Space at VT Google Summer of Code
Applying to Space @ VT's GSoC 2019 Program
Interested students that want to apply to Space @ Virginia Tech's GSoC 2019 program need to complete the following:
- A simple coding task related to space science and engineering, for examples please see our proposal guide.
- Fill out this Google form in order to tell us a little about yourself
This will help our mentors in working with you to develop proposals that are to be submitted via the Google Summer of Code website starting March 25th, 2019.
Once the two items above are submitted it may take mentors a week or more to review your material, in this time please begin working on a proposal as noted below.
As you are preparing your proposal, please use our guide for our proposal format as well as info on completing a simple coding task related to space science and engineering.
Below is a list of ideas that students can propose to and/or gives a sense of the types of projects we're looking at for the Summer of Code program for 2019. You may contact the mentors listed on each idea if there is something that isn't completely clear or if you need a little more information to see if that idea interest you.
1. Web based visualization tools to access space science datasets
Description: The Virginia Tech SuperDARN website hosts several data visualization tools that are used by the Space Science community. Currently, most of the existing visualization tools use IDL as the core backend language (http://www.harrisgeospatial.com/SoftwareTechnology/IDL.aspx) and generate static plots. The main goal of the project is to use our open source data analysis toolkit pydarn and the latest data visualization software such as Python (matplotlib, seaborn, bokeh) and d3.js to develop interactive and dynamic visualization tools.
Expected Outcome : Develop data visualization tools for space science datasets that will be hosted on the Virginia Tech SuperDARN website.
Possible Mentors : Bharat Kunduri, Kevin Sterne
Difficulty Level : Medium
2. Develop big data tools for advanced querying capabilities of space science datasets
Description: An important part of the analysis carried by researchers and students working in Space@VT is querying several datasets and retrieving data that satisfy certain criteria. However, the data is stored as binary files with no indexing, making it extremely slow and arduous to search through and filter the data. The main goal of the current project will be to experiment with new data storage and indexing tools such as Apache Parquet and Elasticsearch and develop a framework that enhances the querying capabilities, especially for geo-spatial and time series data.
Expected Outcome: Use big data frameworks to develop tools that enable faster queries over large geo-spatial and time series data with optimal use of disk space.
Skills Required/Preferred: Experience working with big data tools is preferred.
Possible Mentors: Bharat Kunduri, Xueling Shi, Muhammad Rafiq, Shibaji Chakraborty, Kevin Sterne
Difficulty Level: Difficult
3. Real-time Space@VT data display for web
Description: In order to better promote activities and check on conditions of instruments supported by Space@VT, a real-time data display is needed. These instruments can include: (1) meteorlogical conditions with a local weather station (temperature, pressure, wind speed, direction, humidity, and precipitation); (2) NASA AERONET measurements of total optical depth of aerosols; (3) full-sky camera for detecting clouds and fireballs; (4) state of health and/or magnetometer data plots from AAL-PIP systems on Antarctica.
Expected Outcome: Expect a display that can ingest these data streams and displays on the web to be added to the space.vt.edu website. Also expect the platform/display to be flexible or easily extendible to include new datasets.
Skills Required/Preferred: Python, java, php
Possible Mentors: Elena Lind, Kevin Sterne, Zach Leffke, Shane Coyle
Difficulty Level: Easy to medium
4. Satellite ground track display
Description: Space@VT has a ground station with several others currently in development. There is a need to determine upcoming access times to these ground stations to satellites of interest to Space@VT like the Fox 1-D and Fox 1-C AMSAT satellites and the upcoming Virginia Cubesat Consortium (VCC) satellite. This display would highlight the ground station's activity as well as facilitate other parts under development with the ground station.
Expected Outcome: Expect this project to come up with a real-time way to display satellite passes and access times. In addition to a list of upcoming access times, a display of the satellite’s footprint in relation to Space@VT in either a 2D or 3d format. This display would be accessible via web (with possible posting on space.vt.edu website) or local display that could be displayed in the Space@VT building.
Skills Required/Preferred: Working with ephemeris data, Python, HTML5
Possible Mentors: Kevin Sterne, Zach Leffke
Difficulty Level: Medium to difficult
5. Parallelizing computations of SuperDARN processing routines
Description: There are many ways in which code can run more efficiently and quickly, one of which may be parallelizing functions where results of one part of code are not directly necessary to continue running following code. Many of the core SuperDARN data processing routines were written prior to multi-core computing was widespread. This project idea has a student analyzing data processing code (mostly written in C) and parallelizing where possible in order to increase the overall speed of data processing. Other multi-core or multi-threading techniques can be considered in order to improve the efficiency of the data processing code.
Expected Outcome: Expect an overhaul of current data processing routines in order to increase the speed of producing data products for the SuperDARN and space science community. Code will be added to SuperDARN’s RST github repo.
Skills Required/Preferred: C, parallel processing
Possible Mentors: Kevin Sterne, Bharat Kunduri, Pasha Ponomarenko, Keith Kotyk, Marina Schmidt, Evan Thomas, Simon Shepherd
Difficulty Level: Difficult