rowan¶

Welcome to the documentation for rowan, a package for working with quaternions! Quaternions form a number system with various interesting properties, and they have a number of uses. This package provides tools for standard algebraic operations on quaternions as well as a number of additional tools for e.g. measuring distances between quaternions, interpolating between them, and performing basic point-cloud mapping. A particular focus of the rowan package is working with unit quaternions, which are a popular means of representing rotations in 3D. In order to provide a unified framework for working with the various rotation formalisms in 3D, rowan allows easy interconversion between these formalisms.

To install rowan, first clone the repository from source. Once installed, the package can be installed using setuptools:

$ python setup.py install --user

rowan¶

Overview

`rowan.conjugate`	Conjugates an array of quaternions
`rowan.inverse`	Computes the inverse of an array of quaternions
`rowan.exp`	Computes the natural exponential function \(e^q\).
`rowan.expb`	Computes the exponential function \(b^q\).
`rowan.exp10`	Computes the exponential function \(10^q\).
`rowan.log`	Computes the quaternion natural logarithm.
`rowan.logb`	Computes the quaternion logarithm to some base b.
`rowan.log10`	Computes the quaternion logarithm base 10.
`rowan.multiply`	Multiplies two arrays of quaternions
`rowan.divide`	Divides two arrays of quaternions
`rowan.norm`	Compute the quaternion norm
`rowan.normalize`	Normalize quaternions
`rowan.rotate`	Rotate a list of vectors by a corresponding set of quaternions
`rowan.vector_vector_rotation`	Find the quaternion to rotate one vector onto another
`rowan.from_euler`	Convert Euler angles to quaternions
`rowan.to_euler`	Convert quaternions to Euler angles
`rowan.from_matrix`	Convert the rotation matrices mat to quaternions
`rowan.to_matrix`	Convert quaternions into rotation matrices.
`rowan.from_axis_angle`	Find quaternions to rotate a specified angle about a specified axis
`rowan.to_axis_angle`	Convert the quaternions in q to axis angle representations
`rowan.from_mirror_plane`	Generate quaternions from mirror plane equations.
`rowan.reflect`	Reflect a list of vectors by a corresponding set of quaternions
`rowan.equal`	Check whether two sets of quaternions are equal.
`rowan.not_equal`	Check whether two sets of quaternions are not equal.
`rowan.isfinite`	Test element-wise for finite quaternions.
`rowan.isinf`	Test element-wise for infinite quaternions.
`rowan.isnan`	Test element-wise for NaN quaternions.

Details

The core rowan package contains functions for operating on quaternions. The core package is focused on robust implementations of key functions like multiplication, exponentiation, norms, and others. Simple functionality such as addition is inherited directly from numpy due to the representation of quaternions as numpy arrays. Many core numpy functions implemented for normal arrays are reimplemented to work on quaternions ( such as allclose() and isfinite()). Additionally, numpy broadcasting is enabled throughout rowan unless otherwise specified. This means that any function of 2 (or more) quaternions can take arrays of shapes that do not match and return results according to numpy’s broadcasting rules.

rowan.conjugate(q)¶

Conjugates an array of quaternions

Parameters:	q ((..,4) np.array) – Array of quaternions
Returns:	An array containing the conjugates of q

Example:

q_star = conjugate(q)

rowan.exp(q)¶

Computes the natural exponential function \(e^q\).

The exponential of a quaternion in terms of its scalar and vector parts \(q = a + \boldsymbol{v}\) is defined by exponential power series: formula \(e^x = \sum_{k=0}^{\infty} \frac{x^k}{k!}\) as follows:

\[\begin{split}\begin{align} e^q &= e^{a+v} \\ &= e^a \left(\sum_{k=0}^{\infty} \frac{v^k}{k!} \right) \\ &= e^a \left(\cos \lvert \lvert \boldsymbol{v} \rvert \rvert + \frac{\boldsymbol{v}}{\lvert \lvert \boldsymbol{v} \rvert \rvert} \sin \lvert \lvert \boldsymbol{v} \rvert \rvert \right) \end{align}\end{split}\]

Parameters:	q ((..,4) np.array) – Quaternions
Returns:	Array of shape (…) containing exponentials of q

Example:

q_exp = exp(q)

rowan.expb(q, b)¶

Computes the exponential function \(b^q\).

