Plurigaussian fields

[LSchueler]

Plurigaussian simulations (PGS) are a great way to easily increase the flexibility of Gaussian random fields. With this PR I want to directly incorporate them into GSTools. It not only includes the implementation, but also unittests and a few examples, with which users can familiarize themselves with PGS, as I do not find them very intuitive at first.

However, I still have a few open questions:

• Should PGS really be a class? I mostly did this for GSTools to keep a more or less constant structure. But I don't see any benefits of using a class here. It only makes it a bit more complicated to use PGSs, as you have to first create an instance and then call it, instead of only calling a pgs function. Any opinion on this point @MuellerSeb ?

• At one point we might have to think about a new structure for the examples. In 01_random_field we have examples for general field generation, but based on the randomization method and some examples showing the Fourier method. Then we have 04_vector_field with the vector field generation. And now we have 11_plurigaussian. What do you think @MuellerSeb , should we discuss this soon?

• Is the argument facies the best name for a method which is used in many different fields?

• Any other paper(s) we should cite?

[LSchueler]

Wow, that's a curious detail I stumbled upon...

And for some reason, the old numpy.AxisError didn't work for me with numpy 2.x, which was moved to numpy.exceptions.AxisError, but for backwards compatibility was supposed to be kept in the old place too.

[LSchueler]

Thanks to @EJRicketts's feedback, I updated the examples and fixed a bug, where the L field could have an offset.

[EJRicketts]

Thank you or sharing this with me, it's nice to see that the offset has been sorted :) This was my only major comment.

In general, I think it would be nice to include an example that shows the use of conditional random fields and also periodic random fields.

For field scale problems with associated experimental observations, combining a well chosen lithotype with conditional random fields can be quite powerful in getting nice realistic representations.

With respect to the periodic case, periodicity in the input fields will result in periodicity in the final field, which for my research purposes have been useful in material characterisation. I published some work on this for cementitious materials recently: https://doi.org/10.1007/s11242-024-02074-z
It would be easy to replicate these results for the example, but perhaps a more geoenvironmental example would be best! In any case, a small mention of the paper in the example would be much appreciated 😊

Regarding citing works, there is a nice book on PGS: https://link.springer.com/book/10.1007/978-3-642-19607-2
The authors are (mostly) from the birth place of PGS (Mines-Paristech), some of which are the original authors who took the idea of truncated random fields and extended it to PGS. It feels important to include this.
Xavier Emery has also had quite a big influence on PGS, so it might be worth citing their work also: https://doi.org/10.1016/j.cageo.2007.01.006