diff --git a/docs/Makefile b/docs/Makefile
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index d0c3cbf1020d5c292abdedf27627c6abe25e2293..0000000000000000000000000000000000000000
--- a/docs/Makefile
+++ /dev/null
@@ -1,20 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS ?=
-SPHINXBUILD ?= sphinx-build
-SOURCEDIR = source
-BUILDDIR = build
-
-# Put it first so that "make" without argument is like "make help".
-help:
- @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
- @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/build/doctrees/discrepancy_GP.doctree b/docs/build/doctrees/discrepancy_GP.doctree
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diff --git a/docs/build/doctrees/mcmc.doctree b/docs/build/doctrees/mcmc.doctree
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diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo
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index 145138a4d50656fe26087aff4d8dc812e5870eaf..0000000000000000000000000000000000000000
--- a/docs/build/html/.buildinfo
+++ /dev/null
@@ -1,4 +0,0 @@
-# Sphinx build info version 1
-# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: c83e204b85f6ea5688aa5e0f5b58147d
-tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/build/html/_sources/discrepancy_GP.md.txt b/docs/build/html/_sources/discrepancy_GP.md.txt
deleted file mode 100644
index 94cff5234c8e04cb1658d0fbcbb1cdf56a7c5776..0000000000000000000000000000000000000000
--- a/docs/build/html/_sources/discrepancy_GP.md.txt
+++ /dev/null
@@ -1,69 +0,0 @@
-<!-- markdownlint-disable -->
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP.py#L0"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-# <kbd>module</kbd> `discrepancy_GP`
-Created on Sun Nov 7 09:42:33 2021
-
-@author: farid
-
-
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP.py#L14"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-## <kbd>class</kbd> `Bias`
-
-
-
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP.py#L15"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `__init__`
-
-```python
-__init__(verbose=False)
-```
-
-
-
-
-
-
-
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP.py#L18"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `fit_bias`
-
-```python
-fit_bias(EDX, ED_Y, Data)
-```
-
-
-
-
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP.py#L73"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `predict`
-
-```python
-predict(X, Output, return_std=True)
-```
-
-
-
-
-
-
-
-
----
-
-_This file was automatically generated via [lazydocs](https://github.com/ml-tooling/lazydocs)._
diff --git a/docs/build/html/_sources/discrepancy_GP_v1.md.txt b/docs/build/html/_sources/discrepancy_GP_v1.md.txt
deleted file mode 100644
index 67ec6b8b804b2b99d7bf4b5cb8091d76f593b98e..0000000000000000000000000000000000000000
--- a/docs/build/html/_sources/discrepancy_GP_v1.md.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-<!-- markdownlint-disable -->
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP_v1.py#L0"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-# <kbd>module</kbd> `discrepancy_GP_v1`
-Created on Sun Nov 7 09:42:33 2021
-
-@author: farid
-
-
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP_v1.py#L14"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-## <kbd>class</kbd> `Bias`
-
-
-
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP_v1.py#L15"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `__init__`
-
-```python
-__init__(verbose=False)
-```
-
-
-
-
-
-
-
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP_v1.py#L18"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `fit_bias`
-
-```python
-fit_bias(BiasInputs, ED_Y, Data)
-```
-
-
-
-
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP_v1.py#L74"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `oldpredict`
-
-```python
-oldpredict(Y, Output, BiasInputs=None, return_cov=True)
-```
-
-
-
-
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/discrepancy_GP_v1.py#L81"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `predict`
-
-```python
-predict(Y, Output, BiasInputs=None, return_cov=True)
-```
-
-
-
-
-
-
-
-
----
-
-_This file was automatically generated via [lazydocs](https://github.com/ml-tooling/lazydocs)._
diff --git a/docs/build/html/_sources/example.md.txt b/docs/build/html/_sources/example.md.txt
deleted file mode 100644
index 65b132ad46d79feeb1fa62b90d91aa77f9d55fb1..0000000000000000000000000000000000000000
--- a/docs/build/html/_sources/example.md.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-# My nifty title
-
-Some **text**!
-
-```{admonition} Here's my title
-:class: warning
-
-Here's my admonition content
-```
diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt
deleted file mode 100644
index e53f4bfb9f37de2bbb9718c532a55ccc142b1ea5..0000000000000000000000000000000000000000
--- a/docs/build/html/_sources/index.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-.. bayesvalidrox documentation master file, created by
- sphinx-quickstart on Fri Mar 11 08:09:15 2022.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-Welcome to bayesvalidrox's documentation!
-=========================================
-
-.. toctree::
- example.md
- pylink.html
- :maxdepth: 2
- :caption: Contents:
-
-
-This is a normal text paragraph. The next paragraph is a code sample::
-
- It is not processed in any way, except
- that the indentation is removed.
-
- It can span multiple lines.
-
-This is a normal text paragraph again.
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-* :ref:`pylink`
diff --git a/docs/build/html/_sources/mcmc.md.txt b/docs/build/html/_sources/mcmc.md.txt
deleted file mode 100644
index f78d0d2ad78d2cb3f2cf2b3975e5ed79583b9c30..0000000000000000000000000000000000000000
--- a/docs/build/html/_sources/mcmc.md.txt
+++ /dev/null
@@ -1,187 +0,0 @@
-<!-- markdownlint-disable -->
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L0"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-# <kbd>module</kbd> `mcmc`
-MCMC class for Bayes inference with emcee package using an Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler [1].
-
-1. Foreman-Mackey, D., Hogg, D.W., Lang, D. and Goodman, J., 2013. emcee: the MCMC hammer. Publications of the Astronomical Society of the Pacific, 125(925), p.306. https://emcee.readthedocs.io/en/stable/
-
-Author: Farid Mohammadi, M.Sc. E-Mail: farid.mohammadi@iws.uni-stuttgart.de Department of Hydromechanics and Modelling of Hydrosystems (LH2) Institute for Modelling Hydraulic and Environmental Systems (IWS), University of Stuttgart, www.iws.uni-stuttgart.de/lh2/ Pfaffenwaldring 61 70569 Stuttgart
-
-Created on Wed Jun 3 2020
-
-
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L36"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-## <kbd>class</kbd> `MCMC`
-A class for bayesian inference using a Markov-Chain Monte-Carlo Sampler.
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L41"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `__init__`
-
-```python
-__init__(BayesOpts)
-```
-
-
-
-
-
-
-
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L543"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `eval_model`
-
-```python
-eval_model(theta)
-```
-
-Evaluates the (meta-) model at the given theta.
-
-Parameters
----------- theta : array of shape (n_samples, n_params) Parameter set, i.e. proposals of the MCMC chains.
-
-Returns
-------- mean_pred : dict Mean model prediction. std_pred : dict Std of model prediction.
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L650"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `gelman_rubin`
-
-```python
-gelman_rubin(chain, return_var=False)
-```
-
-The potential scale reduction factor (PSRF) defined by the variance within one chain, W, with the variance between chains B. Both variances are combined in a weighted sum to obtain an estimate of the variance of a parameter θ.The square root of the ratio of this estimates variance to the within chain variance is called the potential scale reduction. For a well converged chain it should approach 1. Values greater than typically 1.1 indicate that the chains have not yet fully converged.
-
-Source: http://joergdietrich.github.io/emcee-convergence.html
-
-https://github.com/jwalton3141/jwalton3141.github.io/blob/master/assets/posts/ESS/rwmh.py
-
-Parameters
----------- chain : array (n_walkers, n_steps, n_params) DESCRIPTION.
-
-Returns
-------- R_hat : float The Gelman-Robin values.
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L446"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `log_likelihood`
-
-```python
-log_likelihood(theta)
-```
-
-Computes likelihood p(y|theta, obs) of the performance of the (meta-)model in reproducing the observation data.
-
-Parameters
----------- theta : array of shape (n_samples, n_params) Parameter set, i.e. proposals of the MCMC chains.
-
-Returns
-------- log_like : array of shape (n_samples) Log likelihood.
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L495"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `log_posterior`
-
-```python
-log_posterior(theta)
-```
-
-Computes the posterior likelihood p(theta| obs) for the given parameterset.
-
-Parameters
----------- theta : array of shape (n_samples, n_params) Parameter set, i.e. proposals of the MCMC chains.
-
-Returns
-------- log_like : array of shape (n_samples) Log posterior likelihood.
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L381"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `log_prior`
-
-```python
-log_prior(theta)
-```
-
-Calculates the log prior likelihood for the given parameter set(s) theta.
-
-Parameters
----------- theta : array of shape (n_samples, n_params) Parameter sets, i.e. proposals of MCMC chains.
-
-Returns
-------- logprior: float or array of shape n_samples Log prior likelihood. If theta has only one row, a single value is returned otherwise an array.
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L700"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `marginal_llk_emcee`
-
-```python
-marginal_llk_emcee(sampler, nburn=None, logp=None, maxiter=1000)
-```
-
-The Bridge Sampling Estimator of the Marginal Likelihood based on https://gist.github.com/junpenglao/4d2669d69ddfe1d788318264cdcf0583
-
-Parameters
----------- sampler : TYPE MultiTrace, result of MCMC run. nburn : int, optional Number of burn-in step. The default is None. logp : TYPE, optional Model Log-probability function. The default is None. maxiter : int, optional Maximum number of iterations. The default is 1000.
-
-Returns
-------- marg_llk : dict Estimated Marginal log-Likelihood.
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L45"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `run_sampler`
-
-```python
-run_sampler(observation, total_sigma2)
-```
-
-
-
-
-
----
-
-<a href="../src/bayesvalidrox/bayes_inference/mcmc.py#L604"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
-
-### <kbd>method</kbd> `train_error_model`
-
-```python
-train_error_model(sampler)
-```
-
-Trains an error model using a Gaussian Process Regression.
-
-Parameters
----------- sampler : obj emcee sampler.
-
-Returns
-------- error_MetaModel : obj A error model.
-
-
-
-
----
-
-_This file was automatically generated via [lazydocs](https://github.com/ml-tooling/lazydocs)._
diff --git a/docs/build/html/_static/alabaster.css b/docs/build/html/_static/alabaster.css
deleted file mode 100644
index 0eddaeb07d19bffb50884d7bd7996418b6461a09..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/alabaster.css
+++ /dev/null
@@ -1,701 +0,0 @@
-@import url("basic.css");
-
-/* -- page layout ----------------------------------------------------------- */
-
-body {
- font-family: Georgia, serif;
- font-size: 17px;
- background-color: #fff;
- color: #000;
- margin: 0;
- padding: 0;
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-div.document {
- width: 940px;
- margin: 30px auto 0 auto;
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- margin: 0 0 0 220px;
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- font-size: 14px;
- line-height: 1.5;
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- border: 1px solid #B1B4B6;
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-div.body {
- background-color: #fff;
- color: #3E4349;
- padding: 0 30px 0 30px;
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-div.body > .section {
- text-align: left;
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- width: 940px;
- margin: 20px auto 30px auto;
- font-size: 14px;
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deleted file mode 100644
index bf18350b65c61f31b2f9f717c03e02f17c0ab4f1..0000000000000000000000000000000000000000
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- margin-bottom: 0px;
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-
-ol.simple ol p,
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- margin-top: 0;
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- margin-top: 0;
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- margin-bottom: 0;
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-dl.footnote > dt,
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- float: left;
- margin-right: 0.5em;
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- margin-bottom: 0em;
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- padding-right: 5px;
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- content: ":";
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- padding-left: 0.5em;
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- margin-bottom: 15px;
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- margin-left: 30px;
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-.versionmodified {
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-.system-message {
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-.footnote:target {
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-.line-block {
- display: block;
- margin-top: 1em;
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-.guilabel, .menuselection {
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-.accelerator {
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-.classifier {
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-.classifier:before {
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- margin: 0 0.5em;
- content: ":";
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- border-bottom: dotted 1px;
- cursor: help;
-}
-
-/* -- code displays --------------------------------------------------------- */
-
-pre {
- overflow: auto;
- overflow-y: hidden; /* fixes display issues on Chrome browsers */
-}
-
-pre, div[class*="highlight-"] {
- clear: both;
-}
-
-span.pre {
- -moz-hyphens: none;
- -ms-hyphens: none;
- -webkit-hyphens: none;
- hyphens: none;
- white-space: nowrap;
-}
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-div[class*="highlight-"] {
- margin: 1em 0;
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-td.linenos pre {
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- background-color: transparent;
- color: #aaa;
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-}
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-.highlight .hll {
- display: block;
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- margin-top: 0;
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- padding: 2px 5px;
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- background-color: transparent;
-}
-
-table.highlighttable td.linenos,
-span.linenos,
-div.highlight span.gp { /* gp: Generic.Prompt */
- user-select: none;
- -webkit-user-select: text; /* Safari fallback only */
- -webkit-user-select: none; /* Chrome/Safari */
- -moz-user-select: none; /* Firefox */
- -ms-user-select: none; /* IE10+ */
-}
-
-div.code-block-caption span.caption-number {
- padding: 0.1em 0.3em;
- font-style: italic;
-}
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-}
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-div.literal-block-wrapper {
- margin: 1em 0;
-}
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-code.xref, a code {
- background-color: transparent;
- font-weight: bold;
-}
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-h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
- background-color: transparent;
-}
-
-.viewcode-link {
- float: right;
-}
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-.viewcode-back {
- float: right;
- font-family: sans-serif;
-}
-
-div.viewcode-block:target {
- margin: -1px -10px;
- padding: 0 10px;
-}
-
-/* -- math display ---------------------------------------------------------- */
-
-img.math {
- vertical-align: middle;
-}
-
-div.body div.math p {
- text-align: center;
-}
-
-span.eqno {
- float: right;
-}
-
-span.eqno a.headerlink {
- position: absolute;
- z-index: 1;
-}
-
-div.math:hover a.headerlink {
- visibility: visible;
-}
-
-/* -- printout stylesheet --------------------------------------------------- */
-
-@media print {
- div.document,
- div.documentwrapper,
- div.bodywrapper {
- margin: 0 !important;
- width: 100%;
- }
-
- div.sphinxsidebar,
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- display: none;
- }
-}
\ No newline at end of file
diff --git a/docs/build/html/_static/custom.css b/docs/build/html/_static/custom.css
deleted file mode 100644
index 2a924f1d6a8bc930c5296bdb2d5c2d3e39b04a1c..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/custom.css
+++ /dev/null
@@ -1 +0,0 @@
-/* This file intentionally left blank. */
diff --git a/docs/build/html/_static/doctools.js b/docs/build/html/_static/doctools.js
deleted file mode 100644
index e509e48349c55c7bbf1015cdf9721915cd357370..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/doctools.js
+++ /dev/null
@@ -1,326 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for all documentation.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-/**
- * make the code below compatible with browsers without
- * an installed firebug like debugger
-if (!window.console || !console.firebug) {
- var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
- "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
- "profile", "profileEnd"];
- window.console = {};
- for (var i = 0; i < names.length; ++i)
- window.console[names[i]] = function() {};
-}
- */
-
-/**
- * small helper function to urldecode strings
- *
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
- */
-jQuery.urldecode = function(x) {
- if (!x) {
- return x
- }
- return decodeURIComponent(x.replace(/\+/g, ' '));
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
- if (typeof s === 'undefined')
- s = document.location.search;
- var parts = s.substr(s.indexOf('?') + 1).split('&');
- var result = {};
- for (var i = 0; i < parts.length; i++) {
- var tmp = parts[i].split('=', 2);
- var key = jQuery.urldecode(tmp[0]);
- var value = jQuery.urldecode(tmp[1]);
- if (key in result)
- result[key].push(value);
- else
- result[key] = [value];
- }
- return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
- function highlight(node, addItems) {
- if (node.nodeType === 3) {
- var val = node.nodeValue;
- var pos = val.toLowerCase().indexOf(text);
- if (pos >= 0 &&
- !jQuery(node.parentNode).hasClass(className) &&
- !jQuery(node.parentNode).hasClass("nohighlight")) {
- var span;
- var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.className = className;
- }
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- node.parentNode.insertBefore(span, node.parentNode.insertBefore(
- document.createTextNode(val.substr(pos + text.length)),
- node.nextSibling));
- node.nodeValue = val.substr(0, pos);
- if (isInSVG) {
- var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
- var bbox = node.parentElement.getBBox();
- rect.x.baseVal.value = bbox.x;
- rect.y.baseVal.value = bbox.y;
- rect.width.baseVal.value = bbox.width;
- rect.height.baseVal.value = bbox.height;
- rect.setAttribute('class', className);
- addItems.push({
- "parent": node.parentNode,
- "target": rect});
- }
- }
- }
- else if (!jQuery(node).is("button, select, textarea")) {
- jQuery.each(node.childNodes, function() {
- highlight(this, addItems);
- });
- }
- }
- var addItems = [];
- var result = this.each(function() {
- highlight(this, addItems);
- });
- for (var i = 0; i < addItems.length; ++i) {
- jQuery(addItems[i].parent).before(addItems[i].target);
- }
- return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
- jQuery.uaMatch = function(ua) {
- ua = ua.toLowerCase();
-
- var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
- /(webkit)[ \/]([\w.]+)/.exec(ua) ||
- /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
- /(msie) ([\w.]+)/.exec(ua) ||
- ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
- [];
-
- return {
- browser: match[ 1 ] || "",
- version: match[ 2 ] || "0"
- };
- };
- jQuery.browser = {};
- jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
-
-/**
- * Small JavaScript module for the documentation.
- */
-var Documentation = {
-
- init : function() {
- this.fixFirefoxAnchorBug();
- this.highlightSearchWords();
- this.initIndexTable();
- if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
- this.initOnKeyListeners();
- }
- },
-
- /**
- * i18n support
- */
- TRANSLATIONS : {},
- PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
- LOCALE : 'unknown',
-
- // gettext and ngettext don't access this so that the functions
- // can safely bound to a different name (_ = Documentation.gettext)
- gettext : function(string) {
- var translated = Documentation.TRANSLATIONS[string];
- if (typeof translated === 'undefined')
- return string;
- return (typeof translated === 'string') ? translated : translated[0];
- },
-
- ngettext : function(singular, plural, n) {
- var translated = Documentation.TRANSLATIONS[singular];
- if (typeof translated === 'undefined')
- return (n == 1) ? singular : plural;
- return translated[Documentation.PLURALEXPR(n)];
- },
-
- addTranslations : function(catalog) {
- for (var key in catalog.messages)
- this.TRANSLATIONS[key] = catalog.messages[key];
- this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
- this.LOCALE = catalog.locale;
- },
-
- /**
- * add context elements like header anchor links
- */
- addContextElements : function() {
- $('div[id] > :header:first').each(function() {
- $('<a class="headerlink">\u00B6</a>').
- attr('href', '#' + this.id).
- attr('title', _('Permalink to this headline')).
- appendTo(this);
- });
- $('dt[id]').each(function() {
- $('<a class="headerlink">\u00B6</a>').
- attr('href', '#' + this.id).
- attr('title', _('Permalink to this definition')).
- appendTo(this);
- });
- },
-
- /**
- * workaround a firefox stupidity
- * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
- */
- fixFirefoxAnchorBug : function() {
- if (document.location.hash && $.browser.mozilla)
- window.setTimeout(function() {
- document.location.href += '';
- }, 10);
- },
-
- /**
- * highlight the search words provided in the url in the text
- */
- highlightSearchWords : function() {
- var params = $.getQueryParameters();
- var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
- if (terms.length) {
- var body = $('div.body');
- if (!body.length) {
- body = $('body');
- }
- window.setTimeout(function() {
- $.each(terms, function() {
- body.highlightText(this.toLowerCase(), 'highlighted');
- });
- }, 10);
- $('<p class="highlight-link"><a href="javascript:Documentation.' +
- 'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
- .appendTo($('#searchbox'));
- }
- },
-
- /**
- * init the domain index toggle buttons
- */
- initIndexTable : function() {
- var togglers = $('img.toggler').click(function() {
- var src = $(this).attr('src');
- var idnum = $(this).attr('id').substr(7);
- $('tr.cg-' + idnum).toggle();
- if (src.substr(-9) === 'minus.png')
- $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
- else
- $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
- }).css('display', '');
- if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
- togglers.click();
- }
- },
-
- /**
- * helper function to hide the search marks again
- */
- hideSearchWords : function() {
- $('#searchbox .highlight-link').fadeOut(300);
- $('span.highlighted').removeClass('highlighted');
- var url = new URL(window.location);
- url.searchParams.delete('highlight');
- window.history.replaceState({}, '', url);
- },
-
- /**
- * make the url absolute
- */
- makeURL : function(relativeURL) {
- return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
- },
-
- /**
- * get the current relative url
- */
- getCurrentURL : function() {
- var path = document.location.pathname;
- var parts = path.split(/\//);
- $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
- if (this === '..')
- parts.pop();
- });
- var url = parts.join('/');
- return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
- },
-
- initOnKeyListeners: function() {
- $(document).keydown(function(event) {
- var activeElementType = document.activeElement.tagName;
- // don't navigate when in search box, textarea, dropdown or button
- if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
- && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
- && !event.shiftKey) {
- switch (event.keyCode) {
- case 37: // left
- var prevHref = $('link[rel="prev"]').prop('href');
- if (prevHref) {
- window.location.href = prevHref;
- return false;
- }
- break;
- case 39: // right
- var nextHref = $('link[rel="next"]').prop('href');
- if (nextHref) {
- window.location.href = nextHref;
- return false;
- }
- break;
- }
- }
- });
- }
-};
-
-// quick alias for translations
-_ = Documentation.gettext;
-
-$(document).ready(function() {
- Documentation.init();
-});
diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js
deleted file mode 100644
index cfb312604e5a3b88125f54533601a95b10d3d6cc..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/documentation_options.js
+++ /dev/null
@@ -1,12 +0,0 @@
-var DOCUMENTATION_OPTIONS = {
- URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
- VERSION: '0.0.3',
- LANGUAGE: 'python',
- COLLAPSE_INDEX: false,
- BUILDER: 'html',
- FILE_SUFFIX: '.html',
- LINK_SUFFIX: '.html',
- HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt',
- NAVIGATION_WITH_KEYS: false
-};
\ No newline at end of file
diff --git a/docs/build/html/_static/jquery-3.5.1.js b/docs/build/html/_static/jquery-3.5.1.js
deleted file mode 100644
index 50937333b99a5e168ac9e8292b22edd7e96c3e6a..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/jquery-3.5.1.js
+++ /dev/null
@@ -1,10872 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.5.1
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2020-05-04T22:49Z
- */
-( function( global, factory ) {
-
- "use strict";
-
- if ( typeof module === "object" && typeof module.exports === "object" ) {
-
- // For CommonJS and CommonJS-like environments where a proper `window`
- // is present, execute the factory and get jQuery.
- // For environments that do not have a `window` with a `document`
- // (such as Node.js), expose a factory as module.exports.
- // This accentuates the need for the creation of a real `window`.
- // e.g. var jQuery = require("jquery")(window);
- // See ticket #14549 for more info.
- module.exports = global.document ?
- factory( global, true ) :
- function( w ) {
- if ( !w.document ) {
- throw new Error( "jQuery requires a window with a document" );
- }
- return factory( w );
- };
- } else {
- factory( global );
- }
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
- return arr.flat.call( array );
-} : function( array ) {
- return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
- // Support: Chrome <=57, Firefox <=52
- // In some browsers, typeof returns "function" for HTML <object> elements
- // (i.e., `typeof document.createElement( "object" ) === "function"`).
- // We don't want to classify *any* DOM node as a function.
- return typeof obj === "function" && typeof obj.nodeType !== "number";
- };
-
-
-var isWindow = function isWindow( obj ) {
- return obj != null && obj === obj.window;
- };
-
-
-var document = window.document;
-
-
-
- var preservedScriptAttributes = {
- type: true,
- src: true,
- nonce: true,
- noModule: true
- };
-
- function DOMEval( code, node, doc ) {
- doc = doc || document;
-
- var i, val,
- script = doc.createElement( "script" );
-
- script.text = code;
- if ( node ) {
- for ( i in preservedScriptAttributes ) {
-
- // Support: Firefox 64+, Edge 18+
- // Some browsers don't support the "nonce" property on scripts.
- // On the other hand, just using `getAttribute` is not enough as
- // the `nonce` attribute is reset to an empty string whenever it
- // becomes browsing-context connected.
- // See https://github.com/whatwg/html/issues/2369
- // See https://html.spec.whatwg.org/#nonce-attributes
- // The `node.getAttribute` check was added for the sake of
- // `jQuery.globalEval` so that it can fake a nonce-containing node
- // via an object.
- val = node[ i ] || node.getAttribute && node.getAttribute( i );
- if ( val ) {
- script.setAttribute( i, val );
- }
- }
- }
- doc.head.appendChild( script ).parentNode.removeChild( script );
- }
-
-
-function toType( obj ) {
- if ( obj == null ) {
- return obj + "";
- }
-
- // Support: Android <=2.3 only (functionish RegExp)
- return typeof obj === "object" || typeof obj === "function" ?
- class2type[ toString.call( obj ) ] || "object" :
- typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
- version = "3.5.1",
-
- // Define a local copy of jQuery
- jQuery = function( selector, context ) {
-
- // The jQuery object is actually just the init constructor 'enhanced'
- // Need init if jQuery is called (just allow error to be thrown if not included)
- return new jQuery.fn.init( selector, context );
- };
-
-jQuery.fn = jQuery.prototype = {
-
- // The current version of jQuery being used
- jquery: version,
-
- constructor: jQuery,
-
- // The default length of a jQuery object is 0
- length: 0,
-
- toArray: function() {
- return slice.call( this );
- },
-
- // Get the Nth element in the matched element set OR
- // Get the whole matched element set as a clean array
- get: function( num ) {
-
- // Return all the elements in a clean array
- if ( num == null ) {
- return slice.call( this );
- }
-
- // Return just the one element from the set
- return num < 0 ? this[ num + this.length ] : this[ num ];
- },
-
- // Take an array of elements and push it onto the stack
- // (returning the new matched element set)
- pushStack: function( elems ) {
-
- // Build a new jQuery matched element set
- var ret = jQuery.merge( this.constructor(), elems );
-
- // Add the old object onto the stack (as a reference)
- ret.prevObject = this;
-
- // Return the newly-formed element set
- return ret;
- },
-
- // Execute a callback for every element in the matched set.
- each: function( callback ) {
- return jQuery.each( this, callback );
- },
-
- map: function( callback ) {
- return this.pushStack( jQuery.map( this, function( elem, i ) {
- return callback.call( elem, i, elem );
- } ) );
- },
-
- slice: function() {
- return this.pushStack( slice.apply( this, arguments ) );
- },
-
- first: function() {
- return this.eq( 0 );
- },
-
- last: function() {
- return this.eq( -1 );
- },
-
- even: function() {
- return this.pushStack( jQuery.grep( this, function( _elem, i ) {
- return ( i + 1 ) % 2;
- } ) );
- },
-
- odd: function() {
- return this.pushStack( jQuery.grep( this, function( _elem, i ) {
- return i % 2;
- } ) );
- },
-
- eq: function( i ) {
- var len = this.length,
- j = +i + ( i < 0 ? len : 0 );
- return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
- },
-
- end: function() {
- return this.prevObject || this.constructor();
- },
-
- // For internal use only.
- // Behaves like an Array's method, not like a jQuery method.
- push: push,
- sort: arr.sort,
- splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
- var options, name, src, copy, copyIsArray, clone,
- target = arguments[ 0 ] || {},
- i = 1,
- length = arguments.length,
- deep = false;
-
- // Handle a deep copy situation
- if ( typeof target === "boolean" ) {
- deep = target;
-
- // Skip the boolean and the target
- target = arguments[ i ] || {};
- i++;
- }
-
- // Handle case when target is a string or something (possible in deep copy)
- if ( typeof target !== "object" && !isFunction( target ) ) {
- target = {};
- }
-
- // Extend jQuery itself if only one argument is passed
- if ( i === length ) {
- target = this;
- i--;
- }
-
- for ( ; i < length; i++ ) {
-
- // Only deal with non-null/undefined values
- if ( ( options = arguments[ i ] ) != null ) {
-
- // Extend the base object
- for ( name in options ) {
- copy = options[ name ];
-
- // Prevent Object.prototype pollution
- // Prevent never-ending loop
- if ( name === "__proto__" || target === copy ) {
- continue;
- }
-
- // Recurse if we're merging plain objects or arrays
- if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
- ( copyIsArray = Array.isArray( copy ) ) ) ) {
- src = target[ name ];
-
- // Ensure proper type for the source value
- if ( copyIsArray && !Array.isArray( src ) ) {
- clone = [];
- } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
- clone = {};
- } else {
- clone = src;
- }
- copyIsArray = false;
-
- // Never move original objects, clone them
- target[ name ] = jQuery.extend( deep, clone, copy );
-
- // Don't bring in undefined values
- } else if ( copy !== undefined ) {
- target[ name ] = copy;
- }
- }
- }
- }
-
- // Return the modified object
- return target;
-};
-
-jQuery.extend( {
-
- // Unique for each copy of jQuery on the page
- expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
- // Assume jQuery is ready without the ready module
- isReady: true,
-
- error: function( msg ) {
- throw new Error( msg );
- },
-
- noop: function() {},
-
- isPlainObject: function( obj ) {
- var proto, Ctor;
-
- // Detect obvious negatives
- // Use toString instead of jQuery.type to catch host objects
- if ( !obj || toString.call( obj ) !== "[object Object]" ) {
- return false;
- }
-
- proto = getProto( obj );
-
- // Objects with no prototype (e.g., `Object.create( null )`) are plain
- if ( !proto ) {
- return true;
- }
-
- // Objects with prototype are plain iff they were constructed by a global Object function
- Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
- return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
- },
-
- isEmptyObject: function( obj ) {
- var name;
-
- for ( name in obj ) {
- return false;
- }
- return true;
- },
-
- // Evaluates a script in a provided context; falls back to the global one
- // if not specified.
- globalEval: function( code, options, doc ) {
- DOMEval( code, { nonce: options && options.nonce }, doc );
- },
-
- each: function( obj, callback ) {
- var length, i = 0;
-
- if ( isArrayLike( obj ) ) {
- length = obj.length;
- for ( ; i < length; i++ ) {
- if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
- break;
- }
- }
- } else {
- for ( i in obj ) {
- if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
- break;
- }
- }
- }
-
- return obj;
- },
-
- // results is for internal usage only
- makeArray: function( arr, results ) {
- var ret = results || [];
-
- if ( arr != null ) {
- if ( isArrayLike( Object( arr ) ) ) {
- jQuery.merge( ret,
- typeof arr === "string" ?
- [ arr ] : arr
- );
- } else {
- push.call( ret, arr );
- }
- }
-
- return ret;
- },
-
- inArray: function( elem, arr, i ) {
- return arr == null ? -1 : indexOf.call( arr, elem, i );
- },
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- merge: function( first, second ) {
- var len = +second.length,
- j = 0,
- i = first.length;
-
- for ( ; j < len; j++ ) {
- first[ i++ ] = second[ j ];
- }
-
- first.length = i;
-
- return first;
- },
-
- grep: function( elems, callback, invert ) {
- var callbackInverse,
- matches = [],
- i = 0,
- length = elems.length,
- callbackExpect = !invert;
-
- // Go through the array, only saving the items
- // that pass the validator function
- for ( ; i < length; i++ ) {
- callbackInverse = !callback( elems[ i ], i );
- if ( callbackInverse !== callbackExpect ) {
- matches.push( elems[ i ] );
- }
- }
-
- return matches;
- },
-
- // arg is for internal usage only
- map: function( elems, callback, arg ) {
- var length, value,
- i = 0,
- ret = [];
-
- // Go through the array, translating each of the items to their new values
- if ( isArrayLike( elems ) ) {
- length = elems.length;
- for ( ; i < length; i++ ) {
- value = callback( elems[ i ], i, arg );
-
- if ( value != null ) {
- ret.push( value );
- }
- }
-
- // Go through every key on the object,
- } else {
- for ( i in elems ) {
- value = callback( elems[ i ], i, arg );
-
- if ( value != null ) {
- ret.push( value );
- }
- }
- }
-
- // Flatten any nested arrays
- return flat( ret );
- },
-
- // A global GUID counter for objects
- guid: 1,
-
- // jQuery.support is not used in Core but other projects attach their
- // properties to it so it needs to exist.
- support: support
-} );
-
-if ( typeof Symbol === "function" ) {
- jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( _i, name ) {
- class2type[ "[object " + name + "]" ] = name.toLowerCase();
-} );
-
-function isArrayLike( obj ) {
-
- // Support: real iOS 8.2 only (not reproducible in simulator)
- // `in` check used to prevent JIT error (gh-2145)
- // hasOwn isn't used here due to false negatives
- // regarding Nodelist length in IE
- var length = !!obj && "length" in obj && obj.length,
- type = toType( obj );
-
- if ( isFunction( obj ) || isWindow( obj ) ) {
- return false;
- }
-
- return type === "array" || length === 0 ||
- typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.5
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2020-03-14
- */
-( function( window ) {
-var i,
- support,
- Expr,
- getText,
- isXML,
- tokenize,
- compile,
- select,
- outermostContext,
- sortInput,
- hasDuplicate,
-
- // Local document vars
- setDocument,
- document,
- docElem,
- documentIsHTML,
- rbuggyQSA,
- rbuggyMatches,
- matches,
- contains,
-
- // Instance-specific data
- expando = "sizzle" + 1 * new Date(),
- preferredDoc = window.document,
- dirruns = 0,
- done = 0,
- classCache = createCache(),
- tokenCache = createCache(),
- compilerCache = createCache(),
- nonnativeSelectorCache = createCache(),
- sortOrder = function( a, b ) {
- if ( a === b ) {
- hasDuplicate = true;
- }
- return 0;
- },
-
- // Instance methods
- hasOwn = ( {} ).hasOwnProperty,
- arr = [],
- pop = arr.pop,
- pushNative = arr.push,
- push = arr.push,
- slice = arr.slice,
-
- // Use a stripped-down indexOf as it's faster than native
- // https://jsperf.com/thor-indexof-vs-for/5
- indexOf = function( list, elem ) {
- var i = 0,
- len = list.length;
- for ( ; i < len; i++ ) {
- if ( list[ i ] === elem ) {
- return i;
- }
- }
- return -1;
- },
-
- booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
- "ismap|loop|multiple|open|readonly|required|scoped",
-
- // Regular expressions
-
- // http://www.w3.org/TR/css3-selectors/#whitespace
- whitespace = "[\\x20\\t\\r\\n\\f]",
-
- // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
- identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
- "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
- // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
- attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
- // Operator (capture 2)
- "*([*^$|!~]?=)" + whitespace +
-
- // "Attribute values must be CSS identifiers [capture 5]
- // or strings [capture 3 or capture 4]"
- "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
- whitespace + "*\\]",
-
- pseudos = ":(" + identifier + ")(?:\\((" +
-
- // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
- // 1. quoted (capture 3; capture 4 or capture 5)
- "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
- // 2. simple (capture 6)
- "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
- // 3. anything else (capture 2)
- ".*" +
- ")\\)|)",
-
- // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
- rwhitespace = new RegExp( whitespace + "+", "g" ),
- rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
- whitespace + "+$", "g" ),
-
- rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
- rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
- "*" ),
- rdescend = new RegExp( whitespace + "|>" ),
-
- rpseudo = new RegExp( pseudos ),
- ridentifier = new RegExp( "^" + identifier + "$" ),
-
- matchExpr = {
- "ID": new RegExp( "^#(" + identifier + ")" ),
- "CLASS": new RegExp( "^\\.(" + identifier + ")" ),
- "TAG": new RegExp( "^(" + identifier + "|[*])" ),
- "ATTR": new RegExp( "^" + attributes ),
- "PSEUDO": new RegExp( "^" + pseudos ),
- "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
- whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
- whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
- "bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
- // For use in libraries implementing .is()
- // We use this for POS matching in `select`
- "needsContext": new RegExp( "^" + whitespace +
- "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
- "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
- },
-
- rhtml = /HTML$/i,
- rinputs = /^(?:input|select|textarea|button)$/i,
- rheader = /^h\d$/i,
-
- rnative = /^[^{]+\{\s*\[native \w/,
-
- // Easily-parseable/retrievable ID or TAG or CLASS selectors
- rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
- rsibling = /[+~]/,
-
- // CSS escapes
- // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
- runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
- funescape = function( escape, nonHex ) {
- var high = "0x" + escape.slice( 1 ) - 0x10000;
-
- return nonHex ?
-
- // Strip the backslash prefix from a non-hex escape sequence
- nonHex :
-
- // Replace a hexadecimal escape sequence with the encoded Unicode code point
- // Support: IE <=11+
- // For values outside the Basic Multilingual Plane (BMP), manually construct a
- // surrogate pair
- high < 0 ?
- String.fromCharCode( high + 0x10000 ) :
- String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
- },
-
- // CSS string/identifier serialization
- // https://drafts.csswg.org/cssom/#common-serializing-idioms
- rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
- fcssescape = function( ch, asCodePoint ) {
- if ( asCodePoint ) {
-
- // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
- if ( ch === "\0" ) {
- return "\uFFFD";
- }
-
- // Control characters and (dependent upon position) numbers get escaped as code points
- return ch.slice( 0, -1 ) + "\\" +
- ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
- }
-
- // Other potentially-special ASCII characters get backslash-escaped
- return "\\" + ch;
- },
-
- // Used for iframes
- // See setDocument()
- // Removing the function wrapper causes a "Permission Denied"
- // error in IE
- unloadHandler = function() {
- setDocument();
- },
-
- inDisabledFieldset = addCombinator(
- function( elem ) {
- return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
- },
- { dir: "parentNode", next: "legend" }
- );
-
-// Optimize for push.apply( _, NodeList )
-try {
- push.apply(
- ( arr = slice.call( preferredDoc.childNodes ) ),
- preferredDoc.childNodes
- );
-
- // Support: Android<4.0
- // Detect silently failing push.apply
- // eslint-disable-next-line no-unused-expressions
- arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
- push = { apply: arr.length ?
-
- // Leverage slice if possible
- function( target, els ) {
- pushNative.apply( target, slice.call( els ) );
- } :
-
- // Support: IE<9
- // Otherwise append directly
- function( target, els ) {
- var j = target.length,
- i = 0;
-
- // Can't trust NodeList.length
- while ( ( target[ j++ ] = els[ i++ ] ) ) {}
- target.length = j - 1;
- }
- };
-}
-
-function Sizzle( selector, context, results, seed ) {
- var m, i, elem, nid, match, groups, newSelector,
- newContext = context && context.ownerDocument,
-
- // nodeType defaults to 9, since context defaults to document
- nodeType = context ? context.nodeType : 9;
-
- results = results || [];
-
- // Return early from calls with invalid selector or context
- if ( typeof selector !== "string" || !selector ||
- nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
- return results;
- }
-
- // Try to shortcut find operations (as opposed to filters) in HTML documents
- if ( !seed ) {
- setDocument( context );
- context = context || document;
-
- if ( documentIsHTML ) {
-
- // If the selector is sufficiently simple, try using a "get*By*" DOM method
- // (excepting DocumentFragment context, where the methods don't exist)
- if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
- // ID selector
- if ( ( m = match[ 1 ] ) ) {
-
- // Document context
- if ( nodeType === 9 ) {
- if ( ( elem = context.getElementById( m ) ) ) {
-
- // Support: IE, Opera, Webkit
- // TODO: identify versions
- // getElementById can match elements by name instead of ID
- if ( elem.id === m ) {
- results.push( elem );
- return results;
- }
- } else {
- return results;
- }
-
- // Element context
- } else {
-
- // Support: IE, Opera, Webkit
- // TODO: identify versions
- // getElementById can match elements by name instead of ID
- if ( newContext && ( elem = newContext.getElementById( m ) ) &&
- contains( context, elem ) &&
- elem.id === m ) {
-
- results.push( elem );
- return results;
- }
- }
-
- // Type selector
- } else if ( match[ 2 ] ) {
- push.apply( results, context.getElementsByTagName( selector ) );
- return results;
-
- // Class selector
- } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
- context.getElementsByClassName ) {
-
- push.apply( results, context.getElementsByClassName( m ) );
- return results;
- }
- }
-
- // Take advantage of querySelectorAll
- if ( support.qsa &&
- !nonnativeSelectorCache[ selector + " " ] &&
- ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
- // Support: IE 8 only
- // Exclude object elements
- ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
- newSelector = selector;
- newContext = context;
-
- // qSA considers elements outside a scoping root when evaluating child or
- // descendant combinators, which is not what we want.
- // In such cases, we work around the behavior by prefixing every selector in the
- // list with an ID selector referencing the scope context.
- // The technique has to be used as well when a leading combinator is used
- // as such selectors are not recognized by querySelectorAll.
- // Thanks to Andrew Dupont for this technique.
- if ( nodeType === 1 &&
- ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
- // Expand context for sibling selectors
- newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
- context;
-
- // We can use :scope instead of the ID hack if the browser
- // supports it & if we're not changing the context.
- if ( newContext !== context || !support.scope ) {
-
- // Capture the context ID, setting it first if necessary
- if ( ( nid = context.getAttribute( "id" ) ) ) {
- nid = nid.replace( rcssescape, fcssescape );
- } else {
- context.setAttribute( "id", ( nid = expando ) );
- }
- }
-
- // Prefix every selector in the list
- groups = tokenize( selector );
- i = groups.length;
- while ( i-- ) {
- groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
- toSelector( groups[ i ] );
- }
- newSelector = groups.join( "," );
- }
-
- try {
- push.apply( results,
- newContext.querySelectorAll( newSelector )
- );
- return results;
- } catch ( qsaError ) {
- nonnativeSelectorCache( selector, true );
- } finally {
- if ( nid === expando ) {
- context.removeAttribute( "id" );
- }
- }
- }
- }
- }
-
- // All others
- return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- * deleting the oldest entry
- */
-function createCache() {
- var keys = [];
-
- function cache( key, value ) {
-
- // Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
- if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
- // Only keep the most recent entries
- delete cache[ keys.shift() ];
- }
- return ( cache[ key + " " ] = value );
- }
- return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
- fn[ expando ] = true;
- return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
- var el = document.createElement( "fieldset" );
-
- try {
- return !!fn( el );
- } catch ( e ) {
- return false;
- } finally {
-
- // Remove from its parent by default
- if ( el.parentNode ) {
- el.parentNode.removeChild( el );
- }
-
- // release memory in IE
- el = null;
- }
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
- var arr = attrs.split( "|" ),
- i = arr.length;
-
- while ( i-- ) {
- Expr.attrHandle[ arr[ i ] ] = handler;
- }
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
- var cur = b && a,
- diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
- a.sourceIndex - b.sourceIndex;
-
- // Use IE sourceIndex if available on both nodes
- if ( diff ) {
- return diff;
- }
-
- // Check if b follows a
- if ( cur ) {
- while ( ( cur = cur.nextSibling ) ) {
- if ( cur === b ) {
- return -1;
- }
- }
- }
-
- return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
- return function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return name === "input" && elem.type === type;
- };
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
- return function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return ( name === "input" || name === "button" ) && elem.type === type;
- };
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
- // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
- return function( elem ) {
-
- // Only certain elements can match :enabled or :disabled
- // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
- // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
- if ( "form" in elem ) {
-
- // Check for inherited disabledness on relevant non-disabled elements:
- // * listed form-associated elements in a disabled fieldset
- // https://html.spec.whatwg.org/multipage/forms.html#category-listed
- // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
- // * option elements in a disabled optgroup
- // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
- // All such elements have a "form" property.
- if ( elem.parentNode && elem.disabled === false ) {
-
- // Option elements defer to a parent optgroup if present
- if ( "label" in elem ) {
- if ( "label" in elem.parentNode ) {
- return elem.parentNode.disabled === disabled;
- } else {
- return elem.disabled === disabled;
- }
- }
-
- // Support: IE 6 - 11
- // Use the isDisabled shortcut property to check for disabled fieldset ancestors
- return elem.isDisabled === disabled ||
-
- // Where there is no isDisabled, check manually
- /* jshint -W018 */
- elem.isDisabled !== !disabled &&
- inDisabledFieldset( elem ) === disabled;
- }
-
- return elem.disabled === disabled;
-
- // Try to winnow out elements that can't be disabled before trusting the disabled property.
- // Some victims get caught in our net (label, legend, menu, track), but it shouldn't
- // even exist on them, let alone have a boolean value.
- } else if ( "label" in elem ) {
- return elem.disabled === disabled;
- }
-
- // Remaining elements are neither :enabled nor :disabled
- return false;
- };
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
- return markFunction( function( argument ) {
- argument = +argument;
- return markFunction( function( seed, matches ) {
- var j,
- matchIndexes = fn( [], seed.length, argument ),
- i = matchIndexes.length;
-
- // Match elements found at the specified indexes
- while ( i-- ) {
- if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
- seed[ j ] = !( matches[ j ] = seed[ j ] );
- }
- }
- } );
- } );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
- return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
- var namespace = elem.namespaceURI,
- docElem = ( elem.ownerDocument || elem ).documentElement;
-
- // Support: IE <=8
- // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
- // https://bugs.jquery.com/ticket/4833
- return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
- var hasCompare, subWindow,
- doc = node ? node.ownerDocument || node : preferredDoc;
-
- // Return early if doc is invalid or already selected
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
- return document;
- }
-
- // Update global variables
- document = doc;
- docElem = document.documentElement;
- documentIsHTML = !isXML( document );
-
- // Support: IE 9 - 11+, Edge 12 - 18+
- // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( preferredDoc != document &&
- ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
- // Support: IE 11, Edge
- if ( subWindow.addEventListener ) {
- subWindow.addEventListener( "unload", unloadHandler, false );
-
- // Support: IE 9 - 10 only
- } else if ( subWindow.attachEvent ) {
- subWindow.attachEvent( "onunload", unloadHandler );
- }
- }
-
- // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
- // Safari 4 - 5 only, Opera <=11.6 - 12.x only
- // IE/Edge & older browsers don't support the :scope pseudo-class.
- // Support: Safari 6.0 only
- // Safari 6.0 supports :scope but it's an alias of :root there.
- support.scope = assert( function( el ) {
- docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
- return typeof el.querySelectorAll !== "undefined" &&
- !el.querySelectorAll( ":scope fieldset div" ).length;
- } );
-
- /* Attributes
- ---------------------------------------------------------------------- */
-
- // Support: IE<8
- // Verify that getAttribute really returns attributes and not properties
- // (excepting IE8 booleans)
- support.attributes = assert( function( el ) {
- el.className = "i";
- return !el.getAttribute( "className" );
- } );
-
- /* getElement(s)By*
- ---------------------------------------------------------------------- */
-
- // Check if getElementsByTagName("*") returns only elements
- support.getElementsByTagName = assert( function( el ) {
- el.appendChild( document.createComment( "" ) );
- return !el.getElementsByTagName( "*" ).length;
- } );
-
- // Support: IE<9
- support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
- // Support: IE<10
- // Check if getElementById returns elements by name
- // The broken getElementById methods don't pick up programmatically-set names,
- // so use a roundabout getElementsByName test
- support.getById = assert( function( el ) {
- docElem.appendChild( el ).id = expando;
- return !document.getElementsByName || !document.getElementsByName( expando ).length;
- } );
-
- // ID filter and find
- if ( support.getById ) {
- Expr.filter[ "ID" ] = function( id ) {
- var attrId = id.replace( runescape, funescape );
- return function( elem ) {
- return elem.getAttribute( "id" ) === attrId;
- };
- };
- Expr.find[ "ID" ] = function( id, context ) {
- if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
- var elem = context.getElementById( id );
- return elem ? [ elem ] : [];
- }
- };
- } else {
- Expr.filter[ "ID" ] = function( id ) {
- var attrId = id.replace( runescape, funescape );
- return function( elem ) {
- var node = typeof elem.getAttributeNode !== "undefined" &&
- elem.getAttributeNode( "id" );
- return node && node.value === attrId;
- };
- };
-
- // Support: IE 6 - 7 only
- // getElementById is not reliable as a find shortcut
- Expr.find[ "ID" ] = function( id, context ) {
- if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
- var node, i, elems,
- elem = context.getElementById( id );
-
- if ( elem ) {
-
- // Verify the id attribute
- node = elem.getAttributeNode( "id" );
- if ( node && node.value === id ) {
- return [ elem ];
- }
-
- // Fall back on getElementsByName
- elems = context.getElementsByName( id );
- i = 0;
- while ( ( elem = elems[ i++ ] ) ) {
- node = elem.getAttributeNode( "id" );
- if ( node && node.value === id ) {
- return [ elem ];
- }
- }
- }
-
- return [];
- }
- };
- }
-
- // Tag
- Expr.find[ "TAG" ] = support.getElementsByTagName ?
- function( tag, context ) {
- if ( typeof context.getElementsByTagName !== "undefined" ) {
- return context.getElementsByTagName( tag );
-
- // DocumentFragment nodes don't have gEBTN
- } else if ( support.qsa ) {
- return context.querySelectorAll( tag );
- }
- } :
-
- function( tag, context ) {
- var elem,
- tmp = [],
- i = 0,
-
- // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
- results = context.getElementsByTagName( tag );
-
- // Filter out possible comments
- if ( tag === "*" ) {
- while ( ( elem = results[ i++ ] ) ) {
- if ( elem.nodeType === 1 ) {
- tmp.push( elem );
- }
- }
-
- return tmp;
- }
- return results;
- };
-
- // Class
- Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
- if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
- return context.getElementsByClassName( className );
- }
- };
-
- /* QSA/matchesSelector
- ---------------------------------------------------------------------- */
-
- // QSA and matchesSelector support
-
- // matchesSelector(:active) reports false when true (IE9/Opera 11.5)
- rbuggyMatches = [];
-
- // qSa(:focus) reports false when true (Chrome 21)
- // We allow this because of a bug in IE8/9 that throws an error
- // whenever `document.activeElement` is accessed on an iframe
- // So, we allow :focus to pass through QSA all the time to avoid the IE error
- // See https://bugs.jquery.com/ticket/13378
- rbuggyQSA = [];
-
- if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
- // Build QSA regex
- // Regex strategy adopted from Diego Perini
- assert( function( el ) {
-
- var input;
-
- // Select is set to empty string on purpose
- // This is to test IE's treatment of not explicitly
- // setting a boolean content attribute,
- // since its presence should be enough
- // https://bugs.jquery.com/ticket/12359
- docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
- "<select id='" + expando + "-\r\\' msallowcapture=''>" +
- "<option selected=''></option></select>";
-
- // Support: IE8, Opera 11-12.16
- // Nothing should be selected when empty strings follow ^= or $= or *=
- // The test attribute must be unknown in Opera but "safe" for WinRT
- // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
- if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
- rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
- }
-
- // Support: IE8
- // Boolean attributes and "value" are not treated correctly
- if ( !el.querySelectorAll( "[selected]" ).length ) {
- rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
- }
-
- // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
- if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
- rbuggyQSA.push( "~=" );
- }
-
- // Support: IE 11+, Edge 15 - 18+
- // IE 11/Edge don't find elements on a `[name='']` query in some cases.
- // Adding a temporary attribute to the document before the selection works
- // around the issue.
- // Interestingly, IE 10 & older don't seem to have the issue.
- input = document.createElement( "input" );
- input.setAttribute( "name", "" );
- el.appendChild( input );
- if ( !el.querySelectorAll( "[name='']" ).length ) {
- rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
- whitespace + "*(?:''|\"\")" );
- }
-
- // Webkit/Opera - :checked should return selected option elements
- // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
- // IE8 throws error here and will not see later tests
- if ( !el.querySelectorAll( ":checked" ).length ) {
- rbuggyQSA.push( ":checked" );
- }
-
- // Support: Safari 8+, iOS 8+
- // https://bugs.webkit.org/show_bug.cgi?id=136851
- // In-page `selector#id sibling-combinator selector` fails
- if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
- rbuggyQSA.push( ".#.+[+~]" );
- }
-
- // Support: Firefox <=3.6 - 5 only
- // Old Firefox doesn't throw on a badly-escaped identifier.
- el.querySelectorAll( "\\\f" );
- rbuggyQSA.push( "[\\r\\n\\f]" );
- } );
-
- assert( function( el ) {
- el.innerHTML = "<a href='' disabled='disabled'></a>" +
- "<select disabled='disabled'><option/></select>";
-
- // Support: Windows 8 Native Apps
- // The type and name attributes are restricted during .innerHTML assignment
- var input = document.createElement( "input" );
- input.setAttribute( "type", "hidden" );
- el.appendChild( input ).setAttribute( "name", "D" );
-
- // Support: IE8
- // Enforce case-sensitivity of name attribute
- if ( el.querySelectorAll( "[name=d]" ).length ) {
- rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
- }
-
- // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
- // IE8 throws error here and will not see later tests
- if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
- rbuggyQSA.push( ":enabled", ":disabled" );
- }
-
- // Support: IE9-11+
- // IE's :disabled selector does not pick up the children of disabled fieldsets
- docElem.appendChild( el ).disabled = true;
- if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
- rbuggyQSA.push( ":enabled", ":disabled" );
- }
-
- // Support: Opera 10 - 11 only
- // Opera 10-11 does not throw on post-comma invalid pseudos
- el.querySelectorAll( "*,:x" );
- rbuggyQSA.push( ",.*:" );
- } );
- }
-
- if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
- docElem.webkitMatchesSelector ||
- docElem.mozMatchesSelector ||
- docElem.oMatchesSelector ||
- docElem.msMatchesSelector ) ) ) ) {
-
- assert( function( el ) {
-
- // Check to see if it's possible to do matchesSelector
- // on a disconnected node (IE 9)
- support.disconnectedMatch = matches.call( el, "*" );
-
- // This should fail with an exception
- // Gecko does not error, returns false instead
- matches.call( el, "[s!='']:x" );
- rbuggyMatches.push( "!=", pseudos );
- } );
- }
-
- rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
- rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
- /* Contains
- ---------------------------------------------------------------------- */
- hasCompare = rnative.test( docElem.compareDocumentPosition );
-
- // Element contains another
- // Purposefully self-exclusive
- // As in, an element does not contain itself
- contains = hasCompare || rnative.test( docElem.contains ) ?
- function( a, b ) {
- var adown = a.nodeType === 9 ? a.documentElement : a,
- bup = b && b.parentNode;
- return a === bup || !!( bup && bup.nodeType === 1 && (
- adown.contains ?
- adown.contains( bup ) :
- a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
- ) );
- } :
- function( a, b ) {
- if ( b ) {
- while ( ( b = b.parentNode ) ) {
- if ( b === a ) {
- return true;
- }
- }
- }
- return false;
- };
-
- /* Sorting
- ---------------------------------------------------------------------- */
-
- // Document order sorting
- sortOrder = hasCompare ?
- function( a, b ) {
-
- // Flag for duplicate removal
- if ( a === b ) {
- hasDuplicate = true;
- return 0;
- }
-
- // Sort on method existence if only one input has compareDocumentPosition
- var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
- if ( compare ) {
- return compare;
- }
-
- // Calculate position if both inputs belong to the same document
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
- a.compareDocumentPosition( b ) :
-
- // Otherwise we know they are disconnected
- 1;
-
- // Disconnected nodes
- if ( compare & 1 ||
- ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
- // Choose the first element that is related to our preferred document
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( a == document || a.ownerDocument == preferredDoc &&
- contains( preferredDoc, a ) ) {
- return -1;
- }
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( b == document || b.ownerDocument == preferredDoc &&
- contains( preferredDoc, b ) ) {
- return 1;
- }
-
- // Maintain original order
- return sortInput ?
- ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
- 0;
- }
-
- return compare & 4 ? -1 : 1;
- } :
- function( a, b ) {
-
- // Exit early if the nodes are identical
- if ( a === b ) {
- hasDuplicate = true;
- return 0;
- }
-
- var cur,
- i = 0,
- aup = a.parentNode,
- bup = b.parentNode,
- ap = [ a ],
- bp = [ b ];
-
- // Parentless nodes are either documents or disconnected
- if ( !aup || !bup ) {
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- /* eslint-disable eqeqeq */
- return a == document ? -1 :
- b == document ? 1 :
- /* eslint-enable eqeqeq */
- aup ? -1 :
- bup ? 1 :
- sortInput ?
- ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
- 0;
-
- // If the nodes are siblings, we can do a quick check
- } else if ( aup === bup ) {
- return siblingCheck( a, b );
- }
-
- // Otherwise we need full lists of their ancestors for comparison
- cur = a;
- while ( ( cur = cur.parentNode ) ) {
- ap.unshift( cur );
- }
- cur = b;
- while ( ( cur = cur.parentNode ) ) {
- bp.unshift( cur );
- }
-
- // Walk down the tree looking for a discrepancy
- while ( ap[ i ] === bp[ i ] ) {
- i++;
- }
-
- return i ?
-
- // Do a sibling check if the nodes have a common ancestor
- siblingCheck( ap[ i ], bp[ i ] ) :
-
- // Otherwise nodes in our document sort first
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- /* eslint-disable eqeqeq */
- ap[ i ] == preferredDoc ? -1 :
- bp[ i ] == preferredDoc ? 1 :
- /* eslint-enable eqeqeq */
- 0;
- };
-
- return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
- return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
- setDocument( elem );
-
- if ( support.matchesSelector && documentIsHTML &&
- !nonnativeSelectorCache[ expr + " " ] &&
- ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
- ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) {
-
- try {
- var ret = matches.call( elem, expr );
-
- // IE 9's matchesSelector returns false on disconnected nodes
- if ( ret || support.disconnectedMatch ||
-
- // As well, disconnected nodes are said to be in a document
- // fragment in IE 9
- elem.document && elem.document.nodeType !== 11 ) {
- return ret;
- }
- } catch ( e ) {
- nonnativeSelectorCache( expr, true );
- }
- }
-
- return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
- // Set document vars if needed
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( ( context.ownerDocument || context ) != document ) {
- setDocument( context );
- }
- return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
- // Set document vars if needed
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( ( elem.ownerDocument || elem ) != document ) {
- setDocument( elem );
- }
-
- var fn = Expr.attrHandle[ name.toLowerCase() ],
-
- // Don't get fooled by Object.prototype properties (jQuery #13807)
- val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
- fn( elem, name, !documentIsHTML ) :
- undefined;
-
- return val !== undefined ?
- val :
- support.attributes || !documentIsHTML ?
- elem.getAttribute( name ) :
- ( val = elem.getAttributeNode( name ) ) && val.specified ?
- val.value :
- null;
-};
-
-Sizzle.escape = function( sel ) {
- return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
- throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
- var elem,
- duplicates = [],
- j = 0,
- i = 0;
-
- // Unless we *know* we can detect duplicates, assume their presence
- hasDuplicate = !support.detectDuplicates;
- sortInput = !support.sortStable && results.slice( 0 );
- results.sort( sortOrder );
-
- if ( hasDuplicate ) {
- while ( ( elem = results[ i++ ] ) ) {
- if ( elem === results[ i ] ) {
- j = duplicates.push( i );
- }
- }
- while ( j-- ) {
- results.splice( duplicates[ j ], 1 );
- }
- }
-
- // Clear input after sorting to release objects
- // See https://github.com/jquery/sizzle/pull/225
- sortInput = null;
-
- return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
- var node,
- ret = "",
- i = 0,
- nodeType = elem.nodeType;
-
- if ( !nodeType ) {
-
- // If no nodeType, this is expected to be an array
- while ( ( node = elem[ i++ ] ) ) {
-
- // Do not traverse comment nodes
- ret += getText( node );
- }
- } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
- // Use textContent for elements
- // innerText usage removed for consistency of new lines (jQuery #11153)
- if ( typeof elem.textContent === "string" ) {
- return elem.textContent;
- } else {
-
- // Traverse its children
- for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
- ret += getText( elem );
- }
- }
- } else if ( nodeType === 3 || nodeType === 4 ) {
- return elem.nodeValue;
- }
-
- // Do not include comment or processing instruction nodes
-
- return ret;
-};
-
-Expr = Sizzle.selectors = {
-
- // Can be adjusted by the user
- cacheLength: 50,
-
- createPseudo: markFunction,
-
- match: matchExpr,
-
- attrHandle: {},
-
- find: {},
-
- relative: {
- ">": { dir: "parentNode", first: true },
- " ": { dir: "parentNode" },
- "+": { dir: "previousSibling", first: true },
- "~": { dir: "previousSibling" }
- },
-
- preFilter: {
- "ATTR": function( match ) {
- match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
- // Move the given value to match[3] whether quoted or unquoted
- match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
- match[ 5 ] || "" ).replace( runescape, funescape );
-
- if ( match[ 2 ] === "~=" ) {
- match[ 3 ] = " " + match[ 3 ] + " ";
- }
-
- return match.slice( 0, 4 );
- },
-
- "CHILD": function( match ) {
-
- /* matches from matchExpr["CHILD"]
- 1 type (only|nth|...)
- 2 what (child|of-type)
- 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
- 4 xn-component of xn+y argument ([+-]?\d*n|)
- 5 sign of xn-component
- 6 x of xn-component
- 7 sign of y-component
- 8 y of y-component
- */
- match[ 1 ] = match[ 1 ].toLowerCase();
-
- if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
- // nth-* requires argument
- if ( !match[ 3 ] ) {
- Sizzle.error( match[ 0 ] );
- }
-
- // numeric x and y parameters for Expr.filter.CHILD
- // remember that false/true cast respectively to 0/1
- match[ 4 ] = +( match[ 4 ] ?
- match[ 5 ] + ( match[ 6 ] || 1 ) :
- 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
- match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
- // other types prohibit arguments
- } else if ( match[ 3 ] ) {
- Sizzle.error( match[ 0 ] );
- }
-
- return match;
- },
-
- "PSEUDO": function( match ) {
- var excess,
- unquoted = !match[ 6 ] && match[ 2 ];
-
- if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
- return null;
- }
-
- // Accept quoted arguments as-is
- if ( match[ 3 ] ) {
- match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
- // Strip excess characters from unquoted arguments
- } else if ( unquoted && rpseudo.test( unquoted ) &&
-
- // Get excess from tokenize (recursively)
- ( excess = tokenize( unquoted, true ) ) &&
-
- // advance to the next closing parenthesis
- ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
- // excess is a negative index
- match[ 0 ] = match[ 0 ].slice( 0, excess );
- match[ 2 ] = unquoted.slice( 0, excess );
- }
-
- // Return only captures needed by the pseudo filter method (type and argument)
- return match.slice( 0, 3 );
- }
- },
-
- filter: {
-
- "TAG": function( nodeNameSelector ) {
- var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
- return nodeNameSelector === "*" ?
- function() {
- return true;
- } :
- function( elem ) {
- return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
- };
- },
-
- "CLASS": function( className ) {
- var pattern = classCache[ className + " " ];
-
- return pattern ||
- ( pattern = new RegExp( "(^|" + whitespace +
- ")" + className + "(" + whitespace + "|$)" ) ) && classCache(
- className, function( elem ) {
- return pattern.test(
- typeof elem.className === "string" && elem.className ||
- typeof elem.getAttribute !== "undefined" &&
- elem.getAttribute( "class" ) ||
- ""
- );
- } );
- },
-
- "ATTR": function( name, operator, check ) {
- return function( elem ) {
- var result = Sizzle.attr( elem, name );
-
- if ( result == null ) {
- return operator === "!=";
- }
- if ( !operator ) {
- return true;
- }
-
- result += "";
-
- /* eslint-disable max-len */
-
- return operator === "=" ? result === check :
- operator === "!=" ? result !== check :
- operator === "^=" ? check && result.indexOf( check ) === 0 :
- operator === "*=" ? check && result.indexOf( check ) > -1 :
- operator === "$=" ? check && result.slice( -check.length ) === check :
- operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
- operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
- false;
- /* eslint-enable max-len */
-
- };
- },
-
- "CHILD": function( type, what, _argument, first, last ) {
- var simple = type.slice( 0, 3 ) !== "nth",
- forward = type.slice( -4 ) !== "last",
- ofType = what === "of-type";
-
- return first === 1 && last === 0 ?
-
- // Shortcut for :nth-*(n)
- function( elem ) {
- return !!elem.parentNode;
- } :
-
- function( elem, _context, xml ) {
- var cache, uniqueCache, outerCache, node, nodeIndex, start,
- dir = simple !== forward ? "nextSibling" : "previousSibling",
- parent = elem.parentNode,
- name = ofType && elem.nodeName.toLowerCase(),
- useCache = !xml && !ofType,
- diff = false;
-
- if ( parent ) {
-
- // :(first|last|only)-(child|of-type)
- if ( simple ) {
- while ( dir ) {
- node = elem;
- while ( ( node = node[ dir ] ) ) {
- if ( ofType ?
- node.nodeName.toLowerCase() === name :
- node.nodeType === 1 ) {
-
- return false;
- }
- }
-
- // Reverse direction for :only-* (if we haven't yet done so)
- start = dir = type === "only" && !start && "nextSibling";
- }
- return true;
- }
-
- start = [ forward ? parent.firstChild : parent.lastChild ];
-
- // non-xml :nth-child(...) stores cache data on `parent`
- if ( forward && useCache ) {
-
- // Seek `elem` from a previously-cached index
-
- // ...in a gzip-friendly way
- node = parent;
- outerCache = node[ expando ] || ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- cache = uniqueCache[ type ] || [];
- nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
- diff = nodeIndex && cache[ 2 ];
- node = nodeIndex && parent.childNodes[ nodeIndex ];
-
- while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
- // Fallback to seeking `elem` from the start
- ( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
- // When found, cache indexes on `parent` and break
- if ( node.nodeType === 1 && ++diff && node === elem ) {
- uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
- break;
- }
- }
-
- } else {
-
- // Use previously-cached element index if available
- if ( useCache ) {
-
- // ...in a gzip-friendly way
- node = elem;
- outerCache = node[ expando ] || ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- cache = uniqueCache[ type ] || [];
- nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
- diff = nodeIndex;
- }
-
- // xml :nth-child(...)
- // or :nth-last-child(...) or :nth(-last)?-of-type(...)
- if ( diff === false ) {
-
- // Use the same loop as above to seek `elem` from the start
- while ( ( node = ++nodeIndex && node && node[ dir ] ||
- ( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
- if ( ( ofType ?
- node.nodeName.toLowerCase() === name :
- node.nodeType === 1 ) &&
- ++diff ) {
-
- // Cache the index of each encountered element
- if ( useCache ) {
- outerCache = node[ expando ] ||
- ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- uniqueCache[ type ] = [ dirruns, diff ];
- }
-
- if ( node === elem ) {
- break;
- }
- }
- }
- }
- }
-
- // Incorporate the offset, then check against cycle size
- diff -= last;
- return diff === first || ( diff % first === 0 && diff / first >= 0 );
- }
- };
- },
-
- "PSEUDO": function( pseudo, argument ) {
-
- // pseudo-class names are case-insensitive
- // http://www.w3.org/TR/selectors/#pseudo-classes
- // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
- // Remember that setFilters inherits from pseudos
- var args,
- fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
- Sizzle.error( "unsupported pseudo: " + pseudo );
-
- // The user may use createPseudo to indicate that
- // arguments are needed to create the filter function
- // just as Sizzle does
- if ( fn[ expando ] ) {
- return fn( argument );
- }
-
- // But maintain support for old signatures
- if ( fn.length > 1 ) {
- args = [ pseudo, pseudo, "", argument ];
- return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
- markFunction( function( seed, matches ) {
- var idx,
- matched = fn( seed, argument ),
- i = matched.length;
- while ( i-- ) {
- idx = indexOf( seed, matched[ i ] );
- seed[ idx ] = !( matches[ idx ] = matched[ i ] );
- }
- } ) :
- function( elem ) {
- return fn( elem, 0, args );
- };
- }
-
- return fn;
- }
- },
-
- pseudos: {
-
- // Potentially complex pseudos
- "not": markFunction( function( selector ) {
-
- // Trim the selector passed to compile
- // to avoid treating leading and trailing
- // spaces as combinators
- var input = [],
- results = [],
- matcher = compile( selector.replace( rtrim, "$1" ) );
-
- return matcher[ expando ] ?
- markFunction( function( seed, matches, _context, xml ) {
- var elem,
- unmatched = matcher( seed, null, xml, [] ),
- i = seed.length;
-
- // Match elements unmatched by `matcher`
- while ( i-- ) {
- if ( ( elem = unmatched[ i ] ) ) {
- seed[ i ] = !( matches[ i ] = elem );
- }
- }
- } ) :
- function( elem, _context, xml ) {
- input[ 0 ] = elem;
- matcher( input, null, xml, results );
-
- // Don't keep the element (issue #299)
- input[ 0 ] = null;
- return !results.pop();
- };
- } ),
-
- "has": markFunction( function( selector ) {
- return function( elem ) {
- return Sizzle( selector, elem ).length > 0;
- };
- } ),
-
- "contains": markFunction( function( text ) {
- text = text.replace( runescape, funescape );
- return function( elem ) {
- return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
- };
- } ),
-
- // "Whether an element is represented by a :lang() selector
- // is based solely on the element's language value
- // being equal to the identifier C,
- // or beginning with the identifier C immediately followed by "-".
- // The matching of C against the element's language value is performed case-insensitively.
- // The identifier C does not have to be a valid language name."
- // http://www.w3.org/TR/selectors/#lang-pseudo
- "lang": markFunction( function( lang ) {
-
- // lang value must be a valid identifier
- if ( !ridentifier.test( lang || "" ) ) {
- Sizzle.error( "unsupported lang: " + lang );
- }
- lang = lang.replace( runescape, funescape ).toLowerCase();
- return function( elem ) {
- var elemLang;
- do {
- if ( ( elemLang = documentIsHTML ?
- elem.lang :
- elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
- elemLang = elemLang.toLowerCase();
- return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
- }
- } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
- return false;
- };
- } ),
-
- // Miscellaneous
- "target": function( elem ) {
- var hash = window.location && window.location.hash;
- return hash && hash.slice( 1 ) === elem.id;
- },
-
- "root": function( elem ) {
- return elem === docElem;
- },
-
- "focus": function( elem ) {
- return elem === document.activeElement &&
- ( !document.hasFocus || document.hasFocus() ) &&
- !!( elem.type || elem.href || ~elem.tabIndex );
- },
-
- // Boolean properties
- "enabled": createDisabledPseudo( false ),
- "disabled": createDisabledPseudo( true ),
-
- "checked": function( elem ) {
-
- // In CSS3, :checked should return both checked and selected elements
- // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
- var nodeName = elem.nodeName.toLowerCase();
- return ( nodeName === "input" && !!elem.checked ) ||
- ( nodeName === "option" && !!elem.selected );
- },
-
- "selected": function( elem ) {
-
- // Accessing this property makes selected-by-default
- // options in Safari work properly
- if ( elem.parentNode ) {
- // eslint-disable-next-line no-unused-expressions
- elem.parentNode.selectedIndex;
- }
-
- return elem.selected === true;
- },
-
- // Contents
- "empty": function( elem ) {
-
- // http://www.w3.org/TR/selectors/#empty-pseudo
- // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
- // but not by others (comment: 8; processing instruction: 7; etc.)
- // nodeType < 6 works because attributes (2) do not appear as children
- for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
- if ( elem.nodeType < 6 ) {
- return false;
- }
- }
- return true;
- },
-
- "parent": function( elem ) {
- return !Expr.pseudos[ "empty" ]( elem );
- },
-
- // Element/input types
- "header": function( elem ) {
- return rheader.test( elem.nodeName );
- },
-
- "input": function( elem ) {
- return rinputs.test( elem.nodeName );
- },
-
- "button": function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return name === "input" && elem.type === "button" || name === "button";
- },
-
- "text": function( elem ) {
- var attr;
- return elem.nodeName.toLowerCase() === "input" &&
- elem.type === "text" &&
-
- // Support: IE<8
- // New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
- ( ( attr = elem.getAttribute( "type" ) ) == null ||
- attr.toLowerCase() === "text" );
- },
-
- // Position-in-collection
- "first": createPositionalPseudo( function() {
- return [ 0 ];
- } ),
-
- "last": createPositionalPseudo( function( _matchIndexes, length ) {
- return [ length - 1 ];
- } ),
-
- "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
- return [ argument < 0 ? argument + length : argument ];
- } ),
-
- "even": createPositionalPseudo( function( matchIndexes, length ) {
- var i = 0;
- for ( ; i < length; i += 2 ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "odd": createPositionalPseudo( function( matchIndexes, length ) {
- var i = 1;
- for ( ; i < length; i += 2 ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
- var i = argument < 0 ?
- argument + length :
- argument > length ?
- length :
- argument;
- for ( ; --i >= 0; ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
- var i = argument < 0 ? argument + length : argument;
- for ( ; ++i < length; ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } )
- }
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
- Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
- Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
- var matched, match, tokens, type,
- soFar, groups, preFilters,
- cached = tokenCache[ selector + " " ];
-
- if ( cached ) {
- return parseOnly ? 0 : cached.slice( 0 );
- }
-
- soFar = selector;
- groups = [];
- preFilters = Expr.preFilter;
-
- while ( soFar ) {
-
- // Comma and first run
- if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
- if ( match ) {
-
- // Don't consume trailing commas as valid
- soFar = soFar.slice( match[ 0 ].length ) || soFar;
- }
- groups.push( ( tokens = [] ) );
- }
-
- matched = false;
-
- // Combinators
- if ( ( match = rcombinators.exec( soFar ) ) ) {
- matched = match.shift();
- tokens.push( {
- value: matched,
-
- // Cast descendant combinators to space
- type: match[ 0 ].replace( rtrim, " " )
- } );
- soFar = soFar.slice( matched.length );
- }
-
- // Filters
- for ( type in Expr.filter ) {
- if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
- ( match = preFilters[ type ]( match ) ) ) ) {
- matched = match.shift();
- tokens.push( {
- value: matched,
- type: type,
- matches: match
- } );
- soFar = soFar.slice( matched.length );
- }
- }
-
- if ( !matched ) {
- break;
- }
- }
-
- // Return the length of the invalid excess
- // if we're just parsing
- // Otherwise, throw an error or return tokens
- return parseOnly ?
- soFar.length :
- soFar ?
- Sizzle.error( selector ) :
-
- // Cache the tokens
- tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
- var i = 0,
- len = tokens.length,
- selector = "";
- for ( ; i < len; i++ ) {
- selector += tokens[ i ].value;
- }
- return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
- var dir = combinator.dir,
- skip = combinator.next,
- key = skip || dir,
- checkNonElements = base && key === "parentNode",
- doneName = done++;
-
- return combinator.first ?
-
- // Check against closest ancestor/preceding element
- function( elem, context, xml ) {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- return matcher( elem, context, xml );
- }
- }
- return false;
- } :
-
- // Check against all ancestor/preceding elements
- function( elem, context, xml ) {
- var oldCache, uniqueCache, outerCache,
- newCache = [ dirruns, doneName ];
-
- // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
- if ( xml ) {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- if ( matcher( elem, context, xml ) ) {
- return true;
- }
- }
- }
- } else {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ elem.uniqueID ] ||
- ( outerCache[ elem.uniqueID ] = {} );
-
- if ( skip && skip === elem.nodeName.toLowerCase() ) {
- elem = elem[ dir ] || elem;
- } else if ( ( oldCache = uniqueCache[ key ] ) &&
- oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
- // Assign to newCache so results back-propagate to previous elements
- return ( newCache[ 2 ] = oldCache[ 2 ] );
- } else {
-
- // Reuse newcache so results back-propagate to previous elements
- uniqueCache[ key ] = newCache;
-
- // A match means we're done; a fail means we have to keep checking
- if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
- return true;
- }
- }
- }
- }
- }
- return false;
- };
-}
-
-function elementMatcher( matchers ) {
- return matchers.length > 1 ?
- function( elem, context, xml ) {
- var i = matchers.length;
- while ( i-- ) {
- if ( !matchers[ i ]( elem, context, xml ) ) {
- return false;
- }
- }
- return true;
- } :
- matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
- var i = 0,
- len = contexts.length;
- for ( ; i < len; i++ ) {
- Sizzle( selector, contexts[ i ], results );
- }
- return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
- var elem,
- newUnmatched = [],
- i = 0,
- len = unmatched.length,
- mapped = map != null;
-
- for ( ; i < len; i++ ) {
- if ( ( elem = unmatched[ i ] ) ) {
- if ( !filter || filter( elem, context, xml ) ) {
- newUnmatched.push( elem );
- if ( mapped ) {
- map.push( i );
- }
- }
- }
- }
-
- return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
- if ( postFilter && !postFilter[ expando ] ) {
- postFilter = setMatcher( postFilter );
- }
- if ( postFinder && !postFinder[ expando ] ) {
- postFinder = setMatcher( postFinder, postSelector );
- }
- return markFunction( function( seed, results, context, xml ) {
- var temp, i, elem,
- preMap = [],
- postMap = [],
- preexisting = results.length,
-
- // Get initial elements from seed or context
- elems = seed || multipleContexts(
- selector || "*",
- context.nodeType ? [ context ] : context,
- []
- ),
-
- // Prefilter to get matcher input, preserving a map for seed-results synchronization
- matcherIn = preFilter && ( seed || !selector ) ?
- condense( elems, preMap, preFilter, context, xml ) :
- elems,
-
- matcherOut = matcher ?
-
- // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
- postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
- // ...intermediate processing is necessary
- [] :
-
- // ...otherwise use results directly
- results :
- matcherIn;
-
- // Find primary matches
- if ( matcher ) {
- matcher( matcherIn, matcherOut, context, xml );
- }
-
- // Apply postFilter
- if ( postFilter ) {
- temp = condense( matcherOut, postMap );
- postFilter( temp, [], context, xml );
-
- // Un-match failing elements by moving them back to matcherIn
- i = temp.length;
- while ( i-- ) {
- if ( ( elem = temp[ i ] ) ) {
- matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
- }
- }
- }
-
- if ( seed ) {
- if ( postFinder || preFilter ) {
- if ( postFinder ) {
-
- // Get the final matcherOut by condensing this intermediate into postFinder contexts
- temp = [];
- i = matcherOut.length;
- while ( i-- ) {
- if ( ( elem = matcherOut[ i ] ) ) {
-
- // Restore matcherIn since elem is not yet a final match
- temp.push( ( matcherIn[ i ] = elem ) );
- }
- }
- postFinder( null, ( matcherOut = [] ), temp, xml );
- }
-
- // Move matched elements from seed to results to keep them synchronized
- i = matcherOut.length;
- while ( i-- ) {
- if ( ( elem = matcherOut[ i ] ) &&
- ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
- seed[ temp ] = !( results[ temp ] = elem );
- }
- }
- }
-
- // Add elements to results, through postFinder if defined
- } else {
- matcherOut = condense(
- matcherOut === results ?
- matcherOut.splice( preexisting, matcherOut.length ) :
- matcherOut
- );
- if ( postFinder ) {
- postFinder( null, results, matcherOut, xml );
- } else {
- push.apply( results, matcherOut );
- }
- }
- } );
-}
-
-function matcherFromTokens( tokens ) {
- var checkContext, matcher, j,
- len = tokens.length,
- leadingRelative = Expr.relative[ tokens[ 0 ].type ],
- implicitRelative = leadingRelative || Expr.relative[ " " ],
- i = leadingRelative ? 1 : 0,
-
- // The foundational matcher ensures that elements are reachable from top-level context(s)
- matchContext = addCombinator( function( elem ) {
- return elem === checkContext;
- }, implicitRelative, true ),
- matchAnyContext = addCombinator( function( elem ) {
- return indexOf( checkContext, elem ) > -1;
- }, implicitRelative, true ),
- matchers = [ function( elem, context, xml ) {
- var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
- ( checkContext = context ).nodeType ?
- matchContext( elem, context, xml ) :
- matchAnyContext( elem, context, xml ) );
-
- // Avoid hanging onto element (issue #299)
- checkContext = null;
- return ret;
- } ];
-
- for ( ; i < len; i++ ) {
- if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
- matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
- } else {
- matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
- // Return special upon seeing a positional matcher
- if ( matcher[ expando ] ) {
-
- // Find the next relative operator (if any) for proper handling
- j = ++i;
- for ( ; j < len; j++ ) {
- if ( Expr.relative[ tokens[ j ].type ] ) {
- break;
- }
- }
- return setMatcher(
- i > 1 && elementMatcher( matchers ),
- i > 1 && toSelector(
-
- // If the preceding token was a descendant combinator, insert an implicit any-element `*`
- tokens
- .slice( 0, i - 1 )
- .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
- ).replace( rtrim, "$1" ),
- matcher,
- i < j && matcherFromTokens( tokens.slice( i, j ) ),
- j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
- j < len && toSelector( tokens )
- );
- }
- matchers.push( matcher );
- }
- }
-
- return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
- var bySet = setMatchers.length > 0,
- byElement = elementMatchers.length > 0,
- superMatcher = function( seed, context, xml, results, outermost ) {
- var elem, j, matcher,
- matchedCount = 0,
- i = "0",
- unmatched = seed && [],
- setMatched = [],
- contextBackup = outermostContext,
-
- // We must always have either seed elements or outermost context
- elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
- // Use integer dirruns iff this is the outermost matcher
- dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
- len = elems.length;
-
- if ( outermost ) {
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- outermostContext = context == document || context || outermost;
- }
-
- // Add elements passing elementMatchers directly to results
- // Support: IE<9, Safari
- // Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
- for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
- if ( byElement && elem ) {
- j = 0;
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( !context && elem.ownerDocument != document ) {
- setDocument( elem );
- xml = !documentIsHTML;
- }
- while ( ( matcher = elementMatchers[ j++ ] ) ) {
- if ( matcher( elem, context || document, xml ) ) {
- results.push( elem );
- break;
- }
- }
- if ( outermost ) {
- dirruns = dirrunsUnique;
- }
- }
-
- // Track unmatched elements for set filters
- if ( bySet ) {
-
- // They will have gone through all possible matchers
- if ( ( elem = !matcher && elem ) ) {
- matchedCount--;
- }
-
- // Lengthen the array for every element, matched or not
- if ( seed ) {
- unmatched.push( elem );
- }
- }
- }
-
- // `i` is now the count of elements visited above, and adding it to `matchedCount`
- // makes the latter nonnegative.
- matchedCount += i;
-
- // Apply set filters to unmatched elements
- // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
- // equals `i`), unless we didn't visit _any_ elements in the above loop because we have
- // no element matchers and no seed.
- // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
- // case, which will result in a "00" `matchedCount` that differs from `i` but is also
- // numerically zero.
- if ( bySet && i !== matchedCount ) {
- j = 0;
- while ( ( matcher = setMatchers[ j++ ] ) ) {
- matcher( unmatched, setMatched, context, xml );
- }
-
- if ( seed ) {
-
- // Reintegrate element matches to eliminate the need for sorting
- if ( matchedCount > 0 ) {
- while ( i-- ) {
- if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
- setMatched[ i ] = pop.call( results );
- }
- }
- }
-
- // Discard index placeholder values to get only actual matches
- setMatched = condense( setMatched );
- }
-
- // Add matches to results
- push.apply( results, setMatched );
-
- // Seedless set matches succeeding multiple successful matchers stipulate sorting
- if ( outermost && !seed && setMatched.length > 0 &&
- ( matchedCount + setMatchers.length ) > 1 ) {
-
- Sizzle.uniqueSort( results );
- }
- }
-
- // Override manipulation of globals by nested matchers
- if ( outermost ) {
- dirruns = dirrunsUnique;
- outermostContext = contextBackup;
- }
-
- return unmatched;
- };
-
- return bySet ?
- markFunction( superMatcher ) :
- superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
- var i,
- setMatchers = [],
- elementMatchers = [],
- cached = compilerCache[ selector + " " ];
-
- if ( !cached ) {
-
- // Generate a function of recursive functions that can be used to check each element
- if ( !match ) {
- match = tokenize( selector );
- }
- i = match.length;
- while ( i-- ) {
- cached = matcherFromTokens( match[ i ] );
- if ( cached[ expando ] ) {
- setMatchers.push( cached );
- } else {
- elementMatchers.push( cached );
- }
- }
-
- // Cache the compiled function
- cached = compilerCache(
- selector,
- matcherFromGroupMatchers( elementMatchers, setMatchers )
- );
-
- // Save selector and tokenization
- cached.selector = selector;
- }
- return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- * selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- * selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
- var i, tokens, token, type, find,
- compiled = typeof selector === "function" && selector,
- match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
- results = results || [];
-
- // Try to minimize operations if there is only one selector in the list and no seed
- // (the latter of which guarantees us context)
- if ( match.length === 1 ) {
-
- // Reduce context if the leading compound selector is an ID
- tokens = match[ 0 ] = match[ 0 ].slice( 0 );
- if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
- context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
- context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
- .replace( runescape, funescape ), context ) || [] )[ 0 ];
- if ( !context ) {
- return results;
-
- // Precompiled matchers will still verify ancestry, so step up a level
- } else if ( compiled ) {
- context = context.parentNode;
- }
-
- selector = selector.slice( tokens.shift().value.length );
- }
-
- // Fetch a seed set for right-to-left matching
- i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
- while ( i-- ) {
- token = tokens[ i ];
-
- // Abort if we hit a combinator
- if ( Expr.relative[ ( type = token.type ) ] ) {
- break;
- }
- if ( ( find = Expr.find[ type ] ) ) {
-
- // Search, expanding context for leading sibling combinators
- if ( ( seed = find(
- token.matches[ 0 ].replace( runescape, funescape ),
- rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
- context
- ) ) ) {
-
- // If seed is empty or no tokens remain, we can return early
- tokens.splice( i, 1 );
- selector = seed.length && toSelector( tokens );
- if ( !selector ) {
- push.apply( results, seed );
- return results;
- }
-
- break;
- }
- }
- }
- }
-
- // Compile and execute a filtering function if one is not provided
- // Provide `match` to avoid retokenization if we modified the selector above
- ( compiled || compile( selector, match ) )(
- seed,
- context,
- !documentIsHTML,
- results,
- !context || rsibling.test( selector ) && testContext( context.parentNode ) || context
- );
- return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
- // Should return 1, but returns 4 (following)
- return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
- el.innerHTML = "<a href='#'></a>";
- return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
- addHandle( "type|href|height|width", function( elem, name, isXML ) {
- if ( !isXML ) {
- return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
- }
- } );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
- el.innerHTML = "<input/>";
- el.firstChild.setAttribute( "value", "" );
- return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
- addHandle( "value", function( elem, _name, isXML ) {
- if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
- return elem.defaultValue;
- }
- } );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
- return el.getAttribute( "disabled" ) == null;
-} ) ) {
- addHandle( booleans, function( elem, name, isXML ) {
- var val;
- if ( !isXML ) {
- return elem[ name ] === true ? name.toLowerCase() :
- ( val = elem.getAttributeNode( name ) ) && val.specified ?
- val.value :
- null;
- }
- } );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
- var matched = [],
- truncate = until !== undefined;
-
- while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
- if ( elem.nodeType === 1 ) {
- if ( truncate && jQuery( elem ).is( until ) ) {
- break;
- }
- matched.push( elem );
- }
- }
- return matched;
-};
-
-
-var siblings = function( n, elem ) {
- var matched = [];
-
- for ( ; n; n = n.nextSibling ) {
- if ( n.nodeType === 1 && n !== elem ) {
- matched.push( n );
- }
- }
-
- return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
- return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-};
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
- if ( isFunction( qualifier ) ) {
- return jQuery.grep( elements, function( elem, i ) {
- return !!qualifier.call( elem, i, elem ) !== not;
- } );
- }
-
- // Single element
- if ( qualifier.nodeType ) {
- return jQuery.grep( elements, function( elem ) {
- return ( elem === qualifier ) !== not;
- } );
- }
-
- // Arraylike of elements (jQuery, arguments, Array)
- if ( typeof qualifier !== "string" ) {
- return jQuery.grep( elements, function( elem ) {
- return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
- } );
- }
-
- // Filtered directly for both simple and complex selectors
- return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
- var elem = elems[ 0 ];
-
- if ( not ) {
- expr = ":not(" + expr + ")";
- }
-
- if ( elems.length === 1 && elem.nodeType === 1 ) {
- return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
- }
-
- return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
- return elem.nodeType === 1;
- } ) );
-};
-
-jQuery.fn.extend( {
- find: function( selector ) {
- var i, ret,
- len = this.length,
- self = this;
-
- if ( typeof selector !== "string" ) {
- return this.pushStack( jQuery( selector ).filter( function() {
- for ( i = 0; i < len; i++ ) {
- if ( jQuery.contains( self[ i ], this ) ) {
- return true;
- }
- }
- } ) );
- }
-
- ret = this.pushStack( [] );
-
- for ( i = 0; i < len; i++ ) {
- jQuery.find( selector, self[ i ], ret );
- }
-
- return len > 1 ? jQuery.uniqueSort( ret ) : ret;
- },
- filter: function( selector ) {
- return this.pushStack( winnow( this, selector || [], false ) );
- },
- not: function( selector ) {
- return this.pushStack( winnow( this, selector || [], true ) );
- },
- is: function( selector ) {
- return !!winnow(
- this,
-
- // If this is a positional/relative selector, check membership in the returned set
- // so $("p:first").is("p:last") won't return true for a doc with two "p".
- typeof selector === "string" && rneedsContext.test( selector ) ?
- jQuery( selector ) :
- selector || [],
- false
- ).length;
- }
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
- // A simple way to check for HTML strings
- // Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
- // Strict HTML recognition (#11290: must start with <)
- // Shortcut simple #id case for speed
- rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
- init = jQuery.fn.init = function( selector, context, root ) {
- var match, elem;
-
- // HANDLE: $(""), $(null), $(undefined), $(false)
- if ( !selector ) {
- return this;
- }
-
- // Method init() accepts an alternate rootjQuery
- // so migrate can support jQuery.sub (gh-2101)
- root = root || rootjQuery;
-
- // Handle HTML strings
- if ( typeof selector === "string" ) {
- if ( selector[ 0 ] === "<" &&
- selector[ selector.length - 1 ] === ">" &&
- selector.length >= 3 ) {
-
- // Assume that strings that start and end with <> are HTML and skip the regex check
- match = [ null, selector, null ];
-
- } else {
- match = rquickExpr.exec( selector );
- }
-
- // Match html or make sure no context is specified for #id
- if ( match && ( match[ 1 ] || !context ) ) {
-
- // HANDLE: $(html) -> $(array)
- if ( match[ 1 ] ) {
- context = context instanceof jQuery ? context[ 0 ] : context;
-
- // Option to run scripts is true for back-compat
- // Intentionally let the error be thrown if parseHTML is not present
- jQuery.merge( this, jQuery.parseHTML(
- match[ 1 ],
- context && context.nodeType ? context.ownerDocument || context : document,
- true
- ) );
-
- // HANDLE: $(html, props)
- if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
- for ( match in context ) {
-
- // Properties of context are called as methods if possible
- if ( isFunction( this[ match ] ) ) {
- this[ match ]( context[ match ] );
-
- // ...and otherwise set as attributes
- } else {
- this.attr( match, context[ match ] );
- }
- }
- }
-
- return this;
-
- // HANDLE: $(#id)
- } else {
- elem = document.getElementById( match[ 2 ] );
-
- if ( elem ) {
-
- // Inject the element directly into the jQuery object
- this[ 0 ] = elem;
- this.length = 1;
- }
- return this;
- }
-
- // HANDLE: $(expr, $(...))
- } else if ( !context || context.jquery ) {
- return ( context || root ).find( selector );
-
- // HANDLE: $(expr, context)
- // (which is just equivalent to: $(context).find(expr)
- } else {
- return this.constructor( context ).find( selector );
- }
-
- // HANDLE: $(DOMElement)
- } else if ( selector.nodeType ) {
- this[ 0 ] = selector;
- this.length = 1;
- return this;
-
- // HANDLE: $(function)
- // Shortcut for document ready
- } else if ( isFunction( selector ) ) {
- return root.ready !== undefined ?
- root.ready( selector ) :
-
- // Execute immediately if ready is not present
- selector( jQuery );
- }
-
- return jQuery.makeArray( selector, this );
- };
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
- // Methods guaranteed to produce a unique set when starting from a unique set
- guaranteedUnique = {
- children: true,
- contents: true,
- next: true,
- prev: true
- };
-
-jQuery.fn.extend( {
- has: function( target ) {
- var targets = jQuery( target, this ),
- l = targets.length;
-
- return this.filter( function() {
- var i = 0;
- for ( ; i < l; i++ ) {
- if ( jQuery.contains( this, targets[ i ] ) ) {
- return true;
- }
- }
- } );
- },
-
- closest: function( selectors, context ) {
- var cur,
- i = 0,
- l = this.length,
- matched = [],
- targets = typeof selectors !== "string" && jQuery( selectors );
-
- // Positional selectors never match, since there's no _selection_ context
- if ( !rneedsContext.test( selectors ) ) {
- for ( ; i < l; i++ ) {
- for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
- // Always skip document fragments
- if ( cur.nodeType < 11 && ( targets ?
- targets.index( cur ) > -1 :
-
- // Don't pass non-elements to Sizzle
- cur.nodeType === 1 &&
- jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
- matched.push( cur );
- break;
- }
- }
- }
- }
-
- return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
- },
-
- // Determine the position of an element within the set
- index: function( elem ) {
-
- // No argument, return index in parent
- if ( !elem ) {
- return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
- }
-
- // Index in selector
- if ( typeof elem === "string" ) {
- return indexOf.call( jQuery( elem ), this[ 0 ] );
- }
-
- // Locate the position of the desired element
- return indexOf.call( this,
-
- // If it receives a jQuery object, the first element is used
- elem.jquery ? elem[ 0 ] : elem
- );
- },
-
- add: function( selector, context ) {
- return this.pushStack(
- jQuery.uniqueSort(
- jQuery.merge( this.get(), jQuery( selector, context ) )
- )
- );
- },
-
- addBack: function( selector ) {
- return this.add( selector == null ?
- this.prevObject : this.prevObject.filter( selector )
- );
- }
-} );
-
-function sibling( cur, dir ) {
- while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
- return cur;
-}
-
-jQuery.each( {
- parent: function( elem ) {
- var parent = elem.parentNode;
- return parent && parent.nodeType !== 11 ? parent : null;
- },
- parents: function( elem ) {
- return dir( elem, "parentNode" );
- },
- parentsUntil: function( elem, _i, until ) {
- return dir( elem, "parentNode", until );
- },
- next: function( elem ) {
- return sibling( elem, "nextSibling" );
- },
- prev: function( elem ) {
- return sibling( elem, "previousSibling" );
- },
- nextAll: function( elem ) {
- return dir( elem, "nextSibling" );
- },
- prevAll: function( elem ) {
- return dir( elem, "previousSibling" );
- },
- nextUntil: function( elem, _i, until ) {
- return dir( elem, "nextSibling", until );
- },
- prevUntil: function( elem, _i, until ) {
- return dir( elem, "previousSibling", until );
- },
- siblings: function( elem ) {
- return siblings( ( elem.parentNode || {} ).firstChild, elem );
- },
- children: function( elem ) {
- return siblings( elem.firstChild );
- },
- contents: function( elem ) {
- if ( elem.contentDocument != null &&
-
- // Support: IE 11+
- // <object> elements with no `data` attribute has an object
- // `contentDocument` with a `null` prototype.
- getProto( elem.contentDocument ) ) {
-
- return elem.contentDocument;
- }
-
- // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
- // Treat the template element as a regular one in browsers that
- // don't support it.
- if ( nodeName( elem, "template" ) ) {
- elem = elem.content || elem;
- }
-
- return jQuery.merge( [], elem.childNodes );
- }
-}, function( name, fn ) {
- jQuery.fn[ name ] = function( until, selector ) {
- var matched = jQuery.map( this, fn, until );
-
- if ( name.slice( -5 ) !== "Until" ) {
- selector = until;
- }
-
- if ( selector && typeof selector === "string" ) {
- matched = jQuery.filter( selector, matched );
- }
-
- if ( this.length > 1 ) {
-
- // Remove duplicates
- if ( !guaranteedUnique[ name ] ) {
- jQuery.uniqueSort( matched );
- }
-
- // Reverse order for parents* and prev-derivatives
- if ( rparentsprev.test( name ) ) {
- matched.reverse();
- }
- }
-
- return this.pushStack( matched );
- };
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
- var object = {};
- jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
- object[ flag ] = true;
- } );
- return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- * options: an optional list of space-separated options that will change how
- * the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- * once: will ensure the callback list can only be fired once (like a Deferred)
- *
- * memory: will keep track of previous values and will call any callback added
- * after the list has been fired right away with the latest "memorized"
- * values (like a Deferred)
- *
- * unique: will ensure a callback can only be added once (no duplicate in the list)
- *
- * stopOnFalse: interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
- // Convert options from String-formatted to Object-formatted if needed
- // (we check in cache first)
- options = typeof options === "string" ?
- createOptions( options ) :
- jQuery.extend( {}, options );
-
- var // Flag to know if list is currently firing
- firing,
-
- // Last fire value for non-forgettable lists
- memory,
-
- // Flag to know if list was already fired
- fired,
-
- // Flag to prevent firing
- locked,
-
- // Actual callback list
- list = [],
-
- // Queue of execution data for repeatable lists
- queue = [],
-
- // Index of currently firing callback (modified by add/remove as needed)
- firingIndex = -1,
-
- // Fire callbacks
- fire = function() {
-
- // Enforce single-firing
- locked = locked || options.once;
-
- // Execute callbacks for all pending executions,
- // respecting firingIndex overrides and runtime changes
- fired = firing = true;
- for ( ; queue.length; firingIndex = -1 ) {
- memory = queue.shift();
- while ( ++firingIndex < list.length ) {
-
- // Run callback and check for early termination
- if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
- options.stopOnFalse ) {
-
- // Jump to end and forget the data so .add doesn't re-fire
- firingIndex = list.length;
- memory = false;
- }
- }
- }
-
- // Forget the data if we're done with it
- if ( !options.memory ) {
- memory = false;
- }
-
- firing = false;
-
- // Clean up if we're done firing for good
- if ( locked ) {
-
- // Keep an empty list if we have data for future add calls
- if ( memory ) {
- list = [];
-
- // Otherwise, this object is spent
- } else {
- list = "";
- }
- }
- },
-
- // Actual Callbacks object
- self = {
-
- // Add a callback or a collection of callbacks to the list
- add: function() {
- if ( list ) {
-
- // If we have memory from a past run, we should fire after adding
- if ( memory && !firing ) {
- firingIndex = list.length - 1;
- queue.push( memory );
- }
-
- ( function add( args ) {
- jQuery.each( args, function( _, arg ) {
- if ( isFunction( arg ) ) {
- if ( !options.unique || !self.has( arg ) ) {
- list.push( arg );
- }
- } else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
- // Inspect recursively
- add( arg );
- }
- } );
- } )( arguments );
-
- if ( memory && !firing ) {
- fire();
- }
- }
- return this;
- },
-
- // Remove a callback from the list
- remove: function() {
- jQuery.each( arguments, function( _, arg ) {
- var index;
- while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
- list.splice( index, 1 );
-
- // Handle firing indexes
- if ( index <= firingIndex ) {
- firingIndex--;
- }
- }
- } );
- return this;
- },
-
- // Check if a given callback is in the list.
- // If no argument is given, return whether or not list has callbacks attached.
- has: function( fn ) {
- return fn ?
- jQuery.inArray( fn, list ) > -1 :
- list.length > 0;
- },
-
- // Remove all callbacks from the list
- empty: function() {
- if ( list ) {
- list = [];
- }
- return this;
- },
-
- // Disable .fire and .add
- // Abort any current/pending executions
- // Clear all callbacks and values
- disable: function() {
- locked = queue = [];
- list = memory = "";
- return this;
- },
- disabled: function() {
- return !list;
- },
-
- // Disable .fire
- // Also disable .add unless we have memory (since it would have no effect)
- // Abort any pending executions
- lock: function() {
- locked = queue = [];
- if ( !memory && !firing ) {
- list = memory = "";
- }
- return this;
- },
- locked: function() {
- return !!locked;
- },
-
- // Call all callbacks with the given context and arguments
- fireWith: function( context, args ) {
- if ( !locked ) {
- args = args || [];
- args = [ context, args.slice ? args.slice() : args ];
- queue.push( args );
- if ( !firing ) {
- fire();
- }
- }
- return this;
- },
-
- // Call all the callbacks with the given arguments
- fire: function() {
- self.fireWith( this, arguments );
- return this;
- },
-
- // To know if the callbacks have already been called at least once
- fired: function() {
- return !!fired;
- }
- };
-
- return self;
-};
-
-
-function Identity( v ) {
- return v;
-}
-function Thrower( ex ) {
- throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
- var method;
-
- try {
-
- // Check for promise aspect first to privilege synchronous behavior
- if ( value && isFunction( ( method = value.promise ) ) ) {
- method.call( value ).done( resolve ).fail( reject );
-
- // Other thenables
- } else if ( value && isFunction( ( method = value.then ) ) ) {
- method.call( value, resolve, reject );
-
- // Other non-thenables
- } else {
-
- // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
- // * false: [ value ].slice( 0 ) => resolve( value )
- // * true: [ value ].slice( 1 ) => resolve()
- resolve.apply( undefined, [ value ].slice( noValue ) );
- }
-
- // For Promises/A+, convert exceptions into rejections
- // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
- // Deferred#then to conditionally suppress rejection.
- } catch ( value ) {
-
- // Support: Android 4.0 only
- // Strict mode functions invoked without .call/.apply get global-object context
- reject.apply( undefined, [ value ] );
- }
-}
-
-jQuery.extend( {
-
- Deferred: function( func ) {
- var tuples = [
-
- // action, add listener, callbacks,
- // ... .then handlers, argument index, [final state]
- [ "notify", "progress", jQuery.Callbacks( "memory" ),
- jQuery.Callbacks( "memory" ), 2 ],
- [ "resolve", "done", jQuery.Callbacks( "once memory" ),
- jQuery.Callbacks( "once memory" ), 0, "resolved" ],
- [ "reject", "fail", jQuery.Callbacks( "once memory" ),
- jQuery.Callbacks( "once memory" ), 1, "rejected" ]
- ],
- state = "pending",
- promise = {
- state: function() {
- return state;
- },
- always: function() {
- deferred.done( arguments ).fail( arguments );
- return this;
- },
- "catch": function( fn ) {
- return promise.then( null, fn );
- },
-
- // Keep pipe for back-compat
- pipe: function( /* fnDone, fnFail, fnProgress */ ) {
- var fns = arguments;
-
- return jQuery.Deferred( function( newDefer ) {
- jQuery.each( tuples, function( _i, tuple ) {
-
- // Map tuples (progress, done, fail) to arguments (done, fail, progress)
- var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
- // deferred.progress(function() { bind to newDefer or newDefer.notify })
- // deferred.done(function() { bind to newDefer or newDefer.resolve })
- // deferred.fail(function() { bind to newDefer or newDefer.reject })
- deferred[ tuple[ 1 ] ]( function() {
- var returned = fn && fn.apply( this, arguments );
- if ( returned && isFunction( returned.promise ) ) {
- returned.promise()
- .progress( newDefer.notify )
- .done( newDefer.resolve )
- .fail( newDefer.reject );
- } else {
- newDefer[ tuple[ 0 ] + "With" ](
- this,
- fn ? [ returned ] : arguments
- );
- }
- } );
- } );
- fns = null;
- } ).promise();
- },
- then: function( onFulfilled, onRejected, onProgress ) {
- var maxDepth = 0;
- function resolve( depth, deferred, handler, special ) {
- return function() {
- var that = this,
- args = arguments,
- mightThrow = function() {
- var returned, then;
-
- // Support: Promises/A+ section 2.3.3.3.3
- // https://promisesaplus.com/#point-59
- // Ignore double-resolution attempts
- if ( depth < maxDepth ) {
- return;
- }
-
- returned = handler.apply( that, args );
-
- // Support: Promises/A+ section 2.3.1
- // https://promisesaplus.com/#point-48
- if ( returned === deferred.promise() ) {
- throw new TypeError( "Thenable self-resolution" );
- }
-
- // Support: Promises/A+ sections 2.3.3.1, 3.5
- // https://promisesaplus.com/#point-54
- // https://promisesaplus.com/#point-75
- // Retrieve `then` only once
- then = returned &&
-
- // Support: Promises/A+ section 2.3.4
- // https://promisesaplus.com/#point-64
- // Only check objects and functions for thenability
- ( typeof returned === "object" ||
- typeof returned === "function" ) &&
- returned.then;
-
- // Handle a returned thenable
- if ( isFunction( then ) ) {
-
- // Special processors (notify) just wait for resolution
- if ( special ) {
- then.call(
- returned,
- resolve( maxDepth, deferred, Identity, special ),
- resolve( maxDepth, deferred, Thrower, special )
- );
-
- // Normal processors (resolve) also hook into progress
- } else {
-
- // ...and disregard older resolution values
- maxDepth++;
-
- then.call(
- returned,
- resolve( maxDepth, deferred, Identity, special ),
- resolve( maxDepth, deferred, Thrower, special ),
- resolve( maxDepth, deferred, Identity,
- deferred.notifyWith )
- );
- }
-
- // Handle all other returned values
- } else {
-
- // Only substitute handlers pass on context
- // and multiple values (non-spec behavior)
- if ( handler !== Identity ) {
- that = undefined;
- args = [ returned ];
- }
-
- // Process the value(s)
- // Default process is resolve
- ( special || deferred.resolveWith )( that, args );
- }
- },
-
- // Only normal processors (resolve) catch and reject exceptions
- process = special ?
- mightThrow :
- function() {
- try {
- mightThrow();
- } catch ( e ) {
-
- if ( jQuery.Deferred.exceptionHook ) {
- jQuery.Deferred.exceptionHook( e,
- process.stackTrace );
- }
-
- // Support: Promises/A+ section 2.3.3.3.4.1
- // https://promisesaplus.com/#point-61
- // Ignore post-resolution exceptions
- if ( depth + 1 >= maxDepth ) {
-
- // Only substitute handlers pass on context
- // and multiple values (non-spec behavior)
- if ( handler !== Thrower ) {
- that = undefined;
- args = [ e ];
- }
-
- deferred.rejectWith( that, args );
- }
- }
- };
-
- // Support: Promises/A+ section 2.3.3.3.1
- // https://promisesaplus.com/#point-57
- // Re-resolve promises immediately to dodge false rejection from
- // subsequent errors
- if ( depth ) {
- process();
- } else {
-
- // Call an optional hook to record the stack, in case of exception
- // since it's otherwise lost when execution goes async
- if ( jQuery.Deferred.getStackHook ) {
- process.stackTrace = jQuery.Deferred.getStackHook();
- }
- window.setTimeout( process );
- }
- };
- }
-
- return jQuery.Deferred( function( newDefer ) {
-
- // progress_handlers.add( ... )
- tuples[ 0 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onProgress ) ?
- onProgress :
- Identity,
- newDefer.notifyWith
- )
- );
-
- // fulfilled_handlers.add( ... )
- tuples[ 1 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onFulfilled ) ?
- onFulfilled :
- Identity
- )
- );
-
- // rejected_handlers.add( ... )
- tuples[ 2 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onRejected ) ?
- onRejected :
- Thrower
- )
- );
- } ).promise();
- },
-
- // Get a promise for this deferred
- // If obj is provided, the promise aspect is added to the object
- promise: function( obj ) {
- return obj != null ? jQuery.extend( obj, promise ) : promise;
- }
- },
- deferred = {};
-
- // Add list-specific methods
- jQuery.each( tuples, function( i, tuple ) {
- var list = tuple[ 2 ],
- stateString = tuple[ 5 ];
-
- // promise.progress = list.add
- // promise.done = list.add
- // promise.fail = list.add
- promise[ tuple[ 1 ] ] = list.add;
-
- // Handle state
- if ( stateString ) {
- list.add(
- function() {
-
- // state = "resolved" (i.e., fulfilled)
- // state = "rejected"
- state = stateString;
- },
-
- // rejected_callbacks.disable
- // fulfilled_callbacks.disable
- tuples[ 3 - i ][ 2 ].disable,
-
- // rejected_handlers.disable
- // fulfilled_handlers.disable
- tuples[ 3 - i ][ 3 ].disable,
-
- // progress_callbacks.lock
- tuples[ 0 ][ 2 ].lock,
-
- // progress_handlers.lock
- tuples[ 0 ][ 3 ].lock
- );
- }
-
- // progress_handlers.fire
- // fulfilled_handlers.fire
- // rejected_handlers.fire
- list.add( tuple[ 3 ].fire );
-
- // deferred.notify = function() { deferred.notifyWith(...) }
- // deferred.resolve = function() { deferred.resolveWith(...) }
- // deferred.reject = function() { deferred.rejectWith(...) }
- deferred[ tuple[ 0 ] ] = function() {
- deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
- return this;
- };
-
- // deferred.notifyWith = list.fireWith
- // deferred.resolveWith = list.fireWith
- // deferred.rejectWith = list.fireWith
- deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
- } );
-
- // Make the deferred a promise
- promise.promise( deferred );
-
- // Call given func if any
- if ( func ) {
- func.call( deferred, deferred );
- }
-
- // All done!
- return deferred;
- },
-
- // Deferred helper
- when: function( singleValue ) {
- var
-
- // count of uncompleted subordinates
- remaining = arguments.length,
-
- // count of unprocessed arguments
- i = remaining,
-
- // subordinate fulfillment data
- resolveContexts = Array( i ),
- resolveValues = slice.call( arguments ),
-
- // the master Deferred
- master = jQuery.Deferred(),
-
- // subordinate callback factory
- updateFunc = function( i ) {
- return function( value ) {
- resolveContexts[ i ] = this;
- resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
- if ( !( --remaining ) ) {
- master.resolveWith( resolveContexts, resolveValues );
- }
- };
- };
-
- // Single- and empty arguments are adopted like Promise.resolve
- if ( remaining <= 1 ) {
- adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
- !remaining );
-
- // Use .then() to unwrap secondary thenables (cf. gh-3000)
- if ( master.state() === "pending" ||
- isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
- return master.then();
- }
- }
-
- // Multiple arguments are aggregated like Promise.all array elements
- while ( i-- ) {
- adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
- }
-
- return master.promise();
- }
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
- // Support: IE 8 - 9 only
- // Console exists when dev tools are open, which can happen at any time
- if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
- window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
- }
-};
-
-
-
-
-jQuery.readyException = function( error ) {
- window.setTimeout( function() {
- throw error;
- } );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
- readyList
- .then( fn )
-
- // Wrap jQuery.readyException in a function so that the lookup
- // happens at the time of error handling instead of callback
- // registration.
- .catch( function( error ) {
- jQuery.readyException( error );
- } );
-
- return this;
-};
-
-jQuery.extend( {
-
- // Is the DOM ready to be used? Set to true once it occurs.
- isReady: false,
-
- // A counter to track how many items to wait for before
- // the ready event fires. See #6781
- readyWait: 1,
-
- // Handle when the DOM is ready
- ready: function( wait ) {
-
- // Abort if there are pending holds or we're already ready
- if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
- return;
- }
-
- // Remember that the DOM is ready
- jQuery.isReady = true;
-
- // If a normal DOM Ready event fired, decrement, and wait if need be
- if ( wait !== true && --jQuery.readyWait > 0 ) {
- return;
- }
-
- // If there are functions bound, to execute
- readyList.resolveWith( document, [ jQuery ] );
- }
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
- document.removeEventListener( "DOMContentLoaded", completed );
- window.removeEventListener( "load", completed );
- jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
- ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
- // Handle it asynchronously to allow scripts the opportunity to delay ready
- window.setTimeout( jQuery.ready );
-
-} else {
-
- // Use the handy event callback
- document.addEventListener( "DOMContentLoaded", completed );
-
- // A fallback to window.onload, that will always work
- window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
- var i = 0,
- len = elems.length,
- bulk = key == null;
-
- // Sets many values
- if ( toType( key ) === "object" ) {
- chainable = true;
- for ( i in key ) {
- access( elems, fn, i, key[ i ], true, emptyGet, raw );
- }
-
- // Sets one value
- } else if ( value !== undefined ) {
- chainable = true;
-
- if ( !isFunction( value ) ) {
- raw = true;
- }
-
- if ( bulk ) {
-
- // Bulk operations run against the entire set
- if ( raw ) {
- fn.call( elems, value );
- fn = null;
-
- // ...except when executing function values
- } else {
- bulk = fn;
- fn = function( elem, _key, value ) {
- return bulk.call( jQuery( elem ), value );
- };
- }
- }
-
- if ( fn ) {
- for ( ; i < len; i++ ) {
- fn(
- elems[ i ], key, raw ?
- value :
- value.call( elems[ i ], i, fn( elems[ i ], key ) )
- );
- }
- }
- }
-
- if ( chainable ) {
- return elems;
- }
-
- // Gets
- if ( bulk ) {
- return fn.call( elems );
- }
-
- return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
- rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
- return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
- return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
- // Accepts only:
- // - Node
- // - Node.ELEMENT_NODE
- // - Node.DOCUMENT_NODE
- // - Object
- // - Any
- return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
- this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
- cache: function( owner ) {
-
- // Check if the owner object already has a cache
- var value = owner[ this.expando ];
-
- // If not, create one
- if ( !value ) {
- value = {};
-
- // We can accept data for non-element nodes in modern browsers,
- // but we should not, see #8335.
- // Always return an empty object.
- if ( acceptData( owner ) ) {
-
- // If it is a node unlikely to be stringify-ed or looped over
- // use plain assignment
- if ( owner.nodeType ) {
- owner[ this.expando ] = value;
-
- // Otherwise secure it in a non-enumerable property
- // configurable must be true to allow the property to be
- // deleted when data is removed
- } else {
- Object.defineProperty( owner, this.expando, {
- value: value,
- configurable: true
- } );
- }
- }
- }
-
- return value;
- },
- set: function( owner, data, value ) {
- var prop,
- cache = this.cache( owner );
-
- // Handle: [ owner, key, value ] args
- // Always use camelCase key (gh-2257)
- if ( typeof data === "string" ) {
- cache[ camelCase( data ) ] = value;
-
- // Handle: [ owner, { properties } ] args
- } else {
-
- // Copy the properties one-by-one to the cache object
- for ( prop in data ) {
- cache[ camelCase( prop ) ] = data[ prop ];
- }
- }
- return cache;
- },
- get: function( owner, key ) {
- return key === undefined ?
- this.cache( owner ) :
-
- // Always use camelCase key (gh-2257)
- owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
- },
- access: function( owner, key, value ) {
-
- // In cases where either:
- //
- // 1. No key was specified
- // 2. A string key was specified, but no value provided
- //
- // Take the "read" path and allow the get method to determine
- // which value to return, respectively either:
- //
- // 1. The entire cache object
- // 2. The data stored at the key
- //
- if ( key === undefined ||
- ( ( key && typeof key === "string" ) && value === undefined ) ) {
-
- return this.get( owner, key );
- }
-
- // When the key is not a string, or both a key and value
- // are specified, set or extend (existing objects) with either:
- //
- // 1. An object of properties
- // 2. A key and value
- //
- this.set( owner, key, value );
-
- // Since the "set" path can have two possible entry points
- // return the expected data based on which path was taken[*]
- return value !== undefined ? value : key;
- },
- remove: function( owner, key ) {
- var i,
- cache = owner[ this.expando ];
-
- if ( cache === undefined ) {
- return;
- }
-
- if ( key !== undefined ) {
-
- // Support array or space separated string of keys
- if ( Array.isArray( key ) ) {
-
- // If key is an array of keys...
- // We always set camelCase keys, so remove that.
- key = key.map( camelCase );
- } else {
- key = camelCase( key );
-
- // If a key with the spaces exists, use it.
- // Otherwise, create an array by matching non-whitespace
- key = key in cache ?
- [ key ] :
- ( key.match( rnothtmlwhite ) || [] );
- }
-
- i = key.length;
-
- while ( i-- ) {
- delete cache[ key[ i ] ];
- }
- }
-
- // Remove the expando if there's no more data
- if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
- // Support: Chrome <=35 - 45
- // Webkit & Blink performance suffers when deleting properties
- // from DOM nodes, so set to undefined instead
- // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
- if ( owner.nodeType ) {
- owner[ this.expando ] = undefined;
- } else {
- delete owner[ this.expando ];
- }
- }
- },
- hasData: function( owner ) {
- var cache = owner[ this.expando ];
- return cache !== undefined && !jQuery.isEmptyObject( cache );
- }
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-// Implementation Summary
-//
-// 1. Enforce API surface and semantic compatibility with 1.9.x branch
-// 2. Improve the module's maintainability by reducing the storage
-// paths to a single mechanism.
-// 3. Use the same single mechanism to support "private" and "user" data.
-// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-// 5. Avoid exposing implementation details on user objects (eg. expando properties)
-// 6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
- rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
- if ( data === "true" ) {
- return true;
- }
-
- if ( data === "false" ) {
- return false;
- }
-
- if ( data === "null" ) {
- return null;
- }
-
- // Only convert to a number if it doesn't change the string
- if ( data === +data + "" ) {
- return +data;
- }
-
- if ( rbrace.test( data ) ) {
- return JSON.parse( data );
- }
-
- return data;
-}
-
-function dataAttr( elem, key, data ) {
- var name;
-
- // If nothing was found internally, try to fetch any
- // data from the HTML5 data-* attribute
- if ( data === undefined && elem.nodeType === 1 ) {
- name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
- data = elem.getAttribute( name );
-
- if ( typeof data === "string" ) {
- try {
- data = getData( data );
- } catch ( e ) {}
-
- // Make sure we set the data so it isn't changed later
- dataUser.set( elem, key, data );
- } else {
- data = undefined;
- }
- }
- return data;
-}
-
-jQuery.extend( {
- hasData: function( elem ) {
- return dataUser.hasData( elem ) || dataPriv.hasData( elem );
- },
-
- data: function( elem, name, data ) {
- return dataUser.access( elem, name, data );
- },
-
- removeData: function( elem, name ) {
- dataUser.remove( elem, name );
- },
-
- // TODO: Now that all calls to _data and _removeData have been replaced
- // with direct calls to dataPriv methods, these can be deprecated.
- _data: function( elem, name, data ) {
- return dataPriv.access( elem, name, data );
- },
-
- _removeData: function( elem, name ) {
- dataPriv.remove( elem, name );
- }
-} );
-
-jQuery.fn.extend( {
- data: function( key, value ) {
- var i, name, data,
- elem = this[ 0 ],
- attrs = elem && elem.attributes;
-
- // Gets all values
- if ( key === undefined ) {
- if ( this.length ) {
- data = dataUser.get( elem );
-
- if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
- i = attrs.length;
- while ( i-- ) {
-
- // Support: IE 11 only
- // The attrs elements can be null (#14894)
- if ( attrs[ i ] ) {
- name = attrs[ i ].name;
- if ( name.indexOf( "data-" ) === 0 ) {
- name = camelCase( name.slice( 5 ) );
- dataAttr( elem, name, data[ name ] );
- }
- }
- }
- dataPriv.set( elem, "hasDataAttrs", true );
- }
- }
-
- return data;
- }
-
- // Sets multiple values
- if ( typeof key === "object" ) {
- return this.each( function() {
- dataUser.set( this, key );
- } );
- }
-
- return access( this, function( value ) {
- var data;
-
- // The calling jQuery object (element matches) is not empty
- // (and therefore has an element appears at this[ 0 ]) and the
- // `value` parameter was not undefined. An empty jQuery object
- // will result in `undefined` for elem = this[ 0 ] which will
- // throw an exception if an attempt to read a data cache is made.
- if ( elem && value === undefined ) {
-
- // Attempt to get data from the cache
- // The key will always be camelCased in Data
- data = dataUser.get( elem, key );
- if ( data !== undefined ) {
- return data;
- }
-
- // Attempt to "discover" the data in
- // HTML5 custom data-* attrs
- data = dataAttr( elem, key );
- if ( data !== undefined ) {
- return data;
- }
-
- // We tried really hard, but the data doesn't exist.
- return;
- }
-
- // Set the data...
- this.each( function() {
-
- // We always store the camelCased key
- dataUser.set( this, key, value );
- } );
- }, null, value, arguments.length > 1, null, true );
- },
-
- removeData: function( key ) {
- return this.each( function() {
- dataUser.remove( this, key );
- } );
- }
-} );
-
-
-jQuery.extend( {
- queue: function( elem, type, data ) {
- var queue;
-
- if ( elem ) {
- type = ( type || "fx" ) + "queue";
- queue = dataPriv.get( elem, type );
-
- // Speed up dequeue by getting out quickly if this is just a lookup
- if ( data ) {
- if ( !queue || Array.isArray( data ) ) {
- queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
- } else {
- queue.push( data );
- }
- }
- return queue || [];
- }
- },
-
- dequeue: function( elem, type ) {
- type = type || "fx";
-
- var queue = jQuery.queue( elem, type ),
- startLength = queue.length,
- fn = queue.shift(),
- hooks = jQuery._queueHooks( elem, type ),
- next = function() {
- jQuery.dequeue( elem, type );
- };
-
- // If the fx queue is dequeued, always remove the progress sentinel
- if ( fn === "inprogress" ) {
- fn = queue.shift();
- startLength--;
- }
-
- if ( fn ) {
-
- // Add a progress sentinel to prevent the fx queue from being
- // automatically dequeued
- if ( type === "fx" ) {
- queue.unshift( "inprogress" );
- }
-
- // Clear up the last queue stop function
- delete hooks.stop;
- fn.call( elem, next, hooks );
- }
-
- if ( !startLength && hooks ) {
- hooks.empty.fire();
- }
- },
-
- // Not public - generate a queueHooks object, or return the current one
- _queueHooks: function( elem, type ) {
- var key = type + "queueHooks";
- return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
- empty: jQuery.Callbacks( "once memory" ).add( function() {
- dataPriv.remove( elem, [ type + "queue", key ] );
- } )
- } );
- }
-} );
-
-jQuery.fn.extend( {
- queue: function( type, data ) {
- var setter = 2;
-
- if ( typeof type !== "string" ) {
- data = type;
- type = "fx";
- setter--;
- }
-
- if ( arguments.length < setter ) {
- return jQuery.queue( this[ 0 ], type );
- }
-
- return data === undefined ?
- this :
- this.each( function() {
- var queue = jQuery.queue( this, type, data );
-
- // Ensure a hooks for this queue
- jQuery._queueHooks( this, type );
-
- if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
- jQuery.dequeue( this, type );
- }
- } );
- },
- dequeue: function( type ) {
- return this.each( function() {
- jQuery.dequeue( this, type );
- } );
- },
- clearQueue: function( type ) {
- return this.queue( type || "fx", [] );
- },
-
- // Get a promise resolved when queues of a certain type
- // are emptied (fx is the type by default)
- promise: function( type, obj ) {
- var tmp,
- count = 1,
- defer = jQuery.Deferred(),
- elements = this,
- i = this.length,
- resolve = function() {
- if ( !( --count ) ) {
- defer.resolveWith( elements, [ elements ] );
- }
- };
-
- if ( typeof type !== "string" ) {
- obj = type;
- type = undefined;
- }
- type = type || "fx";
-
- while ( i-- ) {
- tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
- if ( tmp && tmp.empty ) {
- count++;
- tmp.empty.add( resolve );
- }
- }
- resolve();
- return defer.promise( obj );
- }
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
- var isAttached = function( elem ) {
- return jQuery.contains( elem.ownerDocument, elem );
- },
- composed = { composed: true };
-
- // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
- // Check attachment across shadow DOM boundaries when possible (gh-3504)
- // Support: iOS 10.0-10.2 only
- // Early iOS 10 versions support `attachShadow` but not `getRootNode`,
- // leading to errors. We need to check for `getRootNode`.
- if ( documentElement.getRootNode ) {
- isAttached = function( elem ) {
- return jQuery.contains( elem.ownerDocument, elem ) ||
- elem.getRootNode( composed ) === elem.ownerDocument;
- };
- }
-var isHiddenWithinTree = function( elem, el ) {
-
- // isHiddenWithinTree might be called from jQuery#filter function;
- // in that case, element will be second argument
- elem = el || elem;
-
- // Inline style trumps all
- return elem.style.display === "none" ||
- elem.style.display === "" &&
-
- // Otherwise, check computed style
- // Support: Firefox <=43 - 45
- // Disconnected elements can have computed display: none, so first confirm that elem is
- // in the document.
- isAttached( elem ) &&
-
- jQuery.css( elem, "display" ) === "none";
- };
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
- var adjusted, scale,
- maxIterations = 20,
- currentValue = tween ?
- function() {
- return tween.cur();
- } :
- function() {
- return jQuery.css( elem, prop, "" );
- },
- initial = currentValue(),
- unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
- // Starting value computation is required for potential unit mismatches
- initialInUnit = elem.nodeType &&
- ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
- rcssNum.exec( jQuery.css( elem, prop ) );
-
- if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
- // Support: Firefox <=54
- // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
- initial = initial / 2;
-
- // Trust units reported by jQuery.css
- unit = unit || initialInUnit[ 3 ];
-
- // Iteratively approximate from a nonzero starting point
- initialInUnit = +initial || 1;
-
- while ( maxIterations-- ) {
-
- // Evaluate and update our best guess (doubling guesses that zero out).
- // Finish if the scale equals or crosses 1 (making the old*new product non-positive).
- jQuery.style( elem, prop, initialInUnit + unit );
- if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
- maxIterations = 0;
- }
- initialInUnit = initialInUnit / scale;
-
- }
-
- initialInUnit = initialInUnit * 2;
- jQuery.style( elem, prop, initialInUnit + unit );
-
- // Make sure we update the tween properties later on
- valueParts = valueParts || [];
- }
-
- if ( valueParts ) {
- initialInUnit = +initialInUnit || +initial || 0;
-
- // Apply relative offset (+=/-=) if specified
- adjusted = valueParts[ 1 ] ?
- initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
- +valueParts[ 2 ];
- if ( tween ) {
- tween.unit = unit;
- tween.start = initialInUnit;
- tween.end = adjusted;
- }
- }
- return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
- var temp,
- doc = elem.ownerDocument,
- nodeName = elem.nodeName,
- display = defaultDisplayMap[ nodeName ];
-
- if ( display ) {
- return display;
- }
-
- temp = doc.body.appendChild( doc.createElement( nodeName ) );
- display = jQuery.css( temp, "display" );
-
- temp.parentNode.removeChild( temp );
-
- if ( display === "none" ) {
- display = "block";
- }
- defaultDisplayMap[ nodeName ] = display;
-
- return display;
-}
-
-function showHide( elements, show ) {
- var display, elem,
- values = [],
- index = 0,
- length = elements.length;
-
- // Determine new display value for elements that need to change
- for ( ; index < length; index++ ) {
- elem = elements[ index ];
- if ( !elem.style ) {
- continue;
- }
-
- display = elem.style.display;
- if ( show ) {
-
- // Since we force visibility upon cascade-hidden elements, an immediate (and slow)
- // check is required in this first loop unless we have a nonempty display value (either
- // inline or about-to-be-restored)
- if ( display === "none" ) {
- values[ index ] = dataPriv.get( elem, "display" ) || null;
- if ( !values[ index ] ) {
- elem.style.display = "";
- }
- }
- if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
- values[ index ] = getDefaultDisplay( elem );
- }
- } else {
- if ( display !== "none" ) {
- values[ index ] = "none";
-
- // Remember what we're overwriting
- dataPriv.set( elem, "display", display );
- }
- }
- }
-
- // Set the display of the elements in a second loop to avoid constant reflow
- for ( index = 0; index < length; index++ ) {
- if ( values[ index ] != null ) {
- elements[ index ].style.display = values[ index ];
- }
- }
-
- return elements;
-}
-
-jQuery.fn.extend( {
- show: function() {
- return showHide( this, true );
- },
- hide: function() {
- return showHide( this );
- },
- toggle: function( state ) {
- if ( typeof state === "boolean" ) {
- return state ? this.show() : this.hide();
- }
-
- return this.each( function() {
- if ( isHiddenWithinTree( this ) ) {
- jQuery( this ).show();
- } else {
- jQuery( this ).hide();
- }
- } );
- }
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
- var fragment = document.createDocumentFragment(),
- div = fragment.appendChild( document.createElement( "div" ) ),
- input = document.createElement( "input" );
-
- // Support: Android 4.0 - 4.3 only
- // Check state lost if the name is set (#11217)
- // Support: Windows Web Apps (WWA)
- // `name` and `type` must use .setAttribute for WWA (#14901)
- input.setAttribute( "type", "radio" );
- input.setAttribute( "checked", "checked" );
- input.setAttribute( "name", "t" );
-
- div.appendChild( input );
-
- // Support: Android <=4.1 only
- // Older WebKit doesn't clone checked state correctly in fragments
- support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
- // Support: IE <=11 only
- // Make sure textarea (and checkbox) defaultValue is properly cloned
- div.innerHTML = "<textarea>x</textarea>";
- support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
- // Support: IE <=9 only
- // IE <=9 replaces <option> tags with their contents when inserted outside of
- // the select element.
- div.innerHTML = "<option></option>";
- support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
- // XHTML parsers do not magically insert elements in the
- // same way that tag soup parsers do. So we cannot shorten
- // this by omitting <tbody> or other required elements.
- thead: [ 1, "<table>", "</table>" ],
- col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
- tr: [ 2, "<table><tbody>", "</tbody></table>" ],
- td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
- _default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
- wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
- // Support: IE <=9 - 11 only
- // Use typeof to avoid zero-argument method invocation on host objects (#15151)
- var ret;
-
- if ( typeof context.getElementsByTagName !== "undefined" ) {
- ret = context.getElementsByTagName( tag || "*" );
-
- } else if ( typeof context.querySelectorAll !== "undefined" ) {
- ret = context.querySelectorAll( tag || "*" );
-
- } else {
- ret = [];
- }
-
- if ( tag === undefined || tag && nodeName( context, tag ) ) {
- return jQuery.merge( [ context ], ret );
- }
-
- return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
- var i = 0,
- l = elems.length;
-
- for ( ; i < l; i++ ) {
- dataPriv.set(
- elems[ i ],
- "globalEval",
- !refElements || dataPriv.get( refElements[ i ], "globalEval" )
- );
- }
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
- var elem, tmp, tag, wrap, attached, j,
- fragment = context.createDocumentFragment(),
- nodes = [],
- i = 0,
- l = elems.length;
-
- for ( ; i < l; i++ ) {
- elem = elems[ i ];
-
- if ( elem || elem === 0 ) {
-
- // Add nodes directly
- if ( toType( elem ) === "object" ) {
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
- // Convert non-html into a text node
- } else if ( !rhtml.test( elem ) ) {
- nodes.push( context.createTextNode( elem ) );
-
- // Convert html into DOM nodes
- } else {
- tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
- // Deserialize a standard representation
- tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
- wrap = wrapMap[ tag ] || wrapMap._default;
- tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
- // Descend through wrappers to the right content
- j = wrap[ 0 ];
- while ( j-- ) {
- tmp = tmp.lastChild;
- }
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( nodes, tmp.childNodes );
-
- // Remember the top-level container
- tmp = fragment.firstChild;
-
- // Ensure the created nodes are orphaned (#12392)
- tmp.textContent = "";
- }
- }
- }
-
- // Remove wrapper from fragment
- fragment.textContent = "";
-
- i = 0;
- while ( ( elem = nodes[ i++ ] ) ) {
-
- // Skip elements already in the context collection (trac-4087)
- if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
- if ( ignored ) {
- ignored.push( elem );
- }
- continue;
- }
-
- attached = isAttached( elem );
-
- // Append to fragment
- tmp = getAll( fragment.appendChild( elem ), "script" );
-
- // Preserve script evaluation history
- if ( attached ) {
- setGlobalEval( tmp );
- }
-
- // Capture executables
- if ( scripts ) {
- j = 0;
- while ( ( elem = tmp[ j++ ] ) ) {
- if ( rscriptType.test( elem.type || "" ) ) {
- scripts.push( elem );
- }
- }
- }
- }
-
- return fragment;
-}
-
-
-var
- rkeyEvent = /^key/,
- rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
- rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
- return true;
-}
-
-function returnFalse() {
- return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
- return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
- try {
- return document.activeElement;
- } catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
- var origFn, type;
-
- // Types can be a map of types/handlers
- if ( typeof types === "object" ) {
-
- // ( types-Object, selector, data )
- if ( typeof selector !== "string" ) {
-
- // ( types-Object, data )
- data = data || selector;
- selector = undefined;
- }
- for ( type in types ) {
- on( elem, type, selector, data, types[ type ], one );
- }
- return elem;
- }
-
- if ( data == null && fn == null ) {
-
- // ( types, fn )
- fn = selector;
- data = selector = undefined;
- } else if ( fn == null ) {
- if ( typeof selector === "string" ) {
-
- // ( types, selector, fn )
- fn = data;
- data = undefined;
- } else {
-
- // ( types, data, fn )
- fn = data;
- data = selector;
- selector = undefined;
- }
- }
- if ( fn === false ) {
- fn = returnFalse;
- } else if ( !fn ) {
- return elem;
- }
-
- if ( one === 1 ) {
- origFn = fn;
- fn = function( event ) {
-
- // Can use an empty set, since event contains the info
- jQuery().off( event );
- return origFn.apply( this, arguments );
- };
-
- // Use same guid so caller can remove using origFn
- fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
- }
- return elem.each( function() {
- jQuery.event.add( this, types, fn, data, selector );
- } );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
- global: {},
-
- add: function( elem, types, handler, data, selector ) {
-
- var handleObjIn, eventHandle, tmp,
- events, t, handleObj,
- special, handlers, type, namespaces, origType,
- elemData = dataPriv.get( elem );
-
- // Only attach events to objects that accept data
- if ( !acceptData( elem ) ) {
- return;
- }
-
- // Caller can pass in an object of custom data in lieu of the handler
- if ( handler.handler ) {
- handleObjIn = handler;
- handler = handleObjIn.handler;
- selector = handleObjIn.selector;
- }
-
- // Ensure that invalid selectors throw exceptions at attach time
- // Evaluate against documentElement in case elem is a non-element node (e.g., document)
- if ( selector ) {
- jQuery.find.matchesSelector( documentElement, selector );
- }
-
- // Make sure that the handler has a unique ID, used to find/remove it later
- if ( !handler.guid ) {
- handler.guid = jQuery.guid++;
- }
-
- // Init the element's event structure and main handler, if this is the first
- if ( !( events = elemData.events ) ) {
- events = elemData.events = Object.create( null );
- }
- if ( !( eventHandle = elemData.handle ) ) {
- eventHandle = elemData.handle = function( e ) {
-
- // Discard the second event of a jQuery.event.trigger() and
- // when an event is called after a page has unloaded
- return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
- jQuery.event.dispatch.apply( elem, arguments ) : undefined;
- };
- }
-
- // Handle multiple events separated by a space
- types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
- t = types.length;
- while ( t-- ) {
- tmp = rtypenamespace.exec( types[ t ] ) || [];
- type = origType = tmp[ 1 ];
- namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
- // There *must* be a type, no attaching namespace-only handlers
- if ( !type ) {
- continue;
- }
-
- // If event changes its type, use the special event handlers for the changed type
- special = jQuery.event.special[ type ] || {};
-
- // If selector defined, determine special event api type, otherwise given type
- type = ( selector ? special.delegateType : special.bindType ) || type;
-
- // Update special based on newly reset type
- special = jQuery.event.special[ type ] || {};
-
- // handleObj is passed to all event handlers
- handleObj = jQuery.extend( {
- type: type,
- origType: origType,
- data: data,
- handler: handler,
- guid: handler.guid,
- selector: selector,
- needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
- namespace: namespaces.join( "." )
- }, handleObjIn );
-
- // Init the event handler queue if we're the first
- if ( !( handlers = events[ type ] ) ) {
- handlers = events[ type ] = [];
- handlers.delegateCount = 0;
-
- // Only use addEventListener if the special events handler returns false
- if ( !special.setup ||
- special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
- if ( elem.addEventListener ) {
- elem.addEventListener( type, eventHandle );
- }
- }
- }
-
- if ( special.add ) {
- special.add.call( elem, handleObj );
-
- if ( !handleObj.handler.guid ) {
- handleObj.handler.guid = handler.guid;
- }
- }
-
- // Add to the element's handler list, delegates in front
- if ( selector ) {
- handlers.splice( handlers.delegateCount++, 0, handleObj );
- } else {
- handlers.push( handleObj );
- }
-
- // Keep track of which events have ever been used, for event optimization
- jQuery.event.global[ type ] = true;
- }
-
- },
-
- // Detach an event or set of events from an element
- remove: function( elem, types, handler, selector, mappedTypes ) {
-
- var j, origCount, tmp,
- events, t, handleObj,
- special, handlers, type, namespaces, origType,
- elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
- if ( !elemData || !( events = elemData.events ) ) {
- return;
- }
-
- // Once for each type.namespace in types; type may be omitted
- types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
- t = types.length;
- while ( t-- ) {
- tmp = rtypenamespace.exec( types[ t ] ) || [];
- type = origType = tmp[ 1 ];
- namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
- // Unbind all events (on this namespace, if provided) for the element
- if ( !type ) {
- for ( type in events ) {
- jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
- }
- continue;
- }
-
- special = jQuery.event.special[ type ] || {};
- type = ( selector ? special.delegateType : special.bindType ) || type;
- handlers = events[ type ] || [];
- tmp = tmp[ 2 ] &&
- new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
- // Remove matching events
- origCount = j = handlers.length;
- while ( j-- ) {
- handleObj = handlers[ j ];
-
- if ( ( mappedTypes || origType === handleObj.origType ) &&
- ( !handler || handler.guid === handleObj.guid ) &&
- ( !tmp || tmp.test( handleObj.namespace ) ) &&
- ( !selector || selector === handleObj.selector ||
- selector === "**" && handleObj.selector ) ) {
- handlers.splice( j, 1 );
-
- if ( handleObj.selector ) {
- handlers.delegateCount--;
- }
- if ( special.remove ) {
- special.remove.call( elem, handleObj );
- }
- }
- }
-
- // Remove generic event handler if we removed something and no more handlers exist
- // (avoids potential for endless recursion during removal of special event handlers)
- if ( origCount && !handlers.length ) {
- if ( !special.teardown ||
- special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
- jQuery.removeEvent( elem, type, elemData.handle );
- }
-
- delete events[ type ];
- }
- }
-
- // Remove data and the expando if it's no longer used
- if ( jQuery.isEmptyObject( events ) ) {
- dataPriv.remove( elem, "handle events" );
- }
- },
-
- dispatch: function( nativeEvent ) {
-
- var i, j, ret, matched, handleObj, handlerQueue,
- args = new Array( arguments.length ),
-
- // Make a writable jQuery.Event from the native event object
- event = jQuery.event.fix( nativeEvent ),
-
- handlers = (
- dataPriv.get( this, "events" ) || Object.create( null )
- )[ event.type ] || [],
- special = jQuery.event.special[ event.type ] || {};
-
- // Use the fix-ed jQuery.Event rather than the (read-only) native event
- args[ 0 ] = event;
-
- for ( i = 1; i < arguments.length; i++ ) {
- args[ i ] = arguments[ i ];
- }
-
- event.delegateTarget = this;
-
- // Call the preDispatch hook for the mapped type, and let it bail if desired
- if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
- return;
- }
-
- // Determine handlers
- handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
- // Run delegates first; they may want to stop propagation beneath us
- i = 0;
- while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
- event.currentTarget = matched.elem;
-
- j = 0;
- while ( ( handleObj = matched.handlers[ j++ ] ) &&
- !event.isImmediatePropagationStopped() ) {
-
- // If the event is namespaced, then each handler is only invoked if it is
- // specially universal or its namespaces are a superset of the event's.
