Hi again,

Yesterday a PR was opened to sample uniformly from the surface of the unit sphere: ENH: Random direction distribution by dschmitz89 · Pull Request #17277 · scipy/scipy (github.com)

The API is the following: a multivariate distribution scipy.stats.random_direction(dim) with a .rvs() method. Setting the dimension is required. The .rvs(size) method follows the convention of numpy's multivariate_normal: for size=(m, n) it will generate samples of the shape (m, n, dim).

If this sounds interesting to you, please join the discussion on github.

Best

Am So., 5. Juni 2022 um 20:09 Uhr schrieb Pamphile Roy <roy.pamphile@gmail.com>:
Hi Daniel,

I also think these would be interesting.

I am myself very interested in the sampling part, as we actually recently added poisson disk sampling which internally uses hypersphere sampling. I would be happy to help with this work.

Cheers,
Pamphile (@tupui)

On 5 Jun 2022, at 17:04, Robert Kern <robert.kern@gmail.com> wrote:


Yes, I think both have a place in scipy.stats. Thanks!

On Sun, Jun 5, 2022 at 5:55 AM Daniel Schmitz <danielschmitzsiegen@googlemail.com> wrote:
Hi everyone,

recently questions came up with regards to statistics on the unit sphere in scipy.

Issue 12041 raises the question if spherical mean/variance should be added as descriptive statistics similar to circmean/circvar . The canonical reference for all things related to statistics on the unit sphere is "Directional Statistics" by Mardia & Jupp.

Another important basic functionality is the generation of random samples on the unit sphere: discussion was started in this issue .

Any strong opinion against these? I think at least the random number generation is reimplemented every day around the world (done so myself at least 2 times) and would strongly benefit from being part of scipy.

Best
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Robert Kern
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