Skip to main content

Kernel density estimation on a sphere

Project description

Build Status codecov PyPI version Documentation Status DOI

Spherical Kernel Density Estimation

These packages allow you to do rudimentary kernel density estimation on a sphere. Suggestions for improvements/extensions welcome.

The fundamental principle is to replace the traditional Gaussian function used in kernel density estimation with the Von Mises-Fisher distribution.

Bandwidth estimation is still rough-and-ready.

image1

Example Usage

import numpy
from spherical_kde import SphericalKDE
import matplotlib.pyplot as plt
import cartopy.crs
from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec

# Choose a seed for deterministic plot
numpy.random.seed(seed=0)

# Set up a grid of figures
fig = plt.figure(figsize=(10, 10))
gs_vert = GridSpec(3, 1)
gs_lower = GridSpecFromSubplotSpec(1, 2, subplot_spec=gs_vert[1])

fig.add_subplot(gs_vert[0], projection=cartopy.crs.Mollweide())
fig.add_subplot(gs_lower[0], projection=cartopy.crs.Orthographic())
fig.add_subplot(gs_lower[1], projection=cartopy.crs.Orthographic(-10, 45))
fig.add_subplot(gs_vert[2], projection=cartopy.crs.PlateCarree())

# Choose parameters for samples
nsamples = 100
pi = numpy.pi

# Generate some samples centered on (1,1) +/- 0.3 radians
theta_samples = numpy.random.normal(loc=1, scale=0.3, size=nsamples)
phi_samples = numpy.random.normal(loc=1, scale=0.3, size=nsamples)
phi_samples = numpy.mod(phi_samples, pi*2)
kde_green = SphericalKDE(phi_samples, theta_samples)

# Generate some samples centered on (-1,1) +/- 0.4 radians
theta_samples = numpy.random.normal(loc=1, scale=0.4, size=nsamples)
phi_samples = numpy.random.normal(loc=-1, scale=0.4, size=nsamples)
phi_samples = numpy.mod(phi_samples, pi*2)
kde_red = SphericalKDE(phi_samples, theta_samples)

# Generate a spread of samples along latitude 2, height 0.1
theta_samples = numpy.random.normal(loc=2, scale=0.1, size=nsamples)
phi_samples = numpy.random.uniform(low=-pi/2, high=pi/2, size=nsamples)
phi_samples = numpy.mod(phi_samples, pi*2)
kde_blue = SphericalKDE(phi_samples, theta_samples, bandwidth=0.1)

for ax in fig.axes:
    ax.set_global()
    ax.gridlines()
    ax.coastlines(linewidth=0.1)
    kde_green.plot(ax, 'g')
    kde_green.plot_samples(ax)
    kde_red.plot(ax, 'r')
    kde_blue.plot(ax, 'b')

# Save to plot
fig.tight_layout()
fig.savefig('plot.png')

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

spherical_kde-0.1.2.tar.gz (8.1 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page