CircleCI build openturns 20983

openturns · Nov 22, 2024 · 778a201 · 778a201
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diff --git a/.../master/_downloads/0cbc0c7f31f83b522c07a946f2a2edc5/plot_graphs_loglikelihood_contour.zip b/.../master/_downloads/0cbc0c7f31f83b522c07a946f2a2edc5/plot_graphs_loglikelihood_contour.zip
diff --git a/openturns/master/_downloads/0e331c7db07837c249c7578327e83050/plot_python_function.zip b/openturns/master/_downloads/0e331c7db07837c249c7578327e83050/plot_python_function.zip
diff --git a/openturns/master/_downloads/0eb193ea6e2b1393ff289eed787f56d0/plot_random_generator.zip b/openturns/master/_downloads/0eb193ea6e2b1393ff289eed787f56d0/plot_random_generator.zip
diff --git a/openturns/master/_downloads/0ef2e737c16c571c8945b202ec11aae3/auto_calibration_jupyter.zip b/openturns/master/_downloads/0ef2e737c16c571c8945b202ec11aae3/auto_calibration_jupyter.zip
diff --git a/openturns/master/_downloads/0f702cd5ca398d19b8b874101df7ecb3/KrigingAlgorithm.png b/openturns/master/_downloads/0f702cd5ca398d19b8b874101df7ecb3/KrigingAlgorithm.png
diff --git a/...urns/master/_downloads/10f74d073c1cde25410fe41807be2000/plot_truncated_distribution.ipynb b/...urns/master/_downloads/10f74d073c1cde25410fe41807be2000/plot_truncated_distribution.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import openturns as ot\nimport openturns.viewer as viewer\nfrom matplotlib import pylab as plt\n\not.Log.Show(ot.Log.NONE)\n\n# The original distribution\ndistribution = ot.Gumbel(0.45, 0.6)\ngraph = distribution.drawPDF()\nview = viewer.View(graph)"
+        "import openturns as ot\nimport openturns.viewer as viewer\n\not.Log.Show(ot.Log.NONE)\n\n# The original distribution\ndistribution = ot.Gumbel(0.45, 0.6)\ngraph = distribution.drawPDF()\nview = viewer.View(graph)"
       ]
     },
     {
@@ -130,7 +130,25 @@
       },
       "outputs": [],
       "source": [
-        "graph = truncatedKS.drawPDF([-2.5] * dimension, [2.5] * dimension, [256] * dimension)\ngraph.add(ot.Cloud(truncatedKS.getSample(200)))\ngraph.setColors([\"blue\", \"red\"])\nview = viewer.View(graph)\nplt.show()"
+        "graph = truncatedKS.drawPDF([-2.5] * dimension, [2.5] * dimension, [256] * dimension)\ngraph.add(ot.Cloud(truncatedKS.getSample(200)))\nview = viewer.View(graph)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Display all the graphs\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "view.ShowAll()"
       ]
     }
   ],

diff --git a/...ster/_downloads/113bcf8fb299a8a1ffa667d986351fb5/plot_quick_start_guide_distributions.zip b/...ster/_downloads/113bcf8fb299a8a1ffa667d986351fb5/plot_quick_start_guide_distributions.zip
diff --git a/...turns/master/_downloads/13532f1cc71ed6a9093a0a7b9e049a88/plot_kriging_branin_function.zip b/...turns/master/_downloads/13532f1cc71ed6a9093a0a7b9e049a88/plot_kriging_branin_function.zip
diff --git a/...s/master/_downloads/1355ecb2368bdad10b7fd5fa0ba1e09a/FunctionalBasisProcess.png b/...s/master/_downloads/1355ecb2368bdad10b7fd5fa0ba1e09a/FunctionalBasisProcess.png
diff --git a/openturns/master/_downloads/13868e5a95906ad1cc6c5b66f0d709d9/plot_kronecker_covmodel.zip b/openturns/master/_downloads/13868e5a95906ad1cc6c5b66f0d709d9/plot_kronecker_covmodel.zip
diff --git a/openturns/master/_downloads/1452b2f84a346743a73120a05ce72f48/plot_kriging_sequential.zip b/openturns/master/_downloads/1452b2f84a346743a73120a05ce72f48/plot_kriging_sequential.zip
diff --git a/openturns/master/_downloads/15b5e038756e3e378616202b34e58a4c/plot_stepwise.zip b/openturns/master/_downloads/15b5e038756e3e378616202b34e58a4c/plot_stepwise.zip
diff --git a/...er/_downloads/173583f0938fbf36d5d6a5dc669b49ec/plot_create_extreme_value_distribution.zip b/...er/_downloads/173583f0938fbf36d5d6a5dc669b49ec/plot_create_extreme_value_distribution.zip
diff --git a/...master/_downloads/17ca44f47afc394d93c2db982c9f93b2/plot_bayesian_calibration_flooding.zip b/...master/_downloads/17ca44f47afc394d93c2db982c9f93b2/plot_bayesian_calibration_flooding.zip
diff --git a/openturns/master/_downloads/197458c280068d64ab56bc7ee7330a02/plot_chi2_fitting_test.zip b/openturns/master/_downloads/197458c280068d64ab56bc7ee7330a02/plot_chi2_fitting_test.zip
diff --git a/openturns/master/_downloads/19a33b90e2c031175f68cfc1bf9afea1/plot_estimate_gpd_wooster.zip b/openturns/master/_downloads/19a33b90e2c031175f68cfc1bf9afea1/plot_estimate_gpd_wooster.zip
diff --git a/openturns/master/_downloads/1b308928b7a23c67e3d6cb0ae7d526b1/plot_stiffened_panel.zip b/openturns/master/_downloads/1b308928b7a23c67e3d6cb0ae7d526b1/plot_stiffened_panel.zip
diff --git a/openturns/master/_downloads/1c5d65bed76ea6701ccef2f6dce38abf/plot_ifs.zip b/openturns/master/_downloads/1c5d65bed76ea6701ccef2f6dce38abf/plot_ifs.zip
diff --git a/openturns/master/_downloads/1ca67e19bd35e2f16d1542c8fc304b95/plot_multidimensional_basis.zip b/openturns/master/_downloads/1ca67e19bd35e2f16d1542c8fc304b95/plot_multidimensional_basis.zip
diff --git a/...s/master/_downloads/1d23c3bfcd467857296564274c1ba1a7/plot_linear_combination_function.zip b/...s/master/_downloads/1d23c3bfcd467857296564274c1ba1a7/plot_linear_combination_function.zip
diff --git a/openturns/master/_downloads/1d3f4da281aaa57d7633000b66ef0209/plot_python_distribution.ipynb b/openturns/master/_downloads/1d3f4da281aaa57d7633000b66ef0209/plot_python_distribution.ipynb
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "graph = distribution.drawCDF()\ngraph.setColors([\"blue\"])\nview = otv.View(graph)"
+        "graph = distribution.drawCDF()\nview = otv.View(graph)"
       ]
     },
     {

diff --git a/.../master/_downloads/1e03ed8b4262701cb865dbb89097b467/plot_sensitivity_par_coo_ishigami.zip b/.../master/_downloads/1e03ed8b4262701cb865dbb89097b467/plot_sensitivity_par_coo_ishigami.zip
diff --git a/openturns/master/_downloads/1f3fdd119dea0ff44977bff738927e21/plot_quadratic_function.zip b/openturns/master/_downloads/1f3fdd119dea0ff44977bff738927e21/plot_quadratic_function.zip
diff --git a/openturns/master/_downloads/2064f581be390f9371dd219c67339431/plot_create_mesh.zip b/openturns/master/_downloads/2064f581be390f9371dd219c67339431/plot_create_mesh.zip
diff --git a/openturns/master/_downloads/20d6b1e525620257e3f3f3d838fd3375/plot_multi_form.zip b/openturns/master/_downloads/20d6b1e525620257e3f3f3d838fd3375/plot_multi_form.zip
diff --git a/...turns/master/_downloads/2175ba7aab8f07c635d269c0f6ec9f86/plot_visualize_empirical_cdf.zip b/...turns/master/_downloads/2175ba7aab8f07c635d269c0f6ec9f86/plot_visualize_empirical_cdf.zip
diff --git a/openturns/master/_downloads/2242e461c01f576f8cea78922bdc5863/plot_smoothing_mixture.py b/openturns/master/_downloads/2242e461c01f576f8cea78922bdc5863/plot_smoothing_mixture.py
@@ -132,7 +132,6 @@
 graph = distribution.drawPDF()
 curve = fit.drawPDF()
 graph.add(curve)
-graph.setColors(["dodgerblue3", "darkorange1"])
 graph.setLegends(["Mixture", "Kernel smoothing"])
 graph.setLegendPosition("upper left")
 view = otv.View(graph)

