On Sun, Sep 23, 2012 at 10:20 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 ....
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