Module ultraviolet::rotor

Rotors, i.e. constructs that describe and perform rotations.

A rotor is the geometric algebra analog of the Quaternion, and they end up being mathematically equivalent. They are good for doing the same sorts of things, and for the most part you can use rotors just like you would a quaternion, if you're already familiar with using those. However, they are significantly easier to derive yourself and build intuition for, and they generalize to both lower and higher dimensions than just 3, which is the only space for which quaternions are valuable.

A rotor can be thought of in multiple ways, the first of which is that a rotor is the result of the 'geometric product' of two vectors, denoted for two vectors u and v as simply uv. This operation is defined as

uv = u · v + u ∧ v

As can be seen, this operation results in the addition of two different types of values: first, the dot product will result in a scalar, and second, the exterior (wedge) product will result in a bivector. The addition of these two different types is not defined, but can be understood in a similar way as complex numbers, i.e. as a 'bundle' of two different kinds of values.

The reason we call this type of value a 'rotor' is that if you both left- and right-multiply (using the geometric product) a rotor with a vector, you will rotate the sandwiched vector. For example, if you start with two vectors, a and b, and create a rotor ab from them, then rotate a vector u with this rotor by doing ba u ab, you will end up rotating the vector u by in the plane that corresponds to a ∧ b (i.e. the plane which is parallel with both vectors), by twice the angle between a and b, in the opposite direction of the one that would bring a towards b within that plane.

In ultraviolet, the Mul trait is implemented for Rotors such that doing

rotor * vec

will rotate the Vector vec by the Rotor rotor.

To compose rotations, simply left-multiply the rotor by another one in the same way that matrix composition works. For example,

rotor_ab = rotor_b * rotor_a

Will result in the composition of rotor_b and rotor_a such that rotor_ab encodes a rotation as though rotor_a was applied and then rotor_b was applied.

Note that composition of rotors is more efficient than composition of matrices, however, the operation of rotating a vector by a rotor, i.e. the rotor * vec product, is more expensive to compute than the matrix * vec product. So, rotors are excellent for building and interpolating rotations, but it may be preferrable to convert them into matrices before applying them to vectors/points, if the same rotation will be applied to many vectors.

Structs

Rotor2	A Rotor in 2d space.
Rotor3	A Rotor in 3d space.
WRotor2	A Rotor in 2d space.
WRotor3	A Rotor in 3d space.