Embedded Files
Legacy Tutorial
Legacy Tutorial
- Jupyter notebooks allowing to reproduce the in-house analysis for planet c
- This data set is from the actual challenge
- Make sure you switch to the analysis branch of the Github repo
- This analysis will be detailed in our upcoming Zimmerman et al. paper
Previous Tutorials
Previous Tutorials
May 2020 Update:
The Tutorial material (notebooks and data) is now in a Github Repo which you can clone or download.
It is based on the second rehearsal data set (not the actual challenge data).
All paths are relative in that folder to simplify things, run the notebooks on your local machine immediately
All notebooks are coded in Jupyter Python and require rather standard packages except for the starshade image analysis demo script (SS_Photometry_Tutorial.m) written in MatLab
The hlc_VIP_demo.ipynb - Demo application of the VIP high-contrast image analysis package - is more difficult to get to work, you may want to start with a fresh environment.
A more detailed description on how to use this repo is available in the README.
New York City Hack-a-thon
New York City Hack-a-thon
From the tutorial event we held in October 16-17 2019
Videos
Videos
NY Flatiron event - Day 1 - Full recording
NY Flatiron event - Day 2 - Full recording
Introduction to Participants
Introduction to Participants
Margaret Turnbull (SIT PI) and Julien Girard (DC Coordinator) welcome participants.
General Presentation
General Presentation
Presented by Julien Girard
What the 2019 Data Challenge is about, its aims, what we hope to learn from running it. PDF of the slides
NeilZ_flatiron_cgi_day1_intro.pdfIntro to CGI Simulations
Intro to CGI Simulations
Presented by Neil Zimmerman
HLC OS6 Simulations presented by Neil Zimmerman. PDF of the slides
HLC Data Tour
HLC Data Tour
Presented by Neil Zimmerman
Download the Jupyter python notebook (hlc_data_tour.ipynb) or simply view it on nbviewer
Parallax & Proper Motion
Parallax & Proper Motion
Presented by Neil Zimmerman
Download the Jupyter python notebook or simply view it on nbviewer
Orbital fitting with orbitize!
Orbital fitting with orbitize!
Presented by Junellie Gonzalez Quiles
orbitize!/OFTI
orbitize!/OFTI
OFTI (Orbits For The Impatient) is an orbit-generating algorithm designed specifically to handle data covering short fractions of long-period exoplanets (Blunt et al. 2017). Here we go through steps of using OFTI within orbitize!
Download the Jupyter python notebook or simply view it on nbviewer
orbital parameters
orbital parameters
Obtaining orbital solutions using orbitize!
Download the Jupyter python notebook or simply view it on nbviewer
plotting orbits
plotting orbits
Code to define the orbitize! coordinate system and help you visualize orbits
Download the Jupyter python notebook or simply view it on nbviewer
RadVel to analyze precursor RV data
RadVel to analyze precursor RV data
Presented by Neil Zimmerman
RadVel is a Python package for modeling and fitting radial velocity time series data.
Download the Jupyter python notebook or simply view it on nbviewer
SISTER_DataChallenge_Official.pdfStar Shade Simulations & Calibrations
Star Shade Simulations & Calibrations
Presented (remotely) by Sergi Hildebrandt
New! HLC Quick-Look Photometry & Flux Ratio Calibration
New! HLC Quick-Look Photometry & Flux Ratio Calibration
Demo prepared by Neil Zimmerman (not included in the videos)
We realized that many participants encountered trouble to provide flux ratios in the range we expected them to be: exoplanets in reflected light! To help participants revise their photometry, Neil had come up with this demo.
Jupyter python notebook or simply view it on nbviewer
New! Starshade Photometry
New! Starshade Photometry
Demo prepared by Sergi Hildebrandt (not included in the video)
This demo derives the flux ratios of the same astronomical scenario as with the HLC before, but for the Starshade.
Get the PDF presentation, data and Matlab script in WFIRST-CGI-2019-DC-Tutorial.
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