diff --git a/openturns/master/_downloads/2548166ff0f041abf7ec593b1baca6dc/plot_draw_survival.zip b/openturns/master/_downloads/2548166ff0f041abf7ec593b1baca6dc/plot_draw_survival.zip
diff --git a/...s/master/_downloads/261f8c49581bb6c0afc0c456b07a133c/plot_maximumlikelihood_estimator.zip b/...s/master/_downloads/261f8c49581bb6c0afc0c456b07a133c/plot_maximumlikelihood_estimator.zip
diff --git a/openturns/master/_downloads/26baeadae7900ba95b78b45bca4a3252/plot_kriging_likelihood.zip b/openturns/master/_downloads/26baeadae7900ba95b78b45bca4a3252/plot_kriging_likelihood.zip
diff --git a/...urns/master/_downloads/275d1277620ff27befff82a67c7da38e/plot_conditional_distribution.zip b/...urns/master/_downloads/275d1277620ff27befff82a67c7da38e/plot_conditional_distribution.zip
diff --git a/openturns/master/_downloads/27b2d7f8f943ebb58e07bcf850879309/plot_compute_ell2_error.zip b/openturns/master/_downloads/27b2d7f8f943ebb58e07bcf850879309/plot_compute_ell2_error.zip
diff --git a/...turns/master/_downloads/28f110e8d4187d671c9f5bf471450057/plot_estimate_moments_taylor.zip b/...turns/master/_downloads/28f110e8d4187d671c9f5bf471450057/plot_estimate_moments_taylor.zip
diff --git a/openturns/master/_downloads/2a035e01b88cacb07ef908a22d7310d8/plot_sample_correlation.zip b/openturns/master/_downloads/2a035e01b88cacb07ef908a22d7310d8/plot_sample_correlation.zip
diff --git a/openturns/master/_downloads/2a1e7a17fdd66aa7667a38a867dec696/plot_functional_chaos.zip b/openturns/master/_downloads/2a1e7a17fdd66aa7667a38a867dec696/plot_functional_chaos.zip
diff --git a/openturns/master/_downloads/2a67214ed45c837a4dd891677b8429e1/plot_python_distribution.zip b/openturns/master/_downloads/2a67214ed45c837a4dd891677b8429e1/plot_python_distribution.zip
diff --git a/openturns/master/_downloads/2a7cf8c224eca448791e340e83b10c16/plot_aggregated_function.zip b/openturns/master/_downloads/2a7cf8c224eca448791e340e83b10c16/plot_aggregated_function.zip
diff --git a/...turns/master/_downloads/2af161990124b4c84b5d10bcd84d34a9/plot_minmax_by_random_design.zip b/...turns/master/_downloads/2af161990124b4c84b5d10bcd84d34a9/plot_minmax_by_random_design.zip
diff --git a/openturns/master/_downloads/2be3c5987a62796d7d04ea443d3eefda/plot_chaos_ishigami.zip b/openturns/master/_downloads/2be3c5987a62796d7d04ea443d3eefda/plot_chaos_ishigami.zip
diff --git a/openturns/master/_downloads/2c2d1da58482f34a8de2f9b9956e96d2/plot_kriging.zip b/openturns/master/_downloads/2c2d1da58482f34a8de2f9b9956e96d2/plot_kriging.zip
diff --git a/openturns/master/_downloads/2c7edf7675deb263148b74debd92a02c/plot_event_process.zip b/openturns/master/_downloads/2c7edf7675deb263148b74debd92a02c/plot_event_process.zip
diff --git a/...rns/master/_downloads/2d6ec7e8bc3e372e75a298df17c1ba39/plot_distribution_manipulation.zip b/...rns/master/_downloads/2d6ec7e8bc3e372e75a298df17c1ba39/plot_distribution_manipulation.zip
diff --git a/openturns/master/_downloads/2e93b8c6a2a654d5f374cd0b16c9e4af/plot_quick_start_functions.zip b/openturns/master/_downloads/2e93b8c6a2a654d5f374cd0b16c9e4af/plot_quick_start_functions.zip
diff --git a/...master/_downloads/303d36bfc8a1a98d40a96b57535ed6f8/plot_fit_extreme_value_distribution.py b/...master/_downloads/303d36bfc8a1a98d40a96b57535ed6f8/plot_fit_extreme_value_distribution.py
@@ -70,7 +70,6 @@
 # We draw the fitted distribution and a histogram of the data.
 graph = myDistribution.drawPDF()
 graph.add(ot.HistogramFactory().build(sample).drawPDF())
-graph.setColors(["black", "red"])
 graph.setLegends(["GEV fitting", "histogram"])
 graph.setLegendPosition("upper right")
 
@@ -103,7 +102,6 @@
 curveWeibullMax.setLineStyle("dashed")
 graph.add(curveWeibullMax)
 
-graph.setColors(["black", "red", "green", "blue", "orange"])
 graph.setLegends(
     [
         "GEV fitting",
@@ -159,7 +157,6 @@
 graph = myFittedDist.drawPDF()
 graph.add(ot.HistogramFactory().build(sample).drawPDF())
 graph.setTitle("Generalized Pareto distribution fitting on a sample")
-graph.setColors(["black", "red"])
 graph.setLegends(["GPD fitting", "histogram"])
 graph.setLegendPosition("upper right")
 
@@ -190,13 +187,11 @@
 graph = myFittedDist.drawPDF()
 graph.add(ot.HistogramFactory().build(sample).drawPDF())
 graph.setTitle("Generalized Pareto distribution fitting on a sample")
-graph.setColors(["black", "red"])
 graph.setLegends(["GPD fitting", "histogram"])
 graph.setLegendPosition("upper right")
 
 view = viewer.View(graph)
 axes = view.getAxes()
 _ = axes[0].set_xlim(-1.0, 10.0)
 
