensitivity/reliability/plot_nais.py``) - - 00:00.493 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_multi_form.py` (``examples/reliability_sensitivity/reliability/plot_multi_form.py``) + - 00:00.517 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_fields_metamodels_plot_karhunenloeve_validation.py` (``examples/meta_modeling/fields_metamodels/plot_karhunenloeve_validation.py``) - - 00:00.483 + * - :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``) + - 00:00.509 - 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``) - - 00:00.482 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_truncated_distribution.py` (``examples/probabilistic_modeling/distributions/plot_truncated_distribution.py``) + - 00:00.498 - 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``) - - 00:00.462 + - 00:00.473 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_process_manipulation.py` (``examples/probabilistic_modeling/stochastic_processes/plot_process_manipulation.py``) - - 00:00.462 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_branin_function.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_branin_function.py``) + - 00:00.472 - 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``) - - 00:00.446 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_stepwise.py` (``examples/meta_modeling/general_purpose_metamodels/plot_stepwise.py``) + - 00:00.470 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_sobol.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_sobol.py``) - - 00:00.423 + * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_ordinal_sum_copula.py` (``examples/probabilistic_modeling/copulas/plot_ordinal_sum_copula.py``) + - 00:00.468 + - 0.0 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_nais.py` (``examples/reliability_sensitivity/reliability/plot_nais.py``) + - 00:00.463 - 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``) - - 00:00.420 + - 00:00.462 - 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``) - - 00:00.409 + * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_expectation_simulation_algorithm.py` (``examples/reliability_sensitivity/central_dispersion/plot_expectation_simulation_algorithm.py``) + - 00:00.443 + - 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``) + - 00:00.443 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_regression_sinus.py` (``examples/numerical_methods/general_methods/plot_regression_sinus.py``) - - 00:00.403 + - 00:00.436 - 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``) - - 00:00.401 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_dlib.py` (``examples/numerical_methods/optimization/plot_optimization_dlib.py``) + - 00:00.434 - 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``) - - 00:00.395 + * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_sobol.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_sobol.py``) + - 00:00.428 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_ordinal_sum_copula.py` (``examples/probabilistic_modeling/copulas/plot_ordinal_sum_copula.py``) - - 00:00.389 + * - :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``) + - 00:00.422 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_truncated_distribution.py` (``examples/probabilistic_modeling/distributions/plot_truncated_distribution.py``) - - 00:00.388 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_propagate_kriging_ishigami.py` (``examples/meta_modeling/kriging_metamodel/plot_propagate_kriging_ishigami.py``) + - 00:00.404 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_dlib.py` (``examples/numerical_methods/optimization/plot_optimization_dlib.py``) - - 00:00.387 + * - :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``) + - 00:00.398 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_pce_design.py` (``examples/numerical_methods/general_methods/plot_pce_design.py``) - - 00:00.383 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_axial_stressed_beam_quickstart.py` (``examples/reliability_sensitivity/reliability/plot_axial_stressed_beam_quickstart.py``) + - 00:00.390 - 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``) + * - :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``) - 00:00.374 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_overview_univariate_distributions.py` (``examples/probabilistic_modeling/distributions/plot_overview_univariate_distributions.py``) - - 00:00.365 + * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_pce_design.py` (``examples/numerical_methods/general_methods/plot_pce_design.py``) + - 00:00.373 - 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``) - - 00:00.359 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_extreme_value_distribution.py` (``examples/probabilistic_modeling/distributions/plot_create_extreme_value_distribution.py``) + - 00:00.371 - 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``) - - 00:00.353 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_manipulation.py` (``examples/probabilistic_modeling/distributions/plot_distribution_manipulation.py``) + - 00:00.369 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_stepwise.py` (``examples/meta_modeling/general_purpose_metamodels/plot_stepwise.py``) - - 00:00.351 + * - :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``) + - 00:00.369 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_form.py` (``examples/reliability_sensitivity/reliability/plot_estimate_probability_form.py``) - - 00:00.349 + - 00:00.368 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_1d.