Memory leak when assigning a numpy array to .vec atribute?

[jsbtic]

Dear all,
I ran into some memory problems when changing the values of a quaternion object's .vec -attribute. This becomes exceedingly problematic in for loops, where memory consumptions increases steadily after every iteration step.

C.f. using memory_profiler package, v. 0.41 ( conda install -c anaconda memory_profiler )

$ mprof run quaternion_minimal_example.py 
mprof: Sampling memory every 0.1s
running as a Python program...
Filename: quaternion_minimal_example.py

Line #    Mem usage    Increment   Line Contents
================================================
    10     66.8 MiB      0.0 MiB   @profile
    11                             def main():
    12     66.8 MiB      0.0 MiB       N = int(1e6)
    13     66.8 MiB      0.0 MiB       q = np.quaternion(0, 0, 0, 0)
    14                             
    15    136.5 MiB     69.8 MiB       for n in xrange(N):
    16    136.5 MiB      0.0 MiB           q.vec = np.array([0, 0, 0])

(use mprof plot to show the memory consumption trend.)

Here's the code for quaternion_minimal_example.py

import numpy as np
import quaternion
from memory_profiler import profile


@profile
def main():
    N = int(1e5)
    q = np.quaternion(0, 0, 0, 0)

    for n in xrange(N):
        q.vec = np.array([0, 0, 0])


main()

The problem can be solved if one replaces np.array([0, 0, 0]) with np.array([0, 0, 0]).tolist(). Is there perhaps a bug in the quaternion package, or am I just doing something wrong...(?)

My best,

• J.

[moble]

Thanks for the bug report!

I found an example in the docs showing that you really need to use a separate pointer for PySequence_GetItem, and Py_DECREF it after you're done using it. I guess this was just lucky with lists, but numpy arrays are a little too clever for [our] own good.

[jsbtic]

Thanks for the fix!
J.

P.S. I was thinking, could you add the some examples how to convert M x 4 numppy arrays to quaternion arrays and vice versa to the Usage section in the README.md for new users (took some time for me to find out).

E.g.

>>> M = 5
>>> a = np.round(np.random.rand(M, 4),3)
>>> a
array([[ 0.909,  0.671,  0.079,  0.228],
       [ 0.762,  0.492,  0.096,  0.128],
       [ 0.543,  0.574,  0.273,  0.941],
       [ 0.307,  0.434,  0.512,  0.556],
       [ 0.885,  0.538,  0.614,  0.122]])
>>> qs = quaternion.as_quat_array(a)
>>> qs
array([quaternion(0.909, 0.671, 0.079, 0.228),
       quaternion(0.762, 0.492, 0.096, 0.128),
       quaternion(0.543, 0.574, 0.273, 0.941),
       quaternion(0.307, 0.434, 0.512, 0.556),
       quaternion(0.885, 0.538, 0.614, 0.122)], dtype=quaternion)
>>> b = quaternion.as_float_array(qs)
>>> b
array([[ 0.909,  0.671,  0.079,  0.228],
       [ 0.762,  0.492,  0.096,  0.128],
       [ 0.543,  0.574,  0.273,  0.941],
       [ 0.307,  0.434,  0.512,  0.556],
       [ 0.885,  0.538,  0.614,  0.122]])

[moble]

Good idea. I myself frequently forget how to do these conversions. I've added it to the README. Thanks again!