- if ( !event.rnamespace || handleObj.namespace === false ||
- event.rnamespace.test( handleObj.namespace ) ) {
-
- event.handleObj = handleObj;
- event.data = handleObj.data;
-
- ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
- handleObj.handler ).apply( matched.elem, args );
-
- if ( ret !== undefined ) {
- if ( ( event.result = ret ) === false ) {
- event.preventDefault();
- event.stopPropagation();
- }
- }
- }
- }
- }
-
- // Call the postDispatch hook for the mapped type
- if ( special.postDispatch ) {
- special.postDispatch.call( this, event );
- }
-
- return event.result;
- },
-
- handlers: function( event, handlers ) {
- var i, handleObj, sel, matchedHandlers, matchedSelectors,
- handlerQueue = [],
- delegateCount = handlers.delegateCount,
- cur = event.target;
-
- // Find delegate handlers
- if ( delegateCount &&
-
- // Support: IE <=9
- // Black-hole SVG <use> instance trees (trac-13180)
- cur.nodeType &&
-
- // Support: Firefox <=42
- // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
- // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
- // Support: IE 11 only
- // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
- !( event.type === "click" && event.button >= 1 ) ) {
-
- for ( ; cur !== this; cur = cur.parentNode || this ) {
-
- // Don't check non-elements (#13208)
- // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
- if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
- matchedHandlers = [];
- matchedSelectors = {};
- for ( i = 0; i < delegateCount; i++ ) {
- handleObj = handlers[ i ];
-
- // Don't conflict with Object.prototype properties (#13203)
- sel = handleObj.selector + " ";
-
- if ( matchedSelectors[ sel ] === undefined ) {
- matchedSelectors[ sel ] = handleObj.needsContext ?
- jQuery( sel, this ).index( cur ) > -1 :
- jQuery.find( sel, this, null, [ cur ] ).length;
- }
- if ( matchedSelectors[ sel ] ) {
- matchedHandlers.push( handleObj );
- }
- }
- if ( matchedHandlers.length ) {
- handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
- }
- }
- }
- }
-
- // Add the remaining (directly-bound) handlers
- cur = this;
- if ( delegateCount < handlers.length ) {
- handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
- }
-
- return handlerQueue;
- },
-
- addProp: function( name, hook ) {
- Object.defineProperty( jQuery.Event.prototype, name, {
- enumerable: true,
- configurable: true,
-
- get: isFunction( hook ) ?
- function() {
- if ( this.originalEvent ) {
- return hook( this.originalEvent );
- }
- } :
- function() {
- if ( this.originalEvent ) {
- return this.originalEvent[ name ];
- }
- },
-
- set: function( value ) {
- Object.defineProperty( this, name, {
- enumerable: true,
- configurable: true,
- writable: true,
- value: value
- } );
- }
- } );
- },
-
- fix: function( originalEvent ) {
- return originalEvent[ jQuery.expando ] ?
- originalEvent :
- new jQuery.Event( originalEvent );
- },
-
- special: {
- load: {
-
- // Prevent triggered image.load events from bubbling to window.load
- noBubble: true
- },
- click: {
-
- // Utilize native event to ensure correct state for checkable inputs
- setup: function( data ) {
-
- // For mutual compressibility with _default, replace `this` access with a local var.
- // `|| data` is dead code meant only to preserve the variable through minification.
- var el = this || data;
-
- // Claim the first handler
- if ( rcheckableType.test( el.type ) &&
- el.click && nodeName( el, "input" ) ) {
-
- // dataPriv.set( el, "click", ... )
- leverageNative( el, "click", returnTrue );
- }
-
- // Return false to allow normal processing in the caller
- return false;
- },
- trigger: function( data ) {
-
- // For mutual compressibility with _default, replace `this` access with a local var.
- // `|| data` is dead code meant only to preserve the variable through minification.
- var el = this || data;
-
- // Force setup before triggering a click
- if ( rcheckableType.test( el.type ) &&
- el.click && nodeName( el, "input" ) ) {
-
- leverageNative( el, "click" );
- }
-
- // Return non-false to allow normal event-path propagation
- return true;
- },
-
- // For cross-browser consistency, suppress native .click() on links
- // Also prevent it if we're currently inside a leveraged native-event stack
- _default: function( event ) {
- var target = event.target;
- return rcheckableType.test( target.type ) &&
- target.click && nodeName( target, "input" ) &&
- dataPriv.get( target, "click" ) ||
- nodeName( target, "a" );
- }
- },
-
- beforeunload: {
- postDispatch: function( event ) {
-
- // Support: Firefox 20+
- // Firefox doesn't alert if the returnValue field is not set.
- if ( event.result !== undefined && event.originalEvent ) {
- event.originalEvent.returnValue = event.result;
- }
- }
- }
- }
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
- // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
- if ( !expectSync ) {
- if ( dataPriv.get( el, type ) === undefined ) {
- jQuery.event.add( el, type, returnTrue );
- }
- return;
- }
-
- // Register the controller as a special universal handler for all event namespaces
- dataPriv.set( el, type, false );
- jQuery.event.add( el, type, {
- namespace: false,
- handler: function( event ) {
- var notAsync, result,
- saved = dataPriv.get( this, type );
-
- if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
- // Interrupt processing of the outer synthetic .trigger()ed event
- // Saved data should be false in such cases, but might be a leftover capture object
- // from an async native handler (gh-4350)
- if ( !saved.length ) {
-
- // Store arguments for use when handling the inner native event
- // There will always be at least one argument (an event object), so this array
- // will not be confused with a leftover capture object.
- saved = slice.call( arguments );
- dataPriv.set( this, type, saved );
-
- // Trigger the native event and capture its result
- // Support: IE <=9 - 11+
- // focus() and blur() are asynchronous
- notAsync = expectSync( this, type );
- this[ type ]();
- result = dataPriv.get( this, type );
- if ( saved !== result || notAsync ) {
- dataPriv.set( this, type, false );
- } else {
- result = {};
- }
- if ( saved !== result ) {
-
- // Cancel the outer synthetic event
- event.stopImmediatePropagation();
- event.preventDefault();
- return result.value;
- }
-
- // If this is an inner synthetic event for an event with a bubbling surrogate
- // (focus or blur), assume that the surrogate already propagated from triggering the
- // native event and prevent that from happening again here.
- // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
- // bubbling surrogate propagates *after* the non-bubbling base), but that seems
- // less bad than duplication.
- } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
- event.stopPropagation();
- }
-
- // If this is a native event triggered above, everything is now in order
- // Fire an inner synthetic event with the original arguments
- } else if ( saved.length ) {
-
- // ...and capture the result
- dataPriv.set( this, type, {
- value: jQuery.event.trigger(
-
- // Support: IE <=9 - 11+
- // Extend with the prototype to reset the above stopImmediatePropagation()
- jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
- saved.slice( 1 ),
- this
- )
- } );
-
- // Abort handling of the native event
- event.stopImmediatePropagation();
- }
- }
- } );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
- // This "if" is needed for plain objects
- if ( elem.removeEventListener ) {
- elem.removeEventListener( type, handle );
- }
-};
-
-jQuery.Event = function( src, props ) {
-
- // Allow instantiation without the 'new' keyword
- if ( !( this instanceof jQuery.Event ) ) {
- return new jQuery.Event( src, props );
- }
-
- // Event object
- if ( src && src.type ) {
- this.originalEvent = src;
- this.type = src.type;
-
- // Events bubbling up the document may have been marked as prevented
- // by a handler lower down the tree; reflect the correct value.
- this.isDefaultPrevented = src.defaultPrevented ||
- src.defaultPrevented === undefined &&
-
- // Support: Android <=2.3 only
- src.returnValue === false ?
- returnTrue :
- returnFalse;
-
- // Create target properties
- // Support: Safari <=6 - 7 only
- // Target should not be a text node (#504, #13143)
- this.target = ( src.target && src.target.nodeType === 3 ) ?
- src.target.parentNode :
- src.target;
-
- this.currentTarget = src.currentTarget;
- this.relatedTarget = src.relatedTarget;
-
- // Event type
- } else {
- this.type = src;
- }
-
- // Put explicitly provided properties onto the event object
- if ( props ) {
- jQuery.extend( this, props );
- }
-
- // Create a timestamp if incoming event doesn't have one
- this.timeStamp = src && src.timeStamp || Date.now();
-
- // Mark it as fixed
- this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
- constructor: jQuery.Event,
- isDefaultPrevented: returnFalse,
- isPropagationStopped: returnFalse,
- isImmediatePropagationStopped: returnFalse,
- isSimulated: false,
-
- preventDefault: function() {
- var e = this.originalEvent;
-
- this.isDefaultPrevented = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.preventDefault();
- }
- },
- stopPropagation: function() {
- var e = this.originalEvent;
-
- this.isPropagationStopped = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.stopPropagation();
- }
- },
- stopImmediatePropagation: function() {
- var e = this.originalEvent;
-
- this.isImmediatePropagationStopped = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.stopImmediatePropagation();
- }
-
- this.stopPropagation();
- }
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
- altKey: true,
- bubbles: true,
- cancelable: true,
- changedTouches: true,
- ctrlKey: true,
- detail: true,
- eventPhase: true,
- metaKey: true,
- pageX: true,
- pageY: true,
- shiftKey: true,
- view: true,
- "char": true,
- code: true,
- charCode: true,
- key: true,
- keyCode: true,
- button: true,
- buttons: true,
- clientX: true,
- clientY: true,
- offsetX: true,
- offsetY: true,
- pointerId: true,
- pointerType: true,
- screenX: true,
- screenY: true,
- targetTouches: true,
- toElement: true,
- touches: true,
-
- which: function( event ) {
- var button = event.button;
-
- // Add which for key events
- if ( event.which == null && rkeyEvent.test( event.type ) ) {
- return event.charCode != null ? event.charCode : event.keyCode;
- }
-
- // Add which for click: 1 === left; 2 === middle; 3 === right
- if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
- if ( button & 1 ) {
- return 1;
- }
-
- if ( button & 2 ) {
- return 3;
- }
-
- if ( button & 4 ) {
- return 2;
- }
-
- return 0;
- }
-
- return event.which;
- }
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
- jQuery.event.special[ type ] = {
-
- // Utilize native event if possible so blur/focus sequence is correct
- setup: function() {
-
- // Claim the first handler
- // dataPriv.set( this, "focus", ... )
- // dataPriv.set( this, "blur", ... )
- leverageNative( this, type, expectSync );
-
- // Return false to allow normal processing in the caller
- return false;
- },
- trigger: function() {
-
- // Force setup before trigger
- leverageNative( this, type );
-
- // Return non-false to allow normal event-path propagation
- return true;
- },
-
- delegateType: delegateType
- };
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
- mouseenter: "mouseover",
- mouseleave: "mouseout",
- pointerenter: "pointerover",
- pointerleave: "pointerout"
-}, function( orig, fix ) {
- jQuery.event.special[ orig ] = {
- delegateType: fix,
- bindType: fix,
-
- handle: function( event ) {
- var ret,
- target = this,
- related = event.relatedTarget,
- handleObj = event.handleObj;
-
- // For mouseenter/leave call the handler if related is outside the target.
- // NB: No relatedTarget if the mouse left/entered the browser window
- if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
- event.type = handleObj.origType;
- ret = handleObj.handler.apply( this, arguments );
- event.type = fix;
- }
- return ret;
- }
- };
-} );
-
-jQuery.fn.extend( {
-
- on: function( types, selector, data, fn ) {
- return on( this, types, selector, data, fn );
- },
- one: function( types, selector, data, fn ) {
- return on( this, types, selector, data, fn, 1 );
- },
- off: function( types, selector, fn ) {
- var handleObj, type;
- if ( types && types.preventDefault && types.handleObj ) {
-
- // ( event ) dispatched jQuery.Event
- handleObj = types.handleObj;
- jQuery( types.delegateTarget ).off(
- handleObj.namespace ?
- handleObj.origType + "." + handleObj.namespace :
- handleObj.origType,
- handleObj.selector,
- handleObj.handler
- );
- return this;
- }
- if ( typeof types === "object" ) {
-
- // ( types-object [, selector] )
- for ( type in types ) {
- this.off( type, selector, types[ type ] );
- }
- return this;
- }
- if ( selector === false || typeof selector === "function" ) {
-
- // ( types [, fn] )
- fn = selector;
- selector = undefined;
- }
- if ( fn === false ) {
- fn = returnFalse;
- }
- return this.each( function() {
- jQuery.event.remove( this, types, fn, selector );
- } );
- }
-} );
-
-
-var
-
- // Support: IE <=10 - 11, Edge 12 - 13 only
- // In IE/Edge using regex groups here causes severe slowdowns.
- // See https://connect.microsoft.com/IE/feedback/details/1736512/
- rnoInnerhtml = /<script|<style|<link/i,
-
- // checked="checked" or checked
- rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
- rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
- if ( nodeName( elem, "table" ) &&
- nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
- return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
- }
-
- return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
- elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
- return elem;
-}
-function restoreScript( elem ) {
- if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
- elem.type = elem.type.slice( 5 );
- } else {
- elem.removeAttribute( "type" );
- }
-
- return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
- var i, l, type, pdataOld, udataOld, udataCur, events;
-
- if ( dest.nodeType !== 1 ) {
- return;
- }
-
- // 1. Copy private data: events, handlers, etc.
- if ( dataPriv.hasData( src ) ) {
- pdataOld = dataPriv.get( src );
- events = pdataOld.events;
-
- if ( events ) {
- dataPriv.remove( dest, "handle events" );
-
- for ( type in events ) {
- for ( i = 0, l = events[ type ].length; i < l; i++ ) {
- jQuery.event.add( dest, type, events[ type ][ i ] );
- }
- }
- }
- }
-
- // 2. Copy user data
- if ( dataUser.hasData( src ) ) {
- udataOld = dataUser.access( src );
- udataCur = jQuery.extend( {}, udataOld );
-
- dataUser.set( dest, udataCur );
- }
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
- var nodeName = dest.nodeName.toLowerCase();
-
- // Fails to persist the checked state of a cloned checkbox or radio button.
- if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
- dest.checked = src.checked;
-
- // Fails to return the selected option to the default selected state when cloning options
- } else if ( nodeName === "input" || nodeName === "textarea" ) {
- dest.defaultValue = src.defaultValue;
- }
-}
-
-function domManip( collection, args, callback, ignored ) {
-
- // Flatten any nested arrays
- args = flat( args );
-
- var fragment, first, scripts, hasScripts, node, doc,
- i = 0,
- l = collection.length,
- iNoClone = l - 1,
- value = args[ 0 ],
- valueIsFunction = isFunction( value );
-
- // We can't cloneNode fragments that contain checked, in WebKit
- if ( valueIsFunction ||
- ( l > 1 && typeof value === "string" &&
- !support.checkClone && rchecked.test( value ) ) ) {
- return collection.each( function( index ) {
- var self = collection.eq( index );
- if ( valueIsFunction ) {
- args[ 0 ] = value.call( this, index, self.html() );
- }
- domManip( self, args, callback, ignored );
- } );
- }
-
- if ( l ) {
- fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
- first = fragment.firstChild;
-
- if ( fragment.childNodes.length === 1 ) {
- fragment = first;
- }
-
- // Require either new content or an interest in ignored elements to invoke the callback
- if ( first || ignored ) {
- scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
- hasScripts = scripts.length;
-
- // Use the original fragment for the last item
- // instead of the first because it can end up
- // being emptied incorrectly in certain situations (#8070).
- for ( ; i < l; i++ ) {
- node = fragment;
-
- if ( i !== iNoClone ) {
- node = jQuery.clone( node, true, true );
-
- // Keep references to cloned scripts for later restoration
- if ( hasScripts ) {
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( scripts, getAll( node, "script" ) );
- }
- }
-
- callback.call( collection[ i ], node, i );
- }
-
- if ( hasScripts ) {
- doc = scripts[ scripts.length - 1 ].ownerDocument;
-
- // Reenable scripts
- jQuery.map( scripts, restoreScript );
-
- // Evaluate executable scripts on first document insertion
- for ( i = 0; i < hasScripts; i++ ) {
- node = scripts[ i ];
- if ( rscriptType.test( node.type || "" ) &&
- !dataPriv.access( node, "globalEval" ) &&
- jQuery.contains( doc, node ) ) {
-
- if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) {
-
- // Optional AJAX dependency, but won't run scripts if not present
- if ( jQuery._evalUrl && !node.noModule ) {
- jQuery._evalUrl( node.src, {
- nonce: node.nonce || node.getAttribute( "nonce" )
- }, doc );
- }
- } else {
- DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
- }
- }
- }
- }
- }
- }
-
- return collection;
-}
-
-function remove( elem, selector, keepData ) {
- var node,
- nodes = selector ? jQuery.filter( selector, elem ) : elem,
- i = 0;
-
- for ( ; ( node = nodes[ i ] ) != null; i++ ) {
- if ( !keepData && node.nodeType === 1 ) {
- jQuery.cleanData( getAll( node ) );
- }
-
- if ( node.parentNode ) {
- if ( keepData && isAttached( node ) ) {
- setGlobalEval( getAll( node, "script" ) );
- }
- node.parentNode.removeChild( node );
- }
- }
-
- return elem;
-}
-
-jQuery.extend( {
- htmlPrefilter: function( html ) {
- return html;
- },
-
- clone: function( elem, dataAndEvents, deepDataAndEvents ) {
- var i, l, srcElements, destElements,
- clone = elem.cloneNode( true ),
- inPage = isAttached( elem );
-
- // Fix IE cloning issues
- if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
- !jQuery.isXMLDoc( elem ) ) {
-
- // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
- destElements = getAll( clone );
- srcElements = getAll( elem );
-
- for ( i = 0, l = srcElements.length; i < l; i++ ) {
- fixInput( srcElements[ i ], destElements[ i ] );
- }
- }
-
- // Copy the events from the original to the clone
- if ( dataAndEvents ) {
- if ( deepDataAndEvents ) {
- srcElements = srcElements || getAll( elem );
- destElements = destElements || getAll( clone );
-
- for ( i = 0, l = srcElements.length; i < l; i++ ) {
- cloneCopyEvent( srcElements[ i ], destElements[ i ] );
- }
- } else {
- cloneCopyEvent( elem, clone );
- }
- }
-
- // Preserve script evaluation history
- destElements = getAll( clone, "script" );
- if ( destElements.length > 0 ) {
- setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
- }
-
- // Return the cloned set
- return clone;
- },
-
- cleanData: function( elems ) {
- var data, elem, type,
- special = jQuery.event.special,
- i = 0;
-
- for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
- if ( acceptData( elem ) ) {
- if ( ( data = elem[ dataPriv.expando ] ) ) {
- if ( data.events ) {
- for ( type in data.events ) {
- if ( special[ type ] ) {
- jQuery.event.remove( elem, type );
-
- // This is a shortcut to avoid jQuery.event.remove's overhead
- } else {
- jQuery.removeEvent( elem, type, data.handle );
- }
- }
- }
-
- // Support: Chrome <=35 - 45+
- // Assign undefined instead of using delete, see Data#remove
- elem[ dataPriv.expando ] = undefined;
- }
- if ( elem[ dataUser.expando ] ) {
-
- // Support: Chrome <=35 - 45+
- // Assign undefined instead of using delete, see Data#remove
- elem[ dataUser.expando ] = undefined;
- }
- }
- }
- }
-} );
-
-jQuery.fn.extend( {
- detach: function( selector ) {
- return remove( this, selector, true );
- },
-
- remove: function( selector ) {
- return remove( this, selector );
- },
-
- text: function( value ) {
- return access( this, function( value ) {
- return value === undefined ?
- jQuery.text( this ) :
- this.empty().each( function() {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- this.textContent = value;
- }
- } );
- }, null, value, arguments.length );
- },
-
- append: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- var target = manipulationTarget( this, elem );
- target.appendChild( elem );
- }
- } );
- },
-
- prepend: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- var target = manipulationTarget( this, elem );
- target.insertBefore( elem, target.firstChild );
- }
- } );
- },
-
- before: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.parentNode ) {
- this.parentNode.insertBefore( elem, this );
- }
- } );
- },
-
- after: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.parentNode ) {
- this.parentNode.insertBefore( elem, this.nextSibling );
- }
- } );
- },
-
- empty: function() {
- var elem,
- i = 0;
-
- for ( ; ( elem = this[ i ] ) != null; i++ ) {
- if ( elem.nodeType === 1 ) {
-
- // Prevent memory leaks
- jQuery.cleanData( getAll( elem, false ) );
-
- // Remove any remaining nodes
- elem.textContent = "";
- }
- }
-
- return this;
- },
-
- clone: function( dataAndEvents, deepDataAndEvents ) {
- dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
- deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
- return this.map( function() {
- return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
- } );
- },
-
- html: function( value ) {
- return access( this, function( value ) {
- var elem = this[ 0 ] || {},
- i = 0,
- l = this.length;
-
- if ( value === undefined && elem.nodeType === 1 ) {
- return elem.innerHTML;
- }
-
- // See if we can take a shortcut and just use innerHTML
- if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
- !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
- value = jQuery.htmlPrefilter( value );
-
- try {
- for ( ; i < l; i++ ) {
- elem = this[ i ] || {};
-
- // Remove element nodes and prevent memory leaks
- if ( elem.nodeType === 1 ) {
- jQuery.cleanData( getAll( elem, false ) );
- elem.innerHTML = value;
- }
- }
-
- elem = 0;
-
- // If using innerHTML throws an exception, use the fallback method
- } catch ( e ) {}
- }
-
- if ( elem ) {
- this.empty().append( value );
- }
- }, null, value, arguments.length );
- },
-
- replaceWith: function() {
- var ignored = [];
-
- // Make the changes, replacing each non-ignored context element with the new content
- return domManip( this, arguments, function( elem ) {
- var parent = this.parentNode;
-
- if ( jQuery.inArray( this, ignored ) < 0 ) {
- jQuery.cleanData( getAll( this ) );
- if ( parent ) {
- parent.replaceChild( elem, this );
- }
- }
-
- // Force callback invocation
- }, ignored );
- }
-} );
-
-jQuery.each( {
- appendTo: "append",
- prependTo: "prepend",
- insertBefore: "before",
- insertAfter: "after",
- replaceAll: "replaceWith"
-}, function( name, original ) {
- jQuery.fn[ name ] = function( selector ) {
- var elems,
- ret = [],
- insert = jQuery( selector ),
- last = insert.length - 1,
- i = 0;
-
- for ( ; i <= last; i++ ) {
- elems = i === last ? this : this.clone( true );
- jQuery( insert[ i ] )[ original ]( elems );
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // .get() because push.apply(_, arraylike) throws on ancient WebKit
- push.apply( ret, elems.get() );
- }
-
- return this.pushStack( ret );
- };
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
- // Support: IE <=11 only, Firefox <=30 (#15098, #14150)
- // IE throws on elements created in popups
- // FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
- var view = elem.ownerDocument.defaultView;
-
- if ( !view || !view.opener ) {
- view = window;
- }
-
- return view.getComputedStyle( elem );
- };
-
-var swap = function( elem, options, callback ) {
- var ret, name,
- old = {};
-
- // Remember the old values, and insert the new ones
- for ( name in options ) {
- old[ name ] = elem.style[ name ];
- elem.style[ name ] = options[ name ];
- }
-
- ret = callback.call( elem );
-
- // Revert the old values
- for ( name in options ) {
- elem.style[ name ] = old[ name ];
- }
-
- return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
- // Executing both pixelPosition & boxSizingReliable tests require only one layout
- // so they're executed at the same time to save the second computation.
- function computeStyleTests() {
-
- // This is a singleton, we need to execute it only once
- if ( !div ) {
- return;
- }
-
- container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
- "margin-top:1px;padding:0;border:0";
- div.style.cssText =
- "position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
- "margin:auto;border:1px;padding:1px;" +
- "width:60%;top:1%";
- documentElement.appendChild( container ).appendChild( div );
-
- var divStyle = window.getComputedStyle( div );
- pixelPositionVal = divStyle.top !== "1%";
-
- // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
- reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
- // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
- // Some styles come back with percentage values, even though they shouldn't
- div.style.right = "60%";
- pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
- // Support: IE 9 - 11 only
- // Detect misreporting of content dimensions for box-sizing:border-box elements
- boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
- // Support: IE 9 only
- // Detect overflow:scroll screwiness (gh-3699)
- // Support: Chrome <=64
- // Don't get tricked when zoom affects offsetWidth (gh-4029)
- div.style.position = "absolute";
- scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
- documentElement.removeChild( container );
-
- // Nullify the div so it wouldn't be stored in the memory and
- // it will also be a sign that checks already performed
- div = null;
- }
-
- function roundPixelMeasures( measure ) {
- return Math.round( parseFloat( measure ) );
- }
-
- var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
- reliableTrDimensionsVal, reliableMarginLeftVal,
- container = document.createElement( "div" ),
- div = document.createElement( "div" );
-
- // Finish early in limited (non-browser) environments
- if ( !div.style ) {
- return;
- }
-
- // Support: IE <=9 - 11 only
- // Style of cloned element affects source element cloned (#8908)
- div.style.backgroundClip = "content-box";
- div.cloneNode( true ).style.backgroundClip = "";
- support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
- jQuery.extend( support, {
- boxSizingReliable: function() {
- computeStyleTests();
- return boxSizingReliableVal;
- },
- pixelBoxStyles: function() {
- computeStyleTests();
- return pixelBoxStylesVal;
- },
- pixelPosition: function() {
- computeStyleTests();
- return pixelPositionVal;
- },
- reliableMarginLeft: function() {
- computeStyleTests();
- return reliableMarginLeftVal;
- },
- scrollboxSize: function() {
- computeStyleTests();
- return scrollboxSizeVal;
- },
-
- // Support: IE 9 - 11+, Edge 15 - 18+
- // IE/Edge misreport `getComputedStyle` of table rows with width/height
- // set in CSS while `offset*` properties report correct values.
- // Behavior in IE 9 is more subtle than in newer versions & it passes
- // some versions of this test; make sure not to make it pass there!
- reliableTrDimensions: function() {
- var table, tr, trChild, trStyle;
- if ( reliableTrDimensionsVal == null ) {
- table = document.createElement( "table" );
- tr = document.createElement( "tr" );
- trChild = document.createElement( "div" );
-
- table.style.cssText = "position:absolute;left:-11111px";
- tr.style.height = "1px";
- trChild.style.height = "9px";
-
- documentElement
- .appendChild( table )
- .appendChild( tr )
- .appendChild( trChild );
-
- trStyle = window.getComputedStyle( tr );
- reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
-
- documentElement.removeChild( table );
- }
- return reliableTrDimensionsVal;
- }
- } );
-} )();
-
-
-function curCSS( elem, name, computed ) {
- var width, minWidth, maxWidth, ret,
-
- // Support: Firefox 51+
- // Retrieving style before computed somehow
- // fixes an issue with getting wrong values
- // on detached elements
- style = elem.style;
-
- computed = computed || getStyles( elem );
-
- // getPropertyValue is needed for:
- // .css('filter') (IE 9 only, #12537)
- // .css('--customProperty) (#3144)
- if ( computed ) {
- ret = computed.getPropertyValue( name ) || computed[ name ];
-
- if ( ret === "" && !isAttached( elem ) ) {
- ret = jQuery.style( elem, name );
- }
-
- // A tribute to the "awesome hack by Dean Edwards"
- // Android Browser returns percentage for some values,
- // but width seems to be reliably pixels.
- // This is against the CSSOM draft spec:
- // https://drafts.csswg.org/cssom/#resolved-values
- if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
- // Remember the original values
- width = style.width;
- minWidth = style.minWidth;
- maxWidth = style.maxWidth;
-
- // Put in the new values to get a computed value out
- style.minWidth = style.maxWidth = style.width = ret;
- ret = computed.width;
-
- // Revert the changed values
- style.width = width;
- style.minWidth = minWidth;
- style.maxWidth = maxWidth;
- }
- }
-
- return ret !== undefined ?
-
- // Support: IE <=9 - 11 only
- // IE returns zIndex value as an integer.
- ret + "" :
- ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
- // Define the hook, we'll check on the first run if it's really needed.
- return {
- get: function() {
- if ( conditionFn() ) {
-
- // Hook not needed (or it's not possible to use it due
- // to missing dependency), remove it.
- delete this.get;
- return;
- }
-
- // Hook needed; redefine it so that the support test is not executed again.
- return ( this.get = hookFn ).apply( this, arguments );
- }
- };
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
- emptyStyle = document.createElement( "div" ).style,
- vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
- // Check for vendor prefixed names
- var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
- i = cssPrefixes.length;
-
- while ( i-- ) {
- name = cssPrefixes[ i ] + capName;
- if ( name in emptyStyle ) {
- return name;
- }
- }
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
- var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
- if ( final ) {
- return final;
- }
- if ( name in emptyStyle ) {
- return name;
- }
- return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
- // Swappable if display is none or starts with table
- // except "table", "table-cell", or "table-caption"
- // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
- rdisplayswap = /^(none|table(?!-c[ea]).+)/,
- rcustomProp = /^--/,
- cssShow = { position: "absolute", visibility: "hidden", display: "block" },
- cssNormalTransform = {
- letterSpacing: "0",
- fontWeight: "400"
- };
-
-function setPositiveNumber( _elem, value, subtract ) {
-
- // Any relative (+/-) values have already been
- // normalized at this point
- var matches = rcssNum.exec( value );
- return matches ?
-
- // Guard against undefined "subtract", e.g., when used as in cssHooks
- Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
- value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
- var i = dimension === "width" ? 1 : 0,
- extra = 0,
- delta = 0;
-
- // Adjustment may not be necessary
- if ( box === ( isBorderBox ? "border" : "content" ) ) {
- return 0;
- }
-
- for ( ; i < 4; i += 2 ) {
-
- // Both box models exclude margin
- if ( box === "margin" ) {
- delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
- }
-
- // If we get here with a content-box, we're seeking "padding" or "border" or "margin"
- if ( !isBorderBox ) {
-
- // Add padding
- delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
- // For "border" or "margin", add border
- if ( box !== "padding" ) {
- delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
- // But still keep track of it otherwise
- } else {
- extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
- }
-
- // If we get here with a border-box (content + padding + border), we're seeking "content" or
- // "padding" or "margin"
- } else {
-
- // For "content", subtract padding
- if ( box === "content" ) {
- delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
- }
-
- // For "content" or "padding", subtract border
- if ( box !== "margin" ) {
- delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
- }
- }
- }
-
- // Account for positive content-box scroll gutter when requested by providing computedVal
- if ( !isBorderBox && computedVal >= 0 ) {
-
- // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
- // Assuming integer scroll gutter, subtract the rest and round down
- delta += Math.max( 0, Math.ceil(
- elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
- computedVal -
- delta -
- extra -
- 0.5
-
- // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
- // Use an explicit zero to avoid NaN (gh-3964)
- ) ) || 0;
- }
-
- return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
- // Start with computed style
- var styles = getStyles( elem ),
-
- // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
- // Fake content-box until we know it's needed to know the true value.
- boxSizingNeeded = !support.boxSizingReliable() || extra,
- isBorderBox = boxSizingNeeded &&
- jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
- valueIsBorderBox = isBorderBox,
-
- val = curCSS( elem, dimension, styles ),
- offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
- // Support: Firefox <=54
- // Return a confounding non-pixel value or feign ignorance, as appropriate.
- if ( rnumnonpx.test( val ) ) {
- if ( !extra ) {
- return val;
- }
- val = "auto";
- }
-
-
- // Support: IE 9 - 11 only
- // Use offsetWidth/offsetHeight for when box sizing is unreliable.
- // In those cases, the computed value can be trusted to be border-box.
- if ( ( !support.boxSizingReliable() && isBorderBox ||
-
- // Support: IE 10 - 11+, Edge 15 - 18+
- // IE/Edge misreport `getComputedStyle` of table rows with width/height
- // set in CSS while `offset*` properties report correct values.
- // Interestingly, in some cases IE 9 doesn't suffer from this issue.
- !support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
- // Fall back to offsetWidth/offsetHeight when value is "auto"
- // This happens for inline elements with no explicit setting (gh-3571)
- val === "auto" ||
-
- // Support: Android <=4.1 - 4.3 only
- // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
- !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
- // Make sure the element is visible & connected
- elem.getClientRects().length ) {
-
- isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
- // Where available, offsetWidth/offsetHeight approximate border box dimensions.
- // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
- // retrieved value as a content box dimension.
- valueIsBorderBox = offsetProp in elem;
- if ( valueIsBorderBox ) {
- val = elem[ offsetProp ];
- }
- }
-
- // Normalize "" and auto
- val = parseFloat( val ) || 0;
-
- // Adjust for the element's box model
- return ( val +
- boxModelAdjustment(
- elem,
- dimension,
- extra || ( isBorderBox ? "border" : "content" ),
- valueIsBorderBox,
- styles,
-
- // Provide the current computed size to request scroll gutter calculation (gh-3589)
- val
- )
- ) + "px";
-}
-
-jQuery.extend( {
-
- // Add in style property hooks for overriding the default
- // behavior of getting and setting a style property
- cssHooks: {
- opacity: {
- get: function( elem, computed ) {
- if ( computed ) {
-
- // We should always get a number back from opacity
- var ret = curCSS( elem, "opacity" );
- return ret === "" ? "1" : ret;
- }
- }
- }
- },
-
- // Don't automatically add "px" to these possibly-unitless properties
- cssNumber: {
- "animationIterationCount": true,
- "columnCount": true,
- "fillOpacity": true,
- "flexGrow": true,
- "flexShrink": true,
- "fontWeight": true,
- "gridArea": true,
- "gridColumn": true,
- "gridColumnEnd": true,
- "gridColumnStart": true,
- "gridRow": true,
- "gridRowEnd": true,
- "gridRowStart": true,
- "lineHeight": true,
- "opacity": true,
- "order": true,
- "orphans": true,
- "widows": true,
- "zIndex": true,
- "zoom": true
- },
-
- // Add in properties whose names you wish to fix before
- // setting or getting the value
- cssProps: {},
-
- // Get and set the style property on a DOM Node
- style: function( elem, name, value, extra ) {
-
- // Don't set styles on text and comment nodes
- if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
- return;
- }
-
- // Make sure that we're working with the right name
- var ret, type, hooks,
- origName = camelCase( name ),
- isCustomProp = rcustomProp.test( name ),
- style = elem.style;
-
- // Make sure that we're working with the right name. We don't
- // want to query the value if it is a CSS custom property
- // since they are user-defined.
- if ( !isCustomProp ) {
- name = finalPropName( origName );
- }
-
- // Gets hook for the prefixed version, then unprefixed version
- hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
- // Check if we're setting a value
- if ( value !== undefined ) {
- type = typeof value;
-
- // Convert "+=" or "-=" to relative numbers (#7345)
- if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
- value = adjustCSS( elem, name, ret );
-
- // Fixes bug #9237
- type = "number";
- }
-
- // Make sure that null and NaN values aren't set (#7116)
- if ( value == null || value !== value ) {
- return;
- }
-
- // If a number was passed in, add the unit (except for certain CSS properties)
- // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
- // "px" to a few hardcoded values.
- if ( type === "number" && !isCustomProp ) {
- value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
- }
-
- // background-* props affect original clone's values
- if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
- style[ name ] = "inherit";
- }
-
- // If a hook was provided, use that value, otherwise just set the specified value
- if ( !hooks || !( "set" in hooks ) ||
- ( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
- if ( isCustomProp ) {
- style.setProperty( name, value );
- } else {
- style[ name ] = value;
- }
- }
-
- } else {
-
- // If a hook was provided get the non-computed value from there
- if ( hooks && "get" in hooks &&
- ( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
- return ret;
- }
-
- // Otherwise just get the value from the style object
- return style[ name ];
- }
- },
-
- css: function( elem, name, extra, styles ) {
- var val, num, hooks,
- origName = camelCase( name ),
- isCustomProp = rcustomProp.test( name );
-
- // Make sure that we're working with the right name. We don't
- // want to modify the value if it is a CSS custom property
- // since they are user-defined.
- if ( !isCustomProp ) {
- name = finalPropName( origName );
- }
-
- // Try prefixed name followed by the unprefixed name
- hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
- // If a hook was provided get the computed value from there
- if ( hooks && "get" in hooks ) {
- val = hooks.get( elem, true, extra );
- }
-
- // Otherwise, if a way to get the computed value exists, use that
- if ( val === undefined ) {
- val = curCSS( elem, name, styles );
- }
-
- // Convert "normal" to computed value
- if ( val === "normal" && name in cssNormalTransform ) {
- val = cssNormalTransform[ name ];
- }
-
- // Make numeric if forced or a qualifier was provided and val looks numeric
- if ( extra === "" || extra ) {
- num = parseFloat( val );
- return extra === true || isFinite( num ) ? num || 0 : val;
- }
-
- return val;
- }
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
- jQuery.cssHooks[ dimension ] = {
- get: function( elem, computed, extra ) {
- if ( computed ) {
-
- // Certain elements can have dimension info if we invisibly show them
- // but it must have a current display style that would benefit
- return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
- // Support: Safari 8+
- // Table columns in Safari have non-zero offsetWidth & zero
- // getBoundingClientRect().width unless display is changed.
- // Support: IE <=11 only
- // Running getBoundingClientRect on a disconnected node
- // in IE throws an error.
- ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
- swap( elem, cssShow, function() {
- return getWidthOrHeight( elem, dimension, extra );
- } ) :
- getWidthOrHeight( elem, dimension, extra );
- }
- },
-
- set: function( elem, value, extra ) {
- var matches,
- styles = getStyles( elem ),
-
- // Only read styles.position if the test has a chance to fail
- // to avoid forcing a reflow.
- scrollboxSizeBuggy = !support.scrollboxSize() &&
- styles.position === "absolute",
-
- // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
- boxSizingNeeded = scrollboxSizeBuggy || extra,
- isBorderBox = boxSizingNeeded &&
- jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
- subtract = extra ?
- boxModelAdjustment(
- elem,
- dimension,
- extra,
- isBorderBox,
- styles
- ) :
- 0;
-
- // Account for unreliable border-box dimensions by comparing offset* to computed and
- // faking a content-box to get border and padding (gh-3699)
- if ( isBorderBox && scrollboxSizeBuggy ) {
- subtract -= Math.ceil(
- elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
- parseFloat( styles[ dimension ] ) -
- boxModelAdjustment( elem, dimension, "border", false, styles ) -
- 0.5
- );
- }
-
- // Convert to pixels if value adjustment is needed
- if ( subtract && ( matches = rcssNum.exec( value ) ) &&
- ( matches[ 3 ] || "px" ) !== "px" ) {
-
- elem.style[ dimension ] = value;
- value = jQuery.css( elem, dimension );
- }
-
- return setPositiveNumber( elem, value, subtract );
- }
- };
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
- function( elem, computed ) {
- if ( computed ) {
- return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
- elem.getBoundingClientRect().left -
- swap( elem, { marginLeft: 0 }, function() {
- return elem.getBoundingClientRect().left;
- } )
- ) + "px";
- }
- }
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
- margin: "",
- padding: "",
- border: "Width"
-}, function( prefix, suffix ) {
- jQuery.cssHooks[ prefix + suffix ] = {
- expand: function( value ) {
- var i = 0,
- expanded = {},
-
- // Assumes a single number if not a string
- parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
- for ( ; i < 4; i++ ) {
- expanded[ prefix + cssExpand[ i ] + suffix ] =
- parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
- }
-
- return expanded;
- }
- };
-
- if ( prefix !== "margin" ) {
- jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
- }
-} );
-
-jQuery.fn.extend( {
- css: function( name, value ) {
- return access( this, function( elem, name, value ) {
- var styles, len,
- map = {},
- i = 0;
-
- if ( Array.isArray( name ) ) {
- styles = getStyles( elem );
- len = name.length;
-
- for ( ; i < len; i++ ) {
- map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
- }
-
- return map;
- }
-
- return value !== undefined ?
- jQuery.style( elem, name, value ) :
- jQuery.css( elem, name );
- }, name, value, arguments.length > 1 );
- }
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
- return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
- constructor: Tween,
- init: function( elem, options, prop, end, easing, unit ) {
- this.elem = elem;
- this.prop = prop;
- this.easing = easing || jQuery.easing._default;
- this.options = options;
- this.start = this.now = this.cur();
- this.end = end;
- this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
- },
- cur: function() {
- var hooks = Tween.propHooks[ this.prop ];
-
- return hooks && hooks.get ?
- hooks.get( this ) :
- Tween.propHooks._default.get( this );
- },
- run: function( percent ) {
- var eased,
- hooks = Tween.propHooks[ this.prop ];
-
- if ( this.options.duration ) {
- this.pos = eased = jQuery.easing[ this.easing ](
- percent, this.options.duration * percent, 0, 1, this.options.duration
- );
- } else {
- this.pos = eased = percent;
- }
- this.now = ( this.end - this.start ) * eased + this.start;
-
- if ( this.options.step ) {
- this.options.step.call( this.elem, this.now, this );
- }
-
- if ( hooks && hooks.set ) {
- hooks.set( this );
- } else {
- Tween.propHooks._default.set( this );
- }
- return this;
- }
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
- _default: {
- get: function( tween ) {
- var result;
-
- // Use a property on the element directly when it is not a DOM element,
- // or when there is no matching style property that exists.
- if ( tween.elem.nodeType !== 1 ||
- tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
- return tween.elem[ tween.prop ];
- }
-
- // Passing an empty string as a 3rd parameter to .css will automatically
- // attempt a parseFloat and fallback to a string if the parse fails.
- // Simple values such as "10px" are parsed to Float;
- // complex values such as "rotate(1rad)" are returned as-is.
- result = jQuery.css( tween.elem, tween.prop, "" );
-
- // Empty strings, null, undefined and "auto" are converted to 0.
- return !result || result === "auto" ? 0 : result;
- },
- set: function( tween ) {
-
- // Use step hook for back compat.
- // Use cssHook if its there.
- // Use .style if available and use plain properties where available.
- if ( jQuery.fx.step[ tween.prop ] ) {
- jQuery.fx.step[ tween.prop ]( tween );
- } else if ( tween.elem.nodeType === 1 && (
- jQuery.cssHooks[ tween.prop ] ||
- tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
- jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
- } else {
- tween.elem[ tween.prop ] = tween.now;
- }
- }
- }
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
- set: function( tween ) {
- if ( tween.elem.nodeType && tween.elem.parentNode ) {
- tween.elem[ tween.prop ] = tween.now;
- }
- }
-};
-
-jQuery.easing = {
- linear: function( p ) {
- return p;
- },
- swing: function( p ) {
- return 0.5 - Math.cos( p * Math.PI ) / 2;
- },
- _default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
- fxNow, inProgress,
- rfxtypes = /^(?:toggle|show|hide)$/,
- rrun = /queueHooks$/;
-
-function schedule() {
- if ( inProgress ) {
- if ( document.hidden === false && window.requestAnimationFrame ) {
- window.requestAnimationFrame( schedule );
- } else {
- window.setTimeout( schedule, jQuery.fx.interval );
- }
-
- jQuery.fx.tick();
- }
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
- window.setTimeout( function() {
- fxNow = undefined;
- } );
- return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
- var which,
- i = 0,
- attrs = { height: type };
-
- // If we include width, step value is 1 to do all cssExpand values,
- // otherwise step value is 2 to skip over Left and Right
- includeWidth = includeWidth ? 1 : 0;
- for ( ; i < 4; i += 2 - includeWidth ) {
- which = cssExpand[ i ];
- attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
- }
-
- if ( includeWidth ) {
- attrs.opacity = attrs.width = type;
- }
-
- return attrs;
-}
-
-function createTween( value, prop, animation ) {
- var tween,
- collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
- index = 0,
- length = collection.length;
- for ( ; index < length; index++ ) {
- if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
- // We're done with this property
- return tween;
- }
- }
-}
-
-function defaultPrefilter( elem, props, opts ) {
- var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
- isBox = "width" in props || "height" in props,
- anim = this,
- orig = {},
- style = elem.style,
- hidden = elem.nodeType && isHiddenWithinTree( elem ),
- dataShow = dataPriv.get( elem, "fxshow" );
-
- // Queue-skipping animations hijack the fx hooks
- if ( !opts.queue ) {
- hooks = jQuery._queueHooks( elem, "fx" );
- if ( hooks.unqueued == null ) {
- hooks.unqueued = 0;
- oldfire = hooks.empty.fire;
- hooks.empty.fire = function() {
- if ( !hooks.unqueued ) {
- oldfire();
- }
- };
- }
- hooks.unqueued++;
-
- anim.always( function() {
-
- // Ensure the complete handler is called before this completes
- anim.always( function() {
- hooks.unqueued--;
- if ( !jQuery.queue( elem, "fx" ).length ) {
- hooks.empty.fire();
- }
- } );
- } );
- }
-
- // Detect show/hide animations
- for ( prop in props ) {
- value = props[ prop ];
- if ( rfxtypes.test( value ) ) {
- delete props[ prop ];
- toggle = toggle || value === "toggle";
- if ( value === ( hidden ? "hide" : "show" ) ) {
-
- // Pretend to be hidden if this is a "show" and
- // there is still data from a stopped show/hide
- if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
- hidden = true;
-
- // Ignore all other no-op show/hide data
- } else {
- continue;
- }
- }
- orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
- }
- }
-
- // Bail out if this is a no-op like .hide().hide()
- propTween = !jQuery.isEmptyObject( props );
- if ( !propTween && jQuery.isEmptyObject( orig ) ) {
- return;
- }
-
- // Restrict "overflow" and "display" styles during box animations
- if ( isBox && elem.nodeType === 1 ) {
-
- // Support: IE <=9 - 11, Edge 12 - 15
- // Record all 3 overflow attributes because IE does not infer the shorthand
- // from identically-valued overflowX and overflowY and Edge just mirrors
- // the overflowX value there.
- opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
- // Identify a display type, preferring old show/hide data over the CSS cascade
- restoreDisplay = dataShow && dataShow.display;
- if ( restoreDisplay == null ) {
- restoreDisplay = dataPriv.get( elem, "display" );
- }
- display = jQuery.css( elem, "display" );
- if ( display === "none" ) {
- if ( restoreDisplay ) {
- display = restoreDisplay;
- } else {
-
- // Get nonempty value(s) by temporarily forcing visibility
- showHide( [ elem ], true );
- restoreDisplay = elem.style.display || restoreDisplay;
- display = jQuery.css( elem, "display" );
- showHide( [ elem ] );
- }
- }
-
- // Animate inline elements as inline-block
- if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
- if ( jQuery.css( elem, "float" ) === "none" ) {
-
- // Restore the original display value at the end of pure show/hide animations
- if ( !propTween ) {
- anim.done( function() {
- style.display = restoreDisplay;
- } );
- if ( restoreDisplay == null ) {
- display = style.display;
- restoreDisplay = display === "none" ? "" : display;
- }
- }
- style.display = "inline-block";
- }
- }
- }
-
- if ( opts.overflow ) {
- style.overflow = "hidden";
- anim.always( function() {
- style.overflow = opts.overflow[ 0 ];
- style.overflowX = opts.overflow[ 1 ];
- style.overflowY = opts.overflow[ 2 ];
- } );
- }
-
- // Implement show/hide animations
- propTween = false;
- for ( prop in orig ) {
-
- // General show/hide setup for this element animation
- if ( !propTween ) {
- if ( dataShow ) {
- if ( "hidden" in dataShow ) {
- hidden = dataShow.hidden;
- }
- } else {
- dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
- }
-
- // Store hidden/visible for toggle so `.stop().toggle()` "reverses"
- if ( toggle ) {
- dataShow.hidden = !hidden;
- }
-
- // Show elements before animating them
- if ( hidden ) {
- showHide( [ elem ], true );
- }
-
- /* eslint-disable no-loop-func */
-
- anim.done( function() {
-
- /* eslint-enable no-loop-func */
-
- // The final step of a "hide" animation is actually hiding the element
- if ( !hidden ) {
- showHide( [ elem ] );
- }
- dataPriv.remove( elem, "fxshow" );
- for ( prop in orig ) {
- jQuery.style( elem, prop, orig[ prop ] );
- }
- } );
- }
-
- // Per-property setup
- propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
- if ( !( prop in dataShow ) ) {
- dataShow[ prop ] = propTween.start;
- if ( hidden ) {
- propTween.end = propTween.start;
- propTween.start = 0;
- }
- }
- }
-}
-
-function propFilter( props, specialEasing ) {
- var index, name, easing, value, hooks;
-
- // camelCase, specialEasing and expand cssHook pass
- for ( index in props ) {
- name = camelCase( index );
- easing = specialEasing[ name ];
- value = props[ index ];
- if ( Array.isArray( value ) ) {
- easing = value[ 1 ];
- value = props[ index ] = value[ 0 ];
- }
-
- if ( index !== name ) {
- props[ name ] = value;
- delete props[ index ];
- }
-
- hooks = jQuery.cssHooks[ name ];
- if ( hooks && "expand" in hooks ) {
- value = hooks.expand( value );
- delete props[ name ];
-
- // Not quite $.extend, this won't overwrite existing keys.
- // Reusing 'index' because we have the correct "name"
- for ( index in value ) {
- if ( !( index in props ) ) {
- props[ index ] = value[ index ];
- specialEasing[ index ] = easing;
- }
- }
- } else {
- specialEasing[ name ] = easing;
- }
- }
-}
-
-function Animation( elem, properties, options ) {
- var result,
- stopped,
- index = 0,
- length = Animation.prefilters.length,
- deferred = jQuery.Deferred().always( function() {
-
- // Don't match elem in the :animated selector
- delete tick.elem;
- } ),
- tick = function() {
- if ( stopped ) {
- return false;
- }
- var currentTime = fxNow || createFxNow(),
- remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
- // Support: Android 2.3 only
- // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
- temp = remaining / animation.duration || 0,
- percent = 1 - temp,
- index = 0,
- length = animation.tweens.length;
-
- for ( ; index < length; index++ ) {
- animation.tweens[ index ].run( percent );
- }
-
- deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
- // If there's more to do, yield
- if ( percent < 1 && length ) {
- return remaining;
- }
-
- // If this was an empty animation, synthesize a final progress notification
- if ( !length ) {
- deferred.notifyWith( elem, [ animation, 1, 0 ] );
- }
-
- // Resolve the animation and report its conclusion
- deferred.resolveWith( elem, [ animation ] );
- return false;
- },
- animation = deferred.promise( {
- elem: elem,
- props: jQuery.extend( {}, properties ),
- opts: jQuery.extend( true, {
- specialEasing: {},
- easing: jQuery.easing._default
- }, options ),
- originalProperties: properties,
- originalOptions: options,
- startTime: fxNow || createFxNow(),
- duration: options.duration,
- tweens: [],
- createTween: function( prop, end ) {
- var tween = jQuery.Tween( elem, animation.opts, prop, end,
- animation.opts.specialEasing[ prop ] || animation.opts.easing );
- animation.tweens.push( tween );
- return tween;
- },
- stop: function( gotoEnd ) {
- var index = 0,
-
- // If we are going to the end, we want to run all the tweens
- // otherwise we skip this part
- length = gotoEnd ? animation.tweens.length : 0;
- if ( stopped ) {
- return this;
- }
- stopped = true;
- for ( ; index < length; index++ ) {
- animation.tweens[ index ].run( 1 );
- }
-
- // Resolve when we played the last frame; otherwise, reject
- if ( gotoEnd ) {
- deferred.notifyWith( elem, [ animation, 1, 0 ] );
- deferred.resolveWith( elem, [ animation, gotoEnd ] );
- } else {
- deferred.rejectWith( elem, [ animation, gotoEnd ] );
- }
- return this;
- }
- } ),
- props = animation.props;
-
- propFilter( props, animation.opts.specialEasing );
-
- for ( ; index < length; index++ ) {
- result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
- if ( result ) {
- if ( isFunction( result.stop ) ) {
- jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
- result.stop.bind( result );
- }
- return result;
- }
- }
-
- jQuery.map( props, createTween, animation );
-
- if ( isFunction( animation.opts.start ) ) {
- animation.opts.start.call( elem, animation );
- }
-
- // Attach callbacks from options
- animation
- .progress( animation.opts.progress )
- .done( animation.opts.done, animation.opts.complete )
- .fail( animation.opts.fail )
- .always( animation.opts.always );
-
- jQuery.fx.timer(
- jQuery.extend( tick, {
- elem: elem,
- anim: animation,
- queue: animation.opts.queue
- } )
- );
-
- return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
- tweeners: {
- "*": [ function( prop, value ) {
- var tween = this.createTween( prop, value );
- adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
- return tween;
- } ]
- },
-
- tweener: function( props, callback ) {
- if ( isFunction( props ) ) {
- callback = props;
- props = [ "*" ];
- } else {
- props = props.match( rnothtmlwhite );
- }
-
- var prop,
- index = 0,
- length = props.length;
-
- for ( ; index < length; index++ ) {
- prop = props[ index ];
- Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
- Animation.tweeners[ prop ].unshift( callback );
- }
- },
-
- prefilters: [ defaultPrefilter ],
-
- prefilter: function( callback, prepend ) {
- if ( prepend ) {
- Animation.prefilters.unshift( callback );
- } else {
- Animation.prefilters.push( callback );
- }
- }
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
- var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
- complete: fn || !fn && easing ||
- isFunction( speed ) && speed,
- duration: speed,
- easing: fn && easing || easing && !isFunction( easing ) && easing
- };
-
- // Go to the end state if fx are off
- if ( jQuery.fx.off ) {
- opt.duration = 0;
-
- } else {
- if ( typeof opt.duration !== "number" ) {
- if ( opt.duration in jQuery.fx.speeds ) {
- opt.duration = jQuery.fx.speeds[ opt.duration ];
-
- } else {
- opt.duration = jQuery.fx.speeds._default;
- }
- }
- }
-
- // Normalize opt.queue - true/undefined/null -> "fx"
- if ( opt.queue == null || opt.queue === true ) {
- opt.queue = "fx";
- }
-
- // Queueing
- opt.old = opt.complete;
-
- opt.complete = function() {
- if ( isFunction( opt.old ) ) {
- opt.old.call( this );
- }
-
- if ( opt.queue ) {
- jQuery.dequeue( this, opt.queue );
- }
- };
-
- return opt;
-};
-
-jQuery.fn.extend( {
- fadeTo: function( speed, to, easing, callback ) {
-
- // Show any hidden elements after setting opacity to 0
- return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
- // Animate to the value specified
- .end().animate( { opacity: to }, speed, easing, callback );
- },
- animate: function( prop, speed, easing, callback ) {
- var empty = jQuery.isEmptyObject( prop ),
- optall = jQuery.speed( speed, easing, callback ),
- doAnimation = function() {
-
- // Operate on a copy of prop so per-property easing won't be lost
- var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
- // Empty animations, or finishing resolves immediately
- if ( empty || dataPriv.get( this, "finish" ) ) {
- anim.stop( true );
- }
- };
- doAnimation.finish = doAnimation;
-
- return empty || optall.queue === false ?
- this.each( doAnimation ) :
- this.queue( optall.queue, doAnimation );
- },
- stop: function( type, clearQueue, gotoEnd ) {
- var stopQueue = function( hooks ) {
- var stop = hooks.stop;
- delete hooks.stop;
- stop( gotoEnd );
- };
-
- if ( typeof type !== "string" ) {
- gotoEnd = clearQueue;
- clearQueue = type;
- type = undefined;
- }
- if ( clearQueue ) {
- this.queue( type || "fx", [] );
- }
-
- return this.each( function() {
- var dequeue = true,
- index = type != null && type + "queueHooks",
- timers = jQuery.timers,
- data = dataPriv.get( this );
-
- if ( index ) {
- if ( data[ index ] && data[ index ].stop ) {
- stopQueue( data[ index ] );
- }
- } else {
- for ( index in data ) {
- if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
- stopQueue( data[ index ] );
- }
- }
- }
-
- for ( index = timers.length; index--; ) {
- if ( timers[ index ].elem === this &&
- ( type == null || timers[ index ].queue === type ) ) {
-
- timers[ index ].anim.stop( gotoEnd );
- dequeue = false;
- timers.splice( index, 1 );
- }
- }
-
- // Start the next in the queue if the last step wasn't forced.
- // Timers currently will call their complete callbacks, which
- // will dequeue but only if they were gotoEnd.
- if ( dequeue || !gotoEnd ) {
- jQuery.dequeue( this, type );
- }
- } );
- },
- finish: function( type ) {
- if ( type !== false ) {
- type = type || "fx";
- }
- return this.each( function() {
- var index,
- data = dataPriv.get( this ),
- queue = data[ type + "queue" ],
- hooks = data[ type + "queueHooks" ],
- timers = jQuery.timers,
- length = queue ? queue.length : 0;
-
- // Enable finishing flag on private data
- data.finish = true;
-
- // Empty the queue first
- jQuery.queue( this, type, [] );
-
- if ( hooks && hooks.stop ) {
- hooks.stop.call( this, true );
- }
-
- // Look for any active animations, and finish them
- for ( index = timers.length; index--; ) {
- if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
- timers[ index ].anim.stop( true );
- timers.splice( index, 1 );
- }
- }
-
- // Look for any animations in the old queue and finish them
- for ( index = 0; index < length; index++ ) {
- if ( queue[ index ] && queue[ index ].finish ) {
- queue[ index ].finish.call( this );
- }
- }
-
- // Turn off finishing flag
- delete data.finish;
- } );
- }
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
- var cssFn = jQuery.fn[ name ];
- jQuery.fn[ name ] = function( speed, easing, callback ) {
- return speed == null || typeof speed === "boolean" ?
- cssFn.apply( this, arguments ) :
- this.animate( genFx( name, true ), speed, easing, callback );
- };
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
- slideDown: genFx( "show" ),
- slideUp: genFx( "hide" ),
- slideToggle: genFx( "toggle" ),
- fadeIn: { opacity: "show" },
- fadeOut: { opacity: "hide" },
- fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
- jQuery.fn[ name ] = function( speed, easing, callback ) {
- return this.animate( props, speed, easing, callback );
- };
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
- var timer,
- i = 0,
- timers = jQuery.timers;
-
- fxNow = Date.now();
-
- for ( ; i < timers.length; i++ ) {
- timer = timers[ i ];
-
- // Run the timer and safely remove it when done (allowing for external removal)
- if ( !timer() && timers[ i ] === timer ) {
- timers.splice( i--, 1 );
- }
- }
-
- if ( !timers.length ) {
- jQuery.fx.stop();
- }
- fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
- jQuery.timers.push( timer );
- jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
- if ( inProgress ) {
- return;
- }
-
- inProgress = true;
- schedule();
-};
-
-jQuery.fx.stop = function() {
- inProgress = null;
-};
-
-jQuery.fx.speeds = {
- slow: 600,
- fast: 200,
-
- // Default speed
- _default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
- time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
- type = type || "fx";
-
- return this.queue( type, function( next, hooks ) {
- var timeout = window.setTimeout( next, time );
- hooks.stop = function() {
- window.clearTimeout( timeout );
- };
- } );
-};
-
-
-( function() {
- var input = document.createElement( "input" ),
- select = document.createElement( "select" ),
- opt = select.appendChild( document.createElement( "option" ) );
-
- input.type = "checkbox";
-
- // Support: Android <=4.3 only
- // Default value for a checkbox should be "on"
- support.checkOn = input.value !== "";
-
- // Support: IE <=11 only
- // Must access selectedIndex to make default options select
- support.optSelected = opt.selected;
-
- // Support: IE <=11 only
- // An input loses its value after becoming a radio
- input = document.createElement( "input" );
- input.value = "t";
- input.type = "radio";
- support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
- attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
- attr: function( name, value ) {
- return access( this, jQuery.attr, name, value, arguments.length > 1 );
- },
-
- removeAttr: function( name ) {
- return this.each( function() {
- jQuery.removeAttr( this, name );
- } );
- }
-} );
-
-jQuery.extend( {
- attr: function( elem, name, value ) {
- var ret, hooks,
- nType = elem.nodeType;
-
- // Don't get/set attributes on text, comment and attribute nodes
- if ( nType === 3 || nType === 8 || nType === 2 ) {
- return;
- }
-
- // Fallback to prop when attributes are not supported
- if ( typeof elem.getAttribute === "undefined" ) {
- return jQuery.prop( elem, name, value );
- }
-
- // Attribute hooks are determined by the lowercase version
- // Grab necessary hook if one is defined
- if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
- hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
- ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
- }
-
- if ( value !== undefined ) {
- if ( value === null ) {
- jQuery.removeAttr( elem, name );
- return;
- }
-
- if ( hooks && "set" in hooks &&
- ( ret = hooks.set( elem, value, name ) ) !== undefined ) {
- return ret;
- }
-
- elem.setAttribute( name, value + "" );
- return value;
- }
-
- if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
- return ret;
- }
-
- ret = jQuery.find.attr( elem, name );
-
- // Non-existent attributes return null, we normalize to undefined
- return ret == null ? undefined : ret;
- },
-
- attrHooks: {
- type: {
- set: function( elem, value ) {
- if ( !support.radioValue && value === "radio" &&
- nodeName( elem, "input" ) ) {
- var val = elem.value;
- elem.setAttribute( "type", value );
- if ( val ) {
- elem.value = val;
- }
- return value;
- }
- }
- }
- },
-
- removeAttr: function( elem, value ) {
- var name,
- i = 0,
-
- // Attribute names can contain non-HTML whitespace characters
- // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
- attrNames = value && value.match( rnothtmlwhite );
-
- if ( attrNames && elem.nodeType === 1 ) {
- while ( ( name = attrNames[ i++ ] ) ) {
- elem.removeAttribute( name );
- }
- }
- }
-} );
-
-// Hooks for boolean attributes
-boolHook = {
- set: function( elem, value, name ) {
- if ( value === false ) {
-
- // Remove boolean attributes when set to false
- jQuery.removeAttr( elem, name );
- } else {
- elem.setAttribute( name, name );
- }
- return name;
- }
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
- var getter = attrHandle[ name ] || jQuery.find.attr;
-
- attrHandle[ name ] = function( elem, name, isXML ) {
- var ret, handle,
- lowercaseName = name.toLowerCase();
-
- if ( !isXML ) {
-
- // Avoid an infinite loop by temporarily removing this function from the getter
- handle = attrHandle[ lowercaseName ];
- attrHandle[ lowercaseName ] = ret;
- ret = getter( elem, name, isXML ) != null ?
- lowercaseName :
- null;
- attrHandle[ lowercaseName ] = handle;
- }
- return ret;
- };
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
- rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
- prop: function( name, value ) {
- return access( this, jQuery.prop, name, value, arguments.length > 1 );
- },
-
- removeProp: function( name ) {
- return this.each( function() {
- delete this[ jQuery.propFix[ name ] || name ];
- } );
- }
-} );
-
-jQuery.extend( {
- prop: function( elem, name, value ) {
- var ret, hooks,
- nType = elem.nodeType;
-
- // Don't get/set properties on text, comment and attribute nodes
- if ( nType === 3 || nType === 8 || nType === 2 ) {
- return;
- }
-
- if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
- // Fix name and attach hooks
- name = jQuery.propFix[ name ] || name;
- hooks = jQuery.propHooks[ name ];
- }
-
- if ( value !== undefined ) {
- if ( hooks && "set" in hooks &&
- ( ret = hooks.set( elem, value, name ) ) !== undefined ) {
- return ret;
- }
-
- return ( elem[ name ] = value );
- }
-
- if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
- return ret;
- }
-
- return elem[ name ];
- },
-
- propHooks: {
- tabIndex: {
- get: function( elem ) {
-
- // Support: IE <=9 - 11 only
- // elem.tabIndex doesn't always return the
- // correct value when it hasn't been explicitly set
- // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
- // Use proper attribute retrieval(#12072)
- var tabindex = jQuery.find.attr( elem, "tabindex" );
-
- if ( tabindex ) {
- return parseInt( tabindex, 10 );
- }
-
- if (
- rfocusable.test( elem.nodeName ) ||
- rclickable.test( elem.nodeName ) &&
- elem.href
- ) {
- return 0;
- }
-
- return -1;
- }
- }
- },
-
- propFix: {
- "for": "htmlFor",
- "class": "className"
- }
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
- jQuery.propHooks.selected = {
- get: function( elem ) {
-
- /* eslint no-unused-expressions: "off" */
-
- var parent = elem.parentNode;
- if ( parent && parent.parentNode ) {
- parent.parentNode.selectedIndex;
- }
- return null;
- },
- set: function( elem ) {
-
- /* eslint no-unused-expressions: "off" */
-
- var parent = elem.parentNode;
- if ( parent ) {
- parent.selectedIndex;
-
- if ( parent.parentNode ) {
- parent.parentNode.selectedIndex;
- }
- }
- }
- };
-}
-
-jQuery.each( [
- "tabIndex",
- "readOnly",
- "maxLength",
- "cellSpacing",
- "cellPadding",
- "rowSpan",
- "colSpan",
- "useMap",
- "frameBorder",
- "contentEditable"
-], function() {
- jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
- // Strip and collapse whitespace according to HTML spec
- // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
- function stripAndCollapse( value ) {
- var tokens = value.match( rnothtmlwhite ) || [];
- return tokens.join( " " );
- }
-
-
-function getClass( elem ) {
- return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
- if ( Array.isArray( value ) ) {
- return value;
- }
- if ( typeof value === "string" ) {
- return value.match( rnothtmlwhite ) || [];
- }
- return [];
-}
-
-jQuery.fn.extend( {
- addClass: function( value ) {
- var classes, elem, cur, curValue, clazz, j, finalValue,
- i = 0;
-
- if ( isFunction( value ) ) {
- return this.each( function( j ) {
- jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
- } );
- }
-
- classes = classesToArray( value );
-
- if ( classes.length ) {
- while ( ( elem = this[ i++ ] ) ) {
- curValue = getClass( elem );
- cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
- if ( cur ) {
- j = 0;
- while ( ( clazz = classes[ j++ ] ) ) {
- if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
- cur += clazz + " ";
- }
- }
-
- // Only assign if different to avoid unneeded rendering.
- finalValue = stripAndCollapse( cur );
- if ( curValue !== finalValue ) {
- elem.setAttribute( "class", finalValue );
- }
- }
- }
- }
-
- return this;
- },
-
- removeClass: function( value ) {
- var classes, elem, cur, curValue, clazz, j, finalValue,
- i = 0;
-
- if ( isFunction( value ) ) {
- return this.each( function( j ) {
- jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
- } );
- }
-
- if ( !arguments.length ) {
- return this.attr( "class", "" );
- }
-
- classes = classesToArray( value );
-
- if ( classes.length ) {
- while ( ( elem = this[ i++ ] ) ) {
- curValue = getClass( elem );
-
- // This expression is here for better compressibility (see addClass)
- cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
- if ( cur ) {
- j = 0;
- while ( ( clazz = classes[ j++ ] ) ) {
-
- // Remove *all* instances
- while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
- cur = cur.replace( " " + clazz + " ", " " );
- }
- }
-
- // Only assign if different to avoid unneeded rendering.
- finalValue = stripAndCollapse( cur );
- if ( curValue !== finalValue ) {
- elem.setAttribute( "class", finalValue );
- }
- }
- }
- }
-
- return this;
- },
-
- toggleClass: function( value, stateVal ) {
- var type = typeof value,
- isValidValue = type === "string" || Array.isArray( value );
-
- if ( typeof stateVal === "boolean" && isValidValue ) {
- return stateVal ? this.addClass( value ) : this.removeClass( value );
- }
-
- if ( isFunction( value ) ) {
- return this.each( function( i ) {
- jQuery( this ).toggleClass(
- value.call( this, i, getClass( this ), stateVal ),
- stateVal
- );
- } );
- }
-
- return this.each( function() {
- var className, i, self, classNames;
-
- if ( isValidValue ) {
-
- // Toggle individual class names
- i = 0;
- self = jQuery( this );
- classNames = classesToArray( value );
-
- while ( ( className = classNames[ i++ ] ) ) {
-
- // Check each className given, space separated list
- if ( self.hasClass( className ) ) {
- self.removeClass( className );
- } else {
- self.addClass( className );
- }
- }
-
- // Toggle whole class name
- } else if ( value === undefined || type === "boolean" ) {
- className = getClass( this );
- if ( className ) {
-
- // Store className if set
- dataPriv.set( this, "__className__", className );
- }
-
- // If the element has a class name or if we're passed `false`,
- // then remove the whole classname (if there was one, the above saved it).
- // Otherwise bring back whatever was previously saved (if anything),
- // falling back to the empty string if nothing was stored.
- if ( this.setAttribute ) {
- this.setAttribute( "class",
- className || value === false ?
- "" :
- dataPriv.get( this, "__className__" ) || ""
- );
- }
- }
- } );
- },
-
- hasClass: function( selector ) {
- var className, elem,
- i = 0;
-
- className = " " + selector + " ";
- while ( ( elem = this[ i++ ] ) ) {
- if ( elem.nodeType === 1 &&
- ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
- return true;
- }
- }
-
- return false;
- }
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
- val: function( value ) {
- var hooks, ret, valueIsFunction,
- elem = this[ 0 ];
-
- if ( !arguments.length ) {
- if ( elem ) {
- hooks = jQuery.valHooks[ elem.type ] ||
- jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
- if ( hooks &&
- "get" in hooks &&
- ( ret = hooks.get( elem, "value" ) ) !== undefined
- ) {
- return ret;
- }
-
- ret = elem.value;
-
- // Handle most common string cases
- if ( typeof ret === "string" ) {
- return ret.replace( rreturn, "" );
- }
-
- // Handle cases where value is null/undef or number
- return ret == null ? "" : ret;
- }
-
- return;
- }
-
- valueIsFunction = isFunction( value );
-
- return this.each( function( i ) {
- var val;
-
- if ( this.nodeType !== 1 ) {
- return;
- }
-
- if ( valueIsFunction ) {
- val = value.call( this, i, jQuery( this ).val() );
- } else {
- val = value;
- }
-
- // Treat null/undefined as ""; convert numbers to string
- if ( val == null ) {
- val = "";
-
- } else if ( typeof val === "number" ) {
- val += "";
-
- } else if ( Array.isArray( val ) ) {
- val = jQuery.map( val, function( value ) {
- return value == null ? "" : value + "";
- } );
- }
-
- hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
- // If set returns undefined, fall back to normal setting
- if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
- this.value = val;
- }
- } );
- }
-} );
-
-jQuery.extend( {
- valHooks: {
- option: {
- get: function( elem ) {
-
- var val = jQuery.find.attr( elem, "value" );
- return val != null ?
- val :
-
- // Support: IE <=10 - 11 only
- // option.text throws exceptions (#14686, #14858)
- // Strip and collapse whitespace
- // https://html.spec.whatwg.org/#strip-and-collapse-whitespace
- stripAndCollapse( jQuery.text( elem ) );
- }
- },
- select: {
- get: function( elem ) {
- var value, option, i,
- options = elem.options,
- index = elem.selectedIndex,
- one = elem.type === "select-one",
- values = one ? null : [],
- max = one ? index + 1 : options.length;
-
- if ( index < 0 ) {
- i = max;
-
- } else {
- i = one ? index : 0;
- }
-
- // Loop through all the selected options
- for ( ; i < max; i++ ) {
- option = options[ i ];
-
- // Support: IE <=9 only
- // IE8-9 doesn't update selected after form reset (#2551)
- if ( ( option.selected || i === index ) &&
-
- // Don't return options that are disabled or in a disabled optgroup
- !option.disabled &&
- ( !option.parentNode.disabled ||
- !nodeName( option.parentNode, "optgroup" ) ) ) {
-
- // Get the specific value for the option
- value = jQuery( option ).val();
-
- // We don't need an array for one selects
- if ( one ) {
- return value;
- }
-
- // Multi-Selects return an array
- values.push( value );
- }
- }
-
- return values;
- },
-
- set: function( elem, value ) {
- var optionSet, option,
- options = elem.options,
- values = jQuery.makeArray( value ),
- i = options.length;
-
- while ( i-- ) {
- option = options[ i ];
-
- /* eslint-disable no-cond-assign */
-
- if ( option.selected =
- jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
- ) {
- optionSet = true;
- }
-
- /* eslint-enable no-cond-assign */
- }
-
- // Force browsers to behave consistently when non-matching value is set
- if ( !optionSet ) {
- elem.selectedIndex = -1;
- }
- return values;
- }
- }
- }
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
- jQuery.valHooks[ this ] = {
- set: function( elem, value ) {
- if ( Array.isArray( value ) ) {
- return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
- }
- }
- };
- if ( !support.checkOn ) {
- jQuery.valHooks[ this ].get = function( elem ) {
- return elem.getAttribute( "value" ) === null ? "on" : elem.value;
- };
- }
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
- stopPropagationCallback = function( e ) {
- e.stopPropagation();
- };
-
-jQuery.extend( jQuery.event, {
-
- trigger: function( event, data, elem, onlyHandlers ) {
-
- var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
- eventPath = [ elem || document ],
- type = hasOwn.call( event, "type" ) ? event.type : event,
- namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
- cur = lastElement = tmp = elem = elem || document;
-
- // Don't do events on text and comment nodes
- if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
- return;
- }
-
- // focus/blur morphs to focusin/out; ensure we're not firing them right now
- if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
- return;
- }
-
- if ( type.indexOf( "." ) > -1 ) {
-
- // Namespaced trigger; create a regexp to match event type in handle()
- namespaces = type.split( "." );
- type = namespaces.shift();
- namespaces.sort();
- }
- ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
- // Caller can pass in a jQuery.Event object, Object, or just an event type string
- event = event[ jQuery.expando ] ?
- event :
- new jQuery.Event( type, typeof event === "object" && event );
-
- // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
- event.isTrigger = onlyHandlers ? 2 : 3;
- event.namespace = namespaces.join( "." );
- event.rnamespace = event.namespace ?
- new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
- null;
-
- // Clean up the event in case it is being reused
- event.result = undefined;
- if ( !event.target ) {
- event.target = elem;
- }
-
- // Clone any incoming data and prepend the event, creating the handler arg list
- data = data == null ?
- [ event ] :
- jQuery.makeArray( data, [ event ] );
-
- // Allow special events to draw outside the lines
- special = jQuery.event.special[ type ] || {};
- if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
- return;
- }
-
- // Determine event propagation path in advance, per W3C events spec (#9951)
- // Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
- if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
- bubbleType = special.delegateType || type;
- if ( !rfocusMorph.test( bubbleType + type ) ) {
- cur = cur.parentNode;
- }
- for ( ; cur; cur = cur.parentNode ) {
- eventPath.push( cur );
- tmp = cur;
- }
-
- // Only add window if we got to document (e.g., not plain obj or detached DOM)
- if ( tmp === ( elem.ownerDocument || document ) ) {
- eventPath.push( tmp.defaultView || tmp.parentWindow || window );
- }
- }
-
- // Fire handlers on the event path
- i = 0;
- while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
- lastElement = cur;
- event.type = i > 1 ?
- bubbleType :
- special.bindType || type;
-
- // jQuery handler
- handle = (
- dataPriv.get( cur, "events" ) || Object.create( null )
- )[ event.type ] &&
- dataPriv.get( cur, "handle" );
- if ( handle ) {
- handle.apply( cur, data );
- }
-
- // Native handler
- handle = ontype && cur[ ontype ];
- if ( handle && handle.apply && acceptData( cur ) ) {
- event.result = handle.apply( cur, data );
- if ( event.result === false ) {
- event.preventDefault();
- }
- }
- }
- event.type = type;
-
- // If nobody prevented the default action, do it now
- if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
- if ( ( !special._default ||
- special._default.apply( eventPath.pop(), data ) === false ) &&
- acceptData( elem ) ) {
-
- // Call a native DOM method on the target with the same name as the event.
- // Don't do default actions on window, that's where global variables be (#6170)
- if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
- // Don't re-trigger an onFOO event when we call its FOO() method
- tmp = elem[ ontype ];
-
- if ( tmp ) {
- elem[ ontype ] = null;
- }
-
- // Prevent re-triggering of the same event, since we already bubbled it above
- jQuery.event.triggered = type;
-
- if ( event.isPropagationStopped() ) {
- lastElement.addEventListener( type, stopPropagationCallback );
- }
-
- elem[ type ]();
-
- if ( event.isPropagationStopped() ) {
- lastElement.removeEventListener( type, stopPropagationCallback );
- }
-
- jQuery.event.triggered = undefined;
-
- if ( tmp ) {
- elem[ ontype ] = tmp;
- }
- }
- }
- }
-
- return event.result;
- },
-
- // Piggyback on a donor event to simulate a different one
- // Used only for `focus(in | out)` events
- simulate: function( type, elem, event ) {
- var e = jQuery.extend(
- new jQuery.Event(),
- event,
- {
- type: type,
- isSimulated: true
- }
- );
-
- jQuery.event.trigger( e, null, elem );
- }
-
-} );
-
-jQuery.fn.extend( {
-
- trigger: function( type, data ) {
- return this.each( function() {
- jQuery.event.trigger( type, data, this );
- } );
- },
- triggerHandler: function( type, data ) {
- var elem = this[ 0 ];
- if ( elem ) {
- return jQuery.event.trigger( type, data, elem, true );
- }
- }
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
- jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
- // Attach a single capturing handler on the document while someone wants focusin/focusout
- var handler = function( event ) {
- jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
- };
-
- jQuery.event.special[ fix ] = {
- setup: function() {
-
- // Handle: regular nodes (via `this.ownerDocument`), window
- // (via `this.document`) & document (via `this`).
- var doc = this.ownerDocument || this.document || this,
- attaches = dataPriv.access( doc, fix );
-
- if ( !attaches ) {
- doc.addEventListener( orig, handler, true );
- }
- dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
- },
- teardown: function() {
- var doc = this.ownerDocument || this.document || this,
- attaches = dataPriv.access( doc, fix ) - 1;
-
- if ( !attaches ) {
- doc.removeEventListener( orig, handler, true );
- dataPriv.remove( doc, fix );
-
- } else {
- dataPriv.access( doc, fix, attaches );
- }
- }
- };
- } );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
- var xml;
- if ( !data || typeof data !== "string" ) {
- return null;
- }
-
- // Support: IE 9 - 11 only
- // IE throws on parseFromString with invalid input.
- try {
- xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
- } catch ( e ) {
- xml = undefined;
- }
-
- if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
- jQuery.error( "Invalid XML: " + data );
- }
- return xml;
-};
-
-
-var
- rbracket = /\[\]$/,
- rCRLF = /\r?\n/g,
- rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
- rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
- var name;
-
- if ( Array.isArray( obj ) ) {
-
- // Serialize array item.
- jQuery.each( obj, function( i, v ) {
- if ( traditional || rbracket.test( prefix ) ) {
-
- // Treat each array item as a scalar.
- add( prefix, v );
-
- } else {
-
- // Item is non-scalar (array or object), encode its numeric index.
- buildParams(
- prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
- v,
- traditional,
- add
- );
- }
- } );
-
- } else if ( !traditional && toType( obj ) === "object" ) {
-
- // Serialize object item.
- for ( name in obj ) {
- buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
- }
-
- } else {
-
- // Serialize scalar item.
- add( prefix, obj );
- }
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
- var prefix,
- s = [],
- add = function( key, valueOrFunction ) {
-
- // If value is a function, invoke it and use its return value
- var value = isFunction( valueOrFunction ) ?
- valueOrFunction() :
- valueOrFunction;
-
- s[ s.length ] = encodeURIComponent( key ) + "=" +
- encodeURIComponent( value == null ? "" : value );
- };
-
- if ( a == null ) {
- return "";
- }
-
- // If an array was passed in, assume that it is an array of form elements.
- if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
- // Serialize the form elements
- jQuery.each( a, function() {
- add( this.name, this.value );
- } );
-
- } else {
-
- // If traditional, encode the "old" way (the way 1.3.2 or older
- // did it), otherwise encode params recursively.
- for ( prefix in a ) {
- buildParams( prefix, a[ prefix ], traditional, add );
- }
- }
-
- // Return the resulting serialization
- return s.join( "&" );
-};
-
-jQuery.fn.extend( {
- serialize: function() {
- return jQuery.param( this.serializeArray() );
- },
- serializeArray: function() {
- return this.map( function() {
-
- // Can add propHook for "elements" to filter or add form elements
- var elements = jQuery.prop( this, "elements" );
- return elements ? jQuery.makeArray( elements ) : this;
- } )
- .filter( function() {
- var type = this.type;
-
- // Use .is( ":disabled" ) so that fieldset[disabled] works
- return this.name && !jQuery( this ).is( ":disabled" ) &&
- rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
- ( this.checked || !rcheckableType.test( type ) );
- } )
- .map( function( _i, elem ) {
- var val = jQuery( this ).val();
-
- if ( val == null ) {
- return null;
- }
-
- if ( Array.isArray( val ) ) {
- return jQuery.map( val, function( val ) {
- return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
- } );
- }
-
- return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
- } ).get();
- }
-} );
-
-
-var
- r20 = /%20/g,
- rhash = /#.*$/,
- rantiCache = /([?&])_=[^&]*/,
- rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
- // #7653, #8125, #8152: local protocol detection
- rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
- rnoContent = /^(?:GET|HEAD)$/,
- rprotocol = /^\/\//,
-
- /* Prefilters
- * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
- * 2) These are called:
- * - BEFORE asking for a transport
- * - AFTER param serialization (s.data is a string if s.processData is true)
- * 3) key is the dataType
- * 4) the catchall symbol "*" can be used
- * 5) execution will start with transport dataType and THEN continue down to "*" if needed
- */
- prefilters = {},
-
- /* Transports bindings
- * 1) key is the dataType
- * 2) the catchall symbol "*" can be used
- * 3) selection will start with transport dataType and THEN go to "*" if needed
- */
- transports = {},
-
- // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
- allTypes = "*/".concat( "*" ),
-
- // Anchor tag for parsing the document origin
- originAnchor = document.createElement( "a" );
- originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
- // dataTypeExpression is optional and defaults to "*"
- return function( dataTypeExpression, func ) {
-
- if ( typeof dataTypeExpression !== "string" ) {
- func = dataTypeExpression;
- dataTypeExpression = "*";
- }
-
- var dataType,
- i = 0,
- dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
- if ( isFunction( func ) ) {
-
- // For each dataType in the dataTypeExpression
- while ( ( dataType = dataTypes[ i++ ] ) ) {
-
- // Prepend if requested
- if ( dataType[ 0 ] === "+" ) {
- dataType = dataType.slice( 1 ) || "*";
- ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
- // Otherwise append
- } else {
- ( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
- }
- }
- }
- };
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
- var inspected = {},
- seekingTransport = ( structure === transports );
-
- function inspect( dataType ) {
- var selected;
- inspected[ dataType ] = true;
- jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
- var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
- if ( typeof dataTypeOrTransport === "string" &&
- !seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
- options.dataTypes.unshift( dataTypeOrTransport );
- inspect( dataTypeOrTransport );
- return false;
- } else if ( seekingTransport ) {
- return !( selected = dataTypeOrTransport );
- }
- } );
- return selected;
- }
-
- return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
- var key, deep,
- flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
- for ( key in src ) {
- if ( src[ key ] !== undefined ) {
- ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
- }
- }
- if ( deep ) {
- jQuery.extend( true, target, deep );
- }
-
- return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
- var ct, type, finalDataType, firstDataType,
- contents = s.contents,
- dataTypes = s.dataTypes;
-
- // Remove auto dataType and get content-type in the process
- while ( dataTypes[ 0 ] === "*" ) {
- dataTypes.shift();
- if ( ct === undefined ) {
- ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
- }
- }
-
- // Check if we're dealing with a known content-type
- if ( ct ) {
- for ( type in contents ) {
- if ( contents[ type ] && contents[ type ].test( ct ) ) {
- dataTypes.unshift( type );
- break;
- }
- }
- }
-
- // Check to see if we have a response for the expected dataType
- if ( dataTypes[ 0 ] in responses ) {
- finalDataType = dataTypes[ 0 ];
- } else {
-
- // Try convertible dataTypes
- for ( type in responses ) {
- if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
- finalDataType = type;
- break;
- }
- if ( !firstDataType ) {
- firstDataType = type;
- }
- }
-
- // Or just use first one
- finalDataType = finalDataType || firstDataType;
- }
-
- // If we found a dataType
- // We add the dataType to the list if needed
- // and return the corresponding response
- if ( finalDataType ) {
- if ( finalDataType !== dataTypes[ 0 ] ) {
- dataTypes.unshift( finalDataType );
- }
- return responses[ finalDataType ];
- }
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
- var conv2, current, conv, tmp, prev,
- converters = {},
-
- // Work with a copy of dataTypes in case we need to modify it for conversion
- dataTypes = s.dataTypes.slice();
-
- // Create converters map with lowercased keys
- if ( dataTypes[ 1 ] ) {
- for ( conv in s.converters ) {
- converters[ conv.toLowerCase() ] = s.converters[ conv ];
- }
- }
-
- current = dataTypes.shift();
-
- // Convert to each sequential dataType
- while ( current ) {
-
- if ( s.responseFields[ current ] ) {
- jqXHR[ s.responseFields[ current ] ] = response;
- }
-
- // Apply the dataFilter if provided
- if ( !prev && isSuccess && s.dataFilter ) {
- response = s.dataFilter( response, s.dataType );
- }
-
- prev = current;
- current = dataTypes.shift();
-
- if ( current ) {
-
- // There's only work to do if current dataType is non-auto
- if ( current === "*" ) {
-
- current = prev;
-
- // Convert response if prev dataType is non-auto and differs from current
- } else if ( prev !== "*" && prev !== current ) {
-
- // Seek a direct converter
- conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
- // If none found, seek a pair
- if ( !conv ) {
- for ( conv2 in converters ) {
-
- // If conv2 outputs current
- tmp = conv2.split( " " );
- if ( tmp[ 1 ] === current ) {
-
- // If prev can be converted to accepted input
- conv = converters[ prev + " " + tmp[ 0 ] ] ||
- converters[ "* " + tmp[ 0 ] ];
- if ( conv ) {
-
- // Condense equivalence converters
- if ( conv === true ) {
- conv = converters[ conv2 ];
-
- // Otherwise, insert the intermediate dataType
- } else if ( converters[ conv2 ] !== true ) {
- current = tmp[ 0 ];
- dataTypes.unshift( tmp[ 1 ] );
- }
- break;
- }
- }
- }
- }
-
- // Apply converter (if not an equivalence)
- if ( conv !== true ) {
-
- // Unless errors are allowed to bubble, catch and return them
- if ( conv && s.throws ) {
- response = conv( response );
- } else {
- try {
- response = conv( response );
- } catch ( e ) {
- return {
- state: "parsererror",
- error: conv ? e : "No conversion from " + prev + " to " + current
- };
- }
- }
- }
- }
- }
- }
-
- return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
- // Counter for holding the number of active queries
- active: 0,
-
- // Last-Modified header cache for next request
- lastModified: {},
- etag: {},
-
- ajaxSettings: {
- url: location.href,
- type: "GET",
- isLocal: rlocalProtocol.test( location.protocol ),
- global: true,
- processData: true,
- async: true,
- contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
- /*
- timeout: 0,
- data: null,
- dataType: null,
- username: null,
- password: null,
- cache: null,
- throws: false,
- traditional: false,
- headers: {},
- */
-
- accepts: {
- "*": allTypes,
- text: "text/plain",
- html: "text/html",
- xml: "application/xml, text/xml",
- json: "application/json, text/javascript"
- },
-
- contents: {
- xml: /\bxml\b/,
- html: /\bhtml/,
- json: /\bjson\b/
- },
-
- responseFields: {
- xml: "responseXML",
- text: "responseText",
- json: "responseJSON"
- },
-
- // Data converters
- // Keys separate source (or catchall "*") and destination types with a single space
- converters: {
-
- // Convert anything to text
- "* text": String,
-
- // Text to html (true = no transformation)
- "text html": true,
-
- // Evaluate text as a json expression
- "text json": JSON.parse,
-
- // Parse text as xml
- "text xml": jQuery.parseXML
- },
-
- // For options that shouldn't be deep extended:
- // you can add your own custom options here if
- // and when you create one that shouldn't be
- // deep extended (see ajaxExtend)
- flatOptions: {
- url: true,
- context: true
- }
- },
-
- // Creates a full fledged settings object into target
- // with both ajaxSettings and settings fields.
- // If target is omitted, writes into ajaxSettings.
- ajaxSetup: function( target, settings ) {
- return settings ?
-
- // Building a settings object
- ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
- // Extending ajaxSettings
- ajaxExtend( jQuery.ajaxSettings, target );
- },
-
- ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
- ajaxTransport: addToPrefiltersOrTransports( transports ),
-
- // Main method
- ajax: function( url, options ) {
-
- // If url is an object, simulate pre-1.5 signature
- if ( typeof url === "object" ) {
- options = url;
- url = undefined;
- }
-
- // Force options to be an object
- options = options || {};
-
- var transport,
-
- // URL without anti-cache param
- cacheURL,
-
- // Response headers
- responseHeadersString,
- responseHeaders,
-
- // timeout handle
- timeoutTimer,
-
- // Url cleanup var
- urlAnchor,
-
- // Request state (becomes false upon send and true upon completion)
- completed,
-
- // To know if global events are to be dispatched
- fireGlobals,
-
- // Loop variable
- i,
-
- // uncached part of the url
- uncached,
-
- // Create the final options object
- s = jQuery.ajaxSetup( {}, options ),
-
- // Callbacks context
- callbackContext = s.context || s,
-
- // Context for global events is callbackContext if it is a DOM node or jQuery collection
- globalEventContext = s.context &&
- ( callbackContext.nodeType || callbackContext.jquery ) ?
- jQuery( callbackContext ) :
- jQuery.event,
-
- // Deferreds
- deferred = jQuery.Deferred(),
- completeDeferred = jQuery.Callbacks( "once memory" ),
-
- // Status-dependent callbacks
- statusCode = s.statusCode || {},
-
- // Headers (they are sent all at once)
- requestHeaders = {},
- requestHeadersNames = {},
-
- // Default abort message
- strAbort = "canceled",
-
- // Fake xhr
- jqXHR = {
- readyState: 0,
-
- // Builds headers hashtable if needed
- getResponseHeader: function( key ) {
- var match;
- if ( completed ) {
- if ( !responseHeaders ) {
- responseHeaders = {};
- while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
- responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
- ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
- .concat( match[ 2 ] );
- }
- }
- match = responseHeaders[ key.toLowerCase() + " " ];
- }
- return match == null ? null : match.join( ", " );
- },
-
- // Raw string
- getAllResponseHeaders: function() {
- return completed ? responseHeadersString : null;
- },
-
- // Caches the header
- setRequestHeader: function( name, value ) {
- if ( completed == null ) {
- name = requestHeadersNames[ name.toLowerCase() ] =
- requestHeadersNames[ name.toLowerCase() ] || name;
- requestHeaders[ name ] = value;
- }
- return this;
- },
-
- // Overrides response content-type header
- overrideMimeType: function( type ) {
- if ( completed == null ) {
- s.mimeType = type;
- }
- return this;
- },
-
- // Status-dependent callbacks
- statusCode: function( map ) {
- var code;
- if ( map ) {
- if ( completed ) {
-
- // Execute the appropriate callbacks
- jqXHR.always( map[ jqXHR.status ] );
- } else {
-
- // Lazy-add the new callbacks in a way that preserves old ones
- for ( code in map ) {
- statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
- }
- }
- }
- return this;
- },
-
- // Cancel the request
- abort: function( statusText ) {
- var finalText = statusText || strAbort;
- if ( transport ) {
- transport.abort( finalText );
- }
- done( 0, finalText );
- return this;
- }
- };
-
- // Attach deferreds
- deferred.promise( jqXHR );
-
- // Add protocol if not provided (prefilters might expect it)
- // Handle falsy url in the settings object (#10093: consistency with old signature)
- // We also use the url parameter if available
- s.url = ( ( url || s.url || location.href ) + "" )
- .replace( rprotocol, location.protocol + "//" );
-
- // Alias method option to type as per ticket #12004
- s.type = options.method || options.type || s.method || s.type;
-
- // Extract dataTypes list
- s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
- // A cross-domain request is in order when the origin doesn't match the current origin.
- if ( s.crossDomain == null ) {
- urlAnchor = document.createElement( "a" );
-
- // Support: IE <=8 - 11, Edge 12 - 15
- // IE throws exception on accessing the href property if url is malformed,
- // e.g. http://example.com:80x/
- try {
- urlAnchor.href = s.url;
-
- // Support: IE <=8 - 11 only
- // Anchor's host property isn't correctly set when s.url is relative
- urlAnchor.href = urlAnchor.href;
- s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
- urlAnchor.protocol + "//" + urlAnchor.host;
- } catch ( e ) {
-
- // If there is an error parsing the URL, assume it is crossDomain,
- // it can be rejected by the transport if it is invalid
- s.crossDomain = true;
- }
- }
-
- // Convert data if not already a string
- if ( s.data && s.processData && typeof s.data !== "string" ) {
- s.data = jQuery.param( s.data, s.traditional );
- }
-
- // Apply prefilters
- inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
- // If request was aborted inside a prefilter, stop there
- if ( completed ) {
- return jqXHR;
- }
-
- // We can fire global events as of now if asked to
- // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
- fireGlobals = jQuery.event && s.global;
-
- // Watch for a new set of requests
- if ( fireGlobals && jQuery.active++ === 0 ) {
- jQuery.event.trigger( "ajaxStart" );
- }
-
- // Uppercase the type
- s.type = s.type.toUpperCase();
-
- // Determine if request has content
- s.hasContent = !rnoContent.test( s.type );
-
- // Save the URL in case we're toying with the If-Modified-Since
- // and/or If-None-Match header later on
- // Remove hash to simplify url manipulation
- cacheURL = s.url.replace( rhash, "" );
-
- // More options handling for requests with no content
- if ( !s.hasContent ) {
-
- // Remember the hash so we can put it back
- uncached = s.url.slice( cacheURL.length );
-
- // If data is available and should be processed, append data to url
- if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
- cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
- // #9682: remove data so that it's not used in an eventual retry
- delete s.data;
- }
-
- // Add or update anti-cache param if needed
- if ( s.cache === false ) {
- cacheURL = cacheURL.replace( rantiCache, "$1" );
- uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
- uncached;
- }
-
- // Put hash and anti-cache on the URL that will be requested (gh-1732)
- s.url = cacheURL + uncached;
-
- // Change '%20' to '+' if this is encoded form body content (gh-2658)
- } else if ( s.data && s.processData &&
- ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
- s.data = s.data.replace( r20, "+" );
- }
-
- // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
- if ( s.ifModified ) {
- if ( jQuery.lastModified[ cacheURL ] ) {
- jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
- }
- if ( jQuery.etag[ cacheURL ] ) {
- jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
- }
- }
-
- // Set the correct header, if data is being sent
- if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
- jqXHR.setRequestHeader( "Content-Type", s.contentType );
- }
-
- // Set the Accepts header for the server, depending on the dataType
- jqXHR.setRequestHeader(
- "Accept",
- s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
- s.accepts[ s.dataTypes[ 0 ] ] +
- ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
- s.accepts[ "*" ]
- );
-
- // Check for headers option
- for ( i in s.headers ) {
- jqXHR.setRequestHeader( i, s.headers[ i ] );
- }
-
- // Allow custom headers/mimetypes and early abort
- if ( s.beforeSend &&
- ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
- // Abort if not done already and return
- return jqXHR.abort();
- }
-
- // Aborting is no longer a cancellation
- strAbort = "abort";
-
- // Install callbacks on deferreds
- completeDeferred.add( s.complete );
- jqXHR.done( s.success );
- jqXHR.fail( s.error );
-
- // Get transport
- transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
- // If no transport, we auto-abort
- if ( !transport ) {
- done( -1, "No Transport" );
- } else {
- jqXHR.readyState = 1;
-
- // Send global event
- if ( fireGlobals ) {
- globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
- }
-
- // If request was aborted inside ajaxSend, stop there
- if ( completed ) {
- return jqXHR;
- }
-
- // Timeout
- if ( s.async && s.timeout > 0 ) {
- timeoutTimer = window.setTimeout( function() {
- jqXHR.abort( "timeout" );
- }, s.timeout );
- }
-
- try {
- completed = false;
- transport.send( requestHeaders, done );
- } catch ( e ) {
-
- // Rethrow post-completion exceptions
- if ( completed ) {
- throw e;
- }
-
- // Propagate others as results
- done( -1, e );
- }
- }
-
- // Callback for when everything is done
- function done( status, nativeStatusText, responses, headers ) {
- var isSuccess, success, error, response, modified,
- statusText = nativeStatusText;
-
- // Ignore repeat invocations
- if ( completed ) {
- return;
- }
-
- completed = true;
-
- // Clear timeout if it exists
- if ( timeoutTimer ) {
- window.clearTimeout( timeoutTimer );
- }
-
- // Dereference transport for early garbage collection
- // (no matter how long the jqXHR object will be used)
- transport = undefined;
-
- // Cache response headers
- responseHeadersString = headers || "";
-
- // Set readyState
- jqXHR.readyState = status > 0 ? 4 : 0;
-
- // Determine if successful
- isSuccess = status >= 200 && status < 300 || status === 304;
-
- // Get response data
- if ( responses ) {
- response = ajaxHandleResponses( s, jqXHR, responses );
- }
-
- // Use a noop converter for missing script
- if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
- s.converters[ "text script" ] = function() {};
- }
-
- // Convert no matter what (that way responseXXX fields are always set)
- response = ajaxConvert( s, response, jqXHR, isSuccess );
-
- // If successful, handle type chaining
- if ( isSuccess ) {
-
- // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
- if ( s.ifModified ) {
- modified = jqXHR.getResponseHeader( "Last-Modified" );
- if ( modified ) {
- jQuery.lastModified[ cacheURL ] = modified;
- }
- modified = jqXHR.getResponseHeader( "etag" );
- if ( modified ) {
- jQuery.etag[ cacheURL ] = modified;
- }
- }
-
- // if no content
- if ( status === 204 || s.type === "HEAD" ) {
- statusText = "nocontent";
-
- // if not modified
- } else if ( status === 304 ) {
- statusText = "notmodified";
-
- // If we have data, let's convert it
- } else {
- statusText = response.state;
- success = response.data;
- error = response.error;
- isSuccess = !error;
- }
- } else {
-
- // Extract error from statusText and normalize for non-aborts
- error = statusText;
- if ( status || !statusText ) {
- statusText = "error";
- if ( status < 0 ) {
- status = 0;
- }
- }
- }
-
- // Set data for the fake xhr object
- jqXHR.status = status;
- jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
- // Success/Error
- if ( isSuccess ) {
- deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
- } else {
- deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
- }
-
- // Status-dependent callbacks
- jqXHR.statusCode( statusCode );
- statusCode = undefined;
-
- if ( fireGlobals ) {
- globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
- [ jqXHR, s, isSuccess ? success : error ] );
- }
-
- // Complete
- completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
- if ( fireGlobals ) {
- globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
- // Handle the global AJAX counter
- if ( !( --jQuery.active ) ) {
- jQuery.event.trigger( "ajaxStop" );
- }
- }
- }
-
- return jqXHR;
- },
-
- getJSON: function( url, data, callback ) {
- return jQuery.get( url, data, callback, "json" );
- },
-
- getScript: function( url, callback ) {
- return jQuery.get( url, undefined, callback, "script" );
- }
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
- jQuery[ method ] = function( url, data, callback, type ) {
-
- // Shift arguments if data argument was omitted
- if ( isFunction( data ) ) {
- type = type || callback;
- callback = data;
- data = undefined;
- }
-
- // The url can be an options object (which then must have .url)
- return jQuery.ajax( jQuery.extend( {
- url: url,
- type: method,
- dataType: type,
- data: data,
- success: callback
- }, jQuery.isPlainObject( url ) && url ) );
- };
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
- var i;
- for ( i in s.headers ) {
- if ( i.toLowerCase() === "content-type" ) {
- s.contentType = s.headers[ i ] || "";
- }
- }
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
- return jQuery.ajax( {
- url: url,
-
- // Make this explicit, since user can override this through ajaxSetup (#11264)
- type: "GET",
- dataType: "script",
- cache: true,
- async: false,
- global: false,
-
- // Only evaluate the response if it is successful (gh-4126)
- // dataFilter is not invoked for failure responses, so using it instead
- // of the default converter is kludgy but it works.
- converters: {
- "text script": function() {}
- },
- dataFilter: function( response ) {
- jQuery.globalEval( response, options, doc );
- }
- } );
-};
-
-
-jQuery.fn.extend( {
- wrapAll: function( html ) {
- var wrap;
-
- if ( this[ 0 ] ) {
- if ( isFunction( html ) ) {
- html = html.call( this[ 0 ] );
- }
-
- // The elements to wrap the target around
- wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
- if ( this[ 0 ].parentNode ) {
- wrap.insertBefore( this[ 0 ] );
- }
-
- wrap.map( function() {
- var elem = this;
-
- while ( elem.firstElementChild ) {
- elem = elem.firstElementChild;
- }
-
- return elem;
- } ).append( this );
- }
-
- return this;
- },
-
- wrapInner: function( html ) {
- if ( isFunction( html ) ) {
- return this.each( function( i ) {
- jQuery( this ).wrapInner( html.call( this, i ) );
- } );
- }
-
- return this.each( function() {
- var self = jQuery( this ),
- contents = self.contents();
-
- if ( contents.length ) {
- contents.wrapAll( html );
-
- } else {
- self.append( html );
- }
- } );
- },
-
- wrap: function( html ) {
- var htmlIsFunction = isFunction( html );
-
- return this.each( function( i ) {
- jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
- } );
- },
-
- unwrap: function( selector ) {
- this.parent( selector ).not( "body" ).each( function() {
- jQuery( this ).replaceWith( this.childNodes );
- } );
- return this;
- }
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
- return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
- return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
- try {
- return new window.XMLHttpRequest();
- } catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
- // File protocol always yields status code 0, assume 200
- 0: 200,
-
- // Support: IE <=9 only
- // #1450: sometimes IE returns 1223 when it should be 204
- 1223: 204
- },
- xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
- var callback, errorCallback;
-
- // Cross domain only allowed if supported through XMLHttpRequest
- if ( support.cors || xhrSupported && !options.crossDomain ) {
- return {
- send: function( headers, complete ) {
- var i,
- xhr = options.xhr();
-
- xhr.open(
- options.type,
- options.url,
- options.async,
- options.username,
- options.password
- );
-
- // Apply custom fields if provided
- if ( options.xhrFields ) {
- for ( i in options.xhrFields ) {
- xhr[ i ] = options.xhrFields[ i ];
- }
- }
-
- // Override mime type if needed
- if ( options.mimeType && xhr.overrideMimeType ) {
- xhr.overrideMimeType( options.mimeType );
- }
-
- // X-Requested-With header
- // For cross-domain requests, seeing as conditions for a preflight are
- // akin to a jigsaw puzzle, we simply never set it to be sure.
- // (it can always be set on a per-request basis or even using ajaxSetup)
- // For same-domain requests, won't change header if already provided.
- if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
- headers[ "X-Requested-With" ] = "XMLHttpRequest";
- }
-
- // Set headers
- for ( i in headers ) {
- xhr.setRequestHeader( i, headers[ i ] );
- }
-
- // Callback
- callback = function( type ) {
- return function() {
- if ( callback ) {
- callback = errorCallback = xhr.onload =
- xhr.onerror = xhr.onabort = xhr.ontimeout =
- xhr.onreadystatechange = null;
-
- if ( type === "abort" ) {
- xhr.abort();
- } else if ( type === "error" ) {
-
- // Support: IE <=9 only
- // On a manual native abort, IE9 throws
- // errors on any property access that is not readyState
- if ( typeof xhr.status !== "number" ) {
- complete( 0, "error" );
- } else {
- complete(
-
- // File: protocol always yields status 0; see #8605, #14207
- xhr.status,
- xhr.statusText
- );
- }
- } else {
- complete(
- xhrSuccessStatus[ xhr.status ] || xhr.status,
- xhr.statusText,
-
- // Support: IE <=9 only
- // IE9 has no XHR2 but throws on binary (trac-11426)
- // For XHR2 non-text, let the caller handle it (gh-2498)
- ( xhr.responseType || "text" ) !== "text" ||
- typeof xhr.responseText !== "string" ?
- { binary: xhr.response } :
- { text: xhr.responseText },
- xhr.getAllResponseHeaders()
- );
- }
- }
- };
- };
-
- // Listen to events
- xhr.onload = callback();
- errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
- // Support: IE 9 only
- // Use onreadystatechange to replace onabort
- // to handle uncaught aborts
- if ( xhr.onabort !== undefined ) {
- xhr.onabort = errorCallback;
- } else {
- xhr.onreadystatechange = function() {
-
- // Check readyState before timeout as it changes
- if ( xhr.readyState === 4 ) {
-
- // Allow onerror to be called first,
- // but that will not handle a native abort
- // Also, save errorCallback to a variable
- // as xhr.onerror cannot be accessed
- window.setTimeout( function() {
- if ( callback ) {
- errorCallback();
- }
- } );
- }
- };
- }
-
- // Create the abort callback
- callback = callback( "abort" );
-
- try {
-
- // Do send the request (this may raise an exception)
- xhr.send( options.hasContent && options.data || null );
- } catch ( e ) {
-
- // #14683: Only rethrow if this hasn't been notified as an error yet
- if ( callback ) {
- throw e;
- }
- }
- },
-
- abort: function() {
- if ( callback ) {
- callback();
- }
- }
- };
- }
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
- if ( s.crossDomain ) {
- s.contents.script = false;
- }
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
- accepts: {
- script: "text/javascript, application/javascript, " +
- "application/ecmascript, application/x-ecmascript"
- },
- contents: {
- script: /\b(?:java|ecma)script\b/
- },
- converters: {
- "text script": function( text ) {
- jQuery.globalEval( text );
- return text;
- }
- }
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
- if ( s.cache === undefined ) {
- s.cache = false;
- }
- if ( s.crossDomain ) {
- s.type = "GET";
- }
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
- // This transport only deals with cross domain or forced-by-attrs requests
- if ( s.crossDomain || s.scriptAttrs ) {
- var script, callback;
- return {
- send: function( _, complete ) {
- script = jQuery( "<script>" )
- .attr( s.scriptAttrs || {} )
- .prop( { charset: s.scriptCharset, src: s.url } )
- .on( "load error", callback = function( evt ) {
- script.remove();
- callback = null;
- if ( evt ) {
- complete( evt.type === "error" ? 404 : 200, evt.type );
- }
- } );
-
- // Use native DOM manipulation to avoid our domManip AJAX trickery
- document.head.appendChild( script[ 0 ] );
- },
- abort: function() {
- if ( callback ) {
- callback();
- }
- }
- };
- }
-} );
-
-
-
-
-var oldCallbacks = [],
- rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
- jsonp: "callback",
- jsonpCallback: function() {
- var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
- this[ callback ] = true;
- return callback;
- }
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
- var callbackName, overwritten, responseContainer,
- jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
- "url" :
- typeof s.data === "string" &&
- ( s.contentType || "" )
- .indexOf( "application/x-www-form-urlencoded" ) === 0 &&
- rjsonp.test( s.data ) && "data"
- );
-
- // Handle iff the expected data type is "jsonp" or we have a parameter to set
- if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
- // Get callback name, remembering preexisting value associated with it
- callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
- s.jsonpCallback() :
- s.jsonpCallback;
-
- // Insert callback into url or form data
- if ( jsonProp ) {
- s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
- } else if ( s.jsonp !== false ) {
- s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
- }
-
- // Use data converter to retrieve json after script execution
- s.converters[ "script json" ] = function() {
- if ( !responseContainer ) {
- jQuery.error( callbackName + " was not called" );
- }
- return responseContainer[ 0 ];
- };
-
- // Force json dataType
- s.dataTypes[ 0 ] = "json";
-
- // Install callback
- overwritten = window[ callbackName ];
- window[ callbackName ] = function() {
- responseContainer = arguments;
- };
-
- // Clean-up function (fires after converters)
- jqXHR.always( function() {
-
- // If previous value didn't exist - remove it
- if ( overwritten === undefined ) {
- jQuery( window ).removeProp( callbackName );
-
- // Otherwise restore preexisting value
- } else {
- window[ callbackName ] = overwritten;
- }
-
- // Save back as free
- if ( s[ callbackName ] ) {
-
- // Make sure that re-using the options doesn't screw things around
- s.jsonpCallback = originalSettings.jsonpCallback;
-
- // Save the callback name for future use
- oldCallbacks.push( callbackName );
- }
-
- // Call if it was a function and we have a response
- if ( responseContainer && isFunction( overwritten ) ) {
- overwritten( responseContainer[ 0 ] );
- }
-
- responseContainer = overwritten = undefined;
- } );
-
- // Delegate to script
- return "script";
- }
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
- var body = document.implementation.createHTMLDocument( "" ).body;
- body.innerHTML = "<form></form><form></form>";
- return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
- if ( typeof data !== "string" ) {
- return [];
- }
- if ( typeof context === "boolean" ) {
- keepScripts = context;
- context = false;
- }
-
- var base, parsed, scripts;
-
- if ( !context ) {
-
- // Stop scripts or inline event handlers from being executed immediately
- // by using document.implementation
- if ( support.createHTMLDocument ) {
- context = document.implementation.createHTMLDocument( "" );
-
- // Set the base href for the created document
- // so any parsed elements with URLs
- // are based on the document's URL (gh-2965)
- base = context.createElement( "base" );
- base.href = document.location.href;
- context.head.appendChild( base );
- } else {
- context = document;
- }
- }
-
- parsed = rsingleTag.exec( data );
- scripts = !keepScripts && [];
-
- // Single tag
- if ( parsed ) {
- return [ context.createElement( parsed[ 1 ] ) ];
- }
-
- parsed = buildFragment( [ data ], context, scripts );
-
- if ( scripts && scripts.length ) {
- jQuery( scripts ).remove();
- }
-
- return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
- var selector, type, response,
- self = this,
- off = url.indexOf( " " );
-
- if ( off > -1 ) {
- selector = stripAndCollapse( url.slice( off ) );
- url = url.slice( 0, off );
- }
-
- // If it's a function
- if ( isFunction( params ) ) {
-
- // We assume that it's the callback
- callback = params;
- params = undefined;
-
- // Otherwise, build a param string
- } else if ( params && typeof params === "object" ) {
- type = "POST";
- }
-
- // If we have elements to modify, make the request
- if ( self.length > 0 ) {
- jQuery.ajax( {
- url: url,
-
- // If "type" variable is undefined, then "GET" method will be used.
- // Make value of this field explicit since
- // user can override it through ajaxSetup method
- type: type || "GET",
- dataType: "html",
- data: params
- } ).done( function( responseText ) {
-
- // Save response for use in complete callback
- response = arguments;
-
- self.html( selector ?
-
- // If a selector was specified, locate the right elements in a dummy div
- // Exclude scripts to avoid IE 'Permission Denied' errors
- jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
- // Otherwise use the full result
- responseText );
-
- // If the request succeeds, this function gets "data", "status", "jqXHR"
- // but they are ignored because response was set above.
- // If it fails, this function gets "jqXHR", "status", "error"
- } ).always( callback && function( jqXHR, status ) {
- self.each( function() {
- callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
- } );
- } );
- }
-
- return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
- return jQuery.grep( jQuery.timers, function( fn ) {
- return elem === fn.elem;
- } ).length;
-};
-
-
-
-
-jQuery.offset = {
- setOffset: function( elem, options, i ) {
- var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
- position = jQuery.css( elem, "position" ),
- curElem = jQuery( elem ),
- props = {};
-
- // Set position first, in-case top/left are set even on static elem
- if ( position === "static" ) {
- elem.style.position = "relative";
- }
-
- curOffset = curElem.offset();
- curCSSTop = jQuery.css( elem, "top" );
- curCSSLeft = jQuery.css( elem, "left" );
- calculatePosition = ( position === "absolute" || position === "fixed" ) &&
- ( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
- // Need to be able to calculate position if either
- // top or left is auto and position is either absolute or fixed
- if ( calculatePosition ) {
- curPosition = curElem.position();
- curTop = curPosition.top;
- curLeft = curPosition.left;
-
- } else {
- curTop = parseFloat( curCSSTop ) || 0;
- curLeft = parseFloat( curCSSLeft ) || 0;
- }
-
- if ( isFunction( options ) ) {
-
- // Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
- options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
- }
-
- if ( options.top != null ) {
- props.top = ( options.top - curOffset.top ) + curTop;
- }
- if ( options.left != null ) {
- props.left = ( options.left - curOffset.left ) + curLeft;
- }
-
- if ( "using" in options ) {
- options.using.call( elem, props );
-
- } else {
- if ( typeof props.top === "number" ) {
- props.top += "px";
- }
- if ( typeof props.left === "number" ) {
- props.left += "px";
- }
- curElem.css( props );
- }
- }
-};
-
-jQuery.fn.extend( {
-
- // offset() relates an element's border box to the document origin
- offset: function( options ) {
-
- // Preserve chaining for setter
- if ( arguments.length ) {
- return options === undefined ?
- this :
- this.each( function( i ) {
- jQuery.offset.setOffset( this, options, i );
- } );
- }
-
- var rect, win,
- elem = this[ 0 ];
-
- if ( !elem ) {
- return;
- }
-
- // Return zeros for disconnected and hidden (display: none) elements (gh-2310)
- // Support: IE <=11 only
- // Running getBoundingClientRect on a
- // disconnected node in IE throws an error
- if ( !elem.getClientRects().length ) {
- return { top: 0, left: 0 };
- }
-
- // Get document-relative position by adding viewport scroll to viewport-relative gBCR
- rect = elem.getBoundingClientRect();
- win = elem.ownerDocument.defaultView;
- return {
- top: rect.top + win.pageYOffset,
- left: rect.left + win.pageXOffset
- };
- },
-
- // position() relates an element's margin box to its offset parent's padding box
- // This corresponds to the behavior of CSS absolute positioning
- position: function() {
- if ( !this[ 0 ] ) {
- return;
- }
-
- var offsetParent, offset, doc,
- elem = this[ 0 ],
- parentOffset = { top: 0, left: 0 };
-
- // position:fixed elements are offset from the viewport, which itself always has zero offset
- if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
- // Assume position:fixed implies availability of getBoundingClientRect
- offset = elem.getBoundingClientRect();
-
- } else {
- offset = this.offset();
-
- // Account for the *real* offset parent, which can be the document or its root element
- // when a statically positioned element is identified
- doc = elem.ownerDocument;
- offsetParent = elem.offsetParent || doc.documentElement;
- while ( offsetParent &&
- ( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
- jQuery.css( offsetParent, "position" ) === "static" ) {
-
- offsetParent = offsetParent.parentNode;
- }
- if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
- // Incorporate borders into its offset, since they are outside its content origin
- parentOffset = jQuery( offsetParent ).offset();
- parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
- parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
- }
- }
-
- // Subtract parent offsets and element margins
- return {
- top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
- left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
- };
- },
-
- // This method will return documentElement in the following cases:
- // 1) For the element inside the iframe without offsetParent, this method will return
- // documentElement of the parent window
- // 2) For the hidden or detached element
- // 3) For body or html element, i.e. in case of the html node - it will return itself
- //
- // but those exceptions were never presented as a real life use-cases
- // and might be considered as more preferable results.
- //
- // This logic, however, is not guaranteed and can change at any point in the future
- offsetParent: function() {
- return this.map( function() {
- var offsetParent = this.offsetParent;
-
- while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
- offsetParent = offsetParent.offsetParent;
- }
-
- return offsetParent || documentElement;
- } );
- }
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
- var top = "pageYOffset" === prop;
-
- jQuery.fn[ method ] = function( val ) {
- return access( this, function( elem, method, val ) {
-
- // Coalesce documents and windows
- var win;
- if ( isWindow( elem ) ) {
- win = elem;
- } else if ( elem.nodeType === 9 ) {
- win = elem.defaultView;
- }
-
- if ( val === undefined ) {
- return win ? win[ prop ] : elem[ method ];
- }
-
- if ( win ) {
- win.scrollTo(
- !top ? val : win.pageXOffset,
- top ? val : win.pageYOffset
- );
-
- } else {
- elem[ method ] = val;
- }
- }, method, val, arguments.length );
- };
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
- jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
- function( elem, computed ) {
- if ( computed ) {
- computed = curCSS( elem, prop );
-
- // If curCSS returns percentage, fallback to offset
- return rnumnonpx.test( computed ) ?
- jQuery( elem ).position()[ prop ] + "px" :
- computed;
- }
- }
- );
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
- jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
- function( defaultExtra, funcName ) {
-
- // Margin is only for outerHeight, outerWidth
- jQuery.fn[ funcName ] = function( margin, value ) {
- var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
- extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
- return access( this, function( elem, type, value ) {
- var doc;
-
- if ( isWindow( elem ) ) {
-
- // $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
- return funcName.indexOf( "outer" ) === 0 ?
- elem[ "inner" + name ] :
- elem.document.documentElement[ "client" + name ];
- }
-
- // Get document width or height
- if ( elem.nodeType === 9 ) {
- doc = elem.documentElement;
-
- // Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
- // whichever is greatest
- return Math.max(
- elem.body[ "scroll" + name ], doc[ "scroll" + name ],
- elem.body[ "offset" + name ], doc[ "offset" + name ],
- doc[ "client" + name ]
- );
- }
-
- return value === undefined ?
-
- // Get width or height on the element, requesting but not forcing parseFloat
- jQuery.css( elem, type, extra ) :
-
- // Set width or height on the element
- jQuery.style( elem, type, value, extra );
- }, type, chainable ? margin : undefined, chainable );
- };
- } );
-} );
-
-
-jQuery.each( [
- "ajaxStart",
- "ajaxStop",
- "ajaxComplete",
- "ajaxError",
- "ajaxSuccess",
- "ajaxSend"
-], function( _i, type ) {
- jQuery.fn[ type ] = function( fn ) {
- return this.on( type, fn );
- };
-} );
-
-
-
-
-jQuery.fn.extend( {
-
- bind: function( types, data, fn ) {
- return this.on( types, null, data, fn );
- },
- unbind: function( types, fn ) {
- return this.off( types, null, fn );
- },
-
- delegate: function( selector, types, data, fn ) {
- return this.on( types, selector, data, fn );
- },
- undelegate: function( selector, types, fn ) {
-
- // ( namespace ) or ( selector, types [, fn] )
- return arguments.length === 1 ?
- this.off( selector, "**" ) :
- this.off( types, selector || "**", fn );
- },
-
- hover: function( fnOver, fnOut ) {
- return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
- }
-} );
-
-jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
- "mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
- "change select submit keydown keypress keyup contextmenu" ).split( " " ),
- function( _i, name ) {
-
- // Handle event binding
- jQuery.fn[ name ] = function( data, fn ) {
- return arguments.length > 0 ?
- this.on( name, null, data, fn ) :
- this.trigger( name );
- };
- } );
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
- var tmp, args, proxy;
-
- if ( typeof context === "string" ) {
- tmp = fn[ context ];
- context = fn;
- fn = tmp;
- }
-
- // Quick check to determine if target is callable, in the spec
- // this throws a TypeError, but we will just return undefined.
- if ( !isFunction( fn ) ) {
- return undefined;
- }
-
- // Simulated bind
- args = slice.call( arguments, 2 );
- proxy = function() {
- return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
- };
-
- // Set the guid of unique handler to the same of original handler, so it can be removed
- proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
- return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
- if ( hold ) {
- jQuery.readyWait++;
- } else {
- jQuery.ready( true );
- }
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
- // As of jQuery 3.0, isNumeric is limited to
- // strings and numbers (primitives or objects)
- // that can be coerced to finite numbers (gh-2662)
- var type = jQuery.type( obj );
- return ( type === "number" || type === "string" ) &&
-
- // parseFloat NaNs numeric-cast false positives ("")
- // ...but misinterprets leading-number strings, particularly hex literals ("0x...")
- // subtraction forces infinities to NaN
- !isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
- return text == null ?
- "" :
- ( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
-if ( typeof define === "function" && define.amd ) {
- define( "jquery", [], function() {
- return jQuery;
- } );
-}
-
-
-
-
-var
-
- // Map over jQuery in case of overwrite
- _jQuery = window.jQuery,
-
- // Map over the $ in case of overwrite
- _$ = window.$;
-
-jQuery.noConflict = function( deep ) {
- if ( window.$ === jQuery ) {
- window.$ = _$;
- }
-
- if ( deep && window.jQuery === jQuery ) {
- window.jQuery = _jQuery;
- }
-
- return jQuery;
-};
-
-// Expose jQuery and $ identifiers, even in AMD
-// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
-// and CommonJS for browser emulators (#13566)
-if ( typeof noGlobal === "undefined" ) {
- window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/docs/build/html/_static/jquery.js b/docs/build/html/_static/jquery.js
deleted file mode 100644
index b0614034ad3a95e4ae9f53c2b015eeb3e8d68bde..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/jquery.js
+++ /dev/null
@@ -1,2 +0,0 @@
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diff --git a/docs/build/html/_static/language_data.js b/docs/build/html/_static/language_data.js
deleted file mode 100644
index ebe2f03bf03b7f72481f8f483039ef9b7013f062..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/language_data.js
+++ /dev/null
@@ -1,297 +0,0 @@
-/*
- * language_data.js
- * ~~~~~~~~~~~~~~~~
- *
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
-
-
-/* Non-minified version is copied as a separate JS file, is available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
- var step2list = {
- ational: 'ate',
- tional: 'tion',
- enci: 'ence',
- anci: 'ance',
- izer: 'ize',
- bli: 'ble',
- alli: 'al',
- entli: 'ent',
- eli: 'e',
- ousli: 'ous',
- ization: 'ize',
- ation: 'ate',
- ator: 'ate',
- alism: 'al',
- iveness: 'ive',
- fulness: 'ful',
- ousness: 'ous',
- aliti: 'al',
- iviti: 'ive',
- biliti: 'ble',
- logi: 'log'
- };
-
- var step3list = {
- icate: 'ic',
- ative: '',
- alize: 'al',
- iciti: 'ic',
- ical: 'ic',
- ful: '',
- ness: ''
- };
-
- var c = "[^aeiou]"; // consonant
- var v = "[aeiouy]"; // vowel
- var C = c + "[^aeiouy]*"; // consonant sequence
- var V = v + "[aeiou]*"; // vowel sequence
-
- var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
- var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
- var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
- var s_v = "^(" + C + ")?" + v; // vowel in stem
-
- this.stemWord = function (w) {
- var stem;
- var suffix;
- var firstch;
- var origword = w;
-
- if (w.length < 3)
- return w;
-
- var re;
- var re2;
- var re3;
- var re4;
-
- firstch = w.substr(0,1);
- if (firstch == "y")
- w = firstch.toUpperCase() + w.substr(1);
-
- // Step 1a
- re = /^(.+?)(ss|i)es$/;
- re2 = /^(.+?)([^s])s$/;
-
- if (re.test(w))
- w = w.replace(re,"$1$2");
- else if (re2.test(w))
- w = w.replace(re2,"$1$2");
-
- // Step 1b
- re = /^(.+?)eed$/;
- re2 = /^(.+?)(ed|ing)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- re = new RegExp(mgr0);
- if (re.test(fp[1])) {
- re = /.$/;
- w = w.replace(re,"");
- }
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1];
- re2 = new RegExp(s_v);
- if (re2.test(stem)) {
- w = stem;
- re2 = /(at|bl|iz)$/;
- re3 = new RegExp("([^aeiouylsz])\\1$");
- re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re2.test(w))
- w = w + "e";
- else if (re3.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
- else if (re4.test(w))
- w = w + "e";
- }
- }
-
- // Step 1c
- re = /^(.+?)y$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(s_v);
- if (re.test(stem))
- w = stem + "i";
- }
-
- // Step 2
- re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step2list[suffix];
- }
-
- // Step 3
- re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step3list[suffix];
- }
-
- // Step 4
- re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
- re2 = /^(.+?)(s|t)(ion)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- if (re.test(stem))
- w = stem;
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1] + fp[2];
- re2 = new RegExp(mgr1);
- if (re2.test(stem))
- w = stem;
- }
-
- // Step 5
- re = /^(.+?)e$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- re2 = new RegExp(meq1);
- re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
- w = stem;
- }
- re = /ll$/;
- re2 = new RegExp(mgr1);
- if (re.test(w) && re2.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
-
- // and turn initial Y back to y
- if (firstch == "y")
- w = firstch.toLowerCase() + w.substr(1);
- return w;
- }
-}
-
-
-
-
-var splitChars = (function() {
- var result = {};
- var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
- 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
- 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
- 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
- 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
- 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
- 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
- 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
- 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
- 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
- var i, j, start, end;
- for (i = 0; i < singles.length; i++) {
- result[singles[i]] = true;
- }
- var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
- [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
- [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
- [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
- [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
- [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
- [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
- [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
- [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
- [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
- [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
- [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
- [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
- [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
- [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
- [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
- [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
- [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
- [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
- [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
- [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
- [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
- [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
- [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
- [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
- [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
- [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
- [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
- [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
- [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
- [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
- [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
- [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
- [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
- [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
- [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
- [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
- [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
- [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
- [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
- [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
- [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
- [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
- [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
- [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
- [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
- [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
- [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
- [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
- for (i = 0; i < ranges.length; i++) {
- start = ranges[i][0];
- end = ranges[i][1];
- for (j = start; j <= end; j++) {
- result[j] = true;
- }
- }
- return result;
-})();
-
-function splitQuery(query) {
- var result = [];
- var start = -1;
- for (var i = 0; i < query.length; i++) {
- if (splitChars[query.charCodeAt(i)]) {
- if (start !== -1) {
- result.push(query.slice(start, i));
- start = -1;
- }
- } else if (start === -1) {
- start = i;
- }
- }
- if (start !== -1) {
- result.push(query.slice(start));
- }
- return result;
-}
-
-
diff --git a/docs/build/html/_static/pygments.css b/docs/build/html/_static/pygments.css
deleted file mode 100644
index 87f8bd121b2352751b730a3d04fff0e1ecd946b2..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/pygments.css
+++ /dev/null
@@ -1,82 +0,0 @@
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-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-.highlight .hll { background-color: #ffffcc }
-.highlight { background: #f8f8f8; }
-.highlight .c { color: #8f5902; font-style: italic } /* Comment */
-.highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */
-.highlight .g { color: #000000 } /* Generic */
-.highlight .k { color: #004461; font-weight: bold } /* Keyword */
-.highlight .l { color: #000000 } /* Literal */
-.highlight .n { color: #000000 } /* Name */
-.highlight .o { color: #582800 } /* Operator */
-.highlight .x { color: #000000 } /* Other */
-.highlight .p { color: #000000; font-weight: bold } /* Punctuation */
-.highlight .ch { color: #8f5902; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #8f5902; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #8f5902 } /* Comment.Preproc */
-.highlight .cpf { color: #8f5902; font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: #8f5902; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #8f5902; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #a40000 } /* Generic.Deleted */
-.highlight .ge { color: #000000; font-style: italic } /* Generic.Emph */
-.highlight .gr { color: #ef2929 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #00A000 } /* Generic.Inserted */
-.highlight .go { color: #888888 } /* Generic.Output */
-.highlight .gp { color: #745334 } /* Generic.Prompt */
-.highlight .gs { color: #000000; font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
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-.highlight .ld { color: #000000 } /* Literal.Date */
-.highlight .m { color: #990000 } /* Literal.Number */
-.highlight .s { color: #4e9a06 } /* Literal.String */
-.highlight .na { color: #c4a000 } /* Name.Attribute */
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-.highlight .no { color: #000000 } /* Name.Constant */
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-.highlight .ni { color: #ce5c00 } /* Name.Entity */
-.highlight .ne { color: #cc0000; font-weight: bold } /* Name.Exception */
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diff --git a/docs/build/html/_static/searchtools.js b/docs/build/html/_static/searchtools.js
deleted file mode 100644
index 2d7785937bb75d4c04aca720525940817bf5f92e..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/searchtools.js
+++ /dev/null
@@ -1,529 +0,0 @@
-/*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-if (!Scorer) {
- /**
- * Simple result scoring code.
- */
- var Scorer = {
- // Implement the following function to further tweak the score for each result
- // The function takes a result array [filename, title, anchor, descr, score]
- // and returns the new score.
- /*
- score: function(result) {
- return result[4];
- },
- */
-
- // query matches the full name of an object
- objNameMatch: 11,
- // or matches in the last dotted part of the object name
- objPartialMatch: 6,
- // Additive scores depending on the priority of the object
- objPrio: {0: 15, // used to be importantResults
- 1: 5, // used to be objectResults
- 2: -5}, // used to be unimportantResults
- // Used when the priority is not in the mapping.
- objPrioDefault: 0,
-
- // query found in title
- title: 15,
- partialTitle: 7,
- // query found in terms
- term: 5,
- partialTerm: 2
- };
-}
-
-if (!splitQuery) {
- function splitQuery(query) {
- return query.split(/\s+/);
- }
-}
-
-/**
- * Search Module
- */
-var Search = {
-
- _index : null,
- _queued_query : null,
- _pulse_status : -1,
-
- htmlToText : function(htmlString) {
- var virtualDocument = document.implementation.createHTMLDocument('virtual');
- var htmlElement = $(htmlString, virtualDocument);
- htmlElement.find('.headerlink').remove();
- docContent = htmlElement.find('[role=main]')[0];
- if(docContent === undefined) {
- console.warn("Content block not found. Sphinx search tries to obtain it " +
- "via '[role=main]'. Could you check your theme or template.");
- return "";
- }
- return docContent.textContent || docContent.innerText;
- },
-
- init : function() {
- var params = $.getQueryParameters();
- if (params.q) {
- var query = params.q[0];
- $('input[name="q"]')[0].value = query;
- this.performSearch(query);
- }
- },
-
- loadIndex : function(url) {
- $.ajax({type: "GET", url: url, data: null,
- dataType: "script", cache: true,
- complete: function(jqxhr, textstatus) {
- if (textstatus != "success") {
- document.getElementById("searchindexloader").src = url;
- }
- }});
- },
-
- setIndex : function(index) {
- var q;
- this._index = index;
- if ((q = this._queued_query) !== null) {
- this._queued_query = null;
- Search.query(q);
- }
- },
-
- hasIndex : function() {
- return this._index !== null;
- },
-
- deferQuery : function(query) {
- this._queued_query = query;
- },
-
- stopPulse : function() {
- this._pulse_status = 0;
- },
-
- startPulse : function() {
- if (this._pulse_status >= 0)
- return;
- function pulse() {
- var i;
- Search._pulse_status = (Search._pulse_status + 1) % 4;
- var dotString = '';
- for (i = 0; i < Search._pulse_status; i++)
- dotString += '.';
- Search.dots.text(dotString);
- if (Search._pulse_status > -1)
- window.setTimeout(pulse, 500);
- }
- pulse();
- },
-
- /**
- * perform a search for something (or wait until index is loaded)
- */
- performSearch : function(query) {
- // create the required interface elements
- this.out = $('#search-results');
- this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
- this.dots = $('<span></span>').appendTo(this.title);
- this.status = $('<p class="search-summary"> </p>').appendTo(this.out);
- this.output = $('<ul class="search"/>').appendTo(this.out);
-
- $('#search-progress').text(_('Preparing search...'));
- this.startPulse();
-
- // index already loaded, the browser was quick!
- if (this.hasIndex())
- this.query(query);
- else
- this.deferQuery(query);
- },
-
- /**
- * execute search (requires search index to be loaded)
- */
- query : function(query) {
- var i;
-
- // stem the searchterms and add them to the correct list
- var stemmer = new Stemmer();
- var searchterms = [];
- var excluded = [];
- var hlterms = [];
- var tmp = splitQuery(query);
- var objectterms = [];
- for (i = 0; i < tmp.length; i++) {
- if (tmp[i] !== "") {
- objectterms.push(tmp[i].toLowerCase());
- }
-
- if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
- // skip this "word"
- continue;
- }
- // stem the word
- var word = stemmer.stemWord(tmp[i].toLowerCase());
- // prevent stemmer from cutting word smaller than two chars
- if(word.length < 3 && tmp[i].length >= 3) {
- word = tmp[i];
- }
- var toAppend;
- // select the correct list
- if (word[0] == '-') {
- toAppend = excluded;
- word = word.substr(1);
- }
- else {
- toAppend = searchterms;
- hlterms.push(tmp[i].toLowerCase());
- }
- // only add if not already in the list
- if (!$u.contains(toAppend, word))
- toAppend.push(word);
- }
- var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
-
- // console.debug('SEARCH: searching for:');
- // console.info('required: ', searchterms);
- // console.info('excluded: ', excluded);
-
- // prepare search
- var terms = this._index.terms;
- var titleterms = this._index.titleterms;
-
- // array of [filename, title, anchor, descr, score]
- var results = [];
- $('#search-progress').empty();
-
- // lookup as object
- for (i = 0; i < objectterms.length; i++) {
- var others = [].concat(objectterms.slice(0, i),
- objectterms.slice(i+1, objectterms.length));
- results = results.concat(this.performObjectSearch(objectterms[i], others));
- }
-
- // lookup as search terms in fulltext
- results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
-
- // let the scorer override scores with a custom scoring function
- if (Scorer.score) {
- for (i = 0; i < results.length; i++)
- results[i][4] = Scorer.score(results[i]);
- }
-
- // now sort the results by score (in opposite order of appearance, since the
- // display function below uses pop() to retrieve items) and then
- // alphabetically
- results.sort(function(a, b) {
- var left = a[4];
- var right = b[4];
- if (left > right) {
- return 1;
- } else if (left < right) {
- return -1;
- } else {
- // same score: sort alphabetically
- left = a[1].toLowerCase();
- right = b[1].toLowerCase();
- return (left > right) ? -1 : ((left < right) ? 1 : 0);
- }
- });
-
- // for debugging
- //Search.lastresults = results.slice(); // a copy
- //console.info('search results:', Search.lastresults);
-
- // print the results
- var resultCount = results.length;
- function displayNextItem() {
- // results left, load the summary and display it
- if (results.length) {
- var item = results.pop();
- var listItem = $('<li></li>');
- var requestUrl = "";
- var linkUrl = "";
- if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
- // dirhtml builder
- var dirname = item[0] + '/';
- if (dirname.match(/\/index\/$/)) {
- dirname = dirname.substring(0, dirname.length-6);
- } else if (dirname == 'index/') {
- dirname = '';
- }
- requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
- linkUrl = requestUrl;
-
- } else {
- // normal html builders
- requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
- linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
- }
- listItem.append($('<a/>').attr('href',
- linkUrl +
- highlightstring + item[2]).html(item[1]));
- if (item[3]) {
- listItem.append($('<span> (' + item[3] + ')</span>'));
- Search.output.append(listItem);
- setTimeout(function() {
- displayNextItem();
- }, 5);
- } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
- $.ajax({url: requestUrl,
- dataType: "text",
- complete: function(jqxhr, textstatus) {
- var data = jqxhr.responseText;
- if (data !== '' && data !== undefined) {
- var summary = Search.makeSearchSummary(data, searchterms, hlterms);
- if (summary) {
- listItem.append(summary);
- }
- }
- Search.output.append(listItem);
- setTimeout(function() {
- displayNextItem();
- }, 5);
- }});
- } else {
- // no source available, just display title
- Search.output.append(listItem);
- setTimeout(function() {
- displayNextItem();
- }, 5);
- }
- }
- // search finished, update title and status message
- else {
- Search.stopPulse();
- Search.title.text(_('Search Results'));
- if (!resultCount)
- Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
- else
- Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
- Search.status.fadeIn(500);
- }
- }
- displayNextItem();
- },
-
- /**
- * search for object names
- */
- performObjectSearch : function(object, otherterms) {
- var filenames = this._index.filenames;
- var docnames = this._index.docnames;
- var objects = this._index.objects;
- var objnames = this._index.objnames;
- var titles = this._index.titles;
-
- var i;
- var results = [];
-
- for (var prefix in objects) {
- for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) {
- var match = objects[prefix][iMatch];
- var name = match[4];
- var fullname = (prefix ? prefix + '.' : '') + name;
- var fullnameLower = fullname.toLowerCase()
- if (fullnameLower.indexOf(object) > -1) {
- var score = 0;
- var parts = fullnameLower.split('.');
- // check for different match types: exact matches of full name or
- // "last name" (i.e. last dotted part)
- if (fullnameLower == object || parts[parts.length - 1] == object) {
- score += Scorer.objNameMatch;
- // matches in last name
- } else if (parts[parts.length - 1].indexOf(object) > -1) {
- score += Scorer.objPartialMatch;
- }
- var objname = objnames[match[1]][2];
- var title = titles[match[0]];
- // If more than one term searched for, we require other words to be
- // found in the name/title/description
- if (otherterms.length > 0) {
- var haystack = (prefix + ' ' + name + ' ' +
- objname + ' ' + title).toLowerCase();
- var allfound = true;
- for (i = 0; i < otherterms.length; i++) {
- if (haystack.indexOf(otherterms[i]) == -1) {
- allfound = false;
- break;
- }
- }
- if (!allfound) {
- continue;
- }
- }
- var descr = objname + _(', in ') + title;
-
- var anchor = match[3];
- if (anchor === '')
- anchor = fullname;
- else if (anchor == '-')
- anchor = objnames[match[1]][1] + '-' + fullname;
- // add custom score for some objects according to scorer
- if (Scorer.objPrio.hasOwnProperty(match[2])) {
- score += Scorer.objPrio[match[2]];
- } else {
- score += Scorer.objPrioDefault;
- }
- results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
- }
- }
- }
-
- return results;
- },
-
- /**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
- */
- escapeRegExp : function(string) {
- return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
- },
-
- /**
- * search for full-text terms in the index
- */
- performTermsSearch : function(searchterms, excluded, terms, titleterms) {
- var docnames = this._index.docnames;
- var filenames = this._index.filenames;
- var titles = this._index.titles;
-
- var i, j, file;
- var fileMap = {};
- var scoreMap = {};
- var results = [];
-
- // perform the search on the required terms
- for (i = 0; i < searchterms.length; i++) {
- var word = searchterms[i];
- var files = [];
- var _o = [
- {files: terms[word], score: Scorer.term},
- {files: titleterms[word], score: Scorer.title}
- ];
- // add support for partial matches
- if (word.length > 2) {
- var word_regex = this.escapeRegExp(word);
- for (var w in terms) {
- if (w.match(word_regex) && !terms[word]) {
- _o.push({files: terms[w], score: Scorer.partialTerm})
- }
- }
- for (var w in titleterms) {
- if (w.match(word_regex) && !titleterms[word]) {
- _o.push({files: titleterms[w], score: Scorer.partialTitle})
- }
- }
- }
-
- // no match but word was a required one
- if ($u.every(_o, function(o){return o.files === undefined;})) {
- break;
- }
- // found search word in contents
- $u.each(_o, function(o) {
- var _files = o.files;
- if (_files === undefined)
- return
-
- if (_files.length === undefined)
- _files = [_files];
- files = files.concat(_files);
-
- // set score for the word in each file to Scorer.term
- for (j = 0; j < _files.length; j++) {
- file = _files[j];
- if (!(file in scoreMap))
- scoreMap[file] = {};
- scoreMap[file][word] = o.score;
- }
- });
-
- // create the mapping
- for (j = 0; j < files.length; j++) {
- file = files[j];
- if (file in fileMap && fileMap[file].indexOf(word) === -1)
- fileMap[file].push(word);
- else
- fileMap[file] = [word];
- }
- }
-
- // now check if the files don't contain excluded terms
- for (file in fileMap) {
- var valid = true;
-
- // check if all requirements are matched
- var filteredTermCount = // as search terms with length < 3 are discarded: ignore
- searchterms.filter(function(term){return term.length > 2}).length
- if (
- fileMap[file].length != searchterms.length &&
- fileMap[file].length != filteredTermCount
- ) continue;
-
- // ensure that none of the excluded terms is in the search result
- for (i = 0; i < excluded.length; i++) {
- if (terms[excluded[i]] == file ||
- titleterms[excluded[i]] == file ||
- $u.contains(terms[excluded[i]] || [], file) ||
- $u.contains(titleterms[excluded[i]] || [], file)) {
- valid = false;
- break;
- }
- }
-
- // if we have still a valid result we can add it to the result list
- if (valid) {
- // select one (max) score for the file.
- // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
- var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
- results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
- }
- }
- return results;
- },
-
- /**
- * helper function to return a node containing the
- * search summary for a given text. keywords is a list
- * of stemmed words, hlwords is the list of normal, unstemmed
- * words. the first one is used to find the occurrence, the
- * latter for highlighting it.
- */
- makeSearchSummary : function(htmlText, keywords, hlwords) {
- var text = Search.htmlToText(htmlText);
- if (text == "") {
- return null;
- }
- var textLower = text.toLowerCase();
- var start = 0;
- $.each(keywords, function() {
- var i = textLower.indexOf(this.toLowerCase());
- if (i > -1)
- start = i;
- });
- start = Math.max(start - 120, 0);
- var excerpt = ((start > 0) ? '...' : '') +
- $.trim(text.substr(start, 240)) +
- ((start + 240 - text.length) ? '...' : '');
- var rv = $('<p class="context"></p>').text(excerpt);
- $.each(hlwords, function() {
- rv = rv.highlightText(this, 'highlighted');
- });
- return rv;
- }
-};
-
-$(document).ready(function() {
- Search.init();
-});
diff --git a/docs/build/html/_static/underscore-1.13.1.js b/docs/build/html/_static/underscore-1.13.1.js
deleted file mode 100644
index ffd77af9648a47d389f2d6976d4aa1c44d7ce7ce..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/underscore-1.13.1.js
+++ /dev/null
@@ -1,2042 +0,0 @@
-(function (global, factory) {
- typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
- typeof define === 'function' && define.amd ? define('underscore', factory) :
- (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
- var current = global._;
- var exports = global._ = factory();
- exports.noConflict = function () { global._ = current; return exports; };
- }()));
-}(this, (function () {
- // Underscore.js 1.13.1
- // https://underscorejs.org
- // (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
- // Underscore may be freely distributed under the MIT license.
-
- // Current version.
- var VERSION = '1.13.1';
-
- // Establish the root object, `window` (`self`) in the browser, `global`
- // on the server, or `this` in some virtual machines. We use `self`
- // instead of `window` for `WebWorker` support.
- var root = typeof self == 'object' && self.self === self && self ||
- typeof global == 'object' && global.global === global && global ||
- Function('return this')() ||
- {};
-
- // Save bytes in the minified (but not gzipped) version:
- var ArrayProto = Array.prototype, ObjProto = Object.prototype;
- var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
-
- // Create quick reference variables for speed access to core prototypes.
- var push = ArrayProto.push,
- slice = ArrayProto.slice,
- toString = ObjProto.toString,
- hasOwnProperty = ObjProto.hasOwnProperty;
-
- // Modern feature detection.
- var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
- supportsDataView = typeof DataView !== 'undefined';
-
- // All **ECMAScript 5+** native function implementations that we hope to use
- // are declared here.
- var nativeIsArray = Array.isArray,
- nativeKeys = Object.keys,
- nativeCreate = Object.create,
- nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
- // Create references to these builtin functions because we override them.
- var _isNaN = isNaN,
- _isFinite = isFinite;
-
- // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
- var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
- var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
- 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
- // The largest integer that can be represented exactly.
- var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
- // Some functions take a variable number of arguments, or a few expected
- // arguments at the beginning and then a variable number of values to operate
- // on. This helper accumulates all remaining arguments past the function’s
- // argument length (or an explicit `startIndex`), into an array that becomes
- // the last argument. Similar to ES6’s "rest parameter".
- function restArguments(func, startIndex) {
- startIndex = startIndex == null ? func.length - 1 : +startIndex;
- return function() {
- var length = Math.max(arguments.length - startIndex, 0),
- rest = Array(length),
- index = 0;
- for (; index < length; index++) {
- rest[index] = arguments[index + startIndex];
- }
- switch (startIndex) {
- case 0: return func.call(this, rest);
- case 1: return func.call(this, arguments[0], rest);
- case 2: return func.call(this, arguments[0], arguments[1], rest);
- }
- var args = Array(startIndex + 1);
- for (index = 0; index < startIndex; index++) {
- args[index] = arguments[index];
- }
- args[startIndex] = rest;
- return func.apply(this, args);
- };
- }
-
- // Is a given variable an object?
- function isObject(obj) {
- var type = typeof obj;
- return type === 'function' || type === 'object' && !!obj;
- }
-
- // Is a given value equal to null?
- function isNull(obj) {
- return obj === null;
- }
-
- // Is a given variable undefined?
- function isUndefined(obj) {
- return obj === void 0;
- }
-
- // Is a given value a boolean?
- function isBoolean(obj) {
- return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
- }
-
- // Is a given value a DOM element?
- function isElement(obj) {
- return !!(obj && obj.nodeType === 1);
- }
-
- // Internal function for creating a `toString`-based type tester.
- function tagTester(name) {
- var tag = '[object ' + name + ']';
- return function(obj) {
- return toString.call(obj) === tag;
- };
- }
-
- var isString = tagTester('String');
-
- var isNumber = tagTester('Number');
-
- var isDate = tagTester('Date');
-
- var isRegExp = tagTester('RegExp');
-
- var isError = tagTester('Error');
-
- var isSymbol = tagTester('Symbol');
-
- var isArrayBuffer = tagTester('ArrayBuffer');
-
- var isFunction = tagTester('Function');
-
- // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
- // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
- var nodelist = root.document && root.document.childNodes;
- if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
- isFunction = function(obj) {
- return typeof obj == 'function' || false;
- };
- }
-
- var isFunction$1 = isFunction;
-
- var hasObjectTag = tagTester('Object');
-
- // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
- // In IE 11, the most common among them, this problem also applies to
- // `Map`, `WeakMap` and `Set`.
- var hasStringTagBug = (
- supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
- ),
- isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
- var isDataView = tagTester('DataView');
-
- // In IE 10 - Edge 13, we need a different heuristic
- // to determine whether an object is a `DataView`.
- function ie10IsDataView(obj) {
- return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
- }
-
- var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
- // Is a given value an array?
- // Delegates to ECMA5's native `Array.isArray`.
- var isArray = nativeIsArray || tagTester('Array');
-
- // Internal function to check whether `key` is an own property name of `obj`.
- function has$1(obj, key) {
- return obj != null && hasOwnProperty.call(obj, key);
- }
-
- var isArguments = tagTester('Arguments');
-
- // Define a fallback version of the method in browsers (ahem, IE < 9), where
- // there isn't any inspectable "Arguments" type.
- (function() {
- if (!isArguments(arguments)) {
- isArguments = function(obj) {
- return has$1(obj, 'callee');
- };
- }
- }());
-
- var isArguments$1 = isArguments;
-
- // Is a given object a finite number?
- function isFinite$1(obj) {
- return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
- }
-
- // Is the given value `NaN`?
- function isNaN$1(obj) {
- return isNumber(obj) && _isNaN(obj);
- }
-
- // Predicate-generating function. Often useful outside of Underscore.
- function constant(value) {
- return function() {
- return value;
- };
- }
-
- // Common internal logic for `isArrayLike` and `isBufferLike`.
- function createSizePropertyCheck(getSizeProperty) {
- return function(collection) {
- var sizeProperty = getSizeProperty(collection);
- return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
- }
- }
-
- // Internal helper to generate a function to obtain property `key` from `obj`.
- function shallowProperty(key) {
- return function(obj) {
- return obj == null ? void 0 : obj[key];
- };
- }
-
- // Internal helper to obtain the `byteLength` property of an object.
- var getByteLength = shallowProperty('byteLength');
-
- // Internal helper to determine whether we should spend extensive checks against
- // `ArrayBuffer` et al.
- var isBufferLike = createSizePropertyCheck(getByteLength);
-
- // Is a given value a typed array?
- var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
- function isTypedArray(obj) {
- // `ArrayBuffer.isView` is the most future-proof, so use it when available.
- // Otherwise, fall back on the above regular expression.
- return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
- isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
- }
-
- var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
- // Internal helper to obtain the `length` property of an object.
- var getLength = shallowProperty('length');
-
- // Internal helper to create a simple lookup structure.
- // `collectNonEnumProps` used to depend on `_.contains`, but this led to
- // circular imports. `emulatedSet` is a one-off solution that only works for
- // arrays of strings.
- function emulatedSet(keys) {
- var hash = {};
- for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
- return {
- contains: function(key) { return hash[key]; },
- push: function(key) {
- hash[key] = true;
- return keys.push(key);
- }
- };
- }
-
- // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
- // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
- // needed.
- function collectNonEnumProps(obj, keys) {
- keys = emulatedSet(keys);
- var nonEnumIdx = nonEnumerableProps.length;
- var constructor = obj.constructor;
- var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
- // Constructor is a special case.
- var prop = 'constructor';
- if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
- while (nonEnumIdx--) {
- prop = nonEnumerableProps[nonEnumIdx];
- if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
- keys.push(prop);
- }
- }
- }
-
- // Retrieve the names of an object's own properties.
- // Delegates to **ECMAScript 5**'s native `Object.keys`.
- function keys(obj) {
- if (!isObject(obj)) return [];
- if (nativeKeys) return nativeKeys(obj);
- var keys = [];
- for (var key in obj) if (has$1(obj, key)) keys.push(key);
- // Ahem, IE < 9.
- if (hasEnumBug) collectNonEnumProps(obj, keys);
- return keys;
- }
-
- // Is a given array, string, or object empty?
- // An "empty" object has no enumerable own-properties.
- function isEmpty(obj) {
- if (obj == null) return true;
- // Skip the more expensive `toString`-based type checks if `obj` has no
- // `.length`.
- var length = getLength(obj);
- if (typeof length == 'number' && (
- isArray(obj) || isString(obj) || isArguments$1(obj)
- )) return length === 0;
- return getLength(keys(obj)) === 0;
- }
-
- // Returns whether an object has a given set of `key:value` pairs.
- function isMatch(object, attrs) {
- var _keys = keys(attrs), length = _keys.length;
- if (object == null) return !length;
- var obj = Object(object);
- for (var i = 0; i < length; i++) {
- var key = _keys[i];
- if (attrs[key] !== obj[key] || !(key in obj)) return false;
- }
- return true;
- }
-
- // If Underscore is called as a function, it returns a wrapped object that can
- // be used OO-style. This wrapper holds altered versions of all functions added
- // through `_.mixin`. Wrapped objects may be chained.
- function _$1(obj) {
- if (obj instanceof _$1) return obj;
- if (!(this instanceof _$1)) return new _$1(obj);
- this._wrapped = obj;
- }
-
- _$1.VERSION = VERSION;
-
- // Extracts the result from a wrapped and chained object.
- _$1.prototype.value = function() {
- return this._wrapped;
- };
-
- // Provide unwrapping proxies for some methods used in engine operations
- // such as arithmetic and JSON stringification.
- _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
-
- _$1.prototype.toString = function() {
- return String(this._wrapped);
- };
-
- // Internal function to wrap or shallow-copy an ArrayBuffer,
- // typed array or DataView to a new view, reusing the buffer.
- function toBufferView(bufferSource) {
- return new Uint8Array(
- bufferSource.buffer || bufferSource,
- bufferSource.byteOffset || 0,
- getByteLength(bufferSource)
- );
- }
-
- // We use this string twice, so give it a name for minification.
- var tagDataView = '[object DataView]';
-
- // Internal recursive comparison function for `_.isEqual`.
- function eq(a, b, aStack, bStack) {
- // Identical objects are equal. `0 === -0`, but they aren't identical.
- // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
- if (a === b) return a !== 0 || 1 / a === 1 / b;
- // `null` or `undefined` only equal to itself (strict comparison).
- if (a == null || b == null) return false;
- // `NaN`s are equivalent, but non-reflexive.
- if (a !== a) return b !== b;
- // Exhaust primitive checks
- var type = typeof a;
- if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
- return deepEq(a, b, aStack, bStack);
- }
-
- // Internal recursive comparison function for `_.isEqual`.
- function deepEq(a, b, aStack, bStack) {
- // Unwrap any wrapped objects.
- if (a instanceof _$1) a = a._wrapped;
- if (b instanceof _$1) b = b._wrapped;
- // Compare `[[Class]]` names.
- var className = toString.call(a);
- if (className !== toString.call(b)) return false;
- // Work around a bug in IE 10 - Edge 13.
- if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
- if (!isDataView$1(b)) return false;
- className = tagDataView;
- }
- switch (className) {
- // These types are compared by value.
- case '[object RegExp]':
- // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
- case '[object String]':
- // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
- // equivalent to `new String("5")`.
- return '' + a === '' + b;
- case '[object Number]':
- // `NaN`s are equivalent, but non-reflexive.
- // Object(NaN) is equivalent to NaN.
- if (+a !== +a) return +b !== +b;
- // An `egal` comparison is performed for other numeric values.
- return +a === 0 ? 1 / +a === 1 / b : +a === +b;
- case '[object Date]':
- case '[object Boolean]':
- // Coerce dates and booleans to numeric primitive values. Dates are compared by their
- // millisecond representations. Note that invalid dates with millisecond representations
- // of `NaN` are not equivalent.
- return +a === +b;
- case '[object Symbol]':
- return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
- case '[object ArrayBuffer]':
- case tagDataView:
- // Coerce to typed array so we can fall through.
- return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
- }
-
- var areArrays = className === '[object Array]';
- if (!areArrays && isTypedArray$1(a)) {
- var byteLength = getByteLength(a);
- if (byteLength !== getByteLength(b)) return false;
- if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
- areArrays = true;
- }
- if (!areArrays) {
- if (typeof a != 'object' || typeof b != 'object') return false;
-
- // Objects with different constructors are not equivalent, but `Object`s or `Array`s
- // from different frames are.
- var aCtor = a.constructor, bCtor = b.constructor;
- if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
- isFunction$1(bCtor) && bCtor instanceof bCtor)
- && ('constructor' in a && 'constructor' in b)) {
- return false;
- }
- }
- // Assume equality for cyclic structures. The algorithm for detecting cyclic
- // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
- // Initializing stack of traversed objects.
- // It's done here since we only need them for objects and arrays comparison.
- aStack = aStack || [];
- bStack = bStack || [];
- var length = aStack.length;
- while (length--) {
- // Linear search. Performance is inversely proportional to the number of
- // unique nested structures.
- if (aStack[length] === a) return bStack[length] === b;
- }
-
- // Add the first object to the stack of traversed objects.
- aStack.push(a);
- bStack.push(b);
-
- // Recursively compare objects and arrays.
- if (areArrays) {
- // Compare array lengths to determine if a deep comparison is necessary.
- length = a.length;
- if (length !== b.length) return false;
- // Deep compare the contents, ignoring non-numeric properties.
- while (length--) {
- if (!eq(a[length], b[length], aStack, bStack)) return false;
- }
- } else {
- // Deep compare objects.
- var _keys = keys(a), key;
- length = _keys.length;
- // Ensure that both objects contain the same number of properties before comparing deep equality.
- if (keys(b).length !== length) return false;
- while (length--) {
- // Deep compare each member
- key = _keys[length];
- if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
- }
- }
- // Remove the first object from the stack of traversed objects.
- aStack.pop();
- bStack.pop();
- return true;
- }
-
- // Perform a deep comparison to check if two objects are equal.
- function isEqual(a, b) {
- return eq(a, b);
- }
-
- // Retrieve all the enumerable property names of an object.
- function allKeys(obj) {
- if (!isObject(obj)) return [];
- var keys = [];
- for (var key in obj) keys.push(key);
- // Ahem, IE < 9.
- if (hasEnumBug) collectNonEnumProps(obj, keys);
- return keys;
- }
-
- // Since the regular `Object.prototype.toString` type tests don't work for
- // some types in IE 11, we use a fingerprinting heuristic instead, based
- // on the methods. It's not great, but it's the best we got.
- // The fingerprint method lists are defined below.
- function ie11fingerprint(methods) {
- var length = getLength(methods);
- return function(obj) {
- if (obj == null) return false;
- // `Map`, `WeakMap` and `Set` have no enumerable keys.
- var keys = allKeys(obj);
- if (getLength(keys)) return false;
- for (var i = 0; i < length; i++) {
- if (!isFunction$1(obj[methods[i]])) return false;
- }
- // If we are testing against `WeakMap`, we need to ensure that
- // `obj` doesn't have a `forEach` method in order to distinguish
- // it from a regular `Map`.
- return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
- };
- }
-
- // In the interest of compact minification, we write
- // each string in the fingerprints only once.
- var forEachName = 'forEach',
- hasName = 'has',
- commonInit = ['clear', 'delete'],
- mapTail = ['get', hasName, 'set'];
-
- // `Map`, `WeakMap` and `Set` each have slightly different
- // combinations of the above sublists.
- var mapMethods = commonInit.concat(forEachName, mapTail),
- weakMapMethods = commonInit.concat(mapTail),
- setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
- var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
- var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
- var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
- var isWeakSet = tagTester('WeakSet');
-
- // Retrieve the values of an object's properties.
- function values(obj) {
- var _keys = keys(obj);
- var length = _keys.length;
- var values = Array(length);
- for (var i = 0; i < length; i++) {
- values[i] = obj[_keys[i]];
- }
- return values;
- }
-
- // Convert an object into a list of `[key, value]` pairs.
- // The opposite of `_.object` with one argument.
- function pairs(obj) {
- var _keys = keys(obj);
- var length = _keys.length;
- var pairs = Array(length);
- for (var i = 0; i < length; i++) {
- pairs[i] = [_keys[i], obj[_keys[i]]];
- }
- return pairs;
- }
-
- // Invert the keys and values of an object. The values must be serializable.
- function invert(obj) {
- var result = {};
- var _keys = keys(obj);
- for (var i = 0, length = _keys.length; i < length; i++) {
- result[obj[_keys[i]]] = _keys[i];
- }
- return result;
- }
-
- // Return a sorted list of the function names available on the object.
- function functions(obj) {
- var names = [];
- for (var key in obj) {
- if (isFunction$1(obj[key])) names.push(key);
- }
- return names.sort();
- }
-
- // An internal function for creating assigner functions.
- function createAssigner(keysFunc, defaults) {
- return function(obj) {
- var length = arguments.length;
- if (defaults) obj = Object(obj);
- if (length < 2 || obj == null) return obj;
- for (var index = 1; index < length; index++) {
- var source = arguments[index],
- keys = keysFunc(source),
- l = keys.length;
- for (var i = 0; i < l; i++) {
- var key = keys[i];
- if (!defaults || obj[key] === void 0) obj[key] = source[key];
- }
- }
- return obj;
- };
- }
-
- // Extend a given object with all the properties in passed-in object(s).
- var extend = createAssigner(allKeys);
-
- // Assigns a given object with all the own properties in the passed-in
- // object(s).
- // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
- var extendOwn = createAssigner(keys);
-
- // Fill in a given object with default properties.
- var defaults = createAssigner(allKeys, true);
-
- // Create a naked function reference for surrogate-prototype-swapping.
- function ctor() {
- return function(){};
- }
-
- // An internal function for creating a new object that inherits from another.
- function baseCreate(prototype) {
- if (!isObject(prototype)) return {};
- if (nativeCreate) return nativeCreate(prototype);
- var Ctor = ctor();
- Ctor.prototype = prototype;
- var result = new Ctor;
- Ctor.prototype = null;
- return result;
- }
-
- // Creates an object that inherits from the given prototype object.
- // If additional properties are provided then they will be added to the
- // created object.
- function create(prototype, props) {
- var result = baseCreate(prototype);
- if (props) extendOwn(result, props);
- return result;
- }
-
- // Create a (shallow-cloned) duplicate of an object.
- function clone(obj) {
- if (!isObject(obj)) return obj;
- return isArray(obj) ? obj.slice() : extend({}, obj);
- }
-
- // Invokes `interceptor` with the `obj` and then returns `obj`.
- // The primary purpose of this method is to "tap into" a method chain, in
- // order to perform operations on intermediate results within the chain.
- function tap(obj, interceptor) {
- interceptor(obj);
- return obj;
- }
-
- // Normalize a (deep) property `path` to array.
- // Like `_.iteratee`, this function can be customized.
- function toPath$1(path) {
- return isArray(path) ? path : [path];
- }
- _$1.toPath = toPath$1;
-
- // Internal wrapper for `_.toPath` to enable minification.
- // Similar to `cb` for `_.iteratee`.
- function toPath(path) {
- return _$1.toPath(path);
- }
-
- // Internal function to obtain a nested property in `obj` along `path`.
- function deepGet(obj, path) {
- var length = path.length;
- for (var i = 0; i < length; i++) {
- if (obj == null) return void 0;
- obj = obj[path[i]];
- }
- return length ? obj : void 0;
- }
-
- // Get the value of the (deep) property on `path` from `object`.
- // If any property in `path` does not exist or if the value is
- // `undefined`, return `defaultValue` instead.
- // The `path` is normalized through `_.toPath`.
- function get(object, path, defaultValue) {
- var value = deepGet(object, toPath(path));
- return isUndefined(value) ? defaultValue : value;
- }
-
- // Shortcut function for checking if an object has a given property directly on
- // itself (in other words, not on a prototype). Unlike the internal `has`
- // function, this public version can also traverse nested properties.
- function has(obj, path) {
- path = toPath(path);
- var length = path.length;
- for (var i = 0; i < length; i++) {
- var key = path[i];
- if (!has$1(obj, key)) return false;
- obj = obj[key];
- }
- return !!length;
- }
-
- // Keep the identity function around for default iteratees.
- function identity(value) {
- return value;
- }
-
- // Returns a predicate for checking whether an object has a given set of
- // `key:value` pairs.
- function matcher(attrs) {
- attrs = extendOwn({}, attrs);
- return function(obj) {
- return isMatch(obj, attrs);
- };
- }
-
- // Creates a function that, when passed an object, will traverse that object’s
- // properties down the given `path`, specified as an array of keys or indices.
- function property(path) {
- path = toPath(path);
- return function(obj) {
- return deepGet(obj, path);
- };
- }
-
- // Internal function that returns an efficient (for current engines) version
- // of the passed-in callback, to be repeatedly applied in other Underscore
- // functions.
- function optimizeCb(func, context, argCount) {
- if (context === void 0) return func;
- switch (argCount == null ? 3 : argCount) {
- case 1: return function(value) {
- return func.call(context, value);
- };
- // The 2-argument case is omitted because we’re not using it.
- case 3: return function(value, index, collection) {
- return func.call(context, value, index, collection);
- };
- case 4: return function(accumulator, value, index, collection) {
- return func.call(context, accumulator, value, index, collection);
- };
- }
- return function() {
- return func.apply(context, arguments);
- };
- }
-
- // An internal function to generate callbacks that can be applied to each
- // element in a collection, returning the desired result — either `_.identity`,
- // an arbitrary callback, a property matcher, or a property accessor.
- function baseIteratee(value, context, argCount) {
- if (value == null) return identity;
- if (isFunction$1(value)) return optimizeCb(value, context, argCount);
- if (isObject(value) && !isArray(value)) return matcher(value);
- return property(value);
- }
-
- // External wrapper for our callback generator. Users may customize
- // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
- // This abstraction hides the internal-only `argCount` argument.
- function iteratee(value, context) {
- return baseIteratee(value, context, Infinity);
- }
- _$1.iteratee = iteratee;
-
- // The function we call internally to generate a callback. It invokes
- // `_.iteratee` if overridden, otherwise `baseIteratee`.
- function cb(value, context, argCount) {
- if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
- return baseIteratee(value, context, argCount);
- }
-
- // Returns the results of applying the `iteratee` to each element of `obj`.
- // In contrast to `_.map` it returns an object.
- function mapObject(obj, iteratee, context) {
- iteratee = cb(iteratee, context);
- var _keys = keys(obj),
- length = _keys.length,
- results = {};
- for (var index = 0; index < length; index++) {
- var currentKey = _keys[index];
- results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
- }
- return results;
- }
-
- // Predicate-generating function. Often useful outside of Underscore.
- function noop(){}
-
- // Generates a function for a given object that returns a given property.
- function propertyOf(obj) {
- if (obj == null) return noop;
- return function(path) {
- return get(obj, path);
- };
- }
-
- // Run a function **n** times.
- function times(n, iteratee, context) {
- var accum = Array(Math.max(0, n));
- iteratee = optimizeCb(iteratee, context, 1);
- for (var i = 0; i < n; i++) accum[i] = iteratee(i);
- return accum;
- }
-
- // Return a random integer between `min` and `max` (inclusive).
- function random(min, max) {
- if (max == null) {
- max = min;
- min = 0;
- }
- return min + Math.floor(Math.random() * (max - min + 1));
- }
-
- // A (possibly faster) way to get the current timestamp as an integer.
- var now = Date.now || function() {
- return new Date().getTime();
- };
-
- // Internal helper to generate functions for escaping and unescaping strings
- // to/from HTML interpolation.
- function createEscaper(map) {
- var escaper = function(match) {
- return map[match];
- };
- // Regexes for identifying a key that needs to be escaped.
- var source = '(?:' + keys(map).join('|') + ')';
- var testRegexp = RegExp(source);
- var replaceRegexp = RegExp(source, 'g');
- return function(string) {
- string = string == null ? '' : '' + string;
- return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
- };
- }
-
- // Internal list of HTML entities for escaping.
- var escapeMap = {
- '&': '&',
- '<': '<',
- '>': '>',
- '"': '"',
- "'": ''',
- '`': '`'
- };
-
- // Function for escaping strings to HTML interpolation.
- var _escape = createEscaper(escapeMap);
-
- // Internal list of HTML entities for unescaping.
- var unescapeMap = invert(escapeMap);
-
- // Function for unescaping strings from HTML interpolation.
- var _unescape = createEscaper(unescapeMap);
-
- // By default, Underscore uses ERB-style template delimiters. Change the
- // following template settings to use alternative delimiters.
- var templateSettings = _$1.templateSettings = {
- evaluate: /<%([\s\S]+?)%>/g,
- interpolate: /<%=([\s\S]+?)%>/g,
- escape: /<%-([\s\S]+?)%>/g
- };
-
- // When customizing `_.templateSettings`, if you don't want to define an
- // interpolation, evaluation or escaping regex, we need one that is
- // guaranteed not to match.
- var noMatch = /(.)^/;
-
- // Certain characters need to be escaped so that they can be put into a
- // string literal.
- var escapes = {
- "'": "'",
- '\\': '\\',
- '\r': 'r',
- '\n': 'n',
- '\u2028': 'u2028',
- '\u2029': 'u2029'
- };
-
- var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
- function escapeChar(match) {
- return '\\' + escapes[match];
- }
-
- // In order to prevent third-party code injection through
- // `_.templateSettings.variable`, we test it against the following regular
- // expression. It is intentionally a bit more liberal than just matching valid
- // identifiers, but still prevents possible loopholes through defaults or
- // destructuring assignment.
- var bareIdentifier = /^\s*(\w|\$)+\s*$/;
-
- // JavaScript micro-templating, similar to John Resig's implementation.
- // Underscore templating handles arbitrary delimiters, preserves whitespace,
- // and correctly escapes quotes within interpolated code.
- // NB: `oldSettings` only exists for backwards compatibility.
- function template(text, settings, oldSettings) {
- if (!settings && oldSettings) settings = oldSettings;
- settings = defaults({}, settings, _$1.templateSettings);
-
- // Combine delimiters into one regular expression via alternation.
- var matcher = RegExp([
- (settings.escape || noMatch).source,
- (settings.interpolate || noMatch).source,
- (settings.evaluate || noMatch).source
- ].join('|') + '|$', 'g');
-
- // Compile the template source, escaping string literals appropriately.
- var index = 0;
- var source = "__p+='";
- text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
- source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
- index = offset + match.length;
-
- if (escape) {
- source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
- } else if (interpolate) {
- source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
- } else if (evaluate) {
- source += "';\n" + evaluate + "\n__p+='";
- }
-
- // Adobe VMs need the match returned to produce the correct offset.
- return match;
- });
- source += "';\n";
-
- var argument = settings.variable;
- if (argument) {
- // Insure against third-party code injection. (CVE-2021-23358)
- if (!bareIdentifier.test(argument)) throw new Error(
- 'variable is not a bare identifier: ' + argument
- );
- } else {
- // If a variable is not specified, place data values in local scope.
- source = 'with(obj||{}){\n' + source + '}\n';
- argument = 'obj';
- }
-
- source = "var __t,__p='',__j=Array.prototype.join," +
- "print=function(){__p+=__j.call(arguments,'');};\n" +
- source + 'return __p;\n';
-
- var render;
- try {
- render = new Function(argument, '_', source);
- } catch (e) {
- e.source = source;
- throw e;
- }
-
- var template = function(data) {
- return render.call(this, data, _$1);
- };
-
- // Provide the compiled source as a convenience for precompilation.
- template.source = 'function(' + argument + '){\n' + source + '}';
-
- return template;
- }
-
- // Traverses the children of `obj` along `path`. If a child is a function, it
- // is invoked with its parent as context. Returns the value of the final
- // child, or `fallback` if any child is undefined.
- function result(obj, path, fallback) {
- path = toPath(path);
- var length = path.length;
- if (!length) {
- return isFunction$1(fallback) ? fallback.call(obj) : fallback;
- }
- for (var i = 0; i < length; i++) {
- var prop = obj == null ? void 0 : obj[path[i]];
- if (prop === void 0) {
- prop = fallback;
- i = length; // Ensure we don't continue iterating.
- }
- obj = isFunction$1(prop) ? prop.call(obj) : prop;
- }
- return obj;
- }
-
- // Generate a unique integer id (unique within the entire client session).
- // Useful for temporary DOM ids.
- var idCounter = 0;
- function uniqueId(prefix) {
- var id = ++idCounter + '';
- return prefix ? prefix + id : id;
- }
-
- // Start chaining a wrapped Underscore object.
- function chain(obj) {
- var instance = _$1(obj);
- instance._chain = true;
- return instance;
- }
-
- // Internal function to execute `sourceFunc` bound to `context` with optional
- // `args`. Determines whether to execute a function as a constructor or as a
- // normal function.
- function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
- if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
- var self = baseCreate(sourceFunc.prototype);
- var result = sourceFunc.apply(self, args);
- if (isObject(result)) return result;
- return self;
- }
-
- // Partially apply a function by creating a version that has had some of its
- // arguments pre-filled, without changing its dynamic `this` context. `_` acts
- // as a placeholder by default, allowing any combination of arguments to be
- // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
- var partial = restArguments(function(func, boundArgs) {
- var placeholder = partial.placeholder;
- var bound = function() {
- var position = 0, length = boundArgs.length;
- var args = Array(length);
- for (var i = 0; i < length; i++) {
- args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
- }
- while (position < arguments.length) args.push(arguments[position++]);
- return executeBound(func, bound, this, this, args);
- };
- return bound;
- });
-
- partial.placeholder = _$1;
-
- // Create a function bound to a given object (assigning `this`, and arguments,
- // optionally).
- var bind = restArguments(function(func, context, args) {
- if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
- var bound = restArguments(function(callArgs) {
- return executeBound(func, bound, context, this, args.concat(callArgs));
- });
- return bound;
- });
-
- // Internal helper for collection methods to determine whether a collection
- // should be iterated as an array or as an object.
- // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
- // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
- var isArrayLike = createSizePropertyCheck(getLength);
-
- // Internal implementation of a recursive `flatten` function.
- function flatten$1(input, depth, strict, output) {
- output = output || [];
- if (!depth && depth !== 0) {
- depth = Infinity;
- } else if (depth <= 0) {
- return output.concat(input);
- }
- var idx = output.length;
- for (var i = 0, length = getLength(input); i < length; i++) {
- var value = input[i];
- if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
- // Flatten current level of array or arguments object.
- if (depth > 1) {
- flatten$1(value, depth - 1, strict, output);
- idx = output.length;
- } else {
- var j = 0, len = value.length;
- while (j < len) output[idx++] = value[j++];
- }
- } else if (!strict) {
- output[idx++] = value;
- }
- }
- return output;
- }
-
- // Bind a number of an object's methods to that object. Remaining arguments
- // are the method names to be bound. Useful for ensuring that all callbacks
- // defined on an object belong to it.
- var bindAll = restArguments(function(obj, keys) {
- keys = flatten$1(keys, false, false);
- var index = keys.length;
- if (index < 1) throw new Error('bindAll must be passed function names');
- while (index--) {
- var key = keys[index];
- obj[key] = bind(obj[key], obj);
- }
- return obj;
- });
-
- // Memoize an expensive function by storing its results.
- function memoize(func, hasher) {
- var memoize = function(key) {
- var cache = memoize.cache;
- var address = '' + (hasher ? hasher.apply(this, arguments) : key);
- if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
- return cache[address];
- };
- memoize.cache = {};
- return memoize;
- }
-
- // Delays a function for the given number of milliseconds, and then calls
- // it with the arguments supplied.
- var delay = restArguments(function(func, wait, args) {
- return setTimeout(function() {
- return func.apply(null, args);
- }, wait);
- });
-
- // Defers a function, scheduling it to run after the current call stack has
- // cleared.
- var defer = partial(delay, _$1, 1);
-
- // Returns a function, that, when invoked, will only be triggered at most once
- // during a given window of time. Normally, the throttled function will run
- // as much as it can, without ever going more than once per `wait` duration;
- // but if you'd like to disable the execution on the leading edge, pass
- // `{leading: false}`. To disable execution on the trailing edge, ditto.
- function throttle(func, wait, options) {
- var timeout, context, args, result;
- var previous = 0;
- if (!options) options = {};
-
- var later = function() {
- previous = options.leading === false ? 0 : now();
- timeout = null;
- result = func.apply(context, args);
- if (!timeout) context = args = null;
- };
-
- var throttled = function() {
- var _now = now();
- if (!previous && options.leading === false) previous = _now;
- var remaining = wait - (_now - previous);
- context = this;
- args = arguments;
- if (remaining <= 0 || remaining > wait) {
- if (timeout) {
- clearTimeout(timeout);
- timeout = null;
- }
- previous = _now;
- result = func.apply(context, args);
- if (!timeout) context = args = null;
- } else if (!timeout && options.trailing !== false) {
- timeout = setTimeout(later, remaining);
- }
- return result;
- };
-
- throttled.cancel = function() {
- clearTimeout(timeout);
- previous = 0;
- timeout = context = args = null;
- };
-
- return throttled;
- }
-
- // When a sequence of calls of the returned function ends, the argument
- // function is triggered. The end of a sequence is defined by the `wait`
- // parameter. If `immediate` is passed, the argument function will be
- // triggered at the beginning of the sequence instead of at the end.
- function debounce(func, wait, immediate) {
- var timeout, previous, args, result, context;
-
- var later = function() {
- var passed = now() - previous;
- if (wait > passed) {
- timeout = setTimeout(later, wait - passed);
- } else {
- timeout = null;
- if (!immediate) result = func.apply(context, args);
- // This check is needed because `func` can recursively invoke `debounced`.
- if (!timeout) args = context = null;
- }
- };
-
- var debounced = restArguments(function(_args) {
- context = this;
- args = _args;
- previous = now();
- if (!timeout) {
- timeout = setTimeout(later, wait);
- if (immediate) result = func.apply(context, args);
- }
- return result;
- });
-
- debounced.cancel = function() {
- clearTimeout(timeout);
- timeout = args = context = null;
- };
-
- return debounced;
- }
-
- // Returns the first function passed as an argument to the second,
- // allowing you to adjust arguments, run code before and after, and
- // conditionally execute the original function.
- function wrap(func, wrapper) {
- return partial(wrapper, func);
- }
-
- // Returns a negated version of the passed-in predicate.
- function negate(predicate) {
- return function() {
- return !predicate.apply(this, arguments);
- };
- }
-
- // Returns a function that is the composition of a list of functions, each
- // consuming the return value of the function that follows.
- function compose() {
- var args = arguments;
- var start = args.length - 1;
- return function() {
- var i = start;
- var result = args[start].apply(this, arguments);
- while (i--) result = args[i].call(this, result);
- return result;
- };
- }
-
- // Returns a function that will only be executed on and after the Nth call.
- function after(times, func) {
- return function() {
- if (--times < 1) {
- return func.apply(this, arguments);
- }
- };
- }
-
- // Returns a function that will only be executed up to (but not including) the
- // Nth call.
- function before(times, func) {
- var memo;
- return function() {
- if (--times > 0) {
- memo = func.apply(this, arguments);
- }
- if (times <= 1) func = null;
- return memo;
- };
- }
-
- // Returns a function that will be executed at most one time, no matter how
- // often you call it. Useful for lazy initialization.
- var once = partial(before, 2);
-
- // Returns the first key on an object that passes a truth test.
- function findKey(obj, predicate, context) {
- predicate = cb(predicate, context);
- var _keys = keys(obj), key;
- for (var i = 0, length = _keys.length; i < length; i++) {
- key = _keys[i];
- if (predicate(obj[key], key, obj)) return key;
- }
- }
-
- // Internal function to generate `_.findIndex` and `_.findLastIndex`.
- function createPredicateIndexFinder(dir) {
- return function(array, predicate, context) {
- predicate = cb(predicate, context);
- var length = getLength(array);
- var index = dir > 0 ? 0 : length - 1;
- for (; index >= 0 && index < length; index += dir) {
- if (predicate(array[index], index, array)) return index;
- }
- return -1;
- };
- }
-
- // Returns the first index on an array-like that passes a truth test.
- var findIndex = createPredicateIndexFinder(1);
-
- // Returns the last index on an array-like that passes a truth test.
- var findLastIndex = createPredicateIndexFinder(-1);
-
- // Use a comparator function to figure out the smallest index at which
- // an object should be inserted so as to maintain order. Uses binary search.
- function sortedIndex(array, obj, iteratee, context) {
- iteratee = cb(iteratee, context, 1);
- var value = iteratee(obj);
- var low = 0, high = getLength(array);
- while (low < high) {
- var mid = Math.floor((low + high) / 2);
- if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
- }
- return low;
- }
-
- // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
- function createIndexFinder(dir, predicateFind, sortedIndex) {
- return function(array, item, idx) {
- var i = 0, length = getLength(array);
- if (typeof idx == 'number') {
- if (dir > 0) {
- i = idx >= 0 ? idx : Math.max(idx + length, i);
- } else {
- length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
- }
- } else if (sortedIndex && idx && length) {
- idx = sortedIndex(array, item);
- return array[idx] === item ? idx : -1;
- }
- if (item !== item) {
- idx = predicateFind(slice.call(array, i, length), isNaN$1);
- return idx >= 0 ? idx + i : -1;
- }
- for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
- if (array[idx] === item) return idx;
- }
- return -1;
- };
- }
-
- // Return the position of the first occurrence of an item in an array,
- // or -1 if the item is not included in the array.
- // If the array is large and already in sort order, pass `true`
- // for **isSorted** to use binary search.
- var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
- // Return the position of the last occurrence of an item in an array,
- // or -1 if the item is not included in the array.
- var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
- // Return the first value which passes a truth test.
- function find(obj, predicate, context) {
- var keyFinder = isArrayLike(obj) ? findIndex : findKey;
- var key = keyFinder(obj, predicate, context);
- if (key !== void 0 && key !== -1) return obj[key];
- }
-
- // Convenience version of a common use case of `_.find`: getting the first
- // object containing specific `key:value` pairs.
- function findWhere(obj, attrs) {
- return find(obj, matcher(attrs));
- }
-
- // The cornerstone for collection functions, an `each`
- // implementation, aka `forEach`.
- // Handles raw objects in addition to array-likes. Treats all
- // sparse array-likes as if they were dense.
- function each(obj, iteratee, context) {
- iteratee = optimizeCb(iteratee, context);
- var i, length;
- if (isArrayLike(obj)) {
- for (i = 0, length = obj.length; i < length; i++) {
- iteratee(obj[i], i, obj);
- }
- } else {
- var _keys = keys(obj);
- for (i = 0, length = _keys.length; i < length; i++) {
- iteratee(obj[_keys[i]], _keys[i], obj);
- }
- }
- return obj;
- }
-
- // Return the results of applying the iteratee to each element.
- function map(obj, iteratee, context) {
- iteratee = cb(iteratee, context);
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length,
- results = Array(length);
- for (var index = 0; index < length; index++) {
- var currentKey = _keys ? _keys[index] : index;
- results[index] = iteratee(obj[currentKey], currentKey, obj);
- }
- return results;
- }
-
- // Internal helper to create a reducing function, iterating left or right.
- function createReduce(dir) {
- // Wrap code that reassigns argument variables in a separate function than
- // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
- var reducer = function(obj, iteratee, memo, initial) {
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length,
- index = dir > 0 ? 0 : length - 1;
- if (!initial) {
- memo = obj[_keys ? _keys[index] : index];
- index += dir;
- }
- for (; index >= 0 && index < length; index += dir) {
- var currentKey = _keys ? _keys[index] : index;
- memo = iteratee(memo, obj[currentKey], currentKey, obj);
- }
- return memo;
- };
-
- return function(obj, iteratee, memo, context) {
- var initial = arguments.length >= 3;
- return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
- };
- }
-
- // **Reduce** builds up a single result from a list of values, aka `inject`,
- // or `foldl`.
- var reduce = createReduce(1);
-
- // The right-associative version of reduce, also known as `foldr`.
- var reduceRight = createReduce(-1);
-
- // Return all the elements that pass a truth test.
- function filter(obj, predicate, context) {
- var results = [];
- predicate = cb(predicate, context);
- each(obj, function(value, index, list) {
- if (predicate(value, index, list)) results.push(value);
- });
- return results;
- }
-
- // Return all the elements for which a truth test fails.
- function reject(obj, predicate, context) {
- return filter(obj, negate(cb(predicate)), context);
- }
-
- // Determine whether all of the elements pass a truth test.
- function every(obj, predicate, context) {
- predicate = cb(predicate, context);
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length;
- for (var index = 0; index < length; index++) {
- var currentKey = _keys ? _keys[index] : index;
- if (!predicate(obj[currentKey], currentKey, obj)) return false;
- }
- return true;
- }
-
- // Determine if at least one element in the object passes a truth test.
- function some(obj, predicate, context) {
- predicate = cb(predicate, context);
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length;
- for (var index = 0; index < length; index++) {
- var currentKey = _keys ? _keys[index] : index;
- if (predicate(obj[currentKey], currentKey, obj)) return true;
- }
- return false;
- }
-
- // Determine if the array or object contains a given item (using `===`).
- function contains(obj, item, fromIndex, guard) {
- if (!isArrayLike(obj)) obj = values(obj);
- if (typeof fromIndex != 'number' || guard) fromIndex = 0;
- return indexOf(obj, item, fromIndex) >= 0;
- }
-
- // Invoke a method (with arguments) on every item in a collection.
- var invoke = restArguments(function(obj, path, args) {
- var contextPath, func;
- if (isFunction$1(path)) {
- func = path;
- } else {
- path = toPath(path);
- contextPath = path.slice(0, -1);
- path = path[path.length - 1];
- }
- return map(obj, function(context) {
- var method = func;
- if (!method) {
- if (contextPath && contextPath.length) {
- context = deepGet(context, contextPath);
- }
- if (context == null) return void 0;
- method = context[path];
- }
- return method == null ? method : method.apply(context, args);
- });
- });
-
- // Convenience version of a common use case of `_.map`: fetching a property.
- function pluck(obj, key) {
- return map(obj, property(key));
- }
-
- // Convenience version of a common use case of `_.filter`: selecting only
- // objects containing specific `key:value` pairs.
- function where(obj, attrs) {
- return filter(obj, matcher(attrs));
- }
-
- // Return the maximum element (or element-based computation).
- function max(obj, iteratee, context) {
- var result = -Infinity, lastComputed = -Infinity,
- value, computed;
- if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
- obj = isArrayLike(obj) ? obj : values(obj);
- for (var i = 0, length = obj.length; i < length; i++) {
- value = obj[i];
- if (value != null && value > result) {
- result = value;
- }
- }
- } else {
- iteratee = cb(iteratee, context);
- each(obj, function(v, index, list) {
- computed = iteratee(v, index, list);
- if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
- result = v;
- lastComputed = computed;
- }
- });
- }
- return result;
- }
-
- // Return the minimum element (or element-based computation).
- function min(obj, iteratee, context) {
- var result = Infinity, lastComputed = Infinity,
- value, computed;
- if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
- obj = isArrayLike(obj) ? obj : values(obj);
- for (var i = 0, length = obj.length; i < length; i++) {
- value = obj[i];
- if (value != null && value < result) {
- result = value;
- }
- }
- } else {
- iteratee = cb(iteratee, context);
- each(obj, function(v, index, list) {
- computed = iteratee(v, index, list);
- if (computed < lastComputed || computed === Infinity && result === Infinity) {
- result = v;
- lastComputed = computed;
- }
- });
- }
- return result;
- }
-
- // Sample **n** random values from a collection using the modern version of the
- // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
- // If **n** is not specified, returns a single random element.
- // The internal `guard` argument allows it to work with `_.map`.
- function sample(obj, n, guard) {
- if (n == null || guard) {
- if (!isArrayLike(obj)) obj = values(obj);
- return obj[random(obj.length - 1)];
- }
- var sample = isArrayLike(obj) ? clone(obj) : values(obj);
- var length = getLength(sample);
- n = Math.max(Math.min(n, length), 0);
- var last = length - 1;
- for (var index = 0; index < n; index++) {
- var rand = random(index, last);
- var temp = sample[index];
- sample[index] = sample[rand];
- sample[rand] = temp;
- }
- return sample.slice(0, n);
- }
-
- // Shuffle a collection.
- function shuffle(obj) {
- return sample(obj, Infinity);
- }
-
- // Sort the object's values by a criterion produced by an iteratee.
- function sortBy(obj, iteratee, context) {
- var index = 0;
- iteratee = cb(iteratee, context);
- return pluck(map(obj, function(value, key, list) {
- return {
- value: value,
- index: index++,
- criteria: iteratee(value, key, list)
- };
- }).sort(function(left, right) {
- var a = left.criteria;
- var b = right.criteria;
- if (a !== b) {
- if (a > b || a === void 0) return 1;
- if (a < b || b === void 0) return -1;
- }
- return left.index - right.index;
- }), 'value');
- }
-
- // An internal function used for aggregate "group by" operations.
- function group(behavior, partition) {
- return function(obj, iteratee, context) {
- var result = partition ? [[], []] : {};
- iteratee = cb(iteratee, context);
- each(obj, function(value, index) {
- var key = iteratee(value, index, obj);
- behavior(result, value, key);
- });
- return result;
- };
- }
-
- // Groups the object's values by a criterion. Pass either a string attribute
- // to group by, or a function that returns the criterion.
- var groupBy = group(function(result, value, key) {
- if (has$1(result, key)) result[key].push(value); else result[key] = [value];
- });
-
- // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
- // when you know that your index values will be unique.
- var indexBy = group(function(result, value, key) {
- result[key] = value;
- });
-
- // Counts instances of an object that group by a certain criterion. Pass
- // either a string attribute to count by, or a function that returns the
- // criterion.
- var countBy = group(function(result, value, key) {
- if (has$1(result, key)) result[key]++; else result[key] = 1;
- });
-
- // Split a collection into two arrays: one whose elements all pass the given
- // truth test, and one whose elements all do not pass the truth test.
- var partition = group(function(result, value, pass) {
- result[pass ? 0 : 1].push(value);
- }, true);
-
- // Safely create a real, live array from anything iterable.
- var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
- function toArray(obj) {
- if (!obj) return [];
- if (isArray(obj)) return slice.call(obj);
- if (isString(obj)) {
- // Keep surrogate pair characters together.
- return obj.match(reStrSymbol);
- }
- if (isArrayLike(obj)) return map(obj, identity);
- return values(obj);
- }
-
- // Return the number of elements in a collection.
- function size(obj) {
- if (obj == null) return 0;
- return isArrayLike(obj) ? obj.length : keys(obj).length;
- }
-
- // Internal `_.pick` helper function to determine whether `key` is an enumerable
- // property name of `obj`.
- function keyInObj(value, key, obj) {
- return key in obj;
- }
-
- // Return a copy of the object only containing the allowed properties.
- var pick = restArguments(function(obj, keys) {
- var result = {}, iteratee = keys[0];
- if (obj == null) return result;
- if (isFunction$1(iteratee)) {
- if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
- keys = allKeys(obj);
- } else {
- iteratee = keyInObj;
- keys = flatten$1(keys, false, false);
- obj = Object(obj);
- }
- for (var i = 0, length = keys.length; i < length; i++) {
- var key = keys[i];
- var value = obj[key];
- if (iteratee(value, key, obj)) result[key] = value;
- }
- return result;
- });
-
- // Return a copy of the object without the disallowed properties.
- var omit = restArguments(function(obj, keys) {
- var iteratee = keys[0], context;
- if (isFunction$1(iteratee)) {
- iteratee = negate(iteratee);
- if (keys.length > 1) context = keys[1];
- } else {
- keys = map(flatten$1(keys, false, false), String);
- iteratee = function(value, key) {
- return !contains(keys, key);
- };
- }
- return pick(obj, iteratee, context);
- });
-
- // Returns everything but the last entry of the array. Especially useful on
- // the arguments object. Passing **n** will return all the values in
- // the array, excluding the last N.
- function initial(array, n, guard) {
- return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
- }
-
- // Get the first element of an array. Passing **n** will return the first N
- // values in the array. The **guard** check allows it to work with `_.map`.
- function first(array, n, guard) {
- if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
- if (n == null || guard) return array[0];
- return initial(array, array.length - n);
- }
-
- // Returns everything but the first entry of the `array`. Especially useful on
- // the `arguments` object. Passing an **n** will return the rest N values in the
- // `array`.
- function rest(array, n, guard) {
- return slice.call(array, n == null || guard ? 1 : n);
- }
-
- // Get the last element of an array. Passing **n** will return the last N
- // values in the array.
- function last(array, n, guard) {
- if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
- if (n == null || guard) return array[array.length - 1];
- return rest(array, Math.max(0, array.length - n));
- }
-
- // Trim out all falsy values from an array.
- function compact(array) {
- return filter(array, Boolean);
- }
-
- // Flatten out an array, either recursively (by default), or up to `depth`.
- // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
- function flatten(array, depth) {
- return flatten$1(array, depth, false);
- }
-
- // Take the difference between one array and a number of other arrays.
- // Only the elements present in just the first array will remain.
- var difference = restArguments(function(array, rest) {
- rest = flatten$1(rest, true, true);
- return filter(array, function(value){
- return !contains(rest, value);
- });
- });
-
- // Return a version of the array that does not contain the specified value(s).
- var without = restArguments(function(array, otherArrays) {
- return difference(array, otherArrays);
- });
-
- // Produce a duplicate-free version of the array. If the array has already
- // been sorted, you have the option of using a faster algorithm.
- // The faster algorithm will not work with an iteratee if the iteratee
- // is not a one-to-one function, so providing an iteratee will disable
- // the faster algorithm.
- function uniq(array, isSorted, iteratee, context) {
- if (!isBoolean(isSorted)) {
- context = iteratee;
- iteratee = isSorted;
- isSorted = false;
- }
- if (iteratee != null) iteratee = cb(iteratee, context);
- var result = [];
- var seen = [];
- for (var i = 0, length = getLength(array); i < length; i++) {
- var value = array[i],
- computed = iteratee ? iteratee(value, i, array) : value;
- if (isSorted && !iteratee) {
- if (!i || seen !== computed) result.push(value);
- seen = computed;
- } else if (iteratee) {
- if (!contains(seen, computed)) {
- seen.push(computed);
- result.push(value);
- }
- } else if (!contains(result, value)) {
- result.push(value);
- }
- }
- return result;
- }
-
- // Produce an array that contains the union: each distinct element from all of
- // the passed-in arrays.
- var union = restArguments(function(arrays) {
- return uniq(flatten$1(arrays, true, true));
- });
-
- // Produce an array that contains every item shared between all the
- // passed-in arrays.
- function intersection(array) {
- var result = [];
- var argsLength = arguments.length;
- for (var i = 0, length = getLength(array); i < length; i++) {
- var item = array[i];
- if (contains(result, item)) continue;
- var j;
- for (j = 1; j < argsLength; j++) {
- if (!contains(arguments[j], item)) break;
- }
- if (j === argsLength) result.push(item);
- }
- return result;
- }
-
- // Complement of zip. Unzip accepts an array of arrays and groups
- // each array's elements on shared indices.
- function unzip(array) {
- var length = array && max(array, getLength).length || 0;
- var result = Array(length);
-
- for (var index = 0; index < length; index++) {
- result[index] = pluck(array, index);
- }
- return result;
- }
-
- // Zip together multiple lists into a single array -- elements that share
- // an index go together.
- var zip = restArguments(unzip);
-
- // Converts lists into objects. Pass either a single array of `[key, value]`
- // pairs, or two parallel arrays of the same length -- one of keys, and one of
- // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
- function object(list, values) {
- var result = {};
- for (var i = 0, length = getLength(list); i < length; i++) {
- if (values) {
- result[list[i]] = values[i];
- } else {
- result[list[i][0]] = list[i][1];
- }
- }
- return result;
- }
-
- // Generate an integer Array containing an arithmetic progression. A port of
- // the native Python `range()` function. See
- // [the Python documentation](https://docs.python.org/library/functions.html#range).
- function range(start, stop, step) {
- if (stop == null) {
- stop = start || 0;
- start = 0;
- }
- if (!step) {
- step = stop < start ? -1 : 1;
- }
-
- var length = Math.max(Math.ceil((stop - start) / step), 0);
- var range = Array(length);
-
- for (var idx = 0; idx < length; idx++, start += step) {
- range[idx] = start;
- }
-
- return range;
- }
-
- // Chunk a single array into multiple arrays, each containing `count` or fewer
- // items.
- function chunk(array, count) {
- if (count == null || count < 1) return [];
- var result = [];
- var i = 0, length = array.length;
- while (i < length) {
- result.push(slice.call(array, i, i += count));
- }
- return result;
- }
-
- // Helper function to continue chaining intermediate results.
- function chainResult(instance, obj) {
- return instance._chain ? _$1(obj).chain() : obj;
- }
-
- // Add your own custom functions to the Underscore object.
- function mixin(obj) {
- each(functions(obj), function(name) {
- var func = _$1[name] = obj[name];
- _$1.prototype[name] = function() {
- var args = [this._wrapped];
- push.apply(args, arguments);
- return chainResult(this, func.apply(_$1, args));
- };
- });
- return _$1;
- }
-
- // Add all mutator `Array` functions to the wrapper.
- each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
- var method = ArrayProto[name];
- _$1.prototype[name] = function() {
- var obj = this._wrapped;
- if (obj != null) {
- method.apply(obj, arguments);
- if ((name === 'shift' || name === 'splice') && obj.length === 0) {
- delete obj[0];
- }
- }
- return chainResult(this, obj);
- };
- });
-
- // Add all accessor `Array` functions to the wrapper.
- each(['concat', 'join', 'slice'], function(name) {
- var method = ArrayProto[name];
- _$1.prototype[name] = function() {
- var obj = this._wrapped;
- if (obj != null) obj = method.apply(obj, arguments);
- return chainResult(this, obj);
- };
- });
-
- // Named Exports
-
- var allExports = {
- __proto__: null,
- VERSION: VERSION,
- restArguments: restArguments,
- isObject: isObject,
- isNull: isNull,
- isUndefined: isUndefined,
- isBoolean: isBoolean,
- isElement: isElement,
- isString: isString,
- isNumber: isNumber,
- isDate: isDate,
- isRegExp: isRegExp,
- isError: isError,
- isSymbol: isSymbol,
- isArrayBuffer: isArrayBuffer,
- isDataView: isDataView$1,
- isArray: isArray,
- isFunction: isFunction$1,
- isArguments: isArguments$1,
- isFinite: isFinite$1,
- isNaN: isNaN$1,
- isTypedArray: isTypedArray$1,
- isEmpty: isEmpty,
- isMatch: isMatch,
- isEqual: isEqual,
- isMap: isMap,
- isWeakMap: isWeakMap,
- isSet: isSet,
- isWeakSet: isWeakSet,
- keys: keys,
- allKeys: allKeys,
- values: values,
- pairs: pairs,
- invert: invert,
- functions: functions,
- methods: functions,
- extend: extend,
- extendOwn: extendOwn,
- assign: extendOwn,
- defaults: defaults,
- create: create,
- clone: clone,
- tap: tap,
- get: get,
- has: has,
- mapObject: mapObject,
- identity: identity,
- constant: constant,
- noop: noop,
- toPath: toPath$1,
- property: property,
- propertyOf: propertyOf,
- matcher: matcher,
- matches: matcher,
- times: times,
- random: random,
- now: now,
- escape: _escape,
- unescape: _unescape,
- templateSettings: templateSettings,
- template: template,
- result: result,
- uniqueId: uniqueId,
- chain: chain,
- iteratee: iteratee,
- partial: partial,
- bind: bind,
- bindAll: bindAll,
- memoize: memoize,
- delay: delay,
- defer: defer,
- throttle: throttle,
- debounce: debounce,
- wrap: wrap,
- negate: negate,
- compose: compose,
- after: after,
- before: before,
- once: once,
- findKey: findKey,
- findIndex: findIndex,
- findLastIndex: findLastIndex,
- sortedIndex: sortedIndex,
- indexOf: indexOf,
- lastIndexOf: lastIndexOf,
- find: find,
- detect: find,
- findWhere: findWhere,
- each: each,
- forEach: each,
- map: map,
- collect: map,
- reduce: reduce,
- foldl: reduce,
- inject: reduce,
- reduceRight: reduceRight,
- foldr: reduceRight,
- filter: filter,
- select: filter,
- reject: reject,
- every: every,
- all: every,
- some: some,
- any: some,
- contains: contains,
- includes: contains,
- include: contains,
- invoke: invoke,
- pluck: pluck,
- where: where,
- max: max,
- min: min,
- shuffle: shuffle,
- sample: sample,
- sortBy: sortBy,
- groupBy: groupBy,
- indexBy: indexBy,
- countBy: countBy,
- partition: partition,
- toArray: toArray,
- size: size,
- pick: pick,
- omit: omit,
- first: first,
- head: first,
- take: first,
- initial: initial,
- last: last,
- rest: rest,
- tail: rest,
- drop: rest,
- compact: compact,
- flatten: flatten,
- without: without,
- uniq: uniq,
- unique: uniq,
- union: union,
- intersection: intersection,
- difference: difference,
- unzip: unzip,
- transpose: unzip,
- zip: zip,
- object: object,
- range: range,
- chunk: chunk,
- mixin: mixin,
- 'default': _$1
- };
-
- // Default Export
-
- // Add all of the Underscore functions to the wrapper object.
- var _ = mixin(allExports);
- // Legacy Node.js API.
- _._ = _;
-
- return _;
-
-})));
-//# sourceMappingURL=underscore-umd.js.map
diff --git a/docs/build/html/_static/underscore.js b/docs/build/html/_static/underscore.js
deleted file mode 100644
index cf177d4285ab55fbc16406a5ec827b80e7eecd53..0000000000000000000000000000000000000000
--- a/docs/build/html/_static/underscore.js
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/docs/build/html/example.html b/docs/build/html/example.html
deleted file mode 100644
index 5e9d170f5f2782113fafe2d734fabfee104f59dc..0000000000000000000000000000000000000000
--- a/docs/build/html/example.html
+++ /dev/null
@@ -1,112 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="python">
- <head>
- <meta charset="utf-8" />
- <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
-
- <title>My nifty title — bayesvalidrox 0.0.3 documentation</title>
- <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
- <link rel="stylesheet" type="text/css" href="_static/alabaster.css" />
- <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
- <script src="_static/jquery.js"></script>
- <script src="_static/underscore.js"></script>
- <script src="_static/doctools.js"></script>
- <link rel="index" title="Index" href="genindex.html" />
- <link rel="search" title="Search" href="search.html" />
- <link rel="prev" title="Welcome to bayesvalidrox's documentation!" href="index.html" />
-
- <link rel="stylesheet" href="_static/custom.css" type="text/css" />
-
-
- <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
- </head><body>
-
-
- <div class="document">
- <div class="documentwrapper">
- <div class="bodywrapper">
-
-
- <div class="body" role="main">
-
- <section id="my-nifty-title">
-<h1>My nifty title<a class="headerlink" href="#my-nifty-title" title="Permalink to this headline">¶</a></h1>
-<p>Some <strong>text</strong>!</p>
-<div class="warning admonition">
-<p class="admonition-title">Here's my title</p>
-<p>Here's my admonition content</p>
-</div>
-</section>
-
-
- </div>
-
- </div>
- </div>
- <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
- <div class="sphinxsidebarwrapper">
-<h1 class="logo"><a href="index.html">bayesvalidrox</a></h1>
-
-
-
-
-
-
-
-
-<h3>Navigation</h3>
-<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul class="current">
-<li class="toctree-l1 current"><a class="current reference internal" href="#">My nifty title</a></li>
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-<div class="relations">
-<h3>Related Topics</h3>
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-</ul>
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-<div id="searchbox" style="display: none" role="search">
- <h3 id="searchlabel">Quick search</h3>
- <div class="searchformwrapper">
- <form class="search" action="search.html" method="get">
- <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
- <input type="submit" value="Go" />
- </form>
- </div>
-</div>
-<script>$('#searchbox').show(0);</script>
-
-
-
-
-
-
-
-
- </div>
- </div>
- <div class="clearer"></div>
- </div>
- <div class="footer">
- ©2022, Farid Mohammadi.
-
- |
- Powered by <a href="http://sphinx-doc.org/">Sphinx 4.4.0</a>
- & <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-
- |
- <a href="_sources/example.md.txt"
- rel="nofollow">Page source</a>
- </div>
-
-
-
-
- </body>
-</html>
\ No newline at end of file
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
deleted file mode 100644
index 1ff415690d8027e91ffcf627a659cd70a1c8612a..0000000000000000000000000000000000000000
--- a/docs/build/html/genindex.html
+++ /dev/null
@@ -1,104 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="python">
- <head>
- <meta charset="utf-8" />
- <meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>Index — bayesvalidrox 0.0.3 documentation</title>
- <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
- <link rel="stylesheet" type="text/css" href="_static/alabaster.css" />
- <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
- <script src="_static/jquery.js"></script>
- <script src="_static/underscore.js"></script>
- <script src="_static/doctools.js"></script>
- <link rel="index" title="Index" href="#" />
- <link rel="search" title="Search" href="search.html" />
-
- <link rel="stylesheet" href="_static/custom.css" type="text/css" />
-
-
- <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
- </head><body>
-
-
- <div class="document">
- <div class="documentwrapper">
- <div class="bodywrapper">
-
-
- <div class="body" role="main">
-
-
-<h1 id="index">Index</h1>
-
-<div class="genindex-jumpbox">
-
-</div>
-
-
- </div>
-
- </div>
- </div>
- <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
- <div class="sphinxsidebarwrapper">
-<h1 class="logo"><a href="index.html">bayesvalidrox</a></h1>
-
-
-
-
-
-
-
-
-<h3>Navigation</h3>
-<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="example.html">My nifty title</a></li>
-</ul>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
- <li><a href="index.html">Documentation overview</a><ul>
- </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
- <h3 id="searchlabel">Quick search</h3>
- <div class="searchformwrapper">
- <form class="search" action="search.html" method="get">
- <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
- <input type="submit" value="Go" />
- </form>
- </div>
-</div>
-<script>$('#searchbox').show(0);</script>
-
-
-
-
-
-
-
-
- </div>
- </div>
- <div class="clearer"></div>
- </div>
- <div class="footer">
- ©2022, Farid Mohammadi.
-
- |
- Powered by <a href="http://sphinx-doc.org/">Sphinx 4.4.0</a>
- & <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-
- </div>
-
-
-
-
- </body>
-</html>
\ No newline at end of file
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
deleted file mode 100644
index 5995ac4dcf04a9ef84c1d92b9a5906b90049766c..0000000000000000000000000000000000000000
--- a/docs/build/html/index.html
+++ /dev/null
@@ -1,130 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="python">
- <head>
- <meta charset="utf-8" />
- <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
-
- <title>Welcome to bayesvalidrox's documentation! — bayesvalidrox 0.0.3 documentation</title>
- <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
- <link rel="stylesheet" type="text/css" href="_static/alabaster.css" />
- <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
- <script src="_static/jquery.js"></script>
- <script src="_static/underscore.js"></script>
- <script src="_static/doctools.js"></script>
- <link rel="index" title="Index" href="genindex.html" />
- <link rel="search" title="Search" href="search.html" />
- <link rel="next" title="My nifty title" href="example.html" />
-
- <link rel="stylesheet" href="_static/custom.css" type="text/css" />
-
-
- <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
- </head><body>
-
-
- <div class="document">
- <div class="documentwrapper">
- <div class="bodywrapper">
-
-
- <div class="body" role="main">
-
- <section id="welcome-to-bayesvalidrox-s-documentation">
-<h1>Welcome to bayesvalidrox's documentation!<a class="headerlink" href="#welcome-to-bayesvalidrox-s-documentation" title="Permalink to this headline">¶</a></h1>
-<div class="toctree-wrapper compound">
-<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="example.html">My nifty title</a></li>
-</ul>
-</div>
-<p>This is a normal text paragraph. The next paragraph is a code sample:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">It</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">processed</span> <span class="ow">in</span> <span class="nb">any</span> <span class="n">way</span><span class="p">,</span> <span class="k">except</span>
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-
-<span class="n">It</span> <span class="n">can</span> <span class="n">span</span> <span class="n">multiple</span> <span class="n">lines</span><span class="o">.</span>
-</pre></div>
-</div>
-<p>This is a normal text paragraph again.</p>
-</section>
-<section id="indices-and-tables">
-<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
-<ul class="simple">
-<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
-<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
-<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
-<li><p><span class="xref std std-ref">pylink</span></p></li>
-</ul>
-</section>
-
-
- </div>
-
- </div>
- </div>
- <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
- <div class="sphinxsidebarwrapper">
-<h1 class="logo"><a href="#">bayesvalidrox</a></h1>
-
-
-
-
-
-
-
-
-<h3>Navigation</h3>
-<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="example.html">My nifty title</a></li>
-</ul>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
- <li><a href="#">Documentation overview</a><ul>
- <li>Next: <a href="example.html" title="next chapter">My nifty title</a></li>
- </ul></li>
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-</div>
-<div id="searchbox" style="display: none" role="search">
- <h3 id="searchlabel">Quick search</h3>
- <div class="searchformwrapper">
- <form class="search" action="search.html" method="get">
- <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
- <input type="submit" value="Go" />
- </form>
- </div>
-</div>
-<script>$('#searchbox').show(0);</script>
-
-
-
-
-
-
-
-
- </div>
- </div>
- <div class="clearer"></div>
- </div>
- <div class="footer">
- ©2022, Farid Mohammadi.
-
- |
- Powered by <a href="http://sphinx-doc.org/">Sphinx 4.4.0</a>
- & <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-
- |
- <a href="_sources/index.rst.txt"
- rel="nofollow">Page source</a>
- </div>
-
-
-
-
- </body>
-</html>
\ No newline at end of file
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
deleted file mode 100644
index d8003617a63c550d09d36d1084476cb88cd223df..0000000000000000000000000000000000000000
--- a/docs/build/html/objects.inv
+++ /dev/null
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\ No newline at end of file
diff --git a/docs/build/html/pylink.html b/docs/build/html/pylink.html
deleted file mode 100755
index 87ce4f1822ee2789f229e15b7cbd2991df7f85a8..0000000000000000000000000000000000000000
--- a/docs/build/html/pylink.html
+++ /dev/null
@@ -1,1991 +0,0 @@
-<!doctype html>
-<html lang="en">
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-<article id="content">
-<header>
-<h1 class="title">Module <code>pylink</code></h1>
-</header>
-<section id="section-intro">
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-import os
-import shutil
-import h5py
-import numpy as np
-import time
-import zipfile
-import pandas as pd
-import multiprocessing
-import tqdm
-
-
-class PyLinkForwardModel(object):
- r"""
- A forward model binder
-
- This calss serves as a code wrapper. This wrapper allows the execution of
- a third-party software/solver within the scope of BayesValidRox.
-
- Attributes
- ----------
- link_type : str
- The type of the wrapper. The default is `'pylink'`. This runs the
- third-party software or an executable using a sell command with given
- input files.
- Second option is `'function'` which assumed that model can be run using
- a function written separately in a Python script.
- name : str
- Name of the model.
- shell_command : str
- Shell command to be executed for the `'pylink'` wrapper.
- py_file : str
- Python file name without `.py` extension to be run for the `'function'`
- wrapper. Note that the name of the python file and that of the function
- must be simillar. This function must recieve the parameters in an array
- of shape `(n_samples, n_params)` and returns a dictionary with the
- x_values and output arrays for given output names.
- input_file : str or list
- The input file to be passed to the `'pylink'` wrapper.
- input_template : str or list
- A template input file to be passed to the `'pylink'` wrapper. This file
- must be a copy of `input_file` with `<Xi>` place holder for the input
- parameters defined using `inputs` class, with i being the number of
- parameter. The file name ending should include `.tpl` before the actual
- extension of the input file, for example, `params.tpl.input`.
- aux_file : str or list
- The list of auxiliary files needed for the `'pylink'` wrapper.
- exe_path : str
- Execution path if you wish to run the model for the `'pylink'` wrapper
- in another directory. The default is `None`, which corresponds to the
- currecnt working directory.
- output_names : list of str
- List of the model outputs to be used for the analysis.
- output_file_names : list of str
- List of the name of the model output text files for the `'pylink'`
- wrapper.
- output_parser : str
- Name of the model parser file (without `.py` extension) that recieves
- the `output_file_names` and returns a 2d-array with the first row being
- the x_values, e.g. x coordinates or time and the rest of raws pass the
- simulation output for each model output defined in `output_names`. Note
- that again here the name of the file and that of the function must be
- the same.
- multi_process: bool
- Whether the model runs to be executed in parallel for the `'pylink'`
- wrapper. The default is `True`.
- n_cpus: int
- The number of cpus to be used for the parallel model execution for the
- `'pylink'` wrapper. The default is `None`, which corresponds to all
- available cpus.
- meas_file : str
- The name of the measurement text-based file. This file must contain
- x_values as the first column and one column for each model output. The
- default is `None`. Only needed for the Bayesian Inference.
- meas_file_valid : str
- The name of the measurement text-based file for the validation. The
- default is `None`. Only needed for the validation with Bayesian
- Inference.
- mc_ref_file : str
- The name of the text file for the Monte-Carlo reference (mean and
- standard deviation) values. It must contain `x_values` as the first
- column, `mean` as the second column and `std` as the third. It can be
- used to compare the estimated moments using meta-model in the post-
- processing step.
- obs_dict : dict
- A dictionary containing the measurement text-based file. It must
- contain `x_values` as the first item and one item for each model output
- . The default is `{}`. Only needed for the Bayesian Inference.
- obs_dict_valid : dict
- A dictionary containing the validation measurement text-based file. It
- must contain `x_values` as the first item and one item for each model
- output. The default is `{}`.
- mc_ref_dict : dict
- A dictionary containing the Monte-Carlo reference (mean and standard
- deviation) values. It must contain `x_values` as the first item and
- `mean` as the second item and `std` as the third. The default is `{}`.
- """
-
- def __init__(self, link_type='pylink', name=None, shell_command='',
- py_file=None, input_file=None, input_template=None,
- aux_file=None, exe_path='', output_names=[], output_parser='',
- output_file_names=[], multi_process=True, n_cpus=None,
- meas_file=None, meas_file_valid=None, mc_ref_file=None,
- obs_dict={}, obs_dict_valid={}, mc_ref_dict={}):
- self.link_type = link_type
- self.name = name
- self.shell_command = shell_command
- self.py_file = py_file
- self.input_file = input_file
- self.input_template = input_template
- self.aux_file = aux_file
- self.exe_path = exe_path
- self.multi_process = multi_process
- self.n_cpus = n_cpus
- self.Output.parser = output_parser
- self.Output.names = output_names
- self.Output.file_names = output_file_names
- self.meas_file = meas_file
- self.meas_file_valid = meas_file_valid
- self.mc_ref_file = mc_ref_file
- self.observations = obs_dict
- self.observations_valid = obs_dict_valid
- self.mc_reference = mc_ref_dict
-
- # Nested class
- class Output:
- def __init__(self):
- self.parser = None
- self.names = None
- self.file_names = None
-
- # -------------------------------------------------------------------------
- def within_range(self, out, minout, maxout):
- inside = False
- if (out > minout).all() and (out < maxout).all():
- inside = True
- return inside
-
- # -------------------------------------------------------------------------
- def read_observation(self, case='calib'):
- """
- Reads/prepare the observation/measurement data for
- calibration.
-
- Returns
- -------
- DataFrame
- A dataframe with the calibration data.
-
- """
- if case.lower() == 'calib':
- if bool(self.observations):
- obs = pd.DataFrame.from_dict(self.observations)
- elif self.meas_file is not None:
- file_path = os.path.join(os.getcwd(), self.meas_file)
- obs = pd.read_csv(file_path, delimiter=',')
- else:
- raise Exception("Please provide the observation data as a "
- "dictionary via observations attribute or pass"
- " the csv-file path to MeasurementFile "
- "attribute")
- elif case.lower() == 'valid':
- if bool(self.observations_valid):
- obs = pd.DataFrame.from_dict(self.observations_valid)
- elif self.meas_file_valid is not None:
- file_path = os.path.join(os.getcwd(), self.meas_file_valid)
- obs = pd.read_csv(file_path, delimiter=',')
- else:
- raise Exception("Please provide the observation data as a "
- "dictionary via Observations attribute or pass"
- " the csv-file path to MeasurementFile "
- "attribute")
-
- # Compute the number of observation
- n_obs = obs[self.Output.names].notnull().sum().values.sum()
-
- if case.lower() == 'calib':
- self.observations = obs
- self.n_obs = n_obs
- return self.observations
- elif case.lower() == 'valid':
- self.observations_valid = obs
- self.n_obs_valid = n_obs
- return self.observations_valid
-
- # -------------------------------------------------------------------------
- def read_mc_reference(self):
- """
- Is used, if a Monte-Carlo reference is available for
- further in-depth post-processing after meta-model training.
-
- Returns
- -------
- None
-
- """
- if self.mc_ref_file is None and not hasattr(self, 'mc_reference'):
- return
- elif isinstance(self.mc_reference, dict) and bool(self.mc_reference):
- self.mc_reference = pd.DataFrame.from_dict(self.mc_reference)
- elif self.mc_ref_file is not None:
- file_path = os.path.join(os.getcwd(), self.mc_ref_file)
- self.mc_reference = pd.read_csv(file_path, delimiter=',')
- else:
- raise Exception("Please provide the MC reference data as a "
- "dictionary via mc_reference attribute or pass the"
- " csv-file path to mc_ref_file attribute")
- return self.mc_reference
-
- # -------------------------------------------------------------------------
- def read_output(self):
- """
- Reads the the parser output file and returns it as an
- executable function. It is required when the models returns the
- simulation outputs in csv files.
-
- Returns
- -------
- Output : func
- Output parser function.
-
- """
- output_func_name = self.Output.parser
-
- output_func = getattr(__import__(output_func_name), output_func_name)
-
- file_names = []
- for File in self.Output.file_names:
- file_names.append(os.path.join(self.exe_path, File))
- try:
- output = output_func(self.name, file_names)
- except TypeError:
- output = output_func(file_names)
- return output
-
- # -------------------------------------------------------------------------
- def update_input_params(self, new_input_file, param_sets):
- """
- Finds this pattern with <X1> in the new_input_file and replace it with
- the new value from the array param_sets.
-
- Parameters
- ----------
- new_input_file : TYPE
- DESCRIPTION.
- param_sets : TYPE
- DESCRIPTION.
-
- Returns
- -------
- None.
-
- """
- NofPa = param_sets.shape[0]
- text_to_search_list = [f'<X{i+1}>' for i in range(NofPa)]
-
- for filename in new_input_file:
- # Read in the file
- with open(filename, 'r') as file:
- filedata = file.read()
-
- # Replace the target string
- for text_to_search, params in zip(text_to_search_list, param_sets):
- filedata = filedata.replace(text_to_search, f'{params:0.4e}')
-
- # Write the file out again
- with open(filename, 'w') as file:
- file.write(filedata)
-
- # -------------------------------------------------------------------------
- def run_command(self, command, output_file_names):
- """
- Runs the execution command given by the user to run the given model.
- It checks if the output files have been generated. If yes, the jobe is
- done and it extracts and returns the requested output(s). Otherwise,
- it executes the command again.
-
- Parameters
- ----------
- command : string
- The command to be executed.
- output_file_names : list
- Name of the output file names.
-
- Returns
- -------
- simulation_outputs : array of shape (n_obs, n_outputs)
- Simulation outputs.
-
- """
-
- # Check if simulation is finished
- while True:
- time.sleep(3)
- files = os.listdir(".")
- if all(elem in files for elem in output_file_names):
- break
- else:
- # Run command
- Process = os.system(f'./../{command}')
- if Process != 0:
- print('\nMessage 1:')
- print(f'\tIf value of \'{Process}\' is a non-zero value, '
- 'then compilation problems \n' % Process)
-
- os.chdir("..")
-
- # Read the output
- simulation_outputs = self.read_output()
-
- return simulation_outputs
-
- # -------------------------------------------------------------------------
- def run_forwardmodel(self, xx):
- """
- This function creates subdirectory for the current run and copies the
- necessary files to this directory and renames them. Next, it executes
- the given command.
- """
- c_points, run_no, key_str = xx
-
- # Handle if only one imput file is provided
- if not isinstance(self.input_template, list):
- self.input_template = [self.input_template]
- if not isinstance(self.input_file, list):
- self.input_file = [self.input_file]
-
- new_input_file = []
- # Loop over the InputTemplates:
- for in_temp in self.input_template:
- if '/' in in_temp:
- in_temp = in_temp.split('/')[-1]
- new_input_file.append(in_temp.split('.tpl')[0] + key_str +
- f"_{run_no+1}" + in_temp.split('.tpl')[1])
-
- # Create directories
- newpath = self.name + key_str + f'_{run_no+1}'
- if not os.path.exists(newpath):
- os.makedirs(newpath)
-
- # Copy the necessary files to the directories
- for in_temp in self.input_template:
- # Input file(s) of the model
- shutil.copy2(in_temp, newpath)
- # Auxiliary file
- if self.aux_file is not None:
- shutil.copy2(self.aux_file, newpath) # Auxiliary file
-
- # Rename the Inputfile and/or auxiliary file
- os.chdir(newpath)
- for input_tem, input_file in zip(self.input_template, new_input_file):
- if '/' in input_tem:
- input_tem = input_tem.split('/')[-1]
- os.rename(input_tem, input_file)
-
- # Update the parametrs in Input file
- self.update_input_params(new_input_file, c_points)
-
- # Update the user defined command and the execution path
- try:
- new_command = self.shell_command.replace(self.input_file[0],
- new_input_file[0])
- new_command = new_command.replace(self.input_file[1],
- new_input_file[1])
- except:
- new_command = self.shell_command.replace(self.input_file[0],
- new_input_file[0])
- # Set the exe path if not provided
- if not bool(self.exe_path):
- self.exe_path = os.getcwd()
-
- # Run the model
- output = self.run_command(new_command, self.Output.file_names)
-
- return output
-
- # -------------------------------------------------------------------------
- def run_model_parallel(self, c_points, prevRun_No=0, key_str='',
- mp=True):
- """
- Runs model simulations. If mp is true (default), then the simulations
- are started in parallel.
-
- Parameters
- ----------
- c_points : array like of shape (n_samples, n_params)
- Collocation points (training set).
- prevRun_No : int, optional
- Previous run number, in case the sequential design is selected.
- The default is 0.
- key_str : string, optional
- A descriptive string for validation runs. The default is ''.
- mp : bool, optional
- Multiprocessing. The default is True.
-
- Returns
- -------
- all_outputs : dict
- A dictionary with x values (time step or point id) and all outputs.
- Each key contains an array of the shape (n_samples, n_obs).
- new_c_points : array
- Updated collocation points (training set). If a simulation does not
- executed successfully, the parameter set is removed.
-
- """
-
- # Create hdf5 metadata
- hdf5file = f'ExpDesign_{self.name}.hdf5'
- hdf5_exist = os.path.exists(hdf5file)
- file = h5py.File(hdf5file, 'a')
-
- # Initilization
- n_c_points = len(c_points)
- self.n_outputs = len(self.Output.names)
- all_outputs = {}
-
- # Extract the function
- if self.link_type.lower() == 'function':
- # Prepare the function
- Function = getattr(__import__(self.py_file), self.py_file)
- # ---------------------------------------------------------------
- # -------------- Multiprocessing with Pool Class ----------------
- # ---------------------------------------------------------------
- # Start a pool with the number of CPUs
- if self.n_cpus is None:
- n_cpus = multiprocessing.cpu_count()
- else:
- n_cpus = self.n_cpus
-
- # Run forward model either normal or with multiprocessing
- if not self.multi_process:
- group_results = list([self.run_forwardmodel((c_points,
- prevRun_No,
- key_str))])
- else:
- with multiprocessing.Pool(n_cpus) as p:
- desc = f'Running forward model {key_str}'
- if self.link_type.lower() == 'function':
- imap_var = p.imap(Function, c_points[:, np.newaxis])
- else:
- args = zip(c_points,
- [prevRun_No+i for i in range(n_c_points)],
- [key_str]*n_c_points)
- imap_var = p.imap(self.run_forwardmodel, args)
-
- group_results = list(tqdm.tqdm(imap_var, total=n_c_points,
- desc=desc))
-
- # Save time steps or x-values
- x_values = group_results[0][0]
- all_outputs["x_values"] = x_values
- if not hdf5_exist:
- if type(x_values) is dict:
- grp_x_values = file.create_group("x_values/")
- for varIdx, var in enumerate(self.Output.names):
- grp_x_values.create_dataset(var, data=x_values[var])
- else:
- file.create_dataset("x_values", data=x_values)
-
- # save each output in their corresponding array
- NaN_idx = []
- for varIdx, var in enumerate(self.Output.names):
-
- if not hdf5_exist:
- grpY = file.create_group("EDY/"+var)
- else:
- grpY = file.get("EDY/"+var)
-
- Outputs = np.asarray([item[varIdx+1] for item in group_results],
- dtype=np.float64)
-
- if prevRun_No == 0 and key_str == '':
- grpY.create_dataset(f'init_{key_str}', data=Outputs)
- else:
- try:
- oldEDY = np.array(file[f'EDY/{var}/adaptive_{key_str}'])
- del file[f'EDY/{var}/adaptive_{key_str}']
- data = np.vstack((oldEDY, Outputs))
- except KeyError:
- data = Outputs
- grpY.create_dataset('adaptive_'+key_str, data=data)
-
- NaN_idx = np.unique(np.argwhere(np.isnan(Outputs))[:, 0])
- all_outputs[var] = np.delete(Outputs, NaN_idx, axis=0)
-
- if prevRun_No == 0 and key_str == '':
- grpY.create_dataset(f"New_init_{key_str}",
- data=all_outputs[var])
- else:
- try:
- name = f'EDY/{var}/New_adaptive_{key_str}'
- oldEDY = np.array(file[name])
- del file[f'EDY/{var}/New_adaptive_{key_str}']
- data = np.vstack((oldEDY, all_outputs[var]))
- except KeyError:
- data = all_outputs[var]
- grpY.create_dataset(f'New_adaptive_{key_str}', data=data)
-
- # Print the collocation points whose simulations crashed
- if len(NaN_idx) != 0:
- print('\n')
- print('*'*20)
- print("\nThe following parametersets have been removed:\n",
- c_points[NaN_idx])
- print("\n")
- print('*'*20)
-
- # Pass it to the attribute
- new_c_points = np.delete(c_points, NaN_idx, axis=0)
- self.OutputMatrix = all_outputs
-
- # Save CollocationPoints
- grpX = file.create_group("EDX") if not hdf5_exist else file.get("EDX")
- if prevRun_No == 0 and key_str == '':
- grpX.create_dataset("init_"+key_str, data=c_points)
- if len(NaN_idx) != 0:
- grpX.create_dataset("New_init_"+key_str, data=new_c_points)
-
- else:
- try:
- name = f'EDX/adaptive_{key_str}'
- oldCollocationPoints = np.array(file[name])
- del file[f'EDX/adaptive_{key_str}']
- data = np.vstack((oldCollocationPoints, new_c_points))
- except KeyError:
- data = new_c_points
- grpX.create_dataset('adaptive_'+key_str, data=data)
-
- if len(NaN_idx) != 0:
- try:
- name = f'EDX/New_adaptive_{key_str}'
- oldCollocationPoints = np.array(file[name])
- del file[f'EDX/New_adaptive_{key_str}']
- data = np.vstack((oldCollocationPoints, new_c_points))
- except KeyError:
- data = new_c_points
- grpX.create_dataset('New_adaptive_'+key_str, data=data)
-
- # Close h5py file
- file.close()
-
- return all_outputs, new_c_points
-
- # -------------------------------------------------------------------------
- def zip_subdirs(self, dir_name, key):
- """
- Zips all the files containing the key(word).
-
- Parameters
- ----------
- dir_name : string
- Directory name.
- key : string
- Keyword to search for.
-
- Returns
- -------
- None.
-
- """
- # setup file paths variable
- dir_list = []
- file_paths = []
-
- # Read all directory, subdirectories and file lists
- dir_path = os.getcwd()
-
- for root, directories, files in os.walk(dir_path):
- for directory in directories:
- # Create the full filepath by using os module.
- if key in directory:
- folderPath = os.path.join(dir_path, directory)
- dir_list.append(folderPath)
-
- # Loop over the identified directories to store the file paths
- for direct_name in dir_list:
- for root, directories, files in os.walk(direct_name):
- for filename in files:
- # Create the full filepath by using os module.
- filePath = os.path.join(root, filename)
- file_paths.append('.'+filePath.split(dir_path)[1])
-
- # writing files to a zipfile
- if len(file_paths) != 0:
- zip_file = zipfile.ZipFile(dir_name+'.zip', 'w')
- with zip_file:
- # writing each file one by one
- for file in file_paths:
- zip_file.write(file)
-
- file_paths = [path for path in os.listdir('.') if key in path]
-
- for path in file_paths:
- shutil.rmtree(path)
-
- print("\n")
- print(f'{dir_name}.zip file has been created successfully!\n')
-
- return</code></pre>
-</details>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="pylink.PyLinkForwardModel"><code class="flex name class">
-<span>class <span class="ident">PyLinkForwardModel</span></span>
-<span>(</span><span>link_type='pylink', name=None, shell_command='', py_file=None, input_file=None, input_template=None, aux_file=None, exe_path='', output_names=[], output_parser='', output_file_names=[], multi_process=True, n_cpus=None, meas_file=None, meas_file_valid=None, mc_ref_file=None, obs_dict={}, obs_dict_valid={}, mc_ref_dict={})</span>
-</code></dt>
-<dd>
-<div class="desc"><p>A forward model binder</p>
-<p>This calss serves as a code wrapper. This wrapper allows the execution of
-a third-party software/solver within the scope of BayesValidRox.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>link_type</code></strong> : <code>str</code></dt>
-<dd>The type of the wrapper. The default is <code>'pylink'</code>. This runs the
-third-party software or an executable using a sell command with given
-input files.
-Second option is <code>'function'</code> which assumed that model can be run using
-a function written separately in a Python script.</dd>
-<dt><strong><code>name</code></strong> : <code>str</code></dt>
-<dd>Name of the model.</dd>
-<dt><strong><code>shell_command</code></strong> : <code>str</code></dt>
-<dd>Shell command to be executed for the <code>'pylink'</code> wrapper.</dd>
-<dt><strong><code>py_file</code></strong> : <code>str</code></dt>
-<dd>Python file name without <code>.py</code> extension to be run for the <code>'function'</code>
-wrapper. Note that the name of the python file and that of the function
-must be simillar. This function must recieve the parameters in an array
-of shape <code>(n_samples, n_params)</code> and returns a dictionary with the
-x_values and output arrays for given output names.</dd>
-<dt><strong><code>input_file</code></strong> : <code>str</code> or <code>list</code></dt>
-<dd>The input file to be passed to the <code>'pylink'</code> wrapper.</dd>
-<dt><strong><code>input_template</code></strong> : <code>str</code> or <code>list</code></dt>
-<dd>A template input file to be passed to the <code>'pylink'</code> wrapper. This file
-must be a copy of <code>input_file</code> with <code><Xi></code> place holder for the input
-parameters defined using <code>inputs</code> class, with i being the number of
-parameter. The file name ending should include <code>.tpl</code> before the actual
-extension of the input file, for example, <code>params.tpl.input</code>.</dd>
-<dt><strong><code>aux_file</code></strong> : <code>str</code> or <code>list</code></dt>
-<dd>The list of auxiliary files needed for the <code>'pylink'</code> wrapper.</dd>
-<dt><strong><code>exe_path</code></strong> : <code>str</code></dt>
-<dd>Execution path if you wish to run the model for the <code>'pylink'</code> wrapper
-in another directory. The default is <code>None</code>, which corresponds to the
-currecnt working directory.</dd>
-<dt><strong><code>output_names</code></strong> : <code>list</code> of <code>str</code></dt>
-<dd>List of the model outputs to be used for the analysis.</dd>
-<dt><strong><code>output_file_names</code></strong> : <code>list</code> of <code>str</code></dt>
-<dd>List of the name of the model output text files for the <code>'pylink'</code>
-wrapper.</dd>
-<dt><strong><code>output_parser</code></strong> : <code>str</code></dt>
-<dd>Name of the model parser file (without <code>.py</code> extension) that recieves
-the <code>output_file_names</code> and returns a 2d-array with the first row being
-the x_values, e.g. x coordinates or time and the rest of raws pass the
-simulation output for each model output defined in <code>output_names</code>. Note
-that again here the name of the file and that of the function must be
-the same.</dd>
-<dt><strong><code>multi_process</code></strong> : <code>bool</code></dt>
-<dd>Whether the model runs to be executed in parallel for the <code>'pylink'</code>
-wrapper. The default is <code>True</code>.</dd>
-<dt><strong><code>n_cpus</code></strong> : <code>int</code></dt>
-<dd>The number of cpus to be used for the parallel model execution for the
-<code>'pylink'</code> wrapper. The default is <code>None</code>, which corresponds to all
-available cpus.</dd>
-<dt><strong><code>meas_file</code></strong> : <code>str</code></dt>
-<dd>The name of the measurement text-based file. This file must contain
-x_values as the first column and one column for each model output. The
-default is <code>None</code>. Only needed for the Bayesian Inference.</dd>
-<dt><strong><code>meas_file_valid</code></strong> : <code>str</code></dt>
-<dd>The name of the measurement text-based file for the validation. The
-default is <code>None</code>. Only needed for the validation with Bayesian
-Inference.</dd>
-<dt><strong><code>mc_ref_file</code></strong> : <code>str</code></dt>
-<dd>The name of the text file for the Monte-Carlo reference (mean and
-standard deviation) values. It must contain <code>x_values</code> as the first
-column, <code>mean</code> as the second column and <code>std</code> as the third. It can be
-used to compare the estimated moments using meta-model in the post-
-processing step.</dd>
-<dt><strong><code>obs_dict</code></strong> : <code>dict</code></dt>
-<dd>A dictionary containing the measurement text-based file. It must
-contain <code>x_values</code> as the first item and one item for each model output
-. The default is <code>{}</code>. Only needed for the Bayesian Inference.</dd>
-<dt><strong><code>obs_dict_valid</code></strong> : <code>dict</code></dt>
-<dd>A dictionary containing the validation measurement text-based file. It
-must contain <code>x_values</code> as the first item and one item for each model
-output. The default is <code>{}</code>.</dd>
-<dt><strong><code>mc_ref_dict</code></strong> : <code>dict</code></dt>
-<dd>A dictionary containing the Monte-Carlo reference (mean and standard
-deviation) values. It must contain <code>x_values</code> as the first item and
-<code>mean</code> as the second item and <code>std</code> as the third. The default is <code>{}</code>.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class PyLinkForwardModel(object):
- r"""
- A forward model binder
-
- This calss serves as a code wrapper. This wrapper allows the execution of
- a third-party software/solver within the scope of BayesValidRox.
-
- Attributes
- ----------
- link_type : str
- The type of the wrapper. The default is `'pylink'`. This runs the
- third-party software or an executable using a sell command with given
- input files.
- Second option is `'function'` which assumed that model can be run using
- a function written separately in a Python script.
- name : str
- Name of the model.
- shell_command : str
- Shell command to be executed for the `'pylink'` wrapper.
- py_file : str
- Python file name without `.py` extension to be run for the `'function'`
- wrapper. Note that the name of the python file and that of the function
- must be simillar. This function must recieve the parameters in an array
- of shape `(n_samples, n_params)` and returns a dictionary with the
- x_values and output arrays for given output names.
- input_file : str or list
- The input file to be passed to the `'pylink'` wrapper.
- input_template : str or list
- A template input file to be passed to the `'pylink'` wrapper. This file
- must be a copy of `input_file` with `<Xi>` place holder for the input
- parameters defined using `inputs` class, with i being the number of
- parameter. The file name ending should include `.tpl` before the actual
- extension of the input file, for example, `params.tpl.input`.
- aux_file : str or list
- The list of auxiliary files needed for the `'pylink'` wrapper.
- exe_path : str
- Execution path if you wish to run the model for the `'pylink'` wrapper
- in another directory. The default is `None`, which corresponds to the
- currecnt working directory.
- output_names : list of str
- List of the model outputs to be used for the analysis.
- output_file_names : list of str
- List of the name of the model output text files for the `'pylink'`
- wrapper.
- output_parser : str
- Name of the model parser file (without `.py` extension) that recieves
- the `output_file_names` and returns a 2d-array with the first row being
- the x_values, e.g. x coordinates or time and the rest of raws pass the
- simulation output for each model output defined in `output_names`. Note
- that again here the name of the file and that of the function must be
- the same.
- multi_process: bool
- Whether the model runs to be executed in parallel for the `'pylink'`
- wrapper. The default is `True`.
- n_cpus: int
- The number of cpus to be used for the parallel model execution for the
- `'pylink'` wrapper. The default is `None`, which corresponds to all
- available cpus.
- meas_file : str
- The name of the measurement text-based file. This file must contain
- x_values as the first column and one column for each model output. The
- default is `None`. Only needed for the Bayesian Inference.
- meas_file_valid : str
- The name of the measurement text-based file for the validation. The
- default is `None`. Only needed for the validation with Bayesian
- Inference.
- mc_ref_file : str
- The name of the text file for the Monte-Carlo reference (mean and
- standard deviation) values. It must contain `x_values` as the first
- column, `mean` as the second column and `std` as the third. It can be
- used to compare the estimated moments using meta-model in the post-
- processing step.
- obs_dict : dict
- A dictionary containing the measurement text-based file. It must
- contain `x_values` as the first item and one item for each model output
- . The default is `{}`. Only needed for the Bayesian Inference.
- obs_dict_valid : dict
- A dictionary containing the validation measurement text-based file. It
- must contain `x_values` as the first item and one item for each model
- output. The default is `{}`.
- mc_ref_dict : dict
- A dictionary containing the Monte-Carlo reference (mean and standard
- deviation) values. It must contain `x_values` as the first item and
- `mean` as the second item and `std` as the third. The default is `{}`.
- """
-
- def __init__(self, link_type='pylink', name=None, shell_command='',
- py_file=None, input_file=None, input_template=None,
- aux_file=None, exe_path='', output_names=[], output_parser='',
- output_file_names=[], multi_process=True, n_cpus=None,
- meas_file=None, meas_file_valid=None, mc_ref_file=None,
- obs_dict={}, obs_dict_valid={}, mc_ref_dict={}):
- self.link_type = link_type
- self.name = name
- self.shell_command = shell_command
- self.py_file = py_file
- self.input_file = input_file
- self.input_template = input_template
- self.aux_file = aux_file
- self.exe_path = exe_path
- self.multi_process = multi_process
- self.n_cpus = n_cpus
- self.Output.parser = output_parser
- self.Output.names = output_names
- self.Output.file_names = output_file_names
- self.meas_file = meas_file
- self.meas_file_valid = meas_file_valid
- self.mc_ref_file = mc_ref_file
- self.observations = obs_dict
- self.observations_valid = obs_dict_valid
- self.mc_reference = mc_ref_dict
-
- # Nested class
- class Output:
- def __init__(self):
- self.parser = None
- self.names = None
- self.file_names = None
-
- # -------------------------------------------------------------------------
- def within_range(self, out, minout, maxout):
- inside = False
- if (out > minout).all() and (out < maxout).all():
- inside = True
- return inside
-
- # -------------------------------------------------------------------------
- def read_observation(self, case='calib'):
- """
- Reads/prepare the observation/measurement data for
- calibration.
-
- Returns
- -------
- DataFrame
- A dataframe with the calibration data.
-
- """
- if case.lower() == 'calib':
- if bool(self.observations):
- obs = pd.DataFrame.from_dict(self.observations)
- elif self.meas_file is not None:
- file_path = os.path.join(os.getcwd(), self.meas_file)
- obs = pd.read_csv(file_path, delimiter=',')
- else:
- raise Exception("Please provide the observation data as a "
- "dictionary via observations attribute or pass"
- " the csv-file path to MeasurementFile "
- "attribute")
- elif case.lower() == 'valid':
- if bool(self.observations_valid):
- obs = pd.DataFrame.from_dict(self.observations_valid)
- elif self.meas_file_valid is not None:
- file_path = os.path.join(os.getcwd(), self.meas_file_valid)
- obs = pd.read_csv(file_path, delimiter=',')
- else:
- raise Exception("Please provide the observation data as a "
- "dictionary via Observations attribute or pass"
- " the csv-file path to MeasurementFile "
- "attribute")
-
- # Compute the number of observation
- n_obs = obs[self.Output.names].notnull().sum().values.sum()
-
- if case.lower() == 'calib':
- self.observations = obs
- self.n_obs = n_obs
- return self.observations
- elif case.lower() == 'valid':
- self.observations_valid = obs
- self.n_obs_valid = n_obs
- return self.observations_valid
-
- # -------------------------------------------------------------------------
- def read_mc_reference(self):
- """
- Is used, if a Monte-Carlo reference is available for
- further in-depth post-processing after meta-model training.
-
- Returns
- -------
- None
-
- """
- if self.mc_ref_file is None and not hasattr(self, 'mc_reference'):
- return
- elif isinstance(self.mc_reference, dict) and bool(self.mc_reference):
- self.mc_reference = pd.DataFrame.from_dict(self.mc_reference)
- elif self.mc_ref_file is not None:
- file_path = os.path.join(os.getcwd(), self.mc_ref_file)
- self.mc_reference = pd.read_csv(file_path, delimiter=',')
- else:
- raise Exception("Please provide the MC reference data as a "
- "dictionary via mc_reference attribute or pass the"
- " csv-file path to mc_ref_file attribute")
- return self.mc_reference
-
- # -------------------------------------------------------------------------
- def read_output(self):
- """
- Reads the the parser output file and returns it as an
- executable function. It is required when the models returns the
- simulation outputs in csv files.
-
- Returns
- -------
- Output : func
- Output parser function.
-
- """
- output_func_name = self.Output.parser
-
- output_func = getattr(__import__(output_func_name), output_func_name)
-
- file_names = []
- for File in self.Output.file_names:
- file_names.append(os.path.join(self.exe_path, File))
- try:
- output = output_func(self.name, file_names)
- except TypeError:
- output = output_func(file_names)
- return output
-
- # -------------------------------------------------------------------------
- def update_input_params(self, new_input_file, param_sets):
- """
- Finds this pattern with <X1> in the new_input_file and replace it with
- the new value from the array param_sets.
-
- Parameters
- ----------
- new_input_file : TYPE
- DESCRIPTION.
- param_sets : TYPE
- DESCRIPTION.
-
- Returns
- -------
- None.
-
- """
- NofPa = param_sets.shape[0]
- text_to_search_list = [f'<X{i+1}>' for i in range(NofPa)]
-
- for filename in new_input_file:
- # Read in the file
- with open(filename, 'r') as file:
- filedata = file.read()
-
- # Replace the target string
- for text_to_search, params in zip(text_to_search_list, param_sets):
- filedata = filedata.replace(text_to_search, f'{params:0.4e}')
-
- # Write the file out again
- with open(filename, 'w') as file:
- file.write(filedata)
-
- # -------------------------------------------------------------------------
- def run_command(self, command, output_file_names):
- """
- Runs the execution command given by the user to run the given model.
- It checks if the output files have been generated. If yes, the jobe is
- done and it extracts and returns the requested output(s). Otherwise,
- it executes the command again.
-
- Parameters
- ----------
- command : string
- The command to be executed.
- output_file_names : list
- Name of the output file names.
-
- Returns
- -------
- simulation_outputs : array of shape (n_obs, n_outputs)
- Simulation outputs.
-
- """
-
- # Check if simulation is finished
- while True:
- time.sleep(3)
- files = os.listdir(".")
- if all(elem in files for elem in output_file_names):
- break
- else:
- # Run command
- Process = os.system(f'./../{command}')
- if Process != 0:
- print('\nMessage 1:')
- print(f'\tIf value of \'{Process}\' is a non-zero value, '
- 'then compilation problems \n' % Process)
-
- os.chdir("..")
-
- # Read the output
- simulation_outputs = self.read_output()
-
- return simulation_outputs
-
- # -------------------------------------------------------------------------
- def run_forwardmodel(self, xx):
- """
- This function creates subdirectory for the current run and copies the
- necessary files to this directory and renames them. Next, it executes
- the given command.
- """
- c_points, run_no, key_str = xx
-
- # Handle if only one imput file is provided
- if not isinstance(self.input_template, list):
- self.input_template = [self.input_template]
- if not isinstance(self.input_file, list):
- self.input_file = [self.input_file]
-
- new_input_file = []
- # Loop over the InputTemplates:
- for in_temp in self.input_template:
- if '/' in in_temp:
- in_temp = in_temp.split('/')[-1]
- new_input_file.append(in_temp.split('.tpl')[0] + key_str +
- f"_{run_no+1}" + in_temp.split('.tpl')[1])
-
- # Create directories
- newpath = self.name + key_str + f'_{run_no+1}'
- if not os.path.exists(newpath):
- os.makedirs(newpath)
-
- # Copy the necessary files to the directories
- for in_temp in self.input_template:
- # Input file(s) of the model
- shutil.copy2(in_temp, newpath)
- # Auxiliary file
- if self.aux_file is not None:
- shutil.copy2(self.aux_file, newpath) # Auxiliary file
-
- # Rename the Inputfile and/or auxiliary file
- os.chdir(newpath)
- for input_tem, input_file in zip(self.input_template, new_input_file):
- if '/' in input_tem:
- input_tem = input_tem.split('/')[-1]
- os.rename(input_tem, input_file)
-
- # Update the parametrs in Input file
- self.update_input_params(new_input_file, c_points)
-
- # Update the user defined command and the execution path
- try:
- new_command = self.shell_command.replace(self.input_file[0],
- new_input_file[0])
- new_command = new_command.replace(self.input_file[1],
- new_input_file[1])
- except:
- new_command = self.shell_command.replace(self.input_file[0],
- new_input_file[0])
- # Set the exe path if not provided
- if not bool(self.exe_path):
- self.exe_path = os.getcwd()
-
- # Run the model
- output = self.run_command(new_command, self.Output.file_names)
-
- return output
-
- # -------------------------------------------------------------------------
- def run_model_parallel(self, c_points, prevRun_No=0, key_str='',
- mp=True):
- """
- Runs model simulations. If mp is true (default), then the simulations
- are started in parallel.
-
- Parameters
- ----------
- c_points : array like of shape (n_samples, n_params)
- Collocation points (training set).
- prevRun_No : int, optional
- Previous run number, in case the sequential design is selected.
- The default is 0.
- key_str : string, optional
- A descriptive string for validation runs. The default is ''.
- mp : bool, optional
- Multiprocessing. The default is True.
-
- Returns
- -------
- all_outputs : dict
- A dictionary with x values (time step or point id) and all outputs.
- Each key contains an array of the shape (n_samples, n_obs).
- new_c_points : array
- Updated collocation points (training set). If a simulation does not
- executed successfully, the parameter set is removed.
-
- """
-
- # Create hdf5 metadata
- hdf5file = f'ExpDesign_{self.name}.hdf5'
- hdf5_exist = os.path.exists(hdf5file)
- file = h5py.File(hdf5file, 'a')
-
- # Initilization
- n_c_points = len(c_points)
- self.n_outputs = len(self.Output.names)
- all_outputs = {}
-
- # Extract the function
- if self.link_type.lower() == 'function':
- # Prepare the function
- Function = getattr(__import__(self.py_file), self.py_file)
- # ---------------------------------------------------------------
- # -------------- Multiprocessing with Pool Class ----------------
- # ---------------------------------------------------------------
- # Start a pool with the number of CPUs
- if self.n_cpus is None:
- n_cpus = multiprocessing.cpu_count()
- else:
- n_cpus = self.n_cpus
-
- # Run forward model either normal or with multiprocessing
- if not self.multi_process:
- group_results = list([self.run_forwardmodel((c_points,
- prevRun_No,
- key_str))])
- else:
- with multiprocessing.Pool(n_cpus) as p:
- desc = f'Running forward model {key_str}'
- if self.link_type.lower() == 'function':
- imap_var = p.imap(Function, c_points[:, np.newaxis])
- else:
- args = zip(c_points,
- [prevRun_No+i for i in range(n_c_points)],
- [key_str]*n_c_points)
- imap_var = p.imap(self.run_forwardmodel, args)
-
- group_results = list(tqdm.tqdm(imap_var, total=n_c_points,
- desc=desc))
-
- # Save time steps or x-values
- x_values = group_results[0][0]
- all_outputs["x_values"] = x_values
- if not hdf5_exist:
- if type(x_values) is dict:
- grp_x_values = file.create_group("x_values/")
- for varIdx, var in enumerate(self.Output.names):
- grp_x_values.create_dataset(var, data=x_values[var])
- else:
- file.create_dataset("x_values", data=x_values)
-
- # save each output in their corresponding array
- NaN_idx = []
- for varIdx, var in enumerate(self.Output.names):
-
- if not hdf5_exist:
- grpY = file.create_group("EDY/"+var)
- else:
- grpY = file.get("EDY/"+var)
-
- Outputs = np.asarray([item[varIdx+1] for item in group_results],
- dtype=np.float64)
-
- if prevRun_No == 0 and key_str == '':
- grpY.create_dataset(f'init_{key_str}', data=Outputs)
- else:
- try:
- oldEDY = np.array(file[f'EDY/{var}/adaptive_{key_str}'])
- del file[f'EDY/{var}/adaptive_{key_str}']
- data = np.vstack((oldEDY, Outputs))
- except KeyError:
- data = Outputs
- grpY.create_dataset('adaptive_'+key_str, data=data)
-
- NaN_idx = np.unique(np.argwhere(np.isnan(Outputs))[:, 0])
- all_outputs[var] = np.delete(Outputs, NaN_idx, axis=0)
-
- if prevRun_No == 0 and key_str == '':
- grpY.create_dataset(f"New_init_{key_str}",
- data=all_outputs[var])
- else:
- try:
- name = f'EDY/{var}/New_adaptive_{key_str}'
- oldEDY = np.array(file[name])
- del file[f'EDY/{var}/New_adaptive_{key_str}']
- data = np.vstack((oldEDY, all_outputs[var]))
- except KeyError:
- data = all_outputs[var]
- grpY.create_dataset(f'New_adaptive_{key_str}', data=data)
-
- # Print the collocation points whose simulations crashed
- if len(NaN_idx) != 0:
- print('\n')
- print('*'*20)
- print("\nThe following parametersets have been removed:\n",
- c_points[NaN_idx])
- print("\n")
- print('*'*20)
-
- # Pass it to the attribute
- new_c_points = np.delete(c_points, NaN_idx, axis=0)
- self.OutputMatrix = all_outputs
-
- # Save CollocationPoints
- grpX = file.create_group("EDX") if not hdf5_exist else file.get("EDX")
- if prevRun_No == 0 and key_str == '':
- grpX.create_dataset("init_"+key_str, data=c_points)
- if len(NaN_idx) != 0:
- grpX.create_dataset("New_init_"+key_str, data=new_c_points)
-
- else:
- try:
- name = f'EDX/adaptive_{key_str}'
- oldCollocationPoints = np.array(file[name])
- del file[f'EDX/adaptive_{key_str}']
- data = np.vstack((oldCollocationPoints, new_c_points))
- except KeyError:
- data = new_c_points
- grpX.create_dataset('adaptive_'+key_str, data=data)
-
- if len(NaN_idx) != 0:
- try:
- name = f'EDX/New_adaptive_{key_str}'
- oldCollocationPoints = np.array(file[name])
- del file[f'EDX/New_adaptive_{key_str}']
- data = np.vstack((oldCollocationPoints, new_c_points))
- except KeyError:
- data = new_c_points
- grpX.create_dataset('New_adaptive_'+key_str, data=data)
-
- # Close h5py file
- file.close()
-
- return all_outputs, new_c_points
-
- # -------------------------------------------------------------------------
- def zip_subdirs(self, dir_name, key):
- """
- Zips all the files containing the key(word).
-
- Parameters
- ----------
- dir_name : string
- Directory name.
- key : string
- Keyword to search for.
-
- Returns
- -------
- None.
-
- """
- # setup file paths variable
- dir_list = []
- file_paths = []
-
- # Read all directory, subdirectories and file lists
- dir_path = os.getcwd()
-
- for root, directories, files in os.walk(dir_path):
- for directory in directories:
- # Create the full filepath by using os module.
- if key in directory:
- folderPath = os.path.join(dir_path, directory)
- dir_list.append(folderPath)
-
- # Loop over the identified directories to store the file paths
- for direct_name in dir_list:
- for root, directories, files in os.walk(direct_name):
- for filename in files:
- # Create the full filepath by using os module.
- filePath = os.path.join(root, filename)
- file_paths.append('.'+filePath.split(dir_path)[1])
-
- # writing files to a zipfile
- if len(file_paths) != 0:
- zip_file = zipfile.ZipFile(dir_name+'.zip', 'w')
- with zip_file:
- # writing each file one by one
- for file in file_paths:
- zip_file.write(file)
-
- file_paths = [path for path in os.listdir('.') if key in path]
-
- for path in file_paths:
- shutil.rmtree(path)
-
- print("\n")
- print(f'{dir_name}.zip file has been created successfully!\n')
-
- return</code></pre>
-</details>
-<h3>Class variables</h3>
-<dl>
-<dt id="pylink.PyLinkForwardModel.Output"><code class="name">var <span class="ident">Output</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-<h3>Methods</h3>
-<dl>
-<dt id="pylink.PyLinkForwardModel.read_mc_reference"><code class="name flex">
-<span>def <span class="ident">read_mc_reference</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Is used, if a Monte-Carlo reference is available for
-further in-depth post-processing after meta-model training.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>None</code></dt>
-<dd> </dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def read_mc_reference(self):
- """
- Is used, if a Monte-Carlo reference is available for
- further in-depth post-processing after meta-model training.
-
- Returns
- -------
- None
-
- """
- if self.mc_ref_file is None and not hasattr(self, 'mc_reference'):
- return
- elif isinstance(self.mc_reference, dict) and bool(self.mc_reference):
- self.mc_reference = pd.DataFrame.from_dict(self.mc_reference)
- elif self.mc_ref_file is not None:
- file_path = os.path.join(os.getcwd(), self.mc_ref_file)
- self.mc_reference = pd.read_csv(file_path, delimiter=',')
- else:
- raise Exception("Please provide the MC reference data as a "
- "dictionary via mc_reference attribute or pass the"
- " csv-file path to mc_ref_file attribute")
- return self.mc_reference</code></pre>
-</details>
-</dd>
-<dt id="pylink.PyLinkForwardModel.read_observation"><code class="name flex">
-<span>def <span class="ident">read_observation</span></span>(<span>self, case='calib')</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Reads/prepare the observation/measurement data for
-calibration.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>DataFrame</code></dt>
-<dd>A dataframe with the calibration data.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def read_observation(self, case='calib'):
- """
- Reads/prepare the observation/measurement data for
- calibration.
-
- Returns
- -------
- DataFrame
- A dataframe with the calibration data.
-
- """
- if case.lower() == 'calib':
- if bool(self.observations):
- obs = pd.DataFrame.from_dict(self.observations)
- elif self.meas_file is not None:
- file_path = os.path.join(os.getcwd(), self.meas_file)
- obs = pd.read_csv(file_path, delimiter=',')
- else:
- raise Exception("Please provide the observation data as a "
- "dictionary via observations attribute or pass"
- " the csv-file path to MeasurementFile "
- "attribute")
- elif case.lower() == 'valid':
- if bool(self.observations_valid):
- obs = pd.DataFrame.from_dict(self.observations_valid)
- elif self.meas_file_valid is not None:
- file_path = os.path.join(os.getcwd(), self.meas_file_valid)
- obs = pd.read_csv(file_path, delimiter=',')
- else:
- raise Exception("Please provide the observation data as a "
- "dictionary via Observations attribute or pass"
- " the csv-file path to MeasurementFile "
- "attribute")
-
- # Compute the number of observation
- n_obs = obs[self.Output.names].notnull().sum().values.sum()
-
- if case.lower() == 'calib':
- self.observations = obs
- self.n_obs = n_obs
- return self.observations
- elif case.lower() == 'valid':
- self.observations_valid = obs
- self.n_obs_valid = n_obs
- return self.observations_valid</code></pre>
-</details>
-</dd>
-<dt id="pylink.PyLinkForwardModel.read_output"><code class="name flex">
-<span>def <span class="ident">read_output</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Reads the the parser output file and returns it as an
-executable function. It is required when the models returns the
-simulation outputs in csv files.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><strong><code>Output</code></strong> : <code>func</code></dt>
-<dd>Output parser function.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def read_output(self):
- """
- Reads the the parser output file and returns it as an
- executable function. It is required when the models returns the
- simulation outputs in csv files.
-
- Returns
- -------
- Output : func
- Output parser function.
-
- """
- output_func_name = self.Output.parser
-
- output_func = getattr(__import__(output_func_name), output_func_name)
-
- file_names = []
- for File in self.Output.file_names:
- file_names.append(os.path.join(self.exe_path, File))
- try:
- output = output_func(self.name, file_names)
- except TypeError:
- output = output_func(file_names)
- return output</code></pre>
-</details>
-</dd>
-<dt id="pylink.PyLinkForwardModel.run_command"><code class="name flex">
-<span>def <span class="ident">run_command</span></span>(<span>self, command, output_file_names)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Runs the execution command given by the user to run the given model.
-It checks if the output files have been generated. If yes, the jobe is
-done and it extracts and returns the requested output(s). Otherwise,
-it executes the command again.</p>
-<h2 id="parameters">Parameters</h2>
-<dl>
-<dt><strong><code>command</code></strong> : <code>string</code></dt>
-<dd>The command to be executed.</dd>
-<dt><strong><code>output_file_names</code></strong> : <code>list</code></dt>
-<dd>Name of the output file names.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><strong><code>simulation_outputs</code></strong> : <code>array</code> of <code>shape (n_obs, n_outputs)</code></dt>
-<dd>Simulation outputs.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def run_command(self, command, output_file_names):
- """
- Runs the execution command given by the user to run the given model.
- It checks if the output files have been generated. If yes, the jobe is
- done and it extracts and returns the requested output(s). Otherwise,
- it executes the command again.
-
- Parameters
- ----------
- command : string
- The command to be executed.
- output_file_names : list
- Name of the output file names.
-
- Returns
- -------
- simulation_outputs : array of shape (n_obs, n_outputs)
- Simulation outputs.
-
- """
-
- # Check if simulation is finished
- while True:
- time.sleep(3)
- files = os.listdir(".")
- if all(elem in files for elem in output_file_names):
- break
- else:
- # Run command
- Process = os.system(f'./../{command}')
- if Process != 0:
- print('\nMessage 1:')
- print(f'\tIf value of \'{Process}\' is a non-zero value, '
- 'then compilation problems \n' % Process)
-
- os.chdir("..")
-
- # Read the output
- simulation_outputs = self.read_output()
-
- return simulation_outputs</code></pre>
-</details>
-</dd>
-<dt id="pylink.PyLinkForwardModel.run_forwardmodel"><code class="name flex">
-<span>def <span class="ident">run_forwardmodel</span></span>(<span>self, xx)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>This function creates subdirectory for the current run and copies the
-necessary files to this directory and renames them. Next, it executes
-the given command.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def run_forwardmodel(self, xx):
- """
- This function creates subdirectory for the current run and copies the
- necessary files to this directory and renames them. Next, it executes
- the given command.
- """
- c_points, run_no, key_str = xx
-
- # Handle if only one imput file is provided
- if not isinstance(self.input_template, list):
- self.input_template = [self.input_template]
- if not isinstance(self.input_file, list):
- self.input_file = [self.input_file]
-
- new_input_file = []
- # Loop over the InputTemplates:
- for in_temp in self.input_template:
- if '/' in in_temp:
- in_temp = in_temp.split('/')[-1]
- new_input_file.append(in_temp.split('.tpl')[0] + key_str +
- f"_{run_no+1}" + in_temp.split('.tpl')[1])
-
- # Create directories
- newpath = self.name + key_str + f'_{run_no+1}'
- if not os.path.exists(newpath):
- os.makedirs(newpath)
-
- # Copy the necessary files to the directories
- for in_temp in self.input_template:
- # Input file(s) of the model
- shutil.copy2(in_temp, newpath)
- # Auxiliary file
- if self.aux_file is not None:
- shutil.copy2(self.aux_file, newpath) # Auxiliary file
-
- # Rename the Inputfile and/or auxiliary file
- os.chdir(newpath)
- for input_tem, input_file in zip(self.input_template, new_input_file):
- if '/' in input_tem:
- input_tem = input_tem.split('/')[-1]
- os.rename(input_tem, input_file)
-
- # Update the parametrs in Input file
- self.update_input_params(new_input_file, c_points)
-
- # Update the user defined command and the execution path
- try:
- new_command = self.shell_command.replace(self.input_file[0],
- new_input_file[0])
- new_command = new_command.replace(self.input_file[1],
- new_input_file[1])
- except:
- new_command = self.shell_command.replace(self.input_file[0],
- new_input_file[0])
- # Set the exe path if not provided
- if not bool(self.exe_path):
- self.exe_path = os.getcwd()
-
- # Run the model
- output = self.run_command(new_command, self.Output.file_names)
-
- return output</code></pre>
-</details>
-</dd>
-<dt id="pylink.PyLinkForwardModel.run_model_parallel"><code class="name flex">
-<span>def <span class="ident">run_model_parallel</span></span>(<span>self, c_points, prevRun_No=0, key_str='', mp=True)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Runs model simulations. If mp is true (default), then the simulations
-are started in parallel.</p>
-<h2 id="parameters">Parameters</h2>
-<dl>
-<dt><strong><code>c_points</code></strong> : <code>array like</code> of <code>shape (n_samples, n_params)</code></dt>
-<dd>Collocation points (training set).</dd>
-<dt><strong><code>prevRun_No</code></strong> : <code>int</code>, optional</dt>
-<dd>Previous run number, in case the sequential design is selected.
-The default is 0.</dd>
-<dt><strong><code>key_str</code></strong> : <code>string</code>, optional</dt>
-<dd>A descriptive string for validation runs. The default is ''.</dd>
-<dt><strong><code>mp</code></strong> : <code>bool</code>, optional</dt>
-<dd>Multiprocessing. The default is True.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><strong><code>all_outputs</code></strong> : <code>dict</code></dt>
-<dd>A dictionary with x values (time step or point id) and all outputs.
-Each key contains an array of the shape (n_samples, n_obs).</dd>
-<dt><strong><code>new_c_points</code></strong> : <code>array</code></dt>
-<dd>Updated collocation points (training set). If a simulation does not
-executed successfully, the parameter set is removed.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def run_model_parallel(self, c_points, prevRun_No=0, key_str='',
- mp=True):
- """
- Runs model simulations. If mp is true (default), then the simulations
- are started in parallel.
-
- Parameters
- ----------
- c_points : array like of shape (n_samples, n_params)
- Collocation points (training set).
- prevRun_No : int, optional
- Previous run number, in case the sequential design is selected.
- The default is 0.
- key_str : string, optional
- A descriptive string for validation runs. The default is ''.
- mp : bool, optional
- Multiprocessing. The default is True.
-
- Returns
- -------
- all_outputs : dict
- A dictionary with x values (time step or point id) and all outputs.
- Each key contains an array of the shape (n_samples, n_obs).
- new_c_points : array
- Updated collocation points (training set). If a simulation does not
- executed successfully, the parameter set is removed.
-
- """
-
- # Create hdf5 metadata
- hdf5file = f'ExpDesign_{self.name}.hdf5'
- hdf5_exist = os.path.exists(hdf5file)
- file = h5py.File(hdf5file, 'a')
-
- # Initilization
- n_c_points = len(c_points)
- self.n_outputs = len(self.Output.names)
- all_outputs = {}
-
- # Extract the function
- if self.link_type.lower() == 'function':
- # Prepare the function
- Function = getattr(__import__(self.py_file), self.py_file)
- # ---------------------------------------------------------------
- # -------------- Multiprocessing with Pool Class ----------------
- # ---------------------------------------------------------------
- # Start a pool with the number of CPUs
- if self.n_cpus is None:
- n_cpus = multiprocessing.cpu_count()
- else:
- n_cpus = self.n_cpus
-
- # Run forward model either normal or with multiprocessing
- if not self.multi_process:
- group_results = list([self.run_forwardmodel((c_points,
- prevRun_No,
- key_str))])
- else:
- with multiprocessing.Pool(n_cpus) as p:
- desc = f'Running forward model {key_str}'
- if self.link_type.lower() == 'function':
- imap_var = p.imap(Function, c_points[:, np.newaxis])
- else:
- args = zip(c_points,
- [prevRun_No+i for i in range(n_c_points)],
- [key_str]*n_c_points)
- imap_var = p.imap(self.run_forwardmodel, args)
-
- group_results = list(tqdm.tqdm(imap_var, total=n_c_points,
- desc=desc))
-
- # Save time steps or x-values
- x_values = group_results[0][0]
- all_outputs["x_values"] = x_values
- if not hdf5_exist:
- if type(x_values) is dict:
- grp_x_values = file.create_group("x_values/")
- for varIdx, var in enumerate(self.Output.names):
- grp_x_values.create_dataset(var, data=x_values[var])
- else:
- file.create_dataset("x_values", data=x_values)
-
- # save each output in their corresponding array
- NaN_idx = []
- for varIdx, var in enumerate(self.Output.names):
-
- if not hdf5_exist:
- grpY = file.create_group("EDY/"+var)
- else:
- grpY = file.get("EDY/"+var)
-
- Outputs = np.asarray([item[varIdx+1] for item in group_results],
- dtype=np.float64)
-
- if prevRun_No == 0 and key_str == '':
- grpY.create_dataset(f'init_{key_str}', data=Outputs)
- else:
- try:
- oldEDY = np.array(file[f'EDY/{var}/adaptive_{key_str}'])
- del file[f'EDY/{var}/adaptive_{key_str}']
- data = np.vstack((oldEDY, Outputs))
- except KeyError:
- data = Outputs
- grpY.create_dataset('adaptive_'+key_str, data=data)
-
- NaN_idx = np.unique(np.argwhere(np.isnan(Outputs))[:, 0])
- all_outputs[var] = np.delete(Outputs, NaN_idx, axis=0)
-
- if prevRun_No == 0 and key_str == '':
- grpY.create_dataset(f"New_init_{key_str}",
- data=all_outputs[var])
- else:
- try:
- name = f'EDY/{var}/New_adaptive_{key_str}'
- oldEDY = np.array(file[name])
- del file[f'EDY/{var}/New_adaptive_{key_str}']
- data = np.vstack((oldEDY, all_outputs[var]))
- except KeyError:
- data = all_outputs[var]
- grpY.create_dataset(f'New_adaptive_{key_str}', data=data)
-
- # Print the collocation points whose simulations crashed
- if len(NaN_idx) != 0:
- print('\n')
- print('*'*20)
- print("\nThe following parametersets have been removed:\n",
- c_points[NaN_idx])
- print("\n")
- print('*'*20)
-
- # Pass it to the attribute
- new_c_points = np.delete(c_points, NaN_idx, axis=0)
- self.OutputMatrix = all_outputs
-
- # Save CollocationPoints
- grpX = file.create_group("EDX") if not hdf5_exist else file.get("EDX")
- if prevRun_No == 0 and key_str == '':
- grpX.create_dataset("init_"+key_str, data=c_points)
- if len(NaN_idx) != 0:
- grpX.create_dataset("New_init_"+key_str, data=new_c_points)
-
- else:
- try:
- name = f'EDX/adaptive_{key_str}'
- oldCollocationPoints = np.array(file[name])
- del file[f'EDX/adaptive_{key_str}']
- data = np.vstack((oldCollocationPoints, new_c_points))
- except KeyError:
- data = new_c_points
- grpX.create_dataset('adaptive_'+key_str, data=data)
-
- if len(NaN_idx) != 0:
- try:
- name = f'EDX/New_adaptive_{key_str}'
- oldCollocationPoints = np.array(file[name])
- del file[f'EDX/New_adaptive_{key_str}']
- data = np.vstack((oldCollocationPoints, new_c_points))
- except KeyError:
- data = new_c_points
- grpX.create_dataset('New_adaptive_'+key_str, data=data)
-
- # Close h5py file
- file.close()
-
- return all_outputs, new_c_points</code></pre>
-</details>
-</dd>
-<dt id="pylink.PyLinkForwardModel.update_input_params"><code class="name flex">
-<span>def <span class="ident">update_input_params</span></span>(<span>self, new_input_file, param_sets)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Finds this pattern with <X1> in the new_input_file and replace it with
-the new value from the array param_sets.</p>
-<h2 id="parameters">Parameters</h2>
-<dl>
-<dt><strong><code>new_input_file</code></strong> : <code>TYPE</code></dt>
-<dd>DESCRIPTION.</dd>
-<dt><strong><code>param_sets</code></strong> : <code>TYPE</code></dt>
-<dd>DESCRIPTION.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<p>None.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def update_input_params(self, new_input_file, param_sets):
- """
- Finds this pattern with <X1> in the new_input_file and replace it with
- the new value from the array param_sets.
-
- Parameters
- ----------
- new_input_file : TYPE
- DESCRIPTION.
- param_sets : TYPE
- DESCRIPTION.
-
- Returns
- -------
- None.
-
- """
- NofPa = param_sets.shape[0]
- text_to_search_list = [f'<X{i+1}>' for i in range(NofPa)]
-
- for filename in new_input_file:
- # Read in the file
- with open(filename, 'r') as file:
- filedata = file.read()
-
- # Replace the target string
- for text_to_search, params in zip(text_to_search_list, param_sets):
- filedata = filedata.replace(text_to_search, f'{params:0.4e}')
-
- # Write the file out again
- with open(filename, 'w') as file:
- file.write(filedata)</code></pre>
-</details>
-</dd>
-<dt id="pylink.PyLinkForwardModel.within_range"><code class="name flex">
-<span>def <span class="ident">within_range</span></span>(<span>self, out, minout, maxout)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def within_range(self, out, minout, maxout):
- inside = False
- if (out > minout).all() and (out < maxout).all():
- inside = True
- return inside</code></pre>
-</details>
-</dd>
-<dt id="pylink.PyLinkForwardModel.zip_subdirs"><code class="name flex">
-<span>def <span class="ident">zip_subdirs</span></span>(<span>self, dir_name, key)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Zips all the files containing the key(word).</p>
-<h2 id="parameters">Parameters</h2>
-<dl>
-<dt><strong><code>dir_name</code></strong> : <code>string</code></dt>
-<dd>Directory name.</dd>
-<dt><strong><code>key</code></strong> : <code>string</code></dt>
-<dd>Keyword to search for.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<p>None.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def zip_subdirs(self, dir_name, key):
- """
- Zips all the files containing the key(word).
-
- Parameters
- ----------
- dir_name : string
- Directory name.
- key : string
- Keyword to search for.
-
- Returns
- -------
- None.
-
- """
- # setup file paths variable
- dir_list = []
- file_paths = []
-
- # Read all directory, subdirectories and file lists
- dir_path = os.getcwd()
-
- for root, directories, files in os.walk(dir_path):
- for directory in directories:
- # Create the full filepath by using os module.
- if key in directory:
- folderPath = os.path.join(dir_path, directory)
- dir_list.append(folderPath)
-
- # Loop over the identified directories to store the file paths
- for direct_name in dir_list:
- for root, directories, files in os.walk(direct_name):
- for filename in files:
- # Create the full filepath by using os module.
- filePath = os.path.join(root, filename)
- file_paths.append('.'+filePath.split(dir_path)[1])
-
- # writing files to a zipfile
- if len(file_paths) != 0:
- zip_file = zipfile.ZipFile(dir_name+'.zip', 'w')
- with zip_file:
- # writing each file one by one
- for file in file_paths:
- zip_file.write(file)
-
- file_paths = [path for path in os.listdir('.') if key in path]
-
- for path in file_paths:
- shutil.rmtree(path)
-
- print("\n")
- print(f'{dir_name}.zip file has been created successfully!\n')
-
- return</code></pre>
-</details>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<h1>Index</h1>
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="pylink.PyLinkForwardModel" href="#pylink.PyLinkForwardModel">PyLinkForwardModel</a></code></h4>
-<ul class="two-column">
-<li><code><a title="pylink.PyLinkForwardModel.Output" href="#pylink.PyLinkForwardModel.Output">Output</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.read_mc_reference" href="#pylink.PyLinkForwardModel.read_mc_reference">read_mc_reference</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.read_observation" href="#pylink.PyLinkForwardModel.read_observation">read_observation</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.read_output" href="#pylink.PyLinkForwardModel.read_output">read_output</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.run_command" href="#pylink.PyLinkForwardModel.run_command">run_command</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.run_forwardmodel" href="#pylink.PyLinkForwardModel.run_forwardmodel">run_forwardmodel</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.run_model_parallel" href="#pylink.PyLinkForwardModel.run_model_parallel">run_model_parallel</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.update_input_params" href="#pylink.PyLinkForwardModel.update_input_params">update_input_params</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.within_range" href="#pylink.PyLinkForwardModel.within_range">within_range</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.zip_subdirs" href="#pylink.PyLinkForwardModel.zip_subdirs">zip_subdirs</a></code></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</nav>
-</main>
-<footer id="footer">
-<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
-</footer>
-</body>
-</html>
\ No newline at end of file
diff --git a/docs/build/html/search.html b/docs/build/html/search.html
deleted file mode 100644
index 946e9ed6b60251af4392ce2fc6645cb4a37f06df..0000000000000000000000000000000000000000
--- a/docs/build/html/search.html
+++ /dev/null
@@ -1,123 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="python">
- <head>
- <meta charset="utf-8" />
- <meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>Search — bayesvalidrox 0.0.3 documentation</title>
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-
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- <p>
- Please activate JavaScript to enable the search
- functionality.
- </p>
- </div>
- </noscript>
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- <p>
- Searching for multiple words only shows matches that contain
- all words.
- </p>
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-
- <form action="" method="get">
- <input type="text" name="q" aria-labelledby="search-documentation" value="" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
- <input type="submit" value="search" />
- <span id="search-progress" style="padding-left: 10px"></span>
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-
-
-
-<h3>Navigation</h3>
-<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="example.html">My nifty title</a></li>
-</ul>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
- <li><a href="index.html">Documentation overview</a><ul>
- </ul></li>
-</ul>
-</div>
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-
- </div>
- </div>
- <div class="clearer"></div>
- </div>
- <div class="footer">
- ©2022, Farid Mohammadi.
-
- |
- Powered by <a href="http://sphinx-doc.org/">Sphinx 4.4.0</a>
- & <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
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- </body>
-</html>
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diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
deleted file mode 100644
index 7489ea94ff4815f9bc11b3300692d6115f86139b..0000000000000000000000000000000000000000
--- a/docs/build/html/searchindex.js
+++ /dev/null
@@ -1 +0,0 @@
-Search.setIndex({docnames:["example","index"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":4,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,sphinx:56},filenames:["example.md","index.rst"],objects:{},objnames:{},objtypes:{},terms:{"09":[],"1":[],"1000":[],"125":[],"2013":[],"2020":[],"2021":[],"3":[],"306":[],"33":[],"42":[],"4d2669d69ddfe1d788318264cdcf0583":[],"61":[],"7":[],"70569":[],"925":[],"\u03b8":[],"default":[],"float":[],"function":[],"int":[],"public":[],"return":[],"true":[],A:[],For:[],If:[],It:1,The:1,admonit:0,affin:[],again:1,an:[],ani:1,approach:[],ar:[],arrai:[],asset:[],astronom:[],author:[],automat:[],b:[],base:[],bay:[],bayesian:[],bayesopt:[],between:[],biasinput:[],blob:[],both:[],bridg:[],burn:[],calcul:[],call:[],can:1,carlo:[],chain:[],code:1,com:[],combin:[],comput:[],content:0,converg:[],creat:[],d:[],data:[],de:[],defin:[],depart:[],descript:[],dict:[],e:[],ed_i:[],edx:[],emce:[],en:[],ensembl:[],environment:[],error:[],error_metamodel:[],ess:[],estim:[],evalu:[],exampl:[],except:1,factor:[],fals:[],farid:[],file:[],foreman:[],fulli:[],gaussian:[],gelman:[],gener:[],gist:[],github:[],given:[],goodman:[],greater:[],ha:[],hammer:[],have:[],hogg:[],html:[],http:[],hydraul:[],hydromechan:[],hydrosystem:[],i:[],indent:1,index:1,indic:[],infer:[],institut:[],invari:[],io:[],iter:[],iw:[],j:[],joergdietrich:[],jun:[],junpenglao:[],jwalton3141:[],lang:[],lazydoc:[],lh2:[],likelihood:[],line:1,log:[],log_lik:[],logp:[],logprior:[],m:[],mackei:[],mail:[],marg_llk:[],margin:[],markov:[],master:[],maximum:[],maxit:[],mean:[],mean_pr:[],meta:[],model:[],modul:1,mohammadi:[],mont:[],multipl:1,multitrac:[],my:1,n_param:[],n_sampl:[],n_step:[],n_walker:[],nburn:[],next:1,nifti:1,none:[],normal:1,nov:[],number:[],ob:[],obj:[],observ:[],obtain:[],one:[],onli:[],option:[],otherwis:[],output:[],p:[],pacif:[],packag:[],page:1,paragraph:1,paramet:[],parameterset:[],perform:[],pfaffenwaldr:[],post:[],posterior:[],potenti:[],predict:[],prior:[],probabl:[],process:1,propos:[],psrf:[],py:[],pylink:1,r_hat:[],ratio:[],readthedoc:[],reduct:[],regress:[],remov:1,reproduc:[],result:[],return_cov:[],return_std:[],return_var:[],robin:[],root:[],row:[],run:[],rwmh:[],s:[],sampl:1,sampler:[],sc:[],scale:[],search:1,set:[],shape:[],should:[],singl:[],societi:[],some:0,sourc:[],span:1,squar:[],stabl:[],std:[],std_pred:[],step:[],stuttgart:[],sum:[],sun:[],system:[],text:[0,1],than:[],theta:[],thi:1,titl:1,total_sigma2:[],train:[],type:[],typic:[],uni:[],univers:[],us:[],valu:[],varianc:[],verbos:[],via:[],w:[],wa:[],wai:1,wed:[],weight:[],well:[],within:[],www:[],x:[],y:[],yet:[]},titles:["My nifty title","Welcome to bayesvalidrox's documentation!"],titleterms:{"class":[],__init__:[],bayesvalidrox:1,bia:[],content:1,discrepancy_gp:[],discrepancy_gp_v1:[],document:1,eval_model:[],fit_bia:[],gelman_rubin:[],here:0,indic:1,kbd:[],log_likelihood:[],log_posterior:[],log_prior:[],marginal_llk_emce:[],mcmc:[],method:[],modul:[],my:0,nifti:0,oldpredict:[],predict:[],run_sampl:[],s:[0,1],tabl:1,titl:0,train_error_model:[],welcom:1}})
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diff --git a/docs/html/bayes_inference.html b/docs/html/bayes_inference.html
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index 0000000000000000000000000000000000000000..5fd3ac603907ba8b997dc75f0cb0cba2e587e69b
--- /dev/null
+++ b/docs/html/bayes_inference.html
@@ -0,0 +1,3653 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
+<meta name="generator" content="pdoc 0.10.0" />
+<title>bayes_inference API documentation</title>
+<meta name="description" content="" />
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+<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
+<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
+<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
+<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
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+<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
+<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
+</head>
+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>bayes_inference</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import os
+import copy
+import pandas as pd
+from tqdm import tqdm
+from scipy import stats
+import scipy.linalg as spla
+
+import seaborn as sns
+import corner
+import h5py
+import gc
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn import preprocessing
+from matplotlib.patches import Patch
+import matplotlib.lines as mlines
+from matplotlib.backends.backend_pdf import PdfPages
+import matplotlib.pylab as plt
+
+from mcmc import MCMC
+
+# Load the mplstyle
+plt.style.use(os.path.join(os.path.split(__file__)[0],
+ '../', 'bayesvalidrox.mplstyle'))
+
+
+class BayesInference:
+ """
+ A class to perform Bayesian Analysis.