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_1d.py``) - - 00:00.348 + * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_visualize_clouds.py` (``examples/data_analysis/graphics/plot_visualize_clouds.py``) + - 00:00.367 - 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``) - - 00:00.345 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_smolyak_indices.py` (``examples/reliability_sensitivity/design_of_experiments/plot_smolyak_indices.py``) + - 00:00.364 - 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 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_kronecker_covmodel.py` (``examples/probabilistic_modeling/stochastic_processes/plot_kronecker_covmodel.py``) + - 00:00.355 - 0.0 * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_function_manipulation.py` (``examples/functional_modeling/field_functions/plot_function_manipulation.py``) - - 00:00.338 + - 00:00.354 - 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``) - - 00:00.320 + * - :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``) + - 00:00.350 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_threshold.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_threshold.py``) - - 00:00.317 + * - :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``) + - 00:00.347 - 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``) - - 00:00.291 - - 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``) - - 00:00.290 - - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_pagmo.py` (``examples/numerical_methods/optimization/plot_optimization_pagmo.py``) - - 00:00.289 + - 00:00.338 - 0.0 - * - :ref:`sphx_glr_auto_calibration_bayesian_calibration_plot_imh_python_distribution.py` (``examples/calibration/bayesian_calibration/plot_imh_python_distribution.py``) - - 00:00.287 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_1d.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_1d.py``) + - 00:00.338 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_distribution_manipulation.py` (``examples/probabilistic_modeling/distributions/plot_distribution_manipulation.py``) - - 00:00.286 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_multioutput_firesatellite.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_multioutput_firesatellite.py``) + - 00:00.336 - 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``) - - 00:00.281 + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_normal.py` (``examples/data_analysis/distribution_fitting/plot_estimate_normal.py``) + - 00:00.335 - 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 + - 00:00.323 - 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``) - - 00:00.276 + * - :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``) + - 00:00.323 - 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``) - - 00:00.272 + * - :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``) + - 00:00.314 - 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``) - - 00:00.271 + * - :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``) + - 00:00.308 - 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``) - - 00:00.268 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_isotropic.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_isotropic.py``) + - 00:00.306 - 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``) - - 00:00.259 + * - :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``) + - 00:00.301 - 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``) - - 00:00.258 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_bayes_distribution.py` (``examples/probabilistic_modeling/distributions/plot_bayes_distribution.py``) + - 00:00.297 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_kronecker_covmodel.py` (``examples/probabilistic_modeling/stochastic_processes/plot_kronecker_covmodel.py``) - - 00:00.256 + * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_threshold.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_threshold.py``) + - 00:00.288 - 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 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_deterministic_design.py` (``examples/reliability_sensitivity/design_of_experiments/plot_deterministic_design.py``) + - 00:00.287 - 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_probabilistic_modeling_distributions_plot_create_random_mixture.py` (``examples/probabilistic_modeling/distributions/plot_create_random_mixture.py``) + - 00:00.287 - 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``) - - 00:00.248 + * - :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_data_analysis_graphics_plot_visualize_clouds.py` (``examples/data_analysis/graphics/plot_visualize_clouds.py``) - - 00:00.245 + * - :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``) + - 00:00.276 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_isotropic.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_isotropic.py``) - - 00:00.242 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_simulate.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_simulate.py``) + - 00:00.270 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_univariate_functions_plot_createUnivariateFunction.py` (``examples/functional_modeling/univariate_functions/plot_createUnivariateFunction.py``) - - 00:00.238 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_mixture_distribution.py` (``examples/probabilistic_modeling/distributions/plot_mixture_distribution.py``) + - 00:00.258 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_random_mixture.py` (``examples/probabilistic_modeling/distributions/plot_create_random_mixture.py``) - - 00:00.237 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_hyperparameters_optimization.