Piff 1.4.1

PSFs in the Full FOV

Piff is a Python software package for modeling the point-spread function (PSF) across multiple detectors in the full field of view (FOV).

Features: (Some are aspirational. We’re still working on the code!)

• Has multiple basis sets for the underlying PSF model, including pixel-based, shapelets, Gaussian mixture, maybe also Moffat and/or Kolmogorov.

• Can build the models in either chip or sky coordinates, properly accounting for the WCS of the image.

• Can interpolate across the full field-of-view, or across each chip separately, or a combination of both.

• Can do the fitting in either real or Fourier space.

• Has multiple interpolation functions including polynomials, gaussian processes, and others.

• Can take knowledge of the optical aberrations as input to convolve the model of the atmospheric PSF.

• Performs outlier rejection to detect and remove stars that are not good exemplars of the PSF. Outputs the list of stars that were actually used to build the final model.

• Allows the centroid to be fixed or floating.

• In general, allow any value to be fixed rather than fit for.

• Uses highly readable YAML configuration files to set the various options.

• Includes Python code to read in the PSF files and use it to draw an image of the PSF at an arbitrary location.

• Currently, the lead developers are: - Mike Jarvis (mikejarvis17 at gmail) - Josh Meyers (jmeyers314 at gmail) - Pierre-Francois Leget (pierrefrancois.leget at gmail) - Chris Davis (chris.pa.davis at gmail) If you’d like to join the development effort, or if you have any other questions or comments about the code, feel free to contact us at the above email addresses.

Installation

The easiest way to install Piff is with pip:

pip install piff

If you have previously installed Piff and want to uprade to a new released version, you should do:

pip install piff --upgrade

Depending on the write permissions of the python distribution for your specific system, you might need to use one of the following variants:

sudo pip install piff
pip install piff --user

The latter installs the Python module into ~/.local/lib/python3.7/site-packages, which is normally already in your PYTHONPATH, but it puts the executables piffify and meanify into ~/.local/bin which is probably not in your PATH. To use these scripts, you should add this directory to your PATH. If you would rather install into a different prefix rather than ~/.local, you can use:

pip install piff --install-option="--prefix=PREFIX"

This would install the executables into PREFIX/bin and the Python module into PREFIX/lib/python3.7/site-packages.

If you need the bleeding edge version on the main branch, you can download or clone the repo and install with:

pip install -r requirements.txt
pip install .

Depending on your system, you might prefer/need one of these variants:

sudo pip install .
pip install . --user
pip install . --install-option="--prefix=PREFIX"

Running Tests

After installing Piff, you can run the unit tests by doing:

cd tests
nosetests

Using Piff

A tutorial notebook giving an overview of how to use Piff is available in the examples directory, called Tutorial.ipynb

This is not a comprehensive tour of Piff’s capabilities of course, but it should provide a rough guide to the basic structure.

Full documentation is available at:

http://rmjarvis.github.io/Piff/

Reporting bugs

If you have any trouble installing or using the code, or if you find a bug, an error in the documentation, or have any other problem, please report it at:

https://github.com/rmjarvis/Piff/issues

Click “New Issue”, which will open up a form for you to fill in with the details of the problem you are having.

Requesting features

If you would like to request a new feature, do the same thing. Open a new issue and fill in the details of the feature you would like added to Piff. Or if there is already an issue for your desired feature, please add to the discussion, describing your use case. The more people who say they want a feature, the more likely we are to get around to it sooner than later.