We define the exponential of a quaternion to an arbitrary base relative to the exponential function \(e^q\) using the change of base formula as follows:

\[\begin{split}\begin{align} b^q &= y \\ q &= \log_b y = \frac{\ln y}{\ln b}\\ y &= e^{q\ln b} \end{align}\end{split}\]

Parameters:	q ((..,4) np.array) – Quaternions
Returns:	Array of shape (…) containing exponentials of q

Example:

q_exp = exp(q, 2)

rowan.exp10(q)¶

Computes the exponential function \(10^q\).

Wrapper around expb().

Parameters:	q ((..,4) np.array) – Quaternions
Returns:	Array of shape (…) containing exponentials of q

Example:

q_exp = exp(q, 2)

rowan.log(q)¶

Computes the quaternion natural logarithm.

The natural of a quaternion in terms of its scalar and vector parts \(q = a + \boldsymbol{v}\) is defined by inverting the exponential formula (see exp()), and is defined by the formula :math:` frac{x^k}{k!}` as follows:

\[\begin{equation} \ln(q) = \ln\lvert\lvert q \rvert\rvert + \frac{\boldsymbol{v}}{\lvert\lvert \boldsymbol{v} \rvert\rvert} \arccos\left(\frac{a}{q}\right) \end{equation}\]

Parameters:	q ((..,4) np.array) – Quaternions
Returns:	Array of shape (…) containing logarithms of q

Example:

ln_q = log(q)

rowan.logb(q, b)¶

Computes the quaternion logarithm to some base b.

The quaternion logarithm for arbitrary bases is defined using the standard change of basis formula relative to the natural logarithm.

\[\begin{split}\begin{align} \log_b q &= y \\ q &= b^y \\ \ln q &= y \ln b \\ y &= \log_b q = \frac{\ln q}{\ln b} \end{align}\end{split}\]

Parameters:	q ((..,4) np.array) – Quaternions n ((..) np.array) – Scalars to use as log bases
Returns:	Array of shape (…) containing logarithms of q

Example:

log_q = log(q, 2)

rowan.log10(q)¶

Computes the quaternion logarithm base 10.

Wrapper around logb().

Parameters:	q ((..,4) np.array) – Quaternions
Returns:	Array of shape (…) containing logarithms of q

Example:

log_q = log(q, 2)

rowan.power(q, n)¶

Computes the power of a quaternion \(q^n\).

Quaternions raised to a scalar power are defined according to the polar decomposition angle \(\theta\) and vector \(\hat{u}\): \(q^n = \lvert\lvert q \rvert\rvert^n \left( \cos(n\theta) + \hat{u} \sin(n\theta)\right)\). However, this can be computed more efficiently by noting that \(q^n = \exp(n \ln(q))\).

Parameters:	q ((..,4) np.array) – Quaternions. n ((..) np.arrray) – Scalars to exponentiate quaternions with.
Returns:	Array of shape (…) containing of q

Example:

q_n = pow(q^n)

rowan.multiply(qi, qj)¶

Multiplies two arrays of quaternions

Note that quaternion multiplication is generally non-commutative.

Parameters:	qi ((..,4) np.array) – First set of quaternions qj ((..,4) np.array) – Second set of quaternions
Returns:	An array containing the products of row i of qi with column j of qj

Example:

qi = np.array([[1, 0, 0, 0]])
qj = np.array([[1, 0, 0, 0]])
prod = multiply(qi, qj)

rowan.norm(q)¶

Compute the quaternion norm

Parameters:	q ((..,4) np.array) – Quaternions to find norms for
Returns:	An array containing the norms for qi in q

Example:

q = np.random.rand(10, 4)
norms = norm(q)

rowan.normalize(q)¶

Normalize quaternions

Parameters:	q ((..,4) np.array) – Array of quaternions to normalize
Returns:	An array containing the unit quaternions q/norm(q)

Example:

q = np.random.rand(10, 4)
u = normalize(q)

rowan.from_mirror_plane(x, y, z)¶

Generate quaternions from mirror plane equations.

Reflection quaternions can be constructed of the from \((0, x, y, z)\), i.e. with zero real component. The vector \((x, y, z)\) is the normal to the mirror plane.

Parameters:	x ((..) np.array) – First planar component y ((..) np.array) – Second planar component z ((..) np.array) – Third planar component
Returns:	An array of quaternions corresponding to the provided reflections.