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_hyperparameters_optimization.py``) + - 00:00.243 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_estimate_normal.py` (``examples/data_analysis/distribution_fitting/plot_estimate_normal.py``) - - 00:00.231 + * - :ref:`sphx_glr_auto_functional_modeling_univariate_functions_plot_createUnivariateFunction.py` (``examples/functional_modeling/univariate_functions/plot_createUnivariateFunction.py``) + - 00:00.242 - 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``) - - 00:00.231 + - 00:00.241 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_simulate.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_simulate.py``) - - 00:00.225 + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_expert_mixture.py` (``examples/meta_modeling/general_purpose_metamodels/plot_expert_mixture.py``) + - 00:00.232 - 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``) - - 00:00.213 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_mix_rv_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_mix_rv_process.py``) + - 00:00.223 + - 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``) + - 00:00.214 - 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``) - - 00:00.208 + - 00:00.214 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_bayes_distribution.py` (``examples/probabilistic_modeling/distributions/plot_bayes_distribution.py``) - - 00:00.207 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_generate_by_inversion.py` (``examples/probabilistic_modeling/distributions/plot_generate_by_inversion.py``) + - 00:00.211 - 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``) - - 00:00.202 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_cantilever_beam.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_cantilever_beam.py``) + - 00:00.198 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_mixture_distribution.py` (``examples/probabilistic_modeling/distributions/plot_mixture_distribution.py``) - - 00:00.188 + * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_visualize_pairs.py` (``examples/data_analysis/graphics/plot_visualize_pairs.py``) + - 00:00.192 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_extrema.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_extrema.py``) - - 00:00.183 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_pagmo.py` (``examples/numerical_methods/optimization/plot_optimization_pagmo.py``) + - 00:00.185 - 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``) - - 00:00.179 + * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_src_confidence.py` (``examples/data_analysis/sample_analysis/plot_src_confidence.py``) + - 00:00.185 - 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``) - - 00:00.177 + - 00:00.180 - 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``) + * - :ref:`sphx_glr_auto_meta_modeling_general_purpose_metamodels_plot_taylor_approximation.py` (``examples/meta_modeling/general_purpose_metamodels/plot_taylor_approximation.py``) + - 00:00.180 + - 0.0 + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_qqplot_graph.py` (``examples/data_analysis/statistical_tests/plot_qqplot_graph.py``) - 00:00.176 - 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``) - - 00:00.174 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_timeseries_manipulation.py` (``examples/probabilistic_modeling/stochastic_processes/plot_timeseries_manipulation.py``) + - 00:00.176 - 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``) - - 00:00.173 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_beam_trend.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_beam_trend.py``) + - 00:00.175 - 0.0 - * - :ref:`sphx_glr_auto_graphs_plot_graphs_fill_area.py` (``examples/graphs/plot_graphs_fill_area.py``) - - 00:00.167 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_export_field_vtk.py` (``examples/probabilistic_modeling/stochastic_processes/plot_export_field_vtk.py``) + - 00:00.175 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_graphics_plot_visualize_pairs.py` (``examples/data_analysis/graphics/plot_visualize_pairs.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_aggregated_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_aggregated_process.py``) + - 00:00.170 + - 0.0 + * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_extrema.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_extrema.py``) + - 00:00.166 + - 0.0 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_add_trend.py` (``examples/probabilistic_modeling/stochastic_processes/plot_add_trend.py``) + - 00:00.166 + - 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``) - 00:00.165 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_moments.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_moments.py``) - - 00:00.164 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_trend_transform.py` (``examples/probabilistic_modeling/stochastic_processes/plot_trend_transform.py``) + - 00:00.163 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_generate_by_inversion.py` (``examples/probabilistic_modeling/distributions/plot_generate_by_inversion.