-
 plt.show()
diff --git a/openturns/master/_downloads/3081f83078246821224dfb502b3c93c8/plot_kriging_beam_trend.zip b/openturns/master/_downloads/3081f83078246821224dfb502b3c93c8/plot_kriging_beam_trend.zip
diff --git a/...urns/master/_downloads/309f49c10d5e51bde01d1de415c7ec7f/plot_optimization_constraints.zip b/...urns/master/_downloads/309f49c10d5e51bde01d1de415c7ec7f/plot_optimization_constraints.zip
diff --git a/openturns/master/_downloads/30e24c36a04f3aadeb40470360b53cae/plot_white_noise_process.zip b/openturns/master/_downloads/30e24c36a04f3aadeb40470360b53cae/plot_white_noise_process.zip
diff --git a/...er/_downloads/3108aad049cef16e4606db52461bd010/plot_chaos_distribution_transformation.zip b/...er/_downloads/3108aad049cef16e4606db52461bd010/plot_chaos_distribution_transformation.zip
diff --git a/...s/master/_downloads/32764d685caaf9dcc0d31be3ef384e64/auto_functional_modeling_jupyter.zip b/...s/master/_downloads/32764d685caaf9dcc0d31be3ef384e64/auto_functional_modeling_jupyter.zip
diff --git a/openturns/master/_downloads/336d62d4430f0091c8578c7beb523dd1/plot_gibbs_simus.zip b/openturns/master/_downloads/336d62d4430f0091c8578c7beb523dd1/plot_gibbs_simus.zip
diff --git a/...ns/master/_downloads/3474b74c1da08b61d88daa3eee93697e/plot_createMultivariateFunction.zip b/...ns/master/_downloads/3474b74c1da08b61d88daa3eee93697e/plot_createMultivariateFunction.zip
diff --git a/openturns/master/_downloads/34c8a9a569c038b5fc28d75a54ed57d0/plot_generate_by_inversion.zip b/openturns/master/_downloads/34c8a9a569c038b5fc28d75a54ed57d0/plot_generate_by_inversion.zip
diff --git a/openturns/master/_downloads/3640dd049ad71e6f6218fb31fc47958a/plot_sensitivity_rank_sobol.zip b/openturns/master/_downloads/3640dd049ad71e6f6218fb31fc47958a/plot_sensitivity_rank_sobol.zip
diff --git a/...ownloads/3661d2a740be879b048fc346a710eb73/plot_estimate_non_parametric_distribution.ipynb b/...ownloads/3661d2a740be879b048fc346a710eb73/plot_estimate_non_parametric_distribution.ipynb
@@ -564,7 +564,7 @@
       },
       "outputs": [],
       "source": [
-        "grid = ot.GridLayout(2, 3)\not.RandomGenerator.SetSeed(0)\nfor i, distribution in enumerate(distCollection):\n    sample = distribution.getSample(5000)\n\n    # We draw the real distribution\n    graph = distribution.drawPDF()\n    graph.setLegends([distribution.getClassName()])\n    # We choose the default kernel\n    kernel = ot.KernelSmoothing()\n\n    # We activate no particular treatment\n    fitted = kernel.build(sample)\n    curve = fitted.drawPDF()\n    curve.setLegends([\"Fitted\"])\n    graph.add(curve)\n\n    # We activate the log-transform treatment\n    kernel.setUseLogTransform(True)\n    fitted = kernel.build(sample)\n    curve = fitted.drawPDF()\n    curve.setLegends([\"Fitted LogTransform\"])\n    curve = curve.getDrawable(0)\n    curve.setLineStyle(\"dashed\")\n\n    graph.add(curve)\n    graph.setColors(ot.Drawable.BuildDefaultPalette(3))\n    grid.setGraph(i // 3, i % 3, graph)\n\nview = viewer.View(grid)\n\nplt.show()"
+        "grid = ot.GridLayout(2, 3)\not.RandomGenerator.SetSeed(0)\nfor i, distribution in enumerate(distCollection):\n    sample = distribution.getSample(5000)\n\n    # We draw the real distribution\n    graph = distribution.drawPDF()\n    graph.setLegends([distribution.getClassName()])\n    # We choose the default kernel\n    kernel = ot.KernelSmoothing()\n\n    # We activate no particular treatment\n    fitted = kernel.build(sample)\n    curve = fitted.drawPDF()\n    curve.setLegends([\"Fitted\"])\n    graph.add(curve)\n\n    # We activate the log-transform treatment\n    kernel.setUseLogTransform(True)\n    fitted = kernel.build(sample)\n    curve = fitted.drawPDF()\n    curve.setLegends([\"Fitted LogTransform\"])\n    curve = curve.getDrawable(0)\n    curve.setLineStyle(\"dashed\")\n\n    graph.add(curve)\n    grid.setGraph(i // 3, i % 3, graph)\n\nview = viewer.View(grid)\n\nplt.show()"
       ]
     }
   ],

diff --git a/...aster/_downloads/37021e17efa0109060d56870a1de49bc/plot_sensitivity_sobol_multivariate.zip b/...aster/_downloads/37021e17efa0109060d56870a1de49bc/plot_sensitivity_sobol_multivariate.zip
diff --git a/...ns/master/_downloads/37073062f085e74d35ad582dfd3686cd/StationaryCovarianceModelFactory.py b/...ns/master/_downloads/37073062f085e74d35ad582dfd3686cd/StationaryCovarianceModelFactory.py
@@ -38,7 +38,6 @@ def fEst(X):
 
 cov_graph = func.draw(-a / 4, a / 4, 1024)
 cov_graph.add(funcEst.draw(-a / 4, a / 4, 1024))
-cov_graph.setColors(["blue", "red"])
 cov_graph.setTitle("Stationary covariance model estimation")
 
 fig = plt.figure(figsize=(10, 4))

diff --git a/...s/master/_downloads/37120f4a904c669e53219ba61820aa9f/plot_parametric_spectral_density.zip b/...s/master/_downloads/37120f4a904c669e53219ba61820aa9f/plot_parametric_spectral_density.zip
diff --git a/...ns/master/_downloads/377004ff464ff0c78517cf2cb249a26e/plot_quantilematching_estimator.zip b/...ns/master/_downloads/377004ff464ff0c78517cf2cb249a26e/plot_quantilematching_estimator.zip
diff --git a/openturns/master/_downloads/377968eba2ffc6abbeeb24f6178efd7c/plot_chaos_cv.py b/openturns/master/_downloads/377968eba2ffc6abbeeb24f6178efd7c/plot_chaos_cv.py
@@ -489,7 +489,6 @@ def compute_R2_score_by_kfold(
 cloud.setLegend("K-Fold")
 graph.add(cloud)
 graph.setLegendPosition("topleft")
-graph.setColors(ot.Drawable().BuildDefaultPalette(2))
 graph.setBoundingBox(boundingBox)
 view = otv.View(graph, figure_kw={"figsize": (5.0, 4.0)})
 # sphinx_gallery_thumbnail_number = 4

diff --git a/...rns/master/_downloads/37d5037027e9b9ecf6fece334817cc36/plot_estimate_probability_form.zip b/...rns/master/_downloads/37d5037027e9b9ecf6fece334817cc36/plot_estimate_probability_form.zip
diff --git a/openturns/master/_downloads/3823208149c1cb7a7cd4406f69c79a3b/plot_test_independence.zip b/openturns/master/_downloads/3823208149c1cb7a7cd4406f69c79a3b/plot_test_independence.zip
diff --git a/openturns/master/_downloads/38d23062a999be7e0413aa4f37e3abee/plot_chaos_cv.zip b/openturns/master/_downloads/38d23062a999be7e0413aa4f37e3abee/plot_chaos_cv.zip
diff --git a/...aster/_downloads/394a8bc0fc351cd956596e970c3ec2a3/plot_probability_simulation_results.zip b/...aster/_downloads/394a8bc0fc351cd956596e970c3ec2a3/plot_probability_simulation_results.zip
diff --git a/openturns/master/_downloads/39550032910307046da1a55b1df30071/plot_draw_survival.ipynb b/openturns/master/_downloads/39550032910307046da1a55b1df30071/plot_draw_survival.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "import openturns as ot\nimport openturns.viewer as viewer\nfrom matplotlib import pylab as plt\n\not.Log.Show(ot.Log.NONE)"
+        "import openturns as ot\nimport openturns.viewer as viewer\n\not.Log.Show(ot.Log.NONE)"
       ]
     },
     {
@@ -372,7 +372,25 @@
       },
       "outputs": [],
       "source": [
-        "xmin = sample.getMin()[0]\nxmax = sample.getMax()[0] - 1  # To avoid log(0) because P(X>Xmax)=0\nnpoints = 50\nlogScaleX = True\ngraph = userdefined.drawSurvivalFunction(xmin, xmax, pointNumber, logScaleX)\ngraph.setLogScale(ot.GraphImplementation.LOGXY)\ngraph.setColors([\"blue\"])\ngraph.setLegends([\"Sample\"])\ngraphDistribution = distribution.drawSurvivalFunction(xmin, xmax, npoints, logScaleX)\ngraphDistribution.setLegends([\"GPD\"])\ngraph.add(graphDistribution)\ngraph.setLegendPosition(\"upper right\")\ngraph.setTitle(\"GPD against the sample - n=%d\" % (sample.getSize()))\nview = viewer.View(graph)\n# graph\nplt.show()"
+        "xmin = sample.getMin()[0]\nxmax = sample.getMax()[0] - 1  # To avoid log(0) because P(X>Xmax)=0\nnpoints = 50\nlogScaleX = True\ngraph = userdefined.drawSurvivalFunction(xmin, xmax, pointNumber, logScaleX)\ngraph.setLogScale(ot.GraphImplementation.LOGXY)\ngraph.setLegends([\"Sample\"])\ngraphDistribution = distribution.drawSurvivalFunction(xmin, xmax, npoints, logScaleX)\ngraphDistribution.setLegends([\"GPD\"])\ngraph.add(graphDistribution)\ngraph.setLegendPosition(\"upper right\")\ngraph.setTitle(\"GPD against the sample - n=%d\" % (sample.getSize()))\nview = viewer.View(graph)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Show all the graphs.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "view.ShowAll()"
       ]
     }
   ],