Example:

plane = (1, 2, 3)
quat_ref = from_mirror_plane(*plane)

rowan.reflect(q, v)¶

Reflect a list of vectors by a corresponding set of quaternions

For help constructing a mirror plane, see from_mirror_plane().

Parameters:	q ((..,4) np.array) – Quaternions to use for reflection v ((..,3) np.array) – Vectors to reflect.
Returns:	An array of the vectors in v reflected by q

Example:

q = np.random.rand(1, 4)
v = np.random.rand(1, 3)
v_rot = rotate(q, v)

rowan.rotate(q, v)¶

Rotate a list of vectors by a corresponding set of quaternions

Parameters:	q ((..,4) np.array) – Quaternions to rotate by. v ((..,3) np.array) – Vectors to rotate.
Returns:	An array of the vectors in v rotated by q

Example:

q = np.random.rand(1, 4)
v = np.random.rand(1, 3)
v_rot = rotate(q, v)

rowan.vector_vector_rotation(v1, v2)¶

Find the quaternion to rotate one vector onto another

Parameters:	v1 ((..,3) np.array) – Vector to rotate v2 ((..,3) np.array) – Desired vector
Returns:	Array (…, 4) of quaternions that rotate v1 onto v2.

rowan.from_euler(alpha, beta, gamma, convention='zyx', axis_type='intrinsic')¶

Convert Euler angles to quaternions

For generality, the rotations are computed by composing a sequence of quaternions corresponding to axis-angle rotations. While more efficient implementations are possible, this method was chosen to prioritize flexibility since it works for essentially arbitrary Euler angles as long as intrinsic and extrinsic rotations are not intermixed.

Parameters:

alpha ((..) np.array) – Array of \(\alpha\) values in radians.
beta ((..) np.array) – Array of \(\beta\) values in radians.
gamma ((..) np.array) – Array of \(\gamma\) values in radians.
convention (str) – One of the 12 valid conventions xzx, xyx, yxy, yzy, zyz, zxz, xzy, xyz, yxz, yzx, zyx, zxy
axes (str) – Whether to use extrinsic or intrinsic rotations

Returns:

An array containing the converted quaternions

Example:

rands = np.random.rand(100, 3)
alpha, beta, gamma = rands.T
ql.from_euler(alpha, beta, gamma)

rowan.to_euler(q, convention='zyx', axis_type='intrinsic')¶

Convert quaternions to Euler angles

Euler angles are returned in the sequence provided, so in, e.g., the default case (‘zyx’), the angles returned are for a rotation \(Z(\alpha) Y(\beta) X(\gamma)\).

Note

In all cases, the \(\alpha\) and \(\gamma\) angles are between \(\pm \pi\). For proper Euler angles, \(\beta\) is between \(0\) and \(pi\) degrees. For Tait-Bryan angles, \(\beta\) lies between \(\pm\pi/2\).

For simplicity, quaternions are converted to matrices, which are then converted to their Euler angle representations. All equations for rotations are derived by considering compositions of the three elemental rotations about the three Cartesian axes:

\begin{eqnarray*} R_x(\theta) =& \left(\begin{array}{ccc} 1 & 0 & 0 \\ 0 & \cos \theta & -\sin \theta \\ 0 & \sin \theta & \cos \theta \\ \end{array}\right)\\ R_y(\theta) =& \left(\begin{array}{ccc} \cos \theta & 0 & \sin \theta \\ 0 & 1 & 0\\ -\sin \theta & 1 & \cos \theta \\ \end{array}\right)\\ R_z(\theta) =& \left(\begin{array}{ccc} \cos \theta & -\sin \theta & 0 \\ \sin \theta & \cos \theta & 0 \\ 0 & 0 & 1 \\ \end{array}\right)\\ \end{eqnarray*}

Extrinsic rotations are represented by matrix multiplications in the proper order, so \(z-y-x\) is represented by the multiplication \(XYZ\) so that the system is rotated first about \(Z\), then about \(y\), then finally \(X\). For intrinsic rotations, the order of rotations is reversed, meaning that it matches the order in which the matrices actually appear i.e. the \(z-y'-x''\) convention (yaw, pitch, roll) corresponds to the multiplication of matrices \(ZYX\). For proof of the relationship between intrinsic and extrinsic rotations, see the Wikipedia page on Davenport chained rotations.

