spherical-stats 1.3

Spherical statistics in Python

spherical_stats

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.

Installation

pip install spherical_stats

Documentation

Refer to the online documentation for examples and API reference.

Features:

• 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)