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_normal_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_normal_process.py``) - 00:00.162 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_src_confidence.py` (``examples/data_analysis/sample_analysis/plot_src_confidence.py``) + * - :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``) - 00:00.159 - 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``) - - 00:00.157 - - 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``) + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_importance_sampling.py` (``examples/reliability_sensitivity/reliability/plot_estimate_probability_importance_sampling.py``) - 00:00.155 - 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``) + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_smirnov_test.py` (``examples/data_analysis/statistical_tests/plot_smirnov_test.py``) + - 00:00.154 + - 0.0 + * - :ref:`sphx_glr_auto_graphs_plot_graphs_fill_area.py` (``examples/graphs/plot_graphs_fill_area.py``) - 00:00.153 - 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``) + * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_quantilematching_estimator.py` (``examples/data_analysis/distribution_fitting/plot_quantilematching_estimator.py``) - 00:00.151 - 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``) - - 00:00.146 + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_test_normality.py` (``examples/data_analysis/statistical_tests/plot_test_normality.py``) + - 00:00.150 - 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``) - - 00:00.146 + * - :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``) + - 00:00.149 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_smirnov_test.py` (``examples/data_analysis/statistical_tests/plot_smirnov_test.py``) - - 00:00.143 + * - :ref:`sphx_glr_auto_numerical_methods_iterative_statistics_plot_iterative_moments.py` (``examples/numerical_methods/iterative_statistics/plot_iterative_moments.py``) + - 00:00.148 - 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_data_analysis_sample_analysis_plot_visualize_empirical_cdf.py` (``examples/data_analysis/sample_analysis/plot_visualize_empirical_cdf.py``) + - 00:00.146 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_probability_simulation_results.py` (``examples/reliability_sensitivity/reliability/plot_probability_simulation_results.py``) - - 00:00.139 + - 00:00.145 - 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``) - - 00:00.135 + * - :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``) + - 00:00.143 + - 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``) + - 00:00.137 - 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``) + - 00:00.137 + - 0.0 + * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_central_tendency.py` (``examples/reliability_sensitivity/central_dispersion/plot_central_tendency.py``) - 00:00.133 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_distribution_fitting_plot_quantilematching_estimator.py` (``examples/data_analysis/distribution_fitting/plot_quantilematching_estimator.py``) + * - :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``) - 00:00.132 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_test_normality.py` (``examples/data_analysis/statistical_tests/plot_test_normality.py``) - - 00:00.130 - - 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``) - - 00:00.130 - - 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``) - - 00:00.128 + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_likelihood.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_likelihood.py``) + - 00:00.127 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_add_trend.py` (``examples/probabilistic_modeling/stochastic_processes/plot_add_trend.py``) + * - :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``) - 00:00.126 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_central_tendency.py` (``examples/reliability_sensitivity/central_dispersion/plot_central_tendency.py``) - - 00:00.125 - - 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``) - - 00:00.123 + * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_ancova.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_ancova.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``) - - 00:00.121 + - 00:00.123 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_trend_transform.py` (``examples/probabilistic_modeling/stochastic_processes/plot_trend_transform.py``) - - 00:00.120 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_flood_model.py` (``examples/reliability_sensitivity/reliability/plot_flood_model.py``) + - 00:00.123 - 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``) - - 00:00.119 + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_symbolic_function.py` (``examples/functional_modeling/vectorial_functions/plot_symbolic_function.py``) + - 00:00.122 - 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``) - - 00:00.119 + - 00:00.122 - 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_data_analysis_statistical_tests_plot_kolmogorov_pvalue.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_pvalue.py``) - 00:00.117 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_symbolic_function.py` (``examples/functional_modeling/vectorial_functions/plot_symbolic_function.py``) - - 00:00.115 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_python_distribution.py` (``examples/probabilistic_modeling/distributions/plot_python_distribution.py``) + - 00:00.116 + - 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``) + - 00:00.108 - 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``) - - 00:00.115 + - 00:00.105 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_flood_model.py` (``examples/reliability_sensitivity/reliability/plot_flood_model.py``) - - 00:00.114 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_conditional_distribution.py` (``examples/probabilistic_modeling/distributions/plot_conditional_distribution.py``) + - 00:00.101 - 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``) - - 