CircleCI build openturns 19499

openturns · Sep 5, 2024 · 88a68fc · 88a68fc
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diff --git a/openturns/master/_downloads/d6c47184f1d21b3182aaaf2fecc45634/plot_sensitivity_wingweight.zip b/openturns/master/_downloads/d6c47184f1d21b3182aaaf2fecc45634/plot_sensitivity_wingweight.zip
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diff --git a/...r/_downloads/d850ff3d4c3e655d472703f9ee467be6/plot_asymptotic_estimators_distribution.zip b/...r/_downloads/d850ff3d4c3e655d472703f9ee467be6/plot_asymptotic_estimators_distribution.zip
diff --git a/openturns/master/_downloads/d93395f04410d47f60b9edc5a851cb82/plot_mixed_design.zip b/openturns/master/_downloads/d93395f04410d47f60b9edc5a851cb82/plot_mixed_design.zip
diff --git a/...rns/master/_downloads/dac0617e2074689405acbf7f15fdbb8c/plot_quantile_estimation_wilks.zip b/...rns/master/_downloads/dac0617e2074689405acbf7f15fdbb8c/plot_quantile_estimation_wilks.zip
diff --git a/...turns/master/_downloads/db227e2d269c18c21e6f81f19e8b6f32/plot_hsic_estimators_ishigami.py b/...turns/master/_downloads/db227e2d269c18c21e6f81f19e8b6f32/plot_hsic_estimators_ishigami.py
@@ -13,7 +13,6 @@
 # HSIC estimators rely on a reproducing kernel of a Hilbert space. We can use them to compute sensitivity
 # indices. We present the methods on the :ref:`Ishigami function<use-case-ishigami>`.
 
-
 # %%
 # Definition of the model
 # -----------------------
@@ -148,7 +147,7 @@
 dist2criticalDomain = ot.DistanceToDomainFunction(criticalDomain)
 
 # %%
-# We define the parameters in our function from the output sample
+# We define the empirical parameter values in our function from the output sample
 s = 0.1 * Y.computeStandardDeviation()[0]
 
 # %%
@@ -158,6 +157,11 @@
 phi = ot.ParametricFunction(f, [1], [s])
 filterFunction = ot.ComposedFunction(phi, dist2criticalDomain)
 
+# %%
+# We modify the output covariance kernel so as to adapt it to the filtered output
+Y_filtered = filterFunction(Y)
+outputCovariance.setScale(Y_filtered.computeStandardDeviation())
+covarianceModelCollection[-1] = outputCovariance
 
 # %%
 # We choose an unbiased estimator
@@ -214,6 +218,11 @@
 # Here the weight function is the filter function we used previously.
 weightFunction = filterFunction
 
+# %%
+# We revert to the covariance kernel associated to the unfiltered output
+outputCovariance.setScale(Y.computeStandardDeviation())
+covarianceModelCollection[-1] = outputCovariance
+
 # %%
 # We have to select a biased -but asymptotically unbiased- estimator
 estimatorType = ot.HSICVStat()

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diff --git a/openturns/master/_downloads/f0f00ddb34d9f84de83c72e139840e77/plot_maximum_distribution.zip b/openturns/master/_downloads/f0f00ddb34d9f84de83c72e139840e77/plot_maximum_distribution.zip
diff --git a/openturns/master/_downloads/f16773021e98cca583d5086726d787c6/plot_optimization_bonmin.zip b/openturns/master/_downloads/f16773021e98cca583d5086726d787c6/plot_optimization_bonmin.zip
diff --git a/openturns/master/_downloads/f2378c8b98e8066aa34824c9f907d085/plot_probabilistic_design.zip b/openturns/master/_downloads/f2378c8b98e8066aa34824c9f907d085/plot_probabilistic_design.zip
diff --git a/openturns/master/_downloads/f40824946e1a848fb8dccfb6e36c15ee/plot_create_threshold_event.zip b/openturns/master/_downloads/f40824946e1a848fb8dccfb6e36c15ee/plot_create_threshold_event.zip
diff --git a/...ns/master/_downloads/f4228a290d3fe75a60b0f4b230184c7c/plot_hsic_estimators_ishigami.ipynb b/...ns/master/_downloads/f4228a290d3fe75a60b0f4b230184c7c/plot_hsic_estimators_ishigami.ipynb
@@ -313,7 +313,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We define the parameters in our function from the output sample\n\n"
+        "We define the empirical parameter values in our function from the output sample\n\n"
       ]
     },
     {
@@ -345,6 +345,24 @@
         "f = ot.SymbolicFunction([\"x\", \"s\"], [\"exp(-x/s)\"])\nphi = ot.ParametricFunction(f, [1], [s])\nfilterFunction = ot.ComposedFunction(phi, dist2criticalDomain)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We modify the output covariance kernel so as to adapt it to the filtered output\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "Y_filtered = filterFunction(Y)\noutputCovariance.setScale(Y_filtered.computeStandardDeviation())\ncovarianceModelCollection[-1] = outputCovariance"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -489,6 +507,24 @@
         "weightFunction = filterFunction"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We revert to the covariance kernel associated to the unfiltered output\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "outputCovariance.setScale(Y.computeStandardDeviation())\ncovarianceModelCollection[-1] = outputCovariance"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

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diff --git a/openturns/master/_downloads/fd77a1f5c8e5345443fc835d6bf2caa2/plot_iterative_threshold.zip b/openturns/master/_downloads/fd77a1f5c8e5345443fc835d6bf2caa2/plot_iterative_threshold.zip
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diff --git a/...s/master/_downloads/ffa68333970578e3e2a3c4af5685642c/plot_viscous_fall_field_function.zip b/...s/master/_downloads/ffa68333970578e3e2a3c4af5685642c/plot_viscous_fall_field_function.zip
diff --git a/openturns/master/_downloads/ffa9d199d7551d68b3799354df3b5bf5/auto_data_analysis_python.zip b/openturns/master/_downloads/ffa9d199d7551d68b3799354df3b5bf5/auto_data_analysis_python.zip
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