From bce706410b1970bb0b9c520b7791fa7d3f244a43 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Thu, 18 Jul 2024 17:23:54 +0200
Subject: [PATCH] [docs] Update user guide to match changed example code

---
 CHANGELOG.md                                  |  28 +++++++++++++++-
 docs/source/bayes_description.rst             |   6 +++-
 examples/user_guide/example_user_guide.py     |   1 +
 public/.doctrees/bayes_description.doctree    | Bin 25020 -> 25890 bytes
 public/.doctrees/bmc_description.doctree      | Bin 14220 -> 14201 bytes
 public/.doctrees/environment.pickle           | Bin 3054356 -> 3057524 bytes
 public/.doctrees/model_description.doctree    | Bin 13067 -> 14616 bytes
 public/_sources/bayes_description.rst.txt     |   8 +++--
 public/_sources/bmc_description.rst.txt       |   2 +-
 public/_sources/model_description.rst.txt     |  14 ++++++--
 public/bayes_description.html                 |   9 +++--
 public/bmc_description.html                   |   2 +-
 public/model_description.html                 |  16 ++++++---
 public/searchindex.js                         |   2 +-
 .../post_processing/post_processing.py        |  31 +++++++-----------
 15 files changed, 83 insertions(+), 36 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 42c974fb5..d1669ae56 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,32 @@
 # CHANGELOG
 
-## [Unreleased]
+## [1.1.0]
+### Requirements
+* numpy now at 1.23.5
+
+### Added
+Features
+* class `SeqDesign` for sequential training
+
+Examples
+* Example `user_guide` to go along with the user guide on the website
+* Example `principal_component_analysis` to show application of pca on metamodel outputs
+* Example 'only_model' for use of inference and model comparison without a metamodel
+
+### Changed
+* Moved functions for sequential training from `Engine` to `SeqDesign`
+* Moved `hellinger_distance`, `logpdf`, `subdomain` into `surrogate_models/seq_design`
+* Early stop in `BayesInf` for `BayesModelComp`
+
+Bug fixes
+* Import of `ExpDesign` allowed
+* Images in `PostProcessing` only saved, not opened
+
+
+### Removed
+* Disabled exploration with `voronoi`
+
+## [1.0.0]
 ### Requirements
 * numpy now at 1.23.3
 * ....
diff --git a/docs/source/bayes_description.rst b/docs/source/bayes_description.rst
index 6dac51ad1..5017bceea 100644
--- a/docs/source/bayes_description.rst
+++ b/docs/source/bayes_description.rst
@@ -48,10 +48,14 @@ Next we define the uncertainty on the observation with the class :any:`bayesvali
 For this example we set the uncertainty to be zero-mean gaussian and dependent on the values in the observation, i.e. larger values have a larger uncertainty associated with them.
 The ``parameters`` contain the variance for each point in the observation.
 
+.. warning::
+   For models with only a single uncertain input parameter, numerical issues can appear when the discrepancy is set only depending on the observed data.
+   To resolve this, a small value can be added to the variance of the discrepancy.
+
 >>> obsData = pd.DataFrame(Model.observations, columns=Model.Output.names)
 >>> DiscrepancyOpts = Discrepancy('')
 >>> DiscrepancyOpts.type = 'Gaussian'
->>> DiscrepancyOpts.parameters = obsData**2
+>>> DiscrepancyOpts.parameters = obsData**2+0.01
 
 Now we can initialize an object of class :any:`bayesvalidrox.bayes_inference.bayes_inference.BayesInference` with all the wanted properties.
 This object has to be given our ``Engine``.
diff --git a/examples/user_guide/example_user_guide.py b/examples/user_guide/example_user_guide.py
index aa583c5fa..aa573997e 100644
--- a/examples/user_guide/example_user_guide.py
+++ b/examples/user_guide/example_user_guide.py
@@ -168,6 +168,7 @@ if __name__ == '__main__':
     Model2.Output.names = ['A']
     Model2.func_args = {'x_values': x_values}
     Model2.store = False
+    Model2.observations = Model.observations
     
     MetaMod2 = MetaModel(Inputs)
     MetaMod2.meta_model_type = 'aPCE'
diff --git a/public/.doctrees/bayes_description.doctree b/public/.doctrees/bayes_description.doctree
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diff --git a/public/.doctrees/model_description.doctree b/public/.doctrees/model_description.doctree
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diff --git a/public/_sources/bayes_description.rst.txt b/public/_sources/bayes_description.rst.txt
index 0cb36d10f..5017bceea 100644
--- a/public/_sources/bayes_description.rst.txt
+++ b/public/_sources/bayes_description.rst.txt
@@ -48,10 +48,14 @@ Next we define the uncertainty on the observation with the class :any:`bayesvali
 For this example we set the uncertainty to be zero-mean gaussian and dependent on the values in the observation, i.e. larger values have a larger uncertainty associated with them.
 The ``parameters`` contain the variance for each point in the observation.
 