00:00.114 + * - :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``) + - 00:00.097 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_regression_interval.py` (``examples/numerical_methods/general_methods/plot_regression_interval.py``) - - 00:00.112 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_timeseries_manipulation.py` (``examples/probabilistic_modeling/stochastic_processes/plot_timeseries_manipulation.py``) - - 00:00.110 + - 00:00.093 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_aggregated_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_aggregated_process.py``) - - 00:00.109 - - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_python_distribution.py` (``examples/probabilistic_modeling/distributions/plot_python_distribution.py``) - - 00:00.102 - - 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``) - - 00:00.101 + * - :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``) + - 00:00.090 - 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_probabilistic_modeling_stochastic_processes_plot_functional_basis_process.py` (``examples/probabilistic_modeling/stochastic_processes/plot_functional_basis_process.py``) + - 00:00.089 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_pvalue.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_pvalue.py``) - - 00:00.095 + * - :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_calibration_least_squares_and_gaussian_calibration_plot_calibration_withoutobservedinputs.py` (``examples/calibration/least_squares_and_gaussian_calibration/plot_calibration_withoutobservedinputs.py``) - - 00:00.094 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_maximum_distribution.py` (``examples/probabilistic_modeling/distributions/plot_maximum_distribution.py``) + - 00:00.086 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_conditional_distribution.py` (``examples/probabilistic_modeling/distributions/plot_conditional_distribution.py``) - - 00:00.092 + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_create_your_own_dist.py` (``examples/probabilistic_modeling/distributions/plot_create_your_own_dist.py``) + - 00:00.085 - 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``) - - 00:00.091 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_create_threshold_event.py` (``examples/reliability_sensitivity/reliability/plot_create_threshold_event.py``) + - 00:00.085 - 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``) - - 00:00.090 + * - :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``) + - 00:00.079 - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_sensitivity_analysis_plot_sensitivity_fast.py` (``examples/reliability_sensitivity/sensitivity_analysis/plot_sensitivity_fast.py``) - - 00:00.082 + - 00:00.078 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_create_threshold_event.py` (``examples/reliability_sensitivity/reliability/plot_create_threshold_event.py``) - - 00:00.081 + * - :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``) + - 00:00.078 - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging_likelihood.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging_likelihood.py``) + * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging.py``) - 00:00.077 - 0.0 - * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_bonmin.py` (``examples/numerical_methods/optimization/plot_optimization_bonmin.py``) - - 00:00.077 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_user_stationary_covmodel.py` (``examples/probabilistic_modeling/stochastic_processes/plot_user_stationary_covmodel.py``) + - 00:00.076 - 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``) - - 00:00.075 + * - :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``) + - 00:00.076 - 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``) - - 00:00.074 + * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_statistics.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_statistics.py``) + - 00:00.075 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_constraints.py` (``examples/numerical_methods/optimization/plot_optimization_constraints.py``) - - 00:00.074 - - 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 + - 00:00.071 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_nlopt.py` (``examples/numerical_methods/optimization/plot_optimization_nlopt.py``) - 00:00.071 - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_statistics.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_statistics.py``) - - 00:00.070 - - 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``) - - 00:00.070 - - 0.0 - * - :ref:`sphx_glr_auto_meta_modeling_kriging_metamodel_plot_kriging.py` (``examples/meta_modeling/kriging_metamodel/plot_kriging.py``) - - 00:00.067 + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_quadratic_function.py` (``examples/functional_modeling/vectorial_functions/plot_quadratic_function.py``) + - 00:00.068 - 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``) + * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_visualize_histogram.py` (``examples/data_analysis/sample_analysis/plot_visualize_histogram.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``) - - 00:00.066 - - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_quadratic_function.py` (``examples/functional_modeling/vectorial_functions/plot_quadratic_function.