Setting up and using your development environment

Testing builds

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 you specify -n, the tests are run against the version of NumPy (if any) found on current PYTHONPATH.

When specifying a target using -s, -t, or --python, additional 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 the --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"

Note

Remember that all tests of NumPy should pass before committing your changes.

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.

Note

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 environment variable NPY_AVAILABLE_MEM, for example NPY_AVAILABLE_MEM=32GB, or using pytest --available-memory=32GB target option.

Building in-place

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

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 -j option; 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 _numpyconfig.h and config.h files using HAVE_XXX definitions. These tests are run during the build_src phase of the _multiarray_umath module in the generate_config_h and 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

Using virtualenvs

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.

Running tests

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

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: 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 3.7, 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

Debugging

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 mytest.py:

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.

  • print: Print value of expression EXP.

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!