+
+
+ Attributes
+ ----------
+ MetaModel : obj
+ Meta model object.
+ discrepancy : obj
+ The discrepancy object for the sigma2s, i.e. the diagonal entries
+ of the variance matrix for a multivariate normal likelihood.
+ name : str, optional
+ The type of analysis, either calibration (`Calib`) or validation
+ (`Valid`). The default is `'Calib'`.
+ emulator : bool, optional
+ Analysis with emulator (MetaModel). The default is `True`.
+ bootstrap : bool, optional
+ Bootstrap the analysis. The default is `False`.
+ req_outputs : list, optional
+ The list of requested output to be used for the analysis.
+ The default is `None`. If None, all the defined outputs for the model
+ object is used.
+ selected_indices : dict, optional
+ A dictionary with the selected indices of each model output. The
+ default is `None`. If `None`, all measurement points are used in the
+ analysis.
+ samples : array of shape (n_samples, n_params), optional
+ The samples to be used in the analysis. The default is `None`. If
+ None the samples are drawn from the probablistic input parameter
+ object of the MetaModel object.
+ n_samples : int, optional
+ Number of samples to be used in the analysis. The default is `500000`.
+ If samples is not `None`, this argument will be assigned based on the
+ number of samples given.
+ measured_data : dict, optional
+ A dictionary containing the observation data. The default is `None`.
+ if `None`, the observation defined in the Model object of the
+ MetaModel is used.
+ inference_method : str, optional
+ A method for approximating the posterior distribution in the Bayesian
+ inference step. The default is `'rejection'`, which stands for
+ rejection sampling. A Markov Chain Monte Carlo sampler can be simply
+ selected by passing `'MCMC'`.
+ mcmc_params : dict, optional
+ A dictionary with args required for the Bayesian inference with
+ `MCMC`. The default is `None`.
+
+ Pass the mcmc_params like the following:
+
+ >>> mcmc_params:{
+ 'init_samples': None, # initial samples
+ 'n_walkers': 100, # number of walkers (chain)
+ 'n_steps': 100000, # number of maximum steps
+ 'n_burn': 200, # number of burn-in steps
+ 'moves': None, # Moves for the emcee sampler
+ 'multiprocessing': False, # multiprocessing
+ 'verbose': False # verbosity
+ }
+ The items shown above are the default values. If any parmeter is
+ not defined, the default value will be assigned to it.
+ bayes_loocv : bool, optional
+ Bayesian Leave-one-out Cross Validation. The default is `False`. If
+ `True`, the LOOCV procedure is used to estimate the bayesian Model
+ Evidence (BME).
+ n_bootstrap_itrs : int, optional
+ Number of bootstrap iteration. The default is `1`. If bayes_loocv is
+ `True`, this is qualt to the total length of the observation data
+ set.
+ perturbed_data : array of shape (n_bootstrap_itrs, n_obs), optional
+ User defined perturbed data. The default is `[]`.
+ bootstrap_noise : float, optional
+ A noise level to perturb the data set. The default is `0.05`.
+ plot_post_pred : bool, optional
+ Plot posterior predictive plots. The default is `True`.
+ plot_map_pred : bool, optional
+ Plot the model outputs vs the metamodel predictions for the maximum
+ a posteriori (defined as `max_a_posteriori`) parameter set. The
+ default is `False`.
+ max_a_posteriori : str, optional
+ Maximum a posteriori. `'mean'` and `'mode'` are available. The default
+ is `'mean'`.
+ corner_title_fmt : str, optional
+ Title format for the posterior distribution plot with python
+ package `corner`. The default is `'.3e'`.
+
+ """
+
+ def __init__(self, MetaModel, discrepancy=None, emulator=True,
+ name='Calib', bootstrap=False, req_outputs=None,
+ selected_indices=None, samples=None, n_samples=500000,
+ measured_data=None, inference_method='rejection',
+ mcmc_params=None, bayes_loocv=False, n_bootstrap_itrs=1,
+ perturbed_data=[], bootstrap_noise=0.05, plot_post_pred=True,
+ plot_map_pred=False, max_a_posteriori='mean',
+ corner_title_fmt='.3e'):
+
+ self.MetaModel = MetaModel
+ self.Discrepancy = discrepancy
+ self.emulator = emulator
+ self.name = name
+ self.bootstrap = bootstrap
+ self.req_outputs = req_outputs
+ self.selected_indices = selected_indices
+ self.samples = samples
+ self.n_samples = n_samples
+ self.measured_data = measured_data
+ self.inference_method = inference_method
+ self.mcmc_params = mcmc_params
+ self.perturbed_data = perturbed_data
+ self.bayes_loocv = bayes_loocv
+ self.n_bootstrap_itrs = n_bootstrap_itrs
+ self.bootstrap_noise = bootstrap_noise
+ self.plot_post_pred = plot_post_pred
+ self.plot_map_pred = plot_map_pred
+ self.max_a_posteriori = max_a_posteriori
+ self.corner_title_fmt = corner_title_fmt
+
+ # -------------------------------------------------------------------------
+ def create_inference(self):
+ """
+ Starts the inference.
+
+ Returns
+ -------
+ BayesInference : obj
+ The Bayes inference object.
+
+ """
+
+ # Set some variables
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+ n_params = MetaModel.n_params
+ output_names = Model.Output.names
+ par_names = MetaModel.ExpDesign.par_names
+
+ # If the prior is set by the user, take it.
+ if self.samples is None:
+ self.samples = MetaModel.ExpDesign.generate_samples(
+ self.n_samples, 'random')
+ else:
+ try:
+ samples = self.samples.values
+ except AttributeError:
+ samples = self.samples
+
+ # Take care of an additional Sigma2s
+ self.samples = samples[:, :n_params]
+
+ # Update number of samples
+ self.n_samples = self.samples.shape[0]
+
+ # ---------- Preparation of observation data ----------
+ # Read observation data and perturb it if requested.
+ if self.measured_data is None:
+ self.measured_data = Model.read_observation(case=self.name)
+ # Convert measured_data to a data frame
+ if not isinstance(self.measured_data, pd.DataFrame):
+ self.measured_data = pd.DataFrame(self.measured_data)
+
+ # Extract the total number of measurement points
+ if self.name.lower() == 'calib':
+ self.n_tot_measurement = Model.n_obs
+ else:
+ self.n_tot_measurement = Model.n_obs_valid
+
+ # Find measurement error (if not given) for post predictive plot
+ if not hasattr(self, 'measurement_error'):
+ if isinstance(self.Discrepancy, dict):
+ Disc = self.Discrepancy['known']
+ else:
+ Disc = self.Discrepancy
+ if isinstance(Disc.parameters, dict):
+ self.measurement_error = {k: np.sqrt(Disc.parameters[k]) for k
+ in Disc.parameters.keys()}
+ else:
+ try:
+ self.measurement_error = np.sqrt(Disc.parameters)
+ except TypeError:
+ pass
+
+ # ---------- Preparation of variance for covariance matrix ----------
+ # Independent and identically distributed
+ total_sigma2 = dict()
+ opt_sigma_flag = isinstance(self.Discrepancy, dict)
+ opt_sigma = None
+ for key_idx, key in enumerate(output_names):
+
+ # Find opt_sigma
+ if opt_sigma_flag and opt_sigma is None:
+ # Option A: known error with unknown bias term
+ opt_sigma = 'A'
+ known_discrepancy = self.Discrepancy['known']
+ self.Discrepancy = self.Discrepancy['infer']
+ sigma2 = np.array(known_discrepancy.parameters[key])
+
+ elif opt_sigma == 'A' or self.Discrepancy.parameters is not None:
+ # Option B: The sigma2 is known (no bias term)
+ if opt_sigma == 'A':
+ sigma2 = np.array(known_discrepancy.parameters[key])
+ else:
+ opt_sigma = 'B'
+ sigma2 = np.array(self.Discrepancy.parameters[key])
+
+ elif not isinstance(self.Discrepancy.InputDisc, str):
+ # Option C: The sigma2 is unknown (bias term including error)
+ opt_sigma = 'C'
+ self.Discrepancy.opt_sigma = opt_sigma
+ n_measurement = self.measured_data[key].values.shape
+ sigma2 = np.zeros((n_measurement[0]))
+
+ total_sigma2[key] = sigma2
+
+ self.Discrepancy.opt_sigma = opt_sigma
+ self.Discrepancy.total_sigma2 = total_sigma2
+
+ # If inferred sigma2s obtained from e.g. calibration are given
+ try:
+ self.sigma2s = self.Discrepancy.get_sample(self.n_samples)
+ except:
+ pass
+
+ # ---------------- Bootstrap & TOM --------------------
+ if self.bootstrap or self.bayes_loocv:
+ if len(self.perturbed_data) == 0:
+ # zero mean noise Adding some noise to the observation function
+ self.perturbed_data = self._perturb_data(
+ self.measured_data, output_names
+ )
+ else:
+ self.n_bootstrap_itrs = len(self.perturbed_data)
+
+ # -------- Model Discrepancy -----------
+ if hasattr(self, 'error_model') and self.error_model \
+ and self.name.lower() != 'calib':
+ # Select posterior mean as MAP
+ MAP_theta = self.samples.mean(axis=0).reshape((1, n_params))
+ # MAP_theta = stats.mode(self.samples,axis=0)[0]
+
+ # Evaluate the (meta-)model at the MAP
+ y_MAP, y_std_MAP = MetaModel.eval_metamodel(samples=MAP_theta)
+
+ # Train a GPR meta-model using MAP
+ self.error_MetaModel = MetaModel.create_model_error(
+ self.bias_inputs, y_MAP, Name=self.name
+ )
+
+ # -----------------------------------------------------
+ # ----- Loop over the perturbed observation data ------
+ # -----------------------------------------------------
+ # Initilize arrays
+ logLikelihoods = np.zeros((self.n_samples, self.n_bootstrap_itrs),
+ dtype=np.float16)
+ BME_Corr = np.zeros((self.n_bootstrap_itrs))
+ log_BME = np.zeros((self.n_bootstrap_itrs))
+ KLD = np.zeros((self.n_bootstrap_itrs))
+ inf_entropy = np.zeros((self.n_bootstrap_itrs))
+
+ # Compute the prior predtions
+ # Evaluate the MetaModel
+ if self.emulator:
+ y_hat, y_std = MetaModel.eval_metamodel(samples=self.samples)
+ self.__mean_pce_prior_pred = y_hat
+ self._std_pce_prior_pred = y_std
+
+ # Correct the predictions with Model discrepancy
+ if hasattr(self, 'error_model') and self.error_model:
+ y_hat_corr, y_std = self.error_MetaModel.eval_model_error(
+ self.bias_inputs, self.__mean_pce_prior_pred
+ )
+ self.__mean_pce_prior_pred = y_hat_corr
+ self._std_pce_prior_pred = y_std
+
+ # Surrogate model's error using RMSE of test data
+ if hasattr(MetaModel, 'rmse'):
+ surrError = MetaModel.rmse
+ else:
+ surrError = None
+
+ else:
+ # Evaluate the original model
+ self.__model_prior_pred = self._eval_model(
+ samples=self.samples, key='PriorPred'
+ )
+
+ # Start the likelihood-BME computations for the perturbed data
+ for itr_idx, data in tqdm(
+ enumerate(self.perturbed_data), ascii=True,
+ desc="Boostraping the BME calculations"
+ ):
+
+ # ---------------- Likelihood calculation ----------------
+ if self.emulator:
+ model_evals = self.__mean_pce_prior_pred
+ else:
+ model_evals = self.__model_prior_pred
+
+ # Leave one out
+ if self.bayes_loocv:
+ self.selected_indices = np.nonzero(data)[0]
+
+ # Prepare data dataframe
+ nobs = list(self.measured_data.count().values[1:])
+ numbers = list(map(sum, zip([0] + nobs, nobs)))
+ indices = list(zip([0] + numbers, numbers))
+ data_dict = {
+ output_names[i]: data[j:k] for i, (j, k) in
+ enumerate(indices)
+ }
+
+ # Unknown sigma2
+ if opt_sigma == 'C' or hasattr(self, 'sigma2s'):
+ logLikelihoods[:, itr_idx] = self.normpdf(
+ model_evals, data_dict, total_sigma2,
+ sigma2=self.sigma2s, std=surrError
+ )
+ else:
+ # known sigma2
+ logLikelihoods[:, itr_idx] = self.normpdf(
+ model_evals, data_dict, total_sigma2,
+ std=surrError
+ )
+
+ # ---------------- BME Calculations ----------------
+ # BME (log)
+ log_BME[itr_idx] = np.log(
+ np.nanmean(np.exp(logLikelihoods[:, itr_idx],
+ dtype=np.float128))
+ )
+
+ # Rejection Step
+ # Random numbers between 0 and 1
+ unif = np.random.rand(1, self.n_samples)[0]
+
+ # Reject the poorly performed prior
+ Likelihoods = np.exp(logLikelihoods[:, itr_idx],
+ dtype=np.float64)
+ accepted = (Likelihoods/np.max(Likelihoods)) >= unif
+ posterior = self.samples[accepted]
+
+ # Posterior-based expectation of likelihoods
+ postExpLikelihoods = np.mean(
+ logLikelihoods[:, itr_idx][accepted]
+ )
+
+ # Posterior-based expectation of prior densities
+ postExpPrior = np.mean(
+ np.log([MetaModel.ExpDesign.JDist.pdf(posterior.T)])
+ )
+
+ # Calculate Kullback-Leibler Divergence
+ KLD[itr_idx] = postExpLikelihoods - log_BME[itr_idx]
+
+ # Information Entropy based on Entropy paper Eq. 38
+ inf_entropy[itr_idx] = log_BME[itr_idx] - postExpPrior - \
+ postExpLikelihoods
+
+ # TODO: BME correction when using Emulator
+ # if self.emulator:
+ # BME_Corr[itr_idx] = self._corr_factor_BME(
+ # data, total_sigma2, posterior
+ # )
+
+ # Clear memory
+ gc.collect(generation=2)
+
+ # ---------------- Store BME, Likelihoods for all ----------------
+ # Likelihoods (Size: n_samples, n_bootstrap_itr)
+ self.log_likes = logLikelihoods
+
+ # BME (log), KLD, infEntropy (Size: 1,n_bootstrap_itr)
+ self.log_BME = log_BME
+ self.KLD = KLD
+ self.inf_entropy = inf_entropy
+
+ # TODO: BMECorrFactor (log) (Size: 1,n_bootstrap_itr)
+ # if self.emulator: self.BMECorrFactor = BME_Corr
+
+ # BME = BME + BMECorrFactor
+ if self.emulator:
+ self.log_BME = self.log_BME # + self.BMECorrFactor
+
+ # ---------------- Parameter Bayesian inference ----------------
+ if self.inference_method.lower() == 'mcmc':
+ # Instantiate the MCMC object
+ MCMC_Obj = MCMC(self)
+ self.posterior_df = MCMC_Obj.run_sampler(
+ self.measured_data, total_sigma2
+ )
+
+ elif self.name.lower() == 'valid':
+ # Convert to a dataframe if samples are provided after calibration.
+ self.posterior_df = pd.DataFrame(self.samples, columns=par_names)
+
+ else:
+ # Rejection sampling
+ self.posterior_df = self._rejection_sampling()
+
+ # Provide posterior's summary
+ print('\n')
+ print('-'*15 + 'Posterior summary' + '-'*15)
+ pd.options.display.max_columns = None
+ pd.options.display.max_rows = None
+ print(self.posterior_df.describe())
+ print('-'*50)
+
+ # -------- Model Discrepancy -----------
+ if hasattr(self, 'error_model') and self.error_model \
+ and self.name.lower() == 'calib':
+ if self.inference_method.lower() == 'mcmc':
+ self.error_MetaModel = MCMC_Obj.error_MetaModel
+ else:
+ # Select posterior mean as MAP
+ if opt_sigma == "B":
+ posterior_df = self.posterior_df.values
+ else:
+ posterior_df = self.posterior_df.values[:, :-Model.n_outputs]
+
+ # Select posterior mean as Maximum a posteriori
+ map_theta = posterior_df.mean(axis=0).reshape((1, n_params))
+ # map_theta = stats.mode(Posterior_df,axis=0)[0]
+
+ # Evaluate the (meta-)model at the MAP
+ y_MAP, y_std_MAP = MetaModel.eval_metamodel(samples=map_theta)
+
+ # Train a GPR meta-model using MAP
+ self.error_MetaModel = MetaModel.create_model_error(
+ self.bias_inputs, y_MAP, Name=self.name
+ )
+
+ # -------- Posterior perdictive -----------
+ self._posterior_predictive()
+
+ # -----------------------------------------------------
+ # ------------------ Visualization --------------------
+ # -----------------------------------------------------
+ # Create Output directory, if it doesn't exist already.
+ out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ os.makedirs(out_dir, exist_ok=True)
+
+ # -------- Posteior parameters --------
+ if opt_sigma != "B":
+ par_names.extend(
+ [self.Discrepancy.InputDisc.Marginals[i].name for i
+ in range(len(self.Discrepancy.InputDisc.Marginals))]
+ )
+ # Pot with corner
+ figPosterior = corner.corner(self.posterior_df.to_numpy(),
+ labels=par_names,
+ quantiles=[0.15, 0.5, 0.85],
+ show_titles=True,
+ title_fmt=self.corner_title_fmt,
+ labelpad=0.2,
+ use_math_text=True,
+ title_kwargs={"fontsize": 28},
+ plot_datapoints=False,
+ plot_density=False,
+ fill_contours=True,
+ smooth=0.5,
+ smooth1d=0.5)
+
+ # Loop over axes and set x limits
+ if opt_sigma == "B":
+ axes = np.array(figPosterior.axes).reshape(
+ (len(par_names), len(par_names))
+ )
+ for yi in range(len(par_names)):
+ ax = axes[yi, yi]
+ ax.set_xlim(MetaModel.bound_tuples[yi])
+ for xi in range(yi):
+ ax = axes[yi, xi]
+ ax.set_xlim(MetaModel.bound_tuples[xi])
+
+ # Turn off gridlines
+ for ax in figPosterior.axes:
+ ax.grid(False)
+
+ if self.emulator:
+ plotname = f'/Posterior_Dist_{Model.name}_emulator'
+ else:
+ plotname = f'/Posterior_Dist_{Model.name}'
+
+ figPosterior.set_size_inches((24, 16))
+ figPosterior.savefig(f'./{out_dir}{plotname}.pdf',
+ bbox_inches='tight')
+
+ # -------- Plot MAP --------
+ if self.plot_map_pred:
+ self._plot_max_a_posteriori()
+
+ # -------- Plot log_BME dist --------
+ if self.bootstrap and self.n_bootstrap_itrs > 1:
+ # Computing the TOM performance
+ self.log_BME_tom = stats.chi2.rvs(
+ self.n_tot_measurement, size=self.log_BME.shape[0]
+ )
+
+ fig, ax = plt.subplots()
+ sns.kdeplot(self.log_BME_tom, ax=ax, color="green", shade=True)
+ sns.kdeplot(
+ self.log_BME, ax=ax, color="blue", shade=True,
+ label='Model BME')
+
+ ax.set_xlabel('log$_{10}$(BME)')
+ ax.set_ylabel('Probability density')
+
+ legend_elements = [
+ Patch(facecolor='green', edgecolor='green', label='TOM BME'),
+ Patch(facecolor='blue', edgecolor='blue', label='Model BME')
+ ]
+ ax.legend(handles=legend_elements)
+
+ if self.emulator:
+ plotname = f'/BME_hist_{Model.name}_emulator'
+ else:
+ plotname = f'/BME_hist_{Model.name}'
+
+ plt.savefig(f'./{out_dir}{plotname}.pdf', bbox_inches='tight')
+ plt.show()
+ plt.close()
+
+ # -------- Posteior perdictives --------
+ if self.plot_post_pred:
+ # Plot the posterior predictive
+ self._plot_post_predictive()
+
+ return self
+
+ # -------------------------------------------------------------------------
+ def _perturb_data(self, data, output_names):
+ """
+ Returns an array with n_bootstrap_itrs rowsof perturbed data.
+ The first row includes the original observation data.
+ If `self.bayes_loocv` is True, a 2d-array will be returned with
+ repeated rows and zero diagonal entries.
+
+ Parameters
+ ----------
+ data : pandas DataFrame
+ Observation data.
+ output_names : list
+ List of the output names.
+
+ Returns
+ -------
+ final_data : array
+ Perturbed data set.
+
+ """
+ noise_level = self.bootstrap_noise
+ obs_data = data[output_names].values
+ n_measurement, n_outs = obs_data.shape
+ self.n_tot_measurement = obs_data[~np.isnan(obs_data)].shape[0]
+ # Number of bootstrap iterations
+ if self.bayes_loocv:
+ self.n_bootstrap_itrs = self.n_tot_measurement
+
+ # Pass loocv dataset
+ if self.bayes_loocv:
+ obs = obs_data.T[~np.isnan(obs_data.T)]
+ final_data = np.repeat(np.atleast_2d(obs), self.n_bootstrap_itrs,
+ axis=0)
+ np.fill_diagonal(final_data, 0)
+ return final_data
+
+ else:
+ final_data = np.zeros(
+ (self.n_bootstrap_itrs, self.n_tot_measurement)
+ )
+ final_data[0] = obs_data.T[~np.isnan(obs_data.T)]
+ for itrIdx in range(1, self.n_bootstrap_itrs):
+ data = np.zeros((n_measurement, n_outs))
+ for idx in range(len(output_names)):
+ std = np.nanstd(obs_data[:, idx])
+ if std == 0:
+ std = 0.001
+ noise = std * noise_level
+ data[:, idx] = np.add(
+ obs_data[:, idx],
+ np.random.normal(0, 1, obs_data.shape[0]) * noise,
+ )
+
+ final_data[itrIdx] = data.T[~np.isnan(data.T)]
+
+ return final_data
+
+ # -------------------------------------------------------------------------
+ def _logpdf(self, x, mean, cov):
+ """
+ computes the likelihood based on a multivariate normal distribution.
+
+ Parameters
+ ----------
+ x : TYPE
+ DESCRIPTION.
+ mean : array_like
+ Observation data.
+ cov : 2d array
+ Covariance matrix of the distribution.
+
+ Returns
+ -------
+ log_lik : float
+ Log likelihood.
+
+ """
+ n = len(mean)
+ L = spla.cholesky(cov, lower=True)
+ beta = np.sum(np.log(np.diag(L)))
+ dev = x - mean
+ alpha = dev.dot(spla.cho_solve((L, True), dev))
+ log_lik = -0.5 * alpha - beta - n / 2. * np.log(2 * np.pi)
+ return log_lik
+
+ # -------------------------------------------------------------------------
+ def _eval_model(self, samples=None, key='MAP'):
+ """
+ Evaluates Forward Model.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params), optional
+ Parameter sets. The default is None.
+ key : str, optional
+ Key string to be passed to the run_model_parallel method.
+ The default is 'MAP'.
+
+ Returns
+ -------
+ model_outputs : TYPE
+ DESCRIPTION.
+
+ """
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+
+ if samples is None:
+ self.samples = MetaModel.ExpDesign.generate_samples(
+ self.n_samples, 'random')
+ else:
+ self.samples = samples
+ self.n_samples = len(samples)
+
+ model_outputs, _ = Model.run_model_parallel(
+ self.samples, key_str=key+self.name)
+
+ # Clean up
+ # Zip the subdirectories
+ try:
+ dir_name = f'{Model.name}MAP{self.name}'
+ key = dir_name + '_'
+ Model.zip_subdirs(dir_name, key)
+ except:
+ pass
+
+ return model_outputs
+
+ # -------------------------------------------------------------------------
+ def _kernel_rbf(self, X, hyperparameters):
+ """
+ Isotropic squared exponential kernel.
+
+ Higher l values lead to smoother functions and therefore to coarser
+ approximations of the training data. Lower l values make functions
+ more wiggly with wide uncertainty regions between training data points.
+
+ sigma_f controls the marginal variance of b(x)
+
+ Parameters
+ ----------
+ X : ndarray of shape (n_samples_X, n_features)
+
+ hyperparameters : Dict
+ Lambda characteristic length
+ sigma_f controls the marginal variance of b(x)
+ sigma_0 unresolvable error nugget term, interpreted as random
+ error that cannot be attributed to measurement error.
+ Returns
+ -------
+ var_cov_matrix : ndarray of shape (n_samples_X,n_samples_X)
+ Kernel k(X, X).
+
+ """
+ from sklearn.gaussian_process.kernels import RBF
+ min_max_scaler = preprocessing.MinMaxScaler()
+ X_minmax = min_max_scaler.fit_transform(X)
+
+ nparams = len(hyperparameters)
+ # characteristic length (0,1]
+ Lambda = hyperparameters[0]
+ # sigma_f controls the marginal variance of b(x)
+ sigma2_f = hyperparameters[1]
+
+ # cov_matrix = sigma2_f*rbf_kernel(X_minmax, gamma = 1/Lambda**2)
+
+ rbf = RBF(length_scale=Lambda)
+ cov_matrix = sigma2_f * rbf(X_minmax)
+ if nparams > 2:
+ # (unresolvable error) nugget term that is interpreted as random
+ # error that cannot be attributed to measurement error.
+ sigma2_0 = hyperparameters[2:]
+ for i, j in np.ndindex(cov_matrix.shape):
+ cov_matrix[i, j] += np.sum(sigma2_0) if i == j else 0
+
+ return cov_matrix
+
+ # -------------------------------------------------------------------------
+ def normpdf(self, outputs, obs_data, total_sigma2s, sigma2=None, std=None):
+ """
+ Calculates the likelihood of simulation outputs compared with
+ observation data.
+
+ Parameters
+ ----------
+ outputs : dict
+ A dictionary containing the simulation outputs as array of shape
+ (n_samples, n_measurement) for each model output.
+ obs_data : dict
+ A dictionary/dataframe containing the observation data.
+ total_sigma2s : dict
+ A dictionary with known values of the covariance diagonal entries,
+ a.k.a sigma^2.
+ sigma2 : array, optional
+ An array of the sigma^2 samples, when the covariance diagonal
+ entries are unknown and are being jointly inferred. The default is
+ None.
+ std : dict, optional
+ A dictionary containing the root mean squared error as array of
+ shape (n_samples, n_measurement) for each model output. The default
+ is None.
+
+ Returns
+ -------
+ logLik : array of shape (n_samples)
+ Likelihoods.
+
+ """
+ Model = self.MetaModel.ModelObj
+ logLik = 0.0
+
+ # Extract the requested model outputs for likelihood calulation
+ if self.req_outputs is None:
+ req_outputs = Model.Output.names
+ else:
+ req_outputs = list(self.req_outputs)
+
+ # Loop over the outputs
+ for idx, out in enumerate(req_outputs):
+
+ # (Meta)Model Output
+ nsamples, nout = outputs[out].shape
+
+ # Prepare data and remove NaN
+ try:
+ data = obs_data[out].values[~np.isnan(obs_data[out])]
+ except AttributeError:
+ data = obs_data[out][~np.isnan(obs_data[out])]
+
+ # Prepare sigma2s
+ tot_sigma2s = total_sigma2s[out][~np.isnan(
+ total_sigma2s[out])][:nout]
+
+ # Add the std of the PCE is chosen as emulator.
+ if self.emulator:
+ if std is not None:
+ std_pce = std[out]
+ else:
+ std_pce = np.mean(
+ self._std_pce_prior_pred[out], axis=0)
+ # Expected value of variance (Assump: i.i.d stds)
+ tot_sigma2s += std_pce**2
+
+ # If sigma2 is not given, use given total_sigma2s
+ if sigma2 is None:
+ logLik += stats.multivariate_normal.logpdf(
+ outputs[out], data, np.diag(tot_sigma2s))
+ continue
+
+ # Loop over each run/sample and calculate logLikelihood
+ logliks = np.zeros(nsamples)
+ for s_idx in range(nsamples):
+
+ # Simulation run
+ tot_outputs = outputs[out]
+
+ # Covariance Matrix
+ covMatrix = np.diag(tot_sigma2s)
+
+ if sigma2 is not None:
+ # Check the type error term
+ if hasattr(self, 'bias_inputs') and \
+ not hasattr(self, 'error_model'):
+ # Infer a Bias model usig Gaussian Process Regression
+ bias_inputs = np.hstack(
+ (self.bias_inputs[out],
+ tot_outputs[s_idx].reshape(-1, 1)))
+
+ params = sigma2[s_idx, idx*3:(idx+1)*3]
+ covMatrix = self._kernel_rbf(bias_inputs, params)
+ else:
+ # Infer equal sigma2s
+ try:
+ sigma_2 = sigma2[s_idx, idx]
+ except TypeError:
+ sigma_2 = 0.0
+
+ covMatrix += sigma_2 * np.eye(nout)
+ # covMatrix = np.diag(sigma2 * total_sigma2s)
+
+ # Select the data points to compare
+ if self.selected_indices is not None:
+ indices = self.selected_indices[out]
+ covMatrix = np.diag(covMatrix[indices, indices])
+ else:
+ indices = list(range(nout))
+
+ # Compute loglikelihood
+ logliks[s_idx] = self._logpdf(
+ tot_outputs[s_idx, indices], data[indices], covMatrix
+ )
+
+ logLik += logliks
+ return logLik
+
+ # -------------------------------------------------------------------------
+ def _corr_factor_BME(self, Data, total_sigma2s, posterior):
+ """
+ Calculates the correction factor for BMEs.
+ """
+ MetaModel = self.MetaModel
+ OrigModelOutput = MetaModel.ExpDesign.Y
+ Model = MetaModel.ModelObj
+
+ # Posterior with guassian-likelihood
+ postDist = stats.gaussian_kde(posterior.T)
+
+ # Remove NaN
+ Data = Data[~np.isnan(Data)]
+ total_sigma2s = total_sigma2s[~np.isnan(total_sigma2s)]
+
+ # Covariance Matrix
+ covMatrix = np.diag(total_sigma2s[:self.n_tot_measurement])
+
+ # Extract the requested model outputs for likelihood calulation
+ if self.req_outputs is None:
+ OutputType = Model.Output.names
+ else:
+ OutputType = list(self.req_outputs)
+
+ # SampleSize = OrigModelOutput[OutputType[0]].shape[0]
+
+
+ # Flatten the OutputType for OrigModel
+ TotalOutputs = np.concatenate([OrigModelOutput[x] for x in OutputType], 1)
+
+ NrofBayesSamples = self.n_samples
+ # Evaluate MetaModel on the experimental design
+ Samples = MetaModel.ExpDesign.X
+ OutputRS, stdOutputRS = MetaModel.eval_metamodel(samples=Samples)
+
+ # Reset the NrofSamples to NrofBayesSamples
+ self.n_samples = NrofBayesSamples
+
+ # Flatten the OutputType for MetaModel
+ TotalPCEOutputs = np.concatenate([OutputRS[x] for x in OutputRS], 1)
+ TotalPCEstdOutputRS= np.concatenate([stdOutputRS[x] for x in stdOutputRS], 1)
+
+ logweight = 0
+ for i,sample in enumerate(Samples):
+ # Compute likelilhood output vs RS
+ covMatrix = np.diag(TotalPCEstdOutputRS[i]**2)
+ logLik = self._logpdf(TotalOutputs[i], TotalPCEOutputs[i], covMatrix)
+ # Compute posterior likelihood of the collocation points
+ logpostLik = np.log(postDist.pdf(sample[:,None]))[0]
+ if logpostLik != -np.inf:
+ logweight += logLik + logpostLik
+ return logweight
+# # Initialization
+# covMatrix=np.zeros((NofMeasurements, NofMeasurements), float)
+# BME_RM_Model_Weight = np.zeros((SampleSize))
+# BME_RM_Data_Weight = np.zeros((SampleSize))
+# BME_Corr = np.zeros((1))
+
+
+# # Deviation Computations
+# RM_Model_Deviation = np.zeros((SampleSize,NofMeasurements))
+# RM_Data_Deviation = np.zeros((SampleSize,NofMeasurements))
+# for i in range(SampleSize):
+# RM_Model_Deviation[i] = TotalOutputs[i][:NofMeasurements] - TotalPCEOutputs[i, :] # Reduce model- Full Model
+# RM_Data_Deviation[i] = Observations - TotalPCEOutputs[i, :] # Reduce model- Measurement Data
+
+
+# # Initialization of Co-Variance Matrix
+# # For BME_RM_ModelWeight
+# if NofMeasurements == 1:
+# RM_Model_Error = np.zeros((NofMeasurements, NofMeasurements), float)
+# np.fill_diagonal(RM_Model_Error, np.cov(RM_Model_Deviation.T))
+# else:
+# RM_Model_Error = np.cov(RM_Model_Deviation.T)
+
+
+# # Computation of Weight according to the deviations
+# for i in range(SampleSize):
+# # For BME_RM_DataWeight
+# try:
+# var = Sigma[i]
+# if len(var)==1:
+# np.fill_diagonal(covMatrix, var)
+# else:
+# row,col = np.diag_indices(covMatrix.shape[0])
+# covMatrix[row,col] = np.hstack((np.repeat(var[0], NofMeasurements*0.5),np.repeat(var[1], NofMeasurements*0.5)))
+
+# except:
+# var = Sigma
+
+# np.fill_diagonal(covMatrix, var)
+
+# # Add the std of the PCE is emulator is chosen.
+# # if self.emulator:
+# # covMatrix_PCE = np.zeros((NofMeasurements, NofMeasurements), float)
+# # stdPCE = np.empty((SampleSize,0))
+# # for outputType in OutputType:
+# # stdPCE = np.hstack((stdPCE, stdOutputRS[outputType]))
+# #
+# # stdPCE = np.mean(stdPCE, axis=1)
+# # np.fill_diagonal(covMatrix_PCE, stdPCE**2)
+# #
+# # covMatrix = covMatrix + covMatrix_PCE
+
+# # Calculate the denomitor
+# denom1 = (np.sqrt(2*np.pi)) ** NofMeasurements
+# denom2 = (((2*np.pi)**(NofMeasurements/2)) * np.sqrt(np.linalg.det(covMatrix)))
+
+# BME_RM_Model_Weight[i] = (np.exp(-0.5 * np.dot(np.dot(RM_Model_Deviation[i], np.linalg.pinv(RM_Model_Error)), RM_Model_Deviation[i])))/denom1
+# BME_RM_Data_Weight[i] = (np.exp(-0.5 * np.dot(np.dot(RM_Data_Deviation[i], np.linalg.pinv(covMatrix)), RM_Data_Deviation[i][:,np.newaxis])))/denom2
+
+# for i in range(SampleSize):
+# BME_Corr[0] += BME_RM_Model_Weight[i] * BME_RM_Data_Weight[i] / np.nansum(BME_RM_Data_Weight)
+
+# return np.log(BME_Corr[0])
+
+ # -------------------------------------------------------------------------
+ def _rejection_sampling(self):
+ """
+ Performs rejection sampling to update the prior distribution on the
+ input parameters.
+
+ Returns
+ -------
+ posterior : pandas.dataframe
+ Posterior samples of the input parameters.
+
+ """
+
+ MetaModel = self.MetaModel
+ try:
+ sigma2_prior = self.Discrepancy.sigma2_prior
+ except:
+ sigma2_prior = None
+
+ # Check if the discrepancy is defined as a distribution:
+ samples = self.samples
+
+ if sigma2_prior is not None:
+ samples = np.hstack((samples, sigma2_prior))
+
+ # Take the first column of Likelihoods (Observation data without noise)
+ likelihoods = np.exp(self.log_likes[:, 0], dtype=np.float128)
+ n_samples = len(likelihoods)
+ norm_ikelihoods = likelihoods / np.max(likelihoods)
+
+ # Normalize based on min if all Likelihoods are zero
+ if all(likelihoods == 0.0):
+ likelihoods = self.log_likes[:, 0]
+ norm_ikelihoods = likelihoods / np.min(likelihoods)
+
+ # Random numbers between 0 and 1
+ unif = np.random.rand(1, n_samples)[0]
+
+ # Reject the poorly performed prior
+ accepted_samples = samples[norm_ikelihoods >= unif]
+
+ # Output the Posterior
+ par_names = MetaModel.ExpDesign.par_names
+ if sigma2_prior is not None:
+ for name in self.Discrepancy.name:
+ par_names.append(name)
+
+ return pd.DataFrame(accepted_samples, columns=sigma2_prior)
+
+ # -------------------------------------------------------------------------
+ def _posterior_predictive(self):
+ """
+ Stores the prior- and posterior predictive samples, i.e. model
+ evaluations using the samples, into hdf5 files.
+
+ priorPredictive.hdf5 : Prior predictive samples.
+ postPredictive_wo_noise.hdf5 : Posterior predictive samples without
+ the additive noise.
+ postPredictive.hdf5 : Posterior predictive samples with the additive
+ noise.
+
+ Returns
+ -------
+ None.
+
+ """
+
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+
+ # Make a directory to save the prior/posterior predictive
+ out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ os.makedirs(out_dir, exist_ok=True)
+
+ # Read observation data and perturb it if requested
+ if self.measured_data is None:
+ self.measured_data = Model.read_observation(case=self.name)
+
+ if not isinstance(self.measured_data, pd.DataFrame):
+ self.measured_data = pd.DataFrame(self.measured_data)
+
+ # X_values
+ x_values = MetaModel.ExpDesign.x_values
+
+ try:
+ sigma2_prior = self.Discrepancy.sigma2_prior
+ except:
+ sigma2_prior = None
+
+ # Extract posterior samples
+ posterior_df = self.posterior_df
+
+ # Take care of the sigma2
+ if sigma2_prior is not None:
+ try:
+ sigma2s = posterior_df[self.Discrepancy.name].values
+ posterior_df = posterior_df.drop(
+ labels=self.Discrepancy.name, axis=1
+ )
+ except:
+ sigma2s = self.sigma2s
+
+ # Posterior predictive
+ if self.emulator:
+ if self.inference_method == 'rejection':
+ prior_pred = self.__mean_pce_prior_pred
+ if self.name.lower() != 'calib':
+ post_pred = self.__mean_pce_prior_pred
+ post_pred_std = self._std_pce_prior_pred
+ else:
+ post_pred, post_pred_std = MetaModel.eval_metamodel(
+ samples=posterior_df.values
+ )
+
+ else:
+ if self.inference_method == 'rejection':
+ prior_pred = self.__model_prior_pred
+ if self.name.lower() != 'calib':
+ post_pred = self.__mean_pce_prior_pred,
+ post_pred_std = self._std_pce_prior_pred
+ else:
+ post_pred = self._eval_model(
+ samples=posterior_df.values, key='PostPred'
+ )
+ # Correct the predictions with Model discrepancy
+ if hasattr(self, 'error_model') and self.error_model:
+ y_hat, y_std = self.error_MetaModel.eval_model_error(
+ self.bias_inputs, post_pred
+ )
+ post_pred, post_pred_std = y_hat, y_std
+
+ # Add discrepancy from likelihood Sample to the current posterior runs
+ total_sigma2 = self.Discrepancy.total_sigma2
+ post_pred_withnoise = copy.deepcopy(post_pred)
+ for varIdx, var in enumerate(Model.Output.names):
+ for i in range(len(post_pred[var])):
+ pred = post_pred[var][i]
+
+ # Known sigma2s
+ clean_sigma2 = total_sigma2[var][~np.isnan(total_sigma2[var])]
+ tot_sigma2 = clean_sigma2[:len(pred)]
+ cov = np.diag(tot_sigma2)
+
+ # Check the type error term
+ if sigma2_prior is not None:
+ # Inferred sigma2s
+ if hasattr(self, 'bias_inputs') and \
+ not hasattr(self, 'error_model'):
+ # TODO: Infer a Bias model usig GPR
+ bias_inputs = np.hstack((
+ self.bias_inputs[var], pred.reshape(-1, 1)))
+ params = sigma2s[i, varIdx*3:(varIdx+1)*3]
+ cov = self._kernel_rbf(bias_inputs, params)
+ else:
+ # Infer equal sigma2s
+ try:
+ sigma2 = sigma2s[i, varIdx]
+ except TypeError:
+ sigma2 = 0.0
+
+ # Convert biasSigma2s to a covMatrix
+ cov += sigma2 * np.eye(len(pred))
+
+ if self.emulator:
+ if hasattr(MetaModel, 'rmse') and \
+ MetaModel.rmse is not None:
+ stdPCE = MetaModel.rmse[var]
+ else:
+ stdPCE = post_pred_std[var][i]
+ # Expected value of variance (Assump: i.i.d stds)
+ cov += np.diag(stdPCE**2)
+
+ # Sample a multivariate normal distribution with mean of
+ # prediction and variance of cov
+ post_pred_withnoise[var][i] = np.random.multivariate_normal(
+ pred, cov, 1
+ )
+
+ # ----- Prior Predictive -----
+ if self.inference_method.lower() == 'rejection':
+ # Create hdf5 metadata
+ hdf5file = f'{out_dir}/priorPredictive.hdf5'
+ hdf5_exist = os.path.exists(hdf5file)
+ if hdf5_exist:
+ os.remove(hdf5file)
+ file = h5py.File(hdf5file, 'a')
+
+ # Store x_values
+ if type(x_values) is dict:
+ grp_x_values = file.create_group("x_values/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grp_x_values.create_dataset(var, data=x_values[var])
+ else:
+ file.create_dataset("x_values", data=x_values)
+
+ # Store posterior predictive
+ grpY = file.create_group("EDY/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grpY.create_dataset(var, data=prior_pred[var])
+
+ # ----- Posterior Predictive only model evaluations -----
+ # Create hdf5 metadata
+ hdf5file = out_dir+'/postPredictive_wo_noise.hdf5'
+ hdf5_exist = os.path.exists(hdf5file)
+ if hdf5_exist:
+ os.remove(hdf5file)
+ file = h5py.File(hdf5file, 'a')
+
+ # Store x_values
+ if type(x_values) is dict:
+ grp_x_values = file.create_group("x_values/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grp_x_values.create_dataset(var, data=x_values[var])
+ else:
+ file.create_dataset("x_values", data=x_values)
+
+ # Store posterior predictive
+ grpY = file.create_group("EDY/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grpY.create_dataset(var, data=post_pred[var])
+
+ # ----- Posterior Predictive with noise -----
+ # Create hdf5 metadata
+ hdf5file = out_dir+'/postPredictive.hdf5'
+ hdf5_exist = os.path.exists(hdf5file)
+ if hdf5_exist:
+ os.remove(hdf5file)
+ file = h5py.File(hdf5file, 'a')
+
+ # Store x_values
+ if type(x_values) is dict:
+ grp_x_values = file.create_group("x_values/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grp_x_values.create_dataset(var, data=x_values[var])
+ else:
+ file.create_dataset("x_values", data=x_values)
+
+ # Store posterior predictive
+ grpY = file.create_group("EDY/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grpY.create_dataset(var, data=post_pred_withnoise[var])
+
+ return
+
+ # -------------------------------------------------------------------------
+ def _plot_max_a_posteriori(self):
+ """
+ Plots the response of the model output against that of the metamodel at
+ the maximum a posteriori sample (mean or mode of posterior.)
+
+ Returns
+ -------
+ None.
+
+ """
+
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+ out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ opt_sigma = self.Discrepancy.opt_sigma
+
+ # -------- Find MAP and run MetaModel and origModel --------
+ # Compute the MAP
+ if self.max_a_posteriori.lower() == 'mean':
+ if opt_sigma == "B":
+ Posterior_df = self.posterior_df.values
+ else:
+ Posterior_df = self.posterior_df.values[:, :-Model.n_outputs]
+ map_theta = Posterior_df.mean(axis=0).reshape(
+ (1, MetaModel.n_params))
+ else:
+ map_theta = stats.mode(Posterior_df.values, axis=0)[0]
+ # Prin report
+ print("\nPoint estimator:\n", map_theta[0])
+
+ # Run the models for MAP
+ # MetaModel
+ map_metamodel_mean, map_metamodel_std = MetaModel.eval_metamodel(
+ samples=map_theta)
+ self.map_metamodel_mean = map_metamodel_mean
+ self.map_metamodel_std = map_metamodel_std
+
+ # origModel
+ map_orig_model = self._eval_model(samples=map_theta)
+ self.map_orig_model = map_orig_model
+
+ # Extract slicing index
+ x_values = map_orig_model['x_values']
+
+ # List of markers and colors
+ Color = ['k', 'b', 'g', 'r']
+ Marker = 'x'
+
+ # Create a PdfPages object
+ pdf = PdfPages(f'./{out_dir}MAP_PCE_vs_Model_{self.name}.pdf')
+ fig = plt.figure()
+ for i, key in enumerate(Model.Output.names):
+
+ y_val = map_orig_model[key]
+ y_pce_val = map_metamodel_mean[key]
+ y_pce_val_std = map_metamodel_std[key]
+
+ plt.plot(x_values, y_val, color=Color[i], marker=Marker,
+ lw=2.0, label='$Y_{MAP}^{M}$')
+
+ plt.plot(
+ x_values, y_pce_val[i], color=Color[i], lw=2.0,
+ marker=Marker, linestyle='--', label='$Y_{MAP}^{PCE}$'
+ )
+ # plot the confidence interval
+ plt.fill_between(
+ x_values, y_pce_val[i] - 1.96*y_pce_val_std[i],
+ y_pce_val[i] + 1.96*y_pce_val_std[i],
+ color=Color[i], alpha=0.15
+ )
+
+ # Calculate the adjusted R_squared and RMSE
+ R2 = r2_score(y_pce_val.reshape(-1, 1), y_val.reshape(-1, 1))
+ rmse = np.sqrt(mean_squared_error(y_pce_val, y_val))
+
+ plt.ylabel(key)
+ plt.xlabel("Time [s]")
+ plt.title(f'Model vs MetaModel {key}')
+
+ ax = fig.axes[0]
+ leg = ax.legend(loc='best', frameon=True)
+ fig.canvas.draw()
+ p = leg.get_window_extent().inverse_transformed(ax.transAxes)
+ ax.text(
+ p.p0[1]-0.05, p.p1[1]-0.25,
+ f'RMSE = {rmse:.3f}\n$R^2$ = {R2:.3f}',
+ transform=ax.transAxes, color='black',
+ bbox=dict(facecolor='none', edgecolor='black',
+ boxstyle='round,pad=1'))
+
+ plt.show()
+
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+
+ # Destroy the current plot
+ plt.clf()
+
+ pdf.close()
+
+ # -------------------------------------------------------------------------
+ def _plot_post_predictive(self):
+ """
+ Plots the posterior predictives against the observation data.
+
+ Returns
+ -------
+ None.
+
+ """
+
+ Model = self.MetaModel.ModelObj
+ out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ # Plot the posterior predictive
+ for out_idx, out_name in enumerate(Model.Output.names):
+ fig, ax = plt.subplots()
+ with sns.axes_style("ticks"):
+ x_key = list(self.measured_data)[0]
+
+ # --- Read prior and posterior predictive ---
+ if self.inference_method == 'rejection':
+ # --- Prior ---
+ # Load posterior predictive
+ f = h5py.File(
+ f'{out_dir}/priorPredictive.hdf5', 'r+')
+
+ try:
+ x_coords = np.array(f[f"x_values/{out_name}"])
+ except:
+ x_coords = np.array(f["x_values"])
+
+ X_values = np.repeat(x_coords, 10000)
+
+ prior_pred_df = {}
+ prior_pred_df[x_key] = X_values
+ prior_pred_df[out_name] = np.array(
+ f[f"EDY/{out_name}"])[:10000].flatten('F')
+ prior_pred_df = pd.DataFrame(prior_pred_df)
+
+ tags_post = ['prior'] * len(prior_pred_df)
+ prior_pred_df.insert(
+ len(prior_pred_df.columns), "Tags", tags_post,
+ True)
+ f.close()
+
+ # --- Posterior ---
+ f = h5py.File(f"{out_dir}/postPredictive.hdf5", 'r+')
+
+ X_values = np.repeat(
+ x_coords, np.array(f[f"EDY/{out_name}"]).shape[0])
+
+ post_pred_df = {}
+ post_pred_df[x_key] = X_values
+ post_pred_df[out_name] = np.array(
+ f[f"EDY/{out_name}"]).flatten('F')
+
+ post_pred_df = pd.DataFrame(post_pred_df)
+
+ tags_post = ['posterior'] * len(post_pred_df)
+ post_pred_df.insert(
+ len(post_pred_df.columns), "Tags", tags_post, True)
+ f.close()
+ # Concatenate two dataframes based on x_values
+ frames = [prior_pred_df, post_pred_df]
+ all_pred_df = pd.concat(frames)
+
+ # --- Plot posterior predictive ---
+ sns.violinplot(
+ x_key, y=out_name, data=all_pred_df, hue="Tags",
+ legend=False, ax=ax, split=True, inner=None,
+ color=".8")
+
+ # --- Plot Data ---
+ # Find the x,y coordinates for each point
+ x_coords = np.arange(x_coords.shape[0])
+ first_header = list(self.measured_data)[0]
+ obs_data = self.measured_data.round({first_header: 6})
+ sns.pointplot(
+ x=first_header, y=out_name, color='g', markers='x',
+ linestyles='', capsize=16, data=obs_data, ax=ax)
+
+ ax.errorbar(
+ x_coords, obs_data[out_name].values,
+ yerr=1.96*self.measurement_error[out_name],
+ ecolor='g', fmt=' ', zorder=-1)
+
+ # Add labels to the legend
+ handles, labels = ax.get_legend_handles_labels()
+ labels.append('Data')
+
+ data_marker = mlines.Line2D(
+ [], [], color='lime', marker='+', linestyle='None',
+ markersize=10)
+ handles.append(data_marker)
+
+ # Add legend
+ ax.legend(handles=handles, labels=labels, loc='best',
+ fontsize='large', frameon=True)
+
+ else:
+ # Load posterior predictive
+ f = h5py.File(f"{out_dir}/postPredictive.hdf5", 'r+')
+
+ try:
+ x_coords = np.array(f["x_values"])
+ except:
+ x_coords = np.array(f[f"x_values/{out_name}"])
+
+ mu = np.mean(np.array(f[f"EDY/{out_name}"]), axis=0)
+ std = np.std(np.array(f[f"EDY/{out_name}"]), axis=0)
+
+ # --- Plot posterior predictive ---
+ plt.plot(
+ x_coords, mu, marker='o', color='b',
+ label='Mean Post. Predictive')
+ plt.fill_between(
+ x_coords, mu-1.96*std, mu+1.96*std, color='b',
+ alpha=0.15)
+
+ # --- Plot Data ---
+ ax.plot(
+ x_coords, self.measured_data[out_name].values,
+ 'ko', label='data', markeredgecolor='w')
+
+ # --- Plot ExpDesign ---
+ orig_ED_Y = self.MetaModel.ExpDesign.Y[out_name]
+ for output in orig_ED_Y:
+ plt.plot(
+ x_coords, output, color='grey', alpha=0.15
+ )
+
+ # Add labels for axes
+ plt.xlabel('Time [s]')
+ plt.ylabel(out_name)
+
+ # Add labels to the legend
+ handles, labels = ax.get_legend_handles_labels()
+
+ patch = Patch(color='b', alpha=0.15)
+ handles.insert(1, patch)
+ labels.insert(1, '95 $\\%$ CI')
+
+ # Add legend
+ ax.legend(handles=handles, labels=labels, loc='best',
+ frameon=True)
+
+ # Save figure in pdf format
+ if self.emulator:
+ plotname = f'/Post_Prior_Perd_{Model.name}_emulator'
+ else:
+ plotname = f'/Post_Prior_Perd_{Model.name}'
+
+ fig.savefig(f'./{out_dir}{plotname}_{out_name}.pdf',
+ bbox_inches='tight')</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="bayes_inference.BayesInference"><code class="flex name class">
+<span>class <span class="ident">BayesInference</span></span>
+<span>(</span><span>MetaModel, discrepancy=None, emulator=True, name='Calib', bootstrap=False, req_outputs=None, selected_indices=None, samples=None, n_samples=500000, measured_data=None, inference_method='rejection', mcmc_params=None, bayes_loocv=False, n_bootstrap_itrs=1, perturbed_data=[], bootstrap_noise=0.05, plot_post_pred=True, plot_map_pred=False, max_a_posteriori='mean', corner_title_fmt='.3e')</span>
+</code></dt>
+<dd>
+<div class="desc"><p>A class to perform Bayesian Analysis.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>MetaModel</code></strong> : <code>obj</code></dt>
+<dd>Meta model object.</dd>
+<dt><strong><code>discrepancy</code></strong> : <code>obj</code></dt>
+<dd>The discrepancy object for the sigma2s, i.e. the diagonal entries
+of the variance matrix for a multivariate normal likelihood.</dd>
+<dt><strong><code>name</code></strong> : <code>str</code>, optional</dt>
+<dd>The type of analysis, either calibration (<code>Calib</code>) or validation
+(<code>Valid</code>). The default is <code>'Calib'</code>.</dd>
+<dt><strong><code>emulator</code></strong> : <code>bool</code>, optional</dt>
+<dd>Analysis with emulator (MetaModel). The default is <code>True</code>.</dd>
+<dt><strong><code>bootstrap</code></strong> : <code>bool</code>, optional</dt>
+<dd>Bootstrap the analysis. The default is <code>False</code>.</dd>
+<dt><strong><code>req_outputs</code></strong> : <code>list</code>, optional</dt>
+<dd>The list of requested output to be used for the analysis.
+The default is <code>None</code>. If None, all the defined outputs for the model
+object is used.</dd>
+<dt><strong><code>selected_indices</code></strong> : <code>dict</code>, optional</dt>
+<dd>A dictionary with the selected indices of each model output. The
+default is <code>None</code>. If <code>None</code>, all measurement points are used in the
+analysis.</dd>
+<dt><strong><code>samples</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code>, optional</dt>
+<dd>The samples to be used in the analysis. The default is <code>None</code>. If
+None the samples are drawn from the probablistic input parameter
+object of the MetaModel object.</dd>
+<dt><strong><code>n_samples</code></strong> : <code>int</code>, optional</dt>
+<dd>Number of samples to be used in the analysis. The default is <code>500000</code>.
+If samples is not <code>None</code>, this argument will be assigned based on the
+number of samples given.</dd>
+<dt><strong><code>measured_data</code></strong> : <code>dict</code>, optional</dt>
+<dd>A dictionary containing the observation data. The default is <code>None</code>.
+if <code>None</code>, the observation defined in the Model object of the
+MetaModel is used.</dd>
+<dt><strong><code>inference_method</code></strong> : <code>str</code>, optional</dt>
+<dd>A method for approximating the posterior distribution in the Bayesian
+inference step. The default is <code>'rejection'</code>, which stands for
+rejection sampling. A Markov Chain Monte Carlo sampler can be simply
+selected by passing <code>'MCMC'</code>.</dd>
+<dt><strong><code>mcmc_params</code></strong> : <code>dict</code>, optional</dt>
+<dd>
+<p>A dictionary with args required for the Bayesian inference with
+<code>MCMC</code>. The default is <code>None</code>.</p>
+<p>Pass the mcmc_params like the following:</p>
+<pre><code>>>> mcmc_params:{
+ 'init_samples': None, # initial samples
+ 'n_walkers': 100, # number of walkers (chain)
+ 'n_steps': 100000, # number of maximum steps
+ 'n_burn': 200, # number of burn-in steps
+ 'moves': None, # Moves for the emcee sampler
+ 'multiprocessing': False, # multiprocessing
+ 'verbose': False # verbosity
+ }
+</code></pre>
+<p>The items shown above are the default values. If any parmeter is
+not defined, the default value will be assigned to it.</p>
+</dd>
+<dt><strong><code>bayes_loocv</code></strong> : <code>bool</code>, optional</dt>
+<dd>Bayesian Leave-one-out Cross Validation. The default is <code>False</code>. If
+<code>True</code>, the LOOCV procedure is used to estimate the bayesian Model
+Evidence (BME).</dd>
+<dt><strong><code>n_bootstrap_itrs</code></strong> : <code>int</code>, optional</dt>
+<dd>Number of bootstrap iteration. The default is <code>1</code>. If bayes_loocv is
+<code>True</code>, this is qualt to the total length of the observation data
+set.</dd>
+<dt><strong><code>perturbed_data</code></strong> : <code>array</code> of <code>shape (n_bootstrap_itrs, n_obs)</code>, optional</dt>
+<dd>User defined perturbed data. The default is <code>[]</code>.</dd>
+<dt><strong><code>bootstrap_noise</code></strong> : <code>float</code>, optional</dt>
+<dd>A noise level to perturb the data set. The default is <code>0.05</code>.</dd>
+<dt><strong><code>plot_post_pred</code></strong> : <code>bool</code>, optional</dt>
+<dd>Plot posterior predictive plots. The default is <code>True</code>.</dd>
+<dt><strong><code>plot_map_pred</code></strong> : <code>bool</code>, optional</dt>
+<dd>Plot the model outputs vs the metamodel predictions for the maximum
+a posteriori (defined as <code>max_a_posteriori</code>) parameter set. The
+default is <code>False</code>.</dd>
+<dt><strong><code>max_a_posteriori</code></strong> : <code>str</code>, optional</dt>
+<dd>Maximum a posteriori. <code>'mean'</code> and <code>'mode'</code> are available. The default
+is <code>'mean'</code>.</dd>
+<dt><strong><code>corner_title_fmt</code></strong> : <code>str</code>, optional</dt>
+<dd>Title format for the posterior distribution plot with python
+package <code>corner</code>. The default is <code>'.3e'</code>.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class BayesInference:
+ """
+ A class to perform Bayesian Analysis.
+
+
+ Attributes
+ ----------
+ MetaModel : obj
+ Meta model object.
+ discrepancy : obj
+ The discrepancy object for the sigma2s, i.e. the diagonal entries
+ of the variance matrix for a multivariate normal likelihood.
+ name : str, optional
+ The type of analysis, either calibration (`Calib`) or validation
+ (`Valid`). The default is `'Calib'`.
+ emulator : bool, optional
+ Analysis with emulator (MetaModel). The default is `True`.
+ bootstrap : bool, optional
+ Bootstrap the analysis. The default is `False`.
+ req_outputs : list, optional
+ The list of requested output to be used for the analysis.
+ The default is `None`. If None, all the defined outputs for the model
+ object is used.
+ selected_indices : dict, optional
+ A dictionary with the selected indices of each model output. The
+ default is `None`. If `None`, all measurement points are used in the
+ analysis.
+ samples : array of shape (n_samples, n_params), optional
+ The samples to be used in the analysis. The default is `None`. If
+ None the samples are drawn from the probablistic input parameter
+ object of the MetaModel object.
+ n_samples : int, optional
+ Number of samples to be used in the analysis. The default is `500000`.
+ If samples is not `None`, this argument will be assigned based on the
+ number of samples given.
+ measured_data : dict, optional
+ A dictionary containing the observation data. The default is `None`.
+ if `None`, the observation defined in the Model object of the
+ MetaModel is used.
+ inference_method : str, optional
+ A method for approximating the posterior distribution in the Bayesian
+ inference step. The default is `'rejection'`, which stands for
+ rejection sampling. A Markov Chain Monte Carlo sampler can be simply
+ selected by passing `'MCMC'`.
+ mcmc_params : dict, optional
+ A dictionary with args required for the Bayesian inference with
+ `MCMC`. The default is `None`.
+
+ Pass the mcmc_params like the following:
+
+ >>> mcmc_params:{
+ 'init_samples': None, # initial samples
+ 'n_walkers': 100, # number of walkers (chain)
+ 'n_steps': 100000, # number of maximum steps
+ 'n_burn': 200, # number of burn-in steps
+ 'moves': None, # Moves for the emcee sampler
+ 'multiprocessing': False, # multiprocessing
+ 'verbose': False # verbosity
+ }
+ The items shown above are the default values. If any parmeter is
+ not defined, the default value will be assigned to it.
+ bayes_loocv : bool, optional
+ Bayesian Leave-one-out Cross Validation. The default is `False`. If
+ `True`, the LOOCV procedure is used to estimate the bayesian Model
+ Evidence (BME).
+ n_bootstrap_itrs : int, optional
+ Number of bootstrap iteration. The default is `1`. If bayes_loocv is
+ `True`, this is qualt to the total length of the observation data
+ set.
+ perturbed_data : array of shape (n_bootstrap_itrs, n_obs), optional
+ User defined perturbed data. The default is `[]`.
+ bootstrap_noise : float, optional
+ A noise level to perturb the data set. The default is `0.05`.
+ plot_post_pred : bool, optional
+ Plot posterior predictive plots. The default is `True`.
+ plot_map_pred : bool, optional
+ Plot the model outputs vs the metamodel predictions for the maximum
+ a posteriori (defined as `max_a_posteriori`) parameter set. The
+ default is `False`.
+ max_a_posteriori : str, optional
+ Maximum a posteriori. `'mean'` and `'mode'` are available. The default
+ is `'mean'`.
+ corner_title_fmt : str, optional
+ Title format for the posterior distribution plot with python
+ package `corner`. The default is `'.3e'`.
+
+ """
+
+ def __init__(self, MetaModel, discrepancy=None, emulator=True,
+ name='Calib', bootstrap=False, req_outputs=None,
+ selected_indices=None, samples=None, n_samples=500000,
+ measured_data=None, inference_method='rejection',
+ mcmc_params=None, bayes_loocv=False, n_bootstrap_itrs=1,
+ perturbed_data=[], bootstrap_noise=0.05, plot_post_pred=True,
+ plot_map_pred=False, max_a_posteriori='mean',
+ corner_title_fmt='.3e'):
+
+ self.MetaModel = MetaModel
+ self.Discrepancy = discrepancy
+ self.emulator = emulator
+ self.name = name
+ self.bootstrap = bootstrap
+ self.req_outputs = req_outputs
+ self.selected_indices = selected_indices
+ self.samples = samples
+ self.n_samples = n_samples
+ self.measured_data = measured_data
+ self.inference_method = inference_method
+ self.mcmc_params = mcmc_params
+ self.perturbed_data = perturbed_data
+ self.bayes_loocv = bayes_loocv
+ self.n_bootstrap_itrs = n_bootstrap_itrs
+ self.bootstrap_noise = bootstrap_noise
+ self.plot_post_pred = plot_post_pred
+ self.plot_map_pred = plot_map_pred
+ self.max_a_posteriori = max_a_posteriori
+ self.corner_title_fmt = corner_title_fmt
+
+ # -------------------------------------------------------------------------
+ def create_inference(self):
+ """
+ Starts the inference.
+
+ Returns
+ -------
+ BayesInference : obj
+ The Bayes inference object.
+
+ """
+
+ # Set some variables
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+ n_params = MetaModel.n_params
+ output_names = Model.Output.names
+ par_names = MetaModel.ExpDesign.par_names
+
+ # If the prior is set by the user, take it.
+ if self.samples is None:
+ self.samples = MetaModel.ExpDesign.generate_samples(
+ self.n_samples, 'random')
+ else:
+ try:
+ samples = self.samples.values
+ except AttributeError:
+ samples = self.samples
+
+ # Take care of an additional Sigma2s
+ self.samples = samples[:, :n_params]
+
+ # Update number of samples
+ self.n_samples = self.samples.shape[0]
+
+ # ---------- Preparation of observation data ----------
+ # Read observation data and perturb it if requested.
+ if self.measured_data is None:
+ self.measured_data = Model.read_observation(case=self.name)
+ # Convert measured_data to a data frame
+ if not isinstance(self.measured_data, pd.DataFrame):
+ self.measured_data = pd.DataFrame(self.measured_data)
+
+ # Extract the total number of measurement points
+ if self.name.lower() == 'calib':
+ self.n_tot_measurement = Model.n_obs
+ else:
+ self.n_tot_measurement = Model.n_obs_valid
+
+ # Find measurement error (if not given) for post predictive plot
+ if not hasattr(self, 'measurement_error'):
+ if isinstance(self.Discrepancy, dict):
+ Disc = self.Discrepancy['known']
+ else:
+ Disc = self.Discrepancy
+ if isinstance(Disc.parameters, dict):
+ self.measurement_error = {k: np.sqrt(Disc.parameters[k]) for k
+ in Disc.parameters.keys()}
+ else:
+ try:
+ self.measurement_error = np.sqrt(Disc.parameters)
+ except TypeError:
+ pass
+
+ # ---------- Preparation of variance for covariance matrix ----------
+ # Independent and identically distributed
+ total_sigma2 = dict()
+ opt_sigma_flag = isinstance(self.Discrepancy, dict)
+ opt_sigma = None
+ for key_idx, key in enumerate(output_names):
+
+ # Find opt_sigma
+ if opt_sigma_flag and opt_sigma is None:
+ # Option A: known error with unknown bias term
+ opt_sigma = 'A'
+ known_discrepancy = self.Discrepancy['known']
+ self.Discrepancy = self.Discrepancy['infer']
+ sigma2 = np.array(known_discrepancy.parameters[key])
+
+ elif opt_sigma == 'A' or self.Discrepancy.parameters is not None:
+ # Option B: The sigma2 is known (no bias term)
+ if opt_sigma == 'A':
+ sigma2 = np.array(known_discrepancy.parameters[key])
+ else:
+ opt_sigma = 'B'
+ sigma2 = np.array(self.Discrepancy.parameters[key])
+
+ elif not isinstance(self.Discrepancy.InputDisc, str):
+ # Option C: The sigma2 is unknown (bias term including error)
+ opt_sigma = 'C'
+ self.Discrepancy.opt_sigma = opt_sigma
+ n_measurement = self.measured_data[key].values.shape
+ sigma2 = np.zeros((n_measurement[0]))
+
+ total_sigma2[key] = sigma2
+
+ self.Discrepancy.opt_sigma = opt_sigma
+ self.Discrepancy.total_sigma2 = total_sigma2
+
+ # If inferred sigma2s obtained from e.g. calibration are given
+ try:
+ self.sigma2s = self.Discrepancy.get_sample(self.n_samples)
+ except:
+ pass
+
+ # ---------------- Bootstrap & TOM --------------------
+ if self.bootstrap or self.bayes_loocv:
+ if len(self.perturbed_data) == 0:
+ # zero mean noise Adding some noise to the observation function
+ self.perturbed_data = self._perturb_data(
+ self.measured_data, output_names
+ )
+ else:
+ self.n_bootstrap_itrs = len(self.perturbed_data)
+
+ # -------- Model Discrepancy -----------
+ if hasattr(self, 'error_model') and self.error_model \
+ and self.name.lower() != 'calib':
+ # Select posterior mean as MAP
+ MAP_theta = self.samples.mean(axis=0).reshape((1, n_params))
+ # MAP_theta = stats.mode(self.samples,axis=0)[0]
+
+ # Evaluate the (meta-)model at the MAP
+ y_MAP, y_std_MAP = MetaModel.eval_metamodel(samples=MAP_theta)
+
+ # Train a GPR meta-model using MAP
+ self.error_MetaModel = MetaModel.create_model_error(
+ self.bias_inputs, y_MAP, Name=self.name
+ )
+
+ # -----------------------------------------------------
+ # ----- Loop over the perturbed observation data ------
+ # -----------------------------------------------------
+ # Initilize arrays
+ logLikelihoods = np.zeros((self.n_samples, self.n_bootstrap_itrs),
+ dtype=np.float16)
+ BME_Corr = np.zeros((self.n_bootstrap_itrs))
+ log_BME = np.zeros((self.n_bootstrap_itrs))
+ KLD = np.zeros((self.n_bootstrap_itrs))
+ inf_entropy = np.zeros((self.n_bootstrap_itrs))
+
+ # Compute the prior predtions
+ # Evaluate the MetaModel
+ if self.emulator:
+ y_hat, y_std = MetaModel.eval_metamodel(samples=self.samples)
+ self.__mean_pce_prior_pred = y_hat
+ self._std_pce_prior_pred = y_std
+
+ # Correct the predictions with Model discrepancy
+ if hasattr(self, 'error_model') and self.error_model:
+ y_hat_corr, y_std = self.error_MetaModel.eval_model_error(
+ self.bias_inputs, self.__mean_pce_prior_pred
+ )
+ self.__mean_pce_prior_pred = y_hat_corr
+ self._std_pce_prior_pred = y_std
+
+ # Surrogate model's error using RMSE of test data
+ if hasattr(MetaModel, 'rmse'):
+ surrError = MetaModel.rmse
+ else:
+ surrError = None
+
+ else:
+ # Evaluate the original model
+ self.__model_prior_pred = self._eval_model(
+ samples=self.samples, key='PriorPred'
+ )
+
+ # Start the likelihood-BME computations for the perturbed data
+ for itr_idx, data in tqdm(
+ enumerate(self.perturbed_data), ascii=True,
+ desc="Boostraping the BME calculations"
+ ):
+
+ # ---------------- Likelihood calculation ----------------
+ if self.emulator:
+ model_evals = self.__mean_pce_prior_pred
+ else:
+ model_evals = self.__model_prior_pred
+
+ # Leave one out
+ if self.bayes_loocv:
+ self.selected_indices = np.nonzero(data)[0]
+
+ # Prepare data dataframe
+ nobs = list(self.measured_data.count().values[1:])
+ numbers = list(map(sum, zip([0] + nobs, nobs)))
+ indices = list(zip([0] + numbers, numbers))
+ data_dict = {
+ output_names[i]: data[j:k] for i, (j, k) in
+ enumerate(indices)
+ }
+
+ # Unknown sigma2
+ if opt_sigma == 'C' or hasattr(self, 'sigma2s'):
+ logLikelihoods[:, itr_idx] = self.normpdf(
+ model_evals, data_dict, total_sigma2,
+ sigma2=self.sigma2s, std=surrError
+ )
+ else:
+ # known sigma2
+ logLikelihoods[:, itr_idx] = self.normpdf(
+ model_evals, data_dict, total_sigma2,
+ std=surrError
+ )
+
+ # ---------------- BME Calculations ----------------
+ # BME (log)
+ log_BME[itr_idx] = np.log(
+ np.nanmean(np.exp(logLikelihoods[:, itr_idx],
+ dtype=np.float128))
+ )
+
+ # Rejection Step
+ # Random numbers between 0 and 1
+ unif = np.random.rand(1, self.n_samples)[0]
+
+ # Reject the poorly performed prior
+ Likelihoods = np.exp(logLikelihoods[:, itr_idx],
+ dtype=np.float64)
+ accepted = (Likelihoods/np.max(Likelihoods)) >= unif
+ posterior = self.samples[accepted]
+
+ # Posterior-based expectation of likelihoods
+ postExpLikelihoods = np.mean(
+ logLikelihoods[:, itr_idx][accepted]
+ )
+
+ # Posterior-based expectation of prior densities
+ postExpPrior = np.mean(
+ np.log([MetaModel.ExpDesign.JDist.pdf(posterior.T)])
+ )
+
+ # Calculate Kullback-Leibler Divergence
+ KLD[itr_idx] = postExpLikelihoods - log_BME[itr_idx]
+
+ # Information Entropy based on Entropy paper Eq. 38
+ inf_entropy[itr_idx] = log_BME[itr_idx] - postExpPrior - \
+ postExpLikelihoods
+
+ # TODO: BME correction when using Emulator
+ # if self.emulator:
+ # BME_Corr[itr_idx] = self._corr_factor_BME(
+ # data, total_sigma2, posterior
+ # )
+
+ # Clear memory
+ gc.collect(generation=2)
+
+ # ---------------- Store BME, Likelihoods for all ----------------
+ # Likelihoods (Size: n_samples, n_bootstrap_itr)
+ self.log_likes = logLikelihoods
+
+ # BME (log), KLD, infEntropy (Size: 1,n_bootstrap_itr)
+ self.log_BME = log_BME
+ self.KLD = KLD
+ self.inf_entropy = inf_entropy
+
+ # TODO: BMECorrFactor (log) (Size: 1,n_bootstrap_itr)
+ # if self.emulator: self.BMECorrFactor = BME_Corr
+
+ # BME = BME + BMECorrFactor
+ if self.emulator:
+ self.log_BME = self.log_BME # + self.BMECorrFactor
+
+ # ---------------- Parameter Bayesian inference ----------------
+ if self.inference_method.lower() == 'mcmc':
+ # Instantiate the MCMC object
+ MCMC_Obj = MCMC(self)
+ self.posterior_df = MCMC_Obj.run_sampler(
+ self.measured_data, total_sigma2
+ )
+
+ elif self.name.lower() == 'valid':
+ # Convert to a dataframe if samples are provided after calibration.
+ self.posterior_df = pd.DataFrame(self.samples, columns=par_names)
+
+ else:
+ # Rejection sampling
+ self.posterior_df = self._rejection_sampling()
+
+ # Provide posterior's summary
+ print('\n')
+ print('-'*15 + 'Posterior summary' + '-'*15)
+ pd.options.display.max_columns = None
+ pd.options.display.max_rows = None
+ print(self.posterior_df.describe())
+ print('-'*50)
+
+ # -------- Model Discrepancy -----------
+ if hasattr(self, 'error_model') and self.error_model \
+ and self.name.lower() == 'calib':
+ if self.inference_method.lower() == 'mcmc':
+ self.error_MetaModel = MCMC_Obj.error_MetaModel
+ else:
+ # Select posterior mean as MAP
+ if opt_sigma == "B":
+ posterior_df = self.posterior_df.values
+ else:
+ posterior_df = self.posterior_df.values[:, :-Model.n_outputs]
+
+ # Select posterior mean as Maximum a posteriori
+ map_theta = posterior_df.mean(axis=0).reshape((1, n_params))
+ # map_theta = stats.mode(Posterior_df,axis=0)[0]
+
+ # Evaluate the (meta-)model at the MAP
+ y_MAP, y_std_MAP = MetaModel.eval_metamodel(samples=map_theta)
+
+ # Train a GPR meta-model using MAP
+ self.error_MetaModel = MetaModel.create_model_error(
+ self.bias_inputs, y_MAP, Name=self.name
+ )
+
+ # -------- Posterior perdictive -----------
+ self._posterior_predictive()
+
+ # -----------------------------------------------------
+ # ------------------ Visualization --------------------
+ # -----------------------------------------------------
+ # Create Output directory, if it doesn't exist already.
+ out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ os.makedirs(out_dir, exist_ok=True)
+
+ # -------- Posteior parameters --------
+ if opt_sigma != "B":
+ par_names.extend(
+ [self.Discrepancy.InputDisc.Marginals[i].name for i
+ in range(len(self.Discrepancy.InputDisc.Marginals))]
+ )
+ # Pot with corner
+ figPosterior = corner.corner(self.posterior_df.to_numpy(),
+ labels=par_names,
+ quantiles=[0.15, 0.5, 0.85],
+ show_titles=True,
+ title_fmt=self.corner_title_fmt,
+ labelpad=0.2,
+ use_math_text=True,
+ title_kwargs={"fontsize": 28},
+ plot_datapoints=False,
+ plot_density=False,
+ fill_contours=True,
+ smooth=0.5,
+ smooth1d=0.5)
+
+ # Loop over axes and set x limits
+ if opt_sigma == "B":
+ axes = np.array(figPosterior.axes).reshape(
+ (len(par_names), len(par_names))
+ )
+ for yi in range(len(par_names)):
+ ax = axes[yi, yi]
+ ax.set_xlim(MetaModel.bound_tuples[yi])
+ for xi in range(yi):
+ ax = axes[yi, xi]
+ ax.set_xlim(MetaModel.bound_tuples[xi])
+
+ # Turn off gridlines
+ for ax in figPosterior.axes:
+ ax.grid(False)
+
+ if self.emulator:
+ plotname = f'/Posterior_Dist_{Model.name}_emulator'
+ else:
+ plotname = f'/Posterior_Dist_{Model.name}'
+
+ figPosterior.set_size_inches((24, 16))
+ figPosterior.savefig(f'./{out_dir}{plotname}.pdf',
+ bbox_inches='tight')
+
+ # -------- Plot MAP --------
+ if self.plot_map_pred:
+ self._plot_max_a_posteriori()
+
+ # -------- Plot log_BME dist --------
+ if self.bootstrap and self.n_bootstrap_itrs > 1:
+ # Computing the TOM performance
+ self.log_BME_tom = stats.chi2.rvs(
+ self.n_tot_measurement, size=self.log_BME.shape[0]
+ )
+
+ fig, ax = plt.subplots()
+ sns.kdeplot(self.log_BME_tom, ax=ax, color="green", shade=True)
+ sns.kdeplot(
+ self.log_BME, ax=ax, color="blue", shade=True,
+ label='Model BME')
+
+ ax.set_xlabel('log$_{10}$(BME)')
+ ax.set_ylabel('Probability density')
+
+ legend_elements = [
+ Patch(facecolor='green', edgecolor='green', label='TOM BME'),
+ Patch(facecolor='blue', edgecolor='blue', label='Model BME')
+ ]
+ ax.legend(handles=legend_elements)
+
+ if self.emulator:
+ plotname = f'/BME_hist_{Model.name}_emulator'
+ else:
+ plotname = f'/BME_hist_{Model.name}'
+
+ plt.savefig(f'./{out_dir}{plotname}.pdf', bbox_inches='tight')
+ plt.show()
+ plt.close()
+
+ # -------- Posteior perdictives --------
+ if self.plot_post_pred:
+ # Plot the posterior predictive
+ self._plot_post_predictive()
+
+ return self
+
+ # -------------------------------------------------------------------------
+ def _perturb_data(self, data, output_names):
+ """
+ Returns an array with n_bootstrap_itrs rowsof perturbed data.
+ The first row includes the original observation data.
+ If `self.bayes_loocv` is True, a 2d-array will be returned with
+ repeated rows and zero diagonal entries.
+
+ Parameters
+ ----------
+ data : pandas DataFrame
+ Observation data.
+ output_names : list
+ List of the output names.
+
+ Returns
+ -------
+ final_data : array
+ Perturbed data set.
+
+ """
+ noise_level = self.bootstrap_noise
+ obs_data = data[output_names].values
+ n_measurement, n_outs = obs_data.shape
+ self.n_tot_measurement = obs_data[~np.isnan(obs_data)].shape[0]
+ # Number of bootstrap iterations
+ if self.bayes_loocv:
+ self.n_bootstrap_itrs = self.n_tot_measurement
+
+ # Pass loocv dataset
+ if self.bayes_loocv:
+ obs = obs_data.T[~np.isnan(obs_data.T)]
+ final_data = np.repeat(np.atleast_2d(obs), self.n_bootstrap_itrs,
+ axis=0)
+ np.fill_diagonal(final_data, 0)
+ return final_data
+
+ else:
+ final_data = np.zeros(
+ (self.n_bootstrap_itrs, self.n_tot_measurement)
+ )
+ final_data[0] = obs_data.T[~np.isnan(obs_data.T)]
+ for itrIdx in range(1, self.n_bootstrap_itrs):
+ data = np.zeros((n_measurement, n_outs))
+ for idx in range(len(output_names)):
+ std = np.nanstd(obs_data[:, idx])
+ if std == 0:
+ std = 0.001
+ noise = std * noise_level
+ data[:, idx] = np.add(
+ obs_data[:, idx],
+ np.random.normal(0, 1, obs_data.shape[0]) * noise,
+ )
+
+ final_data[itrIdx] = data.T[~np.isnan(data.T)]
+
+ return final_data
+
+ # -------------------------------------------------------------------------
+ def _logpdf(self, x, mean, cov):
+ """
+ computes the likelihood based on a multivariate normal distribution.
+
+ Parameters
+ ----------
+ x : TYPE
+ DESCRIPTION.
+ mean : array_like
+ Observation data.
+ cov : 2d array
+ Covariance matrix of the distribution.
+
+ Returns
+ -------
+ log_lik : float
+ Log likelihood.
+
+ """
+ n = len(mean)
+ L = spla.cholesky(cov, lower=True)
+ beta = np.sum(np.log(np.diag(L)))
+ dev = x - mean
+ alpha = dev.dot(spla.cho_solve((L, True), dev))
+ log_lik = -0.5 * alpha - beta - n / 2. * np.log(2 * np.pi)
+ return log_lik
+
+ # -------------------------------------------------------------------------
+ def _eval_model(self, samples=None, key='MAP'):
+ """
+ Evaluates Forward Model.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params), optional
+ Parameter sets. The default is None.
+ key : str, optional
+ Key string to be passed to the run_model_parallel method.
+ The default is 'MAP'.
+
+ Returns
+ -------
+ model_outputs : TYPE
+ DESCRIPTION.
+
+ """
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+
+ if samples is None:
+ self.samples = MetaModel.ExpDesign.generate_samples(
+ self.n_samples, 'random')
+ else:
+ self.samples = samples
+ self.n_samples = len(samples)
+
+ model_outputs, _ = Model.run_model_parallel(
+ self.samples, key_str=key+self.name)
+
+ # Clean up
+ # Zip the subdirectories
+ try:
+ dir_name = f'{Model.name}MAP{self.name}'
+ key = dir_name + '_'
+ Model.zip_subdirs(dir_name, key)
+ except:
+ pass
+
+ return model_outputs
+
+ # -------------------------------------------------------------------------
+ def _kernel_rbf(self, X, hyperparameters):
+ """
+ Isotropic squared exponential kernel.
+
+ Higher l values lead to smoother functions and therefore to coarser
+ approximations of the training data. Lower l values make functions
+ more wiggly with wide uncertainty regions between training data points.
+
+ sigma_f controls the marginal variance of b(x)
+
+ Parameters
+ ----------
+ X : ndarray of shape (n_samples_X, n_features)
+
+ hyperparameters : Dict
+ Lambda characteristic length
+ sigma_f controls the marginal variance of b(x)
+ sigma_0 unresolvable error nugget term, interpreted as random
+ error that cannot be attributed to measurement error.
+ Returns
+ -------
+ var_cov_matrix : ndarray of shape (n_samples_X,n_samples_X)
+ Kernel k(X, X).
+
+ """
+ from sklearn.gaussian_process.kernels import RBF
+ min_max_scaler = preprocessing.MinMaxScaler()
+ X_minmax = min_max_scaler.fit_transform(X)
+
+ nparams = len(hyperparameters)
+ # characteristic length (0,1]
+ Lambda = hyperparameters[0]
+ # sigma_f controls the marginal variance of b(x)
+ sigma2_f = hyperparameters[1]
+
+ # cov_matrix = sigma2_f*rbf_kernel(X_minmax, gamma = 1/Lambda**2)
+
+ rbf = RBF(length_scale=Lambda)
+ cov_matrix = sigma2_f * rbf(X_minmax)
+ if nparams > 2:
+ # (unresolvable error) nugget term that is interpreted as random
+ # error that cannot be attributed to measurement error.
+ sigma2_0 = hyperparameters[2:]
+ for i, j in np.ndindex(cov_matrix.shape):
+ cov_matrix[i, j] += np.sum(sigma2_0) if i == j else 0
+
+ return cov_matrix
+
+ # -------------------------------------------------------------------------
+ def normpdf(self, outputs, obs_data, total_sigma2s, sigma2=None, std=None):
+ """
+ Calculates the likelihood of simulation outputs compared with
+ observation data.
+
+ Parameters
+ ----------
+ outputs : dict
+ A dictionary containing the simulation outputs as array of shape
+ (n_samples, n_measurement) for each model output.
+ obs_data : dict
+ A dictionary/dataframe containing the observation data.
+ total_sigma2s : dict
+ A dictionary with known values of the covariance diagonal entries,
+ a.k.a sigma^2.
+ sigma2 : array, optional
+ An array of the sigma^2 samples, when the covariance diagonal
+ entries are unknown and are being jointly inferred. The default is
+ None.
+ std : dict, optional
+ A dictionary containing the root mean squared error as array of
+ shape (n_samples, n_measurement) for each model output. The default
+ is None.
+
+ Returns
+ -------
+ logLik : array of shape (n_samples)
+ Likelihoods.
+
+ """
+ Model = self.MetaModel.ModelObj
+ logLik = 0.0
+
+ # Extract the requested model outputs for likelihood calulation
+ if self.req_outputs is None:
+ req_outputs = Model.Output.names
+ else:
+ req_outputs = list(self.req_outputs)
+
+ # Loop over the outputs
+ for idx, out in enumerate(req_outputs):
+
+ # (Meta)Model Output
+ nsamples, nout = outputs[out].shape
+
+ # Prepare data and remove NaN
+ try:
+ data = obs_data[out].values[~np.isnan(obs_data[out])]
+ except AttributeError:
+ data = obs_data[out][~np.isnan(obs_data[out])]
+
+ # Prepare sigma2s
+ tot_sigma2s = total_sigma2s[out][~np.isnan(
+ total_sigma2s[out])][:nout]
+
+ # Add the std of the PCE is chosen as emulator.
+ if self.emulator:
+ if std is not None:
+ std_pce = std[out]
+ else:
+ std_pce = np.mean(
+ self._std_pce_prior_pred[out], axis=0)
+ # Expected value of variance (Assump: i.i.d stds)
+ tot_sigma2s += std_pce**2
+
+ # If sigma2 is not given, use given total_sigma2s
+ if sigma2 is None:
+ logLik += stats.multivariate_normal.logpdf(
+ outputs[out], data, np.diag(tot_sigma2s))
+ continue
+
+ # Loop over each run/sample and calculate logLikelihood
+ logliks = np.zeros(nsamples)
+ for s_idx in range(nsamples):
+
+ # Simulation run
+ tot_outputs = outputs[out]
+
+ # Covariance Matrix
+ covMatrix = np.diag(tot_sigma2s)
+
+ if sigma2 is not None:
+ # Check the type error term
+ if hasattr(self, 'bias_inputs') and \
+ not hasattr(self, 'error_model'):
+ # Infer a Bias model usig Gaussian Process Regression
+ bias_inputs = np.hstack(
+ (self.bias_inputs[out],
+ tot_outputs[s_idx].reshape(-1, 1)))
+
+ params = sigma2[s_idx, idx*3:(idx+1)*3]
+ covMatrix = self._kernel_rbf(bias_inputs, params)
+ else:
+ # Infer equal sigma2s
+ try:
+ sigma_2 = sigma2[s_idx, idx]
+ except TypeError:
+ sigma_2 = 0.0
+
+ covMatrix += sigma_2 * np.eye(nout)
+ # covMatrix = np.diag(sigma2 * total_sigma2s)
+
+ # Select the data points to compare
+ if self.selected_indices is not None:
+ indices = self.selected_indices[out]
+ covMatrix = np.diag(covMatrix[indices, indices])
+ else:
+ indices = list(range(nout))
+
+ # Compute loglikelihood
+ logliks[s_idx] = self._logpdf(
+ tot_outputs[s_idx, indices], data[indices], covMatrix
+ )
+
+ logLik += logliks
+ return logLik
+
+ # -------------------------------------------------------------------------
+ def _corr_factor_BME(self, Data, total_sigma2s, posterior):
+ """
+ Calculates the correction factor for BMEs.
+ """
+ MetaModel = self.MetaModel
+ OrigModelOutput = MetaModel.ExpDesign.Y
+ Model = MetaModel.ModelObj
+
+ # Posterior with guassian-likelihood
+ postDist = stats.gaussian_kde(posterior.T)
+
+ # Remove NaN
+ Data = Data[~np.isnan(Data)]
+ total_sigma2s = total_sigma2s[~np.isnan(total_sigma2s)]
+
+ # Covariance Matrix
+ covMatrix = np.diag(total_sigma2s[:self.n_tot_measurement])
+
+ # Extract the requested model outputs for likelihood calulation
+ if self.req_outputs is None:
+ OutputType = Model.Output.names
+ else:
+ OutputType = list(self.req_outputs)
+
+ # SampleSize = OrigModelOutput[OutputType[0]].shape[0]
+
+
+ # Flatten the OutputType for OrigModel
+ TotalOutputs = np.concatenate([OrigModelOutput[x] for x in OutputType], 1)
+
+ NrofBayesSamples = self.n_samples
+ # Evaluate MetaModel on the experimental design
+ Samples = MetaModel.ExpDesign.X
+ OutputRS, stdOutputRS = MetaModel.eval_metamodel(samples=Samples)
+
+ # Reset the NrofSamples to NrofBayesSamples
+ self.n_samples = NrofBayesSamples
+
+ # Flatten the OutputType for MetaModel
+ TotalPCEOutputs = np.concatenate([OutputRS[x] for x in OutputRS], 1)
+ TotalPCEstdOutputRS= np.concatenate([stdOutputRS[x] for x in stdOutputRS], 1)
+
+ logweight = 0
+ for i,sample in enumerate(Samples):
+ # Compute likelilhood output vs RS
+ covMatrix = np.diag(TotalPCEstdOutputRS[i]**2)
+ logLik = self._logpdf(TotalOutputs[i], TotalPCEOutputs[i], covMatrix)
+ # Compute posterior likelihood of the collocation points
+ logpostLik = np.log(postDist.pdf(sample[:,None]))[0]
+ if logpostLik != -np.inf:
+ logweight += logLik + logpostLik
+ return logweight
+# # Initialization
+# covMatrix=np.zeros((NofMeasurements, NofMeasurements), float)
+# BME_RM_Model_Weight = np.zeros((SampleSize))
+# BME_RM_Data_Weight = np.zeros((SampleSize))
+# BME_Corr = np.zeros((1))
+
+
+# # Deviation Computations
+# RM_Model_Deviation = np.zeros((SampleSize,NofMeasurements))
+# RM_Data_Deviation = np.zeros((SampleSize,NofMeasurements))
+# for i in range(SampleSize):
+# RM_Model_Deviation[i] = TotalOutputs[i][:NofMeasurements] - TotalPCEOutputs[i, :] # Reduce model- Full Model
+# RM_Data_Deviation[i] = Observations - TotalPCEOutputs[i, :] # Reduce model- Measurement Data
+
+
+# # Initialization of Co-Variance Matrix
+# # For BME_RM_ModelWeight
+# if NofMeasurements == 1:
+# RM_Model_Error = np.zeros((NofMeasurements, NofMeasurements), float)
+# np.fill_diagonal(RM_Model_Error, np.cov(RM_Model_Deviation.T))
+# else:
+# RM_Model_Error = np.cov(RM_Model_Deviation.T)
+
+
+# # Computation of Weight according to the deviations
+# for i in range(SampleSize):
+# # For BME_RM_DataWeight
+# try:
+# var = Sigma[i]
+# if len(var)==1:
+# np.fill_diagonal(covMatrix, var)
+# else:
+# row,col = np.diag_indices(covMatrix.shape[0])
+# covMatrix[row,col] = np.hstack((np.repeat(var[0], NofMeasurements*0.5),np.repeat(var[1], NofMeasurements*0.5)))
+
+# except:
+# var = Sigma
+
+# np.fill_diagonal(covMatrix, var)
+
+# # Add the std of the PCE is emulator is chosen.
+# # if self.emulator:
+# # covMatrix_PCE = np.zeros((NofMeasurements, NofMeasurements), float)
+# # stdPCE = np.empty((SampleSize,0))
+# # for outputType in OutputType:
+# # stdPCE = np.hstack((stdPCE, stdOutputRS[outputType]))
+# #
+# # stdPCE = np.mean(stdPCE, axis=1)
+# # np.fill_diagonal(covMatrix_PCE, stdPCE**2)
+# #
+# # covMatrix = covMatrix + covMatrix_PCE
+
+# # Calculate the denomitor
+# denom1 = (np.sqrt(2*np.pi)) ** NofMeasurements
+# denom2 = (((2*np.pi)**(NofMeasurements/2)) * np.sqrt(np.linalg.det(covMatrix)))
+
+# BME_RM_Model_Weight[i] = (np.exp(-0.5 * np.dot(np.dot(RM_Model_Deviation[i], np.linalg.pinv(RM_Model_Error)), RM_Model_Deviation[i])))/denom1
+# BME_RM_Data_Weight[i] = (np.exp(-0.5 * np.dot(np.dot(RM_Data_Deviation[i], np.linalg.pinv(covMatrix)), RM_Data_Deviation[i][:,np.newaxis])))/denom2
+
+# for i in range(SampleSize):
+# BME_Corr[0] += BME_RM_Model_Weight[i] * BME_RM_Data_Weight[i] / np.nansum(BME_RM_Data_Weight)
+
+# return np.log(BME_Corr[0])
+
+ # -------------------------------------------------------------------------
+ def _rejection_sampling(self):
+ """
+ Performs rejection sampling to update the prior distribution on the
+ input parameters.
+
+ Returns
+ -------
+ posterior : pandas.dataframe
+ Posterior samples of the input parameters.
+
+ """
+
+ MetaModel = self.MetaModel
+ try:
+ sigma2_prior = self.Discrepancy.sigma2_prior
+ except:
+ sigma2_prior = None
+
+ # Check if the discrepancy is defined as a distribution:
+ samples = self.samples
+
+ if sigma2_prior is not None:
+ samples = np.hstack((samples, sigma2_prior))
+
+ # Take the first column of Likelihoods (Observation data without noise)
+ likelihoods = np.exp(self.log_likes[:, 0], dtype=np.float128)
+ n_samples = len(likelihoods)
+ norm_ikelihoods = likelihoods / np.max(likelihoods)
+
+ # Normalize based on min if all Likelihoods are zero
+ if all(likelihoods == 0.0):
+ likelihoods = self.log_likes[:, 0]
+ norm_ikelihoods = likelihoods / np.min(likelihoods)
+
+ # Random numbers between 0 and 1
+ unif = np.random.rand(1, n_samples)[0]
+
+ # Reject the poorly performed prior
+ accepted_samples = samples[norm_ikelihoods >= unif]
+
+ # Output the Posterior
+ par_names = MetaModel.ExpDesign.par_names
+ if sigma2_prior is not None:
+ for name in self.Discrepancy.name:
+ par_names.append(name)
+
+ return pd.DataFrame(accepted_samples, columns=sigma2_prior)
+
+ # -------------------------------------------------------------------------
+ def _posterior_predictive(self):
+ """
+ Stores the prior- and posterior predictive samples, i.e. model
+ evaluations using the samples, into hdf5 files.
+
+ priorPredictive.hdf5 : Prior predictive samples.
+ postPredictive_wo_noise.hdf5 : Posterior predictive samples without
+ the additive noise.
+ postPredictive.hdf5 : Posterior predictive samples with the additive
+ noise.
+
+ Returns
+ -------
+ None.
+
+ """
+
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+
+ # Make a directory to save the prior/posterior predictive
+ out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ os.makedirs(out_dir, exist_ok=True)
+
+ # Read observation data and perturb it if requested
+ if self.measured_data is None:
+ self.measured_data = Model.read_observation(case=self.name)
+
+ if not isinstance(self.measured_data, pd.DataFrame):
+ self.measured_data = pd.DataFrame(self.measured_data)
+
+ # X_values
+ x_values = MetaModel.ExpDesign.x_values
+
+ try:
+ sigma2_prior = self.Discrepancy.sigma2_prior
+ except:
+ sigma2_prior = None
+
+ # Extract posterior samples
+ posterior_df = self.posterior_df
+
+ # Take care of the sigma2
+ if sigma2_prior is not None:
+ try:
+ sigma2s = posterior_df[self.Discrepancy.name].values
+ posterior_df = posterior_df.drop(
+ labels=self.Discrepancy.name, axis=1
+ )
+ except:
+ sigma2s = self.sigma2s
+
+ # Posterior predictive
+ if self.emulator:
+ if self.inference_method == 'rejection':
+ prior_pred = self.__mean_pce_prior_pred
+ if self.name.lower() != 'calib':
+ post_pred = self.__mean_pce_prior_pred
+ post_pred_std = self._std_pce_prior_pred
+ else:
+ post_pred, post_pred_std = MetaModel.eval_metamodel(
+ samples=posterior_df.values
+ )
+
+ else:
+ if self.inference_method == 'rejection':
+ prior_pred = self.__model_prior_pred
+ if self.name.lower() != 'calib':
+ post_pred = self.__mean_pce_prior_pred,
+ post_pred_std = self._std_pce_prior_pred
+ else:
+ post_pred = self._eval_model(
+ samples=posterior_df.values, key='PostPred'
+ )
+ # Correct the predictions with Model discrepancy
+ if hasattr(self, 'error_model') and self.error_model:
+ y_hat, y_std = self.error_MetaModel.eval_model_error(
+ self.bias_inputs, post_pred
+ )
+ post_pred, post_pred_std = y_hat, y_std
+
+ # Add discrepancy from likelihood Sample to the current posterior runs
+ total_sigma2 = self.Discrepancy.total_sigma2
+ post_pred_withnoise = copy.deepcopy(post_pred)
+ for varIdx, var in enumerate(Model.Output.names):
+ for i in range(len(post_pred[var])):
+ pred = post_pred[var][i]
+
+ # Known sigma2s
+ clean_sigma2 = total_sigma2[var][~np.isnan(total_sigma2[var])]
+ tot_sigma2 = clean_sigma2[:len(pred)]
+ cov = np.diag(tot_sigma2)
+
+ # Check the type error term
+ if sigma2_prior is not None:
+ # Inferred sigma2s
+ if hasattr(self, 'bias_inputs') and \
+ not hasattr(self, 'error_model'):
+ # TODO: Infer a Bias model usig GPR
+ bias_inputs = np.hstack((
+ self.bias_inputs[var], pred.reshape(-1, 1)))
+ params = sigma2s[i, varIdx*3:(varIdx+1)*3]
+ cov = self._kernel_rbf(bias_inputs, params)
+ else:
+ # Infer equal sigma2s
+ try:
+ sigma2 = sigma2s[i, varIdx]
+ except TypeError:
+ sigma2 = 0.0
+
+ # Convert biasSigma2s to a covMatrix
+ cov += sigma2 * np.eye(len(pred))
+
+ if self.emulator:
+ if hasattr(MetaModel, 'rmse') and \
+ MetaModel.rmse is not None:
+ stdPCE = MetaModel.rmse[var]
+ else:
+ stdPCE = post_pred_std[var][i]
+ # Expected value of variance (Assump: i.i.d stds)
+ cov += np.diag(stdPCE**2)
+
+ # Sample a multivariate normal distribution with mean of
+ # prediction and variance of cov
+ post_pred_withnoise[var][i] = np.random.multivariate_normal(
+ pred, cov, 1
+ )
+
+ # ----- Prior Predictive -----
+ if self.inference_method.lower() == 'rejection':
+ # Create hdf5 metadata
+ hdf5file = f'{out_dir}/priorPredictive.hdf5'
+ hdf5_exist = os.path.exists(hdf5file)
+ if hdf5_exist:
+ os.remove(hdf5file)
+ file = h5py.File(hdf5file, 'a')
+
+ # Store x_values
+ if type(x_values) is dict:
+ grp_x_values = file.create_group("x_values/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grp_x_values.create_dataset(var, data=x_values[var])
+ else:
+ file.create_dataset("x_values", data=x_values)
+
+ # Store posterior predictive
+ grpY = file.create_group("EDY/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grpY.create_dataset(var, data=prior_pred[var])
+
+ # ----- Posterior Predictive only model evaluations -----
+ # Create hdf5 metadata
+ hdf5file = out_dir+'/postPredictive_wo_noise.hdf5'
+ hdf5_exist = os.path.exists(hdf5file)
+ if hdf5_exist:
+ os.remove(hdf5file)
+ file = h5py.File(hdf5file, 'a')
+
+ # Store x_values
+ if type(x_values) is dict:
+ grp_x_values = file.create_group("x_values/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grp_x_values.create_dataset(var, data=x_values[var])
+ else:
+ file.create_dataset("x_values", data=x_values)
+
+ # Store posterior predictive
+ grpY = file.create_group("EDY/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grpY.create_dataset(var, data=post_pred[var])
+
+ # ----- Posterior Predictive with noise -----
+ # Create hdf5 metadata
+ hdf5file = out_dir+'/postPredictive.hdf5'
+ hdf5_exist = os.path.exists(hdf5file)
+ if hdf5_exist:
+ os.remove(hdf5file)
+ file = h5py.File(hdf5file, 'a')
+
+ # Store x_values
+ if type(x_values) is dict:
+ grp_x_values = file.create_group("x_values/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grp_x_values.create_dataset(var, data=x_values[var])
+ else:
+ file.create_dataset("x_values", data=x_values)
+
+ # Store posterior predictive
+ grpY = file.create_group("EDY/")
+ for varIdx, var in enumerate(Model.Output.names):
+ grpY.create_dataset(var, data=post_pred_withnoise[var])
+
+ return
+
+ # -------------------------------------------------------------------------
+ def _plot_max_a_posteriori(self):
+ """
+ Plots the response of the model output against that of the metamodel at
+ the maximum a posteriori sample (mean or mode of posterior.)
+
+ Returns
+ -------
+ None.
+
+ """
+
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+ out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ opt_sigma = self.Discrepancy.opt_sigma
+
+ # -------- Find MAP and run MetaModel and origModel --------
+ # Compute the MAP
+ if self.max_a_posteriori.lower() == 'mean':
+ if opt_sigma == "B":
+ Posterior_df = self.posterior_df.values
+ else:
+ Posterior_df = self.posterior_df.values[:, :-Model.n_outputs]
+ map_theta = Posterior_df.mean(axis=0).reshape(
+ (1, MetaModel.n_params))
+ else:
+ map_theta = stats.mode(Posterior_df.values, axis=0)[0]
+ # Prin report
+ print("\nPoint estimator:\n", map_theta[0])
+
+ # Run the models for MAP
+ # MetaModel
+ map_metamodel_mean, map_metamodel_std = MetaModel.eval_metamodel(
+ samples=map_theta)
+ self.map_metamodel_mean = map_metamodel_mean
+ self.map_metamodel_std = map_metamodel_std
+
+ # origModel
+ map_orig_model = self._eval_model(samples=map_theta)
+ self.map_orig_model = map_orig_model
+
+ # Extract slicing index
+ x_values = map_orig_model['x_values']
+
+ # List of markers and colors
+ Color = ['k', 'b', 'g', 'r']
+ Marker = 'x'
+
+ # Create a PdfPages object
+ pdf = PdfPages(f'./{out_dir}MAP_PCE_vs_Model_{self.name}.pdf')
+ fig = plt.figure()
+ for i, key in enumerate(Model.Output.names):
+
+ y_val = map_orig_model[key]
+ y_pce_val = map_metamodel_mean[key]
+ y_pce_val_std = map_metamodel_std[key]
+
+ plt.plot(x_values, y_val, color=Color[i], marker=Marker,
+ lw=2.0, label='$Y_{MAP}^{M}$')
+
+ plt.plot(
+ x_values, y_pce_val[i], color=Color[i], lw=2.0,
+ marker=Marker, linestyle='--', label='$Y_{MAP}^{PCE}$'
+ )
+ # plot the confidence interval
+ plt.fill_between(
+ x_values, y_pce_val[i] - 1.96*y_pce_val_std[i],
+ y_pce_val[i] + 1.96*y_pce_val_std[i],
+ color=Color[i], alpha=0.15
+ )
+
+ # Calculate the adjusted R_squared and RMSE
+ R2 = r2_score(y_pce_val.reshape(-1, 1), y_val.reshape(-1, 1))
+ rmse = np.sqrt(mean_squared_error(y_pce_val, y_val))
+
+ plt.ylabel(key)
+ plt.xlabel("Time [s]")
+ plt.title(f'Model vs MetaModel {key}')
+
+ ax = fig.axes[0]
+ leg = ax.legend(loc='best', frameon=True)
+ fig.canvas.draw()
+ p = leg.get_window_extent().inverse_transformed(ax.transAxes)
+ ax.text(
+ p.p0[1]-0.05, p.p1[1]-0.25,
+ f'RMSE = {rmse:.3f}\n$R^2$ = {R2:.3f}',
+ transform=ax.transAxes, color='black',
+ bbox=dict(facecolor='none', edgecolor='black',
+ boxstyle='round,pad=1'))
+
+ plt.show()
+
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+
+ # Destroy the current plot
+ plt.clf()
+
+ pdf.close()
+
+ # -------------------------------------------------------------------------
+ def _plot_post_predictive(self):
+ """
+ Plots the posterior predictives against the observation data.
+
+ Returns
+ -------
+ None.
+
+ """
+
+ Model = self.MetaModel.ModelObj
+ out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ # Plot the posterior predictive
+ for out_idx, out_name in enumerate(Model.Output.names):
+ fig, ax = plt.subplots()
+ with sns.axes_style("ticks"):
+ x_key = list(self.measured_data)[0]
+
+ # --- Read prior and posterior predictive ---
+ if self.inference_method == 'rejection':
+ # --- Prior ---
+ # Load posterior predictive
+ f = h5py.File(
+ f'{out_dir}/priorPredictive.hdf5', 'r+')
+
+ try:
+ x_coords = np.array(f[f"x_values/{out_name}"])
+ except:
+ x_coords = np.array(f["x_values"])
+
+ X_values = np.repeat(x_coords, 10000)
+
+ prior_pred_df = {}
+ prior_pred_df[x_key] = X_values
+ prior_pred_df[out_name] = np.array(
+ f[f"EDY/{out_name}"])[:10000].flatten('F')
+ prior_pred_df = pd.DataFrame(prior_pred_df)
+
+ tags_post = ['prior'] * len(prior_pred_df)
+ prior_pred_df.insert(
+ len(prior_pred_df.columns), "Tags", tags_post,
+ True)
+ f.close()
+
+ # --- Posterior ---
+ f = h5py.File(f"{out_dir}/postPredictive.hdf5", 'r+')
+
+ X_values = np.repeat(
+ x_coords, np.array(f[f"EDY/{out_name}"]).shape[0])
+
+ post_pred_df = {}
+ post_pred_df[x_key] = X_values
+ post_pred_df[out_name] = np.array(
+ f[f"EDY/{out_name}"]).flatten('F')
+
+ post_pred_df = pd.DataFrame(post_pred_df)
+
+ tags_post = ['posterior'] * len(post_pred_df)
+ post_pred_df.insert(
+ len(post_pred_df.columns), "Tags", tags_post, True)
+ f.close()
+ # Concatenate two dataframes based on x_values
+ frames = [prior_pred_df, post_pred_df]
+ all_pred_df = pd.concat(frames)
+
+ # --- Plot posterior predictive ---
+ sns.violinplot(
+ x_key, y=out_name, data=all_pred_df, hue="Tags",
+ legend=False, ax=ax, split=True, inner=None,
+ color=".8")
+
+ # --- Plot Data ---
+ # Find the x,y coordinates for each point
+ x_coords = np.arange(x_coords.shape[0])
+ first_header = list(self.measured_data)[0]
+ obs_data = self.measured_data.round({first_header: 6})
+ sns.pointplot(
+ x=first_header, y=out_name, color='g', markers='x',
+ linestyles='', capsize=16, data=obs_data, ax=ax)
+
+ ax.errorbar(
+ x_coords, obs_data[out_name].values,
+ yerr=1.96*self.measurement_error[out_name],
+ ecolor='g', fmt=' ', zorder=-1)
+
+ # Add labels to the legend
+ handles, labels = ax.get_legend_handles_labels()
+ labels.append('Data')
+
+ data_marker = mlines.Line2D(
+ [], [], color='lime', marker='+', linestyle='None',
+ markersize=10)
+ handles.append(data_marker)
+
+ # Add legend
+ ax.legend(handles=handles, labels=labels, loc='best',
+ fontsize='large', frameon=True)
+
+ else:
+ # Load posterior predictive
+ f = h5py.File(f"{out_dir}/postPredictive.hdf5", 'r+')
+
+ try:
+ x_coords = np.array(f["x_values"])
+ except:
+ x_coords = np.array(f[f"x_values/{out_name}"])
+
+ mu = np.mean(np.array(f[f"EDY/{out_name}"]), axis=0)
+ std = np.std(np.array(f[f"EDY/{out_name}"]), axis=0)
+
+ # --- Plot posterior predictive ---
+ plt.plot(
+ x_coords, mu, marker='o', color='b',
+ label='Mean Post. Predictive')
+ plt.fill_between(
+ x_coords, mu-1.96*std, mu+1.96*std, color='b',
+ alpha=0.15)
+
+ # --- Plot Data ---
+ ax.plot(
+ x_coords, self.measured_data[out_name].values,
+ 'ko', label='data', markeredgecolor='w')
+
+ # --- Plot ExpDesign ---
+ orig_ED_Y = self.MetaModel.ExpDesign.Y[out_name]
+ for output in orig_ED_Y:
+ plt.plot(
+ x_coords, output, color='grey', alpha=0.15
+ )
+
+ # Add labels for axes
+ plt.xlabel('Time [s]')
+ plt.ylabel(out_name)
+
+ # Add labels to the legend
+ handles, labels = ax.get_legend_handles_labels()
+
+ patch = Patch(color='b', alpha=0.15)
+ handles.insert(1, patch)
+ labels.insert(1, '95 $\\%$ CI')
+
+ # Add legend
+ ax.legend(handles=handles, labels=labels, loc='best',
+ frameon=True)
+
+ # Save figure in pdf format
+ if self.emulator:
+ plotname = f'/Post_Prior_Perd_{Model.name}_emulator'
+ else:
+ plotname = f'/Post_Prior_Perd_{Model.name}'
+
+ fig.savefig(f'./{out_dir}{plotname}_{out_name}.pdf',
+ bbox_inches='tight')</code></pre>
+</details>
+<h3>Methods</h3>
+<dl>
+<dt id="bayes_inference.BayesInference.create_inference"><code class="name flex">
+<span>def <span class="ident">create_inference</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Starts the inference.</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>BayesInference</code></strong> : <code>obj</code></dt>
+<dd>The Bayes inference object.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_inference(self):
+ """
+ Starts the inference.
+
+ Returns
+ -------
+ BayesInference : obj
+ The Bayes inference object.
+
+ """
+
+ # Set some variables
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+ n_params = MetaModel.n_params
+ output_names = Model.Output.names
+ par_names = MetaModel.ExpDesign.par_names
+
+ # If the prior is set by the user, take it.
+ if self.samples is None:
+ self.samples = MetaModel.ExpDesign.generate_samples(
+ self.n_samples, 'random')
+ else:
+ try:
+ samples = self.samples.values
+ except AttributeError:
+ samples = self.samples
+
+ # Take care of an additional Sigma2s
+ self.samples = samples[:, :n_params]
+
+ # Update number of samples
+ self.n_samples = self.samples.shape[0]
+
+ # ---------- Preparation of observation data ----------
+ # Read observation data and perturb it if requested.
+ if self.measured_data is None:
+ self.measured_data = Model.read_observation(case=self.name)
+ # Convert measured_data to a data frame
+ if not isinstance(self.measured_data, pd.DataFrame):
+ self.measured_data = pd.DataFrame(self.measured_data)
+
+ # Extract the total number of measurement points
+ if self.name.lower() == 'calib':
+ self.n_tot_measurement = Model.n_obs
+ else:
+ self.n_tot_measurement = Model.n_obs_valid
+
+ # Find measurement error (if not given) for post predictive plot
+ if not hasattr(self, 'measurement_error'):
+ if isinstance(self.Discrepancy, dict):
+ Disc = self.Discrepancy['known']
+ else:
+ Disc = self.Discrepancy
+ if isinstance(Disc.parameters, dict):
+ self.measurement_error = {k: np.sqrt(Disc.parameters[k]) for k
+ in Disc.parameters.keys()}
+ else:
+ try:
+ self.measurement_error = np.sqrt(Disc.parameters)
+ except TypeError:
+ pass
+
+ # ---------- Preparation of variance for covariance matrix ----------
+ # Independent and identically distributed
+ total_sigma2 = dict()
+ opt_sigma_flag = isinstance(self.Discrepancy, dict)
+ opt_sigma = None
+ for key_idx, key in enumerate(output_names):
+
+ # Find opt_sigma
+ if opt_sigma_flag and opt_sigma is None:
+ # Option A: known error with unknown bias term
+ opt_sigma = 'A'
+ known_discrepancy = self.Discrepancy['known']
+ self.Discrepancy = self.Discrepancy['infer']
+ sigma2 = np.array(known_discrepancy.parameters[key])
+
+ elif opt_sigma == 'A' or self.Discrepancy.parameters is not None:
+ # Option B: The sigma2 is known (no bias term)
+ if opt_sigma == 'A':
+ sigma2 = np.array(known_discrepancy.parameters[key])
+ else:
+ opt_sigma = 'B'
+ sigma2 = np.array(self.Discrepancy.parameters[key])
+
+ elif not isinstance(self.Discrepancy.InputDisc, str):
+ # Option C: The sigma2 is unknown (bias term including error)
+ opt_sigma = 'C'
+ self.Discrepancy.opt_sigma = opt_sigma
+ n_measurement = self.measured_data[key].values.shape
+ sigma2 = np.zeros((n_measurement[0]))
+
+ total_sigma2[key] = sigma2
+
+ self.Discrepancy.opt_sigma = opt_sigma
+ self.Discrepancy.total_sigma2 = total_sigma2
+
+ # If inferred sigma2s obtained from e.g. calibration are given
+ try:
+ self.sigma2s = self.Discrepancy.get_sample(self.n_samples)
+ except:
+ pass
+
+ # ---------------- Bootstrap & TOM --------------------
+ if self.bootstrap or self.bayes_loocv:
+ if len(self.perturbed_data) == 0:
+ # zero mean noise Adding some noise to the observation function
+ self.perturbed_data = self._perturb_data(
+ self.measured_data, output_names
+ )
+ else:
+ self.n_bootstrap_itrs = len(self.perturbed_data)
+
+ # -------- Model Discrepancy -----------
+ if hasattr(self, 'error_model') and self.error_model \
+ and self.name.lower() != 'calib':
+ # Select posterior mean as MAP
+ MAP_theta = self.samples.mean(axis=0).reshape((1, n_params))
+ # MAP_theta = stats.mode(self.samples,axis=0)[0]
+
+ # Evaluate the (meta-)model at the MAP
+ y_MAP, y_std_MAP = MetaModel.eval_metamodel(samples=MAP_theta)
+
+ # Train a GPR meta-model using MAP
+ self.error_MetaModel = MetaModel.create_model_error(
+ self.bias_inputs, y_MAP, Name=self.name
+ )
+
+ # -----------------------------------------------------
+ # ----- Loop over the perturbed observation data ------
+ # -----------------------------------------------------
+ # Initilize arrays
+ logLikelihoods = np.zeros((self.n_samples, self.n_bootstrap_itrs),
+ dtype=np.float16)
+ BME_Corr = np.zeros((self.n_bootstrap_itrs))
+ log_BME = np.zeros((self.n_bootstrap_itrs))
+ KLD = np.zeros((self.n_bootstrap_itrs))
+ inf_entropy = np.zeros((self.n_bootstrap_itrs))
+
+ # Compute the prior predtions
+ # Evaluate the MetaModel
+ if self.emulator:
+ y_hat, y_std = MetaModel.eval_metamodel(samples=self.samples)
+ self.__mean_pce_prior_pred = y_hat
+ self._std_pce_prior_pred = y_std
+
+ # Correct the predictions with Model discrepancy
+ if hasattr(self, 'error_model') and self.error_model:
+ y_hat_corr, y_std = self.error_MetaModel.eval_model_error(
+ self.bias_inputs, self.__mean_pce_prior_pred
+ )
+ self.__mean_pce_prior_pred = y_hat_corr
+ self._std_pce_prior_pred = y_std
+
+ # Surrogate model's error using RMSE of test data
+ if hasattr(MetaModel, 'rmse'):
+ surrError = MetaModel.rmse
+ else:
+ surrError = None
+
+ else:
+ # Evaluate the original model
+ self.__model_prior_pred = self._eval_model(
+ samples=self.samples, key='PriorPred'
+ )
+
+ # Start the likelihood-BME computations for the perturbed data
+ for itr_idx, data in tqdm(
+ enumerate(self.perturbed_data), ascii=True,
+ desc="Boostraping the BME calculations"
+ ):
+
+ # ---------------- Likelihood calculation ----------------
+ if self.emulator:
+ model_evals = self.__mean_pce_prior_pred
+ else:
+ model_evals = self.__model_prior_pred
+
+ # Leave one out
+ if self.bayes_loocv:
+ self.selected_indices = np.nonzero(data)[0]
+
+ # Prepare data dataframe
+ nobs = list(self.measured_data.count().values[1:])
+ numbers = list(map(sum, zip([0] + nobs, nobs)))
+ indices = list(zip([0] + numbers, numbers))
+ data_dict = {
+ output_names[i]: data[j:k] for i, (j, k) in
+ enumerate(indices)
+ }
+
+ # Unknown sigma2
+ if opt_sigma == 'C' or hasattr(self, 'sigma2s'):
+ logLikelihoods[:, itr_idx] = self.normpdf(
+ model_evals, data_dict, total_sigma2,
+ sigma2=self.sigma2s, std=surrError
+ )
+ else:
+ # known sigma2
+ logLikelihoods[:, itr_idx] = self.normpdf(
+ model_evals, data_dict, total_sigma2,
+ std=surrError
+ )
+
+ # ---------------- BME Calculations ----------------
+ # BME (log)
+ log_BME[itr_idx] = np.log(
+ np.nanmean(np.exp(logLikelihoods[:, itr_idx],
+ dtype=np.float128))
+ )
+
+ # Rejection Step
+ # Random numbers between 0 and 1
+ unif = np.random.rand(1, self.n_samples)[0]
+
+ # Reject the poorly performed prior
+ Likelihoods = np.exp(logLikelihoods[:, itr_idx],
+ dtype=np.float64)
+ accepted = (Likelihoods/np.max(Likelihoods)) >= unif
+ posterior = self.samples[accepted]
+
+ # Posterior-based expectation of likelihoods
+ postExpLikelihoods = np.mean(
+ logLikelihoods[:, itr_idx][accepted]
+ )
+
+ # Posterior-based expectation of prior densities
+ postExpPrior = np.mean(
+ np.log([MetaModel.ExpDesign.JDist.pdf(posterior.T)])
+ )
+
+ # Calculate Kullback-Leibler Divergence
+ KLD[itr_idx] = postExpLikelihoods - log_BME[itr_idx]
+
+ # Information Entropy based on Entropy paper Eq. 38
+ inf_entropy[itr_idx] = log_BME[itr_idx] - postExpPrior - \
+ postExpLikelihoods
+
+ # TODO: BME correction when using Emulator
+ # if self.emulator:
+ # BME_Corr[itr_idx] = self._corr_factor_BME(
+ # data, total_sigma2, posterior
+ # )
+
+ # Clear memory
+ gc.collect(generation=2)
+
+ # ---------------- Store BME, Likelihoods for all ----------------
+ # Likelihoods (Size: n_samples, n_bootstrap_itr)
+ self.log_likes = logLikelihoods
+
+ # BME (log), KLD, infEntropy (Size: 1,n_bootstrap_itr)
+ self.log_BME = log_BME
+ self.KLD = KLD
+ self.inf_entropy = inf_entropy
+
+ # TODO: BMECorrFactor (log) (Size: 1,n_bootstrap_itr)
+ # if self.emulator: self.BMECorrFactor = BME_Corr
+
+ # BME = BME + BMECorrFactor
+ if self.emulator:
+ self.log_BME = self.log_BME # + self.BMECorrFactor
+
+ # ---------------- Parameter Bayesian inference ----------------
+ if self.inference_method.lower() == 'mcmc':
+ # Instantiate the MCMC object
+ MCMC_Obj = MCMC(self)
+ self.posterior_df = MCMC_Obj.run_sampler(
+ self.measured_data, total_sigma2
+ )
+
+ elif self.name.lower() == 'valid':
+ # Convert to a dataframe if samples are provided after calibration.
+ self.posterior_df = pd.DataFrame(self.samples, columns=par_names)
+
+ else:
+ # Rejection sampling
+ self.posterior_df = self._rejection_sampling()
+
+ # Provide posterior's summary
+ print('\n')
+ print('-'*15 + 'Posterior summary' + '-'*15)
+ pd.options.display.max_columns = None
+ pd.options.display.max_rows = None
+ print(self.posterior_df.describe())
+ print('-'*50)
+
+ # -------- Model Discrepancy -----------
+ if hasattr(self, 'error_model') and self.error_model \
+ and self.name.lower() == 'calib':
+ if self.inference_method.lower() == 'mcmc':
+ self.error_MetaModel = MCMC_Obj.error_MetaModel
+ else:
+ # Select posterior mean as MAP
+ if opt_sigma == "B":
+ posterior_df = self.posterior_df.values
+ else:
+ posterior_df = self.posterior_df.values[:, :-Model.n_outputs]
+
+ # Select posterior mean as Maximum a posteriori
+ map_theta = posterior_df.mean(axis=0).reshape((1, n_params))
+ # map_theta = stats.mode(Posterior_df,axis=0)[0]
+
+ # Evaluate the (meta-)model at the MAP
+ y_MAP, y_std_MAP = MetaModel.eval_metamodel(samples=map_theta)
+
+ # Train a GPR meta-model using MAP
+ self.error_MetaModel = MetaModel.create_model_error(
+ self.bias_inputs, y_MAP, Name=self.name
+ )
+
+ # -------- Posterior perdictive -----------
+ self._posterior_predictive()
+
+ # -----------------------------------------------------
+ # ------------------ Visualization --------------------
+ # -----------------------------------------------------
+ # Create Output directory, if it doesn't exist already.
+ out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ os.makedirs(out_dir, exist_ok=True)
+
+ # -------- Posteior parameters --------
+ if opt_sigma != "B":
+ par_names.extend(
+ [self.Discrepancy.InputDisc.Marginals[i].name for i
+ in range(len(self.Discrepancy.InputDisc.Marginals))]
+ )
+ # Pot with corner
+ figPosterior = corner.corner(self.posterior_df.to_numpy(),
+ labels=par_names,
+ quantiles=[0.15, 0.5, 0.85],
+ show_titles=True,
+ title_fmt=self.corner_title_fmt,
+ labelpad=0.2,
+ use_math_text=True,
+ title_kwargs={"fontsize": 28},
+ plot_datapoints=False,
+ plot_density=False,
+ fill_contours=True,
+ smooth=0.5,
+ smooth1d=0.5)
+
+ # Loop over axes and set x limits
+ if opt_sigma == "B":
+ axes = np.array(figPosterior.axes).reshape(
+ (len(par_names), len(par_names))
+ )
+ for yi in range(len(par_names)):
+ ax = axes[yi, yi]
+ ax.set_xlim(MetaModel.bound_tuples[yi])
+ for xi in range(yi):
+ ax = axes[yi, xi]
+ ax.set_xlim(MetaModel.bound_tuples[xi])
+
+ # Turn off gridlines
+ for ax in figPosterior.axes:
+ ax.grid(False)
+
+ if self.emulator:
+ plotname = f'/Posterior_Dist_{Model.name}_emulator'
+ else:
+ plotname = f'/Posterior_Dist_{Model.name}'
+
+ figPosterior.set_size_inches((24, 16))
+ figPosterior.savefig(f'./{out_dir}{plotname}.pdf',
+ bbox_inches='tight')
+
+ # -------- Plot MAP --------
+ if self.plot_map_pred:
+ self._plot_max_a_posteriori()
+
+ # -------- Plot log_BME dist --------
+ if self.bootstrap and self.n_bootstrap_itrs > 1:
+ # Computing the TOM performance
+ self.log_BME_tom = stats.chi2.rvs(
+ self.n_tot_measurement, size=self.log_BME.shape[0]
+ )
+
+ fig, ax = plt.subplots()
+ sns.kdeplot(self.log_BME_tom, ax=ax, color="green", shade=True)
+ sns.kdeplot(
+ self.log_BME, ax=ax, color="blue", shade=True,
+ label='Model BME')
+
+ ax.set_xlabel('log$_{10}$(BME)')
+ ax.set_ylabel('Probability density')
+
+ legend_elements = [
+ Patch(facecolor='green', edgecolor='green', label='TOM BME'),
+ Patch(facecolor='blue', edgecolor='blue', label='Model BME')
+ ]
+ ax.legend(handles=legend_elements)
+
+ if self.emulator:
+ plotname = f'/BME_hist_{Model.name}_emulator'
+ else:
+ plotname = f'/BME_hist_{Model.name}'
+
+ plt.savefig(f'./{out_dir}{plotname}.pdf', bbox_inches='tight')
+ plt.show()
+ plt.close()
+
+ # -------- Posteior perdictives --------
+ if self.plot_post_pred:
+ # Plot the posterior predictive
+ self._plot_post_predictive()
+
+ return self</code></pre>
+</details>
+</dd>
+<dt id="bayes_inference.BayesInference.normpdf"><code class="name flex">
+<span>def <span class="ident">normpdf</span></span>(<span>self, outputs, obs_data, total_sigma2s, sigma2=None, std=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Calculates the likelihood of simulation outputs compared with
+observation data.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>outputs</code></strong> : <code>dict</code></dt>
+<dd>A dictionary containing the simulation outputs as array of shape
+(n_samples, n_measurement) for each model output.</dd>
+<dt><strong><code>obs_data</code></strong> : <code>dict</code></dt>
+<dd>A dictionary/dataframe containing the observation data.</dd>
+<dt><strong><code>total_sigma2s</code></strong> : <code>dict</code></dt>
+<dd>A dictionary with known values of the covariance diagonal entries,
+a.k.a sigma^2.</dd>
+<dt><strong><code>sigma2</code></strong> : <code>array</code>, optional</dt>
+<dd>An array of the sigma^2 samples, when the covariance diagonal
+entries are unknown and are being jointly inferred. The default is
+None.</dd>
+<dt><strong><code>std</code></strong> : <code>dict</code>, optional</dt>
+<dd>A dictionary containing the root mean squared error as array of
+shape (n_samples, n_measurement) for each model output. The default
+is None.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>logLik</code></strong> : <code>array</code> of <code>shape (n_samples)</code></dt>
+<dd>Likelihoods.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def normpdf(self, outputs, obs_data, total_sigma2s, sigma2=None, std=None):
+ """
+ Calculates the likelihood of simulation outputs compared with
+ observation data.
+
+ Parameters
+ ----------
+ outputs : dict
+ A dictionary containing the simulation outputs as array of shape
+ (n_samples, n_measurement) for each model output.
+ obs_data : dict
+ A dictionary/dataframe containing the observation data.
+ total_sigma2s : dict
+ A dictionary with known values of the covariance diagonal entries,
+ a.k.a sigma^2.
+ sigma2 : array, optional
+ An array of the sigma^2 samples, when the covariance diagonal
+ entries are unknown and are being jointly inferred. The default is
+ None.
+ std : dict, optional
+ A dictionary containing the root mean squared error as array of
+ shape (n_samples, n_measurement) for each model output. The default
+ is None.
+
+ Returns
+ -------
+ logLik : array of shape (n_samples)
+ Likelihoods.
+
+ """
+ Model = self.MetaModel.ModelObj
+ logLik = 0.0
+
+ # Extract the requested model outputs for likelihood calulation
+ if self.req_outputs is None:
+ req_outputs = Model.Output.names
+ else:
+ req_outputs = list(self.req_outputs)
+
+ # Loop over the outputs
+ for idx, out in enumerate(req_outputs):
+
+ # (Meta)Model Output
+ nsamples, nout = outputs[out].shape
+
+ # Prepare data and remove NaN
+ try:
+ data = obs_data[out].values[~np.isnan(obs_data[out])]
+ except AttributeError:
+ data = obs_data[out][~np.isnan(obs_data[out])]
+
+ # Prepare sigma2s
+ tot_sigma2s = total_sigma2s[out][~np.isnan(
+ total_sigma2s[out])][:nout]
+
+ # Add the std of the PCE is chosen as emulator.
+ if self.emulator:
+ if std is not None:
+ std_pce = std[out]
+ else:
+ std_pce = np.mean(
+ self._std_pce_prior_pred[out], axis=0)
+ # Expected value of variance (Assump: i.i.d stds)
+ tot_sigma2s += std_pce**2
+
+ # If sigma2 is not given, use given total_sigma2s
+ if sigma2 is None:
+ logLik += stats.multivariate_normal.logpdf(
+ outputs[out], data, np.diag(tot_sigma2s))
+ continue
+
+ # Loop over each run/sample and calculate logLikelihood
+ logliks = np.zeros(nsamples)
+ for s_idx in range(nsamples):
+
+ # Simulation run
+ tot_outputs = outputs[out]
+
+ # Covariance Matrix
+ covMatrix = np.diag(tot_sigma2s)
+
+ if sigma2 is not None:
+ # Check the type error term
+ if hasattr(self, 'bias_inputs') and \
+ not hasattr(self, 'error_model'):
+ # Infer a Bias model usig Gaussian Process Regression
+ bias_inputs = np.hstack(
+ (self.bias_inputs[out],
+ tot_outputs[s_idx].reshape(-1, 1)))
+
+ params = sigma2[s_idx, idx*3:(idx+1)*3]
+ covMatrix = self._kernel_rbf(bias_inputs, params)
+ else:
+ # Infer equal sigma2s
+ try:
+ sigma_2 = sigma2[s_idx, idx]
+ except TypeError:
+ sigma_2 = 0.0
+
+ covMatrix += sigma_2 * np.eye(nout)
+ # covMatrix = np.diag(sigma2 * total_sigma2s)
+
+ # Select the data points to compare
+ if self.selected_indices is not None:
+ indices = self.selected_indices[out]
+ covMatrix = np.diag(covMatrix[indices, indices])
+ else:
+ indices = list(range(nout))
+
+ # Compute loglikelihood
+ logliks[s_idx] = self._logpdf(
+ tot_outputs[s_idx, indices], data[indices], covMatrix
+ )
+
+ logLik += logliks
+ return logLik</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="bayes_inference.BayesInference" href="#bayes_inference.BayesInference">BayesInference</a></code></h4>
+<ul class="">
+<li><code><a title="bayes_inference.BayesInference.create_inference" href="#bayes_inference.BayesInference.create_inference">create_inference</a></code></li>
+<li><code><a title="bayes_inference.BayesInference.normpdf" href="#bayes_inference.BayesInference.normpdf">normpdf</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
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\ No newline at end of file
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+<article id="content">
+<header>
+<h1 class="title">Module <code>discrepancy</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import scipy.stats as stats
+# from src.bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
+
+
+class Discrepancy:
+ """
+ Discrepancy class for Bayesian inference method.
+ We define the reference or reality to be equal to what we can model and a
+ descripancy term \\( \\epsilon \\). We consider the followin format:
+
+ $$\\textbf{y}_{\\text{reality}} = \\mathcal{M}(\\theta) + \\epsilon,$$
+
+ where \\( \\epsilon \\in R^{N_{out}} \\) represents the the effects of
+ measurement error and model inaccuracy. For simplicity, it can be defined
+ as an additive Gaussian disrepancy with zeromean and given covariance
+ matrix \\( \\Sigma \\):
+
+ $$\\epsilon \\sim \\mathcal{N}(\\epsilon|0, \\Sigma). $$
+
+ In the context of model inversion or calibration, an observation point
+ \\( \\textbf{y}_i \\in \\mathcal{y} \\) is a realization of a Gaussian
+ distribution with mean value of \\(\\mathcal{M}(\\theta) \\) and covariance
+ matrix of \\( \\Sigma \\).
+
+ $$ p(\\textbf{y}|\\theta) = \\mathcal{N}(\\textbf{y}|\\mathcal{M}
+ (\\theta))$$
+
+ The following options are available:
+
+ * Option A: With known redidual covariance matrix \\(\\Sigma\\) for
+ independent measurements.
+
+ * Option B: With unknown redidual covariance matrix \\(\\Sigma\\),
+ paramethrized as \\(\\Sigma(\\theta_{\\epsilon})=\\sigma^2 \\textbf{I}_
+ {N_{out}}\\) with unknown residual variances \\(\\sigma^2\\).
+ This term will be jointly infer with the uncertain input parameters. For
+ the inversion, you need to define a prior marginal via `Input` class. Note
+ that \\(\\sigma^2\\) is only a single scalar multiplier for the diagonal
+ entries of the covariance matrix \\(\\Sigma\\).
+
+ Attributes
+ ----------
+ InputDisc : obj
+ Input object. When the \\(\\sigma^2\\) is expected to be inferred
+ jointly with the parameters (`Option B`).If multiple output groups are
+ defined by `Model.Output.names`, each model output needs to have.
+ a prior marginal using the `Input` class. The default is `''`.
+ disc_type : str
+ Type of the noise definition. `'Gaussian'` is only supported so far.
+ parameters : dict or pandas.DataFrame
+ Known residual variance \\(\\sigma^2\\), i.e. diagonal entry of the
+ covariance matrix of the multivariate normal likelihood in case of
+ `Option A`.
+
+ """
+
+ def __init__(self, InputDisc='', disc_type='Gaussian', parameters=None):
+ self.InputDisc = InputDisc
+ self.disc_type = disc_type
+ self.parameters = parameters
+
+ # -------------------------------------------------------------------------
+ def get_sample(self, n_samples):
+ """
+ Generate samples for the \\(\\sigma^2\\), i.e. the diagonal entries of
+ the variance-covariance matrix in the multivariate normal distribution.
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of samples (parameter sets).
+
+ Returns
+ -------
+ sigma2_prior: array of shape (n_samples, n_params)
+ \\(\\sigma^2\\) samples.
+
+ """
+ self.n_samples = n_samples
+ ExpDesign = ExpDesigns(self.InputDisc)
+ self.sigma2_prior = ExpDesign.generate_ED(
+ n_samples, sampling_method='random', max_pce_deg=1
+ )
+ # Store BoundTuples
+ self.ExpDesign = ExpDesign
+
+ # Naive approach: Fit a gaussian kernel to the provided data
+ self.ExpDesign.JDist = stats.gaussian_kde(ExpDesign.raw_data)
+
+ # Save the names of sigmas
+ if len(self.InputDisc.Marginals) != 0:
+ self.name = []
+ for Marginalidx in range(len(self.InputDisc.Marginals)):
+ self.name.append(self.InputDisc.Marginals[Marginalidx].name)
+
+ return self.sigma2_prior</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="discrepancy.Discrepancy"><code class="flex name class">
+<span>class <span class="ident">Discrepancy</span></span>
+<span>(</span><span>InputDisc='', disc_type='Gaussian', parameters=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Discrepancy class for Bayesian inference method.
+We define the reference or reality to be equal to what we can model and a
+descripancy term <span><span class="MathJax_Preview"> \epsilon </span><script type="math/tex"> \epsilon </script></span>. We consider the followin format:</p>
+<p><span><span class="MathJax_Preview">\textbf{y}_{\text{reality}} = \mathcal{M}(\theta) + \epsilon,</span><script type="math/tex; mode=display">\textbf{y}_{\text{reality}} = \mathcal{M}(\theta) + \epsilon,</script></span></p>
+<p>where <span><span class="MathJax_Preview"> \epsilon \in R^{N_{out}} </span><script type="math/tex"> \epsilon \in R^{N_{out}} </script></span> represents the the effects of
+measurement error and model inaccuracy. For simplicity, it can be defined
+as an additive Gaussian disrepancy with zeromean and given covariance
+matrix <span><span class="MathJax_Preview"> \Sigma </span><script type="math/tex"> \Sigma </script></span>:</p>
+<p><span><span class="MathJax_Preview">\epsilon \sim \mathcal{N}(\epsilon|0, \Sigma). </span><script type="math/tex; mode=display">\epsilon \sim \mathcal{N}(\epsilon|0, \Sigma). </script></span></p>
+<p>In the context of model inversion or calibration, an observation point
+<span><span class="MathJax_Preview"> \textbf{y}_i \in \mathcal{y} </span><script type="math/tex"> \textbf{y}_i \in \mathcal{y} </script></span> is a realization of a Gaussian
+distribution with mean value of <span><span class="MathJax_Preview">\mathcal{M}(\theta) </span><script type="math/tex">\mathcal{M}(\theta) </script></span> and covariance
+matrix of <span><span class="MathJax_Preview"> \Sigma </span><script type="math/tex"> \Sigma </script></span>.</p>
+<p><span><span class="MathJax_Preview"> p(\textbf{y}|\theta) = \mathcal{N}(\textbf{y}|\mathcal{M}
+(\theta))</span><script type="math/tex; mode=display"> p(\textbf{y}|\theta) = \mathcal{N}(\textbf{y}|\mathcal{M}
+(\theta))</script></span></p>
+<p>The following options are available:</p>
+<ul>
+<li>
+<p>Option A: With known redidual covariance matrix <span><span class="MathJax_Preview">\Sigma</span><script type="math/tex">\Sigma</script></span> for
+independent measurements.</p>
+</li>
+<li>
+<p>Option B: With unknown redidual covariance matrix <span><span class="MathJax_Preview">\Sigma</span><script type="math/tex">\Sigma</script></span>,
+paramethrized as <span><span class="MathJax_Preview">\Sigma(\theta_{\epsilon})=\sigma^2 \textbf{I}_
+{N_{out}}</span><script type="math/tex">\Sigma(\theta_{\epsilon})=\sigma^2 \textbf{I}_
+{N_{out}}</script></span> with unknown residual variances <span><span class="MathJax_Preview">\sigma^2</span><script type="math/tex">\sigma^2</script></span>.
+This term will be jointly infer with the uncertain input parameters. For
+the inversion, you need to define a prior marginal via <code>Input</code> class. Note
+that <span><span class="MathJax_Preview">\sigma^2</span><script type="math/tex">\sigma^2</script></span> is only a single scalar multiplier for the diagonal
+entries of the covariance matrix <span><span class="MathJax_Preview">\Sigma</span><script type="math/tex">\Sigma</script></span>.</p>
+</li>
+</ul>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>InputDisc</code></strong> : <code>obj</code></dt>
+<dd>Input object. When the <span><span class="MathJax_Preview">\sigma^2</span><script type="math/tex">\sigma^2</script></span> is expected to be inferred
+jointly with the parameters (<code>Option B</code>).If multiple output groups are
+defined by <code>Model.Output.names</code>, each model output needs to have.