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_userdefined_spectral_model.py` (``examples/probabilistic_modeling/stochastic_processes/plot_userdefined_spectral_model.py``) - 00:00.065 - 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``) - - 00:00.062 - - 0.0 - * - :ref:`sphx_glr_auto_data_analysis_sample_analysis_plot_visualize_histogram.py` (``examples/data_analysis/sample_analysis/plot_visualize_histogram.py``) + * - :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``) - 00:00.060 - 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``) - - 00:00.059 + * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_optimization_bonmin.py` (``examples/numerical_methods/optimization/plot_optimization_bonmin.py``) + - 00:00.057 - 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``) - - 00:00.059 + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_quick_start_functions.py` (``examples/functional_modeling/vectorial_functions/plot_quick_start_functions.py``) + - 00:00.057 - 0.0 - * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_maximum_distribution.py` (``examples/probabilistic_modeling/distributions/plot_maximum_distribution.py``) - - 00:00.058 + * - :ref:`sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_composite_experiment.py` (``examples/reliability_sensitivity/design_of_experiments/plot_composite_experiment.py``) + - 00:00.056 - 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``) - 00:00.056 - 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``) - - 00:00.054 - - 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``) + * - :ref:`sphx_glr_auto_reliability_sensitivity_central_dispersion_plot_estimate_moments_taylor.py` (``examples/reliability_sensitivity/central_dispersion/plot_estimate_moments_taylor.py``) - 00:00.051 - 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``) - 00:00.049 - 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``) - - 00:00.047 - - 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``) - - 00:00.033 + * - :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``) + - 00:00.034 - 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 + - 00:00.033 - 0.0 * - :ref:`sphx_glr_auto_data_analysis_statistical_tests_plot_kolmogorov_test.py` (``examples/data_analysis/statistical_tests/plot_kolmogorov_test.py``) - - 00:00.030 + - 00:00.033 - 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``) - - 00:00.026 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_monte_carlo.py` (``examples/reliability_sensitivity/reliability/plot_estimate_probability_monte_carlo.py``) + - 00:00.032 - 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``) - - 00:00.024 - - 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``) - - 00:00.023 + - 00:00.029 - 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``) - - 00:00.020 + - 00:00.028 + - 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``) + - 00:00.025 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_control_termination.py` (``examples/numerical_methods/optimization/plot_control_termination.py``) - 00:00.014 - 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``) + * - :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``) - - 00:00.009 + * - :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 + - 00:00.010 - 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``) + * - :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``) - 00:00.008 - 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``) - 00:00.007 - 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``) - - 00:00.005 - - 0.0 * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_strong_maximum_test.py` (``examples/reliability_sensitivity/reliability/plot_strong_maximum_test.py``) - - 00:00.005 + - 00:00.006 - 0.0 * - :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``) + - 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``) + - 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``) - 00:00.004 - 0.0 * - :ref:`sphx_glr_auto_numerical_methods_optimization_plot_minmax_optimization.py` (``examples/numerical_methods/optimization/plot_minmax_optimization.py``) - 00:00.004 - 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_functional_modeling_vectorial_functions_plot_createMultivariateFunction.py` (``examples/functional_modeling/vectorial_functions/plot_createMultivariateFunction.py``) - 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 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_manipulation.py` (``examples/reliability_sensitivity/reliability/plot_event_manipulation.py``) + - 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_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_functional_modeling_functional_basis_plot_multidimensional_basis.py` (``examples/functional_modeling/functional_basis/plot_multidimensional_basis.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``) - - 00:00.002 + * - :ref:`sphx_glr_auto_probabilistic_modeling_random_vectors_plot_python_randomvector.py` (``examples/probabilistic_modeling/random_vectors/plot_python_randomvector.py``) + - 00:00.003 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_plot_event_manipulation.py` (``examples/reliability_sensitivity/reliability/plot_event_manipulation.py``) - - 00:00.002 + * - :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``) + - 00:00.003 - 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 + * - :ref:`sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_create_stationary_covmodel.