How to Install PINT

There are two kinds of PINT installation you might be interested in. The first is a simple PINT installation for someone who just wants to use PINT. The second is an installation for someone who wants to be able to run the tests and develop PINT code. The latter naturally requires more other python packages and is more complicated (but not too much).


PINT requires Python 3.8+ [1]

Your Python must have the package installation tool pip installed. Also make sure your setuptools are up to date (e.g. pip install -U setuptools).

We highly recommend using a Conda/Anaconda environment or the package isolation tool virtualenv.


Naming conflict

PINT has a naming conflict with the pint units package available from PyPI (i.e. using pip) and conda. Do NOT pip install pint or conda install pint! See Basic Install via pip or Install with Anaconda.

Apple M1/M2 processors

PINT requires longdouble (80- or 128-bit floating point) arithmetic within numpy, which is currently not supported natively on M1/M2 Macs. However, you can use an x86 version of conda even on an M1/M2 Mac: see instructions for using Apple Intel packages on Apple silicon. It’s possible to have parallel versions of conda for x86 and ARM.

Basic Install via pip

PINT is now available via PyPI as the package pint-pulsar, so it is now simple to install via pip. This will get you the latest released version of PINT.

For most users, who don’t want to develop the PINT code, installation should just be a matter of:

$ pip install pint-pulsar

By default this will install in your system site-packages. Depending on your system and preferences, you may want to append --user to install it for just yourself (e.g. if you don’t have permission to write in the system site-packages), or you may want to create a virtualenv to work on PINT (using a virtualenv is highly recommended by the PINT developers). In that case, you just activate your virtualenv before running the pip command above.

Install with Anaconda

If you use Anaconda environments to manage your python packages, PINT is also available for Anaconda python under the conda-forge channel:

$ conda install -c conda-forge pint-pulsar

Install from Source

If you want access to the latest development version of PINT, or want to be able to make any edits to the code, you can install from source by cloning the git repository.

If your python setup is “nice”, you should be able to install as easily as:

$ git clone
$ cd PINT
$ mkvirtualenv -p `which python3` pint
(pint) $ pip install -e .
(pint) $ python
>>> import pint

Note that you can use your own method to activate your virtualenv if you don’t have virtualenvwrapper installed. This should install PINT along with any python packages it needs to run. (If you want to run the test suite or work on PINT code, see below.) Note that the -e installs PINT in “editable” or “develop” mode. This means that the source code is what is actually being run, rather than making a copy in a site-packages directory. Thus, if you edit any .py file, or do a git pull to update the code this will take effect immediately rather than having to run pip install again. This is a choice, but is the way most developers work.

Unfortunately there are a number of reasons the install can go wrong. Most have to do with not having a “nice” python environment. See the next section for some tips.

Potential Install Issues

Old setuptools (egg-info error message)

PINT’s setup.cfg is written in a declarative style that does not work with older versions of setuptools. The lack of a sufficiently recent version of setuptools is often signalled by the otherwise impenetrable error message error: 'egg_base' must be a directory name (got src). You can upgrade with pip:

$ pip install -U pip setuptools

If this does not help, check your versions of installed things:

$ pip list

You should be able to upgrade to setuptools version at least 0.41. If running pip does not change the version that appears on this list, or if your version changes but the problem persists, you may have a problem with your python setup; read on.


The virtualenv mechanism uses environment variables to create an isolated python environment into which you can install and upgrade packages without affecting or being affected by anything in any other environment. Unfortunately it is possible to defeat this by setting the PYTHONPATH environment variable. Double unfortunately, setting the PYTHONPATH environment used to be the Right Way to use python things that weren’t part of your operating system. So many of us have PYTHONPATH set in our shells. You can check this:

$ printenv PYTHONPATH

If you see any output, chances are that’s causing problems with your virtualenvs. You probably need to go look in your .bashrc and/or .bash_profile to see where that variable is being set and remove it. Yes, it is very annoying that you have to do this.

Previous use of pip install --user

Similarly, it used to be recommended to install packages locally as your user by running pip install --user thing. Unfortunately this causes something of the same problem as having a PYTHONPATH set, where packages installed outside your virtualenv can obscure the ones you have inside, producing bizarre error messages. Record your current packages with pip freeze, then try, outside a virtualenv, doing pip list with various options, and pip uninstall; you shouldn’t be able to uninstall anything system-wise (do not use sudo!) and you shouldn’t be able to uninstall anything in an inactive virtualenv. So once you’ve blown away all those packages, you should be able to work in clean virtualenvs. If you saved the output of pip freeze above, you should be able to use it to create a virtualenv with all the same packages you used to have in your user directory.

Bad conda setup

Conda is a tool that attempts to create isolated environments, like a combination of virtualenv and pip. It should make installing scientific software with lots of dependencies easy and reliable, and you should just be able to set up an appropriate conda environment and use the basic install instructions above. But it may not work.

Specifically, for some reason the python 3 version of conda does not provide the gdbm module, which astropy needs to work on Linux. Good luck.

Installing PINT for Developers

You will need to be able to carry out a basic install of PINT as above. You very likely want to install in a virtualenv and using the develop mode pip -e. Then you will need to install the additional development dependencies:

$ pip install -Ur requirements_dev.txt

PINT development (building the documentation) requires pandoc, which isn’t a python package and therefore needs to be installed in some way appropriate for your operating system. On Linux you may be able to just run:

$ apt install pandoc

On a Mac using MacPorts this would be:

$ sudo port install pandoc

Otherwise, there are several ways to install pandoc

For further development instructions see How to Set Up Your Environment For PINT Development