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.
If you are having trouble building NumPy from source or setting up your local development environment, you can try to build NumPy with GitHub Codespaces. It allows you to create the correct development environment right in your browser, reducing the need to install local development environments and deal with incompatible dependencies.
If you have good internet connectivity and want a temporary set-up, it is
often faster to work on NumPy in a Codespaces environment. For documentation
on how to get started with Codespaces, see
the Codespaces docs.
When creating a codespace for the
numpy/numpy repository, the default
2-core machine type works; 4-core will build and work a bit faster (but of
course at a cost of halving your number of free usage hours). Once your
codespace has started, you can run
conda activate numpy-dev and your
development environment is completely set up - you can then follow the
relevant parts of the NumPy documentation to build, test, develop, write
docs, and contribute to NumPy.
Using virtual environments#
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 pip or conda for example, and set up the development version in a virtual environment.
If you use conda, we recommend creating a separate virtual environment for
numpy development using the
environment.yml file in the root of the repo
(this will create the environment and install all development dependencies at
$ conda env create -f environment.yml # `mamba` works too for this command $ conda activate numpy-dev
$ 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.
Before running the tests, first install the test dependencies:
$ python -m pip install -r test_requirements.txt $ python -m pip install asv # only for running benchmarks
To build the development version of NumPy and run tests, spawn interactive shells with the Python import paths properly set up etc., use the spin utility. To run tests, do one of:
$ spin test -v $ spin test numpy/random # to run the tests in a specific module $ spin test -v -t numpy/core/tests/test_nditer.py::test_iter_c_order
This builds NumPy first, so the first time it may take a few minutes.
You can also use
spin bench for benchmarking. See
spin --help for more
command line options.
If the above commands result in
RuntimeError: Cannot parse version 0+untagged.xxxxx,
git pull upstream main --tags.
Additional arguments may be forwarded to
pytest 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:
$ spin test -t numpy/tests/test_scripts.py::test_f2py -- --pdb
You can also match test names using python operators by passing the
argument to pytest:
$ spin test -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.
Some of the tests in the test suite require a large amount of memory, and are skipped if your system does not have enough.
Other build options#
For more options including selecting compilers, setting custom compiler flags and controlling parallelism, see Compiler selection and customizing a build (from the SciPy documentation.)
spin, 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 3.9, use:
$ tox -e py39
For more extensive information, see Testing Guidelines.
Note: do not run the tests from the root directory of your numpy git repo without
that will result in strange test errors.
Lint checks can be performed on newly added lines of Python code.
Install all dependent packages using pip:
$ python -m pip install -r linter_requirements.txt
To run lint checks before committing new code, run:
$ python tools/linter.py
To check all changes in newly added Python code of current branch with target branch, run:
$ python tools/linter.py --branch main
If there are no errors, the script exits with no message. In case of errors, check the error message for details:
$ python tools/linter.py --branch main ./numpy/core/tests/test_scalarmath.py:34:5: E303 too many blank lines (3) 1 E303 too many blank lines (3)
It is advisable to run lint checks before pushing commits to a remote branch since the linter runs as part of the CI pipeline.
For more details on Style Guidelines:
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)
Most python builds do not include debug symbols and are built with compiler optimizations enabled. To get the best debugging experience using a debug build of Python is encouraged, see Advanced debugging tools.
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:
$ spin gdb 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, to see the
python traceback, use
py-bt. 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.
Rich support for Python debugging requires that the
distributed with Python is installed in a path where gdb can find it. If you
installed your Python build from your system package manager, you likely do
not need to manually do anything. However, if you built Python from source,
you will likely need to create a
.gdbinit file in your home directory
pointing gdb at the location of your Python installation. For example, a
version of python installed via pyenv
.gdbinit file with the following contents:
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!