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).

Prerequisites

PINT requires Python 3.9+ [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).

In most cases, we recommend using an isolated environment, such as virtualenv, Pixi, or Conda/Anaconda/Miniconda.

IMPORTANT Notes!

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 Silicon (M1/M2/M3/M4 …) processors

PINT requires longdouble (80- or 128-bit floating point) arithmetic within numpy. On native macOS ARM Python builds, numpy.longdouble is usually aliased to float64, which is not enough for high-precision timing.

Recommended: native-speed Linux containers (arm64)

Use a multi-arch Linux container. On Apple Silicon Docker pulls the linux/arm64 image and runs it at native speed (not Rosetta). Such images provide true IEEE binary128 numpy.longdouble — more precision than typical x86_64 80-bit longdouble:

# NANOGrav 20-year analysis environment
docker pull nanograv/ng20:cpu
docker run --rm -it \
  -e HOST_UID=$(id -u) -e HOST_GID=$(id -g) \
  -v "$PWD":/work -w /work \
  nanograv/ng20:cpu bash

Quick check inside the container:

python -c "import numpy as np; print(np.finfo(np.longdouble))"

You should see a float128 / binary128-class result (eps around 1e-34), not float64.

Image and further docs:

nanograv/ng20

This image also works as a VS Code / Cursor Dev Container.

Optional: ``pintk`` GUI via X11

To display pintk from the container on a Mac, run an X server (e.g. XQuartz), allow local connections (xhost + 127.0.0.1), and pass DISPLAY into the container, for example:

docker run --rm -it \
  -e HOST_UID=$(id -u) -e HOST_GID=$(id -g) \
  -e DISPLAY=host.docker.internal:0 \
  -v "$PWD":/work -w /work \
  nanograv/ng20:cpu \
  pintk your.par your.tim

For Dev Containers, set the same DISPLAY value (and any needed X11 mounts) in a local override such as .devcontainer/devcontainer.local.json; keep that file untracked.

Alternative: Rosetta / osx-64 conda or Pixi

An x86_64 Python stack under Rosetta also provides 80-bit longdouble, but is slower than a native arm64 Linux container. With Pixi, set platforms = ["osx-64"] in pixi.toml, or follow conda-forge tips for Apple Intel packages on Apple silicon (parallel arm64 and x86 conda installs are possible).

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 or Pixi environment to work on PINT. In that case, you just activate your virtualenv before running the pip command above.

Install with Pixi

Pixi is a fast and simple package management tool that uses conda under the hood. It is very quick and easy to install. Once you have it installed, you can set up a PINT environment (here called “PINT”) like this. First cd to where you want the environment directory to live, then:

$ pixi init PINT
$ cd PINT
$ pixi add pint-pulsar
$ pixi shell

Now you can run PINT commands and import pint in the Python that lives in the Pixi environment you just created and activated.

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 https://github.com/nanograv/PINT.git
$ 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 (or Pixi or conda environment) 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

Bad `PYTHONPATH`

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.

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

Footnotes