diff --git a/openturns/master/_downloads/398cba8baeb494f3230b195cb1856dc3/plot_gibbs.zip b/openturns/master/_downloads/398cba8baeb494f3230b195cb1856dc3/plot_gibbs.zip
diff --git a/openturns/master/_downloads/39f704dc2aaa220bd4ad0237387c8f7f/GaussianProcessFitter.py b/openturns/master/_downloads/39f704dc2aaa220bd4ad0237387c8f7f/GaussianProcessFitter.py
@@ -22,7 +22,6 @@
 fhat = result.getMetaModel()
 
 graph.add(fhat.draw(0.0, 10.0))
-graph.setColors(["blue", "red", "black", "pink"])
 graph.setLegends(["model", "GP Fitter", "sample", "GP Regression"])
 graph.setLegendPosition("upper left")
 graph.setTitle(r"$y(x)=x * sin(x)$")

diff --git a/...s/master/_downloads/3b736a15b18a14ecb45c9a923b720669/plot_distribution_transformation.zip b/...s/master/_downloads/3b736a15b18a14ecb45c9a923b720669/plot_distribution_transformation.zip
diff --git a/...turns/master/_downloads/3b980ef69e4e49ae98082750379327c2/plot_imh_python_distribution.zip b/...turns/master/_downloads/3b980ef69e4e49ae98082750379327c2/plot_imh_python_distribution.zip
diff --git a/...rns/master/_downloads/3d7f62ef891c86f71ee7f00f9b32d7d0/plot_functional_chaos_graphs.ipynb b/...rns/master/_downloads/3d7f62ef891c86f71ee7f00f9b32d7d0/plot_functional_chaos_graphs.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import openturns as ot\nimport openturns.viewer as viewer\nfrom matplotlib import pylab as plt\n\not.Log.Show(ot.Log.NONE)"
+        "import openturns as ot\nimport openturns.viewer as viewer\n\not.Log.Show(ot.Log.NONE)"
       ]
     },
     {
@@ -80,7 +80,18 @@
       },
       "outputs": [],
       "source": [
-        "graph = drawFamily(ot.HermiteFactory())\nview = viewer.View(graph)\nplt.show()"
+        "graph = drawFamily(ot.HermiteFactory())\nview = viewer.View(graph)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "view.ShowAll()"
       ]
     }
   ],

diff --git a/openturns/master/_downloads/3d83f6648f1ba3ecb4170d64831f5712/plot_sensitivity_fast.zip b/openturns/master/_downloads/3d83f6648f1ba3ecb4170d64831f5712/plot_sensitivity_fast.zip
diff --git a/...urns/master/_downloads/3d8f8d0d25e076f26b7a1be7acc254d0/plot_gauss_product_experiment.zip b/...urns/master/_downloads/3d8f8d0d25e076f26b7a1be7acc254d0/plot_gauss_product_experiment.zip
diff --git a/openturns/master/_downloads/3dff3a51e49cef20fef09e8fb88915b8/plot_kolmogorov_pvalue.zip b/openturns/master/_downloads/3dff3a51e49cef20fef09e8fb88915b8/plot_kolmogorov_pvalue.zip
diff --git a/...r/_downloads/3e27b64af96523e66d6c830896eb942a/plot_estimate_multivariate_distribution.zip b/...r/_downloads/3e27b64af96523e66d6c830896eb942a/plot_estimate_multivariate_distribution.zip
diff --git a/openturns/master/_downloads/417ee1d880400bcc23124e57b79d4a77/plot_gpr_1d.zip b/openturns/master/_downloads/417ee1d880400bcc23124e57b79d4a77/plot_gpr_1d.zip
diff --git a/openturns/master/_downloads/4180131959eebe6f6c89b9576a10e606/plot_randomize_sample_lines.zip b/openturns/master/_downloads/4180131959eebe6f6c89b9576a10e606/plot_randomize_sample_lines.zip
diff --git a/...urns/master/_downloads/428d8159786be815d768f28306e57a52/plot_hsic_estimators_ishigami.zip b/...urns/master/_downloads/428d8159786be815d768f28306e57a52/plot_hsic_estimators_ishigami.zip
diff --git a/...ster/_downloads/429f3c67c06c69801722e094f3535e57/plot_functional_chaos_advanced_ctors.zip b/...ster/_downloads/429f3c67c06c69801722e094f3535e57/plot_functional_chaos_advanced_ctors.zip
diff --git a/...turns/master/_downloads/42dd0e7e013e63e6a4c8d82fc2a12400/plot_fieldfunction_metamodel.zip b/...turns/master/_downloads/42dd0e7e013e63e6a4c8d82fc2a12400/plot_fieldfunction_metamodel.zip
diff --git a/openturns/master/_downloads/42e3a3d02e1f50697b5d769ccc5b1102/plot_visualize_clouds.zip b/openturns/master/_downloads/42e3a3d02e1f50697b5d769ccc5b1102/plot_visualize_clouds.zip
diff --git a/.../_downloads/437c175b9578c47e60ba2c069be55866/plot_estimate_non_parametric_distribution.py b/.../_downloads/437c175b9578c47e60ba2c069be55866/plot_estimate_non_parametric_distribution.py
@@ -316,7 +316,6 @@
     curve.setLineStyle("dashed")
 
     graph.add(curve)
-    graph.setColors(ot.Drawable.BuildDefaultPalette(3))
     grid.setGraph(i // 3, i % 3, graph)
 
 view = viewer.View(grid)

diff --git a/openturns/master/_downloads/4393e3e045390dcc039fbe344171f354/plot_calibration_flooding.zip b/openturns/master/_downloads/4393e3e045390dcc039fbe344171f354/plot_calibration_flooding.zip
diff --git a/openturns/master/_downloads/43fb221c5767618d3d15aab4a7542e00/plot_functional_chaos_graphs.py b/openturns/master/_downloads/43fb221c5767618d3d15aab4a7542e00/plot_functional_chaos_graphs.py
@@ -10,7 +10,6 @@
 # %%
 import openturns as ot
 import openturns.viewer as viewer
-from matplotlib import pylab as plt
 
 ot.Log.Show(ot.Log.NONE)
 
@@ -61,4 +60,6 @@ def drawFamily(factory, degreeMax=5):
 # %%
 graph = drawFamily(ot.HermiteFactory())
 view = viewer.View(graph)
-plt.show()
+
+# %%
+view.ShowAll()
diff --git a/openturns/master/_downloads/45a49a91371b4d1663f101ef6f040a4e/plot_estimate_gev_venice.zip b/openturns/master/_downloads/45a49a91371b4d1663f101ef6f040a4e/plot_estimate_gev_venice.zip
diff --git a/openturns/master/_downloads/45ec538ac1a770b03a6efa7962014f15/plot_sensitivity_sobol.zip b/openturns/master/_downloads/45ec538ac1a770b03a6efa7962014f15/plot_sensitivity_sobol.zip
diff --git a/openturns/master/_downloads/462d5b130441e94031e28fe82c2c9d16/plot_kolmogorov_statistics.zip b/openturns/master/_downloads/462d5b130441e94031e28fe82c2c9d16/plot_kolmogorov_statistics.zip
diff --git a/openturns/master/_downloads/4677004cbfcd3c7d648bd7a9c4273fe9/plot_taylor_approximation.py b/openturns/master/_downloads/4677004cbfcd3c7d648bd7a9c4273fe9/plot_taylor_approximation.py
@@ -27,7 +27,6 @@
 # %%
 import openturns as ot
 import openturns.viewer as viewer
-from matplotlib import pylab as plt
 
 ot.Log.Show(ot.Log.NONE)
 
