# PSFMachine
*PRF photometry with Kepler*
<a href="https://github.com/ssdatalab/psfmachine/workflows/tests.yml"><img src="https://github.com/ssdatalab/psfmachine/workflows/pytest/badge.svg" alt="Test status"/></a>
<a href="https://pypi.python.org/pypi/psfmachine"><img src="https://img.shields.io/pypi/v/psfmachine" alt="pypi status"></a>
<a href="https://zenodo.org/record/4784073"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.4784073.svg"></a>
Check out the [documentation](https://ssdatalab.github.io/psfmachine/).
Check out the [paper](#)
`PSFMachine` is an open source Python tool for creating models of instrument effective Point Spread Functions (ePSFs), a.k.a Pixel Response Functions (PRFs). These models are then used to fit a scene in a stack of astronomical images. `PSFMachine` is able to quickly derive photometry from stacks of *Kepler* images and separate crowded sources.
# Installation
```
pip install psfmachine
```
# Example use
Below is an example script that shows how to use `PSFMachine`. Depending on the speed or your computer fitting this sort of model will probably take ~10 minutes to build 200 light curves. You can speed this up by changing some of the input parameters.
```python
import psfmachine as psf
import lightkurve as lk
tpfs = lk.search_targetpixelfile('Kepler-16', mission='Kepler', quarter=12, radius=1000, limit=200, cadence='long').download_all(quality_bitmask=None)
machine = psf.TPFMachine.from_TPFs(tpfs, n_r_knots=10, n_phi_knots=12)
machine.fit_lightcurves()
```
Funding for this project is provided by NASA ROSES grant number 80NSSC20K0874.
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