For more information, see the Wikipedia page for Euler angles (specifically the section on converting between representations).

Parameters:	q ((..,4) np.array) – Quaternions to transform convention (str) – One of the 6 valid conventions zxz, xyx, yzy, zyz, xzx, yxy axes (str) – Whether to use extrinsic or intrinsic
Returns:	An array with Euler angles \((\alpha, \beta, \gamma)\) as the last dimension (in radians)

Example:

rands = np.random.rand(100, 3)
alpha, beta, gamma = rands.T
ql.from_euler(alpha, beta, gamma)
alpha_return, beta_return, gamma_return = ql.to_euler(full)

rowan.from_matrix(mat, require_orthogonal=True)¶

Convert the rotation matrices mat to quaternions

Thhis method uses the algorithm described by Bar-Itzhack in [Itzhack00]. The idea is to construct a matrix K whose largest eigenvalue corresponds to the desired quaternion. One of the strengths of the algorithm is that for nonorthogonal matrices it gives the closest quaternion representation rather than failing outright.

[Itzhack00]

Itzhack Y. Bar-Itzhack. “New Method for Extracting the Quaternion from a Rotation Matrix”, Journal of Guidance, Control, and Dynamics, Vol. 23, No. 6 (2000), pp. 1085-1087 https://doi.org/10.2514/2.4654

Parameters:	mat ((..,3,3) np.array) – An array of rotation matrices
Returns:	An array containing the quaternion representations of the elements of mat (i.e. the same elements of SO(3))

rowan.to_matrix(q, require_unit=True)¶

Convert quaternions into rotation matrices.

Uses the conversion described on Wikipedia.

Parameters:	q ((..,4) np.array) – An array of quaternions
Returns:	The array containing the matrix representations of the elements of q (i.e. the same elements of SO(3))

rowan.from_axis_angle(axes, angles)¶

Find quaternions to rotate a specified angle about a specified axis

Parameters:	axes ((..,3) np.array) – An array of vectors (the axes) angles (float or (..,1) np.array) – An array of angles in radians. Will be broadcast to match shape of v as needed
Returns:	An array of the desired rotation quaternions

Example:

import numpy as np
axis = np.array([[1, 0, 0]])
ang = np.pi/3
quat = about_axis(axis, ang)

rowan.to_axis_angle(q)¶

Convert the quaternions in q to axis angle representations

Parameters:	q ((..,4) np.array) – An array of quaternions
Returns:	A tuple of np.arrays (axes, angles) where axes has shape (…,3) and angles has shape (…,1). The angles are in radians

rowan.isnan(q)¶

Test element-wise for NaN quaternions.

A quaternion is defined as NaN if any elements are NaN.

Parameters:	q ((..,4) np.array) – Quaternions to check
Returns:	A boolean array of shape (…) indicating NaN.

rowan.isinf(q)¶

Test element-wise for infinite quaternions.

A quaternion is defined as infinite if any elements are infinite.

Parameters:	q ((..,4) np.array) – Quaternions to check
Returns:	A boolean array of shape (…) indicating infinite quaternions.

rowan.isfinite(q)¶

Test element-wise for finite quaternions.

A quaternion is defined as finite if all elements are finite.

Parameters:	q ((..,4) np.array) – Quaternions to check
Returns:	A boolean array of shape (…) indicating finite quaternions.

rowan.equal(p, q)¶

Check whether two sets of quaternions are equal.

This function is a simple wrapper that checks array equality and then aggregates along the quaternion axis.

Parameters:	p ((..,4) np.array) – First set of quaternions q ((..,4) np.array) – First set of quaternions
Returns:	A boolean array of shape (…) indicating equality.

rowan.not_equal(p, q)¶

Check whether two sets of quaternions are not equal.

This function is a simple wrapper that checks array equality and then aggregates along the quaternion axis.

Parameters:	p ((..,4) np.array) – First set of quaternions q ((..,4) np.array) – First set of quaternions
Returns:	A boolean array of shape (…) indicating inequality.

rowan.allclose(p, q, **kwargs)¶

Check whether two sets of quaternions are all close.

This is a direct wrapper of the corresponding numpy function.

Parameters:	p ((..,4) np.array) – First set of quaternions q ((..,4) np.array) – First set of quaternions **kwargs – Keyword arguments to pass to np.allclose
Returns:	Whether or not all quaternions are close

rowan.isclose(p, q, **kwargs)¶

Element-wise check of whether two sets of quaternions close.