+a prior marginal using the <code>Input</code> class. The default is <code>''</code>.</dd>
+<dt><strong><code>disc_type</code></strong> : <code>str</code></dt>
+<dd>Type of the noise definition. <code>'Gaussian'</code> is only supported so far.</dd>
+<dt><strong><code>parameters</code></strong> : <code>dict</code> or <code>pandas.DataFrame</code></dt>
+<dd>Known residual variance <span><span class="MathJax_Preview">\sigma^2</span><script type="math/tex">\sigma^2</script></span>, i.e. diagonal entry of the
+covariance matrix of the multivariate normal likelihood in case of
+<code>Option A</code>.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class Discrepancy:
+ """
+ Discrepancy class for Bayesian inference method.
+ We define the reference or reality to be equal to what we can model and a
+ descripancy term \\( \\epsilon \\). We consider the followin format:
+
+ $$\\textbf{y}_{\\text{reality}} = \\mathcal{M}(\\theta) + \\epsilon,$$
+
+ where \\( \\epsilon \\in R^{N_{out}} \\) represents the the effects of
+ measurement error and model inaccuracy. For simplicity, it can be defined
+ as an additive Gaussian disrepancy with zeromean and given covariance
+ matrix \\( \\Sigma \\):
+
+ $$\\epsilon \\sim \\mathcal{N}(\\epsilon|0, \\Sigma). $$
+
+ In the context of model inversion or calibration, an observation point
+ \\( \\textbf{y}_i \\in \\mathcal{y} \\) is a realization of a Gaussian
+ distribution with mean value of \\(\\mathcal{M}(\\theta) \\) and covariance
+ matrix of \\( \\Sigma \\).
+
+ $$ p(\\textbf{y}|\\theta) = \\mathcal{N}(\\textbf{y}|\\mathcal{M}
+ (\\theta))$$
+
+ The following options are available:
+
+ * Option A: With known redidual covariance matrix \\(\\Sigma\\) for
+ independent measurements.
+
+ * Option B: With unknown redidual covariance matrix \\(\\Sigma\\),
+ paramethrized as \\(\\Sigma(\\theta_{\\epsilon})=\\sigma^2 \\textbf{I}_
+ {N_{out}}\\) with unknown residual variances \\(\\sigma^2\\).
+ This term will be jointly infer with the uncertain input parameters. For
+ the inversion, you need to define a prior marginal via `Input` class. Note
+ that \\(\\sigma^2\\) is only a single scalar multiplier for the diagonal
+ entries of the covariance matrix \\(\\Sigma\\).
+
+ Attributes
+ ----------
+ InputDisc : obj
+ Input object. When the \\(\\sigma^2\\) is expected to be inferred
+ jointly with the parameters (`Option B`).If multiple output groups are
+ defined by `Model.Output.names`, each model output needs to have.
+ a prior marginal using the `Input` class. The default is `''`.
+ disc_type : str
+ Type of the noise definition. `'Gaussian'` is only supported so far.
+ parameters : dict or pandas.DataFrame
+ Known residual variance \\(\\sigma^2\\), i.e. diagonal entry of the
+ covariance matrix of the multivariate normal likelihood in case of
+ `Option A`.
+
+ """
+
+ def __init__(self, InputDisc='', disc_type='Gaussian', parameters=None):
+ self.InputDisc = InputDisc
+ self.disc_type = disc_type
+ self.parameters = parameters
+
+ # -------------------------------------------------------------------------
+ def get_sample(self, n_samples):
+ """
+ Generate samples for the \\(\\sigma^2\\), i.e. the diagonal entries of
+ the variance-covariance matrix in the multivariate normal distribution.
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of samples (parameter sets).
+
+ Returns
+ -------
+ sigma2_prior: array of shape (n_samples, n_params)
+ \\(\\sigma^2\\) samples.
+
+ """
+ self.n_samples = n_samples
+ ExpDesign = ExpDesigns(self.InputDisc)
+ self.sigma2_prior = ExpDesign.generate_ED(
+ n_samples, sampling_method='random', max_pce_deg=1
+ )
+ # Store BoundTuples
+ self.ExpDesign = ExpDesign
+
+ # Naive approach: Fit a gaussian kernel to the provided data
+ self.ExpDesign.JDist = stats.gaussian_kde(ExpDesign.raw_data)
+
+ # Save the names of sigmas
+ if len(self.InputDisc.Marginals) != 0:
+ self.name = []
+ for Marginalidx in range(len(self.InputDisc.Marginals)):
+ self.name.append(self.InputDisc.Marginals[Marginalidx].name)
+
+ return self.sigma2_prior</code></pre>
+</details>
+<h3>Methods</h3>
+<dl>
+<dt id="discrepancy.Discrepancy.get_sample"><code class="name flex">
+<span>def <span class="ident">get_sample</span></span>(<span>self, n_samples)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Generate samples for the <span><span class="MathJax_Preview">\sigma^2</span><script type="math/tex">\sigma^2</script></span>, i.e. the diagonal entries of
+the variance-covariance matrix in the multivariate normal distribution.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>n_samples</code></strong> : <code>int</code></dt>
+<dd>Number of samples (parameter sets).</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>sigma2_prior</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
+<dd><span><span class="MathJax_Preview">\sigma^2</span><script type="math/tex">\sigma^2</script></span> samples.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def get_sample(self, n_samples):
+ """
+ Generate samples for the \\(\\sigma^2\\), i.e. the diagonal entries of
+ the variance-covariance matrix in the multivariate normal distribution.
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of samples (parameter sets).
+
+ Returns
+ -------
+ sigma2_prior: array of shape (n_samples, n_params)
+ \\(\\sigma^2\\) samples.
+
+ """
+ self.n_samples = n_samples
+ ExpDesign = ExpDesigns(self.InputDisc)
+ self.sigma2_prior = ExpDesign.generate_ED(
+ n_samples, sampling_method='random', max_pce_deg=1
+ )
+ # Store BoundTuples
+ self.ExpDesign = ExpDesign
+
+ # Naive approach: Fit a gaussian kernel to the provided data
+ self.ExpDesign.JDist = stats.gaussian_kde(ExpDesign.raw_data)
+
+ # Save the names of sigmas
+ if len(self.InputDisc.Marginals) != 0:
+ self.name = []
+ for Marginalidx in range(len(self.InputDisc.Marginals)):
+ self.name.append(self.InputDisc.Marginals[Marginalidx].name)
+
+ return self.sigma2_prior</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="discrepancy.Discrepancy" href="#discrepancy.Discrepancy">Discrepancy</a></code></h4>
+<ul class="">
+<li><code><a title="discrepancy.Discrepancy.get_sample" href="#discrepancy.Discrepancy.get_sample">get_sample</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
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+</html>
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+<article id="content">
+<header>
+<h1 class="title">Module <code>exp_designs</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import math
+import itertools
+import chaospy
+import scipy.stats as st
+from tqdm import tqdm
+
+from apoly_construction import apoly_construction
+
+
+class ExpDesigns:
+ """
+ This class generates samples from the prescribed marginals for the model
+ parameters using the `Input` object.
+
+ Attributes
+ ----------
+ Input : obj
+ Input object containing the parameter marginals, i.e. name,
+ distribution type and distribution parameters or available raw data.
+ method : str
+ Type of the experimental design. The default is `'normal'`. Other
+ option is `'sequential'`.
+ meta_Model : str
+ Type of the meta_model.
+ sampling_method : str
+ Name of the sampling method for the experimental design. The following
+ sampling method are supported:
+
+ * random
+ * latin_hypercube
+ * sobol
+ * halton
+ * hammersley
+ * korobov
+ * chebyshev(FT)
+ * grid(FT)
+ * nested_grid(FT)
+ * user
+ hdf5_file : str
+ Name of the hdf5 file that contains the experimental design.
+ n_new_samples : int
+ Number of (initial) training points.
+ n_max_samples : int
+ Number of maximum training points.
+ mod_LOO_threshold : float
+ The modified leave-one-out cross validation threshold where the
+ sequential design stops.
+ tradeoff_scheme : str
+ Trade-off scheme to assign weights to the exploration and exploitation
+ scores in the sequential design.
+ n_canddidate : int
+ Number of candidate training sets to calculate the scores for.
+ explore_method : str
+ Type of the exploration method for the sequential design. The following
+ methods are supported:
+
+ * Voronoi
+ * random
+ * latin_hypercube
+ * LOOCV
+ * dual annealing
+ exploit_method : str
+ Type of the exploitation method for the sequential design. The
+ following methods are supported:
+
+ * BayesOptDesign
+ * BayesActDesign
+ * VarOptDesign
+ * alphabetic
+ * Space-filling
+ util_func : str or list
+ The utility function to be specified for the `exploit_method`. For the
+ available utility functions see Note section.
+ n_cand_groups : int
+ Number of candidate groups. Each group of candidate training sets will
+ be evaulated separately in parallel.
+ n_replication : int
+ Number of replications. Only for comparison. The default is 1.
+ post_snapshot : int
+ Whether to plot the posterior in the sequential design. The default is
+ `True`.
+ step_snapshot : int
+ The number of steps to plot the posterior in the sequential design. The
+ default is 1.
+ max_a_post : list or array
+ Maximum a posteriori of the posterior distribution, if known. The
+ default is `[]`.
+ adapt_verbose : bool
+ Whether to plot the model response vs that of metamodel for the new
+ trining point in the sequential design.
+
+ Note
+ ----------
+ The following utiliy functions for the **exploitation** methods are
+ supported:
+
+ #### BayesOptDesign (when data is available)
+ - DKL (Kullback-Leibler Divergence)
+ - DPP (D-Posterior-percision)
+ - APP (A-Posterior-percision)
+
+ #### VarBasedOptDesign -> when data is not available
+ - Entropy (Entropy/MMSE/active learning)
+ - EIGF (Expected Improvement for Global fit)
+ - LOOCV (Leave-one-out Cross Validation)
+
+ #### alphabetic
+ - D-Opt (D-Optimality)
+ - A-Opt (A-Optimality)
+ - K-Opt (K-Optimality)
+ """
+
+ def __init__(self, Input, method='normal', meta_Model='pce',
+ sampling_method='random', hdf5_file=None,
+ n_new_samples=1, n_max_samples=None, mod_LOO_threshold=1e-16,
+ tradeoff_scheme=None, n_canddidate=1, explore_method='random',
+ exploit_method='Space-filling', util_func='Space-filling',
+ n_cand_groups=4, n_replication=1, post_snapshot=False,
+ step_snapshot=1, max_a_post=[], adapt_verbose=False):
+
+ self.InputObj = Input
+ self.method = method
+ self.meta_Model = meta_Model
+ self.sampling_method = sampling_method
+ self.hdf5_file = hdf5_file
+ self.n_new_samples = n_new_samples
+ self.n_max_samples = n_max_samples
+ self.mod_LOO_threshold = mod_LOO_threshold
+ self.explore_method = explore_method
+ self.exploit_method = exploit_method
+ self.util_func = util_func
+ self.tradeoff_scheme = tradeoff_scheme
+ self.n_canddidate = n_canddidate
+ self.n_cand_groups = n_cand_groups
+ self.n_replication = n_replication
+ self.post_snapshot = post_snapshot
+ self.step_snapshot = step_snapshot
+ self.max_a_post = max_a_post
+ self.adapt_verbose = adapt_verbose
+
+ # -------------------------------------------------------------------------
+ def generate_samples(self, n_samples, sampling_method='random',
+ transform=False):
+ """
+ Generates samples with given sampling method
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of requested samples.
+ sampling_method : str, optional
+ Sampling method. The default is `'random'`.
+ transform : bool, optional
+ Transformation via an isoprobabilistic transformation method. The
+ default is `False`.
+
+ Returns
+ -------
+ samples: array of shape (n_samples, n_params)
+ Generated samples from defined model input object.
+
+ """
+ try:
+ samples = chaospy.generate_samples(
+ int(n_samples), domain=self.JDist, rule=sampling_method
+ )
+ except:
+ samples = self.JDist.resample(int(n_samples))
+
+ # Transform samples to the original space
+ if transform:
+ tr_samples = self.transform(samples.T)
+ return samples.T, tr_samples
+ else:
+ return samples.T
+
+ # -------------------------------------------------------------------------
+ def generate_ED(self, n_samples, sampling_method='random', transform=False,
+ max_pce_deg=None):
+ """
+ Generates experimental designs (training set) with the given method.
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of requested training points.
+ sampling_method : str, optional
+ Sampling method. The default is `'random'`.
+ transform : bool, optional
+ Isoprobabilistic transformation. The default is `False`.
+ max_pce_deg : int, optional
+ Maximum PCE polynomial degree. The default is `None`.
+
+ Returns
+ -------
+ samples : array of shape (n_samples, n_params)
+ Selected training samples.
+
+ """
+ Inputs = self.InputObj
+ self.ndim = len(Inputs.Marginals)
+ if not hasattr(self, 'n_init_samples'):
+ self.n_init_samples = self.ndim + 1
+ n_samples = int(n_samples)
+
+ # Check if PCE or aPCE metamodel is selected.
+ if self.meta_Model.lower() == 'apce':
+ self.apce = True
+ else:
+ self.apce = False
+
+ # Check if input is given as dist or input_data.
+ if len(Inputs.Marginals[0].input_data):
+ self.input_data_given = True
+ else:
+ self.input_data_given = False
+
+ # Get the bounds if input_data are directly defined by user:
+ if self.input_data_given:
+ for i in range(self.ndim):
+ low_bound = np.min(Inputs.Marginals[i].input_data)
+ up_bound = np.max(Inputs.Marginals[i].input_data)
+ Inputs.Marginals[i].parameters = [low_bound, up_bound]
+
+ # Generate the samples based on requested method
+ self.raw_data, self.bound_tuples = self.init_param_space(max_pce_deg)
+
+ # Pass user-defined samples as ED
+ if sampling_method == 'user':
+ samples = self.X
+ self.n_samples = len(samples)
+
+ # Sample the distribution of parameters
+ elif self.input_data_given:
+ # Case II: Input values are directly given by the user.
+
+ if sampling_method == 'random':
+ samples = self.random_sampler(n_samples)
+
+ elif sampling_method == 'PCM' or \
+ sampling_method == 'LSCM':
+ samples = self.pcm_sampler(max_pce_deg)
+
+ else:
+ # Create ExpDesign in the actual space using chaospy
+ try:
+ samples = chaospy.generate_samples(n_samples,
+ domain=self.JDist,
+ rule=sampling_method).T
+ except:
+ samples = self.JDist.sample(n_samples)
+
+ elif not self.input_data_given:
+ # Case I = User passed known distributions
+ samples = chaospy.generate_samples(n_samples, domain=self.JDist,
+ rule=sampling_method).T
+
+ # Transform samples to the original space
+ if transform:
+ tr_samples = self.transform(samples)
+ return samples, tr_samples
+ else:
+ return samples
+
+ # -------------------------------------------------------------------------
+ def init_param_space(self, max_deg=None):
+ """
+ Initializes parameter space.
+
+ Parameters
+ ----------
+ max_deg : int, optional
+ Maximum degree. The default is `None`.
+
+ Returns
+ -------
+ raw_data : array of shape (n_params, n_samples)
+ Raw data.
+ bound_tuples : list of tuples
+ A list containing lower and upper bounds of parameters.
+
+ """
+ Inputs = self.InputObj
+ ndim = self.ndim
+ rosenblatt_flag = Inputs.Rosenblatt
+ mc_size = 50000
+
+ # Save parameter names
+ self.par_names = []
+ for parIdx in range(ndim):
+ self.par_names.append(Inputs.Marginals[parIdx].name)
+
+ # Create a multivariate probability distribution
+ if max_deg is not None:
+ JDist, poly_types = self.build_dist(rosenblatt=rosenblatt_flag)
+ self.JDist, self.poly_types = JDist, poly_types
+
+ if self.input_data_given:
+
+ self.MCSize = len(Inputs.Marginals[0].input_data)
+ self.raw_data = np.zeros((ndim, self.MCSize))
+
+ for parIdx in range(ndim):
+ # Save parameter names
+ try:
+ self.raw_data[parIdx] = np.array(
+ Inputs.Marginals[parIdx].input_data)
+ except:
+ self.raw_data[parIdx] = self.JDist[parIdx].sample(mc_size)
+
+ else:
+ # Generate random samples based on parameter distributions
+ self.raw_data = chaospy.generate_samples(mc_size,
+ domain=self.JDist)
+
+ # Create orthogonal polynomial coefficients if necessary
+ if self.apce and max_deg is not None and Inputs.poly_coeffs_flag:
+ self.polycoeffs = {}
+ for parIdx in tqdm(range(ndim), ascii=True,
+ desc="Computing orth. polynomial coeffs"):
+ poly_coeffs = apoly_construction(self.raw_data[parIdx],
+ max_deg)
+ self.polycoeffs[f'p_{parIdx+1}'] = poly_coeffs
+
+ # Extract moments
+ for parIdx in range(ndim):
+ mu = np.mean(self.raw_data[parIdx])
+ std = np.std(self.raw_data[parIdx])
+ self.InputObj.Marginals[parIdx].moments = [mu, std]
+
+ # Generate the bounds based on given inputs for marginals
+ bound_tuples = []
+ for i in range(ndim):
+ if Inputs.Marginals[i].dist_type == 'unif':
+ low_bound, up_bound = Inputs.Marginals[i].parameters
+ else:
+ low_bound = np.min(self.raw_data[i])
+ up_bound = np.max(self.raw_data[i])
+
+ bound_tuples.append((low_bound, up_bound))
+
+ self.bound_tuples = tuple(bound_tuples)
+
+ return self.raw_data, self.bound_tuples
+
+ # -------------------------------------------------------------------------
+ def build_dist(self, rosenblatt):
+ """
+ Creates the polynomial types to be passed to univ_basis_vals method of
+ the MetaModel object.
+
+ Parameters
+ ----------
+ rosenblatt : bool
+ Rosenblatt transformation flag.
+
+ Returns
+ -------
+ orig_space_dist : object
+ A chaospy JDist object or a gaussian_kde object.
+ poly_types : list
+ List of polynomial types for the parameters.
+
+ """
+ Inputs = self.InputObj
+ all_data = []
+ all_dist_types = []
+ orig_joints = []
+ poly_types = []
+
+ for parIdx in range(self.ndim):
+
+ if Inputs.Marginals[parIdx].dist_type is None:
+ data = Inputs.Marginals[parIdx].input_data
+ all_data.append(data)
+ dist_type = None
+ else:
+ dist_type = Inputs.Marginals[parIdx].dist_type
+ params = Inputs.Marginals[parIdx].parameters
+
+ if rosenblatt:
+ polytype = 'hermite'
+ dist = chaospy.Normal()
+
+ elif dist_type is None:
+ polytype = 'arbitrary'
+ dist = None
+
+ elif 'unif' in dist_type.lower():
+ polytype = 'legendre'
+ dist = chaospy.Uniform(lower=params[0], upper=params[1])
+
+ elif 'norm' in dist_type.lower() and \
+ 'log' not in dist_type.lower():
+ polytype = 'hermite'
+ dist = chaospy.Normal(mu=params[0], sigma=params[1])
+
+ elif 'gamma' in dist_type.lower():
+ polytype = 'laguerre'
+ dist = chaospy.Gamma(shape=params[0],
+ scale=params[1],
+ shift=params[2])
+
+ elif 'beta' in dist_type.lower():
+ polytype = 'jacobi'
+ dist = chaospy.Beta(alpha=params[0], beta=params[1],
+ lower=params[2], upper=params[3])
+
+ elif 'lognorm' in dist_type.lower():
+ polytype = 'hermite'
+ Mu = np.log(params[0]**2/np.sqrt(params[0]**2 + params[1]**2))
+ Sigma = np.sqrt(np.log(1 + params[1]**2 / params[0]**2))
+ dist = chaospy.LogNormal(mu=Mu, sigma=Sigma)
+
+ elif 'expon' in dist_type.lower():
+ polytype = 'arbitrary'
+ dist = chaospy.Exponential(scale=params[0], shift=params[1])
+
+ elif 'weibull' in dist_type.lower():
+ polytype = 'arbitrary'
+ dist = chaospy.Weibull(shape=params[0], scale=params[1],
+ shift=params[2])
+
+ else:
+ message = (f"DistType {dist_type} for parameter"
+ f"{parIdx+1} is not available.")
+ raise ValueError(message)
+
+ if self.input_data_given or self.apce:
+ polytype = 'arbitrary'
+
+ # Store dists and poly_types
+ orig_joints.append(dist)
+ poly_types.append(polytype)
+ all_dist_types.append(dist_type)
+
+ # Prepare final output to return
+ if None in all_dist_types:
+ # Naive approach: Fit a gaussian kernel to the provided data
+ Data = np.asarray(all_data)
+ orig_space_dist = st.gaussian_kde(Data)
+ self.prior_space = orig_space_dist
+ else:
+ orig_space_dist = chaospy.J(*orig_joints)
+ self.prior_space = st.gaussian_kde(orig_space_dist.sample(10000))
+
+ return orig_space_dist, poly_types
+
+ # -------------------------------------------------------------------------
+ def random_sampler(self, n_samples):
+ """
+ Samples the given raw data randomly.
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of requested samples.
+
+ Returns
+ -------
+ samples: array of shape (n_samples, n_params)
+ The sampling locations in the input space.
+
+ """
+ samples = np.zeros((n_samples, self.ndim))
+
+ for idxPa in range(self.ndim):
+ # input_data given
+ sample_size = len(self.raw_data[idxPa])
+ randIdx = np.random.randint(0, sample_size, n_samples)
+ samples[:, idxPa] = self.raw_data[idxPa, randIdx]
+
+ return samples
+
+ # -------------------------------------------------------------------------
+ def pcm_sampler(self, max_deg):
+ """
+ Generates collocation points based on the root of the polynomial
+ degrees.
+
+ Parameters
+ ----------
+ max_deg : int
+ Maximum degree defined by user.
+
+ Returns
+ -------
+ opt_col_points: array of shape (n_samples, n_params)
+ Collocation points.
+
+ """
+
+ raw_data = self.raw_data
+
+ # Guess the closest degree to self.n_samples
+ def M_uptoMax(deg):
+ result = []
+ for d in range(1, deg+1):
+ result.append(math.factorial(self.ndim+d) //
+ (math.factorial(self.ndim) * math.factorial(d)))
+ return np.array(result)
+
+ guess_Deg = np.where(M_uptoMax(max_deg) > self.n_samples)[0][0]
+
+ c_points = np.zeros((guess_Deg+1, self.ndim))
+
+ def PolynomialPa(parIdx):
+ return apoly_construction(self.raw_data[parIdx], max_deg)
+
+ for i in range(self.ndim):
+ poly_coeffs = PolynomialPa(i)[guess_Deg+1][::-1]
+ c_points[:, i] = np.trim_zeros(np.roots(poly_coeffs))
+
+ # Construction of optimal integration points
+ Prod = itertools.product(np.arange(1, guess_Deg+2), repeat=self.ndim)
+ sort_dig_unique_combos = np.array(list(filter(lambda x: x, Prod)))
+
+ # Ranking relatively mean
+ Temp = np.empty(shape=[0, guess_Deg+1])
+ for j in range(self.ndim):
+ s = abs(c_points[:, j]-np.mean(raw_data[j]))
+ Temp = np.append(Temp, [s], axis=0)
+ temp = Temp.T
+
+ index_CP = np.sort(temp, axis=0)
+ sort_cpoints = np.empty((0, guess_Deg+1))
+
+ for j in range(self.ndim):
+ sort_cp = c_points[index_CP[:, j], j]
+ sort_cpoints = np.vstack((sort_cpoints, sort_cp))
+
+ # Mapping of Combination to Cpoint Combination
+ sort_unique_combos = np.empty(shape=[0, self.ndim])
+ for i in range(len(sort_dig_unique_combos)):
+ sort_un_comb = []
+ for j in range(self.ndim):
+ SortUC = sort_cpoints[j, sort_dig_unique_combos[i, j]-1]
+ sort_un_comb.append(SortUC)
+ sort_uni_comb = np.asarray(sort_un_comb)
+ sort_unique_combos = np.vstack((sort_unique_combos, sort_uni_comb))
+
+ # Output the collocation points
+ if self.sampling_method.lower() == 'lscm':
+ opt_col_points = sort_unique_combos
+ else:
+ opt_col_points = sort_unique_combos[0:self.n_samples]
+
+ return opt_col_points
+
+ # -------------------------------------------------------------------------
+ def transform(self, X, params=None):
+ """
+ Transform the samples via either a Rosenblatt or an isoprobabilistic
+ transformation.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples,n_params)
+ Samples to be transformed.
+
+ Returns
+ -------
+ tr_X: array of shape (n_samples,n_params)
+ Transformed samples.
+
+ """
+ if self.InputObj.Rosenblatt:
+ self.origJDist, _ = self.build_dist(False)
+ tr_X = self.origJDist.inv(self.JDist.fwd(X.T)).T
+ else:
+ # Transform samples via an isoprobabilistic transformation
+ n_samples, n_params = X.shape
+ Inputs = self.InputObj
+ origJDist = self.JDist
+ poly_types = self.poly_types
+
+ disttypes = []
+ for par_i in range(n_params):
+ disttypes.append(Inputs.Marginals[par_i].dist_type)
+
+ # Pass non-transformed X, if arbitrary PCE is selected.
+ if None in disttypes or self.input_data_given or self.apce:
+ return X
+
+ cdfx = np.zeros((X.shape))
+ tr_X = np.zeros((X.shape))
+
+ for par_i in range(n_params):
+
+ # Extract the parameters of the original space
+ disttype = disttypes[par_i]
+ if disttype is not None:
+ dist = origJDist[par_i]
+ else:
+ dist = None
+ polytype = poly_types[par_i]
+ cdf = np.vectorize(lambda x: dist.cdf(x))
+
+ # Extract the parameters of the transformation space based on
+ # polyType
+ if polytype == 'legendre' or disttype == 'uniform':
+ # Generate Y_Dists based
+ params_Y = [-1, 1]
+ dist_Y = st.uniform(loc=params_Y[0],
+ scale=params_Y[1]-params_Y[0])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ elif polytype == 'hermite' or disttype == 'norm':
+ params_Y = [0, 1]
+ dist_Y = st.norm(loc=params_Y[0], scale=params_Y[1])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ elif polytype == 'laguerre' or disttype == 'gamma':
+ params_Y = [1, params[1]]
+ dist_Y = st.gamma(loc=params_Y[0], scale=params_Y[1])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ # Compute CDF_x(X)
+ cdfx[:, par_i] = cdf(X[:, par_i])
+
+ # Compute invCDF_y(cdfx)
+ tr_X[:, par_i] = inv_cdf(cdfx[:, par_i])
+
+ return tr_X
+
+ # -------------------------------------------------------------------------
+ def fit_dist(self, y):
+ """
+ Fits the known distributions to the data.
+
+ Parameters
+ ----------
+ y : array of shape (n_samples)
+ Data to be fitted.
+
+ Returns
+ -------
+ sel_dist: string
+ Selected distribution type from `lognorm`, `norm`, `uniform` or
+ `expon`.
+ params : list
+ Parameters corresponding to the selected distibution type.
+
+ """
+ dist_results = []
+ params = {}
+ dist_names = ['lognorm', 'norm', 'uniform', 'expon']
+ for dist_name in dist_names:
+ dist = getattr(st, dist_name)
+
+ try:
+ if dist_name != 'lognorm':
+ param = dist.fit(y)
+ else:
+ param = dist.fit(np.exp(y), floc=0)
+ except:
+ param = dist.fit(y)
+
+ params[dist_name] = param
+ # Applying the Kolmogorov-Smirnov test
+ D, p = st.kstest(y, dist_name, args=param)
+ dist_results.append((dist_name, D))
+
+ # select the best fitted distribution
+ sel_dist, D = (min(dist_results, key=lambda item: item[1]))
+
+ if sel_dist == 'uniform':
+ params[sel_dist] = [params[sel_dist][0], params[sel_dist][0] +
+ params[sel_dist][1]]
+ if D < 0.05:
+ return sel_dist, params[sel_dist]
+ else:
+ return None, None</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="exp_designs.ExpDesigns"><code class="flex name class">
+<span>class <span class="ident">ExpDesigns</span></span>
+<span>(</span><span>Input, method='normal', meta_Model='pce', sampling_method='random', hdf5_file=None, n_new_samples=1, n_max_samples=None, mod_LOO_threshold=1e-16, tradeoff_scheme=None, n_canddidate=1, explore_method='random', exploit_method='Space-filling', util_func='Space-filling', n_cand_groups=4, n_replication=1, post_snapshot=False, step_snapshot=1, max_a_post=[], adapt_verbose=False)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>This class generates samples from the prescribed marginals for the model
+parameters using the <code>Input</code> object.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>Input</code></strong> : <code>obj</code></dt>
+<dd>Input object containing the parameter marginals, i.e. name,
+distribution type and distribution parameters or available raw data.</dd>
+<dt><strong><code>method</code></strong> : <code>str</code></dt>
+<dd>Type of the experimental design. The default is <code>'normal'</code>. Other
+option is <code>'sequential'</code>.</dd>
+<dt><strong><code>meta_Model</code></strong> : <code>str</code></dt>
+<dd>Type of the meta_model.</dd>
+<dt><strong><code>sampling_method</code></strong> : <code>str</code></dt>
+<dd>
+<p>Name of the sampling method for the experimental design. The following
+sampling method are supported:</p>
+<ul>
+<li>random</li>
+<li>latin_hypercube</li>
+<li>sobol</li>
+<li>halton</li>
+<li>hammersley</li>
+<li>korobov</li>
+<li>chebyshev(FT)</li>
+<li>grid(FT)</li>
+<li>nested_grid(FT)</li>
+<li>user</li>
+</ul>
+</dd>
+<dt><strong><code>hdf5_file</code></strong> : <code>str</code></dt>
+<dd>Name of the hdf5 file that contains the experimental design.</dd>
+<dt><strong><code>n_new_samples</code></strong> : <code>int</code></dt>
+<dd>Number of (initial) training points.</dd>
+<dt><strong><code>n_max_samples</code></strong> : <code>int</code></dt>
+<dd>Number of maximum training points.</dd>
+<dt><strong><code>mod_LOO_threshold</code></strong> : <code>float</code></dt>
+<dd>The modified leave-one-out cross validation threshold where the
+sequential design stops.</dd>
+<dt><strong><code>tradeoff_scheme</code></strong> : <code>str</code></dt>
+<dd>Trade-off scheme to assign weights to the exploration and exploitation
+scores in the sequential design.</dd>
+<dt><strong><code>n_canddidate</code></strong> : <code>int</code></dt>
+<dd>Number of candidate training sets to calculate the scores for.</dd>
+<dt><strong><code>explore_method</code></strong> : <code>str</code></dt>
+<dd>
+<p>Type of the exploration method for the sequential design. The following
+methods are supported:</p>
+<ul>
+<li>Voronoi</li>
+<li>random</li>
+<li>latin_hypercube</li>
+<li>LOOCV</li>
+<li>dual annealing</li>
+</ul>
+</dd>
+<dt><strong><code>exploit_method</code></strong> : <code>str</code></dt>
+<dd>
+<p>Type of the exploitation method for the sequential design. The
+following methods are supported:</p>
+<ul>
+<li>BayesOptDesign</li>
+<li>BayesActDesign</li>
+<li>VarOptDesign</li>
+<li>alphabetic</li>
+<li>Space-filling</li>
+</ul>
+</dd>
+<dt><strong><code>util_func</code></strong> : <code>str</code> or <code>list</code></dt>
+<dd>The utility function to be specified for the <code>exploit_method</code>. For the
+available utility functions see Note section.</dd>
+<dt><strong><code>n_cand_groups</code></strong> : <code>int</code></dt>
+<dd>Number of candidate groups. Each group of candidate training sets will
+be evaulated separately in parallel.</dd>
+<dt><strong><code>n_replication</code></strong> : <code>int</code></dt>
+<dd>Number of replications. Only for comparison. The default is 1.</dd>
+<dt><strong><code>post_snapshot</code></strong> : <code>int</code></dt>
+<dd>Whether to plot the posterior in the sequential design. The default is
+<code>True</code>.</dd>
+<dt><strong><code>step_snapshot</code></strong> : <code>int</code></dt>
+<dd>The number of steps to plot the posterior in the sequential design. The
+default is 1.</dd>
+<dt><strong><code>max_a_post</code></strong> : <code>list</code> or <code>array</code></dt>
+<dd>Maximum a posteriori of the posterior distribution, if known. The
+default is <code>[]</code>.</dd>
+<dt><strong><code>adapt_verbose</code></strong> : <code>bool</code></dt>
+<dd>Whether to plot the model response vs that of metamodel for the new
+trining point in the sequential design.</dd>
+</dl>
+<h2 id="note">Note</h2>
+<p>The following utiliy functions for the <strong>exploitation</strong> methods are
+supported:</p>
+<h4 id="bayesoptdesign-when-data-is-available">BayesOptDesign (when data is available)</h4>
+<ul>
+<li>DKL (Kullback-Leibler Divergence)</li>
+<li>DPP (D-Posterior-percision)</li>
+<li>APP (A-Posterior-percision)</li>
+</ul>
+<h4 id="varbasedoptdesign-when-data-is-not-available">VarBasedOptDesign -> when data is not available</h4>
+<ul>
+<li>Entropy (Entropy/MMSE/active learning)</li>
+<li>EIGF (Expected Improvement for Global fit)</li>
+<li>LOOCV (Leave-one-out Cross Validation)</li>
+</ul>
+<h4 id="alphabetic">alphabetic</h4>
+<ul>
+<li>D-Opt (D-Optimality)</li>
+<li>A-Opt (A-Optimality)</li>
+<li>K-Opt (K-Optimality)</li>
+</ul></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class ExpDesigns:
+ """
+ This class generates samples from the prescribed marginals for the model
+ parameters using the `Input` object.
+
+ Attributes
+ ----------
+ Input : obj
+ Input object containing the parameter marginals, i.e. name,
+ distribution type and distribution parameters or available raw data.
+ method : str
+ Type of the experimental design. The default is `'normal'`. Other
+ option is `'sequential'`.
+ meta_Model : str
+ Type of the meta_model.
+ sampling_method : str
+ Name of the sampling method for the experimental design. The following
+ sampling method are supported:
+
+ * random
+ * latin_hypercube
+ * sobol
+ * halton
+ * hammersley
+ * korobov
+ * chebyshev(FT)
+ * grid(FT)
+ * nested_grid(FT)
+ * user
+ hdf5_file : str
+ Name of the hdf5 file that contains the experimental design.
+ n_new_samples : int
+ Number of (initial) training points.
+ n_max_samples : int
+ Number of maximum training points.
+ mod_LOO_threshold : float
+ The modified leave-one-out cross validation threshold where the
+ sequential design stops.
+ tradeoff_scheme : str
+ Trade-off scheme to assign weights to the exploration and exploitation
+ scores in the sequential design.
+ n_canddidate : int
+ Number of candidate training sets to calculate the scores for.
+ explore_method : str
+ Type of the exploration method for the sequential design. The following
+ methods are supported:
+
+ * Voronoi
+ * random
+ * latin_hypercube
+ * LOOCV
+ * dual annealing
+ exploit_method : str
+ Type of the exploitation method for the sequential design. The
+ following methods are supported:
+
+ * BayesOptDesign
+ * BayesActDesign
+ * VarOptDesign
+ * alphabetic
+ * Space-filling
+ util_func : str or list
+ The utility function to be specified for the `exploit_method`. For the
+ available utility functions see Note section.
+ n_cand_groups : int
+ Number of candidate groups. Each group of candidate training sets will
+ be evaulated separately in parallel.
+ n_replication : int
+ Number of replications. Only for comparison. The default is 1.
+ post_snapshot : int
+ Whether to plot the posterior in the sequential design. The default is
+ `True`.
+ step_snapshot : int
+ The number of steps to plot the posterior in the sequential design. The
+ default is 1.
+ max_a_post : list or array
+ Maximum a posteriori of the posterior distribution, if known. The
+ default is `[]`.
+ adapt_verbose : bool
+ Whether to plot the model response vs that of metamodel for the new
+ trining point in the sequential design.
+
+ Note
+ ----------
+ The following utiliy functions for the **exploitation** methods are
+ supported:
+
+ #### BayesOptDesign (when data is available)
+ - DKL (Kullback-Leibler Divergence)
+ - DPP (D-Posterior-percision)
+ - APP (A-Posterior-percision)
+
+ #### VarBasedOptDesign -> when data is not available
+ - Entropy (Entropy/MMSE/active learning)
+ - EIGF (Expected Improvement for Global fit)
+ - LOOCV (Leave-one-out Cross Validation)
+
+ #### alphabetic
+ - D-Opt (D-Optimality)
+ - A-Opt (A-Optimality)
+ - K-Opt (K-Optimality)
+ """
+
+ def __init__(self, Input, method='normal', meta_Model='pce',
+ sampling_method='random', hdf5_file=None,
+ n_new_samples=1, n_max_samples=None, mod_LOO_threshold=1e-16,
+ tradeoff_scheme=None, n_canddidate=1, explore_method='random',
+ exploit_method='Space-filling', util_func='Space-filling',
+ n_cand_groups=4, n_replication=1, post_snapshot=False,
+ step_snapshot=1, max_a_post=[], adapt_verbose=False):
+
+ self.InputObj = Input
+ self.method = method
+ self.meta_Model = meta_Model
+ self.sampling_method = sampling_method
+ self.hdf5_file = hdf5_file
+ self.n_new_samples = n_new_samples
+ self.n_max_samples = n_max_samples
+ self.mod_LOO_threshold = mod_LOO_threshold
+ self.explore_method = explore_method
+ self.exploit_method = exploit_method
+ self.util_func = util_func
+ self.tradeoff_scheme = tradeoff_scheme
+ self.n_canddidate = n_canddidate
+ self.n_cand_groups = n_cand_groups
+ self.n_replication = n_replication
+ self.post_snapshot = post_snapshot
+ self.step_snapshot = step_snapshot
+ self.max_a_post = max_a_post
+ self.adapt_verbose = adapt_verbose
+
+ # -------------------------------------------------------------------------
+ def generate_samples(self, n_samples, sampling_method='random',
+ transform=False):
+ """
+ Generates samples with given sampling method
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of requested samples.
+ sampling_method : str, optional
+ Sampling method. The default is `'random'`.
+ transform : bool, optional
+ Transformation via an isoprobabilistic transformation method. The
+ default is `False`.
+
+ Returns
+ -------
+ samples: array of shape (n_samples, n_params)
+ Generated samples from defined model input object.
+
+ """
+ try:
+ samples = chaospy.generate_samples(
+ int(n_samples), domain=self.JDist, rule=sampling_method
+ )
+ except:
+ samples = self.JDist.resample(int(n_samples))
+
+ # Transform samples to the original space
+ if transform:
+ tr_samples = self.transform(samples.T)
+ return samples.T, tr_samples
+ else:
+ return samples.T
+
+ # -------------------------------------------------------------------------
+ def generate_ED(self, n_samples, sampling_method='random', transform=False,
+ max_pce_deg=None):
+ """
+ Generates experimental designs (training set) with the given method.
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of requested training points.
+ sampling_method : str, optional
+ Sampling method. The default is `'random'`.
+ transform : bool, optional
+ Isoprobabilistic transformation. The default is `False`.
+ max_pce_deg : int, optional
+ Maximum PCE polynomial degree. The default is `None`.
+
+ Returns
+ -------
+ samples : array of shape (n_samples, n_params)
+ Selected training samples.
+
+ """
+ Inputs = self.InputObj
+ self.ndim = len(Inputs.Marginals)
+ if not hasattr(self, 'n_init_samples'):
+ self.n_init_samples = self.ndim + 1
+ n_samples = int(n_samples)
+
+ # Check if PCE or aPCE metamodel is selected.
+ if self.meta_Model.lower() == 'apce':
+ self.apce = True
+ else:
+ self.apce = False
+
+ # Check if input is given as dist or input_data.
+ if len(Inputs.Marginals[0].input_data):
+ self.input_data_given = True
+ else:
+ self.input_data_given = False
+
+ # Get the bounds if input_data are directly defined by user:
+ if self.input_data_given:
+ for i in range(self.ndim):
+ low_bound = np.min(Inputs.Marginals[i].input_data)
+ up_bound = np.max(Inputs.Marginals[i].input_data)
+ Inputs.Marginals[i].parameters = [low_bound, up_bound]
+
+ # Generate the samples based on requested method
+ self.raw_data, self.bound_tuples = self.init_param_space(max_pce_deg)
+
+ # Pass user-defined samples as ED
+ if sampling_method == 'user':
+ samples = self.X
+ self.n_samples = len(samples)
+
+ # Sample the distribution of parameters
+ elif self.input_data_given:
+ # Case II: Input values are directly given by the user.
+
+ if sampling_method == 'random':
+ samples = self.random_sampler(n_samples)
+
+ elif sampling_method == 'PCM' or \
+ sampling_method == 'LSCM':
+ samples = self.pcm_sampler(max_pce_deg)
+
+ else:
+ # Create ExpDesign in the actual space using chaospy
+ try:
+ samples = chaospy.generate_samples(n_samples,
+ domain=self.JDist,
+ rule=sampling_method).T
+ except:
+ samples = self.JDist.sample(n_samples)
+
+ elif not self.input_data_given:
+ # Case I = User passed known distributions
+ samples = chaospy.generate_samples(n_samples, domain=self.JDist,
+ rule=sampling_method).T
+
+ # Transform samples to the original space
+ if transform:
+ tr_samples = self.transform(samples)
+ return samples, tr_samples
+ else:
+ return samples
+
+ # -------------------------------------------------------------------------
+ def init_param_space(self, max_deg=None):
+ """
+ Initializes parameter space.
+
+ Parameters
+ ----------
+ max_deg : int, optional
+ Maximum degree. The default is `None`.
+
+ Returns
+ -------
+ raw_data : array of shape (n_params, n_samples)
+ Raw data.
+ bound_tuples : list of tuples
+ A list containing lower and upper bounds of parameters.
+
+ """
+ Inputs = self.InputObj
+ ndim = self.ndim
+ rosenblatt_flag = Inputs.Rosenblatt
+ mc_size = 50000
+
+ # Save parameter names
+ self.par_names = []
+ for parIdx in range(ndim):
+ self.par_names.append(Inputs.Marginals[parIdx].name)
+
+ # Create a multivariate probability distribution
+ if max_deg is not None:
+ JDist, poly_types = self.build_dist(rosenblatt=rosenblatt_flag)
+ self.JDist, self.poly_types = JDist, poly_types
+
+ if self.input_data_given:
+
+ self.MCSize = len(Inputs.Marginals[0].input_data)
+ self.raw_data = np.zeros((ndim, self.MCSize))
+
+ for parIdx in range(ndim):
+ # Save parameter names
+ try:
+ self.raw_data[parIdx] = np.array(
+ Inputs.Marginals[parIdx].input_data)
+ except:
+ self.raw_data[parIdx] = self.JDist[parIdx].sample(mc_size)
+
+ else:
+ # Generate random samples based on parameter distributions
+ self.raw_data = chaospy.generate_samples(mc_size,
+ domain=self.JDist)
+
+ # Create orthogonal polynomial coefficients if necessary
+ if self.apce and max_deg is not None and Inputs.poly_coeffs_flag:
+ self.polycoeffs = {}
+ for parIdx in tqdm(range(ndim), ascii=True,
+ desc="Computing orth. polynomial coeffs"):
+ poly_coeffs = apoly_construction(self.raw_data[parIdx],
+ max_deg)
+ self.polycoeffs[f'p_{parIdx+1}'] = poly_coeffs
+
+ # Extract moments
+ for parIdx in range(ndim):
+ mu = np.mean(self.raw_data[parIdx])
+ std = np.std(self.raw_data[parIdx])
+ self.InputObj.Marginals[parIdx].moments = [mu, std]
+
+ # Generate the bounds based on given inputs for marginals
+ bound_tuples = []
+ for i in range(ndim):
+ if Inputs.Marginals[i].dist_type == 'unif':
+ low_bound, up_bound = Inputs.Marginals[i].parameters
+ else:
+ low_bound = np.min(self.raw_data[i])
+ up_bound = np.max(self.raw_data[i])
+
+ bound_tuples.append((low_bound, up_bound))
+
+ self.bound_tuples = tuple(bound_tuples)
+
+ return self.raw_data, self.bound_tuples
+
+ # -------------------------------------------------------------------------
+ def build_dist(self, rosenblatt):
+ """
+ Creates the polynomial types to be passed to univ_basis_vals method of
+ the MetaModel object.
+
+ Parameters
+ ----------
+ rosenblatt : bool
+ Rosenblatt transformation flag.
+
+ Returns
+ -------
+ orig_space_dist : object
+ A chaospy JDist object or a gaussian_kde object.
+ poly_types : list
+ List of polynomial types for the parameters.
+
+ """
+ Inputs = self.InputObj
+ all_data = []
+ all_dist_types = []
+ orig_joints = []
+ poly_types = []
+
+ for parIdx in range(self.ndim):
+
+ if Inputs.Marginals[parIdx].dist_type is None:
+ data = Inputs.Marginals[parIdx].input_data
+ all_data.append(data)
+ dist_type = None
+ else:
+ dist_type = Inputs.Marginals[parIdx].dist_type
+ params = Inputs.Marginals[parIdx].parameters
+
+ if rosenblatt:
+ polytype = 'hermite'
+ dist = chaospy.Normal()
+
+ elif dist_type is None:
+ polytype = 'arbitrary'
+ dist = None
+
+ elif 'unif' in dist_type.lower():
+ polytype = 'legendre'
+ dist = chaospy.Uniform(lower=params[0], upper=params[1])
+
+ elif 'norm' in dist_type.lower() and \
+ 'log' not in dist_type.lower():
+ polytype = 'hermite'
+ dist = chaospy.Normal(mu=params[0], sigma=params[1])
+
+ elif 'gamma' in dist_type.lower():
+ polytype = 'laguerre'
+ dist = chaospy.Gamma(shape=params[0],
+ scale=params[1],
+ shift=params[2])
+
+ elif 'beta' in dist_type.lower():
+ polytype = 'jacobi'
+ dist = chaospy.Beta(alpha=params[0], beta=params[1],
+ lower=params[2], upper=params[3])
+
+ elif 'lognorm' in dist_type.lower():
+ polytype = 'hermite'
+ Mu = np.log(params[0]**2/np.sqrt(params[0]**2 + params[1]**2))
+ Sigma = np.sqrt(np.log(1 + params[1]**2 / params[0]**2))
+ dist = chaospy.LogNormal(mu=Mu, sigma=Sigma)
+
+ elif 'expon' in dist_type.lower():
+ polytype = 'arbitrary'
+ dist = chaospy.Exponential(scale=params[0], shift=params[1])
+
+ elif 'weibull' in dist_type.lower():
+ polytype = 'arbitrary'
+ dist = chaospy.Weibull(shape=params[0], scale=params[1],
+ shift=params[2])
+
+ else:
+ message = (f"DistType {dist_type} for parameter"
+ f"{parIdx+1} is not available.")
+ raise ValueError(message)
+
+ if self.input_data_given or self.apce:
+ polytype = 'arbitrary'
+
+ # Store dists and poly_types
+ orig_joints.append(dist)
+ poly_types.append(polytype)
+ all_dist_types.append(dist_type)
+
+ # Prepare final output to return
+ if None in all_dist_types:
+ # Naive approach: Fit a gaussian kernel to the provided data
+ Data = np.asarray(all_data)
+ orig_space_dist = st.gaussian_kde(Data)
+ self.prior_space = orig_space_dist
+ else:
+ orig_space_dist = chaospy.J(*orig_joints)
+ self.prior_space = st.gaussian_kde(orig_space_dist.sample(10000))
+
+ return orig_space_dist, poly_types
+
+ # -------------------------------------------------------------------------
+ def random_sampler(self, n_samples):
+ """
+ Samples the given raw data randomly.
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of requested samples.
+
+ Returns
+ -------
+ samples: array of shape (n_samples, n_params)
+ The sampling locations in the input space.
+
+ """
+ samples = np.zeros((n_samples, self.ndim))
+
+ for idxPa in range(self.ndim):
+ # input_data given
+ sample_size = len(self.raw_data[idxPa])
+ randIdx = np.random.randint(0, sample_size, n_samples)
+ samples[:, idxPa] = self.raw_data[idxPa, randIdx]
+
+ return samples
+
+ # -------------------------------------------------------------------------
+ def pcm_sampler(self, max_deg):
+ """
+ Generates collocation points based on the root of the polynomial
+ degrees.
+
+ Parameters
+ ----------
+ max_deg : int
+ Maximum degree defined by user.
+
+ Returns
+ -------
+ opt_col_points: array of shape (n_samples, n_params)
+ Collocation points.
+
+ """
+
+ raw_data = self.raw_data
+
+ # Guess the closest degree to self.n_samples
+ def M_uptoMax(deg):
+ result = []
+ for d in range(1, deg+1):
+ result.append(math.factorial(self.ndim+d) //
+ (math.factorial(self.ndim) * math.factorial(d)))
+ return np.array(result)
+
+ guess_Deg = np.where(M_uptoMax(max_deg) > self.n_samples)[0][0]
+
+ c_points = np.zeros((guess_Deg+1, self.ndim))
+
+ def PolynomialPa(parIdx):
+ return apoly_construction(self.raw_data[parIdx], max_deg)
+
+ for i in range(self.ndim):
+ poly_coeffs = PolynomialPa(i)[guess_Deg+1][::-1]
+ c_points[:, i] = np.trim_zeros(np.roots(poly_coeffs))
+
+ # Construction of optimal integration points
+ Prod = itertools.product(np.arange(1, guess_Deg+2), repeat=self.ndim)
+ sort_dig_unique_combos = np.array(list(filter(lambda x: x, Prod)))
+
+ # Ranking relatively mean
+ Temp = np.empty(shape=[0, guess_Deg+1])
+ for j in range(self.ndim):
+ s = abs(c_points[:, j]-np.mean(raw_data[j]))
+ Temp = np.append(Temp, [s], axis=0)
+ temp = Temp.T
+
+ index_CP = np.sort(temp, axis=0)
+ sort_cpoints = np.empty((0, guess_Deg+1))
+
+ for j in range(self.ndim):
+ sort_cp = c_points[index_CP[:, j], j]
+ sort_cpoints = np.vstack((sort_cpoints, sort_cp))
+
+ # Mapping of Combination to Cpoint Combination
+ sort_unique_combos = np.empty(shape=[0, self.ndim])
+ for i in range(len(sort_dig_unique_combos)):
+ sort_un_comb = []
+ for j in range(self.ndim):
+ SortUC = sort_cpoints[j, sort_dig_unique_combos[i, j]-1]
+ sort_un_comb.append(SortUC)
+ sort_uni_comb = np.asarray(sort_un_comb)
+ sort_unique_combos = np.vstack((sort_unique_combos, sort_uni_comb))
+
+ # Output the collocation points
+ if self.sampling_method.lower() == 'lscm':
+ opt_col_points = sort_unique_combos
+ else:
+ opt_col_points = sort_unique_combos[0:self.n_samples]
+
+ return opt_col_points
+
+ # -------------------------------------------------------------------------
+ def transform(self, X, params=None):
+ """
+ Transform the samples via either a Rosenblatt or an isoprobabilistic
+ transformation.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples,n_params)
+ Samples to be transformed.
+
+ Returns
+ -------
+ tr_X: array of shape (n_samples,n_params)
+ Transformed samples.
+
+ """
+ if self.InputObj.Rosenblatt:
+ self.origJDist, _ = self.build_dist(False)
+ tr_X = self.origJDist.inv(self.JDist.fwd(X.T)).T
+ else:
+ # Transform samples via an isoprobabilistic transformation
+ n_samples, n_params = X.shape
+ Inputs = self.InputObj
+ origJDist = self.JDist
+ poly_types = self.poly_types
+
+ disttypes = []
+ for par_i in range(n_params):
+ disttypes.append(Inputs.Marginals[par_i].dist_type)
+
+ # Pass non-transformed X, if arbitrary PCE is selected.
+ if None in disttypes or self.input_data_given or self.apce:
+ return X
+
+ cdfx = np.zeros((X.shape))
+ tr_X = np.zeros((X.shape))
+
+ for par_i in range(n_params):
+
+ # Extract the parameters of the original space
+ disttype = disttypes[par_i]
+ if disttype is not None:
+ dist = origJDist[par_i]
+ else:
+ dist = None
+ polytype = poly_types[par_i]
+ cdf = np.vectorize(lambda x: dist.cdf(x))
+
+ # Extract the parameters of the transformation space based on
+ # polyType
+ if polytype == 'legendre' or disttype == 'uniform':
+ # Generate Y_Dists based
+ params_Y = [-1, 1]
+ dist_Y = st.uniform(loc=params_Y[0],
+ scale=params_Y[1]-params_Y[0])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ elif polytype == 'hermite' or disttype == 'norm':
+ params_Y = [0, 1]
+ dist_Y = st.norm(loc=params_Y[0], scale=params_Y[1])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ elif polytype == 'laguerre' or disttype == 'gamma':
+ params_Y = [1, params[1]]
+ dist_Y = st.gamma(loc=params_Y[0], scale=params_Y[1])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ # Compute CDF_x(X)
+ cdfx[:, par_i] = cdf(X[:, par_i])
+
+ # Compute invCDF_y(cdfx)
+ tr_X[:, par_i] = inv_cdf(cdfx[:, par_i])
+
+ return tr_X
+
+ # -------------------------------------------------------------------------
+ def fit_dist(self, y):
+ """
+ Fits the known distributions to the data.
+
+ Parameters
+ ----------
+ y : array of shape (n_samples)
+ Data to be fitted.
+
+ Returns
+ -------
+ sel_dist: string
+ Selected distribution type from `lognorm`, `norm`, `uniform` or
+ `expon`.
+ params : list
+ Parameters corresponding to the selected distibution type.
+
+ """
+ dist_results = []
+ params = {}
+ dist_names = ['lognorm', 'norm', 'uniform', 'expon']
+ for dist_name in dist_names:
+ dist = getattr(st, dist_name)
+
+ try:
+ if dist_name != 'lognorm':
+ param = dist.fit(y)
+ else:
+ param = dist.fit(np.exp(y), floc=0)
+ except:
+ param = dist.fit(y)
+
+ params[dist_name] = param
+ # Applying the Kolmogorov-Smirnov test
+ D, p = st.kstest(y, dist_name, args=param)
+ dist_results.append((dist_name, D))
+
+ # select the best fitted distribution
+ sel_dist, D = (min(dist_results, key=lambda item: item[1]))
+
+ if sel_dist == 'uniform':
+ params[sel_dist] = [params[sel_dist][0], params[sel_dist][0] +
+ params[sel_dist][1]]
+ if D < 0.05:
+ return sel_dist, params[sel_dist]
+ else:
+ return None, None</code></pre>
+</details>
+<h3>Methods</h3>
+<dl>
+<dt id="exp_designs.ExpDesigns.generate_samples"><code class="name flex">
+<span>def <span class="ident">generate_samples</span></span>(<span>self, n_samples, sampling_method='random', transform=False)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Generates samples with given sampling method</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>n_samples</code></strong> : <code>int</code></dt>
+<dd>Number of requested samples.</dd>
+<dt><strong><code>sampling_method</code></strong> : <code>str</code>, optional</dt>
+<dd>Sampling method. The default is <code>'random'</code>.</dd>
+<dt><strong><code>transform</code></strong> : <code>bool</code>, optional</dt>
+<dd>Transformation via an isoprobabilistic transformation method. The
+default is <code>False</code>.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>samples</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
+<dd>Generated samples from defined model input object.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def generate_samples(self, n_samples, sampling_method='random',
+ transform=False):
+ """
+ Generates samples with given sampling method
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of requested samples.
+ sampling_method : str, optional
+ Sampling method. The default is `'random'`.
+ transform : bool, optional
+ Transformation via an isoprobabilistic transformation method. The
+ default is `False`.
+
+ Returns
+ -------
+ samples: array of shape (n_samples, n_params)
+ Generated samples from defined model input object.
+
+ """
+ try:
+ samples = chaospy.generate_samples(
+ int(n_samples), domain=self.JDist, rule=sampling_method
+ )
+ except:
+ samples = self.JDist.resample(int(n_samples))
+
+ # Transform samples to the original space
+ if transform:
+ tr_samples = self.transform(samples.T)
+ return samples.T, tr_samples
+ else:
+ return samples.T</code></pre>
+</details>
+</dd>
+<dt id="exp_designs.ExpDesigns.generate_ED"><code class="name flex">
+<span>def <span class="ident">generate_ED</span></span>(<span>self, n_samples, sampling_method='random', transform=False, max_pce_deg=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Generates experimental designs (training set) with the given method.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>n_samples</code></strong> : <code>int</code></dt>
+<dd>Number of requested training points.</dd>
+<dt><strong><code>sampling_method</code></strong> : <code>str</code>, optional</dt>
+<dd>Sampling method. The default is <code>'random'</code>.</dd>
+<dt><strong><code>transform</code></strong> : <code>bool</code>, optional</dt>
+<dd>Isoprobabilistic transformation. The default is <code>False</code>.</dd>
+<dt><strong><code>max_pce_deg</code></strong> : <code>int</code>, optional</dt>
+<dd>Maximum PCE polynomial degree. The default is <code>None</code>.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>samples</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
+<dd>Selected training samples.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def generate_ED(self, n_samples, sampling_method='random', transform=False,
+ max_pce_deg=None):
+ """
+ Generates experimental designs (training set) with the given method.
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of requested training points.
+ sampling_method : str, optional
+ Sampling method. The default is `'random'`.
+ transform : bool, optional
+ Isoprobabilistic transformation. The default is `False`.
+ max_pce_deg : int, optional
+ Maximum PCE polynomial degree. The default is `None`.
+
+ Returns
+ -------
+ samples : array of shape (n_samples, n_params)
+ Selected training samples.
+
+ """
+ Inputs = self.InputObj
+ self.ndim = len(Inputs.Marginals)
+ if not hasattr(self, 'n_init_samples'):
+ self.n_init_samples = self.ndim + 1
+ n_samples = int(n_samples)
+
+ # Check if PCE or aPCE metamodel is selected.
+ if self.meta_Model.lower() == 'apce':
+ self.apce = True
+ else:
+ self.apce = False
+
+ # Check if input is given as dist or input_data.
+ if len(Inputs.Marginals[0].input_data):
+ self.input_data_given = True
+ else:
+ self.input_data_given = False
+
+ # Get the bounds if input_data are directly defined by user:
+ if self.input_data_given:
+ for i in range(self.ndim):
+ low_bound = np.min(Inputs.Marginals[i].input_data)
+ up_bound = np.max(Inputs.Marginals[i].input_data)
+ Inputs.Marginals[i].parameters = [low_bound, up_bound]
+
+ # Generate the samples based on requested method
+ self.raw_data, self.bound_tuples = self.init_param_space(max_pce_deg)
+
+ # Pass user-defined samples as ED
+ if sampling_method == 'user':
+ samples = self.X
+ self.n_samples = len(samples)
+
+ # Sample the distribution of parameters
+ elif self.input_data_given:
+ # Case II: Input values are directly given by the user.
+
+ if sampling_method == 'random':
+ samples = self.random_sampler(n_samples)
+
+ elif sampling_method == 'PCM' or \
+ sampling_method == 'LSCM':
+ samples = self.pcm_sampler(max_pce_deg)
+
+ else:
+ # Create ExpDesign in the actual space using chaospy
+ try:
+ samples = chaospy.generate_samples(n_samples,
+ domain=self.JDist,
+ rule=sampling_method).T
+ except:
+ samples = self.JDist.sample(n_samples)
+
+ elif not self.input_data_given:
+ # Case I = User passed known distributions
+ samples = chaospy.generate_samples(n_samples, domain=self.JDist,
+ rule=sampling_method).T
+
+ # Transform samples to the original space
+ if transform:
+ tr_samples = self.transform(samples)
+ return samples, tr_samples
+ else:
+ return samples</code></pre>
+</details>
+</dd>
+<dt id="exp_designs.ExpDesigns.init_param_space"><code class="name flex">
+<span>def <span class="ident">init_param_space</span></span>(<span>self, max_deg=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Initializes parameter space.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>max_deg</code></strong> : <code>int</code>, optional</dt>
+<dd>Maximum degree. The default is <code>None</code>.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>raw_data</code></strong> : <code>array</code> of <code>shape (n_params, n_samples)</code></dt>
+<dd>Raw data.</dd>
+<dt><strong><code>bound_tuples</code></strong> : <code>list</code> of <code>tuples</code></dt>
+<dd>A list containing lower and upper bounds of parameters.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def init_param_space(self, max_deg=None):
+ """
+ Initializes parameter space.
+
+ Parameters
+ ----------
+ max_deg : int, optional
+ Maximum degree. The default is `None`.
+
+ Returns
+ -------
+ raw_data : array of shape (n_params, n_samples)
+ Raw data.
+ bound_tuples : list of tuples
+ A list containing lower and upper bounds of parameters.
+
+ """
+ Inputs = self.InputObj
+ ndim = self.ndim
+ rosenblatt_flag = Inputs.Rosenblatt
+ mc_size = 50000
+
+ # Save parameter names
+ self.par_names = []
+ for parIdx in range(ndim):
+ self.par_names.append(Inputs.Marginals[parIdx].name)
+
+ # Create a multivariate probability distribution
+ if max_deg is not None:
+ JDist, poly_types = self.build_dist(rosenblatt=rosenblatt_flag)
+ self.JDist, self.poly_types = JDist, poly_types
+
+ if self.input_data_given:
+
+ self.MCSize = len(Inputs.Marginals[0].input_data)
+ self.raw_data = np.zeros((ndim, self.MCSize))
+
+ for parIdx in range(ndim):
+ # Save parameter names
+ try:
+ self.raw_data[parIdx] = np.array(
+ Inputs.Marginals[parIdx].input_data)
+ except:
+ self.raw_data[parIdx] = self.JDist[parIdx].sample(mc_size)
+
+ else:
+ # Generate random samples based on parameter distributions
+ self.raw_data = chaospy.generate_samples(mc_size,
+ domain=self.JDist)
+
+ # Create orthogonal polynomial coefficients if necessary
+ if self.apce and max_deg is not None and Inputs.poly_coeffs_flag:
+ self.polycoeffs = {}
+ for parIdx in tqdm(range(ndim), ascii=True,
+ desc="Computing orth. polynomial coeffs"):
+ poly_coeffs = apoly_construction(self.raw_data[parIdx],
+ max_deg)
+ self.polycoeffs[f'p_{parIdx+1}'] = poly_coeffs
+
+ # Extract moments
+ for parIdx in range(ndim):
+ mu = np.mean(self.raw_data[parIdx])
+ std = np.std(self.raw_data[parIdx])
+ self.InputObj.Marginals[parIdx].moments = [mu, std]
+
+ # Generate the bounds based on given inputs for marginals
+ bound_tuples = []
+ for i in range(ndim):
+ if Inputs.Marginals[i].dist_type == 'unif':
+ low_bound, up_bound = Inputs.Marginals[i].parameters
+ else:
+ low_bound = np.min(self.raw_data[i])
+ up_bound = np.max(self.raw_data[i])
+
+ bound_tuples.append((low_bound, up_bound))
+
+ self.bound_tuples = tuple(bound_tuples)
+
+ return self.raw_data, self.bound_tuples</code></pre>
+</details>
+</dd>
+<dt id="exp_designs.ExpDesigns.build_dist"><code class="name flex">
+<span>def <span class="ident">build_dist</span></span>(<span>self, rosenblatt)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Creates the polynomial types to be passed to univ_basis_vals method of
+the MetaModel object.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>rosenblatt</code></strong> : <code>bool</code></dt>
+<dd>Rosenblatt transformation flag.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>orig_space_dist</code></strong> : <code>object</code></dt>
+<dd>A chaospy JDist object or a gaussian_kde object.</dd>
+<dt><strong><code>poly_types</code></strong> : <code>list</code></dt>
+<dd>List of polynomial types for the parameters.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def build_dist(self, rosenblatt):
+ """
+ Creates the polynomial types to be passed to univ_basis_vals method of
+ the MetaModel object.
+
+ Parameters
+ ----------
+ rosenblatt : bool
+ Rosenblatt transformation flag.
+
+ Returns
+ -------
+ orig_space_dist : object
+ A chaospy JDist object or a gaussian_kde object.
+ poly_types : list
+ List of polynomial types for the parameters.
+
+ """
+ Inputs = self.InputObj
+ all_data = []
+ all_dist_types = []
+ orig_joints = []
+ poly_types = []
+
+ for parIdx in range(self.ndim):
+
+ if Inputs.Marginals[parIdx].dist_type is None:
+ data = Inputs.Marginals[parIdx].input_data
+ all_data.append(data)
+ dist_type = None
+ else:
+ dist_type = Inputs.Marginals[parIdx].dist_type
+ params = Inputs.Marginals[parIdx].parameters
+
+ if rosenblatt:
+ polytype = 'hermite'
+ dist = chaospy.Normal()
+
+ elif dist_type is None:
+ polytype = 'arbitrary'
+ dist = None
+
+ elif 'unif' in dist_type.lower():
+ polytype = 'legendre'
+ dist = chaospy.Uniform(lower=params[0], upper=params[1])
+
+ elif 'norm' in dist_type.lower() and \
+ 'log' not in dist_type.lower():
+ polytype = 'hermite'
+ dist = chaospy.Normal(mu=params[0], sigma=params[1])
+
+ elif 'gamma' in dist_type.lower():
+ polytype = 'laguerre'
+ dist = chaospy.Gamma(shape=params[0],
+ scale=params[1],
+ shift=params[2])
+
+ elif 'beta' in dist_type.lower():
+ polytype = 'jacobi'
+ dist = chaospy.Beta(alpha=params[0], beta=params[1],
+ lower=params[2], upper=params[3])
+
+ elif 'lognorm' in dist_type.lower():
+ polytype = 'hermite'
+ Mu = np.log(params[0]**2/np.sqrt(params[0]**2 + params[1]**2))
+ Sigma = np.sqrt(np.log(1 + params[1]**2 / params[0]**2))
+ dist = chaospy.LogNormal(mu=Mu, sigma=Sigma)
+
+ elif 'expon' in dist_type.lower():
+ polytype = 'arbitrary'
+ dist = chaospy.Exponential(scale=params[0], shift=params[1])
+
+ elif 'weibull' in dist_type.lower():
+ polytype = 'arbitrary'
+ dist = chaospy.Weibull(shape=params[0], scale=params[1],
+ shift=params[2])
+
+ else:
+ message = (f"DistType {dist_type} for parameter"
+ f"{parIdx+1} is not available.")
+ raise ValueError(message)
+
+ if self.input_data_given or self.apce:
+ polytype = 'arbitrary'
+
+ # Store dists and poly_types
+ orig_joints.append(dist)
+ poly_types.append(polytype)
+ all_dist_types.append(dist_type)
+
+ # Prepare final output to return
+ if None in all_dist_types:
+ # Naive approach: Fit a gaussian kernel to the provided data
+ Data = np.asarray(all_data)
+ orig_space_dist = st.gaussian_kde(Data)
+ self.prior_space = orig_space_dist
+ else:
+ orig_space_dist = chaospy.J(*orig_joints)
+ self.prior_space = st.gaussian_kde(orig_space_dist.sample(10000))
+
+ return orig_space_dist, poly_types</code></pre>
+</details>
+</dd>
+<dt id="exp_designs.ExpDesigns.random_sampler"><code class="name flex">
+<span>def <span class="ident">random_sampler</span></span>(<span>self, n_samples)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Samples the given raw data randomly.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>n_samples</code></strong> : <code>int</code></dt>
+<dd>Number of requested samples.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>samples</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
+<dd>The sampling locations in the input space.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def random_sampler(self, n_samples):
+ """
+ Samples the given raw data randomly.
+
+ Parameters
+ ----------
+ n_samples : int
+ Number of requested samples.
+
+ Returns
+ -------
+ samples: array of shape (n_samples, n_params)
+ The sampling locations in the input space.
+
+ """
+ samples = np.zeros((n_samples, self.ndim))
+
+ for idxPa in range(self.ndim):
+ # input_data given
+ sample_size = len(self.raw_data[idxPa])
+ randIdx = np.random.randint(0, sample_size, n_samples)
+ samples[:, idxPa] = self.raw_data[idxPa, randIdx]
+
+ return samples</code></pre>
+</details>
+</dd>
+<dt id="exp_designs.ExpDesigns.pcm_sampler"><code class="name flex">
+<span>def <span class="ident">pcm_sampler</span></span>(<span>self, max_deg)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Generates collocation points based on the root of the polynomial
+degrees.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>max_deg</code></strong> : <code>int</code></dt>
+<dd>Maximum degree defined by user.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>opt_col_points</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
+<dd>Collocation points.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def pcm_sampler(self, max_deg):
+ """
+ Generates collocation points based on the root of the polynomial
+ degrees.
+
+ Parameters
+ ----------
+ max_deg : int
+ Maximum degree defined by user.
+
+ Returns
+ -------
+ opt_col_points: array of shape (n_samples, n_params)
+ Collocation points.
+
+ """
+
+ raw_data = self.raw_data
+
+ # Guess the closest degree to self.n_samples
+ def M_uptoMax(deg):
+ result = []
+ for d in range(1, deg+1):
+ result.append(math.factorial(self.ndim+d) //
+ (math.factorial(self.ndim) * math.factorial(d)))
+ return np.array(result)
+
+ guess_Deg = np.where(M_uptoMax(max_deg) > self.n_samples)[0][0]
+
+ c_points = np.zeros((guess_Deg+1, self.ndim))
+
+ def PolynomialPa(parIdx):
+ return apoly_construction(self.raw_data[parIdx], max_deg)
+
+ for i in range(self.ndim):
+ poly_coeffs = PolynomialPa(i)[guess_Deg+1][::-1]
+ c_points[:, i] = np.trim_zeros(np.roots(poly_coeffs))
+
+ # Construction of optimal integration points
+ Prod = itertools.product(np.arange(1, guess_Deg+2), repeat=self.ndim)
+ sort_dig_unique_combos = np.array(list(filter(lambda x: x, Prod)))
+
+ # Ranking relatively mean
+ Temp = np.empty(shape=[0, guess_Deg+1])
+ for j in range(self.ndim):
+ s = abs(c_points[:, j]-np.mean(raw_data[j]))
+ Temp = np.append(Temp, [s], axis=0)
+ temp = Temp.T
+
+ index_CP = np.sort(temp, axis=0)
+ sort_cpoints = np.empty((0, guess_Deg+1))
+
+ for j in range(self.ndim):
+ sort_cp = c_points[index_CP[:, j], j]
+ sort_cpoints = np.vstack((sort_cpoints, sort_cp))
+
+ # Mapping of Combination to Cpoint Combination
+ sort_unique_combos = np.empty(shape=[0, self.ndim])
+ for i in range(len(sort_dig_unique_combos)):
+ sort_un_comb = []
+ for j in range(self.ndim):
+ SortUC = sort_cpoints[j, sort_dig_unique_combos[i, j]-1]
+ sort_un_comb.append(SortUC)
+ sort_uni_comb = np.asarray(sort_un_comb)
+ sort_unique_combos = np.vstack((sort_unique_combos, sort_uni_comb))
+
+ # Output the collocation points
+ if self.sampling_method.lower() == 'lscm':
+ opt_col_points = sort_unique_combos
+ else:
+ opt_col_points = sort_unique_combos[0:self.n_samples]
+
+ return opt_col_points</code></pre>
+</details>
+</dd>
+<dt id="exp_designs.ExpDesigns.transform"><code class="name flex">
+<span>def <span class="ident">transform</span></span>(<span>self, X, params=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Transform the samples via either a Rosenblatt or an isoprobabilistic
+transformation.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>X</code></strong> : <code>array</code> of <code>shape (n_samples,n_params)</code></dt>
+<dd>Samples to be transformed.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>tr_X</code></strong> : <code>array</code> of <code>shape (n_samples,n_params)</code></dt>
+<dd>Transformed samples.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def transform(self, X, params=None):
+ """
+ Transform the samples via either a Rosenblatt or an isoprobabilistic
+ transformation.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples,n_params)
+ Samples to be transformed.
+
+ Returns
+ -------
+ tr_X: array of shape (n_samples,n_params)
+ Transformed samples.
+
+ """
+ if self.InputObj.Rosenblatt:
+ self.origJDist, _ = self.build_dist(False)
+ tr_X = self.origJDist.inv(self.JDist.fwd(X.T)).T
+ else:
+ # Transform samples via an isoprobabilistic transformation
+ n_samples, n_params = X.shape
+ Inputs = self.InputObj
+ origJDist = self.JDist
+ poly_types = self.poly_types
+
+ disttypes = []
+ for par_i in range(n_params):
+ disttypes.append(Inputs.Marginals[par_i].dist_type)
+
+ # Pass non-transformed X, if arbitrary PCE is selected.
+ if None in disttypes or self.input_data_given or self.apce:
+ return X
+
+ cdfx = np.zeros((X.shape))
+ tr_X = np.zeros((X.shape))
+
+ for par_i in range(n_params):
+
+ # Extract the parameters of the original space
+ disttype = disttypes[par_i]
+ if disttype is not None:
+ dist = origJDist[par_i]
+ else:
+ dist = None
+ polytype = poly_types[par_i]
+ cdf = np.vectorize(lambda x: dist.cdf(x))
+
+ # Extract the parameters of the transformation space based on
+ # polyType
+ if polytype == 'legendre' or disttype == 'uniform':
+ # Generate Y_Dists based
+ params_Y = [-1, 1]
+ dist_Y = st.uniform(loc=params_Y[0],
+ scale=params_Y[1]-params_Y[0])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ elif polytype == 'hermite' or disttype == 'norm':
+ params_Y = [0, 1]
+ dist_Y = st.norm(loc=params_Y[0], scale=params_Y[1])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ elif polytype == 'laguerre' or disttype == 'gamma':
+ params_Y = [1, params[1]]
+ dist_Y = st.gamma(loc=params_Y[0], scale=params_Y[1])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ # Compute CDF_x(X)
+ cdfx[:, par_i] = cdf(X[:, par_i])
+
+ # Compute invCDF_y(cdfx)
+ tr_X[:, par_i] = inv_cdf(cdfx[:, par_i])
+
+ return tr_X</code></pre>
+</details>
+</dd>
+<dt id="exp_designs.ExpDesigns.fit_dist"><code class="name flex">
+<span>def <span class="ident">fit_dist</span></span>(<span>self, y)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Fits the known distributions to the data.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>y</code></strong> : <code>array</code> of <code>shape (n_samples)</code></dt>
+<dd>Data to be fitted.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>sel_dist</code></strong> : <code>string</code></dt>
+<dd>Selected distribution type from <code>lognorm</code>, <code>norm</code>, <code>uniform</code> or
+<code>expon</code>.</dd>
+<dt><strong><code>params</code></strong> : <code>list</code></dt>
+<dd>Parameters corresponding to the selected distibution type.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def fit_dist(self, y):
+ """
+ Fits the known distributions to the data.
+
+ Parameters
+ ----------
+ y : array of shape (n_samples)
+ Data to be fitted.
+
+ Returns
+ -------
+ sel_dist: string
+ Selected distribution type from `lognorm`, `norm`, `uniform` or
+ `expon`.
+ params : list
+ Parameters corresponding to the selected distibution type.
+
+ """
+ dist_results = []
+ params = {}
+ dist_names = ['lognorm', 'norm', 'uniform', 'expon']
+ for dist_name in dist_names:
+ dist = getattr(st, dist_name)
+
+ try:
+ if dist_name != 'lognorm':
+ param = dist.fit(y)
+ else:
+ param = dist.fit(np.exp(y), floc=0)
+ except:
+ param = dist.fit(y)
+
+ params[dist_name] = param
+ # Applying the Kolmogorov-Smirnov test
+ D, p = st.kstest(y, dist_name, args=param)
+ dist_results.append((dist_name, D))
+
+ # select the best fitted distribution
+ sel_dist, D = (min(dist_results, key=lambda item: item[1]))
+
+ if sel_dist == 'uniform':
+ params[sel_dist] = [params[sel_dist][0], params[sel_dist][0] +
+ params[sel_dist][1]]
+ if D < 0.05:
+ return sel_dist, params[sel_dist]
+ else:
+ return None, None</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="exp_designs.ExpDesigns" href="#exp_designs.ExpDesigns">ExpDesigns</a></code></h4>
+<ul class="two-column">
+<li><code><a title="exp_designs.ExpDesigns.generate_samples" href="#exp_designs.ExpDesigns.generate_samples">generate_samples</a></code></li>
+<li><code><a title="exp_designs.ExpDesigns.generate_ED" href="#exp_designs.ExpDesigns.generate_ED">generate_ED</a></code></li>
+<li><code><a title="exp_designs.ExpDesigns.init_param_space" href="#exp_designs.ExpDesigns.init_param_space">init_param_space</a></code></li>
+<li><code><a title="exp_designs.ExpDesigns.build_dist" href="#exp_designs.ExpDesigns.build_dist">build_dist</a></code></li>
+<li><code><a title="exp_designs.ExpDesigns.random_sampler" href="#exp_designs.ExpDesigns.random_sampler">random_sampler</a></code></li>
+<li><code><a title="exp_designs.ExpDesigns.pcm_sampler" href="#exp_designs.ExpDesigns.pcm_sampler">pcm_sampler</a></code></li>
+<li><code><a title="exp_designs.ExpDesigns.transform" href="#exp_designs.ExpDesigns.transform">transform</a></code></li>
+<li><code><a title="exp_designs.ExpDesigns.fit_dist" href="#exp_designs.ExpDesigns.fit_dist">fit_dist</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>
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+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>exploration</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import numpy as np
+from scipy.spatial import distance
+
+# from .exp_designs import ExpDesigns
+
+
+class Exploration:
+ """
+ Created based on the Surrogate Modeling Toolbox (SUMO) [1].
+
+ [1] Gorissen, D., Couckuyt, I., Demeester, P., Dhaene, T. and Crombecq, K.,
+ 2010. A surrogate modeling and adaptive sampling toolbox for computer
+ based design. Journal of machine learning research.-Cambridge, Mass.,
+ 11, pp.2051-2055. sumo@sumo.intec.ugent.be - http://sumo.intec.ugent.be
+
+ Attributes
+ ----------
+ MetaModel : obj
+ MetaModel object.
+ n_candidate : int
+ Number of candidate samples.
+ mc_criterion : str
+ Selection crieterion. The default is `'mc-intersite-proj-th'`. Another
+ option is `'mc-intersite-proj'`.
+ w : int
+ Number of random points in the domain for each sample of the
+ training set.
+ """
+
+ def __init__(self, MetaModel, n_candidate,
+ mc_criterion='mc-intersite-proj-th'):
+ self.MetaModel = MetaModel
+ self.Marginals = []
+ self.n_candidate = n_candidate
+ self.mc_criterion = mc_criterion
+ self.w = 100
+
+ def get_exploration_samples(self):
+ """
+ This function generates candidates to be selected as new design and
+ their associated exploration scores.
+
+ Returns
+ -------
+ all_candidates : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
+ """
+ MetaModel = self.MetaModel
+ explore_method = MetaModel.ExpDesign.explore_method
+
+ print("\n")
+ print(f' The {explore_method}-Method is selected as the exploration'
+ 'method.')
+ print("\n")
+
+ if explore_method == 'Voronoi':
+ # Generate samples using the Voronoi method
+ all_candidates, exploration_scores = self.get_vornoi_samples()
+ else:
+ # Generate samples using the MC method
+ all_candidates, exploration_scores = self.get_mc_samples()
+
+ return all_candidates, exploration_scores
+
+ # -------------------------------------------------------------------------
+ def get_vornoi_samples(self):
+ """
+ This function generates samples based on voronoi cells and their
+ corresponding scores
+
+ Returns
+ -------
+ new_samples : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
+ """
+
+ mc_criterion = self.mc_criterion
+ n_candidate = self.n_candidate
+ # Get the Old ExpDesign #samples
+ old_ED_X = self.MetaModel.ExpDesign.X
+ ndim = old_ED_X.shape[1]
+
+ # calculate error #averageErrors
+ error_voronoi, all_candidates = self.approximate_voronoi(
+ self.w, old_ED_X
+ )
+
+ # Pick the best candidate point in the voronoi cell
+ # for each best sample
+ selected_samples = np.empty((0, ndim))
+ bad_samples = []
+
+ for index in range(len(error_voronoi)):
+
+ # get candidate new samples from voronoi tesselation
+ candidates = self.closestPoints[index]
+
+ # get total number of candidates
+ n_new_samples = candidates.shape[0]
+
+ # still no candidate samples around this one, skip it!
+ if n_new_samples == 0:
+ print('The following sample has been skipped because there '
+ 'were no candidate samples around it...')
+ print(old_ED_X[index])
+ bad_samples.append(index)
+ continue
+
+ # find candidate that is farthest away from any existing sample
+ max_min_distance = 0
+ best_candidate = 0
+ min_intersite_dist = np.zeros((n_new_samples))
+ min_projected_dist = np.zeros((n_new_samples))
+
+ for j in range(n_new_samples):
+
+ new_samples = np.vstack((old_ED_X, selected_samples))
+
+ # find min distorted distance from all other samples
+ euclidean_dist = self._build_dist_matrix_point(
+ new_samples, candidates[j], do_sqrt=True)
+ min_euclidean_dist = np.min(euclidean_dist)
+ min_intersite_dist[j] = min_euclidean_dist
+
+ # Check if this is the maximum minimum distance from all other
+ # samples
+ if min_euclidean_dist >= max_min_distance:
+ max_min_distance = min_euclidean_dist
+ best_candidate = j
+
+ # Projected distance
+ projected_dist = distance.cdist(
+ new_samples, [candidates[j]], 'chebyshev')
+ min_projected_dist[j] = np.min(projected_dist)
+
+ if mc_criterion == 'mc-intersite-proj':
+ weight_euclidean_dist = 0.5 * ((n_new_samples+1)**(1/ndim) - 1)
+ weight_projected_dist = 0.5 * (n_new_samples+1)
+ total_dist_scores = weight_euclidean_dist * min_intersite_dist
+ total_dist_scores += weight_projected_dist * min_projected_dist
+
+ elif mc_criterion == 'mc-intersite-proj-th':
+ alpha = 0.5 # chosen (tradeoff)
+ d_min = 2 * alpha / n_new_samples
+ if any(min_projected_dist < d_min):
+ candidates = np.delete(
+ candidates, [min_projected_dist < d_min], axis=0
+ )
+ total_dist_scores = np.delete(
+ min_intersite_dist, [min_projected_dist < d_min],
+ axis=0
+ )
+ else:
+ total_dist_scores = min_intersite_dist
+ else:
+ raise NameError(
+ 'The MC-Criterion you requested is not available.'
+ )
+
+ # Add the best candidate to the list of new samples
+ best_candidate = np.argsort(total_dist_scores)[::-1][:n_candidate]
+ selected_samples = np.vstack(
+ (selected_samples, candidates[best_candidate])
+ )
+
+ self.new_samples = selected_samples
+ self.exploration_scores = np.delete(error_voronoi, bad_samples, axis=0)
+
+ return self.new_samples, self.exploration_scores
+
+ # -------------------------------------------------------------------------
+ def get_mc_samples(self, all_candidates=None):
+ """
+ This function generates random samples based on Global Monte Carlo
+ methods and their corresponding scores, based on [1].
+
+ [1] Crombecq, K., Laermans, E. and Dhaene, T., 2011. Efficient
+ space-filling and non-collapsing sequential design strategies for
+ simulation-based modeling. European Journal of Operational Research
+ , 214(3), pp.683-696.
+ DOI: https://doi.org/10.1016/j.ejor.2011.05.032
+
+ Implemented methods to compute scores:
+ 1) mc-intersite-proj
+ 2) mc-intersite-proj-th
+
+ Arguments
+ ---------
+ all_candidates : array, optional
+ Samples to compute the scores for. The default is `None`. In this
+ case, samples will be generated by defined model input marginals.
+
+ Returns
+ -------
+ new_samples : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
+ """
+ MetaModel = self.MetaModel
+ explore_method = MetaModel.ExpDesign.explore_method
+ mc_criterion = self.mc_criterion
+ if all_candidates is None:
+ n_candidate = self.n_candidate
+ else:
+ n_candidate = all_candidates.shape[0]
+
+ # Get the Old ExpDesign #samples
+ old_ED_X = MetaModel.ExpDesign.X
+ ndim = old_ED_X.shape[1]
+
+ # ----- Compute the number of random points -----
+ if all_candidates is None:
+ # Generate MC Samples
+ all_candidates = MetaModel.ExpDesign.generate_samples(
+ self.n_candidate, explore_method
+ )
+ self.all_candidates = all_candidates
+
+ # initialization
+ new_samples = np.empty((0, ndim))
+ min_intersite_dist = np.zeros((n_candidate))
+ min_projected_dist = np.zeros((n_candidate))
+
+ for i, candidate in enumerate(all_candidates):
+
+ # find candidate that is farthest away from any existing sample
+ maxMinDistance = 0
+
+ new_samples = np.vstack((old_ED_X, new_samples))
+ # find min distorted distance from all other samples
+ euclidean_dist = self._build_dist_matrix_point(
+ new_samples, candidate, do_sqrt=True
+ )
+ min_euclidean_dist = np.min(euclidean_dist)
+ min_intersite_dist[i] = min_euclidean_dist
+
+ # Check if this is the maximum minimum distance from all other
+ # samples
+ if min_euclidean_dist >= maxMinDistance:
+ maxMinDistance = min_euclidean_dist
+
+ # Projected distance
+ projected_dist = distance.cdist(
+ new_samples, [candidate], 'chebyshev'
+ )
+ min_projected_dist[i] = np.min(projected_dist)
+
+ if mc_criterion == 'mc-intersite-proj':
+ weight_euclidean_dist = ((n_candidate+1)**(1/ndim) - 1) * 0.5
+ weight_projected_dist = (n_candidate+1) * 0.5
+ total_dist_scores = weight_euclidean_dist * min_intersite_dist
+ total_dist_scores += weight_projected_dist * min_projected_dist
+
+ elif mc_criterion == 'mc-intersite-proj-th':
+ alpha = 0.5 # chosen (tradeoff)
+ d_min = 2 * alpha / n_candidate
+ if any(min_projected_dist < d_min):
+ all_candidates = np.delete(
+ all_candidates, [min_projected_dist < d_min], axis=0
+ )
+ total_dist_scores = np.delete(
+ min_intersite_dist, [min_projected_dist < d_min], axis=0
+ )
+ else:
+ total_dist_scores = min_intersite_dist
+ else:
+ raise NameError('The MC-Criterion you requested is not available.')
+
+ self.new_samples = all_candidates
+ self.exploration_scores = total_dist_scores
+ self.exploration_scores /= np.nansum(total_dist_scores)
+
+ return self.new_samples, self.exploration_scores
+
+ # -------------------------------------------------------------------------
+ def approximate_voronoi(self, w, samples):
+ """
+ An approximate (monte carlo) version of Matlab's voronoi command.
+
+ Arguments
+ ---------
+ samples : array
+ Old experimental design to be used as center points for voronoi
+ cells.
+
+ Returns
+ -------
+ areas : array
+ An approximation of the voronoi cells' areas.
+ all_candidates: list of arrays
+ A list of samples in each voronoi cell.
+ """
+ MetaModel = self.MetaModel
+
+ n_samples = samples.shape[0]
+ ndim = samples.shape[1]
+
+ # Compute the number of random points
+ n_points = w * samples.shape[0]
+ # Generate w random points in the domain for each sample
+ ExpDesign = ExpDesigns(MetaModel.Inputs)
+ points = ExpDesign.generate_samples(n_points, 'random')
+ self.all_candidates = points
+
+ # Calculate the nearest sample to each point
+ self.areas = np.zeros((n_samples))
+ self.closestPoints = [np.empty((0, ndim)) for i in range(n_samples)]
+
+ # Compute the minimum distance from all the samples of old_ED_X for
+ # each test point
+ for idx in range(n_points):
+ # calculate the minimum distance
+ distances = self._build_dist_matrix_point(
+ samples, points[idx], do_sqrt=True
+ )
+ closest_sample = np.argmin(distances)
+
+ # Add to the voronoi list of the closest sample
+ self.areas[closest_sample] = self.areas[closest_sample] + 1
+ prevclosestPoints = self.closestPoints[closest_sample]
+ self.closestPoints[closest_sample] = np.vstack(
+ (prevclosestPoints, points[idx])
+ )
+
+ # Divide by the amount of points to get the estimated volume of each
+ # voronoi cell
+ self.areas = self.areas / n_points
+
+ self.perc = np.max(self.areas * 100)
+
+ self.errors = self.areas
+
+ return self.areas, self.all_candidates
+
+ # -------------------------------------------------------------------------
+ def _build_dist_matrix_point(self, samples, point, do_sqrt=False):
+ """
+ Calculates the intersite distance of all points in samples from point.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params)
+ The old experimental design.
+ point : array
+ A candidate point.
+ do_sqrt : bool, optional
+ Whether to return distances or squared distances. The default is
+ `False`.
+
+ Returns
+ -------
+ distances : array
+ Distances.
+
+ """
+ distances = distance.cdist(samples, np.array([point]), 'euclidean')
+
+ # do square root?
+ if do_sqrt:
+ return distances
+ else:
+ return distances**2
+
+#if __name__ == "__main__":
+# import scipy.stats as stats
+# import matplotlib.pyplot as plt
+# import matplotlib as mpl
+# import matplotlib.cm as cm
+# plt.rc('font', family='sans-serif', serif='Arial')
+# plt.rc('figure', figsize = (12, 8))
+#
+# def plotter(old_ED_X, all_candidates, exploration_scores):
+# global Bounds
+#
+# from scipy.spatial import Voronoi, voronoi_plot_2d
+# vor = Voronoi(old_ED_X)
+#
+# fig = voronoi_plot_2d(vor)
+#
+# # find min/max values for normalization
+## minima = min(exploration_scores)
+## maxima = max(exploration_scores)
+##
+## # normalize chosen colormap
+## norm = mpl.colors.Normalize(vmin=minima, vmax=maxima, clip=True)
+## mapper = cm.ScalarMappable(norm=norm, cmap=cm.Blues_r)
+##
+## for r in range(len(vor.point_region)):
+## region = vor.regions[vor.point_region[r]]
+## if not -1 in region:
+## polygon = [vor.vertices[i] for i in region]
+## plt.fill(*zip(*polygon), color=mapper.to_rgba(exploration_scores[r]))
+#
+#
+# ax1 = fig.add_subplot(111)
+#
+# ax1.scatter(old_ED_X[:,0], old_ED_X[:,1], s=10, c='r', marker="s", label='Old Design Points')
+# for i in range(old_ED_X.shape[0]):
+# txt = 'p'+str(i+1)
+# ax1.annotate(txt, (old_ED_X[i,0],old_ED_X[i,1]))
+#
+## for i in range(NrofCandGroups):
+## Candidates = all_candidates['group_'+str(i+1)]
+## ax1.scatter(Candidates[:,0],Candidates[:,1], s=10, c='b', marker="o", label='Design candidates')
+# ax1.scatter(all_candidates[:,0],all_candidates[:,1], s=10, c='b', marker="o", label='Design candidates')
+#
+# ax1.set_xlim(Bounds[0][0], Bounds[0][1])
+# ax1.set_ylim(Bounds[1][0], Bounds[1][1])
+#
+# plt.legend(loc='best');
+# plt.show()
+#
+# def voronoi_volumes(points):
+# from scipy.spatial import Voronoi, ConvexHull
+# v = Voronoi(points)
+# vol = np.zeros(v.npoints)
+#
+# for i, reg_num in enumerate(v.point_region):
+# indices = v.regions[reg_num]
+# if -1 in indices: # some regions can be opened
+# vol[i] = np.inf
+# else:
+#
+# #print("reg_num={0: 3.3f} X1={1: 3.3f} X2={2: 3.3f}".format(reg_num, v.points[reg_num-1, 0], v.points[reg_num-1, 1]))
+# vol[i] = ConvexHull(v.vertices[indices]).volume
+#
+# print('-'*40)
+# for i in range(nrofSamples):
+# print("idx={0:d} X1={1: 3.3f} X2={2: 3.3f} Volume={3: 3.3f}".format(i+1, v.points[i, 0], v.points[i, 1], vol[i]))
+#
+# return vol
+#
+# NofPa = 2
+#
+# Bounds = ((-5,10), (0,15))
+#
+# nrofSamples = 10
+# old_ED_X = np.zeros((nrofSamples, NofPa))
+# for idx in range(NofPa):
+# Loc = Bounds[idx][0]
+# Scale = Bounds[idx][1] - Bounds[idx][0]
+# old_ED_X[:,idx] = stats.uniform(loc=Loc, scale=Scale).rvs(size=nrofSamples)
+#
+#
+# nNewCandidate = 40
+#
+# # New Function
+# volumes = voronoi_volumes(old_ED_X)
+#
+#
+# # SUMO
+# Exploration = Exploration(Bounds, old_ED_X, nNewCandidate)
+#
+# #all_candidates, Score = Exploration.get_vornoi_samples()
+# all_candidates, Score = Exploration.get_mc_samples()
+#
+# print('-'*40)
+## for i in range(nrofSamples):
+## print("idx={0:d} X1={1: 3.3f} X2={2: 3.3f} Volume={3: 3.3f}".format(i+1, old_ED_X[i,0], old_ED_X[i,1], vornoi.areas[i]))
+#
+# plotter(old_ED_X, all_candidates, volumes)
+ </code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="exploration.Exploration"><code class="flex name class">
+<span>class <span class="ident">Exploration</span></span>
+<span>(</span><span>MetaModel, n_candidate, mc_criterion='mc-intersite-proj-th')</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Created based on the Surrogate Modeling Toolbox (SUMO) [1].</p>
+<p>[1] Gorissen, D., Couckuyt, I., Demeester, P., Dhaene, T. and Crombecq, K.,
+2010. A surrogate modeling and adaptive sampling toolbox for computer
+based design. Journal of machine learning research.-Cambridge, Mass.,
+11, pp.2051-2055. sumo@sumo.intec.ugent.be - <a href="http://sumo.intec.ugent.be">http://sumo.intec.ugent.be</a></p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>MetaModel</code></strong> : <code>obj</code></dt>
+<dd>MetaModel object.</dd>
+<dt><strong><code>n_candidate</code></strong> : <code>int</code></dt>
+<dd>Number of candidate samples.</dd>
+<dt><strong><code>mc_criterion</code></strong> : <code>str</code></dt>
+<dd>Selection crieterion. The default is <code>'mc-intersite-proj-th'</code>. Another
+option is <code>'mc-intersite-proj'</code>.</dd>
+<dt><strong><code>w</code></strong> : <code>int</code></dt>
+<dd>Number of random points in the domain for each sample of the
+training set.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class Exploration:
+ """
+ Created based on the Surrogate Modeling Toolbox (SUMO) [1].
+
+ [1] Gorissen, D., Couckuyt, I., Demeester, P., Dhaene, T. and Crombecq, K.,
+ 2010. A surrogate modeling and adaptive sampling toolbox for computer
+ based design. Journal of machine learning research.-Cambridge, Mass.,
+ 11, pp.2051-2055. sumo@sumo.intec.ugent.be - http://sumo.intec.ugent.be
+
+ Attributes
+ ----------
+ MetaModel : obj
+ MetaModel object.
+ n_candidate : int
+ Number of candidate samples.
+ mc_criterion : str
+ Selection crieterion. The default is `'mc-intersite-proj-th'`. Another
+ option is `'mc-intersite-proj'`.
+ w : int
+ Number of random points in the domain for each sample of the
+ training set.
+ """
+
+ def __init__(self, MetaModel, n_candidate,
+ mc_criterion='mc-intersite-proj-th'):
+ self.MetaModel = MetaModel
+ self.Marginals = []
+ self.n_candidate = n_candidate
+ self.mc_criterion = mc_criterion
+ self.w = 100
+
+ def get_exploration_samples(self):
+ """
+ This function generates candidates to be selected as new design and
+ their associated exploration scores.
+
+ Returns
+ -------
+ all_candidates : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
+ """
+ MetaModel = self.MetaModel
+ explore_method = MetaModel.ExpDesign.explore_method
+
+ print("\n")
+ print(f' The {explore_method}-Method is selected as the exploration'
+ 'method.')
+ print("\n")
+
+ if explore_method == 'Voronoi':
+ # Generate samples using the Voronoi method
+ all_candidates, exploration_scores = self.get_vornoi_samples()
+ else:
+ # Generate samples using the MC method
+ all_candidates, exploration_scores = self.get_mc_samples()
+
+ return all_candidates, exploration_scores
+
+ # -------------------------------------------------------------------------
+ def get_vornoi_samples(self):
+ """
+ This function generates samples based on voronoi cells and their
+ corresponding scores
+
+ Returns
+ -------
+ new_samples : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
+ """
+
+ mc_criterion = self.mc_criterion
+ n_candidate = self.n_candidate
+ # Get the Old ExpDesign #samples
+ old_ED_X = self.MetaModel.ExpDesign.X
+ ndim = old_ED_X.shape[1]
+
+ # calculate error #averageErrors
+ error_voronoi, all_candidates = self.approximate_voronoi(
+ self.w, old_ED_X
+ )
+
+ # Pick the best candidate point in the voronoi cell
+ # for each best sample
+ selected_samples = np.empty((0, ndim))
+ bad_samples = []
+
+ for index in range(len(error_voronoi)):
+
+ # get candidate new samples from voronoi tesselation
+ candidates = self.closestPoints[index]
+
+ # get total number of candidates
+ n_new_samples = candidates.shape[0]
+
+ # still no candidate samples around this one, skip it!
+ if n_new_samples == 0:
+ print('The following sample has been skipped because there '
+ 'were no candidate samples around it...')
+ print(old_ED_X[index])
+ bad_samples.append(index)
+ continue
+
+ # find candidate that is farthest away from any existing sample
+ max_min_distance = 0
+ best_candidate = 0
+ min_intersite_dist = np.zeros((n_new_samples))
+ min_projected_dist = np.zeros((n_new_samples))
+
+ for j in range(n_new_samples):
+
+ new_samples = np.vstack((old_ED_X, selected_samples))
+
+ # find min distorted distance from all other samples
+ euclidean_dist = self._build_dist_matrix_point(
+ new_samples, candidates[j], do_sqrt=True)
+ min_euclidean_dist = np.min(euclidean_dist)
+ min_intersite_dist[j] = min_euclidean_dist
+
+ # Check if this is the maximum minimum distance from all other
+ # samples
+ if min_euclidean_dist >= max_min_distance:
+ max_min_distance = min_euclidean_dist
+ best_candidate = j
+
+ # Projected distance
+ projected_dist = distance.cdist(
+ new_samples, [candidates[j]], 'chebyshev')
+ min_projected_dist[j] = np.min(projected_dist)
+
+ if mc_criterion == 'mc-intersite-proj':
+ weight_euclidean_dist = 0.5 * ((n_new_samples+1)**(1/ndim) - 1)
+ weight_projected_dist = 0.5 * (n_new_samples+1)
+ total_dist_scores = weight_euclidean_dist * min_intersite_dist
+ total_dist_scores += weight_projected_dist * min_projected_dist
+
+ elif mc_criterion == 'mc-intersite-proj-th':
+ alpha = 0.5 # chosen (tradeoff)
+ d_min = 2 * alpha / n_new_samples
+ if any(min_projected_dist < d_min):
+ candidates = np.delete(
+ candidates, [min_projected_dist < d_min], axis=0
+ )
+ total_dist_scores = np.delete(
+ min_intersite_dist, [min_projected_dist < d_min],
+ axis=0
+ )
+ else:
+ total_dist_scores = min_intersite_dist
+ else:
+ raise NameError(
+ 'The MC-Criterion you requested is not available.'
+ )
+
+ # Add the best candidate to the list of new samples
+ best_candidate = np.argsort(total_dist_scores)[::-1][:n_candidate]
+ selected_samples = np.vstack(
+ (selected_samples, candidates[best_candidate])
+ )
+
+ self.new_samples = selected_samples
+ self.exploration_scores = np.delete(error_voronoi, bad_samples, axis=0)
+
+ return self.new_samples, self.exploration_scores
+
+ # -------------------------------------------------------------------------
+ def get_mc_samples(self, all_candidates=None):
+ """
+ This function generates random samples based on Global Monte Carlo
+ methods and their corresponding scores, based on [1].
+
+ [1] Crombecq, K., Laermans, E. and Dhaene, T., 2011. Efficient
+ space-filling and non-collapsing sequential design strategies for
+ simulation-based modeling. European Journal of Operational Research
+ , 214(3), pp.683-696.
+ DOI: https://doi.org/10.1016/j.ejor.2011.05.032
+
+ Implemented methods to compute scores:
+ 1) mc-intersite-proj
+ 2) mc-intersite-proj-th
+
+ Arguments
+ ---------
+ all_candidates : array, optional
+ Samples to compute the scores for. The default is `None`. In this
+ case, samples will be generated by defined model input marginals.
+
+ Returns
+ -------
+ new_samples : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
+ """
+ MetaModel = self.MetaModel
+ explore_method = MetaModel.ExpDesign.explore_method
+ mc_criterion = self.mc_criterion
+ if all_candidates is None:
+ n_candidate = self.n_candidate
+ else:
+ n_candidate = all_candidates.shape[0]
+
+ # Get the Old ExpDesign #samples
+ old_ED_X = MetaModel.ExpDesign.X
+ ndim = old_ED_X.shape[1]
+
+ # ----- Compute the number of random points -----
+ if all_candidates is None:
+ # Generate MC Samples
+ all_candidates = MetaModel.ExpDesign.generate_samples(
+ self.n_candidate, explore_method
+ )
+ self.all_candidates = all_candidates
+
+ # initialization
+ new_samples = np.empty((0, ndim))
+ min_intersite_dist = np.zeros((n_candidate))
+ min_projected_dist = np.zeros((n_candidate))
+
+ for i, candidate in enumerate(all_candidates):
+
+ # find candidate that is farthest away from any existing sample
+ maxMinDistance = 0
+
+ new_samples = np.vstack((old_ED_X, new_samples))
+ # find min distorted distance from all other samples
+ euclidean_dist = self._build_dist_matrix_point(
+ new_samples, candidate, do_sqrt=True
+ )
+ min_euclidean_dist = np.min(euclidean_dist)
+ min_intersite_dist[i] = min_euclidean_dist
+
+ # Check if this is the maximum minimum distance from all other
+ # samples
+ if min_euclidean_dist >= maxMinDistance:
+ maxMinDistance = min_euclidean_dist
+
+ # Projected distance
+ projected_dist = distance.cdist(
+ new_samples, [candidate], 'chebyshev'
+ )
+ min_projected_dist[i] = np.min(projected_dist)
+
+ if mc_criterion == 'mc-intersite-proj':
+ weight_euclidean_dist = ((n_candidate+1)**(1/ndim) - 1) * 0.5
+ weight_projected_dist = (n_candidate+1) * 0.5
+ total_dist_scores = weight_euclidean_dist * min_intersite_dist
+ total_dist_scores += weight_projected_dist * min_projected_dist
+
+ elif mc_criterion == 'mc-intersite-proj-th':
+ alpha = 0.5 # chosen (tradeoff)
+ d_min = 2 * alpha / n_candidate
+ if any(min_projected_dist < d_min):
+ all_candidates = np.delete(
+ all_candidates, [min_projected_dist < d_min], axis=0
+ )
+ total_dist_scores = np.delete(
+ min_intersite_dist, [min_projected_dist < d_min], axis=0
+ )
+ else:
+ total_dist_scores = min_intersite_dist
+ else:
+ raise NameError('The MC-Criterion you requested is not available.')
+
+ self.new_samples = all_candidates
+ self.exploration_scores = total_dist_scores
+ self.exploration_scores /= np.nansum(total_dist_scores)
+
+ return self.new_samples, self.exploration_scores
+
+ # -------------------------------------------------------------------------
+ def approximate_voronoi(self, w, samples):
+ """
+ An approximate (monte carlo) version of Matlab's voronoi command.
+
+ Arguments
+ ---------
+ samples : array
+ Old experimental design to be used as center points for voronoi
+ cells.
+
+ Returns
+ -------
+ areas : array
+ An approximation of the voronoi cells' areas.
+ all_candidates: list of arrays
+ A list of samples in each voronoi cell.
+ """
+ MetaModel = self.MetaModel
+
+ n_samples = samples.shape[0]
+ ndim = samples.shape[1]
+
+ # Compute the number of random points
+ n_points = w * samples.shape[0]
+ # Generate w random points in the domain for each sample
+ ExpDesign = ExpDesigns(MetaModel.Inputs)
+ points = ExpDesign.generate_samples(n_points, 'random')
+ self.all_candidates = points
+
+ # Calculate the nearest sample to each point
+ self.areas = np.zeros((n_samples))
+ self.closestPoints = [np.empty((0, ndim)) for i in range(n_samples)]
+
+ # Compute the minimum distance from all the samples of old_ED_X for
+ # each test point
+ for idx in range(n_points):
+ # calculate the minimum distance
+ distances = self._build_dist_matrix_point(
+ samples, points[idx], do_sqrt=True
+ )
+ closest_sample = np.argmin(distances)
+
+ # Add to the voronoi list of the closest sample
+ self.areas[closest_sample] = self.areas[closest_sample] + 1
+ prevclosestPoints = self.closestPoints[closest_sample]
+ self.closestPoints[closest_sample] = np.vstack(
+ (prevclosestPoints, points[idx])
+ )
+
+ # Divide by the amount of points to get the estimated volume of each
+ # voronoi cell
+ self.areas = self.areas / n_points
+
+ self.perc = np.max(self.areas * 100)
+
+ self.errors = self.areas
+
+ return self.areas, self.all_candidates
+
+ # -------------------------------------------------------------------------
+ def _build_dist_matrix_point(self, samples, point, do_sqrt=False):
+ """
+ Calculates the intersite distance of all points in samples from point.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params)
+ The old experimental design.
+ point : array
+ A candidate point.
+ do_sqrt : bool, optional
+ Whether to return distances or squared distances. The default is
+ `False`.
+
+ Returns
+ -------
+ distances : array
+ Distances.
+
+ """
+ distances = distance.cdist(samples, np.array([point]), 'euclidean')
+
+ # do square root?
+ if do_sqrt:
+ return distances
+ else:
+ return distances**2</code></pre>
+</details>
+<h3>Methods</h3>
+<dl>
+<dt id="exploration.Exploration.get_exploration_samples"><code class="name flex">
+<span>def <span class="ident">get_exploration_samples</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>This function generates candidates to be selected as new design and
+their associated exploration scores.</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>all_candidates</code></strong> : <code>array</code> of <code>shape (n_candidate, n_params)</code></dt>
+<dd>A list of samples.</dd>
+<dt><strong><code>exploration_scores</code></strong> : <code>arrays</code> of <code>shape (n_candidate)</code></dt>
+<dd>Exploration scores.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def get_exploration_samples(self):
+ """
+ This function generates candidates to be selected as new design and
+ their associated exploration scores.
+
+ Returns
+ -------
+ all_candidates : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
+ """
+ MetaModel = self.MetaModel
+ explore_method = MetaModel.ExpDesign.explore_method
+
+ print("\n")
+ print(f' The {explore_method}-Method is selected as the exploration'
+ 'method.')
+ print("\n")
+
+ if explore_method == 'Voronoi':
+ # Generate samples using the Voronoi method
+ all_candidates, exploration_scores = self.get_vornoi_samples()
+ else:
+ # Generate samples using the MC method
+ all_candidates, exploration_scores = self.get_mc_samples()
+
+ return all_candidates, exploration_scores</code></pre>
+</details>
+</dd>
+<dt id="exploration.Exploration.get_vornoi_samples"><code class="name flex">
+<span>def <span class="ident">get_vornoi_samples</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>This function generates samples based on voronoi cells and their
+corresponding scores</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>new_samples</code></strong> : <code>array</code> of <code>shape (n_candidate, n_params)</code></dt>
+<dd>A list of samples.</dd>
+<dt><strong><code>exploration_scores</code></strong> : <code>arrays</code> of <code>shape (n_candidate)</code></dt>
+<dd>Exploration scores.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def get_vornoi_samples(self):
+ """
+ This function generates samples based on voronoi cells and their
+ corresponding scores
+
+ Returns
+ -------
+ new_samples : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
+ """
+
+ mc_criterion = self.mc_criterion
+ n_candidate = self.n_candidate
+ # Get the Old ExpDesign #samples
+ old_ED_X = self.MetaModel.ExpDesign.X
+ ndim = old_ED_X.shape[1]
+
+ # calculate error #averageErrors
+ error_voronoi, all_candidates = self.approximate_voronoi(
+ self.w, old_ED_X
+ )
+
+ # Pick the best candidate point in the voronoi cell
+ # for each best sample
+ selected_samples = np.empty((0, ndim))
+ bad_samples = []
+
+ for index in range(len(error_voronoi)):
+
+ # get candidate new samples from voronoi tesselation
+ candidates = self.closestPoints[index]
+
+ # get total number of candidates
+ n_new_samples = candidates.shape[0]
+
+ # still no candidate samples around this one, skip it!
+ if n_new_samples == 0:
+ print('The following sample has been skipped because there '
+ 'were no candidate samples around it...')
+ print(old_ED_X[index])
+ bad_samples.append(index)
+ continue
+
+ # find candidate that is farthest away from any existing sample
+ max_min_distance = 0
+ best_candidate = 0
+ min_intersite_dist = np.zeros((n_new_samples))
+ min_projected_dist = np.zeros((n_new_samples))
+
+ for j in range(n_new_samples):
+
+ new_samples = np.vstack((old_ED_X, selected_samples))
+
+ # find min distorted distance from all other samples
+ euclidean_dist = self._build_dist_matrix_point(
+ new_samples, candidates[j], do_sqrt=True)
+ min_euclidean_dist = np.min(euclidean_dist)
+ min_intersite_dist[j] = min_euclidean_dist
+
+ # Check if this is the maximum minimum distance from all other
+ # samples
+ if min_euclidean_dist >= max_min_distance:
+ max_min_distance = min_euclidean_dist
+ best_candidate = j
+
+ # Projected distance
+ projected_dist = distance.cdist(
+ new_samples, [candidates[j]], 'chebyshev')
+ min_projected_dist[j] = np.min(projected_dist)
+
+ if mc_criterion == 'mc-intersite-proj':
+ weight_euclidean_dist = 0.5 * ((n_new_samples+1)**(1/ndim) - 1)
+ weight_projected_dist = 0.5 * (n_new_samples+1)
+ total_dist_scores = weight_euclidean_dist * min_intersite_dist
+ total_dist_scores += weight_projected_dist * min_projected_dist
+
+ elif mc_criterion == 'mc-intersite-proj-th':
+ alpha = 0.5 # chosen (tradeoff)
+ d_min = 2 * alpha / n_new_samples
+ if any(min_projected_dist < d_min):
+ candidates = np.delete(
+ candidates, [min_projected_dist < d_min], axis=0
+ )
+ total_dist_scores = np.delete(
+ min_intersite_dist, [min_projected_dist < d_min],
+ axis=0
+ )
+ else:
+ total_dist_scores = min_intersite_dist
+ else:
+ raise NameError(
+ 'The MC-Criterion you requested is not available.'
+ )
+
+ # Add the best candidate to the list of new samples
+ best_candidate = np.argsort(total_dist_scores)[::-1][:n_candidate]
+ selected_samples = np.vstack(
+ (selected_samples, candidates[best_candidate])
+ )
+
+ self.new_samples = selected_samples
+ self.exploration_scores = np.delete(error_voronoi, bad_samples, axis=0)
+
+ return self.new_samples, self.exploration_scores</code></pre>
+</details>
+</dd>
+<dt id="exploration.Exploration.get_mc_samples"><code class="name flex">
+<span>def <span class="ident">get_mc_samples</span></span>(<span>self, all_candidates=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>This function generates random samples based on Global Monte Carlo
+methods and their corresponding scores, based on [1].</p>
+<p>[1] Crombecq, K., Laermans, E. and Dhaene, T., 2011. Efficient
+space-filling and non-collapsing sequential design strategies for
+simulation-based modeling. European Journal of Operational Research
+, 214(3), pp.683-696.
+DOI: <a href="https://doi.org/10.1016/j.ejor.2011.05.032">https://doi.org/10.1016/j.ejor.2011.05.032</a></p>
+<p>Implemented methods to compute scores:
+1) mc-intersite-proj
+2) mc-intersite-proj-th</p>
+<h2 id="arguments">Arguments</h2>
+<dl>
+<dt><strong><code>all_candidates</code></strong> : <code>array</code>, optional</dt>
+<dd>Samples to compute the scores for. The default is <code>None</code>. In this
+case, samples will be generated by defined model input marginals.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>new_samples</code></strong> : <code>array</code> of <code>shape (n_candidate, n_params)</code></dt>
+<dd>A list of samples.</dd>
+<dt><strong><code>exploration_scores</code></strong> : <code>arrays</code> of <code>shape (n_candidate)</code></dt>
+<dd>Exploration scores.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def get_mc_samples(self, all_candidates=None):
+ """
+ This function generates random samples based on Global Monte Carlo
+ methods and their corresponding scores, based on [1].
+
+ [1] Crombecq, K., Laermans, E. and Dhaene, T., 2011. Efficient
+ space-filling and non-collapsing sequential design strategies for
+ simulation-based modeling. European Journal of Operational Research
+ , 214(3), pp.683-696.
+ DOI: https://doi.org/10.1016/j.ejor.2011.05.032
+
+ Implemented methods to compute scores:
+ 1) mc-intersite-proj
+ 2) mc-intersite-proj-th
+
+ Arguments
+ ---------
+ all_candidates : array, optional
+ Samples to compute the scores for. The default is `None`. In this
+ case, samples will be generated by defined model input marginals.
+
+ Returns
+ -------
+ new_samples : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
+ """
+ MetaModel = self.MetaModel
+ explore_method = MetaModel.ExpDesign.explore_method
+ mc_criterion = self.mc_criterion
+ if all_candidates is None:
+ n_candidate = self.n_candidate
+ else:
+ n_candidate = all_candidates.shape[0]
+
+ # Get the Old ExpDesign #samples
+ old_ED_X = MetaModel.ExpDesign.X
+ ndim = old_ED_X.shape[1]
+
+ # ----- Compute the number of random points -----
+ if all_candidates is None:
+ # Generate MC Samples
+ all_candidates = MetaModel.ExpDesign.generate_samples(
+ self.n_candidate, explore_method
+ )
+ self.all_candidates = all_candidates
+
+ # initialization
+ new_samples = np.empty((0, ndim))
+ min_intersite_dist = np.zeros((n_candidate))
+ min_projected_dist = np.zeros((n_candidate))
+
+ for i, candidate in enumerate(all_candidates):
+
+ # find candidate that is farthest away from any existing sample
+ maxMinDistance = 0
+
+ new_samples = np.vstack((old_ED_X, new_samples))
+ # find min distorted distance from all other samples
+ euclidean_dist = self._build_dist_matrix_point(
+ new_samples, candidate, do_sqrt=True
+ )
+ min_euclidean_dist = np.min(euclidean_dist)
+ min_intersite_dist[i] = min_euclidean_dist
+
+ # Check if this is the maximum minimum distance from all other
+ # samples
+ if min_euclidean_dist >= maxMinDistance:
+ maxMinDistance = min_euclidean_dist
+
+ # Projected distance
+ projected_dist = distance.cdist(
+ new_samples, [candidate], 'chebyshev'
+ )
+ min_projected_dist[i] = np.min(projected_dist)
+
+ if mc_criterion == 'mc-intersite-proj':
+ weight_euclidean_dist = ((n_candidate+1)**(1/ndim) - 1) * 0.5
+ weight_projected_dist = (n_candidate+1) * 0.5
+ total_dist_scores = weight_euclidean_dist * min_intersite_dist
+ total_dist_scores += weight_projected_dist * min_projected_dist
+
+ elif mc_criterion == 'mc-intersite-proj-th':
+ alpha = 0.5 # chosen (tradeoff)
+ d_min = 2 * alpha / n_candidate
+ if any(min_projected_dist < d_min):
+ all_candidates = np.delete(
+ all_candidates, [min_projected_dist < d_min], axis=0
+ )
+ total_dist_scores = np.delete(
+ min_intersite_dist, [min_projected_dist < d_min], axis=0
+ )
+ else:
+ total_dist_scores = min_intersite_dist
+ else:
+ raise NameError('The MC-Criterion you requested is not available.')
+
+ self.new_samples = all_candidates
+ self.exploration_scores = total_dist_scores
+ self.exploration_scores /= np.nansum(total_dist_scores)
+
+ return self.new_samples, self.exploration_scores</code></pre>
+</details>
+</dd>
+<dt id="exploration.Exploration.approximate_voronoi"><code class="name flex">
+<span>def <span class="ident">approximate_voronoi</span></span>(<span>self, w, samples)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>An approximate (monte carlo) version of Matlab's voronoi command.</p>
+<h2 id="arguments">Arguments</h2>
+<dl>
+<dt><strong><code>samples</code></strong> : <code>array</code></dt>
+<dd>Old experimental design to be used as center points for voronoi
+cells.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>areas</code></strong> : <code>array</code></dt>
+<dd>An approximation of the voronoi cells' areas.</dd>
+<dt><strong><code>all_candidates</code></strong> : <code>list</code> of <code>arrays</code></dt>
+<dd>A list of samples in each voronoi cell.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def approximate_voronoi(self, w, samples):
+ """
+ An approximate (monte carlo) version of Matlab's voronoi command.
+
+ Arguments
+ ---------
+ samples : array
+ Old experimental design to be used as center points for voronoi
+ cells.
+
+ Returns
+ -------
+ areas : array
+ An approximation of the voronoi cells' areas.
+ all_candidates: list of arrays
+ A list of samples in each voronoi cell.
+ """
+ MetaModel = self.MetaModel
+
+ n_samples = samples.shape[0]
+ ndim = samples.shape[1]
+
+ # Compute the number of random points
+ n_points = w * samples.shape[0]
+ # Generate w random points in the domain for each sample
+ ExpDesign = ExpDesigns(MetaModel.Inputs)
+ points = ExpDesign.generate_samples(n_points, 'random')
+ self.all_candidates = points
+
+ # Calculate the nearest sample to each point
+ self.areas = np.zeros((n_samples))
+ self.closestPoints = [np.empty((0, ndim)) for i in range(n_samples)]
+
+ # Compute the minimum distance from all the samples of old_ED_X for
+ # each test point
+ for idx in range(n_points):
+ # calculate the minimum distance
+ distances = self._build_dist_matrix_point(
+ samples, points[idx], do_sqrt=True
+ )
+ closest_sample = np.argmin(distances)
+
+ # Add to the voronoi list of the closest sample
+ self.areas[closest_sample] = self.areas[closest_sample] + 1
+ prevclosestPoints = self.closestPoints[closest_sample]
+ self.closestPoints[closest_sample] = np.vstack(
+ (prevclosestPoints, points[idx])
+ )
+
+ # Divide by the amount of points to get the estimated volume of each
+ # voronoi cell
+ self.areas = self.areas / n_points
+
+ self.perc = np.max(self.areas * 100)
+
+ self.errors = self.areas
+
+ return self.areas, self.all_candidates</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="exploration.Exploration" href="#exploration.Exploration">Exploration</a></code></h4>
+<ul class="">
+<li><code><a title="exploration.Exploration.get_exploration_samples" href="#exploration.Exploration.get_exploration_samples">get_exploration_samples</a></code></li>
+<li><code><a title="exploration.Exploration.get_vornoi_samples" href="#exploration.Exploration.get_vornoi_samples">get_vornoi_samples</a></code></li>
+<li><code><a title="exploration.Exploration.get_mc_samples" href="#exploration.Exploration.get_mc_samples">get_mc_samples</a></code></li>
+<li><code><a title="exploration.Exploration.approximate_voronoi" href="#exploration.Exploration.approximate_voronoi">approximate_voronoi</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>
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diff --git a/docs/build/html/inputs.html b/docs/html/inputs.html
similarity index 90%
rename from docs/build/html/inputs.html
rename to docs/html/inputs.html
index f97e314db0a107d73a0aeefa4280a649096d3248..e518ad65394a1acbebbac4ec35254a61ae13c753 100644
--- a/docs/build/html/inputs.html
+++ b/docs/html/inputs.html
@@ -12,6 +12,7 @@
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
+<script async src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML" integrity="sha256-kZafAc6mZvK3W3v1pHOcUix30OHQN6pU/NO2oFkqZVw=" crossorigin></script>
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</head>
@@ -57,10 +58,10 @@ class Input:
>>> Inputs.Marginals[0].name = 'X_1'
>>> Inputs.Marginals[0].input_data = input_data
"""
+ poly_coeffs_flag = True
def __init__(self):
self.Marginals = []
- self.__poly_coeffs_flag = True
self.Rosenblatt = False
def add_marginals(self):
@@ -84,14 +85,14 @@ class Marginal:
Attributes
----------
name : string
- Name of the parameter. The default is '$x_1$'.
+ Name of the parameter. The default is `'$x_1$'`.
dist_type : string
- Name of the distribution. The default is None.
+ Name of the distribution. The default is `None`.
parameters : list
List of the parameters corresponding to the distribution type. The
- default is None.
- input_data : array_like
- The available data corresponding to the parameters. The default is [].
+ default is `None`.
+ input_data : array
+ Available input data. The default is `[]`.
moments : list
List of the moments.
"""
@@ -171,10 +172,10 @@ as following:</p>
>>> Inputs.Marginals[0].name = 'X_1'
>>> Inputs.Marginals[0].input_data = input_data
"""
+ poly_coeffs_flag = True
def __init__(self):
self.Marginals = []
- self.__poly_coeffs_flag = True
self.Rosenblatt = False
def add_marginals(self):
@@ -188,6 +189,13 @@ as following:</p>
"""
self.Marginals.append(Marginal())</code></pre>
</details>
+<h3>Class variables</h3>
+<dl>
+<dt id="inputs.Input.poly_coeffs_flag"><code class="name">var <span class="ident">poly_coeffs_flag</span></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+</dl>
<h3>Methods</h3>
<dl>
<dt id="inputs.Input.add_marginals"><code class="name flex">
@@ -224,14 +232,14 @@ parameter.</p>
<h2 id="attributes">Attributes</h2>
<dl>
<dt><strong><code>name</code></strong> : <code>string</code></dt>
-<dd>Name of the parameter. The default is '$x_1$'.</dd>
+<dd>Name of the parameter. The default is <code>'$x_1$'</code>.</dd>
<dt><strong><code>dist_type</code></strong> : <code>string</code></dt>
-<dd>Name of the distribution. The default is None.</dd>
+<dd>Name of the distribution. The default is <code>None</code>.</dd>
<dt><strong><code>parameters</code></strong> : <code>list</code></dt>
<dd>List of the parameters corresponding to the distribution type. The
-default is None.</dd>
-<dt><strong><code>input_data</code></strong> : <code>array_like</code></dt>
-<dd>The available data corresponding to the parameters. The default is [].</dd>
+default is <code>None</code>.</dd>
+<dt><strong><code>input_data</code></strong> : <code>array</code></dt>
+<dd>Available input data. The default is <code>[]</code>.</dd>
<dt><strong><code>moments</code></strong> : <code>list</code></dt>
<dd>List of the moments.</dd>
</dl></div>
@@ -247,14 +255,14 @@ default is None.</dd>
Attributes
----------
name : string
- Name of the parameter. The default is '$x_1$'.
+ Name of the parameter. The default is `'$x_1$'`.
dist_type : string
- Name of the distribution. The default is None.
+ Name of the distribution. The default is `None`.
parameters : list
List of the parameters corresponding to the distribution type. The
- default is None.
- input_data : array_like
- The available data corresponding to the parameters. The default is [].
+ default is `None`.
+ input_data : array
+ Available input data. The default is `[]`.
moments : list
List of the moments.
"""
@@ -282,6 +290,7 @@ default is None.</dd>
<h4><code><a title="inputs.Input" href="#inputs.Input">Input</a></code></h4>
<ul class="">
<li><code><a title="inputs.Input.add_marginals" href="#inputs.Input.add_marginals">add_marginals</a></code></li>
+<li><code><a title="inputs.Input.poly_coeffs_flag" href="#inputs.Input.poly_coeffs_flag">poly_coeffs_flag</a></code></li>
</ul>
</li>
<li>
diff --git a/docs/html/mcmc.html b/docs/html/mcmc.html
index 6f71c6c1614999e949660a8e357e49bbee5aad3e..9b025da0e76601eb20291b23eb2bf35af8ff558b 100644
--- a/docs/html/mcmc.html
+++ b/docs/html/mcmc.html
@@ -5,14 +5,14 @@
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.10.0" />
<title>mcmc API documentation</title>
-<meta name="description" content="MCMC class for Bayes inference with emcee package using an Affine Invariant
-Markov chain Monte Carlo (MCMC) Ensemble sampler [1] …" />
+<meta name="description" content="" />
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
+<script async src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML" integrity="sha256-kZafAc6mZvK3W3v1pHOcUix30OHQN6pU/NO2oFkqZVw=" crossorigin></script>
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
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@@ -23,45 +23,12 @@ Markov chain Monte Carlo (MCMC) Ensemble sampler [1] …" />
<h1 class="title">Module <code>mcmc</code></h1>
</header>
<section id="section-intro">
-<p>MCMC class for Bayes inference with emcee package using an Affine Invariant
-Markov chain Monte Carlo (MCMC) Ensemble sampler [1].</p>
-<ol>
-<li>Foreman-Mackey, D., Hogg, D.W., Lang, D. and Goodman, J., 2013. emcee: the
-MCMC hammer. Publications of the Astronomical Society of the Pacific, 125(925),
-p.306. <a href="https://emcee.readthedocs.io/en/stable/">https://emcee.readthedocs.io/en/stable/</a></li>
-</ol>
-<p>Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart, www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart</p>
-<p>Created on Wed Jun 3 2020</p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-"""
-MCMC class for Bayes inference with emcee package using an Affine Invariant
-Markov chain Monte Carlo (MCMC) Ensemble sampler [1].
-
-1. Foreman-Mackey, D., Hogg, D.W., Lang, D. and Goodman, J., 2013. emcee: the
-MCMC hammer. Publications of the Astronomical Society of the Pacific, 125(925),
-p.306. https://emcee.readthedocs.io/en/stable/
-
-Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart, www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart
-
-Created on Wed Jun 3 2020
-"""
import os
import numpy as np
@@ -79,7 +46,27 @@ os.environ["OMP_NUM_THREADS"] = "1"
class MCMC:
"""
- A class for bayesian inference using a Markov-Chain Monte-Carlo Sampler.
+ A class for bayesian inference via a Markov-Chain Monte-Carlo (MCMC)
+ Sampler to approximate the posterior distribution of the Bayes theorem:
+ $$p(\\theta|\\mathcal{y}) = \\frac{p(\\mathcal{y}|\\theta) p(\\theta)}
+ {p(\\mathcal{y})}.$$
+
+ This class make inference with emcee package [1] using an Affine Invariant
+ Ensemble sampler (AIES) [2].
+
+ [1] Foreman-Mackey, D., Hogg, D.W., Lang, D. and Goodman, J., 2013.emcee:
+ the MCMC hammer. Publications of the Astronomical Society of the
+ Pacific, 125(925), p.306. https://emcee.readthedocs.io/en/stable/
+
+ [2] Goodman, J. and Weare, J., 2010. Ensemble samplers with affine
+ invariance. Communications in applied mathematics and computational
+ science, 5(1), pp.65-80.
+
+
+ Attributes
+ ----------
+ BayesOpts : obj
+ Bayes object.
"""
def __init__(self, BayesOpts):
@@ -424,8 +411,8 @@ class MCMC:
# -------------------------------------------------------------------------
def log_prior(self, theta):
"""
- Calculates the log prior likelihood for the given parameter set(s)
- theta.
+ Calculates the log prior likelihood \\( p(\\theta)\\) for the given
+ parameter set(s) \\( \\theta \\).
Parameters
----------
@@ -489,8 +476,8 @@ class MCMC:
# -------------------------------------------------------------------------
def log_likelihood(self, theta):
"""
- Computes likelihood p(y|theta, obs) of the performance of the
- (meta-)model in reproducing the observation data.
+ Computes likelihood \\( p(\\mathcal{Y}|\\theta)\\) of the performance
+ of the (meta-)model in reproducing the observation data.
Parameters
----------
@@ -538,8 +525,8 @@ class MCMC:
# -------------------------------------------------------------------------
def log_posterior(self, theta):
"""
- Computes the posterior likelihood p(theta| obs) for the given
- parameterset.
+ Computes the posterior likelihood \\(p(\\theta| \\mathcal{Y})\\) for
+ the given parameterset.
Parameters
----------
@@ -696,11 +683,11 @@ class MCMC:
The potential scale reduction factor (PSRF) defined by the variance
within one chain, W, with the variance between chains B.
Both variances are combined in a weighted sum to obtain an estimate of
- the variance of a parameter θ.The square root of the ratio of this
- estimates variance to the within chain variance is called the potential
- scale reduction.
+ the variance of a parameter \\( \\theta \\).The square root of the
+ ratio of this estimates variance to the within chain variance is called
+ the potential scale reduction.
For a well converged chain it should approach 1. Values greater than
- typically 1.1 indicate that the chains have not yet fully converged.
+ 1.1 typically indicate that the chains have not yet fully converged.
Source: http://joergdietrich.github.io/emcee-convergence.html
@@ -709,7 +696,7 @@ class MCMC:
Parameters
----------
chain : array (n_walkers, n_steps, n_params)
- DESCRIPTION.
+ The emcee ensamples.
Returns
-------
@@ -849,36 +836,6 @@ class MCMC:
Iterative scheme as proposed in Meng and Wong (1996) to estimate the
marginal likelihood
- Parameters
- ----------
- N1 : TYPE
- DESCRIPTION.
- N2 : TYPE
- DESCRIPTION.
- q11 : TYPE
- DESCRIPTION.
- q12 : TYPE
- DESCRIPTION.
- q21 : TYPE
- DESCRIPTION.
- q22 : TYPE
- DESCRIPTION.
- r0 : TYPE
- DESCRIPTION.
- neff : TYPE
- DESCRIPTION.
- tol : TYPE
- DESCRIPTION.
- maxiter : TYPE
- DESCRIPTION.
- criterion : TYPE
- DESCRIPTION.
-
- Returns
- -------
- TYPE
- DESCRIPTION.
-
"""
l1 = q11 - q12
l2 = q21 - q22
@@ -1003,14 +960,51 @@ class MCMC:
<span>(</span><span>BayesOpts)</span>
</code></dt>
<dd>
-<div class="desc"><p>A class for bayesian inference using a Markov-Chain Monte-Carlo Sampler.</p></div>
+<div class="desc"><p>A class for bayesian inference via a Markov-Chain Monte-Carlo (MCMC)
+Sampler to approximate the posterior distribution of the Bayes theorem:
+<span><span class="MathJax_Preview">p(\theta|\mathcal{y}) = \frac{p(\mathcal{y}|\theta) p(\theta)}
+{p(\mathcal{y})}.</span><script type="math/tex; mode=display">p(\theta|\mathcal{y}) = \frac{p(\mathcal{y}|\theta) p(\theta)}
+{p(\mathcal{y})}.</script></span></p>
+<p>This class make inference with emcee package [1] using an Affine Invariant
+Ensemble sampler (AIES) [2].</p>
+<p>[1] Foreman-Mackey, D., Hogg, D.W., Lang, D. and Goodman, J., 2013.emcee:
+the MCMC hammer. Publications of the Astronomical Society of the
+Pacific, 125(925), p.306. <a href="https://emcee.readthedocs.io/en/stable/">https://emcee.readthedocs.io/en/stable/</a></p>
+<p>[2] Goodman, J. and Weare, J., 2010. Ensemble samplers with affine
+invariance. Communications in applied mathematics and computational
+science, 5(1), pp.65-80.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>BayesOpts</code></strong> : <code>obj</code></dt>
+<dd>Bayes object.</dd>
+</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class MCMC:
"""
- A class for bayesian inference using a Markov-Chain Monte-Carlo Sampler.
+ A class for bayesian inference via a Markov-Chain Monte-Carlo (MCMC)
+ Sampler to approximate the posterior distribution of the Bayes theorem:
+ $$p(\\theta|\\mathcal{y}) = \\frac{p(\\mathcal{y}|\\theta) p(\\theta)}
+ {p(\\mathcal{y})}.$$
+
+ This class make inference with emcee package [1] using an Affine Invariant
+ Ensemble sampler (AIES) [2].
+
+ [1] Foreman-Mackey, D., Hogg, D.W., Lang, D. and Goodman, J., 2013.emcee:
+ the MCMC hammer. Publications of the Astronomical Society of the
+ Pacific, 125(925), p.306. https://emcee.readthedocs.io/en/stable/
+
+ [2] Goodman, J. and Weare, J., 2010. Ensemble samplers with affine
+ invariance. Communications in applied mathematics and computational
+ science, 5(1), pp.65-80.
+
+
+ Attributes
+ ----------
+ BayesOpts : obj
+ Bayes object.
"""
def __init__(self, BayesOpts):
@@ -1355,8 +1349,8 @@ class MCMC:
# -------------------------------------------------------------------------
def log_prior(self, theta):
"""
- Calculates the log prior likelihood for the given parameter set(s)
- theta.
+ Calculates the log prior likelihood \\( p(\\theta)\\) for the given
+ parameter set(s) \\( \\theta \\).
Parameters
----------
@@ -1420,8 +1414,8 @@ class MCMC:
# -------------------------------------------------------------------------
def log_likelihood(self, theta):
"""
- Computes likelihood p(y|theta, obs) of the performance of the
- (meta-)model in reproducing the observation data.
+ Computes likelihood \\( p(\\mathcal{Y}|\\theta)\\) of the performance
+ of the (meta-)model in reproducing the observation data.
Parameters
----------
@@ -1469,8 +1463,8 @@ class MCMC:
# -------------------------------------------------------------------------
def log_posterior(self, theta):
"""
- Computes the posterior likelihood p(theta| obs) for the given
- parameterset.
+ Computes the posterior likelihood \\(p(\\theta| \\mathcal{Y})\\) for
+ the given parameterset.
Parameters
----------
@@ -1627,11 +1621,11 @@ class MCMC:
The potential scale reduction factor (PSRF) defined by the variance
within one chain, W, with the variance between chains B.
Both variances are combined in a weighted sum to obtain an estimate of
- the variance of a parameter θ.The square root of the ratio of this
- estimates variance to the within chain variance is called the potential
- scale reduction.
+ the variance of a parameter \\( \\theta \\).The square root of the
+ ratio of this estimates variance to the within chain variance is called
+ the potential scale reduction.
For a well converged chain it should approach 1. Values greater than
- typically 1.1 indicate that the chains have not yet fully converged.
+ 1.1 typically indicate that the chains have not yet fully converged.
Source: http://joergdietrich.github.io/emcee-convergence.html
@@ -1640,7 +1634,7 @@ class MCMC:
Parameters
----------
chain : array (n_walkers, n_steps, n_params)
- DESCRIPTION.
+ The emcee ensamples.
Returns
-------
@@ -1780,36 +1774,6 @@ class MCMC:
Iterative scheme as proposed in Meng and Wong (1996) to estimate the
marginal likelihood
- Parameters
- ----------
- N1 : TYPE
- DESCRIPTION.
- N2 : TYPE
- DESCRIPTION.
- q11 : TYPE
- DESCRIPTION.
- q12 : TYPE
- DESCRIPTION.
- q21 : TYPE
- DESCRIPTION.
- q22 : TYPE
- DESCRIPTION.
- r0 : TYPE
- DESCRIPTION.
- neff : TYPE
- DESCRIPTION.
- tol : TYPE
- DESCRIPTION.
- maxiter : TYPE
- DESCRIPTION.
- criterion : TYPE
- DESCRIPTION.
-
- Returns
- -------
- TYPE
- DESCRIPTION.
-
"""
l1 = q11 - q12
l2 = q21 - q22
@@ -2010,17 +1974,17 @@ class MCMC:
<div class="desc"><p>The potential scale reduction factor (PSRF) defined by the variance
within one chain, W, with the variance between chains B.
Both variances are combined in a weighted sum to obtain an estimate of
-the variance of a parameter θ.The square root of the ratio of this
-estimates variance to the within chain variance is called the potential
-scale reduction.
+the variance of a parameter <span><span class="MathJax_Preview"> \theta </span><script type="math/tex"> \theta </script></span>.The square root of the
+ratio of this estimates variance to the within chain variance is called
+the potential scale reduction.
For a well converged chain it should approach 1. Values greater than
-typically 1.1 indicate that the chains have not yet fully converged.</p>
+1.1 typically indicate that the chains have not yet fully converged.</p>
<p>Source: <a href="http://joergdietrich.github.io/emcee-convergence.html">http://joergdietrich.github.io/emcee-convergence.html</a></p>
<p><a href="https://github.com/jwalton3141/jwalton3141.github.io/blob/master/assets/posts/ESS/rwmh.py">https://github.com/jwalton3141/jwalton3141.github.io/blob/master/assets/posts/ESS/rwmh.py</a></p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>chain</code></strong> : <code>array (n_walkers, n_steps, n_params)</code></dt>
-<dd>DESCRIPTION.</dd>
+<dd>The emcee ensamples.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
@@ -2036,11 +2000,11 @@ typically 1.1 indicate that the chains have not yet fully converged.</p>
The potential scale reduction factor (PSRF) defined by the variance
within one chain, W, with the variance between chains B.
Both variances are combined in a weighted sum to obtain an estimate of
- the variance of a parameter θ.The square root of the ratio of this
- estimates variance to the within chain variance is called the potential
- scale reduction.
+ the variance of a parameter \\( \\theta \\).The square root of the
+ ratio of this estimates variance to the within chain variance is called
+ the potential scale reduction.
For a well converged chain it should approach 1. Values greater than
- typically 1.1 indicate that the chains have not yet fully converged.
+ 1.1 typically indicate that the chains have not yet fully converged.
Source: http://joergdietrich.github.io/emcee-convergence.html
@@ -2049,7 +2013,7 @@ typically 1.1 indicate that the chains have not yet fully converged.</p>
Parameters
----------
chain : array (n_walkers, n_steps, n_params)
- DESCRIPTION.
+ The emcee ensamples.