+.. warning::
+   For models with only a single uncertain input parameter, numerical issues can appear when the discrepancy is set only depending on the observed data.
+   To resolve this, a small value can be added to the variance of the discrepancy.
+
 >>> obsData = pd.DataFrame(Model.observations, columns=Model.Output.names)
 >>> DiscrepancyOpts = Discrepancy('')
 >>> DiscrepancyOpts.type = 'Gaussian'
->>> DiscrepancyOpts.parameters = obsData**2
+>>> DiscrepancyOpts.parameters = obsData**2+0.01
 
 Now we can initialize an object of class :any:`bayesvalidrox.bayes_inference.bayes_inference.BayesInference` with all the wanted properties.
 This object has to be given our ``Engine``.
@@ -76,7 +80,7 @@ For this example we use the python package ``emcee`` to define the MCMC moves.
 >>> BayesObj.inference_method = 'MCMC'
 >>> import emcee
 >>> BayesObj.mcmc_params = {
->>>     'n_steps': 1e4,#5,
+>>>     'n_steps': 1e4,
 >>>     'n_walkers': 30,
 >>>     'moves': emcee.moves.KDEMove(),
 >>>     'multiprocessing': False,
diff --git a/public/_sources/bmc_description.rst.txt b/public/_sources/bmc_description.rst.txt
index 2ef20019c..3ebd9d525 100644
--- a/public/_sources/bmc_description.rst.txt
+++ b/public/_sources/bmc_description.rst.txt
@@ -79,5 +79,5 @@ Now we can run the full model comparison.
 
 >>> output_dict = BayesOpts.model_comparison_all(meta_models, opts_bootstrap)
 
-The created plots are saved in the folder `Outputs_Comparison`.
+The created plots are saved in the folder ``Outputs_Comparison``.
 
diff --git a/public/_sources/model_description.rst.txt b/public/_sources/model_description.rst.txt
index 923f20a7c..42103caa6 100644
--- a/public/_sources/model_description.rst.txt
+++ b/public/_sources/model_description.rst.txt
@@ -25,9 +25,11 @@ This function takes a single realization of the uncertain parameter as a 2-dimen
 Here we use the key ``A`` for the sample values and ``B`` for their squares.
 Under the key ``x_values`` a list should be given that is of the same length as each output of the model for a single input.
 The values in this list can denote e.g. timesteps and are used in postprocessing as labels of the x-axis.
+If we want to set the ``x_values`` outside of the model, it can also be given as an additional parameter
 
->>> def model(sample):
->>>     square = sample*sample
+>>> def model(samples, x_values):
+>>>     sample = samples[0]*x_values
+>>>     square = np.power(samples[0]*x_values, 2)
 >>>     outputs = {'A': sample, 'B': square, 'x_values': [0]}
 >>>     return outputs
 
@@ -43,7 +45,13 @@ Lastly we list the keys of the outputs that we are interested in.
 >>> Model.link_type = 'Function'
 >>> Model.py_file = 'model'
 >>> Model.name = 'model'
->>> Model.Output.names = ['A', 'B']
+>>> Model.Output.names = ['A']
+
+Any parameters to the model function, that are not the samples, can be set via the ``func_args`` argument.
+In this case we define ``x_values`` as a ``np.array`` and include it.
+
+>>> x_values = np.arange(0,1,0.1)
+>>> Model.func_args = {'x_values':x_values}
 