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_stationary_covmodel.py``) + - 00:00.003 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_field_functions_plot_vertexvalue_function.py` (``examples/functional_modeling/field_functions/plot_vertexvalue_function.py``) - - 00:00.002 + * - :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``) + - 00:00.003 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_aggregated_function.py` (``examples/functional_modeling/vectorial_functions/plot_aggregated_function.py``) - - 00:00.002 + * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_event_process.py` (``examples/reliability_sensitivity/reliability_processes/plot_event_process.py``) + - 00:00.003 - 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_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_functional_modeling_field_functions_plot_value_function.py` (``examples/functional_modeling/field_functions/plot_value_function.py``) + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_parametric_function.py` (``examples/functional_modeling/vectorial_functions/plot_parametric_function.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_field_functions_plot_vertexvalue_function.py` (``examples/functional_modeling/field_functions/plot_vertexvalue_function.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``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_composed_function.py` (``examples/functional_modeling/vectorial_functions/plot_composed_function.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_probabilistic_modeling_random_vectors_plot_python_randomvector.py` (``examples/probabilistic_modeling/random_vectors/plot_python_randomvector.py``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_distributions_plot_conditional_random_vector.py` (``examples/probabilistic_modeling/distributions/plot_conditional_random_vector.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_composite_random_vector.py` (``examples/probabilistic_modeling/random_vectors/plot_composite_random_vector.py``) - 00:00.002 - 0.0 - * - :ref:`sphx_glr_auto_reliability_sensitivity_reliability_processes_plot_event_process.py` (``examples/reliability_sensitivity/reliability_processes/plot_event_process.py``) + * - :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_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_functional_modeling_vectorial_functions_plot_functions_outputDim.py` (``examples/functional_modeling/vectorial_functions/plot_functions_outputDim.py``) + * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_composed_function.py` (``examples/functional_modeling/vectorial_functions/plot_composed_function.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``) + * - :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_probabilistic_modeling_stochastic_processes_plot_create_stationary_covmodel.py` (``examples/probabilistic_modeling/stochastic_processes/plot_create_stationary_covmodel.py``) - - 00:00.001 - - 0.0 - * - :ref:`sphx_glr_auto_functional_modeling_vectorial_functions_plot_parametric_function.py` (``examples/functional_modeling/vectorial_functions/plot_parametric_function.py``) - - 00:00.001 - - 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``) - - 00:00.001 + - 00:00.002 - 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``) + * - :ref:`sphx_glr_auto_numerical_methods_general_methods_plot_random_generator.py` (``examples/numerical_methods/general_methods/plot_random_generator.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``) + * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_extract_copula.py` (``examples/probabilistic_modeling/copulas/plot_extract_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``) - - 00:00.001 - - 0.0 * - :ref:`sphx_glr_auto_probabilistic_modeling_copulas_plot_composed_copula.py` (``examples/probabilistic_modeling/copulas/plot_composed_copula.py``) - 00:00.001 - 0.0 diff --git a/openturns/master/_sources/user_manual/_generated/openturns.experimental.PosteriorDistribution.rst.txt b/openturns/master/_sources/user_manual/_generated/openturns.experimental.PosteriorDistribution.rst.txt new file mode 100644 index 00000000000..1955647b528 --- /dev/null +++ b/openturns/master/_sources/user_manual/_generated/openturns.experimental.PosteriorDistribution.rst.txt @@ -0,0 +1,14 @@ +PosteriorDistribution +============================================ + +.. currentmodule:: openturns.experimental + +.. autoclass:: PosteriorDistribution + + + .. automethod:: __init__ + + + +.. minigallery:: openturns.experimental.PosteriorDistribution + :add-heading: Examples using the class \ No newline at end of file diff --git a/openturns/master/_sources/user_manual/probabilistic_modelling.rst.txt b/openturns/master/_sources/user_manual/probabilistic_modelling.rst.txt index 435ad8627f5..69cf833d53e 100644 --- a/openturns/master/_sources/user_manual/probabilistic_modelling.rst.txt +++ b/openturns/master/_sources/user_manual/probabilistic_modelling.rst.txt @@ -187,7 +187,7 @@ Combining and transforming distributions MixedHistogramUserDefined OrderStatisticsMarginalChecker - PosteriorDistribution + experimental.PosteriorDistribution .. autosummary:: :toctree: _generated/ diff --git a/openturns/master/auto_calibration/bayesian_calibration/plot_gibbs.html b/openturns/master/auto_calibration/bayesian_calibration/plot_gibbs.html index d24dde287ed..e239fee1c80 100644 --- a/openturns/master/auto_calibration/bayesian_calibration/plot_gibbs.html +++ b/openturns/master/auto_calibration/bayesian_calibration/plot_gibbs.html @@ -245,7 +245,7 @@

Quick search

View.ShowAll() -Posterior density

Total running time of the script: (0 minutes 12.846 seconds)

+Posterior density

Total running time of the script: (0 minutes 12.487 seconds)

 -Bayesian inference (with log-pdf)

Total running time of the script: (0 minutes 5.693 seconds)

+Bayesian inference (with log-pdf)

Total running time of the script: (0 minutes 5.585 seconds)