[TIP] tox and pypy and numpypy

[Albert-Jan Roskam]

[Re-post, as I did not see my reply on the list. Apologies if you see this message twice]

-----------------------------
On Fri, Nov 21, 2014 8:49 PM CET Albert-Jan Roskam wrote:

>
>
>----- Original Message -----
>
>> From: Tres Seaver <tseaver at palladion.com>
>> To: testing-in-python at lists.idyll.org
>> Cc: 
>> Sent: Friday, November 21, 2014 5:54 PM
>> Subject: Re: [TIP] tox and pypy and numpypy
>> 
>> -----BEGIN PGP SIGNED MESSAGE-----
>> Hash: SHA1
>> 
>> On 11/21/2014 03:08 AM, Albert-Jan Roskam wrote:
>> 
>>  I am using the latest version of Tox and I would like to include Pypy
>>  in my list of environments. One of the required packages is numpy. 
>>  Currently, tox tries to pip install the standard numpy (which fails), 
>>  not numpypy. What is the correct way to solve this? I just read about 
>>  'use_sitepackages=True', which might be an option.
>> 
>> You can set up the 'deps' for the environment differently.  E.g.:
>> 
>> 
>> - ------------------------- %< --------------------------
>> [tox]
>> envlist = py26,py27,pypy
>> 
>> [testenv]
>> deps =
>>     nose
>>     numpy
>> commands =
>>     nosetests
>> 
>> [testenv:pypy]
>> deps =
>>     nose
>> 
>>     numpypy
>> commands =
>>     nosetests
>> - ------------------------- %< --------------------------
>> 
>
>
>hi,
>
>Thanks, you put me on the right track. Numpypy and the normal numpy are both called 'numpy' though. 
>
>I got it working with a separate 'dep', like you suggested, and by using the (experimental) 'install_command'
>
>
>[tox]
>envlist = pypy,py27,py33,py34,docs
>skipsdist = True
>
>[testenv]
>deps = 
>           setuptools
>     nose
>     gocept.testing
>     numpy 
>commands =
>nosetests --with-xunit --xunit-file=junit-{envname}.xml --verbosity=3
>
>[testenv:pypy]
>deps = 
>    setuptools
>    nose
>    gocept.testing
>install_command = 
>    pip install git+https://bitbucket.org/pypy/numpy.git {packages}
>
>
>And it was worth the effort, because numpypy apparently does not 'upcast' arrays with mixed data types. This is with regular numpy (notice the ints become str, upcasting):
>>> numpy.array([['a', 1], ['b', 2]])
>array([['a', '1'],
>['b', '2']], 
>dtype='|S1')
>
>regards,
>Albert-Jan