 With this we have completed an interface to our model.
 We can now evaluate this model on the samples created in the input example.
diff --git a/public/bayes_description.html b/public/bayes_description.html
index 09045e6aa..de6cd6dae 100644
--- a/public/bayes_description.html
+++ b/public/bayes_description.html
@@ -385,10 +385,15 @@ As this expects a 1D-array for each output key, we need to change the format sli
 <p>Next we define the uncertainty on the observation with the class <a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html#bayesvalidrox.bayes_inference.discrepancy.Discrepancy" title="bayesvalidrox.bayes_inference.discrepancy.Discrepancy"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.discrepancy.Discrepancy</span></code></a>.
 For this example we set the uncertainty to be zero-mean gaussian and dependent on the values in the observation, i.e. larger values have a larger uncertainty associated with them.
 The <code class="docutils literal notranslate"><span class="pre">parameters</span></code> contain the variance for each point in the observation.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>For models with only a single uncertain input parameter, numerical issues can appear when the discrepancy is set only depending on the observed data.
+To resolve this, a small value can be added to the variance of the discrepancy.</p>
+</div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">obsData</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">Model</span><span class="o">.</span><span class="n">observations</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">Model</span><span class="o">.</span><span class="n">Output</span><span class="o">.</span><span class="n">names</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">DiscrepancyOpts</span> <span class="o">=</span> <span class="n">Discrepancy</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">DiscrepancyOpts</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="s1">&#39;Gaussian&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">DiscrepancyOpts</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">obsData</span><span class="o">**</span><span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">DiscrepancyOpts</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">obsData</span><span class="o">**</span><span class="mi">2</span><span class="o">+</span><span class="mf">0.01</span>
 </pre></div>
 </div>
 <p>Now we can initialize an object of class <a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.bayes_inference.BayesInference</span></code></a> with all the wanted properties.
@@ -413,7 +418,7 @@ For this example we use the python package <code class="docutils literal notrans
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">inference_method</span> <span class="o">=</span> <span class="s1">&#39;MCMC&#39;</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">emcee</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">mcmc_params</span> <span class="o">=</span> <span class="p">{</span>
-<span class="gp">&gt;&gt;&gt; </span>    <span class="s1">&#39;n_steps&#39;</span><span class="p">:</span> <span class="mf">1e4</span><span class="p">,</span><span class="c1">#5,</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="s1">&#39;n_steps&#39;</span><span class="p">:</span> <span class="mf">1e4</span><span class="p">,</span>
 <span class="gp">&gt;&gt;&gt; </span>    <span class="s1">&#39;n_walkers&#39;</span><span class="p">:</span> <span class="mi">30</span><span class="p">,</span>
 <span class="gp">&gt;&gt;&gt; </span>    <span class="s1">&#39;moves&#39;</span><span class="p">:</span> <span class="n">emcee</span><span class="o">.</span><span class="n">moves</span><span class="o">.</span><span class="n">KDEMove</span><span class="p">(),</span>
 <span class="gp">&gt;&gt;&gt; </span>    <span class="s1">&#39;multiprocessing&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
diff --git a/public/bmc_description.html b/public/bmc_description.html
index f406cd5fc..9d570df35 100644
--- a/public/bmc_description.html
+++ b/public/bmc_description.html
@@ -418,7 +418,7 @@ In this example we use the following settings.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">output_dict</span> <span class="o">=</span> <span class="n">BayesOpts</span><span class="o">.</span><span class="n">model_comparison_all</span><span class="p">(</span><span class="n">meta_models</span><span class="p">,</span> <span class="n">opts_bootstrap</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>The created plots are saved in the folder <cite>Outputs_Comparison</cite>.</p>
+<p>The created plots are saved in the folder <code class="docutils literal notranslate"><span class="pre">Outputs_Comparison</span></code>.</p>
 </section>
 </section>
 
