On Sun, Dec 21, 2014 at 5:37 PM, Maniteja Nandana < [email protected]> wrote:
> Hello Ralf, > > On Sun, Dec 21, 2014 at 9:17 PM, Ralf Gommers <[email protected]> > wrote: > >> Hi Maniteja, >> >> On Sun, Dec 21, 2014 at 4:04 PM, Maniteja Nanda >> You don't need a virtualenv. If you want to only run the tests and make >> sure your changes pass the test suite, the easiest option is ``python >> runtests.py`` in your numpy repo root dir. You can also run tests for a >> particular module that way - see the docstring of runtests.py for more >> details. >> >> > Thank you for the help. I couldn't find a way out in many discussion > threads. I saw the testing guide in the development workflow. As I > understood 'tests/test_xxx.py' is used to test the 'xxx' function. > Almost. test_xxx.py contains tests for all functions in the file xxx.py > > If you want to use your modified numpy to for example import in IPython >> and play with it, I would use an in-place build. So ``python setup.py >> build_ext -i``, and then you can make python find that in-place build by >> adding the repo to your PYTHONPATH or by running ``python setup.py >> develop``. If you then make changes to Python code they're immediately >> visible, if you change compiled code you have to rebuild in-place again. >> > Note that there is also a variant which does use virtualenvs documented at https://github.com/scipy/scipy/blob/master/HACKING.rst.txt#faq (under "*How do I set up a development version of SciPy in parallel to a released version that I use to do my job/research?").* > Cheers, >> Ralf >> >> As you told me , I have built a in-place copy of numpy and added it to > the Python path. > > maniteja@ubuntu:~/FOSS/numpy$ echo $PYTHONPATH > /home/maniteja/FOSS/numpy/numpy > Maybe that's one /numpy too many? If it's right, you should have a dir /home/maniteja/FOSS/numpy/ numpy/numpy/core. > Correct me please if I am wrong. I don't think this is causing the desired > change, since a simple print statement in *count* function in *ma* is not > printing anything when creating an masked array object. > An easy way to check which numpy you're using is "import numpy; print(numpy.__file__)". > In addition to this, I had a doubt in which branch should I do the > modifications, master or testing branch in numpy. I used the testing > branch to create the build because that the master branch keeps getting > updated regularly. Would this be fine or should I use the master branch to > create the build? > This is fine. You should not develop directly on your own master branch. Rather, keep your master branch in sync with numpy master, and create a new feature branch for every new feature that you want to work on. Ralf
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