Easyvvuq

Test for PyPi.

A few minor but important updates in this release:

* Fixed a wide range of tests, library and dependency issues.
* Incorporated a range of documentation improvements.
* Added an example for using PCE with aleatoric uncertainty

Note that the readthedocs page is likely to be further expanded and updated in the weeks after this release.

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

**Fixes and updates**
* Fixed several tests for newer Python versions.
* Updated integration with QCG-PilotJob

**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

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)