python numpy vector math
numpy, python
Solution
You can just use numpy arrays. Look at the numpy for matlab users page for a detailed overview of the pros and cons of arrays w.r.t. matrices.
As I mentioned in the comment, having to use the `dot()` function or method for mutiplication of vectors is the biggest pitfall. But then again, numpy arrays are consistent. All operations are element-wise. So adding or subtracting arrays and multiplication with a scalar all work as expected of vectors.
Edit2: Starting with Python 3.5 and numpy 1.10 you can use the `@` infix-operator for matrix multiplication, thanks to pep 465.
Edit: Regarding your comment:
Yes. The whole of numpy is based on arrays.
Yes. `linalg.norm(v)` is a good way to get the length of a vector. But what you get depends on the possible second argument to norm! Read the docs.
To normalize a vector, just divide it by the length you calculated in (2). Division of arrays by a scalar is also element-wise.
An example in ipython:
In [1]: import math
In [2]: import numpy as np
In [3]: a = np.array([4,2,7])
In [4]: np.linalg.norm(a)
Out[4]: 8.3066238629180749
In [5]: math.sqrt(sum([n**2 for n in a]))
Out[5]: 8.306623862918075
In [6]: b = a/np.linalg.norm(a)
In [7]: np.linalg.norm(b)
Out[7]: 1.0
Note that `In [5]` is an alternative way to calculate the length. `In [6]` shows normalizing the vector.
Problem
What is the `numpy` equivalent to `euclid`'s 2d vector classes / operations ? ( like: `euclid.Vector2` ) So far I have this. Create two vectors ``` import numpy as np loc = np.array([100., 100.]) vel = np.array([30., 10]) loc += vel # reseting speed to a default value, maintaining direction vel.normalize() vel *= 200 loc += vel ```