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. Building
NumPy as of version
1.17 requires a C99 compliant compiler.
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 Git for development.
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_nditer.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
-n, the tests are run against the version of NumPy (if
any) found on current PYTHONPATH.
When specifying a target using
arguments may be forwarded to the target embedded by
runtests.py by passing
the extra arguments after a bare
--. For example, to run a test method with
--pdb flag forwarded to the target, run the following:
$ python runtests.py -t numpy/tests/test_scripts.py::test_f2py -- --pdb
When using pytest as a target (the default), you can
match test names using python operators by passing the
-k argument to pytest:
$ python runtests.py -v -t numpy/core/tests/test_multiarray.py -- -k "MatMul and not vector"
Remember that all tests of NumPy should pass before committing your changes.
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.
Some of the tests in the test suite require a large amount of memory, and are skipped if your system does not have enough.
To override the automatic detection of available memory, set the
NPY_AVAILABLE_MEM, for example
NPY_AVAILABLE_MEM=32GB, or using pytest
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
Note that another way to do an inplace build visible outside the repo base dir
python setup.py develop. Instead of adjusting
.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¶
Build options can be discovered by running any of:
$ python setup.py --help $ python setup.py --help-commands
It’s possible to do a parallel build with
numpy.distutils with the
see Parallel builds for more details.
A similar approach to in-place builds and use of
PYTHONPATH but outside the
source tree is to use:
$ pip install . --prefix /some/owned/folder $ export PYTHONPATH=/some/owned/folder/lib/python3.4/site-packages
NumPy uses a series of tests to probe the compiler and libc libraries for
funtions. The results are stored in
HAVE_XXX definitions. These tests are run during the
phase of the
_multiarray_umath module in the
generate_numpyconfig_h functions. Since the output of these calls includes
many compiler warnings and errors, by default it is run quietly. If you wish
to see this output, you can run the
build_src stage verbosely:
$ python build build_src -v
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
source numpy-dev/bin/activate, and
deactivate to exit from the
virtual environment and back to your previous shell.
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 An example of a successful test : ``4686 passed, 362 skipped, 9 xfailed, 5 warnings in 213.99 seconds``
Or a similar way from the command line:
$ python -c "import numpy as np; np.test()"
Tests can also be run with
pytest numpy, however then the NumPy-specific
plugin is not found which causes strange side effects
Running individual test files can be useful; it’s much faster than running the
whole test suite or that of a whole module (example:
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 3.7, use:
$ tox -e py37
For more extensive information, see Testing Guidelines
Note: do not run the tests from the root directory of your numpy git repo without ``runtests.py``, 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?”. First, ensure that you have gdb installed on your system with the Python extensions (often the default on Linux). You can see which version of Python is running inside gdb to verify your setup:
(gdb) python >import sys >print(sys.version_info) >end sys.version_info(major=3, minor=7, micro=0, releaselevel='final', serial=0)
Next you need to write a Python script that invokes the C code whose execution
you want to debug. For instance
import numpy as np 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. 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. Here are some commonly used commands:
list: List specified function or line.
next: Step program, proceeding through subroutine calls.
step: Continue program being debugged, after signal or breakpoint.
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.
Understanding the code & getting started¶
The best strategy to better understand the code base is to pick something you want to change and start reading the code to figure out how it works. When in doubt, you can ask questions on the mailing list. It is perfectly okay if your pull requests aren’t perfect, the community is always happy to help. As a volunteer project, things do sometimes get dropped and it’s totally fine to ping us if something has sat without a response for about two to four weeks.
So go ahead and pick something that annoys or confuses you about numpy, experiment with the code, hang around for discussions or go through the reference documents to try to fix it. Things will fall in place and soon you’ll have a pretty good understanding of the project as a whole. Good Luck!