diff --git a/public/model_description.html b/public/model_description.html
index cc6d6cce5..16ff2a7b8 100644
--- a/public/model_description.html
+++ b/public/model_description.html
@@ -360,9 +360,11 @@ We define this model as a function <code class="docutils literal notranslate"><s
 This function takes a single realization of the uncertain parameter as a 2-dimensional <code class="docutils literal notranslate"><span class="pre">np.array</span></code> and returns a dictionary of model results.
 Here we use the key <code class="docutils literal notranslate"><span class="pre">A</span></code> for the sample values and <code class="docutils literal notranslate"><span class="pre">B</span></code> for their squares.
 Under the key <code class="docutils literal notranslate"><span class="pre">x_values</span></code> a list should be given that is of the same length as each output of the model for a single input.
-The values in this list can denote e.g. timesteps and are used in postprocessing as labels of the x-axis.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">model</span><span class="p">(</span><span class="n">sample</span><span class="p">):</span>
-<span class="gp">&gt;&gt;&gt; </span>    <span class="n">square</span> <span class="o">=</span> <span class="n">sample</span><span class="o">*</span><span class="n">sample</span>
+The values in this list can denote e.g. timesteps and are used in postprocessing as labels of the x-axis.
+If we want to set the <code class="docutils literal notranslate"><span class="pre">x_values</span></code> outside of the model, it can also be given as an additional parameter</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">model</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">x_values</span><span class="p">):</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">sample</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">x_values</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">square</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">x_values</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span>    <span class="n">outputs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="n">sample</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="n">square</span><span class="p">,</span> <span class="s1">&#39;x_values&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">]}</span>
 <span class="gp">&gt;&gt;&gt; </span>    <span class="k">return</span> <span class="n">outputs</span>
 </pre></div>
@@ -378,7 +380,13 @@ Lastly we list the keys of the outputs that we are interested in.</p>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Model</span><span class="o">.</span><span class="n">link_type</span> <span class="o">=</span> <span class="s1">&#39;Function&#39;</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Model</span><span class="o">.</span><span class="n">py_file</span> <span class="o">=</span> <span class="s1">&#39;model&#39;</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;model&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">Model</span><span class="o">.</span><span class="n">Output</span><span class="o">.</span><span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Model</span><span class="o">.</span><span class="n">Output</span><span class="o">.</span><span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>Any parameters to the model function, that are not the samples, can be set via the <code class="docutils literal notranslate"><span class="pre">func_args</span></code> argument.
+In this case we define <code class="docutils literal notranslate"><span class="pre">x_values</span></code> as a <code class="docutils literal notranslate"><span class="pre">np.array</span></code> and include it.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Model</span><span class="o">.</span><span class="n">func_args</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x_values&#39;</span><span class="p">:</span><span class="n">x_values</span><span class="p">}</span>
 </pre></div>
 </div>
 <p>With this we have completed an interface to our model.
diff --git a/public/searchindex.js b/public/searchindex.js
index 65862bbbd..8fe061015 100644
--- a/public/searchindex.js
