'Recommendations for Low Discrepancy (e.g. Sobol) quasi-random sequences in Python/SciPy?

I would like to use a quasi-random sequence, specifically Sobol, within a SciPy based simulation. Any recommendations on existing, efficient packages?

pythonrandomscipy


Solution 1:[1]

Is this project any use? If not, the relevant Wikipedia article mentions C and Fortran routines in NAG and Numerical Recipes, which probably shouldn't be difficult to wrap or reimplement in Python. There are also C routines in GSL.

Solution 2:[2]

PyTorch provides some options now. One of them is scrambled sobol number generator that can generate quasi random number of higher dimensions of upto ~1k

https://pytorch.org/docs/stable/generated/torch.quasirandom.SobolEngine.html

Another option is to use Scipy that has this option now https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.qmc.Sobol.html

Solution 3:[3]

For Sobol Sequences try sobol_seq.

Generally speaking the best package I've found for dealing with quasirandom sequences is diversipy.

There are also packages that focus on specific implementations, for example sudoku_lhs deals with Latin Hypercubes and the Sudoku-type Constraint variant.

pyDOE implements at least Latin Hypercube (maybe more).

The most interesting package I found is py-design, which creates a wrapper for Fortran 90 codes on 15 or so methods. Unfortunately it does not seem to work (some assets seem to be missing).

Solution 4:[4]

Chaospy is also a valid option. It allows to select several approaches towards low-discrepancy sampling (including 'Sobol, latin hypercube, etc) - for more details see the documentation.

Solution 5:[5]

In the context of sensivity analysis SALib library seems interesting It has a Sobol sample generator and uses SciPy. Link here : https://salib.readthedocs.io/en/latest/

Sources

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Solution Source
Solution 1 James
Solution 2
Solution 3 mikkokotila
Solution 4
Solution 5 Sam

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