Skip to main content

Evaluate and transform D matrices, 3-j symbols, and (scalar or spin-weighted) spherical harmonics

Project description

Test Status Test Coverage Documentation Status PyPI Version Conda Version DOI

Spherical Functions

Python/numba package for evaluating and transforming Wigner's 𝔇 matrices, Wigner's 3-j symbols, and spin-weighted (and scalar) spherical harmonics. These functions are evaluated directly in terms of quaternions, as well as in the more standard forms of spherical coordinates and Euler angles.1

These quantities are computed using recursion relations, which makes it possible to compute to very high ℓ values. Unlike direct evaluation of individual elements, which will generally cause overflow or underflow beyond ℓ≈30, these recursion relations should be accurate for ℓ values beyond 1000.

The conventions for this package are described in detail on this page.

Installation

Because this package is pure python code, installation is very simple. In particular, with a reasonably modern installation, you can just run a command like

conda install -c conda-forge spherical

or

python -m pip install spherical

Either of these will download and install the package.

Usage

Functions of angles or rotations

Currently, due to the nature of recursions, this module does not allow calculation of individual elements, but returns ranges of results. For example, when computing Wigner's 𝔇 matrix, all matrices up to a given ℓ will be returned; when evaluating a spin-weighted spherical harmonic, all harmonics up to a given ℓ will be returned. Fortunately, this is usually what is required in any case.

To calculate Wigner's d or 𝔇 matrix or spin-weighted spherical harmonics, first construct a Wigner object.

import quaternionic
import spherical
ell_max = 16  # Use the largest ℓ value you expect to need
wigner = spherical.Wigner(ell_max)

This module takes input as quaternions. The quaternionic module has various ways of constructing quaternions, including direct construction or conversion from rotation matrices, axis-angle representation, Euler angles,1 or spherical coordinates, among others:

R = quaternionic.array([1, 2, 3, 4]).normalized
R = quaternionic.array.from_axis_angle(vec)
R = quaternionic.array.from_euler_angles(alpha, beta, gamma)
R = quaternionic.array.from_spherical_coordinates(theta, phi)

Mode weights can be rotated as

wigner.rotate(modes, R)

or evaluated as

wigner.evaluate(modes, R)

We can compute the 𝔇 matrix as

D = wigner.D(R)

which can be indexed as

D[wigner.Dindex(ell, mp, m)]

or we can compute the spin-weighted spherical harmonics as

Y = wigner.sYlm(s, R)

which can be indexed as

Y[wigner.Yindex(ell, m)]

Note that, if relevant, it is probably more efficient to use the rotate and evaluate methods than to use D or Y.

Clebsch-Gordan and 3-j symbols

It is possible to compute individual values of the 3-j or Clebsch-Gordan symbols:

w3j = spherical.Wigner3j(j_1, j_2, j_3, m_1, m_2, m_3)
cg = spherical.clebsch_gordan(j_1, m_1, j_2, m_2, j_3, m_3)

However, when more than one element is needed (as is typically the case), it is much more efficient to compute a range of values:

calc3j = spherical.Wigner3jCalculator(j2_max, j3_max)
w3j = calc3j.calculate(j2, j3, m2, m3)

Acknowledgments

I very much appreciate Barry Wardell's help in sorting out the relationships between my conventions and those of other people and software packages (especially Mathematica's crazy conventions).

This code is, of course, hosted on github. Because it is an open-source project, the hosting is free, and all the wonderful features of github are available, including free wiki space and web page hosting, pull requests, a nice interface to the git logs, etc.

The work of creating this code was supported in part by the Sherman Fairchild Foundation and by NSF Grants No. PHY-1306125 and AST-1333129.



1 Euler angles are awful

Euler angles are pretty much the worst things ever and it makes me feel bad even supporting them. Quaternions are faster, more accurate, basically free of singularities, more intuitive, and generally easier to understand. You can work entirely without Euler angles (I certainly do). You absolutely never need them. But if you're so old fashioned that you really can't give them up, they are fully supported.

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-1.0.18.tar.gz (82.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

spherical-1.0.18-py3-none-any.whl (55.7 kB view details)

Uploaded Python 3

File details

Details for the file spherical-1.0.18.tar.gz.

File metadata

  • Download URL: spherical-1.0.18.tar.gz
  • Upload date:
  • Size: 82.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-httpx/0.28.1

File hashes

Hashes for spherical-1.0.18.tar.gz
Algorithm Hash digest
SHA256 cd1ea04957e50fa35105d2a019080d9475a09187fab70c386f459b19d1edbfa4
MD5 e12135c4744efd426f0a0053f0777388
BLAKE2b-256 88150896f2ac88f52675a5a1a48018f55cd52ceeb3a3fd17f621b651689ef892

See more details on using hashes here.

File details

Details for the file spherical-1.0.18-py3-none-any.whl.

File metadata

  • Download URL: spherical-1.0.18-py3-none-any.whl
  • Upload date:
  • Size: 55.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-httpx/0.28.1

File hashes

Hashes for spherical-1.0.18-py3-none-any.whl
Algorithm Hash digest
SHA256 4ed6ccc3964c5338b197f70e2c68e36e73fea9da7b3f53c9f24d6922f687242b
MD5 29b96ae0436d3392ba77f1e86a0364d7
BLAKE2b-256 7d4a75aadc3b17430d4d9f3ad7bf0332a22b7ef78312e2b5b70774d2a301b9ec

See more details on using hashes here.

Supported by

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