@@ -132,4 +131,6 @@
 graph.add(curve)
 graph.setLegendPosition("upper right")
 view = viewer.View(graph)
-plt.show()
+
+# %%
+view.ShowAll()
diff --git a/openturns/master/_downloads/47547e012ed7e340f91c528bdfe5a45c/plot_create_normal_process.zip b/openturns/master/_downloads/47547e012ed7e340f91c528bdfe5a45c/plot_create_normal_process.zip
diff --git a/openturns/master/_downloads/476a3a538ee372f1023b36057684a222/BoxCoxTransform.png b/openturns/master/_downloads/476a3a538ee372f1023b36057684a222/BoxCoxTransform.png
diff --git a/openturns/master/_downloads/485c87f02609b7283ad609fc4dcac031/plot_proba_system_event.zip b/openturns/master/_downloads/485c87f02609b7283ad609fc4dcac031/plot_proba_system_event.zip
diff --git a/.../_downloads/48605cedb59db98b9e0ff233588256b2/plot_post_analytical_importance_sampling.zip b/.../_downloads/48605cedb59db98b9e0ff233588256b2/plot_post_analytical_importance_sampling.zip
diff --git a/openturns/master/_downloads/49c2560f5aa47dda8eb13deacb3e6026/plot_truncated_distribution.py b/openturns/master/_downloads/49c2560f5aa47dda8eb13deacb3e6026/plot_truncated_distribution.py
@@ -22,7 +22,6 @@
 # %%
 import openturns as ot
 import openturns.viewer as viewer
-from matplotlib import pylab as plt
 
 ot.Log.Show(ot.Log.NONE)
 
@@ -65,6 +64,8 @@
 # Draw its PDF
 graph = truncatedKS.drawPDF([-2.5] * dimension, [2.5] * dimension, [256] * dimension)
 graph.add(ot.Cloud(truncatedKS.getSample(200)))
-graph.setColors(["blue", "red"])
 view = viewer.View(graph)
-plt.show()
+
+# %%
+# Display all the graphs
+view.ShowAll()
diff --git a/openturns/master/_downloads/4bcdfd6a56e4819967b046d52ebdeabc/GeneralizedExtremeValue.py b/openturns/master/_downloads/4bcdfd6a56e4819967b046d52ebdeabc/GeneralizedExtremeValue.py
@@ -9,8 +9,6 @@
     distribution = ot.GeneralizedExtremeValue(0.0, 1.0, p)
     pdf_curve = distribution.drawPDF().getDrawable(0)
     cdf_curve = distribution.drawCDF().getDrawable(0)
-    pdf_curve.setColor(palette[i])
-    cdf_curve.setColor(palette[i])
     legend = f"xi={p}"
     pdf_curve.setLegend(legend)
     cdf_curve.setLegend(legend)

diff --git a/openturns/master/_downloads/4c092f03e8153061b73a10d65edc89b1/plot_smoothing_mixture.zip b/openturns/master/_downloads/4c092f03e8153061b73a10d65edc89b1/plot_smoothing_mixture.zip
diff --git a/.../_downloads/4c14280c61fb64fcaa3b8c4bc0810b9b/plot_estimate_probability_randomized_qmc.zip b/.../_downloads/4c14280c61fb64fcaa3b8c4bc0810b9b/plot_estimate_probability_randomized_qmc.zip
diff --git a/openturns/master/_downloads/4cc066dd5a38eb59c54af5033ebd388f/plot_regression_interval.zip b/openturns/master/_downloads/4cc066dd5a38eb59c54af5033ebd388f/plot_regression_interval.zip
diff --git a/openturns/master/_downloads/4d306ed8d90ff5d51c2f72c8f3f475a1/plot_parametric_function.zip b/openturns/master/_downloads/4d306ed8d90ff5d51c2f72c8f3f475a1/plot_parametric_function.zip
diff --git a/openturns/master/_downloads/4d8faa63fdd3f872206132c1bb17eda4/plot_chaos_cv.ipynb b/openturns/master/_downloads/4d8faa63fdd3f872206132c1bb17eda4/plot_chaos_cv.ipynb
@@ -302,7 +302,7 @@
       },
       "outputs": [],
       "source": [
-        "graph = ot.Graph(\"CV : split vs K-Fold\", \"Degree\", \"$R^2$\", True)\ncloud = ot.Cloud(ot.Sample.BuildFromPoint(degree_list), scoreSampleSplit)\ncloud.setPointStyle(\"circle\")\ncloud.setLegend(\"Split\")\ngraph.add(cloud)\ncloud = ot.Cloud(ot.Sample.BuildFromPoint(degree_list), scoreSampleKFold)\ncloud.setPointStyle(\"square\")\ncloud.setLegend(\"K-Fold\")\ngraph.add(cloud)\ngraph.setLegendPosition(\"topleft\")\ngraph.setColors(ot.Drawable().BuildDefaultPalette(2))\ngraph.setBoundingBox(boundingBox)\nview = otv.View(graph, figure_kw={\"figsize\": (5.0, 4.0)})\n# sphinx_gallery_thumbnail_number = 4"
+        "graph = ot.Graph(\"CV : split vs K-Fold\", \"Degree\", \"$R^2$\", True)\ncloud = ot.Cloud(ot.Sample.BuildFromPoint(degree_list), scoreSampleSplit)\ncloud.setPointStyle(\"circle\")\ncloud.setLegend(\"Split\")\ngraph.add(cloud)\ncloud = ot.Cloud(ot.Sample.BuildFromPoint(degree_list), scoreSampleKFold)\ncloud.setPointStyle(\"square\")\ncloud.setLegend(\"K-Fold\")\ngraph.add(cloud)\ngraph.setLegendPosition(\"topleft\")\ngraph.setBoundingBox(boundingBox)\nview = otv.View(graph, figure_kw={\"figsize\": (5.0, 4.0)})\n# sphinx_gallery_thumbnail_number = 4"
       ]
     },
     {

diff --git a/...ns/master/_downloads/4df3e0c0f81319718eb3fb7cc1e6e39e/auto_functional_modeling_python.zip b/...ns/master/_downloads/4df3e0c0f81319718eb3fb7cc1e6e39e/auto_functional_modeling_python.zip
diff --git a/openturns/master/_downloads/4e370018a0b7f35074ef9a32ecac2d09/plot_advanced_mle_estimator.zip b/openturns/master/_downloads/4e370018a0b7f35074ef9a32ecac2d09/plot_advanced_mle_estimator.zip
diff --git a/...rns/master/_downloads/4ee390b5ccc95498710f43a77cb4b6a7/plot_conditional_random_vector.zip b/...rns/master/_downloads/4ee390b5ccc95498710f43a77cb4b6a7/plot_conditional_random_vector.zip
diff --git a/openturns/master/_downloads/4eea463a8688d668770d903a8c72fc5e/plot_function_manipulation.zip b/openturns/master/_downloads/4eea463a8688d668770d903a8c72fc5e/plot_function_manipulation.zip
diff --git a/...master/_downloads/5054b46f4cdf35d7731b997adbac5056/plot_estimate_conditional_quantile.zip b/...master/_downloads/5054b46f4cdf35d7731b997adbac5056/plot_estimate_conditional_quantile.zip
diff --git a/openturns/master/_downloads/50d90b78a3b3e22bfaf29dba34e97f34/Dirichlet.py b/openturns/master/_downloads/50d90b78a3b3e22bfaf29dba34e97f34/Dirichlet.py
@@ -10,16 +10,13 @@
 theta_2 = (0.5, 0.7, 0.9)
 pdf_2d_1 = ot.Graph(title_2d.format(theta_1), "x1", "x2", True)
 pdf_2d_2 = ot.Graph(title_2d.format(theta_2), "x1", "x2", True)
-palette = ot.Drawable.BuildDefaultPalette(10)
 list_theta = [(2, 2), (7, 5), (2, 6), (3, 4)]
 for theta in list_theta:
     distribution = ot.Dirichlet(theta)
     pdf_curve = distribution.drawPDF()
     cdf_curve = distribution.drawCDF()
     pdf_graph.add(pdf_curve)
     cdf_graph.add(cdf_curve)
-pdf_graph.setColors([palette[i] for i in range(4)])
-cdf_graph.setColors([palette[i] for i in range(4)])
 legends = [f"theta={theta}" for theta in list_theta]
 pdf_graph.setLegends(legends)
 cdf_graph.setLegends(legends)

