On Sun, Sep 23, 2012 at 10:20 PM, Nathaniel Smith <njs@pobox.com> wrote:
On Sat, Sep 22, 2012 at 1:18 PM, Ralf Gommers <ralf.gommers@gmail.com> wrote:
On Fri, Sep 21, 2012 at 11:39 PM, Nathaniel Smith <njs@pobox.com> wrote:
So the question is, how do we get a .egg-info? For the specific case Ralf ran into, I'm pretty sure the solution is just that if you're clever enough to do an in-place build and add it to your PYTHONPATH, you should be clever enough to also run 'python setupegg.py egg_info' which will create a .egg-info to go with your in-place build and everything will be fine.
That command first starts rebuilding numpy.
No, it just seems to run the config and source-generation bits, not build anything. It also leaves the .egg-info in the source directory, which is what you want.
You're right, sorry. I saw output like "building extension "numpy.core._dotblas" sources" scrolling by and hit Ctrl-C.
P.S.: yeah the thing where pip decides to upgrade the world is REALLY OBNOXIOUS. It also appears to be on the list to be fixed in the next release or the next release+1, so I guess there's hope?: https://github.com/pypa/pip/pull/571
Good to know. Let's hope that does make it in. Given it's development model, I'm less optimistic that easy_install will receive the same fix though ....
Yeah, easy_install is abandoned and bit-rotting, which is why people usually recommend pip :-). But in this case, I thought that easy_install already doesn't upgrade the world when it runs? Is there something to fix here?
It does, as Josef said above. It has the same -U and --no-deps flags.
Until both pip and easy_install are fixed, this alone should be enough for the advice to be "don't use install_requires". It's not like my alternative suggestion takes away any information or valuable functionality.
pandas, for example, requires several other packages, and I found it quite convenient the other day when I wanted to try out a new version and pip automatically took care of setting all that up for me. It even correctly upgraded numpy, since the virtualenv I was using for testing had inherited my system-installed 1.5.2, but this was the first version of pandas that needed 1.6.
So this saved you from reading "pandas requires numpy >= 1.6.1" and typing "pip install -U numpy". Not my definition of valuable functionality, and certainly not worth the risk of upgrading numpy silently for users. Python packaging tools make me feel grumpy and traumatized too but I
don't see how the solution is to just give up on computer-readable dependency-tracking altogether.
Proper dependency tracking would be preferable, but none at all is better than the current situation imho. Ralf