# pydis: A simple longslit spectroscopy pipeline in Python¶

An easy to use reduction package for one dimensional longslit spectroscopy using Python.

The goal of pyDIS is to provide a turn-key solution for reducing and understanding longslit spectroscopy, which could ideally be done in real time. Currently we are using many simple assumptions to get a quick-and-dirty solution, and modeling the workflow after the robust industry standards set by IRAF. Additionally, we have only used data from the low/medium resolution APO 3.5-m “Dual Imaging Spectrograph” (DIS). Therefore, many instrument specific assumptions are being made. So far PyDIS has also been successfully used (with hacking/modification) on data from MMT and DCT. If you use PyDIS, please send me feedback!

Some background motivation on why I made this package is given here

# Contents¶

## Motivation¶

Really slick tools exist for on-the-fly photometry analysis. However, no turn-key, easy to use spectra toolkit for Python (without IRAF or PyRAF) was available (that we were aware of). Here are some mission statements:

• Being able to extract and see data in real time at the telescope would be extremely helpful!
• This pipeline doesn’t have to give perfect results to be very useful
• Don’t try to build a One Size Fits All solution for every possible instrument or science case. We cannot beat IRAF at it’s own game. IRAF is the industry standard
• The pipeline does need to handle:
• Flats
• Biases
• Spectrum Tracing
• Wavelength Calibration using HeNeAr arc lamp spectra
• Sky Subtraction
• Extraction
• basic Flux Calibration
• The more hands-free the better, a full reduction script needs to be available
• A fully interactive mode (a la IRAF) should be available for each task

So far pyDIS can do a rough job of all the reduction tasks for single point sources objects! We are seeking more data to test it against, to help refine the solution and find bugs.

## How to Help¶

• Check out the Issues page if you think you can help code, or want to requst a feature!
• If you have some data already reduced in IRAF that you trust and would be willing to share, let us know!