Returns
-------
@@ -2085,8 +2049,8 @@ typically 1.1 indicate that the chains have not yet fully converged.</p>
<span>def <span class="ident">log_likelihood</span></span>(<span>self, theta)</span>
</code></dt>
<dd>
-<div class="desc"><p>Computes likelihood p(y|theta, obs) of the performance of the
-(meta-)model in reproducing the observation data.</p>
+<div class="desc"><p>Computes likelihood <span><span class="MathJax_Preview"> p(\mathcal{Y}|\theta)</span><script type="math/tex"> p(\mathcal{Y}|\theta)</script></span> of the performance
+of the (meta-)model in reproducing the observation data.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>theta</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
@@ -2103,8 +2067,8 @@ typically 1.1 indicate that the chains have not yet fully converged.</p>
</summary>
<pre><code class="python">def log_likelihood(self, theta):
"""
- Computes likelihood p(y|theta, obs) of the performance of the
- (meta-)model in reproducing the observation data.
+ Computes likelihood \\( p(\\mathcal{Y}|\\theta)\\) of the performance
+ of the (meta-)model in reproducing the observation data.
Parameters
----------
@@ -2154,8 +2118,8 @@ typically 1.1 indicate that the chains have not yet fully converged.</p>
<span>def <span class="ident">log_posterior</span></span>(<span>self, theta)</span>
</code></dt>
<dd>
-<div class="desc"><p>Computes the posterior likelihood p(theta| obs) for the given
-parameterset.</p>
+<div class="desc"><p>Computes the posterior likelihood <span><span class="MathJax_Preview">p(\theta| \mathcal{Y})</span><script type="math/tex">p(\theta| \mathcal{Y})</script></span> for
+the given parameterset.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>theta</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
@@ -2172,8 +2136,8 @@ parameterset.</p>
</summary>
<pre><code class="python">def log_posterior(self, theta):
"""
- Computes the posterior likelihood p(theta| obs) for the given
- parameterset.
+ Computes the posterior likelihood \\(p(\\theta| \\mathcal{Y})\\) for
+ the given parameterset.
Parameters
----------
@@ -2222,8 +2186,8 @@ parameterset.</p>
<span>def <span class="ident">log_prior</span></span>(<span>self, theta)</span>
</code></dt>
<dd>
-<div class="desc"><p>Calculates the log prior likelihood for the given parameter set(s)
-theta.</p>
+<div class="desc"><p>Calculates the log prior likelihood <span><span class="MathJax_Preview"> p(\theta)</span><script type="math/tex"> p(\theta)</script></span> for the given
+parameter set(s) <span><span class="MathJax_Preview"> \theta </span><script type="math/tex"> \theta </script></span>.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>theta</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
@@ -2241,8 +2205,8 @@ returned otherwise an array.</dd>
</summary>
<pre><code class="python">def log_prior(self, theta):
"""
- Calculates the log prior likelihood for the given parameter set(s)
- theta.
+ Calculates the log prior likelihood \\( p(\\theta)\\) for the given
+ parameter set(s) \\( \\theta \\).
Parameters
----------
diff --git a/docs/html/post_processing.html b/docs/html/post_processing.html
new file mode 100644
index 0000000000000000000000000000000000000000..af165b3d4f984bbc1353f474ddc8abf600b237d9
--- /dev/null
+++ b/docs/html/post_processing.html
@@ -0,0 +1,3859 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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+<title>post_processing API documentation</title>
+<meta name="description" content="" />
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+<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
+<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
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+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>post_processing</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import math
+import os
+from itertools import combinations, cycle
+import pandas as pd
+import scipy.stats as stats
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+from statsmodels.graphics.gofplots import qqplot
+from matplotlib.backends.backend_pdf import PdfPages
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+from matplotlib.offsetbox import AnchoredText
+from matplotlib.patches import Patch
+# Load the mplstyle
+plt.style.use(os.path.join(os.path.split(__file__)[0],
+ '../', 'bayesvalidrox.mplstyle'))
+
+
+class PostProcessing:
+ """
+ This class provides many helper functions to post-process the trained
+ meta-model.
+
+ Attributes
+ ----------
+ MetaModel : obj
+ MetaModel object to do postprocessing on.
+ name : str
+ Type of the anaylsis. The default is `'calib'`. If a validation is
+ expected to be performed change this to `'valid'`.
+ """
+
+ def __init__(self, MetaModel, name='calib'):
+ self.MetaModel = MetaModel
+ self.name = name
+
+ # -------------------------------------------------------------------------
+ def plot_moments(self, xlabel='Time [s]', plot_type=None, save_fig=True):
+ """
+ Plots the moments in a pdf format in the directory
+ `Outputs_PostProcessing`.
+
+ Parameters
+ ----------
+ xlabel : str, optional
+ String to be displayed as x-label. The default is `'Time [s]'`.
+ plot_type : str, optional
+ Options: bar or line. The default is `None`.
+ save_fig : bool, optional
+ Save figure or not. The default is `True`.
+
+ Returns
+ -------
+ pce_means: dict
+ Mean of the model outputs.
+ pce_means: dict
+ Standard deviation of the model outputs.
+
+ """
+
+ bar_plot = True if plot_type == 'bar' else False
+ meta_model_type = self.MetaModel.meta_model_type
+ Model = self.MetaModel.ModelObj
+
+ # Read Monte-Carlo reference
+ self.mc_reference = Model.read_mc_reference()
+ print(self.mc_reference)
+
+ # Set the x values
+ x_values_orig = self.MetaModel.ExpDesign.x_values
+
+ # Compute the moments with the PCEModel object
+ self._compute_pce_moments()
+
+ # Get the variables
+ out_names = Model.Output.names
+
+ # Open a pdf for the plots
+ if save_fig:
+ newpath = (f'Outputs_PostProcessing_{self.name}/')
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # create a PdfPages object
+ pdf = PdfPages(f'./{newpath}Mean_Std_PCE.pdf')
+
+ # Plot the best fit line, set the linewidth (lw), color and
+ # transparency (alpha) of the line
+ for idx, key in enumerate(out_names):
+ fig, ax = plt.subplots(nrows=1, ncols=2)
+
+ # Extract mean and std
+ mean_data = self.pce_means[key]
+ std_data = self.pce_stds[key]
+
+ # Extract a list of x values
+ if type(x_values_orig) is dict:
+ x = x_values_orig[key]
+ else:
+ x = x_values_orig
+
+ # Plot: bar plot or line plot
+ if bar_plot:
+ ax[0].bar(list(map(str, x)), mean_data, color='b',
+ width=0.25)
+ ax[1].bar(list(map(str, x)), std_data, color='b',
+ width=0.25)
+ ax[0].legend(labels=[meta_model_type])
+ ax[1].legend(labels=[meta_model_type])
+ else:
+ ax[0].plot(x, mean_data, lw=3, color='k', marker='x',
+ label=meta_model_type)
+ ax[1].plot(x, std_data, lw=3, color='k', marker='x',
+ label=meta_model_type)
+
+ if self.mc_reference is not None:
+ if bar_plot:
+ ax[0].bar(list(map(str, x)), self.mc_reference['mean'],
+ color='r', width=0.25)
+ ax[1].bar(list(map(str, x)), self.mc_reference['std'],
+ color='r', width=0.25)
+ ax[0].legend(labels=[meta_model_type])
+ ax[1].legend(labels=[meta_model_type])
+ else:
+ ax[0].plot(x, self.mc_reference['mean'], lw=3, marker='x',
+ color='r', label='Ref.')
+ ax[1].plot(x, self.mc_reference['std'], lw=3, marker='x',
+ color='r', label='Ref.')
+
+ # Label the axes and provide a title
+ ax[0].set_xlabel(xlabel)
+ ax[1].set_xlabel(xlabel)
+ ax[0].set_ylabel(key)
+ ax[1].set_ylabel(key)
+
+ # Provide a title
+ ax[0].set_title('Mean of ' + key)
+ ax[1].set_title('Std of ' + key)
+
+ if not bar_plot:
+ ax[0].legend(loc='best')
+ ax[1].legend(loc='best')
+
+ plt.tight_layout()
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+
+ # Destroy the current plot
+ plt.clf()
+
+ pdf.close()
+
+ return self.pce_means, self.pce_stds
+
+ # -------------------------------------------------------------------------
+ def valid_metamodel(self, n_samples=1, samples=None, x_axis='Time [s]'):
+ """
+ Evaluates and plots the meta model and the PCEModel outputs for the
+ given number of samples or the given samples.
+
+ Parameters
+ ----------
+ n_samples : int, optional
+ Number of samples to be evaluated. The default is 1.
+ samples : array of shape (n_samples, n_params), optional
+ Samples to be evaluated. The default is None.
+ x_axis : str, optional
+ Label of x axis. The default is `'Time [s]'`.
+
+ Returns
+ -------
+ None.
+
+ """
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+
+ if samples is None:
+ self.n_samples = n_samples
+ samples = self._get_sample()
+ else:
+ self.n_samples = samples.shape[0]
+
+ # Extract x_values
+ x_values = MetaModel.ExpDesign.x_values
+
+ self.model_out_dict = self._eval_model(samples, key_str='valid')
+ self.pce_out_mean, self.pce_out_std = MetaModel.eval_metamodel(samples)
+
+ try:
+ key = Model.Output.names[1]
+ except IndexError:
+ key = Model.Output.names[0]
+
+ n_obs = self.model_out_dict[key].shape[1]
+
+ if n_obs == 1:
+ self._plot_validation()
+ else:
+ self._plot_validation_multi(x_values=x_values, x_axis=x_axis)
+
+ # -------------------------------------------------------------------------
+ def check_accuracy(self, n_samples=None, samples=None, outputs=None):
+ """
+ Checks accuracy of the metamodel by computing the root mean square
+ error and validation error for all outputs.
+
+ Parameters
+ ----------
+ n_samples : int, optional
+ Number of samples. The default is None.
+ samples : array of shape (n_samples, n_params), optional
+ Parameter sets to be checked. The default is None.
+ outputs : dict, optional
+ Output dictionary with model outputs for all given output types in
+ `Model.Output.names`. The default is None.
+
+ Raises
+ ------
+ Exception
+ When neither n_samples nor samples are provided.
+
+ Returns
+ -------
+ rmse: dict
+ Root mean squared error for each output.
+ valid_error : dict
+ Validation error for each output.
+
+ """
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+
+ # Set the number of samples
+ if n_samples:
+ self.n_samples = n_samples
+ elif samples is not None:
+ self.n_samples = samples.shape[0]
+ else:
+ raise Exception("Please provide either samples or pass number of "
+ "samples!")
+
+ # Generate random samples if necessary
+ Samples = self._get_sample() if samples is None else samples
+
+ # Run the original model with the generated samples
+ if outputs is None:
+ outputs = self._eval_model(Samples, key_str='validSet')
+
+ # Run the PCE model with the generated samples
+ pce_outputs, _ = MetaModel.eval_metamodel(samples=Samples)
+
+ self.rmse = {}
+ self.valid_error = {}
+ # Loop over the keys and compute RMSE error.
+ for key in Model.Output.names:
+ # Root mena square
+ self.rmse[key] = mean_squared_error(outputs[key], pce_outputs[key],
+ squared=False,
+ multioutput='raw_values')
+ # Validation error
+ self.valid_error[key] = (self.rmse[key]**2 / self.n_samples) / \
+ np.var(outputs[key], ddof=1, axis=0)
+
+ # Print a report table
+ print("\n>>>>> Errors of {} <<<<<".format(key))
+ print("\nIndex | RMSE | Validation Error")
+ print('-'*35)
+ print('\n'.join(f'{i+1} | {k:.3e} | {j:.3e}' for i, (k, j)
+ in enumerate(zip(self.rmse[key],
+ self.valid_error[key]))))
+ # Save error dicts in PCEModel object
+ self.MetaModel.rmse = self.rmse
+ self.MetaModel.valid_error = self.valid_error
+
+ return self.rmse, self.valid_error
+
+ # -------------------------------------------------------------------------
+ def plot_seq_design_diagnostics(self, ref_BME_KLD=None, save_fig=True):
+ """
+ Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence
+ (KLD) for the sequential design.
+
+ Parameters
+ ----------
+ ref_BME_KLD : array, optional
+ Reference BME and KLD . The default is `None`.
+ save_fig : bool, optional
+ Whether to save the figures. The default is `True`.
+
+ Returns
+ -------
+ None.
+
+ """
+ PCEModel = self.MetaModel
+ n_init_samples = PCEModel.ExpDesign.n_init_samples
+ n_total_samples = PCEModel.ExpDesign.X.shape[0]
+
+ if save_fig:
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # create a PdfPages object
+ pdf = PdfPages(f'./{newpath}/seqPCEModelDiagnostics.pdf')
+
+ plotList = ['Modified LOO error', 'Validation error', 'KLD', 'BME',
+ 'RMSEMean', 'RMSEStd', 'Hellinger distance']
+ seqList = [PCEModel.SeqModifiedLOO, PCEModel.seqValidError,
+ PCEModel.SeqKLD, PCEModel.SeqBME, PCEModel.seqRMSEMean,
+ PCEModel.seqRMSEStd, PCEModel.SeqDistHellinger]
+
+ markers = ('x', 'o', 'd', '*', '+')
+ colors = ('k', 'darkgreen', 'b', 'navy', 'darkred')
+
+ # Plot the evolution of the diagnostic criteria of the
+ # Sequential Experimental Design.
+ for plotidx, plot in enumerate(plotList):
+ fig, ax = plt.subplots()
+ seq_dict = seqList[plotidx]
+ name_util = list(seq_dict.keys())
+
+ if len(name_util) == 0:
+ continue
+
+ # Box plot when Replications have been detected.
+ if any(int(name.split("rep_", 1)[1]) > 1 for name in name_util):
+ # Extract the values from dict
+ sorted_seq_opt = {}
+ # Number of replications
+ n_reps = PCEModel.ExpDesign.nReprications
+
+ # Get the list of utility function names
+ # Handle if only one UtilityFunction is provided
+ if not isinstance(PCEModel.ExpDesign.UtilityFunction, list):
+ util_funcs = [PCEModel.ExpDesign.UtilityFunction]
+ else:
+ util_funcs = PCEModel.ExpDesign.UtilityFunction
+
+ for util in util_funcs:
+ sortedSeq = {}
+ # min number of runs available from reps
+ n_runs = min([seq_dict[f'{util}_rep_{i+1}'].shape[0]
+ for i in range(n_reps)])
+
+ for runIdx in range(n_runs):
+ values = []
+ for key in seq_dict.keys():
+ if util in key:
+ values.append(seq_dict[key][runIdx].mean())
+ sortedSeq['SeqItr_'+str(runIdx)] = np.array(values)
+ sorted_seq_opt[util] = sortedSeq
+
+ # BoxPlot
+ def draw_plot(data, labels, edge_color, fill_color, idx):
+ pos = labels - (idx-1)
+ bp = plt.boxplot(data, positions=pos, labels=labels,
+ patch_artist=True, sym='', widths=0.75)
+ elements = ['boxes', 'whiskers', 'fliers', 'means',
+ 'medians', 'caps']
+ for element in elements:
+ plt.setp(bp[element], color=edge_color[idx])
+
+ for patch in bp['boxes']:
+ patch.set(facecolor=fill_color[idx])
+
+ if PCEModel.ExpDesign.n_new_samples != 1:
+ step1 = PCEModel.ExpDesign.n_new_samples
+ step2 = 1
+ else:
+ step1 = 5
+ step2 = 5
+ edge_color = ['red', 'blue', 'green']
+ fill_color = ['tan', 'cyan', 'lightgreen']
+ plot_label = plot
+ # Plot for different Utility Functions
+ for idx, util in enumerate(util_funcs):
+ all_errors = np.empty((n_reps, 0))
+
+ for key in list(sorted_seq_opt[util].keys()):
+ errors = sorted_seq_opt.get(util, {}).get(key)[:, None]
+ all_errors = np.hstack((all_errors, errors))
+
+ # Special cases for BME and KLD
+ if plot == 'KLD' or plot == 'BME':
+ # BME convergence if refBME is provided
+ if ref_BME_KLD is not None:
+ if plot == 'BME':
+ refValue = ref_BME_KLD[0]
+ plot_label = r'$BME/BME^{Ref.}$'
+ if plot == 'KLD':
+ refValue = ref_BME_KLD[1]
+ plot_label = '$D_{KL}[p(\theta|y_*),p(\theta)]'\
+ ' / D_{KL}^{Ref.}[p(\theta|y_*), '\
+ 'p(\theta)]$'
+
+ # Difference between BME/KLD and the ref. values
+ all_errors = np.divide(all_errors,
+ np.full((all_errors.shape),
+ refValue))
+
+ # Plot baseline for zero, i.e. no difference
+ plt.axhline(y=1.0, xmin=0, xmax=1, c='green',
+ ls='--', lw=2)
+
+ # Plot each UtilFuncs
+ labels = np.arange(n_init_samples, n_total_samples+1, step1)
+ draw_plot(all_errors[:, ::step2], labels, edge_color,
+ fill_color, idx)
+
+ plt.xticks(labels, labels)
+ # Set the major and minor locators
+ ax.xaxis.set_major_locator(ticker.AutoLocator())
+ ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ ax.xaxis.grid(True, which='major', linestyle='-')
+ ax.xaxis.grid(True, which='minor', linestyle='--')
+
+ # Legend
+ legend_elements = []
+ for idx, util in enumerate(util_funcs):
+ legend_elements.append(Patch(facecolor=fill_color[idx],
+ edgecolor=edge_color[idx],
+ label=util))
+ plt.legend(handles=legend_elements[::-1], loc='best')
+
+ if plot != 'BME' and plot != 'KLD':
+ plt.yscale('log')
+ plt.autoscale(True)
+ plt.xlabel('\\# of training samples')
+ plt.ylabel(plot_label)
+ plt.title(plot)
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+ # Save arrays into files
+ f = open(f'./{newpath}/Seq{plot}.txt', 'w')
+ f.write(str(sorted_seq_opt))
+ f.close()
+ else:
+ for idx, name in enumerate(name_util):
+ seq_values = seq_dict[name]
+ if PCEModel.ExpDesign.n_new_samples != 1:
+ step = PCEModel.ExpDesign.n_new_samples
+ else:
+ step = 1
+ x_idx = np.arange(n_init_samples, n_total_samples+1, step)
+ if n_total_samples not in x_idx:
+ x_idx = np.hstack((x_idx, n_total_samples))
+
+ if plot == 'KLD' or plot == 'BME':
+ # BME convergence if refBME is provided
+ if ref_BME_KLD is not None:
+ if plot == 'BME':
+ refValue = ref_BME_KLD[0]
+ plot_label = r'$BME/BME^{Ref.}$'
+ if plot == 'KLD':
+ refValue = ref_BME_KLD[1]
+ plot_label = '$D_{KL}[p(\theta|y_*),p(\theta)]'\
+ ' / D_{KL}^{Ref.}[p(\theta|y_*), '\
+ 'p(\theta)]$'
+
+ # Difference between BME/KLD and the ref. values
+ values = np.divide(seq_values,
+ np.full((seq_values.shape),
+ refValue))
+
+ # Plot baseline for zero, i.e. no difference
+ plt.axhline(y=1.0, xmin=0, xmax=1, c='green',
+ ls='--', lw=2)
+
+ # Set the limits
+ plt.ylim([1e-1, 1e1])
+
+ # Create the plots
+ plt.semilogy(x_idx, values, marker=markers[idx],
+ color=colors[idx], ls='--', lw=2,
+ label=name.split("_rep", 1)[0])
+ else:
+ plot_label = plot
+
+ # Create the plots
+ plt.plot(x_idx, seq_values, marker=markers[idx],
+ color=colors[idx], ls='--', lw=2,
+ label=name.split("_rep", 1)[0])
+
+ else:
+ plot_label = plot
+ seq_values = np.nan_to_num(seq_values)
+
+ # Plot the error evolution for each output
+ for i in range(seq_values.shape[1]):
+ plt.semilogy(x_idx, seq_values[:, i], ls='--',
+ lw=2, marker=markers[idx],
+ color=colors[idx], alpha=0.15)
+
+ plt.semilogy(x_idx, seq_values, marker=markers[idx],
+ ls='--', lw=2, color=colors[idx],
+ label=name.split("_rep", 1)[0])
+
+ # Set the major and minor locators
+ ax.xaxis.set_major_locator(ticker.AutoLocator())
+ ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ ax.xaxis.grid(True, which='major', linestyle='-')
+ ax.xaxis.grid(True, which='minor', linestyle='--')
+
+ ax.tick_params(axis='both', which='major', direction='in',
+ width=3, length=10)
+ ax.tick_params(axis='both', which='minor', direction='in',
+ width=2, length=8)
+ plt.xlabel('Number of runs')
+ plt.ylabel(plot_label)
+ plt.title(plot)
+ plt.legend(frameon=True)
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ---------------- Saving arrays into files ---------------
+ np.save(f'./{newpath}/Seq{plot}.npy', seq_values)
+
+ # Close the pdf
+ pdf.close()
+ return
+
+ # -------------------------------------------------------------------------
+ def sobol_indices(self, xlabel='Time [s]', plot_type=None, save_fig=True):
+ """
+ Provides Sobol indices as a sensitivity measure to infer the importance
+ of the input parameters. See Eq. 27 in [1] for more details. For the
+ case with Principal component analysis refer to [2].
+
+ [1] Global sensitivity analysis: A flexible and efficient framework
+ with an example from stochastic hydrogeology S. Oladyshkin, F.P.
+ de Barros, W. Nowak https://doi.org/10.1016/j.advwatres.2011.11.001
+
+ [2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
+ component analysis and sparse polynomial chaos expansions for global
+ sensitivity analysis and model calibration: Application to urban
+ drainage simulation. Reliability Engineering & System Safety, 195,
+ p.106737.
+
+ Parameters
+ ----------
+ xlabel : str, optional
+ Label of the x-axis. The default is `'Time [s]'`.
+ plot_type : str, optional
+ Plot type. The default is `None`. This corresponds to line plot.
+ Bar chart can be selected by `bar`.
+ save_fig : bool, optional
+ Whether to save the figures. The default is `True`.
+
+ Returns
+ -------
+ sobol_cell: dict
+ Sobol indices.
+ total_sobol: dict
+ Total Sobol indices.
+
+ """
+ # Extract the necessary variables
+ PCEModel = self.MetaModel
+ basis_dict = PCEModel.basis_dict
+ coeffs_dict = PCEModel.coeffs_dict
+ n_params = PCEModel.n_params
+ max_order = np.max(PCEModel.pce_deg)
+ self.sobol_cell = {}
+ self.total_sobol = {}
+
+ for Output in PCEModel.ModelObj.Output.names:
+
+ n_meas_points = len(coeffs_dict[Output])
+
+ # Initialize the (cell) array containing the (total) Sobol indices.
+ sobol_array = dict.fromkeys(range(1, max_order+1), [])
+ sobol_cell_array = dict.fromkeys(range(1, max_order+1), [])
+
+ for i_order in range(1, max_order+1):
+ n_comb = math.comb(n_params, i_order)
+
+ sobol_cell_array[i_order] = np.zeros((n_comb, n_meas_points))
+
+ total_sobol_array = np.zeros((n_params, n_meas_points))
+
+ # Initialize the cell to store the names of the variables
+ TotalVariance = np.zeros((n_meas_points))
+
+ # Loop over all measurement points and calculate sobol indices
+ for pIdx in range(n_meas_points):
+
+ # Extract the basis indices (alpha) and coefficients
+ Basis = basis_dict[Output][f'y_{pIdx+1}']
+
+ try:
+ clf_poly = PCEModel.clf_poly[Output][f'y_{pIdx+1}']
+ PCECoeffs = clf_poly.coef_
+ except:
+ PCECoeffs = coeffs_dict[Output][f'y_{pIdx+1}']
+
+ # Compute total variance
+ TotalVariance[pIdx] = np.sum(np.square(PCECoeffs[1:]))
+
+ nzidx = np.where(PCECoeffs != 0)[0]
+ # Set all the Sobol indices equal to zero in the presence of a
+ # null output.
+ if len(nzidx) == 0:
+ # This is buggy.
+ for i_order in range(1, max_order+1):
+ sobol_cell_array[i_order][:, pIdx] = 0
+
+ # Otherwise compute them by summing well-chosen coefficients
+ else:
+ nz_basis = Basis[nzidx]
+ for i_order in range(1, max_order+1):
+ idx = np.where(np.sum(nz_basis > 0, axis=1) == i_order)
+ subbasis = nz_basis[idx]
+ Z = np.array(list(combinations(range(n_params), i_order)))
+
+ for q in range(Z.shape[0]):
+ Zq = Z[q]
+ subsubbasis = subbasis[:, Zq]
+ subidx = np.prod(subsubbasis, axis=1) > 0
+ sum_ind = nzidx[idx[0][subidx]]
+ if TotalVariance[pIdx] == 0.0:
+ sobol_cell_array[i_order][q, pIdx] = 0.0
+ else:
+ sobol = np.sum(np.square(PCECoeffs[sum_ind]))
+ sobol /= TotalVariance[pIdx]
+ sobol_cell_array[i_order][q, pIdx] = sobol
+
+ # Compute the TOTAL Sobol indices.
+ for ParIdx in range(n_params):
+ idx = nz_basis[:, ParIdx] > 0
+ sum_ind = nzidx[idx]
+
+ if TotalVariance[pIdx] == 0.0:
+ total_sobol_array[ParIdx, pIdx] = 0.0
+ else:
+ sobol = np.sum(np.square(PCECoeffs[sum_ind]))
+ sobol /= TotalVariance[pIdx]
+ total_sobol_array[ParIdx, pIdx] = sobol
+
+ # ----- if PCA selected: Compute covariance -----
+ if PCEModel.dim_red_method.lower() == 'pca':
+ cov_Z_p_q = np.zeros((n_params))
+ # Extract the basis indices (alpha) and coefficients for
+ # next component
+ if pIdx < n_meas_points-1:
+ nextBasis = basis_dict[Output][f'y_{pIdx+2}']
+
+ try:
+ clf_poly = PCEModel.clf_poly[Output][f'y_{pIdx+2}']
+ nextPCECoeffs = clf_poly.coef_
+ except:
+ nextPCECoeffs = coeffs_dict[Output][f'y_{pIdx+2}']
+
+ # Choose the common non-zero basis
+ mask = (Basis[:, None] == nextBasis).all(-1).any(-1)
+ similar_basis = Basis[mask]
+ # Compute the TOTAL Sobol indices.
+ for ParIdx in range(n_params):
+ idx = similar_basis[:, ParIdx] > 0
+ try:
+ sum_is = nzidx[idx]
+ cov_Z_p_q[ParIdx] = np.sum(PCECoeffs[sum_ind] *
+ nextPCECoeffs[sum_is])
+ except:
+ cov_Z_p_q[ParIdx] = 0.0
+
+ # Compute the sobol indices according to Ref. 2
+ if PCEModel.dim_red_method.lower() == 'pca':
+ n_c_points = PCEModel.ExpDesign.Y[Output].shape[1]
+ PCA = PCEModel.pca[Output]
+ compPCA = PCA.components_
+ nComp = compPCA.shape[0]
+ var_Z_p = PCA.explained_variance_
+
+ # Extract the sobol index of the components
+ for i_order in range(1, max_order+1):
+ n_comb = math.comb(n_params, i_order)
+ sobol_array[i_order] = np.zeros((n_comb, n_c_points))
+ Z = np.array(list(combinations(range(n_params), i_order)))
+
+ for q in range(Z.shape[0]):
+ S_Z_i = sobol_cell_array[i_order][q]
+
+ for tIdx in range(n_c_points):
+ var_Y_t = np.var(PCEModel.ExpDesign.Y[Output][:, tIdx])
+ if var_Y_t == 0.0:
+ term1, term2 = 0.0, 0.0
+ else:
+ term1 = np.sum([S_Z_i[i]*(var_Z_p[i]*(compPCA[i, tIdx]**2)/var_Y_t) for i in range(nComp)])
+
+ # Term 2
+ # cov_Z_p_q = np.ones((nComp))# TODO: from coeffs
+ Phi_t_p = compPCA[:nComp-1]
+ Phi_t_q = compPCA
+ term2 = 2 * np.sum([cov_Z_p_q[ParIdx] * Phi_t_p[i,tIdx] * Phi_t_q[i,tIdx]/var_Y_t for i in range(nComp-1)])
+
+ sobol_array[i_order][q, tIdx] = term1 #+ term2
+
+ # Compute the TOTAL Sobol indices.
+ total_sobol = np.zeros((n_params, n_c_points))
+ for ParIdx in range(n_params):
+ S_Z_i = total_sobol_array[ParIdx]
+
+ for tIdx in range(n_c_points):
+ var_Y_t = np.var(PCEModel.ExpDesign.Y[Output][:, tIdx])
+ if var_Y_t == 0.0:
+ term1, term2 = 0.0, 0.0
+ else:
+ term1 = 0
+ for i in range(nComp):
+ term1 += S_Z_i[i] * var_Z_p[i] * \
+ (compPCA[i, tIdx]**2) / var_Y_t
+
+ # Term 2
+ # cov_Z_p_q = np.ones((nComp))# TODO: from coeffs
+ Phi_t_p = compPCA[:nComp-1]
+ Phi_t_q = compPCA
+ term2 = 0
+ for i in range(nComp-1):
+ term2 += cov_Z_p_q[ParIdx] * Phi_t_p[i, tIdx] \
+ * Phi_t_q[i, tIdx] / var_Y_t
+ term2 *= 2
+
+ total_sobol[ParIdx, tIdx] = term1 + term2
+
+ self.sobol_cell[Output] = sobol_array
+ self.total_sobol[Output] = total_sobol
+ else:
+ self.sobol_cell[Output] = sobol_cell_array
+ self.total_sobol[Output] = total_sobol_array
+
+ # ---------------- Plot -----------------------
+ par_names = PCEModel.ExpDesign.par_names
+ x_values_orig = PCEModel.ExpDesign.x_values
+
+ cases = ['']
+
+ for case in cases:
+ newpath = (f'Outputs_PostProcessing_{self.name}/')
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ if save_fig:
+ # create a PdfPages object
+ name = case+'_' if 'Valid' in cases else ''
+ pdf = PdfPages('./'+newpath+name+'Sobol_indices.pdf')
+
+ fig = plt.figure()
+
+ for outIdx, Output in enumerate(PCEModel.ModelObj.Output.names):
+
+ # Extract total Sobol indices
+ total_sobol = self.total_sobol[Output]
+
+ # Extract a list of x values
+ if type(x_values_orig) is dict:
+ x = x_values_orig[Output]
+ else:
+ x = x_values_orig
+
+ if plot_type == 'bar':
+ ax = fig.add_axes([0, 0, 1, 1])
+ dict1 = {xlabel: x}
+ dict2 = {param: sobolIndices for param, sobolIndices
+ in zip(par_names, total_sobol)}
+
+ df = pd.DataFrame({**dict1, **dict2})
+ df.plot(x=xlabel, y=par_names, kind="bar", ax=ax, rot=0,
+ colormap='Dark2')
+ ax.set_ylabel('Total Sobol indices, $S^T$')
+
+ else:
+ for i, sobolIndices in enumerate(total_sobol):
+ plt.plot(x, sobolIndices, label=par_names[i],
+ marker='x', lw=2.5)
+
+ plt.ylabel('Total Sobol indices, $S^T$')
+ plt.xlabel(xlabel)
+
+ plt.title(f'Sensitivity analysis of {Output}')
+ if plot_type != 'bar':
+ plt.legend(loc='best', frameon=True)
+
+ # Save indices
+ np.savetxt(f'./{newpath}{name}totalsobol_' +
+ Output.replace('/', '_') + '.csv',
+ total_sobol.T, delimiter=',',
+ header=','.join(par_names), comments='')
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+
+ # Destroy the current plot
+ plt.clf()
+
+ pdf.close()
+
+ return self.sobol_cell, self.total_sobol
+
+ # -------------------------------------------------------------------------
+ def check_reg_quality(self, n_samples=1000, samples=None, save_fig=True):
+ """
+ Checks the quality of the metamodel for single output models based on:
+ https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685
+
+
+ Parameters
+ ----------
+ n_samples : int, optional
+ Number of parameter sets to use for the check. The default is 1000.
+ samples : array of shape (n_samples, n_params), optional
+ Parameter sets to use for the check. The default is None.
+ save_fig : bool, optional
+ Whether to save the figures. The default is True.
+
+ Returns
+ -------
+ None.
+
+ """
+ MetaModel = self.MetaModel
+
+ if samples is None:
+ self.n_samples = n_samples
+ samples = self._get_sample()
+ else:
+ self.n_samples = samples.shape[0]
+
+ # Evaluate the original and the surrogate model
+ y_val = self._eval_model(samples, key_str='valid')
+ y_pce_val, _ = MetaModel.eval_metamodel(samples=samples)
+
+ # Open a pdf for the plots
+ if save_fig:
+ newpath = (r'Outputs_PostProcessing_{0}/'.format(self.name))
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # Fit the data(train the model)
+ for key in y_pce_val.keys():
+
+ y_pce_val_ = y_pce_val[key]
+ y_val_ = y_val[key]
+
+ # ------ Residuals vs. predicting variables ------
+ # Check the assumptions of linearity and independence
+ fig1 = plt.figure()
+ plt.title(key+": Residuals vs. Predicting variables")
+ residuals = y_val_ - y_pce_val_
+ plt.scatter(x=y_val_, y=residuals, color='blue', edgecolor='k')
+ plt.grid(True)
+ xmin, xmax = min(y_val_), max(y_val_)
+ plt.hlines(y=0, xmin=xmin*0.9, xmax=xmax*1.1, color='red', lw=3,
+ linestyle='--')
+ plt.xlabel(key)
+ plt.ylabel('Residuals')
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig1.savefig(f'./{newpath}/Residuals_vs_PredVariables.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ------ Fitted vs. residuals ------
+ # Check the assumptions of linearity and independence
+ fig2 = plt.figure()
+ plt.title(key+": Residuals vs. predicting variables")
+ plt.scatter(x=y_pce_val_, y=residuals, color='blue', edgecolor='k')
+ plt.grid(True)
+ xmin, xmax = min(y_val_), max(y_val_)
+ plt.hlines(y=0, xmin=xmin*0.9, xmax=xmax*1.1, color='red', lw=3,
+ linestyle='--')
+ plt.xlabel(key)
+ plt.ylabel('Residuals')
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig2.savefig(f'./{newpath}/Fitted_vs_Residuals.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ------ Histogram of normalized residuals ------
+ fig3 = plt.figure()
+ resid_pearson = residuals / (max(residuals)-min(residuals))
+ plt.hist(resid_pearson, bins=20, edgecolor='k')
+ plt.ylabel('Count')
+ plt.xlabel('Normalized residuals')
+ plt.title(f"{key}: Histogram of normalized residuals")
+
+ # Normality (Shapiro-Wilk) test of the residuals
+ ax = plt.gca()
+ _, p = stats.shapiro(residuals)
+ if p < 0.01:
+ annText = "The residuals seem to come from Gaussian process."
+ else:
+ annText = "The normality assumption may not hold."
+ at = AnchoredText(annText, prop=dict(size=30), frameon=True,
+ loc='upper left')
+ at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
+ ax.add_artist(at)
+
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig3.savefig(f'./{newpath}/Hist_NormResiduals.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ------ Q-Q plot of the normalized residuals ------
+ plt.figure()
+ fig4 = qqplot(resid_pearson, line='45', fit='True')
+ plt.xticks()
+ plt.yticks()
+ plt.xlabel("Theoretical quantiles")
+ plt.ylabel("Sample quantiles")
+ plt.title(key+": Q-Q plot of normalized residuals")
+ plt.grid(True)
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig4.savefig(f'./{newpath}/QQPlot_NormResiduals.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # -------------------------------------------------------------------------
+ def eval_pce_model_3d(self, save_fig=True):
+
+ self.n_samples = 1000
+
+ PCEModel = self.MetaModel
+ Model = self.MetaModel.ModelObj
+ n_samples = self.n_samples
+
+ # Create 3D-Grid
+ # TODO: Make it general
+ x = np.linspace(-5, 10, n_samples)
+ y = np.linspace(0, 15, n_samples)
+
+ X, Y = np.meshgrid(x, y)
+ PCE_Z = np.zeros((self.n_samples, self.n_samples))
+ Model_Z = np.zeros((self.n_samples, self.n_samples))
+
+ for idxMesh in range(self.n_samples):
+ sample_mesh = np.vstack((X[:, idxMesh], Y[:, idxMesh])).T
+
+ univ_p_val = PCEModel.univ_basis_vals(sample_mesh)
+
+ for Outkey, ValuesDict in PCEModel.coeffs_dict.items():
+
+ pce_out_mean = np.zeros((len(sample_mesh), len(ValuesDict)))
+ pce_out_std = np.zeros((len(sample_mesh), len(ValuesDict)))
+ model_outs = np.zeros((len(sample_mesh), len(ValuesDict)))
+
+ for Inkey, InIdxValues in ValuesDict.items():
+ idx = int(Inkey.split('_')[1]) - 1
+ basis_deg_ind = PCEModel.basis_dict[Outkey][Inkey]
+ clf_poly = PCEModel.clf_poly[Outkey][Inkey]
+
+ PSI_Val = PCEModel.create_psi(basis_deg_ind, univ_p_val)
+
+ # Perdiction with error bar
+ y_mean, y_std = clf_poly.predict(PSI_Val, return_std=True)
+
+ pce_out_mean[:, idx] = y_mean
+ pce_out_std[:, idx] = y_std
+
+ # Model evaluation
+ model_out_dict, _ = Model.run_model_parallel(sample_mesh,
+ key_str='Valid3D')
+ model_outs[:, idx] = model_out_dict[Outkey].T
+
+ PCE_Z[:, idxMesh] = y_mean
+ Model_Z[:, idxMesh] = model_outs[:, 0]
+
+ # ---------------- 3D plot for PCEModel -----------------------
+ fig_PCE = plt.figure()
+ ax = plt.axes(projection='3d')
+ ax.plot_surface(X, Y, PCE_Z, rstride=1, cstride=1,
+ cmap='viridis', edgecolor='none')
+ ax.set_title('PCEModel')
+ ax.set_xlabel('$x_1$')
+ ax.set_ylabel('$x_2$')
+ ax.set_zlabel('$f(x_1,x_2)$')
+
+ plt.grid()
+ plt.show()
+
+ if save_fig:
+ # Saving the figure
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # save the figure to file
+ fig_PCE.savefig(f'./{newpath}/3DPlot_PCEModel.pdf', format="pdf",
+ bbox_inches='tight')
+ plt.close(fig_PCE)
+
+ # ---------------- 3D plot for Model -----------------------
+ fig_Model = plt.figure()
+ ax = plt.axes(projection='3d')
+ ax.plot_surface(X, Y, PCE_Z, rstride=1, cstride=1,
+ cmap='viridis', edgecolor='none')
+ ax.set_title('Model')
+ ax.set_xlabel('$x_1$')
+ ax.set_ylabel('$x_2$')
+ ax.set_zlabel('$f(x_1,x_2)$')
+
+ plt.grid()
+ plt.show()
+
+ if save_fig:
+ # Save the figure
+ fig_Model.savefig(f'./{newpath}/3DPlot_Model.pdf', format="pdf",
+ bbox_inches='tight')
+ plt.close(fig_Model)
+
+ return
+
+ # -------------------------------------------------------------------------
+ def _compute_pce_moments(self):
+ """
+ Computes the first two moments using the PCE-based meta-model.
+
+ Returns
+ -------
+ None.
+
+ """
+
+ MetaModel = self.MetaModel
+ self.pce_means = {}
+ self.pce_stds = {}
+
+ for Outkey, ValuesDict in MetaModel.coeffs_dict.items():
+
+ pce_mean = np.zeros((len(ValuesDict)))
+ pce_var = np.zeros((len(ValuesDict)))
+
+ for Inkey, InIdxValues in ValuesDict.items():
+ idx = int(Inkey.split('_')[1]) - 1
+ coeffs = MetaModel.coeffs_dict[Outkey][Inkey]
+
+ # Mean = c_0
+ if coeffs[0] != 0:
+ pce_mean[idx] = coeffs[0]
+ else:
+ pce_mean[idx] = MetaModel.clf_poly[Outkey][Inkey].intercept_
+
+ # Var = sum(coeffs[1:]**2)
+ pce_var[idx] = np.sum(np.square(coeffs[1:]))
+
+ # Back transformation if PCA is selected.
+ if MetaModel.dim_red_method.lower() == 'pca':
+ PCA = MetaModel.pca[Outkey]
+ self.pce_means[Outkey] = PCA.mean_
+ self.pce_means[Outkey] += np.dot(pce_mean, PCA.components_)
+ self.pce_stds[Outkey] = np.sqrt(np.dot(pce_var,
+ PCA.components_**2))
+ else:
+ self.pce_means[Outkey] = pce_mean
+ self.pce_stds[Outkey] = np.sqrt(pce_var)
+
+ # Print a report table
+ print("\n>>>>> Moments of {} <<<<<".format(Outkey))
+ print("\nIndex | Mean | Std. deviation")
+ print('-'*35)
+ print('\n'.join(f'{i+1} | {k:.3e} | {j:.3e}' for i, (k, j)
+ in enumerate(zip(self.pce_means[Outkey],
+ self.pce_stds[Outkey]))))
+ print('-'*40)
+
+ # -------------------------------------------------------------------------
+ def _get_sample(self, n_samples=None):
+ """
+ Generates random samples taken from the input parameter space.
+
+ Returns
+ -------
+ samples : array of shape (n_samples, n_params)
+ Generated samples.
+
+ """
+ if n_samples is None:
+ n_samples = self.n_samples
+ PCEModel = self.MetaModel
+ self.samples = PCEModel.ExpDesign.generate_samples(n_samples, 'random')
+ return self.samples
+
+ # -------------------------------------------------------------------------
+ def _eval_model(self, samples=None, key_str='Valid'):
+ """
+ Evaluates Forward Model for the given number of self.samples or given
+ samples.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params), optional
+ Samples to evaluate the model at. The default is None.
+ key_str : str, optional
+ Key string pass to the model. The default is 'Valid'.
+
+ Returns
+ -------
+ model_outs : dict
+ Dictionary of results.
+
+ """
+ Model = self.MetaModel.ModelObj
+
+ if samples is None:
+ samples = self._get_sample()
+ self.samples = samples
+ else:
+ self.n_samples = len(samples)
+
+ model_outs, _ = Model.run_model_parallel(samples, key_str=key_str)
+
+ return model_outs
+
+ # -------------------------------------------------------------------------
+ def _plot_validation(self, save_fig=True):
+ """
+ Plots outputs for visual comparison of metamodel outputs with that of
+ the (full) original model.
+
+ Parameters
+ ----------
+ save_fig : bool, optional
+ Save the plots. The default is True.
+
+ Returns
+ -------
+ None.
+
+ """
+ PCEModel = self.MetaModel
+
+ # get the samples
+ x_val = self.samples
+ y_pce_val = self.pce_out_mean
+ y_val = self.model_out_dict
+
+ # Open a pdf for the plots
+ if save_fig:
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # create a PdfPages object
+ pdf1 = PdfPages(f'./{newpath}/Model_vs_PCEModel.pdf')
+
+ fig = plt.figure()
+ # Fit the data(train the model)
+ for key in y_pce_val.keys():
+
+ y_pce_val_ = y_pce_val[key]
+ y_val_ = y_val[key]
+
+ regression_model = LinearRegression()
+ regression_model.fit(y_pce_val_, y_val_)
+
+ # Predict
+ x_new = np.linspace(np.min(y_pce_val_), np.max(y_val_), 100)
+ y_predicted = regression_model.predict(x_new[:, np.newaxis])
+
+ plt.scatter(y_pce_val_, y_val_, color='gold', linewidth=2)
+ plt.plot(x_new, y_predicted, color='k')
+
+ # Calculate the adjusted R_squared and RMSE
+ # the total number of explanatory variables in the model
+ # (not including the constant term)
+ length_list = []
+ for key, value in PCEModel.coeffs_dict[key].items():
+ length_list.append(len(value))
+ n_predictors = min(length_list)
+ n_samples = x_val.shape[0]
+
+ R2 = r2_score(y_pce_val_, y_val_)
+ AdjR2 = 1 - (1 - R2) * (n_samples - 1) / \
+ (n_samples - n_predictors - 1)
+ rmse = mean_squared_error(y_pce_val_, y_val_, squared=False)
+
+ plt.annotate(f'RMSE = {rmse:.3f}\n Adjusted $R^2$ = {AdjR2:.3f}',
+ xy=(0.05, 0.85), xycoords='axes fraction')
+
+ plt.ylabel("Original Model")
+ plt.xlabel("PCE Model")
+ plt.grid()
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ pdf1.savefig(fig, bbox_inches='tight')
+
+ # Destroy the current plot
+ plt.clf()
+
+ # Close the pdfs
+ pdf1.close()
+
+ # -------------------------------------------------------------------------
+ def _plot_validation_multi(self, x_values=[], x_axis="x [m]", save_fig=True):
+ """
+ Plots outputs for visual comparison of metamodel outputs with that of
+ the (full) multioutput original model
+
+ Parameters
+ ----------
+ x_values : list or array, optional
+ List of x values. The default is [].
+ x_axis : str, optional
+ Label of the x axis. The default is "x [m]".
+ save_fig : bool, optional
+ Whether to save the figures. The default is True.
+
+ Returns
+ -------
+ None.
+
+ """
+ Model = self.MetaModel.ModelObj
+
+ if save_fig:
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # create a PdfPages object
+ pdf = PdfPages(f'./{newpath}/Model_vs_PCEModel.pdf')
+
+ # List of markers and colors
+ color = cycle((['b', 'g', 'r', 'y', 'k']))
+ marker = cycle(('x', 'd', '+', 'o', '*'))
+
+ fig = plt.figure()
+ # Plot the model vs PCE model
+ for keyIdx, key in enumerate(Model.Output.names):
+
+ y_pce_val = self.pce_out_mean[key]
+ y_pce_val_std = self.pce_out_std[key]
+ y_val = self.model_out_dict[key]
+ try:
+ x = self.model_out_dict['x_values'][key]
+ except IndexError:
+ x = x_values
+
+ for idx in range(y_val.shape[0]):
+ Color = next(color)
+ Marker = next(marker)
+
+ plt.plot(x, y_val[idx], color=Color, marker=Marker,
+ label='$Y_{%s}^M$'%(idx+1))
+
+ plt.plot(x, y_pce_val[idx], color=Color, marker=Marker,
+ linestyle='--',
+ label='$Y_{%s}^{PCE}$'%(idx+1))
+ plt.fill_between(x, y_pce_val[idx]-1.96*y_pce_val_std[idx],
+ y_pce_val[idx]+1.96*y_pce_val_std[idx],
+ color=Color, alpha=0.15)
+
+ # Calculate the RMSE
+ rmse = mean_squared_error(y_pce_val, y_val, squared=False)
+ R2 = r2_score(y_pce_val[idx].reshape(-1, 1),
+ y_val[idx].reshape(-1, 1))
+
+ plt.annotate(f'RMSE = {rmse:.3f}\n $R^2$ = {R2:.3f}',
+ xy=(0.2, 0.75), xycoords='axes fraction')
+
+ plt.ylabel(key)
+ plt.xlabel(x_axis)
+ plt.legend(loc='best')
+ plt.grid()
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ pdf.close()
+
+ # Zip the subdirectories
+ Model.zip_subdirs(f'{Model.name}valid', f'{Model.name}valid_')</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="post_processing.PostProcessing"><code class="flex name class">
+<span>class <span class="ident">PostProcessing</span></span>
+<span>(</span><span>MetaModel, name='calib')</span>
+</code></dt>
+<dd>
+<div class="desc"><p>This class provides many helper functions to post-process the trained
+meta-model.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>MetaModel</code></strong> : <code>obj</code></dt>
+<dd>MetaModel object to do postprocessing on.</dd>
+<dt><strong><code>name</code></strong> : <code>str</code></dt>
+<dd>Type of the anaylsis. The default is <code>'calib'</code>. If a validation is
+expected to be performed change this to <code>'valid'</code>.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class PostProcessing:
+ """
+ This class provides many helper functions to post-process the trained
+ meta-model.
+
+ Attributes
+ ----------
+ MetaModel : obj
+ MetaModel object to do postprocessing on.
+ name : str
+ Type of the anaylsis. The default is `'calib'`. If a validation is
+ expected to be performed change this to `'valid'`.
+ """
+
+ def __init__(self, MetaModel, name='calib'):
+ self.MetaModel = MetaModel
+ self.name = name
+
+ # -------------------------------------------------------------------------
+ def plot_moments(self, xlabel='Time [s]', plot_type=None, save_fig=True):
+ """
+ Plots the moments in a pdf format in the directory
+ `Outputs_PostProcessing`.
+
+ Parameters
+ ----------
+ xlabel : str, optional
+ String to be displayed as x-label. The default is `'Time [s]'`.
+ plot_type : str, optional
+ Options: bar or line. The default is `None`.
+ save_fig : bool, optional
+ Save figure or not. The default is `True`.
+
+ Returns
+ -------
+ pce_means: dict
+ Mean of the model outputs.
+ pce_means: dict
+ Standard deviation of the model outputs.
+
+ """
+
+ bar_plot = True if plot_type == 'bar' else False
+ meta_model_type = self.MetaModel.meta_model_type
+ Model = self.MetaModel.ModelObj
+
+ # Read Monte-Carlo reference
+ self.mc_reference = Model.read_mc_reference()
+ print(self.mc_reference)
+
+ # Set the x values
+ x_values_orig = self.MetaModel.ExpDesign.x_values
+
+ # Compute the moments with the PCEModel object
+ self._compute_pce_moments()
+
+ # Get the variables
+ out_names = Model.Output.names
+
+ # Open a pdf for the plots
+ if save_fig:
+ newpath = (f'Outputs_PostProcessing_{self.name}/')
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # create a PdfPages object
+ pdf = PdfPages(f'./{newpath}Mean_Std_PCE.pdf')
+
+ # Plot the best fit line, set the linewidth (lw), color and
+ # transparency (alpha) of the line
+ for idx, key in enumerate(out_names):
+ fig, ax = plt.subplots(nrows=1, ncols=2)
+
+ # Extract mean and std
+ mean_data = self.pce_means[key]
+ std_data = self.pce_stds[key]
+
+ # Extract a list of x values
+ if type(x_values_orig) is dict:
+ x = x_values_orig[key]
+ else:
+ x = x_values_orig
+
+ # Plot: bar plot or line plot
+ if bar_plot:
+ ax[0].bar(list(map(str, x)), mean_data, color='b',
+ width=0.25)
+ ax[1].bar(list(map(str, x)), std_data, color='b',
+ width=0.25)
+ ax[0].legend(labels=[meta_model_type])
+ ax[1].legend(labels=[meta_model_type])
+ else:
+ ax[0].plot(x, mean_data, lw=3, color='k', marker='x',
+ label=meta_model_type)
+ ax[1].plot(x, std_data, lw=3, color='k', marker='x',
+ label=meta_model_type)
+
+ if self.mc_reference is not None:
+ if bar_plot:
+ ax[0].bar(list(map(str, x)), self.mc_reference['mean'],
+ color='r', width=0.25)
+ ax[1].bar(list(map(str, x)), self.mc_reference['std'],
+ color='r', width=0.25)
+ ax[0].legend(labels=[meta_model_type])
+ ax[1].legend(labels=[meta_model_type])
+ else:
+ ax[0].plot(x, self.mc_reference['mean'], lw=3, marker='x',
+ color='r', label='Ref.')
+ ax[1].plot(x, self.mc_reference['std'], lw=3, marker='x',
+ color='r', label='Ref.')
+
+ # Label the axes and provide a title
+ ax[0].set_xlabel(xlabel)
+ ax[1].set_xlabel(xlabel)
+ ax[0].set_ylabel(key)
+ ax[1].set_ylabel(key)
+
+ # Provide a title
+ ax[0].set_title('Mean of ' + key)
+ ax[1].set_title('Std of ' + key)
+
+ if not bar_plot:
+ ax[0].legend(loc='best')
+ ax[1].legend(loc='best')
+
+ plt.tight_layout()
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+
+ # Destroy the current plot
+ plt.clf()
+
+ pdf.close()
+
+ return self.pce_means, self.pce_stds
+
+ # -------------------------------------------------------------------------
+ def valid_metamodel(self, n_samples=1, samples=None, x_axis='Time [s]'):
+ """
+ Evaluates and plots the meta model and the PCEModel outputs for the
+ given number of samples or the given samples.
+
+ Parameters
+ ----------
+ n_samples : int, optional
+ Number of samples to be evaluated. The default is 1.
+ samples : array of shape (n_samples, n_params), optional
+ Samples to be evaluated. The default is None.
+ x_axis : str, optional
+ Label of x axis. The default is `'Time [s]'`.
+
+ Returns
+ -------
+ None.
+
+ """
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+
+ if samples is None:
+ self.n_samples = n_samples
+ samples = self._get_sample()
+ else:
+ self.n_samples = samples.shape[0]
+
+ # Extract x_values
+ x_values = MetaModel.ExpDesign.x_values
+
+ self.model_out_dict = self._eval_model(samples, key_str='valid')
+ self.pce_out_mean, self.pce_out_std = MetaModel.eval_metamodel(samples)
+
+ try:
+ key = Model.Output.names[1]
+ except IndexError:
+ key = Model.Output.names[0]
+
+ n_obs = self.model_out_dict[key].shape[1]
+
+ if n_obs == 1:
+ self._plot_validation()
+ else:
+ self._plot_validation_multi(x_values=x_values, x_axis=x_axis)
+
+ # -------------------------------------------------------------------------
+ def check_accuracy(self, n_samples=None, samples=None, outputs=None):
+ """
+ Checks accuracy of the metamodel by computing the root mean square
+ error and validation error for all outputs.
+
+ Parameters
+ ----------
+ n_samples : int, optional
+ Number of samples. The default is None.
+ samples : array of shape (n_samples, n_params), optional
+ Parameter sets to be checked. The default is None.
+ outputs : dict, optional
+ Output dictionary with model outputs for all given output types in
+ `Model.Output.names`. The default is None.
+
+ Raises
+ ------
+ Exception
+ When neither n_samples nor samples are provided.
+
+ Returns
+ -------
+ rmse: dict
+ Root mean squared error for each output.
+ valid_error : dict
+ Validation error for each output.
+
+ """
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+
+ # Set the number of samples
+ if n_samples:
+ self.n_samples = n_samples
+ elif samples is not None:
+ self.n_samples = samples.shape[0]
+ else:
+ raise Exception("Please provide either samples or pass number of "
+ "samples!")
+
+ # Generate random samples if necessary
+ Samples = self._get_sample() if samples is None else samples
+
+ # Run the original model with the generated samples
+ if outputs is None:
+ outputs = self._eval_model(Samples, key_str='validSet')
+
+ # Run the PCE model with the generated samples
+ pce_outputs, _ = MetaModel.eval_metamodel(samples=Samples)
+
+ self.rmse = {}
+ self.valid_error = {}
+ # Loop over the keys and compute RMSE error.
+ for key in Model.Output.names:
+ # Root mena square
+ self.rmse[key] = mean_squared_error(outputs[key], pce_outputs[key],
+ squared=False,
+ multioutput='raw_values')
+ # Validation error
+ self.valid_error[key] = (self.rmse[key]**2 / self.n_samples) / \
+ np.var(outputs[key], ddof=1, axis=0)
+
+ # Print a report table
+ print("\n>>>>> Errors of {} <<<<<".format(key))
+ print("\nIndex | RMSE | Validation Error")
+ print('-'*35)
+ print('\n'.join(f'{i+1} | {k:.3e} | {j:.3e}' for i, (k, j)
+ in enumerate(zip(self.rmse[key],
+ self.valid_error[key]))))
+ # Save error dicts in PCEModel object
+ self.MetaModel.rmse = self.rmse
+ self.MetaModel.valid_error = self.valid_error
+
+ return self.rmse, self.valid_error
+
+ # -------------------------------------------------------------------------
+ def plot_seq_design_diagnostics(self, ref_BME_KLD=None, save_fig=True):
+ """
+ Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence
+ (KLD) for the sequential design.
+
+ Parameters
+ ----------
+ ref_BME_KLD : array, optional
+ Reference BME and KLD . The default is `None`.
+ save_fig : bool, optional
+ Whether to save the figures. The default is `True`.
+
+ Returns
+ -------
+ None.
+
+ """
+ PCEModel = self.MetaModel
+ n_init_samples = PCEModel.ExpDesign.n_init_samples
+ n_total_samples = PCEModel.ExpDesign.X.shape[0]
+
+ if save_fig:
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # create a PdfPages object
+ pdf = PdfPages(f'./{newpath}/seqPCEModelDiagnostics.pdf')
+
+ plotList = ['Modified LOO error', 'Validation error', 'KLD', 'BME',
+ 'RMSEMean', 'RMSEStd', 'Hellinger distance']
+ seqList = [PCEModel.SeqModifiedLOO, PCEModel.seqValidError,
+ PCEModel.SeqKLD, PCEModel.SeqBME, PCEModel.seqRMSEMean,
+ PCEModel.seqRMSEStd, PCEModel.SeqDistHellinger]
+
+ markers = ('x', 'o', 'd', '*', '+')
+ colors = ('k', 'darkgreen', 'b', 'navy', 'darkred')
+
+ # Plot the evolution of the diagnostic criteria of the
+ # Sequential Experimental Design.
+ for plotidx, plot in enumerate(plotList):
+ fig, ax = plt.subplots()
+ seq_dict = seqList[plotidx]
+ name_util = list(seq_dict.keys())
+
+ if len(name_util) == 0:
+ continue
+
+ # Box plot when Replications have been detected.
+ if any(int(name.split("rep_", 1)[1]) > 1 for name in name_util):
+ # Extract the values from dict
+ sorted_seq_opt = {}
+ # Number of replications
+ n_reps = PCEModel.ExpDesign.nReprications
+
+ # Get the list of utility function names
+ # Handle if only one UtilityFunction is provided
+ if not isinstance(PCEModel.ExpDesign.UtilityFunction, list):
+ util_funcs = [PCEModel.ExpDesign.UtilityFunction]
+ else:
+ util_funcs = PCEModel.ExpDesign.UtilityFunction
+
+ for util in util_funcs:
+ sortedSeq = {}
+ # min number of runs available from reps
+ n_runs = min([seq_dict[f'{util}_rep_{i+1}'].shape[0]
+ for i in range(n_reps)])
+
+ for runIdx in range(n_runs):
+ values = []
+ for key in seq_dict.keys():
+ if util in key:
+ values.append(seq_dict[key][runIdx].mean())
+ sortedSeq['SeqItr_'+str(runIdx)] = np.array(values)
+ sorted_seq_opt[util] = sortedSeq
+
+ # BoxPlot
+ def draw_plot(data, labels, edge_color, fill_color, idx):
+ pos = labels - (idx-1)
+ bp = plt.boxplot(data, positions=pos, labels=labels,
+ patch_artist=True, sym='', widths=0.75)
+ elements = ['boxes', 'whiskers', 'fliers', 'means',
+ 'medians', 'caps']
+ for element in elements:
+ plt.setp(bp[element], color=edge_color[idx])
+
+ for patch in bp['boxes']:
+ patch.set(facecolor=fill_color[idx])
+
+ if PCEModel.ExpDesign.n_new_samples != 1:
+ step1 = PCEModel.ExpDesign.n_new_samples
+ step2 = 1
+ else:
+ step1 = 5
+ step2 = 5
+ edge_color = ['red', 'blue', 'green']
+ fill_color = ['tan', 'cyan', 'lightgreen']
+ plot_label = plot
+ # Plot for different Utility Functions
+ for idx, util in enumerate(util_funcs):
+ all_errors = np.empty((n_reps, 0))
+
+ for key in list(sorted_seq_opt[util].keys()):
+ errors = sorted_seq_opt.get(util, {}).get(key)[:, None]
+ all_errors = np.hstack((all_errors, errors))
+
+ # Special cases for BME and KLD
+ if plot == 'KLD' or plot == 'BME':
+ # BME convergence if refBME is provided
+ if ref_BME_KLD is not None:
+ if plot == 'BME':
+ refValue = ref_BME_KLD[0]
+ plot_label = r'$BME/BME^{Ref.}$'
+ if plot == 'KLD':
+ refValue = ref_BME_KLD[1]
+ plot_label = '$D_{KL}[p(\theta|y_*),p(\theta)]'\
+ ' / D_{KL}^{Ref.}[p(\theta|y_*), '\
+ 'p(\theta)]$'
+
+ # Difference between BME/KLD and the ref. values
+ all_errors = np.divide(all_errors,
+ np.full((all_errors.shape),
+ refValue))
+
+ # Plot baseline for zero, i.e. no difference
+ plt.axhline(y=1.0, xmin=0, xmax=1, c='green',
+ ls='--', lw=2)
+
+ # Plot each UtilFuncs
+ labels = np.arange(n_init_samples, n_total_samples+1, step1)
+ draw_plot(all_errors[:, ::step2], labels, edge_color,
+ fill_color, idx)
+
+ plt.xticks(labels, labels)
+ # Set the major and minor locators
+ ax.xaxis.set_major_locator(ticker.AutoLocator())
+ ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ ax.xaxis.grid(True, which='major', linestyle='-')
+ ax.xaxis.grid(True, which='minor', linestyle='--')
+
+ # Legend
+ legend_elements = []
+ for idx, util in enumerate(util_funcs):
+ legend_elements.append(Patch(facecolor=fill_color[idx],
+ edgecolor=edge_color[idx],
+ label=util))
+ plt.legend(handles=legend_elements[::-1], loc='best')
+
+ if plot != 'BME' and plot != 'KLD':
+ plt.yscale('log')
+ plt.autoscale(True)
+ plt.xlabel('\\# of training samples')
+ plt.ylabel(plot_label)
+ plt.title(plot)
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+ # Save arrays into files
+ f = open(f'./{newpath}/Seq{plot}.txt', 'w')
+ f.write(str(sorted_seq_opt))
+ f.close()
+ else:
+ for idx, name in enumerate(name_util):
+ seq_values = seq_dict[name]
+ if PCEModel.ExpDesign.n_new_samples != 1:
+ step = PCEModel.ExpDesign.n_new_samples
+ else:
+ step = 1
+ x_idx = np.arange(n_init_samples, n_total_samples+1, step)
+ if n_total_samples not in x_idx:
+ x_idx = np.hstack((x_idx, n_total_samples))
+
+ if plot == 'KLD' or plot == 'BME':
+ # BME convergence if refBME is provided
+ if ref_BME_KLD is not None:
+ if plot == 'BME':
+ refValue = ref_BME_KLD[0]
+ plot_label = r'$BME/BME^{Ref.}$'
+ if plot == 'KLD':
+ refValue = ref_BME_KLD[1]
+ plot_label = '$D_{KL}[p(\theta|y_*),p(\theta)]'\
+ ' / D_{KL}^{Ref.}[p(\theta|y_*), '\
+ 'p(\theta)]$'
+
+ # Difference between BME/KLD and the ref. values
+ values = np.divide(seq_values,
+ np.full((seq_values.shape),
+ refValue))
+
+ # Plot baseline for zero, i.e. no difference
+ plt.axhline(y=1.0, xmin=0, xmax=1, c='green',
+ ls='--', lw=2)
+
+ # Set the limits
+ plt.ylim([1e-1, 1e1])
+
+ # Create the plots
+ plt.semilogy(x_idx, values, marker=markers[idx],
+ color=colors[idx], ls='--', lw=2,
+ label=name.split("_rep", 1)[0])
+ else:
+ plot_label = plot
+
+ # Create the plots
+ plt.plot(x_idx, seq_values, marker=markers[idx],
+ color=colors[idx], ls='--', lw=2,
+ label=name.split("_rep", 1)[0])
+
+ else:
+ plot_label = plot
+ seq_values = np.nan_to_num(seq_values)
+
+ # Plot the error evolution for each output
+ for i in range(seq_values.shape[1]):
+ plt.semilogy(x_idx, seq_values[:, i], ls='--',
+ lw=2, marker=markers[idx],
+ color=colors[idx], alpha=0.15)
+
+ plt.semilogy(x_idx, seq_values, marker=markers[idx],
+ ls='--', lw=2, color=colors[idx],
+ label=name.split("_rep", 1)[0])
+
+ # Set the major and minor locators
+ ax.xaxis.set_major_locator(ticker.AutoLocator())
+ ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ ax.xaxis.grid(True, which='major', linestyle='-')
+ ax.xaxis.grid(True, which='minor', linestyle='--')
+
+ ax.tick_params(axis='both', which='major', direction='in',
+ width=3, length=10)
+ ax.tick_params(axis='both', which='minor', direction='in',
+ width=2, length=8)
+ plt.xlabel('Number of runs')
+ plt.ylabel(plot_label)
+ plt.title(plot)
+ plt.legend(frameon=True)
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ---------------- Saving arrays into files ---------------
+ np.save(f'./{newpath}/Seq{plot}.npy', seq_values)
+
+ # Close the pdf
+ pdf.close()
+ return
+
+ # -------------------------------------------------------------------------
+ def sobol_indices(self, xlabel='Time [s]', plot_type=None, save_fig=True):
+ """
+ Provides Sobol indices as a sensitivity measure to infer the importance
+ of the input parameters. See Eq. 27 in [1] for more details. For the
+ case with Principal component analysis refer to [2].
+
+ [1] Global sensitivity analysis: A flexible and efficient framework
+ with an example from stochastic hydrogeology S. Oladyshkin, F.P.
+ de Barros, W. Nowak https://doi.org/10.1016/j.advwatres.2011.11.001
+
+ [2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
+ component analysis and sparse polynomial chaos expansions for global
+ sensitivity analysis and model calibration: Application to urban
+ drainage simulation. Reliability Engineering & System Safety, 195,
+ p.106737.
+
+ Parameters
+ ----------
+ xlabel : str, optional
+ Label of the x-axis. The default is `'Time [s]'`.
+ plot_type : str, optional
+ Plot type. The default is `None`. This corresponds to line plot.
+ Bar chart can be selected by `bar`.
+ save_fig : bool, optional
+ Whether to save the figures. The default is `True`.
+
+ Returns
+ -------
+ sobol_cell: dict
+ Sobol indices.
+ total_sobol: dict
+ Total Sobol indices.
+
+ """
+ # Extract the necessary variables
+ PCEModel = self.MetaModel
+ basis_dict = PCEModel.basis_dict
+ coeffs_dict = PCEModel.coeffs_dict
+ n_params = PCEModel.n_params
+ max_order = np.max(PCEModel.pce_deg)
+ self.sobol_cell = {}
+ self.total_sobol = {}
+
+ for Output in PCEModel.ModelObj.Output.names:
+
+ n_meas_points = len(coeffs_dict[Output])
+
+ # Initialize the (cell) array containing the (total) Sobol indices.
+ sobol_array = dict.fromkeys(range(1, max_order+1), [])
+ sobol_cell_array = dict.fromkeys(range(1, max_order+1), [])
+
+ for i_order in range(1, max_order+1):
+ n_comb = math.comb(n_params, i_order)
+
+ sobol_cell_array[i_order] = np.zeros((n_comb, n_meas_points))
+
+ total_sobol_array = np.zeros((n_params, n_meas_points))
+
+ # Initialize the cell to store the names of the variables
+ TotalVariance = np.zeros((n_meas_points))
+
+ # Loop over all measurement points and calculate sobol indices
+ for pIdx in range(n_meas_points):
+
+ # Extract the basis indices (alpha) and coefficients
+ Basis = basis_dict[Output][f'y_{pIdx+1}']
+
+ try:
+ clf_poly = PCEModel.clf_poly[Output][f'y_{pIdx+1}']
+ PCECoeffs = clf_poly.coef_
+ except:
+ PCECoeffs = coeffs_dict[Output][f'y_{pIdx+1}']
+
+ # Compute total variance
+ TotalVariance[pIdx] = np.sum(np.square(PCECoeffs[1:]))
+
+ nzidx = np.where(PCECoeffs != 0)[0]
+ # Set all the Sobol indices equal to zero in the presence of a
+ # null output.
+ if len(nzidx) == 0:
+ # This is buggy.
+ for i_order in range(1, max_order+1):
+ sobol_cell_array[i_order][:, pIdx] = 0
+
+ # Otherwise compute them by summing well-chosen coefficients
+ else:
+ nz_basis = Basis[nzidx]
+ for i_order in range(1, max_order+1):
+ idx = np.where(np.sum(nz_basis > 0, axis=1) == i_order)
+ subbasis = nz_basis[idx]
+ Z = np.array(list(combinations(range(n_params), i_order)))
+
+ for q in range(Z.shape[0]):
+ Zq = Z[q]
+ subsubbasis = subbasis[:, Zq]
+ subidx = np.prod(subsubbasis, axis=1) > 0
+ sum_ind = nzidx[idx[0][subidx]]
+ if TotalVariance[pIdx] == 0.0:
+ sobol_cell_array[i_order][q, pIdx] = 0.0
+ else:
+ sobol = np.sum(np.square(PCECoeffs[sum_ind]))
+ sobol /= TotalVariance[pIdx]
+ sobol_cell_array[i_order][q, pIdx] = sobol
+
+ # Compute the TOTAL Sobol indices.
+ for ParIdx in range(n_params):
+ idx = nz_basis[:, ParIdx] > 0
+ sum_ind = nzidx[idx]
+
+ if TotalVariance[pIdx] == 0.0:
+ total_sobol_array[ParIdx, pIdx] = 0.0
+ else:
+ sobol = np.sum(np.square(PCECoeffs[sum_ind]))
+ sobol /= TotalVariance[pIdx]
+ total_sobol_array[ParIdx, pIdx] = sobol
+
+ # ----- if PCA selected: Compute covariance -----
+ if PCEModel.dim_red_method.lower() == 'pca':
+ cov_Z_p_q = np.zeros((n_params))
+ # Extract the basis indices (alpha) and coefficients for
+ # next component
+ if pIdx < n_meas_points-1:
+ nextBasis = basis_dict[Output][f'y_{pIdx+2}']
+
+ try:
+ clf_poly = PCEModel.clf_poly[Output][f'y_{pIdx+2}']
+ nextPCECoeffs = clf_poly.coef_
+ except:
+ nextPCECoeffs = coeffs_dict[Output][f'y_{pIdx+2}']
+
+ # Choose the common non-zero basis
+ mask = (Basis[:, None] == nextBasis).all(-1).any(-1)
+ similar_basis = Basis[mask]
+ # Compute the TOTAL Sobol indices.
+ for ParIdx in range(n_params):
+ idx = similar_basis[:, ParIdx] > 0
+ try:
+ sum_is = nzidx[idx]
+ cov_Z_p_q[ParIdx] = np.sum(PCECoeffs[sum_ind] *
+ nextPCECoeffs[sum_is])
+ except:
+ cov_Z_p_q[ParIdx] = 0.0
+
+ # Compute the sobol indices according to Ref. 2
+ if PCEModel.dim_red_method.lower() == 'pca':
+ n_c_points = PCEModel.ExpDesign.Y[Output].shape[1]
+ PCA = PCEModel.pca[Output]
+ compPCA = PCA.components_
+ nComp = compPCA.shape[0]
+ var_Z_p = PCA.explained_variance_
+
+ # Extract the sobol index of the components
+ for i_order in range(1, max_order+1):
+ n_comb = math.comb(n_params, i_order)
+ sobol_array[i_order] = np.zeros((n_comb, n_c_points))
+ Z = np.array(list(combinations(range(n_params), i_order)))
+
+ for q in range(Z.shape[0]):
+ S_Z_i = sobol_cell_array[i_order][q]
+
+ for tIdx in range(n_c_points):
+ var_Y_t = np.var(PCEModel.ExpDesign.Y[Output][:, tIdx])
+ if var_Y_t == 0.0:
+ term1, term2 = 0.0, 0.0
+ else:
+ term1 = np.sum([S_Z_i[i]*(var_Z_p[i]*(compPCA[i, tIdx]**2)/var_Y_t) for i in range(nComp)])
+
+ # Term 2
+ # cov_Z_p_q = np.ones((nComp))# TODO: from coeffs
+ Phi_t_p = compPCA[:nComp-1]
+ Phi_t_q = compPCA
+ term2 = 2 * np.sum([cov_Z_p_q[ParIdx] * Phi_t_p[i,tIdx] * Phi_t_q[i,tIdx]/var_Y_t for i in range(nComp-1)])
+
+ sobol_array[i_order][q, tIdx] = term1 #+ term2
+
+ # Compute the TOTAL Sobol indices.
+ total_sobol = np.zeros((n_params, n_c_points))
+ for ParIdx in range(n_params):
+ S_Z_i = total_sobol_array[ParIdx]
+
+ for tIdx in range(n_c_points):
+ var_Y_t = np.var(PCEModel.ExpDesign.Y[Output][:, tIdx])
+ if var_Y_t == 0.0:
+ term1, term2 = 0.0, 0.0
+ else:
+ term1 = 0
+ for i in range(nComp):
+ term1 += S_Z_i[i] * var_Z_p[i] * \
+ (compPCA[i, tIdx]**2) / var_Y_t
+
+ # Term 2
+ # cov_Z_p_q = np.ones((nComp))# TODO: from coeffs
+ Phi_t_p = compPCA[:nComp-1]
+ Phi_t_q = compPCA
+ term2 = 0
+ for i in range(nComp-1):
+ term2 += cov_Z_p_q[ParIdx] * Phi_t_p[i, tIdx] \
+ * Phi_t_q[i, tIdx] / var_Y_t
+ term2 *= 2
+
+ total_sobol[ParIdx, tIdx] = term1 + term2
+
+ self.sobol_cell[Output] = sobol_array
+ self.total_sobol[Output] = total_sobol
+ else:
+ self.sobol_cell[Output] = sobol_cell_array
+ self.total_sobol[Output] = total_sobol_array
+
+ # ---------------- Plot -----------------------
+ par_names = PCEModel.ExpDesign.par_names
+ x_values_orig = PCEModel.ExpDesign.x_values
+
+ cases = ['']
+
+ for case in cases:
+ newpath = (f'Outputs_PostProcessing_{self.name}/')
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ if save_fig:
+ # create a PdfPages object
+ name = case+'_' if 'Valid' in cases else ''
+ pdf = PdfPages('./'+newpath+name+'Sobol_indices.pdf')
+
+ fig = plt.figure()
+
+ for outIdx, Output in enumerate(PCEModel.ModelObj.Output.names):
+
+ # Extract total Sobol indices
+ total_sobol = self.total_sobol[Output]
+
+ # Extract a list of x values
+ if type(x_values_orig) is dict:
+ x = x_values_orig[Output]
+ else:
+ x = x_values_orig
+
+ if plot_type == 'bar':
+ ax = fig.add_axes([0, 0, 1, 1])
+ dict1 = {xlabel: x}
+ dict2 = {param: sobolIndices for param, sobolIndices
+ in zip(par_names, total_sobol)}
+
+ df = pd.DataFrame({**dict1, **dict2})
+ df.plot(x=xlabel, y=par_names, kind="bar", ax=ax, rot=0,
+ colormap='Dark2')
+ ax.set_ylabel('Total Sobol indices, $S^T$')
+
+ else:
+ for i, sobolIndices in enumerate(total_sobol):
+ plt.plot(x, sobolIndices, label=par_names[i],
+ marker='x', lw=2.5)
+
+ plt.ylabel('Total Sobol indices, $S^T$')
+ plt.xlabel(xlabel)
+
+ plt.title(f'Sensitivity analysis of {Output}')
+ if plot_type != 'bar':
+ plt.legend(loc='best', frameon=True)
+
+ # Save indices
+ np.savetxt(f'./{newpath}{name}totalsobol_' +
+ Output.replace('/', '_') + '.csv',
+ total_sobol.T, delimiter=',',
+ header=','.join(par_names), comments='')
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+
+ # Destroy the current plot
+ plt.clf()
+
+ pdf.close()
+
+ return self.sobol_cell, self.total_sobol
+
+ # -------------------------------------------------------------------------
+ def check_reg_quality(self, n_samples=1000, samples=None, save_fig=True):
+ """
+ Checks the quality of the metamodel for single output models based on:
+ https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685
+
+
+ Parameters
+ ----------
+ n_samples : int, optional
+ Number of parameter sets to use for the check. The default is 1000.
+ samples : array of shape (n_samples, n_params), optional
+ Parameter sets to use for the check. The default is None.
+ save_fig : bool, optional
+ Whether to save the figures. The default is True.
+
+ Returns
+ -------
+ None.
+
+ """
+ MetaModel = self.MetaModel
+
+ if samples is None:
+ self.n_samples = n_samples
+ samples = self._get_sample()
+ else:
+ self.n_samples = samples.shape[0]
+
+ # Evaluate the original and the surrogate model
+ y_val = self._eval_model(samples, key_str='valid')
+ y_pce_val, _ = MetaModel.eval_metamodel(samples=samples)
+
+ # Open a pdf for the plots
+ if save_fig:
+ newpath = (r'Outputs_PostProcessing_{0}/'.format(self.name))
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # Fit the data(train the model)
+ for key in y_pce_val.keys():
+
+ y_pce_val_ = y_pce_val[key]
+ y_val_ = y_val[key]
+
+ # ------ Residuals vs. predicting variables ------
+ # Check the assumptions of linearity and independence
+ fig1 = plt.figure()
+ plt.title(key+": Residuals vs. Predicting variables")
+ residuals = y_val_ - y_pce_val_
+ plt.scatter(x=y_val_, y=residuals, color='blue', edgecolor='k')
+ plt.grid(True)
+ xmin, xmax = min(y_val_), max(y_val_)
+ plt.hlines(y=0, xmin=xmin*0.9, xmax=xmax*1.1, color='red', lw=3,
+ linestyle='--')
+ plt.xlabel(key)
+ plt.ylabel('Residuals')
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig1.savefig(f'./{newpath}/Residuals_vs_PredVariables.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ------ Fitted vs. residuals ------
+ # Check the assumptions of linearity and independence
+ fig2 = plt.figure()
+ plt.title(key+": Residuals vs. predicting variables")
+ plt.scatter(x=y_pce_val_, y=residuals, color='blue', edgecolor='k')
+ plt.grid(True)
+ xmin, xmax = min(y_val_), max(y_val_)
+ plt.hlines(y=0, xmin=xmin*0.9, xmax=xmax*1.1, color='red', lw=3,
+ linestyle='--')
+ plt.xlabel(key)
+ plt.ylabel('Residuals')
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig2.savefig(f'./{newpath}/Fitted_vs_Residuals.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ------ Histogram of normalized residuals ------
+ fig3 = plt.figure()
+ resid_pearson = residuals / (max(residuals)-min(residuals))
+ plt.hist(resid_pearson, bins=20, edgecolor='k')
+ plt.ylabel('Count')
+ plt.xlabel('Normalized residuals')
+ plt.title(f"{key}: Histogram of normalized residuals")
+
+ # Normality (Shapiro-Wilk) test of the residuals
+ ax = plt.gca()
+ _, p = stats.shapiro(residuals)
+ if p < 0.01:
+ annText = "The residuals seem to come from Gaussian process."
+ else:
+ annText = "The normality assumption may not hold."
+ at = AnchoredText(annText, prop=dict(size=30), frameon=True,
+ loc='upper left')
+ at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
+ ax.add_artist(at)
+
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig3.savefig(f'./{newpath}/Hist_NormResiduals.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ------ Q-Q plot of the normalized residuals ------
+ plt.figure()
+ fig4 = qqplot(resid_pearson, line='45', fit='True')
+ plt.xticks()
+ plt.yticks()
+ plt.xlabel("Theoretical quantiles")
+ plt.ylabel("Sample quantiles")
+ plt.title(key+": Q-Q plot of normalized residuals")
+ plt.grid(True)
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig4.savefig(f'./{newpath}/QQPlot_NormResiduals.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # -------------------------------------------------------------------------
+ def eval_pce_model_3d(self, save_fig=True):
+
+ self.n_samples = 1000
+
+ PCEModel = self.MetaModel
+ Model = self.MetaModel.ModelObj
+ n_samples = self.n_samples
+
+ # Create 3D-Grid
+ # TODO: Make it general
+ x = np.linspace(-5, 10, n_samples)
+ y = np.linspace(0, 15, n_samples)
+
+ X, Y = np.meshgrid(x, y)
+ PCE_Z = np.zeros((self.n_samples, self.n_samples))
+ Model_Z = np.zeros((self.n_samples, self.n_samples))
+
+ for idxMesh in range(self.n_samples):
+ sample_mesh = np.vstack((X[:, idxMesh], Y[:, idxMesh])).T
+
+ univ_p_val = PCEModel.univ_basis_vals(sample_mesh)
+
+ for Outkey, ValuesDict in PCEModel.coeffs_dict.items():
+
+ pce_out_mean = np.zeros((len(sample_mesh), len(ValuesDict)))
+ pce_out_std = np.zeros((len(sample_mesh), len(ValuesDict)))
+ model_outs = np.zeros((len(sample_mesh), len(ValuesDict)))
+
+ for Inkey, InIdxValues in ValuesDict.items():
+ idx = int(Inkey.split('_')[1]) - 1
+ basis_deg_ind = PCEModel.basis_dict[Outkey][Inkey]
+ clf_poly = PCEModel.clf_poly[Outkey][Inkey]
+
+ PSI_Val = PCEModel.create_psi(basis_deg_ind, univ_p_val)
+
+ # Perdiction with error bar
+ y_mean, y_std = clf_poly.predict(PSI_Val, return_std=True)
+
+ pce_out_mean[:, idx] = y_mean
+ pce_out_std[:, idx] = y_std
+
+ # Model evaluation
+ model_out_dict, _ = Model.run_model_parallel(sample_mesh,
+ key_str='Valid3D')
+ model_outs[:, idx] = model_out_dict[Outkey].T
+
+ PCE_Z[:, idxMesh] = y_mean
+ Model_Z[:, idxMesh] = model_outs[:, 0]
+
+ # ---------------- 3D plot for PCEModel -----------------------
+ fig_PCE = plt.figure()
+ ax = plt.axes(projection='3d')
+ ax.plot_surface(X, Y, PCE_Z, rstride=1, cstride=1,
+ cmap='viridis', edgecolor='none')
+ ax.set_title('PCEModel')
+ ax.set_xlabel('$x_1$')
+ ax.set_ylabel('$x_2$')
+ ax.set_zlabel('$f(x_1,x_2)$')
+
+ plt.grid()
+ plt.show()
+
+ if save_fig:
+ # Saving the figure
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # save the figure to file
+ fig_PCE.savefig(f'./{newpath}/3DPlot_PCEModel.pdf', format="pdf",
+ bbox_inches='tight')
+ plt.close(fig_PCE)
+
+ # ---------------- 3D plot for Model -----------------------
+ fig_Model = plt.figure()
+ ax = plt.axes(projection='3d')
+ ax.plot_surface(X, Y, PCE_Z, rstride=1, cstride=1,
+ cmap='viridis', edgecolor='none')
+ ax.set_title('Model')
+ ax.set_xlabel('$x_1$')
+ ax.set_ylabel('$x_2$')
+ ax.set_zlabel('$f(x_1,x_2)$')
+
+ plt.grid()
+ plt.show()
+
+ if save_fig:
+ # Save the figure
+ fig_Model.savefig(f'./{newpath}/3DPlot_Model.pdf', format="pdf",
+ bbox_inches='tight')
+ plt.close(fig_Model)
+
+ return
+
+ # -------------------------------------------------------------------------
+ def _compute_pce_moments(self):
+ """
+ Computes the first two moments using the PCE-based meta-model.
+
+ Returns
+ -------
+ None.
+
+ """
+
+ MetaModel = self.MetaModel
+ self.pce_means = {}
+ self.pce_stds = {}
+
+ for Outkey, ValuesDict in MetaModel.coeffs_dict.items():
+
+ pce_mean = np.zeros((len(ValuesDict)))
+ pce_var = np.zeros((len(ValuesDict)))
+
+ for Inkey, InIdxValues in ValuesDict.items():
+ idx = int(Inkey.split('_')[1]) - 1
+ coeffs = MetaModel.coeffs_dict[Outkey][Inkey]
+
+ # Mean = c_0
+ if coeffs[0] != 0:
+ pce_mean[idx] = coeffs[0]
+ else:
+ pce_mean[idx] = MetaModel.clf_poly[Outkey][Inkey].intercept_
+
+ # Var = sum(coeffs[1:]**2)
+ pce_var[idx] = np.sum(np.square(coeffs[1:]))
+
+ # Back transformation if PCA is selected.
+ if MetaModel.dim_red_method.lower() == 'pca':
+ PCA = MetaModel.pca[Outkey]
+ self.pce_means[Outkey] = PCA.mean_
+ self.pce_means[Outkey] += np.dot(pce_mean, PCA.components_)
+ self.pce_stds[Outkey] = np.sqrt(np.dot(pce_var,
+ PCA.components_**2))
+ else:
+ self.pce_means[Outkey] = pce_mean
+ self.pce_stds[Outkey] = np.sqrt(pce_var)
+
+ # Print a report table
+ print("\n>>>>> Moments of {} <<<<<".format(Outkey))
+ print("\nIndex | Mean | Std. deviation")
+ print('-'*35)
+ print('\n'.join(f'{i+1} | {k:.3e} | {j:.3e}' for i, (k, j)
+ in enumerate(zip(self.pce_means[Outkey],
+ self.pce_stds[Outkey]))))
+ print('-'*40)
+
+ # -------------------------------------------------------------------------
+ def _get_sample(self, n_samples=None):
+ """
+ Generates random samples taken from the input parameter space.
+
+ Returns
+ -------
+ samples : array of shape (n_samples, n_params)
+ Generated samples.
+
+ """
+ if n_samples is None:
+ n_samples = self.n_samples
+ PCEModel = self.MetaModel
+ self.samples = PCEModel.ExpDesign.generate_samples(n_samples, 'random')
+ return self.samples
+
+ # -------------------------------------------------------------------------
+ def _eval_model(self, samples=None, key_str='Valid'):
+ """
+ Evaluates Forward Model for the given number of self.samples or given
+ samples.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params), optional
+ Samples to evaluate the model at. The default is None.
+ key_str : str, optional
+ Key string pass to the model. The default is 'Valid'.
+
+ Returns
+ -------
+ model_outs : dict
+ Dictionary of results.
+
+ """
+ Model = self.MetaModel.ModelObj
+
+ if samples is None:
+ samples = self._get_sample()
+ self.samples = samples
+ else:
+ self.n_samples = len(samples)
+
+ model_outs, _ = Model.run_model_parallel(samples, key_str=key_str)
+
+ return model_outs
+
+ # -------------------------------------------------------------------------
+ def _plot_validation(self, save_fig=True):
+ """
+ Plots outputs for visual comparison of metamodel outputs with that of
+ the (full) original model.
+
+ Parameters
+ ----------
+ save_fig : bool, optional
+ Save the plots. The default is True.
+
+ Returns
+ -------
+ None.
+
+ """
+ PCEModel = self.MetaModel
+
+ # get the samples
+ x_val = self.samples
+ y_pce_val = self.pce_out_mean
+ y_val = self.model_out_dict
+
+ # Open a pdf for the plots
+ if save_fig:
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # create a PdfPages object
+ pdf1 = PdfPages(f'./{newpath}/Model_vs_PCEModel.pdf')
+
+ fig = plt.figure()
+ # Fit the data(train the model)
+ for key in y_pce_val.keys():
+
+ y_pce_val_ = y_pce_val[key]
+ y_val_ = y_val[key]
+
+ regression_model = LinearRegression()
+ regression_model.fit(y_pce_val_, y_val_)
+
+ # Predict
+ x_new = np.linspace(np.min(y_pce_val_), np.max(y_val_), 100)
+ y_predicted = regression_model.predict(x_new[:, np.newaxis])
+
+ plt.scatter(y_pce_val_, y_val_, color='gold', linewidth=2)
+ plt.plot(x_new, y_predicted, color='k')
+
+ # Calculate the adjusted R_squared and RMSE
+ # the total number of explanatory variables in the model
+ # (not including the constant term)
+ length_list = []
+ for key, value in PCEModel.coeffs_dict[key].items():
+ length_list.append(len(value))
+ n_predictors = min(length_list)
+ n_samples = x_val.shape[0]
+
+ R2 = r2_score(y_pce_val_, y_val_)
+ AdjR2 = 1 - (1 - R2) * (n_samples - 1) / \
+ (n_samples - n_predictors - 1)
+ rmse = mean_squared_error(y_pce_val_, y_val_, squared=False)
+
+ plt.annotate(f'RMSE = {rmse:.3f}\n Adjusted $R^2$ = {AdjR2:.3f}',
+ xy=(0.05, 0.85), xycoords='axes fraction')
+
+ plt.ylabel("Original Model")
+ plt.xlabel("PCE Model")
+ plt.grid()
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ pdf1.savefig(fig, bbox_inches='tight')
+
+ # Destroy the current plot
+ plt.clf()
+
+ # Close the pdfs
+ pdf1.close()
+
+ # -------------------------------------------------------------------------
+ def _plot_validation_multi(self, x_values=[], x_axis="x [m]", save_fig=True):
+ """
+ Plots outputs for visual comparison of metamodel outputs with that of
+ the (full) multioutput original model
+
+ Parameters
+ ----------
+ x_values : list or array, optional
+ List of x values. The default is [].
+ x_axis : str, optional
+ Label of the x axis. The default is "x [m]".
+ save_fig : bool, optional
+ Whether to save the figures. The default is True.
+
+ Returns
+ -------
+ None.
+
+ """
+ Model = self.MetaModel.ModelObj
+
+ if save_fig:
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # create a PdfPages object
+ pdf = PdfPages(f'./{newpath}/Model_vs_PCEModel.pdf')
+
+ # List of markers and colors
+ color = cycle((['b', 'g', 'r', 'y', 'k']))
+ marker = cycle(('x', 'd', '+', 'o', '*'))
+
+ fig = plt.figure()
+ # Plot the model vs PCE model
+ for keyIdx, key in enumerate(Model.Output.names):
+
+ y_pce_val = self.pce_out_mean[key]
+ y_pce_val_std = self.pce_out_std[key]
+ y_val = self.model_out_dict[key]
+ try:
+ x = self.model_out_dict['x_values'][key]
+ except IndexError:
+ x = x_values
+
+ for idx in range(y_val.shape[0]):
+ Color = next(color)
+ Marker = next(marker)
+
+ plt.plot(x, y_val[idx], color=Color, marker=Marker,
+ label='$Y_{%s}^M$'%(idx+1))
+
+ plt.plot(x, y_pce_val[idx], color=Color, marker=Marker,
+ linestyle='--',
+ label='$Y_{%s}^{PCE}$'%(idx+1))
+ plt.fill_between(x, y_pce_val[idx]-1.96*y_pce_val_std[idx],
+ y_pce_val[idx]+1.96*y_pce_val_std[idx],
+ color=Color, alpha=0.15)
+
+ # Calculate the RMSE
+ rmse = mean_squared_error(y_pce_val, y_val, squared=False)
+ R2 = r2_score(y_pce_val[idx].reshape(-1, 1),
+ y_val[idx].reshape(-1, 1))
+
+ plt.annotate(f'RMSE = {rmse:.3f}\n $R^2$ = {R2:.3f}',
+ xy=(0.2, 0.75), xycoords='axes fraction')
+
+ plt.ylabel(key)
+ plt.xlabel(x_axis)
+ plt.legend(loc='best')
+ plt.grid()
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ pdf.close()
+
+ # Zip the subdirectories
+ Model.zip_subdirs(f'{Model.name}valid', f'{Model.name}valid_')</code></pre>
+</details>
+<h3>Methods</h3>
+<dl>
+<dt id="post_processing.PostProcessing.plot_moments"><code class="name flex">
+<span>def <span class="ident">plot_moments</span></span>(<span>self, xlabel='Time [s]', plot_type=None, save_fig=True)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Plots the moments in a pdf format in the directory
+<code>Outputs_PostProcessing</code>.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>xlabel</code></strong> : <code>str</code>, optional</dt>
+<dd>String to be displayed as x-label. The default is <code>'Time [s]'</code>.</dd>
+<dt><strong><code>plot_type</code></strong> : <code>str</code>, optional</dt>
+<dd>Options: bar or line. The default is <code>None</code>.</dd>
+<dt><strong><code>save_fig</code></strong> : <code>bool</code>, optional</dt>
+<dd>Save figure or not. The default is <code>True</code>.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>pce_means</code></strong> : <code>dict</code></dt>
+<dd>Mean of the model outputs.</dd>
+<dt><strong><code>pce_means</code></strong> : <code>dict</code></dt>
+<dd>Standard deviation of the model outputs.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def plot_moments(self, xlabel='Time [s]', plot_type=None, save_fig=True):
+ """
+ Plots the moments in a pdf format in the directory
+ `Outputs_PostProcessing`.
+
+ Parameters
+ ----------
+ xlabel : str, optional
+ String to be displayed as x-label. The default is `'Time [s]'`.
+ plot_type : str, optional
+ Options: bar or line. The default is `None`.
+ save_fig : bool, optional
+ Save figure or not. The default is `True`.
+
+ Returns
+ -------
+ pce_means: dict
+ Mean of the model outputs.
+ pce_means: dict
+ Standard deviation of the model outputs.
+
+ """
+
+ bar_plot = True if plot_type == 'bar' else False
+ meta_model_type = self.MetaModel.meta_model_type
+ Model = self.MetaModel.ModelObj
+
+ # Read Monte-Carlo reference
+ self.mc_reference = Model.read_mc_reference()
+ print(self.mc_reference)
+
+ # Set the x values
+ x_values_orig = self.MetaModel.ExpDesign.x_values
+
+ # Compute the moments with the PCEModel object
+ self._compute_pce_moments()
+
+ # Get the variables
+ out_names = Model.Output.names
+
+ # Open a pdf for the plots
+ if save_fig:
+ newpath = (f'Outputs_PostProcessing_{self.name}/')
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # create a PdfPages object
+ pdf = PdfPages(f'./{newpath}Mean_Std_PCE.pdf')
+
+ # Plot the best fit line, set the linewidth (lw), color and
+ # transparency (alpha) of the line
+ for idx, key in enumerate(out_names):
+ fig, ax = plt.subplots(nrows=1, ncols=2)
+
+ # Extract mean and std
+ mean_data = self.pce_means[key]
+ std_data = self.pce_stds[key]
+
+ # Extract a list of x values
+ if type(x_values_orig) is dict:
+ x = x_values_orig[key]
+ else:
+ x = x_values_orig
+
+ # Plot: bar plot or line plot
+ if bar_plot:
+ ax[0].bar(list(map(str, x)), mean_data, color='b',
+ width=0.25)
+ ax[1].bar(list(map(str, x)), std_data, color='b',
+ width=0.25)
+ ax[0].legend(labels=[meta_model_type])
+ ax[1].legend(labels=[meta_model_type])
+ else:
+ ax[0].plot(x, mean_data, lw=3, color='k', marker='x',
+ label=meta_model_type)
+ ax[1].plot(x, std_data, lw=3, color='k', marker='x',
+ label=meta_model_type)
+
+ if self.mc_reference is not None:
+ if bar_plot:
+ ax[0].bar(list(map(str, x)), self.mc_reference['mean'],
+ color='r', width=0.25)
+ ax[1].bar(list(map(str, x)), self.mc_reference['std'],
+ color='r', width=0.25)
+ ax[0].legend(labels=[meta_model_type])
+ ax[1].legend(labels=[meta_model_type])
+ else:
+ ax[0].plot(x, self.mc_reference['mean'], lw=3, marker='x',
+ color='r', label='Ref.')
+ ax[1].plot(x, self.mc_reference['std'], lw=3, marker='x',
+ color='r', label='Ref.')
+
+ # Label the axes and provide a title
+ ax[0].set_xlabel(xlabel)
+ ax[1].set_xlabel(xlabel)
+ ax[0].set_ylabel(key)
+ ax[1].set_ylabel(key)
+
+ # Provide a title
+ ax[0].set_title('Mean of ' + key)
+ ax[1].set_title('Std of ' + key)
+
+ if not bar_plot:
+ ax[0].legend(loc='best')
+ ax[1].legend(loc='best')
+
+ plt.tight_layout()
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+
+ # Destroy the current plot
+ plt.clf()
+
+ pdf.close()
+
+ return self.pce_means, self.pce_stds</code></pre>
+</details>
+</dd>
+<dt id="post_processing.PostProcessing.valid_metamodel"><code class="name flex">
+<span>def <span class="ident">valid_metamodel</span></span>(<span>self, n_samples=1, samples=None, x_axis='Time [s]')</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Evaluates and plots the meta model and the PCEModel outputs for the
+given number of samples or the given samples.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>n_samples</code></strong> : <code>int</code>, optional</dt>
+<dd>Number of samples to be evaluated. The default is 1.</dd>
+<dt><strong><code>samples</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code>, optional</dt>
+<dd>Samples to be evaluated. The default is None.</dd>
+<dt><strong><code>x_axis</code></strong> : <code>str</code>, optional</dt>
+<dd>Label of x axis. The default is <code>'Time [s]'</code>.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<p>None.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def valid_metamodel(self, n_samples=1, samples=None, x_axis='Time [s]'):
+ """
+ Evaluates and plots the meta model and the PCEModel outputs for the
+ given number of samples or the given samples.
+
+ Parameters
+ ----------
+ n_samples : int, optional
+ Number of samples to be evaluated. The default is 1.
+ samples : array of shape (n_samples, n_params), optional
+ Samples to be evaluated. The default is None.
+ x_axis : str, optional
+ Label of x axis. The default is `'Time [s]'`.
+
+ Returns
+ -------
+ None.
+
+ """
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+
+ if samples is None:
+ self.n_samples = n_samples
+ samples = self._get_sample()
+ else:
+ self.n_samples = samples.shape[0]
+
+ # Extract x_values
+ x_values = MetaModel.ExpDesign.x_values
+
+ self.model_out_dict = self._eval_model(samples, key_str='valid')
+ self.pce_out_mean, self.pce_out_std = MetaModel.eval_metamodel(samples)
+
+ try:
+ key = Model.Output.names[1]
+ except IndexError:
+ key = Model.Output.names[0]
+
+ n_obs = self.model_out_dict[key].shape[1]
+
+ if n_obs == 1:
+ self._plot_validation()
+ else:
+ self._plot_validation_multi(x_values=x_values, x_axis=x_axis)</code></pre>
+</details>
+</dd>
+<dt id="post_processing.PostProcessing.check_accuracy"><code class="name flex">
+<span>def <span class="ident">check_accuracy</span></span>(<span>self, n_samples=None, samples=None, outputs=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Checks accuracy of the metamodel by computing the root mean square
+error and validation error for all outputs.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>n_samples</code></strong> : <code>int</code>, optional</dt>
+<dd>Number of samples. The default is None.</dd>
+<dt><strong><code>samples</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code>, optional</dt>
+<dd>Parameter sets to be checked. The default is None.</dd>
+<dt><strong><code>outputs</code></strong> : <code>dict</code>, optional</dt>
+<dd>Output dictionary with model outputs for all given output types in
+<code>Model.Output.names</code>. The default is None.</dd>
+</dl>
+<h2 id="raises">Raises</h2>
+<dl>
+<dt><code>Exception</code></dt>
+<dd>When neither n_samples nor samples are provided.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>rmse</code></strong> : <code>dict</code></dt>
+<dd>Root mean squared error for each output.</dd>
+<dt><strong><code>valid_error</code></strong> : <code>dict</code></dt>
+<dd>Validation error for each output.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def check_accuracy(self, n_samples=None, samples=None, outputs=None):
+ """
+ Checks accuracy of the metamodel by computing the root mean square
+ error and validation error for all outputs.
+
+ Parameters
+ ----------
+ n_samples : int, optional
+ Number of samples. The default is None.
+ samples : array of shape (n_samples, n_params), optional
+ Parameter sets to be checked. The default is None.
+ outputs : dict, optional
+ Output dictionary with model outputs for all given output types in
+ `Model.Output.names`. The default is None.
+
+ Raises
+ ------
+ Exception
+ When neither n_samples nor samples are provided.
+
+ Returns
+ -------
+ rmse: dict
+ Root mean squared error for each output.
+ valid_error : dict
+ Validation error for each output.
+
+ """
+ MetaModel = self.MetaModel
+ Model = MetaModel.ModelObj
+
+ # Set the number of samples
+ if n_samples:
+ self.n_samples = n_samples
+ elif samples is not None:
+ self.n_samples = samples.shape[0]
+ else:
+ raise Exception("Please provide either samples or pass number of "
+ "samples!")
+
+ # Generate random samples if necessary
+ Samples = self._get_sample() if samples is None else samples
+
+ # Run the original model with the generated samples
+ if outputs is None:
+ outputs = self._eval_model(Samples, key_str='validSet')
+
+ # Run the PCE model with the generated samples
+ pce_outputs, _ = MetaModel.eval_metamodel(samples=Samples)
+
+ self.rmse = {}
+ self.valid_error = {}
+ # Loop over the keys and compute RMSE error.
+ for key in Model.Output.names:
+ # Root mena square
+ self.rmse[key] = mean_squared_error(outputs[key], pce_outputs[key],
+ squared=False,
+ multioutput='raw_values')
+ # Validation error
+ self.valid_error[key] = (self.rmse[key]**2 / self.n_samples) / \
+ np.var(outputs[key], ddof=1, axis=0)
+
+ # Print a report table
+ print("\n>>>>> Errors of {} <<<<<".format(key))
+ print("\nIndex | RMSE | Validation Error")
+ print('-'*35)
+ print('\n'.join(f'{i+1} | {k:.3e} | {j:.3e}' for i, (k, j)
+ in enumerate(zip(self.rmse[key],
+ self.valid_error[key]))))
+ # Save error dicts in PCEModel object
+ self.MetaModel.rmse = self.rmse
+ self.MetaModel.valid_error = self.valid_error
+
+ return self.rmse, self.valid_error</code></pre>
+</details>
+</dd>
+<dt id="post_processing.PostProcessing.plot_seq_design_diagnostics"><code class="name flex">
+<span>def <span class="ident">plot_seq_design_diagnostics</span></span>(<span>self, ref_BME_KLD=None, save_fig=True)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence
+(KLD) for the sequential design.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>ref_BME_KLD</code></strong> : <code>array</code>, optional</dt>
+<dd>Reference BME and KLD . The default is <code>None</code>.</dd>
+<dt><strong><code>save_fig</code></strong> : <code>bool</code>, optional</dt>
+<dd>Whether to save the figures. The default is <code>True</code>.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<p>None.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def plot_seq_design_diagnostics(self, ref_BME_KLD=None, save_fig=True):
+ """
+ Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence
+ (KLD) for the sequential design.
+
+ Parameters
+ ----------
+ ref_BME_KLD : array, optional
+ Reference BME and KLD . The default is `None`.
+ save_fig : bool, optional
+ Whether to save the figures. The default is `True`.
+
+ Returns
+ -------
+ None.
+
+ """
+ PCEModel = self.MetaModel
+ n_init_samples = PCEModel.ExpDesign.n_init_samples
+ n_total_samples = PCEModel.ExpDesign.X.shape[0]
+
+ if save_fig:
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # create a PdfPages object
+ pdf = PdfPages(f'./{newpath}/seqPCEModelDiagnostics.pdf')
+
+ plotList = ['Modified LOO error', 'Validation error', 'KLD', 'BME',
+ 'RMSEMean', 'RMSEStd', 'Hellinger distance']
+ seqList = [PCEModel.SeqModifiedLOO, PCEModel.seqValidError,
+ PCEModel.SeqKLD, PCEModel.SeqBME, PCEModel.seqRMSEMean,
+ PCEModel.seqRMSEStd, PCEModel.SeqDistHellinger]
+
+ markers = ('x', 'o', 'd', '*', '+')
+ colors = ('k', 'darkgreen', 'b', 'navy', 'darkred')
+
+ # Plot the evolution of the diagnostic criteria of the
+ # Sequential Experimental Design.
+ for plotidx, plot in enumerate(plotList):
+ fig, ax = plt.subplots()
+ seq_dict = seqList[plotidx]
+ name_util = list(seq_dict.keys())
+
+ if len(name_util) == 0:
+ continue
+
+ # Box plot when Replications have been detected.
+ if any(int(name.split("rep_", 1)[1]) > 1 for name in name_util):
+ # Extract the values from dict
+ sorted_seq_opt = {}
+ # Number of replications
+ n_reps = PCEModel.ExpDesign.nReprications
+
+ # Get the list of utility function names
+ # Handle if only one UtilityFunction is provided
+ if not isinstance(PCEModel.ExpDesign.UtilityFunction, list):
+ util_funcs = [PCEModel.ExpDesign.UtilityFunction]
+ else:
+ util_funcs = PCEModel.ExpDesign.UtilityFunction
+
+ for util in util_funcs:
+ sortedSeq = {}
+ # min number of runs available from reps
+ n_runs = min([seq_dict[f'{util}_rep_{i+1}'].shape[0]
+ for i in range(n_reps)])
+
+ for runIdx in range(n_runs):
+ values = []
+ for key in seq_dict.keys():
+ if util in key:
+ values.append(seq_dict[key][runIdx].mean())
+ sortedSeq['SeqItr_'+str(runIdx)] = np.array(values)
+ sorted_seq_opt[util] = sortedSeq
+
+ # BoxPlot
+ def draw_plot(data, labels, edge_color, fill_color, idx):
+ pos = labels - (idx-1)
+ bp = plt.boxplot(data, positions=pos, labels=labels,
+ patch_artist=True, sym='', widths=0.75)
+ elements = ['boxes', 'whiskers', 'fliers', 'means',
+ 'medians', 'caps']
+ for element in elements:
+ plt.setp(bp[element], color=edge_color[idx])
+
+ for patch in bp['boxes']:
+ patch.set(facecolor=fill_color[idx])
+
+ if PCEModel.ExpDesign.n_new_samples != 1:
+ step1 = PCEModel.ExpDesign.n_new_samples
+ step2 = 1
+ else:
+ step1 = 5
+ step2 = 5
+ edge_color = ['red', 'blue', 'green']
+ fill_color = ['tan', 'cyan', 'lightgreen']
+ plot_label = plot
+ # Plot for different Utility Functions
+ for idx, util in enumerate(util_funcs):
+ all_errors = np.empty((n_reps, 0))
+
+ for key in list(sorted_seq_opt[util].keys()):
+ errors = sorted_seq_opt.get(util, {}).get(key)[:, None]
+ all_errors = np.hstack((all_errors, errors))
+
+ # Special cases for BME and KLD
+ if plot == 'KLD' or plot == 'BME':
+ # BME convergence if refBME is provided
+ if ref_BME_KLD is not None:
+ if plot == 'BME':
+ refValue = ref_BME_KLD[0]
+ plot_label = r'$BME/BME^{Ref.}$'
+ if plot == 'KLD':
+ refValue = ref_BME_KLD[1]
+ plot_label = '$D_{KL}[p(\theta|y_*),p(\theta)]'\
+ ' / D_{KL}^{Ref.}[p(\theta|y_*), '\
+ 'p(\theta)]$'
+
+ # Difference between BME/KLD and the ref. values
+ all_errors = np.divide(all_errors,
+ np.full((all_errors.shape),
+ refValue))
+
+ # Plot baseline for zero, i.e. no difference
+ plt.axhline(y=1.0, xmin=0, xmax=1, c='green',
+ ls='--', lw=2)
+
+ # Plot each UtilFuncs
+ labels = np.arange(n_init_samples, n_total_samples+1, step1)
+ draw_plot(all_errors[:, ::step2], labels, edge_color,
+ fill_color, idx)
+
+ plt.xticks(labels, labels)
+ # Set the major and minor locators
+ ax.xaxis.set_major_locator(ticker.AutoLocator())
+ ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ ax.xaxis.grid(True, which='major', linestyle='-')
+ ax.xaxis.grid(True, which='minor', linestyle='--')
+
+ # Legend
+ legend_elements = []
+ for idx, util in enumerate(util_funcs):
+ legend_elements.append(Patch(facecolor=fill_color[idx],
+ edgecolor=edge_color[idx],
+ label=util))
+ plt.legend(handles=legend_elements[::-1], loc='best')
+
+ if plot != 'BME' and plot != 'KLD':
+ plt.yscale('log')
+ plt.autoscale(True)
+ plt.xlabel('\\# of training samples')
+ plt.ylabel(plot_label)
+ plt.title(plot)
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+ # Save arrays into files
+ f = open(f'./{newpath}/Seq{plot}.txt', 'w')
+ f.write(str(sorted_seq_opt))
+ f.close()
+ else:
+ for idx, name in enumerate(name_util):
+ seq_values = seq_dict[name]
+ if PCEModel.ExpDesign.n_new_samples != 1:
+ step = PCEModel.ExpDesign.n_new_samples
+ else:
+ step = 1
+ x_idx = np.arange(n_init_samples, n_total_samples+1, step)
+ if n_total_samples not in x_idx:
+ x_idx = np.hstack((x_idx, n_total_samples))
+
+ if plot == 'KLD' or plot == 'BME':
+ # BME convergence if refBME is provided
+ if ref_BME_KLD is not None:
+ if plot == 'BME':
+ refValue = ref_BME_KLD[0]
+ plot_label = r'$BME/BME^{Ref.}$'
+ if plot == 'KLD':
+ refValue = ref_BME_KLD[1]
+ plot_label = '$D_{KL}[p(\theta|y_*),p(\theta)]'\
+ ' / D_{KL}^{Ref.}[p(\theta|y_*), '\
+ 'p(\theta)]$'
+
+ # Difference between BME/KLD and the ref. values
+ values = np.divide(seq_values,
+ np.full((seq_values.shape),
+ refValue))
+
+ # Plot baseline for zero, i.e. no difference
+ plt.axhline(y=1.0, xmin=0, xmax=1, c='green',
+ ls='--', lw=2)
+
+ # Set the limits
+ plt.ylim([1e-1, 1e1])
+
+ # Create the plots
+ plt.semilogy(x_idx, values, marker=markers[idx],
+ color=colors[idx], ls='--', lw=2,
+ label=name.split("_rep", 1)[0])
+ else:
+ plot_label = plot
+
+ # Create the plots
+ plt.plot(x_idx, seq_values, marker=markers[idx],
+ color=colors[idx], ls='--', lw=2,
+ label=name.split("_rep", 1)[0])
+
+ else:
+ plot_label = plot
+ seq_values = np.nan_to_num(seq_values)
+
+ # Plot the error evolution for each output
+ for i in range(seq_values.shape[1]):
+ plt.semilogy(x_idx, seq_values[:, i], ls='--',
+ lw=2, marker=markers[idx],
+ color=colors[idx], alpha=0.15)
+
+ plt.semilogy(x_idx, seq_values, marker=markers[idx],
+ ls='--', lw=2, color=colors[idx],
+ label=name.split("_rep", 1)[0])
+
+ # Set the major and minor locators
+ ax.xaxis.set_major_locator(ticker.AutoLocator())
+ ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ ax.xaxis.grid(True, which='major', linestyle='-')
+ ax.xaxis.grid(True, which='minor', linestyle='--')
+
+ ax.tick_params(axis='both', which='major', direction='in',
+ width=3, length=10)
+ ax.tick_params(axis='both', which='minor', direction='in',
+ width=2, length=8)
+ plt.xlabel('Number of runs')
+ plt.ylabel(plot_label)
+ plt.title(plot)
+ plt.legend(frameon=True)
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ---------------- Saving arrays into files ---------------
+ np.save(f'./{newpath}/Seq{plot}.npy', seq_values)
+
+ # Close the pdf
+ pdf.close()
+ return</code></pre>
+</details>
+</dd>
+<dt id="post_processing.PostProcessing.sobol_indices"><code class="name flex">
+<span>def <span class="ident">sobol_indices</span></span>(<span>self, xlabel='Time [s]', plot_type=None, save_fig=True)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Provides Sobol indices as a sensitivity measure to infer the importance
+of the input parameters. See Eq. 27 in [1] for more details. For the
+case with Principal component analysis refer to [2].</p>
+<p>[1] Global sensitivity analysis: A flexible and efficient framework
+with an example from stochastic hydrogeology S. Oladyshkin, F.P.
+de Barros, W. Nowak
+<a href="https://doi.org/10.1016/j.advwatres.2011.11.001">https://doi.org/10.1016/j.advwatres.2011.11.001</a></p>
+<p>[2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
+component analysis and sparse polynomial chaos expansions for global
+sensitivity analysis and model calibration: Application to urban
+drainage simulation. Reliability Engineering & System Safety, 195,
+p.106737.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>xlabel</code></strong> : <code>str</code>, optional</dt>
+<dd>Label of the x-axis. The default is <code>'Time [s]'</code>.</dd>
+<dt><strong><code>plot_type</code></strong> : <code>str</code>, optional</dt>
+<dd>Plot type. The default is <code>None</code>. This corresponds to line plot.
+Bar chart can be selected by <code>bar</code>.</dd>
+<dt><strong><code>save_fig</code></strong> : <code>bool</code>, optional</dt>
+<dd>Whether to save the figures. The default is <code>True</code>.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>sobol_cell</code></strong> : <code>dict</code></dt>
+<dd>Sobol indices.</dd>
+<dt><strong><code>total_sobol</code></strong> : <code>dict</code></dt>
+<dd>Total Sobol indices.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def sobol_indices(self, xlabel='Time [s]', plot_type=None, save_fig=True):
+ """
+ Provides Sobol indices as a sensitivity measure to infer the importance
+ of the input parameters. See Eq. 27 in [1] for more details. For the
+ case with Principal component analysis refer to [2].
+
+ [1] Global sensitivity analysis: A flexible and efficient framework
+ with an example from stochastic hydrogeology S. Oladyshkin, F.P.
+ de Barros, W. Nowak https://doi.org/10.1016/j.advwatres.2011.11.001
+
+ [2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
+ component analysis and sparse polynomial chaos expansions for global
+ sensitivity analysis and model calibration: Application to urban
+ drainage simulation. Reliability Engineering & System Safety, 195,
+ p.106737.
+
+ Parameters
+ ----------
+ xlabel : str, optional
+ Label of the x-axis. The default is `'Time [s]'`.
+ plot_type : str, optional
+ Plot type. The default is `None`. This corresponds to line plot.
+ Bar chart can be selected by `bar`.
+ save_fig : bool, optional
+ Whether to save the figures. The default is `True`.
+
+ Returns
+ -------
+ sobol_cell: dict
+ Sobol indices.
+ total_sobol: dict
+ Total Sobol indices.
+
+ """
+ # Extract the necessary variables
+ PCEModel = self.MetaModel
+ basis_dict = PCEModel.basis_dict
+ coeffs_dict = PCEModel.coeffs_dict
+ n_params = PCEModel.n_params
+ max_order = np.max(PCEModel.pce_deg)
+ self.sobol_cell = {}
+ self.total_sobol = {}
+
+ for Output in PCEModel.ModelObj.Output.names:
+
+ n_meas_points = len(coeffs_dict[Output])
+
+ # Initialize the (cell) array containing the (total) Sobol indices.
+ sobol_array = dict.fromkeys(range(1, max_order+1), [])
+ sobol_cell_array = dict.fromkeys(range(1, max_order+1), [])
+
+ for i_order in range(1, max_order+1):
+ n_comb = math.comb(n_params, i_order)
+
+ sobol_cell_array[i_order] = np.zeros((n_comb, n_meas_points))
+
+ total_sobol_array = np.zeros((n_params, n_meas_points))
+
+ # Initialize the cell to store the names of the variables
+ TotalVariance = np.zeros((n_meas_points))
+
+ # Loop over all measurement points and calculate sobol indices
+ for pIdx in range(n_meas_points):
+
+ # Extract the basis indices (alpha) and coefficients
+ Basis = basis_dict[Output][f'y_{pIdx+1}']
+
+ try:
+ clf_poly = PCEModel.clf_poly[Output][f'y_{pIdx+1}']
+ PCECoeffs = clf_poly.coef_
+ except:
+ PCECoeffs = coeffs_dict[Output][f'y_{pIdx+1}']
+
+ # Compute total variance
+ TotalVariance[pIdx] = np.sum(np.square(PCECoeffs[1:]))
+
+ nzidx = np.where(PCECoeffs != 0)[0]
+ # Set all the Sobol indices equal to zero in the presence of a
+ # null output.
+ if len(nzidx) == 0:
+ # This is buggy.
+ for i_order in range(1, max_order+1):
+ sobol_cell_array[i_order][:, pIdx] = 0
+
+ # Otherwise compute them by summing well-chosen coefficients
+ else:
+ nz_basis = Basis[nzidx]
+ for i_order in range(1, max_order+1):
+ idx = np.where(np.sum(nz_basis > 0, axis=1) == i_order)
+ subbasis = nz_basis[idx]
+ Z = np.array(list(combinations(range(n_params), i_order)))
+
+ for q in range(Z.shape[0]):
+ Zq = Z[q]
+ subsubbasis = subbasis[:, Zq]
+ subidx = np.prod(subsubbasis, axis=1) > 0
+ sum_ind = nzidx[idx[0][subidx]]
+ if TotalVariance[pIdx] == 0.0:
+ sobol_cell_array[i_order][q, pIdx] = 0.0
+ else:
+ sobol = np.sum(np.square(PCECoeffs[sum_ind]))
+ sobol /= TotalVariance[pIdx]
+ sobol_cell_array[i_order][q, pIdx] = sobol
+
+ # Compute the TOTAL Sobol indices.
+ for ParIdx in range(n_params):
+ idx = nz_basis[:, ParIdx] > 0
+ sum_ind = nzidx[idx]
+
+ if TotalVariance[pIdx] == 0.0:
+ total_sobol_array[ParIdx, pIdx] = 0.0
+ else:
+ sobol = np.sum(np.square(PCECoeffs[sum_ind]))
+ sobol /= TotalVariance[pIdx]
+ total_sobol_array[ParIdx, pIdx] = sobol
+
+ # ----- if PCA selected: Compute covariance -----
+ if PCEModel.dim_red_method.lower() == 'pca':
+ cov_Z_p_q = np.zeros((n_params))
+ # Extract the basis indices (alpha) and coefficients for
+ # next component
+ if pIdx < n_meas_points-1:
+ nextBasis = basis_dict[Output][f'y_{pIdx+2}']
+
+ try:
+ clf_poly = PCEModel.clf_poly[Output][f'y_{pIdx+2}']
+ nextPCECoeffs = clf_poly.coef_
+ except:
+ nextPCECoeffs = coeffs_dict[Output][f'y_{pIdx+2}']
+
+ # Choose the common non-zero basis
+ mask = (Basis[:, None] == nextBasis).all(-1).any(-1)
+ similar_basis = Basis[mask]
+ # Compute the TOTAL Sobol indices.
+ for ParIdx in range(n_params):
+ idx = similar_basis[:, ParIdx] > 0
+ try:
+ sum_is = nzidx[idx]
+ cov_Z_p_q[ParIdx] = np.sum(PCECoeffs[sum_ind] *
+ nextPCECoeffs[sum_is])
+ except:
+ cov_Z_p_q[ParIdx] = 0.0
+
+ # Compute the sobol indices according to Ref. 2
+ if PCEModel.dim_red_method.lower() == 'pca':
+ n_c_points = PCEModel.ExpDesign.Y[Output].shape[1]
+ PCA = PCEModel.pca[Output]
+ compPCA = PCA.components_
+ nComp = compPCA.shape[0]
+ var_Z_p = PCA.explained_variance_
+
+ # Extract the sobol index of the components
+ for i_order in range(1, max_order+1):
+ n_comb = math.comb(n_params, i_order)
+ sobol_array[i_order] = np.zeros((n_comb, n_c_points))
+ Z = np.array(list(combinations(range(n_params), i_order)))
+
+ for q in range(Z.shape[0]):
+ S_Z_i = sobol_cell_array[i_order][q]
+
+ for tIdx in range(n_c_points):
+ var_Y_t = np.var(PCEModel.ExpDesign.Y[Output][:, tIdx])
+ if var_Y_t == 0.0:
+ term1, term2 = 0.0, 0.0
+ else:
+ term1 = np.sum([S_Z_i[i]*(var_Z_p[i]*(compPCA[i, tIdx]**2)/var_Y_t) for i in range(nComp)])
+
+ # Term 2
+ # cov_Z_p_q = np.ones((nComp))# TODO: from coeffs
+ Phi_t_p = compPCA[:nComp-1]
+ Phi_t_q = compPCA
+ term2 = 2 * np.sum([cov_Z_p_q[ParIdx] * Phi_t_p[i,tIdx] * Phi_t_q[i,tIdx]/var_Y_t for i in range(nComp-1)])
+
+ sobol_array[i_order][q, tIdx] = term1 #+ term2
+
+ # Compute the TOTAL Sobol indices.
+ total_sobol = np.zeros((n_params, n_c_points))
+ for ParIdx in range(n_params):
+ S_Z_i = total_sobol_array[ParIdx]
+
+ for tIdx in range(n_c_points):
+ var_Y_t = np.var(PCEModel.ExpDesign.Y[Output][:, tIdx])
+ if var_Y_t == 0.0:
+ term1, term2 = 0.0, 0.0
+ else:
+ term1 = 0
+ for i in range(nComp):
+ term1 += S_Z_i[i] * var_Z_p[i] * \
+ (compPCA[i, tIdx]**2) / var_Y_t
+
+ # Term 2
+ # cov_Z_p_q = np.ones((nComp))# TODO: from coeffs
+ Phi_t_p = compPCA[:nComp-1]
+ Phi_t_q = compPCA
+ term2 = 0
+ for i in range(nComp-1):
+ term2 += cov_Z_p_q[ParIdx] * Phi_t_p[i, tIdx] \
+ * Phi_t_q[i, tIdx] / var_Y_t
+ term2 *= 2
+
+ total_sobol[ParIdx, tIdx] = term1 + term2
+
+ self.sobol_cell[Output] = sobol_array
+ self.total_sobol[Output] = total_sobol
+ else:
+ self.sobol_cell[Output] = sobol_cell_array
+ self.total_sobol[Output] = total_sobol_array
+
+ # ---------------- Plot -----------------------
+ par_names = PCEModel.ExpDesign.par_names
+ x_values_orig = PCEModel.ExpDesign.x_values
+
+ cases = ['']
+
+ for case in cases:
+ newpath = (f'Outputs_PostProcessing_{self.name}/')
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ if save_fig:
+ # create a PdfPages object
+ name = case+'_' if 'Valid' in cases else ''
+ pdf = PdfPages('./'+newpath+name+'Sobol_indices.pdf')
+
+ fig = plt.figure()
+
+ for outIdx, Output in enumerate(PCEModel.ModelObj.Output.names):
+
+ # Extract total Sobol indices
+ total_sobol = self.total_sobol[Output]
+
+ # Extract a list of x values
+ if type(x_values_orig) is dict:
+ x = x_values_orig[Output]
+ else:
+ x = x_values_orig
+
+ if plot_type == 'bar':
+ ax = fig.add_axes([0, 0, 1, 1])
+ dict1 = {xlabel: x}
+ dict2 = {param: sobolIndices for param, sobolIndices
+ in zip(par_names, total_sobol)}
+
+ df = pd.DataFrame({**dict1, **dict2})
+ df.plot(x=xlabel, y=par_names, kind="bar", ax=ax, rot=0,
+ colormap='Dark2')
+ ax.set_ylabel('Total Sobol indices, $S^T$')
+
+ else:
+ for i, sobolIndices in enumerate(total_sobol):
+ plt.plot(x, sobolIndices, label=par_names[i],
+ marker='x', lw=2.5)
+
+ plt.ylabel('Total Sobol indices, $S^T$')
+ plt.xlabel(xlabel)
+
+ plt.title(f'Sensitivity analysis of {Output}')
+ if plot_type != 'bar':
+ plt.legend(loc='best', frameon=True)
+
+ # Save indices
+ np.savetxt(f'./{newpath}{name}totalsobol_' +
+ Output.replace('/', '_') + '.csv',
+ total_sobol.T, delimiter=',',
+ header=','.join(par_names), comments='')
+
+ if save_fig:
+ # save the current figure
+ pdf.savefig(fig, bbox_inches='tight')
+
+ # Destroy the current plot
+ plt.clf()
+
+ pdf.close()
+
+ return self.sobol_cell, self.total_sobol</code></pre>
+</details>
+</dd>
+<dt id="post_processing.PostProcessing.check_reg_quality"><code class="name flex">
+<span>def <span class="ident">check_reg_quality</span></span>(<span>self, n_samples=1000, samples=None, save_fig=True)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Checks the quality of the metamodel for single output models based on:
+<a href="https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685">https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685</a></p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>n_samples</code></strong> : <code>int</code>, optional</dt>
+<dd>Number of parameter sets to use for the check. The default is 1000.</dd>
+<dt><strong><code>samples</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code>, optional</dt>
+<dd>Parameter sets to use for the check. The default is None.</dd>
+<dt><strong><code>save_fig</code></strong> : <code>bool</code>, optional</dt>
+<dd>Whether to save the figures. The default is True.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<p>None.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def check_reg_quality(self, n_samples=1000, samples=None, save_fig=True):
+ """
+ Checks the quality of the metamodel for single output models based on:
+ https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685
+
+
+ Parameters
+ ----------
+ n_samples : int, optional
+ Number of parameter sets to use for the check. The default is 1000.
+ samples : array of shape (n_samples, n_params), optional
+ Parameter sets to use for the check. The default is None.
+ save_fig : bool, optional
+ Whether to save the figures. The default is True.
+
+ Returns
+ -------
+ None.
+
+ """
+ MetaModel = self.MetaModel
+
+ if samples is None:
+ self.n_samples = n_samples
+ samples = self._get_sample()
+ else:
+ self.n_samples = samples.shape[0]
+
+ # Evaluate the original and the surrogate model
+ y_val = self._eval_model(samples, key_str='valid')
+ y_pce_val, _ = MetaModel.eval_metamodel(samples=samples)
+
+ # Open a pdf for the plots
+ if save_fig:
+ newpath = (r'Outputs_PostProcessing_{0}/'.format(self.name))
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # Fit the data(train the model)
+ for key in y_pce_val.keys():
+
+ y_pce_val_ = y_pce_val[key]
+ y_val_ = y_val[key]
+
+ # ------ Residuals vs. predicting variables ------
+ # Check the assumptions of linearity and independence
+ fig1 = plt.figure()
+ plt.title(key+": Residuals vs. Predicting variables")
+ residuals = y_val_ - y_pce_val_
+ plt.scatter(x=y_val_, y=residuals, color='blue', edgecolor='k')
+ plt.grid(True)
+ xmin, xmax = min(y_val_), max(y_val_)
+ plt.hlines(y=0, xmin=xmin*0.9, xmax=xmax*1.1, color='red', lw=3,
+ linestyle='--')
+ plt.xlabel(key)
+ plt.ylabel('Residuals')
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig1.savefig(f'./{newpath}/Residuals_vs_PredVariables.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ------ Fitted vs. residuals ------
+ # Check the assumptions of linearity and independence
+ fig2 = plt.figure()
+ plt.title(key+": Residuals vs. predicting variables")
+ plt.scatter(x=y_pce_val_, y=residuals, color='blue', edgecolor='k')
+ plt.grid(True)
+ xmin, xmax = min(y_val_), max(y_val_)
+ plt.hlines(y=0, xmin=xmin*0.9, xmax=xmax*1.1, color='red', lw=3,
+ linestyle='--')
+ plt.xlabel(key)
+ plt.ylabel('Residuals')
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig2.savefig(f'./{newpath}/Fitted_vs_Residuals.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ------ Histogram of normalized residuals ------
+ fig3 = plt.figure()
+ resid_pearson = residuals / (max(residuals)-min(residuals))
+ plt.hist(resid_pearson, bins=20, edgecolor='k')
+ plt.ylabel('Count')
+ plt.xlabel('Normalized residuals')
+ plt.title(f"{key}: Histogram of normalized residuals")
+
+ # Normality (Shapiro-Wilk) test of the residuals
+ ax = plt.gca()
+ _, p = stats.shapiro(residuals)
+ if p < 0.01:
+ annText = "The residuals seem to come from Gaussian process."
+ else:
+ annText = "The normality assumption may not hold."
+ at = AnchoredText(annText, prop=dict(size=30), frameon=True,
+ loc='upper left')
+ at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
+ ax.add_artist(at)
+
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig3.savefig(f'./{newpath}/Hist_NormResiduals.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
+
+ # ------ Q-Q plot of the normalized residuals ------
+ plt.figure()
+ fig4 = qqplot(resid_pearson, line='45', fit='True')
+ plt.xticks()
+ plt.yticks()
+ plt.xlabel("Theoretical quantiles")
+ plt.ylabel("Sample quantiles")
+ plt.title(key+": Q-Q plot of normalized residuals")
+ plt.grid(True)
+ plt.show()
+
+ if save_fig:
+ # save the current figure
+ fig4.savefig(f'./{newpath}/QQPlot_NormResiduals.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()</code></pre>
+</details>
+</dd>
+<dt id="post_processing.PostProcessing.eval_pce_model_3d"><code class="name flex">
+<span>def <span class="ident">eval_pce_model_3d</span></span>(<span>self, save_fig=True)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def eval_pce_model_3d(self, save_fig=True):
+
+ self.n_samples = 1000
+
+ PCEModel = self.MetaModel
+ Model = self.MetaModel.ModelObj
+ n_samples = self.n_samples
+
+ # Create 3D-Grid
+ # TODO: Make it general
+ x = np.linspace(-5, 10, n_samples)
+ y = np.linspace(0, 15, n_samples)
+
+ X, Y = np.meshgrid(x, y)
+ PCE_Z = np.zeros((self.n_samples, self.n_samples))
+ Model_Z = np.zeros((self.n_samples, self.n_samples))
+
+ for idxMesh in range(self.n_samples):
+ sample_mesh = np.vstack((X[:, idxMesh], Y[:, idxMesh])).T
+
+ univ_p_val = PCEModel.univ_basis_vals(sample_mesh)
+
+ for Outkey, ValuesDict in PCEModel.coeffs_dict.items():
+
+ pce_out_mean = np.zeros((len(sample_mesh), len(ValuesDict)))
+ pce_out_std = np.zeros((len(sample_mesh), len(ValuesDict)))
+ model_outs = np.zeros((len(sample_mesh), len(ValuesDict)))
+
+ for Inkey, InIdxValues in ValuesDict.items():
+ idx = int(Inkey.split('_')[1]) - 1
+ basis_deg_ind = PCEModel.basis_dict[Outkey][Inkey]
+ clf_poly = PCEModel.clf_poly[Outkey][Inkey]
+
+ PSI_Val = PCEModel.create_psi(basis_deg_ind, univ_p_val)
+
+ # Perdiction with error bar
+ y_mean, y_std = clf_poly.predict(PSI_Val, return_std=True)
+
+ pce_out_mean[:, idx] = y_mean
+ pce_out_std[:, idx] = y_std
+
+ # Model evaluation
+ model_out_dict, _ = Model.run_model_parallel(sample_mesh,
+ key_str='Valid3D')
+ model_outs[:, idx] = model_out_dict[Outkey].T
+
+ PCE_Z[:, idxMesh] = y_mean
+ Model_Z[:, idxMesh] = model_outs[:, 0]
+
+ # ---------------- 3D plot for PCEModel -----------------------
+ fig_PCE = plt.figure()
+ ax = plt.axes(projection='3d')
+ ax.plot_surface(X, Y, PCE_Z, rstride=1, cstride=1,
+ cmap='viridis', edgecolor='none')
+ ax.set_title('PCEModel')
+ ax.set_xlabel('$x_1$')
+ ax.set_ylabel('$x_2$')
+ ax.set_zlabel('$f(x_1,x_2)$')
+
+ plt.grid()
+ plt.show()
+
+ if save_fig:
+ # Saving the figure
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # save the figure to file
+ fig_PCE.savefig(f'./{newpath}/3DPlot_PCEModel.pdf', format="pdf",
+ bbox_inches='tight')
+ plt.close(fig_PCE)
+
+ # ---------------- 3D plot for Model -----------------------
+ fig_Model = plt.figure()
+ ax = plt.axes(projection='3d')
+ ax.plot_surface(X, Y, PCE_Z, rstride=1, cstride=1,
+ cmap='viridis', edgecolor='none')
+ ax.set_title('Model')
+ ax.set_xlabel('$x_1$')
+ ax.set_ylabel('$x_2$')
+ ax.set_zlabel('$f(x_1,x_2)$')
+
+ plt.grid()
+ plt.show()
+
+ if save_fig:
+ # Save the figure
+ fig_Model.savefig(f'./{newpath}/3DPlot_Model.pdf', format="pdf",
+ bbox_inches='tight')
+ plt.close(fig_Model)
+
+ return</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="post_processing.PostProcessing" href="#post_processing.PostProcessing">PostProcessing</a></code></h4>
+<ul class="">
+<li><code><a title="post_processing.PostProcessing.plot_moments" href="#post_processing.PostProcessing.plot_moments">plot_moments</a></code></li>
+<li><code><a title="post_processing.PostProcessing.valid_metamodel" href="#post_processing.PostProcessing.valid_metamodel">valid_metamodel</a></code></li>
+<li><code><a title="post_processing.PostProcessing.check_accuracy" href="#post_processing.PostProcessing.check_accuracy">check_accuracy</a></code></li>
+<li><code><a title="post_processing.PostProcessing.plot_seq_design_diagnostics" href="#post_processing.PostProcessing.plot_seq_design_diagnostics">plot_seq_design_diagnostics</a></code></li>
+<li><code><a title="post_processing.PostProcessing.sobol_indices" href="#post_processing.PostProcessing.sobol_indices">sobol_indices</a></code></li>
+<li><code><a title="post_processing.PostProcessing.check_reg_quality" href="#post_processing.PostProcessing.check_reg_quality">check_reg_quality</a></code></li>
+<li><code><a title="post_processing.PostProcessing.eval_pce_model_3d" href="#post_processing.PostProcessing.eval_pce_model_3d">eval_pce_model_3d</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/html/pylink.html b/docs/html/pylink.html
old mode 100644
new mode 100755
index 78bb5bf01ecf808e383142550a4c0eb8a1d2a73f..4131ebbf8e2d5874c48e210f3eb80992c4a3d4dc
--- a/docs/html/pylink.html
+++ b/docs/html/pylink.html
@@ -5,13 +5,14 @@
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.10.0" />
<title>pylink API documentation</title>
-<meta name="description" content="This program runs the model defined by the user …" />
+<meta name="description" content="" />
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
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+<script async src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML" integrity="sha256-kZafAc6mZvK3W3v1pHOcUix30OHQN6pU/NO2oFkqZVw=" crossorigin></script>
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</head>
@@ -22,37 +23,12 @@
<h1 class="title">Module <code>pylink</code></h1>
</header>
<section id="section-intro">
-<p>This program runs the model defined by the user.</p>
-<p>Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart, www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart</p>
-<p>Created in July 2019</p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-
-"""
-This program runs the model defined by the user.
-
-Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart, www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart
-
-Created in July 2019
-
-"""
-
import os
import shutil
import h5py
@@ -60,33 +36,104 @@ import numpy as np
import time
import zipfile
import pandas as pd
-from functools import reduce
import multiprocessing
import tqdm
class PyLinkForwardModel(object):
- """A forward model binder
+ """
+ A forward model binder
+
This calss serves as a code wrapper. This wrapper allows the execution of
- a third-party software/solver within the scope of BayesValidRox.
- The wrapper provides two options:
- 1) link_type='PyLink':
- Runs the third-party software using a sell command with given input
- files.
- 2) link_type='function':
- For this case, it is assumed that model can be run using a function
- written separately in a Python script. This function recieves the
- parameters in an array of shape (n_samples, n_params) and returns
- a dictionary with the x_values and output arrays for given output
- names.
+ a third-party software/solver within the scope of BayesValidRox.
+
+ Attributes
+ ----------
+ link_type : str
+ The type of the wrapper. The default is `'pylink'`. This runs the
+ third-party software or an executable using a sell command with given
+ input files.
+ Second option is `'function'` which assumed that model can be run using
+ a function written separately in a Python script.
+ name : str
+ Name of the model.
+ py_file : str
+ Python file name without `.py` extension to be run for the `'function'`
+ wrapper. Note that the name of the python file and that of the function
+ must be simillar. This function must recieve the parameters in an array
+ of shape `(n_samples, n_params)` and returns a dictionary with the
+ x_values and output arrays for given output names.
+ shell_command : str
+ Shell command to be executed for the `'pylink'` wrapper.
+ input_file : str or list
+ The input file to be passed to the `'pylink'` wrapper.
+ input_template : str or list
+ A template input file to be passed to the `'pylink'` wrapper. This file
+ must be a copy of `input_file` with `<Xi>` place holder for the input
+ parameters defined using `inputs` class, with i being the number of
+ parameter. The file name ending should include `.tpl` before the actual
+ extension of the input file, for example, `params.tpl.input`.
+ aux_file : str or list
+ The list of auxiliary files needed for the `'pylink'` wrapper.
+ exe_path : str
+ Execution path if you wish to run the model for the `'pylink'` wrapper
+ in another directory. The default is `None`, which corresponds to the
+ currecnt working directory.
+ output_file_names : list of str
+ List of the name of the model output text files for the `'pylink'`
+ wrapper.
+ output_names : list of str
+ List of the model outputs to be used for the analysis.
+ output_parser : str
+ Name of the model parser file (without `.py` extension) that recieves
+ the `output_file_names` and returns a 2d-array with the first row being
+ the x_values, e.g. x coordinates or time and the rest of raws pass the
+ simulation output for each model output defined in `output_names`. Note
+ that again here the name of the file and that of the function must be
+ the same.
+ multi_process: bool
+ Whether the model runs to be executed in parallel for the `'pylink'`
+ wrapper. The default is `True`.
+ n_cpus: int
+ The number of cpus to be used for the parallel model execution for the
+ `'pylink'` wrapper. The default is `None`, which corresponds to all
+ available cpus.
+ meas_file : str
+ The name of the measurement text-based file. This file must contain
+ x_values as the first column and one column for each model output. The
+ default is `None`. Only needed for the Bayesian Inference.
+ meas_file_valid : str
+ The name of the measurement text-based file for the validation. The
+ default is `None`. Only needed for the validation with Bayesian
+ Inference.
+ mc_ref_file : str
+ The name of the text file for the Monte-Carlo reference (mean and
+ standard deviation) values. It must contain `x_values` as the first
+ column, `mean` as the second column and `std` as the third. It can be
+ used to compare the estimated moments using meta-model in the post-
+ processing step. This is only available for one output.
+ obs_dict : dict
+ A dictionary containing the measurement text-based file. It must
+ contain `x_values` as the first item and one item for each model output
+ . The default is `{}`. Only needed for the Bayesian Inference.
+ obs_dict_valid : dict
+ A dictionary containing the validation measurement text-based file. It
+ must contain `x_values` as the first item and one item for each model
+ output. The default is `{}`.
