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Python modules and scripts used for CDIPS project.

Project description

cdips

Cluster Difference Imaging Photometric Survey

Description

This repository contains some of the code developed for the CDIPS project. If you are interested in light curves, they can be accessed through MAST; planet-finding vetting reports are available at ExoFOP-TESS and have documentation available here.

The tools in this repo can be understood as answers to the question "what do you do once the cdips-pipeline gives you light curves?".

Some possible answers include "find planets", "measure stellar rotation periods", and "find weird variable stars". This question is also in many cases applicable to any time-series data, and some of the tools in this repository are developed with that generality in mind.

Directory contents are as follows.

  • /drivers/ is the interface to make target catalogs, find planet candidates and variable stars, classify, vet the classifications, and make plots and tables for papers. See /drivers/HOWTO.md for instructions.

  • /cdips/ is a python package with tools to make target catalogs, process light curves, optimize cdips-pipeline parameters, plot and analyze light curves.

  • /paper_I/ contains the CDIPS method paper.

  • /tests/ has testing scripts for vetting report generation, HLSP light curve generation, and detrending method comparison. pytest & CI are not set up for this repo.

Author

Luke Bouma

License

MIT

Install

pip install cdips

git clone https://github.com/lgbouma/cdips python setup.py install

to verify you have the dependencies (with apologies for not making this automatic in the installation; some of the dependencies are fairly obscure)

cd cdips/tests python test_environment.py

One place to start if you are building an environment from scratch:

conda env create -f environment.yml -n py38

Update env

conda env export --no-builds > environment.yml

Relevant reading

  1. CDIPS-I: Methods

  2. CDIPS-II: TOI-837b

  3. CDIPS-III: NGC-2516

Project details


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cdips-0.4.tar.gz (44.4 MB view hashes)

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