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Vector math utilities for Python built on NumPy

The vectormath package provides a fast, simple library of vector math utilities by leveraging NumPy. This allows explicit geometric constructs to be created (for example, Vector3 and Plane) without redefining the underlying array math.

The vectormath package includes Vector3/Vector2 and Vector3Array/Vector2Array.

• Speed: All low-level operations rely on NumPy arrays. These are densely packed, typed, and partially implemented in C. The VectorArray classes in particular take advantage of this speed by performing vector operations on all Vectors at once, rather than in a loop.
• Simplicty: High-level operations are explicit and straight-forward. This library should be usable by Programmers, Mathematicians, and Geologists.

• NumPy can be used for any array operations
• Many small libraries on PyPI (e.g. vectors) implement vector math operations but are are only built with single vectors in mind.

• properties uses vectormath as the underlying framework for Vector properties.

To install the repository, ensure that you have pip installed and run:

pip install vectormath

For the development version:

git clone https://github.com/seequent/vectormath.git
cd vectormath
pip install -e .

This example gives a brief demonstration of some of the notable features of Vector3 and Vector3Array

import numpy as np
import vectormath as vmath

# Single Vectors
v = vmath.Vector3(5, 0, 0)
v.normalize()
print(v)                          # >> [1, 0, 0]
print(v.x)                        # >> 1.0

# VectorArrays are much faster than a for loop over Vectors
v_array = vmath.Vector3Array([[4, 0, 0], [0, 2, 0], [0, 0, 3]])
print(v_array.x)                  # >> [4, 0, 0]
print(v_array.length)             # >> [4, 2, 3]
print(v_array.normalize())        # >> [[1, 0, 0], [0, 1, 0], [0, 0, 1]]

# Vectors can be accessed individually or in slices
print(type(v_array[1:]))          # >> vectormath.Vector3Array
print(type(v_array[2]))           # >> vectormath.Vector3

# All these classes are just numpy arrays
print(isinstance(v, np.ndarray))  # >> True
print(type(v_array[1:, 1:]))      # >> numpy.ndarray

Current version: v0.2.2