ninMonkey - 1 year ago 158

Python Question

What is the

`numpy`

`euclid`

`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

Answer

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.

Source (Stackoverflow)