Kernel density estimation on a sphere
Project description
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.
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
Release history Release notifications | RSS feed
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 details)
File details
Details for the file spherical_kde-0.1.2.tar.gz
.
File metadata
- Download URL: spherical_kde-0.1.2.tar.gz
- Upload date:
- Size: 8.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: Python-urllib/3.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3d87d7d7ca8a55ff61b01459aefbc76c26d0bb4e9d846cda1d243c61d72bb621 |
|
MD5 | 22407321c7d5bb1caff2d559e704428f |
|
BLAKE2b-256 | dcbceac61d967e359fcb61c4b16db023a7b78239e6ddf677d809a54102f1910d |