+++ b/public/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"API": [[71, "api"]], "Active learning: iteratively expanding the training set": [[69, "active-learning-iteratively-expanding-the-training-set"]], "Arguments": [[42, "arguments"], [42, "id2"], [60, "arguments"]], "Attributes": [[3, "attributes"], [5, "attributes"], [7, "attributes"], [9, "attributes"], [13, "attributes"], [16, "attributes"], [25, "attributes"], [26, "attributes"], [39, "attributes"], [42, "attributes"], [47, "attributes"], [49, "attributes"], [50, "attributes"], [52, "attributes"], [55, "attributes"], [58, "attributes"], [65, "attributes"]], "Bayesian Inference": [[87, "bayesian-inference"]], "Bayesian inference": [[72, "bayesian-inference"]], "Bayesian multi-model comparison": [[74, "bayesian-multi-model-comparison"]], "Contribution": [[77, "contribution"]], "Define probabilistic input model": [[87, "define-probabilistic-input-model"]], "Define surrogate (meta) model": [[87, "define-surrogate-meta-model"]], "Define the data uncertainty": [[87, "define-the-data-uncertainty"]], "Define the model with PyLinkForwardModel": [[87, "define-the-model-with-pylinkforwardmodel"]], "Discrepancy": [[70, "id3"], [73, "id3"]], "EXAMPLES": [[76, "examples"]], "Example": [[69, "example"], [72, "example"], [74, "example"], [78, "example"], [80, "example"], [85, "example"], [86, "example"]], "Example: Analytical function": [[70, "example-analytical-function"]], "Example: OHagan-function": [[82, "example-ohagan-function"]], "Example: beam": [[73, "example-beam"]], "Example: borehole": [[75, "example-borehole"]], "Example: ishigami": [[79, "example-ishigami"]], "Example: model comparison": [[81, "example-model-comparison"]], "Example: pollution": [[84, "example-pollution"]], "Examples": [[49, "examples"]], "Exploration, exploitation and tradeoff": [[69, "exploration-exploitation-and-tradeoff"]], "Further contents": [[77, "further-contents"]], "Import necessary libraries": [[87, "import-necessary-libraries"]], "Indices and 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\ No newline at end of file
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"util_alphoptdesign": 60, "util_bayesianactivedesign": 60, "util_bayesiandesign": 60, "util_func": [39, 60, 69, 87], "util_varbaseddesign": 60, "utilii": 39, "v": [3, 22, 24, 25, 26, 39, 47, 52, 55, 65], "v1": 33, "valid": [3, 13, 16, 39, 47, 65, 66, 69, 78, 83, 85, 87], "valid_error": 13, "valid_metamodel": [13, 85, 87], "valid_metr": 3, "valid_model_run": 87, "valid_sampl": 87, "validlikelihoods_": 87, "valu": [3, 5, 7, 9, 10, 16, 17, 24, 25, 26, 34, 35, 45, 52, 55, 56, 58, 65, 66, 68, 70, 72, 73, 78, 80, 86, 87], "valueerror": 67, "var": 60, "var_hat": [25, 26, 55, 58], "var_pca_threshold": 65, "var_pr": [24, 25, 26], "varbasedoptdesign": 39, "variabl": [25, 26, 52, 55, 58, 82, 87], "varianc": [3, 7, 10, 24, 25, 26, 55, 58, 65, 68, 69, 70, 72, 73, 82, 84], "variant": 86, "variat": [24, 26, 65, 86], "varidx": [65, 68], "varieti": 80, "variou": [65, 87], "varoptdesign": [39, 60, 69], "vbl": [65, 86], "vector": [25, 26, 65], "venv": 83, "verbos": [3, 9, 16, 24, 25, 26, 29, 52, 55, 58, 60, 65, 72, 87], "veri": [25, 80], "versa": [30, 61], "version": [24, 25, 26, 42, 52, 55, 65, 75, 77, 79, 83, 84], "via": [5, 7, 9, 39, 47, 65, 72, 73, 75, 78, 79, 80, 82, 83, 86, 87], "vice": [30, 61], "view": 65, "virtual": 83, "visual": [5, 39, 72, 78, 83, 85, 87], "voronoi": [39, 42, 69, 87], "vulner": 33, "w": [9, 10, 13, 22, 24, 25, 26, 42, 52, 55], "wa": 65, "wai": [60, 65, 86, 87], "walker": [3, 72], "want": [72, 77, 78, 80, 85, 86, 87], "warranti": [75, 79, 84], "wavelet": 52, "wb": 87, "we": [7, 25, 69, 72, 74, 75, 76, 77, 79, 80, 84, 85, 86, 87], "wear": 9, "websit": [], "weibul": 78, "weight": [5, 10, 24, 25, 26, 39, 52, 55, 58, 60, 69, 74, 83], "well": [10, 24, 25, 26, 52, 55, 72, 74], "were": 85, "what": [3, 7, 76, 86], "when": [3, 7, 9, 13, 16, 24, 25, 26, 30, 39, 52, 55, 58, 61, 72, 78, 87], "where": [5, 7, 24, 25, 26, 34, 35, 39, 44, 52, 55, 65, 78, 87], "whether": [5, 16, 39, 52, 55, 58], "which": [3, 16, 22, 24, 25, 26, 30, 58, 61, 72, 87], "while": [45, 69, 83, 85, 86, 87], "width": 73, "wiener": 37, "wight": 5, "wish": 16, "witch": 44, "within": [10, 16, 74, 76], "without": [16, 69, 73, 75, 77, 79, 84, 87], "wolfgang": 60, "word": 16, "work": [16, 24, 25, 26, 52, 55, 75, 77, 79, 84, 87], "workflow": [77, 87], "wors": [24, 25, 26, 52, 55], "would": [24, 25, 26, 52, 55, 77], "wrapper": 16, "write": 9, "written": [16, 72], "wrong": 60, "www": [22, 33, 55, 58, 75, 79, 84], "x": [13, 16, 24, 25, 26, 31, 34, 35, 36, 39, 44, 47, 52, 53, 55, 58, 62, 65, 68, 78, 80, 86, 87], "x1": 16, "x_1": [49, 50, 79, 87], "x_2": [79, 87], "x_3": 79, "x_axi": 13, "x_can": 60, "x_i": [44, 