+ mc_ref_dict : dict
+ A dictionary containing the Monte-Carlo reference (mean and standard
+ deviation) values. It must contain `x_values` as the first item and
+ `mean` as the second item and `std` as the third. The default is `{}`.
+ This is only available for one output.
"""
- def __init__(self, link_type='PyLink', name=None, shell_command='',
- py_file=None, input_file=None, input_template=None,
- aux_file=None, exe_path='', multi_process=True, n_cpus=None,
- output_parser='', output_names=[], output_file_names=[],
- meas_file=None, meas_file_valid=None, mc_ref_file=None,
- obs_dict={}, obs_dict_valid={}, mc_ref_dict={}):
+ def __init__(self, link_type='pylink', name=None, py_file=None,
+ shell_command='', input_file=None, input_template=None,
+ aux_file=None, exe_path='', output_file_names=[],
+ output_names=[], output_parser='', multi_process=True,
+ n_cpus=None, meas_file=None, meas_file_valid=None,
+ mc_ref_file=None, obs_dict={}, obs_dict_valid={},
+ mc_ref_dict={}):
self.link_type = link_type
self.name = name
self.shell_command = shell_command
@@ -219,24 +266,24 @@ class PyLinkForwardModel(object):
return output
# -------------------------------------------------------------------------
- def update_input_params(self, new_input_file, param_sets):
+ def update_input_params(self, new_input_file, param_set):
"""
Finds this pattern with <X1> in the new_input_file and replace it with
the new value from the array param_sets.
Parameters
----------
- new_input_file : TYPE
- DESCRIPTION.
- param_sets : TYPE
- DESCRIPTION.
+ new_input_file : list
+ List of the input files with the adapted names.
+ param_set : array of shape (n_params)
+ Parameter set.
Returns
-------
None.
"""
- NofPa = param_sets.shape[0]
+ NofPa = param_set.shape[0]
text_to_search_list = [f'<X{i+1}>' for i in range(NofPa)]
for filename in new_input_file:
@@ -245,7 +292,7 @@ class PyLinkForwardModel(object):
filedata = file.read()
# Replace the target string
- for text_to_search, params in zip(text_to_search_list, param_sets):
+ for text_to_search, params in zip(text_to_search_list, param_set):
filedata = filedata.replace(text_to_search, f'{params:0.4e}')
# Write the file out again
@@ -257,13 +304,13 @@ class PyLinkForwardModel(object):
"""
Runs the execution command given by the user to run the given model.
It checks if the output files have been generated. If yes, the jobe is
- done and it extracts and returns the requested output(s). Otherwise,
- it executes the command again.
+ done and it extracts and returns the requested output(s). Otherwise,
+ it executes the command again.
Parameters
----------
- command : string
- The command to be executed.
+ command : str
+ The shell command to be executed.
output_file_names : list
Name of the output file names.
@@ -301,6 +348,19 @@ class PyLinkForwardModel(object):
This function creates subdirectory for the current run and copies the
necessary files to this directory and renames them. Next, it executes
the given command.
+
+ Parameters
+ ----------
+ xx : tuple
+ A tuple including parameter set, simulation number and key string.
+
+ Returns
+ -------
+ output : array of shape (n_outputs+1, n_obs)
+ An array passed by the output paraser containing the x_values as
+ the first row and the simulations results stored in the the rest of
+ the array.
+
"""
c_points, run_no, key_str = xx
@@ -368,21 +428,21 @@ class PyLinkForwardModel(object):
Parameters
----------
- c_points : array like of shape (n_samples, n_params)
+ c_points : array of shape (n_samples, n_params)
Collocation points (training set).
prevRun_No : int, optional
Previous run number, in case the sequential design is selected.
- The default is 0.
- key_str : string, optional
- A descriptive string for validation runs. The default is ''.
+ The default is `0`.
+ key_str : str, optional
+ A descriptive string for validation runs. The default is `''`.
mp : bool, optional
- Multiprocessing. The default is True.
+ Multiprocessing. The default is `True`.
Returns
-------
all_outputs : dict
A dictionary with x values (time step or point id) and all outputs.
- Each key contains an array of the shape (n_samples, n_obs).
+ Each key contains an array of the shape `(n_samples, n_obs)`.
new_c_points : array
Updated collocation points (training set). If a simulation does not
executed successfully, the parameter set is removed.
@@ -533,9 +593,9 @@ class PyLinkForwardModel(object):
Parameters
----------
- dir_name : string
+ dir_name : str
Directory name.
- key : string
+ key : str
Keyword to search for.
Returns
@@ -595,48 +655,189 @@ class PyLinkForwardModel(object):
<dl>
<dt id="pylink.PyLinkForwardModel"><code class="flex name class">
<span>class <span class="ident">PyLinkForwardModel</span></span>
-<span>(</span><span>link_type='PyLink', name=None, shell_command='', py_file=None, input_file=None, input_template=None, aux_file=None, exe_path='', multi_process=True, n_cpus=None, output_parser='', output_names=[], output_file_names=[], meas_file=None, meas_file_valid=None, mc_ref_file=None, obs_dict={}, obs_dict_valid={}, mc_ref_dict={})</span>
+<span>(</span><span>link_type='pylink', name=None, py_file=None, shell_command='', input_file=None, input_template=None, aux_file=None, exe_path='', output_file_names=[], output_names=[], output_parser='', multi_process=True, n_cpus=None, meas_file=None, meas_file_valid=None, mc_ref_file=None, obs_dict={}, obs_dict_valid={}, mc_ref_dict={})</span>
</code></dt>
<dd>
-<div class="desc"><p>A forward model binder
-This calss serves as a code wrapper. This wrapper allows the execution of
-a third-party software/solver within the scope of BayesValidRox.
-The wrapper provides two options:
-1) link_type='PyLink':
-Runs the third-party software using a sell command with given input
-files.
-2) link_type='function':
-For this case, it is assumed that model can be run using a function
-written separately in a Python script. This function recieves the
-parameters in an array of shape (n_samples, n_params) and returns
-a dictionary with the x_values and output arrays for given output
-names.</p></div>
+<div class="desc"><p>A forward model binder</p>
+<p>This calss serves as a code wrapper. This wrapper allows the execution of
+a third-party software/solver within the scope of BayesValidRox.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>link_type</code></strong> : <code>str</code></dt>
+<dd>The type of the wrapper. The default is <code>'pylink'</code>. This runs the
+third-party software or an executable using a sell command with given
+input files.
+Second option is <code>'function'</code> which assumed that model can be run using
+a function written separately in a Python script.</dd>
+<dt><strong><code>name</code></strong> : <code>str</code></dt>
+<dd>Name of the model.</dd>
+<dt><strong><code>py_file</code></strong> : <code>str</code></dt>
+<dd>Python file name without <code>.py</code> extension to be run for the <code>'function'</code>
+wrapper. Note that the name of the python file and that of the function
+must be simillar. This function must recieve the parameters in an array
+of shape <code>(n_samples, n_params)</code> and returns a dictionary with the
+x_values and output arrays for given output names.</dd>
+<dt><strong><code>shell_command</code></strong> : <code>str</code></dt>
+<dd>Shell command to be executed for the <code>'pylink'</code> wrapper.</dd>
+<dt><strong><code>input_file</code></strong> : <code>str</code> or <code>list</code></dt>
+<dd>The input file to be passed to the <code>'pylink'</code> wrapper.</dd>
+<dt><strong><code>input_template</code></strong> : <code>str</code> or <code>list</code></dt>
+<dd>A template input file to be passed to the <code>'pylink'</code> wrapper. This file
+must be a copy of <code>input_file</code> with <code><Xi></code> place holder for the input
+parameters defined using <code>inputs</code> class, with i being the number of
+parameter. The file name ending should include <code>.tpl</code> before the actual
+extension of the input file, for example, <code>params.tpl.input</code>.</dd>
+<dt><strong><code>aux_file</code></strong> : <code>str</code> or <code>list</code></dt>
+<dd>The list of auxiliary files needed for the <code>'pylink'</code> wrapper.</dd>
+<dt><strong><code>exe_path</code></strong> : <code>str</code></dt>
+<dd>Execution path if you wish to run the model for the <code>'pylink'</code> wrapper
+in another directory. The default is <code>None</code>, which corresponds to the
+currecnt working directory.</dd>
+<dt><strong><code>output_file_names</code></strong> : <code>list</code> of <code>str</code></dt>
+<dd>List of the name of the model output text files for the <code>'pylink'</code>
+wrapper.</dd>
+<dt><strong><code>output_names</code></strong> : <code>list</code> of <code>str</code></dt>
+<dd>List of the model outputs to be used for the analysis.</dd>
+<dt><strong><code>output_parser</code></strong> : <code>str</code></dt>
+<dd>Name of the model parser file (without <code>.py</code> extension) that recieves
+the <code>output_file_names</code> and returns a 2d-array with the first row being
+the x_values, e.g. x coordinates or time and the rest of raws pass the
+simulation output for each model output defined in <code>output_names</code>. Note
+that again here the name of the file and that of the function must be
+the same.</dd>
+<dt><strong><code>multi_process</code></strong> : <code>bool</code></dt>
+<dd>Whether the model runs to be executed in parallel for the <code>'pylink'</code>
+wrapper. The default is <code>True</code>.</dd>
+<dt><strong><code>n_cpus</code></strong> : <code>int</code></dt>
+<dd>The number of cpus to be used for the parallel model execution for the
+<code>'pylink'</code> wrapper. The default is <code>None</code>, which corresponds to all
+available cpus.</dd>
+<dt><strong><code>meas_file</code></strong> : <code>str</code></dt>
+<dd>The name of the measurement text-based file. This file must contain
+x_values as the first column and one column for each model output. The
+default is <code>None</code>. Only needed for the Bayesian Inference.</dd>
+<dt><strong><code>meas_file_valid</code></strong> : <code>str</code></dt>
+<dd>The name of the measurement text-based file for the validation. The
+default is <code>None</code>. Only needed for the validation with Bayesian
+Inference.</dd>
+<dt><strong><code>mc_ref_file</code></strong> : <code>str</code></dt>
+<dd>The name of the text file for the Monte-Carlo reference (mean and
+standard deviation) values. It must contain <code>x_values</code> as the first
+column, <code>mean</code> as the second column and <code>std</code> as the third. It can be
+used to compare the estimated moments using meta-model in the post-
+processing step. This is only available for one output.</dd>
+<dt><strong><code>obs_dict</code></strong> : <code>dict</code></dt>
+<dd>A dictionary containing the measurement text-based file. It must
+contain <code>x_values</code> as the first item and one item for each model output
+. The default is <code>{}</code>. Only needed for the Bayesian Inference.</dd>
+<dt><strong><code>obs_dict_valid</code></strong> : <code>dict</code></dt>
+<dd>A dictionary containing the validation measurement text-based file. It
+must contain <code>x_values</code> as the first item and one item for each model
+output. The default is <code>{}</code>.</dd>
+<dt><strong><code>mc_ref_dict</code></strong> : <code>dict</code></dt>
+<dd>A dictionary containing the Monte-Carlo reference (mean and standard
+deviation) values. It must contain <code>x_values</code> as the first item and
+<code>mean</code> as the second item and <code>std</code> as the third. The default is <code>{}</code>.
+This is only available for one output.</dd>
+</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class PyLinkForwardModel(object):
- """A forward model binder
+ """
+ A forward model binder
+
This calss serves as a code wrapper. This wrapper allows the execution of
- a third-party software/solver within the scope of BayesValidRox.
- The wrapper provides two options:
- 1) link_type='PyLink':
- Runs the third-party software using a sell command with given input
- files.
- 2) link_type='function':
- For this case, it is assumed that model can be run using a function
- written separately in a Python script. This function recieves the
- parameters in an array of shape (n_samples, n_params) and returns
- a dictionary with the x_values and output arrays for given output
- names.
+ a third-party software/solver within the scope of BayesValidRox.
+
+ Attributes
+ ----------
+ link_type : str
+ The type of the wrapper. The default is `'pylink'`. This runs the
+ third-party software or an executable using a sell command with given
+ input files.
+ Second option is `'function'` which assumed that model can be run using
+ a function written separately in a Python script.
+ name : str
+ Name of the model.
+ py_file : str
+ Python file name without `.py` extension to be run for the `'function'`
+ wrapper. Note that the name of the python file and that of the function
+ must be simillar. This function must recieve the parameters in an array
+ of shape `(n_samples, n_params)` and returns a dictionary with the
+ x_values and output arrays for given output names.
+ shell_command : str
+ Shell command to be executed for the `'pylink'` wrapper.
+ input_file : str or list
+ The input file to be passed to the `'pylink'` wrapper.
+ input_template : str or list
+ A template input file to be passed to the `'pylink'` wrapper. This file
+ must be a copy of `input_file` with `<Xi>` place holder for the input
+ parameters defined using `inputs` class, with i being the number of
+ parameter. The file name ending should include `.tpl` before the actual
+ extension of the input file, for example, `params.tpl.input`.
+ aux_file : str or list
+ The list of auxiliary files needed for the `'pylink'` wrapper.
+ exe_path : str
+ Execution path if you wish to run the model for the `'pylink'` wrapper
+ in another directory. The default is `None`, which corresponds to the
+ currecnt working directory.
+ output_file_names : list of str
+ List of the name of the model output text files for the `'pylink'`
+ wrapper.
+ output_names : list of str
+ List of the model outputs to be used for the analysis.
+ output_parser : str
+ Name of the model parser file (without `.py` extension) that recieves
+ the `output_file_names` and returns a 2d-array with the first row being
+ the x_values, e.g. x coordinates or time and the rest of raws pass the
+ simulation output for each model output defined in `output_names`. Note
+ that again here the name of the file and that of the function must be
+ the same.
+ multi_process: bool
+ Whether the model runs to be executed in parallel for the `'pylink'`
+ wrapper. The default is `True`.
+ n_cpus: int
+ The number of cpus to be used for the parallel model execution for the
+ `'pylink'` wrapper. The default is `None`, which corresponds to all
+ available cpus.
+ meas_file : str
+ The name of the measurement text-based file. This file must contain
+ x_values as the first column and one column for each model output. The
+ default is `None`. Only needed for the Bayesian Inference.
+ meas_file_valid : str
+ The name of the measurement text-based file for the validation. The
+ default is `None`. Only needed for the validation with Bayesian
+ Inference.
+ mc_ref_file : str
+ The name of the text file for the Monte-Carlo reference (mean and
+ standard deviation) values. It must contain `x_values` as the first
+ column, `mean` as the second column and `std` as the third. It can be
+ used to compare the estimated moments using meta-model in the post-
+ processing step. This is only available for one output.
+ obs_dict : dict
+ A dictionary containing the measurement text-based file. It must
+ contain `x_values` as the first item and one item for each model output
+ . The default is `{}`. Only needed for the Bayesian Inference.
+ obs_dict_valid : dict
+ A dictionary containing the validation measurement text-based file. It
+ must contain `x_values` as the first item and one item for each model
+ output. The default is `{}`.
+ mc_ref_dict : dict
+ A dictionary containing the Monte-Carlo reference (mean and standard
+ deviation) values. It must contain `x_values` as the first item and
+ `mean` as the second item and `std` as the third. The default is `{}`.
+ This is only available for one output.
"""
- def __init__(self, link_type='PyLink', name=None, shell_command='',
- py_file=None, input_file=None, input_template=None,
- aux_file=None, exe_path='', multi_process=True, n_cpus=None,
- output_parser='', output_names=[], output_file_names=[],
- meas_file=None, meas_file_valid=None, mc_ref_file=None,
- obs_dict={}, obs_dict_valid={}, mc_ref_dict={}):
+ def __init__(self, link_type='pylink', name=None, py_file=None,
+ shell_command='', input_file=None, input_template=None,
+ aux_file=None, exe_path='', output_file_names=[],
+ output_names=[], output_parser='', multi_process=True,
+ n_cpus=None, meas_file=None, meas_file_valid=None,
+ mc_ref_file=None, obs_dict={}, obs_dict_valid={},
+ mc_ref_dict={}):
self.link_type = link_type
self.name = name
self.shell_command = shell_command
@@ -769,24 +970,24 @@ names.</p></div>
return output
# -------------------------------------------------------------------------
- def update_input_params(self, new_input_file, param_sets):
+ def update_input_params(self, new_input_file, param_set):
"""
Finds this pattern with <X1> in the new_input_file and replace it with
the new value from the array param_sets.
Parameters
----------
- new_input_file : TYPE
- DESCRIPTION.
- param_sets : TYPE
- DESCRIPTION.
+ new_input_file : list
+ List of the input files with the adapted names.
+ param_set : array of shape (n_params)
+ Parameter set.
Returns
-------
None.
"""
- NofPa = param_sets.shape[0]
+ NofPa = param_set.shape[0]
text_to_search_list = [f'<X{i+1}>' for i in range(NofPa)]
for filename in new_input_file:
@@ -795,7 +996,7 @@ names.</p></div>
filedata = file.read()
# Replace the target string
- for text_to_search, params in zip(text_to_search_list, param_sets):
+ for text_to_search, params in zip(text_to_search_list, param_set):
filedata = filedata.replace(text_to_search, f'{params:0.4e}')
# Write the file out again
@@ -807,13 +1008,13 @@ names.</p></div>
"""
Runs the execution command given by the user to run the given model.
It checks if the output files have been generated. If yes, the jobe is
- done and it extracts and returns the requested output(s). Otherwise,
- it executes the command again.
+ done and it extracts and returns the requested output(s). Otherwise,
+ it executes the command again.
Parameters
----------
- command : string
- The command to be executed.
+ command : str
+ The shell command to be executed.
output_file_names : list
Name of the output file names.
@@ -851,6 +1052,19 @@ names.</p></div>
This function creates subdirectory for the current run and copies the
necessary files to this directory and renames them. Next, it executes
the given command.
+
+ Parameters
+ ----------
+ xx : tuple
+ A tuple including parameter set, simulation number and key string.
+
+ Returns
+ -------
+ output : array of shape (n_outputs+1, n_obs)
+ An array passed by the output paraser containing the x_values as
+ the first row and the simulations results stored in the the rest of
+ the array.
+
"""
c_points, run_no, key_str = xx
@@ -918,21 +1132,21 @@ names.</p></div>
Parameters
----------
- c_points : array like of shape (n_samples, n_params)
+ c_points : array of shape (n_samples, n_params)
Collocation points (training set).
prevRun_No : int, optional
Previous run number, in case the sequential design is selected.
- The default is 0.
- key_str : string, optional
- A descriptive string for validation runs. The default is ''.
+ The default is `0`.
+ key_str : str, optional
+ A descriptive string for validation runs. The default is `''`.
mp : bool, optional
- Multiprocessing. The default is True.
+ Multiprocessing. The default is `True`.
Returns
-------
all_outputs : dict
A dictionary with x values (time step or point id) and all outputs.
- Each key contains an array of the shape (n_samples, n_obs).
+ Each key contains an array of the shape `(n_samples, n_obs)`.
new_c_points : array
Updated collocation points (training set). If a simulation does not
executed successfully, the parameter set is removed.
@@ -1083,9 +1297,9 @@ names.</p></div>
Parameters
----------
- dir_name : string
+ dir_name : str
Directory name.
- key : string
+ key : str
Keyword to search for.
Returns
@@ -1142,43 +1356,20 @@ names.</p></div>
</dl>
<h3>Methods</h3>
<dl>
-<dt id="pylink.PyLinkForwardModel.read_mc_reference"><code class="name flex">
-<span>def <span class="ident">read_mc_reference</span></span>(<span>self)</span>
+<dt id="pylink.PyLinkForwardModel.within_range"><code class="name flex">
+<span>def <span class="ident">within_range</span></span>(<span>self, out, minout, maxout)</span>
</code></dt>
<dd>
-<div class="desc"><p>Is used, if a Monte-Carlo reference is available for
-further in-depth post-processing after meta-model training.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>None</code></dt>
-<dd> </dd>
-</dl></div>
+<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
-<pre><code class="python">def read_mc_reference(self):
- """
- Is used, if a Monte-Carlo reference is available for
- further in-depth post-processing after meta-model training.
-
- Returns
- -------
- None
-
- """
- if self.mc_ref_file is None and not hasattr(self, 'mc_reference'):
- return
- elif isinstance(self.mc_reference, dict) and bool(self.mc_reference):
- self.mc_reference = pd.DataFrame.from_dict(self.mc_reference)
- elif self.mc_ref_file is not None:
- file_path = os.path.join(os.getcwd(), self.mc_ref_file)
- self.mc_reference = pd.read_csv(file_path, delimiter=',')
- else:
- raise Exception("Please provide the MC reference data as a "
- "dictionary via mc_reference attribute or pass the"
- " csv-file path to mc_ref_file attribute")
- return self.mc_reference</code></pre>
+<pre><code class="python">def within_range(self, out, minout, maxout):
+ inside = False
+ if (out > minout).all() and (out < maxout).all():
+ inside = True
+ return inside</code></pre>
</details>
</dd>
<dt id="pylink.PyLinkForwardModel.read_observation"><code class="name flex">
@@ -1243,6 +1434,45 @@ calibration.</p>
return self.observations_valid</code></pre>
</details>
</dd>
+<dt id="pylink.PyLinkForwardModel.read_mc_reference"><code class="name flex">
+<span>def <span class="ident">read_mc_reference</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Is used, if a Monte-Carlo reference is available for
+further in-depth post-processing after meta-model training.</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>None</code></dt>
+<dd> </dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def read_mc_reference(self):
+ """
+ Is used, if a Monte-Carlo reference is available for
+ further in-depth post-processing after meta-model training.
+
+ Returns
+ -------
+ None
+
+ """
+ if self.mc_ref_file is None and not hasattr(self, 'mc_reference'):
+ return
+ elif isinstance(self.mc_reference, dict) and bool(self.mc_reference):
+ self.mc_reference = pd.DataFrame.from_dict(self.mc_reference)
+ elif self.mc_ref_file is not None:
+ file_path = os.path.join(os.getcwd(), self.mc_ref_file)
+ self.mc_reference = pd.read_csv(file_path, delimiter=',')
+ else:
+ raise Exception("Please provide the MC reference data as a "
+ "dictionary via mc_reference attribute or pass the"
+ " csv-file path to mc_ref_file attribute")
+ return self.mc_reference</code></pre>
+</details>
+</dd>
<dt id="pylink.PyLinkForwardModel.read_output"><code class="name flex">
<span>def <span class="ident">read_output</span></span>(<span>self)</span>
</code></dt>
@@ -1285,6 +1515,59 @@ simulation outputs in csv files.</p>
return output</code></pre>
</details>
</dd>
+<dt id="pylink.PyLinkForwardModel.update_input_params"><code class="name flex">
+<span>def <span class="ident">update_input_params</span></span>(<span>self, new_input_file, param_set)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Finds this pattern with <X1> in the new_input_file and replace it with
+the new value from the array param_sets.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>new_input_file</code></strong> : <code>list</code></dt>
+<dd>List of the input files with the adapted names.</dd>
+<dt><strong><code>param_set</code></strong> : <code>array</code> of <code>shape (n_params)</code></dt>
+<dd>Parameter set.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<p>None.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def update_input_params(self, new_input_file, param_set):
+ """
+ Finds this pattern with <X1> in the new_input_file and replace it with
+ the new value from the array param_sets.
+
+ Parameters
+ ----------
+ new_input_file : list
+ List of the input files with the adapted names.
+ param_set : array of shape (n_params)
+ Parameter set.
+
+ Returns
+ -------
+ None.
+
+ """
+ NofPa = param_set.shape[0]
+ text_to_search_list = [f'<X{i+1}>' for i in range(NofPa)]
+
+ for filename in new_input_file:
+ # Read in the file
+ with open(filename, 'r') as file:
+ filedata = file.read()
+
+ # Replace the target string
+ for text_to_search, params in zip(text_to_search_list, param_set):
+ filedata = filedata.replace(text_to_search, f'{params:0.4e}')
+
+ # Write the file out again
+ with open(filename, 'w') as file:
+ file.write(filedata)</code></pre>
+</details>
+</dd>
<dt id="pylink.PyLinkForwardModel.run_command"><code class="name flex">
<span>def <span class="ident">run_command</span></span>(<span>self, command, output_file_names)</span>
</code></dt>
@@ -1295,8 +1578,8 @@ done and it extracts and returns the requested output(s). Otherwise,
it executes the command again.</p>
<h2 id="parameters">Parameters</h2>
<dl>
-<dt><strong><code>command</code></strong> : <code>string</code></dt>
-<dd>The command to be executed.</dd>
+<dt><strong><code>command</code></strong> : <code>str</code></dt>
+<dd>The shell command to be executed.</dd>
<dt><strong><code>output_file_names</code></strong> : <code>list</code></dt>
<dd>Name of the output file names.</dd>
</dl>
@@ -1313,13 +1596,13 @@ it executes the command again.</p>
"""
Runs the execution command given by the user to run the given model.
It checks if the output files have been generated. If yes, the jobe is
- done and it extracts and returns the requested output(s). Otherwise,
- it executes the command again.
+ done and it extracts and returns the requested output(s). Otherwise,
+ it executes the command again.
Parameters
----------
- command : string
- The command to be executed.
+ command : str
+ The shell command to be executed.
output_file_names : list
Name of the output file names.
@@ -1358,7 +1641,19 @@ it executes the command again.</p>
<dd>
<div class="desc"><p>This function creates subdirectory for the current run and copies the
necessary files to this directory and renames them. Next, it executes
-the given command.</p></div>
+the given command.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>xx</code></strong> : <code>tuple</code></dt>
+<dd>A tuple including parameter set, simulation number and key string.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>output</code></strong> : <code>array</code> of <code>shape (n_outputs+1, n_obs)</code></dt>
+<dd>An array passed by the output paraser containing the x_values as
+the first row and the simulations results stored in the the rest of
+the array.</dd>
+</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
@@ -1368,6 +1663,19 @@ the given command.</p></div>
This function creates subdirectory for the current run and copies the
necessary files to this directory and renames them. Next, it executes
the given command.
+
+ Parameters
+ ----------
+ xx : tuple
+ A tuple including parameter set, simulation number and key string.
+
+ Returns
+ -------
+ output : array of shape (n_outputs+1, n_obs)
+ An array passed by the output paraser containing the x_values as
+ the first row and the simulations results stored in the the rest of
+ the array.
+
"""
c_points, run_no, key_str = xx
@@ -1435,21 +1743,21 @@ the given command.</p></div>
are started in parallel.</p>
<h2 id="parameters">Parameters</h2>
<dl>
-<dt><strong><code>c_points</code></strong> : <code>array like</code> of <code>shape (n_samples, n_params)</code></dt>
+<dt><strong><code>c_points</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
<dd>Collocation points (training set).</dd>
<dt><strong><code>prevRun_No</code></strong> : <code>int</code>, optional</dt>
<dd>Previous run number, in case the sequential design is selected.
-The default is 0.</dd>
-<dt><strong><code>key_str</code></strong> : <code>string</code>, optional</dt>
-<dd>A descriptive string for validation runs. The default is ''.</dd>
+The default is <code>0</code>.</dd>
+<dt><strong><code>key_str</code></strong> : <code>str</code>, optional</dt>
+<dd>A descriptive string for validation runs. The default is <code>''</code>.</dd>
<dt><strong><code>mp</code></strong> : <code>bool</code>, optional</dt>
-<dd>Multiprocessing. The default is True.</dd>
+<dd>Multiprocessing. The default is <code>True</code>.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>all_outputs</code></strong> : <code>dict</code></dt>
<dd>A dictionary with x values (time step or point id) and all outputs.
-Each key contains an array of the shape (n_samples, n_obs).</dd>
+Each key contains an array of the shape <code>(n_samples, n_obs)</code>.</dd>
<dt><strong><code>new_c_points</code></strong> : <code>array</code></dt>
<dd>Updated collocation points (training set). If a simulation does not
executed successfully, the parameter set is removed.</dd>
@@ -1466,21 +1774,21 @@ executed successfully, the parameter set is removed.</dd>
Parameters
----------
- c_points : array like of shape (n_samples, n_params)
+ c_points : array of shape (n_samples, n_params)
Collocation points (training set).
prevRun_No : int, optional
Previous run number, in case the sequential design is selected.
- The default is 0.
- key_str : string, optional
- A descriptive string for validation runs. The default is ''.
+ The default is `0`.
+ key_str : str, optional
+ A descriptive string for validation runs. The default is `''`.
mp : bool, optional
- Multiprocessing. The default is True.
+ Multiprocessing. The default is `True`.
Returns
-------
all_outputs : dict
A dictionary with x values (time step or point id) and all outputs.
- Each key contains an array of the shape (n_samples, n_obs).
+ Each key contains an array of the shape `(n_samples, n_obs)`.
new_c_points : array
Updated collocation points (training set). If a simulation does not
executed successfully, the parameter set is removed.
@@ -1625,75 +1933,6 @@ executed successfully, the parameter set is removed.</dd>
return all_outputs, new_c_points</code></pre>
</details>
</dd>
-<dt id="pylink.PyLinkForwardModel.update_input_params"><code class="name flex">
-<span>def <span class="ident">update_input_params</span></span>(<span>self, new_input_file, param_sets)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Finds this pattern with <X1> in the new_input_file and replace it with
-the new value from the array param_sets.</p>
-<h2 id="parameters">Parameters</h2>
-<dl>
-<dt><strong><code>new_input_file</code></strong> : <code>TYPE</code></dt>
-<dd>DESCRIPTION.</dd>
-<dt><strong><code>param_sets</code></strong> : <code>TYPE</code></dt>
-<dd>DESCRIPTION.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<p>None.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def update_input_params(self, new_input_file, param_sets):
- """
- Finds this pattern with <X1> in the new_input_file and replace it with
- the new value from the array param_sets.
-
- Parameters
- ----------
- new_input_file : TYPE
- DESCRIPTION.
- param_sets : TYPE
- DESCRIPTION.
-
- Returns
- -------
- None.
-
- """
- NofPa = param_sets.shape[0]
- text_to_search_list = [f'<X{i+1}>' for i in range(NofPa)]
-
- for filename in new_input_file:
- # Read in the file
- with open(filename, 'r') as file:
- filedata = file.read()
-
- # Replace the target string
- for text_to_search, params in zip(text_to_search_list, param_sets):
- filedata = filedata.replace(text_to_search, f'{params:0.4e}')
-
- # Write the file out again
- with open(filename, 'w') as file:
- file.write(filedata)</code></pre>
-</details>
-</dd>
-<dt id="pylink.PyLinkForwardModel.within_range"><code class="name flex">
-<span>def <span class="ident">within_range</span></span>(<span>self, out, minout, maxout)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">def within_range(self, out, minout, maxout):
- inside = False
- if (out > minout).all() and (out < maxout).all():
- inside = True
- return inside</code></pre>
-</details>
-</dd>
<dt id="pylink.PyLinkForwardModel.zip_subdirs"><code class="name flex">
<span>def <span class="ident">zip_subdirs</span></span>(<span>self, dir_name, key)</span>
</code></dt>
@@ -1701,9 +1940,9 @@ the new value from the array param_sets.</p>
<div class="desc"><p>Zips all the files containing the key(word).</p>
<h2 id="parameters">Parameters</h2>
<dl>
-<dt><strong><code>dir_name</code></strong> : <code>string</code></dt>
+<dt><strong><code>dir_name</code></strong> : <code>str</code></dt>
<dd>Directory name.</dd>
-<dt><strong><code>key</code></strong> : <code>string</code></dt>
+<dt><strong><code>key</code></strong> : <code>str</code></dt>
<dd>Keyword to search for.</dd>
</dl>
<h2 id="returns">Returns</h2>
@@ -1718,9 +1957,9 @@ the new value from the array param_sets.</p>
Parameters
----------
- dir_name : string
+ dir_name : str
Directory name.
- key : string
+ key : str
Keyword to search for.
Returns
@@ -1785,16 +2024,16 @@ the new value from the array param_sets.</p>
<li>
<h4><code><a title="pylink.PyLinkForwardModel" href="#pylink.PyLinkForwardModel">PyLinkForwardModel</a></code></h4>
<ul class="two-column">
-<li><code><a title="pylink.PyLinkForwardModel.Output" href="#pylink.PyLinkForwardModel.Output">Output</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.read_mc_reference" href="#pylink.PyLinkForwardModel.read_mc_reference">read_mc_reference</a></code></li>
+<li><code><a title="pylink.PyLinkForwardModel.within_range" href="#pylink.PyLinkForwardModel.within_range">within_range</a></code></li>
<li><code><a title="pylink.PyLinkForwardModel.read_observation" href="#pylink.PyLinkForwardModel.read_observation">read_observation</a></code></li>
+<li><code><a title="pylink.PyLinkForwardModel.read_mc_reference" href="#pylink.PyLinkForwardModel.read_mc_reference">read_mc_reference</a></code></li>
<li><code><a title="pylink.PyLinkForwardModel.read_output" href="#pylink.PyLinkForwardModel.read_output">read_output</a></code></li>
+<li><code><a title="pylink.PyLinkForwardModel.update_input_params" href="#pylink.PyLinkForwardModel.update_input_params">update_input_params</a></code></li>
<li><code><a title="pylink.PyLinkForwardModel.run_command" href="#pylink.PyLinkForwardModel.run_command">run_command</a></code></li>
<li><code><a title="pylink.PyLinkForwardModel.run_forwardmodel" href="#pylink.PyLinkForwardModel.run_forwardmodel">run_forwardmodel</a></code></li>
<li><code><a title="pylink.PyLinkForwardModel.run_model_parallel" href="#pylink.PyLinkForwardModel.run_model_parallel">run_model_parallel</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.update_input_params" href="#pylink.PyLinkForwardModel.update_input_params">update_input_params</a></code></li>
-<li><code><a title="pylink.PyLinkForwardModel.within_range" href="#pylink.PyLinkForwardModel.within_range">within_range</a></code></li>
<li><code><a title="pylink.PyLinkForwardModel.zip_subdirs" href="#pylink.PyLinkForwardModel.zip_subdirs">zip_subdirs</a></code></li>
+<li><code><a title="pylink.PyLinkForwardModel.Output" href="#pylink.PyLinkForwardModel.Output">Output</a></code></li>
</ul>
</li>
</ul>
diff --git a/docs/html/surrogate_models.html b/docs/html/surrogate_models.html
new file mode 100644
index 0000000000000000000000000000000000000000..f1cb980f7a9cc8f8fd800606cf156e824d51c1c5
--- /dev/null
+++ b/docs/html/surrogate_models.html
@@ -0,0 +1,4282 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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+<title>surrogate_models API documentation</title>
+<meta name="description" content="" />
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+<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
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+<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
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+<script async src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML" integrity="sha256-kZafAc6mZvK3W3v1pHOcUix30OHQN6pU/NO2oFkqZVw=" crossorigin></script>
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+<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
+</head>
+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>surrogate_models</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import warnings
+import numpy as np
+import math
+import h5py
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+import scipy as sp
+from tqdm import tqdm
+from sklearn.decomposition import PCA as sklearnPCA
+import sklearn.linear_model as lm
+from sklearn.gaussian_process import GaussianProcessRegressor
+import sklearn.gaussian_process.kernels as kernels
+import os
+import sys
+from joblib import Parallel, delayed
+
+from exp_designs import ExpDesigns
+from glexindex import glexindex
+from eval_rec_rule import eval_univ_basis
+from reg_fast_ard import RegressionFastARD
+from reg_fast_laplace import RegressionFastLaplace
+from bayes_linear import VBLinearRegression, EBLinearRegression
+warnings.filterwarnings("ignore")
+# Load the mplstyle
+plt.style.use(os.path.join(os.path.split(__file__)[0],
+ '../', 'bayesvalidrox.mplstyle'))
+
+
+class MetaModel:
+ """
+ Meta (surrogate) model
+
+ This class trains a surrogate model. It accepts an input object (input_obj)
+ containing the specification of the distributions for uncertain parameters
+ and a model object with instructions on how to run the computational model.
+
+ Attributes
+ ----------
+ input_obj : obj
+ Input object with the information on the model input parameters.
+ meta_model_type : str
+ Surrogate model types. Three surrogate model types are supported:
+ polynomial chaos expansion (`PCE`), arbitrary PCE (`aPCE`) and
+ Gaussian process regression (`GPE`). Default is PCE.
+ pce_reg_method : str
+ PCE regression method to compute the coefficients. The following
+ regression methods are available:
+
+ 1. OLS: Ordinary Least Square method
+ 2. BRR: Bayesian Ridge Regression
+ 3. LARS: Least angle regression
+ 4. ARD: Bayesian ARD Regression
+ 5. FastARD: Fast Bayesian ARD Regression
+ 6. VBL: Variational Bayesian Learning
+ 7. EBL: Emperical Bayesian Learning
+ Default is `OLS`.
+ pce_deg : int or list of int
+ Polynomial degree(s). If a list is given, an adaptive algorithm is used
+ to find the best degree with the lowest Leave-One-Out cross-validation
+ (LOO) error (or the highest score=1-LOO). Default is `1`.
+ pce_q_norm : float
+ Hyperbolic (or q-norm) truncation for multi-indices of multivariate
+ polynomials. Default is `1.0`.
+ dim_red_method : str
+ Dimensionality reduction method for the output space. The available
+ method is based on principal component analysis (PCA). The Default is
+ `'no'`. There are two ways to select number of components: use
+ percentage of the explainable variance threshold (between 0 and 100)
+ (Option A) or direct prescription of components' number (Option B):
+
+ >>> MetaModelOpts.dim_red_method = 'PCA'
+ >>> MetaModelOpts.var_pca_threshold = 99.999 # Option A
+ >>> MetaModelOpts.n_pca_components = 12 # Option B
+
+ verbose : bool
+ Prints summary of the regression results. Default is `False`.
+
+ Note
+ -------
+ To define the sampling methods and the training set, an experimental design
+ instance shall be defined. This can be done by:
+
+ >>> MetaModelOpts.add_ExpDesign()
+
+ Two experimental design schemes are supported: one-shot (`normal`) and
+ adaptive sequential (`sequential`) designs.
+ For experimental design refer to `ExpDesigns`.
+
+ """
+
+ def __init__(self, input_obj, meta_model_type='PCE', pce_reg_method='OLS',
+ pce_deg=1, pce_q_norm=1.0, dim_red_method='no',
+ verbose=False):
+
+ self.input_obj = input_obj
+ self.meta_model_type = meta_model_type
+ self.pce_reg_method = pce_reg_method
+ self.pce_deg = pce_deg
+ self.pce_q_norm = pce_q_norm
+ self.dim_red_method = dim_red_method
+ self.verbose = False
+
+ # -------------------------------------------------------------------------
+ def create_metamodel(self, Model):
+ """
+ Starts the training of the meta-model for the model objects containg
+ the given computational model.
+
+ Parameters
+ ----------
+ Model : obj
+ Model object.
+
+ Returns
+ -------
+ metamodel : obj
+ The meta model object.
+
+ """
+ self.ModelObj = Model
+ self.n_params = len(self.input_obj.Marginals)
+ self.ExpDesignFlag = 'normal'
+ # --- Prepare pce degree ---
+ if self.meta_model_type.lower() == 'pce':
+ if type(self.pce_deg) is not np.ndarray:
+ self.pce_deg = np.array(self.pce_deg)
+
+ if self.ExpDesign.method == 'sequential':
+ from .sequential_design import SeqDesign
+ seq_design = SeqDesign(self)
+ metamodel = seq_design.train_seq_design(Model)
+
+ elif self.ExpDesign.method == 'normal':
+ self.ExpDesignFlag = 'normal'
+ metamodel = self.train_norm_design(Model)
+
+ else:
+ raise Exception("The method for experimental design you requested"
+ " has not been implemented yet.")
+
+ # Zip the model run directories
+ if self.ModelObj.link_type.lower() == 'pylink':
+ Model.zip_subdirs(Model.name, f'{Model.name}_')
+
+ return metamodel
+
+ # -------------------------------------------------------------------------
+ def train_norm_design(self, Model, verbose=False):
+ """
+ This function loops over the outputs and each time step/point and fits
+ the meta model.
+
+ Parameters
+ ----------
+ Model : obj
+ Model object.
+ verbose : bool, optional
+ Flag for a sequential design in silent mode. The default is False.
+
+ Returns
+ -------
+ self: obj
+ Meta-model object.
+
+ """
+
+ # Get the collocation points to run the forward model
+ CollocationPoints, OutputDict = self.generate_ExpDesign(Model)
+
+ # Initialize the nested dictionaries
+ self.deg_dict = self.auto_vivification()
+ self.q_norm_dict = self.auto_vivification()
+ self.coeffs_dict = self.auto_vivification()
+ self.basis_dict = self.auto_vivification()
+ self.score_dict = self.auto_vivification()
+ self.clf_poly = self.auto_vivification()
+ self.gp_poly = self.auto_vivification()
+ self.pca = self.auto_vivification()
+ self.LCerror = self.auto_vivification()
+ self.x_scaler = {}
+
+ # Define the DegreeArray
+ nSamples, ndim = CollocationPoints.shape
+ self.DegreeArray = self.__select_degree(ndim, nSamples)
+
+ # Generate all basis indices
+ self.allBasisIndices = self.auto_vivification()
+ for deg in self.DegreeArray:
+ keys = self.allBasisIndices.keys()
+ if deg not in np.fromiter(keys, dtype=float):
+ # Generate the polynomial basis indices
+ for qidx, q in enumerate(self.pce_q_norm):
+ basis_indices = self.create_basis_indices(degree=deg,
+ q_norm=q)
+ self.allBasisIndices[str(deg)][str(q)] = basis_indices
+
+ # Evaluate the univariate polynomials on ExpDesign
+ if self.meta_model_type.lower() != 'gpe':
+ self.univ_p_val = self.univ_basis_vals(CollocationPoints)
+
+ if 'x_values' in OutputDict:
+ self.ExpDesign.x_values = OutputDict['x_values']
+ del OutputDict['x_values']
+
+ # --- Loop through data points and fit the surrogate ---
+ if not verbose:
+ print(f"\n>>>> Training the {self.meta_model_type} metamodel "
+ "started. <<<<<<\n")
+ items = tqdm(OutputDict.items(), desc="Fitting regression")
+ else:
+ items = OutputDict.items()
+
+ # For loop over the components/outputs
+ for key, Output in items:
+
+ # Dimensionality reduction with PCA, if specified
+ if self.dim_red_method.lower() == 'pca':
+ self.pca[key], target = self.pca_transformation(Output)
+ else:
+ target = Output
+
+ # Parallel fit regression
+ if self.meta_model_type.lower() == 'gpe':
+ # Prepare the input matrix
+ scaler = MinMaxScaler()
+ X_S = scaler.fit_transform(CollocationPoints)
+
+ self.x_scaler[key] = scaler
+
+ out = Parallel(n_jobs=-1, prefer='threads')(
+ delayed(self.gaussian_process_emulator)(X_S, target[:, idx])
+ for idx in range(target.shape[1]))
+
+ for idx in range(target.shape[1]):
+ self.gp_poly[key][f"y_{idx+1}"] = out[idx]
+
+ else:
+ out = Parallel(n_jobs=-1, prefer='threads')(
+ delayed(self.adaptive_regression)(CollocationPoints,
+ target[:, idx], idx)
+ for idx in range(target.shape[1]))
+
+ for i in range(target.shape[1]):
+ # Create a dict to pass the variables
+ self.deg_dict[key][f"y_{i+1}"] = out[i]['degree']
+ self.q_norm_dict[key][f"y_{i+1}"] = out[i]['qnorm']
+ self.coeffs_dict[key][f"y_{i+1}"] = out[i]['coeffs']
+ self.basis_dict[key][f"y_{i+1}"] = out[i]['multi_indices']
+ self.score_dict[key][f"y_{i+1}"] = out[i]['LOOCVScore']
+ self.clf_poly[key][f"y_{i+1}"] = out[i]['clf_poly']
+ self.LCerror[key][f"y_{i+1}"] = out[i]['LCerror']
+
+ if not verbose:
+ print(f"\n>>>> Training the {self.meta_model_type} metamodel"
+ " sucessfully completed. <<<<<<\n")
+
+ return self
+
+ # -------------------------------------------------------------------------
+ def create_basis_indices(self, degree, q_norm):
+ """
+ Creates set of selected multi-indices of multivariate polynomials for
+ certain parameter numbers, polynomial degree, hyperbolic (or q-norm)
+ truncation scheme.
+
+ Parameters
+ ----------
+ degree : int
+ Polynomial degree.
+ q_norm : float
+ hyperbolic (or q-norm) truncation.
+
+ Returns
+ -------
+ basis_indices : array of shape (n_terms, n_params)
+ Multi-indices of multivariate polynomials.
+
+ """
+ basis_indices = glexindex(start=0, stop=degree+1,
+ dimensions=self.n_params,
+ cross_truncation=q_norm,
+ reverse=False, graded=True)
+ return basis_indices
+
+ # -------------------------------------------------------------------------
+ def add_ExpDesign(self):
+ """
+ Instanciates experimental design object.
+
+ Returns
+ -------
+ None.
+
+ """
+ self.ExpDesign = ExpDesigns(self.input_obj,
+ meta_Model=self.meta_model_type)
+
+ # -------------------------------------------------------------------------
+ def generate_ExpDesign(self, Model):
+ """
+ Prepares the experimental design either by reading from the prescribed
+ data or running simulations.
+
+ Parameters
+ ----------
+ Model : obj
+ Model object.
+
+ Raises
+ ------
+ Exception
+ If model sumulations are not provided properly.
+
+ Returns
+ -------
+ ED_X_tr: array of shape (n_samples, n_params)
+ Training samples transformed by an isoprobabilistic transformation.
+ ED_Y: dict
+ Model simulations (target) for all outputs.
+ """
+ ExpDesign = self.ExpDesign
+ if self.ExpDesignFlag != 'sequential':
+ # Read ExpDesign (training and targets) from the provided hdf5
+ if ExpDesign.hdf5_file is not None:
+
+ # Read hdf5 file
+ f = h5py.File(ExpDesign.hdf5_file, 'r+')
+
+ # Read EDX and pass it to ExpDesign object
+ try:
+ ExpDesign.X = np.array(f["EDX/New_init_"])
+ except KeyError:
+ ExpDesign.X = np.array(f["EDX/init_"])
+
+ # Update number of initial samples
+ ExpDesign.n_init_samples = ExpDesign.X.shape[0]
+
+ # Read EDX and pass it to ExpDesign object
+ out_names = self.ModelObj.Output.names
+ ExpDesign.Y = {}
+
+ # Extract x values
+ try:
+ ExpDesign.Y["x_values"] = dict()
+ for varIdx, var in enumerate(out_names):
+ x = np.array(f[f"x_values/{var}"])
+ ExpDesign.Y["x_values"][var] = x
+ except KeyError:
+ ExpDesign.Y["x_values"] = np.array(f["x_values"])
+
+ # Store the output
+ for varIdx, var in enumerate(out_names):
+ try:
+ y = np.array(f[f"EDY/{var}/New_init_"])
+ except KeyError:
+ y = np.array(f[f"EDY/{var}/init_"])
+ ExpDesign.Y[var] = y
+ f.close()
+ else:
+ # Check if an old hdf5 file exists: if yes, rename it
+ hdf5file = f'ExpDesign_{self.ModelObj.name}.hdf5'
+ if os.path.exists(hdf5file):
+ os.rename(hdf5file, 'old_'+hdf5file)
+
+ # ---- Prepare X samples ----
+ ED_X, ED_X_tr = ExpDesign.generate_ED(ExpDesign.n_init_samples,
+ ExpDesign.sampling_method,
+ transform=True,
+ max_pce_deg=np.max(self.pce_deg))
+ ExpDesign.X = ED_X
+ ExpDesign.collocationPoints = ED_X_tr
+ self.bound_tuples = ExpDesign.bound_tuples
+
+ # ---- Run simulations at X ----
+ if not hasattr(ExpDesign, 'Y') or ExpDesign.Y is None:
+ print('\n Now the forward model needs to be run!\n')
+ ED_Y, up_ED_X = Model.run_model_parallel(ED_X)
+ ExpDesign.X = up_ED_X
+ self.ModelOutputDict = ED_Y
+ ExpDesign.Y = ED_Y
+ else:
+ # Check if a dict has been passed.
+ if type(ExpDesign.Y) is dict:
+ self.ModelOutputDict = ExpDesign.Y
+ else:
+ raise Exception('Please provide either a dictionary or a hdf5'
+ 'file to ExpDesign.hdf5_file argument.')
+
+ return ED_X_tr, self.ModelOutputDict
+
+ # -------------------------------------------------------------------------
+ def univ_basis_vals(self, samples, n_max=None):
+ """
+ Evaluates univariate regressors along input directions.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params)
+ Samples.
+ n_max : int, optional
+ Maximum polynomial degree. The default is `None`.
+
+ Returns
+ -------
+ univ_basis: array of shape (n_samples, n_params, n_max+1)
+ All univariate regressors up to n_max.
+ """
+ # Extract information
+ poly_types = self.ExpDesign.poly_types
+ if samples.ndim != 2:
+ samples = samples.reshape(1, len(samples))
+ n_max = np.max(self.pce_deg) if n_max is None else n_max
+
+ # Extract poly coeffs
+ if self.ExpDesign.input_data_given or self.ExpDesign.apce:
+ apolycoeffs = self.ExpDesign.polycoeffs
+ else:
+ apolycoeffs = None
+
+ # Evaluate univariate basis
+ univ_basis = eval_univ_basis(samples, n_max, poly_types, apolycoeffs)
+
+ return univ_basis
+
+ # -------------------------------------------------------------------------
+ def create_psi(self, basis_indices, univ_p_val):
+ """
+ This function assemble the design matrix Psi from the given basis index
+ set INDICES and the univariate polynomial evaluations univ_p_val.
+
+ Parameters
+ ----------
+ basis_indices : array of shape (n_terms, n_params)
+ Multi-indices of multivariate polynomials.
+ univ_p_val : array of (n_samples, n_params, n_max+1)
+ All univariate regressors up to `n_max`.
+
+ Raises
+ ------
+ ValueError
+ n_terms in arguments do not match.
+
+ Returns
+ -------
+ psi : array of shape (n_samples, n_terms)
+ Multivariate regressors.
+
+ """
+ # Check if BasisIndices is a sparse matrix
+ sparsity = sp.sparse.issparse(basis_indices)
+ if sparsity:
+ basis_indices = basis_indices.toarray()
+
+ # Initialization and consistency checks
+ # number of input variables
+ n_params = univ_p_val.shape[1]
+
+ # Size of the experimental design
+ n_samples = univ_p_val.shape[0]
+
+ # number of basis terms
+ n_terms = basis_indices.shape[0]
+
+ # check that the variables have consistent sizes
+ if n_params != basis_indices.shape[1]:
+ raise ValueError("The shapes of basis_indices and univ_p_val don't"
+ " match!!")
+
+ # Preallocate the Psi matrix for performance
+ psi = np.ones((n_samples, n_terms))
+ # Assemble the Psi matrix
+ for m in range(basis_indices.shape[1]):
+ aa = np.where(basis_indices[:, m] > 0)[0]
+ try:
+ basisIdx = basis_indices[aa, m]
+ bb = np.reshape(univ_p_val[:, m, basisIdx], psi[:, aa].shape)
+ psi[:, aa] = np.multiply(psi[:, aa], bb)
+ except ValueError as err:
+ raise err
+
+ return psi
+
+ # -------------------------------------------------------------------------
+ def fit(self, X, y, basis_indices, reg_method=None):
+ """
+ Fit regression using the regression method provided.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples, n_features)
+ Training vector, where n_samples is the number of samples and
+ n_features is the number of features.
+ y : array of shape (n_samples,)
+ Target values.
+ basis_indices : array of shape (n_terms, n_params)
+ Multi-indices of multivariate polynomials.
+ reg_method : str, optional
+ DESCRIPTION. The default is None.
+
+ Returns
+ -------
+ return_out_dict : Dict
+ Fitted estimator, spareMulti-Index, sparseX and coefficients.
+
+ """
+ if reg_method is None:
+ reg_method = self.pce_reg_method
+
+ # Check if BasisIndices is a sparse matrix
+ sparsity = sp.sparse.issparse(basis_indices)
+
+ clf_poly = []
+ compute_score = True if self.verbose else False
+
+ # inverse of the observed variance of the data
+ if np.var(y) != 0:
+ Lambda = 1 / np.var(y)
+ else:
+ Lambda = 1e-6
+
+ # Bayes sparse adaptive aPCE
+ if reg_method.lower() != 'ols':
+ if reg_method.lower() == 'brr' or np.var(y) == 0:
+ clf_poly = lm.BayesianRidge(n_iter=1000, tol=1e-7,
+ fit_intercept=True,
+ normalize=True,
+ compute_score=compute_score,
+ alpha_1=1e-04, alpha_2=1e-04,
+ lambda_1=Lambda, lambda_2=Lambda)
+ clf_poly.converged = True
+
+ elif reg_method.lower() == 'ard':
+ clf_poly = lm.ARDRegression(fit_intercept=True,
+ normalize=True,
+ compute_score=compute_score,
+ n_iter=1000, tol=0.0001,
+ alpha_1=1e-3, alpha_2=1e-3,
+ lambda_1=Lambda, lambda_2=Lambda)
+
+ elif reg_method.lower() == 'fastard':
+ clf_poly = RegressionFastARD(fit_intercept=True,
+ normalize=True,
+ compute_score=compute_score,
+ n_iter=300, tol=1e-10)
+
+ elif reg_method.lower() == 'bcs':
+ clf_poly = RegressionFastLaplace(fit_intercept=False,
+ n_iter=1000, tol=1e-7)
+
+ elif reg_method.lower() == 'lars':
+ clf_poly = lm.LassoLarsCV(fit_intercept=False)
+
+ elif reg_method.lower() == 'sgdr':
+ clf_poly = lm.SGDRegressor(fit_intercept=False,
+ max_iter=5000, tol=1e-7)
+
+ elif reg_method.lower() == 'omp':
+ clf_poly = lm.OrthogonalMatchingPursuitCV(fit_intercept=False,
+ max_iter=10)
+
+ elif reg_method.lower() == 'vbl':
+ clf_poly = VBLinearRegression(fit_intercept=False)
+
+ elif reg_method.lower() == 'ebl':
+ clf_poly = EBLinearRegression(optimizer='em')
+
+ # Fit
+ clf_poly.fit(X, y)
+
+ # Select the nonzero entries of coefficients
+ # The first column must be kept (For mean calculations)
+ nnz_idx = np.nonzero(clf_poly.coef_)[0]
+
+ if len(nnz_idx) == 0 or nnz_idx[0] != 0:
+ nnz_idx = np.insert(np.nonzero(clf_poly.coef_)[0], 0, 0)
+ # Remove the zero entries for Bases and PSI if need be
+ if sparsity:
+ sparse_basis_indices = basis_indices.toarray()[nnz_idx]
+ else:
+ sparse_basis_indices = basis_indices[nnz_idx]
+ sparse_X = X[:, nnz_idx]
+
+ # Store the coefficients of the regression model
+ clf_poly.fit(sparse_X, y)
+ coeffs = clf_poly.coef_
+ else:
+ # This is for the case where all outputs are zero, thereby
+ # all coefficients are zero
+ if sparsity:
+ sparse_basis_indices = basis_indices.toarray()
+ else:
+ sparse_basis_indices = basis_indices
+ sparse_X = X
+ coeffs = clf_poly.coef_
+
+ # Ordinary least square method (OLS)
+ else:
+ if sparsity:
+ sparse_basis_indices = basis_indices.toarray()
+ else:
+ sparse_basis_indices = basis_indices
+ sparse_X = X
+
+ X_T_X = np.dot(sparse_X.T, sparse_X)
+
+ if np.linalg.cond(X_T_X) > 1e-12 and \
+ np.linalg.cond(X_T_X) < 1 / sys.float_info.epsilon:
+ # faster
+ coeffs = sp.linalg.solve(X_T_X, np.dot(sparse_X.T, y))
+ else:
+ # stabler
+ coeffs = np.dot(np.dot(np.linalg.pinv(X_T_X), sparse_X.T), y)
+
+ # Create a dict to pass the outputs
+ return_out_dict = dict()
+ return_out_dict['clf_poly'] = clf_poly
+ return_out_dict['spareMulti-Index'] = sparse_basis_indices
+ return_out_dict['sparePsi'] = sparse_X
+ return_out_dict['coeffs'] = coeffs
+ return return_out_dict
+
+ # --------------------------------------------------------------------------------------------------------
+ def adaptive_regression(self, ED_X, ED_Y, varIdx, verbose=False):
+ """
+ Adaptively fits the PCE model by comparing the scores of different
+ degrees and q-norm.
+
+ Parameters
+ ----------
+ ED_X : array of shape (n_samples, n_params)
+ Experimental design.
+ ED_Y : array of shape (n_samples,)
+ Target values, i.e. simulation results for the Experimental design.
+ varIdx : int
+ Index of the output.
+ verbose : bool, optional
+ Print out summary. The default is False.
+
+ Returns
+ -------
+ returnVars : Dict
+ Fitted estimator, best degree, best q-norm, LOOCVScore and
+ coefficients.
+
+ """
+
+ NrSamples, n_params = ED_X.shape
+ # Initialization
+ qAllCoeffs, AllCoeffs = {}, {}
+ qAllIndices_Sparse, AllIndices_Sparse = {}, {}
+ qAllclf_poly, Allclf_poly = {}, {}
+ qAllnTerms, AllnTerms = {}, {}
+ qAllLCerror, AllLCerror = {}, {}
+
+ # Extract degree array and qnorm array
+ DegreeArray = np.array([*self.allBasisIndices], dtype=int)
+ qnorm = [*self.allBasisIndices[str(int(DegreeArray[0]))]]
+
+ # Some options for EarlyStop
+ errorIncreases = False
+ # Stop degree, if LOO error does not decrease n_checks_degree times
+ n_checks_degree = 3
+ # Stop qNorm, if criterion isn't fulfilled n_checks_qNorm times
+ n_checks_qNorm = 2
+ nqnorms = len(qnorm)
+ qNormEarlyStop = True
+ if nqnorms < n_checks_qNorm+1:
+ qNormEarlyStop = False
+
+ # =====================================================================
+ # basis adaptive polynomial chaos: repeat the calculation by increasing
+ # polynomial degree until the highest accuracy is reached
+ # =====================================================================
+ # For each degree check all q-norms and choose the best one
+ scores = -np.inf * np.ones(DegreeArray.shape[0])
+ qNormScores = -np.inf * np.ones(nqnorms)
+
+ for degIdx, deg in enumerate(DegreeArray):
+
+ for qidx, q in enumerate(qnorm):
+
+ # Extract the polynomial basis indices from the pool of
+ # allBasisIndices
+ BasisIndices = self.allBasisIndices[str(deg)][str(q)]
+
+ # Assemble the Psi matrix
+ Psi = self.create_psi(BasisIndices, self.univ_p_val)
+
+ # Calulate the cofficients of the meta model
+ outs = self.fit(Psi, ED_Y, BasisIndices)
+
+ # Calculate and save the score of LOOCV
+ score, LCerror = self.corr_loocv_error(outs['clf_poly'],
+ outs['sparePsi'],
+ outs['coeffs'],
+ ED_Y)
+
+ # Check the convergence of noise for FastARD
+ if self.pce_reg_method == 'FastARD' and \
+ outs['clf_poly'].alpha_ < np.finfo(np.float32).eps:
+ score = -np.inf
+
+ qNormScores[qidx] = score
+ qAllCoeffs[str(qidx+1)] = outs['coeffs']
+ qAllIndices_Sparse[str(qidx+1)] = outs['spareMulti-Index']
+ qAllclf_poly[str(qidx+1)] = outs['clf_poly']
+ qAllnTerms[str(qidx+1)] = BasisIndices.shape[0]
+ qAllLCerror[str(qidx+1)] = LCerror
+
+ # EarlyStop check
+ # if there are at least n_checks_qNorm entries after the
+ # best one, we stop
+ if qNormEarlyStop and \
+ sum(np.isfinite(qNormScores)) > n_checks_qNorm:
+ # If the error has increased the last two iterations, stop!
+ qNormScores_nonInf = qNormScores[np.isfinite(qNormScores)]
+ deltas = np.sign(np.diff(qNormScores_nonInf))
+ if sum(deltas[-n_checks_qNorm+1:]) == 2:
+ # stop the q-norm loop here
+ break
+ if np.var(ED_Y) == 0:
+ break
+
+ # Store the score in the scores list
+ best_q = np.nanargmax(qNormScores)
+ scores[degIdx] = qNormScores[best_q]
+
+ AllCoeffs[str(degIdx+1)] = qAllCoeffs[str(best_q+1)]
+ AllIndices_Sparse[str(degIdx+1)] = qAllIndices_Sparse[str(best_q+1)]
+ Allclf_poly[str(degIdx+1)] = qAllclf_poly[str(best_q+1)]
+ AllnTerms[str(degIdx+1)] = qAllnTerms[str(best_q+1)]
+ AllLCerror[str(degIdx+1)] = qAllLCerror[str(best_q+1)]
+
+ # Check the direction of the error (on average):
+ # if it increases consistently stop the iterations
+ if len(scores[scores != -np.inf]) > n_checks_degree:
+ scores_nonInf = scores[scores != -np.inf]
+ ss = np.sign(scores_nonInf - np.max(scores_nonInf))
+ # ss<0 error decreasing
+ errorIncreases = np.sum(np.sum(ss[-2:])) <= -1*n_checks_degree
+
+ if errorIncreases:
+ break
+
+ # Check only one degree, if target matrix has zero variance
+ if np.var(ED_Y) == 0:
+ break
+
+ # ------------------ Summary of results ------------------
+ # Select the one with the best score and save the necessary outputs
+ best_deg = np.nanargmax(scores)+1
+ coeffs = AllCoeffs[str(best_deg)]
+ basis_indices = AllIndices_Sparse[str(best_deg)]
+ clf_poly = Allclf_poly[str(best_deg)]
+ LOOCVScore = np.nanmax(scores)
+ P = AllnTerms[str(best_deg)]
+ LCerror = AllLCerror[str(best_deg)]
+ degree = DegreeArray[np.nanargmax(scores)]
+ qnorm = float(qnorm[best_q])
+
+ # ------------------ Print out Summary of results ------------------
+ if self.verbose:
+ # Create PSI_Sparse by removing redundent terms
+ nnz_idx = np.nonzero(coeffs)[0]
+ BasisIndices_Sparse = basis_indices[nnz_idx]
+
+ print(f'Output variable {varIdx+1}:')
+ print('The estimation of PCE coefficients converged at polynomial '
+ f'degree {DegreeArray[best_deg-1]} with '
+ f'{len(BasisIndices_Sparse)} terms (Sparsity index = '
+ f'{round(len(BasisIndices_Sparse)/P, 3)}).')
+
+ print(f'Final ModLOO error estimate: {1-max(scores):.3e}')
+ print('\n'+'-'*50)
+
+ if verbose:
+ print('='*50)
+ print(' '*10 + ' Summary of results ')
+ print('='*50)
+
+ print("scores:\n", scores)
+ print("Best score's degree:", self.DegreeArray[best_deg-1])
+ print("NO. of terms:", len(basis_indices))
+ print("Sparsity index:", round(len(basis_indices)/P, 3))
+ print("Best Indices:\n", basis_indices)
+
+ if self.pce_reg_method in ['BRR', 'ARD']:
+ fig, ax = plt.subplots(figsize=(12, 10))
+ plt.title("Marginal log-likelihood")
+ plt.plot(clf_poly.scores_, color='navy', linewidth=2)
+ plt.ylabel("Score")
+ plt.xlabel("Iterations")
+ if self.pce_reg_method.lower() == 'bbr':
+ text = f"$\\alpha={clf_poly.alpha_:.1f}$\n"
+ f"$\\lambda={clf_poly.lambda_:.3f}$\n"
+ f"$L={clf_poly.scores_[-1]:.1f}$"
+ else:
+ text = f"$\\alpha={clf_poly.alpha_:.1f}$\n$"
+ f"\\L={clf_poly.scores_[-1]:.1f}$"
+
+ plt.text(0.75, 0.5, text, fontsize=18, transform=ax.transAxes)
+ plt.show()
+ print('='*80)
+
+ # Create a dict to pass the outputs
+ returnVars = dict()
+ returnVars['clf_poly'] = clf_poly
+ returnVars['degree'] = degree
+ returnVars['qnorm'] = qnorm
+ returnVars['coeffs'] = coeffs
+ returnVars['multi_indices'] = basis_indices
+ returnVars['LOOCVScore'] = LOOCVScore
+ returnVars['LCerror'] = LCerror
+
+ return returnVars
+
+ # -------------------------------------------------------------------------
+ def corr_loocv_error(self, clf, psi, coeffs, y):
+ """
+ Calculates the corrected LOO error for regression on regressor
+ matrix `psi` that generated the coefficients based on [1] and [2].
+
+ [1] Blatman, G., 2009. Adaptive sparse polynomial chaos expansions for
+ uncertainty propagation and sensitivity analysis (Doctoral
+ dissertation, Clermont-Ferrand 2).
+
+ [2] Blatman, G. and Sudret, B., 2011. Adaptive sparse polynomial chaos
+ expansion based on least angle regression. Journal of computational
+ Physics, 230(6), pp.2345-2367.
+
+ Parameters
+ ----------
+ clf : object
+ Fitted estimator.
+ psi : array of shape (n_samples, n_features)
+ The multivariate orthogonal polynomials (regressor).
+ coeffs : array-like of shape (n_features,)
+ Estimated cofficients.
+ y : array of shape (n_samples,)
+ Target values.
+
+ Returns
+ -------
+ Q_2 : float
+ LOOCV Validation score (1-LOOCV erro).
+ residual : array of shape (n_samples,)
+ Residual values (y - predicted targets).
+
+ """
+ psi = np.array(psi, dtype=float)
+
+ # Create PSI_Sparse by removing redundent terms
+ nnz_idx = np.nonzero(coeffs)[0]
+ if len(nnz_idx) == 0:
+ nnz_idx = [0]
+ psi_sparse = psi[:, nnz_idx]
+
+ # NrCoeffs of aPCEs
+ P = len(nnz_idx)
+ # NrEvaluation (Size of experimental design)
+ N = psi.shape[0]
+
+ # Build the projection matrix
+ PsiTPsi = np.dot(psi_sparse.T, psi_sparse)
+
+ if np.linalg.cond(PsiTPsi) > 1e-12 and \
+ np.linalg.cond(PsiTPsi) < 1/sys.float_info.epsilon:
+ # faster
+ M = sp.linalg.solve(PsiTPsi,
+ sp.sparse.eye(PsiTPsi.shape[0]).toarray())
+ else:
+ # stabler
+ M = np.linalg.pinv(PsiTPsi)
+
+ # h factor (the full matrix is not calculated explicitly,
+ # only the trace is, to save memory)
+ PsiM = np.dot(psi_sparse, M)
+
+ h = np.sum(np.multiply(PsiM, psi_sparse), axis=1, dtype=np.float128)
+
+ # ------ Calculate Error Loocv for each measurement point ----
+ # Residuals
+ if isinstance(clf, list):
+ residual = np.dot(psi, coeffs) - y
+ else:
+ residual = clf.predict(psi) - y
+
+ # Variance
+ varY = np.var(y)
+
+ if varY == 0:
+ normEmpErr = 0
+ ErrLoo = 0
+ LCerror = np.zeros((y.shape))
+ else:
+ normEmpErr = np.mean(residual**2)/varY
+
+ # LCerror = np.divide(residual, (1-h))
+ LCerror = residual / (1-h)[:, np.newaxis]
+ ErrLoo = np.mean(np.square(LCerror)) / varY
+ # if there are NaNs, just return an infinite LOO error (this
+ # happens, e.g., when a strongly underdetermined problem is solved)
+ if np.isnan(ErrLoo):
+ ErrLoo = np.inf
+
+ # Corrected Error for over-determined system
+ trM = np.trace(M)
+ if trM < 0 or abs(trM) > 1e6:
+ trM = np.trace(np.linalg.pinv(np.dot(psi.T, psi)))
+
+ # Over-determined system of Equation
+ if N > P:
+ T_factor = N/(N-P) * (1 + trM)
+
+ # Under-determined system of Equation
+ else:
+ T_factor = np.inf
+
+ CorrectedErrLoo = ErrLoo * T_factor
+
+ Q_2 = 1 - CorrectedErrLoo
+
+ return Q_2, residual
+
+ # -------------------------------------------------------------------------
+ def pca_transformation(self, Output):
+ """
+ Transforms the targets (outputs) via Principal Component Analysis
+
+ Parameters
+ ----------
+ Output : array of shape (n_samples,)
+ Target values.
+
+ Returns
+ -------
+ pca : obj
+ Fitted sklearnPCA object.
+ OutputMatrix : array of shape (n_samples,)
+ Transformed target values.
+
+ """
+ # Transform via Principal Component Analysis
+ if hasattr(self, 'var_pca_threshold'):
+ var_pca_threshold = self.var_pca_threshold
+ else:
+ var_pca_threshold = 100.0
+ n_samples, n_features = Output.shape
+
+ if hasattr(self, 'n_pca_components'):
+ n_pca_components = self.n_pca_components
+ else:
+ # Instantiate and fit sklearnPCA object
+ covar_matrix = sklearnPCA(n_components=None)
+ covar_matrix.fit(Output)
+ var = np.cumsum(np.round(covar_matrix.explained_variance_ratio_,
+ decimals=5)*100)
+ # Find the number of components to explain self.varPCAThreshold of
+ # variance
+ try:
+ n_components = np.where(var >= var_pca_threshold)[0][0] + 1
+ except IndexError:
+ n_components = min(n_samples, n_features)
+
+ n_pca_components = min(n_samples, n_features, n_components)
+
+ # Print out a report
+ print()
+ print('-' * 50)
+ print(f"PCA transformation is performed with {n_pca_components}"
+ " components.")
+ print('-' * 50)
+ print()
+
+ # Fit and transform with the selected number of components
+ pca = sklearnPCA(n_components=n_pca_components,
+ svd_solver='randomized')
+ OutputMatrix = pca.fit_transform(Output)
+
+ return pca, OutputMatrix
+
+ # -------------------------------------------------------------------------
+ def gaussian_process_emulator(self, X, y, nug_term=None, autoSelect=False,
+ varIdx=None):
+ """
+ Fits a Gaussian Process Emulator to the target given the training
+ points.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples, n_params)
+ Training points.
+ y : array of shape (n_samples,)
+ Target values.
+ nug_term : float, optional
+ Nugget term. The default is None, i.e. variance of y.
+ autoSelect : bool, optional
+ Loop over some kernels and select the best. The default is False.
+ varIdx : int, optional
+ The index number. The default is None.
+
+ Returns
+ -------
+ gp : object
+ Fitted estimator.
+
+ """
+
+ nug_term = nug_term if nug_term else np.var(y)
+
+ Kernels = [nug_term * kernels.RBF(length_scale=1.0,
+ length_scale_bounds=(1e-25, 1e15)),
+ nug_term * kernels.RationalQuadratic(length_scale=0.2,
+ alpha=1.0),
+ nug_term * kernels.Matern(length_scale=1.0,
+ length_scale_bounds=(1e-15, 1e5),
+ nu=1.5)]
+
+ # Automatic selection of the kernel
+ if autoSelect:
+ gp = {}
+ BME = []
+ for i, kernel in enumerate(Kernels):
+ gp[i] = GaussianProcessRegressor(kernel=kernel,
+ n_restarts_optimizer=3,
+ normalize_y=False)
+
+ # Fit to data using Maximum Likelihood Estimation
+ gp[i].fit(X, y)
+
+ # Store the MLE as BME score
+ BME.append(gp[i].log_marginal_likelihood())
+
+ gp = gp[np.argmax(BME)]
+
+ else:
+ gp = GaussianProcessRegressor(kernel=Kernels[0],
+ n_restarts_optimizer=3,
+ normalize_y=False)
+ gp.fit(X, y)
+
+ # Compute score
+ if varIdx is not None:
+ Score = gp.score(X, y)
+ print('-'*50)
+ print(f'Output variable {varIdx}:')
+ print('The estimation of GPE coefficients converged,')
+ print(f'with the R^2 score: {Score:.3f}')
+ print('-'*50)
+
+ return gp
+
+ # -------------------------------------------------------------------------
+ def eval_metamodel(self, samples=None, nsamples=None,
+ sampling_method='random', return_samples=False):
+ """
+ Evaluates meta-model at the requested samples. One can also generate
+ nsamples.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params), optional
+ Samples to evaluate meta-model at. The default is None.
+ nsamples : int, optional
+ Number of samples to generate, if no `samples` is provided. The
+ default is None.
+ sampling_method : str, optional
+ Type of sampling, if no `samples` is provided. The default is
+ 'random'.
+ return_samples : bool, optional
+ Retun samples, if no `samples` is provided. The default is False.
+
+ Returns
+ -------
+ mean_pred : dict
+ Mean of the predictions.
+ std_pred : dict
+ Standard deviatioon of the predictions.
+ """
+ if self.meta_model_type.lower() == 'gpe':
+ model_dict = self.gp_poly
+ else:
+ model_dict = self.coeffs_dict
+
+ if samples is None:
+ if nsamples is None:
+ self.n_samples = 100000
+ else:
+ self.n_samples = nsamples
+
+ samples = self.ExpDesign.generate_samples(self.n_samples,
+ sampling_method)
+ else:
+ self.samples = samples
+ self.n_samples = len(samples)
+
+ # Transform samples
+ samples = self.ExpDesign.transform(samples)
+
+ if self.meta_model_type.lower() != 'gpe':
+ univ_p_val = self.univ_basis_vals(samples,
+ n_max=np.max(self.pce_deg))
+
+ mean_pred = {}
+ std_pred = {}
+
+ # Loop over outputs
+ for ouput, values in model_dict.items():
+
+ mean = np.zeros((len(samples), len(values)))
+ std = np.zeros((len(samples), len(values)))
+ idx = 0
+ for in_key, InIdxValues in values.items():
+
+ # Perdiction with GPE
+ if self.meta_model_type.lower() == 'gpe':
+ X_T = self.x_scaler[ouput].transform(samples)
+ gp = self.gp_poly[ouput][in_key]
+ y_mean, y_std = gp.predict(X_T, return_std=True)
+
+ else:
+ # Perdiction with PCE or pcekriging
+ # Assemble Psi matrix
+ psi = self.create_psi(self.basis_dict[ouput][in_key],
+ univ_p_val)
+ # Perdiction
+ try:
+ # with error bar
+ clf_poly = self.clf_poly[ouput][in_key]
+ y_mean, y_std = clf_poly.predict(psi, return_std=True)
+
+ except:
+ # without error bar
+ coeffs = self.coeffs_dict[ouput][in_key]
+ y_mean = np.dot(psi, coeffs)
+ y_std = np.zeros_like(y_mean)
+
+ mean[:, idx] = y_mean
+ std[:, idx] = y_std
+ idx += 1
+
+ if self.dim_red_method.lower() == 'pca':
+ PCA = self.pca[ouput]
+ mean_pred[ouput] = PCA.mean_ + np.dot(mean, PCA.components_)
+ std_pred[ouput] = np.sqrt(np.dot(std**2, PCA.components_**2))
+ else:
+ mean_pred[ouput] = mean
+ std_pred[ouput] = std
+
+ if return_samples:
+ return mean_pred, std_pred, samples
+ else:
+ return mean_pred, std_pred
+
+ # -------------------------------------------------------------------------
+ def create_model_error(self, X, y, name='Calib'):
+ """
+ Fits a GPE-based model error.
+
+ Parameters
+ ----------
+ X : array of shape (n_outputs, n_inputs)
+ Input array. It can contain any forcing inputs or coordinates of
+ extracted data.
+ y : array of shape (n_outputs,)
+ The model response for the MAP parameter set.
+ name : str, optional
+ Calibration or validation. The default is `'Calib'`.
+
+ Returns
+ -------
+ self: object
+ Self object.
+
+ """
+ Model = self.ModelObj
+ outputNames = Model.Output.Names
+ self.errorRegMethod = 'GPE'
+ self.errorclf_poly = self.auto_vivification()
+ self.errorScale = self.auto_vivification()
+
+ # Read data
+ MeasuredData = Model.read_observation(case=name)
+
+ # Fitting GPR based bias model
+ for out in outputNames:
+ nan_idx = ~np.isnan(MeasuredData[out])
+ # Select data
+ try:
+ data = MeasuredData[out].values[nan_idx]
+ except AttributeError:
+ data = MeasuredData[out][nan_idx]
+
+ # Prepare the input matrix
+ scaler = MinMaxScaler()
+ delta = data # - y[out][0]
+ BiasInputs = np.hstack((X[out], y[out].reshape(-1, 1)))
+ X_S = scaler.fit_transform(BiasInputs)
+ gp = self.gaussian_process_emulator(X_S, delta)
+
+ self.errorScale[out]["y_1"] = scaler
+ self.errorclf_poly[out]["y_1"] = gp
+
+ return self
+
+ # -------------------------------------------------------------------------
+ def eval_model_error(self, X, y_pred):
+ """
+ Evaluates the error model.
+
+ Parameters
+ ----------
+ X : array
+ Inputs.
+ y_pred : dict
+ Predictions.
+
+ Returns
+ -------
+ mean_pred : dict
+ Mean predition of the GPE-based error model.
+ std_pred : dict
+ standard deviation of the GPE-based error model.
+
+ """
+ mean_pred = {}
+ std_pred = {}
+
+ for Outkey, ValuesDict in self.errorclf_poly.items():
+
+ pred_mean = np.zeros_like(y_pred[Outkey])
+ pred_std = np.zeros_like(y_pred[Outkey])
+
+ for Inkey, InIdxValues in ValuesDict.items():
+
+ gp = self.errorclf_poly[Outkey][Inkey]
+ scaler = self.errorScale[Outkey][Inkey]
+
+ # Transform Samples using scaler
+ for j, pred in enumerate(y_pred[Outkey]):
+ BiasInputs = np.hstack((X[Outkey], pred.reshape(-1, 1)))
+ Samples_S = scaler.transform(BiasInputs)
+ y_hat, y_std = gp.predict(Samples_S, return_std=True)
+ pred_mean[j] = y_hat
+ pred_std[j] = y_std
+ # pred_mean[j] += pred
+
+ mean_pred[Outkey] = pred_mean
+ std_pred[Outkey] = pred_std
+
+ return mean_pred, std_pred
+
+ # -------------------------------------------------------------------------
+ class auto_vivification(dict):
+ """
+ Implementation of perl's AutoVivification feature.
+
+ Source: https://stackoverflow.com/a/651879/18082457
+ """
+
+ def __getitem__(self, item):
+ try:
+ return dict.__getitem__(self, item)
+ except KeyError:
+ value = self[item] = type(self)()
+ return value
+
+ # -------------------------------------------------------------------------
+ def __select_degree(self, ndim, nSamples):
+ """
+ Selects degree based on the number of samples and parameters in the
+ sequential design.
+
+ Parameters
+ ----------
+ ndim : TYPE
+ DESCRIPTION.
+ nSamples : TYPE
+ DESCRIPTION.
+
+ Returns
+ -------
+ TYPE
+ DESCRIPTION.
+
+ """
+ # Define the DegreeArray
+ max_deg = np.max(self.pce_deg)
+ min_Deg = np.min(self.pce_deg)
+ nitr = nSamples - self.ExpDesign.n_init_samples
+
+ # Check q-norm
+ if not np.isscalar(self.pce_q_norm):
+ self.pce_q_norm = np.array(self.pce_q_norm)
+ else:
+ self.pce_q_norm = np.array([self.pce_q_norm])
+
+ def M_uptoMax(maxDeg):
+ n_combo = np.zeros(maxDeg)
+ for i, d in enumerate(range(1, maxDeg+1)):
+ n_combo[i] = math.factorial(ndim+d)
+ n_combo[i] /= math.factorial(ndim) * math.factorial(d)
+ return n_combo
+
+ if self.ExpDesignFlag != 'sequential':
+ degNew = max_deg
+ else:
+ d = nitr if nitr != 0 and self.n_params > 5 else 1
+ min_index = np.argmin(abs(M_uptoMax(max_deg)-ndim*nSamples*d))
+ degNew = range(1, max_deg+1)[min_index]
+
+ if degNew > min_Deg and self.pce_reg_method.lower() != 'fastard':
+ DegreeArray = np.arange(min_Deg, degNew+1)
+ else:
+ DegreeArray = np.array([degNew])
+
+ return DegreeArray</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="surrogate_models.MetaModel"><code class="flex name class">
+<span>class <span class="ident">MetaModel</span></span>
+<span>(</span><span>input_obj, meta_model_type='PCE', pce_reg_method='OLS', pce_deg=1, pce_q_norm=1.0, dim_red_method='no', verbose=False)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Meta (surrogate) model</p>
+<p>This class trains a surrogate model. It accepts an input object (input_obj)
+containing the specification of the distributions for uncertain parameters
+and a model object with instructions on how to run the computational model.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>input_obj</code></strong> : <code>obj</code></dt>
+<dd>Input object with the information on the model input parameters.</dd>
+<dt><strong><code>meta_model_type</code></strong> : <code>str</code></dt>
+<dd>Surrogate model types. Three surrogate model types are supported:
+polynomial chaos expansion (<code>PCE</code>), arbitrary PCE (<code>aPCE</code>) and
+Gaussian process regression (<code>GPE</code>). Default is PCE.</dd>
+<dt><strong><code>pce_reg_method</code></strong> : <code>str</code></dt>
+<dd>
+<p>PCE regression method to compute the coefficients. The following
+regression methods are available:</p>
+<ol>
+<li>OLS: Ordinary Least Square method</li>
+<li>BRR: Bayesian Ridge Regression</li>
+<li>LARS: Least angle regression</li>
+<li>ARD: Bayesian ARD Regression</li>
+<li>FastARD: Fast Bayesian ARD Regression</li>
+<li>VBL: Variational Bayesian Learning</li>
+<li>EBL: Emperical Bayesian Learning
+Default is <code>OLS</code>.</li>
+</ol>
+</dd>
+<dt><strong><code>pce_deg</code></strong> : <code>int</code> or <code>list</code> of <code>int</code></dt>
+<dd>Polynomial degree(s). If a list is given, an adaptive algorithm is used
+to find the best degree with the lowest Leave-One-Out cross-validation
+(LOO) error (or the highest score=1-LOO). Default is <code>1</code>.</dd>
+<dt><strong><code>pce_q_norm</code></strong> : <code>float</code></dt>
+<dd>Hyperbolic (or q-norm) truncation for multi-indices of multivariate
+polynomials. Default is <code>1.0</code>.</dd>
+<dt><strong><code>dim_red_method</code></strong> : <code>str</code></dt>
+<dd>Dimensionality reduction method for the output space. The available
+method is based on principal component analysis (PCA). The Default is
+<code>'no'</code>. There are two ways to select number of components: use
+percentage of the explainable variance threshold (between 0 and 100)
+(Option A) or direct prescription of components' number (Option B):<pre><code>>>> MetaModelOpts.dim_red_method = 'PCA'
+>>> MetaModelOpts.var_pca_threshold = 99.999 # Option A
+>>> MetaModelOpts.n_pca_components = 12 # Option B
+</code></pre>
+</dd>
+<dt><strong><code>verbose</code></strong> : <code>bool</code></dt>
+<dd>Prints summary of the regression results. Default is <code>False</code>.</dd>
+</dl>
+<h2 id="note">Note</h2>
+<p>To define the sampling methods and the training set, an experimental design
+instance shall be defined. This can be done by:</p>
+<pre><code class="language-python-repl">>>> MetaModelOpts.add_ExpDesign()
+</code></pre>
+<p>Two experimental design schemes are supported: one-shot (<code>normal</code>) and
+adaptive sequential (<code>sequential</code>) designs.
+For experimental design refer to <code>ExpDesigns</code>.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class MetaModel:
+ """
+ Meta (surrogate) model
+
+ This class trains a surrogate model. It accepts an input object (input_obj)
+ containing the specification of the distributions for uncertain parameters
+ and a model object with instructions on how to run the computational model.
+
+ Attributes
+ ----------
+ input_obj : obj
+ Input object with the information on the model input parameters.
+ meta_model_type : str
+ Surrogate model types. Three surrogate model types are supported:
+ polynomial chaos expansion (`PCE`), arbitrary PCE (`aPCE`) and
+ Gaussian process regression (`GPE`). Default is PCE.
+ pce_reg_method : str
+ PCE regression method to compute the coefficients. The following
+ regression methods are available:
+
+ 1. OLS: Ordinary Least Square method
+ 2. BRR: Bayesian Ridge Regression
+ 3. LARS: Least angle regression
+ 4. ARD: Bayesian ARD Regression
+ 5. FastARD: Fast Bayesian ARD Regression
+ 6. VBL: Variational Bayesian Learning
+ 7. EBL: Emperical Bayesian Learning
+ Default is `OLS`.
+ pce_deg : int or list of int
+ Polynomial degree(s). If a list is given, an adaptive algorithm is used
+ to find the best degree with the lowest Leave-One-Out cross-validation
+ (LOO) error (or the highest score=1-LOO). Default is `1`.
+ pce_q_norm : float
+ Hyperbolic (or q-norm) truncation for multi-indices of multivariate
+ polynomials. Default is `1.0`.
+ dim_red_method : str
+ Dimensionality reduction method for the output space. The available
+ method is based on principal component analysis (PCA). The Default is
+ `'no'`. There are two ways to select number of components: use
+ percentage of the explainable variance threshold (between 0 and 100)
+ (Option A) or direct prescription of components' number (Option B):
+
+ >>> MetaModelOpts.dim_red_method = 'PCA'
+ >>> MetaModelOpts.var_pca_threshold = 99.999 # Option A
+ >>> MetaModelOpts.n_pca_components = 12 # Option B
+
+ verbose : bool
+ Prints summary of the regression results. Default is `False`.
+
+ Note
+ -------
+ To define the sampling methods and the training set, an experimental design
+ instance shall be defined. This can be done by:
+
+ >>> MetaModelOpts.add_ExpDesign()
+
+ Two experimental design schemes are supported: one-shot (`normal`) and
+ adaptive sequential (`sequential`) designs.
+ For experimental design refer to `ExpDesigns`.
+
+ """
+
+ def __init__(self, input_obj, meta_model_type='PCE', pce_reg_method='OLS',
+ pce_deg=1, pce_q_norm=1.0, dim_red_method='no',
+ verbose=False):
+
+ self.input_obj = input_obj
+ self.meta_model_type = meta_model_type
+ self.pce_reg_method = pce_reg_method
+ self.pce_deg = pce_deg
+ self.pce_q_norm = pce_q_norm
+ self.dim_red_method = dim_red_method
+ self.verbose = False
+
+ # -------------------------------------------------------------------------
+ def create_metamodel(self, Model):
+ """
+ Starts the training of the meta-model for the model objects containg
+ the given computational model.
+
+ Parameters
+ ----------
+ Model : obj
+ Model object.
+
+ Returns
+ -------
+ metamodel : obj
+ The meta model object.
+
+ """
+ self.ModelObj = Model
+ self.n_params = len(self.input_obj.Marginals)
+ self.ExpDesignFlag = 'normal'
+ # --- Prepare pce degree ---
+ if self.meta_model_type.lower() == 'pce':
+ if type(self.pce_deg) is not np.ndarray:
+ self.pce_deg = np.array(self.pce_deg)
+
+ if self.ExpDesign.method == 'sequential':
+ from .sequential_design import SeqDesign
+ seq_design = SeqDesign(self)
+ metamodel = seq_design.train_seq_design(Model)
+
+ elif self.ExpDesign.method == 'normal':
+ self.ExpDesignFlag = 'normal'
+ metamodel = self.train_norm_design(Model)
+
+ else:
+ raise Exception("The method for experimental design you requested"
+ " has not been implemented yet.")
+
+ # Zip the model run directories
+ if self.ModelObj.link_type.lower() == 'pylink':
+ Model.zip_subdirs(Model.name, f'{Model.name}_')
+
+ return metamodel
+
+ # -------------------------------------------------------------------------
+ def train_norm_design(self, Model, verbose=False):
+ """
+ This function loops over the outputs and each time step/point and fits
+ the meta model.
+
+ Parameters
+ ----------
+ Model : obj
+ Model object.
+ verbose : bool, optional
+ Flag for a sequential design in silent mode. The default is False.
+
+ Returns
+ -------
+ self: obj
+ Meta-model object.
+
+ """
+
+ # Get the collocation points to run the forward model
+ CollocationPoints, OutputDict = self.generate_ExpDesign(Model)
+
+ # Initialize the nested dictionaries
+ self.deg_dict = self.auto_vivification()
+ self.q_norm_dict = self.auto_vivification()
+ self.coeffs_dict = self.auto_vivification()
+ self.basis_dict = self.auto_vivification()
+ self.score_dict = self.auto_vivification()
+ self.clf_poly = self.auto_vivification()
+ self.gp_poly = self.auto_vivification()
+ self.pca = self.auto_vivification()
+ self.LCerror = self.auto_vivification()
+ self.x_scaler = {}
+
+ # Define the DegreeArray
+ nSamples, ndim = CollocationPoints.shape
+ self.DegreeArray = self.__select_degree(ndim, nSamples)
+
+ # Generate all basis indices
+ self.allBasisIndices = self.auto_vivification()
+ for deg in self.DegreeArray:
+ keys = self.allBasisIndices.keys()
+ if deg not in np.fromiter(keys, dtype=float):
+ # Generate the polynomial basis indices
+ for qidx, q in enumerate(self.pce_q_norm):
+ basis_indices = self.create_basis_indices(degree=deg,
+ q_norm=q)
+ self.allBasisIndices[str(deg)][str(q)] = basis_indices
+
+ # Evaluate the univariate polynomials on ExpDesign
+ if self.meta_model_type.lower() != 'gpe':
+ self.univ_p_val = self.univ_basis_vals(CollocationPoints)
+
+ if 'x_values' in OutputDict:
+ self.ExpDesign.x_values = OutputDict['x_values']
+ del OutputDict['x_values']
+
+ # --- Loop through data points and fit the surrogate ---
+ if not verbose:
+ print(f"\n>>>> Training the {self.meta_model_type} metamodel "
+ "started. <<<<<<\n")
+ items = tqdm(OutputDict.items(), desc="Fitting regression")
+ else:
+ items = OutputDict.items()
+
+ # For loop over the components/outputs
+ for key, Output in items:
+
+ # Dimensionality reduction with PCA, if specified
+ if self.dim_red_method.lower() == 'pca':
+ self.pca[key], target = self.pca_transformation(Output)
+ else:
+ target = Output
+
+ # Parallel fit regression
+ if self.meta_model_type.lower() == 'gpe':
+ # Prepare the input matrix
+ scaler = MinMaxScaler()
+ X_S = scaler.fit_transform(CollocationPoints)
+
+ self.x_scaler[key] = scaler
+
+ out = Parallel(n_jobs=-1, prefer='threads')(
+ delayed(self.gaussian_process_emulator)(X_S, target[:, idx])
+ for idx in range(target.shape[1]))
+
+ for idx in range(target.shape[1]):
+ self.gp_poly[key][f"y_{idx+1}"] = out[idx]
+
+ else:
+ out = Parallel(n_jobs=-1, prefer='threads')(
+ delayed(self.adaptive_regression)(CollocationPoints,
+ target[:, idx], idx)
+ for idx in range(target.shape[1]))
+
+ for i in range(target.shape[1]):
+ # Create a dict to pass the variables
+ self.deg_dict[key][f"y_{i+1}"] = out[i]['degree']
+ self.q_norm_dict[key][f"y_{i+1}"] = out[i]['qnorm']
+ self.coeffs_dict[key][f"y_{i+1}"] = out[i]['coeffs']
+ self.basis_dict[key][f"y_{i+1}"] = out[i]['multi_indices']
+ self.score_dict[key][f"y_{i+1}"] = out[i]['LOOCVScore']
+ self.clf_poly[key][f"y_{i+1}"] = out[i]['clf_poly']
+ self.LCerror[key][f"y_{i+1}"] = out[i]['LCerror']
+
+ if not verbose:
+ print(f"\n>>>> Training the {self.meta_model_type} metamodel"
+ " sucessfully completed. <<<<<<\n")
+
+ return self
+
+ # -------------------------------------------------------------------------
+ def create_basis_indices(self, degree, q_norm):
+ """
+ Creates set of selected multi-indices of multivariate polynomials for
+ certain parameter numbers, polynomial degree, hyperbolic (or q-norm)
+ truncation scheme.
+
+ Parameters
+ ----------
+ degree : int
+ Polynomial degree.
+ q_norm : float
+ hyperbolic (or q-norm) truncation.
+
+ Returns
+ -------
+ basis_indices : array of shape (n_terms, n_params)
+ Multi-indices of multivariate polynomials.
+
+ """
+ basis_indices = glexindex(start=0, stop=degree+1,
+ dimensions=self.n_params,
+ cross_truncation=q_norm,
+ reverse=False, graded=True)
+ return basis_indices
+
+ # -------------------------------------------------------------------------
+ def add_ExpDesign(self):
+ """
+ Instanciates experimental design object.
+
+ Returns
+ -------
+ None.
+
+ """
+ self.ExpDesign = ExpDesigns(self.input_obj,
+ meta_Model=self.meta_model_type)
+
+ # -------------------------------------------------------------------------
+ def generate_ExpDesign(self, Model):
+ """
+ Prepares the experimental design either by reading from the prescribed
+ data or running simulations.
+
+ Parameters
+ ----------
+ Model : obj
+ Model object.
+
+ Raises
+ ------
+ Exception
+ If model sumulations are not provided properly.
+
+ Returns
+ -------
+ ED_X_tr: array of shape (n_samples, n_params)
+ Training samples transformed by an isoprobabilistic transformation.
+ ED_Y: dict
+ Model simulations (target) for all outputs.
+ """
+ ExpDesign = self.ExpDesign
+ if self.ExpDesignFlag != 'sequential':
+ # Read ExpDesign (training and targets) from the provided hdf5
+ if ExpDesign.hdf5_file is not None:
+
+ # Read hdf5 file
+ f = h5py.File(ExpDesign.hdf5_file, 'r+')
+
+ # Read EDX and pass it to ExpDesign object
+ try:
+ ExpDesign.X = np.array(f["EDX/New_init_"])
+ except KeyError:
+ ExpDesign.X = np.array(f["EDX/init_"])
+
+ # Update number of initial samples
+ ExpDesign.n_init_samples = ExpDesign.X.shape[0]
+
+ # Read EDX and pass it to ExpDesign object
+ out_names = self.ModelObj.Output.names
+ ExpDesign.Y = {}
+
+ # Extract x values
+ try:
+ ExpDesign.Y["x_values"] = dict()
+ for varIdx, var in enumerate(out_names):
+ x = np.array(f[f"x_values/{var}"])
+ ExpDesign.Y["x_values"][var] = x
+ except KeyError:
+ ExpDesign.Y["x_values"] = np.array(f["x_values"])
+
+ # Store the output
+ for varIdx, var in enumerate(out_names):
+ try:
+ y = np.array(f[f"EDY/{var}/New_init_"])
+ except KeyError:
+ y = np.array(f[f"EDY/{var}/init_"])
+ ExpDesign.Y[var] = y
+ f.close()
+ else:
+ # Check if an old hdf5 file exists: if yes, rename it
+ hdf5file = f'ExpDesign_{self.ModelObj.name}.hdf5'
+ if os.path.exists(hdf5file):
+ os.rename(hdf5file, 'old_'+hdf5file)
+
+ # ---- Prepare X samples ----
+ ED_X, ED_X_tr = ExpDesign.generate_ED(ExpDesign.n_init_samples,
+ ExpDesign.sampling_method,
+ transform=True,
+ max_pce_deg=np.max(self.pce_deg))
+ ExpDesign.X = ED_X
+ ExpDesign.collocationPoints = ED_X_tr
+ self.bound_tuples = ExpDesign.bound_tuples
+
+ # ---- Run simulations at X ----
+ if not hasattr(ExpDesign, 'Y') or ExpDesign.Y is None:
+ print('\n Now the forward model needs to be run!\n')
+ ED_Y, up_ED_X = Model.run_model_parallel(ED_X)
+ ExpDesign.X = up_ED_X
+ self.ModelOutputDict = ED_Y
+ ExpDesign.Y = ED_Y
+ else:
+ # Check if a dict has been passed.
+ if type(ExpDesign.Y) is dict:
+ self.ModelOutputDict = ExpDesign.Y
+ else:
+ raise Exception('Please provide either a dictionary or a hdf5'
+ 'file to ExpDesign.hdf5_file argument.')
+
+ return ED_X_tr, self.ModelOutputDict
+
+ # -------------------------------------------------------------------------
+ def univ_basis_vals(self, samples, n_max=None):
+ """
+ Evaluates univariate regressors along input directions.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params)
+ Samples.
+ n_max : int, optional
+ Maximum polynomial degree. The default is `None`.
+
+ Returns
+ -------
+ univ_basis: array of shape (n_samples, n_params, n_max+1)
+ All univariate regressors up to n_max.
+ """
+ # Extract information
+ poly_types = self.ExpDesign.poly_types
+ if samples.ndim != 2:
+ samples = samples.reshape(1, len(samples))
+ n_max = np.max(self.pce_deg) if n_max is None else n_max
+
+ # Extract poly coeffs
+ if self.ExpDesign.input_data_given or self.ExpDesign.apce:
+ apolycoeffs = self.ExpDesign.polycoeffs
+ else:
+ apolycoeffs = None
+
+ # Evaluate univariate basis
+ univ_basis = eval_univ_basis(samples, n_max, poly_types, apolycoeffs)
+
+ return univ_basis
+
+ # -------------------------------------------------------------------------
+ def create_psi(self, basis_indices, univ_p_val):
+ """
+ This function assemble the design matrix Psi from the given basis index
+ set INDICES and the univariate polynomial evaluations univ_p_val.
+
+ Parameters
+ ----------
+ basis_indices : array of shape (n_terms, n_params)
+ Multi-indices of multivariate polynomials.
+ univ_p_val : array of (n_samples, n_params, n_max+1)
+ All univariate regressors up to `n_max`.
+
+ Raises
+ ------
+ ValueError
+ n_terms in arguments do not match.
+
+ Returns
+ -------
+ psi : array of shape (n_samples, n_terms)
+ Multivariate regressors.
+
+ """
+ # Check if BasisIndices is a sparse matrix
+ sparsity = sp.sparse.issparse(basis_indices)
+ if sparsity:
+ basis_indices = basis_indices.toarray()
+
+ # Initialization and consistency checks
+ # number of input variables
+ n_params = univ_p_val.shape[1]
+
+ # Size of the experimental design
+ n_samples = univ_p_val.shape[0]
+
+ # number of basis terms
+ n_terms = basis_indices.shape[0]
+
+ # check that the variables have consistent sizes
+ if n_params != basis_indices.shape[1]:
+ raise ValueError("The shapes of basis_indices and univ_p_val don't"
+ " match!!")
+
+ # Preallocate the Psi matrix for performance
+ psi = np.ones((n_samples, n_terms))
+ # Assemble the Psi matrix
+ for m in range(basis_indices.shape[1]):
+ aa = np.where(basis_indices[:, m] > 0)[0]
+ try:
+ basisIdx = basis_indices[aa, m]
+ bb = np.reshape(univ_p_val[:, m, basisIdx], psi[:, aa].shape)
+ psi[:, aa] = np.multiply(psi[:, aa], bb)
+ except ValueError as err:
+ raise err
+
+ return psi
+
+ # -------------------------------------------------------------------------
+ def fit(self, X, y, basis_indices, reg_method=None):
+ """
+ Fit regression using the regression method provided.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples, n_features)
+ Training vector, where n_samples is the number of samples and
+ n_features is the number of features.
+ y : array of shape (n_samples,)
+ Target values.
+ basis_indices : array of shape (n_terms, n_params)
+ Multi-indices of multivariate polynomials.
+ reg_method : str, optional
+ DESCRIPTION. The default is None.
+
+ Returns
+ -------
+ return_out_dict : Dict
+ Fitted estimator, spareMulti-Index, sparseX and coefficients.
+
+ """
+ if reg_method is None:
+ reg_method = self.pce_reg_method
+
+ # Check if BasisIndices is a sparse matrix
+ sparsity = sp.sparse.issparse(basis_indices)
+
+ clf_poly = []
+ compute_score = True if self.verbose else False
+
+ # inverse of the observed variance of the data
+ if np.var(y) != 0:
+ Lambda = 1 / np.var(y)
+ else:
+ Lambda = 1e-6
+
+ # Bayes sparse adaptive aPCE
+ if reg_method.lower() != 'ols':
+ if reg_method.lower() == 'brr' or np.var(y) == 0:
+ clf_poly = lm.BayesianRidge(n_iter=1000, tol=1e-7,
+ fit_intercept=True,
+ normalize=True,
+ compute_score=compute_score,
+ alpha_1=1e-04, alpha_2=1e-04,
+ lambda_1=Lambda, lambda_2=Lambda)
+ clf_poly.converged = True
+
+ elif reg_method.lower() == 'ard':
+ clf_poly = lm.ARDRegression(fit_intercept=True,
+ normalize=True,
+ compute_score=compute_score,
+ n_iter=1000, tol=0.0001,
+ alpha_1=1e-3, alpha_2=1e-3,
+ lambda_1=Lambda, lambda_2=Lambda)
+
+ elif reg_method.lower() == 'fastard':
+ clf_poly = RegressionFastARD(fit_intercept=True,
+ normalize=True,
+ compute_score=compute_score,
+ n_iter=300, tol=1e-10)
+
+ elif reg_method.lower() == 'bcs':
+ clf_poly = RegressionFastLaplace(fit_intercept=False,
+ n_iter=1000, tol=1e-7)
+
+ elif reg_method.lower() == 'lars':
+ clf_poly = lm.LassoLarsCV(fit_intercept=False)
+
+ elif reg_method.lower() == 'sgdr':
+ clf_poly = lm.SGDRegressor(fit_intercept=False,
+ max_iter=5000, tol=1e-7)
+
+ elif reg_method.lower() == 'omp':
+ clf_poly = lm.OrthogonalMatchingPursuitCV(fit_intercept=False,
+ max_iter=10)
+
+ elif reg_method.lower() == 'vbl':
+ clf_poly = VBLinearRegression(fit_intercept=False)
+
+ elif reg_method.lower() == 'ebl':
+ clf_poly = EBLinearRegression(optimizer='em')
+
+ # Fit
+ clf_poly.fit(X, y)
+
+ # Select the nonzero entries of coefficients
+ # The first column must be kept (For mean calculations)
+ nnz_idx = np.nonzero(clf_poly.coef_)[0]
+
+ if len(nnz_idx) == 0 or nnz_idx[0] != 0:
+ nnz_idx = np.insert(np.nonzero(clf_poly.coef_)[0], 0, 0)
+ # Remove the zero entries for Bases and PSI if need be
+ if sparsity:
+ sparse_basis_indices = basis_indices.toarray()[nnz_idx]
+ else:
+ sparse_basis_indices = basis_indices[nnz_idx]
+ sparse_X = X[:, nnz_idx]
+
+ # Store the coefficients of the regression model
+ clf_poly.fit(sparse_X, y)
+ coeffs = clf_poly.coef_
+ else:
+ # This is for the case where all outputs are zero, thereby
+ # all coefficients are zero
+ if sparsity:
+ sparse_basis_indices = basis_indices.toarray()
+ else:
+ sparse_basis_indices = basis_indices
+ sparse_X = X
+ coeffs = clf_poly.coef_
+
+ # Ordinary least square method (OLS)
+ else:
+ if sparsity:
+ sparse_basis_indices = basis_indices.toarray()
+ else:
+ sparse_basis_indices = basis_indices
+ sparse_X = X
+
+ X_T_X = np.dot(sparse_X.T, sparse_X)
+
+ if np.linalg.cond(X_T_X) > 1e-12 and \
+ np.linalg.cond(X_T_X) < 1 / sys.float_info.epsilon:
+ # faster
+ coeffs = sp.linalg.solve(X_T_X, np.dot(sparse_X.T, y))
+ else:
+ # stabler
+ coeffs = np.dot(np.dot(np.linalg.pinv(X_T_X), sparse_X.T), y)
+
+ # Create a dict to pass the outputs
+ return_out_dict = dict()
+ return_out_dict['clf_poly'] = clf_poly
+ return_out_dict['spareMulti-Index'] = sparse_basis_indices
+ return_out_dict['sparePsi'] = sparse_X
+ return_out_dict['coeffs'] = coeffs
+ return return_out_dict
+
+ # --------------------------------------------------------------------------------------------------------
+ def adaptive_regression(self, ED_X, ED_Y, varIdx, verbose=False):
+ """
+ Adaptively fits the PCE model by comparing the scores of different
+ degrees and q-norm.
+
+ Parameters
+ ----------
+ ED_X : array of shape (n_samples, n_params)
+ Experimental design.
+ ED_Y : array of shape (n_samples,)
+ Target values, i.e. simulation results for the Experimental design.
+ varIdx : int
+ Index of the output.
+ verbose : bool, optional
+ Print out summary. The default is False.
+
+ Returns
+ -------
+ returnVars : Dict
+ Fitted estimator, best degree, best q-norm, LOOCVScore and
+ coefficients.
+
+ """
+
+ NrSamples, n_params = ED_X.shape
+ # Initialization
+ qAllCoeffs, AllCoeffs = {}, {}
+ qAllIndices_Sparse, AllIndices_Sparse = {}, {}
+ qAllclf_poly, Allclf_poly = {}, {}
+ qAllnTerms, AllnTerms = {}, {}
+ qAllLCerror, AllLCerror = {}, {}
+
+ # Extract degree array and qnorm array
+ DegreeArray = np.array([*self.allBasisIndices], dtype=int)
+ qnorm = [*self.allBasisIndices[str(int(DegreeArray[0]))]]
+
+ # Some options for EarlyStop
+ errorIncreases = False
+ # Stop degree, if LOO error does not decrease n_checks_degree times
+ n_checks_degree = 3
+ # Stop qNorm, if criterion isn't fulfilled n_checks_qNorm times
+ n_checks_qNorm = 2
+ nqnorms = len(qnorm)
+ qNormEarlyStop = True
+ if nqnorms < n_checks_qNorm+1:
+ qNormEarlyStop = False
+
+ # =====================================================================
+ # basis adaptive polynomial chaos: repeat the calculation by increasing
+ # polynomial degree until the highest accuracy is reached
+ # =====================================================================
+ # For each degree check all q-norms and choose the best one
+ scores = -np.inf * np.ones(DegreeArray.shape[0])
+ qNormScores = -np.inf * np.ones(nqnorms)
+
+ for degIdx, deg in enumerate(DegreeArray):
+
+ for qidx, q in enumerate(qnorm):
+
+ # Extract the polynomial basis indices from the pool of
+ # allBasisIndices
+ BasisIndices = self.allBasisIndices[str(deg)][str(q)]
+
+ # Assemble the Psi matrix
+ Psi = self.create_psi(BasisIndices, self.univ_p_val)
+
+ # Calulate the cofficients of the meta model
+ outs = self.fit(Psi, ED_Y, BasisIndices)
+
+ # Calculate and save the score of LOOCV
+ score, LCerror = self.corr_loocv_error(outs['clf_poly'],
+ outs['sparePsi'],
+ outs['coeffs'],
+ ED_Y)
+
+ # Check the convergence of noise for FastARD
+ if self.pce_reg_method == 'FastARD' and \
+ outs['clf_poly'].alpha_ < np.finfo(np.float32).eps:
+ score = -np.inf
+
+ qNormScores[qidx] = score
+ qAllCoeffs[str(qidx+1)] = outs['coeffs']
+ qAllIndices_Sparse[str(qidx+1)] = outs['spareMulti-Index']
+ qAllclf_poly[str(qidx+1)] = outs['clf_poly']
+ qAllnTerms[str(qidx+1)] = BasisIndices.shape[0]
+ qAllLCerror[str(qidx+1)] = LCerror
+
+ # EarlyStop check
+ # if there are at least n_checks_qNorm entries after the
+ # best one, we stop
+ if qNormEarlyStop and \
+ sum(np.isfinite(qNormScores)) > n_checks_qNorm:
+ # If the error has increased the last two iterations, stop!
+ qNormScores_nonInf = qNormScores[np.isfinite(qNormScores)]
+ deltas = np.sign(np.diff(qNormScores_nonInf))
+ if sum(deltas[-n_checks_qNorm+1:]) == 2:
+ # stop the q-norm loop here
+ break
+ if np.var(ED_Y) == 0:
+ break
+
+ # Store the score in the scores list
+ best_q = np.nanargmax(qNormScores)
+ scores[degIdx] = qNormScores[best_q]
+
+ AllCoeffs[str(degIdx+1)] = qAllCoeffs[str(best_q+1)]
+ AllIndices_Sparse[str(degIdx+1)] = qAllIndices_Sparse[str(best_q+1)]
+ Allclf_poly[str(degIdx+1)] = qAllclf_poly[str(best_q+1)]
+ AllnTerms[str(degIdx+1)] = qAllnTerms[str(best_q+1)]
+ AllLCerror[str(degIdx+1)] = qAllLCerror[str(best_q+1)]
+
+ # Check the direction of the error (on average):
+ # if it increases consistently stop the iterations
+ if len(scores[scores != -np.inf]) > n_checks_degree:
+ scores_nonInf = scores[scores != -np.inf]
+ ss = np.sign(scores_nonInf - np.max(scores_nonInf))
+ # ss<0 error decreasing
+ errorIncreases = np.sum(np.sum(ss[-2:])) <= -1*n_checks_degree
+
+ if errorIncreases:
+ break
+
+ # Check only one degree, if target matrix has zero variance
+ if np.var(ED_Y) == 0:
+ break
+
+ # ------------------ Summary of results ------------------
+ # Select the one with the best score and save the necessary outputs
+ best_deg = np.nanargmax(scores)+1
+ coeffs = AllCoeffs[str(best_deg)]
+ basis_indices = AllIndices_Sparse[str(best_deg)]
+ clf_poly = Allclf_poly[str(best_deg)]
+ LOOCVScore = np.nanmax(scores)
+ P = AllnTerms[str(best_deg)]
+ LCerror = AllLCerror[str(best_deg)]
+ degree = DegreeArray[np.nanargmax(scores)]
+ qnorm = float(qnorm[best_q])
+
+ # ------------------ Print out Summary of results ------------------
+ if self.verbose:
+ # Create PSI_Sparse by removing redundent terms
+ nnz_idx = np.nonzero(coeffs)[0]
+ BasisIndices_Sparse = basis_indices[nnz_idx]
+
+ print(f'Output variable {varIdx+1}:')
+ print('The estimation of PCE coefficients converged at polynomial '
+ f'degree {DegreeArray[best_deg-1]} with '
+ f'{len(BasisIndices_Sparse)} terms (Sparsity index = '
+ f'{round(len(BasisIndices_Sparse)/P, 3)}).')
+
+ print(f'Final ModLOO error estimate: {1-max(scores):.3e}')
+ print('\n'+'-'*50)
+
+ if verbose:
+ print('='*50)
+ print(' '*10 + ' Summary of results ')
+ print('='*50)
+
+ print("scores:\n", scores)
+ print("Best score's degree:", self.DegreeArray[best_deg-1])
+ print("NO. of terms:", len(basis_indices))
+ print("Sparsity index:", round(len(basis_indices)/P, 3))
+ print("Best Indices:\n", basis_indices)
+
+ if self.pce_reg_method in ['BRR', 'ARD']:
+ fig, ax = plt.subplots(figsize=(12, 10))
+ plt.title("Marginal log-likelihood")
+ plt.plot(clf_poly.scores_, color='navy', linewidth=2)
+ plt.ylabel("Score")
+ plt.xlabel("Iterations")
+ if self.pce_reg_method.lower() == 'bbr':
+ text = f"$\\alpha={clf_poly.alpha_:.1f}$\n"
+ f"$\\lambda={clf_poly.lambda_:.3f}$\n"
+ f"$L={clf_poly.scores_[-1]:.1f}$"
+ else:
+ text = f"$\\alpha={clf_poly.alpha_:.1f}$\n$"
+ f"\\L={clf_poly.scores_[-1]:.1f}$"
+
+ plt.text(0.75, 0.5, text, fontsize=18, transform=ax.transAxes)
+ plt.show()
+ print('='*80)
+
+ # Create a dict to pass the outputs
+ returnVars = dict()
+ returnVars['clf_poly'] = clf_poly
+ returnVars['degree'] = degree
+ returnVars['qnorm'] = qnorm
+ returnVars['coeffs'] = coeffs
+ returnVars['multi_indices'] = basis_indices
+ returnVars['LOOCVScore'] = LOOCVScore
+ returnVars['LCerror'] = LCerror
+
+ return returnVars
+
+ # -------------------------------------------------------------------------
+ def corr_loocv_error(self, clf, psi, coeffs, y):
+ """
+ Calculates the corrected LOO error for regression on regressor
+ matrix `psi` that generated the coefficients based on [1] and [2].
+
+ [1] Blatman, G., 2009. Adaptive sparse polynomial chaos expansions for
+ uncertainty propagation and sensitivity analysis (Doctoral
+ dissertation, Clermont-Ferrand 2).
+
+ [2] Blatman, G. and Sudret, B., 2011. Adaptive sparse polynomial chaos
+ expansion based on least angle regression. Journal of computational
+ Physics, 230(6), pp.2345-2367.
+
+ Parameters
+ ----------
+ clf : object
+ Fitted estimator.
+ psi : array of shape (n_samples, n_features)
+ The multivariate orthogonal polynomials (regressor).
+ coeffs : array-like of shape (n_features,)
+ Estimated cofficients.
+ y : array of shape (n_samples,)
+ Target values.
+
+ Returns
+ -------
+ Q_2 : float
+ LOOCV Validation score (1-LOOCV erro).
+ residual : array of shape (n_samples,)
+ Residual values (y - predicted targets).
+
+ """
+ psi = np.array(psi, dtype=float)
+
+ # Create PSI_Sparse by removing redundent terms
+ nnz_idx = np.nonzero(coeffs)[0]
+ if len(nnz_idx) == 0:
+ nnz_idx = [0]
+ psi_sparse = psi[:, nnz_idx]
+
+ # NrCoeffs of aPCEs
+ P = len(nnz_idx)
+ # NrEvaluation (Size of experimental design)
+ N = psi.shape[0]
+
+ # Build the projection matrix
+ PsiTPsi = np.dot(psi_sparse.T, psi_sparse)
+
+ if np.linalg.cond(PsiTPsi) > 1e-12 and \
+ np.linalg.cond(PsiTPsi) < 1/sys.float_info.epsilon:
+ # faster
+ M = sp.linalg.solve(PsiTPsi,
+ sp.sparse.eye(PsiTPsi.shape[0]).toarray())
+ else:
+ # stabler
+ M = np.linalg.pinv(PsiTPsi)
+
+ # h factor (the full matrix is not calculated explicitly,
+ # only the trace is, to save memory)
+ PsiM = np.dot(psi_sparse, M)
+
+ h = np.sum(np.multiply(PsiM, psi_sparse), axis=1, dtype=np.float128)
+
+ # ------ Calculate Error Loocv for each measurement point ----
+ # Residuals
+ if isinstance(clf, list):
+ residual = np.dot(psi, coeffs) - y
+ else:
+ residual = clf.predict(psi) - y
+
+ # Variance
+ varY = np.var(y)
+
+ if varY == 0:
+ normEmpErr = 0
+ ErrLoo = 0
+ LCerror = np.zeros((y.shape))
+ else:
+ normEmpErr = np.mean(residual**2)/varY
+
+ # LCerror = np.divide(residual, (1-h))
+ LCerror = residual / (1-h)[:, np.newaxis]
+ ErrLoo = np.mean(np.square(LCerror)) / varY
+ # if there are NaNs, just return an infinite LOO error (this
+ # happens, e.g., when a strongly underdetermined problem is solved)
+ if np.isnan(ErrLoo):
+ ErrLoo = np.inf
+
+ # Corrected Error for over-determined system
+ trM = np.trace(M)
+ if trM < 0 or abs(trM) > 1e6:
+ trM = np.trace(np.linalg.pinv(np.dot(psi.T, psi)))
+
+ # Over-determined system of Equation
+ if N > P:
+ T_factor = N/(N-P) * (1 + trM)
+
+ # Under-determined system of Equation
+ else:
+ T_factor = np.inf
+
+ CorrectedErrLoo = ErrLoo * T_factor
+
+ Q_2 = 1 - CorrectedErrLoo
+
+ return Q_2, residual
+
+ # -------------------------------------------------------------------------
+ def pca_transformation(self, Output):
+ """
+ Transforms the targets (outputs) via Principal Component Analysis
+
+ Parameters
+ ----------
+ Output : array of shape (n_samples,)
+ Target values.
+
+ Returns
+ -------
+ pca : obj
+ Fitted sklearnPCA object.
+ OutputMatrix : array of shape (n_samples,)
+ Transformed target values.
+
+ """
+ # Transform via Principal Component Analysis
+ if hasattr(self, 'var_pca_threshold'):
+ var_pca_threshold = self.var_pca_threshold
+ else:
+ var_pca_threshold = 100.0
+ n_samples, n_features = Output.shape
+
+ if hasattr(self, 'n_pca_components'):
+ n_pca_components = self.n_pca_components
+ else:
+ # Instantiate and fit sklearnPCA object
+ covar_matrix = sklearnPCA(n_components=None)
+ covar_matrix.fit(Output)
+ var = np.cumsum(np.round(covar_matrix.explained_variance_ratio_,
+ decimals=5)*100)
+ # Find the number of components to explain self.varPCAThreshold of
+ # variance
+ try:
+ n_components = np.where(var >= var_pca_threshold)[0][0] + 1
+ except IndexError:
+ n_components = min(n_samples, n_features)
+
+ n_pca_components = min(n_samples, n_features, n_components)
+
+ # Print out a report
+ print()
+ print('-' * 50)
+ print(f"PCA transformation is performed with {n_pca_components}"
+ " components.")
+ print('-' * 50)
+ print()
+
+ # Fit and transform with the selected number of components
+ pca = sklearnPCA(n_components=n_pca_components,
+ svd_solver='randomized')
+ OutputMatrix = pca.fit_transform(Output)
+
+ return pca, OutputMatrix
+
+ # -------------------------------------------------------------------------
+ def gaussian_process_emulator(self, X, y, nug_term=None, autoSelect=False,
+ varIdx=None):
+ """
+ Fits a Gaussian Process Emulator to the target given the training
+ points.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples, n_params)
+ Training points.
+ y : array of shape (n_samples,)
+ Target values.
+ nug_term : float, optional
+ Nugget term. The default is None, i.e. variance of y.
+ autoSelect : bool, optional
+ Loop over some kernels and select the best. The default is False.
+ varIdx : int, optional
+ The index number. The default is None.
+
+ Returns
+ -------
+ gp : object
+ Fitted estimator.
+
+ """
+
+ nug_term = nug_term if nug_term else np.var(y)
+
+ Kernels = [nug_term * kernels.RBF(length_scale=1.0,
+ length_scale_bounds=(1e-25, 1e15)),
+ nug_term * kernels.RationalQuadratic(length_scale=0.2,
+ alpha=1.0),
+ nug_term * kernels.Matern(length_scale=1.0,
+ length_scale_bounds=(1e-15, 1e5),
+ nu=1.5)]
+
+ # Automatic selection of the kernel
+ if autoSelect:
+ gp = {}
+ BME = []
+ for i, kernel in enumerate(Kernels):
+ gp[i] = GaussianProcessRegressor(kernel=kernel,
+ n_restarts_optimizer=3,
+ normalize_y=False)
+
+ # Fit to data using Maximum Likelihood Estimation
+ gp[i].fit(X, y)
+
+ # Store the MLE as BME score
+ BME.append(gp[i].log_marginal_likelihood())
+
+ gp = gp[np.argmax(BME)]
+
+ else:
+ gp = GaussianProcessRegressor(kernel=Kernels[0],
+ n_restarts_optimizer=3,
+ normalize_y=False)
+ gp.fit(X, y)
+
+ # Compute score
+ if varIdx is not None:
+ Score = gp.score(X, y)
+ print('-'*50)
+ print(f'Output variable {varIdx}:')
+ print('The estimation of GPE coefficients converged,')
+ print(f'with the R^2 score: {Score:.3f}')
+ print('-'*50)
+
+ return gp
+
+ # -------------------------------------------------------------------------
+ def eval_metamodel(self, samples=None, nsamples=None,
+ sampling_method='random', return_samples=False):
+ """
+ Evaluates meta-model at the requested samples. One can also generate
+ nsamples.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params), optional
+ Samples to evaluate meta-model at. The default is None.
+ nsamples : int, optional
+ Number of samples to generate, if no `samples` is provided. The
+ default is None.
+ sampling_method : str, optional
+ Type of sampling, if no `samples` is provided. The default is
+ 'random'.
+ return_samples : bool, optional
+ Retun samples, if no `samples` is provided. The default is False.
+
+ Returns
+ -------
+ mean_pred : dict
+ Mean of the predictions.
+ std_pred : dict
+ Standard deviatioon of the predictions.
+ """
+ if self.meta_model_type.lower() == 'gpe':
+ model_dict = self.gp_poly
+ else:
+ model_dict = self.coeffs_dict
+
+ if samples is None:
+ if nsamples is None:
+ self.n_samples = 100000
+ else:
+ self.n_samples = nsamples
+
+ samples = self.ExpDesign.generate_samples(self.n_samples,
+ sampling_method)
+ else:
+ self.samples = samples
+ self.n_samples = len(samples)
+
+ # Transform samples
+ samples = self.ExpDesign.transform(samples)
+
+ if self.meta_model_type.lower() != 'gpe':
+ univ_p_val = self.univ_basis_vals(samples,
+ n_max=np.max(self.pce_deg))
+
+ mean_pred = {}
+ std_pred = {}
+
+ # Loop over outputs
+ for ouput, values in model_dict.items():
+
+ mean = np.zeros((len(samples), len(values)))
+ std = np.zeros((len(samples), len(values)))
+ idx = 0
+ for in_key, InIdxValues in values.items():
+
+ # Perdiction with GPE
+ if self.meta_model_type.lower() == 'gpe':
+ X_T = self.x_scaler[ouput].transform(samples)
+ gp = self.gp_poly[ouput][in_key]
+ y_mean, y_std = gp.predict(X_T, return_std=True)
+
+ else:
+ # Perdiction with PCE or pcekriging
+ # Assemble Psi matrix
+ psi = self.create_psi(self.basis_dict[ouput][in_key],
+ univ_p_val)
+ # Perdiction
+ try:
+ # with error bar
+ clf_poly = self.clf_poly[ouput][in_key]
+ y_mean, y_std = clf_poly.predict(psi, return_std=True)
+
+ except:
+ # without error bar
+ coeffs = self.coeffs_dict[ouput][in_key]
+ y_mean = np.dot(psi, coeffs)
+ y_std = np.zeros_like(y_mean)
+
+ mean[:, idx] = y_mean
+ std[:, idx] = y_std
+ idx += 1
+
+ if self.dim_red_method.lower() == 'pca':
+ PCA = self.pca[ouput]
+ mean_pred[ouput] = PCA.mean_ + np.dot(mean, PCA.components_)
+ std_pred[ouput] = np.sqrt(np.dot(std**2, PCA.components_**2))
+ else:
+ mean_pred[ouput] = mean
+ std_pred[ouput] = std
+
+ if return_samples:
+ return mean_pred, std_pred, samples
+ else:
+ return mean_pred, std_pred
+
+ # -------------------------------------------------------------------------
+ def create_model_error(self, X, y, name='Calib'):
+ """
+ Fits a GPE-based model error.
+
+ Parameters
+ ----------
+ X : array of shape (n_outputs, n_inputs)
+ Input array. It can contain any forcing inputs or coordinates of
+ extracted data.
+ y : array of shape (n_outputs,)
+ The model response for the MAP parameter set.
+ name : str, optional
+ Calibration or validation. The default is `'Calib'`.
+
+ Returns
+ -------
+ self: object
+ Self object.
+
+ """
+ Model = self.ModelObj
+ outputNames = Model.Output.Names
+ self.errorRegMethod = 'GPE'
+ self.errorclf_poly = self.auto_vivification()
+ self.errorScale = self.auto_vivification()
+
+ # Read data
+ MeasuredData = Model.read_observation(case=name)
+
+ # Fitting GPR based bias model
+ for out in outputNames:
+ nan_idx = ~np.isnan(MeasuredData[out])
+ # Select data
+ try:
+ data = MeasuredData[out].values[nan_idx]
+ except AttributeError:
+ data = MeasuredData[out][nan_idx]
+
+ # Prepare the input matrix
+ scaler = MinMaxScaler()
+ delta = data # - y[out][0]
+ BiasInputs = np.hstack((X[out], y[out].reshape(-1, 1)))
+ X_S = scaler.fit_transform(BiasInputs)
+ gp = self.gaussian_process_emulator(X_S, delta)
+
+ self.errorScale[out]["y_1"] = scaler
+ self.errorclf_poly[out]["y_1"] = gp
+
+ return self
+
+ # -------------------------------------------------------------------------
+ def eval_model_error(self, X, y_pred):
+ """
+ Evaluates the error model.
+
+ Parameters
+ ----------
+ X : array
+ Inputs.
+ y_pred : dict
+ Predictions.
+
+ Returns
+ -------
+ mean_pred : dict
+ Mean predition of the GPE-based error model.
+ std_pred : dict
+ standard deviation of the GPE-based error model.
+
+ """
+ mean_pred = {}
+ std_pred = {}
+
+ for Outkey, ValuesDict in self.errorclf_poly.items():
+
+ pred_mean = np.zeros_like(y_pred[Outkey])
+ pred_std = np.zeros_like(y_pred[Outkey])
+
+ for Inkey, InIdxValues in ValuesDict.items():
+
+ gp = self.errorclf_poly[Outkey][Inkey]
+ scaler = self.errorScale[Outkey][Inkey]
+
+ # Transform Samples using scaler
+ for j, pred in enumerate(y_pred[Outkey]):
+ BiasInputs = np.hstack((X[Outkey], pred.reshape(-1, 1)))
+ Samples_S = scaler.transform(BiasInputs)
+ y_hat, y_std = gp.predict(Samples_S, return_std=True)
+ pred_mean[j] = y_hat
+ pred_std[j] = y_std
+ # pred_mean[j] += pred
+
+ mean_pred[Outkey] = pred_mean
+ std_pred[Outkey] = pred_std
+
+ return mean_pred, std_pred
+
+ # -------------------------------------------------------------------------
+ class auto_vivification(dict):
+ """
+ Implementation of perl's AutoVivification feature.
+
+ Source: https://stackoverflow.com/a/651879/18082457
+ """
+
+ def __getitem__(self, item):
+ try:
+ return dict.__getitem__(self, item)
+ except KeyError:
+ value = self[item] = type(self)()
+ return value
+
+ # -------------------------------------------------------------------------
+ def __select_degree(self, ndim, nSamples):
+ """
+ Selects degree based on the number of samples and parameters in the
+ sequential design.
+
+ Parameters
+ ----------
+ ndim : TYPE
+ DESCRIPTION.
+ nSamples : TYPE
+ DESCRIPTION.
+
+ Returns
+ -------
+ TYPE
+ DESCRIPTION.
+
+ """
+ # Define the DegreeArray
+ max_deg = np.max(self.pce_deg)
+ min_Deg = np.min(self.pce_deg)
+ nitr = nSamples - self.ExpDesign.n_init_samples
+
+ # Check q-norm
+ if not np.isscalar(self.pce_q_norm):
+ self.pce_q_norm = np.array(self.pce_q_norm)
+ else:
+ self.pce_q_norm = np.array([self.pce_q_norm])
+
+ def M_uptoMax(maxDeg):
+ n_combo = np.zeros(maxDeg)
+ for i, d in enumerate(range(1, maxDeg+1)):
+ n_combo[i] = math.factorial(ndim+d)
+ n_combo[i] /= math.factorial(ndim) * math.factorial(d)
+ return n_combo
+
+ if self.ExpDesignFlag != 'sequential':
+ degNew = max_deg
+ else:
+ d = nitr if nitr != 0 and self.n_params > 5 else 1
+ min_index = np.argmin(abs(M_uptoMax(max_deg)-ndim*nSamples*d))
+ degNew = range(1, max_deg+1)[min_index]
+
+ if degNew > min_Deg and self.pce_reg_method.lower() != 'fastard':
+ DegreeArray = np.arange(min_Deg, degNew+1)
+ else:
+ DegreeArray = np.array([degNew])
+
+ return DegreeArray</code></pre>
+</details>
+<h3>Class variables</h3>
+<dl>
+<dt id="surrogate_models.MetaModel.auto_vivification"><code class="name">var <span class="ident">auto_vivification</span></code></dt>
+<dd>
+<div class="desc"><p>Implementation of perl's AutoVivification feature.</p>
+<p>Source: <a href="https://stackoverflow.com/a/651879/18082457">https://stackoverflow.com/a/651879/18082457</a></p></div>
+</dd>
+</dl>
+<h3>Methods</h3>
+<dl>
+<dt id="surrogate_models.MetaModel.create_metamodel"><code class="name flex">
+<span>def <span class="ident">create_metamodel</span></span>(<span>self, Model)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Starts the training of the meta-model for the model objects containg
+the given computational model.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>Model</code></strong> : <code>obj</code></dt>
+<dd>Model object.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>metamodel</code></strong> : <code>obj</code></dt>
+<dd>The meta model object.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_metamodel(self, Model):
+ """
+ Starts the training of the meta-model for the model objects containg
+ the given computational model.
+
+ Parameters
+ ----------
+ Model : obj
+ Model object.
+
+ Returns
+ -------
+ metamodel : obj
+ The meta model object.
+
+ """
+ self.ModelObj = Model
+ self.n_params = len(self.input_obj.Marginals)
+ self.ExpDesignFlag = 'normal'
+ # --- Prepare pce degree ---
+ if self.meta_model_type.lower() == 'pce':
+ if type(self.pce_deg) is not np.ndarray:
+ self.pce_deg = np.array(self.pce_deg)
+
+ if self.ExpDesign.method == 'sequential':
+ from .sequential_design import SeqDesign
+ seq_design = SeqDesign(self)
+ metamodel = seq_design.train_seq_design(Model)
+
+ elif self.ExpDesign.method == 'normal':
+ self.ExpDesignFlag = 'normal'
+ metamodel = self.train_norm_design(Model)
+
+ else:
+ raise Exception("The method for experimental design you requested"
+ " has not been implemented yet.")
+
+ # Zip the model run directories
+ if self.ModelObj.link_type.lower() == 'pylink':
+ Model.zip_subdirs(Model.name, f'{Model.name}_')
+
+ return metamodel</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.train_norm_design"><code class="name flex">
+<span>def <span class="ident">train_norm_design</span></span>(<span>self, Model, verbose=False)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>This function loops over the outputs and each time step/point and fits
+the meta model.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>Model</code></strong> : <code>obj</code></dt>
+<dd>Model object.</dd>
+<dt><strong><code>verbose</code></strong> : <code>bool</code>, optional</dt>
+<dd>Flag for a sequential design in silent mode. The default is False.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>self</code></strong> : <code>obj</code></dt>
+<dd>Meta-model object.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def train_norm_design(self, Model, verbose=False):
+ """
+ This function loops over the outputs and each time step/point and fits
+ the meta model.
+
+ Parameters
+ ----------
+ Model : obj
+ Model object.
+ verbose : bool, optional
+ Flag for a sequential design in silent mode. The default is False.
+
+ Returns
+ -------
+ self: obj
+ Meta-model object.
+
+ """
+
+ # Get the collocation points to run the forward model
+ CollocationPoints, OutputDict = self.generate_ExpDesign(Model)
+
+ # Initialize the nested dictionaries
+ self.deg_dict = self.auto_vivification()
+ self.q_norm_dict = self.auto_vivification()
+ self.coeffs_dict = self.auto_vivification()
+ self.basis_dict = self.auto_vivification()
+ self.score_dict = self.auto_vivification()
+ self.clf_poly = self.auto_vivification()
+ self.gp_poly = self.auto_vivification()
+ self.pca = self.auto_vivification()
+ self.LCerror = self.auto_vivification()
+ self.x_scaler = {}
+
+ # Define the DegreeArray
+ nSamples, ndim = CollocationPoints.shape
+ self.DegreeArray = self.__select_degree(ndim, nSamples)
+
+ # Generate all basis indices
+ self.allBasisIndices = self.auto_vivification()
+ for deg in self.DegreeArray:
+ keys = self.allBasisIndices.keys()
+ if deg not in np.fromiter(keys, dtype=float):
+ # Generate the polynomial basis indices
+ for qidx, q in enumerate(self.pce_q_norm):
+ basis_indices = self.create_basis_indices(degree=deg,
+ q_norm=q)
+ self.allBasisIndices[str(deg)][str(q)] = basis_indices
+
+ # Evaluate the univariate polynomials on ExpDesign
+ if self.meta_model_type.lower() != 'gpe':
+ self.univ_p_val = self.univ_basis_vals(CollocationPoints)
+
+ if 'x_values' in OutputDict:
+ self.ExpDesign.x_values = OutputDict['x_values']
+ del OutputDict['x_values']
+
+ # --- Loop through data points and fit the surrogate ---
+ if not verbose:
+ print(f"\n>>>> Training the {self.meta_model_type} metamodel "
+ "started. <<<<<<\n")
+ items = tqdm(OutputDict.items(), desc="Fitting regression")
+ else:
+ items = OutputDict.items()
+
+ # For loop over the components/outputs
+ for key, Output in items:
+
+ # Dimensionality reduction with PCA, if specified
+ if self.dim_red_method.lower() == 'pca':
+ self.pca[key], target = self.pca_transformation(Output)
+ else:
+ target = Output
+
+ # Parallel fit regression
+ if self.meta_model_type.lower() == 'gpe':
+ # Prepare the input matrix
+ scaler = MinMaxScaler()
+ X_S = scaler.fit_transform(CollocationPoints)
+
+ self.x_scaler[key] = scaler
+
+ out = Parallel(n_jobs=-1, prefer='threads')(
+ delayed(self.gaussian_process_emulator)(X_S, target[:, idx])
+ for idx in range(target.shape[1]))
+
+ for idx in range(target.shape[1]):
+ self.gp_poly[key][f"y_{idx+1}"] = out[idx]
+
+ else:
+ out = Parallel(n_jobs=-1, prefer='threads')(
+ delayed(self.adaptive_regression)(CollocationPoints,
+ target[:, idx], idx)
+ for idx in range(target.shape[1]))
+
+ for i in range(target.shape[1]):
+ # Create a dict to pass the variables
+ self.deg_dict[key][f"y_{i+1}"] = out[i]['degree']
+ self.q_norm_dict[key][f"y_{i+1}"] = out[i]['qnorm']
+ self.coeffs_dict[key][f"y_{i+1}"] = out[i]['coeffs']
+ self.basis_dict[key][f"y_{i+1}"] = out[i]['multi_indices']
+ self.score_dict[key][f"y_{i+1}"] = out[i]['LOOCVScore']
+ self.clf_poly[key][f"y_{i+1}"] = out[i]['clf_poly']
+ self.LCerror[key][f"y_{i+1}"] = out[i]['LCerror']
+
+ if not verbose:
+ print(f"\n>>>> Training the {self.meta_model_type} metamodel"
+ " sucessfully completed. <<<<<<\n")
+
+ return self</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.create_basis_indices"><code class="name flex">
+<span>def <span class="ident">create_basis_indices</span></span>(<span>self, degree, q_norm)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Creates set of selected multi-indices of multivariate polynomials for
+certain parameter numbers, polynomial degree, hyperbolic (or q-norm)
+truncation scheme.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>degree</code></strong> : <code>int</code></dt>
+<dd>Polynomial degree.</dd>
+<dt><strong><code>q_norm</code></strong> : <code>float</code></dt>
+<dd>hyperbolic (or q-norm) truncation.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>basis_indices</code></strong> : <code>array</code> of <code>shape (n_terms, n_params)</code></dt>
+<dd>Multi-indices of multivariate polynomials.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_basis_indices(self, degree, q_norm):
+ """
+ Creates set of selected multi-indices of multivariate polynomials for
+ certain parameter numbers, polynomial degree, hyperbolic (or q-norm)
+ truncation scheme.
+
+ Parameters
+ ----------
+ degree : int
+ Polynomial degree.
+ q_norm : float
+ hyperbolic (or q-norm) truncation.
+
+ Returns
+ -------
+ basis_indices : array of shape (n_terms, n_params)
+ Multi-indices of multivariate polynomials.
+
+ """
+ basis_indices = glexindex(start=0, stop=degree+1,
+ dimensions=self.n_params,
+ cross_truncation=q_norm,
+ reverse=False, graded=True)
+ return basis_indices</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.add_ExpDesign"><code class="name flex">
+<span>def <span class="ident">add_ExpDesign</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Instanciates experimental design object.</p>
+<h2 id="returns">Returns</h2>
+<p>None.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def add_ExpDesign(self):
+ """
+ Instanciates experimental design object.
+
+ Returns
+ -------
+ None.
+
+ """
+ self.ExpDesign = ExpDesigns(self.input_obj,
+ meta_Model=self.meta_model_type)</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.generate_ExpDesign"><code class="name flex">
+<span>def <span class="ident">generate_ExpDesign</span></span>(<span>self, Model)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Prepares the experimental design either by reading from the prescribed
+data or running simulations.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>Model</code></strong> : <code>obj</code></dt>
+<dd>Model object.</dd>
+</dl>
+<h2 id="raises">Raises</h2>
+<dl>
+<dt><code>Exception</code></dt>
+<dd>If model sumulations are not provided properly.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>ED_X_tr</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
+<dd>Training samples transformed by an isoprobabilistic transformation.</dd>
+<dt><strong><code>ED_Y</code></strong> : <code>dict</code></dt>
+<dd>Model simulations (target) for all outputs.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def generate_ExpDesign(self, Model):
+ """
+ Prepares the experimental design either by reading from the prescribed
+ data or running simulations.
+
+ Parameters
+ ----------
+ Model : obj
+ Model object.
+
+ Raises
+ ------
+ Exception
+ If model sumulations are not provided properly.
+
+ Returns
+ -------
+ ED_X_tr: array of shape (n_samples, n_params)
+ Training samples transformed by an isoprobabilistic transformation.
+ ED_Y: dict
+ Model simulations (target) for all outputs.