diff --git a/.../master/_downloads/50eb8d70654816c928f9d642a2ef4a76/plot_quick_start_point_and_sample.zip b/.../master/_downloads/50eb8d70654816c928f9d642a2ef4a76/plot_quick_start_point_and_sample.zip
diff --git a/...urns/master/_downloads/518be0da6f937a76737e11eea3ba1508/plot_createUnivariateFunction.zip b/...urns/master/_downloads/518be0da6f937a76737e11eea3ba1508/plot_createUnivariateFunction.zip
diff --git a/...turns/master/_downloads/52878e7a369d4e78d5357bed0031e830/plot_timeseries_manipulation.zip b/...turns/master/_downloads/52878e7a369d4e78d5357bed0031e830/plot_timeseries_manipulation.zip
diff --git a/...urns/master/_downloads/52af42ee236d1fb67799a76c7ba89173/plot_estimate_dependence_wind.zip b/...urns/master/_downloads/52af42ee236d1fb67799a76c7ba89173/plot_estimate_dependence_wind.zip
diff --git a/...993c0aeab9708075323f3dbe9bf10/plot_estimate_probability_adaptive_directional_sampling.zip b/...993c0aeab9708075323f3dbe9bf10/plot_estimate_probability_adaptive_directional_sampling.zip
diff --git a/openturns/master/_downloads/541c748d6ce6ab3bbe2b01d37f67d57e/plot_generate_flooding.zip b/openturns/master/_downloads/541c748d6ce6ab3bbe2b01d37f67d57e/plot_generate_flooding.zip
diff --git a/openturns/master/_downloads/545c537577c0514acf664d660c3d6673/plot_event_manipulation.zip b/openturns/master/_downloads/545c537577c0514acf664d660c3d6673/plot_event_manipulation.zip
diff --git a/openturns/master/_downloads/58127b3948c7459557773bdd5881ef20/plot_smirnov_test.zip b/openturns/master/_downloads/58127b3948c7459557773bdd5881ef20/plot_smirnov_test.zip
diff --git a/openturns/master/_downloads/589122e5e0a996cebc673636c01ac7a9/plot_mix_rv_process.zip b/openturns/master/_downloads/589122e5e0a996cebc673636c01ac7a9/plot_mix_rv_process.zip
diff --git a/openturns/master/_downloads/58b7cedd7592157d47e84db4a498c83f/auto_data_analysis_jupyter.zip b/openturns/master/_downloads/58b7cedd7592157d47e84db4a498c83f/auto_data_analysis_jupyter.zip
diff --git a/openturns/master/_downloads/591696734b2f30020c482b44bc81f7bd/plot_functions_inputDim.zip b/openturns/master/_downloads/591696734b2f30020c482b44bc81f7bd/plot_functions_inputDim.zip
diff --git a/openturns/master/_downloads/59635bed452be4cc3968bb7852d409aa/KernelSmoothing.py b/openturns/master/_downloads/59635bed452be4cc3968bb7852d409aa/KernelSmoothing.py
@@ -8,7 +8,5 @@
 
 graph = fittedDist.drawPDF()
 graph.add(ot.Gamma(6.0, 1.0).drawPDF())
-graph.setColors(ot.Drawable.BuildDefaultPalette(2))
 graph.setLegends(["KS dist", "Gamma"])
 View(graph)
-# View(graph, figure_kw={'figsize': (8, 4)})
diff --git a/openturns/master/_downloads/599573e70640a0f39bc3497cb30cc18a/plot_random_walk_process.zip b/openturns/master/_downloads/599573e70640a0f39bc3497cb30cc18a/plot_random_walk_process.zip
diff --git a/...master/_downloads/5a308943de755c6667a6ef3e92623e41/plot_gaussian_processes_comparison.zip b/...master/_downloads/5a308943de755c6667a6ef3e92623e41/plot_gaussian_processes_comparison.zip
diff --git a/...er/_downloads/5b4293c2d959aa20763107c6ad85215c/plot_link_computer_code_coupling_tools.zip b/...er/_downloads/5b4293c2d959aa20763107c6ad85215c/plot_link_computer_code_coupling_tools.zip
diff --git a/...ter/_downloads/5b77f050d7914a7573f4f53d9232e83f/plot_fit_extreme_value_distribution.ipynb b/...ter/_downloads/5b77f050d7914a7573f4f53d9232e83f/plot_fit_extreme_value_distribution.ipynb
@@ -169,7 +169,7 @@
       },
       "outputs": [],
       "source": [
-        "graph = myDistribution.drawPDF()\ngraph.add(ot.HistogramFactory().build(sample).drawPDF())\ngraph.setColors([\"black\", \"red\"])\ngraph.setLegends([\"GEV fitting\", \"histogram\"])\ngraph.setLegendPosition(\"upper right\")\n\nview = viewer.View(graph)\naxes = view.getAxes()\n_ = axes[0].set_xlim(-20.0, 20.0)"
+        "graph = myDistribution.drawPDF()\ngraph.add(ot.HistogramFactory().build(sample).drawPDF())\ngraph.setLegends([\"GEV fitting\", \"histogram\"])\ngraph.setLegendPosition(\"upper right\")\n\nview = viewer.View(graph)\naxes = view.getAxes()\n_ = axes[0].set_xlim(-20.0, 20.0)"
       ]
     },
     {
@@ -187,7 +187,7 @@
       },
       "outputs": [],
       "source": [
-        "graph = myDistribution.drawPDF()\ngraph.add(ot.HistogramFactory().build(sample).drawPDF())\n\ndistFrechet = ot.FrechetFactory().buildAsFrechet(sample)\ngraph.add(distFrechet.drawPDF())\n\ndistGumbel = ot.GumbelFactory().buildAsGumbel(sample)\ngraph.add(distGumbel.drawPDF())\n\n# We change the line style of the WeibullMax.\ndistWeibullMax = ot.WeibullMaxFactory().buildAsWeibullMax(sample)\ncurveWeibullMax = distWeibullMax.drawPDF().getDrawable(0)\ncurveWeibullMax.setLineStyle(\"dashed\")\ngraph.add(curveWeibullMax)\n\ngraph.setColors([\"black\", \"red\", \"green\", \"blue\", \"orange\"])\ngraph.setLegends(\n    [\n        \"GEV fitting\",\n        \"histogram\",\n        \"Frechet fitting\",\n        \"Gumbel fitting\",\n        \"WeibullMax fitting\",\n    ]\n)\ngraph.setLegendPosition(\"upper right\")\nview = viewer.View(graph)\naxes = view.getAxes()  # axes is a matplotlib object\n_ = axes[0].set_xlim(-20.0, 20.0)"
+        "graph = myDistribution.drawPDF()\ngraph.add(ot.HistogramFactory().build(sample).drawPDF())\n\ndistFrechet = ot.FrechetFactory().buildAsFrechet(sample)\ngraph.add(distFrechet.drawPDF())\n\ndistGumbel = ot.GumbelFactory().buildAsGumbel(sample)\ngraph.add(distGumbel.drawPDF())\n\n# We change the line style of the WeibullMax.\ndistWeibullMax = ot.WeibullMaxFactory().buildAsWeibullMax(sample)\ncurveWeibullMax = distWeibullMax.drawPDF().getDrawable(0)\ncurveWeibullMax.setLineStyle(\"dashed\")\ngraph.add(curveWeibullMax)\n\ngraph.setLegends(\n    [\n        \"GEV fitting\",\n        \"histogram\",\n        \"Frechet fitting\",\n        \"Gumbel fitting\",\n        \"WeibullMax fitting\",\n    ]\n)\ngraph.setLegendPosition(\"upper right\")\nview = viewer.View(graph)\naxes = view.getAxes()  # axes is a matplotlib object\n_ = axes[0].set_xlim(-20.0, 20.0)"
       ]
     },
     {
@@ -266,7 +266,7 @@
       },
       "outputs": [],
       "source": [
-        "graph = myFittedDist.drawPDF()\ngraph.add(ot.HistogramFactory().build(sample).drawPDF())\ngraph.setTitle(\"Generalized Pareto distribution fitting on a sample\")\ngraph.setColors([\"black\", \"red\"])\ngraph.setLegends([\"GPD fitting\", \"histogram\"])\ngraph.setLegendPosition(\"upper right\")\n\nview = viewer.View(graph)\naxes = view.getAxes()\n_ = axes[0].set_xlim(-1.0, 10.0)"
+        "graph = myFittedDist.drawPDF()\ngraph.add(ot.HistogramFactory().build(sample).drawPDF())\ngraph.setTitle(\"Generalized Pareto distribution fitting on a sample\")\ngraph.setLegends([\"GPD fitting\", \"histogram\"])\ngraph.setLegendPosition(\"upper right\")\n\nview = viewer.View(graph)\naxes = view.getAxes()\n_ = axes[0].set_xlim(-1.0, 10.0)"
       ]
     },
     {
@@ -320,7 +320,7 @@
       },
       "outputs": [],
       "source": [
-        "graph = myFittedDist.drawPDF()\ngraph.add(ot.HistogramFactory().build(sample).drawPDF())\ngraph.setTitle(\"Generalized Pareto distribution fitting on a sample\")\ngraph.setColors([\"black\", \"red\"])\ngraph.setLegends([\"GPD fitting\", \"histogram\"])\ngraph.setLegendPosition(\"upper right\")\n\nview = viewer.View(graph)\naxes = view.getAxes()\n_ = axes[0].set_xlim(-1.0, 10.0)\n\n\nplt.show()"
+        "graph = myFittedDist.drawPDF()\ngraph.add(ot.HistogramFactory().build(sample).drawPDF())\ngraph.setTitle(\"Generalized Pareto distribution fitting on a sample\")\ngraph.setLegends([\"GPD fitting\", \"histogram\"])\ngraph.setLegendPosition(\"upper right\")\n\nview = viewer.View(graph)\naxes = view.getAxes()\n_ = axes[0].set_xlim(-1.0, 10.0)\n\nplt.show()"
       ]
     }
   ],

