tianluyuan / sphere Goto Github PK

Hi! I stumbled upon this package, and it looks great. For my application, I have spatial ellipses (lat/long, major/minor axes, orientation) on the globe representing locations of some entities. I want to map these ellipses to distributions on a sphere so I can sample from them. I'm struggling to understand how to map the parameters I have to one of the constructors you provide. Are you aware of any references that may help? I've tried Googling to no avail! Either way, thank you so much for this package!

I've moved the HGM code from master into a new branch git checkout --track origin/hgm. At some point we should get that working.

Hello! I am using your package to sample quaternions in non isotropic distributions, in particular Kent distribution.

I would like to suggest a small addition to make sampling from seeded generators easier by changing the class method rvs(self, n_samples=None) to rvs(self, n_samples=None,random_gen=None) by adding the possibility to pass a Numpy generator as argument like the model of the rvs_continuous class of scipy.stats.

For instance, if using scipy.stats:

    def _rvs_helper(self,random_gen=None):
        num_samples = 10000
        xs = gauss(0, 1).rvs((num_samples, 3),random_state=random_gen)
        xs = np.divide(xs, np.reshape(norm(xs, 1), (num_samples, 1)))
        lpvalues = self.log_pdf(xs, normalize=False)
        lfmax = self.log_pdf_max(normalize=False)
        shifted = lpvalues - lfmax
        return xs[uniform(0, 1).rvs(num_samples,random_state=random_gen) < np.exp(shifted)]

Where random_state is eitheir a None, int or numpy.random.generator .

another possibility if using numpy.random

from numpy.random import default_gen

# [...]

    def _rvs_helper(self,random_gen=None):
        num_samples = 10000
        if random_gen is None:
            random_gen = default_gen()
        xs = random_gen.normal(0, 1,(num_samples,3))
        xs = np.divide(xs, np.reshape(norm(xs, 1), (num_samples, 1)))
        lpvalues = self.log_pdf(xs, normalize=False)
        lfmax = self.log_pdf_max(normalize=False)
        shifted = lpvalues - lfmax
        return xs[random_gen.uniform(0, 1, num_samples) < np.exp(shift)] 

Thank you for your package !