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Plurigaussian fields #370
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Plurigaussian fields #370
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I'm not able to install old numpy versions locally and the exception documentation of numpy is non existent and I can't bother to go through its source code, so I'll just see what the actions show.
I don't understand why `np.AxisError` doesn't work for np 2.x
Wow, that's a curious detail I stumbled upon... And for some reason, the old |
try: | ||
np.AxisError = np.exceptions.AxisError | ||
except AttributeError: | ||
... |
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I did something similar for emcee. I would import this at the top: https://github.com/dfm/emcee/pull/517/files#diff-1697674b19133cd11b2fbdf0037fcac453954401163ba710c3c0788fa19402f4
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 apgs
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 have04_vector_field
with the vector field generation. And now we have11_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?