Setting up and using your development environment¶
Recommended development setup¶
Since NumPy contains parts written in C and Cython that need to be compiled before use, make sure you have the necessary compilers and Python development headers installed - see Building from source.
Having compiled code also means that importing Numpy from the development sources needs some additional steps, which are explained below. For the rest of this chapter we assume that you have set up your git repo as described in Working with NumPy source code.
To build the development version of NumPy and run tests, spawn interactive shells with the Python import paths properly set up etc., do one of:
$ python runtests.py -v $ python runtests.py -v -s random $ python runtests.py -v -t numpy/core/tests/test_iter.py:test_iter_c_order $ python runtests.py --ipython $ python runtests.py --python somescript.py $ python runtests.py --bench $ python runtests.py -g -m full
This builds Numpy first, so the first time it may take a few minutes. If you specify -n, the tests are run against the version of NumPy (if any) found on current PYTHONPATH.
Using runtests.py is the recommended approach to running tests. There are also a number of alternatives to it, for example in-place build or installing to a virtualenv. See the FAQ below for details.
For development, you can set up an in-place build so that changes made to .py files have effect without rebuild. First, run:
$ python setup.py build_ext -i
This allows you to import the in-place built NumPy from the repo base directory only. If you want the in-place build to be visible outside that base dir, you need to point your PYTHONPATH environment variable to this directory. Some IDEs (Spyder for example) have utilities to manage PYTHONPATH. On Linux and OSX, you can run the command:
$ export PYTHONPATH=$PWD
and on Windows:
$ set PYTHONPATH=/path/to/numpy
Now editing a Python source file in NumPy allows you to immediately test and use your changes (in .py files), by simply restarting the interpreter.
Note that another way to do an inplace build visible outside the repo base dir is with python setup.py develop. Instead of adjusting PYTHONPATH, this installs a .egg-link file into your site-packages as well as adjusts the easy-install.pth there, so its a more permanent (and magical) operation.
Other build options¶
It’s possible to do a parallel build with numpy.distutils with the -j option; see Parallel builds for more details.
In order to install the development version of NumPy in site-packages, use python setup.py install --user.
A similar approach to in-place builds and use of PYTHONPATH but outside the source tree is to use:
$ python setup.py install --prefix /some/owned/folder $ export PYTHONPATH=/some/owned/folder/lib/python3.4/site-packages
A frequently asked question is “How do I set up a development version of NumPy in parallel to a released version that I use to do my job/research?”.
One simple way to achieve this is to install the released version in site-packages, by using a binary installer or pip for example, and set up the development version in a virtualenv. First install virtualenv (optionally use virtualenvwrapper), then create your virtualenv (named numpy-dev here) with:
$ virtualenv numpy-dev
Now, whenever you want to switch to the virtual environment, you can use the command source numpy-dev/bin/activate, and deactivate to exit from the virtual environment and back to your previous shell.
Besides using runtests.py, there are various ways to run the tests. Inside the interpreter, tests can be run like this:
>>> np.test() >>> np.test('full') # Also run tests marked as slow >>> np.test('full', verbose=2) # Additionally print test name/file
Or a similar way from the command line:
$ python -c "import numpy as np; np.test()"
Tests can also be run with nosetests numpy, however then the NumPy-specific nose plugin is not found which causes tests marked as KnownFailure to be reported as errors.
Running individual test files can be useful; it’s much faster than running the whole test suite or that of a whole module (example: np.random.test()). This can be done with:
$ python path_to_testfile/test_file.py
That also takes extra arguments, like --pdb which drops you into the Python debugger when a test fails or an exception is raised.
Running tests with tox is also supported. For example, to build NumPy and run the test suite with Python 3.4, use:
$ tox -e py34
For more extensive info on running and writing tests, see https://github.com/numpy/numpy/blob/master/doc/TESTS.rst.txt .
Note: do not run the tests from the root directory of your numpy git repo, that will result in strange test errors.
Rebuilding & cleaning the workspace¶
Rebuilding NumPy after making changes to compiled code can be done with the same build command as you used previously - only the changed files will be re-built. Doing a full build, which sometimes is necessary, requires cleaning the workspace first. The standard way of doing this is (note: deletes any uncommitted files!):
$ git clean -xdf
When you want to discard all changes and go back to the last commit in the repo, use one of:
$ git checkout . $ git reset --hard
Another frequently asked question is “How do I debug C code inside Numpy?”. The easiest way to do this is to first write a Python script that invokes the C code whose execution you want to debug. For instance mytest.py:
from numpy import linspace x = np.arange(5) np.empty_like(x)
Now, you can run:
$ gdb --args python runtests.py -g --python mytest.py
And then in the debugger:
(gdb) break array_empty_like (gdb) run
The execution will now stop at the corresponding C function and you can step through it as usual. With the Python extensions for gdb installed (often the default on Linux), a number of useful Python-specific commands are available. For example to see where in the Python code you are, use py-list. For more details, see DebuggingWithGdb.
Instead of plain gdb you can of course use your favourite alternative debugger; run it on the python binary with arguments runtests.py -g --python mytest.py.
Building NumPy with a Python built with debug support (on Linux distributions typically packaged as python-dbg) is highly recommended.