86], "x_mc": 60, "x_new": 60, "x_valu": [16, 20, 72, 74, 77, 80, 86], "xi": 16, "xiu": 87, "xlabel": 13, "xnew": 60, "xx": 16, "xxa": 55, "xya": 55, "y": [7, 9, 24, 25, 26, 39, 52, 53, 55, 65, 66, 68], "y_hat": [25, 26, 52, 55, 58, 60], "y_i": 86, "y_pc_val": 20, "y_pc_val_std": 20, "y_pred": [24, 25, 26, 52, 55, 65], "y_true": [24, 25, 26, 52, 55], "y_val": 20, "ye": [16, 70, 73, 84], "yet": 10, "yew": 60, "you": [7, 13, 16, 24, 25, 26, 52, 55, 71, 75, 77, 79, 81, 82, 84], "young": 73, "your": [13, 75, 79, 82, 83], "yr": [75, 79], "z": [77, 87], "zero": [30, 52, 55, 58, 61, 72], "zeromean": 7, "zip": 16, "zip_subdir": 16, "zurich": 33}, "titles": ["bayesvalidrox", "bayesvalidrox.bayes_inference", "bayesvalidrox.bayes_inference.bayes_inference", "bayesvalidrox.bayes_inference.bayes_inference.BayesInference", "bayesvalidrox.bayes_inference.bayes_model_comparison", "bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison", "bayesvalidrox.bayes_inference.discrepancy", "bayesvalidrox.bayes_inference.discrepancy.Discrepancy", "bayesvalidrox.bayes_inference.mcmc", "bayesvalidrox.bayes_inference.mcmc.MCMC", "bayesvalidrox.bayes_inference.mcmc.gelman_rubin", "bayesvalidrox.post_processing", "bayesvalidrox.post_processing.post_processing", "bayesvalidrox.post_processing.post_processing.PostProcessing", "bayesvalidrox.pylink", "bayesvalidrox.pylink.pylink", "bayesvalidrox.pylink.pylink.PyLinkForwardModel", "bayesvalidrox.pylink.pylink.within_range", "bayesvalidrox.surrogate_models", "bayesvalidrox.surrogate_models.adaptPlot", "bayesvalidrox.surrogate_models.adaptPlot.adaptPlot", "bayesvalidrox.surrogate_models.apoly_construction", "bayesvalidrox.surrogate_models.apoly_construction.apoly_construction", "bayesvalidrox.surrogate_models.bayes_linear", "bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression", "bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression", "bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression", "bayesvalidrox.surrogate_models.bayes_linear.gamma_mean", "bayesvalidrox.surrogate_models.engine", "bayesvalidrox.surrogate_models.engine.Engine", "bayesvalidrox.surrogate_models.engine.hellinger_distance", "bayesvalidrox.surrogate_models.engine.logpdf", "bayesvalidrox.surrogate_models.engine.subdomain", "bayesvalidrox.surrogate_models.eval_rec_rule", "bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule", "bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary", "bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis", "bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs", "bayesvalidrox.surrogate_models.exp_designs", "bayesvalidrox.surrogate_models.exp_designs.ExpDesigns", "bayesvalidrox.surrogate_models.exp_designs.check_ranges", "bayesvalidrox.surrogate_models.exploration", "bayesvalidrox.surrogate_models.exploration.Exploration", "bayesvalidrox.surrogate_models.glexindex", "bayesvalidrox.surrogate_models.glexindex.cross_truncate", "bayesvalidrox.surrogate_models.glexindex.glexindex", "bayesvalidrox.surrogate_models.input_space", "bayesvalidrox.surrogate_models.input_space.InputSpace", "bayesvalidrox.surrogate_models.inputs", "bayesvalidrox.surrogate_models.inputs.Input", "bayesvalidrox.surrogate_models.inputs.Marginal", "bayesvalidrox.surrogate_models.orthogonal_matching_pursuit", "bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit", "bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr", "bayesvalidrox.surrogate_models.reg_fast_ard", "bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD", "bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions", "bayesvalidrox.surrogate_models.reg_fast_laplace", "bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace", "bayesvalidrox.surrogate_models.sequential_design", "bayesvalidrox.surrogate_models.sequential_design.SequentialDesign", "bayesvalidrox.surrogate_models.sequential_design.hellinger_distance", "bayesvalidrox.surrogate_models.sequential_design.logpdf", "bayesvalidrox.surrogate_models.sequential_design.subdomain", "bayesvalidrox.surrogate_models.surrogate_models", "bayesvalidrox.surrogate_models.surrogate_models.MetaModel", "bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error", "bayesvalidrox.surrogate_models.surrogate_models.create_psi", "bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator", "Active learning: iteratively expanding the training set", "Example: Analytical function", "API", "Bayesian inference", "Example: beam", "Bayesian multi-model comparison", "Example: