Spherical statistics in Python with a focus on accuracy and speed. Unlike many other implementations of spherical distributions, all probability distributions in spherical_stats are fitted exactly using numerical optimization and root-finding techniques and do not rely on approximations. Computationally intensive parts are accelerated using numba.
pip install spherical_statsRefer to the online documentation for examples and API reference.
- Visualization helper functions to quickly generate data to be plotted with plotly/matplotlib/ipyvolume:
- Sphere creation and evaluation of a function over its surface
- Spherical histogram
- Descriptive statistics:
- Spherical mean and spherical variance
- Orientation tensor
- Parametric distributions with scipy.stats like API:
- Modeling axial data: Watson distribution, Angular central gaussian distribution (ACG)
- Modeling vector data: Von Mises-Fisher distribution (VMF), Elliptically symmetrical angular gausian distribution (ESAG),
Example usage of the distributions:
from spherical_stats import ESAG
import numpy as np
esag_params = np.array([1,3,5,2,6])
#Instantiate ESAG class with known parameters
esag_known = ESAG(esag_params)
#generate 500 ESAG samples and calculate their PDF vals
samples = esag_known.rvs(500)
pdf_vals = esag.pdf(samples)
#Instantiate ESAG class and fit distribution parameters given samples
esag_unknown = ESAG()
esag_unknown.fit(samples, verbose = True)