diff --git a/openturns/master/_downloads/5c3ed7990c196edd3d0d82732c3a52f0/plot_kolmogorov_distribution.py b/openturns/master/_downloads/5c3ed7990c196edd3d0d82732c3a52f0/plot_kolmogorov_distribution.py
@@ -23,7 +23,6 @@
 # %%
 import openturns as ot
 import openturns.viewer as viewer
-from matplotlib import pylab as plt
 
 ot.Log.Show(ot.Log.NONE)
 
@@ -199,12 +198,15 @@ def generateKSSampleEstimatedParameters(nrepeat, samplesize):
 graph.setLegends(["Known parameters"])
 graphP = ot.KernelSmoothing().build(sampleDP).drawPDF()
 graphP.setLegends(["Estimated parameters"])
-graphP.setColors(["blue"])
 graph.add(graphP)
 graph.setTitle("Kolmogorov-Smirnov distribution")
 graph.setXTitle("KS-Statistics")
 view = viewer.View(graph)
-plt.show()
+
+# %%
+# Display the graphs
+view.ShowAll()
+
 
 # %%
 # We see that the distribution of the KS distances when the parameters are

diff --git a/...s/master/_downloads/5d0c7b03067e5614ac647ab1e8bcde4b/plot_calibration_deflection_tube.zip b/...s/master/_downloads/5d0c7b03067e5614ac647ab1e8bcde4b/plot_calibration_deflection_tube.zip
diff --git a/..._downloads/5e7302bdc094106801271d8bba8a6aa7/plot_estimate_stationary_covariance_model.zip b/..._downloads/5e7302bdc094106801271d8bba8a6aa7/plot_estimate_stationary_covariance_model.zip
diff --git a/...s/master/_downloads/600d226fbd631c396cb99a496f237188/InverseBoxCoxTransform.png b/...s/master/_downloads/600d226fbd631c396cb99a496f237188/InverseBoxCoxTransform.png
diff --git a/openturns/master/_downloads/60642898d696254e40ed2c2f8d0d885f/plot_calibration_quickstart.zip b/openturns/master/_downloads/60642898d696254e40ed2c2f8d0d885f/plot_calibration_quickstart.zip
diff --git a/openturns/master/_downloads/60a4d760fd43f421838e34b483ce05ee/plot_smolyak_merge.zip b/openturns/master/_downloads/60a4d760fd43f421838e34b483ce05ee/plot_smolyak_merge.zip
diff --git a/openturns/master/_downloads/62116af902203365c4c062bcc54f3f8a/plot_estimate_gpd_rain.zip b/openturns/master/_downloads/62116af902203365c4c062bcc54f3f8a/plot_estimate_gpd_rain.zip
diff --git a/openturns/master/_downloads/623afba8553c4c1b94d6fef9e7b4ee5d/plot_form_explained.zip b/openturns/master/_downloads/623afba8553c4c1b94d6fef9e7b4ee5d/plot_form_explained.zip
diff --git a/...er/_downloads/63042d3025a409300f5967643cc18f4c/plot_calibration_withoutobservedinputs.zip b/...er/_downloads/63042d3025a409300f5967643cc18f4c/plot_calibration_withoutobservedinputs.zip
diff --git a/openturns/master/_downloads/63d936015b9fe8c1084d030a9bdcaf21/plot_smolyak_quadrature.zip b/openturns/master/_downloads/63d936015b9fe8c1084d030a9bdcaf21/plot_smolyak_quadrature.zip
diff --git a/openturns/master/_downloads/6419bb826c2a13b8b1a53ba3ea2c3dec/plot_test_copula.zip b/openturns/master/_downloads/6419bb826c2a13b8b1a53ba3ea2c3dec/plot_test_copula.zip
diff --git a/openturns/master/_downloads/655eb768eb526d2af1ea0e0433eeac49/plot_ackley_distribution.zip b/openturns/master/_downloads/655eb768eb526d2af1ea0e0433eeac49/plot_ackley_distribution.zip
diff --git a/openturns/master/_downloads/6562cb5f440391f8b800d51ffb554a83/plot_study_save_load.zip b/openturns/master/_downloads/6562cb5f440391f8b800d51ffb554a83/plot_study_save_load.zip
diff --git a/...urns/master/_downloads/661a148897102186e052aa62075814d0/plot_rwmh_python_distribution.zip b/...urns/master/_downloads/661a148897102186e052aa62075814d0/plot_rwmh_python_distribution.zip
diff --git a/...downloads/66926eb6e8be139898cde3d6d796cb7d/StationaryCovarianceModelFactory.png b/...downloads/66926eb6e8be139898cde3d6d796cb7d/StationaryCovarianceModelFactory.png
diff --git a/.../master/_downloads/669c88e63bc2f77f36b4dd5143b089d9/plot_userdefined_covariance_model.zip b/.../master/_downloads/669c88e63bc2f77f36b4dd5143b089d9/plot_userdefined_covariance_model.zip
diff --git a/openturns/master/_downloads/68559af0df72168ca0cda598cfe34f34/plot_trend_transform.zip b/openturns/master/_downloads/68559af0df72168ca0cda598cfe34f34/plot_trend_transform.zip
diff --git a/openturns/master/_downloads/6869db87f34b684e38dda5f58b080bc2/plot_optimization_dlib.zip b/openturns/master/_downloads/6869db87f34b684e38dda5f58b080bc2/plot_optimization_dlib.zip
diff --git a/openturns/master/_downloads/69a7d9a687b1401899213788f81c5c70/plot_smolyak_experiment.zip b/openturns/master/_downloads/69a7d9a687b1401899213788f81c5c70/plot_smolyak_experiment.zip
diff --git a/...ter/_downloads/6b3dc8c62080028f6fe87ecac8bb1afb/plot_estimate_probability_monte_carlo.zip b/...ter/_downloads/6b3dc8c62080028f6fe87ecac8bb1afb/plot_estimate_probability_monte_carlo.zip
diff --git a/...er/_downloads/6bd45d367567d19dd3fb62fb724a75b9/plot_overview_univariate_distributions.zip b/...er/_downloads/6bd45d367567d19dd3fb62fb724a75b9/plot_overview_univariate_distributions.zip
