Automated and reproducible target selection for large collaborations
Project description
sort-a-survey
automated target selection for large astronomical surveys
Installation
Install sortasurvey
using pip:
$ pip install sortasurvey
The survey
binary should have been automatically placed in your system's path by the
command. If your system can not find the survey
executable, cd
into the
top-level sortasurvey
directory and try running the following command:
$ python setup.py install
You may test your installation by using survey --help
to see available command-line options:
$ survey --help
usage: sort-a-survey [-h] [-version] {setup,rank} ...
sort-a-survey: automated, optimizable and reproducible target selection
optional arguments:
-h, --help show this help message and exit
-version, --version Print version number and exit.
subcommands:
{setup,rank}
setup Easy setup for directories and files
rank Rank targets for a given survey
Quickstart
Once you've successfully installed the software, we suggest to create a new directory to keep all your survey-related stuff in one place. The package also provides a convenient setup
feature, so to get started:
$ mkdir survey
$ cd survey
$ survey setup
This last command will set up the proper directories and download a couple example files to get you started via command line. Below is a link to a different tutorial if jupyter notebook is more your thing. To run the software, simply execute:
$ survey rank
Features
Can't converge on a final target list? Do multiple science goals have you tripped up? Are you having a hard time balancing several programs within a set allocation? Have several nights of observing coming up?
Let sortasurvey
do the heavy lifting for you.
Imagine a survey sample that can be:
- Automated
- takes what would be an otherwise-complicated process and make it totally hands-off
- Optimized
- can be ran many times with many different scenarios to create a sample that best fits your survey needs
- Reproduced
- the randomized selection process and sample can be easily reproduced with our random seed feature
- Tested
- Monte-carlo-like simulation capabilities to test how robust your survey sample is
- Visualized
- creates helpful summary tables and stats
Tutorial
Follow example in
sort-a-survey/examples/TKS.ipynb
The work presented here was motivated by the TESS-Keck Survey (TKS), a large, dedicated radial velocity program using over 100 nights on Keck/HIRES to study transiting planets identified by the NASA TESS mission. TKS is a collaboration between researchers at the California Institute of Technology, the University of California, the University of Hawai'i, the University of Kansas, NASA, the NASA Exoplanet Science Institute and the W. M. Keck Observatory. Please visit this repo for more specific details on the application of this algorithm to the final TKS sample.
Attribution
Written by Ashley Chontos, with contributions from BJ Fulton, Erik Petigura, Joey Murphy, Ryan Rubenzahl, Sarah Blunt, Corey Beard, Tara Fetherolf, and Judah van Zandt.
Please cite the [TKS paper] if you make use of this software or the TKS sample in your work.
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