vectormath

vectormath

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Vector math utilities for Python built on NumPy <http://www.numpy.org/>_

Why

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

Scope

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

Goals

• Speed: All low-level operations rely on NumPy arrays. These are densely packed, typed, and partially implemented in C. The :code: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.

Alternatives

• NumPy <http://www.numpy.org/>_ can be used for any array operations
• Many small libraries on PyPI (e.g. vectors <https://github.com/allelos/vectors>_) implement vector math operations but are are only built with single vectors in mind.

Connections

• properties <https://github.com/seequent/properties>_ uses :code:vectormath as the underlying framework for Vector properties.

Installation

To install the repository, ensure that you have pip installed <https://pip.pypa.io/en/stable/installing/>_ and run:

.. code::

pip install vectormath

For the development version:

.. code::

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

Examples

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

.. code:: python

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