This function is a simple wrapper that checks using the corresponding numpy function and then aggregates along the quaternion axis.

Parameters:	p ((..,4) np.array) – First set of quaternions q ((..,4) np.array) – First set of quaternions **kwargs – Keyword arguments to pass to np.isclose
Returns:	A boolean array of shape (…)

rowan.inverse(q)¶

Computes the inverse of an array of quaternions

Parameters:	q ((..,4) np.array) – Array of quaternions
Returns:	An array containing the inverses of q

Example:

q_inv = inverse(q)

rowan.divide(qi, qj)¶

Divides two arrays of quaternions

Division is non-commutative; this function returns \(q_i q_j^{-1}\).

Parameters:	qi ((..,4) np.array) – Dividend quaternion qj ((..,4) np.array) – Divisors quaternions
Returns:	An array containing the quotients of row i of qi with column j of qj

Example:

qi = np.array([[1, 0, 0, 0]])
qj = np.array([[1, 0, 0, 0]])
prod = divide(qi, qj)

random¶

Overview

`rowan.random.rand`	Generate random rotations uniformly
`rowan.random.random_sample`	Generate random rotations unifo

Details

Various functions for generating random sets of rotation quaternions. Note that if you simply want random quaternions not restricted to \(SO(3)\) you can just generate these directly using numpy.random.rand(… 4). This subpackage is entirely focused on generating rotation quaternions.

rowan.random.rand(*args)¶

Generate random rotations uniformly

This is a convenience function a la np.random.rand. If you want a function that takes a tuple as input, use random_sample() instead.

Parameters:	shape (tuple) – The shape of the array to generate.
Returns:	Random quaternions of the shape provided with an additional axis of length 4.

rowan.random.random_sample(size=None)¶

Generate random rotations unifo

In general, sampling from the space of all quaternions will not generate uniform rotations. What we want is a distribution that accounts for the density of rotations, i.e., a distribution that is uniform with respect to the appropriate measure. The algorithm used here is detailed in [Shoe92].

[Shoe92]

Shoemake, K.: Uniform random rotations. In: D. Kirk, editor, Graphics Gems III, pages 124-132. Academic, New York, 1992.

Parameters:	size (tuple) – The shape of the array to generate
Returns:	Random quaternions of the shape provided with an additional axis of length 4

Development Guide¶

Philosophy¶

The goal of rowan is to provide a flexible, easy-to-use, and scalable approach to dealing with rotation representations. To ensure maximum flexibility, rowan operates entirely on numpy arrays, which serve as the de facto standard for efficient multi-dimensional arrays in Python. To be available for a wide variety of applications, rowan aims to work for arbitrarily shaped numpy arrays, mimicking numpy broadcasting to the extent possible. Functions for which this broadcasting is not available should be documented as such.

Since rowan is designed to work everywhere, all hard dependencies aside from numpy are avoided, although soft dependencies for specific functions are allowed. To avoid any dependencies on compilers or other software, all rowan code is written in pure Python. This means that while rowan is intended to provide good performance, it may not be the correct choice in cases where performance is critical. The package was written principally for use-cases where quaternion operations are not the primary bottleneck, so it prioritizes portability, maintainability, and flexibility over optimization.

PEP 20¶

In general, all code in rowan should follow the principles in PEP 20. In particular, prefer simple, explicit code where possible, avoiding unnecessary convolution or complicated code that could be written more simply. Avoid writing code that is not easy to parse up front.

Inline comments are highly encouraged; however, code should be written in a way that it could be understood without comments. Comments such as “Set x to 10” are not helpful and simply clutter code. The most useful comments in a package such as rowan are the ones that explain the underlying algorithm rather than the implementations, which should be simple. For example, the comment “compute the spectral decomposition of A” is uninformative, since the code itself should make this obvious, e.g, np.linalg.eigh. On the other hand, the comment “the eigenvector corresponding to the largest eigenvalue of the A matrix is the quaternion” is instructive.

Source Code Conventions¶

All code in rowan should follow PEP 8 guidelines, which are the de facto standard for Python code. In addition, follow the Google Python Style Guide, which is largely a superset of PEP 8. Note that Google has amended their standards to match PEP 8’s 4 spaces guideline, so write code accordingly. In particular, write docstrings in the Google style.

