Skip to content

Latest commit

 

History

History
75 lines (49 loc) · 2.18 KB

HOWTO_BUILD_DOCS.rst.txt

File metadata and controls

75 lines (49 loc) · 2.18 KB

Building the NumPy API and reference docs

We currently use Sphinx for generating the API and reference documentation for Numpy. You will need Sphinx 1.0.1 or newer.

If you only want to get the documentation, note that pre-built versions can be found at

http://docs.scipy.org/

in several different formats.

Instructions

If you obtained Numpy via git, get also the git submodules that contain additional parts required for building the documentation:

git submodule init
git submodule update

Since large parts of the main documentation are stored in docstrings, you will need to first build Numpy, and install it so that the correct version is imported by

>>> import numpy

Note that you can eg. install Numpy to a temporary location and set the PYTHONPATH environment variable appropriately. Also note that if you have a system Numpy installed via Python eggs, you will also need to use setupegg.py to install the temporary Numpy.

After Numpy is installed, write:

make html

in this doc/ directory. If all goes well, this will generate a build/html subdirectory containing the built documentation. Note that building the documentation on Windows is currently not actively supported, though it should be possible. (See Sphinx documentation for more information.)

To build the PDF documentation, do instead:

make latex
make -C build/latex all-pdf

You will need to have Latex installed for this.

In addition to the above, you can also do:

make dist

which will rebuild Numpy, install it to a temporary location, and build the documentation in all formats. This will most likely again only work on Unix platforms.

Sphinx extensions

Numpy's documentation uses several custom extensions to Sphinx. These are shipped in the sphinxext/ directory, and are automatically enabled when building Numpy's documentation.

If you want to make use of these extensions in third-party projects, they are available on PyPi as the numpydoc package.