diff --git a/...ns/master/_downloads/6d03e4dee2b08ccd62bae5dfb55b68ac/GaussianProcessFitter.png b/...ns/master/_downloads/6d03e4dee2b08ccd62bae5dfb55b68ac/GaussianProcessFitter.png
diff --git a/openturns/master/_downloads/6d40c35de5da53e59e3736b961357f44/plot_ordinal_sum_copula.zip b/openturns/master/_downloads/6d40c35de5da53e59e3736b961357f44/plot_ordinal_sum_copula.zip
diff --git a/openturns/master/_downloads/6e04fbf02802d048d6f2f49b2cdb54c1/plot_estimate_moments.zip b/openturns/master/_downloads/6e04fbf02802d048d6f2f49b2cdb54c1/plot_estimate_moments.zip
diff --git a/...master/_downloads/6e1be84ade199a4f6af4c22825566ae9/plot_functional_chaos_exploitation.zip b/...master/_downloads/6e1be84ade199a4f6af4c22825566ae9/plot_functional_chaos_exploitation.zip
diff --git a/...urns/master/_downloads/6e20a49211011af7e84a81b89b2ac70f/plot_chaos_build_distribution.zip b/...urns/master/_downloads/6e20a49211011af7e84a81b89b2ac70f/plot_chaos_build_distribution.zip
diff --git a/...aster/_downloads/6e518ec737e8c5eb60654ad7ec3146cf/auto_probabilistic_modeling_jupyter.zip b/...aster/_downloads/6e518ec737e8c5eb60654ad7ec3146cf/auto_probabilistic_modeling_jupyter.zip
diff --git a/...nloads/6eaa9cec49834fa341fe3c12979d425e/plot_model_singular_multivariate_distribution.zip b/...nloads/6eaa9cec49834fa341fe3c12979d425e/plot_model_singular_multivariate_distribution.zip
diff --git a/...aster/_downloads/6f5e8023d44074bc0dea8e1a8077bc41/plot_distribution_linear_regression.zip b/...aster/_downloads/6f5e8023d44074bc0dea8e1a8077bc41/plot_distribution_linear_regression.zip
diff --git a/...ter/_downloads/708d09b7bdfce339e18287966c732912/plot_expectation_simulation_algorithm.zip b/...ter/_downloads/708d09b7bdfce339e18287966c732912/plot_expectation_simulation_algorithm.zip
diff --git a/openturns/master/_downloads/70aeb039c9b0fabc4a4522c8f23b7b2f/plot_kriging_categorical.zip b/openturns/master/_downloads/70aeb039c9b0fabc4a4522c8f23b7b2f/plot_kriging_categorical.zip
diff --git a/openturns/master/_downloads/70b03af17c7f5138c806a95f8899e0db/plot_field_fca_sobol.zip b/openturns/master/_downloads/70b03af17c7f5138c806a95f8899e0db/plot_field_fca_sobol.zip
diff --git a/openturns/master/_downloads/71aec4e01968ab2c8be52211336adbb2/auto_meta_modeling_python.zip b/openturns/master/_downloads/71aec4e01968ab2c8be52211336adbb2/auto_meta_modeling_python.zip
diff --git a/openturns/master/_downloads/7214c34045afefc34c7ebdea60927f49/plot_calibration_logistic.zip b/openturns/master/_downloads/7214c34045afefc34c7ebdea60927f49/plot_calibration_logistic.zip
diff --git a/openturns/master/_downloads/7217e654adb9d5b26143bb34b3bb2c87/plot_general_linear_model.py b/openturns/master/_downloads/7217e654adb9d5b26143bb34b3bb2c87/plot_general_linear_model.py
@@ -10,7 +10,6 @@
 # %%
 import openturns as ot
 import openturns.viewer as viewer
-from matplotlib import pylab as plt
 
 ot.Log.Show(ot.Log.NONE)
 
@@ -64,14 +63,14 @@
 xmax = 5.0
 x1value = 0.5
 parametricModelGraph = ot.ParametricFunction(model, [0], [x1value]).draw(xmin, xmax)
-parametricModelGraph.setColors(["red"])
 graphMetamodel = ot.ParametricFunction(responseSurface, [0], [x1value]).draw(xmin, xmax)
-graphMetamodel.setColors(["blue"])
 parametricModelGraph.add(graphMetamodel)
 parametricModelGraph.setLegends(["Model", "Meta-Model"])
 parametricModelGraph.setLegendPosition("upper right")
 view = viewer.View(parametricModelGraph)
-plt.show()
+
+# %%
+view.ShowAll()
 
 # %%
 # We see that the metamodel is equal to the trend because it takes into account the fact that the mean of the Gaussian process is zero.

diff --git a/openturns/master/_downloads/72192f8af5c145ba2976f2de3f2563e3/plot_python_distribution.py b/openturns/master/_downloads/72192f8af5c145ba2976f2de3f2563e3/plot_python_distribution.py
@@ -140,7 +140,6 @@ def computeQuantile(self, prob, tail=False):
 # %%
 # And plot the CDF:
 graph = distribution.drawCDF()
-graph.setColors(["blue"])
 view = otv.View(graph)
 
 # %%

diff --git a/openturns/master/_downloads/721ea9b85a5cdd56b948dfb35e84d99b/plot_sample_pandas.zip b/openturns/master/_downloads/721ea9b85a5cdd56b948dfb35e84d99b/plot_sample_pandas.zip
diff --git a/openturns/master/_downloads/72470bd840148df8d813b0ca21b3d8c5/plot_kolmogorov_test.zip b/openturns/master/_downloads/72470bd840148df8d813b0ca21b3d8c5/plot_kolmogorov_test.zip
diff --git a/...rns/master/_downloads/7263f63092b58313bb0867d9aebe4337/auto_numerical_methods_jupyter.zip b/...rns/master/_downloads/7263f63092b58313bb0867d9aebe4337/auto_numerical_methods_jupyter.zip
diff --git a/...ownloads/72a10ca6fbae493981b89b8ef45af136/plot_probability_simulation_parametrization.zip b/...ownloads/72a10ca6fbae493981b89b8ef45af136/plot_probability_simulation_parametrization.zip
diff --git a/openturns/master/_downloads/7300e74c30077a98a88316bee7ad1e6f/plot_gpr_cantilever_beam.zip b/openturns/master/_downloads/7300e74c30077a98a88316bee7ad1e6f/plot_gpr_cantilever_beam.zip