Easyvvuq

This is the March 2023 release of EasyVVUQ, as part of the SEAVEAtk, with the following updates:

**New features**

* New Simplex Stochastic Collocation sampler for irregular outputs, e.g. with discontinuities or high gradients in the stochastic input space. Works for scalar QoI only thus far.
* Grid-Search sampler, (e.g. for neural-network hyper parameter tuning).
* HDF5 decoder to allow for reading HDF5 output files, useful when dealing with outputs of different size.

**Tutorials**

* SSC tutorial: https://github.com/UCL-CCS/EasyVVUQ/blob/dev/tutorials/simplex_stochastic_collocation_tutorial.ipynb
* Hyperparameter tuning tutorial, local sampling: https://github.com/UCL-CCS/EasyVVUQ/blob/dev/tutorials/hyperparameter_tuning_tutorial.ipynb
* Hyperparameter tuning tutorial, remote sampling with FabSim3: https://github.com/UCL-CCS/EasyVVUQ/blob/dev/tutorials/hyperparameter_tuning_tutorial_with_fabsim.ipynb

**Usability updates**
* Make it more obvious how to import a pandas dataframe containing cases to be considered
* Make it more obvious how to massage the results from the runs before performing the PCE/SC/MC analysis

Overhaul of SC sampler / analysis class:

* Made isotropic sparse-grid subroutines more scalable to higher input dimensions. Reused dimension-adaptive subroutines for this purpose, instead of having (slower) separate isotropic routines.
* Rewrote dimension-adaptive SC expansion as a standard PCE expansion with generalized PCE coefficients. See adaptive sparse-grid tutorial.

Documentation:

* Extensive methodological sparse-grid tutorial: https://www.researchgate.net/publication/359296270_Adaptive_sparse-grid_tutorial
* New tutorial on using mathematical expressions involving parameters in template files using the Jinja encoder: https://github.com/UCL-CCS/EasyVVUQ/blob/dev/tutorials/jinja_tutorial.ipynb

**New features:**

* Updated the documentation in a range of places.

**Bug fixes:**

*Fixed direct integration of EasyVVUQ with QCG-PilotJob. Previously there was an issue with large campaigns where the integration could fail due to an excessively long command-line argument.
*Fixed bug where unsuitable models could be applied with QCG-PilotJob integration.
*Fixed MC sampler for use with 1 parameter: https://github.com/UCL-CCS/EasyVVUQ/commit/fac0b5701db2fefed00b6a81120854ed0109fdc6

**Tutorials:**

* Added an example for including noise in an EasyVVUQ campaign ( easyvvuq_Ishigami_with_noise_tutorial.ipynb)

New features:

- Ability to add external runs via a DataFrame
- Ability to execute EasyVVUQ workflows from R

Tutorials:
- Updates to Dimension Adaptive Fusion tutorial.

New Features

* Better support to execute pure Python simulations.
* Added a surrogate method to AnalysisResults classes.
* QCG-PJ support.
* Gaussian Process Surrogate analysis method.
* Reworked Campaign and hand optimised database.
* Re-implemented Actions system for modular execution options.
* DataFrameSampler for uploading new

Updates

* Large scale code refactoring.
* Docstring and documentation updates.
* Additional testing and benchmarking.
* Continuous benchmarking.

A bug-fix release.