borehole", "EXAMPLES", "Surrogate-assisted\u00a0Bayesian validation of computational models", "Priors, input space and experimental design", "Example: ishigami", "Models", "Example: model comparison", "Example: OHagan-function", "USER GUIDE", "Example: pollution", "Postprocessing", "Training surrogate models", "TUTORIAL"], "titleterms": {"1": 81, "3": 81, "activ": 69, "adaptplot": [19, 20], "also": [], "an": 87, "analyt": 70, "api": 71, "apoly_construct": [21, 22], "argument": [42, 60], "assist": 77, "attribut": [3, 5, 7, 9, 13, 16, 25, 26, 39, 42, 47, 49, 50, 52, 55, 58, 65], "bay": [], "bayes_infer": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "bayes_linear": [23, 24, 25, 26, 27], "bayes_model_comparison": [4, 5], "bayesian": [72, 74, 77, 87], "bayesianlinearregress": 24, "bayesinfer": 3, "bayesmodelcomparison": 5, "bayesvalidrox": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68], "beam": 73, "borehol": 75, "check_rang": 40, "choic": [70, 73, 75, 79, 81, 82, 84], "class": [], "comparison": [74, 81], "comput": 77, "contact": [], "content": 77, "contribut": 77, "corr": 53, "corr_loocv_error": 66, "create_psi": 67, "cross_trunc": 44, "data": [70, 73, 75, 79, 82, 84, 87], "defin": 87, "descript": [], "design": [78, 87], "discrep": [6, 7, 70, 73], "eblinearregress": 25, "engin": [28, 29, 30, 31, 32, 86, 87], "eval_rec_rul": [33, 34, 35, 36, 37], "eval_rec_rule_arbitrari": 35, "eval_univ_basi": 36, "exampl": [49, 69, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 84, 85, 86], "exp_design": [38, 39, 40], "expand": 69, "expdesign": 39, "experiment": [78, 87], "exploit": 69, "explor": [41, 42, 69], "function": [70, 82], "further": 77, "gamma_mean": 27, "gaussian_process_emul": 68, "gelman_rubin": 10, "glexindex": [43, 44, 45], "guid": 83, "hellinger_dist": [30, 61], "import": 87, "indic": 77, "infer": [72, 87], "input": [48, 49, 50, 78, 87], "input_spac": [46, 47], "inputspac": 47, "instal": [77, 83], "introductori": [], "ishigami": 79, "iter": 69, "l2_model": 81, "learn": 69, "librari": 87, "licens": 77, "link": 77, "logpdf": [31, 62], "margin": 50, "mcmc": [8, 9, 10], "meta": 87, "meta_model_engin": [], "metamodel": [65, 70, 73, 75, 79, 81, 82, 84, 86], "model": [70, 73, 74, 75, 77, 79, 80, 81, 82, 84, 86, 87], "model1": 81, "multi": 74, "necessari": 87, "nl2_model": 81, "nl4_model": 81, "note": [24, 25, 26, 39, 52, 55, 65], "ohagan": 82, "option": 86, "orthogonal_matching_pursuit": [51, 52, 53], "orthogonalmatchingpursuit": 52, "overview": 83, "packag": [], "paramet": [3, 5, 7, 9, 10, 13, 16, 17, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 47, 52, 55, 58, 60, 61, 62, 63, 65, 66, 67, 68], "pollut": 84, "poly_rec_coeff": 37, "post": 87, "post_process": [11, 12, 13], "postprocess": [13, 85], "prior": [70, 73, 75, 78, 79, 82, 84], "priors1": 81, "probabilist": 87, "process": 87, "pylink": [14, 15, 16, 17, 70, 73, 75, 79, 81, 82, 84], "pylinkforwardmodel": [16, 87], "quickstart": 77, "rais": [13, 60, 67], "refer": [52, 55, 58], "reg_fast_ard": [54, 55, 56], "reg_fast_laplac": [57, 58], "regressionfastard": 55, "regressionfastlaplac": 58, "return": [3, 5, 7, 9, 10, 13, 16, 17, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 42, 47, 49, 52, 55, 58, 60, 61, 62, 63, 65, 66, 67, 68], "see": [], "sequenti": 87, "sequential_design": [59, 60, 61, 62, 63], "sequentialdesign": 60, "set": [69, 70, 73, 75, 79, 81, 82, 84, 87], "space": 78, "subdomain": [32, 63], "surrog": [70, 73, 75, 77, 79, 81, 82, 84, 86, 87], "surrogate_model": [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68], "tabl": 77, "theori": [], "tradeoff": 69, "train": [69, 70, 73, 75, 79, 81, 82, 84, 86, 87], "tutori": 87, "uncertainti": 87, "update_precis": 56, "user": 83, "valid": 77, "vblinearregress": 26, "within_rang": 17}})
\ No newline at end of file
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 50b32dbea..795371651 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -437,7 +437,7 @@ class PostProcessing:
                     bbox_inches='tight'
                     )
                 # Destroy the current plot
-                plt.clf()
+                plt.close()
                 # Save arrays into files
                 f = open(f'./{newpath}/seq_{plot_name}.txt', 'w')
                 f.write(str(sorted_seq_opt))
@@ -523,7 +523,7 @@ class PostProcessing:
                     bbox_inches='tight'
                     )
                 # Destroy the current plot
-                plt.clf()
+                plt.close()
 