+ """
+ ExpDesign = self.ExpDesign
+ if self.ExpDesignFlag != 'sequential':
+ # Read ExpDesign (training and targets) from the provided hdf5
+ if ExpDesign.hdf5_file is not None:
+
+ # Read hdf5 file
+ f = h5py.File(ExpDesign.hdf5_file, 'r+')
+
+ # Read EDX and pass it to ExpDesign object
+ try:
+ ExpDesign.X = np.array(f["EDX/New_init_"])
+ except KeyError:
+ ExpDesign.X = np.array(f["EDX/init_"])
+
+ # Update number of initial samples
+ ExpDesign.n_init_samples = ExpDesign.X.shape[0]
+
+ # Read EDX and pass it to ExpDesign object
+ out_names = self.ModelObj.Output.names
+ ExpDesign.Y = {}
+
+ # Extract x values
+ try:
+ ExpDesign.Y["x_values"] = dict()
+ for varIdx, var in enumerate(out_names):
+ x = np.array(f[f"x_values/{var}"])
+ ExpDesign.Y["x_values"][var] = x
+ except KeyError:
+ ExpDesign.Y["x_values"] = np.array(f["x_values"])
+
+ # Store the output
+ for varIdx, var in enumerate(out_names):
+ try:
+ y = np.array(f[f"EDY/{var}/New_init_"])
+ except KeyError:
+ y = np.array(f[f"EDY/{var}/init_"])
+ ExpDesign.Y[var] = y
+ f.close()
+ else:
+ # Check if an old hdf5 file exists: if yes, rename it
+ hdf5file = f'ExpDesign_{self.ModelObj.name}.hdf5'
+ if os.path.exists(hdf5file):
+ os.rename(hdf5file, 'old_'+hdf5file)
+
+ # ---- Prepare X samples ----
+ ED_X, ED_X_tr = ExpDesign.generate_ED(ExpDesign.n_init_samples,
+ ExpDesign.sampling_method,
+ transform=True,
+ max_pce_deg=np.max(self.pce_deg))
+ ExpDesign.X = ED_X
+ ExpDesign.collocationPoints = ED_X_tr
+ self.bound_tuples = ExpDesign.bound_tuples
+
+ # ---- Run simulations at X ----
+ if not hasattr(ExpDesign, 'Y') or ExpDesign.Y is None:
+ print('\n Now the forward model needs to be run!\n')
+ ED_Y, up_ED_X = Model.run_model_parallel(ED_X)
+ ExpDesign.X = up_ED_X
+ self.ModelOutputDict = ED_Y
+ ExpDesign.Y = ED_Y
+ else:
+ # Check if a dict has been passed.
+ if type(ExpDesign.Y) is dict:
+ self.ModelOutputDict = ExpDesign.Y
+ else:
+ raise Exception('Please provide either a dictionary or a hdf5'
+ 'file to ExpDesign.hdf5_file argument.')
+
+ return ED_X_tr, self.ModelOutputDict</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.univ_basis_vals"><code class="name flex">
+<span>def <span class="ident">univ_basis_vals</span></span>(<span>self, samples, n_max=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Evaluates univariate regressors along input directions.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>samples</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
+<dd>Samples.</dd>
+<dt><strong><code>n_max</code></strong> : <code>int</code>, optional</dt>
+<dd>Maximum polynomial degree. The default is <code>None</code>.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>univ_basis</code></strong> : <code>array</code> of <code>shape (n_samples, n_params, n_max+1)</code></dt>
+<dd>All univariate regressors up to n_max.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def univ_basis_vals(self, samples, n_max=None):
+ """
+ Evaluates univariate regressors along input directions.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params)
+ Samples.
+ n_max : int, optional
+ Maximum polynomial degree. The default is `None`.
+
+ Returns
+ -------
+ univ_basis: array of shape (n_samples, n_params, n_max+1)
+ All univariate regressors up to n_max.
+ """
+ # Extract information
+ poly_types = self.ExpDesign.poly_types
+ if samples.ndim != 2:
+ samples = samples.reshape(1, len(samples))
+ n_max = np.max(self.pce_deg) if n_max is None else n_max
+
+ # Extract poly coeffs
+ if self.ExpDesign.input_data_given or self.ExpDesign.apce:
+ apolycoeffs = self.ExpDesign.polycoeffs
+ else:
+ apolycoeffs = None
+
+ # Evaluate univariate basis
+ univ_basis = eval_univ_basis(samples, n_max, poly_types, apolycoeffs)
+
+ return univ_basis</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.create_psi"><code class="name flex">
+<span>def <span class="ident">create_psi</span></span>(<span>self, basis_indices, univ_p_val)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>This function assemble the design matrix Psi from the given basis index
+set INDICES and the univariate polynomial evaluations univ_p_val.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>basis_indices</code></strong> : <code>array</code> of <code>shape (n_terms, n_params)</code></dt>
+<dd>Multi-indices of multivariate polynomials.</dd>
+<dt><strong><code>univ_p_val</code></strong> : <code>array</code> of <code>(n_samples, n_params, n_max+1)</code></dt>
+<dd>All univariate regressors up to <code>n_max</code>.</dd>
+</dl>
+<h2 id="raises">Raises</h2>
+<dl>
+<dt><code>ValueError</code></dt>
+<dd>n_terms in arguments do not match.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>psi</code></strong> : <code>array</code> of <code>shape (n_samples, n_terms)</code></dt>
+<dd>Multivariate regressors.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_psi(self, basis_indices, univ_p_val):
+ """
+ This function assemble the design matrix Psi from the given basis index
+ set INDICES and the univariate polynomial evaluations univ_p_val.
+
+ Parameters
+ ----------
+ basis_indices : array of shape (n_terms, n_params)
+ Multi-indices of multivariate polynomials.
+ univ_p_val : array of (n_samples, n_params, n_max+1)
+ All univariate regressors up to `n_max`.
+
+ Raises
+ ------
+ ValueError
+ n_terms in arguments do not match.
+
+ Returns
+ -------
+ psi : array of shape (n_samples, n_terms)
+ Multivariate regressors.
+
+ """
+ # Check if BasisIndices is a sparse matrix
+ sparsity = sp.sparse.issparse(basis_indices)
+ if sparsity:
+ basis_indices = basis_indices.toarray()
+
+ # Initialization and consistency checks
+ # number of input variables
+ n_params = univ_p_val.shape[1]
+
+ # Size of the experimental design
+ n_samples = univ_p_val.shape[0]
+
+ # number of basis terms
+ n_terms = basis_indices.shape[0]
+
+ # check that the variables have consistent sizes
+ if n_params != basis_indices.shape[1]:
+ raise ValueError("The shapes of basis_indices and univ_p_val don't"
+ " match!!")
+
+ # Preallocate the Psi matrix for performance
+ psi = np.ones((n_samples, n_terms))
+ # Assemble the Psi matrix
+ for m in range(basis_indices.shape[1]):
+ aa = np.where(basis_indices[:, m] > 0)[0]
+ try:
+ basisIdx = basis_indices[aa, m]
+ bb = np.reshape(univ_p_val[:, m, basisIdx], psi[:, aa].shape)
+ psi[:, aa] = np.multiply(psi[:, aa], bb)
+ except ValueError as err:
+ raise err
+
+ return psi</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.fit"><code class="name flex">
+<span>def <span class="ident">fit</span></span>(<span>self, X, y, basis_indices, reg_method=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Fit regression using the regression method provided.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>X</code></strong> : <code>array</code> of <code>shape (n_samples, n_features)</code></dt>
+<dd>Training vector, where n_samples is the number of samples and
+n_features is the number of features.</dd>
+<dt><strong><code>y</code></strong> : <code>array</code> of <code>shape (n_samples,)</code></dt>
+<dd>Target values.</dd>
+<dt><strong><code>basis_indices</code></strong> : <code>array</code> of <code>shape (n_terms, n_params)</code></dt>
+<dd>Multi-indices of multivariate polynomials.</dd>
+<dt><strong><code>reg_method</code></strong> : <code>str</code>, optional</dt>
+<dd>DESCRIPTION. The default is None.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>return_out_dict</code></strong> : <code>Dict</code></dt>
+<dd>Fitted estimator, spareMulti-Index, sparseX and coefficients.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def fit(self, X, y, basis_indices, reg_method=None):
+ """
+ Fit regression using the regression method provided.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples, n_features)
+ Training vector, where n_samples is the number of samples and
+ n_features is the number of features.
+ y : array of shape (n_samples,)
+ Target values.
+ basis_indices : array of shape (n_terms, n_params)
+ Multi-indices of multivariate polynomials.
+ reg_method : str, optional
+ DESCRIPTION. The default is None.
+
+ Returns
+ -------
+ return_out_dict : Dict
+ Fitted estimator, spareMulti-Index, sparseX and coefficients.
+
+ """
+ if reg_method is None:
+ reg_method = self.pce_reg_method
+
+ # Check if BasisIndices is a sparse matrix
+ sparsity = sp.sparse.issparse(basis_indices)
+
+ clf_poly = []
+ compute_score = True if self.verbose else False
+
+ # inverse of the observed variance of the data
+ if np.var(y) != 0:
+ Lambda = 1 / np.var(y)
+ else:
+ Lambda = 1e-6
+
+ # Bayes sparse adaptive aPCE
+ if reg_method.lower() != 'ols':
+ if reg_method.lower() == 'brr' or np.var(y) == 0:
+ clf_poly = lm.BayesianRidge(n_iter=1000, tol=1e-7,
+ fit_intercept=True,
+ normalize=True,
+ compute_score=compute_score,
+ alpha_1=1e-04, alpha_2=1e-04,
+ lambda_1=Lambda, lambda_2=Lambda)
+ clf_poly.converged = True
+
+ elif reg_method.lower() == 'ard':
+ clf_poly = lm.ARDRegression(fit_intercept=True,
+ normalize=True,
+ compute_score=compute_score,
+ n_iter=1000, tol=0.0001,
+ alpha_1=1e-3, alpha_2=1e-3,
+ lambda_1=Lambda, lambda_2=Lambda)
+
+ elif reg_method.lower() == 'fastard':
+ clf_poly = RegressionFastARD(fit_intercept=True,
+ normalize=True,
+ compute_score=compute_score,
+ n_iter=300, tol=1e-10)
+
+ elif reg_method.lower() == 'bcs':
+ clf_poly = RegressionFastLaplace(fit_intercept=False,
+ n_iter=1000, tol=1e-7)
+
+ elif reg_method.lower() == 'lars':
+ clf_poly = lm.LassoLarsCV(fit_intercept=False)
+
+ elif reg_method.lower() == 'sgdr':
+ clf_poly = lm.SGDRegressor(fit_intercept=False,
+ max_iter=5000, tol=1e-7)
+
+ elif reg_method.lower() == 'omp':
+ clf_poly = lm.OrthogonalMatchingPursuitCV(fit_intercept=False,
+ max_iter=10)
+
+ elif reg_method.lower() == 'vbl':
+ clf_poly = VBLinearRegression(fit_intercept=False)
+
+ elif reg_method.lower() == 'ebl':
+ clf_poly = EBLinearRegression(optimizer='em')
+
+ # Fit
+ clf_poly.fit(X, y)
+
+ # Select the nonzero entries of coefficients
+ # The first column must be kept (For mean calculations)
+ nnz_idx = np.nonzero(clf_poly.coef_)[0]
+
+ if len(nnz_idx) == 0 or nnz_idx[0] != 0:
+ nnz_idx = np.insert(np.nonzero(clf_poly.coef_)[0], 0, 0)
+ # Remove the zero entries for Bases and PSI if need be
+ if sparsity:
+ sparse_basis_indices = basis_indices.toarray()[nnz_idx]
+ else:
+ sparse_basis_indices = basis_indices[nnz_idx]
+ sparse_X = X[:, nnz_idx]
+
+ # Store the coefficients of the regression model
+ clf_poly.fit(sparse_X, y)
+ coeffs = clf_poly.coef_
+ else:
+ # This is for the case where all outputs are zero, thereby
+ # all coefficients are zero
+ if sparsity:
+ sparse_basis_indices = basis_indices.toarray()
+ else:
+ sparse_basis_indices = basis_indices
+ sparse_X = X
+ coeffs = clf_poly.coef_
+
+ # Ordinary least square method (OLS)
+ else:
+ if sparsity:
+ sparse_basis_indices = basis_indices.toarray()
+ else:
+ sparse_basis_indices = basis_indices
+ sparse_X = X
+
+ X_T_X = np.dot(sparse_X.T, sparse_X)
+
+ if np.linalg.cond(X_T_X) > 1e-12 and \
+ np.linalg.cond(X_T_X) < 1 / sys.float_info.epsilon:
+ # faster
+ coeffs = sp.linalg.solve(X_T_X, np.dot(sparse_X.T, y))
+ else:
+ # stabler
+ coeffs = np.dot(np.dot(np.linalg.pinv(X_T_X), sparse_X.T), y)
+
+ # Create a dict to pass the outputs
+ return_out_dict = dict()
+ return_out_dict['clf_poly'] = clf_poly
+ return_out_dict['spareMulti-Index'] = sparse_basis_indices
+ return_out_dict['sparePsi'] = sparse_X
+ return_out_dict['coeffs'] = coeffs
+ return return_out_dict</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.adaptive_regression"><code class="name flex">
+<span>def <span class="ident">adaptive_regression</span></span>(<span>self, ED_X, ED_Y, varIdx, verbose=False)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Adaptively fits the PCE model by comparing the scores of different
+degrees and q-norm.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>ED_X</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
+<dd>Experimental design.</dd>
+<dt><strong><code>ED_Y</code></strong> : <code>array</code> of <code>shape (n_samples,)</code></dt>
+<dd>Target values, i.e. simulation results for the Experimental design.</dd>
+<dt><strong><code>varIdx</code></strong> : <code>int</code></dt>
+<dd>Index of the output.</dd>
+<dt><strong><code>verbose</code></strong> : <code>bool</code>, optional</dt>
+<dd>Print out summary. The default is False.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>returnVars</code></strong> : <code>Dict</code></dt>
+<dd>Fitted estimator, best degree, best q-norm, LOOCVScore and
+coefficients.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def adaptive_regression(self, ED_X, ED_Y, varIdx, verbose=False):
+ """
+ Adaptively fits the PCE model by comparing the scores of different
+ degrees and q-norm.
+
+ Parameters
+ ----------
+ ED_X : array of shape (n_samples, n_params)
+ Experimental design.
+ ED_Y : array of shape (n_samples,)
+ Target values, i.e. simulation results for the Experimental design.
+ varIdx : int
+ Index of the output.
+ verbose : bool, optional
+ Print out summary. The default is False.
+
+ Returns
+ -------
+ returnVars : Dict
+ Fitted estimator, best degree, best q-norm, LOOCVScore and
+ coefficients.
+
+ """
+
+ NrSamples, n_params = ED_X.shape
+ # Initialization
+ qAllCoeffs, AllCoeffs = {}, {}
+ qAllIndices_Sparse, AllIndices_Sparse = {}, {}
+ qAllclf_poly, Allclf_poly = {}, {}
+ qAllnTerms, AllnTerms = {}, {}
+ qAllLCerror, AllLCerror = {}, {}
+
+ # Extract degree array and qnorm array
+ DegreeArray = np.array([*self.allBasisIndices], dtype=int)
+ qnorm = [*self.allBasisIndices[str(int(DegreeArray[0]))]]
+
+ # Some options for EarlyStop
+ errorIncreases = False
+ # Stop degree, if LOO error does not decrease n_checks_degree times
+ n_checks_degree = 3
+ # Stop qNorm, if criterion isn't fulfilled n_checks_qNorm times
+ n_checks_qNorm = 2
+ nqnorms = len(qnorm)
+ qNormEarlyStop = True
+ if nqnorms < n_checks_qNorm+1:
+ qNormEarlyStop = False
+
+ # =====================================================================
+ # basis adaptive polynomial chaos: repeat the calculation by increasing
+ # polynomial degree until the highest accuracy is reached
+ # =====================================================================
+ # For each degree check all q-norms and choose the best one
+ scores = -np.inf * np.ones(DegreeArray.shape[0])
+ qNormScores = -np.inf * np.ones(nqnorms)
+
+ for degIdx, deg in enumerate(DegreeArray):
+
+ for qidx, q in enumerate(qnorm):
+
+ # Extract the polynomial basis indices from the pool of
+ # allBasisIndices
+ BasisIndices = self.allBasisIndices[str(deg)][str(q)]
+
+ # Assemble the Psi matrix
+ Psi = self.create_psi(BasisIndices, self.univ_p_val)
+
+ # Calulate the cofficients of the meta model
+ outs = self.fit(Psi, ED_Y, BasisIndices)
+
+ # Calculate and save the score of LOOCV
+ score, LCerror = self.corr_loocv_error(outs['clf_poly'],
+ outs['sparePsi'],
+ outs['coeffs'],
+ ED_Y)
+
+ # Check the convergence of noise for FastARD
+ if self.pce_reg_method == 'FastARD' and \
+ outs['clf_poly'].alpha_ < np.finfo(np.float32).eps:
+ score = -np.inf
+
+ qNormScores[qidx] = score
+ qAllCoeffs[str(qidx+1)] = outs['coeffs']
+ qAllIndices_Sparse[str(qidx+1)] = outs['spareMulti-Index']
+ qAllclf_poly[str(qidx+1)] = outs['clf_poly']
+ qAllnTerms[str(qidx+1)] = BasisIndices.shape[0]
+ qAllLCerror[str(qidx+1)] = LCerror
+
+ # EarlyStop check
+ # if there are at least n_checks_qNorm entries after the
+ # best one, we stop
+ if qNormEarlyStop and \
+ sum(np.isfinite(qNormScores)) > n_checks_qNorm:
+ # If the error has increased the last two iterations, stop!
+ qNormScores_nonInf = qNormScores[np.isfinite(qNormScores)]
+ deltas = np.sign(np.diff(qNormScores_nonInf))
+ if sum(deltas[-n_checks_qNorm+1:]) == 2:
+ # stop the q-norm loop here
+ break
+ if np.var(ED_Y) == 0:
+ break
+
+ # Store the score in the scores list
+ best_q = np.nanargmax(qNormScores)
+ scores[degIdx] = qNormScores[best_q]
+
+ AllCoeffs[str(degIdx+1)] = qAllCoeffs[str(best_q+1)]
+ AllIndices_Sparse[str(degIdx+1)] = qAllIndices_Sparse[str(best_q+1)]
+ Allclf_poly[str(degIdx+1)] = qAllclf_poly[str(best_q+1)]
+ AllnTerms[str(degIdx+1)] = qAllnTerms[str(best_q+1)]
+ AllLCerror[str(degIdx+1)] = qAllLCerror[str(best_q+1)]
+
+ # Check the direction of the error (on average):
+ # if it increases consistently stop the iterations
+ if len(scores[scores != -np.inf]) > n_checks_degree:
+ scores_nonInf = scores[scores != -np.inf]
+ ss = np.sign(scores_nonInf - np.max(scores_nonInf))
+ # ss<0 error decreasing
+ errorIncreases = np.sum(np.sum(ss[-2:])) <= -1*n_checks_degree
+
+ if errorIncreases:
+ break
+
+ # Check only one degree, if target matrix has zero variance
+ if np.var(ED_Y) == 0:
+ break
+
+ # ------------------ Summary of results ------------------
+ # Select the one with the best score and save the necessary outputs
+ best_deg = np.nanargmax(scores)+1
+ coeffs = AllCoeffs[str(best_deg)]
+ basis_indices = AllIndices_Sparse[str(best_deg)]
+ clf_poly = Allclf_poly[str(best_deg)]
+ LOOCVScore = np.nanmax(scores)
+ P = AllnTerms[str(best_deg)]
+ LCerror = AllLCerror[str(best_deg)]
+ degree = DegreeArray[np.nanargmax(scores)]
+ qnorm = float(qnorm[best_q])
+
+ # ------------------ Print out Summary of results ------------------
+ if self.verbose:
+ # Create PSI_Sparse by removing redundent terms
+ nnz_idx = np.nonzero(coeffs)[0]
+ BasisIndices_Sparse = basis_indices[nnz_idx]
+
+ print(f'Output variable {varIdx+1}:')
+ print('The estimation of PCE coefficients converged at polynomial '
+ f'degree {DegreeArray[best_deg-1]} with '
+ f'{len(BasisIndices_Sparse)} terms (Sparsity index = '
+ f'{round(len(BasisIndices_Sparse)/P, 3)}).')
+
+ print(f'Final ModLOO error estimate: {1-max(scores):.3e}')
+ print('\n'+'-'*50)
+
+ if verbose:
+ print('='*50)
+ print(' '*10 + ' Summary of results ')
+ print('='*50)
+
+ print("scores:\n", scores)
+ print("Best score's degree:", self.DegreeArray[best_deg-1])
+ print("NO. of terms:", len(basis_indices))
+ print("Sparsity index:", round(len(basis_indices)/P, 3))
+ print("Best Indices:\n", basis_indices)
+
+ if self.pce_reg_method in ['BRR', 'ARD']:
+ fig, ax = plt.subplots(figsize=(12, 10))
+ plt.title("Marginal log-likelihood")
+ plt.plot(clf_poly.scores_, color='navy', linewidth=2)
+ plt.ylabel("Score")
+ plt.xlabel("Iterations")
+ if self.pce_reg_method.lower() == 'bbr':
+ text = f"$\\alpha={clf_poly.alpha_:.1f}$\n"
+ f"$\\lambda={clf_poly.lambda_:.3f}$\n"
+ f"$L={clf_poly.scores_[-1]:.1f}$"
+ else:
+ text = f"$\\alpha={clf_poly.alpha_:.1f}$\n$"
+ f"\\L={clf_poly.scores_[-1]:.1f}$"
+
+ plt.text(0.75, 0.5, text, fontsize=18, transform=ax.transAxes)
+ plt.show()
+ print('='*80)
+
+ # Create a dict to pass the outputs
+ returnVars = dict()
+ returnVars['clf_poly'] = clf_poly
+ returnVars['degree'] = degree
+ returnVars['qnorm'] = qnorm
+ returnVars['coeffs'] = coeffs
+ returnVars['multi_indices'] = basis_indices
+ returnVars['LOOCVScore'] = LOOCVScore
+ returnVars['LCerror'] = LCerror
+
+ return returnVars</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.corr_loocv_error"><code class="name flex">
+<span>def <span class="ident">corr_loocv_error</span></span>(<span>self, clf, psi, coeffs, y)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Calculates the corrected LOO error for regression on regressor
+matrix <code>psi</code> that generated the coefficients based on [1] and [2].</p>
+<p>[1] Blatman, G., 2009. Adaptive sparse polynomial chaos expansions for
+uncertainty propagation and sensitivity analysis (Doctoral
+dissertation, Clermont-Ferrand 2).</p>
+<p>[2] Blatman, G. and Sudret, B., 2011. Adaptive sparse polynomial chaos
+expansion based on least angle regression. Journal of computational
+Physics, 230(6), pp.2345-2367.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>clf</code></strong> : <code>object</code></dt>
+<dd>Fitted estimator.</dd>
+<dt><strong><code>psi</code></strong> : <code>array</code> of <code>shape (n_samples, n_features)</code></dt>
+<dd>The multivariate orthogonal polynomials (regressor).</dd>
+<dt><strong><code>coeffs</code></strong> : <code>array-like</code> of <code>shape (n_features,)</code></dt>
+<dd>Estimated cofficients.</dd>
+<dt><strong><code>y</code></strong> : <code>array</code> of <code>shape (n_samples,)</code></dt>
+<dd>Target values.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>Q_2</code></strong> : <code>float</code></dt>
+<dd>LOOCV Validation score (1-LOOCV erro).</dd>
+<dt><strong><code>residual</code></strong> : <code>array</code> of <code>shape (n_samples,)</code></dt>
+<dd>Residual values (y - predicted targets).</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def corr_loocv_error(self, clf, psi, coeffs, y):
+ """
+ Calculates the corrected LOO error for regression on regressor
+ matrix `psi` that generated the coefficients based on [1] and [2].
+
+ [1] Blatman, G., 2009. Adaptive sparse polynomial chaos expansions for
+ uncertainty propagation and sensitivity analysis (Doctoral
+ dissertation, Clermont-Ferrand 2).
+
+ [2] Blatman, G. and Sudret, B., 2011. Adaptive sparse polynomial chaos
+ expansion based on least angle regression. Journal of computational
+ Physics, 230(6), pp.2345-2367.
+
+ Parameters
+ ----------
+ clf : object
+ Fitted estimator.
+ psi : array of shape (n_samples, n_features)
+ The multivariate orthogonal polynomials (regressor).
+ coeffs : array-like of shape (n_features,)
+ Estimated cofficients.
+ y : array of shape (n_samples,)
+ Target values.
+
+ Returns
+ -------
+ Q_2 : float
+ LOOCV Validation score (1-LOOCV erro).
+ residual : array of shape (n_samples,)
+ Residual values (y - predicted targets).
+
+ """
+ psi = np.array(psi, dtype=float)
+
+ # Create PSI_Sparse by removing redundent terms
+ nnz_idx = np.nonzero(coeffs)[0]
+ if len(nnz_idx) == 0:
+ nnz_idx = [0]
+ psi_sparse = psi[:, nnz_idx]
+
+ # NrCoeffs of aPCEs
+ P = len(nnz_idx)
+ # NrEvaluation (Size of experimental design)
+ N = psi.shape[0]
+
+ # Build the projection matrix
+ PsiTPsi = np.dot(psi_sparse.T, psi_sparse)
+
+ if np.linalg.cond(PsiTPsi) > 1e-12 and \
+ np.linalg.cond(PsiTPsi) < 1/sys.float_info.epsilon:
+ # faster
+ M = sp.linalg.solve(PsiTPsi,
+ sp.sparse.eye(PsiTPsi.shape[0]).toarray())
+ else:
+ # stabler
+ M = np.linalg.pinv(PsiTPsi)
+
+ # h factor (the full matrix is not calculated explicitly,
+ # only the trace is, to save memory)
+ PsiM = np.dot(psi_sparse, M)
+
+ h = np.sum(np.multiply(PsiM, psi_sparse), axis=1, dtype=np.float128)
+
+ # ------ Calculate Error Loocv for each measurement point ----
+ # Residuals
+ if isinstance(clf, list):
+ residual = np.dot(psi, coeffs) - y
+ else:
+ residual = clf.predict(psi) - y
+
+ # Variance
+ varY = np.var(y)
+
+ if varY == 0:
+ normEmpErr = 0
+ ErrLoo = 0
+ LCerror = np.zeros((y.shape))
+ else:
+ normEmpErr = np.mean(residual**2)/varY
+
+ # LCerror = np.divide(residual, (1-h))
+ LCerror = residual / (1-h)[:, np.newaxis]
+ ErrLoo = np.mean(np.square(LCerror)) / varY
+ # if there are NaNs, just return an infinite LOO error (this
+ # happens, e.g., when a strongly underdetermined problem is solved)
+ if np.isnan(ErrLoo):
+ ErrLoo = np.inf
+
+ # Corrected Error for over-determined system
+ trM = np.trace(M)
+ if trM < 0 or abs(trM) > 1e6:
+ trM = np.trace(np.linalg.pinv(np.dot(psi.T, psi)))
+
+ # Over-determined system of Equation
+ if N > P:
+ T_factor = N/(N-P) * (1 + trM)
+
+ # Under-determined system of Equation
+ else:
+ T_factor = np.inf
+
+ CorrectedErrLoo = ErrLoo * T_factor
+
+ Q_2 = 1 - CorrectedErrLoo
+
+ return Q_2, residual</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.pca_transformation"><code class="name flex">
+<span>def <span class="ident">pca_transformation</span></span>(<span>self, Output)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Transforms the targets (outputs) via Principal Component Analysis</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>Output</code></strong> : <code>array</code> of <code>shape (n_samples,)</code></dt>
+<dd>Target values.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>pca</code></strong> : <code>obj</code></dt>
+<dd>Fitted sklearnPCA object.</dd>
+<dt><strong><code>OutputMatrix</code></strong> : <code>array</code> of <code>shape (n_samples,)</code></dt>
+<dd>Transformed target values.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def pca_transformation(self, Output):
+ """
+ Transforms the targets (outputs) via Principal Component Analysis
+
+ Parameters
+ ----------
+ Output : array of shape (n_samples,)
+ Target values.
+
+ Returns
+ -------
+ pca : obj
+ Fitted sklearnPCA object.
+ OutputMatrix : array of shape (n_samples,)
+ Transformed target values.
+
+ """
+ # Transform via Principal Component Analysis
+ if hasattr(self, 'var_pca_threshold'):
+ var_pca_threshold = self.var_pca_threshold
+ else:
+ var_pca_threshold = 100.0
+ n_samples, n_features = Output.shape
+
+ if hasattr(self, 'n_pca_components'):
+ n_pca_components = self.n_pca_components
+ else:
+ # Instantiate and fit sklearnPCA object
+ covar_matrix = sklearnPCA(n_components=None)
+ covar_matrix.fit(Output)
+ var = np.cumsum(np.round(covar_matrix.explained_variance_ratio_,
+ decimals=5)*100)
+ # Find the number of components to explain self.varPCAThreshold of
+ # variance
+ try:
+ n_components = np.where(var >= var_pca_threshold)[0][0] + 1
+ except IndexError:
+ n_components = min(n_samples, n_features)
+
+ n_pca_components = min(n_samples, n_features, n_components)
+
+ # Print out a report
+ print()
+ print('-' * 50)
+ print(f"PCA transformation is performed with {n_pca_components}"
+ " components.")
+ print('-' * 50)
+ print()
+
+ # Fit and transform with the selected number of components
+ pca = sklearnPCA(n_components=n_pca_components,
+ svd_solver='randomized')
+ OutputMatrix = pca.fit_transform(Output)
+
+ return pca, OutputMatrix</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.gaussian_process_emulator"><code class="name flex">
+<span>def <span class="ident">gaussian_process_emulator</span></span>(<span>self, X, y, nug_term=None, autoSelect=False, varIdx=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Fits a Gaussian Process Emulator to the target given the training
+points.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>X</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code></dt>
+<dd>Training points.</dd>
+<dt><strong><code>y</code></strong> : <code>array</code> of <code>shape (n_samples,)</code></dt>
+<dd>Target values.</dd>
+<dt><strong><code>nug_term</code></strong> : <code>float</code>, optional</dt>
+<dd>Nugget term. The default is None, i.e. variance of y.</dd>
+<dt><strong><code>autoSelect</code></strong> : <code>bool</code>, optional</dt>
+<dd>Loop over some kernels and select the best. The default is False.</dd>
+<dt><strong><code>varIdx</code></strong> : <code>int</code>, optional</dt>
+<dd>The index number. The default is None.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>gp</code></strong> : <code>object</code></dt>
+<dd>Fitted estimator.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def gaussian_process_emulator(self, X, y, nug_term=None, autoSelect=False,
+ varIdx=None):
+ """
+ Fits a Gaussian Process Emulator to the target given the training
+ points.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples, n_params)
+ Training points.
+ y : array of shape (n_samples,)
+ Target values.
+ nug_term : float, optional
+ Nugget term. The default is None, i.e. variance of y.
+ autoSelect : bool, optional
+ Loop over some kernels and select the best. The default is False.
+ varIdx : int, optional
+ The index number. The default is None.
+
+ Returns
+ -------
+ gp : object
+ Fitted estimator.
+
+ """
+
+ nug_term = nug_term if nug_term else np.var(y)
+
+ Kernels = [nug_term * kernels.RBF(length_scale=1.0,
+ length_scale_bounds=(1e-25, 1e15)),
+ nug_term * kernels.RationalQuadratic(length_scale=0.2,
+ alpha=1.0),
+ nug_term * kernels.Matern(length_scale=1.0,
+ length_scale_bounds=(1e-15, 1e5),
+ nu=1.5)]
+
+ # Automatic selection of the kernel
+ if autoSelect:
+ gp = {}
+ BME = []
+ for i, kernel in enumerate(Kernels):
+ gp[i] = GaussianProcessRegressor(kernel=kernel,
+ n_restarts_optimizer=3,
+ normalize_y=False)
+
+ # Fit to data using Maximum Likelihood Estimation
+ gp[i].fit(X, y)
+
+ # Store the MLE as BME score
+ BME.append(gp[i].log_marginal_likelihood())
+
+ gp = gp[np.argmax(BME)]
+
+ else:
+ gp = GaussianProcessRegressor(kernel=Kernels[0],
+ n_restarts_optimizer=3,
+ normalize_y=False)
+ gp.fit(X, y)
+
+ # Compute score
+ if varIdx is not None:
+ Score = gp.score(X, y)
+ print('-'*50)
+ print(f'Output variable {varIdx}:')
+ print('The estimation of GPE coefficients converged,')
+ print(f'with the R^2 score: {Score:.3f}')
+ print('-'*50)
+
+ return gp</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.eval_metamodel"><code class="name flex">
+<span>def <span class="ident">eval_metamodel</span></span>(<span>self, samples=None, nsamples=None, sampling_method='random', return_samples=False)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Evaluates meta-model at the requested samples. One can also generate
+nsamples.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>samples</code></strong> : <code>array</code> of <code>shape (n_samples, n_params)</code>, optional</dt>
+<dd>Samples to evaluate meta-model at. The default is None.</dd>
+<dt><strong><code>nsamples</code></strong> : <code>int</code>, optional</dt>
+<dd>Number of samples to generate, if no <code>samples</code> is provided. The
+default is None.</dd>
+<dt><strong><code>sampling_method</code></strong> : <code>str</code>, optional</dt>
+<dd>Type of sampling, if no <code>samples</code> is provided. The default is
+'random'.</dd>
+<dt><strong><code>return_samples</code></strong> : <code>bool</code>, optional</dt>
+<dd>Retun samples, if no <code>samples</code> is provided. The default is False.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>mean_pred</code></strong> : <code>dict</code></dt>
+<dd>Mean of the predictions.</dd>
+<dt><strong><code>std_pred</code></strong> : <code>dict</code></dt>
+<dd>Standard deviatioon of the predictions.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def eval_metamodel(self, samples=None, nsamples=None,
+ sampling_method='random', return_samples=False):
+ """
+ Evaluates meta-model at the requested samples. One can also generate
+ nsamples.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params), optional
+ Samples to evaluate meta-model at. The default is None.
+ nsamples : int, optional
+ Number of samples to generate, if no `samples` is provided. The
+ default is None.
+ sampling_method : str, optional
+ Type of sampling, if no `samples` is provided. The default is
+ 'random'.
+ return_samples : bool, optional
+ Retun samples, if no `samples` is provided. The default is False.
+
+ Returns
+ -------
+ mean_pred : dict
+ Mean of the predictions.
+ std_pred : dict
+ Standard deviatioon of the predictions.
+ """
+ if self.meta_model_type.lower() == 'gpe':
+ model_dict = self.gp_poly
+ else:
+ model_dict = self.coeffs_dict
+
+ if samples is None:
+ if nsamples is None:
+ self.n_samples = 100000
+ else:
+ self.n_samples = nsamples
+
+ samples = self.ExpDesign.generate_samples(self.n_samples,
+ sampling_method)
+ else:
+ self.samples = samples
+ self.n_samples = len(samples)
+
+ # Transform samples
+ samples = self.ExpDesign.transform(samples)
+
+ if self.meta_model_type.lower() != 'gpe':
+ univ_p_val = self.univ_basis_vals(samples,
+ n_max=np.max(self.pce_deg))
+
+ mean_pred = {}
+ std_pred = {}
+
+ # Loop over outputs
+ for ouput, values in model_dict.items():
+
+ mean = np.zeros((len(samples), len(values)))
+ std = np.zeros((len(samples), len(values)))
+ idx = 0
+ for in_key, InIdxValues in values.items():
+
+ # Perdiction with GPE
+ if self.meta_model_type.lower() == 'gpe':
+ X_T = self.x_scaler[ouput].transform(samples)
+ gp = self.gp_poly[ouput][in_key]
+ y_mean, y_std = gp.predict(X_T, return_std=True)
+
+ else:
+ # Perdiction with PCE or pcekriging
+ # Assemble Psi matrix
+ psi = self.create_psi(self.basis_dict[ouput][in_key],
+ univ_p_val)
+ # Perdiction
+ try:
+ # with error bar
+ clf_poly = self.clf_poly[ouput][in_key]
+ y_mean, y_std = clf_poly.predict(psi, return_std=True)
+
+ except:
+ # without error bar
+ coeffs = self.coeffs_dict[ouput][in_key]
+ y_mean = np.dot(psi, coeffs)
+ y_std = np.zeros_like(y_mean)
+
+ mean[:, idx] = y_mean
+ std[:, idx] = y_std
+ idx += 1
+
+ if self.dim_red_method.lower() == 'pca':
+ PCA = self.pca[ouput]
+ mean_pred[ouput] = PCA.mean_ + np.dot(mean, PCA.components_)
+ std_pred[ouput] = np.sqrt(np.dot(std**2, PCA.components_**2))
+ else:
+ mean_pred[ouput] = mean
+ std_pred[ouput] = std
+
+ if return_samples:
+ return mean_pred, std_pred, samples
+ else:
+ return mean_pred, std_pred</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.create_model_error"><code class="name flex">
+<span>def <span class="ident">create_model_error</span></span>(<span>self, X, y, name='Calib')</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Fits a GPE-based model error.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>X</code></strong> : <code>array</code> of <code>shape (n_outputs, n_inputs)</code></dt>
+<dd>Input array. It can contain any forcing inputs or coordinates of
+extracted data.</dd>
+<dt><strong><code>y</code></strong> : <code>array</code> of <code>shape (n_outputs,)</code></dt>
+<dd>The model response for the MAP parameter set.</dd>
+<dt><strong><code>name</code></strong> : <code>str</code>, optional</dt>
+<dd>Calibration or validation. The default is <code>'Calib'</code>.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>self</code></strong> : <code>object</code></dt>
+<dd>Self object.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_model_error(self, X, y, name='Calib'):
+ """
+ Fits a GPE-based model error.
+
+ Parameters
+ ----------
+ X : array of shape (n_outputs, n_inputs)
+ Input array. It can contain any forcing inputs or coordinates of
+ extracted data.
+ y : array of shape (n_outputs,)
+ The model response for the MAP parameter set.
+ name : str, optional
+ Calibration or validation. The default is `'Calib'`.
+
+ Returns
+ -------
+ self: object
+ Self object.
+
+ """
+ Model = self.ModelObj
+ outputNames = Model.Output.Names
+ self.errorRegMethod = 'GPE'
+ self.errorclf_poly = self.auto_vivification()
+ self.errorScale = self.auto_vivification()
+
+ # Read data
+ MeasuredData = Model.read_observation(case=name)
+
+ # Fitting GPR based bias model
+ for out in outputNames:
+ nan_idx = ~np.isnan(MeasuredData[out])
+ # Select data
+ try:
+ data = MeasuredData[out].values[nan_idx]
+ except AttributeError:
+ data = MeasuredData[out][nan_idx]
+
+ # Prepare the input matrix
+ scaler = MinMaxScaler()
+ delta = data # - y[out][0]
+ BiasInputs = np.hstack((X[out], y[out].reshape(-1, 1)))
+ X_S = scaler.fit_transform(BiasInputs)
+ gp = self.gaussian_process_emulator(X_S, delta)
+
+ self.errorScale[out]["y_1"] = scaler
+ self.errorclf_poly[out]["y_1"] = gp
+
+ return self</code></pre>
+</details>
+</dd>
+<dt id="surrogate_models.MetaModel.eval_model_error"><code class="name flex">
+<span>def <span class="ident">eval_model_error</span></span>(<span>self, X, y_pred)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Evaluates the error model.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>X</code></strong> : <code>array</code></dt>
+<dd>Inputs.</dd>
+<dt><strong><code>y_pred</code></strong> : <code>dict</code></dt>
+<dd>Predictions.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><strong><code>mean_pred</code></strong> : <code>dict</code></dt>
+<dd>Mean predition of the GPE-based error model.</dd>
+<dt><strong><code>std_pred</code></strong> : <code>dict</code></dt>
+<dd>standard deviation of the GPE-based error model.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def eval_model_error(self, X, y_pred):
+ """
+ Evaluates the error model.
+
+ Parameters
+ ----------
+ X : array
+ Inputs.
+ y_pred : dict
+ Predictions.
+
+ Returns
+ -------
+ mean_pred : dict
+ Mean predition of the GPE-based error model.
+ std_pred : dict
+ standard deviation of the GPE-based error model.
+
+ """
+ mean_pred = {}
+ std_pred = {}
+
+ for Outkey, ValuesDict in self.errorclf_poly.items():
+
+ pred_mean = np.zeros_like(y_pred[Outkey])
+ pred_std = np.zeros_like(y_pred[Outkey])
+
+ for Inkey, InIdxValues in ValuesDict.items():
+
+ gp = self.errorclf_poly[Outkey][Inkey]
+ scaler = self.errorScale[Outkey][Inkey]
+
+ # Transform Samples using scaler
+ for j, pred in enumerate(y_pred[Outkey]):
+ BiasInputs = np.hstack((X[Outkey], pred.reshape(-1, 1)))
+ Samples_S = scaler.transform(BiasInputs)
+ y_hat, y_std = gp.predict(Samples_S, return_std=True)
+ pred_mean[j] = y_hat
+ pred_std[j] = y_std
+ # pred_mean[j] += pred
+
+ mean_pred[Outkey] = pred_mean
+ std_pred[Outkey] = pred_std
+
+ return mean_pred, std_pred</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="surrogate_models.MetaModel" href="#surrogate_models.MetaModel">MetaModel</a></code></h4>
+<ul class="">
+<li><code><a title="surrogate_models.MetaModel.create_metamodel" href="#surrogate_models.MetaModel.create_metamodel">create_metamodel</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.train_norm_design" href="#surrogate_models.MetaModel.train_norm_design">train_norm_design</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.create_basis_indices" href="#surrogate_models.MetaModel.create_basis_indices">create_basis_indices</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.add_ExpDesign" href="#surrogate_models.MetaModel.add_ExpDesign">add_ExpDesign</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.generate_ExpDesign" href="#surrogate_models.MetaModel.generate_ExpDesign">generate_ExpDesign</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.univ_basis_vals" href="#surrogate_models.MetaModel.univ_basis_vals">univ_basis_vals</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.create_psi" href="#surrogate_models.MetaModel.create_psi">create_psi</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.fit" href="#surrogate_models.MetaModel.fit">fit</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.adaptive_regression" href="#surrogate_models.MetaModel.adaptive_regression">adaptive_regression</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.corr_loocv_error" href="#surrogate_models.MetaModel.corr_loocv_error">corr_loocv_error</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.pca_transformation" href="#surrogate_models.MetaModel.pca_transformation">pca_transformation</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.gaussian_process_emulator" href="#surrogate_models.MetaModel.gaussian_process_emulator">gaussian_process_emulator</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.eval_metamodel" href="#surrogate_models.MetaModel.eval_metamodel">eval_metamodel</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.create_model_error" href="#surrogate_models.MetaModel.create_model_error">create_model_error</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.eval_model_error" href="#surrogate_models.MetaModel.eval_model_error">eval_model_error</a></code></li>
+<li><code><a title="surrogate_models.MetaModel.auto_vivification" href="#surrogate_models.MetaModel.auto_vivification">auto_vivification</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/make.bat b/docs/make.bat
deleted file mode 100644
index 6fcf05b4b76f8b9774c317ac8ada402f8a7087de..0000000000000000000000000000000000000000
--- a/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
- set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=source
-set BUILDDIR=build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
- echo.
- echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
- echo.installed, then set the SPHINXBUILD environment variable to point
- echo.to the full path of the 'sphinx-build' executable. Alternatively you
- echo.may add the Sphinx directory to PATH.
- echo.
- echo.If you don't have Sphinx installed, grab it from
- echo.https://www.sphinx-doc.org/
- exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
diff --git a/docs/source/conf.py b/docs/source/conf.py
deleted file mode 100644
index d0454bf0422df1e3e8b5184e0f3638234eaa8530..0000000000000000000000000000000000000000
--- a/docs/source/conf.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-# import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
-
-
-# -- Project information -----------------------------------------------------
-
-project = 'bayesvalidrox'
-copyright = '2022, Farid Mohammadi'
-author = 'Farid Mohammadi'
-
-# The full version, including alpha/beta/rc tags
-release = '0.0.3'
-
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = ['myst_parser']
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = 'python'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = []
-
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages. See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'alabaster'
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
diff --git a/docs/source/example.md b/docs/source/example.md
deleted file mode 100644
index 65b132ad46d79feeb1fa62b90d91aa77f9d55fb1..0000000000000000000000000000000000000000
--- a/docs/source/example.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# My nifty title
-
-Some **text**!
-
-```{admonition} Here's my title
-:class: warning
-
-Here's my admonition content
-```
diff --git a/docs/source/index.rst b/docs/source/index.rst
deleted file mode 100644
index e53f4bfb9f37de2bbb9718c532a55ccc142b1ea5..0000000000000000000000000000000000000000
--- a/docs/source/index.rst
+++ /dev/null
@@ -1,32 +0,0 @@
-.. bayesvalidrox documentation master file, created by
- sphinx-quickstart on Fri Mar 11 08:09:15 2022.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-Welcome to bayesvalidrox's documentation!
-=========================================
-
-.. toctree::
- example.md
- pylink.html
- :maxdepth: 2
- :caption: Contents:
-
-
-This is a normal text paragraph. The next paragraph is a code sample::
-
- It is not processed in any way, except
- that the indentation is removed.
-
- It can span multiple lines.
-
-This is a normal text paragraph again.
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-* :ref:`pylink`
diff --git a/src/bayesvalidrox/bayes_inference/bayes_inference.py b/src/bayesvalidrox/bayes_inference/bayes_inference.py
index f3d0417f216cf020a3ee0308782d51724b0eeb38..0e82c5901dd54ab7b1de38a9603c9ae152d8bd95 100644
--- a/src/bayesvalidrox/bayes_inference/bayes_inference.py
+++ b/src/bayesvalidrox/bayes_inference/bayes_inference.py
@@ -1,17 +1,6 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-"""
-
-Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart, www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart
-
-Created on Sat Aug 24 2019
-"""
+
import numpy as np
import os
import copy
@@ -50,83 +39,80 @@ class BayesInference:
discrepancy : obj
The discrepancy object for the sigma2s, i.e. the diagonal entries
of the variance matrix for a multivariate normal likelihood.
- name : string, optional
- The type of analysis, either calibration (Calib) or validation
- (Valid). The default is 'Calib'.
+ name : str, optional
+ The type of analysis, either calibration (`Calib`) or validation
+ (`Valid`). The default is `'Calib'`.
emulator : bool, optional
- Analysis with emulator (MetaModel). The default is True.
+ Analysis with emulator (MetaModel). The default is `True`.
bootstrap : bool, optional
- Bootstrap the analysis. The default is False.
+ Bootstrap the analysis. The default is `False`.
req_outputs : list, optional
The list of requested output to be used for the analysis.
- The default is None. If None, all the defined outputs for the model
+ The default is `None`. If None, all the defined outputs for the model
object is used.
selected_indices : dict, optional
A dictionary with the selected indices of each model output. The
- default is None. If None, all measurement points are used in the
+ default is `None`. If `None`, all measurement points are used in the
analysis.
samples : array of shape (n_samples, n_params), optional
- The samples to be used in the analysis. The default is None. If
+ The samples to be used in the analysis. The default is `None`. If
None the samples are drawn from the probablistic input parameter
object of the MetaModel object.
n_samples : int, optional
- Number of samples to be used in the analysis. The default is 5e5.
- If samples is not None, this argument will be assigned based on the
+ Number of samples to be used in the analysis. The default is `500000`.
+ If samples is not `None`, this argument will be assigned based on the
number of samples given.
measured_data : dict, optional
- A dictionary containing the observation data. The default is None.
- if None, the observation defined in the Model object of the
+ A dictionary containing the observation data. The default is `None`.
+ if `None`, the observation defined in the Model object of the
MetaModel is used.
- inference_method : string, optional
- A method for Bayesian inference. The default is 'rejection'. A
- Markov Chain Monte Carlo sampler can be simply selected by passing
- 'MCMC'.
+ inference_method : str, optional
+ A method for approximating the posterior distribution in the Bayesian
+ inference step. The default is `'rejection'`, which stands for
+ rejection sampling. A Markov Chain Monte Carlo sampler can be simply
+ selected by passing `'MCMC'`.
mcmc_params : dict, optional
A dictionary with args required for the Bayesian inference with
- MCMC. The default is None.
-
- Pass the mcmc_params like the following::
-
- mcmc_params:{
- 'init_samples': None, # initial samples
- 'n_walkers': 100, # number of walkers (chain)
- 'n_steps': 100000, # number of maximum steps
- 'n_burn': 200, # number of burn-in steps
- 'moves': None, # Moves for the emcee sampler
- 'multiprocessing': False, # multiprocessing
- 'verbose': False # verbosity
- }
+ `MCMC`. The default is `None`.
+
+ Pass the mcmc_params like the following:
+
+ >>> mcmc_params:{
+ 'init_samples': None, # initial samples
+ 'n_walkers': 100, # number of walkers (chain)
+ 'n_steps': 100000, # number of maximum steps
+ 'n_burn': 200, # number of burn-in steps
+ 'moves': None, # Moves for the emcee sampler
+ 'multiprocessing': False, # multiprocessing
+ 'verbose': False # verbosity
+ }
The items shown above are the default values. If any parmeter is
not defined, the default value will be assigned to it.
bayes_loocv : bool, optional
- Bayesian Leave-one-out Cross Validation. The default is False. If
- True, the LOOCV procedure is used to estimate the bayesian Model
+ Bayesian Leave-one-out Cross Validation. The default is `False`. If
+ `True`, the LOOCV procedure is used to estimate the bayesian Model
Evidence (BME).
n_bootstrap_itrs : int, optional
- Number of bootstrap iteration. The default is 1. If bayes_loocv is
- True, this is qualt to the total length of the observation data
+ Number of bootstrap iteration. The default is `1`. If bayes_loocv is
+ `True`, this is qualt to the total length of the observation data
set.
perturbed_data : array of shape (n_bootstrap_itrs, n_obs), optional
- User defined perturbed data. The default is [].
+ User defined perturbed data. The default is `[]`.
bootstrap_noise : float, optional
- A noise level to perturb the data set. The default is 0.05.
+ A noise level to perturb the data set. The default is `0.05`.
plot_post_pred : bool, optional
- Plot posterior predictive plots. The default is True.
+ Plot posterior predictive plots. The default is `True`.
plot_map_pred : bool, optional
Plot the model outputs vs the metamodel predictions for the maximum
- a posteriori (defined as max_a_posteriori) parameter set. The
- default is False.
- max_a_posteriori : string, optional
- Maximum a posteriori. 'mean' and 'mode' are available. The default
- is 'mean'.
- corner_title_fmt : string, optional
+ a posteriori (defined as `max_a_posteriori`) parameter set. The
+ default is `False`.
+ max_a_posteriori : str, optional
+ Maximum a posteriori. `'mean'` and `'mode'` are available. The default
+ is `'mean'`.
+ corner_title_fmt : str, optional
Title format for the posterior distribution plot with python
- package corner. The default is '.3f'.
+ package `corner`. The default is `'.3e'`.
- Methods
- -------
- create_inference():
- This method starts the analysis.
"""
def __init__(self, MetaModel, discrepancy=None, emulator=True,
@@ -665,7 +651,7 @@ class BayesInference:
----------
samples : array of shape (n_samples, n_params), optional
Parameter sets. The default is None.
- key : string, optional
+ key : str, optional
Key string to be passed to the run_model_parallel method.
The default is 'MAP'.
@@ -801,8 +787,8 @@ class BayesInference:
data = obs_data[out][~np.isnan(obs_data[out])]
# Prepare sigma2s
- tot_sigma2s = total_sigma2s[out][~np.isnan(
- total_sigma2s[out])][:nout]
+ non_nan_indices = ~np.isnan(total_sigma2s[out])
+ tot_sigma2s = total_sigma2s[out][non_nan_indices][:nout]
# Add the std of the PCE is chosen as emulator.
if self.emulator:
diff --git a/src/bayesvalidrox/bayes_inference/discrepancy.py b/src/bayesvalidrox/bayes_inference/discrepancy.py
index a812f2995479e5c42457e408e6eee51abb546942..5e24aa03325a6edbceb1839c6d75b2fb9e7613a5 100644
--- a/src/bayesvalidrox/bayes_inference/discrepancy.py
+++ b/src/bayesvalidrox/bayes_inference/discrepancy.py
@@ -1,55 +1,72 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-"""
-Discrepancy class for Bayesian inference method
-Option A:
- With no explicitly-specified model
-
-Option B:
- With known redidual variance sigma2 (Gaussian or others)
-
-Option C:
- With unknown residual variance sigma2 (Gaussian)
- with given distribution
-
-Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart, www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart
-
-Created on Mon Sep 2 10:48:35 2019
-"""
import scipy.stats as stats
-from surrogate_models.exp_designs import ExpDesigns
-from surrogate_models.inputs import Input
+from src.bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
class Discrepancy:
- def __init__(self, InputDisc):
- self.type = 'Gaussian'
- self.parameters = None
- self.name = 'Sigma2'
- self.prior = None
- self.Marginals = []
+ """
+ Discrepancy class for Bayesian inference method.
+ We define the reference or reality to be equal to what we can model and a
+ descripancy term \\( \\epsilon \\). We consider the followin format:
+
+ $$\\textbf{y}_{\\text{reality}} = \\mathcal{M}(\\theta) + \\epsilon,$$
+
+ where \\( \\epsilon \\in R^{N_{out}} \\) represents the the effects of
+ measurement error and model inaccuracy. For simplicity, it can be defined
+ as an additive Gaussian disrepancy with zeromean and given covariance
+ matrix \\( \\Sigma \\):
+
+ $$\\epsilon \\sim \\mathcal{N}(\\epsilon|0, \\Sigma). $$
+
+ In the context of model inversion or calibration, an observation point
+ \\( \\textbf{y}_i \\in \\mathcal{y} \\) is a realization of a Gaussian
+ distribution with mean value of \\(\\mathcal{M}(\\theta) \\) and covariance
+ matrix of \\( \\Sigma \\).
+
+ $$ p(\\textbf{y}|\\theta) = \\mathcal{N}(\\textbf{y}|\\mathcal{M}
+ (\\theta))$$
+
+ The following options are available:
+
+ * Option A: With known redidual covariance matrix \\(\\Sigma\\) for
+ independent measurements.
+
+ * Option B: With unknown redidual covariance matrix \\(\\Sigma\\),
+ paramethrized as \\(\\Sigma(\\theta_{\\epsilon})=\\sigma^2 \\textbf{I}_
+ {N_{out}}\\) with unknown residual variances \\(\\sigma^2\\).
+ This term will be jointly infer with the uncertain input parameters. For
+ the inversion, you need to define a prior marginal via `Input` class. Note
+ that \\(\\sigma^2\\) is only a single scalar multiplier for the diagonal
+ entries of the covariance matrix \\(\\Sigma\\).
+
+ Attributes
+ ----------
+ InputDisc : obj
+ Input object. When the \\(\\sigma^2\\) is expected to be inferred
+ jointly with the parameters (`Option B`).If multiple output groups are
+ defined by `Model.Output.names`, each model output needs to have.
+ a prior marginal using the `Input` class. The default is `''`.
+ disc_type : str
+ Type of the noise definition. `'Gaussian'` is only supported so far.
+ parameters : dict or pandas.DataFrame
+ Known residual variance \\(\\sigma^2\\), i.e. diagonal entry of the
+ covariance matrix of the multivariate normal likelihood in case of
+ `Option A`.
+
+ """
+
+ def __init__(self, InputDisc='', disc_type='Gaussian', parameters=None):
self.InputDisc = InputDisc
- self.n_samples = 10000
- self.sigma2_prior = None
-
- def create_inputDisc(self):
- InputClass = Input()
- InputClass.addMarginals()
-
- return InputClass
+ self.disc_type = disc_type
+ self.parameters = parameters
# -------------------------------------------------------------------------
def get_sample(self, n_samples):
"""
- Generate samples for the sigma2s, i.e. the diagonal entries of the
- variance-covariance matrix in the multivariate normal distribution.
+ Generate samples for the \\(\\sigma^2\\), i.e. the diagonal entries of
+ the variance-covariance matrix in the multivariate normal distribution.
Parameters
----------
@@ -59,7 +76,7 @@ class Discrepancy:
Returns
-------
sigma2_prior: array of shape (n_samples, n_params)
- Sigma2 samples.
+ \\(\\sigma^2\\) samples.
"""
self.n_samples = n_samples
diff --git a/src/bayesvalidrox/bayes_inference/discrepancy_GP.py b/src/bayesvalidrox/bayes_inference/discrepancy_GP.py
deleted file mode 100644
index 4eb5d0023aeab8f18f6fa9274a687c73350b8b9e..0000000000000000000000000000000000000000
--- a/src/bayesvalidrox/bayes_inference/discrepancy_GP.py
+++ /dev/null
@@ -1,91 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Sun Nov 7 09:42:33 2021
-
-@author: farid
-"""
-import numpy as np
-from sklearn.gaussian_process import GaussianProcessRegressor
-from sklearn.gaussian_process.kernels import Matern,RBF,RationalQuadratic
-from tqdm import tqdm
-from sklearn.preprocessing import MinMaxScaler
-
-class Bias():
- def __init__(self, verbose=False):
- self.verbose = verbose
-
- def fit_bias(self, EDX, ED_Y, Data):
- print('')
- outNames = [*ED_Y][1:]
- self.norm_weights = {}
- self.all_gp = {}
- self.all_kernels = {}
- self.best_gp = {}
- self.all_scales = {}
- for out in tqdm(outNames, ascii=True, desc ="Fitting the GPR based bias model"):
- scale = {}
- gp = {}
- kernels = {}
- weights = []
- # Select kernel
- # kernel = Matern(length_scale=1.0, length_scale_bounds=(1e-15, 1e5),
- # nu=0.5)
- kernel = RBF(length_scale=1.0, length_scale_bounds=(1e-15, 1e5))
- for i in range(ED_Y[out].shape[1]):
-
- # Prepare the input matrix
- scale[i] = MinMaxScaler()
- EDX_S = scale[i].fit_transform(EDX)
-
- # Prepare target from data and Remove NaN
- try:
- data = Data[out].to_numpy()[~np.isnan(Data[out])]
- except:
- data = Data[out][~np.isnan(Data[out])]
-
- delta = data[i] - ED_Y[out][:,i]
-
- # Initiate the GPR class
- gp[i] = GaussianProcessRegressor(kernel=np.var(data)*kernel, n_restarts_optimizer=5,
- alpha = 1e-15,
- normalize_y=False, random_state=5)
-
- # Fit the GPR
- gp[i].fit(EDX_S, delta)
-
- # Save log_marginal_likelihood as a weight
- weights.append(1)
- # weights.append(gp[i].log_marginal_likelihood_value_)
-
- # kernels
- kernels[i] = gp[i].kernel_
-
- # Save GPR objects and normalized weights in dicts
- self.all_scales[out] = scale
- self.all_gp[out] = gp
- self.all_kernels[out] = kernels
- self.norm_weights[out] = weights#/np.max(weights)
- self.best_gp[out] = gp[np.argmax(weights/np.sum(weights))]
-
- return
-
- def predict(self,X,Output,return_std=True):
- X = np.atleast_2d(X)
- all_gp = self.all_gp[Output]
- all_scale = self.all_scales[Output]
- def predict_GPE(args):
- (idx,gp),weight = args
- D_new_T = all_scale[idx].transform(X)
- return gp.predict(D_new_T,return_std)
- map_f = map(predict_GPE, zip(list(all_gp.items()),self.norm_weights[Output]))
- all_results = list(map_f)
-
- if not return_std:
- y_hat = np.array(all_results).reshape(1,-1)
- return y_hat
- else:
- y_hats = np.array([predict[0] for predict in all_results])
- covs = np.array([predict[1][0]**2 for predict in all_results])
-
- return y_hats.reshape(1,-1), np.diag(covs)
\ No newline at end of file
diff --git a/src/bayesvalidrox/bayes_inference/discrepancy_GP_v1.py b/src/bayesvalidrox/bayes_inference/discrepancy_GP_v1.py
deleted file mode 100644
index dae0e447f59e7f786a91485270e92f04edd91f41..0000000000000000000000000000000000000000
--- a/src/bayesvalidrox/bayes_inference/discrepancy_GP_v1.py
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Sun Nov 7 09:42:33 2021
-
-@author: farid
-"""
-import numpy as np
-from sklearn.gaussian_process import GaussianProcessRegressor
-from sklearn.gaussian_process.kernels import Matern,RBF,RationalQuadratic
-from tqdm import tqdm
-from sklearn.preprocessing import MinMaxScaler
-
-class Bias():
- def __init__(self, verbose=False):
- self.verbose = verbose
-
- def fit_bias(self, BiasInputs, ED_Y, Data):
- print('')
- self.BiasInputs = BiasInputs
- outNames = [*ED_Y][1:]
- self.norm_weights = {}
- self.all_gp = {}
- self.all_kernels = {}
- self.best_gp = {}
- self.all_scales = {}
- for out in tqdm(outNames, ascii=True, desc ="Fitting the GPR based bias model"):
- scale = {}
- gp = {}
- kernels = {}
- weights = []
- # Select kernel
- kernel = Matern(length_scale=1.0, length_scale_bounds=(1e-15, 1e5),
- nu=0.5)
- # kernel = RBF(length_scale=1.0, length_scale_bounds=(1e-15, 1e5))
- for i, y in enumerate(ED_Y[out]):
-
- # Prepare the input matrix
- D = np.hstack((BiasInputs[out],y.reshape(-1,1)))
-
- scale[i] = MinMaxScaler()
- D_T = scale[i].fit_transform(D)
-
- # Prepare data Remove NaN
- try:
- data = Data[out].to_numpy()[~np.isnan(Data[out])]
- except:
- data = Data[out][~np.isnan(Data[out])]
-
- # Initiate the GPR class
- gp[i] = GaussianProcessRegressor(kernel=np.var(data)*kernel, n_restarts_optimizer=5,
- alpha = 1e-15,
- normalize_y=False, random_state=5)
-
- # Fit the GPR
- gp[i].fit(D_T, data)
-
- # Save log_marginal_likelihood as a weight
- weights.append(1)
- # weights.append(gp[i].log_marginal_likelihood_value_)
-
- # kernels
- kernels[i] = gp[i].kernel_
-
- # Save GPR objects and normalized weights in dicts
- self.all_scales[out] = scale
- self.all_gp[out] = gp
- self.all_kernels[out] = kernels
- self.norm_weights[out] = weights#/np.max(weights)
- self.best_gp[out] = gp[np.argmax(weights/np.sum(weights))]
-
- return
-
- def oldpredict(self,Y,Output,BiasInputs=None,return_cov=True):
- Y = np.atleast_2d(Y)
- BiasInputs = self.BiasInputs if BiasInputs is None else BiasInputs
- D_new = np.hstack((BiasInputs[Output],Y.reshape(-1,1)))
-
- return self.best_gp[Output].predict(D_new,return_cov=return_cov)
-
- def predict(self,Y,Output,BiasInputs=None,return_cov=True):
- Y = np.atleast_2d(Y)
- BiasInputs = self.BiasInputs if BiasInputs is None else BiasInputs
- all_gp = self.all_gp[Output]
- all_scale = self.all_scales[Output]
- D_new = np.hstack((BiasInputs[Output],Y.reshape(-1,1)))
- def predict_GPE(args):
- (idx,gp),weight = args
- D_new_T = all_scale[idx].transform(D_new)
- return gp.predict(D_new_T,return_cov=True)
- map_f = map(predict_GPE, zip(list(all_gp.items()),self.norm_weights[Output]))
- all_results = list(map_f)
-
- if not return_cov:
- y_hat = np.mean(np.array(all_results),axis=0)
- return y_hat
- else:
- formatting_function = np.vectorize(lambda f: format(f, '6.2E'))
- # print(formatting_function(np.array([predict[0] for predict in all_results])))
- y_hat = np.average(np.array([predict[0] for predict in all_results]),axis=0,
- weights=self.norm_weights[Output])
-
- # Equation 13c Gardner et al. (2021)
- y_hats = np.array([predict[0] for predict in all_results])
- covs = np.array([predict[1] for predict in all_results])
- cov=0
- print(formatting_function(covs))
- for cov_,y_ in zip(covs,y_hats):
- cov += cov_ + np.dot( y_.reshape(-1,1), y_.reshape(1,-1))
- cov /= len(covs)
- cov -= np.dot(y_hat.reshape(-1,1),y_hat.reshape(1,-1))
-
- return y_hat.reshape(1,-1), cov
\ No newline at end of file
diff --git a/src/bayesvalidrox/bayes_inference/mcmc.py b/src/bayesvalidrox/bayes_inference/mcmc.py
index c52a5f2ce4f2e1ffa2bd0a13f7dadef43a60b17e..8a7a44b8fadb327959ee9c4320bb7490617ffc9d 100755
--- a/src/bayesvalidrox/bayes_inference/mcmc.py
+++ b/src/bayesvalidrox/bayes_inference/mcmc.py
@@ -1,23 +1,5 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-"""
-MCMC class for Bayes inference with emcee package using an Affine Invariant
-Markov chain Monte Carlo (MCMC) Ensemble sampler [1].
-
-1. Foreman-Mackey, D., Hogg, D.W., Lang, D. and Goodman, J., 2013. emcee: the
-MCMC hammer. Publications of the Astronomical Society of the Pacific, 125(925),
-p.306. https://emcee.readthedocs.io/en/stable/
-
-Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart, www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart
-
-Created on Wed Jun 3 2020
-"""
import os
import numpy as np
@@ -35,7 +17,27 @@ os.environ["OMP_NUM_THREADS"] = "1"
class MCMC:
"""
- A class for bayesian inference using a Markov-Chain Monte-Carlo Sampler.
+ A class for bayesian inference via a Markov-Chain Monte-Carlo (MCMC)
+ Sampler to approximate the posterior distribution of the Bayes theorem:
+ $$p(\\theta|\\mathcal{y}) = \\frac{p(\\mathcal{y}|\\theta) p(\\theta)}
+ {p(\\mathcal{y})}.$$
+
+ This class make inference with emcee package [1] using an Affine Invariant
+ Ensemble sampler (AIES) [2].
+
+ [1] Foreman-Mackey, D., Hogg, D.W., Lang, D. and Goodman, J., 2013.emcee:
+ the MCMC hammer. Publications of the Astronomical Society of the
+ Pacific, 125(925), p.306. https://emcee.readthedocs.io/en/stable/
+
+ [2] Goodman, J. and Weare, J., 2010. Ensemble samplers with affine
+ invariance. Communications in applied mathematics and computational
+ science, 5(1), pp.65-80.
+
+
+ Attributes
+ ----------
+ BayesOpts : obj
+ Bayes object.
"""
def __init__(self, BayesOpts):
@@ -380,8 +382,8 @@ class MCMC:
# -------------------------------------------------------------------------
def log_prior(self, theta):
"""
- Calculates the log prior likelihood for the given parameter set(s)
- theta.
+ Calculates the log prior likelihood \\( p(\\theta)\\) for the given
+ parameter set(s) \\( \\theta \\).
Parameters
----------
@@ -445,8 +447,8 @@ class MCMC:
# -------------------------------------------------------------------------
def log_likelihood(self, theta):
"""
- Computes likelihood p(y|theta, obs) of the performance of the
- (meta-)model in reproducing the observation data.
+ Computes likelihood \\( p(\\mathcal{Y}|\\theta)\\) of the performance
+ of the (meta-)model in reproducing the observation data.
Parameters
----------
@@ -494,8 +496,8 @@ class MCMC:
# -------------------------------------------------------------------------
def log_posterior(self, theta):
"""
- Computes the posterior likelihood p(theta| obs) for the given
- parameterset.
+ Computes the posterior likelihood \\(p(\\theta| \\mathcal{Y})\\) for
+ the given parameterset.
Parameters
----------
@@ -652,11 +654,11 @@ class MCMC:
The potential scale reduction factor (PSRF) defined by the variance
within one chain, W, with the variance between chains B.
Both variances are combined in a weighted sum to obtain an estimate of
- the variance of a parameter θ.The square root of the ratio of this
- estimates variance to the within chain variance is called the potential
- scale reduction.
+ the variance of a parameter \\( \\theta \\).The square root of the
+ ratio of this estimates variance to the within chain variance is called
+ the potential scale reduction.
For a well converged chain it should approach 1. Values greater than
- typically 1.1 indicate that the chains have not yet fully converged.
+ 1.1 typically indicate that the chains have not yet fully converged.
Source: http://joergdietrich.github.io/emcee-convergence.html
@@ -665,7 +667,7 @@ class MCMC:
Parameters
----------
chain : array (n_walkers, n_steps, n_params)
- DESCRIPTION.
+ The emcee ensamples.
Returns
-------
@@ -805,36 +807,6 @@ class MCMC:
Iterative scheme as proposed in Meng and Wong (1996) to estimate the
marginal likelihood
- Parameters
- ----------
- N1 : TYPE
- DESCRIPTION.
- N2 : TYPE
- DESCRIPTION.
- q11 : TYPE
- DESCRIPTION.
- q12 : TYPE
- DESCRIPTION.
- q21 : TYPE
- DESCRIPTION.
- q22 : TYPE
- DESCRIPTION.
- r0 : TYPE
- DESCRIPTION.
- neff : TYPE
- DESCRIPTION.
- tol : TYPE
- DESCRIPTION.
- maxiter : TYPE
- DESCRIPTION.
- criterion : TYPE
- DESCRIPTION.
-
- Returns
- -------
- TYPE
- DESCRIPTION.
-
"""
l1 = q11 - q12
l2 = q21 - q22
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 644c837ddbc2e374a884be6786980af74f16e3aa..e857c59274048829b0a376679408959b4e22f0af 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -1,18 +1,5 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-"""
-This class offers helper functions for post-processing of the metamodels.
-
-Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart, www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart
-
-Created on Sat Aug 24 2019
-"""
import numpy as np
import math
@@ -35,34 +22,16 @@ plt.style.use(os.path.join(os.path.split(__file__)[0],
class PostProcessing:
"""
- This class provies many helper functions to pos process the trained
+ This class provides many helper functions to post-process the trained
meta-model.
Attributes
----------
MetaModel : obj
MetaModel object to do postprocessing on.
- Rosenblatt : bool
- If Rossenblatt transformation is required for the dependent input
- parameters.
- Methods
- -------
- plot_moments():
- Plots the moments in a pdf format in the directory
- `Outputs_PostProcessing`.
- valid_metamodel(n_samples=1, samples=None, x_axis="Time [s]"):
- Evaluates and plots the meta model and the PCEModel outputs for the
- given number of samples or the given samples.
- check_accuracy(n_samples=None, samples=None, outputs=None):
- Checks accuracy of the metamodel by computing the root mean square
- error and validation error for all outputs.
- plot_seq_design_diagnostics(ref_BME_KLD=None, save_fig=True):
- Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence
- (KLD) for the sequential design.
- sobol_indices(xlabel='Time [s]', plot_type=None, save_fig=True):
- Performs a global sensitivity analysis via Sobol indices.
- check_reg_quality(n_samples=1000, samples=None, save_fig=True):
- Checks the quality of the metamodel for single output models.
+ name : str
+ Type of the anaylsis. The default is `'calib'`. If a validation is
+ expected to be performed change this to `'valid'`.
"""
def __init__(self, MetaModel, name='calib'):
@@ -77,12 +46,12 @@ class PostProcessing:
Parameters
----------
- xlabel : string, optional
- String to be displayed as x-label. The default is 'Time [s]'.
- plot_type : string, optional
- Options: bar or line. The default is None.
+ xlabel : str, optional
+ String to be displayed as x-label. The default is `'Time [s]'`.
+ plot_type : str, optional
+ Options: bar or line. The default is `None`.
save_fig : bool, optional
- Save figure or not. The default is True.
+ Save figure or not. The default is `True`.
Returns
-------
@@ -190,7 +159,7 @@ class PostProcessing:
return self.pce_means, self.pce_stds
# -------------------------------------------------------------------------
- def valid_metamodel(self, n_samples=1, samples=None, x_axis="Time [s]"):
+ def valid_metamodel(self, n_samples=1, samples=None, x_axis='Time [s]'):
"""
Evaluates and plots the meta model and the PCEModel outputs for the
given number of samples or the given samples.
@@ -201,8 +170,8 @@ class PostProcessing:
Number of samples to be evaluated. The default is 1.
samples : array of shape (n_samples, n_params), optional
Samples to be evaluated. The default is None.
- x_axis : string, optional
- Label of x axis. The default is "Time [s]".
+ x_axis : str, optional
+ Label of x axis. The default is `'Time [s]'`.
Returns
-------
@@ -321,9 +290,9 @@ class PostProcessing:
Parameters
----------
ref_BME_KLD : array, optional
- Reference BME and KLD . The default is None.
+ Reference BME and KLD . The default is `None`.
save_fig : bool, optional
- Whether to save the figures. The default is True.
+ Whether to save the figures. The default is `True`.
Returns
-------
@@ -570,13 +539,14 @@ class PostProcessing:
def sobol_indices(self, xlabel='Time [s]', plot_type=None, save_fig=True):
"""
Provides Sobol indices as a sensitivity measure to infer the importance
- of the input parameters. See Eq. 27 in [1] for more details.
+ of the input parameters. See Eq. 27 in [1] for more details. For the
+ case with Principal component analysis refer to [2].
- 1. Global sensitivity analysis: A flexible and efficient framework with
- an example from stochastic hydrogeology S. Oladyshkin, F.P. de Barros,
- W. Nowak https://doi.org/10.1016/j.advwatres.2011.11.001
+ [1] Global sensitivity analysis: A flexible and efficient framework
+ with an example from stochastic hydrogeology S. Oladyshkin, F.P.
+ de Barros, W. Nowak https://doi.org/10.1016/j.advwatres.2011.11.001
- 2. Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
+ [2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
component analysis and sparse polynomial chaos expansions for global
sensitivity analysis and model calibration: Application to urban
drainage simulation. Reliability Engineering & System Safety, 195,
@@ -584,12 +554,13 @@ class PostProcessing:
Parameters
----------
- xlabel : TYPE, optional
- DESCRIPTION. The default is 'Time [s]'.
- plot_type : TYPE, optional
- DESCRIPTION. The default is None.
- save_fig : TYPE, optional
- DESCRIPTION. The default is True.
+ xlabel : str, optional
+ Label of the x-axis. The default is `'Time [s]'`.
+ plot_type : str, optional
+ Plot type. The default is `None`. This corresponds to line plot.
+ Bar chart can be selected by `bar`.
+ save_fig : bool, optional
+ Whether to save the figures. The default is `True`.
Returns
-------
@@ -845,7 +816,7 @@ class PostProcessing:
# -------------------------------------------------------------------------
def check_reg_quality(self, n_samples=1000, samples=None, save_fig=True):
"""
- Checks the quality of the metamodel for single output models.
+ Checks the quality of the metamodel for single output models based on:
https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685
@@ -855,7 +826,7 @@ class PostProcessing:
Number of parameter sets to use for the check. The default is 1000.
samples : array of shape (n_samples, n_params), optional
Parameter sets to use for the check. The default is None.
- save_fig : Bool, optional
+ save_fig : bool, optional
Whether to save the figures. The default is True.
Returns
@@ -1150,7 +1121,7 @@ class PostProcessing:
----------
samples : array of shape (n_samples, n_params), optional
Samples to evaluate the model at. The default is None.
- key_str : string, optional
+ key_str : str, optional
Key string pass to the model. The default is 'Valid'.
Returns
@@ -1262,7 +1233,7 @@ class PostProcessing:
----------
x_values : list or array, optional
List of x values. The default is [].
- x_axis : string, optional
+ x_axis : str, optional
Label of the x axis. The default is "x [m]".
save_fig : bool, optional
Whether to save the figures. The default is True.
diff --git a/src/bayesvalidrox/pylink/pylink.py b/src/bayesvalidrox/pylink/pylink.py
index 4782568213fa39776bbdf25d82db6248396758bc..3d200513a598e5634b956f8e483655d146df94ba 100644
--- a/src/bayesvalidrox/pylink/pylink.py
+++ b/src/bayesvalidrox/pylink/pylink.py
@@ -12,7 +12,7 @@ import tqdm
class PyLinkForwardModel(object):
- r"""
+ """
A forward model binder
This calss serves as a code wrapper. This wrapper allows the execution of
@@ -100,10 +100,11 @@ class PyLinkForwardModel(object):
def __init__(self, link_type='pylink', name=None, py_file=None,
shell_command='', input_file=None, input_template=None,
- aux_file=None, exe_path='', output_file_names=[],
+ aux_file=None, exe_path='', output_file_names=[],
output_names=[], output_parser='', multi_process=True,
n_cpus=None, meas_file=None, meas_file_valid=None,
- mc_ref_file=None, obs_dict={}, obs_dict_valid={}, mc_ref_dict={}):
+ mc_ref_file=None, obs_dict={}, obs_dict_valid={},
+ mc_ref_dict={}):
self.link_type = link_type
self.name = name
self.shell_command = shell_command
@@ -151,22 +152,27 @@ class PyLinkForwardModel(object):
"""
if case.lower() == 'calib':
- if bool(self.observations):
+ if isinstance(self.observations, dict) and bool(self.observations):
obs = pd.DataFrame.from_dict(self.observations)
elif self.meas_file is not None:
file_path = os.path.join(os.getcwd(), self.meas_file)
obs = pd.read_csv(file_path, delimiter=',')
+ elif isinstance(self.observations, pd.DataFrame):
+ obs = self.observations
else:
raise Exception("Please provide the observation data as a "
"dictionary via observations attribute or pass"
" the csv-file path to MeasurementFile "
"attribute")
elif case.lower() == 'valid':
- if bool(self.observations_valid):
+ if isinstance(self.observations_valid, dict) and \
+ bool(self.observations_valid):
obs = pd.DataFrame.from_dict(self.observations_valid)
elif self.meas_file_valid is not None:
file_path = os.path.join(os.getcwd(), self.meas_file_valid)
obs = pd.read_csv(file_path, delimiter=',')
+ elif isinstance(self.observations_valid, pd.DataFrame):
+ obs = self.observations_valid
else:
raise Exception("Please provide the observation data as a "
"dictionary via Observations attribute or pass"
@@ -236,24 +242,24 @@ class PyLinkForwardModel(object):
return output
# -------------------------------------------------------------------------
- def update_input_params(self, new_input_file, param_sets):
+ def update_input_params(self, new_input_file, param_set):
"""
Finds this pattern with <X1> in the new_input_file and replace it with
the new value from the array param_sets.
Parameters
----------
- new_input_file : TYPE
- DESCRIPTION.
- param_sets : TYPE
- DESCRIPTION.
+ new_input_file : list
+ List of the input files with the adapted names.
+ param_set : array of shape (n_params)
+ Parameter set.
Returns
-------
None.
"""
- NofPa = param_sets.shape[0]
+ NofPa = param_set.shape[0]
text_to_search_list = [f'<X{i+1}>' for i in range(NofPa)]
for filename in new_input_file:
@@ -262,7 +268,7 @@ class PyLinkForwardModel(object):
filedata = file.read()
# Replace the target string
- for text_to_search, params in zip(text_to_search_list, param_sets):
+ for text_to_search, params in zip(text_to_search_list, param_set):
filedata = filedata.replace(text_to_search, f'{params:0.4e}')
# Write the file out again
@@ -274,13 +280,13 @@ class PyLinkForwardModel(object):
"""
Runs the execution command given by the user to run the given model.
It checks if the output files have been generated. If yes, the jobe is
- done and it extracts and returns the requested output(s). Otherwise,
- it executes the command again.
+ done and it extracts and returns the requested output(s). Otherwise,
+ it executes the command again.
Parameters
----------
- command : string
- The command to be executed.
+ command : str
+ The shell command to be executed.
output_file_names : list
Name of the output file names.
@@ -318,6 +324,19 @@ class PyLinkForwardModel(object):
This function creates subdirectory for the current run and copies the
necessary files to this directory and renames them. Next, it executes
the given command.
+
+ Parameters
+ ----------
+ xx : tuple
+ A tuple including parameter set, simulation number and key string.
+
+ Returns
+ -------
+ output : array of shape (n_outputs+1, n_obs)
+ An array passed by the output paraser containing the x_values as
+ the first row and the simulations results stored in the the rest of
+ the array.
+
"""
c_points, run_no, key_str = xx
@@ -385,21 +404,21 @@ class PyLinkForwardModel(object):
Parameters
----------
- c_points : array like of shape (n_samples, n_params)
+ c_points : array of shape (n_samples, n_params)
Collocation points (training set).
prevRun_No : int, optional
Previous run number, in case the sequential design is selected.
- The default is 0.
- key_str : string, optional
- A descriptive string for validation runs. The default is ''.
+ The default is `0`.
+ key_str : str, optional
+ A descriptive string for validation runs. The default is `''`.
mp : bool, optional
- Multiprocessing. The default is True.
+ Multiprocessing. The default is `True`.
Returns
-------
all_outputs : dict
A dictionary with x values (time step or point id) and all outputs.
- Each key contains an array of the shape (n_samples, n_obs).
+ Each key contains an array of the shape `(n_samples, n_obs)`.
new_c_points : array
Updated collocation points (training set). If a simulation does not
executed successfully, the parameter set is removed.
@@ -550,9 +569,9 @@ class PyLinkForwardModel(object):
Parameters
----------
- dir_name : string
+ dir_name : str
Directory name.
- key : string
+ key : str
Keyword to search for.
Returns
diff --git a/src/bayesvalidrox/surrogate_models/exp_designs.py b/src/bayesvalidrox/surrogate_models/exp_designs.py
index d71e92e79ae279da4976bdcdcf265122ea7932c3..31b9fde86a16ae94afbd8405eb31882c076d46f6 100644
--- a/src/bayesvalidrox/surrogate_models/exp_designs.py
+++ b/src/bayesvalidrox/surrogate_models/exp_designs.py
@@ -1,17 +1,5 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-"""
-Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart, www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart
-
-Created on Sat Aug 24 2019
-
-"""
import numpy as np
import math
@@ -24,15 +12,120 @@ from .apoly_construction import apoly_construction
class ExpDesigns:
- def __init__(self, Input, meta_Model='pce', hdf5_file=None,
+ """
+ This class generates samples from the prescribed marginals for the model
+ parameters using the `Input` object.
+
+ Attributes
+ ----------
+ Input : obj
+ Input object containing the parameter marginals, i.e. name,
+ distribution type and distribution parameters or available raw data.
+ method : str
+ Type of the experimental design. The default is `'normal'`. Other
+ option is `'sequential'`.
+ meta_Model : str
+ Type of the meta_model.
+ sampling_method : str
+ Name of the sampling method for the experimental design. The following
+ sampling method are supported:
+
+ * random
+ * latin_hypercube
+ * sobol
+ * halton
+ * hammersley
+ * korobov
+ * chebyshev(FT)
+ * grid(FT)
+ * nested_grid(FT)
+ * user
+ hdf5_file : str
+ Name of the hdf5 file that contains the experimental design.
+ n_new_samples : int
+ Number of (initial) training points.
+ n_max_samples : int
+ Number of maximum training points.
+ mod_LOO_threshold : float
+ The modified leave-one-out cross validation threshold where the
+ sequential design stops.
+ tradeoff_scheme : str
+ Trade-off scheme to assign weights to the exploration and exploitation
+ scores in the sequential design.
+ n_canddidate : int
+ Number of candidate training sets to calculate the scores for.
+ explore_method : str
+ Type of the exploration method for the sequential design. The following
+ methods are supported:
+
+ * Voronoi
+ * random
+ * latin_hypercube
+ * LOOCV
+ * dual annealing
+ exploit_method : str
+ Type of the exploitation method for the sequential design. The
+ following methods are supported:
+
+ * BayesOptDesign
+ * BayesActDesign
+ * VarOptDesign
+ * alphabetic
+ * Space-filling
+ util_func : str or list
+ The utility function to be specified for the `exploit_method`. For the
+ available utility functions see Note section.
+ n_cand_groups : int
+ Number of candidate groups. Each group of candidate training sets will
+ be evaulated separately in parallel.
+ n_replication : int
+ Number of replications. Only for comparison. The default is 1.
+ post_snapshot : int
+ Whether to plot the posterior in the sequential design. The default is
+ `True`.
+ step_snapshot : int
+ The number of steps to plot the posterior in the sequential design. The
+ default is 1.
+ max_a_post : list or array
+ Maximum a posteriori of the posterior distribution, if known. The
+ default is `[]`.
+ adapt_verbose : bool
+ Whether to plot the model response vs that of metamodel for the new
+ trining point in the sequential design.
+
+ Note
+ ----------
+ The following utiliy functions for the **exploitation** methods are
+ supported:
+
+ #### BayesOptDesign (when data is available)
+ - DKL (Kullback-Leibler Divergence)
+ - DPP (D-Posterior-percision)
+ - APP (A-Posterior-percision)
+
+ #### VarBasedOptDesign -> when data is not available
+ - Entropy (Entropy/MMSE/active learning)
+ - EIGF (Expected Improvement for Global fit)
+ - LOOCV (Leave-one-out Cross Validation)
+
+ #### alphabetic
+ - D-Opt (D-Optimality)
+ - A-Opt (A-Optimality)
+ - K-Opt (K-Optimality)
+ """
+
+ def __init__(self, Input, method='normal', meta_Model='pce',
+ sampling_method='random', hdf5_file=None,
n_new_samples=1, n_max_samples=None, mod_LOO_threshold=1e-16,
tradeoff_scheme=None, n_canddidate=1, explore_method='random',
exploit_method='Space-filling', util_func='Space-filling',
n_cand_groups=4, n_replication=1, post_snapshot=False,
- step_snapshot=1, max_a_post=[]):
+ step_snapshot=1, max_a_post=[], adapt_verbose=False):
self.InputObj = Input
+ self.method = method
self.meta_Model = meta_Model
+ self.sampling_method = sampling_method
self.hdf5_file = hdf5_file
self.n_new_samples = n_new_samples
self.n_max_samples = n_max_samples
@@ -47,6 +140,7 @@ class ExpDesigns:
self.post_snapshot = post_snapshot
self.step_snapshot = step_snapshot
self.max_a_post = max_a_post
+ self.adapt_verbose = adapt_verbose
# -------------------------------------------------------------------------
def generate_samples(self, n_samples, sampling_method='random',
@@ -58,11 +152,11 @@ class ExpDesigns:
----------
n_samples : int
Number of requested samples.
- sampling_method : TYPE, optional
- DESCRIPTION. The default is 'random'.
+ sampling_method : str, optional
+ Sampling method. The default is `'random'`.
transform : bool, optional
Transformation via an isoprobabilistic transformation method. The
- default is False.
+ default is `False`.
Returns
-------
@@ -94,12 +188,12 @@ class ExpDesigns:
----------
n_samples : int
Number of requested training points.
- sampling_method : string, optional
- Sampling method. The default is 'random'.
+ sampling_method : str, optional
+ Sampling method. The default is `'random'`.
transform : bool, optional
- Isoprobabilistic transformation. The default is False.
- max_pce_deg : TYPE, optional
- Maximum PCE polynomial degree. The default is None.
+ Isoprobabilistic transformation. The default is `False`.
+ max_pce_deg : int, optional
+ Maximum PCE polynomial degree. The default is `None`.
Returns
-------
@@ -180,7 +274,7 @@ class ExpDesigns:
Parameters
----------
max_deg : int, optional
- Maximum degree. The default is None.
+ Maximum degree. The default is `None`.
Returns
-------
@@ -224,9 +318,7 @@ class ExpDesigns:
domain=self.JDist)
# Create orthogonal polynomial coefficients if necessary
- if self.meta_Model.lower() != 'gpe' and self.apce\
- and max_deg is not None \
- and Inputs.polycoeffsFlag:
+ if self.apce and max_deg is not None and Inputs.poly_coeffs_flag:
self.polycoeffs = {}
for parIdx in tqdm(range(ndim), ascii=True,
desc="Computing orth. polynomial coeffs"):
@@ -467,12 +559,12 @@ class ExpDesigns:
Parameters
----------
- X : ndarray of shape (n_samples,n_params)
+ X : array of shape (n_samples,n_params)
Samples to be transformed.
Returns
-------
- tr_X: ndarray of shape (n_samples,n_params)
+ tr_X: array of shape (n_samples,n_params)
Transformed samples.
"""
diff --git a/src/bayesvalidrox/surrogate_models/exploration.py b/src/bayesvalidrox/surrogate_models/exploration.py
index 2504a15d5285acc930b13db30422e2acf613e004..d2e90c362cf2e11e7e90de9f1026758c1a9c3101 100644
--- a/src/bayesvalidrox/surrogate_models/exploration.py
+++ b/src/bayesvalidrox/surrogate_models/exploration.py
@@ -1,366 +1,370 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-"""
-Created based on the Surrogate Modeling Toolbox ("SUMO Toolbox")
-
-publication:
-> - A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
-> D. Gorissen, K. Crombecq, I. Couckuyt, T. Dhaene, P. Demeester,
-> Journal of Machine Learning Research,
-> Vol. 11, pp. 2051-2055, July 2010.
->
-> Contact : sumo@sumo.intec.ugent.be - http://sumo.intec.ugent.be
-
-@author: farid
-"""
import numpy as np
-import copy
-import scipy.stats as stats
from scipy.spatial import distance
-from .ExpDesigns import ExpDesigns
class Exploration:
- def __init__(self, PCEModel, NCandidate):
- self.PCEModel = PCEModel
+ """
+ Created based on the Surrogate Modeling Toolbox (SUMO) [1].
+
+ [1] Gorissen, D., Couckuyt, I., Demeester, P., Dhaene, T. and Crombecq, K.,
+ 2010. A surrogate modeling and adaptive sampling toolbox for computer
+ based design. Journal of machine learning research.-Cambridge, Mass.,
+ 11, pp.2051-2055. sumo@sumo.intec.ugent.be - http://sumo.intec.ugent.be
+
+ Attributes
+ ----------
+ MetaModel : obj
+ MetaModel object.
+ n_candidate : int
+ Number of candidate samples.
+ mc_criterion : str
+ Selection crieterion. The default is `'mc-intersite-proj-th'`. Another
+ option is `'mc-intersite-proj'`.
+ w : int
+ Number of random points in the domain for each sample of the
+ training set.
+ """
+
+ def __init__(self, MetaModel, n_candidate,
+ mc_criterion='mc-intersite-proj-th'):
+ self.MetaModel = MetaModel
self.Marginals = []
- self.OldExpDesign = PCEModel.ExpDesign.X
- self.Bounds = PCEModel.BoundTuples
- self.numNewSamples = NCandidate
- self.mcCriterion = 'mc-intersite-proj-th' #'mc-intersite-proj'
-
- self.allCandidates = []
- self.newSamples = []
-
- self.areas = []
- self.closestPoints = []
- self.perc = None
- self.errors = None
-
+ self.n_candidate = n_candidate
+ self.mc_criterion = mc_criterion
self.w = 100
-
- def getExplorationSamples(self):
+
+ def get_exploration_samples(self):
"""
- This function generates prospective candidates to be selected as new design
- and their associated exploration scores.
+ This function generates candidates to be selected as new design and
+ their associated exploration scores.
+
+ Returns
+ -------
+ all_candidates : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
"""
- PCEModel = self.PCEModel
- explore_method = PCEModel.ExpDesign.explore_method
-
+ MetaModel = self.MetaModel
+ explore_method = MetaModel.ExpDesign.explore_method
+
+ print("\n")
+ print(f' The {explore_method}-Method is selected as the exploration '
+ 'method.')
+ print("\n")
+
if explore_method == 'Voronoi':
- print("\n")
- print(' The Voronoi-based method is selected as the exploration method.')
- print("\n")
-
# Generate samples using the Voronoi method
- allCandidates, scoreExploration = self.getVornoiSamples()
-
+ all_candidates, exploration_scores = self.get_vornoi_samples()
else:
- print("\n")
- print(f' The {explore_method}-Method is selected as the exploration method.')
- print("\n")
# Generate samples using the MC method
- allCandidates, scoreExploration = self.getMCSamples()
+ all_candidates, exploration_scores = self.get_mc_samples()
- return allCandidates, scoreExploration
-
- #--------------------------------------------------------------------------------------------------------
-
- def getVornoiSamples(self):
-
- mcCriterion = self.mcCriterion
- numNewSamples = self.numNewSamples
+ return all_candidates, exploration_scores
+
+ # -------------------------------------------------------------------------
+ def get_vornoi_samples(self):
+ """
+ This function generates samples based on voronoi cells and their
+ corresponding scores
+
+ Returns
+ -------
+ new_samples : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
+ """
+
+ mc_criterion = self.mc_criterion
+ n_candidate = self.n_candidate
# Get the Old ExpDesign #samples
- OldExpDesign = self.OldExpDesign
- ndim = OldExpDesign.shape[1]
-
+ old_ED_X = self.MetaModel.ExpDesign.X
+ ndim = old_ED_X.shape[1]
+
# calculate error #averageErrors
- errorVoronoi, allCandidates = self.approximateVoronoi(self.w,OldExpDesign)
-
-
- # get amount of samples
- sortederrorVoronoi = errorVoronoi #np.sort(copy.copy(errorVoronoi))[::-1]
- bestSamples = range(len(errorVoronoi)) #np.argsort(errorVoronoi)[::-1]
-
-
- # for each best sample, pick the best candidate point in the voronoi cell
- selectedSamples = np.empty((0, ndim))
- badSamples = []
-
- for i, index in enumerate(bestSamples):
+ error_voronoi, all_candidates = self.approximate_voronoi(
+ self.w, old_ED_X
+ )
+
+ # Pick the best candidate point in the voronoi cell
+ # for each best sample
+ selected_samples = np.empty((0, ndim))
+ bad_samples = []
+
+ for index in range(len(error_voronoi)):
# get candidate new samples from voronoi tesselation
- candidates = self.closestPoints[index]
-
+ candidates = self.closest_points[index]
+
# get total number of candidates
- nNewSamples = candidates.shape[0]
-
-
+ n_new_samples = candidates.shape[0]
+
# still no candidate samples around this one, skip it!
- if nNewSamples == 0:
- print('Sample %s skipped because there were no candidate samples around it...'%OldExpDesign[index])
- badSamples.append(index)
+ if n_new_samples == 0:
+ print('The following sample has been skipped because there '
+ 'were no candidate samples around it...')
+ print(old_ED_X[index])
+ bad_samples.append(index)
continue
-
+
# find candidate that is farthest away from any existing sample
- maxMinDistance = 0
- bestCandidate = 0
- minIntersiteDist = np.zeros((nNewSamples))
- minprojectedDist = np.zeros((nNewSamples))
-
- for j in range(nNewSamples):
- NewSamples = np.vstack((OldExpDesign, selectedSamples))
+ max_min_distance = 0
+ best_candidate = 0
+ min_intersite_dist = np.zeros((n_new_samples))
+ min_projected_dist = np.zeros((n_new_samples))
+
+ for j in range(n_new_samples):
+
+ new_samples = np.vstack((old_ED_X, selected_samples))
+
# find min distorted distance from all other samples
- euclideanDist = self.buildDistanceMatrixPoint(NewSamples, candidates[j], doSqrt=True)
- mineuclideanDist = np.min(euclideanDist)
- minIntersiteDist[j] = mineuclideanDist
-
- # see if this is the maximal minimum distance from all other samples
- if mineuclideanDist >= maxMinDistance:
- maxMinDistance = mineuclideanDist
- bestCandidate = j
-
+ euclidean_dist = self._build_dist_matrix_point(
+ new_samples, candidates[j], do_sqrt=True)
+ min_euclidean_dist = np.min(euclidean_dist)
+ min_intersite_dist[j] = min_euclidean_dist
+
+ # Check if this is the maximum minimum distance from all other
+ # samples
+ if min_euclidean_dist >= max_min_distance:
+ max_min_distance = min_euclidean_dist
+ best_candidate = j
+
# Projected distance
- projectedDist = distance.cdist(NewSamples, [candidates[j]], 'chebyshev')
- minprojectedDist[j] = np.min(projectedDist)
-
-
- if mcCriterion == 'mc-intersite-proj':
- weightEuclideanDist = 0.5 * ((nNewSamples+1)**(1/ndim) - 1)
- weightProjectedDist = 0.5 * (nNewSamples+1)
- totalDistScores= weightEuclideanDist * minIntersiteDist + weightProjectedDist * minprojectedDist
-
- elif mcCriterion == 'mc-intersite-proj-th':
- alpha = 0.5 # chosen (tradeoff)
- d_min = 2 * alpha / nNewSamples
- if any(minprojectedDist < d_min):
- candidates = np.delete(candidates, [minprojectedDist < d_min], axis=0)
- totalDistScores = np.delete(minIntersiteDist, [minprojectedDist < d_min], axis=0)
+ projected_dist = distance.cdist(
+ new_samples, [candidates[j]], 'chebyshev')
+ min_projected_dist[j] = np.min(projected_dist)
+
+ if mc_criterion == 'mc-intersite-proj':
+ weight_euclidean_dist = 0.5 * ((n_new_samples+1)**(1/ndim) - 1)
+ weight_projected_dist = 0.5 * (n_new_samples+1)
+ total_dist_scores = weight_euclidean_dist * min_intersite_dist
+ total_dist_scores += weight_projected_dist * min_projected_dist
+
+ elif mc_criterion == 'mc-intersite-proj-th':
+ alpha = 0.5 # chosen (tradeoff)
+ d_min = 2 * alpha / n_new_samples
+ if any(min_projected_dist < d_min):
+ candidates = np.delete(
+ candidates, [min_projected_dist < d_min], axis=0
+ )
+ total_dist_scores = np.delete(
+ min_intersite_dist, [min_projected_dist < d_min],
+ axis=0
+ )
else:
- totalDistScores = minIntersiteDist
-
+ total_dist_scores = min_intersite_dist
else:
- raise NameError('The MC-Criterion you requested is not available.')
-
-
- # add the best candidate to the list of new samples
- bestCandidate = np.argsort(totalDistScores)[::-1][:numNewSamples]
- selectedSamples = np.vstack((selectedSamples, candidates[bestCandidate]))
-
- #print('\nBest candidate around sample %s was chosen to be %s, with minDistance %s'%(OldExpDesign[index], candidates[bestCandidate], totalDistScores[bestCandidate]))
-
- self.newSamples = selectedSamples #candidates
- self.explorationScore = np.delete(sortederrorVoronoi, badSamples, axis=0)
-
-
- return self.newSamples, self.explorationScore
-
- #--------------------------------------------------------------------------------------------------------
-
- def getMCSamples(self, allCandidates=None):
+ raise NameError(
+ 'The MC-Criterion you requested is not available.'
+ )
+
+ # Add the best candidate to the list of new samples
+ best_candidate = np.argsort(total_dist_scores)[::-1][:n_candidate]
+ selected_samples = np.vstack(
+ (selected_samples, candidates[best_candidate])
+ )
+
+ self.new_samples = selected_samples
+ self.exploration_scores = np.delete(error_voronoi, bad_samples, axis=0)
+
+ return self.new_samples, self.exploration_scores
+
+ # -------------------------------------------------------------------------
+ def get_mc_samples(self, all_candidates=None):
"""
- This function generates random samples based on Global Monte Carlo methods
- and their corresponding scores.
-
+ This function generates random samples based on Global Monte Carlo
+ methods and their corresponding scores, based on [1].
+
+ [1] Crombecq, K., Laermans, E. and Dhaene, T., 2011. Efficient
+ space-filling and non-collapsing sequential design strategies for
+ simulation-based modeling. European Journal of Operational Research
+ , 214(3), pp.683-696.
+ DOI: https://doi.org/10.1016/j.ejor.2011.05.032
+
Implemented methods to compute scores:
1) mc-intersite-proj
2) mc-intersite-proj-th
-
- Based on the following paper:
- Crombecq, K., Laermans, E., & Dhaene, T. (2011). Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling.
- European Journal of Operational Research, 214(3), 683-696.
- DOI: https://doi.org/10.1016/j.ejor.2011.05.032
-
+
+ Arguments
+ ---------
+ all_candidates : array, optional
+ Samples to compute the scores for. The default is `None`. In this
+ case, samples will be generated by defined model input marginals.
+
+ Returns
+ -------
+ new_samples : array of shape (n_candidate, n_params)
+ A list of samples.
+ exploration_scores: arrays of shape (n_candidate)
+ Exploration scores.
"""
- PCEModel = self.PCEModel
- explore_method = PCEModel.ExpDesign.explore_method
- mcCriterion = self.mcCriterion
- if allCandidates is None:
- nNewSamples = self.numNewSamples
- else:
- nNewSamples = allCandidates.shape[0]
-
+ MetaModel = self.MetaModel
+ explore_method = MetaModel.ExpDesign.explore_method
+ mc_criterion = self.mc_criterion
+ if all_candidates is None:
+ n_candidate = self.n_candidate
+ else:
+ n_candidate = all_candidates.shape[0]
+
# Get the Old ExpDesign #samples
- OldExpDesign = self.OldExpDesign
- ndim = OldExpDesign.shape[1]
-
+ old_ED_X = MetaModel.ExpDesign.X
+ ndim = old_ED_X.shape[1]
+
# ----- Compute the number of random points -----
- if allCandidates is None:
+ if all_candidates is None:
# Generate MC Samples
- allCandidates = PCEModel.ExpDesign.generate_samples(self.numNewSamples,
- explore_method)
- self.allCandidates = allCandidates
-
+ all_candidates = MetaModel.ExpDesign.generate_samples(
+ self.n_candidate, explore_method
+ )
+ self.all_candidates = all_candidates
+
# initialization
- newSamples = np.empty((0, ndim))
- minIntersiteDist = np.zeros((nNewSamples))
- minprojectedDist = np.zeros((nNewSamples))
-
-
- for i, candidate in enumerate(allCandidates):
-
+ new_samples = np.empty((0, ndim))
+ min_intersite_dist = np.zeros((n_candidate))
+ min_projected_dist = np.zeros((n_candidate))
+
+ for i, candidate in enumerate(all_candidates):
+
# find candidate that is farthest away from any existing sample
maxMinDistance = 0
- bestCandidate = 0
-
-
- NewSamples = np.vstack((OldExpDesign, newSamples))
+
+ new_samples = np.vstack((old_ED_X, new_samples))
# find min distorted distance from all other samples
- euclideanDist = self.buildDistanceMatrixPoint(NewSamples, candidate, doSqrt=True)
- mineuclideanDist = np.min(euclideanDist)
- minIntersiteDist[i] = mineuclideanDist
-
- # see if this is the maximal minimum distance from all other samples
- if mineuclideanDist >= maxMinDistance:
- maxMinDistance = mineuclideanDist
- bestCandidate = i
-
+ euclidean_dist = self._build_dist_matrix_point(
+ new_samples, candidate, do_sqrt=True
+ )
+ min_euclidean_dist = np.min(euclidean_dist)
+ min_intersite_dist[i] = min_euclidean_dist
+
+ # Check if this is the maximum minimum distance from all other
+ # samples
+ if min_euclidean_dist >= maxMinDistance:
+ maxMinDistance = min_euclidean_dist
+
# Projected distance
- projectedDist = distance.cdist(NewSamples, [candidate], 'chebyshev')
- minprojectedDist[i] = np.min(projectedDist)
-
-
- if mcCriterion == 'mc-intersite-proj':
- weightEuclideanDist = ((nNewSamples+1)**(1/ndim) - 1) * 0.5
- weightProjectedDist = (nNewSamples+1) * 0.5
- totalDistScores= weightEuclideanDist * minIntersiteDist + weightProjectedDist * minprojectedDist
-
- elif mcCriterion == 'mc-intersite-proj-th':
- alpha = 0.5 # chosen (tradeoff)
- d_min = 2 * alpha / nNewSamples
- if any(minprojectedDist < d_min):
- allCandidates = np.delete(allCandidates, [minprojectedDist < d_min], axis=0)
- totalDistScores = np.delete(minIntersiteDist, [minprojectedDist < d_min], axis=0)
+ projected_dist = distance.cdist(
+ new_samples, [candidate], 'chebyshev'
+ )
+ min_projected_dist[i] = np.min(projected_dist)
+
+ if mc_criterion == 'mc-intersite-proj':
+ weight_euclidean_dist = ((n_candidate+1)**(1/ndim) - 1) * 0.5
+ weight_projected_dist = (n_candidate+1) * 0.5
+ total_dist_scores = weight_euclidean_dist * min_intersite_dist
+ total_dist_scores += weight_projected_dist * min_projected_dist
+
+ elif mc_criterion == 'mc-intersite-proj-th':
+ alpha = 0.5 # chosen (tradeoff)
+ d_min = 2 * alpha / n_candidate
+ if any(min_projected_dist < d_min):
+ all_candidates = np.delete(
+ all_candidates, [min_projected_dist < d_min], axis=0
+ )
+ total_dist_scores = np.delete(
+ min_intersite_dist, [min_projected_dist < d_min], axis=0
+ )
else:
- totalDistScores= minIntersiteDist
-
+ total_dist_scores = min_intersite_dist
else:
raise NameError('The MC-Criterion you requested is not available.')
-
-
- self.newSamples = allCandidates
- self.explorationScore = totalDistScores / np.nansum(totalDistScores)
-
-
- return self.newSamples, self.explorationScore
-
- #--------------------------------------------------------------------------------------------------------
-
- def approximateVoronoi(self, w, constraints=[]):
+
+ self.new_samples = all_candidates
+ self.exploration_scores = total_dist_scores
+ self.exploration_scores /= np.nansum(total_dist_scores)
+
+ return self.new_samples, self.exploration_scores
+
+ # -------------------------------------------------------------------------
+ def approximate_voronoi(self, w, samples):
"""
- An approximate (monte carlo) version of Matlab's voronoi command.
- The samples are assumed to lie within the LB and UB bounds (=vectors,
- one lower and upper bound per dimension).
-
- If LB,UB are not given [-1 1] is assumed.
-
+ An approximate (monte carlo) version of Matlab's voronoi command.
+
Arguments
---------
- constraints : string
- A set of constraints that have to be satisfied.
- Voronoi cells which partly violate constraints are estimated at their
- size within the allowed area.
-
+ samples : array
+ Old experimental design to be used as center points for voronoi
+ cells.
+
Returns
-------
- areas: numpy array
+ areas : array
An approximation of the voronoi cells' areas.
-
- allCandidates: list of numpy arrays
+ all_candidates: list of arrays
A list of samples in each voronoi cell.
"""
- PCEModel = self.PCEModel
- # Get the Old ExpDesign #samples
- samples = self.OldExpDesign
-
- nSamples = samples.shape[0]
- dim = samples.shape[1]
-
- # Get the bounds
- Bounds = self.Bounds
-
-
- # Compute the number of random points
- # 100 random points in the domain for each sample
- nPoints = w * samples.shape[0]
-
- # Generate random points to estimate the voronoi decomposition
- # points = np.zeros((nPoints, dim))
- # for i in range(dim):
- # points[:,i] = stats.uniform(loc=0, scale=1).rvs(size=nPoints)
-
- # # Scale each column to the correct range
- # for i in range(dim):
- # points[:,i] = self.scaleColumns(points[:,i],Bounds[i][0],Bounds[i][1])
-
- ExpDesign = ExpDesigns(PCEModel.Inputs)
- points = ExpDesign.generate_samples(nPoints, 'random')
-
-
- self.allCandidates = points
-
+ MetaModel = self.MetaModel
+
+ n_samples = samples.shape[0]
+ ndim = samples.shape[1]
+
+ # Compute the number of random points
+ n_points = w * samples.shape[0]
+ # Generate w random points in the domain for each sample
+ points = MetaModel.ExpDesign.generate_samples(n_points, 'random')
+ self.all_candidates = points
+
# Calculate the nearest sample to each point
- self.areas = np.zeros((nSamples))
- self.closestPoints = [np.empty((0,dim)) for i in range(nSamples)] #cell(nSamples, 1)
-
- # Compute the minimum distance from all the samples of OldExpDesign for each test point
- for idx in range(nPoints):
+ self.areas = np.zeros((n_samples))
+ self.closest_points = [np.empty((0, ndim)) for i in range(n_samples)]
+
+ # Compute the minimum distance from all the samples of old_ED_X for
+ # each test point
+ for idx in range(n_points):
# calculate the minimum distance
- distances = self.buildDistanceMatrixPoint(samples, points[idx,:], doSqrt=True)
- closestSample = np.argmin(distances)
-
- #Add to the voronoi list of the closest sample
- #print('Point %s found to be closest to sample %s' %(points[idx], samples[closestSample]))
- self.areas[closestSample] = self.areas[closestSample] + 1
- prevclosestPoints = self.closestPoints[closestSample]
- self.closestPoints[closestSample] = np.vstack((prevclosestPoints, points[idx]))
-
-
- # divide by the amount of points to get the estimated volume of each
+ distances = self._build_dist_matrix_point(
+ samples, points[idx], do_sqrt=True
+ )
+ closest_sample = np.argmin(distances)
+
+ # Add to the voronoi list of the closest sample
+ self.areas[closest_sample] = self.areas[closest_sample] + 1
+ prev_closest_points = self.closest_points[closest_sample]
+ self.closest_points[closest_sample] = np.vstack(
+ (prev_closest_points, points[idx])
+ )
+
+ # Divide by the amount of points to get the estimated volume of each
# voronoi cell
- self.areas = self.areas / nPoints
+ self.areas /= n_points
self.perc = np.max(self.areas * 100)
- self.errors = self.areas
+ self.errors = self.areas
-
- return self.areas, self.allCandidates
-
- #--------------------------------------------------------------------------------------------------------
-
- def buildDistanceMatrixPoint(self, samples, point, doSqrt=False):
+ return self.areas, self.all_candidates
+
+ # -------------------------------------------------------------------------
+ def _build_dist_matrix_point(self, samples, point, do_sqrt=False):
"""
Calculates the intersite distance of all points in samples from point.
-
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params)
+ The old experimental design.
+ point : array
+ A candidate point.
+ do_sqrt : bool, optional
+ Whether to return distances or squared distances. The default is
+ `False`.
+
+ Returns
+ -------
+ distances : array
+ Distances.
+
"""
-
distances = distance.cdist(samples, np.array([point]), 'euclidean')
-
+
# do square root?
- if doSqrt: return distances
- else:
+ if do_sqrt:
+ return distances
+ else:
return distances**2
-
- #--------------------------------------------------------------------------------------------------------
-
- def scaleColumns(self, x,lowerLimit,upperLimit):
- """
- Scale all columns of X to [c,d], defaults to [-1,1]
- If mn and mx are given they are used as the original range of each column of x
-
- """
-
- n = x.shape[0]
-
- mn, mx = min(x), max(x)
-
- # repmat( mn,n,1 ) , repmat(mx-mn,n,1)
- res = np.divide((x - np.tile(np.array(mn), n)) , np.tile(np.array(mx-mn), n)) * (upperLimit - lowerLimit) + lowerLimit
-
-
- return res
#if __name__ == "__main__":
# import scipy.stats as stats
@@ -370,17 +374,17 @@ class Exploration:
# plt.rc('font', family='sans-serif', serif='Arial')
# plt.rc('figure', figsize = (12, 8))
#
-# def plotter(OldExpDesign, allCandidates, explorationScore):
+# def plotter(old_ED_X, all_candidates, exploration_scores):
# global Bounds
#
# from scipy.spatial import Voronoi, voronoi_plot_2d
-# vor = Voronoi(OldExpDesign)
+# vor = Voronoi(old_ED_X)
#
# fig = voronoi_plot_2d(vor)
#
# # find min/max values for normalization
-## minima = min(explorationScore)
-## maxima = max(explorationScore)
+## minima = min(exploration_scores)
+## maxima = max(exploration_scores)
##
## # normalize chosen colormap
## norm = mpl.colors.Normalize(vmin=minima, vmax=maxima, clip=True)
@@ -390,20 +394,20 @@ class Exploration:
## region = vor.regions[vor.point_region[r]]
## if not -1 in region:
## polygon = [vor.vertices[i] for i in region]
-## plt.fill(*zip(*polygon), color=mapper.to_rgba(explorationScore[r]))
+## plt.fill(*zip(*polygon), color=mapper.to_rgba(exploration_scores[r]))
#
#
# ax1 = fig.add_subplot(111)
#
-# ax1.scatter(OldExpDesign[:,0], OldExpDesign[:,1], s=10, c='r', marker="s", label='Old Design Points')
-# for i in range(OldExpDesign.shape[0]):
+# ax1.scatter(old_ED_X[:,0], old_ED_X[:,1], s=10, c='r', marker="s", label='Old Design Points')
+# for i in range(old_ED_X.shape[0]):
# txt = 'p'+str(i+1)
-# ax1.annotate(txt, (OldExpDesign[i,0],OldExpDesign[i,1]))
+# ax1.annotate(txt, (old_ED_X[i,0],old_ED_X[i,1]))
#
## for i in range(NrofCandGroups):
-## Candidates = allCandidates['group_'+str(i+1)]
+## Candidates = all_candidates['group_'+str(i+1)]
## ax1.scatter(Candidates[:,0],Candidates[:,1], s=10, c='b', marker="o", label='Design candidates')
-# ax1.scatter(allCandidates[:,0],allCandidates[:,1], s=10, c='b', marker="o", label='Design candidates')
+# ax1.scatter(all_candidates[:,0],all_candidates[:,1], s=10, c='b', marker="o", label='Design candidates')
#
# ax1.set_xlim(Bounds[0][0], Bounds[0][1])
# ax1.set_ylim(Bounds[1][0], Bounds[1][1])
@@ -436,28 +440,28 @@ class Exploration:
# Bounds = ((-5,10), (0,15))
#
# nrofSamples = 10
-# OldExpDesign = np.zeros((nrofSamples, NofPa))
+# old_ED_X = np.zeros((nrofSamples, NofPa))
# for idx in range(NofPa):
# Loc = Bounds[idx][0]
# Scale = Bounds[idx][1] - Bounds[idx][0]
-# OldExpDesign[:,idx] = stats.uniform(loc=Loc, scale=Scale).rvs(size=nrofSamples)
+# old_ED_X[:,idx] = stats.uniform(loc=Loc, scale=Scale).rvs(size=nrofSamples)
#
#
# nNewCandidate = 40
#
# # New Function
-# volumes = voronoi_volumes(OldExpDesign)
+# volumes = voronoi_volumes(old_ED_X)
#
#
# # SUMO
-# Exploration = Exploration(Bounds, OldExpDesign, nNewCandidate)
+# Exploration = Exploration(Bounds, old_ED_X, nNewCandidate)
#
-# #allCandidates, Score = Exploration.getVornoiSamples()
-# allCandidates, Score = Exploration.getMCSamples()
+# #all_candidates, Score = Exploration.get_vornoi_samples()
+# all_candidates, Score = Exploration.get_mc_samples()
#
# print('-'*40)
## for i in range(nrofSamples):
-## print("idx={0:d} X1={1: 3.3f} X2={2: 3.3f} Volume={3: 3.3f}".format(i+1, OldExpDesign[i,0], OldExpDesign[i,1], vornoi.areas[i]))
+## print("idx={0:d} X1={1: 3.3f} X2={2: 3.3f} Volume={3: 3.3f}".format(i+1, old_ED_X[i,0], old_ED_X[i,1], vornoi.areas[i]))
#
-# plotter(OldExpDesign, allCandidates, volumes)
+# plotter(old_ED_X, all_candidates, volumes)
diff --git a/src/bayesvalidrox/surrogate_models/inputs.py b/src/bayesvalidrox/surrogate_models/inputs.py
index 667b2dffe15d88562ce7114a20e67889ab2fa489..783e82b053cc458be712b588b7fde3a0f3c8decb 100644
--- a/src/bayesvalidrox/surrogate_models/inputs.py
+++ b/src/bayesvalidrox/surrogate_models/inputs.py
@@ -29,10 +29,10 @@ class Input:
>>> Inputs.Marginals[0].name = 'X_1'
>>> Inputs.Marginals[0].input_data = input_data
"""
+ poly_coeffs_flag = True
def __init__(self):
self.Marginals = []
- self.__poly_coeffs_flag = True
self.Rosenblatt = False
def add_marginals(self):
@@ -56,14 +56,14 @@ class Marginal:
Attributes
----------
name : string
- Name of the parameter. The default is '$x_1$'.
+ Name of the parameter. The default is `'$x_1$'`.
dist_type : string
- Name of the distribution. The default is None.
+ Name of the distribution. The default is `None`.
parameters : list
List of the parameters corresponding to the distribution type. The
- default is None.
- input_data : array_like
- The available data corresponding to the parameters. The default is [].
+ default is `None`.
+ input_data : array
+ Available input data. The default is `[]`.
moments : list
List of the moments.
"""
diff --git a/src/bayesvalidrox/surrogate_models/sequential_design.py b/src/bayesvalidrox/surrogate_models/sequential_design.py
index 99a2e780a443978bd770c9967c3a0d58c56ab0e5..bc3a9089a75c061aac9737e9c43a3732cb970956 100644
--- a/src/bayesvalidrox/surrogate_models/sequential_design.py
+++ b/src/bayesvalidrox/surrogate_models/sequential_design.py
@@ -103,7 +103,7 @@ class SeqDesign():
# ------ Prepare diagonal enteries for co-variance matrix ---------
for keyIdx, key in enumerate(Model.Output.names):
# optSigma = 'B'
- sigma2 = np.array(PCEModel.Discrepancy.Parameters[key])
+ sigma2 = np.array(PCEModel.Discrepancy.parameters[key])
TotalSigma2 = np.append(TotalSigma2, sigma2)
# Calculate the initial BME
@@ -226,7 +226,7 @@ class SeqDesign():
# ------ Plot the surrogate model vs Origninal Model ------
if hasattr(PCEModel, 'adapt_verbose') and \
PCEModel.adapt_verbose:
- from PostProcessing.adaptPlot import adaptPlot
+ from post_processing.adaptPlot import adaptPlot
y_hat, std_hat = PCEModel.eval_metamodel(samples=Xnew)
adaptPlot(PCEModel, Ynew, y_hat, std_hat, plotED=False)
@@ -253,15 +253,15 @@ class SeqDesign():
Yprev = PCEModel.ModelOutputDict
# Pass the new prior as the input
- PCEModel.input_obj.__poly_coeffs_flag = False
+ PCEModel.input_obj.poly_coeffs_flag = False
if updatedPrior is not None:
- PCEModel.Inputs.__poly_coeffs_flag = True
+ PCEModel.input_obj.poly_coeffs_flag = True
print("updatedPrior:", updatedPrior.shape)
# Arbitrary polynomial chaos
for i in range(updatedPrior.shape[1]):
- PCEModel.Inputs.Marginals[i].DistType = None
+ PCEModel.Inputs.Marginals[i].dist_type = None
x = updatedPrior[:, i]
- PCEModel.Inputs.Marginals[i].InputValues = x
+ PCEModel.Inputs.Marginals[i].raw_data = x
prevPCEModel = PCEModel
PCEModel = PCEModel.train_norm_design(Model)
@@ -1179,8 +1179,8 @@ class SeqDesign():
explore = Exploration(PCEModel, n_candidates)
explore.w = 100 # * ndim #500
# Select criterion (mc-intersite-proj-th, mc-intersite-proj)
- explore.mcCriterion = 'mc-intersite-proj'
- allCandidates, scoreExploration = explore.getExplorationSamples()
+ explore.mc_criterion = 'mc-intersite-proj'
+ allCandidates, scoreExploration = explore.get_exploration_samples()
# Temp: ---- Plot all candidates -----
if ndim == 2:
@@ -1291,7 +1291,7 @@ class SeqDesign():
X_can = explore.closestPoints[idx]
# Calculate the maxmin score for the region of interest
- newSamples, maxminScore = explore.getMCSamples(X_can)
+ newSamples, maxminScore = explore.get_mc_samples(X_can)
# select the requested number of samples
Xnew[i] = newSamples[np.argmax(maxminScore)]
@@ -1313,12 +1313,6 @@ class SeqDesign():
# Generate candidate samples from Exploration class
nMeasurement = old_EDY[OutputNames[0]].shape[1]
- # Find indices of the Vornoi cells with samples
- goodSampleIdx = []
- for idx in range(len(explore.closestPoints)):
- if len(explore.closestPoints[idx]) != 0:
- goodSampleIdx.append(idx)
-
# Find sensitive region
if UtilMethod == 'LOOCV':
LCerror = PCEModel.LCerror
@@ -1343,7 +1337,12 @@ class SeqDesign():
axis=0)
goodSampleIdx = range(n_cand_groups)
else:
- split_cand = explore.closestPoints
+ # Find indices of the Vornoi cells with samples
+ goodSampleIdx = []
+ for idx in range(len(explore.closest_points)):
+ if len(explore.closest_points[idx]) != 0:
+ goodSampleIdx.append(idx)
+ split_cand = explore.closest_points
# Split the candidates in groups for multiprocessing
args = []
@@ -1360,8 +1359,12 @@ class SeqDesign():
pool.close()
# Retrieve the results and append them
- ExploitScore = np.concatenate([results[NofE][1] for NofE in
- range(len(goodSampleIdx))])
+ if explore_method == 'Voronoi':
+ ExploitScore = [np.mean(results[k][1]) for k in
+ range(len(goodSampleIdx))]
+ else:
+ ExploitScore = np.concatenate(
+ [results[k][1] for k in range(len(goodSampleIdx))])
else:
raise NameError('The requested utility function is not '
@@ -1386,12 +1389,12 @@ class SeqDesign():
Xnew = allCandidates[bestIdx]
else:
for i, idx in enumerate(bestIdx.flatten()):
- X_can = explore.closestPoints[idx]
+ X_can = explore.closest_points[idx]
# plotter(self.ExpDesign.X, X_can, explore_method,
# scoreExploration=None)
# Calculate the maxmin score for the region of interest
- newSamples, maxminScore = explore.getMCSamples(X_can)
+ newSamples, maxminScore = explore.get_mc_samples(X_can)
# select the requested number of samples
Xnew[i] = newSamples[np.argmax(maxminScore)]
diff --git a/src/bayesvalidrox/surrogate_models/surrogate_models.py b/src/bayesvalidrox/surrogate_models/surrogate_models.py
index cb6fd21f358157f67661bd997a5e122734d22815..c751d9b5761ae41dcf008659ff8d112a65ff1632 100644
--- a/src/bayesvalidrox/surrogate_models/surrogate_models.py
+++ b/src/bayesvalidrox/surrogate_models/surrogate_models.py
@@ -1,16 +1,6 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-"""
-Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart
-
-Created on Sat Aug 24 2019
-"""
+
import warnings
import numpy as np
import math
@@ -44,65 +34,61 @@ class MetaModel:
Meta (surrogate) model
This class trains a surrogate model. It accepts an input object (input_obj)
- containing the distribution specification of uncertain parameters and a
- model object with instructions on how to run the computational model.
-
-
- Experimental design::
-
- MetaModelOpts.add_ExpDesign()
-
- Two experimental design schemes are supported: one-shot (`normal`) and
- adaptive sequential (`sequential`) designs.
- For experimental design refer to ... .
+ containing the specification of the distributions for uncertain parameters
+ and a model object with instructions on how to run the computational model.
Attributes
----------
input_obj : obj
Input object with the information on the model input parameters.
- meta_model_type : string
+ meta_model_type : str
Surrogate model types. Three surrogate model types are supported:
polynomial chaos expansion (`PCE`), arbitrary PCE (`aPCE`) and
Gaussian process regression (`GPE`). Default is PCE.
- pce_reg_method : string
+ pce_reg_method : str
PCE regression method to compute the coefficients. The following
- regression methods are available::
-
- 1. OLS: Ordinary Least Square method
- 2. BRR: Bayesian Ridge Regression
- 3. LARS: Least angle regression
- 4. ARD: Bayesian ARD Regression
- 5. FastARD: Fast Bayesian ARD Regression
- 6. VBL: Variational Bayesian Learning
- 7. EBL: Emperical Bayesian Learning
- Default is OLS.
+ regression methods are available:
+
+ 1. OLS: Ordinary Least Square method
+ 2. BRR: Bayesian Ridge Regression
+ 3. LARS: Least angle regression
+ 4. ARD: Bayesian ARD Regression
+ 5. FastARD: Fast Bayesian ARD Regression
+ 6. VBL: Variational Bayesian Learning
+ 7. EBL: Emperical Bayesian Learning
+ Default is `OLS`.
pce_deg : int or list of int
Polynomial degree(s). If a list is given, an adaptive algorithm is used
to find the best degree with the lowest Leave-One-Out cross-validation
- (LOO) error (or the highest score=1-LOO). Default is 1.
+ (LOO) error (or the highest score=1-LOO). Default is `1`.
pce_q_norm : float
Hyperbolic (or q-norm) truncation for multi-indices of multivariate
- polynomials. Default is 1.0.
- dim_red_method : string
+ polynomials. Default is `1.0`.
+ dim_red_method : str
Dimensionality reduction method for the output space. The available
- method is based on principal component analysis (PCA). Default is 'no'.
- Example::
-
- # Select method
- MetaModelOpts.dim_red_method = 'PCA'
- # There is two ways to select number of components.
- # A: use explainable variance threshold (between 0 and 100)
- MetaModelOpts.var_pca_threshold = 99.999
- # B: Direct prescription
- MetaModelOpts.n_pca_components = 12
+ method is based on principal component analysis (PCA). The Default is
+ `'no'`. There are two ways to select number of components: use
+ percentage of the explainable variance threshold (between 0 and 100)
+ (Option A) or direct prescription of components' number (Option B):
+
+ >>> MetaModelOpts.dim_red_method = 'PCA'
+ >>> MetaModelOpts.var_pca_threshold = 99.999 # Option A
+ >>> MetaModelOpts.n_pca_components = 12 # Option B
+
verbose : bool
- Prints summary of the regression results. Default is False.
+ Prints summary of the regression results. Default is `False`.
- Methods
+ Note
-------
- create_metamodel(Model):
- Starts the training of the meta-model for the model objects containg
- the given computational model.
+ To define the sampling methods and the training set, an experimental design
+ instance shall be defined. This can be done by:
+
+ >>> MetaModelOpts.add_ExpDesign()
+
+ Two experimental design schemes are supported: one-shot (`normal`) and
+ adaptive sequential (`sequential`) designs.
+ For experimental design refer to `ExpDesigns`.
+
"""
def __init__(self, input_obj, meta_model_type='PCE', pce_reg_method='OLS',
@@ -125,12 +111,12 @@ class MetaModel:
Parameters
----------
- Model : object
+ Model : obj
Model object.
Returns
-------
- metamodel : object
+ metamodel : obj
The meta model object.
"""
@@ -169,14 +155,14 @@ class MetaModel:
Parameters
----------
- Model : object
+ Model : obj
Model object.
verbose : bool, optional
Flag for a sequential design in silent mode. The default is False.
Returns
-------
- self: object
+ self: obj
Meta-model object.
"""
@@ -289,7 +275,7 @@ class MetaModel:
Returns
-------
- basis_indices : array (n_terms, n_params)
+ basis_indices : array of shape (n_terms, n_params)
Multi-indices of multivariate polynomials.
"""
@@ -320,7 +306,7 @@ class MetaModel:
Parameters
----------
- Model : object
+ Model : obj
Model object.
Raises
@@ -330,7 +316,7 @@ class MetaModel:
Returns
-------
- ED_X_tr: array (n_samples, n_params)
+ ED_X_tr: array of shape (n_samples, n_params)
Training samples transformed by an isoprobabilistic transformation.
ED_Y: dict
Model simulations (target) for all outputs.
@@ -415,13 +401,12 @@ class MetaModel:
samples : array of shape (n_samples, n_params)
Samples.
n_max : int, optional
- Maximum polynomial degree. The default is None.
+ Maximum polynomial degree. The default is `None`.
Returns
-------
- univ_basis: array of (n_samples, n_params, n_max+1)
+ univ_basis: array of shape (n_samples, n_params, n_max+1)
All univariate regressors up to n_max.
-
"""
# Extract information
poly_types = self.ExpDesign.poly_types
@@ -451,7 +436,7 @@ class MetaModel:
basis_indices : array of shape (n_terms, n_params)
Multi-indices of multivariate polynomials.
univ_p_val : array of (n_samples, n_params, n_max+1)
- All univariate regressors up to n_max.
+ All univariate regressors up to `n_max`.
Raises
------
@@ -505,19 +490,19 @@ class MetaModel:
Parameters
----------
- X : array-like of shape (n_samples, n_features)
+ X : array of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
- y : array-like of shape (n_samples,)
+ y : array of shape (n_samples,)
Target values.
- basis_indices : array-like of shape (n_terms, n_params)
+ basis_indices : array of shape (n_terms, n_params)
Multi-indices of multivariate polynomials.
- reg_method : string, optional
+ reg_method : str, optional
DESCRIPTION. The default is None.
Returns
-------
- returnOuts : Dict
+ return_out_dict : Dict
Fitted estimator, spareMulti-Index, sparseX and coefficients.
"""
@@ -630,12 +615,12 @@ class MetaModel:
coeffs = np.dot(np.dot(np.linalg.pinv(X_T_X), sparse_X.T), y)
# Create a dict to pass the outputs
- returnOuts = dict()
- returnOuts['clf_poly'] = clf_poly
- returnOuts['spareMulti-Index'] = sparse_basis_indices
- returnOuts['sparePsi'] = sparse_X
- returnOuts['coeffs'] = coeffs
- return returnOuts
+ return_out_dict = dict()
+ return_out_dict['clf_poly'] = clf_poly
+ return_out_dict['spareMulti-Index'] = sparse_basis_indices
+ return_out_dict['sparePsi'] = sparse_X
+ return_out_dict['coeffs'] = coeffs
+ return return_out_dict
# --------------------------------------------------------------------------------------------------------
def adaptive_regression(self, ED_X, ED_Y, varIdx, verbose=False):
@@ -645,9 +630,9 @@ class MetaModel:
Parameters
----------
- ED_X : array-like of shape (n_samples, n_params)
+ ED_X : array of shape (n_samples, n_params)
Experimental design.
- ED_Y : array-like of shape (n_samples,)
+ ED_Y : array of shape (n_samples,)
Target values, i.e. simulation results for the Experimental design.
varIdx : int
Index of the output.
@@ -835,31 +820,33 @@ class MetaModel:
# -------------------------------------------------------------------------
def corr_loocv_error(self, clf, psi, coeffs, y):
"""
- Calculates the corrected LOO error for the OLS regression on regressor
- matrix PSI that generated the coefficients.
- (based on Blatman, 2009 (PhD Thesis), pg. 115-116).
+ Calculates the corrected LOO error for regression on regressor
+ matrix `psi` that generated the coefficients based on [1] and [2].
- This is based on the following paper:
- ""Blatman, G., & Sudret, B. (2011). Adaptive sparse polynomial
- chaos expansion based on least angle regression.
- Journal of Computational Physics, 230(6), 2345-2367.""
+ [1] Blatman, G., 2009. Adaptive sparse polynomial chaos expansions for
+ uncertainty propagation and sensitivity analysis (Doctoral
+ dissertation, Clermont-Ferrand 2).
+
+ [2] Blatman, G. and Sudret, B., 2011. Adaptive sparse polynomial chaos
+ expansion based on least angle regression. Journal of computational
+ Physics, 230(6), pp.2345-2367.
Parameters
----------
clf : object
Fitted estimator.
- psi : array-like of shape (n_samples, n_features)
+ psi : array of shape (n_samples, n_features)
The multivariate orthogonal polynomials (regressor).
coeffs : array-like of shape (n_features,)
Estimated cofficients.
- y : array-like of shape (n_samples,)
+ y : array of shape (n_samples,)
Target values.
Returns
-------
Q_2 : float
LOOCV Validation score (1-LOOCV erro).
- residual : array-like of shape (n_samples,)
+ residual : array of shape (n_samples,)
Residual values (y - predicted targets).
"""
@@ -945,14 +932,14 @@ class MetaModel:
Parameters
----------
- Output : array-like of shape (n_samples,)
+ Output : array of shape (n_samples,)
Target values.
Returns
-------
- pca : object
+ pca : obj
Fitted sklearnPCA object.
- OutputMatrix : array-like of shape (n_samples,)
+ OutputMatrix : array of shape (n_samples,)
Transformed target values.
"""
@@ -996,7 +983,7 @@ class MetaModel:
return pca, OutputMatrix
# -------------------------------------------------------------------------
- def gaussian_process_emulator(self, X, y, nugTerm=None, autoSelect=False,
+ def gaussian_process_emulator(self, X, y, nug_term=None, autoSelect=False,
varIdx=None):
"""
Fits a Gaussian Process Emulator to the target given the training
@@ -1004,11 +991,11 @@ class MetaModel:
Parameters
----------
- X : array-like of shape (n_samples, n_params)
+ X : array of shape (n_samples, n_params)
Training points.
- y : array-like of shape (n_samples,)
+ y : array of shape (n_samples,)
Target values.
- nugTerm : float, optional
+ nug_term : float, optional
Nugget term. The default is None, i.e. variance of y.
autoSelect : bool, optional
Loop over some kernels and select the best. The default is False.
@@ -1022,15 +1009,15 @@ class MetaModel:
"""
- nugTerm = nugTerm if nugTerm else np.var(y)
+ nug_term = nug_term if nug_term else np.var(y)
- Kernels = [nugTerm * kernels.RBF(length_scale=1.0,
- length_scale_bounds=(1e-25, 1e15)),
- nugTerm * kernels.RationalQuadratic(length_scale=0.2,
- alpha=1.0),
- nugTerm * kernels.Matern(length_scale=1.0,
- length_scale_bounds=(1e-15, 1e5),
- nu=1.5)]
+ Kernels = [nug_term * kernels.RBF(length_scale=1.0,
+ length_scale_bounds=(1e-25, 1e15)),
+ nug_term * kernels.RationalQuadratic(length_scale=0.2,
+ alpha=1.0),
+ nug_term * kernels.Matern(length_scale=1.0,
+ length_scale_bounds=(1e-15, 1e5),
+ nu=1.5)]
# Automatic selection of the kernel
if autoSelect:
@@ -1080,7 +1067,7 @@ class MetaModel:
nsamples : int, optional
Number of samples to generate, if no `samples` is provided. The
default is None.
- sampling_method : string, optional
+ sampling_method : str, optional
Type of sampling, if no `samples` is provided. The default is
'random'.
return_samples : bool, optional
@@ -1088,9 +1075,10 @@ class MetaModel:
Returns
-------
- TYPE
- DESCRIPTION.
-
+ mean_pred : dict
+ Mean of the predictions.
+ std_pred : dict
+ Standard deviatioon of the predictions.
"""
if self.meta_model_type.lower() == 'gpe':
model_dict = self.gp_poly
@@ -1174,13 +1162,13 @@ class MetaModel:
Parameters
----------
- X : array-like of shape (n_outputs, n_inputs)
+ X : array of shape (n_outputs, n_inputs)
Input array. It can contain any forcing inputs or coordinates of
extracted data.
- y : array-like of shape (n_outputs,)
+ y : array of shape (n_outputs,)
The model response for the MAP parameter set.
- name : string, optional
- Calibration or validation. The default is 'Calib'.
+ name : str, optional
+ Calibration or validation. The default is `'Calib'`.
Returns
-------
@@ -1267,7 +1255,11 @@ class MetaModel:
# -------------------------------------------------------------------------
class auto_vivification(dict):
- """Implementation of perl's AutoVivification feature."""
+ """
+ Implementation of perl's AutoVivification feature.
+
+ Source: https://stackoverflow.com/a/651879/18082457
+ """
def __getitem__(self, item):
try:
diff --git a/tests/analytical_function/test_analytical_function.py b/tests/analytical_function/test_analytical_function.py
index e857d5f9f63d041c01921e226845a18b52e95551..c832c4f0483a49beb3ee0430a291932e7a693b93 100755
--- a/tests/analytical_function/test_analytical_function.py
+++ b/tests/analytical_function/test_analytical_function.py
@@ -132,7 +132,7 @@ if __name__ == "__main__":
# One-shot (normal) or Sequential Adaptive (sequential) Design
MetaModelOpts.ExpDesign.method = 'normal'
- MetaModelOpts.ExpDesign.n_init_samples = 50 # 5*ndim
+ MetaModelOpts.ExpDesign.n_init_samples = 75 # 5*ndim
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) korobov
@@ -147,7 +147,7 @@ if __name__ == "__main__":
# ------------------------------------------------
# Set the sampling parameters
MetaModelOpts.ExpDesign.n_new_samples = 1
- MetaModelOpts.ExpDesign.n_max_samples = 15 # 150
+ MetaModelOpts.ExpDesign.n_max_samples = 25 # 150
MetaModelOpts.ExpDesign.mod_LOO_threshold = 1e-16
MetaModelOpts.adapt_verbose = True
@@ -156,7 +156,7 @@ if __name__ == "__main__":
# MetaModelOpts.ExpDesign.n_replication = 20
# -------- Exploration ------
# 1)'Voronoi' 2)'random' 3)'latin_hypercube' 4)'LOOCV' 5)'dual annealing'
- MetaModelOpts.ExpDesign.explore_method = 'latin_hypercube'
+ MetaModelOpts.ExpDesign.explore_method = 'Voronoi'
# Use when 'dual annealing' chosen
MetaModelOpts.ExpDesign.max_func_itr = 200
@@ -176,7 +176,7 @@ if __name__ == "__main__":
# MetaModelOpts.ExpDesign.util_func = 'DKL'
# VarBasedOptDesign -> when data is not available
- # Only with Vornoi >>> 1)Entropy 2)EIGF, 3)LOOCV
+ # 1)Entropy 2)EIGF, 3)LOOCV
# or a combination as a list
MetaModelOpts.ExpDesign.util_func = 'Entropy'
diff --git a/tests/beam/test_beam.py b/tests/beam/test_beam.py
index a1f023610d40f38870cd1747d343bf3416d0c3d0..e1d1ababc8da4b3bd6d96e14dbc9e82c48259549 100644
--- a/tests/beam/test_beam.py
+++ b/tests/beam/test_beam.py
@@ -131,10 +131,10 @@ if __name__ == "__main__":
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley
# 6) chebyshev(FT) 7) korobov 8) grid(FT) 9) nested_grid(FT) 10)user
- MetaModelOpts.ExpDesign.sampling_method = 'user'
+ MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
# Provide the experimental design object with a hdf5 file
- MetaModelOpts.ExpDesign.hdf5_file = 'ExpDesign_Beam9points.hdf5'
+ # MetaModelOpts.ExpDesign.hdf5_file = 'ExpDesign_Beam9points.hdf5'
# ------ Train the surrogate model ------
PCEModel = MetaModelOpts.create_metamodel(Model)