Overview

points is a library for performing geometry and linear algebra calculations.

Linear Algebra

Vectors

The Vector is the simplest linear algebra object. They can represent a point in space, or the attributes of an object.

>>> import points
>>> vector = points.Vector(4, 3, 12)
>>> vector.values()
(4, 3, 12)
>>> vector[2]
12
>>> vector.length()
3

Note that ‘length’ here refers to the number of values in the Vector - to get the size of the line the Vector represents in space, you need the magnitude:

>>> vector.magnitude()
13.0

You can get the component vectors like so:

>>> vector.components()
(<Vector [4, 0, 0]>, <Vector [0, 3, 0]>, <Vector [0, 0, 12]>)

You can add and remove values to the Vector in the same way you would with a list:

>>> vector.add(17)
>>> vector.values()
(4, 3, 12, 17)
>>> vector.remove(17)
>>> vector.values()
(4, 3, 12)
>>> vector.insert(1, 9)
>>> vector.values()
(4, 9, 3, 12)
>>> vector.pop(1)
9
>>> vector.values()
(4, 3, 12)

Vectors can be multiplied by scalar values (numbers) to get new Vectors:

>>> vector * 5
<Vector [20, 15, 60]>

Vectors can also be combined with other Vectors, with basic arithmetic, and also with the dot product and angle between them:

>>> vector2 = points.Vector(9, -1, 4)
>>> vector + vector2
<Vector [13, 2, 16]>
>>> vector - vector2
<Vector [-5, 4, 8]>
>>> vector.distance_to(vector2)
10.246950765959598
>>> vector.dot(vector2)
81
>>> vector.cross(vector2)
<Vector [24, 92, -31]>
>>> vector.angle_with(vector2)
0.8900119515744306
>>> vector.angle_with(vector2, degrees=True)
50.99392854141668

A Vector’s ‘span’ is the set of all Vectors which can be created by scaling it, and the span of a set of Vectors is all the Vectors which can be created from linear combinations of those Vectors. A set of vectors are linearly independent if none of them are in the span of the others…

>>> span = vector.span()
>>> vector in span
True
>>> vector2 in span
False
>>> span = vector.span_with(vector2)
>>> vector in span
True
>>> vector2 in span
True
>>> points.Vector(1, 2, 3) in span
True
>>> vector.linearly_independent_of(vector2)
True

Matrices

A Matrix is a rectangular array of numbers, often used to represent linear transformations. They are created by passing in rows:

>>> matrix = points.Matrix([1, 2, 3], [4, 5, 6])
>>> matrix.rows()
((1, 2, 3), (4, 5, 6))
>>> matrix.columns()
((1, 4), (2, 5), (3, 6))

You can also pass it vector, which will be interpreted as columns:

>>> col1 = points.Vector(1, 4, 7)
>>> col2 = points.Vector(2, 5, 8)
>>> col3 = points.Vector(3, 6, 9)
>>> matrix2 = points.Matrix(col1, col2, col3)
>>> matrix2.rows()
((1, 2, 3), (4, 5, 6), (7, 8, 9))
>>> matrix2.columns()
((1, 4, 7), (2, 5, 8), (3, 6, 9))

You can add matrices together with + or multiply them by scalars with *. The @ operator is used to multiply a Matrix with another Matrix, or with a Vector.

Matrices currently support the concepts of inversion, adjoin, cofactors, minors, determinants, transposition, Gaussian elimination, and checks for row echelon form and reduced row echelon form. See the full documentation for more details.