                 # ---------------- Saving arrays into files ---------------
                 np.save(f'./{newpath}/seq_{plot_name}.npy', seq_values)
@@ -830,7 +830,7 @@ class PostProcessing:
                 )
 
             # Destroy the current plot
-            plt.clf()
+            plt.close()
 
         return self.total_sobol
 
@@ -890,13 +890,12 @@ class PostProcessing:
                            lw=3, linestyle='--')
                 plt.xlabel(par)
                 plt.ylabel('Residuals')
-                plt.show()
 
                 # save the current figure
                 fig1.savefig(f'./{newpath}/Residuals_vs_Par_{i+1}.pdf',
                              bbox_inches='tight')
                 # Destroy the current plot
-                plt.clf()
+                plt.close()
 
             # ------ Fitted vs. residuals ------
             # Check the assumptions of linearity and independence
@@ -909,13 +908,12 @@ class PostProcessing:
                        linestyle='--')
             plt.xlabel(key)
             plt.ylabel('Residuals')
-            plt.show()
 
             # save the current figure
             fig2.savefig(f'./{newpath}/Fitted_vs_Residuals.pdf',
                          bbox_inches='tight')
             # Destroy the current plot
-            plt.clf()
+            plt.close()
 
             # ------ Histogram of normalized residuals ------
             fig3 = plt.figure()
@@ -937,13 +935,11 @@ class PostProcessing:
             at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
             ax.add_artist(at)
 
-            plt.show()
-
             # save the current figure
             fig3.savefig(f'./{newpath}/Hist_NormResiduals.pdf',
                          bbox_inches='tight')
             # Destroy the current plot
-            plt.clf()
+            plt.close()
 
             # ------ Q-Q plot of the normalized residuals ------
             plt.figure()
@@ -954,13 +950,12 @@ class PostProcessing:
             plt.ylabel("Sample quantiles")
             plt.title(f"{key}: Q-Q plot of normalized residuals")
             plt.grid(True)
-            plt.show()
 
             # save the current figure
             plt.savefig(f'./{newpath}/QQPlot_NormResiduals.pdf',
                         bbox_inches='tight')
             # Destroy the current plot
-            plt.clf()
+            plt.close()
 
     # -------------------------------------------------------------------------
     def eval_pce_model_3d(self):
@@ -1023,7 +1018,6 @@ class PostProcessing:
         ax.set_zlabel('$f(x_1,x_2)$')
 
         plt.grid()
-        plt.show()
 
         #  Saving the figure
         newpath = f'Outputs_PostProcessing_{self.name}/'
@@ -1044,10 +1038,8 @@ class PostProcessing:
         ax.set_xlabel('$x_1$')
         ax.set_ylabel('$x_2$')
         ax.set_zlabel('$f(x_1,x_2)$')
-
         plt.grid()
-        plt.show()
-
+        
         # Save the figure
         fig_Model.savefig(f'./{newpath}/3DPlot_Model.pdf',
                           bbox_inches='tight')
@@ -1252,15 +1244,14 @@ class PostProcessing:
             plt.ylabel("Original Model")
             plt.xlabel("PCE Model")
             plt.grid()
-            plt.show()
-
+            
             # save the current figure
             plot_name = key.replace(' ', '_')
             fig.savefig(f'./{newpath}/Model_vs_PCEModel_{plot_name}.pdf',
                         bbox_inches='tight')
 
             # Destroy the current plot
-            plt.clf()
+            plt.close()
 
     # -------------------------------------------------------------------------
     def _plot_validation_multi(self, x_values=[], x_axis="x [m]"):
@@ -1335,7 +1326,7 @@ class PostProcessing:
                         bbox_inches='tight')
 
             # Destroy the current plot
-            plt.clf()
+            plt.close()
 
         # Zip the subdirectories
         Model.zip_subdirs(f'{Model.name}valid', f'{Model.name}valid_')
-- 
GitLab