test_closing_fid essentially calls this to ensure close() is called when
a NpzFile object goes out of context:
for i in range(1, 1025):
np.load(tmp)["data"]
This raises a ResourceWarning on python 3, and fails on pypy since the
garbage collector works differently.
It seems to be a clas
Hi,
This is Florin Papa from the Dynamic Scripting Languages Optimizations team in
Intel Corporation.
Our team is working on optimizing the PyPy interpreter and part of this work is
to find and fix incompatibilities between NumPy and PyPy. Does anyone have
knowledge of real life workloads that
Fri, 05 Aug 2016 10:06:02 +0300, Matti Picus kirjoitti:
[clip]
> I can submit a pull request to skip on pypy, or should this be solved in
> a more substantial way?
Should also be safe to just skip it on Pypy, it's testing that the wrong
way to use np.load also works on CPython.
--
Pauli Virtane
Don't know if it is what you are looking for, but NumPy has a built-in
suite of benchmarks:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.testing.Tester.bench.html
Also, some projects have taken to utilizing the "airspeed velocity" utility
to track benchmarking stats for their projects
