Perform quaternion operations using NumPy arrays
Project description
rowan
Welcome to rowan, a python package for quaternions.
The package is built entirely on top of NumPy and represents quaternions using NumPy arrays, meaning that all functions support arbitrarily highdimensional arrays of quaternions.
Quaternions are encoded as arrays of shape (...,4)
, with the convention that the final dimension of an array (a, b, c, d)
represents the quaternion a + bi + cj + dk
.
The package covers all basic quaternion algebraic and calculus operations, and also provides features for measuring distances, performing point cloud mapping, and interpolating.
If you have any questions about how to work with rowan, please visit the
ReadTheDocs page.
Authors
 Vyas Ramasubramani mailto:vramasub@umich.edu (Lead developer)
Setup
The recommended methods for installing rowan are using pip or conda.
Installation via pip
To install the package from PyPI, execute:
pip install rowan user
Installation via conda
To install the package from conda, first add the condaforge channel:
conda config add channels condaforge
After the condaforge channel has been added, you can install rowan by executing
conda install rowan
Installation from source
To install from source, execute:
git clone https://github.com/glotzerlab/rowan.git
cd rowan
python setup.py install user
Requirements
 Python = 2.7, >= 3.3
 NumPy >= 1.10
Testing
The package is currently tested for Python versions 2.7 and Python >= 3.3 on Unixlike systems. Continuous integrated testing is performed using CircleCI on these Python versions with NumPy versions 1.10 and above.
To run the packaged unit tests, execute the following line from the root of the repository:
python m unittest discover tests
To check test coverage, make sure the coverage module is installed:
pip install coverage
and then run the packaged unit tests with the coverage module:
coverage run m unittest discover tests
Quickstart
This library can be used to work with quaternions by simply instantiating the appropriate NumPy arrays and passing them to the required functions. For example:
import rowan import numpy as np one = np.array([10, 0, 0, 0]) one_unit = rowan.normalize(one) assert(np.all(one_unit == np.array([1, 0, 0, 0]))) if not np.all(one_unit == rowan.multiply(one_unit, one_unit)): raise RuntimeError("Multiplication failed!") one_vec = np.array([1, 0, 0]) rotated_vector = rowan.rotate(one_unit, one_vec) mat = np.eye(3) quat_rotate = rowan.from_matrix(mat) alpha, beta, gamma = rowan.to_euler(quat_rotate) quat_rotate_returned = rowan.from_euler(alpha, beta, gamma) identity = rowan.to_matrix(quat_rotate_returned)
Documentation
Documentation for rowan is written in reStructuredText and compiled using Sphinx. To build the documentation, first install Sphinx:
pip install sphinx sphinx_rtd_theme
You can then use Sphinx to create the actual documentation in either PDF or HTML form by running the following commands in the rowan root directory:
cd doc make html # For html output make latexpdf # For a LaTeX compiled PDF file open build/html/index.html
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