Python example:

# This is the correct style
def multiply(x, y):
    """Multiply two numbers

    Args:
        x (float): The first number
        y (float): The second number

    Returns:
        The product
    """

# This is the incorrect style
def multiply(x, y):
    """Multiply two numbers

    :param x: The first number
    :type x: float
    :param y: The second number
    :type y: float
    :returns: The product
    :rtype: float
    """

Documentation must be included for all files, and is then generated from the docstrings using sphinx.

Unit Tests¶

All code should include a set of unit tests which test for correct behavior. All tests should be placed in the tests folder at the root of the project. These tests should be as simple as possible, testing a single function each, and they should be kept as short as possible. Tests should also be entirely deterministic: if you are using a random set of objects for testing, they should either be generated once and then stored in the tests/files folder, or the random number generator in use should be seeded explicitly (e.g, numpy.random.seed or random.seed). Tests should be written in the style of the standard Python unittest framework. At all times, tests should be executable by simply running python -m unittest discover tests from the root of the project.

General Notes¶

For consistency, NumPy should always be imported as np in code: import numpy as np.

Avoid external dependencies where possible, and avoid introducing any hard dependencies. Dependencies other than NumPy should always be soft, enabling the rest of the package to function as is.

License¶

rowan Open Source Software License Copyright 2010-2018 The Regents of
the University of Michigan All rights reserved.

rowan may contain modifications ("Contributions") provided, and to which
copyright is held, by various Contributors who have granted The Regents of the
University of Michigan the right to modify and/or distribute such Contributions.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice,
   this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors
   may be used to endorse or promote products derived from this software without
   specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Changelog¶

The format is based on Keep a Changelog. This project adheres to Semantic Versioning <http://semver.org/spec/v2.0.0.html>`_.

Unreleased¶

Added¶

Various distance metrics on quaternion space.
Quaternion interpolation.

Fixed¶

Update empty __all__ variable in geometry to export functions.

v0.4.4 - 2018-04-10¶

Added¶

Rewrote internals for upload to PyPI.

v0.4.3 - 2018-04-10¶

Fixed¶

Typos in documentation.

v0.4.2 - 2018-04-09¶

Added¶

Support for Read The Docs and Codecov.
Simplify CircleCI testing suite.
Minor changes to README.
Properly update this document.

v0.4.1 - 2018-04-08¶

Fixed¶

Exponential for bases other than e are calculated correctly.

v0.4.0 - 2018-04-08¶

Added¶

Add functions relating to exponentiation: exp, expb, exp10, log, logb, log10, power.
Add core comparison functions for equality, closeness, finiteness.

v0.3.0 - 2018-03-31¶

Added¶

Broadcasting works for all methods.
Quaternion reflections.
Random quaternion generation.

Changed¶

Converting from Euler now takes alpha, beta, and gamma as separate args.
Ensure more complete coverage.

v0.2.0 - 2018-03-08¶

Added¶

Added documentation.
Add tox support.
Add support for range of python and numpy versions.
Add coverage support.

Changed¶

Clean up CI.
Ensure pep8 compliance.

v0.1.0 - 2018-02-26¶

Added¶

Initial implementation of all functions.

Credits¶

The following people contributed to the rowan package.

Vyas Ramasubramani, University of Michigan - Lead developer.

Initial design
Core quaternion operations
Sphinx docs support

Support and Contribution¶

This package is hosted on Bitbucket. Please report any bugs or problems that you find on the issue tracker.

All contributions to rowan are welcomed! Please see the development guide for more information.

rowan¶

rowan¶

random¶

Development Guide¶

Philosophy¶

PEP 20¶

Source Code Conventions¶

Unit Tests¶

General Notes¶

License¶

Changelog¶

Unreleased¶

Added¶

Fixed¶

v0.4.4 - 2018-04-10¶

Added¶

v0.4.3 - 2018-04-10¶

Fixed¶

v0.4.2 - 2018-04-09¶

Added¶

v0.4.1 - 2018-04-08¶

Fixed¶

v0.4.0 - 2018-04-08¶

Added¶

v0.3.0 - 2018-03-31¶

Added¶

Changed¶

v0.2.0 - 2018-03-08¶

Added¶

Changed¶

v0.1.0 - 2018-02-26¶

Added¶

Credits¶

Support and Contribution¶

Indices and tables¶